Instituto Pedro Nunes

Working with large amounts of text data has become hectic and time-consuming. In order to reduce human effort, costs, and make the process more efficient, companies and organizations resort to intelligent algorithms to automate and assist the manual work. This problem is also present in the field of toxicological analysis of chemical substances, where information needs to be searched from multiple documents. That said, we propose an approach that relies on Question Answering for acquiring information from unstructured data, in our case, English PDF documents containing information about physicochemical and toxicological properties of chemical substances. Experimental results confirm that our approach achieves promising results which can be applicable in the business scenario, especially if further revised by humans.

Additive manufacturing (AM) of construction materials has been one of the emerging advanced technologies that aim to minimise the supply chain in the construction industry through autonomous production of building components directly from digital models without human intervention and complicated formworks. However, technical challenges needs to be addressed for the industrial implementation of AM, e.g. materials formulation standardization, and interfacial bonding quality between the deposited layers amongst others. AM as one of the most highlighted key enabling technologies has the potential to create disruptive solutions, the key for its successful implementation is multidisciplinary effort in synergy involving materials science, architecture/design, computation, and robotics. There are crucial links between the material design formulations and the printing system for the manufacturing of the complex 3D geometries. Understanding and optimising the mix design for fresh rheology of materials and sufficient adhesion/cohesion of interface can allow the incorporation of complexity in the geometry.

Industrial hemp (Cannabis sativa) is one of the most available and widely produced bast fibers with high cellulose content. Interest in these fibers is warranted due to environmental protection challenges as well as their inherent properties such as low density, high specific strength, and stiffness. In addition, advanced research and progress have gone into increasing their mechanical performance through surface treatments and in the development of new materials. The most promising application for hemp fibers is as reinforcement in polymeric composites or through hybridization. Nonetheless, more research is needed to improve their properties and expand their range of applications. The biodegradability issue is one problem that must be addressed when considering long life-cycle applications as the reproducibility of these composites’ final properties. This review is a comprehensive literature review on hemp fibers. It includes hemp fibers’ chemical and mechanical properties, surface modifications, hybrid composites, as well as current and future applications.

Polymers and their composites are widely used for designing structures in aerospace, automotive, electronic, sport industries due to their lightweight, cost, and processing advantages. However, the surface of polymeric materials typically exhibits intrinsic deficiencies, limiting their durability and functionalities, e.g., low wear resistance, low thermal and electrical conductivity, low adhesion, low bioactivity, low reflectiveness, and weak photochemical resistance. Polymer metallization is an emerging concept that addresses these deficiencies by forming a metallic skin on polymeric surfaces. Herein, the working principles, recent advances, challenges, functional capabilities, and applications of the state-of-the-art polymer metallization methods in the fields of additive manufacturing, coating technologies, and material science are reviewed on nano-, micro-, and macroscales. The polymer metallization methods applied to polymeric and polymer composite substrates are physical vapor deposition, electrochemical plating, a family of thermal spray methods (such as flame spaying, arc spraying, plasma spraying, and cold spraying), and a series of polymer–metal direct bonding methods (such as adhesive bonding, injection overmolding, and fusion joining techniques, including ultrasonic joining, friction spot joining, electromagnetic induction joining, and laser joining). Understanding the key aspects within these approaches would guide scientist and engineers for optimizing the design and durability of structural materials made of polymers/composites.

Reactive oxygen species (ROS) are continuously produced in living cells due to metabolic and biochemical reactions and due to exposure to physical, chemical and biological agents. Excessive ROS cause oxidative stress and lead to oxidative DNA damage. Within ROS-mediated DNA lesions, 8-oxoguanine (8-oxoG) and its nucleotide 8-oxo-2'-deoxyguanosine (8-oxodG)-the guanine and deoxyguanosine oxidation products, respectively, are regarded as the most significant biomarkers for oxidative DNA damage. The quantification of 8-oxoG and 8-oxodG in urine, blood, tissue and saliva is essential, being employed to determine the overall effects of oxidative stress and to assess the risk, diagnose, and evaluate the treatment of autoimmune, inflammatory, neurodegenerative and cardiovascular diseases, diabetes, cancer and other age-related diseases. High-performance liquid chromatography with electrochemical detection (HPLC-ECD) is largely employed for 8-oxoG and 8-oxodG determination in biological samples due to its high selectivity and sensitivity, down to the femtomolar range. This review seeks to provide an exhaustive analysis of the most recent reports on the HPLC-ECD determination of 8-oxoG and 8-oxodG in cellular DNA and body fluids, which is relevant for health research.

Machine learning-based systems have presented increasing learning performance, in a wide variety of tasks. However, the problem with some state-of-the-art models is their lack of transparency, trustworthiness, and explainability. To address this problem, eXplainable Artificial Intelligence (XAI) appeared. It is a research field that aims to make black-box models more understandable to humans. The research on this topic has increased in recent years, and many methods, such as LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive exPlanations) have been proposed. Machine learning-based Intrusion Detection Systems (IDS) are one of the many application domains of XAI. However, most of the works about model interpretation focus on other fields, like computer vision, natural language processing, biology, healthcare, etc. This poses a challenge for cybersecurity professionals tasked with analyzing IDS results, thereby impeding their capacity to make informed decisions. In an attempt to address this problem, we have selected two XAI methods, LIME, and SHAP. Using the methods, we have retrieved explanations for the results of a black-box model, part of an IDS solution that performs intrusion detection on IoT devices, increasing its interpretability. In order to validate the explanations, we carried out a perturbation analysis where we tried to obtain a different classification based on the features present in the explanations. With the explanations and the perturbation analysis we were able to draw conclusions about the negative impact of particular features on the model results when present in the input data, making it easier for cybersecurity experts when analyzing the model results and it serves as an aid to the continuous improvement the model. The perturbations also serve as a comparison of performance between LIME and SHAP. To evaluate the degree of interpretability increase, and the explanations provided by each XAI method of the model and directly compare the XAI methods, we have performed a survey analysis.

Abstract Within the next decades, robots will need to be able to execute a large variety of tasks autonomously in a large variety of environments. To relax the resulting programming effort, a knowledge-enabled approach to robot programming can be adopted to organize information in re-usable knowledge pieces. However, for the ease of reuse, there needs to be an agreement on the meaning of terms. A common approach is to represent these terms using ontology languages that conceptualize the respective domain. In this work, we will review projects that use ontologies to support robot autonomy. We will systematically search for projects that fulfill a set of inclusion criteria and compare them with each other with respect to the scope of their ontology, what types of cognitive capabilities are supported by the use of ontologies, and which is their application domain.

Drug design is an important area of study for pharmaceutical businesses. However, low efficacy, off-target delivery, time consumption, and high cost are challenges and can create barriers that impact this process. Deep Learning models are emerging as a promising solution to perform de novo drug design, i.e., to generate drug-like molecules tailored to specific needs. However, stereochemistry was not explicitly considered in the generated molecules, which is inevitable in targeted-oriented molecules. This paper proposes a framework based on Feedback Generative Adversarial Network (GAN) that includes optimization strategy by incorporating Encoder-Decoder, GAN, and Predictor deep models interconnected with a feedback loop. The Encoder-Decoder converts the string notations of molecules into latent space vectors, effectively creating a new type of molecular representation. At the same time, the GAN can learn and replicate the training data distribution and, therefore, generate new compounds. The feedback loop is designed to incorporate and evaluate the generated molecules according to the multiobjective desired property at every epoch of training to ensure a steady shift of the generated distribution towards the space of the targeted properties. Moreover, to develop a more precise set of molecules, we also incorporate a multiobjective optimization selection technique based on a non-dominated sorting genetic algorithm. The results demonstrate that the proposed framework can generate realistic, novel molecules that span the chemical space. The proposed Encoder-Decoder model correctly reconstructs 99% of the datasets, including stereochemical information. The model's ability to find uncharted regions of the chemical space was successfully shown by optimizing the unbiased GAN to generate molecules with a high binding affinity to the Kappa Opioid and Adenosine [Formula: see text] receptor. Furthermore, the generated compounds exhibit high internal and external diversity levels 0.88 and 0.94, respectively, and uniqueness.

Invasion by alien species is a worldwide phenomenon with negative consequences at both natural and production areas. Acacia longifolia is an invasive shrub/small tree well known for its negative ecological impacts in several places around the world. The recent introduction of a biocontrol agent (Trichilogaster acaciaelongifoliae), an Australian bud-galling wasp which decreases flowering of A. longifolia, in Portugal, demands the development of a cost-efficient method to monitor its establishment. We tested how Unmanned Aerial Vehicles (UAV) can be used to map A. longifolia flowering. Our core assumption is as the population of the biocontrol agent increases, its impacts on the reduction of A. longifolia flowering will be increasingly visible. Additionally, we tested if there is a simple linear correlation between the number of flowers of A. longifolia counted in field and the area covered by flowers in the UAV imagery. UAV imagery was acquired over seven coastal areas including frontal dunes, interior sand dunes and pine forests considering two phenological stages: peak and off-peak flowering season. The number of flowers of A. longifolia was counted, in a minimum of 60 1m2 quadrats per study area. For each study area, flower presence/absence maps were obtained using supervised Random Forest. The correlation between the number of flowers and the area covered by flowering plants could then be tested. The flowering of A. longifolia was mapped using UAV mounted with RGB and CIR Cannon IXUS/ELPH cameras (Overall Accuracy > 0.96; Cohen’s Kappa > 0.85) varying according to habitat type and flowering season. The correlation between the number of flowers counted and the area covered by flowering was weak (r2 between 0.0134 and 0.156). This is probably explained, at least partially, by the high variability of A. longifolia in what regards flowering morphology and distribution. The very high accuracy of our approach to map A. longifolia flowering proved to be cost efficient and replicable, showing great potential for detecting the future decrease in flowering promoted by the biocontrol agent. The attempt to provide a low-cost method to estimate A. longifolia flower productivity using UAV failed, but it provided valuable insights on the future steps.

Photodynamic therapy (PDT) is a promising cancer treatment which involves a photosensitizer (PS), light at a specific wavelength for PS activation and oxygen, which combine to elicit cell death. While the illumination required to activate a PS imparts a certain amount of selectivity to PDT treatments, poor tumor accumulation and cell internalization are still inherent properties of most intravenously administered PSs. As a result, common consequences of PDT include skin photosensitivity. To overcome the mentioned issues, PSs may be tailored to specifically target overexpressed biomarkers of tumors. This active targeting can be achieved by direct conjugation of the PS to a ligand with enhanced affinity for a target overexpressed on cancer cells and/or other cells of the tumor microenvironment. Alternatively, PSs may be incorporated into ligand-targeted nanocarriers, which may also encompass multi-functionalities, including diagnosis and therapy. In this review, we highlight the major advances in active targeting of PSs, either by means of ligand-derived bioconjugates or by exploiting ligand-targeting nanocarriers.

Abstract An exhaustive and integrative overview of recent developments in 3D and 4D textiles based on Additive Manufacturing (AM) were provided in order to identify the current state‐of‐the‐art. Despite all scientific progress, AM applied on textiles is a challenging technique and is still at an embryonic stage of research and technological development (R&TD), mainly due to the technological gap between featured prototypes and scalability in manufacturing. Despite its full potential across a range of different applications, such as development of functional filament fibres/wires, 3D printing on textiles, 3D printing completed garments and 4D textiles, needs future developments. Although, AM applied on textiles, enables cost and resource efficiency for small scale production through localised production, shorten supply chain and demand driven manufacture, both customisable and scalable, embracing cost and environmental sustainability. The opportunities and limits of 3D and 4D printing textiles are also discussed. Finally, the conclusion highlights the potential future development and application of the convergence of advanced computational design techniques, product customization, mathematical modelling, simulation, and digital modelling within multifunctional textiles. Graphical Abstract

The interleukin-1 receptor type 1 (IL-1R1) holds pivotal roles in the immune system, as it is positioned at the "epicenter" of the inflammatory signaling networks. Increased levels of the cytokine IL-1 are a recognized feature of the immune response in the central nervous system (CNS) during injury and disease, i.e., neuroinflammation. Despite IL-1/IL-1R1 signaling within the CNS having been the subject of several studies, the roles of IL-1R1 in the CNS cellular milieu still cause controversy. Without much doubt, however, the persistent activation of the IL-1/IL-1R1 signaling pathway is intimately linked with the pathogenesis of a plethora of CNS disease states, ranging from Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS), all the way to schizophrenia and prion diseases. Importantly, a growing body of evidence is showing that blocking IL-1R1 signaling via pharmacological or genetic means in different experimental models of said CNS diseases leads to reduced neuroinflammation and delayed disease progression. The aim of this paper is to review the recent progress in the study of the biological roles of IL-1R1, as well as to highlight key aspects that render IL-1R1 a promising target for the development of novel disease-modifying treatments for multiple CNS indications.

Carbon-based coatings are used in many applications, particularly in sliding contacts to reduce friction and wear. To improve the tribological properties, these coatings are usually alloyed with metals; W is one of the most used since it helps improve the tribological performance at high temperatures. In this work, we compared the tribological performance of Diamond-Like Carbon alloyed with tungsten (DLC-W) films deposited by direct current magnetron sputtering (DCMS) with films deposited in a hybrid configuration DCMS + high power impulse magnetron sputtering (HiPIMS). The DLC-W coatings were produced with approximately the same W content. One hydrogenated film was deposited with the hybrid configuration for comparison purposes. Microstructure, structure, mechanical properties, and tribological behaviour were used to compare the coatings. All the films displayed a low-order structure of tungsten carbide embedded in an amorphous carbon matrix. The use of the hybrid HiPIMS/DCMS results in coatings with more compact morphologies due to the high ionization fraction of the species produced on the W target (W and Ar ionized species), which primarily will oppose the shadowing effect as the ions will reach the substrate at angles close to 90°. HiPIMS non-hydrogenated film is the more tribological, performing either at room or high temperature (150 °C) due to the much more compact morphology, which avoids the detachment of hard W-C particles, which are responsible for more efficiently scratching the film surface. Experiments revealed that wear behaviour in all the films is governed by the contact of the tribolayer formed on the counterpart composed of W–C, C and W–O against the surface of the film.

The use of bismuth(III) salts as catalysts for the Wagner-Meerwein rearrangement of lupane derivatives with expansion of ring E and formation of an additional O-containing ring is reported. This process has also been extended to other terpenes, such as the sesquiterpene (-)-caryophyllene oxide. When the reaction was performed with oleanonic acid, 28,13beta-lactonization occurred, without Wagner-Meerwein rearrangement. Under more vigorous reaction conditions, dehydration of the 3beta-hydroxyl group and subsequent additional Wagner-Meerwein rearrangement led to the selective synthesis of A-neo-18alpha-oleanene compounds, in very high yields.

Bacteria in the genus Xanthomonas infect a wide range of crops and wild plants, with most species responsible for plant diseases that have a global economic and environmental impact on the seed, plant, and food trade. Infections by Xanthomonas spp. cause a wide variety of non-specific symptoms, making their identification difficult. The coexistence of phylogenetically close strains, but drastically different in their phenotype, poses an added challenge to diagnosis. Data on future climate change scenarios predict an increase in the severity of epidemics and a geographical expansion of pathogens, increasing pressure on plant health services. In this context, the effectiveness of integrated disease management strategies strongly depends on the availability of rapid, sensitive, and specific diagnostic methods. The accumulation of genomic information in recent years has facilitated the identification of new DNA markers, a cornerstone for the development of more sensitive and specific methods. Nevertheless, the challenges that the taxonomic complexity of this genus represents in terms of diagnosis together with the fact that within the same bacterial species, groups of strains may interact with distinct host species demonstrate that there is still a long way to go. In this review, we describe and discuss the current molecular-based methods for the diagnosis and detection of regulated Xanthomonas, taxonomic and diversity studies in Xanthomonas and genomic approaches for molecular diagnosis.

Flavonoids represent a large group of aromatic amino acids that are extensively disseminated in plants. More than six thousand different flavonoids have been isolated and identified. They are important components of the human diet, presenting a broad spectrum of health benefits, including antibacterial, antiviral, antimicrobial, antineoplastic, anti-mutagenic, anti-inflammatory, anti-allergic, immunomodulatory, vasodilatory and cardioprotective properties. They are now considered indispensable compounds in the healthcare, food, pharmaceutical, cosmetic and biotechnology industries. All flavonoids are electroactive, and a relationship between their electron-transfer properties and radical-scavenging activity has been highlighted. This review seeks to provide a comprehensive overview concerning the electron-transfer reactions in flavonoids, from the point of view of their in-vitro antioxidant mode of action. Flavonoid redox behavior is related to the oxidation of the phenolic hydroxy groups present in their structures. The fundamental principles concerning the redox behavior of flavonoids will be described, and the phenol moiety oxidation pathways and the effect of substituents and experimental conditions on flavonoid electrochemical behavior will be discussed. The final sections will focus on the electroanalysis of flavonoids in natural products and their identification in highly complex matrixes, such as fruits, vegetables, beverages, food supplements, pharmaceutical compounds and human body fluids, relevant for food quality control, nutrition, and healthcare research.

This paper aims to contribute to situation, activity, and goal awareness in cyber-physical human-machine systems (HMS) by presenting a new information model and specifications for a decision-making component that can be integrated in current system architectures. The objective of this work is to improve the efficacy, acceptance, adaptability, and overall performance of HMS and human-system interaction (HSI) applications using a context-based approach. Our hypothesis is that we can enhance current interaction functionalities by integrating context and interaction information models into a decision-making component that behaves as a supervision process for controlling interaction. In HSI, we aim to define a general human model that may lead to principles and algorithms, allowing more natural and effective interaction between humans and artificial agents. The approach was implemented and tested targeting application in the domain of active and assisted living. The challenge of user acceptance is of vital importance for future solutions and is still one of the major reasons for reluctance to adopt cyber-physical systems in this domain.

Breast cancer is the second leading cause of cancer deaths in women worldwide; therefore, there is an increased need for the discovery, development, optimization, and quantification of diagnostic biomarkers that can improve the disease diagnosis, prognosis, and therapeutic outcome. Circulating cell-free nucleic acids biomarkers such as microRNAs (miRNAs) and breast cancer susceptibility gene 1 (BRCA1) allow the characterization of the genetic features and screening breast cancer patients. Electrochemical biosensors offer excellent platforms for the detection of breast cancer biomarkers due to their high sensitivity and selectivity, low cost, use of small analyte volumes, and easy miniaturization. In this context, this article provides an exhaustive review concerning the electrochemical methods of characterization and quantification of different miRNAs and BRCA1 breast cancer biomarkers using electrochemical DNA biosensors based on the detection of hybridization events between a DNA or peptide nucleic acid probe and the target nucleic acid sequence. The fabrication approaches, the biosensors architectures, the signal amplification strategies, the detection techniques, and the key performance parameters, such as the linearity range and the limit of detection, were discussed.

The purpose of this paper was to understand how an agent's performance is affected when interaction workflows are incorporated in its information model and decision-making process. Our expectation was that this incorporation could reduce errors and faults on agent's operation, improving its interaction performance. We based this expectation on the existing challenges in designing and implementing artificial social agents, where an approach based on predefined user scenarios and action scripts is insufficient to account for uncertainty in perception or unclear expectations from the user. Therefore, we developed a framework that captures the expected behavior of the agent into descriptive scenarios and then translated these into the agent's information model and used the resulting representation in probabilistic planning and decision making to control interaction. Our results indicated an improvement in terms of specificity while maintaining precision and recall, suggesting that the hypothesis being proposed in our approach is plausible. We believe the presented framework will contribute to the field of cognitive robotics, e.g., by improving the usability of artificial social companions, thus overcoming the limitations imposed by approaches that use predefined static models for an agent's behavior resulting in non-natural interaction.

This article reports a study aiming to determine the perceptions of older adults needing formal care about the usefulness, satisfaction, and ease of use of CaMeLi, a virtual companion based on an embodied conversational agent, and the perceptions of formal caregivers about the potential of virtual companions to support care provision. An observational study involving older adults needing formal care was conducted to assess CaMeLi using a multi-method approach (i.e., an auto-reported questionnaire—the Usefulness, Satisfaction, and Ease of use questionnaire; a scale for the usability assessment based on the opinion of observers—the International Classification of Functioning Disability and Health-based Usability Scale; and critical incident registration). Moreover, a focus group was conducted to collect data regarding the perceived utility of virtual companions to support care provision. The observational study was conducted with 46 participants with an average age of 63.6 years, and the results were associated with a high level of usefulness, satisfaction, and ease of use of CaMeLi. Furthermore, the focus group composed of four care providers considered virtual companions a promising solution to support care provision and to prevent loneliness and social isolation. The results of both the observational study and the focus group revealed good perceptions regarding the role of virtual companions to support the care provision for older adults.