bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimization of the effects of biocontrol agents on broccoli head rot using meta-analysis
Authors: Nishimura Fumihiro , Katayama Takahiro , Komobuchi Keizo , IKEDA Ken-ichi 
Published: June 2023

Abstract
Meta-analysis was performed to evaluate the effects of biopesticides on broccoli head rot. Three registered biopesticides, Masterpiece, Vegekeeper, and Biokeeper, were effective in preventing disease as compared to the untreated control but were less effective under conditions of high disease frequency. Masterpiece was more effective in control when the frequency of application was increased rather than the concentration. However, Vegekeeper, and Biokeeper were more effectively controlled by treatments with higher concentration ratios. In the rainfall experiment, Masterpiece and Vegekeeper were more effective than Biokeeper.
Multi-level supervised network for Lung segmentation
Authors: ma yanshan 
Published: June 2023

Abstract
The segmentation of lung regions in CT images is an important technique in computer-assisted medical care. While convolutional neural networks have improved the ability to segment lung areas in CT images, the accuracy of current methods is limited by challenges such as low contrast and abnormal appearance. This paper describes a new approach using a multi-level supervised lung segmentation network with a U-Net backbone and a module called MSDC to address these issues. The MSDC module incorporates multi-scale dilated convolutions to extract lung edge information and address the issue of weak contrast. Additionally, the network includes spatial attention modules to improve its focus on lung areas within CT images. Using several standard CT lung image datasets, this approach demonstrated superior performance compared to recent image segmentation methods, both qualitatively and quantitatively
Risk Factors for Lymph Node Metastasis in Hilar Cholangiocarcinoma: A SEER-based Prediction Model by Nomogram
Authors: Yang Jie , Jia Yong-Tao , Yan Zheng , Luo Jia , Su Ming-Li , Yu Xi , Mao Shu-Qi , Liu Ai-Ming , Lu Cai-De 
Published: June 2023

Abstract
Background: Lymph node metastasis (LNM) is an important independent factor affecting the prognosis of patients with hilar cholangiocarcinoma. The correct evaluation of lymph node status is very important in the clinic. This study aimed to investigate the risk factors for LNM in patients with hilar cholangiocarcinoma and establish a nomogram model that can effectively predict LNM.
Methods: A total of 2683 patients diagnosed with hilar cholangiocarcinoma from 2000 to 2019 in the Surveillance, Epidemiology, and End Results (SEER) database were retrospectively analyzed. Their clinicopathological data were extracted and randomly divided into a training cohort (n = 1879) and a validation cohort (n = 804) with a ratio of 7 : 3. Univariate and multivariate logistic regression analyses were used to evaluate the significant predictors of LNM. Based on the above prediction factors, a nomogram prediction model was constructed. Calibration maps, receiver operating curves (ROC), and the area under ROC curve (AUC) were used to validate the working power of the nomogram model using the two cohorts mentioned above.
Results: LNM occurred in 1244 (46.37%) Of the total 2683 patients. Univariate regression analysis showed that age, T stage, tumor size, and histological grade were associated with LNM (p < 0.05). Multivariate regression analysis showed that T stage, tumor size, and histological grade were independent risk factors for LNM (p < 0.05). The nomogram prediction model showed good predictive power for LNM. The C-indices of the training and validation cohort were 0.725 (95% CI: 0.702-0.747) and 0.711 (95% CI: 0.676-0.746), respectively. The AUC value was 0.736 (95% CI: 0.713-0.758). The calibration curve showed high consistency between the prediction of the model and the actual transition situation, which verified the accuracy and discriminative ability of the nomogram. Decision curve analysis(DCA) showed that the nomogram model could predict clinical outcomes with satisfied accuracy.
Conclusion: As predicted, T stage, tumor size, and histological grade were independent factors influencing LNM in patients with hilar cholangiocarcinoma. The visualized nomogram model can effectively predict the risk of LNM in patients with hilar cholangiocarcinoma and will help physicians make individualized treatment decisions.
Cell cycle gene expression is restored in eutopic primary stromal cells after endometriosis surgery
Authors: Invitti Adriana L , Asanuma Fernando Y , Carvalho Cristina V , Kamergorodsky Gil , Kopelman Alexander , Schor Eduardo 
Published: June 2023

Abstract
The role of the endometrium in the pathogenesis of endometriosis has assumed prominence. Abnormality of gene and protein expression, apoptosis and changes in cell cycle have been extensively studied in endometriosis. We evaluated the cell cycle genes expression on primary stromal endometrial cells (EC) isolated from eutopic endometrium in two different moments: before and after deep infiltrating endometriosis (DIE) surgical treatment. We analysed five ECs from healthy patients (group control) and 9 from women with diagnosis of DIE. Were identified 7 cell cycle genes (p53, TFDP1, TFDP2, KPNA2, RB1, RBL2, SERTAD1) differentially expressed between pre, post-operative and controls. The p53 and KPNA2 genes were downregulated 3.34 fold (p = 0.006) and 2.62, (p = 0.042), respectively, in the endometrium of DIE compared to control. Both were upregulated (p53 - Fold 2.22, p = 0.157; KPNA2 - Fold 4.36, p = 0.017) in the post-operative DIE group in comparison to pre-operative one. Also, the RB1 gene was downregulated 9.36 fold (p = 0.029) in the DIE-post group in comparison to DIE-pre group, having no difference between DIE-pre and control group (p = 0.311). The proteins coded by these genes have association between each other, indicating that the surgical treatment could change the cell cycle regulation in the endometrium of women with endometriosis and that the changes remain after the cell isolation from the tissue.
Exploring Key Parameters Influencing the Inhibitory Effect of Volume Dielectric Barrier Discharge on Phytopathogenic Fungi
Authors: Rotondo Palma Rosa , Aceto Domenico , Rotolo Caterina , Ambrico Marianna , Dilecce Giorgio , Faretra Francesco , Angelini Rita Milvia De Miccolis , Ambrico Paolo Francesco 
Published: June 2023

Abstract
The application of Low-Temperature Plasmas has shown great potential as an effective and alternative tool for microbial inactivation in recent years. Nevertheless, further investigations are required to fully understand the possible factors influencing these processes. The present study aims to investigate the effectiveness of square wave modulated Volume Dielectric Barrier Discharge (VDBD) on the direct inhibition of conidia germination in five different fungal species, various discharge conditions, and substrates used for conidial germination. The five different fungal species studied were Botrytis cinerea, Monilinia fructicola, Aspergillus carbonarius, Fusarium graminearum, and Alternaria alternata. On water agar, the inhibition of Botrytis cinerea was influenced by the applied voltage, which mainly reflects the uniformity of the treatment. Under the selected voltage condition, the inhibition increased with exposure time and decreased with fungal complexity. B. cinerea and M. fructicola, with unicellular conidia and similar melanin content, showed similar behaviour and sensitivity to the treatment. F. graminearum and A. alternata, both having multicellular conidia, were more resistant to the plasma treatment exhibiting different sensitivity. However, after 1 min of treatment, complete inhibition of conidial germination was achieved for all the tested species. Inhibition of A. carbonarius conidia was tested on different agarized media containing dextrose or malt extract. It was found to depend on the medium used, with potato dextrose agar hindering the plasma treatment the most. In this case, the key factor shaping the inhibition was the agarized media selected. Each medium exhibited a different electrical response studied by Electrical Impedance Spectroscopy and morphology observed by SEM images. These differences translated into a different response to the applied electrical field, influencing plasma generation and uniformity.
Dispositional mindfulness and fatigue in Chinese nurses during the COVID-19 pandemic: a possible mediating role of sleep quality
Authors: Dai Caijun , Hu Pinglang , Yan Feifan , He Xuejiao , Cheng Weizhen , Yu Lihua , Fang Achang , Meng Xiaoling , Lou Meiyang , Chen Youying , Chi Danli , Zhou Huasu , Chen Qiaoge , Fang Zhenhong , Ni Shuhong , Huang Qiqi 
Published: June 2023

Abstract
Purpose: During the COVID-19 epidemicin China, clinical nurses are at an elevated risk of suffering fatigue. This research sought to investigate the correlation between dispositional mindfulness and fatigue among nurses, as well as the potential mediation role of sleep quality in this relationship.
Methods: This online cross-sectional survey of nurses was performed from August to September 2022 after the re-emergence of COVID-19 in China. The Mindful Attention Awareness Scale (MAAS), 14-item Fatigue Scale (FS-14), and Pittsburgh Sleep Quality Index (PSQI) were employed to assess the levels of dispositional mindfulness, fatigue, and sleep quality, respectively. The significance of the mediation effect was determined through a bootstrap approach with SPSS PROCESS macro.
Results: A total of 2143 nurses completed the survey. Higher levels of dispositional mindfulness were significantly negatively related to fatigue (r = -0.518, P < 0.001) and sleep disturbance (r = -0.344, P < 0.001). Besides, there was a positive relationship between insufficient sleep and fatigue (r = 0.547, P < 0.001). Analyses of mediation revealed that sleep quality partly mediated the correlation between dispositional mindfulness and fatigue (β= -0.551, 95% Confidence Interval = [-0.630, -0.474]).
Conclusions: Chinese nurses' dispositional awareness was related to the reduction of fatigue during the COVID-19 pandemic, and this relationship indirectly operates through sleep quality. Intervention strategies and measures should be adapted to improve dispositional mindfulness and sleep quality to reduce fatigue in nurses during the pandemic.
Credit Risk Evaluation Model of Supply Chain Finance Based on Deep Dimension Reduction
Authors: Liu Ying , Li SiZhe , Li Huidi , Lv Mingli , Li Ye 
Published: June 2023

Abstract
【Objective】A credit risk assessment model is named SAE_DE_SVM based on deep learning dimension reduction is proposed to solve the problems of multi-heterogeneous and dynamic high-dimensional characteristics in credit risk assessment of supply chain finance.
【Methods】This study obtains samples from CSMAR database, Sina Finance and Economics Network and Shenzhen Stock Exchange official website, and uses Stacked Auto-Encoder (SAE) to reduce the dimension of supply chain financial risk assessment features. Considering the imbalance between the positive and negative proportions of the evaluation samples, the Synthetic Minority Oversampling Technique (SMOTE) oversampling technique is used to balance the samples. Finally, the differential evolution (DE) algorithm is used to optimize the support vector machine (SVM), and SAE_DE_SVM algorithm as supply chain financial credit risk evaluation model is constructed.
【Results】 The results show that the accuracy and time complexity of SAE_DE_SVM model on supply chain financial sample data are 95.83 % and 5.56 s, respectively, which is the best in the comparison model.
【Limitations】 In the process of deep learning dimension reduction, a part of the feature data and information will be lost. However, the related research on the accurate calculation and utilization of these data and information loss is still very lacking.
【Conclusion】The experimental results show that credit risk assessment model of supply chain finance based on SAE_DE_SVM has good performance in predicting the possibility of default of Small and Medium-sized Enterprises (SMEs).
Modulation of Multidrug Resistance Protein 1-mediated transport processes by the antiviral drug ritonavir in cultured primary astrocytes
Authors: Arend Christian , Grothaus Isabell L. , Waespy Mario , Ciacchi Lucio Colombi , Dringen Ralf 
Published: June 2023

AbstractThe Multidrug Resistance Protein 1 (Mrp1) is an ATP-dependent efflux transporter and a major facilitator of drug resistance in mammalian cells during cancer and HIV therapy. In brain, Mrp1-mediated GSH export from astrocytes is the first step in the supply of GSH precursors to neurons. To reveal potential mechanisms underlying the drug-induced modulation of Mrp1-mediated transport processes, we investigated the effects of the antiviral drug ritonavir on cultured rat primary astrocytes. Ritonavir strongly stimulated the Mrp1-mediated export of glutathione (GSH) by decreasing the Kmvalue from 200 nmol/mg to 28 nmol/mg. In contrast, ritonavir decreased the export of the other Mrp1 substrates glutathione disulfide (GSSG) and glutathione-bimane. To give explanation for these apparently contradictory observations, we performedin silicodocking analysis and molecular dynamics simulations using a homology model of rat Mrp1 to predict the binding modes of ritonavir, GSH and GSSG to Mrp1. The results suggest that ritonavir binds to the hydrophilic part of the bipartite binding site of Mrp1 and thereby differently affects the binding and transport of the Mrp1 substrates. These new insights into the modulation of Mrp1-mediated export processes by ritonavir provide a new model to better understand GSH-dependent detoxification processes in brain cells.
Intergenerational financial transfers of older adults in India: Evidence from Longitudinal Ageing Study in India wave-1
Authors: Nagargoje Varsha P. , Dilip T. R. , James K. S. 
Published: June 2023

AbstractIntergenerational resource transfers are fabric of families. This study examined proportion of older adults' direct and indirect financial contributions through various resource transfers in the household. Using bivariate analysis total 25,201 households containing at least one older adult (60+ age) from Longitudinal Ageing Study in India wave-1 were analyzed. The highest contribution was made through work engagement (22%), followed by agriculture work (20.4%), loan-repayment (15.2%) and pension-income (12%). Contribution via business engagement was merely 2.5%. Contribution of older person via loan repayment was only 7% in urban residents, but it is more than double (19%) among rural residents. In terms of indirect financial transfer, the highest contribution was made through sharing the current residential house (62%), followed by cultivable land (34.5%), housing and commercial units (7.7%), and non-cultivable land (5.2%). Even in old-age, Indian seniors have potential to support families by sharing resources, reflecting strong intergenerational bonds and functional solidarity.
Metagenomics analysis of water samples collected from the Yamuna River of Agra city, India
Authors: Raghav Nupur , Saraswat Pooja , Kumar Sunil , Chaurasia Anurag , Ranjan Rajiv 
Published: June 2023

Abstract
Yamuna River water in Agra city of India is heavily contaminated with toxic pollutants, including heavy metals that cause severe damage to ecological and social aspects of life. At present, the direct use of river water for drinking purposes cause severe hazards due to anthropogenic activities and environmental pollution. The quality assessments in terms of physical, chemical, and biological attributes of water samples are essential to understand their impact on humans. In the present study, Yamuna River water samples were collected from three different sites in Agra city during the monsoon, summer, and winter seasons. Various physico-chemical parameters were estimated, and the concentration of heavy metals was measured. In the case of physico-chemical parameter characterization, the values found were mostly above the permissible limits. The results suggest that the Yamuna River water samples contain high levels of cadmium, chromium, lead, and nickel above the desirable levels in most cases. The water samples were subjected to metagenomic analysis using the Illumina platform, which revealed that Proteobacteria, Bacteroidetes, Verrucomicrobia, Actinobacteria, and Planctobacteria were the most abundant phyla with a relative abundance of 61%, 9.34%, 5.23%, 4.64%, and 4.3%, respectively. The Comamonadaceae was the most abundant family, which consists of the genera involved in hydrogen oxidation, iron reduction, degraders of polycyclic aromatic hydrocarbons, and fermentation. Functional prediction showed the presence of genes responsible for different metabolic pathways. The study concludes the status of water contamination at three different sites in Agra and suggests the futuristic use of this research in the field of bioremediation.
Visual Question Answering: A Survey on Techniques and Common Trends in Recent Literature
Authors: Sants Claudio Filipi Goncalves dos , Bastos Felype de Castro , de Faria Ana Claudia Akemi Matsuki , Silva Jose Victor Nogueira Alves da , Uchoa Valeska de Sousa , Neto Decio Goncalves de Aguiar 
Published: June 2023

Abstract
Visual Question Answering (VQA) is an emerging area of interest for researches, being a recent problem in natural language processing and image prediction. In this area, an algorithm needs to answer questions about certain images. As of the writing of this survey, 25 recent studies were analyzed. Besides, 6 datasets were analyzed and provided their link to download. In this work, several recent pieces of research in this area were investigated and a deeper analysis and comparison among them were provided, including results, the state-of-the-art, common errors, and possible points of improvement for future researchers.
An automated approach for binary classification on imbalanced data
Authors: Vieira Pedro Marques , Rodrigues Fátima 
Published: June 2023

Abstract
Imbalanced data is present in various business areas and must be dealt with the appropriate resampling techniques and classification algorithms. However, there is a magnitude of multiple combinations of resampling and learning methods to handle imbalanced data that require specialised knowledge to be used correctly. In this paper, several approaches, ranging from more accessible and more advanced in the domains of data resampling and cost-sensitive techniques, will be considered to handle imbalanced data. The application developed delivers recommendations of the most suited combinations of techniques for a specific dataset, by extracting and comparing dataset meta-features values recorded in a knowledge base. It facilitates effortless classification and automates part of the machine learning pipeline with comparable or better results to a state-of-the-art solution and with a much smaller execution time.
Effects of central pathway and postural control technique on stroke patients: a randomized controlled trial
Authors: He Weichun , Li Ke , Li Hongyu , Xu Ning , Zhu Ning 
Published: June 2023

Abstract
Objective
We investigated the effects of central pathway and postural control (CPPC) technique on motor function, balance function, activities of daily living, degree of neurological deficit, nerve cytokine content and hemorheology indexes of stroke patients.
Method
78 stroke patients were randomly divided into an experimental group added the CPPC technique on the basis of routine rehabilitation therapy and a control group receiving routine rehabilitation therapy. All interventions were 1 time / day for 50 days in both groups. Outcome measures were collected at baseline and post-intervention. The primary outcome measures included the National Institutes of Health Stroke Scale (NIHSS) and the Fugel-Meyer assessment scale (FMA). The secondary outcome measures included Berg Balance Scale (BBS), Tinetti Balance and Gait Analysis, Barthel Index (BI), neuronal cytokine content and hemorheological Indices.
Result
The results of this study show that NIHSS scores of the two groups decreased and FMA scores increased after treatment. Compared with before treatment, BBS score, Tinetti score and the scores of each item of BI scale of the two groups were increased, and there were significant differences between the two groups. The content of NT-3 and NGF in serum of the two groups increased and the hemorheology indexes of HBV, LBV, PV, HCT and fibrinogen decreased after treatment, and there were significant differences between the two groups.
Conclusions
The CPPC technique can alleviate limb dysfunction and motor limitation and can improve balance function, activities of daily life and blood circulation in stroke patients. The CPPC technique is a promising method for future neuropsychological research and can also be used in clinical treatment of stroke patients.
Evaluating the Ability of ChatGPT in Generating Differential Diagnosis in Clinicopathological Conferences of Neurodegenerative Disorders
Authors: Koga Shunsuke , Martin Nicholas B. , Dickson Dennis W. 
Published: June 2023

Abstract
This study explores the utility of the artificial intelligence model ChatGPT in predicting neuropathologic diagnoses from clinical summaries presented at clinicopathological conferences. Twenty-five cases of various neurodegenerative disorders were analyzed using ChatGPT versions 3.5 and 4. The primary neuropathological diagnoses predicted by the models aligned with the official neuropathologic diagnoses in 32% (ChatGPT-3.5) and 52% (ChatGPT-4) of cases. When considering all proposed diagnoses, accuracy increased to 76% and 84% for ChatGPT-3.5 and ChatGPT-4, respectively. These findings highlight the potential of artificial intelligence tools like ChatGPT in neuropathology, suggesting they may facilitate more comprehensive discussions in clinicopathological conferences.
Importance of GWAS Risk Loci and Clinical Data in Predicting Asthma
Using Machine-learning Approaches
Authors: Qin Zan-Mei , Liang Si-Qiao , Long Jian-Xiong , Deng Jing-Min , Wei Xuan , Yang Mei-Ling , Tang Shao-Jie , Li Hai-Li 
Published: June 2023

Introduction:
To understand the risk factors of asthma, we combined genome-wide association
study (GWAS) risk loci and clinical data in predicting asthma using machine-learning
approaches.

Methods:
A case-control study with 123 asthmatics and 100 controls was conducted in the Zhuang
population in Guangxi. GWAS risk loci were detected using polymerase chain reaction, and clinical
data were collected. Machine-learning approaches were used to identify the major factors that
contribute to asthma.

Results:
A total of 14 GWAS risk loci with clinical data were analyzed on the basis of 10 times the
10-fold cross-validation for all machine-learning models. Using GWAS risk loci or clinical data,
the best performances exhibited area under the curve (AUC) values of 64.3% and 71.4%, respectively.
Combining GWAS risk loci and clinical data, the XGBoost established the best model with
an AUC of 79.7%, indicating that the combination of genetics and clinical data can enable improved
performance. We then sorted the importance of features and found the top six risk factors
for predicting asthma to be rs3117098, rs7775228, family history, rs2305480, rs4833095, and body
mass index.

Conclusion:
Asthma-prediction models based on GWAS risk loci and clinical data can accurately
predict asthma, and thus provide insights into the disease pathogenesis.