Are there pollination syndromes in the Australian epacrids (Ericaceae: Styphelioideae)? A novel statistical method to identify key floral traits per syndrome

Background and Aims

Convergent floral traits hypothesized as attracting particular pollinators are known as pollination syndromes. Floral diversity suggests that the Australian epacrid flora may be adapted to pollinator type. Currently there are empirical data on the pollination systems for 87 species (approx. 15 % of Australian epacrids). This provides an opportunity to test for pollination syndromes and their important morphological traits in an iconic element of the Australian flora.

Methods

Data on epacrid–pollinator relationships were obtained from published literature and field observation. A multivariate approach was used to test whether epacrid floral attributes related to pollinator profiles. Statistical classification was then used to rank floral attributes according to their predictive value. Data sets excluding mixed pollination systems were used to test the predictive power of statistical classification to identify pollination models.

Key Results

Floral attributes are correlated with bird, fly and bee pollination. Using floral attributes identified as correlating with pollinator type, bird pollination is classified with 86 % accuracy, red flowers being the most important predictor. Fly and bee pollination are classified with 78 and 69 % accuracy, but have a lack of individually important floral predictors. Excluding mixed pollination systems improved the accuracy of the prediction of both bee and fly pollination systems.

Conclusions

Although most epacrids have generalized pollination systems, a correlation between bird pollination and red, long-tubed epacrids is found. Statistical classification highlights the relative importance of each floral attribute in relation to pollinator type and proves useful in classifying epacrids to bird, fly and bee pollination systems.     

The interaction between pollinator size and the bristle staminode of Penstemon digitalis (Scrophulariaceae)

Penstemon digitalis, a prairie species whose flowers possess a large bristle staminode, is visited by eight bee species of varying size. Two sets of field experiments involving staminode removal were performed to test pollination efficiency in relation to bee size. Our data indicate that bristle staminode presence and function are influenced by size-dependent selection on bee body size and associated pollen transporting attributes. The first experiment compared staminode presence and removal in open-pollinated flowers. Staminode removal significantly reduced total pollen deposition but had no effect on total pollen removal. The second experiment utilized single bee visits to assess the interaction between pollinator size and staminode presence on the rate of pollen deposition and removal. This experiment indicated that staminode removal resulted in fewer pollen grains deposited on stigmas and less pollen removed from anthers for both large and small bees (the contrary was true for medium bees). Although the number of pollen grains deposited was greatly reduced for large bees, staminode removal reduced deposition efficiency most notably for small bees by 41.3%, reducing female reproductive success. Staminode removal increased pollen removal efficiency most notably for medium bees by 22.7%, reducing male reproductive success. Mechanisms of staminode function are discussed.     

CORAL: Building up QSAR models for the chromosome aberration test

A high level of chromosomal aberrations in peripheral blood lymphocytes may be an early marker of cancer risk, but data on risk of specific cancers and types of chromosomal aberrations are limited. Consequently, the development of predictive models for chromosomal aberrations test is important task. Majority of models for chromosomal aberrations test are so-called knowledge-based rules system. The CORAL software (http://www.insilico.eu/coral, abbreviation of “CORrelation And Logic”) is an alternative for knowledge-based rules system. In contrast to knowledge-based rules system, the CORAL software gives possibility to estimate the influence upon the predictive potential of a model of different molecular alerts as well as different splits into the training set and validation set. This possibility is not available for the approaches based on the knowledge-based rules system. Quantitative Structure–Activity Relationships (QSAR) for chromosome aberration test are established for five random splits into the training, calibration, and validation sets. The QSAR approach is based on representation of the molecular structure by simplified molecular input-line entry system (SMILES) without data on physicochemical and/or biochemical parameters. In spite of this limitation, the statistical quality of these models is quite good.     

Classifying Cinnamomums using rough sets classifier based on interval-discretization

Classification, which is the task of assigning objects to one of several predefined categories, is a pervasive problem that encompasses many diverse applications. Decision tree classifier, which is a simple yet widely used classification technique, employs training data to yield decision rules; moreover, it can create thresholds and then split the list of continuous attributes into descrete intervals for handling continuous attributes (Quinlan in Journal of Artificial Intelligence Research 4:77–90, 1996). Rough set theory (Pawlak in International Journal of Computer and Information Sciences 11:341–356, 1982; International Journal of Man-Machine Studies 20:469–483, 1984; Rough sets: theoretical aspects of reasoning about data. Kluwer, Dordrecht, 1991) has been applied to a wide variety of decision analysis problems for the extraction of rules from databases. This paper proposes a hybrid approach that takes advantage of combining decision tree and rough sets classifier and applies it to plant classification. The introduced approach starts with decision tree classifier (C4.5) as preprocessing technique to make interval-discretization, subsequently, and uses rough set method for extracting rules. The proposed approach aims at finding out classification rules via analyzing lamina attributes (leaf stalk, leaf width, leaf length, length/width ratio) of Cinnamomum, which are gathered and measured by plant specialists in the field of Taiwan. A comparison with the widely used algorithms (e.g., decision tree, multilayer perceptrons, naïve Bayes, and rough sets classifier) is carried out to show numerous advantages of the proposed approach. Finally, employing with test data in which species are unknown, results of classification are approved by consulting the relative plant specialists.     

Tyramine functions as a toxin in honey bee larvae during Varroa-transmitted infection by Melissococcus pluton

From wounds of honey bee pupae, caused by the mite Varroa destructor, coccoid bacteria were isolated and identified as Melissococcus pluton. The bacterial isolate was grown anaerobically in sorbitol medium to produce a toxic compound that was purified on XAD columns, gelfiltration and preparative HPLC. The toxic agent was identified by GC-MS and FTICR-MS as tyramine. The toxicity of the isolated tyramine was tested by a novel mobility test using the protozoon Stylonychia lemnae. A concentration of 0.2 mg/ml led to immediate inhibition of mobility. In addition the toxicity was studied on honey bee larvae by feeding tyramine/water mixtures added to the larval jelly. The lethal dosis of tyramine on 4-5 days old bee larvae was determined as 0.3 mg/larvae when added as a volume of 20 microl to the larval food in brood cells. Several other biogenic amines, such as phenylethylamine, histamine, spermine, cadaverine, putrescine and trimethylamine, were tested as their hydrochloric salts for comparison and were found to be inhibitory in the Stylonychia mobility test at similar concentrations. A quantitative hemolysis test with human red blood cells revealed that tyramine and histamine showed the highest membranolytic activity, followed by the phenylethylamine, trimethylamine and spermine, while the linear diamines, cadaverine and putrescine, showed a significantly lower hemolysis when calculated on a molar amine basis. The results indicate that tyramine which is a characteristic amine produced by M. pluton in culture, is the causative agent of the observed toxic symptoms in bee larvae. Thus this disease, known as European foulbrood, is possibly an infection transmitted by the Varroa destructor mite.     

Fungicide tests on adult alfalfa leafcutting bees (Hymenoptera: Megachilidae)

Chalkbrood is a serious disease of alfalfa leafcutting bee Megachile rotundata (F.) (Hymenoptera: Megachilidae) larvae, causing upward of 20% infection in the field. The causative agent is the fungus Ascosphaera aggregata. This bee is used extensively for alfalfa seed pollination in the United States. Using laboratory bioassays, we previously demonstrated that fungicides can reduce chalkbrood levels in the larvae. Here, we evaluate the toxicity of four fungicides, Benlate, Captan, Orbit, and Rovral, to adult bees by using three different bioassays. In the first test, fungicides were applied to bees' thoraces. In the second test, mimicking foliage residue, a piece of filter paper soaked in fungicide was placed on the bottom of a container of bees. The third test evaluated oral toxicity by incorporating fungicides into a sugar-water solution that was fed to the bees. The filter paper test did not discriminate among the fungicides well, and the oral test resulted in the greatest mortality. Toxicity to males was greater than to females. The use of fungicides for chalkbrood control is a logical choice, but caution should be used in how they are applied in the presence of bees.     

Using multidimensional patterns of amino acid attributes for QSAR analysis of peptides

On the basis of exploratory factor analysis, six multidimensional patterns of 516 amino acid attributes, namely, factor analysis scales of generalized amino acid information (FASGAI) involving hydrophobicity, alpha and turn propensities, bulky properties, compositional characteristics, local flexibility and electronic properties, are proposed to represent structures of 48 bitter-tasting dipeptides and 58 angiotensin-converting enzyme inhibitors. Characteristic parameters related to bioactivities of the peptides studied are selected by genetic algorithm, and quantitative structure–activity relationship (QSAR) models are constructed by partial least square (PLS). Our results by a leave-one-out cross validation are compared with the previously known structure representation method and are shown to give slightly superior or comparative performance. Further, two data sets are divided into training sets and test sets to validate the characterization repertoire of FASGAI. Performance of the PLS models developed by training samples by a leave-one-out cross validation and external validation for test samples are satisfying. These results demonstrate that FASGAI is an effective representation technique of peptide structures, and that FASGAI vectors have many preponderant characteristics such as straightforward physicochemical information, high characterization competence and easy manipulation. They can be further applied to investigate the relationship between structures and functions of various peptides, even proteins.     

Larval Apis mellifera L. (Hymenoptera: Apidae) mortality after topical application of antibiotics and dusts

Beekeepers apply various dusts to honey bee, Apis mellifera L., colonies to dislodge parasitic mites and control bacterial brood diseases. Anecdotal reports by beekeepers indicate that the antibiotic oxytetracycline (OTC) can be toxic when applied in powdered sugar to cells containing immature bee brood, but it was not known whether the purported toxicity is caused by the antibiotic or the sugar carrier. Additionally, the toxicity of various dusts, proposed for parasitic mite control, is poorly known. In the current studies, we tested OTC and two other antibiotics (tylosin and lincomycin, candidate compounds for use in honey bee colonies) in a powdered sugar carrier for larval toxicity. We also tested for larval toxicity, several dusts that have been proposed for mite control. OTC caused significant brood mortality of approximately 80% at the concentrations used in the hive (200 mg in 20 g sugar). In contrast, tylosin and lincomycin at the 200 mg dose were both similar to untreated controls, and only five times that concentration (1000 mg) caused significant brood mortality of approximately 65%. The addition of dusts, wheat flour, talc, and a commercially available protein supplement, BeePro, resulted in mortality levels between 65 and 80%, similar to that seen with OTC. The common antibiotic carrier, powered confectioners sugar, was nontoxic. The use of 100 unsealed brood cells was demonstrated to be a reliable means of assessing potential adverse affects of dry compounds on larval honey bees. Two new candidate antibiotics for use in honey bee colonies were less toxic to larval bees than the currently labeled antibiotic, OTC.     

Using support vector machines to distinguish enzymes: Approached by incorporating wavelet transform

The enzymatic attributes of newly found protein sequences are usually determined either by biochemical analysis of eukaryotic and prokaryotic genomes or by microarray chips. These experimental methods are both time-consuming and costly. With the explosion of protein sequences registered in the databanks, it is highly desirable to develop an automated method to identify whether a given new sequence belongs to enzyme or non-enzyme. The discrete wavelet transform (DWT) and support vector machine (SVM) have been used in this study for distinguishing enzyme structures from non-enzymes. The networks have been trained and tested on two datasets of proteins with different wavelet basis functions, decomposition scales and hydrophobicity data types. Maximum accuracy has been obtained using SVM with a wavelet function of Bior2.4, a decomposition scale j=5, and Kyte-Doolittle hydrophobicity scales. The results obtained by the self-consistency test, jackknife test and independent dataset test are encouraging, which indicates that the proposed method can be employed as a useful assistant technique for distinguishing enzymes from non-enzymes.     

FUZZY SET THEORY APPLIED TO PRODUCT CLASSIFICATION BY A SENSORY PANEL1

It is frequently impossible to meet the assumptions underlying the statistical approach to classification of food products by a sensory panel. To find an alternative, we have investigated the applicability of the fuzzy set theory. Within a fuzzy set framework it is acceptable that a product belongs to several classes simultaneously and no assumptions regarding the distribution of sensory properties for a product class are made. Fuzzy classification models can be constructed from a set of training objects by linking the soft class labels to the sensory attributes applying an inference procedure based on fuzzy logic. A number of fuzzy inference procedures has been evaluated using a number of attribute sets. A satisfactory classification has been found using a very simple implication rule and a set of three attributes.     

Is non‐host pollen suitable for generalist bumblebees?

Current evidence suggests that pollen is both chemically and structurally protected. Despite increasing interest in studying bee–flower networks, the constraints for bee development related to pollen nutritional content, toxicity and digestibility as well as their role in the shaping of bee–flower interactions have been poorly studied. In this study we combined bioassays of the generalist bee Bombus terrestris on pollen of Cirsium, Trifolium, Salix, and Cistus genera with an assessment of nutritional content, toxicity, and digestibility of pollen. Microcolonies showed significant differences in their development, non‐host pollen of Cirsium being the most unfavorable. This pollen was characterized by the presence of quite rare δ7‐sterols and a low digestibility. Cirsium consumption seemed increase syrup collection, which is probably related to a detoxification mixing behavior. These results strongly suggest that pollen traits may act as drivers of plant selection by bees and partly explain why Asteraceae pollen is rare in bee generalist diet.     

Physical performance and cardiovascular responses to an acute bout of heavy resistance circuit training versus traditional strength training

Circuit training effectively reduces the time devoted to strength training while allowing an adequate training volume to be achieved. Nonetheless, circuit training has traditionally been performed using relatively low loads for a relatively high number of repetitions, which is not conducive to maximal muscle size and strength gain. This investigation compared physical performance parameters and cardiovascular load during heavy-resistance circuit (HRC) training to the responses during a traditional, passive rest strength training set (TS). Ten healthy subjects (age, 26 +/- 1.6 years; weight, 80.2 +/- 8.78 kg) with strength training experience volunteered for the study. Testing was performed once weekly for 3 weeks. On day 1, subjects were familiarized with the test and training exercises. On the subsequent 2 test days, subjects performed 1 of 2 strength training programs: HRC (5 sets x (bench press + leg extensions + ankle extensions); 35-second interset rest; 6 repetition maximum [6RM] loads) or TS (5 sets x bench press; 3-minute interset rest, 6RM loads). The data confirm that the maximum and average bar velocity and power and the number of repetitions performed of the bench press in the 2 conditions was the same; however, the average heart rate was significantly greater in the HRC compared to the TS condition (HRC = 129 +/- 15.6 beats x min(-1), approximately 71% maximum heart rate (HRmax), TS = 113 +/- 13.1 beats x min(-1), approximately 62% HRmax; P < 0.05). Thus, HRC sets are quantitatively similar to traditional strength training sets, but the cardiovascular load is substantially greater. HRC may be an effective training strategy for the promotion of both strength and cardiovascular adaptations.     

What the honeybee sees: a review of the recognition system of Apis mellifera

Abstract. For many years, two opposing theories have dominated our ideas of what honeybees see. The earliest proposal based on training experiments was that bees detected only simple attributes or features, irrespective of the actual pattern. The features demonstrated experimentally before 1940 were the disruption of the pattern (related to spatial frequency), the area of black or colour, the length of edge, and the angle of orientation of a bar or grating. Cues discovered recently are the range, and radial and tangential edges, and symmetry, relative to the fixation point, which is usually the reward hole. This theory could not explain why recognition failed when the pattern was moved. In the second theory, proposed in 1969, the bee detected the retinotopic directions of black or coloured areas, and estimated the areas of overlap and nonoverlap on each test pattern with the corresponding positions in the training pattern. This proposal explained the progressive loss of recognition as a test pattern was moved or reduced in size, but required that the bees saw and remembered the layout of every learned pattern and calculated the mismatch with each test image. Even so, the same measure of the mismatch was given by many test patterns and could not detect a pattern uniquely. Moreover, this theory could not explain the abundant evidence of simple feature detectors. Recent work has shown that bees learn one or more of a limited number of simple cues. A newly discovered cue is the position, mainly in the vertical direction, of the common centre (centroid) of black areas combined together. Significantly, however, the trained bees look for the cues mentioned above only in the range of places where they had occurred during the training. These two observations made possible a synthesis of both theories. There is no experimental evidence that the bees detect or re-assemble the layout of patterns in space; instead, they look for a cue in the expected place. With an array of detectors of the known cues, together with their directions, this mechanism would enable bees to recognize each familiar place from the coincidences of cues in different directions around the head.     

Adaptive neuro-fuzzy inference technique for estimation of light penetration in reservoirs

An adaptive neuro-fuzzy inference technique has been adopted to estimate light levels in a reservoir. The data were collected randomly from Doğanci Dam Reservoir over a number of years. The input data set is a matrix with vectors of time, depth, sampling location, and incident solar radiation. The output data set is a vector representing light measured at various depths. Randomization and logarithmic transformations have been applied as preprocessing. One-half of the data have been utilized for training; testing and validation steps utilized one-fourth each. An adaptive neuro-fuzzy inference system (ANFIS) has been built as a prediction model for light penetration. Very high correlation values between predictions and real values on light measurements with relatively low root mean square error values have been obtained for training, test, and validation data sets. Elimination of the overtraining problem was ensured by satisfying close root mean square error values for all sets.     

Screening for dihydrofolate reductase inhibitors using MOLPRINT 2D, a fast fragment-based method employing the naïve Bayesian classifier: limitations of the descriptor and the importance of balanced chemistry in training and test sets

A fragment-based similarity searching method, MOLPRINT 2D, was employed for virtual screening of Escherichia coli dihydrofolate reductase inhibitors. Using the original training set of 50,000 compounds, only marginal enrichment factors (between 1 and 3) could be achieved on the test library. The active structures contained in the training and test libraries represented different types of "chemistry", that is, different substructural features associated with activity. Training and test sets were pooled in a 2nd step and randomly split into training and test of equal size, with the objective of smoothing out the different chemical characteristics of both libraries. In a 10-fold cross-validation study on the new training and test sets, typically 10-fold enrichment could be found in the first 96 positions, 4-fold enrichment in the first 384 positions, and 3-fold enrichment in the first 1536 positions, corresponding to 6, 10, and 28 hits, respectively (out of a total of 307; activity defined as average residual activity of less than 80%). The conclusions are 2-fold. On one hand, the exact fragment-matching similarity searching method employed here is not capable of finding completely novel hit structures. On the other hand, this study emphasizes the requirement for a comparable distribution of chemical features of the training and test sets. MOLPRINT 2D is freely downloadable from http://www.cheminformatics.org.     

Mediation of pyrethroid insecticide toxicity to honey bees (Hymenoptera: Apidae) by cytochrome P450 monooxygenases

Honey bees, Apis mellifera L., often thought to be extremely susceptible to insecticides in general, exhibit considerable variation in tolerance to pyrethroid insecticides. Although some pyrethroids, such as cyfluthrin and lambda-cyhalothrin, are highly toxic to honey bees, the toxicity of tau-fluvalinate is low enough to warrant its use to control parasitic mites inside honey bee colonies. Metabolic insecticide resistance in other insects is mediated by three major groups of detoxifying enzymes: the cytochrome P450 monooxygenases (P450s), the carboxylesterases (COEs), and the glutathione S-transferases (GSTs). To test the role of metabolic detoxification in mediating the relatively low toxicity of tau-fluvalinate compared with more toxic pyrethroid insecticides, we examined the effects of piperonyl butoxide (PBO), S,S,S-tributylphosphorotrithioate (DEF), and diethyl maleate (DEM) on the toxicity of these pyrethroids. The toxicity of the three pyrethroids to bees was greatly synergized by the P450 inhibitor PBO and synergized at low levels by the carboxylesterase inhibitor DEF. Little synergism was observed with DEM. These results suggest that metabolic detoxification, especially that mediated by P450s, contributes significantly to honey bee tolerance of pyrethroid insecticides. The potent synergism between tau-fluvalinate and PBO suggests that P450s are especially important in the detoxification of this pyrethroid and explains the ability of honey bees to tolerate its presence.     

转基因植物对传粉蜂类影响的研究进展

随着转基因作物的大规模商业种植,其对非靶生物的安全性问题已经成为转基因生物风险评估的重要内容。有些转基因植物是需要蜂类传粉的显花植物．有些则可作为蜂类的食物来源。抗性转基因植物可能对蜂类产生直接和间接的影响。转基因植物的花粉或者花蜜可能对蜂类产生直接影响,测试化学杀虫剂安全性的相同方法被用来评估这种影响。很多研究测试了纯化的转基因蛋白质(如Bt,蛋白酶抑制剂)对蜜蜂和熊蜂的影响。间接影响来自于遗传转化可能使转基因植物的表型产生的异常变化,特别是花的改变。这种影响是用转基因植物进行评估的。转基因植物对蜂类的影响研究所测定的参数包括蜂类的肠道生理、取食和嗅觉学习行为、经口毒性和寿命等。对在实验室中以幼蜂或者成年蜂为对象、在封闭条件下针对蜂群的研究以及蜂类在转基因植物上的取食行为方面的研究进行了总结。结果表明,转基因植物是否对传粉蜂类产生影响以及影响的大小主要取决于转基因植物的生物学特征、转基因蛋白的性质和表达量。一般来说,Bt毒素蛋白和葡聚糖酶等蛋白质对蜂类没有影响;而某些蛋白酶抑制剂和几丁质酶则可能对蜂类产生不利影响,蜂类所摄取到这类蛋白质的剂量将决定影响的程度。然而,由于采用的研究方法各不相同,难以对不同的研究结果进行分析和比较,并得出明确的结论。