El Niño 2015/2016 impact analysis, monthly outlook March 2016

During the summer and autumn of 2015, El Niño conditions in the east and central Pacific strengthened, disrupting weather patterns throughout the tropics and into the mid-latitudes. For example, rainfall during the summer’s Indian monsoon was approximately 15% below normal. The continued strong El Niño conditions have the potential to trigger damaging impacts (e.g., droughts, famines, floods), particularly in less-developed tropical countries, which would require a swift and effective humanitarian response to mitigate damage to life and property (e.g., health, migration, infrastructure). This analysis uses key climatic variables (temperature, soil moisture and precipitation) as measures to monitor the ongoing risk of these potentially damaging impacts. The previous 2015-2016 El Niño Impact Analysis was based on observations over the past 35 years and produced Impact Tables showing the likelihood and severity of the impacts on temperature and rainfall by season. The current report is an extension of this work, providing information from observations and seasonal forecast models to give a more detailed outlook of the potential near-term impacts of the current El Niño conditions by region. This information has been added to the Impact Tables in the form of an ‘Observations and Outlook’ row. This consists of observational information for the past seasons of JJA 2015, SON 2015 and DJF 2015/2016, a detailed monthly outlook from 5 modeling centres for Mar 2016 and then longer-term seasonal forecast information from 2 modeling centres for the future seasons of AM 2016 and JJA 2016. The seasonal outlook information is an indication of the average likely conditions for that coming month (or season) and region and is not a definite prediction of weather impacts. This report has been produced by University of Reading for Evidence on Demand with the assistance of the UK Department for International Development (DFID) contracted through the Climate, Environment, Infrastructure and Livelihoods Professional Evidence and Applied Knowledge Services (CEIL PEAKS) programme, jointly managed by DAI (which incorporates HTSPE Limited) and IMC Worldwide Limited.

Bayesian nonlinear regression models with scale mixtures of skew-normal distributions: Estimation and case influence diagnostics

The purpose of this paper is to develop a Bayesian analysis for nonlinear regression models under scale mixtures of skew-normal distributions. This novel class of models provides a useful generalization of the symmetrical nonlinear regression models since the error distributions cover both skewness and heavy-tailed distributions such as the skew-t, skew-slash and the skew-contaminated normal distributions. The main advantage of these class of distributions is that they have a nice hierarchical representation that allows the implementation of Markov chain Monte Carlo (MCMC) methods to simulate samples from the joint posterior distribution. In order to examine the robust aspects of this flexible class, against outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence. Further, some discussions on the model selection criteria are given. The newly developed procedures are illustrated considering two simulations study, and a real data previously analyzed under normal and skew-normal nonlinear regression models. (C) 2010 Elsevier B.V. All rights reserved.

Bayesian analysis of skew-t multivariate null intercept measurement error model

The multivariate skew-t distribution (J Multivar Anal 79:93-113, 2001; J R Stat Soc, Ser B 65:367-389, 2003; Statistics 37:359-363, 2003) includes the Student t, skew-Cauchy and Cauchy distributions as special cases and the normal and skew-normal ones as limiting cases. In this paper, we explore the use of Markov Chain Monte Carlo (MCMC) methods to develop a Bayesian analysis of repeated measures, pretest/post-test data, under multivariate null intercept measurement error model (J Biopharm Stat 13(4):763-771, 2003) where the random errors and the unobserved value of the covariate (latent variable) follows a Student t and skew-t distribution, respectively. The results and methods are numerically illustrated with an example in the field of dentistry.

On estimation and influence diagnostics for log-Birnbaum-Saunders Student-t regression models: Full Bayesian analysis

The purpose of this paper is to develop a Bayesian approach for log-Birnbaum-Saunders Student-t regression models under right-censored survival data. Markov chain Monte Carlo (MCMC) methods are used to develop a Bayesian procedure for the considered model. In order to attenuate the influence of the outlying observations on the parameter estimates, we present in this paper Birnbaum-Saunders models in which a Student-t distribution is assumed to explain the cumulative damage. Also, some discussions on the model selection to compare the fitted models are given and case deletion influence diagnostics are developed for the joint posterior distribution based on the Kullback-Leibler divergence. The developed procedures are illustrated with a real data set. (C) 2010 Elsevier B.V. All rights reserved.

Bayesian analysis for a skew extension of the multivariate null intercept measurement error model

Skew-normal distribution is a class of distributions that includes the normal distributions as a special case. In this paper, we explore the use of Markov Chain Monte Carlo (MCMC) methods to develop a Bayesian analysis in a multivariate, null intercept, measurement error model [R. Aoki, H. Bolfarine, J.A. Achcar, and D. Leao Pinto Jr, Bayesian analysis of a multivariate null intercept error-in -variables regression model, J. Biopharm. Stat. 13(4) (2003b), pp. 763-771] where the unobserved value of the covariate (latent variable) follows a skew-normal distribution. The results and methods are applied to a real dental clinical trial presented in [A. Hadgu and G. Koch, Application of generalized estimating equations to a dental randomized clinical trial, J. Biopharm. Stat. 9 (1999), pp. 161-178].

Analysis of the combined effect of lasers of different wavelengths for PDT outcome using 600, 630, and 660 nm

We investigated the effects of photodynamic therapy (PDT) outcome when combining three laser systems that produce light in three different wavelengths (600, 630, and 660 nm). Cooperative as well as independent effects can be observed. We compared the results of the combined wavelengths of light with the effect of single laser for the excitation of the photosensitizer. In the current experiment, the used photosensitizer was Photogem (R) (1.5 mg/kg). Combining two wavelengths for PDT, their cumulative dose and different penetrability may change the overall effect of the fluence of light, which can be effective for increasing the depth of necrosis. This evaluation was performed by comparing the depth and specific aspect of necrosis obtained by using single and dual wavelengths for irradiation of healthy liver of male Wistar rats. We used 15 animals and divided them in five groups of three animals. First, Photogem (R) was administered; follow by measurement of the fluorescence spectrum of the liver before PDT to confirm the level of accumulation of photosensitizer in the tissue. After that, an area of 1 cm(2) of the liver was illuminated using different laser combinations. Qualitative analysis of the necrosis was carried out through histological and morphological study. [GRAPHICS] (a) - microscopic images of rat liver cells, (b) - superficial necrosis caused by PDT using dual-wavelength illumination, (c) - neutrophilic infiltration around the vessel inside the necrosis, and (d) - neutrophilic infiltration around the vessel between necrosis and live tissue (C) 2011 by Astro Ltd. Published exclusively by WILEY-VCH Verlag GmbH & Co. KGaA

Bayesian inference for a skew-normal IRT model under the centred parameterization

Item response theory (IRT) comprises a set of statistical models which are useful in many fields, especially when there is interest in studying latent variables. These latent variables are directly considered in the Item Response Models (IRM) and they are usually called latent traits. A usual assumption for parameter estimation of the IRM, considering one group of examinees, is to assume that the latent traits are random variables which follow a standard normal distribution. However, many works suggest that this assumption does not apply in many cases. Furthermore, when this assumption does not hold, the parameter estimates tend to be biased and misleading inference can be obtained. Therefore, it is important to model the distribution of the latent traits properly. In this paper we present an alternative latent traits modeling based on the so-called skew-normal distribution; see Genton (2004). We used the centred parameterization, which was proposed by Azzalini (1985). This approach ensures the model identifiability as pointed out by Azevedo et al. (2009b). Also, a Metropolis Hastings within Gibbs sampling (MHWGS) algorithm was built for parameter estimation by using an augmented data approach. A simulation study was performed in order to assess the parameter recovery in the proposed model and the estimation method, and the effect of the asymmetry level of the latent traits distribution on the parameter estimation. Also, a comparison of our approach with other estimation methods (which consider the assumption of symmetric normality for the latent traits distribution) was considered. The results indicated that our proposed algorithm recovers properly all parameters. Specifically, the greater the asymmetry level, the better the performance of our approach compared with other approaches, mainly in the presence of small sample sizes (number of examinees). Furthermore, we analyzed a real data set which presents indication of asymmetry concerning the latent traits distribution. The results obtained by using our approach confirmed the presence of strong negative asymmetry of the latent traits distribution. (C) 2010 Elsevier B.V. All rights reserved.

Bayesian Analysis for Nonlinear Regression Model Under Skewed Errors, with Application in Growth Curves

We have considered a Bayesian approach for the nonlinear regression model by replacing the normal distribution on the error term by some skewed distributions, which account for both skewness and heavy tails or skewness alone. The type of data considered in this paper concerns repeated measurements taken in time on a set of individuals. Such multiple observations on the same individual generally produce serially correlated outcomes. Thus, additionally, our model does allow for a correlation between observations made from the same individual. We have illustrated the procedure using a data set to study the growth curves of a clinic measurement of a group of pregnant women from an obstetrics clinic in Santiago, Chile. Parameter estimation and prediction were carried out using appropriate posterior simulation schemes based in Markov Chain Monte Carlo methods. Besides the deviance information criterion (DIC) and the conditional predictive ordinate (CPO), we suggest the use of proper scoring rules based on the posterior predictive distribution for comparing models. For our data set, all these criteria chose the skew-t model as the best model for the errors. These DIC and CPO criteria are also validated, for the model proposed here, through a simulation study. As a conclusion of this study, the DIC criterion is not trustful for this kind of complex model.

Bayesian density estimation using Skew Student-t-Normal mixtures

We present a Bayesian approach for modeling heterogeneous data and estimate multimodal densities using mixtures of Skew Student-t-Normal distributions [Gomez, H.W., Venegas, O., Bolfarine, H., 2007. Skew-symmetric distributions generated by the distribution function of the normal distribution. Environmetrics 18, 395-407]. A stochastic representation that is useful for implementing a MCMC-type algorithm and results about existence of posterior moments are obtained. Marginal likelihood approximations are obtained, in order to compare mixture models with different number of component densities. Data sets concerning the Gross Domestic Product per capita (Human Development Report) and body mass index (National Health and Nutrition Examination Survey), previously studied in the related literature, are analyzed. (c) 2008 Elsevier B.V. All rights reserved.

Bayesian Analysis for the Generalized Lognormal Distribution Applied to Failure Time Analysis

There are several versions of the lognormal distribution in the statistical literature, one is based in the exponential transformation of generalized normal distribution (GN). This paper presents the Bayesian analysis for the generalized lognormal distribution (logGN) considering independent non-informative Jeffreys distributions for the parameters as well as the procedure for implementing the Gibbs sampler to obtain the posterior distributions of parameters. The results are used to analyze failure time models with right-censored and uncensored data. The proposed method is illustrated using actual failure time data of computers.

Functional Analysis of saxophone, the Drosophila Gene Encoding the BMP Type I Receptor Ortholog of Human ALK1/ACVRL1 and ACVR1/ALK2

In metazoans, bone morphogenetic proteins (BMPS) direct a myriad of developmental and adult homeostatic evens through their heterotetrameric type I and type II receptor complexes. We examined 3 existing and 12 newly generated mutations in the Drosophila type I receptor gene, saxophone (sax), the ortholog of the human Activin Receptor-Like. Kinasel and -2 (ALK1/ACVR1 and ALK2/ACVR1) genes. Our genetic analyses identified two distinct classes of sax alleles. The first class consists of homozygous viable gain-of-function (GOF) alleles that exhibit (1) synthetic lethality in combination with mutations in BMP pathway components, and (2) significant maternal effect lethality that can be rescued by an increased dosage of the BMP encoding gene, dpp(+). In contrast, the second class consists of alleles that are recessive lethal and do not exhibit lethality in combination with mutations in other BMP pathway components. The alleles in this second class are clearly loss-of-function (LOF) with both complete and partial loss-of-function mutations represented. We find that one allele in the second class of recessive lethals exhibits dominant-negative behavior, albeit distinct from the GOF activity of the first class of viable alleles. On the basis of the fact that the first class of viable alleles can be reverted to lethality and on our ability to independently generate recessive lethal sat mutations, our analysis demonstrates that sax is an essential gene. Consistent with this conclusion, we find that a normal sax transcript is produced by sax(P), a viable allele previously reported to be mill, and that this allele can be reverted to lethality. Interestingly, we determine that two mutations in the first: class of sax alleles show the same amino acid substitutions as mutations in the human receptors ALK1/ACVR1-1 and ACVR1/ALK2, responsible for cases of hereditary hemorrhagic telangiectasia type 2 (HHT2) and fibrodysplasia ossificans progressiva (FOP), respectively. Finally, the data presented here identify different functional requirements for the Sax receptor, support the proposal that Sax participates in a heteromeric receptor complex, and provide a mechanistic framework for future investigations into disease states that arise from defects in BMP/TGF-beta signaling.

Detailed analysis of the charge transfer complex N,N-dimethylaniline-SO(2) by Raman spectroscopy and density functional theory calculations

Although the amine sulfur dioxide chemistry was well characterized in the past both experimentally and theoretically, no systematic Raman spectroscopic study describes the interaction between N,N-dimethylaniline (DMA) and sulfur dioxide (SO(2)). The formation of a deep red oil by the reaction of SO(2) with DMA is an evidence of the charge transfer (CT) nature of the DMA-SO(2) interaction. The DMA -SO(2) normal Raman spectrum shows the appearance of two intense bands at 1110 and 1151 cm(-1), which are enhanced when resonance is approached. These bands are assigned to nu(s)(SO(2)) and nu(phi-N) vibrational modes, respectively, confirming the interaction between SO(2) and the amine via the nitrogen atom. The dimethyl group steric effect favors the interaction of SO(2) with the ring pi electrons, which gives rise to a pi-pi* low-energy CT electronic transition, as confirmed by time-dependent density functional theory (TDDFT) calculations. In addition, the calculated Raman DMA-SO(2) spectrum at the B3LYP/6-311++g(3df,3pd) level shows good agreement with the experimental results (vibrational wavenumbers and relative intensities), allowing a complete assignment of the vibrational modes. A better understanding of the intermolecular interactions in this model system can be extremely useful in designing new materials to absorb, detect, or even quantify SO(2). Copyright (C) 2009 John Wiley & Sons, Ltd.

Synthesis, spectroscopic characterization and X-ray structures of five-coordinate diorganotin(IV) complexes containing 5-hydroxypyrazoline derivatives as ligands

Four new diorganotin(IV) complexes have been prepared from R(2)SnCl(2) (R = Me, Ph) with the ligands 5-hydroxy-3-metyl-5-phenyl-1-(S-benzildithiocarbazate)-pyrazoline (H(2)L(1)) and 5-hydroxy-3-methyl-5-phenyl-1-(2-thiophenecarboxylic)-pyrazoline (H(2)L(2)). The complexes were characterized by elemental analysis, IR. (1)H (13)C, (119)Sn NMR and Mossbauer spectroscopes The complexes [Me(2)SnL(1)], [Ph(2)SnL(1)] and [Me(2)SnL(2)] were also studied by single crystal X-ray diffraction and the results showed that the Sn(IV) central atom of the complexes adopts a distorted trigonal bipyramidal (TBP) geometry with the N atom of the ONX-tridentate (X = O and S) ligand and two organic groups occupying equatorial sites. The C-Sn-C angles for [Me(2)Sn(L(1))] and [Ph(2)Sn(L(1))] were calculated using a correlation between (119)Sn Mossbauer and X-ray crystallographic data based on the point-charge model Theoretical calculations were performed with the B3LYP density functional employing 3-21G(*) and DZVP all electron basis sets showing good agreement with experimental findings General and Sn(IV) specific IR harmonic frequency scale factors for both basis sets were obtained from comparison with selected experimental frequencies (C) 2010 Elsevier B V All rights reserved

Lifestyle and health status in a sample of Swedish women four years after pregnancy : a comparison of women with a history of normal pregnancy and women with a history of gestational diabetes mellitus

Background: Despite the recommendations to continue the regime of healthy food and physical activity (PA) postpartum for women with previous gestational diabetes mellitus (GDM), the scientific evidence reveals that these recommendations may not be complied to. This study compared lifestyle and health status in women whose pregnancy was complicated by GDM with women who had a normal pregnancy and delivery. Methods: The inclusion criteria were women with GDM (ICD-10: O24.4 A and O24.4B) and women with uncomplicated pregnancy and delivery in 2005 (ICD-10: O80.0). A random sample of women fulfilling the criteria (n = 882) were identified from the Swedish Medical Birth Register. A questionnaire was sent by mail to eligible women approximately four years after the pregnancy. A total of 444 women (50.8%) agreed to participate, 111 diagnosed with GDM in their pregnancy and 333 with normal pregnancy/ delivery. Results: Women with previous GDM were significantly older, reported higher body weight and less PA before the index pregnancy. No major differences between the groups were noticed regarding lifestyle at the follow-up. Overall, few participants fulfilled the national recommendations of PA and diet. At the follow-up, 19 participants had developed diabetes, all with previous GDM. Women with previous GDM reported significantly poorer self-rated health (SRH), higher level of sick-leave and more often using medication on regular basis. However, a history of GDM or having overt diabetes mellitus showed no association with poorer SRH in the multivariate analysis. Irregular eating habits, no regular PA, overweight/obesity, and regular use of medication were associated with poorer SRH in all participants. Conclusions: Suboptimal levels of PA, and fruit and vegetable consumption were found in a sample of women with a history of GDM as well as for women with normal pregnancy approximately four years after index pregnancy. Women with previous GDM seem to increase their PA after childbirth, but still they perform their PA at lower intensity than women with a history of normal pregnancy. Having GDM at index pregnancy or being diagnosed with overt diabetes mellitus at follow-up did not demonstrate associations with poorer SRH four years after delivery.

Motion Cues Analysis for Parkinson Gait Recognition

Background: Previous assessment methods for PG recognition used sensor mechanisms for PG that may cause discomfort. In order to avoid stress of applying wearable sensors, computer vision (CV) based diagnostic systems for PG recognition have been proposed. Main constraints in these methods are the laboratory setup procedures: Novel colored dresses for the patients were specifically designed to segment the test body from a specific colored background. Objective: To develop an image processing tool for home-assessment of Parkinson Gait(PG) by analyzing motion cues extracted during the gait cycles. Methods: The system is based on the idea that a normal body attains equilibrium during the gait by aligning the body posture with the axis of gravity. Due to the rigidity in muscular tone, persons with PD fail to align their bodies with the axis of gravity. The leaned posture of PD patients appears to fall forward. Whereas a normal posture exhibits a constant erect posture throughout the gait. Patients with PD walk with shortened stride angle (less than 15 degrees on average) with high variability in the stride frequency. Whereas a normal gait exhibits a constant stride frequency with an average stride angle of 45 degrees. In order to analyze PG, levodopa-responsive patients and normal controls were videotaped with several gait cycles. First, the test body is segmented in each frame of the gait video based on the pixel contrast from the background to form a silhouette. Next, the center of gravity of this silhouette is calculated. This silhouette is further skeletonized from the video frames to extract the motion cues. Two motion cues were stride frequency based on the cyclic leg motion and the lean frequency based on the angle between the leaned torso tangent and the axis of gravity. The differences in the peaks in stride and lean frequencies between PG and normal gait are calculated using Cosine Similarity measurements. Results: High cosine dissimilarity was observed in the stride and lean frequencies between PG and normal gait. High variations are found in the stride intervals of PG whereas constant stride intervals are found in the normal gait. Conclusions: We propose an algorithm as a source to eliminate laboratory constraints and discomfort during PG analysis. Installing this tool in a home computer with a webcam allows assessment of gait in the home environment.