bluuebunny/crossref_metadata_2025_split huggingface dataset:
Neural general circulation models for weather and climate
Authors: Kochkov Dmitrii , Yuval Janni , Langmore Ian , Norgaard Peter , Smith Jamie , Mooers Griffin , Klöwer Milan , Lottes James , Rasp Stephan , Düben Peter , Hatfield Sam , Battaglia Peter , Sanchez-Gonzalez Alvaro , Willson Matthew , Brenner Michael P. , Hoyer Stephan 
Published: July 2024

AbstractGeneral circulation models (GCMs) are the foundation of weather and climate prediction1,2. GCMs are physics-based simulators that combine a numerical solver for large-scale dynamics with tuned representations for small-scale processes such as cloud formation. Recently, machine-learning models trained on reanalysis data have achieved comparable or better skill than GCMs for deterministic weather forecasting3,4. However, these models have not demonstrated improved ensemble forecasts, or shown sufficient stability for long-term weather and climate simulations. Here we present a GCM that combines a differentiable solver for atmospheric dynamics with machine-learning components and show that it can generate forecasts of deterministic weather, ensemble weather and climate on par with the best machine-learning and physics-based methods. NeuralGCM is competitive with machine-learning models for one- to ten-day forecasts, and with the European Centre for Medium-Range Weather Forecasts ensemble prediction for one- to fifteen-day forecasts. With prescribed sea surface temperature, NeuralGCM can accurately track climate metrics for multiple decades, and climate forecasts with 140-kilometre resolution show emergent phenomena such as realistic frequency and trajectories of tropical cyclones. For both weather and climate, our approach offers orders of magnitude computational savings over conventional GCMs, although our model does not extrapolate to substantially different future climates. Our results show that end-to-end deep learning is compatible with tasks performed by conventional GCMs and can enhance the large-scale physical simulations that are essential for understanding and predicting the Earth system.
Genome-wide association meta-analysis identifies five loci associated with postpartum hemorrhage
Authors: Westergaard David , Steinthorsdottir Valgerdur , Stefansdottir Lilja , Rohde Palle Duun , Wu Xiaoping , Geller Frank , Tyrmi Jaakko , Havulinna Aki S. , Solé-Navais Pol , Flatley Christopher , Ostrowski Sisse Rye , Pedersen Ole Birger , Erikstrup Christian , Sørensen Erik , Mikkelsen Christina , Bruun Mie Topholm , Aagaard Jensen Bitten , Brodersen Thorsten , Ullum Henrik ,   FinnGen,   Danish Blood Donor Study Genomic Consortium,   Estonian Biobank Research Team,   Nordic Collaboration for Womens and Reproductive Health, Magnus Per , Andreassen Ole A. , Njolstad Pål R. , Kolte Astrid Marie , Krebs Lone , Nyegaard Mette , Hansen Thomas Folkmann , Feenstra Bjarke , Daly Mark , Lindgren Cecilia M. , Thorleifsson Gudmar , Stefansson Olafur A. , Sveinbjornsson Gardar , Gudbjartsson Daniel F. , Thorsteinsdottir Unnur , Banasik Karina , Jacobsson Bo , Laisk Triin , Laivuori Hannele , Stefansson Kari , Brunak Søren , Nielsen Henriette Svarre 
Published: July 2024

AbstractBleeding in early pregnancy and postpartum hemorrhage (PPH) bear substantial risks, with the former closely associated with pregnancy loss and the latter being the foremost cause of maternal death, underscoring the severe impact on maternal–fetal health. We identified five genetic loci linked to PPH in a meta-analysis. Functional annotation analysis indicated candidate genesHAND2,TBX3andRAP2C/FRMD7at three loci and showed that at each locus, associated variants were located within binding sites for progesterone receptors. There were strong genetic correlations with birth weight, gestational duration and uterine fibroids. Bleeding in early pregnancy yielded no genome-wide association signals but showed strong genetic correlation with various human traits, suggesting a potentially complex, polygenic etiology. Our results suggest that PPH is related to progesterone signaling dysregulation, whereas early bleeding is a complex trait associated with underlying health and possibly socioeconomic status and may include genetic factors that have not yet been identified.
Proteomic signatures improve risk prediction for common and rare diseases
Authors: Carrasco-Zanini Julia , Pietzner Maik , Davitte Jonathan , Surendran Praveen , Croteau-Chonka Damien C. , Robins Chloe , Torralbo Ana , Tomlinson Christopher , Grünschläger Florian , Fitzpatrick Natalie , Ytsma Cai , Kanno Tokuwa , Gade Stephan , Freitag Daniel , Ziebell Frederik , Haas Simon , Denaxas Spiros , Betts Joanna C. , Wareham Nicholas J. , Hemingway Harry , Scott Robert A. , Langenberg Claudia 
Published: July 2024

AbstractFor many diseases there are delays in diagnosis due to a lack of objective biomarkers for disease onset. Here, in 41,931 individuals from the United Kingdom Biobank Pharma Proteomics Project, we integrated measurements of ~3,000 plasma proteins with clinical information to derive sparse prediction models for the 10-year incidence of 218 common and rare diseases (81–6,038 cases). We then compared prediction models developed using proteomic data with models developed using either basic clinical information alone or clinical information combined with data from 37 clinical assays. The predictive performance of sparse models including as few as 5 to 20 proteins was superior to the performance of models developed using basic clinical information for 67 pathologically diverse diseases (median delta C-index = 0.07; range = 0.02–0.31). Sparse protein models further outperformed models developed using basic information combined with clinical assay data for 52 diseases, including multiple myeloma, non-Hodgkin lymphoma, motor neuron disease, pulmonary fibrosis and dilated cardiomyopathy. For multiple myeloma, single-cell RNA sequencing from bone marrow in newly diagnosed patients showed that four of the five predictor proteins were expressed specifically in plasma cells, consistent with the strong predictive power of these proteins. External replication of sparse protein models in the EPIC-Norfolk study showed good generalizability for prediction of the six diseases tested. These findings show that sparse plasma protein signatures, including both disease-specific proteins and protein predictors shared across several diseases, offer clinically useful prediction of common and rare diseases.
On exploiting nonparametric kernel-based probabilistic machine learning over the large compositional space of high entropy alloys for optimal nanoscale ballistics
Authors: Gupta K. K. , Barman S. , Dey S. , Naskar S. , Mukhopadhyay T. 
Published: July 2024

AbstractThe large compositional space of high entropy alloys (HEA) often presents significant challenges in comprehensively deducing the critical influence of atomic composition on their mechanical responses. We propose an efficient nonparametric kernel-based probabilistic computational mapping to obtain the optimal composition of HEAs under ballistic conditions by exploiting the emerging capabilities of machine learning (ML) coupled with molecular-level simulations. Compared to conventional ML models, the present Gaussian approach is a Bayesian paradigm that can have several advantages, including small training datasets concerning computationally intensive simulations and the ability to provide uncertainty measurements of molecular dynamics simulations therein. The data-driven analysis reveals that a lower concentration of Ni with a higher concentration of Al leads to higher dissipation of kinetic energy and lower residual velocity, but with higher penetration depth of the projectile. To deal with such conflicting computationally intensive functional objectives, the ML-based simulation framework is further extended in conjunction with multi-objective genetic algorithm for identifying the critical elemental compositions to enhance kinetic energy dissipation with minimal penetration depth and residual velocity of the projectile simultaneously. The computational framework proposed here is generic in nature, and it can be extended to other HEAs with a range of non-aligned multi-physical property demands.
Circulating extracellular vesicular microRNA signatures in early gestation show an association with subsequent clinical features of pre-eclampsia
Authors: Ghosh Shubhamoy , Thamotharan Shanthie , Fong Jeanette , Lei Margarida Y. Y. , Janzen Carla , Devaskar Sherin U. 
Published: July 2024

AbstractIn a prospective cohort of subjects who subsequently developed preeclampsia (PE, n = 14) versus remaining healthy (NORM, n = 12), early gestation circulating extracellular vesicles (EVs) containing a panel of microRNA signatures were characterized and their biological networks of targets deciphered. Multiple microRNAs of which some arose from the placenta (19MC and 14MC) demonstrated changes in association with advancing gestation, while others expressed were pathognomonic of the subsequent development of characteristic clinical features of PE which set in as a late-onset subtype. This panel of miRNAs demonstrated a predictability with an area under the curve of 0.96 using leave-one-out cross-validation training in a logistic regression model with elastic-net regularization and precautions against overfitting. In addition, this panel of miRNAs, some of which were previously detected in either placental tissue or as maternal cell-free non-coding transcripts, lent further validation to our EV studies and the observed association with PE. Further, the identified biological networks of targets of these detected miRNAs revealed biological functions related to vascular remodeling, cellular proliferation, growth, VEGF, EGF and the PIP3/Akt signaling pathways, all mediating key cellular functions. We conclude that we have demonstrated a proof-of-principle by detecting a panel of EV packaged miRNAs in the maternal circulation early in gestation with possibilities of biological function in the placenta and other maternal tissues, along with the probability of predicting the subsequent clinical appearance of PE, particularly the late-onset subtype.
Reconciling shared versus context-specific information in a neural network model of latent causes
Authors: Lu Qihong , Nguyen Tan T. , Zhang Qiong , Hasson Uri , Griffiths Thomas L. , Zacks Jeffrey M. , Gershman Samuel J. , Norman Kenneth A. 
Published: July 2024

AbstractIt has been proposed that, when processing a stream of events, humans divide their experiences in terms of inferred latent causes (LCs) to support context-dependent learning. However, when shared structure is present across contexts, it is still unclear how the “splitting” of LCs and learning of shared structure can be simultaneously achieved. Here, we present the Latent Cause Network (LCNet), a neural network model of LC inference. Through learning, it naturally stores structure that is shared across tasks in the network weights. Additionally, it represents context-specific structure using a context module, controlled by a Bayesian nonparametric inference algorithm, which assigns a unique context vector for each inferred LC. Across three simulations, we found that LCNet could (1) extract shared structure across LCs in a function learning task while avoiding catastrophic interference, (2) capture human data on curriculum effects in schema learning, and (3) infer the underlying event structure when processing naturalistic videos of daily events. Overall, these results demonstrate a computationally feasible approach to reconciling shared structure and context-specific structure in a model of LCs that is scalable from laboratory experiment settings to naturalistic settings.
Structure based functional identification of an uncharacterized protein from Coxiella burnetii involved in adipogenesis
Authors: Mamun Tajul Islam , Bourhia Mohammed , Neoaj Taufiq , Akash Shopnil , Azad Md. A. K. , Hossain Md. Sarowar , Rahman Md. Masudur , Bin Jardan Yousef A. , Ibenmoussa Samir , Sitotaw Baye 
Published: July 2024

AbstractCoxiella burnetii, the causative agent of Q fever, is an intracellular pathogen posing a significant global public health threat. There is a pressing need for dependable and effective treatments, alongside an urgency for further research into the molecular characterization of its genome. Within the genomic landscape of Coxiella burnetii, numerous hypothetical proteins remain unidentified, underscoring the necessity for in-depth study. In this study, we conducted comprehensive in silico analyses to identify and prioritize potential hypothetical protein of Coxiella burnetii, aiming to elucidate the structure and function of uncharacterized protein. Furthermore, we delved into the physicochemical properties, localization, and molecular dynamics and simulations, and assessed the primary, secondary, and tertiary structures employing a variety of bioinformatics tools. The in-silico analysis revealed that the uncharacterized protein contains a conserved Mth938-like domain, suggesting a role in preadipocyte differentiation and adipogenesis. Subcellular localization predictions indicated its presence in the cytoplasm, implicating a significant role in cellular processes. Virtual screening identified ligands with high binding affinities, suggesting the protein’s potential as a drug target against Q fever. Molecular dynamics simulations confirmed the stability of these complexes, indicating their therapeutic relevance. The findings provide a structural and functional overview of an uncharacterized protein from C. burnetii, implicating it in adipogenesis. This study underscores the power of in-silico approaches in uncovering the biological roles of uncharacterized proteins and facilitating the discovery of new therapeutic strategies. The findings provide valuable preliminary data for further investigation into the protein’s role in adipogenesis.
Energy efficient cluster-based routing protocol for WSN using multi-strategy fusion snake optimizer and minimum spanning tree
Authors: Yang Le , Zhang Damin , Li Lun , He Qing 
Published: July 2024

AbstractIn recent years, the widespread adoption of wireless sensor networks (WSN) has resulted in the growing integration of the internet of things (IoT). However, WSN encounters limitations related to energy and sensor node lifespan, making the development of an efficient routing protocol a critical concern. Cluster technology offers a promising solution to this challenge. This study introduces a novel cluster routing protocol for WSN. The system selects cluster heads and relay nodes utilizing the multi-strategy fusion snake optimizer (MSSO) and employs the minimum spanning tree algorithm for inter-cluster routing planning, thereby extending the system’s lifecycle and conserving network energy. In pursuit of an optimal clustering scheme, the paper also introduces tactics involving dynamic parameter updating, adaptive alpha mutation, and bi-directional search optimization within MSSO. These techniques significantly increase the algorithm convergence speed and expand the available search space. Furthermore, a novel efficient clustering routing model for WSN is presented. The model generates different objective functions for selecting cluster heads and relay nodes, considering factors such as location, energy, base station distance, intra-cluster compactness, inter-cluster separation, and other relevant criteria. When selecting cluster heads, the fuzzy c-means (FCM) algorithm is integrated into MSSO to improve the optimization performance of the algorithm. When planning inter-cluster routing, the next hop node is selected for the relay node based on distance, residual energy, and direction.The experimental results demonstrate that the proposed protocol reduces energy consumption by at least 26.64% compared to other cluster routing protocols including LEACH, ESO, EEWC, GWO, and EECHS-ISSADE. Additionally, it increases the network lifetime of WSN by at least 25.84%, extends the stable period by at least 52.43%, and boosts the network throughput by at least 40.99%.
Resonant noise amplification in a predator-prey model with quasi-discrete generations
Authors: Giannakou M. , Waclaw B. 
Published: July 2024

AbstractPredator-prey models have been shown to exhibit resonance-like behaviour, in which random fluctuations in the number of organisms (demographic noise) are amplified when their frequency is close to the natural oscillatory frequency of the system. This behaviour has been traditionally studied in models with exponentially distributed replication and death times. Here we consider a biologically more realistic model, in which organisms replicate quasi-synchronously such that the distribution of replication times has a narrow maximum at some $$T>0$$

T
>
0

corresponding to the mean doubling time. We show that when the frequency of replication $$f=1/T$$

f
=
1
/
T

is tuned to the natural oscillatory frequency of the predator-prey model, the system exhibits oscillations that are much stronger than in the model with Poissonian (non-synchronous) replication and death. These oscillations lead to population instability and the extinction of one of the species much sooner than in the case of Poissonian replication. The effect can be explained by resonant amplification of coloured noise generated by quasi-synchronous replication events.
Dynamics of PM2.5 and network activity during extreme pollution events
Authors: Bashan Nail F. , Li Weiyu , Wang Qi R. 
Published: July 2024

AbstractIn an era where air pollution poses a significant threat to both the environment and public health, we present a network-based approach to unravel the dynamics of extreme pollution events. Leveraging data from 741 monitoring stations in the contiguous United States, we have created dynamic networks using time-lagged correlations of hourly particulate matter (PM2.5) data. The established spatial correlation networks reveal significant PM2.5 anomalies during the 2020 and 2021 wildfire seasons, demonstrating the approach’s sensitivity to detecting regional pollution phenomena. The methodology also provides insights into smoke transport and network response, highlighting the persistence of air quality issues beyond visible smoke periods. Additionally, we explored meteorological variables’ impacts on network connectivity. This study enhances understanding of spatiotemporal pollution patterns, positioning spatial correlation networks as valuable tools for environmental monitoring and public health surveillance.
A unique warm–water oasis in the Siberian Arctic’s Chaun Bay sustained by hydrothermal groundwater discharge
Authors: Charkin Alexander N. , Kosobokova Ksenia N. , Ershova Elizaveta A. , Syomin Vitaly L. , Kolbasova Glafira D. , Semkin Pavel Yu. , Leusov Andrey E. , Dudarev Oleg V. , Gulenko Timofey A. , Yaroshchuk Elena I. , Startsev Anatoly M. , Fayman Pavel A. , Krasikov Vladislav A. , Zverev Sergey A. , Bessonova Elena A. , Ulyantsev Alexander S. , Elovsky Evgeny V. , Yurikova Daria A. , Kobyakov Kirill A. , Zimina Olga L. , Gerasimova Alexandra V. , Tishchenko Peter P. , Didov Alexander A. 
Published: July 2024

AbstractChaun Bay, located on the fringe of the East Siberian Sea, has been described since the mid-20th century to support a unique marine ecosystem that is atypical for the local Siberian Arctic. Here we use ship-board physical, biogeochemical and geological measurements taken in October 2020, along with hydrographic observations taken from land-fast ice in April 2023, to demonstrate that these warm-water biological communities are supported by hydrothermal submarine groundwater discharge that delivers heat, salinity, nutrients, and trace elements to the bay. We identify a cyclonic eddy that mixes the warm nutrient-rich groundwater with oxygen-rich surface water, resulting in a water mass within Chaun Bay that has similar physical and chemical properties to the highly productive waters of the North Pacific and Southern Chukchi Sea. The bay showed elevated concentrations of chlorophyll-a and zooplankton, and the abundance and species diversity of epibenthos significantly exceeded values observed elsewhere in the East Siberian Sea. The benthic communities contained a number of boreal species that are not typically found in the Arctic Ocean. We also observed Thysanoessa krill populations, a pelagic species generally considered an expatriate in Arctic waters.
A megaton-scale industrial demonstration study on hydrothermal mineralization enabled silty waste upcycling
Authors: Du Yao , Liu Chuang , Qiu Qihan , Zhang Haidong , Li Bo , Chen Xincai , Lou Honghai , Zeng Qiang 
Published: July 2024

AbstractThe continual large-scale urbanization and urban renewal in coastal cities of China have accumulated massive silty residue (SR) that may alter the coastal lines and always exerts high stresses to local environments. To meet China’s sustainable development policy, the need for green and high-efficient industrial treatments and reuse of SR is urgent. In this work, we may, for the first time, report a megaton-scale industrial project to upcycle low-quality SR and recycled aggregate (RA) to manufacture construction materials with hydrothermal mineralization (HM). In-situ pilot tests on five batches of SR-RA blocks were conducted. Results demonstrate that the produced blocks possess the compressive strengths of 11.4–15.8 MPa, densities of 1280–1430 kg/m3, porosities of 35–44%, CO2 emissions of 170.22–187.29 kg e-CO2/m3 and costs of 126.49–156.51 CNY/m3, comparable with or superior than the commercial blocks. The silica in SR could react with lime to produce tobermorite with stable pseudohexagonal plate under HM treatment, which improved the microstructure of the material. The findings validate the industrial practicability of upcycling low-quality SR and RA with HM for valuable construction block manufacture.
Fam134c and Fam134b shape axonal endoplasmic reticulum architecture in vivo
Authors: Iavarone Francescopaolo , Zaninello Marta , Perrone Michela , Monaco Mariagrazia , Barth Esther , Gaedke Felix , Pizzo Maria Teresa , Di Lorenzo Giorgia , Desiderio Vincenzo , Sommella Eduardo , Merciai Fabrizio , Salviati Emanuela , Campiglia Pietro , Luongo Livio , De Leonibus Elvira , Rugarli Elena , Settembre Carmine 
Published: July 2024

AbstractEndoplasmic reticulum (ER) remodeling is vital for cellular organization. ER-phagy, a selective autophagy targeting ER, plays an important role in maintaining ER morphology and function. The FAM134 protein family, including FAM134A, FAM134B, and FAM134C, mediates ER-phagy. While FAM134B mutations are linked to hereditary sensory and autonomic neuropathy in humans, the physiological role of the other FAM134 proteins remains unknown. To address this, we investigate the roles of FAM134 proteins using single and combined knockouts (KOs) in mice. Single KOs in young mice show no major phenotypes; however, combined Fam134b and Fam134c deletion (Fam134b/cdKO), but not the combination including Fam134a deletion, leads to rapid neuromuscular and somatosensory degeneration, resulting in premature death. Fam134b/cdKO mice show rapid loss of motor and sensory axons in the peripheral nervous system. Long axons from Fam134b/cdKO mice exhibit expanded tubular ER with a transverse ladder-like appearance, whereas no obvious abnormalities are present in cortical ER. Our study unveils the critical roles of FAM134C and FAM134B in the formation of tubular ER network in axons of both motor and sensory neurons.