bluuebunny/crossref_metadata_2025 huggingface dataset:
Deep Long‐Period Earthquakes at Akutan Volcano From 2005 to 2017 Better Track Magma Influxes Compared to Volcano‐Tectonic Earthquakes
Authors: Song Zilin , Tan Yen Joe , Roman Diana C.
Published: May 2023

Both volcano‐tectonic (VTs) and deep long‐period earthquakes (DLPs) have been documented at Akutan Volcano, Alaska and may reflect different active processes helpful for eruption forecasting. In this study, we perform high‐resolution earthquake detection, classification, and relocation using seismic data from 2005 to 2017 to investigate their relationship with underlying magmatic processes. We find that the 2,787 VTs and 787 DLPs are concentrated above and below the inferred magma reservoir respectively. They both are clustered as swarms and occur preferentially during inflation episodes with no spatial migrations. However, moment release rates of DLP swarms show a stronger correlation with inflation and their low‐frequency content is likely a source instead of a path effect. Therefore, we infer that DLPs are directly related to unsteady magma movement through a complex pathway. In comparison, repeating events are observed in VTs. Thus, we conclude that they represent fault rupture triggered by magma/fluid movement or larger earthquakes.
Upward Lightning at the Gaisberg Tower: The Larger‐Scale Meteorological Influence on the Triggering Mode and Flash Type
Authors: Stucke Isabell , Morgenstern Deborah , Diendorfer Gerhard , Mayr Georg J. , Pichler Hannes , Schulz Wolfgang , Simon Thorsten , Zeileis Achim
Published: May 2023

Upward lightning is rarer than downward lightning and requires tall (100+ m) structures to initiate. It may be either self‐initiated or triggered by other lightning discharges. While conventional lightning location systems (LLSs) detect most of the upward lightning flashes superimposed by pulses or return strokes, they miss a specific flash type that consists only of a continuous current. Globally, only few specially instrumented towers can record this flash type. The proliferation of wind turbines in combination with damages from upward lightning necessitates an improved understanding under which conditions self‐initiated upward lightning and the continuous‐current‐only subtype occur. This study uses a random forest machine learning model to find the larger‐scale meteorological conditions favoring the occurrence of the different phenomena. It combines ground truth lightning current measurements at the specially instrumented tower at Gaisberg mountain in Austria with variables from larger‐scale meteorological reanalysis data (ERA5). These variables reliably explain whether upward lightning is self‐initiated or triggered by other lightning discharges. The most important variable is the height of the −10°C isotherm above the tall structure: the closer it is, the higher is the probability of self‐initiated upward lightning. For the different flash types, this study finds a relationship to the larger‐scale electrification conditions and the LLS‐detected lightning situation in the vicinity. Lower amounts of supercooled liquid water, solid, and liquid differently sized particles and no LLS‐detected lightning events nearby favor the continuous‐current‐only subtype compared to the other subtypes, which preferentially occur with LLS‐detected lightning events within 3 km from the Gaisberg Tower.
Deep Learning for Downscaling Tropical Cyclone Rainfall to Hazard‐Relevant Spatial Scales
Authors: Vosper Emily , Watson Peter , Harris Lucy , McRae Andrew , Santos‐Rodriguez Raul , Aitchison Laurence , Mitchell Dann
Published: May 2023

Flooding, driven in part by intense rainfall, is the leading cause of mortality and damages from the most intense tropical cyclones (TCs). With rainfall from TCs set to increase under anthropogenic climate change, it is critical to accurately estimate extreme rainfall to better support short‐term and long‐term resilience efforts. While high‐resolution climate models capture TC statistics better than low‐resolution models, they are computationally expensive. This leads to a trade‐off between capturing TC features accurately, and generating large enough simulation data sets to sufficiently sample high‐impact, low‐probability events. Downscaling can assist by predicting high‐resolution features from relatively cheap, low‐resolution models. Here, we develop and evaluate a set of three deep learning models for downscaling TC rainfall to hazard‐relevant spatial scales. We use rainfall from the Multi‐Source Weighted‐Ensemble Precipitation observational product at a coarsened resolution of ∼100 km, and apply our downscaling model to reproduce the original resolution of ∼10 km. We find that the Wasserstein Generative Adversarial Network is able to capture realistic spatial structures and power spectra and performs the best overall, with mean biases within 5% of observations. We also show that the model can perform well at extrapolating to the most extreme storms, which were not used in training.
Temporal Evolution of Dune Number Density in a Barchan Dune Field
Authors: Xiao Xiang , Liu Hongyou , Zheng Xiaojing
Published: May 2023

The present work examines the temporal evolution of dune number density in desert barchan dune fields and the effects of wind velocity, particle diameter and sand supply thickness using the Comprehensive Scale‐Coupled Model of dune field considering turbulence. Results show that the dune number density exhibits a temporal evolution trend of “increase—decrease—tend to be stable,” which is similar to the “overshoot” phenomenon of the per unit width sand transport rate evolving with time. The larger the wind velocity and the smaller the particle diameter are, the enhanced surface sand transport by wind causes the dune number density peak to be larger and appear earlier, but the more active dune interactions result in a higher decay rate during the decline stage. In addition, a small sand supply thickness slows sand accumulation and thus delays the initial development of barchans. With an increase in the sand supply thickness, more small‐scale barchans are formed earlier in dune fields due to gradually sufficient sand amounts, which promotes dune interactions and thus accelerates the decay of dune number density. Nevertheless, as the sand supply thickness continuously increases, the barchans link together to form dune chains, resulting in a slowed migration velocity and a reduced spatial density of interactions. The present work makes up for the lack of understanding of the initial evolution stage of barchan dune fields and the dune interactions in existing studies, and thus may be helpful for gaining insight into the dynamic evolution process of aeolian dune systems.
Methane Emissions in Seagrass Meadows as a Small Offset to Carbon Sequestration
Authors: Yau Yvonne Y. Y. , Reithmaier Gloria , Majtényi‐Hill Claudia , Serrano Oscar , Piñeiro‐Juncal Nerea , Dahl Martin , Mateo Miguel Angel , Bonaglia Stefano , Santos Isaac R.
Published: May 2023

Seagrass meadows are effective carbon sinks due to high primary production and sequestration in sediments. However, methane (CH4) emissions can partially counteract their carbon sink capacity. Here, we measured diffusive sediment‐water and sea‐air CO2 and CH4 fluxes in a coastal embayment dominated by Posidonia oceanica in the Mediterranean Sea. High‐resolution timeseries observations revealed large spatial and temporal variability in CH4 concentrations (2–36 nM). Lower sea‐air CH4 emissions were observed in an area with dense seagrass meadows compared to patchy seagrass. A 6%−40% decrease of CH4 concentration in the surface water around noon indicates that photosynthesis likely limits CH4 fluxes. Sediments were the major CH4 source as implied from radon (a natural porewater tracer) observations and evidence for methanogenesis in deeper sediments. CH4 sediment‐water fluxes (0.1 ± 0.1–0.4 ± 0.1 μmol m−2 d−1) were higher than average sea‐air CH4 emissions (0.12 ± 0.10 μmol m−2 d−1), suggesting that dilution and CH4 oxidation in the water column could reduce net CH4 fluxes into the atmosphere. Overall, relatively low sea‐air CH4 fluxes likely represent the net emissions from subtidal seagrass habitat not influenced by allochthonous CH4 sources. The local CH4 emissions in P. oceanica can offset less than 1% of the carbon burial in sediments (142 ± 69 g CO2eq m−2 yr−1). Combining our results with earlier observations in other seagrass meadows worldwide reveals that global CH4 emissions only offset a small fraction (<2%) of carbon sequestration in sediments from seagrass meadows.
We Need a Better Way to Share Earth Observations
Authors: Liu Zhong , Wen Yixin , Mantas Vasco , Meyer David
Published: May 2023

A more accessible, open data-sharing infrastructure will engage a broader community of contributors, helping to develop satellite data products that benefit Earth science research and applications.
Constraints on Bend‐Faulting and Mantle Hydration at the Marianas Trench From Seismic Anisotropy
Authors: Mark Hannah F. , Lizarralde Daniel , Wiens Douglas A.
Published: May 2023

Subduction zones are a key link between the surface water cycle and the solid Earth, as the incoming plate carries pore water and hydrous minerals into the subsurface. However, water fluxes from surface to subsurface reservoirs over geologic time are highly uncertain because the volume of water carried in hydrous minerals in the slab mantle is poorly constrained. Estimates of slab mantle hydration based on seismic tomography assume bulk serpentinization, representing an upper bound on water volume. We measure azimuthal seismic anisotropy near the Marianas Trench, use spatial variations in anisotropy to constrain the extent and geometry of bend‐related faulting, and place a lower bound on slab mantle water content for the case where serpentinization is confined within fault zones. The seismic observations can be explained by a minimum of ∼0.85 wt% water in the slab mantle, compared to the upper bound of ∼2 wt% obtained from tomography.
Asymmetry in the Seasonal Cycle of Zonal‐Mean Surface Air Temperature
Authors: Roach Lettie A. , Eisenman Ian , Wagner Till J. W. , Donohoe Aaron
Published: May 2023

At most latitudes, the seasonal cycle of zonal‐mean surface air temperature is notably asymmetric: the length of the warming season is not equal to the length of the cooling season. The asymmetry varies spatially, with the cooling season being ∼40 days shorter than the warming season in the subtropics and the warming season being ∼100 days shorter than the cooling season at the poles. Furthermore, the asymmetry differs between the Northern Hemisphere and the Southern Hemisphere. Here, we show that these observed features are broadly captured in a simple model for the evolution of temperature forced by realistic insolation. The model suggests that Earth's orbital eccentricity largely determines the hemispheric contrast, and obliquity broadly dictates the meridional structure. Clouds, atmospheric heat flux convergence, and time‐invariant effective surface heat capacity have minimal impacts on seasonal asymmetry. This simple, first‐order picture has been absent from previous discussions of the surface temperature seasonal cycle.
Magnetic Resonance Imaging of Multi‐Phase Lava Flow Analogs: Velocity and Rheology
Authors: Birnbaum Janine , Zia Wasif , Bordbar Alireza , Lee Ray F. , Boyce Christopher M. , Lev Einat
Published: May 2023

The rheology of lavas and magmas exerts a strong control on the dynamics and hazards posed by volcanic eruptions. Magmas and lavas are complex mixtures of silicate melt, suspended crystals, and gas bubbles. To improve the understanding of the dynamics and effective rheology of magmas and lavas, we performed dam‐break flow experiments using suspensions of silicone oil, sesame seeds, and N2O bubbles. Experiments were run inside a magnetic resonance imaging (MRI) scanner to provide imaging of the flow interior. We varied the volume fraction of sesame seeds between 0 and 0.48, and of bubbles between 0 and 0.21. MRI phase‐contrast velocimetry was used to measure liquid velocity. We fit an effective viscosity to the velocity data by approximating the stress using lubrication theory and the imaged shape of the free surface. In experiments with both particles and bubbles (three‐phase suspensions), we observed shear banding in which particle‐poor regions deform with a lower effective viscosity and dominate flow propagation speed. Our observations demonstrate the importance of considering variations in phase distributions within magmatic fluids and their implications on the dynamics of volcanic eruptions.
Obesity impairs cardiolipin-dependent mitophagy and therapeutic intercellular mitochondrial transfer ability of mesenchymal stem cells
Authors: Sagar Shakti , Faizan Md Imam , Chaudhary Nisha , Singh Vandana , Singh Praveen , Gheware Atish , Sharma Khushboo , Azmi Iqbal , Singh Vijay Pal , Kharya Gaurav , Mabalirajan Ulaganathan , Agrawal Anurag , Ahmad Tanveer , Sinha Roy Soumya
Published: May 2023

Mesenchymal stem cell (MSC) transplantation alleviates metabolic defects in diseased recipient cells by intercellular mitochondrial transport (IMT). However, the effect of host metabolic conditions on IMT and thereby on the therapeutic efficacy of MSCs has largely remained unexplored. Here we found impaired mitophagy, and reduced IMT in MSCs derived from high-fat diet (HFD)-induced obese mouse (MSC-Ob). MSC-Ob failed to sequester their damaged mitochondria into LC3-dependent autophagosomes due to decrease in mitochondrial cardiolipin content, which we propose as a putative mitophagy receptor for LC3 in MSCs. Functionally, MSC-Ob exhibited diminished potential to rescue mitochondrial dysfunction and cell death in stress-induced airway epithelial cells. Pharmacological modulation of MSCs enhanced cardiolipin-dependent mitophagy and restored their IMT ability to airway epithelial cells. Therapeutically, these modulated MSCs attenuated features of allergic airway inflammation (AAI) in two independent mouse models by restoring healthy IMT. However, unmodulated MSC-Ob failed to do so. Notably, in human (h)MSCs, induced metabolic stress associated impaired cardiolipin-dependent mitophagy was restored upon pharmacological modulation. In summary, we have provided the first comprehensive molecular understanding of impaired mitophagy in obese-derived MSCs and highlight the importance of pharmacological modulation of these cells for therapeutic intervention.
Pathological convergence of APP and SNCA deficiency in hippocampal degeneration of young rats
Authors: Wang Yajie , Miao Zhikang , Xu Chang , Cai Ying , Yang Yuting , Hu Yue , Zhao Mengna , Shao Yue , Li Zhiqiang , Chen Jincao , Chen Shi , Wang Lianrong
Published: May 2023

The common pathogenesis of Alzheimer’s disease (AD) and Parkinson’s disease (PD) has been supported by biochemical, genetic and molecular evidence. Mitochondrial dysfunction is considered to be the common pathology in early AD and PD. The physiological regulation of APP and α-synuclein on mitochondria remains unclear, let alone whether they share common regulatory mechanisms affecting the development of neurodegenerative diseases. By studying gene knockout rats, the commonality of physiological APP and α-synuclein in maintaining mitochondrial function through calcium homeostasis regulation was revealed, which was critical in inhibiting hippocampal degeneration in young rats. APP and α-synuclein both control hippocampal mitochondrial calcium intake and outflow. In the mitochondrial calcium influx regulation, APP and α-synuclein are located on the mitochondrial-associated endoplasmic reticulum membrane (MAM) and converge to regulate the IP3R1-Grp75-VDAC2 axis. Mitochondrial calcium outflow is redundantly promoted by both α-synuclein and APP. Loss of APP or SNCA leads to mitochondrial calcium overload, thus enhancing aerobic respiration and ER stress, and ultimately causing excessive apoptosis in the hippocampus and spatial memory impairment in young rats. Based on this study, we believe that the physiological function impairment of APP and SNCA is the early core pathology to induce mitochondrial dysfunction at the early stage of AD and PD, while the IP3R1-Grp75-VDAC2 axis might be the common drug target of these two diseases.
Cytoprotective autophagy as a pro-survival strategy in ART-resistant malaria parasites
Authors: Kannan Deepika , Joshi Nishant , Gupta Sonal , Pati Soumya , Bhattacharjee Souvik , Langsley Gordon , Singh Shailja
Published: May 2023

Despite several initiatives to subside the global malaria burden, the spread of artemisinin-resistant parasites poses a big threat to malaria elimination. Mutations in PfKelch13 are predictive of ART resistance, whose underpinning molecular mechanism remains obscure. Recently, endocytosis and stress response pathways such as the ubiquitin-proteasome machinery have been linked to artemisinin resistance. With Plasmodium, however, ambiguity persists regarding a role in ART resistance for another cellular stress defence mechanism called autophagy. Therefore, we investigated whether, in the absence of ART treatment, basal autophagy is augmented in PfK13-R539T mutant ART-resistant parasites and analyzed whether PfK13-R539T endowed mutant parasites with an ability to utilize autophagy as a pro-survival strategy. We report that in the absence of any ART treatment, PfK13-R539T mutant parasites exhibit increased basal autophagy compared to PfK13-WT parasites and respond aggressively through changes in autophagic flux. A clear cytoprotective role of autophagy in parasite resistance mechanism is evident by the observation that a suppression of PI3-Kinase (PI3K) activity (a master autophagy regulator) rendered difficulty in the survival of PfK13-R539T ART-resistant parasites. In conclusion, we now show that higher PI3P levels reported for mutant PfKelch13 backgrounds led to increased basal autophagy that acts as a pro-survival response to ART treatment. Our results highlight PfPI3K as a druggable target with the potential to re-sensitize ART-resistant parasites and identify autophagy as a pro-survival function that modulates ART-resistant parasite growth.
Circ_0020256 induces fibroblast activation to drive cholangiocarcinoma development via recruitment of EIF4A3 protein to stabilize KLF4 mRNA
Authors: Li Zongyan , Chen Zuxiao , Li Shiying , Qian Xiangjun , Zhang Lei , Long Guojie , Xie Jiancong , Huang Xiaoming , Zheng Zheyu , Pan Weidong , Li Haiyan , Zhang Dawei
Published: May 2023

Cancer-associated fibroblasts (CAFs) are a kind of stromal cells in the cholangiocarcinoma (CCA) microenvironment, playing crucial roles in cancer development. However, the potential mechanisms of the interaction between CCA cells and CAFs remain obscure. This work investigated the role of circ_0020256 in CAFs activation. We proved circ_0020256 was up-regulated in CCA. High circ_0020256 expression facilitated TGF-β1 secretion from CCA cells, which activated CAFs via the phosphorylation of Smad2/3. Mechanistically, circ_0020256 recruited EIF4A3 protein to stabilize KLF4 mRNA and upregulate its expression, then KLF4 bound to TGF-β1 promoter and induced its transcription in CCA cells. KLF4 overexpression abrogated the inhibition of circ_0020256 silencing in TGF-β1/Smad2/3-induced CAFs activation. Furthermore, CCA cell growth, migration, and epithelial-mesenchymal transition were favored by CAFs-secreted IL-6 via autophagy inhibition. We also found circ_0020256 accelerated CCA tumor growth in vivo. In conclusion, circ_0020256 promoted fibroblast activation to facilitate CCA progression via EIF4A3/KLF4 pathway, providing a potential intervention for CCA progression.
Regorafenib inhibits EphA2 phosphorylation and leads to liver damage via the ERK/MDM2/p53 axis
Authors: Yan Hao , Wu Wentong , Hu Yuhuai , Li Jinjin , Xu Jiangxin , Chen Xueqin , Xu Zhifei , Yang Xiaochun , Yang Bo , He Qiaojun , Luo Peihua
Published: May 2023

The hepatotoxicity of regorafenib is one of the most noteworthy concerns for patients, however the mechanism is poorly understood. Hence, there is a lack of effective intervention strategies. Here, by comparing the target with sorafenib, we show that regorafenib-induced liver injury is mainly due to its nontherapeutic target Eph receptor A2 (EphA2). EphA2 deficiency attenuated liver damage and cell apoptosis under regorafenib treatment in male mice. Mechanistically, regorafenib inhibits EphA2 Ser897 phosphorylation and reduces ubiquitination of p53 by altering the intracellular localization of mouse double minute 2 (MDM2) by affecting the extracellular signal-regulated kinase (ERK)/MDM2 axis. Meanwhile, we found that schisandrin C, which can upregulate the phosphorylation of EphA2 at Ser897 also has protective effect against the toxicity in vivo. Collectively, our findings identify the inhibition of EphA2 Ser897 phosphorylation as a key cause of regorafenib-induced hepatotoxicity, and chemical activation of EphA2 Ser897 represents a potential therapeutic strategy to prevent regorafenib-induced hepatotoxicity.
Endothelial cell-derived stem cell factor promotes lipid accumulation through c-Kit-mediated increase of lipogenic enzymes in brown adipocytes
Authors: Lee Hyuek Jong , Lee Jueun , Yang Myung Jin , Kim Young-Chan , Hong Seon Pyo , Kim Jung Mo , Hwang Geum-Sook , Koh Gou Young
Published: May 2023

Active thermogenesis in the brown adipose tissue (BAT) facilitating the utilization of lipids and glucose is critical for maintaining body temperature and reducing metabolic diseases, whereas inactive BAT accumulates lipids in brown adipocytes (BAs), leading to BAT whitening. Although cellular crosstalk between endothelial cells (ECs) and adipocytes is essential for the transport and utilization of fatty acid in BAs, the angiocrine roles of ECs mediating this crosstalk remain poorly understood. Using single-nucleus RNA sequencing and knock-out male mice, we demonstrate that stem cell factor (SCF) derived from ECs upregulates gene expressions and protein levels of the enzymes for de novo lipogenesis, and promotes lipid accumulation by activating c-Kit in BAs. In the early phase of lipid accumulation induced by denervation or thermoneutrality, transiently expressed c-Kit on BAs increases the protein levels of the lipogenic enzymes via PI3K and AKT signaling. EC-specific SCF deletion and BA-specific c-Kit deletion attenuate the induction of the lipogenic enzymes and suppress the enlargement of lipid droplets in BAs after denervation or thermoneutrality in male mice. These data provide insight into SCF/c-Kit signaling as a regulator that promotes lipid accumulation through the increase of lipogenic enzymes in BAT when thermogenesis is inhibited.
Image analysis-based tumor infiltrating lymphocytes measurement predicts breast cancer pathologic complete response in SWOG S0800 neoadjuvant chemotherapy trial
Authors: Fanucci Kristina A. , Bai Yalai , Pelekanou Vasiliki , Nahleh Zeina A. , Shafi Saba , Burela Sneha , Barlow William E. , Sharma Priyanka , Thompson Alastair M. , Godwin Andrew K. , Rimm David L. , Hortobagyi Gabriel N. , Liu Yihan , Wang Leona , Wei Wei , Pusztai Lajos , Blenman Kim R. M.
Published: May 2023

We assessed the predictive value of an image analysis-based tumor-infiltrating lymphocytes (TILs) score for pathologic complete response (pCR) and event-free survival in breast cancer (BC). About 113 pretreatment samples were analyzed from patients with stage IIB-IIIC HER-2-negative BC randomized to neoadjuvant chemotherapy ± bevacizumab. TILs quantification was performed on full sections using QuPath open-source software with a convolutional neural network cell classifier (CNN11). We used easTILs% as a digital metric of TILs score defined as [sum of lymphocytes area (mm2)/stromal area(mm2)] × 100. Pathologist-read stromal TILs score (sTILs%) was determined following published guidelines. Mean pretreatment easTILs% was significantly higher in cases with pCR compared to residual disease (median 36.1 vs.14.8%, p < 0.001). We observed a strong positive correlation (r = 0.606, p < 0.0001) between easTILs% and sTILs%. The area under the prediction curve (AUC) was higher for easTILs% than sTILs%, 0.709 and 0.627, respectively. Image analysis-based TILs quantification is predictive of pCR in BC and had better response discrimination than pathologist-read sTILs%.
Immune landscape of breast tumors with low and intermediate estrogen receptor expression
Authors: Voorwerk Leonie , Sanders Joyce , Keusters Milou S. , Balduzzi Sara , Cornelissen Sten , Duijst Maxime , Lips Esther H. , Sonke Gabe S. , Linn Sabine C. , Horlings Hugo M. , Kok Marleen
Published: May 2023

Immune checkpoint blockade (ICB) is currently approved for patients with triple-negative breast cancer (TNBC), whereas responses to ICB are also observed in a small subgroup of Estrogen Receptor (ER)-positive breast cancer. The cut-off for ER-positivity (≥1%) is based on likelihood of endocrine treatment response, but ER-positive breast cancer represents a very heterogeneous group. This raises the question whether selection based on ER-negativity should be revisited to select patients for ICB treatment in the context of clinical trials. Stromal tumor-infiltrating lymphocytes (sTILs) and other immune parameters are higher in TNBC compared to ER-positive breast cancer, but it is unknown whether lower ER levels are associated with more inflamed tumor microenvironments (TME). We collected a consecutive series of primary tumors from 173 HER2-negative breast cancer patients, enriched for tumors with ER expression between 1 and 99% and found levels of stromal TILs, CD8 + T cells, and PD-L1 positivity in breast tumors with ER 1–9% and ER 10–50% to be comparable to tumors with ER 0%. Expression of immune-related gene signatures in tumors with ER 1–9% and ER 10–50% was comparable to ER 0%, and higher than in tumors with ER 51–99% and ER 100%. Our results suggest that the immune landscape of ER low tumors (1–9%) and ER intermediate tumors (10–50%) mimic that of primary TNBC.
Intranasal COVID-19 vaccine induces respiratory memory T cells and protects K18-hACE mice against SARS-CoV-2 infection
Authors: Diallo Béré K. , Ní Chasaide Caitlín , Wong Ting Y. , Schmitt Pauline , Lee Katherine S. , Weaver Kelly , Miller Olivia , Cooper Melissa , Jazayeri Seyed D. , Damron F. Heath , Mills Kingston H. G.
Published: May 2023

Current COVID-19 vaccines prevent severe disease, but do not induce mucosal immunity or prevent infection with SARS-CoV-2, especially with recent variants. Furthermore, serum antibody responses wane soon after immunization. We assessed the immunogenicity and protective efficacy of an experimental COVID-19 vaccine based on the SARS-CoV-2 Spike trimer formulated with a novel adjuvant LP-GMP, comprising TLR2 and STING agonists. We demonstrated that immunization of mice twice by the intranasal (i.n.) route or by heterologous intramuscular (i.m.) prime and i.n. boost with the Spike-LP-GMP vaccine generated potent Spike-specific IgG, IgA and tissue-resident memory (TRM) T cells in the lungs and nasal mucosa that persisted for at least 3 months. Furthermore, Spike-LP-GMP vaccine delivered by i.n./i.n., i.m./i.n., or i.m./i.m. routes protected human ACE-2 transgenic mice against respiratory infection and COVID-19-like disease following lethal challenge with ancestral or Delta strains of SARS-CoV-2. Our findings underscore the potential for nasal vaccines in preventing infection with SARS-CoV-2 and other respiratory pathogen.
PTB-XL+, a comprehensive electrocardiographic feature dataset
Authors: Strodthoff Nils , Mehari Temesgen , Nagel Claudia , Aston Philip J. , Sundar Ashish , Graff Claus , Kanters Jørgen K. , Haverkamp Wilhelm , Dössel Olaf , Loewe Axel , Bär Markus , Schaeffter Tobias
Published: May 2023

Machine learning (ML) methods for the analysis of electrocardiography (ECG) data are gaining importance, substantially supported by the release of large public datasets. However, these current datasets miss important derived descriptors such as ECG features that have been devised in the past hundred years and still form the basis of most automatic ECG analysis algorithms and are critical for cardiologists’ decision processes. ECG features are available from sophisticated commercial software but are not accessible to the general public. To alleviate this issue, we add ECG features from two leading commercial algorithms and an open-source implementation supplemented by a set of automatic diagnostic statements from a commercial ECG analysis software in preprocessed format. This allows the comparison of ML models trained on clinically versus automatically generated label sets. We provide an extensive technical validation of features and diagnostic statements for ML applications. We believe this release crucially enhances the usability of thePTB-XLdataset as a reference dataset for ML methods in the context of ECG data.
Watershed carbon yield derived from gauge observations and river network connectivity in the United States
Authors: Qiu Han , Zhang Xuesong , Yang Anni , Wickland Kimberly P. , Stets Edward G. , Chen Min
Published: May 2023

River networks play a critical role in the global carbon cycle. Although global/continental scale riverine carbon cycle studies demonstrate the significance of rivers and streams for linking land and coastal regions, the lack of spatially distributed riverine carbon load data represents a gap for quantifying riverine carbon net gain or net loss in different regions, understanding mechanisms and factors that influence the riverine carbon cycle, and testing simulations of aquatic carbon cycle models at fine scales. Here, we (1) derive the riverine load of particulate organic carbon (POC) and dissolved organic carbon (DOC) for over 1,000 hydrologic stations across the Conterminous United States (CONUS) and (2) use the river network connectivity information for over 80,000 catchment units within the National Hydrography Dataset Plus (NHDPlus) to estimate riverine POC and DOC net gain or net loss for watersheds controlled between upstream-downstream hydrologic stations. The new riverine carbon load and watershed net gain/loss represent a unique contribution to support future studies for better understanding and quantification of riverine carbon cycles.
An inventory of greenhouse gas emissions due to natural gas pipeline incidents in the United States and Canada from 1980s to 2021
Authors: Lu Hongfang , Xu Zhao-Dong , Cheng Y. Frank , Peng Haoyan , Xi Dongmin , Jiang Xinmeng , Ma Xin , Dai Jun , Shan Yuli
Published: May 2023

Natural gas is believed to be a critical transitional energy source. However, natural gas pipelines, once failed, will contribute to a large amount of greenhouse gas (GHG) emissions, including methane from uncontrolled natural gas venting and carbon dioxide from flared natural gas. However, the GHG emissions caused by pipeline incidents are not included in the regular inventories, making the counted GHG amount deviate from the reality. This study, for the first time, establishes an inventory framework for GHG emissions including all natural gas pipeline incidents in the two of the largest gas producers and consumers in North America (United States and Canada) from 1980s to 2021. The inventory comprises GHG emissions resulting from gathering and transmission pipeline incidents in a total of 24 states or regions in the United States between 1970 and 2021, local distribution pipeline incidents in 22 states or regions between 1970 and 2021, as well as natural gas pipeline incidents in a total of 7 provinces or regions in Canada between 1979 and 2021. These datasets can improve the accuracy of regular emission inventories by covering more emission sources in the United States and Canada and provide essential information for climate-oriented pipeline integrity management.
Long-term daily hydrometeorological drought indices, soil moisture, and evapotranspiration for ICOS sites
Authors: Pohl Felix , Rakovec Oldrich , Rebmann Corinna , Hildebrandt Anke , Boeing Friedrich , Hermanns Floris , Attinger Sabine , Samaniego Luis , Kumar Rohini
Published: May 2023

Eddy covariance sites are ideally suited for the study of extreme events on ecosystems as they allow the exchange of trace gases and energy fluxes between ecosystems and the lower atmosphere to be directly measured on a continuous basis. However, standardized definitions of hydroclimatic extremes are needed to render studies of extreme events comparable across sites. This requires longer datasets than are available from on-site measurements in order to capture the full range of climatic variability. We present a dataset of drought indices based on precipitation (Standardized Precipitation Index, SPI), atmospheric water balance (Standardized Precipitation Evapotranspiration Index, SPEI), and soil moisture (Standardized Soil Moisture Index, SSMI) for 101 ecosystem sites from the Integrated Carbon Observation System (ICOS) with daily temporal resolution from 1950 to 2021. Additionally, we provide simulated soil moisture and evapotranspiration for each site from the Mesoscale Hydrological Model (mHM). These could be utilised for gap-filling or long-term research, among other applications. We validate our data set with measurements from ICOS and discuss potential research avenues.
Association between excessive fetal growth and maternal cancer in Shanghai, China: a large, population-based cohort study
Authors: Qian Naisi , Yang Qing , Chen Lei , Jin Shan , Qiao Jiaying , Cai Renzhi , Wu Chunxiao , Yu Huiting , Gu Kai , Wang Chunfang
Published: May 2023

The prevalence of high birth weight or large for gestational age (LGA) infants is increasing, with increasing evidence of pregnancy-related factors that may have long-term impacts on the health of the mother and baby. We aimed to determine the association between excessive fetal growth, specifically LGA and macrosomia, and subsequent maternal cancer by performing a prospective population-based cohort study. The data set was based on the Shanghai Birth Registry and Shanghai Cancer Registry, with medical records from the Shanghai Health Information Network as a supplement. Macrosomia and LGA prevalence was higher in women who developed cancer than in women who did not. Having an LGA child in the first delivery was associated with a subsequently increased risk of maternal cancer (hazard ratio [HR] = 1.08, 95% confidence interval [CI] 1.04–1.11). Additionally, in the last and heaviest deliveries, there were similar associations between LGA births and maternal cancer rates (HR = 1.08, 95% CI 1.04–1.12; HR = 1.08, 95% CI 1.05–1.12, respectively). Furthermore, a substantially increased trend in the risk of maternal cancer was associated with birth weights exceeding 2500 g. Our study supports the association between LGA births and increased risks of maternal cancer, but this risk requires further investigation.
Prevalence of BRCA homopolymeric indels in an ION Torrent-based tumour-to-germline testing workflow in high-grade ovarian carcinoma
Authors: Azzollini Jacopo , Agnelli Luca , Conca Elena , Torelli Tommaso , Busico Adele , Capone Iolanda , Angelini Marta , Tamborini Elena , Perrone Federica , Vingiani Andrea , Lorenzini Daniele , Peissel Bernard , Pruneri Giancarlo , Manoukian Siranoush
Published: May 2023

Tumour DNA sequencing is essential for precision medicine since it guides therapeutic decisions but also fosters the identification of patients who may benefit from germline testing. Notwithstanding, the tumour-to-germline testing workflow presents a few caveats. The low sensitivity for indels at loci with sequences of identical bases (homopolymers) of ion semiconductor-based sequencing techniques represents a well-known limitation, but the prevalence of indels overlooked by these techniques in high-risk populations has not been investigated. In our study, we addressed this issue at the homopolymeric regions of BRCA1/2 in a retrospectively selected cohort of 157 patients affected with high-grade ovarian cancer and negative at tumour testing by ION Torrent sequencing. Variant allele frequency (VAF) of indels at each of the 29 investigated homopolymers was systematically revised with the IGV software. Thresholds to discriminate putative germline variants were defined by scaling the VAF to a normal distribution and calculating the outliers that exceeded the mean + 3 median-adjusted deviations of a control population. Sanger sequencing of the outliers confirmed the occurrence of only one of the five putative indels in both tumour and blood from a patient with a family history of breast cancer. Our results indicated that the prevalence of homopolymeric indels overlooked by ion semiconductor techniques is seemingly low. A careful evaluation of clinical and family history data would further help minimise this technique-bound limitation, highlighting cases in which a deeper look at these regions would be recommended.
Optimal low voltage ride through of wind turbine doubly fed induction generator based on bonobo optimization algorithm
Authors: Mostafa M. Abdelateef , El-Hay Enas A. , Elkholy Mahmoud M.
Published: May 2023

The large-scale wind energy conversion system (WECS) based on a doubly fed induction generator (DFIG) has gained popularity in recent years because of its various economic and technical merits. The fast integration of WECS with existing power grids has caused negative influence on the stability and reliability of power systems. Grid voltage sags produce a high overcurrent in the DFIG rotor circuit. Such these challenges emphasise the necessity of the low voltage ride through (LVRT) capability of a DFIG for ensuring power grid stability during voltage dips. To deal with these issues simultaneously, this paper aims to obtain the optimal values of injected rotor phase voltage for DFIG and wind turbine pitch angles for all operating wind speeds in order to achieve LVRT capability. Bonobo optimizer (BO) is a new optimization algorithm that is applied to crop the optimum values of injected rotor phase voltage for DFIG and wind turbine pitch angles. These optimal values provide the maximum possible DFIG mechanical power to guarantee rotor and stator currents do not exceed the rated values and also deliver the maximum reactive power for supporting grid voltage during faults. The ideal power curve of a 2.4 MW wind turbine has been estimated to get the allowable maximum wind power for all wind speeds. To validate the results accuracy, the BO results are compared to two other optimization algorithms: particle swarm optimizer and driving training optimizer. Adaptive neuro fuzzy inference system is employed as an adaptive controller for the prediction of the values of rotor voltage and wind turbine pitch angle for any stator voltage dip and any wind speed.
Immune gene variation associated with chromosome-scale differences among individual zebrafish genomes
Authors: McConnell Sean C. , Hernandez Kyle M. , Andrade Jorge , de Jong Jill L. O.
Published: May 2023

Immune genes have evolved to maintain exceptional diversity, offering robust defense against pathogens. We performed genomic assembly to examine immune gene variation in zebrafish. Gene pathway analysis identified immune genes as significantly enriched among genes with evidence of positive selection. A large subset of genes was absent from analysis of coding sequences due to apparent lack of reads, prompting us to examine genes overlapping zero coverage regions (ZCRs), defined as 2 kb stretches without mapped reads. Immune genes were identified as highly enriched within ZCRs, including over 60% of major histocompatibility complex (MHC) genes and NOD-like receptor (NLR) genes, mediators of direct and indirect pathogen recognition. This variation was most highly concentrated throughout one arm of chromosome 4 carrying a large cluster of NLR genes, associated with large-scale structural variation covering more than half of the chromosome. Our genomic assemblies uncovered alternative haplotypes and distinct complements of immune genes among individual zebrafish, including the MHC Class II locus on chromosome 8 and the NLR gene cluster on chromosome 4. While previous studies have shown marked variation in NLR genes between vertebrate species, our study highlights extensive variation in NLR gene regions between individuals of the same species. Taken together, these findings provide evidence of immune gene variation on a scale previously unknown in other vertebrate species and raise questions about potential impact on immune function.
FUS fibrillation occurs through a nucleation-based process below the critical concentration required for liquid–liquid phase separation
Authors: Bertrand Emilie , Demongin Clément , Dobra Ioana , Rengifo-Gonzalez Juan Carlos , Singatulina Anastasia S. , Sukhanova Maria V. , Lavrik Olga I. , Pastré David , Hamon Loic
Published: May 2023

FUS is an RNA-binding protein involved in familiar forms of ALS and FTLD that also assembles into fibrillar cytoplasmic aggregates in some neurodegenerative diseases without genetic causes. The self-adhesive prion-like domain in FUS generates reversible condensates via the liquid–liquid phase separation process (LLPS) whose maturation can lead to the formation of insoluble fibrillar aggregates in vitro, consistent with the appearance of cytoplasmic inclusions in ageing neurons. Using a single-molecule imaging approach, we reveal that FUS can assemble into nanofibrils at concentrations in the nanomolar range. These results suggest that the formation of fibrillar aggregates of FUS could occur in the cytoplasm at low concentrations of FUS, below the critical ones required to trigger the liquid-like condensate formation. Such nanofibrils may serve as seeds for the formation of pathological inclusions. Interestingly, the fibrillation of FUS at low concentrations is inhibited by its binding to mRNA or after the phosphorylation of its prion-like domain, in agreement with previous models.
Assessment of potential pathogenic bacterial load and multidrug resistance in locally manufactured cosmetics commonly used in Dhaka metropolis
Authors: Nusrat Namira , Ahmad Zahra Maftuha , Ahmed Akash , Haque Fahim
Published: May 2023

In Bangladesh cosmetics are being produced disregarding the Good Manufacturing Practices. So, this study aimed to test the level and nature of bacterial contamination of such cosmetics. A total of 27 cosmetics comprising eight lipsticks, nine powders, and ten creams were bought from New Market and Tejgaon areas of Dhaka city and tested. Bacteria was detected in 85.2% of samples. Majority of the samples (77.8%) exceeded the limit given by the Bangladesh Standards and Testing Institution (BSTI), Food and Drug Administration (FDA) and the International Organization for Standardization (ISO). Both Gram-negative (Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae and Salmonella spp.) and Gram-positive bacteria (species of Streptococcus, Staphylococcus, Bacillus and Listeria monocytogenes) were identified. Hemolysis was observed in 66.7% Gram-positive and 25% Gram-negative bacteria. Multidrug resistance was tested in 165 randomly selected isolates. Every species of Gram-positive and Gram-negative bacteria exhibited varying levels of multidrug resistance. The highest levels of antibiotic resistance were in broad-spectrum antibiotics (ampicillin, azithromycin, cefepime, ciprofloxacin and meropenem) and narrow-spectrum Gram-negative antibiotics (aztreonam and colistin). Multidrug resistance was 12–78% in Gram-negative bacteria and 12–100% in Gram-positive bacteria. Coagulase and DNase were identified in 97.5% and 5.1% of Staphylococcus aureus isolates respectively. Our findings indicate that these cosmetics pose a risk to the public’s health.
Proof of concept of the potential of a machine learning algorithm to extract new information from conventional SARS-CoV-2 rRT-PCR results
Authors: Cabrera Alvargonzález Jorge , Larrañaga Janeiro Ana , Pérez Castro Sonia , Martínez Torres Javier , Martínez Lamas Lucía , Daviña Nuñez Carlos , Del Campo-Pérez Víctor , Suarez Luque Silvia , Regueiro García Benito , Porteiro Fresco Jacobo
Published: May 2023

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been and remains one of the major challenges modern society has faced thus far. Over the past few months, large amounts of information have been collected that are only now beginning to be assimilated. In the present work, the existence of residual information in the massive numbers of rRT-PCRs that tested positive out of the almost half a million tests that were performed during the pandemic is investigated. This residual information is believed to be highly related to a pattern in the number of cycles that are necessary to detect positive samples as such. Thus, a database of more than 20,000 positive samples was collected, and two supervised classification algorithms (a support vector machine and a neural network) were trained to temporally locate each sample based solely and exclusively on the number of cycles determined in the rRT-PCR of each individual. Overall, this study suggests that there is valuable residual information in the rRT-PCR positive samples that can be used to identify patterns in the development of the SARS-CoV-2 pandemic. The successful application of supervised classification algorithms to detect these patterns demonstrates the potential of machine learning techniques to aid in understanding the spread of the virus and its variants.
Application of supervised machine learning algorithms for classification and prediction of type-2 diabetes disease status in Afar regional state, Northeastern Ethiopia 2021
Authors: Ebrahim Oumer Abdulkadir , Derbew Getachew
Published: May 2023

Ethiopia has been challenged by the growing magnitude of diabetes in general and type-2 diabetes in particular. Knowledge extraction from stored dataset can be an important base for better decision on diabetes rapid diagnosis, suggestive on prediction for early intervention. Thus, this study was addressed these problem by application of supervised machine learning algorithms for classification and prediction of type 2 diabetes disease status and might provide context-specific information to program planners and policy makers so that, priority will be given to the more affected groups. To apply supervised machine learning algorithms; compare these algorithms and select the best algorithm based on their performance for classification and prediction of type-2 diabetic disease status (positive or negative) in public hospitals of Afar regional state, Northeastern Ethiopia. This study was conducted at Afar regional state from February to June of 2021. Decision tree; pruned J 48, Artificial neural network, K-nearest neighbor, Support vector machine, Binary logistic regression, Random forest, and Naïve Bayes supervised machine learning algorithms were applied using secondary data from the medical database record review. A total of 2239 sample Dataset diagnosed for diabetes from 2012 to April 22/2020 (1523 with type-2 diabetes and 716 without type-2 diabetes) was checked for its completeness prior to analysis. For all algorithms, WEKA3.7 tool was used for analysis purposes. Moreover, all algorithms were compared based on their correctly classification rate, kappa statistics, confusion matrix, area under the curve, sensitivity, and specificity. From the seven major supervised machine learning algorithms, the best classification and prediction results were obtained from random forest [correctly classified rate (93.8%), kappa statistics (0.85), sensitivity (0.98), area under the curve (0.97) and confusion matrix (out of 454 actual positive prediction for 446)] which was followed by decision tree pruned J 48 [correctly classified rate (91.8%), kappa statistics (0.80), sensitivity (0.96), area under the curve (0.91) and confusion matrices (out of 454 actual positive prediction for 438)] and k-nearest neighbor [correctly classified rate (89.8%), kappa statistics (0.76), sensitivity (0.92), area under the curve (0.88) and confusion matrices (out of 454 actual positive prediction for 421)]. Random forest, Decision tree pruned J48 and k-nearest neighbor algorithms have better classification and prediction performance for classifying and predicting type-2 diabetes disease status. Therefore, based on this performance, random forest algorithm can be judged as suggestive and supportive for clinicians at the time of type-2 diabetes diagnosis.