bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prediction of 316 stainless steel low-cycle fatigue life based on machine learning
Authors: Duan Hongyan , Cao Mengjie , Liu Lin , Yue Shunqiang , He Hong , Zhao Yingjian , Zhang Zengwang , liu Yang 
Published: April 2023

AbstractThe low-cycle fatigue life of 316 stainless steel is a significant basis for safety assessment. Usually, many factors affect the low-cycle fatigue life of stainless steel, and the relationship between the influencing factors and fatigue life is complicated and nonlinear. Therefore, it is hard to predict fatigue life using the traditional empirical formula. Based on this, a machine learning algorithm is proposed. In this paper, based on the large amount of existing experimental data, machine learning methods are used to predict the low circumferential fatigue life of 316 stainless steel. The results show that the prediction accuracy of nu-SVR and ELM models is high and can meet engineering needs.
Predictive biosignature of major depressive disorder derived from physiological measurements of outpatients using machine learning
Authors: Ricka Nicolas , Pellegrin Gauthier , Fompeyrine Denis A. , Lahutte Bertrand , Geoffroy Pierre A. 
Published: April 2023

AbstractMajor Depressive Disorder (MDD) has heterogeneous manifestations, leading to difficulties in predicting the evolution of the disease and in patient's follow-up. We aimed to develop a machine learning algorithm that identifies a biosignature to provide a clinical score of depressive symptoms using individual physiological data. We performed a prospective, multicenter clinical trial where outpatients diagnosed with MDD were enrolled and wore a passive monitoring device constantly for 6 months. A total of 101 physiological measures related to physical activity, heart rate, heart rate variability, breathing rate, and sleep were acquired. For each patient, the algorithm was trained on daily physiological features over the first 3 months as well as corresponding standardized clinical evaluations performed at baseline and months 1, 2 and 3. The ability of the algorithm to predict the patient's clinical state was tested using the data from the remaining 3 months. The algorithm was composed of 3 interconnected steps: label detrending, feature selection, and a regression predicting the detrended labels from the selected features. Across our cohort, the algorithm predicted the daily mood status with 86% accuracy, outperforming the baseline prediction using MADRS alone. These findings suggest the existence of a predictive biosignature of depressive symptoms with at least 62 physiological features involved for each patient. Predicting clinical states through an objective biosignature could lead to a new categorization of MDD phenotypes.
ANGPTL4 is a potential driver of HCV-induced peripheral insulin resistance
Authors: Gomes Diana , Sobolewski Cyril , Conzelmann Stéphanie , Schaer Tifany , Lefai Etienne , Alfaiate Dulce , Tseligka Eirini D. , Goossens Nicolas , Tapparel Caroline , Negro Francesco , Foti Michelangelo , Clément Sophie 
Published: April 2023

AbstractChronic hepatitis C (CHC) is associated with the development of metabolic disorders, including both hepatic and extra-hepatic insulin resistance (IR). Here, we aimed at identifying liver-derived factor(s) potentially inducing peripheral IR and uncovering the mechanisms whereby HCV can regulate the action of these factors. We found ANGPTL4 (Angiopoietin Like 4) mRNA expression levels to positively correlate with HCV RNA (r = 0.46, p < 0.03) and HOMA-IR score (r = 0.51, p = 0.01) in liver biopsies of lean CHC patients. Moreover, we observed an upregulation of ANGPTL4 expression in two models recapitulating HCV-induced peripheral IR, i.e. mice expressing core protein of HCV genotype 3a (HCV-3a core) in hepatocytes and hepatoma cells transduced with HCV-3a core. Treatment of differentiated myocytes with recombinant ANGPTL4 reduced insulin-induced Akt-Ser473 phosphorylation. In contrast, conditioned medium from ANGPTL4-KO hepatoma cells prevented muscle cells from HCV-3a core induced IR. Treatment of HCV-3a core expressing HepG2 cells with PPARγ antagonist resulted in a decrease of HCV-core induced ANGPTL4 upregulation. Together, our data identified ANGPTL4 as a potential driver of HCV-induced IR and may provide working hypotheses aimed at understanding the pathogenesis of IR in the setting of other chronic liver disorders.
CEA dynamics for predicting response after anti-EGFR monoclonal antibody treatment in metastatic colorectal cancer
Authors: Kang Sora , Kim Sun Young , Hong Yong Sang , Kim Tae Won , Choi Ki Eun , Kim Min Jung , Kim Jeong Eun 
Published: April 2023

AbstractCarcinoembryonic antigen (CEA) is the most widely used tumor marker in metastatic colorectal cancer (mCRC). However, its potential as a predictive marker of progression in mCRC during systemic chemotherapy, particularly in patients receiving monoclonal antibodies as a combination therapy, has remained of interest. Herein, we investigated whether CEA changes could predict disease progression and clinical outcomes in patients with mCRC cotreated with systemic chemotherapy and/or biologic agents. A total of 1261 patients with mCRC undergoing a first-line systemic treatment were included in this retrospective study. We analyzed the optimal cut-off value for CEA changes to predict progression at the first response evaluation by the treatment arm (chemotherapy alone, chemotherapy plus anti-vascular endothelial growth factor (VEGF) monoclonal antibody [mAb], and chemotherapy plus anti-epidermal growth factor receptor [EGFR] mAb). These cut-off values were then used to predict overall survival (OS) and progression-free survival (PFS). When stratified by their treatment arm, 891 (70.6%), 266 (21.0%), and 104 (8.2%) of the study patients were included in the chemotherapy alone-, anti-VEGF mAb, and anti-EGFR mAb groups, respectively. The optimal CEA cut-off values were 16.5% and 38.9% increase in the whole cohort and anti-EGFR mAb group, respectively, and these values showed high sensitivity and specificity for predicting disease progression. The patients in the entire population and anti-EGFR mAb group with CEA changes below these cut-off values showed significantly better OS and PFS outcomes compared those whose changes were above cut-off values. Among the patients with mCRC treated with anti-VEGF mAb, no associations were found between OS or PFS outcomes and CEA changes. CEA is potentially a good surrogate marker for predicting disease progression and survival outcomes in patients with mCRC receiving first-line systemic chemotherapy alone or chemotherapy with anti-EGFR mAb, whereas it is less effective in those treated with anti-VEGF mAb.
Efficient low temperature Monte Carlo sampling using quantum annealing
Authors: Sandt Roland , Spatschek Robert 
Published: April 2023

AbstractQuantum annealing is an efficient technology to determine ground state configurations of discrete binary optimization problems, described through Ising Hamiltonians. Here we show that—at very low computational cost—finite temperature properties can be calculated. The approach is most efficient at low temperatures, where conventional approaches like Metropolis Monte Carlo sampling suffer from high rejection rates and therefore large statistical noise. To demonstrate the general approach, we apply it to spin glasses and Ising chains.
Deep learning-based automated detection and multiclass classification of focal interictal epileptiform discharges in scalp electroencephalograms
Authors: Chung Yoon Gi , Lee Woo-Jin , Na Sung Min , Kim Hunmin , Hwang Hee , Yun Chang-Ho , Kim Ki Joong 
Published: April 2023

AbstractDetection and spatial distribution analyses of interictal epileptiform discharges (IEDs) are important for diagnosing, classifying, and treating focal epilepsy. This study proposes deep learning-based models to detect focal IEDs in electroencephalography (EEG) recordings of the frontal, temporal, and occipital scalp regions. This study included 38 patients with frontal (n = 15), temporal (n = 13), and occipital (n = 10) IEDs and 232 controls without IEDs from a single tertiary center. All the EEG recordings were segmented into 1.5-s epochs and fed into 1- or 2-dimensional convolutional neural networks to construct binary classification models to detect IEDs in each focal region and multiclass classification models to categorize IEDs into frontal, temporal, and occipital regions. The binary classification models exhibited accuracies of 79.3–86.4%, 93.3–94.2%, and 95.5–97.2% for frontal, temporal, and occipital IEDs, respectively. The three- and four-class models exhibited accuracies of 87.0–88.7% and 74.6–74.9%, respectively, with temporal, occipital, and non-IEDs F1-scores of 89.9–92.3%, 84.9–90.6%, and 84.3–86.0%; and 86.6–86.7%, 86.8–87.2%, and 67.8–69.2% for the three- and four-class (frontal, 50.3–58.2%) models, respectively. The deep learning-based models could help enhance EEG interpretation. Although they performed well, the resolution of region-specific focal IED misinterpretations and further model improvement are needed.
Ensemble machine learning algorithm for predicting acute kidney injury in patients admitted to the neurointensive care unit following brain surgery
Authors: Wu Muying , Jiang Xuandong , Du Kailei , Xu Yingting , Zhang Weimin 
Published: April 2023

AbstractAcute kidney injury (AKI) is a common postoperative complication among patients in the neurological intensive care unit (NICU), often resulting in poor prognosis and high mortality. In this retrospective cohort study, we established a model for predicting AKI following brain surgery based on an ensemble machine learning algorithm using data from 582 postoperative patients admitted to the NICU at the Dongyang People's Hospital from March 1, 2017, to January 31, 2020. Demographic, clinical, and intraoperative data were collected. Four machine learning algorithms (C5.0, support vector machine, Bayes, and XGBoost) were used to develop the ensemble algorithm. The AKI incidence in critically ill patients after brain surgery was 20.8%. Intraoperative blood pressure; postoperative oxygenation index; oxygen saturation; and creatinine, albumin, urea, and calcium levels were associated with the postoperative AKI occurrence. The area under the curve value for the ensembled model was 0.85. The accuracy, precision, specificity, recall, and balanced accuracy values were 0.81, 0.86, 0.44, 0.91, and 0.68, respectively, indicating good predictive ability. Ultimately, the models using perioperative variables exhibited good discriminatory ability for early prediction of postoperative AKI risk in patients admitted to the NICU. Thus, the ensemble machine learning algorithm may be a valuable tool for forecasting AKI.
Marital status and long-term cardiovascular risk in general population (Gubbio, Italy)
Authors: Humbert Xavier , Rabiaza Andry , Fedrizzi Sophie , Alexandre Joachim , Menotti Alessandro , Touzé Emmanuel , Laurenzi Martino , Terradura-Vagnarelli Oscar , Puddu Paolo E. 
Published: April 2023

AbstractTo investigate whether marital status is associated to long-term major fatal and non-fatal cardiovascular events in men and women from the Gubbio Population Study. The incidence of cardiovascular disease (CVD), including stroke and coronary heart disease (CHD) and CVD death together with all-cause mortality were analyzed. The analysis included 2832 persons (44% men, 54 ± 11 years old). Marital status was defined at entry as married (married or living conjugally) versus unmarried subjects (widowed, separated, divorced or single). Married and unmarried subjects did not differ concerning socio-demographic, anthropometric and biological variables at baseline. Over 191 months median follow-up, the incidence of CHD was lower among married versus unmarried women [HR: 0.63 (95% CI 0.41–0.96)] only; the same was true for CHD mortality [HR: 0.43 (95% CI 0.22–0.84)] and all-cause mortality [HR: 0.75 (95% CI 0.59–0.96)] independently of traditional risk factors (age, SBP, total and HDL cholesterol, cigarette smoke and BMI). In men, marital status was not associated to any of the investigated outcomes. In primary care, marital status should be investigated as it can be associated with long-term CHD and all-cause incidence and mortality risks among women.
Beat-to-beat finger photoplethysmography in atrial fibrillation patients undergoing electrical cardioversion
Authors: Saglietto Andrea , Scarsoglio Stefania , Canova Daniela , De Ferrari Gaetano Maria , Ridolfi Luca , Anselmino Matteo 
Published: April 2023

AbstractAtrial fibrillation (AF)-induced peripheral microcirculatory alterations have poorly been investigated. The present study aims to expand current knowledge through a beat-to-beat analysis of non-invasive finger photoplethysmography (PPG) in AF patients restoring sinus rhythm by electrical cardioversion (ECV). Continuous non-invasive arterial blood pressure and left middle finger PPG pulse oximetry waveform (POW) signals were continuously recorded before and after elective ECV of consecutive AF or atrial flutter (AFL) patients. The main metrics (mean, standard deviation, coefficient of variation), as well as a beat-to-beat analysis of the pulse pressure (PP) and POW beat-averaged value (aPOW), were computed to compare pre- and post-ECV phases. 53 patients (mean age 69 ± 8 years, 79% males) were enrolled; cardioversion was successful in restoring SR in 51 (96%) and signal post-processing was feasible in 46 (87%) patients. In front of a non-significant difference in mean PP (pre-ECV: 51.96 ± 13.25, post-ECV: 49.58 ± 10.41 mmHg; p = 0.45), mean aPOW significantly increased after SR restoration (pre-ECV: 0.39 ± 0.09, post-ECV: 0.44 ± 0.06 a.u.; p < 0.001). Moreover, at beat-to-beat analysis linear regression yielded significantly different slope (m) for the PP (RR) relationship compared to aPOW(RR) [PP(RR): 0.43 ± 0.18; aPOW(RR): 1.06 ± 0.17; p < 0.001]. Long (> 95th percentile) and short (< 5th percentile) RR intervals were significantly more irregular in the pre-ECV phases for both PP and aPOW; however, aPOW signal suffered more fluctuations compared to PP (p < 0.001 in both phases). Present findings suggest that AF-related hemodynamic alterations are more manifest at the peripheral (aPOW) rather than at the upstream macrocirculatory level (PP). Restoring sinus rhythm increases mean peripheral microvascular perfusion and decreases variability of the microvascular hemodynamic signals. Future dedicated studies are required to determine if AF-induced peripheral microvascular alterations might relate to long-term prognostic effects.
Impact of adenomyosis and endometriosis on IVF/ICSI pregnancy outcome in patients undergoing gonadotropin-releasing hormone agonist treatment and frozen embryo transfer
Authors: Wang Yu , Yi Yu-Chiao , Guu Hwa-Fen , Chen Ya-Fang , Kung Hsiao-Fan , Chang Jui-Chun , Chen Li-Yu , Chuan Shih-Ting , Chen Ming-Jer 
Published: April 2023

AbstractAlthough numerous studies have attempted to establish the relationship between adenomyosis and infertility, no consensus has emerged. Our aim was to investigate whether adenomyosis and endometriosis affected IVF outcomes in our patients. This was a retrospective study of 1720 patients from January 2016 to December 2019. In total, 1389 cycles were included: 229 cycles in the endometriosis group (group E), 89 cycles in the adenomyosis group (group A), 69 cycles in the endometriosis and adenomyosis group (group EA), and 1002 cycles in the control group (group C). Most patients in groups A and EA received GnRH agonist treatment before FET. The 1st FET live birth rates (LBR) were 39.3%, 32.1%, 25% and 48.1% in groups E, A, EA, and C. The miscarriage rates were 19.9%, 34.7%, 39%, and 17.6%. The per retrieval cycle cumulative live birth rates (cLBRs) in patients < 38 y/o were 56.4%, 58.1%, 44.8%, and 63%. The per retrieval cycle cLBRs in patients ≥ 38 y/o were 25%, 9.8%, 17.2%, and 29.5%. Among groups A and EA, LBRs were 25.58% and 18.89% in patients with a ≥ sevenfold decrease and a < sevenfold decrease in CA-125 level, respectively, after GnRH agonist treatment. Endometriosis was not associated with a poorer pregnancy outcome. Patients with adenomyosis with/without endometriosis had higher miscarriage rates, lower LBRs, and lower cLBRs, especially in patients aged ≥ 38 years, even after GnRH agonist treatment before FET cycles. Patients who have a greater than sevenfold decrease in CA-125 level after GnRH agonist treatment might have better clinical pregnancy outcomes.
FDG PET texture indices as imaging biomarkers for epidermal growth factor receptor mutation status in lung adenocarcinoma
Authors: Ishimura Mariko , Norikane Takashi , Mitamura Katsuya , Yamamoto Yuka , Manabe Yuri , Murao Mitsumasa , Murota Makiko , Kanaji Nobuhiro , Nishiyama Yoshihiro 
Published: April 2023

AbstractIdentifying the epidermal growth factor receptor (EGFR) mutation status is important for the optimal treatment of patients with EGFR mutations. We investigated the relationship between 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) texture indices and EGFR mutation status in patients with newly diagnosed lung adenocarcinoma. We retrospectively analyzed data of patients with newly diagnosed lung adenocarcinoma who underwent pretreatment FDG PET/computed tomography and EGFR mutation testing between August 2014 and November 2020. Patients were divided into mutated EGFR and wild-type EGFR groups. The maximum standardized uptake value (SUVmax) and 31 texture indices for the primary tumor were calculated from PET images and compared between the two groups. Of the 66 patients included, 22 had mutated EGFR and 44 had wild-type EGFR. The SUVmax did not significantly differ between the two groups. Among the 31 evaluated texture indices, the following five showed a statistically significant difference between the groups: correlation (P = 0.003), gray-level nonuniformity for run (P = 0.042), run length nonuniformity (P = 0.02), coarseness (P = 0.006), and gray-level nonuniformity for zone (P = 0.04). Based on the preliminary results of this study in a small patient population, FDG PET texture indices may be potential imaging biomarkers for the EGFR mutation status in patients with newly diagnosed lung adenocarcinoma.
Structural insights into the broad protection against H1 influenza viruses by a computationally optimized hemagglutinin vaccine
Authors: Dzimianski John V. , Han Julianna , Sautto Giuseppe A. , O’Rourke Sara M. , Cruz Joseph M. , Pierce Spencer R. , Ecker Jeffrey W. , Carlock Michael A. , Nagashima Kaito A. , Mousa Jarrod J. , Ross Ted M. , Ward Andrew B. , DuBois Rebecca M. 
Published: April 2023

AbstractInfluenza virus poses an ongoing human health threat with pandemic potential. Due to mutations in circulating strains, formulating effective vaccines remains a challenge. The use of computationally optimized broadly reactive antigen (COBRA) hemagglutinin (HA) proteins is a promising vaccine strategy to protect against a wide range of current and future influenza viruses. Though effective in preclinical studies, the mechanistic basis driving the broad reactivity of COBRA proteins remains to be elucidated. Here, we report the crystal structure of the COBRA HA termed P1 and identify antigenic and glycosylation properties that contribute to its immunogenicity. We further report the cryo-EM structure of the P1-elicited broadly neutralizing antibody 1F8 bound to COBRA P1, revealing 1F8 to recognize an atypical receptor binding site epitope via an unexpected mode of binding.
Creating measurement-based oil and gas sector methane inventories using source-resolved aerial surveys
Authors: Johnson Matthew R. , Conrad Bradley M. , Tyner David R. 
Published: April 2023

AbstractCritical mitigation of methane emissions from the oil and gas (OG) sector is hampered by inaccurate official inventories and limited understanding of contributing sources. Here we present a framework for incorporating aerial measurements into comprehensive OG sector methane inventories that achieves robust, independent quantification of measurement and sample size uncertainties, while providing timely source-level insights. This hybrid inventory combines top-down, source-resolved, multi-pass aerial measurements with bottom-up estimates of unmeasured sources leveraging continuous probability of detection and quantification models for a chosen aerial technology. Notably, the technique explicitly considers skewed source distributions and finite facility populations that have not been previously addressed. The protocol is demonstrated to produce a comprehensive upstream OG sector methane inventory for British Columbia, Canada, which while approximately 1.7 times higher than the most recent official bottom-up inventory, reveals a lower methane intensity of produced natural gas (<0.5%) than comparable estimates for several other regions. Finally, the method and data are used to upper bound the potential influence of source variability/intermittency, directly addressing an open question in the literature. Results demonstrate that even for an extreme case, variability/intermittency effects can be addressed by sample size and survey design and have a minor impact on overall inventory uncertainty.
Functional annotation and importance of marine bacterial transporters of plankton exometabolites
Authors: Schroer William F , Kepner Hannah E , Uchimiya Mario , Mejia Catalina , Rodriguez Lidimarie Trujillo , Reisch Christopher R , Moran Mary Ann 
Published: April 2023

Abstract
Metabolite exchange within marine microbial communities transfers carbon and other major elements through global cycles and forms the basis of microbial interactions. Yet lack of gene annotations and concern about the quality of existing ones remain major impediments to revealing currencies of carbon flux. We employed an arrayed mutant library of the marine bacterium Ruegeria pomeroyi DSS-3 to experimentally annotate substrates of organic compound transporter systems, using mutant growth and compound drawdown analyses to link transporters to their cognate substrates. Mutant experiments verified substrates for thirteen R. pomeroyi transporters. Four were previously hypothesized based on gene expression data (taurine, glucose/xylose, isethionate, and cadaverine/putrescine/spermidine); five were previously hypothesized based on homology to experimentally annotated transporters in other bacteria (citrate, glycerol, N-acetylglucosamine, fumarate/malate/succinate, and dimethylsulfoniopropionate); and four had no previous annotations (thymidine, carnitine, cysteate, and 3-hydroxybutyrate). These bring the total number of experimentally-verified organic carbon influx transporters to 18 of 126 in the R. pomeroyi genome. In a longitudinal study of a coastal phytoplankton bloom, expression patterns of the experimentally annotated transporters linked them to different stages of the bloom, and also led to the hypothesis that citrate and 3-hydroxybutyrate were among the most highly available bacterial substrates. Improved functional annotation of the gatekeepers of organic carbon uptake is critical for deciphering carbon flux and fate in microbial ecosystems.