bluuebunny/crossref_metadata_2025_split huggingface dataset:
From Delta to Omicron: analysing the SARS-CoV-2 epidemic in France using variant-specific screening tests (September 1 to December 18, 2021)
Authors: Sofonea Mircea T. , Roquebert Bénédicte , Foulongne Vincent , Verdurme Laura , Trombert-Paolantoni Sabine , Roussel Mathilde , Haim-Boukobza Stéphanie , Alizon Samuel 
Published: January 2022

AbstractWe analysed 131,478 SARS-CoV-2 variant screening tests performed in France from September 1st to December 18, 2021. Tests consistent with the presence of the Omicron variant exhibit significantly higher cycle threshold Ct values, which could indicate lower amounts of virus genetic material. We estimate that the transmission advantage of the Omicron variant over the Delta variant is +105% (95% confidence interval: 96-114%). Based on these data, we use mechanistic mathematical modelling to explore scenarios for early 2022.
Social isolation and psychological distress among southern US college students in the era of COVID-19
Authors: Giovenco Danielle , Shook-Sa Bonnie E , Hutson Bryant , Buchanan Laurie , Fisher Edwin B , Pettifor Audrey 
Published: January 2022

ABSTRACTObjectiveTo examine the prevalence of psychological distress and its association with social isolation among University of North Carolina Chapel Hill (UNC-CH) students.MethodsA cross-sectional survey was emailed to all students in June 2020. Students reported self-isolating none, some, most, or all of the time and were screened for clinically significant symptoms of depression (CSSD). Data were weighted to the UNC-CH population.Results7,012 students completed surveys-64% reported self-isolating most or all of the time and 64% reported CSSD. Compared to those self-isolating none of the time, students self-isolating some of the time were 1.78 (95% CI 1.37-2.30) times as likely to report CSSD, and students self-isolating most and all of the time were 2.12 (95% CI 1.64-2.74) and 2.27 (95% CI 1.75-2.94) times as likely to report CSSD, respectively.ConclusionsUniversities should prioritize student mental health and prepare support services to mitigate mental health consequences of the pandemic.
Inter-individual deep image reconstruction
Authors: Ho Jun Kai , Horikawa Tomoyasu , Majima Kei , Cheng Fan , Kamitani Yukiyasu 
Published: January 2022

AbstractThe sensory cortex is characterized by general organizational principles such as topography and hierarchy. However, measured brain activity given identical input exhibits substantially different patterns across individuals. Although anatomical and functional alignment methods have been proposed in functional magnetic resonance imaging (fMRI) studies, it remains unclear whether and how hierarchical and fine-grained representations can be converted between individuals while preserving the encoded perceptual content. In this study, we trained a method of functional alignment called neural code converter that predicts a target subject’s brain activity pattern from a source subject given the same stimulus, and analyzed the converted patterns by decoding hierarchical visual features and reconstructing perceived images. The converters were trained on fMRI responses to identical sets of natural images presented to pairs of individuals, using the voxels on the visual cortex that covers from V1 through the ventral object areas without explicit labels of the visual areas. We decoded the converted brain activity patterns into the hierarchical visual features of a deep neural network using decoders pre-trained on the target subject and then reconstructed images via the decoded features. Without explicit information about the visual cortical hierarchy, the converters automatically learned the correspondence between visual areas of the same levels. Deep neural network feature decoding at each layer showed higher decoding accuracies from corresponding levels of visual areas, indicating that hierarchical representations were preserved after conversion. The visual images were reconstructed with recognizable silhouettes of objects even with relatively small numbers of data for converter training. The decoders trained on pooled data from multiple individuals through conversions lead to a slight improvement over those trained on a single individual. These results demonstrate that the hierarchical and fine-grained representation can be converted by functional alignment, while preserving sufficient visual information to enable inter-individual visual image reconstruction.Highlights● Neural code converters convert brain activity patterns across individuals with moderate conversion accuracy and learn reasonable visual area correspondences at the same level between individuals.● The converted brain activity patterns can be decoded into hierarchical DNN features to reconstruct visual images, even though the converter is trained on a small number of data samples.● The information of hierarchical and fine-scale visual features that enable visual image reconstruction are preserved after functional alignment.
Testing the state-dependent model of subsecond time perception against experimental evidence
Authors: Ravichandran-Schmidt Pirathitha , Hass Joachim 
Published: January 2022

AbstractCoordinated movements, speech, and other actions are impossible without precise timing. Computational models of interval timing are expected to provide key insights into the underlying mechanisms of timing, which are currently largely unknown. So far, existing models have only been partially replicating key experimental observations, such as the linear psychophysical law, the linear increase of the standard deviation (the scalar property or Weber’s law), and the modulation of subjective duration via dopamine. Here, we incorporate the state-dependent model for subsecond timing as proposed by Buonomano (2000) into a strongly data-driven computational network model of PFC We show that this model variant, the state-dependent PFC model, successfully encodes time up to 750 milliseconds and reproduces all key experimental observations mentioned above, including many of its details. Investigating the underlying mechanisms, we find that the representations of different intervals are based on the natural heterogeneity in the parameters of the network, leading to stereotypic responses of subsets of neurons. Furthermore, we propose a theory for the mechanism underlying subsecond timing in this model based on correlation and ablation analyses as well as mathematical analyses explaining the emergence of the scalar property and Vierordt law. The state-dependent PFC model proposed here constitutes the first data-driven model of subsecond timing in the range of hundreds of milliseconds that has been thoroughly tested against a variety of experimental data, providing an ideal starting point for further investigations of subsecond timing.HighlightsThe state-dependent model of time perception also encodes durations in the subsecond range when embedded into a data-driven network model of the prefrontal cortex.The model reproduces three key experimental findings of time perception: the linear psychophysical law, the scalar property (Weber’s law) and dopaminergic modulation of subjective durations.Many details of experimental observations can be reproduced and explained by the model without specific parameter tuning.A new theory for the emergence of Weber’s law and Vierordt’s law (overestimation of short durations and underestimation of long durations) is provided.The current variant of the state-dependent model is the first model of time perception to be thoroughly tested against a wide range of experimental evidence and can now be considered to be experimentally validated.
P2RX7 inhibition reduces breast cancer induced osteolytic lesions - implications for bone metastasis
Authors: Shah Karan M. , Tattersall Luke , Hussain Aleana , Macfarlane Sarah C. , Williamson Alexander , Acosta-Martin Adelina E. , Erler Janine T. , Ottewell Penelope D. , Gartland Alison 
Published: January 2022

AbstractBreast cancer metastasis to bone is a major contributor to morbidity and mortality in patients and remains an unmet clinical need. Purinergic signalling via the P2X7 receptor (P2RX7) in the primary tumour microenvironment is associated with progression of several cancers. It has also now become evident that intra-tumoural hypoxia facilitates cancer metastasis and reduces patient survival. In this study, we present data suggesting that hypoxia regulates the expression of P2RX7 in the primary tumour microenvironment; and importantly, inhibition with a selective antagonist (10mg/kg A740003) increased cancer cell death via apoptosis in a E0771/C57BL-6J syngeneic murine model. Furthermore, micro-computed tomography demonstrated reduced number of osteolytic lesions and lesion area following P2RX7 inhibition in absence of overt metastases by decreasing osteoclast numbers. We also demonstrate that activation of P2RX7 plays a role in the secretion of extracellular vesicles (EVs) from breast cancer cells. Mass-spectrometric analyses showed a distinct protein signature for EVs derived from hypoxic compared with normoxic cancer cells which elicit specific responses in bone cells that are associated with pre-metastatic niche formation. Thus, inhibiting P2RX7 provides a novel opportunity to preferentially target the hypoxic breast cancer cells preventing tumour progression and subsequent metastasis to bone
Northward Expansion ofAmblyomma americanum(Acari: Ixodidae) into Southern Michigan
Authors: Fowler Peter D. , Nguyentran S. , Quatroche L. , Porter M.L. , Kobbekaduwa V. , Tippin S. , Miller Guy , Dinh E. , Foster E. , Tsao J.I. 
Published: January 2022

AbstractAmblyomma americanum(Linnaeus) (Acari: Ixodidae) (lone star tick) is an aggressive, generalist parasite that vectors numerous important human and animal pathogens. In recent decades its geographic range has expanded northwards from endemic regions in the southeastern and southcentral US. In 2019 five questingA. americanumcomprising two life stages were detected at one site in Berrien County, in southwestern Michigan, satisfying one CDC criterium for an established population for the first time in the state. To better characterize the northern extent of emergingA. americanum, we conducted active surveillance (i.e., drag sampling) in summer 2020 throughout Michigan’s southern counties and detected one adultA. americanumfrom each of six widespread sites, including where they had been detected in 2019. A larger established population was identified at another site in Berrien County, which yielded 691A. americanumcomprising three life stages. Questing tick phenologies at this site were similar to that reported for other regions. Statewide surveillance in 2021 revealed noA. americanumoutside of Berrien County, but establishment criteria were met again at the two sites where established populations were first detected respectively in 2019 and 2020. These observations may represent the initial successful invasion ofA. americanuminto Michigan. Data from passive (1999-2020) and active surveillance (2004-2021) efforts, including a domestic animal sentinel program (2015-2018), are reported to provide context for this nascent invasion. Continued active surveillance is needed to help inform the public, medical professionals, and public health officials of the health risks associated with this vector.
The TOG protein Stu2 is regulated by acetylation
Authors: Greenlee Matt , Witt Braden , Sabo Jeremy , Morris Savannah , Miller Rita K. 
Published: January 2022

ABSTRACTStu2 in S. cerevisiae is a member of the XMAP215/Dis1/Alp14/Msps/CKAP5/ch-TOG family of MAPs and has multiple functions in controlling microtubules, including microtubule polymerization, microtubule depolymerization, linking chromosomes to the kinetochore, and assembly of γ-TuSCs at the SPB. Whereas phosphorylation has been shown to be critical for Stu2 localization at the kinetochore, other regulatory mechanisms that control Stu2 function are still poorly understood. Here, we show that a novel form of Stu2 regulation occurs through the acetylation of three lysine residues at K252, K469, and K870, which are located in three distinct domains of Stu2. Alteration of acetylation through acetyl-mimetic and acetyl-blocking mutations did not impact the essential function of Stu2. Instead, these mutations lead to both positive and negative changes in chromosome stability, as well as changes in resistance to the microtubule depolymerization drug, benomyl. In agreement with our in silico modeling, several acetylation-mimetic mutants displayed increased interactions with γ-tubulin. Taken together, these data suggest that Stu2 acetylation can govern multiple Stu2 functions in both a positive and negative manner, including chromosome stability and interactions at the SPB.
Analyzing the relationship between distracted driving and eye movement using multimodal data collected during car driving
Authors: Uraki Daigo , Tanioka Kensuke , Hiwa Satoru , Furutani Hiroshi , Hiroyasu Tomoyuki 
Published: January 2022

AbstractHuman error is the leading cause of traffic accidents and originates from the distraction caused by various factors, such as the driver’s physical condition and mental state. One of the significant factors causing driver distraction is the presence of stress. In a previous study, multiple stressors were used to examine distraction while driving. Multiple stressors were given to the driver and the corresponding driver biometric data were obtained, and a multimodal dataset was published thereafter. In this study, we reiterate the results of existing studies and investigated the relationship between gaze variability while driving and stressor intervention, which has not yet been examined. We also examined whether biometric and vehicle information can estimate the presence or absence of secondary tasks during driving.
PlasmidMaker: a Versatile, Automated, and High Throughput End-to-End Platform for Plasmid Construction
Authors: Enghiad Behnam , Xue Pu , Singh Nilmani , Boob Aashutosh , Shi Chengyou , Petrov Vassily Andrew , Liu Roy , Peri Siddhartha Suryanarayana , Lane Stephan Thomas , Gaither Emily Danielle , Zhao Huimin 
Published: January 2022

AbstractPlasmids are used extensively in basic and applied biology. However, design and construction of plasmids, specifically the ones carrying complex genetic information, remains one of the most time-consuming, labor-intensive, and rate-limiting steps in performing sophisticated biological experiments. Here, we report the development of a versatile, robust, automated end-to-end platform named PlasmidMaker that allows error-free construction of plasmids with virtually any sequences in a high-throughput manner. This platform consists of a most versatile DNA assembly method using Pyrococcus furiosus Argonaute (PfAgo)-based artificial restriction enzymes, a user-friendly frontend for plasmid design, and a backend that streamlines the workflow and integration with a robotic system. As a proof of concept, we used this platform to generate 101 plasmids from six different species ranging from 5 to 18 kb in size from up to 11 DNA fragments within 3 days. PlasmidMaker should greatly expand the potential of synthetic biology.
Single-cell transcriptome reveals the redifferentiation trajectories of the early stage of de novo shoot regeneration in Arabidopsis thaliana
Authors: Liu Guangyu , Li Jie , Li Ji-Ming , Chen Zhiyong , Yuan Peisi , Chen Ruiying , Yin Ruilian , Liao Zhiting , Li Xinyue , Gu Ying , Sun Hai-Xi , Xia Keke 
Published: January 2022

AbstractDe novo shoot regeneration from a callus plays a crucial role in both plant biotechnology and the fundamental research of plant cell totipotency. Recent studies have revealed many regulatory factors involved in this developmental process. However. our knowledge of the cell heterogeneity and cell fate transition during de novo shoot regeneration is still limited. Here, we performed time-series single-cell transcriptome experiments to reveal the cell heterogeneity and redifferentiation trajectories during the early stage of de novo shoot regeneration. Based on the single-cell transcriptome data of 35,669 cells at five-time points, we successfully determined seven major cell populations in this developmental process and reconstructed the redifferentiation trajectories. We found that all cell populations resembled root identities and undergone gradual cell-fate transitions. In detail, the totipotent callus cells differentiated into pluripotent QC-like cells and then gradually developed into less differentiated cells that have multiple root-like cell identities, such as pericycle-like cells. According to the reconstructed redifferentiation trajectories, we discovered that the canonical regeneration-related genes were dynamically expressed at certain stages of the redifferentiation process. Moreover, we also explored potential transcription factors and regulatory networks that might be involved in this process. The transcription factors detected at the initial stage, QC-like cells, and the end stage provided a valuable resource for future functional verifications. Overall, this dataset offers a unique glimpse into the early stages of de novo shoot regeneration, providing a foundation for a comprehensive analysis of the mechanism of de novo shoot regeneration.
Efficient simulation of 3D reaction-diffusion in models of neurons and networks
Authors: McDougal Robert A. , Conte Cameron , Eggleston Lia , Newton Adam J.H. , Galijasevic Hana 
Published: January 2022

AbstractNeuronal activity is the result of both the electrophysiology and chemophysiology. A neuron can be well represented for the purposes of electrophysiological simulation as a tree composed of connected cylinders. This representation is also apt for 1D simulations of their chemophysiology, provided the spatial scale is larger than the diameter of the cylinders and there is radial symmetry. Higher dimensional simulation is necessary to accurately capture the dynamics when these criteria are not met, such as with wave curvature, spines, or diffusion near the soma.We have developed a solution to enable efficient finite volume method simulation of reaction-diffusion kinetics in intracellular 3D regions in neuron and network models and provide an implementation within the NEURON simulator. An accelerated version of the CTNG 3D reconstruction algorithm transforms morphologies suitable for ion-channel based simulations into consistent 3D voxelized regions. Kinetics are then solved using a parallel algorithm based on Douglas-Gunn that handles the irregular 3D geometry of a neuron; these kinetics are coupled to NEURON’s 1D mechanisms for ion channels, synapses, etc. The 3D domain may cover the entire cell or selected regions of interest. Simulations with dendritic spines and of the soma reveal details of dynamics that would be missed in a pure 1D simulation. We describe and validate the methods and discuss their performance.
Temporal control of the integrated stress response by a stochastic molecular switch
Authors: Klein Philipp , Kallenberger Stefan M. , Roth Hanna , Roth Karsten , Ly-Hartig Thi Bach Nga , Magg Vera , Aleš Janez , Talemi Soheil Rastgou , Qiang Yu , Wolf Steffen , Oleksiuk Olga , Kurilov Roma , Di Ventura Barbara , Bartenschlager Ralf , Eils Roland , Rohr Karl , Hamprecht Fred A. , Höfer Thomas , Fackler Oliver T. , Stoecklin Georg , Ruggieri Alessia 
Published: January 2022

AbstractStress granules (SGs) are formed in the cytosol as an acute response to environmental cues and activation of the integrated stress response (ISR), a central signaling pathway controlling protein synthesis. Using chronic virus infection as stress model, we previously uncovered a unique temporal control of the ISR resulting in recurrent phases of SG assembly and disassembly. Here, we elucidate the molecular network generating this fluctuating stress response, by integrating quantitative experiments with mathematical modeling, and find that the ISR operates as a stochastic switch. Key elements controlling this switch are the cooperative activation of the stress-sensing kinase PKR, the ultrasensitive response of SG formation to the phosphorylation of the translation initiation factor eIF2α, and negative feedback via GADD34, a stress-induced subunit of protein phosphatase 1. We identify GADD34 mRNA levels as the molecular memory of the ISR that plays a central role in cell adaptation to acute and chronic stress.
Synthetic Data Resource and Benchmarks for Time Cell Analysis and Detection Algorithms
Authors: Ananthamurthy Kambadur Gundu , Bhalla Upinder S 
Published: January 2022

AbstractHippocampal CA1 cells take part in reliable, time-locked activity sequences in tasks that involve an association between temporally separated stimuli, in a manner that tiles the interval between the stimuli. Such cells have been termed time cells. Here we adopt a first-principles approach to comparing diverse analysis and detection algorithms for identifying time cells. We generated synthetic activity datasets using calcium signals recordedin vivofrom the mouse hippocampus using 2-Photon imaging, as template response waveforms. We assigned known, ground truth values to perturbations applied to perfect activity signals, including noise, calcium event width, timing imprecision, hit-trial ratio and background (untuned) activity. We tested a range of published and new algorithms and their variants on this dataset. We find that most algorithms correctly classify over 80% of cells, but have different balances between true and false positives, and different sensitivity to the five categories of perturbation. Reassuringly, most methods are reasonably robust to perturbations, including background activity, and show good concordance in classification of time cells. The same algorithms were also used to analyse and identify time cells in experimental physiology datasets recordedin vivoand most show good concordance.Significance StatementNumerous approaches have been developed to analyze time cells and neuronal activity sequences, but it is not clear if their classifications match, nor how sensitive they are to various sources of data variability. We provide two main contributions to address this: 1) A resource to generate ground truth labelled synthetic 2-P Calcium activity data with defined distributions for confounds such as noise and background activity, and 2) a survey of several methods for analyzing time-cell data using our synthetic data as ground truth. As a further resource, we provide a library of efficient C++ implementations of several algorithms with a Python interface. The synthetic dataset and its generation code are useful for profiling future methods, testing analysis toolchains, and as input to computational and experimental models of sequence detection.