Decision‐making and best practices for taxonomy‐free environmental DNA metabarcoding in biomonitoring using Hill numbers

Environmental DNA (eDNA) metabarcoding is raising expectations for biomonitoring of organisms that have hitherto been neglected. To bypass current limitations in taxonomic assignments due to incomplete or erroneous reference databases, taxonomy-free approaches are proposed for biomonitoring at the level of operational taxonomic units (OTUs). This is challenging, because OTUs cannot be annotated and directly compared against classically derived taxonomic data. The application of good stringency treatments to infer the validity of OTUs and clear understanding of the consequences of such treatments is especially relevant for biodiversity assessments. We investigated how common practices of stringency filtering affect eDNA diversity estimates in the statistical framework of Hill numbers. We collected water eDNA samples at 61 sites across a 740-km2 river catchment, reflecting a spatially realistic scenario in biomonitoring. After bioinformatic processing of the data, we studied how different stringency treatments affect conclusions with respect to biodiversity at the catchment and site levels. The applied stringency treatments were based on the consistent appearance of OTUs across filter replicates, a relative abundance cut-off and rarefaction. We detected large differences in diversity estimates when accounting for presence/absence only, such that detected diversity at the catchment scale differed by an order of magnitude between the treatments. These differences disappeared when using stringency treatments with increasing weighting of the OTU abundances. Our study demonstrated the usefulness of Hill numbers for biodiversity analyses and comparisons of eDNA data sets that strongly differ in diversity. We recommend best practice for data stringency filtering for biomonitoring using eDNA

Orchestration of distributed ingestion and processing of IoT data for fog platforms

In recent years there has been an extraordinary growth of the Internet of Things (IoT) and its protocols. The increasing diffusion of electronic devices with identification, computing and communication capabilities is laying ground for the emergence of a highly distributed service and networking environment. The above mentioned situation implies that there is an increasing demand for advanced IoT data management and processing platforms. Such platforms require support for multiple protocols at the edge for extended connectivity with the objects, but also need to exhibit uniform internal data organization and advanced data processing capabilities to fulfill the demands of the application and services that consume IoT data. One of the initial approaches to address this demand is the integration between IoT and the Cloud computing paradigm. There are many benefits of integrating IoT with Cloud computing. The IoT generates massive amounts of data, and Cloud computing provides a pathway for that data to travel to its destination. But today’s Cloud computing models do not quite fit for the volume, variety, and velocity of data that the IoT generates. Among the new technologies emerging around the Internet of Things to provide a new whole scenario, the Fog Computing paradigm has become the most relevant. Fog computing was introduced a few years ago in response to challenges posed by many IoT applications, including requirements such as very low latency, real-time operation, large geo-distribution, and mobility. Also this low latency, geo-distributed and mobility environments are covered by the network architecture MEC (Mobile Edge Computing) that provides an IT service environment and Cloud-computing capabilities at the edge of the mobile network, within the Radio Access Network (RAN) and in close proximity to mobile subscribers. Fog computing addresses use cases with requirements far beyond Cloud-only solution capabilities. The interplay between Cloud and Fog computing is crucial for the evolution of the so-called IoT, but the reach and specification of such interplay is an open problem. This thesis aims to find the right techniques and design decisions to build a scalable distributed system for the IoT under the Fog Computing paradigm to ingest and process data. The final goal is to explore the trade-offs and challenges in the design of a solution from Edge to Cloud to address opportunities that current and future technologies will bring in an integrated way. This thesis describes an architectural approach that addresses some of the technical challenges behind the convergence between IoT, Cloud and Fog with special focus on bridging the gap between Cloud and Fog. To that end, new models and techniques are introduced in order to explore solutions for IoT environments. This thesis contributes to the architectural proposals for IoT ingestion and data processing by 1) proposing the characterization of a platform for hosting IoT workloads in the Cloud providing multi-tenant data stream processing capabilities, the interfaces over an advanced data-centric technology, including the building of a state-of-the-art infrastructure to evaluate the performance and to validate the proposed solution. 2) studying an architectural approach following the Fog paradigm that addresses some of the technical challenges found in the first contribution. The idea is to study an extension of the model that addresses some of the central challenges behind the converge of Fog and IoT. 3) Design a distributed and scalable platform to perform IoT operations in a moving data environment. The idea after study data processing in Cloud, and after study the convenience of the Fog paradigm to solve the IoT close to the Edge challenges, is to define the protocols, the interfaces and the data management to solve the ingestion and processing of data in a distributed and orchestrated manner for the Fog Computing paradigm for IoT in a moving data environment.En els últims anys hi ha hagut un gran creixement del Internet of Things (IoT) i els seus protocols. La creixent difusió de dispositius electrònics amb capacitats d'identificació, computació i comunicació esta establint les bases de l’aparició de serveis altament distribuïts i del seu entorn de xarxa. L’esmentada situació implica que hi ha una creixent demanda de plataformes de processament i gestió avançada de dades per IoT. Aquestes plataformes requereixen suport per a múltiples protocols al Edge per connectivitat amb el objectes, però també necessiten d’una organització de dades interna i capacitats avançades de processament de dades per satisfer les demandes de les aplicacions i els serveis que consumeixen dades IoT. Una de les aproximacions inicials per abordar aquesta demanda és la integració entre IoT i el paradigma del Cloud computing. Hi ha molts avantatges d'integrar IoT amb el Cloud. IoT genera quantitats massives de dades i el Cloud proporciona una via perquè aquestes dades viatgin a la seva destinació. Però els models actuals del Cloud no s'ajusten del tot al volum, varietat i velocitat de les dades que genera l'IoT. Entre les noves tecnologies que sorgeixen al voltant del IoT per proporcionar un escenari nou, el paradigma del Fog Computing s'ha convertit en la més rellevant. Fog Computing es va introduir fa uns anys com a resposta als desafiaments que plantegen moltes aplicacions IoT, incloent requisits com baixa latència, operacions en temps real, distribució geogràfica extensa i mobilitat. També aquest entorn està cobert per l'arquitectura de xarxa MEC (Mobile Edge Computing) que proporciona serveis de TI i capacitats Cloud al edge per la xarxa mòbil dins la Radio Access Network (RAN) i a prop dels subscriptors mòbils. El Fog aborda casos d?us amb requisits que van més enllà de les capacitats de solucions només Cloud. La interacció entre Cloud i Fog és crucial per a l'evolució de l'anomenat IoT, però l'abast i especificació d'aquesta interacció és un problema obert. Aquesta tesi té com objectiu trobar les decisions de disseny i les tècniques adequades per construir un sistema distribuït escalable per IoT sota el paradigma del Fog Computing per a ingerir i processar dades. L'objectiu final és explorar els avantatges/desavantatges i els desafiaments en el disseny d'una solució des del Edge al Cloud per abordar les oportunitats que les tecnologies actuals i futures portaran d'una manera integrada. Aquesta tesi descriu un enfocament arquitectònic que aborda alguns dels reptes tècnics que hi ha darrere de la convergència entre IoT, Cloud i Fog amb especial atenció a reduir la bretxa entre el Cloud i el Fog. Amb aquesta finalitat, s'introdueixen nous models i tècniques per explorar solucions per entorns IoT. Aquesta tesi contribueix a les propostes arquitectòniques per a la ingesta i el processament de dades IoT mitjançant 1) proposant la caracterització d'una plataforma per a l'allotjament de workloads IoT en el Cloud que proporcioni capacitats de processament de flux de dades multi-tenant, les interfícies a través d'una tecnologia centrada en dades incloent la construcció d'una infraestructura avançada per avaluar el rendiment i validar la solució proposada. 2) estudiar un enfocament arquitectònic seguint el paradigma Fog que aborda alguns dels reptes tècnics que es troben en la primera contribució. La idea és estudiar una extensió del model que abordi alguns dels reptes centrals que hi ha darrere de la convergència de Fog i IoT. 3) Dissenyar una plataforma distribuïda i escalable per a realitzar operacions IoT en un entorn de dades en moviment. La idea després d'estudiar el processament de dades a Cloud, i després d'estudiar la conveniència del paradigma Fog per resoldre el IoT prop dels desafiaments Edge, és definir els protocols, les interfícies i la gestió de dades per resoldre la ingestió i processament de dades en un distribuït i orquestrat per al paradigma Fog Computing per a l'IoT en un entorn de dades en moviment

MicroRNA Profiling in Cartilage Ageing

Osteoarthritis (OA) is the most common age-related joint disorder in man. MicroRNAs (miRNA), a class of small noncoding RNAs, are potential therapeutic targets for regulating molecular mechanisms in both disease and ageing. Whilst there is an increasing amount of research on the roles of miRNAs in ageing, there has been scant research on age-related changes in miRNA in a cartilage. We undertook a microarray study on young and old human cartilages. Findings were validated in an independent cohort. Contrasts between these samples identified twenty differentially expressed miRNAs in a cartilage from old donors, derived from an OA environment which clustered based on OA severity. We identified a number of recognised and novel miRNAs changing in cartilage ageing and OA including miR-126: a potential new candidate with a role in OA pathogenesis. These analyses represent important candidates that have the potential as cartilage ageing and OA biomarkers and therapeutic targets

Landscape transcriptomics as a tool for addressing global change effects across diverse species

Landscape transcriptomics is an emerging field studying how genome-wide expression patterns reflect dynamic landscape-scale environmental drivers, including habitat, weather, climate, and contaminants, and the subsequent effects on organismal function. This field is benefitting from advancing and increasingly accessible molecular technologies, which in turn are allowing the necessary characterization of transcriptomes from wild individuals distributed across natural landscapes. This research is especially important given the rapid pace of anthropogenic environmental change and potential impacts that span levels of biological organization. We discuss three major themes in landscape transcriptomic research: connecting transcriptome variation across landscapes to environmental variation, generating and testing hypotheses about the mechanisms and evolution of transcriptomic responses to the environment, and applying this knowledge to species conservation and management. We discuss challenges associated with this approach and suggest potential solutions. We conclude that landscape transcriptomics has great promise for addressing fundamental questions in organismal biology, ecology, and evolution, while providing tools needed for conservation and management of species

State management in coreless mobile networks

The number of mobile Internet users has skyrocketed and with the advent of the Internet of things we are reaching the limits of the current telecommunications standard 4G. The improvements and goals set for the next standard, 5G, are not trivial and research is in progress to reach them. Improvements across all involved technology fields is needed. In this thesis we present a novel mobile network architecture-coreless mobile networks- and develop state management concepts, which we base on the analysis of the current 4G/LTE architecture. The coreless mobile network focuses on the redesign of the state management in mobile networks, more precisely, removal of state from 4G core network entities into an eternal ubiquitous data store. The architecture follows trends in current research, particularly network function virtualisation, software defined networking and mobile edge computing. The new network architecture requires a data storage solution that is capable of functioning as the state store in the mobile network environment. Thus, we present an overview of promising data stores and evaluate their suitability. Further in this thesis we present the results of benchmarking the Apache Geode data store, as an example of a state management solution that could be leveraged in realising the coreless mobile network architecture. We discovered that the Apache Geode data store is, depending on configuration, capable of delivering the data model, consistency, high availability, scaling, throughput and latency that are required in our proposed architecture. ACM Computing Classification System (CCS): - Information systems ~ Distributed storage - Information systems ~ Hierarchical storage management - Networks ~ Middle boxes / network appliances - Networks ~ Mobile network

Environmental DNA effectively captures functional diversity of coastal fish communities.

Robust assessments of taxonomic and functional diversity are essential components of research programmes aimed at understanding current biodiversity patterns and forecasting trajectories of ecological changes. Yet, evaluating marine biodiversity along its dimensions is challenging and dependent on the power and accuracy of the available data collection methods. Here we combine three traditional survey methodologies (underwater visual census strip transects [UVCt], baited underwater videos [BUV] and small-scale fishery catches [SSFc]), and one novel molecular technique (environmental DNA metabarcoding [eDNA]-12S rRNA and cytochrome oxidase subunit 1 [COI]) to investigate their efficiency and complementarity in assessing fish diversity. We analysed 1,716 multimethod replicates at a basin scale to measure the taxonomic and functional diversity of Mediterranean fish assemblages. Taxonomic identities were investigated at species, genus and family levels. Functional identities were assessed using combinations of morphological, behavioural and trophic traits. We show that: (a) SSFc provided the higher taxonomic diversity estimates followed by eDNA, and then UVCt and BUV; (b) eDNA was the only method able to gather the whole spectrum of considered functional traits, showing the most functionally diversified and least redundant fish assemblages; and (c) the effectiveness of eDNA in describing functional structure reflected its lack of selectivity towards any considered functional trait. Our findings suggest that the reach of eDNA analysis stretches beyond taxon detection efficiency and provides new insights into the potential of metabarcoding in ecological studies

Fog Computing

Everything that is not a computer, in the traditional sense, is being connected to the Internet. These devices are also referred to as the Internet of Things and they are pressuring the current network infrastructure. Not all devices are intensive data producers and part of them can be used beyond their original intent by sharing their computational resources. The combination of those two factors can be used either to perform insight over the data closer where is originated or extend into new services by making available computational resources, but not exclusively, at the edge of the network. Fog computing is a new computational paradigm that provides those devices a new form of cloud at a closer distance where IoT and other devices with connectivity capabilities can offload computation. In this dissertation, we have explored the fog computing paradigm, and also comparing with other paradigms, namely cloud, and edge computing. Then, we propose a novel architecture that can be used to form or be part of this new paradigm. The implementation was tested on two types of applications. The first application had the main objective of demonstrating the correctness of the implementation while the other application, had the goal of validating the characteristics of fog computing.Tudo o que não é um computador, no sentido tradicional, está sendo conectado à Internet. Esses dispositivos também são chamados de Internet das Coisas e estão pressionando a infraestrutura de rede atual. Nem todos os dispositivos são produtores intensivos de dados e parte deles pode ser usada além de sua intenção original, compartilhando seus recursos computacionais. A combinação desses dois fatores pode ser usada para realizar processamento dos dados mais próximos de onde são originados ou estender para a criação de novos serviços, disponibilizando recursos computacionais periféricos à rede. Fog computing é um novo paradigma computacional que fornece a esses dispositivos uma nova forma de nuvem a uma distância mais próxima, onde “Things” e outros dispositivos com recursos de conectividade possam delegar processamento. Nesta dissertação, exploramos fog computing e também comparamos com outros paradigmas, nomeadamente cloud e edge computing. Em seguida, propomos uma nova arquitetura que pode ser usada para formar ou fazer parte desse novo paradigma. A implementação foi testada em dois tipos de aplicativos. A primeira aplicação teve o objetivo principal de demonstrar a correção da implementação, enquanto a outra aplicação, teve como objetivo validar as características de fog computing