bluuebunny/crossref_metadata_2025_split huggingface dataset:
E-bike riding: A metabolic evaluation in the context of exercise intensity domains
Authors: Bonardi Alberto , Negro Francesco , Iannetta Danilo 
Published: November 2024

AbstractIntroductionE-bikes are being promoted as a mode of transportation that can aid with meeting current physical activity guidelines. This study evaluated exertional intensity associated with E-Biking within the exercise intensity domain framework. We hypothesized that exertional intensity of E-bikes is insufficient to evoke a metabolic demand associated with the minimum intensity needed to improve cardiorespiratory fitness.MethodsForty-four participants (22 females) of varying activity levels completed two experimental sessions. The first session involved a lab-based ramp-incremental exercise test to determine V̇O2max, gas exchange threshold (GET), and respiratory compensation point (RCP). The second session involved the completion of two outdoor rides on the same bike equipped with an electrical motor E-bike. The first ride was completed without electrical assistance, while the second was. The power output (PO) and speed were set at an intensity corresponding to ∼10% above GET.ResultsAs expected, using E-bike assistance resulted in a lower metabolic demand, falling well below GET. While the absolute power output (PO) was different between sexes, relative heart rate (HR) and relative PO were similar between the rides without and with assistance. This suggests that when riding an E-bike, the internal load is similar between sexes.ConclusionDespite E-bikes facilitate a more active lifestyle and help to reduce the emission of pollutants, when interpreted within the context of the exercise intensity domain schema, their associated exertional intensity is likely insufficient to confidently elicit cardiorespiratory benefits.
An Open-Source Neurodynamic Model of the Bladder
Authors: Lister Elliot , McConnell-Trevillion Aidan , Jabbari Milad , Erfanian Abbas , Nazarpour Kianoush 
Published: November 2024

AbstractBackgroundLower urinary tract (LUT) symptoms affect a significant proportion of the population. In-silico medicine can help understand these conditions and develop treatments. However, current LUT computational models are closed source, too deterministic, and do not allow for simple use of modelling neural intervention.MethodsAn open-source Python-based model was developed to simulate bladder, sphincter, and kidney dynamics using normalised neural signals to predict pressure and volume. The model was verified against animal bladder data, assessed for noise sensitivity, and evaluated against known physiological factors.ResultsThe animal data comparison yielded a significantly more similar pattern than existing models, with a correlation coefficient ofr= 0.93 (p< 0.001). All physiological factors were within bounds, and the model remained stable with noise under the described boundaries.ConclusionThe proposed model advances the field of computational medicine by providing an open-source model for researchers and developers. It improves upon existing models by being accessible, including a built-in neural model that better replicates smooth bladder filling results, and incorporating a novel kidney function that alters bladder function by time of day in line with circadian rhythm. Future applications include personalised medicine, treating LUT symptoms with in-silico models and adaptive neural interventions.
M2 Macrophages are Major Mediators of Germline Risk of Endometriosis and Explain Pleiotropy with Comorbid Traits
Authors: Ochoa Soledad , Rasquel-Oliveira Fernanda S. , McKinnon Brett , Haro Marcela , Subramaniam Sugarniya , Yu Pak , Coetzee Simon , Anglesio Michael S. , Wright Kelly N. , Meyer Raanan , Gargett Caroline E. , Mortlock Sally , Montgomery Grant W. , Rogers Michael S. , Lawrenson Kate 
Published: November 2024

AbstractEndometriosis is a common gynecologic condition that causes chronic life-altering symptoms including pain, infertility, and elevated cancer risk. There is an urgent need for new non-hormonal targeted therapeutics to treat endometriosis, but until very recently, the cellular and molecular signatures of endometriotic lesions were undefined, severely hindering the development of clinical advances. Integrating inherited risk data from analyses of >450,000 individuals with ∼350,000 single cell transcriptomes from 21 patients, we uncover M2-macrophages as candidate drivers of disease susceptibility, and nominate IL1 signaling as a central hub impacted by germline genetic variation associated with endometriosis. Extensive functional follow-up confirmed these associations and revealed a pleiotropic role for this pathway in endometriosis. Population-scale expression quantitative trail locus analysis demonstrated that genetic variation controllingIL1Aexpression is also associated with endometriosis risk variants. Manipulation of IL1 signaling in state-of-the-artin vitrodecidualized assembloids impacted epithelial differentiation, and in anin vivoendometriosis model, treatment with anakinra (an interleukin-1 receptor antagonist) resulted in a significant, dose-dependent reduction in both spontaneous pain and evoked pain. Together these studies highlight non-diagnostic cell types as central to endometriosis susceptibility and support IL1 signaling as an important actionable pathway for this disease.
Phylogeographic patterns ofEscherichia coliisolated from white-tailed deer,Odocoileus virginianus, during necropsy in a farmed deer surveillance program
Authors: Metrailer Morgan C. , Cheng An-Chi , Bluhm Andrew P. , Jiranantasak Treenate , Norris Michael H. , Surphlis Austin , Subramaniam Kuttichantran , Jeong Kwangcheol Casey , Wisely Samantha M. , Krauer Juan Campos , Blackburn Jason K. 
Published: November 2024

ABSTRACTWhite-tailed deer (Odocoileus virginianus) (WTD) farms are distributed throughout Florida and number nearly 400. The Cervidae Health Research Initiative (CHeRI) is an initiative that investigates disease and health of farmed cervids statewide. Within the Florida industry, Epizootic Hemorrhagic disease virus (EHDV) and Bluetongue virus (BTV) are major causes of disease and associated economic losses. Often, deer survive EHDV or BTV infections but succumb to secondary bacterial infections, includingEscherichia coli. E. colican be opportunistic with severe illness or death occurring in hosts weakened from viral infection. We investigated presumptiveE. colistrains isolated from WTD during necropsy investigations. Genomic DNA was extracted and whole genome sequenced from 61 suspectE. coliisolates. Exploratory spatial data analysis (ESDA) of the distribution ofE. coliphylotypes, distribution ofE. coli, and deer between ranches was performed. We examined phylogenetic relationships between isolates, categorized by ranch and year and compared to all reportedE. coliisolates in Florida from EnteroBase. One strain wasEnterobacter hormaecheiand the other 60 wereE. coli. TwoE. colistrains were toxigenic. Deer isolates represented 7 phylogroups with B1 being the most prevalent (45/60) and geographically widespread (14/16 counties reportingE. coli). Phylogroup A was the second most prevalent phylogroup. In at least two instances two phylogroups were present within a single animal. We found that deer isolates spanned many of known phylotypes in Florida from animals and humans. Diversity patterns suggest deer are infected locally with most animals on ranches having high genomic similarity within phylogroups.*Sequence data from this article have been deposited in the National Center for Biotechnology Information (NCBI) database under BioProject PRJNA1183872.
Identifying genomic adaptation to local climate using a mechanistic evolutionary model
Authors: Goel Nikunj , Bossu Christen M. , Van Ee Justin J. , Zavaleta Erika , Ruegg Kristen C. , Hooten Mevin B. 
Published: November 2024

AbstractIdentifying genomic adaptation is key to understanding species’ evolutionary responses to climate change. However, current methods to identify adaptive variation have two major limitations. First, when estimating genetic variation, most methods do not account for observational uncertainty in genetic data because of finite sampling and missing genotypes. Second, current methods use phenomenological models to partition genetic variation into adaptive and non-adaptive components. These phenomenological models are not mechanistic models of evolution and, therefore, do not faithfully capture the demographic history of the species.We address these limitations by developing a hierarchical Bayesian model that explicitly accounts for both the observational uncertainty and underlying evolutionary processes. The first layer of the hierarchy is the data model that captures observational uncertainty by probabilistically linking RAD-sequence data to genetic variation. The second layer is a process model that provides a mechanistic explanation of how evolutionary forces, such as local adaptation, mutation, migration, and drift, maintain genetic variation. The third layer is the parameter model, which incorporates our knowledge about biological processes. For example, because most loci in the genome are expected to be neutral, the environmental sensitivity coefficients are assigned a regularized prior centered at zero. Together, the three models provide a rigorous probabilistic framework to identify local adaptation in wild organisms.Analysis of simulated RAD-seq data shows that our statistical model can reliably infer adaptive genetic variation. To show the real-world applicability of our method, we re-analyzed RAD-seq data from Willow Flycatchers (Empidonax traillii) in the USA. We found 30 genes close to loci that showed a statistically significant association with temperature seasonality. Gene ontology suggests that several of these genes play a crucial role in egg mineralization, feather development, and the ability to withstand extreme temperatures.Moreover, biogeographers can easily modify the data and process models to accommodate a wide range of genetic datasets (e.g., pool and low coverage genome sequencing) and demographic histories (e.g., range shifts), allowing them to construct statistical models specific to their study system.
The role of carbonic anhydrase in rock weathering and pH regulation by the soil bacteriumBurkholderia thailandensisE264
Authors: Bell Derek S. , Leake Jonathan R. , Beerling David J. , Ton Jurriaan , Epihov Dimitar Z. 
Published: November 2024

AbstractEnhanced rock weathering (ERW) is increasingly recognized as a way to sequester atmospheric carbon dioxide (CO2) to slow global warming, but its effectiveness needs to be optimized. Carbonic anhydrase (CA), an enzyme capable of accelerating rock weathering bothin vitroand in soil, offers a valuable target due to its ability to convert CO2into carbonic acid. This conversion promotes rock dissolution, enabling immediate CO2absorption through cation release and charge-balance mechanisms. Studies have shown that bacteria grown in axenic, rock-amended media increase CA gene expression, but the influence of bacterial CA on rock dissolution rates remains unclear. To investigate this, we used a reverse genetics approach with the phosphate-solubilizing bacteriumBurkholderia thailandensisE264. We examined three CA-inactivated mutants alongside the wildtype, growing them in minimal media with basalt rock dust (0-10% w/v) at an initial pH of 6.0. After 7 days, we measured weathering potential through elemental concentrations, pH, and dissolved inorganic carbon. In 1% basalt medium, inactivation of the CA1 gene (BTH_I1052) significantly reduced base cation weathering by 41% compared to the wildtype, whereas inactivation of CA2 (BTH_I0345) and CA3 (BTH_I1199) had no significant effect. In the highly buffered, 10% basalt medium, CA1 had a minor role in weathering, and both CA2 and CA3 had no effect. These findings suggest that CA genes inB. thailandensisoperate differently and that CA1’s effect is pH-dependent. Surprisingly, CA1 was localized intracellularly, raising questions about how intracellular CAs might influence mineral dissolution, potentially through acidity export or abiontic enzyme activity after cell lysis.ImportanceWhile purified carbonic anhydrase (CA) protein has been shown to increase mineral dissolution rates in mineral-amended mediain vitro, it remains unclear if the bacterial CA gene directly drives this process. This study used CA-inactivated mutants of the soil bacteriumBurkholderia thailandensisin basalt-amended liquid media and found that only one of the three CA genes influenced mineral dissolution rates. This finding supports prior evidence that bacterial CAs may contribute to mineral dissolution in soils. Importantly, it also showed that not all CA genes in a bacterium may activate under the same conditions, which could impact how soil bacterial CAs are leveraged to enhance weathering. Furthermore, cellular localisation predictions indicated that all three CA genes inB. thailandensisare cytosolic, challenging the common focus on extracellular CAs and suggesting that CA proteins may influence the external environment without needing to be actively exported from the cell.
Conformational ensemble-based framework enables rapid development of Lassa virus vaccine candidates
Authors: Mishra Nitesh , Avillion Gabriel , Callaghan Sean , DiBiase Charlotte , Hurtado Jonathan , Liendo Nathan , Burbach Sarah , Messmer Terrence , Briney Bryan 
Published: November 2024

ABSTRACTLassa virus (LASV), an arenavirus endemic to West Africa, poses a significant public health threat due to its high pathogenicity and expanding geographic risk zone. LASV glycoprotein complex (GPC) is the only known target of neutralizing antibodies, but its inherent metastability and conformational flexibility have hindered the development of GPC-based vaccines. We employed a variant of AlphaFold2 (AF2), called subsampled AF2, to generate diverse structures of LASV GPC that capture an array of potential conformational states using MSA subsampling and dropout layers. Conformational ensembles identified several metamorphic domains—areas of significant conformational flexibility—that could be targeted to stabilize the GPC in its immunogenic prefusion state. ProteinMPNN was then used to redesign GPC sequences to minimize the mobility of metamorphic domains. These redesigned sequences were further filtered using subsampled AF2, leading to the identification of promising GPC variants for further testing. A small library of redesigned GPC sequences was experimentally validated and showed significantly increased protein yields compared to controls. Antigenic profiles indicated these variants preserved essential epitopes for effective immune response, suggesting their potential for broad protective efficacy. Our results demonstrate that AI-driven approaches can predict the conformational landscape of complex pathogens. This knowledge can be used to stabilize viral proteins, such as LASV GPC, in their prefusion conformation, optimizing them for stability and expression, and offering a streamlined framework for vaccine design. Our deep learning / machine learning enabled framework contributes to global efforts to combat LASV and has broader implications for vaccine design and pandemic preparedness.
Genetic screen identifies cell wall enzyme is key for freshwater survival ofFrancisella tularensis
Authors: Macaraeg Aisling , Trautmann Hannah S. , Ramsey Kathryn M. 
Published: November 2024

AbstractHuman infection withFrancisella tularensis, a potentially lethal bacterial pathogen, typically occurs after exposure to contaminated water, soil, food, or an infected animal. WhileF. tularensiscan persist in environmental sources over long periods of time, the genetic requirements that permit its long-term viability are not understood. To address this question, we developed a laboratory model for persistence ofF. tularensisin fresh water, finding that viable cells could be recovered for 3 – 8 weeks after incubation at 4°C. Using this model, we took an unbiased, transposon insertion sequencing approach to identify genes critical for this persistence ofF. tularensiscells. We found that mutants inmpl, a gene encoding murein peptide ligase, are defective for persistence in fresh water. Previous studies had identifiedmplas critical for intramacrophage survival. Murein peptide ligase plays a role in peptidoglycan recycling, suggesting thatF. tularensisuses this enzyme to maintain cell wall integrity during hypoosmotic and intramacrophage stress conditions. Our results highlight the importance of understanding how bacterial cell envelopes have evolved and adapted to maintain their integrity in a variety of stress conditions.
Developmental functions ofrapgef1bin neural crest specification and presomitic mesoderm patterning
Authors: Prasad Tuhina , Iyer Sharada , D’silva Sian , Mathew Reuben J. , Sowpati Divya Tej , Radha Vegesna , Kumar Megha 
Published: November 2024

AbstractRapgef1, a cell fate determinant and effector of multiple signaling events is essential for mammalian embryonic development. Here, we investigated the developmental role ofrapgef1using zebrafish as a model. We show thatrapgef1is maternally expressed and alternately spliced isoforms of its two paralogs,rapgef1aandrapgef1bshow development and tissue-specific expression. CRISPR-Cas9 and morpholino-based targeting ofrapgef1bresulted in developmental defects in the embryonic brain and somites. Therapgef1bmorphants showed altered expression of lineage determinants of the cranial neural crest. Comparative transcriptome and altered expression analysis of morphants revealed fresh insights into the developmental functions ofrapgef1bin presomitic mesoderm, and somitogenesis. During early embryonic mitoses, the morphants showed mitotic defects such as diffused spindle poles and chromosome mis-congression. Our results demonstrate thatrapgef1bis required for normal embryonic mitoses, cranial neural crest specification, somitogenesis, and myogenesis during embryonic development.SignificanceRAPGEF1 is an important signaling factor, essential for cytoskeletal remodeling, signaling, and cell adhesion in cultured mammalian cells. TheRapgef1knockout mice embryos fail to survive beyond implantation, strongly suggesting its essential role in embryonic development. Further, mutations inRAPGEF1are associated with many neurological disorders like schizophrenia and intellectual disability with behavioral defects. However, the role of rapgef1 in regulating embryonic events is poorly understood. This study highlights the developmental functions ofrapgef1in lineage determination during embryonic development. We show thatrapgef1bacts as an activator of canonical Wnt signaling and is essential for early neurodevelopment and somite formation. Further,rapgef1bis also required to maintain mitotic fidelity, spindle pole integrity, and chromosome congression during embryonic mitoses.
The sicker sex is plastic: Thermal plasticity determines sex biases in pathogen transmission
Authors: Butterworth Nathan J. , Lush Jared , Moore Kerri J. , Hall Matthew D. 
Published: November 2024

ABSTRACTSex differences are predicted to play an important role in the spread and evolution of pathogens. However, attempts to generalize the ‘sicker’ sex have been challenged by intraspecific variability of sex-biases across the infection process. Sex specific plasticity provides a framework to resolve this by elucidating how infection is shaped at the sex-pathogen-environment interface. Using theDaphnia magnaandPasteuria ramosasystem, we measure infection outcomes for males and females across three temperatures and seven pathogen densities to quantify how sex specific plasticity shapes susceptibility, pathogen loads, and ultimately transmission. We find unique forms of plasticity at each stage of infection – including equivalent, sex-specific, and divergent plasticity. Integrating these into a single estimate of transmission reveals a clear pattern – male-biased transmission at cold temperatures, and female-biased transmission at warm temperatures. Sex specific thermal plasticity thus determines the ‘sicker’ sex, with implications for pathogen spread and evolution in a warming world.
Trait-space patterning is dictated by the tempo and mode of mutation
Authors: Martis Stephen , Schwab David J. , GrandPre Trevor 
Published: November 2024

AbstractIn large, natural ecosystems, many (≳ 1) phenotypically relevant mutants can emerge over the characteristic turnover time of the population. When this is the case, there can be ‘eco-evolutionary feedback’ between the dynamical processes that underlie mutation, selection and ecology. In this paper, we show that, owing to such feedback, the precise details of the mutational process can have a qualitative impact on the long-term behavior of an eco-evolutionary system, in contrast to the classical population genetic assumption that all mutations can be modeled with an effective, homogeneous rate. We demonstrate this in the context of a version of MacArthur’s consumer-resource model in which consumers mutate along a resource preference trait-space. Starting from a stochastic individual-based model, we simulate the system in the case where mutations are exogenously generated at a fixed rate (e.g. via external mutagens) and in the case where mutations are coupled to replication (e.g. via DNA copying errors). We find that, surprisingly, replication-coupled mutations are capable of generating a patterned phase in the limit of fast ecological relaxation – precisely the regime where classical population genetic models are expected to operate. We derive the appropriate mean-field description of the stochastic model and use it to show that the patterned phase comes about due to a Turing-like mechanism driven by the non-reciprocal and nonlinear nature of replicative mutations. We demonstrate that these results are robust to demographic noise and model choices and we discuss systems in which this phenomenology might be relevant.
Semi-Automated High Content Analysis of Pollen Performance Using TubeTracker
Authors: Yimga Ouonkap Sorel V. , Oseguera Yahir , Okihiro Bryce , Johnson Mark A. 
Published: November 2024

AbstractPollen function is critical for successful plant reproduction and crop productivity and it is important to develop accessible methods to quantitatively analyze pollen performance to enhance reproductive resilience. Here we introduce TubeTracker as a method to quantify key parameters of pollen performance such as, time to pollen grain germination, pollen tube tip velocity and pollen tube survival. TubeTracker integrates manual and automatic image processing routines and the graphical user interface allows the user to interact with the software to make manual corrections of automated steps. TubeTracker does not depend on training data sets required to implement machine learning approaches and thus can be immediately implemented using readily available imaging systems. Furthermore, TubeTracker is an excellent tool to produce the pollen performance data sets necessary to take advantage of emerging AI-based methods to fully automate analysis. We tested TubeTracker and found it to be accurate in measuring pollen tube germination and pollen tube tip elongation across multiple cultivars of tomato.Abstract FigureGraphical AbstractGraphical user interface of TubeTracker showing all supported functionalities.