Detecting Factor Ⅺ Deficiency in Holstein Cattle Using PCR Analysis

[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.

Anomalous Cell Detection with Kernel Density-Based Local Outlier Factor

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.

DETECTION OF LEUKEMIA INHIBITORY FACTOR (LIF) BY A ENZYME-LINKED IMMUNOSORBENT ASSAY

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-

An Efficient WRF Framework for Discovering Risk Genes and Abnormal Brain Regions in Parkinson's Disease Based on Imaging Genetics Data

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.