Production of Offspring from Azoospermic Mice with Meiotic Failure: Precise Biparental Meiosis within Halved Oocytes While the large volume of mammalian oocytes is necessary for embryo development, it can lead to error-prone chromosomal segregation during meiosis. Conversely, we hypothesized that smaller oocytes would have a great unidentified potential to stabilize unstable meiosis and improve the development of the resultant embryos. Here, we show that reducing ooplasmic volume can rescue highly error-prone fertilization using primary spermatocytes by preventing segregation errors of chromosomes during biparental meiosis. High-resolution live-imaging analysis revealed that erroneous chromosome segregation occurred in most (90%) spermatocyte-injected oocytes of normal size, but could be ameliorated to 40% in halved oocytes. The birth rate improved remarkably from 1% to 19% (P < 0.0001). Importantly, this technique enabled the production of offspring from azoospermic mice with spermatocyte arrest caused by STX2 deficiency, an azoospermia factor also found in humans. Thus, contrary to popular opinion, oocytes inherently have a strong potential for precise meiotic divisions, which can be evoked by reduction of the ooplasmic volume. Their potential might help rescue cases of untreatable human azoospermia with spermatocyte arrest.
Sox8 remodels the cranial ectoderm to generate the ear The vertebrate inner ear arises from a pool of progenitors with the potential to contribute to all the sense organs and cranial ganglia in the head. Here we explore the molecular mechanisms that control ear specification from these precursors. Using a multi-omics approach combined with loss-of-function experiments we identify a core transcriptional circuit that imparts ear identity, along with the first genome-wide characterization of non-coding elements that integrate this information. This analysis places the transcription factor Sox8 at the top of the ear determination network. Introducing Sox8 into cranial ectoderm not only converts non-ear cells into ear progenitors, but also activates the cellular programs for ear morphogenesis and neurogenesis. Thus, Sox8 has the unique ability to remodel transcriptional networks in the cranial ectoderm towards ear identity.
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. Membrane contact probability: an essential and predictive character for the structural and functional studies of membrane proteins One of the unique traits of membrane proteins is that a significant fraction of their hydrophobic amino acids is exposed to the hydrophobic core of lipid bilayers rather than being embedded in the protein interior, which is often not explicitly considered in the protein structure and function predictions. Here, we propose a characteristic and predictive quantity, the membrane contact probability (MCP), to describe the likelihood of the amino acids of a given sequence being in direct contact with the acyl chains of lipid molecules. We show that MCP is complementary to solvent accessibility in characterizing the outer surface of membrane proteins, and it can be predicted for any given sequence with a machine learning-based method by utilizing a training dataset extracted from MemProtMD, a database generated from molecular dynamics simulations for the membrane proteins with a known structure. As the first of many potential applications, we demonstrate that MCP can be used to systematically improve the prediction precision of the protein contact maps and structures. Molecular basis for gating of cardiac ryanodine receptor: underlying mechanisms for gain- and loss-of function mutations Cardiac ryanodine receptor (RyR2) is a large Ca2+ release channel in the sarcoplasmic reticulum and indispensable for excitation-contraction coupling in the heart. RyR2 is activated by Ca2+ and RyR2 mutations are implicated in severe arrhythmogenic diseases. Yet, the structural basis underlying channel opening and how mutations affect the channel remain unknown. Here, we addressed gating mechanism of RyR2 by combining high-resolution structures determined by cryo-electron microscopy with quantitative functional analysis of channels carrying various mutations in specific residues. We demonstrated two fundamental mechanisms for channel gating: interactions close to the channel pore stabilize the channel to prevent hyperactivity and a series of interactions in the surrounding regions is necessary for channel opening upon Ca2+ binding. Mutations at the residues involved in the former and the latter mechanisms cause gain-of-function and loss-of-function, respectively. Our results reveal gating mechanisms of the RyR2 channel and alterations by pathogenic mutations at the atomic level. Abscisic Acid Drives Stomatal Closure through Increases in Hydrogen Peroxide in Distinct Subcellular Compartments Including Mitochondria Abscisic acid (ABA) closes stomata to minimize transpiration and gas exchange in response to drought. ABA signaling increases accumulation of reactive oxygen species (ROS) in guard cells to drive stomatal closure. We used chemical dyes and genetically-encoded biosensors to identify the subcellular localizations where ABA increases hydrogen peroxide (H2O2) in guard cells. The general redox sensor, dichlorofluorescein (DCF), revealed ABA-driven ROS increases in distinct subcellular locations including chloroplasts, cytosol, and nuclei. ABA also increased fluorescence of both DCF and an H2O2-selective probe, Peroxy Orange 1, in mitochondria, which colocalize with Mitotracker Red. Whole cell and mitochondrial localized ABA-increased ROS signals were lost in an ABA-insensitive quintuple ABA receptor mutant and accentuated in an ABA-hypersensitive mutant. The ABA-induced ROS accumulation in mitochondria was lost in guard cells of mutants with defects in genes encoding H2O2 elevating respiratory burst oxidase homolog (RBOH) enzymes and reduced by treatment with the RBOH inhibitor VAS2870. ABA treatment of guard cells transformed with the genetically-encoded H2O2 reporter targeted to the cytoplasm, roGFP2-Orp1, and the mitochondrial targeted, mt-roGFP2-Orp1, revealed significant ABA-induced H2O2 increases. Furthermore, H2O2 treatment is sufficient to close stomata. These results reveal that ABA elevates H2O2 accumulation in guard cell cytosol and mitochondria to drive stomatal closure.

One sentence summaryGenetically encoded biosensors and chemical probes revealed ABA-dependent increases in hydrogen peroxide, a reactive oxygen species with signaling activity, in guard cell cytoplasm and mitochondria. OrganoID: a versatile deep learning platform for tracking and analysis of single-organoid dynamics Organoids have immense potential as ex vivo disease models for drug discovery and personalized drug screening. Dynamic changes in individual organoid morphology, number, and size can indicate important drug responses, however these metrics are difficult and labor-intensive to obtain for high-throughput image datasets. Here, we present OrganoID, a robust image analysis platform that automatically recognizes, labels, and tracks single organoids, pixel-by-pixel, in brightfield and phase-contrast microscopy experiments. The platform was trained on images of pancreatic cancer organoids and validated on separate images of pancreatic, lung, colon, and adenoid cystic carcinoma organoids, which showed excellent agreement with manual measurements of organoid count (96%) and size (95%) without any parameter adjustments. Single-organoid tracking accuracy remained above 89% over a four-day time-lapse microscopy study. Automated single-organoid morphology analysis of a chemotherapy dose-response experiment identified decreased organoid circularity as an important morphological feature reflecting drug response. OrganoID enables straightforward, detailed, and accurate image analysis to accelerate the use of organoids in high-throughput, data-intensive biomedical applications. Imbalanced segregation of recombinant haplotypes in hybrid populations reveals inter- and intrachromosomal Dobzhansky-Muller incompatibilities Dobzhansky-Muller incompatibilities (DMIs) are a major component of reproductive isolation between species. DMIs imply negative epistasis, and are exposed when two diverged populations hybridize. Mapping the locations of DMIs has largely relied on classical genetic mapping. Approaches to date are hampered by low power and the challenge of identifying DMI loci on the same chromosome, because strong initial linkage of parental haplotypes weakens statistical tests. Here, we propose new statistics to infer negative epistasis from haplotype frequencies in hybrid populations. When two divergent populations hybridize, the variance in heterozygosity at two loci decreases faster with time at DMI loci than at random pairs of loci. When two populations hybridize at near-even admixture proportions, the deviation of the observed variance from its expectation becomes negative for the DMI pair. This negative deviation enables us to detect intermediate to strong negative epistasis both within and between chromosomes. In practice, the detection window in hybrid populations depends on the demographic scenario, the recombination rate, and the strength of epistasis. When the initial proportion of the two parental populations is uneven, only strong DMIs can be detected with our method unless migration prevents parental haplotypes from being lost. We use the new statistics to infer candidate DMIs from three hybrid populations of swordtail fish. We identify numerous new DMI candidates, some of which are inferred to interact with several loci within and between chromosomes. Moreover, we discuss our results in the context of an expected enrichment in intrachromosomal over interchromosomal DMIs.

Author SummaryGenetic incompatibility in the form of (Bateson-)Dobzhansky-Muller incompatibilities (DMIs) is an important component of reproductive isolation between species. However, the evolutionary role of DMIs during the process of speciation is contentious. DMIs occur when two or more genetic variants interact to reduce their carriers fitness. Once recombination combines incompatible variants in hybrids, selection acts to remove these variants from the population. One step towards addressing the evolutionary role of DMIs is to quantify the prevalence of DMIs in incipient and hybridizing species. Here, we present statistics that are sensitive to the resulting recombinant imbalance and that can indicate the location of DMIs in hybrid genomes in various demographic scenarios. We use simulations to show that the time window during which a DMI is detectable depends on its genomic location, the severity of the DMI, and the populations demography. Importantly, our statistic distinguishes genetic associations arising due to physical linkage from those arising due to gene interactions, which allows for the inference of both inter- and intrachromosomal DMIs. Applying our statistics to three hybrid populations of swordtail fish, we confirm previously known DMIs and identify new candidate incompatibilities. Second Heart Field-derived Cells Contribute to Angiotensin II-mediated Ascending Aortopathies BackgroundThe ascending aorta is a common location for aneurysm and dissection. This aortic region is populated by a mosaic of medial and adventitial cells that are embryonically derived from either the second heart field (SHF) or the cardiac neural crest. SHF-derived cells populate areas that coincide with the spatial specificity of thoracic aortopathies. The purpose of this study was to determine whether and how SHF-derived cells contribute to ascending aortopathies.

MethodsAscending aortic pathologies were examined in patients with sporadic thoracic aortopathies and angiotensin II (AngII)-infused mice. Ascending aortas without overt pathology from AngII-infused mice were subjected to mass spectrometry assisted proteomics, and molecular features of SHF-derived cells were determined by single cell transcriptomic analyses. Genetic deletion of either low-density lipoprotein receptor-related protein 1 (Lrp1) or transforming growth factor-{beta} receptor 2 (Tgfbr2) in SHF- derived cells was conducted to examine the impact of SHF-derived cells on vascular integrity.

ResultsPathologies in human ascending aortic aneurysmal tissues were predominant in outer medial layers and adventitia. This gradient was mimicked in mouse aortas following AngII infusion that was coincident with the distribution of SHF-derived cells. Proteomics indicated that brief AngII infusion, prior to overt pathology, evoked downregulation of SMC proteins and differential expression of extracellular matrix proteins, including several LRP1 ligands. LRP1 deletion in SHF-derived cells augmented AngII-induced ascending aortic aneurysm and rupture. Single cell transcriptomic analysis revealed that brief AngII infusion decreased Lrp1 and Tgfbr2 mRNA abundance in SHF-derived cells and induced a unique fibroblast population with low abundance of Tgfbr2 mRNA. SHF-specific Tgfbr2 deletion led to embryonic lethality at E12.5 with dilatation of the outflow tract and retroperitoneal hemorrhage. Integration of proteomic and single cell transcriptomics results identified plasminogen activator inhibitor 1 (PAI1) as the most increased protein in SHF-derived SMCs and fibroblasts during AngII infusion. Immunostaining revealed a transmural gradient of PAI1 in both ascending aortas of AngII-infused mice and human ascending aneurysmal aortas that mimicked the gradient of medial and adventitial pathologies.

ConclusionSHF-derived cells exert a critical role in maintaining vascular integrity through LRP1 and TGF-{beta} signaling associated with increases of aortic PAI1.



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org.highwire.dtl.DTLVardef@185e3d9org.highwire.dtl.DTLVardef@131d702org.highwire.dtl.DTLVardef@11afa42org.highwire.dtl.DTLVardef@242786_HPS_FORMAT_FIGEXP  M_FIG C_FIG Clinical PerspectiveO_ST_ABSWhat is new?C_ST_ABS- SHF-derived SMCs and fibroblasts associate with AngII-induced aortic pathologies.
- AngII induces a distinct fibroblast sub-cluster that is less abundant for mRNAs related to major extracellular components and TGF{beta} ligands and receptors, but more abundant for proliferative genes.
- TGFBR2 deletion in SHF-derived cells are embryonic lethal with significant dilatation of the outflow tract in mice.
- SHF-specific deletion of LRP1 leads to aortic pathologies in mice, supporting the importance of SHF-derived cells in maintaining ascending aortic wall integrity.


What are the clinical implications?- Heterogeneity of the embryonic origins of SMCs and fibroblasts contributes to complex mechanisms of vasculopathy formation, which should be considered when investigating the pathogenesis of thoracic aortopathies. 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. TAS-Seq: a robust and sensitive amplification method for bead-based scRNA-seq Single-cell RNA-sequencing (scRNA-seq) is valuable for analyzing cellular heterogeneity. Cell composition accuracy is critical for analyzing cell-cell interaction networks from scRNA-seq data. We developed terminator-assisted solid-phase cDNA amplification and sequencing (TAS-Seq) for scRNA-seq based on a terminator, terminal transferase, and nanowell/bead-based scRNA-seq platform; TAS-Seq showed high tolerance to variations in the terminal transferase reaction, which complicate the handling of existing terminal transferase-based scRNA-seq methods. In murine and human lung samples, TAS-Seq yielded scRNA-seq data that were highly correlated with flow-cytometric data, showing higher gene-detection sensitivity and more robust detection of important cell-cell interactions and expression of growth factors/interleukins in cell subsets than 10X Chromium v2 and Smart-seq2. Expanding TAS-Seq application will improve understanding and atlas construction of lung biology at the single-cell level. Population differences in the length and early-life dynamics of telomeres among European pied flycatchers Telomere length and shortening rate are increasingly used as biomarkers for long-term costs in ecological and evolutionary studies because of their relationships with survival and fitness. Both early-life conditions and growth, and later-life stressors can create variation in telomere shortening rate. Studies on between- population telomere length and dynamics are scarce, despite the expectation that populations exposed to varying environmental constraints would present divergent telomere length patterns. The pied flycatcher (Ficedula hypoleuca) is a passerine bird breeding across Eurasia (from Spain to western Siberia) and migrating through the Iberian Peninsula to spend the non-breeding period in sub-Saharan Africa. Thus, different populations show marked differences in migration distance. We studied the large-scale variation of telomere length and early-life dynamics in the pied flycatcher by comparing six European populations across a north- south gradient (Finland, Estonia, England, and Spain) predicting negative effect of migration distance on adult telomere length, and of nestling growth on nestling telomere dynamics. There were clear population differences in telomere length, with English birds from mid-latitudes having the longest telomeres. Telomere length did not thus show consistent latitudinal variation and was not linearly linked to differences in migration distance. Early-life telomere shortening rate tended to vary between populations. Fast growth was associated with shorter telomeres in the early life, but faster nestling growth affected telomeres more negatively in northern than southern populations. While the sources of between-population differences in telomere-related biology remain to be more intensively studied, our study illustrates the need to expand telomere studies at the between-population level. Oncogenic protein condensates suppress growth factor perception and modulate drug tolerance Drug resistance remains a central challenge towards durable cancer therapy, including for cancers driven by the EML4-ALK oncogene. EML4-ALK and related fusion oncogenes form cytoplasmic protein condensates that transmit oncogenic signals through the Ras/Erk pathway. However, whether such condensates play a role in drug response is unclear. Here, we used optogenetics to find that condensates suppress signaling through endogenous RTKs including EGFR. Notably, ALK inhibition hypersensitized RTK signals, which are known to drive resistance. Suppression of RTKs occurred because condensates sequestered downstream adapter proteins that are required for RTK signal transmission. Strikingly, EGFR hypersensitization resulted in rapid and pulsatile Erk signal reactivation, which originated from neighboring apoptotic cells. Paracrine signals promoted survival during ALK inhibition, and blockade of paracrine signals suppressed drug tolerance. Our results uncover a regulatory role for RTK fusion condensates in cancer drug response and demonstrate the potential of optogenetics for uncovering functional biomarkers of cancer cells. Naming of human diseases on the wrong side of history BackgroundIn the medical sphere, understanding naming conventions strengthen the integrity of naming human diseases remains nominal rather than substantial yet. Since the current nosology-based standard for human diseases could not offer a one-size-fits-all corrective mechanism, many idiomatic but flawed names frequently appear in scientific literature and news outlets at the cost of sociocultural impacts.

ObjectiveWe attempt to examine the ethical oversights of current naming practices and propose heuristic rationales and approaches to determine a pithy name instead of an inopportune nosology.

MethodsFirst, we examined the compiled global online news volumes and emotional tones on some inopportune nosology like German measles, Middle Eastern Respiratory Syndrome, Spanish flu, Hong Kong flu, and Huntingtons disease in the wake of COVID-19. Second, we prototypically scrutinize the lexical dynamics and pathological differentials of German measles and common synonyms by leveraging the capacity of the Google Books Ngram Corpus. Third, we demonstrated the empirical approaches to curate an exclusive substitute for an anachronistic nosology German measles based on deep learning models and post-hoc explanations.

ResultsThe infodemiological study shows that the public informed the offensive names with extremely negative tones in textual and visual narratives. The findings of the historiographical study indicate that many synonyms of German measles did not survive, while German measles became an anachronistic usage, and rubella has taken the dominant place since 1994. The PubMedBERT model could identify rubella as a potential substitution for German measles with the highest semantic similarity. The results of the semantic drift experiments further indicate that rubella tends to survive during the ebb and flow of semantic drift.

ConclusionsOur findings indicate that the nosological evolution of anachronistic names could result in sociocultural impacts without a corrective mechanism. To mitigate such impacts, we introduce some ethical principles for formulating an improved naming scheme. Based on deep learning models and post-hoc explanations, our illustrated experiments could provide hallmark references to the remedial mechanism of naming practices and pertinent credit allocations. Minimizing spectral overlap in multicolor flow cytometry experiments Selecting fluorochromes for polychromatic panels for flow cytometry is complex and time-consuming. Poorly designed panels can result in overlap between the emission spectra of the different fluorochromes, making their signals difficult to separate. While assessing all possible panels is simple to do programmatically, the combinatorial complexity of most real world problems renders brute force computation impractical. Here we present a novel complexity optimization algorithm for fast design of minimal spectral overlap fluorochrome combinations. To aid researchers in designing fluorochrome panels we implemented the algorithm in a web server, Spectracular, that also considers instrument laser configuration and allows users to define various constraints related to antibody-fluorochrome availability and marker co-expression. Genome-scale metabolic modeling reveals SARS-CoV-2-induced metabolic changes and antiviral targets Tremendous progress has been made to control the COVID-19 pandemic caused by the SARS-CoV-2 virus. However, effective therapeutic options are still rare. Drug repurposing and combination represent practical strategies to address this urgent unmet medical need. Viruses, including coronaviruses, are known to hijack host metabolism to facilitate viral proliferation, making targeting host metabolism a promising antiviral approach. Here, we describe an integrated analysis of 12 published in vitro and human patient gene expression datasets on SARS-CoV-2 infection using genome-scale metabolic modeling (GEM), revealing complicated host metabolism reprogramming during SARS-CoV-2 infection. We next applied the GEM-based metabolic transformation algorithm to predict anti-SARS-CoV-2 targets that counteract the virus-induced metabolic changes. We successfully validated these targets using published drug and genetic screen data and by performing an siRNA assay in Caco-2 cells. Further generating and analyzing RNA-sequencing data of remdesivir-treated Vero E6 cell samples, we predicted metabolic targets acting in combination with remdesivir, an approved anti-SARS-CoV-2 drug. Our study provides clinical data-supported candidate anti-SARS-CoV-2 targets for future evaluation, demonstrating host metabolism-targeting as a promising antiviral strategy. The obstacles and potential clues of prime editing applications in tomato, a dicot plant Precision genome editing is highly desired for crop improvement. The recently emerged CRISPR/Cas technology offers great potential applications in precision plant genome engineering. A prime editing (PE) approach combining a reverse transcriptase (RT) with a Cas9 nickase and a "priming" extended guide RNA has shown a high frequency for precise genome modification in mammalian cells and several plant species. Nevertheless, the applications of the PE approach in dicot plants are still limited and inefficient. We designed and tested prime editors for precision editing of a synthetic sequence in a transient assay and for desirable alleles of 10 loci in tomato by stable transformation. Our data obtained by targeted deep sequencing also revealed only low PE efficiencies in both the tobacco and tomato systems. Further assessment of the activities of the PE components uncovered potential reasons for the inefficiency of the PE complexes. Modifying the pegRNA sequences by shortening or introducing mismatches to the primer binding sequences (PBS) in order to reduce their melting temperatures (Tm) did not enhance the PE efficiency at the SlBMP21, SlALC and SlALS1 loci. Our data show challenges of PE approach in tomato, indicating a further improvement of the PE system for the successful applications such as use of improved expression systems. Our work provides an important clue for the successful application of the PE approach in crop improvement. Multispecific DARPin(R) therapeutics demonstrate very high potency against SARS-CoV-2 variants in vitro SARS-CoV-2 has infected millions of people globally and continues to undergo evolution. Emerging variants can be partially resistant to vaccine induced immunity and therapeutic antibodies, emphasizing the urgent need for accessible, broad-spectrum therapeutics. Here, we report a comprehensive study of ensovibep, the first trispecific clinical DARPin candidate, that can simultaneously engage all three units of the spike protein trimer to potently inhibit ACE2 interaction, as revealed by structural analyses. The cooperative binding of the individual modules enables ensovibep to retain inhibitory potency against all frequent SARS-CoV-2 variants, including Omicron BA.1 and BA.2, as of February 2022. Moreover, viral passaging experiments show that ensovibep, when used as a single agent, can prevent development of escape mutations comparably to a cocktail of monoclonal antibodies (mAb). Finally, we demonstrate that the very high in vitro antiviral potency also translates into significant therapeutic protection and reduction of pathogenesis in Roborovski dwarf hamsters infected with either the SARS-CoV-2 wild-type or the Alpha variant. In this model, ensovibep prevents fatality and provides substantial protection equivalent to the standard of care mAb cocktail. These results support further clinical evaluation and indicate that ensovibep could be a valuable alternative to mAb cocktails and other treatments for COVID-19. The effect of resource depletion on the thermal response of mosquito population fitness The temperature-dependencies of life history traits are increasingly being used to predict how climatic warming will affect vector-borne disease dynamics, partially by affecting the abundance dynamics of the vector population. Such predictions generally arise from mathematical models that incorporate the temperature dependence of traits measured under laboratory conditions. These temperature-trait relationships are typically estimated from juvenile populations reared under optimal resource conditions, even though natural populations experience intermittent resource depletion. Using laboratory experiments on the mosquito Aedes aegypti, combined with a stage-structured population model, we show that resource depletion in the juvenile habitat can significantly depress the vectors maximal population growth rate (rm) across the entire temperature range, cause it to peak at a lower temperature, and narrow its thermal niche width. Our results provide compelling evidence for future studies to consider resource depletion when predicting the effects of global change on vector-borne disease transmission, disease vectors and other arthropods. Mendelian Randomization for causal inference accounting for pleiotropy and sample structure using genome-wide summary statistics Mendelian Randomization (MR) is a valuable tool for inferring causal relationships among a wide range of traits using summary statistics from genome-wide association studies (GWASs). Existing summary-level MR methods often rely on strong assumptions, resulting in many false positive findings. To relax MR assumptions, ongoing research has been primarily focused on accounting for confounding due to pleiotropy. Here we show that sample structure is another major confounding factor, including population stratification, cryptic relatedness, and sample overlap. We propose a unified MR approach, MR-APSS, which (i) accounts for pleiotropy and sample structure simultaneously by leveraging genome-wide information; and (ii) allows to include more genetic variants with moderate effects as instrument variables (IVs) to improve statistical power without inflating type I errors. We first evaluated MR-APSS using comprehensive simulations and negative controls, and then applied MR-APSS to study the causal relationships among a collection of diverse complex traits. The results suggest that MR-APSS can better identify plausible causal relationships with high reliability. In particular, MR-APSS can perform well for highly polygenic traits, where the IV strengths tend to be relatively weak and existing summary-level MR methods for causal inference are vulnerable to confounding effects. HOW CONCERNING IS A SARS-COV-2 VARIANT OF CONCERN? COMPUTATIONAL PREDICTIONS AND THE VARIANTS LABELING SYSTEM We herein report a study to evaluate the use of computational prediction of SARS-CoV-2 genetic variations in improving the current variants labeling system. First, we reviewed the basis of the system developed by the World Health Organization (WHO) for the labeling of SARS-CoV-2 genetic variants and the adaptations made to it by the United States Center of Diseases Control (CDC). We observed that the labeling system is based upon the virus major attributes. However, we found that the labeling criteria of the SARS-CoV-2 variants derived from these attributes are not accurately defined and are used differently by the two health management agencies. Consequently, discrepancies exist between the labels given by WHO and CDC to same variants. Our observations suggest that giving the VOC label to a new variant is premature and might not be appropriate. Therefore, we carried out a comparative computational study to predict the effects of the mutations on the virus structure and functions of five VOCs. By linking these data to the criteria used by WHO and the CDC for variant labeling, we ascertained that comparative computational predictions of the impact of genetic variations are a better ground for rapid and more accurate labelling of SARS-CoV-2 variants. We propose to label all emergent variants VUM or VBM and to carry out computational predictive studies and thorough variants comparison, upon which more appropriate and informative labels can be attributed. Furthermore, harmonization of the variants labeling system would be globally beneficial to communicate about and fight COVID-19 pandemic. Sensing their plasma membrane curvature allows migrating cells to circumvent obstacles Cell migration is a hallmark out-of-equilibrium process in biology. In addition to persistent self-propelled motion, many cells display remarkable adaptive behaviors when they navigate complex environments within the body. Combining theory and experiments, we identify a curvature-sensing mechanism underlying obstacle avoidance in immune-like cells. The genetic perturbation of this machinery leads to a reduced capacity to evade obstructions combined with faster and more persistent cell migration in obstacle-free environments. We propose that the active polymerization of the actin cytoskeleton at the advancing edge of migrating cells is locally inhibited by the curvature-sensitive BAR protein Snx33 in regions with inward plasma membrane curvature. This coupling between actin and membrane dynamics leads to a mechanochemical instability that generates complex protrusive patterns at the cellular front. Adaptive motility thus arises from two simultaneous curvature-dependent effects, i) the specific reduction of propulsion in regions where external objects deform the plasma membrane and ii) the intrinsic patterning capacity due to the membrane-actin coupling that promotes spontaneous changes in the cells protrusions. Our results show how cells utilize actin- and plasma membrane biophysics to sense their environment, allowing them to adaptively decide if they should move ahead or turn away. On the basis of our findings, we propose that the natural diversity of BAR proteins may allow cells to tune their curvature sensing machinery to match the shape characteristics in their environment. Fronto-Parietal Network Segregation Predicts Maintained Processing Speed in the Cognitively Healthy Oldest-old Evaluating brain network interactions in cognitively healthy older adults can help us understand how brain characteristics vary with age and how these variations affect cognitive functioning. Functional connections among groups of brain areas give insight into the brains organization, and the cognitive effects of aging may relate to this large-scale organization. We investigated functional network properties in 146 cognitively healthy participants aged 85+ in the McKnight Brain Aging Registry. We found that the segregation of both the cortical association system and the segregation of the fronto-parietal network (FPN) were strong predictors of cognition and processing speed. We also provide a healthy oldest-old (85+) cortical parcellation that can be used in future work in this age group. This study shows that network segregation of the oldest-old brain is closely linked with cognitive performance. In particular, segregation of the FPN plays an important role in supporting overall cognition and processing speed in this aging cohort. This work adds to the growing body of knowledge about age-related dedifferentiation by demonstrating that cognitive ability is associated with differentiated functional networks even in very old individuals experiencing successful cognitive aging. Ultrahigh-Throughput Ambient MS: Direct Analysis at 22 Samples per Second by Infrared Matrix-Assisted Desorption Electrospray Ionization Mass Spectrometry Infrared Matrix-Assisted Laser Desorption Electrospray Ionization (IR-MALDESI) mass spectrometry is an ambient-direct sampling method being developed for high-throughput, label-free, biochemical screening of large-scale compound libraries. Here, we report the development of an ultrahigh-throughput continuous motion IR-MALDESI sampling approach capable of acquiring data at rates up to 22.7 samples per second in a 384-well microtiter plate. At top speed, less than 1% analyte carryover is observed from well-to-well and signal intensity relative standard deviations (RSD) of 11.5% and 20.9% for 3 M 1-hydroxymidazolam and 12 M dextrorphan, respectively, are achieved. The ability to perform parallel kinetics studies on 384 samples with ~30s time resolution using an isocitrate dehydrogenase 1 (IDH1) enzyme assay is shown. Finally, we demonstrate the repeatability and throughput of our approach by measuring 115,200 samples from 300 microtiter plate reads consecutively over 5.54 hours with RSDs under 8.14% for each freshly introduced plate. Taken together, these results demonstrate the use of IR-MALDESI at sample acquisition rates that surpass other currently reported direct sampling mass spectrometry approaches used for high throughput compound screening.

For Table of Contents Only

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org.highwire.dtl.DTLVardef@5f916corg.highwire.dtl.DTLVardef@4473b4org.highwire.dtl.DTLVardef@82e8forg.highwire.dtl.DTLVardef@128b57b_HPS_FORMAT_FIGEXP  M_FIG C_FIG Prevention and therapy of SARS-CoV-2 and the B.1.351 variant in mice Improving the standard of clinical care for individuals infected with SARS-CoV-2 variants is a global health priority. Small molecule antivirals like remdesivir (RDV) and biologics such as human monoclonal antibodies (mAb) have demonstrated therapeutic efficacy against SARS-CoV-2, the causative agent of COVID-19. However, it is not known if combination RDV/mAb will improve outcomes over single agent therapies or whether antibody therapies will remain efficacious against variants. In kinetic studies in a mouse-adapted model of ancestral SARS-CoV-2 pathogenesis, we show that a combination of two mAbs in clinical trials, C144 and C135, have potent antiviral effects against even when initiated 48 hours after infection. The same antibody combination was also effective in prevention and therapy against the B.1.351 variant of concern (VOC). Combining RDV and antibodies provided a modest improvement in outcomes compared to single agents. These data support the continued use of RDV to treat SARS-CoV-2 infections and support the continued clinical development of the C144 and C135 antibody combination to treat patients infected with SARS-CoV-2 variants. Task-independent metrics of computational hardness predict human cognitive performance The survival of human organisms depends on our ability to solve complex tasks in the face of limited cognitive resources. However, little is known about the factors that drive the complexity of those tasks. Here, building on insights from computational complexity theory, we quantify the computational hardness of cognitive tasks using a set of task-independent metrics related to the computational resource requirements of individual instances of a task. We then examine the relation between those metrics and human behavior and find that they predict both time spent on a task as well as accuracy in three canonical cognitive tasks. Our findings demonstrate that performance in cognitive tasks can be predicted based on generic metrics of their inherent computational hardness.

TeaserThe ability of humans to solve cognitive tasks is affected by generic mathematical properties of problems related to their computational complexity.