Uncertainty Quantification Of A Semi-Supervised Support Vector Regression Reservoir Model

Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. Recent advances in machine learning offer a novel approach to model spatial distribution of petrophysical properties in complex reservoirs alternative to geostatistics. The approach is based of semisupervised learning, which handles both ?labelled? observed data and ?unlabelled? data, which have no measured value but describe prior knowledge and other relevant data in forms of manifolds in the input space where the modelled property is continuous. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic geological features and describe stochastic variability and non-uniqueness of spatial properties. On the other hand, it is able to capture and preserve key spatial dependencies such as connectivity of high permeability geo-bodies, which is often difficult in contemporary petroleum reservoir studies. Semi-supervised SVR as a data driven algorithm is designed to integrate various kind of conditioning information and learn dependences from it. The semi-supervised SVR model is able to balance signal/noise levels and control the prior belief in available data. In this work, stochastic semi-supervised SVR geomodel is integrated into Bayesian framework to quantify uncertainty of reservoir production with multiple models fitted to past dynamic observations (production history). Multiple history matched models are obtained using stochastic sampling and/or MCMC-based inference algorithms, which evaluate posterior probability distribution. Uncertainty of the model is described by posterior probability of the model parameters that represent key geological properties: spatial correlation size, continuity strength, smoothness/variability of spatial property distribution. The developed approach is illustrated with a fluvial reservoir case. The resulting probabilistic production forecasts are described by uncertainty envelopes. The paper compares the performance of the models with different combinations of unknown parameters and discusses sensitivity issues.

Variation in haematological parameters in children less than five years of age with asymptomatic Plasmodium infection: implication for malaria field studies

During the season of high malaria transmission, most children are infected by Plasmodium, which targets red blood cells (RBCs), affecting haematological parameters. To describe these variations, we examined the haematological profiles of two groups of children living in a malaria-endemic area. A cross-sectional survey was conducted at the peak of the malaria transmission season in a rural area of Burkina Faso. After informed consent and clinical examination, blood samples were obtained from the participants for malaria diagnosis and a full blood count. Of the 414 children included in the analysis, 192 were not infected with Plasmodium, whereas 222 were asymptomatic carriers of Plasmodium infection. The mean age of the infected children was 41.8 months (range of 26.4-57.2) compared to 38.8 months (range of 22.4-55.2) for the control group (p = 0.06). The asymptomatic infected children tended to have a significantly lower mean haemoglobin level (10.8 g/dL vs. 10.4 g/dL; p < 0.001), mean lymphocyte count (4592/µL vs. 5141/µL; p = 0.004), mean platelet count (266 x 103/µL vs. 385 x 103/µL; p < 0.001) and mean RBC count (4.388 x 106/µL vs. 4.158 x 106/µL; p < 0.001) and a higher mean monocyte count (1403/µL vs. 1192/µL; p < 0.001) compared to the control group. Special attention should be applied when interpreting haematological parameters and evaluating immune responses in asymptomatic infected children living in malaria-endemic areas and enrolled in vaccine trials.

Lifelong dynamics of human CD4+CD25+ regulatory T cells: insights from in vivo data and mathematical modeling.

Despite their limited proliferation capacity, regulatory T cells (T(regs)) constitute a population maintained over the entire lifetime of a human organism. The means by which T(regs) sustain a stable pool in vivo are controversial. Using a mathematical model, we address this issue by evaluating several biological scenarios of the origins and the proliferation capacity of two subsets of T(regs): precursor CD4(+)CD25(+)CD45RO(-) and mature CD4(+)CD25(+)CD45RO(+) cells. The lifelong dynamics of T(regs) are described by a set of ordinary differential equations, driven by a stochastic process representing the major immune reactions involving these cells. The model dynamics are validated using data from human donors of different ages. Analysis of the data led to the identification of two properties of the dynamics: (1) the equilibrium in the CD4(+)CD25(+)FoxP3(+)T(regs) population is maintained over both precursor and mature T(regs) pools together, and (2) the ratio between precursor and mature T(regs) is inverted in the early years of adulthood. Then, using the model, we identified three biologically relevant scenarios that have the above properties: (1) the unique source of mature T(regs) is the antigen-driven differentiation of precursors that acquire the mature profile in the periphery and the proliferation of T(regs) is essential for the development and the maintenance of the pool; there exist other sources of mature T(regs), such as (2) a homeostatic density-dependent regulation or (3) thymus- or effector-derived T(regs), and in both cases, antigen-induced proliferation is not necessary for the development of a stable pool of T(regs). This is the first time that a mathematical model built to describe the in vivo dynamics of regulatory T cells is validated using human data. The application of this model provides an invaluable tool in estimating the amount of regulatory T cells as a function of time in the blood of patients that received a solid organ transplant or are suffering from an autoimmune disease.

Alterations in cytokines and haematological parameters during the acute and convalescent phases of Plasmodium falciparum and Plasmodium vivax infections

Haematological and cytokine alterations in malaria are a broad and controversial subject in the literature. However, few studies have simultaneously evaluated various cytokines in a single patient group during the acute and convalescent phases of infection. The aim of this study was to sequentially characterise alterations in haematological patters and circulating plasma cytokine and chemokine levels in patients infected with Plasmodium vivax or Plasmodium falciparum from a Brazilian endemic area during the acute and convalescent phases of infection. During the acute phase, thrombocytopaenia, eosinopaenia, lymphopaenia and an increased number of band cells were observed in the majority of the patients. During the convalescent phase, the haematologic parameters returned to normal. During the acute phase, P. vivax and P. falciparum patients had significantly higher interleukin (IL)-6, IL-8, IL-17, interferon-γ, tumour necrosis factor (TNF)-α, macrophage inflammatory protein-1β and granulocyte-colony stimulating factor levels than controls and maintained high levels during the convalescent phase. IL-10 was detected at high concentrations during the acute phase, but returned to normal levels during the convalescent phase. Plasma IL-10 concentration was positively correlated with parasitaemia in P. vivax and P. falciparum-infected patients. The same was true for the TNF-α concentration in P. falciparum-infected patients. Finally, the haematological and cytokine profiles were similar between uncomplicated P. falciparum and P. vivax infections.

Association between magnesium-deficient status and anthropometric and clinical-nutritional parameters in posmenopausal women.

Background: During menopause occurs weight gain and bone loss occurs due to the hormone decline during this period and other factors such as nutrition. Magnesium deficiency suggests a risk factor for obesity and osteoporosis. OBJECTIVE: To evaluate the clinical and nutritional magnesium status in a population of postmenopausal women, assessing intake and serum levels of magnesium in the study population and correlation with anthropometric parameters such as body mass index(BMI) and body fat, and biochemical parameters associated. SUBJECTS AND METHOD: The study involved 78 healthy women aged 44-76, with postmenopausal status, from the province of Grenade, Spain. The sample was divided into two age groups: group1, aged < 58, and group 2 aged >/= 58. Anthropometric parameters were recorded and nutritional intake was assessed by 72-hour recall, getting the RDAs through Nutriber(R) program. To assess the biochemical parameters was performed a blood sample was taken. Magnesium was analyzed by flame atomic absorption spectrophotometry (FAAS) in erythrocyte and plasma wet-mineralized samples. RESULTS: Our results show that 37.85% of the total subjects have an overweight status. Magnesium intake found in our population is insufficient in 36% of women,while plasma magnesium deficiency corresponds to 23% of the population and 72% of women have deficient levels of magnesium in erythrocyte. Positive correlations were found between magnesium intake and dietary intake of calcium, of phosphorus,and with prealbumin plasma levels, as well as with a lower waist / hip ratio Magnesium levels in erythrocyte were correlated with lower triglycerides and urea values. CONCLUSION: It is important to control and monitor the nutritional status of magnesium in postmenopausal women to prevent nutritional alterations and possible clinical and chronic degenerative diseases associated with magnesium deficiency and with menopause.

Community introduction of practice parameters for autistic spectrum disorders: Advancing early recognition.

OBJECTIVES: Within a strong interdisciplinary framework, improvement in the quality of care for children with autistic spectrum disorders through a 2 year implementation program of Practice Parameters, aimed principally at improving early detection and intervention. METHOD: We developed Practice Parameters (PPs) for Pervasive Developmental Disorders and circulated the PPs to all child and adolescent psychiatrists practicing in the region. RESULTS: PP development and parallel information strategies resulted in a significant decrease of 1.5 years in the mean-age-at-diagnosis. However, further analysis indicated that improvement was only transient. CONCLUSION: Despite the encouraging improvement in mean-age-at-diagnosis 2 years after PP implementation, other indicators showed a failure to maintain the improvements. A systematic screening program would be the most reliable method to reinforce the PPs.

Is there an efficient trap or collection method for sampling Anopheles darlingi and other malaria vectors that can describe the essential parameters affecting transmission dynamics as effectively as human landing catches? - A Review

Distribution, abundance, feeding behaviour, host preference, parity status and human-biting and infection rates are among the medical entomological parameters evaluated when determining the vector capacity of mosquito species. To evaluate these parameters, mosquitoes must be collected using an appropriate method. Malaria is primarily transmitted by anthropophilic and synanthropic anophelines. Thus, collection methods must result in the identification of the anthropophilic species and efficiently evaluate the parameters involved in malaria transmission dynamics. Consequently, human landing catches would be the most appropriate method if not for their inherent risk. The choice of alternative anopheline collection methods, such as traps, must consider their effectiveness in reproducing the efficiency of human attraction. Collection methods lure mosquitoes by using a mixture of olfactory, visual and thermal cues. Here, we reviewed, classified and compared the efficiency of anopheline collection methods, with an emphasis on Neotropical anthropophilic species, especially Anopheles darlingi, in distinct malaria epidemiological conditions in Brazil.

Suitability, success and sinks: how do predictions of nesting distributions relate to fitness parameters in high arctic waders?

AimAlthough habitat suitability maps derived from species distribution models (SDMs) are often assumed to highlight locations that can sustain healthy populations over time, the relationship between suitability scores and fitness parameters has rarely been tested thoroughly. LocationZackenberg Valley, north-east Greenland. MethodsUsing 14years of data (1997-2010) representing three wader species (dunlin Calidris alpina, sanderling Calidris alba and ruddy turnstone Arenaria interpres), we tested the relationships between modelled suitability and fitness parameters at nesting locations. ResultsAmong the three species examined, only the ruddy turnstone exhibited significant relationships between suitability and nest success, but over time rather than space. During years with extensive snow cover in the landscape, the nesting sites of ruddy turnstone occurred in different habitats than were typically used across years. Moreover, in years with extensive snow cover, the ruddy turnstone initiated nests later and suffered from higher egg predation rates. Main conclusionOur results suggest that SDMs derived from species occurrences that include years of low reproductive success may over-estimate the potential suitable habitat in the landscape. Whenever possible, variation in reproductive success should be considered when building models to inform species' response to environmental change. species' response to environmental change.

Study and implementation of some quantitative trading models

Quantitative or algorithmic trading is the automatization of investments decisions obeying a fixed or dynamic sets of rules to determine trading orders. It has increasingly made its way up to 70% of the trading volume of one of the biggest financial markets such as the New York Stock Exchange (NYSE). However, there is not a signi cant amount of academic literature devoted to it due to the private nature of investment banks and hedge funds. This projects aims to review the literature and discuss the models available in a subject that publications are scarce and infrequently. We review the basic and fundamental mathematical concepts needed for modeling financial markets such as: stochastic processes, stochastic integration and basic models for prices and spreads dynamics necessary for building quantitative strategies. We also contrast these models with real market data with minutely sampling frequency from the Dow Jones Industrial Average (DJIA). Quantitative strategies try to exploit two types of behavior: trend following or mean reversion. The former is grouped in the so-called technical models and the later in the so-called pairs trading. Technical models have been discarded by financial theoreticians but we show that they can be properly cast into a well defined scientific predictor if the signal generated by them pass the test of being a Markov time. That is, we can tell if the signal has occurred or not by examining the information up to the current time; or more technically, if the event is F_t-measurable. On the other hand the concept of pairs trading or market neutral strategy is fairly simple. However it can be cast in a variety of mathematical models ranging from a method based on a simple euclidean distance, in a co-integration framework or involving stochastic differential equations such as the well-known Ornstein-Uhlenbeck mean reversal ODE and its variations. A model for forecasting any economic or financial magnitude could be properly defined with scientific rigor but it could also lack of any economical value and be considered useless from a practical point of view. This is why this project could not be complete without a backtesting of the mentioned strategies. Conducting a useful and realistic backtesting is by no means a trivial exercise since the \laws" that govern financial markets are constantly evolving in time. This is the reason because we make emphasis in the calibration process of the strategies' parameters to adapt the given market conditions. We find out that the parameters from technical models are more volatile than their counterpart form market neutral strategies and calibration must be done in a high-frequency sampling manner to constantly track the currently market situation. As a whole, the goal of this project is to provide an overview of a quantitative approach to investment reviewing basic strategies and illustrating them by means of a back-testing with real financial market data. The sources of the data used in this project are Bloomberg for intraday time series and Yahoo! for daily prices. All numeric computations and graphics used and shown in this project were implemented in MATLAB^R scratch from scratch as a part of this thesis. No other mathematical or statistical software was used.

Mathematical modeling and analysis of the interaction of populations of bacteria and bacteriophages within chicken intestine

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Activated protein C consumption and coagulation parameters in severe sepsis and septic shock.

Introduction Activated protein C (APC) deC ciency is prevalent in severe sepsis and septic shock patients. The aim of the study was to relate the anticoagulation activity evaluated by APC with other coagulation parameters adjusted to 28-day mortality. Methods A cohort study of 150 critically ill adults. Age, sex, sources of infection and coagulation markers within 24< hours from severe sepsis or septic shock onset, deC ned according to Surviving Sepsis Campaign (SSC) criteria, were studied. We analyzed APC activity using a hemostasis laboratory analyzer (BCS® XP; Siemens). A descriptive and comparative statistical analysis was performed using SPSS version 15.0 (SPSS Inc., Chicago, IL, USA). Results We analyzed 150 consecutive episodes of severe sepsis (16%) or septic shock (84%) admitted to the UCI. The median age of the study sample was 64 (interquartile range (IQR): 22.30.001). See Figure 1. Conclusion Low levels of PC are associated with poor outcome and severity in severe sepsis, and it is well correlated with antithrombin III and INR.

New perspectives in the use of ink evidence in forensic science: Part II. Development and testing of mathematical algorithms for the automatic comparison of ink samples analysed by HPTLC

In the first part of this research, three stages were stated for a program to increase the information extracted from ink evidence and maximise its usefulness to the criminal and civil justice system. These stages are (a) develop a standard methodology for analysing ink samples by high-performance thin layer chromatography (HPTLC) in reproducible way, when ink samples are analysed at different time, locations and by different examiners; (b) compare automatically and objectively ink samples; and (c) define and evaluate theoretical framework for the use of ink evidence in forensic context. This report focuses on the second of the three stages. Using the calibration and acquisition process described in the previous report, mathematical algorithms are proposed to automatically and objectively compare ink samples. The performances of these algorithms are systematically studied for various chemical and forensic conditions using standard performance tests commonly used in biometrics studies. The results show that different algorithms are best suited for different tasks. Finally, this report demonstrates how modern analytical and computer technology can be used in the field of ink examination and how tools developed and successfully applied in other fields of forensic science can help maximising its impact within the field of questioned documents.

Possible solution of some essential zero problems in compositional data analysis

One of the tantalising remaining problems in compositional data analysis lies in how to deal with data sets in which there are components which are essential zeros. By anessential zero we mean a component which is truly zero, not something recorded as zero simply because the experimental design or the measuring instrument has not been sufficiently sensitive to detect a trace of the part. Such essential zeros occur inmany compositional situations, such as household budget patterns, time budgets,palaeontological zonation studies, ecological abundance studies. Devices such as nonzero replacement and amalgamation are almost invariably ad hoc and unsuccessful insuch situations. From consideration of such examples it seems sensible to build up amodel in two stages, the first determining where the zeros will occur and the secondhow the unit available is distributed among the non-zero parts. In this paper we suggest two such models, an independent binomial conditional logistic normal model and a hierarchical dependent binomial conditional logistic normal model. The compositional data in such modelling consist of an incidence matrix and a conditional compositional matrix. Interesting statistical problems arise, such as the question of estimability of parameters, the nature of the computational process for the estimation of both the incidence and compositional parameters caused by the complexity of the subcompositional structure, the formation of meaningful hypotheses, and the devising of suitable testing methodology within a lattice of such essential zero-compositional hypotheses. The methodology is illustrated by application to both simulated and real compositional data