A gall-inducing arthropod drives declines in canopy tree photosynthesis

Mature forest canopies sustain an enormous diversity of herbivorous arthropods; however, with the exception of species that exhibit large-scale outbreaks, canopy arthropods are thought to have relatively little influence on overall forest productivity. Diminutive gall-inducing mites (Acari; Eriophyoidae) are ubiquitous in forest canopies and are almost always highly host specific, but despite their pervasive occurrence, the impacts of these obligate parasites on canopy physiology have not been examined. We have documented large declines in photosynthetic capacity (approx. 60%) and stomatal conductance (approx. 50%) in canopy leaves of mature sugar maple (Acer saccharum) trees frequently galled by the maple spindle gall mite Vasates aceriscrumena. Remarkably, such large impacts occurred at very low levels of galling, with the presence of only a few galls (occupying approx. 1% of leaf area) compromising gas-exchange across the entire leaf. In contrast to these extreme impacts on the leaves of adult trees, galls had no detectible effect on the gas-exchange of maple saplings, implying large ontogenetic differences in host tolerance to mite galling. We also found a significant negative correlation between canopy tree radial increment growth and levels of mite galling. Increased galling levels and higher physiological susceptibility in older canopy trees thus suggest that gall-inducing mites may be major drivers of “age-dependent” reductions in the physiological performance and growth of older trees.     

Preprocessing of tandem mass spectrometric data based on decision tree classification

In this study, we present a preprocessing method for quadrupole time-of-flight （Q-TOF） tandem mass spectra to increase the accuracy of database searching for peptide （protein） identification. Based on the natural isotopic information inherent in tandem mass spectra, we construct a decision tree after feature selection to classify the noise and ion peaks in tandem spectra. Furthermore, we recognize overlapping peaks to find the monoisotopic masses of ions for the following identification process. The experimental results show that this preprocessing method increases the search speed and the reliability of peptide identification.     

Microbial control of arthropod pests of tropical tree fruits

A multitude of insects and mites attack fruit crops throughout the tropics. The traditional method for controlling most of these pests is the application of chemical pesticides. Growing concern on the negative environmental effects has encouraged the development of alternatives. Inundatively and inoculatively applied microbial control agents (virus, bacteria, fungi, and entomopathogenic nematodes) have been developed as alternative control methods of a wide variety of arthropods including tropical fruit pests. The majority of the research and applications in tropical fruit agroecosystems has been conducted in citrus, banana, coconut, and mango. Successful microbial control initiatives of citrus pests and mites have been reported. Microbial control of arthropod pests of banana includes banana weevil, Cosmopolites sordidus Germar (Coleoptera: Curculionidae) (with EPNs and fungi) among others Oryctes rhinoceros (L.) is one of the most important pests of coconut and one of the most successful uses of non-occluded virus for classical biological control. Key pests of mango that have been controlled with microbial control agents include fruit flies (Diptera: Tephritidae) (with EPNs and fungi), and other pests. Also successful is the microbial control of arthropod pests of guava, papaya and pineapple. The challenge towards a broader application of entomopathogens is the development of successful combinations of entomopathogens, predators, and parasitoids along with other interventions to produce effective and sustainable pest management.     

Examining the large-scale convergence of photosynthesis-weighted tree leaf temperatures through stable oxygen isotope analysis of multiple data sets

The idea that photosynthesis-weighted tree canopy leaf temperature (T(canδ)) can be resolved through analysis of oxygen isotope composition in tree wood cellulose (δ(18) O(wc)) has led to the observation of boreal-to-subtropical convergence of T(canδ) to c. 20°C. To further assess the validity of the large-scale convergence of T(canδ), we used the isotope approach to perform calculation of T(canδ) for independent δ(18) O(wc) data sets that have broad coverage of climates. For the boreal-to-subtropical data sets, we found that the deviation of T(canδ) from the growing season temperature systemically increases with the decreasing mean annual temperature. Across the whole data sets we calculated a mean T(canδ) of 19.48°C and an SD of 2.05°C, while for the tropical data set, the mean T(canδ) was 26.40 ± 1.03°C, significantly higher than the boreal-to-subtropical mean. Our study thus offers independent isotopic support for the concept that boreal-to-subtropical trees display conserved T(canδ) near 20°C. The isotopic analysis cannot distinguish between the possibility that leaf temperatures are generally elevated above ambient air temperatures in cooler environments and the possibility that leaf temperature equals air temperature, whereas the leaf/air temperature at which photosynthesis occurs has a weighted average of near 20°C in cooler environments. Future work will separate these potential explanations.     

A new geometric biclustering algorithm based on the Hough transform for analysis of large-scale microarray data

Biclustering is an important tool in microarray analysis when only a subset of genes co-regulates in a subset of conditions. Different from standard clustering analyses, biclustering performs simultaneous classification in both gene and condition directions in a microarray data matrix. However, the biclustering problem is inherently intractable and computationally complex. In this paper, we present a new biclustering algorithm based on the geometrical viewpoint of coherent gene expression profiles. In this method, we perform pattern identification based on the Hough transform in a column-pair space. The algorithm is especially suitable for the biclustering analysis of large-scale microarray data. Our studies show that the approach can discover significant biclusters with respect to the increased noise level and regulatory complexity. Furthermore, we also test the ability of our method to locate biologically verifiable biclusters within an annotated set of genes.     

COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas

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A new MapReduce associative classifier based on a new storage format for large-scale imbalanced data

The process of knowledge discovery from big and high dimensional datasets has become a popular research topic. The classification problem is a key task in bioinformatics, business intelligence, decision science, astronomy, physics, etc. Building associative classifiers has been a notable research interest in recent years because of their superior accuracy. In associative classifiers, using under-sampling or over-sampling methods for imbalanced big datasets reduces accuracy or increases running time, respectively. Hence, there is a significant need to create efficient associative classifiers for imbalanced big data problems. These classifiers should be able to handle challenges such as memory usage, running time and efficiently exploring the search space. To this end, efficient calculation of measures is a primary objective for associative classifiers. In this paper, we propose a new efficient associative classifier for big imbalanced datasets. The proposed method is based on Rare-PEARs (a multi-objective evolutionary algorithm that efficiently discovers rare and reliable association rules) and is able to evaluate rules in a distributed manner by using a new storing data format. This format simplifies measures calculation and is fully compatible with the MapReduce programming model. We have applied the proposed method (RPII) on a well-known big dataset (ECBDL’14) and have compared our results with seven other learning methods. The experimental results show that RPII outperform other methods in sensitivity and final score measures (the values of sensitivity and final score measures were approximately 0.74 and 0.54 respectively). The results demonstrate that the proposed method is a good candidate for large-scale classification problems; furthermore, it achieves reasonable execution time when the target platform is a typical computer clusters.     

Effects of bird predation on arthropod abundance and tree growth across an elevational gradient

Considerable uncertainty surrounds the conditions under which birds can cause trophic cascades. In a three‐year experiment, we studied the direct and indirect effects of insectivorous birds on arthropod abundance, herbivory, and growth of striped maple Acer pensylvanicum saplings in a northern hardwood forest of central New Hampshire, USA. We manipulated bird predation by erecting exclosures around saplings and directly manipulated herbivory by removing herbivores. We also examined how climate modifies these interactions by replicating the experiment at three locations along an elevational gradient. Effects of bird predation were variable. Overall, mean arthropod biomass was 20% greater on saplings within bird exclosures than on controls (p<0.05). The mean biomass of leaf‐chewing herbivores, primarily Lepidoptera larvae, was 25% greater within exclosures but not statistically different from controls. To a lesser degree, mean herbivore damage to foliage within exclosures exceeded that of controls but differences were not significant. We also did not detect significant treatment effects on sapling shoot growth. The high understory vegetation density relative to bird abundance, and low rate of herbivory during the study (mean 5% leaf area removed, controls), may have limited the ability of birds to affect sapling growth. Climate effects operated at multiple scales, resulting in a complex interplay of interactions within the food web. Regional synchrony of climatic conditions resulted in annual fluctuations in herbivore abundance and tree growth that were shared across elevations. At the same time, local environmental variation resulted in site differences in the plant, herbivore, and bird communities. These patterns resulted in a mosaic of top–down strengths across time and space, suggesting an overall pattern of limited effects of birds on plant growth, possibly interspersed with hotspots of trophic cascades.     

基于DNA分子标记数据构建系统进化树的新策略

结合DPS软件和MEGA软件优点,进行DNA分子标记数据处理和系统发育树构建的新策略：首先使用DPS软件进行0,1数据系统聚类方法获得遗传距离矩阵,然后将此矩阵输入MEGA3,利用NJ或者UPGMA进行系统进化树的构建和树的优化。该方法操作简单,得到的树形美观。     

Application of Bayesian networks on large-scale biological data

The investigation of the interplay between genes, proteins, metabolites and diseases plays a central role in molecular and cellular biology. Whole genome sequencing has made it possible to examine the behavior of all the genes in a genome by high-throughput experimental techniques and to pinpoint molecular interactions on a genome-wide scale, which form the backbone of systems biology. In particular, Bayesian network (BN) is a powerful tool for the ab-initial identification of causal and non-causal relationships between biological factors directly from experimental data. However, scalability is a crucial issue when we try to apply BNs to infer such interactions. In this paper, we not only introduce the Bayesian network formalism and its applications in systems biology, but also review recent technical developments for scaling up or speeding up the structural learning of BNs, which is important for the discovery of causal knowledge from large-scale biological datasets. Specifically, we highlight the basic idea, relative pros and cons of each technique and discuss possible ways to combine different algorithms towards making BN learning more accurate and much faster.     

Detection and application of novel SSR markers from transcriptome data for Astronium fraxinifolium Schott,a threatened Brazilian tree species

Molecular Biology Reports - Astronium fraxinifolium is an endangered tree species from Brazil. Due to its significance in environmental reforestation, as well as the continued exploitation of its...     

Evaluation of light regulatory potential of Calvin cycle steps based on large-scale gene expression profiling data

Although large-scale gene expression data have been studied from many perspectives, they have not been systematically integrated to infer the regulatory potentials of individual genes in specific pathways. Here we report the analysis of expression patterns of genes in the Calvin cycle from 95 Arabidopsis microarray experiments, which revealed a consistent gene regulation pattern in most experiments. This identified pattern, likely due to gene regulation by light rather than feedback regulations of the metabolite fluxes in the Calvin cycle, is remarkably consistent with the rate-limiting roles of the enzymes encoded by these genes reported from both experimental and modeling approaches. Therefore, the regulatory potential of the genes in a pathway may be inferred from their expression patterns. Furthermore, gene expression analysis in the context of a known pathway helps to categorize various biological perturbations that would not be recognized with the prevailing methods.