Anomaly detection in smart city wireless sensor networks

Aquesta tesi proposa una plataforma de detecció d’intrusions per a revelar atacs a les xarxes de sensors sense fils (WSN, per les sigles en anglès) de les ciutats intel·ligents (smart cities). La plataforma està dissenyada tenint en compte les necessitats dels administradors de la ciutat intel·ligent, els quals necessiten accés a una arquitectura centralitzada que pugui gestionar alarmes de seguretat en un sistema altament heterogeni i distribuït. En aquesta tesi s’identifiquen els diversos passos necessaris des de la recollida de dades fins a l’execució de les tècniques de detecció d’intrusions i s’avalua que el procés sigui escalable i capaç de gestionar dades típiques de ciutats intel·ligents. A més, es comparen diversos algorismes de detecció d’anomalies i s’observa que els mètodes de vectors de suport d’una mateixa classe (one-class support vector machines) resulten la tècnica multivariant més adequada per a descobrir atacs tenint en compte les necessitats d’aquest context. Finalment, es proposa un esquema per a ajudar els administradors a identificar els tipus d’atacs rebuts a partir de les alarmes disparades.Esta tesis propone una plataforma de detección de intrusiones para revelar ataques en las redes de sensores inalámbricas (WSN, por las siglas en inglés) de las ciudades inteligentes (smart cities). La plataforma está diseñada teniendo en cuenta la necesidad de los administradores de la ciudad inteligente, los cuales necesitan acceso a una arquitectura centralizada que pueda gestionar alarmas de seguridad en un sistema altamente heterogéneo y distribuido. En esta tesis se identifican los varios pasos necesarios desde la recolección de datos hasta la ejecución de las técnicas de detección de intrusiones y se evalúa que el proceso sea escalable y capaz de gestionar datos típicos de ciudades inteligentes. Además, se comparan varios algoritmos de detección de anomalías y se observa que las máquinas de vectores de soporte de una misma clase (one-class support vector machines) resultan la técnica multivariante más adecuada para descubrir ataques teniendo en cuenta las necesidades de este contexto. Finalmente, se propone un esquema para ayudar a los administradores a identificar los tipos de ataques recibidos a partir de las alarmas disparadas.This thesis proposes an intrusion detection platform which reveals attacks in smart city wireless sensor networks (WSN). The platform is designed taking into account the needs of smart city administrators, who need access to a centralized architecture that can manage security alarms in a highly heterogeneous and distributed system. In this thesis, we identify the various necessary steps from gathering WSN data to running the detection techniques and we evaluate whether the procedure is scalable and capable of handling typical smart city data. Moreover, we compare several anomaly detection algorithms and we observe that one-class support vector machines constitute the most suitable multivariate technique to reveal attacks, taking into account the requirements in this context. Finally, we propose a classification schema to assist administrators in identifying the types of attacks compromising their networks

A Genomic Approach for the Identification and Classification of Genes Involved in Cell Wall Formation and its Regulation in Saccharomyces Cerevisiae

Using a hierarchical approach, 620 non-essential single-gene yeast deletants generated by EUROFAN I were systematically screened for cell-wall-related phenotypes. By analyzing for altered sensitivity to the presence of Calcofluor white or SDS in the growth medium, altered sensitivity to sonication, or abnormal morphology, 145 (23%) mutants showing at least one cell wall-related phenotype were selected. These were screened further to identify genes potentially involved in either the biosynthesis, remodeling or coupling of cell wall macromolecules or genes involved in the overall regulation of cell wall construction and to eliminate those genes with a more general, pleiotropic effect. Ninety percent of the mutants selected from the primary tests showed additional cell wall-related phenotypes. When extrapolated to the entire yeast genome, these data indicate that over 1200 genes may directly or indirectly affect cell wall formation and its regulation. Twenty-one mutants with altered levels of β1,3-glucan synthase activity and five Calcofluor white-resistant mutants with altered levels of chitin synthase activities were found, indicating that the corresponding genes affect β1,3-glucan or chitin synthesis. By selecting for increased levels of specific cell wall components in the growth medium, we identified 13 genes that are possibly implicated in different steps of cell wall assembly. Furthermore, 14 mutants showed a constitutive activation of the cell wall integrity pathway, suggesting that they participate in the modulation of the pathway either directly acting as signaling components or by triggering the Slt2-dependent compensatory mechanism. In conclusion, our screening approach represents a comprehensive functional analysis on a genomic scale of gene products involved in various aspects of fungal cell wall formation

The Fourteenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the Extended Baryon Oscillation Spectroscopic Survey and from the Second Phase of the Apache Point Observatory Galactic Evolution Experiment

The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in operation since 2014 July. This paper describes the second data release from this phase, and the 14th from SDSS overall (making this Data Release Fourteen or DR14). This release makes the data taken by SDSS-IV in its first two years of operation (2014–2016 July) public. Like all previous SDSS releases, DR14 is cumulative, including the most recent reductions and calibrations of all data taken by SDSS since the first phase began operations in 2000. New in DR14 is the first public release of data from the extended Baryon Oscillation Spectroscopic Survey; the first data from the second phase of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2), including stellar parameter estimates from an innovative data-driven machine-learning algorithm known as "The Cannon"; and almost twice as many data cubes from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous release (N = 2812 in total). This paper describes the location and format of the publicly available data from the SDSS-IV surveys. We provide references to the important technical papers describing how these data have been taken (both targeting and observation details) and processed for scientific use. The SDSS web site (www.sdss.org) has been updated for this release and provides links to data downloads, as well as tutorials and examples of data use. SDSS-IV is planning to continue to collect astronomical data until 2020 and will be followed by SDSS-V

Anomaly detection in smart city wireless sensor networks

Aquesta tesi proposa una plataforma de detecció d’intrusions per a revelar atacs a les xarxes de sensors sense fils (WSN, per les sigles en anglès) de les ciutats intel·ligents (smart cities). La plataforma està dissenyada tenint en compte les necessitats dels administradors de la ciutat intel·ligent, els quals necessiten accés a una arquitectura centralitzada que pugui gestionar alarmes de seguretat en un sistema altament heterogeni i distribuït. En aquesta tesi s’identifiquen els diversos passos necessaris des de la recollida de dades fins a l’execució de les tècniques de detecció d’intrusions i s’avalua que el procés sigui escalable i capaç de gestionar dades típiques de ciutats intel·ligents. A més, es comparen diversos algorismes de detecció d’anomalies i s’observa que els mètodes de vectors de suport d’una mateixa classe (one-class support vector machines) resulten la tècnica multivariant més adequada per a descobrir atacs tenint en compte les necessitats d’aquest context. Finalment, es proposa un esquema per a ajudar els administradors a identificar els tipus d’atacs rebuts a partir de les alarmes disparades.Esta tesis propone una plataforma de detección de intrusiones para revelar ataques en las redes de sensores inalámbricas (WSN, por las siglas en inglés) de las ciudades inteligentes (smart cities). La plataforma está diseñada teniendo en cuenta la necesidad de los administradores de la ciudad inteligente, los cuales necesitan acceso a una arquitectura centralizada que pueda gestionar alarmas de seguridad en un sistema altamente heterogéneo y distribuido. En esta tesis se identifican los varios pasos necesarios desde la recolección de datos hasta la ejecución de las técnicas de detección de intrusiones y se evalúa que el proceso sea escalable y capaz de gestionar datos típicos de ciudades inteligentes. Además, se comparan varios algoritmos de detección de anomalías y se observa que las máquinas de vectores de soporte de una misma clase (one-class support vector machines) resultan la técnica multivariante más adecuada para descubrir ataques teniendo en cuenta las necesidades de este contexto. Finalmente, se propone un esquema para ayudar a los administradores a identificar los tipos de ataques recibidos a partir de las alarmas disparadas.This thesis proposes an intrusion detection platform which reveals attacks in smart city wireless sensor networks (WSN). The platform is designed taking into account the needs of smart city administrators, who need access to a centralized architecture that can manage security alarms in a highly heterogeneous and distributed system. In this thesis, we identify the various necessary steps from gathering WSN data to running the detection techniques and we evaluate whether the procedure is scalable and capable of handling typical smart city data. Moreover, we compare several anomaly detection algorithms and we observe that one-class support vector machines constitute the most suitable multivariate technique to reveal attacks, taking into account the requirements in this context. Finally, we propose a classification schema to assist administrators in identifying the types of attacks compromising their networks

Ali-Bey - an open collaborative georeferencing web application

Georeferencing preserved specimens represents a major effort at the Museu de Ciències Naturals de Barcelona (MCNB), given the available resources and limited staff that can be allocated to the task. Georeferencing is a labour-intensive and hard-to-automate task that requires software tools that can help in making it as efficient as possible. The tool we present, Ali-Bey, has been slowly developed over 15 years and its functionalities have been gradually built in a process of development, testing, use in production and refinement, rather than as a single development cycle out of a comprehensive specifications requirement document. At the start, the MCNB could not find a tool that fully satisfied the requirements listed as essential and made the decision to develop a custom tool. At the end, the initiative has proved successful since it has delivered a new georeferencing tool that meets the MCNB's needs, all in a context of yearly scarce availability of funds. The tool has been gradually matured and developed over the years, in line with the scarce financing. Only recently, after reaching a notable set of novel features, we considered to release it as an open-source project. The MCNB has supported its development up until this date and decided to open it in order to give the NHC community the opportunity to contribute to its development.We present the software tool Ali-Bey that provides new functionality for the georeferencing of specimens in Natural History Collections, namely the possibility of cooperation between different institutions, the traceability of georeferences and the capability of managing different versions of a same site name, namely for historical reasons. The tool is an open-source web application implemented in Python and the Django framework that leverages other commonly-used specialised geodatabase and map server tools. An API provides access to the geodatabase to externally-developed tools. In addition, for an easy installation, the tool is provided as a multi-container Docker application

PATJ low frequency variants are associated with worse ischemic stroke functional outcome: a genome-wide meta-analysis

Rationale: Ischemic stroke (IS) is among the leading causes of adult disability. Part of the variability in functional outcome after stroke has been attributed to genetic factors but no locus has been consistently associated with stroke outcome. Objective: Our aim was to identify genetic loci influencing the recovery process using accurate phenotyping to produce the largest genome-wide association study (GWAS) in IS recovery to date. Methods and Results: A 12-cohort, two-phase (discovery-replication and joint) meta-analysis of GWAS included anterior-territory and previously independent IS cases. Functional outcome was recorded using 3-month modified Rankin Scale (mRS). Analyses were adjusted for confounders such as discharge NIHSS. A gene-based burden test was performed. The discovery phase (n=1,225) was followed by open (n=2,482) and stringent joint-analyses (n=1,791). Those cohorts with mRS recorded at timepoints other than 3-month or incomplete data on previous functional status were excluded in the stringent analyses. Novel variants in PATJ gene were associated with worse functional outcome at 3-month after stroke. The top variant was rs76221407 (G allele, beta=0·40, p=1·70x10-9). Conclusions: Our results identify a set of common variants in PATJ gene associated with 3-month functional outcome at genome-wide significance level. Future studies should examine the role of PATJ in stroke recovery and consider stringent phenotyping to enrich the information captured to unveil additional stroke outcome loci