Semaphorin3A-neuropilin1 signalling is involved in the generation of cortical interneurons

Cortical interneurons are generated predominantly in the medial ganglionic eminence of the ventral telencephalon and migrate to the cortex during embryonic development. These cells express neuropilin (Nrp1 and Nrp2) receptors which mediate their response to the chemorepulsive class 3 semaphorin (Sema) ligands. We show here that semaphorins Sema3A and Sema3F are expressed in layers adjacent to cortical interneuron migratory streams as well as in the striatum, suggesting they may have a role in guiding these cells throughout their journey. Analysis of Sema3A (-/-) and Sema3F (-/-) mice during corticogenesis showed that absence of Sema3A, but not Sema3F, leads to aberrant migration of cortical interneurons through the striatum. Reduced number of cortical interneurons was found in the cortex of Sema3A (-/-), Nrp1 (-/-) and Nrp2 (-/-) mice, as well as altered distribution in Sema3F (-/-), Nrp1 (-/-), Nrp2 (-/-) animals and especially in neuropilin double mutants. The observed decrease in interneurons in Sema3A (-/-) and Nrp1 (-/-) mice was due to altered proliferative activity of their progenitors highlighted by changes in their mitotic spindle positioning and angle of cleavage plane during cell division. These findings point to a novel role for Sema3A-Nrp1 signalling in progenitor cell dynamics and in the generation of interneurons in the ventral telencephalon

Cadherin 8 regulates proliferation of cortical interneuron progenitors

Cortical interneurons are born in the ventral forebrain and migrate tangentially in two streams at the levels of the intermediate zone (IZ) and the pre-plate/marginal zone to the developing cortex where they switch to radial migration before settling in their final positions in the cortical plate. In a previous attempt to identify the molecules that regulate stream specification, we performed transcriptomic analysis of GFP-labelled interneurons taken from the two migratory streams during corticogenesis. A number of cadherins were found to be expressed differentially, with Cadherin-8 (Cdh8) selectively present in the IZ stream. We verified this expression pattern at the mRNA and protein levels on tissue sections and found approximately half of the interneurons of the IZ expressed Cdh8. Furthermore, this cadherin was also detected in the germinal zones of the subpallium, suggesting that it might be involved not only in the migration of interneurons but also in their generation. Quantitative analysis of cortical interneurons in animals lacking the cadherin at E18.5 revealed a significant increase in their numbers. Subsequent functional in vitro experiments showed that blocking Cdh8 function led to increased cell proliferation, with the opposite results observed with over-expression, supporting its role in interneuron generation

A Penerapan Metode Subtractive Fuzzy C-means Pada Tingkat Partisipasi Pendidikan Jenjang Sekolah Menengah Atas/sederajat Di Kabupaten/kota Pulau Kalimantan Tahun 2018

Cluster analysis is a data exploration method uses to obtain hidden characteristics by forming data clusters. One of the cluster analysis methods is Subtractive Fuzzy C-Means (SFCM). SFCM is a combination of Subtractive Clustering and Fuzzy C-Means methods. The SFCM method has the advantages of not requiring many iterations and the results obtained are more stable and accurate than the FCM and SC methods. This study aims to determine the result of clustering on the enrollment rate data for Senior High School (SHS) / equivalent. The data used were the enrollment rate data for high school / equivalent level in the Regency / City of Kalimantan Island in 2018 using three variables, namely the Crude Participation Rate (CPR), the School Participation Rate (SPR) and the Net Enrollment Rate (NER). Based on the three validity indices, namely Partition Coefficient Index (PCI) Validity Index, Modified Partition Coefficient Index (MPCI), and Xie & Beni Index (XBI) in the SFCM method, the optimal cluster were two clusters. Keywords: clustering, education, Subtractive Fuzzy C-Mean

Perbandingan Klasifikasi Algoritma C5.0 dengan Classification And Regression Tree (Studi Kasus: Data Sosial Kepala Keluarga Masyarakat Desa Teluk Baru Kecamatan Muara Ancalong Tahun 2019)

Decision tree is a algorithm used as a reasoning procedure to get answers from problems are entered. Many methods can be used in decision trees, including the C5.0 algorithm and Classification and Regression Tree (CART). This research aims to determine the classification results of the C5.0 and CART algorithms and to determine the comparison of the accuracy classification results from these two methods. The variables used in this research are the average monthly income (Y), employment (X1), number of   family members (X2), last education  (X3)  and  gender  (X4).  After analyzing the results obtained that the accuracy rate of C5.0 algorithm is 79,17% while the accuracy rate of CART is 84,63%. So it can be said that the CART method is a better method in classifying the average income of the people of Teluk Baru Village in Muara Ancalong District in 2019 compared to the C5.0 algorithm metho

Analisis Credit Scoring terhadap Status Pembayaran Barang Elektronik dan Furniture Menggunakan Bootstrap Aggregating K-nearest Neighbor

Classification is the process of grouping objects that have the same characteristics into several categories. This study applies a combination of classification algorithms, namely Bootstrap Aggregating K-Nearest Neighbor in credit scoring analysis. The aim is to classify the credit payment status of electronic goods and furniture at PT KB Finansia Multi Finance in 2020 and determine the level of accuracy produced. Credit payment status is grouped into 2 categories, namely smoothly and not smoothly. There are 7 independent variables that are used to describe the characteristics of the debtor, namely age, number of dependents, length of stay, years of service, income, amount of payment, and payment period. The application of the classification algorithm at the credit scoring analysis is expected to assist creditors in making decisions to accept or reject credit applications from prospective debtors. The results showed that the accuracy obtained from the Bootstrap Aggregating K-Nearest Neighbor algorithm with a proportion of 90:10, m=80%, C=73, and K=5 was the best, which was 92.308%