Abstract
Long non-coding RNA (lncRNA) genes have well-established and important impacts on molecular and cellular functions. However, among the thousands of lncRNA genes, it is still a major challenge to identify the subset with disease or trait relevance. To systematically characterize these lncRNA genes, we used Genotype Tissue Expression (GTEx) project v8 genetic and multi-tissue transcriptomic data to profile the expression, genetic regulation, cellular contexts, and trait associations of 14,100 lncRNA genes across 49 tissues for 101 distinct complex genetic traits. Using these approaches, we identified 1,432 lncRNA gene-trait associations, 800 of which were not explained by stronger effects of neighboring protein-coding genes. This included associations between lncRNA quantitative trait loci and inflammatory bowel disease, type 1 and type 2 diabetes, and coronary artery disease, as well as rare variant associations to body mass index.

Abstract
The oceans have the capability to support the current transitions occurring within our societies, including the implementation of clean energy production and storage technologies and new paths for sustainable food production. These transitions are, nonetheless, many times dependent on innovative technologies which require long paths of technology maturation before they can fit the existing ecosystems and markets. One critical step for technology validation is the demonstration stage in real offshore conditions, which is necessary to validate the performance of the proposed technologies, as well as their reliability and economic viability. In this respect, Portugal has been the testbed of several ocean-based technologies, including the Windfloat device, and possesses the necessary infrastructures to implement and test further innovative concepts and designs. Still, these infrastructures are currently underutilized, which means more technology developers could be testing and implementing their technologies in the country. This paper presents the OceanACT initiative, which is being led by five partners, + ATLANTIC, CEIIA, Fórum Oceano, INESC TEC and WavEC, aiming to promote and manage the existing offshore testing infrastructures in the country. The vision and the strategic path for the initiative, as well as the available infrastructures, and its respective metocean conditions, are presented here. This initiative intends to attract new technology developers to the country, and consequently generate relevant socioeconomic benefits, such as the attraction of investment, the inclusion of the national industry into the supply chain of these innovative projects, and the creation of highly qualified jobs.

Abstract
Anaphora resolution is one of the problems in natural language processing. It is the process of disambiguating the antecedent of a referring expression from the set of entities in a discourse. The correct interpretation of pronouns plays an important role in the construction of meaning Thus, the resolution of pronominal anaphors remains a very important task for many natural language processing applications. Additionally, it plays an increasingly significant role in computational linguistics. However, a significant amount of work on anaphora resolution is focused on English; anaphora resolution for other languages, including Arabic, is still limited. In this paper, we present a new set of computational and linguistic features to resolve Arabic anaphors using a machine learning approach. In this paper, an in-depth study was conducted on a set of computational and linguistic features to exploit their effectiveness and investigate their effect on anaphora resolution. The aim was to efficiently integrate different feature sets and classification algorithms to synthesize a more accurate classification procedure. Four well-known machine learning algorithms k-nearest neighbor, maximum entropy, decision tree and meta-classifier, were employed as base-classifiers for each of the feature sets. A wide range of comparative experiments on Quran datasets was conducted, the discussion presented, and conclusions were drawn. The experimental results show that our approach gives satisfactory results. (C) 2021 The Authors. Published by Elsevier B.V.

Abstract
In this work we use the concept of paraxial fluids of light to explore quantum turbulence, probing a turbulent regime induced on an optical beam propagating inside a defocusing nonlinear media. For that purpose, we establish a physical analogue of a two-component quantum fluid by making use of orthogonal polarizations and incoherent beam interaction, obtaining a system for which the perturbative excitations follow a modified Bogoliubov-like dispersion relation. This dispersion relation features regions of instability that define an effective range of energy injection and that are easily tuned by manipulating the relative angle of incidence between the two components. Our numerical results support the predictions and show evidence of direct and inverse turbulent cascades expected from weak wave turbulence theories, which may inspire new ways to explore to quantum turbulence with optical analogues.

Abstract
The materials used in the transport industry have been changing in the last decades. The traditional and heavy steel have been switching by the light alloys like aluminum alloys. However, despite their advantages as low density and high corrosion resistance, the manufacturing process, especially fusion welding, is very demanding and challenging. In the transport industry, most of the hyperstatic components made in aluminum alloys are welded manually with the associate financial costs as well as the lack of quality and repeatability. For these reasons, it is urgent to develop new methodologies to automate this process. The present work intends to show a scientific method to automate the welding process of hyperstatic frames, very common in bicycles, made in aluminum alloy. This methodology involves two steps, the first one in which is performed numerical simulations to determine the optimal welding parameters to minimize the distortion and residual stresses. The second step is experimental one, and it is created an automated welding cell with a robot to weld the frames. It has been proved that it is possible to obtain welding aluminum frames with acceptable quality in agreement with the ASME IX standard. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Abstract
Modern containerized distributed systems, such as big data storage and processing stacks or micro-service based applications, are inherently hard to monitor and optimize, as resource usage does not directly match hardware resources due to multiple virtualization layers. For instance, inter-application traffic is an important factor in as it directly indicates how components interact, it has not been possible to accurately monitor it in an application independent way and without severe overhead, thus putting it out of reach of cloud platforms. In this paper we present an efficient black-box monitoring approach for gathering detailed structural information of collaborating processes in a distributed system that can be queried for various purposes, as it includes both information about processes, containers, and hosts, as well as resource usage and amount of data exchanged. The key to achieving high detail and low overhead without custom application instrumentation is to use a kernel-aided event driven strategy. We validate a prototype implementation by applying it to multi-platform microservice deployments, evaluate its performance with micro-benchmarks, and demonstrate its usefulness for container placement in a distributed data storage and processing stack (i.e., Cassandra and Spark).