bluuebunny/crossref_metadata_2025_split huggingface dataset:
Rhein kills Actinobacillus pleuropneumoniae, reduces biofilm formation, and effectively treats bacterial lung infections in mice
Authors: Ding Haifeng , Bai Yilin , Luo Weiyu , Li Hao , Zhu Chunling , Zhao Xueqin , Sun Huarun , Wen Yuliang , Zhang Wei , Zhang Shouping , Wang Yimin , Zhang Huihui , Chen Shijun , Zhang Gaiping , Wei Zhanyong , Hu Jianhe , Wang Lei , Bai Yueyu 
Published: August 2023

Abstract

Actinobacillus pleuropneumoniae, a member of the Pasteurellaceae family, is known for its highly infectious nature and is the primary causative agent of infectious pleuropneumonia in pigs. This disease poses a considerable threat to the global pig industry and leads to substantial economic losses due to reduced productivity, increased mortality rates, and the need for extensive veterinary care and treatment. Rhein, as an antimicrobial compound, is one of the effective components extracted from Polygonaceae plants which has effects against kinds of bacteria. Here, in this study, we found that Rhein had a good killing effect on A. pleuropneumoniae and that the minimum inhibitory concentration (MIC) was 25 µg/mL. After 3h of action, Rhein (4×MIC) completely kills A. pleuropneumoniae and Rhein has good stability. In addition, the treatment with Rhein (1×MIC) significantly reduced the formation of bacterial biofilms. Therapeutic evaluation in a murine model showed that Rhein protects mice from A. pleuropneumoniae and relieves lung inflammation. Quantitative RT-PCR results showed that Rhein treatment significantly downregulated the expression of the IL-18ཤTNF-αཤp65ཤp38 gene. This study reports the activity of Rhein against A. pleuropneumoniae and its mechanism and reveals, the ability of Rhein to treat A. pleuropneumoniae infection in mice, laying the foundation for the development of new drugs for bacterial infections.
Spontaneous segregation of visual information between parallel streams of a multi-stream convolutional neural network
Authors: Tamura Hiroshi 
Published: August 2023

Abstract
Visual information is processed in hierarchically organized parallel pathways in the primate brain. In lower cortical areas, color information and shape information are processed in a parallel manner, while in higher cortical areas, various types of visual information, such as color, face, animate/inanimate, are processed in a parallel manner. In the present study, the possibility of spontaneous segregation of visual information in parallel streams was examined by constructing a convolutional neural network with parallel architecture in all of the convolutional layers. The results revealed that color information was segregated from shape information in most model instances. Deletion of the color-related stream decreased recognition accuracy in the inanimate category, whereas deletion of the shape-related stream decreased recognition accuracy in the animate category. The results suggest that properties of filters and functions of a stream are spontaneously segregated in parallel streams of neural networks.
Insights on sustainability of Earth Science data infrastructure projects
Authors: Virapongse Arika , Gallagher James , Tikoff Basil 
Published: August 2023

Abstract
In this paper, we studied eleven long-term data infrastructure projects, most focused on the Earth Sciences, to understand characteristics that contributed to their project sustainability. Among our sample group, we noted the existence of three different types of project groupings: Database, Framework, and Middleware. Our results show that most efforts started as funded research projects, and nearly all became organizations in order to become sustainable. Projects were often funded for short time scales, but had the long-term burden of sustaining and supporting open science, interoperability, and community building–activities that are difficult to fund directly. This transition from “project” to “organization” was challenging for most efforts, and specifically in regards to leadership change and funding issues. Some common approaches to sustainability were identified within each project grouping. Framework and Database projects both relied heavily on the commitment to, and contribution from, a disciplinary community. Framework projects often used bottom-up governance approaches to maintain the active participation and interest of their community. Database projects succeeded when they were able to position themselves as part of the core workflow for disciplinary-specific scientific research. Middleware projects borrowed heavily from sustainability models used by software companies, while maintaining strong scientific partnerships. Cyberinfrastructure for science requires considerable resources to develop and sustain itself, and much of these resources are provided through in-kind support from academics, researchers, and their institutes. It is imperative that more work is done to find appropriate models that help sustain key data infrastructure for Earth Science over the long-term.
Multi-Objective Feature Selection for Structure Preservation in Multi-Label Data Using Particle Swarm Optimization
Authors: Karimzadeh Masoud , Moradi Parham , Ghaderzadeh Abdulbaghi 
Published: August 2023

Abstract

In order to make the data more manageable and improve the accuracy of machine learning models, data need to be preprocessed. Data preprocessing involves a series of steps such as data cleaning, data transformation, feature selection, and feature extraction. Data cleaning involves removing or correcting data that is incorrect, incomplete, irrelevant, or duplicated. Data transformation includes normalization, standardization, aggregation, and discretization. Feature selection involves selecting the most relevant features from the dataset while feature extraction involves transforming existing features into new features. Our proposed evaluation function combines a multi-label accuracy measure with a feature selection measure. In this paper a new assessment method based on swarm intelligence for feature selection. The suggested technique utilizes a multi-objective fitness function by utilizing mutual information between features and labels. We conduct experiments on five real-world datasets. The results show that proposed evaluation function in some cases outperforms the existing methods.
The Impact of Typical Plant Community Characteristics in the Langya Mountain Urban Forest Park on Climate Effects
Authors: Ren Liying , Hua Rui , Gu Shiguo , Gu Liuwan , Li Yinuo 
Published: August 2023

Abstract
With the rapid development of urbanization, the urban heat island effect poses a huge threat to the urban climate environment, which objectively reflects the microclimate effect of urban forest parks on urban climate. The paper investigates the characteristics of typical plant communities and the relationship between plant communities and microclimate regulation in Langya mountain urban forest park, and explores the transformation of plant communities on their occupied environment. By monitoring the impact of typical characteristics of different plant communities on the microclimate of forest parks, the microclimate effect of plant communities of different forest types was studied, and the regulation of plant communities in urban forest parks on local Microclimate was analyzed. Our findings indicate that the community forest density and plant coverage at sampling point C are relatively high, with a canopy density close to 0.85, which has no significant impact on refrigeration and humidification, but can effectively increase the concentration of negative ions in the air. Moreover, sampling site C belongs to arbor, bush, and shrub layer structure with high green content, which has a good effect on dust reduction and retention.
Diabetes mellitus and ankylosing spondylitis: A bidirectional Mendelian randomization study.
Authors: Luo hongsheng , Pan bingcheng , Liu shuanghua , Huang weidong , Tan yimei , Gan zhenyu , Pan zhenhua , Huang menghe , Zhong dayuan , Wu shuan 
Published: August 2023

Abstract
Background: Previous studies have shown a higher prevalence of diabetes mellitus (DM) in the ankylosing spondylitis (AS) population, but there are only a very few observational studies of the two diseases, and there is still a lack of research on the causal relationship between ankylosing spondylitis and diabetes mellitus. Therefore, we took an mendelian randomization (MR) approach to explore the causal relationship between AS and DM.
Methods: MR can be used to investigate whether there is a causal relationship between DM and AS using single nucleotide polymorphisms (SNPs). We performed bidirectional Mendelian randomization analysis to explore the causal relationship between AS and DM, and the data associated were obtained from the FinnGen Research Project.
Results: After a rigorous experimental design and instrumental variable screening, we could find a causal relationship between AS and Type 1 diabetes mellitus (T1DM), and AS reduced the risk of T1DM (IVW-MRE: P=2.92E-04; WM: P=3.39E-05; Outlier-corrected: P=8.48E-03,OR for IVW-MRE: 0.910, 95% CI: 0.865-0.958; OR for WM: 0.910, 95% CI: 0.870-0.951). We did not derive reliable results between AS and T2DM, but due to the results of weight median (p=1.74E-02<0.05), and the fact that the OR of all three methods of MR analysis was less than 1, we can conclude that patients with AS are at a lower risk of developing Type 2 diabetes mellitus (T2DM). In the reverse study, it was not concluded that DM increased the risk of AS (P>0.05 for all four MR methods).
Conclusion：The current study does not support the previous view that the prevalence of DM is higher in patients with AS, confirms the conclusion that the risk of T1DM is lower in patients with AS, and that AS may reduce the risk of T2DM, but does not obtain evidence of the relevance of the effect of DM on AS.
Performance Comparison of Improved Machine Learning Algorithms Based on Bayesian Optimization in High-dimensional and Unbalanced COPD Data
Authors: Li Yiting , Wang Xuchun , Qiao Yuchao , Ren Jiahui , Ren Hao , Cui Yu , Liu Jing , Zhao Ruiqing , Qiu Lixia 
Published: August 2023

Abstract
Background and objective: Early identification of individuals at high risk of chronic obstructive pulmonary disease (COPD) is crucial for reducing related mortality rates and economic burden. However, conventional machine learning (ML) models have limitations when making predictions using COPD data that exhibit high-dimensional and unbalanced characteristics. Therefore, to address this issue, this study developed a well-performing Bayesian optimization (BO)-ML hybrid model combined with variable screening and resampling technology to construct a COPD risk prediction model.
Methods: We collected a sample of 4,747 COPD cases with no missing data from the 2019 COPD Surveillance project in Shanxi Province, and extracted 34 potentially relevant variables from the dataset. Firstly, we used the Smoothly Clipped Absolute Deviation (SCAD) method to select variables associated with COPD. Secondly, we oversampling the unbalanced data using Synthetic Minority Over-sampling Technique (SMOTE) algorithm. Thirdly, we construct risk prediction models in the training set using four BO-improved ML models, including BO-Decision Tree (DT), BO-Naive Bayes (NB), BO-Support Vector Machine (SVM) and BO-K-nearest neighbor (KNN). Finally, the predictive performance of the combined models is tested and evaluated.
Result: The SCAD method was used to select 14 variables specifically associated with COPD from a dataset of 34 features. After applying the SMOTE resampling method, the ratio of COPD patients to non-COPD patients in the dataset of this study was balanced at 1:1. In the construction process of the four ML models, this study utilized BO algorithm to identify their optimal hyperparameters. Furthermore, in the comparison of model performance, this study found that combining BO-ML hybrid models with data balancing techniques can improve their performance. Specifically, the combination of SMOTE and BO-NB demonstrated stable performance and attained high scores in the comprehensive evaluation index, with AUC and G-means values of 0.770 and 0.696 respectively.
Conclusion: Despite the challenges posed by high dimensionality, redundancy, and class imbalance in data set, the BO-NB model, when integrated with SCAD and SMOTE, has exhibited excellent performance in accurately identifying individuals at a high risk of COPD. It provides early warnings to clinical doctors, helping them take timely preventive measures.
Mangiferin-Mediated Sirtuin Modulation in Chemotherapy-Induced Cognitive Impairment: Insights from Computational Analysis
Authors: Reddy Shireesha Jannapu , B Harish Kumar. , Mutalik Srinivas , K Sreedhara Ranganath Pai , K Nandakumar 
Published: August 2023

Abstract
Context: Chemobrain, known as chemotherapy-induced cognitive impairment (CICI), is among the adverse effects of chemotherapy frequently observed in chronic breast cancer survivors. This condition is primarily attributed to the increased presence of free oxygen radicals and inflammatory cytokines both peripherally and centrally. SIRT1, a highly conserved evolutionary regulator, plays a pivotal role in protecting organisms from oxidative stress. Notably, elevated levels of SIRT1 are expressed in the hippocampal regions of the brain, a critical structure closely associated with learning and memory in the central nervous system. This correlation suggests that SIRT1 is implicated in the regulation of various cognitive disability models. Therefore, modulating SIRT1 activity has the potential to mitigate CICI by reducing oxidative stress markers within the brain.
Method: The current study involves molecular docking of Mangiferin and other phytochemicals previously established as activators of human SIRT1 protein. Subsequently, molecular dynamics simulations are conducted using the phytochemical that exhibited the highest docking score compared to others. These investigations encompass several key steps, including protein selection, protein and ligand preparation, receptor grid generation, ligand docking, calculation of binding free energy, and molecular dynamic simulation by using using Maestro version 12.1 using Schrodinger, LLC.
Enhancement of Growth Perfection and Optical Transmission in Rhodamine B-doped KDP Single Crystal for Photonic Applications
Authors: dhas sahaya jude Sathiya , Usharani S. , Priyavathani S. Janet , Umarani P. 
Published: August 2023

Abstract
In recent years, because of the rapid development persisting in the modern electronic industries, materials with the ability of producing highly efficient harmonic generation have acquired great demand as they are very much suitable for high-speed devices and for the progressing field of optical communication system. In the present work, we report the growth, diffraction and spectroscopic features of the pure and Rhodamine B (RB)-doped potassium dihydrogen phosphate (KDP) crystals. The title crystal was grown by the conventional slow evaporation technique. The XRD and UV-Visible spectral studies have been carried out to examine the structural and optical behavior of the grown crystals. Optical microscopic technique has been utilized to examine the surface features of the grown crystals and the impedance spectroscopic measurements have been carried out to know the electrical energy storage capability and electrical conductivity behaviour. Based on the growth features and the observed analytical results, it is authenticated that that the RB-doped crystal possesses better crystalline nature and optical transmittance and hence it is strongly suggested for the applications of photonic devices.
Tissue Differentiation and Diagnostic Accuracy of 99m Tc-PSMA and 99m Tc-MDP Bone Scans in Prostate Cancer Patients
Authors: Banouei Farshad 
Published: August 2023

Abstract
Purpose: Prostate cancer is currently the second most common cancer among men worldwide. Given the prevalence of this disease and the costs incurred by society in its diagnosis and treatment, awareness of diagnostic and therapeutic modalities and factors influencing their outcomes is of particular importance.
Methods: This prospective study aimed to investigate the diagnostic accuracy of 99mTc- PSMA scan and 99mTc-MDP Bone scan in 40 patients with prostate cancer and bone metastases. The study was conducted between 2020 and 2023, and the results were compared based on the tissue differentiation of cancerous tissues.
Results: The data analysis revealed that the diagnostic accuracy of 99mTc- PSMA scan and 99mTc-MDP Bone scan for grade groups 1-5 ranged from 95.4% to 100% and 95.4% to 100%, respectively. However, none of the results were statistically significant. Specifically, the 99mTc- PSMA scan demonstrated diagnostic accuracy percentages of 100%, 98.7%, 96.1%, 96.6%, and 95.4%, respectively, for grade groups 1 to 5. Similarly, the diagnostic accuracy of 99mTc-MDP Bone scan for grade groups 1 to 5 was 100%, 96.3%, 98.7%, 96.6%, and 95.4%, respectively.
Conclusion: The statistical analysis of the data suggests that tissue differentiation of prostate cancer does not impact the diagnostic accuracy of 99mTc- PSMA scan and 99mTc-MDP Bone scan. Moreover, the Gleason score of tissue samples did not affect the differentiation of cancerous tissues by the scans. Therefore, the findings suggest that 99mTc- PSMA scan and 99mTc-MDP Bone scan can provide accurate diagnostic results for prostate cancer patients with bone metastases, regardless of the tissue differentiation or Gleason score.
Word Prediction Model using an elaborated RNN integrated with N-gram for efficient Text Input
Authors: Ikegami Yukino , Tsuruta Setsuo , Kutics Andrea , Damiani Ernesto , Knauf Rainer 
Published: August 2023

Abstract
Smartphone users are beyond two billion worldwide. Heavy users of the texting application rely on input prediction to reduce typing effort. In languages based on the Roman alphabet, many techniques are available. However, Japanese text is based on multiple character sets such as Kanji, Hiragana and Katakana. For its time intensive input, next word prediction an open challenge. To tackle this, a hybrid language model is proposed. It integrates a Recurrent Neural Network (RNN) with an n-gram model. RNNs are powerful models for learning long sequences for next word prediction. N-gram models are best at current word completion. Our RNN language model predicts next words. According the “price” of the performance gain paid by a higher time complexity, our model best deploys on a client-server architecture. Heavily-loaded RNN-LM deploys on the server while the n-gram model on the client. It consists of an input layer equipped with word embedding, an output layer, and hidden layers connected with LSTMs (Long Short-Term Memories). Training is done via BPTT (Back Propagation Through Time). For robust training, BPTT is elaborated by learning rate refinement and gradient norm scaling. To avoid overfitting, the dropout technique is applied except for LSTM. Due to synergetic elaboration, it shows 10% lower perplexity than Zaremba’s excellent conventional models in our experiment. Our model has been incorporated into IME (Input Method Editor) we call Flick. In our experiment, Flick outperforms Mozc (Google Japanese Input) by 16% in time and 34% in the number of key strokes.
Accuracy of conventional fusion algorithms for precipitation estimates across the Chinese mainland
Authors: Jiang Qin , Fan Zedong , Xu Yun , Li Weiyue , Zhang Junhao 
Published: August 2023

Abstract

Multi-source data-fusion approaches have been developed for estimating regional precipitation. However, studies considering the specific upper limits of the improved gridded rainfall data for different fusion approaches are limited. Here, the potential ranges of accuracy improvement for satellite and reanalysis rainfall products were addressed using various machine learning fusion approaches, including multivariate linear regression (MLR), feedforward neural network (FNN), random forest (RF), and long short-term memory (LSTM), over the Chinese mainland. All four fusion methods reduce errors in the original precipitation products. The upper limits of accuracy improvement in terms of correlation coefficient (CC) and root mean square error (RMSE) were 30.65 and 15.27%, respectively. M-RF showed the best average CC (0.828) and RMSE (4.62 mm/day) in the four seasons. LSTM performed the best under light rainfall events, whereas MLR and RF exhibited better performance under moderate and heavy rainfall events, respectively. Overall, these results serve as a basis for the fusion approach and technique selection, based on the comprehensive validation in different climate zones, altitudes, and seasons over the Chinese mainland.
An early warning method on the carrying capacity of regional groundwater resources
Authors: Ling Minhua , Chen Jiangyu , Zhang Peize , Wei Xingling , Yu Lili 
Published: August 2023

Abstract

Traditional studies on the carrying capacity of groundwater resources mainly focus on a comprehensive evaluation, and this paper provides quantitative prediction and early warning on the carrying capacity of groundwater resources, which is important for groundwater management and protection. This study constructed an early warning indicator system for the carrying capacity of groundwater resources, established an early warning method on the carrying capacity of groundwater resources, determined the early warning grades of Henan Province and various cities from 2023 to 2025, and analyzed the spatial distribution pattern. The results showed that the early warning grades of Henan Province from 2023 to 2025 were mild warnings. In the subsystems, the early warning grades of the groundwater resources and economic society systems were mild warnings, while the early warning grades of the ecological environment system were moderate warnings. The early warning grades of the carrying capacity of groundwater resources in all cities were basically mild or moderate warnings, showing a downward trend. This study establishes an early warning method on the carrying capacity of groundwater resources that includes the selection of early warning indicators, prediction of indicators, and determination of the early warning grades which can be a reference for other regions.
A novel approach to precipitation prediction using a coupled CEEMDAN-GRU-Transformer model with permutation entropy algorithm
Authors: Zhao Jiwei , Nie Guangzheng , Yan Meng , Wang Yaowen , Wang Luyao 
Published: August 2023

Abstract

The accurate forecasting of precipitation in the upper reaches of the Yellow River is imperative for enhancing water resources in both the local and broader Yellow River basin in the present and future. While many models exist for predicting precipitation by analyzing historical data, few consider the impact of different frequency sequences on model accuracy. In this study, we propose a coupled monthly precipitation prediction model that leverages the adaptive noise complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), gated recurrent unit neural network (GRU), and attention mechanism-based transformer model. The permutation entropy (PE) algorithm is employed to partition the data processed by CEEMDAN into different frequencies, with different models utilized to predict different frequencies. The predicted results are subsequently combined to obtain the monthly precipitation prediction value. The model is applied to precipitation prediction in four regions in the upper reaches of the Yellow River and compared with other models. Evaluation results demonstrate that the CEEMDAN-GRU-Transformer model outperforms other models in predicting precipitation for these regions, with a coefficient of determination R2 greater than 0.8. These findings suggest that the proposed model provides a novel and effective method for improving the accuracy of regional medium and long-term precipitation prediction.