A deep audiovisual approach for human confidence classification

Research on self-efficacy and confidence has spread across several subfields of psychology and neuroscience. The role of one’s confidence is very crucial in the formation of attitude and communication skills. The importance of differentiating the levels of confidence is quite visible in this domain. With the recent advances in extracting behavioral insight from a signal in multiple applications, detecting confidence is found to have great importance. One such prominent application is detecting confidence in interview conversations. We have collected an audiovisual data set of interview conversations with 34 candidates. Every response (from each of the candidate) of this data set is labeled with three levels of confidence: high, medium, and low. Furthermore, we have also developed algorithms to efficiently compute such behavioral confidence from speech and video. A deep learning architecture is proposed for detecting confidence levels (high, medium, and low) from an audiovisual clip recorded during an interview. The achieved unweighted average recall (UAR) reaches 85.9% on audio data and 73.6% on video data captured from an interview session

SYNTHESIS, CHARACTERIZATION AND QUANTITATION OF REGIOISOMERIC IMPURITY IN NIMODIPINE BULK AND FORMULATION

Objective: The present research work was directed towards the synthesis characterization and quantitation of regioisomeric impurity of Nimodipine i.e. diethyl 1, 4-dihydro-2,6-dimethyl pyridine dicarboxylate in bulk and tablet formulation, by UV,IR,NMR and GC-MS techniques and a RP-HPLC method was developed as per ICH Q2B guidelines for quantitation of 1, 4-Dihydro-2, 6-Dimethyl-4-(p-nitro phenyl) pyridine-3,5 dicarboxylate (NI) from bulk and formulation. Methods: The synthesis of NI was carried out by Hantzch pyridine synthesis, by using p-nitrobenzaldehyde, ethylacetoacetate, in presence of ammonia and methanol as a catalyst. The percentage yield was found to be 89.29%. Recrystallization and purification of NI was done. The preliminary evaluation was done on laboratory scale via melting point, elemental analysis and TLC. Results: The melting point of impurity was found to be 156-1580C. The TLC of impurity was carried by using Chloroform: Methanol (9:1) and the Rf was found to be 0.79. The confirmation of structure of NI was carried out by using sophisticated techniques i.e., FT-IR, NMR (13C and 1H), GC-MS etc. The RP-HPLC method was developed to quantify the NI in Nimodipine bulk and formulation as per ICH Q2B guidelines. The method validation was done as per ICH guidelines. Conclusion: The validated optimized method was found to be linear, précised, robust, rugged and accurate. Finally NI was quantified from bulk Nimodipine and its marketed tablet formulation. It was concluded that the amount of NI, present in tablet was found to be 0.1% and in the bulk 0.067% respectively. Thus it was revealed that the NI was found to be within the limit laid down ICH guidelines (Not more than 0.1 %)

AI-based human audio processing for COVID-19: a comprehensive overview

The Coronavirus (COVID-19) pandemic impelled several research efforts, from collecting COVID-19 patients’ data to screening them for virus detection. Some COVID-19 symptoms are related to the functioning of the respiratory system that influences speech production; this suggests research on identifying markers of COVID-19 in speech and other human generated audio signals. In this article, we give an overview of research on human audio signals using ’Artificial Intelligence’ techniques to screen, diagnose, monitor, and spread the awareness about COVID-19. This overview will be useful for developing automated systems that can help in the context of COVID-19, using non-obtrusive and easy to use bio-signals conveyed in human non-speech and speech audio productions

EPMO: A novel medical student assessment tool that integrates entrustable professional activities, prime, and the modified Ottawa coactivity scale

Alignment of workplace-based assessments (WPBA) with core entrustable professional activities (EPAs) for entering residency may provide opportunities to monitor student progress across the continuum of undergraduate medical education. Core EPAs, however, reflect tasks of varying degrees of difficulty and faculty assessors are not accustomed to rating students based on entrustability. Expectations of student progress should vary depending on the complexity of the tasks associated with the EPAs. An assessment tool that orients evaluators to the developmental progression of specific EPA tasks will be critical to fairly evaluate learners. The authors developed an EPA assessment tool combining the frameworks of Professionalism, Reporter, Interpreter, Manager, Educator (PRIME), and Modified Ottawa coactivity scales. Only those EPAs that could be repeatedly observed and assessed across clinical clerkships were included. From July 2019 to March 2020, third-year medical students across multiple clerkships were assessed using this tool. The authors hypothesized that if the tool was applied correctly, ratings of learner independence would be lower with higher complexity tasks and that such ratings would increase over the course of year with ongoing clinical learning. Assessment data for 247 medical students were similar across clerkships suggesting that evaluators in diverse clinical contexts were able to use this tool to assign scores reflective of developing entrustability in the workplace. Faculty rated student entrustability highest in skills emphasized in the pre-clerkship curriculum (professionalism and reporter) and progressively lower in more advanced skills (interpreter and manager). Students' ratings increased over time with more clinical exposure. The authors developed a composite WBPA tool that combines the frameworks of EPAs, PRIME, and Modified Ottawa Co- Activity and demonstrated the usability of applying it for learner assessments in clinical settings. Further multicenter studies with cohorts of pre- and post-clerkship students may provide additional validity evidence for the tool