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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.

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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.

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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.

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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.

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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.

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The Humboldt Archipelago, situated on Chile's north-central coast, is renowned for its exceptional biodiversity. However, lizards of the Liolaemus genus are a particularly understudied group in this archipelago. Liolaemus genus is divided into two clades: chiliensis and nigromaculatus. Within the nigromaculatus clade the zapallarensis group is restricted to the semi-arid and arid coastal habitats of the Atacama Desert in north-central Chile. While it has been reported that lizards from the zapallarensis group inhabit various islands within the Humboldt Archipelago, there has been limited knowledge regarding their specific species identification. To identify the lizard species inhabiting these islands, we conducted phylogenetic analyses using a mitochondrial gene and examined morphological characteristics. Our findings reveal that lizards from the Damas, Choros, and Gaviota islands belong to Liolaemus silvai. In contrast, the lizards on Chañaral Island form a distinct and previously unrecognised group, clearly distinguishable from Liolaemus silvai. In conclusion, our study not only confirms the presence of L. silvai on the Damas, Choros, and Gaviota islands but also describes a new lizard species on Chañaral Island named Liolaemus carezzae sp. nov. These findings contribute valuable insights into the biodiversity of these islands and introduce a newly discovered endemic taxon to the region, enriching our understanding of Chile's unique island ecosystems.

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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.

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BACKGROUND: Triatomines are blood-sucking insects capable of transmitting Trypanosoma cruzi, the parasite that causes Chagas disease in humans. Vectorial transmission entails an infected triatomine feeding on a vertebrate host, release of triatomine infective dejections, and host infection by the entry of parasites through mucous membranes, skin abrasions, or the biting site; therefore, transmission to humans is related to the triatomine-human contact. In this cross-sectional study, we evaluated whether humans were detected in the diet of three sylvatic triatomine species (Mepraia parapatrica, Mepraia spinolai, and Triatoma infestans) present in the semiarid-Mediterranean ecosystem of Chile. METHODS: We used triatomines collected from 32 sites across 1100 km, with an overall T. cruzi infection frequency of 47.1% (N = 4287 total specimens) by conventional PCR or qPCR. First, we amplified the vertebrate cytochrome b gene (cytb) from all DNA samples obtained from triatomine intestinal contents. Then, we sequenced cytb-positive PCR products in pools of 10-20 triatomines each, grouped by site. The filtered sequences were grouped into amplicon sequence variants (ASVs) with a minimum abundance of 100 reads. ASVs were identified by selecting the best BLASTn match against the NCBI nucleotide database. RESULTS: Overall, 16 mammal (including human), 14 bird, and seven reptile species were identified in the diet of sylvatic triatomines. Humans were part of the diet of all analyzed triatomine species, and it was detected in 19 sites representing 12.19% of the sequences. CONCLUSIONS: Sylvatic triatomine species from Chile feed on a variety of vertebrate species; many of them are detected here for the first time in their diet. Our results highlight that the sylvatic triatomine-human contact is noteworthy. Education must be enforced for local inhabitants, workers, and tourists arriving in endemic areas to avoid or minimize the risk of exposure to Chagas disease vectors.

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INTRODUCCIÓN: El osteosarcoma (OS) es el tumor óseo primario maligno más frecuente. En 2014 se incorporó en Chile dentro del plan de garantías explícitas en salud (GES), ley que garantiza el tratamiento desde la sospecha hasta su tratamiento y rehabilitación. OBJETIVOS: Describir las características de la población mayor de 15 años, con diagnóstico histopatológico de OS, definir su sobrevida, complicaciones e identificar variables que afectan estos resultados. MATERIAL Y MÉTODOS: Estudio retrospectivo, se incluyó a todos los pacientes con diagnóstico de OS, con confirmación histológica, desde 2014 a 2020. Datos demográficos, sitio anatómico, hallazgos histopatológicos, estadio de ingreso, presencia de metástasis, tratamiento, complicaciones y sobrevida fueron registrados. Se realizó un estudio multivariante para determinar factores pronósticos. RESULTADOS: 133 pacientes fueron incluidos. 58,8% hombres, la edad promedio fue 31 años y la localización más frecuente fue fémur distal (34,9%). El 56,4% de los pacientes ingresó en estadio Enneking IIB, 36,4% presentó metástasis al ingreso. Un 14,3% presentó complicaciones, siendo la más frecuente la infección periprotésica (6 casos). El 16% evolucionó con recidiva local. El modelo multivariante mostró que la edad y presencia de metástasis al ingreso constituyen variables pronósticas independientes, con un cociente de riesgo de 1,017 para edad (p = 0,016) y de 3,13 para presencia de metástasis (p = 0,0000). En el análisis de subgrupos, los pacientes sin metástasis al diagnóstico presentaron sobrevida a 5 años de 54% versus 11% para el grupo con metástasis; al ajustar por edad, la sobrevida a 5 años para el grupo sin metástasis y con metástasis fue de 73% y 22% respectivamente. CONCLUSIÓN: Este es el primer estudio de descripción demográfica de pacientes con OS que se realiza en nuestro país. Los resultados son similares a lo reportado por la literatura internacional, destacando en nuestra serie la edad de presentación y presencia de metástasis al ingreso como factores que afectan el pronóstico de una manera significativa. Al ajustar por edad, los porcentajes de sobrevida son equiparables con lo reportado en otros centros internacionales especializados en sarcomas.

BACKGROUND: Osteosarcoma (OS) is the most frequent malignant primary bone tumor. The explicit health guarantee (GES) plan in Chile, a law that guarantees diagnosis, treatment, and rehabilitation, incorporated OS in 2014. OBJETIVES: To describe the characteristics of the population over 15 years, with a histopathological diagnosis of OS, to define survival rate and complications, and to identify variables that affect these outcomes. METHODS: A retrospective study from 2014 to 2020, including all patients affiliated with the public health system diagnosed with OS, with histological confirmation. From clinical records, we extracted demographic data, anatomical OS location, histopathological findings, admission stage, presence of metastases, treatment, complications, and survival. We determined prognostic factors by multivariate analysis. Results: 133 patients, 58.8% men, the average age was 31 years, and the most frequent location was the distal femur (34.9%). 56.4% of the patients were admitted in Enneking stage IIB, and 36.4% presented metastasis at admission. 14.3% presented complications, the most frequent periprosthetic infection (6 cases). 16% of patients evolved with local recurrence. The multivariate model showed that age and metastases at admission constitute independent prognostic variables, with a hazard ratio of 1.017 for age (p = 0.016) and 3.13 for metastases (p = 0.0000). In the subgroup analysis, patients without metastases at diagnosis had a 5-year survival of 54% versus 11% for the group with metastases. Adjusting for age, the 5-year survival for the non-metastatic and metastatic groups was 73% and 22%, respectively. CONCLUSION: This is our country's first demographic description study of patients with OS. In our series, the age of presentation and the presence of metastases at admission were factors that significantly affect prognosis. When adjusting for age, the survival percentages are comparable to those reported in other international centers specialized in sarcomas.

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The growing increase in infections caused by C. tropicalis, associated with its drug resistance and consequent high mortality, especially in immunosuppressed people, today generates a serious global public health problem. In the search for new potential drug candidates that can be used as treatments or adjuvants in the control of infections by these pathogenic yeasts, the objective of this research was to evaluate the action of isoespintanol (ISO) against the formation of fungal biofilms, the mitochondrial membrane potential (ΔΨm), and its effect on the integrity of the cell wall. We report the ability of ISO to inhibit the formation of biofilms by up to 89.35%, in all cases higher than the values expressed by amphotericin B (AFB). Flow cytometric experiments using rhodamine 123 (Rh123) showed the ability of ISO to cause mitochondrial dysfunction in these cells. Likewise, experiments using calcofluor white (CFW) and analyzed by flow cytometry showed the ability of ISO to affect the integrity of the cell wall by stimulating chitin synthesis; these changes in the integrity of the wall were also observed through transmission electron microscopy (TEM). These mechanisms are involved in the antifungal action of this monoterpene.

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BACKGROUND: Osteosarcoma (OS) is the most frequent malignant primary bone tumor. The explicit health guarantee (GES) plan in Chile, a law that guarantees diagnosis, treatment, and rehabilitation, incorporated OS in 2014. OBJECTIVES: To describe the characteristics of the population over 15 years, with a histopathological diagnosis of OS, to define survival rate and complications, and to identify variables that affect these outcomes. METHODS: A retrospective study from 2014 to 2020, including all patients affiliated with the public health system diagnosed with OS, with histological confirmation. From clinical records, we extracted demographic data, anatomical OS location, histopathological findings, admission stage, presence of metastases, treatment, complications, and survival. We determined prognostic factors by multivariate analysis. RESULTS: 133 patients, 58.8% men, the average age was 31 years, and the most frequent location was the distal femur (34.9%). 56.4% of the patients were admitted in Enneking stage IIB, and 36.4% presented metastasis at admission. 14.3% presented complications, the most frequent periprosthetic infection (6 cases). 16% of patients evolved with local recurrence. The multivariate model showed that age and metastases at admission constitute independent prognostic variables, with a hazard ratio of 1.017 for age (p = 0.016) and 3.13 for metastases (p = 0.0000). In the subgroup analysis, patients without metastases at diagnosis had a 5-year survival of 54% versus 11% for the group with metastases. Adjusting for age, the 5-year survival for the non-metastatic and metastatic groups was 73% and 22%, respectively. CONCLUSION: This is our country's first demographic description study of patients with OS. In our series, the age of presentation and the presence of metastases at admission were factors that significantly affect prognosis. When adjusting for age, the survival percentages are comparable to those reported in other international centers specialized in sarcomas.

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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.

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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.

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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.

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Mepraia is a genus (Triatominae) endemic to Chile and a vector of Trypanosoma cruzi. Alternative phylogeographic hypotheses have been suggested for Mepraia. We tested different colonization routes hypothesized using mitochondrial sequences and phylogeographic approaches to select the best-supported hypothesis. Our results suggest that, after the split from the sister genus Triatoma at ~4.3 Mya, Mepraia formed two main clades at ~2.1 Mya. The northern clade diverged from Mepraia sp. ~1.7 Mya, giving rise to M. parapatrica and M. gajardoi about ~1.4 Mya. The southern clade originated M. spinolai ~1.68 Mya. We suggest that Mepraia had an origin in the north-central Andes along with orogenic processes, reinforced by hyperaridity during the Pliocene. The hyperarid cycle would have separated the southern and northern clades. Then, in the northern clade, dispersal occurred north and south from the centre through corridors during the Pleistocene Climatic Oscillations. Climate changes may have induced a major speciation process in the Atacama Desert, while the more homogeneous habitat colonized by the southern clade led to only one, but structured, species.

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Mepraia parapatrica is one of the lesser known and less abundant sylvatic triatomine species naturally infected by the protozoan Trypanosoma cruzi, the etiological agent of Chagas disease. M. parapatrica lives in sympatry with T. cruzi-infected rodents, but only birds, reptiles, and marine mammals have been reported as blood-meal sources of this vector species by serology. The distribution range of this kissing bug overlaps with fishers' settlements and tourist areas, and therefore the study of the blood-meal sources of this triatomine species is relevant. Here, we determined the blood-meal sources of M. parapatrica by NGS or standard sequencing from a coastal mainland area and an island in northern Chile, and T. cruzi infection by real-time PCR. The blood-meals of. M parapatrica included 61.3% reptiles, 35.5% mammals (including humans) and 3.2% birds. Feeding on reptiles was more frequent on the mainland, while on the island feeding on mammals was more frequent. The presence of T. cruzi-infected triatomine bugs and humans as part of the diet of M. parapatrica in both areas represents an epidemiological threat and potential risk to the human population visiting or established in these areas. Currently there are no tools to control wild triatomines; these results highlight the potential risk of inhabiting these areas and the necessity of developing information campaigns for the community and surveillance actions.

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The protozoan Trypanosoma cruzi, the causative agent of Chagas disease, is transmitted by infected feces or consumption of blood-sucking triatomine insects to several mammalian orders including Chiroptera. In Chile, the distribution of several insectivorous and one hematophagous bat species overlaps with those of triatomine vectors, but the T. cruzi infection status of local chiropterans is unknown. In 2018, we live-captured bats from two protected areas in Chile to collect plagiopatagium tissue, feces and perianal swab samples, in search for T. cruzi-DNA by real time PCR assays using species-specific primers. In Pan de Azúcar island (∼26°S), we examined a roost of Desmodus rotundus (common vampire bat) and sampled tissue from 17 individuals, detecting T. cruzi-DNA in five of them. In Las Chinchillas National Reserve (∼31°S), we examined two roosts of Histiotus montanus (small big-eared brown bat), collecting feces or perianal swab samples from eight individuals, detecting T. cruzi-DNA in four of them. This is the first report of T. cruzi-DNA evidence in bat species from Chile. Both vector-borne and oral transmission are potential infection routes that can explain our results. Further investigation is needed for a better understanding of the role of bats in the T. cruzi transmission cycle.

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Trypanosoma cruzi, the cause agent of Chagas disease, is transmitted mainly by blood-feeding insects of the subfamily Triatominae. The T. cruzi life cycle alternates between triatomines and mammalian hosts, excluding birds and reptiles. Triatomines of Mepraia genus are wild vectors of T. cruzi in Chile. Mepraia specimens infected with T. cruzi have been detected in Pan de Azúcar and Santa María islands. The most common vertebrates that inhabit these islands are birds and reptiles, and it is unknown whether small mammals are present. Consequently, it is relevant to know whether there are any T. cruzi-infected small mammals on those islands to elucidate the T. cruzi cycle. To clarify this crossroads, islands of northern Chile were explored to determine if T. cruzi-infected triatomines and rodents co-occur in islands of northern Chile. T. cruzi DNA was detected by conventional and real-time PCR in three islands: on Santa María and Pan de Azúcar islands T. cruzi was detected in Mepraia sp samples, while on Pan de Azúcar (6.1%) and Damas islands (15%) was detected in the rodent Abrothrix olivacea. We show for the first time in Chile the occurrence of insular rodents infected with T. cruzi, and a complete T. cruzi life cycle in a coastal island. Our results provide new insights to understand the T. cruzi infection in the wild cycle.

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Chagas disease is one of the main zoonoses mediated by vectors in America. The etiological agent is the protozoan Trypanosoma cruzi, transmitted mainly by hematophagous insects of the subfamily Triatominae. Mepraia species are triatomines endemic to Chile that play an important role in T. cruzi transmission in the wild cycle and are potential vectors for humans. In addition to the continental distribution, populations of Mepraia genus have been reported inhabiting islands of northern Chile. The presence of individuals of Mepraia in insular areas might be explained through passive dispersion by marine birds or by vicariance of an ancestral widespread population. To clarify the biogeographic origin and phylogenetic relationships of island individuals of Mepraia, mitochondrial COI and cyt b genes were sequenced in individuals from island and continental areas. Gene sequences were used to estimate phylogenetic relationships, divergence dates and migration rates between insular and continental populations. The dates of divergence estimates are congruent with sea level and tectonic changes that originated the islands during Pleistocene. Migration rates suggest symmetric historical island-continent gene flow. We suggest that the origin of island triatomines can be explained by both vicariance and dispersion. Phylogenetic relationships show that individuals from Santa María Island and the continent clustered in a clade different from those previously reported, indicating a new lineage of Mepraia genus. This study will contribute to understand the origin of the T. cruzi infection in coastal islands of northern Chile.

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