Search

The neocortex of primates, including humans, contains more abundant and diverse inhibitory neurons compared with rodents, but the molecular foundations of these observations are unknown. Through integrative gene coexpression analysis, we determined a consensus transcriptional profile of GABAergic neurons in mid-gestation human neocortex. By comparing this profile to genes expressed in GABAergic neurons purified from neonatal mouse neocortex, we identified conserved and distinct aspects of gene expression in these cells between the species. We show here that the calcium-binding protein secretagogin (SCGN) is robustly expressed by neocortical GABAergic neurons derived from caudal ganglionic eminences (CGE) and lateral ganglionic eminences during human but not mouse brain development. Through electrophysiological and morphometric analyses, we examined the effects of SCGN expression on GABAergic neuron function and form. Forced expression of SCGN in CGE-derived mouse GABAergic neurons significantly increased total neurite length and arbor complexity following transplantation into mouse neocortex, revealing a molecular pathway that contributes to morphological differences in these cells between rodents and primates.

Study was conducted for six weeks on 240, day-old Cobb-430Y strain broiler chicks randomly assigned to four groups (60 chicks per group) containing three replicates. Group 'A' was Negative Control (NC) contained 3% low protein (LP) than Positive Control (PC) group 'B'. Group B contained normal protein (NP) [22.50, 21.00, and 19.50% CP for broiler pre-starter (BPS), starter (BS) and finisher (BF) phases, respectively] as per strain requirement. Group 'C' (NC+Gly1) was formed by adding 0.640, 0.810, and 0.630% L-Glycine in NC diet to arrive 2.30, 2.30, and 2.00% Digestible (Dig.) Gly + Ser levels, while group 'D' (NC+Gly2) was formed by adding 0.755, 0.985, and 0.740% L-Glycine in NC diet to arrive 2.40, 2.40, and 2.10% Dig. Gly + Ser levels in BPS, BS, and BF diets, respectively. Dig. Gly + Ser levels of NC group were 1.71, 1.56, and 1.43%; and of PC group were 2.02, 1.91, and 1.76%, respectively, for BPS, BS and BF diets. Ratio of Dig. Met, Thr, Trp, Arg, and Val. to Lys. were balanced (AA-balanced) in all diets. All diets were isocaloric (3010, 3100, and 3200 Kcal ME/kg feed for BPS, BS, and BF, respectively). Results showed that the L-Glycine supplementation in AA-balanced 3% LP diet significantly (p=0.05) improved overall BW, WG, and FCR in broilers. Dig. Gly + Ser levels of 2.30, 2.30, and 2.00% in AA-balanced 3% LP diets (0.640, 0.810, and 0.630% L-Glycine) in BPS, BS, and BF phases, respectively, were found to be cost-effective for improving BW, WG, and FCR with less feed intake. Study concludes that the L-Glycine is recommended to maintain Dig. Gly + Ser levels of 2.30, 2.30, and 2.00% in BPS, BS, and BF phases for cost-effective formulation with a reduction of 3% CP in a standard commercial broiler diet. [ABSTRACT FROM AUTHOR]

Proper neurological function in humans requires precise control of levels of the epigenetic regulator methyl CpG-binding protein 2 (MeCP2). MeCP2 protein levels are low in fetal brains, where the predominant MECP2 transcripts have an unusually long 3' untranslated region (UTR). Here, we show that miR-483-5p, an intragenic microRNA of the imprinted IGF2, regulates MeCP2 levels through a human-specific binding site in the MECP2 long 3' UTR. We demonstrate the inverse correlation of miR-483-5p and MeCP2 levels in developing human brains and fibroblasts from Beckwith-Wiedemann syndrome patients. Importantly, expression of miR-483-5p rescues abnormal dendritic spine phenotype of neurons overexpressing human MeCP2. In addition, miR-483-5p modulates the levels of proteins of the MeCP2-interacting corepressor complexes, including HDAC4 and TBL1X. These data provide insight into the role of miR-483-5p in regulating the levels of MeCP2 and interacting proteins during human fetal development.

Land cover (LC) categorization is considered a necessary task of intelligent interpretation technology for remote sensing imagery that is intended to categorize every pixel to perform the predefined LC classification. Land Use and Land Cover (LULC) information has the ability to provide various insights in order to overcome environmental and socioeconomic impacts such as disaster risk, climate change, poverty, and food insecurity. Therefore, image categorization tasks are involved in conventional works, where the classical visual interpretation techniques completely depend upon professional knowledge as well as a professional’s classification experience, which is more susceptible to subjective awareness, inefficient, and time consuming. By overcoming this issue, the latest deep-structured approach is suggested to perform the LC image classification. Initially, the land images are gathered. Further, the collected images are employed for patch splitting, where the images are split into multiple patches. After splitting, the patches are fed to the Ensemble-based Convolutional Neural Network (ECNN), which is constructed with a Fully Convolutional Network (FCN), U-Net, DeepLabv3, and Mask Region-based Convolutional Neural Network (Mask R-CNN) for performing segmentation. Here, the hyperparameters are optimally tuned with the Hybrid Billiards-inspired Water Wave Algorithm (HB-WWA) by integrating the Billiards-inspired Optimization Algorithm (BOA) and Water Wave Algorithm (WWA). Finally, the classification is carried out with a fuzzy classifier. Thus, the performance is validated and measured through diverse metrics. Consequently, the developed work has demonstrated enhanced classification accuracy when tested on other existing algorithms. [ABSTRACT FROM AUTHOR]