A physiological and behavioral mechanism for leaf-herbivore induced systemic root resistance

Indirect plant-mediated interactions between herbivores are important drivers of community composition in terrestrial ecosystems. Among the most striking examples are the strong indirect interactions between spatially separated leaf- and root-feeding insects sharing a host plant. Although leaf feeders generally reduce the performance of root herbivores, little is known about the underlying systemic changes in root physiology and the associated behavioral responses of the root feeders. We investigated the consequences of maize (Zea mays) leaf infestation by Spodoptera littoralis caterpillars for the root-feeding larvae of the beetle Diabrotica virgifera virgifera, a major pest of maize. D. virgifera strongly avoided leaf-infested plants by recognizing systemic changes in soluble root components. The avoidance response occurred within 12 h and was induced by real and mimicked herbivory, but not wounding alone. Roots of leaf-infested plants showed altered patterns in soluble free and soluble conjugated phenolic acids. Biochemical inhibition and genetic manipulation of phenolic acid biosynthesis led to a complete disappearance of the avoidance response of D. virgifera. Furthermore, bioactivity-guided fractionation revealed a direct link between the avoidance response of D. virgifera and changes in soluble conjugated phenolic acids in the roots of leaf-attacked plants. Our study provides a physiological mechanism for a behavioral pattern that explains the negative effect of leaf attack on a root-feeding insect. Furthermore, it opens up the possibility to control D. virgifera in the field by genetically mimicking leaf herbivore-induced changes in root phenylpropanoid patterns.

Changes of Root Length and Root-to-Crown Ratio after Apical Surgery: An Analysis by Using Cone-beam Computed Tomography

INTRODUCTION Apical surgery is an important treatment option for teeth with post-treatment periodontitis. Although apical surgery involves root-end resection, no morphometric data are yet available about root-end resection and its impact on the root-to-crown ratio (RCR). The present study assessed the length of apicectomy and calculated the loss of root length and changes of RCR after apical surgery. METHODS In a prospective clinical study, cone-beam computed tomography scans were taken preoperatively and postoperatively. From these images, the crown and root lengths of 61 roots (54 teeth in 47 patients) were measured before and after apical surgery. Data were collected relative to the cementoenamel junction (CEJ) as well as to the crestal bone level (CBL). One observer took all measurements twice (to calculate the intraobserver variability), and the means were used for further analysis. The following parameters were assessed for all treated teeth as well as for specific tooth groups: length of root-end resection and percentage change of root length, preoperative and postoperative RCRs, and percentage change of RCR after apical surgery. RESULTS The mean length of root-end resection was 3.58 ± 1.43 mm (relative to the CBL). This amounted to a loss of 33.2% of clinical and 26% of anatomic root length. There was an overall significant difference between the tooth groups (P < .05). There was also a statistically significant difference comparing mandibular and maxillary teeth (P < .05), but not for incisors/canines versus premolars/molars (P = .125). The mean preoperative and postoperative RCRs (relative to CEJ) were 1.83 and 1.35, respectively (P < .001). With regard to the CBL reference, the mean preoperative and postoperative RCRs were 1.08 and 0.71 (CBL), respectively (P < .001). The calculated changes of RCR after apical surgery were 24.8% relative to CEJ and 33.3% relative to CBL (P < .001). Across the different tooth groups, the mean RCR was not significantly different (P = .244 for CEJ and 0.114 for CBL). CONCLUSIONS This CBCT-based study demonstrated that the RCR is significantly changed after root-end resection in apical surgery irrespective of the clinical (CBL) or anatomic (CEJ) reference levels. The lowest, and thus clinically most critical, postoperative RCR was observed in maxillary incisors. Future clinical studies need to show the impact of resection length and RCR changes on the outcome of apical surgery.

Assessment of the nonoperated root after apical surgery of the other root in mandibular molars: a 5-year follow-up study

INTRODUCTION If a surgical approach is chosen to treat a multirooted tooth affected by persistent periapical pathosis, usually only the affected roots are operated on. The present study assessed the periapical status of the nonoperated root 5 years after apical surgery of the other root in mandibular molars. METHODS Patients treated with apical surgery of mandibular molars with a follow-up of 5 years were selected. Patient-related and clinical parameters (sex, age, smoking, symptoms, and signs of infection) before surgery were recorded. Preoperative intraoral periapical radiographs and radiographs 5 years after surgery were examined. The following data were collected: tooth, operated root, type and quality of the coronal restoration, marginal bone level, length and homogeneity of the root canal filling, presence of a post/screw, periapical index (PAI) of each root, and radiographic healing of the operated root. The presence of apical pathosis of the nonoperated root was analyzed statistically in relation to the recorded variables. RESULTS Thirty-seven patients fulfilled the inclusion criteria. Signs of periapical pathosis in the nonoperated root 5 years after surgery (PAI ≥ 3) could be observed in only 3 cases (8.1%). Therefore, statistical analysis in relation to the variables was not possible. The PAI of the nonoperated root before surgery had a weak correlation with signs of apical pathosis 5 years after surgery. CONCLUSIONS Nonoperated roots rarely developed signs of new apical pathosis 5 years after apical surgery of the other root in mandibular molars. It appears reasonable to resect and fill only roots with a radiographically evident periapical lesion.

Non-Parametric Tests of Real Exchange rates in the Post-Bretton Woods Era

This paper examines the mean-reverting property of real exchange rates. Earlier studies have generally not been able to reject the null hypothesis of a unit-root in real exchange rates, especially for the post-Bretton Woods floating period. The results imply that long-run purchasing power parity does not hold. More recent studies, especially those using panel unit-root tests, have found more favorable results, however. But, Karlsson and Löthgren (2000) and others have recently pointed out several potential pitfalls of panel unit-root tests. Thus, the panel unit-root test results are suggestive, but they are far from conclusive. Moreover, consistent individual country time series evidence that supports long-run purchasing power parity continues to be scarce. In this paper, we test for long memory using Lo's (1991) modified rescaled range test, and the rescaled variance test of Giraitis, Kokoszka, Leipus, and Teyssière (2003). Our testing procedure provides a non-parametric alternative to the parametric tests commonly used in this literature. Our data set consists of monthly observations from April 1973 to April 2001 of the G-7 countries in the OECD. Our two tests find conflicting results when we use U.S. dollar real exchange rates. However, when non-U.S. dollar real exchange rates are used, we find only two cases out of fifteen where the null hypothesis of an unit-root with short-term dependence can be rejected in favor of the alternative hypothesis of long-term dependence using the modified rescaled range test, and only one case when using the rescaled variance test. Our results therefore provide a contrast to the recent favorable panel unit-root test results.

Statistical Methods for Differential Expressions of Genes Detected in Multiple-Condition Experiment of Microarray

Most studies of differential gene-expressions have been conducted between two given conditions. The two-condition experimental (TCE) approach is simple in that all genes detected display a common differential expression pattern responsive to a common two-condition difference. Therefore, the genes that are differentially expressed under the other conditions other than the given two conditions are undetectable with the TCE approach. In order to address the problem, we propose a new approach called multiple-condition experiment (MCE) without replication and develop corresponding statistical methods including inference of pairs of conditions for genes, new t-statistics, and a generalized multiple-testing method for any multiple-testing procedure via a control parameter C. We applied these statistical methods to analyze our real MCE data from breast cancer cell lines and found that 85 percent of gene-expression variations were caused by genotypic effects and genotype-ANAX1 overexpression interactions, which agrees well with our expected results. We also applied our methods to the adenoma dataset of Notterman et al. and identified 93 differentially expressed genes that could not be found in TCE. The MCE approach is a conceptual breakthrough in many aspects: (a) many conditions of interests can be conducted simultaneously; (b) study of association between differential expressions of genes and conditions becomes easy; (c) it can provide more precise information for molecular classification and diagnosis of tumors; (d) it can save lot of experimental resources and time for investigators.^

It's your game, keep it real. Sexual education study

Purpose: This study translated and adapted the It's Your Game, Keep It Real study currently being implemented with middle school youth in Southeast Texas for a middle school population in rural western Honduras. The study tested the effects of a sexual health education program focused on human immunodeficiency virus, sexually transmitted infections, and pregnancy prevention. We hypothesized that the number of adolescents in the intervention group who initiate sexual activity will reduce in comparison to the control group and there will be an increase consistent condom use in sexually active adolescents in the intervention group. ^ Methods: The target population included Spanish-speaking Hispanic middle school students from a small, semi-urban city in western Honduras. One school was randomly selected to receive the intervention and one to the comparison condition. The intervention curriculum consisted of 10 seventh-grade lessons that included individual and group classroom-based activities and personal journaling. Follow-up surveys were completed three months after the last lesson with 146 students (79.3% of the defined cohort). ^ Results: In the comparison condition, 21.4% of students initiated sex by the post-test follow-up three months after the intervention compared to 7.8% in the intervention condition. ^ Conclusions: A multi-component, curriculum-based program that is theory driven and culturally relevant can increase knowledge about STIs and HIV, increase self-confidence amongst middle school students, and develop communication skills amongst friends and partners. Further research must be conducted to assess delay in sexual initiation and the generalizability of these results.^

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

Placement Methods of Phosphorus and Potassium for Corn and Soybean Managed with No-till and Chisel-plow/Disk Tillage

No-till management limits the incorporation of crop residue and fertilizer with soil resulting in wetter, colder soils and the accumulation of organic matter, phosphorus (P), and potassium (K) near the soil surface. Banding of P and K could be more effective than broadcast fertilization by counteracting stratification, applying nutrients in the root zone (starter effect), and minimizing reactions with the soil that may reduce their availability to plants. Therefore, this long-term study was established in 1994 to evaluate P and K fertilizer placement methods and grain yield of corn-soybean rotations managed with notill and chisel-plow/disk tillage.

Estimación de la evapotranspiración real regional a partir de datos satelitales

El objetivo del presente trabajo fue determinar la Evapotranspiración real (ETR) a nivel regional utilizando la información del satélite meteorológico NOAA-AVHRR y comparar los resultados obtenidos con los calculados a partir de un modelo de simulación de balance hídrico. Para la estimación de la ETR se analizaron 30 imágenes que abarcan el oasis Norte de Mendoza. Con la información de los canales C1 (Visible) y C2 (IRC) se obtuvo el índice verde normalizado (NDVI), a través del cual se siguió la evolución anual de la vegetación y con la correspondiente al Infrarrojo térmico (C4 y C5) se calculó la Temperatura de superficie (Ts) por el método Split - Windows Luego se vinculó la Ts calculada por teledetección con la temperatura del aire (Ta), para finalmente calcular la suma acumulada de las diferencias entre Ts y Ta, conocida como SDD (stress degree day) que permite estimar globalmente las características de stress hídrico a nivel regional. Conociendo (Ts-Ta) se estimó la ETR a partir de la radiación neta y de los coeficientes A y B que se estimaron según las características de la cobertura vegetal, aplicando una relación simplificada a partir del balance de energía, desarrollado por Jackson (1977) y Seguin (1983) según la ecuación: ETR = Rn + A -B ( Ts - Ta ) Posteriormente, se incluyó en los cálculos los valores de Emisividad y se hizo variar el coeficiente B de acuerdo a la ocupación del suelo en cada uno de los polígonos en que fue dividida el área de estudio. En la etapa final se compararon estadísticamente los datos de ETR estimados por los distintos métodos con los simulados por el modelo y se obtuvo como conclusión final que: la estimación de la ETR a nivel regional mediante datos satelitales, se adapta muy bien a la mayoría de los casos y es sencilla de calcular, por lo que la metodología desarrollada es fácilmente extrapolable a otros oasis de la región.

Space-time Reconstruction of Oceanic Sea States via Variational Stereo Methods

We present a remote sensing observational method for the measurement of the spatio-temporal dynamics of ocean waves. Variational techniques are used to recover a coherent space-time reconstruction of oceanic sea states given stereo video imagery. The stereoscopic reconstruction problem is expressed in a variational optimization framework. There, we design an energy functional whose minimizer is the desired temporal sequence of wave heights. The functional combines photometric observations as well as spatial and temporal regularizers. A nested iterative scheme is devised to numerically solve, via 3-D multigrid methods, the system of partial differential equations resulting from the optimality condition of the energy functional. The output of our method is the coherent, simultaneous estimation of the wave surface height and radiance at multiple snapshots. We demonstrate our algorithm on real data collected off-shore. Statistical and spectral analysis are performed. Comparison with respect to an existing sequential method is analyzed.

Nonparametric Message Passing Methods for Cooperative Localization and Tracking

The objective of this thesis is the development of cooperative localization and tracking algorithms using nonparametric message passing techniques. In contrast to the most well-known techniques, the goal is to estimate the posterior probability density function (PDF) of the position of each sensor. This problem can be solved using Bayesian approach, but it is intractable in general case. Nevertheless, the particle-based approximation (via nonparametric representation), and an appropriate factorization of the joint PDFs (using message passing methods), make Bayesian approach acceptable for inference in sensor networks. The well-known method for this problem, nonparametric belief propagation (NBP), can lead to inaccurate beliefs and possible non-convergence in loopy networks. Therefore, we propose four novel algorithms which alleviate these problems: nonparametric generalized belief propagation (NGBP) based on junction tree (NGBP-JT), NGBP based on pseudo-junction tree (NGBP-PJT), NBP based on spanning trees (NBP-ST), and uniformly-reweighted NBP (URW-NBP). We also extend NBP for cooperative localization in mobile networks. In contrast to the previous methods, we use an optional smoothing, provide a novel communication protocol, and increase the efficiency of the sampling techniques. Moreover, we propose novel algorithms for distributed tracking, in which the goal is to track the passive object which cannot locate itself. In particular, we develop distributed particle filtering (DPF) based on three asynchronous belief consensus (BC) algorithms: standard belief consensus (SBC), broadcast gossip (BG), and belief propagation (BP). Finally, the last part of this thesis includes the experimental analysis of some of the proposed algorithms, in which we found that the results based on real measurements are very similar with the results based on theoretical models.

Simultaneous 3D object tracking and camera parameter estimation by Bayesian methods and transdimensional MCMC sampling

Multi-camera 3D tracking systems with overlapping cameras represent a powerful mean for scene analysis, as they potentially allow greater robustness than monocular systems and provide useful 3D information about object location and movement. However, their performance relies on accurately calibrated camera networks, which is not a realistic assumption in real surveillance environments. Here, we introduce a multi-camera system for tracking the 3D position of a varying number of objects and simultaneously refin-ing the calibration of the network of overlapping cameras. Therefore, we introduce a Bayesian framework that combines Particle Filtering for tracking with recursive Bayesian estimation methods by means of adapted transdimensional MCMC sampling. Addi-tionally, the system has been designed to work on simple motion detection masks, making it suitable for camera networks with low transmission capabilities. Tests show that our approach allows a successful performance even when starting from clearly inaccurate camera calibrations, which would ruin conventional approaches.

Two Novel Methods For The Determination Of The Number Of Components In Independent Components Analysis Models

Independent Components Analysis is a Blind Source Separation method that aims to find the pure source signals mixed together in unknown proportions in the observed signals under study. It does this by searching for factors which are mutually statistically independent. It can thus be classified among the latent-variable based methods. Like other methods based on latent variables, a careful investigation has to be carried out to find out which factors are significant and which are not. Therefore, it is important to dispose of a validation procedure to decide on the optimal number of independent components to include in the final model. This can be made complicated by the fact that two consecutive models may differ in the order and signs of similarly-indexed ICs. As well, the structure of the extracted sources can change as a function of the number of factors calculated. Two methods for determining the optimal number of ICs are proposed in this article and applied to simulated and real datasets to demonstrate their performance.

A Multiresolution Image Alignment Technique Based on Direct Methods for Pose Estimation of Aerial Vehicles

In this paper, we seek to expand the use of direct methods in real-time applications by proposing a vision-based strategy for pose estimation of aerial vehicles. The vast majority of approaches make use of features to estimate motion. Conversely, the strategy we propose is based on a MR (Multi-Resolution) implementation of an image registration technique (Inverse Compositional Image Alignment ICIA) using direct methods. An on-board camera in a downwards-looking configuration, and the assumption of planar scenes, are the bases of the algorithm. The motion between frames (rotation and translation) is recovered by decomposing the frame-to-frame homography obtained by the ICIA algorithm applied to a patch that covers around the 80% of the image. When the visual estimation is required (e.g. GPS drop-out), this motion is integrated with the previous known estimation of the vehicles' state, obtained from the on-board sensors (GPS/IMU), and the subsequent estimations are based only on the vision-based motion estimations. The proposed strategy is tested with real flight data in representative stages of a flight: cruise, landing, and take-off, being two of those stages considered critical: take-off and landing. The performance of the pose estimation strategy is analyzed by comparing it with the GPS/IMU estimations. Results show correlation between the visual estimation obtained with the MR-ICIA and the GPS/IMU data, that demonstrate that the visual estimation can be used to provide a good approximation of the vehicle's state when it is required (e.g. GPS drop-outs). In terms of performance, the proposed strategy is able to maintain an estimation of the vehicle's state for more than one minute, at real-time frame rates based, only on visual information.

Some past decision theory models for euro construction model codes and for road drainage in Spain, and modest hints with multi-criteria methods for agro and environmental assurance

The authors are from UPM and are relatively grouped, and all have intervened in different academic or real cases on the subject, at different times as being of different age. With precedent from E. Torroja and A. Páez in Madrid Spain Safety Probabilistic models for concrete about 1957, now in ICOSSAR conferences, author J.M. Antón involved since autumn 1967 for euro-steel construction in CECM produced a math model for independent load superposition reductions, and using it a load coefficient pattern for codes in Rome Feb. 1969, practically adopted for European constructions, giving in JCSS Lisbon Feb. 1974 suggestion of union for concrete-steel-al.. That model uses model for loads like Gumbel type I, for 50 years for one type of load, reduced to 1 year to be added to other independent loads, the sum set in Gumbel theories to 50 years return period, there are parallel models. A complete reliability system was produced, including non linear effects as from buckling, phenomena considered somehow in actual Construction Eurocodes produced from Model Codes. The system was considered by author in CEB in presence of Hydraulic effects from rivers, floods, sea, in reference with actual practice. When redacting a Road Drainage Norm in MOPU Spain an optimization model was realized by authors giving a way to determine the figure of Return Period, 10 to 50 years, for the cases of hydraulic flows to be considered in road drainage. Satisfactory examples were a stream in SE of Spain with Gumbel Type I model and a paper of Ven Te Chow with Mississippi in Keokuk using Gumbel type II, and the model can be modernized with more varied extreme laws. In fact in the MOPU drainage norm the redacting commission acted also as expert to set a table of return periods for elements of road drainage, in fact as a multi-criteria complex decision system. These precedent ideas were used e.g. in wide Codes, indicated in symposia or meetings, but not published in journals in English, and a condensate of contributions of authors is presented. The authors are somehow involved in optimization for hydraulic and agro planning, and give modest hints of intended applications in presence of agro and environment planning as a selection of the criteria and utility functions involved in bayesian, multi-criteria or mixed decision systems. Modest consideration is made of changing in climate, and on the production and commercial systems, and on others as social and financial.