A systematic review of observational studies on oxidative/nitrosative stress involvement in dengue pathogenesis

Objective: Our objective was to systematically review the published observational research related to the role of oxidative-nitrosative stress in pathogenesis of dengue. Methods: We searched electronic databases (PubMed, EMBASE, The COCHRANE library, ScienceDirect, Scopus, SciELO, LILACS via Virtual Health Library, Google Scholar) using the term: dengue, dengue virus, severe dengue, oxidative stress, nitrosative stress, antioxidants, oxidants, free radicals, oxidized lipid products, lipid peroxides, nitric oxide, and nitric oxide synthase. Articles were selected for review by title and abstract excluding letter, review, in vivo and in vitro studies, and duplicates studies. Selected articles were reviewed for study design, original purposes, sample size, main outcomes, methods, and oxidative-nitrosative stress markers values. Results: In total, 4,331 non-duplicates articles were identified from electronic databases searches, of which 16 were eligible for full text searching. Data from the observational studies originate from Asian countries (50%; 8/16), South American countries (31.2%; 5/16), and Central America and the Caribbean countries (18.8%; 3/16). Casecontrol study was the type of design most common in researches reviewed. The 1997 World Health Organization (WHO) dengue case classification criteria were used in all studies included in this review. Conclusions: Based on published data found in peer-reviewed literature, oxidative and nitrosative stress are demonstrated by changes in plasma levels of nitric oxide, antioxidants, lipid peroxidation and protein oxidation markers in patients with dengue infection. Additionally, elevated serum protein carbonyls and malondialdehyde levels appear to be associated with dengue disease severity.

Big data, smart homes and ambient assisted living

Objectives: To discuss how current research in the area of smart homes and ambient assisted living will be influenced by the use of big data. Methods: A scoping review of literature published in scientific journals and conference proceedings was performed, focusing on smart homes, ambient assisted living and big data over the years 2011-2014. Results: The health and social care market has lagged behind other markets when it comes to the introduction of innovative IT solutions and the market faces a number of challenges as the use of big data will increase. First, there is a need for a sustainable and trustful information chain where the needed information can be transferred from all producers to all consumers in a structured way. Second, there is a need for big data strategies and policies to manage the new situation where information is handled and transferred independently of the place of the expertise. Finally, there is a possibility to develop new and innovative business models for a market that supports cloud computing, social media, crowdsourcing etc. Conclusions: The interdisciplinary area of big data, smart homes and ambient assisted living is no longer only of interest for IT developers, it is also of interest for decision makers as customers make more informed choices among today's services. In the future it will be of importance to make information usable for managers and improve decision making, tailor smart home services based on big data, develop new business models, increase competition and identify policies to ensure privacy, security and liability.

The issue of data protection and data security in the (pre-Lisbon) EU third pillar

The key functional operability in the pre-Lisbon PJCCM pillar of the EU is the exchange of intelligence and information amongst the law enforcement bodies of the EU. The twin issues of data protection and data security within what was the EU’s third pillar legal framework therefore come to the fore. With the Lisbon Treaty reform of the EU, and the increased role of the Commission in PJCCM policy areas, and the integration of the PJCCM provisions with what have traditionally been the pillar I activities of Frontex, the opportunity for streamlining the data protection and data security provisions of the law enforcement bodies of the post-Lisbon EU arises. This is recognised by the Commission in their drafting of an amending regulation for Frontex , when they say that they would prefer “to return to the question of personal data in the context of the overall strategy for information exchange to be presented later this year and also taking into account the reflection to be carried out on how to further develop cooperation between agencies in the justice and home affairs field as requested by the Stockholm programme.” The focus of the literature published on this topic, has for the most part, been on the data protection provisions in Pillar I, EC. While the focus of research has recently sifted to the previously Pillar III PJCCM provisions on data protection, a more focused analysis of the interlocking issues of data protection and data security needs to be made in the context of the law enforcement bodies, particularly with regard to those which were based in the pre-Lisbon third pillar. This paper will make a contribution to that debate, arguing that a review of both the data protection and security provision post-Lisbon is required, not only in order to reinforce individual rights, but also inter-agency operability in combating cross-border EU crime. The EC’s provisions on data protection, as enshrined by Directive 95/46/EC, do not apply to the legal frameworks covering developments within the third pillar of the EU. Even Council Framework Decision 2008/977/JHA, which is supposed to cover data protection provisions within PJCCM expressly states that its provisions do not apply to “Europol, Eurojust, the Schengen Information System (SIS)” or to the Customs Information System (CIS). In addition, the post Treaty of Prüm provisions covering the sharing of DNA profiles, dactyloscopic data and vehicle registration data pursuant to Council Decision 2008/615/JHA, are not to be covered by the provisions of the 2008 Framework Decision. As stated by Hijmans and Scirocco, the regime is “best defined as a patchwork of data protection regimes”, with “no legal framework which is stable and unequivocal, like Directive 95/46/EC in the First pillar”. Data security issues are also key to the sharing of data in organised crime or counterterrorism situations. This article will critically analyse the current legal framework for data protection and security within the third pillar of the EU.

Humour, Hate Crimes and British Radical Right Users on Twitter: A longitudinal Post-Brexit study

This dissertation explores the link between hate crimes that occurred in the United Kingdom in June 2017, June 2018 and June 2019 through the posts of a robust sample of Conservative and radical right users on Twitter. In order to avoid the traditional challenges of this kind of research, I adopted a four staged research protocol that enabled me to merge content produced by a group of randomly selected users to observe the phenomenon from different angles. I collected tweets from thirty Conservative/right wing accounts for each month of June over the three years with the help of programming languages such as Python and CygWin tools. I then examined the language of my data focussing on humorous content in order to reveal whether, and if so how, radical users online often use humour as a tool to spread their views in conditions of heightened disgust and wide-spread political instability. A reflection on humour as a moral occurrence, expanding on the works of Christie Davies as well as applying recent findings on the behavioural immune system on online data, offers new insights on the overlooked humorous nature of radical political discourse. An unorthodox take on the moral foundations pioneered by Jonathan Haidt enriched my understanding of the analysed material through the addition of a moral-based layer of enquiry to my more traditional content-based one. This convergence of theoretical, data driven and real life events constitutes a viable “collection of strategies” for academia, data scientists; NGO’s fighting hate crimes and the wider public alike. Bringing together the ideas of Davies, Haidt and others to my data, helps us to perceive humorous online content in terms of complex radical narratives that are all too often compressed into a single tweet.

Reinforcement learning over encrypted data

Reinforcement learning is a particular paradigm of machine learning that, recently, has proved times and times again to be a very effective and powerful approach. On the other hand, cryptography usually takes the opposite direction. While machine learning aims at analyzing data, cryptography aims at maintaining its privacy by hiding such data. However, the two techniques can be jointly used to create privacy preserving models, able to make inferences on the data without leaking sensitive information. Despite the numerous amount of studies performed on machine learning and cryptography, reinforcement learning in particular has never been applied to such cases before. Being able to successfully make use of reinforcement learning in an encrypted scenario would allow us to create an agent that efficiently controls a system without providing it with full knowledge of the environment it is operating in, leading the way to many possible use cases. Therefore, we have decided to apply the reinforcement learning paradigm to encrypted data. In this project we have applied one of the most well-known reinforcement learning algorithms, called Deep Q-Learning, to simple simulated environments and studied how the encryption affects the training performance of the agent, in order to see if it is still able to learn how to behave even when the input data is no longer readable by humans. The results of this work highlight that the agent is still able to learn with no issues whatsoever in small state spaces with non-secure encryptions, like AES in ECB mode. For fixed environments, it is also able to reach a suboptimal solution even in the presence of secure modes, like AES in CBC mode, showing a significant improvement with respect to a random agent; however, its ability to generalize in stochastic environments or big state spaces suffers greatly.

Self-supervised information retrieval: a novel approach based on Deep Metric Learning and Neural Language Models

Most of the existing open-source search engines, utilize keyword or tf-idf based techniques to find relevant documents and web pages relative to an input query. Although these methods, with the help of a page rank or knowledge graphs, proved to be effective in some cases, they often fail to retrieve relevant instances for more complicated queries that would require a semantic understanding to be exploited. In this Thesis, a self-supervised information retrieval system based on transformers is employed to build a semantic search engine over the library of Gruppo Maggioli company. Semantic search or search with meaning can refer to an understanding of the query, instead of simply finding words matches and, in general, it represents knowledge in a way suitable for retrieval. We chose to investigate a new self-supervised strategy to handle the training of unlabeled data based on the creation of pairs of ’artificial’ queries and the respective positive passages. We claim that by removing the reliance on labeled data, we may use the large volume of unlabeled material on the web without being limited to languages or domains where labeled data is abundant.

Abusing data dominance in the digital market. The application of merger control and article 102 TFEU to data-related conducts.

The internet and digital technologies revolutionized the economy. Regulating the digital market has become a priority for the European Union. While promoting innovation and development, EU institutions must assure that the digital market maintains a competitive structure. Among the numerous elements characterizing the digital sector, users’ data are particularly important. Digital services are centered around personal data, the accumulation of which contributed to the centralization of market power in the hands of a few large providers. As a result, data-driven mergers and data-related abuses gained a central role for the purposes of EU antitrust enforcement. In light of these considerations, this work aims at assessing whether EU competition law is well-suited to address data-driven mergers and data-related abuses of dominance. These conducts are of crucial importance to the maintenance of competition in the digital sector, insofar as the accumulation of users’ data constitutes a fundamental competitive advantage. To begin with, part 1 addresses the specific features of the digital market and their impact on the definition of the relevant market and the assessment of dominance by antitrust authorities. Secondly, part 2 analyzes the EU’s case law on data-driven mergers to verify if merger control is well-suited to address these concentrations. Thirdly, part 3 discusses abuses of dominance in the phase of data collection and the legal frameworks applicable to these conducts. Fourthly, part 4 focuses on access to “essential” datasets and the indirect effects of anticompetitive conducts on rivals’ ability to access users’ information. Finally, Part 5 discusses differential pricing practices implemented online and based on personal data. As it will be assessed, the combination of an efficient competition law enforcement and the auspicial adoption of a specific regulation seems to be the best solution to face the challenges raised by “data-related dominance”.

Data sensitivity detection in chat interactions for privacy protection

In recent years, there has been exponential growth in using virtual spaces, including dialogue systems, that handle personal information. The concept of personal privacy in the literature is discussed and controversial, whereas, in the technological field, it directly influences the degree of reliability perceived in the information system (privacy ‘as trust’). This work aims to protect the right to privacy on personal data (GDPR, 2018) and avoid the loss of sensitive content by exploring sensitive information detection (SID) task. It is grounded on the following research questions: (RQ1) What does sensitive data mean? How to define a personal sensitive information domain? (RQ2) How to create a state-of-the-art model for SID?(RQ3) How to evaluate the model? RQ1 theoretically investigates the concepts of privacy and the ontological state-of-the-art representation of personal information. The Data Privacy Vocabulary (DPV) is the taxonomic resource taken as an authoritative reference for the definition of the knowledge domain. Concerning RQ2, we investigate two approaches to classify sensitive data: the first - bottom-up - explores automatic learning methods based on transformer networks, the second - top-down - proposes logical-symbolic methods with the construction of privaframe, a knowledge graph of compositional frames representing personal data categories. Both approaches are tested. For the evaluation - RQ3 – we create SPeDaC, a sentence-level labeled resource. This can be used as a benchmark or training in the SID task, filling the gap of a shared resource in this field. If the approach based on artificial neural networks confirms the validity of the direction adopted in the most recent studies on SID, the logical-symbolic approach emerges as the preferred way for the classification of fine-grained personal data categories, thanks to the semantic-grounded tailor modeling it allows. At the same time, the results highlight the strong potential of hybrid architectures in solving automatic tasks.

Big data analysis systems in IoE environments for managing privacy and data protection: pseudonymity, de-anonymization and the right to be forgotten

The thesis represents the conclusive outcome of the European Joint Doctorate programmein Law, Science & Technology funded by the European Commission with the instrument Marie Skłodowska-Curie Innovative Training Networks actions inside of the H2020, grantagreement n. 814177. The tension between data protection and privacy from one side, and the need of granting further uses of processed personal datails is investigated, drawing the lines of the technological development of the de-anonymization/re-identification risk with an explorative survey. After acknowledging its span, it is questioned whether a certain degree of anonymity can still be granted focusing on a double perspective: an objective and a subjective perspective. The objective perspective focuses on the data processing models per se, while the subjective perspective investigates whether the distribution of roles and responsibilities among stakeholders can ensure data anonymity.

Cross-sensors analysis of multispectral Earth observation data for environmental monitoring

Time Series Analysis of multispectral satellite data offers an innovative way to extract valuable information of our changing planet. This is now a real option for scientists thanks to data availability as well as innovative cloud-computing platforms, such as Google Earth Engine. The integration of different missions would mitigate known issues in multispectral time series construction, such as gaps due to clouds or other atmospheric effects. With this purpose, harmonization among Landsat-like missions is possible through statistical analysis. This research offers an overview of the different instruments from Landsat and Sentinel missions (TM, ETM, OLI, OLI-2 and MSI sensors) and products levels (Collection-2 Level-1 and Surface Reflectance for Landsat and Level-1C and Level-2A for Sentinel-2). Moreover, a cross-sensors comparison was performed to assess the interoperability of the sensors on-board Landsat and Sentinel-2 constellations, having in mind a possible combined use for time series analysis. Firstly, more than 20,000 pairs of images almost simultaneously acquired all over Europe were selected over a period of several years. The study performed a cross-comparison analysis on these data, and provided an assessment of the calibration coefficients that can be used to minimize differences in the combined use. Four of the most popular vegetation indexes were selected for the study: NDVI, EVI, SAVI and NDMI. As a result, it is possible to reconstruct a longer and denser harmonized time series since 1984, useful for vegetation monitoring purposes. Secondly, the spectral characteristics of the recent Landsat-9 mission were assessed for a combined use with Landsat-8 and Sentinel-2. A cross-sensor analysis of common bands of more than 3,000 almost simultaneous acquisitions verified a high consistency between datasets. The most relevant discrepancy has been observed in the blue and SWIRS bands, often used in vegetation and water related studies. This analysis was supported with spectroradiometer ground measurements.

Patient data sharing for the improvement of healthcare and medical research: towards a moral duty of patients to share health-related data?

The project answers to the following central research question: ‘How would a moral duty of patients to transfer (health) data for the benefit of health care improvement, research, and public health in the eHealth sector sit within the existing confidentiality, privacy, and data protection legislations?’. The improvement of healthcare services, research, and public health relies on patient data, which is why one might raise the question concerning a potential moral responsibility of patients to transfer data concerning health. Such a responsibility logically would have subsequent consequences for care providers concerning the further transferring of health data with other healthcare providers or researchers and other organisations (who also possibly transfer the data further with others and other organisations). Otherwise, the purpose of the patients’ moral duty, i.e. to improve the care system and research, would be undermined. Albeit the arguments that may exist in favour of a moral responsibility of patients to share health-related data, there are also some moral hurdles that come with such a moral responsibility. Furthermore, the existing European and national confidentiality, privacy and data protection legislations appear to hamper such a possible moral duty, and they may need to be reconsidered to unlock the full use of data for healthcare and research.

Benchmarking ’Expressing Without Asserting’ approaches on a large Wikidata dump

Semantic Web technologies provide the means to express the knowledge in a formal and standardized manner, enabling machines to automatically derive meaning from the data. Often this knowledge is uncertain or different degrees of certainty may be assigned to the same statements. This is the case in many fields of study such as in Digital Humanities, Science and Arts. The challenge relies on the fact that our knowledge about the surrounding world is dynamic and may evolve based on new data coming from the latest discoveries. Furthermore we should be able to express conflicting, debated or disputed statements in an efficient, effective and consistent way without the need of asserting them. We call this approach 'Expressing Without Asserting' (EWA). In this work we identify all existing methods that are compatible with actual Semantic Web standards and enable us to express EWA. In our research we were able to prove that existing reification methods such as Named Graphs, Singleton Properties, Wikidata Statements and RDF-Star are the most suitable methods to represent in a reliable way EWA. Next we compare these methods with our own method, namely Conjectures from a quantitative perspective. Our main objective was to put Conjectures into stress tests leveraging enormous datasets created ad hoc using art-related Wikidata dumps and measure the performance in various triplestores in relation with similar concurrent methods. Our experiments show that Conjectures are a formidable tool to express efficiently and effectively EWA. In some cases, Conjectures outperform state of the art methods such as singleton and Rdf-Star exposing their great potential. Is our firm belief that Conjectures represent a suitable solution to EWA issues. Conjectures in their weak form are fully compatible with Semantic Web standards, especially with RDF and SPARQL. Furthermore Conjectures benefit from comprehensive syntax and intuitive semantics that make them easy to learn and adapt.

The Crystal-t Algorithm: A New Approach To Calculate The Sle Of Lipidic Mixtures Presenting Solid Solutions.

Lipidic mixtures present a particular phase change profile highly affected by their unique crystalline structure. However, classical solid-liquid equilibrium (SLE) thermodynamic modeling approaches, which assume the solid phase to be a pure component, sometimes fail in the correct description of the phase behavior. In addition, their inability increases with the complexity of the system. To overcome some of these problems, this study describes a new procedure to depict the SLE of fatty binary mixtures presenting solid solutions, namely the Crystal-T algorithm. Considering the non-ideality of both liquid and solid phases, this algorithm is aimed at the determination of the temperature in which the first and last crystal of the mixture melts. The evaluation is focused on experimental data measured and reported in this work for systems composed of triacylglycerols and fatty alcohols. The liquidus and solidus lines of the SLE phase diagrams were described by using excess Gibbs energy based equations, and the group contribution UNIFAC model for the calculation of the activity coefficients of both liquid and solid phases. Very low deviations of theoretical and experimental data evidenced the strength of the algorithm, contributing to the enlargement of the scope of the SLE modeling.

Malpighiaceae: correlations between habit, fruit type and basic chromosome number

The family Malpighiaceae presents species with different habits, fruit types and cytological characters. Climbers are considered the most derived habit, followed, respectively, by the shrubby and arboreal ones. The present study examines the relationship between basic chromosome numbers and the derivation of climbing habit and fruit types in Malpighiaceae. A comparison of all the chromosome number reports for Malpighiaceae showed a predominance of chromosome numbers based on x=5 or 10 in the genera of sub-family Malpighioideae, mainly represented by climbers with winged fruits, whereas non-climbing species with non-winged fruits, which predominate in sub-family Byrsonimoideae, had counts based on x=6, which is considered the less derived basic number for the family. Based on such data, confirmed by statistic assays, and on the monophyletic origin of this family, we admit the hypothesis that morphological derivation of habit and fruit is correlated with chromosome basic number variation in the family Malpighiaceae.

As relativas que são fáceis na aquisição do português brasileiro

The acquisition of Portuguese by two Brazilian children (aged 2;0 -5;0) is discussed in an attempt to describe and explain the first relative clauses produced in naturalistic, observational studies, according to the framework of generative syntax theory. The results show that at around 3;0: a) the child starts to deal with relative clauses as modifiers of N; b) cleft sentences appear before relative clauses, and c) the first relatives confirm the prevalence of the vernacular strategy of relativization in Brazilian Portuguese identified by other studies based on adult data.