bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

AbstractWe 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

AbstractImmune 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

AbstractCurrent 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

AbstractMachine 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

AbstractRiver 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

AbstractNatural 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

AbstractEddy 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

AbstractThe 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

AbstractTumour 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

AbstractThe 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

AbstractImmune 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

AbstractFUS 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

AbstractIn 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

AbstractSevere 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.