Biomechanical properties of non-flight vibrations produced by bees.

Bees use thoracic vibrations produced by their indirect flight muscles for powering wingbeats in flight, but also during mating, pollination, defence and nest building. Previous work on non-flight vibrations has mostly focused on acoustic (airborne vibrations) and spectral properties (frequency domain). However, mechanical properties such as the vibration's acceleration amplitude are important in some behaviours, e.g. during buzz pollination, where higher amplitude vibrations remove more pollen from flowers. Bee vibrations have been studied in only a handful of species and we know very little about how they vary among species. In this study, we conducted the largest survey to date of the biomechanical properties of non-flight bee buzzes. We focused on defence buzzes as they can be induced experimentally and provide a common currency to compare among taxa. We analysed 15,000 buzzes produced by 306 individuals in 65 species and six families from Mexico, Scotland and Australia. We found a strong association between body size and the acceleration amplitude of bee buzzes. Comparison of genera that buzz-pollinate and those that do not suggests that buzz-pollinating bees produce vibrations with higher acceleration amplitude. We found no relationship between bee size and the fundamental frequency of defence buzzes. Although our results suggest that body size is a major determinant of the amplitude of non-flight vibrations, we also observed considerable variation in vibration properties among bees of equivalent size and even within individuals. Both morphology and behaviour thus affect the biomechanical properties of non-flight buzzes.

Effects of anther-stigma position on cross-pollination efficiency in a hermaphroditic plant.

PREMISE: Evolution of cross-pollination efficiency depends on the genetic variation of flower traits, the pollen vector, and flower trait matching between pollen donors and recipients. Trait matching has been almost unexplored among nonheterostylous species, and we examined whether the match of anther length in pollen donors and stigma length in pollen recipients influences the efficiency of cross-pollination. To explore potential constraints for evolutionary response, we also quantified genetic variation and covariation among sepal length, petal length and width, stamen length, style length, and herkogamy. METHODS: We created 58 experimental arrays of Turnera velutina that varied in the extent of mismatch in the position of anthers and stigmas between single-flowered plants. Genetic variation and correlations among flower traits were estimated under greenhouse conditions. RESULTS: Style length, but not herkogamy, influenced the efficiency of cross-pollination. Plants with stamen length that matched the style length of other plants were more efficient pollen donors, whereas those with the style protruding above the stamens of other plants were more efficient pollen recipients. Significant broad-sense heritability (0.22 > hB 2 < 0.42) and moderate genetic correlations (0.33 > r < 0.85) among floral traits were detected. CONCLUSIONS: Our results demonstrated that anther-stigma mismatch between flowers contributed to variation in the efficiency of cross-pollination. The genetic correlations between stamen length and other floral traits suggests that any change in cross-pollination efficiency would be driven by changes in style rather than in stamen length.

The role of within-plant variation in nectar production: an experimental approach.

BACKGROUND AND AIMS: Nectar, a plant reward for pollinators, can be energetically expensive. Hence, a higher investment in nectar production can lead to reduced allocation to other vital functions and/or increased geitonogamous pollination. One possible strategy employed by plants to reduce these costs is to offer variable amounts of nectar among flowers within a plant, to manipulate pollinator behaviour. Using artificial flowers, we tested this hypothesis by examining how pollinator visitation responds to inter- and intra-plant variation in nectar production, assessing how these responses impact the energetic cost per visit. METHODS: We conducted a 2 × 2 factorial experiment using artificial flowers, with two levels of nectar investment (high and low sugar concentration) and two degrees of intra-plant variation in nectar concentration (coefficient of variation 0 and 20 %). The experimental plants were exposed to visits (number and type) from a captive Bombus impatiens colony, and we recorded the total visitation rate, distinguishing geitonogamous from exogamous visits. Additionally, we calculated two estimators of the energetic cost per visit and examined whether flowers with higher nectar concentrations (richer flowers) attracted more bumblebees. KEY RESULTS: Plants in the variable nectar production treatment (coefficient of variation 20 %) had a greater proportion of flowers visited by pollinators, with higher rates of total, geitonogamous and exogamous visitation, compared with plants with invariable nectar production. When assuming no nectar reabsorption, variable plants incurred a lower cost per visit compared with invariable plants. Moreover, highly rewarding flowers on variable plants had higher rates of pollination visits compared with flowers with few rewards. CONCLUSIONS: Intra-plant variation in nectar concentration can represent a mechanism for pollinator manipulation, enabling plants to decrease the energetic costs of the interaction while still ensuring consistent pollinator visitation. However, our findings did not provide support for the hypothesis that intra-plant variation in nectar concentration acts as a mechanism to avoid geitonogamy. Additionally, our results confirmed the hypothesis that increased visitation to variable plants is dependent on the presence of flowers with nectar concentration above the mean.

Geographic isolation and long-distance gene flow influence the genetic structure of the blue fan palm Brahea armata (Arecaceae).

The formation of the Baja California Peninsula (BCP) has impacted the microevolutionary dynamics of different species in ways that depend on biological traits such as dispersal capacity. Plants with relatively low levels of vagility have exhibited high genetic divergence between the BCP and Continental mainland. Brahea armata (Arecaceae) is a palm species inhabiting the northern part of the BCP and Sonora; its distribution occurs in isolated oases of vegetation. We aimed to evaluate the influence of the formation of the BCP on the genetic structure of B. armata using nuclear microsatellites and chloroplast markers (cpDNA) to compare patterns of genetic diversity and structure with previous published studies. Because gene flow through seeds is usually more limited compared to pollen flow, we expect to find stronger genetic structure at (cpDNA) than at nuclear markers. Moreover, larger genetic structure might also be explained by the smaller effective population size of cpDNA. We analyzed six microsatellite markers and two cpDNA regions. The main results indicated high levels of genetic differentiation among isolated populations located in the BCP, while low genetic differentiation was found between southern populations of the BCP and Sonora, suggesting long distance gene flow. In contrast, chloroplast markers indicated high levels of genetic structure between BCP and Sonora populations, suggesting asymmetrical gene flow between pollen (measured by nuclear microsatellites) and seed (cpDNA markers). This study provides valuable information on genetic diversity of B. armata that can be relevant for conservation and management; and develops microsatellites markers that can be transferred to other Brahea species.

Posthoc Interpretability of Neural Responses by Grouping Subject Motor Imagery Skills Using CNN-Based Connectivity.

Motor Imagery (MI) refers to imagining the mental representation of motor movements without overt motor activity, enhancing physical action execution and neural plasticity with potential applications in medical and professional fields like rehabilitation and education. Currently, the most promising approach for implementing the MI paradigm is the Brain-Computer Interface (BCI), which uses Electroencephalogram (EEG) sensors to detect brain activity. However, MI-BCI control depends on a synergy between user skills and EEG signal analysis. Thus, decoding brain neural responses recorded by scalp electrodes poses still challenging due to substantial limitations, such as non-stationarity and poor spatial resolution. Also, an estimated third of people need more skills to accurately perform MI tasks, leading to underperforming MI-BCI systems. As a strategy to deal with BCI-Inefficiency, this study identifies subjects with poor motor performance at the early stages of BCI training by assessing and interpreting the neural responses elicited by MI across the evaluated subject set. Using connectivity features extracted from class activation maps, we propose a Convolutional Neural Network-based framework for learning relevant information from high-dimensional dynamical data to distinguish between MI tasks while preserving the post-hoc interpretability of neural responses. Two approaches deal with inter/intra-subject variability of MI EEG data: (a) Extracting functional connectivity from spatiotemporal class activation maps through a novel kernel-based cross-spectral distribution estimator, (b) Clustering the subjects according to their achieved classifier accuracy, aiming to find common and discriminative patterns of motor skills. According to the validation results obtained on a bi-class database, an average accuracy enhancement of 10% is achieved compared to the baseline EEGNet approach, reducing the number of "poor skill" subjects from 40% to 20%. Overall, the proposed method can be used to help explain brain neural responses even in subjects with deficient MI skills, who have neural responses with high variability and poor EEG-BCI performance.

Chained Deep Learning Using Generalized Cross-Entropy for Multiple Annotators Classification.

Supervised learning requires the accurate labeling of instances, usually provided by an expert. Crowdsourcing platforms offer a practical and cost-effective alternative for large datasets when individual annotation is impractical. In addition, these platforms gather labels from multiple labelers. Still, traditional multiple-annotator methods must account for the varying levels of expertise and the noise introduced by unreliable outputs, resulting in decreased performance. In addition, they assume a homogeneous behavior of the labelers across the input feature space, and independence constraints are imposed on outputs. We propose a Generalized Cross-Entropy-based framework using Chained Deep Learning (GCECDL) to code each annotator's non-stationary patterns regarding the input space while preserving the inter-dependencies among experts through a chained deep learning approach. Experimental results devoted to multiple-annotator classification tasks on several well-known datasets demonstrate that our GCECDL can achieve robust predictive properties, outperforming state-of-the-art algorithms by combining the power of deep learning with a noise-robust loss function to deal with noisy labels. Moreover, network self-regularization is achieved by estimating each labeler's reliability within the chained approach. Lastly, visual inspection and relevance analysis experiments are conducted to reveal the non-stationary coding of our method. In a nutshell, GCEDL weights reliable labelers as a function of each input sample and achieves suitable discrimination performance with preserved interpretability regarding each annotator's trustworthiness estimation.

Combined Immunoglobulin Free Light Chains Are Novel Predictors of Cardiovascular Events in Patients With Abdominal Aortic Aneurysm.

OBJECTIVE: Abdominal aortic aneurysm (AAA) is characterised by the presence of B cells and immunoglobulins in the aortic wall, mainly in the adventitia. Kappa (κ) and lambda (λ) free light chains (FLCs) are produced from B cells during immunoglobulin synthesis. This study investigated the presence and prognostic value of combined FLCs (cFLCs or summed κ and λ) in patients with AAA. METHODS: cFLCs were analysed by a turbidimetric specific assay in tissue conditioned media from AAA samples (n = 34) compared with healthy aortas (n = 34) from France and in plasma samples from patients with AAA (n = 434) and age matched controls (n = 104) selected from the Viborg Vascular (VIVA) AAA screening trial in Denmark. t test, logistic regression, and Cox regression were used to test whether plasma cFLCs serve as a marker for AAA presence and whether cFLCs were predictive of death, major adverse cardiovascular events (MACE), or major adverse lower limb events (MALE). RESULTS: Increased cFLC levels were detected in the AAA adventitial layer compared with the AAA medial layer and healthy media layer (13.65 ± 3.17 vs. 6.57 ± 1.01 vs. 0.49 ± 0.09 mg/L, respectively, p < .050). The upper tertile of plasma cFLCs was independently associated with AAA presence after correcting for confounders (odds ratio [OR] 7.596, 95% confidence intervals [CI] 3.117 - 18.513; p < .001). Of 434 patients with AAA, 89 (20.5%) died, 104 (24.0%) suffered MACE, and 63 (14.5%) suffered MALE, during a five year follow up. In univariable analysis, the cFLC upper tertile was associated with a higher risk of death, MACE, and MALE (p < .001 for all). After adjustment for confounders, cFLCs remained an independent predictor of all cause mortality (hazard ratio [HR] 4.310, 95% CI 2.157 - 8.609; p < .001), MACE (HR 2.153, 95% CI 1.218 - 3.804; p = .008), or MALE (HR 3.442, 95% CI 1.548 - 7.652; p = .002) for those in the upper tertile. CONCLUSION: Increased cFLCs are observed in adventitial tissue of patients with AAA, indicating local activation of B cells. Plasma cFLC levels are an independent predictor of death, MACE, and MALE in patients with AAA.

Comparison of achromatic doublet on glass Fresnel lenses for concentrator photovoltaics.

Silicone on glass (SoG) Fresnel lenses are the reference technology in concentrator photovoltaics (CPV) because of their simplicity and low cost. Nevertheless, their performance is strongly limited by chromatic aberration. As an alternative, in order to overcome such limitation, achromatic doublet on glass (ADG) Fresnel lenses were proposed. Such lenses are achromatic cemented doublet specifically designed for CPV applications. In this paper, a novel ADG architecture is presented and its performance analyzed and compared to previous proposals. The results show that most of the intrinsic optical losses are minimized and a superior optical efficiency can be achieved. The novel ADG design provides an achromatic lens for CPV whose efficiency is almost equal to the reference SoG technology and, at the same time, maintains all the advantages provided by the achromatic design such as the higher maximum attainable concentration and the strongly reduced temperature dependency.

Roll-to-roll nanoimprint lithography of high efficiency Fresnel lenses for micro-concentrator photovoltaics.

Roll-to-roll nanoimprint lithography (R2R-NIL) is an enabling technology for the low-cost mass production of high-quality micro- and nano-sized optical elements. Particularly, the fabrication of Fresnel lenses using R2R-NIL is a promising approach to produce optical arrays for micro-concentrator photovoltaic modules. This work investigates the application of a continuous R2R imprinting process based on ultraviolet curing of transparent photopolymer resins (UV-NIL) to fabricate high-efficiency and low-cost Fresnel lenses. The morphological attributes and the related optical performance of the lenses fabricated using roll-to-roll UV-NIL on flexible PET sheets yielded optical efficiency values up to ∼ 69% at a concentration ratio of 178X, whereas a value of ∼ 77% was obtained for the UV-NIL batch processed on a flat rigid substrate. Further improvement of the optical efficiency has been achieved by adding moth-eye inspired antireflective (AR) features on the side opposite to the Fresnel motifs via a double-sided R2R UV-NIL process. The process developed paves the way for cost-effective mass production of high-efficiency Fresnel lenses for micro-concentrator photovoltaics.

Natural selection acting on integrated phenotypes: covariance among functional leaf traits increases plant fitness.

Plant functional strategies are usually accomplished through the simultaneous expression of different traits, and hence their correlations should be promoted by natural selection. The adaptive value of correlations among leaf functional traits, however, has not been assessed in natural populations. We estimated intraspecific variation in leaf functional traits related to the primary metabolism and anti-herbivore defence in a population of Turnera velutina. We analysed whether natural selection favoured the expression of individual traits, particular combinations of traits or leaf phenotypic integration. Patterns of covariation among traits were related to water and nitrogen economy, and were similar among genotypes, but the magnitude of their phenotypic integration differed by 10-fold. Although families did not differ in the mean values of leaf functional traits, directional selection favoured low nitrogen content and low chemical defence, high content of chlorophyll, sugar in extrafloral nectar and trichome density. Families with higher phenotypic integration among leaf traits grew faster and produced more flowers. We suggest that the coordinated expression of leaf traits has an adaptive value, probably related to optimisation in the expression of traits related to water conservation and nitrogen acquisition.

Ontogenetic changes in the targets of natural selection in three plant defenses.

The evolution of plant defenses has traditionally been studied at single plant ontogenetic stages, overlooking the fact that natural selection acts continuously on organisms along their development, and that the adaptive value of phenotypes can change along ontogeny. We exposed 20 replicated genotypes of Turnera velutina to field conditions to evaluate whether the targets of natural selection on different defenses and their adaptative value change across plant development. We found that low chemical defense was favored in seedlings, which seems to be explained by the assimilation efficiency and the ability of the specialist herbivore to sequester cyanogenic glycosides. Whereas trichome density was unfavored in juvenile plants, it increased relative plant fitness in reproductive plants. At this stage we also found a positive correlative gradient between cyanogenic potential and sugar content in extrafloral nectar. We visualize this complex multi-trait combination as an ontogenetic defensive strategy. The inclusion of whole-plant ontogeny as a key source of variation in plant defense revealed that the targets and intensity of selection change along the development of plants, indicating that the influence of natural selection cannot be inferred without the assessment of ontogenetic strategies in the expression of multiple defenses.

Effects of herbivory and its timing on reproductive success of a tropical deciduous tree.

BACKGROUND AND AIMS: The implications of herbivory for plant reproduction have been widely studied; however, the relationship of defoliation and reproductive success is not linear, as there are many interacting factors that may influence reproductive responses to herbivore damage. In this study we aimed to disentangle how the timing of foliar damage impacts both male and female components of fitness, and to assess when it has greater impacts on plant reproductive success. METHODS: We measured herbivore damage and its effects on floral production, male and female floral attributes as well as fruit yield in three different phenological phases of Casearia nitida (Salicaceae) over the course of two consecutive years. Then we tested two models of multiple causal links among herbivory and reproductive success using piecewise structural equation models. KEY RESULTS: The effects of leaf damage differed between reproductive seasons and between male and female components of fitness. Moreover, the impact of herbivory extended beyond the year when it was exerted. The previous season's cumulated foliar damage had the largest impact on reproductive characters, in particular a negative effect on the numbers of inflorescences, flowers and pollen grains, indirectly affecting the numbers of infructescences and fruits, and a positive one on the amount of foliar damage during flowering. CONCLUSIONS: For perennial and proleptic species, the dynamics of resource acquisition and allocation patterns for reproduction promote and extend the effects of herbivore damage to longer periods than a single reproductive event and growing season, through the interactions among different components of female and male fitness.

Disentangling the effects of local resources, landscape heterogeneity and climatic seasonality on bee diversity and plant-pollinator networks in tropical highlands.

Land-use alteration and climate seasonality have profound effects on bee species diversity by influencing the availability of nesting and floral resources. Here, using twelve sites embedded in an agriculture-forest mosaic in the tropical highlands of Guatemala, we investigated the relative effects of climate seasonality and landscape heterogeneity on bee and floral-resource community structure and on their mutualistic network architecture. We found that climate seasonality affected bee diversity, which was higher in the wet season and associated positively with the availability of floral resources across both seasons. Bee community composition also differed between seasons and it was mainly driven by floral-resource richness and the proportion of agricultural, semi-natural and forest cover. In addition to the effects on bee diversity, climate seasonality also affected flower-bee visitation networks. We documented higher relative (null model corrected) nestedness in the dry season compared to the wet season. Niche partitioning as a result of competition for scarce resources in the dry season could be the process driving the differences in the network structure between seasons. Furthermore, relative nestedness was consistently smaller than zero, and relative modularity and specialization were consistently larger than zero in both seasons, suggesting the existence of isolated groups of interacting partners in all our flower-bee visitation networks. Our results highlight the effect of climatic seasonality and the importance of preserving local floral resources and natural heterogeneous habitats for the conservation of bee communities and their pollination services in tropical highlands.

CLoDSA: a tool for augmentation in classification, localization, detection, semantic segmentation and instance segmentation tasks.

BACKGROUND: Deep learning techniques have been successfully applied to bioimaging problems; however, these methods are highly data demanding. An approach to deal with the lack of data and avoid overfitting is the application of data augmentation, a technique that generates new training samples from the original dataset by applying different kinds of transformations. Several tools exist to apply data augmentation in the context of image classification, but it does not exist a similar tool for the problems of localization, detection, semantic segmentation or instance segmentation that works not only with 2 dimensional images but also with multi-dimensional images (such as stacks or videos). RESULTS: In this paper, we present a generic strategy that can be applied to automatically augment a dataset of images, or multi-dimensional images, devoted to classification, localization, detection, semantic segmentation or instance segmentation. The augmentation method presented in this paper has been implemented in the open-source package CLoDSA. To prove the benefits of using CLoDSA, we have employed this library to improve the accuracy of models for Malaria parasite classification, stomata detection, and automatic segmentation of neural structures. CONCLUSIONS: CLoDSA is the first, at least up to the best of our knowledge, image augmentation library for object classification, localization, detection, semantic segmentation, and instance segmentation that works not only with 2 dimensional images but also with multi-dimensional images.

Evolutionary transition between bee pollination and hummingbird pollination in Salvia: Comparing means, variances and covariances of corolla traits.

Covariation among traits can modify the evolutionary trajectory of complex structures. This process is thought to operate at a microevolutionary scale, but its long-term effects remain controversial because trait covariation can itself evolve. Flower morphology, and particularly floral trait (co)variation, has been envisioned as the product of pollinator-mediated selection. Available evidence suggests that major changes in pollinator assemblages may affect the joint expression of floral traits and their phenotypic integration. We expect species within a monophyletic lineage sharing the same pollinator type will show not only similarity in trait means but also similar phenotypic variance-covariance structures. Here, we tested this expectation using eighteen Salvia species pollinated either by bees or by hummingbirds. Our findings indicated a nonsignificant multivariate phylogenetic signal and a decoupling between means and variance-covariance phenotypic matrices of floral traits during the evolution to hummingbird pollination. Mean trait value analyses revealed significant differences between bee- and hummingbird-pollinated Salvia species although fewer differences were detected in the covariance structure between groups. Variance-covariance matrices were much more similar among bee- than hummingbird-pollinated species. This pattern is consistent with the expectation that, unlike hummingbirds, bees physically manipulate the flower, presumably exerting stronger selection pressures favouring morphological convergence among species. Overall, we conclude that the evolution of hummingbird pollination proceeded through different independent transitions. Thus, although the evolution of hummingbird pollination led to a new phenotypic optimum, the process involved the diversification of the covariance structure.

On the adaptive value of monomorphic versus dimorphic enantiostyly in Solanum rostratum.

Background and Aims: Enantiostyly is a reproductive system with heteromorphic flowers characterized by asymmetrical deflection of the style, either to the left or to the right of the floral axis. There are two types of enantiostyly. In monomorphic enantiostyly, plants produce the two types of flowers in the same individual. Dimorphic enantiostyly is restricted to only seven species and their populations consist of individuals producing either the right or the left flower type. It is hypothesized that the dimorphic form is derived from monomorphic ancestors because it functions as an outcrossing mechanism. We tested this latter hypothesis and investigated if monomorphic enantiostyly is resistant to invasion by individuals with dimorphic enantiostyly, because it functions as a reproductive assurance mechanism. Methods: To determine the conditions favouring the invasion of dimorphic enantiostyly, measurements of reproductive success and outcrossing rates in 15 natural flowering patches of Solanum rostratum were made. To test if monomorphic enantiostyly provides a reproductive assurance mechanism, experimental plants with either manually created dimorphic or natural monomorphic reproductive systems were exposed to two different pollination scenarios (flower density treatments), and reproductive success and outcrossing rates were measured. Key Results: Naturally flowering patches experienced severe pollination limitation, showed marked differences in reproductive success and had relatively high outcrossing rates. Plants in the experimental patches also showed pollination limitation and high outcrossing rates. Individuals with dimorphic enantiostyly expressed higher reproductive and outcrossing advantages under high-density conditions. These advantages disappeared in the low-density treatment, where the monomorphic form attained a higher reproductive success and no differences in outcrossing rates were detected. Conclusions: Monomorphic enantiostyly should be resistant to invasion of the dimorphic form because the prevalent ecological conditions favour the maintenance of geitonogamous individuals that are able to take advantage of ecological heterogeneity and generalized pollination limitation.

DecoFungi: a web application for automatic characterisation of dye decolorisation in fungal strains.

BACKGROUND: Fungi have diverse biotechnological applications in, among others, agriculture, bioenergy generation, or remediation of polluted soil and water. In this context, culture media based on color change in response to degradation of dyes are particularly relevant; but measuring dye decolorisation of fungal strains mainly relies on a visual and semiquantitative classification of color intensity changes. Such a classification is a subjective, time-consuming and difficult to reproduce process. RESULTS: DecoFungi is the first, at least up to the best of our knowledge, application to automatically characterise dye decolorisation level of fungal strains from images of inoculated plates. In order to deal with this task, DecoFungi employs a deep-learning model, accessible through a user-friendly web interface, with an accuracy of 96.5%. CONCLUSIONS: DecoFungi is an easy to use system for characterising dye decolorisation level of fungal strains from images of inoculated plates.

Surveying and benchmarking techniques to analyse DNA gel fingerprint images.

DNA fingerprinting is a genetic typing technique that allows the analysis of the genomic relatedness between samples, and the comparison of DNA patterns. The analysis of DNA gel fingerprint images usually consists of five consecutive steps: image pre-processing, lane segmentation, band detection, normalization and fingerprint comparison. In this article, we firstly survey the main methods that have been applied in the literature in each of these stages. Secondly, we focus on lane-segmentation and band-detection algorithms-as they are the steps that usually require user-intervention-and detect the seven core algorithms used for both tasks. Subsequently, we present a benchmark that includes a data set of images, the gold standards associated with those images and the tools to measure the performance of lane-segmentation and band-detection algorithms. Finally, we implement the core algorithms used both for lane segmentation and band detection, and evaluate their performance using our benchmark. As a conclusion of that study, we obtain that the average profile algorithm is the best starting point for lane segmentation and band detection.

A survey of tools for analysing DNA fingerprints.

DNA fingerprinting is a genetic typing technique that allows the analysis of the genomic relatedness between samples, and the comparison of DNA patterns. This technique has multiple applications in different fields (medical diagnosis, forensic science, parentage testing, food industry, agriculture and many others). An important task in molecular epidemiology of infectious diseases is the analysis and comparison of pulsed-field gel electrophoresis (PFGE) patterns. This is applied to determine the clonal diversity of bacteria in the follow-up of outbreaks or for tracking specific clones of special relevance. The resulting images produced by DNA fingerprinting are sometimes difficult to interpret, and multiple tools have been developed to simplify this task. In this article, we present a survey of tools for analysing DNA fingerprints. In particular, we compare 33 tools using a set of predefined criteria. The comparison was carried out by hands-on experiences-whenever possible-and inspecting the documentation of the tools. As no system is preferred in all the possible scenarios, we have created a spreadsheet that can be customized by researchers to determine the best system for their needs.

Testing the hypothesis that biological modularity is shaped by adaptation: Xylem in the Bursera simaruba clade of tropical trees.

The study of modularity allows recognition of suites of character covariation that potentially diagnose units of evolutionary change. One prominent perspective predicts that natural selection should forge developmental units that maximize mutual functional independence. We examined the module-function relation using secondary xylem (wood) in a clade of tropical trees as a study system. Traditionally, the three main cell types in wood (vessels, fibers, and parenchyma) have respectively been associated with three functions (conduction, mechanical support, and storage). We collected samples from nine species of the simaruba clade of Bursera at fifteen sites and measured thirteen anatomical variables that have traditionally been regarded as reflecting the distinct functions of these cell types. If there are indeed (semi) independently evolving modules associated with functions, and cell types really are associated with these functions, then we should observe greater association between traits within cell types than between traits from different cell types. To map these associations, we calculated correlation coefficients among anatomical variables and identified modules using cluster and factor analysis. Our results were only partially congruent with expectations, with associations between characters of different cell types common. These results suggest causes of covariation, some involving selected function as predicted, but also highlighting the tradeoffs and shared developmental pathways limiting the evolutionary independence of some cell types in the secondary xylem. The evolution of diversity across the simaruba clade appears to have required only limited independence between parts.