bluuebunny/crossref_metadata_2025_split huggingface dataset:
Disentangling the contribution of individual and social learning processes in human advice-taking behavior
Authors: Pereg Maayan , Hertz Uri , Ben-Artzi Ido , Shahar Nitzan 
Published: January 2024

AbstractThe study of social learning examines how individuals learn from others by means of observation, imitation, or compliance with advice. However, it still remains largely unknown whether social learning processes have a distinct contribution to behavior, independent from non-social trial-and-error learning that often occurs simultaneously. 153 participants completed a reinforcement learning task, where they were asked to make choices to gain rewards. Advice from an artificial teacher was presented in 60% of the trials, allowing us to compare choice behavior with and without advice. Results showed a strong and reliable tendency to follow advice (test-retest reliability ~0.73). Computational modeling suggested a unique contribution of three distinct learning strategies: (a) individual learning (i.e., learning the value of actions, independent of advice), (b) informed advice-taking (i.e., learning the value of following advice), and (c) non-informed advice-taking (i.e., a constant bias to follow advice regardless of outcome history). Comparing artificial and empirical data provided specific behavioral regression signatures to both informed and non-informed advice taking processes. We discuss the theoretical implications of integrating internal and external information during the learning process.
Measuring criticality in control of complex biological networks
Authors: Someya Wataru , Akutsu Tatsuya , Schwartz Jean-Marc , Nacher Jose C. 
Published: January 2024

AbstractRecent controllability analyses have demonstrated that driver nodes tend to be associated to genes related to important biological functions as well as human diseases. While researchers have focused on identifying critical nodes, intermittent nodes have received much less attention. Here, we propose a new efficient algorithm based on the Hamming distance for computing the importance of intermittent nodes using a Minimum Dominating Set (MDS)-based control model. We refer to this metric as criticality. The application of the proposed algorithm to compute criticality under the MDS control framework allows us to unveil the biological importance and roles of the intermittent nodes in different network systems, from cellular level such as signaling pathways and cell-cell interactions such as cytokine networks, to the complete nervous system of the nematode worm C. elegans. Taken together, the developed computational tools may open new avenues for investigating the role of intermittent nodes in many biological systems of interest in the context of network control.
The dengue-specific immune response and antibody identification with machine learning
Authors: Natali Eriberto Noel , Horst Alexander , Meier Patrick , Greiff Victor , Nuvolone Mario , Babrak Lmar Marie , Fink Katja , Miho Enkelejda 
Published: January 2024

AbstractDengue virus poses a serious threat to global health and there is no specific therapeutic for it. Broadly neutralizing antibodies recognizing all serotypes may be an effective treatment. High-throughput adaptive immune receptor repertoire sequencing (AIRR-seq) and bioinformatic analysis enable in-depth understanding of the B-cell immune response. Here, we investigate the dengue antibody response with these technologies and apply machine learning to identify rare and underrepresented broadly neutralizing antibody sequences. Dengue immunization elicited the following signatures on the antibody repertoire: (i) an increase of CDR3 and germline gene diversity; (ii) a change in the antibody repertoire architecture by eliciting power-law network distributions and CDR3 enrichment in polar amino acids; (iii) an increase in the expression of JNK/Fos transcription factors and ribosomal proteins. Furthermore, we demonstrate the applicability of computational methods and machine learning to AIRR-seq datasets for neutralizing antibody candidate sequence identification. Antibody expression and functional assays have validated the obtained results.
Development and use of machine learning algorithms in vaccine target selection
Authors: Bravi Barbara 
Published: January 2024

AbstractComputer-aided discovery of vaccine targets has become a cornerstone of rational vaccine design. In this article, I discuss how Machine Learning (ML) can inform and guide key computational steps in rational vaccine design concerned with the identification of B and T cell epitopes and correlates of protection. I provide examples of ML models, as well as types of data and predictions for which they are built. I argue that interpretable ML has the potential to improve the identification of immunogens also as a tool for scientific discovery, by helping elucidate the molecular processes underlying vaccine-induced immune responses. I outline the limitations and challenges in terms of data availability and method development that need to be addressed to bridge the gap between advances in ML predictions and their translational application to vaccine design.
Three immunizations with Novavax’s protein vaccines increase antibody breadth and provide durable protection from SARS-CoV-2
Authors: Lenart Klara , Arcoverde Cerveira Rodrigo , Hellgren Fredrika , Ols Sebastian , Sheward Daniel J. , Kim Changil , Cagigi Alberto , Gagne Matthew , Davis Brandon , Germosen Daritza , Roy Vicky , Alter Galit , Letscher Hélène , Van Wassenhove Jérôme , Gros Wesley , Gallouët Anne-Sophie , Le Grand Roger , Kleanthous Harry , Guebre-Xabier Mimi , Murrell Ben , Patel Nita , Glenn Gregory , Smith Gale , Loré Karin 
Published: January 2024

AbstractThe immune responses to Novavax’s licensed NVX-CoV2373 nanoparticle Spike protein vaccine against SARS-CoV-2 remain incompletely understood. Here, we show in rhesus macaques that immunization with Matrix-MTM adjuvanted vaccines predominantly elicits immune events in local tissues with little spillover to the periphery. A third dose of an updated vaccine based on the Gamma (P.1) variant 7 months after two immunizations with licensed NVX-CoV2373 resulted in significant enhancement of anti-spike antibody titers and antibody breadth including neutralization of forward drift Omicron variants. The third immunization expanded the Spike-specific memory B cell pool, induced significant somatic hypermutation, and increased serum antibody avidity, indicating considerable affinity maturation. Seven months after immunization, vaccinated animals controlled infection by either WA-1 or P.1 strain, mediated by rapid anamnestic antibody and T cell responses in the lungs. In conclusion, a third immunization with an adjuvanted, low-dose recombinant protein vaccine significantly improved the quality of B cell responses, enhanced antibody breadth, and provided durable protection against SARS-CoV-2 challenge.
A new high-resolution global topographic factor dataset calculated based on SRTM
Authors: Sun Yuwei , Zhang Hongming , Yang Qinke , Li Rui , Liu Baoyuan , Zhao Xining , Shi Haijing , Li Hongyi , Ren Yuhan , Fan Xiao , Dong Liang , Xu Yikun , Chang Yi , Yuan Linlin 
Published: January 2024

AbstractTopography is an important factor affecting soil erosion and is measured as a combination of the slope length and slope steepness (LS-factor) in erosion models, like the Chinese Soil Loss Equation. However, global high-resolution LS-factor datasets have rarely been published. Challenges arise when attempting to extract the LS-factor on a global scale. Furthermore, existing LS-factor estimation methods necessitate projecting data from a spherical trapezoidal grid to a planar rectangle, resulting in grid size errors and high time complexity. Here, we present a global 1-arcsec resolution LS-factor dataset (DS-LS-GS1) with an improved method for estimating the LS-factor without projection conversion (LS-WPC), and we integrate it into a software tool (LS-TOOL). Validation of the Himmelblau–Orlandini mathematical surface shows that errors are less than 1%. We assess the LS-WPC method on 20 regions encompassing 5 landform types, and R2 of LS-factor are 0.82, 0.82, 0.83, 0.83, and 0.84. Moreover, the computational efficiency can be enhanced by up to 25.52%. DS-LS-GS1 can be used as high-quality input data for global soil erosion assessment.
A single-stage dual-source inverter using low-power components and microcomputers
Authors: Ghani Varzaneh Majid , Rajaei Amirhossein , Kamali-Omidi Navid , Shams-Panah Ali , Khosravi Mohamadreza 
Published: January 2024

AbstractThis paper is an attempt to provide a dual-source inverter, an intelligent inverter topology that links two isolated DC sources to a single three-phase output through single-stage conversion. The converter is designed to be utilized in hybrid photovoltaic fuel cell systems, among other renewable energy applications. The proposed dual-source inverter employs a single DC-AC converter, as opposed to conventional dual-source hybrid inverters which make use of several input DC-DC modules to obtain the voltage formed across the inverter’s input DC-link. In the proposed topology, the semiconductor count is low, which leads to improved efficiency, cost, complexity, and reliability. The proposed topology makes use of two impedance networks connected by transformers, diodes, and capacitors. The regulation of the electrical power generated by primary sources and the independence of the converter on key factors like voltage and frequency are essential parameters in multi-input converters. This feature becomes highly prominent when the control algorithm is implemented by conventional processors. Viewed from this perspective, the control method described in this paper is worthy of consideration. The research work describes a 220-W/50 Hz prototype that employs Simple Boost-SPWM. Experimental results verify the analyses and corroborate the satisfactory performance of the suggested converter.
Joint extraction model of entity relations based on decomposition strategy
Authors: Li Ran , La kaijun , Lei Jingsheng , Huang Liya , Ouyang Jing , Shu Yu , Yang Shengying 
Published: January 2024

AbstractNamed entity recognition and relation extraction are two important fundamental tasks in natural language processing. The joint entity-relationship extraction model based on parameter sharing can effectively reduce the impact of cascading errors on model performance by performing joint learning of entities and relationships in a single model, but it still cannot essentially get rid of the influence of pipeline models and suffers from entity information redundancy and inability to recognize overlapping entities. To this end, we propose a joint extraction model based on the decomposition strategy of pointer mechanism is proposed. The joint extraction task is divided into two parts. First, identify the head entity, utilizing the positive gain effect of the head entity on tail entity identification.Then, utilize a hierarchical model to improve the accuracy of the tail entity and relationship identification. Meanwhile, we introduce a pointer model to obtain the joint features of entity boundaries and relationship types to achieve boundary-aware classification. The experimental results show that the model achieves better results on both NYT and WebNLG datasets.
Electromagnetic tomographic cerebral angiography
Authors: Semenov Serguei 
Published: January 2024

AbstractWorld Health Organization stated that “Cardiovascular diseases (CVDs) are the leading cause of death globally. Angiography is an important method in diagnostic of CVD. Standard-of-Care methods of angiography, such as X-Ray or CT- or MRI- angiography methods, being accurate and widely adopted in clinical practice, are bulky, expensive and energy in-efficient. X-ray and CT- angiography methods are also potentially hazardous as techniques require the use of ionizing contrast agents. Electromagnetic tomography (EMT) is an emerging medical imaging modality. EMT is applicable for safe functional imaging but suffers from a limited spatial resolution because of relatively large wavelength of electromagnetic radiation as compared to sizes of biological targets of particular interest, such as, for example blood vessels. Novel approach and method, presented in the study is capable to overcome such limitations and provide a mean for a dynamic, on-line EMT angiography. New method of EMT angiography was presented in application to cerebral angiography. Achieved imaging results clearly demonstrate applicability of the method for detecting small cerebral vessels of the diameter as small as 1.3 mm and to distinguish vessels with different dimensions. The technical challenges in the development of angiography capable EMT systems are assessed and discussed.
Reduced plasma GDF10 levels are positively associated with cholesterol impairment and childhood obesity
Authors: Yousof Tamana R. , Mejia-Benitez Aurora , Morrison Katherine M. , Austin Richard C. 
Published: January 2024

AbstractChildhood obesity is a global health concern affecting over 150 million children worldwide, with projections of a rise to 206 million by 2025. Understanding the mechanisms underlying this epidemic is crucial for developing effective interventions. In this study, we investigated circulating levels of Growth Differentiation Factor 10 (GDF10), a novel regulator of adipogenesis. Previous studies report diminished circulating GDF10 levels contribute to obesity and hepatic steatosis in mice. To further understand the role of plasma GDF10 in childhood obesity, a prospective case–control study was conducted. Using an enzyme-linked immunosorbent assay, plasma GDF10 levels were measured in children aged 5–17 years of age with normal (n = 36) and increased (n = 56) body mass index (BMI). Subsequently, plasma GDF10 levels were compared to various cardio-metabolic parameters. Children with increased BMI exhibit significantly lower levels of plasma GDF10 compared to children with normal BMI (p < 0.05). This study not only supports previous mouse data but is the first to report that lower levels of GDF10 is associated with childhood obesity, providing an important human connection for the relevance of GDF10 in obesity. Furthermore, this study revealed a significant correlation between low plasma GDF10 levels and elevated LDL-cholesterol and total cholesterol levels dependent on BMI (95% CI, p < 0.05). This study supports the hypothesis that children with obesity display lower plasma levels of GDF10, which correlates with elevated cholesterol levels. These insights shed light on potential mechanisms contributing to childhood obesity and may lead to future therapeutic interventions targeting GDF10 to mitigate adverse effects of adipogenesis in cardiometabolic health.
Evaluation of a simplified pharmacovigilance tool for general practitioners: 5 years of insight
Authors: Trenque A. , Rabiaza A. , Fedrizzi S. , Chretien B. , Sassier M. , Morello R. , Alexandre J. , Humbert X. 
Published: January 2024

AbstractSpontaneous reporting of adverse drug reactions (ADRs) is the cornerstone of pharmacovigilance. However, major underreporting exists. The main objective of this study was to assess the use of a pharmacovigilance simplified reporting tool (PSRT) by general practitioners (GPs) and, secondarily, to describe the quality of ADR reports during this period. The PSRT was proposed on June 1st, 2015, for the 1290 GPs in the Western Normandy Region. The number and quality of ADRs reported monthly by GPs were prospectively collected from June 1st, 2015, to May 31st, 2020 (Period 2), and compared to those reported during a control period (June 1st, 2010, to May 31st, 2015, Period 1). During all the periods, 920 reports were made by 307 GPs (198 reports in Period 1 and 722 reports in Period 2), with 477 reports (51.8%) using the PSRT. During Period 2, the monthly number of reports was multiplied by 3.5 (p < 0.0001), and the number of GPs was 1.4 compared to that in Period 1 (p = 0.01). Our PSRT showed effectiveness in quantitative and qualitative terms. It must now go further and be integrated into GP software to facilitate ADR reporting nationwide.
High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning
Authors: Dhaubhadel Sayera , Ganguly Kumkum , Ribeiro Ruy M. , Cohn Judith D. , Hyman James M. , Hengartner Nicolas W. , Kolade Beauty , Singley Anna , Bhattacharya Tanmoy , Finley Patrick , Levin Drew , Thelen Haedi , Cho Kelly , Costa Lauren , Ho Yuk-Lam , Justice Amy C. , Pestian John , Santel Daniel , Zamora-Resendiz Rafael , Crivelli Silvia , Tamang Suzanne , Martins Susana , Trafton Jodie , Oslin David W. , Beckham Jean C. , Kimbrel Nathan A. ,   Million Veteran Program Suicide Exemplar Work Group, Agarwal Khushbu , Ashley-Koch Allison E. , Aslan Mihaela , Begoli Edmond , Brown Ben , Calhoun Patrick S. , Cheung Kei-Hoi , Choudhury Sutanay , Cliff Ashley M. , Cuellar-Hengartner Leticia , Deangelis Haedi E. , Dennis Michelle F. , Finley Patrick D. , Garvin Michael R. , Gelernter Joel E. , Hair Lauren P. , Ham Colby , Harvey Phillip D. , Hauser Elizabeth R. , Hauser Michael A. , Hengartner Nick W. , Jacobson Daniel A. , Jones Jessica , Jones Piet C. , Kainer David , Kaplan Alan D. , Katz Ira R. , Kember Rachel L. , Kirby Angela C. , Ko John C. , Lagergren John , Lane Matthew , Levey Daniel F. , Lindquist Jennifer H. , Liu Xianlian , Madduri Ravi K. , Manore Carrie , Martinez Carianne , McCarthy John F. , Cashman Mikaela McDevitt , Miller J. Izaak , Morrow Destinee , Pavicic-Venegas Mirko , Pyarajan Saiju , Qin Xue J. , Rajeevan Nallakkandi , Ramsey Christine M. , Ribeiro Ruy , Rodriguez Alex , Romero Jonathon , Shi Yunling , Stein Murray B. , Sullivan Kyle A. , Sun Ning , Tamang Suzanne R. , Townsend Alice , Trafton Jodie A. , Walker Angelica , Wang Xiange , Wangia-Anderson Victoria , Yang Renji , Yoo Shinjae , Zhao Hongyu , McMahon Benjamin H. 
Published: January 2024

AbstractWe present an ensemble transfer learning method to predict suicide from Veterans Affairs (VA) electronic medical records (EMR). A diverse set of base models was trained to predict a binary outcome constructed from reported suicide, suicide attempt, and overdose diagnoses with varying choices of study design and prediction methodology. Each model used twenty cross-sectional and 190 longitudinal variables observed in eight time intervals covering 7.5 years prior to the time of prediction. Ensembles of seven base models were created and fine-tuned with ten variables expected to change with study design and outcome definition in order to predict suicide and combined outcome in a prospective cohort. The ensemble models achieved c-statistics of 0.73 on 2-year suicide risk and 0.83 on the combined outcome when predicting on a prospective cohort of$$\sim$$∼ 4.2 M veterans. The ensembles rely on nonlinear base models trained using a matched retrospective nested case-control (Rcc) study cohort and show good calibration across a diversity of subgroups, including risk strata, age, sex, race, and level of healthcare utilization. In addition, a linear Rcc base model provided a rich set of biological predictors, including indicators of suicide, substance use disorder, mental health diagnoses and treatments, hypoxia and vascular damage, and demographics.
Non-biofilm-forming Staphylococcus epidermidis planktonic cell supernatant induces alterations in osteoblast biological function
Authors: Gómez-Alonso Itzia Sidney , Betanzos-Cabrera Gabriel , Moreno-Lafont Martha Cecilia , Cancino-Diaz Mario Eugenio , García-Pérez Blanca Estela , Cancino-Diaz Juan Carlos 
Published: January 2024

AbstractStaphylococcal biofilms significantly contribute to prosthetic joint infection (PJI). However, 40% of S. epidermidis PJI isolates do not produce biofilms, which does not explain the role of biofilms in these cases. We studied whether the supernatant from planktonic S. epidermidis alters osteoblast function. Non-biofilm-forming S. epidermidis supernatants (PJI− clinical isolate, healthy skin isolate (HS), and ATCC12228 reference strain) and biofilm-forming supernatants (PJI+ clinical isolate, ATCC35984 reference strain, and Staphylococcus aureus USA300 reference strain) were included. Osteoblasts stimulated with supernatants from non-biofilm-forming isolates for 3, 7, and 14 days showed significantly reduced cellular DNA content compared with unstimulated osteoblasts, and apoptosis was induced in these osteoblasts. Similar results were obtained for biofilm-forming isolates, but with a greater reduction in DNA content and higher apoptosis. Alkaline phosphatase activity and mineralization were significantly reduced in osteoblasts treated with supernatants from non-biofilm-forming isolates compared to the control at the same time points. However, the supernatants from biofilm-forming isolates had a greater effect than those from non-biofilm-forming isolates. A significant decrease in the expression of ATF4, RUNX2, ALP, SPARC, and BGLAP, and a significant increase in RANK-L expression were observed in osteoblasts treated with both supernatants. These results demonstrate that the supernatants of the S. epidermidis isolate from the PJI− and HS (commensal) with a non-biofilm-forming phenotype alter the function of osteoblasts (apoptosis induction, failure of cell differentiation, activation of osteoblasts, and induction of bone resorption), similar to biofilm-forming isolates (PJI+, ATCC35984, and S. aureus USA300), suggesting that biofilm status contributes to impaired osteoblast function and that the planktonic state can do so independently of biofilm production.
Metabolomic signatures of ideal cardiovascular health in black adults
Authors: Islam Shabatun J. , Liu Chang , Mohandas Appesh N. , Rooney Kimberly , Nayak Aditi , Mehta Anurag , Ko Yi-An , Kim Jeong Hwan , Sun Yan V. , Dunbar Sandra B. , Lewis Tené T. , Taylor Herman A. , Uppal Karan , Jones Dean P. , Quyyumi Arshed A. , Searles Charles D. 
Published: January 2024

AbstractPlasma metabolomics profiling is an emerging methodology to identify metabolic pathways underlying cardiovascular health (CVH). The objective of this study was to define metabolomic profiles underlying CVH in a cohort of Black adults, a population that is understudied but suffers from disparate levels of CVD risk factors. The Morehouse-Emory Cardiovascular (MECA) Center for Health Equity study cohort consisted of 375 Black adults (age 53 ± 10, 39% male) without known CVD. CVH was determined by the AHA Life’s Simple 7 (LS7) score, calculated from measured blood pressure, body mass index (BMI), fasting blood glucose and total cholesterol, and self-reported physical activity, diet, and smoking. Plasma metabolites were assessed using untargeted high-resolution metabolomics profiling. A metabolome wide association study (MWAS) identified metabolites associated with LS7 score after adjusting for age and sex. UsingMummichogsoftware, metabolic pathways that were significantly enriched in metabolites associated with LS7 score were identified. Metabolites representative of these pathways were compared across clinical domains of LS7 score and then developed into a metabolomics risk score for prediction of CVH. We identified novel metabolomic signatures and pathways associated with CVH in a cohort of Black adults without known CVD. Representative and highly prevalent metabolites from these pathways included glutamine, glutamate, urate, tyrosine and alanine, the concentrations of which varied with BMI, fasting glucose, and blood pressure levels. When assessed in conjunction, these metabolites were independent predictors of CVH. One SD increase in the novel metabolomics risk score was associated with a 0.88 higher LS7 score, which translates to a 10.4% lower incident CVD risk. We identified novel metabolomic signatures of ideal CVH in a cohort of Black Americans, showing that a core group of metabolites central to nitrogen balance, bioenergetics, gluconeogenesis, and nucleotide synthesis were associated with CVH in this population.