Analysis and spatial modeling of the indoor radon Swiss data

The present research deals with an important public health threat, which is the pollution created by radon gas accumulation inside dwellings. The spatial modeling of indoor radon in Switzerland is particularly complex and challenging because of many influencing factors that should be taken into account. Indoor radon data analysis must be addressed from both a statistical and a spatial point of view. As a multivariate process, it was important at first to define the influence of each factor. In particular, it was important to define the influence of geology as being closely associated to indoor radon. This association was indeed observed for the Swiss data but not probed to be the sole determinant for the spatial modeling. The statistical analysis of data, both at univariate and multivariate level, was followed by an exploratory spatial analysis. Many tools proposed in the literature were tested and adapted, including fractality, declustering and moving windows methods. The use of Quan-tité Morisita Index (QMI) as a procedure to evaluate data clustering in function of the radon level was proposed. The existing methods of declustering were revised and applied in an attempt to approach the global histogram parameters. The exploratory phase comes along with the definition of multiple scales of interest for indoor radon mapping in Switzerland. The analysis was done with a top-to-down resolution approach, from regional to local lev¬els in order to find the appropriate scales for modeling. In this sense, data partition was optimized in order to cope with stationary conditions of geostatistical models. Common methods of spatial modeling such as Κ Nearest Neighbors (KNN), variography and General Regression Neural Networks (GRNN) were proposed as exploratory tools. In the following section, different spatial interpolation methods were applied for a par-ticular dataset. A bottom to top method complexity approach was adopted and the results were analyzed together in order to find common definitions of continuity and neighborhood parameters. Additionally, a data filter based on cross-validation was tested with the purpose of reducing noise at local scale (the CVMF). At the end of the chapter, a series of test for data consistency and methods robustness were performed. This lead to conclude about the importance of data splitting and the limitation of generalization methods for reproducing statistical distributions. The last section was dedicated to modeling methods with probabilistic interpretations. Data transformation and simulations thus allowed the use of multigaussian models and helped take the indoor radon pollution data uncertainty into consideration. The catego-rization transform was presented as a solution for extreme values modeling through clas-sification. Simulation scenarios were proposed, including an alternative proposal for the reproduction of the global histogram based on the sampling domain. The sequential Gaussian simulation (SGS) was presented as the method giving the most complete information, while classification performed in a more robust way. An error measure was defined in relation to the decision function for data classification hardening. Within the classification methods, probabilistic neural networks (PNN) show to be better adapted for modeling of high threshold categorization and for automation. Support vector machines (SVM) on the contrary performed well under balanced category conditions. In general, it was concluded that a particular prediction or estimation method is not better under all conditions of scale and neighborhood definitions. Simulations should be the basis, while other methods can provide complementary information to accomplish an efficient indoor radon decision making.

"Peptabody": a new type of high avidity binding protein.

A new type of high avidity binding molecule, termed "peptabody" was created by harnessing the effect of multivalent interaction. A short peptide ligand was fused via a semi-rigid hinge region with the coiled-coil assembly domain of the cartilage oligomeric matrix protein, resulting in a pentameric multivalent binding molecule. In the first peptabody (Pab-S) described here, a peptide (S) specific for the mouse B-cell lymphoma BCL1 surface Ig idiotype, was selected from a phage display library. A fusion gene was constructed encoding peptide S, followed by the 24 aa hinge region from camel IgG and a modified 55 aa cartilage oligomeric matrix protein pentamerization domain. The Pab-S fusion protein was expressed in Escherichia coli in a soluble form at high levels and purified in a single step by metal-affinity chromatography. Pab-S specifically bound the BCL1 surface idiotype with an avidity of about 1 nM, which corresponds to a 2 x 10(5)-fold increase compared with the affinity of the synthetic peptide S itself. Biochemical characterization showed that Pab-S is a stable homopentamer of about 85 kDa, with interchain disulfide bonds. Pab-S can be dissociated under denaturing and reducing conditions and reassociated as a pentamer with full-binding activity. This intrinsic feature provides an easy way to combine Pab molecules with two different peptide specificities, thus producing heteropentamers with bispecific and/or chelating properties.

Polarity and specific sequence requirements of peroxisome proliferator-activated receptor (PPAR)/retinoid X receptor heterodimer binding to DNA. A functional analysis of the malic enzyme gene PPAR response element.

The malic enzyme (ME) gene is a target for both thyroid hormone receptors and peroxisome proliferator-activated receptors (PPAR). Within the ME promoter, two direct repeat (DR)-1-like elements, MEp and MEd, have been identified as putative PPAR response elements (PPRE). We demonstrate that only MEp and not MEd is able to bind PPAR/retinoid X receptor (RXR) heterodimers and mediate peroxisome proliferator signaling. Taking advantage of the close sequence resemblance of MEp and MEd, we have identified crucial determinants of a PPRE. Using reciprocal mutation analyses of these two elements, we show the preference for adenine as the spacing nucleotide between the two half-sites of the PPRE and demonstrate the importance of the two first bases flanking the core DR1 in 5'. This latter feature of the PPRE lead us to consider the polarity of the PPAR/RXR heterodimer bound to its cognate element. We demonstrate that, in contrast to the polarity of RXR/TR and RXR/RAR bound to DR4 and DR5 elements respectively, PPAR binds to the 5' extended half-site of the response element, while RXR occupies the 3' half-site. Consistent with this polarity is our finding that formation and binding of the PPAR/RXR heterodimer requires an intact hinge T region in RXR while its integrity is not required for binding of the RXR/TR heterodimer to a DR4.

Experimental analysis and computer prediction of CTF/NFI transcription factor DNA binding sites.

Accurate prediction of transcription factor binding sites is needed to unravel the function and regulation of genes discovered in genome sequencing projects. To evaluate current computer prediction tools, we have begun a systematic study of the sequence-specific DNA-binding of a transcription factor belonging to the CTF/NFI family. Using a systematic collection of rationally designed oligonucleotides combined with an in vitro DNA binding assay, we found that the sequence specificity of this protein cannot be represented by a simple consensus sequence or weight matrix. For instance, CTF/NFI uses a flexible DNA binding mode that allows for variations of the binding site length. From the experimental data, we derived a novel prediction method using a generalised profile as a binding site predictor. Experimental evaluation of the generalised profile indicated that it accurately predicts the binding affinity of the transcription factor to natural or synthetic DNA sequences. Furthermore, the in vitro measured binding affinities of a subset of oligonucleotides were found to correlate with their transcriptional activities in transfected cells. The combined computational-experimental approach exemplified in this work thus resulted in an accurate prediction method for CTF/NFI binding sites potentially functioning as regulatory regions in vivo.

MMPBSA decomposition of the binding energy throughout a molecular dynamics simulation of Amyloid-Beta (Aß10-35) aggregation

Recent experiments with amyloid-beta (Aß) peptides indicate that the formation of toxic oligomers may be an important contribution to the onset of Alzheimer's disease. The toxicity of Aß oligomers depend on their structure, which is governed by assembly dynamics. However, a detailed knowledge of the structure of at the atomic level has not been achieved yet due to limitations of current experimental techniques. In this study, replica exchange molecular dynamics simulations are used to identify the expected diversity of dimer conformations of Aß10-35 monomers. The most representative dimer conformation has been used to track the dimer formation process between both monomers. The process has been characterized by means of the evolution of the decomposition of the binding free energy, which provides an energetic profile of the interaction. Dimers undergo a process of reorganization driven basically by inter-chain hydrophobic and hydrophilic interactions and also solvation/desolvation processes.

Spatial pattern and process at the plant neighbourhood scale: insights from communities dominated by the clonal grass Elymus repens (L.) Gould.

Question: How do clonal traits of a locally dominant grass (Elymus repens (L.) Gould.) respond to soil heterogeneity and shape spatial patterns of its tillers? How do tiller spatial patterns constrain seedling recruitment within the community?Locations: Artificial banks of the River Rhone, France.Material and Methods: We examined 45 vegetation patches dominated by Elymus repens. During a first phase we tested relationships between soil variables and three clonal traits (spacer length, number of clumping tillers and branching rate), and between the same clonal traits and spatial patterns (i.e. density and degree of spatial aggregation) of tillers at a very fine scale. During a second phase, we performed a sowing experiment to investigate effects of density and spatial patterns of E. repens on recruitment of eight species selected from the regional species pool.Results: Clonal traits had clear effects - especially spacer length - on densification and aggregation of E. repens tillers and, at the same time, a clear response of these same clonal traits as soil granulometry changed. The density and degree of aggregation of E. repens tillers was positively correlated to total seedling cover and diversity at the finest spatial scales.Conclusions: Spatial patterning of a dominant perennial grass responds to soil heterogeneity through modifications of its clonal morphology as a trade-off between phalanx and guerrilla forms. In turn, spatial patterns have strong effects on abundance and diversity of seedlings. Spatial patterns of tillers most probably led to formation of endogenous gaps in which the recruitment of new plant individuals was enhanced. Interestingly, we also observed more idiosyncratic effects of tiller spatial patterns on seedling cover and diversity when focusing on different growth forms of the sown species.

Catechol-binding serines of beta(2)-adrenergic receptors control the equilibrium between active and inactive receptor states.

The binding free energy for the interaction between serines 204 and 207 of the fifth transmembrane helix of the beta(2)-adrenergic receptor (beta(2)-AR) and catecholic hydroxyl (OH) groups of adrenergic agonists was analyzed using double mutant cycles. Binding affinities for catecholic and noncatecholic agonists were measured in wild-type and mutant receptors, carrying alanine replacement of the two serines (S204A, S207A beta(2)-AR), a constitutive activating mutation, or both. The free energy coupling between the losses of binding energy attributable to OH deletion from the ligand and from the receptor indicates a strong interaction (nonadditivity) as expected for a direct binding between the two sets of groups. However, we also measured a significant interaction between the deletion of OH groups from the receptor and the constitutive activating mutation. This suggests that a fraction of the decrease in agonist affinity caused by serine mutagenesis may involve a shift in the conformational equilibrium of the receptor toward the inactive state. Direct measurements using a transient transfection assay confirm this prediction. The constitutive activity of the (S204A, S207A) beta(2)-AR mutant is 50 to 60% lower than that of the wild-type beta(2)-AR. We conclude that S204 and S207 do not only provide a docking site for the agonist, but also control the equilibrium of the receptor between active (R*) and inactive (R) forms.

The influence of environmental spatial structure on the life history traits and diversity of species in a metacommunity

Several models have been proposed to understand how so many species can coexist in ecosystems. Despite evidence showing that natural habitats are often patchy and fragmented, these models rarely take into account environmental spatial structure. In this study we investigated the influence of spatial structure in habitat and disturbance regime upon species' traits and species' coexistence in a metacommunity. We used a population-based model to simulate competing species in spatially explicit landscapes. The species traits we focused on were dispersal ability, competitiveness, reproductive investment and survival rate. Communities were characterized by their species richness and by the four life-history traits averaged over all the surviving species. Our results show that spatial structure and disturbance have a strong influence on the equilibrium life-history traits within a metacommunity. In the absence of disturbance, spatially structured landscapes favour species investing more in reproduction, but less in dispersal and survival. However, this influence is strongly dependent on the disturbance rate, pointing to an important interaction between spatial structure and disturbance. This interaction also plays a role in species coexistence. While spatial structure tends to reduce diversity in the absence of disturbance, the tendency is reversed when disturbance occurs. In conclusion, the spatial structure of communities is an important determinant of their diversity and characteristic traits. These traits are likely to influence important ecological properties such as resistance to invasion or response to climate change, which in turn will determine the fate of ecosystems facing the current global ecological crisis.

Spatial behavior in groups: an agent-based approach

We present an agent-based model with the aim of studying how macro-level dynamics of spatial distances among interacting individuals in a closed space emerge from micro-level dyadic and local interactions. Our agents moved on a lattice (referred to as a room) using a model implemented in a computer program called P-Space in order to minimize their dissatisfaction, defined as a function of the discrepancy between the real distance and the ideal, or desired, distance between agents. Ideal distances evolved in accordance with the agent's personal and social space, which changed throughout the dynamics of the interactions among the agents. In the first set of simulations we studied the effects of the parameters of the function that generated ideal distances, and in a second set we explored how group macrolevel behavior depended on model parameters and other variables. We learned that certain parameter values yielded consistent patterns in the agents' personal and social spaces, which in turn led to avoidance and approaching behaviors in the agents. We also found that the spatial behavior of the group of agents as a whole was influenced by the values of the model parameters, as well as by other variables such as the number of agents. Our work demonstrates that the bottom-up approach is a useful way of explaining macro-level spatial behavior. The proposed model is also shown to be a powerful tool for simulating the spatial behavior of groups of interacting individuals.

T-cell hybridoma specific for a cytochrome c peptide: specific antigen binding and interleukin 2 production.

T-cell hybridomas were obtained after fusion of BW 5147 thymoma and long-term cultured T cells specific for cytochrome c peptide 66-80 derivatized with a 2,4-dinitroaminophenyl (DNAP) group. The resulting hybridomas were selected for their capacity to specifically bind to soluble radiolabeled peptide antigen. One T-cell hybrid was positive for antigen binding. This hybrid T cell exhibits surface phenotypic markers of the parent antigen-specific T cells. The binding could be inhibited either by an excess of unlabeled homologous antigen or by cytochrome c peptide 11-25 derivatized with a 2-nitrophenylsulfenyl group. Several other peptide antigens tested failed to inhibit binding of the radioactive peptide. This suggests that a specific amino acid sequence, modified by a DNAP group, is the antigenic structure recognized by the putative T-cell receptor. In addition, direct interaction of DNAP-66-80 peptide with the hybridoma cell line induced production of the T-cell growth factor interleukin 2. Furthermore, supernatants derived from syngeneic macrophages pulsed with the relevant peptide also induced the antigen-specific hybridoma to produce interleukin 2.

Quantifying the weight of fingerprint evidence through the spatial relationship, directions and types of minutiae observed on fingermarks

This paper presents a statistical model for the quantification of the weight of fingerprint evidence. Contrarily to previous models (generative and score-based models), our model proposes to estimate the probability distributions of spatial relationships, directions and types of minutiae observed on fingerprints for any given fingermark. Our model is relying on an AFIS algorithm provided by 3M Cogent and on a dataset of more than 4,000,000 fingerprints to represent a sample from a relevant population of potential sources. The performance of our model was tested using several hundreds of minutiae configurations observed on a set of 565 fingermarks. In particular, the effects of various sub-populations of fingers (i.e., finger number, finger general pattern) on the expected evidential value of our test configurations were investigated. The performance of our model indicates that the spatial relationship between minutiae carries more evidential weight than their type or direction. Our results also indicate that the AFIS component of our model directly enables us to assign weight to fingerprint evidence without the need for the additional layer of complex statistical modeling involved by the estimation of the probability distributions of fingerprint features. In fact, it seems that the AFIS component is more sensitive to the sub-population effects than the other components of the model. Overall, the data generated during this research project contributes to support the idea that fingerprint evidence is a valuable forensic tool for the identification of individuals.

Antigen binding to secretory immunoglobulin A results in decreased sensitivity to intestinal proteases and increased binding to cellular Fc receptors.

In intestinal secretions, secretory IgA (SIgA) plays an important sentinel and protective role in the recognition and clearance of enteric pathogens. In addition to serving as a first line of defense, SIgA and SIgA x antigen immune complexes are selectively transported across Peyer's patches to underlying dendritic cells in the mucosa-associated lymphoid tissue, contributing to immune surveillance and immunomodulation. To explain the unexpected transport of immune complexes in face of the large excess of free SIgA in secretions, we postulated that SIgA experiences structural modifications upon antigen binding. To address this issue, we associated specific polymeric IgA and SIgA with antigens of various sizes and complexity (protein toxin, virus, bacterium). Compared with free antibody, we found modified sensitivity of the three antigens assayed after exposure to proteases from intestinal washes. Antigen binding further impacted on the immunoreactivity toward polyclonal antisera specific for the heavy and light chains of the antibody, as a function of the antigen size. These conformational changes promoted binding of the SIgA-based immune complex compared with the free antibody to cellular receptors (Fc alphaRI and polymeric immunoglobulin receptor) expressed on the surface of premyelocytic and epithelial cell lines. These data reveal that antigen recognition by SIgA triggers structural changes that confer to the antibody enhanced receptor binding properties. This identifies immune complexes as particular structural entities integrating the presence of bound antigens and adds to the known function of immune exclusion and mucus anchoring by SIgA.