Miracle-dz/UniFAR-FTUs huggingface dataset:
Herpes Viruses in the Baltimore Longitudinal Study of Aging

Background and Objectives Although an infectious etiology of Alzheimer disease (AD) has received renewed attention with a particular focus on herpes viruses, the longitudinal effects of symptomatic herpes virus (sHHV) infection on brain structure and cognition remain poorly understood, as does the effect of sHHV on AD/neurodegeneration biomarkers. Methods We used a longitudinal, community-based cohort to characterize the association of sHHV diagnoses with changes in 3 T MRI brain volume and cognitive performance. In addition, we related sHHV to cross-sectional differences in plasma biomarkers of AD (β-amyloid [Aβ]42/40), astrogliosis (glial fibrillary acidic protein [GFAP]), and neurodegeneration (neurofilament light [NfL]). Baltimore Longitudinal Study of Aging participants were recruited from the community and assessed with serial brain MRIs and cognitive examinations over an average of 3.4 (SD = 3.2) and 8.6 (SD = 7.7) years, respectively. sHHV classification used International Classification of Diseases, Ninth Revision codes documented at comprehensive health and functional screening evaluations at each study visit. Linear mixed-effects and multivariable linear regression models were used in analyses. Results A total of 1,009 participants were included in the primary MRI analysis, 98% of whom were cognitively normal at baseline MRI (mean age = 65.7 years; 54.8% female). Having a sHHV diagnosis (N = 119) was associated with longitudinal reductions in white matter volume (annual additional rate of change −0.34 cm3/y; p = 0.035), particularly in the temporal lobe. However, there was no association between sHHV and changes in total brain, total gray matter, or AD signature region volumes. Among the 119 participants with sHHV, exposure to antiviral treatment attenuated declines in occipital white matter (p = 0.04). Although the sHHV group had higher cognitive scores at baseline, sHHV diagnosis was associated with accelerated longitudinal declines in attention (annual additional rate of change −0.01 Z-score/year; p = 0.008). In addition, sHHV diagnosis was associated with elevated plasma GFAP, but not related to Aβ42/40 and NfL levels. Discussion These findings suggest an association of sHHV infection with white matter volume loss, attentional decline, and astrogliosis. Although the findings link sHHV to several neurocognitive features, the results do not support an association between sHHV and AD-specific disease processes.
Improvement of fitness and biocontrol properties of Pseudomonas putida via an extracellular heme peroxidase

The extracellular 373‐kDa PehA heme peroxidase of Pseudomonas putida KT2440 has two enzymatic domains which depend on heme cofactor for their peroxidase activity. A null pehA mutant was generated to examine the impact of PehA in rhizosphere colonization competence and the induction of plant systemic resistance (ISR). This mutant was not markedly hampered in colonization efficiency. However, increase in pehA dosage enhanced colonization fitness about 30 fold in the root and 900 fold in the root apex. In vitro assays with purified His‐tagged enzymatic domains of PehA indicated that heme‐dependent peroxidase activity was required for the enhancement of root tip colonization. Evaluation of live/dead cells confirmed that overexpression of pehA had a positive effect on bacterial cell viability. Following root colonization of rice plants by KT2440 strain, the incidence of rice blast caused by Magnaporthe oryzae was reduced by 65% and the severity of this disease was also diminished in comparison to non‐treated plants. An increase in the pehA dosage was also beneficial for the control of rice blast as compared with gene inactivation. The results suggest that PehA helps P. putida to cope with the plant‐imposed oxidative stress leading to enhanced colonization ability and concomitant ISR‐elicitation.
Optimal deployment of sensor–emitter geometries for hybrid localisation using TDOA and AOA measurements

The relative sensor–emitter geometry has a great impact on the localisation accuracy. In this study, optimal deployments of hybrid time-difference-of-arrival (TDOA) and angle-of-arrival (AOA) localisation are studied. The explicit expression of the determinant of the Fisher information matrix is acquired and chosen as the criterion to determine optimal sensor deployments with the assumption of Gaussian measurement noise. The analytic solutions of the optimal sensor geometries are derived in both general and co-located scenarios. Finally, the theoretical developments are corroborated by simulation examples of unmanned aerial vehicles path planning problem.
Maybe they had a bad day: how LGBTQ and BIPOC patients react to bias in healthcare and struggle to speak out

OBJECTIVE People who experience marginalization, including Black, Indigenous, People of Color (BIPOC) and Lesbian, Gay, Bisexual, Transgender, Queer, Plus (ie, all other marginalized genders and sexual orientations) people (LGBTQ+) experience discrimination during healthcare interactions, which negatively impacts patient-provider communication and care. Yet, scarce research examines the lived experience of unfair treatment among patients from marginalized groups to guide patient-centered tools that improve healthcare equity. MATERIALS AND METHODS We interviewed 25 BIPOC and/or LGBTQ+ people about their experiences of unfair treatment and discrimination when visiting healthcare providers. Through thematic analysis, we describe participants' immediate reactions and longer-term consequences of those experiences. RESULTS We identified 4 ways that participants reacted to discrimination in the moment: Fighting, Fleeing, Excusing, and Working Around Bias. Long-term consequences reflect 6 ways they coped: Delaying or Avoiding Care, Changing Healthcare Providers, Self-prescribing, Covering Behaviors, Experiencing Health Complications, and Mistrusting Healthcare Institutions. DISCUSSION By describing how patients react to experiences of unfair treatment and discrimination, our findings enhance the understanding of health disparities as patients cope and struggle to speak out.To combat these problems, we identify 3 future directions for informatics interventions that improve provider behavior, support patient advocacy, and address power dynamics in healthcare. CONCLUSIONS BIPOC and LGBTQ+ patients' perspectives on navigating unfair treatment and discrimination in healthcare offers critical insight into their experiences and long-term consequences of those experiences. Understanding the circumstances and consequences of unfair treatment, discrimination, and the impact of bias through this patient-centered lens is crucial to inform informatics technologies that promote health equity.
An LSTM-based Method for Recognition and Prediction of Aircraft Formation

Aircraft formation recognition and prediction are of great significance in modern air combat. Aiming at the problems of many manual interventions and complex implementation through traditional aircraft formation recognition methods, an intelligent recognition and prediction method of aircraft formation is proposed. First, a formation coding method is designed, which is combined with Support Vector Machine (SVM) to construct a formation recognition model. Then, a formation prediction model is constructed based on the Long-Short-Term Memory network (LSTM) and the recognition model. Finally, a dataset is generated to train the two models, and the trained model can be used for formation recognition and prediction. After experimental verification, the method proposed in the paper has better recognition and prediction effects on formations, the recognition accuracy can reach 95.5%, and the accuracy of formation prediction can reach 95%.
Shallow Water Bathymetry Retrieval by Optical Remote Sensing Based on Depth-Invariant Index and Location Features

Abstract At present, most machine learning bathymetry retrieval models use the band reflectance as the inversion feature only, without considering features related to the water substrate and pixel spatial correlation. In this study, in addition to band reflectance, two features, Depth-Invariant Index (DII) and pixel location, are taken into account. Two machine learning algorithms, Random Forest (RF) and Back Propagation (BP) neural network are used to retrieve bathymetry. The effects of the two features on the accuracy and performance of bathymetry retrieval are explored. The results show that: (i) Machine learning algorithms are generally superior to the widely used Stumpf model. Stumpf model performs better only in the depth range of 8–16 m, with a Root Mean Square Error (RMSE) of 0.85 m, but has poor performance in other depth ranges. (ii) Compared with models that use Band Reflectance (BR) only, DIIb,g (blue-green DII) + BR model, Location and Location + BR models are all superior to the BR model for RF and BP algorithms. It means that DII and location features are very effective in improving the bathymetry retrieval accuracy.
Extended Place Capabilities Summaries for Rust Programs

—The advent of the Rust programming language and its borrowing and ownership model have created new opportunities in Program Analysis and Veriﬁcation. In order to take advantage of Rust’s advanced type system, however, it is necessary to extract detailed information from both the Rust compiler and borrow checker. Once this data has been collected, it then must be assembled into a meaningful format. The Extended Place Capabilities Summary (EPCS) presents type and borrow checking data in just such a format for a meaningful subset of the Rust language, while also performing analysis steps to provide an enriched summary of the data. Furthermore, the EPCS API implementation allows veriﬁcation and analysis tool developers to depend on a centralized, stable API rather than relying on various internal compiler or borrow checker functions and data structures.
UFCG: database of universal fungal core genes and pipeline for genome-wide phylogenetic analysis of fungi

In phylogenomics the evolutionary relationship of organisms is studied by their genomic information. A common approach to phylogenomics is to extract related genes from each organism, build a multiple sequence alignment and then reconstruct evolution relations through a phylogenetic tree. Often a set of highly conserved genes occurring in single-copy, called core genes, are used for this analysis, as they allow efficient automation within a taxonomic clade. Here we introduce the Universal Fungal Core Genes (UFCG) database and pipeline for genome-wide phylogenetic analysis of fungi. The UFCG database consists of 61 curated fungal marker genes, including a novel set of 41 computationally derived core genes and 20 canonical genes derived from literature, as well as marker gene sequences extracted from publicly available fungal genomes. Furthermore, we provide an easy-to-use, fully automated and open-source pipeline for marker gene extraction, training and phylogenetic tree reconstruction. The UFCG pipeline can identify marker genes from genomic, proteomic and transcriptomic data, while producing phylogenies consistent with those previously reported, and is publicly available together with the UFCG database at https://ufcg.steineggerlab. com.
A comparative study of two grasswren species reveals strong genetic divergence between a peninsula and mainland population

Dispersal patterns dictate genetic population structure, and ultimately population resilience, through maintaining critical ecological processes and genetic diversity. Direct observation of dispersal events is not often possible, but genetic methods offer an alternative method of indirectly measuring dispersal. Here, we use 7 652 genome-wide single-nucleotide polymorphisms (SNPs) to evaluate genetic population structure and infer dispersal capabilities of the Western Grasswren (Amytornis textilis textilis; WGW) in Western Australia (n = 118), utilising a sister species, the Thick-billed Grasswren (Amytornis modestus; TBGW) as a comparison dataset (n = 80). We found genetic divergence and low genetic diversity between two populations (Hamelin and Peron) in the WGW, despite evidence of long dispersal distances within populations by females. In addition, the two WGW populations were found to be more genetically divergent than two described subspecies of TBGW, despite the WGW occurring over a smaller spatial scale. By comparing these two grasswren species, our data suggest a narrow strip of land may be acting as a geographic barrier in the WGW, limiting dispersal between a peninsula population to the mainland. We investigate if morphology aligns with genetic divergence, with some estimates of divergence between WGW populations greater than those between subspecies of TBGW. However, confidence intervals were large, preventing definitive conclusions. Our results support the hypothesis that peninsula populations of small, ground-dwelling birds are genetically isolated from adjacent mainland populations. Furthermore, there is evidence to suggest that the limited gene flow is asymmetrical, with directional dispersal occurring from the bounded peninsula population to the mainland. Our study also highlights how substantial genetic divergence does not necessarily coincide with phenotypic differences.
Large-scale network metrics improve the classification performance of rapid-eye-movement sleep behavior disorder patients

Clinical decision support systems based on machine-learning algorithms are largely applied in the context of the diagnosis of neurodegenerative diseases (NDDs). While recent models yield robust classifications in supervised two classes-problems accurately separating Parkinson’s disease (PD) from healthy control (HC) subjects, few works looked at prodromal stages of NDDs. Idiopathic Rapid-eye Movement (REM) sleep behavior disorder (iRBD) is considered a prodromal stage of PD with a high chance of phenoconversion but with heterogeneous symptoms that hinder accurate disease prediction. Machine learning (ML) based methods can be used to develop personalized trajectory models, but these require large amounts of observational points with homogenous features significantly reducing the possible imaging modalities to non-invasive and cost-effective techniques such as high-density electrophysiology (hdEEG). In this work, we aimed at quantifying the increase in accuracy and robustness of the classification model with the inclusion of network-based metrics compared to the classical Fourier-based power spectral density (PSD). We performed a series of analyses to quantify significance in cohort-wise metrics, the performance of classification tasks, and the effect of feature selection on model accuracy. We report that amplitude correlation spectral profiles show the largest difference between iRBD and HC subjects mainly in delta and theta bands. Moreover, the inclusion of amplitude correlation and phase synchronization improves the classification performance by up to 11% compared to using PSD alone. Our results show that hdEEG features alone can be used as potential biomarkers in classification problems using iRBD data and that large-scale network metrics improve the performance of the model. This evidence suggests that large-scale brain network metrics should be considered important tools for investigating prodromal stages of NDD as they yield more information without harming the patient, allowing for constant and frequent longitudinal evaluation of patients at high risk of phenoconversion. Highlights Network-based features are important tools to investigate prodromal stages of PD Amplitude correlation shows the largest difference between two groups in 9/30 bands Amplitude correlation improved up to 11% the performance compared to PSD alone Classification robustness increases when we use both network-based EEG features Classifier performance worsens when PSD is added to network-based EEG features
Homeodomain-less MEIS1 suppresses CUL3-dependent ATOH1 degradation to regulate cerebellar granule cell development

Granule cell progenitors (GCPs) and granule cells (GCs) in the cerebellum are excellent models for studying the differentiation of neural progenitors into neurons. Although gradual degradation of ATOH1 protein in GCPs leads to their differentiation into GCs, the underlying regulatory mechanism is unclear. We show that a homeodomain-less isoform of MEIS1 (MEIS1-HdL) regulates ATOH1 degradation and GCP differentiation in a transcriptional regulation-independent manner. BMP signaling phosphorylates Ser328 of ATOH1 via ERK. CUL3 was identified as an E3-ligase that polyubiquitinates Ser328 phosphorylated ATOH1, leading to ATOH1 degradation. MEIS1-HdL and full-length MEIS1 form a trimeric complex with CUL3 and COP9 signalosome that inhibits ATOH1 ubiquitination and degradation. MEIS1-HdL is exclusively expressed in GCPs and suppresses ATOH1 degradation and GCP differentiation into GCs, despite high BMP signaling activities in the cells. Our study provides insight into the precise regulatory machinery of the degradation of the pivotal protein ATOH1 and differentiation of neural progenitors.
Non-invasive fecal DNA yields whole genome and metagenomic data for species conservation

Non-invasive biological samples benefit studies that investigate rare, elusive, endangered, and/or dangerous species. Integrating genomic techniques that use non-invasive biological samples with advances in computational approaches can benefit and inform wildlife conservation and management. Here we present a molecular pipeline that uses non-invasive fecal DNA samples to generate low- to medium-coverage genomes (e.g., >90% of the complete nuclear genome at 6X coverage) and metagenomic sequences, combining in a novel fashion widely available and accessible DNA collection cards with commonly used DNA extraction and library building approaches. DNA preservation cards are easy to transport and can be stored non-refrigerated, avoiding cumbersome and/or costly sample methods. The genomic library construction and shotgun sequencing approach did not require enrichment or targeted DNA amplification. The utility and potential of the data generated by this pipeline was demonstrated by the application of genome-scale analysis and metagenomics to zoo and free-ranging African savanna elephants (Loxodonta africana). Fecal samples collected from free-ranging individuals contained an average of 12.41% (5.54-21.65%) endogenous elephant DNA. Clustering of these elephants with others from the same geographic region was demonstrated by a principal component analysis of genetic variation using nuclear genome-wide SNPs. Metagenomic analyses generated compositional taxon classifications that included Loxodonta, green plants, fungi, arthropods, bacteria, viruses and archaea, showcasing the utility of our approach for addressing complementary questions based on host-associated DNA, e.g., pathogen and parasite identification. The molecular pipeline presented here extends applications beyond what has previously been shown for target-enriched datasets and contributes towards the expansion and application of genomic techniques to conservation science and practice.
Patterns of NFkB activation resulting from damage, reactive microglia, cytokines, and growth factors in the mouse retina

Müller glia are a cellular source for neuronal regeneration in vertebrate retinas. However, the capacity for retinal regeneration varies widely across species. Understanding the mechanisms that regulate the reprogramming of Müller glia into progenitor cells is key to reversing the loss of vision that occurs with retinal diseases. In the mammalian retina, NFκB signaling promotes glial reactivity and represses the reprogramming of Müller glia into progenitor cells. Here we investigate different cytokines, growth factors, cell signaling pathways, and damage paradigms that influence NFκB-signaling in the mouse retina. We find that exogenous TNF and IL1β potently activate NFκB-signaling in Müller glia in undamaged retinas, and this activation is independent of microglia. By comparison, TLR1/2 agonist indirectly activates NFκB-signaling in Müller glia, and this activation depends on the presence of microglia as Tlr2 is predominantly expressed by microglia, but not other types of retinal cells. Exogenous FGF2 did not activate NFκB-signaling, whereas CNTF, Osteopontin, WNT4, or inhibition of GSK3β activated NFκB in Müller glia in the absence of neuronal damage. By comparison, dexamethasone, a glucocorticoid agonist, suppressed NFκB-signaling in Müller glia in damaged retinas, in addition to reducing numbers of dying cells and the accumulation of reactive microglia. Although NMDA-induced retinal damage activated NFκB in Müller glia, optic nerve crush had no effect on NFκB activation within the retina, whereas glial cells within the optic nerve were responsive. We conclude that the NFκB pathway is activated in retinal Müller glia in response to many different cell signaling pathways, and activation often depends on signals produced by reactive microglia.
Estimating sequence diversity of prion protein gene (PRNP) in Portuguese populations of two cervid species: red deer and fallow deer

Among the transmissible spongiform encephalopathies (TSEs), chronic wasting disease (CWD) in cervids is now a rising concern in wildlife within Europe, after the detection of the first case in Norway in 2016, in a wild reindeer and until June 2022 a total of 34 cases were described in Norway, Sweden and Finland. The definite diagnosis is postmortem, performed in target areas of the brain and lymph nodes. Samples are first screened using a rapid test and, if positive, confirmed by immunohistochemistry and Western immunoblotting. The study of the genetics of the prion protein gene, PRNP, has been proved to be a valuable tool for determining the relative susceptibility to TSEs. In the present study, the exon 3 of PRNP gene of 143 samples from red deer (Cervus elaphus) and fallow deer (Dama dama) of Portugal was analyzed. Three single nucleotide polymorphisms (SNPs) were found in red deer – codon A136A, codon T98A, codon Q226E – and no sequence variation was detected in fallow deer. The low genetic diversity found in our samples is compatible with previous studies in Europe. The comparison with results from North America, suggests that the free-ranging deer from our study may present susceptibility to CWD, although lack of experimental data and the necessity of extensive survey are necessary to evaluate these populations.
ARAX: a graph-based modular reasoning tool for translational biomedicine

Motivation With the rapidly growing volume of knowledge and data in biomedical databases, improved methods for knowledge-graph-based computational reasoning are needed in order to answer translational questions. Previous efforts to solve such challenging computational reasoning problems have contributed tools and approaches, but progress has been hindered by the lack of an expressive analysis workflow language for translational reasoning and by the lack of a reasoning engine—supporting that language—that federates semantically integrated knowledge-bases. Results We introduce ARAX, a new reasoning system for translational biomedicine that provides a web browser user interface and an application programming interface. ARAX enables users to encode translational biomedical questions and to integrate knowledge across sources to answer the user’s query and facilitate exploration of results. For ARAX, we developed new approaches to query planning, knowledge-gathering, reasoning, and result ranking and dynamically integrate knowledge providers for answering biomedical questions. To illustrate ARAX’s application and utility in specific disease contexts, we present several use-case examples. Availability and Implementation The source code and technical documentation for building the ARAX server-side software and its built-in knowledge database are freely available online (https://github.com/RTXteam/RTX). We provide a hosted ARAX service with a web browser interface at arax.rtx. ai and a web application programming interface (API) endpoint at arax.rtx. ai/api/arax/v1.3/ui/. Contact dmk333@psu.edu