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PurposeCoal mining is a vital sector in Colombia, contributing significantly to the nation’s economy and the development of its regions. However, despite its importance, it has led to a gradual decline in the health of mine workers and nearby residents. While the adverse health effects of open-pit coal mining on exposed individuals have been well-documented in Colombia and globally, studies investigating genetic damage in underground coal miners are lacking.MethodsThe aim of our study was to evaluate chromosomal and genotoxic damage, in peripheral blood samples from a group of underground coal miners and residents of areas exposed to coal, in the town of Samacá, Boyacá-Colombia, and in a group of unexposed individuals by using banding and molecular cytogenetic techniques, as well as cytokinesis block micronucleus assays.ResultsOur results suggest that occupational exposure to coal induces chromosomal and genotoxic damage in somatic cells of underground coal miners. Chromosomal and genotoxic damage is an important step in carcinogenesis and the development of many other diseases. Our findings provide valuable insights into the effects of coal dust exposure on chromosomal integrity and genetic stability.ConclusionOur pilot study suggests that occupational exposure to coal induces chromosomal damage in underground coal miners, highlighting the importance of validating these findings with a larger sample size. Our results highlight the need to implement prevention and protection measures, as well as educational programs for underground coal miners. Characterizing and estimating exposure risks are extremely important for the safety of people exposed occupationally and environmentally to coal and its derivatives.

Flux Balance Analysis (FBA) is a constraint-based method that is commonly used to guide metabolites through restricting pathways that often involve conditions such as anaplerotic cycles like Calvin, reversible or irreversible reactions, and nodes where metabolic pathways branch. The method can identify the best conditions for one course but fails when dealing with the pathways of multiple metabolites of interest. Recent studies on metabolism consider it more natural to optimize several metabolites simultaneously rather than just one; moreover, they point out the use of metaheuristics as an attractive alternative that extends FBA to tackle multiple objectives. However, the literature also warns that the use of such techniques must not be wild. Instead, it must be subject to careful fine-tuning and selection processes to achieve the desired results. This work analyses the impact on the quality of the pathways built using the NSGAII and MOEA/D algorithms and several novel optimization models; it conducts a study on two case studies, the pigment biosynthesis and the node in glutamate metabolism of the microalgae Chlorella vulgaris, under three culture conditions (autotrophic, heterotrophic, and mixotrophic) while optimizing for three metabolic intermediaries as independent objective functions simultaneously. The results show varying performances between NSGAII and MOEA/D, demonstrating that the selection of an optimization model can greatly affect predicted phenotypes.

In recent years, many-objective optimization problems (MaOPs) have been challenging. Classically, algorithms obtain first the Pareto front (PF). Next, a decision maker (DM) can choose the best solutions according to their preferences in the region of interest (ROI). However, the DM effort increases with objectives in MaOPs. For this reason, this paper proposes a new C++ software based on simulated annealing, decomposition, differential evolution, and outranking relations. MOSA/D-O and MOSA/D-O-II algorithms from the software can pressure toward the ROI at run-time in MaOPs. Both algorithms were tested with DTLZ and WFG benchmarks, showing promised performance in an ad hoc indicator for 5 and 10 objectives with 10 DMs.

In the department of Boyacá, Colombia, agriculture stands as one of the primary economic activities. However, the escalating utilization of pesticides within this sector has sparked concern regarding its potential correlation with elevated risks of genotoxicity, chromosomal alterations, and carcinogenesis. Furthermore, pesticides have been associated with a broad spectrum of genetic polymorphisms that impact pivotal genes involved in pesticide metabolism and DNA repair, among other processes. Nonetheless, our understanding of the genotoxic effects of pesticides on the chromosomes (as biomarkers of effect) in exposed farmers and the impact of genetic polymorphisms (as susceptibility biomarkers) on the increased risk of chromosomal damage is still limited. The aim of our study was to evaluate chromosomal alterations, chromosomal instability, and clonal heterogeneity, as well as the presence of polymorphic variants in the GSTP1 and XRCC1 genes, in peripheral blood samples of farmers occupationally exposed to pesticides in Aquitania, Colombia, and in an unexposed control group. Our results showed statistically significant differences in the frequency of numerical chromosomal alterations, chromosomal instability, and clonal heterogeneity levels between the exposed and unexposed groups. In addition, we also found a higher frequency of chromosomal instability and clonal heterogeneity in exposed individuals carrying the heterozygous GSTP1 AG and XRCC1 (exon 10) GA genotypes. The evaluation of chromosomal alterations and chromosomal instability resulting from pesticide exposure, combined with the identification of polymorphic variants in the GSTP1 and XRCC1 genes, and further research involving a larger group of individuals exposed to pesticides could enable the identification of effect and susceptibility biomarkers. Such markers could prove valuable for monitoring individuals occupationally exposed to pesticides.

Chromosomal instability (CIN), defined by variations in the number or structure of chromosomes from cell to cell, is recognized as a distinctive characteristic of cancer associated with the ability of tumors to adapt to challenging environments. CIN has been recognized as a source of genetic variation that leads to clonal heterogeneity (CH). Recent findings suggest a potential association between CIN and CH with the prognosis of BC patients, particularly in tumors expressing the epidermal growth factor receptor 2 (HER2+). In fact, information on the role of CIN in other BC subtypes, including luminal B BC, is limited. Additionally, it remains unknown whether CIN in luminal B BC tumors, above a specific threshold, could have a detrimental effect on the growth of human tumors or whether low or intermediate CIN levels could be linked to a more favorable BC patient prognosis when contrasted with elevated levels. Clarifying these relationships could have a substantial impact on risk stratification and the development of future therapeutic strategies aimed at targeting CIN in BC. This study aimed to assess CIN and CH in tumor tissue samples from ten patients with luminal B BC and compare them with established clinicopathological parameters. The results of this study reveal that luminal B BC patients exhibit intermediate CIN and stable aneuploidy, both of which correlate with lymphovascular invasion. Our results also provide valuable preliminary data that could contribute to the understanding of the implications of CIN and CH in risk stratification and the development of future therapeutic strategies in BC.

Simulated annealing is a metaheuristic that balances exploration and exploitation to solve global optimization problems. However, to deal with multi- and many-objective optimization problems, this balance needs to be improved due to diverse factors such as the number of objectives. To deal with this issue, this work proposes MOSA/D, a hybrid framework for multi-objective simulated annealing based on decomposition and evolutionary perturbation functions. According to the literature, the decomposition strategy allows diversity in a population while evolutionary perturbations add convergence toward the Pareto front; however, a question should be asked: What is the effect of such components when included as part of a multi-objective simulated annealing design? Hence, this work studies the performance of the MOSA/D framework considering in its implementation two widely used perturbation operators: classical genetic operators and differential evolution. The proposed algorithms are MOSA/D-CGO, based on classical genetic operators, and MOSA/D-DE, based on differential evolution operators. The main contribution of this work is the performance analysis of MOSA/D using both perturbation operators and identifying the one most suitable for the framework. The approaches were tested using DTLZ on two and three objectives and CEC2009 benchmarks on two, three, five, and ten objectives; the performance analysis considered diversity and convergence measured through the hypervolume (HV) and inverted generational distance (IGD) indicators. The results pointed out that there is a promising improvement in performance in favor of MOSA/D-DE.

MDM2 amplification represents the leading oncogenic pathway and diagnostic hallmark of liposarcoma, whose assessment is based on Fluorescence In Situ Hybridization (FISH) analysis. Despite its diagnostic relevance, no univocal interpretation criteria regarding FISH assessments of MDM2 amplification have been established so far, leading to several different approaches and potential diagnostic misinterpretations. This study aims to address the most common issues and proposes troubleshooting guidelines for MDM2 amplification assessments by FISH. We retrospectively retrieved 51 liposarcomas, 25 Lipomas, 5 Spindle Cell Lipoma/Pleomorphic Lipomas, and 2 Atypical Spindle Cell Lipomatous Tumors and the corresponding MDM2 FISH analysis. We observed MDM2 amplification in liposarcomas cases only (43 out of 51 cases) and identified three MDM2-amplified patterns (scattered (50% of cases), clustered (14% of cases), and mixed (36% of cases)) and two nonamplified patterns (low number of signals (82% of cases) and polysomic (18% of cases)). Based on these data and published evidence in the literature, we propose a set of criteria to guide MDM2 amplification analysis in liposarcoma. Kindled by the compelling importance of MDM2 assessments to improve diagnostic and therapeutic liposarcoma management, these suggestions could represent the first step to develop a univocal interpretation model and consensus guidelines.

Pesticides are a group of environmental pollutants widely used in agriculture to protect crops, and their indiscriminate use has led to a growing public awareness about the health hazards associated with exposure to these substances. In fact, exposure to pesticides has been associated with an increased risk of developing diseases, including cancer. In a study previously published by us, we observed the induction of specific chromosomal alterations and, in general, the deleterious effect of pesticides on the chromosomes of five individuals exposed to pesticides. Considering the importance of our previous findings and their implications in the identification of cytogenetic biomarkers for the monitoring of exposed populations, we decided to conduct a new study with a greater number of individuals exposed to pesticides. Considering the above, the aim of this study was to evaluate the type and frequency of chromosomal alterations, chromosomal variants, the level of chromosomal instability and the clonal heterogeneity in a group of thirty-four farmers occupationally exposed to pesticides in the town of Simijacá, Colombia, and in a control group of thirty-four unexposed individuals, by using Banding Cytogenetics and Molecular Cytogenetics (Fluorescence in situ hybridization). Our results showed that farmers exposed to pesticides had significantly increased frequencies of chromosomal alterations, chromosomal variants, chromosomal instability and clonal heterogeneity when compared with controls. Our results confirm the results previously reported by us, and indicate that occupational exposure to pesticides induces not only chromosomal instability but also clonal heterogeneity in the somatic cells of people exposed to pesticides. This study constitutes, to our knowledge, the first study that reports clonal heterogeneity associated with occupational exposure to pesticides. Chromosomal instability and clonal heterogeneity, in addition to reflecting the instability of the system, could predispose cells to acquire additional instability and, therefore, to an increased risk of developing diseases.

Introducción: El incremento de la actividad sísmica como fuente energética asociada a las erupciones volcánicas de lodo en Urabá (Mayo 2017), Cartagena (Junio 2017) y en Canalete, Córdoba (Diciembre 2017), motivó la realización de un estudio geoeléctrico y microsismológico, que busca evaluar la dinámica del volcán El Totumo en límites de los departamentos de Bolívar y Atlántico en la región Caribe de Colombia, por lo tanto el propósito de éste trabajo es presentar los resultados de un estudio Geofísico para determinar anomalías relativas a la dinámica del volcán. Objetivo: Se espera detectar a través de las tomografías los flujos de lodo en ascenso hacia la superficie situación que generaría riesgos para el turismo de la región. Metodología: Para este estudio se aplican dos métodos Geofísicos: Prospección Geoeléctrica y Monitoreo Sísmológico, con el fin de detectar la distribución de los materiales litológicos en profundidad, así como los rasgos estructurales más importantes relacionados con la distribución y ascenso de lodo. Todo lo anterior se logra mediante el análisis de Tomografías que revelan con claridad el peligro generado por los flujos de lodo fino en ascenso y el método SPAC (por sus siglas del Inglés, Spatial Autocorrelation) que permite correlacionar las velocidades de las señales con los estratos sedimentarios, e identificar con claridad la alteración del soporte estructural del volcán El Totumo debido a la presión ejercida por el lodo en ascenso a la superficie. Resultados: Al llevar a cabo el procesamiento de los datos Geofísicos en ambos casos, se observa una distribución litológica normal característica de las resistividades del subsuelo. No obstante, no se observan anomalías o cambios estructurales en las tomografías eléctricas, como tampoco rastros de actividad sísmica o sismológica que hubiesen alterado el perfil de velocidades de ondas de corte. Conclusiones: Por todo lo anterior se puede afirmar que no se presentan anomalías relativas al comportamiento dinámico del volcán en este periodo de tiempo, se sugiere aplicar la misma metodología para estudios futuros del mismo fenómeno no solo al volcán El Totumo sino a cualquier volcán de lodo con el fin de generar alertas tempranas sobre los riesgos que su actividad implica frente a los personas que habitan en sus proximidades. Palabras clave: Salses, Volcán de lodo, Monitoreo sismológico, Diapirismo, Flujo de lodos, Tomografías Eléctricas, Microtremores, Ondas de Corte

Introducción: El gen p53 codifica para una proteína de 53 KD con importante función reguladora en procesos celulares como la proliferación, la muerte celular y la preservación del material genético. Se le conoce como el "guardián del genoma" y frecuentemente se encuentra mutado en casi el 50% de los carcinomas humanos. Se han identificado variantes en su secuencia o polimorfismos genéticos (en diferentes regiones del gen), varios estudios han mostrado una asociación entre el riesgo de desarrollar cáncer mamario y la presencia de estas variantes. El objetivo del presente estudio de casos y controles fue analizar la asociación de los genotipos y las frecuencias alélicas de tres de los polimorfismos del gen p53 con el riesgo de desarrollar cáncer mamario familiar en una población colombiana. Los polimorfismos estudiados fueron: en el exón 4 del gen que corresponde al codón 72 de la proteína, donde puede haber una arginina o una prolina (Arg72Pro); en el intrón 3 (duplicación de 16pb) y en el intrón 6, donde puede encontrarse una guanina o una adenina (G/A). Materiales y métodos: Se analizaron 186 pacientes con cáncer mamario familiar y 186 individuos como controles, las frecuencias alélicas y genotípicas se determinaron por análisis de RFLPs y se midió la fuerza de asociación mediante la razón de disparidad (Odds ratio: OR), con una significancia de la asociación utilizando el intervalo de confianza del 95% y con el programa estadístico SPSS 11,5. Resultados: Las frecuencias genotípicas del codón 72 en la población estudiada fueron: 61,3% para Arg/Arg, 32,3% para Pro/Arg y 6,5% para Pro/Pro en los casos y 44,1, 45,7 y 10,2% respectivamente en los controles. La frecuencia alélica obtenida para arginina en los casos fue 77,4 y 67,0% en los controles (P=0,004). Se obtuvo un OR=2,19 con un IC de 95% (1,33-3,63). La frecuencia de los genotipos del intrón 6 fueron entre casos y controles W/W (75,3 frente a 82,3%), W/M (24,2 frente a 15,6%) y M/M (0,5 frente a 2,2%) mientras que los valores de las frecuencias genotípicas del intrón 3 en los casos fueron: 90,3% para W/W, 8,6% para W/M y 1,1% para M/M y en los controles 98,9% para W/W y 0,5% para W/M y M/M. Las frecuencias alélicas para el alelo silvestre (W) fueron en los casos 94,6% y en los controles 99,2%. Se obtuvo un OR=17,86 con un IC del 95% (2,25-141,84). Conclusiones: Los resultados obtenidos en la población colombiana estudiada muestran una importante asociación entre el genotipo Arg/Arg del codón 72 (dos veces más) y el genotipo W/M intrón 3 (18 veces más) del gen p53 con el riesgo de desarrollar cáncer mamario familiar.

Studies enabled by metabolic models of different species of microalgae have become significant since they allow us to understand changes in their metabolism and physiological stages. The most used method to study cell metabolism is FBA, which commonly focuses on optimizing a single objective function. However, recent studies have brought attention to the exploration of simultaneous optimization of multiple objectives. Such strategies have found application in optimizing biomass and several other bioproducts of interest; they usually use approaches such as multi-level models or enumerations schemes. This work proposes an alternative in silico multiobjective model based on an evolutionary algorithm that offers a broader approximation of the Pareto frontier, allowing a better angle for decision making in metabolic engineering. The proposed strategy is validated on a reduced metabolic network of the microalgae Chlamydomonas reinhardtii while optimizing for the production of protein, carbohydrates, and CO2 uptake. The results from the conducted experimental design show a favorable difference in the number of solutions achieved compared to a classic tool solving FBA.

Satisfaction is relevant for decision makers (DM, Decision Makers). Satisfaction is the feeling produced in individuals by executing actions to satisfy their needs, for example, the payment of debts, jobs, or academic achievements, and the acquisition of goods or services. In the satisfaction literature, some theories model the satisfaction of individuals from job and customer approaches. However, considering personality elements to influence satisfaction and define preferences in strategies that optimize decision making provides the unique characteristics of a DM. These characteristics favor the scope of solutions closer to the satisfaction expectation. Satisfaction theories do not include specific elements of personality and preferences, so integrating these elements will offer more efficient decisions in computable models. In this work, a model of satisfaction with personality characteristics that influence the preferences of a DM is proposed. The proposed model is integrated into a preference-based optimizer that improves the decision-making process of a Virtual Decision Maker (VDM) in an optimization context. The optimization context addressed in this work is the product selection process within a food product shopping problem. An experimental design is proposed that compares two configurations that represent the cognitive part of an agent’s decision process to validate the operation of the proposed model in the context of optimization: (1) satisfaction, personality, and preferences, and (2) personality and preferences. The results show that considering satisfaction and personality in combination with preferences provides solutions closer to the interests of an individual, reflecting a more realistic behavior. Furthermore, this work demonstrates that it is possible to create a configurable model that allows adapting to different aptitudes and reflecting them in a computable model.

In combinatorial optimization, the more complex a problem is, the more challenging it becomes, usually causing most research to focus on creating solvers for larger cases. However, real-life situations also contain small-sized instances that deserve a researcher’s attention. For example, within a web development context, a developer might face small combinatorial optimization cases that fall in the following situations to solve them: (1) the development of an ad hoc specialized strategy is not justified; (2) the developer could lack the time, or skills, to create the solution; (3) the efficiency of naive brute force strategies might be compromised due to the programming paradigm use. Similar situations in this context, combined with a recent increasing interest in optimization information from databases, open a research area to develop easy-to-implement strategies that compete with those naive approaches and do not require specialized knowledge. Therefore, this work revises Structured Query Language (SQL) approaches and proposes new methods to tackle combinatorial optimization problems such as the Portfolio Selection Problem, Maximum Clique Problem, and Graph Coloring Problem. The performance of the resulting queries is compared against naive approaches; its potential to extend to other optimization problems is studied. The presented examples demonstrate the simplicity and versatility of using a SQL approach to solve small optimization problem instances.

This paper introduces a strategy to enrich swarm intelligence algorithms with the preferences of the Decision Maker (DM) represented in an ordinal classifier based on interval outranking. Ordinal classification is used to bias the search toward the Region of Interest (RoI), the privileged zone of the Pareto frontier containing the most satisfactory solutions according to the DM’s preferences. We applied this hybridising strategy to two swarm intelligence algorithms, i.e., Multi-objective Grey Wolf Optimisation and Indicator-based Multi-objective Ant Colony Optimisation for continuous domains. The resulting hybrid algorithms were called GWO-InClass and ACO-InClass. To validate our strategy, we conducted experiments on the DTLZ problems, the most widely studied test suit in the framework of multi-objective optimisation. According to the results, our approach is suitable when many objective functions are treated. GWO-InClass and ACO-InClass demonstrated the capacity of reaching the RoI better than the original metaheuristics that approximate the complete Pareto frontier.

Abstract Background The role of forkhead-box A1 (FOXA1) and Androgen receptor (AR) in breast cancer (BC) has been extensively studied. However, the prognostic role of their co-expression in Estrogen receptor positive (ER+) BC has not been investigated so far. The aim of the present study was thus to assess the co-expression (protein and mRNA) of FOXA1 and AR in BC patients, in order to evaluate their prognostic impact according to ER status. Methods Immunohistochemical expression of AR and FOXA1 was evaluated on 479 consecutive BC, with complete clinical-pathological and follow up data. Fresh-frozen tissues from 65 cases were available. The expression of AR and FOXA1 with ER was validated using mRNA analyses. Survival and Cox proportional hazard analyses were used to evaluate the relationship between FOXA1, AR and prognosis. Results Expression of ER, AR and FOXA1 was observed in 78, 60 and 85% of cases respectively. Most AR+ cases (97%) were also FOXA1+. The level of FOXA1 mRNA positively correlated with level of both AR mRNA (r = 0.8975; P

The androgen receptor (AR) is frequently expressed in breast cancer (BC), but its association with clinical and biological parameters of BC patients remains unclear. Here, we investigated the association of AR gene expression according to intrinsic BC subtypes by meta-analysis of large-scale microarray transcriptomic datasets. Sixty-two datasets including 10315 BC patients were used in the meta-analyses. Interestingly, AR mRNA level is significantly increased in patients categorized with less aggressive intrinsic molecular subtypes including, Luminal A compared to Basal-like (standardized mean difference, SMD: 2.12; 95% confidence interval, CI: 1.88 to 2.35; p < 0.001) or when comparing Luminal B to Basal-like (SMD: 1.53; CI: 1.33 to 1.72; p < 0.001). The same trend was observed when analyses were performed using immunohistochemistry-based surrogate subtypes. Consistently, the AR mRNA expression was higher in patients with low histological grade (p < 0.001). Furthermore, our data revealed higher levels of AR mRNA in BC patients expressing either estrogen or progesterone receptors (p < 0.001). Together, our findings indicate that high mRNA levels of AR are associated with BC subgroups with the less aggressive clinical features.

Obesity is an increasing health challenge and is recognized as a breast cancer risk factor. Although obesity-related breast cancer mechanisms are not fully understood, this association has been linked to impaired hormone secretion by the dysfunctional obese adipose tissue (hyperplasic and hypertrophic adipocytes). Among these hormones, altered production of androgens and adipokines is observed, and both, are independently associated with breast cancer development. In this review, we describe and comment on the relationships reported between these factors and breast cancer, focusing on the biological associations that have helped to unveil the mechanisms by which signaling from androgens and adipokines modifies the behavior of mammary epithelial cells. Furthermore, we discuss the potential crosstalk between the two most abundant adipokines produced by the adipose tissue (adiponectin and leptin) and the androgen receptor, an emerging marker in breast cancer. The identification and understanding of interactions among adipokines and the androgen receptor in cancer cells are necessary to guide the development of new therapeutic approaches in order to prevent and cure obesity and breast cancer.

This paper addresses group multi-objective optimization under a new perspective. For each point in the feasible decision set, satisfaction or dissatisfaction from each group member is determined by a multi-criteria ordinal classification approach, based on comparing solutions with a limiting boundary between classes “unsatisfactory” and “satisfactory”. The whole group satisfaction can be maximized, finding solutions as close as possible to the ideal consensus. The group moderator is in charge of making the final decision, finding the best compromise between the collective satisfaction and dissatisfaction. Imperfect information on values of objective functions, required and available resources, and decision model parameters are handled by using interval numbers. Two different kinds of multi-criteria decision models are considered: (i) an interval outranking approach and (ii) an interval weighted-sum value function. The proposal is more general than other approaches to group multi-objective optimization since (a) some (even all) objective values may be not the same for different DMs; (b) each group member may consider their own set of objective functions and constraints; (c) objective values may be imprecise or uncertain; (d) imperfect information on resources availability and requirements may be handled; (e) each group member may have their own perception about the availability of resources and the requirement of resources per activity. An important application of the new approach is collective multi-objective project portfolio optimization. This is illustrated by solving a real size group many-objective project portfolio optimization problem using evolutionary computation tools.

The decision-making process can be complex and underestimated, where mismanagement could lead to poor results and excessive spending. This situation appears in highly complex multi-criteria problems such as the project portfolio selection (PPS) problem. Therefore, a recommender system becomes crucial to guide the solution search process. To our knowledge, most recommender systems that use argumentation theory are not proposed for multi-criteria optimization problems. Besides, most of the current recommender systems focused on PPS problems do not attempt to justify their recommendations. This work studies the characterization of cognitive tasks involved in the decision-aiding process to propose a framework for the Decision Aid Interactive Recommender System (DAIRS). The proposed system focuses on a user-system interaction that guides the search towards the best solution considering a decision-maker’s preferences. The developed framework uses argumentation theory supported by argumentation schemes, dialogue games, proof standards, and two state transition diagrams (STD) to generate and explain its recommendations to the user. This work presents a prototype of DAIRS to evaluate the user experience on multiple real-life case simulations through a usability measurement. The prototype and both STDs received a satisfying score and mostly overall acceptance by the test users.

A common issue in the Multi-Objective Portfolio Optimization Problem (MOPOP) is the presence of uncertainty that affects individual decisions, e.g., variations on resources or benefits of projects. Fuzzy numbers are successful in dealing with imprecise numerical quantities, and they found numerous applications in optimization. However, so far, they have not been used to tackle uncertainty in MOPOP. Hence, this work proposes to tackle MOPOP’s uncertainty with a new optimization model based on fuzzy trapezoidal parameters. Additionally, it proposes three novel steady-state algorithms as the model’s solution process. One approach integrates the Fuzzy Adaptive Multi-objective Evolutionary (FAME) methodology; the other two apply the Non-Dominated Genetic Algorithm (NSGA-II) methodology. One steady-state algorithm uses the Spatial Spread Deviation as a density estimator to improve the Pareto fronts’ distribution. This research work’s final contribution is developing a new defuzzification mapping that allows measuring algorithms’ performance using widely known metrics. The results show a significant difference in performance favoring the proposed steady-state algorithm based on the FAME methodology.

It is important to know the properties of an optimization problem and the difficulty an algorithm faces to solve it. Population evolvability obtains information related to both elements by analysing the probability of an algorithm to improve current solutions and the degree of those improvements. DPEM_HH is a dynamic multi-objective hyper-heuristic that uses low-level heuristic (LLH) selection methods that apply population evolvability. DPEM_HH uses dynamic multiobjective evolutionary algorithms (DMOEAs) as LLHs to solve dynamic multi-objective optimization problems (DMOPs). Population evolvability is incorporated in DPEM_HH by calculating it on each candidate DMOEA for a set of sampled generations after a change is detected, using those values to select which LLH will be applied. DPEM_HH is tested on multiple DMOPs with dynamic properties that provide several challenges. Experimental results show that DPEM_HH with a greedy LLH selection method that uses average population evolvability values can produce solutions closer to the Pareto optimal front with equal to or better diversity than previously proposed heuristics. This shows the effectiveness and feasibility of the application of population evolvability on hyperheuristics to solve dynamic optimization problems.

Most evolutionary multi-objective algorithms perform poorly in many objective problems. They normally do not make selective pressure towards the Region of Interest (RoI), the privileged zone in the Pareto frontier that contains solutions important to a DM. Several works have proved that a priori incorporation of preferences improves convergence towards the RoI. The work of (E. Fernandez, E. Lopez, F. Lopez & C.A. Coello Coello, 2011) uses a binary fuzzy outranking relational system to map many-objective problems into a tri-objective optimization problem that searches the RoI; however, it requires the elicitation of many preference parameters, a very hard task. The use of an indirect elicitation approach overcomes such situation by allowing the parameter inference from a battery of examples. Even though the relational system of Fernandez et al. (2011) is based on binary relations, it is more convenient to elicit its parameters from assignment examples. In this sense, this paper proposes an evolutionary-based indirect parameter elicitation method that uses preference information embedded in assignment examples, and it offers an analysis of their impact in a priori incorporation of DM’s preferences. Results show, through an extensive computer experiment over random test sets, that the method estimates properly the model parameter’s values. First published online 7 May 2019

Most real-world problems require the optimization of multiple objective functions simultaneously, which can conflict with each other. The environment of these problems usually involves imprecise information derived from inaccurate measurements or the variability in decision-makers’ (DMs’) judgments and beliefs, which can lead to unsatisfactory solutions. The imperfect knowledge can be present either in objective functions, restrictions, or decision-maker’s preferences. These optimization problems have been solved using various techniques such as multi-objective evolutionary algorithms (MOEAs). This paper proposes a new MOEA called NSGA-III-P (non-nominated sorting genetic algorithm III with preferences). The main characteristic of NSGA-III-P is an ordinal multi-criteria classification method for preference integration to guide the algorithm to the region of interest given by the decision-maker’s preferences. Besides, the use of interval analysis allows the expression of preferences with imprecision. The experiments contrasted several versions of the proposed method with the original NSGA-III to analyze different selective pressure induced by the DM’s preferences. In these experiments, the algorithms solved three-objectives instances of the DTLZ problem. The obtained results showed a better approximation to the region of interest for a DM when its preferences are considered.

Many real-world optimization problems involving several conflicting objective functions frequently appear in current scenarios and it is expected they will remain present in the future. However, approaches combining multi-objective optimization with the incorporation of the decision maker’s (DM’s) preferences through multi-criteria ordinal classification are still scarce. In addition, preferences are rarely associated with a DM’s characteristics; the preference selection is arbitrary. This paper proposes a new hybrid multi-objective optimization algorithm called P-HMCSGA (preference hybrid multi-criteria sorting genetic algorithm) that allows the DM’s preferences to be incorporated in the optimization process’ early phases and updated into the search process. P-HMCSGA incorporates preferences using a multi-criteria ordinal classification to distinguish solutions as good and bad; its parameters are determined with a preference disaggregation method. The main feature of P-HMCSGA is the new method proposed to associate preferences with the characterization profile of a DM and its integration with ordinal classification. This increases the selective pressure towards the desired region of interest more in agreement with the DM’s preferences specified in realistic profiles. The method is illustrated by solving real-size multi-objective PPPs (project portfolio problem). The experimentation aims to answer three questions: (i) To what extent does allowing the DM to express their preferences through a characterization profile impact the quality of the solution obtained in the optimization? (ii) How sensible is the proposal to different profiles? (iii) How much does the level of robustness of a profile impact the quality of final solutions (this question is related with the knowledge level that a DM has about his/her preferences)? Concluding, the proposal fulfills several desirable characteristics of a preferences incorporation method concerning these questions.

The co-expression of androgen (AR) and estrogen (ER) receptors, in terms of higher AR/ER ratio, has been recently associated with poor outcome in ER-positive (ER+) breast cancer (BC) patients. The aim of this study was to analyze if the biological aggressiveness, underlined in ER+ BC tumors with higher AR/ER ratio, could be due to higher expression of genes related to cell proliferation. On a cohort of 47 ER+ BC patients, the AR/ER ratio was assessed by immunohistochemistry and by mRNA analysis. The expression level of five gene proliferation markers was defined through TaqMan®-qPCR assays. Results were validated using 979 BC cases obtained from gene expression public databases. ER+ BC tumors with ratios of AR/ER ≥ 2 have higher expression levels of cellular proliferation genes than tumors with ratios of AR/ER < 2, in both the 47 ER+ BC patients (P < 0.001) and in the validation cohort (P = 0.005). Moreover, BC cases with ratios of AR/ER ≥ 2 of the validation cohort were mainly assigned to luminal B and HER2-enriched molecular subtypes, typically characterized by higher proliferation and poorer prognosis. These data suggest that joint routine evaluation of AR and ER expression may identify a unique subset of tumors, which show higher levels of cellular proliferation and therefore a more aggressive behavior.

In this paper, we develop and apply a genetic algorithm to solve surgery scheduling cases in a Mexican Public Hospital. Here, one of the most challenging issues is to process containers with heterogeneous capacity. Many scheduling problems do not share this restriction; because of this reason, we developed and implemented a strategy for the processing of heterogeneous containers in the genetic algorithm. The final product was named “genetic algorithm for scheduling optimization” (GAfSO). The results of GAfSO were tested with real data of a local hospital. Said hospital assigns different operational time to the operating rooms throughout the week. Also, the computational complexity of GAfSO is analyzed. Results show that GAfSO can assign the corresponding capacity to the operating rooms while optimizing their use.

Cross-shore profiles and environmental forcing were used to analyse morphological change of a headland bay beach: Tenby, West Wales (51.66 N; −4.71 W) over a mesoscale timeframe (1996–2013). Beach profile variations were attuned with longer term shoreline change identified by previous research showing southern erosion and northern accretion within the subaerial zone and were statistically significant in both sectors although centrally there was little or no significance. Conversely a statistically significant volume loss was shown at all profile locations within the intertidal zone. There were negative phase relationships between volume changes at the beach extremities, indicative of beach rotation and results were statistically significant (p < 0.01) within both subaerial (R2 = 0.59) and intertidal (R2 = 0.70) zones. This was confirmed qualitatively by time-series analysis and further cross correlation analysis showed trend reversal time-lagged associations between sediment exchanges at either end of the beach. Wave height and storm events displayed summer/winter trends which explained longer term one directional rotation at this location. In line with previous regional research, environmental forcing suggests that imposed changes are influenced by variations in southwesterly wind regimes. Winter storms are generated by Atlantic southwesterly winds and cause a south toward north sediment exchange, while southeasterly conditions that cause a trend reversal are generally limited to the summer period when waves are less energetic. Natural and man-made embayed beaches are a common coastal feature and many experience shoreline changes, jeopardising protective and recreational beach functions. In order to facilitate effective and sustainable coastal zone management strategies, an understanding of the morphological variability of these systems is needed. Therefore, this macrotidal research dealing with rotational processes across the entire intertidal has significance for other macrotidal coastlines, especially with predicted climate change and sea level rise scenarios, to inform local, regional and national shoreline risk management strategies.

Software test suites based on the concept of interaction testing are very useful for testing software components in an economical way. Test suites of this kind may be created using mathematical objects called covering arrays. A covering array, denoted by CA(N; t, k, v), is an N × k array over [Formula: see text] with the property that every N × t sub-array covers all t-tuples of [Formula: see text] at least once. Covering arrays can be used to test systems in which failures occur as a result of interactions among components or subsystems. They are often used in areas such as hardware Trojan detection, software testing, and network design. Because system testing is expensive, it is critical to reduce the amount of testing required. This paper addresses the Optimal Shortening of Covering ARrays (OSCAR) problem, an optimization problem whose objective is to construct, from an existing covering array matrix of uniform level, an array with dimensions of (N - δ) × (k - Δ) such that the number of missing t-tuples is minimized. Two applications of the OSCAR problem are (a) to produce smaller covering arrays from larger ones and (b) to obtain quasi-covering arrays (covering arrays in which the number of missing t-tuples is small) to be used as input to a meta-heuristic algorithm that produces covering arrays. In addition, it is proven that the OSCAR problem is NP-complete, and twelve different algorithms are proposed to solve it. An experiment was performed on 62 problem instances, and the results demonstrate the effectiveness of solving the OSCAR problem to facilitate the construction of new covering arrays.

The role of nipple discharge cytology (NDc) in the surgical management of breast cancer patients is unclear. We aimed: (i) to evaluate the effect of malignant NDc on the surgical approach to the nipple-areola complex, and (ii) to verify the association between malignant NDc and nipple malignancy.We retrospectively analyzed a case series of 139 patients with NDc who underwent breast surgery. The clinical and histological findings, types of surgery with emphasis on nipple-areola complex amputation, immunohistochemical phenotypes of the carcinomas and measurements of the tumor-nipple distance were recorded. Additionally, in patients who showed HER2-positive lesions on definitive surgery, we evaluated the HER2 immunocytochemistry of the NDc smears.Thirty-two malignant and 107 benign/borderline NDc diagnoses were identified. All 32 malignant-NDc cases were histologically confirmed as malignant. Thirty borderline/benign-NDc cases were histologically diagnosed as malignant (sensitivity 58%). The majority of the patients with malignant NDc were treated with nipple-areola complex amputations in both the mastectomy and conservative surgery groups (P

A priori incorporation of the decision maker’s preferences is a crucial issue in many-objective evolutionary optimization. Some approaches characterize the best compromise solution of this problem through fuzzy outranking relations; however, they require the elicitation of a large number of parameters (weights and different thresholds). This paper proposes a novel metaheuristic-based optimization method to infer the model’s parameters of a fuzzy relational system of preferences, based on a small number of judgments given by the decision maker. The results show a satisfactory rate of error when predicting new outcomes with the parameter values obtained by using small size reference sets.

Introducción: El gen p53 codifica para una proteína de 53 KD con importante función reguladora en procesos celulares como la proliferación, la muerte celular y la preservación del material genético. Se le conoce como el "guardián del genoma" y frecuentemente se encuentra mutado en casi el 50% de los carcinomas humanos. Se han identificado variantes en su secuencia o polimorfismos genéticos (en diferentes regiones del gen), varios estudios han mostrado una asociación entre el riesgo de desarrollar cáncer mamario y la presencia de estas variantes. El objetivo del presente estudio de casos y controles fue analizar la asociación de los genotipos y las frecuencias alélicas de tres de los polimorfismos del gen p53 con el riesgo de desarrollar cáncer mamario familiar en una población colombiana. Los polimorfismos estudiados fueron: en el exón 4 del gen que corresponde al codón 72 de la proteína, donde puede haber una arginina o una prolina (Arg72Pro); en el intrón 3 (duplicación de 16pb) y en el intrón 6, donde puede encontrarse una guanina o una adenina (G/A). Materiales y métodos: Se analizaron 186 pacientes con cáncer mamario familiar y 186 individuos como controles, las frecuencias alélicas y genotípicas se determinaron por análisis de RFLPs y se midió la fuerza de asociación mediante la razón de disparidad (Odds ratio: OR), con una significancia de la asociación utilizando el intervalo de confianza del 95% y con el programa estadístico SPSS 11,5. Resultados: Las frecuencias genotípicas del codón 72 en la población estudiada fueron: 61,3% para Arg/Arg, 32,3% para Pro/Arg y 6,5% para Pro/Pro en los casos y 44,1, 45,7 y 10,2% respectivamente en los controles. La frecuencia alélica obtenida para arginina en los casos fue 77,4 y 67,0% en los controles (P=0,004). Se obtuvo un OR=2,19 con un IC de 95% (1,33-3,63). La frecuencia de los genotipos del intrón 6 fueron entre casos y controles W/W (75,3 frente a 82,3%), W/M (24,2 frente a 15,6%) y M/M (0,5 frente a 2,2%) mientras que los valores de las frecuencias genotípicas del intrón 3 en los casos fueron: 90,3% para W/W, 8,6% para W/M y 1,1% para M/M y en los controles 98,9% para W/W y 0,5% para W/M y M/M. Las frecuencias alélicas para el alelo silvestre (W) fueron en los casos 94,6% y en los controles 99,2%. Se obtuvo un OR=17,86 con un IC del 95% (2,25-141,84). Conclusiones: Los resultados obtenidos en la población colombiana estudiada muestran una importante asociación entre el genotipo Arg/Arg del codón 72 (dos veces más) y el genotipo W/M intrón 3 (18 veces más) del gen p53 con el riesgo de desarrollar cáncer mamario familiar.Introduction: Gen p53 codifies a 53 KD protein, an important regulator of cell processes such as proliferation, cellular death (apoptosis), and the preservation of the genetic material. It is known as the "guardian of the genoma" and is frequently encountered in a mutated stated in as much as 50% of the human carcinomas. Variants in its sequence, or genetic polymorphism (in various regions of the gen), have been identified, and diverse studies have shown an association of the risk of developing breast cancer with the presence of such variants. The purpose of this cases-controls study was to analyze the association of the genotypes and the allelic frequencies of three of the polymorphisms of gen p53 with the risk of developing familial mammary cancer in a population group in Colombia. Polymorphisms studied were: in the gen´s exon 4, corresponding to codon 72 of the protein, where there may be an aginine or a proline (Arg72Pro); in the intron 3 (duplication of 16pb); and in the intron 6, where there could be found a guanine or an adenine (G/A). Materials and methods: 186 patients with familial breast cancer were studied, with 186 subjects as controls, determining the allelic and genotype frequencies by RFLPS analyses and measurement the force of association by the Odds Ratio (OR), with a significance of the association utilizing a Confidence Interval (CI) of 95% and the statistical program SPSS 11.5. Results. The genotype frequencies of codon 72 in the study population were: 61.3% for Arg/Arg, 32.3% for Pro/Arg, and 6.5% for Pro/Pro in the cases studied, and 44.1%, 45.7% and 10.2%, respectively, for the control subjects. The allelic frequency obtained for arginine in the cases studied were 77.4%, and 67.0% in the control subjects (P=0.004). An OR=2.19 with a 95% CI (1.33-3.63) was observed. The frequency of the genotypes of intron 6 were between the cases and the W/W controls (75.3% versus 82.3%), W/M (24.2% versus 15.6%), and M/M (0.5% versus 2.2%), while the values of the genotype intron 3 frequencies in the cases were: 90.3% for W/W, 8.6% for W/M, and 1.1% for M/M, and in the controls: 98.9% for W/W and 0.5% for W/M and M/M. The allelic frequencies for the silvester allelo (W) were 94.6% in the cases and 99.2% in the controls. An OR=17.86 with a 95% CI (2.25-141.84) was achieved. Conclusions: Our results in the Colombian population group studied show an important association of the Arg/Arg genotype of codon 72 (2 x) and the W/M intron 3 genotype (18 x) of gen p53 with the risk of developing familial mammary cancer.

Recently, non-clonal chromosomal alterations previously unappreciated are being proposed to be included in cytogenetic practice. The aim of this inclusion is to obtain a greater understanding of chromosomal instability (CIN) and tumor heterogeneity and their role in cancer evolution and therapy response. Although several genetic assays have allowed the evaluation of the variation in a population of cancer cells, these assays do not provide information at the level of individual cells, therefore limiting the information of the genomic diversity within tumors (heterogeneity). The karyotype is one of the few available cytogenetic techniques that allow us not only to identify the chromosomal alterations present within a single cell, but also allows us to profile both clonal (CCA) and non-clonal chromosomal alterations (NCCAs). A greater understanding of CIN and tumor heterogeneity in cancer could not only improve existing therapeutic regimens but could also be used as targets for the design of new therapeutic approaches. In this review we indicate the importance and significance of karyotypic chaos, NCCAs and CIN in the prognosis of human cancers.

17β-Estradiol (E2) plays a pivotal role in the development and progression of breast cancer. As a result, blockade of the E2 signal through either tamoxifen (TAM) or aromatase inhibitors is an important therapeutic strategy to treat or prevent estrogen receptor (ER) positive breast cancer. However, resistance to TAM is the major obstacle in endocrine therapy. This resistance occurs either de novo or is acquired after an initial beneficial response. The underlying mechanisms for TAM resistance are probably multifactorial and remain largely unknown. Considering that breast cancer is a very heterogeneous disease and patients respond differently to treatment, the molecular analysis of TAM’s biological activity could provide the necessary framework to understand the complex effects of this drug in target cells. Moreover, this could explain, at least in part, the development of resistance and indicate an optimal therapeutic option. This review highlights the implications of TAM in breast cancer as well as the role of receptors/signal pathways recently suggested to be involved in the development of TAM resistance. G protein—coupled estrogen receptor, Androgen Receptor and Hedgehog signaling pathways are emerging as novel therapeutic targets and prognostic indicators for breast cancer, based on their ability to mediate estrogenic signaling in ERα-positive or -negative breast cancer.

The morphological change of a headland bay beach—Tenby, West Wales, UK—was analysed over a 73-year period (1941–2014). Geo-referenced aerial photographs were used to extract shoreline positions which were subsequently compared with wave models based on storm event data. From the 1941 baseline, results showed shoreline change rates reduced over time with regression models enabling a prediction of shoreline equilibrium circa 2061. Further temporal analyses showed southern and central sector erosion and northern accretion, while models identified long-term plan-form rotation, i.e., a negative phase relationship between beach extremities and a change from negative to positive correlation within the more stable central sector. Models were then used in conjunction with an empirical 2nd order polynomial equation to predict the 2061 longshore equilibrium shoreline position under current environmental conditions. Results agreed with previous regional research which showed that dominant south and southwesterly wave regimes influence south to north longshore drift with counter drift generated by less dominant easterly regimes. The equilibrium shoreline was also used to underpin flood and inundation assessments, identifying areas at risk and strategies to increase resilience. UK shoreline management plans evaluate coastal vulnerability based upon temporal epochs of 20, 50 and 100 years. Therefore, this research evaluating datasets spanning 73 years has demonstrated the effectiveness of linear regression in integrating temporal and spatial consequences of sea level rise and storms. The developed models can be used to predict future shoreline positions aligned with shoreline management plan epochs and inform embayed beach shoreline assessments at local, regional and international scales, by identifying locations of vulnerability and enabling the development of management strategies to improve resilience under scenarios of sea level rise and climate change.

En el mundo se presentan aproximadamente 160 mil nuevos casos de melanoma al año y su tasa de mortalidad es mayor que la de otros tipos de cáncer de piel debido al alto potencial metastásico de melanoma. Por esto la detección de micrometástasis en sangre periférica mediante la identificación de biomarcadores tumorales específicos de células de melanoma puede a futuro constituirse en una prometedora herramienta de diagnóstico. El Factor de Transcripción asociado a Microftalmia (MITF) codifica una proteína esencial para la sobrevida y desarrollo de melanocitos y se ha encontrado aumento de expresión y amplificación del gen en tumores melanocíticos. El gen codifica 10 isoformas, siendo MITF-M específica de melanocitos y candidata para la detección de células de melanoma. El objetivo del presente trabajo fue determinar la expresión de tres isoformas del gen MITF en sangre periférica de individuos con melanoma y compararla con la expresión en sangre periférica de personas sin ningún tipo de cáncer. Muestras de sangre de 61 pacientes con melanoma y 61 controles, fueron utilizadas para extracción de ARN y analizadas por RT-PCR tiempo real. Expresión de la región consenso de MITF se detectó en 58 casos y 60 controles, con incremento en el nivel de expresión de 1.31 veces más en el grupo de casos respecto al grupo control sin encontrar diferencias estadísticamente significativas (P=0.258). Las isoformas MITF-A y MITF-B se detectaron en todas las muestras, con un incremento en el nivel de expresión de 1.9 y 1.8 veces más en el grupo de casos, respectivamente y encontrando diferencias estadísticamente significativas entre los dos grupos (P=0.002 en las dos isoformas). Expresión de la isoforma MITF-M se detectó en 16 de los casos y no se encontró su expresión en controles. Estos resultados indican que se está detectando células de melanoma en sangre periférica y que estudios adicionales son necesarios para corroborar el buen uso de las isoformas del gen MITF como biomarcadore s para la detección de esta enfermedad.

En Colombia, en el año 2007 el tipo de cáncer más frecuente fue el de piel, con 895 casos nuevos, de los cuales se registraron 97 casos de melanoma maligno cutáneo (MMC), que lo ubicaron como el tercer tipo de cáncer de piel, después del carcinoma de células basales (481 casos) y carcinoma de células escamosas (134 casos), neoplasias que presentan una mortalidad muy baja y rara vez hacen metástasis, contrario a lo que sucede en melanoma maligno cutáneo donde la mortalidad y el potencial metastático son muy altos. Dado que la exposición incrementada a la radiación ultravioleta es considerada como el factor ambiental que contribuye de manera más importante al desarrollo de MMC, es probable pensar que polimorfismos del gen XPD, que participan en reparación de daño al ADN ocasionado por rayos UVB y polimorfismos del gen MC1R, que regula procesos de pigmentación, se constituyan en variantes alélicas que puedan estar confiriendo susceptibilidad al desarrollo de MMC. Por ésta razón, el objetivo del presente trabajo se determinaron las frecuencias alélicas y genotípicas de los polimorfismos Arg151Cys del gen MC1R y Lys751Gln del gen XPD mediante técnicas de PCR convencional y RFLPs en una población colombiana de 92 pacientes con melanoma maligno cutáneo y en un grupo control. Estas poblaciones se compararon para establecer si existe asociación entre los polimorfismos estudiados, características clinicopatológicas (color piel, color de ojos, color cabello, circunstancias por las que se expone al sol quemaduras severas por el sol, tipo de piel y edad) y el riesgo a desarrollar melanoma maligno cutáneo. No se encontraron diferencias estadísticamente significativas (P< 0.05) utilizando las pruebas de asociación X2 y prueba exacta de Fisher, entre la presencia de los polimorfismos en los genes XPD y MC1R y el desarrollo de Melanoma Maligno Cutáneo. Así mismo, no se presentaron diferencias estadísticamente significativas (P < 0.05) entre las características clínico patológicas y su asociación con el desarrollo de Melanoma Maligno Cutáneo. El análisis entre las características clinicopatológicas, el desarrollo de Melanoma Maligno Cutáneo y la presencia de los polimorfismos, tampoco arrojó diferencias significativas.

La evolución reciente del Caribe colombiano está asociada, entre otras causas, a una marcada influencia antropogénica sobre la morfología litoral. A lo largo de los 30 municipios costeros que conforman esta región, se encuentran áreas intervenidas por el hombre que al mismo tiempo son afectadas por retrocesos significativos en su línea de costa. La tendencia erosiva predominante es influenciada y multiplicada, en muchos de los casos, por una expansión humana desorganizada y los fenómenos que ésta trae consigo. Un análisis general en algunas áreas permitió identificar acciones realizadas por el hombre, su influencia e sus impactos negativos sobre la geomorfofogía y evolución reciente del sistema litoral