Synthesis of fatty acid methyl ester from rice bran oil

Increase in urbanization, standard of living of people and expanding population are the key factors which contribute to the growth in energy demand. India ranks sixth in the world in terms of energy demand. India’s demand for diesel fuel is increasing rapidly and much of the increase has to be met with expensive oil import. The known world wide reserves of petroleum are predicted to last for about 40 years, hence future availability of petroleum is uncertain. Alternative fuels have to be considered in order to undertake energy security and import substitution for diesel crude imports by 5% in 2015 and 10% in 2020. In this circumstances non-edible oil source play a vital role to meet the diesel requirement. In the present study, biodiesel has been prepared from Rice Bran oil by transesterification method.Â

Health Economic and Safety Considerations for Artificial Intelligence Applications in Diabetic Retinopathy Screening

Systematic screening for diabetic retinopathy (DR) has been widely recommended for early detection in patients with diabetes to address preventable vision loss. However, substantial manpower and financial resources are required to deploy opportunistic screening and transition to systematic DR screening programs. The advent of artificial intelligence (AI) technologies may improve access and reduce the financial burden for DR screening while maintaining comparable or enhanced clinical effectiveness. To deploy an AI-based DR screening program in a real-world setting, it is imperative that health economic assessment (HEA) and patient safety analyses are conducted to guide appropriate allocation of resources and design safe, reliable systems. Few studies published to date include these considerations when integrating AI-based solutions into DR screening programs. In this article, we provide an overview of the current state-of-the-art of AI technology (focusing on deep learning systems), followed by an appraisal of existing literature on the applications of AI in ophthalmology. We also discuss practical considerations that drive the development of a successful DR screening program, such as the implications of false-positive or false-negative results and image gradeability. Finally, we examine different plausible methods for HEA and safety analyses that can be used to assess concerns regarding AI-based screening

Acceptance and Perception of Artificial Intelligence Usability in Eye Care (APPRAISE) for Ophthalmologists: A Multinational Perspective

Background: Many artificial intelligence (AI) studies have focused on development of AI models, novel techniques, and reporting guidelines. However, little is understood about clinicians' perspectives of AI applications in medical fields including ophthalmology, particularly in light of recent regulatory guidelines. The aim for this study was to evaluate the perspectives of ophthalmologists regarding AI in 4 major eye conditions: diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD) and cataract. Methods: This was a multi-national survey of ophthalmologists between March 1st, 2020 to February 29th, 2021 disseminated via the major global ophthalmology societies. The survey was designed based on microsystem, mesosystem and macrosystem questions, and the software as a medical device (SaMD) regulatory framework chaired by the Food and Drug Administration (FDA). Factors associated with AI adoption for ophthalmology analyzed with multivariable logistic regression random forest machine learning. Results: One thousand one hundred seventy-six ophthalmologists from 70 countries participated with a response rate ranging from 78.8 to 85.8% per question. Ophthalmologists were more willing to use AI as clinical assistive tools (88.1%, n = 890/1,010) especially those with over 20 years' experience (OR 3.70, 95% CI: 1.10–12.5, p = 0.035), as compared to clinical decision support tools (78.8%, n = 796/1,010) or diagnostic tools (64.5%, n = 651). A majority of Ophthalmologists felt that AI is most relevant to DR (78.2%), followed by glaucoma (70.7%), AMD (66.8%), and cataract (51.4%) detection. Many participants were confident their roles will not be replaced (68.2%, n = 632/927), and felt COVID-19 catalyzed willingness to adopt AI (80.9%, n = 750/927). Common barriers to implementation include medical liability from errors (72.5%, n = 672/927) whereas enablers include improving access (94.5%, n = 876/927). Machine learning modeling predicted acceptance from participant demographics with moderate to high accuracy, and area under the receiver operating curves of 0.63–0.83. Conclusion: Ophthalmologists are receptive to adopting AI as assistive tools for DR, glaucoma, and AMD. Furthermore, ML is a useful method that can be applied to evaluate predictive factors on clinical qualitative questionnaires. This study outlines actionable insights for future research and facilitation interventions to drive adoption and operationalization of AI tools for Ophthalmology

Clinical features and outcomes of patients with tubercular uveitis treated with antitubercular therapy in the collaborative ocular tuberculosis study (COTS)-1

IMPORTANCE Eradication of systemic tuberculosis (TB) has been limited by neglected populations and the HIV pandemic. Whereas ocular TB often presents as uveitis without any prior evidence of systemic TB, the existing uncertainty in the diagnosis of TB uveitis may perpetuate missed opportunities to address systemic TB. OBJECTIVE To examine the clinical features of TB uveitis and the associations with response to antitubercular therapy (ATT). DESIGN, SETTING, AND PARTICIPANTS This retrospective multinational cohort study included patients from 25 ophthalmology referral centers diagnosed with TB uveitis and treated with ATT from January 1, 2004, through December 31, 2014, with a minimum follow-up of 1 year. MAIN OUTCOMES AND MEASURES Treatment failure, defined as a persistence or recurrence of inflammation within 6 months of completing ATT, inability to taper oral corticosteroids to less than 10mg/d or topical corticosteroid drops to less than 2 drops daily, and/or recalcitrant inflammation necessitating corticosteroid-sparing immunosuppressive therapy. RESULTS A total of 801 patients (1272 eyes) were studied (mean [SD] age, 40.5 [14.8] years; 413 [51.6%] male and 388 [48.4%] female; 577 [73.6%] Asian). Most patients had no known history (498 of 661 [75.3%]) of systemic TB. Most patients had bilateral involvement (471 of 801 [58.8%]). Common clinical signs reported include vitreous haze (523 of 1153 [45.4%]), retinal vasculitis (374 of 874 [42.8%]), and choroidal involvement (419 of 651 [64.4%]). Treatment failure developed in 102 of the 801 patients (12.7%). On univariate regression analysis, the hazard ratios (HRs) associated with intermediate uveitis (HR, 2.21; 95%CI, 1.07-4.55; P = .03), anterior uveitis (HR, 2.68; 95%CI, 1.32-2.35; P = .006), and panuveitis (HR, 3.28; 95%CI, 1.89-5.67; P < .001) were significantly higher compared with posterior distribution. The presence of vitreous haze had a statistically significant association (HR, 1.95; 95%CI, 1.26-3.02; P = .003) compared with absence of vitreous haze. Bilaterality had an associated HR of 1.50 (95%CI, 0.96-2.35) compared with unilaterality (HR, 1 [reference]), although this finding was not statistically significant (P = .07). On multivariate Cox proportional hazards regression analysis, the presence of vitreous haze had an adjusted HR of 2.98 (95%CI, 1.50-5.94; P = .002), presence of snow banking had an adjusted HR of 3.71 (95%CI, 1.18-11.62; P = .02), and presence of choroidal involvement had an adjusted HR of 2.88 (95%CI, 1.22-6.78; P = .02). CONCLUSIONS AND RELEVANCE A low treatment failure rate occurred in patients with TB uveitis treated with ATT. Phenotypes and test results are studied whereby patients with panuveitis having vitreous and choroidal involvement had a higher risk of treatment failure. These findings are limited by retrospectivemethods. A prospectively derived composite clinical risk score might address this diagnostic uncertainty through holistic and standardized assessment of the combinations of clinical features and investigation results that may warrant diagnosis of TB uveitis and treatment with ATT

Bio-inspired camera for surveillance in IoT

Using an event-based camera sensor instead of a frame-based camera sensor has many benefits such as reduced power consumption and reduced file output memory size. Adding cognitive abilities onto an event-based camera sensor would further reduce the information that this sensor needs to transmit. This paper seeks to combine the advances in deep neural networks for frame-based videos with the efficiency of event-based sensors. This will be done by comparing existing algorithms used in frame-based videos to identify objects in event-based camera output. Additionally, this paper seeks to reduce the complexity of these algorithms to ensure the practical implementation of the algorithm in resource constrained settings.Bachelor of Engineering (Electrical and Electronic Engineering

Artificial intelligence in ophthalmology in 2020: A technology on the cusp for translation and implementation

10.1097/01.APO.0000656984.56467.2cAsia-Pacific Journal of Ophthalmology9261-6

Social Media Big Data: The Good, The Bad, and the Ugly (Un)truths

10.3389/fdata.2021.623794Frontiers in Big Data462379