bluuebunny/crossref_metadata_2025_split huggingface dataset:
The acute effects of cannabidiol on emotional processing and anxiety: a neurocognitive imaging study
Authors: Bloomfield Michael AP , Yamamori Yumeya , Hindocha Chandni , Jones Augustus PM , Yim Jocelyn LL , Walker Hannah R , Statton Ben , Wall Matthew B , Lees Rachel H , Howes Oliver D , Curran H Valerie , Roiser Jonathan P , Freeman Tom P 
Published: December 2020

BackgroundThere is growing interest in the therapeutic potential of cannabidiol (CBD) across a range of psychiatric disorders. CBD has been found to reduce anxiety during experimentally-induced stress in anxious individuals and healthy controls. However, the mechanisms underlying the putative anxiolytic effects of CBD are unknown. We therefore sought to investigate the behavioural and neural effects of a single dose of CBD vs. placebo on a range of emotion-related measures to test cognitive-mechanistic models of its effects on anxiety.MethodsWe conducted a randomised, double-blind, placebo-controlled, crossover, acute oral challenge of 600 mg of CBD in 24 healthy participants on emotional processing, with neuroimaging (viewing emotional faces during fMRI) and cognitive (emotional appraisal) measures as well as subjective response to experimentally-induced anxiety.ResultsCBD did not produce effects on BOLD responses to emotional faces, cognitive measures of emotional processing, or modulate experimentally-induced anxiety, relative to placebo.ConclusionsGiven the rising popularity of CBD for its putative medical benefits, further research is warranted to investigate the clinical potential of CBD for the treatment of anxiety disorders.
CEM500K – A large-scale heterogeneous unlabeled cellular electron microscopy image dataset for deep learning
Authors: Conrad Ryan , Narayan Kedar 
Published: December 2020

AbstractAutomated segmentation of cellular electron microscopy (EM) datasets remains a challenge. Supervised deep learning (DL) methods that rely on region-of-interest (ROI) annotations yield models that fail to generalize to unrelated datasets. Newer unsupervised DL algorithms require relevant pre-training images, however, pre-training on currently available EM datasets is computationally expensive and shows little value for unseen biological contexts, as these datasets are large and homogeneous. To address this issue, we present CEM500K, a nimble 25 GB dataset of 0.5 x 106unique cellular EM images curated from nearly 600 three-dimensional (3D) and 10,000 two-dimensional (2D) images from >100 unrelated imaging projects. We show that models pre-trained on CEM500K learn features that are biologically relevant and resilient to meaningful image augmentations. Critically, we evaluate transfer learning from these pre-trained models on six publicly available and one newly derived benchmark segmentation task and report state-of-the-art results on each. We release the CEM500K dataset, pre-trained models and curation pipeline for model building and further expansion by the EM community. Data and code are available athttps://www.ebi.ac.uk/pdbe/emdb/empiar/entry/10592/andhttps://git.io/JLLTz.
A Game-Theoretic Model for Co-Adaptive Brain-Machine Interfaces
Authors: Madduri Maneeshika M. , Burden Samuel A. , Orsborn Amy L. 
Published: December 2020

AbstractCo-adaptation in brain-machine interfaces (BMIs) can improve performance and facilitate user learning. We propose and analyze a mathematical model for co-adaptation in BMIs. We model the brain and the decoder as strategic agents who seek to minimize their individual cost functions, leading to a game-theoretic formulation of interaction. We frame our BMI model as a potential game to identify stationary points (Nash equilibria) of the brain-decoder interactions, which correspond to points at which both the brain and the decoder stop adapting. Assuming the brain and the decoder adapt using gradientbased schemes, we analytically show how convergence to these equilibria depends on agent learning rates. This theoretical framework presents a basis for simulating co-adaption using dynamic game theory and can be extended to tasks with multiple dimensions and to different decoder models. This framework can ultimately be used to inform adaptive decoder design to shape brain learning and optimize BMI performance.
Cytosplore-Transcriptomics: a scalable inter-active framework for single-cell RNA sequencing data analysis
Authors: Abdelaal Tamim , Eggermont Jeroen , Höllt Thomas , Mahfouz Ahmed , Reinders Marcel J.T. , Lelieveldt Boudewijn P.F. 
Published: December 2020

SummaryThe ever-increasing number of analyzed cells in Single-cell RNA sequencing (scRNA-seq) experiments imposes several challenges on the data analysis. Current analysis methods lack scalability to large datasets hampering interactive visual exploration of the data. We present Cytosplore-Transcriptomics, a framework to analyze scRNA-seq data, including data preprocessing, visualization and downstream analysis. At its core, it uses a hierarchical, manifold preserving representation of the data that allows the inspection and annotation of scRNA-seq data at different levels of detail. Consequently, Cytosplore-Transcriptomics provides interactive analysis of the data using low-dimensional visualizations that scales to millions of cells.AvailabilityCytosplore-Transcriptomics can be freely downloaded fromtranscriptomics.cytosplore.orgContactb.p.f.lelieveldt@lumc.nl
Benzoquinones in the defensive secretion of a bug (Pamillia behrensii): a common chemical trait retrieved in the Heteroptera
Authors: Wagner Julian M. , Naragon Thomas H. , Brückner Adrian 
Published: December 2020

AbstractBenzoquinones are a phylogenetically widespread compound class within arthropods, appearing in harvestman, millipedes and insects. Whereas the function of benzoquinones as defensive compounds against potential predators and microbes has been well established, the full extent of benzoquinone usage across arthropods, and especially within Insecta, has yet to be established. Adding to the growing list of unique evolutionary origins of benzoquinone employment, we describe in this paper the metathoracic scent gland secretion of the mirid bugPamillia behrensii, which is composed of heptan-2-one, 2-heptyl acetate, 2,3-dimethyl-1-4-benzoquinone, 2,3-dimethyl-1-4-hydroquinone as well as one unknown compound. Similarly, to many other arthropods that use benzoquinones,Pamilliareleases the contents of its gland as a defensive mechanism in response to harassment by other arthropod predators. Morphological investigation of the gland showed that the benzoquinone-producing gland complex ofP. behrensiifollows a similar blueprint to metathoracic scent glands described in other Heteropterans. Overall, our data further underpins the widespread convergent evolution and use of benzoquinones for defense across the Arthropoda, now including the order Hemiptera.
Optogenetic inhibition and activation of Rac and Rap1 using a modified iLID system
Authors: Elston R. Nick , Pablo Michael , Pimenta Frederico M. , Hahn Klaus M. , Watanabe Takashi 
Published: December 2020

AbstractThe small GTPases Rac1 and Rap1 can fulfill multiple cellular functions because their activation kinetics and localization are precisely controlled. To probe the role of their spatio-temporal dynamics, we generated optogenetic tools that activate or inhibit endogenous Rac and Rap1 in living cells. An improved version of the light-induced dimerization (iLID) system was used to control plasma membrane localization of protein domains that specifically activate or inactivate Rap1 and Rac (Tiam1 and Chimerin for Rac, RasGRP2 and Rap1GAP for Rap1). Irradiation yielded a 50-230% increase in the concentration of these domains at the membrane, leading to effects on cell morphodynamics consistent with the known roles of Rac1 and Rap1.
Effects of beta- and gamma-band rhythmic stimulation on motor inhibition
Authors: Leunissen Inge , Van Steenkiste Manon , Heise Kirstin-Friederike , Monteiro Thiago Santos , Dunovan Kyle , Mantini Dante , Coxon James P. , Swinnen Stephan P. 
Published: December 2020

AbstractVoluntary movements are accompanied by an increase in gamma-band oscillatory activity (60-100Hz) and a strong desynchronization of beta-band activity (13-30Hz) in the motor system at both the cortical and subcortical level. Conversely, successful motor inhibition is associated with increased beta power in a fronto-basal-ganglia network. Intriguingly, gamma activity also increases in response to a stop-signal. In this study, we used transcranial alternating current stimulation to drive beta and gamma oscillations to investigate whether these frequencies are causally related to motor inhibition. We found that 20Hz stimulation targeted at the pre-supplementary motor area enhanced inhibition and increased beta oscillatory activity around the time of the stop-signal in trials directly following stimulation. In contrast, 70Hz stimulation seemed to slow down the braking process, and predominantly affected go task performance. These results demonstrate that the effects of tACS are state-dependent and that especially fronto-central beta activity is a functional marker for successful motor inhibition.
An Extensive Sequence Dataset of Gold-Standard Samples for Benchmarking and Development
Authors: Baid Gunjan , Nattestad Maria , Kolesnikov Alexey , Goel Sidharth , Yang Howard , Chang Pi-Chuan , Carroll Andrew 
Published: December 2020

AbstractAccurate standards and extensive development datasets are the foundation of technical progress. To facilitate benchmarking and development, we sequence 9 samples, covering the Genome in a Bottle truth sets on multiple instruments (NovaSeq, HiSeqX, HiSeq4000, PacBio Sequel II System) and sample preparations (PCR-Free, PCR-Positive) for both whole genome and multiple exome kits. We benchmark pipelines, quantifying strengths and limitations for sequencing and analysis methods. We identify variability within and between instruments, preparation methods, and analytical pipelines, across various sequencing depths. We discuss the relevance of this variability to downstream analyses, and strategies to reduce variability.
Toward reproducible, scalable, and robust data analysis across multiplex tissue imaging platforms
Authors: Burlingame Erik Ames , Eng Jennifer , Thibault Guillaume , Chin Koei , Gray Joe W. , Chang Young Hwan 
Published: December 2020

AbstractThe emergence of megascale single-cell multiplex tissue imaging (MTI) datasets necessitates reproducible, scalable, and robust tools for cell phenotyping and spatial analysis. We developed open-source, graphics processing unit (GPU)-accelerated tools for intensity normalization, phenotyping, and microenvironment characterization. We deploy the toolkit on a human breast cancer (BC) tissue microarray stained by cyclic immunofluorescence and benchmark our cell phenotypes against a published MTI dataset. Finally, we demonstrate an integrative analysis revealing BC subtype-specific features.
Millisecond-scale molecular dynamics simulation of spike RBD structure reveals evolutionary adaption of SARS-CoV-2 to stably bind ACE2
Authors: Nelson Gard , Buzko Oleksandr , Bassett Aaron , Spilman Patricia , Niazi Kayvan , Rabizadeh Shahrooz , Soon-Shiong Patrick 
Published: December 2020

AbstractThe Receptor Binding Domain (RBD) of the SARS-CoV-2 surface spike (S) protein interacts with host angiotensin converting enzyme 2 (ACE2) to gain entry to host cells and initiate infection1–3. Detailed, accurate understanding of key interactions between S RBD and ACE2 provides critical information that may be leveraged in the development of strategies for the prevention and treatment of COVID-19. Utilizing the published sequences and cryo-EM structures of both the viral S RBD and ACE24,5, we performedin silicomolecular dynamics (MD) simulations of free S RBD and of its interaction with ACE2 over the exceptionally long durations of 2.9 and 2 milliseconds, respectively, to elucidate the nature and relative affinity of S RBD surface residues for the ACE2 binding region. Our findings reveal that free S RBD has assumed an optimized ACE2 binding-ready conformation, incurring little entropic penalty for binding, an evolutionary adaptation that contributes to its high affinity for the receptor6. We further identified high probability molecular binding interactions that inform both vaccine design and therapeutic development, which may include recombinant ACE2-based spike decoys7and/or allosteric S RBD-ACE2 binding inhibitors8,9to prevent or arrest infection and thus disease.
The connectome predicts resting state functional connectivity across the Drosophila brain
Authors: Turner Maxwell H. , Mann Kevin , Clandinin Thomas R. 
Published: December 2020

SummaryConnectomic datasets have emerged as invaluable tools for understanding neural circuits in many systems. What constraints does the connectome place on information processing and routing in a large scale neural circuit? For mesoscale brain networks, the relationship between cell and synaptic level connectivity and brain function is not well understood. Here, we use data from the Drosophila connectome in conjunction with whole-brainin vivoimaging to relate structural and functional connectivity in the central brain. We find that functional connectivity is strongly associated with the strength of both direct and indirect anatomical pathways. We also show that some brain regions, including the mushroom body and central complex, show considerably higher functional connectivity to other brain regions than is predicted based on their direct anatomical connections. We find several key topological similarities between mesoscale brain networks in flies and mammals, revealing conserved principles relating brain structure and function.
The MERS-CoV receptor gene is among COVID-19 risk factors inherited from Neandertals
Authors: Zeberg Hugo , Pääbo Svante 
Published: December 2020

AbstractIn the current SARS-CoV-2 pandemic, two genetic regions derived from Neandertals have been shown to increase and decrease, respectively, the risk of falling severely ill upon infection. Here, we show that 2-8% of people in Eurasia carry a variant promoter region of theDPP4gene inherited from Neandertals. This gene encodes an enzyme that serves as a receptor for the coronavirus MERS-CoV and is currently not believed to be a receptor for SARS-CoV-2. However, the NeandertalDPP4variant doubles the risk to become critically ill in COVID-19.
Building bridges: the hippocampus constructs narrative memories across distant events
Authors: Cohn-Sheehy Brendan I. , Delarazan Angelique I. , Reagh Zachariah M. , Crivelli-Decker Jordan E. , Kim Kamin , Barnett Alexander J. , Zacks Jeffrey M. , Ranganath Charan 
Published: December 2020

AbstractLife’s events are scattered throughout time, yet we often recall different events in the context of an integrated narrative. Prior research suggests that the hippocampus, which supports memory for past events, can sometimes support integration of overlapping associations or separate events, but the conditions which lead to hippocampus-dependent memory integration are unclear. We used functional brain imaging to test whether the ability to form a larger narrative (narrative coherence) drives hippocampal memory integration. During encoding of fictional stories, the hippocampus supported patterns of activity, including activity at boundaries between events, which were more similar between distant events that formed one coherent narrative, compared with events taken from unrelated narratives. Twenty-four hours later, the hippocampus preferentially supported detailed recall of coherent narrative events, through reinstatement of hippocampal activity patterns from encoding. These findings reveal a key function of the hippocampus: the dynamic integration of events into a narrative structure for memory.
Role of slow temporal dynamics in reliable activity of stochastically driven neurons
Authors: Mokashe Subhadra , Nadkarni Suhita 
Published: December 2020

ABSTRACTNeuronal networks maintain robust patterns of activity despite a backdrop of noise from various sources. Mutually inhibiting neurons is a standard network motif implicated in rhythm generation. In an elementary network motif of two neurons capable of swapping from an active state to a quiescent state, we ask how different sources of stochasticity alter firing patterns. In this system, the alternating activity occurs via combined action of a calcium-dependent potassium current, sAHP (slow afterhyperpolarization), and a fast GABAergic synapse. We show that simulating extrinsic noise arising from background activity extends the dynamical range of neuronal firing. Extrinsic noise also has the effect of increasing the switching frequency via a faster build-up of sAHP current. We show that switching frequency as a function of input current has a non-monotonic behavior. Interestingly the noise tolerance of this system varies with the input current. It shows maximum robustness to noise at an input current that corresponds to the minimum switching frequency between the neurons. The slow decay time scale of sAHP conductance allows neurons to act as a low-pass filter, attenuate noise, and integrate over ion channel fluctuations. Additionally, we show that the slow inactivation time of the sAHP channel allows the neuron to act as an action potential counter. We propose that this intrinsic property of the current allows the network to maintain rhythmic activity critical for various functions, despite the noise, and operate as a temporal integrator.
Simultaneous Single-Cell Genome and Transcriptome Sequencing of Termite Hindgut Protists Reveals Metabolic and Evolutionary Traits of Their Endosymbionts
Authors: Stephens Michael E. , Benjamino Jacquelynn , Graf Joerg , Gage Daniel J. 
Published: December 2020

AbstractDifferent protist species which colonize the hindguts of wood feedingReticulitermestermites are associated with endosymbiotic bacteria belonging to the genusEndomicrobium. In this study, we focused on the endosymbionts of three protist species fromReticulitermes flavipes, which includedPyrsonympha vertens,Trichonympha agilis, andDinenymphaspecies II. Since these protist hosts represented members of difference taxa which colonize different niches within the hindguts of their termite hosts, we investigated if these differences translated to differential gene content and expression in their endosymbionts. Following assembly and comparative genome and transcriptome analyses, we discovered that these endosymbionts differed with respect to possible niche specific traits such carbon metabolism. Our analyses supported that genes related to carbon metabolism were acquired by horizontal gene transfer (HGT) from donor taxa which are present in termite’s hindgut community. In addition, our analyses supported that these endosymbionts have retained and expressed genes related to natural transformation (competence) and recombination. Taken together, the presence of genes acquired by HGT and a putative competence pathway supported that these endosymbionts are not cut-off from gene flow and that competence may be a mechanism by which members of theEndomicrobiumcan acquire new traits.ImportanceThe composition and structure of wood, which contains cellulose, hemicellulose and lignin, prevents most organisms from using this common food source. Termites are a rare exception among animals, and they rely on a complex microbiome housed in their hindguts to use wood as a source of food. The lower termiteR. flavipeshouses a variety of protist and prokaryotes that are the key players the disassembly of lignocellulose. In this paper we describe the genomes and the gene expression profiles of fiveEndomicrobiumendosymbionts living inside three different protist species fromR. flavipes. Data from these genomes suggest that theseEndomicrobiumspecies have different mechanisms for using both carbon and nitrogen. In addition, they harbor genes that may be used to import free DNA from their environment. This process of DNA-uptake may cntribute to the high levels of horizontal gene transfer often seen in theEndomicrobiumspecies.
Removal of rare amplicon sequence variants from 16S rRNA gene sequence surveys biases the interpretation of community structure data
Authors: Schloss Patrick D. 
Published: December 2020

AbstractMethods for remediating PCR and sequencing artifacts in 16S rRNA gene sequence collections are in continuous development and have significant ramifications on the inferences that can be drawn. A common approach is to remove rare amplcon sequence variants (ASVs) from datasets. But, the definition of rarity is generally selected without regard for the number of sequences in the samples or the variation in sequencing depth across samples within a study. I analyzed the impact of removing rare ASVs on metrics of alpha and beta diversity using samples collected across 12 published datasets. Removal of rare ASVs significantly decreased the number of ASVs and operational taxonomic units as well as their diversity. Furthermore, their removal increased the variation in community structure between samples. When simulating a known effect size, removal of rare ASVs reduced the power to detect the effect relative to not removing rare ASVs. Removal of rare ASVs did not affect the false detection rate when samples were randomized to simulate a null model. However, the false detection rate increased when rare ASVs were removed using a null distribution and assignment of samples to simulated treatment groups according to their sequencing depth. The false detection rate did not vary when rare ASVs were retained. This analysis demonstrates the problems inherent in removing rare ASVs. Researchers are encouraged to retain rare ASVs, to select approaches that minimize PCR and sequencing artifacts, and to use rarefaction to control for uneven sequencing effort.ImportanceRemoving rare amplicon sequence variants (ASVs) from 16S rRNA gene sequence collections is an approach that has grown in popularity for limiting PCR and sequencing artifacts. Yet, it is unclear what impact an abundance-based filter has on downstream analyses. To investigate the effects of removing rare ASVs, I analyzed the community distributions found in the samples of 12 published datasets. Analysis of these data and simulations based on them showed that removal of rare ASVs distorts the representation of microbial communities. This has the effect of artificially making it more difficult to detect differences between treatment groups. Also of concern was the observation that if sequencing depth is confounded with the treatment, then the probability of falsely detecting a difference between the treatment groups increased with the removal of rare ASVs. The practice of removing rare ASVs should stop, lest researcher adversely affect the interpretation of their data.