Interferon-beta is one of the most widely prescribed disease-modifying therapies for multiple sclerosis patients. However, this treatment is only partially effective, and a significant proportion of patients do not respond to this drug. This paper proposes an alternative fuzzy logic system based on the opinion of a neurology expert to classify relapsing-remitting multiple sclerosis patients: high, medium, and low responder to interferon-beta. Also, a pipeline prediction model trained with biomarkers associated to interferon-beta response is proposed for predicting whether patients are potential candidates to be treated with this drug, in order to avoid ineffective therapies. The classification results shows that the fuzzy system presents a 100% efficiency compared with an unsupervised hierarchical clustering method (52%). So, the performance of the prediction model is evaluated, and a 0.8 testing accuracy is achieved. Hence, a pipeline model including data standardization, data compression, and a learning algorithm, can be a useful tool for getting reliable predictions about the response to interferon-beta.

Inflammatory Bowel Disease (IBD) presents distinct challenges during pregnancy due to its influence on maternal health and pregnancy outcomes. This literature review aims to dissect the existing scientific evidence on pregnancy in women with IBD and provide evidence-based recommendations for clinical management. A comprehensive search was conducted across scientific databases, selecting clinical studies, systematic reviews, and other pertinent resources. Numerous studies have underscored an increased risk of complications during pregnancy for women with IBD, including preterm birth, low birth weight, neonates small for gestational age, and congenital malformations. Nevertheless, it's evident that proactive disease management before and throughout pregnancy can mitigate these risks. Continuation of IBD treatment during pregnancy and breastfeeding is deemed safe with agents like thiopurines, anti-TNF, vedolizumab, or ustekinumab. However, there's a call for caution when combining treatments due to the heightened risk of severe infections in the first year of life. For small molecules, their use is advised against in both scenarios. Effective disease management, minimizing disease activity, and interdisciplinary care are pivotal in attending to women with IBD. The emphasis is placed on the continual assessment of maternal and infant outcomes and an expressed need for further research to enhance the understanding of the ties between IBD and adverse pregnancy outcomes.

Artificial Intelligence (AI) has emerged as a disruptive technology in various areas, and medicine is no exception. In the last decade, there has been rapid progress in the application of AI in healthcare, from diagnosing and prognosticating diseases to improving operational efficiency in hospitals. This literature review explores the advances of artificial intelligence in the field of medicine, discusses its benefits, and discusses the ethical and regulatory challenges that need to be addressed to maximize its potential.

Polymer-based composites are widely used in the automotive, security, aeronautical and space industries, to mention a few. This is because of their good mechanical properties, which are similar to those of metals but with the attraction of being lightweight. Kevlar® as a reinforcement is extensively used in the security industry owing to its good ballistic properties. This investigation presents the mechanical characterization based on in-plane quasi-static tensile testing of Kevlar® composite using a universal testing system. The methodology developed for the preparation of coupons is based on pressure, temperature and time. As a result of this work, elastic moduli (EL and ET), Poisson’s ratio (νLT), shear modulus (GLT) and strengths (XT, YT, S) were obtained. It is worth mentioning that there is scarce or no characterization of this material in the literature, and those studies that do characterize it do not present the coupon thermoforming conditions or the reasons of the specimen dimensions (width, length and thickness).

In the past years, consumers have increased their interest to buy healthier food products, rejecting those products with more additives and giving preference to the fresh ones. Moreover, the current environmental situation has made society more aware of the importance of reducing the production of plastic and food waste. In this way and considering the Food Industry need of reducing the food spoilage along the food chain, edible coating has been considered as an eco-friendly food packaging that can replace traditional plastic packaging providing an improvement in the product’s shelf-life. Edible coating are thin layers applied straightaway on the food materials’ surface, that are made of biopolymers that usually incorporate other elements, such as nanoparticles or essential oils, to improve their physicochemical properties. These materials must provide a barrier that can prevent the pass of water vapor and other gasses, the microbial growth, the moisture loss, and the oxidation, so the shelf-life could be extended. The aim of this review was to compile the current data available to give a global vision of the formulation process and the different ways to improve the characteristics of the coats applied in both fruits and vegetables. In this way, the suitability of compounds in by-products produced during the Food Industry chain were also considered for the edible coating production.

Data from NASA’s Soil Moisture Active Passive Mission (SMAP) and from the California Cooperative Oceanic Fisheries Investigations (CalCOFI) were used to examine the freshening that occurred during 2015-2016 in the Southern California Current System. Overall the freshening was found to be related to the 2014-2016 Northeast Pacific Warm Anomaly. The primary goal was to determine the feasibility of using SMAP data to observe the surface salinity signal associated with the warming. As a first step direct comparisons were done with salinity from the CalCOFI data at one-meter depth. During 2015 SMAP was saltier than CalCOFI by 0.5 PSU, but biases were reduced to < 0.1 PSU during 2016. South of 33°N, and within 100 km of the coast, SMAP was fresher in 2015 by almost 0.2 PSU. CalCOFI showed freshening of 0.1 PSU. North of 33°N SMAP and CalCOFI saw significant freshening in 2016, SMAP by 0.4 PSU and CalCOFI by 0.2 PSU. Differences between SMAP and CalCOFI are consistent with the increased stratification in 2015 and changes in the mixed layer depth.

This paper presents a comprehensive analysis of the SEIRS COVID-19 pandemic model with saturated incidence rate. By modifying the existing model and proposing a new simplified version, we investigate the dynamics of disease transmission, including the impact of saturation terms on disease spread. Through stability analysis at disease-free and endemic equilibria, we aim to enhance understanding of the spread of COVID-19 and inform effective control strategies. Drawing on mathematical models and epidemiological insights, this study contributes to the ongoing efforts to combat the global pandemic.

Background. It is unclear whether the use of clinical prediction rules is sufficient to rule out infective endocarditis (IE) in patients with Staphylococcus aureus bacteremia (SAB) without an echocardiogram evaluation, either transthoracic (TTE) and/or transesophageal (TEE). Our primary purpose was to test the usefulness of PREDICT, POSITIVE and VIRSTA scores to rule out IE without echocardiography. Our secondary purpose was to evaluate whether not performing an echocardiogram evaluation is associated with higher mortality. Methods. We conducted a unicentric retrospective cohort including all patients with a first SAB episode from January 2015 to December 2020. IE was defined according to modified Duke criteria. We predefined threshold cut-off points to consider that IE was ruled out by means of the mentioned scores. To assess 30-day mortality, we used a multivariable regression model considering performing an echocardiogram as covariate. Results. Out of 404 patients, IE was diagnosed in 50 (12.4%). Prevalence of IE within patients with negative PREDICT, POSITIVE and VIRSTA scores was: 3.6% (95% CI 0.1-6.9%), 4.9% (95% CI 2.2-7.7%), and 2.2% (95% CI 0.2-4.3%), respectively. Patients with negative VIRSTA and negative TTE had an IE prevalence of 0.9% (95% CI 0-2.8%). Performing an echocardiogram was independently associated with lower 30-day mortality (OR 0.24 95%CI 0.10-0.54, p=0.001). Conclusion. PREDICT and POSITIVE scores were not sufficient to rule out IE without TEE. In patients with negative VIRSTA score, it was doubtful if IE could be discarded with a negative TTE. Not performing an echocardiogram was associated with worse outcomes, which might be related to presence of occult IE. Further studies are needed to assess the usefulness of clinical prediction rules in avoiding echocardiographic evaluation in SAB patients.

A recent taxonomic study confirmed the synonymy of Rhodococcus equi (Magnusson 1923) Goodfellow and Alderson 1977 and Corynebacterium hoagii (Morse 1912) Eberson 1918. As a result, both R. equi and C. hoagii were reclassified to Rhodococcus hoagii comb. nov. in application of the principle of priority of the Prokaryotic Code. Being R. equi a well-known animal and zoonotic human pathogen, and the name solidly established in the veterinary and medical literature, we and others argued that the nomenclatural change may cause error and confusion and be potentially perilous. We have now additionally found that the nomenclatural type of the basonym C. hoagii, ATCC 7005^T, does not correspond with the original description of C. hoagii in the early literature. Its inclusion as the C. hoagii type on the Approved Lists 1980 results in a change in the characters of the taxon and in C. hoagii clearly designating two different bacteria. Moreover, ATCC 7005, the only strain in circulation under the name C. hoagii, does not have a well documented history; it is unclear why it was deposited as C. hoagii and a possible mixup with a Corynebacterium (Rhodococcus) equi isolate is a reasonable assumption. We therefore request the rejection of Rhodococcus hoagii as a nomen ambiguum, nomen dubium and nomen perplexum in addition to nomen periculosum, and conservation of the name Rhodococcus equi, according to Rules 56ab of the Code.

Colombia is the world's leading producer of mild washed arabica coffee and produces 12.6 million bags of green coffee, but at the same time 784,000 tons of waste biomass are dumped in open fields, of which only 5% is recovered or used. The objective of this project was to evaluate the production of platform chemicals from these coffee wastes. To achieve this, biomass characterization was carried out using proximate analysis, ultimate analysis and structural analysis. Hydrothermal valorization was carried out at a temperature range of 120-180 °C (LHW) and 180-260 °C (HTC) for one hour, the platform chemicals obtained were quantified by HPLC-IR, monitored by pH and conductivity, and the solid fraction was characterized by monitoring the functional groups in IR spectroscopy, and elemental analysis. Hydrolysis processes were obtained from 150 °C, production of platform chemicals from 180 °C and maximum concentration at 180 °C-4h, over 200 °C degradation of the products in the liquid fraction starts to take place. Homogeneous basic and acid catalysts were used to improve the yields of the reaction. The kinetics of the hydrolysis of lignocellulosic structures to sugars were also analyzed and described, and reaction orders of 1 (LHW), 3 (HTC) and their respective reaction rate equations were reported.

The current emerging COVID-19 pandemic has caused a global impact on every major aspect of our societies. It is known that SARS-Cov-2 can endure harsh environmental conditions for up to 72 h, which may contribute to its rapid spread. Therefore, effective containment strategies, such as sanitizing, are critical. Nanotechnology can represent an alternative to reduce the COVID-19 spread, particularly in critical areas, such as healthcare facilities and public places. Nanotechnology-based products are effective at inhibiting different pathogens, including viruses, regardless of their drug-resistant profile, biological structure, or physiology. Although there are several approved nanotechnology-based antiviral products, this work aims to highlight the use of nanomaterials as sanitizers for the prevention of the spread of mainly SARS-Cov-2. It has been widely demonstrated that nanomaterials are an alternative for sanitizing surfaces to inactivate the virus. Also, antimicrobial nanomaterials can reduce the risk of secondary microbial infections on COVID-19 patients, as they inhibit the bacteria and fungi that can contaminate healthcare-related facilities. Finally, cost-effective, easy-to-synthesize antiviral nanomaterials could reduce the burden of the COVID-19 on challenging environments and in developing countries.

This research proposes an algorithm to sign messages using the ElGamal asymmetric cryptosystem, called: Digital signature algorithm based on ElGamal, Diffie-Hellman protocol and the number π (DSAπ). This proposal has a high level of security like the DSA standard, in addition, execution times are reduced in relation to it. The Diffie-Hellman protocol and the number π were used in the construction of the digital signature algorithm in conjunction with the Hash-Sha-512 function. Some experiments are presented to provide evidence that the bits on the right side of the decimal point of π appear randomly, such as: a color image was constructed of a string of consecutive bits chosen at random, of the number π. Subsequently, parameters such as: entropy, correlation, discrete Fourier transform, goodness-of-fit test χ2 were evaluated. It is mentioned that an S-box 8×8 is involved in the signature. The sensitivity of the signature was measured, that is, when small changes are made to the sender’s private key, or small changes to the message. Finally, an image was constructed using the signatures of random texts, and subsequently the randomness of its pixels was obtained, to verify that there is no relationship between the different signatures.

Proper identification of oriented knots and 2-component links requires a precise link nomenclature. Motivated by questions arising in DNA topology, this study aims to produce a nomenclature unambiguous with respect to link symmetries. For knots, this involves distinguishing a knot type from its mirror image. In the case of 2-component links, there are up to sixteen possible symmetry types for each topology. The study revisits the methods previously used to disambiguate chiral knots and extends them to oriented 2-component links with up to nine crossings. Monte Carlo simulations are used to report on writhe, a geometric indicator of chirality. There are ninety-two prime 2-component links with up to nine crossings. Guided by geometrical data, linking number and the symmetry groups of 2-component links, a canonical link diagram for each link type is proposed. All diagrams but six were unambiguously chosen ( $8_1^{2}5$ , $9_5^2$ , $9_3^{2}4$ , $9_3^{2}5$ , $9_3^{2}9$ , and $9_4^{2}1$ ). We include complete tables for prime knots with up to ten crossings and prime links with up to nine crossings. We also prove a result on the behavior of the writhe under local lattice moves.

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

Although all isolates of the foodborne pathogen Listeria monocytogenes are considered to be pathogenic, epidemiological evidence indicates that certain serovar 4b lineages are more likely to cause severe invasive (neuromeningeal, maternal-fetal) listeriosis. Recently described as L. monocytogenes “hypervirulent” clones, no distinctive bacterial trait has been identified so far that could account for the differential pathogenicity of these strains. Here we discuss some preliminary observations in experimentally infected mice suggesting that serovar 4b hypervirulent strains may have a hitherto unrecognized capacity for prolonged in vivo survival. We propose the hypothesis that protracted survivability in primary infection foci in liver and spleen –first target organs after intestinal translocation– may cause L. monocytogenes serovar 4b hypervirulent clones to have a higher probability of secondary dissemination to brain and placenta.

In Mexico, the incorporation of deaf people into education has been lacking since only 14% of the deaf population in the age group between 3 and 29 years access education with the support of a hearing aid. Additionally, those who have been incorporated frequently face inappropriate educational strategies which poorly develop the use of Mexican Sign Language (MSL) and therefore academical success and opportunities for insertion in the workplace are difficult. This research explores a novel mexican sign language lexicon video dataset containing the dynamical gestures most frequently used by MSL. Each gesture consists of a set of different versions of videos under uncontrolled conditions. MX-ITESO-100 data set is composed of a lexicon of 100 gestures and 5,000 videos from three participants with different grammatical elements. Additionally, the data set is evaluated in a two-step neural network model with an accuracy greater than 99%. and thus serves as a benchmark for future training of machine learning models in computer vision systems. Finally, this research provides an inclusive environment within society and organizations in particular for people with hearing impairment.

Primary Health Care is crucial in the healthcare system, being the first level of contact with health services for users and patients to take action using the available resources. Patient care units are created to facilitate direct user participation through the circuit of complaints, grievances, and suggestions, allowing the analysis of the population's unmet expectations and needs. To study complaints and grievances regarding the treatment and information received by users in the Santiago and Barbanza healthcare area during the year 2022. A descriptive cross-sectional study was conducted, in which complaints filed by patients in the year 2022 in the Santiago and Barbanza healthcare area were analyzed. A total of 326 users who filed a total of 512 complaints were studied in the year 2022, with 358 (70.0%) related to treatment/information. The mean age was 33.5±16.2 years. 72.4% were women (n=236). A Pearson correlation was performed to establish whether there was a dependence between the number of healthcare visits in both primary care and hospital settings in 2022 and the number of complaints filed in the same year. Pearson correlation coefficient was 0.14; 95% CI (0.02 - 0.27), p=0.012. There is a correlation between the number of consultations and the number of complaints filed. Frequent users are more likely to file a complaint. However, there is no correlation between the number of consultations and the likelihood of filing a complaint regarding treatment/information. Therefore, there seems to be a good relationship between healthcare personnel and users, which impacts patient satisfaction.

Despite gender equality being present in the social and political sphere, we still encounter aspects that are characteristic of sexism. Such aspects impact upon gender inequality and different types of violence towards women. The present article aims to examine the behaviour of adolescents from Huelva with regards to ambivalent sexism towards women on social networks and their influence on health. Further, we seek to uncover adolescent’s perceptions with regards to gender differences in the use of social networks, the relationship between sexism and women's emotional well-being was observed. The study sample was formed by young people aged between 14 and 16 years who were residing in rural and urban zones in the south of Spain. A mixed methods approach was taken. At a quantitative level, a sample of 400 young people was recruited. These were administered a questionnaire about sexism which was composed of two scales and has been validated at a national and international level. At a qualitative level, the study counted on 33 young people who participated in in-depth discussions via interviews and discussion groups. The results showed that sexism emerges in adolescence in the analysed sample from the south of Spain. This favoured a digital gender gap and was reinforced through social networks such as Instagram and Snapchat. Rising awareness and a critical view of the aforementioned sexism was shown on the behalf of females, particularly those from urban backgrounds.

Recent advances in artificial intelligence combined with behavioral sciences have led to the development of cutting-edge tools for recognizing human emotions based on text, video, audio, and physiological data. However, these data sources are expensive, intrusive, and regulated, unlike plants which have been shown to be sensitive to human steps and sounds. A methodology is proposed to use plants as human emotion detectors. Electrical signals from plants are tracked and labeled based on video data. Labeled data are then used for classification. MLP, biLSTM, MFCC-CNN, MFCC-ResNet, and additionally, Random Forests, 1-Dimensional CNN, and biLSTM without windowing models are set using a grid search algorithm with cross-validation. Finally, best-parameterized models are trained and used with the test set for classification. The performance of this methodology is measured with a case study with 54 participants, who were watching an emotionally charged video, as ground truth their facial emotions were simultaneously measured using face emotion analysis. The Random Forest model shows the best performance, particularly in recognizing high-arousal emotions, achieving an overall weighted accuracy of 55.2% and demonstrating high weighted recall in emotions such as fear 61.0% and happiness 60.4%. The MFCC-ResNet model offers decently balanced results, with Accuracy(MFCC-ResNet)=0.318 and Recall (MFCC-ResNet)=0.324. With the MFCC-ResNet model fear and anger are recognized with 75% and 50% recall respectively. Thus, using plants as an emotion recognition tool seems worth investigating, addressing both cost and privacy concerns.

We previously reported that corneal fibroblasts within 3D fibrin matrices secrete, bind, and organize fibronectin into tracks that facilitate cell spreading and migration. Other cells use these fibronectin tracks as conduits, which leads to the development of an interconnected cell/fibronectin network. In this study, we investigate how cell induced reorganization of fibrin correlates with fibronectin track formation in response to two growth factors present during wound healing: PDGF BB, which stimulates cell spreading and migration; and TGFβ1, which stimulates cellular contraction and myofibroblast transformation. Both PDGF BB and TGF stimulated global fibrin matrix contraction (P < 0.005), however cell and matrix patterning were different. We found that during PDGF BB induced cell spreading, fibronectin was organized simultaneously with the generation of tractional forces at the leading edge of pseudopodia. Over time this led to the formation of an interconnected network consisting of cells, fibronectin and compacted fibrin tracks. Following culture in TGFβ1, cells were less motile, produced significant local fibrin reorganization, and formed fewer cellular connections as compared to PDGF BB (P < 0.005). Although bands of compacted fibrin tracks developed in between neighboring cells, fibronectin labeling was not generally present along these tracks, and the correlation between fibrin and fibronectin labeling was significantly less than that observed in PDGF BB (P < 0.001). Taken together, our results show that cell-induced ECM reorganization can occur independently from fibronectin patterning. Nonetheless, both events seem to be coordinated, as corneal fibroblasts in PDGF BB secrete and organize fibronectin as they preferentially spread along compacted fibrin tracks between cells, producing an interconnected network in which cells, fibronectin and compacted fibrin tracks are highly correlated. This mechanism of patterning could contribute to the formation of organized cellular networks that have been observed following corneal injury and refractive surgery.

The rye (Secale cereale L.) crop shows a high potential to collaborate in the sustainability of high Andean livestock because it supports the agroclimatic conditions and acid soils in the Peruvian Andes. The production of green forage, hay, and grain of the rye crop in acid soils was studied with the use of different levels of phosphorus and potassium fertilization in four local rye ecotypes (CBI-001, CSM-001, CJS-001, and CCE-001). The green forage yield (FV) ranged from 32.35 to 53.62 t ha-1, dry matter from 6.05 to 8.56 t ha-1, and hay from 7.0 to 10.36 t ha-1; nutritional levels ranged from 9.02 % to 13.56 % protein and 6.50 to 7.75 % ash levels, mainly with differences between ecotypes (p&lt;0.05). No differences existed between fertilization levels for the number of stems per plant, and flowering stems per plant, and grains per ear (p&gt;0.05). Also, CBI-001 and CCE-001 were superior with 1868.4 and 1797.8 kg ha-1 of grain, respectively (p=0.0072); the use of 60 kg ha of Nitrogen, 120 kg ha-1 of P2O5 and 80 kg ha-1 of K2O gave higher grain and residue yields. The high nutritional value and yield of the rye ecotypes studied in acid soil conditions and without irrigation can be an alternative for livestock feeding and grain production in the rainy season in the Andes as a dual-purpose crop.

The development of high complexity geometry parts is one of the main goals of the additive manufacturing technology. However, the failure of printed structures and the joining of different parts to create complex assemblies represents a real challenge in the research of efficient and sustainability techniques for the permanent assembly of polymers. Laser welding processes have been used as a single step method to join metals until years ago. Nowadays, the growing trend in the use of thermoplastics for additive manufacturing has led to the need to adapt this technique to materials with a very specific nature and more sensitive to thermal effects. Also, the possibility of transmitting the laser beam through transparent polymer layers allows to focus the energy supply on internal sections of the assembled components. In this research, an infrared laser marking system was used to join two different samples of polylactic acid manufactured by fused deposited modeling technology. In order to increase the effectiveness of the bonding process, a transparent and a dark sample have been used as assembly material, focusing the laser beam on the interface area of the two parts. By means of tensile tests, dimensional measurement and the use of optical microscopy techniques, a basis was established that links the supplied energy by laser to the joining performance.

Lignocellulosic biomass is a powerful approach for the production of sustainable biofuels and the further achievement of the goal of biomass conversion into a second-generation clean energy that can cope with the depletion of fossil reserves and rising energy requirements. In the conversion process, a pretreatment is essential to overcome the recalcitrance of the lignocellulosic biomass, accelerate its disintegration into cellulose, hemicellulose, and lignin and, in turn, get an optimal yield of fermentable sugars in the enzymatic hydrolysis. In addition to this, it should be industrially scalable and capable of enhancing fuel properties and feedstock processability. Here, steam explosion technology has stood out due to its results and advantages such as wide applicability, high efficiency in the short term, or lack of contamination in spite of its conventionality. This gentle and fast pretreatment incorporates high temperature autohydrolysis and structural alteration by explosive decompression. Steam explosion method has been one of the most effective especially for the hydrolysis of cellulose from agricultural wastes due to the lower quantity of acetyl groups in the composition of hemicellulose. In this aspect, sugarcane bagasse is a promising feedstock for bioethanol production due to its high cellulosic content and elevated availability. The objective of this review has been to compile the latest information on steam explosion pretreatment, stages, equipment, variables involved, by-products generated, as well as the advantages and disadvantages of the technique. At the same time, its feasibility and viability using sugarcane bagasse as feedstock has been discussed. Finally, the effectiveness of the technique with different feedstocks has been evaluated.

This paper describes how to construct a low-cost magnetic levitation system (MagLev). The MagLev has been intensively used in the engineering education, allowing instructors and students to learn through hands-on experiments essential concepts, such as electronics, electromagnetism, and control systems. Built from scratch, the proposed MagLev depends only on simple, low-cost components readily available on the market. In addition to showing how to construct the MagLev, this paper presents a semi-active control strategy which seems novel when applied in the MagLev. Experiments performed in the laboratory have compared the proposed control scheme with the classical PID control. The corresponding real-time experiments illustrate both the effectiveness of the approach and the potential of the MagLev for education.

Surface treatments of metals based on laser marking technology is an important application in a wide range of industrial fields. By specific combinations of laser processing parameters, the modified surface leads to different textures with specific roughness and colored appearance. Most of current works are focused on the modification of color tonality of flat surfaces, or the development of specific topography features, but the combination of both processes is not usually evaluated, mainly due to the complexity to control the optical properties on rough surfaces. This research presents an analysis of the influence of the micro-geometrical characteristics of periodic patterned laser tracks on the chromaticity and reflectance of Ti6Al4V substrates. The samples were irradiated with an infrared nanosecond pulsed laser under air atmosphere, taking as control parameter the scan speed of the beam. A roughness evaluation, microscopic inspection, absorption and chromaticity examination were conducted. Although micro-crack growth was detected in isolated case (10 mm/s), the possibility of adjusting the result color were demonstrated by controlling the thermal affected zone thickness of the textures. Results of rough/colored combined textures allow opening new perspectives in industrial design, particularly in aesthetic applications with special properties.

Intermediate CAG expansions in the gene ataxin-2 (ATXN2) are a known risk factor for ALS, but little is known about their role in FTD risk. Moreover, their contribution to the risk and phenotype of patients might vary in populations with different genetic background. The aim of this study was to assess the relationship of intermediate CAG expansions in ATXN2 with the risk and phenotype of ALS and FTD in the Spanish population. Repeat-Primed PCR was performed in 620 ALS and 137 FTD patients in three referral centers of Spain to determine the exact number of CAG repeats. In our cohort, ≥27 CAG repeats in ATXN2 was associated with a higher risk of developing ALS (odds ratio [OR] = 2.666 [1.471-4.882]; p = 0.0013), but not FTD (odds ratio [OR] = 1.446 [0.558-3.574]; p = 0.44). Moreover, ALS patients with ≥27 CAG repeats in ATXN2 showed shorter survival rate compared to those with &lt;27 repeats (hazard ratio [HR] 1.74 [1.18, 2.56], p = 0.005), more frequent limb onset (odds ratio [OR] = 2.34 [1.093-4.936]; p = 0.028), as well as family history of ALS (odds ratio [OR] = 2.538 [1.375-4.634]; p = 0.002). Intermediate CAG expansions of ≥27 repeats in ATXN2 are associated with ALS risk, but not with FTD, in the Spanish population. ALS patients carrying an intermediate expansion in ATXN2 show more frequent limb onset, but worse prognosis than those without expansions. In patients carrying C9orf72 expansions, the intermediate ATXN2 expansion might increase the penetrance and modify the phenotype.

Background: Electrical conductivity of trabecular bone at 100 kHz was recently reported as a good predictor of bone volume fraction. However, to quantify its relationship with the free water (or physiological solution) content and between the conductivities of its constituents, is still unclear. Methods: In this contribution, in silico models inspired by microCT images of trabecular bovine samples were used to build realistic geometries. Finite Element Method was applied to solve the electrical problem and to robustly fit the conductivity of the constituents with literature data. The obtained effective electrical conductivity was compared to Bruggeman three media mixture model: physiological solution, bone marrow and bone matrix. Results: The values for physiological solution plus bone marrow and bone matrix that captured better the bone volume fraction in the two media Finite Element model were: σps+bm = 298.4 mS/m and σb = 21.0 mS/m, respectively. Additionally, relative good results were obtained by a three media Bruggeman mixture model with σbm= 103 mS/m, σb= 21.0 mS/m, and σps= 1200 mS/m. Simple linear relationships between the proportions of constituents depending on bone volume fraction were tested. Degree of anisotropy and fractal dimension do not show detectable changes in effective conductivity. Conclusions: These results provided some useful findings for simulation purposes: first, a higher value of electrical conductivity of bone marrow has to be used in order to obtain similar values to those experimental published data. Second, anisotropy is not detectable by conductivity measurements for small trabecular samples (cube 5 mm). Finally, the simulations presented here showed a relatively good fitting of the mixture Bruggeman model which potentially would allow both, accounting for the free water content and re-scaling the model for whole bone electrical simulations.

Acute ischemic stroke (AIS) is the loss of neurological function due to a sudden reduction in cerebral blood flow and is a leading cause of disability and death worldwide. In recent years, the field of radiological imaging has experienced an explosion that may reach unprecedented heights with the advent of artificial intelligence. One of the latest innovations in artificial intelligence is radiomics, which is based on the fact that a large amount of quantitative data can be extracted from radiological images, from which patterns can be identified and associated with specific pathologies. Since its inception, radiomics has been particularly associated with the field of oncology, with promising results in a wide range of clinical situations. The performance of radiomics in non-tumour pathologies has been increasingly explored in recent years and the results continue to be promising. The aim of this review is to explore the potential applications of radiomics in AIS patients and to theorise how radiomics may change the paradigm for these patients in the coming years.

A brain-computer interface for controlling elements commonly used at home is presented in this paper. It includes the electroencephalography device needed to acquire signals associated to the brain activity, the algorithms for artefact reduction and event classification, and the communication protocol.

We discuss the topic of the transparency of the Universe in gamma-rays due to the Extragalactic Background Light, and its cosmological and physical implications. Rather than a review, this is a personal account on the development of 30 years of this branch of physical science. Extensive analysis of the currently available information appears to us as revealing a global coherence among the astrophysical, cosmological, and fundamental physics data, or, at least, no evident need so far of substantial modification of our present understanding. Deeper data from future experiments will verify to what extent and in which directions this conclusion should be modified.

The purpose of this paper is to examine the role and impact of public governance on European supranational public policies that seek to foster scientific and economic growth within innovation ecosystems (IEs) through the PITCCH initiative. The PITCCH is a European innovation development initiative funded by Horizon 2020 under the 'Innovation in SMEs' measure.The research methodology for this study is based on a 10-month real-time ethnographic field study of the PITCCH project, which allowed for the observation of the project governance and the capture of the perspectives of innovation-driven small and medium enterprises (i-SMEs) in their engagement with IE stakeholders.The findings of this research demonstrate the role of public governance in providing result-oriented practical support to the IE through the lens of i-SMEs, and they contribute to the existing literature on regional economic growth supported by supranational public policies. These findings have both academic and practical applications. They enrich the current literature on this topic and provide insights for policymakers and governing representatives seeking to implement effective public policies.

Grafting polyethylene glycol (PEG) on polymers surface is widely used to improve biocompatibility by reducing protein and cell adhesion. Although PEG is considered to be bioinert, its incorporation to biomaterials has shown to improve cell viability depending on the amount and molecular weight (MW) used. This phenomenon was studied here by grafting PEG of three MW onto polyurethane (PU) substrata at three molar concentrations to assess their effect on PU surface properties and on the viability of osteoblasts and fibroblasts. PEG formed a covering on the substrata which increased the hydrophilicity and surface energy of PUs. Among the results it was observed that osteoblast viability increased for all MW and grafting densities of PEG employed compared with unmodified PU. However, fibroblast viability only increased at certain combinations of MW and grafting densities of PEG, suggesting an optimal level of these parameters. PEG grafting also promoted a more spread cell morphology than that exhibited by unmodified PU; nevertheless, cells became apoptotic-like as PEG MW and grafting density were increased. These effects on cells could be due to PEG affecting culture medium pH, which became more alkaline at higher MW and concentrations of PEG. Results support the hypothesis that surface energy of PU substrates can be tuned by controlling the MW and grafting density of PEG, but these parameters should be optimized to promote cell viability without inducing apoptotic-like behavior.

The official recommendations to manage COVID-19 in the outpatient setting focus on high-cost drugs (remdesivir, nirmatrelvir-ritonavir, molnupiravir), suffering from many problems. Therefore, one may consider also other treatments meeting these criteria: • promising effectiveness, based on favorable randomized controlled trials (RCTs) of sufficient validity. Even without definitive evidence, physicians and informed patients can consider them if they are also: • safe, to the state of knowledge • with a very favorable opportunity cost • biologically plausible/not implausible • accessible • without major commercial sponsors in the trials promoting them, and with researchers without major conflicts of interests. The main focus is on a limited group of functional foods and some their active ingredients, with further mention of other substances of different origin, yet fully meeting all the aforementioned criteria. The functional foods and substances described and discussed certainly do not cover all the known effective therapies for COVID-19 (in one case also for Long-COVID), but the only therapies proposed in this paper currently satisfy all the above criteria. The supporting scientific evidence will favor meta-analyses of RCTs that include the most definitive outcome: all-cause mortality, i.e. the outcome that most fully informed people tend to prioritize. Furthermore, the reported evidence favors clinical studies of the most valid design, i.e. RCTs.

Plastics, primarily microplastics, are among the greatest pollutants in aquatic environments. Their removal and/or degradation in these environments are crucial to ensure an optimal future of the ecosystems. In this work MnO2 particles were synthesized and characterized for the removal polystyrene microplastics as a model. MnO2 catalyzes the peroxide reaction, resulting in the formation of oxygen bubbles that propel the pollutants to the surface, achieving removal efficiencies of up to 80%. To achieve this, hydrothermal synthesis was employed using various methods. Parameters such as MnO2, microplastics and H2O2 concentrations were varied to determine the optimal conditions for microplastics recovering. The ideal conditions for a low microplastic concentrations of microplastics (10 g L-1) are 0.2 g L-1 MnO2, 1.6% of H2O2 and 0.01 triton as a surfactant. In these conditions the micromotors can recover approximately of 80% 300 nm sized polystyrene microplastic within 40 min.

CVDs are a leading cause of death globally. In CVDs, the heart is unable to deliver enough blood to other body regions. Since effective and accurate diagnosis of CVDs is essential for CVD prevention and treatment, machine learning (ML) techniques can be effectively and reliably used to discern patients suffering from a CVD from those who do not suffer from any heart condition. Namely, machine learning algorithms (MLAs) play a key role in the diagnosis of CVDs through predictive models that allow us to identify the main risks factors influencing CVD development. In this study, we analyze the performance of ten MLAs on two datasets for CVD prediction and two for CVD diagnosis. Algorithm performance is analyzed on top-two and top-four dataset attributes/features with respect to five performance metrics –accuracy, precision, recall, f1-score, and roc-auc – using the train-test split technique and k-fold cross-validation. Our study identifies the top two and four attributes from each CVD diagnosis/prediction dataset. As our main findings, the ten MLAs exhibited appropriate diagnosis and predictive performance; hence, they can be successfully implemented for improving current CVD diagnosis efforts and help patients around the world, especially in regions where medical staff is lacking.

Introduction: Studies conducted in real-life scenarios on vaccine protection against COVID-19 constitute an important global priority, but one that is currently mostly neglected in low- and middle-income countries such as Angola. Here, we analyze for the first-time vaccine protection against COVID-19 in a real-life scenario after 6 months of implementing a multi-vaccination plan in Angola, providing estimation of odds ratios in vaccinated individuals and vaccine efficacy against infection by SARS-CoV-2 in a period that coincided with the identification of the Omicron variant in the country. Methods: We used a negative test case-control design to assess the effectiveness of vaccination against confirmed SARS-CoV-2 infection. A total of 4.232 vaccinated and unvaccinated individuals with the result of a rapid antigen diagnostic test against SARS-CoV-2 performed from December 27 to 28, 2021 were included in the study. Data were extracted from the Digital Vaccination Record Platform (Rediv) of the Ministry of Health of Angola. All ethical procedures related to the authorization necessary to carry out the study were followed. Statistical analyzes were performed using version 7.5.2.0 of CDC's Epi Info. Frequency distributions and measures of central tendency were used to characterize the study universe. The general and sex-adjusted and age-adjusted odds ratios, were evaluated by comparing the chances of vaccination between cases and controls, and their associated 95% CI, which were calculated using the Mantel-Haenszel stratification method. The risk classification of Axel Kroeger, Piscoya and Alarcon was used to interpret the odds ratio. The Breslow-Day statistic was used to assess the homogeneity of the odds ratios. Vaccine efficacy was calculated using the odds ratio applying the accepted statistical vaccine efficacy formula:(1 − odds ratio) × 100. For all estimates, a P value < 0.05 was considered statistically significant. Results: The population consisted of 63.63% male and 36.37% female. The mean age was 36 years with a standard deviation of 13. 83. Regarding vaccination status, 83.27% of individuals were vaccinated and 16.73% were unvaccinated, with 21.81% positive and 78.19% negative for SARS -CoV-2. The odds of SARS-CoV-2 infection were 0.85 (95% CI 0.70 – 1.03) times lower in vaccinated compared to unvaccinated individuals, with P=0.09. The overall vaccine efficacy (VE) was 15% (95% CI -3 – 30). Conclusion: There was no statistically significant decrease in the chances of SARS-CoV-2 infection in vaccinated versus unvaccinated individuals. However, the overall vaccine efficacy was 15%.

Introduction: Overuse of antibiotics has been increasing in recent years and is currently the most prescribed drug in the paediatric age. It is estimated that 72% of patients in the neonatal intensive care unit (NICU) are treated with antibiotics. One of the effects observed with their use is the alteration of the microbiota, which is associated with metabolic disorders including obesity. In this study, the aim was to determine in newborn rats, whether most commonly prescribed antibiotics in Mexican NICUs have a differential effect on weight gain, development of adiposity, and modification of carbohydrate and lipid metabolism. Methods: This is an experimental study in newborn rats, comparing Ampicillin/Meropenem (Access/Watch groups) 100 mg/g every 12 h /10 mg/g every 12 h (each of these two antibiotics was adjusted to the required dosages based on weight); Cefotaxime 200 mg/g every 24 h; (Watch group) and Amikacin 15 mg/g every 24 h (Access group) versus Saline Solution as Control. Each antibiotic was intraperitoneal administered daily for 5 days in males (to avoid gender differences) to 14 newborn rats in each group. A comparison of anthropometric and biochemical parameters registered on day 28 was made using ANOVA analysis. Differences were considered if p < 0.05.Results: Amikacin was the antibiotic with the greatest effect on anthropometric measurements and LDL-cholesterol; as well as Cefotaxime on Glucose and triglycerides, while Meropenem plus Ampicillin showed the weakest effect on measured parameters. Conclusion: The administration of antibiotics in the neonatal stage has repercussions on the body composition of adult rats, as well as on lipids and carbohydrates serum levels. Future studies might evaluate the toxicity of antibiotics on the immature neonatal organs.

Wind technology is considered to be among the most promising types of renewable energy sources, and due to high oil prices and growing concerns about climate change and energy security, it has been the subject of extensive considerations in recent years, including questions related to the relative sustainability of electricity production when the manufacturing, assembly, transportation and dismantling processes of these facilities are taken into account. The present article evaluates the environmental impacts, carbon emissions and water consumption, derived from the production of electric energy of the Villonaco wind farm, located in Loja-Ecuador, during its entire life cycle, using the Life Cycle Analysis method. Finally, it is concluded that wind energy has greater environmental advantages, since it has lower values of carbon and water footprints than other energy sources. Additionally, with the techniques Cumulative Energy Demand and Energy Return on Investment, sustainability in the production of electricity from wind power in Ecuador is demonstrated; and, that due to issues of vulnerability to climate change, the diversification of its energy mix is essential considering the inclusion of non-conventional renewable sources such as solar or wind, this being the only way to reduce both the carbon footprint and the water supply power.

Integrating nanomaterials like carbon nanotubes (CNTs) and iron oxide (Fe₃O₄) into epoxy composites has significant interest due to their potential to enhance mechanical properties. This study evaluates the impact of dispersion quality on the mechanical performance of CNT/Fe₃O₄/Epoxy composites, comparing stirring and sonication methods at three different loadings: 0.1, 0.3, and 0.5 wt.%. Tensile testing revealed that sonicated composites consistently outperformed stirred composites, with a significant increase in elastic modulus and ultimate tensile strength (UTS). However, fracture strain decreased in both composite types compared to pure epoxy, with sonicated composites experiencing a more significant reduction than stirred composites. These results underscore the importance of high-quality dispersion for optimizing mechanical properties.

Current methods for assessing individual well-being in team collaboration at the workplace rely often on manually collected surveys. This limits continuous real-world data collection and proactive measures to improve team member workplace satisfaction. We propose a method to automatically derive social signals related to individual well-being in team collaboration from raw audio and video data collected in teamwork contexts. The goal is to develop computational methods and measurements to facilitate the mirroring of individuals’ well-being to themselves. We are focusing on how speech behavior is perceived by team members to improve their well-being. Our main contribution is the assembly of an integrated toolchain to perform multi-modal extraction of robust speech features in noisy field settings and to find which features are predictors of self-reported satisfaction scores. We apply the toolchain to a case study where we collected videos of 22 teams with 56 participants collaborating over a four-day period in a team project. Our audiovisual speaker diarization extracts individual speech features in a noisy environment. As the dependent variable team members filled out a daily PERMA (positive emotion, engagement, relationships, meaning, and accomplishment) survey. These well-being scores have been predicted with speech features extracted from the videos using machine learning. The results suggest that the proposed toolchain is able to automatically predict individual well-being in teams, leading to better teamwork and happier team members.

Current smartphones incorporate different types of sensors that allow us to know our spatial position, they give us information about pressure, speed, acceleration, time, acoustic level, and other different physical magnitudes. These smartphones measure each component of the magnetic field, bearing in mind that any current perpendicular to a magnetic field produces a small potential difference, transversal to the said current, being this voltage easily measurable by Hall sensors. With the implementation of three Hall sensors, and an appropriate app, we can measure the three components of the magnetic field vector, and with this we can obtain information and deduce properties of the physical systems considered. In this paper we are exploring the use of smartphones in a physics laboratory for freshman students. To do this, we have measured, using Hall sensors, the magnetic field created by a linear quadrature, and we have obtained, first of all, its dependence on the distance between the quadrupole and the magnetic sensor. The second purpose of this work is to show that the laboratory is a powerful tool that increases the significant learning of freshman students through advanced technological tools.

We believe that a natural focus of the Physics Education Research community is on understanding and improving student learning in our physics courses. For this purpose, we are introducing smartphones in the physics laboratory. Current smartphones measure each component of the magnetic field, bearing in mind that any current perpendicular to a magnetic field produces a small potential difference, transversal to the said current, being this voltage easily measurable by Hall sensors. In this work, we have considered the magnetic field created by a linear quadrupole and we have studied its dependence on distance. Using an experimental procedure that is simple we have measured the magnetic field using the Hall sensor that most smartphones have, together with the corresponding app. The purpose of this work is to show that the laboratory is a powerful tool that increases significant learning under three conditions: 1) the practice must not be too sophisticated; 2) students must handle objects in the lab; and 3) the practice must be scientifically accurate, including the adjustments by minimum squares, and the following and necessary error calculation.

Population studies have demonstrated an association between sodium (Na) and po-tassium (K) intake and levels of blood pressure (BP) and cholesterol. The aim of this study was to describe the dietary intake and distribution of Na and K in Mexicans, and their as-sociation with metabolic risk outcomes. We analyzed a national survey that included 4,219 participants. Dietary information was obtained through a 24-hour recall. Foods and beverages were classified based on the degree of processing. BP and biomarkers in blood and urine were measured. The mean intake (mg/d) of Na was 1512 in pre-schoolchildren, 2844 in schoolchildren, 3743 in adolescents, and 3132 in adults. The mean intake of K was 1616 in pre-schoolchildren, 2256 in schoolchildren, 2967 in adolescents, and 3401 in adults. Processed and ultra-processed foods (UPF) contribute to sodium intake: 49% in preschool and schoolchildren, 47% in adolescents, and 39% in adults. Adults in the fourth quartile of sodium intake had lower serum concentrations of cholesterol (181.4 mg/dL) and HDL-c (35.5 mg/dL). The Mexican population has high Na and low K intakes. There is a relationship between Na sodium consumption and cholesterol, and LDL levels. UPF contributes to almost 40% of the sodium consumed by Mexicans.

In this paper the possibility of harvesting energy from the vibrations of a plate is analyzed. The harvester takes the form of a cantilever dynamic vibration absorber equipped with a piezoelectric layer and tuned by means of a tip mass to the first mode of vibration of the plate. A mathematical model of the coupled system composed of the plate and the harvester is presented. The validity of the proposed harvester is proved by means of simulations carried out with the modal expansions approach. Simulation results highlighting the effect of harvester tuning and location are presented as well. Then the validity of the harvester is confirmed by experimental tests carried out both with a concentrated impulsive load and with a distributed pressure load. Simulations and experimental tests show that the cantilever piezoelectric dynamic vibration absorber generates open circuit voltage an order of magnitude larger than the one generated by the same piezoelectric layer when directly bonded to the plate surface.

With a predominantly humid tropical climate and a large area for expanding agricultural activities, Angola has in principle favorable conditions for bioenergy production. The focus of this study was to evaluate the availability of suitable land for producing sugarcane. This crop is highly efficient in converting solar energy into biomass for energy purposes in Angola. To this end, the Global Agro-Ecological Zones database, GAEZ v4, developed by IIASA and FAO, with the aid of the QGIS version 3.22.5, has been used to identify areas suitable for sugarcane cultivation, considering four levels of suitability. Aiming at the sustainability of bioenergy production, areas dedicated to food production, protected areas, and areas with slopes above 16% were excluded. Under these criteria, this study identified the existence of 6.3 Mha in lands of good agricultural suitability, with water resources, corresponding to 5% of the Angolan territory, distributed in seven provinces of the country, especially in the provinces of Cuando Cubango and Cunene, where 85% of the very suitable land under irrigation is located. Adopting a model of agricultural productivity, assuming irrigation and adequate agricultural practices, such area could produce approximately 956 million ton of sugarcane annually, which is significantly higher than the current production in this country, This amount of feedstock, processed using current technology could potentially produce 81.3 GL of ethanol and 176,9 TWh of electricity with low GHG emission per year, able to mitigate, as a whole, circa 60.3 MtCO2-eq/year by displacing gasoline in light vehicles and diesel and natural gas consumed in power generation.

According to the cognitive radio (CR) paradigm, the efficient use of the radio spectrum is achieved by performing four main functions: spectral perception, spectral mobility, decision-making, and cooperation. In this work, a multivariable algorithm that considers geographic mobility (GM) is presented. The decision-making process includes a feedback mechanism for the dynamic selection of reservation channels in a wireless network with cognitive capabilities. Geographic mobility plays a crucial role in how radio takes advantage of its environment; however, it has been scarcely explored in the literature. In the present work, geographic mobility tests were carried out involving the interaction of CR and primary users (PU), where a measurement of the latency time of the proposed algorithm called: Algorithm for Decision-Making with Geographic Mobility (ATDeMoGeo) was evaluated and compared with other algorithms such as: Dijkstra's Algorithm, Analytic Hierarchical Process (AHP), Fuzzy Analytic Hierarchical Process (FAHP) and Decision-Making Algorithm with Modified Dijkstra (ATDDiM). It was found that the proposed algorithm is robust and that it considerably reduces the latency time to accurately determine the best communication channels, thanks to the decision-making function implemented to establish which channels the CRs can occupy. For the selection of the best reserve channels, a series of criteria were considered, such as: signal-to-interference plus noise ratio (SINR), bandwidth (BW), probability of channel availability (AP), estimated channel time of availability (ETA) using the random waypoint mobility model (RWPM). These criteria are evaluated for two types of service, i.e., real time (RT) and best effort (BE). For the evaluation and validation of the ATDeMoGeo algorithm, a network with cognitive characteristics in NS-3 was simulated. The simulation scenarios consisted of a base station (BS) with mobile CRs and PUs. Based on the previous criteria, i.e., BW, SINR, AP and ETA, the BS assesses the spectrum occupancy information obtained by the CRs. The results show that the proposed algorithm can be used without problem for the proposed scenarios with higher speed and accuracy for the selection of better communication channels.

In recent years, numerous research studies have shown the excellent characteristics of silk fibroin nanoparticles as a vehicle for drugs delivery and it is foreseeable that their production could reach industrial scale in the coming years. For this reason, it is essential to know all the parameters that affect the formation of nanoparticles in order to standardize the process. Several studies have stated that the process used for sericin removal (degumming) from silk cocoons has a strong impact in the silk fibroin integrity and their mechanical properties after processing it into biomaterials. In this work, silk cocoons were degummed following four standard methods: autoclaving, short alkaline (Na2CO3) boiling, long alkaline (Na2CO3) boiling and ultrasounds. The resultant silk fibroin fibers were dissolved in the ionic liquid 1-ethyl-3-methylimidazolium acetate and used for nanoparticle synthesis by rapid desolvation in polar organic solvents. The relative efficiencies of the degumming processes and the integrity of the resulting fibroin fibers obtained were analyzed by weight loss, optical microscopy, thermogravimetric analysis, infrared spectroscopy and SDS-PAGE. Particle sizes and morphology were analyzed by Dynamic Light Scattering and Field Emission Scanning Electronic Microscopy. The results showed that the different treatments had a remarkable impact on the integrity of the silk fibroin chains, as confirmed by gel electrophoresis which can be correlated with particle mean size and size distribution changes. The study confirm that all the parameters of the process must be controlled in order to reach an optimum reproducibility of the nanoparticle production.

The melanoma differentiation-associated protein 5 (MDA5; encoded by the IFIH1 gene) mediates the activation of the interferon pathway in response to viral infection. This protein is also upregulated in autoimmune diseases and psoriasis skin lesions. IFIH1 gene variants that increased the MDA5 activity have been associated with increased risk for immune mediated diseases, including psoriasis. Our aim was to determine the association between three IFIH1 variants (rs35337543, intron8 +1G>C; rs35744605, Glu627Stop; and rs1990760, Ala946Thr) and the main clinical findings in a cohort of Spanish patients with psoriatic disease (N=572; 77% early-onset). Early-onset psoriasis (EOPs) had a significant higher frequency of severe disease and Cw6+. Carriers of the 946Thr variant were more common in EOPs (p<0.001), and the effect was more pronounced among Cw6-negatives. This variant was also associated with an increased risk of psoriatic arthritis (PsA) independently of other factors (OR=1.62, 95%CI=1.11-2.37). The rs3533754 and rs35744605 have been reported as risk factors for viral infection and protective for autoimmune diseases, but we did not find significant differences between the two onset age or PsA groups. However, due to the reduced frequency of the two variants (<0.02) the size of our cohort was too low to conclude a significant effect. In conclusion, the common IFIH1 rs1990760 T allele that has been linked to increased gene expression was significantly more frequent in EOPs patients. This variant was also an independent risk factor for PsA in our cohort. This risk allele was in linkage disequilibrium with other variants previously associated with the risk of psoriasis and PsA. Our study reinforces the widely reported role of IFIH1 gene variants on psoriatic disease and other immune mediated diseases.

Gender-based violence is considered a serious social and public health problem. Overcoming this situation implies a process that results in the favorable biopsychosocial rehabilitation, the resilient of women. The objective of this study was to analyze the tools, resources and personal and psychosocial mechanisms used by women survivors of gender-based violence. The design was an interpretative phenomenology. It carried out with 22 women who have overcome gender-based violence. Data was collected through personal interviews and narration. The results were grouped into four themes: "Process of violence", "Social resources for coping and overcoming GBV", "Personal tools for coping and overcoming GBV", and "Feelings identified, from the abuse stage to the survival stage". Several studies concluded that overcoming abuse is influenced by the women social network, and it can be the action of these people determining their survival to gender violence. Despite the recognized usefulness of these available resources, it would be desirable to strengthen them in order to be able to drive more women toward survival, assuming a strengthening of coping and overcoming, without forgetting the importance of other support mechanisms such as their family and group therapies.

This paper presents a novel approach for accurate counting and localization of tropical plants in aerial images that is able to work in new visual domains in which the available data is not labeled. Our approach uses deep learning and domain adaptation, designed to handle domain shift between the training and test data, which is a common challenge in this agricultural applications. This method uses a source dataset with annotated plants and a target dataset without annotations, and adapts a model trained on the source dataset to the target dataset using unsupervised domain alignment and pseudolabeling. The experimental results show the effectiveness of this approach for plant counting in aerial images of pineapples under significative domain shift, achieving a reduction up to 97% in the counting error when compared to the supervised baseline.

Polymer hydrogels are 3D networks consisting of hydrophilic crosslinked macromolecular chains, able to swell and retain water. Since their invention in the 1960s, they have become an outstanding pillar in the design, development, and application of engineered polymer systems suitable for biomedical and pharmaceutical applications (such as drug or cell delivery, regeneration of hard and soft tissues, wound healing, and bleeding prevention, among others). Despite several well-established synthetic routes of polymer hydrogels based on batch polymerization techniques, about fifteen years ago academia started to look for alternative methods involving simpler reaction paths, shorter reaction times, and lower energy consumption. In this context, frontal polymerization (FP) undoubtedly has become an alternative and efficient reaction model that allows for converting monomers into polymers via a localized and propagating reaction, by exploiting the formation and propagation of a “hot” polymerization front, able to self-sustain and propagate throughout the monomeric mixture. Therefore, the present work aims to summarize the main research outcomes, achieved during the last years, concerning the design, preparation, and application of FP-derived polymeric hydrogels, demonstrating the feasibility of this technique for the obtainment of functional 3D networks, and providing the reader with some perspectives for the forthcoming years.

In this article, we analyse the determinants of Regulatory Quality-RQ for 193 countries in the period 2011-2020. We use a database from ESG-Environment Social Governance of the World Bank. We apply OLS, Panel Data with Fixed Effects and Panel Data with Random Effects. We found that the variables that have the most positive impact on RQ, among others, are “GHG Net Emission”, “Mean Drought Index”, and “Heat Index”. We also found that the variables that have the most negative impact on RQ are among others “Renewable Energy Consumption”, “Voice and Accountability” and “Rule of Law”. Furthermore, we have applied the k-Means algorithm optimized with the Elbow Method and we find the presence of five clusters. In adjunct, we confront eight machine learning algorithms to predict the value of RQ and we found that the best predictor is Polynomial Regression. The predictive level of RQ for the analysed countries is expected to diminish of -1,29%. In the end, we present a network analysis with the Euclidean distance and we found the presence of a structure of seven networks using augmented data.

In this work, we propose an easy and a low cost method for the synthesis of Nitrogen-Doped Graphene NDG and its silver nanowires NW functionalization NWGN. The synthesis was performed using the improved graphene oxide method, chemical reduction of graphene oxide in the presence of caffeine as green nitrogen source and the subsequently the silver nanowires growth in the surface, by the chemical reductions salts in the presence of NG. Achieving a homogeneous growing (coating) of graphene sheets. The samples were analyzed using conventional characterization techniques: SEM-EDX, XRD, FT-IR, RAMAN, TEM, HRTEM, STEM and XPS.

Graves' disease (GD) is an autoimmune disorder in which Thyrotropin Receptor Antibodies (TRAb) continuously stimulate thyroid receptors, leading to excessive production of thyroid hormones. TRAb also affect the orbital tissues, causing Graves' ophthalmopathy. Treatment to control Graves' disease consists of antithyroid drugs, radioiodine and thyroidectomy, and through such treatments, TRAb is reduced and the disease is alleviated. The mechanism by which TRAb, which play a critical role in the pathogenesis of Graves’ disease, decreases after GD treatment is still unknown. This paper presents a theoretical mechanism of how GD patients maintain a vicious cycle and how GD treatment can reduce the TRAb levels and breaks the cycle using stress-based ideas.

Somerateofmutationisinevitable,eveningenesthatthemselvesdeterminethemutation rate. Because mutation in these genes can alter the mutation rate, variation in the mutation rate is inevitable. This simple line of reasoning is corroborated by a handful of empirical studies that found mutation-rate variation in natural and laboratory populations. The evolutionary consequences of mutation-rate variation in natural populations can be many and can be difficult to predict, as selection on such variation can be both direct and indirect. For example, lineages with increased mutation rate will have increased adaptability in the long term but decreased overall fitness in the short term because most fitness mutations are immediately deleterious. In spite of its great relevance to basic and applied science, the rate of mutation to mutator phenotype continues to be essentially unknown. The reasons for this gap in our knowledge are largely methodological; in general, a mutator phenotype cannot be observed directly but must instead be inferred from numbers of some neutral “marker” mutation that can be observed directly: different mutation-rate variants will produce this marker mutation at different rates. Here, we derive the expected distribution of numbers of the marker mutants observed, accounting for the fact that some of the mutants will have been produced by a mutator phenotype that itself arose by mutation during the growth of the culture. This development makes possible a novel experimental protocol for estimating the rate of mutation to mutator phenotype. Simulated experiments using biologically-reasonable parameters that employ this protocol show that such experiments in the lab can give us fairly accurate estimates of the rate of mutation to mutator phenotype. Although our ability to estimate mutation-to-mutator rates from simulated experiments is promising, we view this study as a proof-of-concept study and only a first step towards practical empirical estimation.

The crucial stages in the geochemical evolution of the Earth’s crust, ocean, and atmosphere could be explained by the assumed low-energy nuclear reactions (LENR) that are triggered by seismic activity. LENR result in the fission of medium-weight elements accompanied by neutron emissions, involving Fe and Ni as starting elements, and C, N, O as resultants. Geochemical data and experimental evidences support the LENR hypothesis. A spectral analysis of the period 1955-2013 shows common cycles between interannual changes in atmospheric CO2 growth rate and global seismic-moment release, whereas the trending behavior of the atmospheric CO2 was in response to the anthropogenic emissions. Assuming a correlation between such seismic and atmospheric fluctuations, the latter could be explained by cycles of worldwide seismicity, which would trigger massively LENR in the Earth’s Crust. In this framework, LENR from active faults could be considered as a relevant cause of carbon formation and degassing of freshly-formed CO2 during seismic activity. However, further studies are necessary to validate the present hypothesis which, at the present time, mainly aims to stimulate debate on the models which regulates atmospheric CO2.

Background: A new access technique was developed to reduce postoperative adverse events after the extraction of impacted maxillary wisdom teeth. Hence, this study aimed to assess the occurrence of adverse events after extraction of maxillary impacted wisdom teeth using traditional or modified access. Methods: Two different surgical incision designs were used for bilateral wisdom tooth extractions in 30 patients. The traditional incision was performed distal to the second molar in the center of the tuberosity, followed by a buccal releasing incision. After tooth extraction the wound was secured by sutures. The new technique consists of an oblique incision from the distal palatal aspect of the tuberosity towards the buccal aspect of the second molar. After tooth extraction, cyanoacrylate glue was used on the wound. Results: Lower pain was reported by patients at the site treated with the new technique (p&lt;0.01). Edema, postsurgical bleeding, and hematoma were similar in both groups. The surgical time was shorter for the new technique (p&lt;0.01). Conclusions: The new technique applied for the extraction of impacted maxillary wisdom teeth reduced postsurgical pain and the duration of surgery.

Ampelography, the botanical discipline dedicated to the identification and classification of grapevine cultivars, was grounded on the description of morphological characters and more recently is based on the application of DNA polymorphisms. New methods of image analysis may help to optimize morphological approaches in ampelography. The objective of this study was the classification of representative cultivars of Vitis vinifera conserved in the Spanish collection of IMIDRA according to seed shape. Thirty eight cultivars representing the diversity of this collection were analyzed. A consensus seed silhouette was defined for each cultivar representing the geometric figure that better adjusted to their seed shape. All the cultivars tested were classified in ten morphological groups, each corresponding to a new model. The models are geometric figures defined by equations and similarity to each model is evaluated by quantification of percent of the area shared by the two figures, the seed and the model (J index). The comparison of seed images with geometric models is a rapid and convenient method to classify cultivars. A large proportion of the collection may be classified according to the new models described and the method permits to find new models according to seed shape in other cultivars.

We study in this work a dilute granular gas immersed in a thermal bath made of smaller particles with nonnegligible masses as compared with the granular ones. The kinetic theory for this system is developed and described by an Enskog--Fokker--Planck equation for the one-particle velocity distribution function. Granular particles are assumed to have inelastic and hard interactions, losing energy in collisions as accounted by a constant coefficient of normal restitution. The interaction with the thermal bath is based on a nonlinear drag force plus a white-noise stochastic force. To get explicit results of the temperature aging and steady states, Maxwellian and first Sonine approximations are developed. The latter takes into account the coupling of the excess kurtosis with the temperature. Theoretical predictions are compared with direct simulation Monte Carlo and event-driven molecular dynamics simulations. While good results for the granular temperature are obtained from the Maxwellian approximation, a much better agreement, especially as inelasticity and drag nonlinearity increase, is found when using the first Sonine approximation. The latter approximation is, additionally, crucial to account for memory effects like Mpemba and Kovacs-like ones.

We estimate the value of Research and Development Expenditures as a percentage of GDP-RDE in the context of Environmental, Social and Governance-ESG model. We use the ESG World Bank database. We analyze data from193 countries in the period 2011-2020. We apply a set of econometric techniques i.e. Pooled Ordinary Least Squares-OLS, Panel Data with Random Effects, Panel Data with Fixed Effects, Weighted Least Squares-WLS. We found that the level of RDE is positively associated, among others, to “Nitrous Oxide Emissions” and “Scientific and Technical Journal Articles”, and negatively associated, among others to “Heat Index 35”, “Maximum 5-day Rainfall”. Furthermore, we perform a cluster analysis with the application of the k-Means algorithm optimized with the Elbow Method. The results show the presence of four clusters. Finally, we confront eight different machine-learning algorithms to predict the future value of RDE. We find that Linear Regression is the best predictive algorithms. RDE is expected to growth on average of 0.07% for the analysed countries.

After approximately 6 months of age, term breastfed infants are increasingly depending on other sources of iron to avoid iron deficiency anemia. The appropriate complementary feeding must include a balance composition of foods containing an adequate amount of macro- and micronutrients to reduce the risk of iron deficiency anemia. This study aims to compare the anemia and growth status of infants receiving commercial fortified infant cereals (FIC) with infants not receiving them. We use all complete multiple Demographic Health Surveys (DHS) from 2005 to 2018 to understand global infant feeding patterns. To better control for the strong household wealth effect in nutritional choices and possibly health awareness, we use propensity score technique as applied in outcome research to better control the effect of covariates. After matching and controlling for confounders, we did find a significant association between reduced risk of anemia and consumption of FIC. After matching and adjusting for confounders the small but positive effects of consumption of FIC on Height for age z-score and Weight for Height Z-score are no longer statistically significant.

We consider a Cauchy problem for differential equations in a Hilbert space $X$. The problem is stated in a time interval $I$, which can be finite or infinite. Weuse a fixed point argument for history-dependent operators to prove the unique solvability of the problem. Then, we state and prove convergence criteria for both a general fixed point problem and thecorresponding Cauchy problem. These criteria provide necessary and sufficient conditions on a sequence $\{u_n\}$ which guarantee its convergence to the solution of the corresponding problem, in the space of both continuous and continuously differentiable functions. We then specify our results in the study of a particular differential equation governed by two nonlinear operators. Finally, we provide an application in viscoelasticity and give a mechanical interpretation of the corresponding convergence result.

This paper discusses the theoretical and experimental correlations between fatigue and static strength statistical distributions. We use a two-parameter residual strength model that obeys the qualitative Strenght-Life Equal-Rank (SLERA) assumption for guidance. The modeling approach consists of recovering the model's parameters by best fitting the constant amplitude (CA) fatigue data at a given stress ratio, R, and the experimental Weibull parameters of the static strength distribution function. Once the model's parameters are fixed, its capabilities, potential, and limits are discussed by comparing its predictions with residual strength and fatigue data obtained at different stress ratios, R. Moreover, from a preliminary analysis, the theoretical extension of the model's capabilities to variable amplitude loadings is conceptualized. The application of Miner's rule is also discussed and compared with a new damage rule to analyze the fatigue responses under variable amplitude loadings.

Among the many approaches to COVID-19 prevention, the possible role of diet has so far been somewhat marginal. Nutrition is very rich in substances with a potential beneficial effect on health and some of these could have an antiviral action or in any case be important in modulating the immune system and in defending cells from the oxidative stress associated with infection. This short review draws the attention on some components of Citrus fruits and especially of the orange (Citrus sinensis), well known for its vitamin content, but less for the function of its flavonoids. Among the latter, hesperidin has recently attracted the attention of researchers, because it binds to the key proteins of the SARS-CoV-2 virus. Several computational methods, independently applied by different researchers, showed that hesperidin has a low binding energy both with the coronavirus "spike" protein, and with the main protease that transforms the early proteins of the virus (pp1a and ppa1b) into the complex responsible for viral replication. The affinity of hesperidin for these proteins is comparable if not superior to that of common chemical antivirals. The preventive efficacy of vitamin C, at dosage attainable by diet, against viral infections is controversial, but recent reviews suggest that this substance may be useful in case of increased stress on the immune system. Finally, the reasons that suggest undertaking appropriate research on the Citrus fruits addition in the diet, as a complementary prevention and treatment of COVID-19, are discussed.

Blazars are a subclass of Active Galactic Nuclei (AGN) having relativistic jets aligned within a few degrees of our line-of-sight and form the majority of the AGN detected in the TeV regime. The Fermi-Large Area Telescope (LAT) is a pair-conversion telescope, sensitive to photons having energies between 20 MeV-2 TeV, and is capable of scanning the entire gamma-ray sky every three hours. Despite the remarkable success of the Fermi mission, many questions still remain unanswered, such as the site of gamma-ray production and the emission mechanisms involved. The Asteroid Terrestrial-impact Last Alert System (ATLAS) is a high cadence all sky survey system optimized to be efficient for finding potentially dangerous asteroids, as well as in tracking and searching for highly variable and transient sources, such as AGN. In this study, we investigate possible correlations between the \textit{Fermi}-LAT observations in the 100 MeV-300 GeV energy band and the ATLAS optical data in the R-band, centered at 679 nm, for a sample of 18 TeV-detected northern blazars over 8 years of observations between 2015-2022. Under the assumption that the optical and gamma-ray flares are produced by the same outburst propagating down the jet, the strong correlations found for some sources suggest a single-zone leptonic model of emission.

Traditional ways of validating satellite-derived sea surface temperature (SST) and sea surface salinity (SSS) products, using comparisons with buoy measurements, do not allow for evaluating the impact of mesoscale to submesoscale variability. Here we present the validation of remotely-sensed SST and SSS data against the unmanned surface vehicle (USV) – Saildrone – measurements from the Spring 2018 Baja deployment. More specifically, biases and root mean square differences (RMSD) were calculated between USV-derived SST and SSS values, and six satellite-derived SST (MUR, OSTIA, CMC, K10, REMSS, and DMI) and three SSS (JPLSMAP, RSS40, RSS70) products. Biases between the USV SST and OSTIA/CMC/DMI were approximately zero while MUR showed a bias of 0.2C. OSTIA showed the smallest RMSD of 0.36C while DMI had the largest RMSD of 0.5C. An RMSD of 0.4C between Saildrone SST and the satellite-derived products could be explained by the daily variability in USV SST which currently cannot be resolved by remote sensing measurements. For SSS, values from the JPLSMAP product showed saltier biases of 0.2 PSU, while RSS40 and RSS70 showed fresh biases of 0.3 PSU. An RMSD of 0.4 PSU could not be explained solely by the daily variability of the USV-derived SSS. Coherences were significant at the longer wavelengths, with a local maximum at 100 km that is most likely associated with the mesoscale turbulence in the California Current System.

Right atrial isomerism (RAI) is a complex entity whose treatment and outcome are heterogeneous. The aim of this study was to analyze the results obtained after cardiac surgery in patients with RAI. A retrospective study was conducted; it included patients diagnosed with RAI who underwent cardiac surgery; their follow-up was from January 1, 2010 to March 31, 2020. Demographic characteristics and perioperative conditions were described. Thirty-eight patients with RAI were included, the median age was 4 years (IQR 2-9.2), and 57.9% were men. The main diagnoses were atrioventricular canal (63.2%) and pulmonary stenosis (55.3%). The most common surgical procedures were modified Blalock‐Taussig shunt (65.8%) and total cavopulmonary connection with an extracardiac conduit fenestrated without cardiopulmonary bypass (15.9%). The use of inhaled nitric oxide was a marker of postoperative mortality in critically ill patients (OR: 10.33; 95% CI: 1.04 - 102.08; p = 0.02). The overall survival was 86.8%, with a better outcome in those who did not require reintubation (Log Rank, p < 0.01). The survival of RAI was similar to other centers of reference. Individuals with RAI should be evaluated rigorously to determine an adequate repair strategy, considering high morbidity and mortality.

This paper presents a study of different types of parametric signals with application to underwater acoustic communications. In all the signals, the carrier frequency is 200 kHz, which corresponds to the resonance frequency of the transducer under study and different modulations are presented and compared. In this sense, we study modulations with parametric sine sweeps (4 to 40 kHz) that represent binary codes (zeros and ones), getting closer to the application in acoustic communications. The different properties of the transmitting signals in terms of bit rate, directivity, efficiency and power needed are discussed as well.

Seeds from several Ribes taxa were surveyed for phenolic compounds and in vitro antiproliferative activity against HT-29 colorectal cancer cells. Total phenolic compounds were analysed through the Folin-Ciocalteu procedure, while phenolic profiles were analysed by LC coupled to a single mass spectrometer Orbitrap using an electrospray interface (ESI). Antitumor effects were established using the MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay. Total phenolics ranged from 11.4 in R. alpinum to 94.8 mg of caffeic acid equivalents (CAE)/g in R. nigrum 'Koksa'. Concerning phenolic compounds, four were hydroxylated benzoic acids, four cinnamic acid derivatives, eight flavonoids, and nine flavonoid glycosides. The growth inhibition against HT-29 cancer cells was exercised much better by R. nigrum 'Koksa' and Ribes 'Erkeeni' (GI50 37 and 42 µg/ml). All Ribes extracts, except R. nigrum 'Hara katarlik', showed higher activity than that of R. rubrum (GI50 at 72 h: 99 µg/ml). Interestingly, the highly bioactive extract from Ribes 'Erkeeni' contains all detected phenolics, while R. nigrum 'Koksa' lacks only populnin. Therefore, the high bioactivity found for such extracts could be due to a synergy due to all detected compounds. This work constitutes a comprehensive action for expanding knowledge on the phenolic profiles and antitumor activity of GLA-rich Ribes seeds.

Basic features of seed dormancy are illustrated. The seed overall regulatory network governs seed metabolism and development, and it is coordinated by plant hormones. A functional model focused on abscisic acid (ABA), the foremost plant hormone in dormancy, is used as a framework to critically discuss the literature. Gibberellins (GAs) have a main role in germination, and the ABA – GAs balance is a typical feature of the seed state: ABA dominates during dormancy and GAs prevail through germination. Thus, the literature converges toward envisaging the development switch between dormancy and germination as represented by the ABA/GAs ratio. The ABA – GAs antagonism is based on mutual inhibition, a feature of the regulatory network architecture that characterizes development trajectories based on a regulatory circuit with a bistable switch. Properties of such kind of regulatory architecture are introduced step by step, and it is shown that seed development – towards either dormancy or germination – is more properly represented by a tristable regulatory circuit, whose intermediate metastable states ultimately take one or the other development trajectory. Although the ABA/GAs ratio can conveniently represent the state of the seed overall regulatory network along the seed development trajectory, specific (unknown) dormancy factors are required to determine the development trajectory. The development landscape is shown to provide a well-suited representation of seed states travelling along developmental trajectories, particularly when the states are envisioned as regulatory circuits. Looking at seed dormancy in terms of regulatory circuits and development landscapes offers a valuable perspective to improve our understanding of this biological phenomenon.

Background: Differential diagnosis between polymyalgia rheumatica (PMR) and seronegative elderly-onset rheumatoid arthritis (SEORA) is not easy, to the point that in the past they were considered the same entity. In these patients, sleep disorders have been scarcely assessed, and considered as expression of mood disorders such as depression and anxiety. Methods: In 38 Caucasian elderly patients (median age: 73.9 ± 8.06 years) consecutively referred to two outpatient clinics from January to May 2018 with diagnosis of PMR and SEORA, sleep impairment was assessed using the Medical Outcomes Study-Sleep scale (MOS-SS). Depression and anxiety were assessed using the Neuropsychiatric Inventory (NPI) score, with point 0 for absent and point 3 for severe. Comorbidities were assessed using the Cumulative Illness Rating Scale (CIRS). Patients taking medications used to treat sleep disturbance or that could favor sleep disturbances were excluded. The study was approved by the local ethics committee and carried out in accordance with the Helsinki Declaration, revised 2013. Every patient signed an informed consent form at the time of the first visit. Results: MOS-SS total point in PMR patients was significantly higher than in SEORA patients (47.60 ± 8.4 vs 28.26 ± 12.4; P = 0.000). After six-month therapy with prednisone (12.5–15 mg/day, followed after 4 weeks by gradual tapering), MOS-SS total point improved in the two groups of patients, with no significant difference (17.0 ± 6.2 vs 17.8 ± 4.2; P = 0.644). No correlation was found between MOS-SS and comorbidities, and between MOS-SS, anxiety or depression. Conclusions: Our data suggest that the assessment of sleep impairment could be very useful in the differential diagnosis between PMR and SEORA. Up today, the reasons why patients with PMR have—at the time of diagnosis—a sleep impairment higher than SEORA are speculative. Further ad hoc complementary studies in multicenter cohorts are needed.

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.

Water is a fundamental resource for ecosystems, humans, and the development of all economic sectors; it is necessary to identify and evaluate its environmental pressures or impacts. The water footprint (WF) is an appropriate indicator for the consumption of water used to produce a product. The present study uses this tool to evaluate the green and blue water requirements and the sustainability of irrigation water use for agroindustrial avocado production in Ziracuaretiro, Michoacán (2012-2021). Our analysis was based on aggregating weather and soil data at the municipal level and official government databases of avocado cultivated surface, fruit production, and water rights concessions. The analysis considers the homogeneity of information throughout the study area. We estimated that rainfed plantations require 839.03 m³/ton and irrigated plantations require 2355.80 m³/ton, with an average of 1597.47 m³/ton. In addition, we determined that avocado cultivation can demand up to 124.3% of agricultural water concessions in this municipality. In addition to the WF estimates, the analysis indicates that such studies are fundamental for decision-makers to develop and implement water use-efficiency strategies and shows the need for further research related to water consumption of avocado as a crop at more detailed scales to reduce uncertainty.

Since the early 1990s, the European (ESA) and Italian (ASI) space agencies have managed and distributed a huge amount of satellite-recorded SAR data to the research community and private industries. Moreover, the availability of advanced cloud computing services implementing different multi-temporal SAR interferometry techniques allows the generation of deformation time series from massive SAR images. We exploit the information provided by a large PS dataset to determine the temporal trend of ground deformation and the relative deformation rate with millimetric accuracy to analyse the spatial and temporal distribution of land subsidence induced by water pumping from a deep confined aquifer in the Northern Valle Umbra Basin (Central Italy), exploiting 24 years of Permanent Scatterers – Interferometric SAR data archives. The SAR images were acquired between 1992 and 2016 by satellite ERS1/2, ENVISAT and Sentinel 1 ESA missions, and the COSMO-SkyMed ASI mission). We observe ground velocities and deformation geometries between 1992 and 2016, with displacements of more than 70 cm and velocities of up to 55 mm/yr and the results suggest that the shape and position of the surface ground displacement are controlled by the fault activity hidden under the valley deposits.

Well-being is one of the pillars of positive psychology, which is known to have positive effects not only on the personal and professional lives of individuals, but also on teams and organizations. Understanding and promoting individual well-being is essential for staff health and long-term success, but current tools for assessing subjective well-being rely on time-consuming surveys and questionnaires, which limit the possibility of providing the real-time feedback needed to raise awareness and change individual behavior. This paper proposes a framework for understanding the process of non-verbal communication in teamwork, using video data to identify significant predictors of individual well-being in teamwork. It relies on video acquisition technologies and state-of-the-art artificial intelligence tools to extract individual, relative, and environmental characteristics from panoramic video. Statistical analysis is applied to each time series, leading to the generation of a dataset of 125 features, which are then linked to PERMA (Positive Emotion, Engagement, Relationships, Meaning, and Accomplishments) surveys developed in the context of positive psychology. Each pillar of the PERMA model is evaluated as a regression or classification problem using machine learning algorithms. Our approach was applied to a case study, where 80 students collaborated in 20 teams for a week on a team task in a face-to-face setting. This enabled us to formulate several hypotheses identifying factors influencing individual well-being in teamwork. These promising results point to interesting avenues for research, for instance fusing different media for the analysis of individual well-being in teamwork.

As recent history has shown, changing climate not only threatens to increase the spread of known disease, but also the emergence of new and dangerous phenotypes. This occurred most recently with West Nile virus: a virus previously known for mild febrile illness rapidly emerged to become a major cause of mortality and long-term disability throughout the world. As we move forward, into increasingly uncertain times, public health research must begin to incorporate a broader understanding of the determinants of disease emergence – what, how, why, and when. The increasing mainstream availability of high-quality open data and high-powered analytical methods presents promising new opportunities. Up to now, quantitative models of disease outbreak risk have been largely based on just a few key drivers, namely climate and large-scale climatic effects. Such limited assessments, however, often overlook key interacting processes and downstream determinants more likely to drive local manifestation of disease. Such pivotal determinants may include local host abundance, human behavioral variability, and population susceptibility dynamics. The results of such analyses can therefore be misleading in cases where necessary downstream requirements are not fulfilled. It is therefore important to develop models that include climate and higher-level climatic effects alongside the downstream non-climatic factors that ultimately determine individual disease manifestation. Today, few models attempt to comprehensively address such dynamics: up until very recently, the technology simply hasn’t been available. Herein, we present an updated overview of current perspectives on the varying drivers and levels of interactions that drive disease spread. We review the predominant analytical paradigms, discuss their strengths and weaknesses, and highlight promising new analytical solutions. Our focus is on the prediction of arboviruses, particularly West Nile virus, as these diseases represent the pinnacle of epidemiological complexity – solution to which would serve as an effective “gatekeeper”. We present the current state-of-the-art with respect to known drivers of arbovirus outbreak risk and severity, differentially highlighting the impact of climate and non-climatic drivers. The reality of multiple classes of drivers interacting at different geospatial and temporal scales requires advanced new methodologies. We therefore close out by presenting and discussing some promising new applications of AI. Given the reality of accelerating disease risks due to climate change, public health and other related fields must begin the process of updating their research programs to incorporate these much needed, new capabilities.

The work proposes a backtesting analysis in comparison between the Lee-Carter and the Cairns-Blake-Dowd mortality models, employing Italian data. The mortality data come from the Italian National Statistics Institute (ISTAT) database and span the period 1975-2014, over which we computed back-projections evaluating the performances of the models in comparisons with real data. We propose three different backtest approaches, evaluating the goodness of short-run forecast versus medium-length ones. We find that both models were not able to capture the improving shock on the mortality observed for the male population on the analyzed period. Moreover, the results suggest that CBD forecast are reliable prevalently for ages above 75, and that LC forecast are basically more accurate for this data.

This study investigates the impact of real-time emotional feedback on the quality of teamwork conducted over videoconferencing. We developed a framework that provides real-time feedback through a virtual mirror based on facial and voice emotion recognition. In an experiment with 28 teams (84 participants), teams collaborated over Zoom to set up a virtual Mars station using custom simulation software. Participants were divided into 14 experimental teams which were shown the virtual mirror, and 14 control teams without it. Team performance was measured by the improvement in the Mars simulation output quality. Our analysis using correlation, multilevel regression, and machine learning revealed that fewer interruptions but an increasing number over time correlated with higher performance. Higher vocal arousal and happiness also enhanced performance. We confirmed that female presence in teams boosts performance. SHAP values indicated that high variability in happiness, head movement, and positive facial valence—an “emotional rollercoaster”—positively predicted team performance. The experimental group outperformed the control group, suggesting that virtual mirroring improves virtual teamwork and that interrupting each other more, while speaking less, leads to better results.

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”.

Deep learning neural networks require an immense amount of computation, especially in the training phase of the network when networks with multiple layers of intermediate neurons need to be built. In this paper, we will focus on the PSO algorithm with the aim of significantly accelerating the DLNN training phase by taking advantage of the GPGPU architecture and the Apache Spark analytics engine for large-scale data processing tasks. PSO is a bio-inspired stochastic optimization method whose goal is to iteratively improve the solution to a (usually complex) problem by attempting to approximate a given objective. However, parallelizing an efficient PSO is not a straightforward process due to the complexity of the computations performed on the swarm of particles and the iterative execution of the algorithm until a solution close to the objective with minimal error is achieved. In the present work, two parallelizations of the PSO algorithm have been implemented , both designed for a distributed execution environment. The synchronous parallel PSO implementation ensures consistency at the cost of potential idle time due to global synchronization, while the asynchronous parallel PSO approach improves execution time by reducing the need for global synchronization, making it more suitable for large datasets and distributed environments such as Apache Spark. Both variants of PSO have been implemented to distribute the computational load supported by this algorithm –due to the costly fitness evaluation and updating of particle positions– across the different Spark cluster executor nodes to effectively achieve coarse-grained parallelism, resulting in a significant performance increase over current sequential variants of PSO.

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 order to meet the Millennium Development Goals set forth by the United Nations’ Millennium Summit that took place in 2000, in New York, particularly in the area of education, the Mozambican Government decided that, from 2003 onwards, during their primary school formation, students should be passed automatically even if they do not have enough intellectual capacities that justify their progress. In fact, this decision was made during a period when there were many pupils being failed, due to various reasons. After more than 15 years of its implementation, this paper aims to analyzing the results acquired from this decision, from the point of view of the quality of education. This will be done taking into account the prevailing situation of education in Mozambique. Having considered these aspects, the paper will move on to presenting some of the challenges and opportunities that the country should consider in the area of education, as a way to bring about better outcomes and promote development, in the country.

Background: Alzheimer's disease causes great changes, with the prefrontal cortex be-ing frequently damaged zones, affecting physical and cognitive behavioural and compromising autonomy and quality of life. Objective: To evaluate the effectiveness of feedback-based tech-nology on the improvement of physical-cognitive capacities in Alzheimer. Methods: Forty pa-tients with Alzheimer were recruited from the Santa Elena Alzheimer's Association, Seville, of which 15 ended up being pilot participants, divided randomly into controls (8) and experi-mental (7). They were evaluated with: Cognitive Mini-Examination Scale, Oddball Test and At-tention Network, Berg Scale, Tinetti, Timed up and Go and Geriatric Deterioration Scale. The experimental group was treated with feedback-based technology under the supervision of physiotherapists, during 16 thirty-minute sessions, twice a week. The control group performed their usual care. Result: A significant improvement was observed in the experimental group (Cognitive Mini-Examination and Tinetti). In the group of control, a worsening of physical and motor skills was evidenced because of the degenerative disease. Discussion/Conclusion: This treatment could be a strategy to keep the patient in early stages for longer, and improve quali-ty of life delaying dependency. It creates intergenerational bonds and use at home to promote treatment adherence.

In this paper, the power quality of interconnected microgrids is managed using a Model Predictive Control (MPC) methodology which manipulates the power converters of the microgrids in order to achieve the requirements. The control algorithm is developed for the microgrids working modes: grid-connected, islanded and interconnected. The results and simulations are also applied to the transition between the different working modes. In order to show the potential of the control algorithm, a comparison study is carried out with classical Proportional-Integral Pulse Width Modulation (PI-PWM) based controllers. The proposed control algorithm not only improves the transient response in comparison with classical methods but also shows an optimal behavior in all the working modes, minimizing the harmonics content in current and voltage even with the presence of non-balanced and non-harmonic-free three-phase voltage and current systems

Graphene oxide was synthesized by a one-step environmentally friendly mechanochemistry process directly from graphite and characterized by Raman, FT-IR and UV/vis spectroscopies, Atomic Force Microscopy, X-ray Diffraction, Scanning Electron Microscopy, Energy-Dispersive X-ray Spectroscopy and Thermogravimetric Analysis. Spectroscopic analysis shows that the functional groups and oxygen content of the synthesized material are comparable with those of graphene oxide synthesized by other previously reported methods (Hummers). Thermogravimetric analysis reveals thermal stability up to 400 °C.

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.

Ultrasonography is a relevant diagnostic tool extensively used andrology of human and do-mestic animals, including dogs. The present review aimed to describe all the technologies based on ultrasound, starting with the basic B-Mode ultrasonography to the recent contrast-enhanced ultrasonography (CEUS) and the ultrasound elastography, available to assess the testicle of the dog. The principles of the different technologies and the relevant findings in normal and ab-normal testicular conditions were described and discussed. B-mode ultrasonography aims at the examination of the testis detecting focal lesions but lacks objectivization. Other technologies, such as Doppler ultrasonography, B-Flow, and CEUS allow the definition of vascular patterns, that could be measured with specific applications, such as spectral Doppler or quantitative CEUS. Finally, ultrasound elastography allows the estimation of parenchyma stiffness, in both qualitatively and quantitatively manner. Ultrasound-based technologies assist the andrology in the evaluation of the testicular function and integrity, offering valuable information to define pathological conditions that could have an impact on the health and life quality of the male dog.

Silk fibroin is a well-known biopolymer used in several applications in which the interaction with biological tissue is required. In fact, fibroin is extremely versatile and can be shaped to form several constructs useful in tissue engineering applications. Confocal imaging is usually per-formed to test the cells behaviour on the construct and in this context the fibroin autofluorescence is regarded as a problem. In addition, the autofluorescence is not intense enough to provide useful morphological images. In fact, to control study the constructs morphology other techniques are used (i.e. SEM, Micro-CT). In this work we propose a method based on the fluorescence energy transfer (FRET) to suppress the fibroin autofluorescence moving it to higher wavelength accessible to the confocal microscopy for a direct imaging.

The evaluation of robotic hands is a subjectively biased, complex process. The fields pertaining to robotic hands are human-centric in nature, making human hands a good standard for benchmark comparisons of robotic hands. To achieve this, we propose a new evaluation index, where we evaluate robotic hands on three fronts: their form, features and performance. An evaluation on how anthropomorphic robotic hands are in basic mobility, and appearance constitutes the ``Form'', while features that can be read, changed and actuated for effective control of robotic hands constitutes the ``Features''. We derived these key features from an extensive analysis of robotic hands in literature. Finally, the robotic hands carry out a series of tasks that evaluate their ``Performance''. An individual score for each category is drawn and we carry out a three-pronged analysis. We also propose an additional feature in the form of price to provide context when analysing multiple hands.

Definitions of bronchopulmonary dysplasia (BPD) or post-prematurity respiratory disease (PPRD) aim to stratify the risk of mortality and morbidity, with an emphasis on long-term respiratory outcomes. There is no univocal classification of BPD, due to its complex multifactorial nature and the substantial heterogeneity of clinical presentation. Currently, there is no definitive cure available for extremely premature very-low-birth-weight infants with BPD, and challenges in finding targeted preventive therapies persist. However, innovative stem cells-based postnatal therapies targeting BPD-free survival are emerging, which are likely to be offered in the first few days of life to higher-risk subpopulations of premature infants. Hence the need for easy-to-use noninvasive tools for a standardized, precise and reliable BPD assessment at a very early stage, to support clinical decision-making and to predict the response to treatment. In this non-systematic review, we present an overview of strategies for monitoring preterm infants with early and evolving BPD-PPRD, and make some remarks on future prospects, with a focus on near-infrared spectroscopy (NIRS).

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%.

The nursing process in clinical practice can be assessed using standardized language systems. The purpose is to find evidence on the effectiveness of the nursing process with standardized terminology using NANDA International, Nursing Interventions Classification, and Nursing Outcome Classification improving diag-nostic accuracy, nursing interventions, health outcomes, and people’s satisfaction. A systematic review was carried out in Medline and PreMedline, Embase, The Cochrane Library, CINAHL, SCI-EXPANDED, SSCI and Scielo and LILACS including randomized clinical trials, quasi-experimental, cohort and case-control studies. Selection and critical appraisal were conducted by two independent reviewers. The certainty of the evidence was assessed with Grading of Recommendations Assessment, Development and Evaluation Methodology. 17 studies were included with variability in the level and certainty of evidence. According to outcomes, 6 evaluated improvements in diagnostic accuracy and 11 evaluated improvements in individual health outcomes. No studies evaluated improvements in intervention efficacy or population satisfaction. There is a need to increase studies with rigorous methodologies that address diagnostic accuracy and indi-viduals’ health outcomes using NANDA International, Nursing Interventions Classification, and Nursing Outcome Classification; as well as implementing studies that evaluate the use of these terminologies for improvements in the efficacy of nurses' interventions and population satisfaction with the nursing process.

Scientific evidence increasingly supports the strong link between diet and health, acknowledging that a well-balanced diet plays a crucial role in preventing chronic diseases such as obesity, diabetes, cardiovascular issues, and certain types of cancer. This perspective opens the door to developing precision diets, particularly tailored for individuals at risk of developing cancer. It encompasses a vast research area and involves the study of an expanding array of compounds with multi-level "omics" compositions, including genomics, transcriptomics, proteomics, epigenomics, miRNomics, and metabolomics. We review here the components of the Southern European Atlantic Diet (SEAD) from both a chemical and pharmacological standpoint. The information sources consulted, complemented by crystallographic data from the Protein Data Bank, establish a direct link between SEAD and its anticancer properties. The data collected strongly suggest that SEAD offers an exceptionally healthy profile, particularly due to the presence of beneficial biomolecules in its foods. The inclusion of olive oil and paprika in this diet provides numerous health benefits, and science supports the anti-cancer properties of dietary supplements with biomolecules sourced from vegetables of the brassica genus. Nonetheless, further research is warranted in this field to gain deeper insights into the potential benefits of SEAD's bioactive compounds against cancer.

Heart failure with preserved ejection fraction (HFpEF) remains a poorly characterized syndrome with many dark aspects related to different patients profile, various associated risk factors and wide aetiologies. It comprises several pathophysiological pathways related to endothelial dysfunction, myocardial fibrosis, extracellular matrix deposition and high inflammatory response. Up to now, it has been described only for clinical appearance and most common associated risk factors without an effective characterization of biological processes responsible for cardiovascular deteriorations. Recent advances in laboratory and metabolomic researches showed that HFpEF appears strictly related to specific cells and molecular mechanisms dysregulation. Some biomarkers are capable to early identify these processes adding new insights into diagnosis and risk stratification. Additionally recent advances on intermediate metabolites reflecting provide relevant information on intrinsic cellular and energetic substrate alterations. The systematic combination of clinical imaging and laboratory data may lead to a precision medicine approach providing prognostic and therapeutic advantages. Current review reports traditional and emerging biomarkers recently investigated in HFpEF setting, and it purpose a new diagnostic approach based on integrative information achieved from risk factors burden, hemodynamic dysfunction and biomarkers signature partnership.

Optimism and self-efficacy have been associated with psychological health. Empathy has also been found to promote positive functioning and to have a unique role in community health volunteering. This study investigated whether self-efficacy and optimism were associated with psychological and subjective well-being in a group of healthcare volunteers and whether empathy added incrementally to these associations. A sample of 160 Italian clown doctors volunteering in various hospitals completed self-report measures of self-efficacy, optimism, empathy, psychological well-being, and subjective well-being. Results indicated that self-efficacy and optimism were associated with both outcomes and that aspects of empathy, such as others’ perspective-taking and personal distress for others’ difficulties, incrementally added to these associations, although with opposite effects. The present study adds to previous research on the role of self-efficacy, optimism, and empathy for community health volunteers’ psychological health and offers suggestions regarding the training of this type of volunteer.

Selective mutism (SM) is an “Anxiety Disorder” characterized by a child’s “persistent inability” to communicate verbally in some (or all) contexts of social life that is associated with other cognitive-affective disorders. Cognitive-behavioural assessment and psychological intervention may be complex to be performed due to both the difficulty to administered standardized neuropsychological batteries (e.g., language-mediated tests) and the difficulty to involve family and teachers in the intervention program, respectively. Here, a single-case study is described in which a female Filipina child with SM underwent a comprehensive neuropsychological assessment and multimodal therapeutical intervention between the ages of 7 to 11 years. More specifically, the psychological intervention included cognitive-behavioral psychotherapy to both improve social-cognitive skills and learning abilities and to reduce anxiety, and speech therapy. Moreover, the parents and teachers were actively involved in the therapeutic process and they were also administered a psycho-education program. After this specific treatment, at the age of 11 years the girl began to verbalize in both therapy and school contexts, although she still was used to adopt non-verbal strategies. A parallel and gradual improvement in communicative-linguistic skills and school learning was also observed. In conclusion, the present report highlights the importance to apply an "integrated and multimodal intervention" to treat SM in children that includes also psycho-education intervention for both parents and teachers.

We report on the application of femtosecond laser micromachining to the fabrication of complex glass microdevices, for high-order harmonic generation in gas. The three-dimensional capabilities and extreme flexibility of femtosecond laser micromachining allow us to achieve accurate control of gas density inside the micrometer interaction channel. This device gives a considerable increase in harmonics generation efficiency if compared with traditional harmonic generation in gas jets. We propose different chip geometries that allow to control the gas density and driving field intensity inside the interaction channel to achieve quasi-phase matching conditions in the harmonic generation process. We believe that these glass micro-devices will pave the way to future downscaling of High-order Harmonic Generation beamlines.

Collaborative robotics is a major topic in current robotics research posing new challenges, especially in Human-Robot Interaction. The crucial aspect in this area of research focuses on understanding the behavior of robots when engaging with humans, where reinforcement learning is a key discipline that allows us to explore sophisticated emerging reactions. This review aims to delve into the relevance of different sensors and techniques, with special attention to EEG (electroencephalography data on brain activity) and its influence on the behavior of robots interacting with humans. In addition, mechanisms available to mitigate potential risks during the experimentation process such as virtual reality will also be addressed. In the final part of the paper, future lines of research combining the areas of collaborative robotics, reinforcement learning, virtual reality and human factors will be explored, as this last aspect is vital to ensure safe and effective Human-Robot Interactions.

Immortal-time bias (ITB) is known to be common in cohort studies and distorts the association estimates between treated and untreated groups. We used data from the last of two large studies in an Italian province on COVID-19 vaccines safety and effectiveness incurred this bias, and aligned the entire population on a single index date, to correct the ITB. We considered the "all-cause deaths" outcome to compare the survival curves between the unvaccinated group and the various vaccination statuses. The all-cause deaths Hazard Ratios in univariate analysis for unvaccinated (reference) versus vaccinated with 1, 2, 3/4 doses were 0.88 (CI95: 0.78 –1.00; p-value 0.044), 1.23 (1.16–1.32; p-value ≤0.001) and 1.21 (1.14–1.29; p-value ≤0.001), respectively. The multivariate values were 2.40 (2.00–2.88; p-value <0.0001), 1.98 (1.75–2.24; p-value <0.0001), 0.99 (0.90–1.09; ns). The possible explanations of the trend of the Hazard Ratios as vaccinations increase could be a harvesting effect; a calendar-time bias, accounting for seasonality and pandemic waves; a case-counting windows bias; a healthy-vaccinee bias; or some their combination. With two and even with 3/4 doses the calculated Restricted Mean Survival Time and Restricted Mean Time Lost have shown a small but significant downside for the vaccinated populations.

Asparagus shoots are worldwide consumed, although most of such species have a restricted range, and several taxa still remain unstudied. In this work, a total of four taxa from different locations were scrutinized and compared with cultivated A. officinalis. All shoots were screened for saponins by LC-MS, and in vitro antiproliferative activities against the HT-29 colorectal cancer cell line (by MTT assay). The total saponins (TS) contained in the crude extracts ranged from 710.0 (A. officinalis) to 1258.6 mg/100 g dw (A. acutifolius). It stands out the richness of compounds detected in this work: a total of 47 saponins have been detected and quantified in the edible parts (shoots) of 5 taxa of Asparagus. The structure of all the saponins found present skeletons of the furostane and spirostane type. In turn, the structures with a furostane skeleton are divided into unsaturated and dioxygenated, both in the 20-22 position. The sum of dioscin and derivatives varied largely along the studied taxa. It reached the following percentages of TS: 27.11 (A. officinalis), 18.96 (A. aphyllus), 5.37 (A. acutifolius), and 0.59 (A. albus), while in A. horridus such compound remains undetected. Aspachiosde A, D, and M varied largely among samples, while a total of seven aspaspirostanosides were characterized in the analyzed species. Hierarchical cluster analysis of the saponin profiles clearly separated the various taxa and demonstrated that the taxonomic position is more important than the place from which the samples were acquired. Thus, saponin profiles have chemotaxonomic significance in Asparagus taxa. The MTT assay showed dose- and time-dependent inhibitory effects of several saponins extracts on HT-29 cancer cells, standing out the cell growth inhibition exercised by A. albus and A. acutifolius (GI50 of 125 and 175 µg/mL). This work constitutes a whole approach to evaluate the saponins from shoots of different Asparagus taxa and provide arguments for use them as functional foods.