bluuebunny/crossref_metadata_2025_split huggingface dataset:
Genotype by environment interaction for grain yield in foxtail millet (Setaria italica) using AMMI model and GGE Biplot
Authors: Zhang Haiying , Feng Zhiwei , Wang Jin , Yun Xiaodong , Qu Fei , Sun Changqing , Wang Qian 
Published: June 2022

Abstract
Genotype by environment interaction (GEI) is a major limiting factor in the regional planting and breeding of cultivars. The objective of this study was to assess GEI for grain yield in foxtail millet cultivars grown in northwest China and identify promising genotypes and representative seed production environments. The study used 12 genotypes in eight environments, representative of early-maturing growing areas. Additive main effects and multiplicative interaction (AMMI) and genotype and genotype by environment (GGE) biplot analysis were used to analyze the data. AMMI analysis of variance (ANOVA) indicated that genotype (G), environment (E), and GEI had significant effects (p < 0.01). The E contributed the highest percentage of the total sum of squares (SS) (43.4%), followed by G×E (28.6%) and G (19.5%). Six stability parameters based on AMMI were used to analyze yielding stability. YG35, FH9, DT29, and ZZG21 were more stable. The correlation coefficients between the six parameters were > 0.8. The genomic selection index (GSI) was then used to identify the stable and high-yield genotypes (YG35, ZZG21, and DT29). GGE biplot analysis revealed the same cultivars as the best performers for being closest to the ideal cultivar. The test sites were classified into two mega-environments using GGE biplot analysis for genotype assessment and seed production. CF and XJ were identified as the closest to the ideal test site, with relatively strong discriminatory ability and representativeness. Therefore, the findings of this study provide insights for the regional planting and breeding of foxtail millet in the future.
Biolixiviation of metals of computers printed circuit boards by Acidithiobacillus ferrooxidans and metals bioremoval by mixed culture subjected to a magnetic field.
Authors: Silva Jessica , Dias Roseli , Cardoso Vicelma , de Resende Miriam Maria 
Published: June 2022

Abstract
Crushed and ground Printed Circuit Boards (PCBs) samples were characterized to evaluate copper, lead, and aluminum using morphological characterization by Scanning Electron Microscopy (SEM), Energy Dispersive X-Ray Spectroscopy (EDS), and X-Ray Fluorescence Spectroscopy (XRF). Bioleaching experiments using Acidithiobacillus ferrooxidans bacteria with and without pH adjustment in the inoculum were carried out in a shaker at 30 ºC and agitation of 100 rpm for a total duration of 12 days. The XRF characterizations showed 0.12% lead, 3.72% copper, and 12.73% aluminum in the PCBs. The metal solubilization experiments indicated higher values of total metal solubilization when performed with initial pH adjustment in the inoculum preparation. However, these experiments did not show higher values of metal solubilization by bioleaching. Sequential bioleaching assays with A. ferrooxidans followed by bioremoval metals by mixed culture in the presence and absence of field 5Hz magnetic had a total duration of 24h in the bioreactor with recirculation. The sequential experiments of bioremoval using mixed culture after bioleaching with initial adjustment of inoculum pH without field application had 100% removal of Al, 27.34% for Cu, and 96.43% for Pb from the lixiviate medium, and with the field application indicated 100% removal of Al, 83.82% for Cu, and 98.27% for Pb. A similar bioleaching experiment without inoculum pH adjustment and without field application had 99.74% removal for Cu and 91, 92% for Pb. The field application indicated 100% removal for Cu and 95.76% for Pb. Bioreactors with a magnetic field don’t show better removals to all metals analyzed.
Atomic excited states and the related energy levels⊗
Authors: Zadeh Dariush Habibollah 
Published: June 2022

Abstract
The general theory of atoms including the related excited states and energy levels is reviewed. Numerical calculations and detailed works for several elements are presented to establish a better understanding of excited states. The article seeks also for connections between atomic behavior, and the internal structures and inner electrons of atoms. Then the atomic excited energy levels are formulated using the corresponding atomic ionization energies and a new defined parameter called “Characteristic Exponent k”. Furthermore, a small data bank is generated using the calculated “characteristic exponents k” for elements to be utilized for future simulations, studies, research activities and a connection to the type of atoms and their internal atomic structures.
Prediction of Forest Fire Risk for Artillery Military Training using Weighted Support Vector Machine for imbalanced data
Authors: Nam Ji Hyun , Mun Jongmin , Jo Seongil , Kim Jaeoh 
Published: June 2022

Abstract
South Korea has been in a truce with North Korea since 1953, and conventional artillery firing training are still needed considering its geopolitical location. Based on the weather condition, the decision is made whether to conduct artillery training, which might cause forest fires. Wildfires caused by military training not only devastate forests but also worsen public opinion on national defense. While forest fires triggered by artillery training cause substantial damage, the number of occurrences is very low, making it difficult to construct a prediction model in a general manner. In this paper, the weighted support vector machine for imbalanced data is applied to predict the risk of forest fire due to artillery firing training. We employ an over-sampling technique based on a probability distribution for imbalanced data and applied a weighted support vector machine algorithm that enforces a misclassification cost of the minority class. This study not only considerably reduces the probability of forest fires occurring during conventional artillery fire training in the Republic of Korea Army (ROKA) but also encourages the development of practical approaches for wildfires prediction in countries with climates similar to the Korean Peninsula. Furthermore, our proposed method can contribute to the study of the classification model for various imbalanced data. Through Monte Carlo simulations, we demonstrate that our proposed method achieves significantly higher accuracy than traditional methods.
GHDC: A Dual-Centric Data Center Network Architecture by Using Multi-port Servers with Greater Incremental Scalability
Authors: Zhou Peng , Lin Longxin , He Tengjiao , Zhang Zhen 
Published: June 2022

Abstract
As the volume of data keeps growing rapidly, more and more servers are continuously added into data centers. The industry experience indicates that, in order to reduce the cost and energy consumption, data enter network (DCN) can be expanded by adding a small number of servers from time to time instead of adding a huge number of servers at a time. The DCN structures with incremental scalability can be expanded gradually while their topological properites are preserved. In order to improve the performance of the DCN, dual-centric DCN structures are proposed. However, the incremental scalabilities of the current dual-centric DCNs are not good. This paper proposes a new type of dual-centric data center network structure called GHDC (Generalized Hypercube Data Center network architecture), which is constructed by using commodity switches and multi-port servers. After analyzing the shortest distance between any two vertices, a routing algorithm is developed. To achieve incremental scalability, two incomplete GHDC structures are proposed. A small number of servers can be added into the incomplete GHDC structures while their topological properties are maintained. The analysis results show that GHDC significantly outperform FCell, FRectangle and FSquare in terms of the incremental scalability and robustness. The average through-put of GHDC is approximately equal to that of FSquare and highter than that of FCell and FRectangle by about 17.45% and 25.5%. Compared with the FCell, FRectangle and FSquare, GHDC reduces the cost andenergy consumption by about 10.84%, 22.85% and 29.55%, respectively.
A Study into Patient Similarity through Representation Learning from Medical Records
Authors: Memarzadeh Hoda , Ghadiri Nasser , Samwald Matthias , Shahreza Maryam Lotfi 
Published: June 2022

Abstract
Patient similarity assessment, which identifies patients similar to a given patient, can help improve medical care. The assessment can be performed using Electronic Medical Records (EMRs). Patient similarity measurement requires converting heterogeneous EMRs into comparable formats to calculate their distance. While versatile document representation learning methods have been developed in recent years, it is still unclear how complex EMR data should be processed to create the most useful patient representations. This study presents a new data representation method for EMRs that takes the information in clinical narratives into account. To address the limitations of previous approaches in handling complex parts of EMR data, an unsupervised method is proposed for building a patient representation, which integrates unstructured data with structured data extracted from patients' EMRs. In order to model the extracted data, we employed a tree structure that captures the temporal relations of multiple medical events from EMR. We processed clinical notes to extract symptoms, signs, and diseases using different tools such as medspaCy, MetaMap, and scispaCy and mapped entities to the Unified Medical Language System (UMLS). After creating a tree data structure, we utilized two novel relabeling methods for the non-leaf nodes of the tree to capture two temporal aspects of the extracted events. By traversing the tree, we generated a sequence that could create an embedding vector for each patient. The comprehensive evaluation of the proposed method for patient similarity and mortality prediction tasks demonstrated that our proposed model leads to lower mean squared error (MSE), higher precision, and normalized discounted cumulative gain (NDCG) relative to baselines.
Fine mapping and candidate gene analysis of QS_B3.1 conferring clubroot resistance in Brassica rapa
Authors: Pang Wenxing , Zhang Xue , Ma Yinbo , Wang Yingjun , Zhan Zongxiang , Piao Zhongyun 
Published: June 2022

Abstract
Chinese cabbage is an important leafy vegetable rich in many nutrients widely grown in China. Clubroot disease caused by an obligate biotrophic pathogen Plasmodiophora brassicae was rapidly spread and challenged to Chinese cabbage production. A clubroot resistance (CR) gene, QS_B3.1, was mapped on chromosome A03 of B. rapa. A Chinese cabbage line ‘CR510’, which harbor homozygous resistance locus QS_B3.1 was selected from a BC4F3 family. ‘CR510’ was crossed with a clubroot susceptible Chinese cabbage inbred line ‘59-1’. Total 51 recombinant plants were identified from an F2 population including 3000 individuals. These recombinants were selfed and the clubroot resistance of F2/3 families were evaluated. Finally, QS_B3.1 was fine mapped to an interval of approximately 386 kb between marker syau-InDel3024 and syau-InDel3008. According to the reference genome, total 54 genes including five encoding the TIR-NBS-LRR proteins was annotated in the fine mapped region. Further nine candidate’s gene expression in parental lines at 7, 14 and 21 days after inoculation of P.brassicae were evaluated. Bra019376, Bra019401, Bra019403 and Bra019410 are highly expressed in ‘CR510’ than ‘59-1’. Gene sequence of Bra019410 from ‘CR510’ was cloned and identified different from CRa. Therefore, Bra019376, Bra019401, Bra019403 and Bra019410 are the most likely candidates of QS_B3.1. Our research provides a valuable germplasm resource against P. brassicae and QS_B3.1 closely linked marker for marker-assisted selection in CR cultivars breeding.
Does cervical curvature affect neurological outcome after incomplete spinal cord injury without radiographic abnormalities (SCIWORA): 1-year follow-up
Authors: Qi Can , Cao Junming , Xia Hehuan , Miao Dechao , Liu Yaming , Guo Junfei , Li Zequn , Hou Zhiyong 
Published: June 2022

Abstract
Background
At present, surgery is the primary clinical treatment for SCIWORA patients, but conservative treatment still plays an important role in patients with incomplete spinal cord injury. As an important index of cervical spine degeneration, cervical curvature has an impact on the prognosis of spinal cord injury patients. This paper studied the prognosis of conservatively treated patients with SCIWORA and the correlation between cervical curvature and neurological prognosis.
Methods
A retrospective study was conducted in all the patients with SCI admitted to the Third Affiliated Hospital of Hebei Medical University between January 2017 and June 2020. Data were recorded in 106 eligible patients, including sex, age, injury factors, Cobb angle, CCI, CSA, and ASIA motor and sensory scores. The Wilcoxon sign rank sum test was used to analyze the data postinjury and at the 1-year follow-up. Pearson correlation analysis was performed for the Cobb angle, CCI and CSA. Simple linear regression analysis and multiple linear regression analysis were performed for each group of variables.
Results
The Wilcoxon signed rank sum test confirmed that the Cobb angle, the CCI and the CSA of the patients were not significantly different at the 1-year follow-up when compared with the postinjury values, and the ASIA motor and sensory scores were significantly improved. The Pearson correlation analysis showed correlations among the Cobb angle, the CCI and the CSA. Simple linear regression analysis and multiple linear regression analysis showed that the nerve recovery rate was negatively correlated with age and was positively correlated with the Cobb angle.
Conclusion
Conservative treatment of incomplete SCIWORA can achieve a good prognosis. There is a clear correlation between the Cobb angle, CCI and CSA, and the Cobb angle, as an important influencing factor, needs to be considered. For SCIWORA patients undergoing nonsurgical treatment, improving cervical curvature is beneficial to the prognosis of patients. Age negatively affects the neurological prognosis.
DA-UNet: Deep supervision and atrous inception-based U-Net combining CRF for automatic liver segmentation from CT
Authors: Lv Peiqing , Wang Jinke 
Published: June 2022

Abstract
Due to low contrast and the blurred boundary between liver tissue and neighboring organs sharing similar intensity values, the problem of liver segmentation from CT images has not yet achieved satisfactory performance and remains a challenge. To alleviate these problems, we introduce deep supervision (DS) and atrous inception (AI) technologies with conditional random field (CRF) and propose three major improvements that are experimentally shown to have substantive and practical value. First, we replace the encoder's standard convolution with the residual block. Residual block can increase the depth of the network. Second, we provide AI module to connect the encoder and decoder. AI allows us to obtain multi-scale features. Third, we incorporate the DS mechanism into the decoder. This helps to make full use of information of the shallow layers. In addition, we employ the Tversky loss function to balance the segmented and non-segmented regions and perform the further refinement with a dense CRF. Finally, we extensively validate the proposed method on three public databases: LiTS17, 3DIRCADb, and SLiver07. Compared to the state-of-the-art (SOTA) methods, the proposed method achieved increased segmentation accuracy for the livers with low contrast and the fuzzy boundary between liver tissue and neighboring organs and is, therefore, more suited for automatic segmentation of these livers.
Motifizer: a tool for parsing high-throughput sequencing datasets and quantitative comparative analyses of transcription factor-binding sites
Authors: Khan Soumen , Bhattacharjee Abhik , Badhe Pranav , Dongaonkar Chinmay , Gaglani Yash 
Published: June 2022

Abstract
BackgroundComparative analysis of Chromatin immunoprecipitation followed by massively parallel sequencing (ChIP-seq), combined with transcriptomics data (RNA-seq), can provide critical insights into gene regulatory networks controlled by transcription factors in a given biological context. While multiple programs exist, which need to be individually installed and executed to generate such information, a user-friendly tool with low system requirements which combines the various computational processes into one package, using a docker container, is lacking.ResultsIn this study, we present a user-interactive, infrastructure-independent computational pipeline, called Motifizer, which uses open source tools for processing ChIP-Seq and RNA-seq datasets. Motifizer performs extensive analysis of raw input data, performing peak calling, de-novo motif analysis and differential gene expression analysis on ChIP and RNA-seq datasets. Additionally, Motifizer can be used for analysis and quantitative comparison of transcription factor binding sites in user defined genomic regions. Motifizer also allows easy addition and/or changes to analysis parameters, thereby adding to the versatility of the tool.ConclusionThe Motifizer tool is an easy to use tool which uses a docker container system to install and execute ChIP-seq and RNA-seq data parsing. The Analysis module of Motifizer can be employed to identify putative targets involved in gene regulatory networks. Motifizer can be accessed by a large user base and does not require programming skill by the user. Motifizer can be locally installed from the repository (https://github.com/abhikbhattacharjee/Motifizer)
Open and Green Areas as a Gathering Area the Case of Küçükçekmece, Istanbul
Authors: Aksoy Yıldız 
Published: June 2022

Abstract
Gathering areas are needed for protected against possible risks that may arise during and after the earthquake. Green areas, which have an important role in the organic bond to be established between the ecological, physical, and economic functions before the earthquake, and human health and living space, assume the function of gathering areas after earthquakes and other possible disasters. The research aims to calculate, plan and create sufficient green areas that can be used after a possible earthquake taking into account the risk and disaster management approach. A competency assessment of gathering areas at the level of neighborhoods was made with the proposal of a norm of 2 m2 per capita. As a result of the evaluation, it was observed that norm deficit (area deficit) did not occur in the gathering areas in the neighborhood of Atakent, Beşyol, Fatih, Halkalı Merkez, Istasyon, Söğütlüçeşme, Tevfikbey and Yeşilova.
The influence of different glucose tolerance on QTc interval: a population-based study
Authors: Lin Ning , Zhang Hongmei , Li Xiaoyong , Niu Yixin , Gu Hongxia , Lu Shuai , Yang Zhen , Qin Li , Su Qing 
Published: June 2022

Abstract
Background
Corrected QT (QTc) interval has been reported to be associated with type 2 diabetes. This study aimed to explore the relationship between different glucose tolerance and QTc interval among middle-aged and older Chinese individuals.
Methods
We conducted a cross-sectional analysis that included 9898 subjects (3194 men and 6704 women) in a Chinese population. Glucose tolerance was studied during OGTT. Insulin, blood pressure, HbA1c, serum lipids, hepatic transaminases and waist-to-hip ratio were assessed.The QTc interval was derived from ECG recordings, and the subjects were stratified based on different glucose tolerance.
Results
QTc interval levels were increased significantly in the subjects with abnormal glucose metabolism compared with the normal glucose regulation group. Multiple regression analyses QTc interval was significantly associated with fasting plasma glucose, 2-h OGTT plasma glucose and HbA1c. The odds ratios of prolonged QTc was 1.396 for IFG/IGT (95% CI: 0.126–1.730), 1.342 for type 2 diabetes (95% CI: 0.142–1.577) after all the potential confounders were adjusted.
Conclusions
IGR and diabetes are associated with prolonged QTc interval among middle-aged and older Chinese individuals. Abnormal glucose regulation can be used to monitor QTc interval in the population.
Hydrological and water quality simulation and future runoff prediction under CMIP6 scenario in the upstream basin of Miyun Reservoir
Authors: Zhang Jing , Ma Sijie , Song Yongyu 
Published: June 2022

AbstractWater resources are a key factor restricting human survival and social and economic development. The Miyun Reservoir, which is the only surface drinking water source in Beijing and the water storage reservoir of the South-to-North Water Diversion Project, plays a decisive role in ensuring water-use sustainability of the capital. This study focused on the Baihe River Basin, which is one of the important inflows of the Miyun Reservoir. The mathematical data on the climate and hydrological changes of the Baihe River Basin over the past 30 years were obtained, and the evolution law of the Baihe River runoff and its influencing factors was analyzed. Our analysis used the data obtained via multi-source meteorology to establish the Soil and Water Assessment Tool (SWAT) model of the Baihe River Basin. Sequential Uncertainty Fitting version 2 (SUFI-2) and Generalized Likelihood Uncertainty Estimation (GLUE) were used to simulate runoff from 1990 to 2017 on a monthly scale, along with sediment, total nitrogen (TN), and total phosphorus (TP) contents from 1990 to 2010. Then, the parameter uncertainty of the SWAT model and the applicability of the SWAT model in the Baihe River Basin were determined. The land-use transfer matrix showed that land-use changes are complex and the transformation forms are diverse. The simulation results showed that the transformation of land-use patterns in 1990 and 2010 had slight impacts on the hydrologic and water quality of the basin. By identifying the key source areas of non-point source pollution in the basin, the impacts of three management measures on the hydrological and water quality of the basin were simulated. The results showed that the reductions in flow rates, sediment content, TN, and TP (4.37, 31.93, 23.13, and 35.30%, respectively) obtained through terrace engineering were significantly better than those acquired via stubble mulch and contour planting. Additionally, this study uses the Sixth International Coupling Mode Comparison Program (CMIP6) climate change scenario and the BCC-CSM2-MR climate model coupled with the corrected SWAT model to predict future runoff, thereby providing references and suggestions for managing the Baihe River Basin and the Miyun Reservoir.