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Abstract Background Microglia, the resident immune cells of the central nervous system (CNS), have two distinct phenotypes in the developing brain: amoeboid form, known to be amoeboid microglial cells (AMC) and ramified form, known to be ramified microglial cells (RMC). The AMC are characterized by being proliferative, phagocytic and migratory whereas the RMC are quiescent and exhibit a slow turnover rate. The AMC transform into RMC with advancing age, and this transformation is indicative of the gradual shift in the microglial functions. Both AMC and RMC respond to CNS inflammation, and they become hypertrophic when activated by trauma, infection or neurodegenerative stimuli. The molecular mechanisms and functional significance of morphological transformation of microglia during normal development and in disease conditions is not clear. It is hypothesized that AMC and RMC are functionally regulated by a specific set of genes encoding various signaling molecules and transcription factors. Results To address this, we carried out cDNA microarray analysis using lectin-labeled AMC and RMC isolated from frozen tissue sections of the corpus callosum of 5-day and 4-week old rat brain respectively, by laser capture microdissection. The global gene expression profiles of both microglial phenotypes were compared and the differentially expressed genes in AMC and RMC were clustered based on their functional annotations. This genome wide comparative analysis identified genes that are specific to AMC and RMC. Conclusions The novel and specific molecules identified from the trancriptome explains the quiescent state functioning of microglia in its two distinct morphological states.

The number of leaves in maize plant is one of the key traits describing its growth conditions. It is directly related to plant development and leaf counts also give insight into changing plant development stages. Compared with the traditional solutions which need excessive human interventions, the methods of computer vision and machine learning are more efficient. However, leaf counting with computer vision remains a challenging problem. More and more researchers are trying to improve accuracy. To this end, an automated, deep learning based approach for counting leaves in maize plants is developed in this paper. A Convolution Neural Network(CNN) is used to extract leaf features. The CNN model in this paper is inspired by Google Inception Net V3, which using multi-scale convolution kernels in one convolution layer. To compress feature maps generated from some middle layers in CNN, the Fisher Vector (FV) is used to reduce redundant information. Finally, these encoded feature maps are used to regress the leaf numbers by using Random Forests. To boost the related research, a relatively single maize image dataset (Different growth stage with 2845 samples, which 80% for train and 20% for test) is constructed by our team. The proposed algorithm in single maize data set achieves Mean Square Error (MSE) of 0.32. [ABSTRACT FROM AUTHOR]

Introduction: We aimed to develop and implement a short tandem repeat (STR) polymerase chain reaction alternative to fluorescence in situ hybridisation (FISH) for the preimplantation genetic diagnosis (PGD) of chromosomal translocations., Methods: Selected informative STRs located on translocated arms of relevant chromosomes were used to discriminate between normal and unbalanced chromosome states in each embryo., Results: PGD cycles were performed on five couples where one spouse carried a balanced translocation. 27 embryos were analysed, of which 12 were normal/balanced, 12 were abnormal/unbalanced and three were indeterminate. Four PGD cycles proceeded to embryo transfer, of which two led to pregnancy. The first pregnancy showed a normal male karyotype, and a healthy baby was delivered at term. A second pregnancy unexpectedly miscarried in the second trimester from unknown causes., Conclusion: STR analysis is a simple and suitable alternative to FISH for detecting unbalanced chromosomal states in preimplantation embryos.

Background and Results: Embryos from diabetic mice exhibit several forms of neural tube defects, including non-closure of the neural tube. In the present study, embryos collected at embryonic day 11.5 from diabetic pregnancies displayed open neural tube with architectural disruption of the surrounding tissues. The percentage of proliferating cells was found to be increased in the dorsal and ventral domains of the spinal neural tube of embryos from diabetic mice, indicating a defect in the proliferation index. We have analyzed the development of various cell types, including motoneurons, interneurons, oligodendrocytes and migrating neurons, as well as radial glial cells in the open neural tube using specific molecular markers. Immunofluorescence results revealed a significantly reduced number of Pax2+ interneurons and increased number of Isl-1+ motoneurons, as well as Olig2+ oligodendrocytes in the neural tube of embryos from diabetic mice as compared to controls. In addition, these embryos exhibited a decreased number of doublecortin positive migrating neurons and Glast/Blbp positive radial glial cells with shortened processes in the neural tube. Expression levels of several developmental control genes involved in the generation of different neuronal cell types (such as Shh, Ngn, Ngn2, Ascl1) were also found to be altered in the neural tube of embryos from diabetic mice., Conclusions: Overall, the open neural tube in embryos of diabetic mice exhibits defects in the specification of different cell types, including motoneurons and interneurons, as well as glial cells along the dorsoventral axis of the developing spinal cord. Although these defects are associated with altered expression of several development control genes, the exact mechanisms by which maternal diabetes contributes to these changes remain to be investigated.