bluuebunny/crossref_metadata_2025_split huggingface dataset:
MLP-ANN’s Weight Optimization using Velocity Symbiotic Organism Search Algorithm for Datamining
Authors: Noureddine Samia , Betka Abir , Toumi Abida , Benharkat Aicha-Nabila 
Published: September 2022

Abstract
Optimization is an essential tool that improves the problem’s results and leads to the best possible solution in different research domains. In data mining, optimization can be used to formulate a suitable design for the learning model, in order to enhance the decision-making step. Data mining is considered as a promising approach that exploits large volume of data and transforms them into helpful information for an appropriate decision. The diverse use of data mining has shown its needs to a training process, neural networks have been used successfully in this context. They have been also combined with metaheuristic techniques to provide superior results. Symbiotic Organisms Search (SOS) is one of the most important and powerful metaheuristics due to its simplicity and robustness. However, it suffers from the premature convergence. To alleviate this shortcoming, we propose an improved version of the SOS called VSOS (Velocity Symbiotic Organism Search), in which a new term, velocity, is introduced. This term is involved to realise a balance between local search (exploit) and global search (explore). The proposed method is firstly benchmarked on 22 test functions. The VSOS method is then combined with Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) for classification in data mining process. Three popular datasets are employed for this experiment, and the results, in term of classification accuracy, are compared with those obtained with other metaheuristics, such as BBO, GA, PBIL, PSO, HWBA, SOS based on clustering process and the conventional SOS. Experimental results indicate that the proposed approach can provide classification accuracy up to 99% with some datasets, outperforming the other cited methods, which confirms its high performance and credibility of treatment.
DSTN hypomethylation promotes radiotherapy resistance of rectal cancer by activating Wnt/β-catenin signaling pathway
Authors: Wen Rongbo , Zhou Leqi , Jiang Siyuan , Fan Hao , Zheng Kuo , Yu Yue , Gao Xianhua , Hao Liqiang , Lou Zheng , Yu Guanyu , Yang Fu , Zhang Wei 
Published: September 2022

Abstract
Background
Although surgical resection combined with neoadjuvant radiotherapy can reduce the local recurrence rate of rectal cancer, only some patients benefit from neoadjuvant radiotherapy. Therefore, how to screen out rectal cancer patients who are sensitive or resistant to radiotherapy has great clinical significance.
Results
We found that DSTN was highly expressed (P <0.05) and hypomethylated (P <0.01) in neoadjuvant radiotherapy resistant tissues of rectal cancer. Follow-up data confirmed that patients with high expression of DSTNin neoadjuvant radiotherapy resistant tissues of rectal cancer had a shorter disease-free survival (P <0.05). DSTNexpression increased after methyltransferase inhibitor inhibited DNA methylation in DNA promoter region of colorectal cancer cells (P <0.05). In vitro and in vivo experiments showed that knockdown of DSTN could promote the sensitivity of colorectal cancer cells to radiotherapy, and overexpression of DSTNcould promote the resistance of colorectal cancer cells to radiation (P <0.05). The expression of C-Myc and Cyclin D1, which are downstream of Wnt/β-catenin signaling pathway, were up-regulated in colorectal cancer cells with overexpression of DSTN. The expression of β-catenin was highly expressed in radiotherapy resistant tissues, and there was a linear correlation between the expression of DSTN and β-catenin (P <0.0001). Further studies showed that DSTNcould bind to β-catenin and increase the stability of β-catenin.
Conclusion
The degree of DNA methylation and the expression level of DSTN can be used as a biomarker to predict the sensitivity of neoadjuvant radiotherapy for rectal cancer, and DSTN and β-catenin are also expected to become a reference and a new target for the selection of neoadjuvant radiotherapy for rectal cancer.
Comparative Analysis of Supervised Machine and Deep Learning Algorithms for Kyphosis Disease Detection
Authors: Chauhan Alok Singh , Gupta Amit Kumar , Garg Ruchi Rani 
Published: September 2022

Abstract
The spinal curve that represents an abnormally rounded back is known as kyphosis, and it can be caused by stress, infectious disease, developmental abnormalities, genetic disease, and occasionally iatrogenic disease. Due to the fact that it provides technical explanations and a significant standard, the emergence of machine learning and deep learning has demonstrated that they can better characterize data. Machine Learning and Deep Learning are promising approaches that help in the prediction, diagnosing of sickness. Compared to conventional computing algorithms, deep learning algorithms are far more effective in disease detection and diagnosing. In this study, Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Deep Neural Network (DNN) models are applied to detect the presence of kyphosis in youngsters supported medicine information and compared to point out the rise in the potency of predictions associated with kyphosis. Each classification algorithm that makes use of the hyperparameter tuning and control feature enhances the algorithms' overall prediction performance, which enhances the algorithms' overall performance. The potency of the planned models were enforced and checked over medical dataset utilized from Kaggle. Overall, the DNN model performed best among the wide range of different models and achieved maximum accuracy and other performance metrics scores derived from the stratified K-Fold cross-validation. It is advised that after a patient has completed a clinical procedure, the DNN model be trained to identify and forecast kyphosis disease.
Effects of Nitrogen Addition on Rhizosphere Soil Microbial Community and Yield of Wheat in Loess Plateau
Authors: Fan Yaqi , Liu Qi , Huo Ruixuan , Wang Yanan , Guo Laichun , Yang Zhenping , Huang Tingmiao , Gao Zhiqiang , Qiao Yuejing 
Published: September 2022

Abstract
The soil microbial community diversity of wheat rhizosphere was affected by the amount of nitrogen fertilizer. In addition to bacterial community, ammonia-oxidizing archaea, nitrogen-fixing bacteria and denitrifying bacteria also play important roles in nitrogen cycle. At present, the microecological mechanism of its response to nitrogen application is still unclear. In this study, the rhizosphere soil microorganisms of winter wheat were used as the research object. The changes of soil bacteria, ammonia-oxidizing archaea, nitrogen-fixing bacteria and denitrifying bacteria communities under five nitrogen rates of 0 (N0), 90 (N6), 180 (N12), 240 (N16) and 300 (N20) kg N · hm-2 were studied by high-throughput sequencing technology. Among them, under N12 treatment, the Alpha diversity index of bacteria, AOA and nifH nitrogen-fixing bacteria and the Shannon and Simpson indexes of nirK denitrifying bacteria were significantly increased. N12 treatment significantly increased the relative abundance of Proteobacteria, but no significant difference was found at the nifH and nirK bacterial phyla levels. Under the high nitrogen treatment of N16 and N20, the dominant bacteria of nirK-type denitrifying bacteria increased significantly compared with N12 treatment, there was no significant difference in microbial community distribution between N20 and the control group. Therefore, the nitrogen addition under N12 treatment was most conducive to the absorption and utilization of nitrogen fertilizer by soil microorganisms. The effect of nitrogen addition on microbial community was weaker than that of soil properties and wheat yield, and nitrogen addition was significantly correlated with yield, reaching the highest yield at 300 kg·hm-2.
Barriers and facilitators to implementing advance care planning in naïve contexts. Where to look when plowing new terrain?
Authors: Westbye Siri Faerden , Rostoft Siri , Romøren Maria , Thoresen Lisbeth , Wahl Astrid Klopstad , Pedersen Reidar 
Published: September 2022

Abstract
Background: Advance care planning (ACP) is a way of complying modern medicine to the principle of patient autonomy and ensuring that patients receive medical care that is consistent with their values, goals and preferences. Robust evidence supports the benefits of ACP, but it remains an underutilized resource in most countries. This paper goes from the naïve point of view and seeks to identify the barriers and facilitators to implementation in unfamiliarized contexts and in a whole system approach involving the clinical, institutional and policy level to improve the implementation of ACP.
Methods:Qualitative interviews were chosen to enable an explorative, flexible design. 19 interviews were conducted with 40 health care professionals and leaders in hospitals and in municipalities. The thematic analysis was done following Braun and Clarke’s strategy for thematic analysis.
Results: The main reported barriers were the lack of time, the lack of a common understanding on what ACP is, lack of training and lack of culture and leadership legitimating ACP. The most important reported facilitators were finding good arenas, creating meeting places and communication systems that allow ACP, as well as anchoring ACP in the leadership in hospitals and in the municipalities.
Conclusions: The most important barriers reported were the lack of a legitimation of ACP, and not adopting facilitators such as implementing efficient communications systems, standardized approaches, and overall starting policy development and professional and public education on ACP.
Open-label placebos: A systematic review and meta-analysis of experimental studies with non-clinical samples
Authors: Spille Lukas , Fendel Johannes Caspar , Seuling Patrik , Göritz Anja Simone , Schmidt Stefan 
Published: September 2022

Abstract
Background: The use of open-label placebos (OLPs) has been shown to be effective in clinical trials. We conducted a systematic review and meta-analysis to examine whether OLPs are effective in experimental studies with non-clinical populations.
Methods: We searched five electronic databases on April 15, 2021. We conducted separate analyses for self-reported and objective outcomes and examined whether the level of suggestiveness of the instructions influenced the effectiveness of OLPs.
Results: Of 3,573 identified records, 20 studies comprising 1,201 participants were included. We found a significant effect of OLPs for self-reported outcomes (SMD=0.43; 95% CI=0.28, 0.58; I2=7.2%) but not for objective outcomes (SMD=-0.02; 95% CI=-0.25, 0.21; I2=43.6%). The level of suggestiveness of the instructions influenced the effectiveness of OLPs for objective outcomes (p=.02), but not for self-reported outcomes.
Discussion: OLPs appear to be effective when examined in experimental studies. However, the small number of studies highlights the need for further research.
Machine learning driven identification of gene-expression signatures correlated with multiple organ dysfunction trajectories and complex sub-endotypes of pediatric septic shock
Authors: Atreya Mihir R. , Banerjee Shayantan , Lautz Andrew J. , Alder Matthew N. , Varisco Brian M. , wong hector , Muszynski Jennifer A. , Hall Mark W. , Sanchez-Pinto L. Nelson , Kamaleswaran Rishikesan 
Published: September 2022

Abstract
Background
Multiple organ dysfunction syndrome (MODS) disproportionately drives sepsis morbidity and mortality among children. The biology of this heterogeneous syndrome is complex, dynamic, and incompletely understood. Gene expression signatures correlated with MODS trajectories may facilitate identification of molecular targets and predictive enrichment.
Methods
Secondary analyses of publicly available datasets. (1) Supervised machine learning (ML) was used to identify genes correlated with persistent MODS relative to those without in the derivation cohort. Model performances were tested across 4 validation cohorts, among children and adults with differing inciting cause for organ dysfunctions, to identify a stable set of genes and fixed classification model to reliably estimate the risk of MODS. Clinical propensity scores, where available, were used to enhance model performance. (2) We identified organ-specific dysfunction signatures by eliminating redundancies between the shared MODS signature and those of individual organ dysfunctions. (3) Finally, novel patient subclasses were identified through unsupervised hierarchical clustering of genes correlated with persistent MODS and compared with previously established pediatric septic shock endotypes.
Results
568 genes were differentially expressed, among which ML identified 109 genes that were consistently correlated with persistent MODS. The AUROC of a model that incorporated the stable features chosen from repeated cross-validation experiments to estimate risk of MODS was 0.87 (95% CI: 0.85–0.88). Model performance using the top 20 genes and an ExtraTree classification model yielded AUROCs ranging 0.77–0.96 among validation cohorts. Genes correlated with day 3 and 7 cardiovascular, respiratory, and renal dysfunctions were identified. Finally, the top 50 genes were used to discover four novel subclasses, of which patients belonging to M1 and M2 had the worst clinical outcomes. Reactome pathway analyses revealed a potential role of transcription factor RUNX1 in distinguishing subclasses. Interaction with receipt of adjuvant steroids suggested that newly derived M1 and M2 endotypes were biologically distinct relative to established endotypes.
Conclusions
Our data suggest the existence of complex sub-endotypes among children with septic shock wherein overlapping biological pathways may be linked to differential response to therapies. Future studies in cohorts enriched for patients with MODS may facilitate discovery and development of disease modifying therapies for subsets of critically ill children with sepsis.
Identification of Hub Genes and Pathways Associated with Oxidative Stress and Cell Senescence of intervertebral disc between Disc Herniation and Degenerative Spondylolisthesis Using Bioinformatics Analysis
Authors: Lin Run , Xie Tianhang , Wang Xiandi , Li Zhuhai , Huang Shishu , Zeng Jiancheng 
Published: September 2022

AbstractBackground This study aimed to identify the hub genes and pathways of genes related to oxidative stress (OS) and cell senescence (CS) of intervertebral disc between disc herniation (DH) and degenerative spondylolisthesis (DS), and to predict the transcription factors of the hub genes. Methods The GSE146904 dataset was obtained from the Gene Expression Omnibus (GEO) database, including 5 disc herniation samples and 5 degenerative spondylolisthesis samples, and the differentially expressed genes (DEGs) were recognized by TBtools. Genes related to oxidative stress in the Gene Ontology (GO) database and genes related to cell senescence in the CellAge database were obtained. By using DAVID, Metascape, and GSEA, GO and signaling pathways analysis were performed. Protein-protein interaction (PPI) network was constructed using the STRING database, and the degree algorithm of Cytoscape software was used to screen for the hub genes. The NetworkAnalyst web tool was used to find the hub genes’ transcriptional factors (TFs). Results 428 oxidative stress–related genes were obtained in GSE146904 and GO database, among which 33 genes were significantly differentially expressed. 267 cell senescence–related genes were obtained in GSE146904 and CellAge database, among which 18 genes were significantly differentially expressed. OS-related genes were primarily involved in several signaling pathways, such as Calcium signaling pathway and GnRH signaling pathway. The top 10 hub genes were JUN, FOXO3, CASP3, JAK2, RELA, EZH2, ABL1, PTGS2, FBXW7, MCL1. Besides, TFAP2A, GATA2 and SP1. Besides, NFIC and FOXC1 might be the key regulatory factors of hub genes. Meanwhile, CS-related genes were primarily involved in MAPK signaling pathways. The top 10 hub genes were MAPK12, BLK, CAV1, CDK18, PIM1, CXCL1, NOTCH3, NUAK1, MAP3K6 and MAP4K1. Besides, GATA2, FOXC1 and SREBF1 might be the key regulatory factors of hub genes. Conclusions Some hub genes and signaling pathways associated with oxidative stress and cell senescence between DS and DH were identified through a series of bioinformatics analyses. This may play a potential role in explaining disease progression and treatment.
The association of body mass index and health-related quality of life: Data from two weight-loss interventional studies
Authors: Kral Pavol , Holst-Hansen Thomas , Olivieri Anamaria-Vera , Ivanescu Cristina , Lamotte Mark , Larsen Sara 
Published: September 2022

Abstract
Purpose
The correlation between body mass index (BMI) and utility in patients with overweight or obesity was assessed using health-related quality of life (HRQoL) data collected in two weight-loss interventional studies, SCALE and STEP 1.
Methods
Short-Form Health Survey 36-Item (SF-36) scores from SCALE and STEP 1 were mapped to EuroQoL-5 dimensions-3 levels (EQ-5D-3L) using an established algorithm to derive utilities for the UK. SF-36 scores from STEP 1 were converted into Short-Form six-dimensions (SF-6D) utilities for Portugal, using the tool developed by the University of Sheffield. Correlations between utility scores and BMI at baseline were assessed by multiple linear regression analyses, controlling for demographic and clinical parameters.
Results
Higher baseline BMI was correlated with lower EQ-5D-3L/SF-6D utilities in all analyses, although the correlation was nonsignificant. Assuming linearity between BMI ranges 30–40 kg/m2, a unit increase predicted a utility loss of − 0.0041 and − 0.0031 on EQ-5D-3L for males and females, respectively, in SCALE, − 0.0039 and − 0.0047 in STEP 1, and − 0.0027 and − 0.002 on SF-6D males and females, respectively. Presence of hypertension and older age were significantly negatively correlated with SCALE EQ-5D-3L. Age, presence of coronary artery diseases (CADs) and previous smoking were significantly negatively correlated with STEP 1 EQ-5D-3L. Only presence of CADs was significantly negatively correlated with STEP 1 SF-6D. Males had significantly higher utilities compared with females in the UK analyses.
Conclusion
These findings are useful to inform cost-effectiveness analyses in obesity whereby multiple factors, along with BMI, are used to calculate quality-adjusted life-years.
Cumulative genetic score of KIAA0319 affects reading fluency through parental education levels and rapid automatized naming
Authors: Yang Qing , Cheng Chen , Wang Zhengjun 
Published: September 2022

Abstract
KIAA0319, a well-studied candidate gene, has been shownto be associated with dyslexia and reading ability. In the present study, we investigated whether KIAA0319 interacts with the parental education level and whether rapid automatized naming (RAN), phonological awareness and morphological awareness mediate the relationship between the gene and reading ability. A total of 13 SNPs were selected, and the cumulative genetic risk score (CGS) was calculated. The interaction effect suggested that individuals with a low CGS were better at fluent reading in a positive environment than individuals with a high CGS, but not accurate reading. Moreover, the interaction coincided with the differential susceptibility model. The results of the multiple mediator model revealed that digit RAN mediates the impact of the genetic cumulative effect on fluent reading. These findings provide evidence that KIAA0319 is a risk vulnerability gene that interacts with environmental factorsto impact reading ability and demonstratethe reliability of RAN as an endophenotype.
Study on debonding defects detection of CFRP / Al honeycomb structure by Square-wave thermography
Authors: Liu Guozeng , Gao Weicheng , Liu Wei , Zou Xionghui , Zhang Xuekun , Li Rui , Shen Runhong 
Published: September 2022

Abstract
Honeycomb structures are widely used in aerospace and other fields, which are prone to debonding defects during service. Therefore, it is necessary to detect the defects of honeycomb structures in time. In this paper, the square-wave thermography detection principle was studied. The thermal conduction of CFRP / Al honeycomb panel with debonding defects was simulated by the finite element analysis. Pulse phase thermography (PPT), principal component analysis (PCA), thermal signal reconstruction (TSR), and total harmonic distortion (THD) ware studied for the original infrared image sequence. The signal-to-noise ratio (SNR) was calculated and the processing results ware compared. PCA-PC1 had the maximum SNR, which could reduce the background noise interference in square-wave thermography. Square-wave thermography can accurately detect and identify the debonding defects of the honeycomb structure.
A rad50 germline mutation induces the ataxia-telangiectasia phenotype in a transparent medaka model
Authors: Chisada Shinichi , Ohtsuka Kouki , Fujiwara Masachika , Yoshida Masao , Matsushima Satsuki , Watanabe Takashi , Karita Kanae , Ohnishi Hiroaki 
Published: September 2022

Abstract
Mutations in the MRN-ATM pathway-related genes cause MRN-related diseases and ataxia-telangiectasia. However, the effect of RAD50 mutations on these diseases remains unclear. To understand the function of RAD50 in the pathogenesis of ataxia-telangiectasia, we adopted a medaka rad50 mutant model. A two-base deletion in the rad50 gene was introduced into transparent STIII medaka using the CRISPR/Cas9 system. Results of rheotaxic analyses, histological studies, and microsatellite instability tests were compared between mutant and wildtype medaka. Horizontal intensities and macroscopic and microscopic observations of thirteen 40-week-old rad50Δ2/+ medaka, microsatellite instability tests of rad50Δ2/+ and rad50Δ2/Δ2 medaka, and 50% survival rates of rad50Δ2/+ medaka revealed that the medaka rad50 germline mutation model concurrently reproduced most of the major ataxia-telangiectasia phenotypes, including ataxia, telangiectasia, tumorigenesis, and genetic instability phenotypes. This fish model may help in further understanding the molecular mechanism and tumorigenesis underlying ataxia-telangiectasia and in developing novel therapeutic strategies against RAD50 molecular disorders.
Simulating solar radio bursts using generative adversarial networks
Authors: Scully Jeremiah , Flynn Ronan , Gallagher Peter , Carley Eoin , Daly Mark 
Published: September 2022

Abstract
Solar flares are one of the most extreme space weather events in our solar system. The impulsive solar radio emission associated with a solar flare is known as a solar radio burst (SRB). They are generally studied in dynamic spectra and are classified into five major spectral classes, ranging from Type I to Type V, based on their form and frequency, and time duration. Due to their intricate characterisation, generating a training set for object detection and classification models of such phenomena is a difficulty in machine learning. Current algorithms implement parametric modelling where the quantity, grouping, intensity, drift rate, heterogeneity, start-end frequency, and start-end time of Type III and Type II radio bursts are all random. However, this model does not factor in the true shape or general features seen in real dynamic spectra observations of the sun, which can be crucial when training classification or object detection algorithms. In this research, we introduce a methodology named Generative Adversarial Network (GAN) for generating realistic SRB simulations. By using real examples of Type III and Type II SRB data, we can train GANs to generate images almost comparable to real observed data. Furthermore, we evaluate the model’s generated results using human perception, then we compare and contrast the results using a metric known as Fréchet Inception Distance.