m6A-TSHub: unveiling the context-specific m6A methylation and m6A-affecting mutations in 23 human tissues

As the most pervasive epigenetic marker present on mRNA and lncRNA, N6-methyladenosine (m6A) RNA methylation has been shown to participate in essential biological processes. Recent studies revealed the distinct patterns of m6A methylome across human tissues, and a major challenge remains in elucidating the tissue-specific presence and circuitry of m6A methylation. We present here a comprehensive online platform m6A-TSHub for unveiling the context-specific m6A methylation and genetic mutations that potentially regulate m6A epigenetic mark. m6A-TSHub consists of four core components, including (1) m6A-TSDB: a comprehensive database of 184,554 functionally annotated m6A sites derived from 23 human tissues and 499,369 m6A sites from 25 tumor conditions, respectively; (2) m6A-TSFinder: a web server for high-accuracy prediction of m6A methylation sites within a specific tissue from RNA sequences, which was constructed using multi-instance deep neural networks with gated attention; (3) m6A-TSVar: a web server for assessing the impact of genetic variants on tissue-specific m6A RNA modification; and (4) m6A-CAVar: a database of 587,983 TCGA cancer mutations (derived from 27 cancer types) that were predicted to affect m6A modifications in the primary tissue of cancers. The database should make a useful resource for studying the m6A methylome and genetic factor of epitranscriptome disturbance in a specific tissue (or cancer type). m6A-TSHub is accessible at: www.xjtlu.edu.cn/biologicalsciences/m6ats.

CLASH Analyst: A Web Server to Identify In Vivo RNA–RNA Interactions from CLASH Data

Non-coding RNAs, such as miRNAs and piRNAs, play critical roles in gene regulation through base-pairing interactions with their target molecules. The recent development of the crosslinking, ligation, and sequencing of hybrids (CLASH) method has allowed scientists to map transcriptome-wide RNA–RNA interactions by identifying chimeric reads consisting of fragments from regulatory RNAs and their targets. However, analyzing CLASH data requires scientists to use advanced bioinformatics, and currently available tools are limited for users with little bioinformatic experience. In addition, many published CLASH studies do not show the full scope of RNA–RNA interactions that were captured, highlighting the importance of reanalyzing published data. Here, we present CLASH Analyst, a web server that can analyze raw CLASH data within a fully customizable and easy-to-use interface. CLASH Analyst accepts raw CLASH data as input and identifies the RNA chimeras containing the regulatory and target RNAs according to the user’s interest. Detailed annotation of the captured RNA–RNA interactions is then presented for the user to visualize within the server or download for further analysis. We demonstrate that CLASH Analyst can identify miRNA- and piRNA-targeting sites reported from published CLASH data and should be applicable to analyze other RNA–RNA interactions. CLASH Analyst is freely available for academic use.

A two-step computer vision-based framework for bolt loosening detection and its implementation on a smartphone application

Bolt loosening detection is a labor-intensive and time-consuming process for field engineers. This paper develops a two-step computer vision-based framework to quickly identify bolt loosening angle from field images captured by unmanned aerial vehicle (UAV). In step one, a total of 1200 image samples of bolted structures were used to train faster region based convolutional neural network (Faster R-CNN) for bolt detection from UAV captured images. In step two, computer vision-based technologies, including Gaussian filter, perspective transform, and Hough transform (HT), were performed to quantify bolt loosening angle. The developed framework was then integrated into web server and an iOS application (app) was designed to enable fast data communication between field workplace (UAV captured images) and web server (bolt loosening angle quantification), so that field engineers can quickly view the inspection results on their phone screens. The proposed framework and designed smartphone app greatly help field engineers to improve the accuracy and efficiency for onsite inspection and maintenance of bolted structures.

DUNEScan: a web server for uncertainty estimation in skin cancer detection with deep neural networks

Recent years have seen a steep rise in the number of skin cancer detection applications. While modern advances in deep learning made possible reaching new heights in terms of classification accuracy, no publicly available skin cancer detection software provide confidence estimates for these predictions. We present DUNEScan (Deep Uncertainty Estimation for Skin Cancer), a web server that performs an intuitive in-depth analysis of uncertainty in commonly used skin cancer classification models based on convolutional neural networks (CNNs). DUNEScan allows users to upload a skin lesion image, and quickly compares the mean and the variance estimates provided by a number of new and traditional CNN models. Moreover, our web server uses the Grad-CAM and UMAP algorithms to visualize the classification manifold for the user’s input, hence providing crucial information about its closeness to skin lesion images from the popular ISIC database. DUNEScan is freely available at: https://www.dunescan.org.

ASAP 2: a pipeline and web server to analyze marker gene amplicon sequencing data automatically and consistently

Background Amplicon sequencing of marker genes such as 16S rDNA have been widely used to survey and characterize microbial community. However, the complex data analyses have required many interfering manual steps often leading to inconsistencies in results. Results Here, we have developed a pipeline, amplicon sequence analysis pipeline 2 (ASAP 2), to automate and glide through the processes without the usual manual inspections and user’s interference, for instance, in the detection of barcode orientation, selection of high-quality region of reads, and determination of resampling depth and many more. The pipeline integrates all the analytical processes such as importing data, demultiplexing, summarizing read profiles, trimming quality, denoising, removing chimeric sequences and making the feature table among others. The pipeline accepts multiple file formats as input including multiplexed or demultiplexed, paired-end or single-end, barcode inside or outside and raw or intermediate data (e.g. feature table). The outputs include taxonomic classification, alpha/beta diversity, community composition, ordination analysis and statistical tests. ASAP 2 supports merging multiple sequencing runs which helps integrate and compare data from different sources (public databases and collaborators). Conclusions Our pipeline minimizes hands-on interference and runs amplicon sequence variant (ASV)-based amplicon sequencing analysis automatically and consistently. Our web server assists researchers that have no access to high performance computer (HPC) or have limited bioinformatics skills. The pipeline and web server can be accessed at https://github.com/tianrenmaogithub/asap2 and https://hts.iit.edu/asap2, respectively.

Model Sistem Pengendalian Suhu dan Kelembaban Ruangan Produksi Obat Berbasis NodeMCU ESP32

Proses produksi dalam industri farmasi harus mengikuti pedoman yang tertera dalam Cara Pembuatan Obat yang Baik (CPOB). Proses produksi harus dihentikan bila suhu dan kelembaban ruangan produksi tidak memenuhi nilai yang sudah ditentukan dan harus membuat laporan tentang terhentinya produksi. Penelitian ini mengusulkan sebuah model sistem yang dapat mengatur suhu dan kelembaban udara secara otomatis berbasis NodeMCU ESP32 yang dapat memberikan informasi kepada teknisi secara terus menerus. Melalui Web Server phpMyAdmin data suhu dan kelembaban dapat disimpan serta dimonitor dari jarak jauh menggunakan smartphone atau komputer dengan membuka web. Sensor DHT 11 digunakan untuk mendeteksi nilai suhu dan kelembaban dan untuk mengontrolnya digunakan kipas Peltier dan dehumidifier. Dalam penelitian ini dilakukan pengendalian ruangan produksi obat yang memiliki ketentuan kelas C dan D sesuai ketentuan pada CPOB yaitu untuk jenis obat cair di ruang steril. Ruangan produksi obat kelas C memiliki range suhu dari 16˚C sampai dengan 25˚C dan range kelembaban 45% sampai dengan 55%, sedangkan ruangan produksi obat kelas D memiliki range suhu dari 20˚C sampai dengan 27˚C dan range kelembaban dari 40% sampai dengan 60%. Dari hasil pengujian yang dilakukan dapat diketahui bahwa sistem dapat mengendalikan suhu dan kelembaban ruangan secara otomatis bila ada gangguan sekaligus mencatat data waktu kejadian gangguan untuk melengkapi laporan selama proses produksi obat.

COVID-19 Tracking Mobile Application at ITCG

Objectives: Implement a mobile application to control COVID-19 in all its variants, containing or reducing the spread of the virus during the return to face-to-face classes at the ITCG. Methodology: The project was developed in Android Studio using different libraries and technological resources. You have access to a web server with a MySQL database. The application allows scanning the QR code of the visited site from the mobile application, reporting a positive case and vaccine reactions, monitoring any unusual adverse effects, as well as receiving notifications via SMS. Contribution: Contain the advance of the virus, avoiding saturating health systems, monitoring users in the event of any eventuality. The application was distributed to the student community of the Technological Institute of Ciudad Guzmán (ITCG) during the application of the first dose of the AstraZeneca vaccine, to monitor any symptoms, as well as positive cases during the return to face-to-face classes.

An in-silico approach to design potential siRNAs against the ORF57 of Kaposi’s sarcoma-associated herpesvirus

Kaposi's sarcoma-associated herpesvirus (KSHV) is one of the few human oncogenic viruses, which causes a variety of malignancies, including Kaposi's sarcoma, multicentric Castleman disease, and primary effusion lymphoma, particularly in human immunodeficiency virus patients. The currently available treatment options cannot always prevent the invasion and dissemination of this virus. In recent times, siRNA-based therapeutics are gaining prominence over conventional medications as siRNA can be designed to target almost any gene of interest. The ORF57 is a crucial regulatory protein for lytic gene expression of KSHV. Disruption of this gene translation will inevitably inhibit the replication of the virus in the host cell. Therefore, the ORF57 of KSHV could be a potential target for designing siRNA-based therapeutics. Considering both sequence preferences and target site accessibility, several online tools (i-SCORE Designer, Sfold web server) had been utilized to predict the siRNA guide strand against the ORF57. Subsequently, off-target filtration (BLAST), conservancy test (fuzznuc), and thermodynamics analysis (RNAcofold, RNAalifold, and RNA Structure web server) were also performed to select the most suitable siRNA sequences. Finally, two siRNAs were identified that passed all of the filtration phases and fulfilled the thermodynamic criteria. We hope that the siRNAs predicted in this study would be helpful for the development of new effective therapeutics against KSHV.