bluuebunny/crossref_metadata_2025_split huggingface dataset:
Successful Development of a Natural Language Processing Algorithm for Pancreatic Neoplasms and Associated Histologic Features
Authors: Harrison Jon Michael , Yala Adam , Mikhael Peter , Roldan Jorge , Ciprani Debora , Michelakos Theodoros , Bolm Louisa , Qadan Motaz , Ferrone Cristina , Fernandez-del Castillo Carlos , Lillemoe Keith Douglas , Santus Enrico , Hughes Kevin 
Published: September 2023

Objectives
Natural language processing (NLP) algorithms can interpret unstructured text for commonly used terms and phrases. Pancreatic pathologies are diverse and include benign and malignant entities with associated histologic features. Creating a pancreas NLP algorithm can aid in electronic health record coding as well as large database creation and curation.

Methods
Text-based pancreatic anatomic and cytopathologic reports for pancreatic cancer, pancreatic ductal adenocarcinoma, neuroendocrine tumor, intraductal papillary neoplasm, tumor dysplasia, and suspicious findings were collected. This dataset was split 80/20 for model training and development. A separate set was held out for testing purposes. We trained using convolutional neural network to predict each heading.

Results
Over 14,000 reports were obtained from the Mass General Brigham Healthcare System electronic record. Of these, 1252 reports were used for algorithm development. Final accuracy and F1 scores relative to the test set ranged from 95% and 98% for each queried pathology. To understand the dependence of our results to training set size, we also generated learning curves. Scoring metrics improved as more reports were submitted for training; however, some queries had high index performance.

Conclusions
Natural language processing algorithms can be used for pancreatic pathologies. Increased training volume, nonoverlapping terminology, and conserved text structure improve NLP algorithm performance.
Stem nodulation: diversity and occurrence inAeschynomeneandSesbanialegumes from wetlands of Madagascar
Authors: Manantsoa Faustin F. , Rakotoarisoa Marrino F. , Chaintreuil Clémence , Razakatiana Adamson T.E. , Gressent Frédéric , Pervent Marjorie , Bourge Mickaël , Andrianandrasana Martial D. , Nouwen Nico , Randriambanona Herizo , Ramanankierana Heriniaina , Arrighi Jean-François 
Published: September 2023

AbstractAs an adaptation to flooding, few legume species have the original ability to develop nitrogen-fixing nodules on the stem. By surveying wetlands of Madagascar, we found a large occurrence and diversity of stem nodulation in Aeschynomene and Sesbania legumes. They represent opportunities to investigate different modalities of the nitrogen-fixing symbiosis in legumes.
Leveraging Machine Learning to Uncover the Hidden Links between Trusting Behavior and Biological Markers
Authors: Cao Zimu , Setoyama Daiki , Monica-Natsumi Daudelin , Matsushima Toshio , Yada Yuichiro , Watabe Motoki , Hikida Takatoshi , Kato Takahiro A , Naoki Honda 
Published: September 2023

AbstractUnderstanding the decision-making mechanisms underlying trust is essential, particularly for patients with mental disorders who experience difficulties in developing trust. We aimed to explore biomarkers associated with trust-based decision-making by quantitative analysis. However, quantification of decision-making properties is difficult because it cannot be directly observed. Here, we developed a machine learning method based on Bayesian hierarchical model to quantitatively decode the decision-making properties from behavioral data of a trust game. By applying the method to data of patients with MDD and healthy controls, we estimated model parameters regulating trusting decision-making. The estimated model was able to predict behaviors of each participant. Although there is no difference of the estimated parameters between MDD and healthy controls, several biomarkers were associated with the decision-making properties in trusting behavior. Our findings provide valuable insights into the trusting decision-making, offering a basis for developing targeted interventions to improve their social functioning and overall well-being.
Developmental priming of cancer susceptibility
Authors: Panzeri Ilaria , Fagnocchi Luca , Apostle Stefanos , Tompkins Megan , Wolfrum Emily , Madaj Zachary , Hostetter Galen , Liu Yanqing , Schaefer Kristen , Chih-Hsiang Yang , Bergsma Alexis , Drougard Anne , Dror Erez ,   PERMUTE, Chandler Darrell , Schramek Daniel , Triche Timothy J. , Pospisilik J. Andrew 
Published: September 2023

SUMMARYDNA mutations are necessary drivers of cancer, yet only a small subset of mutated cells go on to cause the disease. To date, the mechanisms that determine which rare subset of cells transform and initiate tumorigenesis remain unclear. Here, we take advantage of a unique model of intrinsic developmental heterogeneity (Trim28+/D9) and demonstrate that stochastic early life epigenetic variation can trigger distinct cancer-susceptibility ‘states’ in adulthood. We show that these developmentally primed states are characterized by differential methylation patterns at typically silenced heterochromatin, and that these epigenetic signatures are detectable as early as 10 days of age. The differentially methylated loci are enriched for genes with known oncogenic potential. These same genes are frequently mutated in human cancers, and their dysregulation correlates with poor prognosis. These results provide proof-of-concept that intrinsic developmental heterogeneity can prime individual, life-long cancer risk.
Valency of Ligand-Receptor Binding from Pair Potentials
Authors: Morton William , Vácha Robert , Angioletti-Uberti Stefano 
Published: September 2023

AbstractMolecular dynamics simulations have been crucial for investigating the dynamics of nanoparticle uptake by cell membranes via ligand-receptor interactions. Use of coarsegrained models has enabled evaluation of the effects of nanoparticle size, shape, ligand distribution on nanoparticles surface, or used thoroughly in the past decade, where a percentage of lipid heads, receptors, are attracted to sites on the nanoparticle surface, ligands. However, when pair-potentials are used to represent ligand-receptor interactions, the number of receptors interacting with one ligand, valency, may vary. We demonstrate that the curvature of a nanoparticle, strength of ligand-receptor interactions, and ligand or receptor concentration change the valency - ranging from 3.4 to 5.1 in this study. Such change in valency can create inaccurate comparisons between nanoparticles, or even result in the uptake of smaller nanoparticles than would be expected. To rectify this inconsistency we propose the adoption of a model based on bond-formation and use it to determine the extent to which previous studies may have been effected. This work recommends avoiding pair-potentials for modeling ligandreceptor interactions to ensure methodological consistency in nanoparticle studies.TOC GraphicA rendering of a ligand coated nanoparticle coming into contact with a lipid bilayer membrane. The receptor in the membrane is highlighted for clarity.
Intracellular K+limits T cell exhaustion and preserves antitumor function
Authors: Collier Camille , Wucherer Kelly , McWhorter Matthew , Jenkins Chelsea , Bartlett Alexandra , Roychoudhuri Rahul , Eil Robert 
Published: September 2023

AbstractThe cancer-killing activity of T cells is often compromised within tumors, allowing disease progression. We previously found that intratumoral elevations in extracellular K+related to ongoing cell death constrained CD8+T cell Akt-mTOR signaling and effector function (1,2). To alleviate K+mediated T cell suppression, we pursued genetic means to lower intracellular K+.Transcriptomic analysis of CD8+T cells demonstrated the Na+/K+ATPase to be robustly and dynamically expressed. CRISPR-Cas9 mediated deletion of the catalytic alpha subunit of the Na+/K+ATPase lowered intracellular K+but produced tonic hyperactivity in multiple signal transduction cascades along with the acquisition of co-inhibitory receptors and terminal differentiation in mouse and human CD8+T cells. Mechanistically, Na+/K+ATPase disruption led to ROS accumulation due to depletion of intracellular K+in T cells. Antioxidant treatment or high K+media preventedAtp1a1deficient T cells from exhausted T (TEx) cell formation. Consistent with transcriptional and proteomic data suggesting a TExcell phenotype, T cells lackingAtp1a1had compromised persistence and antitumor activity in a syngeneic model of orthotopic murine melanoma. Translational application of these findings will include efforts to lower intracellular K+while limiting ROS accumulation within tumor specific T cells.SynopsisHigh extracellular K+(↑[K+]e) is found within tumors and suppresses T cell effector function. Collier et al. find that deletion of the Na+/K+ATPase in T cells lowers intracellular K+and promotes ROS accumulation, tonic signal transduction and T cell exhaustion owing to ROS accumulation. Engineering T cell ion transport is an important consideration for cancer immunotherapy.
Evolution of sex differences in cooperation: the role of trade-offs with dispersal
Authors: Capilla-Lasheras Pablo , Bircher Nina , Brown Antony M. , Harrison Xavier , Reed Thomas , York Jennifer E. , Cram Dominic L. , Rutz Christian , Walker Lindsay , Naguib Marc , Young Andrew J. 
Published: September 2023

AbstractExplaining the evolution of sex differences in cooperation remains a major challenge. Comparative studies highlight that offspring of the more philopatric sex tend to be more cooperative within their family groups than those of the more dispersive sex but we do not understand why. The leading ‘Philopatry hypothesis’ proposes that the more philopatric sex cooperates more because their higher likelihood of natal breeding increases the direct fitness benefits of natal cooperation. However, the ‘Dispersal trade-off hypothesis’ proposes that the more dispersive sex cooperates less because preparations for dispersal, such as extra-territorial prospecting, trade-off against natal cooperation. Here, we test both hypotheses in cooperatively breeding white-browed sparrow weavers (Plocepasser mahali), using a novel high-resolution automated radio-tracking method. First, we show that males are the more dispersive sex (a rare reversal of the typical avian sex-difference in dispersal) and that, consistent with the predictions of both hypotheses, females contribute substantially more than males to cooperative care while within the natal group. However, the Philopatry hypothesis cannot readily explain this female-biased cooperation, as females are not more likely than males to breed within their natal group. Instead, our radio-tracking findings support the Dispersal trade-off hypothesis: males conduct pre-dispersal extra-territorial prospecting forays at higher rates than females and prospecting appears to trade-off against natal cooperation. Our findings thus highlight that the evolution of sex differences in cooperation could be widely attributable to trade-offs between cooperation and dispersal; a potentially general explanation that does not demand that cooperation yields direct fitness benefits.
Genome-Scale Metabolic Modeling Identifies Causal Reactions Mediated by SNP-SNP Interactions Influencing Yeast Sporulation
Authors: Sasikumar Srijith , Kumar S Pavan , Bhatt Nirav , Sinha Himanshu 
Published: September 2023

ABSTRACTUnderstanding how genetic variations influence cellular function remains a major challenge in genetics. Genome-scale metabolic models (GEMs) are powerful tools used to understand the functional effects of genetic variants. While GEMs have illuminated genotype-phenotype relationships, the impact of single nucleotide polymorphisms (SNPs) in transcription factors and their interactions on metabolic fluxes remains largely unexplored. We used gene expression data from a yeast allele replacement panel to construct co-expression networks and SNP-specific GEMs. The analysis of these models helped us to understand how genetic interactions affect yeast sporulation efficiency, a quantitative trait. Our findings revealed that SNP-SNP interactions have a significant impact on the connectivity of key metabolic regulators involved in steroid biosynthesis, amino acid metabolism and histidine biosynthesis. By integrating gene expression data into GEMs and conducting genome-scale differential flux analysis, we were able to identify causal reactions within six major metabolic pathways, providing mechanistic explanations for variations in sporulation efficiency. Notably, we found that in specific SNP combinations, the pentose phosphate pathway was differentially regulated. In models where the pentose phosphate pathway was inactive, the autophagy pathway was activated, likely compensating by providing critical precursors such as nucleotides and amino acids. This compensatory mechanism may enhance sporulation efficiency by supporting processes that are dependent on the pentose phosphate pathway. Our study sheds light on how transcription factor polymorphisms interact to shape metabolic pathways in yeast and offers valuable insights into genetic variants associated with metabolic traits in genome-wide association studies.
Multi-omic profiling of pathogen-stimulated primary immune cells
Authors: Salz Renee , Vorsteveld Emil E. , van der Made Caspar I. , Kersten Simone , Stemerdink Merel , Riepe Tabea V. , Hsieh Tsung-han , Mhlanga Musa , Netea Mihai G. , Volders Pieter-Jan , Hoischen Alexander , ’t Hoen Peter A.C. 
Published: September 2023

SummaryWe performed long-read transcriptome and proteome profiling of pathogen-stimulated peripheral blood mononuclear cells (PBMCs) from healthy donors to discover new transcript and protein isoforms expressed during immune responses to diverse pathogens. Long-read transcriptome profiling reveals novel sequences and isoform switching induced upon pathogen stimulation, including transcripts that are difficult to detect using traditional short-read sequencing. Widespread loss of intron retention occurs as a common result of all pathogen stimulations. We highlight novel transcripts ofNFKB1 and CASP1that may indicate novel immunological mechanisms. RNA expression differences did not result in differences in the amounts of secreted proteins. Clustering analysis of secreted proteins revealed a correlation between chemokine (receptor) expression on the RNA and protein levels inC. albicans-and Poly(I:C)-stimulated PBMCs. Isoform aware long-read sequencing of pathogen-stimulated immune cells highlights the potential of these methods to identify novel transcripts, revealing a more complex transcriptome landscape than previously appreciated.
Deep Learning of Ligand-bound RNA Tertiary Structures Diverges from Learning Unbound Ones: A Case Study Using The gRNAde Software
Authors: Dhesi Tajveer S. , Bannister Alyssa W. , Manzourolajdad Amirhossein 
Published: September 2023

AbstractModeling the relationship between the native RNA sequence and itsin-vivostructure is challenging, partly due to the flexible nature of the RNA molecular structure. In addition, the RNA structure can take on different conformations in the presence of specific molecules, metabolites, temperatures or other signaling and environmental factors, making it difficult to construct a universal statistical model for the sequence-structure relationship of the RNA. Using a Geometric-Vector-Perceptron Graph Neural Network architecture, Joshi, et al. predict the RNA sequence from its given 3D structure with good performance and on a dataset including RNA structures of different type and length, namely RNAsolo. In this work, using the Authors open-source software package, gRNAde, we confirm their results on a more updated version of RNAsolo and for structure of different resolution, confirming the ability of the algorithm to capture RNA structural features and generalize to sequences of different lengths. We did observe, however, that performance on riboswitches is lower than expected that RNAs whose structure has been resolved while being bound to a ligand, such as riboswitches, may require a statistical model that diverges from those of native structures.
Searching for Structure: Characterizing the Protein Conformational Landscape with Clustering-based Algorithms
Authors: Macke Amanda C. , Stump Jacob E. , Kelly Maria S. , Rowley Jamie , Herath Vageesha , Mullen Sarah , Dima Ruxandra I. 
Published: September 2023

AbstractThe identification and characterization of the main conformations from a protein population is a challenging, inherently high-dimensional problem. We introduce the Secondary sTructural Ensembles with machine LeArning (StELa) double clustering method, which clusters protein structures based on the underlying Ramachandran plot. Our approach takes advantage of the relationship between the phi and psi dihedral angles in a protein backbone and the secondary structure of the protein. The classification of states as vectors composed of the clusters’ indices arising naturally from the Ramachandran plot, followed by the hierarchical clustering of the vectors, enables the identification of the minima from the corresponding free energy landscape (FEL) by lifting the high structure degeneracy found with existing approaches such as the RMSD-based clustering GROMOS. We compare the performance of StELa with not only GROMOS but also with CATS, the combinatorial averaged transient structure clustering method based on distributions of the phi and psi dihedral angle coordinates. Using ensembles of conformations from molecular dynamics (MD) simulations of either intrinsically disordered proteins (IDPs) of various lengths (tau protein fragments) or from local structures from a globular protein, we show that StELa is the only clustering method that identifies nearly all the minima from the corresponding FELs. In contrast, GROMOS yields a large number of clusters that cover the entire FEL and CATS, even with an additional clustering step, is unable to sample well the FEL for long IDPs and for fragments from globular proteins as it misses important minima.TOC Graphic
Deep-learning and transfer learning identify new breast cancer survival subtypes from single-cell imaging data
Authors: Yadav Shashank , Zhou Shu , He Bing , Du Yuheng , Garmire Lana X 
Published: September 2023

ABSTRACTQuantitative models that explicitly capture single-cell resolution cell-cell interaction features to predict patient survival at population scale are currently missing. Here, we computationally extracted hundreds of features describing single-cell based cell-cell interactions and cellular phenotypes from a large, published cohort of cyto-images of breast cancer patients. We applied these features to a neural-network based Cox-nnet survival model and obtained high accuracy in predicting patient survival in test data (Concordance Index > 0.8). We identified seven survival subtypes using the top survival features, which present distinct profiles of epithelial, immune, fibroblast cells, and their interactions. We identified atypical subpopulations of TNBC patients with moderate prognosis (marked by GATA3 over-expression) and Luminal A patients with poor prognosis (marked by KRT6 and ACTA2 over-expression and CDH1 under-expression). These atypical subpopulations are validated in TCGA-BRCA and METABRIC datasets. This work provides important guidelines on bridging single-cell level information towards population-level survival prediction.STATEMENT OF TRANSLATIONAL RELEVANCEOur findings from a breast cancer population cohort demonstrate the clinical utility of using the single-cell level imaging mass cytometry (IMC) data as a new type of patient prognosis prediction marker. Not only did the prognosis prediction achieve high accuracy with a Concordance index score greater than 0.8, it also enabled the discovery of seven survival subtypes that are more distinguishable than the molecular subtypes. These new subtypes present distinct profiles of epithelial, immune, fibroblast cells, and their interactions. Most importantly, this study identified and validated atypical subpopulations of TNBC patients with moderate prognosis (GATA3 over-expression) and Luminal A patients with poor prognosis (KRT6 and ACTA2 over-expression and CDH1 under-expression), using multiple large breast cancer cohorts.
Perspectives on health, illness, disease and management approaches among Baganda traditional spiritual healers in Central Uganda
Authors: Sekagya Yahaya H. K. , Muchunguzi Charles , Unnikrishnan Payyappallimana , Mulogo Edgar M. 
Published: September 2023

AbstractIn Uganda, spirituality is closely associated with traditional healthcare; however, though prevalent, it is considered controversial, mystical, less-documented and often misunderstood. There is a paucity of literature on the description of health, illness, disease, and management approaches among traditional spiritual healers. This article examines perspectives on health, illness, disease and management approaches among Baganda traditional spiritual healers, theBalubaale, in central Uganda, who engage ancestral spirits in health care and management.The study used a qualitative study design in particular grounded theory. Unstructured, qualitative interviews and observation were used on 12 male and female purposively selectedBalubaalein Central Uganda. Data was transcribed, coded and thematically analyzed using ATLAS ti. 22 Computer software based on an inductive approach.Findings show that the words and concepts describing health, illness, disease and management approaches are descriptive and contextualized to include the problem, the prospected root-causes, and therapeutic approaches involved. The words for illness “olumbe”,disease“obulwadde”and the management approaches such as divination (kulagula), ritual cleansing(kwambulula), amulets (ensiribaandyirizi), and scarification (kusandaga)have spiritual and social dimensions, contextual meanings and attachments.Further research is recommended among other tribes and larger sample size to compare findings and terminologies to facilitate communication and policy considerations.
The predictive value of arterial stiffness combined with hypertension in the index of atherosclerotic cardiovascular disease
Authors: Qin Qian , Yang Yang , Li Jiaoyan , Yang Hang , Chen Jingfeng , Zheng Yansong , Ding Suying 
Published: September 2023

AbstractObjectiveThis study was conducted to investigate the hypertension and arterial stiffness (AS) in predicting future atherosclerotic cardiovascular disease (ASCVD) risk.Methods:We included 6530 participants from Chinese People′s Liberation Army General Hospital. AS was assessed by measuring brachial-ankle pulse wave velocity (baPWV) and participants were stratified into two groups: ASCVD≥10% or ASCVD<10% by a risk threshold of 10%. The Kaplan-Meier survival analysis and Cox proportional risk model were evaluated the risk of ASCVD between participants with ideal vascular function (IVF defined as normal AS with normotension), normotension with AS (NTAS), hypertension with normal baPWV (HTNAS) and hypertension with AS (HTAS). C statistics were used to compare hypertension and AS status in determining ASCVD risk.ResultsAfter a median follow-up of 2.17 years, 672 participants with high risk of ASCVD were identified. Compared to the IVF group, the highest risk of ASCVD was exhibited in the HTAS group (HR=2.252,95%CI=1.733∽2.927), followed by the NTAS group (HR=1.888,95%CI=1.583∽2.252) and HTNAS group (HR=1.827,95%CI=1.144∽2.916). Multiple sensitivity and subgroup analyses yielded similar results. Additionally, compared to the addition of hypertension in the traditional model, the addition of AS elevated the incremental effect on the predicted value of ASCVD (the C statistic was 0.824 vs 0.817, integrated discrimination improvement was 0.80% vs 0.20%, and net reclassification index was 25.00% vs 12.20%).ConclusionsThe individuals with AS had a higher risk of ASCVD, and hypertension amplified these associations after adjusting for cardiovascular confounders. Otherwise, AS showed better predictive power than hypertension in determining ASCVD risk.