Data-Mining Discovery of Pattern and Process in Ecological Systems

ABSTRACT Most ecologists use statistical methods as their main analytical tools when analyzing data to identify relationships between a response and a set of predictors; thus, they treat all analyses as hypothesis tests or exercises in parameter estimation. However, little or no prior knowledge about a system can lead to creation of a statistical model or models that do not accurately describe major sources of variation in the response variable. We suggest that under such circumstances data mining is more appropriate for analysis. In this paper we 1) present the distinctions between data-mining (usually exploratory) analyses and parametric statistical (confirmatory) analyses, 2) illustrate 3 strengths of data-mining tools for generating hypotheses from data, and 3) suggest useful ways in which data mining and statistical analyses can be integrated into a thorough analysis of data to facilitate rapid creation of accurate models and to guide further research.     

Effects of Citrus unshiu Powder on the Cytokine Balance in Peripheral Blood Mononuclear Cells of Patients with Seasonal Allergic Rhinitis to Pollen

We evaluated the effects of a 50% methanol extract of Citrus unshiu powder (MEC) on cytokines in peripheral blood mononuclear cells (PBMCs) obtained from patients with seasonal allergic rhinitis to cedar pollen. The levels of cytokines, such as TNF-α, IFN-γ, IL-2, IL-4, IL-5, IL-10, IL-12 (p70), IL-13, and GM-CSF, produced by pollen-stimulated PBMC were measured. We found that MEC suppressed pollen-induced TNF-α release and increased IFN-γ release from PBMCs. The results suggest that Citrus unshiu powder has an immunomodulatory effect in vitro and that its use could improve seasonal allergic rhinitis symptoms.     

Integrated analysis of genetic,genomic and proteomic data

The rapid expansion of methods for measuring biological data ranging from DNA sequence variations to mRNA expression and protein abundance presents the opportunity to utilize multiple types of information jointly in the study of human health and disease. Organisms are complex systems that integrate inputs at myriad levels to arrive at an observable phenotype. Therefore, it is essential that questions concerning the etiology of phenotypes as complex as common human diseases take the systemic nature of biology into account, and integrate the information provided by each data type in a manner analogous to the operation of the body itself. While limited in scope, the initial forays into the joint analysis of multiple data types have yielded interesting results that would not have been reached had only one type of data been considered. These early successes, along with the aforementioned theoretical appeal of data integration, provide impetus for the development of methods for the parallel, high-throughput analysis of multiple data types. The idea that the integrated analysis of multiple data types will improve the identification of biomarkers of clinical endpoints, such as disease susceptibility, is presented as a working hypothesis.     

Nuclear DNA content and separation of Nicotiana sylvestris vegetative and generative nuclei at various stages of male gametogenesis

At all stages of male gametogenesis, generative and vegetative pollen nuclei of Nicotiana sylvestris can be distinguished without ambiguity after Feulgen or ethidium bromide staining. They differ by their morphology and their apparent DNA content, always lower in vegetative nuclei. These differences provide a basis for their separation by sedimentation and fluorometry. After elimination of the another somatic cells and after crushing the pollen, vegetative and generative nuclei are separated by two successive Percoll gradients (purity 80–90%). Analysis of the gradient fractions and final purification can be done with a cell sorter. DNAs of both types are isolated by a cetyltrimethylammonium method, followed by a RNase treatment. Yields are lower for vegetative than for generative nuclei, and decrease with the age of pollen. Molecular weights and digestibility by restriction enzymes are compatible with molecular analyses.     

Pea Lectin Expressed Transgenically in Oilseed Rape Reduces Growth Rate of Pollen Beetle Larvae

In several studies plant lectins have shown promise as transgenic resistance factors against various insect pests. We have here shown that pea seed lectin is a potential candidate for use against pollen beetle, a serious pest of Brassica oilseeds. In feeding assays where pollen beetle larvae were fed oilseed rape anthers soaked in a 1% solution of pea lectin there was a reduction in survival of 84% compared to larvae on control treatment and the weight of surviving larvae was reduced by 79%. When a 10% solution of pea lectin was used all larvae were dead after 4 days of testing. To further evaluate the potential use of pea lectin, transgenic plants of oilseed rape (Brassica napus cv. Westar) were produced in which the pea lectin gene under control of the pollen-specific promoter Sta44-4 was introduced. In 11 out of 20 tested plants of the T₀-generation there was a significant reduction in larval weight, which ranged up to 46% compared to the control. A small but significant reduction in larval survival rate was also observed. In the T₂-generation significant weight reductions, with a maximum of 32%, were obtained in 10 out of 33 comparisons between transgenic plants and their controls. Pea lectin concentrations in anthers of transgenic T₂-plants ranged up to 1.5% of total soluble protein. There was a negative correlation between lectin concentration and larval growth. Plants from test groups with significant differences in larval weights had a significantly higher mean pea lectin concentration, 0.64% compared to 0.15% for plants from test groups without effect on larval weight. These results support the conclusion that pea lectin is a promising resistance factor for use in Brassica oilseeds against pollen beetles.     

Glacial refugia of temperate, Mediterranean and Ibero-North African flora in south-eastern Spain: new evidence from cave pollen at two Neanderthal man sites

Aim To locate glacial refugia of thermophilous plant species in Spain. Location Two south‐eastern Spanish Neanderthal man sites in Murcia; namely, the inland Cueva Negra del Estrecho del Río Quípar and the coastal Sima de las Palomas del Cabezo Gordo. Methods We use pollen found in cave sediments as a source of palaeobotanical and palaeoecological information. The findings are discussed with regard both to animal remains from both sites, and also to other refugia in south‐eastern Spain and elsewhere in the Iberian Peninsula. Results Both sequences show persistence of abundant mesothermophilous trees during the last glacial stage, suggesting both localities were reservoirs of phytodiversity and woodland species. At both sites, deciduous and evergreen oaks are the most abundant components, followed by a wide variety of deciduous trees and sclerophyllous shrubs, including Ibero‐North African xerothermic scrub near the coast. Conclusions Incomplete information underlies a common misapprehension that Iberian glacial refugia were confined to southernmost parts of the peninsula. A rather different picture of Quaternary refugia emerges from consideration of pollen sequences from caves (and other inputs such as macroscopic charcoal, spatial genetic structure of present‐day populations, faunal remains, and present‐day distribution of thermophilous species). This picture offers a view of numerous viable areas for woodland species in southern Spain, in addition to others in the mountain ranges, both in continental central Spain and those of northern Spain: these stretch from the Mediterranean coast of Catalonia to the westernmost extent of the Bay of Biscay.     

Molecules, development and fossils in the study of metazoan evolution; Articulata versus Ecdysozoa revisited

Two conflicting hypotheses of protostome relationships, Articulata and Ecdysozoa, are reviewed by evaluating the evidence in favor and against each one of them. Understanding early embryonic development and segmentation in non-arthropod non-annelid protostomes seems crucial to the debate. New ways of coding metazoan matrices, avoiding ground-patterns and higher taxa, and incorporating fossil evidence seems the best way to avoid circular debates. Molecular data served as the catalyzer for the Ecdysozoa hypothesis, although morphological support had been implicitly suggested. Most molecular analyses published so far have shown some support for Ecdysozoa, whereas none has ever supported Articulata. Here, new analyses of up to four nuclear loci, including 18S rRNA, myosin heavy chain II, histone H3 and elongation factor 1- are conducted to test the molecular support for Ecdysozoa, and, at least under some parameter sets, most data sets show a clade formed by the molting animals. In contrast, support for Articulata is not found under any analytical conditions.     

CellDepot: A Unified Repository for scRNA-seq Data and Visual Exploration

CellDepot containing over 270 datasets from 8 species and many tissues serves as an integrated web application to empower scientists in exploring single-cell RNA-seq (scRNA-seq) datasets and comparing the datasets among various studies through a user-friendly interface with advanced visualization and analytical capabilities. To begin with, it provides an efficient data management system that users can upload single cell datasets and query the database by multiple attributes such as species and cell types. In addition, the graphical multi-logic, multi-condition query builder and convenient filtering tool backed by MySQL database system, allows users to quickly find the datasets of interest and compare the expression of gene(s) across these. Moreover, by embedding the cellxgene VIP tool, CellDepot enables fast exploration of individual dataset in the manner of interactivity and scalability to gain more refined insights such as cell composition, gene expression profiles, and differentially expressed genes among cell types by leveraging more than 20 frequently applied plotting functions and high-level analysis methods in single cell research. In summary, the web portal available at http://celldepot.bxgenomics.com, prompts large scale single cell data sharing, facilitates meta-analysis and visualization, and encourages scientists to contribute to the single-cell community in a tractable and collaborative way. Finally, CellDepot is released as open-source software under MIT license to motivate crowd contribution, broad adoption, and local deployment for private datasets.     

Integrating multi-platform genomic data using hierarchical Bayesian relevance vector machines

Background

Recent advances in genome technologies and the subsequent collection of genomic information at various molecular resolutions hold promise to accelerate the discovery of new therapeutic targets. A critical step in achieving these goals is to develop efficient clinical prediction models that integrate these diverse sources of high-throughput data. This step is challenging due to the presence of high-dimensionality and complex interactions in the data. For predicting relevant clinical outcomes, we propose a flexible statistical machine learning approach that acknowledges and models the interaction between platform-specific measurements through nonlinear kernel machines and borrows information within and between platforms through a hierarchical Bayesian framework. Our model has parameters with direct interpretations in terms of the effects of platforms and data interactions within and across platforms. The parameter estimation algorithm in our model uses a computationally efficient variational Bayes approach that scales well to large high-throughput datasets.

Results

We apply our methods of integrating gene/mRNA expression and microRNA profiles for predicting patient survival times to The Cancer Genome Atlas (TCGA) based glioblastoma multiforme (GBM) dataset. In terms of prediction accuracy, we show that our non-linear and interaction-based integrative methods perform better than linear alternatives and non-integrative methods that do not account for interactions between the platforms. We also find several prognostic mRNAs and microRNAs that are related to tumor invasion and are known to drive tumor metastasis and severe inflammatory response in GBM. In addition, our analysis reveals several interesting mRNA and microRNA interactions that have known implications in the etiology of GBM.

Conclusions

Our approach gains its flexibility and power by modeling the non-linear interaction structures between and within the platforms. Our framework is a useful tool for biomedical researchers, since clinical prediction using multi-platform genomic information is an important step towards personalized treatment of many cancers. We have a freely available software at: http://odin.mdacc.tmc.edu/~vbaladan.

     

Seed hairs of poplar trees as natural airborne pollen trap for allergenic pollen grains

The present article deals with the efficacy of seed hairs of poplar trees (Populus spp.) as a potent natural airborne pollen trap. Different species of Populus are commonly found planted along the streets in the cities of North China. The seed hairs and pericarp of poplar trees were collected from the trees and on the ground in Beijing Botanical Garden of Chinese Academy of Sciences and around Miyun Reservoir during May 2005 for pollen analysis. Different pollen spectra are recorded from different samples and are characterised by dominant occurrence of pollen grains of arboreal and anemophilous plants. In addition, pollen grains of non‐arboreal plants including grasses are also found trapped. Among the 46 trapped pollen grains, 26 are known to be allergenic. This study suggests that poplar seed hairs possibly make people feel uncomfortable due to the presence of allergenic pollen trapped in the hairs.