bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hybrid immunity expands the functional humoral footprint of both mRNA and vector-based SARS-CoV-2 vaccines
Authors: Kaplonek Paulina , Deng Yixiang , Lee Jessica Shih-Lu , Zar Heather J , Zavadska Dace , Johnson Marina , Lauffenburger Douglas A. , Goldblatt David , Alter Galit 
Published: July 2022

AbstractThe COVID-19 pandemic catalyzed a revolution in vaccine development, leading to the testing and approval of several global vaccine platforms that have shown tremendous promise in curbing the pandemic. Yet, despite these successes, waning immunity, and the emergence of variants of concern linked to rising breakthrough infections among vaccinees, have begun to highlight opportunities to improve vaccine platforms and deployment. Real-world vaccine efficacy has highlighted the reduced risk of breakthrough infection and disease among individuals infected and vaccinated, otherwise referred to as hybrid immunity. Hybrid immunity points to the potential for more vigorous or distinct immunity primed by the infection and may confer enhanced protection from COVID-19. Beyond augmented hybrid induced neutralizing antibody and T cell immune responses, here we sought to define whether hybrid immunity may shape the functional humoral immune response to SARS-CoV-2 following Pfizer/BNT162b2 and Moderna mRNA1273 mRNA-based, and ChadOx1/AZ1222 and Ad26.COV2.S vector-based SARS-CoV-2 vaccination. Each vaccine exhibited a unique functional humoral immune profile in the setting of naïve or hybrid immunity. However, hybrid immunity showed a unique augmentation in S2-domain specific functional humoral immunity that was poorly induced in the setting of naïve immune response. These data highlight the immunodominant effect of the S1-domain in the setting of natural immunity, which is highly variable during viral evolution, and the importance of natural infection in breaking this immunodominance in driving immunity to the S2 region of the SARS-CoV-2 S2 domain that is more conserved across variants of concern.
FMRI complexity correlates with tau-PET in Late-Onset and Autosomal Dominant Alzheimer’s Disease
Authors: Jann Kay , Boudreau Julia , Albrecht Daniel , Cen Steven Y , Cabeen Ryan P , Ringman John M , Wang Danny JJ ,   the Alzheimer’s Disease Neuroimaging Initiative
Published: July 2022

AbstractNeurofibrillary tangle pathology detected with tau-PET correlates closely with neuronal injury and cognitive symptoms in Alzheimer’s disease (AD). Complexity of rs-fMRI time-series, measured by entropy values, have recently been reported to decrease with aging, APOE ε4 genotype and cognitive decline in AD. Here we hypothesize that the complexity of BOLD signals provides an index for tau-related neuronal injury and cognitive decline in the AD process.Data were obtained from the Alzheimer’s Disease Neuroimaging Initiative phase 3 (ADNI3) and the Estudio de la Enfermedad de Alzheimer en Jalisciences (EEAJ) study, including cognitively normal elderly controls, persons with late onset AD (LOAD) and early onset autosomal dominant AD (ADAD) patients and their relatives. Our cohort consisted of a sample of 147 subjects from ADNI3 and 41 subjects from EEAJ with T1 structural, tau-PET (tracer: 18F-AV1451) and fMRI scans. Correlations between SUVR tau-PET and multi-scale entropy (MSE) were calculated voxelwise as well as for standard automated anatomical labeling (AAL) atlas regions while accounting for age, gender, and regional gray matter volume. Potential pathways relating MSE to cognitive function mediated through tau-PET were assessed by path analysis.We found significant negative correlations between low frequency MSE and tau-PET measures in medial temporal lobe, in both ADNI3 and EEAJ cohorts. Furthermore, low frequency MSE showed significant associations with the Clinical Dementia Rating (CDR) scale and the Mini-Mental State Status Exam (MMSE) scores in both ADNI3 and EEAJ cohorts, which were largely mediated through the tau-PET signal.Correlations of MSE with tau-PET in temporal lobes support our hypothesis that the complexity of rs-fMRI is associated with regional tau protein accumulation. Furthermore, the association of MSE with CDR and MMSE, mediated by tau-PET, in disease relevant areas suggests that a reduction in MSE is indicative of decreased information processing capacity and cognitive decline in AD processes.
A Long-Circulating Vector for Aptamers Based upon Polyphosphodiester-Backboned Molecular Brushes
Authors: Wang Yuyan , Wang Dali , Lin Jiachen , Lyu Zidi , Chen Peiru , Sun Tingyu , Xue Chenyang , Mojtabavi Mehrnaz , Vedadghavami Armin , Zhang Zheyu , Wang Ruimeng , Zhang Lei , Park Christopher , Heo Gyu Seong , Liu Yongjian , Dong Sijia , Zhang Ke 
Published: July 2022

AbstractAptamers face challenges for use outside the ideal conditions in which they are developed. These difficulties are most palpable in vivo due to nuclease activities, rapid clearance, and off-target binding. Herein, we demonstrate that a polyphosphodiester-backboned molecular brush can suppress enzymatic digestion, reduce non-specific cell uptake, enable long blood circulation, and rescue the bioactivity of a conjugated aptamer in vivo. The backbone along with the aptamer is assembled via solid-phase synthesis, followed by installation of poly(ethylene glycol) (PEG) side chains using a two-step process with near-quantitative efficiency. The synthesis allows for precise control over polymer size and architecture. Consisting entirely of building blocks that are generally recognized as safe for therapeutics, this novel molecular brush is expected to provide a highly translatable route for aptamer-based therapeutics.
A diverse population of pericoerulear neurons controls arousal and exploratory behaviors
Authors: Luskin Andrew T. , Li Li , Fu Xiaonan , Martin Madison M. , Barcomb Kelsey , Girven Kasey S. , Blackburn Taylor , Wells Bailey A. , Thai Sarah T. , Li Esther M. , Rana Akshay N. , Simon Rhiana C. , Sun Li , Gao Lei , Murry Alexandria D. , Golden Sam A. , Stuber Garret D. , Ford Christopher P. , Gu Liangcai , Bruchas Michael R. 
Published: July 2022

AbstractAs the primary source of norepinephrine (NE) in the brain, the locus coeruleus (LC) regulates arousal, avoidance and stress responses1,2. However, how local neuromodulatory inputs control LC function remains unresolved. Here we identify a population of transcriptionally, spatially and functionally diverse GABAergic neurons in the LC dendritic field that receive distant inputs and modulate modes of LC firing to control global arousal levels and arousal-related processing and behaviors. We define peri-LC anatomy using viral tracing and combine single-cell RNA sequencing with spatial transcriptomics to molecularly define both LC-NE and peri-LC cell types. We identify several neuronal cell types which underlie peri-LC functional diversity using a series of complementary neural circuit approaches in behaving mice. Our findings indicate that LC and peri-LC neurons are transcriptionally, functionally, and anatomically heterogenous neuronal populations which modulate arousal and avoidance states. Defining the molecular, cellular, and functional diversity of the LC and peri-LC provides a road map for understanding the neurobiological basis of arousal, motivation and neuropsychiatric disorders.
Comprehensive Histopathology Imaging in Pancreatic Biopsies: High Definition Infrared Imaging with Machine Learning Approach
Authors: Liberda Danuta , Koziol Paulina , Wrobel Tomasz P. 
Published: July 2022

AbstractInfrared (IR) based histopathology offers a new paradigm in looking at tissues and can provide a complimentary information source for more classical histopathology, which makes it a noteworthy tool given possible clinical application. The goal of this study is to build a robust machine learning model using IR imaging of pancreatic cancer histopathology on a single pixel level. In this article, we report a pancreatic cancer classification model based on data from over 600 biopsies (coming from 250 patients) imaged with IR diffraction-limited spatial resolution. To fully research model’s classification ability, we measured tissues in Standard and High Definition using two optical setups. This forms one of the largest IR datasets analyzed up to now, with almost 700 million spectra of different tissue types. The first classification model, based on six tissue classes, created for comprehensive histopathology achieved AUC values on the pixel (tissue) level above 0.95. We successfully developed a comprehensive histopathology digital staining model for pancreatic tissues based on biochemical information extracted from IR spectra.
Network predictions sharpen the representation of visual features for categorization
Authors: Yan Yuening , Zhan Jiayu , Ince Robin A.A. , Schyns Philippe G. 
Published: July 2022

AbstractModels of visual cognition assume that brain networks predict the contents of a stimulus to facilitate its subsequent categorization. However, the specific network mechanisms of this facilitation remain unclear. Here, we studied them in 11 individual participants cued to the spatial location (left vs. right) and contents (Low vs. High Spatial Frequency, LSF vs. HSF) of an upcoming Gabor stimulus that they categorized. Using concurrent MEG recordings, we reconstructed in each participant the network that communicates the predicted contents and the network that represents these contents from the stimulus for categorization. We show that predictions of LSF vs. HSF propagate top-down from temporal to contra-lateral occipital cortex, with modulatory supervision from frontal cortex. In occipital cortex, predictions sharpen bottom-up stimulus LSF vs. HSF representations, leading to faster categorizations. Our results therefore reveal functional networks that predict visual contents to sharpen their representations from the stimulus to facilitate categorization behavior.
Context and Domain Matter: The Error-Related Negativity in Peer Presence Predicts Fear of Negative Evaluation, not Global Social Anxiety, in Adolescents
Authors: Niu Yanbin , Li Zixuan , Pettit Jeremy W. , Buzzell George A. , Zhao Jingjing 
Published: July 2022

AbstractBackgroundSocial anxiety symptoms are most likely to emerge during adolescence, a developmental window marked by heightened concern over peer evaluation. However, the neurocognitive mechanism(s) underlying adolescent social anxiety remain unclear. Emerging work points to the error-related negativity (ERN) as a potential neural marker of exaggerated self/error-monitoring in social anxiety, particularly for errors committed in front of peers.However, social anxiety symptoms are marked by heterogeneity and it remains unclear exactly what domain(s) of social anxiety symptoms are associated with ERN variation in peer presence, particularly within the adolescent period.MethodsTo advance and deepen the mechanistic understanding of the ERN’s putative role as a neural marker for social anxiety in adolescence, we leveraged a social manipulation procedure and assessed a developmentally-salient domain of social anxiety during adolescence—Fear of Negative Evaluation (FNE). Adolescents residing in Hanzhong, a small city in the southwestern region of mainland China, had EEG recorded while performing a flanker task, twice (peer presence/absence); FNE, as well as global social anxiety symptoms were assessed.ResultsOverall ERN increases in peer presence. FNE specifically, but not global levels of social anxiety symptoms, predicted ERN in peer presence.ConclusionsThese data are the first demonstration that the ERN relates to a specific domain of social anxiety in adolescents, as well as the first evidence of such relations within a non-WEIRD (Western, Educated, Industrialized, Rich and Democratic) sample. Results have important implications for theory and research into adolescent social anxiety.
Integration and evaluation of magnetic stimulation in physiology setups
Authors: Ahlers Malte T. , Block Christoph T. , Winklhofer Michael , Greschner Martin 
Published: July 2022

AbstractA large number of behavioral experiments have demonstrated the existence of a magnetic sense in many animal species. Further, studies with immediate gene expression markers have identified putative brain regions involved in magnetic information processing. In contrast, very little is known about the physiology of the magnetic sense and how the magnetic field is neuronally encoded. In vivo electrophysiological studies reporting neuronal correlates of the magnetic sense either have turned out to be irreproducible for lack of appropriate artifact controls or still await independent replication. Thus far, the research field of magnetoreception has little exploited the power of ex vivo physiological studies, which hold great promise for enabling stringent controls. However, tight space constraints in a recording setup and the presence of magnetizable materials in setup components and microscope objectives make it demanding to generate well-defined magnetic stimuli at the location of the biological specimen. Here, we present a solution based on a miniature vector magnetometer, a coil driver, and a calibration routine for the coil system to compensate for magnetic distortions in the setup. The magnetometer fits in common physiology recording chambers and has a sufficiently small spatial integration area to allow for probing spatial inhomogeneities. The coil-driver allows for the generation of defined non-stationary fast changing magnetic stimuli. Our ex vivo multielectrode array recordings from avian retinal ganglion cells show that artifacts induced by rapid magnetic stimulus changes can mimic the waveform of biological spikes on single electrodes. However, induction artifacts can be separated clearly from biological responses if the spatio-temporal characteristics of the artifact on multiple electrodes is taken into account. We provide the complete hardware design data and software resources for the integrated magnetic stimulation system.
A comprehensive mouse kidney atlas enables rare cell population characterization and robust marker discovery
Authors: Novella-Rausell Claudio , Grudniewska Magda , Peters Dorien J. M. , Mahfouz Ahmed 
Published: July 2022

AbstractThe cellular diversity and complexity of the kidney are on par with its physiological intricacy. Although our anatomical understanding of the different segments and their functions is supported by a plethora of research, the identification of distinct and rare cell populations and their markers remains elusive. Here, we leverage the large number of cells and nuclei profiles using single-cell (scRNA-seq) and single-nuclei (snRNA-seq) RNA-sequencing to build a comprehensive atlas of the adult mouse kidney. We created MKA (Mouse Kidney Atlas) by integrating 59 publicly available single-cell and single-nuclei transcriptomic datasets from eight independent studies. The atlas contains more than 140.000 cells and nuclei covering different single-cell technologies, age, and tissue sections. To harmonize annotations across datasets, we constructed a hierarchical model of the cell populations present in our atlas. Using this hierarchy, we trained a model to automatically identify cells in unannotated datasets and evaluated its performance against well-established methods and annotation references. Our learnt model is dynamic, allowing the incorporation of novel cell populations and refinement of known profiles as more datasets become available. Using MKA and the learned model of cellular hierarchies, we predicted previously missing cell annotations from several studies and characterized well-studied and rare cell populations. This allowed us to identify reproducible markers across studies for poorly understood cell types and transitional states.
PyGenePlexus: A Python package for gene discovery using network-based machine learning
Authors: Mancuso Christopher A , Liu Renming , Krishnan Arjun 
Published: July 2022

SummaryPyGenePlexus is a Python package that enables a user to gain insight into any gene set of interest based on a molecular interaction network using supervised machine learning. PyGenePlexus provides predictions of how associated every gene in the network is to the input gene set, offers interpretability by comparing the model trained on the input gene set to models trained on thousands of known gene sets, and returns the network connectivity of the top predicted genes.Availability and Implementationhttps://pypi.org/project/geneplexus/ and https://github.com/krishnanlab/PyGenePlexusContactarjun@msu.edu
“Rotating Frame Relaxation for Magic Angle Spinning Solid State NMR, A Promising Tool for Characterizing Biopolymer Motion”
Authors: Keeler Eric , McDermott Ann 
Published: July 2022

AbstractMagic angle spinning NMR rotating frame relaxation measurements provide a powerful experimental strategy to probe biomolecules dynamics, as is illustrated by numerous recent applications. We discuss experimental strategies for this class of experiments, with a particular focus on systems where motion-driven modulation of the chemical shift interaction is the main mechanism for relaxation. We also explore and describe common strategies for interpreting the data sets and extracting motion timescale, activation energy, and angle or order parameters from rotating frame relaxation data. Using model free analysis, and numerical simulations, including time domain treatment, we explore conditions under which it is possible to obtain accurate and precise information about the timescales of motions. Overall, with rapid technical advances in solid state NMR, there is a bright future for this class of studies.
tRNA Analysis of eXpression (tRAX): A tool for integrating analysis of tRNAs, tRNA-derived small RNAs, and tRNA modifications
Authors: Holmes Andrew D. , Howard Jonathan M. , Chan Patricia P. , Lowe Todd M. 
Published: July 2022

AbstractRecent high-throughput sequencing protocols have facilitated increased accuracy in measurements of transfer tRNAs (tRNAs) and tRNA-derived small RNAs (tDRs) from biological samples. However, commonly used RNA-seq analysis pipelines overlook special considerations given the unique features of tRNA metabolism. We present tRAX (tRNA Analysis of eXpression), a user-friendly analytic package for streamlined processing and graphic presentation of small-RNA sequencing data. Here, we apply it to both tRNAs and tDRs from mouse tissues to illustrate the extensive analysis and visualization features. Biologically compelling results demonstrate tRAX as an effective and accessible tool for in-depth characterization of tRNA and tDR transcriptomes.
Fast trimer statistics facilitate accurate decoding of large random DNA barcode sets even at large sequencing error rates
Authors: Press William H. 
Published: July 2022

AbstractPredefined sets of short DNA sequences are commonly used as barcodes to identify individual biomolecules in pooled populations. Such use requires either sufficiently small DNA error rates, or else an error-correction methodology. Most existing DNA error-correcting codes (ECCs) correct only one or two errors per barcode in sets of typically ≲ 104 barcodes. We here consider the use of random barcodes of sufficient length that they remain accurately decodable even with ≳ 6 errors and even at 10% or 20% nucleotide error rates. We show that length 34 nt is sufficient even with ≳ 106 barcodes. The obvious objection to this scheme is that it requires comparing every read to every possible barcode by a slow Levenshtein or Needleman-Wunsch comparison. We show that several orders of magnitude speedup can be achieved by (i) a fast triage method that compares only trimer (three consecutive nucleotide) occurence statistics, precomputed in linear time for both reads and barcodes, and (ii) the massive parallelism available on today’s even commodity-grade GPUs. With 106 barcodes of length 34 and 10% DNA errors (substitutions and indels) we achieve in simulation 99.9% precision (decode accuracy) with 98.8% recall (read acceptance rate). Similarly high precision with somewhat smaller recall is achievable even with 20% DNA errors. The amortized computation cost on a commodity workstation with two GPUs (2022 capability and price) is estimated as between US$ 0.15 and US$ 0.60 per million decoded reads.
Gamma-interferon-inducible lysosomal thiol reductase maintains cardiac immuno-metabolic homeostasis in heart failure
Authors: Li Mark , Wang Jinxi , Qian Qingwen , Chen Biyi , Zhang Zeyuan , Barroso Elizabeth , Zhang Yuan , Hall Duane D. , Abel E. Dale , Song Long-Sheng , Yang Ling 
Published: July 2022

ABSTRACTBackgroundThe lysosome is a central player in maintaining immuno-metabolic homeostasis. However, mechanistic insights into the regulation of lysosome-dependent immuno-metabolism in the heart are lacking. Lysosomal reductase Gamma Interferon-Inducible Thiol Reductase (GILT) is the only identified lysosomal reductase that controls diverse sets of lysosomal enzymes and cargoes.MethodsThe role of cardiac GILT was assessed by generating a novel genetic mouse model and employing a multidisciplinary approach including surgical interventions, livein situhigh resolution microscopy, whole-tissue respirometry analysis, unbiased transcriptomic and metabolomic analyses, and various cell biology and biochemical assays.ResultsWe found that expression and activity of GILT are reduced in hearts from humans and mice with heart failure (HF). Mice with cardiac specific loss of GILT develop late onset systolic HF at baseline. In the setting of nutrient-overload and experimental left ventricular pressure overload conditions, loss of GILT in cardiomyocytes accelerates the development of heart dysfunction. Transcriptomic and metabolic analyses further revealed that cardiac GILT deficiency alters adaptive immuno-metabolic signatures in the heart. Finally, at the cellular level, cardiac GILT deletion impaired mitochondrial respiration, which was in part due to NLR Family Pyrin Domain Containing 3 (NLRP3)-mediated elevation of mitochondrial oxidative stress.ConclusionsTogether, these findings identify a causal link between a lysosome-inflammation axis, mitochondrial function and heart failure. Elucidation of these mechanisms will identify novel therapeutic strategies for treating HF.
Efficient recovery of complete gut phage genomes by combined short- and long-sequencing
Authors: Chen Wei-Hua , Chen Jingchao , Sun Chuqing , Dong Yanqi , Jin Menglu , Lai Senying , Jia Longhao , Zhao Xueyang , Gao Na L , Liu Zhi , Bork Peer , Zhao Xing-Ming 
Published: July 2022

AbstractCurrent metagenome-assembled human phage catalogs contained mostly fragmented genomes. Here, we developed a vigorous phage detection method involving phage enrichment and long-read sequencing and applied to 135 fecal samples. With ~10 times more efficient in obtaining complete genomes (~34%) than the Gut Virome Database, we identified the first megabasephage (~1.03Mb), and revealed the hidden diversity of the gut phageome including dozens of phages more prevalent than the crAssphages and Gubaphages.