Multi-Disciplinary Applications of Oceanographic Geophysical Data Collection

Thesis (Ph.D.)--University of Washington, 2016-08

Prevalence and factors associated with anthropometric failure, vitamin A and iron deficiency among adolescents in a Nigerian urban community.

Background: Under nutrition is a problem of severe magnitude in low income countries like Nigeria. Adolescent school children might also be vulnerable. The dearth of data hinders planning of school health and nutrition programmes for school children. Objective: To determine the prevalence of stunting, thinness; vitamin A and iron deficiencies among adolescent students in Nsukka urban, Nigeria and to determine factors that are associated with these nutritional problems. Methods: A total of 400 participants were randomly selected from 717 students aged 12 – 18 years in 3 randomly selected secondary schools. Questionnaires, anthropometric measurements, and blood analyses were the data collection methods employed. Results: The prevalence of stunting was 33.3% and thinness 31.0%. Neither overweight nor obesity was observed. While 64.0% were anaemic; 44.0% had vitamin A deficiency (VAD). A total of 48.0% had both anaemia and stunting, 42% had VAD + thinness; while 40% had anaemia + VAD. Household income was a predictor of vitamin A status. Children from medium/ high income households had higher odds of having VAD than those from low income households (AOR=0.14; 95% CI=0.031, 0.607; P=0.009). Household income (AOR=0.12; 95% CI=0.021, 0.671; P=0.016), and age (AOR=0.09; 95% CI=0.014, 0.587; P=0.012) were independent determinants of height-for-age status. Conclusion: Among urban adolescent students in Nigeria, stunting, thinness, anaemia and VAD were problems of public health significance. Age and household monthly income played major roles.

Evaluation of blood zinc, calcium and blood lead levels among children aged 1-36 months

Background: Early childhood lead exposure is associated with numerous adverse health effects. Biomonitoring among susceptible populations, such as children, has not been previously conducted. The aim of the study is to evaluate the blood lead (Pb) and total blood calcium (Ca) levels; blood zinc (Zn) levels. Methods: A cross-sectional study was designed to collect healthy children age 1-36 months (Mean ± SD: 1.5 ± 0.6 age, 60% boys) in the study from January 2010 to September 2011. Results: The overall mean blood Pb levels were 42.18 ± 12.13 μg/L, the overall mean blood Zn and total blood Ca concentrations were 62.18 ± 12.33 μmol/L and 1.78 ± 0.13 mmol/L, respectively. The prevalence of elevated blood Pb levels in all children was 1.3%. A significant difference was found between female and male subjects for the blood Pb and Zn. After controlling for gender and age, there was a weak positive correlation between total blood Ca and Zn level. Conclusions: The blood Pb levels had a significant negative correlation with total blood Ca level after adjusting for age and gender, and these findings suggest that Pb had effect on positive blood Zn and total blood Ca levels; parents should pay more attention to the nutrition of girls.

The influence of the initial state of nutrition on the lifespan of patients with amyotrophic lateral sclerosis (ALS) during home enteral nutrition

Aim: Amyotrophic lateral sclerosis (ALS) is a chronic, neurodegenerative disease, which leads to development of malnutrition. The main purpose of this research was to analyze the impact of malnutrition on the course of the disease and long-term survival. Material and methods: A retrospective analysis has been performed on 48 patients (22 F [45,83%] and 26 M [54,17%], the average age of patients: 66,2 [43-83]) in 2008-2014.The analysis of the initial state of nutrition was measured by body mass index (BMI), nutritional status according to NRS 2002, SGA and concentration of albumin in blood serum. Patients were divided into two groups, depending on the state of nutrition: well-nourished and malnourished. The groups were created separately for each of these, which allowed an additional comparative analysis of techniques used for the assessment of nutritional status. Results: Proper state of nutrition was interrelated with longer survival (SGA: 456 vs. 679 days, NRS: 312 vs. 659 vs. 835 days, BMI: respectively, 411, 541, 631 days, results were statistically significant for NRS and BMI). Concentration of albumin was not a prognostic factor, but longer survival was observed when level of albumin was increased during nutritional therapy. Conclusions: The initial nutrition state and positive response to enteral feeding is associated with better survival among patients with ALS. For this reason, nutritional therapy should be introduced as soon as possible.

Effect of Astragalus membranaceus (Fisch) Bunge extract on streptozocin-induced diabetic in rats

Purpose: To investigate the effect of Astragalus membranaceus (Fisch.) Bunge. extract (AMBE) on streptozotocin-induced diabetic rats. Methods: The aqueous extract of AMB was obtained by steeping the dried Astragalus membranaceus (Fisch.) Bunge. in water at 60 oC three times, each for 1 h, before first drying in an oven at 100 oC and then freeze-drying the last extract thus obtained. Diabete model rats was induced by a single intraperitoneal injection of a freshly prepared solution of streptozotocin (50 mg/kg). The rats were randomly divided into 6 groups of ten rats each: negative control group, normal control group, reference group (glibenclamide1 mg/kgbody weight) as well as AMB extract groups, namely, 40, 80 and 160 mg/kg body weight. Antihyperglycemic effect was measured by blood glucose and plasma insulin levels. Oxidative stress was evaluated in liver and kidney by antioxidant markers, viz, lipidperoxidation (LPO), superoxide dismutase (SOD), reduced glutathione (GSH), glutathione peroxidase (GPx) and catalase (CAT), while blood serum levels of creatinine and urea were also determined in both diabetic control and treated rats. Results: Compared with diabetic rats, oral administration of AMBE at a concentration of 160 mg/kg daily for 30 days showed a significant decrease in fasting blood glucose (109.438 ± 3.52, p < 0.05) and increased insulin level (13.96 ± 0.74, p < 0.05). Furthermore, it significantly reduced biochemical parameters (serum creatinine, 0.86 ± 0.29, p < 0.05) and serum urea (45.14 ± 1.79, p < 0.05). The treatment also resulted in significant increase in GSH (49.21 ± 2.59, p < 0.05), GPx (11.96 ± 1.16, p < 0.05), SOD (14.13 ± 0.49, p < 0.05), CAT (83.25 ± 3.14, p < 0.05) level in the liver and kidney of diabetic rats. Conclusion: The results suggest that AMBE may effectively normalize impaired antioxidant status in streptozotocin-induced diabetes in a dose-dependent manner. AMBE has a protective effect against lipid peroxidation by scavenging free radicals and is thus capable of reducing the risk of diabetic complications.

From the traditional verification approach to the spatial methods: a study of applicability of the SAL metric in the framework of the WMO project MesoVICT

Increasing in resolution of numerical weather prediction models has allowed more and more realistic forecasts of atmospheric parameters. Due to the growing variability into predicted fields the traditional verification methods are not always able to describe the model ability because they are based on a grid-point-by-grid-point matching between observation and prediction. Recently, new spatial verification methods have been developed with the aim of show the benefit associated to the high resolution forecast. Nested in among of the MesoVICT international project, the initially aim of this work is to compare the newly tecniques remarking advantages and disadvantages. First of all, the MesoVICT basic examples, represented by synthetic precipitation fields, have been examined. Giving an error evaluation in terms of structure, amplitude and localization of the precipitation fields, the SAL method has been studied more thoroughly respect to the others approaches with its implementation in the core cases of the project. The verification procedure has concerned precipitation fields over central Europe: comparisons between the forecasts performed by the 00z COSMO-2 model and the VERA (Vienna Enhanced Resolution Analysis) have been done. The study of these cases has shown some weaknesses of the methodology examined; in particular has been highlighted the presence of a correlation between the optimal domain size and the extention of the precipitation systems. In order to increase ability of SAL, a subdivision of the original domain in three subdomains has been done and the method has been applied again. Some limits have been found in cases in which at least one of the two domains does not show precipitation. The overall results for the subdomains have been summarized on scatter plots. With the aim to identify systematic errors of the model the variability of the three parameters has been studied for each subdomain.

A probabilistic approach to user mobility prediction for wireless services.

Mobile and wireless networks have long exploited mobility predictions, focused on predicting the future location of given users, to perform more efficient network resource management. In this paper, we present a new approach in which we provide predictions as a probability distribution of the likelihood of moving to a set of future locations. This approach provides wireless services a greater amount of knowledge and enables them to perform more effectively. We present a framework for the evaluation of this new type of predictor, and develop 2 new predictors, HEM and G-Stat. We evaluate our predictors accuracy in predicting future cells for mobile users, using two large geolocation data sets, from MDC [11], [12] and Crawdad [13]. We show that our predictors can successfully predict with as low as an average 2.2% inaccuracy in certain scenarios.

Spatial and temporal downscaling of solar global radiation and mean air temperature from weather forecast data - an introductory numerical study and validation

This short paper presents a numerical method for spatial and temporal downscaling of solar global radiation and mean air temperature data from global weather forecast models and its validation. The final objective is to develop a prediction algorithm to be integrated in energy management models and forecast of energy harvesting in solar thermal systems of medium/low temperature. Initially, hourly prediction and measurement data of solar global radiation and mean air temperature were obtained, being then numerically downscaled to half-hourly prediction values for the location where measurements were taken. The differences between predictions and measurements were analyzed for more than one year of data of mean air temperature and solar global radiation on clear sky days, resulting in relative daily deviations of around -0.9±3.8% and 0.02±3.92%, respectively.

Simulation of photoinduced processes in organic chromophores: mapping photochemically relevant states for accurate dynamics

The simulation of ultrafast photoinduced processes is a fundamental step towards the understanding of the underlying molecular mechanism and interpretation/prediction of experimental data. Performing a computer simulation of a complex photoinduced process is only possible introducing some approximations but, in order to obtain reliable results, the need to reduce the complexity must balance with the accuracy of the model, which should include all the relevant degrees of freedom and a quantitatively correct description of the electronic states involved in the process. This work presents new computational protocols and strategies for the parameterisation of accurate models for photochemical/photophysical processes based on state-of-the-art multiconfigurational wavefunction-based methods. The required ingredients for a dynamics simulation include potential energy surfaces (PESs) as well as electronic state couplings, which must be mapped across the wide range of geometries visited during the wavepacket/trajectory propagation. The developed procedures allow to obtain solid and extended databases reducing as much as possible the computational cost, thanks to, e.g., specific tuning of the level of theory for different PES regions and/or direct calculation of only the needed components of vectorial quantities (like gradients or nonadiabatic couplings). The presented approaches were applied to three case studies (azobenzene, pyrene, visual rhodopsin), all requiring an accurate parameterisation but for different reasons. The resulting models and simulations allowed to elucidate the mechanism and time scale of the internal conversion, reproducing or even predicting new transient experiments. The general applicability of the developed protocols to systems with different peculiarities and the possibility to parameterise different types of dynamics on an equal footing (classical vs purely quantum) prove that the developed procedures are flexible enough to be tailored for each specific system, and pave the way for exact quantum dynamics with multiple degrees of freedom.

Variational and learning models for image and time series inverse problems

Inverse problems are at the core of many challenging applications. Variational and learning models provide estimated solutions of inverse problems as the outcome of specific reconstruction maps. In the variational approach, the result of the reconstruction map is the solution of a regularized minimization problem encoding information on the acquisition process and prior knowledge on the solution. In the learning approach, the reconstruction map is a parametric function whose parameters are identified by solving a minimization problem depending on a large set of data. In this thesis, we go beyond this apparent dichotomy between variational and learning models and we show they can be harmoniously merged in unified hybrid frameworks preserving their main advantages. We develop several highly efficient methods based on both these model-driven and data-driven strategies, for which we provide a detailed convergence analysis. The arising algorithms are applied to solve inverse problems involving images and time series. For each task, we show the proposed schemes improve the performances of many other existing methods in terms of both computational burden and quality of the solution. In the first part, we focus on gradient-based regularized variational models which are shown to be effective for segmentation purposes and thermal and medical image enhancement. We consider gradient sparsity-promoting regularized models for which we develop different strategies to estimate the regularization strength. Furthermore, we introduce a novel gradient-based Plug-and-Play convergent scheme considering a deep learning based denoiser trained on the gradient domain. In the second part, we address the tasks of natural image deblurring, image and video super resolution microscopy and positioning time series prediction, through deep learning based methods. We boost the performances of supervised, such as trained convolutional and recurrent networks, and unsupervised deep learning strategies, such as Deep Image Prior, by penalizing the losses with handcrafted regularization terms.

Methods for integrating machine learning and constrained optimization

In the framework of industrial problems, the application of Constrained Optimization is known to have overall very good modeling capability and performance and stands as one of the most powerful, explored, and exploited tool to address prescriptive tasks. The number of applications is huge, ranging from logistics to transportation, packing, production, telecommunication, scheduling, and much more. The main reason behind this success is to be found in the remarkable effort put in the last decades by the OR community to develop realistic models and devise exact or approximate methods to solve the largest variety of constrained or combinatorial optimization problems, together with the spread of computational power and easily accessible OR software and resources. On the other hand, the technological advancements lead to a data wealth never seen before and increasingly push towards methods able to extract useful knowledge from them; among the data-driven methods, Machine Learning techniques appear to be one of the most promising, thanks to its successes in domains like Image Recognition, Natural Language Processes and playing games, but also the amount of research involved. The purpose of the present research is to study how Machine Learning and Constrained Optimization can be used together to achieve systems able to leverage the strengths of both methods: this would open the way to exploiting decades of research on resolution techniques for COPs and constructing models able to adapt and learn from available data. In the first part of this work, we survey the existing techniques and classify them according to the type, method, or scope of the integration; subsequently, we introduce a novel and general algorithm devised to inject knowledge into learning models through constraints, Moving Target. In the last part of the thesis, two applications stemming from real-world projects and done in collaboration with Optit will be presented.