bluuebunny/crossref_metadata_2025_split huggingface dataset:
Psychiatric comorbidities and concurrent substance use among people who inject drugs: a single-centre hospital-based study
Authors: Kar Hadiya , Gania Abdul Majid , Bandy Altaf , ud din Dar Nizam , Rafiq Farhana 
Published: November 2023

AbstractThe management of people who inject drugs (PWID) is compounded by the presence of psychiatric comorbidities leading to frequent relapses and poor treatment outcomes. Early identification and treatment of psychiatric comorbidities should be included in the management to enhance treatment outcomes. The objective of this study was to estimate the prevalence of psychiatric comorbidities and concurrent substance use among opioid injectors. This hospital-based, cross-sectional study was conducted from March 2021 to August 2022. This study included opioid injectors of all ages and both sexes. The Mini International Neuropsychiatric Interview-7 (MINI-7) and WHO-ASSIST were used to determine psychiatric comorbidities and concurrent substance use, respectively. Both crude and adjusted odds ratios were calculated to assess associations among demographic variables, concurrent substance use and psychiatric comorbidities. Among the 328 opioid injectors, the overall prevalence of psychiatric comorbidities was 88.1%, with the majority (68.6%) having more than one comorbidity. The most common psychiatric comorbidities were panic disorder (41.2%), social anxiety disorder (40.5%), and antisocial personality disorder (39.3%). Concurrent use of alcoholic beverages doubled the risk of ASPD (odds ratio 2.14 (1.24–3.72)). Cocaine (odds ratio 2.36 (1.10–5.03)) and amphetamines (odds ratio 7.68 (2.21–26.65)) increased the risk of OCD. Daily heroin injections were negatively associated (odds ratio 0.18 (0.03–0.94)) with psychotic disorders. Younger age (adjusted odds ratio 0.20 (0.79–0.53)) and never married status (adjusted odds ratio 2.62 (1.06–6.47)) were the only significant variables in the regression analysis. In conclusion, opioid injectors had a higher prevalence of numerous psychiatric comorbidities. The most common comorbidity was anxiety disorders. Concurrent use of tobacco, cannabis, cocaine, inhalants, etc., greatly increased the risk of psychiatric comorbidities.
The effects of psychosocial and behavioral interventions on depressive and anxiety symptoms during the COVID-19 pandemic: a systematic review and meta-analysis
Authors: He Jiali , Lin Jingxia , Sun Wen , Cheung Teris , Cao Yuan , Fu Eugene , Chan Sunny H. W. , Tsang Hector W. H. 
Published: November 2023

AbstractPsychosocial and behavioral interventions have been shown to significantly reduce depressive and anxiety symptoms in different populations. Recent evidence suggests that the mental health of the general population has deteriorated significantly since the start of Coronavirus Disease 2019 (COVID-19) pandemic. We conducted a systematic review and meta-analysis of studies on the therapeutic effects of psychosocial and behavioral interventions on depression and anxiety during the COVID-19 pandemic. We systematically searched six electronic databases between December 2019 and February 2022 including PubMed, PsycINFO, Scopus, Web of Science, CNKI, and Wanfang Data. We included randomized clinical trials of psychosocial and behavioral interventions in individuals with depressive or anxiety symptoms during the COVID-19 outbreak compared to various control conditions. A total of 35 eligible studies with 5457 participants were included. The meta-analysis results showed that psychosocial and behavioral interventions had statistically significant moderate effects on depression [SMD =  − 0.73, 95% CI (− 1.01, − 0.45), I2 = 90%] and large effects on anxiety [SMD =  − 0.90, 95% CI (− 1.19, − 0.60), I2 = 92%], especially in the general population and COVID-19 survivors. Exercise and cognitive behavioral therapy were found to be the most effective treatments with moderate-to-large effect size for depression and anxiety during the outbreak of COVID-19. We also found the internet-based approach could also achieve almost equally significant effects on depression and anxiety compared with face-to-face traditional approach. Our findings suggest that cognitive behavioral therapy and physical exercise intervention are significantly effective for depression and anxiety related to the COVID-19 pandemic regardless of the delivery modes, and gender differences should be taken into consideration for better implementation of interventions in clinical and community practice.
Transformer-based convolutional forgetting knowledge tracking
Authors: Liu Tieyuan , Zhang Meng , Zhu Chuangying , Chang Liang 
Published: November 2023

AbstractKnowledge tracking is to analyze the mastery of students' knowledge through the learning track. This is very important for online education, since it can determine a learner’s current knowledge level by analyzing the learning history and then make recommendations for future learning. In the past, the commonly used model for knowledge tracking is the convolutional neural network, but it has long-term sequence dependencies. With the invention of Transformer, it has excellent performance in long-sequence modeling by virtue of the attention mechanism, and is gradually introduced into the field of knowledge tracking. However, through our research, some knowledge tracking data sets have a large number of continuous and repetitive training, which will cause Transformer model to ignore the potential connections between some knowledge points. To overcome this problem, we introduce a convolutional attention mechanism to help the model perceive contextual information better. In addition, we simulate the forgetting phenomenon of students during the learning process by calculating the forgetting factor, and fuse it with the weight matrix generated by the model to improve the accuracy of the model. As a result, a Transformer-based Convolutional Forgetting Knowledge Tracking (TCFKT) model is presented in this paper. According to the experimental results conducted on the real world ASSITments2012, ASSISTments2017, KDD a, STATIC datasets, the TCFKT model outperforms other knowledge tracking models.
Electrochemical etching strategy for shaping monolithic 3D structures from 4H-SiC wafers
Authors: Hochreiter André , Groß Fabian , Möller Morris-Niklas , Krieger Michael , Weber Heiko B. 
Published: November 2023

AbstractSilicon Carbide (SiC) is an outstanding material, not only for electronic applications, but also for projected functionalities in the realm of spin-based quantum technologies, nano-mechanical resonators and photonics-on-a-chip. For shaping 3D structures out of SiC wafers, predominantly dry-etching techniques are used. SiC is nearly inert with respect to wet etching, occasionally photoelectrochemical etching strategies have been applied. Here, we propose an electrochemical etching strategy that solely relies on defining etchable volumina by implantation of p-dopants. Together with the inertness of the n-doped regions, very sharp etching contrasts can be achieved. We present devices as different as monolithic cantilevers, disk-shaped optical resonators and membranes etched out of a single crystal wafer. The high quality of the resulting surfaces can even be enhanced by thermal treatment, with shape-stable devices up to and even beyond 1550°C. The versatility of our approach paves the way for new functionalities on SiC as high-performance multi-functional wafer platform.
Water filtration by endobenthic sandprawns enhances resilience against eutrophication under experimental global change conditions
Authors: Thomas C. M. , de Cerff C. , Maniel G. A. V. , Oyatoye A. E. , Rocke E. , Marco H. G. , Pillay D. 
Published: November 2023

AbstractIdentifying processes that confer resilience against global change is a scientific challenge but is central to managing ecosystem functionality in future. Detecting resilience-enhancing mechanisms is especially relevant in coastal ecosystems, where multi-stressor interactions can drive degradation over time. Here, we quantify the resilience-conferring potential of endobenthic sandprawns against eutrophication, including under high temperatures. We show using a global change mesocosm experiment that sandprawn presence was associated with declines in phytoplankton biomass, particularly under eutrophic conditions, where sandprawns reduced phytoplankton biomass by approximately 74% and prevented a shift to extreme eutrophy. Eutrophic waters were nanophytoplankton-dominated, but sandprawn presence countered this, resulting in even contributions of pico- and nanophytoplankton. Our findings highlight the potential for sandprawns to increase resilience against eutrophication by limiting phytoplankton blooms, preventing extreme eutrophy and counteracting nanophytoplankton dominance. Incorporating endobenthic crustaceans into resilience-based management practices can assist in arresting future water quality declines in coastal ecosystems.
Radiomics as a non-invasive adjunct to Chest CT in distinguishing benign and malignant lung nodules
Authors: Selvam Minmini , Chandrasekharan Anupama , Sadanandan Abjasree , Anand Vikas Kumar , Murali Arunan , Krishnamurthi Ganapathy 
Published: November 2023

AbstractIn an observational study conducted from 2016 to 2021, we assessed the utility of radiomics in differentiating between benign and malignant lung nodules detected on computed tomography (CT) scans. Patients in whom a final diagnosis regarding the lung nodules was available according to histopathology and/or 2017 Fleischner Society guidelines were included. The radiomics workflow included lesion segmentation, region of interest (ROI) definition, pre-processing, and feature extraction. Employing random forest feature selection, we identified ten important radiomic features for distinguishing between benign and malignant nodules. Among the classifiers tested, the Decision Tree model demonstrated superior performance, achieving 79% accuracy, 75% sensitivity, 85% specificity, 82% precision, and 90% F1 score. The implementation of the XGBoost algorithm further enhanced these results, yielding 89% accuracy, 89% sensitivity, 89% precision, and an F1 score of 89%, alongside a specificity of 85%. Our findings highlight tumor texture as the primary predictor of malignancy, emphasizing the importance of texture-based features in computational oncology. Thus, our study establishes radiomics as a powerful, non-invasive adjunct to CT scans in the differentiation of lung nodules, with significant implications for clinical decision-making, especially for indeterminate nodules, and the enhancement of diagnostic and predictive accuracy in this clinical context.
Construction and application of flow pressure drop model of perforated well considering pressure loss of perforation hole
Authors: Jiang Hongfeng , Wu Muwang , Zheng Yongjian , Zhao Qibin , Gao Yongde 
Published: November 2023

AbstractPerforating well is one of the main production wells in reservoir development. Perforating effect directly affects well production, so the optimization of perforating parameters has attracted wide attention. Because pressure difference serves as the driving force for fluid flowing from formation to wellbore, it is important to understand the composition of production pressure difference in perforating well, which can guide the optimization of perforating parameters and the evaluation of perforating effect. In order to clarify the composition of production pressure difference during the production process of perforated wells, a pressure drop model pressure drop model is established based on fluid mechanics theory, which includes a pressure drop model of formation and a pressure drop model of perforation hole. The pressure drop model of formation is firstly constructed based on the Darcy's law and the equivalent resistance method, and the pressure drop model of perforation hole is built by the fluid tube-flow theory. Secondly, the numerical calculation method is adopted to realize the coupling solution of models, and the accuracy of this model is verified by comparison of the Karakas-Tariq model. Finally, the effects of formation physical properties and perforating parameters on flow pressure drop are discussed. The results show that there is a difference of more than 2 orders of magnitude between the pressure drop generated in perforation hole and flow pressure difference, and pressure drop of perforation hole can be neglected in practical applications. Comparing with medium–high permeability reservoirs, optimizing perforation parameters in low permeability reservoirs has a more significant impact on flow pressure drop. Among perforating parameters, perforation length and perforation density have great influence on flow pressure difference, while perforation diameter and phase angle have relatively little influence. These results have certain guiding significance for optimizing perforating parameters in different permeability reservoirs.
A non-invasive method for concurrent detection of multiple early-stage cancers in women
Authors: Gupta Ankur , Siddiqui Zaved , Sagar Ganga , Rao Kanury V. S. , Saquib Najmuddin 
Published: November 2023

AbstractUntargeted serum metabolomics was combined with machine learning-powered data analytics to develop a test for the concurrent detection of multiple cancers in women. A total of fifteen cancers were tested where the resulting metabolome data was sequentially analysed using two separate algorithms. The first algorithm successfully identified all the cancer-positive samples with an overall accuracy of > 99%. This result was particularly significant given that the samples tested were predominantly from early-stage cancers. Samples identified as cancer-positive were next analysed using a multi-class algorithm, which then enabled accurate discernment of the tissue of origin for the individual samples. Integration of serum metabolomics with appropriate data analytical tools, therefore, provides a powerful screening platform for early-stage cancers.
Altered Gut Microbiota and Short-chain Fatty Acids in Chinese Children with Constipated Autism Spectrum Disorder
Authors: He Jianquan , Gong Xiuhua , Hu Bing , Lin Lin , Lin Xiujuan , Gong Wenxiu , Zhang Bangzhou , Cao Man , Xu Yanzhi , Xia Rongmu , Zheng Guohua , Wu Shuijin , Zhang Yuying 
Published: November 2023

AbstractGastrointestinal symptoms are more prevalent in children with autism spectrum disorder (ASD) than in typically developing (TD) children. Constipation is a significant gastrointestinal comorbidity of ASD, but the associations among constipated autism spectrum disorder (C-ASD), microbiota and short-chain fatty acids (SCFAs) are still debated. We enrolled 80 children, divided into the C-ASD group (n = 40) and the TD group (n = 40). In this study, an integrated 16S rRNA gene sequencing and gas chromatography–mass spectrometry-based metabolomics approach was applied to explore the association of the gut microbiota and SCFAs in C-ASD children in China. The community diversity estimated by the Observe, Chao1, and ACE indices was significantly lower in the C-ASD group than in the TD group. We observed that Ruminococcaceae_UCG_002, Erysipelotrichaceae_UCG_003, Phascolarctobacterium, Megamonas, Ruminiclostridium_5, Parabacteroides, Prevotella_2, Fusobacterium, and Prevotella_9 were enriched in the C-ASD group, and Anaerostipes, Lactobacillus, Ruminococcus_gnavus_group, Lachnospiraceae_NK4A136_group, Ralstonia, Eubacterium_eligens_group, and Ruminococcus_1 were enriched in the TD group. The propionate levels, which were higher in the C-ASD group, were negatively correlated with the abundance of Lactobacillus taxa, but were positively correlated with the severity of ASD symptoms. The random forest model, based on the 16 representative discriminant genera, achieved a high accuracy (AUC = 0.924). In conclusion, we found that C-ASD is related to altered gut microbiota and SCFAs, especially decreased abundance of Lactobacillus and excessive propionate in faeces, which provide new clues to understand C-ASD and biomarkers for the diagnosis and potential strategies for treatment of the disorder. This study was registered in the Chinese Clinical Trial Registry (www.chictr.org.cn; trial registration number ChiCTR2100052106; date of registration: October 17, 2021).
Genome mining conformance to metabolite profile of Bacillus strains to control potato pathogens
Authors: Lagzian Arezoo , Riseh Roohallah Saberi , Sarikhan Sajjad , Ghorbani Abozar , Khodaygan Pejman , Borriss Rainer , Guzzi Pietro Hiram , Veltri Pierangelo 
Published: November 2023

AbstractBiocontrol agents are safe and effective methods for controlling plant disease pathogens, such as Fusarium solani, which causes dry wilt, and Pectobacterium spp., responsible for potato soft rot disease. Discovering agents that can effectively control both fungal and bacterial pathogens in potatoes has always presented a challenge. Biological controls were investigated using 500 bacterial strains isolated from rhizospheric microbial communities, along with two promising biocontrol strains: Pseudomonas (T17-4 and VUPf5). Bacillus velezensis (Q12 and US1) and Pseudomonas chlororaphis VUPf5 exhibited the highest inhibition of fungal growth and pathogenicity in both laboratory (48%, 48%, 38%) and greenhouse (100%, 85%, 90%) settings. Q12 demonstrated better control against bacterial pathogens in vivo (approximately 50%). Whole-genome sequencing of Q12 and US1 revealed a genome size of approximately 4.1 Mb. Q12 had 4413 gene IDs and 4300 coding sequences, while US1 had 4369 gene IDs and 4255 coding sequences. Q12 exhibited a higher number of genes classified under functional subcategories related to stress response, cell wall, capsule, levansucrase synthesis, and polysaccharide metabolism. Both Q12 and US1 contained eleven secondary metabolite gene clusters as identified by the antiSMASH and RAST servers. Notably, Q12 possessed the antibacterial locillomycin and iturin A gene clusters, which were absent in US1. This genetic information suggests that Q12 may have a more pronounced control over bacterial pathogens compared to US1. Metabolic profiling of the superior strains, as determined by LC/MS/MS, validated our genetic findings. The investigated strains produced compounds such as iturin A, bacillomycin D, surfactin, fengycin, phenazine derivatives, etc. These compounds reduced spore production and caused deformation of the hyphae in F. solani. In contrast, B. velezensis UR1, which lacked the production of surfactin, fengycin, and iturin, did not affect these structures and failed to inhibit the growth of any pathogens. Our findings suggest that locillomycin and iturin A may contribute to the enhanced control of bacterial pectolytic rot by Q12.
Relay learning: a physically secure framework for clinical multi-site deep learning
Authors: Bo Zi-Hao , Guo Yuchen , Lyu Jinhao , Liang Hengrui , He Jianxing , Deng Shijie , Xu Feng , Lou Xin , Dai Qionghai 
Published: November 2023

AbstractBig data serves as the cornerstone for constructing real-world deep learning systems across various domains. In medicine and healthcare, a single clinical site lacks sufficient data, thus necessitating the involvement of multiple sites. Unfortunately, concerns regarding data security and privacy hinder the sharing and reuse of data across sites. Existing approaches to multi-site clinical learning heavily depend on the security of the network firewall and system implementation. To address this issue, we propose Relay Learning, a secure deep-learning framework that physically isolates clinical data from external intruders while still leveraging the benefits of multi-site big data. We demonstrate the efficacy of Relay Learning in three medical tasks of different diseases and anatomical structures, including structure segmentation of retina fundus, mediastinum tumors diagnosis, and brain midline localization. We evaluate Relay Learning by comparing its performance to alternative solutions through multi-site validation and external validation. Incorporating a total of 41,038 medical images from 21 medical hosts, including 7 external hosts, with non-uniform distributions, we observe significant performance improvements with Relay Learning across all three tasks. Specifically, it achieves an average performance increase of 44.4%, 24.2%, and 36.7% for retinal fundus segmentation, mediastinum tumor diagnosis, and brain midline localization, respectively. Remarkably, Relay Learning even outperforms central learning on external test sets. In the meanwhile, Relay Learning keeps data sovereignty locally without cross-site network connections. We anticipate that Relay Learning will revolutionize clinical multi-site collaboration and reshape the landscape of healthcare in the future.
Non-isocyanate polyurethanes synthesized from terpenes using thiourea organocatalysis and thiol-ene-chemistry
Authors: Scheelje Frieda Clara M. , Meier Michael A. R. 
Published: November 2023

AbstractThe depletion of fossil resources as well as environmental concerns contribute to an increasing focus on finding more sustainable approaches for the synthesis of polymeric materials. In this work, a synthesis route towards non-isocyanate polyurethanes (NIPUs) using renewable starting materials is presented. Based on the terpenes limonene and carvone as renewable resources, five-membered cyclic carbonates are synthesized and ring-opened with allylamine, using thiourea compounds as benign and efficient organocatalysts. Thus, five renewable AA monomers are obtained, bearing one or two urethane units. Taking advantage of the terminal double bonds of these AA monomers, step-growth thiol-ene polymerization is performed using different dithiols, to yield NIPUs with molecular weights of above 10 kDa under mild conditions. Variation of the dithiol and amine leads to polymers with different properties, with Mn of up to 31 kDa and Tg’s ranging from 1 to 29 °C.
A new risk assessment approach for design of hybrid microgrids considering stability issues
Authors: Azizi Ali , Blaabjerg Frede , Peyghami Saeed 
Published: November 2023

AbstractStability issues can significantly increase the risk of hybrid microgrids (HMGs), particularly during island mode operation. The dynamic performance of the system can induce constraints and stability margins that may elevate the loss of load probability. This paper presents a new stability‐oriented risk assessment model that bridges the conventional reliability models, stability, and system risk. The proposed model ensures the risk of the system by considering the redesign or reconfiguration of HMGs to address stability issues. First, the interlinking converters (ICs) DC‐link voltage stability is analysed to determine the acceptable power flow margins in rectifying and inversion mode. Next, the new general risk assessment model is introduced. The results show that the stability margin significantly increases the risk of the HMG, particularly when considering the aging of converters. The study also examines the impact of various load characteristics and ICs with different numbers but the same total size. In some cases, the risk is acceptable for the desired loads, or it can be reduced to an admissible level by reconfiguring the ICs. Finally, the paper demonstrates the effectiveness of the proposed model in the optimal design of HMGs, aiming to guarantee the system's risk.