bluuebunny/crossref_metadata_2025_split huggingface dataset:
Identifying maximally informative signal-aware representations of single-cell data using the Information Bottleneck
Authors: Dubnov Serafima , Piran Zoe , Soreq Hermona , Nitzan Mor 
Published: May 2024

AbstractRapid advancements in single-cell RNA-sequencing (scRNA-seq) technologies revealed the richness of myriad attributes encompassing cell identity, such as diversity of cell types, organ-of-origin, or developmental stage. However, due to the large scale of the data, obtaining an interpretable compressed representation of cellular states remains a computational challenge. For this task we introduce bioIB, a method based on the Information Bottleneck algorithm, designed to extract an optimal compressed representation of scRNA-seq data with respect to a desired biological signal, such as cell type or disease state. BioIB generates a hierarchy of weighted gene clusters, termed metagenes, that maximize the information regarding the signal of interest. Applying bioIB to a scRNA-seq atlas of differentiating macrophages and setting either the organ-of-origin or the developmental stage as the signal of interest provided two distinct signal-specific sets of metagenes that captured the attributes of the respective signal. BioIB’s representation can also be used to expose specific cellular subpopulations, for example, when applied to a single-nucleus RNA-sequencing dataset of an Alzheimer’s Disease mouse model, it identified a subpopulation of disease-associated astrocytes. Lastly, the hierarchical structure of metagenes revealed interconnections between the corresponding biological processes and cellular populations. We demonstrate this over hematopoiesis scRNA-seq data, where the metagene hierarchy reflects the developmental hierarchy of hematopoietic cell types.SignificanceSingle-cell gene expression represents an invaluable resource, encoding multiple aspects of cellular identity. However, its high complexity poses a challenge for downstream analyses. We introduce bioIB, a methodology based on the Information Bottleneck, that compresses data while maximizing the information about a biological signal-of-interest, such as disease state. bioIB generates a hierarchy of metagenes, probabilistic gene clusters, which compress the data at gradually changing resolutions, exposing signal-related processes and informative connections between gene programs and their corresponding cellular populations. Across diverse single-cell datasets, bioIB generates distinct metagene representations of the same dataset, each maximally informative relative to a different signal; uncovers signal-associated cellular populations; and produces a metagene hierarchy that reflects the developmental hierarchy of the underlying cell types.
Leveraging Descriptor Learning and Functional Map-based Shape Matching for Automatic Landmark Acquisition
Authors: Thomas Oshane O. , Maga A. Murat 
Published: May 2024

AbstractGeometric morphometrics is widely employed across the biological sciences for the quantification of morphological traits. However, the scalability of these methods to large datasets is hampered by the requisite placement of landmarks, which can be laborious and time consuming if done manually. Additionally, the selected landmarks embody a particular hypothesis regarding the critical geometry pertinent to the biological inquiry at hand. Modifying this hypothesis lacks flexibility, necessitating the acquisition of an entirely new set of landmarks on the entire dataset to reflect any theoretical adjustments. In our research, we investigate the precision and accuracy of landmarks derived from the comprehensive set of functional correspondences acquired through the functional map framework of geometry processing. We use a deep functional map network to learn shape descriptors that effectively yield functional map-based and point-to-point correspondences between the specimens in our dataset. We then interrogate these maps to identify corresponding landmarks given manually placed landmarks from the entire dataset. We assess our method by automating the landmarking process on a dataset comprising mandibles from various rodent species, comparing its efficacy against MALPACA, a cutting-edge technique for automatic landmark placement. Compared to MALPACA, our model is notably faster and maintains competitive accuracy. The Root Mean Square Error (RMSE) analysis reveals that while MALPACA generally exhibits the lowest RMSE, our models perform comparably, especially with smaller training datasets, suggesting strong generalizability. Visual evaluations confirm the precision of our landmark placements, with deviations remaining within an acceptable range. These findings underscore the potential of unsupervised learning models in anatomical landmark placement, providing a viable and efficient alternative to traditional methods.
Skeletal muscle PGC-1α remodels mitochondrial phospholipidome but does not alter energy efficiency for ATP synthesis
Authors: Karasawa Takuya , Choi Ran Hee , Meza Cesar A. , Maschek J. Alan , Cox James E. , Funai Katsuhiko 
Published: May 2024

AbstractBackgroundExercise training is thought to improve the mitochondrial energy efficiency of skeletal muscle. Some studies suggest exercise training increases the efficiency for ATP synthesis by oxidative phosphorylation (OXPHOS), but the molecular mechanisms are unclear. We have previously shown that exercise remodels the lipid composition of mitochondrial membranes, and some of these changes could contribute to improved OXPHOS efficiency (ATP produced by O2consumed or P/O). Peroxisome proliferator-activated receptor gamma coactivator-1 alpha (PGC-1α) is a transcriptional co-activator that coordinately regulates exercise-induced adaptations including mitochondria. We hypothesized that increased PGC-1α activity is sufficient to remodel mitochondrial membrane lipids and promote energy efficiency.MethodsMice with skeletal muscle-specific overexpression of PGC-1α (MCK-PGC-1α) and their wildtype littermates were used for this study. Lipid mass spectrometry and quantitative PCR were used to assess muscle mitochondrial lipid composition and their biosynthesis pathway. The abundance of OXPHOS enzymes was determined by western blot assay. High-resolution respirometry and fluorometry analysis were used to characterize mitochondrial bioenergetics (ATP production, O2consumption, and P/O) for permeabilized fibers and isolated mitochondria.ResultsLipidomic analyses of skeletal muscle mitochondria from wildtype and MCK-PGC-1α mice revealed that PGC-1α increases the concentrations of cone-shaped lipids such as phosphatidylethanolamine (PE), cardiolipin (CL), and lysophospholipids, while decreases the concentrations of phosphatidylcholine (PC), phosphatidylinositol (PI) and phosphatidic acid (PA). However, while PGC-1α overexpression increased the abundance of OXPHOS enzymes in skeletal muscle and the rate of O2consumption (JO2), P/O values were unaffected with PGC-1α in permeabilized fibers or isolated mitochondria.ConclusionsCollectively, overexpression of PGC-1α promotes the biosynthesis of mitochondrial PE and CL but neither PGC-1α nor the mitochondrial membrane lipid remodeling induced in MCK-PGC-1α mice is sufficient to increase the efficiency for mitochondrial ATP synthesis. These findings suggest that exercise training may increase OXPHOS efficiency by a PGC-1α-independent mechanism, and question the hypothesis that mitochondrial lipids directly affect OXPHOS enzymes to improve efficiency for ATP synthesis.
A Rigorous Multi-Laboratory Study of Known PDAC Biomarkers Identifies Increased Sensitivity and Specificity Over CA19-9 Alone
Authors: Haab Brian , Qian Lu , Staal Ben , Jain Maneesh , Fahrmann Johannes , Worthington Christine , Prosser Denise , Velokokhatnaya Liudmila , Lopez Camden , Tang Runlong , Hurd Mark W. , Natarajan Gopalakrishnan , Kumar Sushil , Smith Lynette , Hanash Sam , Batra Surinder K. , Maitra Anirban , Lokshin Anna , Huang Ying , Brand Randall E. 
Published: May 2024

AbstractA blood test that enables surveillance for early-stage pancreatic ductal adenocarcinoma (PDAC) is an urgent need. Independent laboratories have reported PDAC biomarkers that could improve biomarker performance over CA19-9 alone, but the performance of the previously reported biomarkers in combination is not known. Therefore, we conducted a coordinated case/control study across multiple laboratories using common sets of blinded training and validation samples (132 and 295 plasma samples, respectively) from PDAC patients and non-PDAC control subjects representing conditions under which surveillance occurs. We analyzed the training set to identify candidate biomarker combination panels using biomarkers across laboratories, and we applied the fixed panels to the validation set. The panels identified in the training set, CA19-9 with CA199.STRA, LRG1, TIMP-1, TGM2, THSP2, ANG, and MUC16.STRA, achieved consistent performance in the validation set. The panel of CA19-9 with the glycan biomarker CA199.STRA improved sensitivity from 0.44 with 0.98 specificity for CA19-9 alone to 0.71 with 0.98 specificity (p < 0.001, 1000-fold bootstrap). Similarly, CA19-9 combined with the protein biomarker LRG1 and CA199.STRA improved specificity from 0.16 with 0.94 sensitivity for CA19-9 to 0.65 with 0.89 sensitivity (p < 0.001, 1000-fold bootstrap). We further validated significantly improved performance using biomarker panels that did not include CA19-9. This study establishes the effectiveness of a coordinated study of previously discovered biomarkers and identified panels of those biomarkers that significantly increased the sensitivity and specificity of early-stage PDAC detection in a rigorous validation trial.
Analytic Challenges in Clinical Trials of Early Alzheimer’s Disease
Authors: Mallinckrodt Craig , Lipkovich Ilya , Hendrix Suzanne , Dickson Sam , Molenberghs Geert 
Published: May 2024

AbstractThe present investigation assessed how the heavily right-skewed data seen in recently reported results in Alzheimer’s Disease (AD) clinical trials influenced treatment contrasts when data were analyzed via the typical mixed-effects model for repeated measures (MMRM) versus robust regression (RR) and the non-parametric Hodges-Lehman estimator (HL).Results in simulated data patterned after AD trials showed that imbalance across treatment arms in the number of patients in the extreme right tail (those with rapid disease progression) frequently occurred by chance alone. Each analysis method controlled Type I error at or below the nominal level. The RR analysis yielded smaller standard errors, and more power than MMRM and HL. In datasets with appreciable imbalance in the number of rapid progressing patients, MMRM results favored the treatment arm with fewer rapid progressors. Results from HL showed the same trend, but to a lesser degree. Robust regression yielded similar results regardless of the ratio of rapid progressors. Although more research is needed over a wider range of scenarios, it should not be assumed that MMRM is the optimal approach for trials in early Alzheimer’s Disease.
Detection of anti-nucleocapsid antibodies associated with primary SARS-CoV-2 infection in unvaccinated and vaccinated blood donors
Authors: Grebe Eduard , Stone Mars , Spencer Bryan R. , Akinseye Akintunde , Wright David , Di Germanio Clara , Bruhn Roberta , Zurita Karla G. , Contestable Paul , Green Valerie , Lanteri Marion C. , Saa Paula , Biggerstaff Brad J. , Coughlin Melissa M. , Kleinman Steve , Custer Brian , Jones Jefferson M. , Busch Michael P. 
Published: May 2024

AbstractAnti-nucleocapsid (N) antibody assays can be used to estimate SARS-CoV-2 infection prevalence in regions employing anti-spike based COVID-19 vaccines. However, poor sensitivity of anti-N assays in detecting infections after vaccination (VI) has been reported. To support serological monitoring of infections, including VI, in a large blood donor cohort (N=142,599), we derived a lower cutoff for identifying previous infection using the Ortho VITROS Anti-SARS-CoV-2 Total-N Antibody assay, improving sensitivity while maintaining specificity >98%. Sensitivity was validated in samples donated after self-reported infections diagnosed by a swab-based test. Sensitivity for first infections in unvaccinated donors was 98.1% (95% CI: 98.0,98.2) and for VI was 95.6% (95.6,95.7), using the standard cutoff. Regression analysis showed sensitivity was reduced in the Delta compared to Omicron period, in older donors, asymptomatic infections, ≤30 days after infection and for VI. The standard Ortho anti-N threshold demonstrated good sensitivity, which was modestly improved with the revised cutoff.
Spatial variation in housing construction material in low- and middle-income countries: a Bayesian spatial prediction model of a key infectious diseases risk factor and social determinant of health
Authors: Colston Josh M , Fang Bin , Nong Malena K , Chernyavskiy Pavel , Annapareddy Navya , Lakshmi Venkataraman , Kosek Margaret N 
Published: May 2024

AbstractHousing infrastructure and quality is a major determinant of infectious disease risk and other health outcomes in regions of the world where vector borne, waterborne and neglected tropical diseases are endemic. It is important to quantify the geographical distribution of improvements to the major dwelling components to identify and target resources towards populations at risk. The aim of this study was to model the sub-national spatial variation in housing materials using covariates with quasi-global coverage and use the resulting estimates to map the predicted coverage across the world’s low- and middle-income countries (LMICs). Data relating to the materials used in dwelling construction were sourced from nationally representative household surveys conducted since 2005. Materials used for construction of flooring, walls, and roof were reclassified as improved or unimproved. Households lacking location information were georeferenced using a novel methodology, and a suite of environmental and demographic spatial covariates were extracted at those locations for use as model predictors. Integrated nested Laplace approximation (INLA) models were fitted to obtain and map predicted probabilities for each dwelling component. The dataset compiled included information from households in 283,000 clusters from 350 surveys. Low coverage of improved housing was predicted across the Sahel and southern Sahara regions of Africa, much of inland Amazonia, and areas of the Tibetan plateau. Coverage of improved roofs and walls was high in the Central Asia, East Asia and Pacific and Latin America and the Caribbean regions, while improvements in all three components, but most notably floors, was low in Sub-Saharan Africa. Human development was by far the strongest determinant of dwelling component quality, though vegetation greenness and land use were also relevant markers These findings are made available to the reader as files that can be imported into a GIS for integration into relevant analysis to derive improved estimates of preventable health burdens attributed to housing.
EndoPRS: Incorporating Endophenotype Information to Improve Polygenic Risk Scores for Clinical Endpoints
Authors: Kharitonova Elena V. , Sun Quan , Ockerman Frank , Chen Brian , Zhou Laura Y. , Cao Hongyuan , Mathias Rasika A. , Auer Paul L. , Ober Carole , Raffield Laura M. , Reiner Alexander P. , Cox Nancy J. , Kelada Samir , Tao Ran , Li Yun 
Published: May 2024

AbstractPolygenic risk score (PRS) prediction of complex diseases can be improved by leveraging related phenotypes. This has motivated the development of several multi-trait PRS methods that jointly model information from genetically correlated traits. However, these methods do not account for vertical pleiotropy between traits, in which one trait acts as a mediator for another. Here, we introduce endoPRS, a weighted lasso model that incorporates information from relevant endophenotypes to improve disease risk prediction without making assumptions about the genetic architecture underlying the endophenotype-disease relationship. Through extensive simulation analysis, we demonstrate the robustness of endoPRS in a variety of complex genetic frameworks. We also apply endoPRS to predict the risk of childhood onset asthma in UK Biobank by leveraging a paired GWAS of eosinophil count, a relevant endophenotype. We find that endoPRS significantly improves prediction compared to many existing PRS methods, including multi-trait PRS methods, MTAG and wMT-BLUP, which suggests advantages of endoPRS in real-life clinical settings.
Predicting the infecting dengue serotype from antibody titre data using machine learning
Authors: Cracknell Daniels Bethan , Buddhari Darunee , Hunsawong Taweewun , Iamsirithaworn Sopon , Farmer Aaron R , Cummings Derek A.T. , Anderson Kathryn B. , Dorigatti Ilaria 
Published: May 2024

AbstractThe development of a safe and efficacious vaccine that provides immunity against all four dengue virus serotypes is a priority, and a significant challenge for vaccine development has been defining and measuring serotype-specific outcomes and correlates of protection. The plaque reduction neutralisation test (PRNT) is the gold standard assay for measuring serotype-specific antibodies, but this test cannot differentiate homotypic and heterotypic antibodies and characterising the infection history is challenging. To address this, we present an analysis of pre- and post-infection antibody titres measured using the PRNT, collected from a prospective cohort of Thai children. We applied four machine learning classifiers and multinomial logistic regression to the titre data to predict the infecting serotype. The models were validated against the true infecting serotype, identified using RT-PCR. Model performance was calculated using 100 bootstrap samples of the train and out-of-sample test sets. Our analysis showed that, on average, the greatest change in titre was against the infecting serotype. However, in 53.4% (109/204) of the subjects, the highest titre change did not correspond to the infecting serotype, including in 34.3% (12/35) of dengue-naïve individuals. The highest post-infection titres of seropositive cases were more likely to match the serotype of the highest pre-infection titre than the infecting serotype, consistent with original antigenic sin. Despite these challenges, the best performing machine learning algorithm achieved 76.3% (95% CI 57.9-89.5%) accuracy on the out-of-sample test set in predicting the infecting serotype from PRNT data. Incorporating additional spatiotemporal data improved accuracy to 80.6% (95% CI 63.2-94.7%), while using only post-infection titres as predictor variables yielded an accuracy of 71.7% (95% CI 57.9-84.2%). These results show that machine learning classifiers can be used to overcome challenges in interpreting PRNT titres, making them useful tools in investigating dengue immune dynamics, infection history and identifying serotype-specific correlates of protection, which in turn can support the evaluation of clinical trial endpoints and vaccine development.
Compound Mutations in the Abl1 Kinase Cause Inhibitor Resistance by Shifting DFG Flip Mechanisms and Relative State Populations
Authors: Monteiro da Silva Gabriel , Lam Kyle , Dalgarno David C. , Rubenstein Brenda M. 
Published: May 2024

AbstractThe intrinsic dynamics of most proteins are central to their function. Protein tyrosine kinases such as Abl1 undergo significant conformational changes that modulate their activity in response to different stimuli. These conformational changes constitute a conserved mechanism for self-regulation that dramatically impacts kinases’ affinities for inhibitors. Few studies have attempted to extensively sample the pathways and elucidate the mechanisms that underlie kinase inactivation. In large part, this is a consequence of the steep energy barriers associated with many kinase conformational changes, which present a significant obstacle for computational studies using traditional simulation methods. Seeking to bridge this knowledge gap, we present a thorough analysis of the “DFG flip” inactivation pathway in Abl1 kinase. By leveraging the power of the Weighted Ensemble methodology, which accelerates sampling without the use of biasing forces, we have comprehensively simulated DFG flip events in Abl1 and its inhibitor-resistant variants, revealing a rugged landscape punctuated by potentially druggable intermediate states. Through our strategy, we successfully simulated dozens of uncorrelated DFG flip events distributed along two principal pathways, identified the molecular mechanisms that govern them, and measured their relative probabilities. Further, we show that the compound Glu255Lys/Val Thr315Ile Abl1 variants owe their inhibitor resistance phenotype to an increase in the free energy barrier associated with completing the DFG flip. This barrier stabilizes Abl1 variants in conformations that can lead to loss of binding for Type-II inhibitors such as Imatinib or Ponatinib. Finally, we contrast our Abl1 observations with the relative state distributions and propensity for undergoing a DFG flip of evolutionarily-related protein tyrosine kinases with diverging Type-II inhibitor binding affinities. Altogether, we expect that our work will be of significant importance for protein tyrosine kinase inhibitor discovery, while also furthering our understanding of how enzymes self-regulate through highly-conserved molecular switches.
Different state-dependence of population codes across cortex
Authors: Bandi Akhil C. , Runyan Caroline A. 
Published: May 2024

SummaryDuring perceptual decision-making, behavioral performance varies with changes in internal states such as arousal, motivation, and strategy. Yet it is unknown how these internal states affect information coding across cortical regions involved in differing aspects of sensory perception and decision-making. We recorded neural activity from the primary auditory cortex (AC) and posterior parietal cortex (PPC) in mice performing a navigation-based sound localization task. We then modeled transitions in the behavioral strategies mice used during task performance. Mice transitioned between three latent performance states with differing decision-making strategies: an ‘optimal’ state and two ‘sub-optimal’ states characterized by choice bias and frequent errors. Performance states strongly influenced population activity patterns in association but not sensory cortex. Surprisingly, activity of individual PPC neurons was better explained by external inputs and behavioral variables during suboptimal behavioral performance than in the optimal performance state. Furthermore, shared variability across neurons (coupling) in PPC was strongest in the optimal state. In AC, shared variability was similarly weak across all performance states. Together, these findings indicate that neural activity in association cortex is more strongly linked to internal state than in sensory cortex.