Miracle-dz/UniFAR-FTUs huggingface dataset:
The mitochondrial Na+/Ca2+ exchanger, NCLX, mediates PDE2 dependent neuronal survival and learning

Impaired phosphodiesterase (PDE) function and mitochondrial Ca2+ - [Ca2+]m signaling leads to cardiac failure, ischemic damage and dysfunctional learning and memory. Yet, a causative link between these pathways is unknown. Here, we fluorescently monitored [Ca2+]m transients in hippocampal neurons evoked by caffeine followed by depolarization. [Ca2+]m efflux was apparent in WT but diminished in neurons deficient in the mitochondrial Na+/Ca2+ exchanger NCLX. Surprisingly, neuronal depolarization-induced Ca2+ transients alone failed to evoke strong [Ca2+]m efflux in WT neurons. Caffeine is also a PDE inhibitor. Pretreatment with the PDE2 inhibitor Bay 60-7550 rescued [Ca2+]m efflux triggered by neuronal depolarization. Inhibition of PDE2 acted by diminishing the Ca2+ dependent reduction of mitochondrial cAMP, thereby promoting NCLX phosphorylation. Selective PDE2 inhibition also enhanced [Ca2+]m efflux triggered by neuromodulators. We found that protection of neurons against excitotoxic insults, conferred by PDE2 inhibition, was diminished in NCLX KO neurons, thus is NCLX dependent. Finally, administration of Bay 60-7550 enhanced new object recognition learning in WT but not in NCLX KO mice. Our results identify a long-sought link between PDE and [Ca2+]m signaling thereby providing new therapeutic targets.
MuscleNET: mapping electromyography to kinematic and dynamic biomechanical variables by machine learning

Objective. This paper proposes machine learning models for mapping surface electromyography (sEMG) signals to regression of joint angle, joint velocity, joint acceleration, joint torque, and activation torque. Approach. The regression models, collectively known as MuscleNET, take one of four forms: ANN (forward artificial neural network), RNN (recurrent neural network), CNN (convolutional neural network), and RCNN (recurrent convolutional neural network). Inspired by conventional biomechanical muscle models, delayed kinematic signals were used along with sEMG signals as the machine learning model’s input; specifically, the CNN and RCNN were modeled with novel configurations for these input conditions. The models’ inputs contain either raw or filtered sEMG signals, which allowed evaluation of the filtering capabilities of the models. The models were trained using human experimental data and evaluated with different individual data. Main results. Results were compared in terms of regression error (using the root-mean-square) and model computation delay. The results indicate that the RNN (with filtered sEMG signals) and RCNN (with raw sEMG signals) models, both with delayed kinematic data, can extract underlying motor control information (such as joint activation torque or joint angle) from sEMG signals in pick-and-place tasks. The CNNs and RCNNs were able to filter raw sEMG signals. Significance. All forms of MuscleNET were found to map sEMG signals within 2 ms, fast enough for real-time applications such as the control of exoskeletons or active prostheses. The RNN model with filtered sEMG and delayed kinematic signals is particularly appropriate for applications in musculoskeletal simulation and biomechatronic device control.
Gene co-expression reveals the modularity and integration of C4 and CAM in Portulaca

C4 photosynthesis and Crassulacean acid metabolism (CAM) have been considered as largely independent adaptations in spite of sharing key biochemical modules. Portulaca is a geographically widespread clade of over 100 annual and perennial angiosperm species that primarily use C4, but facultatively exhibit CAM when drought stressed, a photosynthetic system known as C4+CAM. It has been hypothesized that C4+CAM is rare because of pleiotropic constraints, but these have not been deeply explored. We generated a chromosome-level genome assembly of P. amilis and sampled mRNA from P. amilis and P. oleracea during CAM induction. Gene co-expression network analyses identified C4 and CAM gene modules shared and unique to both Portulaca species. A conserved CAM module linked phosphoenolpyruvate carboxylase (PEPC) to starch turnover during the day-night transition and was enriched in circadian clock regulatory motifs in the P. amilis genome. Preservation of this co-expression module regardless of water status suggests that Portulaca constitutively operate a weak CAM cycle that is transcriptionally and post-transcriptionally upregulated during drought. C4 and CAM mostly used mutually exclusive genes for primary carbon fixation and it is likely that nocturnal CAM malate stores are shuttled into diurnal C4 decarboxylation pathways, but we find evidence that metabolite cycling may occur at low levels. C4 likely evolved in Portulaca through co-option of redundant genes and integration of the diurnal portion of CAM. Thus, the ancestral CAM system did not strongly constrain C4 evolution because photosynthetic gene networks are not co-regulated for both daytime and nighttime functions.
Decoding Images from Multi-Region, High Resolution, Electrode Recordings In the Mouse Visual System

We hypothesize that deep networks are superior to linear decoders at recovering visual stimuli from neural activity. Using high-resolution, multielectrode Neuropixels recordings, we verify this is the case for a simple feed-forward deep neural network having just 7 layers. These results suggest that these feed-forward neural networks and perhaps more complex deep architectures will give superior performance in a visual brain-machine interface.
The Ubiquitous Vigilance Signal in fMRI Time Series Data

Vigilance and wakefulness modulate estimates of functional connectivity, and, if unaccounted for, they can become a substantial confound in resting-state fMRI. Unfortunately, wakefulness is rarely monitored due to the need for additional concurrent recordings (e.g., eye tracking, EEG). Recent work has shown that strong fluctuations around 0.05Hz, hypothesized to be CSF inflow, appear in the fourth ventricle (FV) when subjects fall asleep. The analysis of these fluctuations could provide an easy way to evaluate wakefulness in fMRI-only data. Here we evaluate this possibility using the 7T resting-state sample from the Human Connectome Project. Our results confirm the presence of those fluctuations in the HCP sample despite this data having relatively small inflow weighting. Moreover, we show that fluctuations of a similar frequency appear in large portions of grey matter with different temporal delays, and that they can substantially influence estimates of functional connectivity. Finally, we demonstrate that the temporal evolution of this signal cannot only help us reproduce previously reported overall sleep patterns in resting-state data, but also predict individual periods of eye closure with 70% accuracy, matching predictions attainable using the amplitude of the global signal (a common fMRI marker of arousal). In summary, our results demonstrate the ubiquitous presence of this signal in a large, publicly available, fMRI sample, its value as a marker of arousal in absence of a better metric, its relationship to the global signal, and its potential nuisance effects on functional connectivity estimates when ignored.
Research Progress on Catalpol as Treatment for Atherosclerosis

Coronary atherosclerotic heart disease, cerebrovascular disease, and peripheral artery disease are common diseases with high morbidity and mortality rates and must be addressed. Their most frequent complications, including myocardial infarction and stroke, are caused by spontaneous thrombotic occlusion and are the most frequent cause of death worldwide. Atherosclerosis (AS) is the most widespread underlying pathological change for the above diseases. Therefore, drugs that interfere with this pathophysiological process must be incorporated in the treatment. Chinese traditional and herbal drugs can effectively treat AS. With the development of traditional Chinese medicine, the active ingredients in common Chinese medicinal materials must be thoroughly purified prior to their application in western medicine. Various proprietary Chinese medicine preparations with remarkable effects have been used in AS treatment. Catalpol, the active component of Rehmannia glutinosa, belongs to iridoid terpene and has anti-inflammatory, antioxidant, insulin resistance improvement, and other related effects. Several reviews have been conducted on this compound and its actions against osteoporosis, neurodegenerative diseases, Alzheimer's disease (AD), Parkinson's disease (PD) and diabetes and its complications. The current review focused on catalpol’s effect on atherosclerotic plaque formation in different animal models. The potential mechanisms of catalpol to ameliorate AS were also summarized in terms of oxidative stress, inflammation, cell aging, apoptosis, and activation of the silent information regulator factor 2-related enzyme 1 (SIRT1) pathway.
Screening and Isolation of a Novel Polyene-Producing Streptomyces Strain Inhibiting Phytopathogenic Fungi in the Soil Environment

Microbial-based eco-friendly biological substances are needed to protect crops from phytopathogenic fungi and replace toxic chemical fungicides that cause serious environmental issues. This study screened for soil antifungal Streptomyces strains, which produce rich, diverse, and valuable bioactive metabolites in the soil environment. Bioassay-based antifungal screening of approximately 2,400 Streptomyces strains led to the isolation of 149 strains as tentative antifungal producers. One Streptomyces strain showing the most potent antifungal activities against Candida albicans and Fusarium oxysporum was identified as a putative anti-phytopathogenic soil isolate that is highly homologous to Streptomyces rubrisoli (named S. rubrisoli Inha 501). An in vitro antifungal assay, pot-test, and field-test against various phytopathogenic fungi confirmed that S. rubrisoli Inha 501 is a potential novel phytopathogenic fungicide producer to protect various crops in the soil environment. Whole-genome sequencing of S. rubrisoli Inha 501 and an anti-SMASH genome mining approach revealed an approximately 150-kb polyene biosynthetic gene cluster (BGC) in the chromosome. The target compound isolation and its BGC analysis confirmed that the giant linear polyene compound exhibiting the anti-phytopathogenic activity in S. rubrisoli Inha 501 was highly homologous to the previously reported compound, neotetrafibricin A. These results suggest that a bioassay-based screening of a novel antifungal Streptomyces strain followed by its genome mining for target compound BGC characterization would be an efficient approach to isolating a novel candidate phytopathogenic fungicide that can protect crops in the soil environment.
Intelligent Mobility in the City: The Influence of System and Context Factors on Drivers’ Takeover Willingness and Trust in Automated Vehicles

Automated driving in urban environments not only has the potential to improve traffic flow and heighten driver comfort but also to increase traffic safety, particularly for vulnerable road users such as pedestrians. For these benefits to take effect, drivers need to trust and use automated vehicles. This decision is influenced by both system and context factors. However, it is not yet clear how these factors interact with each other, especially for automated driving in city scenarios with crossing pedestrians. Therefore, we conducted an online experiment in which participants (N = 68) experienced short automated rides from the driver’s perspective through an urban environment. In each of the presented videos, a pedestrian crossed the street in front of the automated vehicle while system and context factors were varied: 1) the crossing pedestrian’s intention was either visualized correctly (as crossing) or incorrectly (visualization missing) by the automated vehicle (system factor), 2) the pedestrian was either distracted by using a smartphone while crossing or not (context factor), and 3) the scenario was either more or less complex depending on the number of other vehicles and pedestrians being present (context factor). In situations with a system malfunction where the crossing pedestrian’s intention was not visualized, participants perceived the situation as more critical, had less trust in the automated system, and a higher willingness to take over control regardless of any context factors. However, when the system worked correctly, the crossing pedestrian’s smartphone usage came into play, especially in the less complex scenario. Participants perceived situations with a distracted pedestrian as more critical, trusted the system less, indicated a higher willingness to take over control, and were more uncertain about their decision. As this study demonstrates the influence of distracted pedestrians, more research is needed on context factors and their inclusion in the design of interfaces to keep drivers informed during automated driving in urban environments.
Small Molecule Microcrystal Electron Diffraction for the Pharmaceutical Industry–Lessons Learned From Examining Over Fifty Samples

The emerging field of microcrystal electron diffraction (MicroED) is of great interest to industrial researchers working in the drug discovery and drug development space. The promise of being able to routinely solve high-resolution crystal structures without the need to grow large crystals is very appealing. Despite MicroED’s exciting potential, adoption across the pharmaceutical industry has been slow, primarily owing to a lack of access to specialized equipment and expertise. Here we present our experience building a small molecule MicroED service pipeline for members of the pharmaceutical industry. In the past year, we have examined more than fifty small molecule samples submitted by our clients, the majority of which have yielded data suitable for structure solution. We also detail our experience determining small molecule MicroED structures of pharmaceutical interest and offer some insights into the typical experimental outcomes. This experience has led us to conclude that small molecule MicroED adoption will continue to grow within the pharmaceutical industry where it is able to rapidly provide structures inaccessible by other methods.
Allo-Specific Humoral Responses: New Methods for Screening Donor-Specific Antibody and Characterization of HLA-Specific Memory B Cells

Antibody-mediated allograft rejection (AMR) causes more kidney transplant failure than any other single cause. AMR is mediated by antibodies recognizing antigens expressed by the graft, and antibodies generated against major histocompatibility complex (MHC) mismatches are especially problematic. Most research directed towards the management of clinical AMR has focused on identifying and characterizing circulating donor-specific HLA antibody (DSA) and optimizing therapies that reduce B-cell activation and/or block antibody secretion by inhibiting plasmacyte survival. Here we describe a novel set of reagents and techniques to allow more specific measurements of MHC sensitization across different animal transplant models. Additionally, we have used these approaches to isolate and clone individual HLA-specific B cells from patients sensitized by pregnancy or transplantation. We have identified and characterized the phenotypes of individual HLA-specific B cells, determined the V(D)J rearrangements of their paired H and L chains, and generated recombinant antibodies to determine affinity and specificity. Knowledge of the BCR genes of individual HLA-specific B cells will allow identification of clonally related B cells by high-throughput sequence analysis of peripheral blood mononuclear cells and permit us to re-construct the origins of HLA-specific B cells and follow their somatic evolution by mutation and selection.
What Type of Body Shape Moves Children? An Experimental Exploration of the Impact of Narrative Cartoon Character Body Shape on Children’s Narrative Engagement, Wishful Identification, and Exercise Motivation

Background The incorporation of narratives helps to enhance children’s engagement in active video games (AVGs), thus increasing moderate-to-vigorous physical activity (MVPA). Specific narrative elements, such as the visual representation of the characters’ body shape, have been rarely manipulated to explore their role in modulating children’s narrative engagement (NE) and exercise motivation. Objective To investigate the effects of character body shape manipulation (overweight/obese, average, or athletic slim) on children’s narrative immersion (NI), NE, wishful identification (WI), as well as their mediating effect on AVG and PA motivation. Methods Children ages 8–12 years old (N = 87) were randomly assigned to watch a 15-min animated video (designed for an existing AVG) in which the main characters had an overweight/obese, or average∗, or athletic slim body shape (all other elements were identical). Children’s NI, NE, WI, and AVG and physical activity (PA) motivation were then assessed. Results Controlling for social desirability, the analysis indicated that participants with a BMI of greater than the 75th percentile had a significantly higher NI, NE, WI, and PA motivation when video characters were set to the overweight/obese condition, than they did for video characters set to the average or athletic slim conditions. On the other hand, children of equal or less than the 75th percentile exposed to the average character body condition had a greater NE, WI, and PA motivation than overweight/obese or athletic slim conditions. A mediation analysis with structural equation modeling indicated that NE mediated the effect between character body shape and AVG and PA motivation. Conclusion Narrative cartoon characters that mirror the target participant’s body shape can increase NE, which in turn mediates AVG and PA motivation. Content producers should identify optimal strategies in character body shape design to encourage children of different weight status to participate in PA with engaging stories to maximize health narratives’ persuasive potentials. ∗The term average in this sense is not in reference to the national average body weight, but rather an average of the body weights represented in conditions A and C.
Prevalence and Risk Factors of Mental Health Symptoms and Suicidal Behavior Among University Students in Wuhan, China During the COVID-19 Pandemic

Background: University students who are exposed to coronavirus disease 2019 (COVID-19) could be mentally distressed. We aimed to evaluate the pattern and risk factors of mental health and suicidal behavior among students who experienced long-term school closure due to the COVID-19 pandemic. Methods: This large-sample, cross-sectional, online survey was conducted from June 29, 2020, to July 18, 2020. Eleven thousand two hundred fifty four participants were recruited from 30 universities located in Wuhan, Hubei Province, China. The prevalence of symptoms of depression, anxiety, insomnia, and posttraumatic stress disorder (PTSD) and suicidal behavior was evaluated using the Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, Insomnia Severity Index, Posttraumatic Stress Disorder Checklist for DSM-5, and questions about suicidal ideation and attempts, respectively. Logistic regression was used to explore risk factors for mental health problems and suicidal behavior. Results: The prevalence of mental health problems was 41.5% for depressive symptoms, 32.6% for anxiety symptoms, 35.0% for insomnia symptoms, 8.5% for PTSD symptoms, and 2.0% for suicidal behavior. Participants with high stress during the pandemic were at higher risk of symptoms of depression [adjusted odds ratio (OR) = 1.67, 95% confidence interval (CI) = 1.43–1.95, p < 0.01), anxiety (adjusted OR = 1.90, 95% CI = 1.63–2.23, p < 0.01), insomnia (adjusted OR = 1.64, 95% CI = 1.44–1.87, p < 0.01), PTSD (adjusted OR = 1.71, 95% CI = 1.38–2.11, p < 0.01) and suicidal behavior (adjusted OR = 3.51, 95% CI = 2.28–5.40, p < 0.01). Distant relationship with parents, changes in lifestyle and alcohol use during the pandemic were associated with higher risk of mental health symptoms and suicidal behavior, whereas regular physical exercise reduced the risk of mental health problems. Conclusions: The psychological symptoms and suicidal behavior were relatively high among students who attended university in Wuhan, China after 6 months of the COVID-19 outbreak in China. Comprehensive mental health services and suicide prevention strategies are essential for university students during the COVID-19 pandemic.
Distributed Melt on a Debris-Covered Glacier: Field Observations and Melt Modeling on the Lirung Glacier in the Himalaya

Debris-covered glaciers, especially in high-mountain Asia, have received increased attention in recent years. So far, few field-based observations of distributed mass loss exist and both the properties of the debris layer as well as the atmospheric drivers of melt below debris remain poorly understood. Using multi-year observations of on-glacier atmospheric data, debris properties and spatial surface elevation changes from repeat flights with an unmanned aerial vehicle (UAV), we quantify the necessary variables to compute melt for the Lirung Glacier in the Himalaya. By applying an energy balance model we reproduce observed mass loss during one monsoon season in 2013. We show that melt is especially sensitive to thermal conductivity and thickness of debris. Our observations show that previously used values in literature for the thermal conductivity through debris are valid but variability in space on a single glacier remains high. We also present a simple melt model, which is calibrated based on the results of energy balance model, that is only dependent on air temperature and debris thickness and is therefore applicable for larger scale studies. This simple melt model reproduces melt under thin debris (<0.5 m) well at an hourly resolution, but fails to represent melt under thicker debris accurately at this high temporal resolution. On the glacier scale and using only off-glacier forcing data we however are able to reproduce the total melt volume of a debris-covered tongue. This is a promising result for catchment scale studies, where quantifying melt from debris covered glaciers remains a challenge.
Disrupted Topological Organization in White Matter Networks in Unilateral Sudden Sensorineural Hearing Loss

Sudden sensorineural hearing loss (SSNHL) is a sudden-onset hearing impairment that rapidly develops within 72 h and is mostly unilateral. Only a few patients can be identified with a defined cause by routine clinical examinations. Recently, some studies have shown that unilateral SSNHL is associated with alterations in the central nervous system. However, little is known about the topological organization of white matter (WM) networks in unilateral SSNHL patients in the acute phase. In this study, 145 patients with SSNHL and 91 age-, gender-, and education-matched healthy controls were evaluated using diffusion tensor imaging (DTI) and graph theoretical approaches. The topological properties of WM networks, including global and nodal parameters, were investigated. At the global level, SSNHL patients displayed decreased clustering coefficient, local efficiency, global efficiency, normalized clustering coefficient, normalized characteristic path length, and small-worldness and increased characteristic path length (p < 0.05) compared with healthy controls. At the nodal level, altered nodal centralities in brain regions involved the auditory network, visual network, attention network, default mode network (DMN), sensorimotor network, and subcortical network (p < 0.05, Bonferroni corrected). These findings indicate a shift of the WM network topology in SSNHL patients toward randomization, which is characterized by decreased global network integration and segregation and is reflected by decreased global connectivity and altered nodal centralities. This study could help us understand the potential pathophysiology of unilateral SSNHL.