Use of Two-Part Regression Calibration Model to Correct for Measurement Error in Episodically Consumed Foods in a Single-Replicate Study Design: EPIC Case Study.

In epidemiologic studies, measurement error in dietary variables often attenuates association between dietary intake and disease occurrence. To adjust for the attenuation caused by error in dietary intake, regression calibration is commonly used. To apply regression calibration, unbiased reference measurements are required. Short-term reference measurements for foods that are not consumed daily contain excess zeroes that pose challenges in the calibration model. We adapted two-part regression calibration model, initially developed for multiple replicates of reference measurements per individual to a single-replicate setting. We showed how to handle excess zero reference measurements by two-step modeling approach, how to explore heteroscedasticity in the consumed amount with variance-mean graph, how to explore nonlinearity with the generalized additive modeling (GAM) and the empirical logit approaches, and how to select covariates in the calibration model. The performance of two-part calibration model was compared with the one-part counterpart. We used vegetable intake and mortality data from European Prospective Investigation on Cancer and Nutrition (EPIC) study. In the EPIC, reference measurements were taken with 24-hour recalls. For each of the three vegetable subgroups assessed separately, correcting for error with an appropriately specified two-part calibration model resulted in about three fold increase in the strength of association with all-cause mortality, as measured by the log hazard ratio. Further found is that the standard way of including covariates in the calibration model can lead to over fitting the two-part calibration model. Moreover, the extent of adjusting for error is influenced by the number and forms of covariates in the calibration model. For episodically consumed foods, we advise researchers to pay special attention to response distribution, nonlinearity, and covariate inclusion in specifying the calibration model.

When should clinicians search for GLUT1 deficiency syndrome in childhood generalized epilepsies?

UNLABELLED: GLUT1 deficiency (GLUT1D) has recently been identified as an important cause of generalized epilepsies in childhood. As it is a treatable condition, it is crucial to determine which patients should be investigated. METHODS: We analyzed SLC2A1 for mutations in a group of 93 unrelated children with generalized epilepsies. Fasting lumbar puncture was performed following the identification of a mutation. We compared our results with a systematic review of 7 publications of series of patients with generalized epilepsies screened for SLC2A1 mutations. RESULTS: We found 2/93 (2.1%) patients with a SLC2A1 mutation. One, carrying a novel de novo deletion had epilepsy with myoclonic-atonic seizures (MAE), mild slowing of head growth, choreiform movements and developmental delay. The other, with a paternally inherited missense mutation, had childhood absence epilepsy with atypical EEG features and paroxysmal exercise-induced dyskinesia (PED) initially misdiagnosed as myoclonic seizures. Out of a total of 1110 screened patients with generalized epilepsies from 7 studies, 2.4% (29/1110) had GLUT1D. This rate was higher (5.6%) among 303 patients with early onset absence epilepsy (EOAE) from 4 studies. About 50% of GLUT1D patients had abnormal movements and 41% a family history of seizures, abnormal movements or both. CONCLUSION: GLUT1D is most likely to be found in MAE and in EOAE. The probability of finding GLUT1D in the classical idiopathic generalized epilepsies is very low. Pointers to GLUT1D include an increase in seizures before meals, cognitive impairment, or PED which can easily be overlooked.

Generalized food-deceptive orchid species flower earlier and occur at lower altitudes than rewarding ones

Aims Food-deceptive pollination, in which plants do not offer any food reward to their pollinators, is common within the Orchidaceae. As food-deceptive orchids are poorer competitors for pollinator visitation than rewarding orchids, their occurrence in a given habitat may be more constrained than that of rewarding orchids. In particular, the success of deceptive orchids strongly relies on several biotic factors such as interactions with co-flowering rewarding species and pollinators, which may vary with altitude and over time. Our study compares generalized food-deceptive (i.e. excluding sexually deceptive) and rewarding orchids to test whether (i) deceptive orchids flower earlier compared to their rewarding counterparts and whether (ii) the relative occurrence of deceptive orchids decreases with increasing altitude. Methods To compare the flowering phenology of rewarding and deceptive orchids, we analysed data compiled from the literature at the species level over the occidental Palaearctic area. Since flowering phenology can be constrained by the latitudinal distribution of the species and by their phylogenetic relationships, we accounted for these factors in our analysis. To compare the altitudinal distribution of rewarding and deceptive orchids, we used field observations made over the entire Swiss territory and over two Swiss mountain ranges. Important Findings We found that deceptive orchid species start flowering earlier than rewarding orchids do, which is in accordance with the hypotheses of exploitation of naive pollinators and/or avoidance of competition with rewarding co-occurring species. Also, the relative frequency of deceptive orchids decreases with altitude, suggesting that deception may be less profitable at high compared to low altitude.

Genetics of cadmium hyperaccumulation variance in Thlaspi caerulescens natural populations from Switzerland

SUMMARY Heavy metal presence in the environment is a serious concern since some of them can be toxic to plants, animals and humans once accumulated along the food chain. Cadmium (Cd) is one of the most toxic heavy metal. It is naturally present in soils at various levels and its concentration can be increased by human activities. Several plants however have naturally developed strategies allowing them to grow on heavy metal enriched soils. One of them consists in the accumulation and sequestration of heavy metals in the above-ground biomass. Some plants present in addition an extreme strategy by which they accumulate a limited number of heavy metals in their shoots in amounts 100 times superior to those expected for a non-accumulating plant in the same conditions. Understanding the genetic basis of the hyperaccumulation trait - particularly for Cd - remains an important challenge which may lead to biotechnological applications in the soil phytoremediation. In this thesis, Thlaspi caerulescens J. & C. Presl (Brassicaceae) was used as a model plant to study the Cd hyperaccumulation trait, owing to its physiological and genetic characteristics. Twenty-four wild populations were sampled in different regions of Switzerland. They were characterized for environmental and soil parameters as well as intrinsic characteristics of plants (i.e. metal concentrations in shoots). They were as well genetically characterized by AFLPs, plastid DNA polymorphism and genes markers (CAPS and microsatellites) mainly developed in this thesis. Some of the investigated genes were putatively linked to the Cd hyperaccumulation trait. Since the study of the Cd hyperaccumulation in the field is important as it allows the identification of patterns of selection, the present work offered a methodology to define the Cd hyperaccumulation capacity of populations from different habitats permitting thus their comparison in the field. We showed that Cd, Zn, Fe and Cu accumulations were linked and that populations with higher Cd hyperaccumulation capacity had higher shoot and reproductive fitness. Using our genetic data, statistical methods (Beaumont & Nichols's procedure, partial Mantel tests) were applied to identify genomic signatures of natural selection related to the Cd hyperaccumulation capacity. A significant genetic difference between populations related to their Cd hyperaccumulation capacity was revealed based on somè specific markers (AFLP and candidate genes). Polymorphism at the gene encoding IRTl (Iron-transporter also participating to the transport of Zn) was suggested as explaining part of the variation in Cd hyperaccumulation capacity of populations supporting previous physiological investigations. RÉSUMÉ La présence de métaux lourds dans l'environnement est un phénomène préoccupant. En effet, certains métaux lourds - comme le cadmium (Cd) -sont toxiques pour les plantes, les animaux et enfin, accumulés le long de la chaîne alimentaire, pour les hommes. Le Cd est naturellement présent dans le sol et sa concentration peut être accrue par différentes activités humaines. Certaines plantes ont cependant développé des stratégies leur permettant de pousser sur des sols contaminés en métaux lourds. Parmi elles, certaines accumulent et séquestrent les métaux lourds dans leurs parties aériennes. D`autres présentent une stratégie encore plus extrême. Elles accumulent un nombre limité de métaux lourds en quantités 100 fois supérieures à celles attendues pour des espèces non-accumulatrices sous de mêmes conditions. La compréhension des bases génétiques de l'hyperaccumulation -particulièrement celle du Cd - représente un défi important avec des applications concrètes en biotechnologies, tout particulièrement dans le but appliqué de la phytoremediation des sols contaminés. Dans cette thèse, Thlaspi caerulescens J. & C. Presl (Brassicaceae) a été utilisé comme modèle pour l'étude de l'hyperaccumulation du Cd de par ses caractéristiques physiologiques et génétiques. Vingt-quatre populations naturelles ont été échantillonnées en Suisse et pour chacune d'elles les paramètres environnementaux, pédologique et les caractéristiques intrinsèques aux plantes (concentrations en métaux lourds) ont été déterminés. Les populations ont été caractérisées génétiquement par des AFLP, des marqueurs chloroplastiques et des marqueurs de gènes spécifiques, particulièrement ceux potentiellement liés à l'hyperaccumulation du Cd (CAPS et microsatellites). La plupart ont été développés au cours de cette thèse. L'étude de l'hyperaccumulation du Cd en conditions naturelles est importante car elle permet d'identifier la marque, éventuelle de sélection naturelle. Ce travail offre ainsi une méthodologie pour définir et comparer la capacité des populations à hyperaccumuler le Cd dans différents habitats. Nous avons montré que les accumulations du Cd, Zn, Fe et Cu sont liées et que les populations ayant une grande capacité d'hyperaccumuler le Cd ont également une meilleure fitness végétative et reproductive. Des méthodes statistiques (l'approche de Beaumont & Nichols, tests de Martel partiels) ont été utilisées sur les données génétiques pour identifier la signature génomique de la sélection naturelle liée à la capacité d'hyperaccumuler le Cd. Une différenciation génétique des populations liée à leur capacité d'hyperaccumuler le Cd a été mise en évidence sur certains marqueurs spécifiques. En accord avec les études physiologiques connues, le polymorphisme au gène codant IRT1 (un transporteur de Fe impliqué dans le transport du Zn) pourrait expliquer une partie de la variance de la capacité des populations à hyperaccumuler le Cd.

A generalized mechanistic codon model.

Models of codon evolution have attracted particular interest because of their unique capabilities to detect selection forces and their high fit when applied to sequence evolution. We described here a novel approach for modeling codon evolution, which is based on Kronecker product of matrices. The 61 × 61 codon substitution rate matrix is created using Kronecker product of three 4 × 4 nucleotide substitution matrices, the equilibrium frequency of codons, and the selection rate parameter. The entities of the nucleotide substitution matrices and selection rate are considered as parameters of the model, which are optimized by maximum likelihood. Our fully mechanistic model allows the instantaneous substitution matrix between codons to be fully estimated with only 19 parameters instead of 3,721, by using the biological interdependence existing between positions within codons. We illustrate the properties of our models using computer simulations and assessed its relevance by comparing the AICc measures of our model and other models of codon evolution on simulations and a large range of empirical data sets. We show that our model fits most biological data better compared with the current codon models. Furthermore, the parameters in our model can be interpreted in a similar way as the exchangeability rates found in empirical codon models.

Generalized low-density codes with BCH constituents for full-diversity near-outage performance

A new graph-based construction of generalized low density codes (GLD-Tanner) with binary BCH constituents is described. The proposed family of GLD codes is optimal on block erasure channels and quasi-optimal on block fading channels. Optimality is considered in the outage probability sense. Aclassical GLD code for ergodic channels (e.g., the AWGN channel,the i.i.d. Rayleigh fading channel, and the i.i.d. binary erasure channel) is built by connecting bitnodes and subcode nodes via a unique random edge permutation. In the proposed construction of full-diversity GLD codes (referred to as root GLD), bitnodes are divided into 4 classes, subcodes are divided into 2 classes, and finally both sides of the Tanner graph are linked via 4 random edge permutations. The study focuses on non-ergodic channels with two states and can be easily extended to channels with 3 states or more.

Generalized linear and generalized additive models in studies of species distributions: setting the scene

An important statistical development of the last 30 years has been the advance in regression analysis provided by generalized linear models (GLMs) and generalized additive models (GAMs). Here we introduce a series of papers prepared within the framework of an international workshop entitled: Advances in GLMs/GAMs modeling: from species distribution to environmental management, held in Riederalp, Switzerland, 6-11 August 2001.We first discuss some general uses of statistical models in ecology, as well as provide a short review of several key examples of the use of GLMs and GAMs in ecological modeling efforts. We next present an overview of GLMs and GAMs, and discuss some of their related statistics used for predictor selection, model diagnostics, and evaluation. Included is a discussion of several new approaches applicable to GLMs and GAMs, such as ridge regression, an alternative to stepwise selection of predictors, and methods for the identification of interactions by a combined use of regression trees and several other approaches. We close with an overview of the papers and how we feel they advance our understanding of their application to ecological modeling.

Biotic and abiotic variables show little redundancy in explaining tree species distributions

Abiotic factors such as climate and soil determine the species fundamental niche, which is further constrained by biotic interactions such as interspecific competition. To parameterize this realized niche, species distribution models (SDMs) most often relate species occurrence data to abiotic variables, but few SDM studies include biotic predictors to help explain species distributions. Therefore, most predictions of species distributions under future climates assume implicitly that biotic interactions remain constant or exert only minor influence on large-scale spatial distributions, which is also largely expected for species with high competitive ability. We examined the extent to which variance explained by SDMs can be attributed to abiotic or biotic predictors and how this depends on species traits. We fit generalized linear models for 11 common tree species in Switzerland using three different sets of predictor variables: biotic, abiotic, and the combination of both sets. We used variance partitioning to estimate the proportion of the variance explained by biotic and abiotic predictors, jointly and independently. Inclusion of biotic predictors improved the SDMs substantially. The joint contribution of biotic and abiotic predictors to explained deviance was relatively small (similar to 9%) compared to the contribution of each predictor set individually (similar to 20% each), indicating that the additional information on the realized niche brought by adding other species as predictors was largely independent of the abiotic (topo-climatic) predictors. The influence of biotic predictors was relatively high for species preferably growing under low disturbance and low abiotic stress, species with long seed dispersal distances, species with high shade tolerance as juveniles and adults, and species that occur frequently and are dominant across the landscape. The influence of biotic variables on SDM performance indicates that community composition and other local biotic factors or abiotic processes not included in the abiotic predictors strongly influence prediction of species distributions. Improved prediction of species' potential distributions in future climates and communities may assist strategies for sustainable forest management.

Spanning tests in return and stochastic discount factor mean-variance frontiers: A unifying approach

We propose new spanning tests that assess if the initial and additional assets share theeconomically meaningful cost and mean representing portfolios. We prove their asymptoticequivalence to existing tests under local alternatives. We also show that unlike two-step oriterated procedures, single-step methods such as continuously updated GMM yield numericallyidentical overidentifyng restrictions tests, so there is arguably a single spanning test.To prove these results, we extend optimal GMM inference to deal with singularities in thelong run second moment matrix of the influence functions. Finally, we test for spanningusing size and book-to-market sorted US stock portfolios.

Shared knowledge

Agents use their knowledge on the history of the economy in orderto choose what is the optimal action to take at any given moment of time,but each individual observes history with some noise. This paper showsthat the amount of information available on the past evolution of theeconomy is an endogenous variable, and that this leads to overconcentrationof the investment, which can be interpreted as underinvestment in research.It presents a model in which agents have to invest at each period in one of$K$ sectors, each of them paying an exogenous return that follows a welldefined stochastic path. At any moment of time each agent receives an unbiasednoisy signal on the payoff of each sector. The signals differ across agents,but all of them have the same variance, which depends on the aggregate investmentin that particular sector (so that if almost everybody invests in it theperceptions of everybody will be very accurate, but if almost nobody doesthe perceptions of everybody will be very noisy). The degree of hetereogeneityacross agents is then an endogenous variable, evolving across time determining,and being determined by, the amount of information disclosed.As long as both the level of social interaction and the underlying precisionof the observations are relatively large agents behave in a very preciseway. This behavior is unmodified for a huge range of informational parameters,and it is characterized by an excessive concentration of the investment ina few sectors. Additionally the model shows that generalized improvements in thequality of the information that each agent gets may lead to a worse outcomefor all the agents due to the overconcentration of the investment that thisproduces.

Glut-1 deficiency syndrome masquerading as idiopathic generalized epilepsy.

To report the case of a child with short absences and occasional myoclonias since infancy who was first diagnosed with an idiopathic generalized epilepsy, but was documented at follow-up to have a mild phenotype of glucose transporter type 1 deficiency syndrome. Unlike other reported cases of Glut-1 DS and epilepsy, this child had a normal development as well as a normal head growth and neurological examination. Early onset of seizures and later recognized episodes of mild confusion before meals together with persistent atypical EEG features and unexpected learning difficulties led to the diagnosis. Seizure control and neuropsychological improvements were obtained with a ketogenic diet.

Improving generalized regression analysis for the spatial prediction of forest communities

Aim This study used data from temperate forest communities to assess: (1) five different stepwise selection methods with generalized additive models, (2) the effect of weighting absences to ensure a prevalence of 0.5, (3) the effect of limiting absences beyond the environmental envelope defined by presences, (4) four different methods for incorporating spatial autocorrelation, and (5) the effect of integrating an interaction factor defined by a regression tree on the residuals of an initial environmental model. Location State of Vaud, western Switzerland. Methods Generalized additive models (GAMs) were fitted using the grasp package (generalized regression analysis and spatial predictions, http://www.cscf.ch/grasp). Results Model selection based on cross-validation appeared to be the best compromise between model stability and performance (parsimony) among the five methods tested. Weighting absences returned models that perform better than models fitted with the original sample prevalence. This appeared to be mainly due to the impact of very low prevalence values on evaluation statistics. Removing zeroes beyond the range of presences on main environmental gradients changed the set of selected predictors, and potentially their response curve shape. Moreover, removing zeroes slightly improved model performance and stability when compared with the baseline model on the same data set. Incorporating a spatial trend predictor improved model performance and stability significantly. Even better models were obtained when including local spatial autocorrelation. A novel approach to include interactions proved to be an efficient way to account for interactions between all predictors at once. Main conclusions Models and spatial predictions of 18 forest communities were significantly improved by using either: (1) cross-validation as a model selection method, (2) weighted absences, (3) limited absences, (4) predictors accounting for spatial autocorrelation, or (5) a factor variable accounting for interactions between all predictors. The final choice of model strategy should depend on the nature of the available data and the specific study aims. Statistical evaluation is useful in searching for the best modelling practice. However, one should not neglect to consider the shapes and interpretability of response curves, as well as the resulting spatial predictions in the final assessment.