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The quantification of the radiative impacts of light absorbing ambient black carbon (BC) particles strongly depends on accurate measurements of BC mass concentration and absorption coefficient (β(abs)). In this study, an experiment has been conducted to quantify the influence of hygroscopic growth of ambient particles on light absorption. Using the hygroscopic growth factor (i.e., Zdanovskii-Stokes-Robinson (ZSR) approach), a model has been developed to predict the chemical composition of particles based on measurements, and the absorption and scattering coefficients are derived using a core-shell assumption with light extinction estimates based on Mie theory. The estimated optical properties agree within 7% for absorption coefficient and 30% for scattering coefficient with that of measured values. The enhancement of absorption is found to vary according to the thickness of the shell and BC mass, with a maximum of 2.3 for a shell thickness of 18 nm for the particles. The findings of this study underline the importance of considering aerosol-mixing states while calculating their radiative forcing.

The airborne transmission of the COVID-19 virus has been suggested as a major mode of transmission in recent studies. In this context, we studied the spatial transmission of COVID-19 vectors in an indoor setting representative of a typical office room. Computational fluid dynamics (CFD) simulations were performed to study the airborne dispersion of particles ejected due to different respiratory mechanisms, i.e., coughing, sneezing, normal talking, and loud talking. Number concentration profiles at a distance of 2 m in front of the emitter at the ventilation rates of 4, 6, and 8 air changes per hour (ACH) were estimated for different combinations of inlet–outlet positions and emitter–receptor configurations. Apart from respiratory events, viz., coughing and sneezing characterized by higher velocity and concentration of ejected particles, normal as well as loud talking was seen to be carrying particles to the receptor for some airflow patterns in the room. This study indicates that the ″rule of thumb based safe distance approach″ cannot be a general mitigation strategy for infection control. Under some scenarios, events with a lower release rate of droplets such as talking (i.e., asymptomatic transmission) can lead to a high concentration of particles persisting for long times. For better removal, the study suggests ″air curtains″ as an appropriate approach, simultaneously highlighting the pitfalls in the ″higher ventilation rate for better removal″ strategy. The inferences for talking-induced particle transmissions are crucial considering that large populations of COVID-19-infected persons are projected to be asymptomatic transmitters.

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Betulinic acid (BA) has been shown to cause apoptosis in neuroblastoma and melanoma cell lines. We evaluated the cytotoxicity of BA in two breast cancer cell lines MCF-7 and T47D differing in their p53 status. Treatment with BA resulted in a dose dependent inhibition of cell proliferation and induction of apoptosis. This indicates p53-independent apoptotic pathway, because response of both p53 mutant and wild type cell line were found unaffected after treatment with pifithrin-α, an inhibitor of p53. Cells were significantly protected when treated by tocopherol suggesting involvement of membrane centered lipid peroxidation-mediated mechanism in BA-induced apoptosis.

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We propose a data driven epidemic model using the real data on the infection, recovery and death cases for the analysis of COVID-19 progression in India. The model assumes continuation of existing control measures such as lockdown and quarantines, the suspected and confirmed cases and does not consider the scenario of 2nd surge of the epidemic due to any reason. The model is arrived after least square fitting of epidemic behaviour model based on theoretical formulation to the real data of cumulative infection cases reported between 24 March 2020 and 15 May 2020. The predictive capability of the model has been validated with real data of infection cases reported during May 15-30, 2020. A detailed analysis of model predictions in terms of future trend of COVID-19 progress individually in 18 states of India and India as a whole has been attempted. Infection rate in India as a whole is continuously decreasing with time and has reached 3 times lower than the initial infection rate after 6 weeks of lock down suggesting the effectiveness of the lockdown in containing the epidemic. Results suggest that India as a whole could see the peak and end of the epidemic in the month of July 2020 and January 2021. As per the current trend in the data, active infected cases in India may reach 2 lakhs at the peak time and total infected cases may reach around 14 lakhs. State-wise results have been discussed in the manuscript. However, the prediction may deviate particularly for longer dates, as assumptions of model cannot be met always in a real scenario. In view of this, a real time application (COV-IND Predictor) has been developed which automatically syncs the latest data from COVID19 dash board on daily basis and update the model input parameters and predictions of relevant results on daily basis. This application can serve as a practical tool for epidemic management decisions