Methylation of the hTERT Promoter: A Novel Cancer Biomarker for Leptomeningeal Metastasis Detection in Cerebrospinal Fluids.

PURPOSE: The diagnosis of leptomeningeal metastases is usually confirmed by the finding of malignant cells by cytologic examination in the cerebrospinal fluid (CSF). More sensitive and specific cancer biomarkers may improve the detection of tumor cells in the CSF. Promoter methylation of the human telomerase reverse transcriptase (hTERT) gene characterizes most cancer cells. The aim of this study was to develop a sensitive method to detect hTERT methylation and to explore its use as a cancer biomarker in CSF. EXPERIMENTAL DESIGN: In 77 CSF specimens from 67 patients, hTERT promoter methylation was evaluated using real-time methylation-sensitive high-resolution melting (MS-HRM) and real-time TaqMan PCR and MS-HRM in a single-tube assay. RESULTS: Real-time MS-HRM assay was able to detect down to 1% hTERT-methylated DNA in a background of unmethylated DNA. PCR products were obtained from 90% (69/77) of CSF samples. No false positive hTERT was detected in the 21 non-neoplastic control cases, given to the method a specificity of 100%. The sensitivity of the real-time MS-HRM compared with the cytologic gold standard analysis was of 92% (11/12). Twenty-six CSFs from 22 patients with an hTERT-methylated primary tumor showed cytologic results suspicious for malignancy; in 17 (65%) of them, a diagnosis of leptomeningeal metastases could be confirmed by the hTERT methylation test. CONCLUSION: The hTERT real-time MS-HRM approach is fast, specific, sensitive, and could therefore become a valuable tool for diagnosis of leptomeningeal metastases as an adjunct to the traditional examination of CSF. Clin Cancer Res; 19(8); 2216-23. ©2013 AACR.

Large-scale clustering through functional embedding

We present a new framework for large-scale data clustering. The main idea is to modify functional dimensionality reduction techniques to directly optimize over discrete labels using stochastic gradient descent. Compared to methods like spectral clustering our approach solves a single optimization problem, rather than an ad-hoc two-stage optimization approach, does not require a matrix inversion, can easily encode prior knowledge in the set of implementable functions, and does not have an ?out-of-sample? problem. Experimental results on both artificial and real-world datasets show the usefulness of our approach.

Workplace Aggressive Behaviors: Three Essays

Abstract¦This thesis examines through three essays the role of the social context and of people concern for justice in explaining workplace aggressive behaviors.¦In the first essay, I argue that a work group instrumental climate - a climate emphasizing respect of organizational procedures -deters employees to manifest counterproductive work behaviors through informal sanctions (i.e., socio-emotional disapproval) they anticipate from it for misbehaving. A contrario, a work group affective climate - a climate concerned about others' well-being - leads employees to infer less informal sanctions and thus indirectly facilitates counterproductive work behaviors. I additionally expect these indirect effects to be conditional on employees' level of conscientiousness and agreeableness. Cross-level structural equations on cross-sectional data obtained from 158 employees in 26 work groups supported my expectations. By promoting collective responsibility for the respect of organizational rules and by knowing what their work group considers threatening their well-being, leaders may be able to prevent counterproductive work behaviors.¦Adopting an organizational justice perspective, the second essay provides a theoretical explanation of why and how collective deviance can emerge in a collective. In interdependent situations, employees use justice perceptions to infer others' cooperative intent. Even if moral transgressions (e.g., injustice) are ambiguous, their repetition and configuration within a team can lead employees to assign blame and develop collective cynicism toward the transgressor. Over time, collective cynicism - a shared belief about the transgressor's intentional lack of integrity - progressively constrains the diversity of employees' response to blame and leads collective deviance to emerge. This essay contributes to workplace deviance research by offering a theoretical framework for investigations of the phenomenon at the collective level. It organizations effort to manage and prevent deviance should consider.¦In the third essay, I solve an apparent contradiction in the literature showing that justice concerns sometimes lead employees to react aggressively to injustice and sometimes to refrain from it. Drawing from just-world theory, a cross-sectional field study and an experiment provide evidence that retaliatory tendencies following injustice are moderated by personal and general just-world beliefs. Whereas a high personal just-world belief facilitates retaliatory reactions to injustice, a high general just-world belief attenuates such reactions. This essay uncovers a dark side of personal just-world belief and a bright one of general just-world belief, and participates to extend just-world theory to the working context.

Gene transfer of a soluble IL-1 type 2 receptor-Ig fusion protein improves cardiac allograft survival in rats.

OBJECTIVE: Interleukin-1 (IL-1) mediates ischemia-reperfusion injury and graft inflammation after heart transplantation. IL-1 affects target cells through two distinct types of transmembrane receptors, type-1 receptor (IL-1R1), which transduces the signal, and the non-signaling type-2 receptor (IL-1R2), which acts as a ligand sink that subtracts IL-1beta from IL-1R1. We analyzed the efficacy of adenovirus (Ad)-mediated gene transfer of a soluble IL-1R2-Ig fusion protein in delaying cardiac allograft rejection and the mechanisms underlying the protective effect. METHODS: IL-1 inhibition by IL-1R2-Ig was tested using an in vitro functional assay whereby endothelial cells preincubated with AdIL-1R2-Ig or control virus were stimulated with recombinant IL-1beta or tumor necrosis factor-alpha (TNF-alpha), and urokinase-type plasminogen activator (u-PA) induction was measured by zymography. AdIL-1R2-Ig was delivered to F344 rat donor hearts ex vivo, which were placed in the abdominal position in LEW hosts. Intragraft inflammatory cell infiltrates and proinflammatory cytokine expression were analyzed by immunohistochemistry and real-time reverse transcriptase-polymerase chain reaction (RT-PCR), respectively. RESULTS: IL-1R2-Ig specifically inhibited IL-1beta-induced u-PA responses in vitro. IL-1R2-Ig gene transfer reduced intragraft monocytes/macrophages and CD4(+) cell infiltrates (p<0.05), TNF-alpha and transforming growth factor-beta (TGF-beta) expression (p<0.05), and prolonged graft survival (15.6+/-5.7 vs 10.3+/-2.5 days with control vector and 10.1+/-2.1 days with buffer alone; p<0.01). AdIL-1R2-Ig combined with a subtherapeutic regimen of cyclosporin A (CsA) was superior to CsA alone (19.4+/-3.0 vs 15.9+/-1.8 days; p<0.05). CONCLUSIONS: Soluble IL-1 type-2 receptor gene transfer attenuates cardiac allograft rejection in a rat model. IL-1 inhibition may be useful as an adjuvant therapy in heart transplantation.

Dynamics of multiple lin gene expression in Sphingomonas paucimobilis B90A in response to different hexachlorocyclohexane isomers.

Sphingomonas paucimobilis B90A is able to degrade the alpha-, beta-, gamma-, and delta-isomers of hexachlorocyclohexane (HCH). It contains the genes linA, linB, linC, linD, linE, and linR, which have been implicated in HCH degradation. In this study, dynamic expression of the lin genes was measured in chemostat-grown S. paucimobilis B90A by RNA dot blot hybridization and real-time reverse transcriptase PCR upon exposure to a pulse of different HCH isomers. Irrespective of the addition of HCH, linA, linB, and linC were all expressed constitutively. In contrast, linD and linE were induced with alpha-HCH (2 mg/liter) and gamma-HCH (7 mg/liter). A sharp increase in mRNA levels for linD and linE was observed from 10 to 45 min after the addition of alpha- or gamma-HCH. Induction of linD and linE was not detectable upon the addition of 0.7 mg of gamma-HCH per liter, although the compound was degraded by the cells. The addition of beta-HCH (5 mg/liter) or delta-HCH (20 mg/liter) did not lead to linE and linD induction, despite the fact that 50% of the compounds were degraded. This suggests that degradation of beta- and delta-HCH proceeds by a different pathway than that of alpha- and gamma-HCH.

Label-free detection of tobramycin in serum by transmission-localized surface plasmon resonance.

In order to improve the efficacy and safety of treatments, drug dosage needs to be adjusted to the actual needs of each patient in a truly personalized medicine approach. Key for widespread dosage adjustment is the availability of point-of-care devices able to measure plasma drug concentration in a simple, automated, and cost-effective fashion. In the present work, we introduce and test a portable, palm-sized transmission-localized surface plasmon resonance (T-LSPR) setup, comprised of off-the-shelf components and coupled with DNA-based aptamers specific to the antibiotic tobramycin (467 Da). The core of the T-LSPR setup are aptamer-functionalized gold nanoislands (NIs) deposited on a glass slide covered with fluorine-doped tin oxide (FTO), which acts as a biosensor. The gold NIs exhibit localized plasmon resonance in the visible range matching the sensitivity of the complementary metal oxide semiconductor (CMOS) image sensor employed as a light detector. The combination of gold NIs on the FTO substrate, causing NIs size and pattern irregularity, might reduce the overall sensitivity but confers extremely high stability in high-ionic solutions, allowing it to withstand numerous regeneration cycles without sensing losses. With this rather simple T-LSPR setup, we show real-time label-free detection of tobramycin in buffer, measuring concentrations down to 0.5 μM. We determined an affinity constant of the aptamer-tobramycin pair consistent with the value obtained using a commercial propagating-wave based SPR. Moreover, our label-free system can detect tobramycin in filtered undiluted blood serum, measuring concentrations down to 10 μM with a theoretical detection limit of 3.4 μM. While the association signal of tobramycin onto the aptamer is masked by the serum injection, the quantification of the captured tobramycin is possible during the dissociation phase and leads to a linear calibration curve for the concentrations over the tested range (10-80 μM). The plasmon shift following surface binding is calculated in terms of both plasmon peak location and hue, with the latter allowing faster data elaboration and real-time display of the results. The presented T-LSPR system shows for the first time label-free direct detection and quantification of a small molecule in the complex matrix of filtered undiluted blood serum. Its uncomplicated construction and compact size, together with the remarkable performances, represent a leap forward toward effective point-of-care devices for therapeutic drug concentration monitoring.

Structured sparsity for spatially coherent fibre orientation estimation in diffusion MRI.

We propose a novel formulation to solve the problem of intra-voxel reconstruction of the fibre orientation distribution function (FOD) in each voxel of the white matter of the brain from diffusion MRI data. The majority of the state-of-the-art methods in the field perform the reconstruction on a voxel-by-voxel level, promoting sparsity of the orientation distribution. Recent methods have proposed a global denoising of the diffusion data using spatial information prior to reconstruction, while others promote spatial regularisation through an additional empirical prior on the diffusion image at each q-space point. Our approach reconciles voxelwise sparsity and spatial regularisation and defines a spatially structured FOD sparsity prior, where the structure originates from the spatial coherence of the fibre orientation between neighbour voxels. The method is shown, through both simulated and real data, to enable accurate FOD reconstruction from a much lower number of q-space samples than the state of the art, typically 15 samples, even for quite adverse noise conditions.

What role do just-world beliefs play in harmful responses to injustice?

Research suggests that employees sometimes retaliate and sometimes refrain from retaliation for the same reason, namely because they care about justice. In two studies, we seek to solve this apparent inconsistency. Drawing on just world theory, we argue that retaliatory, harmful behavioral strategies to deal with injustice are associated to individual differences in personal belief in a just world (personal BJW). In contrast, individual differences in believing that the world is just in general (general BJW) are linked to the inhibition of these reactions. As a consequence, the relation between injustice and harmful behaviors is stronger for people with a high personal BJW than for those with a low one. General BJW is associated with their inhibition such that the relation between injustice and harmful reactions is weaker for people with a high general BJW than for those with a low one. We found evidence for our hypotheses in a cross-sectional field study and an experiment. We discuss our findings in light of their implications for just-world theory and suggest avenues for future research integrating organizational justice literature.

An efficient total variation algorithm for super-resolution in fetal brain MRI with adaptive regularization.

Although fetal anatomy can be adequately viewed in new multi-slice MR images, many critical limitations remain for quantitative data analysis. To this end, several research groups have recently developed advanced image processing methods, often denoted by super-resolution (SR) techniques, to reconstruct from a set of clinical low-resolution (LR) images, a high-resolution (HR) motion-free volume. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has been quite attracted by Total Variation energies because of their ability in edge preserving but only standard explicit steepest gradient techniques have been applied for optimization. In a preliminary work, it has been shown that novel fast convex optimization techniques could be successfully applied to design an efficient Total Variation optimization algorithm for the super-resolution problem. In this work, two major contributions are presented. Firstly, we will briefly review the Bayesian and Variational dual formulations of current state-of-the-art methods dedicated to fetal MRI reconstruction. Secondly, we present an extensive quantitative evaluation of our SR algorithm previously introduced on both simulated fetal and real clinical data (with both normal and pathological subjects). Specifically, we study the robustness of regularization terms in front of residual registration errors and we also present a novel strategy for automatically select the weight of the regularization as regards the data fidelity term. Our results show that our TV implementation is highly robust in front of motion artifacts and that it offers the best trade-off between speed and accuracy for fetal MRI recovery as in comparison with state-of-the art methods.

Analysis of space-time environmental data in complex regions

This thesis develops a comprehensive and a flexible statistical framework for the analysis and detection of space, time and space-time clusters of environmental point data. The developed clustering methods were applied in both simulated datasets and real-world environmental phenomena; however, only the cases of forest fires in Canton of Ticino (Switzerland) and in Portugal are expounded in this document. Normally, environmental phenomena can be modelled as stochastic point processes where each event, e.g. the forest fire ignition point, is characterised by its spatial location and occurrence in time. Additionally, information such as burned area, ignition causes, landuse, topographic, climatic and meteorological features, etc., can also be used to characterise the studied phenomenon. Thereby, the space-time pattern characterisa- tion represents a powerful tool to understand the distribution and behaviour of the events and their correlation with underlying processes, for instance, socio-economic, environmental and meteorological factors. Consequently, we propose a methodology based on the adaptation and application of statistical and fractal point process measures for both global (e.g. the Morisita Index, the Box-counting fractal method, the multifractal formalism and the Ripley's K-function) and local (e.g. Scan Statistics) analysis. Many measures describing the space-time distribution of environmental phenomena have been proposed in a wide variety of disciplines; nevertheless, most of these measures are of global character and do not consider complex spatial constraints, high variability and multivariate nature of the events. Therefore, we proposed an statistical framework that takes into account the complexities of the geographical space, where phenomena take place, by introducing the Validity Domain concept and carrying out clustering analyses in data with different constrained geographical spaces, hence, assessing the relative degree of clustering of the real distribution. Moreover, exclusively to the forest fire case, this research proposes two new methodologies to defining and mapping both the Wildland-Urban Interface (WUI) described as the interaction zone between burnable vegetation and anthropogenic infrastructures, and the prediction of fire ignition susceptibility. In this regard, the main objective of this Thesis was to carry out a basic statistical/- geospatial research with a strong application part to analyse and to describe complex phenomena as well as to overcome unsolved methodological problems in the characterisation of space-time patterns, in particular, the forest fire occurrences. Thus, this Thesis provides a response to the increasing demand for both environmental monitoring and management tools for the assessment of natural and anthropogenic hazards and risks, sustainable development, retrospective success analysis, etc. The major contributions of this work were presented at national and international conferences and published in 5 scientific journals. National and international collaborations were also established and successfully accomplished. -- Cette thèse développe une méthodologie statistique complète et flexible pour l'analyse et la détection des structures spatiales, temporelles et spatio-temporelles de données environnementales représentées comme de semis de points. Les méthodes ici développées ont été appliquées aux jeux de données simulées autant qu'A des phénomènes environnementaux réels; nonobstant, seulement le cas des feux forestiers dans le Canton du Tessin (la Suisse) et celui de Portugal sont expliqués dans ce document. Normalement, les phénomènes environnementaux peuvent être modélisés comme des processus ponctuels stochastiques ou chaque événement, par ex. les point d'ignition des feux forestiers, est déterminé par son emplacement spatial et son occurrence dans le temps. De plus, des informations tels que la surface bru^lée, les causes d'ignition, l'utilisation du sol, les caractéristiques topographiques, climatiques et météorologiques, etc., peuvent aussi être utilisées pour caractériser le phénomène étudié. Par conséquent, la définition de la structure spatio-temporelle représente un outil puissant pour compren- dre la distribution du phénomène et sa corrélation avec des processus sous-jacents tels que les facteurs socio-économiques, environnementaux et météorologiques. De ce fait, nous proposons une méthodologie basée sur l'adaptation et l'application de mesures statistiques et fractales des processus ponctuels d'analyse global (par ex. l'indice de Morisita, la dimension fractale par comptage de boîtes, le formalisme multifractal et la fonction K de Ripley) et local (par ex. la statistique de scan). Des nombreuses mesures décrivant les structures spatio-temporelles de phénomènes environnementaux peuvent être trouvées dans la littérature. Néanmoins, la plupart de ces mesures sont de caractère global et ne considèrent pas de contraintes spatiales com- plexes, ainsi que la haute variabilité et la nature multivariée des événements. A cet effet, la méthodologie ici proposée prend en compte les complexités de l'espace géographique ou le phénomène a lieu, à travers de l'introduction du concept de Domaine de Validité et l'application des mesures d'analyse spatiale dans des données en présentant différentes contraintes géographiques. Cela permet l'évaluation du degré relatif d'agrégation spatiale/temporelle des structures du phénomène observé. En plus, exclusif au cas de feux forestiers, cette recherche propose aussi deux nouvelles méthodologies pour la définition et la cartographie des zones périurbaines, décrites comme des espaces anthropogéniques à proximité de la végétation sauvage ou de la forêt, et de la prédiction de la susceptibilité à l'ignition de feu. A cet égard, l'objectif principal de cette Thèse a été d'effectuer une recherche statistique/géospatiale avec une forte application dans des cas réels, pour analyser et décrire des phénomènes environnementaux complexes aussi bien que surmonter des problèmes méthodologiques non résolus relatifs à la caractérisation des structures spatio-temporelles, particulièrement, celles des occurrences de feux forestières. Ainsi, cette Thèse fournit une réponse à la demande croissante de la gestion et du monitoring environnemental pour le déploiement d'outils d'évaluation des risques et des dangers naturels et anthro- pogéniques. Les majeures contributions de ce travail ont été présentées aux conférences nationales et internationales, et ont été aussi publiées dans 5 revues internationales avec comité de lecture. Des collaborations nationales et internationales ont été aussi établies et accomplies avec succès.