bluuebunny/crossref_metadata_2025_split huggingface dataset:
High WEE1 expression is independently linked to poor survival in multiple myeloma
Authors: Simhal Anish K. , Firestone Ross , Oh Jung Hun , Avutu Viswatej , Norton Larry , Hultcrantz Malin , Usmani Saad Z. , Maclachlan Kylee H. , Deasy Joseph O. 
Published: September 2024

AbstractCurrent prognostic scores in multiple myeloma (MM) currently rely on disease burden and a limited set of genomic alterations. Some studies have suggested gene expression panels may predict clinical outcomes, but none are presently utilized in clinical practice. We therefore analyzed the MMRF CoMMpass dataset (N=659) and identified a high-risk group (top tertile) and a low-risk group (bottom tertile) based on WEE1 expression sorted in descending order. The tyrosine kinase WEE1 is a critical cell cycle regulator during the S-phase and G2M-checkpoint. Abnormal WEE1 expression has been implicated in multiple cancers including breast, ovarian, and gastric cancers, but has not until this time been implicated in MM. PFS was significantly different (p <1e-9) between the groups, which was validated in two independent microarray gene expression profiling (GEP) datasets from the Total Therapy 2 (N=341) and 3 (N=214) trials. Our results show WEE1 expression is prognostic independent of known biomarkers, differentiates outcomes associated with known markers, is upregulated independently of its interacting neighbors, and is associated with dysregulated P53 pathways. This suggests that WEE1 expression levels may have clinical utility in prognosticating outcomes in newly diagnosed MM and may support the application of WEE1 inhibitors to MM preclinical models. Determining the causes of abnormal WEE1 expression may uncover novel therapeutic pathways.
Metabolic modeling identifies determinants of thermal growth responses inArabidopsis thaliana
Authors: Wendering Philipp , Andreou Gregory M. , Laitinen Roosa A. E. , Nikoloski Zoran 
Published: September 2024

AbstractTemperature is a critical environmental factor affecting nearly all plant processes, including growth, development, and yield. Yet, despite decades of research, we lack the ability to predict plant performance at different temperatures, limiting the development of climate-resilient crops. Further, there is a pressing need to bridge the gap between the prediction of physiological and molecular traits to improve our understanding and manipulation of plant temperature responses. Here, we developed the first enzyme-constrained model ofArabidopsis thaliana’s metabolism, facilitating predictions of growth-related phenotypes at different temperatures. We showed that the model can be employed forin silicoidentification of genes that affect plant growth at suboptimal growth temperature. Using mutant lines, we validated the genes predicted to affect plant growth, demonstrating the potential of metabolic modeling in accurately predicting plant thermal responses. The temperature-dependent enzyme-constrained metabolic model provides a template that can be used for developing sophisticated strategies to engineer climate-resilient crops.
Exploring group-specific technical variation patterns of single-cell data
Authors: Zhou Yang , Sheng Qiongyu , Jin Shuilin 
Published: September 2024

AbstractConstructing single-cell atlases requires preserving differences attributable to biological variables, such as cell types, tissue origins, and disease states, while eliminating batch effects. However, existing methods are inadequate in explicitly modeling these biological variables. Here, we introduce SIGNAL, a general framework designed to disentangle biological and technical effects by learning group-specific technical variation patterns, thereby linking these metadata to data integration. SIGNAL employs a novel variant of principal component analysis (PCA) to align multiple batches, enabling the integration of 1 million cells in approximately 2 minutes. SIGNAL, despite its computational simplicity, surpasses state-of-the-art methods across multiple integration scenarios: (1) heterogeneous datasets, (2) cross-species datasets, (3) simulated datasets, (4) integration on low-quality cell annotations, and (5) reference-based integration. Furthermore, we demonstrate that SIGNAL accurately transfers knowledge from reference to query datasets. Notably, we propose a self-adjustment strategy to restore annotated cell labels potentially distorted during integration. Finally, we apply SIGNAL to multiple large-scale atlases, including a human heart cell atlas containing 2.7 million cells, identifying tissue- and developmental stage-specific subtypes, as well as condition-specific cell states. This underscores SIGNAL’s exceptional capability in multi-scale analysis.
Spatiotemporal dynamics and assembly process differ in fungal communities across contiguous habitats in tropical forests
Authors: Lin Chieh-Ping , Lin Yu-Fei , Liu Yu-Ching , Lu Mei-Yeh Jade , Ke Huei-Mien , Tsai Isheng Jason 
Published: September 2024

ABSTRACTBackgroundThe variation in fungal community composition within a single habitat space has been extensively studied in forest ecosystems. However, the spatial and temporal distribution of fungi across contiguous habitats, particularly at a local scale and in tropical regions, remains underexplored. In this study, we examined the fungal community composition across multiple habitats proximal to each other over two seasons in seven Fagaceae species in Taiwanese broadleaf forests. We tested how local spatial scale and habitat influence community assembly.ResultsUsing a metabarcoding approach, we sequenced ITS3/ITS4 amplicons from 864 samples collected from four distinct habitats—leaves, twigs, litter, and soil. We identified 11,600 fungal amplicon sequence variants (ASVs), with community composition differing significantly between habitats proximal to each other. Phyllosphere (leaves and twigs) fungi exhibited higher diversity compared to soil. Habitat type and long-term precipitation emerged as the most influential factors driving fungal diversity and composition, with a clear distance-decay relationship observed in leaf and twig but not in soil. Random forest analysis accurately classified habitats based on ASVs’ relative abundances, with strong predictors were mostly endemic ASVs prevalent in soil. Misclassified samples were due to secondary contact of fungi between adjacent habitats. Co-occurrence network analysis revealed more complex and deterministic networks in leaf and twig habitats, while soil was driven by stochastic processes and contained most endemic ASVs. ACladosporiumsp. emerged as a keystone species, maintaining network stability across forests.ConclusionThis study reveals how local spatial variation and habitat shape distinct fungal communities in tropical forests, with deterministic processes dominating in some habitats and stochasticity playing a key role in others. We show local fungal taxa were strong habitat predictors and drivers of community cohesion. These findings highlight the importance of studying coexisting habitats to gain a deeper understanding of fungal biogeography and ecosystem function.
Exploring the Flexible Penalization of Bayesian Survival Analysis Using Beta Process Prior for Baseline Hazard
Authors: Dauda Kazeem A. , Adeniyi Ebenezer J. , Lamidi Rasheed K. , Wahab Olalekan T. 
Published: September 2024

AbstractHigh-dimensional data has significantly captured the interest of many researchers, particularly in the context of variable selection. However, when dealing with time-to-event data in survival analysis, where censoring is a key consideration, progress in addressing this complex problem has remained somewhat limited. More-over, in microarray research, it is common to identify groupings of genes involved in the same biological pathways. These gene groupings frequently collaborate and operate as a unified entity. Therefore, this study is motivated to adopt the idea of a Penalized semi-parametric Bayesian Cox (PSBC) model through elastic-net and group lasso penalty functions (PSBC-EN-G and PSBC-GL-G) to incorporate the grouping structure of the covariates (genes) and optimally perform variable selection. The proposed methods assign a beta prior process to the cumulative baseline hazard function (PSBC-EN-B and PSBC-GL-B), instead of the gamma prior process used in existing methods (PSBC-EN-G and PSBC-GL-G). Three real-life datasets and simulation scenarios were considered to compare and validate the efficiency of the modified methods with existing techniques, using Bayesian Information Criteria (BIC). The results of the simulated studies provided empirical evidence that the proposed methods performed better than the existing methods across a wide range of data scenarios. Similarly, the results of the real-life study showed that the proposed methods revealed a substantial improvement over the existing techniques in terms of feature selection and grouping behavior.
Machine learning-optimized targeted detection of alternative splicing
Authors: Yang Kevin , Islas Nathaniel , Jewell San , Jha Anupama , Radens Caleb M. , Pleiss Jeffrey A. , Lynch Kristen W. , Barash Yoseph , Choi Peter S. 
Published: September 2024

ABSTRACTRNA-sequencing (RNA-seq) is widely adopted for transcriptome analysis but has inherent biases which hinder the comprehensive detection and quantification of alternative splicing. To address this, we present an efficient targeted RNA-seq method that greatly enriches for splicing-informative junction-spanning reads. Local Splicing Variation sequencing (LSV-seq) utilizes multiplexed reverse transcription from highly scalable pools of primers anchored near splicing events of interest. Primers are designed using Optimal Prime, a novel machine learning algorithm trained on the performance of thousands of primer sequences. In experimental benchmarks, LSV-seq achieves high on-target capture rates and concordance with RNA-seq, while requiring significantly lower sequencing depth. Leveraging deep learning splicing code predictions, we used LSV-seq to target events with low coverage in GTEx RNA-seq data and newly discover hundreds of tissue-specific splicing events. Our results demonstrate the ability of LSV-seq to quantify splicing of events of interest at high-throughput and with exceptional sensitivity.
Effect of heterozygous deletions on phenotypic changes and dosage compensation inArabidopsis thaliana
Authors: Ikoma Takuya , Nishijima Ryo , Ikeda Miho , Ishii Kotaro , Nagalla Asanga Deshappriya , Abe Tomoko , Kazama Yusuke 
Published: September 2024

ABSTRACTHeterozygous deletions, which include a large number of genes, are often caused by the induction of mutations. The induction of gene dosage compensation should be considered when assessing the effects of heterozygous deletions on phenotypic changes. This mechanism is known to balance the expression levels of genes with different copy numbers in sex chromosomes, but it is also known to operate in autosomes. In the present study, 12Arabidopsis thalianaBC1mutants with heterozygous deletions were produced by crossing wild-type Col-0 plants with mutants induced by heavy ion beams. The sizes of the deletions ranged from 50.9 kb to 2.03 Mb, and the number of deleted genes ranged from 8 to 92. Nine of the 12 BC1mutants showed phenotypic changes in fresh weight 14 days after cultivation or during the flowering period. RNA-sequencing (RNA-seq) analyses of 14-day-old leaves, 40-day-old leaves, and flower buds showed that dosage compensation did not occur in any stage or tissue tested. These results indicate that heterozygous deletions cause phenotypic changes owing to the absence of dosage compensation.Article SummaryIn this study, we examined the impact of heterozygous deletions inArabidopsis thaliana. By crossing wild-type Col-0 plants with mutants induced by heavy ion beams, we created 12 BC1 mutants, each having heterozygous deletions. The deletions ranged from 50.9 kb to 2.03 Mb, affecting 8 to 92 genes. Nine mutants showed changes in fresh weight or flowering time. RNA-seq analyses of leaves and flower buds revealed no gene dosage compensation. Our findings indicate that phenotypic changes result from the lack of dosage compensation in heterozygous deletions.
Sample size considerations for species co-occurrence models
Authors: Cowans Amber , Bigata Albert Bonet , Sutherland Chris 
Published: September 2024

AbstractThe multi-species occupancy model of Rotaet al. (2016) is widely applied for inferences about interactions in the occurrence of different species, but convergence and estimation issues under realistic sample sizes are common. We conducted a simulation study to evaluate the ability to recover co-occurrence estimates using standard and penalised likelihood under varying sample size and interaction scenarios while increasing model complexity in two dimensions: the number of interacting species and the number of covariates. We demonstrate that the ability to quantify interactions in species occupancy using these models is highly sensitive to sample size and interaction strength. Even in the simplest scenario, there is high bias and low coverage in natural parameters (used for inference) for sample sizes below 400 sites. Strong co-occurrence is detected consistently above 400 sites, but weak co-occurrence is never consistently detected even with 3000 sites. We demonstrate that mean predictive ability is less affected by sample size and complexity, with low bias in derived probabilities at 100 sites. We caution the use of these models for inference in small datasets or when co-occurrence is expected to be weak, but show they are suitable to detect strong co-occurrence in larger datasets and generate predictions of site occupancy states.
Tau P301S Transgenic Mice Develop Gait and Eye Movement Impairments That Mimic Progressive Supranuclear Palsy
Authors: Creed Rose B. , Harris Scott C. , Sridhar Sadhana , du Lac Sascha , Zee David S. , Dunn Felice A. , Bouvier Guy , Nelson Alexandra B. 
Published: September 2024

AbstractProgressive supranuclear palsy (PSP) is a neurodegenerative disorder with an estimated prevalence of 5-7 people in 100,000. Clinically characterized by impairments in gait, balance, and eye movements, as well as aggregated Tau pathology, PSP leads to death in approximately 5-8 years. No disease-modifying treatments are currently available. The contribution of Tau pathology to the symptoms of patients with PSP is poorly understood, in part due to lack of a rodent model that recapitulates characteristic aspects of PSP. Here, we assessed the hTau.P301S mouse for key clinical features of PSP, finding progressive impairments in balance and gait coordination. Additionally, we found impairments in fast vertical eye movements, one of the most distinctive features of PSP. Across animals, we found that Tau pathology in motor control regions correlated with motor deficits. These findings highlight the utility of the hP301S mouse in modeling key aspects of PSP.
Application and Significance of SIRVB Model in Analyzing COVID-19 Dynamics
Authors: Ariyaratne Pavithra , Ramasinghe Lumbini P. , Ayyash Johathan S. , Kelley Tyler M. , Plant-Collins Terry A. , Shinkle Logan W. , Zuercher Aoife M. , Chen Jixin 
Published: September 2024

AbstractIn the summer of 2024, COVID-19 positive cases spiked in many countries, but it is no longer a deadly pandemic thanks to global herd immunity to the SARS-CoV-2 viruses. In our physical chemistry lab in spring 2024, students practice kinetic models, SIR (Susceptible, Infected, and Recovered) and SIRV (Susceptible, Infected, Recovered, Vaccinated) using COVID-19 positive cases and vaccination data from World Health Organization (WHO). In this report, we further introduce virus breakthrough to the existing model updating it the SIRVB (Susceptible, Infectious, Recovered, Vaccinated, Breakthrough) model. We believe this is the simplest model possible to explain the COVID-19 kinetics in all countries in the past four years. Parameters obtained from such practice correlate with many indices of different countries. These models and parameters have significant value to researchers and policymakers in predicting the stages of future outbreaks of infectious diseases.TOC
Emergence to dominance: Estimating time to dominance of SARS-CoV-2 variants using nonlinear statistical models
Authors: Awasthi Srishti , Dehkharghani Maryam Zolfaghari , Fudolig Miguel 
Published: September 2024

AbstractBackground/ObjectiveRelative proportion of cases in a multi-strain pandemic like the COVID-19 pandemic provides insight on how fast a newly emergent variant dominates the infected population. However, the behavior of relative proportion of emerging variants is an understudied field. We investigated the emerging behavior of dominant COVID-19 variants using nonlinear statistical methods and calculated the time to dominance of each variant.MethodWe used a phenomenological approach to model national- and regional-level variant share data from the national genomic surveillance system provided by the Centers for Disease Control and Prevention to determine the best model to describe the emergence of two recent dominant variants of the SARS-CoV-2 virus: XBB.1.5 and JN.1. The proportions were modeled using logistic, Weibull, and generalized additive models. Model performance was evaluated using the Akaike Information Criteria (AIC) and the root mean square error (RMSE).FindingsThe Weibull model performed the worst out of all three approaches. The generalized additive model approach slightly outperformed the logistic model based on fit statistics, but lacked in interpretability compared to the logistic model. These models were then used to estimate the time elapsed from emergence to dominance in the infected population, denoted by the time to dominance (TTD). All three models yielded similar TTD estimates. The XBB.1.5 variant was found to dominate the population faster compared to the JN.1 variant, especially in HHS Region 2 (New York) where the XBB.1.5 was believed to emerge. This research expounds on how emerging viral strains transition to dominance, informing public health interventions against future emergent COVID-19 variants and other infectious diseases.
Do pyrethroid insecticides influence sex ratios in agile frogs (Rana dalmatina)?
Authors: Balogh Emese , Hócza Szabolcs , Ujhegyi Nikolett , Kásler Andrea , Holly Dóra , Herczeg Dávid , Ujszegi János , Gál Zoltán , Hoffmann Orsolya I. , Bókony Veronika , Mikó Zsanett 
Published: September 2024

ABSTRACTEnvironmental pollutants have the potential to alter sex ratios in wildlife through sex-biased mortality. Furthermore, endocrine disruptors may cause sex reversal during early ontogeny in ectothermic vertebrates, resulting in a phenotypic sex that is not concordant with the genotypic sex encoded by the sex chromosomes. Despite the wide-ranging implications of these sex-ratio biasing effects, they are rarely studied in ecotoxicology, especially in a way that allows for disentangling the two mechanisms. We investigated these effects of two synthetic pyrethroids, deltamethrin and etofenprox, that are commonly used insecticides and have been linked to adverse effects on fish and amphibian biodiversity. We assessed the effects of environmentally relevant concentrations of these two pyrethroids on phenotypic sex ratio, sex-dependent mortality, and sex reversal in agile frogs (Rana dalmatina). Tadpoles from field-collected eggs were reared in mesocosm until metamorphosis by adding 0.03 or 0.3 μg/L of deltamethrin or etofenprox three times to the water. We observed no effect in three of the four treatment groups. However, in the lower-concentration etofenprox treatment, phenotypic sex ratio was male-biased two months post-metamorphosis, and genotypic sexing revealed that this was due to female-biased mortality during metamorphosis and not to sex reversal. Although the estimation certainty of these effects was somewhat limited, they highlight that not all sex-ratio distorting effects are caused by sex reversal. Therefore, ecotoxicological studies aiming to understand the endocrine distruptor effects of environmental contaminants should strive to separate the effects on sex determination and sex-dependent mortality.
Effects of Inosine-5’-Monophosphate Dehydrogenase (IMPDH/GuaB) Inhibitors onBorrelia burgdorferiGrowth in Standard and Modified Culture Conditions
Authors: Siegel Eric L. , Rich Connor , Saravanan Sanchana , Pearson Patrick , Xu Guang , Rich Stephen M. 
Published: September 2024

AbstractBorrelia burgdorferi’s Inosine-5’-Monophosphate dehydrogenase (IMPDH, GuaB) is a potential therapeutic target. GuaB is not needed forB. burgdorferisurvival and replication in standard growth media, therefore our ability to test novel drugs against the whole organism is limited to tests using mammalian hosts. Modifications to standard culture conditions that can induce borreliastatic effects of GuaB inhibitors will enhance our ability to evaluate these inhibitors. This study aimed to evaluate basic modifications to standard growth media that can achieve this objective. Effects of GuaB inhibitors (mycophenolic acid, 6-Chloropurine) were compared in standard Barbour-Stoenner-Kelly-II (BSK-II) medium with BSK-II modified to 60% concentration rabbit serum, usingB. burgdorferi5A1. Effects were also evaluated with another BSK-II modification with the direct supplementation of hypoxanthine, adenine, and nicotinamide (75 μM each). In standard BSK-II, neither mycophenolic acid nor 6-Chloropurine riboside affectedB. burgdorferigrowth. An ANOVA showed significant, dose-dependent interactions between culture condition and treatment considering modified growth conditions (F = 4.471, P = 0.001). Mycophenolic acid at 250 μM reduced replication by 48 and 50% (P < 0.001 each). 6-Chloropurine riboside was more effective in both conditions at 250 μM, reducing growth by 64 and 65% (P < 0.001 each). Effects of GuaB inhibitors were shown against whole organismBorrelia burgdorferifor the first time. Modification of BSK-II medium with physiologically relevant levels of mammalian serum supports replication and induces GuaB inhibitor-mediated suppression of replication. Effects of 6-Chloropurine riboside were demonstrated for the first time, indicating that B. burgdorferi can salvage and phosphorylate these analogue purine derivatives. These molecules can be manipulated to better inhibit replication. Optimisation will allow the assessment of novel Borrelia-specific IMPDH inhibitor molecules in culture for Lyme disease interventions.HighlightsEffects of IMPDH inhibitors are shown against whole organismBorrelia burgdorferifor the first time.Modification of BSK-II medium with physiologically relevant levels of mammalian serum supports replication and induces IMPDH inhibitor-mediated suppression of replication.Effects of 6-Chloropurine riboside are demonstrated for the first time, indicating thatB. burgdorferican salvage and phosphorylate these analogue purine derivatives. These molecules can be manipulated to better inhibit replication.Optimisation of modified BSK-II will allow the assessment of novelBorrelia-specific IMPDH inhibitor molecules in culture for Lyme disease interventions.