Cycles of transcription and local translation support molecular long-term memory in the hippocampus Long-term memory requires transcription and translation of activity-regulated genes. Many of these are immediate early genes (IEGs) with short-lived mRNAs and proteins, decaying rapidly after stimulation. It remains unknown how an IEG with rapid mRNA and protein turnover can impact long-lasting changes at the synapses. Using fluorescently tagged endogenous Arc, an IEG important for memory consolidation, we performed high-resolution imaging of transcription and translation in individual neurons to identify the long-term gene dynamics after stimulation. Unexpectedly, once induced, Arc underwent transcriptional reactivation often at the same allele. Cycles of transcription were coordinated with localized translation. This cyclical regulation of an IEG, dependent on protein synthesis, reactivates subsequent transcription for maintaining mRNA supply to dendrites. The ensuing Arc mRNAs were preferentially localized at sites marked by previous Arc protein, thereby consolidating local "hubs" of dendritic Arc. These findings revealed the spatio-temporal dynamics of transcription-translation coupling of an IEG and provide a mechanism by which short-lived synaptic proteins can be sustained over the long-time scales of memory consolidation.
Population receptive fields in non-human primates from whole-brain fMRI and large-scale neurophysiology in visual cortex Population receptive field (pRF) modeling is a popular fMRI method to map the retinotopic organization of the human brain. While fMRI-based pRF-maps are qualitatively similar to invasively recorded single-cell receptive fields in animals, it remains unclear what neuronal signal they represent. We addressed this question in awake non-human primates comparing whole-brain fMRI and large-scale neurophysiological recordings in areas V1 and V4 of the visual cortex. We examined the fits of several pRF-models based on the fMRI BOLD-signal, multi-unit spiking activity (MUA) and local field potential (LFP) power in different frequency bands. We found that pRFs derived from BOLD-fMRI were most similar to MUA-pRFs in V1 and V4, while pRFs based on LFP gamma power also gave a good approximation. FMRI-based pRFs thus reliably reflect neuronal receptive field properties in the primate brain. In addition to our results in V1 and V4, the whole-brain fMRI measurements revealed retinotopic tuning in many other cortical and subcortical areas with a consistent increase in pRF-size with increasing eccentricity, as well as a retinotopically specific deactivation of default-mode network nodes similar to previous observations in humans.
A chemical screen based on an interruption of zebrafish gastrulation identifies the HTR2C inhibitor Pizotifen as a suppressor of EMT-mediated metastasis Metastasis is responsible for approximately 90% of cancer-associated mortality but few models exist that allow for rapid and effective screening of anti-metastasis drugs. Current mouse models of metastasis are too expensive and time consuming to use for rapid and high-throughput screening. Therefore, we created a unique screening concept utilizing conserved mechanisms between zebrafish gastrulation and cancer metastasis for identification of potential anti-metastatic drugs. We hypothesized that small chemicals that interrupt zebrafish gastrulation might also suppress metastatic progression of cancer cells and developed a phenotype-based chemical screen to test the hypothesis. The screen used epiboly, the first morphogenetic movement in gastrulation, as a marker and enabled 100 chemicals to be tested in five hours. The screen tested 1280 FDA-approved drugs and identified Pizotifen, an antagonist for serotonin receptor 2C (HTR2C) as an epiboly-interrupting drug. Pharmacologic and genetic inhibition of HTR2C suppressed metastatic progression in a mouse model. Blocking HTR2C with Pizotifen restored epithelial properties to metastatic cells through inhibition of Wnt-signaling. In contrast, HTR2C induced epithelial to mesenchymal transition (EMT) through activation of Wnt-signaling and promoted metastatic dissemination of human cancer cells in a zebrafish xenotransplantation model. Taken together, our concept offers a novel platform for discovery of anti-metastasis drugs. Citation needed?Wikipedia bibliometrics during the first waveof the COVID-19 pandemic. BackgroundWith the COVID-19 pandemics outbreak, millions flocked to Wikipedia for updated information. Amid growing concerns regarding an "infodemic", ensuring the quality of information is a crucial vector of public health. Investigating if and how Wikipedia remained up to date and in line with science is key to formulating strategies to counter misinformation. Using citation analyses, we asked: which sources informed Wikipedias COVID-19-related articles before and during the pandemics first wave (January-May 2020).

ResultsWe found that coronavirus-related articles referenced trusted media sources and high-quality academic research. Moreover, despite a surge in COVID-19 preprints, Wikipedia had a clear preference for open-access studies published in respected journals and made little use of preprints. Building a timeline of English COVID-19 articles from 2001-2020 revealed a nuanced trade-off between quality and timeliness. It further showed how preexisting articles on key topics related to the virus created a framework for integrating new knowledge. Supported by a rigid sourcing policy, this "scientific infrastructure" facilitated contextualization and regulated the influx of new information. Lastly, we constructed a network of DOI-Wikipedia articles, which showed the shifting landscape of pandemic-related knowledge on Wikipedia and how academic citations create a web of shared knowledge supporting topics like COVID-19 vaccine development.

ConclusionsUnderstanding how scientific research interacts with the digital knowledge-sphere during the pandemic provides insight into how Wikipedia can facilitate access to science. It also reveals how, aided by what we term its "citizen encyclopedists", it successfully fended off COVID-19 disinformation and how this unique model may be deployed in other contexts. Optimization of avian perching manoeuvres Perching at speed is amongst the most challenging flight behaviours that birds perform1,2, and beyond the capability of current autonomous vehicles. Smaller birds may touchdown by hovering3-8, but larger birds typically swoop upward to perch1,2 - presumably because the adverse scaling of their power margin prohibits slow flapping flight9, and because swooping transfers excess kinetic to potential energy1,2,10. Perching is risky in larger birds6,11, demanding precise control of velocity and pose12-15, but it is unknown how they optimize this challenging manoeuvre. More generally, whereas cruising flight behaviours such as migration and commuting are adapted to minimize cost-of-transport or time-of-flight16, the optimization of unsteady flight manoeuvres remains largely unexplored7,17. Here we show that swooping minimizes neither the time nor energy required to perch safely in Harris hawks Parabuteo unicinctus, but instead minimizes the distance flown under hazardous post-stall conditions. By combining motion capture data from 1,563 flights with flight dynamics modelling, we found that the birds choice of where to transition from powered dive to unpowered climb minimizes the distance from the landing perch over which very high lift coefficients are required. Time and energy are therefore invested to maintain the control authority needed to execute a safe landing, rather than being minimized continuously as in technical applications of autonomous perching under nonlinear feedback control13 and deep reinforcement learning18,19. Naive birds learn this behaviour on-the-fly, so our findings suggest an alternative reward function for reinforcement learning of autonomous perching in air vehicles. 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. Host evolutionary history and ecological context modulate the adaptive potential of the microbiome Can the microbiome serve as a reservoir of adaptive potential for hosts? To address this question, we leveraged [~]150 generations of experimental evolution in Drosophila melanogaster in a stressful high sugar (HS) diet. We performed a fully reciprocal transplant experiment using the dominant control and HS bacteria and measured life-history and metabolic traits. If the microbiome can indeed confer benefits to hosts, then transplant recipients should gain fitness benefits compared to controls. Interestingly, we found that these benefits do exist, but are non-linear. The benefits depend on specific interactions between host genotype and the microbiome, and these interactions are modulated by the environment. Mismatches between fly evolution and microbiome exerted fitness costs by slowing development and reducing fecundity, especially in the stressful HS diet. The dominant HS bacteria (Acetobacter pasteurianus) uniquely encoded several genes to enable uric acid degradation, mediated the toxic effects of uric acid accumulation due to the HS diet for flies. Our study demonstrates that host genotype x microbiome x environment interactions have substantial effects on host phenotype, highlighting how host evolution and ecological context together shape the adaptive potential of the microbiome. Cellular feedback to organotelluranes displays modulation of antioxidant proteins gene expression Organotelluranes RT3 and RT4 are thiol reagents that induce mitochondrial transition pore (MTP) opening in a sensitive and insensitive manner to cyclosporin A. Although RT3 and RT4 promote glutathione depletion, paradoxically, they are also an efficient antioxidant for membrane lipids. These compounds antagonistic effects elicited the challenging question of how the gene expression of antioxidant enzymes would respond to treatment with these compounds. The influence of RT3 and RT4 on antioxidant enzyme expression was investigated in cultured aortic smooth muscle cells (ASMC). RT3 and RT4 promoted disruption of ionic calcium homeostasis, mitochondrial transmembrane potential ({Delta}{Psi}), and cell death in a dose-dependent manner. The cell death mechanisms responded qualitatively to the increase of the organotellurane concentration and changed from apoptosis to necrosis. RT3 and RT4 increased the expression of thioredoxin significantly. RT3 also increased the expression of glutaredoxin and glutathione peroxidase, slightly the catalase expression without significant effects on SOD expression. The results are consistent with GSH and protein thiol depletion and discussed based on the cell toxicity mechanism exhibited by these compounds.



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org.highwire.dtl.DTLVardef@52bb82org.highwire.dtl.DTLVardef@19476d9org.highwire.dtl.DTLVardef@6aa99org.highwire.dtl.DTLVardef@1aa605e_HPS_FORMAT_FIGEXP  M_FIG C_FIG A flux-based machine learning model to simulate the impact of pathogen metabolic heterogeneity on drug interactions AO_SCPLOWBSTRACTC_SCPLOWDrug combinations are a promising strategy to counter antibiotic resistance. However, current experimental and computational approaches do not account for the entire complexity involved in combination therapy design, such as the effect of pathogen metabolic heterogeneity, changes in the growth environment, drug treatment order and time interval. To address these limitations, we present a comprehensive approach that uses genome-scale metabolic modeling and machine learning to guide combination therapy design. Our mechanistic approach (a) accommodates diverse data types, (b) accounts for time- and order-specific interactions, and (c) accurately predicts drug interactions in various growth conditions and their robustness to pathogen metabolic heterogeneity. Our approach achieved high accuracy (AUC = 0.83 for synergy, AUC = 0.98 for antagonism) in predicting drug interactions for E. coli cultured in 57 metabolic conditions based on experimental validation. The entropy in bacterial metabolic response was predictive of combination therapy outcomes across time scales and growth conditions. Simulation of metabolic heterogeneity using population-FBA identified two sub-populations of E. coli cells defined by regulation of cis- to trans-isomerization of unsaturated fatty acids with distinct effects on drug interactions. Analysis of the vast landscape of condition-specific drug interactions revealed a set of 24 robustly synergistic drug combinations with potential for clinical use.

SO_SCPLOWIGNIFICANCEC_SCPLOWWorldwide, 700,000 people die each year from drug-resistant infections. Drug combinations have great potential to reduce the spread of drug-resistant bacteria. However, their potency is impacted by both the pathogen growth environment and the heterogeneity in pathogen metabolism. The metabolic heterogeneity in a pathogen population allows them to survive antibiotic treatment. Here we present a flexible machine-learning framework that utilizes diverse data types to effectively search through the large design space of both sequential and simultaneous combination therapies across hundreds of simulated growth conditions and pathogen metabolic states. Our approach can serve as a useful guide for the selection of robustly synergistic drug combinations. N-VEGF, the forgotten isoform of VEGF-A, activate cellular stress survival circuits Vascular endothelial growth factor A (VEGF-A) is a secreted protein that stimulates angiogenesis in response to hypoxia. Under hypoxic conditions, a non-canonical long isoform called L-VEGF is concomitantly expressed with VEGF-A. Once translated, L-VEGF it is proteolytically cleaved to generate N-VEGF and VEGF-A. Interestingly, while VEGF-A is secreted and affects the surrounding cells, N-VEGF is mobilized to the nucleus. This suggests that N-VEGF participates in transcriptional response to hypoxia. In this study, we performed a series of complementary experiments to examine the functional role of N-VEGF. Strikingly, we found that the mere expression of N-VEGF followed by its hypoxia-independent mobilization to the nucleus was sufficient to induce key genes associated with angiogenesis, such as Hif1, VEGF-A isoforms, as well as genes associated with cell survival under hypoxia. Complementarily, when N-VEGF was genetically depleted, key hypoxia-induced genes were downregulated and cells were significantly susceptible to hypoxia-mediated apoptosis. This is the first reports of N-VEGF serving as an autoregulatory arm of VEGF-A. Further experiments will be needed to determine the role of N-VEGF in cancer and embryogenesis. Cytokinin oxidase/dehydrogenase family genes exhibit functional divergence and overlap in rice growth and development Cytokinins play key roles in plant growth and development; hence, cytokinin biosynthesis and degradation have been extensively studied. Cytokinin oxidase/dehydrogenases (CKXs) are a group of enzymes that regulate oxidative cleavage to maintain cytokinin homeostasis. In rice, 11 OsCKX genes have been identified to date; however, most of their functions remain unknown. Here, we comprehensively analyzed the expression patterns and functions of OsCKX genes. Using CRISPR/Cas9 technology, we constructed mutants of all OsCKX genes to determine the functions of OsCKXs in rice development. The results revealed that the single osckx and higher-order osckx4 osckx9 mutant lines showed functional overlap and subfunctionalization. Notably, the osckx1 osckx2 and osckx4 osckx9 double mutants displayed contrasting phenotypic changes in tiller number and panicle size compared to the wild type. Moreover, we identified several genes with significantly altered expression in osckx4 and osckx9 single and double mutant plants. Many differentially expressed genes were found to be associated with auxin and cytokinin pathways. Additionally, the cytokinins in osckx4 osckx9 mutants were increased compared to the wild type. Overall, our findings provide new insights into the functions of OsCKX genes in rice growth and may be used as a foundation for future studies aimed at improving rice yield. Key words: Cytokinin, expression pattern, OsCKX, panicle, phenotype, rice, tiller

HighlightThe osckx4 osckx9 double mutant had a significantly greater number of tillers, whereas the osckx1 osckx2 double mutant showed the opposite phenotypic change, compared to the wild type A network approach to genetic circuit designs As genetic circuits become more sophisticated, the size and complexity of data about their designs increases. This data captured goes beyond monolithic genetic sequences and towards circuit modularity and functional details, which are beneficial for analyzing circuit performance and establishing design automation techniques. However, the accessibility, visualisation and usability of design data (and metadata) have received relatively little attention to date. Here, we present a method to turn circuit designs into networks and showcase its potential to enhance the utility of design data. Since networks are dynamic structures, initial graphs can be interactively shaped into sub-networks of relevant information based on requirements such as abstraction, hierarchy and protein interactions. Additionally, several visual changes can be applied, such as colouring or clustering nodes based on types (e.g., genes or promoters), resulting in easier comprehension from a user perspective. This approach allows circuit designs to be coupled to other networks, such as metabolic pathways or implementation protocols captured in graph-like formats. Therefore, we advocate using networks to structure, access and improve synthetic biology information. Substrate dynamics contribute to enzymatic specificity in human and bacterial methionine adenosyltransferases Protein conformational change can facilitate the binding of non-cognate substrates and underlie promiscuous activities. However, the contribution of substrate conformational dynamics to this process is comparatively poorly understood. Here we analyse human (hMAT2A) and Escherichia coli (eMAT) methionine adenosyltransferases that have identical active sites but different substrate specificity. In the promiscuous hMAT2A, non-cognate substrates bind in a stable conformation to allow catalysis. In contrast, non-cognate substrates rarely sample stable productive binding modes in eMAT owing to altered mobility in the enzyme active site. Different cellular concentrations of substrate likely drove the evolutionary divergence of substrate specificity in these orthologs. The observation of catalytic promiscuity in hMAT2A led to the detection of a new human metabolite, methyl thioguanosine, that is produced at elevated level in a cancer cell line. This work establishes that identical active sites can result in different substrate specificity owing to the effects of substrate and enzyme dynamics. OncoLoop: A network-based precision cancer medicine framework At present, prioritizing cancer treatments at the individual patient level remains challenging, and performing co-clinical studies using patient-derived models in real-time is often not feasible. To circumvent these challenges, we introduce OncoLoop, a precision medicine framework to predict and validate drug sensitivity in human tumors and their pre-existing high-fidelity (cognate) model(s) by leveraging perturbational profiles of clinically-relevant oncology drugs. As proof-of-concept, we applied OncoLoop to prostate cancer (PCa) using a series of genetically-engineered mouse models (GEMMs) that recapitulate a broad spectrum of disease states, including castration-resistant, metastatic, and neuroendocrine prostate cancer. Interrogation of published cohorts using Master Regulator (MR) conservation analysis revealed that most patients were represented by at least one cognate GEMM-derived tumor (GEMM-DT). Drugs predicted to invert MR activity in patients and their cognate GEMM-DTs were successfully validated, including in two cognate allografts and one cognate patient-derived xenograft (PDX). OncoLoop is a highly generalizable framework that can be extended to other cancers and potentially other diseases.

Significance StatementOncoLoop is a transcriptomic-based experimental and computational framework that can support rapid-turnaround co-clinical studies to identify and validate drugs for individual patients, which can then be readily adapted to clinical practice. This framework should be applicable in many cancer contexts for which appropriate models and drug perturbation data are available. Genomic evidence of paternal genome elimination in the globular springtail Allacma fusca Paternal genome elimination (PGE) - a type of reproduction in which males inherit but fail to pass on their fathers genome - evolved independently in six to eight arthropod clades. Thousands of species, including several important for agriculture, reproduce via this mode of reproduction. While PGE is well established in some of the clades, the evidence in globular springtails (Symphypleona) remains elusive, even though they represent the oldest and most species rich clade putatively reproducing via PGE. We sequenced genomic DNA from whole bodies of Allacma fusca males with high fractions (>27.5%) of sperm to conclusively confirm that all the sperm carry one parental haplotype only. Although it is suggestive that the single haplotype present in sperm is maternally inherited, definitive genetic proof of the parent of origin is still needed. The genomic approach we developed allows for detection of genotypic differences between germline and soma in all species with sufficiently high fraction of germline in their bodies. This opens new opportunities for scans of reproductive modes in small organisms. Liver ductal organoids reconstruct intrahepatic biliary trees in decellularized liver grafts Three-dimensional scaffolds decellularized from native organs are a promising technique to establish engineered liver grafts and overcome the current shortage of donor organs. However, limited sources of bile duct cells and inappropriate cell distribution in bioengineered liver grafts have hindered their practical application. Organoid technology is anticipated to be an excellent tool for the advancement of regenerative medicine. In the present study, we reconstructed intrahepatic bile ducts in a rat decellularized liver graft by recellularization with liver ductal organoids. Using an ex vivo perfusion culture system, we demonstrated the biliary characteristics of repopulated mouse liver organoids, which maintained bile duct markers and reconstructed biliary tree-like networks with luminal structures. We also established a method for the co-recellularization with engineered bile ducts and primary hepatocytes, revealing the appropriate cell distribution to mimic the native liver. We then utilized this model in human organoids to demonstrate the reconstructed bile ducts. Our results show that liver ductal organoids are a potential cell source for bile ducts from bioengineered liver grafts using three-dimensional scaffolds. Multiparametric profiling of cellular lipid metabolism in hypercholesterolemia Systematic insight into cellular dysfunctions can improve understanding of disease etiology, risk assessment and patient stratification. We present a multiparametric high-content imaging platform enabling quantification of low-density lipoprotein (LDL) uptake and lipid storage in cytoplasmic droplets of primary leukocyte subpopulations. We validated this platform with samples from 65 individuals with variable blood LDL-cholesterol (LDL-c) levels, including familial hypercholesterolemia (FH) and non-FH subjects. We integrated lipid storage data into a novel readout, lipid mobilization, measuring the efficiency with which cells deplete lipid reservoirs. Lipid mobilization correlated positively with LDL uptake and negatively with hypercholesterolemia and age, improving differentiation of individuals with normal and elevated LDL-c. Moreover, combination of cell-based readouts with a polygenic risk score for LDL-c explained hypercholesterolemia better than the genetic risk score alone. This platform provides functional insights into cellular lipid trafficking from a few mls of blood and is applicable to dissect metabolic disorders, such as hypercholesterolemia.

MotivationWe have limited information on how cellular lipid uptake and processing differ between individuals and influence the development of metabolic diseases, such as hypercholesterolemia. Available assays are labor intensive, require skilled personnel and are difficult to scale to higher throughput, making it challenging to obtain systematic functional cell-based data from individuals. To overcome this problem, we established a scalable automated analysis pipeline enabling reliable quantification of multiple cellular readouts, including lipid uptake, storage and mobilization, from different white blood cell populations. This approach provides new personalized insights into the cellular basis of hypercholesterolemia and obesity.

Graphical Abstract

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org.highwire.dtl.DTLVardef@9222c1org.highwire.dtl.DTLVardef@27e289org.highwire.dtl.DTLVardef@89ba82org.highwire.dtl.DTLVardef@33c9cd_HPS_FORMAT_FIGEXP  M_FIG C_FIG Changes in Behavior and Salivary Serum Amyloid A in cows with Subclinical Mastitis The aim of this study was to identify detailed changes in behavior, and in salivary serum amyloid A (SAA), associated with subclinical mastitis. This included standard sickness behaviors (which form part of an adaptive response to conserve energy, minimize heat loss and facilitate recovery following infection and inflammation) and lesser-studied behaviors, that are biologically predicted to change at low-level infection, and therefore particularly relevant for detecting subclinical mastitis (here labelled  luxury behaviors). SAA is a physiological marker of systemic inflammation, with levels in milk and serum already known to increase during subclinical mastitis. We investigated whether the same was true for SAA in cow saliva. Data were collected for n = 34 commercial barn-housed dairy cows, tested in 17 matched-pairs. Each pair comprised a cow with subclinical mastitis (SCM) and a healthy control (CTRL), identified using somatic cell count (SCC) (SCM: SCC >200 x1000 cells/ml; CTRL: SCC <100 x1000 cells/ml). SCM cows were selected for study ad-hoc, at which point they were paired with a CTRL cow, based upon parity and calving date; consequently, the full data set was accrued over several months. Data were collected for each pair over 3 days: SCC (l4:00-l5:00h) Day 1; behavior (24h from 00:00h) Day 2; salivary serum amyloid-A (SAA) Day 3. We report, for the first time, that an increase in salivary SAA occurs during subclinical mastitis; SAA was higher in SCM cows and demonstrated a positive (weak) correlation with SCC. The behavioral comparisons revealed that SCM cows had reductions in activity (behavioral transitions and distance moved), social exploration, social reactivity (here: likelihood to move away/be displaced following receipt of agonism), performance of social grooming and head butts, and the receipt of agonistic non-contact challenges. In addition, SCM cows received more head swipes, and spent a greater proportion of time lying with their head on their flank than CTRL cows. SCM cows also displayed an altered feeding pattern; they spent a greater proportion of feeding time in direct contact with two conspecifics, and a lower proportion of feeding time at self-locking feed barriers, than CTRL cows. Behavioral measures were found to correlate, albeit loosely, with serum SAA in a direction consistent with predictions for sickness behavior. These included positive correlations with both lying duration and the receipt of all agonistic behavior, and negative correlations with feeding, drinking, the performance of all social and all agonistic behavior, and social reactivity. We conclude that changes in salivary SAA, social behavior, and activity offer potential in the detection of subclinical mastitis and recommend further investigation to substantiate and refine our findings. Detecting DNA of novel fungal pathogens using ResNets and a curated fungi-hosts data collection BackgroundEmerging pathogens are a growing threat, but large data collections and approaches for predicting the risk associated with novel agents are limited to bacteria and viruses. Pathogenic fungi, which also pose a constant threat to public health, remain understudied. Relevant data remains comparatively scarce and scattered among many different sources, hindering the development of sequencing-based detection workflows for novel fungal pathogens. No prediction method working for agents across all three groups is available, even though the cause of an infection is often difficult to identify from symptoms alone.

ResultsWe present a curated collection of fungal host range data, comprising records on human, animal and plant pathogens, as well as other plant-associated fungi, linked to publicly available genomes. We show that it can be used to predict the pathogenic potential of novel fungal species directly from DNA sequences with either sequence homology or deep learning. We develop learned, numerical representations of the collected genomes and visualize the landscape of fungal pathogenicity. Finally, we train multi-class models predicting if next-generation sequencing reads originate from novel fungal, bacterial or viral threats.

ConclusionsThe neural networks trained using our data collection enable accurate detection of novel fungal pathogens. A curated set of over 1,400 genomes with host and pathogenicity metadata supports training of machine learning models and sequence comparison, not limited to the pathogen detection task.

AvailabilityThe data, models and code are hosted at https://zenodo.org/record/5846345, https://zenodo.org/record/5711877, and https://gitlab.com/dacs-hpi/deepac. Omicron BA.1 and BA.2 Variants Increase the Interactions of SARS-CoV-2 Spike Glycoprotein with ACE2 SARS-CoV-2 infection is initiated by binding of the receptor-binding domain (RBD) of its spike glycoprotein to the peptidase domain (PD) of angiotensin-converting enzyme 2 (ACE2) receptors in host cells. Recently detected Omicron variant of SARS-CoV-2 (B.1.1.529) is heavily mutated on RBD. Currently, the most common Omicron variants are the original BA.1 Omicron strain and the BA.2 variant, which became more prevalent since it first appeared. To investigate how these mutations affect RBD-PD interactions, we performed all-atom molecular dynamics simulations of the BA.1 and BA.2 RBD-PD in the presence of full-length glycans, explicit water and ions. Simulations revealed that RBDs of BA.1 and BA.2 variants exhibit a more dispersed interaction network and make an increased number of salt bridges and hydrophobic interactions with PD compared to wild-type RBD. Although BA.1 and BA.2 differ in two residues at the RBD-ACE2 interface, no major difference in RBD-PD interactions and binding strengths were observed between these variants. Using the conformations sampled in each trajectory, the Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) method estimated ~34% and ~51% stronger binding free energies for BA.1 and BA.2 RBD, respectively, than wild-type RBD, which may result in higher binding efficiency of the Omicron variant to infect host cells. Unsupervised clustering analysis of SARS-Cov-2population structure reveals six major subtypes atearly stage across the world Identifying the population structure of the newly emerged coronavirus SARS-CoV-2 has significant potential to inform public health management and diagnosis. As SARS-CoV-2 sequencing data accrued, grouping them into clusters is important for organizing the landscape of the population structure of the virus. Due to the limited prior information on the newly emerged coronavirus, we utilized four different clustering algorithms to group 16,873 SARS-CoV-2 strains, which automatically enables the identification of spatial structure for SARS-CoV-2. A total of six distinct genomic clusters were identified using mutation profiles as input features. Comparison of the clustering results reveals that the four algorithms produced highly consistent results, but the state-of-the-art unsupervised deep learning clustering algorithm performed best and produced the smallest intra-cluster pairwise genetic distances. The varied proportions of the six clusters within different continents revealed specific geographical distributions. In particular, our analysis found that Oceania was the only continent on which the strains were dispersively distributed into six clusters. In summary, this study provides a concrete framework for the use of clustering methods to study the global population structure of SARS-CoV-2. In addition, clustering methods can be used for future studies of variant population structures in specific regions of these fast-growing viruses. Fractal polymer with loops recapitulates key features of chromosome organization Chromosomes are exceedingly long polymers compacted in a cell nucleus. While it has long been suggested that mammalian chromosomes during interphase are folded into loops, experimental detection of such loops has remained a daunting task. The most comprehensive experimental information about chromosome spatial organization is provided by Hi-C experiments that measure the frequency of contacts between all chromosomal positions. The lack of a tractable physical model of a polymer folded into loops limits our ability to interpret experimental data. It thus remains unknown how to obtain accurate and quantitative information about the nature of chromosomal looping from Hi-C. Here, we introduce a model of a polymer with random loops, solve it analytically and extend it by simulations for real chains. Remarkably, our model faithfully reproduces complex shapes of experimental contact probability curves universal among mammalian cells. Furthermore, our model allows one to estimate sizes of randomly positioned chromosomal loops from experimental data. We also show that excluded volume in real chains can induce osmotic and topological repulsion between loops, stiffening interphase chromosomes. Thus, our new framework allows interpretation of experimental data and suggests that interphase chromosomes are crumpled polymers further folded into a sequence of randomly positioned loops. Glucose inhibits haemostasis and accelerates diet-induced hyperlipidaemia in zebrafish larvae Hyperglycaemia damages the microvasculature in part through the reduced recruitment of immune cells and interference with platelet signalling, leading to poor wound healing and accelerated lipid deposition in mammals. We investigated the utility of zebrafish larvae to model the effect of glucose on neutrophil and macrophage recruitment to a tail wound, wound-induced haemostasis, and chicken egg yolk feed challenge-induced hyperlipidaemia by supplementing larvae with exogenous glucose by immersion or injection. Neither method of glucose supplementation affected the recruitment of neutrophils and macrophages following tail transection. Glucose injection reduced thrombocyte retention and fibrin plug formation while only thrombocyte retention was reduced by glucose immersion following tail transection. We observed accelerated lipid accumulation in glucose-injected larvae challenged with high fat chicken egg yolk feeding. Our study identifies conserved and divergent effects of high glucose on inflammation, haemostasis, and hyperlipidaemia in zebrafish larvae compared to mammals. 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. 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. YfiB: An Outer Membrane Protein Involved in the Virulence of Shigella flexneri The intracellular pathogen Shigella flexneri, which is the causative agent of bacillary dysentery, significantly influences the worldwide implication of diarrheal infections, consequentially causing about 1.1 million deaths each year. Due to a non-availability of an authorized vaccine and the upsurge of multidrug resistance amongst Shigella strains, there has been a huge demand for further genetic analyses which could help in the advancement of new/improved drugs, and finding vaccine candidates against the pathogen. Whilst many features about the invasion of colonic cells by Shigella have been identified, fundamental gaps in information concerning in what way the bacteria transit, survive, and control gene expression, remain. The present study aims to illustrate the role of yfiB gene in Shigella virulence, which is a part of the periplasmic YfiBNR tripartite signaling system. This system is involved in the regulation of cyclic-di-GMP levels inside the bacterial cells, which is a vital messenger molecule impacting varied cellular processes such as biofilm formation, cytotoxicity, motility, synthesis of exopolysaccharide, and other virulence mechanisms like adhesion and invasion of the bacteria. Through a combination of genetic, biochemical, and virulence assays, we show how knocking out the yfiB gene can disrupt the entire YfiBNR system and affect biofilm formation, bacterial invasion, host-surface attachment, and the overall virulence of Shigella. This study eventually improves our understanding of the in-vivo persistence and survival of Shigella, brings light to the c-di-GMP lead regulation of Shigella virulence, and provides a prospective new target to design anti-infective drugs and vaccines against S. flexneri and other bacterial pathogens.