bluuebunny/crossref_metadata_2025_split huggingface dataset:
Erythrobacter oryzae sp. nov., isolated from rice paddy soil
Authors: Lee Hyo-Jin , Hwang Ji-Soo , Lee Eun-kyung , Whang Kyung-Sook 
Published: March 2024

Two novel bacterial strains, designated as COR-2T and CR-8, were isolated from paddy soil. These isolates were aerobic, Gram-stain-negative, non-spore-forming, non-motile, rod-shaped, and formed orange-coloured colonies. Phylogenetic analysis based on 16S rRNA gene sequences showed that two strains formed a clear phylogenetic lineage with the genus Erythrobacter. Strains COR-2T and CR-8 showed 99.9 % 16S rRNA gene sequence similarity. Both strains had the highest 16S rRNA gene similarity of 99.1–99.7 % to Erythrobacter colymbi TPW-24T, Erythrobacter donghaensis SW-132T and Erythrobacter tepidarius DSM 10594T, respectively. The genome of strain COR-2T comprised 3 559 918 bp and the genomic DNA G + C content was 67.7 mol%. The average nucleotide identity and digital DNA–DNA hybridization values between strain COR-2T and its closely related species of the genus Erythrobacter were 79.3–85.5% and 24.1–29.1 %, respectively. The major respiratory quinone was Q-10, while the major fatty acids were C18 : 1
ω7c and C17 : 1
ω6c. The major polar lipids were phosphatidylethanolamine, phosphatidylglycerol, phosphatidylcholine, two unidentified phospholipids and eight unidentified lipids. Based on phylogenetic and phenotypic considerations, the two strains [COR-2T (type strain; = KACC 22941T=JCM 35529T) and CR-8 (= KACC 22945=JCM 35530)] are considered to represent novel species of the genus Erythrobacter, for which the name Erythrobacter oryzae sp. nov. is proposed.
Molecular hydrogen for outpatients with Covid-19 (Hydro-Covid): a phase 3, randomised, triple-blinded, adaptive, placebo-controlled, multicentre trial
Authors: Gaboreau Yoann , Milovančev Aleksandra , Rolland Carole , Eychenne Claire , Alcaraz Jean-Pierre , Ihl Cordelia , Mazet Roseline , Boucher François , Vermorel Celine , Ostojic Sergej M. , Borel Jean Christian , Cinquin Philippe , Bosson Jean-Luc ,   the HydroCovid Investigators
Published: March 2024

AbstractBackgroundDue to its antioxidative, anti-inflammatory, anti-apoptosis, and antifatigue properties, molecular hydrogen (H2) is potentially a novel therapeutic gas for acute coronavirus disease 2019 (COVID-19) patients.AimTo determine the efficacy and safety profile of hydrogen rich water (HRW) to reduce the risk of progression of COVID-19.Design and settingsWe conducted a phase 3, triple-blind, randomized, placebo-controlled trial to evaluate treatment with HRW started within 5 days after the onset of signs or symptoms in primary care patients with mild-to-moderate, laboratory-confirmed COVID-19 and at least one risk factor for severe COVID-19 illness.MethodParticipants were randomly assigned to receive HRW or placebo twice daily for 21 days. The composite primary endpoint was the incidence of clinical worsening (dyspnea, fatigue) associated with a need for oxygen therapy, hospitalization or death at day-14; the incidence of adverse events was the primary safety end point.ResultsA total of 675 participants were followed up until day-30. 337 in the HRW group and 338 in the placebo group. Baseline characteristics were similar in the two groups. HRW was not superior to placebo in preventing clinical worsening at day-14: in H2group, 46.1% met a clinical deterioration, 43.5% in the placebo group, Hazard Ratio 1.09, 90% confidence interval [0.90-1.31]. One death was reported in the H2group and 2 in the placebo group at day-30. Adverse events were reported in 91 (27%) and 89 (26.2%) participants respectively.ConclusionTwice-daily ingestion of HRW from the onset of COVID-19 symptoms for 21 days did not reduce clinical worsening.How this fits inOnly a few molecules specially developed against SARS-CoV-2 can limit impact of COVID-19 (vaccines, monoclonal antibodies or antiviral drugs)Using their multiple properties, H2 may play a key role in preventing the severe and post-acute forms of COVID-19Taking twice daily Hydrogen Rich Water (HRW) was not efficacious to prevent severe COVID-19 in at risk COVID-19 patients.HRW confirmed a very safe profil
Improving Heart Disease Probability Prediction Sensitivity with a Grow Network Model
Authors: Akter Simon Bin , Hasan Rakibul , Akter Sumya , Hasan Md. Mahadi , Sarkar Tanmoy 
Published: March 2024

AbstractThe traditional approaches in heart disease prediction across a vast amount of data encountered a huge amount of class imbalances. Applying the conventional approaches that are available to resolve the class imbalances provides a low recall for the minority class or results in imbalance outcomes. A lightweight GrowNet-based architecture has been proposed that can obtain higher recall for the minority class using the Behavioral Risk Factor Surveillance System (BRFSS) 2022 dataset. A Synthetic Refinement Pipeline using Adaptive-TomekLinks has been employed to resolve the class imbalances. The proposed model has been tested in different versions of BRFSS datasets including BRFSS 2022, BRFSS 2021, and BRFSS 2020. The proposed model has obtained the highest specificity and sensitivity of 0.74 and 0.81 respectively across the BRFSS 2022 dataset. The proposed approach achieved an Area Under the Curve (AUC) of 0.8709. Additionally, applying explainable AI (XAI) to the proposed model has revealed the impacts of transitioning from smoking to e-cigarettes and chewing tobacco on heart disease.
An integrative approach to protein sequence design through multiobjective optimization
Authors: Hong Lu , Kortemme Tanja 
Published: March 2024

AbstractWith recent methodological advances in the field of computational protein design, in particular those based on deep learning, there is an increasing need for frameworks that allow for coherent, direct integration of different models and objective functions into the generative design process. Here we demonstrate how evolutionary multiobjective optimization techniques can be adapted to provide such an approach. With the established Non-dominated Sorting Genetic Algorithm II (NSGA-II) as the optimization framework, we use AlphaFold2 and ProteinMPNN confidence metrics to define the objective space, and a mutation operator composed of ESM-1v and ProteinMPNN to rank and then redesign the least favorable positions. Using the multistate design problem of the foldswitching protein RfaH as an in-depth case study, we show that the evolutionary multiobjective optimization approach leads to significant reduction in the bias and variance in RfaH native sequence recovery, compared to a direct application of ProteinMPNN. We suggest that this improvement is due to three factors: (i) the use of an informative mutation operator that accelerates the sequence space exploration, (ii) the parallel, iterative design process inherent to the genetic algorithm that improves upon the ProteinMPNN autoregressive sequence decoding scheme, and (iii) the explicit approximation of the Pareto front that leads to optimal design candidates representing diverse tradeoff conditions. We anticipate this approach to be readily adaptable to different models and broadly relevant for protein design tasks with complex specifications.Author summaryProteins are the fundamental building blocks of life, and engineering them has broad applications in medicine and biotechnology. Computational methods that seek to model and predict the protein sequence-structure-function relationship have seen significant advancement from the recent development in deep learning. As more models become available, it remains an open question how to effectively combine them into a coherent computational design approach. One approach is to perform computational design with one model, and filter the design candidates with the others. In this work, we demonstrate how an optimization algorithm inspired by evolution can be adapted to provide an alternative framework that outperforms the post hoc filtering approach, especially for problems with multiple competing design specifications. Such a framework may enable researchers to more effectively integrate the strengths of different modeling approaches to tackle more complex design problems.
The power and limits of predicting exon-exon interactions using protein 3D structures
Authors: Liebold Jeanine , Del Moral-Morales Aylin , Manalastas-Cantos Karen , Tsoy Olga , Kurtz Stefan , Baumbach Jan , Newaz Khalique 
Published: March 2024

AbstractAlternative splicing (AS) effects on cellular functions can be captured by studying changes in the underlying protein-protein interactions (PPIs). Because AS results in the gain or loss of exons, existing methods for predicting AS-related PPI changes utilize known PPI interfacing exon-exon interactions (EEIs), which only cover ∼5% of known human PPIs. Hence, there is a need to extend the existing limited EEI knowledge to advance the functional understanding of AS. In this study, we explore whether existing computational PPI interface prediction (PPIIP) methods, originally designed to predict residue-residue interactions (RRIs), can be used to predict EEIs. We evaluate three recent state-of-the-art PPIIP methods for the RRI- as well as EEI-prediction tasks using known protein complex structures, covering ∼230,000 RRIs and ∼27,000 EEIs. Our results provide the first evidence that existing PPIIP methods can be extended for the EEI prediction task, showing F-score, precision, and recall performances of up to ∼38%, ∼63%, and ∼28%, respectively, with a false discovery rate of less than 5%. Our study provides insights into the power and limits of existing PPIIP methods to predict EEIs, thus guiding future developments of computational methods for the EEI prediction task. We provide streamlined computational pipelines integrating each of the three considered PPIIP methods for the EEI prediction task to be utilized by the scientific community.
A Targeted Genome-scale Overexpression Platform for Proteobacteria
Authors: Banta Amy B. , Myers Kevin S. , Ward Ryan D. , Cuellar Rodrigo A. , Place Michael , Freeh Claire C. , Bacon Emily E. , Peters Jason M. 
Published: March 2024

AbstractTargeted, genome-scale gene perturbation screens using Clustered Regularly Interspaced Short Palindromic Repeats interference (CRISPRi) and activation (CRISPRa) have revolutionized eukaryotic genetics, advancing medical, industrial, and basic research. Although CRISPRi knockdowns have been broadly applied in bacteria, options for genome-scale overexpression face key limitations. Here, we develop a facile approach for genome-scale gene overexpression in bacteria we call, “CRISPRtOE” (CRISPR transposition and OverExpression). We create a platform for comprehensive gene targeting using CRISPR-associated transposition (CAST) and show that transposition occurs at a higher frequency in non-transcribed DNA. We then demonstrate that CRISPRtOE can upregulate gene expression in Proteobacteria with medical and industrial relevance by integrating synthetic promoters of varying strength upstream of target genes. Finally, we employ CRISPRtOE screening at the genome-scale in Escherichia coli, recovering known antibiotic targets and genes with unexplored roles in antibiotic function. We envision that CRISPRtOE will be a valuable overexpression tool for antibiotic mode of action, industrial strain optimization, and gene function discovery in bacteria.
Communityassesses differential cell communication using large multi-sample case-control scRNAseq datasets
Authors: Solovey Maria , Celik Muhammet A. , Salcher Felix R. , Abdalfattah Mohmed , Ismail Mostafa , Scialdone Antonio , Ziemann Frank , Colomé-Tatché Maria 
Published: March 2024

AbstractCell-cell communication is essential for physiological tissue function. In disease, this communication often gets disbalances by changes in the tissue cell type composition, fraction of cell engaged in communication and the rising or dropping expression levels of ligands, receptors and adhesion molecules. The changes in all these components of communication can be studied using single cell RNA-sequencing (scRNAseq) methods. With dropping sequencing costs, it is now possible to perform scRNAseq studies in larger cohorts of case and control samples to better address the heterogeneity of diseases. Here we presentcommunity, an R-based tool that is designed to perform differential communication analysis using scRNAseq between large cohorts of cases and controls.Communityis able to reconstruct communication between different cell types both in the case and the control cohort of a dataset, and subsequently analyze which communication channels are affected in disease.Communityis the first tool that integrates cell type abundance into the calculation of an interaction strength.Communityis also able to disentangle the mechanisms underlying these changes, as well as detect interactions that are kept compensated by a sender or a receiver despite the disbalanced signaling from the counterpart. We testedcommunityon two disease entities, ulcerative colitis and acute myeloid leukemia, using published scRNAseq datasets. We compared the performance of our tool to other differential communication pipelines, whichcommunityoutperformed in speed and robustness. Overall,communityis a fast, well-scalable, user-friendly R tool to assess differential cell-cell communication using large case-control scRNAseq datasets disentangling the driving mechanisms of communication shifts in disease.
Temporal dynamics of color processing measured using a continuous tracking task
Authors: Barnett Michael A. , Chin Benjamin M. , Aguirre Geoffrey K. , Burge Johannes , Brainard David H. 
Published: March 2024

AbstractWe characterized the temporal dynamics of color processing using a continuous tracking paradigm by estimating temporal impulse response functions associated with tracking chromatic Gabor patches. We measured how the lag of these functions changes as a function of chromatic direction and contrast for stimuli in the LS cone contrast plane. In the same set of subjects, we also measured detection thresholds for stimuli with matched spatial, temporal, and chromatic properties. We created a model of tracking and detection performance to test if a common representation of chromatic contrast accounts for both measures. The model summarizes the effect of chromatic contrast over different chromatic directions through elliptical isoresponse contours, the shapes of which are contrast independent. The fitted elliptical isoresponse contours have essentially the same orientation in the detection and tracking tasks. For the tracking task, however, there is a striking reduction in sensitivity to signals originating in the S cones. The results are consistent with common chromatic mechanisms mediating performance on the two tasks, but with task-dependent relative weighting of signals from L and S cones.
Artificial neural network for brain-machine interface consistently produces more naturalistic finger movements than linear methods
Authors: Temmar Hisham , Willsey Matthew S. , Costello Joseph T. , Mender Matthew J. , Cubillos Luis H. , Lam Jordan LW , Wallace Dylan M , Kelberman Madison M. , Patil Parag G. , Chestek Cynthia A. 
Published: March 2024

AbstractBrain-machine interfaces (BMI) aim to restore function to persons living with spinal cord injuries by ‘decoding’ neural signals into behavior. Recently, nonlinear BMI decoders have outperformed previous state-of-the-art linear decoders, but few studies have investigated what specific improvements these nonlinear approaches provide. In this study, we compare how temporally convolved feedforward neural networks (tcFNNs) and linear approaches predict individuated finger movements in open and closed-loop settings. We show that nonlinear decoders generate more naturalistic movements, producing distributions of velocities 85.3% closer to true hand control than linear decoders. Addressing concerns that neural networks may come to inconsistent solutions, we find that regularization techniques improve the consistency of tcFNN convergence by 194.6%, along with improving average performance, and training speed. Finally, we show that tcFNN can leverage training data from multiple task variations to improve generalization. The results of this study show that nonlinear methods produce more naturalistic movements and show potential for generalizing over less constrained tasks.TeaserA neural network decoder produces consistent naturalistic movements and shows potential for real-world generalization through task variations.
A Panoramic View of Cell Population Dynamics in Mammalian Aging
Authors: Zhang Zehao , Schaefer Chloe , Jiang Weirong , Lu Ziyu , Lee Jasper , Sziraki Andras , Abdulraouf Abdulraouf , Wick Brittney , Haeussler Maximilian , Li Zhuoyan , Molla Gesmira , Satija Rahul , Zhou Wei , Cao Junyue 
Published: March 2024

AbstractTo elucidate the aging-associated cellular population dynamics throughout the body, here we present PanSci, a single-cell transcriptome atlas profiling over 20 million cells from 623 mouse tissue samples, encompassing a range of organs across different life stages, sexes, and genotypes. This comprehensive dataset allowed us to identify more than 3,000 unique cellular states and catalog over 200 distinct aging-associated cell populations experiencing significant depletion or expansion. Our panoramic analysis uncovered temporally structured, organ- and lineage-specific shifts of cellular dynamics during lifespan progression. Moreover, we investigated aging-associated alterations in immune cell populations, revealing both widespread shifts and organ-specific changes. We further explored the regulatory roles of the immune system on aging and pinpointed specific age-related cell population expansions that are lymphocyte-dependent. The breadth and depth of our ‘cell-omics’ methodology not only enhance our comprehension of cellular aging but also lay the groundwork for exploring the complex regulatory networks among varied cell types in the context of aging and aging-associated diseases.One Sentence SummaryPanSci, a single-cell transcriptome atlas of over 20 million cells throughout the mouse lifespan, unveils the temporal architecture of aging-associated cellular population dynamics, organ-specific immune cell shifts, and the lymphocyte’s role in organismal aging.
Effects of electroconvulsive shock on the function, circuitry, and transcriptome of dentate gyrus granule neurons
Authors: Santiago Adrienne N. , Nguyen Phi , Castello-Saval Julia , Chung Hannah M. , Luna Victor M. , Hen Rene , Chang Wei-li 
Published: March 2024

AbstractTherapeutic use of electroconvulsive shock (ECS) is particularly effective for treatment-resistant depression. Like other more common forms of antidepressant treatment such as SSRIs, ECS has been shown to increase neurogenesis in the hippocampal dentate gyrus of rodent models. Yet the question of how ECS-induced neurogenesis supports improvement of depressive symptoms remains unknown. Here, we show that ECS-induced neurogenesis is necessary to improve depressive-like behavior of mice exposed to chronic corticosterone (Cort). We then use slice electrophysiology to show that optogenetic stimulation of adult-born neurons produces a greater hyperpolarization in mature granule neurons after ECS vs Sham treatment. We identify that this hyperpolarization requires the activation of group II metabotropic glutamate receptors. Consistent with this finding, we observe reduced expression of the immediate early gene cFos in the granule cell layer of ECS vs Sham subjects. Using single nucleus RNA sequencing, we reveal major transcriptomic shifts in granule neurons after treatment with ECS+Cort or fluoxetine+Cort vs Cort alone. We identify a population of immature cells which has greater representation in both ECS+Cort and fluoxetine+Cort treated samples vs Cort alone. We also find global differences in ECS-vs fluoxetine-induced transcriptomic shifts. Together, these findings highlight a critical role for immature granule cells in the antidepressant action of ECS.
Bacteria in honeybee crops are decoupled from those in floral nectar and bee mouths
Authors: Warren Magdalena L. , Tsuji Kaoru , Decker Leslie E. , Kishi Manabu , Yang Jihoon , Howe Adina C. , Fukami Tadashi 
Published: March 2024

AbstractBacteria in the honeybee gut are a well-recognized factor affecting bee health. However, the primary focus of this research has been the hindgut, while the crop, or honey stomach, is assumed to be dominated by environmentally acquired transient taxa that matter little to the bees. To evaluate this assumption, we examined bacterial taxa in the crop and mouth ofApis melliferaandA. cerana japonicaforagers and in the nectar ofPrunus mumeflowers visited by the bees in the Minabe-Tanabe region of Japan. We found that in bacterial composition, the crop was distinct from both the mouth and the nectar, whereas mouth and nectar samples were indistinguishable. Furthermore, the crop remained similar in bacterial composition and diversity, while the mouth showed a sharp drop in alpha diversity and a large increase in beta diversity, from summer to winter. These results refute the conventional assumption, suggesting instead that the crop contains a conserved bacterial community largely distinct from environmental taxa. We also found that strains of a crop-associated species,Apilactobacillus kunkeei, could be season- and host species-specific. Together, these findings suggest that crop-associated bacterial communities should be studied further to better understand the relationship between honeybees and their gut bacteria.
A comprehensive investigation of intracortical and corticothalamic models of the alpha rhythm
Authors: Bastiaens Sorenza P. , Momi Davide , Griffiths John D. 
Published: March 2024

AbstractThe electroencephalographic alpha rhythm is one of the most robustly observed and widely studied empirical phenomena in all of neuroscience. However, despite its extensive implication in a wide range of cognitive processes and clinical pathologies, the mechanisms underlying alpha generation in neural circuits remain poorly understood. In this paper we offer a renewed foundation for research on this question, by undertaking a systematic comparison and synthesis of the most prominent theoretical models of alpha rhythmogenesis in the published literature. We focus on four models, each studied intensively by multiple authors over the past three decades: i) Jansen-Rit, ii) Moran-David-Friston, iii) Robinson-Rennie-Wright, and iv) Liley-Wright. Several common elements are identified, such as the use of second-order differential equations and sigmoidal potential-to-rate operators to represent population-level neural activity. Major differences are seen in other features such as wiring topologies and conduction delays. Through a series of mathematical analyses and numerical simulations, we nevertheless demonstrate that the selected models can be meaningfully compared, by associating parameters and circuit motifs of analogous biological significance. With this established, we conduct explorations of rate constant and synaptic connectivity parameter spaces, with the aim of identifying common patterns in key behaviours, such as the role of excitatory-inhibitory interactions in the generation of oscillations. Finally, using linear stability analysis we identify two qualitatively different alpha-generating dynamical regimes across the models: i) noise-driven fluctuations, and ii) self-sustained limit-cycle oscillations, emerging due to an Andronov-Hopf bifurcation. The comprehensive survey and synthesis developed here can, we suggest, be used to help guide future theoretical and experimental work aimed at disambiguating these and other candidate theories of alpha rhythmogenesis.