bluuebunny/crossref_metadata_2025_split huggingface dataset:
Plant And Soil Microbial Communities Regulate Soil Respiration In Response To Precipitation And Land Use In An Inner Mongolian Grassland
Authors: Zhang Chi , Song Chao , Wang Donghui , Qin Wenkuan , Zhu Biao , Li Frank Yonghong , Wang Yonghui , Ma Wenhong 
Published: November 2021

Abstract
Purpose: Changes in precipitation amount and land use are expected to greatly impact soil respiration (Rs) of grassland ecosystems. However, little is known about whether they can interactively impact Rs and how plant and soil microbial communities regulate the response of Rs. Methods: Here, we investigated the impacts of altered precipitation amount (–50%, ambient and +50%) and land-use regime (fencing, mowing and grazing) on Rs with a field experiment in the Inner Mongolian grassland.Results: We found that altered precipitation amount impacted Rs and its components across the 3-year study period, while land-use regime alone or its interaction with precipitation amount impacted them in certain years. In addition, changed soil microclimate, especially soil moisture, under altered precipitation amount and land-use regime can impact the components of Rs either directly or indirectly via influencing plant and soil microbial communities.Conclusions: Integrating changing precipitation amount and land-use regime within experiment can produce more accurate insights into grassland Rs, and chronically shifted plant and soil microbial communities under these changes may result in distinct long-term impacts on Rs.
Sex Differences in Rumen Fermentation and Microbiota of Tibetan Goat
Authors: Guo Xinyu , Sha Yuzhu , Lv Weibing , Pu Xiaoning , Liu Xiu , Luo Yuzhu , Hu Jiang , Wang Jiqing , Li Shaobin , Zhao Zhidong 
Published: November 2021

Abstract
Background: Gut microbiota play an important role in maintaining host metabolism, immune system and health, while sex, genotype, diet and health have certain effects on composition of gut microbiota. Therefore, in order to explore the sex differences in the structure and function of rumen microbiota in Tibetan goat, the study analyzed the sex differences in rumen fermentation parameters, rumen microbiota and the expression of genes related to VFAs transport in Tibetan goat. Results: The results showed that the content of acetic acid in the rumen of rams was significantly higher than that of ewes (P <0.05), and propionic acid and butyric acid were higher than that of ewes, but the difference was not significant (P >0.05); The expression of VFAs transport related genes DRA, AE2, MCT-1, NHE1, NHE2 in rumen epithelium of ewes was significantly higher than that of rams. Analysis of the composition and structure of rumen microbiota showed that there were significant sex differences in structure of rumen microbiota, and the abundance of rumen microbiota in ewes was higher than that in rams (P>0.05). At the phylum level, Firmicutes and Bacteroidetes were the dominant phyla of Tibetan goat, while Firmicutes was significantly higher in ewes than in rams (P<0.05). At the genus level, the relative abundance of Fibrobacter, Ruminococcus_1 and Pyramidobacter in ewes was significantly higher than that in rams (P<0.05). The function prediction results showed that Replication, recombination and repair, RNA processing and modification were mainly enriched in ewes (P<0.05). Conclusions: Correlation analysis revealed significant associations of some rumen microbiota with the fermentation product VFAs and VFAs transport-related genes. It is concluded that rams and ewes have a strong ability for fermenting and metabolizing when adapting to the plateau environment, which provides a certain sex reference basis for Tibetan goat adaptation to the plateau environment.
Physical Property Characterization of the Waipapa Greywacke: An Important Geothermal Reservoir Basement Rock in New Zealand
Authors: Melia Aurelio , Faulkner Daniel Roy , McNamara David Daniel 
Published: November 2021

Abstract
Greywacke basement rocks in New Zealand host conventional geothermal reservoirs and may supply important hotter and deeper geothermal energy resources in the future. This work combines petrological analyses and physical property measurements of Waipapa greywacke, a basement unit hosting New Zealand geothermal reservoirs, in order to understand better how structurally controlled flow networks develop and channel geothermal fluids within it. Results show intact Waipapa greywacke has high tensile and triaxial compressive strengths, and low intrinsic permeability (~10-21 m2). Permeability of intact Waipapa greywacke does not increase significantly during triaxial loading to failure and is accompanied by minimal changes ultrasonic wave velocities. These data taken together suggest that microcrack development during brittle deformation is very limited. Upon failure, the permeability increases by two orders of magnitude and shows similar permeability to tests performed on synthetic, single, mode I fractures in intact Waipapa greywacke. Permeability persists in Waipapa greywacke fractures under confining pressures of at least 150 MPa. It is concluded that Waipapa greywacke rocks will not allow fluid flow through the matrix of the rock and that substantial geothermal fluid flow will only occur through macrofracture networks.
Analysis of Superradiation Stability of Kerr-Newman Black Hole Using Curve integral
Authors: Chen Wen-Xiang 
Published: November 2021

Abstract
In this article, a new variable y is added here to expand the results of the above article.We use the properties of the Laurent series and the Cauchy integral. When y is greater than a certain limit, the effective potential of the equation does not have a pole, then there is no potential well outside the event horizon, when p 2(a 2 + Q2)/r2 + < ω < mΩH + qΦH,so the Kerr-Newman black hole is superradiantly stable at that time.
Classification of Painful or Painless Diabetic Peripheral Neuropathy and Identification of the Most Powerful Predictors Using Machine Learning Models in Large Cross-Sectional Cohorts
Authors: Baskozos Georgios , Themistocleous Andreas , Hebert Harry L , Pascal Mathilde , John Jishi , Callaghan Brian , Laycock Helen , Crombez Geert , Rice Andrew SC , Smith Blair H , Bennett David L.H 
Published: November 2021

Abstract
Background: To improve the treatment of painful Diabetic Peripheral Neuropathy (DPN) and associated co-morbidities, a better understanding of the pathophysiology and risk factors for painful DPN is required. Using harmonised cohorts (N = 1230) we have built models that classify painful versus painless DPN. Methods: The Random Forest, Adaptive Regression Splines and Naive Bayes machine learning models were trained for classifying painful/painless DPN. Their performance was estimated using cross-validation in large cross-sectional cohorts (N = 935). Models were externally validated in a large population-based cohort (N = 295) in the presence of missing values. Variables were ranked for importance using model specific metrics and marginal effects of predictors were aggregated and assessed at the global level. Model selection was carried out using the Mathews Correlation Coefficient (MCC) and model performance was quantified in the validation set using MCC, the area under the precision/recall curve (AUPRC) and accuracy.Results: Random Forest (MCC=0.28, AUPRC = 0.76) and Adaptive Regression Splines (MCC = 0.29, AUPRC = 0.77) were the best performing models and showed the smallest reduction in performance between the training and validation dataset. EQ5D index, the 10-item personality dimensions, HbA1c, Depression and Anxiety t-scores, age and Body Mass Index were consistently amongst the most powerful predictors in classifying painful vs painless DPN. Conclusions: Machine learning models trained on large cross-sectional cohorts were able to accurately classify painful or painless DPN on an independent population-based dataset. Painful DPN is associated with more depression, anxiety and certain personality traits. It is also associated with poorer self-reported quality of life, younger age, poor glucose control and high Body Mass Index (BMI). The models showed good performance in realistic conditions in the presence of missing values and noisy datasets. These models can be used either in the clinical context to assist patient stratification based on the risk of painful DPN or return broad risk categories based on user input. Model’s performance and calibration suggest that in both cases they could potentially improve diagnosis and outcomes by changing modifiable factors like BMI and HbA1c control and institute earlier preventive or supportive measures like psychological interventions.
Trajectories and perceptual precursors of intelligence in minimally verbal autistic children from preschool to school age
Authors: Girard Dominique , Courchesne Valérie , Cimon-Paquet Catherine , Jacques Claudine , Soulières Isabelle 
Published: November 2021

Abstract
Background

The question of cognitive prognosis is frequently asked at the time of autism diagnosis, often at preschool age. It remains however difficult to answer this question at such a young age, given the considerable heterogeneity of cognitive development trajectories and the challenges associated with intellectual assessment in autistic children, particularly among minimally verbal children.
Methods

The current prospective cohort study investigated whether early perceptual abilities measured at preschool age could predict later intellectual abilities at school age in a group of 41 autistic (9 girls, 32 boys) and 57 neurotypical children (29 girls, 28 boys). Participants were assessed at three time points during the childhood period (between the age of 2 and 8 years old) using the Wechsler Preschool and Primary Scales of Intelligence – Fourth edition as a measure of full-scale IQ and the Raven’s Colored Progressive Matrices as a measure of non-verbal IQ. The performance on two perceptual tests (Visual Search and Children Embedded Figures Test) as well as the frequency of perceptual behaviors served as predictors of later intellectual abilities.
Results

Early performance on perceptual tests measured at preschool age was positively related to later full-scale IQ in both autistic and neurotypical children. Furthermore, both perceptual behaviors and performance on perceptual tests measured at preschool age were associated with later non-verbal IQ in the autistic group. In contrast, only the performance on Children Embedded Figures Test was associated with later non-verbal IQ in the neurotypical group.
Limitations:

The sample size was relatively modest, with some attrition across time points, as expected in a sample including preschool minimally and non-verbal children.
Conclusions

Our findings support the important role of perception in autistic cognition. Early perceptual abilities may be a valid avenue for estimating general intelligence and non-verbal abilities at preschool age, particularly for minimally verbal autistic children.
Systematic Pan-Cancer Analysis Reveals the Prognostic Value and Immunological Role of EPHX2
Authors: Hu Weiquan , Zhao Qinfei , Xu Jing , Li Huaying , Zhu Longyu , Hu Suping , Li Juan , Chen Fangfang 
Published: November 2021

Abstract
Background: Accumulating evidence indicates the essential role of EPHX2 in tumorigenesis. However, to date, no studies have performed a systematic evaluation of EPHX2 gene in human cancers and the predictive role of EPHX2 in cancer immunotherapy response has still not been explored. Methods: In the present study, Oncomine, TIMER2, UALCAN, GEPIA2, PrognoScan, HPA and Kaplan-Meier Plotter database were utilized to comprehensively analyze the expression landscape and prognostic clinical value of EPHX2 across 33 human cancers. To gain a better understanding of the role of EPHX2 in cancer immunotherapy, the correlations between EPHX2 and tumor immune microenvironment (TME) such as immune cell infiltrations, immune modulators, and the major histocompatibility complex were demonstrated. The underlying EPHX2-associated signaling pathways in cancer were also analysed. Moreover, the correlation between EPHX2 and immunotherapeutic biomarkers such as tumor mutational burden (TMB) and microsatellite instability (MSI) was explored. At last, the potential immune checkpoint blockers (ICB) response was predicted using tumor immune dysfunction and exclusion (TIDE) algorithm. Results: Overall, the mRNA expression of EPHX2 was significantly downregulated in the majority of tumors compared with normal tissues. Despite the significant prognostic value of EPHX2 expression across cancers, EPHX2 played a protective or detrimental role in different kinds of cancers. Generally speaking, immune cell infiltrations, immune modulators and immunotherapeutic biomarkers were all strongly related to the expression of EPHX2. Besides, EPHX2 expression was significantly related to immune-relevant pathways, especially in PAAD, THYM and UVM. Furthermore, our study demonstrated diverse response patterns of ICB in response to EPHX2 expression in different tumor types. Conclusion: Our findings here suggest that EPHX2 could be a prognostic factor in multiple cancers and play an important role in tumor immunity by affecting infiltrating immune cells, TMB and MSI. This study provides further insight into the role of EPHX2 in tumor immunotherapy.
Peripheral blood CD4+CCR6+ compartment differentiates HIV-1 infected or seropositive elite controllers from long-term successfully treated individuals
Authors: Akusjärvi Sara Svensson , Krishnan Shuba , Jütte Bianca , Ambikan Anoop , Gupta Soham , Rodriguez Jimmy , Vegvari Akos , Sperk Maike , Nowak Piotr , Vesterbacka Jan , Svensson J. Peter , Sonnerborg Anders , Neogi Ujjwal 
Published: November 2021

Abstract
HIV-1 infection induces a chronic inflammatory environment not restored by suppressive antiretroviral therapy (ART). As of today, the effect of viral suppression and immune reconstitution in people living with HIV-1 (PLWH) on long-term suppressive ART (PLWHART) is not well-described. Herein, we show how PLWH who naturally control the virus (PLWHEC) have a reduced proportion of CD4+CCR6+ cells compared to PLWHART and HIV-1 negative controls. Specifically, the CD4+CCR6+/CCR6- cells exhibited a uniqe proteomic profile with a modulated energy metabolism in PLWHEC compared to PLWHART independent of CCR6 status. Furthermore, the CD4+CCR6+ cells were enriched in proteins involved in apoptosis and p53 signalling in PLWHEC compared to PLWHART, indicative of increased sensitivity towards cell death mechanisms. Collectively, this data shows how PLWHEC have a unique chemokine receptor profile that may aid in facilitating natural control of HIV-1 infection.
Construction and Analysis of a Prognostic Model of Osteosarcoma Based on m6A-Related lncRNA
Authors: Ling Zhian , Liang Yuting , Wei Suping , Chen Yuanming , Zhao Jinmin 
Published: November 2021

Abstract
Background

N6-methylandenosine (m6A) methylation is one of the most common methylation modifications in RNA. At present, a large number of studies have found that m6A methylation can regulate the occurrence and development of tumors by modifying mRNA. However, it is still unclear how m6A modifies Long non-coding RNA (lncRNA) that regulates mRNA expression by interacting with miRNA to affect the occurrence and development of osteosarcoma(OS). Therefore, exploring the lncRNAs related to m6A methylation and identifying lncRNAs that have both prognostic effects and immune functions are things that need to be solved urgently.
Methods

The published gene expression data of OS and complete clinical annotation files were obtained from the TARGET database. LncRNAs with P <0.001 from the results of Pearson correlation coefficient analysis as m6A-related lncRNAs were screened. Single-factor Cox regression analysis was used to screening prognostic- related lncRNA combined with the clinical information of patients and constructed a prognostic model based on lasso regression analysis. Then we explored the differences in survival and immune function of different subtypes that be obtained using the Consensus Cluster. The enrichment of differential genes between high and low risk groups in the KEGG pathway is achieved through Gene set enrichment analysis(GSEA).
Results

We obtained 706 lncRNAs in the TARGET database. Consensus clustering method were used to divide patients with OS into subgroups based on the expression of 26 prognostic-related lncRNAs. Through Kaplan-Meier survival analysis, there are significant differences between the two subgroups. The average immune score (P = 0.02), stromal score(P =0.027), and estimate score༈P = 0.015༉were higher in cluster 1 than in cluster 2. We found that compared with cluster 2, SIGLEC15, HAVCR2, LAG3, and PDCD1 were highly expressed in cluster 1.We obtain a prognostic model by lasso regression analysis. In the training group and the text group, the OS curve showed that patients in the high-risk group had a poorer prognosis than those in the low-risk group. In the training set, univariate Cox regression analysis and multivariate Cox regression analysis showed that the risk score was correlated with the prognosis of OS patients. In the high-risk group, the Linoleic acid metabolism and the Glycine, serine and threonine metabolism pathway were mainly involved by Gene Set Enrichment analysis. The abundance of Mast cells activated (P ≦0.024) and T cells CD4 (P ≦0.0044) naive were positively association the risk score.
Conclusions

This study clarified the important role of m6A-related lncRNAs in the prognosis and immune microenvironment of patients with OS, and indicate that m6A-related prognostic lncRNA signals may provide new targets for the diagnosis and treatment of OS.
Effectiveness of Information and Communication Technology on Obesity in Childhood and Adolescence: Systematic Review and Meta-analysis
Authors: Park Jihyun , Park Mi-Jeong , Seo Young-Gyun 
Published: November 2021

Background
Internet or mobile device use as a form of information and communication technology (ICT) can be more effective in weight loss and weight maintenance than traditional obesity interventions.

Objective
The study aims to assess the effectiveness of child-centered ICT interventions on obesity-related outcomes.

Methods
Articles were retrieved from the Cochrane Central Register of Controlled Trials, Embase, and PubMed web-based databases. We selected randomized controlled trials in which the participants were aged <18 years. The primary outcomes were BMI, body weight, BMI z-score, waist circumference, and percentage body fat.

Results
In total, 10 of the initial 14,867 studies identified in the databases were selected according to the inclusion criteria. A total of 640 participants were included in the intervention group and 619 in the comparator group. Meta-analyses were conducted considering various subgroups (intervention type, comparator type, target participants, mean age, sex, BMI status, and follow-up period). Overall, ICT interventions demonstrated no significant effect on BMI, body weight, BMI z-score, waist circumference, and percentage body fat. Subgroup analyses revealed that the effect of the intervention was statistically significant for the following: web intervention (weighted mean difference [WMD]=−1.26 kg/m2, 95% CI −2.24 to −0.28), lifestyle modification comparator (WMD=−1.75, 95% CI −2.76 to −0.74), intervention involving both boys and girls (WMD=−1.30, 95% CI −2.14 to −0.46), and intervention involving obesity only (WMD=−1.92, 95% CI −3.75 to −0.09).

Conclusions
The meta-analysis results for children with obesity who used the web intervention program confirmed significant effects on BMI reduction compared with lifestyle modification. Evidence from the meta-analysis identified internet technology as a useful tool for weight loss in children with obesity.
Machine Learning–Based Hospital Discharge Prediction for Patients With Cardiovascular Diseases: Development and Usability Study
Authors: Ahn Imjin , Gwon Hansle , Kang Heejun , Kim Yunha , Seo Hyeram , Choi Heejung , Cho Ha Na , Kim Minkyoung , Jun Tae Joon , Kim Young-Hak 
Published: November 2021

Background
Effective resource management in hospitals can improve the quality of medical services by reducing labor-intensive burdens on staff, decreasing inpatient waiting time, and securing the optimal treatment time. The use of hospital processes requires effective bed management; a stay in the hospital that is longer than the optimal treatment time hinders bed management. Therefore, predicting a patient’s hospitalization period may support the making of judicious decisions regarding bed management.

Objective
First, this study aims to develop a machine learning (ML)–based predictive model for predicting the discharge probability of inpatients with cardiovascular diseases (CVDs). Second, we aim to assess the outcome of the predictive model and explain the primary risk factors of inpatients for patient-specific care. Finally, we aim to evaluate whether our ML-based predictive model helps manage bed scheduling efficiently and detects long-term inpatients in advance to improve the use of hospital processes and enhance the quality of medical services.

Methods
We set up the cohort criteria and extracted the data from CardioNet, a manually curated database that specializes in CVDs. We processed the data to create a suitable data set by reindexing the date-index, integrating the present features with past features from the previous 3 years, and imputing missing values. Subsequently, we trained the ML-based predictive models and evaluated them to find an elaborate model. Finally, we predicted the discharge probability within 3 days and explained the outcomes of the model by identifying, quantifying, and visualizing its features.

Results
We experimented with 5 ML-based models using 5 cross-validations. Extreme gradient boosting, which was selected as the final model, accomplished an average area under the receiver operating characteristic curve score that was 0.865 higher than that of the other models (ie, logistic regression, random forest, support vector machine, and multilayer perceptron). Furthermore, we performed feature reduction, represented the feature importance, and assessed prediction outcomes. One of the outcomes, the individual explainer, provides a discharge score during hospitalization and a daily feature influence score to the medical team and patients. Finally, we visualized simulated bed management to use the outcomes.

Conclusions
In this study, we propose an individual explainer based on an ML-based predictive model, which provides the discharge probability and relative contributions of individual features. Our model can assist medical teams and patients in identifying individual and common risk factors in CVDs and can support hospital administrators in improving the management of hospital beds and other resources.