Ontology-based Access Control in Open Scenarios: Applications to Social Networks and the Cloud

La integració d'Internet a la societat actual ha fet possible compartir fàcilment grans quantitats d'informació electrònica i recursos informàtics (que inclouen maquinari, serveis informàtics, etc.) en entorns distribuïts oberts. Aquests entorns serveixen de plataforma comuna per a usuaris heterogenis (per exemple, empreses, individus, etc.) on es proporciona allotjament d'aplicacions i sistemes d'usuari personalitzades; i on s'ofereix un accés als recursos compartits des de qualsevol lloc i amb menys esforços administratius. El resultat és un entorn que permet a individus i empreses augmentar significativament la seva productivitat. Com ja s'ha dit, l'intercanvi de recursos en entorns oberts proporciona importants avantatges per als diferents usuaris, però, també augmenta significativament les amenaces a la seva privacitat. Les dades electròniques compartides poden ser explotades per tercers (per exemple, entitats conegudes com "Data Brokers"). Més concretament, aquestes organitzacions poden agregar la informació compartida i inferir certes característiques personals sensibles dels usuaris, la qual cosa pot afectar la seva privacitat. Una manera de del.liar aquest problema consisteix a controlar l'accés dels usuaris als recursos potencialment sensibles. En concret, la gestió de control d'accés regula l'accés als recursos compartits d'acord amb les credencials dels usuaris, el tipus de recurs i les preferències de privacitat dels propietaris dels recursos/dades. La gestió eficient de control d'accés és crucial en entorns grans i dinàmics. D'altra banda, per tal de proposar una solució viable i escalable, cal eliminar la gestió manual de regles i restriccions (en la qual, la majoria de les solucions disponibles depenen), atès que aquesta constitueix una pesada càrrega per a usuaris i administradors . Finalment, la gestió del control d'accés ha de ser intuïtiu per als usuaris finals, que en general no tenen grans coneixements tècnics.La integración de Internet en la sociedad actual ha hecho posible compartir fácilmente grandes cantidades de información electrónica y recursos informáticos (que incluyen hardware, servicios informáticos, etc.) en entornos distribuidos abiertos. Estos entornos sirven de plataforma común para usuarios heterogéneos (por ejemplo, empresas, individuos, etc.) donde se proporciona alojamiento de aplicaciones y sistemas de usuario personalizadas; y donde se ofrece un acceso ubicuo y con menos esfuerzos administrativos a los recursos compartidos. El resultado es un entorno que permite a individuos y empresas aumentar significativamente su productividad. Como ya se ha dicho, el intercambio de recursos en entornos abiertos proporciona importantes ventajas para los distintos usuarios, no obstante, también aumenta significativamente las amenazas a su privacidad. Los datos electrónicos compartidos pueden ser explotados por terceros (por ejemplo, entidades conocidas como “Data Brokers”). Más concretamente, estas organizaciones pueden agregar la información compartida e inferir ciertas características personales sensibles de los usuarios, lo cual puede afectar a su privacidad. Una manera de paliar este problema consiste en controlar el acceso de los usuarios a los recursos potencialmente sensibles. En concreto, la gestión de control de acceso regula el acceso a los recursos compartidos de acuerdo con las credenciales de los usuarios, el tipo de recurso y las preferencias de privacidad de los propietarios de los recursos/datos. La gestión eficiente de control de acceso es crucial en entornos grandes y dinámicos. Por otra parte, con el fin de proponer una solución viable y escalable, es necesario eliminar la gestión manual de reglas y restricciones (en la cual, la mayoría de las soluciones disponibles dependen), dado que ésta constituye una pesada carga para usuarios y administradores. Por último, la gestión del control de acceso debe ser intuitivo para los usuarios finales, que por lo general carecen de grandes conocimientos técnicos.Thanks to the advent of the Internet, it is now possible to easily share vast amounts of electronic information and computer resources (which include hardware, computer services, etc.) in open distributed environments. These environments serve as a common platform for heterogeneous users (e.g., corporate, individuals etc.) by hosting customized user applications and systems, providing ubiquitous access to the shared resources and requiring less administrative efforts; as a result, they enable users and companies to increase their productivity. Unfortunately, sharing of resources in open environments has significantly increased the privacy threats to the users. Indeed, shared electronic data may be exploited by third parties, such as Data Brokers, which may aggregate, infer and redistribute (sensitive) personal features, thus potentially impairing the privacy of the individuals. A way to palliate this problem consists on controlling the access of users over the potentially sensitive resources. Specifically, access control management regulates the access to the shared resources according to the credentials of the users, the type of resource and the privacy preferences of the resource/data owners. The efficient management of access control is crucial in large and dynamic environments such as the ones described above. Moreover, in order to propose a feasible and scalable solution, we need to get rid of manual management of rules/constraints (in which most available solutions rely) that constitutes a serious burden for the users and the administrators. Finally, access control management should be intuitive for the end users, who usually lack technical expertise, and they may find access control mechanism more difficult to understand and rigid to apply due to its complex configuration settings

Eco-Evolutionary Implications of Environmental Change Across Heterogeneous Landscapes

Species use a variety of mechanisms to adapt to environmental change. These range from spatially tracking optimal environments, to phenotypically plastic responses and evolutionary adaptation. Due to increases in anthropogenic influence on environments, characteristics of change such as their duration and magnitude are undergoing fundamental shifts away from the natural disturbance regimes that shaped species’ evolution. This dissertation uses empirical data and simulation models to examine the ecological and evolutionary consequences of environmental change across real, heterogeneous landscapes for multiple species, with an emphasis on anthropogenic changes. I used landscape genetics to evaluate the effects of urbanization on two native amphibian species, spotted salamanders (Ambystoma maculatum) and wood frogs (Lithobates sylvaticus). Population isolation was positively associated with local urbanization and lessened genetic diversity for both species. Resistance surface modelling revealed connectivity was diminished by developed land cover, light roads, interstates, and topography for both species, plus secondary roads and rivers for wood frogs, highlighting the influence of anthropogenic landscape features relative to natural features. Further study of a subset of wood frog populations revealed adaptive evolution associated with urban environments. I identified a set of 37 loci with the capacity to correctly reassign individuals into rural or urban populations with 87.5 and 93.8% accuracy, respectively. I developed an agent-based model to examine how gene flow, rates of change, and strength of landscape spatial and temporal autocorrelation influence abundance outcomes for species experiencing an environmental shift. Analysis of 36 environmental scenarios suggests that environmental variation, which is an emergent property of landscape autocorrelation, is negatively associated with the magnitude and duration of abundance declines following environmental change. Higher levels of gene flow lessened this effect, particularly in abrupt change scenarios, although gradual changes also resulted in demographic costs. Lastly, I used an investigation of an emerging disease in American lobsters (Homarus americanus) to study within-generation responses to environmental pressures. Using whole transcriptome shotgun sequencing I identified eight differentially expressed unigenes associated with the disease and seven related to environmental differences. Collectively, my dissertation provides numerous examples of how anthropogenically induced environmental change can direct ecological and evolutionary processes

Deep Learning Methods for Detection and Tracking of Particles in Fluorescence Microscopy Images

Studying the dynamics of sub-cellular structures such as receptors, filaments, and vesicles is a prerequisite for investigating cellular processes at the molecular level. In addition, it is important to characterize the dynamic behavior of virus structures to gain a better understanding of infection mechanisms and to develop novel drugs. To investigate the dynamics of fluorescently labeled sub-cellular and viral structures, time-lapse fluorescence microscopy is the most often used imaging technique. Due to the limited spatial resolution of microscopes caused by diffraction, these very small structures appear as bright, blurred spots, denoted as particles, in microscopy images. To draw statistically meaningful biological conclusions, a large number of such particles need to be analyzed. However, since manual analysis of fluorescent particles is very time consuming, fully automated computer-based methods are indispensable. We introduce novel deep learning methods for detection and tracking of multiple particles in fluorescence microscopy images. We propose a particle detection method based on a convolutional neural network which performs image-to-image mapping by density map regression and uses the adaptive wing loss. For particle tracking, we present a recurrent neural network that exploits past and future information in both forward and backward direction. Assignment probabilities across multiple detections as well as the probabilities for missing detections are computed jointly. To resolve tracking ambiguities using future information, several track hypotheses are propagated to later time points. In addition, we developed a novel probabilistic deep learning method for particle tracking, which is based on a recurrent neural network mimicking classical Bayesian filtering. The method includes both aleatoric and epistemic uncertainty, and provides valuable information about the reliability of the computed trajectories. Short and long-term temporal dependencies of individual object dynamics are exploited for state prediction, and assigned detections are used to update the predicted states. Moreover, we developed a convolutional Long Short-Term Memory neural network for combined particle tracking and colocalization analysis in two-channel microscopy image sequences. The network determines colocalization probabilities, and colocalization information is exploited to improve tracking. Short and long-term temporal dependencies of object motion as well as image intensities are taken into account to compute assignment probabilities jointly across multiple detections. We also introduce a deep learning method for probabilistic particle detection and tracking. For particle detection, temporal information is integrated to regress a density map and determine sub-pixel particle positions. For tracking, a fully Bayesian neural network is presented that mimics classical Bayesian filtering and takes into account both aleatoric and epistemic uncertainty. Uncertainty information of individual particle detections is considered. Network training for the developed deep learning-based particle tracking methods relies only on synthetic data, avoiding the need of time-consuming manual annotation. We performed an extensive evaluation of our methods based on image data of the Particle Tracking Challenge as well as on fluorescence microscopy images displaying virus proteins of HCV and HIV, chromatin structures, and cell-surface receptors. It turned out that the methods outperform previous methods

A Genomic Portrait of Hepatitis C Virus and MicroRNA-122

Hepatitis C virus (HCV) uniquely requires the liver specific microRNA-122 (miR- 122) for replication, yet global effects on endogenous microRNA (miRNA) targets during infection are unexplored. In this body of work, we employed highthroughput sequencing and crosslinking immunoprecipitation (HITS-CLIP) experiments of human Argonaute (AGO) during HCV infection. We demonstrate robust AGO binding on the 5\u27 untranslated region of HCV RNA at known and predicted miR-122 sites, thereby establishing conclusive biochemical evidence of endogenous miR-122 action on HCV RNA that firmly agrees with previous genetic evidence. We further characterize novel AGO binding on HCV RNA to determine its dependence on miR-122, miRNAs generally, replication competence and time. These results establish an unbiased interaction landscape between HCV RNA and cellular miRNAs, mostly miR-122. On the human transcriptome, we observed reduced AGO binding and functional mRNA de-repression of miR-122 targets during virus infection. This miR-122 sponge effect was relieved and redirected to miR-15 targets by swapping the miRNA tropism of the virus. Single-cell expression data from reporters containing miR-122 sites showed significant de-repression during HCV infection depending on expression level and site number. Based on these results, we describe a quantitative mathematical model of HCV induced miR-122 sequestration and propose that such miR-122 inhibition by HCV RNA may result in global de-repression of host miR-122 targets. This in turn may provide an environment fertile for the long-term oncogenic potential of HCV. This last point presented a fitting entree into miR-122 biology, given its known tumor suppressive activity in the liver. To conclude this work, we performed AGO-CLIP in miR-122 knockout mouse livers as well as in human liver samples, to determine the in vivo targetome for this miRNA across two species. Surprisingly, we discovered widespread and non-canonical miR-122 binding throughout the transcriptome. Furthermore, a substantial fraction of this binding was not conserved between mouse and human transcriptomes, despite the fact that miR-122 is highly conserved. These results, in concert with AGOCLIP in HCV infected cells, point to a model where HCV may have evolved the use of miR-122 for its high abundance and its well buffered capacity to be inhibited with minimal detrimental effects to the host, and perhaps benefits for the virus. In sum, this thesis reveals how miR-122 is redistributed in the cell following HCV infection. As a molecular mechanism, chronic inhibition of miR-122 by HCV RNA is proposed to impact, and may very well help induce, the complex constellation of liver diseases that characterize this infection in humans

11th International Conference on Predictive Modelling in Food: book of abstracts

It is our great pleasure to welcome you in Bragança, Portugal, for the 11th International Conference of Predictive Modelling in Food (ICPMF11). Since 1992, ten ICPMF editions have taken place, providing a forum for the exchange of ideas, identification of research needs and novel approaches for the advancement of predictive modelling towards ensuring safety and quality of foods. Bragança is a typically-Portuguese old town (Romanic origin dates back to the 10th century), located by the Natural Park of Montesinho – one of the wildest forest zones of Europe – and the Douro Valley – the third oldest protected wine region in the world; and surrounded by traditional villages of a distinctive rustic beauty. Bragança houses several traditional industries producing a myriad of local foods, such as cheese, fermented meats, wine, chestnuts and honey, which provide substantial economic sustainability to the region. ICPMF11 reunites food researchers, stakeholders, risk assessors and users of predictive models to present recent developments and trends in modelling approaches for food quality, safety and sustainability. We succeeded to gather a significant number of delegates from over the world to participate in a comprehensive scientific programme that includes keynote lectures, oral communications and posters, allocated in sessions focusing on: . Advances in predictive microbiology modelling . Predictive modelling in innovative food processing and preservation technologies . Advances in microbial dynamics and interactions . Advances in software and database tools . Meta-analysis protocols and applications . Advances in risk assessment methods and integration of omics techniques . Advances in predictive modelling in food quality and safety . Predictive mycology . Individual cell and whole-cell modelling Apart from those, ICPMF11 features for the first time a special session dedicated to “Innovative approaches for ensuring safety of traditional foods” and the Round Table: “Assuring the Safety of Traditional Foods: A Scientific Contribution to Protecting our Cultural Heritage”. We, as food researchers based in a Mediterranean mountain region, are aware that the production of traditional foods plays a key role in the development of rural regions, since the agricultural commodities used as raw materials are generally produced locally, allowing and stimulating local commercialisation, thus contributing to a sustainable environment, and employment in rural populations. It was inspiring for us to have received many submissions from both developed and developing countries on the valorisation of traditional foods through the application of up-to-date modelling research. Besides that, one morning workshop and three afternoon tutorials were programmed during the day before the scientific programme. The workshop “How to benefit from the Risk Assessment Modelling and Knowledge Integration Platform (RAKIP)” was organised by Matthias Filter. The parallel tutorials “Towards an integrated predictive software map: Practical examples of use of predictive microbiology software tools for food safety and quality”; “Advanced methods in predictive microbiology” and “Topics in quantitative microbial risk assessment using R” were organised by Fernando Pérez-Rodríguez, Pablo Fernández, Alberto Garre and Mariem Ellouze; by Lihan Huang, Cheng-An Hwang and Vasco Cadavez; and by Patrick Njage and Ana Sofia Ribeiro Duarte, respectively. We thank these organisers for their proposals. Abstracts, reviewed by the ICPMF11 Scientific Committee, are published in the present Book of Abstracts while peer-reviewed original research articles will be invited to be published in ICPMF11 Special Issues in the International Journal of Food Microbiology and Microbial Risk Analysis. To stimulate the participation of postgraduate students and young researchers, two kinds of awards were arranged: the Young Researcher Best Oral Presentation prizes, sponsored by Elsevier; and the Developing Scientist Best Poster prizes, sponsored by the International Committee on Food Microbiology and Hygiene (ICFMH) of the International Union of Microbiological Societies (IUMS). For the first time, this ICPMF edition gives out two awards for the Senior Researcher Best Oral Presentation, sponsored by the open-access journal Foods – MDPI. In addition to the scientific programme, we prepared an exciting social programme for delegates to appreciate the rich culture, gastronomy and traditions of Bragança, w includes welcome reception, live music, tasting of regional food and a gala dinner in the Castle of Bragança. We look forward to lively discussions, and hope that this meeting will give you the opportunity to strengthen friendship and cooperation, and build new contacts for future research endeavours.info:eu-repo/semantics/publishedVersio

Integrating Human Population Genetics And Genomics To Elucidate The Etiology Of Brain Disorders

Brain disorders present a significant burden on affected individuals, their families and society at large. Existing diagnostic tests suffer from a lack of genetic biomarkers, particularly for substance use disorders, such as alcohol dependence (AD). Numerous studies have demonstrated that AD has a genetic heritability of 40-60%. The existing genetics literature of AD has primarily focused on linkage analyses in small family cohorts and more recently on genome-wide association analyses (GWAS) in large case-control cohorts, fueled by rapid advances in next generation sequencing (NGS). Numerous AD-associated genomic variations are present at a common frequency in the general population, making these variants of public health significance. However, known AD-associated variants explain only a fraction of the expected heritability. In this dissertation, we demonstrate that systems biology applications that integrate evolutionary genomics, rare variants and structural variation can dissect the genetic architecture of AD and elucidate its heritability. We identified several complex human diseases, including AD and other brain disorders, as potential targets of natural selection forces in diverse world populations. Further evidence of natural selection forces affecting AD was revealed when we identified an association between eye color, a trait under strong selection, and AD. These findings provide strong support for conducting GWAS on brain disorder phenotypes. However, with the ever-increasing abundance of rare genomic variants and large cohorts of multi-ethnic samples, population stratification becomes a serious confounding factor for GWAS. To address this problem, we designed a novel approach to identify ancestry informative single nucleotide polymorphisms (SNPs) for population stratification adjustment in association analyses. Furthermore, to leverage untyped variants from genotyping arrays – particularly rare variants – for GWAS and meta-analysis through rapid imputation, we designed a tool that converts genotype definitions across various array platforms. To further elucidate the genetic heritability of brain disorders, we designed approaches aimed at identifying Copy Number Variations (CNVs) and viral insertions into the human genome. We conducted the first CNV-based whole genome meta-analysis for AD. We also designed an integrated approach to estimate the sensitivity of NGS-based methods of viral insertion detection. For the first time in the literature, we identified herpesvirus in NGS data from an Alzheimer’s disease brain sample. The work in this dissertation represents a three-faceted advance in our understanding of brain disease etiology: 1) evolutionary genomic insights, 2) novel resources and tools to leverage rare variants, and 3) the discovery of disease-associated structural genomic aberrations. Our findings have broad implications on the genetics of complex human disease and hold promise for delivering clinically useful knowledge and resources

Human Adaptation in the Light of Ancient and Modern Genomes

Modern humans originated in Africa around 200,000 years ago and today have settled in nearly every corner of earth. During migrations humans became exposed to new pathogens, food sources and have encountered vastly different environments. Natural selection likely contributed to the survival under such diverse conditions by promoting the raise in frequency of advantageous alleles. Thereby natural selection leaves genetic footprints that we can identify. The thesis at hand is about understanding how natural selection has shaped different human populations by analyzing these genetic footprints. In the first study, I infer the evolutionary history of an insertion-substitution variant using present-day human genomic data. This variant is interesting because the ancestral allele encodes a previously unannotated open-reading frame for a gene with antiviral ac- tivity (IFNL4 ), while the derived allele truncates this open-reading frame and is strongly associated with improved clearance of Hepatitis C, a major health care problem. Using an approximate bayesian computation approach I infer a complex evolutionary history, where the derived, truncating allele evolved under weak positive selection in Africa, with selection strength increasing in non-African populations, especially in East Asian popu- lations where the truncating allele is nearly fixed today. Hence, the changes in selection and resulting population differences in allele frequency contribute to the variation in Hep- atitis C clearance observed across human populations today. In the second study, I use ancient human genomes to estimate genome-wide allele frequencies in the past to understand present-day population differentiation. I develop a new statistic and incorporate the genome of Ust’-Ishim, a modern human that lived 45,000 year ago in Siberia, to study to what extent natural selection and drift have contributed to human population differentiation. The results suggest that European populations carry high frequency alleles in protein-coding (genic) regions that evolved under strong, recent positive selection. Further, the genic alleles that rose in frequency recently in Europeans were already present in ancient hunter-gatherers more often than in ancient farmers. This suggests that during the colonization of Europe local, positive selection changed the frequency of advantageous alleles in hunter-gatherer populations prior to the influx of farming individuals and those alleles remained beneficial also in the later admixed populations

Ancient and modern DNA reveal dynamics of domestication and cross-continental dispersal of the dromedary

Dromedaries have been fundamental to the development of human societies in arid landscapes and for long-distance trade across hostile hot terrains for 3,000 y. Today they continue to be an important livestock resource in marginal agro-ecological zones. However, the history of dromedary domestication and the influence of ancient trading networks on their genetic structure have remained elusive. We combined ancient DNA sequences of wild and early-domesticated dromedary samples from arid regions with nuclear microsatellite and mitochondrial genotype information from 1,083 extant animals collected across the species’ range. We observe little phylogeographic signal in the modern population, indicative of extensive gene flow and virtually affecting all regions except East Africa, where dromedary populations have remained relatively isolated. In agreement with archaeological findings, we identify wild dromedaries from the southeast Arabian Peninsula among the founders of the domestic dromedary gene pool. Approximate Bayesian computations further support the “restocking from the wild” hypothesis, with an initial domestication followed by introgression from individuals from wild, now-extinct populations. Compared with other livestock, which show a long history of gene flow with their wild ancestors, we find a high initial diversity relative to the native distribution of the wild ancestor on the Arabian Peninsula and to the brief coexistence of early-domesticated and wild individuals. This study also demonstrates the potential to retrieve ancient DNA sequences from osseous remains excavated in hot and dry desert environments

Knowledge Management approaches to model pathophysiological mechanisms and discover drug targets in Multiple Sclerosis

Multiple Sclerosis (MS) is one of the most prevalent neurodegenerative diseases for which a cure is not yet available. MS is a complex disease for numerous reasons; its etiology is unknown, the diagnosis is not exclusive, the disease course is unpredictable and therapeutic response varies from patient to patient. There are four established subtypes of MS, which are segregated based on different characteristics. Many environmental and genetic factors are considered to play a role in MS etiology, including viral infection, vitamin D deficiency, epigenetical changes and some genes. Despite the large body of diverse scientific knowledge, from laboratory findings to clinical trials, no integrated model which portrays the underlying mechanisms of the disease state of MS is available. Contemporary therapies only provide reduction in the severity of the disease, and there is an unmet need of efficient drugs. The present thesis provides a knowledge-based rationale to model MS disease mechanisms and identify potential drug candidates by using systems biology approaches. Systems biology is an emerging field which utilizes the computational methods to integrate datasets of various granularities and simulate the disease outcome. It provides a framework to model molecular dynamics with their precise interaction and contextual details. The proposed approaches were used to extract knowledge from literature by state of the art text mining technologies, integrate it with proprietary data using semantic platforms, and build different models (molecular interactions map, agent based models to simulate disease outcome, and MS disease progression model with respect to time). For better information representation, disease ontology was also developed and a methodology of automatic enrichment was derived. The models provide an insight into the disease, and several pathways were explored by combining the therapeutics and the disease-specific prescriptions. The approaches and models developed in this work resulted in the identification of novel drug candidates that are backed up by existing experimental and clinical knowledge

Exploring the pre-immune landscape of antigen-specific T cells

Abstract Background Adaptive immune responses to newly encountered pathogens depend on the mobilization of antigen-specific clonotypes from a vastly diverse pool of naive T cells. Using recent advances in immune repertoire sequencing technologies, models of the immune receptor rearrangement process, and a database of annotated T cell receptor (TCR) sequences with known specificities, we explored the baseline frequencies of T cells specific for defined human leukocyte antigen (HLA) class I-restricted epitopes in healthy individuals. Methods We used a database of TCR sequences with known antigen specificities and a probabilistic TCR rearrangement model to estimate the baseline frequencies of TCRs specific to distinct antigens epitopespecificT-cells. We verified our estimates using a publicly available collection of TCR repertoires from healthy individuals. We also interrogated a database of immunogenic and non-immunogenic peptides is used to link baseline T-cell frequencies with epitope immunogenicity. Results Our findings revealed a high degree of variability in the prevalence of T cells specific for different antigens that could be explained by the physicochemical properties of the corresponding HLA class I-bound peptides. The occurrence of certain rearrangements was influenced by ancestry and HLA class I restriction, and umbilical cord blood samples contained higher frequencies of common pathogen-specific TCRs. We also identified a quantitative link between specific T cell frequencies and the immunogenicity of cognate epitopes presented by defined HLA class I molecules. Conclusions Our results suggest that the population frequencies of specific T cells are strikingly non-uniform across epitopes that are known to elicit immune responses. This inference leads to a new definition of epitope immunogenicity based on specific TCR frequencies, which can be estimated with a high degree of accuracy in silico, thereby providing a novel framework to integrate computational and experimental genomics with basic and translational research efforts in the field of T cell immunology