The relative prominence of developmental bias versus natural selection is a long standing controversy in evolutionary biology. Here we demonstrate quantitatively that developmental bias is the primary explanation for the occupation of the morphospace of RNA secondary structure (SS) shapes. By using the RNAshapes method to define coarse-grained SS classes, we can directly measure the frequencies that non-coding RNA SS shapes appear in nature. Our main findings are, firstly, that only the most frequent structures appear in nature: The vast majority of possible structures in the morphospace have not yet been explored. Secondly, and perhaps more surprisingly, these frequencies are accurately predicted by the likelihood that structures appear upon uniform random sampling of sequences. The ultimate cause of these patterns is not natural selection, but rather strong phenotype bias in the RNA genotype-phenotype (GP) map, a type of developmental bias that tightly constrains evolutionary dynamics to only act within a reduced subset of structures which are easy to “find”.

Morphospaces –representations of phenotypic characteristics– are often populated unevenly, leaving large parts unoccupied. Such patterns are typically ascribed to contingency, or else to natural selection disfavouring certain parts of the morphospace. The extent to which developmental bias, the tendency of certain phenotypes to preferentially appear as potential variation, also explains these patterns is hotly debated. Here we demonstrate quantitatively that developmental bias is the primary explanation for the occupation of the morphospace of RNA secondary structure (SS) shapes. Upon random mutations, some RNA SS shapes (the frequent ones) are much more likely to appear than others. By using the RNAshapes method to define coarse-grained SS classes, we can directly compare the frequencies that non-coding RNA SS shapes appear in the RNAcentral database to frequencies obtained upon random sampling of sequences. We show that: a) Only the most frequent structures appear in nature; the vast majority of possible structures in the morphospace have not yet been explored. b) Remarkably small numbers of random sequences are needed to produce all the RNA SS shapes found in nature so far. c) Perhaps most surprisingly, the natural frequencies are accurately predicted, over several orders of magnitude in variation, by the likelihood that structures appear upon uniform random sampling of sequences. The ultimate cause of these patterns is not natural selection, but rather strong phenotype bias in the RNA genotype-phenotype map, a type of developmental bias or “findability constraint”, which limits evolutionary dynamics to a hugely reduced subset of structures that are easy to “find”.

This study examined the effect of collaborative learning (CL) versus traditional lecture-based learning (TL) pedagogies and gender group composition in effecting positive or negative attitudes of biology major and nonmajor men and women students. The experimental research method was administered in experimental and control groups to test the hypotheses. Students’ attitudes refer to their positive or negative feelings and inclinations to learn biology. A nine-factor attitude scale was administered in (1) single-gender nonmajor biology, (2) mixed-gender nonmajor biology, (3) single-gender major biology, and (4) mixed-gender biology major groups. Men (221) and women (219) were randomly assigned into single and mixed-gender classes without groups and single-gender groups (4M) or (4W) and mix-gender (2M+2W) groups. In CL nonmajor and major single-gender groups, women demonstrated significantly higher positive attitudes than men. In contrast, men’s attitudes were significantly improved in mixed-gender CL groups for major and nonmajor sections, and the effect size was larger in mix-gender classes. Women feel less anxious in single-gender groups but more anxious in mixed-gender groups. In mixed-gender groups, men’s self-efficacy, general interest, and motivation enhanced significantly; overall, men experienced greater satisfaction and triggered their desire to collaborate better, affecting all nine attitudinal factors. There was an interaction effect demonstrating the teaching pedagogy’s impact on improving students’ attitudes toward biology; students’ gender and gender-specific group composition have been the most influential factor for nonmajor students. These findings suggest that there is a need for developing gender-specific and context-specific learning pedagogies, and instructors carefully select gender grouping in teaching undergraduate science subjects.

An important question in evolutionary biology is whether and in what ways genotype-phenotype (GP) map biases can influence evolutionary trajectories. Untangling the relative roles of natural selection and biases (and other factors) in shaping phenotypes can be difficult. Because RNA secondary structure (SS) can be analysed in detail mathematically and computationally, is biologically relevant, and a wealth of bioinformatic data is available, it offers a good model system for studying the role of bias. For quite short RNA (length L ≤ 126), it has recently been shown that natural and random RNA are structurally very similar, suggesting that bias strongly constrains evolutionary dynamics. Here we extend these results with emphasis on much larger RNA with length up to 3000 nucleotides. By examining both abstract shapes and structural motif frequencies (ie the numbers of helices, bonds, bulges, junctions, and loops), we find that large natural and random structures are also very similar, especially when contrasted to typical structures sampled from the space of all possible RNA structures. Our motif frequency study yields another result, that the frequencies of different motifs can be used in machine learning algorithms to classify random and natural RNA with quite high accuracy, especially for longer RNA (eg ROC AUC 0.86 for L = 1000). The most important motifs for classification are found to be the number of bulges, loops, and bonds. This finding may be useful in using SS to detect candidates for functional RNA within `junk' DNA regions.

An important question in evolutionary biology is whether (and in what ways) genotype–phenotype (GP) map biases can influence evolutionary trajectories. Untangling the relative roles of natural selection and biases (and other factors) in shaping phenotypes can be difficult. Because the RNA secondary structure (SS) can be analyzed in detail mathematically and computationally, is biologically relevant, and a wealth of bioinformatic data are available, it offers a good model system for studying the role of bias. For quite short RNA (length L≤126), it has recently been shown that natural and random RNA types are structurally very similar, suggesting that bias strongly constrains evolutionary dynamics. Here, we extend these results with emphasis on much larger RNA with lengths up to 3000 nucleotides. By examining both abstract shapes and structural motif frequencies (i.e., the number of helices, bonds, bulges, junctions, and loops), we find that large natural and random structures are also very similar, especially when contrasted to typical structures sampled from the spaces of all possible RNA structures. Our motif frequency study yields another result, where the frequencies of different motifs can be used in machine learning algorithms to classify random and natural RNA with high accuracy, especially for longer RNA (e.g., ROC AUC 0.86 for L = 1000). The most important motifs for classification are the number of bulges, loops, and bonds. This finding may be useful in using SS to detect candidates for functional RNA within ‘junk’ DNA regions.

Many metaheuristic approaches are inherently stochastic. In order to compare such methods, statistical tests are needed. However, choosing an appropriate test is not trivial, given that each test has some assumptions about the distribution of the underlying data that must be true before it can be used. Permutation tests (P-Tests) are statistical tests with minimal number of assumptions. These tests are simple, intuitive and nonparametric. In this paper, we argue researchers in the field of metaheuristics to adopt P-Tests to compare their algorithms. We define two statistic tests and then present an algorithm that uses them to compute the p-value. The proposed process is used to compare 5 metaheuristic algorithms on 10 benchmark functions. The resulting p-values are compared with the p-values of two widely used statistical tests. The results show that the proposed P-test is generally consistent with the classical tests, but more conservative in few cases.

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