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Open Access Publications from the University of California

Open Access Policy Deposits

This series is automatically populated with publications deposited by UC Irvine Donald Bren School of Information and Computer Sciences Department of Computer Science researchers in accordance with the University of California’s open access policies. For more information see Open Access Policy Deposits and the UC Publication Management System.
Cover page of UCI CANSAT

UCI CANSAT

(2022)

Mission Summary: Launch & Ascent Phase: The CanSat is mission ready inside the rocket. It is launched to an altitude of 670-725 meters for the Deployment Phase to proceed. Deployment Phase: The rocket separates via ejection charges causing the CanSat and nose cone to fall out. Here, telemetry begins and the CanSat descends at 15 m/s. Descent Phases: 1. An altitude of 400 meters, a larger parachute is deployed to reduce the CanSat’s descent to 5 m/2/. 2. At 300 meters, the Cansat releases a tethered payload to a distance of 10 meters for 20 secs. Said payload will record via a camera oriented in the South direction at 45° downwards. Landing Phase: CanSat and all internal components must land safely intact for recovery.

Hippocampal ensembles represent sequential relationships among an extended sequence of nonspatial events.

(2022)

The hippocampus is critical to the temporal organization of our experiences. Although this fundamental capacity is conserved across modalities and species, its underlying neuronal mechanisms remain unclear. Here we recorded hippocampal activity as rats remembered an extended sequence of nonspatial events unfolding over several seconds, as in daily life episodes in humans. We then developed statistical machine learning methods to analyze the ensemble activity and discovered forms of sequential organization and coding important for order memory judgments. Specifically, we found that hippocampal ensembles provide significant temporal coding throughout nonspatial event sequences, differentiate distinct types of task-critical information sequentially within events, and exhibit theta-associated reactivation of the sequential relationships among events. We also demonstrate that nonspatial event representations are sequentially organized within individual theta cycles and precess across successive cycles. These findings suggest a fundamental function of the hippocampal network is to encode, preserve, and predict the sequential order of experiences.

Commentary: How Do Microglia Regulate Neural Circuit Connectivity and Activity in the Adult Brain?

(2022)

Microglia are the primary immune cells in CNS. Recent work shows that microglia are also essential for proper brain development through synaptic pruning and remodeling during early life development. But the question of whether and how microglia regulate synaptic connectivity in the adult brain remains open. Our recently published study provides new insights into the functional roles of microglia in the adult mouse brain. We find that chronic depletion of microglia via CSF1R inhibitors in the visual cortex in adult mice induces a dramatic increase in perineuronal nets, and enhances neural activities of both excitatory neurons and parvalbumin interneurons. These findings highlight new potential therapeutic avenues to enhance adult neural plasticity by manipulating microglia.

A novel H129-based anterograde monosynaptic tracer exhibits features of strong labeling intensity, high tracing efficiency, and reduced retrograde labeling.

(2022)

Background

Viral tracers are important tools for mapping brain connectomes. The feature of predominant anterograde transneuronal transmission offers herpes simplex virus-1 (HSV-1) strain H129 (HSV1-H129) as a promising candidate to be developed as anterograde viral tracers. In our earlier studies, we developed H129-derived anterograde polysynaptic tracers and TK deficient (H129-dTK) monosynaptic tracers. However, their broad application is limited by some intrinsic drawbacks of the H129-dTK tracers, such as low labeling intensity due to TK deficiency and potential retrograde labeling caused by axon terminal invasion. The glycoprotein K (gK) of HSV-1 plays important roles in virus entry, egress, and virus-induced cell fusion. Its deficiency severely disables virus egress and spread, while only slightly limits viral genome replication and expression of viral proteins. Therefore, we created a novel H129-derived anterograde monosynaptic tracer (H129-dgK) by targeting gK, which overcomes the limitations of H129-dTK.

Methods

Using our established platform and pipeline for developing viral tracers, we generated a novel tracer by deleting the gK gene from the H129-G4. The gK-deleted virus (H129-dgK-G4) was reconstituted and propagated in the Vero cell expressing wildtype H129 gK (gKwt) or the mutant gK (gKmut, A40V, C82S, M223I, L224V, V309M), respectively. Then the obtained viral tracers of gKmut pseudotyped and gKwt coated H129-dgK-G4 were tested in vitro and in vivo to characterize their tracing properties.

Results

H129-dgK-G4 expresses high levels of fluorescent proteins, eliminating the requirement of immunostaining for imaging detection. Compared to the TK deficient monosynaptic tracer H129-dTK-G4, H129-dgK-G4 labeled neurons with 1.76-fold stronger fluorescence intensity, and visualized 2.00-fold more postsynaptic neurons in the downstream brain regions. gKmut pseudotyping leads to a 77% decrease in retrograde labeling by reducing axon terminal invasion, and thus dramatically improves the anterograde-specific tracing of H129-dgK-G4. In addition, assisted by the AAV helper trans-complementarily expressing gKwt, H129-dgK-G4 allows for mapping monosynaptic connections and quantifying the circuit connectivity difference in the Alzheimer's disease and control mouse brains.

Conclusions

gKmut pseudotyped H129-dgK-G4, a novel anterograde monosynaptic tracer, overcomes the limitations of H129-dTK tracers, and demonstrates desirable features of strong labeling intensity, high tracing efficiency, and improved anterograde specificity.

DEGnext: classification of differentially expressed genes from RNA-seq data using a convolutional neural network with transfer learning.

(2022)

Background

A limitation of traditional differential expression analysis on small datasets involves the possibility of false positives and false negatives due to sample variation. Considering the recent advances in deep learning (DL) based models, we wanted to expand the state-of-the-art in disease biomarker prediction from RNA-seq data using DL. However, application of DL to RNA-seq data is challenging due to absence of appropriate labels and smaller sample size as compared to number of genes. Deep learning coupled with transfer learning can improve prediction performance on novel data by incorporating patterns learned from other related data. With the emergence of new disease datasets, biomarker prediction would be facilitated by having a generalized model that can transfer the knowledge of trained feature maps to the new dataset. To the best of our knowledge, there is no Convolutional Neural Network (CNN)-based model coupled with transfer learning to predict the significant upregulating (UR) and downregulating (DR) genes from both trained and untrained datasets.

Results

We implemented a CNN model, DEGnext, to predict UR and DR genes from gene expression data obtained from The Cancer Genome Atlas database. DEGnext uses biologically validated data along with logarithmic fold change values to classify differentially expressed genes (DEGs) as UR and DR genes. We applied transfer learning to our model to leverage the knowledge of trained feature maps to untrained cancer datasets. DEGnext's results were competitive (ROC scores between 88 and 99[Formula: see text]) with those of five traditional machine learning methods: Decision Tree, K-Nearest Neighbors, Random Forest, Support Vector Machine, and XGBoost. DEGnext was robust and effective in terms of transferring learned feature maps to facilitate classification of unseen datasets. Additionally, we validated that the predicted DEGs from DEGnext were mapped to significant Gene Ontology terms and pathways related to cancer.

Conclusions

DEGnext can classify DEGs into UR and DR genes from RNA-seq cancer datasets with high performance. This type of analysis, using biologically relevant fine-tuning data, may aid in the exploration of potential biomarkers and can be adapted for other disease datasets.

Noncanonical projections to the hippocampal CA3 regulate spatial learning and memory by modulating the feedforward hippocampal trisynaptic pathway.

(2021)

The hippocampal formation (HF) is well documented as having a feedforward, unidirectional circuit organization termed the trisynaptic pathway. This circuit organization exists along the septotemporal axis of the HF, but the circuit connectivity across septal to temporal regions is less well described. The emergence of viral genetic mapping techniques enhances our ability to determine the detailed complexity of HF circuitry. In earlier work, we mapped a subiculum (SUB) back projection to CA1 prompted by the discovery of theta wave back propagation from the SUB to CA1 and CA3. We reason that this circuitry may represent multiple extended noncanonical pathways involving the subicular complex and hippocampal subregions CA1 and CA3. In the present study, multiple retrograde viral tracing approaches produced robust mapping results, which supports this prediction. We find significant noncanonical synaptic inputs to dorsal hippocampal CA3 from ventral CA1 (vCA1), perirhinal cortex (Prh), and the subicular complex. Thus, CA1 inputs to CA3 run opposite the trisynaptic pathway and in a temporal to septal direction. Our retrograde viral tracing results are confirmed by anterograde-directed viral mapping of projections from input mapped regions to hippocampal dorsal CA3 (dCA3). We find that genetic inactivation of the projection of vCA1 to dCA3 impairs object-related spatial learning and memory but does not modulate anxiety-related behaviors. Our data provide a circuit foundation to explore novel functional roles contributed by these noncanonical hippocampal circuit connections to hippocampal circuit dynamics and learning and memory behaviors.

Cover page of Deep underground neutrino experiment (DUNE) near detector conceptual design report

Deep underground neutrino experiment (DUNE) near detector conceptual design report

(2021)

The Deep Underground Neutrino Experiment (DUNE) is an international, world-class experiment aimed at exploring fundamental questions about the universe that are at the forefront of astrophysics and particle physics research. DUNE will study questions pertaining to the preponderance of matter over antimatter in the early universe, the dynamics of supernovae, the subtleties of neutrino interaction physics, and a number of beyond the Standard Model topics accessible in a powerful neutrino beam. A critical component of the DUNE physics program involves the study of changes in a powerful beam of neutrinos, i.e., neutrino oscillations, as the neutrinos propagate a long distance. The experiment consists of a near detector, sited close to the source of the beam, and a far detector, sited along the beam at a large distance. This document, the DUNE Near Detector Conceptual Design Report (CDR), describes the design of the DUNE near detector and the science program that drives the design and technology choices. The goals and requirements underlying the design, along with projected performance are given. It serves as a starting point for a more detailed design that will be described in future documents.

A Technology-Based Pregnancy Health and Wellness Intervention (Two Happy Hearts): Case Study.

(2021)

Background

The physical and emotional well-being of women is critical for healthy pregnancy and birth outcomes. The Two Happy Hearts intervention is a personalized mind-body program coached by community health workers that includes monitoring and reflecting on personal health, as well as practicing stress management strategies such as mindful breathing and movement.

Objective

The aims of this study are to (1) test the daily use of a wearable device to objectively measure physical and emotional well-being along with subjective assessments during pregnancy, and (2) explore the user's engagement with the Two Happy Hearts intervention prototype, as well as understand their experiences with various intervention components.

Methods

A case study with a mixed design was used. We recruited a 29-year-old woman at 33 weeks of gestation with a singleton pregnancy. She had no medical complications or physical restrictions, and she was enrolled in the Medi-Cal public health insurance plan. The participant engaged in the Two Happy Hearts intervention prototype from her third trimester until delivery. The Oura smart ring was used to continuously monitor objective physical and emotional states, such as resting heart rate, resting heart rate variability, sleep, and physical activity. In addition, the participant self-reported her physical and emotional health using the Two Happy Hearts mobile app-based 24-hour recall surveys (sleep quality and level of physical activity) and ecological momentary assessment (positive and negative emotions), as well as the Perceived Stress Scale, Center for Epidemiologic Studies Depression Scale, and State-Trait Anxiety Inventory. Engagement with the Two Happy Hearts intervention was recorded via both the smart ring and phone app, and user experiences were collected via Research Electronic Data Capture satisfaction surveys. Objective data from the Oura ring and subjective data on physical and emotional health were described. Regression plots and Pearson correlations between the objective and subjective data were presented, and content analysis was performed for the qualitative data.

Results

Decreased resting heart rate was significantly correlated with increased heart rate variability (r=-0.92, P<.001). We found significant associations between self-reported responses and Oura ring measures: (1) positive emotions and heart rate variability (r=0.54, P<.001), (2) sleep quality and sleep score (r=0.52, P<.001), and (3) physical activity and step count (r=0.77, P<.001). In addition, deep sleep appeared to increase as light and rapid eye movement sleep decreased. The psychological measures of stress, depression, and anxiety appeared to decrease from baseline to post intervention. Furthermore, the participant had a high completion rate of the components of the Two Happy Hearts intervention prototype and shared several positive experiences, such as an increased self-efficacy and a normal delivery.

Conclusions

The Two Happy Hearts intervention prototype shows promise for potential use by underserved pregnant women.

Transcriptome Profiling of Dysregulated GPCRs Reveals Overlapping Patterns across Psychiatric Disorders and Age-Disease Interactions.

(2021)

G-protein-coupled receptors (GPCRs) play an integral role in the neurobiology of psychiatric disorders. Almost all neurotransmitters involved in psychiatric disorders act through GPCRs, and GPCRs are the most common targets of therapeutic drugs currently used in the treatment of psychiatric disorders. However, the roles of GPCRs in the etiology and pathophysiology of psychiatric disorders are not fully understood. Using publically available datasets, we performed a comprehensive analysis of the transcriptomic signatures of G-protein-linked signaling across the major psychiatric disorders: autism spectrum disorder (ASD), schizophrenia (SCZ), bipolar disorder (BP), and major depressive disorder (MDD). We also used the BrainSpan transcriptomic dataset of the developing human brain to examine whether GPCRs that exhibit chronological age-associated expressions have a higher tendency to be dysregulated in psychiatric disorders than age-independent GPCRs. We found that most GPCR genes were differentially expressed in the four disorders and that the GPCR superfamily as a gene cluster was overrepresented in the four disorders. We also identified a greater amplitude of gene expression changes in GPCRs than other gene families in the four psychiatric disorders. Further, dysregulated GPCRs overlapped across the four psychiatric disorders, with SCZ exhibiting the highest overlap with the three other disorders. Finally, the results revealed a greater tendency of age-associated GPCRs to be dysregulated in ASD than random GPCRs. Our results substantiate the central role of GPCR signaling pathways in the etiology and pathophysiology of psychiatric disorders. Furthermore, our study suggests that common GPCRs' signaling may mediate distinct phenotypic presentations across psychiatric disorders. Consequently, targeting these GPCRs could serve as a common therapeutic strategy to treat specific clinical symptoms across psychiatric disorders.

Deep Learning-Assisted Multiphoton Microscopy to Reduce Light Exposure and Expedite Imaging in Tissues With High and Low Light Sensitivity.

(2021)

Purpose

Two-photon excitation fluorescence (2PEF) reveals information about tissue function. Concerns for phototoxicity demand lower light exposure during imaging. Reducing excitation light reduces the quality of the image by limiting fluorescence emission. We applied deep learning (DL) super-resolution techniques to images acquired from low light exposure to yield high-resolution images of retinal and skin tissues.

Methods

We analyzed two methods: a method based on U-Net and a patch-based regression method using paired images of skin (550) and retina (1200), each with low- and high-resolution paired images. The retina dataset was acquired at low and high laser powers from retinal organoids, and the skin dataset was obtained from averaging 7 to 15 frames or 70 frames. Mean squared error (MSE) and the structural similarity index measure (SSIM) were outcome measures for DL algorithm performance.

Results

For the skin dataset, the patches method achieved a lower MSE (3.768) compared with U-Net (4.032) and a high SSIM (0.824) compared with U-Net (0.783). For the retinal dataset, the patches method achieved an average MSE of 27,611 compared with 146,855 for the U-Net method and an average SSIM of 0.636 compared with 0.607 for the U-Net method. The patches method was slower (303 seconds) than the U-Net method (<1 second).

Conclusions

DL can reduce excitation light exposure in 2PEF imaging while preserving image quality metrics.

Translational relevance

DL methods will aid in translating 2PEF imaging from benchtop systems to in vivo imaging of light-sensitive tissues such as the retina.