Background: Current drug for the treatment of endometriosis is not able of completely cure and significant side effects hinder the continuation of treatment. So, the search for new drug candidates is necessary, and the use of plants extracts is highlighted. Babassu oil and copaiba oilresin have several therapeutic properties. We investigated the in vitro effects of two nanoemulsions containing babassu oil (Orbignya speciosa) (SNEDDS-18) and/or copaiba oilresin (Copaifera langsdorffii) (SNEDDS-18/COPA) on cultured human eutopic endometrium stromal cells from endometrial biopsies of patients without (CESC) and with (EuESC) endometriosis, and human stromal cells from biopsies of endometriotic lesions (EctESC). Methods: CESC, EuESC and EctESC were established and treated with SNEDDS-18 and SNEDDS-18/COPA to evaluate its effects on cytotoxicity, cell morphology, proliferation, and signaling pathways. Results: After 48 hours incubation with SNEDDS-18 and SNEDDS-18/COPA, cell viability and proliferation were inhibited, especially in EctESC. The lowest concentration of both nanoemulsions reduced cell viability and proliferation and breakdown the cytoskeleton in EctESCs. After 24h treatment it was observed a decrease in IL-1, TNF-α, and MCP-1, and an increase in IL-10 production. Conclusions: Both nanoemulsions can affect the endometriotic stromal cell behaviors, thus revealing two potential candidates for new phytotherapeutic agents on the management of endometriosis.

Recent advancements in deep learning have spurred the development of numerous novel semantic segmentation models for land cover mapping, showcasing exceptional performance in delineating precise boundaries and producing highly accurate land cover maps. However, to date, no systematic literature review has comprehensively examined semantic segmentation models in the context of land cover mapping. This paper addresses this gap by synthesizing recent advancements in semantic segmentation models for land cover mapping from 2017 to 2023, drawing insights on trends, data sources, model structures, and performance metrics based on a review of 106 extracted articles. Our analysis identifies top journals in the field, including MDPI Remote Sensing, IEEE Journal of Selected Topics in Earth Science, and IEEE Transactions on Geoscience and Remote Sensing, IEEE Geoscience and Remote Sensing Letters, ISPRS Journal Of Photogrammetry And Remote Sensing as the leading journals. We find that research predominantly focuses on land cover, urban areas, precision agriculture, environment, coastal areas, and forests. Geographically, 35.29% of the study areas are located in China, followed by USA (11.76%), France (5.88 %), Spain (4%) and others. Sentinel-2, Sentinel-1, and Landsat satellites emerge as the most commonly used data sources. Benchmark datasets such as ISPRS Vaihingen & Potsdam, LandCover.ai, DeepGlobe, and GID datasets are frequently employed. Model architectures predominantly utilize encoder-decoder, and hybrid convolutional neural network-based structures because of their impressive performances, with limited adoption of transformer-based architectures due to its computational complexity issue, and slow convergence speed. Lastly, this paper highlights existing key research gaps in the field to guide future research directions.

The article delves into the crucial realm of compressing hydrogen in its gaseous state, a pivotal process for enhancing its viability in both civil and industrial sectors. The study initiates by providing a concise overview and comparison of diverse hydrogen storage methodologies, laying the groundwork with an in-depth analysis of hydrogen's thermophysical properties. It scrutinizes plausible configurations for hydrogen compression, aiming to strike a delicate balance between energy consumption, predominantly derived from the fuel itself, and the requisite number of compression stages. Notably, to render hydrogen storage competitive in terms of volume, pressures of at least 350 bar are deemed essential, albeit at an energy cost amounting to approximately 10% of the fuel's calorific value. Multi-stage compression emerges as a crucial strategy, not solely for energy efficiency, but also to curtail temperature rise, with an upper limit set at 200°C. This nuanced approach is underlined by the exploration of compression levels commonly cited in the literature, particularly 350 bar and 700 bar. Ultimately, the study advocates for a three-stage compression system as a pragmatic compromise, capable of achieving high-pressure solutions while keeping compression work below 10 MJ/kg, a threshold indicative of sustainable energy utilization.

The transition to cleaner energy sources, particularly in hard-to-abate industrial sectors, often requires gradual integration of new technologies. Hydrogen, crucial for decarbonization, is explored as a fuel in blended combustions. Blending or replacing fuels impacts combustion stability and heat transfer rates due to differing densities. An extensive literature review examines blended combustion, focusing on hydrogen-methane mixtures. While industrial burners claim to accommodate up to 20% hydrogen, theoretical support is lacking. A novel thermodynamic analysis methodology is introduced, evaluating methane-hydrogen combustion using the Wobbe index. Findings highlight practical limitations beyond 25% hydrogen volume, necessitating a shift to "totally hydrogen" combustion. Blended combustion can be proposed as a medium-term strategy, acknowledging hydrogen's limited penetration. Higher percentages require burner and infrastructure redesign.

We use oblate coordinates to study its resulting orbit equations. Their related solutions of Einstein's vacuum equations can be written as a linear combination of Legendre polynomials of positive denite integers $l$. Starting from solutions of the zeroth order $l=0$ in a nearly newtonian regime, we obtain a non-trivial formula favoring both retrograde and advanced solutions for the apsidal precession depending on parameters related to the metric coecients, particularly applied to the apsidal precessions of Mercury and asteroids (Icarus and 2 Pallas). As a realization of the equivalence problem in general Relativity, a comparison is made with the resulting perihelion shift produced by Weyl cylindric coordinates and the Schwarzschild solution analyzing how different geometries of space-time influence on solutions in astrophysical phenomena.

Introduction: Thyroid nodules are extremely common and require complex management to prevent unnecessary surgical intervention and ensure that no malignant disease is overlooked. Several diagnostic tools and scoring systems are available to evaluate the Risk of Malignancy (ROM). The goal is to assess variables that can aid and support the clinical recommendations suggested by the updated Bethesda System for Reporting Thyroid Cytopathology (TBSRTC), such as the ultrasonographic features of thyroid nodules, particularly for indeterminate categories III (atypia of undetermined significance) and IV (follicular neoplasm). Methods: We retrospectively analysed the correlation of the demographic and ultrasonographic characteristics of thyroid nodules with the cytopathological and histopathological diagnoses of TBSRTC categories III (atypia of undetermined significance), IV (follicular neoplasm), V (suspicious for malignancy), and VI (malignant) in patients who underwent surgery in a single Portuguese centre over a 10-year period. Results: In total, 360 nodules were evaluated in 341 patients and 57% were histopathologically malignant or borderline. The majority were included in the TBSRTC indeterminate categories III and IV with an ROM of 44% and 43%, respectively. The ultrasonographic characteristics associated with a higher TBSRTC category and a greater ROM value were hypoechogenicity, presence of microcalcifications, irregular margins, and the presence of cervical adenopathy. When correlating with a malignant histology, only adenopathy and the presence of microcalcifications were observed to be statistically significant. Discussion: The indeterminate categories of the TBSRTC have been the most challenging ones to manage. The new TBSRTC (2023) guidelines as well as the ultrasonographic characteristics of a patient can be helpful in assessing the ROM and deciding on an appropriate course of treatment. Other resources, such as molecular tests, are also gaining a more important role in the clinical decision process and may become crucial in the future. Conclusion: The clinical management of thyroid nodules requires a careful analysis of clinical history and an evaluation of demographic details, the symptoms of the patient, comorbidities, personal and family history, ultrasonographic features, and the results of cytopathology, thyroid function, and molecular/genetic tests to provide the best care possible. Patients with thyroid nodules of an indeterminate character can obtain a definitive diagnosis through surgery. However, by employing a complete analysis of all relevant data, some patients may be suitable for active surveillance instead of a surgical intervention.

In view of the preparation for a post-LHC collider, the high-energy physics (HEP) community started from 2010 to discuss various options, including the use of HTS for very high field dipoles. Therefore, a small program was set in Europe aiming at exploring the possibility of using HTS for accelerator quality magnets. Based on various EU funded programs, though at modest levels, has enabled the European community of accelerator magnets to start getting experience in HTS and addressing a few issues. The program was based on use of REBCO tapes to form 10 kA Roebel cables, to be used to wind small dipoles of 30-40 mm aperture in the 5 T range. The dipoles are designed to be later inserted in a background dipole field (in Nb3Sn), to reach eventually a field level in the 16-20 T range, beyond the reach of LTS. The program is currently underway: more than 1 km tape of high performance (Je > 500 A/mm2 at 20 T, 4.2 K has been manufactured and characterized, various 30 m long Roebel cables have been assembled and validated up to 13 kA, a few dipoles have been wound and tested, reaching at present 4.5 T in stand-alone (while a dipole made from race track coils with no-bore exceeded 5 T using stacked tape cable) and a test in a background field is being organized.

From the linear Nash-Green fluctuations of background metric, we present the perturbation equations in an embedded four space-time. In the context of a five-dimensional bulk, we show that the perturbations are only propagated by the gravitational tensorial field equation. In a Newtonian conformal gauge, we study the matter density evolution in sub-horizon regime and on how such scale may be affected by the extrinsic curvature. We use the "extended Gold 2018'' growth dataset with 25 datapoints and the best fit Planck2018/LambdaCDM parameters. Hence, we determine the evolution equation for the density growth delta(a) as a result from the embedded equations of the background geometry. By using solar constraints, we analyse the evolution of the effective Newtonian constant G_eff and showing that applying Taylor expansion to G_eff (a) under the constraint of time-derivative of G_eff(0)=G at a= 1 in matter domination era, we get an agreement with Big Bang Nucleosynthesis (BBN) and also an alleviation of the 3-sigma tension to 1-sigma contour between (sigma8-Omega_m) of the observations from Cosmic Microwave Background (CMB) and Large Scale Structure (LSS) probes.

The Gram-negative, strictly anaerobic bacterium Megasphaera elsdenii was first isolated from the rumen in 1953 and is common in the mammalian gastrointestinal tract. Its ability to use either lactate or glucose as its major energy source for growth has been well documented, although it can also ferment amino acids to ammonia and branched-chain fatty acids, which are growth factors for other bacteria. The ruminal abundance of M. elsdenii usually increases in animals fed grain-based diets due to its ability to use lactate (the product of rapid ruminal sugar fermentation), especially at low ruminal pH (< 5.5). M. elsdenii has been proposed as a potential dietary probiotic to prevent ruminal acidosis in feedlot cattle and high-producing dairy cows. However, this bacterium has also been associated with milk fat depression (MFD) in dairy cows, although proving a causative role has remained elusive. This review summarizes the unique physiology of this intriguing bacterium, and its role of in ruminal community function and in the health and productivity of the host animal. In addition to its effects in the rumen, the ability of M. elsdenii to produce C2-C7 carboxylic acids – potential precursors for industrial fuel and chemical production, is examined.

The evolution of digital media in adolescents has changed the patterns and motives of use and the impact on their communication choices in their social and family networks. The objectives of this study are to understand how peers communicate adopting a social network (SN) or by voice and their social desirability. After the informant consent signature, the adolescents completed a series of self-report questionnaires on the use of SN, on communication preferences, and on social desirability through online. Most of the adolescents belonged to the 17-19 age group (83.6%) and were female (68.9%). Adolescents spent more than 3 hours/day on Whatsapp and more than 2 hours/day on Instagram, while the use of Facebook was on average only 35 minutes/day. Females used digital media for longer than males. Adolescents aged 17-19 years choose more Facebook and voice modes compared to adolescents aged 14 and 16 years. The alternative modes of Whatsapp and voice were chosen more than the social networks in their communication strategies, especially for negative topics. Motives for use were, in addition to boredom, related to maintaining one's social sphere with peers. Some educative considerations were made based on these results.

Mining for resources extraction may lead to several geological and associated environmental changes due to ground movements, collision with mining cavities and deformation of aquifers. Geological changes may continue in a reclaimed mine area, and the deformed aquifers may entail a breakdown of substrates and an increase in ground water tables, which may cause surface area inundation. Consequently, a reclaimed mine area may experience surface area collapse, i.e. subsidence, and degradation of vegetation health. Thus, monitoring short-term landscape dynamics in a reclaimed mine area may provide important information on the long-term geological and environmental impacts of mining activities. We studied landscape dynamics in Kirchheller Heide, Germany, which experienced extensive soil movement due to longwall mining without stowing, using Landsat imageries between 2013 and 2016. A Random Forest image classification technique was applied to analyse land-use and land-cover dynamics and the growth of wetland areas was assessed using a Spectral Mixture Analysis (SMA). We also analyzed the changes in vegetation health using a Normalized Difference Vegetation Index (NDVI). We observed a 19.9% growth of wetland area within the four years with 87.2% of growth in the coverage of two major waterbodies in the reclaimed mine area. NDVI values indicate that 66.5% of the vegetation of the study area was degraded due to changes in ground water tables and surface flooding. Our results inform environmental management and mining reclamation authorities about the subsidence spots and priority mitigation areas from land surface and vegetation degradation in Kirchheller Heide.

In this article we investigate the determinants of marketing or organizational innovators in Europe for 36 countries in the period 2010-2019. We have used data from the European Innovation Scoreboard-EIS of the European Commission. We perform different econometric models i.e. Dynamic Panel, Pooled OLS, Panel Data with Fixed Effects, Panel Data with Random Effects, WLS. Results show that the level of marketing or organizational innovators in positively associated, among others variables to “Innovation Index”, “Innovators” and “Knowledge Intensive Service Exports”, while is negatively associated with “Sales Impacts”, “Foreign Controlled Enterprises Share of Value Added” and “Government procurement of advanced technology products”.

Rapid and extensive urbanization has adversely impacted humans and ecological entities in the recent decades through a decrease in surface permeability and the emergence of urban heat islands (UHI). While detailed and continuous assessments of surface permeability and UHI are crucial for urban planning and management of landuse zones, they have mostly involved time consuming and expensive field studies, and single sensor derived large scale aerial and satellite imageries. We demonstrated the advantage of fusing imageries from multiple sensors for landuse and landcover (LULC) change assessments as well as for assessing surface permeability and UHI emergence in Tirunelveli, Tamilnadu, India. Cartosat-2 and Landsat-7 ETM+ imageries from 2007 and 2017 were fused and classified using a Rotation Forest (RF), while surface permeability and temperature were quantified using Soil-Adjusted Vegetation Index (SAVI) and Land Surface Temperature (LST) index, respectively. Fused images exhibited higher classification accuracies than non-fused images, i.e. overall kappa coefficient values 0.83 and 0.75, respectively. We observed an overall increase of 20 km2 (45%) in the coverage of urban (dry, real estate plots and built-up) areas, while a decrease of 27 km2 (37%) for vegetated (cropland and forest) areas in Tirunelveli between 2007 and 2017. The SAVI values indicated an extensive decrease in surface permeability for Tirunelveli overall (0.4) and also for almost all LULC zones. The LST values exhibited an associated overall increase (1.30C) of surface temperature in Tirunelveli with the highest increase (2.40C) for urban built-up areas between 2007 and 2017. The SAVI-LST combined metric depicted the Southeastern built-up areas in Tirunelveli as a potential UHI hotspot, while a caution for the Western riparian zone for UHI emergence in 2017. Our results provide important metrics for surface permeability and UHI monitoring, and inform urban and zonal planning authorities about the advantages of satellite image fusion.

Turbulence is a fluid dynamic problem refractory to mathematical treatment. Examining a theoretical model of liquid water flowing in a cylinder at different Raleigh numbers, we propose a novel approach to elucidate the first stages of turbulent flows. The weakly bonded molecular assemblies of liquid water form a fluctuating branched polymer in which every micro-cluster displays different density. Against the common view of liquid water as an incompressible and continuous fluid, we suggest that the occurrence of transient local aggregates could be able to generate the vortices and eddies that are the hallmarks of turbulence. We quantify the local changes in velocity, diameter and density required to engender “obstacles” to the average flow. Then, we show how these microstructures, equipped with different Raleigh numbers and characterized by high percolation index, could generate boundary layers that contribute to micro-vortices production. We conclude that the genesis of turbulence cannot be assessed in terms of collective phenomena, rather is sustained, among many other factors, by the underrated microscopic inhomogeneities of fluids like liquid water.

The major challenge in the current context of a rising world’s energy demand is to limit the global temperature increase for mitigating climate change. This goal requires a large reduction of CO2 emissions, mainly produced by power generation and industrial processes using fossil fuels. In this study, a novel methodology for K2CO3-doped Li4SiO4 sorbents production for CO2 capture at high temperature was adopted based on the Design of Experiments (DoE). Different synthesis (temperature and K2CO3 content) and adsorption conditions (sorption temperature and CO2 concentration) were systematically tested by Response Surface Methodology (RSM) to obtain predictive models of CO2 uptake and Li4SiO4 conversion. The results of RSM analysis evidenced a maximum adsorption capacity of 196.4 mg/g for a sorbent produced at 600 °C and with 36.9 wt% of K2CO3, tested at 500 °C and 4 vol% of CO2. Whereas at 50 vol% of CO2, the best uptake of 295.6 mg/g was obtained with a sorbent synthesized at 600 °C, containing less K2CO3 (17.1 wt%) and tested at higher temperature (662 °C). The obtained results highlight that K2CO3-doped Li4SiO4 sorbents can be tailored for maximizing CO2 capture at different operating conditions, making them suitable for use in industrial processes.

In this article we analyze the determinants and the export trend of European countries of medium and high technology products. The data were analyzed using various econometric models, namely WLS, Pooled OLS, Dynamic Panel, Panel Data with Fixed Effects, Panel Data with Random Effects. The results show that exports of medium and high-tech products are positively associated, among other variables, with the value of “Average Annual GDP Growth”, “Total Entrepreneurial Activity” and “Sales Impacts”, and negatively associated with, among other variables, “Human Resources”, “Government and Procurement of Advanced Technology Products” and “Buyer Sophistication”. A cluster analysis was realized with the k-Means algorithm optimized with the Silhouette coefficient. The result showed the presence of only two clusters. Since this result was considered poorly representative of the industrial complexity of the European Union countries, a further analysis was carried out with the Elbow method. The result showed the presence of 6 clusters with the dominance of Germany and the economies connected to the German economy. In addition, a network analysis was carried out using the distance to Manhattan. Four complex network structures and two simplified network structures were detected. A comparison was then made between 10 machine learning algorithms for predicting the value of exports of medium and high-tech products. The result shows that the best performing algorithm is the SGD. An analysis with Augmented Data-AD was implemented with a comparison between 10 machine learning algorithms for prediction and the result shows that the Linear Regression algorithm is the best predictor. The prediction with the Augmented Data-AD allows to reduce the MAE by about 0.0022131 compared to the prediction with the Original Data-OD.

In this article we investigate the determinants of the European “Most Cited Publications”. We use data from the European Innovation Scoreboard-EIS of the European Commission for the period 2010-2019. Data are analyzed with Panel Data with Fixed Effects, Panel Data with Random Effects, WLS, and Pooled OLS. Results show that the level of “Most Cited Publications” is positively associated, among others, to “Innovation Index” and “Enterprise Birth” and negatively associated, among others, to “Government Procurement of Advanced Technology Products” and “Human Resources”. Furthermore, we perform a cluster analysis with the k-Means algorithm either with the Silhouette Coefficient and the Elbow Method. We find that the Elbow Method shows better results than the Silhouette Coefficient with a number of clusters equal to 3. In adjunct we perform a network analysis with the Manhattan distance, and we find the presence of 4 complex and 2 simplified network structures. Finally, we present a confrontation among 10 machine learning algorithms to predict the level of “Most Cited Publication” either with Original Data-OD either with Augmented Data-AD. Results show that the best machine learning algorithm to predict the level of “Most Cited Publication” with Original Data-OD is SGD, while Linear Regression is the best machine learning algorithm for the prediction of “Most Cited Publications” with Augmented Data-AD.

This paper presents high-resolution digital measurements at low cost, combining oversample and averaging. Experiments of processing data between samples and between multiple temperature sensors in the same environment are conducted. They provide results with effective increase in the analog to digital converter bits, in correspondence with the minimization on the effects of noise. It is shown that averaging a set of small, low-cost sensors at a small microcontroller can give flexible access to improved measurement accuracy.

In this article we investigate the role of “Renewable Energy Consumption” in the context of Circular Economy. We use data from the World Bank for 193 countries in the period 2011-2020. We perform several econometric techniques i.e., Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled OLS, WLS. Our results show that “Renewable Energy Consumption” is positively associated among others to “Cooling Degree Days” and “Adjusted savings: net forest depletion” and negatively associated among others to “GHG net emissions/removals by LUCF” and “Mean Drought Index”. Furthermore, we perform a cluster analysis with the application of the k-Means algorithm optimized with the Silhouette Coefficient and we find the presence of two clusters. Finally, we compare eight different machine learning algorithms to predict the value of Renewable Energy Consumption. Our results show that the Polynomial Regression is the best algorithm in the sense of prediction and that on average the renewable energy consumption is expected to growth of 2.61%.

Globally mangrove forests are substantially declining and a globally synthesized database of the drivers of deforestation and drivers’ interaction is scarce. Here we synthesized the key social-ecological drivers of global mangrove deforestation by reviewing about two hundred published scientific studies over the last four decades (from 1980 to 2021). Our focus was on both natural and anthropogenic drivers with gradual and abrupt impacts and their geographic ranges of effects and how these drivers interact. We also summarized the patterns of global mangrove coverage decline between 1990 and 2020 and identified the threatened mangrove species and their geographic ranges. Our consolidated studies reported a 8,600 km2 decline in the global mangrove coverage between 1990 and 2020 with the highest decline occurring in South and Southeast Asia (3870 km2). We could identify 11 threatened mangrove species, two of which are critically endangered (Sonneratia griffithii and Bruguiera hainseii). Our reviewed studies pointed to aquaculture and agriculture as the predominant driver of global mangrove deforestation though the spatial distribution of their impacts varied. Gradual climate variations, i.e. seal-level rise, long-term precipitation and temperature changes and driven coastline erosion, constitute the second major group of drivers. Our findings underline a strong interaction across natural and anthropogenic drivers with the strongest interaction between the driver groups aquaculture and agriculture and industrialization and pollution. Our results suggest prioritizing globally coordinated empirical studies linking drivers and mangrove changes and a global development of policies for mangrove conservation.

Duplex Stainless Steels (DSS) and Superduplex Stainless Steels (SDSS) are an important class of stainless steels because they combine the benefits of austenite and ferrite phases, resulting in steels with better mechanical properties and higher corrosion resistance. Due to these characteristics are widely employed in various industries. However, the appearance of deleterious phases in their microstructure impairs the properties of DSS and SDSS. Among the deleterious phases, the main one is the sigma phase (σ), which can be nucleated when the steel is exposed to the temperature range between 650 °C and 900 °C, reducing its toughness and resistance to corrosion. In a previous work, Fonseca and collaborators used two descriptors of the microstructural path to analyze the formation of sigma phase (σ), SV, interfacial area per unit volume between sigma phase and austenite, and <λ>, mean chord length of sigma, both in function of the VV, volume fraction of sigma, known in the literature as microstructural partial path (MP). In this work, the contiguity ratio is applied for the first time to describe the microstructural path in the study of sigma phase precipitation in SDSS. The contiguity ratio showed that the distribution of the ferrite/sigma boundaries is homogeneous. Thus, it is reasonable to infer that one has a uniform distribution of sigma phase nuclei within the ferrite. About the kinetics of sigma phase formation, the DSS can be described by the classical JMAK equation, whereas for the SDSS, the kinetics tends to follow the Cahn model for grain edge nucleation. Finally, we present the 3D reconstruction of the sigma phase in SDSS. The results demonstrate that the sigma phase nucleates at the edges of the ferrite/austenite interfaces. Moreover, the sigma phase grows consuming the ferrite, but it is not fully interconnected.

Concatemeric viral DNA is packaged into Bacteriophage P22 procapsids via a headful packaging mechanism mediated by a molecular machine consisting of small (gp3) and large (gp2) terminase subunits. Although a negative stain reconstruction exists for the terminase holoenzyme, it is not clear how this complex binds dodecameric portal protein located at a 5-fold mismatch vertex. Herein, we describe new assemblies for the holoenzyme. Both native mass spectrometry and transmission electron microscopy reveal that the P22 terminase complex adopts three main assemblies, which include a nonameric S-terminase bound to two L-terminase 1(gp3)9:2(gp2), two nonameric S-terminase bound to five L-terminase 2(gp3)9:5(gp2), and three nonameric S-terminase bound to seven L-terminase 3(gp3)9:7(gp2). Native agarose gel electrophoresis shows that the terminase complex interacts with procapsid with mild crosslinking. These results herein illustrate the P22 terminase complex can adopt a variety of confirmations and assembly states.

Soils provide important regulating ecosystem services and have crucial implications for human well-being and environmental conservation. However, soil degradation and particularly soil erosion jeopardize the maintenance and existence of these services. This study explores the spatio-temporal relationships of soil erosion to understand the distribution patterns of sediment retention services in mainland Portugal. Based on Corine Land Cover maps from 1990 to 2018, the InVEST Sediment Delivery Ratio (SDR) model was used to evaluate the influence of sediment dynamics for soil and water conservation. Spatial differences in the sediment retention levels were observed within the NUTS III boundaries, showing which areas are more vulnerable to soil erosion processes. Results indicated that the Region of Leiria, Douro and the coastal regions have decreased importantly sediment retention capacity over the years. However, in most of the territory (77.52%) changes in sediment retention were little or not important (i.e., less than 5%). The statistical validation of the model proved the consistency of the results, highlighting the usefulness of this methodology to analyse the state of soil erosion in the country. These findings can be relevant to support strategies for more efficient land use planning regarding soil erosion mitigation practices.

Autoencoders are neural networks that have applications in denoising processes. Their use is widely reported in imaging (2D), though 1D series can also benefit from this function. Here, three canonical waveforms are used to train a neural network and achieve a signal-to-noise reduction with curves whose noise energy was above that of the signals. A real-world test is carried out with the same autoencoder subjected to a set of time series corrupted by noise generated by a zener diode, biased on the avalanche region. Results showed that, observed some guidelines, the autoencoder can indeed denoise 1D waveforms usually observed in electronics, particularly square waves found in digital circuits.

Fluxgate magnetometers require calibration methods appropriate to their application levels and particularities; however the development of fully controlled calibration procedures presents a particular challenge regarding the inevitable influence of the local geomagnetic field and other external interferences when a laboratory with magnetically shielded walls is not available. In that context, we discussed the development of an automated calibration method for fluxgate magnetometers, considering those limitations in time and space, and avoiding some of the problems commonly found in other proposed solutions for the same challenge. For this task, we designed and built a new set of high level procedures, electronic systems and software, which perform active testing and automated calibration of fluxgate magnetometers, considering some resource constraints and employing instruments commonly found in electronic calibration laboratories.

Dental-implant-supported reconstructions provide comfort and improvements in prosthetic function, adaptation, and stability over conventional treatment options. The objective of this study was to evaluate the effect of multiple cleaning solutions on dentures and their influence on the deterioration and loss of retention of overdenture attachments in a 12-month clinical-use simulation. In this way, four different brands of retentive caps made of Teflon (OT Equator, Locator®, Kerator®, and Locator R-Tx®) were immersed in five different cleaning solutions (Kukident®, Benfix®, Corega®, and Protefix®), and tap water was used as the control group, in a simulation that lasted 12 months. Data were analyzed using two-way ANOVA and a Tukey HSD. Furthermore, a Levene Test and Shapiro–Wilk tests were performed to assess the validation of the ANOVA assumptions. The statistical analysis was performed using R version 4.2.2 software with the significance level set to p &lt; 0.05. There were significant statistical differences between the different manufacturers on the retention forces of the attachment’s retentive caps. For the cleaning solution groups, different statistical results between Kukident® and Benfix® were observed. There were no significant statistical differences between Corega®, Protefix®, and tap water despite the fact that the retention forces decreased in all of them.

: The paper aims to present a conceptual framework for developing territorial development strategies based on crowdsourcing technologies to enhance intangible heritage, setting this framework within the context of Italian inner areas defined by a policy framework oriented towards the territorial development of marginal areas. For this reason, the work intends to provide the necessary background information for the development of such strategies, examining the technological aspects, a precise definition of the concept of intangible heritage, the application of a socially innovative strategy for marginal territories, and a framework of strategic actions necessary to implement solutions capable of involving local communities from the bottom up and ensuring a widespread redistribution of the benefits in the development of these technologies. The paper's objective is, therefore, to bridge the gap between different debates: technological innovation, the enhancement of intangible heritage, and territorial development based on socially innovative strategies, providing a key information framework for experts and scholars who intend to approach these topics or develop projects based on these tools and objectives.

In this article we estimate the level of Government Effectiveness-GE in 193 countries in the period 2011-2020 using data of the ESG World Bank Database. Different econometric techniques are used i.e. Panel Data with Random Effects, Panel Data with Fixed Effects, and Pooled OLS. Results show that GE is positively related among others to “Control of Corruption”, “Political Stability and Absence of Violence/Terrorism”, and negatively associated with “Percentage Annual GDP Growth”. We perform a cluster analysis with the k-Means algorithm optimized with the Elbow Method and we find the presence of four clusters. Finally, we confront eight machine learning algorithms for the prediction of GE. Results show that the Polynomial Regression is the best predictive algorithm. The value of GE is expected to growth on average by 15.97%.

In this article we estimate the level of Control of Corruption for 193 countries in the period 2011-2020 using data from the ESG World Bank Database. Various econometric techniques are applied i.e.: Panel Data with Random Effects, Panel Data with Fixed Effects, Pooled OLS, WLS. Results show that “Control of Corruption” is positively associated, among others, to “Government Effectiveness” and “Political Stability and Absence of Violence/Terrorism”, while it is negatively associated among others to “Agriculture, Forestry, and Fishing Value Added as Percentage of GDP” and “GHG Net Emissions/Removals by LUCF”. A cluster analysis implemented with the k-Means algorithm optimized with the Elbow Method shows four clusters. A confrontation among eight Machine Learning algorithms is proposed for the prediction of Control of Corruption. Polynomial Regression is the best predictor for the training data. The level of Control of Corruption is expected to growth by 10.36% on average.

In this article we investigate the impact of “Renewable Electricity Output” on green economy in the context of circular economy for 193 countries in the period 2011-2020. We use data from World Bank ESG framework. We perform Panel Data with Fixed Effects, Panel Data with Random Effects, WLS, and Pooled OLS. Our results show that Renewable Electricity Output is positively associated, among others, to “Adjusted Savings-Net Forest Depletion” and “Renewable Energy Consumption” and negatively associated, among others, to “CO2 Emission” and “Cooling Degree Days”. Furthermore, we perform a cluster analysis implementing the k-Means algorithm optimized with the Elbow Method and we find the presence of 4 clusters. Finally, we confront seven different machine learning algorithms to predict the future level of “Renewable Electricity Output”. Our results show that Linear Regression is the best algorithm and that the future value of renewable electricity output is predicted to growth on average at a rate of 0.83% for the selected countries.

We investigate the innovational determinants of “Patent Applications” in Europe. We use data from the European Innovation Scoreboard-EIS of the European Commission for 36 countries in the period 2010-2019. We use Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled OLS, WLS and Dynamic Panel. We found that the variables that have a deeper positive association with “Patent Applications” are “Human Resources” and “Intellectual Assets”, while the variables that show a more intense negative relation with Patent Applications are “Employment Share in Manufacturing” and “Total Entrepreneurial Activity”. A cluster analysis with the k-Means algorithm optimized with the Silhouette Coefficient has been realized. The results show the presence of two clusters. A network analysis with the distance of Manhattan has been performed and we find three different complex network structures. Finally, a comparison is made among eight machine learning algorithms for the prediction of the future value of the “Patent Applications”. We found that PNN-Probabilistic Neural Network is the best performing algorithm. Using PNN the results show that the mean future value of “Patent Applications” in the estimated countries is expected to decrease of -0.1%.

In this article we have estimated the value of “Opportunity Driven Entrepreneurship” in Europe. We use data from European Innovation Scoreboard-EIS of the European Commission for 36 countries in the period 2010-2019. We use Panel Data with Fixed Effects, Panel Data with Random Effects, WLS, Pooled OLS, and Dynamic Panel. Our results show that “Opportunity Driven Entrepreneurship” is positively associated, among others, to “Innovation Friendly Environment” and “Turnover Share Large Enterprises”, while it is negatively associated, among others, to “Sales Impacts” and “R&D Expenditure Business Sectors”.

In this article we estimate the value of “Non-R&D Innovation Expenditures” in Europe. We use data from the European Innovation Scoreboard-EIS of the European Commission from the period 2010-2019. We test data with the following econometric models i.e.: Pooled OLS, Dynamic Panel, Panel Data with Fixed Effects, Panel Data with Random Effects, WLS. We found that “Non-R&D Innovation Expenditures” is positively associated among others to “Innovation Index” and “Firm Investments” and negatively associated among others to “Human Resources” and “Government Procurement of Advanced Technology Products”. We use the k-Means algorithm with either the Silhouette Coefficient and the Elbow Method in a confrontation with the network analysis optimized with the Distance of Manhattan and we find that the optimal number of clusters is four. Furthermore, we propose a confrontation among eight machine learning algorithms to predict the level of “Non-R&D Innovation Expenditures” either with Original Data-OD either with Augmented Data-AD. We found that Gradient Boost Trees Regression is the best predictor for OD while Tree Ensemble Regression is the best Predictor for AD. Finally, we verify that the prediction with AD is more efficient of that with OD with a reduction in the average value of statistical errors equal to 40,50%.

In this article we investigate the determinants of “New Doctorate Graduates” in Europe. We use data from the EIS-European Innovation Scoreboard of the European Commission for 36 countries in the period 2010-2019 with Pooled OLS, Dynamic Panel, WLS, Panel Data with Fixed Effects and Panel Data with Random Effects. We found that “New Doctorate Graduates” is positively associated, among others, with “Human Resources” and “Government Procurement of Advanced Technology Products” and negatively, associated among others, with “Total Entrepreneurial Activity” and “Innovation Index”. We apply a clusterization with k-Means algorithm either with the Silhouette Coefficient either with the Elbow Method and we found that in both cases the optimal number of clusters is three. Furthermore, we use the Network Analysis with the Distance of Manhattan, and we find the presence of seven network structures. Finally, we propose a confrontation among ten machine learning algorithms to predict the value of “New Doctorate Graduates” either with Original Data-OD either with Augmented Data-AD. Results show that SGD-Stochastic Gradient Descendent is the best predictor for OD while Linear Regression performs better for AD.

The article affords the question of lifelong learning in Europe using data from the European Innovation Scoreboard-EIS in the period 2010-2019 for 36 countries. The econometric analysis is realized using WLS, Dynamic Panel, Pooled OLS, Panel Data with Fixed Effects and Random Effects. The results show that lifelong learning is, among other variables, positively associated to “Human Resources” and “Government procurement of advanced technology products” and is negatively associated, among others, to “Average annual GDP growth” and “Innovation Index”. A clusterization is realized using the k-Means algorithm with a confrontation between the Elbow Method and the Silhouette Coefficient. Subsequently, a Network Analysis was applied with the distance of Manhattan. The results show the presence of 4 complex and 2 simplified network structures. Finally, a comparison was made among eight machine learning algorithms for the prediction of the value of lifelong learning. The results show that the linear regression is the best predictor algorithm and that the level of lifelong learning is expected to growth on average by 1.12%.

In the following article, the value of the "Knowledge Intensive Services Exports in Europe" in 36 European countries is estimated. The data were analyzed through a set of econometric models or: Poled OLS, Dynamic Panel, Panel Data with Fixed Effects, Panel Data with Random Effects, WLS. The results show that “Knowledge Intensive Services Exports” is negatively associated, among others, with "Buyer Sophistication", "Government Procurement of Advanced Technology Products", and positively associated with the following variables i.e. "Innovation Index", "Sales Impacts" and "Total Entrepreneurial Activity". Then a clusterization with k-Means algorithm was made with the Elbow method. The results show the presence of 3 clusters. A network analysis was later built and 4 complex network structures and three structures with simplified networks were detected. To predict the future trend of the variable, a comparison was made with eight different machine learning algorithms. The results show that prediction with Augmented Data-AD is more efficient than prediction with Original Data-AD with a reduction of the mean of statistical errors equal to 55,94%.

The following article analyzes the determinants of the innovation index in Europe. The data refer to the European Innovation Scoreboard-EIS of the European Commission for the period between 2010 and 2019 for 36 countries. The data are analyzed using the following econometric techniques: Panel Data with Random Effects, Panel Data with Fixed Effects, Dynamic Panel Data, Pooled OLS, WLS. The results show that the Innovation Index is negatively connected to some variables, among which the most significant are "GDP per capita", "R&D expenditure public sector", "Venture capital", "Tertiary education", and positively connected to some variables among which the most relevant are: "Government procurement of advanced technology products", "Average annual population growth", "Finance and support", "Human resources", "Marketing or organisational innovators", "Linkages". A clustering was then carried out using the unsupervised k-Means algorithm optimized with the Silhouette coefficient which shows the presence of 2 clusters per value of the Innovation Index. Eight machine learning algorithms has been used for prediction with real data. The Tree Ensemble Regression algorithm has been chosen as best performer. A further prediction has been made with the augmented data. The result shows that the best performing algorithm is Linear Regression with an innovation index value predicted to grow by approximately 3.38%.

The Combined Inversion of Surface and AeRosols (CISAR) algorithm for the joint retrieval of surface and aerosol single scattering properties has been further developed in order to extend the retrieval to clouds and overcome the need for an external cloud mask. Pixels located in the transition zone between pure cloud and pure aerosol are often discarded by both aerosol and cloud algorithms, despite being essential for studying aerosol-cloud interactions, which still represent the largest source of uncertainty in climate predictions. The proposed approach aims at filling this gap and deepening the understanding of aerosol properties in cloudy environments. The new CISAR version is applied to Sentinel-3A/SLSTR observations and evaluated against different satellite products and ground measurements. The spatial coverage is greatly improved with respect to algorithms processing only pixels flagged as clear sky by the SLSTR cloud mask. The continuous retrieval of aerosol properties without any safety zone around clouds opens new possibilities for studying aerosol properties in cloudy environments.

One of the most broadly used models for membrane fouling is the Hermia model, which separates this phenomenon into four blocking mechanisms, each with an associated parameter n. These mechanisms are complete blocking (n=2), intermediate blocking (n=1), standard blocking (n=3/2), and cake formation (n=3/2). The original model, which was obtained through experimental data, is given by an Ordinary Differential Equation (ODE) dependent on n. At the time, this ODE was only solved for these four values of n, which limits the effectiveness of the model when adjusted to experimental data. This paper aims to not only mathematically prove the original Hermia model but also to broaden the scope of this model for any real number n by using the original ODE, the equations of fluid mechanics, and the properties of single and multivariable calculus. The extended Hermia model (EHM) is given by a power law for any n≠2 and is given by an exponential function at n=2. To better test the model, we have performed the model fitting of the EHM and compared its performance to the original four pore-blocking mechanisms in 6 micro- and ultrafiltration examples. In all examples, the EHM performed consistently better than the four original pore-blocking mechanisms. Changes in the blocking mechanisms concerning transmembrane pressure (TMP), crossflow rate (CFR), crossflow velocity (CFV), membrane composition, and pretreatments are also discussed.

Synthesis of nickel oxide nanoparticles (NiO NPs) is a low cost and ecofriendly route that brings great benefits over chemical and physical methods of synthesis. Our approach consisted of using aqueous extracts from treated waste of Manihot Esculenta (Cassava) as reducing, stabilizing and capping agents for the synthesis of NiO NPs. The results proved this approach might be a viable alternative. Extracts were mixed during 30 minutes with Ni(NO3)2.6H2O leading to NPs formation. NiO NPs were characterized through FTIR, XRD, TEM and Raman spectroscopy. NiO NPs showed different shapes and sizes around 5-10 nm in agreement with the particle size calculated by XRD Scherrer equation.

Titica vine fibers (TVFs) extracted from aerial roots of Heteropsis flexuosa, from the Amazon region, were 10, 20, 30 and 40 vol% incorporated into an epoxy matrix for applications in ballistic multilayered armor systems (MASs) and stand-alone tests for personal protection against high-velocity 7.62 mm ammunition. The back-face signature (BFS) depth measured for composites with 20 and 40 vol% TVFs used as an intermediate layer in MASs was 25.6 and 32.5 mm, respectively, below the maximum limit set by the international standard. Fracture mechanisms found by scanning electron microscopy (SEM) attested the relevance of increasing the fiber fraction for applications in MASs. The results of stand-alone tests showed that the control (0 vol%) and samples with 20 vol% TVFs absorbed the highest impact energy (Eabs) (212 – 176 J), and consequently limit velocity (VL) values (213 – 194 m/s), when compared with 40 vol% fiber fractions. However, the macroscopic evaluation found that the plain epoxy, referring to control samples, shattered completely. In addition, for 10 and 20 vol% TVFs, the composites were fragmented or exhibited delamination fracture, which contributed to their physical integrity. On the other hand, the composite with 30 and 40 vol% TVFs, whose Eabs and VL varied between 166 – 130 J and 189 – 167 m/s, respectively, showed the best dimensional stability. The SEM images indicated that for composites with 10 and 20 vol% TVFs the fracture mode was predominantly brittle, due to the greater performance of the epoxy resin and the discrete action of the fibers. While for composites with 30 and 40 vol% TVFs, there was the activation of more complex mechanisms such as pullout, shearing and fiber rupture.

Neonatal palliative care (NPC) is an integrated and holistic approach that is an integral part of the contemporary neonatal treatment delivery paradigm. It is the highest fulfillment of the notion of beneficence (doing or creating 'good') that has otherwise been neglected/underestimated by the focus of modern medicine on technology and instrumental treatments by its commitment to alleviating patient pain and quality of life. For decades, the Double-Effect Doctrine (DDE) has been used to consider and address a range of ethically questionable circumstances, often at the end of life, including euthanasia, termination of pregnancy to save maternal life, and morally justified warfare. The theory has continued to be mired in controversy as ethicists, legal scholars, theologians, and philosophers discuss the abstract concepts of moral reasoning, purpose, foresight, and other underlying moral theories. In this paper, the moral theory of DDE is discussed in its clinical application to the ethical decision-making process in neonatal palliative and end-of-life treatment, specifically 1) the administration of opioids as required for symptom control and 2) the use of palliative sedation for intractable symptom care.

Abstract: This paper is focusing on the stabilisation of soil using jute fibre as soil stabilizer. Stabilisation is the process of modifying the properties of a soil to improve its engineering performance and used it for a variety of engineering works. This study examines the potential of soil stabilization with jute fibre when it is cut into roughly 30mm lengths as stabilizer. The varying percentages like 0.5%, 1%, 1.5 and 2% of pieces of jute fibre were used and mixed it with soil. The laboratory tests such as California Bearing Ratio (CBR) test, modified compaction tests and direct shear strength tests have been conducted to observe the change in engineering properties of soil. On the basis of the experiments performed, it can be concluded that the stabilization of soil using 30mm pieces of jute as stabilizer improves the strength characteristics of the soil so that it becomes usable as one of the reinforcing material for the construction of roadways, parking areas, site development projects, airports and many other situations where sub-soils are not suitable for construction.

The efficacy of the product development process is measured by the ability to launch a project with product and production process specifications that could guarantee that the manufacturing can produce it with the least impact. If a problem is detected late, they bring consequences beyond the high cost of the solution, if related to physical ergonomics, which will influence the well-being of operators, productivity, and quality. Virtual Reality (VR) and Digital Human Modeling (DHM) are ones of the enabling technologies of Industry 4.0 and has already been applied on a large scale in industries such as automotive, construction, and aeronautics. However, even though the huge applications, these technologies are not yet applied by these industries for the analysis of physical ergonomics during product development phases. This study aims to characterize the state of the art and technology about the application of Virtual Reality and Digital Human Modeling for the physical ergonomics analysis in the during product development phases in the industry through a systematic review of the literature and patents. In patent documents recovery, we used Derwent Innovation database. The research is based on searching the selected terms in the title, summary, and claims of the documents through a search strategy containing IPC code and keywords. In articles recovery, we searched ScienceDirect, Springer, and IEEExplore databases for scientific publications. The search resulted in 311 patents documents and 16 articles in the scientific database. This study analyzed the patents to map out the technological progress in this area, where we found in the charts and data an increasing number of publications per year and a spread application with a considerable number of new technologies presented in these recent patents. The literature review indicated that Virtual Reality technology complements the Digital Human Modeling during physical ergonomics analysis for manufacturing process already designed. The majority of research on the use of VR and DHM technologies for physical ergonomics analysis focus on the automotive industry and the ergonomic assessment of workstations and current processes. Further research is needed to investigate how Virtual Reality and Digital Human Modeling might assist in the understanding of physical ergonomics in certain tasks throughout the product development process, such as the simulation of worker posture or effort when assembling parts.

The predictability of wind information in a given location is essential for the evaluation of a wind power project. Predicting wind speed accurately improves the planning of wind power generation, reducing costs and improving the use of resources. This paper seeks to predict the mean hourly wind speed in anemometric towers (at a height of 50 meters) at two locations: a coastal region and one with complex terrain characteristics. To this end, the Holt-Winters (HW), Artificial Neural Networks (ANN) and Hybrid time-series models were used. Observational data evaluated by the Modern-Era Retrospective analysis for Research and Applications-Version 2 (MERRA-2) reanalysis at the same height of the towers. The results show that the hybrid model had a better performance in relation to the others, including when compared to the evaluation with MERRA-2. For example, in terms of statistical residuals, RMSE and MAE were 0.91 and 0.62 m/s, respectively. As such, the hybrid models are a good method to forecast wind speed data for wind generation.

The hazard and the carcinogenic risks due to the exposure to some potentially toxic elements by the Santiago Island (Cape Verde) population where calculated, considering soil ingestion, inhalation and dermal contact as exposure pathways. The topsoil of Santiago Island is enriched in Co, Cr, Cu, Ni, V, Zn, Mn and Cd to upper crust values. Hazard indices (HI) were calculated for these metals and As exposures, of Santiago Island population and the calculations were performed for children and adults. For children HI are higher than 1 for Co, Cr and Mn. So there is indication of potential non-carcinogenic risk for children, due to the high Co (HI=2.995), Cr (HI=1.329) and Mn (HI=1.126), values in soils. For the other elements and for adults there is no potential non-carcinogenic risk. Cancer risk was calculated for As, Cd, Cr and Ni exposures, for adults and children and the results are always lower than the carcinogenic target risk of 1x10-6, for As, Cd, and Ni. However, cancer risk are higher than the carcinogenic target risk for Cr, for adults. Regarding As, for children the fraction due to Riskingestion represents 51.6%, while Riskinhalation represents 48.0% and Riskdermalcontact represents only 0.4% of total risk. For adults Riskinhalation represents 81.3%, Riskingestion represents 16.6% and Riskdermal contact represents 2.1%. These results reflect the higher daily ingestion dose for children and the higher inhalation rate and higher dermal contact surface for adults. For the other elements and for adults the cancer risk due to Cr, Ni and Cd inhalation is always higher than for children, reflecting the higher inhalation rate for adults.

The complexity of the current social context leads healthcare professionals to deal daily with users who are increasingly varied in terms of culture, language and religion. Transcultural nursing is a practical discipline that aims to respond to the patient's need for assistance, but must not ignore the study and knowledge of the patient, with his history, his culture, but also with his expectations, in order to respond to his care needs with effectiveness, efficiency, adequacy and respect. This work originates from the desire to investigate the "state of health" of the relationship between nurses/health workers and migrant patients, researching the variables that improve or worsen it. It aims to investigate how diversity affects the care provided to migrant people, highlighting the difficulties felt by nurses and define the areas of possible care improvements. It seems possible to affirm that the nurses and health workers interviewed still respond to the problem of migrant patients with a strong spirit of participation and availability, without, however, this motivational charge being accompanied by a widespread "nursing" awareness of the problem.

Atopic dermatitis (AD) is a chronic eczematous inflammatory disease that may arise from environmental, genetic and immunological factors. Despite the efficacy of current treatment options like corticosteroids, such approaches are mainly focused on symptoms relief, and may present certain undesirable side effects. In recent years, investigation regarding isolated natural compounds, oils, mixtures and/or extracts, have gained scientific attention because of their high efficiency and moderate to low toxicity. Despite their promising therapeutic effects, the applicability of such natural health care solutions is somehow limited by their instability, poor solubility, and low bioavailability. Therefore, novel nanoformulation-based systems have been designed to overpass these limitations, thus enhancing the therapeutic potential, by promoting the capacity of these natural drugs to properly exert their action in AD-like skin lesions. For the best of our knowledge, this is the first literature review that focused on summarizing the last nanoformulation-based solutions loaded with natural ingredients, and specifically for the management of AD. We suggest that future studies should focus on robust clinical trials that may confirm the security and effectiveness of such natural-based nanosystems, thus paving the way for more reliable AD treatments.

In the bioprinting process, controlling the motion of bioprinters involves a computer-aided design (CAD) model, converting that model into g-code, and transmitting the motion commands to the bioprinters. The g-code file contains information about the motion of the axes and can be generated using various software. However, a challenge arises when using g-code between different bioprinters. Therefore, the objective of this study was to propose a reliable method for analyzing the differences in 3D bioprinting devices by parameterizing a crucial variable for extrusion bioprinting: the E-value. Four bioprinters from three different manufacturers were used to investigate the extrusion behavior when subjected to the same E-value.

Ventricina del Vastese is a traditional dry fermented sausage from Central Italy not yet charac-terized for occurrence, identity and safety of coagulase negative staphylococci (CNS), a bacterial group technologically important for this kind of products. Therefore, in this study, 98 CNS isolates from four manufacturers were differentiated by Repetitive element palindromic PCR (Rep-PCR) and identified by 16S rRNA gene sequencing. These were examined for genes encoding biogenic amine (BA) production, resistance to aminoglycosides, β-lactams and tetracyclines and staphylococcal enterotoxins (SEs). Staphylococcus succinus (55%) predominated, followed by S. xylosus (30%), S. epidermidis (7.4%), S. equorum (3.1%), S. saprophyticus (3.1%) and S. warneri (1%). One S. succinus subsp. casei isolate was slightly β-hemolytic. SEs and the histidine decarboxylase gene hdcA were not detected, while the tyrosine decarboxylase gene tdcA was detected in four S. xylosus isolates. A blaZ beta-lactamase gene and tetracycline resistance genes tetK (six isolates) and tetA (one isolate also bearing tetK) were found, respectively. However, fewer hazardous and AR traits compared to CNS examined in other studies were found, as a probable consequence of using meat from animals reared in conditions that allow to minimize pathogen circulation and antibiotic use. Therefore, appropriate production conditions can reduce these threats.

This study was aimed to examine the recent trends of antibiotic resistance (AR) prevalence in Staphylococcus aureus isolated from milk of animals with clinical mastitis in areas of the Abruzzo and Molise regions in central Italy. Fifty-four S. aureus isolates could be obtained from routine testing for clinical mastitis agents carried out in the author institution in years 2021 and 2022. These were analyzed for phenotypic resistance to eight antibiotics recommended for testing by European norms and belonging to the antibiotic classes used for mastitis treatment in milk producing animals. Moreover, the presence of 14 transferable genetic determinants encoding resistance to the same antibiotics was analyzed by qPCR tests developed in this study. Phenotypic resistance to non-β-lactams was infrequent, with only one 2022 isolate resistant to clindamycin. However, low level resistance to the β-lactam cefoxitin was observed in 59.2% isolates in both years making these isolates classifiable as methicillin resistant. The AR genotypes detected were blaZ gene (50% 2021 isolates and 44.4% 2022 isolates), ermC/T- aphA3-blaZ (one 2021 isolate), ant6-ermC/T-aphA3-blaZ (one 2021 isolate), ermB-blaZ (one 2022 isolate) and mecA-mph (one 2022 isolate). An interview to the veterinarians who conferred the samples, regarding antimicrobials prescribed for mastitis treatment and criteria of usage, indicated a possible causal relation with the AR test results. The low prevalence of AR genotypes, not increasing in time, most probably reflecting the reported management of antibiotic therapies in farms. However, the frequently observed cefoxitin resistance needs to be explained genotypically, further monitored and limited by modifying antibiotic usage practices. The identification of a mecA positive isolate in 2022 suggests to investigate further if this genotype is emerging locally.

Latest evidence revealed a possible association between Parkinson’s disease (PD) and periodontitis. We explored the causal relationship of this association through two-sample Mendelian randomization (MR) in European ancestry populations. To this end, we used openly accessible data of genome-wide association studies (GWAS) on PD and periodontitis. As instrumental variables for periodontitis, seventeen single-nucleotide polymorphisms (SNPs) from a GWAS of periodontitis (1817 periodontitis cases vs. 2215 controls) and forty-five SNPs from a GWAS of PD (20,184 cases and 397,324 controls). Eight non-overlapping SNPs of periodontitis from an additional GWAS assisted in the validation of association being studied. Multiple approaches of MR were carried-out. There was no evidence of genetic liability of periodontitis being associated with a higher risk of PD (B= -0.0003, Standard Error [SE] 0.0003, P = 0.26). The eight independent SNPs (B= -0.0000, SE 0.0001, P = 0.99) validated this outcome. We found no association of genetically primed PD towards periodontitis (B= -0.0001, SE 0.0001, P = 0.19). This MR study found no conclusive evidence to support a bidirectional causal genetic liability between PD and periodontitis. Further GWAS studies are needed to confirm the consistency of these results.

Recent studies supported a clinical association between Parkinson’s Disease (PD) and periodontitis. Hence, investigating possible protein interactions between these two conditions is of interest. In this study, we conducted a protein-protein network interaction analysis with recognized genes encoding proteins for PD and periodontitis. Genes of interest were collected via GWAS database. Then, we conducted a protein interaction analysis using STRING database, with a highest confidence cut-off of 0.9. Our protein network casted a comprehensive analysis of potential protein-protein interactions between PD and periodontitis. This analysis may underpin valuable information for new candidate molecular mechanisms between PD and periodontitis and may serve new potential targets for research purposes. These results should be carefully interpreted giving the limitations of this approach.

The safe and reliable operation of autonomous vehicles in complex and dynamic environments is heavily dependent upon exteroceptive and proprioceptive sensors. A LiDAR system generates accurate 3D point clouds, which are crucial for the detection, classification, and tracking of multiple targets. LiDAR data, however, presents significant challenges due to its density, noise, and varying sampling rates. In this study, various clustering and MTT techniques for LiDAR point clouds will be identified and classified within the context of autonomous driving to assess the state-of-the-art methods’ key challenges and their performances. We have categorized clustering and MTT methods used in AV applications, identified research gaps and challenges and analyzed existing algorithms and their challenges in detail. Researchers and practitioners in the field of autonomous driving will find this review to be a valuable resource.

Background: Triple negative breast cancer (TNBC) is the most aggressive form of breast cancer with poor outcomes. The molecular basis of TNBC remains poorly understood. The objective of this study was to explore the relationship between obesity and TNBC in premenopausal and postmenopausal Caucasian women using whole genome transcription profiling. Methods: We compared gene expression levels of tumor samples drawn from normal weight, overweight and obese in pre and postmenopausal women diagnosed with TNBC. We performed hierarchical clustering to assess similarity in patterns of gene expression profiles, and conducted network and pathway analysis to identify molecular networks and biological pathways. Results: We discovered gene signatures distinguishing normal weight from obese, normal weight from overweight and overweight from obese individuals in both premenopausal and postmenopausal women. The analysis revealed molecular networks and biological pathways dysregulated in response to obesity. Among the discovered pathways included the unfolded protein response, endoplasmatic reticulum stress, B cell receptor and the autophagy signaling pathways in obese premenopausal women and the integrin, axonal guidance, ERK/MAPK and Glutathione biosynthesis signaling pathways obese postmenopausal women. Conclusions: The results suggest that both overweight and obesity are associated with TNBC, highlighting the need for conformation of these results in independent studies.

In this study, the distribution patterns of southern horse mackerel are examined using commercial fine-scale resolution data. Using landings by size category and VMS data from the Portuguese commercial bottom trawl fishery, which consistently targets horse mackerel, this study provides a comprehensive analysis of horse mackerel age distributions spanning a decade (2010-2020). Importantly, the study addresses potential biases in commercial effort data and establishes the usefulness of commercial bottom-trawl gear as a suitable method for sampling and evaluate southern horse mackerel stock dynamics. Ordered regression models were applied to allow for the modelling of the distribution of multiple age categories and investigate spatio-temporal migrations off the Portuguese coast. Southern horse mackerel shows a widespread age distribution range and stable abundance with indications of seasonal and spatial patterns in the distribution of specific age groups. The insights derived from this research contribute valuable knowledge for understanding the dynamics and distribution patterns of fish populations.

Deep learning is emerging in many industrial sectors in hand with big data analytics to streamline production. In the biomanufacturing sector, big data infrastructure is lagging comparatively to other industries. A promising approach is to combine Deep Neural Networks (DNN) with prior knowledge in Hybrid Neural Network (HNN) workflows that are less dependent on the quality and quantity of data. This paper reviews published articles over the past 30 years on the topic of HNN applications to bioprocesses. It revealed that HNNs were applied to various bioprocesses, including microbial cultures, animal cells cultures, mixed microbial cultures, and enzyme biocatalysis. HNNs were mainly applied for process analysis, process monitoring, development of software sensors, open- and closed-loop control, batch-to-batch control, model predictive control, intensified design of experiments, quality-by-design, and recently for the development of digital twins. Most previous HNN studies combined shallow Feedforward Neural Networks (FFNNs) with physical laws, such as macroscopic material balance equations, following the semiparametric design principle. Only recently, deep HNNs based on deep FFNNs, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM) networks and Physics Informed Neural Networks (PINNs) have been reported. The biopharma sector is currently a major driver but applications to biologics quality attributes, new modalities, and downstream processing are significant research gaps.

We have estimated the level of Risk Weighted Assets among 30 countries in Europe, in 30 trimesters, using data of the European Banking Authority-EBA of 139 variables. We perform an econometric model using Pooled OLS, Panel Data with Fixed Effects, Panel Data with Random Effects, Weighted Least Squares. We found that Risk Weighted Assets is negatively associated, among others, to the level of NFC loans in mining and quarrying, in public administration and defence, and in financial and insurance activities and positively associated, among others to distribution of NFC loans in human health services and social work activities, in education and the level of net fee and commission income. Furthermore, we apply a cluster analysis with the k-Means algorithm, and we find the presence of two clusters. A comparison was then made between eight different machine learning algorithms for predicting the value of the RWAs and we found that the best predictor is the linear regression. The RWA value is predicted to increase by 1.5%.

The determinants of the presence of “Foreign Doctorate Students” among 36 European Countries for the period 2010-2019 are analyzed in this article. Panel Data with Fixed Effects, Random Effects, WLS, Pooled OLS, and Dynamic Panel are used to investigate the data. We found that the presence of Foreign Doctorate Students is positively associated to “Attractive Research Systems”, “Finance and Support”, “Rule of Law”, “Sales Impacts”, “New Doctorate Graduates”, “Basic School Entrepreneurial Education and Training”, “Tertiary Education” and negatively associated to “Innovative Sales Share”, “Innovation Friendly Environment”, “Linkages”, “Trademark Applications”, “Government Procurement of Advanced Technology Products”, “R&D Expenditure Public Sectors”. A cluster analysis was then carried out through the application of the unsupervised k-Means algorithm optimized using the Silhouette coefficient with the identification of 5 clusters. Finally, eight different machine learning algorithms were used to predict the value of the "Foreign Doctorate Students" variable. The results show that the best predictor algorithm is the "Tree Ensemble Regression" with a predicted value growing at a rate of 114.03%.

The determinants of enterprises providing ICT training in Europe are analyzed in this article. Data are collected from the European Innovation Scoreboard-EIS of the European Commission for 36 European countries in the period 2000-2019. Data are analyzed with Panel Data with Fixed Effects, Panel Data with Random Effects, Dynamic Panel, WLS and Pooled OLS. Results show that the number of enterprises providing ICT training in Europe is positively associate with “Innovation Index”, “Innovators”, “New Doctorate Graduates”, “Tertiary Education” and negatively associated with “Government Procurement of Advanced Technology Products”, “Human Resources”, and “Marketing or Organisational Innovators”. In adjunct a cluster analysis is performed by using k-Means algorithm optimized with the Silhouette Coefficient and we find the presence of four clusters. Finally, we use eight different machine learning algorithms to predict the value of the enterprises providing ICT training in Europe. We found that the Simple Tree Regression is the best predictor and that the number of enterprises providing ICT training in Europe is expected to growth of the 5,02%.

Genetic and environmental factors including lifestyle are thought to play an important role in the pathophysiology of rheumatoid arthritis (RA). There is evidence that diet can enhance the inflammatory response in genetically predisposed individuals. On the other hand, certain types of diets are thought to alleviate RA symptoms due to their anti-inflammatory and antioxidant activities. Also, natural compounds with potential effectiveness in RA management belong to different chemical classes such as flavonoids, polyphenols, carotenoids, and alkaloids with their antioxidant characteristics. In this paper, the nutritional approaches to prevent or extenuate the disease progress were examined in the light of current literature. Mediterranean and vegan diets equally have been shown to exhibit positive effects on RA as the consumption of dietary fiber, antioxidants and anti-inflammatory compounds from fruits, vegetables, grains, nuts, and seeds are high. Whereas Mediterranean diet additionally includes beneficial nutrients of animal origin such as omega-3 polyunsaturated fatty acids from fish and seafood, patients on vegan diet need to be monitored closely for intake of all critical nutrients. Certain calorie restrictions and intermittent fasting diets have been shown to benefit RA patients although there is an obvious need for further studies to establish solid evidence-based recommendations and guidelines. The research data available strongly suggest that dietary approaches may help delay the onset of RA and/or improve symptoms and thus nutrition should be routinely addressed to facilitate management of the disease.

Introduction: The purposes of this study were to seek for overall ratio (OR) and anterior ratio (AR) patients data in normal occlusion and Angle’s malocclusion studies, and to assess if such results support Bolton’s standards as general references. Methods: Pubmed, Medline, CENTRAL and Scholar databases were searched up to February 2018 (CRD42018088438). Gray literature was explored through OpenGray. Non-randomized clinical studies, published in English and assessing Bolton’s OR and AR in normal occlusion and Angle’s malocclusion groups (Class I, Class II, Class II division 1, Class 2 division 2, Class III) patients were included. OR and AR means and standard deviations (SD) were collected. Potential covariates (study design, publication year, country where the study was conducted, number of cases, gender, mesiodistal measurement method, and calibration method) were also extracted. The National Health Heart Lung, and Blood Institute’s Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies was used to assess each included studies quality. Pairwise Random-Effects and Multilevel Bayesian Network Meta-Analyses were used to synthesize available data. Results: Fifty-two observational studies were included (8872 participants; male/females 2674/3272; 16 studies lacked gender information). For normal occlusion, global pooled estimates for OR and AR means were 91.74% (95% CI: 91.37-92.10) and 78.24% (95% CI: 77.85-78.63), respectively. We could identify on Angle’s Class III patients meaningful OR and AR mean deviations from normal occlusion (0.89, 95% credible interval [CrI], 0.66-1.12, and 0.66, 95% CrI, 0.38-0.94, respectively), while on Class I patients we found a meaningful mean deviation from normal occlusion only for OR (0.25, 95% CrI, 0.03-0.47). Concerning gender impact, male patients presented higher OR (0.30, 95% CI 0.00-0.59) and AR (0.41, 95% CI 0.00-0.83) mean values than females in Class I. Conclusions: The results show that global pooled OR and AR mean values for normal occlusion patients are slightly above Bolton’s original values. Class I, for OR mean values, and Class III, for both OR and AR, are proportionally larger than normal occlusion patients. Gender had almost no impact on teeth mesiodistal proportion.

Genetic Programming (GP) is a powerful Machine Learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in Remote Sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs Feature Construction by evolving hyper-features from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyper-feature from satellite bands to improve the classification of land cover types. We add the evolved hyper-features to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (Decision Trees, Random Forests and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyper-features to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI and NBR. We also compare the performance of the M3GP hyper-features in the binary classification problems with those created by other Feature Construction methods like FFX and EFS.

Traditional dry fermented meat products are obtained artisanally in many countries, where they represent a gastronomic heritage well distinguished from industrial counterparts. This food category is most often obtained from red meat, a food commodity that is under attack because of evidences of increased risk of cancer and degenerative diseases with high consumption. However, traditional fermented meat products are intended for moderate consumption and gastronomic experience and, as such, their production must be continued also to safeguard culture and economy of the geographical areas of origin. In this review, the main risks attributed to these products are considered and how these risks are minimized by following precise production norms, respectful of the ancient manufacturing processes is described. Moreover, results obtained in studies reporting use of autochthonous microbial cultures of lactic acid bacteria (LAB), coagulase negative staphylococci (CNS), Debaryomyces hansenii and Penicillium nalgiovense to improve safety and quality of traditional fermented meats are summarized. The role of these products as a source of microorganisms that can be beneficial to the host are also considered. From the results of the studies reviewed here it appears that the development of autochthonous cultures for these foods ensure safety and stabilize sensory characteristics with possibility to be extended to the large variety of traditional productions.

Evidence regarding the effect of the onlay preparation design for different CAD/CAM restorative materials considering the preservation of cusps is lacking. Molars were 3D modeled in four preparation designs for onlay restoration: traditional design with functional cusp coverage (TFC), non-retentive design with functional cusp coverage (NFC), traditional design with non-functional cusp coverage (TNFC), non-retentive design with non-functional cusp coverage (NNFC). Restorations were simulated with two CAD/CAM restorative materials: LD – lithium disilicate (IPS e.max CAD) and RC - resin composite (GrandioBloc). A 100 N axial load was applied to the occlusal surface simulating the centric contact point. Von Mises (VM) and maximum principal (Pmax) stresses were evaluated for restorations, cement layer and dental substrate. The non-retentive preparation design reduced the stress concentration in the tooth structure in comparison to the conventional retentive design. For LD onlays, the stress distribution on the restoration intaglio surface showed that the preparation design as well as the prepared cusp, influenced the stress magnitude. The non-retentive preparation design provided better load distribution in both restorative materials and more advantageous for tooth structure. The resin composite restoration on non-functional cusp is recommended when functional cusp is preserved, in order to associate conservative dentistry and low stress magnitude.

Understanding and controlling reproductive cycles and gonadal maturation in cultivated species are crucial in aquaculture. The sea urchin Paracentrotus lividus, known for its edible gonads, requires careful maturation control for both reproduction and commercialization. This study explores the impact of temperature, photoperiod, and diet on gonadal development to enhance year-round aquaculture practices. Using two independent cultivation systems, we manipulated environmental conditions to mimic different seasons. Sea urchins were exposed to natural or manipulated temperature and photoperiod conditions and fed either natural (Ulva spp.) or formulated diets. The gonadosomatic index (GI) and histological analysis assessed gonadal development. Results revealed a clear correlation between environmental conditions, diet, and gonadal maturation. Manipulated conditions accelerated maturation, with sea urchins showing advanced stages compared to natural conditions. Furthermore, sea urchins fed formulated diets exhibited higher GI values, indicating enhanced maturation. Histological analysis confirmed accelerated maturation, particularly in females. The study underscores the feasibility of controlling gonadal maturation through environmental manipulation and diet, enabling year-round marketable gonad production. Providing formulated diets rich in polyunsaturated fatty acids, notably docosahexaenoic acid (DHA), enhances commercial value. These findings optimize aquaculture practices for P. lividus, highlighting its adaptability to yield-maximizing production strategies. Understanding and controlling reproductive cycles and gonadal maturation in cultivated species are crucial in aquaculture. The sea urchin Paracentrotus lividus, known for its edible gonads, requires careful maturation control for both reproduction and commercialization. This study explores the impact of temperature, photoperiod, and diet on gonadal development to enhance year-round aquaculture practices. Using two independent cultivation systems, we manipulated environmental conditions to mimic different seasons. Sea urchins were exposed to natural or manipulated temperature and photoperiod conditions and fed either natural (Ulva spp.) or formulated diets. The gonadosomatic index (GI) and histological analysis assessed gonadal development. Results revealed a clear correlation between environmental conditions, diet, and gonadal maturation. Manipulated conditions accelerated maturation, with sea urchins showing advanced stages compared to natural conditions. Furthermore, sea urchins fed formulated diets exhibited higher GI values, indicating enhanced maturation. Histological analysis confirmed accelerated maturation, particularly in females. The study underscores the feasibility of controlling gonadal maturation through environmental manipulation and diet, enabling year-round marketable gonad production. Providing formulated diets rich in polyunsaturated fatty acids, notably docosahexaenoic acid (DHA), enhances commercial value. These findings optimize aquaculture practices for P. lividus, highlighting its adaptability to yield-maximizing production strategies.

Introdução: Os fatores de risco vasculares são decisivos na evolução da doença aterosclerótica e a ecografia Doppler carotídea e vertebral permite acompanhar o seu aparecimento e evolução. A medida do espessamento da parede arterial permite o diagnóstico precoce da doença potencializando seu tratamento e controle dos fatores de risco vasculares. Objetivo: Analisar a presença de doença ateromatosa em indivíduos com idade > 65 anos e compreender sua transformação com fatores de risco vascular. Materiais e Métodos: Estudo observacional transversal, em indivíduos com idade > 65 anos manifestados ao ecoDoppler carotídeo entre 1º de janeiro de 2012 e 31 de dezembro de 2021. Foi calculado o índice médio-intimal, a presença de placas ateromatosas, sua repercussão hemodinâmica e fatores de risco vascular foram registrados. Resultados: Obteve-se uma amostra de 5885 indivíduos, sendo 41,8% do sexo feminino e 58,2% do sexo masculino. A média de idade foi de 76,59 anos, variando entre 65 e 98 anos (+6,69). A hipertensão arterial foi a mais prevalente 81,3%. Houve relação positiva significativa entre índice médio-intimal, idade e história pessoal de doenças cardiovasculares e cerebrovasculares (p=0,001). Na presença de placas, sexo masculino, hipertensão arterial, diabetes, dislipidemia, tabagismo e história pessoal de doenças cardiovasculares e cerebrovasculares apresentadas de forma positiva. Conclusões: Nesta amostra, fatores de risco vascular não modificáveis ​​parecem ser determinantes na presença de aumento da espessura da parede arterial. Na presença de sinais de um estágio mais avançado da doença aterosclerótica, os fatores de risco vascular modificáveis ​​​​são decisivos,

This literature review was aimed to identifying the opportunistic bacterial pathogens that can be transmitted by contaminated food and represent a current threat for patients particularly suscep-tible to infections because of underlying conditions or predisposing factors. The analysis was focused on recent case or outbreak reports, and systematic reviews published in years 2019 - 2023 and resulted in sorting 24 bacterial groups comprising genera or species able to cause a variety of systemic or invasive infections if ingested with food or drinking water. These included both bacteria known to cause mild infections in immunocompetent persons and bacteria considered innocuous that are used in food fermentation or as probiotics. No recent cases from dietary routes were reported for bacteria of critical importance as nosocomial pathogens and widely occurring in food products, primarily, Acinetobacter baumanii and Klebsiella pneumoniae. However, the very first sources of their introduction in the clinical environment still need to be estab-lished. In many instances, risky dietary habits such as eating raw fish, seafood, raw meat, unpasteurized milk and derived products or the lack of control on fermentation processes led to the reported illnesses, pointing out to the necessity to improve the hygiene of production and consumer’s awareness of risks.

Deploying UAVs as aerial base stations is an exceptional approach to reinforce terrestrial infrastructure owing to their remarkable flexibility and superior agility. However, it is essential to design their flight trajectory effectively to make the most of UAV-assisted wireless communications. This paper presents a novel method for improving wireless connectivity between UAVs and terrestrial users through effective path planning. This is achieved by developing a goal-directed trajectory planning method using active inference. First, we create a global dictionary using TSPWP instances executed on various training examples. This dictionary contains letters representing available hotspots, tokens representing local paths, and words depicting complete trajectories and hotspot order. By using this world model, the UAV can understand the TSPWP’s decision-making grammar and how to use the available letters to form tokens and words at various levels of abstraction and time scales. With this knowledge, the UAV can assess encountered situations and deduce optimal routes based on the belief encoded in the world model. Our proposed method outperforms traditional Q-learning by providing fast, stable, and reliable solutions with good generalization ability.

Chitin and its derivative, chitosan, have diverse applications in fields such as agriculture, medicine, and biosensors, amongst others. Extraction is primarily conducted from marine sources, such as crustaceans, which have been the focus of optimization process studies. However, there are other sources that are more readily available, such as insects, where insufficient research has been conducted. The house cricket (Acheta domesticus) is a promising source for chitin extraction because of its high chitin content, availability, and short lifespan. Modern chemical chitin extraction methods have not been standardized due to the use of different reagents, molar concentrations, temperatures, and reaction times across publications. Therefore, in this study, the compositional analysis of Acheta domesticus cricket flour was determined: 2.62% humidity, 4.3% ash content, 56.29% protein, 13.35% fat, 23.44% carbohydrates, and 15.71% crude fiber content. After a drying, defatting, demineralization, deproteinization, and bleaching process, chitin extraction was performed, and chitosan was obtained via a deacetylation reaction. The demineralization process was standardized at 30ºC for 3 hours using HCl 2M, resulting in 95.85 ± 0.012%. The deproteinization process was optimized at 80ºC for 45 minutes using NaOH 2.56 M, yielding 46.37 ± 3.86%. Finally, the identity and physicochemical characteristics of the compounds were determined through characterization with FTIR, XRD, SEM and DSC.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition, characterized by persistent deficits in communication/social interaction and restricted and repetitive patterns of behavior, interests or activities. It is usually identified in childhood, despite late diagnoses in cases of greater functionality. Havighurst proposes a set of fundamental tasks throughout the life cycle, whose achievement is probably compromised in the ASD, even though it lacks scientific evidence. This study innovates, as it is based on Havighurst's theoretical model for understanding the development process of three adults and one young person, diagnosed with ASD level I, at different life stages. Through the methodology of life histories, interviews were subject to content analysis. Results pointed out the early detection and interventions importance, minimizing adversities, above all by those who had late diagnoses. Self-perceived success, not expected, was found in some development tasks (e.g. conscience and morality development). Also were noticed expected limitations related to ASD (e.g. difficulties in relationships with peers and in the labor market inclusion). Probably, in cases of greater severity and less functionality/autonomy, there would be greater failure in development tasks. It is crucial to improve research on this developmental model and on the factors that delay the diagnosis, including cases of different levels of severity.

Equipping autonomous agents for dynamic interaction and navigation is a significant challenge in intelligent transportation systems. This study aims to address this by implementing a brain-inspired model for decision-making in autonomous vehicles. We employ active inference, a Bayesian approach that models decision-making processes similar to the human brain, focusing on the agent's preferences and the principle of free energy. This approach is combined with imitation learning to enhance the vehicle's ability to adapt to new observations and make human-like decisions. The research involved developing a multi-modal self-awareness architecture for autonomous driving systems and testing this model in driving scenarios, including abnormal observations. The results demonstrated the model's effectiveness in enabling the vehicle to make safe decisions, particularly in unobserved or dynamic environments. The study concludes that the integration of active inference with imitation learning significantly improves the performance of autonomous vehicles, offering a promising direction for future developments in intelligent transportation systems.

The Interdisciplinary Method of Musical Literacy, Education and Artistic Awareness (MILMESA) is developed and applied essentially in higher education (artistic, cultural and teacher training courses), promoting effective endogenous (interarts) and exogenous interconnection (artistic and other non-artistic areas) of an inter and pluridisciplinary matrix, as well as assuming itself as a privileged vehicle of relationship and extension to the community in the frame of the making of artistic-cultural and pedagogical-didactic events in the European, Luso-Brazilian, Luso-African and Hispanic American territories. It was created, tested and evaluated in five consecutive quadrennia (2002–2023), being regularly adapted, adapted and added according to the observed realities, evolving into the deepening of new concepts and models of intervention in these domains (e.g., Expressive and Proximal Didactics). Using the Ethnographic Method and the Case Study, and having the source of data collection as Document Analysis of written and iconographic or visual sources (n = 1386) and Participant Observation, this study aimed to describe and analyse the application and evolution of MILMESA in terms of education and extension to the community. It was concluded that there was a need to promote, with greater regularity and scope, greater technological interaction via digital platforms and social networks, within the framework of the participation of external specialists (e-DEP) and the very experience of MILMESA’s experiences and initiatives, giving it a more meaningful and participatory range.

This study aimed to analyze the effect of a flow training program based on mindfulness applied to young and adult elite handball athletes. A quantitative, quasi-experimental, descriptive data analysis approach was carried out. The sample included 105 athletes (51 female and 54 male). The athletes were divided into two groups: i) experimental (n = 53) and ii) control (n = 52). The results of the repeated ANOVA indicated that the experimental group achieved significant improvements compared to the control group in decision making (n2p=.086), mental well-being (n2p=.045), dispositional flow state (n2p=.103), non-judgment (n2p=.040), refocusing (n2p=.052) and mindful trait in daily life (n2p=.058). In the linear regression test, the factor non-judgment was identified as a predictor of decision making (F(103)=7.99, p=.006) and the predictor for the mental well-being were dispositional flow state (F(102)=29.2, p

(1) Introduction: The COVID-19 pandemic presented unique challenges for patients and healthcare providers, especially for those working with obesity and related health problems. E-health has emerged as a crucial tool for the follow-up of users undergoing bariatric surgery during this period, allowing remote monitoring of users' health status and providing access to virtual consultation with health professionals. This study aims to analyze the impact of the use of telemedicine during the COVID-19 pandemic on the results of bariatric surgery and user satisfac-tion with this follow-up. (2) Method: Observational study with retrospective data collection. The variables were divided into several groups: health data, associated comorbidities, quality of life, quality of sleep and satisfaction with telemedicine monitoring. (3) Results: Surgery significantly affected weight loss, in general more than 75% loss of excess weight. Comorbidities, like the qual-ity of life, were reversed over time, with 50% of subjects maintaining excellent levels. Although most users were satisfied with the monitoring, the variables had no relationship. (4) Conclusions: The follow-up assured by telemedicine did not alter the results of bariatric surgery, which may allow us to infer that it may be an option to consider for regular monitoring of the post-bariatric surgery process.

This study aims to evaluate the potential of machine learning algorithms (Random Forest and Support Vector Machine) in predicting the open porosity of a general-use industrial mortar applied to different substrates based on the characteristics of both the mortar and substrates. For this purpose, an experimental database comprising 1592 samples of industrial mortar applied to five different substrates (hollowed ceramic brick, solid ceramic brick, concrete block, concrete slab, and lightweight concrete block) was generated by an experimental program. The samples were characterized by bulk density, open porosity, capillary water absorption coefficient, drying index, and compressive strength. This database was then used to train and test the machine learning algorithms to predict the open porosity of the mortar. The results indicate that it is possible to predict the open porosity of the mortar with good prediction accuracy and that both Random Forest (RF) and Support Vector Machine (SVM) algorithms (RF =0.880; SVM = 0.896) are suitable for this task. Regarding the main characteristics that influence the open porosity of the mortar, bulk density and the open porosity of the substrate are significant factors. Furthermore, the study employs a straightforward methodology with a machine learning no-code platform, enhancing the replicability of its findings for future research and practical implementations.

Epithelial-mesenchymal transition (EMT) networks are essential in acquiring the drug resistance and cancer malignant features in cancer stem cells (CSCs). In this regard, gene expression profiles in diffuse- and intestinal-type gastric cancer (GC) have been analyzed to reveal the network pathways in EMT and CSCs, since the diffuse-type GC has much more mesenchymal features than intestinal-type GC that has the intestinal features. The study results revealed that the activation state of several canonical pathways related to cell cycle regulation was altered. The canonical pathway on Cell cycle: G1/S checkpoint regulation was activated in diffuse-type GC, and canonical pathways on Cell cycle control of chromosomal replication and Cyclins and cell cycle regulation were activated in intestinal-type GC. Canonical pathway related to Role of BRCA1 in DNA damage response was activated in intestinal-type GC, where BRCA1, which is related to G1/S phase transition was up-regulated in intestinal-type GC. Several microRNAs (miRNAs), including mir-10, mir-17, mir-19, mir-194, mir-224, mir-25, mir-34, mir-451, and mir-605, were identified to have direct relationships of RNA-RNA interaction in Cell cycle: G1/S checkpoint regulation pathway. Additionally, cell cycle regulation may be altered in EMT conditions. The alterations in activation states of the pathways related to cell cycle regulation in diffuse- and intestinal-type GC would indicate the significance of cell cycle regulation in EMT.

In the last three decades the presence of phospholipids in the nucleus has been shown and thoroughly investigated. A great interest raised about nuclear inositol lipids mainly because of their role in signalling acting. Here we review the main issues of nuclear phospholipid localization and the role of nuclear inositol lipids and their related enzymes in cellular signaling both in physiological and pathological conditions.

In this paper we derived an expression that allows the determination of the thermo-optic coefficient of weakly-guiding germanium-doped silica fibers, based on the thermal behavior of optical fiber devices, such as, fiber Bragg gratings (FBGs). The calculations rely on the full knowledge of the fiber parameters and on the temperature sensitivity of FBGs. In order to validate the results, we estimated the thermo-optic coefficient of bulk GeO2 glass at 293 K and 1.55 m to be 18.3x10-6 K-1. The determination of this value required to calculate a correction factor which is based on the knowledge of the thermal expansion coefficient of the fiber core, the Pockels’ coefficients (p11=0.125, p12=0.258 and p44=-0.0662) and the Poisson ratio (=0.161) of the SMF-28 fiber. To achieve that goal, we estimated the temperature dependence of the thermal expansion coefficient of GeO2 and we discussed the dispersion and temperature dependence of Pockels’ coefficients. We have presented expressions for the dependence of the longitudinal and transverse acoustic velocities on the GeO2 concentration used to calculate the Poisson ratio. We have also discussed the dispersion of the photoelastic constant. An estimate for the temperature dependence of the thermo-optic coefficient of bulk GeO2 glass is presented for the 200-300 K temperature range.

Mycotoxins are fungal secondary metabolites, which can cause acute and chronic toxic effects in animals and humans. The present study aimed to determine multi-mycotoxin levels in Algerian workers using urine as target. A method based on a QuEChERS (quick, easy, cheap, effective, rugged, and safe) extraction procedure followed by LC-MS/MS (liquid-chromatography tandem mass spectrometry) was optimized and validated for determination of 11 mycotoxins in 96 urine samples. Different sorbents were tested to be used in the dispersive solid-phase extraction (d-SPE) cleanup step of QuEChERS. The final method was fit-for-purpose and showed good analytical performance in terms of specificity, linearity and precision. All samples contained at least 2 mycotoxins, being toxin-2 (T-2) the most common, which was found in 92.7% of the samples, followed by zearalenone (ZEN) with 90.6% of positive samples, and ochratoxin A (OTA) with 86.4%. T-2 levels ranged from 0.3 μg/L to 36.3 μg/L, while OTA ranged from 0.3 μg/L to 3.5 μg/L and ZEN ranged from 7.6 μg/L to 126.8 μg/L. This was the first mycotoxin biomonitoring study carried out in the Algerian population. The findings highlight the need of accurate data for better risk assessment and the development of better regulation to manage mycotoxin contamination in this country.

The soil-structure interaction represents an important parameter when analyzing the dynamic behavior of bridges, taking into account the integration between soil, foundation, and structure to ensure an adequate structural representation. In this paper, a computational modeling strategy using soil-structure interaction (SSI) methodology was applied to represent the dynamic behavior of a pedestrian bridge (LOP). The computational models were validated using monitoring results from an extensive structural health monitoring campaign. The bridge monitoring program was characterized using embedded sensors and cutting-edge technology for remote monitoring: ground-based radar interferometry and video motion amplification. The instrumentation results were compared to establish the capabilities and advantages of this novel remote monitoring technique. The experimental results obtained from free and forced vibrations in the asset served as a basis for validating the developed computational model and allowed evaluating the asset be-havior in service for different loading conditions. This paper emphasizes the SSI approach to represent the pedestrian bridge dynamic behavior more accurately, allowing computational models to represent the LOP structural responses over the course of its life-cycle.

The aim of this study was to develop and validate a predictive early tooth loss multivariable model for periodontitis patients before periodontal treatment. A total of 544 patients seeking periodontal care at a university dental hospital were enrolled in the study. Teeth extracted after periodontal diagnosis and due to periodontal reasons were recorded. Clinical and sociodemographic variables were analyzed, considering the risk of short-term tooth loss. This study followed the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines for development and validation, with two cohorts considered as follows: 455 patients in the development phase and 99 in the validation phase. As a result, it was possible to compute a predictive model based on tooth type and clinical attachment loss. The model explained 25.3% of the total variability and correctly ranked 98.9% of the cases. The final reduced model area under the curve (AUC) was 0.809 (95% Confidence Interval (95% CI): 0.629 - 0.989) for the validation sample and 0.920 (95% CI: 0.891 - 0.950) for the development cohort. The established model presented adequate prediction potential of early tooth loss due to periodontitis. This model may have clinical and epidemiologic relevance towards the prediction of tooth loss burden.

Drainage systems are usually dimensioned for design storms based on intensity-duration-frequency (IDF) curves of extreme precipitation. For each location, different IDF curves are established based on local hydrological conditions. Recent research shows that these curves also vary with time, and should be updated with recent data. The purpose of this study is to evaluate IDF curves obtained from precipitation simulations from the Eta RCM, comparing them with IDF curves obtained from data of a rainfall station. Climate models can be a useful tool for assessing the impacts of climate changes on drainage systems, referring precipitation forecasts. In this study, the Eta RCM was forced by two global climate models: HadGEM2-ES and MIROC5. The bias of the precipitation data, generated by RCM models, was corrected using a Gamma distribution. The Juqueriquerê River Basin, in the cities of Caraguatatuba and São Sebastião, São Paulo State, Brazil, was chosen as a case study. The results show a good correlation between the IDF curves of simulated and observed rainfall for the control period (1960-2005), indicating the strong possibility of using the Eta RCM precipitation forecasts for 2007 - 2099 to establish future IDFs thereby, taking into account climate changes in urban drainage design.

The global consumption of vegan foods is experiencing an expressive upward trend, underscoring the critical need for quality control measures based on nutritional and functional considerations. This study aimed to evaluate the functional quality of caviar and salmon analogs food inks, based on pulses combined with nano ingredients. The primary objective of this work was to determine the total antioxidant compounds contained in these samples using a voltammetric technique with a glassy carbon electrode. Approximately 100 mg of samples underwent ethanolic extraction (70%) with 1 hour of stirring. The voltammograms were acquired under specific conditions: 10 mL of phosphate buffer electrolyte with a pH of 3.0; an initial potential (Ei) of 0.0 V; a final potential (Ef) of 1.0 V; a pulse amplitude of 50 mV; and a scan rate of 50 mV s-1. The reference electrode used was Ag/AgCl (KCl 3 mol L-1), and the auxiliary electrode was a platinum wire. The voltammograms revealed prominent anodic current peaks at 0.76-0.78 V, which are attributed to isoflavones. Isoflavones, known secondary metabolites with substantial antioxidant potential commonly found in pulses, were identified. The total isoflavone concentrations obtained ranged from 31.5 to 64.3 mg Eq genistein 100 g-1. The results not only validated the efficacy of the electrochemical sensor for quantifying total antioxidant compounds in the samples but also demonstrated that the concentration of total isoflavones in caviar and salmon analogs fell within the expected limits.

Oral health surveys are essential to assess the dental caries experience and to influence national policies. This retrospective cross-sectional study aims to analysis dental caries experience who sought dental treatment in a reference university dental hospital at the Lisbon Metropolitan Area between January 2016 and March 2020. Full-mouth examination, sociodemographic, behaviors and medical information were included. Descriptive analyses and logistic regression analysis were applied to ascertain risk indicators associated with dental caries experience. A final sample of 9,349 participants (5,592 females/3,757 males) were included, aged 18 to 99 years old. In this population, caries experience was 91.1%, higher in female participants. Age (OR= 1.01, 95% CI [1.00-1.02], occupation (OR= 2.94, 95% CI [2.37-3.65], OR= 3.35, 95% CI [2.40-4.67], OR= 2.55, 95% CI [1.66-3.91], for employed, unemployed and retired, respectively), overweight (OR= 1.52, 95% CI [1.18-1.96]), reporting to never visited a dentist (OR= 0.38, 95% CI [0.23-0.64] and self-reported week teeth status (OR= 2.14, 95% CI [1.40-3.28]) were identified as risk indicators for the presence of dental caries, according to adjusted multivariable logistic analyses. These results highlight a substantial rate of dental experience in a Portuguese cohort and will pave the way for future tailored oral public health programs in Portugal.

Having as objective to reach a circular economy, it is important to maximize the Physical Asset’s Life Cycle. The evaluation of Physical Assets Life Cycle may have several approaches which may provide different results. These differences may not be very significant, but must be taken into consideration, because they have consequences in the manager decision. This permits to have a wider time interval to decide when to withdrawal the Physical Asset or to renewal it, and or if this ought to continue functioning because the profits are higher than the expenses, what allows to diminish waste and increase sustainability. These are some aspects that are discussed in this paper, which presents several models to evaluate the Physical Assets Life Cycle, considering the market value, devaluations methods and a more generalized way of Fisher’s Equation, which can include the Risk tax, among others. The results are discussed supported in data for simulation, which are used for each Econometric Model aiming to evaluate the differences among them. In all Models they are considered not only the expenses, namely of Investment and Functioning, but also the Profits, which permit to evaluate the Physical Asset Life Cycle in a holistic way. The models are very versatile, allowing to evaluate quantitatively the changing in the maintenance policies, the energy prices variations, the risk evaluation, the variation of profits according to the real market, and so on. The results demonstrated the robustness of the approach described and that maximize the Physical Assets Life Cycle allowing to minimize the consumption of world resources and, by consequence, it contributes for a more sustainable world.

Epithelial-mesenchymal transition (EMT) plays an important role in the acquisition of cancer stem cell (CSC) feature and drug resistance, which are the main hallmarks of cancer malignancy. Although previous findings have shown that several signaling pathways are activated in cancer progression, the precise mechanism of signaling pathways in EMT and CSCs are not fully understood. In this study, we focused on the intestinal and diffuse-type gastric cancer (GC), and analyzed the gene expression of public RNAseq data to understand the molecular pathway regulation in different subtypes of gastric cancer. Network pathway analysis was performed by Ingenuity Pathway Analysis (IPA). Total 2815 probe set IDs were significantly different between intestinal- and diffuse-type GC data in cBioPortal Cancer Genomics. The 10 genes including male-specific lethal 3 homolog (Drosophila) pseudogene 1 (MSL3P1), CDC28 protein kinase regulatory subunit 1B (CKS1B), DEAD-box helicase 27 (DDX27), golgi to ER traffic protein 4 (GET4), chromosome segregation 1 like (CSE1L), translocase of outer mitochondrial membrane 34 (TOMM34), YTH N6-methyladenosine RNA binding protein 1 (YTHDF1), ribonucleic acid export 1 (RAE1), par-6 family cell polarity regulator beta (PARD6B), and MRG domain binding protein (MRGBP) were found to have difference in gene expression in intestinal- and diffuse-type GC. Total 463 direct relationships with 3 molecules (MYC, NTRK1, UBE2M) were found in the biomarker-filtered network generated by network pathway analysis. The networks and features in intestinal- and diffuse-type GC have been investigated and profiled in bioinformatics. Our results revealed the signaling pathways networks in intestinal- and diffuse-type GC, bringing new light for the elucidation of drug resistance mechanisms in CSCs.

This study analysed through the perspective of networks the association between physical activity (PA), sedentary behaviour (SB), and cardiovascular risk factors in overweight low-income schoolchildren from the perspective of complex systems. Methods: The sample consisted of 41 overweight children between 6 and 10 years old (56% girls). PA and SB were analysed by accelerometer, and the measures of cardiovascular factor risk were: anthropometric and hemodynamic measurements, body fat, lipid profile and glucose, cardiorespiratory fitness (CRF), left ven-tricular mass (LVM), and Sokolov-Lyon. Network analysis with Boot-strap-1000 was performed to analyze the association between PA, SB and the cardiovascular risk factors. Results: Moderate to vigorous PA was positive related with Waist circumference (WC; 0.499), HDL-C (0.307), and CRF (0.276), and negatively associated with BMI (-0.251) and Fat (-0.341). For SB, positive associations were seen with WC (0.326), CRF (0.296), LVM (0.250) and Sokolov (0.215). Adicionalment, the centrality indicators highlighted WC as the most important variable in the network. Conclu-sion: Interventions that aim to mitigate the harmful effects of obesity on cardiovascular risk factors in overweight children should consider WC as an important variable in of system.

The paper analyzes the relationship between urban waste recycling and technological innovation systems in Europe. Data from the Global Innovation Index for 34 European countries in the period 2013-2022 was used. To analyze the characteristics of European countries in terms of waste recy-cling capacity, the k-Means algorithm optimized with the Elbow method and the Silhouette Coef-ficient was used. The results show that the optimal number of clusters is 3. Panel data results show that waste recycling increases with domestic market scale, gross capital formation, and the diffu-sion of Information and Communication Technologies (ICTs), while it decreases with the infra-structure index, business sophistication index, and the average expenditure in research and devel-opment of large companies.

Epidemiologic studies suggest an inverse correlation between coffee consumption and the occurrence of neurodegenerative diseases, but the role of caffeine and roasting degree are still matter of debate. The objective of this work was to evaluate the effects of caffeinated (light, medium, and dark roast) and decaffeinated instant coffee samples in acetylcholinesterase inhibition and antioxidant assays, as well as in animal models of Parkinson’s disease. Caffeinated coffees inhibited the acetylcholinesterase in much smaller concentrations than decaffeinated coffee. All coffee samples showed antioxidant capacity without relation with the caffeine content. Dopaminergic-like activity in the haloperidol-induced catalepsy test was observed with caffeinated coffee, but not in the decaffeinated sample. The medium roast coffee reduced the number of rotations of rats after methamphetamine administration on the 6-hydroxydopamine unilateral lesion of the medial forebrain bundle. However, the coffee treatment did not avoid the loss of dopaminergic neurons on substantia nigra pars compact and only the smallest dose of coffee was able to avoid the decrease of dopamine levels in the lesioned side of the striatum. Altogether, these results suggest that coffee exerts moderate pro-cholinergic and pro-dopaminergic effects and caffeine seems to be the main responsible for these effects.

This review summarizes the most recent developments of research on protozoan parasite infections ac-quired through food and drinking water and is aimed at gathering updated knowledge on the risk factors, illnesses caused, and measures for prevention. Reports included dated from 2019 to the present and regarded recent outbreaks and cases of severe infec-tions, prevalence in different population segments and food handlers, and occurrence in food and drinking water. Cryptosporidium spp., Entamoeba histolytica, Giardia intestinalis, Trypanosoma cruzi and Toxoplasma gondii were the microorganisms most frequently involved in large outbreaks and/or severe infections, while Cyclospora cayetanensis was most frequently involved in single case reports. Cystoisospora belli was implied in severe infections in immunocompromised patients, while Blastocystis homi-nis, Sarcocystis spp., Dientamoeba fragilis, Endolimax nana and Pentatrichomonas hominis appeared to be less relevant for number of symptomatic infections and pathogenic potential. A progressive improvement in surveillance of protozoan infections and infection sources was highlighted, especially in developed countries. The apparent increase in cases was concomitant with the implementa-tion of reporting systems and the application of molecular diagnostic methods that represent valuable in-struments for establishing epidemiological links and preventing or limiting outbreaks. The pathogenic role and sources of infection should be better elucidated for some species.

(1) Background: To estimate resistance, reactance and phase angle values among moderate preterm infants and their variation according to neonatal and maternal characteristics and nutritional intake. (2) Methods: This was a cohort that evaluated 43 moderate preterm infants using bioelectrical impedance analysis. The study variables included resistance, reactance and PA measurements, in addition to classification of nutritional intake. (3) Results: The mean resistance was 602.0+118.2 Ω, reactance was 57.2 Ω (IIQ = 42.6-65.2) and phase angle was 522° (IIQ = 4.1-6.6). Lower resistance values were found in the presence of risky pregnancy (532.2+111.9 Ω vs 650.9+97.9 Ω, p&lt;0.001) and lower reactance values, in the presence of harmful maternal lifestyle habits at both the first (p=0.01) and second assessments (p=0.01). Eight preterm infants were considered to have insufficient nutritional intake (23.5%); 17, sufficient (50.0%) and 9, partially sufficient (26.5%). There was less reactance among preterm infants with insufficient nutritional intake (p &lt; 0.001). (4) Conclusions: The bioelectrical impedance analysis measurements were within the range of values reported in other studies. There was an association between full diet and adequate nutritional intake with higher resistance values, and a lower reactance value was associated with the presence of risky pregnancy and harmful maternal lifestyle.

In recent years, many studies have highlighted the consistent finding of amoxicillin in waters destined for wastewater treatment plants, in addition to superficial waters of rivers and lakes in both Europe and North America. In this paper, the amoxicillin degradation pathway was investigated by simulating the chlorination process normally used in a wastewater treatment plant to reduce similar emerging pollutants at three different pH values. The structures of 16 isolated degradation byproducts (DPs), one of which was isolated for the first time, were separated on a C-18 column via a gradient HPLC method. Then, combining mass spectrometry (MALDI-MS/TOF) and nuclear magnetic resonance, we compared commercial standards and justified a proposed formation mechanism beginning from the parent drug. Microbial growth inhibition bioassays with Escherichia coli, Klebsiella pneumoniae, and Staphylococcus aureus were performed to determine the potential loss of antibacterial activity in isolated degradation byproducts. An increase of antibacterial activity in the DPs was observed compared to the parent compound.

Over the last decade, the therapeutic scenario for advanced non-small-cell lung cancer (NSCLC) has undergone a major paradigm shift. Immune checkpoint inhibitors (ICIs) have shown a meaningful clinical and survival improvement in different settings of the disease. However, the real benefit of this therapeutic approach remains controversial in selected NSCLC subsets, such as those of the elderly with active brain metastases or oncogene-addicted mutations. This is mainly due to the exclusion or underrepresentation of these patient subpopulations in most of the pivotal phase III studies; this precludes the generalization of the ICI efficacy in this context. Moreover, no predictive biomarkers of ICI response exist that can help with patient selection for this therapeutic approach. Here, we critically summarize the current state of ICI efficacy in the most common “special” NSCLC subpopulations.

This literature review was aimed to collect investigations on the in vivo evidences for bacteria associated to fermented dairy foods to behave as probiotics with beneficial effects in the prevention and treatment of various diseases. All main bacterial groups commonly present in high numbers in fermented milks or cheeses were taken into account, namely starter lactic acid bacteria (SLAB) Lactobacillus delbrueckii subsp. bulgaricus and lactis, L. helveticus, Lactococcus lactis, Strepto-coccus thermophilus, non starter LAB (NSLAB) Lacticaseibacillus spp., Lactiplantibacillus plantarum, dairy propionibacteria and other less frequently encountered species. Only studies regarding strains of proven dairy origin were considered. Studies in animal models and clinical studies showed that dairy bacteria ameliorate symptoms of inflammatory bowel disease (IBD), mucositis, metabolic syndrome, aging and oxidative stress, cancer, bone diseases, atopic dermatitis, allergies, infections and damages caused by pollutants, mild stress and depression. Immunomodulation and changes in the intestinal microbiota were the mechanisms most often involved in the observed effects. The results of the studies considered indicated that milk and dairy products are a rich source of beneficial bacteria that should be further exploited to the advantage of human and animal health.

Obesity is a complex chronic disease characterized by excess of body fat. It represents a significant public health problem due to the health-related risk factors. There are growing evidences showing that maternal obesity can program the offspring, which influence neonatal phenotype and predispose offspring to a higher prevalence of metabolic disorders such as obesity. This increased risk may also be epigenetically transmitted across generations. Thus, there is an urgent need to find effective reprogramming approaches in order to resume normal fetal development. Polyphenols are bioactive compounds found in fruits and vegetables that exert their anti-obesity effect through its powerful anti-oxidant and anti-inflammatory activities. Polyphenols supplementation has been proven to counteract the deleterious effects of maternal obesity programming on offspring. Indeed, some polyphenols can cross the placenta and protect the fetal predisposition against obesity. The present review summarizes the effects of dietary polyphenols on obesity-induced maternal reprogramming as an offspring anti-obesity approach.

Using the sustainable livelihoods analytical framework, adaptability of cattle raising to multiple stressors (e.g. climate change and market conditions) in the dry tropics of Chiapas, Mexico was evaluated. Three case studies located in the Frailesca region of Chiapas were analyzed: (I) peasant cattle raising in a rural village in the Frailesca Valley; (II) peasant cattle raising in a rural village in a natural protected area in the Frailesca Highlands; and (III) holistic cattle raising by farmers with private land ownership in the Frailesca Valley. Adaptability was evaluated using an index on a scale of one to a hundred; average values were: case I = 20.9 ± 1.4; case II = 32.1 ± 1.8; and case III = 63.6 ± 3.5. In order to increase farms adaptability and reduce the vulnerability of cattle raising families, there is a need to modify public policy to take into account the conditions of the most vulnerable farmers (cases I and II). Given the economic, environmental, and social context of Mexico´s dry tropics, establishing ecological or organic cattle raising and silvopastoral systems may reduce the vulnerability of farm families and increase their level of adaptability of their farms to multiple stressors.

This systematic review investigates the failure rate and marginal bone loss (MBL) of dental implants placed in Solid-organ transplant (SOT) patients compared to healthy controls. Three databases (PubMed, Web of Sciences and the Cochrane Library) were searched up to June 2020 (PROSPERO CRD42019124896). Case-control and cohort studies reporting data failure rate and marginal bone loss (MBL) of dental implants placed in SOT patients were included. The risk of bias of observational studies was assessed through the Newcastle-Ottawa Scale (NOS). Four case-control studies fulfilled the inclusion criteria, all of low risk of bias. Meta-analyses revealed consistently lower implant failure rate than control populations at patient and implant levels. SOT patients had a significant difference of -18% (p-value <0.001) of MLB towards healthy patients. SOT status poses no serious threat to implant survival. Overall, this group of patients presented lower levels of dental implant failure rate and marginal bone loss compared to otherwise healthy patients. Further intervention trials with wider sample size and longer follow-ups are necessary to confirm these summary results.

We explored the statistics of filtering of simple patterns on a number of deterministic and random graphs as a tractable simple example of information processing in complex systems. In this problem, multiple inputs map to the same output, and the statistics of filtering is represented by the distribution of this degeneracy. For a few simple filter patterns on a ring we obtained an exact solution of the problem and described numerically more difficult filter setups. For each of the filter patterns and networks we found a few numbers essentially describing the statistics of filtering and compared them for different networks. Our results for networks with diverse architectures appear to be essentially determined by two factors: whether the graphs structure is deterministic or random, and the vertex degree. We find that filtering in random graphs produces a much richer statistics than in deterministic graphs. This statistical richness is reduced by increasing the graph’s degree.

In 2020, following the World Health Organization's declaration of COVID-19 as a pandemic, dental practices faced unprecedented disruptions globally. This retrospective cross-sectional study, conducted at a leading university dental hospital in the Lisbon Metropolitan Area, investigated the impact of these disruptions on dental caries. The study, spanning June 2019 to June 2021, included a full-mouth X-ray examination, along with sociodemographic, behavioral, and medical data. Our sample comprised 3,380 participants (2,007 females and 1,373 males), aged 18 to 99 years, divided into pre-lockdown and post-lockdown groups. We employed the Decayed, Missing, and Filled Teeth (DMFT) index to measure dental caries. Our findings revealed that nearly 90% of participants in both groups experienced dental caries, indicating a statistically significant difference between the pre-lockdown and post-lockdown periods. However, the impact of COVID-19 confinement varied among different demographic sectors, with age, educational background, and professional status emerging as the key determinants. This study highlights the need for a more nuanced understanding of the impact of the pandemic on oral health, emphasizing the role of sociodemographic factors in shaping dental health outcomes during such global health crises.