Review on Big Data Analysis on COVID-19

Big data analytics is in transforming stage and it will continue to grow and contribute in different area like health. In recent past we have been gone through biggest health issue of the century and it taught us a lesson that in this modern world, technology with relevant data can help to us reduce any human challenge. COVID 19 is that health issue which we faced and suffered a lot. Big data provided us a platform which help us to create an accurate and most trusted data base to analyze, track and tackle this situation. Big data provides an elaborate set of attributes, details of infected patient in very explanatory

Big Data Analysis on COVID-19

Over the past 2 years, the Coronavirus has rapidly spread to all parts of the world. Scientist and researchers are continuing their research to find a permanent cure. As the number of cases are increasing, so the tests are for Coronavirus is increasing rapidly, it is impossible to maintain data of test due to the time and cost factors. Big data is very helpful to maintain the track record of the COVID-19 infected patients in a very systematic way and will reduce the time delay for the results of the medical tests and modulate doctors to give proper medical treatment to the infected person. Big data analytics play an important role in building knowledge, studies required in making decisions and precautionary measures. However, due to the vast amount of data available on COVID-19 from various sources, there is a need to review the roles of big data analysis in controlling and tracking the spread of COVID-19, presenting the main challenges and directions of COVID-19 data analysis, as well as providing a framework on the related existing applications and studies to facilitate future research on COVID-19 analysis. Keywords-big data analytics, 2019 novel coronavirus disease (COVID-19)

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Music in our ears: the biological bases of musical timbre perception.

Timbre is the attribute of sound that allows humans and other animals to distinguish among different sound sources. Studies based on psychophysical judgments of musical timbre, ecological analyses of sound's physical characteristics as well as machine learning approaches have all suggested that timbre is a multifaceted attribute that invokes both spectral and temporal sound features. Here, we explored the neural underpinnings of musical timbre. We used a neuro-computational framework based on spectro-temporal receptive fields, recorded from over a thousand neurons in the mammalian primary auditory cortex as well as from simulated cortical neurons, augmented with a nonlinear classifier. The model was able to perform robust instrument classification irrespective of pitch and playing style, with an accuracy of 98.7%. Using the same front end, the model was also able to reproduce perceptual distance judgments between timbres as perceived by human listeners. The study demonstrates that joint spectro-temporal features, such as those observed in the mammalian primary auditory cortex, are critical to provide the rich-enough representation necessary to account for perceptual judgments of timbre by human listeners, as well as recognition of musical instruments

Machine Learning Styles for Diabetic Retinopathy Detection: A Review and Bibliometric Analysis

Diabetic retinopathy (DR) is a medical condition caused by diabetes. The development of retinopathy significantly depends on how long a person has had diabetes. Initially, there may be no symptoms or just a slight vision problem due to impairment of the retinal blood vessels. Later, it may lead to blindness. Recognizing the early clinical signs of DR is very important for intervening in and effectively treating DR. Thus, regular eye check-ups are necessary to direct the person to a doctor for a comprehensive ocular examination and treatment as soon as possible to avoid permanent vision loss. Nevertheless, due to limited resources, it is not feasible for screening. As a result, emerging technologies, such as artificial intelligence, for the automatic detection and classification of DR are alternative screening methodologies and thereby make the system cost-effective. People have been working on artificial-intelligence-based technologies to detect and analyze DR in recent years. This study aimed to investigate different machine learning styles that are chosen for diagnosing retinopathy. Thus, a bibliometric analysis was systematically done to discover different machine learning styles for detecting diabetic retinopathy. The data were exported from popular databases, namely, Web of Science (WoS) and Scopus. These data were analyzed using Biblioshiny and VOSviewer in terms of publications, top countries, sources, subject area, top authors, trend topics, co-occurrences, thematic evolution, factorial map, citation analysis, etc., which form the base for researchers to identify the research gaps in diabetic retinopathy detection and classification

Association of sugary foods and drinks consumption with behavioral risk and oral health status of 12- and 15-year-old Indian school children

Aim: This study aims to assess the association of sugary foods and drinks consumption with behavioral risk and oral health status of 12- and 15-year-old government school children in Udaipur. Materials and Methods: A descriptive cross-sectional study was conducted among of 12- and 15-year-old government schoolchildren of Udaipur. A survey pro forma designed based on HBSC (Health behaviour in School-aged Children) study protocol and WHO Oral Health Assessment Form for Children (2013) was used. Chi-Square test, Independent Sample t-test, and Multinomial Logistic Regression analysis were used with 95% confidence interval and 5% significance level. Results: Out of 710 participants, 455 (64.1%) were males and 255 females (35.9%). Majority of 15 years age (57.3%) consumed more soft drinks than 12-year-old. Males showed a comparatively greater tendency to have sugar sweetened products than females. The decayed, missing, and filled teeth (dmft) and DMFT scores were relatively higher for subjects who consumed sugary substances more than once/day than who had less than once/day. Gingivitis was associated with high sugar diet. Conclusion: Sugary foods and drinks consumption is significantly associated with behavioral habits of children and is a clear behavioral risk for oral health