bluuebunny/crossref_metadata_2025_split huggingface dataset:
Disrupted functional connectivity between the nucleus accumbens and posterior cingulate cortex in autism spectrum disorder
Authors: Polk MaryScott , Ikuta Toshikazu 
Published: December 2021

Objectives
Dysfunctions in the basal ganglia have been repeatedly found in autism spectrum disorder (ASD). The nucleus accumbens (NAcc) is known for its central role in social functions and also in its abnormality in ASD. In this study, whole-brain functional connectivity of the NAcc was examined to isolate brain regions that are differently connected to the NAcc in autism, using resting-state functional MRI (rs-fMRI) data.

Methods
In the initial dataset, 68 individuals with ASD (13.13 ± 2.41 years old) and 77 typically developing individuals (14.79 ± 3.57 years old) were compared.

Results
The precuneus cortex, lingual gyrus, thalamus, dorsal striatum, anterior cingulate cortex and posterior cingulate cortex showed weaker connectivity to the NAcc in the group with ASD. Among these regions, dysconnectivity of the posterior cingulate cortex (PCC) was replicated in the replication dataset, with 78 individuals with ASD and 105 without ASD.

Conclusions
This study suggests that the dysconnectivity between the NAcc and PCC may account for social dysfunctions in ASD.
Microevidence for microdosing with psilocybin mushrooms: a double-blind placebo-controlled study of subjective effects, behavior, creativity, perception, cognition, and brain activity
Authors: Cavanna Federico , Muller Stephanie , de la Fuente Laura Alethia , Zamberlan Federico , Palmucci Matías , Janeckova Lucie , Kuchar Martin , Pallavicini Carla , Tagliazucchi Enzo 
Published: December 2021

AbstractThe use of low sub-hallucinogenic doses of psychedelics (“microdosing”) has gained popularity in recent years. Although anecdotal reports claim multiple benefits associated with this practice, the lack of placebo-controlled studies limits our knowledge of microdosing and its effects. Moreover, research conducted in laboratory settings might fail to capture the motivation of individuals engaged in microdosing protocols. We recruited 34 individuals planning to microdose with psilocybin mushrooms (Psilocybe cubensis), one of the materials most frequently used for this purpose. Following a double-blind placebo-controlled design, we investigated the effects of 0.5 g dried mushrooms on subjective experience, behavior, creativity, perception, cognition, and brain activity. The reported acute effects were significantly more intense for the active dose compared to the placebo, which could be explained by unblinding. For the other measurements, we observed either null effects or a trend towards cognitive impairment and, in the case of EEG, towards reduced theta band spectral power. Our findings support the possibility that expectation effects underlie at least some of the anecdotal benefits attributed to microdosing with psilocybin mushrooms.
The Reputational Dividends of Collaborating with a Highly Reputable Agency: The Case of the FDA and Its Domestic Partner Agencies
Authors: Maor Moshe , Sulitzeanu-Kenan Raanan , Balmas Meital 
Published: December 2021

AbstractWhat, if any, dividends do agencies reap from collaboration with a highly reputable agency, such as the FDA? Utilizing a dataset covering 30 U.S. federal agencies over a period of 34 years (1980–2013), we estimate the short and long-term reputational effects of interagency collaboration. Collaboration is measured by the number of memorandums of understanding (MOUs) in effect between each agency and the FDA, while agency reputation is assessed using an automated measure of media-coverage valence (positive/negative tone) for each agency-year. To account for potential reverse and reciprocal causality, we utilize cross-lagged fixed-effects models. We find evidence of moderate rises in reputation due to increased collaboration with the FDA. These effects persist significantly for two years, before decaying to null after four years. Employing similar analyses, we furthermore estimate reversed causality – of reputation on the level of consequent collaboration – finding no evidence of such effects.Research SupportMoshe Maor and Raanan Sulitzeanu-Kenan gratefully acknowledge financial support from the Israel Science Foundation under grant 1002/11.
The Petasites hybridus CO2-extract (Ze 339) blocks SARS-CoV-2 replication in vitro
Authors: Urda Lorena , Kreuter Matthias Heinrich , Drewe Jürgen , Boonen Georg , Butterweck Veronika , Klimkait Thomas 
Published: December 2021

AbstractThe coronavirus disease 2019 (COVID-19), caused by a novel coronavirus (SARS-CoV-2), has spread worldwide, affecting over 250 million people and resulting in over five million deaths. Antivirals that are effective are still limited. The antiviral activities of the Petasites hybdridus CO2-extract Ze 339 were previously reported. Thus, to assess the anti-SARS-CoV-2 activity of Ze 339 as well as isopetasin and neopetasin as major active compounds, a CPE- and plaque reduction assay in Vero E6 cells was used for viral output. Antiviral effects were tested using the original virus (Wuhan) and the Delta variant of SARS-CoV-2. The antiviral drug remdesivir was used as control. Pre-treatment with Ze 339 in SARS-CoV-2 infected Vero E6 cells with either virus variant significantly inhibited virus replication with IC50 values of 0.10 and 0.40 μg/mL, repectively. The IC50 values obtained for isopetasin ranged between 0.37-0.88 μM for both virus variants, that of remdesivir between 1.53-2.37 μM. In conclusion, Ze 339 as well as the petasins potently inhibited SARS-Cov-2 replication in vitro of the Wuhan and Delta variants. Since time is of essence in finding effective treatments, clinical studies will have to demonstrate if Ze339 can become a therapeutic option to treat SARS-CoV-2 infections.
Modeling the trajectory of SARS-CoV-2 spike protein evolution in continuous latent space using a neural network and Gaussian process
Authors: King Samuel , Chen Xinyi E. , Ng Sarah W. S. , Rostin Kimia , Roberts Tylo , Hahn Samuel V. , Schwab Janella C. , Sekhon Parneet , Kagieva Madina , Reilly Taylor , Qi Ruo Chen , Salman Paarsa , Hong Ryan J. , Ma Eric J. , Hallam Steven J. 
Published: December 2021

AbstractViral vaccines can lose their efficacy as the genomes of targeted viruses rapidly evolve, resulting in new variants that may evade vaccine-induced immunity. This process is apparent in the emergence of new SARS-CoV-2 variants which have the potential to undermine vaccination efforts and cause further outbreaks. Predictive vaccinology points to a future of pandemic preparedness in which vaccines can be developed preemptively based in part on predictive models of viral evolution. Thus, modeling the trajectory of SARS-CoV-2 spike protein evolution could have value for mRNA vaccine development. Traditionally, in silico sequence evolution has been modeled discretely, while there has been limited investigation into continuous models. Here we present the Viral Predictor for mRNA Evolution (VPRE), an open-source software tool which learns from mutational patterns in viral proteins and models their most statistically likely evolutionary trajectories. We trained a variational autoencoder with real-time and simulated SARS-CoV-2 genome data from Australia to encode discrete spike protein sequences into continuous numerical variables. To simulate evolution along a phylogenetic path, we trained a Gaussian process model with the numerical variables to project spike protein evolution up to five months in advance. Our predictions mapped primarily to a sequence that differed by a single amino acid from the most reported spike protein in Australia within the prediction timeframe, indicating the utility of deep learning and continuous latent spaces for modeling viral protein evolution. VPRE can be readily adapted to investigate and predict the evolution of viruses other than SARS-CoV-2 in temporal, geographic, and lineage-specific pathways.
An antibody-escape calculator for mutations to the SARS-CoV-2 receptor-binding domain
Authors: Greaney Allison J. , Starr Tyler N. , Bloom Jesse D. 
Published: December 2021

ABSTRACTA key goal of SARS-CoV-2 surveillance is to rapidly identify viral variants with mutations that reduce neutralization by polyclonal antibodies elicited by vaccination or infection. Unfortunately, direct experimental characterization of new viral variants lags their sequence-based identification. Here we help address this challenge by aggregating deep mutational scanning data into an “escape calculator” that estimates the antigenic effects of arbitrary combinations of mutations to the virus’s spike receptor-binding domain (RBD). The calculator can be used to intuitively visualize how mutations impact polyclonal antibody recognition, and score the expected antigenic effect of combinations of mutations. These scores correlate with neutralization assays performed on SARS-CoV-2 variants, and emphasize the ominous antigenic properties of the recently described Omicron variant. An interactive version of the calculator is at https://jbloomlab.github.io/SARS2_RBD_Ab_escape_maps/escape-calc/, and we provide a Python module for batch processing.
Low-affinity CAR T cells exhibit reduced trogocytosis, preventing fratricide and antigen-negative tumor escape while preserving anti-tumor activity
Authors: Olson Michael L. , Vander Mause Erica R. , Radhakrishnan Sabarinath V. , Brody Joshua D. , Rapoport Aaron P. , Welm Alana L. , Atanackovic Djordje , Luetkens Tim 
Published: December 2021

ABSTRACTChimeric antigen receptor (CAR) T cells using the high-affinity CD19 binding domain FMC63 are an effective treatment for patients with relapsed and aggressive B cell lymphoma. However, antigen loss and poor CAR T cell persistence remain common causes for relapse in these patients. Using primary patient samples, we now show that FMC63-based CAR T cells confer rapid antigen loss in all major tumor types currently approved for treatment with CD19 CAR T cells via trogocytosis, the stripping of antigen from tumor cells by CAR T cells. We show that CAR T cell-mediated trogocytosis can be dramatically reduced across a wide range of B cell malignancies by replacing FMC63 with a low affinity CD19 antibody. This reduction in trogocytosis does not alter the direct anti-tumor activity of CD19 CAR T cells but prevents the emergence of antigen-negative tumor cells and significantly increases CAR T cell viability by reducing fratricide of CD19 CAR T cells following trogocytosis.TEASERA reduction in CAR affinity does not affect tumor killing but prolongs T cell persistence and prevents antigen-negative tumor escape.
Control of nuclear size by osmotic forces in Schizosaccharomyces pombe
Authors: Lemière Joёl , Real-Calderon Paula , Holt Liam J. , Fai Thomas G. , Chang Fred 
Published: December 2021

AbstractThe size of the nucleus scales robustly with cell size so that the nuclear-to-cell volume ratio (N/C ratio) is maintained during cell growth in many cell types. The mechanism responsible for this scaling remains mysterious. Previous studies have established that the N/C ratio is not determined by DNA amount but is instead influenced by factors such as nuclear envelope mechanics and nuclear transport. Here, we developed a quantitative model for nuclear size control based upon colloid osmotic pressure and tested key predictions in the fission yeast Schizosaccharomyces pombe. This model posits that the N/C ratio is determined by the numbers of macromolecules in the nucleoplasm and cytoplasm. Osmotic shift experiments showed that the fission yeast nucleus behaves as an ideal osmometer whose volume is primarily dictated by osmotic forces. Inhibition of nuclear export caused accumulation of macromolecules and an increase in crowding in the nucleoplasm, leading to nuclear swelling. We further demonstrated that the N/C ratio is maintained by a homeostasis mechanism based upon synthesis of macromolecules during growth. These studies demonstrate the functions of colloid osmotic pressure in intracellular organization and size control.
“A novel paradigm for optimal mass feature peak picking in large scale LC-MS datasets using the ‘isopair’: isoLock, autoCredential and anovAlign”
Authors: Hubbard Allen , Connelly Louis , Kambhampati Shrikaar , Evans Brad , Baxter Ivan 
Published: December 2021

AbstractUntargeted metabolomics enables direct quantification of metabolites without apriori knowledge of their identity. Liquid chromatography mass spectrometry (LC-MS), a popular method to implement untargeted metabolomics, identifies metabolites via combined mass/charge (m/z) and retention time as mass features. Improvements in the sensitivity of mass spectrometers has increased the complexity of data produced, leading to computational obstacles. One outstanding challenge is calling metabolite mass feature peaks rapidly and accurately in large LC-MS datasets (dozens to thousands of samples) in the presence of measurement and other noise. While existing algorithms are useful, they have limitations that become pronounced at scale and lead to false positive metabolite predictions as well as signal dropouts. To overcome some of these shortcomings, biochemists have developed hybrid computational and carbon labeling techniques, such as credentialing. Credentialing can validate metabolite signals, but is laborious and its applicability is limited. We have developed a suite of three computational tools to overcome the challenges of unreliable algorithms and inefficient validation protocols: isolock, autoCredential and anovAlign. Isolock uses isopairs, or metabolite-istopologue pairs, to calculate and correct for mass drift noise across LC-MS runs. autoCredential leverages statistical features of LC-MS data to amplify naturally present 13C isotopologues and validate metabolites through isopairs. This obviates the need to artificially introduce carbon labeling. anovAlign, an anova-derived algorithm, is used to align retention time windows across samples to accurately delineate retention time windows for mass features. Using a large published clinical dataset as well as a plant dataset with biological replicates across time, genotype and treatment, we demonstrate that this suite of tools is more sensitive and reproducible than both an open source metabolomics pipelines, XCMS, and the commercial software progenesis QI. This software suite opens a new era for enhanced accuracy and increased throughput for untargeted metabolomics.
Heterozygous transcriptional signatures unmask variable premature termination codon (PTC) burden alongside pathway-specific adaptations in blood outgrowth endothelial cells from patients with nonsense DNA variants causing hereditary hemorrhagic telangiectasia
Authors: Bernabeu-Herrero Maria E , Patel Dilip , Bielowka Adrianna , Guerrero Patricia Chaves , Marciniak Stefan J , Noseda Michela , Aldred Micheala A. , Shovlin Claire L 
Published: December 2021

ABSTRACTFrameshift and nonsense DNA variants represent the commonest causes of monogenic inherited diseases. They usually generate premature termination codon (PTC)-containing RNA transcripts that produce truncated proteins in recombinant systems, but endogenously are subject to nonsense mediated decay. To examine native consequences of these variants, we derived cells from pre-genotyped patients. Blood outgrowth endothelial cells (BOECs) were established from individuals with hereditary hemorrhagic telangiectasia (HHT) due to a heterozygous nonsense variant inACVRL1,ENGorSMAD4that each encode an endothelial cell-expressed protein mediating bone morphogenetic protein (BMP)/ transforming growth factor (TGF)-β signalling. RNA sequencing alignments to PTC alleles varied from 8-23% of expected, and differed between same-donor replicates. Differential gene expression analyses were validated by single cell qRT-PCR, and identification of changes in wider, disease-specific BMP/TGF-β pathway components. However, the most differentially expressed genes clustered to disease-independent terms for post translational protein modification (isopeptide bond; ubiquitin-like conjugation). They were the only terms meeting Benjamini significance after clustering Bonferroni-ranked, differentially expressed genes from the 5,013 meeting 10% intraassay coefficients of variation, and significance was robust to normalisation methods. Optimised pulse chase experiments supported perturbed wildtype protein maturation, but no PTC-truncated protein was identified. Unexpectedly, BOEC cultures with highest PTC persistence were discriminated in unsupervised hierarchical clustering of low GINI coefficient ‘invariant’ housekeeper genes, and patterns were compatible with higher cellular stress. The findings support a model whereby PTCs are more of a burden in stressed cells, and lead us to conclude that overlooked and varying PTC burdens contribute to biological variability.
Traditional martial arts and shooting training have different effects on auditory fine structure processing ability——Evidence from behavioral tests and fMRI
Authors: Ding Yu , Zhang Keying , Cao Chunmei 
Published: December 2021

AbstractExplore the influence of traditional martial arts and shooting training on the ability of auditory temporal fine structure (TFS) processing. Twenty-five college students participated in the experiment, including 8 traditional martial arts practitioners, 8 high-level shooting athletes, and 9 control groups without any regular exercise habits. The BIC (break in interaural correlation) delay threshold and TFS1 test were used to evaluate the temporary storage capacity and sensitivity of TFS, respectively, and a fMRI test was performed after the test. The results found that the traditional martial arts group had stronger TFS sensitivity, while the shooting group had stronger TFS retention ability, and the performance of the behavioral test of the shooting group is related to the fALFF value of the brain area of interest. Traditional martial arts and shooting training have improved the ability of auditory information processing from different angles, diversified exercise habits will lead to the development of diversity in brain structure and function.
Subcellular spatial transcriptomics identifies three mechanistically different classes of localizing RNAs
Authors: Cassella Lucia , Ephrussi Anne 
Published: December 2021

AbstractIntracellular RNA localization is a widespread and dynamic phenomenon that compartmentalizes gene expression and contributes to the functional polarization of cells. Thus far, mechanisms of RNA localization identified in Drosophila have been based on a few RNAs in different tissues, and a comprehensive mechanistic analysis of RNA localization in a single tissue is lacking. Here, by subcellular spatial transcriptomics we identify RNAs localized in the apical and basal domains of the columnar follicular epithelium (FE) and we analyze the mechanisms mediating their localization. Whereas the dynein/BicD/Egl machinery controls apical RNA localization, basally-targeted RNAs require kinesin-1 to overcome a “default” dynein-mediated transport. Moreover, a non-canonical, translation- and dynein-dependent mechanism mediates apical localization of a subgroup of dynein-activating adaptor RNAs (BicD, Bsg25D, hook). Altogether, our study identifies at least three mechanisms underlying RNA localization in the FE, and suggests a possible link between RNA localization and dynein/dynactin/adaptor complex formation in vivo.
Utilizing occupancy-detection models with museum specimen data: promise and pitfalls
Authors: Shirey Vaughn , Khelifa Rassim , M’Gonigle Leithen K. , Guzman Laura Melissa 
Published: December 2021

AbstractHistorical museum records provide potentially useful data for identifying drivers of change in species occupancy. However, because museum records are typically obtained via many collection methods, methodological developments are needed in order to enable robust inferences. Occupancy-detection models, a relatively new and powerful suite of methods, are a potentially promising avenue because they can account for changes in collection effort through space and time. Here we present a methodological road-map for using occupancy models to analyze historical museum records. We use simulated data-sets to identify how and when patterns in data and/or modelling decisions can bias inference. We focus primarily on the consequences of contrasting methodological approaches for dealing with species’ ranges and inferring species’ non-detections in both space and time. We find that not all data-sets are suitable for occupancy-detection analysis but, under the right conditions (namely, data-sets that span long durations and contain a high fraction of community-wide collections, or collection events that focus on communities of organisms), models can accurately estimate trends. Finally, we present a case-study on eastern North American odonates where we calculate long-term trends of occupancy by using our most robust workflow.
Inference of Brain States under Anesthesia with Meta Learning Based Deep Learning Models
Authors: Wang Qihang , Liu Feng , Wan Guihong , Chen Ying 
Published: December 2021

AbstractMonitoring the depth of unconsciousness during anesthesia is useful in both clinical settings and neuroscience investigations to understand brain mechanisms. Electroencephalogram (EEG) has been used as an objective means of characterizing brain altered arousal and/or cognition states induced by anesthetics in real-time. Different general anesthetics affect cerebral electrical activities in different ways. However, the performance of conventional machine learning models on EEG data is unsatisfactory due to the low Signal to Noise Ratio (SNR) in the EEG signals, especially in the office-based anesthesia EEG setting. Deep learning models have been used widely in the field of Brain Computer Interface (BCI) to perform classification and pattern recognition tasks due to their capability of good generalization and handling noises. Compared to other BCI applications, where deep learning has demonstrated encouraging results, the deep learning approach for classifying different brain consciousness states under anesthesia has been much less investigated. In this paper, we propose a new framework based on meta-learning using deep neural networks, named Anes-MetaNet, to classify brain states under anesthetics. The Anes-MetaNet is composed of Convolutional Neural Networks (CNN) to extract power spectrum features, and a time consequence model based on Long Short-Term Memory (LSTM) Networks to capture the temporal dependencies, and a meta-learning framework to handle large cross-subject variability. We used a multi-stage training paradigm to improve the performance, which is justified by visualizing the high-level feature mapping. Experiments on the office-based anesthesia EEG dataset demonstrate the effectiveness of our proposed Anes-MetaNet by comparison of existing methods.
Genome-wide association study across five cohorts identifies five novel loci associated with idiopathic pulmonary fibrosis
Authors: Allen Richard J , Stockwell Amy , Oldham Justin M , Guillen-Guio Beatriz , Flores Carlos , Noth Imre , Yaspan Brian L , Jenkins R Gisli , Wain Louise V ,   International IPF Genetics Consortium
Published: December 2021

AbstractIdiopathic pulmonary fibrosis (IPF) is a chronic lung condition with poor survival times. We previously published a genome-wide meta-analysis of IPF risk across three studies with independent replication of associated variants in two additional studies. To maximise power and to generate more accurate effect size estimates, we performed a genome-wide meta-analysis across all five studies included in the previous IPF risk GWAS. We utilised the distribution of effect sizes across the five studies to assess the replicability of the results and identified five robust novel genetic association signals implicating mTOR signalling, telomere maintenance and spindle assembly genes in IPF risk.