(1) Background: The aim of the present study was to evaluate the effect of different types of warm-ups on the strength and skin temperature of Paralympic powerlifting athletes; (2) Methods: The participants were 15 male Paralympic powerlifting athletes. It was analyzed the effects of three different types of warm-up (without warm-up (WW), traditional warm-up (TW), or stretching warm-up (SW)) on static and dynamic strength tests as well as in the skin temperature, which was monitored by thermal imaging; (3) Results: show no differences in the dynamic and static indicators of the force in relation to the different types of warm-up. No significant differences were found in relation to the Peak Torque (p = 0.055, F=4.560, η2p= 0.246 medium effect), and 1-Repetition Maximum (p = 0.139, F=3.191, η2p = 0.186, medium effect) between the different types of warm-up. In the thermographic analysis, there was a significant difference only in the Pectoral muscle clavicular portion between the TW (33.04 ± 0.71ºC) and the WW (32.51 ± 0.74ºC) (p = 0.038). The TW method also presented slightly higher values than the SW and WW in the Pectoral Muscles Sternal portion and in the Deltoid anterior portion, but with p-value > 0.05; (4) Conclusions: that the types of warm-up studied do not seem to interfere with the performance of Paralympic Powerlifting athletes. However, the thermal images showed that traditional warm-up best meets the objectives expected for this preparation phase.

Introduction and Purpose: Nurses are expected to be competent in practice based on the domain of standards and interpersonal soft skills (ISS). Nurses are faced with inadequate physical assessment 28%, formulating diagnoses 56%, plans 34%, implementation 31%, evaluation 37,5%, and communication skills 41%. Aims to develop an ISS learning model based on transformative learning theory and digitalization (TLTD) to improve nurses’ competencies. Method: Used Explanatory cross-sectional involved 190 practitioner nurses. Variables of individual characters (X1), facilities (X2), Social environment support/SES (X3), TLTD (X4), ISS (X5), and nursing competencies (Y) were measured. Data was collected using a checklist questionnaire, conducted on 6 June to 10 August 2022 at Dili, Baucau, and Maliana hospitals. Used descriptive and inferential analysis with SEM-PLS. Result: Five of the 11 hypotheses of statistical p-value < 0,05 and T-Test > 1,96, including X2>X4, X3>X5, X4>X5, and X5>y, to form ISS learning model. The characteristic of respondents were 81,8% young adults, 67,9% women, 57,8% undergraduates, judging the previous training 35% was unsuccessful, and 45,8% did not meet expectations. Learning facilities that are still lacking include policies 23,7%, curriculum 23,4%, guideline 22,7%, modules 27,9%, infrastructure 20,3%, social environment support 24,2% low, organizational support, 30,0% low peer support and 26,3% low family support. The average TLTD is 25,3% to 34,2%, ISS 22,1% to 30,5%, and nurse competence 25,3% to 30,5%.Suggestion: Result of the study become base to improve nurse’s competencies. Conclusion: Learning model was formed by facilities, SES, TLTD, ISS, and nurses’ competencies.

The city of Lisbon, has any other capital of a European country, has a large number of issues while managing the waste and recycling containers spread throughout the city. This document presents the results of a study promoted by the Lisbon City Council for trialing LPWAN technology on the waste management vertical under the Lisbon Smart City initiative. Current waste management is done using GSM sensors, and the aim is to use LPWAN to reduce the costs, improve range and reduce provisioning times when changing the communications provider. After an initial study, LoRa was selected as the LPWAN of choice for the trials. The study is composed of multiple use cases at different distances, types of recycling waste containers, placements (underground and surface) and different kinds of waste level measurement LoRa sensors, deployed in order to assess the impact of the different use cases on the LoRa sensor usage. The results shown that the underground waste containers present the most difficult challenge, where the container itself imposes attenuation levels of 26dB on the link budget. The results promoted the deployment of a city wide LoRa network available to all departments inside the Lisbon City Council, and considering the network capacity the network, the network is also available to citizens to be used freely.

This paper presents a review on numerical and experimental methods to predict fracture of ad-vanced high strength steels (AHSS). AHSS are widely processed through sheet metal forming processes. Although being an excellent choice when lightweight, high strength and ductility are critical factors, their multi-phase microstructure accentuates forming challenges. To accurately model forming behavior, necking based failure criteria as well as direct fracture models require improvements. As a necking based failure model, the conventional forming limit diagram/curve (FLD/FLC) presents limitations such as in estimating direct fracture (surface cracks, edge cracks, shear cracks), as well as deformation histories under nonlinear strain paths. Thus, significant re-search efforts are being made towards the needs of advanced fracture constitutive models capa-ble of predicting fracture scenarios without necking, more frequently observed in the realm of AHSS. Scientific community research is divided into several directions aiming at improving the forming and fracture behavior accuracy of parts subjected to sheet metal forming operations. In this review paper, a comprehensive overview on ductile fracture modeling is presented. Firstly, the FLD/FLC limitations modeling fracture behavior in sheet metal forming operations are stud-ied. Followed by the recent trends in constitutive material modeling. Afterwards, material char-acterization methods advancements to cover a broad range of stress states are comprised. Final-ly, damage and fracture models predicting failure in AHSS are investigated. This review paper supplies relevant information on the current issues the sheet metal forming community is chal-lenged with due to the trend towards AHSS employment in the automotive industry.

Notwithstanding the advances achieved in the last decades in the field of synthetic bone substitutes, the development of biodegradable 3D scaffolds with ideal mechanical and biological properties remains an unattained challenge. In this work, a novel approach is explored to produce synthetic bone grafts mimicking the complex bone structure using additive manufacturing. For the first time, scaffolds were produced, using an extrusion technique, composed of a thermoplastic polymer, polycaprolactone (PCL), hydroxyapatite nanoparticles (HANp), and polyethylene glycol diacrylate (PEGDA). These scaffolds were further compared with two groups of scaffolds: one composed of PCL and another of PCL and HANp. After production, optimisation and characterisation of these scaffolds, an in vitro evaluation was performed using human dental pulp stem/stromal cells (hDPSCs). Through the findings it was possible to conclude that PEGDA scaffolds were successfully produced presenting networks of interconnected channels, presenting hydrophilic properties (15.15 4.06°), adequate mechanical performance (10.41MPa 0.934), and allowing a cell viability significantly superior to the other groups analysed. To conclude, findings in this study demonstrated that PCL, HANp and PEGDA scaffolds may have promising effects on bone regeneration and might open new insights for 3D tissue substitutes.

With the recent advances in science and technology, more processing capability and data have become available, allowing a more straightforward implementation of data analysis techniques. Fortunately, the available online data storage capacity follows this trend, and vast amounts of data can be stored online freely or at accessible costs. As happens with every evolution (or revolution) in any science field, organizing and sharing this data is essential to contribute to new studies or validate the obtained results quickly. To facilitate this, we must guarantee interoperability between the existing datasets and the developed software, whether commercial or open source. This article explores this issue, analyzing the current initiatives to establish data standards and comparing some of the existing dataset online storage platforms. Through a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, it is possible to understand better the strategy that should be taken to improve the efficiency in this field, which directly depends on the data characteristics.

Artificial Intelligence (AI) is an automation mechanism that runs in a computer system performing tasks that normally require human intelligence, such as visual perception, speech recognition, decision making or translation [1]. Some authors argue that recent developments in AI are leading to a wave of innovation in organizational design and changes to institutionalized norms of the workplace [2]. Techno-optimists even named this present phase the ‘second machine age’, arguing that it now involves the substitution of the human brain (Brynjolfsson and McAfee 2014). Potentially, the ability to apply AI in a generalized way can produce significant technical, economic and social effects in firms. But how many of these AI applications are ready and how far can they be from reaching the manufacturing industry market? The paper will answer the question: what are the implications on industrial productivity and employment in the automotive sector with the recent automation trends in Portugal? We will focus on AI as the most relevant emergent technology to understand the development of automation in areas related to robotics, software, and data communications in Europe (Moniz 2018). R&amp;D investments in industrial processes in general may reflect productivity improvements derived from the increased automation process. Our results will be based on case studies from the automotive and components sector combined with database search by keywords that signal intelligence automation developments and AI applications selected from national R&amp;D projects (on robotics, machine learning, collaborative tools, human-machine interaction, autonomous systems, etc) supported by European structural funds. The implications on industrial productivity and employment will be discussed in relation to automation trends in the automotive sector.

It is known for decades that viruses from the Coronoviridae family can adapt to human-to-human transmission. In 2020, SARS-CoV-2 caused a global pandemic of unprecedented scale imposing the loss of millions of human lives and being at the heart of a global economic crisis. Thus, we overviewed key research advances generated from the identification of the etiological agent to a better understanding of its origin, evolution and factors underlying global spread. Furthermore, we analyze the scientific productivity using the PubMed database. We found that the total number of publications increased more than 8% in 2020 when compared with 2019 or the average publications per year in the previous quinquennial. Remarkably, 86,638 publications related with COVID-19 and SARS-CoV-2 were published in 2020. Furthermore, there was also an increase in 2020 of publications in other major infectious diseases, such as AIDS, tuberculosis, or malaria. This success is likely the result from the vigorous, international, collaborative, and multidisciplinary response by the research community. During 2020 it was demonstrated, that with adequate support, it is possible to boost the rate of scientific progress in infectious diseases. Sustained investment in science will be key to address existing and future pandemics as the human population increases.

Sensors available on mobile devices allow the automatic identification of Activities of Daily Living (ADL). This paper describes an approach for the creation of a framework for the identification of ADL, taking in account several concepts, including data acquisition, data processing, data fusion, pattern recognition, and machine learning. These concepts can be mapped in a module of the framework, including the use and creation of several algorithms. For the development of a framework that works in several conditions, e.g., without Internet connection, these algorithms should take in account the hardware and software limitations of the mobile devices to run all main tasks locally. The main purpose of this paper is related to the presentation the sensors, algorithms, and architecture of the proposed approach.

In this study, the physical properties of briquettes produced from two different biomass feedstocks (sawdust and date palm trunk) and different plastic wastes were investigated, without using any external binding agent. The biomass feedstocks were blended with different ratios of two WEEE plastics (halogen-free wire and print circuit boards (PCB)) and automotive shredder residues (ASR). The briquettes production is studied at different waste proportions (10-30%), pressures (22-67 MPa) and temperatures (room-130 ˚C). Physical properties as density and durability rating were measured, usually increasing with temperature. Palm trunk gave better results than sawdust in most cases, due to its moisture content and the extremely fine particles that are easily obtained.

Maritime ports represent an important ecosystem for pollutant emissions and, taking into account the ongoing energy transition, need to adopt new solutions to mitigate current emission levels. These emissions are partially avoidable if ships and vessels docked at the port use electric energy to feed their internal power needs instead of using their internal combustion engines. In Europe, there is an ongoing discussion on including such emissions in the European Emissions Trading System which will represent added costs for maritime operators. Onshore Power Supply systems can contribute to the ongoing energy transition by allowing the use of electric power to feed docked ships. As a first step to contribute to the development of Onshore Power Supply solutions, it is necessary to evaluate the added power needs that these systems would represent for the port. This paper presents a methodology that allows port operators to verify, straightforwardly and transparently, their power needs for Onshore Power Supply applications. The methodology is based on the historical data of docked ships at the port or quay level and provides an energy analysis of each type of vessel to determine the power to be installed at the port so that it is possible to supply energy to different types of ships and vessels simultaneously. Additionally, the implemented methodology provides economic and technical decision support factors by comparing the fuel costs with electric power costs, assessing the potential for this transition to Onshore Power Supply. The methodology is validated using a real case study for the port of Lisbon and obtained results demonstrate the potential for the installation of Onshore Power Supply in medium to large-dimension maritime ports.

The application of machine learning techniques to satellite imagery has been the subject of interest in recent years. The increase in quality and quantity of images, made available by Earth observation programs, such as the Copernicus program, led to the generation of large amounts of data. Among the various applications of this data is the creation of land cover maps. The present work aimed to create machine learning models capable of accurately segment and classify satellite images, to automatically generate a land cover map of the Portuguese territory. Several experiments were carried out with the spectral bands of the Sentinel-2 satellite, with vegetation indices, and with several sets of land cover classes. Three machine learning architectures were evaluated, which adopt two different techniques for image classification. One of the classification techniques follows an object-oriented approach, and in this case the architecture adopted in our models was a U-Net artificial neural network. The other classification technique is pixel-oriented, and the machine learning models tested were random forest and support vector machine. The overall accuracy of the results obtained ranged from 68.6% to 94.75%, depending strongly on the number of classes into which the land cover is classified. The result of 94.75% was obtained when classifying the land cover only into 5 classes. However, a very interesting accuracy of 92.37% was achieved by the model when trained to classify 8 classes. These results are superior to those reported in the related bibliography.

This paper questions the potential shift of reproduction towards assisted reproductive technologies due to benefits provided by genetic manipulation of embryos. In order to examine the viability of such a shift and its implications from a regulatory perspective, we relied on two panels of experts from Israel and Spain, using the Delphi method and a series of in-depth interviews. We anticipate, at a first stage, a continuous-steady growth in the use of IVF, supplemented by preimplantation genetic diagnosis and the introduction of CRISPR/Cas. At a second stage, attracting a growing share of fertile people would require developments in genomics. While it is unclear whether these developments will fully materialize, they could be replaced by technoscientific imaginaries generating perceived benefits. We conclude that the regulation of reproductive genetics is becoming more critical and complex. The aim should be to ensure good practices and equity, while providing more information to the public. A broad and inclusive societal debate may overcome the difficulty of drawing a clear line between medical uses and non-medical uses of genetic selection and engineering and may contribute to finding the right balance between allowing autonomous decisions of patients and protecting the public interest.

Cryopreservation is a stressful process for sperm as it is associated with an increased production of reactive oxygen species (ROS). Elevated ROS levels, which create an imbalance with antioxidant capacity, may result in membrane lipid peroxidation (LPO), protein damage and DNA fragmentation. This study aimed to determine whether membrane LPO and DNA fragmentation of frozen-thawed horse sperm relies upon the antioxidant activity, including enzymes (superoxide dismutase (SOD), glutathione peroxidase (GPX), catalase (CAT), and paraoxonase type 1 (PON1)); non-enzymatic antioxidant capacity (Trolox equivalent antioxidant capacity (TEAC), plasma ferric reducing antioxidant capacity (FRAP), and cupric reducing antioxidant capacity (CUPRAC)); and the oxidative stress index (OSI), of their seminal plasma (SP). Based on total motility and plasma membrane integrity (SYBR14+/PI−) after thawing, ejaculates were hierarchically (p &lt; 0.001) clustered into two groups of good (GFE) and poor (PFE) freezability. LPO and DNA fragmentation (global DNA breaks) were higher (p &lt; 0.05) in PFE than in GFE, with LPO and DNA fragmentation (global DNA breaks) after thawing showing a positive relationship (p &lt; 0.05) with SP-OSI levels and ROS production. In addition, sperm motility and membrane integrity after thawing were negatively (p &lt; 0.05) correlated with the activity levels of SP antioxidants (PON1 and TEAC). The present results indicate that LPO and DNA fragmentation in frozen-thawed horse sperm vary between ejaculates. These differences could result from variations in the activity of antioxidants (PON1 and TEAC) and the balance between the oxidant and antioxidant components present in the SP.

Polyalcohol liquefaction can be done by acid or basic catalysis producing polyols with different properties. This study compared the mechanical properties of foams produced using polyols from liquefied Cytisus scoparius obtained by acid and basic catalysis and using two different foam catalysts. The differences were monitored using FTIR analysis. Acid-catalyzed liquefaction yielded 95.1 %, with the resultant polyol having an OH index of 1081 mg KOH/g while basic catalysis yielded 82.5 %, with a similar OH index of 1070 mg KOH/g. Generally, compressive strength with dibutyltin dilaurate (DBTDL) ranged from 16 - 31 kPa (acid-liquefied polyol) and 12 -21 kPa (base-liquefied polyol), while with stannous octoate (TIN), it ranged from 17 - 42 kPa (acid) and 29 - 68 kPa (base). Increasing water content generally decreased the compressive modulus and strength of foams. Higher water content led to a higher absorption at 1670 cm-1 in the FTIR spectrum due to the formation of urea. Higher isocyanate indices generally improved compressive strength, but high amounts led to unreacted isocyanate that could be seen by a higher absorption at 2265 cm-1 and 3290 cm-1. DBTL was shown to be the best foam catalyst due to higher trimer conversion seen in the spectra by a higher absorption at 1410 cm-1. Acid and base-derived polyols lead to different polyurethane foams with different FTIR spectra, particularly with a higher absorption at 1670 cm-1 for foams from acid-derived liquefaction.

With the latest advances in information and communication technologies, greater amounts of sensitive user and corporate information are constantly shared across the network making it susceptible to an attack that can compromise data confidentiality, integrity and availability. Intrusion Detection Systems (IDS) are important security mechanisms that can perform a timely detection of malicious events through the inspection of network traffic or host-based logs. Throughout the years, many machine learning techniques have proven to be successful at conducting anomaly detection but only a few considered the sequential nature of data. This work proposes a sequential approach and evaluates the performance of a Random Forest (RF), a Multi-Layer Perceptron (MLP) and a Long-Short Term Memory (LSTM) on the CIDDS-001 dataset. The resulting performance measures of this particular approach are compared with the ones obtained from a more traditional one, that only considers individual flow information, in order to determine which methodology best suits the concerned scenario. The experimental outcomes lead to believe that anomaly detection can be better addressed from a sequential perspective and that the LSTM is a very reliable model for acquiring sequential patterns in network traffic data, achieving an accuracy of 99.94% and a f1-score of 91.66%.

Nowadays, frameworks and models are critical to enabling organizations to identify their current sustainability integration into business and to follow up on these initiatives over time. In this context, the maturity models offer a structured way of analyzing how a supply chain meets specific sustainability requirements and which areas demand attention to reach maturity levels. This study proposes a five-level maturity model to help supply chains identify their level of engagement with sustainability practices combining three perspectives: intra and inter-organizational sustainability practices, triple-bottom-line approach and critical areas for sustainability. All the steps followed in constructing the maturity model were based on a literature review, and case studies supported its improvement, application, and testing. The proposed model presents many advantages, such as being used as a self-assessment tool, a roadmap for sustainability behaviour improvement, and a benchmarking tool to evaluate and compare standards and best practices among organizations and supply chains.

LoRa and its upper layers definition LoRaWAN is one of the most promising LPWAN technologies for implementing the Internet of Things (IoT). Although being a popular technology, several works in the literature have revealed various weaknesses regarding the security of LoRaWAN v1.0 (the official 1st draft). By using all these recommendations from the academia and industry, the LoRa-Alliance has worked on the v1.0 to develop an enhanced version and provide more secure and trustable architecture. The result of these efforts ended-up with LoRaWAN v1.1, which was released on Oct 11, 2017. This manuscript aims at demystifying the security aspects and provide a comprehensive Security Risk Analysis related to latest version of LoRaWAN. Besides, it provides several remedies to the recognized vulnerabilities. To the best of authors’ knowledge, this work is one of its first kind by providing a detailed security analysis related to latest version of LoRaWAN. According to our analysis, end-device physical capture, rogue gateway and replay attacks are found to be threating for safety operation of the network. Eventually, v1.1 of LoRaWAN is found to be less vulnerable to attacks compared to v1.0, yet possesses several security implications that need to be addressed and fixed for the upcoming releases.

Institutional sustainability is a dynamic and multifaceted concept that relies on the contributions of various institutions, including the legal and the scientific system. On the other side, consumers boycotts are an expression of political consumerism by which consumers can use their market power to attain sustainability objectives. This article explores institutional trust on the legal system and trust on scientists as potential drivers for consumers’ boycotts. Using data retrieved from the European Social Survey covering twenty-five countries, the study employs binary logistic regression to assess the importance of institutional trust and other potential drivers of product boycotts in Europe. Results confirm that boycotting behaviour is affected by institutional trust, as well as other individual variables including gender, age and life-cycle effects, education, interest in politics and level of satisfaction with the political system, generalized trust, personal well-being, and consumers’ use and perceptions of information and communication technologies. The results of this study enrich the literature on consumer boycotts and have implications for policymakers involved in sustainability initiatives.

This study investigates the relationship between clean and dirty energy markets, specifically focusing on clean energy stock indexes and their potential as hedging assets and safe havens during periods of global economic uncertainty. The research analyzes five clean energy indexes and five dirty energy indexes from May 2018 to May 2023, considering events like the global pandemic and the Russian invasion of Ukraine. The main objective is to examine the causal relationship among different stock indexes pertaining to dirty and clean energy by using the Granger causality test (VAR Granger Causality/Block Exogeneity Wald Test) to determine whether clean energy indexes can predict future prices of dirty energy indexes. However, the findings reveal that clean and dirty energy indexes do not exhibit hedging characteristics or serve as safe havens during times of economic uncertainty, rejecting the research question. These results have important implications for investment strategies, as assets lacking safe haven characteristics may not preserve portfolio efficiency in uncertain times. The study's insights provide valuable guidance for investors, poli-cymakers, and participants in energy financial markets. It highlights the need to adapt investment approaches and seek alternative options to navigate uncertain economic conditions effectively.

Intrusion Detection Systems play a crucial role in a network. They can detect different network attacks and raise warnings on them. Machine learning-based IDSs are trained on datasets that, due to the context, are inherently large, since they can contain network traffic from different time periods and often include a large number of features. In this paper, we present two contributions: the study of the importance of feature selection when using an IDS dataset, while striking a balance between performance and the number of features; and the study of the feasibility of using a low-capacity device, the Nvidia Jetson Nano, to implement an IDS in a low-capacity network. The results, comparing the GA with other well-known techniques in feature selection and reduction, show that the GA has a higher F1-score of 76%, although the time to find the optimal set of features surpasses other methods; the reduction of the number of features reduces the processing time without a significant impact in f1-score. The Jetson Nano allows the classification of network traffic with an overhead of 10 times in comparison to a traditional server.

Cork oak mortality has reached alarming proportions in the last decades, exacerbated by climate change. Understanding this phenomenon is crucial in finding mitigation or adaptation strategies. This study conducts a diachronic analysis of cork oak mortality over 10 years using GIS tools, focusing on Portugal's Tagus Lezíria region. Topographic, edaphic and climatic variables were employed to create maps of edaphoclimatic aptitude for cork oaks. Dead trees were identified using remote sensing techniques and crown coverage was determined to calculate the mortality index. The diachronic analysis aimed to explore climate change effects on cork oak mortality. A decrease in precipitation was observed, significantly impacting stands with canopy cover below 40%. Furthermore, a negative effect of solar radiation identified only in stands with canopy cover of 40% suggested its role in cork oak decline. This study introduces a novel perspective, highlighting the protective effect of denser canopy cover (above 40%) against excessive solar radiation and the impact of reduced precipitation. The integrated and diachronic approach provides valuable information for adapting management strategies to climate change challenges.

The increasing social pressure for decarbonization has placed businesses under considerable scrutiny to actively reduce carbon emissions. A critical step towards achieving this objective is to shift conventional production and consumption systems to more sustainable alternatives. Thus, there is the emergent need to understand the patterns and drivers of the transformative business models (BMs) that underpin that shift. This study adopts a mixed-methods approach that integrates different literature streams--including Sustainability Transitions Theory (STT), Strategic Niche Management (SNM), and the Business Models approach--and stakeholders’ interviews to investigate the key elements of business models that lead towards sustainable practices. This research examines the organizational arrangements of European start-ups operating between 2014 and 2020. The transformation towards decarbonized production and consumption is characterized by efficient combination of business strategies that incorporate advanced technologies (ATs), such as artificial intelligence (AI), machine learning (ML) and its algorithms, along with sustainable elements, resulting in transformative business models. By exploring the driving elements behind the transition to low-carbon approaches, this study fills a significant gap in the existing literature on business models. The findings from this research also hold relevance for policymakers to promote decarbonization.

Non-typhoid salmonellosis is a common and problematic foodborne zoonotic disease in which pork and pork products can be an important potential source of infection. In order to prevent this disease important efforts to monitor the situation in the main source, livestock, are conducted in most developed countries. In the European Union EFSA and ECDC compile information at the member state level, even though important differences in production systems and surveillance systems exist. Here, Salmonella surveillance systems in one of the main sources of foodborne salmonellosis, swine, and humans in Spain were reviewed to identify potential gaps and discuss potential ways of integration under a One Health approach. Despite the extensive information generated through the surveillance activities source attribution can be only routinely performed through ad-hoc outbreak investigations, and national reports on human outbreaks do not provide sufficiently detailed information to gain a better understanding of the epidemiology of the pathogen. Human and animal monitoring of Salmonella would benefit from a better exchange of information and collaboration. Analysis of spatio-temporal trends in livestock and humans could help to identify likely sources of infection and to target surveillance efforts in areas with higher prevalence or where specific strains are found.

TomoSim comes as part of project ATMOS, a miniaturised DOAS tomographic atmospheric evaluation device, designed to fit a small drone. During the development of the project, it became necessary to write a simulation tool for system validation. TomoSim is the answer to this problem. The software has two main goals: to mathematically validate the tomographic acquisition method; and to allow some adjustments to the system before reaching final product stages. This measurement strategy was based on a drone performing a sequential trajectory and gathering projections arranged in fan beams, before using some classical tomographic methods to reconstruct a spectral image. The team tested three different reconstruction algorithms, all of which were able to produce an image, validating the team’s initial assumptions regarding the trajectory and acquisition strategy. All algorithms were assessed on their computational performance and their ability for reconstructing spectral "images", using two phantoms, one of which custom made for this purpose. In the end, the team was also able to uncover certain limitations of the TomoSim approach that should be addressed before the final stages of the system.

In this research paper we describe the development phase of a low-cost LoRa IoT solution applied to a kindergarten school with three years results. A set of sensors solution was developed in a LoRa communication board, battery powered, providing a simplified setup process. These sensors were used in order to measure temperature, humidity, luminosity, air quality and presence. Also, energy monitor solutions were integrated. The acquired data is transmitted and analysed for knowledge extraction, identifying savings and other related KPIs. From data, automatic saving actions were performed towards heating and cooling systems, lighting and a set of if-then actions were developed for automatic cost-saving actions, based on infrared signals to heating/cooling systems using some procedure of external command devices. This approach avoids the usage of proprietary vendor solutions in a flexible approach that can easily be deployed to any building facility. This is an important achievement since most of the building consumption is based on heating and cooling systems. In a three years test of the solution, the total energy consumption savings surpassed 20%

The pharmaceutical industry is facing the pressure of a global economy, loss of value in local markets and the highly intense innovation that characterizes this sector. This has a heavy impact, particularly in smaller economies. With this investigation, we intend to identify the determinants of internationalization as levers for sustainability in the pharmaceutical export sector of a small economy. Data was collected from a sample representing 63% of the total universe, Portuguese pharmaceutical organizations with exporting activity. Contextualization of the sector and a bibliographic review were previously carried out, which laid the groundwork for the empirical framework. This study revealed a deeply internationalized sector conditioned by a few shortcomings, namely a certain lack of sustainable competitive advantages, relatively low investment in research and development (R&D), insufficient innovation in internationalization strategies as well as scarce institutional support. Our findings may help pave the way for a more complete understanding of the dynamics of internationalization in highly competitive sectors.

Mannosylerythritol lipids (MELs), one of the most promising biosurfactants (BS), are glycolipids produced by yeasts or fungi, with great environmental performance and a high compatibility with the human body. MELs besides working as typical surfactants, can form diverse structures when in the critical aggregation concentration (CAC), reducing the surface tension of water and other solutions, and being stable over a wide range of conditions. Among others, MELs present anti-microbial, antitumor, antioxidant and anti-inflammatory activity, skin and hair repair capacity, which opens possibilities for their use in applications from cosmetics and pharmaceutics to bio-remediation and agriculture. However, their market share is still low when compared to other glycolipids, due to their less developed production process and higher production cost. This re-view gathers information on the potential applications of MELs mentioned on the literature since 1993. Furthermore, it is also explored the current strategies being developed to enhance the market presence of MELs, in parallel with the ones developed for rhamnolipids and sophorolipids.

Background: The Ibizan Hound is a canine breed native to the Mediterranean region, where leishmaniasis is an endemic zoonosis. Several studies indicate a low prevalence of this disease in dogs, whereas other canine breeds present a high prevalence. However, the molecular underlaying mechanisms yet remains unknown. Methods: In this study, we analyze the haplotypes of genes encode cytokines related to immune response of Leishmania infantum infection in twenty-four Boxer and twenty-four Ibizan hound apparently healthy using CanineHD DNA Analysis BeadChip including 165,480 mapped positions. Results: The results show that several haplotypes of genes encoding Interleukin 6 (IL6) and Interferon gamma (IFNG) are related to Interferon gamma (IFN-γ), and Interleukins (IL) 2 and 18 serum levels. Our results indicate that the regulation of immune response is different in the two canine breeds analyzed and are related to the haplotype compositions of the genes encoding these cytokines. Conclusions: Future studies are needed to elucidate whether these differences and haplotypes are related to different phenotypes in immune response and expression gene regulation to L. infantum infection in dogs.

The increased demand and use of autonomous vehicles and advanced driver-assistance systems has been constrained by an incidence of accidents involving errors with the perception layer’s functionality. In tandem, recent papers have noted the lack of standardized, independent testing formats and insufficient methods with which to analyze, verify and qualify LiDAR-based data and categorization. While camera-based approaches benefit from an ample amount of research, camera images can be unreliable in situations with impaired visibility such as dim lighting and fog. This paper aims to introduce a novel method based entirely on LiDAR data with the capability to detect anomalous patterns as well as complementing other performance evaluators using a Copula-based approach. With a promising set of preliminary results, this methodology may be used to evaluate an algorithm’s confidence score, the impact conditions may have on LiDAR data and detect cases in which LiDAR data may be insufficient or otherwise unusable.

Agricultural production needs technologies that assist the management of natural resources, for example, the collection of real-time data on soil, water, weather, crops, and biodiversity conditions. Sensor technology solutions and open-source software are appropriate for promoting more sustainable agricultural production. Among the advantages of using open-source technologies and software is its potential for extension, collaboration, customization, flexibility, maintenance cost, transparency, speed, and better security. Given the above, the objective of this research was to find, in different electronic databases, exclusively open-source software for precision agriculture, offering a systematic review, and addressing considerations and challenges. This survey considers up-to-date open-source software available in repositories such as GitHub and GitLab, to understand its characteristics and application formats.

Circular Economy (CE) is a growing topic among scholars, industry and government aiming at decoupling economic growth and development from the consumption of finite resources. CE incorporates different meanings, from reduce, reuse and recycle activities to environmental degradation or resource scarcity, and supported by specific indicators to attain sustainable development. However, so far, there has been no agreement to measure how effective an industry/product is in making the transition from linear to circular approaches, particularly those affected the society. This research work aims to perform a systematic literature review (n=60) to analyze and discuss how social aspects have been considered and integrated in the CE research so far. Moreover, this review provides and overview of the literature on social impact within CE, that resulted in three main outputs: a knowledge map of the CE, an analysis of social aspects within CE, and the theories/frameworks used to evaluate social impact of CE. Finally, this study brings to light how CE implementation can affect society and highlights the importance of social dimension in the domains of CE and policy-making community, which could help moving CE towards a sustainable development.

Bovine mastitis is the most impacting disease of dairy industry, and it is characterized by a complexity of causal agents, which have revealed a geographical variation among regions and countries. The mastitis-related pathogens have been traditionally classified as contagious or environmental, based on habits of the microorganisms and transmission routes. In addition, the severity of mammary infections has been associated with the virulence of the pathogens, and immune and nutritional status of the hosts. Considering this scenario, we investigated the etiological nature, clinical severity scores, and days in milk (DIM) data in 4,273 clinical cases of bovine mastitis among ten large-dairy farms located in the Southeast region of Brazil. Streptococcus dysgalactiae (283/4,273=6.6%), Escherichia coli (190/4,273=4.4%), Prototheca spp. (112/4,273=2.6%), and Streptococcus uberis (95/4,273=2.2%) were the predominant pathogens isolated, all from the environmental origin. Among 4,273 clinical cases, clinical gravity score was available in 43.8% (1,871/4,273) animals. From these, 69.8% (1,306/1,871), 27.3% (510/1,871) and 2.9% (55/1,871) were scored as mild, moderate, and severe, respectively. Most of isolation of pathogens were observed in the first 100 days in milk, and their clinical severity scored as mild (3,612/4,273=84.5%). Our results contribute to the etiological identification, clinical severity scoring, and milking aspects of bovine clinical mastitis in dairy farms with a history of clinical mammary infections.

The COVID-19 pandemic brought about a significant change in mobility habits within cities, with a significant decrease in the use of collective urban transport that has only been partially and unevenly reversed. To recover the previous rates of use, many cities are analyzing which characteristics of the urban transport network are hindering this recovery process in order to design actions to promote sustainable mobility. This article shows a study carried out in the city of Almería (Spain), where the operation variables of the municipal bus network have been analyzed (frequency, average vehicle occupancy, length of the lines, urban or suburban character, circularity, nodality or intermodality) to detect what aspects are more determinant on the number of users recovered. The results show that the lines that have recovered the most users in average are suburban lines, as well as the nodal lines that serve key centroids such as the university. Thus, the dependence of users on public transport would be the most important determinant in recovery, more than other variables representative of the service´s intrinsic quality. This result shows that, without compromising the necessary quality of the service, the restrictions on less sustainable transportation alternatives will be those that, to a greater extent, allow for the prompt recovery of the space lost by urban public transportation.

The current research investigated the environmental risk of the polycyclic musk compounds, Galaxolide® (HHCB) and Tonalide® (AHTN), in the marine environments. These substances are lipophilic, bioaccumulated and potentially biomagnified in aquatic organisms. To understand the toxicity of HHCB and AHTN, we performed acute toxicity tests by exposing marine microalgae (Phaeodactylum tricornutum, Tretraselmis chuii and Isochrysis galbana), crustaceans (Artemia franciscana), echinoderms (Paracentrotus lividus), bivalves (Mytilus galloprovincialis), fish (Sparus aurata) and a candidate freshwater microalga (Raphidocelis subcapitata) to environmentally relevant concentrations (0.005 - 5 µg/L) following standardized protocols. The effects of both substances on microalgae growth were incipient and only I. galbana was sensitive to HHCB and AHTN, with IC10 values of 5.22 µg/L and 0.328 µg/L, respectively. Significant (p < 0.01) concentration dependent responses were measured in P. lividus and M. galloprovincialis larvae developments as well as S. aurata mortality tested with HHCB. The effect of HHCB on P. lividus larvae development was the most sensitive endpoint recorded, producing an EC50 value of 4.07 µg/L. Our results show that HHCB represents a high risk to P. lividus larvae development for early life stages in marine environments.

Green investors have expressed concerns about the environment and sustainability due to the high energy consumption involved in cryptocurrency mining and transactions. This article investigates the safe haven characteristics of clean energy stock indexes in relation to 3 cryptocurrencies, taking into account their respective levels of "dirty" energy consumption from May 16, 2018, to May 15, 2023. By virtue of analyzing a tumultuous era in the global economy, the level of integration between clean energy stock indexes and cryptocurrencies will be inferred by using Gregory and Hansen's methodology. Furthermore, to assess the presence of a volatility spillover effect between clean energy stock indexes and “dirty-classified” cryptocurrencies, the ?-test of heteroscedasticity of two samples from Forbes and Rigobon will be employed. The empirical findings show that clean energy stock indexes may offer a viable safe haven for dirty energy cryptocurrencies. However, the precise associations differ depending on the cryptocurrency under examination. The implications of the study's results are significant for investment strategies, this knowledge can inform decision-making procedures and facilitate the adoption of sustainable investment practices. Investors and policymakers can gain a deeper understanding of the interplay between investments in renewable energy and the cryptocurrency market.

Low Power Wide Area Networks (LPWAN) enable a growing number of Internet-of-Things (IoT) applications with large geographical coverage, low bit-rate, and long lifetime requirements. LoRa (Long Range) is a well-known LPWAN technology that uses a proprietary Chirp Spread Spectrum (CSS) physical layer, while the upper layers are defined by an open standard - LoRaWAN. In this paper, we propose a simple yet effective method to improve the Quality-of-Service (QoS) of LoRa networks by fine-tuning specific radio parameters. Through a Mixed Integer Linear Programming (MILP) problem formulation, we find optimal settings for the Spreading Factor (SF) and Carrier Frequency (CF) radio parameters, considering the network traffic specifications as a whole, to improve the Data Extraction Rate (DER) and to reduce the packet collision rate and the energy consumption in LoRa networks. The effectiveness of the optimization procedure is demonstrated by simulations, using LoRaSim for different network scales. In relation to the traditional LoRa radio parameter assignment policies, our solution leads to an average increase of 6% in DER, and a number of collisions 13 times smaller. In comparison to networks with dynamic radio parameter assignment policies, there is an increase of 5%, 2.8%, and 2% of DER, and a number of collisions 11, 7.8 and 2.5 times smaller than equal-distribution, Tiurlikova's (SoTa), and random distribution, respectively. Regarding the network energy consumption metric, the proposed optimization obtained an average consumption similar to Tiurlikova's, and 2.8 times lower than the equal-distribution and random dynamic allocation policies. Furthermore, we approach the practical aspects of how to implement and integrate the optimization mechanism proposed in LoRa, guaranteeing backward compatibility with the standard protocol.

Signaling modules that integrate the diverse extra- and intracellular inputs to control cell proliferation are essential during both development and adult stages to guarantee organism homeostasis. Mobs are small adaptor proteins that participate in several of these signalling pathways. Here we review recent advances unraveling Mob4 cellular functions, a highly conserved non-catalytic protein, that plays a diversity of roles in cell proliferation, sperm cell differentiation and simultaneously is involved in synapse formation and neural development. In addition, the gene is often overexpressed in a wide range of tumors and is linked to poor clinical outcomes. Nevertheless, Mob4 molecular functions remain poorly defined, although it integrates the core structure of STRIPAK, a kinase/phosphatase protein complex, that can act upstream of the Hippo pathway. In this review we focus on the recent findings of Mob4 functions, that have begun to clarify its critical role on cell proliferation and development of tissues and individuals.

A method for infrared and cameras sensor fusion, applied to indoor positioning in intelligent spaces, is proposed in this work. The fused position is obtained with a maximum likelihood estimator from infrared and camera independent observations. Specific models are proposed for variance propagation from infrared and camera observations (phase shifts and image respectively) to their respective position estimates and to the final fused estimation. Model simulations are compared with real measurements in a setup designed to validate the system. The difference between theoretical prediction and real measurements is between 0.4 cm (fusion) and 2.5 cm (camera), within a 95% confidence margin. The positioning precision is in the cm level (sub-cm level can be achieved at most tested positions) in a 4x3 m locating cell with 5 infrared detectors on the ceiling and one single camera, at distances from target up to 5 m and 7 m respectively. Due to the low cost system design and the results observed, the system is expected to be feasible and scalable to large real spaces.

Background: Tuberculosis (TB) continues to be a serious public health threat that affects the most vulnerable population. Patients who are LTFU after TB diagnosis still represent one of the biggest challenges to TB control. Method: In this prospective observational study, we aimed to identify and analyse the risk factors associated with LTFU among TB patients who started first-line TB treatment in the Sanatorium Hospital in Luanda. Result: A total of 113 patients with TB were included between August 2018 and September 2019. Seventy-six (67.3%) patients were cured, 27 (23.9%) were LTFU, 5 (4.4%) died, 4 (3.5%) were transferred and 1 (0.9%) presented a treatment failure. After excluding those dead, transferred or who failed to treatment, we observed that severe TB at the time of diagnosis (OR 9.24, 95% CI 2.18-39.04) and food insecurity were significantly associated with LTFU (OR 5.96, 95% CI 1.66-21.41). Conclusion: The findings of our study are of enormous value to understand the reasons for the LTFU of patients with TB and can guide polices and facilitate designing measures to allow better adherence, and therefore, greater treatment success

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder characterised by poor social interaction and communication, as well as restricted and stereotyped interests. Due of the high prevalence of gastrointestinal disorders in individuals with ASD, researchers have investigated the gut microbiota as a potential contributor to its aetiology. The relationship between the microbiome, gut, and brain (microbiome-gut-brain axis) has been acknowledged as a key factor in modulating brain function and social behaviour, but its connection to the aetiology of ASD is not well understood. Recently, there has been increasing attention on the relationship between the immune system, gastrointestinal disorders, and neurological issues in ASD, particularly in relation to the loss of specific species or a decrease in microbial diversity. It focuses on how gut microbiota dysbiosis can affect gut permeability, immune function, and microbiota metabolites in ASD. Although a very complete study suggests that dysbiosis is a consequence of the disease and that it has practically no effect on autistic manifestations. This is a review of the relationship between the immune system, microbial diversity, and the microbiome-gut-brain axis in the development of autistic symptoms severity and, a proposal of a novel role of gut microbiome in ASD, where dysbiosis is a consequence of ASD-related behaviour and where dysbiosis in turn accentuates the autistic manifestations of the patients via microbiome-gut-brain axis in a feedback circuit.

RNA pol II assembly occurs in the cytoplasm before the translocation of the enzyme to the nucleus. Affecting assembly influences mRNA transcription in the nucleus and mRNA decay in the cytoplasm. However, very little is known about the consequences on ncRNA synthesis. In this work, we show that impairment of RNA pol II assembly leads to a decrease in cryptic non-coding RNAs (preferentially CUTs and SUTs). This alteration is partially restored upon overcoming the assembly defect. Notably, this drop in ncRNAs is only partially dependent on the nuclear exosome, which suggests a major specific effect of enzyme assembly. Our data also point out a defect in transcription termination and lead as to proposes that CTD phosphatase Rtr1 could be involved in this process.

N-(diisopropylphosphanyl)benzamide, PhC(O)NHPiPr2, has been synthesized in good yield following two alternative procedures that employ benzamide as the starting material. The first one is a two-step preparation, in which N-(trimetilsilyl)benzamide is reacted with PiPr2Cl, to give the title compound in good yield, whereas the second one is a straightforward synthesis which converts benzamide into N-(diisopropylphosphanyl)benzamide by reaction with PiPr2Cl in the presence of N,N-dimethylpyridin-4-amine (DMAP) and triethylamine. NMR spectroscopy and X-ray diffraction analyses have been performed to characterize the new compound and elucidate its molecular structure in the solid state. N-(diisopropylphosphanyl)benzamide adds to the limited family of amido-substituted phosphines, RC(O)NHPR’2, which can be classified as bidentate hybrid P,O-ligands, both in their neutral and anionic forms, the latter achievable by deprotonation of the NH group.

The diagnosis and the pathology of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) remain under debate. However, there is a growing body of evidence for an autoimmune component in ME/CFS caused by the Epstein-Barr virus (EBV) and other viral infections. In this work, we took advantage of a large public dataset on the IgG antibodies to 3,054 EBV peptides to understand whether these immune responses could be used as putative biomarkers for disease diagnosis and triggers of pathological autoimmunity in ME/CFS patients using healthy controls (HCs) as a comparator cohort. We then aimed at predicting disease status of study participants using a Super Learner algorithm targeting an accuracy of 85% when splitting data into train and test datasets. When we compared data of all ME/CFS patients or data of a subgroup of these patients with non-infectious or unknown disease trigger to the dataset of HC, we could not find an antibody-based classifier that would meet the desired accuracy in the test dataset. In contrast, we could identify a 26-antibody classifier that could distinguish ME/CFS patients with an infectious disease trigger from HCs with 100% and 90% accuracies on the train and test sets, respectively. We finally performed a bioinformatic analysis of the EBV peptides associated with these 26 antibodies. We found no correlation between the importance metric of the selected antibodies in the classifier and the maximal sequence homology between human proteins and each EBV peptide recognized by these antibodies. In conclusion, these 26 antibodies against EBV have an effective potential for disease diagnosis of a subset of patients, but they are less likely to trigger pathological autoimmune responses that could explain the pathogenesis of ME/CFS.

This study explores the cocrystallization between the drug praziquantel (PZQ) and the nutraceutical curcumin (CU). The investigation revealed two novel solid forms: a cocrystal solvate with ethyl acetate and a non-solvated cocrystal. This novel drug-nutraceutical cocrystal is identi-fied as “generally recognized as safe” (GRAS) praziquantel-curcumin-ethyl acetate (2:1:1) co-crystal solvate. This latter undergoes desolvation, leading to the formation of the praziquan-tel-curcumin (2:1) cocrystal. The cocrystal solvate has ethyl acetate molecules occupying the voids with minimal interactions within the crystal lattice. The application of heat treatment induces solvent removal and prompts the transition to the non-solvated cocrystal, as highlighted by VT-XRD. Thermal analyses demonstrate the stability of the cocrystal solvate up to approximately 100°C, beyond which it transforms into the non-solvated phase, which eventually melts at 130°C.

Background: Gait analysis systems offer invaluable insights for rehabilitation, yet their expense limits clinician access. We developed GAITWell®, a low-cost modular system for capturing spatiotemporal gait variables, and evaluated its measurement properties. Methods: The GAITWell® uses discreet binary sensors on interconnected boards to collect gait data, which is then analyzed using the DBSCAN algorithm on the Cartesian points from sensor outputs. Reliability was assessed using ICC2,1, standard error of the mean (SEM) and Bland-Altman plots. Validity was determined by comparing GAITWell® with the Qualisys Pro-Reflex system. Results: Participated 38 healthy adults, with an average age of 33.2 years (SD 13.0). Correlations between GAITWell® and the Qualisys system ranged from moderate to very high for most gait variables, with the exception of stride length, which demonstrated a low but significant correlation (r = .360, p &lt; .05). The ICC2,1 indicated moderate to good agreement for most gait variables. Stance and double support times, cadence, and base of support exhibited poor reliability, characterized by larger SEM and limits of agreement. Conclusions: Our findings indicate that the GAITWell® system is a promising tool for gait analysis. Future research will enhance sensor accuracy and refine algorithms to improve reliability.

(1) Background: Nursing homes’ preparedness in managing a public health crisis has been fragile, with effects on safety culture. The objective of this study was to assess nursing homes’ COVID-19 preparedness in Southern Portugal, including personnel’s work experiences during the pandemic. (2) Methods: We used a COVID-19 preparedness checklist to be completed by management teams, followed by follow-up calls to nursing homes. Thereafter, a survey to personnel was applied. Data analysis included descriptive statistics, exploratory factor analysis, and thematic analysis of open-end questions. (3) Results: In total, 71% (138/195) of eligible nursing homes returned the preparedness checklist. We conducted 83 follow-up calls and received 720 replies to the personnel survey. On average, 25% of nursing homes did not have an adequate decision-making structure to respond to the pandemic. Outbreak capacity and training were fragile areas among nursing homes’ contingency plans. We identified compliance with procedures and nonpunitive response to mistakes as fragile areas of safety culture, and teamwork as a strong safety area. (4) Conclusions: To strengthen how nursing homes cope with upcoming phases of the COVID-19 pandemic or future public health emergencies, nursing homes’ preparedness and safety culture should be fostered and closely monitored.

Bimetallic (or multimetallic) catalysis has emerged as a powerful tool in modern chemical synthesis, offering improved reaction control and versatility. This review focuses on the recent de-velopments in bimetallic sequential catalysis for the synthesis of nitrogen heterocycles, essential building blocks in pharmaceuticals and fine chemicals. The cooperative action of two (and sometimes more) different metal catalysts enables intricate control over reaction pathways, enhancing selectivity and efficiency of N-heterocyclic compounds synthesis. By activating less reactive substrates, this multimetal catalytic strategy opens new synthetic possibilities for challenging compounds. The use of catalytic materials in bimetallic systems reduces waste and improves atom efficiency, aligning with green chemistry principles. With a diverse range of metal combinations and reaction conditions, bimetallic catalysis provides access to a broad array of N-heterocyclic compounds with various functionalities. This paper highlights the significant progress made in the past decade in this topic, emphasizing the promising potential of bimetallic catalysis in drug discovery and the fine chemical industries.

In this paper we investigate in different scenarios the feasibility of using massive multiple-input multiple-output (mMIMO) with Reconfigurable Intelligent Surfaces (RISs) to boost the throughput and coverage performances with high energy efficiency, considering sub-6 GHz, mmWave and THz bands. With that objective, a centralized radio access network (C-RAN) suitable for beyond fifth generation (B5G) systems is considered, where we integrate the base stations (BSs) with multiple RISs and devices or user equipment. RISs with a large number of quasi-passive reflecting elements constitute a low-cost approach capable of shaping the radio wave propagation and improve wireless connectivity. We consider a scenario where multiple RISs are combined with mMIMO in the uplink in order to provide connectivity to Smart City (thousands of active low power devices) wirelessly, in the 3.6GHz and 28GHz bands. We also address a scenario where RISs are adopted with mMIMO in the downlink so as to offer connectivity to a Stadium with Pitch, (thousands of active user equipment) in the 28GHz band. Finally, we have also studied the connectivity at 100GHz of a Factory where several RIS panels replacing most of the BSs equipped with mMIMO assure improved throughput and coverage. We concluded that RISs are capable of improving the performance in any of these analyzed scenarios at the different frequency bands, justifying that they are a key enabling technology for 6G.

Head and neck cancer is the sixth most frequent cancer type. Drug resistance and toxicity are common challenges of the existing therapies, making the development of reliable preclinical models essential for the study of the involved molecular mechanisms as well as for eventual intervention approaches that improve the clinical outcome. Preclinical models of head and neck squamous cell carcinoma have been traditionally based in cell lines and murine models. In this review, we will go over the most frequently used preclinical models, from immortalized-cell and primary tumour cultures in monolayer or 3D, to the currently available animal models. We will scrutinize their efficiency in mimicking the molecular and cellular complexity of head and neck squamous cell carcinoma. Finally, the challenges and opportunities of other envisaged putative approaches, as well as the potential of the preclinical models to further develop customized therapies will be discussed.

Deep neural networks are hegemonic approaches to many machine learning areas, including natural language processing (NLP). Thanks to the availability of large corpora collections and the capability of deep architectures to shape internal language mechanisms in self-supervised learning processes (also known as "pre-training"), versatile and performing models are released continuously for every new network design. But these networks, somehow, learn a probability distribution of words and relations across the training collection used, inheriting the potential flaws, inconsistencies and biases contained in such a collection. As pre-trained models have found to be very useful approaches to transfer learning, dealing with bias has become a relevant issue in this new scenario. We introduce bias in a formal way and explore how it has been treated in several networks, in terms of detection and correction. Also, available resources are identified and a strategy to deal with bias in deep NLP is proposed.

Three dietary treatments were assayed in 350 milking Murciano-Granadina multiparous goats in full-lactation: a control diet and two experimental diets, one including flaked linseed (FL) and the other salmon oil (SO). Neither dietary treatment affected the daily milk yield, cheese yield or the physicochemical parameters of the milk and cheese. With regard to the fatty acid profile (FA), the milk and cheese from animals whose diet was supplemented with SO had a higher percentage of fatty acids than those obtained with the FL supplemented diet, except for C18:0, C18:1, C18:2 n-6, trans-9, trans-12 C18:2, cis-9, trans-11 C18:2, C18:3 and C19:0, which reached their highest levels in milk obtained with the diet supplemented with FL. The decrease in the percentage of C16:0 was greater in the milk derived from the FL diet than from the SO diet. The FL supplemented diet improved the nutritional value of milk due to a reduction in saturated fatty acids (SFA) and an increase in polyunsaturated acids (PUFA) and conjugated linoleic acid (CLA). The decrease in n-6/n-3 in observed milk was more pronounced with the FL diet. No differences in the sensory profile were found for the milk and cheese derived from the different dietary treatments. Keywords: goat, flaked linseed, salmon oil, fatty acid, sensory profile.

Purpose: The goal of this article was to introduce a new technique for root coverage (RC), the Mixed-Thickness Tunnel access (MiTT), approaching a full-split design, showing the results obtained in a case series and demonstrating it step-by-step. Methods: Healthy individuals, non-smokers, and non-diabetics, who were diagnosed with gingival recession (GR) type 1 or 2 (RT1 or RT2). After all necessary evaluations, prophylaxis was performed 14 days before the surgical procedure. Then, after the basics clinical conduct and anesthesia, one or two vertical incision(s) on the mucosa (around 1-2 mm apical to the MGJ), lateral to the papilla base, were done. Initial detachment of the mucosa from the muscles (split design); it was permitted (but not mandatory) to do intrasulcular incisions; from the MGJ to coronal, subperiosteal access was done to raise the full-thickness tunnel. It must communicate up to the gingival sulcus area and the papilla’s base, keeping the papilla’s tip intact. A connective tissue graft was harvested, which was inserted through the linear incision or intrasulcularly. Interrupted sutures were done and may be used as adjunctive material. Results: Nine healthy individuals (7 females and 2 males) aged 19 and 43 years were enrolled. They were treated following the MiTT steps. There were four cases with a single GR, 2 patients with 2 teeth involved, and three with 3 or 4 GR. Seven cases were RT1 and two RT2. All RT1 cases achieved 100% RC, while the mean RC obtained for RT2 was around 80%. Conclusion: MiTT can be considered a more straightforward approach for minimally invasive surgical technique, which is a feasible option to treat RC with a high success rate, predictability, and esthetic preservation. There is a technical sensitivity to performing the full-split design procedure.

Introduction: Within primary ARDS, SARS-CoV-2-associated ARDS (C-ARDS) emerged in late 2019, reaching its peak during the subsequent two years. Recent efforts in ARDS research have concentrated on phenotyping this heterogeneous syndrome to enhance comprehension of its pathophysiology. Methods and Results: A retrospective study was conducted on C-ARDS patients from April 2020 to February 2021, encompassing 110 participants with a mean age of 63.2±11.92 (26-83 years). Of these, 61.2% (68) were male, and 25% (17) experienced severe ARDS, resulting in a mortality rate of 47.3% (52). Ventilation settings, arterial blood gases, and chest X-ray (CXR) were evaluated on the first day of invasive mechanical ventilation and between days two and three. CXR images were scrutinized using a convolutional neural network (CNN). A binary logistic regression model for predicting C-ARDS mortality was developed based on the most influential variables: age, PaO2/FiO2 ratio (P/F) on days one and three, CNN-extracted CXR features, and age. Initial performance assessment on test data (23 patients out of the 110) revealed an area under the receiver operating characteristic (ROC) curve of 0.862 CI (0.654-0.969). Conclusion: Integrating data available in all intensive care units enables the prediction of C-ARDS mortality by utilizing evolving P/F ratios and CXR. This approach can assist in tailoring treatment plans and initiating early discussions to escalate care and extracorporeal life support. Machine learning algorithms for imaging classification can uncover otherwise inaccessible patterns, potentially evolving into another form of ARDS phenotyping. The combined features of these algorithms and clinical variables demonstrate superior performance compared to either element alone.

In recent years, using pre-sprouted seedlings for sugarcane establishment has been developed in Brazil. Despite yielding promising results, the optimal irrigation management for this method during the Brazilian winter planting season has yet to be determined. Additionally, information regarding water demand and seedling behavior when subjected to different irrigation levels remains unclear. This study aimed to assess the response of pre-sprouted sugarcane seedlings to different irrigation levels during the winter in southeastern Brazil. Pre-sprouted sugarcane seedlings were subjected to different irrigation levels during the winter season in the region of Piracicaba, using a sprinkler irrigation system. Periodic evaluations were conducted, such as seedlings mortality rate, root size, root dry mass, number of sugarcane tillers sprouted per meter, total number of tillers, plant height, leaf area index, shoot dry mass, and total leaf chlorophyll index. The findings of this investigation indicate that a 10 mm irrigation level facilitated optimal fixation depth and achieved a 97.8% survival rate. Moreover, a correlation between seedling mortality rate and irrigation management was observed, emphasizing the critical role of irrigation in establishing and developing seedlings. Based on these findings, a 10 mm irrigation may represent the optimal irrigation strategy for pre-sprouted sugarcane seedlings in this scenario.

Rapid developments in the field of underwater photogrammetry have given scientists1the ability to produce accurate 3-dimensional (3D) models which are now increasingly used in the representation and study of local areas of interest. This paper addresses the lack of systematic analysis of 3D reconstruction and navigation fusion strategies, as well as associated error evaluation of models produced at larger scales in GPS-denied environments using a monocular camera (often in deep-sea scenarios). Based on our prior work on automatic scale estimation of Structure from Motion (SfM)-based 3D models using laser scalers, an automatic scale accuracy framework is presented. The confidence level for each of the scale error estimates is independently assessed through the propagation of the uncertainties associated with image features and laser spot detections using a Monte Carlo simulation. The number of iterations used in the simulation was validated through the analysis of the final estimate behaviour. To facilitate the detection and uncertainty estimation of even greatly attenuated laser beams, an automatic laser spot detection method, mitigating the effects of scene texture, was developed, with the main novelty of estimating the uncertainties based on the recovered characteristic shapes of laser spots with radially decreasing1 intensities. The effects of four different reconstruction strategies resulting from the combinations of Incremental/GlobalSfM, and thea priori/a posterioriuse of navigation data were analyzed using two distinct survey scenarios captured during the SUBSAINTES 2017 cruise (doi: 10.17600/17001000). The study demonstrates that surveys with multiple overlaps of non-sequential images result in a nearly identical solution regardless of the strategy (SfM or navigation fusion), while surveys with weakly connected sequentially acquired images are prone to produce broad-scale deformation (doming effect) when navigation is not included in the optimization. Thus the scenarios with complex survey patterns substantially benefit from using multi-objective BA navigation fusion. In all cases, the errors in the models are inferior to 5%, with errors often being around 1%. The effects of combining data from multiple surveys were also evaluated. The introduction of additional vectors in the optimization of multi-survey problems successfully accounted for offset changes present in the underwater USBL-based navigation data and thus minimize the effect of contradicting navigation priors. Our results also illustrate the importance of collecting a multitude of evaluation data at different locations and moments during the survey.

Business Intelligence, defined by [1] as "the ability to understand the interrelations of the facts that are presented in such a way that it can guide the action towards achieving a desired goal", has been used since 1958 for the transformation of data into information, and of information into knowledge, to be used when making decisions in a business environment. But, what would happen if we took the same principles of business intelligence and applied them to the academic environment? The answer would be the creation of Academic Analytics, a term defined by [2] as the process of evaluating and analyzing organizational information from university systems for reporting and making decisions, whose characteristics allow it to be used more and more in institutions, since the information they accumulate about their students and teachers gathers data such as academic performance, student success, persistence, and retention [5]. Academic Analytics enables an analysis of data that is very important for making decisions in the educational institutional environment, aggregating valuable information in the academic research activity and providing easy to use business intelligence tools. This article shows a proposal for creating an information system based on Academic Analytics, using ASP.Net technology and trusting storage in the database engine Microsoft SQL Server, designing a model that is supported by Academic Analytics for the collection and analysis of data from the information systems of educational institutions. The idea that was conceived proposes a system that is capable of displaying statistics on the historical data of students and teachers taken over academic periods, without having direct access to institutional databases, with the purpose of gathering the information that the director, the teacher, and finally the student need for making decisions. The model was validated with information taken from students and teachers during the last five years, and the export format of the data was pdf, csv, and xls files. The findings allow us to state that it is extremely important to analyze the data that is in the information systems of the educational institutions for making decisions. After the validation of the model, it was established that it is a must for students to know the reports of their academic performance in order to carry out a process of self-evaluation, as well as for teachers to be able to see the results of the data obtained in order to carry out processes of self-evaluation, and adaptation of content and dynamics in the classrooms, and finally for the head of the program to make decisions.

Hydrothermal pyrite is an important source of thallium (Tl) but this rare element can be also detected in the secondary iron goethite-lepidocrocite and jarosite ores, associated to hydrothermal kerogen, manganese oxides, accessorial potassium minerals and tiny Tl-bearing clusters shielded in quartz masses. We studied Tl-bearing samples collected from Fe-Mn ores hosted in dolostone rocks sited along ca. 15 km of fossil thermal springs following a geological fault in the Lodares surroundings. Samples were analyzed by environmental scanning electron microscopy and energy dispersive spectroscopy (ESEM-EDS), electron probe micro-analysis (EPMA), inductively coupled plasma-mass spectroscopy (ICP-MS), X-ray diffraction analysis (XRD), Tl LIII-edge X-ray absorption near-edge structure (XANES) spectroscopy, Micro-Raman and Fourier transform infrared spectroscopy (FTIR). We found epithermal pyrite, sphalerite, galena and baryte and secondary gypsum, jarosite, scorodite, anglesite, goethite, epsomite and elemental sulfur produced by both hydroxylation and bacterial processes. The highest Tl contents were found in hydrothermal pyrite (200 mg kg-1), kerogen (13 mg kg-1), manganese ores (27 mg kg-1) and iron sulfate-hydroxides (142 mg kg-1). The aquatic kerogen was formed in a marine environment and later heavily carbonized during the hydrothermal processes. The Lodares outcrops exhibit interesting details on the geochemical cycle of Tl in a genetic frame of epithermal formation of pyrite and kerogen.

The human-centric approach is a leading trend for future production processes, and collaborative roboticists are key to its realization. This article addresses the challenge of designing a new cus-tom-made non-conventional machine or robot involving toolpath control (interpolated axes) with collaborative functionalities but by using “general-purpose standard" safety and motion control technologies. This is done on a non-conventional Cable-Driven Parallel Robot (CDPR). Safety is assured by safe commands to individual axes, known as Safe Motion Monitoring Functionalities, which limits the axes speed in the event of human intrusion. At the same time, the robot's Motion Controller applies an override to the toolpath speed to accommodate the robot's path speed to the axis’s limitations. The implementation of a new Pre-Warning Zone, prevent unnecessary stops due to the approach of the human operator. The article also details a real experiment that validates the effectiveness of the proposed strategy.

Grapevine Trunk Diseases (GTDs) are caused by a wide variety of phytopathogenic fungal species that compromise grapevine productivity and wine quality. Each year they lead to major economic losses. Most GTD treatments currently available on the market are chemical-based and environ-mentally harmful. Given that one of the goals proposed by the European Green Deal is to develop sustainable agriculture which does not harm the environment, reduces pesticide use, and pro-motes rural area economies, an alternative to those treatments may be the use of elicitors such as oligosaccharides from fungal organisms. Indeed, many studies have confirmed that oligosaccha-rides play important roles in activating plants' innate immunity. Mannans are one possible type of such oligosaccharides. The present work analysed Vitis vinifera L. (cv. Tempranillo and cv. Airén) leaves and grapes to evaluate their physiological and molecular response to GTDs and how this response might be conditioned using different concentrations of mannans as elicitors. It was found that mannan addition to diseased vines was more positive for the leaves than for the grapes. In general, mannans increased the expression levels of chalcone synthase (CHS1, CHS3), phenylalanine ammonia lyase (PAL), superoxide dismutase (SOD), and polyphenol oxidase (PPO) in healthy leaves of both varieties. In diseased leaves, CHS3 and PAL decreased in both varieties, while CHS1 and PPO increased in Tempranillo but not in Airén. In the grapes, the expression pat-terns of the genes studied varied greatly due to the development of the disease. The mannan treatment seemed to reduce the oxidative stress caused by GTDs, although this response depend-ed on the variety and on the plant's phytosanitary status.

New advances in creating effective and affordable copper-based catalysts for water splitting represent a promising strategy for driving sustainable energy technologies. However, ma- terials containing copper are susceptible to corrosion and agglomeration. In this study, we describe a synthesis route to produce olamine-functionalized CuO nanoparticles using the Hot Injection method. Nanoparticles were obtained and subsequently deposited on FTO substrates, undergoing thermal treatment at 500°C and 600°C in atmospheres containing O2 and N2, aiming to enhance the adhesion of the active material to the substrate and modify its oxidation state. Microstructural properties were analyzed using XRD, SEM, and TEM, while electrochemical properties were studied using CV, LSV, and EIS. Electrodes treated thermally in an N2 atmosphere at 600°C exhibited higher ESCA, indicating a greater area of active sites exposed to surface reactions and, therefore, superior catalytic activity in Hy- drogen Evolution Reaction (HER). These findings show an interesting strategy for avoiding rapid electron-hole recombination and, consequently, CuO corrosion. For Oxygen Evolution Reaction (OER), samples treated thermally in O2 atmosphere at 500°C and 600°C appear to be more effective.

Scale drop disease virus (SDDV), a double-stranded DNA virus in the family Iridoviridae, has been reported widely in southeast Asian countries as a causative agent of scale drop syndrome (SDS) in Asian seabass. SDS have resulted in high mortality and significant economic losses to the aquaculture industry. This study demonstrated the use of metagenomic methods to investigate bacterial and viral communities present in infected fish tissues and recovering a complete genome of the causative agent named SDDV TH7_2019. Characterization of the TH7_2019 genome revealed a genome size of 131 kb with 134 putative ORFs encoding viral proteins potentially associated with host apoptosis manipulation. A comparative genome analysis showed a high degree of amino acid identity across SDDV strains, with variations in number of repeat sequences and mutations within core genes. Phylogenetic analyses indicate a close relationship among SDDV genomes. This research enhances our understanding of the genetic diversity and evolutionary relationship of SDDV, contributing valuable insights for further development of effective control strategies of SDDV.

This study investigates the relationship between energy metals and precious metals to assess their suitability as safe haven assets in clean energy investment portfolios. It focuses on the impact of events in 2020 and 2022, characterized by substantial investments in clean energy. The research reveals that, except for Nickel Futures (NICKELc1), energy metals are positively linked with clean energy indexes. This suggests that they can serve as a secure investment option for green investors looking to diversify their portfolios. As a result, the study dismisses the initial question, indicating that energy metals can indeed be considered safe havens within the context of clean energy investments. Additionally, the research reinforces prior findings that precious metals like gold, silver, and platinum possess safe haven characteristics in relation to specific clean energy stock indexes. These results have significant implications, particularly given the increasing investments in clean energy stocks and the recurring uncertainties in the market. In summary, this study supports the idea that both energy metals and precious metals can play valuable roles in clean energy portfolios, providing stability during turbulent times in the market.

The non-existence of a land ownership database in most of the developing countries moves the inhabitants to the occupation of public lands. Some of this situation are the origin to areas of informal housing, commerce and agriculture and in the end into new ​​informal settlements. Informal settlements become a serious problem in developing countries. The most common typology of informal settlements is that they are the population settled in public lands without any infrastructures and against the administrator's will. Thought this action the result in an uncontrolled land occupation process that promotes new informal areas without any proper built-up utilities, located in risk areas on the territory, barely ensuring the minimum requirements for a heaty living of the population and in various cases incentives to an informal economy. The process of build a cadastral map in informal settlement areas is a fundamental base to support the future transformation of illegal areas and to regulate the occupation of new subdivision planning and into the creation of new expansion areas. In this paper, it is presented a methodology developed to be applied to support a new register of land and to management. The transformation of informal settlement areas. The model to register the land tenure has been associated with allows the process application to multiple typology of informal settlements. The model to register land tenure has developed on a series of qualitative and quantitative data that determine the identification and classification of the buildings and its physical and functional description. The model was developed using Geographic Information System and with an initial survey of existing land titles of possession and public proposals to develop new expansion areas. A case study of the method is presented, where the land management model was implemented - Chã da Caldeiras in Ilha do Fogo an informal settlement in Cape Verde. The results are a great acceptance of the proposal by the population and local authorities and the starting of the implementation phase.

In the Northeast of Portugal, like in many parts of the world, most soils are acidic, which may hamper crop productivity. This study presents the findings of a factorial experiment involving three factors: i) soil type [schist (Sch) and granite (Gra)]; ii) cultivars [Cobrançosa (Cob) and Arbequina (Arb)]; and iii) fertilizer treatments [liming (CaCO3) plus Mg (LMg), P application (+P), B application (+B), all fertilizing materials combined (Con+), and an untreated control (Con-)]. Dry matter yield (DMY) did not show significant differences between cultivars, but plants grown in schist soil exhibited significantly higher biomass compared to those in granite soil. Among the treatments, +B and Con+ resulted in the highest DMY (50.8 and 47.2 g pot-1, respectively), followed by +P (34.3 g pot-1) and Con- (28.6 g pot-1). Treatment LMg yielded significantly lower values (15.6 g pot-1) than Con-. LMg raised the pH above 7 (7.36), leading to a severe B deficiency. Although Con+ also raised the pH above 7 (7.48), it ranked among the most productive treatments for providing B. Therefore, when applying lime to B-poor sandy soils, moderate rates are advised to avoid inducing a B deficiency. Additionally, it seems prudent to apply B after lime application.

Artificial intelligence (AI) is revolutionizing approaches in plant disease management and phy-topathological research. This review analyzes current applications and future directions of AI in addressing evolving agricultural challenges. Plant diseases annually cause 10-16% yield losses in major crops, prompting urgent innovations. Artificial intelligence (AI) shows aptitude for auto-mated disease detection and diagnosis utilizing image recognition techniques, with reported accuracies exceeding 95% and surpassing human visual assessment. Forecasting models inte-grating weather, soil, and crop data enable preemptive interventions by predicting spa-tial-temporal outbreak risks weeks in advance at 81-95% precision, minimizing pesticide usage. Precision agriculture powered by AI optimizes data-driven, tailored crop protection strategies boosting resilience. Real-time monitoring leveraging AI discerns pre-symptomatic anomalies from plant and environmental data for early alerts. These applications highlight AI's proficiency in il-luminating opaque disease patterns within increasingly complex agricultural data. Machine learning techniques overcome human cognitive constraints by discovering multivariate correla-tions unnoticed before. AI is poised to transform in-field decision making around disease pre-vention and precision management. Overall, AI constitutes a strategic innovation pathway to strengthen ecological plant health management amidst climate change, globalization, and agri-cultural intensification pressures. With prudent and ethical implementation, AI-enabled tools promise to enable next-generation phytopathology, enhancing crop resilience worldwide.Artificial Intelligence, Phytopathology, Emerging Disease, Climate Change, Control diseases.

The bivalve mollusk, Anadara tuberculosa (Black Shell, in Spanish Concha prieta, "CP"), is found on America's Pacific coast, where it is harvested for subsistence and commercial markets. This paper aims to diagnose the performance of the black shell's value chain. We also identify several challenges that must be addressed to improve the sustainability of the black shell fishery in Ecuador. The descriptive methodology was quantitative, with a non-experimental, field, cross-sectional, and ex post facto design. Similar questionnaires were designed, validated, and applied to each link in the production chain to collect information. The performance of the value chain was diagnosed in the dimensions of productivity, competitiveness, and quality of life, reaching a 75.1% index, a result that indicates that the value chain has a moderate to good performance level. However, the value chain can be improved if the following challenges are met: 1) Restoration of the mangrove ecosystem, 2) Promotion of low-intensity shellfish aquaculture, 3) Good sanitary management and purification, 4) Promotion of value-focused ventures, 5) Strengthening of organizations and agreements for the use and custody of mangroves, and 6) Strengthening of institutions that contribute to the advancement of these challenges.

A 3D graphene foam made of interconnected multilayer graphene flakes was produced on optical fibres (OF) by laser-induced transformation of a polyimide (PI) film coated on the OF cladding. This material, known as la-ser-induced graphene (LIG), was explored in the electrochemical detection and quantification of dopamine (DA) at physiologically relevant concentrations in the presence of the most relevant interfering molecules in biological fluids, ascorbic acid (AA) and uric acid (UA). The measured limit of detection is 100 nM, the linear range is 0.1 to 5 μM and a maximum sensitivity of 5.0 µA µM−1 cm−2 was obtained for LIG decorated with Pt nanoparticles (NPs). Moreover, immunity to AA and UA interference and to fouling was attained by decorating the LIG elec-trode with Pt NPs and coating with Nafion. These figures of merit underlines the potential of these sensors for the quantification of physiologically relevant concentrations of DA in biological fluids, paving the way for the development of hybrid electrochemical/optical sensing actuating platforms in a lab-on-fibre configuration, with relevant applications in biomedical engineering. The advantages of this hybrid arrangement include the possi-bility of in-situ counterproofing, extended measuring ranges, photoelectrochemical detection and the probing of inaccessible places. This elegant approach can also provide a simple and cost-effective way to fabricate biomed-ical devices with extended functionality, such as medical optical probes with added electrochemical capabilities, optogenetics combined with local electrochemical detection, among others.

Regional heterogeneity of microglia was first described a century ago by Pio del Rio Hortega. Currently, new hints on microglia heterogeneity across central nervous system (CNS) regions are still being unraveled by high-throughput techniques. It remains unclear whether these spatial specificities translate into different microglial behaviors in vitro. We cultured microglia isolated from the cortex and spinal cord and analyzed the effect of the CNS spatial source on in vitro behavior by applying the same experimental protocol and culture conditions. We analyzed microglial cell numbers, function, and morphology and found a distinctive in vitro phenotype. We found that microglia were present in higher numbers in spinal cord-derived glial cultures, presenting different expression of inflammatory genes and a lower phagocytosis rate under basal conditions or after activation with LPS and IFN-. Morphologically, cortical microglial cells are more complex and present longer ramifications, which were also observed in vivo in CX3CR1+/GFP transgenic reporter mice. Collectively, our data demonstrated that microglial behavior in vitro is defined according to specific spatial characteristics acquired in the tissue. Thus, our study highlights the importance of microglia as a source of CNS for in vitro studies.

The present research is embedded in the professional development and research line and in the needs of secondary education and first-year university teachers. We focussed on evidencing the importance of teachers’ professional training to include some specific adaptation and skills in intercultural dialogue and understanding -often called Intercultural Competence- because of its direct impact on the sustainable development of human beings, groups, and ecosystems. We investigated the role played by each of the main competencies linked to the following intercultural dimensions: Professional Identity, Ethics and Axiology, Methodology, and Inclusive Education. We used an integrated methodology and a cross-study of data, performed after the obtention of a three-cornered evaluation of results collected in focus groups, interviews, and questionnaires. We were able to show the impact of intercultural dialogue and understanding in the education for a sustainable development pattern. This is fundamental to set up a new ecology of forms, knowledge, attitudes, and educational meanings, further used to update teachers and students’ training in sustainable ecology and cultural diversity. Progress made in these complementary competencies -Professional Identity, Ethics and Axiology, Methodology- were appraised by teachers participating in the present study; the latter showing an increased interest and demand for the intercultural competence, after increasing their proficiency in the other complementary competencies.

Framed in a context with an emerging economy and with a high percentage of school failure, this study aimed to identify the factors that turn students with socioeconomic disadvantages into resilient students. Two questions guided the research: Can resilience be supported in students in adverse socioeconomic situations? What factors influence building resilient students? A cross-sectional study was carried out from 2011 to 2015 in Chile, using a multilevel logistic regression model with three levels, considering the hierarchical data structure. The behavior of 63100 to 76400 sampled students was analyzed. Results show five relevant factors in building resilience: self-efficacy in language, minor aggressions and violence perceived by students, norms and values of the establishment, interest in study, and self-efficacy. Some risk factors identified were an atmosphere of less respect and trust, engagement in physical education activities and good performance in carrying them out. These results could orient educational leaders interested in supporting the educational community in order to improve the academic success of disadvantaged students.

Forensic anthropologists play a key role in skeletal trauma analysis and commonly use macroscopic features to distinguish between trauma types. However, this approach can be challenging, particularly in cases of highly comminuted or incompletely recovered fractures. Histological analysis of microscopic fracture characteristics in fractured bones may thus help provide additional information on trauma type and bone fracture biomechanics in general. This study analysed the extent of microcrack damage to osteons in long bones with blunt force trauma (BFT) and gunshot trauma (GST), from both traumatic death cases and post-mortem experimental fractures. We identified four types of osteonal damage (OD). In traumatic death cases, OD affecting the inside of the osteon and compromising the Haversian canal (type 1) was found to be indicative of BFT. Moreover, OD affecting the cement line (type 3) and interstitial lamellae (type 4) was more common in the GST samples. OD affecting the inside of the osteon without compromising the Haversian canal (type 2) was not found to be indicative of either trauma type. In cases of experimental fractures, our study revealed that post-mortem fractures in dry bone samples featured the highest amount of OD, particularly of type 4. This study also found that the experimentally produced GST featured similar OD pattern to GST death cases. These findings support our hypothesis that there are distinct osteonal damage patterns in human long bones with BFT and GST, which are of relevant value for trauma analysis in forensic anthropology.

In the context of Shared Autonomous Vehicles, the need to monitor the environment inside the car will be crucial. This article focuses on the application of deep learning algorithms to detect objects, namely lost/forgotten items to inform the passengers, and aggressive items to monitoring if violent actions may arise between passengers. For object detection algorithms was used public datasets (COCO and TAO) to train state-of-the-art algorithms, such as YOLOv5. For violent action detection was used the MoLa InCar dataset to train on state-of-the-art algorithms such as I3D, R(2+1)D, SlowFast, TSN and TSM. At the end an embedded automotive solution was used to demonstrate both methods running in real-time.

When a limb suffers a fracture, rupture, or dislocation, it is traditionally immobilized with plaster. This may induce discomfort in the patient, excessive itching and sweating, which creates the growth of bacteria, leading to an unhygienic and difficulty to keep clean from treatment. Furthermore, if the plaster remains for a long period, it may cause lesions in the joints and ligaments. To overcome all these disadvantages, orthoses have emerged as important medical devices to help patients in rehabilitation, as well as for self-care of deficiencies in clinics and daily life. Traditionally, these devices are produced manually, which becomes time-consuming and error prone. From another point-of-view, it is possible to use imageology (X-ray or computed tomography) to scan the human body; a process that may help orthoses manufacturing but induces radiation to the patient. To overcome this great disadvantage, several types of 3D scanners, without any kind of radiation have emerged. This article describes the use of various types of scanners capable of digitizing the human body, to produce custom orthoses. Studies have shown that photogrammetry is the most used and most suitable 3D scanner for the acquisition of the human body in 3D. With this evolution of technology, it is possible to decrease the scanning time and it will be possible to introduce this technology in clinical environment.

The development in information technology has played an influential role in transforming the restaurant industry services. Therefore, this research’s main agenda is to investigate factors that motivate employees to adopt and continue using information technology services by integrating two famous information system (IS) theories, namely, task technology fit (TTF) and technology continuance theory (TCT). The extant integrative perspective model details the cause-effect relationship between technology adoption and continuance intention. The positivist paradigm forms the basis of this research design, and the approach followed is quantitative research. Using the stratified random sampling technique, the empirical data was collected from 417 restaurant industry employees in the US (United States) on a five-point Likert scale. The PLS-SEM technique was utilized to analyze data while using Smart PLS 3 because of its suitability and wider application currently in the hospitality sector. Results suggest that the recently developed integrated technology continuance research model has considerable influence on predicting pre- and post-adoption behavior with continuance intention for technology usage within the restaurant industry. All hypotheses were found significant except one for the direct association of hedonic motivation and continuance intention of technology adoption. Moreover, the results revealed that factors like perceived security & information privacy and assisting conditions were the most important factors in determining the usage of information technology with continuance intention. Unlike previous research studies that focus majorly only on issues before adoption of informational technology usage, the current focus on investigating continuance intention toward information technology usage by focusing on factors that can also boost post-adoption behavior and pre-adoption usage information technology.

Background: Evidences on the effects of chemotherapy treatment cycles on measures of muscle, mental state, social and cognitive performance are scarce. Objective: To analyze the effects of chemotherapy cycles on muscle strength and activation, functional capacity, quality of life, fatigue and anxiety of women with breast cancer. Methods: Twenty-two women divided into a treatment group (n = 10; 46.6 ± 9.6 years) and control group (n = 12; 51.6 ± 7.0 years) participated in the study. Analysis of muscle performance, quality of life, fatigue and anxiety after the 2nd and 4th cycle of chemotherapy with anthracyclines and cyclophosphamide were performed in women with breast cancer (TRA) and compared to healthy women (CTR). Two-way ANOVA was used to compare the variance of the means and the significance level was set as p≤0.05. Results: Differences were found in the muscular activation of the vastus medialis between the groups at post time (p = 0.038), as well as in the sit and stand test in the baseline (p<0.001) and post moment (p<0.001). Functional capacity performance was different between baseline (p<0.001) and post-time (p<0.001) groups. Additionally, the TRA group worsened the quality of life in the domains of functional capacity (p<0.001) and limitation of physical aspects (p=0.002), besides presenting negative changes in fatigue. Conclusion: Anthracycline and cyclophosphamide chemotherapy cycles reduce muscular performance and affect biopsychosocial variables in women with breast cancer.

In recent years, the advances in sperm morphology and genetic analyses in Perumytilus purpuratus allowed to suggest two evolutionary scenarios for this mussel: 1) the scenario of cryptic species and 2) the scenario of incipient or in progress speciation. To a better understanding of the evolutionary history of P. purpuratus, we performed an extensive sampling effort along a latitudinal gradient of ca. 7,180 km of coastline, from Southern Pacific to Atlantic Oceans and, we have also gone deeper in sperm morphology in P. purpuratus, exploring its association with phylogeny and population genetics to contrast if variability in sperm traits between northern and southern regions are signals of cryptic or incipient species. Overall, our results showed three sperm morphotypes and genetic structure in males of P. purpuratus strongly correlated. Additionally, we found evidence of a genetic admixture between lineages suggesting an introgressive hybridization zone at 38°S. These findings suggest the hypothesis that P. purpuratus is under incomplete reproductive isolation, with the presence of mechanisms of postzygotic isolation between lineages and, therefore, under an incipient or in fraganti speciation process. Finally, we discovered a new long morphotype for this species restricted to southern localities. Overall, our results support the hypothesis of historical events and postglacial recolonization as causal phenomena for the observed divergences.

Salmonella Typhimurium infection in pigs is characterized by an intense inflammatory response in early infection which seems to dysregulate host physiology including the gut microbiota. Both, changes in host physiology and microbiome composition, must entail modifications within the gut environment. This study analyzes the gut metabolome in early infected pigs (48 hours after infection) and non-infected controls by untargeted metabolomics (LC–QTOF MS/MS and GC–TOF MS), supported with microbiome data (16S rRNA abundance and prediction functional analyses). The metabolomic profile in Salmonella-infected pigs differed from healthy controls in 39 of these metabolites, including eight amino acids, four bile acids and carboxylic acids, three carnitines and sugar derivates, eleven fatty acids and six other compounds. Changes in amino acids abundance and the observed decrease of proteolytic bacteria such as Lactobacillus or Bacteroides could favor the niche colonization by Desulfovibrionaceae. In addition, the decrease in short chain fatty acids and tricarboxylic acids cycle was correlated to a decrease in beneficial bacteria in infected pigs. Interestingly, we observed an increase of bile acids concentration and compounds such as succinic acid or pantothenic acid, which may boost the inflammatory response. Altogether, the results reveal how S. Typhimurium infection alters the gut microbiome composition and prompts changes in the metabolism with a potential interaction between intestinal metabolism and microbiota abundance.

Introduction: When analyzing the current situation in Spain, derived from our current lifestyles far from following a Mediterranean lifestyle, there is an alarming prevalence of diabesity, which increases the global risk of suffering from cardiovascular disease (CVD) and decreases the quality of life related to health (QLRH). In order to guarantee, beyond glycemic control of patients with type 2 diabetes (DM2), adequate control of cardiovascular risk factors (CVRF) in DM2, it will be necessary to carry out a community intervention focused on the application of education programs focused on promoting the acquisition of healthy eating habits through the Mediterranean diet (DMED). This dietary pattern, together with physical exercise, has been shown to contribute to improving the QLRH of patients. Objective: To analyze the quality of life related to health, present in patients with poorly controlled type 2 diabetes, to determine the possible relationship between this and the degree of adherence to the Mediterranean diet and to examine whether there are differences between the sexes. Material and methods: Observational descriptive study in 93 patients diagnosed with DM2 with poor glycemic control (1Ac ≥ 7%), carried out in various health centers in Albacete and Cuenca, in which the baseline relationship between adherence to DMed and the HRQoL. They were administered a data collection sheet that included a survey of the degree of adherence to the DMed (MEDAS-14) and QLRH (SF-12v2) in the Primary Care (PC) medical and nursing consultations. The variables were analyzed: age groups, sex, years of evolution of DM2, body mass index (BMI), as well as basal glycemia (GB) and glycosylated hemoglobin (HbA1c). The “MEDAS-14” (adherence to DMED) was the main variable and the “SF-12v2” (QLRH) was the secondary variable. Material and methods: Observational descriptive study in 93 patients diagnosed with DM2 with poor glycemic control (1Ac ≥ 7%), carried out in various health centers in Albacete and Cuenca, in which the baseline relationship between adherence to DMed and the HRQoL. They were administered a data collection sheet that included a survey of the degree of adherence to the DMed (MEDAS-14) and QLRH (SF-12v2) in the Primary Care (PC) medical and nursing consultations. The variables were analyzed: age groups, sex, years of evolution of DM2, body mass index (BMI), as well as basal glycemia (GB) and glycosylated hemoglobin (HbA1c). The “MEDAS-14” (adherence to DMED) was the main variable and the “SF-12v2” (QLRH) was the secondary variable. Results: Patients with poorly controlled DM2 and with low adherence to the MedDM show a non-significantly greater affectation in the physical sphere: fair general state of health, physical function I (limitation to make moderate efforts such as moving a table, vacuuming or walk more than an hour), physical function II (limitation to climb several flights of stairs), physical role (problems at work or daily activities doing less than loved ones), physical role II (they had to stop performing some tasks at work or activities of daily living) and regular body pain. Presenting less affectation in the mental field: emotional role I, emotional role II, mental health I, vitality and mental health II. Without affectation in the social function since they consider that rarely the emotional or physical problems have hindered their social activities. Conclusions: Diabetic patients with poor glycemic control have low adherence to the MedMD (<9 points) and have poor QLRH. Low adherence to the DMed and obesity are related to a greater affectation in the physical dimension and less affectation in the mental dimension without affectation in the "social function".

AbstractBackground: In digitalization era, health care system was conditioned with the health worker’s ability to adapt media of digital technology. Digital technology media was used to strengthening and fasted the health care for patient and community. Aims: This study aimed to explore the accurate information of health professional utilization digital technology during doing the job at the health facilities in Timor-Leste. Methods: This study was used correctional study with 56 respondents was participated in the study. Was collected the data from 1 to 30 February 2023. Was used questioner of google form based for data collection. Data collection was distributed via online with google form to social media as WhatsApp’s, email and face book and twitter to completed by respondents. The inclusive Criteria are: health professional living in Timor-Leste, to able response the questioner. Was used descriptive analysis in this study. Finding: This study was indicated that majority of health professional was participated in the study as 54,2% man then women have 45,8%. About 100% health professional used hand phone to facilitate their daily activities including related with their work activities. Other side indicated in digital technology tools use are able to use Microsoft word is 78,3%, able to use Microsoft Excel is 76,7% and able to use Power Point is 81,7%. E-mail use about 89,2%, WhatsApp’s use 81,7%, About 74,2% was used zoom/webinars/Google meet application, about 72,5% sad that they were used computer in the work place until now. Around 96.4% need to improve or deepen their knowledge and skills in using ICT in the future to facilitate they are activity. Conclusion: Majority of health professional in Timor-Leste was used the computer, internet, hand phone in daily activities. Majority need to gate the training to improve their knowledge, attitude and psychomotor in the future.

Background: Alzheimer's Disease (AD) is a progressive and incurable condition that prompts the exploration of interventions to slow its advancement and enhance the quality of life. The cortical brain waves could have a role in the process of executive function deterioration, increasing dementia symptoms. Objective: To interrogate the effects of physical exercise, and non-invasive brain stimulation, both in electrophysiological behavior of the brain, the executive functions, and dementia symptoms in older adults with AD. Methods: One hundred and sixteen older adults were grouped (n=22 each): GAE (AD, PMED+NIBS), GCP (AD, PMED), GCN (AD, NIBS), GCS (control, no intervention). Binaural beats were used as a non-invasive brain stimulation, physical exercise, or both, were applied during 16 weeks. Pre/post measurements included Alpha Band, and SMR Rhythms, executive functions, and dementia symptoms. Results: GCS showed no significant changes (p>0.05). GCN and GCS slightly enhanced inhibitory control (p>0.05) and working memory (p>0.05). In contrast, GCP and GAE improved executive function (p

The Yellow-legged Hornet (Vespa velutina nigrithorax) (YLH) is an invasive insect that arrived in Europe in 2004 and is now spread across nine countries. It is a threat to the native entomofauna and harmful to beekeeping and agriculture, as it is a ravenous predator of the European Honey Bee (Apis mellifera) and other pollinating species. Its expansion has been unstoppable and all resources are needed to fight against it, including native vertebrate predators. Among these, the European Bee-eater (Merops apiaster) (EBE) is a potential one, but little is known about its predation on YLH. In a study carried out in Portugal, remains of YHL were detected in EBE nesting sites, which to the best of our knowledge is the first such report. This means that this bird could be one more agent in the biological control of this pest (although research on predation intensity is still needed), in conjunction with other natural predators and other strategies. In the Iberian Peninsula both species are allopatric in vast regions, so the role of EBE may be more limited. However, in the rest of Europe, at a country or continent scale, the scenario may be different and sympatry may occur to a greater extent.

Wireless power transfer promises to revolutionize the way we use and power mobile devices. However, low transfer efficiencies prevent this technology from seeing wide scale real-world adoption. The aim of this work is to use quasioptics to develop a system composed of a dielectric lens fed by a phased array to reduce spillover losses, increasing the beam efficiency, while working on the antenna system’s Fresnel zone. The DC-RF electronics, digital beamforming and beam-steering by an FPGA, and radiating 4x4 microstrip patch phased array have been developed and experimented upon, while the lens has been designed and simulated. This paper details these preliminary results, where the phased array radiation pattern was measured, showing that the beam is being generated and steered as expected, prompting the lens construction for the complete system experimentation.

The sustainability of agroecological systems, biodiversity protection, animal welfare and consumer demand for higher-quality products from alternative and extensive farming methods, have reinforced interest in local breeds that are well adapted to low-input environments. However, food safety needs to be safeguarded to reinforce consumer confidence. The aim of this study was to conduct a preliminary investigation on the occurrence of Salmonella spp. in eggshells, hen’s cloaca and litter materials from autochthonous Portuguese laying hens raised in a semi-extensive system for small-scale production. A total of 279 samples were obtained, with 63 samples from the “Preta Lusitânica” breed, and 72 samples each from the remaining autochthonous breeds, namely “Branca”, “Amarela” and “Pedrês Portuguesa”. None (0%) of the samples analyzed were positive for Salmonella spp.. To the best of our knowledge, these are the first results of Salmonella evaluation from hen’s cloaca, eggshells, and litter materials in autochthonous Portuguese chickens, suggesting that a semi-extensive production system can contribute to better food security and a lower risk to public health and the environment.

Smart Grid systems have become popular and necessary for the development of a sustainable power grid. These systems use different technologies to provide optimized services to the users of the network. Regarding computing, these systems optimize electrical services by processing a large amount of data generated. However, privacy and security are essential in this kind of system. With a large amount of data generated, it is necessary to protect the privacy of users, because this data may reveal users’ personal information. Today, blockchain technology has proven to be an efficient architecture for solving privacy and security problems in different scenarios. Over the years, different blockchain platforms have emerged, attempting to solve specific problems in different areas. However, the use of different platforms fragmented the market, which was no different in the smart grid scenario. This work proposes a blockchain architecture that uses sidechains to make the system scalable and adaptable. We used three blockchains to ensure privacy, security, and trust in the system. To universalize the proposed solution, we used the OSGP protocol and smart contracts. The results show that architecture security and privacy are guaranteed, making it feasible for implementation in real systems. Although scalability issues regarding the storage of data generated still exists.

This study analyzed the relationship between anthropometric measures, cardiorespiratory capacity, strength, power and local muscle endurance with performance in the CrossFit® Open 2020. For this, 17 volunteers (6 women) (29.0 ± 7.2 years; 70.5 ± 9.8 kg) completed, on separate weeks, body composition (dual-energy x-ray absorptiometry), maximal oxygen consumption (2km row test), muscle strength (1RM back and front squat, isometric peak torque), and muscle power (1RM snatch and clean &amp; jerk), and muscle endurance (Tibana test), which were compared with performance during the CrossFit® Open 2020. Multiple linear regression showed that for the CrossFit Open 2020.1 and 2020.2 workouts, the score in the Tibana test was the only variable that explained the outcomes (Beta = -0.78, p &lt; 0.01 for 2020.1 workout and Beta = 0.82, p &lt; 0.01 for 2020.2 workout). Performance in the CrossFit Open 2020.3 and 2020.4 workouts were explained through the relative strength (Beta = 0.58, p = 0.02 for 2020.3 workout and Beta = 0.50, p = 0.04 for 2020.4 workout). Lastly, Tibana test had the greatest influence on CrossFit Open 2020.5 workout (Beta = -0.75, p &lt; 0.01). A local muscle endurance and muscle strength may be used to predict CrossFit® open workout performance.

Unmanned vehicles (UVs) have become increasingly important in various scenarios of civil and military operations. The present work aims at the conceptual design of a modular Amphibious Unmanned Ground Vehicle (A-UGV), that can be easily adapted for different types of land and/or water missions, with low monetary cost (&lt;5k€, without sensors). Basing the design on the needs highlighted in the 2021 review of the Strategic Directive of the Portuguese Navy, the necessary specifications and requirements are established for two mission scenarios. Then a market research analysis focused on vehicles currently available, and their technological advances, is conducted to identify existing UVs solutions and respective characteristics/capabilities of interest to the current work. To study and define the geometry of the hull and the configuration of the A-UGV itself, preliminary computational structural and fluid analyses are carried out to ensure it complies with the specifications initially established. As a result, one obtains a fully electric vehicle, with approximate dimensions of 1050×670×450mm (length-width-height), enabled with 6×6 traction capable of reaching 20km/h in land, possesses amphibious capabilities of independent propulsion in water up to 8kts, and with an estimated autonomy of over 60 minutes.

Exosomes have shown promising potential as a therapeutic approach for wound healing. Nevertheless, the translation from experimental studies to commercially available treatments is still lacking. To assess the current state of research in this field, a systematic review was performed to examine the methodological heterogeneity among studies conducted over the past five years. Additionally, the review analyzed the suitability of animal models used and their relevance to human medicine. A PubMed search was performed for english-language, full-text available papers published from 2018 to June 2023, focusing on exosomes derived from mammalian sources and their application in wound healing, particularly those involving in vivo assays. Out of 531 results, 148 papers were selected for analysis. The findings revealed significant variability in terms of cell sources and types, biomaterials, and administration routes under investigation, indicating the need for further research in this field. Additionally, a comparative examination encompassing diverse cellular origins, types, administration pathways, or biomaterials is imperative. Furthermore, the predominance of rodent-based animal models raises concerns, as there have been limited advancements towards more complex in vivo models and scale-up assays. These constraints underscore the substantial efforts that remain necessary before attaining commercially viable and extensively applicable therapeutic approaches using exosomes.

Type 2 Diabetes Mellitus is a chronic metabolic disease that poses a significant long-term health risk due to its debilitating complications. While the diagnosis and monitoring of diabetes are well-established, the monitoring of its numerous complications presents a challenge, which can compromise patients' prognosis and quality of life. We analyzed the proteome of ten saliva samples, five from healthy individuals and five from patients with Type 2 Diabetes Mellitus. The aim was to identify potential biomarkers of Type 2 Diabetes Mellitus and its complications. We used PANTHER and FUNRICH tools to conduct a functional analysis and enrichment approach. Our findings revealed 622 proteins involved in 16 disrupted biological processes, which are linked to Type 2 Diabetes Mellitus. Metabolic processes, response to stimuli, processes of the immune system, and signaling are notable due to their known relation with Type 2 Diabetes Mellitus. Enrichment analysis has revealed that there are eleven biological processes that have been deregulated. These processes involve twenty different salivary proteins that are associated with multiple complications in diabetes. Our findings demonstrate that saliva is a dependable source of possible biomarkers that can be used to monitor diabetes and its complications.

Malignant pleural mesothelioma (MPM) is a locally aggressive disease related to asbestos exposure with a median survival for untreated patients of 4-8 months. The combination of chemotherapy based in platinum and antifolate is the standard treatment and the addition of bevacizumab adds two months of gain in median survival. Recently in first-line treatment, immunotherapy combining nivolumab with ipilimumab has been shown to be superior to chemotherapy in the CheckMate-743 study in terms of overall survival (18.1 months), leading to its approval by the FDA and EMA. The positive results of this study represent a new standard of treatment for patients with MPM, however, not all patients will benefit from immunotherapy treatment. In an effort to improve the selection of patient candidates for immunotherapy in different tumors, biomarkers that have been associated with a greater possibility of response to treatment have been described. MPM is a type of tumor with low mutational load and neo-antigens, making it a relatively non-immunogenic tumor for T cells and possibly less susceptible to responding to immunotherapy. Different retrospective studies have shown that PD-L1 expression occurs in 20-40% of patients and is associated with a poor prognosis; however, the predictive value of PD-L1 in response to immunotherapy has not been confirmed. The purpose of this work is to review state of art of MPM treatment in the year 2023 focusing in the efficacy results of first-line or subsequent immunotherapy studies in patients with MPM and possible chemo-immunotherapy combination strategies. Additionally, potential biomarkers of response to immunotherapy will be reviewed, such as histology, PD-L1, lymphocyte populations, and TMB.

Abstract: COVID-19 in 2020 brought challenges to the Brazilian public health system with an emerging virus with respiratory contagion called SARS-CoV-2. There are few studies in Brazil and in some countries, on the increased incidence of certain viral respiratory infections, includ-ing H1N1 and coronavirus and their association with low levels of vitamin D, zinc and iron. The aim of this study was to demonstrate that the deficit of vitamin D, zinc and iron has an impact on the infectious process of patients with COVID-19 and to establish new forms of prevention for the worsening of COVID-19 in the human body. Data were collected from medical records and test results from patients being followed up during the treatment period for COVID-19. Patients with low blood levels of vitamin D, zinc and iron during the treatment period of COVID-19 had a higher percentage of worsening and complications requiring hospitalization in intensive care beds. The ingestion of vitamin D, zinc and iron in the treatment period of patients with COVID-19 in addition to being an immunological protector against SARS-CoV-2 and alleviating the process of worsening the disease can also act as a biomarker in cases of this disease.

The purpose of this study was to assess if self-regulation of intensity based on rating of perceived exertion (RPE) is a reliable method to control the intensity of metabolic conditioning of functional-fitness session. In addition, the relationship between RPE and changes in heart rate and lactate responses was also analyzed. Eight male participants (age 28.1 ± 5.4 years; body mass 77.2 ± 4.4kg; VO₂max: 52.6 ± 4.6 mL·(kg·min)⁻¹) completed three randomly sessions (5 to 7 days apart) under different conditions: (1) all-out (ALL); (2) self-regulation of intensity based on a RPE of 6 (hard) on the Borg CR-10 scale (RPE6); and (3) a control session. Rate of perceived exertion, LAC and HR response were measured pre, during and immediately after the sessions. The RPE and LAC during the ALL-OUT sessions were higher (p ≤ 0.05) than the RPE6 and control sessions for all the analyzed time points during the sessions. Regarding HR, the 22 min area under the curve of HR during ALL-OUT and RPE6 sessions were significantly higher (p ≤ 0.05) than the control session. The average number of repetitions was lower (p ≤ 0.05) for the RPE6 session (190.5 ± 12.5 repetitions) when compared to the ALL session (214.4 ± 18.6 repetitions). There was a significant correlation between RPE and LAC (p = 0.001; r = 0.76; very large) and number of repetitions during the session (p = 0.026; r = 0.55; large). No correlation was observed between RPE and HR (p = 0.147; r = 0.380). These results indicate that self-regulation of intensity of effort based on RPE may be a useful tool to control exercise intensity during a metabolic conditioning session of functional-fitness.

Control of Tropical Theileriosis, a tick-borne disease with a strong impact on cattle breeding, can be facilitated by using marker-assisted selection in breeding programs. Genome-wide association studies (GWAS) using high-density arrays are extremely important for the ongoing process of identifying genomic variants associated with resistance to Theileria annulata infection. In this work, Single Nucleotide Polymorphisms (SNPs) were analyzed in the Portuguese autochthonous cattle breeds Alentejana and Mertolenga. In total, 14 SNPs suggestive of significance (p ≤ 10-4) were identified for Alentejana cattle and 20 SNPs for Mertolenga cattle. The genomic regions around these SNPs were further investigated for annotated genes and Quantitative trait loci (QTLs) previously described by other authors. Regarding the Alentejana breed, the MAP3K1, CMTM7, SSFA2, and ATG13 genes are located near suggestive SNPs and appear as candidate genes for resistance to Tropical Theileriosis, considering its action the immune response and resistance to other diseases. On the other hand, in the Mertolenga breed, the UOX gene is also a candidate gene due to its apparent link to the pathogenesis of the disease. These results may represent a first step towards the possibility of including genetic markers for resistance to Tropical Theileriosis in current breed selection programs.

The study describes the acute and delayed time course of recovery following the CrossFit® Benchmark Workout Karen. Eight trained men (28.4±6.4 years; 1RM back squat 139.1±26.0 kg) undertook the Karen protocol. The protocol consists of 150 Wall Balls, aiming to hit a target 3 meters high. Countermovement jump height (CMJ), creatine kinase (CK), and perceived recovery status scale (PRS) (general, lower and upper limbs) were assessed pre, post-0h, 24h, 48h and 72h after the session. The CK concentration 24h after was higher than pre-exercise (338.4 U/L vs. 143.3 U/L; effect size: 0.74; p≤0.05). At 48h and 72h following exercise, CK concentration had returned to baseline levels. The PRS general and of the lower limbs were lower in the 24-hours post-exercise compared to pre-exercise (PRS general: 4.7 ±1.5 and 7.9 ±1.7 mmol/L; and PRS of the lower limbs: 4.0 ±2.5 and 7.9 ±0.8, respectively). The PRS general, lower, and upper limbs were reduced at 48-post exercise compared to 72-hours post-exercise scores. Our findings provide insights into the fatigue profile and recovery in acute CrossFit® and can be useful to coaches effectively design the daily session.

Identifying cracks in the incipient state is essential to prevent the failure of engineering structures. Detection methods relying on the analysis of the changes in modal parameters are widely used because of the advantages they present. In our previous research, we have found that eigenfrequencies were capable of indicating the position and depth of damage when sufficient vibration modes were considered. The damage indicator we developed was based on the relative frequency shifts (RFS). To calculate the RFSs for various positions and depths of a crack, we established a mathematical relation that involved the squared modal curvatures in the healthy state and the deflection of the healthy and damaged beam under dead mass, respectively. In this study, we propose to calculate the RFS for beams with several cracks by applying the superposition principle. We demonstrate that this is possible if the cracks are far enough from each other. In fact, if the cracks are close to each other, the superposition method does not work and we distinguish two cases: (i) when the cracks affect the same beam face, the frequency drop is less than the sum of the individual frequency drops, and (ii) on the contrary, cracks on opposite sides cause a decrease in frequency, which is greater than the sum of the frequency drop due to individual damage. When the RFS curves are known, crack assessment becomes an optimization problem, the cost function being the distance between the measured RFSs and all possible RFSs for several vibration modes. Thus, the RFS constitutes a benchmark that characterizes damage using only the eigenfrequencies. We can accurately locate multiple cracks and estimate their severity trough experiments and thus prove the reliability of the proposed method.

This study assesses the efficacy of a microwave colonoscopy algorithm to detect colorectal cancer precursors or polyps in an ex-vivo human colon model. The algorithm works with a device composed of a cylindrical ring-shaped switchable antenna array, which can be attached to the tip of a conventional colonoscope as an accessory. The accessory is connected to an external processing unit that generates an acoustic signal when a polyp is detected. Nowadays, 22% of polyps go undetected with conventional colonoscopy and the risk of cancer after a negative colonoscopy can be up to 7.9%. Fifteen ex-vivo freshly excised human colons with cancer (n=12) or polyps (n=3) were examined with the microwave-assisted colonoscopy system simulating a real colonoscopy exploration. Successive measurements of the colon were taken with the microwave-based colonoscopy device and processed with a microwave imaging algorithm. After the experiment, the dielectric properties of the specimens were measured with a coaxial probe and finally the samples underwent a pathology analysis. The results show that all the neoplasms were detected with a sensitivity of 100% and specificity of 87.4%.

The aim of the present work was to evaluate the effects of Thalassia testudinum hydroethanolic extract, its polyphenolic fraction, and thalassiolin B on the activity of phase I metabolizing enzymes as well as their antimutagenic effects. Spectrofluorometric techniques were used to evaluate the effect of tested products on rat and human CYP1A and CYP2B activity. The antimutagenic effect of tested products was evaluated in benzo[a]pyrene (BP)-induced mutagenicity assay by Ames test. Finally, the antimutagenic effect of Thalassia testudinum (100 mg/kg) was assessed in a BP-induced mutagenesis in mice. The tested products significantly (p<0.05) inhibit rat CYP1A1 activity, acting as mixed-type inhibitors of rat CYP1A1 (Ki = 54.16±9.09 μg/mL, 5.96±1.55 μg/mL and 3.05±0.89 μg/mL, respectively). Inhibition of human CYP1A1 was also observed (Ki = 197.1±63.40 μg/mL and 203.10±17.29 μg/mL for the polyphenolic fraction and for thalassiolin B, respectively). In addition, the evaluated products significantly inhibit (p<0.05) benzo[a]pyrene (BP)-induced mutagenicity in vitro. Furthermore, oral doses of Thalassia testudinum (100 mg/kg) significantly reduced (p<0.05) the BP-induced micronuclei and oxidative damage, together with an increase of glutathione, in mice. In summary, Thalassia testudinum metabolites exhibit antigenotoxic activity mediated, at least, by the inhibition of CYP1A1-mediated BP biotransformation. Thus, the metabolites of T. testudinum may represent a potential source of chemopreventive compounds for adjuvant therapy of cancer.

The design and engineering of antibacterial materials is key for preventing bacterial adherence and proliferation in biomedical and house-hold instruments. Silver nanoparticles (AgNPs) and chitosan (CHI) are broad-spectrum antibacterial materials with different properties, whose combined application is currently under optimization. This study proposes the formation of antibacterial films with AgNPs embedded in carboxymethylcellulose/chitosan multilayers by the layer-by-layer (LbL) method. The films were deposited onto nanoporous silicon (nPSi), an ideal platform for bioengineering applications, due to its biocompatibility, biodegradability, and bioresorbability. We focus on two alternative multilayer deposition processes: cyclic dip coating (CDC) and cyclic spin coating (CSC). The physicochemical properties of the films were the subject of microscopic, microstructural and surface-interface analyses. The antibacterial activity of each film was investigated against Escherichia coli (Gram-negative) and Staphylococcus aureus (Gram-positive) bacteria strains as model microorganisms. According to the findings, CDC technique produced multilayer films with higher antibacterial activity for both bacteria compared to the CSC method. Bacteria adhesion inhibition was observed from only 3 cycles. The developed AgNPs-multilayer composite film offers advantageous antibacterial properties for biomedical applications.

Many signaling pathways, molecular and cellular actors which are critical for wound healing have been implicated in cancer metastasis. These two conditions are a complex succession of cellular biological events and accurate regulation of these events is essential. Apart from inflammation, macrophages-released ROS arise as major regulators of these processes. But, whatever the pathology concerned, oxidative stress is a complicated phenomenon to control and requires a finely tuned balance over the different stages and responding cells. This review provides an overview of the pivotal role of oxidative stress in both wound healing and metastasis, encompassing the contribution of macrophages. Indeed, macrophages are major ROS producers but also appear as their targets since ROS interfere with their differentiation and function. Elucidating ROS functions in wound healing and metastatic spread may allow the development of innovative therapeutic strategies involving redox modulators.

Delayed diagnosis and a lack of adequate care for people with autism spectrum disorder (ASD) are related to worse outcomes and quality of life. This study aimed to identify the profile of service use, barriers to access care, and factors related to those barriers in Brazilian families with children with ASD. A total of 927 families with ASD children (3-17 years) from five Brazilian regions completed an online version of the Caregivers Needs Survey. Results showed that the most used services were behavioral interventions and pharmacotherapy, while the most used professionals were neurologists, nutritionists, speech therapists, psychiatrists, psychologists, and pediatricians. The main barriers included waiting lists, costs, and the absence of services or treatment. Service use varied according to age, the region of residence, the type of health care system used, and the parents/caregivers' education. Access to behavioral interventions was more frequent among users of the private system/health insurance and families whose caregivers had higher education. The absence of specialized services/treatments was less frequent among residents of state capitals and families whose caregivers had higher levels of education. This study highlights how families with children/adolescents with ASD in Brazil face significant barriers to access care related to sociodemographic factors.