bluuebunny/crossref_metadata_2025_split huggingface dataset:
Efficacy and safety of ensitrelvir in patients with mild-to-moderate COVID-19: the phase 2b part of a randomized, placebo-controlled, phase 2/3 study
Authors: Mukae Hiroshi , Yotsuyanagi Hiroshi , Ohmagari Norio , Doi Yohei , Sakaguchi Hiroki , Sonoyama Takuhiro , Ichihashi Genki , Sanaki Takao , Baba Keiko , Tsuge Yuko , Uehara Takeki 
Published: June 2022

AbstractThis phase 2b part of a randomized phase 2/3 study assessed the efficacy and safety of ensitrelvir for mild-to-moderate coronavirus disease 2019 (COVID-19). Patients were randomized (1:1:1) to orally receive ensitrelvir fumaric acid 125 mg (375 mg on day 1; n=140) or 250 mg (750 mg on day 1; n=140) or placebo (n=141) once daily for 5 days. Compared with placebo, the change from baseline in severe acute respiratory syndrome coronavirus 2 titer (measured as log10 50% tissue-culture infectious dose) on day 4 was significantly greater with ensitrelvir 125 mg and 250 mg (differences from placebo: −0.41, P<0.0001 for both). The total score of predefined 12 COVID-19 symptoms showed an improving trend with ensitrelvir treatment without a significant intergroup difference. Most adverse events were mild in severity. Ensitrelvir treatment demonstrated a favorable antiviral efficacy and potential clinical benefit with an acceptable safety profile. (Japan Registry of Clinical Trials identifier: jRCT2031210350)
Replicable multivariate BWAS with moderate sample sizes
Authors: Spisak Tamas , Bingel Ulrike , Wager Tor 
Published: June 2022

AbstractBrain-Wide Association Studies (BWAS) have become a dominant method for linking mind and brain over the past 30 years. Univariate models test tens to hundreds of thousands of brain voxels individually, whereas multivariate models (‘multivariate BWAS’) integrate signals across brain regions into a predictive model. Numerous problems have been raised with univariate BWAS, including lack of power and reliability and an inability to account for pattern-level information embedded in distributed neural circuits1–3. Multivariate predictive models address many of these concerns, and offer substantial promise for delivering brain-based measures of behavioral and clinical states and traits2,3.In their recent paper4, Marek et al. evaluated the effects of sample size on univariate and multivariate BWAS in three large-scale neuroimaging dataset and came to the general conclusion that “BWAS reproducibility requires samples with thousands of individuals”. We applaud their comprehensive analysis, and we agree that (a) large samples are needed when conducting univariate BWAS of individual differences in trait measures, and (b) multivariate BWAS reveal substantially larger effects and are therefore more highly powered. However, we disagree with Marek et al.’s claims that multivariate BWAS provide “inflated in-sample associations” that often fail to replicate (i.e., are underpowered), and that multivariate BWAS consequently require thousands of participants when predicting trait-level individual differences. Here we substantiate that (i) with appropriate methodology, the reported in-sample effect size inflation in multivariate BWAS can be entirely eliminated, and (ii) in most cases, multivariate BWAS effects are replicable with substantially smaller sample sizes (Figure 1).
Pan-cancer analysis reveals the value of FDX1 as a novel biomarker in the prognosis and immunotherapy in human tumors
Authors: Zhang Qingqing , Liu Nanyang , Wu Di , Xu Zhengyu , Wang Yichen , Wang Ping 
Published: June 2022

AbstractEmerging evidence supports FDX1’s important role in the development and progression of cancer, but no extensive cancer analysis is available to date. This study was the first to comprehensively explore the expression of FDX1 in 33 types of cancer and the significance of FDX1 in clinical prognosis by using bioinformatics techniques. Meanwhile, we analyzed the relationship between FDX1 and pathological stage, as well as immune cell infiltration. Based on this, the important role of FDX1 in tumor immunotherapy was proposed. The expression of FDX1 was significantly different between normal and tumor samples in 17 of 33 types of cancer. Besides, Cox regression analysis showed that FDX1 is a protective gene in Kidney renal clear cell carcinoma (KIRC), Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), Liver hepatocellular carcinoma (LIHC), Kidney renal papillary cell carcinoma (KIRP), Mesothelioma (MESO), and Thyroid carcinoma (THCA). Porphyrin and oxidative metabolism pathway regulating integrator complex was involved in the process. Furthermore, high expression of FDX1 promoted infiltration of Eosinophils and monocyte in Adrenocortical carcinoma (ACC) and Kidney Chromophobe (KICH) by affecting the tumor microenvironment (TME) and was significantly correlated with immune checkpoint genes. Our first pan-cancer analysis elucidates the expression characteristics of FDX1 across different cancers and highlights its potential value as a prognostic biomarker, laying a foundation for further study of its immunotherapy mechanism in various cancers.
Evolutionary divergence in sugar valuation shifts Drosophila suzukii oviposition choice towards ripe fruit
Authors: Cavey Matthieu , Charroux Bernard , Travaillard Solène , Manière Gérard , Berthelot-Grosjean Martine , Quitard Sabine , Minervino Caroline , Detailleur Brice , Grosjean Yaël , Prud’homme Benjamin 
Published: June 2022

AbstractBehavior evolution can promote the emergence of agricultural pests via ecological niche changes. The underlying neuronal mechanisms are poorly understood. We investigate the chemosensory changes underlying the evolutionary shift of oviposition substrate of the pest Drosophila suzukii from rotten to ripe fruits. Using a model substrate for fermented fruits and genetic manipulations, we show that an increase in valuation of fruit sugars during oviposition decisions drives D. suzukii to oviposit on ripe fruits as opposed to the model D. melanogaster which prefers rotten fruits. Inter-species comparative in vivo calcium imaging of sugar Gustatory Receptor Neurons suggests that increased sugar valuation in D. suzukii is related to neuronal sensitivity changes at multiple levels of the oviposition circuitry. Our results show that the tuning of sugar valuation has contributed to the evolution of oviposition preference on ripe fruit of D. suzukii.
Involvement of ILC1-like innate lymphocytes in human autoimmunity, lessons from alopecia areata
Authors: Britva Rimma Laufer , Keren Aviad , Bertolini Marta , Ullmann Yehuda , Paus Ralf , Gilhar Amos 
Published: June 2022

AbstractHere, we have explored the involvement of innate lymphoid cells-type 1 (ILC1) in the pathogenesis of alopecia areata (AA), because we found them to be significantly increased around lesional and non-lesional HFs of AA patients. To further explore these unexpected findings, we first co-cultured autologous circulating ILC1-like cells (ILC1lc) with healthy, but stressed, organ-cultured human scalp hair follicles (HFs). ILClc induced all hallmarks of AA ex vivo: they significantly promoted premature, apoptosis-driven HF regression (catagen), HF cytotoxicity/dystrophy and most important for AA pathogenesis, collapse of the HFs physiological immune privilege. NKG2D-blocking or IFNγ-neutralizing antibodies antagonized this. In vivo, intradermal injection of autologous activated, NKG2D+/IFNγ-secreting ILC1lc into healthy human scalp skin xenotransplanted onto SCID/beige mice sufficed to rapidly induce characteristic AA lesions. This provides the first evidence that ILC1lc suffice to induce AA in previously healthy human HFs ex vivo and in vivo, and further questions the conventional wisdom that AA is always an autoantigen-dependent, CD8+ T cell-driven autoimmune disease.
Dominant toxin hypothesis: unravelling the venom phenotype across micro and macroevolution
Authors: Smith Edward G. , Surm Joachim M. , Macrander Jason , Simhi Adi , Amir Guy , Sachkova Maria Y. , Lewandowska Magda , Reitzel Adam M. , Moran Yehu 
Published: June 2022

AbstractVenom is a complex trait with substantial inter- and intraspecific variability resulting from strong selective pressures acting on the expression of many toxic proteins. However, understanding the processes underlying the toxin expression dynamics that determine the venom phenotype remains unresolved. Here, we use comparative genomics and transcriptomics to reveal that toxin expression in sea anemones evolves rapidly with little constraint and that a single toxin family dictates the venom phenotype in each species. This dominant toxin family is characterized by massive gene duplication events. In-depth analysis of the sea anemone,Nematostella vectensis, revealed significant variation in the number of copies of the dominant toxin (Nv1) across populations, corresponding to significant differences inNv1expression at both the transcript and protein levels. These differences inNv1copies are driven by independent expansion events, resulting in distinct haplotypes that have a restricted geographical distribution. Strikingly, one population has undergone a severe contraction event, causing a near-complete loss of Nv1 production. Our findings across micro- and macroevolutionary scales in sea anemones complement observations of single dominant toxin family present in other venomous organisms and establishes the dominant toxin hypothesis whereby venomous animals have convergently evolved a similar strategy in shaping the venom phenotype.
Aging impairs the clustering and reduces the activity of L-type calcium channels in cardiac pacemaker cells
Authors: Baudot Matthias , Vivas Oscar , Choi Sabrina , Serrano Gabriella , Flores Victor A. , Santana L. Fernando , Moreno Claudia M 
Published: June 2022

AbstractEvery heartbeat is initiated by a spontaneous electrical signal generated inside the cardiac pacemaker. The generation of this electrical signal depends on the coordinated opening and closing of different ion channels, where voltage-gated L-type calcium channels play a central role. Despite the reliability of the pacemaker, all mammals experience a linear slowdown of the pacemaker rate with age. In humans, this slowing can become pathological and constitutes the main cause for the requirement of the implantation of artificial pacemakers. However, the mechanisms behind the age-associated slowdown of the pacemaker are not well understood. Here, we show that age directly alters the function of L-type calcium channels in pacemaker cells from mice. These age-associated alterations include: i) a reduction in the density of the channels at the plasma membrane, ii) a reduction in the clustering of the channels, iii) a decrease in channel open probability, and iv) a reduction in the cooperative gating of the calcium channels. Altogether, these age-associated alterations result in a global reduction of the L-type calcium current density and in a slowdown of the pacemaker diastolic depolarization. Remarkably, increasing the open probability of L-type calcium channels pharmacologically was enough to restore pacemaker rate in old cells to the same levels observed in the young. Overall, our findings provide evidence that proper organization and function of L-type calcium channels is impaired by aging and that this dysfunction contributes to the slowdown of pacemaker cells in old animals.
mb-PHENIX: Diffusion and Supervised Uniform Manifold Approximation for denoising microbiota data
Authors: Cristian Padron-Manrique , Aarón Vázquez-Jiménez , Armando Esquivel-Hernandez Diego , Yoscelina Estrella Martinez Lopez , Daniel Neri-Rosario , Jean Paul Sánchez-Castañeda , David Giron-Villalobos , Osbaldo Resendis-Antonio 
Published: June 2022

AbstractMotivationMicrobiota data suffers from technical noise (reflected as excess of zeros in the count matrix) and the curse of dimensionality. This complicates downstream data analysis and compromises the scientific discovery’s reliability. Data sparsity makes it difficult to obtain a well-cluster structure and distorts the abundance distributions. Currently, there is a rised need to develop new algorithms with improved capacities to reduce noise and recover missing information.ResultsWe present mb-PHENIX, an open-source algorithm developed in Python, that recovers taxa abundances from the noisy and sparse microbiota data. Our method deals with sparsity in the count matrix (in 16S microbiota and shotgun studies) by applying imputation via diffusion onto the supervisedUniform Manifold Approximation Projection(sUMAP) space. Our hybrid machine learning approach allows the user to denoise microbiota data. Thus, the differential abundance of microbes is more accurate among study groups, where abundance analysis fails.AvailabilityThe mb-PHENIX algorithm is available athttps://github.com/resendislab/mb-PHENIX. An easy-to-use implementation is available on Google Colab (see GitHub)ContactOresendis@inmegen.gob.mxSupplementary informationSupplementary data are available atBioinformaticsonline.
The Intrinsic Hierarchy of Self – Converging Topography and Dynamics
Authors: Çatal Yasir , Günay Mehmet Akif , Li Chunbo , Wang Jijun , Cui Huiru , Li Wei , Northoff Georg 
Published: June 2022

AbstractThe brain can be characterized by an intrinsic hierarchy in its topography which, as recently shown for the uni-transmodal distinction of core and periphery, converges with its dynamics. Does such intrinsic hierarchical organization in both topography and dynamic also apply to the brain’s inner core itself and its higher-order cognitive functions like self? Applying multiple fMRI data sets, we show how the recently established three-layer topography of self (internal, external, mental) is already present during the resting state and carried over to task states including both task-specific and -unspecific effects. Moreover, the topographic hierarchy converges with corresponding dynamic changes (measured by power-law exponent, autocorrelation window, median frequency, sample entropy, complexity) during both rest and task states. Finally, analogous to the topographic hierarchy, we also demonstrate hierarchy among the different dynamic measures themselves according to background and foreground. Finally, we show task-specific- and un-specific effects in the hierarchies of both dynamics and topography. Together, we demonstrate the existence of an intrinsic topographic hierarchy of self and its convergence with dynamics.
Genomic characterisation of a novel species of Erysipelothrix associated with mortalities among endangered seabirds
Authors: Zhong Jiadong , Medvecky Matej , Tornos Jérémy , Clessin Augustin , Gantelet Hubert , Gamble Amandine , Forde Taya L. , Boulinier Thierry 
Published: June 2022

AbstractInfectious diseases threaten endangered species, particularly in small isolated populations. Seabird populations on the remote Amsterdam Island in the Indian Ocean have been in decline for the past three decades, with avian cholera caused by Pasteurella multocida proposed as the primary driver. However, Erysipelothrix spp. has also been sporadically detected from albatrosses on Amsterdam Island and may be contributing to some of the observed mortality. In this study, we genomically characterised 16 Erysipelothrix spp. isolates obtained from three Indian yellow-nosed albatross chick carcasses in 2019. Two isolates were sequenced using both Illumina short-read and MinION long-read approaches, which – following hybrid assembly – resulted in closed circular genomes. Mapping of Illumina reads from the remaining isolates to one of these new reference genomes revealed that all 16 isolates were closely related, with a maximum of 13 nucleotide differences distinguishing any pair of isolates. The nucleotide diversity of isolates obtained from the same or different carcasses was similar, suggesting all three chicks were likely infected from a common source. These genomes were compared with a global collection of genomes from E. rhusiopathiae and other species from the same genus. The isolates from albatrosses were phylogenetically distinct, sharing a most recent common ancestor with E. rhusiopathiae. Based on phylogenomic analysis and standard thresholds for average nucleotide identity and digital DNA-DNA hybridisation, these isolates represent a novel Erysipelothrix species, for which we propose the name Erysipelothrix amsterdamensis sp. nov. The type strain is E. amsterdamensis A18Y020dT. The implications of this bacterium for albatross conservation will require further study.
Detection of Autism Spectrum Disorder Using Graph Representation Learning Algorithms and Deep Neural Network, Based on fMRI Signals
Authors: Yousedian Ali , Shayegh Farzaneh , Maleki Zeinab 
Published: June 2022

AbstractIn this paper, we are going to apply graph representation learning algorithms to identify autism spectrum disorder (ASD) patients within a large brain imaging dataset. Since ASD is characterized by social deficits and repetitive behavioral symptoms, it is mainly identified by brain functional connectivity patterns. Attempts to unveil the neural patterns that emerged from ASD are the essence of ASD classification. We claim that considering the connectivity patterns of the brain can be appropriately executed by graph representation learning methods. These methods can capture the whole structure of the brain, both local and global properties. The investigation is done for the brain imaging worldwide multi-site database known as ABIDE (Autism Brain Imaging Data Exchange). The classifier adapted to the features embedded in graphs is a LeNet deep neural network. Among different graph representation techniques, we used AWE, Node2vec, Struct2vec, multi node2vec, and Graph2Img. The best approach was Graph2Img, in which after extracting the feature vectors representative of the brain nodes, the PCA algorithm is applied to the matrix of feature vectors. Although we could not outperform the previous 70% accuracy of 10-fold cross-validation in the identification of ASD versus control patients in the dataset, for leave-one-site-out cross-validation, we could obtain better results (our accuracy: 80%). It is evident that the effect of graph embedding methods is making the connectivity matrix more suitable for applying to a deep network.
ResMiCo: increasing the quality of metagenome-assembled genomes with deep learning
Authors: Mineeva Olga , Danciu Daniel , Schölkopf Bernhard , Ley Ruth E. , Rätsch Gunnar , Youngblut Nicholas D. 
Published: June 2022

AbstractThe number of published metagenome assemblies is rapidly growing due to advances in sequencing technologies. However, sequencing errors, variable coverage, repetitive genomic regions, and other factors can produce misassemblies, which are challenging to detect for taxonomically novel genomic data. Assembly errors can affect all downstream analyses of the assemblies. Accuracy for the state of the art in reference-free misassembly prediction does not exceed an AUPRC of 0.57, and it is not clear how well these models generalize to real-world data. Here, we present the Residual neural network for Misassembled Contig identification (ResMiCo), a deep learning approach for reference-free identification of misassembled contigs. To develop ResMiCo, we first generated a training dataset of unprecedented size and complexity that can be used for further benchmarking and developments in the field. Through rigorous validation, we show that ResMiCo is substantially more accurate than the state of the art, and the model is robust to novel taxonomic diversity and varying assembly methods. ResMiCo estimated 4.7% misassembled contigs per metagenome across multiple real-world datasets. We demonstrate how ResMiCo can be used to optimize metagenome assembly hyperparameters to improve accuracy, instead of optimizing solely for contiguity. The accuracy, robustness, and ease-of-use of ResMiCo make the tool suitable for general quality control of metagenome assemblies and assembly methodology optimization.Author summaryMetagenome assembly quality is fundamental to all downstream analyses of such data. The number of metagenome assemblies, especially metagenome-assembled genomes (MAGs), is rapidly increasing, but tools to assess the quality of these assemblies lack the accuracy needed for robust quality control. Moreover, existing models have been trained on datasets lacking complexity and realism, which may limit their generalization to novel data. Due to the limitations of existing models, most studies forgo such approaches and instead rely on CheckM to assess assembly quality, an approach that only utilizes a small portion of all genomic information and does not identify specific misassemblies. We harnessed existing large genomic datasets and high-performance computing to produce a training dataset of unprecedented size and complexity and thereby trained a deep learning model for predicting misassemblies that can robustly generalize to novel taxonomy and varying assembly methodologies.
Early life tolerance depends on a subset of specialized dendritic cells and is reinforced by the skin microbiota
Authors: Weckel Antonin , Dhariwala Miqdad O. , Ly Kevin , Ojewumi Oluwasunmisola T. , Riggs Julianne B. , Gonzalez Jeanmarie R. , Dwyer Laura R. , Okoro Joy N. , Leech John M. , Bacino Margot S. , Cho Grace D. , Merana Geil , Anandasabapathy Niroshana , Kumamoto Yosuke , Scharschmidt Tiffany C. 
Published: June 2022

SummaryEarly life establishment of tolerance to commensal bacteria at barrier surfaces carries enduring implications for immune health but remains poorly understood. Here we show that this process is controlled by microbial interaction with a specialized subset of antigen presenting cells. More particularly, we identify CD301b+ type 2 conventional dendritic cells (DC) as a subset in neonatal skin specifically capable of uptake, presentation and generation of regulatory T cells (Tregs) to commensal antigens. In early life, CD301b+ DC2 are enriched for programs of phagocytosis and maturation, while also expressing tolerogenic markers. In both human and murine skin, these signatures were reinforced by microbial uptake. In contrast to their adult counterparts or other early life DC subsets, neonatal CD301b+ DC2 highly expressed the retinoic acid-producing enzyme, RALDH2, deletion of which limited commensal-specific Tregs. Thus, synergistic interactions between bacteria and a specialized DC subset critically support early life tolerance at the cutaneous interface.
Hardware evaluation of spike detection algorithms towards wireless brain machine interfaces
Authors: Oprea Alexandru , Zhang Zheng , Constandinou Timothy G. 
Published: June 2022

AbstractThe current trend for implantable Brain Machine Interfaces (BMIs) is to increase the channel count, towards next generation devices that improve on information transfer rate. This however increases the raw data bandwidth for wired or wireless systems that ultimately impacts the power budget (and thermal dissipation). On-implant feature extraction and/or compression are therefore becoming essential to reduce the data rate, however the processing power is of concern. One common feature extraction technique for intracortical BMIs is spike detection. In this work, we have empirically compared the performance, resource utilization, and power consumption of three hardware efficient spike emphasizers, Non-linear Energy Operator (NEO), Amplitude Slope Operator (ASO) and Energy of Derivative (ED), and two common statistical thresholding mechanisms (using mean or median). We also propose a novel median approximation to address the issue of the median operator not being hardware-efficient to implement. These have all been implemented and evaluated on reconfigurable hardware (FPGA) to estimate their hardware efficiency in an ultimate ASIC design. Our results suggest that ED with average thresholding provides the most hardware efficient (low power/resource) choice, while using median has the advantage of improved detection accuracy and higher robustness on threshold multiplier settings. This work is significant because it is the first to implement and compare the hardware and algorithm trade-offs that have to be made before translating the algorithms into hardware instances to design wireless implantable BMIs.