Xanthine oxidase (XO) is a member of the molybdopterin-containing enzyme family. It interconverts xanthine to uric acid as the human body’s last step of purine catabolism. The high uric acid concentration in the blood directly leads to human diseases like gout and hyperuricemia. Therefore, drugs that inhibit the biosynthesis of uric acid by human XO have been clinically used for many years to decrease the concentration of uric acid in the blood. This study investigates the inhibition mechanism of XO and a new promising drug, topiroxostat, and a commonly prescribed drug, oxipurinol, by employing molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) calculations. Oxipurinol has been reported to act as a covalent inhibitor, while topiroxostat acts as both a noncovalent and covalent inhibitor and undergoes a stepwise inhibition by all its hydroxylated metabolites. However, the detailed mechanism of inhibition of oxipurinol and each topiroxostat’s metabolite remains elusive and can help design more effective drugs with similar inhibition functions. Hence, herein we present the computational investigation of these drugs’ structural and dynamical effects and the calculated reaction mechanism for all of the oxidation steps catalyzed by the molybdopterin center in the active site. Calculated results for the proposed reaction mechanisms for each metabolite’s inhibition reaction in the enzyme’s active site, binding affinities, and the noncovalent interactions with the surrounding amino acid residues are consistent with previously reported experimental findings. Analysis of the noncovalent interactions via energy decomposition analysis (EDA) and noncovalent interaction (NCI) techniques suggests that residues several residues of the binding pocket can be used as key interacting residues for further hybrid-type inhibitor development.

Management of substances that possess high potential for abuse requires a comprehensive understanding of the temporal effects of a corresponding volume of intake. Cannabis is deemed as one of the most widely used drugs in the United States and studies related to the primary psychoactive compound present in it, Δ-9-tetrahydrocannabinol (THC), have revealed that it causes adverse health effects. In this study, we present a field-deployable electrochemical sensing system that can detect THC at the 5 ng mL−1 cut-off level with a dynamic range of 0.1–100 ng mL−1 in human saliva. Considering the complexity of the human saliva matrix, the specificity study demonstrated selectivity towards THC with minimum interactions with ethanol and cannabidiol (CBD). Surface Plasmon Resonance (SPR) has been implemented to visualize and validate the capture probe as a means for THC detection. A robust, compatible binary classifier model has been shown in this work to effectively group samples into THC+ (high) and THC− (low) groups from human saliva with an accuracy greater than 90% considering a limited dataset. Hence, we demonstrate the potential of an innovative end-to-end system to effectively regulate cannabis use and prevent substance abuse in our surroundings.

Workplace inclusivity is vital for organizational success in today’s competitive business landscape. Research by Nelson and Piatak (2021) suggests that the examination of intersectionality is important to understand different aspects of inclusion to make better efforts in the diversity management area. Workplace inclusivity is vital for organizational success in today’s competitive business landscape. The existing literature provides meaningful information on race, gender, and disability as separate constructs affecting job satisfaction and performance. However, there is a notable lack of research examining the intersectionality of these identities within federal workplaces. By focusing on how these identities intersect and how they might be uniquely experienced by federal employees, our study fills this gap. Moreover, this study extends the current knowledge base by exploring how an inclusive work environment may moderate the effects of this intersectionality, a perspective that has been largely overlooked in the literature to date. Consequently, this research contributes to the understanding of diversity, equity, and inclusion, and it offers actionable recommendations for enhancing inclusivity in federal workplaces. This research aims to use the 2022 FEVS data to understand these dynamics and guide the creation of a more inclusive and engaging work environment. We will use STATA to present our research analysis. We anticipate identifying key areas for improvement and providing actionable insights to enhance workplace inclusivity. This research could contribute to understanding diversity, equity, and inclusion in federal workplaces.

Accurately perceiving gaze direction is crucial for social interactions, but is impaired across psychopathologies characterized by social dysfunction (e.g., schizophrenia, autism spectrum disorder, social anxiety disorder). Altered gaze perception arises from disrupted sensory processing and/or self-referential processing, but their contributions to different psychopathology dimensions is unclear. The current study addresses this question using an experimental approach in a general population. 122 individuals (age: 18-30; 51% female) completed self-report questionnaires assessing psychopathology traits (psychosis proneness, social anxiety, and autism) and an online gaze perception task. They viewed face images with 9 gaze angles, superimposed with 3 levels (no, low, high) of visual noise (manipulating sensory processing) and in 2 (forward, deviated) head orientations (manipulating self-referential belief), and indicated perceived self-directed gaze (yes/no) to each face. Psychophysical metrics (curve width, threshold) indexed perceptual precision and self-referential bias, respectively, in each condition. Visual noise decreased perceptual precision and increased self-referential bias. Higher social anxiety was associated with greater precision overall and increased susceptibility to the precision-reducing effect of sensory noise. Reduced precision was associated with higher autism traits; psychosis proneness was unrelated to precision. These findings suggest differential contributions and manifestations of sensory processing abnormalities in different psychopathological domains during gaze perception.

It is well known that lead glaze on earthenware undergoes blackening in anaerobic environments such as cesspits and canals as a result of the formation of black lead sulfide within the glaze. However, the degradation process is not understood, especially the formation of red and orange colors in the glaze. Conservation treatments, such as exposure to strong summer sun over several weeks, can result in the recovery of the original glaze appearance. However, little is understood about the recuperation process. In this preliminary study, we employ optical microscopy, X-ray photoelectron spectroscopy (XPS) and Raman spectroscopy to investigate the chemical and physical properties of blackened ceramics before and after light treatment using five different light sources. The light sources are an LED (~400 – 700 nm), a halogen bulb (~300 – 1000 nm), a heat therapy lamp (~600 – 850 nm) and two UV sources (254 nm and 385 – 400 nm). The data shows that the degraded lead glazes contain lead sulfide, lead sulfate, and lead sulfonates. The different colors of the lead glazes appear to correlate with the amount of these species in the glaze. In agreement with previous data, we also observed that light can be employed to aid in the recovery of the glaze color. X-ray photoelectron spectra show that upon exposure to light, the lead sulfide in the glaze is photooxidized to white lead sulfate. Further, the process is wavelength dependent: lamps which emit longer wavelengths of light are more effective for the transformation of lead sulfide to lead sulfate. These studies suggest that the use of low-cost light sources may be an effective treatment method for blackened lead glazes and provide a safe, easy alternative to chemical treatments, such as hydrogen peroxide.

Excess production of lactate from glucose is a hallmark of cancer. Most lactate is produced in glycolysis by the reduction of pyruvate to L-lactate but D-lactate can also be generated from glucose via the lesser-known methylglyoxal (MG) pathway. This MG pathway is upregulated in some cancer cells. Since the stereoisomers of lactate cannot be distinguished by conventional 1H NMR spectroscopy, a chiral NMR shift reagent (SR) is used to resolve the CEST signals of D- and L-lactate. The goal of this project is to develop a nanoparticle system that can localize large amounts of this chiral SR into the extracellular space of tumors to allow real-time monitoring of D- versus L-lactate production by CEST. Our approach is to attach a large number of SR’s to tobacco mosaic virus (TMV) particles known to localize in tumors.

This poster presents the challenge of debugging autonomous driving software using the Robot Operating System (ROS) due to non-deterministic behavior when replaying recorded data. To solve this issue, we introduce AutoDbg, a novel mechanism that ensures deterministic debugging without modifying the software. AutoDbg monitors message queues and changes the communication model to achieve this. The results show significant improvements in issue reproduction rates, minimal performance overhead, and reduced storage space requirements. Additionally, we demonstrate the practical applicability of AutoDbg in real-world scenarios, making it a valuable tool for improving the reliability and safety of autonomous systems.

Mammary glands exhibit remarkable epithelial regeneration during reproductive cycles and pregnancy, largely driven by tissue resident stem cells and progenitors. These stem/progenitor cells are regulated by several intrinsic mechanisms and extrinsic niche elements, including sex hormones estrogen and progesterone. Hormone-receptor positive cells are integral niche cells that secrete hormone-triggered factors which in turn regulate hormone-receptor-negative stem/progenitor cells and the regeneration of milk-secreting lobuloalveolar structures. The stroma surrounding the epithelium also harbors diverse cell lineages including immune cells, endothelial cells, fibroblasts, adipocytes and peripheral nerves. However, the influence of specific stromal elements on mammary stem/progenitor cells and tissue regeneration is not well understood. In particular, the contribution of peripheral nerves to mammary epithelial growth and differentiation are not known. Of note, high nerve density is associated with poor prognosis in breast cancer. Thus, it is critical to define the factors that influence nerve innervation and determine the impact of nerves on mammary epithelia. Here, we set out to investigate peripheral nerve innervation during murine mammary epithelial regeneration. During pregnancy, we observed elevated protein gene product 9.5 (PGP 9.5)-positive peripheral innervation in the mammary gland concomitant with lobuloalveolar expansion. In non-pregnant female mice, hormone treatment with estrogen and progesterone was found to significantly increase nerve density, pinpointing hormones as key drivers of peripheral nerve innervation during mammary epithelial growth. Analysis for specific peripheral nerve lineages revealed the presence of tyrosine hydroxylase (TH)-expressing sympathetic neurons proximal to the mammary epithelium. These neurons are capable of secreting neurotransmitters that can influence the activity of effector cells expressing adrenergic receptors. In mouse mammary tissues, we detected the expression of adrenergic receptors in mammary epithelial cells that are predominantly hormone receptor-positive. To study sympathetic nerve-mediated effects on hormone-driven mammary epithelial regeneration, we ablated sympathetic nerves locally in the mouse mammary gland with 6-hydroxydopamine which is known to generate free radicals that specifically induce death of sympathetic neurons. Following hormone-stimulation, sympathetic nerve ablation was found to disrupt the mammary epithelial architecture and branching morphogenesis during lobuloalveologenesis. We are currently analyzing the effects of sympathetic nerve ablation on mammary stem/progenitor cells and the underlying mechanisms by which these peripheral nerves modulate epithelial regeneration. Our findings infer a crucial role for sympathetic nerves in mammary epithelial regeneration, likely through modulatory effects on hormone-sensing niche cells that govern stem and progenitor cell activity in the mammary gland. Advancing our understanding of peripheral nerve-mammary cell interactions will provide new insight into nerve-mediated effects on breast cancer progression and novel cancer therapeutics.

Substance use disorder is a chronic relapsing condition often marked by the inability to cease drug use despite negative outcomes. Environmental stimuli presented during drug taking become hyper-salient reward indicators or cues and can make abstaining from drugs difficult. Extinction is a learning process that can reduce the power of these cues by creating a neutral association with a previously drug-paired cue. Unfortunately, extinction-based therapies have had limited success in long term prevention of relapse. Our lab has previously shown that vagus nerve stimulation (VNS) during extinction training from drug-seeking behavior reduces drug seeking during cue-induced reinstatement, but the mechanisms of how VNS enhances extinction learning are unknown. BDNF is crucial for multiple forms of learning and memory, and it mediates synaptic plasticity associated with cocaine-induced behaviors. Repeated exposure to cocaine and withdrawal can lower the levels of BDNF in the mPFC, and VNS elevates BDNF levels throughout the cortex. Here we investigated the role of BDNF in VNS-enhanced extinction learning. VNS- or Sham-stimulated rats received i.p. injections of the TrkB receptor antagonist ANA-12 during extinction training. VNS reduced drug-seeking during cue induced reinstatement, and VNS rats given ANA-12 during extinction responded similar to Sham-stimulated rats. Following reinstatement animals were sacrificed for whole cell patch clamp experiments to measure how glutamatergic transmission is affected by BDNF. Pyramidal neurons from Sham-stimulated rats animals showed reduced AMPA:NMDA ratios compared to drug-naïve animals, and treatment with VNS reversed this effect. AMPA:NMDA ratios in VNS ANA-12 rats were similar to those in Sham-stimulated rats. Additionally, we investigated how pairing extinction learning with VNS alters neuronal activation during reinstatement in networks that project to the mPFC. We infused a GFP-expressing retrograde AAV into the infralimbic cortex (IL) prior to self-administration and extinction training. Rats were sacrificed after a cue-induced reinstatement and tissue from regions associated with reinstatement containing retrogradely-labeled neurons were stained for cFos as a marker of neuronal activity. We then quantified how VNS altered the total number of cFos-positive cells, as well as the co-localization of cFos+ cells that project to the IL. We found that VNS reduces activity in IL-projecting cells in the basolateral amygdala and ventral hippocampus, but increased activity in the paraventricular nucleus of the thalamus. These results help us gain a better understanding of the mechanisms of how VNS facilitates extinction learning from drug seeking behavior.

In my creative practice, the conceivable seems actual, and the truth exists but has countless iterations. By selecting mainly informal solutions or an asymmetrical balance in the placement of objects, I try to develop forms that do not follow logical measures but are based somewhat on subjective connotations and parallels, which cause the viewer to make new personal associations.

As high-penetration of renewable energy such as solar energy in electricity market decreasing the consumption of environmentally contaminated and expensive disposable energy, it increases the uncertainty nature in the electricity system. Nowadays, most of the research focuses on the overall accuracy of the objective function without considering the local performance. And there are numerous methodologies, while universally best model does not exist. In this poster, a reinforcement learning based dynamic model selection methodology is introduced, with local awareness to avoid over-reliance on global accuracy.

8-Oxoguanine (OG) is one of the deleterious effects of oxidative stress that leads to DNA damage. This oxidized form of guanine forms a stable Hoogsteen base pair with Adenine. The repair of OG:A mispair is initiated by the Adenine-DNA glycosylase enzyme (MutY), followed by the excision of adenine through base excision repair mechanism. Mutations in the MUTYH gene have been associated with the development of colorectal cancer syndrome known as MUTYH-associated Polyposis (MAP), due to their inability to effectively repair OG:A mispairs. Experimental findings have uncovered a hydrogen bond bridge connecting the catalytic residue D236 and the iron-sulfur cluster cofactor [Fe-S]. The key residues involved in this H-bond bridge are D236, N238, R241, and C290. Experimental results show that the cancer-associated mutations R241Q and N238S within the structural bridge, lead to the loss of glycosylase activity without showing drastic changes in affinity of MutY for its DNA substrate. Therefore, the purpose of this study is to evaluate the effect of these mutations on the dynamics and the structural behavior of the active site and Fe-S cluster with molecular dynamics (MD) simulations. We performed MD simulations for 1.5 microseconds separately for wild-type and mutant structures in both human and mouse models. Our simulations, along with analyses of non-bonded interactions, dynamical network behavior, and normal mode analysis, show significant differences and suggest that these mutations affect the dynamics of the structure and the connection among the residues involved in the H-bond bridge. The comparative results of these analyses will be further discussed in the presentation.

Paroxysmal arrhythmias caused by medications are challenging to be prospectively identified. Zebrafish have emerged as an ideal model organism for screening small molecule compounds to study cardiac abnormalities, due to their rapid development, transparency during early stages, and similarities to the human heart. To overcome the challenges associated with observing cardiac abnormalities in zebrafish, we sought to develop a light-field microscope, a rapid imaging method with high photon efficiency, for the volumetric acquisition in a single snapshot. This method, along with its variations, utilizes a multi lenslets array (MLA) to capture angular information. We have customized a light-field hardware system and developed a pipeline to incorporate the MLA into the detection path. A program based on wave optics has also been developed to calculate the point spread function at different depths. The program involves two main steps: calculating the widefield PSF and applying the MLA effect as a mask. Future enhancements can incorporate additional steps and optical instruments to modulate the overall PSF. We aim to capture drug-induced arrhythmias in zebrafish larvae using this method. This research aims to deepen our understanding of the mechanisms underlying these arrhythmias and their connection to drug effects.

Seismic reservoir monitoring can serve as a powerful tool to image fluid flow within a reservoir during production. This is because fluid saturations and pressures in the reservoir change, and the recorded seismic data change accordingly. In this paper, we implement time-lapse seismic Full Waveform Inversion on a marine data from north Australia and get a high-resolution Vp change model with little inversion artifacts, which can reflect the fluid change of the reservoir during the production.

Pseudomonas aeruginosa is a Gram-negative bacterium that causes secondary chronic infections in cystic fibrosis (CF) patients. P. aeruginosa can cause a range of other opportunistic infections and is able to effectively dominate in a polymicrobial community. Bacteria, in general, have been found to modify their membrane lipids in response to external stress. Likewise, P. aeruginosa possesses a range of membrane phospholipids, which can be modified to protect against cationic antimicrobial peptides (CAMPs). An Acidic Bligh-Dyer lipid extraction conducted on P. aeruginosa PA14 revealed a previously uncharacterized glycophospholipid, aminohexosyl-phosphatidylglycerol (AH-PG), in mass spectrometric analysis. The genes likely responsible for AH-PG production were found in a three gene operon containing a glycosyltransferase, a deacetylase and an MprF-like flippase. Previous literature studies in Clostridoides difficile identified a similar three gene organization responsible for resistance against daptomycin and bacitracin. When the putative deacetylase gene was disrupted, AH-PG production by P. aeruginosa was lost. P. aeruginosa also encodes the multiple peptide resistance factor (MprF), which is responsible for aminoacylation of phospholipids and flipping of them across the membrane. In P. aeruginosa, MprF uses phosphatidylglycerol (PG) as substrate, to make significant levels of alanyl-phosphatidylglycerol (Ala-PG) in acidic stress conditions. We found that when mprF was interrupted, there was an increase in AH-PG in an acidic environment (pH = 5.5), indicating a compensatory mechanism for the loss of Ala-PG. Hence, we hypothesize that AH-PG may be playing a role in protecting P. aeruginosa from CAMPs and similar antimicrobials, along with Ala-PG.

Kaposi’s Sarcoma Herpes Virus (KSHV) is the etiological agent of Kaposi’s Sarcoma (KS) and a leading cause of cancer in AIDS patients. With limited treatment for KS, it is essential to understand the mechanics behind KSHV infection. KSHV undergoes latent and lytic viral phases in the host cell. Only a few viral genes are expressed during latency, however, during lytic replication, KSHV encodes for ~90 genes, including a viral G protein-coupled receptor (vGPCR), a chemokine-like receptor with homology to host receptors. vGPCR signaling has been shown to alter host cell survival, angiogenesis, and metabolism. Previous metabolomic analysis showed that KSHV latently-infected endothelial cells modulate central carbon pathways (glycolysis, fatty acid synthesis, and amino-acid metabolism). Drug inhibition of these metabolic pathways resulted in reduced survival of latently infected cells and a significant reduction in virion production during the lytic phase. However, no study has measured the global alterations in the host cell metabolome during lytic KSHV to date. We hypothesize that global host cell metabolism is modulated to support maximal lytic infection. To test this hypothesis, endothelial cells overexpressing vGPCR or undergoing KSHV lytic infection will be analyzed via mass-spectrometry for metabolite levels. We expect to see significant metabolite changes in central carbon metabolic pathways and potentially reveal novel pathways modulated by KSHV lytic infection.

The presence of background noise or competing talkers is one of the main communication challenges for cochlear implant (CI) users in speech understanding in naturalistic spaces. These external factors distort the time-frequency (T-F) content including magnitude spectrum and phase of speech signals. While most existing speech enhancement (SE) solutions focus solely on enhancing the magnitude response, recent research highlights the importance of phase in perceptual speech quality. Motivated by multi-task machine learning, this study proposes a deep complex convolution transformer network (DCCTN) for complex spectral mapping, which simultaneously enhances the magnitude and phase responses of speech. The proposed network leverages a complex-valued U-Net structure with a transformer within the bottleneck layer to capture sufficient low-level detail of contextual information in the T-F domain. To capture the harmonic correlation in speech, DCCTN incorporates a frequency transformation block in the encoder structure of the U-Net architecture. The DCCTN learns a complex transformation matrix to accurately recover speech in the T-F domain from a noisy input spectrogram. Experimental results demonstrate that the proposed DCCTN outperforms existing model solutions such as the convolutional recurrent network (CRN), deep complex convolutional recurrent network (DCCRN), and gated convolutional recurrent network (GCRN) in terms of objective speech intelligibility and quality, both for seen and unseen noise conditions. To evaluate the effectiveness of the proposed SE solution, a formal listener evaluation involving three CI recipients was conducted. Results indicate a significant improvement in speech intelligibility performance for CI recipients in noisy environments. Additionally, DCCTN demonstrates the capability to suppress highly non-stationary noise without introducing musical artifacts commonly observed in conventional SE methods.

The oncogenic virus, Kaposi’s Sarcoma-Associated Herpesvirus (KSHV) causes Kaposi Sarcoma (KS), a malignant cancer. Both latent and lytic stages of KSHV contribute to its pathogenesis. Modulating host cell transcription and ensuring cell survival are critical for the progression of KSHV infection. Viral G Protein-Coupled Receptor (vGPCR), expressed during lytic infection, notably induces angiogenesis, promotes cell growth, inhibits endothelial cell apoptosis, and influences host gene expression and cell survival signaling. The specific role of vGPCR in apoptosis regulation is still understudied. A better understanding of these mechanisms will contribute to the development of effective treatments for KS. We conducted an RNASeq study to evaluate host cell gene expression in vGPCR overexpressing (OE) endothelial cells. Several host cell transcriptional changes were measured, including the upregulation of anoctamin 1 (ANO1), a host calcium-activated chloride channel that is implicated in modulating apoptosis. Preliminary data shows that upon ANO1 knockdown, vGPCR overexpressing endothelial cells show an increase in cell death under apoptotic conditions. Our results show the host protein ANO1 is required for vGPCR-mediated inhibition of host cell death, expanding our understanding of the fundamental mechanisms of vGPCR. Currently, we are using ANO1 drug inhibitors and measuring cell viability and their impact on various cellular processes. We hypothesize that vGPCR-dependent ANO1 signaling mediates the anti-apoptotic pathway which contributes to the intricate network of cellular responses aimed at preserving cell survival. Our goal is to elucidate the molecular pathways involving vGPCR and ANO1, thereby suggesting ANO1 as a therapeutic agent against KS.

Treatment challenges in breast cancer arise from significant heterogeneity found within breast tumors and the surrounding microenvironment. The tumor microenvironment presents a complex ecosystem which comprises diverse stromal cells including immune, endothelial and fibroblast cell lineages and the extracellular matrix that support tumor progression. Adipocytes represent an abundant cell population in the normal breast and prior studies have reported a crosstalk between adipocytes and mammary cancer cells. De-differentiation of adipocytes into adipocyte progenitor-like cells is linked to a pro-tumorigenic microenvironment in mouse mammary tumors. We have previously demonstrated that adipocyte progenitors in the murine mammary stroma have the capacity to generate epithelial lineages during mammary epithelial growth. The influence of adipocyte progenitors in the mammary tumor microenvironment is not known. In this study, we investigated the effects of adipocyte progenitors on mammary cancer cells and their interactions using the 3T3-L1 adipocyte precursor (AP) cell line and the EO771 syngeneic mammary cancer (MCa) cell line. AP and MCa cell lines were co-cultured, after which MCa cell growth, invasion and migration were assessed using established in vitro assays. MCa cells cultured alone were also exposed to AP-conditioned media to determine whether secreted factors derived from APs could modulate MCa cells. AP cells were found to alter the behavior of MCa cells, affecting their cell proliferation, migration, and cellular phenotype. This work infers the potential of adipocyte progenitors to regulate mammary cancer growth and progression, which warrants further investigation into their fate and role in vivo during mammary tumorigenesis.

Sustainable high-performance steam condensation is critical to reducing the size, weight, and cost of water and energy systems. It is well-known that dropwise condensation can provide a significantly higher heat-transfer coefficient than filmwise condensation. Tremendous efforts have been spent to promote dropwise condensation by achieving a nonwetting state on superhydrophobic surfaces and a slippery state on liquid-infused surfaces, but these surfaces suffer from severe durability challenges. Here, we report sustainable high-performance dropwise condensation of steam on newly developed durable quasi-liquid surfaces, which are easily made by chemically bonding quasi-liquid polymer molecules on solid substrates. As a result, the solid/water interface is changed to a quasi-liquid/water interface with minimal adhesion and extraordinary durability. The quasi-liquid surface with ultralow contact angle hysteresis down to 1° showed a heat-transfer coefficient up to 70 and 380% higher than those on conventional hydrophobic and hydrophilic surfaces, respectively. Furthermore, we demonstrated that the quasi-liquid coating exhibited a sustainable heat-transfer coefficient of 71 kW/(m2 K) at a heat flux of 420 kW/m2 under a prolonged period of 39 h in continuous steam condensation. Such a quasi-liquid surface has the potential to sustain high-performance dropwise condensation of steam and address the long-standing durability challenge in the field.

The development of recent global health hazards has introduced challenges in administering in-person subject listening tests. Our goal Is to expand the functionality of existing CCi-Mobile hardware platform by developing a cloud platform to leverage remote applications. We developed CCi-Cloud, a remote assessment platform using Amazon Web Services cloud-computing tools, to address deploying CCi-Mobile hardware in remote modality. This paper discusses the infrastructure of CCi-Cloud as well as explore the results of three listening experiments conducted to (i) Evaluate the effects of natural and artificial Lombard speech on speech intelligibility, (ii) Evaluate speaker identification performance of “famous” or familiar speakers in various noise conditions, (iii) Evaluate non-linguistic identification performance. All three experiments were conducted in-person and remotely to analyze the correlation between results obtained in both modalities. An overview of CCi-Cloud platform is presented followed by the deployment of three Listening experiments in-person and Remote for normal hearing and cochlear implant users.

The classification and scoring of animal behavior play pivotal roles in pre-clinical research. Traditional methods, reliant on video examination, are labor-intensive and susceptible to bias. To address these challenges, research efforts have focused on computational methods and image-processing algorithms for automated behavioral classification. Two primary approaches have emerged: marker- and markerless-based tracking systems. Markerless systems, driven by deep learning and machine learning algorithms, offer high accuracy while tracking multiple points without hindering animal movement. However, these systems require substantial computational resources and extensive data, especially when applied in scenarios involving multiple animals being tracked simultaneously. In contrast, marker-based systems offer a simpler solution that demands fewer computational resources, provide high accessibility for real-time tracking, and offer multi-animal scalability with minimal computational deficits. In this study, we showcase the utility of ArUco markers, binary square fiducial images, in assessing animal engagement during a rat operant conditioning task. In addition, we introduce a two-state engagement model based on ArUco marker data, providing an unbiased method for estimating animal engagement from a go/no-go behavioral task. We hypothesize that ArUco markers can accurately estimate engagement to ensure optimal task duration during behavioral testing. Our method employs a convolution with a five-minute rectangular kernel and a 50% engagement threshold, allowing us to pinpoint critical transitions between states of engagement and distraction. Here, we present the performance of our ArUco tracking program, demonstrating a high classification accuracy of 98% compared to manual curation of video data. Additionally, we highlight the benefits of using dual cameras over a single camera for improved tracking consistency by 6.97 ± 0.94% (p < 0.0001). Overall, our approach offers a scalable, efficient, and accessible solution for automated scoring of rodent engagement during behavioral data collection. Future experiments will assess the potential of the ArUco marker tracking program for classifying additional rodent behaviors such as locomotion, rearing, grooming, and scratching.

Investigation into various effects of telomere length has increased recently, particularly in the context of longevity, senescence, and disease risk. Studies report that possessing either extreme in telomere length may increase the likelihood of developing adverse health issues, with longer lengths associated with development of certain cancers and shorter lengths associated with development of age-related neurodegenerative diseases. This suggests that telomere action may be nonlinear, with an advantage for intermediate length and increased risk for shorter or longer lengths. Telomere length is controlled in part by a single-nucleotide polymorphism (SNP) in the TERT gene (rs2736100) with T/G carriers possessing intermediate length, T/T shortest, and G/G longest telomere lengths. Based on the pattern of disease risk, we hypothesized that TERT may influence cognitive aging, conferring protection for heterozygotes. N=183 healthy adults (aged 20-87 years) completed multiple cognitive tests that were used to create cognitive composite scores for executive function, working memory, episodic memory, and processing speed and an overall “general” cognitive composite score (avgCognition), and were genotyped for rs2736100. Initial analyses using a general linear model identified a significant TERT x Age interaction on “average cognition”. After breaking down cognition into subgroups, executive function and working memory enhanced this effect and each analysis yielded a significant TERT x Age interaction, whereby the superior performance of TERT heterozygotes weakened with increasing age. These results suggest that heterozygote genotype and associated median telomere length may have a protective impact on cognitive aging. Future research investigating frontal and parietal neural correlates for executive function and working memory processes in association with TERT and age will be completed.

Acinetobacter baumannii causes nosocomial infections, which pose a significant threat to human health due to their alarming propensity for antibiotic resistance. Of particular concern is the recent emergence of resistance to Polymyxin E, commonly known as colistin, a last line of defense against A. baumannii infections. As colistin resistance rates are still low in A. baumannii, the mechanistic basis for colistin resistance in A. baumannii is unclear. We therefore sought to define the mechanistic basis for colistin resistance in A. baumannii. In this study, using a machine learning model trained on clinical A. baumannii isolates, we identified 31 A. baumannii genes that were associated with colistin resistance. These genes represent a novel set of genetic determinants previously unrecognized for their involvement in conferring resistance to colistin. To assess their impact, we employed the Manoil AB5075 transposon library and conducted standardized antimicrobial susceptibility testing on each transposon mutant. None of the 31 A. baumannii mutants exhibited resistance to colistin based on their Minimum Inhibitory Concentration (MIC). We further explored the relationship between these genes and resistance through analyzing the fitness of the mutants in the presence of colistin. Surprisingly, despite the lack of significant differences in MIC values, our findings unveiled distinct adaptive fitness phenotypes for the mutant strains in response to colistin. Approximately 48% of the mutants had increased fitness phenotypes, while 26% exhibited decreased fitness phenotypes, in the presence of sub-inhibitory colistin concentrations. Moreover, we found that most mutants show alterations in membrane properties, biofilm formation, efflux pump activity, and oxidative stress response in the presence of colistin. These observations suggest the existence of unidentified mutations that are currently not captured by routine susceptibility tests through which A. baumannii adapts and survives in the presence of colistin. While MIC values remain widely utilized and informative in assessing antimicrobial susceptibility, their ability to accurately reflect true antibiotic efficacies is limited. Consequently, our study emphasizes the need for comprehensive assessments beyond MIC measurements to accurately define colistin resistance in A. baumannii. By uncovering the intricate microbial fitness and dynamic adaptive responses exhibited by A. baumannii in the presence of colistin, our research highlights the need to reassess how we define and identify antibiotic resistance in bacterial pathogens in order to improve diagnostic outcomes in the clinic.

Several studies of contacts between Transition-Metal Dichalcogenides (TMD) and metals have been performed using Density Functional Theory (DFT) but to quantify contact resistance, quantum transport simulations are required. We perform quantum transmitting boundary method (QTBM), fully equivalent to ballistic non-equilibrium Green’s Function (NEGF) method, based transport simulations to calculate RC in metal-MoS2 top contacts. We investigate the effect of spatially non-uniform doping in MoS2 and the surrounding dielectric environment on RC and propose a contact heterostructure which reduces RC and can enhance mobility. Finally, we show the effect of Image Force Barrier Lowering (IFBL) on contact resistance in top contacts.

The conventional hearing aid fitting process consists of obtaining a person’s pure tone hearing thresholds or audiogram and applying a prescriptive fitting rationale. In essence, the hearing aid fitting process involves applying appropriate gain values across a number of frequency bands depending on the input level of sound signals. The target gain values in standard fitting rationales such as DSLv5 or NAL-NL2 reflect an “average” derived from a group of hearing-impaired individuals with similar hearing loss. However, a typical prescription does not take into consideration any individual hearing preferences of a user or the fact that different individuals encounter different audio environments and that their hearing preferences in those environments may not be the same. Hearing aid gain adjustments to accommodate for variations in hearing preferences are typically performed manually in a clinic. Our research team has been developing machine learning solutions for systematic personalization or tuning of the amplification function of hearing aids. In our latest work, the personalization of hearing aid fitting is carried out in an on-the-fly manner in realistic audio environments or in the field by conducting paired comparisons that are driven by a maximum likelihood inverse reinforcement machine learning method. The developed fitting process has been tested on hearing-impaired subjects and the results obtained indicate that our machine learning personalization leads to a hearing aid fitting that is significantly preferred over a prescribed hearing aid fitting.

More than 70% of breast cancer (BCa) patients harbor tumors that are dependent on estrogen receptor alpha (ERα) for growth. Thus, ERα is a BCa therapeutic target. However, patients can develop resistance to ERα-targeting therapy, highlighting the need for alternative therapeutic targets. One hormone receptor that has been investigated as an alternative therapeutic target for hormone-driven cancers is glucocorticoid receptor (GR). However, the role of GR in BCa is subtype specific, where GR is associated with better prognosis in ERα positive tumors, but poorer prognosis in Erα negative tumors. Chronic inflammation promotes cancer initiation and progression, including in BCa, and we previously discovered that chronic exposure to interleukin-1 (IL-1) inflammatory cytokine can lead to HR-independence in prostate cancer cells. To determine if the same is true for BCa, we chronically exposed the MCF7 human BCa cell line to IL-1 for several months to generate chronic IL-1 sublines. The chronic IL-1 sublines remain sensitive to the ERα-targeting drug, fulvestrant, indicating that the chronic IL-1 sublines remain ERα-dependent. Therefore, we have begun investigating GR regulation and function in the chronic IL-1 sublines. We discovered that acute IL-1 exposure represses GR mRNA and protein levels, while chronic IL-1 exposure leads to the restoration of GR levels in MCF7 cells. Furthermore, acute IL-1 no longer represses GR mRNA and protein levels in chronic IL-1 sublines, indicating that chronic IL-1 sublines evolve insensitivity to IL-1 regulation of GR signaling. We also discovered that chronic IL-1 selects for divergent BCa cell subpopulations that show either an ERα+/GR+ or ERα low/GR+ phenotype in response to acute IL-1. To determine if the stable accumulation of GR promotes or inhibits tumorigenicity of the chronic IL-1 sublines, we will test novel GR antagonists developed by the Tambar lab at UTSW. Our studies help us to 1) determine if GR is a rationale therapeutic target for BCa cells that have been chronically exposed to IL-1 in the tumor microenvironment and 2) better understand the role of GR in the progression of ERα low versus ERα+ BCa tumors.

Oral Cancer is the 13th most common cancer affecting 380,000 people globally. The biggest challenge is that in the initial stage cancer can go unnoticed until it reaches the most advanced, difficult-to-treat stages. Though a 90% survival rate is assured when diagnosed earlier, an expensive periodic dental check-up is mandatory for early-stage detection. In such case, What if we convert a smartphone into cancer screening Tool? Mobile Vision Technology is a promising platform for early diagnosis of oral cancer. The aim of this research is to launch an Oral Cancer Detection (OCD) based mobile application with deep learning as a back end. This paper offers detailed implementation of DCNN architectures for mobile application and its results. In the end, the best model is integrated with smartphone, thus acting as screening tool. This approach ensures on-device processing as well as privacy of the user. We believe this application will revolutionize healthcare accessibility and delivery

Significant imbalances in terrestrial water storage (TWS) and severe drought have been observed around the world as a consequence of climate change. Improving our ability to monitor TWS and drought is critical for water-resource management and water-deficit estimation. In this study, we utilize continuous seismic ambient noise to monitor the spatial and temporal evolution of near-surface seismic velocity (dv/v) in central Oklahoma from 2013 to 2022. The temporal variation of dv/v is found to be negatively correlated with gravitational measurements and groundwater levels, showing the impact of groundwater storage on seismic velocities over multi-year timescales, which can be potentially explained through changes of pore-pressure or bulk density near the Earth’s surface. In addition, the seasonal variation of measured dv/v can be explained by using the thermo-elastic response to atmospheric temperature changes. Furthermore, the occurrences of droughts appear coincidently with local peaks of dv/v, suggesting the potential of using near-surface velocity changes to monitor droughts. Leveraging recent progresses on calculating sensitivity kernel of coda wave interferometry, we project dt/t onto the spatial domain. Our result is consistent with Earth’s surface deformation measured by InSAR. This observation further demonstrates the sensitivity of near-surface seismic velocity to the migration of groundwater in central Oklahoma. Considering numerous permanent seismometers deployed all around the world, continuous seismic recordings can potentially improve our capability on monitoring distributions and variations of water resources near the Earth’s surface.

Exploring visual data holds the key to diagnosing fatal diseases like cancer and unravelling Earth’s geological tales hidden in grains of sand. Our innovative methodology, rooted in topological data analysis and machine learning (ML), bridges these diverse domains. Initially devised for scrutinising histopathological images to expedite cancer diagnosis and grading, our approach achieved remarkable success, boasting a 99% Area Under Curve (AUC) across various cancer types. The essence of our method lies in harnessing the power of topological patterns to extract reliable feature vectors, enabling swift and accurate analysis. Venturing beyond medical realms, we adapted this methodology to classify eight types of sand samples. Despite the close resemblance among some samples, our model demonstrated a commendable accuracy of 88.36%, showcasing its potential to decode subtle visual nuances. This unified methodology stands as a testament to the versatile utility of topological data analysis and paves the way for innovative cross-disciplinary applications. Through our poster presentation, we aim to elucidate how the same mathematical lens can unveil critical insights in biomedical and geological domains, shedding light on the boundless possibilities in the confluence of topological analysis and machine learning.

Despite the completion of the Human Genome Project in 2003, significant gaps persist in our understanding of both the genomic and proteomic landscapes. At the proteomic level, there are numerous proteins whose function is still not known and thus categorized as ‘orphan’ proteins. This enigma presents a unique opportunity for researchers to elucidate their functions and potentially harness them for novel medical applications. One such orphan protein is TMEM205, short for transmembrane protein 205. Its physiological role remains elusive, yet it has been observed to undergo overexpression in cancer cells, playing a role in their response to the anti-cancer drug cisplatin. This observation carries profound implications because platinum-containing drugs share common transport pathways with copper, a physiologically relevant element unlike platinum. With this premise in mind, our study employs Molecular Dynamics Simulations and Free Energy Calculations, including the application of Umbrella Sampling, to validate the hypothesis that hTMEM205 functions as a transmembrane copper exporter protein. By leveraging advanced computational techniques, we aim to shed light on the intricate mechanisms of hTMEM205 and its potential significance in copper homeostasis.

Nanoparticles (NPs) are promising agents to absorb external energy and generate heat. Clusters of NPs or NP array heating have found an essential role in several biomedical applications, diagnostic techniques, and chemical catalysis. Various studies have shed light on the heat transfer of nanostructures and greatly advanced our understanding of NP array heating. However, there is a lack of analytical tools and dimensionless parameters to describe the transient heating of NP arrays. Here we demonstrate a comprehensive analysis of the transient NP array heating. Firstly, we develop a set of analytical solutions for the NP array heating and provide a useful mathematical description of the spatial-temporal evolution of temperature for 2D, 3D, and spherical NP array heating. Based on this, we introduce the concept of thermal resolution that quantifies the relationship between minimal heating time, NP array size, energy intensity, and target temperature. Lastly, we define a set of dimensionless parameters that characterize the transition from confined heating to delocalized heating. This study advances the understanding of nanomaterials heating and guides rational design of innovative approaches for NP array heating.

Cystic fibrosis (CF) is a genetic disease that predisposes patients to severe Pseudomonas aeruginosa infections. After initial colonization, P. aeruginosa develops antibiotic resistance as it persists within the host, eventually killing the patient once lung function is inexorably compromised. CF disease arises from mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) which block the efflux of chloride and the biological buffer bicarbonate. We have previously found that bicarbonate can improve antibiotic susceptibilities against multi drug resistant bacteria, and prior studies have linked it to altered host responses. We hypothesize bicarbonate is a perfect host target that can be modified to improve treatment outcomes in CF patients. We conducted antibiotic susceptibility testing in both bacteriological and physiological media, revealing differential responses with various classes of antibiotics against P. aeruginosa specifically in physiologically relevant conditions. We have also performed macrophage killing assays to observe the impact of bicarbonate on the host response to P. aeruginosa. Surprisingly, bicarbonate was found to interfere with the macrophage efficiency in P. aeruginosa eradication. In the future, we plan to investigate the impact of varying bicarbonate concentrations on host-pathogen interactions and in vivo antibiotic efficacies. These studies highlight the potential for improvement of treatment efficacies in CF through modulation of host microenvironmental conditions.

Spinal cord diseases can significantly reduce quality of life by deteriorating motor capabilities and control of basic functions. The blood-spinal cord barrier (BSCB) significantly reduces therapeutic delivery of intravenous drugs to spinal cord tissue. The BSCB is similar to the blood-brain barrier (BBB), with endothelial cells, tight junctions, basal membrane, and astrocyte end-feet constituting a functional neurovascular unit and highly regulated barrier. The BSCB safeguards the spinal cord from potentially harmful substances in the blood. However, it also significantly hinders treatments for diseases in the spinal cord since it only allows specific molecules through to tissue. Thus, there is a critical need for novel approaches to overcome the BSCB for delivery to local spinal cord regions for therapeutic applications. Several methods may overcome the BSCB, such as focused ultrasound (FUS) with microbubbles (MBs) or intrathecal delivery into cerebrospinal fluid (CSF), but these methods have limitations. Transient BSCB modulation mediated by FUS and MBs is an area of intense interest, as encouraged by promising results in the brain. Yet, FUS within the spinal canal creates standing waves due to reflection off the spinal bones with unique geometry, and FUS can cause thermal deposition in the bone, complicating the use of this technique for BSCB modulation. On the other hand, intrathecal CSF delivery allows for treatment of leptomeningeal diseases. However, intrathecal delivery suffers limited penetrability into spinal cord parenchyma and the inability to target specific regions. Therefore, it remains a challenge to locally deliver therapeutics into the spinal cord for disease treatment. In contrast, light can be shone on regions of interest with excellent spatiotemporal control. Clinicians have adapted spinal laser interstitial thermal therapy (LITT) for treating tumors in the spinal cord in humans. Additionally, our recent work demonstrated modulation of the BBB and molecular delivery to brain tissue by the optical stimulation of vascular-targeted gold nanoparticles and short-pulsed laser light. Excitation of plasmonic gold nanoparticles releases mechanical wave energy, impacting endothelial cells. This method results in a transient elevation of calcein that propagates to adjacent endothelial cells after laser excitation and extends the region of BBB opening. Further, actin polymerization and calcein-dependent phosphorylation of ERK1/2 lead to cytoskeletal activation, increasing paracellular permeability. These studies have demonstrated feasibility and provided mechanistic insight into how excitation of endothelial-targeted AuNPs leads to an increase in BBB permeability. In this project, we focus on light delivery to the spinal cord and demonstrate delivery of a systemic biologic (i.e., a peptide) to the spinal cord and itch modulation by optically changing BSCB permeability (mBSCB) locally. We performed mBSCB in a rodent model with a minimally invasive optic fiber and without significantly impacting motor behavior, bypassing the vertebrae and directly delivering light to the spinal cord to modulate BSCB permeability. We anticipate that mBSCB will provide an effective and safe tool for delivering a wide range of neuromodulators locally to enhance the delivery of many different molecules and therapeutics.

The goal of this study is to prepare a guideline to design and optimization of Avalanche Photodiodes using Technology Computer Aided Design in combination with analytical calculation and Bayesian optimization. Analytical calculation is done to investigate the device behavior as a function of different parameters. The result of investigation is used to determine and effectively limit our search space. Bayesian Optimization finds the optimized device in the determined search space and uses a figure of merit using TCAD. In summary, our research has successfully developed a comprehensive guideline for APD design and optimization, resulting in successful design of 5µm APDs with a gain of 100, responsivity of 0.2 A/W and f3dB > 0.5 GHz while the device has a desired breakdown voltage of 30, 40 or 50 V, showcasing the practical impact of our approach on photodetector technology.

Soil plays multiple important roles in Earth’s ecosystem. Healthy soil reduces the impact of floods, droughts, and fires. Soil provides a reservoir to store carbon dioxide from the air thus slowing down global warming. Real time monitoring of several soil parameters is imperative to improving crop yield as well to sustain an ever growing human population. Soil health parameters encompass moisture content, phosphorus, pH, nitrate, ammonium and other parameters. For plants, nitrogen is an essential growth nutrient which is present in soil in multiple forms where nitrate and ammonium are the two that are readily available for plants to use. Current soil nitrate and ammonium assessment techniques require destructive sampling of the soil where a core of soil is collected then treated at a lab before measuring the nutrients concentrations. This method measures nitrate and ammonium at discreet time points and doesn’t provide continuous real time monitoring. This work utilizes commercial screen printed electrodes with ion-selective coatings for low cost continuous in-situ soil total available nitrogen measurement. The coating contains a ligand that allows the target nutrient, whether nitrate or ammonium, in the electrolyte to diffuse through the coating creating an ionic current under an alternating current (AC) voltage. The current signal captures the concentration of the target nutrient.

Use Versal AI Engines to accelerate global placement algorithm

Alcohol-associated liver disease (ALD) contributes significantly to the death and disability worldwide. Accumulating studies have shown that lipid homeostasis at the adipose-liver-axis plays an important role in the development of ALD. Under energy-demanding states, lipolysis is activated in WAT, leading to increased triglyceride hydrolysis and elevated release of free fatty acid (FFA) into circulation. Notably, both our lab and others have observed enhanced adipose tissue lipolysis and increased release of FFA in heavy drinkers and alcohol-fed rodents. These FFAs can enter the liver for triglyceride synthesis, contributing to alcoholic fatty liver disease. However, the molecular mechanisms by which alcohol induces hyper-lipolysis are not completely understood. It has been reported that sympathetic nervous system (SNS) widely innervates the WAT and activated SNS releases norepinephrine (NE) from the nerve terminals. Then NE interacts with β-adrenergic receptors (β-ARs) to stimulate protein kinase A (PKA) and subsequently phosphorylate perilipin and hormone-sensitive lipase (HSL), leading to the hydrolysis of triglycerides. To test our hypothesis that the activation of SNS contributes to enhanced lipolysis of WAT and ALD development, we intraperitoneally injected a widely used SNS inhibitor, 6-hydroxydopamine hydrobromide (6-OHDA), to C57BL/6J male mice once a day for three days at 100mg/kg body weight (BW). On the fourth day, mice were treated with a single binge (5g/kg BW) (EtOH) or maltose dextrin (Control) by oral gavage. We found that alcohol binge caused reduced epidydimal fat pad weight, increased circulating FFA level and enhanced expression of pHSL in WAT. However, these binge drinking-altered parameters were dramatically attenuated in 6-OHDA-treated mice. In addition, elevated liver weight and increased hepatic triglyceride content were observed in mice following alcohol binge. Intriguingly, sympathetic nerve-ablated mice were protected against alcohol-induced increase in liver weight and exhibited dramatically attenuated triglyceride accumulation in the liver. These interesting findings led us to further determine the sensitivity of binge drinking-treated mice to agonist-stimulated lipolysis. C57BL/6J male mice were first given an oral gavage of 5 g/kg BW of alcohol or maltose dextrin. Thirty minutes later, mice were treated with an intraperitoneal injection of CL316,243 (0.1 mg/kg BW), a selective β3-adrenergic receptor agonist. Notably, combined binge drinking and CL316,243 treatment caused higher circulating FFA concentrations and more triglyceride contents in mouse liver. Collectively, our data suggest that SNS plays an important role in acute alcohol-induced adipose tissue lipolysis and fatty liver disease.

Low back pain and knee pain complications have been associated with military personnel carrying heavy loads, which has been known to influence their walking performance during daily tasks. To determine the effects of load carriage, the comparison of bodyweight to a weighted backpack was considered while performing an in-door overground walk. The purpose of this research is to investigate the gait changes induced by walking while carrying a load of 20kg (up to 45 lbs). Eight electromyography sensors (EMG) were used to detect muscle activation in the biceps femoris, vastus lateralis, gastrocnemius, tibialis anterior, and rectus abdominis. Sixteen motion capture cameras were used to analyze the spatiotemporal changes. Eight force plates were used to calculate the heel strikes and analyze the joint mechanics. We hypothesize that walking while carrying a load will decrease spatiotemporal parameters, while simultaneously increase the muscle activation in the lower extremity.

Aquaporin 4 (AQP4) is a water channel expressed in astrocytes in the central nervous system. It links astrocytes to the brain’s blood vessels, maintains water homeostasis in the brain, and clears waste through the glymphatic system, and disfunction of these features can lead to the development of many neurological diseases such as Alzheimer’s disease. Recently Aqp4 was found to show translational readthrough, generating a C-terminally extended form of AQP4, known as AQP4x. AQP4x is mainly localized along the blood vessels in the brain, and its loss disrupts the waste clearance from the brain. To investigate the pathophysiological functions of AQP4x, we have generated a mouse line with 100% AQP4x (AQP4All_X) and another with no AQP4x (AQP4No_X). Here, we utilize these mice to investigate the role of AQP4X in Alzheimer’s disease and brain edema. Using native gel electrophoresis, we find that AQP4x is less prone to aggregate into high-molecular weight complexes compared to the canonical AQP4. In an Alzheimer’s disease mouse model, we find that high-molecular weight complexes and canonical AQP4 are significantly upregulated. Further, we demonstrate that brain water content can be modulated by AQP4X. Finally, our electron microscopy data show an increase in endothelial cell vesicles in AQP4No_X mice. These findings suggest that AQP4X is an indispensable variant of AQP4, with key roles in the neurovasculature and possibly a potential target to intervene neurological diseases such as Alzheimer’s, stroke and edema.

The challenge of halting amyloid-beta plaque accumulation in Alzheimer’s disease (AD) persists with no effective therapy currently available. Our investigation, guided by genomic data, identified two proteases from the Potyviridae family of plant viruses as strong candidates for recognizing amyloid-beta. Computational simulations revealed that these candidate proteases could potentially accommodate amyloid-beta within their substrate-binding pockets, with the catalytic protease residue positioned within 4 Å of the cleavable peptide bond in amyloid-beta, indicating their potential to cleave the amyloid-beta peptide. Initial testing revealed that a mutation made in one of these proteases, sunflower chlorotic mottle virus protease, resulted in it having a partial cleaving ability against amyloid-beta in vivo. Our research aims to use a yeast platform of directed evolution to enhance its cleaving ability against amyloid beta and to evolve the other protease to potentially cleave it as well. This work would highlight the promise of directed evolution in engineering proteases for therapeutic applications, potentially addressing amyloid-beta accumulation in AD and offering novel avenues for tackling neurodegenerative diseases.

This research uses a novel dataset on data breaches to show that a company’s expectations for such significantly influences its operational (working capital) management to mitigate some of the potential effects of a breach on its operations. Specifically, I find evidence that firms use more trade credit and bank credit to build up their cash reserves based on their expectations about the likelihood of a data breach

We report the temperature dependence of important parameters in both gamma and thermal neutron detection of our rugged, efficient, portable, and scalable radiation detection system. The results of the temperature reliability tests show the gamma detector photopeak position at 662keV varies within ± 7.5% from 0°C to 60°C. For the neutron detector an increase in the leakage current of 2 orders of magnitude was observed from RT, to 100°C. At 50°C we see a leakage current of 19.5μA at -5V and 17μA at -2.5V which are well withing operable conditions.

As global temperatures continue to rise rapidly, it has become more important than ever to reduce greenhouse gas emissions while maintaining temperatures within buildings and homes at a range that is comfortable for humans. Current heating and cooling systems employed in residential and commercial buildings contribute to 4% of global greenhouse emissions. To combat this issue, we have created a Solid-Solid Phase Change Material (ssPCM) coupled with Radiative Cooling filler: a composite that can be employed within building walls, providing thermal regulation without any external energy source.

As part of our immune response, the body produces anti-tumorigenic inflammatory proteins to kill cancer cells. However, cancer cells can usurp chronic inflammation for their own survival. Interleukin-1 (IL–1) is an inflammatory protein found in breast cancer (BCa) tumors that is elevated in the BCa patient tumors and serum and promotes metastasis, indicating IL–1 is clinically relevant. But the molecular mechanisms underlying the effect of chronic IL–1 on BCa progression remain unknown. We previously discovered that acute exposure to IL–1 represses essential hormone receptors in cancers cells, but concomitantly upregulates other compensatory pro-survival proteins that contribute to tumor progression. To determine if chronic IL–1 inflammation has the same effect, we generated cell subline models using the human BCa cell line, T47D, by exposing T47D cells chronically to IL–1 for several months. Notably, we found that the T47D chronic IL–1 sublines stably evolve insensitivity to exogenous IL–1. This result suggests that chronic IL–1 causes stable molecular changes that may affect BCa cell response to other exogenous factors. Interleukin-6 (IL-6) is another important cytokine which can play a pivotal in cancer development by reducing cytotoxic reactive oxygen species (ROS) levels in cancer cells. Indeed, T47D parental cells treated with IL-6 show upregulation of antioxidant genes such as HMOX1, GCLC and NQO1 and show reduction in ROS levels. However, the chronic IL-1 sublines are insensitive to IL-6 induction of the antioxidant genes and show little or no IL-6-mediated ROS repression. Interestingly, chronic IL-1 sublines maintain the canonical IL-6/JAK/STAT signaling pathway, indicating that IL-6 ROS regulation is JAK/STAT independent. Moreover, the chronic IL-1 sublines show basally low or no ROS accumulation, indicating that endogenous ROS regulation is altered. Taken together, chronic IL-1 exposure selects for cells that evolve altered ROS/antioxidant regulation and response to exogenous factors. Future experiments include determining the functional significance of ROS/antioxidant signaling in chronic IL-1 subline cell tumorigenicity. Our studies will enable us to better understand the role of chronic IL-1 inflammation in BCa progression.

Virtual Reality (VR) has made an incredible impact in the healthcare industry supporting clients with mental and psychological illnesses such as addiction, phobias, and obsession with the help of cognitive behavioral therapy (CBT) and exposure therapy (ET). Recent studies confirm VR as a certified tool in generating rapid and lasting improvements for mental health patients, it has now shifted gears in creating a more inclusive environment for violent offenders to empathize with abuse and trauma victims. But such studies have been limited to reducing the levels of aggression and improvement in empathy from the perspective of the abuser. Despite the encouraging success of VR applications for the treatment of varied mental disorders, no study has explored the usability of VR specifically for treating the conditions associated with female victims of Gender-Based Violence. The aim of the paper is to present a systematic review of the current successes of VR applications in the psychological research space and to propose a VR-based therapy model exclusively designed for female victims of GBV.

Cystic Fibrosis (CF) is a hereditary condition affecting the respiratory system due to mutations in the CFTR gene, which deprive the lung of chloride and bicarbonate, resulting in chronic respiratory infections primarily caused by Pseudomonas aeruginosa. P. aeruginosa gains antibiotic resistance as infections progress, eventually killing the patient as all treatment options are exhausted. This study investigates how the conditions of the CF lung impact the evolution of antibiotic resistance in P. aeruginosa. We utilized adaptive laboratory evolution (ALE) to drive antibiotic resistance in multidrug resistant P. aeruginosa strain P4 in either bacteriologic or physiologically relevant conditions. In bacteriologic medium, P. aeruginosa exhibited an impressive capacity to develop resistance to the macrolide azithromycin (AZM), becoming 128-fold resistant to the antibiotic over the course of 8 days. However, in physiological medium, P. aeruginosa was unable to evolve resistance to AZM over the same timeframe. Our prior work has found a role for the mammalian buffer bicarbonate, found within humans and our physiologic medium, but absent from the bacteriologic medium, in influencing antibiotic susceptibilities. Suspecting bicarbonate may be influencing the evolution of AZM resistance, we conducted ALE experiments in CAMHB supplemented with bicarbonate. Exogenous bicarbonate impaired the evolution of AZM resistance in P. aeruginosa in CA-MHB. In the future we will examine the genetic basis for bicarbonates’ influence on the evolution of AZM resistance and we will evaluate the evolution of resistance in other macrolide antibiotics. This study highlights how CF host environmental factors influence the evolution of antibiotic resistance in P. aeruginosa.

CAVs are clustered into coalitions each managed by a leader. Within a coalition, collision avoidance is addressed using a Monte Carlo Tree Search (MCTS)-based approach. We propose algorithms for collision avoidance across coalitions. After an initial assessment of the impact of a potential collision on an affected coalition, leaders cooperate to define action plans that are free of intra-coalition and inter-coalition conflicts. The algorithms were validated through extensive realistic simulations in a multi-agent-based traffic simulator. Experimental results discuss the reliability and scalability of the algorithms for coalitions of different sizes. Moreover, we present an analysis to select the optimal coalition size and the optimal number of coalitions given a total number of CAVs.

This poster introduces research that broadens the scope of identifying behaviors related to autism, moving beyond traditional focus areas like repetitive actions to also include social communication and interaction as observed through video analysis. To support this research, we unveil the Audio-Visual Autism Spectrum Dataset (AV-ASD), the most comprehensive dataset available for autism screening in unstructured video settings. With hundreds clips across eight categories, AV-ASD leverages both audio and visual information to capture a wide spectrum of autism-associated behaviors. We also propose baseline models that merge audio, visual, and speech signals, overcoming limitations inherent to single-modality models. Various fusion strategies are explored to effectively combine these modalities with the aim of maximizing information utility. Empirical results validate that integrating audio, visual, and speech modalities enhances autism behavior recognition. Upon acceptance of this work, we commit to releasing the dataset and code to encourage open-source collaboration and accelerate AI-driven autism research.

Most leadership studies assume that leaders are inclusive, but few explore the factors that contribute to a leader’s inclusivity. With the demographics becoming increasingly diverse in terms of gender, race, ethnicity, generation, and culture and greater emphasis on participation, leaders and managers face new challenges in creating a cohesive and enabling work environment that preserves the uniqueness of actors and promotes equitable outcomes, particularly for the marginalized and underrepresented. However, personal biases, workplace politics, and appeasing attitudes can lead to alienation and negatively impact the organization and its stakeholders. This paper aims to broaden the extant understanding of antecedents of inclusive leadership in a public organization beyond its current dyadic and team focus, by exploring them in the broad context of public service.

Fuzzing is a vibrant research area that has been successfully utilized to find vulnerabilities in well-known software. Numerous works are published each year that claim improvements to a fuzzer’s internal mechanisms. However, fuzzing evaluations tend to focus on end metrics, such as the number of bugs found or total coverage gain, without evaluating fine-grained internal metrics. We propose a hierarchical formalization that represents any fuzzing evaluation. Using this formalization, we perform a review of 20 recent fuzzing papers to understand common practices and assumptions in fuzzing. We find there is broad agreement on what the effect of a power schedule or a search strategy is, but that most papers do not directly evaluate this effect. Motivated by this finding, we propose and formalize statistical analyses of low-level fuzzing data to understand the individual effect of internal mechanisms. We instantiate this approach for power schedules and search strategies, and evaluate four fuzzers that claim to improve these mechanisms. Our results are sharply at odds with several claims in the works we studied. In most cases, we found weak-to-negligible correlations between a fuzzer’s prediction of how promising a seed is and its actual quality. For example, none of AFLFast’s power schedules exhibit a direct correlation between their predictions and the outcomes. These results indicate the importance of carefully constructing fuzzing evaluations to directly evaluate internal fuzzing mechanisms.

Spontaneous dyskinesia has been repeatedly reported in psychotic disorders and is hypothesized to be related to striatal dopaminergic dysfunction. However, findings of spontaneous dyskinesia in biological first-degree relatives (FDR) of individuals with psychotic disorders are mixed. Clarity on this issue can provide insights into the association of dyskinesia with genetic liability or disease-specific processes. Using data from the Psychosis Human Connectome Project, this study examined dyskinesia in individuals with psychotic disorders (n=116), FDR, (n=62), and controls (n=42). Dyskinesia was evaluated via a force steadiness task wherein participants applied a constant pressure to a load cell for two sets of three 15s trials with their dominant and non-dominant hands. Increased variability in the force signal reflects dyskinesia, arising from uncoordinated muscle contractions. Repeated-measures ANOVA was used to analyze force variability with group and sex as between-subjects factors, handedness as a within-subjects factor, and age as a covariate. Preliminary results revealed main effects of group (p<.001) and age (p<.001). Bonferroni-corrected post-hoc comparisons revealed greater force variability in patients compared to both relatives and controls (p’s <.001), with essentially identical variability in relatives and controls. These findings suggest dyskinesia may be associated with disease-specific factors in patients rather than genetic liability.

The aviation industry is currently undergoing a significant transformation, moving away from conventional aircraft towards electric-powered alternatives in pursuit of zero emissions. This shift involves a progression from more electric aircraft (MEA) to the development of fully electric aircraft. The US Advanced Research Projects Agency–Energy (ARPA-E) is actively leading this transition, with a specific focus on developing a fully electric powertrain for single-aisle aircraft as part of their Aviation-class Synergistically Cooled Electric-motors with iNtegrated Drives (ASCEND) project. The primary objective of this project is to create a motor capable of delivering a peak power of 250 kW at 5000 RPM during take-off. Axial flux permanent magnet motors are among the appropriate choices for this purpose due to their low weight. This poster introduces and assesses a dual rotor axial flux permanent magnet motor designed specifically for electric aircraft applications. To enhance torque density and efficiency, various innovative features are integrated into the design, including the use of cobalt steels, Halbach arrays, and wires with rectangular cross-sections. One notable design element is the adoption of the yokeless and segmented armature topology, which has demonstrated exceptional performance, particularly in handling high current densities. To further optimize performance, the rotor magnets are arranged in a Halbach array configuration, resulting in a more sinusoidal flux distribution that can minimize losses and torque ripple. The incorporation of wires with rectangular cross-sections is another noteworthy feature, as it increases the copper fill factor compared to conventional round wires, leading to reduced weight and reduced losses. Furthermore, cobalt steel materials are chosen due to their ability to handle stronger magnetic fields, which reduces weight and losses.

Intranasal administration is a non-invasive delivery approach which could serve as a promising alternative to the traditional syringe-and-needle. ZIF-8-based slow-release systems have recently been popular in the field of nanomedicine, but their application for intranasal systems have been sparsely investigated. Here we study the biosafety of a model slow-release system using liposomes encapsulated in ZIF-8.

Mono-nucleosomes are chromatin’s building blocks and their dynamics control access to DNA. Mono-nucleosome structures have allowed us to better understand chromatin activity and mechanisms. However, such structures neglect the importance of potentially important interactions between adjacent nucleosomes. Analysis at the single nucleosome level may not accurately depict the genomic environment in the nucleus of a cell. Multi-nucleosome arrays can fill this gap and allow the investigation of potential nucleosome-nucleosome interactions. Three nucleosomes on a single long DNA, termed tri-nucleosomes, is the minimal array that can act as a model to study chromatin dynamics in a genomic-like environment. Here, we have developed a system to assemble tri-nucleosomes arrays with precise features on the first, second, and/or third nucleosome. Our system consists of three DNA constructs. Each DNA construct can be used to assemble a mono-nucleosome, and subsequently be ligated using specific digestion and ligation enzymes. The tri-nucleosomes can also accommodate the incorporation of different regulatory mechanisms and binding partners at precise positions. These arrays can elucidate the effect of such mechanisms on neighboring nucleosomes, mimicking a genomic environment. The tri-nucleosomes system combined with a validated HDX-MS workflow using stable isotope labeling is a valuable tool for better understanding chromatin compaction at the molecular level.

Hydrogen-deuterium exchange coupled with mass spectrometry (HDX-MS) experiments are carried out as comparative analyses that are technically challenging. In these experiments, all samples must be in the same conditions with regards to pH, temperature, and time. HDX-MS also imposes limitations in monitoring multiple, non-interchanging populations of a protein in a single sample. A novel workflow using mixtures of proteins distinguished by isotopic labeling with 12C, 13C, or 15N can address these limitations. Such a workflow also provides biologically interesting analysis for oligomeric systems in which different copies of the same protein are in unique environments. We are using histone proteins as a system to test the feasibility of an isotopically labeled HDX-MS workflow. We performed HDX-MS time course experiments on a mixture of wild type, 15N, 13C H2A, and H2B in their unfolded state and on a mixture of wild type, 15N, 13C folded H2A-H2B. Results for both experiments showed similar D-uptake rates between the 13C or 15N with the wild type of histone. The results for unfolded and refolded histones suggest that there is no considerable influence on the chemical exchange rate and hydrogen bonding by stable isotopic labeling and thus validate the feasibility of using stable isotopic labeled proteins in HDX-MS workflow. We applied the workflow to study the difference in dynamics between Xenopus H2A-H2B with tailless Xenopus H2A-H2B and Human H2A.Z-H2B. Tailless Xenopus H2A-H2B showed similar dynamics as Xenopus H2A-H2B while Human H2A.Z-H2B showed increased dynamics.

The present study used an altered auditory feedback (AAF) paradigm to investigate how attentional mechanisms affect speech error detection and motor control. Electroencephalography (EEG) and speech signals were recorded from 21 neurologically intact subjects with no history of speech and hearing disorders while they produced the speech vowel /a/ and received randomized ±100 cents pitch-shift stimuli in their real-time auditory feedback. Subjects were instructed to pay focal attention to the auditory feedback and press a button to indicate whether they detected a pitch-shift stimulus. Data for this group was compared with 22 matched subjects who completed the same AAF task without attentional instruction. We found smaller magnitudes of speech compensation in the attention vs. no-attention group and a significant positive linear association between speech compensation magnitude and P2 event-related potential (ERP) amplitude. In addition, our data revealed that the amplitude of P2 component was significantly enhanced for the attention vs. no-attention group. Source localization analysis showed that this effect was accounted for by significantly stronger neural activities in the right hemisphere insula, precentral gyrus, postcentral gyrus, transverse temporal gyrus, and superior temporal gyrus in the attention group. These findings suggest that focal attention to auditory feedback can enhance speech error detection, and subsequently improve sensorimotor control via generating more stable speech outputs (i.e., smaller compensations) in response to pitch-shift alterations. Our data are informative for advancing theoretical models and motivating targeted interventions with a focus on the role of attention for improving treatment outcomes in patients with motor speech disorders.

Metal Organic Frameworks (MOFs) have gained a huge scientific interest due to its properties such as, permanent porosity, crystallinity, structural diversity, and large surface area. Even though this class of materials possesses a high potential for a wide range of applications, the processing of these fragile crystalline powders limits its end use applications. Therefore, it is important to develop fabrication techniques to overcome the limitations. Herein, a new approach is introduced to construct complex structures with fine features by integrating various loading weight percentages of MOF crystals to a photoresin formulation. Through free radical polymerization in a digital light projection (DLP) setup, MOF loaded composites are 3D printed, and the MOF-polymer catalytic performances are studied. By choosing different resins, we can increase the amount of MOF on the surface of a printed structure, increasing access of the MOF for improved catalysis applications. This approach demonstrates how the MOF properties are preserved through 3D printing process and open techniques for a wide library of MOFs to process through additive manufacturing.

For extreme voltage step-down conversion ratios, existing hybrid converter topologies suffer from a high component count and require well-orchestrated timing control between the components. To address this challenge, a new quadratic step-down DC-DC converter is derived through topology superimposition. This design reduces the number of power devices and eases timing control. Delivering the current in parallel paths, it achieves current ripple cancellation, thereby reducing the bulky inductor size and enhancing the power efficiency. An experimental prototype of this design achieves direct 48V-to-1V power conversion with a maximum load of 20A and a peak efficiency of 94.5%.

Polycaprolactones (PCLs) are essential aliphatic polyesters for drug delivery due to their biodegradability, biocompatibility, and synthetic versatility. Introducing different substituents at g-position of e-caprolactone (e-CL) monomers can easily tune the properties of synthetic polymers. Our research aims to synthesize amphiphilic diblock copolymers (AmBCs) from the novel disubstituted g-amide e-CL monomers. These monomers can double the density of substituents on a single polymer chain, directly affecting the properties of polymeric micelles, such as drug load capacity (DLC) and thermodynamic stability. AmBCs were prepared by ring-opening polymerization (ROP) of each monomer using Triazabicyclodecene (TBD) catalysts to generate living polymerization behavior. Self-assembly, thermoresponsive behavior, and DLC of the AmBCs were analyzed. Our research has been focused on two strategies to increase the DLC of the micelles; a) tuning substituents at the hydrophobic polycaprolactone block and b) co-loading with polyphenols, such as resveratrol via p- p stacking and hydrogen bonding interaction between the anticancer drug, doxorubicin, and polyphenol. The co-loading approach can significantly reduce the cardiotoxicity caused by the anticancer drug. The in vitro biocompatibility and cellular uptake of the AmBCs will be thoroughly investigated for breast cancer treatment using MCF-7 cells. Moreover, a microfluidic device will be used to cultivate stem cell-derived organoids to simulate the dynamic microenvironment of the organ and test the toxicity of drug-loaded micelles.

The sigma-2 receptor/TMEM97 is a transmembrane protein found in the endoplasmic reticulum and plasma membrane. Previous studies developed sigma-2 receptor/TMEM97 binding ligands that show anxiolytic/antidepressant-like properties and relieve neuropathic pain in rodents. Despite medical interests, little to no affective and pain behavioral characterization has been done using transgenic mice. This knowledge gap limits the progress of sigma-2 receptor/TMEM97 as a viable therapeutic target. To address this gap, our study aims to investigate the role of sigma-2 receptor/TMEM97 in 1) modulating affective and pain behaviors at baseline and 2) regulating neuropathic pain-induced affective behaviors such as anxiety and depression. We used wild-type (WT) and global TMEM97 knockout (KO) mice (Male and female C57BL6/J, aged 8-9 weeks) and conducted a battery of affective assays: open field, light/dark preference, elevated plus maze, elevated zero maze, forced swim test, and tail suspension test. The same affective battery is performed before and after 10 weeks of neuropathic spared-nerve injury (or sham treatment) to obtain the baseline and post-injury affective behaviors, respectively. Peripheral paw mechanical sensitivity was measured to assess pain hypersensitivity developed after injury between WT and TMEM97 KO mice. The experimenter was blinded to the genotype and treatment. At baseline, our results show that TMEM97 KO mice show statistically significant reduced anxiety/depression-like behaviors in light/dark preference and tail suspension test but not in open field, elevated plus maze, and forced swim test compared to those of WT mice. This suggests that sigma-2 receptor/TMEM97 plays a modest role in modulating anxiety and depression in naïve mice. We next performed spared nerve injury in WT and TMEM97 KO mice to assess the receptor’s role in a model of neuropathic pain with comorbid anxiety/depression-like behaviors. No significant difference was observed in spared nerve injury-induced mechanical hypersensitivity between WT and TMEM97 KO mice. WT mice showed depressive behaviors induced by prolonged neuropathic pain in the forced swim assay, whereas TMEM97 KO mice did not. In the presence of nerve injury, the absence of sigma-2/TMEM97 protects against the development of neuropathic pain-induced depression. In conclusion, our findings indicate that targeting sigma-2 receptor/TMEM97 may hold promise for alleviating pain-related affective comorbidities. The results highlight the importance of further investigating sigma-2 receptor/TMEM97 as a therapeutic target for psychiatric diseases related to pain management.

The optimal technique for probing nanoscale properties of polymers is nanoindentation, owing to easy sample preparation and excellent reproducibility in load response. Polymers possess a unique problem as their response to loading is time-dependent which renders the most common indentation techniques inadequate. This is a well-known issue and despite this, poly(3-hexylthiophene) has been exclusively tested using these time-independent techniques. A viscoelastic nanoindentation method is presented in which the polymer’s response to ramp loading and step loading are considered. Through the determination of viscoelastic functions, Young’s modulus is extracted and determined to be 260 MPa, which is in excellent agreement with specialized mechanical tests for thin films and highlights the promise of this method for use on other organic semiconducting polymers.

A new carboradiofluorination method has been developed for synthesis of a wide variety of homoallylic fluorides. This method holds promise toward PET Imaging Probe Development.

Elastography is a widely used technique for extracting tissue elasticity information from biomedical images. However, its broader application for accurate quantitative analysis s has been limited by human factors, ad hoc assumptions, and noisy/false images. Although data-driven models have shown great promise in addressing these issues, they are heavily relying on large training datasets and are very sensitive to the statistics. In this study, we aim to bridge this critical technological gap by integrating physics-based models with deep neural networks, resulting in a novel PINN for image analysis of tissue elastic properties. Specifically, we establish a computational framework based on finite element method (FEM) and deep neural network. We show that this framework can capture the full field results from ultrasound images under deformation without the need of a training dataset. We demonstrate the robustness of the algorithm in various scenarios such as irregular shaped organs, 3D meshes, image data and a large range of irregular stiffness patterns and boasts a <5% error margin when compared to ideal situations. As a physics informed network, the model is not limited to a range of training data and enforces the physical constraints to ensure the validity of the results. By leveraging the unique strengths of both physics-based models using FEM and deep learning, the proposed PINN leads to significant advancements in the field of elastography with broad implications for improving patient care.

Covalent organic frameworks (COFs) are known to be a promising class of materials for a wide range of applications, yet their poor solution processability limits their utility in many areas. Here we report a pore engineering method using hydrophilic side chains to improve the processability of hydrazone and β-ketoenamine-linked COFs and the production of flexible, crystalline films. Mechanical measurements of the free-standing COF films of COF-PEO-3 (hydrazone-linked) and TFP-PEO-3 (β-ketoenamine-linked), revealed a Young’s modulus of 391.7 MPa and 1034.7 MPa, respectively. The solubility and excellent mechanical properties enabled the use of these COFs in dielectric devices. Specifically, the TFP-PEO-3 film-based dielectric capacitors display simultaneously high dielectric constant and breakdown strength, resulting in a discharged energy density of 11.22 J cm–3. This work offers a general approach for producing solution processable COFs and mechanically flexible COF-based films, which hold great potential for use in energy storage and flexible electronics applications.

Active ingredients are widely used compound to treat skin texture, prevent skin dehydration, stimulate collagen production. Despite their good properties, it comes with unpleasant side effects such as irritation, peeling, and redness of the skin at high concentrations. Designing a nanocarrier for hydrophobic and photosensitive ingredients that allows controlled release over time is of great importance in skincare products. Polyamidoamine (PAMAM) dendrimers are polymeric nanocarriers that can be designed on a specific nano-sized scale with high functionality that allows the conjugation of multiple molecules. Herein, we developed a PAMAM dendrimers with a hydrophobic core, modified on the surface by polyoxazolines to enhance their water solubility and reduce the potential toxicity. In order to increase the skin penetration, nanocarrier is further grafted by collagen stimulating peptide. This system is cleavable by skin enzymes hence slowly release the encapsulated active ingredient over the course of time. The nanocarrier was characterized by DLS, TEM, and SEC. Preliminary cell viability data showed that the nanocarrier has no significant cytotoxicity on human adult fibroblasts. The in vitro release study has shown the overall system has slow release of the active ingredient up to 12h.

Spatial cognition is among the cognitive domains that exhibit decline with advanced normal aging. Deficits in spatial navigation, however, are much more pronounced in Alzheimer’s disease (AD) and it is considered one of the earliest signs of the disease. While research on the relationship of aging and spatial cognition has grown significantly in recent years, studies on the impact of environmental properties are scarce. We recently introduced environmental complexity, an index reflecting the frequency and density of street networks and landmark features, to predict cognitive statuses of each zip-code zone across the United States using data from the National Alzheimer’s Coordinating Center. Here, we explored the indirect and direct relationships between environmental complexity, age, spatial navigation-relevant volumetric brain regions and spatial cognitive abilities of 660 individuals who were cognitively normal, diagnosed with Mild Cognitive Impairment (MCI), and with AD. Greater environmental complexity was associated with larger brain volumes in regions involved in allocentric spatial navigation, lesser diagnosis of MCI and AD, and better spatial cognition performance. These findings suggest that residing in spatially complex environments allows for the routine usage of cognitive neural mapping across time, which may help to stave off the brain atrophy that is associated with spatial navigation difficulties seen in AD, and may be a target for future interventions.

[Background] Both white matter and functional activity show regionally differential aging rates. Studies yoking aging of brain structure and function are accumulating, linking structural connectivity and functional activity in the brain. These multimodal studies have shown mixed results. We hypothesize that these differential results are due, in part, to differences in approach to ROI and tract selection, and lack of consideration of differential aging of networks. Functional activity in default mode network regions (DMN) evidences age-related decline beginning in middle-age in response to increasing task difficulty; while white matter structure connectivity in the multiple demand network (MDN) regions (mostly frontal-parietal) declines earlier than DMN white matter regions. Here we sought to test the hypothesis that the altered functional activity in DMN during task performance might be a compensatory response to declined white matter microstructure in MDN. Further, previous aging studies have shown coupling of MDN and DMN functional activity to support cognition. We hypothesized that for older adults, poorer white matter connectivity in MDN will be accompanied by this coupling in support of increased task demands and should be associated with better cognitive performance, if it is indeed compensatory. [Methods] Participants included 160 healthy adults aged 20-94 (mean 52 +/- 19 yrs) who underwent diffusion-weighted and task-BOLD MRI as well as neuropsychological testing (switching and inhibition from D-KEFS verbal fluency and Stroop interference, and Cattell fluid intelligence tests). We employed a functionally-guided, deterministic tractography approach, using functional clusters (age-related positive BOLD modulation: PosMod; mainly MDN, or negative BOLD modulation: NegMod; mainly DMN) to task difficulty) as tractography seeds to locate the white matter tracts connecting either of the activated networks or between both networks using DSIstudio. To examine the structure-function associations and how they relate to cognition, General Linear Models were specified in R to test the effects of 1) aging of the white matter tracts (fractional anisotropy; FA); 2) structure-function association between tract FA and BOLD modulation to difficulty; 3) Age, PosMod, NegMod, and tract FA on executive functions and fluid intelligence to test for compensation. [Results] Functionally-guided tractography located four tracts — two MDN related tracts: inferior frontal-occipital fasciculus (IFOF) and inferior frontal-insula U-shaped tract; one DMN tract: the cingulum; one cross-network tract: corpus callosum splenium. We found 1) significant age-related FA reduction for the U-shaped tract after controlling for whole brain FA; 2) significant Age × IFOF FA interaction on NegMod: lower FA was associated with strengthened negative BOLD modulation in older adults, but weakened negative BOLD modulation in younger adults; 3) strengthened BOLD modulation compensated for structural decline of the same network for older adults; 4) weakened BOLD modulation was observed as compensation for lower white matter FA in different networks; 5) Significant Age × PosMod × NegMod ×FA2 interaction where high PosMod, coupled with low NegMod, predicted increasingly better inhibition scores in older adults. For those who have poor FA, this effect further strengthened with decreasing FA; and further weakened for those with preserved FA. [Conclusions] We provide supporting evidence for age-related decline of white matter connectivity in the MDN, and add novel evidence for structure-function compensation within and across DMN and MDN regions. Maintaining better cognition in older adults required dynamically strengthening or weakening of functional activity to compensate for declined white matter in the same or different networks, respectively. Future work will test these associations in this longitudinal sample.

Increased energy consumption has triggered the development of various energy storage devices. Supercapacitors have gained attention as energy storage devices due to their high-power density and long cycle life. The energy density of supercapacitor can be increased by combining electric double-layer capacitance (EDLC) and pseudocapacitance (PC). A hybrid material based on rare-earth metal oxide templated carbon will be presented that combines both EDLC and PC within the same material. Specifically, neodymium hydroxide (Nd(OH)3) reacted with acetylene and steam to form carbon coated Nd2O3. Both Nd(OH)3 nanorods and nanospheres were used to prepared templated carbon followed by partial or complete washing of metal oxide. The resulting carbon exhibit the surface area of 1245 m2/g. Asymmetric coin cell was constructed with capacitance of 162 F/g and energy density of 90.6 Wh/kg This work paves the door to develop novel hybrid supercapacitors.

Static analysis tools are critical to ensure the quality and security of software. It is important to comprehensively test these tools, to ensure their correctness and understand their capabilities. The most comprehensive approach to testing static analysis tools involves running them against real-world datasets for which ground truths are known, and comparing the results of the analysis with the ground truths. Ideally, each benchmark in the dataset is associated with a specific analysis or language feature, so that failures can be traced to individual modules of the analysis. However, creating such benchmarks is incredibly difficult, and as a result, very few exist. To address this issue, in this work, we propose a novel approach to automatically generate ground-truth feature benchmarks from real-world programs. Our key idea to realize this idea is to define dynamic analysis that underapproximate the results of static analyses. These \textit{dynamic projections} allow us to automatically generate lower bounds for an analyzer’s results, against which an analysis’ output can be checked. The benefit of doing this is twofold: first, we can use this approach to find unsoundness issues in a static analysis; second, we can use these results to compare multiple analyzers to understand their capabilities. To evaluate our approach, we have implemented dynamic projections for four static analysis features: parameterized call-site sensitivity, parameterized object sensitivity, analysis of static initializers, and analysis of trivially resolvable reflection. We generated ground truths for three benchmarks — one real world benchmark, and two microbenchmarks. We found that microbenchmarks, which are popular targets for testing analyses, often did not expose any behavior that allowed us to generate ground truths, indicating the importance of using real-world datasets for testing static analysis. We then compared these results against the results of three popular static analysis tools for Java: SOOT, WALA, and DOOP. We found that these tools often failed test cases, indiciating that they have significant issues that need to be fixed.

Age-associated grey matter thinning is likely exacerbated by age-related biological risk factors. Here we examined the influence of beta-amyloid burden, hypertension, and inflammation (IL-1beta genotype) on 4-year within-person change in cortical thickness for their independent and synergistic effects and their spatial distribution. In 46 healthy, cognitively normal adults (52-86 years at baseline) with two waves of structural MRI scans and baseline Amyvid-PET scans, change in cortical thickness over four-years was estimated vertex-wise for each hemisphere. Using Freesurfer’s glmfit, cortical amyloid, age, hypertensive status, and inflammatory risk were entered as predictors of regional 4-year cortical thinning, cluster corrected using Monte-Carlo simulation. Results revealed several independent effects of modifying risk factors on regional cortical thinning. Additionally, an AgeAmyloidIL-1beta synergistic effect indicated that the rate of within-person cortical thinning increases as age and amyloid increase in participants with genetic pro-inflammatory risk, highlighting the important and interactive role of individual differences on brain aging.

Metal-organic frameworks (MOFs) are porous crystalline materials with surface areas ranging up to 10,000 m2/g, better than traditional porous materials such as carbons or zeolites. These materials can possess open compartments for guest substrates, functional linkers, active catalytic sites, and tunable metal nodes. These characteristics allow MOFs to be used for gas storage, gas separation, catalysis, sensing, and biological applications. A new mixed linker metal–organic framework (MOF) has been synthesized from a copper-based metal–organic polyhedron (MOP-1) and 2,2’-bipyridine (2,2’-bipy). The CuMOF-Bipy with a formula of [Cu2(2,2’-bipy)2(m-BDC)2]n comprises a binuclear Cu(II) node coordinated to 2,2’-bipy, and isophthalic acid (m-BDC), which bridges to neighboring nodes. The crystal structure of CuMOF-Bipy consists of a stacked two-dimensional framework with the sql topology. CuMOF-bipy was characterized by single-crystal X-ray diffraction (SC-XRD), X-ray photoelectron spectroscopy (XPS), thermogravimetric analysis (TGA), and gas sorption. CuMOF-Bipy was shown to have one-dimensional sinusoidal channels that allow diffusion of CO2 but limits N2. CuMOF-Bipy takes up to 6.4 weight% of CO2.

Enterococcus faecalis is a species of Gram-positive bacterium that natively inhabits the human gastrointestinal tract. This bacterium is associated with the decreased effectiveness of antibiotic treatments for other bacterial infections localized to the gastrointestinal tract. One such antibiotic is metronidazole, a 5-nitroimidazole drug once used commonly in the treatment of Clostridioides difficile infections. E. faecalis is intrinsically resistant to metronidazole and we hypothesize that E. faecalis is able to employ native cellular mechanisms to reduce metronidazole to a form which is non-toxic to organisms such as C. difficile. By elucidating the mechanisms used by E. faecalis to reduce the efficacy of this drug, antibiotic treatments for gastrointestinal infections can be improved. To gain an understanding of the mechanisms at play, many methods and assays have been used. A spectrophotometric assay has been used to assess the amount of unaltered metronidazole in liquid cultures as a function of optical density. This assay has been used on a wide variety of strains of E. faecalis, including many transposon mutants obtained from an E. faecalis transposon library, as well as organisms similar to E. faecalis, such as Enterococcus faecium. This assay has also been used to assess the effects of various culture conditions such as changes to nutrients and growth conditions.These assays have revealed E. faecium’s inability to degrade metronidazole. We further found that E. faecalis requires ferrous iron and anaerobic conditions to degrade metronidazole. We found that transposon-disrupted genes involved in E. faecalis’s extracellular electron transfer system were commonly associated with an inability to degrade metronidazole. Our findings have led us to strongly believe that E. faecalis uses extracellular electron transfer to reduce metronidazole to an inactive form. Despite these and many more findings, we remain exactly unclear how this process happens. We have developed a theoretical model for this process, but further research is required to fully characterize this phenomenon.

This research presents a far-field radiation beam steering model to increase the scanning range of a ultrawideband (UWB) 1×4 Vivaldi antenna array. Along with the electrical beam steering method where phase difference is provided between the array elements, a novel mechanical tilting of the array elements is introduced. Different mechanical tilting is implemented by tilting the array elements along the elevation plane while maintaining a 15^° tilt angle difference. The antenna array along the mechanical tilt combinations achieves 6 – 18+ GHz operating frequency with highest coupling of -22 dB between the array elements. The introduction of electro-mechanical steering increases the scanning range by > 2 times while maintaining a unidirectional radiation pattern and 13.8 – 15.4 dBi gain. Based on the simulated results and experimental validation, the electro-mechanical beam steering model when implemented on the proposed 1×4 Vivaldi antenna array is capable of achieving a ±42^°, ±44^°,±36^°, and ±42^° scanning range along the elevation plane with a < 3 dB scanning loss at 8, 9, 10, and 11.5 GHz respectively.

The neonatal mouse heart in contrast to the adults has the ability to regenerate after myocardial infarction. The investigation of cardiac morphogenesis is critical to uncover the underlying mechanism of cardiac regeneration. Hence, we have developed a light-sheet microscope (LSM) along with the tissue clearing method to investigate the 3-dimensional (3D) architecture of the intact neonatal mouse heart. To achieve a uniform axial resolution and ensure even illumination of the sample, we improved our imaging system by incorporating an axially swept light sheet microscopy with a voice coil actuator for isotropic imaging. The focus of the laser beam is axially swept across the whole field of view and synchronously in conjunction with the sCMOS’s rolling shutter. Our LSM offers a lateral resolution of 2.08 ± 0.11 um and an axial resolution of 2.84 ± 0.19 um. By synchronizing the laser waist, rolling shutter, and translational stages with our custom LabVIEW code, we can take optical sections throughout the whole heart in 3D to reveal the cardiac structure of the neonatal mouse. This provides an entry point to investigate the intricate myocardial trabeculation and compaction during cardiac morphogenesis and regeneration.

Circulator plays a crucial role in the operation of full-duplex radio-telephony systems. In this paper, an active circulator using a standard 0.18 μm CMOS technology using three bridged-T network (BTNs) is designed to boost the isolation over the X-band. As part of the proposed circulator design, a tuning platform is devised to explore the impact of various design variables of the peaking inductor on wireless benchmarks including isolation and insertion loss through the assessment of circulator port parameters. The simulation results indicate a significant isolation of over 30 dB between the transmitter (TX) and the receiver (RX) while insertion loss from the TX to antenna and from the antenna to the RX are maintained below 7.42 dB and 7.27 dB, respectively. The proposed circulator exhibits promising characteristics as a potential choice for duplexers where substantial isolation and minimal insertion loss are of paramount importance.

Theoretically, the use of riblet films on airfoils can improve the airfoils’ aerodynamic performance. Several studies show that with specific geometrical parameters, optimal drag reduction can be achieved. However, the effects of riblets in horizontal axis wind turbine blades for power enhancement, remain to be explored. In this study, we conducted systematically designed wind tunnel experiments to characterize the aerodynamic performance of a DU91-W2-250 airfoil with a riblet film. To quantify the impact of the riblet film on wind turbine performance, experimental results were used as input data for numerical simulations. Large Eddy Simulations were conducted for the smooth and modified airfoils under ideal non-turbulent, and turbulent inflow conditions. For the turbulent inflow simulations, staggered cubes were introduced upstream of the wind turbine to generate velocity fluctuations in the flow. Results from the numerical simulations show that improvements in the aerodynamic performance of the airfoil with riblets enhance the aerodynamic torque that drives the wind turbine, thereby increasing the power output. The percent improvement in the power coefficient with the use of the riblet film is higher in the turbulent inflow case, as compared to the improvement in the ideal, non-turbulent case.

Massive studies concern the development of low-carbon water and energy systems. Specifically, surfaces with special wettability to promote vapor-to-liquid condensation have been widely studied, but current solutions suffer from poor heat transfer performances due to inefficient droplet removal. In this work, we push the limit of condensation on a beetle-inspired biphilic quasi-liquid surface in a steam environment, which provides a heat flux 100 times higher than that in atmospheric condensation. Unlike the beetle-inspired surfaces that have sticky hydrophilic domains, the biphilic quasi-liquid surface consists of PEGylated and siloxane polymers as hydrophilic and hydrophobic quasi-liquid patterns with the contact angle hysteresis of 3 and 1, respectively. More importantly, each hydrophilic slippery pattern behaves like a slippery bridge that accelerates droplet coalescence and removal. As a result, the condensed droplets grow rapidly and shed off. We have demonstrated that the biphilic striped quasi-liquid surface shows a 60% higher water harvesting rate in atmospheric condensation and a 170% heat transfer coefficient in steam condensation than the conventional beetle-inspired surface. Our work provides a new paradigm to push the limit of condensation heat transfer at a high heat flux, which sheds light on the next-generation surface design for water and energy sustainability.

Room temperature solid-state synthesis of halide perovskites is a simple, single-step, efficient, and cost-effective method for synthesizing halide perovskite precursors. These precursors can subsequently be used to deposit thin films using various techniques. Solid-state methods also reduce or, in some cases, eliminate additional time-consuming treatments and purification steps. The success of this approach relies on a deep understanding of the underlying reaction mechanisms, particularly concerning diverse perovskite systems. One significant concern revolves around the stability of CsPbI3, a material crucial in the development of optoelectronic devices, including perovskite solar cells. Its inherent instability has long posed a challenge. This study aims to address this issue by stabilizing the cubic phase of CsPbI3 at room temperature through solid-state reactions. We conducted a comprehensive investigation into the utilization of smaller ionic radius elements as partial substitutes for Pb to enhance the stability of CsPbI3 in the solid-state. Our approach involved excluding any ligands, solvents, or stabilizing agents to examine the pure chemistry of these reactions. We systematically evaluated various elements and identified the most effective ones for stabilizing CsPbI3 in the solid-state. Furthermore, we extensively discussed the degradation mechanisms associated with the other elements studied, such as Ni2+, Mn2+, and Ca2+. Our findings revealed that Sn2+ demonstrated the ability to stabilize CsPbI3 in the solid-state, while we thoroughly explored the degradation mechanisms of the remaining elements using X-ray Photoelectron Spectroscopy (XPS). This study introduces a new approach to enhance the stability of CsPbI3 by focusing on its B sites within solid-state reactions. To the best of our understanding, there has been limited prior research on this aspect through solid-state reactions without the use of solvents or stabilizing agents.

Modern inspection techniques in integrated circuit (IC) manufacturing is based on vision-based deep neural networks. The training of such networks requires the availability of a very large amount of data. However, not much defective data is available from an actual production line. To address the issue of lack of data for such situations, generative adversarial networks can be used to generate synthesized data as a data augmentation approach. In IC manufacturing, images of defective epoxy drops need to get identified for the purpose of performing die attachment. In practice, very few defective epoxy drop images are available. After examining several widely used generative models, the CycleGAN generative model is found to provide closest synthesized images to real images for our application. CycleGAN a variation of the generative adversarial network (GAN). This work involves improving the conventional CycleGAN via enhancing its cycle consistency loss function by two other loss functions based on learned perceptual image patch similarity (LPIPS) and structural similarity index metric (SSIM). The results obtained indicate that our improved CycgleGAN generative model produces more realistic synthesized images in terms of quantitative image quality metrics. An example image classifier is shown to illustrate the improvement in the inspection outcome when using the synthesized images generated by the developed improved generative model.

Enhancing the resilience of a power distribution network holds significant societal importance, especially in the face of increasing cyber-physical threats and extreme weather events. In this study, we introduce an innovative approach for bolstering the resilience of power distribution networks, harnessing the cutting-edge tools of topological data analysis—a novel frontier in graph representation learning. Our method involves the application of multiparameter persistent homology, enabling us to capture the most intricate topological nuances within the power distribution network by incorporating multiple user-defined functions. By leveraging nodal variables such as voltage information and edge variables like current flow, we construct higher-order substructures within the network. We then track topological patterns throughout this construction. The effectiveness of our proposed Topological Machine Learning model, which is both computationally feasible and scalable, is substantiated through contingency classification across various small and large-scale distribution networks. Our model surpasses existing state-of-the-art methods in performance.

Substance use has emerged as a pressing public crisis in the United States, particularly among adolescents. Effective strategies for steering adolescents away from the path leading to substance use disorder are needed. To address this, we developed a Bayesian absolute risk prediction model that predicts the personalized risk of developing cannabis use disorder (CUD) for an adolescent or young adult cannabis user over a given time period. Data from a nationally representative longitudinal study, Add Health, were used to train the model. The proposed model has five risk factors: gender, a measure of delinquency, and scores on personality traits of conscientiousness, neuroticism, and openness. For predicting the risk of developing CUD within 5 years from the age of first cannabis use, the area under the receiver operating characteristic curve (AUC) of the model and the ratio of expected and observed number of cases (E/O), computed via 5-fold cross-validation, were 0.68 and 0.95, respectively. This indicates good discrimination and calibration performance of the model. Furthermore, external validation of the model was conducted using independent test data from Add Health. The AUC and E/O for the same prediction were 0.64 and 0.98, respectively, affirming the model’s robust performance in both discrimination and calibration. The proposed model is the first absolute risk prediction model for a substance use disorder. It can aid clinicians in identifying adolescent/youth substance users with a high risk of developing CUD in future for providing early interventions.

Does expatriates’ work-life balance during international assignments really increase their career satisfaction by enhancing their commitment to the local subsidiary? This study seeks to investigate a moderated mediation model of the relationship between expatriates’ work-life balance during international assignment and their career satisfaction with local commitment as a mediator as well as moderating effects of how marketable expatriates think of themselves on such relationships. To do so, we rely on data collected from 121 expatriates currently on international assignment across five waves of surveys. We find that having a higher work-life balance during international assignments, while on the surface, it appears desirable, is negatively related to their local subsidiary commitment. However, local commitment is positively related to career satisfaction. Thus, work-life balance has a negative impact on career satisfaction, which challenges the assumption that reduced work-life conflict leads to automatic increases in job-related experiences such as local subsidiary commitment and career satisfaction. We also find the moderating effect of external marketability on the relationship between work-life balance and local subsidiary commitment as well as that between local subsidiary commitment and career satisfaction. This prompts a reevaluation of the complexities of work-life dynamics and their impact on expatriates’ well-being and career outcomes.

At times, leaders send mixed signals to their employees, which can be confusing. While it’s clear that employees tend to react negatively when they feel mistreated by their leaders, what happens when these same leaders also maintain a positive relationship with them? What if the positive relationship fluctuates or persists? Our research dives into these intriguing scenarios. We wanted to understand the employee reactions in a setting where a leader might act harshly, but at the same time, exchange well with the employee. We followed 145 nurses interacting with their head nurse for 2 weeks to explore the phenomenon. The outcomes of such a complex relationship can provide valuable insights into workplace dynamics and how employees navigate mixed signals from their leaders.

Space weather physicists describe phenomena in the near-Earth environment. For example, interactions between solar radiation and the Earth’s atmosphere produce a layer of electrically charged particles (i.e., plasma) called the ionosphere. The monitoring of the ionosphere is motivated in part by (1) its potentially deleterious effect on propagating radio signals from communication satellites and satellite navigation systems and (2) the space weather physics revealed by its short- (day-to-day) and long-term variability. Observations made by satellite- and ground-based instruments have contributed significantly to our understanding of the ionospheric variability and its associated physics. Instruments onboard satellites can provide in situ measurements of the ionosphere at different locations around the Earth. However, risks associated with their development, deployment, and relatively high cost can serve as barriers to new satellite observations. Satellite observations also have low temporal resolution. For instance, Low-Earth-Orbit (LEO) satellites provide observations for a given longitude with a cadence of approximately 90 minutes. In contrast, ground-based instruments can be operated continuously with lower risk to study the variability of the ionosphere. For example, a radar operating in the Very-High Frequency (VHF) band has been used since the early 1960s to monitor the ionosphere at the low-latitude Jicamarca Radio Observatory (JRO) in Lima, Peru. VHF observations with the JRO radar have enabled studies of plasma density depletions, referred to as ionospheric irregularities, with ~ 3 m scale sizes. These observations could only be obtained for a single direction (i.e., directly above the JRO). Here, we present results from an ongoing effort to detect and study ionospheric irregularities around the JRO with a new Ultra-High-Frequency (UHF) radar: the 14-panel version of the Advanced Modular Incoherent Scatter Radar (AMISR-14). The new AMISR-14 observations are the result of a collaboration between the University of Texas at Dallas, Instituto Geofísico del Perú, and Universidad Ana G. Méndez. The AMISR-14 operating frequency enables the detection of ~ 0.34 m scale size irregularities previously unobserved at the JRO. Additionally, the electronic beam steering capability of AMISR-14 enables it to create multi-directional (i.e., two-dimensional) observations of the equatorial ionosphere. AMISR-14, then, can complement previous and ongoing single direction observations with the collocated JRO radar. We show examples of two-dimensional observations with AMISR-14 and discuss some of the science questions being addressed with this new observational capability.

Particle accelerators are machines designed to accelerate atomic or subatomic particles for various purposes. In today’s technology, particle accelerators achieve this by accelerating, directing, and focusing charged particles through electromagnetic forces. This versatile technology has applications across numerous civil and military sectors, playing critical roles. Electron guns are widely used in the pre-acceleration phase of many applications, providing the necessary accelerator fields that influence beam dynamics throughout the system. They are utilized in scientific instruments, electron devices, and industrial facilities. However, the specific design requirements vary depending on the application. The primary challenge for DC electron guns is to simultaneously achieve high power and low emittance electron beams. This study introduces a novel method for designing a high-power, low-emittance DC electron gun using numerical methods and image processing. The process begins by determining the electrode shapes of the electron gun in Computer Simulation Technology (CST) program to achieve the desired parameter correlation. Subsequently, a 2D electric field optimization of the electron gun is conducted in MATLAB, employing finite difference numerical methods. Image processing tools are utilized to transfer the determined electrode shapes from CST to MATLAB. Since CST provides a 3D solution, simulations can be time-consuming. However, the axially symmetrical structure of the electron gun allows for a quick and accurate 2D solution, enabling the acquisition of design parameters more efficiently. The optimization results in MATLAB are also validated in CST. As a result, the final electrode structures are designed, achieving both high power and low emittance electron beams.

This study is in the area of speech and language technology. We focus on understanding and quantifying the change in phoneme and prosody information encoded in the Self-Supervised Learning (SSL) model, brought by an accent identification (AID) fine-tuning task. This problem is addressed based on model probing. Specifically, we conduct a systematic layer-wise analysis of the representations of the Transformer layers on a phoneme correlation task, and a novel word-level prosody prediction task. We compare the probing performance of the pre-trained and fine-tuned SSL models. Results show that the AID fine-tuning task steers the top 2 layers to learn richer phoneme and prosody representation. These changes share some similarities with the effects of fine-tuning with an Automatic Speech Recognition task. In addition, we observe strong accent-specific phoneme representations in layer 9. To sum up, this study provides insights into the understanding of SSL features and their interactions with fine-tuning tasks.

Micelles obtained from biodegradable amphiphilic diblock copolymers are one of the widely investigated materials for anticancer drug delivery systems. The hydrophobic nucleus of the micelles provides a solubilizing medium for the hydrophobic drugs, whereas the hydrophilic shell protect them and provides colloidal stability. Additionally, micelles smaller than 100nm diameter can passively target tumors via enhanced permeability and retention (EPR) effect- improving selectivity of the drug. However, synthesizing and characterizing polymeric micelles is an extraneous and cost-demanding task, often leading to suboptimal properties for drug delivery carriers. Moreover, existing experimental methods provide limited insights to molecular-level interactions, hindering the possibility of improving these carriers’ performance. To this end, we have utilized the molecular dynamics (MD) simulations approach to gain mechanistic insights into the intermolecular interactions of these polymeric micelles. We subjected micelles comprising biodegradable amphiphilic polycaprolactones to MD simulation, determining transition temperature and drug-polymer interaction, and compared it with experimental data. We foresight our approach to expedite the development of micellar drug delivery carriers for their potential application in doxorubicin drug delivery.

Methylation is important in the regulation, function, and expression of genes. This is done by a class of enzymes known as methyltransferases. Protein methyltransferases methylate the lysine or arginine residue of another substrate. When methylation is not regulated it can lead to diseases including cancer. Most inhibitors of this process lack specificity due to the similarity of its conformation to the catalytically active configuration to its methyl donors. S-Adenosyl methionine (SAM) is a cofactor that can be a source of the methyl group for this process. Molecular dynamics (MD) simulations are used to study SAM in the binding pocket of methyltransferases. The goal of this study is to probe for inactive SAM conformations in the binding pocket of the methyltransferase. These conformations should be unique to the methyltransferase. This presentation focuses on the conformations of SAM in the SET7/9 protein in complex with a TAF10 peptide. IR data from our collaborator suggests that SAM can occupy different conformations in the SET7/9 binding pocket. Identification of these different conformations of SAM in the binding pocket can be used as a model to design more specific inhibitors. MD can help in the exploration of conformations that SAM may occupy too quickly for NMR studies or conformations that may get averaged out in x-ray crystallography.

A lot of attention has been brought to surfaces with low adhesion that allow shedding of low surface tension liquids, such as water. This is due to their broad applications in refrigeration, water harvesting and miscellaneous commercial use. The goal of this experiment is to computationally test polymers with terminal groups of varying hydrophobicity and polymer length in a liquid bridge to ascertain the correct qualifications for effective droplet shedding. A surface with the polymers is created and mirrored to create a “sandwiched” system. Then water or hexane is placed between the two surface plates. The results will allow us to accurately and directly measure the force being applied by the liquid between the surfaces in order to determine the stability of the system. Highly mobile polydimethylsiloxane is being studied on account of its high mobility that allows water shedding, alongside rigid self-assembled alkanethiols. An advantage MD has in modeling these surfaces is the enhanced way to measure the force excreted on the surface on the water, as well as a higher control on the overall system.