bluuebunny/crossref_metadata_2025_split huggingface dataset:
The neuron mixer and its impact on human brain dynamics
Authors: Luff Charlotte E. , Peach Robert , Mallas Emma-Jane , Rhodes Edward , Laumann Felix , Boyden Edward S. , Sharp David J. , Barahona Mauricio , Grossman Nir 
Published: January 2023

AbstractA signal mixer made of a transistor or a diode facilitates rich computation, which has been the building block of modern telecommunications. The mixing produces new signals at the sum and difference frequencies of the input, thereby enabling powerful operations such as frequency conversion (aka heterodyning), phase detection, and multiplexing. Here, we report that a neural cell is also a signal mixer. We found throughex-vivoandin-vivowhole-cell measurements that neurons mix exogenous (controlled) and endogenous (spontaneous) subthreshold membrane potential oscillations, producing new oscillation frequencies. We show, using pharmacological manipulation, that the neural mixing originates in the voltage-gated ion channels. Furthermore, we demonstrate that the neural mixing dynamic is evident in human brain activity and is associated with cognitive functions. We found that the human electroencephalogram (EEG) displays distinct clusters of local and inter-region mixing interactions. By quantifying the strength of these interactions before a task, we show that converting the salient posterior alpha-beta oscillations into gamma-band oscillations regulates the visual attention state. Neural circuit oscillations have been observed in nearly every cognitive domain and species, and abnormal spectra of neural oscillations have been found in almost all brain disorders. Signal mixing enables individual neurons to actively sculpt the spectrum of their circuit oscillations and utilize them for computational operations, which have only been seen in modern telecommunication until now.
Flow similarity model predicts the allometry and allometric covariation of petiole dimensions
Authors: Price Charles A. 
Published: January 2023

ABSTRACTAllometric relationships for plants, plant organs and plant parts, have long generated interest among biologists. Several prominent theoretical models based on biomechanical and/or hydraulic arguments, have been introduced with mixed support. Here I test a more recent offering, flow similarity, which is based on the conservation of volumetric flow rate and velocity. Using dimensional data for 935 petioles from 43 angiosperm species, I show that both the intraspecific and interspecific petiole allometries are more closely aligned with the predictions of the flow similarity model than that of elastic or geometric similarity. Further, allometric covariation among empirical scaling exponents falls along predicted functions with clustering around the flow similarity predictions. This work adds to the body of literature highlighting the importance of hydraulics in understanding the physiological basis of plant allometries, identifies previously unknown central tendencies in petiole allometry, and helps to delineate the scope within which the flow similarity model may be applicable.
Leveraging family data to design Mendelian Randomization that is provably robust to population stratification
Authors: LaPierre Nathan , Fu Boyang , Turnbull Steven , Eskin Eleazar , Sankararaman Sriram 
Published: January 2023

AbstractMendelian Randomization (MR) has emerged as a powerful approach to leverage genetic instruments to infer causality between pairs of traits in observational studies. However, the results of such studies are susceptible to biases due to weak instruments as well as the confounding effects of population stratification and horizontal pleiotropy. Here, we show that family data can be leveraged to design MR tests that are provably robust to confounding from population stratification, assortative mating, and dynastic effects. We demonstrate in simulations that our approach, MR-Twin, is robust to confounding from population stratification and is not affected by weak instrument bias, while standard MR methods yield inflated false positive rates. We applied MR-Twin to 121 trait pairs in the UK Biobank dataset and found that MR-Twin identifies likely causal trait pairs and does not identify trait pairs that are unlikely to be causal. Our results suggest that confounding from population stratification can lead to false positives for existing MR methods, while MR-Twin is immune to this type of confounding.
The probability of edge existence due to node degree: a baseline for network-based predictions
Authors: Zietz Michael , Himmelstein Daniel S. , Kloster Kyle , Williams Christopher , Nagle Michael W. , Greene Casey S. 
Published: January 2023

AbstractImportant tasks in biomedical discovery such as predicting gene functions, gene-disease associations, and drug repurposing opportunities are often framed as network edge prediction. The number of edges connecting to a node, termed degree, can vary greatly across nodes in real biomedical networks, and the distribution of degrees varies between networks. If degree strongly influences edge prediction, then imbalance or bias in the distribution of degrees could lead to nonspecific or misleading predictions. We introduce a network permutation framework to quantify the effects of node degree on edge prediction. Our framework decomposes performance into the proportions attributable to degree and the network’s specific connections. We discover that performance attributable to factors other than degree is often only a small portion of overall performance. Degree’s predictive performance diminishes when the networks used for training and testing—despite measuring the same biological relationships—were generated using distinct techniques and hence have large differences in degree distribution. We introduce the permutation-derived edge prior as the probability that an edge exists based only on degree. The edge prior shows excellent discrimination and calibration for 20 biomedical networks (16 bipartite, 3 undirected, 1 directed), with AUROCs frequently exceeding 0.85. Researchers seeking to predict new or missing edges in biological networks should use the edge prior as a baseline to identify the fraction of performance that is nonspecific because of degree. We released our methods as an open-source Python package (https://github.com/hetio/xswap/).
An entorhinal-like region in food-caching birds
Authors: Applegate Marissa C. , Gutnichenko Konstantin S. , Mackevicius Emily L. , Aronov Dmitriy 
Published: January 2023

SUMMARYThe mammalian entorhinal cortex routes inputs from diverse sources into the hippocampus. This information is mixed and expressed in the activity of many specialized entorhinal cell types, which are considered indispensable for hippocampal function. However, functionally similar hippocampi exist even in non-mammals that lack an obvious entorhinal cortex, or generally any layered cortex. To address this dilemma, we mapped extrinsic hippocampal connections in chickadees, whose hippocampi are used for remembering numerous food caches. We found a well-delineated structure in these birds that is topologically similar to the entorhinal cortex and interfaces between the hippocampus and other pallial regions. Recordings of this structure revealed entorhinal-like activity, including border and multi-field grid-like cells. These cells were localized to the subregion predicted by anatomical mapping to match the dorsomedial entorhinal cortex. Our findings uncover an anatomical and physiological equivalence of vastly different brains, suggesting a fundamental nature of entorhinal-like computations for hippocampal function.
The Field-Dependent Nature of PageRank Values in Citation Networks
Authors: Heil Benjamin J. , Greene Casey S. 
Published: January 2023

AbstractThe value of scientific research can be easier to assess at the collective level than at the level of individual contributions. Several journal-level and article-level metrics aim to measure the importance of journals or individual manuscripts. However, many are citation-based and citation practices vary between fields. To account for these differences, scientists have devised normalization schemes to make metrics more comparable across fields. We use PageRank as an example metric and examine the extent to which field-specific citation norms drive estimated importance differences. In doing so, we recapitulate differences in journal and article PageRanks between fields. We also find that manuscripts shared between fields have different PageRanks depending on which field’s citation network the metric is calculated in. We implement a degree-preserving graph shuffling algorithm to generate a null distribution of similar networks and find differences more likely attributed to field-specific preferences than citation norms. Our results suggest that while differences exist between fields’ metric distributions, applying metrics in a field-aware manner rather than using normalized global metrics avoids losing important information about article preferences. They also imply that assigning a single importance value to a manuscript may not be a useful construct, as the importance of each manuscript varies by the reader’s field.
Are We Seeing the Unseen of Human Trafficking? A Retrospective Secondary Analysis of the CTDC K-Anonymized Global Victim of Trafficking Data Pool in the Period 2010-2020
Authors: Al-Tammemi Ala’a B. , Nadeem Asma , Kutkut Laila , Ali Manal , Angawi Khadijah , Abdallah Maram H. , Abutaima Rana , Shoumar Rasha , Albakri Rana , Sallam Malik 
Published: January 2023

ABSTRACTBackgroundHuman trafficking is considered a hidden global threat with unsubstantiated numbers despite the estimation of the presence of around 40.3 million victims worldwide. Human trafficking results in severe detrimental impacts on both mental and physical health. Given the sensitivity and negative consequences of human trafficking on the global system and victims, and considering the scarce research in this area, our current study aimed at describing the (i) Sociodemographic profiles of anonymized victims, (ii) Means of control, and (iii) Purpose of trafficking, utilizing the largest anonymized and publicly available dataset on victims of human trafficking.MethodsThis is a retrospective secondary analysis of the Counter-Trafficking Data Collaborative (CTDC) data pool in the period from 2010 to 2020. The utilized dataset is called the k-anonymized global victim of trafficking dataset, and it is considered the largest global dataset on victims of human trafficking. Data from the k-anonymized data pool were extracted and exported to Statistical Package for Social Sciences, SPSS® version 27.0 for Windows (IBM Corp. Version 27.0.Armonk, NY) for quality check and analysis using descriptive statistics.ResultsA total of 87003 victims of human trafficking were identified in the period from 2010 to 2020. The most age category encountered among victims was 9-17 years with around 10326 victims (11.9%), followed by 30-38 years with 8562 victims (9.8%). Females comprised 70% of the sample with 60938 victims. The United States (n=51611), Russia (n=4570), and the Philippines (n=1988) comprised the most countries of exploitation/trafficking. Additionally, the year 2019 witnessed the greatest number of victims registered for assistance by anti-trafficking agencies with around 21312 victims (24.5%). Concerning means of control, threats, psychological abuse, restriction of the victim’s movement, taking the victim’s earnings, and physical abuse were the most reported means. Around 42685 victims (49.1%) reported sexual exploitation as the purpose of their trafficking, followed by forced labor with 18176 victims (20.9%).ConclusionVarious means and methods can be used by traffickers to control the victims to be trafficked for many purposes, with sexual exploitation and forced labor being the most common ones. Global anti-trafficking efforts should be brought together in solidarity through utilizing the paradigm of protection of victims, prosecution of traffickers, prevention of trafficking, and inter-sectoral partnerships. Despite being a global concern with various reports that tried to capture the number of trafficked victims worldwide, human trafficking still has many unseen aspects that impose a significant challenge and adds to the global burden in combatting this threat.
Assessment of distant-site rescue elements for CRISPR toxin-antidote gene drives
Authors: Chen Jingheng , Xu Xuejiao , Champer Jackson 
Published: January 2023

AbstractNew types of gene drives promise to provide increased flexibility, offering many options for confined modification or suppression of target populations. Among the most promising are CRISPR toxin-antidote gene drives, which disrupt essential wild-type genes by targeting them with Cas9/gRNA, resulting in their removal. This increases the frequency of the drive in the population. All these drives, plus homing modification rescue drives, rely on having an effective rescue element, which consists of a recoded version of the target gene. This rescue element can be at the same site as the target gene, which maximizes the chance of efficient rescue, or at a distant site, which allows some other useful options, such as easily disrupting another essential gene or achieving greater confinement. Previously, we developed a homing rescue drive targeting a haplolethal gene and a toxin-antidote drive targeting an essential but haplosufficient gene. These successful drives had functional rescue elements but suboptimal drive efficiency. Here, we attempted to construct new toxin-antidote drives targeting these genes with a distantsite configuration from three different loci. We found that use of additional gRNAs increased cut rates to nearly 100%. However, all distant-site rescue elements failed for both haplolethal and haplosufficient target genes. Furthermore, one rescue element with a minimally recoded rescue element was used as a template for homology-directed repair for the target gene on a different chromosomal arm, resulting in the formation of functional resistance alleles at high frequency. Together, these results can inform the design of future CRISPR-based toxin-antidote gene drives.
Neural characterization of the “totonou” state associated with sauna use
Authors: Chang Ming , Ibaraki Takuya , Naruse Yasushi , Imamura Yasuhiko 
Published: January 2023

AbstractSaunas are becoming increasingly popular worldwide, being an activity that promotes relaxation and health. Intense feelings of happiness have been reported shortly after enjoying a hot sauna and cold water, what is known in Japan as the “totonou” state. However, no research has investigated what occurs in the brain during the “totonou” state. In the present study, participants underwent a sauna phase, consisting of three sets of alternating hot sauna, cold water, and rest. We elucidated changes in brain activity and mood in the “totonou” state by measuring and comparing brain activity and emotional scales before and after the sauna phase and during the rest phase in each set. We found significant increases in theta and alpha power during rest and after the sauna phase compared to before the sauna phase. Moreover, in an auditory oddball task, the p300 amplitude decreased significantly and MMN amplitude increased significantly after the sauna phase. The increase in MMN indicates higher activation of the pre-attentional auditory process, leading to a decrease in attention-related brain activity P300. Hence, the brain reaches in a more efficient state. Further, the response time in behavioral tasks decreased significantly. In addition, the participants’ subjective responses to the questionnaire showed significant changes in physical relaxation and other indicators after being in the sauna. Finally, we developed an artificial intelligence classifier, obtaining an average accuracy of brain state classification of 88.34%. The results have potential for future application.
ATG5 selectively engages virus-tethered BST2/Tetherin in an LC3C-associated pathway
Authors: Judith Delphine , Versapuech Margaux , Bejjani Fabienne , Palaric Marjory , Verlhac Pauline , Kuster Aurelia , Lepont Leslie , Gallois-Montbrun Sarah , Janvier Katy , Berlioz-Torrent Clarisse 
Published: January 2023

AbstractBST2/Tetherin is a restriction factor that reduces HIV-1 dissemination by tethering virus at the cell surface. BST2 also acts as a sensor of HIV-1 budding, establishing a cellular anti-viral state. The HIV-1 Vpu protein antagonizes BST2 antiviral functions, notably by subverting an LC3C-associated pathway, a key cell intrinsic anti-microbial mechanism. Here, we show that ATG5 associates with BST2 and acts as a signaling scaffold to trigger an LC3C-associated pathway induced by HIV-1 infection. This process is initiated at the plasma membrane through the recognition of virus-tethered BST2 by ATG5. ATG5 and BST2 assemble as a complex, independently of the viral protein Vpu and ahead of the recruitment of the ATG protein LC3C. The conjugation of ATG5 with ATG12 is dispensable for this interaction. ATG5 recognizes cysteine-linked homodimerized BST2 and specifically engages phosphorylated BST2 tethering viruses at the plasma membrane, in an LC3C-associated pathway. We also found that this LC3C-associated pathway is used by Vpu to attenuate the inflammatory responses mediated by virion retention. Overall, we highlight that by targeting BST2 tethering viruses, ATG5 acts as a transducer of the LC3C-associated pathway induced by HIV-1 infection.Significance statementThe outcome of viral infection in cells is dependent on the balance between host restriction factors and viral countermeasures. BST2/Tetherin is a restriction factor that reduces HIV-1 dissemination by tethering virions at the cell surface. Its action is counteracted by the viral protein Vpu through multiple mechanisms. Here, we describe the initial step of a non-canonical autophagic pathway, called LC3C-associated pathway, subverted by Vpu to counteract BST2 antiviral activities. We found that the autophagic protein ATG5 acts as a transducer by targeting phosphorylated and dimerized virus-tethered BST2 from cell surface to the degradation. Our discovery opens new avenue in the discovery of unconventional functions of ATG5, as an adaptor for receptor at the plasma membrane initiating an unconventional autophagy process.
Enhanced neuroimaging with a calcium sensor in the live adultDrosophila Melanogasterbrain using closed-loop adaptive optics light-sheet microscopy
Authors: Hubert Antoine , Farkouh Georges , Harms Fabrice , Veilly Cynthia , Imperato Sophia , Mercier Mathias , Loriette Vincent , Rouyer François , Fragola Alexandra 
Published: January 2023

AbstractWe report on an Adaptive Optics (AO) Light-Sheet Fluorescence Microscope compatible with neuroimaging, based on direct wavefront sensing without the requirement of a guide star. We demonstrate fast AO correction, typically within 500ms, of in-depth aberrations of the live adultDrosophilabrain, enabling to double the contrast when imaging with structural or calcium sensors. We quantify the gain in terms of image quality on multiply neuronal structures part of the sleep network in theDrosophilabrain, at various depths, and discuss the optimization of key parameters driving AO such as the number of corrected modes and the photon budget. We present a first design of a compact AO add-on that is compatible with integration into most of reported Light-Sheet setups and neuroimaging.
Identification of protein secretion systems in bacterial genomes using MacSyFinder version 2
Authors: Abby Sophie S , Denise Rémi , Rocha Eduardo PC 
Published: January 2023

AbstractProtein secretion systems are complex molecular machineries that translocate proteins through the outer membrane and sometimes through multiple other barriers. They have evolved by co-option of components from other envelope-associated cellular machineries, making them sometimes difficult to identify and discriminate. Here, we describe how to identify protein secretion systems in bacterial genomes using the MacSyFinder program. This flexible computational tool uses the knowledge gathered from experimental studies to identify homologous systems in genome data. It can be used with a set of pre-defined MacSyFinder models—”TXSScan”, to identify all major secretion systems of diderm bacteria (i.e., with inner and LPS-containing outer membranes) as well as evolutionarily related cell appendages (pili and flagella). For this, it identifies and clusters co-localized genes encoding proteins of secretion systems using sequence similarity search with Hidden Markov Model (HMM) protein profiles. Finally, it checks if the clusters’ genetic content and genomic organization satisfy the constraints of the model. TXSScan models can be altered in the command line or customized to search for variants of known secretion systems. Models can also be built from scratch to identify novel systems. In this chapter, we describe a complete pipeline of analysis, starting from i) the integration of information from a reference set of experimentally studied systems, ii) the identification of conserved proteins and the construction of their HMM protein profiles, iii) the definition and optimization of “macsy-models”, and iv) their use and online distribution as tools to search genomic data for secretion systems of interest. MacSyFinder is available here:https://github.com/gem-pasteur/macsyfinder, and MacSyFinder models here:https://github.com/macsy-models.
Ultrasensitive Proteomics Depicted an In-depth Landscape for Mouse Embryo
Authors: Gu Lei , Li Xumiao , Zhu Wencheng , Shen Yi , Wang Qinqin , Zhang Huiping , Li Jingquan , Li Ziyi , Liu Zhen , Li Chen , Wang Hui 
Published: January 2023

AbstractIn recent years, single-cell or low-input multi-omics techniques have brought a revolution in the study of pre-implantation embryo development. However, single-cell or low-input proteome research in this field is relatively underdeveloped, due to the limited source of mammalian embryo samples, the objective reality of high abundance zona pellucida proteins, and the lack of hypersensitive proteome technology. Here, a comprehensive solution of ultrasensitive proteome technology was developed for single-cell or low-input mouse embryos. Both deep coverage route and high-throughput route could significantly reduce the starting material and enhance the proteomic depth without any customized instrument. Using the deep coverage route, an average of 2,665 or 4,585 protein groups can be identified from 1 or 20 mouse zygotes respectively. Using the high-throughput route, 300 single mouse zygotes can be analysis in 8 days with an average of 2,371 proteins identified. With its popularization, we believe researchers can choose deep coverage or high-throughput technology routes according to their own conditions.