bluuebunny/crossref_metadata_2025_split huggingface dataset:
HIV pre-exposure prophylaxis provision by U.S. health centers in 2021
Authors: Chinbunchorn Tanat , Mayer Kenneth H. , Campbell Juwan , King Dana , Krakower Douglas , Marcus Julia L. , Grasso Chris , Keuroghlian Alex S. 
Published: October 2023

Objectives:
The aim of this study was to assess HIV preexposure prophylaxis (PrEP) provision in U.S. health centers.

Design:
The U.S. Ending the HIV Epidemic (EHE) initiative designated health centers as the main healthcare system through which PrEP scale-up occurs. Health centers offer primary care to over 30 million disproportionately uninsured, racially or ethnically minoritized, and low-income patients. This study is the first to assess PrEP provision across health centers, including characteristics of clinics, patient populations, and policies associated with PrEP prescribing.

Methods:
The Health Resources and Services Administration's Uniform Data System contained aggregate data on PrEP prescriptions and patient sociodemographics at health centers from January 1 through December 31, 2021, in 50 U.S. states, the District of Columbia, and eight U.S. territories. We compared patient demographics and availability of Medicaid expansion and PrEP assistance programs at health centers that prescribed vs. those that did not prescribe PrEP.

Results:
Across 1375 health centers serving 30 193 278 patients, 79 163 patients were prescribed PrEP. Health centers that prescribed any PrEP had higher proportions of sexual, gender, racial, and ethnic minority patient populations compared with health centers that prescribed no PrEP. Compared with health centers that prescribed no PrEP, a higher proportion of health centers that prescribed PrEP were located in designated high-priority jurisdictions of the EHE initiative or states with Medicaid expansion or public PrEP assistance programs.

Conclusion:
Health centers are critical for scaling up PrEP in minoritized populations disproportionately affected by HIV, facilitated through federal and state-level policies. These findings highlight service gaps and inform future interventions to optimize PrEP implementation and support EHE initiative goals.
Antimicrobial and quorum sensing inhibitory activity of epiphytic bacteria isolated from the red alga Halymenia durvillei
Authors: Padayao Mary Hannah Rose , Padayao Francis Reuben Paul , Patalinghug Jenny Marie , Raña Gem Stephen , Yee Jonie , Geraldino Paul John , Quilantang Norman 
Published: October 2023

Halymenia durvillei is a red alga that is commonly utilized in the Philippines as food and as a source of high-value natural products for industrial applications. However, there are no studies regarding the microbial community associated with H. durvillei and its potential applications. This study aimed to isolate and identify the epiphytic bacteria of H. durvillei and determine their antimicrobial and quorum sensing inhibitory (QSI) effects. The thalli of H. durvillei were collected at the shores of Santa Fe, Bantayan, Cebu, Philippines. Bacterial isolates were identified using 16S rRNA, and their ethyl acetate (EtOAc) extracts were subjected to antimicrobial susceptibility tests against representative species of yeast, Gram-negative, and Gram-positive bacteria. Their QSI activity against Chromobacterium violaceum was also determined. Fourteen distinct bacterial colonies belonging to four genera, namely, Alteromonas (3), Bacillus (5), Oceanobacillus (1), and Vibrio (5) were successfully isolated and identified. All fourteen bacterial isolates exhibited antibacterial effects. EPB9, identified as B. safensis, consistently showed the strongest inhibition against Escherichia coli, Staphylococcus aureus, and S. epidermidis having minimum inhibitory concentrations (MIC) ranging from 0.0625 – 1.0 mg mL-1. In contrast, all fourteen isolates showed weak antifungal effects. Both B. safensis (EPB 9) and B. australimaris (EPB15) exhibited QSI effects at 100 mg mL-1 showing opaque zones of 3.1 ± 0.9 mm and 3.8 ± 0.4 mm, respectively. This study is the first to isolate and identify the distinct microbial epiphytic bacterial community of H. durvillei and its potential as an abundant resource for new antibacterial and QSI bioactives.
Durability of Motor Learning by Observing
Authors: Mangos Natalia , Forgaard Christopher J. , Gribble Paul L. 
Published: October 2023

AbstractInformation about another person’s movement kinematics obtained through visual observation activates brain regions involved in motor learning. Observation-related changes in these brain areas are associated with adaptive changes to feedforward neural control of muscle activation and behavioural improvements in limb movement control. However, little is known about the stability of these observation-related effects over time. Here we used force channel trials to probe changes in lateral force production at various time points (1 min, 10 min, 30 min, 60 min, 24 h) after participants either physically performed, or observed another individual performing upper limb reaching movements that were perturbed by novel, robot-generated forces (a velocity-dependent force-field). Observers learned to predictively generate directionally and temporally specific compensatory forces during reaching, consistent with the idea that they acquired an internal representation of the novel dynamics. Participants who physically practiced in the force-field showed adaptation that was detectable at all time points, with some decay detected after 24 h. Observation-related adaptation was less temporally stable in comparison, decaying slightly after 1 h and undetectable at 24 h. Observation induced less adaptation overall than physical practice, which could explain differences in temporal stability. Visually acquired representations of movement dynamics are retained and continue to influence behavior for at least one hour after observation.SignificanceWe used force channel probes in an upper limb force-field reaching task in humans to compare the durability of learning-related changes that occured through visual observation to those after physical movement practice. Visually acquired representations of movement dynamics continued to influence behavior for at least one hour after observation. The significance of the findings is two-fold. First, they broaden our understanding of the potential role of visual observation in naturalistic motor skill acquisition. Our findings point to a (at least) 1-hour window during which visual observation of another person (e.g. a tutor or a conspecific learner) could play a role in motor learning, especially in early learning, presumably (as shown in our previous work) through activation of the neural circuitry that underlies motor skill acquisition. Second, this one-hour window represents an opportunity for observation-based interventions in neurorehabilitation (e.g. interleaving observation with physical practice) to aid in driving cortical reorganization for recovery of motor function.
Identification of Hub Genes and Functional Annotation of Glaucoma Using Bioinformatic Analysis
Authors: Aaron Pavithren , Sivanananthan Navanithan , Kaur Jaspreet , Arumainathan Jeyvisna , Swaminathan Venkataramanan 
Published: October 2023

AbstractGlaucoma is a common eye condition that damages the optic nerve, leading to vision loss and potential blindness if not treated. It affects around 80 million people worldwide and is a major cause of irreversible blindness. This study aims to identify key genes and their functions in glaucoma using bioinformatic analysis. The research follows a five-step approach, including data retrieval, processing, identification of differentially expressed genes (DEGs), protein interaction analysis, functional enrichment analysis, and identification of hub genes. Three GSE datasets (GSE 4316, GSE 53985, and GSE 7144) were selected, resulting in a total of 632 upregulated and 625 downregulated genes. Analysis using the DAVID server revealed significant biological processes related to gene regulation, transcription, and cell adhesion. The protein networks for upregulated and downregulated genes consisted of 581 nodes and 1713 edges, and 575 nodes and 2428 edges, respectively. The Cytohubba plugin identified 10 significant genes for each group. The upregulated genes included PTPRC, INS, SRC, CXCR, FN1, TNF, ACTB, AKT1, ALB, and PECAM1, while the downregulated genes included VEGFA, FN1, TP53, ACTB, AKT1, CCND1, CTNNB1, MYC, CD44, and EGFR. These findings enhance our understanding of glaucoma’s molecular biology and provide valuable insights for prognosis prediction and the development of treatments and medications.
Modulating Subjective Pain Perception with Decoded MNI-space Neurofeedback
Authors: Berman Taryn , Cushing Cody , Manuel Shawn , Vachon-Presseau Étienne , Cortese Aurelio , Kawato Mitsuo , Woo Choong-Wan , Wager Tor D. , Lau Hakwan , Roy Mathieu , Taschereau-Dumouchel Vincent 
Published: October 2023

AbstractPain is a complex emotional experience that still remains challenging to manage. Previous functional magnetic resonance imaging (fMRI) studies have associated pain with distributed patterns of brain activity (i.e., brain decoders), but it is still unclear whether these observations reflect causal mechanisms. To address this question, we devised a new neurofeedback approach leveraging real-time decoding of fMRI data to test if modulating pain-related multivoxel fMRI patterns could lead to changes in subjective pain experience. We first showed that subjective pain ratings can indeed be accurately predicted using a real-time decoding approach based on the stimulus intensity independent pain signature (SIIPS) and the neurologic pain signature (NPS). Next, we trained participants in a double-blinded decoded fMRI neurofeedback experiment to up- or down-regulate the SIIPS. Our results indicate that participants can learn to down-regulate the expression of SIIPS independently from NPS expression. Importantly, the success of this neurofeedback training was associated with the perceived intensity of painful stimulation following the intervention. Taken together, these results indicate that closed-loop brain imaging can be efficiently conducted usinga priorifMRI decoders of pain, potentially opening up a new range of applications for decoded neurofeedback, both for clinical and basic science purposes.
A machine learning-based approach to identify reliable gold standards for protein complex composition prediction
Authors: Yang Pengcheng , Lee Youngwoo , Szymanski Daniel B. , Xie Jun 
Published: October 2023

AbstractCo-Fractionation Mass Spectrometry (CFMS) enables the discovery of protein complexes and the systems-level analyses of multimer dynamics that facilitate responses to environmental and developmental conditions. A major challenge in the CFMS analyses, and other omics approaches in general, is to conduct validation experiments at scale and develop precise methods to evaluate the performance of the analyses. For protein complex composition predictions, CORUM is commonly used as a source of known complexes; however, the subunit pools in cell extracts are very rarely in the assumed fully assembled states. Therefore, a fundamental conflict exists between the assumed multimerization of the CORUM “gold standards” and the CFMS experimental datasets to be evaluated. In this paper, we develop a machine learning-based “small world” data analysis method. This method uses size exclusion chromatography profiles of predicted CORUM complex subunits to identify relatively rare instances of fully assembled complexes, as well as bona fide stable CORUM subcomplexes. Our method involves a two-stage machine learning approach that is designed to leverage evolutionarily conserved sequences among CORUM subunits and integrate it with size exclusion chromatography profile data from CFMS experiments. The generated gold standards are evaluated by both statistical significance and size comparison between calculated and predicted complexes. We expect these gold standards to serve as improved benchmarks to assess the overall reliability of CFMS-based protein complex composition predictions.
Distinct rich and diverse clubs regulate coarse and fine binocular disparity processing: Evidence from stereoscopic task-based fMRI
Authors: Lohia Kritika , Soans Rijul Saurabh , Saxena Rohit , Mahajan Kabir , Gandhi Tapan K. 
Published: October 2023

AbstractWhile cortical regions involved in processing binocular disparities have been studied extensively, little is known on how the human visual system adapts to changing disparity magnitudes. Even though there is an established correlation of BOLD signal with disparity magnitudes, this correlation is not inherent and instead arises from specific causal interactions within an integrated network. Here, we investigate causal mechanisms of coarse and fine binocular disparity processing using fMRI with a clinically validated, custom anaglyph-based stimulus. Therefore, we use degree (D) and participation coefficient (PC) metrics representing rich and diverse properties of the brain network, respectively. Twenty-six healthy participants were asked to indicate hidden 3D shapes through anaglyph filters at four disparity magnitudes. Our findings reveal significant changes at different disparity magnitudes in terms of D and PC of Middle Temporal (MT), V2, V3 and Superior Parietal Lobule (SPL) across both hemispheres. Of these, MT exhibited overlapping rich and diverse club characteristics among other brain regions. Further, diverse clubs outperform rich clubs in decoding disparity magnitudes irrespective of the hemisphere, thereby reinforcing their integrative network properties. These findings imply that distinct rich and diverse clubs exist and provide functional evidence for the variability in human stereopsis.
Evaluating discordant somatic calls across mutation discovery approaches to minimize false negative drug-resistant findings
Authors: Lin Hsin-Fu , Chien Pei-Miao , Cheng Chinyi , Yuan Tzu-Hang , Wang Yu-Bin , Chen Pei-Lung , Chen Chien-Yu , Huang Jia-Hsin , Hsu Jacob Shujui 
Published: October 2023

AbstractEvaluating robustness of somatic mutation detections is essential when utilizing whole exome sequencing (WES) for treatment decision-making. A comprehensive evaluation was conducted using tumor WES from the FDA-led Sequencing Quality Control Phase 2 (SEQC2) project, in which multiple library kits sequenced identical DNA materials across three labs to benchmark analytical validity. These workflows included various read aligner (BWA, Bowtie2, DRAGEN-Aligner, DRAGMAP, and HISAT2) and mutation caller (Mutect2, TNscope, DRAGEN-Caller, and DeepVariant) combinations. The results revealed that DRAGEN exhibited superior performance, achieving mean F1-scores of 0.966 and 0.791 for SNV and INDEL detection, respectively. Among open-source software, BWA Mutect2 and HISAT2 Mutect2 combinations showed the highest mean F1-scores for SNV (0.949) and IN-DEL (0.722), respectively. The analyses indicated that high-quality data can be analyzed as having worse results, and vice versa. Evaluations of COSMIC reported mutations unveiled discrepancies across enrichment kits. IDT enrichment kits showed a higher false negative rate, while Agilent WES kits tended to miss mutations inCBLandIDH1, and Roche library kits tended to miss the mutations inPIK3CB. For drug-related biomarkers, Sentieon TNscope tended to underestimate tumor mutation burden and overlook crucial drug-resistance mutations such asFLT3(c.G1879A: p.A627T) for cytarabine resistance in leukemia andMAP2K1(c.G199A:p.D67N) forBRAFinhibitors in melanoma. The findings highlight the importance of robust bioinformatic analysis in identifying tumor mutations and guiding clinical decision-making.HighlightsMutation callers had a significantly higher effect on overall sensitivity than aligners.Benchmarking analyses demonstrated that high-quality sequencing reads can be analyzed as having worse results, and vice versa.DRAGEN exhibited the best performance among other aligner-caller combinations.The combination of BWA with Mutect2 and HISAT2 with Mutect2 yielded the highest mean F1 scores for detecting SNVs and INDELs by open-source software, respectively.Sentieon TNscope tended to underestimate the tumor mutation burden and missed several drug-resistant mutations.
Protein Language Model Supervised Precise and Efficient Protein Backbone Design Method
Authors: Zhang Bo , Liu Kexin , Zheng Zhuoqi , Liu Yunfeiyang , Mu Junxi , Wei Ting , Chen Hai-Feng 
Published: October 2023

ABSTRACTProteins are essential macromolecules that play crucial roles in nearly every type of biological function. Most of the protein functions are determined by their position topologies, indicating that new functional proteins might be generated by designing their tertiary structures initially. Over the past two decades, numerous energy-based and machine learning algorithms have been proposed forde novoprotein design. However, optimizing these models to achieve a balanced performance among three critical aspects, namely sample fidelity, diversity, and generation speed, remains a challenging task on account of the constrained residue connections and hydrophobic interactions. Here we used the protein language model (pLM) as supervision to develop two complementary deep learning algorithms, Generative Protein Design by Language Model-Inpainting (GPDL-Inpainting) and GPDL-Hallucination, that can sufficiently represent and generate the protein structures and sequences. We compared the models with the state-of-the-art protein design models (RFdiffusion, RF Inpainting, and RF Halluciantion) and demonstrated that our methods can generate more designable structures with more diversity and higher computation efficiency. We also explored the possibility of the combination of hallucination and inpainting which might further improve the model efficiency and diversity. The source code of GPDL is available athttps://github.com/sirius777coder/GPDL.
On the road to losing connectivity: Fecal samples provide genome-wide insights into anthropogenic impacts on two large herbivore species in central India
Authors: Tyagi Abhinav , Yadav Nidhi , Pandit Awadhesh , Ramakrishnan Uma 
Published: October 2023

AbstractHumans have impacted most of the planet, and the ensuing fragmentation results in small, isolated habitat patches posing a risk of genetic diversity loss, inbreeding and genetic load. Understanding how natural and anthropogenic landscape features affect gene flow among habitat patches is critical for maintaining connectivity. Genome-wide data is required to comprehend the impacts of recent fragmentation, which can be challenging when only non-invasive samples are available. Here, we build upon advancements in conservation genomics to address connectivity of two large herbivores, gaur (Bos gaurus) and sambar (Rusa unicolor) in central India. Given their habitat associations, we expected these species to respond similarly to habitat fragmentation. We used fecal-DNA and methylation-based host-DNA enrichment with modified ddRAD protocol to generate genome-wide single nucleotide polymorphism (SNP) data for 124 Gaur and 99 Sambar individuals. Our findings reveal that gaur populations in central India are fragmented, displaying high genetic differentiation, with drift significantly affecting small populations like Umred-Karhandala Wildlife Sanctuary. Although Sambar shows low genetic structure, another small population, Bor Tiger Reserve is genetically differentiated. Our results suggest that although landcover change and roads restrict animal movement, the extent of this impact varies across the two species. We show that different species, respond differently to landscape features, even with similar habitat associations. We highlight small and isolated populations requiring urgent conservation intervention. Such multi-species approaches enhance our understanding of cross-species connectivity patterns. We suggest shifting from single-species to multi-species holistic conservation approach in rapidly developing landscapes to better manage co-occurring endangered species.
Semantic harmonization of Alzheimer’s disease datasets using AD-Mapper
Authors: Wegner Philipp , Balabin Helena , Ay Mehmet Can , Bauermeister Sarah , Killin Lewis , Gallacher John , Hofmann-Apitius Martin , Salimi Yasamin ,   the Alzheimer’s Disease Neuroimaging Initiative,   the Japanese Alzheimer’s Disease Neuroimaging Initiative,   the Aging Brain: Vasculature, Ischemia, and Behavior Study,   the Alzheimer’s Disease Repository Without Borders Investigators,   the European Prevention of Alzheimer’s Disease (EPAD) Consortium
Published: October 2023

AbstractINTRODUCTIONDespite numerous past endeavors for the semantic harmonization of Alzheimer’s disease (AD) cohort studies, an automatic tool has yet to be developed. As cohort studies form the basis of data-driven analysis, harmonizing them is crucial for cross-cohort analysis. We aimed to accelerate this task by constructing an automatic harmonization tool.METHODSWe created a common data model (CDM) through cross-mapping data from 20 cohorts, three CDMs, and ontology terms, which was then used to fine-tune a BioBERT model. Finally, we evaluated the model using three previously unseen cohorts and compared its performance to a string-matching baseline model.RESULTSHere, we present our AD-Mapper interface for automatic harmonization of AD cohort studies, which outperformed a string-matching baseline on previously unseen cohort studies. We showcase our CDM comprising 1218 unique variables.DISCUSSIONAD-Mapper leverages semantic similarities in naming conventions across cohorts to improve mapping performance.
Copy number variation and elevated genetic diversity at immune trait loci in Atlantic and Pacific herring
Authors: Mohamadnejad Sangdehi Fahime , Jamsandekar Minal S. , Enbody Erik D. , Pettersson Mats E. , Andersson Leif 
Published: October 2023

AbstractAtlantic and Pacific herring are sister species that diverged about 2 million years ago. Here we describe a genome comparison of the two species that reveal high genome-wide differentiation as expected for two distinct species but with islands of remarkably low genetic differentiation as measured by anFSTanalysis. However, this is not caused by low interspecies sequence divergence but an exceptionally high estimated intraspecies nucleotide diversity. These high diversity regions are not enriched for repeats but are highly enriched for immune trait-related genes. This enrichment includes classical immunity genes, such as immunoglobulin, T-cell receptor and major histocompatibility complex genes, but also a substantial number of genes with a role in the innate immune system. An analysis of long-read based assemblies from two Atlantic herring individuals revealed extensive copy number variation at these genomic regions, indicating that the elevated intraspecies nucleotide diversities was partially due to the cross-mapping of short reads. This study demonstrates that copy number expansion and variation is a characteristic feature of immune trait loci in herring and that this genetic diversity is likely to contribute to resistance to infectious diseases in extremely abundant species, such as the Atlantic and Pacific herring. Another important implication is that these loci are blind spots in classical genome-wide screens for genetic differentiation using short-read data, not only in herring, likely also in other species harboring qualitatively similar variation at immune trait loci.SignificanceThis study has revealed an extensive copy number variation and high nucleotide diversity at genes related to the immune system in Atlantic herring. Our analysis of previously published data in teleost species indicate that this is probably a widespread pattern among vertebrates. We also document that population genetic parameters estimated using short-read sequencing data are unreliable for these regions due to their complexity. They will also appear as blind spots in genome scans for regions of genetic differentiation based onFSTstatistics due to the very high within population nucleotide diversity. We show how long-read data can be used to decipher the gene organization and genetic diversity at these regions.