Ensemble methods for environmental data modelling with support vector regression

This paper investigates the use of ensemble of predictors in order to improve the performance of spatial prediction methods. Support vector regression (SVR), a popular method from the field of statistical machine learning, is used. Several instances of SVR are combined using different data sampling schemes (bagging and boosting). Bagging shows good performance, and proves to be more computationally efficient than training a single SVR model while reducing error. Boosting, however, does not improve results on this specific problem.

Poxvirus vector-based HIV vaccines.

PURPOSE OF REVIEW: In this review, we will provide the scientific rationale for the use of poxvirus vectors in the field of HIV vaccines, the immunological profile of the vaccine-induced immune responses, an update on the current use of poxvirus vector-based vaccines in HIV vaccine clinical trials, and the development of new modified poxvirus vectors with improved immunological profile. RECENT FINDINGS: An Ad5-HIV vaccine was tested in a phase IIb clinical trial (known as the Step trial). Vaccinations in the Step trial were discontinued because the vaccine did not show any effect on acquisition of infection and on viral load. After the disappointing failure of the Step trial, the field of HIV vaccine has regained enthusiasm and vigour due to the promising protective effect observed in the phase III efficacy trial (known as RV-144) performed in Thailand which has tested a poxvirus-gp120 combination. SUMMARY: The RV-144 phase III has provided for the first time evidence that an HIV vaccine can prevent HIV infection. The results from the RV-144 trial are providing the scientific rationale for the future development of the HIV vaccine field and for designing future efficacy trials.

The neuronal monocarboxylate transporter MCT2: its regulation by BDNF and its role in learning and memory

Glucose has been considered the major, if not the exclusive, energy substrate for the brain. But under certain physiological and pathological conditions other substrates, namely monocarboxylates (lactate, pyruvate and ketone bodies), can contribute significantly to satisfy brain energy demands. These monocarboxylates need to be transported across the blood-brain barrier or out of astrocytes into the extracellular space and taken up into neurons. It has been shown that monocarboxylates are transported by a family of proton-linked transporters called monocarboxylate transporters (MCTs). In the central nervous system, MCT2 is the predominant neuronal isoform and little is known about the regulation of its expression. Noradrenaline (NA), insulin and IGF-1 were previously shown to enhance the expression of MCT2 in cultured cortical neurons via a translational mechanism. Here we demonstrate that the well known brain neurotrophic factor BDNF enhances MCT2 protein expression in cultured cortical neurons and in synaptoneurosome preparations in a time- and concentrationdependent manner without affecting MCT2 mRNA levels. We observed that BDNF induced MCT2 expression by activation of MAPK as well as PI3K/Akt/mTOR signaling pathways. Furthermore, we investigated the possible post-transcriptional regulation of MCT2 expression by a neuronal miRNA. Then, we demonstrated that BDNF enhanced MCT2 expression in the hippocampus in vivo, in parallel with some post-synaptic proteins such as PSD95 and AMPA receptor GluR2/3 subunits, and two immediate early genes Arc and Zif268 known to be expressed in conditions related to synaptic plasticity. In the last part, we demonstrated in vivo that a downregulation of hippocampal MCT2 via silencing with an appropriate lentiviral vector in mice caused an impairment of working memory without reference memory deficit. In conclusion, these results suggest that regulation of neuronal monocarboxylate transporter MCT2 expression could be a key event in the context of synaptic plasticity, allowing an adequate energy substrate supply in situations of altered synaptic efficacy. - Le glucose représente le substrat énergétique majeur pour le cerveau. Cependant, dans certaines conditions physiologiques ou pathologiques, le cerveau a la capacité d'utiliser des substrats énergéiques appartenant à la classe des monocarboxylates (lactate, pyruvate et corps cétoniques) afin de satisfaire ses besoins énergétiques. Ces monocarboxylates doivent être transportés à travers la barrière hématoencéphalique mais aussi hors des astrocytes vers l'espace extracellulaire puis re-captés par les neurones. Leur transport est assuré par une famillle de transporteurs aux monocarboxylates (MCTs). Dans le système nerveux central, les neurones expriment principalement l'isoforme MCT2 mais peu d'informations sont disponibles concernant la régulation de son expression. Il a été montré que la noradrénaline, l'insuline et l'IGF-1 induisent l'expression de MCT2 dans des cultures de neurones corticaux par un mécanisme traductionnel. Dans cette étude nous démontrons dans un premier temps que le facteur neurotrophique BDNF augmente l'expression de MCT2 à la fois dans des cultures de neurones corticaux et dans les préparations synaptoneurosomales selon un décours temporel et une gamme de concentrations propre. Aucun changement n'a été observé concernant les niveaux d'ARNm de MCT2. Nous avons observé que le BDNF induisait l'expression de MCT2 par l'activation simultanée des voies de signalisation MAPK et PI3K/Akt/mTOR. De plus, nous nous sommes intéressés à une potentielle régulation par les micro-ARNs de la synthèse de MCT2. Ensuite, nous avons démontré que le BDNF induit aussi l'expression de MCT2 dans l'hippocampe de la souris en parallèle avec d'autres protéines post-synaptiques telles que PSD95 et GluR2/3 et avec deux « immediate early genes » tels que Arc et Zif268 connus pour être exprimés dans des conditions de plasticité synaptique. Dans un dernier temps, nous avons démontré qu'une diminution d'expression de MCT2 induite par le biais d'un siRNA exprimé via un vecteur lentiviral dans l'hippocampe de souris générait des déficits de mémoire de travail sans affecter la mémoire de référence. En conclusion, ces résultats nous suggèrent que le transporteur aux monocarboxylates neuronal MCT2 serait essentiel pour l'apport énergétique du lactate pour les neurones dans des conditions de haute activité neuronale comme c'est le cas pendant les processus de plasticité synaptique.

Impact of controlled changes in grain size and pore space characteristics on the hydraulic conductivity and spectral induced polarization response of "proxies" of saturated alluvial sediments

Understanding the influence of pore space characteristics on the hydraulic conductivity and spectral induced polarization (SIP) response is critical for establishing relationships between the electrical and hydrological properties of surficial unconsolidated sedimentary deposits, which host the bulk of the world's readily accessible groundwater resources. Here, we present the results of laboratory SIP measurements on industrial-grade, saturated quartz samples with granulometric characteristics ranging from fine sand to fine gravel, which can be regarded as proxies for widespread alluvial deposits. We altered the pore space characteristics by changing (i) the grain size spectra, (ii) the degree of compaction, and (iii) the level of sorting. We then examined how these changes affect the SIP response, the hydraulic conductivity, and the specific surface area of the considered samples. In general, the results indicate a clear connection between the SIP response and the granulometric as well as pore space characteristics. In particular, we observe a systematic correlation between the hydraulic conductivity and the relaxation time of the Cole-Cole model describing the observed SIP effect for the entire range of considered grain sizes. The results do, however, also indicate that the detailed nature of these relations depends strongly on variations in the pore space characteristics, such as, for example, the degree of compaction. The results of this study underline the complexity of the origin of the SIP signal as well as the difficulty to relate it to a single structural factor of a studied sample, and hence raise some fundamental questions with regard to the practical use of SIP measurements as site- and/or sample-independent predictors of the hydraulic conductivity.

Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.

Are niche-based species distribution models transferable in space?

Aim To assess the geographical transferability of niche-based species distribution models fitted with two modelling techniques. Location Two distinct geographical study areas in Switzerland and Austria, in the subalpine and alpine belts. Methods Generalized linear and generalized additive models (GLM and GAM) with a binomial probability distribution and a logit link were fitted for 54 plant species, based on topoclimatic predictor variables. These models were then evaluated quantitatively and used for spatially explicit predictions within (internal evaluation and prediction) and between (external evaluation and prediction) the two regions. Comparisons of evaluations and spatial predictions between regions and models were conducted in order to test if species and methods meet the criteria of full transferability. By full transferability, we mean that: (1) the internal evaluation of models fitted in region A and B must be similar; (2) a model fitted in region A must at least retain a comparable external evaluation when projected into region B, and vice-versa; and (3) internal and external spatial predictions have to match within both regions. Results The measures of model fit are, on average, 24% higher for GAMs than for GLMs in both regions. However, the differences between internal and external evaluations (AUC coefficient) are also higher for GAMs than for GLMs (a difference of 30% for models fitted in Switzerland and 54% for models fitted in Austria). Transferability, as measured with the AUC evaluation, fails for 68% of the species in Switzerland and 55% in Austria for GLMs (respectively for 67% and 53% of the species for GAMs). For both GAMs and GLMs, the agreement between internal and external predictions is rather weak on average (Kulczynski's coefficient in the range 0.3-0.4), but varies widely among individual species. The dominant pattern is an asymmetrical transferability between the two study regions (a mean decrease of 20% for the AUC coefficient when the models are transferred from Switzerland and 13% when they are transferred from Austria). Main conclusions The large inter-specific variability observed among the 54 study species underlines the need to consider more than a few species to test properly the transferability of species distribution models. The pronounced asymmetry in transferability between the two study regions may be due to peculiarities of these regions, such as differences in the ranges of environmental predictors or the varied impact of land-use history, or to species-specific reasons like differential phenotypic plasticity, existence of ecotypes or varied dependence on biotic interactions that are not properly incorporated into niche-based models. The lower variation between internal and external evaluation of GLMs compared to GAMs further suggests that overfitting may reduce transferability. Overall, a limited geographical transferability calls for caution when projecting niche-based models for assessing the fate of species in future environments.

The lower Triassic anachronistic carbonate facies in space and time

The end-Permian mass extinction greatly affected the sedimentary record, but the sedimentary response was not limited to the Permian-Triassic boundary interval. This transformation extended to sedimentation that spanned the entire Early Triassic. Calcimicrobialites play an important role throughout this time interval, and at least four main events of anomalous carbonate deposition can be shown. A post-extinction calcimicrobial unit occurs above the extensive Permian skeletal carbonate platform exposed in the Taurus Mountains (southern Turkey), in south Armenia, north-west north and Central Iran along the Zagros Mountains. The calcimicrobial unit formed during the flooding of the platform that took place during the earliest Triassic. A similar calcimicrobialite formed during late Griesbachian to Dienerian time atop the shallow Permian skeletal carbonate platform largely exposed in south China. A third event occurred during the Early Olenekian on the first Mesozoic isolated pelagic plateau (Baid seamount, Oman Mountains). Here the change in carbonate sedimentation is reflected in the occurrence of thrombolites and carbonate seafloor fans. Near the end of Early Triassic time, unusual carbonate deposition is recorded both on an isolated pelagic plateau of the Western Tethys (Halstatt limestone of Dobrogea, Romania) and on the eastern Panthalassa margin of the western United States. In the western United States, the event is represented by stromatolites and thrombolites in the Virgin Limestone of the Moenkopi Formation and by seafloor fans in the middle and upper members of the Union Wash Formation. These unusual episodes of anomalous carbonate deposition illustrate a fundamental change in sedimentation that occurred in the aftermath of the end-Permian mass extinction.

Modelling ecological niches with support vector machines

1. The ecological niche is a fundamental biological concept. Modelling species' niches is central to numerous ecological applications, including predicting species invasions, identifying reservoirs for disease, nature reserve design and forecasting the effects of anthropogenic and natural climate change on species' ranges. 2. A computational analogue of Hutchinson's ecological niche concept (the multidimensional hyperspace of species' environmental requirements) is the support of the distribution of environments in which the species persist. Recently developed machine-learning algorithms can estimate the support of such high-dimensional distributions. We show how support vector machines can be used to map ecological niches using only observations of species presence to train distribution models for 106 species of woody plants and trees in a montane environment using up to nine environmental covariates. 3. We compared the accuracy of three methods that differ in their approaches to reducing model complexity. We tested models with independent observations of both species presence and species absence. We found that the simplest procedure, which uses all available variables and no pre-processing to reduce correlation, was best overall. Ecological niche models based on support vector machines are theoretically superior to models that rely on simulating pseudo-absence data and are comparable in empirical tests. 4. Synthesis and applications. Accurate species distribution models are crucial for effective environmental planning, management and conservation, and for unravelling the role of the environment in human health and welfare. Models based on distribution estimation rather than classification overcome theoretical and practical obstacles that pervade species distribution modelling. In particular, ecological niche models based on machine-learning algorithms for estimating the support of a statistical distribution provide a promising new approach to identifying species' potential distributions and to project changes in these distributions as a result of climate change, land use and landscape alteration.

Efficient lentiviral gene transfer into corneal stroma cells using a femtosecond laser.

We investigated a new procedure for gene transfer into the stroma of pig cornea for the delivery of therapeutic factors. A delimited space was created at 110 mum depth with a LDV femtosecond laser in pig corneas, and a HIV1-derived lentiviral vector expressing green fluorescent protein (GFP) (LV-CMV-GFP) was injected into the pocket. Corneas were subsequently dissected and kept in culture as explants. After 5 days, histological analysis of the explants revealed that the corneal pockets had closed and that the gene transfer procedure was efficient over the whole pocket area. Almost all the keratocytes were transduced in this area. Vector diffusion at right angles to the pocket's plane encompasses four (endothelium side) to 10 (epithelium side) layers of keratocytes. After 21 days, the level of transduction was similar to the results obtained after 5 days. The femtosecond laser technique allows a reliable injection and diffusion of lentiviral vectors to efficiently transduce stromal cells in a delimited area. Showing the efficacy of this procedure in vivo could represent an important step toward treatment or prevention of recurrent angiogenesis of the corneal stroma.

Predicting the physicochemical profile of diastereoisomeric histidine-containing dipeptides by property space analysis.

OBJECTIVES: This study aimed at measuring the lipophilicity and ionization constants of diastereoisomeric dipeptides, interpreting them in terms of conformational behavior, and developing statistical models to predict them. METHODS: A series of 20 dipeptides of general structure NH(2) -L-X-(L or D)-His-OMe was designed and synthetized. Their experimental ionization constants (pK(1) , pK(2) and pK(3) ) and lipophilicity parameters (log P(N) and log D(7.4) ) were measured by potentiometry. Molecular modeling in three media (vacuum, water, and chloroform) was used to explore and sample their conformational space, and for each stored conformer to calculate their radius of gyration, virtual log P (preferably written as log P(MLP) , meaning obtained by the molecular lipophilicity potential (MLP) method) and polar surface area (PSA). Means and ranges were calculated for these properties, as was their sensitivity (i.e., the ratio between property range and number of rotatable bonds). RESULTS: Marked differences between diastereoisomers were seen in their experimental ionization constants and lipophilicity parameters. These differences are explained by molecular flexibility, configuration-dependent differences in intramolecular interactions, and accessibility of functional groups. Multiple linear equations correlated experimental lipophilicity parameters and ionization constants with PSA range and other calculated parameters. CONCLUSION: This study documents the differences in lipophilicity and ionization constants between diastereoisomeric dipeptides. Such configuration-dependent differences are shown to depend markedly on differences in conformational behavior and to be amenable to multiple linear regression. Chirality 24:566-576, 2012. © 2012 Wiley Periodicals, Inc.