
Acuna, M., Eaton, L., Ramirez, N. R., Cifuentes, L., & Llop, E. (2003). Genetic variants of serum butyrylcholinesterase in Chilean Mapuche Indians. Am. J. Phys. Anthropol., 121(1), 81–85.
Abstract: We estimated the frequencies of serum butyrylcholinesterase (BChE) alleles in three tribes of Mapuche Indians from southern Chile, using enzymatic methods, and we estimated the frequency of allele BCHE*K in one tribe using primer reduced restriction analysis (PCRPIRA). The three tribes have different degrees of European admixture, which is reflected in the observed frequencies of the atypical allele BCHE*A: 1.11% in Huilliches, 0.89% in Cuncos, and 0% in Pehuenches. This result is evidence in favor of the hypothesis that BCHE*A is absent in native Amerindians. The frequencies of BCHE*F were higher than in most reported studies (3.89%, 5.78%, and 4.41%, respectively). These results are probably due to an overestimation of the frequency of allele BCHE*F, since none of the 20 BCHE UF individuals (by the enzymatic test) individuals analyzed showed either of the two DNA base substitutions associated with this allele. Although enzymatic methods rarely detect the presence of allele BCHE*K, PCRPIRA found the allele in an appreciable frequency (5.76%), although lower than that found in other ethnic groups. Since observed frequencies of unusual alleles correspond to estimated percentages of European admixture, it is likely that none of these unusual alleles were present in Mapuche Indians before the arrival of Europeans. (C) 2003 WileyLiss, Inc.



Allende, C., Sohn, E., & Little, C. (2015). Treelink: data integration, clustering and visualization of phylogenetic trees. BMC Bioinformatics, 16, 6 pp.
Abstract: Background: Phylogenetic trees are central to a wide range of biological studies. In many of these studies, tree nodes need to be associated with a variety of attributes. For example, in studies concerned with viral relationships, tree nodes are associated with epidemiological information, such as location, age and subtype. Gene trees used in comparative genomics are usually linked with taxonomic information, such as functional annotations and events. A wide variety of tree visualization and annotation tools have been developed in the past, however none of them are intended for an integrative and comparative analysis. Results: Treelink is a platformindependent software for linking datasets and sequence files to phylogenetic trees. The application allows an automated integration of datasets to trees for operations such as classifying a tree based on a field or showing the distribution of selected data attributes in branches and leafs. Genomic and proteonomic sequences can also be linked to the tree and extracted from internal and external nodes. A novel clustering algorithm to simplify trees and display the most divergent clades was also developed, where validation can be achieved using the data integration and classification function. Integrated geographical information allows ancestral character reconstruction for phylogeographic plotting based on parsimony and likelihood algorithms. Conclusion: Our software can successfully integrate phylogenetic trees with different data sources, and perform operations to differentiate and visualize those differences within a tree. File support includes the most popular formats such as newick and csv. Exporting visualizations as images, cluster outputs and genomic sequences is supported. Treelink is available as a web and desktop application at http://www. treelinkapp. com.



Allende, H., Bravo, D., & Canessa, E. (2010). Robust design in multivariate systems using genetic algorithms. Qual. Quant., 44(2), 315–332.
Abstract: This paper presents a methodology based oil genetic algorithms, which finds feasible and reasonably adequate Solutions to problems of robust design in multivariate systems. We use a genetic algorithm to determine the appropriate control factor levels for simultaneously optimizing all of the responses of the system, considering the noise factors which affect it. The algorithm is guided by a desirability function which works with only one fitness function although the system May have many responses. We validated the methodology using data obtained from a real system and also from a process simulator, considering univariate and multivariate systems. In all cases, the methodology delivered feasible solutions, which accomplished the goals of robust design: obtain responses very close to the target values of each of them, and with minimum variability. Regarding the adjustment of the mean of each response to the target value, the algorithm performed very well. However, only in some of the multivariate cases, the algorithm was able to significantly reduce the variability of the responses.



Altimiras, F., UszczynskaRatajczak, B., Camara, F., Vlasova, A., Palumbo, E., Newhouse, S., et al. (2017). Brain Transcriptome Sequencing of a Natural Model of Alzheimer's Disease. Front. Aging Neurosci., 9, 8 pp.



AlvarezMiranda, E., Pereira, J., TorrezMeruvia H., & Vila, M. (2021). A Hybrid Genetic Algorithm for the Simple Assembly Line Balancing Problem with a Fixed Number of Workstations. Mathematics, 9(17), 2157.
Abstract: The assembly line balancing problem is a classical optimisation problem whose objective is to assign each production task to one of the stations on the assembly line so that the total efficiency of the line is maximized. This study proposes a novel hybrid method to solve the simple version of the problem in which the number of stations is fixed, a problem known as SALBP2. The hybrid differs from previous approaches by encoding individuals of a genetic algorithm as instances of a modified problem that contains only a subset of the solutions to the original formulation. These individuals are decoded to feasible solutions of the original problem during fitness evaluation in which the resolution of the modified problem is conducted using a dynamic programming based approach that uses new bounds to reduce its state space. Computational experiments show the efficiency of the method as it is able to obtain several new bestknown solutions for some of the benchmark instances used in the literature for comparison purposes.



Bachoc, F., Porcu, E., Bevilacqua, M., Furrer, R., & Faouzi, T. (2022). Asymptotically equivalent prediction in multivariate geostatistics. Bernoulli, 28(4), 2518–2545.
Abstract: Cokriging is the common method of spatial interpolation (best linear unbiased prediction) in multivariate geostatistics. While best linear prediction has been well understood in univariate spatial statistics, the literature for the multivariate case has been elusive so far. The new challenges provided by modern spatial datasets, being typically multivariate, call for a deeper study of cokriging. In particular, we deal with the problem of misspecified cokriging prediction within the framework of fixed domain asymptotics. Specifically, we provide conditions for equivalence of measures associated with multivariate Gaussian random fields, with index set in a compact set of a ddimensional Euclidean space. Such conditions have been elusive for over about 50 years of spatial statistics. We then focus on the multivariate Matern and Generalized Wendland classes of matrix valued covariance functions, that have been very popular for having parameters that are crucial to spatial interpolation, and that control the mean square differentiability of the associated Gaussian process. We provide sufficient conditions, for equivalence of Gaussian measures, relying on the covariance parameters of these two classes. This enables to identify the parameters that are crucial to asymptotically equivalent interpolation in multivariate geostatistics. Our findings are then illustrated through simulation studies.



Barroso, L., Munoz, F. D., Bezerra, B., Rudnick, H., & Cunha, G. (2021). ZeroMarginalCost Electricity Market Designs: Lessons Learned From Hydro Systems in Latin America Might Be Applicable for Decarbonization. IEEE Power Energy Mag., 19(1), 64–73.
Abstract: Large reductions in the cost of renewable energy technologies, particularly wind and solar, as well as various instruments used to achieve decarbonization targets (e.g., renewable mandates, renewable auctions, subsidies, and carbon pricing mechanisms) are driving the rapid growth of investments in these generation technologies worldwide.



Becker, F., Montealecre, P., Rapaport, I., & Todinca, I. (2020). The Impact Of Locality In The Broadcast Congested Clique Model. SIAM Discret. Math., 34(1), 682–700.
Abstract: The broadcast congested clique model (BCLIQUE) is a messagepassing model of distributed computation where n nodes communicate with each other in synchronous rounds. First, in this paper we prove that there is a oneround, deterministic algorithm that reconstructs the input graph G if the graph is ddegenerate, and rejects otherwise, using bandwidth b = O(d . log n). Then, we introduce a new parameter to the model. We study the situation where the nodes, initially, instead of knowing their immediate neighbors, know their neighborhood up to a fixed radius r. In this new framework, denoted BCLIQuE[r], we study the problem of detecting, in G, an induced cycle of length at most k (CYCLE <= k) and the problem of detecting an induced cycle of length at least k +1 (CYCLE>k). We give upper and lower bounds. We show that if each node is allowed to see up to distance r = left perpendicular k/2 right perpendicular + 1, then a polylogarithmic bandwidth is sufficient for solving CYCLE>k with only two rounds. Nevertheless, if nodes were allowed to see up to distance r = left perpendicular k/3 right perpendicular, then any oneround algorithm that solves CYCLE>k needs the bandwidth b to be at least Omega(n/ log n). We also show the existence of a oneround, deterministic BCLIQUE algorithm that solves CYCLE <= k with bandwitdh b = O(n(1/left perpendicular k/2 right perpendicular). log n). On the negative side, we prove that, if epsilon <= 1/3 and 0 < r <= k/4, then any epsilonerror, Rround, bbandwidth algorithm in the BCLIQUE[r] model that solves problem CYCLE(<= k )satisfies R . b = Omega(n(1/left perpendicular k/2 right perpendicular)).



Bergen, M., & Munoz, F. D. (2018). Quantifying the effects of uncertain climate and environmental policies on investments and carbon emissions: A case study of Chile. Energy Econ., 75, 261–273.
Abstract: In this article we quantify the effect of uncertainty of climate and environmental policies on transmission and generation investments, as well as on CO2 emissions in Chile. We use a twostage stochastic planning model with recourse or corrective investment options to find optimal portfolios of infrastructure both under perfect information and uncertainty. Under a series of assumptions, this model is equivalent to the equilibrium of a much more complicated bilevel market model, where a transmission planner chooses investments first and generation firms invest afterwards. We find that optimal investment strategies present important differences depending on the policy scenario. By changing our assumption of how agents will react to this uncertainty we compute bounds on the cost that this uncertainty imposes on the system, which we estimate ranges between 3.2% and 5.7% of the minimum expected system cost of $57.6B depending on whether agents will consider or not uncertainty when choosing investments. We also find that, if agents choose investments using a stochastic planning model, uncertain climate policies can result in nearly 18% more CO2 emissions than the equilibrium levels observed under perfect information. Our results highlight the importance of credible and stable longterm regulations for investors in the electric power industry if the goal is to achieve climate and environmental targets in the most costeffective manner and to minimize the risk of asset stranding. (C) 2018 Elsevier B.V. All rights reserved.



Besaury, L., Ouddane, B., Pavissich, J. P., DubrulleBrunaud, C., Gonzalez, B., & Quillet, L. (2012). Impact of copper on the abundance and diversity of sulfatereducing prokaryotes in two chilean marine sediments. Mar. Pollut. Bull., 64(10), 2135–2145.
Abstract: We studied the abundance and diversity of the sulfatereducing prokaryotes (SRPs) in two 30cm marine chilean sediment cores, one with a longterm exposure to coppermining residues, the other being a nonexposed reference sediment. The abundance of SRPs was quantified by qPCR of the dissimilatory sulfite reductase gene betasubunit (dsrB) and showed that SRPs are sensitive to high copper concentrations, as the mean number of SRPs all along the contaminated sediment was two orders of magnitude lower than in the reference sediment. SRP diversity was analyzed by using the dsrBsequencesbased PCRDGGE method and constructing gene libraries for dsrBsequences. Surprisingly, the diversity was comparable in both sediments, with dsrB sequences belonging to Desulfobacteraceae, Syntrophobacteraceae, and Desulfobulbaceae, SRP families previously described in marine sediments, and to a deep branching dsrAB lineage. The hypothesis of the presence of horizontal transfer of copper resistance genes in the microbial population of the polluted sediment is discussed. (C) 2012 Elsevier Ltd. All rights reserved.



Bevilacqua, M., CamanoCarrillo, C., & Porcu, E. (2022). Unifying compactly supported and Matern covariance functions in spatial statistics. J. Multivar. Anal., 189, 104949.
Abstract: The Matern family of covariance functions has played a central role in spatial statistics for decades, being a flexible parametric class with one parameter determining the smoothness of the paths of the underlying spatial field. This paper proposes a family of spatial covariance functions, which stems from a reparameterization of the generalized Wendland family. As for the Matern case, the proposed family allows for a continuous parameterization of the smoothness of the underlying Gaussian random field, being additionally compactly supported.
More importantly, we show that the proposed covariance family generalizes the Matern model which is attained as a special limit case. This implies that the (reparametrized) Generalized Wendland model is more flexible than the Matern model with an extraparameter that allows for switching from compactly to globally supported covariance functions.
Our numerical experiments elucidate the speed of convergence of the proposed model to the Matern model. We also inspect the asymptotic distribution of the maximum likelihood method when estimating the parameters of the proposed covariance models under both increasing and fixed domain asymptotics. The effectiveness of our proposal is illustrated by analyzing a georeferenced dataset of mean temperatures over a region of French, and performing a reanalysis of a large spatial point referenced dataset of yearly total precipitation anomalies.



Campas, O., Rojas, E., Dumais, J., & Mahadevan, L. (2012). Strategies For Cell Shape Control In TipGrowing Cells. Am. J. Bot., 99(9), 1577–1582.
Abstract: Premise of the study: Despite the large diversity in biological cell morphology, the processes that specify and control cell shape are not yet fully understood. Here we study the shape of tipgrowing, walled cells, which have evolved a polar mode of cell morphogenesis leading to characteristic filamentous cell morphologies that extend only apically. Methods: We identified the relevant parameters for the control of cell shape and derived scaling laws based on mass conservation and force balance that connect these parameters to the resulting geometrical phenotypes. These laws provide quantitative testable relations linking morphological phenotypes to the biophysical processes involved in establishing and modulating cell shape in tipgrowing, walled cells. Key results and conclusions: By comparing our theoretical results to the observed morphological variation within and across species, we found that tipgrowing cells from plant and fungal species share a common strategy to shape the cell, whereas oomycete species have evolved a different mechanism.



Canessa, E., & Chaigneau, S. (2015). Calibrating AgentBased Models Using a Genetic Algorithm. Stud. Inform. Control, 24(1), 79–90.
Abstract: We present a Genetic Algorithm (GA)based tool that calibrates Agentbased Models (ABMs). The GA searches through a userdefined set of input parameters of an ABM, delivering values for those parameters so that the output time series of an ABM may match the real system's time series to certain precision. Once that set of possible values has been available, then a domain expert can select among them, the ones that better make sense from a practical point of view and match the explanation of the phenomenon under study. In developing the GA, we have had three main goals in mind. First, the GA should be easily used by nonexpert computer users and allow the seamless integration of the GA with different ABMs. Secondly, the GA should achieve a relatively short convergence time, so that it may be practical to apply it to many situations, even if the corresponding ABMs exhibit complex dynamics. Thirdly, the GA should use a few data points of the real system's time series and even so, achieve a sufficiently good match with the ABM's time series to attaining relational equivalence between the real system under study and the ABM that models it. That feature is important since social science longitudinal studies commonly use few data points. The results show that all of those goals have been accomplished.



Canessa, E., & Chaigneau, S. (2017). Response surface methodology for estimating missing values in a pareto genetic algorithm used in parameter design. Ing. Invest., 37(2), 89–98.
Abstract: We present an improved Pareto Genetic Algorithm (PGA), which finds solutions to problems of robust design in multiresponse systems with 4 responses and as many as 10 control and 5 noise factors. Because some response values might not have been obtained in the robust design experiment and are needed in the search process, the PGA uses Response Surface Methodology (RSM) to estimate them. Not only the PGA delivered solutions that adequately adjusted the response means to their target values, and with low variability, but also found more Pareto efficient solutions than a previous version of the PGA. This improvement makes it easier to find solutions that meet the tradeoff among variance reduction, mean adjustment and economic considerations. Furthermore, RSM allows estimating outputs' means and variances in highly nonlinear systems, making the new PGA appropriate for such systems.



Canessa, E., Droop, C., & Allende, H. (2012). An improved genetic algorithm for robust design in multivariate systems. Qual. Quant., 46(2), 665–678.
Abstract: In a previous article, we presented a genetic algorithm (GA), which finds solutions to problems of robust design in multivariate systems. Based on that GA, we developed a new GA that uses a new desirability function, based on the aggregation of the observed variance of the responses and the squared deviation between the mean of each response and its corresponding target value. Additionally, we also changed the crossover operator from a onepoint to a uniform one. We used three different case studies to evaluate the performance of the new GA and also to compare it with the original one. The first case study involved using data from a univariate real system, and the other two employed data obtained from multivariate process simulators. In each of the case studies, the new GA delivered good solutions, which simultaneously adjusted the mean of each response to its corresponding target value. This performance was similar to the one of the original GA. Regarding variability reduction, the new GA worked much better than the original one. In all the case studies, the new GA delivered solutions that simultaneously decreased the standard deviation of each response to almost the minimum possible value. Thus, we conclude that the new GA performs better than the original one, especially regarding variance reduction, which was the main problem exhibited by the original GA.



Canessa, E., Vera, S., & Allende, H. (2012). A new method for estimating missing values for a genetic algorithm used in robust design. Eng. Optimiz., 44(7), 787–800.
Abstract: This article presents an improved genetic algorithm (GA), which finds solutions to problems of robust design in multivariate systems with many control and noise factors. Since some values of responses of the system might not have been obtained from the robust design experiment, but may be needed in the search process, the GA uses response surface methodology (RSM) to estimate those values. In all test cases, the GA delivered solutions that adequately adjusted the mean of the responses to their corresponding target values and with low variability. The GA found more solutions than the previous versions of the GA, which makes it easier to find a solution that may meet the tradeoff among variance reduction, mean adjustment and economic considerations. Moreover, RSM is a good method for estimating the mean and variance of the outputs of highly nonlinear systems, which makes the new GA appropriate for optimizing such systems.



Collins, C. J., Vivanco, J. F., Sokn, S. A., Williams, B. O., Burgers, T. A., & Ploeg, H. L. (2015). Fracture healing in mice lacking Pten in osteoblasts: a microcomputed tomography imagebased analysis of the mechanical properties of the femur. J. Biomech., 48(2), 310–317.
Abstract: In the United States, approximately eight million osseous fractures are reported annually, of which 510% fail to create a bony union. Osteoblastspecific deletion of the gene Pten in mice has been found to stimulate bone growth and accelerate fracture healing. Healing rates at four weeks increased in femurs from Pten osteoblast conditional knockout mice (PtenCKO) compared to wildtype mice (WT) of the same genetic strain as measured by an increase in mechanical stiffness and failure load in fourpoint bending tests. Preceding mechanical testing, each femur was imaged using a Skyscan 1172 microcomputed tomography (mu CT) scanner (Skyscan, Kontich, Belgium). The present study used μCT imagebased analysis to test the hypothesis that the increased femoral fracture force and stiffness in PtenCKO were due to greater section properties with the same effective material properties as that of the WT. The second moment of area and section modulus were computed in ImageJ 1.46 (National Institutes of Health) and used to predict the effective flexural modulus and the stress at failure for fourteen pairs of intact and callus WT and twelve pairs of intact and callus PtenCKO femurs. For callus and intact femurs, the failure stress and tissue mineral density of the PtenCKO and WT were not different; however, the section properties of the PtenCKO were more than twice as large 28 days postfracture. It was therefore concluded, when the gene Pten was conditionally knockedout in osteoblasts, the resulting increased bending stiffness and force to fracture were due to increased section properties. (C) 2014 Elsevier Ltd. All rights reserved.



Cominetti, R., Quattropani, M., & Scarsini, M. (2022). The BuckPassing Game. Math. Oper. Res., Early Access.
Abstract: We consider two classes of games in which players are the vertices of a directed graph. Initially, nature chooses one player according to some fixed distribution and gives the player a buck. This player passes the buck to one of the player's outneighbors in the graph. The procedure is repeated indefinitely. In one class of games, each player wants to minimize the asymptotic expected frequency of times that the player receives the buck. In the other class of games, the player wants to maximize it. The PageRank game is a particular case of these maximizing games. We consider deterministic and stochastic versions of the game, depending on how players select the neighbor to which to pass the buck. In both cases, we prove the existence of pure equilibria that do not depend on the initial distribution; this is achieved by showing the existence of a generalized ordinal potential. If the graph on which the game is played admits a Hamiltonian cycle, then this is the outcome of priorfive Nash equilibrium in the minimizing game. For the minimizing game, we then use the price of anarchy and stability to measure fairness of these equilibria.



Cordova, S., Canizares, C., Lorca, A., & Olivares, D. E. (2021). An Energy Management System With ShortTerm Fluctuation Reserves and Battery Degradation for Isolated Microgrids. IEEE Trans. Smart Grid, 12(6), 4668–4680.
Abstract: Due to the lowinertia and significant renewable generation variability in isolated microgrids, short timescale fluctuations in the order of seconds can have a large impact on a microgrid's frequency regulation performance. In this context, the present paper presents a mathematical model for an Energy Management System (EMS) that takes into account the operational impact of the shortterm fluctuations stemming from renewable generation rapid changes, and the role that renewable curtailment and batteries, including their degradation, can play to counterbalance these variations. Computational experiments on the real Kasabonika Lake First Nation microgrid and CIGRE benchmark test system show the operational benefits of the proposed EMS, highlighting the need to properly model shortterm fluctuations and battery degradation in EMS for isolated microgrids with significant renewable integration.



Cordova, S., Canizares, C. A., Lorca, A., & Olivares, D. E. (2022). FrequencyConstrained Energy Management System for Isolated Microgrids. IEEE Trans. Smart Grid, 13(5), 3394–3407.
Abstract: Secondtosecond power imbalances stemming from renewable generation can have a large impact on the frequency regulation performance of isolated microgrids, as these are characterized by low inertia and, more commonly nowadays, significant renewable energy penetration. Thus, the present paper develops a novel frequencyconstrained Energy Management System (EMS) that takes into account the impact of shortterm power fluctuations on the microgrid's operation and frequency regulation performance. The proposed EMS model is based on accurate linear equations describing frequency deviation, rateofchangeoffrequency, and regulation provision in daily microgrid operations. Dynamic simulations on a realistic CIGRE benchmark test system show the economic and reliability benefits of the presented EMS model, highlighting the need of incorporating fast power fluctuations and their impact on frequency dynamics in EMSs for sustainable isolated microgrids.

