bluuebunny/crossref_metadata_2025_split huggingface dataset:
A unified framework for measuring selection on cellular lineages and traits
Authors: Yamauchi Shunpei , Nozoe Takashi , Okura Reiko , Kussell Edo , Wakamoto Yuichi 
Published: July 2021

AbstractIntracellular states probed by gene expression profiles and metabolic activities are intrinsically noisy, causing phenotypic variations among cellular lineages. Understanding the adaptive and evolutionary roles of such variations requires clarifying their linkage to population growth rates. Extending a cell lineage statistics framework, here we show that a population’s growth rate can be expanded by fitness cumulants of any cell lineage trait. The expansion enables quantifying the contribution of each fitness cumulant, such as variance and skewness, to population growth. We introduce a function that contains all the essential information of cell lineage statistics, including mean lineage fitness and selection strength. We reveal a relation between fitness heterogeneity and population growth rate response to perturbation. We apply the framework to experimental cell lineage data from bacteria to mammalian cells, revealing that third or higher-order cumulants’ contributions are negligible under constant growth conditions but could be significant in regrowing processes from growth-arrested conditions. Furthermore, we identify cellular populations in which selection leads to an increase of fitness variance among lineages. The framework assumes no particular growth models or environmental conditions, and is thus applicable to various biological phenomena for which phenotypic heterogeneity and cellular proliferation are important.
Traces of semantization - from episodic to semantic memory in a spiking cortical network model
Authors: Chrysanthidis Nikolaos , Fiebig Florian , Lansner Anders , Herman Pawel 
Published: July 2021

AbstractEpisodic memory is the recollection of past personal experiences associated with particular times and places. This kind of memory is commonly subject to loss of contextual information or “semantization”, which gradually decouples the encoded memory items from their associated contexts while transforming them into semantic or gist-like representations. Novel extensions to the classical Remember/Know behavioral paradigm attribute the loss of episodicity to multiple exposures of an item in different contexts. Despite recent advancements explaining semantization at a behavioral level, the underlying neural mechanisms remain poorly understood. In this study, we suggest and evaluate a novel hypothesis proposing that Bayesian-Hebbian synaptic plasticity mechanisms might cause semantization of episodic memory. We implement a cortical spiking neural network model with a Bayesian-Hebbian learning rule called Bayesian Confidence Propagation Neural Network (BCPNN), which captures the semantization phenomenon and offers a mechanistic explanation for it. Encoding items across multiple contexts leads to item-context decoupling akin to semantization. We compare BCPNN plasticity with the more commonly used spike-timing dependent plasticity (STDP) learning rule in the same episodic memory task. Unlike BCPNN, STDP does not explain the decontextualization process. We also examine how selective plasticity modulation of isolated salient events may enhance preferential retention and resistance to semantization. Our model reproduces important features of episodicity on behavioral timescales under various biological constraints whilst also offering a novel neural and synaptic explanation for semantization, thereby casting new light on the interplay between episodic and semantic memory processes.
Ensemble encoding of conditioned fear by prefrontal somatostatin interneurons
Authors: Cummings Kirstie A. , Bayshtok Sabina , Kenny Paul J. , Clem Roger L. 
Published: July 2021

AbstractNeurons preferentially activated by learning have been ascribed the unique potential to encode memory. However, it remains unclear which genetically-defined cell types are recruited as part of such an ensemble, or what role discrete subpopulations play in behavior. Here we show that fear conditioning activates a heterogeneous neural ensemble in the medial prefrontal cortex (mPFC), comprised to a large degree of GABAergic interneurons immunoreactive for somatostatin (SST-INs). Using an intersectional genetic approach, we demonstrate that fear learning-activated SST-INs exhibit distinct circuit properties, are preferentially reactivated during memory retrieval, and mediate the expression of defensive freezing. We further show that a rewarding experience, morphine treatment, activates an orthogonal SST-IN population that exerts opposing control over fear. These results outline an important role for discrete GABAergic ensembles in fear memory encoding, and point to an unappreciated capacity for functional specialization among SST-INs.
Docking Molecular analysis of potential Aldosterone antagonists
Authors: Ferrari Ivan Vito , Patrizio Paolo 
Published: July 2021

AbstractBackgroundAldosterone antagonists (spironolactone, eplerenone) inhibit the action of aldosterone in the collecting duct; as such, these agents cause modest diuresis but inhibit potassium and hydrogen ion secretion. We report first time Potential Aldosterone antagonists by in Silico approach, using AutoDock Vina and AutoDock 4 (or MGL Tool), estimated with Pyrx and AM Dock Software, calculating three different important parameters: Binding Affinity (kcal/mol), estimated Ki (in nM units) and Ligand Efficiency (L.E. in kcal/mol). After a selective analysis of over 1000 drugs, processed with Pyrx (a Virtual Screening software for Computational Drug Discovery) in the Ligand Binding site pocket of the protein (ID PDB 2OAX Chain A:), we noticed high values of Binding Energy, about −13.55 kcal/mol estimated by AutoDock 4 with AM Dock Software, concluding that it could be an excellent candidate drug, compared to everyone else Aldosterone antagonists. Indeed, from the results of AutoDock Vina and AutoDock 4 (or AutoDock 4.2), implemented with Lamarckian genetic algorithm, LGA, trough AMDock Software, our results of Binding Energy are very similar to the crystallized Spironolactone in PDB 2OAX Chain A protein.
X upregulation is not global and extent of upregulation differs between ancestral and acquired X-linked genes
Authors: Naik C H , Hari K , Chandel D , Jolly MK , Gayen S 
Published: July 2021

AbstractEvolution of sex chromosome dosage compensation in mammals remains poorly understood. Ohno’s hypothesis state that evolution of dosage compensation occurred through two steps: first, to compensate the dosage imbalance created due to the degradation of Y chromosome in male, upregulation of X-chromosome happened. Subsequently, transmission of X-chromosome upregulation (XCU) in female led to the evolution of X-chromosome inactivation (XCI) to counteract extra dosage of X-linked genes in female cells. Here, we have profiled gene-wise dynamics of XCU in pre-gastrulation mouse embryos at single cell level and found that XCU is dynamically linked with XCI, however, XCU is not global or chromosome-wide like XCI. Therefore, our result raises question whether XCU driven the evolution of XCI. If so, then why XCI is chromosome wide while XCU is not. We propose that XCI might have evolved independent of XCU and therefore refining the current model is necessary. Separately, we show that higher occupancy of different activating factors at upregulated X-linked genes leads to enhanced transcriptional burst frequency and thereby leads to upregulation. On the other hand, our analysis indicates that extent of upregulation, enrichment of different activating marks differs between ancestral and newly acquired X-linked genes. Altogether, our study provides significant insight into the dynamics and mechanistic basis of evolution of sex chromosome dosage compensation.Abstract Figure
Pleiotropic Variability Score: A Genome Interpretation Metric to Quantify Phenomic Associations of Genomic Variants
Authors: Shameer Khader , Glicksberg Benjamin S. , Badgeley Marcus A. , Johnson Kipp W. , Dudley Joel T. 
Published: July 2021

AbstractA more complete understanding of phenomic space is critical for elucidating genome-phenome relationships and for assessing disease risk from genome sequencing. To incorporate knowledge of how related a variant’s associations are, we developed a new genome interpretation metric called Pleiotropic Variability Score (PVS). PVS uses semantic reasoning to score the relatedness of a genetic variant’s associated phenotypes based on those phenotypes’ relationships in the human phenotype ontology (HPO) and disease ontology (DO). We tested 78 unique semantic similarity methods and integrated six robust metrics to define the pleiotropy score of SNPs. We computed PVS for 12,541 SNPs which were mapped to 382 HPO and 317 DO unique phenotype terms in a genotype-phenotype catalog (10,021 SNPs mapped to DO phenotypes and 8,569 SNPs mapped to HPO phenotypes). We validated the utility of PVS by computing pleiotropy using an electronic health record linked genomic database (BioME, n=11,210). Further we demonstrate the application of PVS in personalized medicine using “personalized pleiotropy score” reports for individuals with genomic data that could potentially aid in variant interpretation. We further developed a software framework to incorporate PVS into VCF files and to consolidate pleiotropy assessment as part of genome interpretation pipelines. As the genome-phenome catalogs are growing, PVS will be a useful metric to assess genetic variation to find SNPs with highly pleiotropic effects. Additionally, variants with varying degree of pleiotropy can be prioritized for explorative studies to understand specific roles of SNPs and pleiotropic hubs in mediating novel phenotypes and drug development.
Facultative variation across a shallow to deep torpor spectrum in hummingbirds
Authors: Shankar A , Cisneros INH , Thompson S , Graham CH , Powers DR 
Published: July 2021

AbstractMany small endotherms use torpor, saving energy by a controlled reduction of their body temperature and metabolic rate. Some species (e.g. arctic ground squirrels, hummingbirds) enter deep torpor, dropping their body temperatures by 23-37 °C, while others can only enter shallow torpor (e.g., pigeons, 3-10 °C reductions). However, deep torpor in mammals can increase predation risk (unless animals are in burrows or caves), inhibit immune function, and result in sleep deprivation, so even for species that can enter deep torpor, facultative shallow torpor might help balance energy savings with these potential costs. Deep torpor occurs in three avian orders. Although the literature hints that some bird species can use both shallow and deep torpor, little empirical evidence of such an avian torpor spectrum exists. We infrared imaged three hummingbird species that are known to use deep torpor, under natural temperature and light cycles, to test if they were also capable of shallow torpor. All three species used both deep and shallow torpor, often on the same night. Depending on the species, they used shallow torpor for 5-35% of the night. The presence of a bird torpor spectrum indicates a capacity for fine-scale physiological and genetic regulation of avian torpid metabolism.
EEG biomarkers of reduced inhibition in human cortical microcircuits in depression
Authors: Mazza Frank , Valiante Taufik A. , Griffiths John D. , Hay Etay 
Published: July 2021

AbstractReduced cortical inhibition by somatostatin-expressing (SST) interneurons has been strongly associated with treatment-resistant depression. However, whether the effects of reduced SST interneuron inhibition on microcircuit activity have signatures detectible in electroencephalography (EEG) signals remains unknown. We simulated resting-state activity and EEG using detailed models of human cortical microcircuits with normal (healthy) or reduced SST interneuron inhibition (depression). Healthy microcircuit models showed emergent key features of resting-state EEG, and depression microcircuits exhibited increased theta, alpha and low beta power (4 – 15 Hz). The changes in depression involved a combination of an aperiodic broadband, and periodic theta components. We then demonstrated the specificity of the EEG signatures of reduced SST interneuron inhibition by showing they were distinct from those corresponding to reduced parvalbumin-expressing (PV) interneuron inhibition. Our study thus links SST interneuron inhibition level to distinct features in EEG simulated from detailed human microcircuits, which can serve to better identify mechanistic subtypes of depression using EEG, and non-invasively monitor modulation of cortical inhibition.
FMRP regulates mRNAs encoding distinct functions in the cell body and dendrites of CA1 pyramidal neurons
Authors: Hale Caryn R. , Sawicka Kirsty , Mora Kevin , Fak John , Kang Jin Joo , Cutrim Paula , Cialowicz Katarzyna , Carroll Thomas , Darnell Robert B. 
Published: July 2021

AbstractNeurons are believed to rely on dendritic localization and translation of mRNAs in order to generate activity-dependent changes in the synaptic plasticity. Here, we develop a strategy combining compartment-specific CLIP and TRAP in conditionally tagged mice to precisely define the ribosome-bound dendritic transcriptome of CA1 pyramidal neurons. This revealed transcripts that have differentially localized alternative 3’UTR and splicing isoforms. FMRP targets are overrepresented among dendritic mRNAs, and compartment-specific FMRP-CLIP defined 383 dendritic FMRP targets, and also allowed for segregation of whole-cell FMRP targets into functional modules that are locally regulated by FMRP. In the absence of FMRP, dendritic FMRP targets show increased ribosome association, consistent with reported roles for FMRP in translational repression. Together, the data support a model in which distinct patterns of FMRP localization allow it to differentially regulate the expression of nuclear proteins and synaptic proteins within different compartments of a single neuronal cell type.
Identifying Louisiana communities at the crossroads of environmental and social vulnerability, COVID-19, and asthma
Authors: Bakshi Arundhati , Van Doren Alicia , Maser Colette , Aubin Kathleen , Stewart Collette , Soileau Shannon , Friedman Kate , Williams Alexis 
Published: July 2021

AbstractThe COVID-19 pandemic has disproportionately affected the socially and environmentally vulnerable, including through indirect effects on other health conditions. Asthma is one such condition, which may be exacerbated by both prolonged adverse in-home exposures if quarantining in unhealthy homes and prolonged outdoor exposures if the ambient air quality is unhealthy or hazardous. As both are often the case in Environmental Justice (EJ) communities, here we have analyzed data at the census tract (CT) level for Louisiana to assess any correlation between social and environmental vulnerability, and health issues like COVID-19 and asthma. Higher Social Vulnerability Index (SVI), Particulate Matter less than 2.5 µm in diameter (PM2.5) and Ozone levels were associated with higher rates of cumulative COVID-19 incidence at various time points during the pandemic, as well as higher average annual asthma hospitalization rates and estimated asthma prevalence. Further, cumulative COVID-19 incidence during the first three months of the pandemic was moderately correlated with both asthma hospitalizations and estimated prevalence, suggesting similar underlying factors may be affecting both conditions. Additionally, 137 CTs were identified where social and environmental vulnerabilities co-existed, of which 75 (55%) had high estimated prevalence of asthma. These areas are likely to benefit from asthma outreach that considers both social and environmental risk factors. Fifteen out of the 137 CTs (11%) not only had higher estimated prevalence of asthma but also a high burden of COVID-19. Further research in these areas may help to elucidate any common social determinants of health that underlie both asthma and COVID-19 burdens, as well as better clarify the possible role of the environment as related to the COVID-19 burden in Louisiana.
Applying mixture model methods to SARS-CoV-2 serosurvey data from Geneva
Authors: Bouman Judith A , Kadelka Sarah , Stringhini Silvia , Pennacchio Francesco , Meyer Benjamin , Yerly Sabine , Kaiser Laurent , Guessous Idris , Azman Andrew S , Bonhoeffer Sebastian , Regoes Roland R 
Published: July 2021

AbstractSerosurveys are an important tool to estimate the true extent of the current SARS-CoV-2 pandemic. So far, most serosurvey data have been analysed with cut-off based methods, which dichotomize individual measurements into sero-positives or negatives based on a predefined cutoff. However, mixture model methods can gain additional information from the same serosurvey data. Such methods refrain from dichotomizing individual values and instead use the full distribution of the serological measurements from pre-pandemic and COVID-19 controls to estimate the cumulative incidence. This study presents an application of mixture model methods to SARS-CoV-2 serosurvey data from the SEROCoV-POP study from April and May 2020 in Geneva (2766 individuals). Besides estimating the total cumulative incidence in these data (8.1% (95% CI: 6.8% - 9.8%)), we applied extended mixture model methods to estimate an indirect indicator of disease severity, which is the fraction of cases with a distribution of antibody levels similar to hospitalised COVID-19 patients. This fraction is 51.2% (95% CI: 15.2% - 79.5%) across the full serosurvey, but differs between three age classes: 21.4% (95% CI: 0% - 59.6%) for individuals between 5 and 40 years old, 60.2% (95% CI: 21.5% - 100%) for individuals between 41 and 65 years old and 100% (95% CI: 20.1% - 100%) for individuals between 66 and 90 years old. Additionally, we find a mismatch between the inferred negative distribution of the serosurvey and the validation data of pre-pandemic controls. Overall, this study illustrates that mixture model methods can provide additional insights from serosurvey data.
Public opinion on sharing data from UK health services for clinical and research purposes without explicit consent
Authors: Jones Linda A. , Nelder Jenny R. , Fryer Joseph M. , Alsop Philip H. , Geary Michael R. , Prince Mark , Cardinal Rudolf N. 
Published: July 2021

1.AbstractBACKGROUNDIn the UK, National Health Service (NHS/HSC) data is variably shared between healthcare organizations for direct care, and increasingly used in de-identified forms for research. Few large-scale studies have examined public opinion on sharing, including the treatment of mental health (MH) versus physical health (PH) data.METHODSPre-registered anonymous online survey open to all UK residents, recruiting Feb–Sep 2020. Participants were randomized to one of three framing statements regarding MH versus PH data.FINDINGSParticipants numbered 29275; 40% had experienced a MH condition. A majority supported identifiable data sharing for direct clinical care without explicit consent, but 20% opposed this. Preference for clinical/identifiable sharing decreased with distance and was slightly less for MH than PH data, with a small framing effect. Preference for research/de-identified data sharing without explicit consent showed the same small PH/MH and framing effects, plus greater preference for sharing structured data than de-identified free text. There was net support for research sharing to the NHS, academic institutions, and national research charities, net ambivalence about sharing to profit-making companies researching treatments, and net opposition to sharing to other companies (similar to sharing publicly). De-identified linkage to non-health data was generally supported, except to data held by private companies. We report demographic influences on preference. A clear majority supported a single NHS mechanism to choose uses of their data. Support for data sharing increased during the pandemic.INTERPRETATIONSupport for healthcare data sharing for direct care without explicit consent is broad but not universal. There is net support for the sharing of de-identified data for research to the NHS, academia, and the charitable sector, but not the commercial sector. A single national NHS-hosted system for patients to control the use of their NHS data for clinical purposes and for research would have broad public support.FUNDINGMRC.
Low seropositivity and sub-optimal neutralisation rates in patients fully vaccinated against COVID-19 with B cell malignancies
Authors: Fox TA , Kirkwood AA , Enfield L , O’Reilly M , Arulogun S , D’Sa S , O’Nions J , Kavi J , Vitsaras E , Townsend W , Burns SO , Gohil SH , Cwynarski K , Thomson KJ , Noursadeghi M , Heyderman RS , Rampling T , Ardeshna KM , McCoy LE , Morris EC 
Published: July 2021

AbstractPatients with haematological malignancies are at increased risk of severe disease and death from COVID-19 and are less likely to mount humoral immune responses to COVID-19 vaccination, with the B cell malignancies a particularly high-risk group.Our COV-VACC study is evaluating the immune response to COVID-19 vaccination in patients with B cell malignancies. Eligible patients were either receiving active treatment or had received treatment within the last 24 months. Patients were vaccinated with either the BNT162b2 (Pfizer-BioNTech) (n=41) or ChAdOx1 nCoV-19 (Oxford-AstraZeneca) (n=14) vaccines. The median age of participants was 60 years (range: 27-82) and 50% were receiving systemic anti-cancer therapy (SACT) at the time of vaccination. This interim analysis from the first 55 participants describes anti-S seropositivity rates, neutralising antibody activity and association with peripheral lymphocyte subsets.After the first vaccine dose, 36% overall had detectable anti-S antibodies rising to 42% after the second dose. Sera from seropositive patients was assessed for neutralisation activity in vitro. Of the seropositive patients after first dose (n=17), only 41% were able to neutralise SARS-CoV-2 pseudotyped virus with a 50% inhibitory dilution factor (ID50) of >1:50. After two doses (n=21) 57% of the seropositive patients had detectable neutralisation activity (median ID50 of 1:469, range 1:70 – 1:3056). Total blood lymphocyte, CD19, CD4 and CD56 counts were significantly associated with seropositivity. Patients vaccinated more than 6 months after completing therapy were significantly more likely to develop antibodies than those within 6 months of treatment or on active treatment; OR: 5.93 (1.29 – 27.28).Our data has important implications for patients with B cell malignancies as we demonstrate a disconnect between anti-S seropositivity and virus neutralisation in vitro following vaccination against COVID-19.Urgent consideration should be given to revaccinating patients with B-cell malignancies after completion of anti-cancer treatment as large numbers currently remain at high risk of infection with the increasing transmission of SARS-CoV-2 in many countries.