Spatial Expression of Assembly Geometric Errors for Multi-axis Machine Tool Based on Kinematic Jacobian-Torsor Model

Assembly geometric error as a part of the machine tool system errors has a significant influence on the machining accuracy of the multi-axis machine tool.And it cannot be eliminated due to the error propagation of components in the assembly process,which is generally non-uniformly distributed in the whole working space.A comprehensive expression model for assembly geometric error is greatly helpful for machining quality control of machine tools to meet the demand for machining accuracy in practice.However,the expression ranges based on the standard quasistatic expression model for assembly geometric errors are far less than those needed in the whole working space of the multi-axis machine tool.To address this issue,a modeling methodology based on the Jacobian-Torsor model is proposed to describe the spatially distributed geometric errors.Firstly,an improved kinematic Jacobian-Torsor model is developed to describe the relative movements such as translation and rotation motion between assembly bodies,respectively.Furthermore,based on the proposed kinematic Jacobian-Torsor model,a spatial expression of geometric errors for the multi-axis machine tool is given.And simulation and experimental verification are taken with the investigation of the spatial distribution of geometric errors on five four-axis machine tools.The results validate the effectiveness of the proposed kinematic Jacobian-Torsor model in dealing with the spatial expression of assembly geometric errors.

Spatial and temporal expression of germ cell nuclear factorin murine epididymis

Aim: To investigate the spatial and temporal expression of germ cell nuclear factor (GCNF) in mouse and rat epididymis during postnatal period. Methods: The epididymal sections from different postnatal days were stained for GCNF by the indirect immunofluorescence technique and digital photographs were taken by a Carl Zeiss confocal microscope. Results: GCNF was first detected on day 12 in mouse epididymis and day 14 in rat epididymis. The highest expression of GCNF was observed on day 35 in both mouse and rat epididymis. In adults, GCNF exhibited a region-specific expression pattern, i.e., it was expressed predominantly in the initial segment, caput and proximal corpus of rat epididymis and was abundant in the proximal corpus of mouse epididymis. GCNF could be found in the nuclei of the principal, apical, narrow, clear and halo cells. Conclusion: GCNF may play an important role in epididymal differentiation and development and in sperm maturation.

Expression of germ cell nuclear factor in mouse germ cells and sperm during postnatal period

Aim: To assess the spatial and temporal expression of germ cell nuclear factor (GCNF) in male mouse germ cells during postnatal development and in sperm before and after capacitation. Methods: The indirect immun-ofluorescence method with anti-GCNF antiserum was used to investigate the GCNF expression in mice at day 8, 10, 14, 17, 20, 28, 35, 70, and 420 after birth and in sperm before and after capacitation. Results: With the proceeding of spermatogenesis, GCNF was first detected in the nuclei of spermatogonia and a few early stage primary sperma-tocytes at day 8, which was increased gradually at day 10 to 14 inclusive. From day 17 to day 20, the GCNF was concentrated in round spermatids, while both spermatogonia and early stage primary spermatocytes became GCNF negative. From day 28 until day 420, strong GCNF expression was shown in round spermatids and pachytene spermatocytes, while spermatogonia, early primary spermatocytes and elongating spermatids were all GCNF negative. In addition, it was also found that GCNF was localized on the acrosomal cap region of spermatozoa and there was a big change in GCNF expression during capacitation, from 98 % GCNF positive before capacitation to about 20 % positive following capacitation. The localization of GCNF in caput and cauda spermatozoa was similar. Conclusion: GCNF may play important roles in spermatogenesis, capacitation and fertilization.

Facial expression recognition with contextualized histograms

A new algorithm taking the spatial context of local features into account by utilizing contextualized histograms was proposed to recognize facial expression. The contextualized histograms were extracted fromtwo widely used descriptors—the local binary pattern（ LBP） and weber local descriptor（ WLD）. The LBP and WLD feature histograms were extracted separately fromeach facial image,and contextualized histogram was generated as feature vectors to feed the classifier. In addition,the human face was divided into sub-blocks and each sub-block was assigned different weights by their different contributions to the intensity of facial expressions to improve the recognition rate. With the support vector machine（SVM） as classifier,the experimental results on the 2D texture images fromthe 3D-BU FE dataset indicated that contextualized histograms improved facial expression recognition performance when local features were employed.

Spatial transcriptome analysis of long non-coding RNAs reveals tissue specificity and functional roles in cancer

Long non-coding RNAs(lncRNAs)play a significant role in maintaining tissue morphology and functions,and their precise regulatory effectiveness is closely related to expression patterns.However,the spatial expression patterns of lncRNAs in humans are poorly characterized.Here,we constructed five comprehensive transcriptomic atlases of human lncRNAs covering thousands of major tissue samples in normal and disease states.The lncRNA transcriptomes exhibited high consistency within the same tissues across resources,and even higher complexity in specialized tissues.Tissue-elevated(TE)lncRNAs were identified in each resource and robust TE lncRNAs were refined by integrative analysis.We detected 1 to 4684 robust TE lncRNAs across tissues;the highest number was in testis tissue,followed by brain tissue.Functional analyses of TE lncRNAs indicated important roles in corresponding tissue-related pathways.Moreover,we found that the expression features of robust TE lncRNAs made them be effective biomarkers to distinguish tissues;TE lncRNAs also tended to be associated with cancer,and exhibited differential expression or were correlated with patient survival.In summary,spatial classification of lncRNAs is the starting point for elucidating the function of lncRNAs in both maintenance of tissue morphology and progress of tissue-constricted diseases.

Cell type specificity of signaling:view from membrane receptors distribution and their downstream transduction networks

Studies on cell signaling pay more attention to spatial dynamics and how such diverse organization can relate to high order of cellular capabilities.To overview the specificity of cell signaling,we integrated human receptome data with proteome spatial expression profiles to systematically investigate the specificity of receptors and receptor-triggered transduction networks across 62 normal cell types and 14 cancer types.Six percent receptors showed cell-type-specific expression,and 4% signaling networks presented enriched cell-specific proteins induced by the receptors.We introduced a concept of“response context”to annotate the cell-type dependent signaling networks.We found that most cells respond similarly to the same stimulus,as the“response contexts”presented high functional similarity.Despite this,the subtle spatial diversity can be observed from the difference in network architectures.The architecture of the signaling networks in nerve cells displayed less completeness than that in glandular cells,which indicated cellular-context dependent signaling patterns are elaborately spatially organized.Likewise,in cancer cells most signaling networks were generally dysfunctional and less complete than that in normal cells.However,glioma emerged hyper-activated transduction mechanism in malignant state.Receptor ATP6AP2 and TNFRSF21 induced rennin-angiotensin and apoptosis signaling were found likely to explain the glioma-specific mechanism.This work represents an effort to decipher context-specific signaling network from spatial dimension.Our results indicated that although a majority of cells engage general signaling response with subtle differences,the spatial dynamics of cell signaling can not only deepen our insights into different signaling mechanisms,but also help understand cell signaling in disease.