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Sex differences have been a rarely addressed aspect in digital game-based learning (DGBL). Likewise, mixed results have been presented regarding the effects according to sex and the conditions that generate these effects. The present work studied the effects of a drill-and-practice mathematical game on primary students. The study focused on an analysis by sex, measuring motivation and learning in the practice activity. Also, two instructional mechanics were considered regarding the question answering to search for possible differences: a multiple-try feedback (MTF) condition and a single-try feedback (STF) condition. A total of 81 students from four courses and two schools participated in the intervention. The study's main findings were as follows: (a) the girls outperformed the boys in terms of the students' learning gains; (b) the girls presented lower levels of competence and autonomy than the boys; (c) under MTF, the girls presented lower levels of autonomy but no differences in competence contrasted with the boys; (d) under STF, the girls presented lower levels of competence but no differences in autonomy contrasted with the boys; (e) no sex differences existed in interest, effort, and value, in general, as per the instructional condition. This study enhances the knowledge of sex differences under diverse instructional settings, in particular providing insights into the possible differences by sex when varying the number of attempts provided to students.

In the complex and dynamic landscape of cyber threats, organizations require sophisticated strategies for managing Cybersecurity Operations Centers and deploying Security Information and Event Management systems. Our study enhances these strategies by integrating the precision of well-known biomimetic optimization algorithms-namely Particle Swarm Optimization, the Bat Algorithm, the Gray Wolf Optimizer, and the Orca Predator Algorithm-with the adaptability of Deep Q-Learning, a reinforcement learning technique that leverages deep neural networks to teach algorithms optimal actions through trial and error in complex environments. This hybrid methodology targets the efficient allocation and deployment of network intrusion detection sensors while balancing cost-effectiveness with essential network security imperatives. Comprehensive computational tests show that versions enhanced with Deep Q-Learning significantly outperform their native counterparts, especially in complex infrastructures. These results highlight the efficacy of integrating metaheuristics with reinforcement learning to tackle complex optimization challenges, underscoring Deep Q-Learning's potential to boost cybersecurity measures in rapidly evolving threat environments.

Background: The protocol described in this paper is part of a research project coordinated between three Spanish universities, where a technology aimed at improving the quality of life of people with cerebral palsy will be developed. Part of the proposed technology will consist of an interface and a series of applications to increase motivation for daily physical activity. The basis of these developments is the measurement of the emotional state of the subjects., Methods: The experimental protocol is designed with two research objectives, on the one hand to identify the emotional state through physiological signals, and on the other to determine whether music can be a motivating factor to promote physical activity. It is specifically designed for subjects with cerebral palsy, taking into account the special characteristics of this population. These are people with whom it is difficult to use questionnaires to have a basis to contrast with the measured physiological signals, so measurements must be taken in carefully chosen daily-life situations., Discussion: We hope our findings show which physiological parameters are the most robust to measure the emotional state and how to design rehabilitation and physical activity promotion routines that are motivating, in addition to being able to avoid risk factors during the performance of these routines., Trial Registration: NCT05621057.

One of the significant challenges in scaling agile software development is organizing software development teams to ensure effective communication among members while equipping them with the capabilities to deliver business value independently. A formal approach to address this challenge involves modeling it as an optimization problem: given a professional staff, how can they be organized to optimize the number of communication channels, considering both intra-team and inter-team channels? In this article, we propose applying a set of bio-inspired algorithms to solve this problem. We introduce an enhancement that incorporates ensemble learning into the resolution process to achieve nearly optimal results. Ensemble learning integrates multiple machine-learning strategies with diverse characteristics to boost optimizer performance. Furthermore, the studied metaheuristics offer an excellent opportunity to explore their linear convergence, contingent on the exploration and exploitation phases. The results produce more precise definitions for team sizes, aligning with industry standards. Our approach demonstrates superior performance compared to the traditional versions of these algorithms.

The set-covering problem aims to find the smallest possible set of subsets that cover all the elements of a larger set. The difficulty of solving the set-covering problem increases as the number of elements and sets grows, making it a complex problem for which traditional integer programming solutions may become inefficient in real-life instances. Given this complexity, various metaheuristics have been successfully applied to solve the set-covering problem and related issues. This study introduces, implements, and analyzes a novel metaheuristic inspired by the well-established Growth Optimizer algorithm. Drawing insights from human behavioral patterns, this approach has shown promise in optimizing complex problems in continuous domains, where experimental results demonstrate the effectiveness and competitiveness of the metaheuristic compared to other strategies. The Growth Optimizer algorithm is modified and adapted to the realm of binary optimization for solving the set-covering problem, resulting in the creation of the Binary Growth Optimizer algorithm. Upon the implementation and analysis of its outcomes, the findings illustrate its capability to achieve competitive and efficient solutions in terms of resolution time and result quality.

Advances in technology and artificial intelligence (smart healthcare) open up a range of possibilities for precision intervention in the field of health sciences. The objectives of this study were to analyse the functionality of using supervised (prediction and classification) and unsupervised (clustering) machine learning techniques to analyse results related to the development of functional skills in patients at developmental ages of 0-6 years. We worked with a sample of 113 patients, of whom 49 were cared for in a specific centre for people with motor impairments (Group 1) and 64 were cared for in a specific early care programme for patients with different impairments (Group 2). The results indicated that in Group 1, chronological age predicted the development of functional skills at 85% and in Group 2 at 65%. The classification variable detected was functional development in the upper extremities. Two clusters were detected within each group that allowed us to determine the patterns of functional development in each patient with respect to functional skills. The use of smart healthcare resources has a promising future in the field of early care. However, data recording in web applications needs to be planned, and the automation of results through machine learning techniques is required.

This document analyzes a survey conducted in three geographical areas in Spain, focusing on centers for individuals with cerebral palsy (CP). The study aims to determine the adherence rate to recommended physical activity guidelines, assess if there is a decline in interest in physical activity over time, identify the stage at which this decline occurs, and explore potential mechanisms, tools, or strategies to sustain long-term engagement in regular physical activity for this population. The 36-item questionnaire comprises multiple-choice, open-ended, and Likert scale-type questions. Data were collected on physical activity frequency and duration, daily living activities, and demographics. Statistical analysis identified patterns and relationships between variables. Findings reveal that only a 17.6% meets the World Health Organization (WHO) recommendations regarding regular physical activity (RPA), decreasing in frequency or number of days a week, (3.7 d/w to 2.9 d/w; p < 0.01) and duration (50.5 min/d to 45.2 min/d; p < 0.001) with age, especially for those with higher Gross Motor Function Classification System (GMFCS) mobility levels. Obesity slightly correlates with session duration (ρ = -0.207; p < 0.05), not mobility limitations. Gender has no significant impact on mobility, communication, or physical activity, while age affects variables such as body mass index (BMI) and engagement ( p < 0.01). A substantial proportion follows regular physical activities based on health professionals' advice, with interest decreasing with age. To improve adherence, focusing on sports-oriented goals, group sessions, and games is recommended. These findings emphasize the importance of personalized programs, particularly for older individuals and those with greater mobility limitations.

In recent years, there has been growing interest in the use of computer-assisted technology for early detection of skin cancer through the analysis of dermatoscopic images. However, the accuracy illustrated behind the state-of-the-art approaches depends on several factors, such as the quality of the images and the interpretation of the results by medical experts. This systematic review aims to critically assess the efficacy and challenges of this research field in order to explain the usability and limitations and highlight potential future lines of work for the scientific and clinical community. In this study, the analysis was carried out over 45 contemporary studies extracted from databases such as Web of Science and Scopus. Several computer vision techniques related to image and video processing for early skin cancer diagnosis were identified. In this context, the focus behind the process included the algorithms employed, result accuracy, and validation metrics. Thus, the results yielded significant advancements in cancer detection using deep learning and machine learning algorithms. Lastly, this review establishes a foundation for future research, highlighting potential contributions and opportunities to improve the effectiveness of skin cancer detection through machine learning.

Parkinson's disease (PD) is a progressive neurodegenerative disorder whose prevalence has steadily been rising over the years. Specialist neurologists across the world assess and diagnose patients with PD, although the diagnostic process is time-consuming and various symptoms take years to appear, which means that the diagnosis is prone to human error. The partial automatization of PD assessment and diagnosis through computational processes has therefore been considered for some time. One well-known tool for PD assessment is finger tapping (FT), which can now be assessed through computer vision (CV). Artificial intelligence and related advances over recent decades, more specifically in the area of CV, have made it possible to develop computer systems that can help specialists assess and diagnose PD. The aim of this study is to review some advances related to CV techniques and FT so as to offer insight into future research lines that technological advances are now opening up.

In this study, we introduce an innovative policy in the field of reinforcement learning, specifically designed as an action selection mechanism, and applied herein as a selector for binarization schemes. These schemes enable continuous metaheuristics to be applied to binary problems, thereby paving new paths in combinatorial optimization. To evaluate its efficacy, we implemented this policy within our BSS framework, which integrates a variety of reinforcement learning and metaheuristic techniques. Upon resolving 45 instances of the Set Covering Problem, our results demonstrate that reinforcement learning can play a crucial role in enhancing the binarization techniques employed. This policy not only significantly outperformed traditional methods in terms of precision and efficiency, but also proved to be extensible and adaptable to other techniques and similar problems. The approach proposed in this article is capable of significantly surpassing traditional methods in precision and efficiency, which could have important implications for a wide range of real-world applications. This study underscores the philosophy behind our approach: utilizing reinforcement learning not as an end in itself, but as a powerful tool for solving binary combinatorial problems, emphasizing its practical applicability and potential to transform the way we address complex challenges across various fields.

Population-based metaheuristics can be seen as a set of agents that smartly explore the space of solutions of a given optimization problem. These agents are commonly governed by movement operators that decide how the exploration is driven. Although metaheuristics have successfully been used for more than 20 years, performing rapid and high-quality parameter control is still a main concern. For instance, deciding the proper population size yielding a good balance between quality of results and computing time is constantly a hard task, even more so in the presence of an unexplored optimization problem. In this paper, we propose a self-adaptive strategy based on the on-line population balance, which aims for improvements in the performance and search process on population-based algorithms. The design behind the proposed approach relies on three different components. Firstly, an optimization-based component which defines all metaheuristic tasks related to carry out the resolution of the optimization problems. Secondly, a learning-based component focused on transforming dynamic data into knowledge in order to influence the search in the solution space. Thirdly, a probabilistic-based selector component is designed to dynamically adjust the population. We illustrate an extensive experimental process on large instance sets from three well-known discrete optimization problems: Manufacturing Cell Design Problem, Set covering Problem, and Multidimensional Knapsack Problem. The proposed approach is able to compete against classic, autonomous, as well as IRace-tuned metaheuristics, yielding interesting results and potential future work regarding dynamically adjusting the number of solutions interacting on different times within the search process.

Feature selection is becoming a relevant problem within the field of machine learning. The feature selection problem focuses on the selection of the small, necessary, and sufficient subset of features that represent the general set of features, eliminating redundant and irrelevant information. Given the importance of the topic, in recent years there has been a boom in the study of the problem, generating a large number of related investigations. Given this, this work analyzes 161 articles published between 2019 and 2023 (20 April 2023), emphasizing the formulation of the problem and performance measures, and proposing classifications for the objective functions and evaluation metrics. Furthermore, an in-depth description and analysis of metaheuristics, benchmark datasets, and practical real-world applications are presented. Finally, in light of recent advances, this review paper provides future research opportunities.

In the optimization field, the ability to efficiently tackle complex and high-dimensional problems remains a persistent challenge. Metaheuristic algorithms, with a particular emphasis on their autonomous variants, are emerging as promising tools to overcome this challenge. The term "autonomous" refers to these variants' ability to dynamically adjust certain parameters based on their own outcomes, without external intervention. The objective is to leverage the advantages and characteristics of an unsupervised machine learning clustering technique to configure the population parameter with autonomous behavior, and emphasize how we incorporate the characteristics of search space clustering to enhance the intensification and diversification of the metaheuristic. This allows dynamic adjustments based on its own outcomes, whether by increasing or decreasing the population in response to the need for diversification or intensification of solutions. In this manner, it aims to imbue the metaheuristic with features for a broader search of solutions that can yield superior results. This study provides an in-depth examination of autonomous metaheuristic algorithms, including Autonomous Particle Swarm Optimization, Autonomous Cuckoo Search Algorithm, and Autonomous Bat Algorithm. We submit these algorithms to a thorough evaluation against their original counterparts using high-density functions from the well-known CEC LSGO benchmark suite. Quantitative results revealed performance enhancements in the autonomous versions, with Autonomous Particle Swarm Optimization consistently outperforming its peers in achieving optimal minimum values. Autonomous Cuckoo Search Algorithm and Autonomous Bat Algorithm also demonstrated noteworthy advancements over their traditional counterparts. A salient feature of these algorithms is the continuous nature of their population, which significantly bolsters their capability to navigate complex and high-dimensional search spaces. However, like all methodologies, there were challenges in ensuring consistent performance across all test scenarios. The intrinsic adaptability and autonomous decision making embedded within these algorithms herald a new era of optimization tools suited for complex real-world challenges. In sum, this research accentuates the potential of autonomous metaheuristics in the optimization arena, laying the groundwork for their expanded application across diverse challenges and domains. We recommend further explorations and adaptations of these autonomous algorithms to fully harness their potential.

Beverages and drinks play a significant role in maintaining the integral health of individuals. The aim of this study is to discover the pattern of beverage consumption in different groups of the Spanish population and to investigate its relationship with other nutritional variables and habits. To achieve the objectives, an observational, descriptive and cross-sectional study was conducted. For data collection, a questionnaire was designed and validated that explored different beverage and food consumption variables as well as socio-demographic and lifestyle variables. The instrument was disseminated, among the Spanish young adult population, through snowball sampling using social networks, collecting a sample of 17,541 valid surveys. Bivariate comparative analyses and correlation analyses were performed, and finally, the principal component analysis (PCA) method was used in order to study the relationships between variables related to drinking and health. The main results show significant differences in the pattern of beverage consumption between the socio-demographic variables of sex, age and educational level, as well as between different areas of Spain, while the PCA model shows the relationship between the consumption of sugar-sweetened beverages with the Healthy Nutrition Index of the population and sport practice. Based on the results of the study, the following conclusions were reached: the beverage consumption pattern of the Spanish population is affected by socio-demographic variables. Healthier drinking habits affect the nutrition and health of the population.

Aim: The aim was to identify, evaluate, and summarize the findings of relevant individual studies on the precision and accuracy of radiological BA assessment procedures among children from different ethnic groups., Materials and Methods: A qualitative systematic review was carried out following the MOOSE statement and previously registered in PROSPERO (CRD42023449512). A search was performed in MEDLINE ( PubMed ) ( n = 561), the Cochrane Library ( n = 261), CINAHL ( n = 103), Web of Science ( WOS ) ( n = 181), and institutional repositories ( n = 37) using MeSH and free terms combined with the Booleans " AND " and " OR ". NOS and ROBINS-E were used to assess the methodological quality and the risk of bias of the included studies, respectively., Results: A total of 51 articles ( n = 20,100) on radiological BA assessment procedures were precise in terms of intra-observer and inter-observer reliability for all ethnic groups. In Caucasian and Hispanic children, the Greulich-Pyle Atlas ( GPA ) was accurate at all ages, but in youths, Tanner-Whitehouse radius-ulna-short bones 3 ( TW3-RUS ) could be an alternative. In Asian and Arab subjects, GPA and Tanner-Whitehouse 3 ( TW3 ) overestimated the BA in adolescents near adulthood. In African youths, GPA overestimated the BA while TW3 was more accurate., Conclusion: GPA and TW3 radiological BA assessment procedures are both precise but their accuracy in estimating CA among children of different ethnic groups can be altered by racial bias.

A healthy lifestyle and proper nutrition have a major impact on the well-being of a population. Therefore, the aim of this research is to describe the behavior of these habits in relation to sociodemographic variables to provide data on the development of effective training and awareness-raising actions., Methods: An observational, descriptive and cross-sectional study was carried out. To study the behavior of a series of variables related to eating habits and lifestyle, a questionnaire was designed and validated and subsequently disseminated online, by means of nonprobabilistic snowball sampling, relying on social networks. The sample collected consisted of 18,070 young adults of Spanish nationality. Bivariate comparative analyses were performed using t -test independent samples, and the effect size (ES) was calculated by determining Cohen's D coefficient. A multivariate analysis were conducted using linear regression and principal component analysis., Results: Adults eat better but have a poorer quality of rest and are more sedentary than young people. No gender differences were found in nutritional habits; however, men engage in more sports and sleep better. People with a higher educational level have better nutritional and sleep habits, but are more sedentary, as are people of a higher socioeconomic level., Conclusions: Higher socioeconomic and educational levels seem to favor a healthier lifestyle. The Spanish population aged 18-45 years needs to make dietary changes but leads an active lifestyle.

Whey is a by-product that represents a cheap source of protein with a high nutritional value, often used to improve food quality. When used as a raw material to produce hypoallergenic infant formulas (HIF), a processing step able to decrease the allergenic potential is required to guarantee their safe use for this purpose. In the present paper, thermal treatments, high hydrostatic pressure (HHP), and enzymatic hydrolysis (EH) were assessed to decrease the antigenicity of whey protein solutions (WPC). For monitoring purposes, a competitive ELISA method, able to detect the major and most allergenic whey protein β-lactoglobulin (BLG), was developed as a first step to evaluate the efficiency of the processes. Results showed that EH together with HHP was the most effective combination to reduce WPC antigenicity. The evaluation method proved useful to monitor the processes and to be employed in the quality control of the final product, to guarantee the efficiency, and in protein antigenicity reduction.

In this work, an approach is proposed to solve binary combinatorial problems using continuous metaheuristics. It focuses on the importance of binarization in the optimization process, as it can have a significant impact on the performance of the algorithm. Different binarization schemes are presented and a set of actions, which combine different transfer functions and binarization rules, under a selector based on reinforcement learning is proposed. The experimental results show that the binarization rules have a greater impact than transfer functions on the performance of the algorithms and that some sets of actions are statistically better than others. In particular, it was found that sets that incorporate the elite or elite roulette binarization rule are the best. Furthermore, exploration and exploitation were analyzed through percentage graphs and a statistical test was performed to determine the best set of actions. Overall, this work provides a practical approach for the selection of binarization schemes in binary combinatorial problems and offers guidance for future research in this field.

High-throughput sequencing of small RNA molecules such as microRNAs (miRNAs) has become a widely used approach for studying gene expression and regulation. However, analyzing miRNA-Seq data can be challenging because it requires multiple steps, from quality control and preprocessing to differential expression and pathway-enrichment analyses, with many tools and databases available for each step. Furthermore, reproducibility of the analysis pipeline is crucial to ensure that the results are accurate and reliable. Here, we present myBrain-Seq, a comprehensive and reproducible pipeline for analyzing miRNA-Seq data that incorporates miRNA-specific solutions at each step of the analysis. The pipeline was designed to be flexible and user-friendly, allowing researchers with different levels of expertise to perform the analysis in a standardized and reproducible manner, using the most common and widely used tools for each step. In this work, we describe the implementation of myBrain-Seq and demonstrate its capacity to consistently and reproducibly identify differentially expressed miRNAs and enriched pathways by applying it to a real case study in which we compared schizophrenia patients who responded to medication with treatment-resistant schizophrenia patients to obtain a 16-miRNA treatment-resistant schizophrenia profile.

Deep learning object-detection models are being successfully applied to develop computer-aided diagnosis systems for aiding polyp detection during colonoscopies. Here, we evidence the need to include negative samples for both (i) reducing false positives during the polyp-finding phase, by including images with artifacts that may confuse the detection models (e.g., medical instruments, water jets, feces, blood, excessive proximity of the camera to the colon wall, blurred images, etc.) that are usually not included in model development datasets, and (ii) correctly estimating a more realistic performance of the models. By retraining our previously developed YOLOv3-based detection model with a dataset that includes 15% of additional not-polyp images with a variety of artifacts, we were able to generally improve its F1 performance in our internal test datasets (from an average F1 of 0.869 to 0.893), which now include such type of images, as well as in four public datasets that include not-polyp images (from an average F1 of 0.695 to 0.722).

The consolidation of telerehabilitation for the treatment of many diseases over the last decades is a consequence of its cost-effective results and its ability to offer access to rehabilitation in remote areas. Telerehabilitation operates over a distance, so vulnerable patients are never exposed to unnecessary risks. Despite its low cost, the need for a professional to assess therapeutic exercises and proper corporal movements online should also be mentioned. The focus of this paper is on a telerehabilitation system for patients suffering from Parkinson's disease in remote villages and other less accessible locations. A full-stack is presented using big data frameworks that facilitate communication between the patient and the occupational therapist, the recording of each session, and real-time skeleton identification using artificial intelligence techniques. Big data technologies are used to process the numerous videos that are generated during the course of treating simultaneous patients. Moreover, the skeleton of each patient can be estimated using deep neural networks for automated evaluation of corporal exercises, which is of immense help to the therapists in charge of the treatment programs.

An attention disturbance is a problem that affects many school-aged children. The assessment in children is usually report-based, and as a result, controversy surrounds the diagnosis. To solve this issue, the aim of this study was to develop a new tool to detect possible attention-related problems and impulsive behavior in 4- and 5-year-old children. This tool was developed as an Android app and could be used to provide an early indicator of possible future development problems. A sample of 103 children (48 girls and 55 boys) was randomly selected from primary schools and assessed by Pinky-Piggy, a videogame application based on a classical paradigm in experimental psychology. Data from this app were compared with a Child Neuropsychological Maturity Questionnaire. The subjects displayed different patterns of response to play a very simple game called Pinky-Piggy. The application discriminated between high-responders and low responders. The results showed a relationship between these two profiles and the levels of attention and neurodevelopment in each group. The tool could identify different types of profiles and demonstrated its potential to evaluate endophenotypes to predict attentional problems related to impulsive behavior. Additionally, it required less time and fewer tests to identify possible at-risk populations, thus assisting in clinical diagnosis.

Colorectal cancer is one of the most frequent malignancies. Colonoscopy is the de facto standard for precancerous lesion detection in the colon, i.e., polyps, during screening studies or after facultative recommendation. In recent years, artificial intelligence, and especially deep learning techniques such as convolutional neural networks, have been applied to polyp detection and localization in order to develop real-time CADe systems. However, the performance of machine learning models is very sensitive to changes in the nature of the testing instances, especially when trying to reproduce results for totally different datasets to those used for model development, i.e., inter-dataset testing. Here, we report the results of testing of our previously published polyp detection model using ten public colonoscopy image datasets and analyze them in the context of the results of other 20 state-of-the-art publications using the same datasets. The F1-score of our recently published model was 0.88 when evaluated on a private test partition, i.e., intra-dataset testing, but it decayed, on average, by 13.65% when tested on ten public datasets. In the published research, the average intra-dataset F1-score is 0.91, and we observed that it also decays in the inter-dataset setting to an average F1-score of 0.83.

The aim of this work was to study the effect of finishing diets including distiller grains (DG) on color and oxidative stability of beef after being exposed to aerobic retail display conditions, with or without previous aging. For this purpose, beef samples from animals fed with finishing diets including 0%, 15%, 30%, and 45% DG (on a dry matter basis), which had been exposed to aerobic retail display conditions, with or without previous aging under vacuum packaging, were evaluated. The content of γ-tocopherol, β-carotene, and lutein in diet samples increased with the level of DG. Beef evaluated at 72 h post-mortem showed greater content of γ-tocopherol and retinol as the DG level increased. Meat color was not affected by DG inclusion, but color parameters decreased with retail conditions. Meat from animals fed with DG showed the lowest values of thiobarbituric acid reactive substances (TBARS), independently of the retail display conditions. However, all samples were below the threshold associated with rancid aromas and above the a* value related to meat color acceptance. Thus, feeding diets including up to 45% of DG improved the antioxidant status of meat, preserving the color, and delaying lipid oxidation in meat samples under the display conditions evaluated.

Bifidobacteria are among the most abundant microorganisms inhabiting the intestine of humans and many animals. Within the genus Bifidobacterium , several beneficial effects have been attributed to strains belonging to the subspecies Bifidobacterium longum subsp. longum and Bifidobacterium longum subsp. infantis , which are often found in infants and adults. The increasing numbers of sequenced genomes belonging to these two subspecies, and the availability of novel computational tools focused on predicting glycolytic abilities, with the aim of understanding the capabilities of degrading specific carbohydrates, allowed us to depict the potential glycoside hydrolases (GH) of these bacteria, with a focus on those GH profiles that differ in the two subspecies. We performed an in silico examination of 188 sequenced B. longum genomes and depicted the commonly present and strain-specific GHs and GH families among representatives of this species. Additionally, GH profiling, genome-based and 16S rRNA-based clustering analyses showed that the subspecies assignment of some strains does not properly match with their genetic background. Furthermore, the analysis of the potential GH component allowed the distinction of clear GH patterns. Some of the GH activities, and their link with the two subspecies under study, are further discussed. Overall, our in silico analysis poses some questions about the suitability of considering the GH activities of B. longum subsp. longum and B. longum subsp. infantis to gain insight into the characterization and classification of these two subspecies with probiotic interest.

Massive sequencing projects executed in Saccharomyces cerevisiae have revealed in detail its population structure. The recent "1002 yeast genomes project" has become the most complete catalogue of yeast genetic diversity and a powerful resource to analyse the evolutionary history of genes affecting specific phenotypes. In this work, we selected 22 nitrogen associated genes and analysed the sequence information from the 1011 strains of the "1002 yeast genomes project". We constructed a total evidence (TE) phylogenetic tree using concatenated information, which showed a 27% topology similarity with the reference (REF) tree of the "1002 yeast genomes project". We also generated individual phylogenetic trees for each gene and compared their topologies, identifying genes with similar topologies (suggesting a shared evolutionary history). Furthermore, we pruned the constructed phylogenetic trees to compare the REF tree topology versus the TE tree and the individual genes trees, considering each phylogenetic cluster/subcluster within the population, observing genes with cluster/subcluster topologies of high similarity to the REF tree. Finally, we used the pruned versions of the phylogenetic trees to compare four strains considered as representatives of S. cerevisiae clean lineages, observing for 15 genes that its cluster topologies match 100% the REF tree, supporting that these strains represent main lineages of yeast population. Altogether, our results showed the potential of tree topologies comparison for exploring the evolutionary history of a specific group of genes.