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Pain is an unpleasant sensation associated with injury, inflammation, and infection. It has been demonstrated that communication between immune cells and neurons plays a vital role in pain and pain-related diseases (e.g. multiple sclerosis, osteoarthritis, irritable bowel syndrome). Growing data from preclinical and clinical studies have established that the bilateral regulations between peripheral immune cells and nociceptive neurons could be beneficial or detrimental for the development of pain and immune defense. We here review the mechanisms underlying neuroimmune crosstalk between circulating immune cells (e.g. macrophages, T cells, mast cells, neutrophils, monocytes) and nociceptors in the peripheral nervous system and the spinal cord. Deciphering the mechanisms by which neuroimmune interaction integrates neuronal inputs and immune responses helps to understand the pathogenesis of pain-related diseases and develop effective medications.

Background The recognition of protein interaction sites is of great significance in many biological processes, signaling pathways and drug designs. However, most sites on protein sequences cannot be defined as interface or non-interface sites because only a small part of protein interactions had been identified, which will cause the lack of prediction accuracy and generalization ability of predictors in protein interaction sites prediction. Therefore, it is necessary to effectively improve prediction performance of protein interaction sites using large amounts of unlabeled data together with small amounts of labeled data and background knowledge today. Results In this work, three semi-supervised support vector machine–based methods are proposed to improve the performance in the protein interaction sites prediction, in which the information of unlabeled protein sites can be involved. Herein, five features related with the evolutionary conservation of amino acids are extracted from HSSP database and Consurf Sever, i.e., residue spatial sequence spectrum, residue sequence information entropy and relative entropy, residue sequence conserved weight and residual Base evolution rate, to represent the residues within the protein sequence. Then three predictors are built for identifying the interface residues from protein surface using three types of semi-supervised support vector machine algorithms. Conclusion The experimental results demonstrated that the semi-supervised approaches can effectively improve prediction performance of protein interaction sites when unlabeled information is involved into the predictors and one of them can achieve the best prediction performance, i.e., the accuracy of 70.7%, the sensitivity of 62.67% and the specificity of 78.72%, respectively. With comparison to the existing studies, the semi-supervised models show the improvement of the predication performance.

Protein-protein interactions play essential roles in various biological progresses. Identifying protein interaction sites can facilitate researchers to understand life activities and therefore will be helpful for drug design. However, the number of experimental determined protein interaction sites is far less than that of protein sites in protein-protein interaction or protein complexes. Therefore, the negative and positive samples are usually imbalanced, which is common but bring result bias on the prediction of protein interaction sites by computational approaches. In this work, we presented three imbalance data processing strategies to reconstruct the original dataset, and then extracted protein features from the evolutionary conservation of amino acids to build a predictor for identification of protein interaction sites. On a dataset with 10,430 surface residues but only 2,299 interface residues, the imbalance dataset processing strategies can obviously reduce the prediction bias, and therefore improve the prediction performance of protein interaction sites. The experimental results show that our prediction models can achieve a better prediction performance, such as a prediction accuracy of 0.758, or a high F-measure of 0.737, which demonstrated the effectiveness of our method.

Protein-protein interactions play essential roles in various biological progresses. Identifying protein interaction sites can facilitate researchers to understand life activities and therefore will be helpful for drug design. However, the unbalance between the negative and positive samples comes from the current definitions of interaction sites places restriction on the prediction of protein interaction sites by computational approaches. In this work, we proposed three imbalance data processing strategies to improve the performance of protein interaction sites prediction. We first proposed the extraction of relevant features based on the evolutionary conservation of amino acids to predict protein interaction sites. At the same time, three methods are proposed to deal with the imbalance of positive and negative samples in data sets. Experimental results demonstrated the effectiveness of our method.

Differing from optical remote sensing image, radar remote sensing image can be used to extract more useful information, and its application is becoming widespread in a variety of fields. Based on the central place theory, the spatial structure of urban system in the Northern China Plain is studied by using Radarsat ScanSAR mosaic image. The results show that: (1) Radarsat ScanSAR data are suitable for automatic extraction of building-up areas and has meaningful potential for urban geographic study. (2) The urban system in the Northern China Plain, which is deeply influenced by physical factors, especially hydrographic factors, can be divided into five categories: urban system of equal distance between central places on fluvial fan region at Mt.Taihangshan; hexagonal urban system in central part of Hebei flood plain; pentagonal urban system in the Yellow River fluvial fan; quadrilateral urban system in the vicinity of Huaihe River system; and scattered new towns in the places of rolling hills in central and southern areas of Shandong Province. (3) An evolution model of central place system from hexagon to pentagon and quadrangle influenced by river is suggested. (4) No matter hexagonal or pentagonal urban systems, this study has demonstrated that there are good relationship between the distance structure model of the central place and the real-life instance.

Suspended sediment is one of the most important parameters for water quality. Numerous experiential or deductive models have been advanced for detecting suspended sediment using remote sensing technology. However, due to the lack of atmospheric parameters and sufficient statistics, the precision or accuracy of these models cannot be guaranteed. In this paper, we take Lake Chaohu as an example area and process its TM/ETM+ data by applying the method of internal average relative reflectance for atmospheric correction and by extracting sediment information according to the value of SI (SI=(TM2+TM3)/(TM2/TM3)). The results show that: (1) an accurate extraction of water information of Lake Chaohu can be obtained by considering the relationship between the spectrums, (2) the data of relative suspended sediment revealed are in accordance with the instrumental data in situ, (3) the high-density suspended sediment area has expanded 1.5 times during the past 13 years, indicating changes of the lake's estuary, shoreline, and its suspended sediment content, and (4) the main sources of suspended sediment of Lake Chaohu are river transportation and erosion of the lakeshore.