College of Sciences Faculty Research

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    Impact of endosymbionts on tick physiology and fitness
    (Cambridge University Press, 2023-08-24) Kolo, Agatha O.; Raghavan, Rahul
    Ticks transmit pathogens and harbour non-pathogenic, vertically transmitted intracellular bacteria termed endosymbionts. Almost all ticks studied to date contain 1 or more of Coxiella, Francisella, Rickettsia or Candidatus Midichloria mitochondrii endosymbionts, indicative of their importance to tick physiology. Genomic and experimental data suggest that endosymbionts promote tick development and reproductive success. Here, we review the limited information currently available on the potential roles endosymbionts play in enhancing tick metabolism and fitness. Future studies that expand on these findings are needed to better understand endosymbionts’ contributions to tick biology. This knowledge could potentially be applied to design novel strategies that target endosymbiont function to control the spread of ticks and pathogens they vector.
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    Thorium: A Language for Bounded Verification of Dynamic Reactive Objects
    (Association for Computing Machinery, 2023-10-19) Baldor, Kevin; Wang, Xiaoyin; Niu, Jianwei
    Developing reliable reactive software is notoriously difficult – particularly when that software reacts by changing its behavior. Some of this difficulty is inherent; software that must respond to external events as they arrive tends to end up in states that are dependent on the value of that input and its order of arrival. This results in complicated corner cases that can be challenging to recognize. However, we find that some of the complexity is an accident of the features of the programming languages widely used in industry. The loops and subroutines of structured programming are well-suited to data transformation, but poorly capture – and sometimes obscure – the flow of data through reactive programs developed using the inversion-of-control paradigm; an event handler that modifies the data flow tends to be declared closer to the definition of the event that activates it than to the initial definition of the data flow that it modifies. This paper approaches both challenges with a language inspired by the declarative modules of languages SIGNAL and Lustre and the semantics of the SodiumFRP Functional Reactive Programming library with a declarative mechanism for self modification through module substitution. These language features lead to software with a code structure that closely matches the flow of data through the running program and thus makes software easier to understand. Further, we demonstrate how those language features enable a bounded model checking approach that can verify that a reactor meets its requirements or present a counterexample trace, a series of states and inputs that lead to a violation. We analyze the runtime performance of the verifier as a function of model size and trace length.
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    MemPerf: Profiling Allocator-Induced Performance Slowdowns
    (Association for Computing Machinery, 2023-10-16) Zhou, Jin; Silvestro, Sam; Tang, Steven (Jiaxun); Yang, Hanmei; Liu, Hongyu; Zeng, Guangming; Wu, Bo; Liu, Cong; Liu, Tongping
    The memory allocator plays a key role in the performance of applications, but none of the existing profilers can pinpoint performance slowdowns caused by a memory allocator. Consequently, programmers may spend time improving application code incorrectly or unnecessarily, achieving low or no performance improvement. This paper designs the first profiler—MemPerf—to identify allocator-induced performance slowdowns without comparing against another allocator. Based on the key observation that an allocator may impact the whole life-cycle of heap objects, including the accesses (or uses) of these objects, MemPerf proposes a life-cycle based detection to identify slowdowns caused by slow memory management operations and slow accesses separately. For the prior one, MemPerf proposes a thread-aware and type-aware performance modeling to identify slow management operations. For slow memory accesses, MemPerf utilizes a top-down approach to identify all possible reasons for slow memory accesses introduced by the allocator, mainly due to cache and TLB misses, and further proposes a unified method to identify them correctly and efficiently. Based on our extensive evaluation, MemPerf reports 98% medium and large allocator-reduced slowdowns (larger than 5%) correctly without reporting any false positives. MemPerf also pinpoints multiple known and unknown design issues in widely-used allocators.
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    PExReport-Maven: Creating Pruned Executable Cross-Project Failure Reports in Maven Build System
    (Association for Computing Machinery, 2023-07-13) Huang, Sunzhou; Wang, Xiaoyin
    Modern Java software development extensively depends on existing libraries written by other developer teams from the same or a different organization. When a developer executes the test, the execution trace may go across the boundaries of multiple dependencies and create cross-project failures (CPFs). A readable, executable, and concise CPF report may enable the most effective communication, but creating such a report is often challenging in Java ecosystems. We developed PExReport-Maven to automatically create the ideal CPF reports in the Maven build system. PExReport-Maven leverages the Maven build system to prune source code, dependencies, and the build environment to create a concise stand-alone executable CPF reproduction package from the original CPF project. The reproduction package includes the source code, dependencies, and build environment necessary to reproduce the CPF, making it an ideal CPF report. We performed an evaluation on 74 software project issues with 198 cross-project failures, and the evaluation results show that PExReport can create pruned reproduction packages for 184 out of the 198 test failures in our dataset, with an average reduction of 72.97% in Java classes. A future study will be conducted based on user feedback from using this tool to report real-world CPFs. PExReport-Maven is publicly available at The tool demo is available on the PExReport website:
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    The Eyes Have It: Visual Feedback Methods to Make Walking in Immersive Virtual Reality More Accessible for People With Mobility Impairments While Utilizing Head-Mounted Displays
    (Association for Computing Machinery, 2023-10-22) Mahmud, M. Rasel; Cordova, Alberto; Quarles, John
    The use of Head-Mounted Displays (HMDs) in Virtual Reality (VR) can cause gait disturbance problems for users because they are unable to see the real world while in VR. This is particularly challenging for individuals with mobility impairments who rely heavily on visual cues to maintain balance. The limited research that has been conducted on this issue has not focused on ways to solve it. IN this study, we investigated how different visual feedback methods affect walking patterns (i.e., gait) in VR. The study involved 50 participants, including 25 individuals with mobility impairments due to multiple sclerosis and 25 without mobility impairments. The participants completed timed walking tasks in both the real world and in VR environments that included various types of visual feedback, such as spatial, static, and rhythmic. The results showed that static and rhythmic visual feedback significantly improved gait performance in VR for people with mobility impairments compared to no visual feedback in VR. The results will help to make more accessible virtual environments for people with mobility impairments.
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    Context Modulation Enables Multi-tasking and Resource Efficiency in Liquid State Machines
    (Association for Computing Machinery, 2023-08-28) Helfer, Peter; Teeter, Corinne; Hill, Aaron; Vineyard, Craig M.; Aimone, James B.; Kudithipudi, Dhireesha
    Memory storage and retrieval are context-sensitive in both humans and animals; memories are more accurately retrieved in the context where they were acquired, and similar stimuli can elicit different responses in different contexts. Researchers have suggested that such effects may be underpinned by mechanisms that modulate the dynamics of neural circuits in a context-dependent fashion. Based on this idea, we design a mechanism for context-dependent modulation of a liquid state machine, a recurrent spiking artificial neural network. We find that context modulation enables a single network to multitask and requires fewer neurons than when several smaller networks are used to perform the tasks individually.
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    What’s (Not) Working in Programmer User Studies?
    (Association for Computing Machinery, 2023-07-24) Davis, Matthew C.; Aghayi, Emad; Latoza, Thomas D.; Wang, Xiaoyin; Myers, Brad A.; Sunshine, Joshua
    A key goal of software engineering research is to improve the environments, tools, languages, and techniques programmers use to efficiently create quality software. Successfully designing these tools and demonstrating their effectiveness involves engaging with tool users—software engineers. Researchers often want to conduct user studies of software engineers to collect direct evidence. However, running user studies can be difficult, and researchers may lack solution strategies to overcome the barriers, so they may avoid user studies. To understand the challenges researchers face when conducting programmer user studies, we interviewed 26 researchers. Based on the analysis of interview data, we contribute (i) a taxonomy of 18 barriers researchers encounter; (ii) 23 solution strategies some researchers use to address 8 of the 18 barriers in their own studies; and (iii) 4 design ideas, which we adapted from the behavioral science community, that may lower 8 additional barriers. To validate the design ideas, we held an in-person all-day focus group with 16 researchers.
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    NEO: Neuron State Dependent Mechanisms for Efficient Continual Learning
    (Association for Computing Machinery, 2023-04-12) Daram, Anurag; Kudithipudi, Dhireesha
    Continual learning (sequential learning of tasks) is challenging for deep neural networks, mainly because of catastrophic forgetting, the tendency for accuracy on previously trained tasks to drop when new tasks are learned. Although several biologically-inspired techniques have been proposed for mitigating catastrophic forgetting, they typically require additional memory and/or computational overhead. Here, we propose a novel regularization approach that combines neuronal activation-based importance measurement with neuron state-dependent learning mechanisms to alleviate catastrophic forgetting in both task-aware and task-agnostic scenarios. We introduce a neuronal state-dependent mechanism driven by neuronal activity traces and selective learning rules, with storage requirements for regularization parameters that grow slower with network size - compared to schemes that calculate weight importance, whose storage grows quadratically. The proposed model, NEO, is able to achieve performance comparable to other state-of-the-art regularization based approaches to catastrophic forgetting, while operating with a reduced memory overhead.
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    Protocol-agnostic IoT Device Classification on Encrypted Traffic Using Link-Level Flows
    (Association for Computing Machinery, 2023-05-09) Morales, Gabriel A.; Bienek-Parrish, Adam; Jenkins, Patrick; Slavin, Rocky
    Convenience is a strong driver for the evolution of technology. Such efforts have given rise to the Internet-of-Things (IoT), defined as the network of everyday devices (i.e., “things”) ranging from light bulbs to smart speakers, connected to the Internet and each other. IoT devices frequently transmit data wirelessly which can be passively collected by an adversary. In this work we present a methodology with which to perform device classification on encrypted traffic in a protocol-agnostic manner by applying network flow analysis to link-level data. Our evaluation demonstrates successful device classification for 15 device categories with an overall weighted F1-Score of 95% on a dataset consisting of Wi-Fi, Bluetooth, and Zigbee traffic. Furthermore, we explore model transferability between encrypted and decrypted datasets on these three networking protocols and present our flow generation tool, ProtoFlow.
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    Extraction-Free Detection of SARS-CoV-2 Viral RNA Using LumiraDx's RNA Star Complete Assay from Clinical Nasal Swabs Stored in a Novel Collection and Transport Medium
    (2023-09-21) Daum, Luke T.; Rodriguez, John D.; Ward, Susan R.; Chambers, James P.
    Background: The rapid detection of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is vital for patient care. The LumiraDx™ SARS-CoV-2 RNA Star Complete (RSC) is an Emergency Use Authorization-recognized molecular test using nasal/nasopharyngeal swabs immersed in a viral/universal transport medium (VTM/UTM). However, there is a critical need for an alternative medium for point-of-care testing (POCT). This study aimed to investigate Xtract-Free (XF), a novel collection medium for transport and direct (extraction-free) use with nucleic acid tests. Methods: Using serially diluted SARS-CoV-2 viral RNA (vRNA) in a routine UTM and XF, a limit of detection (LOD) was established via an RSC test and a quantitative reverse transcription PCR (RT-qPCR). Additionally, the results obtained from a panel of 108 clinical “car-side” nasal swabs collected in XF during the coronavirus pandemic and assessed using the ”gold-standard” RT-qPCR assay were compared to Lumira’s RSC assay. Results: The average replicate RT-qPCR cycle threshold (CT) values for vRNA in XF and UTM were observed to be equivalent. An LOD for which five out of five replicates were detected using XF or VTM was approximately 2000 copies/mL. The nasal swabs collected in XF exhibited 93.9% positive percent agreement (sensitivity) and 100% negative percent agreement (specificity) compared to the RT-qPCR. Three specimens tested positive via an RT-qPCR were negative when tested via RSC; however, all three samples had CT values ≥ 36.4. Conclusions: XF is equivalent to VTM/UTM and is compatible for use with the RSC test. Furthermore, XF can be used directly with RT-qPCRs and rapid antigen testing without the requirement for separate nucleic acid extraction (an extraction-free process), making it ideal for cost-effective high-throughput and decentralized respiratory testing. Impact Statement: This study is the first to evaluate LumiraDx’s SARS-CoV-2 RNA Star Complete assay in concert with Xtract-Free (XF), a novel collection medium containing a proprietary RNase-inactivating technology for the rapid, ”extraction-free” detection of SARS-CoV-2 RNA from clinical nasal swabs. Specimens collected in XF combined with rapid LumiraDx detection provide a safe and sensitive alternative to VTM/UTM, and Molecular Transport medium (MTM) for high throughput, “extraction-free” molecular detection.
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    New Results Achieved for Fractional Differential Equations with Riemann–Liouville Derivatives of Nonlinear Variable Order
    (2023-09-20) Abdelhamid, Hallouz; Stamov, Gani; Souid, Mohammed Said; Stamova, Ivanka
    This paper proposes new existence and uniqueness results for an initial value problem (IVP) of fractional differential equations of nonlinear variable order. Riemann–Liouville-type fractional derivatives are considered in the problem. The new fundamental results achieved in this work are obtained by using the inequalities technique and the fixed point theory. In addition, uniform stability criteria for the solutions are derived. The accomplished results are new and complement the scientific research in the field. A numerical example is composed to show the efficacy and potency of the proposed criteria.
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    New Approach for Generating Synthetic Medical Data to Predict Type 2 Diabetes
    (2023-09-01) Tagmatova, Zarnigor; Abdusalomov, Akmalbek; Nasimov, Rashid; Nasimova, Nigorakhon; Dogru, Ali Hikmet; Cho, Young-Im
    The lack of medical databases is currently the main barrier to the development of artificial intelligence-based algorithms in medicine. This issue can be partially resolved by developing a reliable high-quality synthetic database. In this study, an easy and reliable method for developing a synthetic medical database based only on statistical data is proposed. This method changes the primary database developed based on statistical data using a special shuffle algorithm to achieve a satisfactory result and evaluates the resulting dataset using a neural network. Using the proposed method, a database was developed to predict the risk of developing type 2 diabetes 5 years in advance. This dataset consisted of data from 172,290 patients. The prediction accuracy reached 94.45% during neural network training of the dataset.
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    High-Throughput Screening of the Repurposing Hub Library to Identify Drugs with Novel Inhibitory Activity against Candida albicans and Candida auris Biofilms
    (2023-08-27) Ajetunmobi, Olabayo H.; Wall, Gina; Vidal Bonifacio, Bruna; Martinez Delgado, Lucero A.; Chaturvedi, Ashok K.; Najvar, Laura K.; Wormley, Floyd L.; Patterson, Hoja P.; Wiederhold, Nathan P.; Patterson, Thomas F.; Lopez-Ribot, Jose L.
    Candidiasis is one of the most frequent nosocomial infections affecting an increasing number of at-risk patients. Candida albicans remains the most frequent causative agent of candidiasis, but, in the last decade, C. auris has emerged as a formidable multi-drug-resistant pathogen. Both species are fully capable of forming biofilms, which contribute to resistance, increasing the urgency for new effective antifungal therapies. Repurposing existing drugs could significantly accelerate the development of novel therapies against candidiasis. Here, we have screened the Repurposing Hub library from the Broad Institute, containing over 6000 compounds, in search for inhibitors of C. albicans and C. auris biofilm formation. The primary screen identified 57 initial hits against C. albicans and 33 against C. auris. Confirmatory concentration-dependent assays were used to validate the activity of the initial hits and, at the same time, establish their anti-biofilm potency. Based on these results, ebselen, temsirolimus, and compound BAY 11-7082 emerged as the leading repositionable compounds. Subsequent experiments established their spectrum of antifungal activity against yeasts and filamentous fungi. In addition, their in vivo activity was examined in the murine models of hematogenously disseminated C. albicans and C. auris infections. Although promising, further in vitro and in vivo studies are needed to confirm their potential use for the therapy of candidiasis and possibly other fungal infections.
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    Novel NADPH Oxidase-2 Inhibitors as Potential Anti-Inflammatory and Neuroprotective Agents
    (2023-08-23) Juric, Matea; Rawat, Varun; Amaradhi, Radhika; Zielonka, Jacek; Ganesh, Thota
    A family of seven NADPH oxidase enzymes (Nox1-5, Duox1-2) has been implicated in a variety of diseases, including inflammatory lung diseases, neurodegenerative diseases, cardiovascular diseases, and cancer. Here, we report the results of our studies aimed at developing novel brain-permeable Nox2 inhibitors with potential application as neuroprotective agents. Using cell-based assays, we identified a novel Nox2 inhibitor, TG15-132, that prevents PMA-stimulated oxygen consumption and reactive oxygen species (superoxide radical anion and hydrogen peroxide) formation upon acute treatment in differentiated HL60 cells. Long-term treatment with TG15-132 attenuates the induction of genes encoding Nox2 subunits, several inflammatory cytokines, and iNOS in differentiated THP-1 cells. Moreover, TG15-132 shows a relatively long plasma half-life (5.6 h) and excellent brain permeability, with a brain-to-plasma ratio (>5-fold) in rodent models. Additionally, TG15-132 does not cause any toxic effects on vital organs or blood biomarkers of toxicity in mice upon chronic dosing for seven days. We propose that TG15-132 may be used as a Nox2 inhibitor and a potential neuroprotective agent, with possible further structural modifications to increase its potency.
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    A Customized Human Mitochondrial DNA Database (hMITO DB v1.0) for Rapid Sequence Analysis, Haplotyping and Geo-Mapping
    (2023-08-31) Shen-Gunther, Jane; Gunther, Rutger S.; Cai, Hong; Wang, Yufeng
    The field of mitochondrial genomics has advanced rapidly and has revolutionized disciplines such as molecular anthropology, population genetics, and medical genetics/oncogenetics. However, mtDNA next-generation sequencing (NGS) analysis for matrilineal haplotyping and phylogeographic inference remains hindered by the lack of a consolidated mitogenome database and an efficient bioinformatics pipeline. To address this, we developed a customized human mitogenome database (hMITO DB) embedded in a CLC Genomics workflow for read mapping, variant analysis, haplotyping, and geo-mapping. The database was constructed from 4286 mitogenomes. The macro-haplogroup (A to Z) distribution and representative phylogenetic tree were found to be consistent with published literature. The hMITO DB automated workflow was tested using mtDNA-NGS sequences derived from Pap smears and cervical cancer cell lines. The auto-generated read mapping, variants track, and table of haplotypes and geo-origins were completed in 15 min for 47 samples. The mtDNA workflow proved to be a rapid, efficient, and accurate means of sequence analysis for translational mitogenomics.
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    Ac-Susceptibility Studies of the Energy Barrier to Magnetization Reversal in Frozen Magnetic Nanofluids of Different Concentrations
    (2023-08-19) Botez, Cristian E.; Price, Alex D.
    We used ac-susceptibility to measure the blocking temperature, TB, and energy barrier to the magnetization reversal, EB, of nanomagnetic fluids of different concentrations, c. We collected data on five samples synthesized by dispersing Fe3O4 nanoparticles of average diameter D = 8 nm in different volumes of carrier fluid (hexane). We found that TB increases with the increase in c, a behavior predicted by the Dormann–Bessais–Fiorani (DBF) theory. In addition, our observed TB vs. c dependence is excellently described by a power law TB = A·c^γ, with A = 64 K and γ = 0.056. Our data also show that a Néel–Brown activation law τ(T) = τ0 exp(EB/(kBT)) describes the superspin dynamics in the most diluted sample, whereas an additional energy barrier term, Ead, is needed at higher concentrations, according to the DBF model: τ(T) = τr exp ((EB + Ead)/(kBT)). We found EB/kB = 366 K and additional energy barriers Ead/kB that increase linearly with the common logarithm of the volume concentration, from 138 K at c = 8.3 × 10^(−4)% to 745 K at c = 4 × 10^(−2)%. These results add to our understanding of the contributions by different factors to the superspin dynamics. In addition, the quantitative relations that we established between the TB, Ead, and c support the current efforts towards the rational design of functional nanomaterials.
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    Roles of Oxidative Stress in Synaptic Dysfunction and Neuronal Cell Death in Alzheimer's Disease
    (2023-08-17) Plascencia-Villa, Germán; Perry, George
    Alzheimer’s disease (AD) is a brain disorder that progressively undermines memory and thinking skills by affecting the hippocampus and entorhinal cortex. The main histopathological hallmarks of AD are the presence of abnormal protein aggregates (Aβ and tau), synaptic dysfunction, aberrant proteostasis, cytoskeletal abnormalities, altered energy homeostasis, DNA and RNA defects, inflammation, and neuronal cell death. However, oxidative stress or oxidative damage is also evident and commonly overlooked or considered a consequence of the advancement of dementia symptoms. The control or onset of oxidative stress is linked to the activity of the amyloid-β peptide, which may serve as both antioxidant and pro-oxidant molecules. Furthermore, oxidative stress is correlated with oxidative damage to proteins, nucleic acids, and lipids in vulnerable cell populations, which ultimately lead to neuronal death through different molecular mechanisms. By recognizing oxidative stress as an integral feature of AD, alternative therapeutic or preventive interventions are developed and tested as potential or complementary therapies for this devastating neurodegenerative disease.
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    Exploring Molecular Targets for Mitochondrial Therapies in Neurodegenerative Diseases
    (2023-08-06) Plascencia-Villa, Germán; Perry, George
    The progressive deterioration of function and structure of brain cells in neurodegenerative diseases is accompanied by mitochondrial dysfunction, affecting cellular metabolism, intracellular signaling, cell differentiation, morphogenesis, and the activation of programmed cell death. However, most of the efforts to develop therapies for Alzheimer's and Parkinson's disease have focused on restoring or maintaining the neurotransmitters in affected neurons, removing abnormal protein aggregates through immunotherapies, or simply treating symptomatology. However, none of these approaches to treating neurodegeneration can stop or reverse the disease other than by helping to maintain mental function and manage behavioral symptoms. Here, we discuss alternative molecular targets for neurodegeneration treatments that focus on mitochondrial functions, including regulation of calcium ion (Ca2+) transport, protein modification, regulation of glucose metabolism, antioxidants, metal chelators, vitamin supplementation, and mitochondrial transference to compromised neurons. After pre-clinical evaluation and studies in animal models, some of these therapeutic compounds have advanced to clinical trials and are expected to have positive outcomes in subjects with neurodegeneration. These mitochondria-targeted therapeutic agents are an alternative to established or conventional molecular targets that have shown limited effectiveness in treating neurodegenerative diseases.
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    Fetal brain vulnerability to SARS-CoV-2 infection
    (Elsevier, 2023-08) McMahon, Courtney L.; Castro, Joshua; Silvas, Jesus; Muniz Perez, Aranis; Estrada, Manuel; Carrion, Ricardo Jr.; Hsieh, Jenny
    Whether or not SARS-CoV-2 can cross from mother to fetus during a prenatal infection has been controversial; however, recent evidence such as viral RNA detection in umbilical cord blood and amniotic fluid, as well as the discovery of additional entry receptors in fetal tissues suggests a potential for viral transmission to and infection of the fetus. Furthermore, neonates exposed to maternal COVID-19 during later development have displayed neurodevelopmental and motor skill deficiencies, suggesting the potential for consequential neurological infection or inflammation in utero. Thus, we investigated transmission potential of SARS-CoV-2 and the consequences of infection on the developing brain using human ACE2 knock-in mice. In this model, we found that viral transmission to the fetal tissues, including the brain, occurred at later developmental stages, and that infection primarily targeted male fetuses. In the brain, SARS-CoV-2 infection largely occurred within the vasculature, but also within other cells such as neurons, glia, and choroid plexus cells; however, viral replication and increased cell death were not observed in fetal tissues. Interestingly, early gross developmental differences were observed between infected and mock-infected offspring, and high levels of gliosis were seen in the infected brains 7 days post initial infection despite viral clearance at this time point. In the pregnant mice, we also observed more severe COVID-19 infections, with greater weight loss and viral dissemination to the brain, compared to non-pregnant mice. Surprisingly, we did not observe an increase in maternal inflammation or the antiviral IFN response in these infected mice, despite showing clinical signs of disease. Overall, these findings have concerning implications regarding neurodevelopment and pregnancy complications of the mother following prenatal COVID-19 exposure.
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    Development of an Imaging Flow Cytometry Method for Fungal Cytological Profiling and Its Potential Application in Antifungal Drug Development
    (2023-06-30) McMahon, Courtney L.; Esqueda, Marisol; Yu, Jieh-Juen; Wall, Gina; Romo, Jesus A.; Vila, Taissa; Chaturvedi, Ashok; Lopez-Ribot, Jose L.; Wormley, Floyd; Hung, Chiung-Yu
    Automated imaging techniques have been in increasing demand for the more advanced analysis and efficient characterization of cellular phenotypes. The success of the image-based profiling method hinges on assays that can rapidly and simultaneously capture a wide range of phenotypic features. We have developed an automated image acquisition method for fungal cytological profiling (FCP) using an imaging flow cytometer that can objectively measure over 250 features of a single fungal cell. Fungal cells were labeled with calcofluor white and FM4-64FX, which bind to the cell wall and lipophilic membrane, respectively. Images of single cells were analyzed using IDEAS(R) software. We first acquired FCPs of fungal cells treated with fluconazole, amphotericin B, and caspofungin, each with a distinct mode of action, to establish FCP databases of profiles associated with specific antifungal treatment. Once fully established, we investigated the potential application of this technique as a screening methodology to identify compounds with novel antifungal activity against Candida albicans and Cryptococcus neoformans. Altogether, we have developed a rapid, powerful, and novel image-profiling method for the phenotypic characterization of fungal cells, also with potential applications in antifungal drug development.