Abstract
AbstractNear 20% of the Portuguese population is aged 65 years or above, a value similar to most developed countries. This older adult population also suffers from obesity and obesity-related pathologies. The environment encompasses a set of obesity determinants and knowing the associations between the environment and obesity may help health professionals and caregivers to provide for the older adults.In this study, we aimed to relate anthropometric measures with socio-demographic data in older adults.This is a cross-sectional study using data from the Pronutrisenior project, collected in Vila Nova de Gaia, Portugal. The sample consists of 456 older adults, aged 65 to 92 years without cognitive impairment. The sample consisted of older adults living at their homes. Socio-demographic, clinical, geographical, and anthropometric data was collected. The statistical analysis used IBM-SPSS-22.0 and consisted on descriptive statistics, Pearson correlations and UniANOVA. Significantly results (p < 0.05) are reported.These older adults were mostly females (54.2%) with a mean age of 73.8 years (sd = 6.3), mean body mass index (BMI) of 29.1kg/m2 (sd = 4.8), mean waist-to-height ratio (WHtR) of 0.623 (sd = 0.073) and mean percentage of body fat mass (%BFM) of 40.7% (sd = 8.9%) for females and 30.2% (sd = 8.5%) for males. BMI, WHtR and %BFM were positively correlated. In this sample of older adults, higher values of these measures were associated to being female, younger, less educated; to having articular pains and respiratory problems, and not having insomnia, hypertension, chewing problems nor hearing problems; to drink more liquids but not consuming dairy products daily; to not take nutritional supplements but to take more medicines; to be without somebody to talk to and to be more dependent; and to live in a house without stairs to climb and to live near other older adults, and in a more urbanized area with streets with steeper slopes.In this sample of older adults, obesity is related with health characteristics and those are related with socio-demographic and geographical characteristics of the area of residence. Besides the identification of risk factors for the older adult population, this information may help designing health care policies that takes in consideration the physical and geographical characteristics of the neighbourhood of the area of residence of the older adults.

Abstract
Cancer has an important and considerable gender differential susceptibility confirmed by several epidemiological studies. Gastric (GC) and thyroid cancer (TC) are examples of malignancies with a higher incidence in males and females, respectively. Beyond environmental predisposing factors, it is expected that gender-specific gene deregulation contributes to this differential incidence. We performed a detailed characterization of the transcriptomic differences between genders in normal and tumor tissues from stomach and thyroid using Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) data. We found hundreds of sex-biased genes (SBGs). Most of the SBGs shared by normal and tumor belong to sexual chromosomes, while the normal and tumor-specific tend to be found in the autosomes. Expression of several cancer-associated genes is also found to differ between sexes in both types of tissue. Thousands of differentially expressed genes (DEGs) between paired tumor-normal tissues were identified in GC and TC. For both cancers, in the most susceptible gender, the DEGs were mostly under-expressed in the tumor tissue, with an enrichment for tumor-suppressor genes (TSGs). Moreover, we found gene networks preferentially associated to males in GC and to females in TC and correlated with cancer histological subtypes. Our results shed light on the molecular differences and commonalities between genders and provide novel insights in the differential risk underlying these cancers.

Abstract
This work studies Tor, an anonymous overlay network used to browse the Internet. Apart from its main purpose, this open-source project has gained popularity mainly because it does not hide its implementation. In this way, researchers and security experts can fully examine and confirm its security requirements. Its ease of use has attracted all kinds of people, including ordinary citizens who want to avoid being profiled for targeted advertisements or circumvent censorship, corporations who do not want to reveal information to their competitors, and government intelligence agencies who need to do operations on the Internet without being noticed. In opposition, an anonymous system like this represents a good testbed for attackers, because their actions are naturally untraceable. In this work, the characteristics of Tor traffic are studied in detail in order to devise an inspection methodology able to improve Tor detection. In particular, this methodology considers as new inputs the observer position in the network, the portion of traffic it can monitor, and particularities of the Tor browser for helping in the detection process. In addition, a set of Snort rules were developed as a proof-of-concept for the proposed Tor detection approach. © Springer Nature Switzerland AG 2020.

Abstract
One important problem in Bioinformatics is the discovery of new interactions between cellular lines and chemical compounds. In silico methods for cell-line screening are fundamental to optimize cost and time in the drug discovery processes. In order to build these methods, we need to computationally represent cell lines. Current methods for modeling cell line interactions rely on comparing genetic expression profiles. However, these profiles are usually unknown. In this work, we present a method to characterize and represent cell lines by text processing the related scientific literature. We collect abstracts of scientific papers about cellular lines from Cellosaurus and PubMed. These documents are then represented as TF-IDF vectors. We build a data set for classification with the document vectors having the cell line identifier as the target class. We then apply a multiclass SVM classification method. We use Support Vector Domain Description to describe and characterize each cell line with its corresponding hyperplane obtained with a one-vs-rest training. We evaluated several configurations of classifiers, using micro-averaged precision as metric to choose the best classifier, and were able to differentiate cellular lines from a set of 200+.

Abstract
This paper proposes a Local Energy Market using a P2P blockchain-powered marketplace where agents bilaterally trade energy after the consumption and production period, and not before, as usual in electricity market design. The EU and MIBEL regulatory framework for Renewable Energy Communities potentially creates space for such a market, but some improvements in the settlement procedures and agent's participation must be met. © 2020 IEEE.

Abstract
Recent years witnessed an increase in income inequality. Several explanations have been put forward. In the present paper, we consider a series of technologically related events that have been crucial for the increased income inequality, that is, public R&D incentives, increasing horizontal integration and spillover effects. We found that public R&D incentives and the increasing horizontal integration have biased the income distribution towards the top income group. In particular, the high-skilled workers involved in the R&D process have benefited enormously from this process. Similarly, capital owners have seen an increase in their profits, because of the reduction in product market competition and technological improvements in the production process. We found the effect of knowledge spillovers to be less clear-cut. We conclude discussing the implications of our results and suggesting possible solutions to the increasing income inequality. We call for the creation of supranational institutions, and for stricter legislation on competition and antitrust policy.