bluuebunny/crossref_metadata_2025_split huggingface dataset:
Standardizing Phenotypic Algorithms for the Classification of Degenerative Rotator Cuff Tear from Electronic Health Record Systems
Authors: Herzberg S , Garduno-Rapp NE , Ong H , Gangreddi S , Chandrashekar A , Wei W , LeClere L , Wen W , Hartmann K , Jain N , Giri A 
Published: October 2024

ABSTRACTBackgroundDegenerative rotator cuff tears (DCTs) are the leading cause of shoulder pain, affecting 30% to 50% of individuals over 50. Current phenotyping strategies for DCT rely on heterogeneous combinations of procedural and diagnostic codes, leading to concerns about misclassification. We aimed to create universal phenotypic algorithms to classify DCT status across EHR systems.MethodsUsing Vanderbilt University Medical Center’s de-identified EHR system, we developed and validated two sets of algorithms—one requiring imaging verification and one without imaging verification—to identify DCT cases and controls. The algorithms used combinations of ICD and CPT codes, as well as natural language processing (NLP), to increase diagnostic certainty. Manual chart reviews by trained personnel blinded to case-control determinations were conducted to compute positive (PPV) and negative predictive values (NPV).ResultsThe algorithm development process resulted in five phenotypes with an overall predictive value of 94.5%. By approach: 1) code only cases that required imaging confirmation (PPV = 89%), 2) code only cases that did not require imaging verification (PPV = 92%), 3) NLP-based cases that did not require imaging verification (PPV = 89%), 4) code-based controls that were confirmed by imaging (NPV = 90%), and 5) code and NLP-based controls that did not require imaging verification (NPV = 100%). External validation demonstrated 94% sensitivity and 75% specificity for the code-only algorithms. The addition of NLP increased the number of identified cases without compromising predictive values.ConclusionsThese algorithms represent an improvement over current phenotyping strategies and allows for EHR studies of unprecedented size
Early zygotic engineering promotes targeted large transgene integration and direct production of fully transgenic animals
Authors: Gupta Khusali , Xu Ping , Gallant Judith , Yoon Yeonsoo , Rivera-Pérez Jaime A. , Lawrence Jeanne B. 
Published: October 2024

AbstractGenetic engineering has become increasingly feasible with the advent of site-specific nucleases. This technology is highly effective for the generation of small mutations, however targeted insertion of large sequences to generate transgenic animals remains challenging, time consuming, and laborious. Following several failed attempts using the same reagents, we identified a protocol that achieved very high targeted integration of a 3.2 kb transgene into a single locus of a humanized mouse model of Down syndrome. Moreover, we demonstrate in multiple ways that this procedure directly generates numerous non-mosaic founder animals, which bred true to generate all transgenic progeny.In vitrofertilization of oocytes was followed by AAV-mediated delivery of donor sequence and electroporation of Cas9 ribonucleoprotein, all parameters designed to promoteearlyzygoticengineering (EZE). This strategy can obviate the need to breed mosaic animals for germline transmission and can enable engineering of difficult to breed animals. While efforts to improve genome engineering focus on reagents and delivery techniques, findings here suggest a narrow window of time in the early fertilized oocyte, likely before genome replication, is key to achieve both high integration efficiency and one-step generation of non-mosaic, engineered mice.
Stem water content is crucial to support fruit tree functioning during heatwaves in a Mediterranean climate
Authors: Rez Laura , Missik Justine E. , Bohrer Gil , Mau Yair 
Published: October 2024

AbstractDroughts are expected to intensify in the Mediterranean region due to climate change, yet the effect of these highly variable events on local trees is unknown. To study the particular effect of heatwaves in orchards, where soil-drought can be mitigated by irrigation, we propose a heatwave definition that focuses on atmospheric stress and its consequences, by relating the intensity of high VPD events to losses in tree stem-water storage (StWS). We found that the sensitivity and resilience of StWS to heatwaves is species-specific, and varies among species with different water-management strategies (e.g., isohydric orange and anisohydric mango trees,p< 10−3). Navel orange trees were sensitive to heatwaves starting at the 80th percentile of VPD in early spring, and once irrigation began, despite the harsh Mediterranean summer temperatures, StWC increased to 0.57 g cm−3, slightly greater than the StWC of the earlier wet season (approximately 0.55 g cm−3). Oppositely, there was a net reduction in StWC in Shelly mango trees from 0.75 to 0.69 g cm−3between the two seasons, as sensitivity to heatwaves increased from the 90th to the 80th percentile in spring and summer, respectively. By first quantifying heatwaves and relating this new variable to changes in StWS, we were able to describe the sensitivity of each species according to the rarity of the heatwave events by VPD percentile, and their resilience to heatwaves over seasons based on the corresponding net changes in StWC. Though the experiment in this study was performed in a Mediterranean climate, hotter-droughts are rising globally and the framework developed here for quantifying and measuring the effect of heatwaves can be broadly applied across geographic locations.
Novel AI-powered computational method using tensor decomposition for identification of common optimal bin sizes when integrating multiple Hi-C datasets
Authors: Taguchi Y-H , Turki Turki 
Published: October 2024

AbstractIdentifying the optimal bin sizes (or resolutions) for the integration of multiple Hi-C datasets is a challenge due to the fact that bin sizes must be common over multiple datasets. By contrast, the dependence of quality upon bin sizes can vary from dataset to dataset. Moreover, common structures should not be sought in bin sizes smaller than the optimal bin sizes, below which common structure cannot be the primary structure any more even after increasing the number of mapped short reads per bin. In this case, there are no common structures at finer resolutions, suggesting that individual Hi-C datasets may have to be analyzed separately in the bin sizes smaller than the optimal one. Thus, quality assessments of individual datasets have a limited ability to determine the best bin size for all datasets. In this study, we propose a novel application of tensor decomposition (TD) based unsupervised feature extraction (FE) to choose the optimal bin sizes for the integration of multiple Hi-C datasets. TD-based unsupervised FE exhibit phase transition-like phenomena through which the smallest possible bin size (or the highest resolution) can be automatically estimated empirically, without the need to manually set a threshold value for the integration of multiple Hi-C datasets, retrieved from GEO with GEO ID, GSE260760 and GSE255264. To our knowledge, ours is the first one that can optimize bin sizes over multiple Hi-C profiles without any tunable parameters.
Separating biological variance from noise by applying EM algorithm to modified General Linear Model
Authors: Lee Tien-Wen 
Published: October 2024

ABSTRACTIntroductionThe General Linear Model (GLM) has been widely used in research, where error term has been treated as noise. However, compelling evidence suggests that in biological systems, the target variables may possess their innate variances.MethodsA modified GLM was proposed to explicitly model biological variance and non-biological noise. Employing the Expectation and Maximization (EM) scheme can distinguish biological variance from noise, termed EMSEV (EM for Separating Variances). The performance of EMSEV was evaluated by varying noise levels, dimensions of the design matrix, and covariance structures of the target variables.ResultsThe deviation between EMSEV outputs and the pre-defined distribution parameters increased with noise level. With a proper initial guess, when the noise magnitude and the variance of the target variables were similar, there were deviations of 3% and 10–16% in the estimated mean and covariance of the target variables, respectively, along with a 1.7% deviation in noise estimation.ConclusionEMSEV appears promising for distinguishing signal variance from noise in biological systems. The potential applications and implications in biological science and statistical inference are discussed.
Serotonergic neurons in the dorsal raphe regulate visual attention
Authors: Lehnert Jonas , Cha Kuwook , Forestell Julia , Yang Kerry , Ma Xinyue , Britt Jonathan , Khadra Anmar , Cook Erik P. , Krishnaswamy Arjun 
Published: October 2024

AbstractVisual attention enhances the neural representation of salient stimuli within the visual cortex. It is generally thought that this enhancement is driven by glutamatergic feedback from frontal cortical areas. Here we report the unexpected observation that dorsal raphe (DR) derived serotonin (5HT) controls visual attention. We developed a behavioral model that captured the way mice allocated attention to cued and uncued visual locations and features. Simultaneous photometry showed reduced DR activity when mice deployed attention to the cued locations and features, whereas high DR activity was observed when mice were less attentive. Optogenetic excitation of DR-5HT neurons impaired attention to the cue and degraded behavioral performance, while optogenetic suppression improved attention and performance. A genetically encoded sensor of 5HT release showed reduced 5HT levels in visual cortex when mice attend and detect stimuli. These results demonstrate that DR-5HT neurons are members of the brain’s attentional circuit and suggest that 5HT is a novel biological carrier of visual attention.
UBE3A reinstatement restores behavior and proteome in an Angelman Syndrome mouse model of Imprinting Defects
Authors: Milazzo Claudia , Narayanan Ramanathan , Badillo Solveig , Wang Silvia , Almand Rosaisela , Mientjes Edwin , Chamberlain Stormy , Kremer Thomas , Elgersma Ype 
Published: October 2024

AbstractAngelman Syndrome (AS) is a severe neurodevelopmental disorder wionly symptomatic treatment currently available. Besides mutations within theUBE3Agene, AS is caused by deletions, imprinting center defects (mICD) or uniparental disomy of chromosome 15 (UPD). Current mouse models areUbe3a-centric and do not address expression changes of other 15q11-q13 genes on AS pathophysiology. Here, we studied a mouse line that harbors a mutation affecting the AS-PWS imprinting center, hence modeling mICD/UPD AS subtypes. mICD mice showed significant reduction in UBE3A protein, bi-allelic expression ofUbe3a-ATSandMkrn3-Snord115gene cluster, leading to robust AS behavioral deficits and proteome alterations similar toUbe3aKOmice. Genetic UBE3A overexpression in mICD mice, mimicking therapeutic strategies that effectively activate the biallelic silencedUbe3agene, resulted in a complete rescue of all behavioral and proteome alterations. Subsequently, treatment with an antisense oligonucleotide (ASO) to directly activate the biallelic silencedUbe3agene in mICD mice also resulted in efficient reinstatement of UBE3A, alongside a partial rescue of behavioral phenotypes. Taken together, these findings demonstrate that UBE3A loss is the primary factor underlying AS phenotypes in the mICD/UPD mouse model, and also corroborate that UBE3A reinstatement is an attractive therapeutic strategy for mICD/UPD AS individuals.
Fluid Flow Impacts Endothelial-Monocyte Interactions in a Model of Vascular Inflammatory Fibrosis
Authors: Linares Isabelle , Chen Kaihua , Saffren Ava , Mansouri Mehran , Abhyankar Vinay V. , Miller Benjamin L. , Begolo Stefano , Awad Hani A. , McGrath James L. 
Published: October 2024

AbstractThe aberrant vascular response associated with tendon injury results in circulating immune cell infiltration and a chronic inflammatory feedback loop leading to poor healing outcomes. Studying this dysregulated tendon repair response in human pathophysiology has been historically challenging due to the reliance on animal models. To address this, our group developed the human tendon-on-a-chip (hToC) to model cellular interactions in the injured tendon microenvironment; however, this model lacked the key element of physiological flow in the vascular compartment. Here, we leveraged the modularity of our platform to create a fluidic hToC that enables the study of circulating immune cell and vascular crosstalk in a tendon injury model. Under physiological shear stress consistent with postcapillary venules, we found a significant increase in the endothelial leukocyte activation marker intercellular adhesion molecule 1 (ICAM-1), as well as enhanced adhesion and transmigration of circulating monocytes across the endothelial barrier. The addition of tissue macrophages to the tendon compartment further increased the degree of circulating monocyte infiltration into the tissue matrix. Our findings demonstrate the importance of adding physiological flow to the human tendon-on-a-chip, and more generally, the significance of flow for modeling immune cell interactions in tissue inflammation and disease.
Effects of Host-Tree Foliage on Polymorphism in an Insect Pathogen
Authors: Freedman Ari S. , Huang Amy , Dixon Katherine , Polivka Carlos , Dwyer Greg 
Published: October 2024

AbstractThe theory of host-pathogen interactions has successfully shown that persistent pathogen virulence may be explained through tradeoffs between different pathogen fitness components, but classical theory cannot explain pathogen coexistence. More recent theory invokes both tradeoffs and environmental heterogeneity, but resembles classical theory in focusing on a limited range of possible tradeoffs, and therefore has seen few applications. To better understand the usefulness of tradeoff theory for explaining pathogen coexistence in nature, we measured components of pathogen fitness in two distantly related morphotypes of a baculovirus that infects larvae of the Douglas-fir tussock moth (Orgyia pseudotsugata). We show that the two morphotypes vary in multiple components of fitness, including the probability of infection given exposure to the pathogen, the incubation time of the pathogen, variability in the incubation time of the pathogen, and the detectability of the pathogen. Moreover, because the baculovirus is transmitted when host larvae accidentally consume infectious virus particles while feeding on foliage of the insect’s host trees, the strength and direction of the differences in fitness components of the two morphotypes depends on the host-tree species on which host larvae consume the virus. Through simulations of a model parameterized using our experimental data, we demonstrate how several varying fitness components can work in concert to promote strain coexistence, particularly highlighting the role of variability in incubation time. Our results suggest that the two morphotypes may coexist because of variation in forest tree-species composition, providing important empirical evidence that tradeoffs and environmental heterogeneity can together modulate pathogen competition.
Impact of ligand binding on VEGFR1, VEGFR2, and NRP1 localization in human endothelial cells
Authors: Sarabipour Sarvenaz , Kinghorn Karina , Quigley Kaitlyn M , Kovacs-Kasa Anita , Annex Brian H , Bautch Victoria L , Mac Gabhann Feilim 
Published: October 2024

AbstractThe vascular endothelial growth factor receptors (VEGFRs) bind to cognate ligands to facilitate signaling pathways critical for angiogenesis, the growth of new capillaries from existing vasculature. Intracellular trafficking regulates the availability of receptors on the cell surface to bind ligands, which regulate activation, and the movement of activated receptors between the surface and intracellular pools, where they can initiate different signaling pathways. Using experimental data and computational modeling, we recently demonstrated and quantified the differential trafficking of three VEGF receptors, VEGFR1, VEGFR2, and coreceptor Neuropilin-1 (NRP1). Here, we expand that approach to quantify how the binding of different VEGF ligands alters the trafficking of these VEGF receptors and demonstrate the consequences of receptor localization and ligand binding on the localization and dynamics of signal initiation complexes. We include simulations of four different splice isoforms of VEGF-A and PLGF, each of which binds to different combinations of the VEGF receptors, and we use new experimental data for two of these ligands to parameterize and validate our model. We show that VEGFR2 trafficking is altered in response to ligand binding, but that trafficking of VEGFR1 is not; we also show that the altered trafficking can be explained by a single mechanistic process, increased internalization of the VEGFR2 receptor when bound to ligand; other processes are unaffected. We further show that even though the canonical view of receptor tyrosine kinases is of activation on the cell surface, most of the ligand-receptor complexes for both VEGFR1 and VEGFR2 are intracellular. We also explore the competition between the receptors for ligand binding, the so-called ‘decoy effect’, and show that whilein vitroon the cell surface minimal such effect would be observed, inside the cell the effect can be substantial and may influence signaling. We term this location dependence the ‘reservoir effect’ as the size of the local ligand reservoir (large outside the cell, small inside the cell) plays an integral role in the receptor-receptor competition. These results expand our understanding of receptor-ligand trafficking dynamics and are critical for the design of therapeutic agents to regulate ligand availability to VEGFR1 and hence VEGF receptor signaling in angiogenesis.
The contribution of coding variants to the heritability of multiple cancer types using UK Biobank whole-exome sequencing data
Authors: Wilcox Naomi , Tyrer Jonathan P. , Dennis Joe , Yang Xin , Perry John R. B. , Gardner Eugene J. , Easton Douglas F. 
Published: October 2024

AbstractGenome-wide association studies have been highly successful at identifying common variants associated with cancer, however, they do not explain all the inherited risk of cancer. Family-based studies, targeted sequencing and, more recently, exome-wide association studies have identified rare coding variants in some genes associated with cancer risk, but the overall contribution of these variants to the heritability of cancer is less clear. Here we describe a method to estimate the genome-wide contribution of rare coding variants to heritability that fits models to the burden effect sizes using an empirical Bayesian approach. We apply this method to the burden of protein-truncating variants in over 15,000 genes for 11 cancers in UK Biobank, using whole-exome sequencing data on over 400,000 individuals. We extend the method to consider the overlap of genes contributing to pairs of cancers. We found ovarian cancer to have the greatest proportion of heritability attributable to protein-truncating variants in genes (46%). The joint cancer models highlight significant clustering of cancer types, including a near complete overlap in susceptibility genes for breast, ovarian, prostate and pancreatic cancer. Our results provide insights into the contribution of rare coding variants to the heritability of cancer and identify additional genes with strong evidence of susceptibility to multiple cancer types.
Non-invasive Real-time Detection of Potassium level Changes in Skeletal Muscles during Exercise by Magnetic Resonance Spectroscopy
Authors: Roesli Etienne , Primasová Hedvika Haindrich , Thiede Marc , Mani Laila-Yasmin , Gast Lena , Nagel Armin , Vogt Bruno , Vermathen Peter 
Published: October 2024

AbstractIntroductionPotassium is essential in cellular functions, with specific importance in muscle activity and cardiovascular health. It is the main intracellular cation in the human body with 70% located in muscle. Traditional methods to measure potassium levels are invasive and lack specificity for intracellular concentrations. Recently, non-invasive in vivo investigation of K+ ion homeostasis has become feasible by using39K Magnetic Resonance Imaging (MRI) and MR spectroscopy (MRS) at ultrahigh magnetic fields. However, studies demonstrating the sensitivity of39K MRI or MRS to detect potassium alterations in disease or upon intervention are sparse. This study utilizes39K MRS to non-invasively track real-time intramuscular potassium changes during exercise, providing an assessment of potassium dynamics and explores the potential for technical artifacts in the measurements.MethodsFive healthy subjects (three males, two females) were recruited to perform standardized dynamic knee extensions inside a 7T MR scanner. Potassium levels were measured using a39K MRS protocol that included periods of rest, moderate, and heavy exercise followed by recovery. Additionally, possible measurement artifacts due to muscle movement or changes in coil position relative to the thigh were evaluated using39K MRS and1H MRI monitoring in separate sessions.ResultsThe study revealed a consistent decrease in potassium levels during both moderate and heavy exercise, with an average decrease of 5-6%. These changes were rapidly detectable and were reversed upon cessation of exercise, indicating effective in vivo monitoring capability. Possible experimental artifacts were investigated, and the results suggested not to be responsible for the detected potassium changes during exercise. The results of the non-localized39K MRS measurements during exercise correlated well with expected physiological changes based on previous literature.DiscussionThe application of39K MRS provides a valuable non-invasive tool for studying potassium dynamics in human skeletal muscle. This technique could enhance our understanding of muscle physiology and metabolic disorders. The ability to measure these changes in real time and non-invasively highlights the potential for clinical applications, including monitoring of diseases affecting muscle and cellular metabolism.
Proximity proteomics reveals a co-evolved LRRK2-regulatory network linked to centrosomes
Authors: Eckert Marita , Miglionico Pasquale , Izzi Francesca , De Oliveira Rosa Natalia , Ueffing Marius , Raimondi Francesco , Gloeckner Christian Johannes 
Published: October 2024

AbstractThe Leucine-rich repeat kinase 2 (LRRK2) not only plays a vital role in familial forms of Parkinson’s disease (PD) but is also considered as a risk factor for idiopathic PD. Its multi-domain architecture enables a fine-tuned regulation of its biological function by orchestrating intra- and inter-molecular interactions. Here, we present BioID proximity proteomes of LRRK2 revealing new interactors, which we further characterized by a novel evolutionary and structural bioinformatics pipeline. Co-evolutionary analysis of the protein-protein interaction network identified a structural and functional module enriched in cytoskeletal components linked to the centrosome and microtubules. Furthermore, structural modelling of binary interactions via AlphaFold-multimer revealed distinct groups of interactors engaging with LRRK2 dependent on specific conformations and epitopes. Furthermore, we identified distinct changes in the LRRK2 proximity proteome induced by the type I kinase inhibitor MLi-2 or by co-expression of the LRRK2 upstream effector RAB29. Dependent on its state of activity and conformation, these protein-protein interactions link LRRK2 to defined cellular sub-compartments, including centriolar satellites as well as vesicular sub-compartments.