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
Pokemon is one of the most popular video games in the world, and recent interest has appeared in Pokemon battling as a testbed for AI challenges. This is due to Pokemon battling showing interesting properties which contrast with current AI challenges over other video games. To this end, we implement a Pokemon Battle Environment, which preserves many of the core elements of Pokemon battling, and allows researchers to test isolated learning objectives. Our approach focuses on type advantage in Pokemon battles and on the advantages of delayed rewards through switching, which is considered core strategies for any Pokemon battle. As a competitive multi-agent environment, it has a partially-observable, high-dimensional, and continuous state-space, adheres to the Gym de facto standard reinforcement learning interface, and is performance-oriented, achieving thousands of interactions per second in commodity hardware. We determine whether deep competitive reinforcement learning algorithms, WPL theta and GIGA theta, can learn successful policies in this environment. Both converge to rational and effective strategies, and GIGA theta shows faster convergence, obtaining a 100% win-rate in a disadvantageous test scenario.

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.