Fibronectin distribution in the chick embryo during formation of the blastula.

The distribution of the fibronectin-rich extracellular matrix (ECM) in the chick embryo during formation of the blastula has been evaluated semiquantitatively using an electron microscopical immunogold staining technique. During the first 10 h of postlaying development, fibronectin was found in both embryonic area pellucida and extra-embryonic area opaca of the blastoderm. In the area pellucida, the fibronectin was (1) associated with the basal lamina of the epiblast, (2) present between epiblastic and hypoblastic cells and (3) occasionally internalized in hypoblastic cells. Along the embryonic axis, a transient and high density of ECM was associated with the front of the anteriorly and rapidly expanding hypoblast. Very high density of fibronectin was observed in the marginal zone of the area pellucida, where the epiblastic and deeper cell layers show contacts and intense re-arrangements. In the area opaca, fibronectin was at first found only sporadically between contacting cells, but its density increased steadily and markedly during the first day of development. These rapid and significant changes in the regional distribution of fibronectin-rich ECM are discussed with respect to the early morphogenesis of the chick embryo.

The truncated mean of an asymmetric distribution

This paper investigates a simple procedure to estimate robustly the mean of an asymmetric distribution. The procedure removes the observations which are larger or smaller than certain limits and takes the arithmetic mean of the remaining observations, the limits being determined with the help of a parametric model, e.g., the Gamma, the Weibull or the Lognormal distribution. The breakdown point, the influence function, the (asymptotic) variance, and the contamination bias of this estimator are explored and compared numerically with those of competing estimates.

Adaptive strategy for the statistical analysis of connectomes.

We study an adaptive statistical approach to analyze brain networks represented by brain connection matrices of interregional connectivity (connectomes). Our approach is at a middle level between a global analysis and single connections analysis by considering subnetworks of the global brain network. These subnetworks represent either the inter-connectivity between two brain anatomical regions or by the intra-connectivity within the same brain anatomical region. An appropriate summary statistic, that characterizes a meaningful feature of the subnetwork, is evaluated. Based on this summary statistic, a statistical test is performed to derive the corresponding p-value. The reformulation of the problem in this way reduces the number of statistical tests in an orderly fashion based on our understanding of the problem. Considering the global testing problem, the p-values are corrected to control the rate of false discoveries. Finally, the procedure is followed by a local investigation within the significant subnetworks. We contrast this strategy with the one based on the individual measures in terms of power. We show that this strategy has a great potential, in particular in cases where the subnetworks are well defined and the summary statistics are properly chosen. As an application example, we compare structural brain connection matrices of two groups of subjects with a 22q11.2 deletion syndrome, distinguished by their IQ scores.

An investigation of the potential of DIP-STR markers for DNA mixture analyses

The genetic characterization of unbalanced mixed stains remains an important area where improvement is imperative. In fact, using the standard tools of forensic DNA profiling (i.e., STR markers), the profile of the minor contributor in mixed DNA stains cannot be successfully detected if its quantitative share of DNA is less than 10% of the mixed trace. This is due to the fact that the major contributor's profile "masks" that of the minor contributor. Besides known remedies to this problem, such as Y-STR analysis, a new compound genetic marker that consists of a Deletion/Insertion Polymorphism (DIP) linked to a Short Tandem Repeat (STR) polymorphism, has recently been developed and proposed [1]. These novel markers are called DIP-STR markers. This paper compares, from a statistical and forensic perspective, the potential usefulness of these novel DIP-STR markers (i) with traditional STR markers in cases of moderately unbalanced mixtures, and (ii) with Y-STR markers in cases of female-male mixtures. This is done through a comparison of the distribution of 100,000 likelihood ratio values obtained using each method on simulated mixtures. This procedure is performed assuming, in turn, the prosecution's and the defence's point of view.

Predicted effect of climate change on the invasibility and distribution of the Western corn root-worm

1 Insect pests, biological invasions and climate change are considered to representmajor threats to biodiversity, ecosystem functioning, agriculture and forestry.Deriving hypothesis of contemporary and/or future potential distributions of insectpests and invasive species is becoming an important tool for predicting the spatialstructure of potential threats.2 The western corn rootworm (WCR) Diabrotica virgifera virgifera LeConte is apest of maize in North America that has invaded Europe in recent years, resultingin economic costs in terms of maize yields in both continents. The present studyaimed to estimate the dynamics of potential areas of invasion by the WCR under aclimate change scenario in the Northern Hemisphere. The areas at risk under thisscenario were assessed by comparing, using complementary approaches, the spatialprojections of current and future areas of climatic favourability of the WCR. Spatialhypothesis were generated with respect to the presence records in the native rangeof the WCR and physiological thresholds from previous empirical studies.3 We used a previously developed protocol specifically designed to estimatethe climatic favourability of the WCR. We selected the most biologicallyrelevant climatic predictors and then used multidimensional envelope (MDE) andMahalanobis distances (MD) approaches to derive potential distributions for currentand future climatic conditions.4 The results obtained showed a northward advancement of the upper physiologicallimit as a result of climate change, which might increase the strength of outbreaksat higher latitudes. In addition, both MDE and MD outputs predict the stability ofclimatic favourability for the WCR in the core of the already invaded area in Europe,which suggests that this zone would continue to experience damage from this pestin Europe.

The HSR on chromosome 1 of the house mouse, Mus domesticus: distribution and frequency in Switzerland.

A total of 357 house mice (Mus domesticus) from 83 localities uniformly distributed throughout Switzerland were screened for the presence of a homogenously staining region (HSR) on chromosome 1. Altogether 47 mice from 11 localities were HSR/+ or HSR/HSR. One sample of 11 individuals all had an HSR/HSR karyotype. Almost all mice with the variant were collected from the Rhone valley (HSR frequency: 61%) and Val Bregaglia (HSR frequency: 81%). For samples from most of the area of Switzerland, the HSR was absent. There was no strong association between the geographic distribution of the HSR and the areas of occurrence of metacentrics. However, at Chiggiogna the HSR was found on Rb (1.3). Possible explanations for the HSR polymorphism are discussed.

Sample size and statistical power in the hierarchical analysis of variance: applications in morphometry of the nervous system.

Analysis of variance is commonly used in morphometry in order to ascertain differences in parameters between several populations. Failure to detect significant differences between populations (type II error) may be due to suboptimal sampling and lead to erroneous conclusions; the concept of statistical power allows one to avoid such failures by means of an adequate sampling. Several examples are given in the morphometry of the nervous system, showing the use of the power of a hierarchical analysis of variance test for the choice of appropriate sample and subsample sizes. In the first case chosen, neuronal densities in the human visual cortex, we find the number of observations to be of little effect. For dendritic spine densities in the visual cortex of mice and humans, the effect is somewhat larger. A substantial effect is shown in our last example, dendritic segmental lengths in monkey lateral geniculate nucleus. It is in the nature of the hierarchical model that sample size is always more important than subsample size. The relative weight to be attributed to subsample size thus depends on the relative magnitude of the between observations variance compared to the between individuals variance.

Information security and the enforcement of secrecy : the practical application of quantum key distribution

There is no doubt about the necessity of protecting digital communication: Citizens are entrusting their most confidential and sensitive data to digital processing and communication, and so do governments, corporations, and armed forces. Digital communication networks are also an integral component of many critical infrastructures we are seriously depending on in our daily lives. Transportation services, financial services, energy grids, food production and distribution networks are only a few examples of such infrastructures. Protecting digital communication means protecting confidentiality and integrity by encrypting and authenticating its contents. But most digital communication is not secure today. Nevertheless, some of the most ardent problems could be solved with a more stringent use of current cryptographic technologies. Quite surprisingly, a new cryptographic primitive emerges from the ap-plication of quantum mechanics to information and communication theory: Quantum Key Distribution. QKD is difficult to understand, it is complex, technically challenging, and costly-yet it enables two parties to share a secret key for use in any subsequent cryptographic task, with an unprecedented long-term security. It is disputed, whether technically and economically fea-sible applications can be found. Our vision is, that despite technical difficulty and inherent limitations, Quantum Key Distribution has a great potential and fits well with other cryptographic primitives, enabling the development of highly secure new applications and services. In this thesis we take a structured approach to analyze the practical applicability of QKD and display several use cases of different complexity, for which it can be a technology of choice, either because of its unique forward security features, or because of its practicability.

Improving the prediction of plant species distribution and community composition by adding edaphic to topo-climatic variables

Questions Soil properties have been widely shown to influence plant growth and distribution. However, the degree to which edaphic variables can improve models based on topo-climatic variables is still unclear. In this study, we tested the roles of seven edaphic variables, namely (1) pH; (2) the content of nitrogen and of (3) phosphorus; (4) silt; (5) sand; (6) clay and (7) carbon-to-nitrogen ratio, as predictors of species distribution models in an edaphically heterogeneous landscape. We also tested how the respective influence of these variables in the models is linked to different ecological and functional species characteristics. Location The Western Alps, Switzerland. Methods With four different modelling techniques, we built models for 115 plant species using topo-climatic variables alone and then topo-climatic variables plus each of the seven edaphic variables, one at a time. We evaluated the contribution of each edaphic variable by assessing the change in predictive power of the model. In a second step, we evaluated the importance of the two edaphic variables that yielded the largest increase in predictive power in one final set of models for each species. Third, we explored the change in predictive power and the importance of variables across plant functional groups. Finally, we assessed the influence of the edaphic predictors on the prediction of community composition by stacking the models for all species and comparing the predicted communities with the observed community. Results Among the set of edaphic variables studied, pH and nitrogen content showed the highest contributions to improvement of the predictive power of the models, as well as the predictions of community composition. When considering all topo-climatic and edaphic variables together, pH was the second most important variable after degree-days. The changes in model results caused by edaphic predictors were dependent on species characteristics. The predictions for the species that have a low specific leaf area, and acidophilic preferences, tolerating low soil pH and high humus content, showed the largest improvement by the addition of pH and nitrogen in the model. Conclusions pH was an important predictor variable for explaining species distribution and community composition of the mountain plants considered in our study. pH allowed more precise predictions for acidophilic species. This variable should not be neglected in the construction of species distribution models in areas with contrasting edaphic conditions.

Domain Adaptation in remote sensing: increasing the portability of land-cover classifiers

Among the types of remote sensing acquisitions, optical images are certainly one of the most widely relied upon data sources for Earth observation. They provide detailed measurements of the electromagnetic radiation reflected or emitted by each pixel in the scene. Through a process termed supervised land-cover classification, this allows to automatically yet accurately distinguish objects at the surface of our planet. In this respect, when producing a land-cover map of the surveyed area, the availability of training examples representative of each thematic class is crucial for the success of the classification procedure. However, in real applications, due to several constraints on the sample collection process, labeled pixels are usually scarce. When analyzing an image for which those key samples are unavailable, a viable solution consists in resorting to the ground truth data of other previously acquired images. This option is attractive but several factors such as atmospheric, ground and acquisition conditions can cause radiometric differences between the images, hindering therefore the transfer of knowledge from one image to another. The goal of this Thesis is to supply remote sensing image analysts with suitable processing techniques to ensure a robust portability of the classification models across different images. The ultimate purpose is to map the land-cover classes over large spatial and temporal extents with minimal ground information. To overcome, or simply quantify, the observed shifts in the statistical distribution of the spectra of the materials, we study four approaches issued from the field of machine learning. First, we propose a strategy to intelligently sample the image of interest to collect the labels only in correspondence of the most useful pixels. This iterative routine is based on a constant evaluation of the pertinence to the new image of the initial training data actually belonging to a different image. Second, an approach to reduce the radiometric differences among the images by projecting the respective pixels in a common new data space is presented. We analyze a kernel-based feature extraction framework suited for such problems, showing that, after this relative normalization, the cross-image generalization abilities of a classifier are highly increased. Third, we test a new data-driven measure of distance between probability distributions to assess the distortions caused by differences in the acquisition geometry affecting series of multi-angle images. Also, we gauge the portability of classification models through the sequences. In both exercises, the efficacy of classic physically- and statistically-based normalization methods is discussed. Finally, we explore a new family of approaches based on sparse representations of the samples to reciprocally convert the data space of two images. The projection function bridging the images allows a synthesis of new pixels with more similar characteristics ultimately facilitating the land-cover mapping across images.

The association between inflammatory biomarkers and metabolically healthy obesity depends of the definition used.

BACKGROUND/OBJECTIVES: To assess the distribution of interleukin (IL)-1β, IL-6, tumour necrosis factor (TNF)-α and C-reactive protein (CRP) according to the different definitions of metabolically healthy obesity (MHO). SUBJECTS/METHODS: A total of 881 obese (body mass index (BMI) > or =30 kg/m2) subjects derived from the population-based CoLaus Study participated in this study. MHO was defined using six sets of criteria including different combinations of waist, blood pressure, total high-density lipoprotein cholesterol or low-density lipoprotein -cholesterol, triglycerides, fasting glucose, homeostasis model, high-sensitivity CRP, and personal history of cardiovascular, respiratory or metabolic diseases. IL-1β, IL-6 and TNF-α were assessed by multiplexed flow cytometric assay. CRP was assessed by immunoassay. RESULTS: On bivariate analysis some, but not all, definitions of MHO led to significantly lower levels of IL-6, TNF-α and CRP compared with non-MH obese subjects. Most of these differences became nonsignificant after multivariate analysis. An posteriori analysis showed a statistical power between 9 and 79%, depending on the inflammatory biomarker and MHO definition considered. Further increasing sample size to overweight+obese individuals (BMI > or =25 kg/m2, n=2917) showed metabolically healthy status to be significantly associated with lower levels of CRP, while no association was found for IL-1β. Significantly lower IL-6 and TNF-α levels were also found with some but not all MHO definitions, the differences in IL-6 becoming nonsignificant after adjusting for abdominal obesity or percent body fat. CONCLUSIONS: MHO individuals present with decreased levels of CRP and, depending on MHO definition, also with decreased levels in IL-6 and TNF-α. Conversely, no association with IL-1β levels was found.

Distribution and anatomical localization of the glucose transporter 2 (GLUT2) in the adult rat brain--an immunohistochemical study.

The aim of this work was to study the distribution and cellular localization of GLUT2 in the rat brain by light and electron microscopic immunohistochemistry, whereas our ultrastructural observations will be reported in a second paper. Confirming previous results, we show that GLUT2-immunoreactive profiles are present throughout the brain, especially in the limbic areas and related nuclei, whereas they appear most concentrated in the ventral and medial regions close to the midline. Using cresyl violet counterstaining and double immunohistochemical staining for glial or neuronal markers (GFAp, CAII and NeuN), we show that two limited populations of oligodendrocytes and astrocytes cell bodies and processes are immunoreactive for GLUT2, whereas a cross-reaction with GLUT1 cannot be ruled out. In addition, we report that the nerve cell bodies clearly immunostained for GLUT2 were scarce (although numerous in the dentate gyrus granular layer in particular), whereas the periphery of numerous nerve cells appeared labeled for this transporter. The latter were clustered in the dorsal endopiriform nucleus and neighboring temporal and perirhinal cortex, in the dorsal amygdaloid region, and in the paraventricular and reuniens thalamic nuclei, whereas they were only a few in the hypothalamus. Moreover, a group of GLUT2-immunoreactive nerve cell bodies was localized in the dorsal medulla oblongata while some large multipolar nerve cell bodies peripherally labeled for GLUT2 were scattered in the caudal ventral reticular formation. This anatomical localization of GLUT2 appears characteristic and different from that reported for the neuronal transporter GLUT3 and GLUT4. Indeed, the possibility that GLUT2 may be localized in the sub-plasmalemnal region of neurones and/or in afferent nerve fibres remains to be confirmed by ultrastructural observations. Because of the neuronal localization of GLUT2, and of its distribution relatively similar to glucokinase, it may be hypothesized that this transporter is, at least partially, involved in cerebral glucose sensing.

Temporal distribution and magnitude of the vulnerability period around stroke depend on stroke subtype.

Background: We aimed to analyze the rate and time distribution of pre- and post-morbid cerebrovascular events in a single ischemic stroke population, and whether these depend on the etiology of the index stroke. Methods: In 2,203 consecutive patients admitted to a single stroke center registry (ASTRAL), the ischemic stroke that led to admission was considered the index event. Frequency distribution and cumulative relative distribution graphs of the most recent and first recurrent event (ischemic stroke, transient ischemic attack, intracranial or subarachnoid hemorrhage) were drawn in weekly and daily intervals for all strokes and for all stroke types. Results: The frequency of events at identical time points before and after the index stroke was mostly reduced in the first week after (vs. before) stroke (1.0 vs. 4.2%, p < 0.001) and the first month (2.7 vs. 7.4%, p < 0.001), and then ebbed over the first year (8.4 vs. 13.1%, p < 0.001). On daily basis, the peak frequency was noticed at day -1 (1.6%) with a reduction to 0.7% on the index day and 0.17% 24 h after. The event rate in patients with atherosclerotic stroke was particularly high around the index event, but 1-year cumulative recurrence rate was similar in all stroke types. Conclusions: We confirm a short window of increased vulnerability in ischemic stroke and show a 4-, 3- and 2-fold reduction in post-stroke events at 1 week, 1 month and 1 year, respectively, compared to identical pre-stroke periods. This break in the 'stroke wave' is particularly striking after atherosclerotic and lacunar strokes.

Geostatistical determination of heavy-metal distribution in a contaminated site - A case study

A two stage sampling strategy is necessary in order to optimize the study of distribution of pollution in soils and groundwater. First, detailed sampling from a limited area coupled with statistical analysis of the data are used to determine the microvariability of the parameter(s). The results from this detailed analysis are then used to calculate the optimal spacing between samples for the larger scale study. This two stage sampling strategy can result in significant financial savings during subsequent soil or groundwater remediation. This combined sampling and statistical analysis approach is illustrated with an example from a heavy metal contaminated site.