Phosphoprotein Associated with Glycosphingolipid-Enriched Microdomains Differentially Modulates Src Kinase Activity in Brain Maturation

Src family kinases (SFK) control multiple processes during brain development and function. We show here that the phosphoprotein associated with glycosphigolipid-enriched microdomains (PAG)/Csk binding protein (Cbp) modulates SFK activity in the brain. The timing and localization of PAG expression overlap with Fyn and Src, both of which we find associated to PAG. We demonstrate in newborn (P1) mice that PAG negatively regulates Src family kinases (SFK). P1 Pag1-/- mouse brains show decreased recruitment of Csk into lipid rafts, reduced phosphorylation of the inhibitory tyrosines within SFKs, and an increase in SFK activity of >/ = 50%. While in brain of P1 mice, PAG and Csk are highly and ubiquitously expressed, little Csk is found in adult brain suggesting altered modes of SFK regulation. In adult brain Pag1-deficiency has no effect upon Csk-distribution or inhibitory tyrosine phosphorylation, but kinase activity is now reduced (−20–30%), pointing to the development of a compensatory mechanism that may involve PSD93. The distribution of the Csk-homologous kinase CHK is not altered. Importantly, since the activities of Fyn and Src are decreased in adult Pag1-/- mice, thus presenting the reversed phenotype of P1, this provides the first in vivo evidence for a Csk-independent positive regulatory function for PAG in the brain

Optimal DG Location and Sizing to Minimize Losses and Improve Voltage Profile Using Garra Rufa Optimization

Distributed generation (DG) refers to small generating plants that usually develop green energy and are located close to the load buses. Thus, reducing active as well as reactive power losses, enhancing stability and reliability, and many other benefits arise in the case of a suitable selection in terms of the location and the size of the DGs, especially in smart cities. In this work, a new nature-inspired algorithm called Garra Rufa optimization is selected to determine the optimal DG allocation. The new metaheuristic algorithm stimulates the massage fish activity during finding food using MATLAB software. In addition, three indexes which are apparently powered loss compounds and voltage profile, are considered to estimate the effectiveness of the proposed method. To validate the proposed algorithm, the IEEE 30 and 14 bus standard test systems were employed. Moreover, five cases of DGs number are tested for both standards to provide a set of complex cases. The results significantly show the high performance of the proposed method especially in highly complex cases compared to particle swarm optimization (PSO) algorithm and genetic algorithm (GA). The DG allocation, using the proposed method, reduces the active power losses of the IEEE-14 bus system up to 236.7873%, by assuming 5DGs compared to the active power losses without DG. Furthermore, the GRO increases the maximum voltage stability index of the IEEE-30 bus system by 857% in case of the 4DGs, whereas GA rises the reactive power of 5DGs to benefit the IEEE-14 bus system by 195.1%