bluuebunny/crossref_metadata_2025_split huggingface dataset:
Neutrophil motility is regulated by both cell intrinsic and endothelial cell ARPC1B
Authors: Peterson Ashley , Bennin David , Lasarev Michael , Chini Julia , Beebe David J. , Huttenlocher Anna 
Published: November 2023

AbstractNeutrophil directed motility is necessary for host defense, but its dysregulation can also cause collateral tissue damage. Actinopathies are monogenic disorders that affect the actin cytoskeleton and lead to immune dysregulation. Deficiency in ARPC1B, a component of the ARP2/3 complex, results in vascular neutrophilic inflammation; however, the mechanism remains unclear. Here we generated ARPC1B-deficient human iPSC-derived iNeutrophils that show impaired migration and a switch from pseudopodia to the formation of elongated filopodia. We show, using a blood vessel on a chip model, that primary human neutrophils have impaired movement across an endothelium deficient in APRC1B. We also show that the combined deficiency of ARPC1B in iNeutrophils and endothelium results in further reduction in neutrophil migration. Taken together, these results suggest that ARPC1B in endothelium is sufficient to drive neutrophil behavior.Summary statementThe actin regulator ARPC1B in both neutrophils and endothelium drives neutrophil motility.
Acetyl-CoA Carboxylase Obstructs CD8+T-Cell Lipid Utilization and Energy Synthesis in the Tumor Microenvironment
Authors: Hunt Elizabeth G. , Hurst Katie E. , Kennedy Andrew S. , Riesenberg Brian P. , Gandy Evelyn J. , Andrews Alex M. , Gao Peng , Coleman Michael F. , Wallace Emily D. , Thaxton Jessica E. 
Published: November 2023

SummaryThe solid tumor microenvironment (TME) imprints a compromised metabolic state in tumor infiltrating T cells (TILs) hallmarked by the inability to maintain effective energy synthesis for antitumor function and survival. T cells in the TME must catabolize lipids via mitochondrial fatty acid oxidation (FAO) to supply energy in nutrient stress, and it is established that T cells enriched in FAO are adept at cancer control. However, endogenous TILs and unmodified cellular therapy products fail to sustain bioenergetics in tumors. Using patient samples and mouse models, we reveal that the solid TME imposes perpetual acetyl-CoA carboxylase (ACC) activity, enforcing lipid biogenesis and storage in TILs that directly opposes FAO. Using metabolic, lipidomic, and confocal imaging strategies, we find that restricting ACC wholly rewires T cell metabolism, enabling energy maintenance in TME stress. Moreover, limiting ACC activity potentiates a gene and phenotypic program indicative of T cell memory, engendering TILs with increased survival and polyfunctionality, with the ability to control solid cancer.
Redefining phenotypic intratumor heterogeneity of pancreatic ductal adenocarcinoma: a bottom-up approach
Authors: Hilmi Marc , Delecourt Flore , Raffenne Jérôme , Bourega Taib , Dusetti Nelson , Iovanna Juan , Blum Yuna , Richard Magali , Neuzillet Cindy , Couvelard Anne , de Mestier Louis , Rebours Vinciane , Nicolle Rémy , Cros Jérôme 
Published: November 2023

AbstractBackgroundPancreatic ductal adenocarcinoma (PDAC) tumor inter-patient heterogeneity has been well described with two major prognostic subtypes (classical and basal-like). An important intra-patient heterogeneity has been reported but has not yet been extensively studied due to the lack of standardized, reproducible and easily accessible high throughput methods.Material and MethodsWe built an immunohistochemical (IHC) tool capable of differentiating RNA-defined classical and basal-like tumors by selecting relevant antibodies using a multi-step process. The successive stages of i) anin-silicoselection from a review literature and a bulk transcriptome analysis of 309 PDACs, ii) a tumor-specific selection from 30 patient-derived xenografts followed by iii) the validation on tissue microarrays in 50 PDAC were conducted. We used our final IHC panel on two independent cohorts of resected PDAC (n=95, whole-slide, n=148, tissue microarrays) for external validation. After digitization and registration of pathology slides, we performed a tile-based-analysis in tumor and pre-neoplastic epithelial areas and a k-means clustering to identify relevant marker combinations.ResultsSequential marker selection led to the following panel: GATA6, CLDN18, TFF1, MUC16, S100A2, KRT17, PanBasal. Four different phenotypes were identified: 1 classical, 1 intermediate (KRT17+) and 2 basal-like (MUC16+ vs S100A2+) with specific biological properties. The presence of a minor basal contingent drastically reduced overall survival, even in classical predominant PDACs (HR=2.36, p=0.01). Analysis of preneoplastic lesions suggested that pancreatic carcinogenesis may follow a progressive evolution from classical toward a basal through an early intermediate phenotype.ConclusionOur IHC panel redefined and easily assessed the high degree of intra- and inter-tumoral heterogeneity of PDAC.
Protein-Ligand Binding Site Prediction andDe NovoLigand Generation from Cryo-EM Maps
Authors: Lu Chunyang , Mitra Kaustav , Mitra Kiran , Meng Hanze , Rich-New Shane Thomas , Wang Fengbin , Si Dong 
Published: November 2023

AbstractIdentification of protein-ligand binding sites is one of the most challenging tasks in drug discovery and design. Recent advances in machine learning community, particularly deep learning, inspired considerable research into deep learning-based methods for protein ligand binding site prediction (PLBP) and have achieved promising results. However one limitation of these methods and models is that they are developed and evaluated using conventional databases consisting of relatively small protein structures determined from X-ray crystallography and NMR with high resolution. When current PLBP methods are directly applied to large protein complexes, they either fail or produce very poor results. Given the increasing popularity of protein structures determined from Cryo-EM maps, which usually capture large protein complexes at relatively low resolution, there is a strong need to apply PLBP methods to Cryo-EM maps. For this purpose, we created a novel database (EMD-Ligand Dataset) consisting of Cryo-EM maps and information about their ligand-binding partners. Our study of several state-of-the-art PLBP methods show that even though they perform reasonably well on conventional databases, they do poorly on the novel EMD-Ligand Dataset, which calls for more research and work to be done by the community. As current methods for determining protein structures experimentally from Cryo-EM maps are time-consuming and expensive, we integrated PLBP methods with DeepTracer, an automatic and fast, de novo Cryo-EM protein structure modeling method, and established an end-to-end system capable of predicting ligand binding sites directly from Cryo-EM maps. Additionally, we leveraged an existing ligand generator to generate drug-like ligands from our predicted ligand-binding pockets and demonstrated its effectiveness.
Quantifying potential abiotic drivers of the ‘nurse-plant effect’ in two dominant shrub species of the northern Chihuahuan Desert
Authors: Ferrenberg Scott , Faist Akasha M. , Osborne Brooke , Lee Steven R. 
Published: November 2023

AbstractAggregations of plants surrounded by areas without vegetation cover in dryland ecosystems are thought to arise when larger plants facilitate the recruitment and/or performance of smaller “protégé” plants—a phenomenon referred to as the “nurse-plant” effect. While numerous drivers can generate a nurse-plant effect, efforts to quantify multiple drivers simultaneously are rare. After verifying a higher density of protégés beneath the foundational shrubsLarrea tridentataandProsopis glandulosa, multiple potential mechanisms underlying the nurse-plant effect were quantified in the Chihuahuan Desert of southern New Mexico. Comparisons of properties under shrub canopies relative to unvegetated interspaces revealed significantly greater concentrations of soil nutrients and lower photosynthetically active radiation and soil temperatures beneath shrubs but a consistently higher soil moisture in interspaces despite a greater water holding capacity in soils beneath shrubs. Nutrient concentrations were greater, on average, in soils beneathP. glandulosathanL. tridentatabut protégé plant numbers did not significantly differ among the species. Further, inL. tridentataandP. glandulosa,canopy size was positively related to levels of understory shading, and canopy size ofP. glandulosawas also positively related to soil nitrogen and microbial biomass. Results of this study suggest that a majority of the variance in the abiotic nurse-plant effect of this low-latitude system is explained by radiation interception and the concomitant reduction in temperatures experienced by protégé plants as opposed to direct effects of shrubs on soil water availability. As global change pressures intensify in drylands, a loss of perennial plant cover through mortality or dieback in canopies could have substantial, negative effects on soil biogeochemical pools and plant diversity. Additional quantification of the spatial and temporal variance in different mechanisms driving the nurse-plant effect across environmental and climatic gradients is needed to improve our understanding of plant community dynamics in dryland ecosystems.
CRISPR-Cas13d screens identifyKILR, a breast cancer risk-associated lncRNA that regulates DNA replication and repair
Authors: Wang Lu , Bitar Mainá , Lu Xue , Jacquelin Sebastien , Nair Sneha , Sivakumaran Haran , Hillman Kristine M. , Kaufmann Susanne , Ziegman Rebekah , Casciello Francesco , Gowda Harsha , Rosenbluh Joseph , Edwards Stacey L. , French Juliet D. 
Published: November 2023

ABSTRACTLong noncoding RNAs (lncRNAs) have surpassed the number of protein-coding genes, yet the majority have no known function. We previously discovered >800 lncRNAs at regions identified by breast cancer genome-wide association studies (GWAS). Here, we performed a pooled CRISPR-Cas13d RNA knockdown screen to identify which of these lncRNAs altered cell proliferation. We found thatKILR,a lncRNA that functions as a tumor suppressor, safeguards breast cells against uncontrolled proliferation. The half-life ofKILRis significantly reduced by the risk haplotype, revealing an alternative mechanism by which variants alter cancer risk. We showed thatKILRsequesters RPA1, a subunit of the RPA complex, required for DNA replication and repair. ReducedKILRexpression promotes cell proliferation by increasing the available pool of RPA1 and the speed of DNA replication. Our findings confirm lncRNAs as mediators of breast cancer risk, emphasize the need to annotate noncoding transcripts in relevant cell types when investigating GWAS variants and provide a scalable platform for mapping phenotypes associated with lncRNAs.
Fungi of the order Mucorales express a “sealing-only” tRNA ligase
Authors: Ahammed Khondakar Sayef , van Hoof Ambro 
Published: November 2023

AbstractSome eukaryotic pre-tRNAs contain an intron that is removed by a dedicated set of enzymes. Intron-containing pre-tRNAs are cleaved by tRNA splicing endonuclease (TSEN), followed by ligation of the two exons and release of the intron. Fungi use a “heal and seal” pathway that requires three distinct catalytic domains of the tRNA ligase enzyme, Trl1. In contrast, humans use a “direct ligation” pathway carried out by RTCB, an enzyme completely unrelated to Trl1. Because of these mechanistic differences, Trl1 has been proposed as a promising drug target for fungal infections. To validate Trl1 as a broad-spectrum drug target, we show that fungi from three different phyla contain Trl1 orthologs with all three domains. This includes the major invasive human fungal pathogens, and these proteins each can functionally replace yeast Trl1. In contrast, species from the order Mucorales, including the pathogensRhizopus arrhizusandMucor circinelloides, contain an atypical Trl1 that contains the sealing domain, but lack both healing domains. Although these species contain fewer tRNA introns than other pathogenic fungi, they still require splicing to decode three of the 61 sense codons. These sealing-only Trl1 orthologs can functionally complement defects in the corresponding domain of yeast Trl1 and use a conserved catalytic lysine residue. We conclude that Mucorales use a sealing-only enzyme together with unidentified non-orthologous healing enzymes for their heal and seal pathway. This implies that drugs that target the sealing activity are more likely to be broader-spectrum antifungals than drugs that target the healing domains.
The Neotropical endemic liverwort subfamily Micropterygioideae had circum-Antarctic links to the rest of the Lepidoziaceae during the early Cretaceous
Authors: Rayos Antonio L. , Renner Matthew A. M. , Ho Simon Y. W. 
Published: November 2023

AbstractLepidoziaceae are the third-largest family of liverworts, with about 860 species distributed on all continents. The evolutionary history of this family has not been satisfactorily resolved, with taxa such as Micropterygioideae yet to be included in phylogenetic analyses. We inferred a dated phylogeny of Lepidoziaceae using a data set consisting of 13 genetic markers, sampled from 147 species. Based on our phylogenetic estimate, we used statistical dispersal-vicariance analysis to reconstruct the biogeographic history of the family. We inferred a crown age of 197 Ma (95% credible interval 157–240 Ma) for the family in the Australian region, with most major lineages also originating in the same region. Micropterygioideae are placed as the sister lineage to Lembidioideae, with these two groups diverging from each other about 132 Ma in the South American–Australian region. Our results suggest a circum-Antarctic link between Micropterygioideae and the rest of the family, along with extinction of the lineage in the region. Crown Micropterygioideae were inferred to have arisen 45 million years ago in South America, before the continent separated from Antarctica. Our study reveals the influence of past geological events on the evolution and distribution of a widespread and diverse family of liverworts.
Using digital spatial profiling to analyse onco-immune-related gene expression within oropharyngeal tumours in relation to HPV and p16 status
Authors: Brooks Jill M. , Zheng Yuanning , Hunter Kelly , Willcox Benjamin E. , Gevaert Olivier , Mehanna Hisham 
Published: November 2023

AbstractHuman Papilloma virus (HPV)-mediated oropharyngeal cancer (OPC) has significantly better prognosis compared with HPV-negative, stimulating interest in treatment de-intensification approaches to reduce long-term sequelae. Routine clinical testing frequently utilises immunohistochemistry to detect upregulation of p16 protein as a surrogate marker of HPV-mediation. However, this does not detect discordant HPV+/p16-cases and incorrectly assigns HPV-/p16+ cases, which, given their inferior prognosis compared to HPV+/p16+, may have important clinical implications. The biology underlying poorer prognosis of HPV/p16 discordant OPC requires exploration. Here, we utilised digital spatial profiling to compare the expression patterns of selected immuno-oncology-related genes within the tumour and stromal compartments of HPV+/p16+, HPV-/p16+ and HPV-/p16-OPC tumour tissues (n=12). KRT andHIF1Awere upregulated in HPV-/p16+ and HPV-/p16-tumours relative to HPV+/p16+. Conversely, multiple genes associated with antitumour immune responses (such asCXCL9, CXCL10, CD74) were upregulated in HPV+/p16+ tumours. Of note, HPV-/p16+ and HPV-/p16-tumours displayed highly similar gene expression profiles, with only CXCL9 showing differential expression in stromal regions. These results are consistent with described prognostic patterns (HPV+p16+ > HPV-/p16+ > HPV-/p16-) and underline the need for dual HPV and p16 testing to guide clinical decision making.
Stroke Risk Prediction from Medical Survey Data: AI-Driven Risk Analysis with Insightful Feature Importance using Explainable AI (XAI)
Authors: Akter Simon Bin , Akter Sumya , Pias Tanmoy Sarkar 
Published: November 2023

AbstractPrioritizing dataset dependability, model performance, and interoperability is a compelling demand for improving stroke risk prediction from medical surveys using AI in healthcare. These collective efforts are required to enhance the field of stroke risk assessment and demonstrate the transformational potential of AI in healthcare. This novel study leverages the CDC’s recently published 2022 BRFSS dataset to explore AI-based stroke risk prediction. Numerous substantial and notable contributions have been established from this study. To start with, the dataset’s dependability is improved through a unique RF-based imputation technique that overcomes the challenges of missing data. In order to identify the most promising models, six different AI models are meticulously evaluated including DT, RF, GNB, RusBoost, AdaBoost, and CNN. The study combines top-performing models such as GNB, RF, and RusBoost using fusion approaches such as soft voting, hard voting, and stacking to demonstrate the combined prediction performance. The stacking model demonstrated superior performance, achieving an F1 score of 88%. The work also employs Explainable AI (XAI) approaches to highlight the subtle contributions of important dataset features, improving model interpretability. The comprehensive approach to stroke risk prediction employed in this study enhanced dataset reliability, model performance, and interpretability, demonstrating AI’s fundamental impact in healthcare.
Knowledge of self-care and complications of diabetes mellitus among diabetes mellitus patients in Addis Ababa, Ethiopia
Authors: Getachew Roman , Tewahido Dagmawit 
Published: November 2023

AbstractBackgroundDiabetes mellitus is a progressive disease that compromises the quality of life of the victims. Patients’ knowledge about diabetes mellitus self-care is vital in achieving targeted glycemic control and minimizing complications of the disease. However, there are limited studies in Ethiopia on the subject to guide interventions. Hence, this paper aimed to assess knowledge about diabetic self-care and complications among diabetes mellitus patients in Addis Ababa, Ethiopia.MethodsAn institutional-based cross-sectional study design was employed. A structured questionnaire was used to assess knowledge; participants who responded correctly to at least 6 of the eight questions were classified as having good knowledge about the complications and those who answered <6 questions correctly were classified as having poor knowledge. Participants who correctly responded to ≥30 of the 37 self-care questions were classified as having good knowledge about diabetes self-care; those who correctly responded to <30 questions were classified as having poor knowledge. Multivariate logistic regression analyses were used to identify associated factors.ResultOverall, 202 (51.7%) of the study participants had good knowledge about the complications, and 279 (71.4%) of the participants had a good knowledge of self-care. Receiving diabetic education was a significant modifiable factor for having poor knowledge about both the complication (AOR= 3.078(1.323,7.160)) and the self-care (AOR=7.645(3.153,18.538)).ConclusionAbout half (51.7%) of the participants had good knowledge about the complications, and about three in four (71.4%) had good knowledge about self-care. Receiving diabetic education was significantly associated with both complication and self-care knowledge status. Focusing on the quality and quantity of the information that is delivered to diabetes mellitus patients can improve the quality of care and the outcome.
Analysing biodiversity observation data collected in continuous time: Should we use discrete- or continuous-time occupancy models?
Authors: Pautrel Léa , Moulherat Sylvain , Gimenez Olivier , Etienne Marie-Pierre 
Published: November 2023

AbstractBiodiversity monitoring is undergoing a revolution, with fauna observations data being increasingly gathered continuously over extended periods, through sensors like camera traps and acoustic recorders, or via opportunistic observations. These data are often analysed with discrete-time ecological models, requiring the transformation of continuously collected data into arbitrarily chosen non-independent discrete time intervals. To overcome this issue, ecologists are increasingly turning to the existing continuous-time models in the literature. Closer to the real detection process, they are lesser known than discrete-time models, not always easily accessible, and can be more complex. Focusing on occupancy models, a type of species distribution models, we asked ourselves: Should we dedicate time and effort to learning and using these continuous-time models, or can we go on using discrete-time models?We conducted a comparative simulation study using data generated within a continuous-time framework. We assessed the performance of five static occupancy models with varying detection processes: discrete detection/non-detection process, discrete count process, continuous-time Poisson process, and two types of modulated Poisson processes. Our goal was to assess their abilities to estimate occupancy probability with continuously collected data. We applied all models to empirical lynx data as an illustrative example.In scenarios with easily detectable animals, we found that all models accurately estimated occupancy. All models reached their limits with highly elusive animals. Variation in discretisation intervals had minimal impact on the discrete models’ capacity to estimate occupancy accurately.Our study underscores that opting for continuous-time models with an increased number of parameters, aiming to get closer to the sensor detection process, may not offer substantial advantages over simpler models when the sole aim is to accurately estimate occupancy. Model choice can thus be driven by practical considerations such as data availability or implementation time. However, occupancy models can encompass goals beyond estimating occupancy probability. Continuous-time models, particularly those considering temporal variations in detection, can offer valuable insights into specific species behaviour and broader ecological inquiries. We hope that our findings offer valuable guidance for researchers and practitioners working with continuously collected data in wildlife monitoring and modelling.