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Glaciers supply multiple ecosystem services that are threatened by climate change. The retreat and disappearance of tropical glaciers is an important dynamic that affects ecosystems and local communities. The knowledge of the impacts of this land-change dynamics on the supply of ecosystem services is lacking. In that sense, the assessment developed can provide evidence about the costs and benefits of promoting conservation and human well-being at the same time. Then, the main objective of this research is to determine the spatial–temporal changes and their effects on the economic value of ecosystem services in a glacial retreat environment. We selected the Marangani district as a study area. It comprises the La Raya Mountain range in the Andes. The assessments were carried out across two scales of observation: the municipality and the watershed level. Here, we process spectral information from Landsat Sensor using the Random Forest algorithm in the Google Earth Engine platform to classify 10 biomes. It was carried out over more than 30 years (from 1986 to 2019). After that, ecosystem services provided by the biomes were valued using the transfer method. This research shows that at the municipality level, almost all the LULCs faced variations over time, and the glaciers had the highest change, accumulating a ratio of –85.51%, whereas at the watershed level, a higher tendency of land changes is observed in the areas without glaciers, and those with glacier areas count on permanent larger bofedales. At the municipality level, the economic value of ecosystems shows that bofedales and water surfaces are the LULCs that supply the highest ecosystem services (∼33,000 USD ha−1 yr−1 each). In addition, without the inflation adjustment, the total ESV is on a trajectory of losing ESV (–$9.67 × 106). In the watersheds with glacier retreat, significant quantity of bofedales and natural grasslands controls the fluctuations of ESV. These high-mountain watersheds play an essential role in providing benefits and value to local communities. In general, the municipality level indicates the trajectory of changes in the district, whereas the watershed scale shows the urgency for implementing spatial conservation actions.

Estimation of Distribution Algorithms (EDAs) maintain and iteratively update a probabilistic model to tackle optimization problems. The Boltzmann Probability Distribution Function (Boltzmann-PDF) provides advantages when used in energy based EDAs. However, direct sampling from the Boltzmann-PDF to update the probabilistic model is unpractical, and several EDAs employ an approximation to the Boltzmann-PDF by means of a Gaussian distribution that is usually derived by the minimization of the Kullback-Leibler divergence (KL-divergence) computed between the Gaussian and the Boltzmann-PDFs. The KL-divergence measure is not symmetric, and this causes the Gaussian approximation to fail at correctly modeling the target function for the EDAs, because the parameters of the Gaussian are not optimally estimated. In this paper, we derive an approximation to the Boltzmann-PDF using Jeffreys' divergence (a symmetric measure) in lieu of the KL-divergence and thus improve the performance of the optimization algorithm. Our approach is termed Symmetric-approximation Energy-based Estimation of Distribution (SEED) algorithm. The SEED algorithm is experimentally compared under a univariate approach against two other EDAs (UMDAc and BUMDA) on several benchmark optimization problems. The results show that the SEED algorithm is more effective and more efficient than the other algorithms.

In this work, we focus on the problem of selecting low-level heuristics in a hyperheuristic approach with offline learning, for the solution of instances of different problem domains. The objective is to improve the performance of the offline hyperheuristic approach, identifying equivalence classes in a set of instances of different problems and selecting the best performing heuristics in each of them. A methodology is proposed as the first step of a set of instances of all problems, and the generic characteristics of each instance and the performance of the heuristics in each one of them are considered to define the vectors of characteristics and make a grouping of classes. Metalearning with statistical tests is used to select the heuristics for each class. Finally, we used the Naive Bayes to test the set instances with k-fold cross-validation, and we compared all results statistically with the best-known values. In this research, the methodology was tested by applying it to the problems of capacitated vehicle routing (CVRP) and graph coloring (GCP). The experimental results show that the proposed methodology can improve the performance of the offline hyperheuristic approach, correctly identifying the classes of instances and applying the appropriate heuristics in each case. This is based on the statistical comparison of the results obtained with those of the state of the art of each instance.

Proper tuning of hyper-parameters is essential to the successful application of SVM-classifiers. Several methods have been used for this problem: grid search, random search, estimation of distribution Algorithms (EDAs), bio-inspired metaheuristics, among others. The objective of this paper is to determine the optimal method among those that recently reported good results: Bat algorithm, Firefly algorithm, Fruit-fly optimization algorithm, particle Swarm optimization, Univariate Marginal Distribution Algorithm (UMDA), and Boltzmann-UMDA. The criteria for optimality include measures of effectiveness, generalization, efficiency, and complexity. Experimental results on 15 medical diagnosis problems reveal that EDAs are the optimal strategy under such criteria. Finally, a novel performance index to guide the optimization process, that improves the generalization of the solutions while maintaining their effectiveness, is presented.

Effective texture classification requires image descriptors capable of efficiently detecting, extracting, and describing the most relevant information in the images, so that, for instance, different texture classes can be distinguished despite image distortions such as varying illuminations, viewpoints, scales, and rotations. Designing such an image descriptor is a challenging task that typically involves the intervention of human experts. In this paper, a general method to automatically design effective image descriptors is proposed. Our method is based on grammatical evolution and, using a set of example images from a texture classification problem and a classification algorithm as inputs, generates problem-adapted image descriptors that achieve very competitive classification results. Our method is tested on five well-known texture data sets with different number of classes and image distortions to prove its effectiveness and robustness. Our classification results are statistically compared against those obtained by means of six popular hand-crafted texture descriptors in the state of the art. This statistical analysis shows that our evolutionarily designed descriptors outperform most of those designed by human experts.

La calendarización de tareas en las instituciones educativas tiene como objetivo que los estudiantes tomen sus asignaturas correspondientes apegándose a un conjunto de restricciones. En el presente trabajo se muestra que, utilizando un enfoque Multi-objetivo junto a la metodología API-CARPIO, se generan soluciones aceptables para el problema de calendarización de horarios. Las instancias de prueba provienen de datos reales del Instituto Tecnológico de León (ITL). Los resultados del algoritmo Multi-objetivo NSGAII y sus distintas configuraciones son comparados con los resultados de algoritmos Metaheurísticos, además con los resultados de un Experto Humano.

En las Universidades, se busca un esquema de diseño de horarios que cumpla con las restricciones del alumnado, docentes, plan de estudios de la oferta educativa e inmuebles de la institución. Las Hiperheurísticas nos permiten generar metodologías que solucionen un conjunto de instancias de un problema. En este trabajo se muestra el uso de k-Folds Cross Validation en su versión de Leave-One-Out para el entrenamiento de Hiperheurísticas con aprendizaje fuera de línea aplicadas al problema de Course Timetabling, siendo esto el aporte del artículo. Como órgano rector para la Hiperheurística se utilizó el Iterated Local Search, empleando la metodología API-CARPIO donde las instancias de prueba provienen de datos reales del Instituto Tecnológico de León.

Among the various environmental disturbances caused by wildfires, their impacts within burnt areas and on the downhill aquatic ecosystems has been receiving increased attention. Post-fire rainfalls and subsequent runoffs play an important role in transporting ash and soil to aquatic systems within the burnt areas. These runoffs can be a diffuse source of toxic substances such as metals. The present work aims at assessing the effects of ash-loaded runoff on feeding rates of three representative aquatic invertebrates (Daphnia magna, Corbicula fluminea and Atyaephyra desmarestii) and the mosquitofish, Gambusia holbrooki, through post-exposure feeding inhibition bioassays carried out in-situ and in the laboratory using water collected from the experimental field sites. Four sites were selected in a partially burnt basin for bioassay deployment and sample collection: one site upstream of the burnt area (RUS); three sites receiving runoff directly from the burnt area, one immediately downstream of the burnt area (RDS) and two in permanent tributary streams within the burnt area (BS 1 and BS 2 ). The in-situ exposure lasted four days and began following the first post-fire major rain events. At sites affected by the wildfire, post-exposure feeding rates for D. magna, A. desmarestii and G. holbrooki were lower, which is consistent with the highest levels of metals found at these sites compared to the unaffected site, although the individual concentrations of each metal were generally below corresponding ecological safety benchmarks. Thus, interactions between metals and/or between metals and other environmental parameters certainly played a role in modulating the ecotoxic effects of the runoffs; this was further supported by a Toxic Units Summation exercise. Even if direct causal links between the ecotoxicological effects observed in D. magna, A. desmarestii and G. holbrooki and the physicochemical parameters of the water samples could not be established, the results suggest an important role of major and trace elements in explaining post-exposure feeding rate variation., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Mediterranean forests are highly susceptible to wildfires, which can cause several impacts not only within burnt areas but also on downstream aquatic ecosystems. The ashes' washout from burnt areas by surface runoff can be a diffuse source of toxic substances, such as metals, when reaching the nearby aquatic systems, and can be noxious to aquatic organisms. The present work aimed at assessing the ecotoxicological effects of post-fire contamination on two aquatic producers (the microalgae Raphidocelis subcapitata and the macrophyte Lemna minor) through in-situ bioassays, validating the obtained results with the outcomes of laboratory bioassays with surface water collected simultaneously. Four distinct sites were selected in a basin partially burnt (Ceira river basin; Coimbra district, Portugal) for bioassay deployment: one site upstream the burnt area in the Ceira river (RUS); three sites located under the influence of the burnt area, one immediately downstream of the burnt area in the Ceira river (RDS) and the other two in tributary streams within the burnt area (BS 1 and BS 2 ). The in-situ bioassays lasted for 13 days and began following the first post-fire major rain events. Results showed that the microalgae growth rate was able to distinguish the three sites within and downstream of the burnt area (BS 1 , BS 2 , RDS) from the site upstream (RUS). By contrast, the macrophytes growth rate only allowed to differentiate between the sites within the burnt area (BS 1 and BS 2 ) and those up- and downstream of the burnt area (RUS and RDS). The in-situ results for both species were corroborated with the results of the laboratory experiments, supporting the use of laboratory surrogates for a screening assessment of wildfire impacts in aquatic ecosystems. Direct causal relationships between the observed ecotoxicological effects on R. subcapitata and L. minor and the physical-chemical parameters of the water samples were difficult to establish, although the results suggest (i) a role of differential major and trace metal load in explaining species growth variation; (ii) interaction between metals and/or between metals and other field parameters are likely to modulate the biological responses to the challenges deriving from wildfire runoff., Competing Interests: Declaration of competing interest The authors have no conflict of interest to declare., (Copyright © 2020 Elsevier Inc. All rights reserved.)

This review covers the literature published for marine natural products isolated from macroalgae and addresses the taxonomic details of source organisms, the chemical types of isolated compounds and the location of sampling sites. The emphasis of this review is on the identification of the most bioprospected taxa and regions, as well as on how these trends have shifted over time.

Our need to create a program for individuals at high risk for breast cancer development led us to research the available data on such programs. In this paper, we summarize our findings and our thinking process as we developed our own program. Breast cancer incidence is increasing worldwide. Even though there are known risk factors for breast cancer development, approximately 60% of patients with breast cancer have no known risk factor, although this situation will probably change with further research, especially in genetics. For patients with risk factors based on personal or family history, different models are available for assessing and quantifying risk. Assignment of risk levels permits tailored screening and risk reduction strategies. Potential benefits of specialized programs for women with high breast cancer risk include more cost -effective interventions as a result of patient stratification on the basis of risk; generation of valuable data to advance science; and differentiation of breast programs from other breast cancer units, which can result in increased revenue that can be directed to further improvements in patient care. Guidelines for care of patients at high risk for breast cancer are available from various groups. However, running a high-risk breast program involves much more than applying a guideline. Each high-risk program needs to be designed by its institution with consideration of local resources and country legislation, especially related to genetic issues. Development of a successful high-risk program includes identifying strengths, weaknesses, opportunities, and threats; developing a promotion plan; choosing a risk assessment tool; defining "high risk"; and planning screening and risk reduction strategies for the specific population served by the program. The information in this article may be useful for other institutions considering creation of programs for patients with high breast cancer risk.

Bioprospecting for new marine natural products (NPs) has increased significantly over the last decades, leading to an unprecedented discovery of new molecules. Marine invertebrates have been the most important source of these NPs, with researchers commonly targeting particular taxonomic groups, marine regions and/or molecules from specific chemical groups. The present review focuses on new NPs identified from marine invertebrates between 2000 and 2009, and performs a detailed analysis on: (1) the chemical groups of these NPs; (2) the association of particular chemical groups to specific marine invertebrate taxa; and (3) the yielding of molecules from the same chemical group from organisms occurring in a particular geographic region. Our survey revealed an increasing number of new terpenoids being discovered between 2000 and 2009, contrasting with the decreasing trend in the discovery of new alkaloids and aliphatic molecules. Overall, no particular association was identified between marine invertebrate taxa and chemical groups of new NPs. Nonetheless, it is worth noting that most NPs recorded from cnidarians and mollusks were terpenoids, while most NPs identified in echinoderms were aliphatic compounds or carbohydrates. The geographical trends observed in our study do not support the idea of particular chemical groups of new NPs being associated with marine invertebrates from any specific geographical region, as NPs from different chemical groups were commonly distributed worldwide.

It is acknowledged that marine invertebrates produce bioactive natural products that may be useful for developing new drugs. By exploring untapped geographical sources and/or novel groups of organisms one can maximize the search for new marine drugs to treat human diseases. The goal of this paper is to analyse the trends associated with the discovery of new marine natural products from invertebrates (NMNPI) over the last two decades. The analysis considers different taxonomical levels and geographical approaches of bioprospected species. Additionally, this research is also directed to provide new insights into less bioprospected taxa and world regions. In order to gather the information available on NMNPI, the yearly-published reviews of Marine Natural Products covering 1990-2009 were surveyed. Information on source organisms, specifically taxonomical information and collection sites, was assembled together with additional geographical information collected from the articles originally describing the new natural product. Almost 10000 NMNPI were discovered since 1990, with a pronounced increase between decades. Porifera and Cnidaria were the two dominant sources of NMNPI worldwide. The exception was polar regions where Echinodermata dominated. The majority of species that yielded the new natural products belong to only one class of each Porifera and Cnidaria phyla (Demospongiae and Anthozoa, respectively). Increased bioprospecting efforts were observed in the Pacific Ocean, particularly in Asian countries that are associated with the Japan Biodiversity Hotspot and the Kuroshio Current. Although results show comparably less NMNPI from polar regions, the number of new natural products per species is similar to that recorded for other regions. The present study provides information to future bioprospecting efforts addressing previously unexplored taxonomic groups and/or regions. We also highlight how marine invertebrates, which in some cases have no commercial value, may become highly valuable in the ongoing search for new drugs from the sea.