bluuebunny/crossref_metadata_2025_split huggingface dataset:
Metabolic switch in the aging astrocyte supported via integrative approach comprising network and transcriptome analyses
Authors: Acevedo Alejandro , Torres Felipe , Kiwi Miguel , Baeza-Lehnert Felipe , Felipe Barros L. , Lee-Liu Dasfne , González-Billault Christian 
Published: February 2022

SUMMARYDysregulated central-energy metabolism is a hallmark of brain aging. Supplying enough energy for neurotransmission relies on the neuron-astrocyte metabolic network. To identify genes contributing to age-associated brain functional decline, we formulated an approach to analyze the metabolic network by integrating flux, network structure and transcriptomic databases of neurotransmission and aging. Our findings support that during brain aging: 1) The astrocyte undergoes a metabolic switch from aerobic glycolysis to oxidative phosphorylation, decreasing lactate supply to the neuron, while the neuron suffers intrinsic energetic deficit by downregulation of Krebs cycle genes, including mdh1 and mdh2 (Malate-Aspartate Shuttle); 2) Branched-chain amino acid degradation genes were downregulated, identifying dld as a central regulator; 3) Ketone body synthesis increases in the neuron, while the astrocyte increases their utilization, in line with neuronal energy deficit in favor of astrocytes. We identified candidates for preclinical studies targeting energy metabolism to prevent age-associated cognitive decline.
GLMsingle: a toolbox for improving single-trial fMRI response estimates
Authors: Prince Jacob S. , Charest Ian , Kurzawski Jan W. , Pyles John A. , Tarr Michael J. , Kay Kendrick N. 
Published: February 2022

ABSTRACTAdvances in modern artificial intelligence (AI) have inspired a paradigm shift in human neuroscience, yielding large-scale functional magnetic resonance imaging (fMRI) datasets that provide high-resolution brain responses to tens of thousands of naturalistic visual stimuli. Because such experiments necessarily involve brief stimulus durations and few repetitions of each stimulus, achieving sufficient signal-to-noise ratio can be a major challenge. We address this challenge by introducing GLMsingle, a scalable, user-friendly toolbox available in MATLAB and Python that enables accurate estimation of single-trial fMRI responses (glmsingle.org). Requiring only fMRI time-series data and a design matrix as inputs, GLMsingle integrates three techniques for improving the accuracy of trial-wise general linear model (GLM) beta estimates. First, for each voxel, a custom hemodynamic response function (HRF) is identified from a library of candidate functions. Second, cross-validation is used to derive a set of noise regressors from voxels unrelated to the experimental paradigm. Third, to improve the stability of beta estimates for closely spaced trials, betas are regularized on a voxel-wise basis using ridge regression. Applying GLMsingle to the Natural Scenes Dataset and BOLD5000, we find that GLMsingle substantially improves the reliability of beta estimates across visually-responsive cortex in all subjects. Furthermore, these improvements translate into tangible benefits for higher-level analyses relevant to systems and cognitive neuroscience. Specifically, we demonstrate that GLMsingle: (i) improves the decorrelation of response estimates between trials that are nearby in time; (ii) enhances representational similarity between subjects both within and across datasets; and (iii) boosts one-versus-many decoding of visual stimuli. GLMsingle is a publicly available tool that can significantly improve the quality of past, present, and future neuroimaging datasets that sample brain activity across many experimental conditions.
Genetic diversity analysis and characterization of Ugandan sorghum
Authors: Chakrabarty Subhadra , Mufumbo Raphael , Windpassinger Steffen , Jordan David , Mace Emma , Snowdon Rod J. , Hathorn Adrian 
Published: February 2022

AbstractThe National Genebank of Uganda houses diverse and rich Sorghum bicolor germplasm collection. This genetic diversity resource is untapped, under-utilized and has not been systematically incorporated into sorghum breeding programs. In this study, we characterized the germplasm collection using whole genome SNP markers. Discriminant analysis of principal components (DAPC) was implemented to study racial ancestry of the accessions in comparison to a global sorghum diversity set and characterize sub-groups and admixture in the Ugandan germplasm. Genetic structure and phylogenetic analysis was conducted to identify distinct genotypes in the Ugandan collection and relationships among groups. Furthermore, in a case study for identification of potentially useful adaptive trait variation for breeding, we performed genome-wide association studies for juvenile cold tolerance. Genomic regions potentially involved in adaptation of Ugandan sorghum varieties to cooler climatic conditions were identified that could be of interest for expansion of sorghum production into temperate latitudes. The study demonstrates how genebank genomics can potentially facilitate effective and efficient usage of valuable, untapped germplasm collections for agronomic trait evaluation and subsequent allele mining.
The effect of treatment of auxin producing bacterial culture supernatant in combination with organic and inorganic fertilizers
Authors: Park Seunghye , Shin Ji-Hwan 
Published: February 2022

AbstractBacteria that secret plant growth promoting substances are good candidate for field application to improve crop yield. Especially, bacteria that excrete auxin can enhance root growth and settlement of crops to help adapting new soil environment when transplanted. In this study, we applied culture supernatant of an auxin producing bacteria Ignatzschineria sp. CG20001. The bacterial culture supernatant (BCS) was applied in combination with chicken manure and liquid chemical fertilizer. The treatment of bacterial culture supernatant enhanced growth of red kohlrabi, Brassica oleraceae var. gongyloides, especially in combination with chicken manure. To understand mechanisms related higher growth, we compared photosynthetic efficiency by measuring chlorophyll fluorescence and light harvesting antenna pigments composition. The performance index PS II was higher in BCS treated plants, and recovered faster after leaf harvest. Photoprotective mechanism was also increased in BCS treated plants, however, and DTT infiltration, non-photochemical quenching (NPQ) reduced to similar level, implying higher xanthophyll cycle dependent NPQ induction mechanism in BCS treated plants. However, in liquid chemical fertilized plants, BCS treated and non-treated plants showed the opposite pattern in the change of NPQ. The chlorophyll a/b ratio was highest in no-fertilized non-treated control, and lowest in chicken manure and BCS combination, implying impact of nutrition availability between the two groups. Through these results, we conclude that the appropriate combination of organic fertilizer and plant growth promoting bacterial metabolite can improve plant growth more than chemical fertilizers. This study also provides physiological understanding for strategic fertilization regime of organic fertilizer and biofertilizer combination.
Robust deep learning object recognition models rely on low frequency information in natural images
Authors: Li Zhe , Caro Josue Ortega , Rusak Evgenia , Brendel Wieland , Bethge Matthias , Anselmi Fabio , Patel Ankit B. , Tolias Andreas S. , Pitkow Xaq 
Published: February 2022

ABSTRACTMachine learning models have difficulty generalizing to data outside of the distribution they were trained on. In particular, vision models are usually vulnerable to adversarial attacks or common corruptions, to which the human visual system is robust. Recent studies have found that regularizing machine learning models to favor brain-like representations can improve model robustness, but it is unclear why. We hypothesize that the increased model robustness is partly due to the low spatial frequency preference inherited from the neural representation. We tested this simple hypothesis with several frequency-oriented analyses, including the design and use of hybrid images to probe model frequency sensitivity directly. We also examined many other publicly available robust models that were trained on adversarial images or with data augmentation, and found that all these robust models showed a greater preference to low spatial frequency information. We show that preprocessing by blurring can serve as a defense mechanism against both adversarial attacks and common corruptions, further confirming our hypothesis and demonstrating the utility of low spatial frequency information in robust object recognition.
Evaluation of taxonomic classification and profiling methods for long-read shotgun metagenomic sequencing datasets
Authors: Portik Daniel M. , Brown C. Titus , Pierce-Ward N. Tessa 
Published: February 2022

ABSTRACTBackgroundLong-read shotgun metagenomic sequencing is gaining in popularity and offers many advantages over short-read sequencing. The higher information content in long reads is useful for a variety of metagenomics analyses, including taxonomic classification and profiling. The development of long-read specific tools for taxonomic classification is accelerating, yet there is a lack of information regarding their relative performance. Here, we perform a critical benchmarking study using 11 methods, including five methods designed specifically for long reads. We applied these tools to several mock community datasets generated using Pacific Biosciences (PacBio) HiFi or Oxford Nanopore Technology (ONT) sequencing, and evaluated their performance based on read utilization, detection metrics, and relative abundance estimates.ResultsOur results show that long-read classifiers generally performed best. Several short-read classification and profiling methods produced many false positives (particularly at lower abundances), required heavy filtering to achieve acceptable precision (at the cost of reduced recall), and produced inaccurate abundance estimates. By contrast, two long-read methods (BugSeq, MEGAN-LR & DIAMOND) and one generalized method (sourmash) displayed high precision and recall without any filtering required. Furthermore, in the PacBio HiFi datasets these methods detected all species down to the 0.1% abundance level with high precision. Some long-read methods, such as MetaMaps and MMseqs2, required moderate filtering to reduce false positives to resemble the precision and recall of the top-performing methods. We found read quality affected performance for methods relying on protein prediction or exact k-mer matching, and these methods performed better with PacBio HiFi datasets. We also found that long-read datasets with a large proportion of shorter reads (<2kb length) resulted in lower precision and worse abundance estimates, relative to length-filtered datasets. Finally, for classification methods, we found that the long-read datasets produced significantly better results than short-read datasets, demonstrating clear advantages for long-read metagenomic sequencing.ConclusionsOur critical assessment of available methods provides best-practice recommendations for current research using long reads and establishes a baseline for future benchmarking studies.
JUN Regulation of Injury-induced Enhancers in Schwann Cells
Authors: Ramesh Raghu , Manurung Yanti , Ma Ki H. , Blakely Todd , Won Seongsik , Wyatt Eugene , Awatramani Rajeshwar , Svaren John 
Published: February 2022

AbstractSchwann cells play a critical role after peripheral nerve injury by clearing myelin debris, forming axon-guiding Bands of Bungner, and re-myelinating regenerating axons. Schwann cells undergo epigenomic remodeling to differentiate into a repair state that expresses unique genes, some of which are not expressed at other stages of Schwann cell development. We previously identified a set of enhancers that are activated in Schwann cells after nerve injury, and we determined if these enhancers are pre-programmed into the Schwann cell epigenome as poised enhancers prior to injury. Poised enhancers share many attributes of active enhancers, such as open chromatin, but are marked by repressive H3K27 trimethylation (H3K27me3) rather than H3K27ac. We find that most injury-induced enhancers are not marked as poised enhancers prior to injury indicating that injury-induced enhancers are not pre-programmed in the Schwann cell epigenome. Injury-induced enhancers are enriched with AP-1 binding motifs, and the c-JUN subunit of AP-1 had been shown to be critical to drive the transcriptional response of Schwann cells after injury. Using in vivo ChIP-seq analysis we find that c-JUN binds to a subset of injury-induced enhancers. To test the role of specific injury-induced enhancers, we focused on c-JUN-binding enhancers upstream of the Sonic Hedgehog (Shh) gene, which is only upregulated in repair Schwann cells compared to other stages of Schwann cell development. We confirm that c-JUN regulates these enhancers and also show that the enhancers are required for robust induction of the Shh gene after injury.Significance StatementThe pro-regenerative actions of Schwann cells after nerve injury depends on upon profound reprogramming of the epigenome. The repair state is directed by injury-induced transcription factors, like JUN, which is uniquely required after nerve injury. In this study, we test whether the injury program is pre-programmed into the epigenome as poised enhancers and define which enhancers bind JUN. Finally, we test the roles of these enhancers by performing CRISPR-mediated deletion of JUN-bound injury enhancers in the Sonic hedgehog gene. While many long range enhancers drive expression of Sonic hedgehog at different developmental stages of specific tissues, these studies identify an entirely new set of enhancers that are required for Sonic hedgehog induction in Schwann cells after injury.
Soil amendments and suppression of Phytophthora root rot in avocado (Persea indica)
Authors: Farooq Qurrat Ul Ain , McComb Jen , Hardy Giles StJ. , Burgess Treena 
Published: February 2022

AbstractThe ability of microbial or mineral-based soil additives to suppress root rot caused by Phytophthora cinnamomi was assessed. Phosphite and metalaxyl treatments for the control of disease, and glyphosate for weed control were also assessed. A treatment simulating avocado orchard conditions had chicken manure, wood mulch, and mulch from beneath trees in an avocado orchard added to the pots. Soil treatments (three probiotic and two mineral-based) were applied to 9-month-old saplings growing in containers in a glasshouse. After one-month, half of the plants of each treatment were inoculated with the pathogen. Three months after inoculation, plants were harvested and plant growth and root damage were measured. In the first experiment infestation with P. cinnamomi significantly reduced fine root dry weight in all plants except those in soil treated with one silicon-based mineral mulch. Visible root damage was higher in plants treated with probiotics. In this experiment, and in a repeat experiment the reduction of fine root damage achieved by spraying plants with phosphite or addition of a silica based mineral mulch was similar. Phosphite was preferable to metalaxyl as a chemical treatment, as the latter reduced shoot and root growth of non-infected plants. Glyphosate treatment of wheat seedlings growing in the pots with the avocados reduced shoot and fine root growth of both non-infected and infected plants. These observations need to be confirmed under field conditions.
mSigHdp: hierarchical Dirichlet process mixture modeling for mutational signature discovery
Authors: Liu Mo , Wu Yang , Jiang Nanhai , Boot Arnoud , Rozen Steven G. 
Published: February 2022

AbstractMutational signatures are characteristic patterns of mutations caused by endogenous or exogenous mutational processes. These signatures can be discovered by analyzing mutations in large sets of samples – usually somatic mutations in tumor samples. Most programs for discovering mutational signatures are based on non-negative matrix factorization (NMF). Alternatively, signatures can be discovered using hierarchical Dirichlet process (HDP) mixture models, an approach that has been explored less. These models assign mutations to clusters and view each cluster as being generated from the signature of a particular mutational process. Here we describe mSigHdp, an improved approach to using HDP mixture models to discover mutational signatures. We benchmarked mSigHdp and state-of-the-art NMF-based approaches on 4 realistic synthetic data sets. These data sets encompassed 18 cancer types. In total they contained 3.5×107single-base-substitution mutations representing 32 signatures and 6.1×106small-insertion-and-deletion mutations representing 13 signatures. For 3 of the 4 data sets, mSigHdp had the best positive predictive value for discovering mutational signatures, and for all 4 data sets, it had the best true positive rate. Its CPU usage was similar to that of the NMF-based approaches. Thus, mSigHdp is an important and practical addition to the set of tools available for discovering mutational signatures.Data and code availabilitymSigHdp is available at public repositorieshttps://github.com/steverozen/mSigHdpandhttps://github.com/steverozen/hdpx. The synthetic data, code for generating the synthetic data, code for running the mutational-signature discovery programs, the main outputs of the programs, and code for analyzing their results and for generating the data figures in this paper are available athttps://github.com/Rozen-Lab/mSigHdpsup files. A singularity container with mSigHdp can be downloaded from cloud.sylabs.io with the shell command “singularity pull library://rozen-lab/msighdp/msighdp:2.1.2”. A toy-example Rscript for using this container is athttps://github.com/steverozen/mSigHdp/blob/master/data-raw/container_scripts/test_mSigHdp.R.Supplementary materialOne excel file of supplementary tables and one PDF file of supplementary figures have been submitted along with this manuscript.
An image processing protocol to extract variables predictive of human embryo fitness for assisted reproduction
Authors: Chéles Dóris Spinosa , Ferreira André Satoshi , de Jesus Isabela Sueitt , Fernandez Eleonora Inácio , Pinheiro Gabriel Martins , Dal Molin Eloiza Adriane , Alves Wallace , de Souza Rebeca Colauto Milanezi , Bori Lorena , Meseguer Marcos , Rocha José Celso , Gouveia Nogueira Marcelo Fábio 
Published: February 2022

ABSTRACTOBJECTIVETo study the application of image processing for segmentation of blastocysts images and extraction of potential variables for prediction of embryo fitness.DESIGNRetrospective study.SETTINGSingle reproductive medical center. IVI-RMA (Valencia, Spain) between 2017 and 2019.PATIENTSAn initial dataset including 353 images from EmbryoScope® and 474 images from Geri® incubators was acquired, of which 320 images from EmbryoScope® and 309 images from Geri® incubators were used in this study.INTERVENTION(S)None.MAIN OUTCOME MEASURE(S)Successful segmentation of images into trophectoderm (TE), blastocoel, and inner cell mass (ICM) using the proposed processing steps.RESULTSA total of 33 variables were automatically generated by digital image processing, each representing a different aspect of the embryo and describing a different characteristic of the expanding blastocyst (EX), ICM, or TE. These variables can be categorized into texture, gray level average, gray level standard deviation, modal value, relations, and light level. The automated and directed steps of the proposed processing protocol exclude spurious results, except when image quality (e.g., focus) prevents correct segmentation.CONCLUSIONSThe proposed image processing protocol that can successfully segment human blastocyst images from two distinct sources and extract 33 variables with potential utility in embryo selection for ART.
Plankton energy flows using a global size-structured and trait-based model
Authors: Negrete-García Gabriela , Luo Jessica Y. , Long Matthew C. , Lindsay Keith , Levy Michael , Barton Andrew D. 
Published: February 2022

AbstractPlankton community models are critical tools for understanding the processes that shape marine plankton communities, how plankton communities impact biogeochemical cycles, and the feedbacks between community structure and function. Here, using the flexible Marine Biogeochemistry Library (MARBL), we present the Size-based Plankton Ecological TRAits (MARBL-SPECTRA) model, which is designed to represent a diverse plankton community while remaining computationally tractable. MARBL-SPECTRA is composed of nine phytoplankton and six zooplankton size classes represented using allometric scaling relationships for physiological traits and interactions within multiple functional types. MARBL-SPECTRA is embedded within the global ocean component of the Community Earth System Model (CESM) and simulates large-scale, emergent patterns in phytoplankton growth limitation, plankton phenology, plankton generation time, and trophic transfer efficiency. The model qualitatively reproduces observed global patterns of surface nutrients, chlorophyll biomass, net primary production, and the biogeographies of a range of plankton size classes. In addition, the model simulates how predator:prey dynamics and trophic efficiency vary across gradients in total ecosystem productivity. Shorter food chains that export proportionally more carbon from the surface to the ocean interior occur in productive, eutrophic regions, whereas in oligotrophic regions, the food chains are relatively long and export less organic matter from the surface. The union of functional type modeling with size-resolved, trait-based modeling approaches allows MARBL-SPECTRA to capture both large-scale elemental cycles and the structure of planktonic food webs affecting trophic transfer efficiency.