bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of chronic vapor nicotine exposure on affective and cognitive behavior in male mice
Authors: Murdaugh Laura B. , Miliano Cristina , Chen Irene , Faunce Christine L. , Natividad Luis A. , Gregus Ann M. , Buczynski Matthew W. 
Published: March 2024

AbstractNicotine use is a leading cause of preventable deaths worldwide, and most of those who attempt to quit will relapse. While electronic cigarettes and other electronic nicotine delivery systems (ENDS) were presented as a safer alternative to traditional cigarettes and promoted as devices to help traditional tobacco smokers reduce or quit smoking, they have instead contributed to increasing nicotine use among youths. Despite this, ENDS also represent a useful tool to create novel preclinical animal models of nicotine exposure that more accurately represent human nicotine use. In this study, we validated a chronic, intermittent, ENDS-based passive vapor exposure model in mice, and then measured changes in multiple behaviors related to nicotine abstinence. First, we performed a behavioral dose curve to investigate the effects of different nicotine inter-vape intervals on various measures including body weight, locomotor activity, and pain hypersensitivity. Next, we performed a pharmacokinetic study to measure plasma levels of nicotine and cotinine following chronic exposure for each inter-vape interval. Finally, we utilized a behavior test battery at a single dosing regimen that produces blood levels equivalent to human smokers in order to characterize the effects of chronic nicotine, vehicle, or passive airflow and identified nicotine-induced impairments in cognitive behavior.
Role of plasma process gas on permeate flux augmentation of cellulose nitrate membrane for mud water treatment
Authors: Hazarika Tonmoi , Kakati Bharat , Pal Dipankar , Saikia Rimlee , Rawal Ankit , Mahanta Manoj Kumar , Biswas Subir 
Published: March 2024

AbstractA comparative study between Nitrogen (N2) and Argon (Ar) plasma is carried out to investigate its effect on surface morphology, hydrophilicity, permeate flux and ageing of cellulose nitrate polymeric membranes in the present work. Langmuir probe and Optical Emission Spectroscopy are used to characterize the plasma. The SEM analysis reveals the noticeable macro-void creations and pore enlargement for both N2 and Ar plasma. The AFM analysis shows a higher surface roughness for Ar plasma treatment as compared to N2 plasma treatment. XPS analysis confirms the changes in the polymer matrix along with the incorporation of various functional groups on the membrane surface as a result of the plasma treatment. A better hydrophilic nature with prolonged plasma treatment is observed for Ar plasma as compared to N2 plasma treatment. The present results show a higher permeate flux with a high rejection rate for Ar plasma treatment in comparison to N2 plasma, which might be due to the pore size and pore area enlargement of the membrane. The hydrophobic recovery for both the plasma-treated membranes is found significant for the initial ageing period of 7 days and found almost stable in nature after 7 days. A diffusion-based theoretical model is developed to study the hydrophobic recovery of plasma-treated membranes. A strong alignment between experimental and theoretical results is observed in the present work. The Cake Filtration model, derived from the Hermia model, is identified as the most suitable model for describing the fouling mechanisms for the present work.
Dietary linoleic acid supplementation protects against obesity-induced microglial reactivity in mice
Authors: Jantzen Lucas , Dumontoy Stéphanie , Ramadan Bahrie , Houdayer Christophe , Haffen Emmanuel , Hichami Aziz , Khan Naim Akhtar , Van Waes Vincent , Cabeza Lidia 
Published: March 2024

AbstractWe investigated whether linoleic acid (LA) supplementation could modulate emotional behavior and microglia-related neuroinflammation. For that, male mice of C57BL/6J genetic background fed either a high-fat diet (HFD) or a standard diet (STD) for 12 weeks, were treated with a vehicle or LA solution for 5 weeks before being evaluated for emotional behavior using a battery of behavioral tests. The animals were subsequently sacrificed and their brains collected and processed for immunofluorescence staining, targeting microglia-specific calcium-binding proteins (IBA-1). Neuroinflammation severity was assessed in multiple hypothalamic, cortical and subcortical brain regions. We show an anxio-depressive-like effect of sustained HFD feeding that was neither alleviated nor worsened with LA supplementation. However, increased IBA-1 expression and microgliosis in the HFD group were largely attenuated by LA supplementation. These observations demonstrate that the anti-neuroinflammatory properties of LA are not restricted to hypothalamic areas but are also evident at the cortical and subcortical levels. This study discloses that neuroinflammation plays a role in the genesis of neuropsychiatric disorders in the context of obesity, and that LA supplementation is a useful dietary strategy to alleviate the impact of obesity-related neuroinflammation.
Risk factors for placenta accreta spectrum disorders in women with any prior cesarean and a placenta previa or low lying: a prospective population-based study
Authors: Kayem Gilles , Seco Aurélien , Vendittelli Francoise , Crenn Hebert Catherine , Dupont Corinne , Branger Bernard , Huissoud Cyril , Fresson Jeanne , Winer Norbert , Langer Bruno , Rozenberg Patrick , Morel Olivier , Bonnet Marie Pierre , Perrotin Franck , Azria Elie , Carbillon Lionel , Chiesa Coralie , Raynal Pierre , Rudigoz René Charles , Patrier Sophie , Beucher Gaël , Dreyfus Michel , Sentilhes Loïc , Deneux-Tharaux Catherine 
Published: March 2024

AbstractThis study aimed to identify the risk factors for placenta accreta spectrum (PAS) in women who had at least one previous cesarean delivery and a placenta previa or low-lying. The PACCRETA prospective population-based study took place in 12 regional perinatal networks from 2013 through 2015. All women with one or more prior cesareans and a placenta previa or low lying were included. Placenta accreta spectrum (PAS) was diagnosed at delivery according to standardized clinical and histological criteria. Of the 520,114 deliveries, 396 fulfilled inclusion criteria; 108 were classified with PAS at delivery. Combining the number of prior cesareans and the placental location yielded a rate ranging from 5% for one prior cesarean combined with a posterior low-lying placenta to 63% for three or more prior cesareans combined with placenta previa. The factors independently associated with PAS disorders were BMI ≥ 30, previous uterine surgery, previous postpartum hemorrhage, a higher number of prior cesareans, and a placenta previa. Finally, in this high-risk population, the rate of PAS disorders varies greatly, not only with the number of prior cesareans but also with the exact placental location and some of the women's individual characteristics. Risk stratification is thus possible in this population.
A generalisation of the method of regression calibration and comparison with Bayesian and frequentist model averaging methods
Authors: Little Mark P. , Hamada Nobuyuki , Zablotska Lydia B. 
Published: March 2024

AbstractFor many cancer sites low-dose risks are not known and must be extrapolated from those observed in groups exposed at much higher levels of dose. Measurement error can substantially alter the dose–response shape and hence the extrapolated risk. Even in studies with direct measurement of low-dose exposures measurement error could be substantial in relation to the size of the dose estimates and thereby distort population risk estimates. Recently, there has been considerable attention paid to methods of dealing with shared errors, which are common in many datasets, and particularly important in occupational and environmental settings. In this paper we test Bayesian model averaging (BMA) and frequentist model averaging (FMA) methods, the first of these similar to the so-called Bayesian two-dimensional Monte Carlo (2DMC) method, and both fairly recently proposed, against a very newly proposed modification of the regression calibration method, the extended regression calibration (ERC) method, which is particularly suited to studies in which there is a substantial amount of shared error, and in which there may also be curvature in the true dose response. The quasi-2DMC with BMA method performs well when a linear model is assumed, but very poorly when a linear-quadratic model is assumed, with coverage probabilities both for the linear and quadratic dose coefficients that are under 5% when the magnitude of shared Berkson error is large (50%). For the linear model the bias is generally under 10%. However, using a linear-quadratic model it produces substantially biased (by a factor of 10) estimates of both the linear and quadratic coefficients, with the linear coefficient overestimated and the quadratic coefficient underestimated. FMA performs as well as quasi-2DMC with BMA when a linear model is assumed, and generally much better with a linear-quadratic model, although the coverage probability for the quadratic coefficient is uniformly too high. However both linear and quadratic coefficients have pronounced upward bias, particularly when Berkson error is large. By comparison ERC yields coverage probabilities that are too low when shared and unshared Berkson errors are both large (50%), although otherwise it performs well, and coverage is generally better than the quasi-2DMC with BMA or FMA methods, particularly for the linear-quadratic model. The bias of the predicted relative risk at a variety of doses is generally smallest for ERC, and largest for the quasi-2DMC with BMA and FMA methods (apart from unadjusted regression), with standard regression calibration and Monte Carlo maximum likelihood exhibiting bias in predicted relative risk generally somewhat intermediate between ERC and the other two methods. In general ERC performs best in the scenarios presented, and should be the method of choice in situations where there may be substantial shared error, or suspected curvature in the dose response.
Simulant molten core–concrete interaction experiments in view of understanding Fukushima Daiichi Nuclear Power Station Cs-bearing particles generation mechanism
Authors: Laffolley Hugo , Journeau Christophe , Grambow Bernd 
Published: March 2024

AbstractThe Fukushima Daiichi accident resulted in the release of a novel form of radioactive Cs contamination into the environment, called Cs-bearing microparticles (CsMP). CsMPs constitute a substantial portion of the radioactive pollution near the nuclear power station and traveled beyond several hundred kilometers. Extensive characterization of the CsMPs revealed an amorphous silica matrix, along with Cs and other minor or trace elements such as Fe and Zn. This study explores the unclear generation mechanism of CsMPs by conducting experimental molten core concrete interactions (MCCI) as a source of Si and analyzing the resultant aerosols. The findings demonstrate that MCCI is in capacity to produce spherical submicronic and micronic particles, primarily composed of amorphous silica and incorporating elements akin to CsMPs. A humid atmosphere is found to favour an even closer chemical composition. Examination of the internal structure of the synthesized particles unveils pores and numerous crystalline nanoinclusions possibly serving as nucleation sites for CsMP formation through the condensation of Si-rich vapors.
The effect of a prosocial environment on health and well-being during the first COVID-19 lockdown and a year later
Authors: Trachtenberg Estherina , Ruzal Keren , Forkosh Oren , Ben-Ami Bartal Inbal 
Published: March 2024

AbstractThe outset of the COVID-19 pandemic was characterized by prolonged periods of chronic stress and social isolation. While studies have investigated the changes to well-being (WB) during this period, the impact of the social environment on long-term physical and mental health requires further study. This study aimed to assess the factors influencing WB and health outcomes, with the hypothesis that a positive social environment would play a significant immediate and long-term role in improving WB and preventing the effects of anxiety associated with the pandemic. At time point 1 (April 2020), an Israeli sample of 206 participants (84% female, mean age 31.5) responded to traditional questionnaires assessing mental health and social support. Factors affecting WB were assessed within subjects during the first COVID-19 lockdown for 6 weeks using a daily survey (Beiwe phone application). A year later, in May 2021, at time point 2, the initial questionnaires were readministered to a subset of the same participants (N = 94). We found that anxiety during the first lockdown adversely affected WB and predicted health and WB deterioration a year later. In contrast, a high quality of social relationships was associated with better short- and long-term WB, and mitigated the adverse effects of anxiety. Daily activities, including physical activity, meditation, and romantic relations, were also positively associated with WB during the first lockdown but did not have long-term effects. In summary, our study underscores the enduring health advantages of a positive social environment, particularly during stressful periods. These results have implications for health policymakers: programs which support individuals with high anxiety and low support, by integrating them into community-based interventions, promise to enhance well-being (WB) and health, as well as to fortify the community as a whole.
Suboptimal decision making and interpersonal problems in ADHD: longitudinal evidence from a laboratory task
Authors: Sørensen L. , Adolfsdottir S. , Kvadsheim E. , Eichele H. , Plessen K. J. , Sonuga-Barke E. 
Published: March 2024

AbstractOver half of children with Attention-Deficit/Hyperactivity Disorder (ADHD) display interpersonal and social problems. Several lines of research suggest that suboptimal decision making, the ability to adjust choices to different risk-varying options, influences poorer choices made in social interactions. We thus measured decision making and its prediction of social problems longitudinally with the Cambridge Gambling Task in children with ADHD over four years. Children with ADHD had shown suboptimal decision making driven mainly by delay aversion at baseline and we expected this to be a stabile trait which would predict greater parent-reported social problems. From the baseline assessment (n = 70), 67% participated at the follow-up assessment, 21 from the ADHD group and 26 from the typically developing group. The mean age at the follow-up was 14.5 years old. The results confirmed our expectations that suboptimal decision making was a stabile trait in children and adolescents with ADHD. Although delay aversion did not differ from controls at follow-up it still proved to be the main longitudinal predictor for greater social problems. Our findings indicate that impulsivity in social interactions may be due to a motivational deficit in youth with ADHD.
Identifying American climate change free riders and motivating sustainable behavior
Authors: Magistro Beatrice , Abramson Cecilia , Ebanks Daniel , Debnath Ramit , Alvarez R. Michael 
Published: March 2024

AbstractFree riders, who benefit from collective efforts to mitigate climate change but do not actively contribute, play a key role in shaping behavioral climate action. Using a sample of 2096 registered American voters, we explore the discrepancy between two groups of free riders: cynics, who recognize the significance of environmental issues but do not adopt sustainable behaviors, and doubters, who neither recognize the significance nor engage in such actions. Through statistical analyses, we show these two groups are different. Doubters are predominantly male, younger, with lower income and education, exhibit stronger conspiracy beliefs, lower altruism, and limited environmental knowledge, are more likely to have voted for Trump and lean towards conservative ideology. Cynics are younger, religious, higher in socioeconomic status, environmentally informed, liberal-leaning, and less likely to support Trump. Our research provides insights on who could be most effectively persuaded to make climate-sensitive lifestyle changes and provides recommendations to prompt involvement in individual sustainability behaviors. Our findings suggest that for doubters, incentivizing sustainability through positive incentives, such as financial rewards, may be particularly effective. Conversely, for cynics, we argue that engaging them in more community-driven and social influence initiatives could effectively translate their passive beliefs into active participation.
Antibiotics and probiotics-induced effects on the total fatty acid composition of feces in a rat model
Authors: Marosvölgyi Tamás , Mintál Kitti , Farkas Nelli , Sipos Zoltán , Makszin Lilla , Szabó Éva , Tóth Attila , Kocsis Béla , Kovács Krisztina , Hormay Edina , Lénárd László , Karádi Zoltán , Bufa Anita 
Published: March 2024

AbstractFatty acids (FAs) play important roles as membrane components and signal transduction molecules. Changes in short chain FA (SCFA) composition are associated with gut microbiota modifications. However, the effect of bacteria-driven changes on the detailed FA spectrum has not been explored yet. We investigated the effect of antibiotics (ABx) and/or probiotics, in four treatment groups on rat stool FA composition. Principal component analysis indicated that the chromatogram profiles of the treatment groups differ, which was also observed at different time points. Linear mixed effects models showed that in the parameters compared (sampling times, treatments. and their interactions), both the weight percentage and the concentration of FAs were affected by ABx and probiotic administration. This study found that the gut microbiome defines trans and branched saturated FAs, most saturated FAs, and unsaturated FAs with less carbon atoms. These results are among the first ones to demonstrate the restoring effects of a probiotic mixture on a substantial part of the altered total FA spectrum, and also revealed a previously unknown relationship between gut bacteria and a larger group of FAs. These findings suggest that intestinal bacteria produce not only SCFAs but also other FAs that may affect the host’s physiological processes.
Automated detection and recognition system for chewable food items using advanced deep learning models
Authors: Kumar Yogesh , Koul Apeksha , Kamini  , Woźniak Marcin , Shafi Jana , Ijaz Muhammad Fazal 
Published: March 2024

AbstractIdentifying and recognizing the food on the basis of its eating sounds is a challenging task, as it plays an important role in avoiding allergic foods, providing dietary preferences to people who are restricted to a particular diet, showcasing its cultural significance, etc. In this research paper, the aim is to design a novel methodology that helps to identify food items by analyzing their eating sounds using various deep learning models. To achieve this objective, a system has been proposed that extracts meaningful features from food-eating sounds with the help of signal processing techniques and deep learning models for classifying them into their respective food classes. Initially, 1200 audio files for 20 food items labeled have been collected and visualized to find relationships between the sound files of different food items. Later, to extract meaningful features, various techniques such as spectrograms, spectral rolloff, spectral bandwidth, and mel-frequency cepstral coefficients are used for the cleaning of audio files as well as to capture the unique characteristics of different food items. In the next phase, various deep learning models like GRU, LSTM, InceptionResNetV2, and the customized CNN model have been trained to learn spectral and temporal patterns in audio signals. Besides this, the models have also been hybridized i.e. Bidirectional LSTM + GRU and RNN + Bidirectional LSTM, and RNN + Bidirectional GRU to analyze their performance for the same labeled data in order to associate particular patterns of sound with their corresponding class of food item. During evaluation, the highest accuracy, precision,F1 score, and recall have been obtained by GRU with 99.28%, Bidirectional LSTM + GRU with 97.7% as well as 97.3%, and RNN + Bidirectional LSTM with 97.45%, respectively. The results of this study demonstrate that deep learning models have the potential to precisely identify foods on the basis of their sound by computing the best outcomes.
Investigation of deep learning model for predicting immune checkpoint inhibitor treatment efficacy on contrast-enhanced computed tomography images of hepatocellular carcinoma
Authors: Nakao Yasuhiko , Nishihara Takahito , Sasaki Ryu , Fukushima Masanori , Miuma Satoshi , Miyaaki Hisamitsu , Akazawa Yuko , Nakao Kazuhiko 
Published: March 2024

AbstractAlthough the use of immune checkpoint inhibitors (ICIs)-targeted agents for unresectable hepatocellular carcinoma (HCC) is promising, individual response variability exists. Therefore, we developed an artificial intelligence (AI)-based model to predict treatment efficacy using pre-ICIs contrast-enhanced computed tomography (CT) imaging characteristics. We evaluated the efficacy of atezolizumab and bevacizumab in 43 patients at the Nagasaki University Hospital from 2020 to 2022 using the modified Response Evaluation Criteria in Solid Tumors. A total of 197 Progressive Disease (PD), 271 Partial Response (PR), and 342 Stable Disease (SD) contrast CT images of HCC were used for training. We used ResNet-18 as the Convolutional Neural Network (CNN) model and YOLOv5, YOLOv7, YOLOv8 as the You Only Look Once (YOLO) model with precision-recall curves and class activation maps (CAMs) for diagnostic performance evaluation and model interpretation, respectively. The 3D t-distributed Stochastic Neighbor Embedding was used for image feature analysis. The YOLOv7 model demonstrated Precision 53.7%, Recall 100%, F1 score 69.8%, mAP@0.5 99.5% for PD, providing accurate and clinically versatile predictions by identifying decisive points. The ResNet-18 model had Precision 100% and Recall 100% for PD. However, the CAMs sites did not align with the tumors, suggesting the CNN model is not predicting that a given CT slice is PD, PR, or SD, but that it accurately predicts Individual Patient's CT slices. Preparing substantial training data for tumor drug effect prediction models is challenging compared to general tumor diagnosis models; hence, large-scale validation using an efficient YOLO model is warranted.