bluuebunny/crossref_metadata_2025_split huggingface dataset:
Characterization of Wastewater Quality and Denitrification Potential of Domestic Wastewater
Authors: Khanh Nguyen Quoc , Chuang Shun-Hsing 
Published: May 2024

Abstract

Developing a simulation model requires precision in the parameters of the wastewater quality components. This study investigated the fractions of chemical oxygen demand (COD) and total nitrogen (TN) in influent and effluent at a domestic wastewater treatment plant in Taiwan. As per the COD categorization, raw wastewater includes 12.6% readily biodegradable COD and 45.6% slowly biodegradable COD that can participate in the denitrification process. Meanwhile, up to 62.8% ammonia nitrogen was present in wastewater, according to the TN classification. As a result, when using raw wastewater as a carbon source, the optimal COD/NO3-N was 6, indicating a low denitrification efficiency (merely 50% compared to 95% for acetate as a carbon source). The abundance of slowly biodegradable COD coupled with the denitrification rate of 0.73 mg N/g VSS.hr was approximately half the denitrification rate of readily biodegradable COD (1.67 mg N/g VSS.hr). Furthermore, this study has also shown the potential to use slowly biodegradable carbon sources in the denitrification process by contributing to improving the reduction efficiency by more than 50% when applying both carbon sources.
Contribution of wind and water erosion in the agricultural and pastoral areas of northern China based on 137Cs tracer technology
Authors: Jiang hongtao , Guo Chunrong , Li Xiaojia , Zhang Wanfeng , Du Pengfei , Guo Qiankun , Wang Yousheng , jing Wang 
Published: May 2024

Abstract

This study focuses on the important ecological challenges facing agricultural and pastoral areas in northern China: soil wind and water erosion, both of which contribute significantly to soil degradation. Understanding the relative impacts of these erosion types is crucial for effective erosion control measures. We explored the ratio of soil wind erosion to water erosion in an agro-pastoral zone based on topographic and geomorphic conditions using the 137Cs tracer method. The study showed that cropland is the land type that has suffered the most severe erosion in the region in recent decades. Specifically, on gentle slopes (6–8°), the rate of water erosion exceeded the rate of wind erosion by a factor of about eight, while on steeper slopes (10–15°) this trend was more pronounced, with the rate of water erosion exceeding the rate of wind erosion by a factor of about 27. This result was verified by the measured data in the previous study area. Taken together, water erosion dominates in the agricultural and pastoral areas of northern China, while wind erosion takes second place. Future erosion prevention strategies should prioritize hydraulic erosion control measures on sloping cropland. In addition, it is crucial to promote research on the compound mechanism of wind and water erosion to adopt more effective erosion control measures and ensure the sustainable development of the ecological environment in the region.
A Standardized M3Cs clinical validity framework for microRNA data in microRNA databases with application to MicroRNA Childhood Cancer Catalog (M3Cs) version 2.0
Authors: Rashed Wafaa M. , Abdel-Razek Ahmed H. , Rezk Mohamed A. , Rezk Ahmed A. , Soudy Mohamed , Shawky Sherif M. , Hassan Mohamed Kamel , Matboli Marwa 
Published: May 2024

Abstract

The rapid expansion of miRNA research as cancer biomarkers has spurred the development of user-friendly miRNA-disease databases. However, integrating these bioinformatics tools into clinical practice requires innovative approaches. Digital innovation (DIN) represents the innovative link to integrate proposed ‘M3Cs standardized clinical validity framework’ in digital miRNA-disease databases. Our team diligently adhered to the standard workflow of DIN, following each step of the process: Initiate, Develop, Implement, and Exploit. Here we describe a framework for the accurate and standardized clinical validity categorization of miRNAs in databases and validate it by application to the MicroRNA Childhood Cancer Catalog version 2.0 (M3Cs V.2.0) as the product of DIN. The framework provides a semiquantitative measurement for the strength of evidence of a miRNA-disease relationship that correlates to a qualitative classification: "Strong”, “Moderate”, “Limited”, "No Known Trait/Trait subtype Relationship” or "Contradictory Evidence”. Classifications derived from this framework are reviewed and confirmed or adjusted based on clinical expertise of appropriate disease experts. To our knowledge, this is the first catalog to connect miRNAs data with the clinical impact as a validated tool in childhood cancer. We believe that widespread adoption of this framework will lead to improved analysis, interpretation, and integration of miRNA data toward clinical utility.
A causal relationship between bone mineral density and breast cancer risk: a Mendelian randomization study based on East Asian population
Authors: Cheng Ziyang , Xu Hui , Wang Xin , Teng Tao , Li Bin , Cao Zhong , Li Zhichao , Zhang Jiayi , Xuan Jin , Zhang Fengyi , Chen Yaxin , Li Yujie , Wang Wenbo 
Published: May 2024

Abstract

Background
Breast cancer (BC) poses significant burdens on women globally. While past research suggests a potential link between bone mineral density (BMD) and BC risk, findings remain inconsistent. Our study aims to elucidate the causal relationship between BMD and BC in East Asians using bidirectional Mendelian randomization (MR) and reverse MR analyses.
Methods
Genetic association data for bone mineral density T-scores (BMD-T) and Z-scores (BMD-Z) (Sample size = 92,615) and breast cancer from two different sources (Sample size1 = 98,283; Sample size2 = 79,550) were collected from publicly available genome-wide association studies (GWAS). Single-nucleotide polymorphisms (SNPs) associated with BMD-T and BMD-Z as phenotype-related instrumental variables were used, with breast cancer as the outcome. As the primary means of causal inference, the inverse variance weighted (IVW) approach was employed. Heterogeneity analysis was conducted using Cochran's Q test, while MR-Egger regression analysis was implemented to assess the pleiotropic effects of the instrumental variables. Sensitivity analyses were performed using methods such as MR-Egger, weighted median, and weighted mode to analyze the robustness and reliability of the results. The MR-PRESSO method and the RadialMR were used to detect and remove outliers. The PhenoScanner V2 website was utilized to exclude confounding factors shared between BMD and BC. Then, the meta-analysis method was applied to combine the MR analysis results from the two BC sources. Finally, a reverse MR analysis was conducted.
Results
The results of the IVW method were consolidated through meta-analysis, revealing a positive correlation between genetically predicted BMD-T (OR = 1.22, 95% CI: 1.13–1.33, P < 0.001) and BMD-Z(OR = 1.17, 95% CI: 1.09–1.26, P < 0.001)with increased BC risk.The MR-Egger regression suggested that neither of these causal relationships was affected by heterogeneity or horizontal pleiotropy. The sensitivity analyses supported the IVW results, indicating the robustness of the findings. Reverse MR analysis showed no causal relationship between BC and BMD.
Conclusion
Our MR study results provide evidence for a causal relationship between BMD and BC risk, which suggests the significance of BMD screening in detecting and preventing BC.
Characterization and Expression Analysis of MADS-box gene AGL8 in Cotton: Insights into gene function differentiation in plant growth and stress resistance
Authors: Wang Zhicheng , Cao Yuefen , Jiang Yurong , Ding Mingquan , Rong Junkang 
Published: May 2024

Abstract

Background
AGAMOUS-LIKE 8 (AGL8) belongs to the MADS-box family, which plays an important role in transcriptional regulation, sequence-specific DNA binding and other biological processes and molecular functions. As a representative of polyploid plants, cotton genome contain multiple AGL8 genes. However, their differentiation in function is still unclear.
Methods and Results
This study conducted a comprehensive genomic analysis of AGL8 genes. Cotton AGL8s were subdivided into four subgroups (Groups 1, 2, 3, and 4) based on phylogenetic analysis, and different subgroups of AGL8s showed different characteristics, including structure and conserved motifs. In the promoter region of GhAGL8 genes, we successfully predicted cis-elements that respond to phytohormone signal transduction and stress response of plants. Based on transcriptome data and real-time quantitative PCR validation, the results indicated that three genes, GH_D07G0744, GH_A03G0856 and GH_A07G0749, were highly induced by MeJA, SA, and ABA, which indicated that they function in plant resistance to abiotic and biological stresses.
Conclusions
The information from gene structure, number and types of conserved domains, tissue-specific expression levels, and expression patterns under different treatments indicates the differentiation of sequence and function in cotton AGL8 genes. Different AGL8s play a role in vegetative growth, reproductive development, and plant stress resistance.These results laid a foundation for further study of GhAGL8s in cotton.
RETRACTED: Mechanical overloading induces chondrocyte apoptosis via lysosomal acidification loss and TRPML1 hyperactivation
Authors: Yao Hanyu , Ooi Hon Son , Lan Yujian , Yi Chizhe , Su Yiming 
Published: May 2024

Abstract

The authors have requested the removal of their preprint from Research Square, due to current circumstances that make preprinting challenging at this time. The author's request does not imply errors or misconduct.
COVID-19 Risk Perceptions in Japan: A Cross Sectional Study
Authors: Chiba Asako , Nakata Taisuke , Nguyen Thuy Linh , Takaku Reo 
Published: May 2024

Abstract

We conducted a large-scale online survey in February 2023 to investigate the perceptions of infection and fatality risks from COVID-19 in Japan. Univariate analysis comparing perceived and actual risk suggests prevalence of overestimation as well as non-negligible underestimation of COVID-19 risks in Japan. Multivariate logistic regression analysis reveals that age, income and educational levels, health status, information sources, and experiences related to COVID-19 are associated with the subjective assessments of infection and fatality risks. Given that risk perceptions are closely correlated with daily socio-economic activities and well-being, it is important for policymakers and public health experts to understand how to communicate COVID-19 risks to the public effectively.
Machine learning applied to global scale species distribution models (SDMs)
Authors: Fuster-Alonso Alba , Mestre-Tomás Jorge , Baez Jose Carlos , Pennino Maria Grazia , Barber Xavier , Bellido Jose María , Conesa David , López-Quílez Antonio , Steenbeek Jeroen , Christensen Villy , Coll Marta 
Published: May 2024

Abstract

Species Distribution Models (SDMs) have been widely applied in ecology to analyze the historical and future patterns of marine species' distributions. With the increasing impact of climate change in recent decades, understanding potential shifts in species distributions has become a crucial challenge. Research on alterations in spatial and temporal distributions has revealed an increasing focus on developing different statistical approaches for global-scale and long-term forecasts. One promising approach is Bayesian Additive Regression Trees (BART), a non-parametric machine learning tool based on a sum-of-trees model that could be useful for addressing ecological problems. The goal of this study is to apply BART on a global scale and use it to estimate and predict possible present and future habitats of marine species under different climate change scenarios. Here we show an application of BART focused on the functional group of marine turtles, analyzing their historical and future distributions both individually and as a taxonomic group, their relationship with environmental variables, and BART's capacity to predict long-term distributions at global scales. Furthermore, to assess the capabilities of BART, we conduct a simulation study under two distinct scenarios: 1) simulating a hypothetical cosmopolitan species distribution and 2) simulating a hypothetical persistent species distribution. Our results show that BART is a promising approach to predict the potential distribution of our target species, as well as their relationship with key environmental variables, on a global scale.
Seismic fragility assessment of existing Italian overpass bridges
Authors: d'Aragona Marco Gaetani , Recupero Antonino , Prota Andrea 
Published: May 2024

Abstract

This study introduces a three-dimensional refined finite element model that is suitable for dynamic analysis of existing overpass bridges with Multi-Span Simply-Supported scheme. The proposed modeling approach allows to realistically reproduce the bridge behavior under seismic loadings via a proper simulation of the expected behavior and failure modes of each bridge component, as well as their mutual interaction. Two different Limit States, as well as a definition of system-level collapse, are introduced depending on the behavior of the bridge components and the interaction between the superstructure and the substructure. The proposed modeling approach is adopted to generate Limit State fragility curves adopting a stochastic approach based on cloud analysis which entails the use of real recorded ground motions for a reference bridge, designed and constructed in southern Italy in the 1970s. The results show that the proposed simulation approach is suitable to realistically reproduce the complex behavior of this type of bridge providing significant insights into their vulnerability.
PPMs, PGPB and Microalgae as Three Distinct Biofertilizers on Lettuce Health
Authors: ŞEN Nurgül KITIR 
Published: May 2024

Abstract

The utilization of biofertilizers has emerged as a promising approach in modern agriculture to enhance crop productivity and quality while promoting environmental sustainability. In this study, we investigated the efficacy of three distinct biofertilizers, namely Potential Plant Probiotic Microorganisms (PPMs), Plant Growth-Promoting Bacteria (PGPB), and Microalgae, on the health on lettuce (Lactuca sativa L. var.Crispa). The experiment was conducted in a controlled environment of a high tunnel greenhouse at Gebze Technical University. The study encompassed a comprehensive analysis of various growth parameters, including plant height, stem diameter, leaf count, plant weight, root length, and chlorophyll content related with plant health. Our findings demonstrate that the application of PPMs, PGPB, and Microalgae biofertilizers significantly improved lettuce yield and quality compared to traditional fertilization methods. Moreover, these biofertilizers positively influenced soil and plant health, contributing to enhanced microbial diversity and soil structure. Additionally, our study highlights the environmental and economic benefits associated with biofertilizer usage, paving the way for sustainable agricultural practices. Overall, this research underscores the potential of PPMs, PGPB, and Microalgae biofertilizers as effective alternatives in lettuce cultivation, offering promising prospects for future agricultural innovations and practices.
Development of an m6A subtype classifier to guide precision therapy for patients with bladder cancer
Authors: Zhang Ganghua , Yang Jingxin , Fang Jianing , Yu Rui , Yin Zhijing , Chen Guanjun , Tai Panpan , He Dong , Cao Ke , Jiang Jiaode 
Published: May 2024

Abstract

Purpose: Bladder cancer (BLCA) is a highly heterogeneous tumor. We aim to construct a classifier from the perspective of N6-methyladenosine methylation (m6A) to identify patients with different prognostic risks and treatment responsiveness for precision therapy.
METHODS: Data of gene expression profile, mutation, and clinical characteristics were mainly obtained from the TCGA-BLCA cohort. Unsupervised clustering was performed to construct m6A subtypes. The tumor microenvironment (TME) landscapes was explored by using ssGSEA, ESTIMATE, and MCPcounter algorithms. K-M survival curves and Cox regression analysis were used to demonstrate significance of m6A subtypes in predicting prognosis. pRRophetic, oncoPredict and TIDE algorithms were used to evaluate responsiveness to antitumor therapy. A classifier of m6a subtypes was finally developed based on random forest and artificial neural network (ANN).
RESULTS: The two m6A subtypes have significantly different m6A-related gene expression profiles and mutational landscapes. TME analysis showed a higher level of stromal and Inhibitory immune component in subtype B compared with subtype A. The m6A subtype is a clinically independent prognostic predictor of BLCA, subtype B have a poorer prognosis. Drug sensitivity analysis showed that subtype B have lower IC50 values and AUC values for cisplatin and docetaxel. Efficacy assessment showed significantly poorer radiotherapy efficacy and lower immunotherapy responsiveness in subtype B. We finally constructed an ANN classifier to accurately classify BLCA patients into two m6A subtypes.
CONCLUSION: Our study developed a classifier for identifying subtypes with different m6A characteristics, and BLCA patients with different m6A subtypes have significantly different prognosis and responsiveness to antitumor therapy.
Improving eSource Site Start-Up Practices
Authors: Cramer Amy E. , King Linda S. , Buckley Michael T. , Casteleyn Peter , Ennis Cory , Hamidi Muayad , Rodrigues Gonçalo M. C. , Snyder Denise C. , Vattikola Aruna , Eisenstein Eric L. 
Published: May 2024

Abstract

Background: eSource software that copies patient electronic health record data into a clinical trial electronic case report form holds promise for increasing data quality while reducing data collection, monitoring and source document verification costs. Integrating eSource into multicenter clinical trial start-up procedures could facilitate the use of eSource technologies in clinical trials.
Methods: We conducted a qualitative integrative analysis to identify eSource site start-up key steps, challenges that might occur in executing those steps, and potential solutions to those challenges. We then conducted a value analysis to determine the challenges and solutions with the greatest impacts for eSource implementation teams.
Results: There were 16 workshop participants: 10 pharmaceutical sponsor, 3 academic site, and 1 eSource vendor representatives. Participants identified 36 Site Start-Up Key Steps, 11 Site Start-Up Challenges, and 14 Site Start-Up Solutions for eSource-enabled studies. Participants also identified 77 potential impacts of the Challenges upon the Site Start-Up Key Steps and 70 ways in which the Solutions might impact Site Start-Up Challenges. The most important Challenges were: (1) not being able to identify a site eSource champion and (2) not agreeing on an eSource approach. The most important Solutions were: (1) vendors accepting electronic data in the FHIR standard, (2) creating standard content for eSource-related legal documents, and (3) creating a common eSource site readiness checklist.
Conclusions: Site start-up for eSource-enabled multi-center clinical trials is a complex socio-technical problem. This study’s Start-Up Solutions provide a basic infrastructure for scalable eSource implementation.
RS-YOLO: A YOLO-Based Method for Small Object Detection in Remote Sensing
Authors: Ao Dun , Zhao Wentao , Zhang Yu , Lei Fei 
Published: May 2024

Abstract

Since the objects are usually small and densely distributed, the current mainstream algorithms have poor performance in the task of object detection in remote sensing, and are prone to produce missed detection and false detection. Moreover, remote sensing images(RSI) contain complex background noise, which further exacerbates this phenomenon. To settle these problems, this paper proposes a novel small object detection model in remote sensing, named RS-YOLO. In order to suppress the background noise and enhance the attention to small objects, by integrating Biformer, a dynamic attention mechanism, we construct an improved backbone network. Then, an efficient neck structure based on Gather-and-Distribute(GD) mechanism is introduced to enhance the feature fusion capability of the model. In addition, in order to fully utilize shallow features, we simplify the deep layer structure to reduce its competitiveness in the label assignment process. The strategy simultaneously improves the accuracy and speed of the model. Moreover, we combine Inner-IoU and CIoU loss functions as the new bounding box regression(BBR) loss function, which enhances the localization performance and robustness of the model, and speeds up the training time. Finally, on AI-TOD and DIOR datasets, the mean Average Precision (mAP) of our proposed RS-YOLO reaches 56.3\% and 74.2\% respectively, which exceeds the baseline model and other mainstream algorithms.
The Mms22-Rtt107 axis dampens the DNA damage checkpoint by reducing the stability of the Rad9 checkpoint mediator
Authors: Zhao Xiaolan , Wan Bingbing , Guan Danying , Li Shibai , Chwat-Edelstein Tzippora 
Published: May 2024

Abstract

The DNA damage checkpoint is a highly conserved signaling pathway induced by genotoxin exposure or endogenous genome stress. It alters many cellular processes such as arresting the cell cycle progression and increasing DNA repair capacities. However, cells can downregulate the checkpoint after prolonged stress exposure to allow continued growth and alternative repair. Strategies that can dampen the DNA damage checkpoint are not well understood. Here, we report that budding yeast employs a pathway composed of the scaffold protein Rtt107, its binding partner Mms22, and an Mms22-associated ubiquitin ligase complex to downregulate the DNA damage checkpoint. Mechanistically, this pathway promotes the proteasomal degradation of a key checkpoint factor, Rad9. Furthermore, Rtt107 binding to Mms22 helps to enrich the ubiquitin ligase complex on chromatin and target the chromatin-bound form of Rad9. Finally, we provide evidence that the Rtt107-Mms22 axis operates in parallel with the Rtt107-Slx4 axis, which displaces Rad9 from chromatin. We thus propose that Rtt107 enables a bifurcated “anti-Rad9” strategy to optimally downregulate the DNA damage checkpoint.
Simulating Institutional Change for Governing Natural Common-Pool Resources: An Agent-Based Model Approach
Authors: Mohammadi Maede , Gharakhani Sasan 
Published: May 2024

Abstract

Many local communities rely on natural Common-Pool Resources (CPRs) for their economic well-being. However, these resources often suffer from overexploitation due to the tragedy of the commons. Despite this challenge, evidence shows that local inhabitants can effectively govern CPRs through institutional rules. However, the specific mechanisms driving the emergence and changes in CPR governance still require further exploration. Agent-Based Models (ABMs) offer valuable insights into these dynamics by bridging the micro and macro levels of analysis. In this study, researchers employed the grammar of institutions (ADICO) to systematically model the mechanisms of emergence, institutional changes, and user strategies in CPR governance. The results demonstrate that self-governing institutions, which enforce rules by detecting cheaters and excluding them from resource extraction, outperform open-access arrangements. Furthermore, the study reveals that more complex institutional changes lead to weaker governance performance, while stricter rule enforcement results in higher resource stock levels but lower user payoffs. Lastly, the study suggests that reducing costs imposed on users is vital to prevent resource overexploitation. These findings enhance our understanding of the mechanisms behind successful CPR governance and can inform policy decisions aimed at promoting sustainable resource utilization.
JEL classification: Q57, Q50, C63