MOTIVATION: Data normalization is an important step in processing proteomics data generated in mass spectrometry experiments, which aims to reduce sample-level variation and facilitate comparisons of samples. Previously published methods for normalization primarily depend on the assumption that the distribution of protein expression is similar across all samples. However, this assumption fails when the protein expression data is generated from heterogenous samples, such as from various tissue types. This led us to develop a novel data-driven method for improved normalization to correct the systematic bias meanwhile maintaining underlying biological heterogeneity. RESULTS: To robustly correct the systematic bias, we used the density-power-weight method to down-weigh outliers and extended the one-dimensional robust fitting method described in the previous work to our structured data. We then constructed a robustness criterion and developed a new normalization algorithm, called RobNorm.In simulation studies and analysis of real data from the genotype-tissue expression project, we compared and evaluated the performance of RobNorm against other normalization methods. We found that the RobNorm approach exhibits the greatest reduction in systematic bias while maintaining across-tissue variation, especially for datasets from highly heterogeneous samples. AVAILABILITYAND IMPLEMENTATION: https://github.com/mwgrassgreen/RobNorm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Accurately detecting tissue specificity (TS) in genes helps researchers understand tissue functions at the molecular level. The Genotype-Tissue Expression project is one of the publicly available data resources, providing large-scale gene expressions across multiple tissue types. Multiple tissue comparisons and heterogeneous tissue expression make it challenging to accurately identify tissue specific gene expression. How to distinguish the inlier expression from the outlier expression becomes important to build the population level information and further quantify the TS. There still lacks a robust and data-adaptive TS method taking into account heterogeneities of the data. RESULTS: We found that the key to identify tissue specific gene expression is to properly define a concept of expression population. In a linear regression problem, we developed a novel data-adaptive robust estimation approach (AdaReg) based on density-power-weight under unknown outlier distribution and non-vanishing outlier proportion. The Gaussian-population mixture model was considered in the setting of identifying TS. We took into account heterogeneities of gene expression and applied the robust data-adaptive procedure to estimate the population parameters. With the well-estimated population parameters, we constructed the AdaTiSS algorithm.Our AdaTiSS profiled TS for each gene and each tissue, which standardized the gene expression in terms of TS. We provided a new robust and powerful tool to the literature of defining TS. AVAILABILITY AND IMPLEMENTATION: https://github.com/mwgrassgreen/AdaTiSS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Modern information technologies, such as Internet of Things technology, image recognition technology and artificial intelligence technology, are used to conduct comprehensive intelligent perception and monitoring on the experimental bases distributed all over the country, and an intelligent experimental base management platform is built. All kinds of data acquisition devices are networked through intelligent network to realize automatic data acquisition, advanced computing, collaborative work, data mining and Intelligent and visual management of the experimental base. The construction of intelligent experimental base makes the management of the experimental base more scientific and standardized, avoids repeated labor, improves the efficiency of scientific research and expands the scope of scientific research.

As a hot research field of big data intelligence, knowledge graph is widely concerned and discussed in recent years. This thesis elaborated the definition and architecture of knowledge graph, the development drive and its importance, analyzed the seven technologies in the knowledge graph: knowledge acquisition, knowledge representation, knowledge storage, knowledge fusion, knowledge modeling, knowledge computation and knowledge operation and maintenance, and introduced and prospected the application of knowledge graph in the agricultural information consultation and the challenges faced in its future development.

Human following is an important feature in various human-mobile-robot collaboration applications. Vision-based human following approaches employing visual servoing controls to achieve human following are commonly adopted. Such approaches, however, require to pre-define the desired human-following parameters and need to online extract features from the acquired images to calculate the human-following parameters which serve as the feedback of visual servoing controls. This paper proposes a novel visual servoing control using the non-vector space control theory, which makes the robot be able to personalize its desired human-following parameters as a desired image learnt from human-driven demonstrations. The approach provides an easy and intuitive way for humans to personalize mobile robots to complete human-following tasks in the manners that humans prefer. Experimental results demonstrate the effectiveness and advantage of the proposed approach.