Pedotransfer functions to estimate retention and availability of water in soils of the state of Santa Catarina, Brazil

Studies on water retention and availability are scarce for subtropical or humid temperate climate regions of the southern hemisphere. The aims of this study were to evaluate the relations of the soil physical, chemical, and mineralogical properties with water retention and availability for the generation and validation of continuous point pedotransfer functions (PTFs) for soils of the State of Santa Catarina (SC) in the South of Brazil. Horizons of 44 profiles were sampled in areas under different cover crops and regions of SC, to determine: field capacity (FC, 10 kPa), permanent wilting point (PWP, 1,500 kPa), available water content (AW, by difference), saturated hydraulic conductivity, bulk density, aggregate stability, particle size distribution (seven classes), organic matter content, and particle density. Chemical and mineralogical properties were obtained from the literature. Spearman's rank correlation analysis and path analysis were used in the statistical analyses. The point PTFs for estimation of FC, PWP and AW were generated for the soil surface and subsurface through multiple regression analysis, followed by robust regression analysis, using two sets of predictive variables. Soils with finer texture and/or greater organic matter content retain more moisture, and organic matter is the property that mainly controls the water availability to plants in soil surface horizons. Path analysis was useful in understanding the relationships between soil properties for FC, PWP and AW. The predictive power of the generated PTFs to estimate FC and PWP was good for all horizons, while AW was best estimated by more complex models with better prediction for the surface horizons of soils in Santa Catarina.

Estimation of jump-diffusion processes via empirical characteristic functions

Preface The starting point for this work and eventually the subject of the whole thesis was the question: how to estimate parameters of the affine stochastic volatility jump-diffusion models. These models are very important for contingent claim pricing. Their major advantage, availability T of analytical solutions for characteristic functions, made them the models of choice for many theoretical constructions and practical applications. At the same time, estimation of parameters of stochastic volatility jump-diffusion models is not a straightforward task. The problem is coming from the variance process, which is non-observable. There are several estimation methodologies that deal with estimation problems of latent variables. One appeared to be particularly interesting. It proposes the estimator that in contrast to the other methods requires neither discretization nor simulation of the process: the Continuous Empirical Characteristic function estimator (EGF) based on the unconditional characteristic function. However, the procedure was derived only for the stochastic volatility models without jumps. Thus, it has become the subject of my research. This thesis consists of three parts. Each one is written as independent and self contained article. At the same time, questions that are answered by the second and third parts of this Work arise naturally from the issues investigated and results obtained in the first one. The first chapter is the theoretical foundation of the thesis. It proposes an estimation procedure for the stochastic volatility models with jumps both in the asset price and variance processes. The estimation procedure is based on the joint unconditional characteristic function for the stochastic process. The major analytical result of this part as well as of the whole thesis is the closed form expression for the joint unconditional characteristic function for the stochastic volatility jump-diffusion models. The empirical part of the chapter suggests that besides a stochastic volatility, jumps both in the mean and the volatility equation are relevant for modelling returns of the S&P500 index, which has been chosen as a general representative of the stock asset class. Hence, the next question is: what jump process to use to model returns of the S&P500. The decision about the jump process in the framework of the affine jump- diffusion models boils down to defining the intensity of the compound Poisson process, a constant or some function of state variables, and to choosing the distribution of the jump size. While the jump in the variance process is usually assumed to be exponential, there are at least three distributions of the jump size which are currently used for the asset log-prices: normal, exponential and double exponential. The second part of this thesis shows that normal jumps in the asset log-returns should be used if we are to model S&P500 index by a stochastic volatility jump-diffusion model. This is a surprising result. Exponential distribution has fatter tails and for this reason either exponential or double exponential jump size was expected to provide the best it of the stochastic volatility jump-diffusion models to the data. The idea of testing the efficiency of the Continuous ECF estimator on the simulated data has already appeared when the first estimation results of the first chapter were obtained. In the absence of a benchmark or any ground for comparison it is unreasonable to be sure that our parameter estimates and the true parameters of the models coincide. The conclusion of the second chapter provides one more reason to do that kind of test. Thus, the third part of this thesis concentrates on the estimation of parameters of stochastic volatility jump- diffusion models on the basis of the asset price time-series simulated from various "true" parameter sets. The goal is to show that the Continuous ECF estimator based on the joint unconditional characteristic function is capable of finding the true parameters. And, the third chapter proves that our estimator indeed has the ability to do so. Once it is clear that the Continuous ECF estimator based on the unconditional characteristic function is working, the next question does not wait to appear. The question is whether the computation effort can be reduced without affecting the efficiency of the estimator, or whether the efficiency of the estimator can be improved without dramatically increasing the computational burden. The efficiency of the Continuous ECF estimator depends on the number of dimensions of the joint unconditional characteristic function which is used for its construction. Theoretically, the more dimensions there are, the more efficient is the estimation procedure. In practice, however, this relationship is not so straightforward due to the increasing computational difficulties. The second chapter, for example, in addition to the choice of the jump process, discusses the possibility of using the marginal, i.e. one-dimensional, unconditional characteristic function in the estimation instead of the joint, bi-dimensional, unconditional characteristic function. As result, the preference for one or the other depends on the model to be estimated. Thus, the computational effort can be reduced in some cases without affecting the efficiency of the estimator. The improvement of the estimator s efficiency by increasing its dimensionality faces more difficulties. The third chapter of this thesis, in addition to what was discussed above, compares the performance of the estimators with bi- and three-dimensional unconditional characteristic functions on the simulated data. It shows that the theoretical efficiency of the Continuous ECF estimator based on the three-dimensional unconditional characteristic function is not attainable in practice, at least for the moment, due to the limitations on the computer power and optimization toolboxes available to the general public. Thus, the Continuous ECF estimator based on the joint, bi-dimensional, unconditional characteristic function has all the reasons to exist and to be used for the estimation of parameters of the stochastic volatility jump-diffusion models.

Enhanced degradation of metalaxyl in agricultural soils of São Paulo State, Brazil

This work investigated the effect of repeated applications on enhanced degradation of metalaxyl in two different agricultural soils used for cultivation of orange and lemon from Casa Branca and Itapetininga districts of São Paulo State, Brazil. Soil samples were collected from areas repeatedly treated with commercial ridomil 50GR for six successive years, and from other areas never exposed to this fungicide. At the laboratory, soil samples received a 14C-metalaxyl solution and its degradation was studied through radiometric techniques to measure biomineralization and recovery of extractable- and soil-bound products. Enhanced degradation was verified only in one soil, although partial degradation and mineralization of the fungicide were detected in both soils. The different rates and patterns of metalaxyl degradation in the soils were probably due to their different physical, chemical, and biological characteristics.

Current state of vaccine therapies in non-small-cell lung cancer.

A large variety of cancer vaccines have undergone extensive testing in early-phase clinical trials. A limited number have also been tested in randomized phase II clinical trials. Encouraging trends toward increased survival in the vaccine arms have been recently observed for 2 vaccine candidates in patients with non-small-cell lung cancer. These have provided the impetus for the initiation of phase III trials in large groups of patients with lung cancer. These vaccines target 2 antigens widely expressed in lung carcinomas: melanoma-associated antigen 3, a cancer testis antigen; and mucin 1, an antigen overexpressed in a largely deglycosylated form in advanced tumors. Therapeutic cancer vaccines aim at inducing strong CD8 and CD4 T-cell responses. The majority of vaccines recently tested in phase I clinical trials show efficacy in terms of induction of specific tumor antigen immunity. However, clinical efficacy remains to be determined but appears limited. Efforts are thus aimed at understanding the basis for this apparent lack of effect on tumors. Two major factors are involved. On one hand, current vaccines are suboptimal. Strong adjuvant agents and appropriate tumor antigens are needed. Moreover, dose, route, and schedule also need optimization. On the other hand, it is now clear that large tumors often present a tolerogenic microenvironment that hampers effective antitumor immunity. The partial understanding of the molecular pathways leading to functional inactivation of T cells at tumor sites has provided new targets for intervention. In this regard, blockade of cytotoxic T-lymphocyte antigen-4 and programmed death-1 with humanized monoclonal antibodies has reached the clinical testing stage. In the future, more potent cancer vaccines will benefit from intense research in antigen discovery and adjuvant agents. Furthermore, it is likely that vaccines need to be combined with compounds that reverse major tolerogenic pathways that are constitutively active at the tumor site. Developing these combined approaches to vaccination in cancer promises new, exciting findings and, at the same time, poses important challenges to academic research institutions and the pharmaceutical industry.

Planning under partial observability by classical replanning: theory and experiments

Planning with partial observability can be formulated as a non-deterministic search problem in belief space. The problem is harder than classical planning as keeping track of beliefs is harder than keeping track of states, and searching for action policies is harder than searching for action sequences. In this work, we develop a framework for partial observability that avoids these limitations and leads to a planner that scales up to larger problems. For this, the class of problems is restricted to those in which 1) the non-unary clauses representing the uncertainty about the initial situation are nvariant, and 2) variables that are hidden in the initial situation do not appear in the body of conditional effects, which are all assumed to be deterministic. We show that such problems can be translated in linear time into equivalent fully observable non-deterministic planning problems, and that an slight extension of this translation renders the problem solvable by means of classical planners. The whole approach is sound and complete provided that in addition, the state-space is connected. Experiments are also reported.

Augmented state Kalman filtering for AUV navigation

Addresses the problem of estimating the motion of an autonomous underwater vehicle (AUV), while it constructs a visual map ("mosaic" image) of the ocean floor. The vehicle is equipped with a down-looking camera which is used to compute its motion with respect to the seafloor. As the mosaic increases in size, a systematic bias is introduced in the alignment of the images which form the mosaic. Therefore, this accumulative error produces a drift in the estimation of the position of the vehicle. When the arbitrary trajectory of the AUV crosses over itself, it is possible to reduce this propagation of image alignment errors within the mosaic. A Kalman filter with augmented state is proposed to optimally estimate both the visual map and the vehicle position

Active deformation fields: dense deformation field estimation for atlas-based segmentation using the active contour framework.

This paper presents a new and original variational framework for atlas-based segmentation. The proposed framework integrates both the active contour framework, and the dense deformation fields of optical flow framework. This framework is quite general and encompasses many of the state-of-the-art atlas-based segmentation methods. It also allows to perform the registration of atlas and target images based on only selected structures of interest. The versatility and potentiality of the proposed framework are demonstrated by presenting three diverse applications: In the first application, we show how the proposed framework can be used to simulate the growth of inconsistent structures like a tumor in an atlas. In the second application, we estimate the position of nonvisible brain structures based on the surrounding structures and validate the results by comparing with other methods. In the final application, we present the segmentation of lymph nodes in the Head and Neck CT images, and demonstrate how multiple registration forces can be used in this framework in an hierarchical manner.

Soybean crop area estimation by Modis/Evi data

The objective of this work was to develop a procedure to estimate soybean crop areas in Rio Grande do Sul state, Brazil. Estimations were made based on the temporal profiles of the enhanced vegetation index (Evi) calculated from moderate resolution imaging spectroradiometer (Modis) images. The methodology developed for soybean classification was named Modis crop detection algorithm (MCDA). The MCDA provides soybean area estimates in December (first forecast), using images from the sowing period, and March (second forecast), using images from the sowing and maximum crop development periods. The results obtained by the MCDA were compared with the official estimates on soybean area of the Instituto Brasileiro de Geografia e Estatística. The coefficients of determination ranged from 0.91 to 0.95, indicating good agreement between the estimates. For the 2000/2001 crop year, the MCDA soybean crop map was evaluated using a soybean crop map derived from Landsat images, and the overall map accuracy was approximately 82%, with similar commission and omission errors. The MCDA was able to estimate soybean crop areas in Rio Grande do Sul State and to generate an annual thematic map with the geographic position of the soybean fields. The soybean crop area estimates by the MCDA are in good agreement with the official agricultural statistics.

Bone mineral density (BMD), micro-architecture estimation (TBS) and vertebral fracture assessment (VFA) extracted from a single DXA device in combination with clinical risk factors improve significantly the identification of women at high risk of fracture.

Osteoporosis (OP) is a systemic skeletal disease characterized by a low bone mineral density (BMD) and a micro-architectural (MA) deterioration. Clinical risk factors (CRF) are often used as a MA approximation. MA is yet evaluable in daily practice by the trabecular bone score (TBS) measure. TBS is very simple to obtain, by reanalyzing a lumbar DXA-scan. TBS has proven to have diagnosis and prognosis values, partially independent of CRF and BMD. The aim of the OsteoLaus cohort is to combine in daily practice the CRF and the information given by DXA (BMD, TBS and vertebral fracture assessment (VFA)) to better identify women at high fracture risk. The OsteoLaus cohort (1400 women 50 to 80 years living in Lausanne, Switzerland) started in 2010. This study is derived from the cohort COLAUS who started in Lausanne in 2003. The main goal of COLAUS is to obtain information on the epidemiology and genetic determinants of cardiovascular risk in 6700 men and women. CRF for OP, bone ultrasound of the heel, lumbar spine and hip BMD, VFA by DXA and MA evaluation by TBS are recorded in OsteoLaus. Preliminary results are reported. We included 631 women: mean age 67.4 ± 6.7 years, BMI 26.1 ± 4.6, mean lumbar spine BMD 0.943 ± 0.168 (T-score − 1.4 SD), and TBS 1.271 ± 0.103. As expected, correlation between BMD and site matched TBS is low (r2 = 0.16). Prevalence of VFx grade 2/3, major OP Fx and all OP Fx is 8.4%, 17.0% and 26.0% respectively. Age- and BMI-adjusted ORs (per SD decrease) are 1.8 (1.2-2.5), 1.6 (1.2-2.1), and 1.3 (1.1-1.6) for BMD for the different categories of fractures and 2.0 (1.4-3.0), 1.9 (1.4-2.5), and 1.4 (1.1-1.7) for TBS respectively. Only 32 to 37% of women with OP Fx have a BMD < − 2.5 SD or a TBS < 1.200. If we combine a BMD < − 2.5 SD or a TBS < 1.200, 54 to 60% of women with an osteoporotic Fx are identified. As in the already published studies, these preliminary results confirm the partial independence between BMD and TBS. More importantly, a combination of TBS subsequent to BMD increases significantly the identification of women with prevalent OP Fx which would have been misclassified by BMD alone. For the first time we are able to have complementary information about fracture (VFA), density (BMD), micro- and macro architecture (TBS and HAS) from a simple, low ionizing radiation and cheap device: DXA. Such complementary information is very useful for the patient in the daily practice and moreover will likely have an impact on cost effectiveness analysis.

Maximum likelihood estimation of position in GNSS

In this letter, we obtain the Maximum LikelihoodEstimator of position in the framework of Global NavigationSatellite Systems. This theoretical result is the basis of a completelydifferent approach to the positioning problem, in contrastto the conventional two-steps position estimation, consistingof estimating the synchronization parameters of the in-viewsatellites and then performing a position estimation with thatinformation. To the authors’ knowledge, this is a novel approachwhich copes with signal fading and it mitigates multipath andjamming interferences. Besides, the concept of Position–basedSynchronization is introduced, which states that synchronizationparameters can be recovered from a user position estimation. Weprovide computer simulation results showing the robustness ofthe proposed approach in fading multipath channels. The RootMean Square Error performance of the proposed algorithm iscompared to those achieved with state-of-the-art synchronizationtechniques. A Sequential Monte–Carlo based method is used todeal with the multivariate optimization problem resulting fromthe ML solution in an iterative way.

The micro-architecture estimation by TBS discriminate women with and without osteoporotic fracture independently of age, BMI and BMD: The Osteo-Mobile Vaud cohort study.

Introduction: « Osteo-Mobile Vaud » is a mobile osteoporosis (OP) screening program. The women > 60 years living in the region Vaud will be offered OP screening with new equipment installed in a bus. The main goal is to evaluate the fracture risk with the combination of clinical risk factors (CRF) and informations extracted by a single DXA: bone mineral density (BMD), vertebral fracture assessment (VFA), and micro-architecture (MA) evaluation. MA is yet evaluable in daily practice by the Trabecular Bone Score (TBS) measure. TBS is a novel grey-level texture measurement reflecting bone MA based on the use of experimental variograms of 2D projection images. TBS is very simple to obtain, by reanalyzing a lumbar DXA-scan. TBS has proven to have diagnosis and prognosis value, partially independent of CRF and BMD. A 55-years follow- up is planned. Method: The Osteo-Mobile Vaud cohort (1500 women, > 60 years, living in the region Vaud) started in July 2010. CRF for OP, lumbar spine and hip BMD, VFA by DXA and MA evaluation by TBS are recorded. Preliminary results are reported. Results: In July 31th, we evaluated 510 women: mean age 67 years, BMI 26 kg/m². 72 women had one or more fragility fractures, 39 had vertebral fracture (VFx) grade 2/3. TBS decreases with age (-0.005 / year, p<0.001), and with BMI (-0.011 per kg/m², p<0.001). Correlation between BMD and site matched TBS is low (r=0.4, p<0.001). For the lowest T-score BMD, odds ratio (OR, 95% CI) for VFx grade 2/3 and clinical OP Fx are 1.8 (1.1-2.9) and 2.3 (1.5-3.4). For TBS, age-, BMI- and BMD adjusted ORs (per SD decrease) for VFx grade 2/3 and clinical OP Fx are 1.9 (1.2-3.0) and 1.8 (1.2-2.7). The TBS added value was independent of lumbar spine BMD or the lowest T-score (femoral neck, total hip or lumbar spine). Conclusion: As in the already published studies, these preliminary results confirm the partial independence between BMD and TBS. More importantly, a combination of TBS and BMD may increase significantly the identification of women with prevalent OP Fx. For the first time we are able to have complementary information about fracture (VFA), density (BMD), and micro-architecture (TBS) from a simple, low ionizing radiation and cheap device: DXA. The value of such informations in a screening program will be evaluated.

Effect Estimation of an Innovative Nursing Intervention to Improve Delirium among Home-Dwelling Older Adults: A Randomized Controlled Pilot Trial.

AIMS: Estimating the effect of a nursing intervention in home-dwelling older adults on the occurrence and course of delirium and concomitant cognitive and functional impairment. METHODS: A randomized clinical pilot trial using a before/after design was conducted with older patients discharged from hospital who had a medical prescription to receive home care. A total of 51 patients were randomized into the experimental group (EG) and 52 patients into the control group (CG). Besides usual home care, nursing interventions were offered by a geriatric nurse specialist to the EG at 48 h, 72 h, 7 days, 14 days, and 21 days after discharge. All patients were monitored for symptoms of delirium using the Confusion Assessment Method. Cognitive and functional statuses were measured with the Mini-Mental State Examination and the Katz and Lawton Index. RESULTS: No statistical differences with regard to symptoms of delirium (p = 0.085), cognitive impairment (p = 0.151), and functional status (p = 0.235) were found between the EG and CG at study entry and at 1 month. After adjustment, statistical differences were found in favor of the EG for symptoms of delirium (p = 0.046), cognitive impairment (p = 0.015), and functional status (p = 0.033). CONCLUSION: Nursing interventions to detect delirium at home are feasible and accepted. The nursing interventions produced a promising effect to improve delirium.

Structured sparsity for spatially coherent fibre orientation estimation in diffusion MRI.

We propose a novel formulation to solve the problem of intra-voxel reconstruction of the fibre orientation distribution function (FOD) in each voxel of the white matter of the brain from diffusion MRI data. The majority of the state-of-the-art methods in the field perform the reconstruction on a voxel-by-voxel level, promoting sparsity of the orientation distribution. Recent methods have proposed a global denoising of the diffusion data using spatial information prior to reconstruction, while others promote spatial regularisation through an additional empirical prior on the diffusion image at each q-space point. Our approach reconciles voxelwise sparsity and spatial regularisation and defines a spatially structured FOD sparsity prior, where the structure originates from the spatial coherence of the fibre orientation between neighbour voxels. The method is shown, through both simulated and real data, to enable accurate FOD reconstruction from a much lower number of q-space samples than the state of the art, typically 15 samples, even for quite adverse noise conditions.

On the Adoption of Partial Least Squares in Psychological Research: Caveat Emptor

The partial least squares technique (PLS) has been touted as a viable alternative to latent variable structural equation modeling (SEM) for evaluating theoretical models in the differential psychology domain. We bring some balance to the discussion by reviewing the broader methodological literature to highlight: (1) the misleading characterization of PLS as an SEM method; (2) limitations of PLS for global model testing; (3) problems in testing the significance of path coefficients; (4) extremely high false positive rates when using empirical confidence intervals in conjunction with a new "sign change correction" for path coefficients; (5) misconceptions surrounding the supposedly superior ability of PLS to handle small sample sizes and non-normality; and (6) conceptual and statistical problems with formative measurement and the application of PLS to such models. Additionally, we also reanalyze the dataset provided by Willaby et al. (2015; doi:10.1016/j.paid.2014.09.008) to highlight the limitations of PLS. Our broader review and analysis of the available evidence makes it clear that PLS is not useful for statistical estimation and testing.

Beyond pure public and pure private management models: Partial privatization in the European airport industry

The use of private funding and management is enjoying an increasing trend in airports. The literature has not paid enough attention to the mixed management models in this industry, although many European airports take the form of mixed public-private companies, where ownership is shared between public and private sectors. We examine the determinants of the degree of private participation in the European airport sector. Drawing on a sample of the 100 largest European airports, we estimate a multivariate equation in order to determine the role of airport characteristics, fiscal variables, and political factors on the extent of private involvement. Our results confirm the alignment between public and private interests in partially privatized airports. Fiscal constraints and market attractiveness promote private participation. Integrated governance models and the share of network carriers prevent the presence of private ownership, while the degree of private participation appears to be pragmatic rather than ideological.