The data generated at an exponential rate has resulted in Big Data. This data has many characteristics and consists of structured, unstructured, and semi-structured data formats. It contains valuable information for the different types of stakeholders based on their need however it is not possible to meet them with the help of traditional tools and techniques. Here the big data technologies play a crucial role to handle, store, and process this tremendous amount of data in real-time. Big data analytics is used to extract meaningful information or patterns from the voluminous data. It can be further divided into three types i.e. text analytics, audio analytics, video analytics, and social media analytics. Big data analytics if followed by big data analysis process plays a significant role in generating meaningful information from big data. Big data analysis process consists of data acquisition, data storage, data management, data analytics, and finally data visualization. However, it is not simple and brings many challenges that need to be resolved. This paper presents the issues and challenges related to big data, prominent characteristics of big data, big data analytics, big data analysis process, and technologies used for processing the massive data.
Proprioception is critical to motor control and functional status but has received limited study early after stroke. Patients admitted to an inpatient rehabilitation facility for stroke (n = 18, mean(±SD) 12.5 ± 6.6 days from stroke) and older healthy controls (n = 19) completed the Wrist Position Sense Test (WPST), a validated, quantitative measure of wrist proprioception, as well as motor and cognitive testing. Patients were serially tested when available (n = 12, mean 11 days between assessments). In controls, mean(±SD) WPST error was 9.7 ± 3.5° in the dominant wrist and 8.8 ± 3.8° in the nondominant wrist (p = 0.31). In patients with stroke, WPST error was 18.6 ± 9° in the more-affected wrist, with abnormal values present in 88.2%; and 11.5 ± 5.6° in the less-affected wrist, with abnormal values present in 72.2%. Error in the more-affected wrist was higher than in the less-affected wrist (p = 0.003) or in the dominant (p = 0.001) and nondominant (p < 0.001) wrist of controls. Age and BBT performance correlated with dominant hand WPST error in controls. WPST error in either wrist after stroke was not related to age, BBT, MoCA, or Fugl-Meyer scores. WPST error did not significantly change in retested patients. Wrist proprioception deficits are common, bilateral, and persistent in subacute stroke and not explained by cognitive or motor deficits.
The newly detected virus also called coronavirus spreads the disease Covid19. World Health Organization (WHO) confirmed this virus as a worldwide pandemic as it has infected millions of people and has taken away many lives across the globe. An infection caused by Covid19 disease majorly destroys the respiratory tract of human beings that ends with multiple organ failures or death in the worst case. In the present work, chest radiographs were provided as input to various deep learning CNN architectures for the purpose of feature extraction. After extracting the features, the images were provided as the input to various machine learning classifiers that classify the chest radiographs as Covid-19 positive, pneumonia infection, or healthy scans.
Background & Objective: Ayurveda, the “Mother of all healing”, has existed for over 5,000 years and hence is considered to be the oldest healing science. Ayurveda states that the mind can heal and transform a person's whole being as the mind and body are associated. Herbs are the heart of Ayurvedic belief. They are used to boost defense against diseases and viruses and keep the brain, body, and soul in complete balance. Although ayurvedic medicines and herbs have natural components, they should still be used with certain precautions under the supervision of a medical practitioner. This study aims to manually curate information for the various ayurvedic medicinal herbs that have antiviral activity against harmful viruses. Methods: Detailed information is collected from the literature regarding the following (a) types of viruses (b) which particular category they belong to(c) the respective components of herbs that are responsible for curing viruses. We developed a web interface with the help of php and mysql to get the desired output. Results: The database consists of 104 viruses and 704 natural components. The web server is available at: http://ayurvir.com. Interpretation & Conclusion: We believe that AyurVirDB database will be extremely beneficial for the research community. It not only aids in investigations of Ayurvedic medicinal plants and their components. On the emergence or re-emergence of a virus, one could be able to predict the ayurvedic plants/herbs used for viral treatment based on virus similarity or disease symptoms.
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