A Multiscale Vibrational Spectroscopic Approach for Identification and Biochemical Characterization of Pollen

Background

Analysis of pollen grains reveals valuable information on biology, ecology, forensics, climate change, insect migration, food sources and aeroallergens. Vibrational (infrared and Raman) spectroscopies offer chemical characterization of pollen via identifiable spectral features without any sample pretreatment. We have compared the level of chemical information that can be obtained by different multiscale vibrational spectroscopic techniques.

Methodology

Pollen from 15 different species of Pinales (conifers) were measured by seven infrared and Raman methodologies. In order to obtain infrared spectra, both reflectance and transmission measurements were performed on ground and intact pollen grains (bulk measurements), in addition, infrared spectra were obtained by microspectroscopy of multigrain and single pollen grain measurements. For Raman microspectroscopy measurements, spectra were obtained from the same pollen grains by focusing two different substructures of pollen grain. The spectral data from the seven methodologies were integrated into one data model by the Consensus Principal Component Analysis, in order to obtain the relations between the molecular signatures traced by different techniques.

Results

The vibrational spectroscopy enabled biochemical characterization of pollen and detection of phylogenetic variation. The spectral differences were clearly connected to specific chemical constituents, such as lipids, carbohydrates, carotenoids and sporopollenins. The extensive differences between pollen of Cedrus and the rest of Pinaceae family were unambiguously connected with molecular composition of sporopollenins in pollen grain wall, while pollen of Picea has apparently higher concentration of carotenoids than the rest of the family. It is shown that vibrational methodologies have great potential for systematic collection of data on ecosystems and that the obtained phylogenetic variation can be well explained by the biochemical composition of pollen. Out of the seven tested methodologies, the best taxonomical differentiation of pollen was obtained by infrared measurements on bulk samples, as well as by Raman microspectroscopy measurements of the corpus region of the pollen grain. Raman microspectroscopy measurements indicate that measurement area, as well as the depth of focus, can have crucial influence on the obtained data.     

A Standardized Reference Data Set for Vertebrate Taxon Name Resolution

Taxonomic names associated with digitized biocollections labels have flooded into repositories such as GBIF, iDigBio and VertNet. The names on these labels are often misspelled, out of date, or present other problems, as they were often captured only once during accessioning of specimens, or have a history of label changes without clear provenance. Before records are reliably usable in research, it is critical that these issues be addressed. However, still missing is an assessment of the scope of the problem, the effort needed to solve it, and a way to improve effectiveness of tools developed to aid the process. We present a carefully human-vetted analysis of 1000 verbatim scientific names taken at random from those published via the data aggregator VertNet, providing the first rigorously reviewed, reference validation data set. In addition to characterizing formatting problems, human vetting focused on detecting misspelling, synonymy, and the incorrect use of Darwin Core. Our results reveal a sobering view of the challenge ahead, as less than 47% of name strings were found to be currently valid. More optimistically, nearly 97% of name combinations could be resolved to a currently valid name, suggesting that computer-aided approaches may provide feasible means to improve digitized content. Finally, we associated names back to biocollections records and fit logistic models to test potential drivers of issues. A set of candidate variables (geographic region, year collected, higher-level clade, and the institutional digitally accessible data volume) and their 2-way interactions all predict the probability of records having taxon name issues, based on model selection approaches. We strongly encourage further experiments to use this reference data set as a means to compare automated or computer-aided taxon name tools for their ability to resolve and improve the existing wealth of legacy data.     

A Common Data Set and Framework for Representing Spatial Location Information in the Internet

Many organizations are currently working on how to express and provide location information to services and applications in the Internet. Each of them basically specifies their own way. This raises a problem – the various location information formats, services and applications will not be interoperable in the Internet. Interoperability can be achieved if there is a common way of expressing location information. This paper therefore proposes a common data set and an extensible framework of expressing location information in the Internet. The design aims at bridging various existing/proposed location data representation formats, as well as meeting the requirements of existing/proposed location-aware services.     

A Hybrid Technique for the Periodicity Characterization of Genomic Sequence Data

Many studies of biological sequence data have examined sequence structure in terms of periodicity, and various methods for measuring periodicity have been suggested for this purpose. This paper compares two such methods, autocorrelation and the Fourier transform, using synthetic periodic sequences, and explains the differences in periodicity estimates produced by each. A hybrid autocorrelation—integer period discrete Fourier transform is proposed that combines the advantages of both techniques. Collectively, this representation and a recently proposed variant on the discrete Fourier transform offer alternatives to the widely used autocorrelation for the periodicity characterization of sequence data. Finally, these methods are compared for various tetramers of interest in C. elegans chromosome I.     

A Joint Matrix Completion and Filtering Model for Influenza Serological Data Integration

Antigenic characterization based on serological data, such as Hemagglutination Inhibition (HI) assay, is one of the routine procedures for influenza vaccine strain selection. In many cases, it would be impossible to measure all pairwise antigenic correlations between testing antigens and reference antisera in each individual experiment. Thus, we have to combine and integrate the HI tables from a number of individual experiments. Measurements from different experiments may be inconsistent due to different experimental conditions. Consequently we will observe a matrix with missing data and possibly inconsistent measurements. In this paper, we develop a new mathematical model, which we refer to as Joint Matrix Completion and Filtering, for HI data integration. In this approach, we simultaneously handle the incompleteness and uncertainty of observations by assuming that the underlying merged HI data matrix has low rank, as well as carefully modeling different levels of noises in each individual table. An efficient blockwise coordinate descent procedure is developed for optimization. The performance of our approach is validated on synthetic and real influenza datasets. The proposed joint matrix completion and filtering model can be adapted as a general model for biological data integration, targeting data noises and missing values within and across experiments.     

槭树科植物广义形态学性状分支分析

通过45个广义的形态学性状对槭树科(Aceraceae)尤其是槭属(Acer L.)的主要类群做了分支分析,结果显示：1)槭属内由于各类群分布着较多的同塑性状状态,而难以为属下组间关系的解决提供更多有价值的信息;通过对具体的性状状态分布分析显示,对于象槭属这样在形态上分化较大的类群,由于多数分类性状在不同类群间经历了平行和逆转演化,因而在较低分类阶元水平很难选择合适的性状来通过分支分析构建其系统发育;2)鸡爪槭组(section Palmata)作为整个槭属的基部类群,虽然支持率较低,但与其它类群相比在槭属内维持了较多的原始性状;3)金钱槭属(Dipteronia Oliv.)的两个种作为单系得到了100％的靴带支持,且和槭属作为姐妹群也得到了较好的支持。     

Cherry Picking: A Characterization of the Temporal Hybridization Number for a Set of Phylogenies

Recently, we have shown that calculating the minimum–temporal-hybridization number for a set ${\mathcal{P}}$ of rooted binary phylogenetic trees is NP-hard and have characterized this minimum number when ${\mathcal{P}}$ consists of exactly two trees. In this paper, we give the first characterization of the problem for ${\mathcal{P}}$ being arbitrarily large. The characterization is in terms of cherries and the existence of a particular type of sequence. Furthermore, in an online appendix to the paper, we show that this new characterization can be used to show that computing the minimum–temporal hybridization number for two trees is fixed-parameter tractable.     

An Environmental Data Set for Vector-Borne Disease Modeling and Epidemiology

Understanding the environmental conditions of disease transmission is important in the study of vector-borne diseases. Low- and middle-income countries bear a significant portion of the disease burden; but data about weather conditions in those countries can be sparse and difficult to reconstruct. Here, we describe methods to assemble high-resolution gridded time series data sets of air temperature, relative humidity, land temperature, and rainfall for such areas; and we test these methods on the island of Madagascar. Air temperature and relative humidity were constructed using statistical interpolation of weather station measurements; the resulting median 95^th percentile absolute errors were 2.75°C and 16.6%. Missing pixels from the MODIS11 remote sensing land temperature product were estimated using Fourier decomposition and time-series analysis; thus providing an alternative to the 8-day and 30-day aggregated products. The RFE 2.0 remote sensing rainfall estimator was characterized by comparing it with multiple interpolated rainfall products, and we observed significant differences in temporal and spatial heterogeneity relevant to vector-borne disease modeling.     

A Joint Modeling Approach for Longitudinal Data with Informative Observation Times and a Terminal Event

In this article, we propose a new joint modeling approach for the analysis of longitudinal data with informative observation times and a dependent terminal event. We specify a semiparametric mixed effects model for the longitudinal process, a proportional rate frailty model for the observation process, and a proportional hazards frailty model for the terminal event. The association among the three related processes is modeled via two latent variables. Estimating equation approaches are developed for parameter estimation, and the asymptotic properties of the proposed estimators are established. The finite sample performance of the proposed estimators is examined through simulation studies, and an application to a medical cost study of chronic heart failure patients is illustrated.     

Tibiofemoral kinematics and condylar motion during the stance phase of gait

Accurate knowledge of the dynamic knee motion in-vivo is instrumental for understanding normal and pathological function of the knee joint. However, interpreting motion of the knee joint during gait in other than the sagittal plane remains controversial. In this study, we utilized the dual fluoroscopic imaging technique to investigate the six-degree-of-freedom kinematics and condylar motion of the knee during the stance phase of treadmill gait in eight healthy volunteers at a speed of 0.67 m/s. We hypothesized that the 6DOF knee kinematics measured during gait will be different from those reported for non-weightbearing activities, especially with regards to the phenomenon of femoral rollback. In addition, we hypothesized that motion of the medial femoral condyle in the transverse plane is greater than that of the lateral femoral condyle during the stance phase of treadmill gait. The rotational motion and the anterior–posterior translation of the femur with respect to the tibia showed a clear relationship with the flexion–extension path of the knee during the stance phase. Additionally, we observed that the phenomenon of femoral rollback was reversed, with the femur noted to move posteriorly with extension and anteriorly with flexion. Furthermore, we noted that motion of the medial femoral condyle in the transverse plane was greater than that of the lateral femoral condyle during the stance phase of gait (17.4±2.0 mm vs. 7.4±6.1 mm, respectively; p<0.01). The trend was opposite to what has been observed during non-weightbearing flexion or single-leg lunge in previous studies. These data provide baseline knowledge for the understanding of normal physiology and for the analysis of pathological function of the knee joint during walking. These findings further demonstrate that knee kinematics is activity-dependent and motion patterns of one activity (non-weightbearing flexion or lunge) cannot be generalized to interpret a different one (gait).     

Relationship Between Structure and Mechanical Function of the Tissues of the Intervertebral Joint

This paper reviews our current understanding of the relationshipbetween the structures and properties of the tissues of thespine and their mechanical functions. Emphasis is on the humanlumbar spine. Vertebrae consist of a core of cancellous bone(low density) surrounded by a shell of cortical bone (high stiffness);as a result they have high stiffness but low mass. The intervertebraldisc is able to withstand compression because of the swellingpressure exerted by the nucleus pulposus which is constrained,radially, by the annulus fibrosus. Thus the disc acts as a thick-walledpressure vessel. Collagen fibers within the annulus providereinforcement during compression, bending and torsion of thedisc. Collagen fibers also provide tensile reinforcement andprevent tears spreading across ligaments. The ligamenta flavacontain elastic fibers (low stiffness and low strength) withcollagen fibers (high stiffness and high strength). In the unstretchedligamenta flava, the collagen fibers have almost random orientationsbut they become aligned as the ligament is stretched. This structureenables the high extensibility of elastic fibers to be exploitedbut protects them from damage at high strains. The structureof the interspinous ligament suggests that its main functionis to attach the thoracolumbar fascia to the posterior spine.Thus the fascia is maintained in tension when stretched by theabdominal muscles. This and other observations indicate theimportance of muscles for maintaining the stability of the spinalcolumn.     

MENGA: A New Comprehensive Tool for the Integration of Neuroimaging Data and the Allen Human Brain Transcriptome Atlas

Introduction

Brain-wide mRNA mappings offer a great potential for neuroscience research as they can provide information about system proteomics. In a previous work we have correlated mRNA maps with the binding patterns of radioligands targeting specific molecular systems and imaged with positron emission tomography (PET) in unrelated control groups. This approach is potentially applicable to any imaging modality as long as an efficient procedure of imaging-genomic matching is provided. In the original work we considered mRNA brain maps of the whole human genome derived from the Allen human brain database (ABA) and we performed the analysis with a specific region-based segmentation with a resolution that was limited by the PET data parcellation. There we identified the need for a platform for imaging-genomic integration that should be usable with any imaging modalities and fully exploit the high resolution mapping of ABA dataset.

Aim

In this work we present MENGA (Multimodal Environment for Neuroimaging and Genomic Analysis), a software platform that allows the investigation of the correlation patterns between neuroimaging data of any sort (both functional and structural) with mRNA gene expression profiles derived from the ABA database at high resolution.

Results

We applied MENGA to six different imaging datasets from three modalities (PET, single photon emission tomography and magnetic resonance imaging) targeting the dopamine and serotonin receptor systems and the myelin molecular structure. We further investigated imaging-genomic correlations in the case of mismatch between selected proteins and imaging targets.     

SIMPLEX: Cloud-Enabled Pipeline for the Comprehensive Analysis of Exome Sequencing Data

In recent studies, exome sequencing has proven to be a successful screening tool for the identification of candidate genes causing rare genetic diseases. Although underlying targeted sequencing methods are well established, necessary data handling and focused, structured analysis still remain demanding tasks. Here, we present a cloud-enabled autonomous analysis pipeline, which comprises the complete exome analysis workflow. The pipeline combines several in-house developed and published applications to perform the following steps: (a) initial quality control, (b) intelligent data filtering and pre-processing, (c) sequence alignment to a reference genome, (d) SNP and DIP detection, (e) functional annotation of variants using different approaches, and (f) detailed report generation during various stages of the workflow. The pipeline connects the selected analysis steps, exposes all available parameters for customized usage, performs required data handling, and distributes computationally expensive tasks either on a dedicated high-performance computing infrastructure or on the Amazon cloud environment (EC2). The presented application has already been used in several research projects including studies to elucidate the role of rare genetic diseases. The pipeline is continuously tested and is publicly available under the GPL as a VirtualBox or Cloud image at http://simplex.i-med.ac.at; additional supplementary data is provided at http://www.icbi.at/exome.     

A Predictive Framework for Integrating Disparate Genomic Data Types Using Sample-Specific Gene Set Enrichment Analysis and Multi-Task Learning

Understanding the root molecular and genetic causes driving complex traits is a fundamental challenge in genomics and genetics. Numerous studies have used variation in gene expression to understand complex traits, but the underlying genomic variation that contributes to these expression changes is not well understood. In this study, we developed a framework to integrate gene expression and genotype data to identify biological differences between samples from opposing complex trait classes that are driven by expression changes and genotypic variation. This framework utilizes pathway analysis and multi-task learning to build a predictive model and discover pathways relevant to the complex trait of interest. We simulated expression and genotype data to test the predictive ability of our framework and to measure how well it uncovered pathways with genes both differentially expressed and genetically associated with a complex trait. We found that the predictive performance of the multi-task model was comparable to other similar methods. Also, methods like multi-task learning that considered enrichment analysis scores from both data sets found pathways with both genetic and expression differences related to the phenotype. We used our framework to analyze differences between estrogen receptor (ER) positive and negative breast cancer samples. An analysis of the top 15 gene sets from the multi-task model showed they were all related to estrogen, steroids, cell signaling, or the cell cycle. Although our study suggests that multi-task learning does not enhance predictive accuracy, the models generated by our framework do provide valuable biological pathway knowledge for complex traits.     

Meta-QC-Chain: Comprehensive and Fast Quality Control Method for Metagenomic Data

Next-generation sequencing(NGS) technology has revolutionized and significantly impacted metagenomic research.However,the NGS data usually contains sequencing artifacts such as low-quality reads and contaminating reads,which will significantly compromise downstream analysis.Many quality control(QC) tools have been proposed,however,few of them have been verified to be suitable or efficient for metagenomic data,which are composed of multiple genomes and are more complex than other kinds of NGS data.Here we present a metagenomic data QC method named Meta-QC-Chain.Meta-QC-Chain combines multiple QC functions:technical tests describe input data status and identify potential errors,quality trimming filters poor sequencing-quality bases and reads,and contamination screening identifies higher eukaryotic species,which are considered as contamination for metagenomic data.Most computing processes are optimized based on parallel programming.Testing on an 8-GB real dataset showed that Meta-QC-Chain trimmed low sequencing-quality reads and contaminating reads,and the whole quality control procedure was completed within 20 min.Therefore,Meta-QC-Chain provides a comprehensive,useful and high-performance QC tool for metagenomic data.Meta-QC-Chain is publicly available for free at:http://computationalbioenergy.org/meta-qc-chain.html.     

Development of Multiscale Biological Image Data Analysis: Review of 2006 International Workshop on Multiscale Biological Imaging,Data Mining and Informatics,Santa Barbara,USA (BII06)

The 2006 International Workshop on Multiscale Biological Imaging, Data Mining and Informatics was held at Santa Barbara, on Sept 7-8, 2006. Based on the presentations at the workshop, we selected and compiled this collection of research articles related to novel algorithms and enabling techniques for bio- and biomedical image analysis, mining, visualization, and biology applications.