Variable mutation rates as an adaptive strategy in replicator populations

For evolving populations of replicators, there is much evidence that the effect of mutations on fitness depends on the degree of adaptation to the selective pressures at play. In optimized populations, most mutations have deleterious effects, such that low mutation rates are favoured. In contrast to this, in populations thriving in changing environments a larger fraction of mutations have beneficial effects, providing the diversity necessary to adapt to new conditions. What is more, non-adapted populations occasionally benefit from an increase in the mutation rate. Therefore, there is no optimal universal value of the mutation rate and species attempt to adjust it to their momentary adaptive needs. In this work we have used stationary populations of RNA molecules evolving in silico to investigate the relationship between the degree of adaptation of an optimized population and the value of the mutation rate promoting maximal adaptation in a short time to a new selective pressure. Our results show that this value can significantly differ from the optimal value at mutation-selection equilibrium, being strongly influenced by the structure of the population when the adaptive process begins. In the short-term, highly optimized populations containing little variability respond better to environmental changes upon an increase of the mutation rate, whereas populations with a lower degree of optimization but higher variability benefit from reducing the mutation rate to adapt rapidly. These findings show a good agreement with the behaviour exhibited by actual organisms that replicate their genomes under broadly different mutation rates. © 2010 Stich et al.

Directed Selection using Reinforcement Learning for the Examination Timetabling Problem

Traditional heuristic approaches to the Examination Timetabling Problem normally utilize a stochastic method during Optimization for the selection of the next examination to be considered for timetabling within the neighbourhood search process. This paper presents a technique whereby the stochastic method has been augmented with information from a weighted list gathered during the initial adaptive construction phase, with the purpose of intelligently directing examination selection. In addition, a Reinforcement Learning technique has been adapted to identify the most effective portions of the weighted list in terms of facilitating the greatest potential for overall solution improvement. The technique is tested against the 2007 International Timetabling Competition datasets with solutions generated within a time frame specified by the competition organizers. The results generated are better than those of the competition winner in seven of the twelve examinations, while being competitive for the remaining five examinations. This paper also shows experimentally how using reinforcement learning has improved upon our previous technique.

Multi-objective ant colony optimization for requirements selection

The selection of a set of requirements between all the requirements previously defined by customers is an important process, repeated at the beginning of each development step when an incremental or agile software development approach is adopted. The set of selected requirements will be developed during the actual iteration. This selection problem can be reformulated as a search problem, allowing its treatment with metaheuristic optimization techniques. This paper studies how to apply Ant Colony Optimization algorithms to select requirements. First, we describe this problem formally extending an earlier version of the problem, and introduce a method based on Ant Colony System to find a variety of efficient solutions. The performance achieved by the Ant Colony System is compared with that of Greedy Randomized Adaptive Search Procedure and Non-dominated Sorting Genetic Algorithm, by means of computational experiments carried out on two instances of the problem constructed from data provided by the experts.

Competition and fixation of cohorts of adaptive mutations under Fisher geometrical model

One of the simplest models of adaptation to a new environment is Fisher's Geometric Model (FGM), in which populations move on a multidimensional landscape defined by the traits under selection. The predictions of this model have been found to be consistent with current observations of patterns of fitness increase in experimentally evolved populations. Recent studies investigated the dynamics of allele frequency change along adaptation of microbes to simple laboratory conditions and unveiled a dramatic pattern of competition between cohorts of mutations, i.e., multiple mutations simultaneously segregating and ultimately reaching fixation. Here, using simulations, we study the dynamics of phenotypic and genetic change as asexual populations under clonal interference climb a Fisherian landscape, and ask about the conditions under which FGM can display the simultaneous increase and fixation of multiple mutations-mutation cohorts-along the adaptive walk. We find that FGM under clonal interference, and with varying levels of pleiotropy, can reproduce the experimentally observed competition between different cohorts of mutations, some of which have a high probability of fixation along the adaptive walk. Overall, our results show that the surprising dynamics of mutation cohorts recently observed during experimental adaptation of microbial populations can be expected under one of the oldest and simplest theoretical models of adaptation-FGM.

Energy norm a-posteriori error estimation for hp-adaptive discontinuous Galerkin methods for elliptic problems in three dimensions

We develop the energy norm a-posteriori error estimation for hp-version discontinuous Galerkin (DG) discretizations of elliptic boundary-value problems on 1-irregularly, isotropically refined affine hexahedral meshes in three dimensions. We derive a reliable and efficient indicator for the errors measured in terms of the natural energy norm. The ratio of the efficiency and reliability constants is independent of the local mesh sizes and weakly depending on the polynomial degrees. In our analysis we make use of an hp-version averaging operator in three dimensions, which we explicitly construct and analyze. We use our error indicator in an hp-adaptive refinement algorithm and illustrate its practical performance in a series of numerical examples. Our numerical results indicate that exponential rates of convergence are achieved for problems with smooth solutions, as well as for problems with isotropic corner singularities.

Wave impedance selection for passivity-based bilateral teleoperation

When a task must be executed in a remote or dangerous environment, teleoperation systems may be employed to extend the influence of the human operator. In the case of manipulation tasks, haptic feedback of the forces experienced by the remote (slave) system is often highly useful in improving an operator's ability to perform effectively. In many of these cases (especially teleoperation over the internet and ground-to-space teleoperation), substantial communication latency exists in the control loop and has the strong tendency to cause instability of the system. The first viable solution to this problem in the literature was based on a scattering/wave transformation from transmission line theory. This wave transformation requires the designer to select a wave impedance parameter appropriate to the teleoperation system. It is widely recognized that a small value of wave impedance is well suited to free motion and a large value is preferable for contact tasks. Beyond this basic observation, however, very little guidance exists in the literature regarding the selection of an appropriate value. Moreover, prior research on impedance selection generally fails to account for the fact that in any realistic contact task there will simultaneously exist contact considerations (perpendicular to the surface of contact) and quasi-free-motion considerations (parallel to the surface of contact). The primary contribution of the present work is to introduce an approximate linearized optimum for the choice of wave impedance and to apply this quasi-optimal choice to the Cartesian reality of such a contact task, in which it cannot be expected that a given joint will be either perfectly normal to or perfectly parallel to the motion constraint. The proposed scheme selects a wave impedance matrix that is appropriate to the conditions encountered by the manipulator. This choice may be implemented as a static wave impedance value or as a time-varying choice updated according to the instantaneous conditions encountered. A Lyapunov-like analysis is presented demonstrating that time variation in wave impedance will not violate the passivity of the system. Experimental trials, both in simulation and on a haptic feedback device, are presented validating the technique. Consideration is also given to the case of an uncertain environment, in which an a priori impedance choice may not be possible.

A Mutational Hotspot and Strong Selection Contribute to the Order of Mutations Selected for during Escherichia coli Adaptation to the Gut

The relative role of drift versus selection underlying the evolution of bacterial species within the gut microbiota remains poorly understood. The large sizes of bacterial populations in this environment suggest that even adaptive mutations with weak effects, thought to be the most frequently occurring, could substantially contribute to a rapid pace of evolutionary change in the gut. We followed the emergence of intra-species diversity in a commensal Escherichia coli strain that previously acquired an adaptive mutation with strong effect during one week of colonization of the mouse gut. Following this first step, which consisted of inactivating a metabolic operon, one third of the subsequent adaptive mutations were found to have a selective effect as high as the first. Nevertheless, the order of the adaptive steps was strongly affected by a mutational hotspot with an exceptionally high mutation rate of 10-5. The pattern of polymorphism emerging in the populations evolving within different hosts was characterized by periodic selection, which reduced diversity, but also frequency-dependent selection, actively maintaining genetic diversity. Furthermore, the continuous emergence of similar phenotypes due to distinct mutations, known as clonal interference, was pervasive. Evolutionary change within the gut is therefore highly repeatable within and across hosts, with adaptive mutations of selection coefficients as strong as 12% accumulating without strong constraints on genetic background. In vivo competitive assays showed that one of the second steps (focA) exhibited positive epistasis with the first, while another (dcuB) exhibited negative epistasis. The data shows that strong effect adaptive mutations continuously recur in gut commensal bacterial species.

FROM GENES TO GENOMES: LOCAL ADAPTATION AND ADAPTIVE POTENTIAL IN TWO ARCTIC SEABIRDS

By investigating the mechanisms underlying the evolution and the maintenance of local adaptations we can help predict how species will adapt to future environmental change. In this thesis I investigate local adaptation and adaptive potential in thick-billed and common murres (Uria lomvia and U. aalge), two arctic seabirds of international conservation concern. Thanks to the recent development of new genomic methods, I address three major themes that are relevant for both the development of evolutionary theory and conservation: 1) the role of gene flow in the origin and maintenance of adaptation; 2) levels and distribution of standing genetic variation, and their contribution to adaptive potential; and 3) the genomic mechanisms maintaining an adaptive dimorphism within a single interbreeding population. First, I review the literature on genomics of local adaptation with gene flow and find that adaptation can be maintained despite gene flow, that gene flow itself can promote adaptation, and that genetic architecture is important in the origin and maintenance of local adaptations. Second, I genotype genome-wide markers and toll-like receptor genes (TLRs) to investigate local adaptation and adaptive potential in thick-billed murres. Thick-billed murres do not show signatures of local adaptation to their breeding grounds, but outlier loci group birds according to their non-breeding distributions, suggesting that selection and/or demographic connectivity in the winter may explain patterns of differentiation in this species. Genetic variation at TLRs does not decrease with increasing latitude as predicted, but tests of selection and measures of genetic diversity suggest differences in local selective regimes at most genes. Thick-billed murres show high levels of standing genetic variation and their adaptive potential will mostly depend on rate and magnitude of environmental change. Finally, I improve and annotate the assembly of the highly heterozygous genome of the thick-billed murre. Using this assembly as a reference, I perform whole genome analyses to investigate the genomic basis of an adaptive dimorphism in Atlantic common murres. I show for the first time that a 60 kb complex copy number variant in a non-coding region maintains differences in plumage and cold adaptation despite high gene flow.

Adaptive strategies of two Portuguese grapevines varieties (Aragonês. syn. Tempranillo and Trincadeira) face drought: physiological and structural responses.

At Mediterranean regions and particularly in southern Portugal, it is imperative to identify grape varieties more adapted to warm and dry climates in order to overcome future climatic changes. Two Vitis vinifera genotypes, Aragonez (syn. Tempranillo) and Trincadeira, were selected to assess their physiological responses to soil water stress. Vines were subjected to four irrigation regimes: irrigated during all phenological cycle, non-irrigated during all phenological cycle, non irrigated until veraison, irrigated after veraison. Predawn leaf water potential was much higher in Trincadeira than Aragonez in non- irrigated plants. This result is in accordance with its higher stomatal control efficiency in this variety (Trincadeira). Photosynthetic capacity (Amax at saturating light intensity) decreased due to stomatal and biochemical limitations under water stress. However, recovery capacity of leaf water status after irrigation was faster in Trincadeira. Yield and yield x Brix increased when irrigation occurred after veraison, particularly in Trincadeira. These results show that Trincadeira presents a drought adaptation than Aragonez. Ratio of variable to maximum fluorescence Fv/Fm and total leaf chlorophyll related with leaf water potential for both species. Reflectance Normalized Difference Vegetation Index (NDVI705), Red Edge Inflexion Point Index and Photochemical Reflectance Index were related with irrigation treatment. Relative water content and specific leaf area were similar between varieties. In conclusion, we suggested that there is variation among the genotypes and the main physiological parameters for variety selection, for drought, were leaf water potential, stomatal conductance and reflectance indexes.

Wild carrot differentiation in Europe and positive selection at DcAOX1 gene

By definition, the domestication process leads to an overall reduction of crop genetic diversity. This lead to the current search of genomic regions in wild crop relatives (CWR), an important task for modern carrot breeding. Nowadays massive sequencing possibilities can allow for discovery of novel genetic resources in wild populations, but this quest could be aided by the use of a surrogate gene (to first identify and prioritize novel wild populations for increased sequencing effort). Alternative oxidase (AOX) gene family seems to be linked to all kinds of abiotic and biotic stress reactions in various organisms and thus have the potential to be used in the identification of CWR hotspots of environment-adapted diversity. High variability of DcAOX1 was found in populations of wild carrot sampled across a West-European environmental gradient. Even though no direct relation was found with the analyzed climatic conditions or with physical distance, population differentiation exists and results mainly from the polymorphisms associated with DcAOX1 exon 1 and intron 1. The relatively high number of amino acid changes and the identification of several unusually variable positions (through a likelihood ratio test), suggests that DcAOX1 gene might be under positive selection. However, if positive selection is considered, it only acts on some specific populations (i.e. is in the form of adaptive differences in different population locations) given the observed high genetic diversity. We were able to identify two populations with higher levels of differentiation which are promising as hot spots of specific functional diversity.

Petroleomics By Electrospray Ionization Ft-icr Mass Spectrometry Coupled To Partial Least Squares With Variable Selection Methods: Prediction Of The Total Acid Number Of Crude Oils.

Negative-ion mode electrospray ionization, ESI(-), with Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) was coupled to a Partial Least Squares (PLS) regression and variable selection methods to estimate the total acid number (TAN) of Brazilian crude oil samples. Generally, ESI(-)-FT-ICR mass spectra present a power of resolution of ca. 500,000 and a mass accuracy less than 1 ppm, producing a data matrix containing over 5700 variables per sample. These variables correspond to heteroatom-containing species detected as deprotonated molecules, [M - H](-) ions, which are identified primarily as naphthenic acids, phenols and carbazole analog species. The TAN values for all samples ranged from 0.06 to 3.61 mg of KOH g(-1). To facilitate the spectral interpretation, three methods of variable selection were studied: variable importance in the projection (VIP), interval partial least squares (iPLS) and elimination of uninformative variables (UVE). The UVE method seems to be more appropriate for selecting important variables, reducing the dimension of the variables to 183 and producing a root mean square error of prediction of 0.32 mg of KOH g(-1). By reducing the size of the data, it was possible to relate the selected variables with their corresponding molecular formulas, thus identifying the main chemical species responsible for the TAN values.

Selection And Validation Of Reference Genes For Functional Studies In The Calliphoridae Family.

The genera Cochliomyia and Chrysomya contain both obligate and saprophagous flies, which allows the comparison of different feeding habits between closely related species. Among the different strategies for comparing these habits is the use of qPCR to investigate the expression levels of candidate genes involved in feeding behavior. To ensure an accurate measure of the levels of gene expression, it is necessary to normalize the amount of the target gene with the amount of a reference gene having a stable expression across the compared species. Since there is no universal gene that can be used as a reference in functional studies, candidate genes for qPCR data normalization were selected and validated in three Calliphoridae (Diptera) species, Cochliomyia hominivorax Coquerel, Cochliomyia macellaria Fabricius, and Chrysomya albiceps Wiedemann . The expression stability of six genes ( Actin, Gapdh, Rp49, Rps17, α -tubulin, and GstD1) was evaluated among species within the same life stage and between life stages within each species. The expression levels of Actin, Gapdh, and Rp49 were the most stable among the selected genes. These genes can be used as reliable reference genes for functional studies in Calliphoridae using similar experimental settings.

Strict Criteria For Selection Of Laparoscopy For Women With Adnexal Mass.

We compared the indication of laparoscopy for treatment of adnexal masses based on the risk scores and tumor diameters with the indication based on gynecology-oncologists' experience. This was a prospective study of 174 women who underwent surgery for adnexal tumors (116 laparotomies, 58 laparoscopies). The surgeries begun and completed by laparoscopy, with benign pathologic diagnosis, were considered successful. Laparoscopic surgeries that required conversion to laparotomy, led to a malignant diagnosis, or facilitated cyst rupture were considered failures. Two groups were defined for laparoscopy indication: (1) absence of American College of Obstetrics and Gynecology (ACOG) guideline for referral of high-risk adnexal masses criteria (ACOG negative) associated with 3 different tumor sizes (10, 12, and 14 cm); and (2) Index of Risk of Malignancy (IRM) with cutoffs at 100, 200, and 300, associated with the same 3 tumor sizes. Both groups were compared with the indication based on the surgeon's experience to verify whether the selection based on strict rules would improve the rate of successful laparoscopy. ACOG-negative and tumors ≤10 cm and IRM with a cutoff at 300 points and tumors ≤10cm resulted in the same best performance (78% success = 38/49 laparoscopies). However, compared with the results of the gynecology-oncologists' experience, those were not statistically significant. The selection of patients with adnexal mass to laparoscopy by the use of the ACOG guideline or IRM associated with tumor diameter had similar performance as the experience of gynecology-oncologists. Both methods are reproducible and easy to apply to all women with adnexal masses and could be used by general gynecologists to select women for laparoscopic surgery; however, referral to a gynecology-oncologist is advisable when there is any doubt.

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry

PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.