Missing value estimation for DNA microarray gene expression data by Support Vector Regression imputation and orthogonal coding scheme

BACKGROUND: Gene expression profiling has become a useful biological resource in recent years, and it plays an important role in a broad range of areas in biology. The raw gene expression data, usually in the form of large matrix, may contain missing values. The downstream analysis methods that postulate complete matrix input are thus not applicable. Several methods have been developed to solve this problem, such as K nearest neighbor impute method, Bayesian principal components analysis impute method, etc. In this paper, we introduce a novel imputing approach based on the Support Vector Regression (SVR) method. The proposed approach utilizes an orthogonal coding input scheme, which makes use of multi-missing values in one row of a certain gene expression profile and imputes the missing value into a much higher dimensional space, to obtain better performance. RESULTS: A comparative study of our method with the previously developed methods has been presented for the estimation of the missing values on six gene expression data sets. Among the three different input-vector coding schemes we tried, the orthogonal input coding scheme obtains the best estimation results with the minimum Normalized Root Mean Squared Error (NRMSE). The results also demonstrate that the SVR method has powerful estimation ability on different kinds of data sets with relatively small NRMSE. CONCLUSION: The SVR impute method shows better performance than, or at least comparable with, the previously developed methods in present research. The outstanding estimation ability of this impute method is partly due to the use of the most missing value information by incorporating orthogonal input coding scheme. In addition, the solid theoretical foundation of SVR method also helps in estimation of performance together with orthogonal input coding scheme. The promising estimation ability demonstrated in the results section suggests that the proposed approach provides a proper solution to the missing value estimation problem. The source code of the SVR method is available from for non-commercial use

Development of a trench cutting re-mixing deep wall method model test device

The trench cutting re-mixing deep wall (TRD) is a new type of underground waterproof curtain. Mixing uniformity is the key index affecting the efficiency and quality of this method. However, because of many influencing factors, existing theories cannot be used to express the relationship between various factors and mixing uniformity. By analyzing the cutting and mixing process of the TRD method, the main factors affecting the uniformity of the mixing were obtained. A model test device was designed and manufactured, based on Buckingham's pi theorem. The validity of the model test device was verified through a comparative analysis of model and field test results. The model test device was demonstrated to be able to simulate the mixing process of the TRD method. The results provide guidance for promotion and better application of the TRD method

Root tuber extract of Polygooum cillinerve (Nakai) Ohwi exerts antimicrobial and immunomodulatory activities

Purpose: To investigate the antibacterial and immunomodulatory activities of the root tuber extract of Polygonum ciliinerve (Nakai) ohwi, a traditional Chinese medicine.Methods: Minimum inhibitory concentrations (MICs) of root tuber extract of Polygonum ciliinerve (Nakai) ohwi (rPC) and the synergistic effects between rPC and antibiotics were evaluated by broth dilution. In vitro adhesion and invasion assays were used to determine the effect of rPC on bacterial adhesion and invasion. Furthermore, the immunomodulatory effects of rPC were assessed by western blot.Results: rPC treatment inhibited the growth of Staphylococcus aureus (MIC = 12 mg/ml) and Escherichia coli (MIC = 64 mg/mL). rPC also showed synergistic effects with penicillin (fractional inhibitory concentration, FIC = 0.45), vancomycin (FIC = 0.333), moxifloxacin (FIC = 0.25), and levofloxacin (FIC = 0.356). The adhesion and invasion of bacteria were also suppressed by rPC treatment. Moreover, rPC exhibited an immunomodulatory effect during bacterial infection.Conclusion: rPC shows antibacterial and immunomodulatory activites when assessed by multiple methods, and therefore is a potential therapeutic alternative agent for the treatment of bacterial infections.Keywords: Polygonum ciliinerve root tuber, Synergistic effect, Immunomodulatory, Antibacteria

Door and window detection in 3D point cloud of indoor scenes.

This paper proposes a 3D-2D-3D algorithm for doors and windows detection in 3D indoor environment of point cloud data. Firstly, by setting up a virtual camera in the middle of this 3D environment, a set of pictures are taken from different angles by rotating the camera, so that corresponding 2D images can be generated. Next, these images are used to detect and identify the positions of doors and windows in the space. To obtain point cloud data containing the doors and windows position information, the 2D information are then mapped back to the origin 3D point cloud environment. Finally, by processing the contour lines and crossing points, the features of doors and windows through the position information are optimized. The experimental results show that this "global-local" approach is efficient when detecting and identifying the location of doors and windows in 3D point cloud environment

Causal relationship between gut microbiota and rosacea: a two-sample Mendelian randomization study

BackgroundRosacea, a chronic inflammatory skin condition affecting millions worldwide, is influenced by complex interactions between genetic and environmental factors. Although gut microbiota’s role in skin health is well-acknowledged, definitive causal links between gut microbiota and rosacea remain under-explored.MethodsUsing a two-sample Mendelian randomization (MR) design, this study examined potential causal relationships between gut microbiota and rosacea. Data was sourced from the largest Genome-Wide Association Study (GWAS) for gut microbiota and the FinnGen biobank for rosacea. A total of 2078 single nucleotide polymorphisms (SNPs) associated with gut microbiota were identified and analyzed using a suite of MR techniques to discern causal effects.ResultsThe study identified a protective role against rosacea for two bacterial genera: phylum Actinobacteria and genus Butyrivibrio. Furthermore, 14 gut microbiota taxa were discovered to exert significant causal effects on variant categories of rosacea. While none of these results met the strict False Discovery Rate correction threshold, they retained nominal significance. MR outcomes showed no pleiotropy, with homogeneity observed across selected SNPs. Directionality tests pointed toward a robust causative path from gut microbiota to rosacea.ConclusionThis study provides compelling evidence of the gut microbiota’s nominal causal influence on rosacea, shedding light on the gut-skin axis’s intricacies and offering potential avenues for therapeutic interventions in rosacea management. Further research is warranted to validate these findings and explore their clinical implications