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Objectiveto evaluate the immune response to the SARS-CoV-2 vaccines in adults with immune-mediated rheumatic diseases (IMRDs) in comparison to healthy individuals, observed 1-20 weeks following the fourth vaccine dose. Additionally, to evaluate the impact of immunosuppressive therapies, vaccination schedules, the time interval between vaccination and sample collection on the vaccine’s immune response.MethodsWe designed a longitudinal observational study conducted at the rheumatology department of Hospital de Copiapó. Neutralizing antibodies (Nabs) titers against the Wuhan and Omicron variant were analyzed between 1-20 weeks after administration of the fourth dose of the SARS-CoV-2 vaccine to 341 participants (218 IMRD patients and 123 healthy controls). 218 IMRD patients with rheumatoid arthritis (RA), psoriatic arthritis (PsA), ankylosing spondylitis (AS), systemic lupus erythematosus (SLE), systemic vasculitis (VS) and systemic scleroderma (SS) were analyzed.ResultsPerforming a comparison between the variants, Wuhan vs Omicron, we noticed that there were significant differences (p

BackgroundVaccine effectiveness against SARS-CoV-2 infection has been somewhat limited due to the widespread dissemination of the Omicron variant, its subvariants, and the immune response dynamics of the naturally infected with the virus.MethodsTwelve subjects between 3-17 years old (yo), vaccinated with two doses of CoronaVac®, were followed and diagnosed as breakthrough cases starting 14 days after receiving the second dose. Total IgGs against different SARS-CoV-2 proteins and the neutralizing capacity of these antibodies after infection were measured in plasma. The activation of CD4+ and CD8+ T cells was evaluated in peripheral blood mononuclear cells stimulated with peptides derived from the proteins from the wild-type (WT) virus and Omicron subvariants by flow cytometry, as well as different cytokines secretion by a Multiplex assay.Results2 to 8 weeks post-infection, compared to 4 weeks after 2nd dose of vaccine, there was a 146.5-fold increase in neutralizing antibody titers against Omicron and a 38.7-fold increase against WT SARS-CoV-2. Subjects showed an increase in total IgG levels against the S1, N, M, and NSP8 proteins of the WT virus. Activated CD4+ T cells showed a significant increase in response to the BA.2 subvariant (p

Bridges are exposed daily to environmental and operational factors that may cause weariness, fatigue, and damage. Continuous structural health monitoring (SHM) has been crucial to ensuring public safety, preventing accidents, and avert costly damages. In this regard, advances in Machine Learning and Big Data technologies have enabled automated, real-time structural monitoring. However, challenges persist, notably the scarcity of labeled data, rendering supervised learning impractical. Additionally, state-of-the-art methods demand extensive training data to generalize and achieve satisfactory performance, which can be limited in real-world scenarios. This paper presents a novel three-step method supported by advanced Machine Learning and signal processing techniques aimed at detecting anomalous signals. This method is trained solely on structural acceleration signals, eliminating the need for labeled data. Among the contributions of this work, it can be mentioned that a remarkable accuracy in the detection of structural damage was demonstrated quantitatively. (F1 Score of 93%), while requiring significantly less training data volume than alternative methods (less than 25% of the total) and opening up different lines of research.

Sewage surveillance provides useful epidemiologic and public health information on viral infections at the population level. We detected monkeypox virus DNA from sewage samples covering 85% of the population in Santiago Metropolitan Region Chile. We also isolated infective viruses from those samples. Wastewater surveillance could complement clinical surveillance for monkeypox virus.

This research presents a novel hybrid approach, which combines the White Shark Optimizer (WSO) metaheuristic algorithm with chaotic maps integrated into the binarization process. Inspired by the predatory behavior of white sharks, WSO has shown great potential to navigate complex search spaces for optimization tasks. On the other hand, chaotic maps are nonlinear dynamical systems that generate pseudo-random sequences, allowing for better solution diversification and avoiding local optima. By hybridizing WSO and chaotic maps through adaptive binarization rules, the complementary strengths of both approaches are leveraged to obtain high-quality solutions. We have solved the Set Covering Problem (SCP), a well-known NP-hard combinatorial optimization challenge with real-world applications in several domains, and experimental results indicate that LOG and TENT chaotic maps are better after statistical testing. This hybrid approach could have practical applications in telecommunication network optimization, transportation route planning, and resource-constrained allocation.

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.

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.

Abstract: Increasing prevalence of vaping (e-cigarette use) among youth in Canada and elsewhere has become a serious public health concern. The Teens Talk Vaping project sought to co-produce research about teen vaping with teens to generate in-depth qualitative evidence about the everyday socio-environmental dimensions of teen vaping experiences and exposures across perspectives of both teens who vape and those who do not. Our participatory approach included a capacity-building programme to train teen team members to contribute to the project as ‘co-researchers’, equipping them with the research skills necessary to contribute to all phases of the project, from data collection through to knowledge translation. Paired with adult researchers, teen co-researchers facilitated 7 online focus groups with teens (n=17) from across Canada, including teens who vaped (n=3) and those who did not (n=14). Our participatory thematic analysis generated five themes: (1) Secrecy and surveillance at school; (2) Online omnipresence; (3) Social pressures and positionings; (4) (Un)restricted mobilities and access; and (5) Re-thinking school-based vaping education. Our findings reveal the extent to which exposure to vaping is deeply embedded and normalized in the everyday micro-geographies of teens in Canada as seemingly ‘everywhere.’ Teen vaping prevention efforts must be equity-centred, youth-driven, and take account of the nuanced ways in which vaping is layered into the day-to-day online and offline contexts of young people’s lives.

Data networks and computing devices have experienced exponential growth. Within a short span of time, they have opened new digital frontiers while also bringing forth new threats. These threats have the potential to increase costs and disrupt regular operations. Choosing a cybersecurity plan to address these threats requires balancing direct and indirect costs against the benefits of implementation and subsequent operation. In this study, we propose an efficient strategy for designing networking topologies by incorporating a Security Information and Event Management System. This system consists of a central server and Network Intrusion Detection Sensors, which gather data and promptly transmit information regarding suspicious activities to the server. The server then takes immediate action in case of incidents. To determine the optimal number and placement of sensors, a many-objective optimization approach is employed. The problem is mathematically modeled using linear programming. To solve the optimization problem, swarm intelligence techniques such as the particle swarm optimizer, the bat algorithm, and the black hole method are utilized. Various test scenarios were created by presenting low, medium, and complex instances of conventional networks. The results obtained using the black hole bio-inspired algorithm were particularly satisfying, surpassing the performance and resolution of the other methods.

The COVID-19 pandemic evolves constantly, requiring adaptable solutions to combat emerging SARS-CoV-2 variants. To address this, we created a pentameric scaffold based on a mammalian protein, which can be customized with up to 10 protein binding modules. This molecular scaffold spans roughly 20 nm and can simultaneously neutralize SARS-CoV-2 Spike proteins from one or multiple viral particles. Using only two different modules targeting the Spike’s RBD domain, this construct outcompetes human antibodies from vaccinated individuals’ serum and blocks in vitro cell attachment and pseudotyped virus entry. Additionally, the multibodies inhibit viral replication at low picomolar concentrations, regardless of the variant. This customizable multibody can be easily produced in procaryote systems, providing a new avenue for therapeutic development and detection devices, and contributing to preparedness against rapidly evolving pathogens.

Stagnation at local optima represents a significant challenge in bio-inspired optimization algorithms, often leading to suboptimal solutions. This paper addresses this issue by proposing a hybrid model that combines the Orca predator algorithm with deep Q-learning. The Orca predator algorithm is an optimization technique that mimics the hunting behavior of orcas. It solves complex optimization problems by exploring and exploiting search spaces efficiently. Deep Q-learning is a reinforcement learning technique that combines Q-learning with deep neural networks. This integration aims to turn the stagnation problem into an opportunity for more focused and effective exploitation, enhancing the optimization technique’s performance and accuracy. The proposed hybrid model leverages the biomimetic strengths of the Orca predator algorithm to identify promising regions nearby in the search space, complemented by the fine-tuning capabilities of deep Q-learning to navigate these areas precisely. The practical application of this approach is evaluated using the high-dimensional Heartbeat Categorization Dataset, focusing on the feature selection problem. This dataset, comprising complex electrocardiogram signals, provided a robust platform for testing the feature selection capabilities of our hybrid model. Our experimental results are encouraging, showcasing the hybrid strategy’s capability to identify relevant features without significantly compromising the performance metrics of machine learning models. This analysis was performed by comparing the improved method of the Orca predator algorithm against its native version and a set of state-of-the-art algorithms.

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.

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.

Summary: Background: The Omicron variant has challenged the control of the COVID-19 pandemic due to its immuno-evasive properties. The administration of a booster dose of a SARS-CoV-2 vaccine showed positive effects in the immunogenicity against SARS-CoV-2, effect that is even enhanced after the administration of a second booster. Methods: During a phase-3 clinical trial, we evaluated the effect of a second booster of CoronaVac®, an inactivated vaccine administered 6 months after the first booster, in the neutralization of SARS-CoV-2 (n = 87). In parallel, cellular immunity (n = 45) was analyzed in stimulated peripheral mononuclear cells by flow cytometry and ELISPOT. Findings: Although a 2.5-fold increase in neutralization of the ancestral SARS-CoV-2 was observed after the second booster when compared with prior its administration (Geometric mean units p

Abstract Background Chile was severely affected by COVID19 outbreaks but was also one of the first countries to start a nationwide program to vaccinate against the disease. Furthermore, Chile became one of the fastest countries to inoculate a high percentage of the target population and implemented homologous and heterologous booster schemes in late 2021 to prevent potential immunological waning. The aim of this study is to compare the immunogenicity and time course of the humoral response elicited by the CoronaVac vaccine in combination with homologous versus heterologous boosters. Methods We compared the immunogenicity of two doses of CoronaVac and BNT162b2 vaccines and one homologous or heterologous booster through an ELISA assay directed against the ancestral spike protein of SARS-CoV-2. Sera were collected from individuals during the vaccination schedule and throughout the implementation of homologous and heterologous booster programs in Chile. Results Our findings demonstrate that a two-dose vaccination scheme with CoronaVac induces lower levels of anti-SARS-CoV-2 spike antibodies than BNT162b2 in a broad age range (median age 42 years; interquartile range (IQR) 27-61). Furthermore, antibody production declines with time in individuals vaccinated with CoronaVac and less noticeably, with BNT162b2. Analysis of booster schemes revealed that individuals vaccinated with two doses of CoronaVac generate immunological memory against the SARS-CoV-2 ancestral strain, which can be re-activated with homologous or heterologous (BNT162b2 and ChAdOx1) boosters. Nevertheless, the magnitude of the antibody response with the heterologous booster regime was considerably higher (induction fold BNT162b2: 11.2x; ChAdoX1; 12.4x; CoronaVac: 6.0x) than the responses induced by the homologous scheme. Both homologous and heterologous boosters induced persistent humoral responses (median 122 days, IQR (108-133)), although heterologous boosters remained superior in activating a humoral response after 100 days. Conclusions Two doses of CoronaVac induces antibody titers against the SARS-CoV-2 ancestral strain which are lower in magnitude than those induced by the BNT162b2 vaccine. However, the response induced by CoronaVac can be greatly potentiated with a heterologous booster scheme with BNT162b2 or ChAdOx1 vaccines. Furthermore, the heterologous and homologous booster regimes induce a durable antibody response which does not show signs of decay 3 months after the booster dose.

Chaotic maps are sources of randomness formed by a set of rules and chaotic variables. They have been incorporated into metaheuristics because they improve the balance of exploration and exploitation, and with this, they allow one to obtain better results. In the present work, chaotic maps are used to modify the behavior of the binarization rules that allow continuous metaheuristics to solve binary combinatorial optimization problems. In particular, seven different chaotic maps, three different binarization rules, and three continuous metaheuristics are used, which are the Sine Cosine Algorithm, Grey Wolf Optimizer, and Whale Optimization Algorithm. A classic combinatorial optimization problem is solved: the 0-1 Knapsack Problem. Experimental results indicate that chaotic maps have an impact on the binarization rule, leading to better results. Specifically, experiments incorporating the standard binarization rule and the complement binarization rule performed better than experiments incorporating the elitist binarization rule. The experiment with the best results was STD_TENT, which uses the standard binarization rule and the tent chaotic map.

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.

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.

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.

The digitization of information and technological advancements have enabled us to gather vast amounts of data from various domains, including but not limited to medicine, commerce, and mining. Machine learning techniques use this information to improve decision-making, but they have a big problem: they are very sensitive to data variation, so it is necessary to clean them to remove irrelevant and redundant information. This removal of information is known as the Feature Selection Problem. This work presents the Pendulum Search Algorithm applied to solve the Feature Selection Problem. As the Pendulum Search Algorithm is a metaheuristic designed for continuous optimization problems, a binarization process is performed using the Two-Step Technique. Preliminary results indicate that our proposal obtains competitive results when compared to other metaheuristics extracted from the literature, solving well-known benchmarks.

IntroductionBooster doses of SARS-CoV-2 vaccines improve seroconversion rates in solid organ transplant recipients (SOTRs) but the impact of homologous and heterologous booster doses in neutralizing antibody (NAb) titers and their ability to interfere with the variant of concern Omicron are not well studied.MethodsWe designed a prospective, open-label, observational clinical cohort study. 45 participants received two doses of BNT162b2 or CoronaVac (21-day or 28-day intervals, respectively) followed by a first and second booster with BNT162b2 (5-month apart each) and we analyzed the neutralizing antibody titers against SARSCoV-2 D614G (B.1 lineage) and Omicron (BA.1 lineage).ResultsOur results show that SOTRs receiving an initial two-dose scheme of CoronaVac or BNT162b2 generate lower NAbs titers against the ancestral variant of SARS-CoV-2 when compared with healthy controls. Although these NAb titers were further decreased against the SARS-CoV-2 Omicron, a single BNT162b2 booster in both groups was sufficient to increase NAb titers against the variant of concern. More importantly, this effect was only observed in those participants responding to the first two shots but not in those not responding to the initial vaccination scheme.DiscussionThe data provided here demonstrate the importance of monitoring antibody responses in immunocompromised subjects when planning booster vaccination programs in this risk group.

ABSTRACT Multiple vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been evaluated in clinical trials. However, trials addressing the immune response in the pediatric population are scarce. The inactivated vaccine CoronaVac has been shown to be safe and immunogenic in a phase 1/2 clinical trial in a pediatric cohort in China. Here, we report interim safety and immunogenicity results of a phase 3 clinical trial for CoronaVac in healthy children and adolescents in Chile. Participants 3 to 17 years old received two doses of CoronaVac in a 4-week interval until 31 December 2021. Local and systemic adverse reactions were registered for volunteers who received one or two doses of CoronaVac. Whole-blood samples were collected from a subgroup of 148 participants for humoral and cellular immunity analyses. The main adverse reaction reported after the first and second doses was pain at the injection site. Four weeks after the second dose, an increase in neutralizing antibody titer was observed in subjects relative to their baseline visit. Similar results were found for activation of specific CD4+ T cells. Neutralizing antibodies were identified against the Delta and Omicron variants. However, these titers were lower than those for the D614G strain. Importantly, comparable CD4+ T cell responses were detected against these variants of concern. Therefore, CoronaVac is safe and immunogenic in subjects 3 to 17 years old, inducing neutralizing antibody secretion and activating CD4+ T cells against SARS-CoV-2 and its variants. (This study has been registered at ClinicalTrials.gov under no. NCT04992260.) IMPORTANCE This work evaluated the immune response induced by two doses of CoronaVac separated by 4 weeks in healthy children and adolescents in Chile. To date, few studies have described the effects of CoronaVac in the pediatric population. Therefore, it is essential to generate knowledge regarding the protection of vaccines in this population. Along these lines, we reported the anti-S humoral response and cellular immune response to several SARS-CoV-2 proteins that have been published and recently studied. Here, we show that a vaccination schedule consisting of two doses separated by 4 weeks induces the secretion of neutralizing antibodies against SARS-CoV-2. Furthermore, CoronaVac induces the activation of CD4+ T cells upon stimulation with peptides from the proteome of SARS-CoV-2. These results indicate that, even though the neutralizing antibody response induced by vaccination decreases against the Delta and Omicron variants, the cellular response against these variants is comparable to the response against the ancestral strain D614G, even being significantly higher against Omicron.

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.

Background: The development of vaccines to control the coronavirus disease 2019 (COVID-19) pandemic progression is a worldwide priority. CoronaVac is an inactivated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine approved for emergency use with robust efficacy and immunogenicity data reported in trials in China, Brazil, Indonesia, Turkey, and Chile. Methods: This study is a randomized, multicenter, and controlled phase 3 trial in healthy Chilean adults aged ≥18 years. Volunteers received two doses of CoronaVac separated by 2 (0–14 schedule) or 4 weeks (0–28 schedule); 2302 volunteers were enrolled, 440 were part of the immunogenicity arm, and blood samples were obtained at different times. Samples from a single center are reported. Humoral immune responses were evaluated by measuring the neutralizing capacities of circulating antibodies. Cellular immune responses were assessed by ELISPOT and flow cytometry. Correlation matrixes were performed to evaluate correlations in the data measured. Results: Both schedules exhibited robust neutralizing capacities with the response induced by the 0–28 schedule being better. No differences were found in the concentration of antibodies against the virus and different variants of concern (VOCs) between schedules. Stimulation of peripheral blood mononuclear cells (PBMCs) with Mega pools of Peptides (MPs) induced the secretion of interferon (IFN)-γ and the expression of activation induced markers in CD4+ T cells for both schedules. Correlation matrixes showed strong correlations between neutralizing antibodies and IFN-γ secretion. Conclusions: Immunization with CoronaVac in Chilean adults promotes robust cellular and humoral immune responses. The 0–28 schedule induced a stronger humoral immune response than the 0–14 schedule. Funding: Ministry of Health, Government of Chile, Confederation of Production and Commerce & Millennium Institute on Immunology and Immunotherapy, Chile. Clinical trial number: NCT04651790

ABSTRACT CoronaVac is an inactivated SARS-CoV-2 vaccine approved by the World Health Organization (WHO). Previous studies reported increased levels of neutralizing antibodies and specific T cells 2 and 4 weeks after two doses of CoronaVac; these levels were significantly reduced at 6 to 8 months after the two doses. Here, we report the effect of a booster dose of CoronaVac on the anti-SARS-CoV-2 immune response generated against the variants of concern (VOCs), Delta and Omicron, in adults participating in a phase III clinical trial in Chile. Volunteers immunized with two doses of CoronaVac in a 4-week interval received a booster dose of the same vaccine between 24 and 30 weeks after the second dose. Neutralization capacities and T cell activation against VOCs Delta and Omicron were assessed 4 weeks after the booster dose. We observed a significant increase in neutralizing antibodies 4 weeks after the booster dose. We also observed a rise in anti-SARS-CoV-2-specific CD4+ T cells over time, and these cells reached a peak 4 weeks after the booster dose. Furthermore, neutralizing antibodies and SARS-CoV-2-specific T cells induced by the booster showed activity against VOCs Delta and Omicron. Our results show that a booster dose of CoronaVac increases adults’ humoral and cellular anti-SARS-CoV-2 immune responses. In addition, immunity induced by a booster dose of CoronaVac is active against VOCs, suggesting adequate protection. IMPORTANCE CoronaVac is an inactivated vaccine against SARS-CoV-2 that has been approved by WHO for emergency use. Phase III clinical trials are in progress in several countries, including China, Brazil, Turkey, and Chile, and have shown safety and immunogenicity after two doses of the vaccine. This report characterizes immune responses induced by two doses of CoronaVac followed by a booster dose 5 months after the second dose in healthy Chilean adults. The data reported here show that a booster dose increased the immune responses against SARS-CoV-2, enhancing levels of neutralizing antibodies against the ancestral strain and VOCs. Similarly, anti-SARS-CoV-2 CD4+ T cell responses were increased following the booster dose. In contrast, levels of gamma interferon secretion and T cell activation against the VOCs Delta and Omicron were not significantly different from those for the ancestral strain. Therefore, a third dose of CoronaVac in a homologous vaccination schedule improves its immunogenicity in healthy volunteers.

Screening, testing and contact tracing plays a pivotal role in control of the COVID-19 pandemic. To enable this it is necessary to increase the testing capacity. This study compared a SARS-CoV-2 rapid antigen test (RAT) and RT-PCR in 842 asymptomatic individuals from Tarapacá, Chile. A sensitivity of 69.86%, specificity of 99.61%, PPV of 94.44% and NPP of 97.22% with Ct values (Ct > 27) that were significantly higher among individuals with false-negative RAT were reported. These results support the fact that RAT might have a significant impact on the identification of asymptomatic carriers in areas that lack suitable laboratories to perform SARS-CoV-2 real-time RT-PCR diagnostics, or the results take more than 24–48 h, as well as zones with high traffic of individuals such as border/customs, airports, interregional bus, train stations or in any mass testing campaign requiring rapid results.

In recent years, metaheuristic methods have shown remarkable efficacy in resolving complex combinatorial challenges across a broad spectrum of fields. Nevertheless, the escalating complexity of these problems necessitates the continuous development of innovative techniques to enhance the performance and reliability of these methods. This paper aims to contribute to this endeavor by examining the impact of solution initialization methods on the performance of a hybrid algorithm applied to the set union knapsack problem (SUKP). Three distinct solution initialization methods, random, greedy, and weighted, have been proposed and evaluated. These have been integrated within a sine cosine algorithm employing k-means as a binarization procedure. Through testing on medium- and large-sized SUKP instances, the study reveals that the solution initialization strategy influences the algorithm’s performance, with the weighted method consistently outperforming the other two. Additionally, the obtained results were benchmarked against various metaheuristics that have previously solved SUKP, showing favorable performance in this comparison.

This paper presents a set of adaptive parameter control methods through reinforcement learning for the particle swarm algorithm. The aim is to adjust the algorithm’s parameters during the run, to provide the metaheuristics with the ability to learn and adapt dynamically to the problem and its context. The proposal integrates Q–Learning into the optimization algorithm for parameter control. The applied strategies include a shared Q–table, separate tables per parameter, and flexible state representation. The study was evaluated through various instances of the multidimensional knapsack problem belonging to the NP-hard class. It can be formulated as a mathematical combinatorial problem involving a set of items with multiple attributes or dimensions, aiming to maximize the total value or utility while respecting constraints on the total capacity or available resources. Experimental and statistical tests were carried out to compare the results obtained by each of these hybridizations, concluding that they can significantly improve the quality of the solutions found compared to the native version of the algorithm.

The cryptanalytic resistance of modern block and stream encryption systems mainly depends on the substitution box (S-box). In this context, the problem is thus to create an S-box with higher value of nonlinearity because this property can provide some degree of protection against linear and differential cryptanalysis attacks. In this paper, we design a scheme built on a human behavior-based optimization algorithm, supported with Self-Organizing Maps to prevent premature convergence and improve the nonlinearity property in order to obtain strong $8 \times 8$ substitution boxes. The experiments are compared with S-boxes obtained using other metaheuristic algorithms such as Ant Colony Optimization, Genetic Algorithm and an approach based on chaotic functions and show that the obtained S-boxes have good cryptographic properties. The obtained S-box is investigated against standard tests such as bijectivity, nonlinearity, strict avalanche criterion, bit independence criterion, linear probability and differential probability, proving that the proposed scheme is proficient to discover a strong nonlinear component of encryption systems.

This article proposes an optimization model to set frequencies, vehicle capacities, required fleet and the stops serving each route along a transit corridor which minimize the total user and operating costs. The optimization problem is solved by applying the “Black Hole” algorithm, which imitates the movement of stars (solutions), towards a black hole (Best solution). The main contributions of the model are based on incorporating variable dwell times depending on bus stop demand not only to the passenger perceived journey times but also to the bus cycle times and on considering capacity constraints in both vehicles and bus tops. This led to a more accurate and realistic operating times and user perceived journey times. The application of the model to two case studies and the sensitivity analysis carried out demonstrate that for low levels of demand, constant dwell times can be assumed but being these times different between the different stops of the corridor, considering their demand. However, with high level of demand the difference found in operating costs and travel times strongly recommend incorporating variable dwell times in the model in order to achieve a more realistic design of transit corridor strategies.

The coronavirus disease 2019 (COVID19) pandemic has left researchers scrambling to identify the humoral immune correlates of protection from COVID-19. To date, the antibody mediated correlates of virus neutralization have been extensively studied. However, the extent that non-neutralizing functions contribute to anti-viral responses are ill defined. In this study, we profiled the anti-spike antibody subtype/subclass responses, along with neutralization and antibody-dependent natural killer cell functions in 83 blood samples collected between 4 and 201 days post-symptoms onset from a cohort of COVID-19 outpatients. We observed heterogeneous humoral responses against the acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein. Overall, anti-spike profiles were characterized by a rapid rise of IgA and sustained IgG titers. In addition, strong antibody-mediated natural killer effector responses correlated with milder disease and being female. While higher neutralization profiles were observed in males along with increased severity. These results give an insight into the underlying function of antibodies beyond neutralization and suggest that antibody-mediated natural killer cell activity is a key function of the humoral response against the SARS-CoV-2 spike protein.

CoronaVac vaccine from Sinovac Life Science is currently being used in several countries. In Chile, the effectiveness of preventing hospitalization is higher than 80% with a vaccination schedule. However, to date, there are no data about immune response induction or specific memory. For this reason, we recruited 15 volunteers without previous suspected/diagnosed COVID-19 and with negative PCR over time to evaluate the immune response to CoronaVac 28 and 90 days after the second immunization (dpi). The CoronaVac administration induces total and neutralizing anti-spike antibodies in all vaccinated volunteers at 28 and 90 dpi. Furthermore, using ELISpot analysis to assay cellular immune responses against SARS-CoV-2 spike protein, we found an increase in IFN-gamma- and Granzyme B-producing cells in vaccinated volunteers at 28 and 90 dpi. Together, our results indicate that CoronaVac induces a robust humoral immune response and cellular immune memory of at least 90 dpi.

The widespread application of information and communication technologies in education, especially in the context of learning management platforms, is generating a large amount of data related to the academic activities in which students and teachers participate [...]

In recent years, continuous metaheuristics have been a trend in solving binary-based combinatorial problems due to their good results. However, to use this type of metaheuristics, it is necessary to adapt them to work in binary environments, and in general, this adaptation is not trivial. The method proposed in this work evaluates the use of reinforcement learning techniques in the binarization process. Specifically, the backward Q-learning technique is explored to choose binarization schemes intelligently. This allows any continuous metaheuristic to be adapted to binary environments. The illustrated results are competitive, thus providing a novel option to address different complex problems in the industry.

For years, extensive research has been in the binarization of continuous metaheuristics for solving binary-domain combinatorial problems. This paper is a continuation of a previous review and seeks to draw a comprehensive picture of the various ways to binarize this type of metaheuristics; the study uses a standard systematic review consisting of the analysis of 512 publications from 2017 to January 2022 (5 years). The work will provide a theoretical foundation for novice researchers tackling combinatorial optimization using metaheuristic algorithms and for expert researchers analyzing the binarization mechanism’s impact on the metaheuristic algorithms’ performance. Structuring this information allows for improving the results of metaheuristics and broadening the spectrum of binary problems to be solved. We can conclude from this study that there is no single general technique capable of efficient binarization; instead, there are multiple forms with different performances.

When we face real problems using computational resources, we understand that it is common to find combinatorial problems in binary domains. Moreover, we have to take into account a large number of possible candidate solutions, since these can be numerous and make it complicated for classical algorithmic techniques to address them. When this happens, in most cases, it becomes a problem due to the high resource cost they generate, so it is of utmost importance to solve these problems efficiently. To cope with this problem, we can apply other methods, such as metaheuristics. There are some metaheuristics that allow operation in discrete search spaces; however, in the case of continuous swarm intelligence metaheuristics, it is necessary to adapt them to operate in discrete domains. To perform this adaptation, it is necessary to use a binary scheme to take advantage of the original moves of the metaheuristics designed for continuous problems. In this work, we propose to hybridize the whale optimization algorithm metaheuristic with the Q-learning reinforcement learning technique, which we call (the QBWOA). By using this technique, we are able to realize an smart and fully online binarization scheme selector, the results have been statistically promising thanks to the respective tables and graphs.

N6-methyladenosine (m6A) is the most abundant internal modification described in eukaryotic mRNA and several viral RNA including human respiratory syncytial virus (HRSV). Here, we evaluated the impact of m6A writers, erasers and readers on HRSV genomic RNA accumulation and inclusion bodies assembly during viral replication. We observed that the METTL3/METTL14 m6A writer complex plays a negative role in HRSV protein synthesis and viral titers, while m6A erasers FTO and ALKBH5 had the opposite effect. We also observed that m6A readers YTHDF1-3 bind to the viral genomic RNA inducing a decrease in its intracellular levels and thus, inhibiting viral replication. Finally, we observed that overexpression of YTHDFs proteins caused a decrease in the size of inclusion bodies (IBs), accompanied by an increase in their number. METTL3 knockdown cells showed an opposite effect indicating that the dynamics of IBs assembly and coalescence are strongly affected by m6A readers in a mechanism dependent on m6A writers. Taken together, our results demonstrated that the m6A modification negatively affects HRSV replication, possibly through a mechanism involving the assembly of inclusion bodies, the main factories of viral genomic RNA synthesis.

In recent years, Convolutional Neural Networks (CNN) have been widely used for real-world applications in the field of computer vision. Their class-leading performance, however, depends heavily on the architecture used for a given problem. In most cases, the architectures are manually optimized by the researchers, a time-consuming process hard to achieve without prior knowledge of CNN. In this paper, we propose a new genetic algorithm for the optimization of the CNN architecture for a given image classification problem. This algorithm extends and refines existing research in the field, by allowing depth exploration, introducing a novel sequential crossover operator, using an incremental selective pressure schedule over evolution (favoring higher diversity in early generations) and by evaluating individual performances over the validation set with early stopping. The technique is validated in three image classification dataset, namely, CIFAR10, MNIST and Caltech256 datasets, which are widely used benchmarks for image classification algorithms. We evaluate the performance and total execution time over these datasets, and compare our results with those achieved by the best genetic methods published so far. In all cases, we achieve better results in terms of test accuracy, consistently over different datasets, while remaining in the same orders of magnitude of execution time of the fastest approaches.

Bone fractures are caused by diseases or accidents and are a widespread problem throughout the globe. Worldwide, 1.6 millions of hip fractures occur every year and are expected to rise to 6.3 millions in 2050. The current gold standard to assess fracture risk is the Dual-energy X-ray Absorptiometry (DXA), which provides a projected image of the bone from which areal bone mineral density is extracted. Ultrasound techniques have been proposed as non invasive alternatives. Recently, estimates of cortical thickness and porosity, obtained by Bi-Directional Axial Transmission (BDAT) in a pilot clinical study, have been shown to be associated with non-traumatic fractures in post menopausal women. Cortical parameters were derived from the comparison between experimental and theoretical guided modes. This model-based inverse approach failed for the patients associated with poor guided mode information. Moreover, even if the fracture discrimination ability was found similar to DXA, it remained moderate. The goal of this paper is to explore if these two limitations could be overcome by using automatic classification tools, independent of any waveguide model. To this end, a dynamic machine learning approach based on a Support Vector Machine (SVM) has been used to classify ultrasonic guided wave spectrum images measured by BDAT on post menopausal women with or without non-traumatic fractures. This approach has then been improved using parameters tuned by Bat Algorithm Optimization (BOA). The applied methodology focused on the extraction of texture features through a gray level co-occurrence matrix, structural comparison and histograms. The results accuracy was assessed using a confusion matrix. The effectiveness of the learning approach reached an accuracy of 92.31%.

The need to make good use of resources has allowed metaheuristics to become a tool to achieve this goal. There are a number of complex problems to solve, among which is the Set-Covering Problem, which is a representation of a type of combinatorial optimization problem, which has been applied to several real industrial problems. We use a binary version of the optimization algorithm based on teaching and learning to solve the problem, incorporating various binarization schemes, in order to solve the binary problem. In this paper, several binarization techniques are implemented in the teaching/learning based optimization algorithm, which presents only the minimum parameters to be configured such as the population and number of iterations to be evaluated. The performance of metaheuristic was evaluated through 65 benchmark instances. The results obtained are promising compared to those found in the literature.

In order to minimize execution times, improve the quality of solutions, and address more extensive target situations, optimization techniques, particularly metaheuristics, are continually improved. Hybridizing procedures are one of these noteworthy strategies due to their wide range of applications. This article describes a hybrid algorithm that combines the k-means method to produce a binary version of the cuckoo search and sine cosine algorithms. The binary algorithms are applied on the NP-hard multi-demand multidimensional knapsack problem. This problem is of particular interest because it has two types of constraints. The first group of constraints is related to the capacity of the knapsacks, and a second type is associated with the demand that must be met. Experiments were undertaken to acquire insight into the contribution of the k-means technique and the local search operator to the final results. Additionally, a comparison is made with two other types of binarization, the first based on a random method and the second based on the percentile concept. The results reveal that the k-means hybrid algorithm consistently provides superior results in most cases studied. In particular, incorporating the local search operator improved the results by an average of 0.23%. On the other hand, when comparing the results with 100 items and 30-30 restrictions, k-means was 1.06% better on average than the random operator.

Nature–inspired computing is a promising field of artificial intelligence. This area is mainly devoted to designing computational models based on natural phenomena to address complex problems. Nature provides a rich source of inspiration for designing smart procedures capable of becoming powerful algorithms. Many of these procedures have been successfully developed to treat optimization problems, with impressive results. Nonetheless, for these algorithms to reach their maximum performance, a proper balance between the intensification and the diversification phases is required. The intensification generates a local solution around the best solution by exploiting a promising region. Diversification is responsible for finding new solutions when the main procedure is trapped in a local region. This procedure is usually carryout by non-deterministic fundamentals that do not necessarily provide the expected results. Here, we encounter the stagnation problem, which describes a scenario where the search for the optimum solution stalls before discovering a globally optimal solution. In this work, we propose an efficient technique for detecting and leaving local optimum regions based on Shannon entropy. This component can measure the uncertainty level of the observations taken from random variables. We employ this principle on three well–known population–based bio–inspired optimization algorithms: particle swarm optimization, bat optimization, and black hole algorithm. The proposal’s performance is evidenced by solving twenty of the most challenging instances of the multidimensional knapsack problem. Computational results show that the proposed exploration approach is a legitimate alternative to manage the diversification of solutions since the improved techniques can generate a better distribution of the optimal values found. The best results are with the bat method, where in all instances, the enhanced solver with the Shannon exploration strategy works better than its native version. For the other two bio-inspired algorithms, the proposal operates significantly better in over 70% of instances.

Herpesviruses are double-stranded DNA viruses occurring at a high prevalence in the human population and are responsible for a wide array of clinical manifestations and diseases, from mild to severe. These viruses are classified in three subfamilies (Alpha-, Beta- and Gammaherpesvirinae), with eight members currently known to infect humans. Importantly, all herpesviruses can establish lifelong latent infections with symptomatic or asymptomatic lytic reactivations. Accumulating evidence suggest that chemical modifications of viral RNA and DNA during the lytic and latent phases of the infections caused by these viruses, are likely to play relevant roles in key aspects of the life cycle of these viruses by modulating and regulating their replication, establishment of latency and evasion of the host antiviral response. Here, we review and discuss current evidence regarding epitranscriptomic and epigenetic modifications of herpesviruses and how these can influence their life cycles. While epitranscriptomic modifications such as m6A are the most studied to date and relate to positive effects over the replication of herpesviruses, epigenetic modifications of the viral genome are generally associated with defense mechanisms of the host cells to suppress viral gene transcription. However, herpesviruses can modulate these modifications to their own benefit to persist in the host, undergo latency and sporadically reactivate.

Population-based approaches have given us new search strategies and ideas in order to solve optimization problems. Usually, these methods are based on the performance carried out by a finite number of agents, which by the interaction between them they evolve and work all over the search space. Also, it is well-known that the correct employment of parameter values in this kind of method can positively impact their performance and behavior. In this context, the present work focuses on the design of a hybrid architecture which smartly balances the population size on run-time. In order to smartly balance and control the population size, a modular approach, named Linear Modular Population Balancer (LMPB), is proposed. The main ideas behind the designed architecture include the solving strategy behind a population-based metaheuristic, the influence of learning components based on multiple statistical modeling methods which transform the dynamic data generated into knowledge, and the possibilities to tackle both discrete and continuous optimization problems. In this regard, three modules are proposed for LMPB, which concern tasks such as the management of the population-based algorithm, parameter setting, probabilities, learning methods, and selection mechanism for the population size to employ. In order to test the viability and effectiveness of our proposed approach, we solve a set of well-known benchmark functions and the multidimensional knapsack problem (MKP). Additionally, we illustrate promising solving results, compare them against state-of-the-art methods which have proved to be good options for solving optimization problems, and give solid arguments for future work in the necessity to keep evolving this type of proposed architecture.

BackgroundConvalescent plasma (CP), despite limited evidence on its efficacy, is being widely used as a compassionate therapy for hospitalized patients with COVID-19. We aimed to evaluate the efficacy and safety of early CP therapy in COVID-19 progression.Methods and findingsThe study was an open-label, single-center randomized clinical trial performed in an academic medical center in Santiago, Chile, from May 10, 2020, to July 18, 2020, with final follow-up until August 17, 2020. The trial included patients hospitalized within the first 7 days of COVID-19 symptom onset, presenting risk factors for illness progression and not on mechanical ventilation. The intervention consisted of immediate CP (early plasma group) versus no CP unless developing prespecified criteria of deterioration (deferred plasma group). Additional standard treatment was allowed in both arms. The primary outcome was a composite of mechanical ventilation, hospitalization for >14 days, or death. The key secondary outcomes included time to respiratory failure, days of mechanical ventilation, hospital length of stay, mortality at 30 days, and SARS-CoV-2 real-time PCR clearance rate. Of 58 randomized patients (mean age, 65.8 years; 50% male), 57 (98.3%) completed the trial. A total of 13 (43.3%) participants from the deferred group received plasma based on clinical aggravation. We failed to find benefit in the primary outcome (32.1% versus 33.3%, odds ratio [OR] 0.95, 95% CI 0.32-2.84, p > 0.999) in the early versus deferred CP group. The in-hospital mortality rate was 17.9% versus 6.7% (OR 3.04, 95% CI 0.54-17.17 p = 0.246), mechanical ventilation 17.9% versus 6.7% (OR 3.04, 95% CI 0.54-17.17, p = 0.246), and prolonged hospitalization 21.4% versus 30.0% (OR 0.64, 95% CI, 0.19-2.10, p = 0.554) in the early versus deferred CP group, respectively. The viral clearance rate on day 3 (26% versus 8%, p = 0.204) and day 7 (38% versus 19%, p = 0.374) did not differ between groups. Two patients experienced serious adverse events within 6 hours after plasma transfusion. The main limitation of this study is the lack of statistical power to detect a smaller but clinically relevant therapeutic effect of CP, as well as not having confirmed neutralizing antibodies in donor before plasma infusion.ConclusionsIn the present study, we failed to find evidence of benefit in mortality, length of hospitalization, or mechanical ventilation requirement by immediate addition of CP therapy in the early stages of COVID-19 compared to its use only in case of patient deterioration.Trial registrationNCT04375098.

Tellurium oxyanion, tellurite (TeO3–2), is a highly toxic compound for many organisms. Its presence in the environment has increased over the past years due to industrial manufacturing processes and has been associated with adverse effects on human health. Although tellurite induces the phosphorylation of eIF2α, DNA damage and oxidative stress, the molecular mechanisms related to the cellular responses to tellurite-induced stress are poorly understood. In this work, we evaluated the ability of tellurite to induce phosphorylation of eIF2α, stress granules (SGs) assembly and their relationship with DNA damage in U2OS cells. We demonstrate that tellurite promotes the assembly of bona fide cytoplasmic SGs. Unexpectedly, tellurite also induces the assembly of nuclear SGs. Interestingly, we observed that the presence of tellurite-induced nuclear SGs correlates with γH2AX foci. However, although H2O2 also induce DNA damage, no nuclear SGs were observed. Our data show that tellurite promotes the assembly of cytoplasmic and nuclear SGs in response to oxidative stress and DNA damage, revealing a new aspect of cellular stress response mediated by the assembly of nuclear stress granules.

The main component of a cryptographic system that allows us to ensure its strength against attacks, is the substitution box. The strength of this component can be validated by various metrics, one of them being the nonlinearity. To this end, it is essential to develop a design for substitution boxes that allows us to guarantee compliance with this metric. In this work, we implemented a hybrid between the stochastic fractal search algorithm in conjunction with opposition-based learning. This design is supported by sequential model algorithm configuration for the proper parameters configuration. We obtained substitution boxes of high nonlinearity in comparison with other works based on metaheuristics and chaotic schemes. The proposed substitution box is evaluated using bijectivity, the strict avalanche criterion, nonlinearity, linear probability, differential probability and bit-independence criterion, which demonstrate the excellent performance of the proposed approach.

Los árboles y arbustos son componentes inherentes de cualquier comunidad vegetal, ya sea tropical, desértica, polar o templada. Su densa biomasa actúa como regulador de muchos factores físicos ambientales; también es fuente de alimentación y refugio para miles de organismos heterótrofos. Para el hombre, los bosques formados por los árboles representan valores económicos, sociales y culturales imprescindibles. El estado de Chihuahua cuenta con una gran diversidad de árboles y arbustos cubriendo áreas desérticas, templadas y tropicales. El objetivo de este estudio fue inventariar las especies de árboles y arbustos en las zonas templadas del estado de Chihuahua. El estudio fue realizado en todo el estado, en el cual se establecieron y recorrieron once sitios de observación durante los años 2012, 2013 y 2014. Cada sitio fue visitado por lo menos una vez para obtener los siguientes datos: especies de árboles y arbustos con características templadas, asociación vegetal del sitio, coordenadas geográficas (lat/long) y elevación (msnm). Al analizar dichos datos, se obtuvo un listado de árboles y arbustos compuesto por 27 familias, 57 géneros y 163 especies. Las familias con más especies fueron: Fagaceae (33 y 6 híbridos), Pinaceae (19 y 2 variedades), Rosaceae (10), Ericaceae (10) y Cupressaceae (8). De igual manera, se obtuvo un listado de 11 especies que están enlistadas en la NOM-059-SEMANART-2010 y 7 especies consideradas amenazadas, incluidas en la Lista Roja de UICN (Unión Internacional sobre la Conservacion de la Naturaleza). Las comunidades vegetales del clima templado de Chihuahua son dominadas por dos grupos principales de plantas (encinos y pinos), cuya biomasa, en conjunto, ejerce una gran influencia sobre el ambiente físico, y modifica los efectos de los factores temperatura, agua y suelos para mantener las condiciones mésicas en las regiones templadas en el estado. Abstract Trees and shrubs are inherent components of any plant community, either tropical, desert, polar or temperate. Its dense biomass acts as a regulator of many physical environmental factors as well as being a source of food and shelter for thousands of heterotrophic organisms. For man, the forests formed by trees represent essential economic, social and cultural values. The state of Chihuahua has a wide variety of trees and shrubs covering desert, temperate and tropical areas. The aim of this study was to inventory the species of trees and shrubs in the temperate areas of the state of Chihuahua. The study was conducted throughout the state, in which, eleven observation sites were established and toured in 2012, 2013 and 2014. Each site was visited at least once to obtain the following data: species of trees or shrubs with temperate characteristics, plant association, geographic coordinates (lat/ long) and elevation (meters). After analyzing the data, a list of trees and shrubs comprising 27 families, 57 genera and 163 species was obtained. The dominating families were Fagaceae (33 species and 6 hybrids), Pinaceae (19 and 2 varieties), Rosaceae (10), Ericaceae (10) and Cupressaceae (8). Similarly, it was obtained a list of 11 species of the region that are listed in NOM-059-SEMANART 2010 and 7 species considered threatened, included in the Red List of IUCN (International Union for Conservation of Nature). The temperate plant communities of Chihuahua are dominated by two groups of plants (oaks and pines) whose biomass, overall, have a great influence on the physical environment and modify the effects of factors such as temperature, water and soil and thus maintaining the mesic conditions in the temperate region of the State. Keywords: temperate forest of Chihuahua, trees and shrubs, pines and oaks of Chihuahua, floristic list.

The manufacturing Cell Design Problem (MCDP) is a classical optimization problem that finds application in lines of manufacture. The problem consist in distributing machines in cells, where the parts processed by each machine travels in the production process in such a way that productivity is improved. To solve the MCDP we employ a novel metaheuristic, which is inspired by actions, attitudes, and conducts that people normally have in life, named Human behavior-based optimization (HBBO). An individual try to evolve in life by trying his best in order to be a better human being with a brilliant future, successful at life, and be an example for others. We couple the HBBO with Autonomous Search (AS), which allows the modification of internal components of our approach when exposed to changing external forces and opportunities. We compare our HBBO-AS with the classic HBBO and an implementation using IRace, which is a software package that allows us to automatize the configuration of an algorithm through automatic configuration procedures. Additionally, in order to test the competitiveness of our results, we compare with other algorithms proved to perform well solving the MCDP. We illustrate experimental results, where the proposed approach is able to obtain interesting performance and robustness in the 125 well-known instances of the MCDP.