Blockchain Powered Vaccine Efficacy for Pharma Sector.

Infectious and contagious diseases exist in humanity for many centuries which causes a curb in the growth of the population. Immunization plays a vital role to prevent mortality and morbidity against infectious diseases. COVID-19 pandemic continues to rage the urgency of developing a vaccine that should ensure the safety, efficacy, swift and fair deployment, implementation, and monitoring of vaccines across the globe. In the present context, the vaccine production to immunization campaign is a critical challenge. Therefore, an effective vaccine supply chain mechanism is required to address issues such as counterfeit vaccines, reduce vaccine wastages, and vaccine record fraud. In this paper, a blockchain-enabled vaccine supply chain is proposed to ensure the correctness, transparency, trust, and immutable log and improve the efficiency of vaccine distribution in the cold chain. The uniqueness of the proposed system is to provide distributed system to verify the reliability and efficacy of the vaccine from production to end beneficiaries' feedback about the vaccine. Our proposed system gives a clear view to the users as well as to the healthcare provider about the vaccination and ensures the anticounterfeit vaccine. The proposed system minimizes counterfeit vaccines and records, provides transparent communication between stakeholders in the supply chain, and improves the security of the vaccine supply chain and immutable feedback system about the vaccine.

Evaluation of the established non-MHC multiple sclerosis loci in an Indian population.

BACKGROUND: Multiple sclerosis (MS) is a chronic demyelinating neurodegenerative disorder with a strong genetic component. OBJECTIVE: The prevalence of MS in India is low compared with white populations of Northern European descent. METHODS: In order to ascertain whether disease susceptibility genes are the same across different populations, we completed the first investigation in the Indian MS population of 15 MS loci outside of the major histocompatibility (MHC) region that were previously identified and validated with MS susceptibility through genome-wide association and replication studies in white populations. RESULTS: In total, 197 Indian patients and 197 unrelated controls were analyzed. The most associated single nucleotide polymorphism (SNP) within this study was rs6897932 in the IL7R gene, which showed a strong protective effect in this data set (rs 6897932, OR = 0.5543, 95% CI = 0.37-0.78, p = 0.0009727). Two other SNPs were nominally associated with MS in this dataset, namely CLEC16A rs 12708716 (p = 0.0082, OR = 1.478, 95% CI = 1.106-1.975) and CD226 rs763361 (p = 0.03971, OR = 1.353, CI = 1.014-1.805). For the majority of the remaining SNPs (7/14), the trend for association was in the same direction as in previous studies in the white population. CONCLUSIONS: Although the power of this study was limited, our preliminary data suggest that disease susceptibility genes in MS in the Indian population may be similar to those of western populations.

Predicting carbon and water vapor fluxes using machine learning and novel feature ranking algorithms.

Gap-filling eddy covariance flux data using quantitative approaches has increased over the past decade. Numerous methods have been proposed previously, including look-up table approaches, parametric methods, process-based models, and machine learning. Particularly, the REddyProc package from the Max Planck Institute for Biogeochemistry and ONEFlux package from AmeriFlux have been widely used in many studies. However, there is no consensus regarding the optimal model and feature selection method that could be used for predicting different flux targets (Net Ecosystem Exchange, NEE; or Evapotranspiration -ET), due to the limited systematic comparative research based on the identical site-data. Here, we compared NEE and ET gap-filling/prediction performance of the least-square-based linear model, artificial neural network, random forest (RF), and support vector machine (SVM) using data obtained from four major row-crop and forage agroecosystems located in the subtropical or the climate-transition zones in the US. Additionally, we tested the impacts of different training-testing data partitioning settings, including a 10-fold time-series sequential (10FTS), a 10-fold cross validation (CV) routine with single data point (10FCV), daily (10FCVD), weekly (10FCVW) and monthly (10FCVM) gap length, and a 7/14-day flanking window (FW) approach; and implemented a novel Sliced Inverse Regression-based Recursive Feature Elimination algorithm (SIRRFE). We benchmarked the model performance against REddyProc and ONEFlux-produced results. Our results indicated that accurate NEE and ET prediction models could be systematically constructed using SVM/RF and only a few top informative features. The gap-filling performance of ONEFlux is generally satisfactory (R2 = 0.39-0.71), but results from REddyProc could be very limited or even unreliable in many cases (R2 = 0.01-0.67). Overall, SIRRFE-refined SVM models yielded excellent results for predicting NEE (R2 = 0.46-0.92) and ET (R2 = 0.74-0.91). Finally, the performance of various models was greatly affected by the types of ecosystem, predicting targets, and training algorithms; but was insensitive towards training-testing partitioning. Our research provided more insights into constructing novel gap-filling models and understanding the underlying drivers affecting boundary layer carbon/water fluxes on an ecosystem level.

The Comprehensive Aquatic Systems Model (CASM): Advancing Computational Capability for Ecosystem Simulation.

The comprehensive aquatic systems model (CASM), an aquatic food web-ecosystem model, was developed originally to explore relationships between food web structure and ecosystem function, and was then subsequently adapted to assess potential ecological risks posed by chemical contaminants. The present short communication presents the history of the CASM, describes the model structure, lists the outputs of the model, and introduces user-friendly versions of CASM applications that are being made publicly available. Environ Toxicol Chem 2020;39:2298-2303. © 2020 SETAC.

Comparison of resting and adenosine-free pressure indices with adenosine-induced hyperemic fractional flow reserve in intermediate coronary lesions.

OBJECTIVE: Fractional flow reserve (FFR) using adenosine has been the gold standard in the functional assessment of intermediate coronary stenoses in the catheterization laboratory. We aim to study the correlation of adenosine-free indices such as whole cycle Pd/Pa [the ratio of mean distal coronary pressure (Pd) to the mean pressure observed in the aorta (Pa)], instantaneous wave-free ratio (iFR), and contrast-induced submaximal hyperemia (cFFR) with FFR. METHODS: This multicenter, prospective, observational study included patients with stable angina or acute coronary syndrome (>48 h since onset) with discrete intermediate coronary lesions (40-70% diameter stenosis). All patients underwent assessment of whole cycle Pd/Pa, iFR, cFFR, and FFR. We then evaluated the correlation of these indices with FFR and assessed the diagnostic efficiencies of them against FFR ≤0.80. RESULTS: Of the 103 patients from three different centers, 83 lesions were included for analysis. The correlation coefficient (r value) of whole cycle Pd/Pa, iFR, and cFFR in relation to FFR were +0.84, +0.77, and +0.70 (all p values < 0.001), respectively, and the c-statistic against FFR ≤0.80 were 0.92 (0.86-0.98), 0.89(0.81-0.97), and 0.91 (0.85-0.97) (all p values < 0.001), respectively. The best cut-off values identified by receiver-operator characteristic curve for whole cycle Pd/Pa, iFR, and cFFR were 0.94, 0.90, and 0.88, respectively, for an FFR ≤0.80. By the concept of "adenosine-free zone" (iFR = 0.86-0.93), 59% lesions in this study would not require adenosine. CONCLUSION: All the three adenosine-free indices had good correlation with FFR. There is no difference in the diagnostic accuracies among the indices in functional evaluation of discrete intermediate coronary stenoses. However, further validation is needed before adoption of adenosine-free pressure parameters into clinical practice.

Data quality scoring system for microcosm and mesocosm studies used to derive a level of concern for atrazine.

The US Environmental Protection Agency (USEPA) has historically used different methods to derive an aquatic level of concern (LoC) for atrazine, though all have generally relied on an expanding set of mesocosm and microcosm ("cosm") studies for calibration. The database of results from ecological effects studies with atrazine in cosms now includes 108 data points from 39 studies and forms the basis for assessing atrazine's potential to impact aquatic plant communities. Inclusion of the appropriate cosm studies and accurate interpretation of each data point-delineated as binary scores of "effect" (effect score 1) or "no effect" (effect score 0) of a specific atrazine exposure profile on plant communities in a single study-is critical to USEPA's approach to determining the LoC. We reviewed the atrazine cosm studies in detail and carefully interpreted their results in terms of the binary effect scores. The cosm database includes a wide range of experimental systems and study designs, some of which are more relevant to natural plant communities than others. Moreover, the studies vary in the clarity and consistency of their results. We therefore evaluated each study against objective criteria for relevance and reliability to produce a weighting score that can be applied to the effect scores when calculating the LoC. This approach is useful because studies that are more relevant and reliable have greater influence on the LoC than studies with lower weighting scores. When the current iteration of USEPA's LoC approach, referred to as the plant assemblage toxicity index (PATI), was calibrated with the weighted cosm data set, the result was a 60-day LoC of 21.2 µg/L. Integr Environ Assess Manag 2018;14:489-497. © 2018 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Modeling the effects of thiamethoxam on Midwestern farm ponds and emergent wetlands.

Potential toxic effects of thiamethoxam on nontarget organisms and the community structure of a generic Midwestern farm pond and emergent wetland were assessed using 2 versions of the comprehensive aquatic system model: CASMGFP , a generic farm pond model, and CASMGWL , a generic wetland model. The CASMGFP and CASMGWL are integrated bioenergetics-based and habitat quality models that describe the daily biomass values of selected producer and consumer populations representative of generalized Midwestern farm ponds and emergent wetlands. The CASMGFP demonstrated the ability to reproduce values of population biomass reported for Midwestern (and other) pond ecosystems; the CASMGWL provided a similar modeling capability for Midwestern emergent wetlands. Lethal and sublethal effects of thiamethoxam were modeled as extrapolations of laboratory toxicity assays using the CASMGFP and the CASMGWL . Time series of daily environmental concentrations of thiamethoxam constructed for 6 regional pesticide applications across the United States failed to produce any calculated impacts on modeled population biomass or changes in community structure of modeled trophic guilds in the CASMGFP or the CASMGWL . However, evaluation of systematically increased daily concentrations demonstrated the ability of both models to simulate direct and indirect toxic effects of this pesticide. The present model study suggests that process-based food web/ecosystem models can be used to characterize the potential ecological effects of thiamethoxam on generalized farm pond and emergent wetland ecosystems. Environ Toxicol Chem 2018;37:738-754. © 2017 SETAC.

A comparative study of the modeled effects of atrazine on aquatic plant communities in midwestern streams.

Potential effects of atrazine on the nontarget aquatic plants characteristic of lower-order streams in the Midwestern United States were previously assessed using the Comprehensive Aquatic System Model (CASMATZ ). Another similar bioenergetics-based, mechanistic model, AQUATOX, was examined in the present study, with 3 objectives: 1) to develop an AQUATOX model simulation similar to the CASMATZ model reference simulation in describing temporal patterns of biomass production by modeled plant populations, 2) to examine the implications of the different approaches used by the models in deriving plant community-based levels of concern (LOCs) for atrazine, and 3) to determine the feasibility of implementing alternative ecological models to assess ecological impacts of atrazine on lower-order Midwestern streams. The results of the present comparative modeling study demonstrated that a similar reference simulation to that from the CASMATZ model could be developed using the AQUATOX model. It was also determined that development of LOCs and identification of streams with exposures in excess of the LOCs were feasible with the AQUATOX model. Compared with the CASMATZ model results, however, the AQUATOX model consistently produced higher estimates of LOCs and generated non-monotonic variations of atrazine effects with increasing exposures. The results of the present study suggest an opportunity for harmonizing the treatments of toxicity and toxicity parameter estimation in the CASMATZ and the AQUATOX models. Both models appear useful in characterizing the potential impacts of atrazine on nontarget aquatic plant populations in lower-order Midwestern streams. The present model comparison also suggests that, with appropriate parameterization, these process-based models can be used to assess the potential effects of other xenobiotics on stream ecosystems.

Modeling the potential effects of atrazine on aquatic communities in midwestern streams.

The comprehensive aquatic systems model for atrazine (CASM(ATZ)) estimates the potential toxic effects of atrazine on populations of aquatic plants and consumers in a generic lower-order midwestern stream. The CASM(ATZ) simulates the daily production of 20 periphyton and 6 aquatic vascular plant species. The modeled consumer community consists of 17 functionally defined species of zooplankton, benthic invertebrates, bacteria, and fish. Daily values of population biomass (grams of carbon per square meter) are calculated as nonlinear functions of population bioenergetics, physical-chemical environmental parameters, grazing/predator-prey interactions, and population-specific direct and indirect responses to atrazine. The CASM(ATZ) uses Monte Carlo methods to characterize the implications of phenotypic variability, environmental variability, and uncertainty associated with atrazine toxicity data in estimating the potential impacts of time-varying atrazine exposures on population biomass and community structure. Comparisons of modeled biomass values for plants and consumers with published data indicate that the generic reference simulation realistically describes ecological production in lower-order midwestern streams. Probabilistic assessments were conducted using the CASM(ATZ) to evaluate potential modeled changes in plant community structure resulting from measured atrazine exposure profiles in 3 midwestern US streams representing watersheds highly vulnerable to runoff. Deviation in the median values of maximum 30-d average Steinhaus similarity index ranged from 0.09% to 2.52% from the reference simulation. The CASM(ATZ) could therefore be used for the purposes of risk assessment by comparison of site monitoring-based model output to a biologically relevant Steinhaus similarity index level of concern. Used as a generic screening technology or in site-specific applications, the CASM(AT) provides an effective, coherent, and transparent modeling framework for assessing ecological risks posed by pesticides in lower-order streams.

Establishment risks for invasive species.

This article presents a quantitative methodology for evaluating the probability of invasive pest species establishing persistent populations. The estimation of pest establishment relies on data and information describing the biology and ecology of the pest and its interactions with potential host species and the regional environment. This information is developed using a model construct borrowed from theoretical population ecology. The methodology for estimating the probability of pest establishment is part of an overall framework that explores the implications of reductions in pest invasions on subsequent establishment. The risk reduction framework integrates the engineering aspects of different technologies for reducing pest entry, the biology and ecology of pest species, the suitability of potentially susceptible hosts, and the quality of available habitats. The methodology for estimating the risk of establishment is presented using an example pest, the Asian longhorned beetle (Anoplophora glabripennis), which has been introduced into the United States via solid wood packing materials (SWPM) used in international commerce. Uncertainties inherent to the estimation of model parameters that determine the risk of establishment are defined, quantified, and propagated through the population model. Advantages and limitations of the proposed methodology are discussed along with recommendations to make the approach more useful in the management of risks posed by the establishment of pest populations.