Miracle-dz/UniFAR-FTUs huggingface dataset:
Fox Hunting in Wild Apples: Searching for Novel Genes in Malus Sieversii

Malus sieversii is considered the progenitor of modern apple (Malus pumila) cultivars and to represent a valuable source of genetic diversity. Despite the importance of M. sieversii as a source of disease resistance, stress tolerance, and novel fruit traits, little is known about gene function and diversity in M. sieversii. Notably, a publicly annotated genome sequence for this species is not available. In the current study, the FOX (Full-length cDNA OvereXpressing) gene hunting system was used to construct a library of transgenic lines of Arabidopsis in which each transgenic line overexpresses a full-length gene obtained from a cDNA library of the PI619283 accession of M. sieversii. The cDNA library was constructed from mRNA obtained from bark tissues collected in late fall–early winter, a time at which many abiotic stress-adaptative genes are expressed. Over 4000 apple FOX Arabidopsis lines have been established from the pool of transgenic seeds and cDNA inserts corresponding to various Gene Ontology (GO) categories have been identified. A total of 160 inserts appear to be novel, with no or limited homology to M. pumila, Arabidopsis, or poplar. Over 1300 lines have also been screened for freezing resistance. The constructed library of transgenic lines provides a valuable genetic resource for exploring gene function and diversity in Malus sieversii. Notably, no such library of t-DNA lines currently exists for any Malus species.
HLA-DRB1*07:01 and *08:02 Alleles Confer a Protective Effect Against ACPA-Positive Rheumatoid Arthritis in a Latin American Admixed Population

Simple Summary The focus of the genetic studies on rheumatoid arthritis has been on Europeans or European-derived populations, with limited information available on other populations, especially in Latin America. The aim of the present study was to test previously reported HLA markers, the most important genetic contributors for the risk of developing rheumatoid arthritis, associated with anti-citrullinated protein antibodies-positive rheumatoid arthritis in a Latin American population with admixed ancestry. Our study shows for the first time an association between HLA-DRB1*07:01 and *08:02 alleles and protection against anti-citrullinated protein antibodies-positive rheumatoid arthritis in the Chilean population. In addition, our results seem to support the existence of differentiated genomic patterns in Chilean, and probably other Latin American populations, that are not the same that the found in Europeans regarding to loci repeatedly involved in rheumatoid arthritis. Identifying relationships between HLA-DRB1 alleles and rheumatoid arthritis is important for identifying disease associations in different ethnic groups in order to reach a better understanding of rheumatoid arthritis worldwide. Abstract HLA-DRB1 shared epitope (SE) alleles are important genetic contributors for the risk of developing anti-citrullinated protein antibodies (ACPA)-positive rheumatoid arthritis (RA), particularly in Caucasians. We aimed to analyze the contribution of HLA-DRB1 alleles and single nucleotide polymorphisms (SNPs) within the major histocompatibility complex (MHC) region to the susceptibility to develop ACPA-positive RA in a Latin American (LA) population with admixed ancestry. A total of 289 ACPA-positive RA patients and 510 controls were enrolled in this study. The presence of HLA-DRB1*04:01, *09:01 and *10:01 was increased in ACPA-positive RA patients compared with healthy controls (p < 0.0001, p < 0.001 and p < 0.01, respectively), whereas DRB1*07:01 and *08:02 was associated with a decreased risk of ACPA-positive RA (p < 0.001 and p < 0.01, respectively). These results showed a strong correlation with estimates from studies in Asians but not in Caucasian populations. The present study describes the protective effects of the HLA-DRB1*07:01 and *08:02 alleles in ACPA-positive RA patients in a LA population for the first time. Identifying relationships between HLA-DRB1 alleles and RA is important for identifying disease associations in different ethnic groups in order to reach a better understanding of RA worldwide.
Allosteric Activation of p300 Autoacetylation by the Tumor Suppressor p53

The transcriptional coactivator p300 (KAT3B) is an essential protein with an intrinsic lysine acetyltransferase activity. As a coactivator, p300 is essential for p53 transactivation; however, its precise role in p53 transactivation remains unclear. Here, we report that in addition to being a substrate, p53 allosterically activates the acetyltransferase activity of p300. Mechanistically, the activation occurs through the enhancement of p300 autoacetylation. Cryo-electron microscopic analysis of the p53-p300 complex revealed that the domain organization of p300 is substantially altered upon binding of p53, suggesting a p53-induced ‘conformational switch’ in the p300 structure, between an inactive and a catalytically active form. Analysis of the genome-wide occupancy of autoacetylated p300 after p53-mediated activation revealed enrichment of acetylated p300 near the transcription start sites of many p53-regulated genes. This is accompanied by a similar enrichment of activating histone marks near those sites. We conclude that allosteric activation of p300 by p53 allows p300 to be hyperactive only near the region of p53 regulated genes, thus, imparting an additional layer of specificity enhancement mechanism in the regulation of gene expression. Such a mechanism could be an important component of imparting locale-specific alteration of other epigenetic marks.
Multiscale dynamics and information flow in a data-driven model of the primary motor cortex microcircuit

Abstract We developed a biophysically detailed multiscale model of mouse primary motor cortex (M1) with over 10,000 neurons and 35 million synapses. We focused on intratelencephalic (IT) and pyramidal-tract (PT) neurons of layer 5 (L5), which were modeled at high multicompartment resolution. Wiring densities were based on prior detailed measures from mouse slice, and depended on cell class and cortical depth at sublaminar resolution. Prominent phase-amplitude-coupled delta and gamma activity emerged from the network. Spectral Granger causality analysis revealed the dynamics of information flow through populations at different frequencies. Stimulation of motor vs sensory long-range inputs to M1 demonstrated distinct intra- and inter-laminar dynamics and PT output. Manipulating PT Ih altered PT activity, supporting the hypothesis that Ih neuromodulation is involved in translating motor planning into execution. Our model sheds light on the multiscale dynamics of cell-type-specific M1 circuits and how connectivity relates to dynamics.
Deconvolution of Transcriptional Networks Identified TCF4 as a Master Regulator in Schizophrenia

Tissue-specific reverse engineering of transcriptional networks has led to discoveries uncovering underlying regulators of cellular networks in several cancers and other diseases. However, whether these approaches can be applied to complex psychiatric disorders is largely unexplored, partly due to the general lack of data on appropriate tissues for mental disorders. In this study, using RNA-Seq data on dorsolateral prefrontal cortex from 307 schizophrenia (SCZ) cases and 245 controls, we deconvoluted the transcriptional network to identify transcriptional “master regulators” mediating expression of a large body of target genes. Together with an independent RNA-Seq data on cultured primary neuronal cells derived from olfactory neuroepithelium from a cohort of 143 SCZ cases and 112 controls, we identified TCF4 as a candidate master regulator. TCF4 was previously identified as a SCZ susceptibility gene from genome-wide association studies, but its regulatory subnetworks had been elusive. We confirmed many predicted regulatory targets by TCF4 through additional analysis of predicted transcription factor binding sites and ATAC-Seq data (p=3.251e-05). We further knocked down TCF4 expression in human induced pluripotent stem cells (hiPSC) at 3 and 14 days post neuronal differentiation, confirmed that TCF4 affects axon guidance pathway, and that the collection of predicted target genes were dysregulated on day 3 (p=1.55e-06) and day 14 (p=1.05e-08). In conclusion, multiple lines of evidence suggested that TCF4 serves as a master regulator in SCZ, demonstrating a clear example where network dysregulation involving hundreds of genes can lead to susceptibility to complex psychiatric diseases.
A longitudinal analysis of men’s intrasexual competitiveness, state anxiety, salivary testosterone, and salivary cortisol

Previous research suggests that competition-induced increases in men’s testosterone levels are associated with increases in their intrasexual competitiveness. Whether these relationships are also evident when considering naturally occurring changes in men’s testosterone levels is an open question, however. To investigate this issue, we carried out a longitudinal analysis of steroid hormone levels and men’s responses on the Intrasexual Competitiveness Scale. We found no evidence that men’s intrasexual competitiveness tracked naturally occurring changes in salivary testosterone, cortisol, or their interaction. However, men did report greater current (i.e., state) anxiety when cortisol was high, replicating previous findings. Our null results for steroid hormones and intrasexual competitiveness suggest that findings for relationships between competition-induced changes in testosterone and men’s intrasexual competitiveness may not necessarily generalize well to relationships with naturally occurring variation in steroid hormones.
Gene set-centric search

3 Abstract: 34 Despite increasing availability of sequenced genomes, accurate characterization of gene 35 functions is needed to close the genotype-phenotype gap. Recent advances in gene function 36 prediction rely on ensemble approaches that integrate the results from multiple inference 37 methods to produce superior predictions. Yet, these developments remain largely unexplored 38 in plants. We present Neighbor Counting Ensemble, a gene function prediction method which 39 integrates eleven gene co-function networks for Arabidopsis thaliana, and produces more 40 accurate gene function predictions for a larger fraction of genes with unknown function. We 41 used these predictions to identify genes involved in mitochondrial complex I formation, and 42 for five of them we confirmed the predictions experimentally. The ensemble predictions are 43 provided as a user-friendly online database, EnsembleNet, available at 44 http://www.gene2function. de/ensemblenet.html. 45
Improving the Interpretability of fMRI Decoding Using Deep Neural Networks and Adversarial Robustness

Deep neural networks (DNNs) are being increasingly used to make predictions from functional magnetic resonance imaging (fMRI) data. However, they are widely seen as uninterpretable “black boxes,” as it can be difficult to discover what input information is used by the DNN in the process, something important in both cognitive neuroscience and clinical applications. A saliency map is a common approach for producing interpretable visualizations of the relative importance of input features for a prediction. However, methods for creating maps often fail due to DNNs being sensitive to input noise or by focusing too much on the input and too little on the model. It is also challenging to evaluate how well saliency maps correspond to the truly relevant input information, as ground truth is not always available. In this paper, we review a variety of methods for producing gradient-based saliency maps and present an adversarial training method we developed to make DNNs robust to input noise, with the goal of improving the quality of DNN saliency maps. We introduce two quantitative evaluation procedures for saliency map methods in fMRI, applicable whenever a DNN or linear model is being trained to decode some information from imaging data. We evaluate the procedures using synthetic data sets, where the complex activation structure is known, and on fMRI data from the Human Connectome Project using saliency maps produced for linear models and DNNs doing task decoding in both settings. Our key finding is that saliency maps produced with different methods vary widely in quality in both synthetic and Human Connectome Project fMRI data, even when those methods have similar prediction performance. We found that training both linear and non-linear models using adversarial noise increased the quality of their saliency maps. We also found that, while some linear models generate good saliency maps in the highly controlled, synthetic data, non-linear DNN methods generate better saliency maps in real-world fMRI data. Finally, our experiments give evidence that combining adversarial training with a complex, non-linear model can improve saliency map quality when compared to several methods commonly used in the literature.
Calculation of medium precipitation using the Thiessen method and the ridge

This study proposes a method to calculate hydrological areas of influence based on the Thiessen method and considering the ridge line. The study dealt with the basin of the high Sapucai, upstream of the city of Itajuba-MG, and used data from flood monitoring stations of the Federal University of Itajuba as well as images of the Shuttle Radar Topography Mission (SRTM). Through the use of this data, areas of influence were traced of rainfall stations through a combination of the Thiessen method and the lines ridges nearest mountain of these rainfall measurements. It is believed that, using results of the areas of influence of a meteorological station, this method produces results that are more accurate than other methods recommended by the specialized literature.
Conservation of a gene cluster reveals novel cercosporin biosynthetic mechanisms and extends production to the genus Colletotrichum

Species in the genus Cercospora cause economically devastating diseases in sugar beet, maize, rice, soy bean and other major food crops. Here we sequenced the genome of the sugar beet pathogen C. beticola and found it encodes 63 putative secondary metabolite gene clusters, including the cercosporin toxin biosynthesis (CTB) cluster. We show that the CTB gene cluster has experienced multiple duplications and horizontal transfers across a spectrum of plant pathogenic fungi, including the wide host range Colletotrichum genus as well as the rice pathogen Magnaporthe oryzae. Although cercosporin biosynthesis has been thought to-date to rely on an eight gene CTB cluster, our phylogenomic analysis revealed gene collinearity adjacent to the established cluster in all CTB cluster-harboring species. We demonstrate that the CTB cluster is larger than previously recognized and includes cercosporin facilitator protein (CFP) previously shown to be involved with cercosporin auto-resistance, and four additional genes required for cercosporin biosynthesis including the final pathway enzymes that install the unusual cercosporin methylenedioxy bridge. Finally, we demonstrate production of cercosporin by Colletotrichum fioriniae, the first known cercosporin producer within this agriculturally important genus. Thus, our results provide new insight into the intricate evolution and biology of a toxin critical to agriculture and broaden the production of cercosporin to another fungal genus containing many plant pathogens of important crops worldwide. Significance Statement Species in the fungal genus Cercospora cause diseases in many important crops worldwide. Their success as pathogens is largely due to the secretion of cercosporin during infection. We report that the cercosporin toxin biosynthesis (CTB) cluster is ancient and was horizontally transferred to diverse fungal pathogens on an unprecedented scale. Since these analyses revealed genes adjacent to the established CTB cluster, we evaluated their role in C. beticola to show that four are necessary for cercosporin biosynthesis. Finally, we confirmed that the apple pathogen Colletotrichum fioriniae produces cercosporin, the first case outside the family Mycosphaerellaceae. Other Colletotrichum plant pathogens also harbor the CTB cluster, which points to a wider concern that this toxin may play in virulence and human health.
Large Fork-Join Queues with Nearly Deterministic Arrival and Service Times

In this paper, we study an N server fork-join queue with nearly deterministic arrival and service times. Specifically, we present a fluid limit for the maximum queue length as [Formula: see text]. This fluid limit depends on the initial number of tasks. In order to prove these results, we develop extreme value theory and diffusion approximations for the queue lengths.
Heterogeneous Network Representation Learning: A Unified Framework With Survey and Benchmark

Since real-world objects and their interactions are often multi-modal and multi-typed, heterogeneous networks have been widely used as a more powerful, realistic, and generic superclass of traditional homogeneous networks (graphs). Meanwhile, representation learning (a.k.a. embedding) has recently been intensively studied and shown effective for various network mining and analytical tasks. In this work, we aim to provide a unified framework to deeply summarize and evaluate existing research on heterogeneous network embedding (HNE), which includes but goes beyond a normal survey. Since there has already been a broad body of HNE algorithms, as the first contribution of this article, we provide a generic paradigm for the systematic categorization and analysis over the merits of various existing HNE algorithms. Moreover, existing HNE algorithms, though mostly claimed generic, are often evaluated on different datasets. Understandable due to the application favor of HNE, such indirect comparisons largely hinder the proper attribution of improved task performance towards effective data preprocessing and novel technical design, especially considering the various ways possible to construct a heterogeneous network from real-world application data. Therefore, as the second contribution, we create four benchmark datasets with various properties regarding scale, structure, attribute/label availability, and etc. from different sources, towards handy and fair evaluations of HNE algorithms. As the third contribution, we carefully refactor and amend the implementations and create friendly interfaces for 13 popular HNE algorithms, and provide all-around comparisons among them over multiple tasks and experimental settings. By putting all existing HNE algorithms under a unified framework, we aim to provide a universal reference and guideline for the understanding and development of HNE algorithms. Meanwhile, by open-sourcing all data and code, we envision to serve the community with an ready-to-use benchmark platform to test and compare the performance of existing and future HNE algorithms (https://github.com/yangji9181/HNE).
New equations of state constrained by nuclear physics, observations, and QCD calculations of high-density nuclear matter

We present new equations of state for applications in core-collapse supernova and neutron star merger simulations. We start by introducing an effective mass parametrization that is fit to recent microscopic calculations up to twice saturation density. This is important to capture the predicted thermal effects, which have been shown to determine the proto--neutron star contraction in supernova simulations. The parameter range of the energy-density functional underlying the equation of state is constrained by chiral effective field theory results at nuclear densities as well as by functional renormalization group computations at high densities based on QCD. We further implement observational constraints from measurements of heavy neutron stars, the gravitational wave signal of GW170817, and from the recent NICER results. Finally, we study the resulting allowed ranges for the equation of state and for properties of neutron stars, including the predicted ranges for the neutron star radius and maximum mass.
Web Content and Digitization Patterns of Tribal College Libraries Within the Great Plains Region

The Web content and digitization patterns of twenty-three tribal college libraries from the Great Plains region were examined to determine what types of services, programs, digital resources, and social media were promoted on their websites. The results of this study differed from a previous 1998 study by Kaya, which found that tribal libraries merely support the institutions and are ‘not recognized as an integral part of the activities of a tribal college, that is, as an equal partner in the academic endeavor’ (p. 245). Kaya noted that the success of students enrolled in tribal colleges was not contingent upon libraries and the resources they provide. Findings of this study indicate that today, the library is an integral part of the tribal college and the surrounding communities they serve and they promote lifelong learning and literacy in the community.
The Development of Reading and Operation Span Tasks in Persian as Measures of Working Memory Capacity for Iranian EFL Learners

This paper presented two complex span tasks in Persian as measures of working memory capacity (WMC). Firstly, the construct of working memory (WM) and possible WM measures that could assess this construct efficiently were critically reviewed. Accordingly, as measures of domain-general components of WM, a reading span task and an operation span task which were documented to assess this construct efficiently were chosen and developed. Following this, the developed tasks were conducted on 151 teenage learners in a foreign language institute in West Azerbaijan Province. Then, the administration and scoring of the measures were described step by step. The tasks were validated against each other as well as against a digit span task which assessed the domain-specific aspect of WMC as well. The result showed a strong correlation between the reading span task and the operation span task, and a moderate correlation of either of the functions with the digit span task. Consequently, both of the developed span tasks can be valid indicators of WM capacity for Iranian individuals and can be used in a wide array of research domains in second language acquisition studies.
The genetics of behavioral isolation in an island system

Mating behavior divergence can make significant contributions to reproductive isolation and speciation in various biogeographic contexts. However, whether the genetic architecture underlying mating behavior divergence is related to the biogeographic history and the tempo and mode of speciation remains poorly understood. Here, we use quantitative trait locus (QTL) mapping to infer the number, distribution, and effect size of mating song rhythm variation in the crickets Laupala eukolea and L. cerasina, which occur on different islands (Maui and Hawai’i). We then compare these results with a similar study of an independently evolving species pair that diverged within the same island. Finally, we annotate the L. cerasina transcriptome and test whether QTL fall in functionally enriched genomic regions. We document a polygenic architecture behind song rhythm divergence in the inter-island species pair that is remarkably similar to that previously found for an intra-island species pair in the same genus. Importantly, QTL regions were significantly enriched for potential homologs of genes involved in pathways that may be modulating cricket song rhythm. These clusters of loci could constrain the spatial genomic distribution of genetic variation underlying cricket song variation and harbor several candidate genes that merit further study.