The so-called occupational disease, in fact, refers to enterprises and institutions or individual workers, because in the daily process of employment, contact with radioactive substances or dust and other toxic and ha...The so-called occupational disease, in fact, refers to enterprises and institutions or individual workers, because in the daily process of employment, contact with radioactive substances or dust and other toxic and harmful substances, which will cause a certain degree of threat to their own health, forming a certain harm. Therefore, the disease type of occupational disease will often bring serious physical and mental trauma to the workers, and even cause great harm and impact on their families. To this end, this needs the relevant departments to cause enough attention. The detection and evaluation of occupational diseases is an important way to effectively avoid occupational diseases. For this reason, this paper mainly starts with the routine detection and evaluation of occupational-disease-inductive factors, and from the perspective of a new health investigation. In addition, by combining the 130 researcher cases selected from January 2019 to January 2021, the comprehensive field investigation in the detection and evaluation of occupational-disease-inductive factors is a more comprehensive and objective analysis.展开更多
Since data services are penetrating into our daily life rapidly, the mobile network becomes more complicated, and the amount of data transmission is more and more increasing. In this case, the traditional statistical ...Since data services are penetrating into our daily life rapidly, the mobile network becomes more complicated, and the amount of data transmission is more and more increasing. In this case, the traditional statistical methods for anomalous cell detection cannot adapt to the evolution of networks, and data mining becomes the mainstream. In this paper, we propose a novel kernel density-based local outlier factor(KLOF) to assign a degree of being an outlier to each object. Firstly, the notion of KLOF is introduced, which captures exactly the relative degree of isolation. Then, by analyzing its properties, including the tightness of upper and lower bounds, sensitivity of density perturbation, we find that KLOF is much greater than 1 for outliers. Lastly, KLOFis applied on a real-world dataset to detect anomalous cells with abnormal key performance indicators(KPIs) to verify its reliability. The experiment shows that KLOF can find outliers efficiently. It can be a guideline for the operators to perform faster and more efficient trouble shooting.展开更多
[Objective] This study established a method to detect Factor Ⅺ by polymerase chain reaction analysis.[Method]A pair of primers was designed and synthesized according to sequences of FⅪ gene in Holstein calves,publis...[Objective] This study established a method to detect Factor Ⅺ by polymerase chain reaction analysis.[Method]A pair of primers was designed and synthesized according to sequences of FⅪ gene in Holstein calves,published in Genbank. Polymerase chain reaction was used to analyze FⅪ deficiency of 576 Holstein calves in Henan,and the result was verified by DNA sequencing. [Result] We detect 576 cows,which include two carriers and one F Ⅺ deficiency,and the result was consistent with the DNA sequencing. The frequency of the FⅪ mutant allele was 0.3%,the carrier was 0.3%,the prevalence was 0.2%.[Conclusion]A method detecting FⅪ by polymerase chain reaction analysis was established. This method is not only simple and convenient,but also has a high accuracy and low cost,which is more suitable for large-scale FⅪ investigation.展开更多
LIF is a cytokine with leiotropic activities. In order to understand better the physiological and patho-physiological role of LIF. we have developed a simple and specific enzyme-linked immunosorbent assay (ELISAI for ...LIF is a cytokine with leiotropic activities. In order to understand better the physiological and patho-physiological role of LIF. we have developed a simple and specific enzyme-linked immunosorbent assay (ELISAI for detecting LIF in human plasma and serum and in tissue culture media. A monoclonal ami-LIF antibody 8B11 (IgGl) produced in our laboratory was coated onto microtiter plates. After block-展开更多
As an emerging research field of brain science,multimodal data fusion analysis has attracted broader attention in the study of complex brain diseases such as Parkinson's disease(PD).However,current studies primari...As an emerging research field of brain science,multimodal data fusion analysis has attracted broader attention in the study of complex brain diseases such as Parkinson's disease(PD).However,current studies primarily lie with detecting the association among different modal data and reducing data attributes.The data mining method after fusion and the overall analysis framework are neglected.In this study,we propose a weighted random forest(WRF)model as the feature screening classifier.The interactions between genes and brain regions are detected as input multimodal fusion features by the correlation analysis method.We implement sample classification and optimal feature selection based on WRF,and construct a multimodal analysis framework for exploring the pathogenic factors of PD.The experimental results in Parkinson's Progression Markers Initiative(PPMI)database show that WRF performs better compared with some advanced methods,and the brain regions and genes related to PD are detected.The fusion of multi-modal data can improve the classification of PD patients and detect the pathogenic factors more comprehensively,which provides a novel perspective for the diagnosis and research of PD.We also show the great potential of WRF to perform the multimodal data fusion analysis of other brain diseases.展开更多
文摘The so-called occupational disease, in fact, refers to enterprises and institutions or individual workers, because in the daily process of employment, contact with radioactive substances or dust and other toxic and harmful substances, which will cause a certain degree of threat to their own health, forming a certain harm. Therefore, the disease type of occupational disease will often bring serious physical and mental trauma to the workers, and even cause great harm and impact on their families. To this end, this needs the relevant departments to cause enough attention. The detection and evaluation of occupational diseases is an important way to effectively avoid occupational diseases. For this reason, this paper mainly starts with the routine detection and evaluation of occupational-disease-inductive factors, and from the perspective of a new health investigation. In addition, by combining the 130 researcher cases selected from January 2019 to January 2021, the comprehensive field investigation in the detection and evaluation of occupational-disease-inductive factors is a more comprehensive and objective analysis.
基金supported by the National Basic Research Program of China (973 Program: 2013CB329004)
文摘Since data services are penetrating into our daily life rapidly, the mobile network becomes more complicated, and the amount of data transmission is more and more increasing. In this case, the traditional statistical methods for anomalous cell detection cannot adapt to the evolution of networks, and data mining becomes the mainstream. In this paper, we propose a novel kernel density-based local outlier factor(KLOF) to assign a degree of being an outlier to each object. Firstly, the notion of KLOF is introduced, which captures exactly the relative degree of isolation. Then, by analyzing its properties, including the tightness of upper and lower bounds, sensitivity of density perturbation, we find that KLOF is much greater than 1 for outliers. Lastly, KLOFis applied on a real-world dataset to detect anomalous cells with abnormal key performance indicators(KPIs) to verify its reliability. The experiment shows that KLOF can find outliers efficiently. It can be a guideline for the operators to perform faster and more efficient trouble shooting.
文摘[Objective] This study established a method to detect Factor Ⅺ by polymerase chain reaction analysis.[Method]A pair of primers was designed and synthesized according to sequences of FⅪ gene in Holstein calves,published in Genbank. Polymerase chain reaction was used to analyze FⅪ deficiency of 576 Holstein calves in Henan,and the result was verified by DNA sequencing. [Result] We detect 576 cows,which include two carriers and one F Ⅺ deficiency,and the result was consistent with the DNA sequencing. The frequency of the FⅪ mutant allele was 0.3%,the carrier was 0.3%,the prevalence was 0.2%.[Conclusion]A method detecting FⅪ by polymerase chain reaction analysis was established. This method is not only simple and convenient,but also has a high accuracy and low cost,which is more suitable for large-scale FⅪ investigation.
文摘LIF is a cytokine with leiotropic activities. In order to understand better the physiological and patho-physiological role of LIF. we have developed a simple and specific enzyme-linked immunosorbent assay (ELISAI for detecting LIF in human plasma and serum and in tissue culture media. A monoclonal ami-LIF antibody 8B11 (IgGl) produced in our laboratory was coated onto microtiter plates. After block-
基金This work was supported by the National Natural Science Foundation of China under Grant No.62072173the Natural Science Foundation of Hunan Province of China under Grant No.2020JJ4432+3 种基金the Key Scientific Research Projects of Department of Education of Hunan Province under Grant No.20A296the Degree and Postgraduate Education Reform Project of Hunan Province under Grant No.2019JGYB091Hunan Provincial Science and Technology Project Foundation under Grant No.2018TP1018,and the InnovationEntrepreneurship Training Program of Hunan Xiangjiang Artificial Intelligence Academy.
文摘As an emerging research field of brain science,multimodal data fusion analysis has attracted broader attention in the study of complex brain diseases such as Parkinson's disease(PD).However,current studies primarily lie with detecting the association among different modal data and reducing data attributes.The data mining method after fusion and the overall analysis framework are neglected.In this study,we propose a weighted random forest(WRF)model as the feature screening classifier.The interactions between genes and brain regions are detected as input multimodal fusion features by the correlation analysis method.We implement sample classification and optimal feature selection based on WRF,and construct a multimodal analysis framework for exploring the pathogenic factors of PD.The experimental results in Parkinson's Progression Markers Initiative(PPMI)database show that WRF performs better compared with some advanced methods,and the brain regions and genes related to PD are detected.The fusion of multi-modal data can improve the classification of PD patients and detect the pathogenic factors more comprehensively,which provides a novel perspective for the diagnosis and research of PD.We also show the great potential of WRF to perform the multimodal data fusion analysis of other brain diseases.
