bluuebunny/crossref_metadata_2025_split huggingface dataset:
A series of terribly unfortunate events: How environment and infection synergized to cause the Kihansi spray toad extinction
Authors: Sewell Thomas R , van Dorp Lucy , Ghosh Pria N , Wierzbicki Claudia , Caroe Cristian , Lyakurwa John V , Tonelli Elena , Bowkett Andrew , Marsden Stuart , Cunningham Andrew A , Garner Trenton W J , Gilbert Thomas P , Weldon Ché , Fisher Matthew C 
Published: November 2021

AbstractOutbreaks of emerging infectious diseases are trained by local biotic and abiotic factors, with host declines occurring when conditions favour the pathogen. Extinction of the Tanzanian Kihansi spray toad (Nectophrynoides asperginis) in 2004 was contemporaneous with the construction of a dam, implicating habitat modification in the loss of this species. However, high burdens of a globally emerging infection, Batrachochytrium dendrobatidis (Bd) were synchronously observed implicating infectious disease in this toads extinction. Here, by shotgun sequencing skin DNA from archived toad mortalities and assembling chytrid mitogenomes, we prove this outbreak was caused by the BdCAPE lineage and not the panzootic lineage BdGPL that is widely associated with global amphibian extinctions. Molecular dating showed an invasion of BdCAPE across Southern Africa overlapping with the timing of the extinction event. However, post-outbreak surveillance of conspecific species inhabiting this mountainous region showed widespread infection by BdCAPE yet no signs of amphibian ill-health or species decline. Our findings show that despite efforts to mitigate the environmental impact caused by dams construction, invasion of the pathogen ultimately led to the loss of the Kihansi spray toad; a synergism between emerging infectious disease and environmental change that likely heralds wider negative impacts on biodiversity in the Anthropocene.
Fish diversity drives regional productivity but not stability in southeastern United States coastal marine fishes
Authors: Caughron Juliane G , Plante Craig J , Reichert Marcel JM , Smart Tracey I , McGlinn Daniel J 
Published: November 2021

AbstractAimEcosystem-based management requires accurate predictions on how biotic and environmental factors interact to deliver ecosystem services. Biodiversity-ecosystem function (BEF) theory predicts that as diversity increases, the ecosystem will become more productive (positive diversity-productivity relationship – DPR) and more stable (positive diversity-stability relationship – DSR). Support for BEF has been primarily derived from fine-grained, non-harvested systems. The purpose of this study is to examine the robustness of BEF predictions for the DPR and DSR by examining how well fish diversity predicts productivity and stability of fish, shrimp, and flounder at a regional scale.LocationSoutheast coast of United States.Time Period1989 - 2015Major Taxa StudiedMarine FishesMethodsWe used 27 years of the SEAMAP-SA Coastal Trawl Survey database to derive estimates of fish, shrimp, and flounder biomass (i.e., productivity), temporal stability of biomass (i.e., invariability of productivity), and fish community species richness. We pooled trawls into 22 km x 22 km raster cells and 3-year time bins. We controlled for variation in sampling effort using sample-based rarefaction. We compared the ability of fish species richness, water salinity, and water temperature to predict biomass and stability of all fish, shrimp, and flounder using multiple linear regression.ResultsBoth the DPR and DSR exhibited positive log-log linear trends as expected, but the DPR had a much stronger signal. Species richness outperformed the environmental covariates in both the fish and shrimp DPR models. Surface temperature was the most important variable in both flounder models. Overall, our models better explained productivity than stability.Main ConclusionsThe DPR and DSR are relevant at regional scales in a commercially important fishery although support for the DSR is less justified than DPR. Further investigation into the underlying mechanisms driving the DPR and DSR are necessary to design management around BEF theory.
FEASIBILITY OF VASCULAR REMODELING PARAMETER ESTIMATION FOR ASSESSING HYPERTENSIVE PREGNANCY DISORDERS
Authors: Kissas Georgios , Hwuang Eileen , Thompson Elizabeth W. , Schwartz Nadav , Detre John A. , Witschey Walter R. , Perdikaris Paris 
Published: November 2021

ABSTRACTHypertensive pregnancy disorders, such as preeclampsia, are leading sources of both maternal and fetal morbidity in pregnancy. Non-invasive imaging, such as ultrasound and magnetic resonance imaging (MRI), is an important tool in predicting and monitoring these high risk pregnancies. While imaging can measure hemodynamic parameters, such as uterine artery pulsatility and resistivity indices, the interpretation of such metrics for disease assessment rely on ad-hoc standards, which provide limited insight to the physical mechanisms underlying the emergence of hypertensive pregnancy disorders. To provide meaningful interpretation of measured hemodynamic data in patients, advances in computational fluid dynamics can be brought to bear. In this work, we develop a patient-specific computational framework that combines Bayesian inference with a reduced-order fluid dynamics model to infer remodeling parameters, such as vascular resistance, compliance and vessel cross-sectional area, known to be related to the development of hypertension. The proposed framework enables the prediction of hemodynamic quantities of interest, such as pressure and velocity, directly from sparse and noisy MRI measurements. We illustrate the effectiveness of this approach in two systemic arterial network geometries: an aorta with carotid and a maternal pelvic arterial network. For both cases, the model can reconstruct the provided measurements and infer parameters of interest. In the case of the maternal pelvic arteries, the model can make a distinction between the pregnancies destined to develop hypertension and those that remain normotensive, expressed through the value range of the predicted absolute pressure.
STAT1Gain-of-Function Variants Drive Altered T Cell Prevalence, Metabolism, and Heightened IL-6 Sensitivity
Authors: Kaviany Saara , Bartkowiak Todd , Dulek Daniel E. , Khan Yasmin W. , Hayes Madeline J. , Schaefer Samuel , Dahunsi Debolanle O. , Connelly James A. , Irish Jonathan M. , Rathmell Jeffrey C. 
Published: November 2021

AbstractPatients with Signal Transducer and Activator of Transcription 1 (STAT1) gain-of-function (GOF) pathogenic variants exhibit susceptibility to infections, autoimmunity, and cancer due to enhanced or prolonged STAT1 phosphorylation following cytokine stimulation. While interferons (IFNs) are canonical STAT1 activators, other cytokines that may also contribute to pathology inSTAT1GOF patients have been less well defined. Here we analyzed the immune profiles and cytokine responses of two patients with heterozygous GOF mutations in theSTAT1coiled-coil domain. A systems immunology approach revealed major changes in the T cell compartment and minor changes in the B cells, NK cells, and myeloid cells. Both patients withSTAT1GOF differed from healthy individuals in the abundance and phenotype of effector memory, Th17, and Treg populations.STAT1GOF T cells displayed a pattern of increased activation and had elevated markers of glycolysis and lipid oxidation. Hypersensitivity of T cells to IL-6 was observed with intense, sustained STAT1 phosphorylation in memory T cell populations that exceeded that induced by IFNs. Together, these results show a role for STAT1 in T cell metabolism and suggest that IL-6 may play a critical role to promote T cell memory formation and activation in patients withSTAT1GOF.
Deeplearning based MHC epitope prediction for cancer neoantigen discovery
Authors: Xie Carolyn , Shi Yu , Zhang Chi 
Published: November 2021

AbstractNeoantigens are important for cancer immunotherapies or cancer vaccine development, but identification of neoantigens is challenging. The high binding affinity between the mutated peptide and MHC (major histocompatibility complex) molecules of the patients is a necessary factor for a somatic mutation on the tumor genome to form a neoantigen. MHC epitope prediction tools can be used for the identification of neoantigens. This research investigates MHC epitope prediction by utilizing Tri-peptide similarity as features for the XGBoost classifier. This model was tested on experimentally validated cancer neoantigen peptides.
Community composition of coral-associated Symbiodiniaceae is driven by fine-scale environmental gradients
Authors: de Souza Mariana Rocha , Caruso Carlo , Ruiz-Jones Lupita , Dury Crawford , Gates Ruth , Toonen Robert J. 
Published: November 2021

AbstractThe survival of reef-building corals is dependent upon a symbiosis between the coral and the community of Symbiodiniaceae. Montipora capitata, one of the main reef building coral species in Hawaiʻi, is known to host a diversity of symbionts, but it remains unclear how they change spatially and whether environmental factors drive those changes. Here, we surveyed the Symbiodiniaceae community in 600 M. capitata colonies from 30 sites across Kāneʻohe Bay and tested for host specificity and environmental gradients driving spatial patterns of algal symbiont distribution. We found that the Symbiodiniaceae community differed markedly across sites, with M. capitata in the most open-ocean (northern) site hosting few or none of the genus Durusdinium, whereas individuals at other sites had a mix of Durusdinium and Cladocopium. Our study shows that the algal symbiont community composition responds to fine-scale differences in environmental gradients; depth and temperature variability were the most significant predictor of Symbiodiniaceae community, although environmental factors measured in the study explained only about 20% of observed variation. Identifying and mapping Symbiodiniaceae community distribution at multiple scales is an important step in advancing our understanding of algal symbiont diversity, distribution and evolution, and the potential responses of corals to future environmental change.
T1SEstacker: A tri-layer stacking model effectively predicts bacterial type 1 secreted proteins based on C-terminal non-RTX-motif sequence features
Authors: Chen Zewei , Zhao Ziyi , Hui Xinjie , Zhang Junya , Hu Yixue , Chen Runhong , Cai Xuxia , Hu Yueming , Wang Yejun 
Published: November 2021

AbstractThe proteins secreted through type 1 secretion systems often play important roles in pathogenicity of various gram-negative bacteria. However, the type 1 secretion mechanism remains unknown. In this research, we observed the sequence features of RTX proteins, a major class of type 1 secreted substrates. We found striking non-RTX-motif amino acid composition patterns at the C-termini, most typically exemplified by the enriched ‘[FLI][VAI]’ at the most C-terminal two positions. Machine-learning models, including deep-learning models, were trained using these sequence-based non-RTX-motif features, and further combined into a tri-layer stacking model, T1SEstacker, which predicted the RTX proteins accurately, with a 5-fold cross-validated sensitivity of ~0.89 at the specificity of ~0.94. Besides substrates with RTX motifs, T1SEstacker can also well distinguish non-RTX-motif type 1 secreted proteins, further suggesting their potential existence of common secretion signals. In summary, we made comprehensive sequence analysis on the type 1 secreted RTX proteins, identified common sequence-based features at the C-termini, and developed a stacking model that can predict type 1 secreted proteins accurately.
Urbanization and altitude impact on gut microbiome of an Andean frog (Pristimantis unistrigatus)
Authors: Carphio Elena Catelan , Cisneros-Heredia Diego F. , Caicedo Andrés , Cárdenas Paúl A. 
Published: November 2021

AbstractThe analysis of the intestinal microbiome in amphibians provides insights of the anthropogenic environmental impact. Pristimantis unistrigatus is an Andean amphibian species whose distribution has been recorded in Ecuador and Colombia, ranging from endemic elfin forests to urban gardens. In this study, we focus on the analysis of the P. unistrigatus microbiome 16S rRNA gene sequencing. A total of 32 specimens of P. unistrigatus were collected and analyzed from 4 locations in the Valley of Quito, Ecuador, characterized by several urban environments and altitudes. The results show that the relative abundance of bacteria is significantly different amongst groups. Clostridiales are proportionally more abundant in rural and lower altitude locations, while Erysipelotrichaceae, Desulfovibrionaceae, Enterobacteriaceae, Bacteroidaceae and Lachnospiraceae are found at higher elevations. These results highlight the importance of the evolution of the microbiome as a tool of adaptation and survival of amphibians in the present-day changing ecosystems undergoing anthropogenic stresses.ImportanceAmphibians constitutes one of the groups most vulnerable to environmental alterations. Due to their sophisticated reproductive and breeding requirements and their permeable skins to breathe, amphibians are compulsively studied as ecological indicators. The destruction of pristine habitats occurred all over the planet in recent decades has caused a catastrophic decline in amphibian populations for many species everywhere. Ecuador, being one of the most biodiverse country on Earth, hosts a huge variety of amphibians, thus offering a unique possibility of studying the biology of the amphibian species living in its ecosystems, and how they adapt to changing habitats. A direct way to diagnose the status of an amphibian population is to study the gut microbiome of the individual specimens. The gut microbiome is closely related to the host’s health and so to its ability to adapt and survive. An important output of this study is to offer indications and tools useful to conservation programs before irreversible damages are caused to the habitats and the amphibians’ populations still thriving in them.
Genetic variation within the human papillomavirus type 16 genome is associated with oropharyngeal cancer prognosis
Authors: Lang Kuhs Krystle A. , Faden Daniel , Chen Li , Smith Derek K. , Pinheiro Maisa , Wood C. Burton , Davis Seth , Yeager Meredith , Boland Joseph F. , Cullen Michael , Steinberg Mia , Bass Sara , Wang Xiaowei , Liu Ping , Mehrad Mitra , Tucker Thomas , Lewis James S. , Ferris Robert L. , Mirabello Lisa 
Published: November 2021

ABSTRACTPurposeA significant barrier to adoption of de-escalated treatment protocols for human papillomavirus-driven oropharyngeal cancer (HPV-OPC) is that few predictors of poor prognosis exist. We conducted the first large whole-genome sequencing (WGS) study to characterize the genetic variation of the HPV16 genome and to evaluate its association with HPV-OPC patient survival.Patients and Methods460 OPCs from 2 large US medical centers (1980-2017) underwent HPV16 WGS. Site-specific variable positions (SNPs) across the HPV16 genome were identified. Cox proportional hazards models estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for overall survival by HPV16 SNPs. Harrell C-index and time-dependent positive predictive value (PPV) curves and areas under the PPV curves were used to evaluate the predictive accuracy of HPV16 SNPs for overall survival.Results384 OPCs (83.48%) passed quality control filters with sufficient depth and coverage of HPV16 genome sequencing to be analyzed. 284 HPV16 SNPs with a minor allele frequency >1% were identified. Eight HPV16 SNPs were significantly associated with worse survival after false discovery rate (FDR) correction (individual prevalence:1.0%-5.5%; combined prevalence: 15.10%); E1 gene position 1053 (HR for overall survival [HRos]:3.75,95%CI:1.77-7.95;Pfdr=0.0099); L2 gene positions 4410 (HRos:5.32,95%CI:1.91-14.81;Pfdr=0.0120), 4539 (HRos:6.54,95%CI:2.03-21.08;Pfdr=0.0117); 5050 (HRos:6.53,95%CI:2.34-18.24;Pfdr=0.0030) and 5254 (HRos:7.76,95%CI:2.41-24.98;Pfdr=0.0030); and L1 gene positions 5962 (HRos:4.40,95%CI:1.88-10.31;Pfdr=0.0110) and 6025 (HRos:5.71,95%CI:2.43-13.41;Pfdr=0.0008) and position 7173 within the upstream regulatory region (HRos:9.90,95%CI:3.05-32.12;Pfdr=0.0007). Median survival time for patients with ≥1 high-risk HPV16 SNPs was 3.96 years compared to 18.67 years for patients without a high-risk SNP; log-rank test P<0.001. HPV16 SNPs significantly improved the predictive accuracy for overall survival above traditional factors (age, smoking, stage, treatment): increase in C-index was 0.069 (95% CI: 0.019-0.119, P <0.001); increase in area under the PPV curve for predicting 5-year survival was 0.068 (95%CI: 0.015-0.111, P =0.008).ConclusionsHPV16 genetic variation is associated with HPV-OPC prognosis and can improve prognostic accuracy.
Higher-order interaction of brain microstructural and functional connectome
Authors: Wang Hao , Wu Hui-Jun , Liu Yang-Yu , Lü Linyuan 
Published: November 2021

ABSTRACTDespite a relatively fixed anatomical structure, the human brain can support rich cognitive functions, triggering particular interest in investigating structure-function relationships. Myelin is a vital brain microstructure marker, yet the individual microstructure-function relationship is poorly understood. Here, we explore the brain microstructure-function relationships using a higher-order framework. Global (network-level) higher-order microstructure-function relationships negatively correlate with male participants’ personality scores and decline with aging. Nodal (node-level) higher-order microstructure-function relationships are not aligned uniformly throughout the brain, being stronger in association cortices and lower in sensory cortices, showing gender differences. Notably, higher-order microstructure-function relationships are maintained from the whole-brain to local circuits, which uncovers a compelling and straightforward principle of brain structure-function interactions. Additionally, targeted artificial attacks can disrupt these higher-order relationships, and the main results are robust against several factors. Together, our results increase the collective knowledge of higher-order structure-function interactions that may underlie cognition, individual differences, and aging.
Identifying common transcriptome signatures of cancer by interpreting deep learning models
Authors: Jha Anupama , Quesnel-Vallières Mathieu , Thomas-Tikhonenko Andrei , Lynch Kristen W. , Barash Yoseph 
Published: November 2021

AbstractCancer is a set of diseases characterized by unchecked cell proliferation and invasion of surrounding tissues. The many genes that have been genetically associated with cancer or shown to directly contribute to oncogenesis vary widely between tumor types, but common gene signatures that relate to core cancer pathways have also been identified, signifying that cancer cases display common hallmark molecular features. It is not clear however whether there exist additional sets of genes or transcriptomic features that are less well known in cancer biology but that are also commonly deregulated across several cancer types. Here, in order to agnostically identify transcriptomic features that are commonly shared between cancer types, we used RNA-Seq datasets encompassing thousands of samples from 19 healthy tissue types and 18 solid tumor types to train three feed-forward neural networks, based either on protein-coding gene expression, lncRNA expression or splice junction use, to distinguish between healthy and tumor samples. All three models achieve high precision, recall and accuracy on test sets derived from 13 datasets used during training and on an independent test dataset, indicating that our models recognize transcriptome signatures that are consistent across tumors. Analysis of attribution values extracted from our models reveals that genes that are commonly altered in cancer by expression or splicing variations are under strong evolutionary and selective constraints, suggesting that they have important cellular functions. Importantly, we found that genes composing our cancer transcriptome signatures are not frequently affected by mutations or genomic alterations and that their functions differ widely from the genes genetically associated with cancer. Finally, our results also highlighted that deregulation of RNA-processing genes and aberrant splicing are pervasive features across a large array of solid tumor types. The transcriptomic features that we highlight here define cancer signatures that may reflect causal variations or consequences of disease state, or a combination of both.
Behavioral and postural analyses establish sleep-like states for mosquitoes that can impact host landing and blood feeding
Authors: Ajayi Oluwaseun M. , Marlman Justin M. , Gleitz Lucas A. , Smith Evan S. , Piller Benjamin D. , Krupa Justyna A. , Vinauger Clément , Benoit Joshua B. 
Published: November 2021

AbstractSleep is an evolutionarily conserved process that has been described in different animal systems. For insects, sleep characterization has been primarily achieved using behavioral and electrophysiological correlates in a few systems. Sleep in mosquitoes, which are important vectors of disease-causing pathogens, has not been directly examined. This is surprising as circadian rhythms, which have been well studied in mosquitoes, influence sleep in other systems. In this study, we characterized sleep in mosquitoes using body posture analysis and behavioral correlates, and quantified the effect of sleep deprivation on sleep rebound, host landing and blood-feeding propensity. Body and appendage position metrics revealed a clear distinction between the posture of mosquitoes in their putative sleep and awake states for multiple species, which correlate with a reduction in responsiveness to host cues. Sleep assessment informed by these posture analyses indicated significantly more sleep during periods of low activity. Nighttime and daytime sleep deprivation resulting from the delivery of vibration stimuli induced sleep rebound in the subsequent phase in day and night active mosquitoes, respectively. Lastly, sleep deprivation suppressed host landing in both laboratory and field settings and also impaired blood feeding of a human host when mosquitoes would normally be active. These results suggest that quantifiable sleep states occur in mosquitoes, and highlight the potential epidemiological importance of mosquito sleep.
Haplotype-aware single-cell multiomics uncovers functional effects of somatic structural variation
Authors: Jeong Hyobin , Grimes Karen , Bruch Peter-Martin , Rausch Tobias , Hasenfeld Patrick , Sabarinathan Radhakrishnan , Porubsky David , Herbst Sophie A. , Erarslan-Uysal Büşra , Jann Johann-Christoph , Marschall Tobias , Nowak Daniel , Bourquin Jean-Pierre , Kulozik Andreas E. , Dietrich Sascha , Bornhauser Beat , Sanders Ashley D. , Korbel Jan O. 
Published: November 2021

AbstractSomatic structural variants (SVs) are widespread in cancer genomes, however, their impact on tumorigenesis and intra-tumour heterogeneity is incompletely understood, since methods to functionally characterize the broad spectrum of SVs arising in cancerous single-cells are lacking. We present a computational method, scNOVA, that couples SV discovery with nucleosome occupancy analysis by haplotype-resolved single-cell sequencing, to systematically uncover SV effects on cis-regulatory elements and gene activity. Application to leukemias and cell lines uncovered SV outcomes at several loci, including dysregulated cancer-related pathways and mono-allelic oncogene expression near SV breakpoints. At the intra-patient level, we identified different yet overlapping subclonal SVs that converge on aberrant Wnt signaling. We also deconvoluted the effects of catastrophic chromosomal rearrangements resulting in oncogenic transcription factor dysregulation. scNOVA directly links SVs to their functional consequences, opening the door for single-cell multiomics of SVs in heterogeneous cell populations.