Assessing the Influence of Sensor-Induced Noise on Machine-Learning-Based Changeover Detection in CNC Machines.

The noise in sensor data has a substantial impact on the reliability and accuracy of (ML) algorithms. A comprehensive framework is proposed to analyze the effects of diverse noise inputs in sensor data on the accuracy of ML models. Through extensive experimentation and evaluation, this research examines the resilience of a LightGBM ML model to ten different noise models, namely, Flicker, Impulse, Gaussian, Brown, Periodic, and others. A thorough analytical approach with various statistical metrics in a Monte Carlo simulation setting was followed. It was found that the Gaussian and Colored noise were detrimental when compared to Flicker and Brown, which are identified as safe noise categories. It was interesting to find a safe threshold limit of noise intensity for the case of Gaussian noise, which was missing in other noise types. This research work employed the use case of changeover detection in (CNC) manufacturing machines and the corresponding data from the publicly funded research project (OBerA).

Computational screening of natural compounds from Salvia plebeia R. Br. for inhibition of SARS-CoV-2 main protease.

The novel Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) has emerged to be the reason behind the COVID-19 pandemic. It was discovered in Wuhan, China and then began spreading around the world, impacting the health of millions. Efforts for treatment have been hampered as there are no antiviral drugs that are effective against this virus. In the present study, we have explored the phytochemical constituents of Salvia plebeia R. Br., in terms of its binding affinity by targeting COVID-19 main protease (Mpro) using computational analysis. Molecular docking analysis was performed using PyRx software. The ADMET and drug-likeness properties of the top 10 compounds showing binding affinity greater than or equal to - 8.0 kcal/mol were analysed using pkCSM and DruLiTo, respectively. Based on the docking studies, it was confirmed that Rutin and Plebeiosides B were the most potent inhibitors of the main protease of SARS-CoV-2 with the best binding affinities of - 9.1 kcal/mol and - 8.9 kcal/mol, respectively. Further, the two compounds were analysed by studying their biological activity using the PASS webserver. Molecular dynamics simulation analysis was performed for the selected protein-ligand complexes to confirm their stability at 300 ns. MM-PBSA provided the basis for analyzing the affinity of the phytochemicals towards Mpro by calculating the binding energy, and secondary structure analysis indicated the stability of protease structure when it is bound to Rutin and Plebeiosides B. Altogether, the study identifies Rutin and Plebeiosides B to be potent Mpro inhibitors of SARS-CoV-2. Supplementary Information: The online version contains supplementary material available at 10.1007/s42535-021-00304-z.

Assessment of microsatellite instability for screening bladder cancer in high-risk population.

AIMS: This study aims to determine the diagnostic efficacy of microsatellite markers for screening bladder cancer in population at high risk. MATERIALS AND METHODS: A population of 200 people was screened for bladder cancer using a set of microsatellite markers. Urine samples were obtained from four different types of population groups - Group 1 (healthy population group), Group 2 (current smokers with a smoking history of more than 10 years), Group 3 (bladder cancer group), and Group 4 (bladder cancer group who were former smokers with a history of more than 10 years). Polymerase chain reaction (PCR) was performed to amplify microsatellite sequences at D9S63, D9S156, and D9S283. PCR products were separated on 1.8% agarose gel and were scanned using ultraviolet transilluminator. RESULTS: In Group 2 (high-risk population group, mainly current smokers with a history of more than 10 years), microsatellite alterations were found in 36 out of 50 people. We observed microsatellite alterations in 38 out of 50 people in Group 3 (bladder cancer group) and in 39 out of 50 people in Group 4 (bladder cancer group, mainly former smokers with a history of more than 10 years). The sensitivity of this test in Group 2, Group 3, and Group 4 was found to be 72%, 76% and 78%, respectively. The specificity of this test in each group was found to be 90%. CONCLUSION: Using these set of microsatellite markers, medium sensitivity and high specificity were reported for this test. The current findings suggest that a set of microsatellite markers (D9S63, D9S156, and D9S283) can be used to detect bladder cancer in high-risk population.

Classification, Source, and Effect of Environmental Pollutants and Their Biodegradation.

Any foreign chemical substance that is unusually present within an organism or is unexpectedly found in the environment at a higher concentration than the permissible limits can be termed a xenobiotic or a pollutant. Such substances include carcinogens, drugs, food additives, hydrocarbons, dioxins, polychlorinated biphenyls, pesticides or even some natural compounds. Pollutants are known for their higher persistence and pervasiveness, and along with their transformed products, they can remain in and interact with the environment for prolonged periods. In this article, the classification of such substances based on their nature, use, physical state, pathophysiological effects, and sources is discussed. The effects of pollutants on the environment, their biotransformation in terms of bioaccumulation, and the different types of remediation such as in situ and ex situ remediation, are also presented.

Role of Bioadsorbents in Reducing Toxic Metals.

Industrialization and urbanization have led to the release of increasing amounts of heavy metals into the environment. Metal ion contamination of drinking water and waste water is a serious ongoing problem especially with high toxic metals such as lead and cadmium and less toxic metals such as copper and zinc. Several biological materials have attracted many researchers and scientists as they offer both cheap and effective removal of heavy metals from waste water. Therefore it is urgent to study and explore all possible sources of agrobased inexpensive adsorbents for their feasibility in the removal of heavy metals. The objective was to study inexpensive adsorbents like various agricultural wastes such as sugarcane bagasse, rice husk, oil palm shell, coconut shell, and coconut husk in eliminating heavy metals from waste water and their utilization possibilities based on our research and literature survey. It also shows the significance of developing and evaluating new potential biosorbents in the near future with higher adsorption capacity and greater reusable options.