Comparative efficacy of family mediated intervention versus early intensive behavioural intervention on symptom domains in children with autism spectrum disorder: A randomized controlled trial.

BACKGROUND: Family Mediated Intervention (FMI) and Early Intensive Behavioural Intervention (EIBI) are found to be standard of care for children with Autism Spectrum Disorder (ASD). Comparison of their efficacy were assessed using ISAA as primary outcome measure. METHODS: This study was a parallel arm, open label, randomized active- controlled non-inferiority clinical trial. 50 Children diagnosed with ASD were randomized into FMI and EIBI groups. Clinical status was checked by using Indian scale for assessment of autism (ISAA), Oro- motor and sensory profile at baseline, after three and six months. RESULTS: Difference between change in mean ISAA score between FMI and EIBI group at the end of 6 months as per protocol (PP) analysis was -7.23 (CI=-18.41, 3.94), which was within pre-defined clinically relevant non-inferiority (NI) margin of - 24. FMI was found to be non-inferior to EIBI at the end of 6 months as the lower bound of 95% CI (-18.41) for ISAA score was higher than NI margin. ISAA scores were found to be statistically lower in both FMI and EIBI groups at the end point compared to baseline which indicated improvement in symptom severity. CONCLUSION: FMI was non-inferior to EIBI as therapy for children with ASD at the end of six months. Finding also indicated longer duration of treatment is required for FMI to be superior. FMI can be recommended for children with ASD in view of improved ISAA scores reported in our study. CLINICAL TRIAL REGISTRATION NUMBER: CTRI/2020/08/027099 (Registered with Clinical Trials Registry- India).

Mapping cover crop species in southeastern Michigan using Sentinel-2 satellite data and Google Earth Engine.

Cover crops are a critical agricultural practice that can improve soil quality, enhance crop yields, and reduce nitrogen and phosphorus losses from farms. Yet there is limited understanding of the extent to which cover crops have been adopted across large spatial and temporal scales. Remote sensing offers a low-cost way to monitor cover crop adoption at the field scale and at large spatio-temporal scales. To date, most studies using satellite data have mapped the presence of cover crops, but have not identified specific cover crop species, which is important because cover crops of different plant functional types (e.g., legumes, grasses) perform different ecosystem functions. Here we use Sentinel-2 satellite data and a random forest classifier to map the cover crop species cereal rye and red clover, which represent grass and legume functional types, in the River Raisin watershed in southeastern Michigan. Our maps of agricultural landcover across this region, including the two cover crop species, had moderate to high accuracies, with an overall accuracy of 83%. Red clover and cereal rye achieved F1 scores that ranged from 0.7 to 0.77, and user's and producer's accuracies that ranged from 63.3% to 86.2%. The most common misclassification of cover crops was fallow fields with remaining crop stubble, which often looked similar because these cover crop species are typically planted within existing crop stubble, or interseeded into a grain crop. We found that red-edge bands and images from the end of April and early July were the most important for classification accuracy. Our results demonstrate the potential to map individual cover crop species using Sentinel-2 imagery, which is critical for understanding the environmental outcomes of increasing crop diversity on farms.

Greenhouse gas emissions from agricultural residue burning have increased by 75 % since 2011 across India.

Quantifying crop residue burning across India is imperative, owing to its adverse impacts on public health, the environment, and agricultural productivity. Specific information about the extent and characteristics of agricultural crop burning can verify the emission potential of agricultural systems and thereby facilitate targeted dissemination of agricultural innovations and support policymakers in mitigating the harmful effects. With a focus on district-level burning estimates, our study provides a comprehensive seasonal analysis of agricultural burning in India, including burned area, dry matter burned, and gaseous emissions for seven major crops from 2011 to 2020. To quantify the actual residues burned, we developed a remote sensing-based approach that incorporates the monitoring of agricultural burned area to quantify the actual residues burned. Including this satellite measure of the burned area greatly improves emissions estimates and minimizes error compared to typical approaches, which instead use an assumed fraction of total residues that are burned for each crop type. We estimated that emissions have increased by approximately 75 % for CO and Greenhouse gasses - CO2, CH4 and N2O - from 2011 to 2020. Total CO2e emissions increased from ~19,340 Gg.yr-1 in 2011 to ~33,834 Gg.yr-1 in 2020. Most emissions occurred during end of the Kharif season, followed by Rabi, caused by the burning of rice and wheat residues. Among the Indian states, Punjab has the highest burning activity, with 27 % (2.0 million hectares) of its total cultivated area burned in 2020. Interestingly, Madhya Pradesh has emerged as the second-largest contributor, accounting for 30 % of the total burned area across India in 2020. Our study demonstrates how satellite data can be used to map agricultural residue burning at scale, and this information can provide crucial insights for policy framing, targeting, and interventions to manage agricultural residues without compromising air quality and climate.

Warming temperatures exacerbate groundwater depletion rates in India.

Climate change will likely increase crop water demand, and farmers may adapt by applying more irrigation. Understanding the extent to which this is occurring is of particular importance in India, a global groundwater depletion hotspot, where increased withdrawals may further jeopardize groundwater resources. Using historical data on groundwater levels, climate, and crop water stress, we find that farmers have adapted to warming temperatures by intensifying groundwater withdrawals, substantially accelerating groundwater depletion rates in India. When considering increased withdrawals due to warming, we project that the rates of net groundwater loss for 2041-2080 could be three times current depletion rates, even after considering projected increases in precipitation and possible decreases in irrigation use as groundwater tables fall. These results reveal a previously unquantified cost of adapting to warming temperatures that will likely further threaten India's food and water security over the coming decades.

EFFECT OF PSYCHOLOGICAL FIRST AID ON MENTAL HEALTH IN HOSPITALIZED STABLE COVID-19 PATIENTS: A PRE-POST RESEARCH DESIGN.

BACKGROUND: The COVID-19 pandemic is known to affect mental health of sufferers. Psychological First Aid (PFA) is a mental health service for individuals in crisis, which can be provided to anyone regardless of age and it does not require mental health expertise. Its effect on mental health issues of COVID-19 patients has not been studied effectively. The present study aimed to assess the psychological impact and effect of PFA on mental health in stable COVID-19 hospitalized patients. SUBJECTS AND METHODS: This was an interventional study with a pre-post research design in a tertiary government teaching hospital in eastern India. 93 stable patients who were admitted in a period of a month with COVID-19 were included in the study after obtaining appropriate consent. They were provided PFA (both structured individual and group sessions) by trained nurses. The Depression, Anxiety, and Stress scale (DASS-21) was used to assess depression, anxiety, and stress in the patients before and after intervention. RESULTS: The mean age of study population which comprised of 68.8% males was 56.2 ± 13.7 years. Median scores for depression, anxiety and stress were 4, 6 and 6 on admission and 0, 2 and 2 respectively before discharge after intervention (P<0.001). 13%, 25.9% and 8.6% were the combined percentages scores of patients with varying levels of depression, anxiety and stress at the time of admission which were reduced to 4.3% (P=0.046), 5.4% (P=0.001), 2.2% (P=0.03) respectively before discharge after intervention within one week. CONCLUSION: PFA may be a cost-effective intervention in stable COVID-19 admitted patients who had depression, anxiety, and stress.

Using microsatellite data to estimate the persistence of field-level yield gaps and their drivers in smallholder systems.

One way to meet growing food demand is to increase yields in regions that have large yield gaps, including smallholder systems. To do this, it is important to quantify yield gaps, their persistence, and their drivers at large spatio-temporal scales. Here we use microsatellite data to map field-level yields from 2014 to 2018 in Bihar, India and use these data to assess the magnitude, persistence, and drivers of yield gaps at the landscape scale. We find that overall yield gaps are large (33% of mean yields), but only 17% of yields are persistent across time. We find that sowing date, plot area, and weather are the factors that most explain variation in yield gaps across our study region, with earlier sowing associated with significantly higher yield values. Simulations suggest that if all farmers were able to adopt ideal management strategies, including earlier sowing and more irrigation use, yield gaps could be closed by up to 42%. These results highlight the ability of micro-satellite data to understand yield gaps and their drivers, and can be used to help identify ways to increase production in smallholder systems across the globe.

Exploring the path to pathos "Lived experiences of parents of children with autism spectrum disorder": An interpretative phenomenological analysis.

Background: Children with autism spectrum disorder (ASD) require lifetime support by the family, thus posing a great amount of stress among parents. Understanding lived experiences of parents who provide lifelong support will guide in planning effective treatment for children with ASD. In view of this, the study was aimed to depict and understand the lived experiences of parents of children with ASD and making sense of it. Methods: This interpretative phenomenological analysis research design was carried out on 15 parents of children with ASD coming to the tertiary care referral hospital of eastern zone of India. In-depth interviews were conducted to understand the lived experiences of parents. Results: The current study identified six themes: major symptom recognition; myths, beliefs, and stigma related to children with ASD; help seeking behavior; coping with challenging experiences; support system; uncertainties, insecurities, and gleam of hope. Conclusion: Lived experiences were found to be predominantly difficult for most of the parents of children with ASD, and inadequate services pose a major challenge to them. The findings highlight the need for involving the parents in the treatment programs as early as possible or extending appropriate support to the family.

Using Sentinel-1, Sentinel-2, and Planet satellite data to map field-level tillage practices in smallholder systems.

Remote sensing can be used to map tillage practices at large spatial and temporal scales. However, detecting such management practices in smallholder systems is challenging given that the size of fields is smaller than historical readily-available satellite imagery. In this study we used newer, higher-resolution satellite data from Sentinel-1, Sentinel-2, and Planet to map tillage practices in the Eastern Indo-Gangetic Plains in India. We specifically tested the classification performance of single sensor and multiple sensor random forest models, and the impact of spatial, temporal, or spectral resolution on classification accuracy. We found that when considering a single sensor, the model that used Planet imagery (3 m) had the highest classification accuracy (86.55%) while the model that used Sentinel-1 data (10 m) had the lowest classification accuracy (62.28%). When considering sensor combinations, the model that used data from all three sensors achieved the highest classification accuracy (87.71%), though this model was not statistically different from the Planet only model when considering 95% confidence intervals from bootstrap analyses. We also found that high levels of accuracy could be achieved by only using imagery from the sowing period. Considering the impact of spatial, temporal, and spectral resolution on classification accuracy, we found that improved spatial resolution from Planet contributed the most to improved classification accuracy. Overall, it is possible to use readily-available, high spatial resolution satellite data to map tillage practices of smallholder farms, even in heterogeneous systems with small field sizes.

Research priorities for global food security under extreme events.

Extreme events, such as those caused by climate change, economic or geopolitical shocks, and pest or disease epidemics, threaten global food security. The complexity of causation, as well as the myriad ways that an event, or a sequence of events, creates cascading and systemic impacts, poses significant challenges to food systems research and policy alike. To identify priority food security risks and research opportunities, we asked experts from a range of fields and geographies to describe key threats to global food security over the next two decades and to suggest key research questions and gaps on this topic. Here, we present a prioritization of threats to global food security from extreme events, as well as emerging research questions that highlight the conceptual and practical challenges that exist in designing, adopting, and governing resilient food systems. We hope that these findings help in directing research funding and resources toward food system transformations needed to help society tackle major food system risks and food insecurity under extreme events.

Agricultural burned area detection using an integrated approach utilizing multi spectral instrument based fire and vegetation indices from Sentinel-2 satellite.

This study presents a methodology that focuses on detecting agricultural burned areas using Sentinel-2 multispectral data at 10 m. We developed a simple, locally adapted, straightforward approach of multi-index threshold to extract post-winter agricultural burned areas at high resolution for 2019-21. Further, we design a new method for virtual sample collection using already validated fire location data and visual interpretation conditioned using strict selection criteria to improve sample accuracy. Sampling accuracy showed near-perfect agreement with an average Cohen's Kappa value of 0.98. We retrieved monthly ABAs at a resolution of 10 m, and these products were validated against reference burned sample plots identified using visual interpretation of Planet (3m) satellite data. Overall, we found that our method performed well, with an F1 score of 83.63% and low commission (20%) and omission (7%) errors. When compared to global burnt area products, validation accuracy demonstrated an exceptional subpixel scale detecting capability. The study also addresses the complexity of residue burnings and burn signatures' volatile nature by performing multilevel masking and temporal corrections.•A novel remotely sensed data aided virtual sampling approach to acquire burned and unburned samples.•An integrated method to extract smallholder agricultural burned area using Sentinel-2 multispectral data at a high resolution of 10 m.

Prior crop season management constrains farmer adaptation to warming temperatures: Evidence from the Indo-Gangetic Plains.

Climate change induced heat stress is predicted to negatively impact wheat yields across the Indo-Gangetic Plains (IGP) of India. Research suggests that early sowing of wheat can substantially reduce this impact. However, a large proportion of farmers sow wheat late across this region, likely resulting in large-scale yield loss. We examined the extent of late wheat sowing across the IGP and which perceptional, management, biophysical, and socio-economic factors are associated with delayed sowing using household survey data from 2429 farmers and the cumulative logit model. Our results indicate that despite understanding that early sowing can be helpful to avoid terminal heat stress, over 50% of farmers sow wheat later than their perceived ideal wheat sowing date. We find that variables related to how wheat fields are prepared prior to sowing are associated with wheat sowing date. Specifically, farmers who had shorter fallow periods prior to sowing wheat and those who used zero tillage were 95% and 65% more likely to sow wheat earlier, respectively. In addition, we found that how farmers managed their rice crop in the preceding cropping season impacted wheat sowing date - farmers who transplanted and harvested rice later and/or planted longer duration rice varieties sowed their wheat later. Our results suggest that policies that promote earlier sowing of rice, such as improved access to irrigation and direct seeding machinery, and reduced field preparation time, such as wider adoption of zero tillage technologies, can help farmers across the IGP sow wheat earlier. This is critical given that warming temperatures will only increase the negative impacts of terminal heat stress on wheat yields across this region over the coming decades.

Groundwater depletion will reduce cropping intensity in India.

Groundwater depletion is becoming a global threat to food security, yet the ultimate impacts of depletion on agricultural production and the efficacy of available adaptation strategies remain poorly quantified. We use high-resolution satellite and census data from India, the world's largest consumer of groundwater, to quantify the impacts of groundwater depletion on cropping intensity, a crucial driver of agricultural production. Our results suggest that, given current depletion trends, cropping intensity may decrease by 20% nationwide and by 68% in groundwater-depleted regions. Even if surface irrigation delivery is increased as a supply-side adaptation strategy, which is being widely promoted by the Indian government, cropping intensity will decrease, become more vulnerable to interannual rainfall variability, and become more spatially uneven. We find that groundwater and canal irrigation are not substitutable and that additional adaptation strategies will be necessary to maintain current levels of production in the face of groundwater depletion.

Regional differences in agricultural and socioeconomic factors associated with farmer household dietary diversity in India.

This paper investigated whether there are any regional-level differences in factors associated with farmer household dietary diversity using the Food Consumption Score (FCS), in two states of India: Haryana and Gujarat. Our results suggest that the factors associated with farmer household dietary diversity were region-specific, with diverse drivers across districts. For example, in Vadodara (Gujarat), farmers who had greater crop diversity and planted more cash crops had higher dietary diversity while large landholders in Bhavnagar (Gujarat) had higher dietary diversity. In Karnal (Haryana), more educated farmer households and those who cultivated large landholdings had higher dietary diversity while farmers in Bhiwani (Haryana) who were more educated and sold more crops to market had higher dietary diversity. Thus, factors associated with FCS differed even within the same state. These results suggest that in some regions of India, higher crop diversity and better education could improve farmer household dietary diversity. On the other hand, in some other regions, dietary diversity is best improved through the income generation pathway, where households that earn increased income from selling more crops were able to purchase more diverse food from markets. Our study suggests that the drivers of household dietary diversity across rural India are complex and heterogeneous; thus, future policies and programs to improve farmer household nutrition should be tailored considering regional differences in the factors associated with household nutrition.

Reimagining the potential of Earth observations for ecosystem service assessments.

The benefits nature provides to people, called ecosystem services, are increasingly recognized and accounted for in assessments of infrastructure development, agricultural management, conservation prioritization, and sustainable sourcing. These assessments are often limited by data, however, a gap with tremendous potential to be filled through Earth observations (EO), which produce a variety of data across spatial and temporal extents and resolutions. Despite widespread recognition of this potential, in practice few ecosystem service studies use EO. Here, we identify challenges and opportunities to using EO in ecosystem service modeling and assessment. Some challenges are technical, related to data awareness, processing, and access. These challenges require systematic investment in model platforms and data management. Other challenges are more conceptual but still systemic; they are byproducts of the structure of existing ecosystem service models and addressing them requires scientific investment in solutions and tools applicable to a wide range of models and approaches. We also highlight new ways in which EO can be leveraged for ecosystem service assessments, identifying promising new areas of research. More widespread use of EO for ecosystem service assessment will only be achieved if all of these types of challenges are addressed. This will require non-traditional funding and partnering opportunities from private and public agencies to promote data exploration, sharing, and archiving. Investing in this integration will be reflected in better and more accurate ecosystem service assessments worldwide.

Current understandings about cognition and the neurobiological correlates in schizophrenia.

Schizophrenia is a severe mental disorder. Cognitive deficits are one of the core features of schizophrenia. Multiple domains of cognition (executive function, attention/vigilance, working memory, verbal fluency, visuospatial skills, processing speed, and social cognition) are affected in patients with schizophrenia. Deficits in cognition led to impairment in the real world functioning. Identifying the cognitive deficits and early intervention is required for better functional outcome. This review focuses on conceptual understanding of cognition with its neurobiological correlates in schizophrenia and its different clinical implications.

Sensitivity of crop cover to climate variability: insights from two Indian agro-ecoregions.

Crop productivity in India varies greatly with inter-annual climate variability and is highly dependent on monsoon rainfall and temperature. The sensitivity of yields to future climate variability varies with crop type, access to irrigation and other biophysical and socio-economic factors. To better understand sensitivities to future climate, this study focuses on agro-ecological subregions in Central and Western India that span a range of crops, irrigation, biophysical conditions and socioeconomic characteristics. Climate variability is derived from remotely-sensed data products, Tropical Rainfall Measuring Mission (TRMM - precipitation) and Moderate Resolution Imaging Spectroradiometer (MODIS - temperature). We examined green-leaf phenologies as proxy for crop productivity using the MODIS Enhanced Vegetation Index (EVI) from 2000 to 2012. Using both monsoon and winter growing seasons, we assessed phenological sensitivity to inter-annual variability in precipitation and temperature patterns. Inter-annual EVI phenology anomalies ranged from -25% to 25%, with some highly anomalous values up to 200%. Monsoon crop phenology in the Central India site is highly sensitive to climate, especially the timing of the start and end of the monsoon and intensity of precipitation. In the Western India site, monsoon crop phenology is less sensitive to precipitation variability, yet shows considerable fluctuations in monsoon crop productivity across the years. Temperature is critically important for winter productivity across a range of crop and management types, such that irrigation might not provide a sufficient buffer against projected temperature increases. Better access to weather information and usage of climate-resilient crop types would play pivotal role in maintaining future productivity. Effective strategies to adapt to projected climate changes in the coming decades would also need to be tailored to regional biophysical and socio-economic conditions.

Perceptional and socio-demographic factors associated with household drinking water management strategies in rural Puerto Rico.

Identifying which factors influence household water management can help policy makers target interventions to improve drinking water quality for communities that may not receive adequate water quality at the tap. We assessed which perceptional and socio-demographic factors are associated with household drinking water management strategies in rural Puerto Rico. Specifically, we examined which factors were associated with household decisions to boil or filter tap water before drinking, or to obtain drinking water from multiple sources. We find that households differ in their management strategies depending on the institution that distributes water (i.e. government PRASA vs community-managed non-PRASA), perceptions of institutional efficacy, and perceptions of water quality. Specifically, households in PRASA communities are more likely to boil and filter their tap water due to perceptions of low water quality. Households in non-PRASA communities are more likely to procure water from multiple sources due to perceptions of institutional inefficacy. Based on informal discussions with community members, we suggest that water quality may be improved if PRASA systems improve the taste and odor of tap water, possibly by allowing for dechlorination prior to distribution, and if non-PRASA systems reduce the turbidity of water at the tap, possibly by increasing the degree of chlorination and filtering prior to distribution. Future studies should examine objective water quality standards to identify whether current management strategies are effective at improving water quality prior to consumption.

The importance of rare species: a trait-based assessment of rare species contributions to functional diversity and possible ecosystem function in tall-grass prairies.

The majority of species in ecosystems are rare, but the ecosystem consequences of losing rare species are poorly known. To understand how rare species may influence ecosystem functioning, this study quantifies the contribution of species based on their relative level of rarity to community functional diversity using a trait-based approach. Given that rarity can be defined in several different ways, we use four different definitions of rarity: abundance (mean and maximum), geographic range, and habitat specificity. We find that rarer species contribute to functional diversity when rarity is defined by maximum abundance, geographic range, and habitat specificity. However, rarer species are functionally redundant when rarity is defined by mean abundance. Furthermore, when using abundance-weighted analyses, we find that rare species typically contribute significantly less to functional diversity than common species due to their low abundances. These results suggest that rare species have the potential to play an important role in ecosystem functioning, either by offering novel contributions to functional diversity or via functional redundancy depending on how rare species are defined. Yet, these contributions are likely to be greatest if the abundance of rare species increases due to environmental change. We argue that given the paucity of data on rare species, understanding the contribution of rare species to community functional diversity is an important first step to understanding the potential role of rare species in ecosystem functioning.

Functional and phylogenetic diversity as predictors of biodiversity--ecosystem-function relationships.

How closely does variability in ecologically important traits reflect evolutionary divergence? The use of phylogenetic diversity (PD) to predict biodiversity effects on ecosystem functioning, and more generally the use of phylogenetic information in community ecology, depends in part on the answer to this question. However, comparisons of the predictive power of phylogenetic diversity and functional diversity (FD) have not been conducted across a range of experiments. To address how phylogenetic diversity and functional trait variation control biodiversity effects on biomass production, we summarized the results of 29 grassland plant experiments where both the phylogeny of plant species used in the experiments is well described and where extensive trait data are available. Functional trait variation was only partially related to phylogenetic distances between species, and the resulting FD values therefore correlate only partially with PD. Despite these differences, FD and PD predicted biodiversity effects across all experiments with similar strength, including in subsets that excluded plots with legumes and that focused on fertilization experiments. Two- and three-trait combinations of the five traits used here (leaf nitrogen percentage, height, specific root length, leaf mass per unit area, and nitrogen fixation) resulted in the FD values with the greatest predictive power. Both PD and FD can be valuable predictors of the effect of biodiversity on ecosystem functioning, which suggests that a focus on both community trait diversity and evolutionary history can improve understanding of the consequences of biodiversity loss.