Elsevier最新文献

{"title":"Nano-zirconia modified biochar for efficient removal of arsenite and arsenate from freshwater and seawater.","authors":"Qianyu Zhao, Yun Wu, Yingying Tang, Peng Zhang, Yunxue Guo, Wei Zhang","doi":"10.1016/j.jenvman.2025.125940","DOIUrl":"10.1016/j.jenvman.2025.125940","url":null,"abstract":"<p><p>Arsenic (As) pollution in groundwater and seawater represents a major global environmental and public health issue. This study explores the efficacy of nano-zirconium oxide (N-ZrO₂) loaded biochar (BC) for removing inorganic arsenic (iAs), arsenite [As(III)] and arsenate [As(V)], from both freshwater and seawater. Utilizing scanning electron microscopy, flourier transform infrared spectroscopy, X-ray diffraction, and X-ray photoelectron spectroscopy, successful loading of N-ZrO₂ onto BC was confirmed, significantly boosting its adsorption capacity to 44.1 mg g^-1 for As(III) and 33.5 mg g^-1 for As(V). The adsorption process, following a pseudo-second-order kinetic model, primarily involved chemisorption, with hydroxyl groups playing a crucial role. The N-ZrO₂-modified BC exhibited minimal pH sensitivity, demonstrating optimal adsorption at a concentration of 0.5 g L^-1, surpassing other materials in efficiency and dosage requirements, and exhibiting potential for recyclability. In practical applications, it achieved high removal efficiencies (95 % in freshwater and 86 % in seawater), establishing 700 °C N-ZrO₂-BC as a proficient adsorbent for simultaneous removal of As(III) and As(V) from contaminated freshwater and seawater. This study offers a promising solution to As contamination, with significant implications for public health and environmental sustainability.</p>","PeriodicalId":356,"journal":{"name":"Journal of Environmental Management","volume":"387 ","pages":"125940"},"PeriodicalIF":8.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144172390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to \"A case for the use of deep learning algorithms for individual and population level assessments of mental health disorders: Predicting depression among China's elderly\" [J. Affect. Disord. Volume 369, 15 January 2025, Pages 329-337].","authors":"Yingjie Wang, Xuzhe Wang, Li Zhao, Kyle Jones","doi":"10.1016/j.jad.2025.02.070","DOIUrl":"10.1016/j.jad.2025.02.070","url":null,"abstract":"","PeriodicalId":14963,"journal":{"name":"Journal of affective disorders","volume":" ","pages":"916"},"PeriodicalIF":4.9,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143537162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to \"Childhood maltreatment, trait resilience and prenatal distress among expecting mothers and fathers in the FinnBrain birth cohort study\" [J. Affect. Disord. 344, (2024) pages 41-47].","authors":"Viivi Mondolin, Hasse Karlsson, Jetro J Tuulari, Juho Pelto, Linnea Karlsson, Elisabeth Nordenswan, Eeva-Leena Kataja","doi":"10.1016/j.jad.2025.02.035","DOIUrl":"10.1016/j.jad.2025.02.035","url":null,"abstract":"","PeriodicalId":14963,"journal":{"name":"Journal of affective disorders","volume":" ","pages":"915"},"PeriodicalIF":4.9,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143449219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to \"Trajectories of Chinese adolescent depression before and after COVID-19: A cross-temporal meta-analysis with segmented regression\" [J. Affect. Disord. 373 (2025) 333-344].","authors":"Xiayu Du, Hanzhang Wu, Sailigu Yalikun, Jiayi Li, Jiaojiao Jia, Tieyu Duan, Zongkui Zhou, Zhihong Ren","doi":"10.1016/j.jad.2025.01.106","DOIUrl":"10.1016/j.jad.2025.01.106","url":null,"abstract":"","PeriodicalId":14963,"journal":{"name":"Journal of affective disorders","volume":" ","pages":"914"},"PeriodicalIF":4.9,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143038234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning models for diagnosis and risk prediction in eating disorders, depression, and alcohol use disorder.","authors":"Zuo Zhang, Lauren Robinson, Robert Whelan, Lee Jollans, Zijian Wang, Frauke Nees, Congying Chu, Marina Bobou, Dongping Du, Ilinca Cristea, Tobias Banaschewski, Gareth J Barker, Arun L W Bokde, Antoine Grigis, Hugh Garavan, Andreas Heinz, Rüdiger Brühl, Jean-Luc Martinot, Marie-Laure Paillère Martinot, Eric Artiges, Dimitri Papadopoulos Orfanos, Luise Poustka, Sarah Hohmann, Sabina Millenet, Juliane H Fröhner, Michael N Smolka, Nilakshi Vaidya, Henrik Walter, Jeanne Winterer, M John Broulidakis, Betteke Maria van Noort, Argyris Stringaris, Jani Penttilä, Yvonne Grimmer, Corinna Insensee, Andreas Becker, Yuning Zhang, Sinead King, Julia Sinclair, Gunter Schumann, Ulrike Schmidt, Sylvane Desrivières","doi":"10.1016/j.jad.2024.12.053","DOIUrl":"10.1016/j.jad.2024.12.053","url":null,"abstract":"<p><strong>Background: </strong>Early diagnosis and treatment of mental illnesses is hampered by the lack of reliable markers. This study used machine learning models to uncover diagnostic and risk prediction markers for eating disorders (EDs), major depressive disorder (MDD), and alcohol use disorder (AUD).</p><p><strong>Methods: </strong>Case-control samples (aged 18-25 years), including participants with Anorexia Nervosa (AN), Bulimia Nervosa (BN), MDD, AUD, and matched controls, were used for diagnostic classification. For risk prediction, we used a longitudinal population-based sample (IMAGEN study), assessing adolescents at ages 14, 16 and 19. Regularized logistic regression models incorporated broad data domains spanning psychopathology, personality, cognition, substance use, and environment.</p><p><strong>Results: </strong>The classification of EDs was highly accurate, even when excluding body mass index from the analysis. The area under the receiver operating characteristic curves (AUC-ROC [95 % CI]) reached 0.92 [0.86-0.97] for AN and 0.91 [0.85-0.96] for BN. The classification accuracies for MDD (0.91 [0.88-0.94]) and AUD (0.80 [0.74-0.85]) were also high. The models demonstrated high transdiagnostic potential, as those trained for EDs were also accurate in classifying AUD and MDD from healthy controls, and vice versa (AUC-ROCs, 0.75-0.93). Shared predictors, such as neuroticism, hopelessness, and symptoms of attention-deficit/hyperactivity disorder, were identified as reliable classifiers. In the longitudinal population sample, the models exhibited moderate performance in predicting the development of future ED symptoms (0.71 [0.67-0.75]), depressive symptoms (0.64 [0.60-0.68]), and harmful drinking (0.67 [0.64-0.70]).</p><p><strong>Conclusions: </strong>Our findings demonstrate the potential of combining multi-domain data for precise diagnostic and risk prediction applications in psychiatry.</p>","PeriodicalId":14963,"journal":{"name":"Journal of affective disorders","volume":" ","pages":"889-899"},"PeriodicalIF":4.9,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7617286/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142864262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to \"Neural evidence of implicit emotion regulation deficits: An explorative study of comparing PTSD with and without alcohol dependence\" [J. Affect. Disord. 372 (2025) 548-563].","authors":"Junrong Zhao, Yunxiao Guo, Yafei Tan, Yuyi Zhang, Sijun Liu, Yinong Liu, Jiayi Li, Jun Ruan, Lianzhong Liu, Zhihong Ren","doi":"10.1016/j.jad.2025.01.105","DOIUrl":"10.1016/j.jad.2025.01.105","url":null,"abstract":"","PeriodicalId":14963,"journal":{"name":"Journal of affective disorders","volume":" ","pages":"913"},"PeriodicalIF":4.9,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143037719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interactive effect of sleep duration, lifestyle factors and comorbidity on depressive symptoms: Insights from the China health and retirement longitudinal study.","authors":"Changyu Ju, Chunrong Huang, Xiaodong Liu, Juming Liu","doi":"10.1016/j.jad.2025.01.024","DOIUrl":"10.1016/j.jad.2025.01.024","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;As population aging intensifies, depression emerges as a major global public health issue, especially affecting middle-aged and elderly individuals. While studies have investigated factors like sleep duration, physical activity, smoking, drinking habits, and comorbidity, the complex interplay and cumulative effect of these factors on the risk of depressive symptoms remain not fully understood.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This research utilizes data from the China Health and Retirement Longitudinal Study (CHARLS), encompassing observations from 2015 to 2020. The subjects included 8234 middle-aged and elderly individuals, accounting for a total of 22,570 observations. Lifestyle factors were represented by physical activity, smoking, and drinking habits, with the volume of moderate-to-vigorous physical activity (MVPA) quantified by quoting metabolic equivalents (MET). Multivariate logistic regression models were conducted for baseline analysis, and mixed-effects logistic regression models with random participant intercepts were constructed for the longitudinal analysis of the cohort. Moreover, interaction terms between these factors were included to assess their combined impact on the risk of depressive symptoms.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Longitudinal analysis revealed a notable correlation between short sleep duration (&lt;7 h) and an elevated risk of depressive symptoms, evidenced by an adjusted odds ratio (OR) of 3.13 (95 % CI: 2.73-3.74). Conversely, long sleep duration (&gt;9 h) was not associated with a marked change in risk of depressive symptoms (OR = 1.11, 95 % CI: 0.78-1.59, p = 0.59). High levels of physical activity (192-336 MET-h/week) were significantly linked to an elevated risk of depressive symptoms (OR = 1.70, 95 % CI: 1.19-2.42). Discontinuing smoking was significantly correlated with a lower risk of depressive symptoms (OR = 0.68, 95 % CI: 0.52-0.90). Subjects with two or more concurrent conditions exhibited a substantially higher risk of depressive symptoms (OR = 3.19, 95 % CI: 3.13-3.25). Investigating the combined influence of sleep duration, lifestyle elements, and concurrent conditions revealed that enhanced physical activity levels significantly decreased risk of depressive symptoms in participants with short sleep duration, adjusting the OR from 3.16 to 0.83 (95 % CI, 0.53-1.30). Among participants with short sleep duration, smoking and alcohol consumption patterns were linked to a decreased risk of depressive symptoms, although these associations lacked statistical significance. Relative to subjects without concurrent conditions, those harboring two or more such conditions faced a significantly heightened risk of depressive symptoms in the context of short sleep duration (OR = 3.00, 95 % CI: 2.24-4.03), a risk not observed in subjects with extended sleep duration. Moderate napping (0.5-1 h) among participants with short sleep duration was found to significantly mitigate risk of depressi","PeriodicalId":14963,"journal":{"name":"Journal of affective disorders","volume":" ","pages":"900-912"},"PeriodicalIF":4.9,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142964952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ethnobotanical survey of traditional knowledge and food practices of traditional leafy vegetables consumed within the Bronkhorstspruit, Gauteng province, South Africa","authors":"Petunia Mashiane ,&nbsp;Kara-lee Prinsloo","doi":"10.1016/j.sajb.2025.05.066","DOIUrl":"10.1016/j.sajb.2025.05.066","url":null,"abstract":"<div><div>The United Nations has created the Sustainable Development Goals (SDGs) intending to eliminate poverty and ensure healthy lives and wellbeing. The current study aims to work towards achieving goal number three, which promotes good health and well-being. Traditional leafy vegetables (TLVs) forms part of most traditional communities and plays a vital role in daily diets. Traditional knowledge (TK) on the use and practices is however being lost and this could impact on the food security and health of communities. TLVs remain underutilised, undervalued and neglected crops. The aim of this study was to investigate the popularity, uses, household cooking techniques and recipes of traditional leafy vegetables locally available in the study area, Bronkhorstspruit. Data was gathered among different cultural groups using questionnaires, in-depth interviews and focus groups. Three hundred (300) female participants from different age and cultural groups were interviewed. The relative frequency of citation (RFC), use value (UV), use report (UR) and cooking techniques of locally consumed traditional leafy vegetables (TLVs) were determined. Results indicated that 75 % of the 20 identified traditional leafy vegetables had an RFC of 80–95 %, suggesting their high popularity in the area. In addition, 20 TLVs per cultural group belonging to 10 families, with species belonging to the Cucurbitaceae family, which was the dominant family utilized, <em>Amaranthus spinosus, Cleome</em> and <em>Cucurbita species</em>, being the most popular, with RFC values between 90 and 95 % per cultural group. <em>Momordica balsamina</em> had the highest use reports (UR) and use value (UV) of 0.242 compared to other TLVs. Traditional recipes differed per cultural group. Most participants indicated boiling as the most preferred household cooking technique for TLVs. The current study highlighted the rich ethnobotanical knowledge and diverse culinary practices surrounding traditional leafy vegetables in Bronkhorstspruit, underscoring their cultural significance, nutritional value, and potential role in promoting sustainable food systems and preserving indigenous knowledge in South Africa.</div></div>","PeriodicalId":21919,"journal":{"name":"South African Journal of Botany","volume":"184 ","pages":"Pages 122-132"},"PeriodicalIF":2.7,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144230202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of the current development status and application prospects of coal reservoir microstructure characterisation technology","authors":"Kai Wang ,&nbsp;Lin Guo ,&nbsp;Chao Xu ,&nbsp;Wenjing Wang ,&nbsp;Tong Yang ,&nbsp;Yongwang Yuan ,&nbsp;Yongbo Shi","doi":"10.1016/j.rser.2025.115939","DOIUrl":"10.1016/j.rser.2025.115939","url":null,"abstract":"<div><div>Coal reservoirs serve as the primary storage space for coalbed methane (CBM) accumulation and represent key potential sites for CO₂ sequestration. A comprehensive understanding of the complex multi-scale pore structure is essential for elucidating gas storage and transport mechanisms across diverse pore types. To systematically map research advancements in coal microstructure characterization, this study analyzed 5074 publications on \"coal pore structure methods\" from the Web of Science Core Collection using bibliometric approaches. The results reveal three distinct developmental phases: the initial germination stage (pre-2000), the slow development stage (2000–2014) and the rapid growth stage (2014-present). Geographical contributions show China as the most active research nation (2174 publications), followed by the United States (432) and Australia (375). Keyword clustering analysis highlights how evolving research demands have driven technological innovation, with \"adsorption,\" \"pore structure,\" and \"permeability\" emerging as core themes. Digital imaging reconstruction technologies have enabled a paradigm shift in coal pore structure analysis, from macroscopic investigations to molecular-scale characterization, while also advancing from qualitative descriptions to quantitative or semi-quantitative evaluations. Future priorities center on developing multiscale coupled models that integrate pore structure features with reservoir properties, a critical step for advancing fluid transport predictions in micro-nano pores under complex geological conditions. Characterization technologies for coal reservoir microstructures will play an increasingly vital role in energy development, coal mine hazard control, and environmental protection. Cross-disciplinary collaboration and technological integration will be key drivers of progress in this field.</div></div>","PeriodicalId":418,"journal":{"name":"Renewable and Sustainable Energy Reviews","volume":"221 ","pages":"Article 115939"},"PeriodicalIF":16.3,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144230242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An integrated grade classification model to evaluate raw milk quality","authors":"Xin Yang ,&nbsp;Yan Wang ,&nbsp;Debao Nie ,&nbsp;Qinggang Zhang ,&nbsp;Wei Zheng ,&nbsp;Baisheng Dai ,&nbsp;Weizheng Shen","doi":"10.1016/j.compag.2025.110565","DOIUrl":"10.1016/j.compag.2025.110565","url":null,"abstract":"<div><div>The quality of raw milk is crucial for both dairy farming and the dairy industry. This study presents an integrated grade classification model to evaluate raw milk quality based on fat content, protein content, and somatic cell count. Near-infrared (NIR) technology was employed to develop a rapid classification model. To address the challenge of modeling the complex nonlinear relationship between raw milk quality grades and spectral variables, a novel hybrid variable selection method based on combining Extreme Gradient Boosting (XGBoost) was proposed in this paper. A total of 617 raw milk samples were collected and divided three quality grades. Firstly, various preprocessing methods were applied to raw milk spectral data including Savitzky-Golay smoothing, standard normal variate (SNV), multiplicative scatter correction, and first derivative. SNV was chosen for noise removal according its performance. Then, XGBoost-based forward feature selection (XGBFFS) and further optimized by genetic algorithm (GA) was used to selection variables. For XGBFFS, variable importance values were computed by XGBoost method and variables were selected by forward feature selection. And then GA was employed to further optimize and reduce variable space. The XGBFFS-GA method was applied to quality evaluation of raw milk and compared to traditional variable selections, including ReliefF, uninformative variable elimination, and competitive adaptive reweighted sampling. Integrated models were built by support Vector Machine (SVM) and XGBoost for different variable selection methods. The results indicated that variable selection methods based on XGBoost effectively reduce variable space and the XGBFFS-GA demonstrated the best performance for quality evaluation of raw milk. Finally, the XGBFFS-GA-SVM model achieved the best results, with prediction set accuracy of 94.84% and F1 score of 94.21%. This study introduces a new idea for variable selection in NIR spectroscopy analysis and a rapid integrated grade classification model for raw milk quality evaluation.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110565"},"PeriodicalIF":7.7,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144230298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}