A CNN model with feature integration for MI EEG subject classification in BMI ObjectiveElectroencephalogram (EEG) based motor imagery (MI) classification is an important aspect in brain-machine interfaces (BMIs) which bridges between neural system and computer devices decoding brain signals into recognizable machine commands. However, the MI classification task is challenging due to inherent complex properties, inter-subject variability, and low signal-to-noise ratio (SNR) of EEG signals. To overcome the above-mentioned issues, the current work proposes an efficient multi-scale convolutional neural network (MS-CNN).

ApproachIn the framework, discriminant user-specific features have been extracted and integrated to improve the accuracy and performance of the CNN classifier. Additionally, different data augmentation methods have been implemented to further improve the accuracy and robustness of the model.

Main resultsThe model achieves an average classification accuracy of 93.74% and Cohens kappa-coefficient of 0.92 on the BCI competition IV2b dataset outperforming several baseline and current state-of-the-art EEG-based MI classification models.

SignificanceThe proposed algorithm effectively addresses the shortcoming of existing CNN-based EEG-MI classification models and significantly improves the classification accuracy.
Gymnasts' ability to modulate bioelectric sensorimotor rhythms during kinesthetic motor imagery of sports non-specific movements Previous psychological studies using questionnaires have consistently reported that athletes have superior motor imagery ability, both for sports-specific and sports non-specific movements. However, regarding motor imagery of sports non-specific movements, no physiological studies have demonstrated differences in neural activity between athletes and non-athletes. The purpose of the present study was to examine differences in bioelectric sensorimotor rhythms during kinesthetic motor imagery (KMI) of sports non-specific movements between gymnasts and non-gymnasts. We selected gymnasts as an example population because they are likely to have particularly superior motor imagery ability due to frequent usage of motor imagery including KMI as part of daily practice. Healthy young participants (16 gymnasts and 16 non-gymnasts) performed repeated motor execution and KMI of sports non-specific movements (wrist dorsiflexion and shoulder abduction of the dominant hand). Scalp electroencephalogram (EEG) was recorded over the contralateral sensorimotor cortex. During motor execution and KMI, sensorimotor EEG power is known to decrease in the - (8-15 Hz) and {beta}-bands (16-35 Hz), referred to as event-related desynchronization (ERD). We calculated the maximal peak of ERD both in the - (ERDmax) and {beta}-bands ({beta}ERDmax) as a measure of changes in corticospinal excitability. ERDmax was significantly greater in gymnasts, who subjectively evaluated their KMI as being more vivid, for both KMI tasks. On the other hand, {beta}ERDmax was greater in gymnasts only for shoulder abduction KMI. These findings suggest gymnasts signature of flexibly modulating sensorimotor rhythm with no movements, which may be the basis of their superior ability of KMI for sports non-specific movements.

New & NoteworthyKinesthetic motor imagery of sports non-specific movements was compared between gymnasts and non-gymnasts (i.e., healthy controls) from both physiological and psychological approaches. The EEG sensorimotor rhythms during kinesthetic motor imagery were more desynchronized in gymnasts who subjectively imaged their own movements as being more vivid. The work reveals novel ability in gymnasts to flexibly control their sensorimotor rhythms with no actual movements, which would be the basis of their superior ability of motor imagery.
High school science fair: Experiences of two groups of undergraduate bioscience students Science fairs offer potential opportunities for students to learn first-hand about the practices of science. Over the past six years we have been carrying out voluntary and anonymous surveys with regional and national groups of high school and post high school students to learn about their high school science fair experiences regarding help received, obstacles encountered, and opinions about the value and impact of science fair. Understanding what students think about science fairs will help educators make science fairs more effective learning opportunities. In this paper, we focus on the findings with two national groups of post high school students - undergraduate research fellows (SURF students) who did research at UT Southwestern Medical Center during 2014-2019 and undergraduates biology students attending the 2019 Howard Hughes Medical Institute Science Education Alliance (SEA) summer symposium. About 25% of the students who completed surveys indicated that they had participated in high school science fair, but more than half attended high schools where science fairs were unavailable. Effectively, 6 out of every 10 students participated in science fair if available. Students who could have participated in high school science fair but chose not to do so identified not enough time and coming up with their project idea as major reasons why not. About half the SURF students favored requiring non-competitive science fair regardless whether they themselves had participated in science fair. On the other hand, less than 1 in 5 thought that competitive science fair should be required. Introduction to the scientific process and general learning were mentioned most frequently as the reasons to require non-competitive science fair; these reasons were mentioned rarely in connection with competitive science fair. Unlike the national cohort of high school students we surveyed previously, who mostly did science fair in 9th and 10th grades, SURF students participated in science fair throughout high school and were twice as likely as high school students to have carried out science fair more than once. In conclusion, our findings suggest that participation of the undergraduate bioscience majors in high school science fairs occurs far more frequently than recognized previously. The findings emphasize further the importance of incentivizing rather than requiring science fair participation, especially in 9th and 10th grades, and the potential value of developing non-competitive science fairs. Mitochondrial F1FO ATP synthase determines the local proton motive force in cristae tips The classical view of oxidative phosphorylation is that a proton motive force PMF generated by the respiratory chain complexes fuels ATP synthesis. Under glycolytic conditions, ATP synthase in its reverse mode also can contribute to the PMF. Here, we dissected the two functions of ATP synthase and the role of its inhibitory factor 1 (IF1) under different metabolic conditions in detail. pH profiles of mitochondrial sub-compartments were recorded with high spatial resolution in live mammalian cells by positioning a pH-sensor directly at F1 and FO of ATP synthase, complex IV and in the matrix. Our results clearly show that ATP synthase activity is substantially controlling the PMF and that IF1 is essential under OXPHOS conditions to prevent reverse ATP synthase activity due to an almost negligible {Delta}pH.

GRAPHICAL ABSTRACT

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org.highwire.dtl.DTLVardef@1a975eforg.highwire.dtl.DTLVardef@7ddb49org.highwire.dtl.DTLVardef@c7a128org.highwire.dtl.DTLVardef@2559e8_HPS_FORMAT_FIGEXP  M_FIG C_FIG HIGHLIGHTSO_LIThe {Delta}pH along and across the inner mitochondrial membrane is not homogeneous
C_LIO_LIThe proton motive force at cristae tips is controlled by F1 FO ATP synthase
C_LIO_LIUnder OXPHOS conditions, the pH difference between FO and F1 of active ATP synthase is almost negligible (1.2 proton vs. 1 proton equivalent)
C_LIO_LIIF1 is required to prevent the onset of ATP hydrolysis under OXPHOS conditions
C_LI Robust and Remote Measurement of Heart Rate Based on A Surveillance Camera Arrhythmia is a marked symptom of many cardiovascular diseases (CVDs). The accurate and timely detection of heart rate can greatly reduce the harm of arrhythmia to people. However, it is still a challenge to robustly and remotely measure heart rate in daily life due to the changing environmental conditions during measurement, such as the varying light intensity, the movement of people, and the uncertain distance between the sensor to people. In this study, we propose a method to accurately measure human heart rate within a distance of 4.5 meters under different light intensities by simply using a surveillance camera. After a 20-second color video of a persons hand is captured by the camera, a method based on the Fast Fourier Transform (FFT) algorithm is designed to extract the blood volume pulse wave to calculate the heart rate. According to the comparison between the real heart rate and results measured by electrocardiography (ECG), the proposed method achieves an accuracy of 95.8% when the measurement is performed within a distance of 4.5 meters and 90% when within 5.0 meters. Our experiments show that when the illuminance varies between 100-1000 lux (lighting level indoor), we still get the correct results. Our experiments also demonstrate that the proposed method accurately obtain heart rate even when the light intensity is below 32 lux (300-500 lux in a workplace environment). The methods strong adaptability to changing environmental conditions makes it applicable to many scenarios, such as homes of the elderly, classrooms, and other public spaces. Simulation of foraging behavior using a decision-making agent with Bayesian and inverse Bayesian inference: Temporal correlations and power laws in displacement patterns It has been stated that in human migratory behavior, the step length series may have temporal correlation and that there is some relationship between this time dependency and the fact that the frequency distribution of step length follows a power-law distribution. Furthermore, the frequency of occurrence of the step length in some large marine organisms has been found to switch between power-law and exponential distributions, depending on the difficulty of prey acquisition. However, to date it has not been clarified how the aforementioned three phenomena arise: the positive correlation created in the step length series, the relation between the positive correlation of the step length series and the form of an individuals step length distribution, and the switching between power-law and exponential distributions depending on the abundance of prey. This study simulated foraging behavior using the Bayesian decision-making agent simultaneously performing both knowledge learning and knowledge-based inference to analyze how the aforementioned three phenomena arise. In the agent with learning and inference, past experiences were stored as hypotheses (knowledge) and they were used in current foraging behavior; at the same time, the hypothesis continued to be updated based on new experiences. The simulation results show that the agent with both learning and inference has a mechanism that simultaneously causes all the phenomena. Integrative small and long RNA-omics analysis of human healing and non-healing wounds discovers cooperating microRNAs as therapeutic targets MicroRNAs (miR) are important posttranscriptional regulators and exhibit a high potential to be utilized in diagnosis and therapy. However, our insufficient knowledge of the miR-mediated gene regulation in human skin wound healing severely hinders the identification of clinically relevant miRs. Here, we performed paired small RNA and long RNA sequencing in human tissue samples, including matched skin and acute wounds collected at each healing stage and chronic non-healing venous ulcers (VU). With integrative small and long RNA-omics analysis, we developed a compendium (https://www.xulandenlab.com/humanwounds-mirna-mrna), which will be an open, comprehensive resource to broadly aid wound healing research. With this first clinical, wound-centric resource of miRs and mRNAs, we identified 17 pathologically relevant miRs that exhibited abnormal VU expression and displayed their targets enriched explicitly in the VU gene signature. Intermeshing regulatory networks controlled by these miRs revealed their high cooperativity in contributing to chronic wound pathology characterized by persistent inflammation and proliferative phase initiation failure. Furthermore, we demonstrated that miR-34a, miR-424, and miR-516, upregulated in VU, cooperatively suppressed keratinocyte growth while promoting inflammatory response. Collectively, our study opens the possibility of developing innovative wound treatment that targets pathologically relevant cooperating miRs to attain higher therapeutic efficacy and specificity. A Bayesian model selection approach to mediation analysis Mediation analysis is a powerful tool for discovery of causal relationships. We describe a Bayesian model selection approach to mediation analysis that is implemented in our bmediatR software. Using simulations, we show that bmediatR performs as well or better than established methods including the Sobel test, while allowing greater flexibility in both model specification and in the types of inference that are possible. We applied bmediatR to genetic data from mice and human cell lines to demonstrate its ability to derive biologically meaningful findings. The Bayesian model selection framework is extensible to support a wide variety of mediation models. Quercetin handles cellular oxidant / antioxidant systems and mitigates immunosenescence hallmarks in human PBMCs: an in vitro study Immunosenescence, oxidative stress, and low vaccine efficacy are important symptoms of aging. The goal of our study was to see if quercetin had anti-aging and stimulating effects on PBMC immune cells in vitro. In the presence of concanavalin a, PBMCs were isolated from healthy elderly and young people and cultured in a complete RPMI-1640 medium supplemented with quercetin. Cell proliferation was assessed using the MTT colorimetric assay after a 48-hour incubation period. Spectrophophotometric assays were used to assess oxidative biomarkers (PC, MDA, and GSH). The ELISA method was used to determine the amount of IL-2 released. A Griess reagent was used to investigate iNOS activity. When compared to young control cells, aged PBMCs had lower proliferation potency, lower IL-2 and NO release, and higher MDA and PC levels. Importantly, quercetin-treated aged PBMCs have a high proliferative response comparable to young cells, restored iNOS activity, and increased levels of GSH antioxidant defences. In comparison to untreated aged PBMCs, treated PBMCs have lower lipo-oxidative damage but higher PC levels. Quercetin may be used as a promising dietary vaccinal adjuvant in the elderly, it has significant effects in reducing immunosenescence hallmarks, as well as mitigating the lipo-oxidative stress in PBMCs cells.

Practical abstractQuercetin is a common dietary polyphenol found in fruits and vegetables that is well known for its antioxidant activity in radical scavenging and anticancer properties characterized by immune system stimulation. In vitro, quercetin boosts immune responses in the elderly and reduces immunosenescence symptoms, according to our findings. As a result, this molecule could be a safe and promising vaccine adjuvant for boosting immunity and reducing aging complications in immune cells as well as reducing oxidative stress in PBMC cells. Genetic diversity loss in the Anthropocene More species than ever before are at risk of extinction due to anthropogenic habitat loss and climate change. But even species that are not threatened have seen reductions in their populations and geographic ranges, likely impacting their genetic diversity. Although preserving genetic diversity is key to maintaining adaptability of species, we lack predictive tools and global estimates of genetic diversity loss across ecosystems. By bridging theories of biodiversity and population genetics, we introduce a mathematical framework to understand the loss of naturally occurring DNA mutations within decreasing habitat within a species. Analysing genome-wide variation data of 10,095 geo-referenced individuals from 20 plant and animal species, we show that genome-wide diversity follows a power law with geographic area (the mutations-area relationship), which can predict genetic diversity loss in spatial computer simulations of local population extinctions. Given pre-21st century values of ecosystem transformations, we estimate that over 10% of genetic diversity may already be lost, surpassing the United Nations targets for genetic preservation. These estimated losses could rapidly accelerate with advancing climate change and habitat destruction, highlighting the need for forecasting tools that facilitate implementation of policies to protect genetic resources globally. Teosinte mexicana introgression modulates phosphatidylcholine levels and maize flowering time Native Americans domesticated maize (Zea mays ssp. mays) from lowland teosinte parviglumis (Zea mays ssp.parviglumis) in the warm Mexican southwest and brought it to the highlands of Mexico and South America where it was exposed to lower temperatures that imposed strong selection on flowering time. Phospholipids are important metabolites in plant responses to low-temperature and phosphorus availability, and have been suggested to influence flowering time. Here, we combined linkage mapping with genome scans to identify High PhosphatidylCholine 1 (HPC1), a gene that encodes a phospholipase A1 enzyme, as a major driver of phospholipid variation in highland maize. Common garden experiments demonstrated strong genotype-by-environment interactions associated with variation at HPC1, with the highland HPC1 allele leading to higher fitness in highlands, possibly by hastening flowering. The highland maize HPC1 variant resulted in impaired function of the encoded protein due to a polymorphism in a highly conserved sequence. A meta-analysis across HPC1 orthologs indicated a strong association between the identity of the amino acid at this position and optimal growth in prokaryotes. Mutagenesis of HPC1 via genome editing validated its role in regulating phospholipid metabolism. Finally, we showed that the highland HPC1 allele entered cultivated maize by introgression from the wild highland teosinte Zea mays ssp. mexicana and has been maintained in maize breeding lines from the Northern US, Canada and Europe. Thus, HPC1 introgressed from teosinte mexicana underlies a large metabolic QTL that modulates phosphatidylcholine levels and has an adaptive effect at least in part via induction of early flowering time. Re-convolving the compositional landscape of primary and recurrent glioblastoma reveals prognostic andtargetable tissue states Glioblastoma is an aggressive diffusely infiltrating neoplasm that spreads beyond surgical resection margins, where it intermingles with non-neoplastic brain cells. This complex microenvironment harbouring infiltrating glioma and non-neoplastic brain cells is the origin of tumor recurrence. Thus, understanding the cellular and molecular features of the glioma microenvironment is therapeutically and prognostically important. Here, we used single-nucleus RNA sequencing (snRNAseq) of primary and recurrent glioma to define three compositional  tissue-states rooted in cohabitation of cell-types and transcriptional states. These comprise a state featuring A) abundant normal brain microenvironment cells, B) reactive/inflamed brain microenvironment, and C) cellular/proliferative tumor. All these states exhibited variable degrees of infiltration by glioma cells. Spatial transcriptomics confirmed that the cell-types and transcriptomics states which compositionally cohabitate tissue states also colocalize in space. Tissue states are clinically significant because they correlate with radiographic, histopathologic, and prognostic features. We found that enrichment of tissue state B signature correlated with shorter survival. Importantly, we found that our compositionally defined tissue states are enriched in distinct metabolic pathways. One such pathway is fatty acid biosynthesis, which was enriched in tissue state B - a state enriched in recurrent glioblastoma and composed of astrocyte-like/mesenchymal glioma cells, reactive astrocytes resembling those seen in neurodegeneration, and monocyte-like myeloid cells. We showed that treating acute slices of GBM with a fatty acid synthesis inhibitor is sufficient to deplete the transcriptional signature of tissue state B. Our findings define a novel compositional approach to the glioma-infiltrated tissue which allows us to discover prognostic and targetable features, paving the way to new mechanistic and therapeutic discoveries. Conserved organ-specific microbial assemblages in different populations of a terrestrial crab Brachyuran crabs originated in the oceans and evolved specific morphological and physiological adaptations to live in freshwater, intertidal and even terrestrial habitats but the role of a selection mechanism involving symbiotic microorganisms long these colonization processes are not known. In this work we investigated the associated microbiota of three populations of a terrestrial brachyuran crab, Chiromantes haematocheir, to find evidence of a conserved crab-specific microbiome unrelated to the population of origin and dissimilar from environmental microbial assemblages. Bacterial 16S rRNA gene and fungal ITS sequences were obtained from selected crab organs and environmental matrices to profile microbial communities. In spite of the presence of truly marine larval stages and no gregarious behaviour, favouring microbiota exchanges, we found common, organ-specific microbiota, associated to the gut and the gills of the crabs (with more than 15% of the genera detected specifically enriched only in one organ). Our results suggest an early establishment of a new common, stable microbiota in the transition from water to land. Deep learning can predict multi-omic biomarkers from routine pathology images: A systematic large-scale study We assessed the pan-cancer predictability of multi-omic biomarkers from haematoxylin and eosin (H&E)-stained whole slide images (WSI) using deep learning (DL) throughout a systematic study. A total of 13,443 DL models predicting 4,481 multi-omic biomarkers across 32 cancer types were trained and validated. The investigated biomarkers included a broad range of genetic, transcriptomic, proteomic, and metabolic alterations. Furthermore, established markers relevant for prognosis, molecular subtypes and clinical outcomes were included. Overall, we established the general feasibility of predicting multi-omic markers directly from routine histology images with DL across solid cancer types, where 50% of the models could perform at an area under the curve (AUC) of more than 0.633 (with 25% of the models having an AUC larger than 0.711). A wide range of biomarkers were detectable from routine histology images across all investigated cancer types, with a mean AUC of at least 0.62 in almost all malignancies. Strikingly, we observed that biomarker predictability was mostly consistent and not dependent on sample size and class ratio, suggesting a degree of true predictability inherent in histomorphology. Together, the results of our study show the potential of DL to predict a multitude of biomarkers across the omics spectrum using only routine slides. This paves the way for accelerating diagnosis and developing more precise treatments for cancer patients. Data-Driven Network Models for Genetic Circuits From Time-Series Data with Incomplete Measurements Synthetic gene networks are frequently conceptualized and visualized as static graphs. This view of biological programming stands in stark contrast to the transient nature of biomolecular interaction, which is frequently enacted by labile molecules that are often unmeasured. Thus, the network topology and dynamics of synthetic gene networks can be difficult to verify in vivo or in vitro, due to the presence of unmeasured biological states. Here we introduce the dynamical structure function as a new mesoscopic, data-driven class of models to describe gene networks with incomplete measurements. We introduce a network reconstruction algorithm and a code base for reconstructing the dynamical structure function from data, to enable discovery and visualization of graphical relationships in a genetic circuit diagram as time-dependent functions rather than static, unknown weights. We prove a theorem, showing that dynamical structure functions can provide a data-driven estimate of the size of crosstalk fluctuations from an idealized model. We illustrate this idea with numerical examples. Finally, we show how data-driven estimation of dynamical structure functions can explain failure modes in two experimentally implemented genetic circuits, a historical genetic circuit and a new E. coli based transcriptional event detector. Versatile Cell-Based Assay for Measuring DNA Alkylation Damage and its repair DNA alkylation damage induced by environmental carcinogens, chemotherapy drugs, or endogenous metabolites plays a central role in mutagenesis, carcinogenesis, and cancer therapy. Base excision repair (BER) is a conserved, front line DNA repair pathway that removes alkylation damage from DNA. The capacity of BER to repair DNA alkylation varies markedly between different cell types and tissues, which correlates with cancer risk and cellular responses to alkylation chemotherapy. The ability to measure cellular rates of alkylation damage repair by the BER pathway is critically important for better understanding of the fundamental processes involved in carcinogenesis, and also to advance development of new therapeutic strategies. Methods for assessing the rates of alkylation damage and repair, especially in human cells, are limited, prone to significant variability due to the unstable nature of some of the alkyl adducts, and often rely on indirect measurements of BER activity. Here, we report a highly reproducible and quantitative, cell-based assay, named alk-BER (alkylation Base Excision Repair) for measuring rates of BER following alkylation DNA damage. The alk-BER assay involves specific detection of methyl DNA adducts (7-methyl guanine and 3-methyl adenine) directly in genomic DNA. The assay has been developed and adapted to measure the activity of BER in fungal model systems and human cell lines. Considering the specificity and conserved nature of BER enzymes, the assay can be adapted to virtually any type of cultured cells. Alk-BER offers a cost efficient and reliable method that can effectively complement existing approaches to advance integrative research on mechanisms of alkylation DNA damage and repair. Genetically diverse mouse models of SARS-CoV-2 infection reproduce clinical variation and cytokine responses in COVID-19 Inflammation in response to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection drives severity of coronavirus disease 2019 (COVID-19), with effective versus dysregulated responses influenced by host genetics. To understand mechanisms of inflammation, animal models that reflect genetic diversity and clinical outcomes observed in humans are needed. We report a mouse panel comprising the diverse genetic backgrounds of the Collaborative Cross founder strains crossed to K18-hACE2 transgenic mice that confers high susceptibility to SARS-CoV-2. Infection of CC x K18-hACE2 F1 progeny resulted in a spectrum of weight loss, survival, viral replication kinetics, histopathology, and cytokine profiles, some of which were sex-specific. Importantly, survival was associated with early type I interferon (IFN) expression and a phased proinflammatory response distinct from mice with severe disease. Thus, dynamics of inflammatory responses observed in COVID-19 can be modeled in diverse mouse strains that provide a genetically tractable platform for understanding antiviral immunity and evaluating countermeasures.

One Sentence SummaryGenetically diverse mice display a broad spectrum of clinically relevant responses to SARS-CoV-2 infection, reflecting variability in COVID-19 disease. Natural tolerance to transposition is associated with double-strand break repair and germ-cell differentiation Transposable elements (TE) are mobile genetic parasites whose unregulated activity in the germline causes DNA damage and sterility. While the regulation of TE mobilization by hosts is studied extensively, little is known about mechanisms that could allow germline cells to persist in the face of genotoxic stress imposed by active transposition. Such tolerance mechanisms are predicted to be beneficial when new TEs invade and host repression has not yet evolved. Here we use hybrid dysgenesis--a sterility syndrome of Drosophila caused by transposition of invading DNA transposons--to uncover genetic variants that confer tolerance to transposition. Using a panel of highly recombinant inbred lines of Drosophila melanogaster, we identified two linked quantitative trait loci (QTL), that determine tolerance in young and old females, respectively. Through transcriptomic and phenotypic comparisons, we provide evidence that young tolerant females exhibit enhanced repair of double-stranded breaks, explaining their ability to withstand high germline transposition rates. We furthermore identify the germline differentiation factor brat as an independent tolerance factor, whose activity may promote germline maintenance in aging dysgenic females. Together, our work reveals the diversity of potential tolerance mechanisms across development, as well as tolerant variants that may be beneficial in the context of P-element transposition. Main protease mutants of SARS-CoV-2 variants remain susceptible to nirmatrelvir (PF-07321332) The COVID-19 pandemic continues to be a public health threat. Multiple mutations in the spike protein of emerging variants of SARS-CoV-2 appear to impact on the effectiveness of available vaccines. Specific antiviral agents are keenly anticipated but their efficacy may also be compromised in emerging variants. One of the most attractive coronaviral drug targets is the main protease (Mpro). A promising Mpro inhibitor of clinical relevance is the peptidomimetic nirmatrelvir (PF-07321332). We expressed Mpro of six SARS-CoV-2 lineages (C.37 Lambda, B.1.1.318, B.1.2, B.1.351 Beta, B.1.1.529 Omicron, P.2 Zeta), each of which carries a strongly prevalent missense mutation (G15S, T21I, L89F, K90R, P132H, L205V). Enzyme kinetics showed that these Mpro variants are similarly catalytically competent as the wildtype. We show that nirmatrelvir has similar potency against the variants as against the wildtype. Our in vitro data suggest that the efficacy of the specific Mpro inhibitor nirmatrelvir is not compromised in current COVID-19 variants.

Graphical abstract

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org.highwire.dtl.DTLVardef@182cc6org.highwire.dtl.DTLVardef@1239d10org.highwire.dtl.DTLVardef@11ca6d9org.highwire.dtl.DTLVardef@df53b_HPS_FORMAT_FIGEXP  M_FIG C_FIG Coupled myovascular expansion directs cardiac growth and regeneration BackgroundHeart regeneration requires multiple cell types to enable cardiomyocyte (CM) proliferation. How these cells interact to create growth niches is unclear. Here we profile proliferation kinetics of cardiac endothelial cells (CECs) and CMs in the neonatal mouse heart and find that they are spatiotemporally coupled.

Methods and ResultsWe show that coupled myovascular expansion during cardiac growth or regeneration is dependent upon VEGF-VEGFR2 signaling, as genetic deletion of Vegfr2 from CECs or inhibition of VEGFA abrogates both CEC and CM proliferation. Repair of cryoinjury displays poor spatial coupling of CEC and CM proliferation. Boosting CEC density after cryoinjury with virus encoding Vegfa enhances regeneration. Using Mendelian randomization, we demonstrate that circulating VEGFA levels are positively linked with human myocardial mass, suggesting that Vegfa can stimulate human cardiac growth.

ConclusionsOur work demonstrates the importance of coupled CEC and CM expansion and reveals a myovascular niche that may be therapeutically targeted for heart regeneration. Diverse stem-chondrichthyan oral structures and evidence for an independently acquired acanthodid dentition The teeth of sharks famously form a series of transversely-organised files with conveyor-belt replacement that are borne directly on the jaw cartilages, in contrast to the dermal plate-borne dentition of bony fishes that undergoes site-specific replacement. A major obstacle in understanding how this system evolved is the poorly understood relationships of the earliest chondrichthyans and the profusion of morphologically and terminologically diverse bones, cartilages, splints and whorls that they possess. Here we use tomographic methods to investigate mandibular structures in several early branching  acanthodian-grade stem-chondrichthyans. We show that the dentigerous jaw bones of disparate genera of ischnacanthids are united by a common construction, being growing bones with non-shedding dentition. Mandibular splints, which support the ventro-lateral edge of the Meckels cartilage in some taxa, are formed from dermal bone and may be an acanthodid synapomorphy. We demonstrate that the teeth of Acanthodopsis are borne directly on the mandibular cartilage and that this taxon is deeply nested within an edentulous radiation, representing an unexpected independent origin of teeth. Many or even all of the range of unusual oral structures may be apomorphic, but they should nonetheless be considered when building hypotheses of tooth and jaw evolution, both in chondrichthyans and more broadly. Synthetic Antigen Presenting cells reveal the diversity and functional specialisation of extracellular vesicles composing the fourth signal of T cell immunological synapses. The T cell Immunological Synapse (IS) is a pivotal hub for the regulation of adaptive immunity by endowing the exchange of information between cells engaged in physical contacts. Beyond the integration of antigen (signal one), co-stimulation (signal two), and cytokines (signal three), the IS facilitates the delivery of T-cell effector assemblies including supramolecular attack particles (SMAPs) and extracellular vesicles (EVs). How these particulate outputs differ among T -cell subsets and how subcellular compartments and signals exchanged at the synapse contribute to their composition is not fully understood. Here we harnessed bead-supported lipid bilayers (BSLBs) as a tailorable and versatile technology for the study of synaptic particle biogenesis and composition in different T-cell subsets, including CART. These synthetic antigen-presenting cells (APCs) facilitated the characterisation of trans-synaptic vesicles (tSV) as a heterogeneous population of EVs comprising among others PM-derived synaptic ectosomes and CD63+ exosomes. We harnessed BSLB to unveil the factors influencing the vesicular release of CD40L, as a model effector, identifying CD40 trans presentation, T-cell activation, ESCRT upregulation/recruitment, antigen density/potency, co-repression by PD-1 ligands, and its processing by ADAM10 as major determinants. Further, BSLB made possible the comparison of microRNA (miR) species associated with tSV and steadily released EVs. Altogether, our data provide evidence for a higher specialisation of tSV which are enriched not only in effector immune receptors but also in miR and RNA-binding proteins. Considering the molecular uniqueness and functional complexity of the tSV output, which is also accompanied by SMAPs, we propose their classification as signal four.

Graphical abstract

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org.highwire.dtl.DTLVardef@100f3aorg.highwire.dtl.DTLVardef@57adfforg.highwire.dtl.DTLVardef@6052fcorg.highwire.dtl.DTLVardef@1e85c21_HPS_FORMAT_FIGEXP  M_FIG C_FIG HighlightsO_LIBead Supported Lipid Bilayers (BSLB) reconstituting antigen-presenting cells support synapse assembly by T cells and the release of effector particles.
C_LIO_LIBSLB facilitate the dissection of the cellular machineries and synapse composition shaping the released tSV.
C_LIO_LItSV and their steadily released counterparts have a different composition. TSV show a higher enrichment of effectors including immune receptors, miR, RNA- and other nucleic acid-binding proteins, than EVs.
C_LI 3D Reconstructions of Mouse Skeletal Muscle Reveal a Decrease in the MICOS Complex and Altered Mitochondrial Networks Background: Skeletal muscle gradually loses mass, strength, endurance, and oxidative capacity during aging. Studies of bioenergetics and protein turnover show that mitochondria mediate this decline in function. Mitochondria cristae are essential for the production of ATP. While mitochondrial aging is associated with endoplasmic reticulum stress, fragmented mitochondria, and decreased mitochondrial capacity, the genes associated with morphological changes in mitochondria during aging still requires further elucidation Further, it is not completely understood how 3D mitochondrial networks and the specialization of mitochondria are altered during aging. Methods: We measured changes in mitochondrial morphology and mitochondrial connectivity during the aging of the mouse gastrocnemius muscle through serial block facing-scanning electron microscopy and 3D reconstruction. Nanotunnels were also measured through 3D reconstruction. CRISPR/Cas9 KO was performed to examine changes in mitochondria upon loss of the mitochondrial contact site and cristae organizing system (MICOS) complex and optic atrophy 1 (OPA-1). Metabolomics were used to find key metabolite pathways changed upon off of the MICOS complex. Results: We found changes in mitochondrial network configuration, nanotunneling, size, shape, number, contact sites, cristae organizing system (MICOS) dynamics and gene expression in skeletal muscle during aging. We also found an association of OPA-1 and the MICOS complex in the gastrocnemius with mitochondrial aging. Further, the loss of the MICOS complex was linked with decreased oxidative capacity, and altered mitochondrial metabolism. Conclusions: To our knowledge, studies of mitochondrial changes in skeletal muscle throughout aging remains limited. 3D reconstructions of nanotunnels elucidated novel patterns of mitochondrial aging in skeletal muscle. MICOS proteins decreased with age and mitochondrial morphology was similar between aged skeletal muscle and that of young mice with MICOS protein loss. In tandem, our data suggest a relationship between the MICOS complex and aging, which could be potentially linked to disease states with additional 3D reconstruction. NOMA: a high-throughput microchip for robust, sequential measurements of secretions from the same single-cells Despite recent advances in single-cell analysis technologies, lacking simple methods to keep the live single-cells traceable for longitudinal detection reliably poses a significant obstacle in single-cell secretion analysis. Here we reported the high-density NOMA (narrow-opening microwell array) microchip that realized the retention of[&ge;] 97% of trapped single cells in dedicated spatial locations during repetitive detection procedures, verified with both adherent and suspension cells by two researchers independently. We applied it in monitoring single-cell protein secretions sequentially from the same single cells, and we found the digital protein secretion patterns dominate the protein secretion. We also demonstrated the microchip for longitudinally tracking IL-8 and the CD81+EV secretions from the same single-cells over days, which revealed the presence of "super secretors" within the cell population be more persistent to secrete protein or extracellular vesicle for an extended period. The NOMA platform reported here is simple, robust, and easy to operate for tracking sequential measurements from the same single cells, representing a novel and informative tool to inspire new observations in biomedical research.

Table of Contents Graphic

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org.highwire.dtl.DTLVardef@aa69c7org.highwire.dtl.DTLVardef@6bc79org.highwire.dtl.DTLVardef@3fcbbforg.highwire.dtl.DTLVardef@597f4b_HPS_FORMAT_FIGEXP  M_FIG C_FIG Benchmarking of deep learning algorithms for 3D instance segmentation of confocal image datasets Segmenting three dimensional microscopy images is essential for understanding phenomena like morphogenesis, cell division, cellular growth and genetic expression patterns. Recently, deep learning (DL) pipelines have been developed which claim to provide high accuracy segmentation of cellular images and are increasingly considered as the state-of-the-art for image segmentation problems. However, it remains difficult to define their relative performances as the concurrent diversity and lack of uniform evaluation strategies makes it difficult to know how their results compare. In this paper, we first made an inventory of the available DL methods for 3 dimensional (3D) cell segmentation. We next implemented and quantitatively compared a number of representative DL pipelines, alongside a highly efficient non-DL method named MARS. The DL methods were trained on a common dataset of 3D cellular confocal microscopy images. Their segmentation accuracies were also tested in the presence of different image artifacts. A specific method for segmentation quality evaluation was adopted which isolates segmentation errors due to under/over segmentation. This is complemented with a 3D visualization strategy for interactive exploration of segmentation quality. Our analysis shows that the DL pipelines have different levels of accuracy. Two of them, which are end to end 3D and were originally designed for cell boundary detection, show high performance, and offer clear advantages in terms of adaptability to new data.

Author summaryIn recent years a number of deep learning (DL) algorithms based on computational neural networks have been developed which claim to achieve high accuracy and automatic segmentation of 3D microscopy images. Although these algorithms have received considerable attention in the literature, it is difficult to evaluate their relative performances, while it remains unclear whether they really perform better than other, more classical segmentation methods.

To clarify these issues, we performed a detailed, quantitative analysis of a number of representative DL pipelines for cell instance segmentation from 3D confocal microscopy image datasets. We developed a protocol for benchmarking the performances of such DL based segmentation pipelines using common training and test datasets, evaluation metrics and visualizations. Using this protocol, we evaluated and compared four different DL pipelines to identify their strengths and limitations. A high performance non-DL method was also included in the evaluation. We show that DL pipelines may show significant differences in their performances depending on their model architecture and pipeline components but overall show excellent adaptability to unseen data. We also show that our benchmarking protocol could be extended to a variety of segmentation pipelines and datasets. Emergent evolutionary forces in spatial models of microbial growth in the human gut microbiota The genetic composition of the gut microbiota is constantly reshaped by ecological and evolutionary forces. These strain-level dynamics can be challenging to understand because they emerge from complex spatial growth processes that take place within a host. Here we introduce a general population genetic framework to predict how stochastic evolutionary forces emerge from simple models of microbial growth in spatially extended environments like the intestinal lumen. Our framework shows how fluid flow and longitudinal variation in growth rate combine to shape the frequencies of genetic variants in sequenced fecal samples, yielding analytical expressions for the effective generation times, selection coefficients, and rates of genetic drift. We find that the emergent evolutionary dynamics can often be captured by well-mixed models that lack explicit spatial structure, even when there is substantial spatial variation in species-level composition. By applying these results to the human colon, we find that continuous fluid flow and simple forms of wall growth are unlikely to create sufficient bottlenecks to allow large fluctuations in mutant frequencies within a host. We also find that the effective gener-ation times may be significantly shorter than expected from traditional average growth rate estimates. Our results provide a starting point for qua ntifying genetic turnover in spatially extended settings like the gut microbiota, and may be relevant for other microbial ecosystems where unidirectional fluid flow plays an important role.