Machine Learning for Scientific Data Analysis

Over the last few years, machine learning has revolutionized countless areas and fields. Nowadays, AI bears promise for analyzing, extracting knowledge, and driving discovery across many scientific domains such as chemistry, biology, and genomics. However, the specific challenges posed by scientific data demand to adapt machine learning techniques to new requirements. We investigate machine learning-driven scientific data analysis, focusing on a set of key requirements. These include the management of uncertainty for complex data and models, the estimation of system properties starting from low-volume and imprecise collected data, the support to scientific model development through large-scale analysis of experimental data, and the machine learning-driven integration of complementary experimental technologies.

Potential benefits of Tricetin in medicine for the treatment of cancers and other health-related disorders: Medicinal importance and therapeutic benefit

Background: Medicinal plants have been used in medicine for the treatment of numerous diseases due to their medicinal properties and pharmacological activities. Popularity of herbal based drugs in the health sector has been increasing due to patient compliance and cost effectiveness. Herbal drugs derived from plant and animal source have been used in the Ayurvedic, Homeopathic, and Naturopathic system of medicine. Medicinal plants have been used as fuel, clothing, shelter and food material in worldwide since very early age. Phytoconstituents are pure plant chemicals found in different parts of the plant material. Flavonoids are important class of phytochemical found in medicinal plants and their derived products. Methods: In order to know the biological importance of tricetin, in the present investigation scientific data of tricetin in respect to their medicinal importance and pharmacological activities were collected and analyzed. Literature database such as Google, PubMed, Science Direct and Scopus has been searched using term tricetin and flavonoid. All the scientific information has been collected from these databases to know the biological importance of tricetin. Analytical data of tricetin have been also collected and analyzed in the present work to know the isolation, separation and identification procedure of trice Results: Scientific data analysis of different research work revealed the presence of tricetin in Triticum dicoccum, Lathyrus pratensis, Eucalyptus globules, Thuja occidentalis and Metasequoia glyptostroboides. Scientific data analysis signified biological importance of tricetin against different form of cancerous disorders, human osteosarcoma, glioblastoma multiforme, human breast adenocarcinoma, human non‑small cell lung cancer and liver cancer. Scientific data analysis also signified biological potential of tricetin against inflammation, neurodegenerative diseases, atherosclerosis, diabetes and respiratory syncytial virus infection. Scientific data analysis revealed the biological importance of tricetin against multidrug resistance and free radicals. Conclusions: Scientific data analysis revealed biological importance and pharmacological activities of tricetin against various form of human disorders including cancer, inflammation, neurodegeneration, atherosclerosis and diabetes.

Health benefits of avicularin in the medicine against cancerous disorders and other complications: Biological importance, therapeutic benefit and analytical aspects of the medicine

Background: Herbal drugs and their derived phytochemicals have been used in medicine for the preparation of different types of pharmaceutical products. Pure phytochemicals including flavonoids, alkaloids and terpenoids have been used in medicine for the treatment of different types of human disorders including cancerous disorders. Flavonoids have been well known in medicine for their anti-viral, anti-bacterial, anti-inflammatory, anti-diabetic, anti-cancer, anti-aging and cardioprotective potential. Avicularin, also called quercetin-3-α-l-arabino furanoside, is a pure flavonoid, a class of phytochemicals, found to be present in Lindera erythrocarpa and Lespedeza cuneata. Avicularin has been well known in medicine for its anti-cancer properties. Methods: In the present work, scientific data of avicularin have been collected from different databases such as Google, PubMed, Science Direct, Google Scholar and Scopus and summarized with reference to medicinal importance, pharmacological activities and analytical aspects of avicularin. The present review summarized the health beneficial properties of avicularin in medicine through data analysis of various scientific research works. Further analytical progress in medicine for the qualitative and quantitative analysis of avicularin in medicine has been also discussed in the present work. Results: Scientific data analysis of different literature work revealed the biological importance of flavonoid class of phytochemical ‘avicularin’ in medicine. Scientific data analysis revealed that avicularin was found to be present in the Lindera erythrocarpa, Lespedeza cuneata, Rhododendron schlipenbachii and Psidium guajava. Avicularin has been well known in medicine for its anti-inflammatory, anti-allergic, anti-oxidant, anti-tumor and hepatoprotective activities. Avicularin protects cardiomyocytes and hepatocytes against oxidative stress-induced apoptosis and induces cytotoxicity in cancer lines and tumor tissues. Avicularin has positive influence on human hepatocellular carcinoma and inhibits intracellular lipid accumulation. The role of avicularin in rheumatoid arthritis has been also established with its underlying molecular mechanisms in the scientific work. Recent interest in avicularin has focused on pharmacological investigations for its anti-cancer activity in the medicine. Conclusion: The present work signified the biological importance of avicularin in medicine through its medicinal uses, pharmacological activities and analytical aspects in the biological system.

An analysis of security vulnerabilities in container images for scientific data analysis

Background Software containers greatly facilitate the deployment and reproducibility of scientific data analyses in various platforms. However, container images often contain outdated or unnecessary software packages, which increases the number of security vulnerabilities in the images, widens the attack surface in the container host, and creates substantial security risks for computing infrastructures at large. This article presents a vulnerability analysis of container images for scientific data analysis. We compare results obtained with 4 vulnerability scanners, focusing on the use case of neuroscience data analysis, and quantifying the effect of image update and minification on the number of vulnerabilities. Results We find that container images used for neuroscience data analysis contain hundreds of vulnerabilities, that software updates remove roughly two-thirds of these vulnerabilities, and that removing unused packages is also effective. Conclusions We provide recommendations on how to build container images with fewer vulnerabilities.