Gryphaea and cepaea—case studies for teaching selection

An approach to the teaching of natural selection in evolution using materials related to living creatures instead of artificial models such as toothpicks is described. Some investigations using simple flow tanks to investigate the stability of Gryphaea shells and a simulated non-visual searching for Cepaea are suggested for use in class practical sessions. All the designs for the investigations suggested have been tested with students and found to be workable in the relatively short practical periods available for school sixth-form and College of Education students.     

Parallel high-dimensional multi-objective feature selection for EEG classification with dynamic workload balancing on CPU–GPU architectures

Many bioinformatics applications that analyse large volumes of high-dimensional data comprise complex problems requiring metaheuristics approaches with different types of implicit parallelism. For example, although functional parallelism would be used to accelerate evolutionary algorithms, the fitness evaluation of the population could imply the computation of cost functions with data parallelism. This way, heterogeneous parallel architectures, including central processing unit (CPU) microprocessors with multiple superscalar cores and accelerators such as graphics processing units (GPUs) could be very useful. This paper aims to take advantage of such CPU–GPU heterogeneous architectures to accelerate electroencephalogram classification and feature selection problems by evolutionary multi-objective optimization, in the context of brain computing interface tasks. In this paper, we have used the OpenCL framework to develop parallel master-worker codes implementing an evolutionary multi-objective feature selection procedure in which the individuals of the population are dynamically distributed among the available CPU and GPU cores.     

Dissection of complex traits in forest trees — opportunities for marker-assisted selection

Due to their long reproductive cycles and the time to expression of mature traits, marker-assisted selection is particularly attractive for tree breeding. In this review, we discuss different approaches used for developing markers and propose a method for application of markers in low linkage disequilibrium (LD) populations. Identification of useful markers for application in tree breeding is mainly based on two approaches, quantitative trait locus (QTL) mapping and association genetic studies. While several studies have identified significant markers, effect of the individual markers is low making it difficult to utilize them in breeding programs. Recently, genomic selection (GS) was proposed for overcoming some of these difficulties. In GS, high density markers are used for predicting phenotypes from genotypes. Currently small effective populations with high LD are being tested for GS in tree breeding. For wider application, GS needs to be applied in low LD populations which are found in many tree breeding programs. Here we propose an approach in which the significant markers from association studies may be used for developing prediction models in low LD populations using the same methods as in GS. Preliminary analyses indicate that a modest numbers of markers may be sufficient for developing prediction models in low LD populations. GS based on large numbers of random markers or small numbers of associated markers is poised to make marker-assisted selection a reality in forest tree breeding.     

Prediction of protein–protein interactions based on PseAA composition and hybrid feature selection

Based on pseudo amino acid (PseAA) composition and a novel hybrid feature selection frame, this paper presents a computational system to predict the PPIs (protein–protein interactions) using 8796 protein pairs. These pairs are coded by PseAA composition, resulting in 114 features. A hybrid feature selection system, mRMR–KNNs–wrapper, is applied to obtain an optimized feature set by excluding poor-performed and/or redundant features, resulting in 103 remaining features. Using the optimized 103-feature subset, a prediction model is trained and tested in the k-nearest neighbors (KNNs) learning system. This prediction model achieves an overall accurate prediction rate of 76.18%, evaluated by 10-fold cross-validation test, which is 1.46% higher than using the initial 114 features and is 6.51% higher than the 20 features, coded by amino acid compositions. The PPIs predictor, developed for this research, is available for public use at http://chemdata.shu.edu.cn/ppi.     

The catalytic power of enzymes: conformational selection or transition state stabilization?

The mechanism by which enzymes produce enormous rate enhancements in the reactions they catalyze remains unknown. Two viewpoints, selection of ground state conformations and stabilization of the transition state, are present in the literature in apparent opposition. To provide more insight into current discussion about enzyme efficiency, a two-state model of enzyme catalysis was developed. The model was designed to include both the pre-chemical (ground state conformations) and the chemical (transition state) components of the process for the substrate both in water and in the enzyme. Although the model is of general applicability, the chorismate to prephenate reaction catalyzed by chorismate mutase was chosen for illustrative purposes. The resulting kinetic equations show that the catalytic power of enzymes, quantified as the k(cat)/k(uncat) ratio, is the product of two terms: one including the equilibrium constants for the substrate conformational states and the other including the rate constants for the uncatalyzed and catalyzed chemical reactions. The model shows that these components are not mutually exclusive and can be simultaneously present in an enzymic system, being their relative contribution a property of the enzyme. The developed mathematical expressions reveal that the conformational and reaction components of the process perform differently for the translation of molecular efficiency (changes in energy levels) into observed enzymic efficiency (changes in k(cat)), being, in general, more productive the component involving the transition state.     

Sexual selection in an anadromous population of threespine sticklebacks — no role for parasites

Summary We examined the hypothesis that trematode parasites played a role in the evolution of the red colour of male threespine sticklebacks (Gasterosteus aculeatus L.) and whether the parasites affected female fitness. Parasites (blackspot disease) played no role either in determining the outcome of male—male competition for breeding territories or in female mate choice. Among males with territories, mating success was highly variable. Some males obtained over 3000 eggs (approximately 10 matings) whereas others received none. In 1 year of the 2 year study, males with the greatest amount of red nuptial coloration had the greatest mating success. Although male colour may sometimes affect female choice in this system, this preference has probably not evolved because of the Hamilton—Zuk mechanism of sexual selection. The parasites had small, but statistically significant effects on female fitness. Females with high parasite loads were in poorer condition and produced fewer eggs than less parasitized fish.     

Selection for fertility in mice — the selection plateau and how to overcome it

Summary A long-term experiment for increasing the traits first day litter size (LS1) and litter weight (LW1) was conducted with two populations for 33 generations. The selection plateau was reached in population DU-C (selection and estrus synchronization (h² = 0.02±0.01); in population DU-K (selection) the plateau (h² = 0.05±0.2) was nearly reached. Selection progress per generation was in LS1 b = 0.11±0.02; b = 0.12±0.04 (1st to 18th generation DU-K, DU-C) and b = 0.10±0.03; b = 0.07±0.05 (19th to 33rd generation, DU-K, DU-C) in LW1 b = 0.16±0.04 g; 0.19±0.07 g (DU-K, DU-C) b = 0.20±0.09 g; 0.001±0.09 g (DU-K, DU-C). Reverse and relaxe selection as well as systematic inbreeding was applied for 10 generations. Reverse selection yielded h² = 0.28±0.11 (R-DU-K) and h² = 0.17±0.05 (R-DU-C) and showed that there was still additive genetic variance. Relaxe selection did not cause alterations in the selection parameters, whereas inbreeding lead to inbred depressions (b = LS1 = –0.42±0.15; –0.45±0.12; b = LW1 = –1.13±0.20; –0.82±0.18 I-DU-K, I-DU-C). The plateau was based upon the heterozygote advantage. Several methods for overcoming the plateau were applied. A new selective useful variance could be created by crossing the plateau populations (h² = 0.14±0.04). A short-term progress in overcoming the plateau (1st to 3rd generation) could be obtained by litter size standardization (LS = 388). Tandem selection (selection for body weight — BW42) as well as crossing of inbred strains were not suitable for overcoming the selection plateau. Altering the environmental conditions as a possibility for overcoming the plateau has been discussed.     

Long-term selection for 8-week body weight in chickens — direct and correlated responses

Summary A bidirectional selection experiment for 8-week body weight in chickens was conducted. In addition to 27 generations of selection, random samples were chosen from each selected line in generations 6, 13, 19 and 26 to initiate lines in which selection was relaxed. Genetic change was evident in the selected high-weight line through the first 75% of the study after which response in the direction opposing selection occurred. Selection for low body weight resulted in considerable reduction in body size, particularly in the last quarter of the study. Correlated responses evaluated were body weight at several ages, conformation, age at onset of lay, various reproductive and egg quality traits and ratio (female: male) of some traits. Data from lines where selection was relaxed indicated that natural selection opposed artificial selection with the effects greater in later generations.     

Linkage rules for plant-pollinator networks: trait complementarity or exploitation barriers?

Recent attempts to examine the biological processes responsible for the general characteristics of mutualistic networks focus on two types of explanations: nonmatching biological attributes of species that prevent the occurrence of certain interactions (“forbidden links”), arising from trait complementarity in mutualist networks (as compared to barriers to exploitation in antagonistic ones), and random interactions among individuals that are proportional to their abundances in the observed community (“neutrality hypothesis”). We explored the consequences that simple linkage rules based on the first two hypotheses (complementarity of traits versus barriers to exploitation) had on the topology of plant–pollination networks. Independent of the linkage rules used, the inclusion of a small set of traits (two to four) sufficed to account for the complex topological patterns observed in real-world networks. Optimal performance was achieved by a “mixed model” that combined rules that link plants and pollinators whose trait ranges overlap (“complementarity models”) and rules that link pollinators to flowers whose traits are below a pollinator-specific barrier value (“barrier models”). Deterrence of floral parasites (barrier model) is therefore at least as important as increasing pollination efficiency (complementarity model) in the evolutionary shaping of plant–pollinator networks.     

Identification of autoantibody biomarkers for primary Sjögren's syndrome using protein microarrays

Sj?gren’s syndrome (SS) is a chronic, progressive autoimmune disease primarily affecting women. Diagnosis of SS requires an invasive salivary gland tissue biopsy and a long delay from the start of the symptoms to final diagnosis has been frequently observed. In this study,we aim to identify salivary autoantibody biomarkers for primary SS (pSS) using a protein microarray approach. Immune-response protoarrays were used to profile saliva autoantibodies from patients with pSS (n = 514), patients with systemic lupus erythematosus(SLE, n = 513), and healthy control subjects (n = 513). We identified 24 potential autoantibody biomarkers that can discriminate patients with pSS from both patients with SLE and healthy individuals. Four saliva autoantibody biomarkers, anti-transglutaminase, anti-histone, anti-SSA, and anti-SSB, were further tested in independent pSS (n = 534), SLE (n = 534), and healthy control (n = 534) subjects and all were successfully validated with ELISA. This study has demonstrated the potential of a high-throughput protein microarray approach for the discovery of autoantibody biomarkers. The identified saliva autoantibody biomarkers may lead to a clinical tool for simple, noninvasive detection of pSS at low cost.     

Microclimate of tree cavities during winter nights—implications for roost site selection in birds

We examined the relationships between cavity temperature, ambient temperature outside the cavity and structural characteristics of 70 cavities measured for 1 night to determine if cavity roosting birds may potentially select warmer tree cavities for wintertime roosting. The mean temperature increment of the cavity (=cavity-ambient temperature) varied from −2.4 to 4.9°C and increased with higher day-to-night fluctuations in the ambient temperature, smaller cavity entrance and better health status of the cavity tree. Cavities in healthy trees were warmer than those in dead trees, but this difference disappeared with rising mean ambient temperatures. This interaction between the effects of tree health status and mean ambient temperature, as well as the effect of day-to-night fluctuations in the ambient temperature, were supported by the analysis of repeated measurements of temperature taken on 12 consecutive nights in five cavities. The variability in cavity microclimates makes the selection of warmer roost sites possible, and the predictors of microclimate may provide indirect cues to prospecting birds.     

Analysis and prediction of drug–drug interaction by minimum redundancy maximum relevance and incremental feature selection

Drug–drug interaction (DDI) defines a situation in which one drug affects the activity of another when both are administered together. DDI is a common cause of adverse drug reactions and sometimes also leads to improved therapeutic effects. Therefore, it is of great interest to discover novel DDIs according to their molecular properties and mechanisms in a robust and rigorous way. This paper attempts to predict effective DDIs using the following properties: (1) chemical interaction between drugs; (2) protein interactions between the targets of drugs; and (3) target enrichment of KEGG pathways. The data consisted of 7323 pairs of DDIs collected from the DrugBank and 36,615 pairs of drugs constructed by randomly combining two drugs. Each drug pair was represented by 465 features derived from the aforementioned three categories of properties. The random forest algorithm was adopted to train the prediction model. Some feature selection techniques, including minimum redundancy maximum relevance and incremental feature selection, were used to extract key features as the optimal input for the prediction model. The extracted key features may help to gain insights into the mechanisms of DDIs and provide some guidelines for the relevant clinical medication developments, and the prediction model can give new clues for identification of novel DDIs.