bluuebunny/crossref_metadata_2025 huggingface dataset:
The effects of vulnerability-schema structure on suicidal ideation are enhanced by traumatic stress both directly and through depression
Authors: Luo Mengjie , Yu Yang , Zhao Mengxue , Feng Zhengzhi , Zhang Jingxuan
Published: August 2024

Objective
This study aimed to discover the complicated effects of impulsivity and traumatic stress on suicidal ideation mediated by negative cognitive processing bias and depression, through which we may detect the suicide signs as early as possible, to advance the preventive measures.
Methods
We recruited 1,798 undergraduates as participants. Self-rating Idea of Suicide Scale (SIOSS), Barratt’s Impulsiveness Scale (BIS), Negative Cognitive Processing Bias Questionnaire (NCPBQ), and PTSD Checklist Civilian Version (PCL-C) were applied as measuring tools. A moderated mediation latent path model was conducted, among which negative cognitive processing bias and depression were mediation variables and traumatic stress was moderation variable.
Results
Impulsivity was considered separately as three dimensions, namely, non-planning, motor and cognitive impulsivities. We found that all the models fitted well (RMSEA < 0.08, CFI > 0.90, TLI > 0.90, and SRMR < 0.08), in which traumatic stress moderated the direct effects of depression on suicidal ideation (p < 0.01, p < 0.001). For the model of non-planning and cognitive impulsivities, traumatic stress also moderated the effects of impulsivities on depression (p < 0.01) and of negative cognitive processing bias on suicidal ideation (p < 0.05). The simple indirect paths of “impulsivity→negative cognitive processing bias→suicidal ideation” and “impulsivity→depression→suicidal ideation” and the chain indirect path of “impulsivity→negative cognitive processing bias→depression→suicidal ideation” were all significant (p < 0.001) on different (i.e., high, medium, and low) levels of traumatic stress. Traumatic stress positively moderated the indirect effects (p < 0.001, p < 0.01, p < 0.05) except for the path of “motor impulsivity→negative cognitive processing bias→suicidal ideation”.
Conclusions
The present study confirmed that negative cognitive processing bias and impulsivity were closely correlated and worked together as trait-like vulnerability-schema structures in the development of suicidal ideation. For suicide prevention, we could monitor the dynamics of the vulnerability-schema structure and intervene in negative cognitive processing bias, which was easier and more practical to change compared with impulsivity. This might advance the time course of finding the suicide signs.
Deep Learning Model for Intrusion Detection Based on Weighted Cross Entropy Loss Function and OBiLSTM
Authors: Ding Yong , Xu Shuai , Peng Xinyong , Yang Changsong , Liang Hai , Li Zhenyu
Published: August 2024

Industrial Internet of Things has become an important tool to promote the transformation and upgrading of the manufacturing industry and enhance it's competitiveness. With the popularization of IIOT, the cyber security threats faced by industrial systems are also increasing, and intrusion detection techniques have emerged. However, due to the complex structure of IIOT and the variety of network traffic data generated, the Intrusion detection that has been studied accuracy is not high. To improve the accuracy of intrusion detection, we introduce an intrusion detection method for IIoT utilizing an Optimized Bidirectional Long Short-Term Memory network. We first enhance BiLSTM by substituting the inverse Long Short-term Memory network with a Gated Recurrent Unit, followed by designing a novel neural network, OBiLSTM. We also designed a weighting method for the Cross-entropy Loss function, which makes the detection accuracy further improved. Finally, We implemented the proposed intrusion detection prototype and tested it based on two datasets, UNSW-NB15 and NSL-KDD. The experimental outcomes indicate that our proposed prototype surpasses previous methods in terms of accuracy, recall, precision, and F1_score.
OptiFeat: Enhancing Feature Selection, A Hybrid Approach Combining Subject Matter Expertise and Recursive Feature Elimination Method
Authors: Vijayakumar G. , Bharathi R. K.
Published: August 2024

Optimizing the performance of Java Virtual Machines (JVMs)[1] is crucial for achieving efficient execution of Java applications. Feature selection plays a pivotal role in identifying the most relevant parameters for fine-tuning JVMs, thereby enhancing their overall efficiency. This paper presents a novel hybrid approach that integrates both subject matter expertise and Recursive Feature Elimination (RFE)[2] model to refine feature selection for JVM fine-tuning using machine learning techniques. Traditional feature selection methods often lack the ability to incorporate domain-specific knowledge, resulting in suboptimal selections[3]. In contrast, the hybrid approach leverages the expertise of JVM administrators or developers to guide the feature selection process. By integrating domain knowledge into the feature selection pipeline, ensure the inclusion of crucial JVM parameters that may not be captured by automated techniques alone. Furthermore, employed the RFE model, a powerful recursive feature elimination algorithm, to iteratively identify and eliminate irrelevant features from the initial feature set. This iterative process enhances the efficiency of feature selection by systematically pruning less influential parameters, thereby improving the overall performance of the JVM. To validate the effectiveness of the hybrid approach, conducted experiments using real-world JVM datasets and compare the performance of the method against existing feature selection techniques. The results demonstrate that the approach not only achieves superior performance in terms of JVM fine-tuning but also provides insights into the significance of domain expertise in optimizing JVM performance[4]. It contributes to the field of JVM optimization by proposing a novel hybrid approach that combines subject matter expertise with machine learning-based feature selection techniques. By leveraging both domain knowledge and automated algorithms, the approach offers a comprehensive solution for enhancing feature selection in JVM fine-tuning, ultimately leading to improved performance and efficiency in Java application execution.
DA-BAG: A Multi-Model Fusion Text Classification Method Combining BERT and GCN Using Self-Domain Adversarial Training
Authors: Shao Dangguo , Su Shun , Ma Lei , Yi Sanli , Lai Hua
Published: August 2024

Both pre-training-based methods and GNN-based methods are considered the most advanced techniques in natural language processing tasks, particularly in text classification tasks. However, traditional graph learning methods focus solely on structured information from text to graph, overlooking the hidden local information within the syntactic structure of the text. Conversely, large-scale pre-training model methods tend to overlook global semantic information, potentially introducing new noise and training biases when combined. To tackle these challenges, we introduce DA-BAG, a novel approach that co-trains BERT and graph convolution models. Utilizing a self-domain adversarial training method on a single dataset, DA-BAG extracts multi-domain distribution features across multiple models, enabling self-adversarial domain adaptation training without the need for additional data, thereby enhancing model generalization and robustness. Furthermore, by incorporating an attention mechanism in multiple models, DA-BAG effectively combines the structural semantics of the graph with the token-level semantics of the pre-trained model, leveraging hidden information within the text's syntactic structure. Additionally, a sequential multi-layer graph convolutional neural(GCN) connection structure based on a residual pre-activation variant is employed to stabilize the feature distribution of graph data and adjust the graph data structure accordingly. Extensive evaluations on 5 datasets(20NG, R8, R52, Ohsumed, MR) demonstrate that DA-BAG achieves state-of-the-art performance across a diverse range of datasets.
Magnetic iron-oxide nanoparticles in the brain connected to alcohol-associated liver disease
Authors: Kaub Leon , Milz Stefan , Barapatre Nirav , Büttner Andreas , Michalke Bernhard , Schmitz Christoph , Gilder Stuart A.
Published: August 2024

Magnetic iron-oxide nanoparticles in the form of magnetite (Fe3O4) are present in the human brain. They have been hypothesized to biomineralize in situ, as a result of dysfunctional iron homeostasis related to Alzheimer’s disease, or to enter the brain as airborne pollution particles. Regardless of their origin, magnetic iron-oxides pose a potential hazard to human health due to their high redox activity and surface charge. Here we report measurements on four post-mortem human brainstems, with one brainstem showing approximately 100 times higher magnetite concentrations than the other cases. This brainstem came from a subject with alcohol-associated liver disease (ALD) that manifested in liver cirrhosis and massive hepatic iron overload. Laser ablation – inductively coupled plasma – mass spectrometry showed the highest levels of trace metals (iron, copper and manganese) in the ALD brainstem. It is well established that a dysfunctional liver can result in the accumulation of trace metals in the brain. Our data indicate a similar pathway for magnetite particles, yet liver pathology has not been linked to magnetite occurrence in the brain so far. It may prove to be a crucial factor in understanding the high variation of magnetite concentrations found in human brains.
Coronal offset of C7 is associated with uneven joint degeneration between right and left hips after spinal fusion
Authors: kawai toshiyuki , Shimizu Takayoshi , Okuzu Yaichiro , Kuroda Yutaka , Morita Yugo , Otsuki Bungo , Goto Koji , Fujibayashi Shunsuke , Matsuda Shuichi
Published: August 2024

Introduction:
The aim of the study was to investigate the effects of coronal offset of C7 after spinal fusion on the discrepancy in joint space narrowing between the right and left hips.
Methods: We retrospectively reviewed data from patients who underwent lumbar spinal fusion from 2011 to 2018 at our institute. The rate of hip joint space narrowing after spinal fusion was measured in 190 patients (380 hips). We assessed the effects of the distance between the C7 plumb line and the central sacral vertical line (C7-CSVL) on the discrepancy in joint space narrowing between the right and left hips. Using multivariate regression models, we controlled for the effects of age, sex, body mass index, fusion length, and several spinopelvic alignment parameters (sacral slope, pelvic incidence, lumbar lordosis, pelvic incidence minus lumbar lordosis, and sagittal vertical axis) on the joint space narrowing rate.
Results: Multivariate regression showed that the C7-CSVL was associated with the discrepancy of the joint space narrowing rate between right and left, indicating that when C7 deviated to right, the joint space narrowing was larger in the right than left hip (standardized coefficient, 0.203; p=0.0005).
Conclusion: A larger C7-CSVL was associated with a larger right–left discrepancy in hip joint space narrowing after spinal fusion. These findings indicate that spinal coronal balance affects the distribution of joint degeneration in the right and left hips. Surgeons should understand the potential risk of uneven progression of degeneration between the right and left hips in patients with large coronal offset.
Level of Evidence: Level III
Continuous nuclear envelope surveillance is required for DNA double-strand break repair.
Authors: Machín Félix , Medina-Suárez Sara
Published: August 2024

Precise double-strand break (DSB) repair is paramount for genome stability. Homologous recombination (HR) is preferred to repair DSBs when a nearby sister chromatid ensures an error-free template. In Saccharomyces cerevisiae, this preference extends into anaphase and telophase (late mitosis; late-M), despite sister chromatids having been pulled apart. Previously, we identified the nuclear envelope (NE) protein Msc1 as important for late-M DSB repair. Here, we report that Msc1 faces the NE lumen, and its depletion leads to DSB-independent over-compartmentalization of the nucleus, which hampers approximation of sister loci after DSB. Depletion of Msc1 also leads to nuclear pore complex mislocation, a phenotype shared by the highly conserved NE healing complex ESCRT-III. Critically, we show that both Msc1 and ESCRT-III also ensure DSB repair in G2/M, and that there is synergism between Msc1 and the ESCRT-III subunit Snf7. These findings highlight the essential role of NE health in DSB repair.
Economic impact of TAVI pathway optimisation: the experience of the University Hospital of Parma.
Authors: Fanelli Simone , Lanza Gianluca , Pratici Lorenzo , Benatti Giorgio , Vignali Luigi
Published: August 2024

Background: Transcatheter Aortic Valve Implantation (TAVI) has emerged as a standard of care for patients suffering from symptomatic severe aortic stenosis. However, organisational analyses and investigations of this intervention’s economic impact are lacking. This study aims to assess the potential impact of implementing a process designed to optimise the TAVI pathway regarding organisational, human, material, and economic resources.
Methods: The research is conducted through a case study. The methodology consisted of three stages: 1. mapping of the current TAVI pathway; 2. identification of the organisational changes necessary to optimise it and implementation of the identified action with a multidisciplinary teamwork; 3. simulations of the organisational and economic impact of the optimisation process. Data related to costs, revenues, and activities were provided by the hospital’s Management Control office. The data analysed refer to a 12-month period. The TAVI pathway optimization presented in this analysis is aligned with the best practices described in the BENCHMARK study.
Results: The analysis of the current TAVI pathway in the studied hospital highlighted several critical points during the three phases of the course (pre-procedure, peri-procedure, and post-procedure). The working groups identified five areas for TAVI pathway improvement: patient and family education, coronary risk stratification, conduction disorders management, fast-track discharge eligibility, nurse-led rapid mobilisation and early discharge.
The organisational solutions highlighted by the working groups outline a new TAVI pathway capable of generating a significant impact not only from an organisational point of view, but also from an economic point of view. We estimated that in our cohort TAVI optimisation would have saved approximately 112 ICCU bed-days and have led to an average cost reduction of about €3,900 per patient.
Conclusions: This study showed a process of optimisation of the TAVI pathway highlighting the positive impacts for patients, caregivers, healthcare workers, and the hospital. A Clinical Valve Coordinator may help to manage the procedural programme for individual patients while maintaining seamless communication with the Heart Team throughout the patient care journey, making the process even more streamlined. The case study analysed can be useful to all hospitals wishing to undertake processes aimed at improving the TAVI pathway.
Quantum Deep Learning for Automatic Chronic Kidney Disease Identification and Classification with CT images
Authors: Hussain Sajid , Songhua Xu , Aslam Muhammad , Waqas Muhammad , Hussain Shahid
Published: August 2024

Chronic Kidney Disease (CKD) entails a progressive decline in renal function, often originating from comorbidities such as diabetes and hypertension. Early detection is crucial for managing progression and associated complications. Meanwhile, computed tomography (CT) serves as a vital tool for identifying kidney conditions. However, the accuracy and efficiency of radiological diagnoses are hampered by image quality, especially in early-stage disease. Despite extensive research on artificial intelligence (AI) based diagnostic models, high efficiency and optimal accuracy remains challenging. This study introduces a deep learning (DL) clinical diagnostic system aimed at enhancing the automatic identification and classification of CKD. Through an exploration of standard, advanced, and quantum DL algorithms in the CKD domain, it was selecting a hybrid quantum deep convolutional neural network (QDCNN) as our proposed model due to its high-quality performance. The model was optimized and refined through training with augmented and denoised datasets. This refined iteration yields impressive test performances in terms of accuracy: 99.98%, recall: 99.89%, precision: 99.84%, F1 score: 99.86%, specificity: 99.84%, Micro AUC: 99.99%, and testing time of 0.0641 seconds per image. Positioned to outperform existing methods, our proposed system demonstrates the potential to accurately identify kidney conditions at every stage, providing invaluable assistance to medical professionals and equipping them with an advanced level of accuracy, promptness, and reliability in diagnoses that is unparalleled in its excellence.
Explanation beyond Individual Features: Instance-wise Feature Grouping for EHR Predictive Analytics
Authors: Cheong Chin Wang , Yin Kejing , Cheung William K. , Tsang Ivor
Published: August 2024

Identifying relevant input features which contribute to the output of a clinical prediction model can enhance the model explainability. To allow the explainability to be more personalized, instance-wise feature selection (IWFS) methods can be adopted where features are selected specifically for each input instance. Existing IWFS methods often grapple with feature selection instability, and thus precarious interpretation. As relevant features among the instances in a dataset do overlap, feature grouping tricks have been proposed to regularize the selection, but often at the expense of sacrificing the downstream prediction accuracy. To this end, we propose a novel instance-wise feature grouping method called FlexGPC to achieve robust and stable selection by learning i) flexible representation for feature groups, and ii) flexible combination of feature groups implemented using neural networks. To evaluate the effectiveness of FlexGPC, we explore various feature group combination schemes and conduct extensive experiments for performance comparison using real-world electronic health records (EHR) data. Our experimental results show that FlexGPC outperforms all the SOTA baselines in terms of accuracy and feature selection stability for both downstream mortality and next-admission diagnosis prediction tasks. We also illustrate that computational phenotyping can be achieved at the same time, with the identified feature groups being the potential phenotypes.
Short Pulse Grid and Subthreshold Micropulse Laser (the Sandwich Grid) Plus Intravitreal Ranibizumab for the Treatment of Diabetic Macular Edema
Authors: Peroni Renato , Cardillo José Augusto , Memória Rafael , Pinto Tomás de Oliveira Castro Teixeira , Albieri Lucélia , Scott Ingrid U. , Jorge Rodrigo
Published: August 2024

Objective: To investigate the effects of two laser treatment procedures combined, short pulse grid laser (SP) and subthreshold micropulse laser (MP) (the sandwich grid [SWG] technique), plus intravitreal ranibizumab (IVR) on central subfield thickness (CSFT), best-corrected visual acuity (BCVA) and macular sensitivity in patients with diabetic macular edema (DME).
Methods: Forty-five eyes (of 33 patients) with center-involving DME were treated with the SWG laser technique plus IVR and followed for 12 months. Laser treatment was performed at baseline: SP laser spots were placed in a grid pattern the macular area (500 μm from the fovea) according to the extension of DME; subsequently, MP laser was delivered up to the edge of the fovea. MP laser re-treatment sessions could be performed every 3 months if DME was present and CSFT was ≥ 300 μm on SD-OCT. IVR injection was performed at baseline and repeated monthly if CSFT > 300μm. Preoperatively and monthly, ophthalmological examination was performed including measurements of BCVA, CSFT, and macular sensitivity.
Results: One-year follow-up data is available for 37 eyes of 27 patients. Mean ± SE CSFT (µm) was 509.36 ± 25.14 and 325.76 ± 15.34 at baseline and 12 months, respectively. A significant reduction in mean CSFT was observed at all study visits compared to baseline (p<0.001). Mean ± SE BCVA (logMAR) was 0.62 ± 0.04 and 0.45 ± 0.04 at baseline and 12 months, respectively. A significant improvement in mean BCVA was observed at all study visits compared to baseline (p<0.001). Mean ± SE macular sensitivity (dB) was 17.85 ± 0.80 and improved to 19.05 ± 0.59 after one year of follow-up (p=0.006). The mean number of IVR injections was 8.29 ± 0.63. The mean number of MP laser procedures including the initial SWG laser session was 3.67 ± 0.22. No ocular or systemic adverse effects were observed.
Conclusion: The SWG laser technique plus IVR was associated with significant improvement in macular edema, BCVA, and macular sensitivity in patients with center-involving DME.
Discrete Element Modeling of the Drag Force on a Rigid Body Swimming in a Granular Media
Authors: Guo Hong , Fu Jiangtao , Jiang Hong , Xu Qian , Tao Mingjiang
Published: August 2024

Owing to the difficulties in the accurate calculation of the shear zone area, determining drag force (FD)applied on a rigid body swimming in a granular bed is more complicated compared with that on the semi-immersed flat plate. In this study, we simplified the shear zone, made three assumptions to describe the “wedge,” and introduced the inertial number in the dynamic friction coefficient. The results showed that the boundary conditions have little effect on the overall FD and it increases with the increase in the swimming velocity and immersion depth and decreases as the particle diameter increases. Furthermore, we modified the Coulomb passive-earth pressure model, which is based on various functions, including swimming velocity and immersion depth, to effectively predict FD. Thus, the modified model could successfully predict the respective FD on rigid bodies at different swimming velocities and immersion depths.
Peripheral membrane protein endophilin B1 probes, perturbs and permeabilizes lipid bilayers
Authors: Sundborger-Lunna Anna , Thorlacius Arni , Rulev Maksim , Sundberg Oscar
Published: August 2024

Bin/Amphiphysin/Rvs (BAR) domain containing proteins are cytosolic, peripheral membrane proteins that regulate the curvature of membranes in eukaryotic cells. BAR protein endophilin B1 plays a key role in multiple cellular processes critical for oncogenesis, including autophagy and apoptosis. Amphipathic regions in endophilin B1 drive membrane association and tubulation through membrane scaffolding. Our understanding of exactly how BAR proteins like endophilin B1 promote highly diverse intracellular membrane remodeling events in the cell is severely limited due to lack of high-resolution structural information. Here we present the highest resolution cryo-EM structure of a BAR protein to date and the first structures of a BAR protein bound to nanodiscs. Using neural networks, we can effectively sort particle species of different stoichiometries, revealing the tremendous flexibility of post-membrane binding, pre-polymer BAR dimer organization and membrane deformation. We also show that endophilin B1 efficiently permeabilizes negatively charged liposomes that contain mitochondria-specific lipid cardiolipin and propose a new model for Bax-mediated cell death.
Population Genetic Structure of Phaedranassa Cinerea Ravenna (Amaryllidaceae) and Conservation Implications
Authors: Buenaño María Belén , Ulloa Carmen Ulloa , Francisco-Ortega Javier , Meerow Alan W. , Oleas Nora H.
Published: August 2024

Background
Andean orogeny has shaped the endemism of plant species in montane forests, creating a mosaic of habitats in small and isolated areas. Understanding these endemic species' genetic diversity patterns is crucial for their conservation. Phaedranassa cinerea (Amaryllidaceae), a species restricted to the western Andes of Ecuador, is listed as “vulnerable” according to the IUCN criteria. This study aimed to analyze the genetic structure of P. cinerea to propose conservation measures.
Results
Using 13 microsatellites and a Bayesian approach, we analyzed the genetic differentiation of P. cinerea, evidence of bottlenecks, and possible diversification scenarios. Our results indicate that the genetic diversity of P. cinerea is greater than that of congeneric species. The Bayesian analysis identified two genetic groups, with no evidence of isolation by distance. Populations in the northwest of the Ecuadorean Andes have less allele richness compared to those in the southwest. Additionally, the species exhibits excess homozygosity and evidence of bottlenecks. Our Bayesian analysis suggests that the differentiation between populations was not older than 5000 years and was as recent as 600 years ago for some of the populations. Based on the geographic distribution of the known populations, the species should be listed as endangered instead of vulnerable to extinction.
Conclusions
The findings suggest that conservation efforts should focus on securing genetic exchange between populations to preserve the genetic diversity of P. cinerea.
A Load Forecasting Method of New Power System Based on Personalized Federated Learning
Authors: Shen Yang , Li Zewen , Deng Fangming , Gao Bo
Published: August 2024

The emerging distributed generation technology in new power systems encounters the challenges of unstable efficiency and high accuracy of prediction models. The generalization ability of prediction models is hindered by variations in users’ behavioral characteristics. Furthermore, inability to share power data across regions poses substantial impediments to generation arrangement and power dispatch. This paper proposes a load forecasting technology based on federated learning (FL), which can avoid uploading or sharing the users’ data to protect data privacy. A multi-task module was added to traditional FL to improve user accuracy (UA) rather than global model accuracy, where the client trains a separate personalized model by keeping the local Layer-Normalization (LN) private. Moreover, in order to fast model convergence, the local LSTM prediction algorithm was added with the Grey Wolf optimization (GWO) algorithm and the attention mechanism. The experimental results show that the overall model training time of the improved LSTM algorithm is shortened by 26%. The Mean absolute percentage error (MAPE) of the proposed multi-task FL is 9.79% lower than traditional FL, and the MAPE of clients with small data volume and large feature deviation is reduced by 18.07% at most.
Research on Optimal Selection of Runoff Prediction Models Based on Coupled Machine Learning Methods
Authors: Wei Xing , CHEN Mengen , ZHOU Yulin , ZOU Jianhua , Ran Libo , SHI Ruibo
Published: August 2024

Runoff fluctuations under the influence of climate change and human activities present a significant challenge and valuable application in constructing high-accuracy runoff prediction models. This study aims to address this challenge by taking the Wanzhou station in the Three Gorges Reservoir area as a case study to optimize various prediction models. The study first selects artificial neural network (ANN) and support vector machine (SVM) as the base models. Then, it evaluates and selects from three time-series decomposition methods: Time-Varying Filter-based Empirical Mode Decomposition (TVF-EMD), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and Variational Mode Decomposition (VMD). Subsequently, these decomposition methods are coupled with optimization algorithms, including Whale Optimization Algorithm (WOA), Grasshopper Optimization Algorithm (GOA), and Sparrow Search Algorithm (SSA), to construct various hybrid prediction models. The results indicate that: (1) Among the single prediction models, the Long Short-Term Memory (LSTM) model outperforms the Backpropagation Neural Network (BP) and SVM in terms of prediction accuracy; (2) The hybrid models show superior accuracy compared to the individual models, with the VMD-LSTM model outperforming the CEEMDAN-LSTM and TVF-EMD-LSTM models; (3) Among the coupled machine learning prediction models, the VMD-SSA-LSTM model achieves the highest accuracy. Employing a "decomposition-reconstruction" strategy combined with robust optimization algorithms enhances the performance of machine learning prediction models, thereby significantly improving the runoff prediction capabilities in watershed hydrological models.
Preparation of copolymers based on aniline and 2[2-chloro-1-methylbut-2-en-1-yl]aniline and their application for the removal of methyl orange from aqueous solutions
Authors: Usmanova Gulsum S. , Latypova Lyaysan R. , Yusupova Alfia R. , Mustafin Akhat G.
Published: August 2024

In this work, new copolymers P1-P9 were synthesized by oxidative copolymerization of aniline and 2-[2-chloro-1-methylbut-2-en-1-yl]aniline in HCl solution. The resulting copolymers were characterized with nitrogen adsorption-desorption isotherms, particle size. The equilibrium, kinetics, isotherms and thermodynamics of adsorption of methyl orange azo dye from aqueous solutions on P1-P9 copolymers have been studied. The influence of physicochemical variables (dosage, contact time, pH, dye concentration, temperature, ionic strength of the solution) on the specific adsorption of dye was determined. It is shown that P1-P4 copolymers are able to remove dye from aqueous solutions in a wide range of concentrations with efficiency up to 99.27%. The adsorption of methyl orange follows the Langmuir model (correlation coefficient is 0.9852-0.9990), which corresponds to monolayer adsorption on a homogeneous surface. The kinetics of dye adsorption is described by a pseudo-second-order equation, which is characteristic of chemisorption. Thermodynamic parameters of adsorption have been determined, allowing us to conclude that dye adsorption is a spontaneous endothermic process. It is shown that P1-P4 exhibit good adsorption capacity up to 110 mg/g. These copolymers retain high efficiency in five repeated adsorption-desorption cycles. The developed materials are considered to be suitable as cheap regenerated sorbents for anionic pollutants in wastewater.
Novel Insights into the Relationship Between Glucose-Lowering Drugs and Nonalcoholic Fatty Liver Disease and liver function: a Mendelian Randomization Study
Authors: Lei Gang , Dai Chibing
Published: August 2024

Objectives
The potential risk of nonalcoholic fatty liver disease (NAFLD) and liver toxicity attributed to glucose-lowering medications is uncertain. The objective of this study was to explore the causal relationship between these factors through the implementation of a Mendelian randomization (MR) analysis.
Methods
Two-sample MR, summary-data-based MR (SMR), and colocalization analysis were utilized to investigate the association between ten drug reduce glucose targets (PPARG, DPP4, GLP1R, INSR, SLC5A2, ABCC8, KCNJ11, ETFDH, GPD2, and PRKAB1) to reduce NAFLD and liver function tests (LFTs) levels, including aspartate aminotransferase (AST), gamma glutamyl transferase (GGT), alkaline phosphatase (ALP), alanine aminotransferase (ALT), and bilirubin.
Results
DPP4 is closely associated with GGT and ALT. PPARG is significantly associated with NAFLD and correlated with various liver enzymes GGT, AST, ALT, ALP, total bilirubin, and direct bilirubin. PRKAB1 is linked to total and direct bilirubin levels, while SLC5A2 is associated with total and direct bilirubin levels, ALP levels, and NAFLD risk. Limited evidence suggests that genetic variants in PRKAB1, GLP1R, INSR, GPD2, DPP4, and ABCC8/KCNJ11 are correlated with GGT, ALT, bilirubin, and NAFLD levels. Additional validation through SMR and colocalization analysis further confirmed the causal effects of these findings.
Conclusions
Specific glucose-lowering medications have been associated with an elevated risk of NAFLD and abnormal LFTs results, potentially offering innovative strategies for the management of NAFLD and LFTs abnormalities.
Ultrafast Polymerase Chain Reaction on MEMS Microheater
Authors: Cheng Xing , Liu Hongjun , Liu Yu , Liu Rongyue , Chen Rifei , Li Weihao , Chen Zijun , KIM Ji Tae , Chen Zhongren
Published: August 2024

Ultrafast polymerase chain reaction (PCR) is crucial for the rapid detection of pathogens, particularly in medical emergencies and public health scenarios. Conventional PCR systems, however, require extended processing times due to the inherent mass transfer rates of ~ 10 µL scale liquids. This study aims to achieve ultrafast nucleic acid amplification using a MEMS microheater to significantly reduce reaction volumes from a typical 10 µL PCR system to 3 nL, resulting in a total duration of 304 seconds for 38 thermal cycling. Temperature mapping and calibrations were conducted using infrared microscopy, and COMSOL simulations were employed to analyze thermal behavior and fluid dynamics within the droplets. The droplets were heated at a rate of 254°C/s and cooled at a rate of 122°C/s through natural thermal balance. The calibrated microheater exhibited high-temperature stability with a variation of ± 0.1°C, and efficient PCR amplification of HBV DNA and Coronavirus RNA samples were demonstrated, with Ct values significantly lower than those obtained using commercial equipment. As well, successful reverse transcription and PCR amplification of RNA samples were achieved. However, due to the limitations of the commercial reagents under such a short cycling duration, the amplification efficiency was undermined, being calculated at 88%. This technology offers a viable solution for rapid pathogen detection and holds potential for widespread applications in medical diagnostics and public health, particularly during pandemic outbreaks.
Emotion Recognition in Closed-Cabin Environment: An exploratory Study Using Millimeter-Wave Radar and Respiration Signal
Authors: Wang Hanyu , Chen Dengkai , Gu Sen , Zhou Yao , Sun Yiwei , Sun Jianhua , Huang Yuexin , Zhang Xian , Fan Hao
Published: August 2024

In the field of psychology and cognition within closed cabins, noncontact vital sign detection holds significant potential as it can enhance the user experience by utilizing objective measurements to assess emotions, making the process more sustainable and easier to deploy. To evaluate the capability of noncontact methods for emotion recognition in closed spaces, such as submarines, this study proposes an emotion recognition method that employs a millimeter-wave radar to capture respiration signals and uses a machine-learning framework for emotion classification. Respiration signals were collected while the participants watched videos designed to elicit different emotions. An automatic sparse encoder was used to extract features from respiration signals, and two support vector machines were employed for emotion classification. The proposed method was experimentally validated using the FaceReader software, which is based on audiovisual signals, and achieved an emotion classification accuracy of 68.21%, indicating the feasibility and effectiveness of using respiration signals to recognize and assess the emotional states of individuals in closed cabins.
The Role of Helical and Non-Helical Drives on the evolution of Self-Consistent Dynamos
Authors: BISWAS SHISHIR , Ganesh Rajaraman
Published: August 2024

In the self-consistent dynamo limit, the magnetic feedback on the velocity field is sufficiently strong to induce a change in the topology of the magnetic field. Consequently, the magnetic energy reaches a state of non-linear saturation. Here, we investigate the role played by helical and non-helical drives in the triggering and the eventual saturation of a self-consistent dynamo. Evidence of small-scale dynamo (SSD) activity is found for both helical and non-helical forcing, driven at the largest possible scale. Based on the spectrum analysis, we find that the evolution of kinetic energy follows Kolmogorov’s k-5/3 law while that of magnetic energy follows Kazantsev’s k3/2 scaling. Also, we have verified that the aforementioned scalings remain valid for various magnetic Prandtl numbers (Pm ). Statistical analysis is found to support our numerical finds.
Simulation Research on the Clinical Path of Non -Small Cell Lung Cancer Surgery Based on Time Petri Net
Authors: Da CHEN , Haitao SONG , Jun SONG , ZHONG Hui , Chuangchuang XU , Junli GAO
Published: August 2024

Background Clinical pathways are increasingly adopted to control costs and enhance quality management, becoming a standardized approach in treatment. This study aims to develop a hospital-specific clinical pathway for non-small cell lung cancer surgery, based on national standards and actual treatment practices, and evaluate its service efficiency through simulation. Methods We analyzed 94 electronic medical records of non-small cell lung cancer surgeries conducted from May 2020 to November 2022. The need for a localized clinical pathway was assessed by statistically analyzing delays in recorded treatment processes. A time Petri Net model was subsequently developed for this pathway, and simulations were conducted to determine the impact on hospital length of stay post-implementation. Results The existing treatment processes at the hospital generally conformed to national clinical pathway standards. The analysis of the Petri Net model confirmed its validity and effectiveness. Simulation results indicated that the average hospital stay is reduced to 8.20 days from a baseline of 7.76 days, achieving a total time reduction of approximately 10.28 hours, with reductions in individual diagnostic and treatment processes ranging from 0.15 to 5.04 hours. Conclusion The implementation of a tailored clinical pathway for non-small cell lung cancer surgery significantly improved service efficiency by aligning with national standards. This allows the hospital to better integrate and optimize medical resources, enhancing the overall quality of clinical pathway management.
A straightforward method for disaccharide characterization by a simplified version of time domain nuclear magnetic resonance
Authors: Sultana Afroza , Asghari Ali , Khalloufi Seddik
Published: August 2024

The necessity of identifying and quantifying sugars in food processing is endless for maintaining food quality attributes such as color, taste, and texture, monitoring regulatory compliance, labeling packages, and maintaining authenticity. Despite available analytical methods for characterizing sugar molecules, the limitations of conventional methods drive researchers to seek more convenient alternatives. This study aimed to characterize common disaccharides such as saccharose, lactose, maltose, and trehalose using a simplified version of time domain nuclear magnetic resonance (TD-NMR), facilitating a quick, cost-effective, and user-friendly approach. In the transverse relaxometry, secondary peak(s) were observed for all the disaccharides with a main peak. Although they have similar molecular formulas and weights, lactose exhibited the longest relaxation time for the secondary peak, followed by trehalose, saccharose, and maltose. This behavior was assumed due to the interaction of sugar molecules with water. The increasing concentration of disaccharide in the solution displayed the leftward shifting of peaks. Maltose showed two secondary peaks, which were not observed in other sugar samples. This TD-NMR showed potential to distinguish disaccharides from unknown powders and solutions by analyzing either the relaxation time of the secondary peak or the ratio of the secondary to the total peak. Moreover, quantification is possible from the standard curves of relaxation time and the combined surface area of the main and secondary peaks with the corresponding sugar concentration. However, it shows challenges in discrimination between α- and β-isomers.
Characterizing Shadowing and Fading in Emerging Wireless Communication Systems via the Shadowed Beaulieu-Xie Model
Authors: Olutayo Adebola , Holzman Jonathan F. , Chaaban Anas
Published: August 2024

The drive for wireless communications at higher frequencies has led to growing interest for systems in the millimetre-wave and terahertz bands. However, the performance of such systems has been greatly hampered to date by shadowing and fading, leaving emerging sixth-generation and land mobile satellite systems awaiting solutions. As such, we analyze and characterize the effects of shadowing and fading seen in these systems. We do so by introducing the second-order statistics of the shadowed Beaulieu-Xie model, and then apply the probability density and characteristic function methods to acquire expressions and results for the level crossing rate and average fade duration in these emerging systems. We then fit our modeled results to measured data obtained by the Communication Research Centre in Ottawa, Canada, in collaboration with the National Aeronautics and Space Administration of the United States of America. Our shadowed Beaulieu-Xie model shows an excellent fit to the strong shadowing and fading in the measured data, suggesting that the model has great potential in characterizing emerging communication environments and systems.
Temporal Patterns, Causes, and Risk Determinants of Lower Respiratory Infections in BRICS+ Countries: A Longitudinal Inter-Country Assessment of Disease Burdens
Authors: Chen Jiakang , He Nanhao , Allel Kasim , Fang Xiaoyi , Chen Jiaqiang , Tu Ruiyang , Zhuo Chuyue , Zou Guanyang , Zhuo Chao
Published: August 2024

With the inclusion of new member states, nearly half of the world's population today residence in the BRICS + countries. However, no studies have systematically assessed the disease burden caused by lower respiratory tract infections (LRI) in these countries. Using data from the Global Burden of Disease Study (GBD) 2019 and age-period-cohort models, we estimate the temporal patterns, causes, and risk determinants of LRI burden between 1990 and 2019. The findings showed BRICS + experienced a total of 256821260 LRI events, accounting for 52.5% of the global incidence (488902504), accounting for 34.2% of the global LRI deaths [2493199 (2736184 to 2268183)] In 2019. The age standardized mortality rate of LRIs in BRICS + countries ranged from 13.8 to 86.4 cases per 100,000 people. Local drift showed that the improvement was most pronounced in children. In terms of numbers, pneumococci are the common LRI type in all countries. In terms of risk factors, countries show strong heterogeneity. From 1990 to 2019, the burden of LRI generally decreased as SDI increased. The result shows that BRICS countries should strengthen cooperation and exchanges in health and provide better experience for other developing countries to reduce the burden of LRI.
Impact of Initial Surgical Interventions on Empyema Outcomes: Insights from a Cohort Study in Yemen
Authors: Issa Mohammed Ali , Obaidel Yasser Abdurabo , Galeb Khaled Sultan Ali , Jowah Haitham Mohammed
Published: August 2024

Prupose:
This study aimed to evaluate the effectiveness of different initial interventions, including thoracostomy drain tubes, open thoracotomy with decortication, and VATS (Video-Assisted Thoracoscopic Surgery) thoracoscopy, in the management of empyema.
Patient and Methods:
This prospective cohort study was conducted at two teaching hospitals in Sana'a, Yemen, over a two-year period, from 2022 to 2024. The study included 40 patients diagnosed with empyema. Patients were categorized according to the type of initial intervention received. Demographic data, clinical presentation, imaging findings, intervention details, and outcomes were systematically collected and analyzed. Statistical analyses were performed to identify associations between demographic characteristics, empyema stage, intervention type, and treatment success.
Results
The study included 40 patients, with a higher proportion of males (67.5%) than females (32.5%). The mean age was 47.1 years (SD: 12.85). The overall success rate of the initial interventions was 55%, with significant variation based on empyema stage, comorbidities, and intervention type. Stage I empyema had the highest success rate (80%, followed by Stage II (50%) and Stage III (27.3%), with a statistically significant difference (p-value = 0.034). Smoking history was identified as a significant negative predictor of success (p-value = 0.001). Higher pleural fluid pH was associated with better outcomes (p-value = 0.015*). The most common complications were chest infections (20%) and bronchopleural fistulas (10%), with a mortality rate of 7.5%.
Conclusions
The empyema stage significantly affects the success rate, with early stages showing better outcomes. Early and appropriate intervention, particularly in later stages, is crucial for better outcomes. Effective management of postoperative complications is vital. This study highlights the need for early diagnosis and tailored interventions based on the empyema stage to improve patient outcomes. Future research should focus on larger multicenter studies to validate these findings and develop standardized treatment protocols.
Assessing the Impact of Exports, Imports, and Gross Fixed Capital Formation on Nepal's Economic Growth: an Ardl Approach
Authors: Poudel Anish
Published: August 2024

The Auto-Regressive Distributed Lag (ARDL) method is used in this study to examine the effects of imports, exports, and gross fixed capital formation (GFCF) on Nepal's economic growth. This time series analysis evaluates the long- and short-term relationships between these macroeconomic variables using the ARDL approach. The findings show that while lagged exports harm GDP, exports have a favourable long-term impact on GDP. The error correction model captures the short-run dynamics well. The robustness of the model is confirmed by diagnostic tests, revealing no specification errors, serial correlation, or heteroscedasticity. Stability tests, such as CUSUM and CUSUMSQ, confirm the stability of the model's coefficients over time. These findings provide vital economic development and macroeconomic policy insights, indicating that optimizing capital investments, imports, and exports can significantly boost Nepal's economic growth.
JEL Classification: C22, O11, C32, E60, E22
The Impact of the Covid-19 Pandemic on Children's Rights and Psychosocial Status
Authors: Cenger cüneyt Destan , Doğan Adil Han , Malkoç Mehmet Ali , Özçetin Mustafa , Kılıç Ayşe
Published: August 2024

Objective
Children's rights emerged with the recognition of human rights and constitutional fundamental rights for children under eighteen. This study aimed to evaluate the attitudes and behaviors of parents towards children at home during the COVID-19 pandemic in terms of violation of children's rights.
Material and Method
Between December 2022 and June 2023, researchers administered a 38-question questionnaire to 540 parents at the university hospital general pediatrics outpatient clinic and pediatrics service.
Results
Among the participants, 70.4% were mothers, and 29.6% were fathers. The analysis indicated that fathers who took part in the survey engaged in child abuse less frequently than mothers at a statistically significant level. Furthermore, the severity of punishment administered by fathers to their children was statistically significantly lower than that of mothers.
Conclusion
In our study, they reported that verbal or physical violence occurred in the family during the pandemic period. Hence, it was determined that social isolation and disruption of education experienced with COVID-19 increased the rate of childhood exposure to domestic abuse and violence.