Using Bayesian model selection and calibration to improve the DayCent ecosystem model

2020 Fall.Includes bibliographical references.Process-based biogeochemical models have been developed and used for decades to predict the outcomes of real-world ecological processes. These models are based on a theoretical understanding of relevant ecological processes and approximated using highly complex mathematical equations and hundreds of unknown parameters—requiring calibration using physical observations of the system. These models are then used to test scientific understanding, estimate pools and fluxes, make predictions for future scenarios, and to evaluate management and policy outcomes. To provide a better understanding of the ecological processes, these models need to be simple, make accurate predictions, and account for all sources of uncertainty. The focus of this dissertation is to develop a Bayesian model analysis framework to meet the goal of developing simple and accurate models that fully address uncertainty. This framework includes variance-based global sensitivity analysis (GSA) to identify influential model parameters, a Bayesian calibration method using sampling importance resampling (SIR) to estimate the posterior distribution of unknown model parameters and hyperparameters, and a Monte Carlo analysis to estimate the posterior predictive distribution of model outputs. The framework accounts for all sources of uncertainty, including the remaining uncertainty over the fitted parameters. Additionally, Bayesian model selection is also implemented in the framework to determine the most appropriate level of complexity during model development. The framework is applied to improve the DayCent ecosystem model in agricultural applications. The DayCent model was improved with several model developments, including NH3 volatilization, the release of nitrogen (N) from controlled-release N fertilizers (CRNFs) and the inhibition of the biological process of nitrification and delay the transformation of NH+4 to NO-3 with nitrification inhibitor (NIs). The model development incorporates key 4R management practices that mitigate NH3 and N2O emissions in fertilized upland agricultural soils. In addition, I recalibrated the soil organic matter submodel to improve estimation of soil organic carbon (C) sequestration potentials to a 30 cm depth for several management practices, including organic matter amendment, adoption of no-till management, and addition of synthetic N fertilizers. The results showed that the DayCent model predictions of C sequestration and reduction in N2O flux as well as NH3 volatilization from several management practices were consistent with the field observations. The model result suggested that addition of organic amendments and adoption of no-till are viable management option for C sequestration, however, the addition of synthetic N fertilizer did not produce a significant level of C sequestration. For NH3 volatilization, the model also adequately captures the reduction potential of urease inhibitor along with the incorporation of urea by mechanical means or with immediate irrigation/rainfall. The model also shows promising results in mitigating N2O emissions with both CRNFs and NIs in comparison to field observations. The model prediction focuses on estimating greenhouse gas (GHG) mitigation potential and estimation of uncertainty arising during model prediction—enhancing DayCent as a tool for scientific understanding, regional to global assessments, policy implementation, and carbon emission trading. Overall, the model improvements enhanced the ability of the DayCent model in providing a stronger basis to support policy and management decisions associated with GHG mitigation in agricultural soils

Bacterial ‘Cell’ Phones: Do cell phones carry potential pathogens?

Cell phones are important companions for professionals especially health care workers (HCWs) for better communication in hospital. The present study compared the nature of the growth of potentially pathogenic bacterial flora on cell phones in hospital and community. 75% cell phones from both the categories grew at least one potentially pathogenic organism. Cell phones from HCWs grew significantly more potential pathogens like MRSA (20%), Acinetobacter species (5%), Pseudomonas species (2.5%) as compared to the non HCWs. 97.5% HCWs use their cell phone in the hospital, 57.5% never cleaned their cell phone and 20% admitted that they did not wash their hands before or after attending patients, although majority (77.5%) knows that cell phones can have harmful colonization and act as vector for nosocomial infections. It is recommended, therefore, that cell phones in the hospital should be regularly decontaminated. Moreover, utmost emphasis needs to be paid to hand washing practices among HCWs

Enabling Water-Energy–Food Nexus: a New Approach for Sustainable Agriculture and Food Security in Mountainous Landlocked Countries

Majority of landlocked mountainous countries are poorly ranked in Human Development Index (HDI), mostly due to poor per capita agriculture production, increasing population, unemployment, expensive and delayed transportation including several other factors. Generally, economy of such countries substantially relies on subsistence agriculture, tourism, hydropower and largely on remittance etc. Recently, it has been argued that to utilize scarce suitable land efficiently for food production, poor inland transport, hydropower, irrigation, drinking water in integration with other developmental infrastructures, an overarching policy linking water - energy – food nexus within a country for combating water, energy and food security would be most relevant. Thus, in present paper it has been opined that promotion of such linkage via nexus approach is the key to sustainable development of landlocked mountainous countries. Major land mass in mountainous countries like Nepal remains unsuitable for agriculture, road and other infrastructure profoundly imposing food, nutrition and energy security. However, large pristine snowy mountains function as wildlife sanctuaries, pastures, watershed, recharge areas for regional and global water, food and energy security. In return, landlocked mountainous countries are offered certain International leverages. For more judicious trade off, it is recommended that specific countries aerial coverage of mountains would be more appropriate basis for such leverages. Moreover, for sustainability of mountainous countries an integrated approach enabling water - energy – food nexus via watershed-hydropower-irrigation-aquaculture-agriculture-integrated linking policy model is proposed. This model would enable protection of watershed for pico, micro, and mega hydro power plants and tail waters to be used for aquaculture or irrigation or drinking water purposes for food and nutrition security

RIVER ECOLOGICAL STUDY: BUILDING THE KNOWLEDGE BASE FOR VARIETY OF ASSESSMENTS SUCH AS CLIMATE CHANGE IN NEPAL

Climate change is now universally acknowledged to be taking place across the globe. It is generally presumed that the impacts of climate change would be more severe in the country like Nepal due to its location, physiography, poverty and lack of preparedness to cope with the changes. The last reason is mainly associated with knowledge, information and ability to use technologies based on science.The main objective of this research is to analyze and evaluate the effects of climate change by taking fish as an indicator. However, an even more important outcome is to prepare a solid foundation of fish-based information, which could be used in the future as a reference for a variety of purposes including the study of climate change. Two sets of examples, one in the tributaries of a glacial river and another in the tributary of a rain -fed river are compared in terms of fish ecological attributes to test for effects of climate change. In addition to fish-based information, this research also studies physico-chemical parameters and benthic fauna so as to build up an ecological profile of the rivers

Aspirin for prophylactic use in the primary prevention of cardiovascular disease and cancer : a systematic review and overview of reviews

Background: Prophylactic aspirin has been considered to be beneficial in reducing the risks of heart disease and cancer. However, potential benefits must be balanced against the possible harm from side effects, such as bleeding and gastrointestinal (GI) symptoms. It is particularly important to know the risk of side effects when aspirin is used as primary prevention - that is when used by people as yet free of, but at risk of developing, cardiovascular disease (CVD) or cancer. In this report we aim to identify and re-analyse randomised controlled trials (RCTs), systematic reviews and meta-analyses to summarise the current scientific evidence with a focus on possible harms of prophylactic aspirin in primary prevention of CVD and cancer. Objectives: To identify RCTs, systematic reviews and meta-analyses of RCTs of the prophylactic use of aspirin in primary prevention of CVD or cancer. To undertake a quality assessment of identified systematic reviews and meta-analyses using meta-analysis to investigate study-level effects on estimates of benefits and risks of adverse events; cumulative meta-analysis; exploratory multivariable meta-regression; and to quantify relative and absolute risks and benefits. Methods: We identified RCTs, meta-analyses and systematic reviews, and searched electronic bibliographic databases (from 2008 September 2012) including MEDLINE, Cochrane Central Register of Controlled Trials, Database of Abstracts of Reviews of Effects, NHS Centre for Reviews and Dissemination, and Science Citation Index. We limited searches to publications since 2008, based on timing of the most recent comprehensive systematic reviews. Results: In total, 2572 potentially relevant papers were identified and 27 met the inclusion criteria. Benefits of aspirin ranged from 6% reduction in relative risk (RR) for all-cause mortality [RR 0.94, 95% confidence interval (CI) 0.88 to 1.00] and 10% reduction in major cardiovascular events (MCEs) (RR 0.90, 95% CI 0.85 to 0.96) to a reduction in total coronary heart disease (CHD) of 15% (RR 0.85, 95% CI 0.69 to 1.06). Reported pooled odds ratios (ORs) for total cancer mortality ranged between 0.76 (95% CI 0.66 to 0.88) and 0.93 (95% CI 0.84 to 1.03). Inclusion of the Women's Health Study changed the estimated OR to 0.82 (95% CI 0.69 to 0.97). Aspirin reduced reported colorectal cancer (CRC) incidence (OR 0.66, 95% CI 0.90 to 1.02). However, including studies in which aspirin was given every other day raised the OR to 0.91 (95% CI 0.74 to 1.11). Reported cancer benefits appeared approximately 5 years from start of treatment. Calculation of absolute effects per 100,000 patient-years of follow-up showed reductions ranging from 33 to 46 deaths (all-cause mortality), 60-84 MCEs and 47-64 incidents of CHD and a possible avoidance of 34 deaths from CRC. Reported increased RRs of adverse events from aspirin use were 37% for GI bleeding (RR 1.37, 95% CI 1.15 to 1.62), between 54% (RR 1.54, 95% CI 1.30 to 1.82) and 62% (RR 1.62, 95% CI 1.31 to 2.00) for major bleeds, and between 32% (RR 1.32, 95% CI 1.00 to 1.74) and 38% (RR 1.38, 95% CI 1.01 to 1.82) for haemorrhagic stroke. Pooled estimates of increased RR for bleeding remained stable across trials conducted over several decades. Estimates of absolute rates of harm from aspirin use, per 100,000 patient-years of follow-up, were 99-178 for non-trivial bleeds, 46-49 for major bleeds, 68-117 for GI bleeds and 8-10 for haemorrhagic stroke. Meta-analyses aimed at judging risk of bleed according to sex and in individuals with diabetes were insufficiently powered for firm conclusions to be drawn. Limitations: Searches were date limited to 2008 because of the intense interest that this subject has generated and the cataloguing of all primary research in so many previous systematic reviews. A further limitation was our potential over-reliance on study-level systematic reviews in which the person-years of follow-up were not accurately ascertainable. However, estimates of number of events averted or incurred through aspirin use calculated from data in study-level meta-analyses did not differ substantially from estimates based on individual patient data-level meta-analyses, for which person-years of follow-up were more accurate (although based on less-than-complete assemblies of currently available primary studies). Conclusions: We have found that there is a fine balance between benefits and risks from regular aspirin use in primary prevention of CVD. Effects on cancer prevention have a long lead time and are at present reliant on post hoc analyses. All absolute effects are relatively small compared with the burden of these diseases. Several potentially relevant ongoing trials will be completed between 2013 and 2019, which may clarify the extent of benefit of aspirin in reducing cancer incidence and mortality. Future research considerations include expanding the use of IPD meta-analysis of RCTs by pooling data from available studies and investigating the impact of different dose regimens on cardiovascular and cancer outcomes

An Interactive Visual Tool to Enhance Understanding of Random Forest Predictions

Random forests are known to provide accurate predictions, but the predictions are not easy to understand. In order to provide support for understanding such predictions, an interactive visual tool has been developed. The tool can be used to manipulate selected features to explore “what-if” scenarios. It exploits the internal structure of decision trees in a trained forest model and presents this information as interactive plots and charts. In addition, the tool presents a simple decision rule as an explanation for the prediction. It also presents the recommendation for reassignments of feature values of the example that leads to change in the prediction to a preferred class. An evaluation of the tool was undertaken in a large truck manufacturing company, targeting the fault prediction of a selected component in trucks. A set of domain experts were invited to use the tool and provide feedback in post-task interviews. The result of this investigation suggests that the tool indeed may aid in understanding the predictions of a random forest, and also allows for gaining new insights

Mathematical Model: Comparative Study of Thermal Effects of Laser in Corneal Refractive Surgeries

Lasers have been widely used in ophthalmology. Refractive errors are some of the most common ophthalmic abnormalities worldwide. Laser refractive surgery was developed to correct refractive errors myopia, hyperopia and astigmatism. Two types of laser surgical techniques: lamellar and thermal are available to reshape the corneal curvature. Ultraviolet (UV) emitting argon fluoride (ArF) excimer laser is used to sculpt cornea in lamellar procedures, whereas, infrared (IR) emitting holmium yttrium aluminum garnet (Ho: YAG) laser is used to shrink cornea in thermal procedure. Tissue heating is common in all types of laser surgical techniques. Hence, in this paper, a finite element model is developed to investigate the temperature distribution of cornea in different laser refractive surgeries. Characteristics of optical and thermal processes and influence of the parameters of radiation and tissues on the results of laser action are investigated. The results of mathematical modeling in different surgical techniques are discussed, compared, and validated with experimental results

Explaining Random Forest Predictions with Association Rules

Random forests frequently achieve state-of-the-art predictive performance. However, the logic behind their predictions cannot be easily understood, since they are the result of averaging often hundreds or thousands of, possibly conflicting, individual predictions. Instead of presenting all the individual predictions, an alternative is proposed, by which the predictions are explained using association rules generated from itemsets representing paths in the trees of the forest. An empirical investigation is presented, in which alternative ways of generating the association rules are compared with respect to explainability, as measured by the fraction of predictions for which there is no applicable rule and by the fraction of predictions for which there is at least one applicable rule that conflicts with the forest prediction. For the considered datasets, it can be seen that most predictions can be explained by the discovered association rules, which have a high level of agreement with the underlying forest. The results do not single out a clear winner of the considered alternatives in terms of unexplained and disagreement rates, but show that they are associated with substantial differences in computational cost