bluuebunny/crossref_metadata_2025_split huggingface dataset:
mRNA COVID-19 vaccine effectiveness against SARS-CoV-2 infection in a prospective community cohort, rural Wisconsin, November 2020-December 2021
Authors: McLean Huong Q , McClure David L , King Jennifer P , Meece Jennifer K , Pattinson David , Neumann Gabriele , Kawaoka Yoshihiro , Rolfes Melissa A , Belongia Edward A 
Published: December 2021

AbstractReduced COVID-19 vaccine effectiveness (VE) has been observed with increasing predominance of the Delta variant. In a prospective rural community cohort of 1265 participants, VE against symptomatic and asymptomatic SARS-CoV-2 infection was 56% for mRNA COVID-19 vaccines.
Early detection and improved genomic surveillance of SARS-CoV-2 variants from deep sequencing data
Authors: Ramazzotti Daniele , Maspero Davide , Angaroni Fabrizio , Antoniotti Marco , Piazza Rocco , Graudenzi Alex 
Published: December 2021

In the definition of fruitful strategies to contrast the worldwide diffusion of SARS-CoV-2, maximum efforts must be devoted to the early detection of dangerous variants. An effective help to this end is granted by the analysis of deep sequencing data of viral samples, which are typically discarded after the creation of consensus sequences. Indeed, only with deep sequencing data it is possible to identify intra-host low-frequency mutations, which are a direct footprint of mutational processes that may eventually lead to the origination of functionally advantageous variants. Accordingly, a timely and statistically robust identification of such mutations might inform political decision-making with significant anticipation with respect to standard analyses based on con-sensus sequences.To support our claim, we here present the largest study to date of SARS-CoV-2 deep sequencing data, which involves 220,788 high quality samples, collected over 20 months from 137 distinct studies. Importantly, we show that a rele-vant number of spike and nucleocapsid mutations of interest associated to the most circulating variants, including Beta, Delta and Omicron, might have been intercepted several months in advance, possibly leading to different public-health decisions. In addition, we show that a refined genomic surveillance system involving high- and low-frequency mutations might allow one to pin-point possibly dangerous emerging mutation patterns, providing a data-driven automated support to epidemiologists and virologists.
Microbial dynamics inference at ecosystem-scale
Authors: Gibson Travis E. , Kim Younhun , Acharya Sawal , Kaplan David E. , DiBenedetto Nicholas , Lavin Richard , Berger Bonnie , Allegretti Jessica R. , Bry Lynn , Gerber Georg K. 
Published: December 2021

AbstractDynamical systems models are a powerful tool for analyzing interactions, stability, resilience, and other key properties in biomedically important microbial ecosystems, such as the gut microbiome. Challenges to modeling and inference in this setting include the large number of species present, and data sparsity/noise characteristics. Here, we introduce a Bayesian statistical method, the Microbial Dynamical Systems Inference Engine 2 (MDSINE2), which infers compact and interpretable ecosystems-scale dynamical systems models from microbiome time-series data. We model microbial dynamics as stochastic processes driven by inferred interaction modules, or groups of microbes with similar interaction structure and responses to perturbations. Additionally, we model the noise characteristics of sequencing and qPCR measurements to provide uncertainty quantification for all outputs. To evaluate MDSINE2, and provide a benchmarking resource for the community, we generated the most densely sampled microbiome time-series to date, which consists of a cohort of mice that received fecal transplants from a human donor and were then subjected to dietary and antibiotic perturbations. Benchmarking on simulated and real data demonstrate that MDSINE2 significantly outperforms state-of-the-art methods, and moreover identifies interaction modules that shed new light on ecosystems-scale interactions in the gut microbiome. We provide MDSINE2 as an open-source Python package at:https://github.com/gerberlab/MDSINE2.
Targeted drug release from stable and safe ultrasound-sensitive nanocarriers
Authors: Wilson Matthew G. , Parikh Aarav , Dara Audri , Beaver Alexander , Kubanek Jan 
Published: December 2021

ABSTRACTTargeted delivery of medication has the promise of increasing the effectiveness and safety of current systemic drug treatments. Focused ultrasound is emerging as noninvasive and practical energy for targeted drug release. However, it has yet to be determined which nanocarriers and ultrasound parameters can provide both effective and safe release. Perfluorocarbon nanodroplets have the potential to achieve these goals, but current approaches have either been effective or safe, but not both. We found that nanocarriers with highly stable perfluorocarbon cores mediate effective drug release so long as they are activated by ultrasound of sufficiently low frequency. We demonstrate a favorable safety profile of this formulation in a non-human primate. To facilitate translation of this approach into humans, we provide an optimized method for manufacturing the nanocarriers. This study provides a recipe and release parameters for effective and safe drug release from nanoparticle carriers in the body part specified by focused ultrasonic waves.
Limited plasticity in gene expression for providing or receiving parental care under different temperatures
Authors: Moss Jeanette B. , Cunningham Christopher B. , McKinney Elizabeth C. , Moore Allen J. 
Published: December 2021

ABSTRACTParenting is thought to evolve to buffer offspring from variable, unpredictable, and challenging environmental conditions. In the subsocial carrion beetle, Nicrophorus orbicollis, stressful temperatures during parenting do not affect parental behavior despite imposing steep fitness costs to parents. Here, we ask if plasticity of gene expression underpins this behavioral stability or facilitates independent compensation by larvae. To test this we characterized gene expression of parents and offspring before and during active parenting under benign (20°C) and stressful (24°C) temperatures to identify genes of parents and offspring associated with thermal response, parenting/being parented, and gene expression plasticity associated with behavioral stability of parental care. The main effects of thermal and social condition each shaped patterns of gene expression in females, males, and larvae. In addition, we implicated 79 genes in females as ‘buffering’ parental behavior across environments. The majority of these underwent significant changes in expression in actively parenting mothers at the benign temperature, but not at the stressful temperature. Our results suggest that neither genetic programs for parenting nor their effects on offspring gene expression are fundamentally different under stressful conditions, and that behavioral stability is associated primarily with the maintenance of existing genetic programs rather than replacement or supplementation. Thus, while selection for compensatory gene expression could expand the range of thermal conditions parents will tolerate, without expanding the toolkit of genes involved selection is unlikely to lead to adaptive changes of function.
Evolutionary learning in the brain by heterosynaptic plasticity
Authors: Bi Zedong , Chen Guozhang , Yang Dongping , Zhou Yu , Tian Liang 
Published: December 2021

SummaryHow the brain modifies synapses to improve the performance of complicated networks remains one of the biggest mysteries in neuroscience. Canonical models suppose synaptic weights change according to pre- and post-synaptic activities (i.e., local plasticity rules), implementing gradient-descent algorithms. However, the lack of experimental evidence to confirm these models suggests that there may be important ingredients neglected by these models. For example, heterosynaptic plasticity, non-local rules mediated by inter-cellular signaling pathways, and the biological implementation of evolutionary algorithms (EA), another machine-learning paradigm that successfully trains large-scale neural networks, are seldom explored. Here we propose and systematically investigate an EA model of brain learning with non-local rules alone. Specifically, a population of agents are represented by different information routes in the brain, whose task performances are evaluated through gating on individual routes alternatively. The selection and reproduction of agents are realized by dopamine-guided heterosynaptic plasticity. Our EA model provides a framework to re-interpret the biological functions of dopamine, meta-plasticity of dendritic spines, memory replay, and the cooperative plasticity between the synapses within a dendritic neighborhood from a new and coherent aspect. Neural networks trained with the model exhibit analogous dynamics to the brain in cognitive tasks. Our EA model manifests broad competence to train spiking or analog neural networks with recurrent or feedforward architecture. Our EA model also demonstrates its powerful capability to train deep networks with biologically plausible binary weights in MNIST classification and Atari-game playing tasks with performance comparable with continuous-weight networks trained by gradient-based methods. Overall, our work leads to a fresh understanding of the brain learning mechanism unexplored by local rules and gradient-based algorithms.
Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation
Authors: Ball James , Petrova Katerina , Coomes David A. , Flaxman Seth 
Published: December 2021

AbstractTropical forests are subject to diverse deforestation pressures but their conservation is essential to achieve global climate goals. Predicting the location of deforestation is challenging due to the complexity of the natural and human systems involved but accurate and timely forecasts could enable effective planning and on-the-ground enforcement practices to curb deforestation rates. New computer vision technologies based on deep learning can be applied to the increasing volume of Earth observation data to generate novel insights and make predictions with unprecedented accuracy.Here, we demonstrate the ability of deep convolutional neural networks to learn spatiotemporal patterns of deforestation from a limited set of freely available global data layers, including multispectral satellite imagery, the Hansen maps of historic deforestation (2001-2020) and the ALOS JAXA digital surface model, to forecast future deforestation (2021). We designed four original deep learning model architectures, based on 2D Convolutional Neural Networks (2DCNN), 3D Convolutional Neural Networks (3DCNN), and Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN) to produce spatial maps that indicate the risk to each forested pixel (~30 m) in the landscape of becoming deforested within the next year. They were trained and tested on data from two ~80,000 km2 tropical forest regions in the Southern Peruvian Amazon.We found that the networks could predict the likely location of future deforestation to a high degree of accuracy. Our best performing model – a 3DCNN – had the highest pixel-wise accuracy (80-90%) when validated on 2020 deforestation based 2014-2019 training. Visual examination of the forecasts indicated that the 3DCNN network could automatically discern the drivers of forest loss from the input data. For example, pixels around new access routes (e.g. roads) were assigned high risk whereas this was not the case for recent, concentrated natural loss events (e.g. remote landslides).CNNs can harness limited time-series data to predict near-future deforestation patterns, an important step in using the growing volume of satellite remote sensing data to curb global deforestation. The modelling framework can be readily applied to any tropical forest location and used by governments and conservation organisations to prevent deforestation and plan protected areas.
Inhibition of the sodium-dependent HCO3- transporter SLC4A4, produces a cystic fibrosis-like airway disease phenotype
Authors: Saint-Criq Vinciane , Guequén Anita , Philp Amber , Villanueva Sandra , Apablaza Tábata , Fernández-Moncada Ignacio , Mansilla Agustín , Delpiano Livia , Ruminot Iván , Carrasco Cristian , Gray Michael A. , Flores Carlos A. 
Published: December 2021

ABSTRACTBicarbonate secretion is a fundamental process involved in maintaining acid-base homeostasis. Disruption of bicarbonate entry into airway lumen, as has been observed in cystic fibrosis, produces several defects in lung function due to thick mucus accumulation. Bicarbonate is critical for correct mucin deployment and there is increasing interest in understanding its role in airway physiology, particularly in the initiation of lung disease in children affected by cystic fibrosis, in the absence of detectable bacterial infection. The current model of anion secretion in mammalian airways consists of CFTR and TMEM16A as apical anion exit channels, with limited capacity for bicarbonate transport compared to chloride. However, both channels can couple to SLC26A4 anion exchanger to maximise bicarbonate secretion. Nevertheless, current models lack any details about the identity of the basolateral protein(s) responsible for bicarbonate uptake into airway epithelial cells. We report herein that the electrogenic, sodium-dependent, bicarbonate cotransporter, SLC4A4, is expressed in the basolateral membrane of human and mouse airways, and that it’s pharmacological inhibition or genetic silencing reduces bicarbonate secretion. In fully differentiated primary human airway cells, SLC4A4 inhibition induced an acidification of the airways surface liquid and markedly reduced the capacity of cells to recover from an acid load. Studies in the Slc4a4-null mice revealed a previously unreported lung phenotype, characterized by mucus accumulation and reduced mucociliary clearance. Collectively, our results demonstrate that the reduction of SLC4A4 function induced a CF-like phenotype, even when chloride secretion remained intact, highlighting the important role SLC4A4 plays in bicarbonate secretion and mammalian airway function.
High content screening and computational prediction reveal viral genes that suppress innate immune response
Authors: Ng Tai L. , Olson Erika J. , Yoo Tae Yeon , Weiss H. Sloane , Koide Yukiye , Koch Peter D. , Rollins Nathan J. , Mach Pia , Meisinger Tobias , Bricken Trenton , Chang Timothy Z. , Molloy Colin , Zürcher Jérôme , Mitchison Timothy J. , Glass John I. , Marks Debora S. , Way Jeffrey C. , Silver Pamela A. 
Published: December 2021

ABSTRACTSuppression of the host innate immune response is a critical aspect of viral replication. Upon infection, viruses may introduce one or more proteins that inhibit key immune pathways, such as the type I interferon pathway. However, the ability to predict and evaluate viral protein bioactivity on targeted pathways remains challenging and is typically done on a single virus/gene basis. Here, we present a medium-throughput high-content cell-based assay to reveal the immunosuppressive effects of viral proteins. To test the predictive power of our approach, we developed a library of 800 genes encoding known, predicted, and uncharacterized human viral genes. We find that previously known immune suppressors from numerous viral families such as Picornaviridae and Flaviviridae recorded positive responses. These include a number of viral proteases for which we further confirmed that innate immune suppression depends on protease activity. A class of predicted inhibitors encoded by Rhabdoviridae viruses was demonstrated to block nuclear transport, and several previously uncharacterized proteins from uncultivated viruses were shown to inhibit nuclear transport of the transcription factors NF-κB and IRF3. We propose that this pathway-based assay, together with early sequencing, gene synthesis, and viral infection studies, could partly serve as the basis for rapid in vitro characterization of novel viral proteins.IMPORTANCEInfectious diseases caused by viral pathogens exacerbate healthcare and economic burdens. Numerous viral biomolecules suppress the human innate immune system, enabling viruses to evade an immune response from the host. Despite our current understanding of viral replications and immune evasion, new viral proteins, including those encoded by uncultivated viruses or emerging viruses, are being unearthed at a rapid pace from large scale sequencing and surveillance projects. The use of medium- and high-throughput functional assays to characterize immunosuppressive functions of viral proteins can advance our understanding of viral replication and possibly treatment of infections. In this study we assembled a large viral gene library from diverse viral families and developed a high content assay to test for inhibition of innate immunity pathways. Our work expands the tools that can rapidly link sequence and protein function, representing a practical step towards early-stage evaluation of emerging and understudied viruses.
Shared regulation and functional relevance of local gene co-expression revealed by single cell analysis
Authors: Ribeiro Diogo M. , Ziyani Chaymae , Delaneau Olivier 
Published: December 2021

AbstractMost human genes are co-expressed with a nearby gene. Yet, previous studies only reported this extensive local gene co-expression using bulk RNA-seq. Here, we leverage single cell datasets in >85 individuals to identify gene co-expression across cells, unbiased by cell type heterogeneity and benefiting from the co-occurrence of transcription events in single cells. We discover thousands of co-expressed genes in two cell types and (i) compare single cell to bulk RNA-seq in identifying local gene co-expression, (ii) show that many co-expressed genes – but not the majority – are composed of functionally-related genes and (iii) provide evidence that these genes are transcribed synchronously and their co-expression is maintained up to the protein level. Finally, we identify gene-enhancer associations using multimodal single cell data, which reveal that >95% of co-expressed gene pairs share regulatory elements. Our in-depth view of local gene co-expression and regulatory element co-activity advances our understanding of the shared regulatory architecture between genes.
A mean-field toolbox for spiking neuronal network model analysis
Authors: Layer Moritz , Senk Johanna , Essink Simon , van Meegen Alexander , Bos Hannah , Helias Moritz 
Published: December 2021

ABSTRACTMean-field theory of spiking neuronal networks has led to numerous advances in our analytical and intuitive understanding of the dynamics of neuronal network models during the past decades. But, the elaborate nature of many of the developed methods, as well as the difficulty of implementing them, may limit the wider neuroscientific community from taking maximal advantage of these tools. In order to make them more accessible, we implemented an extensible, easy-to-use open-source Python toolbox that collects a variety of mean-field methods for the widely used leaky integrate-and-fire neuron model. The Neuronal Network Mean-field Toolbox (NNMT) in its current state allows for estimating properties of large neuronal networks, such as firing rates, power spectra, and dynamical stability in mean-field and linear response approximation, without running simulations on high performance systems. In this article we describe how the toolbox is implemented, show how it is used to calculate neuronal network properties, and discuss different use-cases, such as extraction of network mechanisms, parameter space exploration, or hybrid modeling approaches. Although the initial version of the toolbox focuses on methods that are close to our own past and present research, its structure is designed to be open and extensible. It aims to provide a platform for collecting analytical methods for neuronal network model analysis and we discuss how interested scientists can share their own methods via this platform.
Compared with SARS-CoV2 wild type’s spike protein, the SARS-CoV2 omicron’s receptor binding motif has adopted a more SARS-CoV1 and/or bat/civet-like structure
Authors: Glocker Michael O. , Opuni Kwabena F.M. , Thiesen Hans-Juergen 
Published: December 2021

AbstractOur study focuses on free energy calculations of SARS-CoV2 spike protein receptor binding motives (RBMs) from wild type and variants-of-concern with particular emphasis on currently emerging SARS- CoV2 omicron variants of concern (VOC). Our computational free energy analysis underlines the occurrence of positive selection processes that specify omicron host adaption and bring changes on the molecular level into context with clinically relevant observations. Our free energy calculations studies regarding the interaction of omicron’s RBM with human ACE2 shows weaker binding to ACE2 than alpha’s, delta’s, or wild type’s RBM. Thus, less virus is predicted to be generated in time per infected cell. Our mutant analyses predict with focus on omicron variants a reduced spike-protein binding to ACE2-receptor protein possibly enhancing viral fitness / transmissibility and resulting in a delayed induction of danger signals as trade-off. Finally, more virus is produced but less per cell accompanied with delayed Covid-19 immunogenicity and pathogenicity. Regarding the latter, more virus is assumed to be required to initiate inflammatory immune responses.
From Diversity to Complexity: Microbial Networks in Soils
Authors: Guseva Ksenia , Darcy Sean , Simon Eva , Alteio Lauren V. , Montesinos-Navarro Alicia , Kaiser Christina 
Published: December 2021

ABSTRACTNetwork analysis has been used for many years in ecological research to analyze organismal associations, for example in food webs, plant-plant or plant-animal interactions. Although network analysis is widely applied in microbial ecology, only recently has it entered the realms of soil microbial ecology, shown by a rapid rise in studies applying co-occurrence analysis to soil microbial communities. While this application offers great potential for deeper insights into the ecological structure of soil microbial ecosystems, it also brings new challenges related to the specific characteristics of soil datasets and the type of ecological questions that can be addressed. In this Perspectives Paper we assess the challenges of applying network analysis to soil microbial ecology due to the small-scale heterogeneity of the soil environment and the nature of soil microbial datasets. We review the different approaches of network construction that are commonly applied to soil microbial datasets and discuss their features and limitations. Using a test dataset of microbial communities from two depths of a forest soil, we demonstrate how different experimental designs and network constructing algorithms affect the structure of the resulting networks, and how this in turn may influence ecological conclusions. We will also reveal how assumptions of the construction method, methods of preparing the dataset, and definitions of thresholds affect the network structure. Finally, we discuss the particular questions in soil microbial ecology that can be approached by analyzing and interpreting specific network properties. Targeting these network properties in a meaningful way will allow applying this technique not in merely descriptive, but in hypothesis-driven research.