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.