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

Study of Local Curriculum Implementation Imparting Local Scientific Knowledge in Nepal

This study focuses on the status of local curriculum development and implementation with imparting scientific knowledge in Purvakhola, Palpa. Though the education policy of Nepal is authorizing the construction of the local curriculum and its implementation, due to different causes most school clusters are not showing interest in the local curriculum. So, the study explores an existing gap in the educational policy of the government of Nepal and its implementation by local educational authorities. This study is adopting a qualitative method and data generation and its interpretation is based on primary data. The finding is drawn on its status of implementation of local curriculum, local curriculum should be designed based on local needs and covering local scientific knowledge. Also, it should be included local heritages, local autonomy, and the policy of decentralization of curriculum development

Modeling Impact of Temperature and Human Movement on the Persistence of Dengue Disease

Dengue is a vector-borne infectious disease endemic in many parts of the world. The disease is spreading in new places due to human movement into the dengue disease supporting areas. Temperature is the major climatic factor which affects the biological processes of the mosquitoes and their interaction with the viruses. In the present work, we propose a multipatch model to assess the impact of temperature and human movement in the transmission dynamics of dengue disease. The work consists of system of ordinary differential equations that describe the transmission dynamics of dengue disease between humans and mosquitoes. Human population is divided into four classes: susceptible, exposed, infectious, and recovered. Mosquito population is divided into three classes: susceptible, exposed, and infectious. Basic reproduction number R0 of the model is obtained using Next-Generation Matrix method. The qualitative analysis of the model is made in terms of the basic reproduction number. Parameters used in the model are considered temperature dependent. Dynamics of vector and host populations are investigated with different human movement rates and different temperature levels. Numerical results show that proper management of human movement between patches helps reducing the burden of dengue disease. It is also seen that the temperature affects the transmission dynamics of the disease significantly

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

Hepatitis B and C Virus Infections among Blood Donors in Blood Transfusion Center, Pokhara, Nepal: Seroprevalence and its Associated Risk Factors

Hepatitis B Virus (HBV) and Hepatitis C Virus (HCV) infections lead to chronic diseases and are the most common causes of liver cirrhosis and cancer in developing countries like Nepal. The study is carried out to determine the seroprevalence of HBV and HCV by using a Rapid kit method and Elisa Method to find out its risk factors. The cross-sectional study was done among blood donating people from 16th August 2016 to 19th November 2016. Blood donors in Pokhara Valley were screened for anti-HCV antibodies, anti-HBV antibodies using third generation ELISA kits and automated ELISA Processor in serology laboratory at Central Blood Transfusion Service (CBTS) of Nepal Red Cross Society (NRCS) in Pokhara, Nepal. 1777 (87.2%) units were male blood donors and 260 (12.6%) units were female donors out of 2037 participants. Gender wise, the ratio between male and female was 1:0.1. HBV and HCV infection rate in blood donors were detected at 0.7% (15/2037) and 0.5% (8/2037) respectively. HBV infection rate in volunteer blood donor people was 0.7% (14/1881) which was higher than the replacement donors i.e. 0.6% (1/156). Similarly, in HCV infection rate in volunteer donor were 0.4% (8/1881). HBV infected people are detected higher than the HCV infected people among the blood donors. In addition, there was no significant relationship between positive results of HBV and HCV tests with the gender, age, tattoo, donor type

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

The Role of Aspartate Aminotransferase to Platelet Ratio Index as a Non-Invasive Predictor of Variceal Etiology of Upper Gastrointestinal Bleeding

Introduction: Non-invasive strategies to predict variceal from non-variceal bleeding will be highly beneficialfor preemptive management of Upper Gastrointestinal Bleeding (UGIB). This study aimed to assess the roleof aspartate aminotransferase (AST) to platelet ratio index (APRI) as a non-invasive predictor of varicealetiology of UGIB. Methods: This was a retrospective descriptive study conducted at Endoscopy Departmentof Dhulikhel Hospital between January 2017 and December 2019 in patients presenting with acute UGIB.We assessed the diagnostic utility of the APRI score relative to other objective measures by Area Under theReceiver Operating Characteristic (AUROC) curve analysis. Results: A total of 158 patients with historyof UGIB were included in the study. There were total 123 males (77.8%) and the mean age of the patientswas 50.3±16.1 years. The APRI score performed well in predicting a variceal etiology of acute UGIB, withAUROC 0.9. When APRI was used at cut-off of 1.3, it had a sensitivity of 84.1% and specificity of 76.8%,a positive predictive value of 70.7% and a negative predictive value of 89.9% while predicting varicealetiology of UGIB at presentation. The relative risk of varices at an APRI cut-off of 1.3 is 17.5 with a p-valueof <0.0001. Conclusion: The present study highlighted that APRI score can be used as an objective metricthat helps to predict a variceal etiology of acute UGIB

Fecal carriage of Extended Spectrum β-Lactamases (ESBL) Producing Escherichia coli and Klebsiella spp. among School Children in Pokhara, Nepal

Extended-spectrum β-lactamases (ESBL) producing microbes in recent years have been a major problem in developing countries like Nepal, with limited treatment options. This study aimed to determine the prevalence of ESBL producing E. coli and Klebsiella spp. in school children in Pokhara, Nepal. The study was conducted from June to October, 2015 at the microbiology laboratory of Manipal Teaching Hospital, Pokhara, Nepal. Antibiotic Susceptibility Test (AST) was done after isolation and identification of bacterial isolates. Then, presence of ESBL enzymes in E. coli and Klebsiella spp. were tested by combination disc diffusion test using cefotaxime and ceftazidime alone and with clavulanic acid. Out of total 309 school children, 211 (68%) bacterial isolates were detected from stool samples. Among them, E. coli and Klebsiella spp. were detected in 97 (46%) and 39 (19%) stool samples respectively. Bacteria isolated from 14 (5%) stool samples were multi-drug resistant (MDR) positive. After applying combined disk method, 88 (29%) isolates were found to be ESBL producer. Emerging prevalence rate of ESBL producing E. coli and Klebsiella spp. are major problem in medical history. Therefore, rapid need of surveillance for effective management of such MDR-strain is required

Antibiotic Resistance Pattern of Shigella spp. Among Gastroenteritis Patients at Tertiary Care Hospital in Pokhara, Nepal

Shigellosis, a disease caused by Shigella species. It is a major public health problem in developing nations like Nepal, where communities having poverty; poor sanitation, personal hygiene, and water supplies. The main aim of our study is to isolate and identify Shigella spp. from gastroenteritis patients and to find out its drug resistance pattern.A cross-sectional study was carried out based on routinely attending outpatients and inpatients.  A total of 225 stool samples collected from gastroenteritis patients were processed from 20 April to 24 September 2014 in Western Regional Hospital, Pokhara, Nepal. Standard microbiological procedures were followed for the isolation of Shigella spp. After that slide agglutination kit method was used for identification of Shigella spp.  Finally, Kirby-Bauer disc diffusion method was done for an antimicrobial resistance test.Of the total 225 gastroenteritis patients, 133 were detected as bacterial positive cases.  Among positive cases, Shigella spp. was identified in 10.5%.  Age wise, an infection rate of Shigella in patients <15-years old was found higher i.e. 7.3% than in patients ≥ 15 years old i.e. 4.5% with the (p = 0.432) at 95% CI. The infection rate of S. dysenteriae, S. flexneri, and S. sonnei was detected in 28.6%, 57.1%, and 14.3% respectively. For the antimicrobial test, eight types of antibiotics were used.  The most resistance pattern of isolated Shigella spp. was found in nalidixic acid, and co-trimoxazole 92.8% followed by ampicillin 64.3% and ciprofloxacin 42.8% etc.Our study reported that endemicity of Shigellosis with S. flexneri is the predominant group in gastroenteritis patients. This finding suggests that co-trimoxazole, nalidixic acid, ciprofloxacin and ampicillin should not be used experimentally as first-line drugs for shigellosis treatment

The Influence of Demographic and Job-related Characteristics on Nurses’ Compassion Satisfaction and Fatigue

Introduction: Growing stress and declining job satisfaction are the major challenges in nursing. Demographic and work-related factors may influence nurses’ compassion satisfaction and fatigue levels. Therefore, the study examined the impact of demographic and occupational factors on nurses’ professional quality of life. Methods: A cross-sectional study was conducted on 172 nurses working in two tertiary care hospitals in Pokhara using proportionate stratified random sampling. Data was gathered using the Professional Quality of Life (ProQOL) scale version 5 in September 2019. SPSS was used for bivariate and multivariate analysis to determine a significant relationship between socio-demographic and work-related variables and three professional quality-of-life subscales. The ethical approval was taken from the Institutional Review Committee (IRC) of Pokhara University (Reference Number: 83-075-76). Results: Most of the participants showed an average level of compassion satisfaction (79.1%, n=136), burnout (77.9%, n=134), and secondary traumatic stress (85.5%, n=147). The study revealed a significant mean difference between demographic characteristics (marital status and having children at home) and three professional quality of life subscales. Similarly, the study did not yield significant mean differences between the work-related variables and three professional quality-of-life subscales. Conclusion: Nurses in tertiary care hospitals exhibited moderate to high levels of compassion satisfaction while experiencing moderate to low levels of burnout and secondary traumatic stress. Despite moderate to low levels of burnout and secondary traumatic stress, it is imperative to address these issues as they have the potential to lead to medical errors and compromise patient care standards