The multipoint Morisita index for the analysis of spatial patterns

In many fields, the spatial clustering of sampled data points has many consequences. Therefore, several indices have been proposed to assess the level of clustering affecting datasets (e.g. the Morisita index, Ripley's Kfunction and Rényi's generalized entropy). The classical Morisita index measures how many times it is more likely to select two measurement points from the same quadrats (the data set is covered by a regular grid of changing size) than it would be in the case of a random distribution generated from a Poisson process. The multipoint version (k-Morisita) takes into account k points with k >= 2. The present research deals with a new development of the k-Morisita index for (1) monitoring network characterization and for (2) detection of patterns in monitored phenomena. From a theoretical perspective, a connection between the k-Morisita index and multifractality has also been found and highlighted on a mathematical multifractal set.

Advanced mapping of environmental data: Geostatistics, Machine Learning and Bayesian Maximum Entropy

This book combines geostatistics and global mapping systems to present an up-to-the-minute study of environmental data. Featuring numerous case studies, the reference covers model dependent (geostatistics) and data driven (machine learning algorithms) analysis techniques such as risk mapping, conditional stochastic simulations, descriptions of spatial uncertainty and variability, artificial neural networks (ANN) for spatial data, Bayesian maximum entropy (BME), and more.

What do we gain from simplicity versus complexity in species distribution models?

Species distribution models (SDMs) are widely used to explain and predict species ranges and environmental niches. They are most commonly constructed by inferring species' occurrence-environment relationships using statistical and machine-learning methods. The variety of methods that can be used to construct SDMs (e.g. generalized linear/additive models, tree-based models, maximum entropy, etc.), and the variety of ways that such models can be implemented, permits substantial flexibility in SDM complexity. Building models with an appropriate amount of complexity for the study objectives is critical for robust inference. We characterize complexity as the shape of the inferred occurrence-environment relationships and the number of parameters used to describe them, and search for insights into whether additional complexity is informative or superfluous. By building 'under fit' models, having insufficient flexibility to describe observed occurrence-environment relationships, we risk misunderstanding the factors shaping species distributions. By building 'over fit' models, with excessive flexibility, we risk inadvertently ascribing pattern to noise or building opaque models. However, model selection can be challenging, especially when comparing models constructed under different modeling approaches. Here we argue for a more pragmatic approach: researchers should constrain the complexity of their models based on study objective, attributes of the data, and an understanding of how these interact with the underlying biological processes. We discuss guidelines for balancing under fitting with over fitting and consequently how complexity affects decisions made during model building. Although some generalities are possible, our discussion reflects differences in opinions that favor simpler versus more complex models. We conclude that combining insights from both simple and complex SDM building approaches best advances our knowledge of current and future species ranges.

Comparison between computational alanine scanning and per-residue binding free energy decomposition for protein-protein association using MM-GBSA: application to the TCR-p-MHC complex.

Recognition by the T-cell receptor (TCR) of immunogenic peptides (p) presented by Class I major histocompatibility complexes (MHC) is the key event in the immune response against virus-infected cells or tumor cells. A study of the 2C TCR/SIYR/H-2K(b) system using a computational alanine scanning and a much faster binding free energy decomposition based on the Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) method is presented. The results show that the TCR-p-MHC binding free energy decomposition using this approach and including entropic terms provides a detailed and reliable description of the interactions between the molecules at an atomistic level. Comparison of the decomposition results with experimentally determined activity differences for alanine mutants yields a correlation of 0.67 when the entropy is neglected and 0.72 when the entropy is taken into account. Similarly, comparison of experimental activities with variations in binding free energies determined by computational alanine scanning yields correlations of 0.72 and 0.74 when the entropy is neglected or taken into account, respectively. Some key interactions for the TCR-p-MHC binding are analyzed and some possible side chains replacements are proposed in the context of TCR protein engineering. In addition, a comparison of the two theoretical approaches for estimating the role of each side chain in the complexation is given, and a new ad hoc approach to decompose the vibrational entropy term into atomic contributions, the linear decomposition of the vibrational entropy (LDVE), is introduced. The latter allows the rapid calculation of the entropic contribution of interesting side chains to the binding. This new method is based on the idea that the most important contributions to the vibrational entropy of a molecule originate from residues that contribute most to the vibrational amplitude of the normal modes. The LDVE approach is shown to provide results very similar to those of the exact but highly computationally demanding method.

Active Learning of Very-High Resolution Optical Imagery with SVM: Entropy vs Margin Sampling

An active learning method is proposed for the semi-automatic selection of training sets in remote sensing image classification. The method adds iteratively to the current training set the unlabeled pixels for which the prediction of an ensemble of classifiers based on bagged training sets show maximum entropy. This way, the algorithm selects the pixels that are the most uncertain and that will improve the model if added in the training set. The user is asked to label such pixels at each iteration. Experiments using support vector machines (SVM) on an 8 classes QuickBird image show the excellent performances of the methods, that equals accuracies of both a model trained with ten times more pixels and a model whose training set has been built using a state-of-the-art SVM specific active learning method

Fourth order pseudo maximum likelihood methods

We extend PML theory to account for information on the conditional moments up to order four, but without assuming a parametric model, to avoid a risk of misspecification of the conditional distribution. The key statistical tool is the quartic exponential family, which allows us to generalize the PML2 and QGPML1 methods proposed in Gourieroux et al. (1984) to PML4 and QGPML2 methods, respectively. An asymptotic theory is developed. The key numerical tool that we use is the Gauss-Freud integration scheme that solves a computational problem that has previously been raised in several fields. Simulation exercises demonstrate the feasibility and robustness of the methods [Authors]

A deterministic model to assess the impact of AIDS interventions in countries with generalized HIV epidemic. An application to Botswana

General introductionThe Human Immunodeficiency/Acquired Immunodeficiency Syndrome (HIV/AIDS) epidemic, despite recent encouraging announcements by the World Health Organization (WHO) is still today one of the world's major health care challenges.The present work lies in the field of health care management, in particular, we aim to evaluate the behavioural and non-behavioural interventions against HIV/AIDS in developing countries through a deterministic simulation model, both in human and economic terms. We will focus on assessing the effectiveness of the antiretroviral therapies (ART) in heterosexual populations living in lesser developed countries where the epidemic has generalized (formerly defined by the WHO as type II countries). The model is calibrated using Botswana as a case study, however our model can be adapted to other countries with similar transmission dynamics.The first part of this thesis consists of reviewing the main mathematical concepts describing the transmission of infectious agents in general but with a focus on human immunodeficiency virus (HIV) transmission. We also review deterministic models assessing HIV interventions with a focus on models aimed at African countries. This review helps us to recognize the need for a generic model and allows us to define a typical structure of such a generic deterministic model.The second part describes the main feed-back loops underlying the dynamics of HIV transmission. These loops represent the foundation of our model. This part also provides a detailed description of the model, including the various infected and non-infected population groups, the type of sexual relationships, the infection matrices, important factors impacting HIV transmission such as condom use, other sexually transmitted diseases (STD) and male circumcision. We also included in the model a dynamic life expectancy calculator which, to our knowledge, is a unique feature allowing more realistic cost-efficiency calculations. Various intervention scenarios are evaluated using the model, each of them including ART in combination with other interventions, namely: circumcision, campaigns aimed at behavioral change (Abstain, Be faithful or use Condoms also named ABC campaigns), and treatment of other STD. A cost efficiency analysis (CEA) is performed for each scenario. The CEA consists of measuring the cost per disability-adjusted life year (DALY) averted. This part also describes the model calibration and validation, including a sensitivity analysis.The third part reports the results and discusses the model limitations. In particular, we argue that the combination of ART and ABC campaigns and ART and treatment of other STDs are the most cost-efficient interventions through 2020. The main model limitations include modeling the complexity of sexual relationships, omission of international migration and ignoring variability in infectiousness according to the AIDS stage.The fourth part reviews the major contributions of the thesis and discusses model generalizability and flexibility. Finally, we conclude that by selecting the adequate interventions mix, policy makers can significantly reduce the adult prevalence in Botswana in the coming twenty years providing the country and its donors can bear the cost involved.Part I: Context and literature reviewIn this section, after a brief introduction to the general literature we focus in section two on the key mathematical concepts describing the transmission of infectious agents in general with a focus on HIV transmission. Section three provides a description of HIV policy models, with a focus on deterministic models. This leads us in section four to envision the need for a generic deterministic HIV policy model and briefly describe the structure of such a generic model applicable to countries with generalized HIV/AIDS epidemic, also defined as pattern II countries by the WHO.

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.