Secure Integration of Wireless Sensor Networks into Applications

Wireless sensors are small devices that are able to gather, process and deliver information from a physical environment to an external system. By doing so, they open new applications in different domains, such as healthcare, traffc control, defense and agriculture. The integration of Wireless Sensor Networks (WSN) with Business Applications (BA) raises technical and security related challenges. Existing approaches target technical issues such as interoperability between WSN and BAs or heterogeneity of acquired sensor data. In this work, we start by performing an analysis of the risks that such an integration of WSNs with BAs may present using the NIST SP 800-30 recommendations. We then introduce and analyze an effcient security scheme that does not use complex operations and guarantees end-to-end confidentiality of sensor data. Finally, we provide an in silico proof-of-concept and validate it using a real WSN co-developed with Cisco Systems France

The need for standardisation in life science research - an approach to excellence and trust

Today, academic researchers benefit from the changes driven by digital technologies and the enormous growth of knowledge and data, on globalisation, enlargement of the scientific community, and the linkage between different scientific communities and the society. To fully benefit from this development, however, information needs to be shared openly and transparently. Digitalisation plays a major role here because it permeates all areas of business, science and society and is one of the key drivers for innovation and international cooperation. To address the resulting opportunities, the EU promotes the development and use of collaborative ways to produce and share knowledge and data as early as possible in the research process, but also to appropriately secure results with the European strategy for Open Science (OS). It is now widely recognised that making research results more accessible to all societal actors contributes to more effective and efficient science; it also serves as a boost for innovation in the public and private sectors. However for research data to be findable, accessible, interoperable and reusable the use of standards is essential. At the metadata level, considerable efforts in standardisation have already been made (e.g. Data Management Plan and FAIR Principle etc.), whereas in context with the raw data these fundamental efforts are still fragmented and in some cases completely missing. The CHARME consortium, funded by the European Cooperation in Science and Technology (COST) Agency, has identified needs and gaps in the field of standardisation in the life sciences and also discussed potential hurdles for implementation of standards in current practice. Here, the authors suggest four measures in response to current challenges to ensure a high quality of life science research data and their re-usability for research and innovation

Model-based assessment of mammalian cell metabolic functionalities using omics data.

Omics experiments are ubiquitous in biological studies, leading to a deluge of data. However, it is still challenging to connect changes in these data to changes in cell functions because of complex interdependencies between genes, proteins, and metabolites. Here, we present a framework allowing researchers to infer how metabolic functions change on the basis of omics data. To enable this, we curated and standardized lists of metabolic tasks that mammalian cells can accomplish. Genome-scale metabolic networks were used to define gene sets associated with each metabolic task. We further developed a framework to overlay omics data on these sets and predict pathway usage for each metabolic task. We demonstrated how this approach can be used to quantify metabolic functions of diverse biological samples from the single cell to whole tissues and organs by using multiple transcriptomic datasets. To facilitate its adoption, we integrated the approach into GenePattern (www.genepattern.org-CellFie)

Age at onset as stratifier in idiopathic Parkinson’s disease – effect of ageing and polygenic risk score on clinical phenotypes

Several phenotypic differences observed in Parkinson’s disease (PD) patients have been linked to age at onset (AAO). We endeavoured to find out whether these differences are due to the ageing process itself by using a combined dataset of idiopathic PD (n = 430) and healthy controls (HC; n = 556) excluding carriers of known PD-linked genetic mutations in both groups. We found several significant effects of AAO on motor and non-motor symptoms in PD, but when comparing the effects of age on these symptoms with HC (using age at assessment, AAA), only positive associations of AAA with burden of motor symptoms and cognitive impairment were significantly different between PD vs HC. Furthermore, we explored a potential effect of polygenic risk score (PRS) on clinical phenotype and identified a significant inverse correlation of AAO and PRS in PD. No significant association between PRS and severity of clinical symptoms was found. We conclude that the observed non-motor phenotypic differences in PD based on AAO are largely driven by the ageing process itself and not by a specific profile of neurodegeneration linked to AAO in the idiopathic PD patients

Detection and characterization of critical transitions in mitochondrial activity via high content screening

Critical transitions exist in many dynamical systems, ranging from the Earth’s cli- mate system to microcosm populations. During a critical transition, the state of a dynamical system abruptly changes from one stable state to another, typically without obvious prior warning. Preventing such abrupt changes remains a chal- lenge, however recently, several metrics were suggested as early warning signals. These indicators are thought to have predictive value for upcoming critical transi- tions. In Parkinson’s disease, there are no detectable motor symptoms in a patient until neuronal dopaminergic cell death exceeds 60–70%. Being able to define early warning signals in a disease context could open new avenues for both preventive and disease modifying treatments. We hypothesize that the dynamics of progression of some disorders including Parkinson’s disease could be manifested by critical tran- sitions. However, before rushing into medical applications, a thorough framework needs to be developed that aims to describe such nonlinear dynamics in cellular systems. In this thesis, we set out to study critical transitions in a simple cellular model using mitochondrial membrane potential ∆Ψ m as readout. To identify criti- cal transitions, we established a modular high-content screening platform allowing systematic perturbation of oxidative phosphorylation. To increase the probability for detecting a critical transition in ∆Ψ m , five inhibitory compounds were combined in multiple pairwise concentration landscapes. We show that critical transitions, de- tectable via ∆Ψ m , are an intrinsic property of the cellular system studied and that two-component Gaussian mixture models adequately capture the dynamics of the critical transition occurring for the combination of Oligomycin A and Antimycin A. Adding to that, we identified the coefficient of variation as a strong early warning signal for the upcoming of the critical transitions. This thesis should serve as a foundation for a broader application of critical transitions and early warning sig- nals in both cell culture systems and translational studies aiming to understand the nonlinear dynamics of biological systems

The Parkinson’s Disease Map: a framework for integration, curation and exploration of disease related pathways

The pathogenesis of Parkinson's Disease (PD) is multi-factorial and age-related, implicating various genetic and environmental factors. It becomes increasingly important to develop new approaches to organize and explore the exploding knowledge of this field. The published knowledge on pathways implicated in PD, such as synaptic and mitochondrial dysfunction, alpha-synuclein pathobiology, failure of protein degradation systems and neuroinflammation has been organized and represented using CellDesigner. This repository has been linked to a framework of bioinformatics tools including text mining, database annotation, large-scale data integration and network analysis. We present the PD map, a computer-based knowledge repository, which includes molecular mechanisms of PD in a visually structured and standardized way. A bioinformatics framework that facilitates in-depth knowledge exploration, extraction and curation supports the map. We discuss the insights gained from PD map-driven text mining of a corpus of over 50 thousands full text PD-related papers, integration and visualization of gene expression in post mortem brain tissue of PD patients with the map, as well as results of network analysis. Conclusions: The knowledge repository of disease-related mechanisms provides a global insight into relationships between different pathways and allows considering a given pathology in a broad context. Enrichment with available text and bioinformatics databases as well as integration of experimental data supports better understanding of complex mechanisms of PD and formulation of novel research hypotheses

DISTRIBUTED AND COMPONENT ORIENTED TOOLS FOR COMMUNICATION NETWORKS USING WEB SERVICES

Modern communication networks are reaching a high level of complexity. As their global dimensioning and performance evaluation are very hard tasks, a multi-layer approach is generally applied to divide them in smaller problems easier to tackle. Within this approach, tools only address one or two network layers. To perform an enhanced analysis encompassing more layers, several tools, not necessarily available in the same place when the problem arises, are often required. This paper shows how to allow a remote access to various existing tools using Web Services paradigm. In this way, software components modelling network elements, layers, or computing specific functions are turned into Web Services, which are available to remote users over the internet. Accessed through a simplified interface, they can be used sequentially or in parallel to solve a specific task. This dispenses the user to perform a local implementation, allows better code reuse, and offers an easy way to confront results from distinct models. A tool accessed via a web service can be beneficial for either research or educational purposes. Moreover, making tools available on the web increases credibility and visibility of their authors. Calls to Web Service require transmitting and retrieving al

Automated nuclei clump splitting by combining local concavity orientation and graph partitioning

Automated clump decomposition is essential for single cell based analysis of fluorescent microscopy images. This paper presents a new method for automatically splitting clumps of cell nuclei in fluorescence microscopy images. Nuclei are first segmented using histogram concavity analysis. Clumps of nuclei are detected by fitting an ellipse to the segmented objects and examining objects where the fitted ellipse does not overlap accurately with the segmented object. These clumps are then further processed to find concave points on the object boundaries. The orientation of the detected concavities is subsequently calculated based on the local shape of the object border. Finally, a graph segmentation based approach is used to pair concavities that represent best candidates for splitting touching nuclei based on properties derived from the local concavity properties. This approach was validated by manual inspection and has shown promising results in the high throughput analysis of HeLa cell images