Comparative localization of glioma-reactive monoclonal antibodies in vivo in an athymic mouse human glioma xenograft model.

Radioiodinated murine monoclonal antibodies (Mabs) 81C6, Me 1-14, C12, D12, and E9, made against or reactive with human gliomas but not normal brain, and Mab UJ13A, a pan-neuroectodermal Mab reactive with normal human glial and neural cells, were evaluated in paired label studies in the D-54 MG subcutaneous human glioma xenograft model system in nude mice. Following intravenous injection in the tail vein of mice bearing 200-400 mm3 tumors, specific localization of Mabs to tumor over time (6 h-9 days) was evaluated by tissue counting; each Mab demonstrated a unique localization profile. The comparison of localization indices (LI), determined as a ratio of tissue level of Mab to control immunoglobulin with simultaneous correction for blood levels of each, showed Mabs 81C6 and Me 1-14 to steadily accumulate in glioma xenografts, maintaining LI from 5-20 at 7-9 days after Mab injection. Mab UJ13A peaked at day 1, maintaining this level through day 2, and declining thereafter. Mabs D12 and C12 peaked at days 3 and 4, respectively, and E9 maintained an LI of greater than 3 from days 3-9. Percent injected dose localized/g of tumor varied from a peak high of 16% (81C6) to a low of 5% (Me 1-14 and UJ13A). Immunoperoxidase histochemistry, performed with each Mab on a battery of primary human brain neoplasms, revealed that Mabs 81C6 and E9, which demonstrated the highest levels of percent injected dose localized/g of tumor over time, reacted with antigens expressed in the extracellular matrix. This finding suggests that extracellular matrix localization of antigen represents a biologically significant factor affecting localization and/or binding in the xenograft model used. The demonstration of significant localization, varied kinetics and patterns of localization of this localizing Mab panel warrants their continued investigation as potential imaging and therapeutic agents for human trials.

MRI-based prostate brachytherapy seed localization.

A magnetic resonance imaging (MRI) pulse sequence and a corresponding image processing algorithm to localize prostate brachytherapy seeds during or after therapy are presented. Inversion-Recovery with ON-resonant water suppression (IRON) is an MRI methodology that generates positive contrast in regions of magnetic field susceptibility, as created by prostate brachytherapy seeds. Phantoms comprising of several materials found in brachytherapy seeds were created to assess the usability of the IRON pulse sequence for imaging seeds. Resulting images show that seed materials are clearly visible with high contrast using IRON, agreeing with theoretical predictions. A seed localization algorithm to process IRON images demonstrates the potential of this imaging technique for seed localization and dosimetry.

Distinct levels in Pom1 gradients limit Cdr2 activity and localization to time and position division.

Where and when cells divide are fundamental questions. In rod-shaped fission yeast cells, the DYRK-family kinase Pom1 is organized in concentration gradients from cell poles and controls cell division timing and positioning. Pom1 gradients restrict to mid-cell the SAD-like kinase Cdr2, which recruits Mid1/Anillin for medial division. Pom1 also delays mitotic commitment through Cdr2, which inhibits Wee1. Here, we describe quantitatively the distributions of cortical Pom1 and Cdr2. These reveal low profile overlap contrasting with previous whole-cell measurements and Cdr2 levels increase with cell elongation, raising the possibility that Pom1 regulates mitotic commitment by controlling Cdr2 medial levels. However, we show that distinct thresholds of Pom1 activity define the timing and positioning of division. Three conditions-a separation-of-function Pom1 allele, partial downregulation of Pom1 activity, and haploinsufficiency in diploid cells-yield cells that divide early, similar to pom1 deletion, but medially, like wild-type cells. In these cells, Cdr2 is localized correctly at mid-cell. Further, Cdr2 overexpression promotes precocious mitosis only in absence of Pom1. Thus, Pom1 inhibits Cdr2 for mitotic commitment independently of regulating its localization or cortical levels. Indeed, we show Pom1 restricts Cdr2 activity through phosphorylation of a C-terminal self-inhibitory tail. In summary, our results demonstrate that distinct levels in Pom1 gradients delineate a medial Cdr2 domain, for cell division placement, and control its activity, for mitotic commitment.

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.

Operational benefits and challenges of the use of fingerprint statistical models: a field study

Research projects aimed at proposing fingerprint statistical models based on the likelihood ratio framework have shown that low quality finger impressions left on crime scenes may have significant evidential value. These impressions are currently either not recovered, considered to be of no value when first analyzed by fingerprint examiners, or lead to inconclusive results when compared to control prints. There are growing concerns within the fingerprint community that recovering and examining these low quality impressions will result in a significant increase of the workload of fingerprint units and ultimately of the number of backlogged cases. This study was designed to measure the number of impressions currently not recovered or not considered for examination, and to assess the usefulness of these impressions in terms of the number of additional detections that would result from their examination.

Vulnerabilities of Fingerprint Reader to Fake Fingerprint Attacks

The purpose of this research is to assess the vulnerabilities of a high resolution fingerprint sensor when confronted with fake fingerprints. The study has not been focused on the decision outcome of the biometric device, but essentially on the scores obtained following the comparison between a query (genuine or fake) and a template using an AFIS system. To do this, fake fingerprints of 12 subjects have been produced with and without their cooperation. These fake fingerprints have been used alongside with real fingers. The study led to three major observations: First, genuine fingerprints produced scores higher than fake fingers (translating a closer proximity) and this tendency is observed considering each subject separately. Second, scores are however not sufficient as a single measure to differentiate these samples (fake from genuine) given the variation due to the donors themselves. That explains why fingerprint readers without vitality detection can be fooled. Third, production methods and subjects greatly influence the scores obtained for fake fingerprints.

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.

The integrin antagonist cilengitide activates alphaVbeta3, disrupts VE-cadherin localization at cell junctions and enhances permeability in endothelial cells.

Cilengitide is a high-affinity cyclic pentapeptdic alphaV integrin antagonist previously reported to suppress angiogenesis by inducing anoikis of endothelial cells adhering through alphaVbeta3/alphaVbeta5 integrins. Angiogenic endothelial cells express multiple integrins, in particular those of the beta1 family, and little is known on the effect of cilengitide on endothelial cells expressing alphaVbeta3 but adhering through beta1 integrins. Through morphological, biochemical, pharmacological and functional approaches we investigated the effect of cilengitide on alphaVbeta3-expressing human umbilical vein endothelial cells (HUVEC) cultured on the beta1 ligands fibronectin and collagen I. We show that cilengitide activated cell surface alphaVbeta3, stimulated phosphorylation of FAK (Y(397) and Y(576/577)), Src (S(418)) and VE-cadherin (Y(658) and Y(731)), redistributed alphaVbeta3 at the cell periphery, caused disappearance of VE-cadherin from cellular junctions, increased the permeability of HUVEC monolayers and detached HUVEC adhering on low-density beta1 integrin ligands. Pharmacological inhibition of Src kinase activity fully prevented cilengitide-induced phosphorylation of Src, FAK and VE-cadherin, and redistribution of alphaVbeta3 and VE-cadherin and partially prevented increased permeability, but did not prevent HUVEC detachment from low-density matrices. Taken together, these observations reveal a previously unreported effect of cilengitide on endothelial cells namely its ability to elicit signaling events disrupting VE-cadherin localization at cellular contacts and to increase endothelial monolayer permeability. These effects are potentially relevant to the clinical use of cilengitide as anticancer agent.

A two-phase composite in simple shear: Effective mechanical anisotropy development and localization potential

We present a combined shape and mechanical anisotropy evolution model for a two-phase inclusion-bearing rock subject to large deformation. A single elliptical inclusion embedded in a homogeneous but anisotropic matrix is used to represent a simplified shape evolution enforced on all inclusions. The mechanical anisotropy develops due to the alignment of elongated inclusions. The effective anisotropy is quantified using the differential effective medium (DEM) approach. The model can be run for any deformation path and an arbitrary viscosity ratio between the inclusion and host phase. We focus on the case of simple shear and weak inclusions. The shape evolution of the representative inclusion is largely insensitive to the anisotropy development and to parameter variations in the studied range. An initial hardening stage is observed up to a shear strain of gamma = 1 irrespective of the inclusion fraction. The hardening is followed by a softening stage related to the developing anisotropy and its progressive rotation toward the shear direction. The traction needed to maintain a constant shear rate exhibits a fivefold drop at gamma = 5 in the limiting case of an inviscid inclusion. Numerical simulations show that our analytical model provides a good approximation to the actual evolution of a two-phase inclusion-host composite. However, the inclusions develop complex sigmoidal shapes resulting in the formation of an S-C fabric. We attribute the observed drop in the effective normal viscosity to this structural development. We study the localization potential in a rock column bearing varying fraction of inclusions. In the inviscid inclusion case, a strain jump from gamma = 3 to gamma = 100 is observed for a change of the inclusion fraction from 20% to 33%.

Predictive analysis and mapping of indoor radon concentrations in Switzerland

It is estimated that around 230 people die each year due to radon (222Rn) exposure in Switzerland. 222Rn occurs mainly in closed environments like buildings and originates primarily from the subjacent ground. Therefore it depends strongly on geology and shows substantial regional variations. Correct identification of these regional variations would lead to substantial reduction of 222Rn exposure of the population based on appropriate construction of new and mitigation of already existing buildings. Prediction of indoor 222Rn concentrations (IRC) and identification of 222Rn prone areas is however difficult since IRC depend on a variety of different variables like building characteristics, meteorology, geology and anthropogenic factors. The present work aims at the development of predictive models and the understanding of IRC in Switzerland, taking into account a maximum of information in order to minimize the prediction uncertainty. The predictive maps will be used as a decision-support tool for 222Rn risk management. The construction of these models is based on different data-driven statistical methods, in combination with geographical information systems (GIS). In a first phase we performed univariate analysis of IRC for different variables, namely the detector type, building category, foundation, year of construction, the average outdoor temperature during measurement, altitude and lithology. All variables showed significant associations to IRC. Buildings constructed after 1900 showed significantly lower IRC compared to earlier constructions. We observed a further drop of IRC after 1970. In addition to that, we found an association of IRC with altitude. With regard to lithology, we observed the lowest IRC in sedimentary rocks (excluding carbonates) and sediments and the highest IRC in the Jura carbonates and igneous rock. The IRC data was systematically analyzed for potential bias due to spatially unbalanced sampling of measurements. In order to facilitate the modeling and the interpretation of the influence of geology on IRC, we developed an algorithm based on k-medoids clustering which permits to define coherent geological classes in terms of IRC. We performed a soil gas 222Rn concentration (SRC) measurement campaign in order to determine the predictive power of SRC with respect to IRC. We found that the use of SRC is limited for IRC prediction. The second part of the project was dedicated to predictive mapping of IRC using models which take into account the multidimensionality of the process of 222Rn entry into buildings. We used kernel regression and ensemble regression tree for this purpose. We could explain up to 33% of the variance of the log transformed IRC all over Switzerland. This is a good performance compared to former attempts of IRC modeling in Switzerland. As predictor variables we considered geographical coordinates, altitude, outdoor temperature, building type, foundation, year of construction and detector type. Ensemble regression trees like random forests allow to determine the role of each IRC predictor in a multidimensional setting. We found spatial information like geology, altitude and coordinates to have stronger influences on IRC than building related variables like foundation type, building type and year of construction. Based on kernel estimation we developed an approach to determine the local probability of IRC to exceed 300 Bq/m3. In addition to that we developed a confidence index in order to provide an estimate of uncertainty of the map. All methods allow an easy creation of tailor-made maps for different building characteristics. Our work is an essential step towards a 222Rn risk assessment which accounts at the same time for different architectural situations as well as geological and geographical conditions. For the communication of 222Rn hazard to the population we recommend to make use of the probability map based on kernel estimation. The communication of 222Rn hazard could for example be implemented via a web interface where the users specify the characteristics and coordinates of their home in order to obtain the probability to be above a given IRC with a corresponding index of confidence. Taking into account the health effects of 222Rn, our results have the potential to substantially improve the estimation of the effective dose from 222Rn delivered to the Swiss population.

Defining the RNA polymerase III transcriptome: Genome-wide localization of the RNA polymerase III transcription machinery in human cells.

Our view of the RNA polymerase III (Pol III) transcription machinery in mammalian cells arises mostly from studies of the RN5S (5S) gene, the Ad2 VAI gene, and the RNU6 (U6) gene, as paradigms for genes with type 1, 2, and 3 promoters. Recruitment of Pol III onto these genes requires prior binding of well-characterized transcription factors. Technical limitations in dealing with repeated genomic units, typically found at mammalian Pol III genes, have so far hampered genome-wide studies of the Pol III transcription machinery and transcriptome. We have localized, genome-wide, Pol III and some of its transcription factors. Our results reveal broad usage of the known Pol III transcription machinery and define a minimal Pol III transcriptome in dividing IMR90hTert fibroblasts. This transcriptome consists of some 500 actively transcribed genes including a few dozen candidate novel genes, of which we confirmed nine as Pol III transcription units by additional methods. It does not contain any of the microRNA genes previously described as transcribed by Pol III, but reveals two other microRNA genes, MIR886 (hsa-mir-886) and MIR1975 (RNY5, hY5, hsa-mir-1975), which are genuine Pol III transcription units.

Contribution of fine particulate matter sources to indoor exposure in bars, restaurants, and cafes

This study investigated the contribution of sources and establishment characteristics, on the exposure to fine particulate matter (PM(2.5)) in the non-smoking sections of bars, cafes, and restaurants in central Zurich. PM(2.5)-exposure was determined with a nephelometer. A random sample of hospitality establishments was investigated on all weekdays, from morning until midnight. Each visit lasted 30 min. Numbers of smokers and other sources, such as candles and cooking processes, were recorded, as were seats, open windows, and open doors. Ambient air pollution data were obtained from public authorities. Data were analysed using robust MM regression. Over 14 warm, sunny days, 102 establishments were measured. Average establishment PM(2.5) concentrations were 64.7 microg/m(3) (s.d. = 73.2 microg/m(3), 30-min maximum 452.2 microg/m(3)). PM(2.5) was significantly associated with the number of smokers, percentage of seats occupied by smokers, and outdoor PM. Each smoker increased PM(2.5) on average by 15 microg/m(3). No associations were found with other sources, open doors or open windows. Bars had more smoking guests and showed significantly higher concentrations than restaurants and cafes. Smokers were the most important PM(2.5)-source in hospitality establishments, while outdoor PM defined the baseline. Concentrations are expected to be even higher during colder, unpleasant times of the year. PRACTICAL IMPLICATIONS: Smokers and ambient air pollution are the most important sources of fine airborne particulate matter (PM(2.5)) in the non-smoking sections of bars, restaurants, and cafes. Other sources do not significantly contribute to PM(2.5)-levels, while opening doors and windows is not an efficient means of removing pollutants. First, this demonstrates the impact that even a few smokers can have in affecting particle levels. Second, it implies that creating non-smoking sections, and using natural ventilation, is not sufficient to bring PM(2.5) to levels that imply no harm for employees and non-smoking clients. [Authors]

Changes in D-serine levels and localization during postnatal development of the rat vestibular nuclei.

The patterns of development of the vestibular nuclei (VN) and their main connections involving glutamate neurotransmission offer a good model for studying the function of the glial-derived neuromodulator D-serine in synaptic plasticity. In this study we show that D-serine is present in the VN and we analyzed its distribution and the levels of expression of serine racemase and D-amino acid oxidase (D-AAO) at different stages of postnatal (P) development. From birth to P21, high levels of D-serine were detected in glial cells and processes in all parts of the VN. This period corresponded to high expression of serine racemase and low expression of D-AAO. On the other hand, in the mature VN D-serine displayed very low levels and was mainly localized in neuronal cell bodies and dendrites. This drop of D-serine in adult stages corresponded to an increasing expression of D-AAO at mature stages. High levels of glial D-serine during the first 3 weeks of postnatal development correspond to an intense period of plasticity and synaptogenesis and maturation of VN afferents, suggesting that D-serine could be involved in these phenomena. These results demonstrate for the first time that changes in D-serine levels and distribution occur during postnatal development in the central nervous system. The strong decrease of D-serine levels and the glial-to-neuronal switch suggests that D-serine may have distinct functional roles depending on the developmental stage of the vestibular network.