SECURITY AND OTHER VULNERABILITY PREDICTION USING NOVEL DEEP REPRESENTATION OF SOURCE CODE WITH ACTIVE FEEDBACK LOOP

Since the cost of fixing vulnerabilities can be thirty times greater after an application has been deployed, it is recognized that properly-written code can yield potentially large savings. Accordingly, approaches presented herein apply machine learning and Artificial Intelligence (AI) techniques to improve developer experience by enabling developers to avoid introducing potential bugs and/or vulnerabilities while coding. Billions of lines of source code, which have already been written, are utilized as examples of how to write functional and secure code that is easy to read and to debug. By leveraging this wealth of available data, which is complemented with state-of-art machine learning models, enterprise-level software solutions can be developed that have a high standard of coding and are potentially bug-free

Sirtuin 6 inhibition protects against glucocorticoid-induced skeletal muscle atrophy by regulating IGF/PI3K/AKT signaling

Chronic activation of stress hormones such as glucocorticoids leads to skeletal muscle wasting in mammals. However, the molecular events that mediate glucocorticoid-induced muscle wasting are not well understood. Here, we show that SIRT6, a chromatin-associated deacetylase indirectly regulates glucocorticoid-induced muscle wasting by modulating IGF/PI3K/AKT signaling. Our results show that SIRT6 levels are increased during glucocorticoid-induced reduction of myotube size and during skeletal muscle atrophy in mice. Notably, overexpression of SIRT6 spontaneously decreases the size of primary myotubes in a cell-autonomous manner. On the other hand, SIRT6 depletion increases the diameter of myotubes and protects them against glucocorticoid-induced reduction in myotube size, which is associated with enhanced protein synthesis and repression of atrogenes. In line with this, we find that muscle-specific SIRT6 deficient mice are resistant to glucocorticoid-induced muscle wasting. Mechanistically, we find that SIRT6 deficiency hyperactivates IGF/PI3K/AKT signaling through c-Jun transcription factor-mediated increase in IGF2 expression. The increased activation, in turn, leads to nuclear exclusion and transcriptional repression of the FoxO transcription factor, a key activator of muscle atrophy. Further, we find that pharmacological inhibition of SIRT6 protects against glucocorticoid-induced muscle wasting in mice by regulating IGF/PI3K/AKT signaling implicating the role of SIRT6 in glucocorticoid-induced muscle atrophy.Fil: Mishra, Sneha. No especifíca;Fil: Cosentino, Claudia. Harvard Medical School; Estados UnidosFil: Tamta, Ankit Kumar. No especifíca;Fil: Khan, Danish. No especifíca;Fil: Srinivasan, Shalini. No especifíca;Fil: Ravi, Venkatraman. No especifíca;Fil: Abbotto, Elena. Università degli Studi di Genova; ItaliaFil: Arathi, Bangalore Prabhashankar. No especifíca;Fil: Kumar, Shweta. No especifíca;Fil: Jain, Aditi. No especifíca;Fil: Ramaian, Anand S.. No especifíca;Fil: Kizkekra, Shruti M.. No especifíca;Fil: Rajagopal, Raksha. No especifíca;Fil: Rao, Swathi. No especifíca;Fil: Krishna, Swati. No especifíca;Fil: Asirvatham Jeyaraj, Ninitha. Indian Institute of Technology; IndiaFil: Haggerty, Elizabeth R.. Harvard Medical School; Estados UnidosFil: Silberman, Dafne Magalí. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Centro de Estudios Farmacológicos y Botánicos. Universidad de Buenos Aires. Facultad de Medicina. Centro de Estudios Farmacológicos y Botánicos; ArgentinaFil: Kurland, Irwin J.. No especifíca;Fil: Veeranna, Ravindra P.. No especifíca;Fil: Jayavelu, Tamilselvan. No especifíca;Fil: Bruzzone, Santina. Università degli Studi di Genova; ItaliaFil: Mostoslavsky, Raul. Harvard Medical School; Estados UnidosFil: Sundaresan, Nagalingam R.. No especifíca

Optimizing Current Profiles for Efficient Online Estimation of Battery Equivalent Circuit Model Parameters Based on Cramer–Rao Lower Bound

Battery management systems (BMS) are important for ensuring the safety, efficiency and reliability of a battery pack. Estimating the internal equivalent circuit model (ECM) parameters of a battery, such as the internal open circuit voltage, battery resistance and relaxation parameters, is a crucial requirement in BMSs. Numerous approaches to estimating ECM parameters have been reported in the literature. However, existing approaches consider ECM identification as a joint estimation problem that estimates the state of charge together with the ECM parameters. In this paper, an approach is presented to decouple the problem into ECM identification alone. Using the proposed approach, the internal open circuit voltage and the ECM parameters can be estimated without requiring the knowledge of the state of charge of the battery. The proposed approach is applied to estimate the open circuit voltage and internal resistance of a battery