bluuebunny/crossref_metadata_2025_split huggingface dataset:
MUC5AC: A potential biomarker of severity in pediatric patients infected with influenza
Authors: López Eduardo L. , Martín Ferolla Fausto , Meza Santiago N. , Mascardi Normando , Neira Pablo , Chiormi Agustina , Reyero Mariana , Retta Alejandra , Contrini María M. , Acosta Patricio L. 
Published: May 2024

AbstractNumerous factors can increase the risk of severe influenza; however, a majority of severe cases occur in previously healthy children. Identification of high‐risk children is important for targeted preventive interventions and prompt treatment. The aim of this study was to evaluate MUC5AC as a biomarker for influenza disease severity in children. For this, a prospective cohort study was conducted in 2019. Children hospitalized with acute respiratory infection (ARI) with confirmed positive influenza infection were enrolled. Influenza cases were identified by reverse transcriptase‐polymerase chain reaction. Life‐threatening disease (LTD) was defined by the need for intensive care and ventilatory support. MUC5AC, epidemiologic, and clinical risk factors were assessed. Three hundred and forty‐two patients were hospitalized with ARI, of which 49 (14%) had confirmed influenza infection and 6 (12%) of them developed LTD. MUC5AC levels were higher in those patients with mild disease compared to cases with poorer outcomes. Our results show that the severity of influenza infection in children is significantly associated with low levels of MUC5AC. These findings suggest its potential as a suitable biomarker for predicting disease severity.
Integrating dialectical tensions in mindfulness‐informed counseling
Authors: Bellin Zvi 
Published: May 2024

AbstractThis article explores integrating mindfulness within psychotherapy through dialectical humanism. The author integrates Buddhist, Jewish, and contemporary psychology perspectives to explore three dialectical tensions in psychotherapy: striving versus acceptance, becoming versus being, and relevancy versus regularity. The author offers integrative mindfulness techniques for counselors to use with clients.
Achieving sustainable development in Sub‐Saharan Africa: Do human capital and foreign direct investment matter?
Authors: Onatunji Olufemi G. 
Published: May 2024

AbstractOver the past few decades, there has been a noticeable development of interest among scholars and policymakers worldwide regarding the prominent drivers of sustainable development. The role of human capital and foreign direct investment (FDI) in fostering sustainable development, however, has been largely neglected by policymakers and scholars, despite recent emphasis on FDI amidst current economic performance worldwide, particularly in developing countries. This study empirically investigates the effect of human capital and FDI on sustainable development in sub‐Saharan Africa (SSA) spanning 1991–2021. The empirical analysis was implemented by deploying different techniques, such as feasible generalized least squares, panel corrected standard errors, and Lewbel two‐stage least square techniques, with the panel quantile regression approach as a robustness check. The empirical outcomes show that improvement in human capital via educational attainment fosters economic expansion, employment opportunities, and poverty reduction, whereas an increase in health human capital undermines sustainable development in SSA. The findings also reveal that increasing FDI flows to priority sectors in the region are critical for enhancing human capital, thus stimulating economic performance, job opportunities, and poverty alleviation in SSA. This study puts forward important policy implications for achieving sustainable development.
Gallic Acid Attenuates Sepsis‐Induced Liver Injury through C/EBPβ‐Dependent MAPK Signaling Pathway
Authors: Cai Yuwei , Zhao Denghui , Pan Yu , Chen Bingqi , Cao Yifei , Han Shufen , Lian Fuzhi , Zhang Yunlong , Yan Xiao 
Published: May 2024

ScopeLiver injury is a major complication associated with sepsis. Together with others, the study has shown that gallic acid (GA) exerts anti‐inflammatory and antioxidant effects in vivo. However, the role of GA in sepsis‐mediated hepatic impairment and the underlying mechanisms remains to be elucidated.Methods and resultsC57BL/6J mice are pretreated with saline or GA and subjected to sham or cecal ligation and puncture (CLP). The pathological alterations are assessed by hematoxylin and eosin staining as well as immunohistochemical staining. RNA sequencing is employed to analyze hepatic transcriptome modifications. The study finds that GA supplementation significantly ameliorates CLP‐induced mortality, liver dysfunction, and inflammation. RNA sequencing reveals that 1324 genes are markedly differentially regulated in livers of saline‐ or GA‐treated sham or CLP mice. Gene ontology analysis demonstrates that the differentially expressed genes regulated by GA are predominantly correlated with the immune system process, oxidation–reduction process, and inflammatory response. Furthermore, mitogen‐activated protein kinase (MAPK) signaling is localized in the center of the GA‐mediated pathway network. Notably, activation of MAPK by C16‐PAF significantly blocks GA‐mediated protective effects on hepatic injury, inflammation, as well as CCAAT/enhancer‐binding protein‐β (C/EBPβ) dependent extracellular signal‐regulated kinase 1/2 (ERK1/2) and nuclear factor‐κB (NF‐κB) signaling.ConclusionTherefore, this study indicates that GA may offer a promising therapeutic opportunity for sepsis‐associated liver injury.
Uncertainty quantification in multi‐class segmentation: Comparison between Bayesian and non‐Bayesian approaches in a clinical perspective
Authors: Scalco Elisa , Pozzi Silvia , Rizzo Giovanna , Lanzarone Ettore 
Published: May 2024

AbstractBackgroundAutomatic segmentation techniques based on Convolutional Neural Networks (CNNs) are widely adopted to automatically identify any structure of interest from a medical image, as they are not time consuming and not subject to high intra‐ and inter‐operator variability. However, the adoption of these approaches in clinical practice is slowed down by some factors, such as the difficulty in providing an accurate quantification of their uncertainty.PurposeThis work aims to evaluate the uncertainty quantification provided by two Bayesian and two non‐Bayesian approaches for a multi‐class segmentation problem, and to compare the risk propensity among these approaches, considering CT images of patients affected by renal cancer (RC).MethodsFour uncertainty quantification approaches were implemented in this work, based on a benchmark CNN currently employed in medical image segmentation: two Bayesian CNNs with different regularizations (Dropout and DropConnect), named BDR and BDC, an ensemble method (Ens) and a test‐time augmentation (TTA) method. They were compared in terms of segmentation accuracy, using the Dice score, uncertainty quantification, using the ratio of correct‐certain pixels (RCC) and incorrect‐uncertain pixels (RIU), and with respect to inter‐observer variability in manual segmentation. They were trained with the Kidney and Kidney Tumor Segmentation Challenge launched in 2021 (Kits21), for which multi‐class segmentations of kidney, RC, and cyst on 300 CT volumes are available. Moreover, they were tested considering this and other two public renal CT datasets.ResultsAccuracy results achieved large differences across the structures of interest for all approaches, with an average Dice score of 0.92, 0.58, and 0.21 for kidney, tumor, and cyst, respectively. In terms of uncertainties, TTA provided the highest uncertainty, followed by Ens and BDC, whereas BDR provided the lowest, and minimized the number of incorrect certain pixels worse than the other approaches. Again, large differences were seen across the three structures in terms of RCC and RIU. These metrics were associated with different risk propensity, as BDR was the most risk‐taking approach, able to provide higher accuracy in its prediction, but failing to assign uncertainty on incorrect segmentation in every case. The other three approaches were more conservative, providing large uncertainty regions, with the drawback of giving alert also on correct areas. Finally, the analysis of the inter‐observer segmentation variability showed a significant variation among the four approaches on the external dataset, with BDR reporting the lowest agreement (Dice = 0.82), and TTA obtaining the highest score (Dice = 0.94).ConclusionsOur outcomes highlight the importance of quantifying the segmentation uncertainty and that decision‐makers can choose the approach most in line with the risk propensity degree required by the application and their policy.
An analysis of urodynamic parameters in diabetic and nondiabetic women
Authors: Burns Ramzy T. , Arnold Peter J. , Song Leo , Moss Kevin L. , Powell Charles R. 
Published: May 2024

AbstractObjectivesDiabetes is highly prevalent worldwide, with an estimated 536 million living with diabetes in 2021, and that number projected to increase to 783 million by 2045. Diabetic bladder dysfunction is thought to affect up to 60%–90% of individuals with diabetes and can significantly impact quality of life. Despite the prevalence of diabetic bladder dysfunction, the exact pathophysiological mechanism, and resulting clinical presentation, remains debated. Our objective was to compare urodynamic parameters between diabetic and nondiabetic women, assessing the impact of various markers of diabetes severity on bladder function.MethodsA retrospective chart review was conducted on female patients aged 18 and above who underwent urodynamic studies at a single tertiary care university hospital system from 2014 to 2020. Patients were categorized based on diabetes status, and diabetes severity including duration of disease, hemoglobin A1c levels, insulin dependence, and markers of end‐organ dysfunction. Urodynamic variables, including compliance, bladder voided efficiency, bladder contractility index, postvoid residual, maximum flow rate, capacity, voided volume, and detrusor overactivity, were assessed by two independent reviewers. Statistical analyses were performed to assess the impact of diabetes and diabetic severity on urodynamic parameters.ResultsA total of 652 female patients were included in the study, of which, 152 (23.3%) had diabetes, with an average duration of diagnosis of 82.3 months. Diabetic women were older and had higher body mass index compared to nondiabetic women. Diabetic retinopathy and neuropathy were present in 18% and 54.6% of diabetic patients, respectively. Significant differences in urodynamic parameters were observed between diabetic and nondiabetic women, with diabetic women showing higher rates of detrusor overactivity (p = 0.01), particularly associated with increasing BMI (p = 0.03). However, classic markers of diabetes severity including duration, as well as markers of end‐organ damage, showed mixed associations with urodynamic changes.ConclusionsDespite the prevalence of diabetic bladder dysfunction and its impact on patient quality of life, the exact mechanisms and clinical presentation remain elusive. Our study highlights the significant differences in urodynamic parameters between diabetic and nondiabetic women, emphasizing the need for further research into the relationship between diabetes and diabetic bladder dysfunction.
A parareal exponential integrator finite element method for semilinear parabolic equations
Authors: Huang Jianguo , Ju Lili , Xu Yuejin 
Published: May 2024

AbstractIn this article, we present a parareal exponential finite element method, with the help of variational formulation and parareal framework, for solving semilinear parabolic equations in rectangular domains. The model equation is first discretized in space using the finite element method with continuous piecewise multilinear rectangular basis functions, producing the semi‐discrete system. We then discretize the temporal direction using the explicit exponential Runge–Kutta approach accompanied by the parareal framework, resulting in the fully‐discrete numerical scheme. To further improve computational speed, we design a fast solver for our method based on tensor product spectral decomposition and fast Fourier transform. Under certain regularity assumption, we successfully derive optimal error estimates for the proposed parallel‐based method with respect to ‐norm. Extensive numerical experiments in two and three dimensions are also carried out to validate the theoretical results and demonstrate the performance of our method.
Nudging increases take‐up of employment services: Evidence from a large field experiment
Authors: Hopkins Vince , Dorion Jeff 
Published: May 2024

AbstractWhen people lose their job, labor market programs help them get back to work. But administrative burdens can hinder enrollment in such programs. We report results from a mixed‐method project to increase enrollment in employment services during the first 3 months of the COVID‐19 pandemic. First, we interviewed jobseekers and frontline staff to uncover administrative burdens. Second we worked with staff to co‐design a behavioral “nudge” intervention. Finally, in a large field experiment (N = 14,008), we evaluate the impact of this intervention on participation in employment services. We present two main findings. First, reducing administrative burden triples enrollment in the program within the first 30 days. Second, we test two motivational frames—one emphasizing social norms, another using checklist messaging. We find that message framing drives engagement with communications, such as email open rates and website click‐throughs. However, framing generates no statistically significant difference in enrollment rates. Our results demonstrate the potential for applied behavioral science to improve implementation of labor market policy. We also contribute to current debates about the effectiveness of nudging to increase take‐up of public services.
Enhanced electroactive β‐phase and dielectric properties in P(VDF‐HFP) composite flexible films through doping with three calcium chloride salts: CaCl2, CaCl2·2H2O, and CaCl2·6H2O
Authors: Yuennan Jureeporn , Tohluebaji Nikruesong , Putson Chatchai , Muensit Nantakan , Channuie Phongpichit 
Published: May 2024

AbstractFlexible dielectric films with a high dielectric constant, utilizing P(VDF‐HFP)/salt composites, were manufactured through the solution casting method. In this work, we studied the impacts of three calcium chloride salts—CaCl2, CaCl2·2H2O, and CaCl2·6H2O—on the surface morphology, crystalline phase transformation, mechanical properties, and dielectric responses of the P(VDF‐HFP) copolymer. The detailed structure and properties of all samples were assessed via scanning electron microscope (SEM), atomic force microscopy (AFM), Fourier transform infrared spectroscopy (FTIR), X‐ray diffraction (XRD), tensile testing, and LCR meter analysis. The experimental results demonstrated that the addition of calcium salts to the P(VDF‐HFP) composites induced a visibly microporous morphology, with the porosity and surface roughness of the composites gradually increasing with salt content. In comparison to the pure P(VDF‐HFP) film, the composite films exhibited decreased stiffness and heightened flexibility. Furthermore, a higher β‐phase fraction and notable enhancements in the dielectric constant resulting from the inclusion of calcium salts were observed. Consequently, the P(VDF‐HFP) composite filled with 3 wt% of CaCl2·6H2O demonstrated the highest capability for promoting the β‐phase fraction and dielectric constant, achieving values of 98.33% and 21.49% (at 10 Hz), respectively.
Image driven deep learning method with FFT solver for predicting the microscale full field stress of stochastic boundary composites
Authors: Liao Yong , Wang Bing , Wang Hongyue , Meng Songhe , Fang Guodong 
Published: May 2024

AbstractA novel image‐driven deep learning approach embedded with model‐data‐knowledge information can directly predict the full field stress distribution and mechanical properties of composites solely based on initial scanning geometry images. The fiber spatial distribution and morphological features are identified by Scanning Electron Microscopy (SEM) initially. An effective random fiber generation algorithm is further utilized to generate images database comprising equivalent geometric models of various microstructures. Subsequently, the geometry model images database is analyzed by fast Fourier transform (FFT) to produce the database including the elastic modulus parameters and full field stress distributions of composites with distinct microstructures. Finally, an innovative image‐learning comprehensive paradigm uniting convolutional neural networks and convolutional autoencoders is elaborated systematically to learn the inherent laws of geometry images and field images. The results show that the proposed method have capacity to effectively and accurately predict the elastic modulus and full field stress distributions combined with error analysis even for stochastic boundary composites. The proposed methodology is a symbiosis of cutting‐edge image learning and non‐destructive testing techniques for composites, which can directly use the local non‐destructive or scanning images of composite structural components to quickly obtain field information and further predict damage evolution online.Highlights
Proposing a novel deep learning approach combined with FFT directly to predict stress distribution of stochastic boundary composites solely based on micro‐images.
Adopting equivalent geometric models dispersed by higher pixels mesh density considering extreme thin interface.
Incorporate adaptive algorithms for streamlined training optimization.
An innovative integration of deep learning and non‐destructive testing technology for the stress distribution and properties prediction online.
‘Main Mechanical Forces‐Chemical Interactions’ Interplay as a Tool to Elucidate Folding Mechanisms
Authors: Larocca Michele , Floresta Giuseppe , Verderese Daniele , Cilibrizzi Agostino 
Published: May 2024

ABSTRACTThe folding of peptides and proteins is rigidly reliant on the ‘chemical information’ carried by the specific amino acid sequence. In this study, three polypeptides (PDBs: 2jof, 1res and 1prv) were investigated as model systems to assess their folded features, thereby enabling further understanding of mechanisms that play a role in regulating the folding process more widely. A novel physico‐chemical approach of analysis is proposed herein, focusing on chemical interactions and their related mechanical forces that we trust are determinant to drive the folding. Through this methodology, we have predicted the conformations adopted by the three polypeptides and compared the outcomes to those experimentally determined, achieving a substantial structural agreement. Molecular dynamic simulations have been carried out to further support our calculations and structural results. Within the three models, we demonstrate that the interaction of each amino acid residue with its neighbour residues is a crucial determinant for the formation of the 3D stable native structures. This article provides initial evidence that the folding occurs by means of mechanical forces developed upon establishing chemical interactions amongst residues, which, in turn, are peculiar to each specific amino acid present in each position of the peptide chain.
Recent advances in Joule‐heating synthesis of functional nanomaterials for photo and electrocatalysis
Authors: Li Jiaqi , Luo Laiyu , Wang Siyu , Song Haojie , Jiang Baojiang 
Published: May 2024

AbstractBackgroundCatalyst synthesis plays a crucial role in advancing photo and electrocatalysis technologies for sustainable development. However, the traditional thermal radiation heating method suffers from the disadvantages of high energy consumption, low heat transfer efficiency, slow heating speed and long heating time, which leads to the inefficiency and cost increases in catalyst preparation.AimsThe Joule‐heating ultrafast synthesis method with rapid heating/quenching and shorter heating time has attracted much attention. Despite its potential, there is a lack of comprehensive reviews specifically addressing the synthesis of advanced photo and electrocatalysts via Joule‐heating. Therefore, this review aims to help people quickly understand the advantages of Joule‐heating in the synthesis of photo and electrocatalysts.DiscussionHerein, we firstly introduce the principles and devices of Joule‐heating, and then we discuss breakthroughs in defect modulation, heterojunction construction, single‐atom catalysts, bimetallic alloy catalysts, high‐entropy alloy catalysts and metastable catalysts achieved through Joule‐heating technology. The diverse applications of these catalysts include hydrogen evolution, oxygen evolution, oxygen reduction reactions, carbon dioxide reduction reactions, nitrogen reduction reaction and degradation of organic pollutants. Furthermore, this review provides a forward‐looking perspective on future directions for employing Joule‐heating methods in the field of photo and electrocatalysis research.ConclusionThis review highlights the pivotal role played by Joule‐heating techniques in advancing nanomaterial synthesis as well as developing sustainable high‐performance catalyst systems.
Stressful life events among college student: Direct and indirect relationships with insomnia, depression, self‐esteem, and suicide ideation
Authors: Zou Hongyu , Kong Xiaohua , Chen Ziyao , Zhang Wei 
Published: May 2024

AbstractSuicide is a serious public health problem and is the fourth leading cause of death among adolescents. Suicide ideation is the most significant predictor of suicide. However, few empirical studies have examined the relationship between stressful life events, insomnia, depression symptoms, self‐esteem, and suicide ideation among college students. This study builds on O'Connor's The integrated motivational‐volitional model of suicidal behavior theory will verify the relationship between these five factors through a large data study among college students, and further reveal the mechanisms and interventions of suicide ideation among college students. A total of 2717 (mean age = 19.81 years, 22.49% male) college students from South China participated in this behavioral and health survey. Using self‐reported questionnaires which were Adolescent Self Rating Life Events Check‐list, MINI‐C, Insomnia Severity Index, PHQ‐9, and Self‐Esteem Scale to assess the subjects’ demographic information, suicide ideation, stressful life events, insomnia, depressive symptoms, and self‐esteem levels as comprehensively as possible. A moderated chain mediation model was used to validate their relationship. The results showed that stressful life events significant positively predicted insomnia symptoms, depressive symptoms, and suicide ideation. The mediating effect of depression between stressful life events and suicide ideation was significant. The chain mediating effect of insomnia and depression between stressful life events and suicide ideation was significant. However, the mediating effect of insomnia between stressful life events and suicide ideation was not significant. Also, the positive prediction of stressful life events and depression on suicide ideation was weaker when the individual's self‐esteem level was higher, and the moderated chain mediating effect was significant. This study developed a moderated chain mediation model, and proposes a new integrated stress coping‐self‐perception model of suicide ideation to discover the important role of self‐esteem levels in adolescent suicide prevention.