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"Kumar, N."
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Extraction of bioactive compounds from Psidium guajava leaves and its utilization in preparation of jellies
Psidium guajava L. (guava) is predominantly grown throughout the world and known for its medicinal properties in treating various diseases and disorders. The present work focuses on aqueous extraction of bioactive compounds from the guava leaf and its utilization in the formulation of jelly to improve the public health. The guava leaf extract has been used in the preparation of jelly with pectin (1.5 g), sugar (28 g) and lemon juice (2 mL). The prepared guava leaf extract jelly (GJ) and the control jelly (CJ, without extract) were subjected to proximate, nutritional and textural analyses besides determination of antioxidant and antimicrobial activities. GJ was found to contain carbohydrate (45.78 g/100 g), protein (3.0 g/100 g), vitamin C (6.15 mg/100 g), vitamin B3 (2.90 mg/100 g) and energy (120.6 kcal). Further, the texture analysis of CJ and GJ indicated that both the jellies showed similar properties emphasizing that the addition of guava leaf extract does not bring any change in the texture properties of jelly. GJ exhibited antimicrobial activity against various bacteria ranging from 11.4 to 13.6 mm. Similarly, GJ showed antioxidant activity of 42.38% against DPPH radical and 33.45% against hydroxyl radical. Mass spectroscopic analysis of aqueous extract confirmed the presence of esculin, quercetin, gallocatechin, 3-sinapoylquinic acid, gallic acid, citric acid and ellagic acid which are responsible for antioxidant and antimicrobial properties.
Journal Article
Spatio-temporal interactions facilitate large carnivore sympatry across a resource gradient
Species within a guild vary their use of time, space and resources, thereby enabling sympatry. As intra-guild competition intensifies, such behavioural adaptations may become prominent. We assessed mechanisms of facilitating sympatry among dhole (Cuon alpinus), leopard (Panthera pardus) and tiger (Panthera tigris) in tropical forests of India using camera-trap surveys. We examined population-level temporal, spatial and spatio-temporal segregation among them across four reserves representing a gradient of carnivore and prey densities. Temporal and spatial overlaps were higher at lower prey densities. Combined spatio-temporal overlap was minimal, possibly due to chance. We found fine-scale avoidance behaviours at one high-density reserve. Our results suggest that: (i) patterns of spatial, temporal and spatio-temporal segregation in sympatric carnivores do not necessarily mirror each other; (ii) carnivores are likely to adopt temporal, spatial, and spatio-temporal segregation as alternative mechanisms to facilitate sympatry; and (iii) carnivores show adaptability across a gradient of resource availability, a driver of inter-species competition. We discuss behavioural mechanisms that permit carnivores to co-occupy rather than dominate functional niches, and adaptations to varying intensities of competition that are likely to shape structure and dynamics of carnivore guilds.
Journal Article
A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring
Low-cost sensing strategies hold the promise of denser air quality monitoring networks, which could significantly improve our understanding of personal air pollution exposure. Additionally, low-cost air quality sensors could be deployed to areas where limited monitoring exists. However, low-cost sensors are frequently sensitive to environmental conditions and pollutant cross-sensitivities, which have historically been poorly addressed by laboratory calibrations, limiting their utility for monitoring. In this study, we investigated different calibration models for the Real-time Affordable Multi-Pollutant (RAMP) sensor package, which measures CO, NO2, O3, and CO2. We explored three methods: (1) laboratory univariate linear regression, (2) empirical multiple linear regression, and (3) machine-learning-based calibration models using random forests (RF). Calibration models were developed for 16–19 RAMP monitors (varied by pollutant) using training and testing windows spanning August 2016 through February 2017 in Pittsburgh, PA, US. The random forest models matched (CO) or significantly outperformed (NO2, CO2, O3) the other calibration models, and their accuracy and precision were robust over time for testing windows of up to 16 weeks. Following calibration, average mean absolute error on the testing data set from the random forest models was 38 ppb for CO (14 % relative error), 10 ppm for CO2 (2 % relative error), 3.5 ppb for NO2 (29 % relative error), and 3.4 ppb for O3 (15 % relative error), and Pearson r versus the reference monitors exceeded 0.8 for most units. Model performance is explored in detail, including a quantification of model variable importance, accuracy across different concentration ranges, and performance in a range of monitoring contexts including the National Ambient Air Quality Standards (NAAQS) and the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. A key strength of the RF approach is that it accounts for pollutant cross-sensitivities. This highlights the importance of developing multipollutant sensor packages (as opposed to single-pollutant monitors); we determined this is especially critical for NO2 and CO2. The evaluation reveals that only the RF-calibrated sensors meet the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. We also demonstrate that the RF-model-calibrated sensors could detect differences in NO2 concentrations between a near-road site and a suburban site less than 1.5 km away. From this study, we conclude that combining RF models with carefully controlled state-of-the-art multipollutant sensor packages as in the RAMP monitors appears to be a very promising approach to address the poor performance that has plagued low-cost air quality sensors.
Journal Article
Prevalence and severity of secondary traumatic stress and optimism in Indian health care professionals during COVID-19 lockdown
The COVID-19 pandemic has brought to light the lacunae in the preparedness of healthcare systems across the globe. This preparedness also includes the safety of healthcare providers (HCPs) at various levels. Sudden spread of COVID-19 infection has created threatening and vulnerable conditions for the HCPs. The current pandemic situation has not only affected physical health of HCPs but also their mental health.
This study aims to understand the prevalence and severity of secondary traumatic stress, optimism parameters, along with states of mood experienced by the HCPs, viz., doctors, nurses and allied healthcare professionals (including Physiotherapist, Lab technicians, Phlebotomist, dieticians, administrative staff and clinical pharmacist), during the COVID-19 lockdown in India.
The assessment of level of secondary traumatic stress (STS), optimism/pessimism (via Life Orientation Test-Revised) and current mood states experienced by Indian HCPs in the present COVID-19 pandemic situation was done using a primary data of 2,008 HCPs from India during the first lockdown during April-May 2020. Data was collected through snow-ball sampling technique, reaching out to various medical health care professionals through social media platforms.
Amongst the study sample 88.2% of doctors, 79.2 of nurses and 58.6% of allied HCPs were found to have STS in varying severity. There was a female preponderance in the category of Severe STS. Higher optimism on the LOTR scale was observed among doctors at 39.3% followed by nurses at 26.7% and allied health care professionals 22.8%. The mood visual analogue scale which measures the \"mood\" during the survey indicated moderate mood states without any gender bias in the study sample.
The current investigation sheds light on the magnitude of the STSS experienced by the HCPs in the Indian Subcontinent during the pandemic. This hitherto undiagnosed and unaddressed issue, calls for a dire need of creating better and accessible mental health programmes and facilities for the health care providers in India.
Journal Article
Molecular detection and characterization of Clostridium perfringens toxin genes causing necrotic enteritis in broiler chickens
A total of 464 samples comprising of cloacal swabs from necrotic enteritis suspected live birds (191), intestinal scrapings from dead birds with symptoms of necrotic enteritis (91), and apparently healthy birds (182) were collected from selected districts of AP. The samples were subjected to multiplex PCR for simultaneous detection of α, β, and β2 toxin genes and uniplex PCR for the detection of NetB gene. Multiplex PCR screening of samples reveled α toxin gene positives from (cpa) 248/282 (87.94%) necrotic enteritis suspected and 40/182 (21.97%) apparently healthy samples. Among cpa positives 142/248 (57.25%) and 3/40 (7.5%) were positive for β2 toxin gene in necrotic enteritis suspected and apparently healthy birds respectively, indicating the involvement of C. perfringens type A, with minor pore forming toxin gene cpb2 in causing necrotic enteritis in poultry. None of the sample was positive for β toxin gene. The present research indicates C. perfringens type A along with β2 toxin gene was responsible for causing necrotic enteritis in broiler chickens in some parts of Andhra Pradesh in India. Phylogenetic relationship of amplified cpa and cpb2 amino acids sequences from present C. perfringens isolates were studied. The analysis reveals the sequence identity of cpb2 gene of the present isolates and variations at both nucleotide and amino acid level with the published sequences of cpb2 gene of C. perfringens isolates from different animal species of the USA, Iran, Netherlands, and Japan.
Journal Article