SARS-CoV-2-positive patients display considerable differences in proteome diversity in urine, nasopharyngeal, gargle solution and bronchoalveolar lavage fluid samples

Background: Proteome profile changes post-severe acute respiratory syndrome coronavirus 2 (post-SARS-CoV-2) infection in different body sites of humans remains an active scientific investigation whose solutions stand a chance of providing more information on what constitutes SARS-CoV-2 pathogenesis. While proteomics has been used to understand SARS-CoV-2 pathogenesis, there are limited data about the status of proteome profile in different human body sites infected by sarscov2 virus. To bridge this gap, our study aims to profile the proteins secreted in urine, bronchoalveolar lavage fluid (BALF), gargle solution, and nasopharyngeal samples and assess the proteome differences in these body samples collected from SARS-CoV-2-positive patients. Materials and methods: We downloaded publicly available proteomic data from (https://www.ebi.ac.uk/pride/). The data we downloaded had the following identifiers: i) PXD019423, n=3 from Charles Tanford Protein Center in Germany. ii) PXD018970, n=15 from Beijing Proteome Research Centre, China. iii)PXD022085, n=5 from Huazhong University of Science and Technology, China, and iv) PXD022889, n=18 from Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905 USA. MaxQuant was used for the peptide spectral matching using human and SARS-CoV-2 downloaded from UniProt database (access date 13th October 2021). Results: The individuals infected with SARS-CoV-2 viruses displayed a different proteome diversity from the different body sites we investigated. Overally, we identified 1809 proteins across the four different sample types we compared. Urine and BALF samples had significantly more abundant SARS-CoV-2 proteins than the other body sites we compared. Urine samples had 257(33.7%) unique proteins followed by nasopharyngeal with 250(32.8%) unique proteins. Garlge solution and BALF had 38(5%) and 73(9.6%) unique proteins respectively. Conclusions: Urine, gargle solution, nasopharyngeal, and bronchoalveolar lavage fluid samples have different protein diversity in individuals infected with SARS-CoV-2. Moreover, our data also demonstrated that a given body site is characterized by a unique set of proteins in SARS-CoV-2 seropositive individuals. Key words: SARS-CoV-2, body sites,urine,gargle solution, BALF,nasopharyngeal

A Platform for Keyword Search and its Application for COVID-19 Pandemic Data

Keyword search is typically associated with information retrieval systems. However, recently, keyword search has been expanded to relational databases and RDF datasets, as an attractive alternative to traditional database access. This paper introduces DANKE, a platform for keyword search over databases, and discusses how third-party applications can be equipped with DANKE to take advantage of a data retrieval mechanism that does not require users to have specific technical skills for searching, retrieving and exploring data. The paper ends with the description of an application, called CovidKeyS, which uses DANKE to implement keyword search over three COVID-19 data scenarios.

Recruitment Search Engines for Screening Resumes through AI by Using Boolean Search Functions

The days are gone when hard copies of resumes were sent to recruiters and they used to screen and set aside non relevant resumes. Artificial Intelligence has taken up mundane tasks of recruiters by simplifying search algorithms and human computer interaction. Job Boards provide recruiters with a database of candidates powered by a search tool with lot of filters. The demonstration paper actually carries out searches on recruitment database access tools to filter the relevant applicants from a wide pool of data with the help of search engine tools in resume databases of various jobboards. It shows how Boolean operators get through better search results in few minutes. Recruiter job to get to the right candidate is sorted out with Artificial Intelligence so that they can focus more on strategic tasks. Future of AI in recruitment is immense. Already companies are empowering recruiters with 360 degree tools which help with all Human Resource Aspects.

DNA Computing Using Cryptographic and Steganographic Strategies

Information protection and secrecy are major concerns, especially regarding the internet’s rapid growth and widespread usage. Unauthorized database access is becoming more common and is being combated using a variety of encrypted communication methods, such as encryption and data hiding. DNA cryptography and steganography are used as carriers by utilizing the bio-molecular computing properties that have become more common in recent years. This study examines recently published DNA steganography algorithms, which use DNA to encrypt confidential data transmitted through an insecure communication channel. Several DNA-based steganography strategies will be addressed, with a focus on the algorithm’s advantages and drawbacks. Probability cracking, blindness, double layer of security, and other considerations are used to compare steganography algorithms. This research would help and create more effective and accurate DNA steganography strategies in the future.

Database Access Layer Code Generation Directly from Use Case Scenarios

Requirements definition is the first step in the life cycle of a software system. Requirements are formulated as paragraphs of text and appear ambiguous, so they cannot be translated directly into code. For this reason, they are treated as secondary artifacts for software developers. This paper presents a model-driven based approach where requirements are treated as first-class citizens, and can contribute to the final code. In this approach, requirements are formulated as use case models with their textual scenarios, using a precise requirements language called RSL, allowing an automatic transition to executable Java code. The structure of the generated code follows the Model-View-Presenter (MVP) architectural pattern. The work focuses on the Model layer code, which is responsible for the persistence and storage of data in a database system.

Implementation of Multiprocessing and Multithreading for End Node Middleware Control on Internet of Things Devices

Previously, an educational robot system was built by incorporating Internet of Things (IoT) elements. Over time, this educational robot has been implanted with a middleware. Middleware has a role in receiving command data from the real-time database, access sensors, actuators, and sending feedback. Middleware contains protocols that translate commands between high-level programming and Raspberry Pi hardware. The focus of this research is to improve the performance of the middleware to pursue processing time efficiency. For this reason, it is necessary to implement multiprocessing and multithreading in handling several tasks. The CPU division has been adjusted automatically to not work on just one core or block of memory. Several program functions can run in parallel and reduce program execution time efficiently. The tasks handled are sensor reading and actuator control in the form of a motor. Testing has been carried out to perform multiprocessing and multithreading tasks to process six sensors and five actuators. Multiprocessing requires an average of 1.00% to 15.00% CPU usage and 2.70% memory usage. Meanwhile, multithreading involves an average of 1.00% to 71.00% CPU usage and 3.30% memory usage.