RNAi technology has significant potential as a future therapeutic and could theoretically be used to knock down disease-specific RNAs. However, due to frequent off-target effects, low efficiency, and limited accessibility of nuclear transcripts, the clinical application of the technology remains challenging. In this study, we first assessed the stability of Cas13a mRNA and guide RNA. Next, we titrated Cas13a and guide RNA vectors to achieve effective knockdown of firefly luciferase (FLuc) RNA, used as a target transcript. The interference specificity of Cas13a on guide RNA design was next explored. Subsequently, we targeted the EML4-ALK v1 transcript in H3122 lung cancer cells. As determined by FLuc assay, Cas13a exhibited activity only toward the orientation of the crRNA–guide RNA complex residing at the 5′ of the crRNA. The activity of Cas13a was maximal for guide RNAs 24–30 bp in length, with relatively low mismatch tolerance. After knockdown of the EML4-ALK transcript, cell viability was decreased up to 50%. Cas13a could effectively knock down FLuc luminescence (70–76%), mCherry fluorescence (72%), and EML4-ALK at the protein (>80%) and transcript levels (26%). Thus, Cas13a has strong potential for use in RNA regulation and therapeutics, and could contribute to the development of personalized medicine.

No abstract

This study aims to determine the quality of the website of the Program Studi Sistem Informasi Universitas PGRI Madiun (or later called Prodi SI UNIPMA) with the Webqual 4.0 method which has 4 variables, namely quality of use, quality of information, interaction services and overall quality. The population in this study were students of SI UNIPMA Study Program where 21 respondents were taken as samples. Multiple linear regression analysis is used to test the relationship between variables Webqual 4.0 and student satisfaction. From the results of this study, it can be concluded that the most influential variable in satisfaction is the quality of information with a value of 14.131 and the smallest is the variable quality of use with a value of 2.266. So the recommendations for the website can be obtained is to increase the dimensions of the website's usefulness to students.

Deep neural networks have been extensively researched in the field of document image classification to improve classification performance and have shown excellent results. However, there is little research in this area that addresses the question of how well these models would perform in a real-world environment, where the data the models are confronted with often exhibits various types of noise or distortion. In this work, we present two separate benchmark datasets, namely RVL-CDIP-D and Tobacco3482-D, to evaluate the robustness of existing state-of-the-art document image classifiers to different types of data distortions that are commonly encountered in the real world. The proposed benchmarks are generated by inserting 21 different types of data distortions with varying severity levels into the well-known document datasets RVL-CDIP and Tobacco3482, respectively, which are then used to quantitatively evaluate the impact of the different distortion types on the performance of latest document image classifiers. In doing so, we show that while the higher accuracy models also exhibit relatively higher robustness, they still severely underperform on some specific distortions, with their classification accuracies dropping from ~90% to as low as ~40% in some cases. We also show that some of these high accuracy models perform even worse than the baseline AlexNet model in the presence of distortions, with the relative decline in their accuracy sometimes reaching as high as 300-450% that of AlexNet. The proposed robustness benchmarks are made available to the community and may aid future research in this area.

This study aims to determine the implementation of government policies on the discipline of the state civil apparatus at the Village Office in Baranti District, Sidenreng Rappang Regency. The sampling technique used was saturated sampling technique with a total sample of 23 people. Data collection techniques using observation, interviews, questionnaires, and literature study. Quantitative data analysis techniques used are frequency tabulation analysis and simple regression analysis with the help of SPSS 20.0 for windows program and Likert Scale. Based on the results of the questionnaire, it was obtained a recapitulation of the variables of government policy implementation, measured through indicators of the right policy, right implementer, right target, right environment, and right process, 41% included in the "good enough" category. The recapitulation of the discipline variable of the state civil apparatus is measured by indicators of time discipline, regulatory discipline, and responsibility discipline, 33% of which are included in the "less good" category. Based on the results of the processed simple regression analysis using SPSS 20.0 for windows, with the Summary Model obtained a value of 41% in the "good enough" category.

Genetic polymorphisms, including single nucleotide polymorphisms (SNPs), are responsible for inter-individual variability in susceptibility to cancer and other disorders. Both environmental factors (e.g., smoking or carcinogen exposure) and genetic variation underlie the development of cancer; however, studies of twins suggest that genetic variation is more important. Hence, the identification of SNPs makes an important contribution to cancer research. In this study, 13 SNPs in 12 genes were genotyped in HEK 293 and HeLa cells using the simple and inexpensive SNP-RFLP method. Sanger sequencing was performed for one SNP to validate the SNP-RFLP results. Of the 13 SNPs, 10 were homozygous and three were heterozygous (rs10937405, rs12296850, and rs3814113) in HEK 293 cells, while 12 were homozygous and one was heterozygous (rs995030) in HeLa cells. The cells carried eight disease-associated risk alleles (32% of typed alleles), including rs2853677, rs995030, rs2736100, and rs6010620 in HEK 293 cells, and rs10937405, rs3814113, rs4767364, and rs6010620 in HeLa cells. Four SNP loci were homozygous for different alleles in each cell line, with HEK 293 cells having a CC genotype at rs2853677, GG at rs2736100 and rs4767364, and TT at rs3819197, while HeLa cells had TT genotypes at rs2853677 and rs2736100, AA at rs4767364, and CC at rs3819197. In conclusion, these results are potentially applicable for testing of novel gene therapeutic approaches in future experiments where the non-risk alleles of the eight identified risk alleles are substituted into HEK 293 or HeLa cells.

Preprocessing data is needed some methods to get better results. This research is intended to AbstrakPreprocessing data sangat dibutuhkan beberapa metode untuk mendapatkan hasil yang lebih baik. Penelitian ini ditujukan mengolah dataset karyawan sebagai inputan preprocessing. Selanjutnya digunakan model algoritma decision tree, random tree dan random forest. Pohon keputusan digunakan untuk membuat model aturan yang dipilih dalam proses mengambil keputusan. Dengan hasil pendekatan preprocessing dan model aturan yang didapat, dapat menjadi referensi bagi pengambil keputusan untuk mengambil keputusan variabel mana yang harus diperhatikan untuk mendukung peningkatan kinerja karyawan.

Bank Muamalat Indonesia sebagai pionir bank syariah yang ada di Indonesia. Eksistensi Bank Muamalat kian kuat ketika mampu melewati badai krisis moneter di tahun 1997. Namun 5 tahun terakhir, Bank Muamalat mengalami permasalahan permasalahan profitabilitas yang ditandai dengan laba bersih turun signifikan terutama di 2 tahun terakhir. Penelitian ini bertujuan untuk menilai dan menganalisis Rasio Profitabilitas berupa Return On Assets, Return On Equity, Net Profit Margin, Gross Profit Margin di PT. Bank Muamalat Indonesia, Tbk tahun 2015- 2019. Jenis Penelitian ini adalah penelitian kuantitatif deskriptif. Berdasarkan hasil analisis dan pembahasan, kinerja keuangan di PT. Bank Muamalat, Tbk diukur dengan menggunakan Return On Assets (ROA), Return On Equity (ROE), Net Profit Margin (NPM), Gross Profit Margin (GPM), menunjukan kondisi keuangan yang sangat kurang baik. Hal ini dilihat berdasarkan perhitungan rata- rata Return On Equity, Net Profit Margin, Gross Profit Margin selama 5 tahun yang masih berada jauh dibawah standar penilaian cukup dari Bank Indonesia. Hal ini menunjukkan bahwa PT. Bank Muamalat, Tbk belum mampu mengelola modal dan meningkatkan pembiayaan untuk menghasilkan laba perusahaan dan menekan biaya operasional yang dimiliki secara efektif dan efisien. Keywords: Profitabilitas, ROA, ROE, NPM,  GPM.