Estimating prevalence of post-war health disorders using multiple systems data.

Effective surveillance on the long-term public health impact due to war and terrorist attacks remains limited. Such health issues are commonly under-reported, specifically for a large group of individuals. For this purpose, efficient estimation of the size or undercount of the population under the risk of physical and mental health hazards is of utmost necessity. A novel trivariate Bernoulli model is developed allowing heterogeneity among the individuals and dependence between the sources of information, and an estimation methodology using a Monte Carlo-based EM algorithm is proposed. Simulation results show the superiority of the performance of the proposed method over existing competitors and robustness under model mis-specifications. The method is applied to analyse two real case studies on monitoring amyotrophic lateral sclerosis (ALS) cases for the Gulf War veterans and the 9/11 terrorist attack survivors at the World Trade Center, USA. The average annual cumulative incidence rate for ALS disease increases by 33 % and 16 % for deployed and no-deployed military personnel, respectively, after adjusting the undercount. The number of individuals exposed to the risk of physical and mental health effects due to WTC terrorist attacks increased by 42 % . These results provide interesting insights that can assist in effective decision-making and policy formulation for monitoring the health status of post-war survivors.

Ecological restoration at pilot-scale employing site-specific rationales for small-patch degraded mangroves in Indian Sundarbans.

To date, degraded mangrove ecosystem restoration accomplished worldwide primarily aligns towards rehabilitation with monotypic plantations, while ecological restoration principles are rarely followed in these interventions. However, researchers admit that most of these initiatives' success rate is not appreciable often. An integrative framework of ecological restoration for degraded mangroves where site-specific observations could be scientifically rationalized, with co-located reference pristine mangroves as the target ecosystem to achieve is currently distinctively lacking. Through this experimental scale study, we studied the suitability of site-specific strategies to ecologically restore degraded mangrove patches vis-à-vis the conventional mono-species plantations in a highly vulnerable mangrove ecosystem in Indian Sundarbans. This comprehensive restoration framework was trialed in small discrete degraded mangrove patches spanning ~ 65 ha. Site-specific key restoration components applied are statistically validated through RDA analyses and Bayesian t-tests. 25 quantifiable metrics evaluate the restoration success of a ~ 3 ha degraded mangrove patch with Ridgeline distribution, Kolmogorov-Smirnov (K-S) tests, and Mahalanobis Distance (D2) measure to prove the site's near-equivalence to pristine reference in multiple ecosystem attributes. This restoration intervention irrevocably establishes the greater potential of this framework in the recovery of ecosystem functions and self-sustenance compared to that of predominant monoculture practices for vulnerable mangroves.

Forecasting mangrove ecosystem degradation utilizing quantifiable eco-physiological resilience -A study from Indian Sundarbans.

Sundarbans mangrove forest, the world's largest continuous mangrove forests expanding across India and Bangladesh, in recent times, is immensely threatened by degradation stress due to natural stressors and anthropogenic disturbances. The degradation across the 19 mangrove forests in Indian Sundarbans was evaluated by eight environmental criteria typical to mangrove ecosystem. In an attempt to find competent predictors for mangrove ecosystem degradation, key eco-physiological resilience trait complex specific for mangroves from 4922 individuals for physiological analyses with gene expression and 603 individuals for leaf tissue distributions from 16 mangroves and 15 associate species was assessed along the degradation gradient. The degradation data was apparently categorized into four and CDFA discriminates 97% of the eco-physiological resilience data into corresponding four groups. Predictive Bayesian regression models and mixed effects models indicate osmolyte accumulation and thickness of water storage tissue as primary predictors of each of the degradation criteria that appraise the degradation status of mangrove ecosystem. RDA analyses well represented response variables of degradation explained by explanatory resilience variables. We hypothesize that with the help of our predictive models the policy makers could trace even the cryptic process of mangrove degradation and save the respective forests in time by proposing appropriate action plans.