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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.

Aledo, J. A., Goles, E., MontalvaMedel, M., Montealegre, P., & Valverde, J. C. (2023). Symmetrizable Boolean networks. Inf. Sci., 626, 787–804.
Abstract: In this work, we provide a procedure that allows us to transform certain kinds of deterministic Boolean networks on minterm or maxterm functions into symmetric ones, so inferring that such symmetrizable networks can present only periodic points of periods 1 or 2. In particular, we deal with generalized parallel (or synchronous) dynamical systems (GPDS) over undirected graphs, i. e., discrete parallel dynamical systems over undirected graphs where some of the selfloops may not appear. We also study the class of antisymmetric GPDS (which are nonsymmetrizable), proving that their periodic orbits have period 4. In addition, we introduce a class of nonsymmetrizable systems which admit periodic orbits with arbitrary large periods.

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.
Keywords: Phylogenetic tree; Data integration; Clustering; Visualization

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.

Asenjo, F. A., & Hojman, S. A. (2022). Airy heat bullets. Eur. Phys. J. Plus., 137(10), 1201.
Abstract: New localized structured solutions for the threedimensional linear heat (diffusion) equation are presented. These new solutions are written in terms of Airy functions. They are constructed as wave packetlike structures formed by a superposition of Bessel functions through the introduction of spectral functions. These diffusive solutions accelerate along their propagation direction, while in the plane orthogonal to it, they retain their confined structure. These heat (diffusion) densities retain a complete localized form in space as they propagate, and may be considered the heat analogue of Airy light bullets.
Keywords: GAUSSIAN LIGHT BULLETS; WAVES; BEAMS; GENERATION

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.

Balbontin, C., Hensher, D. A., & Ho, C. (2023). Light commercial vehicles destination choice: Understanding preferences relative to the number of stop and tourbased trip type. Transp. Res. ELogist. Transp. Rev., 171, 103042.
Abstract: Freight delivery modelling has made significant progress in the past few decades. In this study we propose to use an aggregate multistep approach to gain a better understanding of the tourbased trips of light commercial vehicles in Sydney, Australia. The paper identifies differences in destination choicemaking given by the number of stop and the stop count of the trip, defined by the total number of stops in the tourbased trip. The findings suggest that estimating a separate model for each number of stops and stop count provides a better understanding on how destination choices are made. Different scenarios were simulated to show how the probability of choosing a certain destination depending on the number of stop and stop count changes due to variations in travel time and distance. Results show that light commercial vehicles are more sensitive to the generalised cost (defined by travel time and distance) in the first stop, and the sensitivity decreases as the trip is completed.

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., Montealegre, 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)).
Keywords: broadcast congested clique; induced cycles; graph degeneracy

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. 
Cáeres, C., Heusser, B., Garnham, A., & Moczko, E. (2023). The Major Hypotheses of Alzheimer's Disease: Related NanotechnologyBased Approaches for Its Diagnosis and Treatment. Cells, 12(23), 2669.
Abstract: Alzheimer's disease (AD) is a wellknown chronic neurodegenerative disorder that leads to the progressive death of brain cells, resulting in memory loss and the loss of other critical body functions. In March 2019, one of the major pharmaceutical companies and its partners announced that currently, there is no drug to cure AD, and all clinical trials of the new ones have been cancelled, leaving many people without hope. However, despite the clear message and startling reality, the research continued. Finally, in the last two years, the Food and Drug Administration (FDA) approved the firstever medications to treat Alzheimer's, aducanumab and lecanemab. Despite researchers' support of this decision, there are serious concerns about their effectiveness and safety. The validation of aducanumab by the Centers for Medicare and Medicaid Services is still pending, and lecanemab was authorized without considering data from the phase III trials. Furthermore, numerous reports suggest that patients have died when undergoing extended treatment. While there is evidence that aducanumab and lecanemab may provide some relief to those suffering from AD, their impact remains a topic of ongoing research and debate within the medical community. The fact is that even though there are considerable efforts regarding pharmacological treatment, no definitive cure for AD has been found yet. Nevertheless, it is strongly believed that modern nanotechnology holds promising solutions and effective clinical strategies for the development of diagnostic tools and treatments for AD. This review summarizes the major hallmarks of AD, its etiological mechanisms, and challenges. It explores existing diagnostic and therapeutic methods and the potential of nanotechnologybased approaches for recognizing and monitoring patients at risk of irreversible neuronal degeneration. Overall, it provides a broad overview for those interested in the evolving areas of clinical neuroscience, AD, and related nanotechnology. With further research and development, nanotechnologybased approaches may offer new solutions and hope for millions of people affected by this devastating disease.

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.
Keywords: cell wall; morphogenesis; morphological variation; tipgrowth; walled cells

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.
