bluuebunny/crossref_metadata_2025_split huggingface dataset:
A new simulation framework to evaluate the suitability of eDNA for marine and aquatic Environmental Impact Assessments
Authors: Coston‐Guarini Jennifer , Hinz Shawn , Mirimin Luca , Guarini Jean‐Marc 
Published: May 2023

AbstractA model was developed to forecast and compare changes in species presence assessed with either eDNA or traditional observations. We use it to explore how ecosystem conditions could affect the suitability of eDNA for Environmental Impact Assessment. First, a deterministic model simulated the dynamics of the impacted population (called “receptor” in EIA) and their shed DNA fragment concentrations. Second, random distributions of receptor organisms and eDNA fragment quantities at steady state were simulated within the impacted spatial domain (called “project area”). Then, simple random samplings were performed for both the receptor and eDNA. Third, post‐sampling processes (eDNA extraction, amplification, and analysis) were simulated to estimate the taxon detection probability. Fourth, we simulated an impact by modifying the growth, mortality, and mobility (null, passive, and active) parameters of the receptor taxon. eDNA detection probability curves were then estimated for a range of environmental sample volumes by fitting a Weibull cumulative distribution function. A F‐like statistic compared detection curves before and after impact. Statistically significant differences were detected with eDNA in impact scenarios where receptor taxon growth rate decreased and receptor mobility was null or passive. In scenarios where the project area accumulates DNA shed from multiple categories of the same taxon (e.g., from dead organisms if mortality increased or when individuals can cross project area boundaries), it is difficult to assess impact. Our study shows that results obtained from eDNA sampling will not always agree with an impact classically assessed on a receptor population. One reason is that sources of the total eDNA pool are not identified. The modeling highlights the need: to do preliminary testing of sample sizes, to develop new approaches that will identify sources from the pool of extracted DNA, and to improve descriptions of the ecogeochemical processes required to forecast shed DNA reactivity.
Electrochemical characterization of 17β‐estradiol with fast‐scan cyclic voltammetry
Authors: Weese‐Myers Moriah E. , Ross Ashley E. 
Published: May 2023

AbstractWe have developed a sensitive and stable electrochemical method for 17β‐estradiol (E2) detection using fast‐scan cyclic voltammetry (FSCV). Recently, E2 was proposed to function as a rapid synaptocrine signaling molecule in the brain; however, methods to directly monitor subsecond fluctuations in E2 are currently unavailable, limiting our understanding of the dynamics and mechanism of rapid E2 release. FSCV at carbon‐fiber microelectrodes enables subsecond detection of electroactive neurochemicals directly in tissues like the brain. Here, we have electrochemically characterized E2 using FSCV for use in a tissue matrix. The limit of detection of E2 is 31.2±2.5 nM with FSCV, which will enable low nanomolar fluctuations in extracellular E2 to be monitored with hundred millisecond temporal resolution. We also identify specific parameters for waveform modification to improve future detection. This method will significantly improve E2 sensing capabilities and will have far‐reaching impacts on improving our understanding of dynamic E2 signaling in the brain.
Seismic damage prediction of RC buildings using machine learning
Authors: Bhatta Sanjeev , Dang Ji 
Published: May 2023

AbstractDecision‐makers and stakeholders require a rapid assessment of potential damage after earthquake events in order to develop and implement disaster risk reduction strategies and to respond systematically in post‐disaster situations. The damage investigated manually after an earthquake are complicated, labor‐intensive, time‐consuming, and error prone process. The development of fragility curves is time consuming and unable to predict the damage for wide classes of structures since it considers few structural properties and only one seismic characteristic. Furthermore, the nonlinear finite element method cannot be utilized for numerous buildings because it involves more time and money. This paper presents the machine learning (ML)‐based seismic damage prediction of RC buildings. It is found that some of the research works only considered seismic parameters or structural parameters to train the ML models and predict the structural damage assessment. However, these ML models may not fully reveal the underlying complexity of the relationship between input parameters and building performance. As a result, their applicability will be limited. This paper evaluates the feasibility of using ML techniques such as K‐nearest neighbor, random forest, decision tree, support vector machine, and artificial neural network to rapidly predict earthquake‐induced reinforced concrete building damage considering both the structural properties and ground motion characteristics. The machine learning models are trained using the simulation results. Due to lack of real earthquake damage datasets or limited access, most of the research works used Scikit Learn train_test_split function to randomly split the entire datasets into training and testing datasets and the performance of the proposed ML technique are evaluated using the testing datasets. However, in this study, the performances of different ML models are evaluated using real earthquake damage datasets of RC buildings collected after 2015 Nepal earthquake. The overall accuracy on testing datasets suggests the capability of machine learning algorithms in successfully predicting the seismic damage of reinforced concrete buildings in quick time with reasonable accuracy. This study is beneficial in emergency response and recovery planning after an earthquake.
Enhanced design and experimental investigation of disc spring‐based self‐centering buckling‐restrained braces with a compounded combination mode
Authors: Chen Peng , Xu Longhe , Xie Xingsi 
Published: May 2023

AbstractThe disc spring‐based self‐centering energy dissipation (SCED) brace is a high‐capacity resilient structural component, characterized by a flag‐shaped hysteresis curve, which can provide both damping and recentering capacity to a structure. However, the conventional design method for the disc spring‐based SCED brace cannot quantitatively take into account the friction effects induced by combined disc springs, which greatly limits the disc spring size selection and combination mode. To address this issue, the friction effect of the combined springs is quantified using an incremental energy method and then introduced into the existing design method. A fully assembled self‐centering steel buckling‐restrained brace (SC‐SBRB) consisting of an energy dissipation (ED) system and a self‐centering (SC) system is proposed. The SC system employs a combination mode of stacking multi‐section springs in parallel and compounding new and old springs together to provide the desired recentering capacity for the brace; however, it is distinguished by an appreciable ED capacity that is different from the SC devices in previously reported SCED braces. With the use of the energy method for the quantification of the spring friction, the differences between the mechanical properties of a group of old springs in earlier and recent tests were systematically analyzed to check their reliability and serviceability. A series of test procedures were sequentially conducted on a pure BRB brace and a pure SC brace, and then on the full SC‐SBRB. A fatigue test was also performed after the damaged brace was repaired. The experimental results demonstrate that the improved design can provide accurate estimations of the spring friction in the brace design, and that the proposed brace can exhibit a stable and favorable flag‐shaped hysteretic response with negligible residual deformations throughout the loading protocol. The old spring used in the tests was found to have stable and reliable mechanical properties and could be reused in a brace requiring repair after an earthquake.
Factors associated with fathers' involvement in infant and young child feeding and nurturing care in the urban slums of Bangladesh: A cross‐sectional study
Authors: Bhattacharyya Dipika Shankar , Sarker Tonmoy , Akter Nargis , Shafique Sohana , Nabi Mohammad Hayatun , Hawlader Mohammad Delwer Hossain , Mitra Dipak Kumar 
Published: May 2023

AbstractOptimal infant and young child feeding (IYCF) and nurturing care during the first years of children's lives are crucial for ensuring their growth and development. The IYCF and nurturing practices are associated with a multifaceted interplay of context‐specific factors where fathers' involvement is necessary. The aim of this study is to explore the current scenario of fathers' involvement in IYCF practices in the urban slums of Bangladesh. A cross‐sectional survey among the residents of the Sat Tola slum in Dhaka, the capital of Bangladesh was conducted. To avoid social desirability bias, 361 mothers of children aged <24 months were interviewed regarding fathers' involvement. An operational definition of father's involvement was developed based on 11 criteria and then scoring was applied to classify ‘Good Involvement’ and associated factors were identified using multivariable logistic regression. Fathers' mean age was around 31 (SD ± 6.09) years and the majority of them (44.32%) completed primary education. Fathers had diversified occupations such as day laborer (32.41%), garment worker (22.71%), and business (14.96%). Factors that were significantly associated with the father's involvement in IYCF practices include educational status (aOR = 3.98, 95% CI = 1.91, 8.26, p < .00) and theiroccupational status (aOR = 0.34, 95% CI = 0.16, 0.70, p = .00). Fathers were more active for their first child (aOR: 1.99, 95% CI = 1.04, 3.79, p = .03). Having child in the age group of 14–20 months (aOR = 2.73, 95% CI = 1.32, 5.64, p = <.01) and wife in the age group of 21–30 years (aOR = 2.34, 95% CI = 1.20, 4.58, p = .01) were significantly associated. The study finding explored that fathers' education and occupation as well as mothers' age and education were significantly associated factors for fathers' involvement in the IYCF practices. Further longitudinal studies are recommended to establish the causal relationship between fathers' involvement with IYCF and their impact on child growth and development.
Who benefits from quality competition in health care? A theory and a laboratory experiment on the relevance of patient characteristics
Authors: Brosig‐Koch Jeannette , Hehenkamp Burkhard , Kokot Johanna 
Published: May 2023

AbstractWe study how competition between physicians affects the provision of medical care. In our theoretical model, physicians are faced with a heterogeneous patient population, in which patients systematically vary with regard to both their responsiveness to the provided quality of care and their state of health. We test the behavioral predictions derived from this model in a controlled laboratory experiment. In line with the model, we observe that competition significantly improves patient benefits as long as patients are able to respond to the quality provided. For those patients, who are not able to choose a physician, competition even decreases the patient benefit compared to a situation without competition. This decrease is in contrast to our theoretical prediction implying no change in benefits for passive patients. Deviations from patient‐optimal treatment are highest for passive patients in need of a low quantity of medical services. With repetition, both, the positive effects of competition for active patients as well as the negative effects of competition for passive patients become more pronounced. Our results imply that competition can not only improve but also worsen patient outcome and that patients' responsiveness to quality is decisive.
The impact of developmental job experience on job performance: The importance of team context
Authors: Cao Jie , Hamori Monika 
Published: May 2023

AbstractDrawing on social resources theory, we examine the impact of developmental job experience (DJE) on employees' job performance and the role of the team context in this relationship. In a multisource, multiwave dataset of 354 employees working on 40 teams in seven Chinese companies, we find that DJE has a positive indirect relationship with job performance through increasing employees' information and support seeking. This positive indirect relationship is stronger for employees on teams with a high average DJE and low variance in DJE; it is significantly weaker for employees on teams with a low average DJE and a high variance in DJE. These results reveal that the work and team contexts play important roles in the relationship between DJE and employees' work outcomes.
Artificial intelligence analysis of electroencephalogram and evoked potential in patients with depression based on machine learning
Authors: Ma Jianqi 
Published: May 2023

AbstractWith the continuous improvement of people's mental pressure and life pace, people's study and life pressure would increase, leading to the increase of people's depression. Depression is a mental illness, a chronic mental illness that is inconsistent with the patient's physical condition. In recent years, as people know more and more about depression, and they have more and more research on depression, many research scholars have provided new ideas for the treatment of depression, and this paper takes this as the research direction and research basis. This paper introduces the background of EEG (electroencephalogram, EEG) and evoked potential and artificial intelligence (Artificial intelligence, AI) methods, and then analyzes the patients with depression based on AI, and summarizes the application of electronics. The concept analysis of depression, EEG and evoked potential is put forward. At the end of the article, the application of machine learning in depression is studied. At the same time, with the continuous development of machine learning in artificial intelligence, the EEG and evoked potential related work in patients with depression are also facing new opportunities and challenges.
Effects of Leptin and Body Weight on Inflammation and Knee Osteoarthritis Phenotypes in Female Rats
Authors: Fu Yao , Batushansky Albert , Kinter Michael , Huebner Janet L. , Kraus Virginia B. , Griffin Timothy M. 
Published: May 2023

ABSTRACTLeptin is a proinflammatory adipokine that contributes to obesity‐associated osteoarthritis (OA), especially in women. However, the extent to which leptin causes knee OA separate from the effect of increased body weight is not clear. We hypothesized that leptin is necessary to induce knee OA in obese female rats but not sufficient to induce knee OA in lean rats lacking systemic metabolic inflammation. The effect of obesity without leptin signaling was modeled by comparing female lean Zucker rats to pair fed obese Zucker rats, which possess mutant fa alleles of the leptin receptor gene. The effect of leptin without obesity was modeled in female F344BN F1 hybrid rats by systemically administering recombinant rat leptin versus saline for 23 weeks via osmotic pumps. Primary OA outcomes included cartilage histopathology and subchondral bone micro‐computed tomography. Secondary outcomes included targeted cartilage proteomics, serum inflammation, and synovial fluid inflammation following an acute intra‐articular challenge with interleukin‐1β (IL‐1β). Compared to lean Zucker rats, obese Zucker rats developed more severe tibial osteophytes and focal cartilage lesions in the medial tibial plateau, with modest changes in proximal tibial epiphysis trabecular bone structure. In contrast, exogenous leptin treatment, which increased plasma leptin sixfold without altering body weight, caused mild generalized cartilage fibrillation and reduced Safranin O staining compared to vehicle‐treated animals. Leptin also significantly increased subchondral and trabecular bone volume and bone mineral density in the proximal tibia. Cartilage metabolic and antioxidant enzyme protein levels were substantially elevated with leptin deficiency and minimally suppressed with leptin treatment. In contrast, leptin treatment induced greater changes in systemic and local inflammatory mediators compared to leptin receptor deficiency, including reduced serum IL‐6 and increased synovial fluid IL‐1β. In conclusion, rat models that separately elevate leptin or body weight develop distinct OA‐associated phenotypes, revealing how obesity increases OA pathology through both leptin‐dependent and independent pathways. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.
Drug design of human dihydrofolate reductase (hDHFR) inhibitors with deep learning
Authors: Wu Yi‐Huan , Cheng Chih‐Yang , Chen Yen‐Yang , Li Feng‐Yin 
Published: May 2023

AbstractHuman dihydrofolate reductase (hDHFR) inhibitors have been a popular research object designed as anti‐cancer, anti‐malarial, and antibacterial drugs for decades. Besides quantitative structure‐activity relationship (QSAR), artificial intelligence (AI) has recently been introduced in numerous professional biological researches, such as molecular drug design and biological activity prediction. In this study, we construct a deep‐learning workflow for designing novel hDHFR inhibitors. This workflow mainly includes two networks, as described in the following: The first one is the artificial neural network trained by the molecules selected from the ChEMBL database with experimental hDHFR inhibitions as the label to evaluate the bioactivity of the designed molecular structures constructed from the second network. The second network utilizes conditional generative and adversarial networks (cGAN) to generate candidate molecules with the desired properties. Finally, the obtained candidate molecules with high hDHFR inhibition are subjected to a molecular docking process to verify their binding patterns and affinity strengths inside the active site of hDHFR. In the end, we have successfully identified several novel drug‐like compounds with hDHFR inhibition comparable to those currently used in clinics. We present a new tool to effectively design new drug‐like compounds through an AI approach.
School partner perspectives on the implementation of the Your Voice Your View sexual assault prevention program for high school students
Authors: Orchowski Lindsay M. , Paszek Claudia , Lopez Richard M. , Oesterle Daniel W. , Pearlman Deborah N. , Rizzo Christie J. , Ghose Elwy Anashua Rani , Berkowitz Alan D. , Malone Sandra , Fortson Beverly L. 
Published: May 2023

AbstractDespite the high risk for sexual assault among adolescents, few sexual assault prevention programs designed for implementation in high schools have sustained rigorous evaluation. The present study sought to better understand the factors that influenced the implementation of Your Voice Your View (YVYV), a four‐session sexual assault prevention program for 10th grade students, which includes a teacher “Lunch and Learn” training as well as a 4‐week school‐specific social norms poster campaign. Following program implementation, eight school partners (i.e., health teachers, guidance counselors, teachers, and principals) participated in an interview to provide feedback on the process of program implementation. The Consolidated Framework for Implementation Research was utilized to examine site‐specific determinants of program implementation. Participants discussed the importance of the design quality and packaging of the program, as well as the relative advantage of offering students a violence prevention program led by an outside team, as opposed to teachers in the school. School partners highlighted the importance of intensive preplanning before implementation, clear communication between staff, the utility of engaging a specific champion to coordinate programming, and the utility of offering incentives for participation. Having resources to support implementation, a desire to address sexual violence in the school, and a positive classroom climate in which to administer the small‐group sessions were seen as school‐specific facilitators of program implementation. These findings can help to support the subsequent implementation of the YVYV program, as well as other sexual assault prevention programs in high schools.
Analyzing the sustainability of intensified side‐stream extractive distillation for binary azeotropic separation: a comparative study
Authors: Kong Zong Yang , Sánchez‐Ramírez Eduardo , Yang Ao , Segovia‐Hernández Juan Gabriel , Sunarso Jaka 
Published: May 2023

AbstractBACKGROUNDThe sustainability performance of extractive distillation (ED) that collectively considers energy efficiency, process safety, economic profitability, process control, and environmental emission has not been widely reported. Motivated by this lack of research, we analyzed and compared the sustainability performance of the intensified side‐stream ED (SSED) with that of the conventional extractive distillation (CED) for the separation of binary azeotropic mixtures. Two different design approaches were analyzed; the first approach involves designing the intensified SSED by preserving the original CED column configuration, while the second approach involves optimizing the original column configuration to become a new intensified SSED configuration.RESULTSWe found that preserving the original configuration usually provides energy‐savings, but this advantage is not guaranteed unless the process is optimized. Generally, the reduction in energy consumption improves economic and environmental performance. However, the optimized design has a higher risk index because it is usually larger in size, for the sake of lowering the energy consumption. In addition, the optimized design usually has a higher condition number, which signifies that a more complex control structure is required.CONCLUSIONAltogether, designing the intensified process via process optimization does not always guarantee significant improvement in all sustainability indicators. © 2023 Society of Chemical Industry (SCI).