LRBmat: A Novel Gut Microbial Interaction and Individual Heterogeneity Inference Method for Colorectal Cancer

Many diseases are considered to be closely related to the changes in the gut microbial community, including colorectal cancer (CRC), which is one of the most common cancers in the world. The diagnostic classification and etiological analysis of CRC are two critical issues worthy of attention. Many methods adopt gut microbiota to solve it, but few of them simultaneously take into account the complex interactions and individual heterogeneity of gut microbiota, which are two common and important issues in genetics and intestinal microbiology, especially in high-dimensional cases. In this paper, a novel method with a Binary matrix based on Logistic Regression (LRBmat) is proposed to deal with the above problem. The binary matrix can directly weakened or avoided the influence of heterogeneity, and also contain the information about gut microbial interactions with any order. Moreover, LRBmat has a powerful generalization, it can combine with any machine learning method and enhance them. The real data analysis on CRC validates the proposed method, which has the best classification performance compared with the state-of-the-art. Furthermore, the association rules extracted from the binary matrix of the real data align well with the biological properties and existing literatures, which are helpful for the etiological analysis of CRC. The source codes for LRBmat are available at https://github.com/tsnm1/LRBmat

STW-MD: A Novel Spatio-Temporal Weighting and Multi-Step Decision Tree Method for Considering Spatial Heterogeneity in Brain Gene Expression Data

Motivation: Gene expression during brain development or abnormal development is a biological process that is highly dynamic in spatio and temporal. Due to the lack of comprehensive integration of spatial and temporal dimensions of brain gene expression data, previous studies have mainly focused on individual brain regions or a certain developmental stage. Our motivation is to address this gap by incorporating spatio-temporal information to gain a more complete understanding of the mechanisms underlying brain development or disorders associated with abnormal brain development, such as Alzheimer's disease (AD), and to identify potential determinants of response. Results: In this study, we propose a novel two-step framework based on spatial-temporal information weighting and multi-step decision trees. This framework can effectively exploit the spatial similarity and temporal dependence between different stages and different brain regions, and facilitate differential gene analysis in brain regions with high heterogeneity. We focus on two datasets: the AD dataset, which includes gene expression data from early, middle, and late stages, and the brain development dataset, spanning fetal development to adulthood. Our findings highlight the advantages of the proposed framework in discovering gene classes and elucidating their impact on brain development and AD progression across diverse brain regions and stages. These findings align with existing studies and provide insights into the processes of normal and abnormal brain development. Availability: The code of STW-MD is available at https://github.com/tsnm1/STW-MD.Comment: 11 pages, 6 figure

Research and application of coal exploration data management method in working face based on GIS

In order to improve the efficiency of dynamic calculation of coal reserves in the mining process of coal working face and enrich the dynamic updated data required in the construction of 3D geological model of working face in intelligent mining, a GIS based coal mining face data management method is proposed through the full analysis and in-depth research on the professional needs, business processes, technical routes and data structure of coal mining face data management. At the data level, the business process of coal exploration data management was optimized, the data structure and storage method of different types of coal exploration data were designed, the coal exploration data sharing and management of mining face based on spatial relational database were realized from using the spatial data organization and management mode. At the presentation level, the interactive management of coal exploration data and graphics was realized based on the domestic geographic information system platform, LongRuanGIS, independently developed from the bottom. At the business level, a drawing algorithm of coal exploration line was proposed to realize the rapid drawing of different coal exploration lines. The data structure, drawing style, drawing method and data management method of coal exploration point were designed to ensure the beautiful mapping of coal exploration data and efficient reuse of data; a method of dynamically updating the geological model of coal mining face by using the data of coal thickness detection was proposed, which enriches the dynamic updating data source of high-precision three-dimensional dynamic geological model of working face. The results of normalization application in many mines show that the management method of coal exploration data based on GIS realizes the unified management and sharing of different types of coal exploration data, realizes the rapid automatic mapping and dynamic updating of coal exploration data and improves the drawing efficiency of coal mine geologists. At the same time, the timely updating of coal exploration data provides convenient and effective data management measures for dynamic calculation of reserves and dynamic updating of high-precision 3D geological model, ensuring the efficient use of coal exploration data in many aspects

TaSnRK2.4, an SNF1-type serine/threonine protein kinase of wheat (Triticum aestivum L.), confers enhanced multistress tolerance in Arabidopsis

Osmotic stresses such as drought, salinity, and cold are major environmental factors that limit agricultural productivity worldwide. Protein phosphorylation/dephosphorylation are major signalling events induced by osmotic stress in higher plants. Sucrose non-fermenting 1-related protein kinase2 family members play essential roles in response to hyperosmotic stresses in Arabidopsis, rice, and maize. In this study, the function of TaSnRK2.4 in drought, salt, and freezing stresses in Arabidopsis was characterized. A translational fusion protein of TaSnRK2.4 with green fluorescent protein showed subcellular localization in the cell membrane, cytoplasm, and nucleus. To examine the role of TaSnRK2.4 under various environmental stresses, transgenic Arabidopsis plants overexpressing wheat TaSnRK2.4 under control of the cauliflower mosaic virus 35S promoter were generated. Overexpression of TaSnRK2.4 resulted in delayed seedling establishment, longer primary roots, and higher yield under normal growing conditions. Transgenic Arabidopsis overexpressing TaSnRK2.4 had enhanced tolerance to drought, salt, and freezing stresses, which were simultaneously supported by physiological results, including decreased rate of water loss, enhanced higher relative water content, strengthened cell membrane stability, improved photosynthesis potential, and significantly increased osmotic potential. The results show that TaSnRK2.4 is involved in the regulation of enhanced osmotic potential, growth, and development under both normal and stress conditions, and imply that TaSnRK2.4 is a multifunctional regulatory factor in Arabidopsis. Since the overexpression of TaSnRK2.4 can significantly strengthen tolerance to drought, salt, and freezing stresses and does not retard the growth of transgenic Arabidopsis plants under well-watered conditions, TaSnRK2.4 could be utilized in transgenic breeding to improve abiotic stresses in crops

A Case Study of an Optimized Intermittent Ventilation Strategy Based on CFD Modeling and the Concept of FCT

With the increasing operation costs and implementation of carbon tax in the underground coal mining systems, a cost-effective ventilation system with well methane removal efficiency becomes highly required. Since the intermittent ventilation provides a novel approach in energy saving, there still exists some scientific issues. This paper adopts the design concept of frequency conversion technology (FCT) to the ventilation pattern design for the first time, and an optimized intermittent ventilation strategy is proposed. Specifically, a real excavation laneway of a coal mine in China is established as the physical model, and computational fluid dynamic (CFD) approaches are utilized to investigate the spatiotemporal characteristics of airflow behavior and methane distribution. Plus, the period of intermittency and appropriate air velocity are scientifically defined based on the conception of FCT by conducting the parametric studies. Therefore, an optimized case is brought out with well methane removal efficiency and remarkable energy reduction of 39.2% in a ventilation period. Furthermore, the simulation result is verified to be reliable by comparing with field measurements. The result demonstrates that a balance of significant energy saving and the methane removal requirement based on the concept of FTC is possible, which could in turn provide good operational support for FTC

An efficient PAM spatial clustering algorithm based on MapReduce

Clustering analysis has been a hot area of spatial data mining for several years. With the rapid development of the spatial information technology, the amount of spatial data is growing exponentially and it makes spatial clustering of massive spatial data a challenging task. Aiming to improve the efficiency of the clustering process on massive spatial data, an implementation of parallel Partitioning Around Medoids (PAM) spatial clustering algorithm based on MapReduce is proposed. The experiments on Hadoop and HBase demonstrate that the proposed algorithm can process massive spatial data efficiently and scale well on commodity hardware.EICPCI-S(ISTP)

A Tightly Coupled GIS and Spatiotemporal Modeling for Methane Emission Simulation in the Underground Coal Mine System

Mine safety is of primary concern in the underground coal mining system. At present, there is a lack of an efficient platform to manage the numerical simulation procedure and inherent spatiotemporal data for coal mine disasters. This necessitates the coupling of spatiotemporal model with geographic information system (GIS) in practical application. Here, a novel spatiotemporal model tightly coupled with GIS is presented to improve the model-data integration. Such tight coupling is achieved by developing a lattice Boltzmann method (LBM) based turbulent model with an underlying shared FluentEntity model within the LongRuanGIS platform. The case study and comparison with the traditional computational fluid dynamics (CFD) method demonstrated that the platform is capable and effective in providing functionalities for lattice domain decomposition, simulation, visualization and analyses, as well as improving the computational efficiency. The proposed approach and platform, promising for the disaster prevention, offer a template for future GIS-Model integration and also applicable for other underground coal mine disasters

A GIS Based Unsteady Network Model and System Applications for Intelligent Mine Ventilation

With the development of state-of-the-art technology, such as the artificial intelligence and the Internet of Things, the construction of “intelligent mine” is being vigorously promoted, where the intelligent mine ventilation is one of the primary concerns that provides the efficient guarantee for safety production in the underground coal mine system. This study aims to integrate the geographical information system (GIS) and the unsteady ventilation network model together, to provide location based information and online real-time support for the decision-making system. Firstly, a GIS based unsteady network model is proposed, and its algorithm steps are brought out. Secondly, a prototype web system, named 3D VentCloud, is designed and developed based on the front and end technique, which effectively integrates the proposed algorithms. Thirdly, the model is validated, and the system is applied to a real coal mine for ventilation solution, which demonstrates that the model is reasonable and practical. The online simulation system is efficient in providing real-time support. The study is potential and is expected to guide the real-time coal mine safety production