Imaging spectrometer fundamentals for researchers in the biosciences--a tutorial.

Over the last 2 years there has been a dramatic increase in the number of bioscience laboratories using wavelength dispersive spectroscopy to study in vivo, in situ fluorescence. Transforming spectral information into an image provides a graphic means of mapping localized ionic, molecular, and protein-protein interactions. Spectroscopy also enables fluorophores with overlapping spectral features to be delineation. In this study, we provide the tools that a researcher needs to put into perspective instrumental contributions to a reported spectrum in order to gain greater understanding of the natural emission of the sample. We also show how to deduce the basic capabilities of a spectral confocal system. Finally, we show how to determine the true spectral bandwidth of an object, the illuminated area of a laser-excited object, and what is needed to optimize light throughput.     

Automatic figure ranking and user interfacing for intelligent figure search

Background

Figures are important experimental results that are typically reported in full-text bioscience articles. Bioscience researchers need to access figures to validate research facts and to formulate or to test novel research hypotheses. On the other hand, the sheer volume of bioscience literature has made it difficult to access figures. Therefore, we are developing an intelligent figure search engine (http://figuresearch.askhermes.org). Existing research in figure search treats each figure equally, but we introduce a novel concept of “figure ranking”: figures appearing in a full-text biomedical article can be ranked by their contribution to the knowledge discovery.

Methodology/Findings

We empirically validated the hypothesis of figure ranking with over 100 bioscience researchers, and then developed unsupervised natural language processing (NLP) approaches to automatically rank figures. Evaluating on a collection of 202 full-text articles in which authors have ranked the figures based on importance, our best system achieved a weighted error rate of 0.2, which is significantly better than several other baseline systems we explored. We further explored a user interfacing application in which we built novel user interfaces (UIs) incorporating figure ranking, allowing bioscience researchers to efficiently access important figures. Our evaluation results show that 92% of the bioscience researchers prefer as the top two choices the user interfaces in which the most important figures are enlarged. With our automatic figure ranking NLP system, bioscience researchers preferred the UIs in which the most important figures were predicted by our NLP system than the UIs in which the most important figures were randomly assigned. In addition, our results show that there was no statistical difference in bioscience researchers'' preference in the UIs generated by automatic figure ranking and UIs by human ranking annotation.

Conclusion/Significance

The evaluation results conclude that automatic figure ranking and user interfacing as we reported in this study can be fully implemented in online publishing. The novel user interface integrated with the automatic figure ranking system provides a more efficient and robust way to access scientific information in the biomedical domain, which will further enhance our existing figure search engine to better facilitate accessing figures of interest for bioscientists.     

A novel quantitative measure of image quality in fluoroscopy

Assessment of fluoroscopic image quality has not kept pace with technological developments in interventional imaging equipment. Access to ‘for presentation’ data on these systems has motivated this investigation into a novel quantitative method of measuring image quality. We have developed a statistical algorithm as an alternative to subjective assessment using threshold contrast detail detectability techniques. Using sets of uniformity exposed fluoroscopy frames, the algorithm estimates the minimum contrast necessary for conspicuity of a range of virtual target object areas A. Pixel mean value distributions in a central image region are Gaussian, with standard deviation σ Pixel binning produces background distributions with area A. For 95% confidence of conspicuity a target object must exhibit a minimum contrast of 3.29σ. A range of threshold contrasts are calculated for a range of virtual areas. Analysis on a few seconds of fluoroscopy data is performed remotely and no test object is required. In this study Threshold Index and Contrast Detail curves were calculated for different incident air kerma rates at the detector, different levels of electronic magnification and different types of image processing. A limited number of direct comparisons were made with subjective assessments using the Leeds TO.10 test object. Results obtained indicate that the statistical algorithm is not only more sensitive to changes in levels of detector dose rate and magnification, but also to levels of image processing, including edge-enhancement. Threshold Index curves thus produced could be used as an interventional system optimisation tool and to objectively compare image quality between vendor systems.     

Migration of isogenic cell lines quantified by dynamic multivariate analysis of single-cell motility

Cell migration is essential in many physiological and pathological processes. To understand this complex behavior, researchers have turned to quantitative, in vitro, image-based measurements to dissect the steps of cellular motility. With the rise of automated microscopy, the bottleneck in these approaches is no longer data acquisition, but data analysis. Using time-lapse microscopy and computer-assisted image analysis, we have developed a novel, quantitative assay that extracts a multivariate profile for cellular mo-tility. This technique measures three dynamic parameters per single cell: speed, surface area, and an in-dex of cell expansion/contraction activity (DECCA). Our assay can be used in combination with a variety of extracellular matrix components, or other soluble agents, to analyze the effects of the microenviron-ment on cellular migration dynamics in vitro. Our application was developed and tested using A431 and HT-1080 cell lines plated on laminin-332 or fibronectin substrates. Our results indicate that HT-1080 cells migrate faster, have a greater surface area, and have a higher DECCA index than A431 cells on both matrices (for all parameters, p < 0.05). Spearman’s correlation coefficients suggest that for these cell lines and matrices, various combinations of the three measurements display low to medium-high levels of correlation. These findings compare well with previous literature. Our approach provides new tools to measure cellular migration dynamics and address questions on the relationship between cell motility and the microenvironment, using only common microscopy techniques, accessible image analysis applica-tions, and a basic desktop computer for image processing.     

Objective quality evaluation of fluorescence images to optimize automatic image acquisition.

BACKGROUND: Because different spectral sensitivities of human eye and image sensor lead to different perception of fluorescence signals, data generation is an important step in image analysis, because following work steps depend on it. METHODS: We developed a method to determine image parameters allowing an objective appraisal of quality of image data as well as a separation of object and background. RESULTS: Calculated parameters can be used for an automated adjustment of camera parameters in image analysis systems. DISCUSSION: Our approach for objective adjusted data generation achieves an improvement of analysis quality.     

Outgroup heteroduplex analysis using temperature gradient gel electrophoresis: high resolution, large scale, screening of DNA variation in the mitochondrial control region

The ability of DNA screening techniques such as Temperature Gradient Gel Electrophoresis (TGGE) and Heteroduplex Analysis to provide resolution approaching that provided by DNA sequencing for a fraction of the time, effort and expense point to them as the logical successor to allozyme electrophoresis for population genetics. Here we present a novel alternative to the standard TGGE/Heteroduplex Analysis protocol - Outgroup Heteroduplex Analysis (OHA). We assess this technique's sensitivity in comparison to previous screening approaches using a known hierarchy of sequence differences. Our data show that Outgroup Heteroduplex Analysis has greatly increased sensitivity for screening DNA variants from that of TGGE used alone and is easily applicable to large numbers of samples. Using this technique we can consistently detect differences of as small as one base change in a 433-base-pair fragment of Control Region mitochondrial DNA from Melomys cerbinipes (an Australian rodent). The approach should easily be extendable to nuclear loci and is not necessarily dependent on the use of a denaturing gradient When combined with a targeted sequencing effort, OHA provides a sensitive and simple means of obtaining allele/haplotype frequencies and their phylogenies for population and phylogeographic studies in molecular ecology.     

A Method for the Evaluation of Image Quality According to the Recognition Effectiveness of Objects in the Optical Remote Sensing Image Using Machine Learning Algorithm

Objective and effective image quality assessment (IQA) is directly related to the application of optical remote sensing images (ORSI). In this study, a new IQA method of standardizing the target object recognition rate (ORR) is presented to reflect quality. First, several quality degradation treatments with high-resolution ORSIs are implemented to model the ORSIs obtained in different imaging conditions; then, a machine learning algorithm is adopted for recognition experiments on a chosen target object to obtain ORRs; finally, a comparison with commonly used IQA indicators was performed to reveal their applicability and limitations. The results showed that the ORR of the original ORSI was calculated to be up to 81.95%, whereas the ORR ratios of the quality-degraded images to the original images were 65.52%, 64.58%, 71.21%, and 73.11%. The results show that these data can more accurately reflect the advantages and disadvantages of different images in object identification and information extraction when compared with conventional digital image assessment indexes. By recognizing the difference in image quality from the application effect perspective, using a machine learning algorithm to extract regional gray scale features of typical objects in the image for analysis, and quantitatively assessing quality of ORSI according to the difference, this method provides a new approach for objective ORSI assessment.     

A semi-localized elastic net for surface reconstruction of objects from multislice images

The traveling salesman problem (TSP) is a prototypical problem of combinatorial optimization and, as such, it has received considerable attention from neural-network researchers seeking quick, heuristic solutions. An early stage in many computer vision tasks is the extraction of object shape from an image consisting of noisy candidate edge points. Since the desired shape will often be a closed contour, this problem can be viewed as a version of the TSP in which we wish to link only a subset of the points/cities (i.e. the "noisefree" ones). None of the extant neural techniques for solving the TSP can deal directly with this case. In this paper, we present a simple but effective modification to the (analog) elastic net of Durbin and Willshaw which shifts emphasis from global to local behavior during convergence, so allowing the net to ignore some image points. Unlike the original elastic net, this semi-localized version is shown to tolerate considerable amounts of noise. As an example practical application, we describe the extraction of "pseudo-3D" human lung outlines from multiple preprocessed magnetic resonance images of the torso. An effectiveness measure (ideally zero) quantifies the difference between the extracted shape and some idealized shape exemplar. Our method produces average effectiveness scores of 0.06 for lung shapes extracted from initial semi-automatic segmentations which define the noisefree case. This deteriorates to 0.1 when extraction is from a noisy edge-point image obtained fully-automatically using a feedforward neural network.     

Some statistical issues in microarray gene expression data

In this paper we discuss some of the statistical issues that should be considered when conducting experiments involving microarray gene expression data. We discuss statistical issues related to preprocessing the data as well as the analysis of the data. Analysis of the data is discussed in three contexts: class comparison, class prediction and class discovery. We also review the methods used in two studies that are using microarray gene expression to assess the effect of exposure to radiofrequency (RF) fields on gene expression. Our intent is to provide a guide for radiation researchers when conducting studies involving microarray gene expression data.     

Sociomics! Using the IssueCrawler to map, monitor and engage with the global proteomics research network

We invite comment upon an experiment to locate proteomics on the WWW using a software tool called the IssueCrawler. We call our research "sociomics" because, like the bioscience omics, it is a semi-automated, computerised approach to the global analysis of data, whose computerised results can be integrated towards the development of a new "systems sociology" approach to the study of society. Our findings are that proteomics on the web is a scale-free network whose nodes display considerable "dynamic range".     

Impact of miscentering on patient dose and image noise in x-ray CT imaging: Phantom and clinical studies

The operation of the bowtie filter in x-ray CT is correct if the object being scanned is properly centered in the scanner’s field-of-view. Otherwise, the dose delivered to the patient and image noise will deviate from optimal setting. We investigate the effect of miscentering on image noise and surface dose on three commercial CT scanners. Six cylindrical phantoms with different size and material were scanned on each scanner. The phantoms were positioned at 0, 2, 4 and 6 cm below the isocenter of the scanner’s field-of-view. Regression models of surface dose and noise were produced as a function of miscentering magnitude and phantom’s size. 480 patients were assessed using the calculated regression models to estimate the influence of patient miscentering on image noise and patient surface dose in seven imaging centers. For the 64-slice CT scanner, the maximum increase of surface dose using the CTDI-32 phantom was 13.5%, 33.3% and 51.1% for miscenterings of 2, 4 and 6 cm, respectively. The analysis of patients’ scout scans showed miscentering of 2.2 cm in average below the isocenter. An average increase of 23% and 7% was observed for patient dose and image noise, respectively. The maximum variation in patient miscentering derived from the comparison of imaging centers using the same scanner was 1.6 cm. Patient miscentering may substantially increase surface dose and image noise. Therefore, technologists are strongly encouraged to pay greater attention to patient centering.     

Identifying the Best-Fitting Factor Structure of the Experience of Close Relations - Revised in a Scandinavian Example

The aim of this study was to enhance the understanding of cultural and sample differences in the assessment of attachment by examining the factor structure of the Experiences in Close Relationships-Revised (ECR-R). The ECR-R is a self-report measure of adult romantic attachment dimensions. The present study used a Danish sample with the purpose of addressing limitations in previous studies, such as the lack of diversity in cultural background, restricted sample characteristics, and poorly fitting structure models. Participants consisted of 253 parents of children between the ages of 7 and 12 years, 53% being mothers. The parents completed the paper version of the questionnaire. Confirmatory Factor Analyses were carried out to determine whether theoretically and empirically established models including one and two factors would also provide adequate fits in a Danish sample. A previous study using the original ECR suggested that Scandinavian samples may best be described using a five-factor solution. Our results indicated that the one- and two-factor models of the ECR-R did not fit the data well. Exploratory Factor Analysis revealed a five-factor model. Our study provides evidence that further investigation is needed to establish which model may provide the best model fit in the Scandinavian countries.     

Data sharing across biobanks: epistemic values,data mutability and data incommensurability

Despite the centrality of epistemic issues in biobank knowledge generation, there is currently a lacuna in research addressing the epistemic assumptions of biobank science and data sharing. The article addresses this lacuna. Using the insights of philosophical and sociological analysis, we examine standardization and harmonization and central challenges biobank data-sharing faces. We defend two central epistemic values, namely “spatial and temporal translatability” and “epistemic adequacy” which foster effective biobank knowledge generation. The first refers to the way in which biobank data need to be re-usable and re-purposable by bioscience researchers who did not create them. Given the perennial issues of data mutability and incommensurability, we also propose “epistemic adequacy.” In uncovering the social and epistemic foundations of biobank science, we emphasize issues essential for achieving effective and transparent biobank practice and productive communication and engagement with the public about the nature, potential and limits of biobanks.     

Object and scene analysis by saccadic eye-movements: an investigation with higher-order statistics

Based on an information theoretical approach, we investigate feature selection processes in saccadic object and scene analysis. Saccadic eye movements of human observers are recorded for a variety of natural and artificial test images. These experimental data are used for a statistical evaluation of the fixated image regions. Analysis of second-order statistics indicates that regions with higher spatial variance have a higher probability to be fixated, but no significant differences beyond these variance effects could be found at the level of power spectra. By contrast, an investigation with higher-order statistics, as reflected in the bispectral density, yielded clear structural differences between the image regions selected by saccadic eye movements as opposed to regions selected by a random process. These results indicate that nonredundant, intrinsically two-dimensional image features like curved lines and edges, occlusions, isolated spots, etc. play an important role in the saccadic selection process which must be integrated with top-down knowledge to fully predict object and scene analysis by human observers.     

利用电子计算机对西双版纳地区遥感图象进行地表景观划分

The earth’s surface landscape classification was obtained by computer analysis of LANDSAT multispectral data. Clustering analysis of spectral data provides an element, repeatable classification based upon spectral characteristics of earth’s surface landscape. The classification procedure repeated until the data are classified into the natural lusters. In this experiment, the clustering algorithm yields 17(0–16) types of earth′s surface landscape by 8 iterations.The accuracy of the classification was verified by a comparison with aerial photography and ground investigation, and the agreement was in the order of 83%. The results from automatic classification were satisfactory.     

BactMAP: An R package for integrating,analyzing and visualizing bacterial microscopy data

High-throughput analyses of single-cell microscopy data are a critical tool within the field of bacterial cell biology. Several programs have been developed to specifically segment bacterial cells from phase-contrast images. Together with spot and object detection algorithms, these programs offer powerful approaches to quantify observations from microscopy data, ranging from cell-to-cell genealogy to localization and movement of proteins. Most segmentation programs contain specific post-processing and plotting options, but these options vary between programs and possibilities to optimize or alter the outputs are often limited. Therefore, we developed BactMAP (Bacterial toolbox for Microscopy Analysis & Plotting), a command-line based R package that allows researchers to transform cell segmentation and spot detection data generated by different programs into various plots. Furthermore, BactMAP makes it possible to perform custom analyses and change the layout of the output. Because BactMAP works independently of segmentation and detection programs, inputs from different sources can be compared within the same analysis pipeline. BactMAP complies with standard practice in R which enables the use of advanced statistical analysis tools, and its graphic output is compatible with ggplot2, enabling adjustable plot graphics in every operating system. User feedback will be used to create a fully automated Graphical User Interface version of BactMAP in the future. Using BactMAP, we visualize key cell cycle parameters in Bacillus subtilis and Staphylococcus aureus, and demonstrate that the DNA replication forks in Streptococcus pneumoniae dissociate and associate before splitting of the cell, after the Z-ring is formed at the new quarter positions. BactMAP is available from https://veeninglab.com/bactmap .     

Microwave imaging of tissue blood content changes

Active microwave imaging gives information on the dielectric properties of the body, allowing the collection of data that are distinct from, but complementary to, those available from other imaging methods based on different radiations. Two types of microwave imaging systems have been developed. The first is a planar system that irradiates the object with a plane wave and collects scattered phase and amplitude data at 1024 points on a parallel plane. The data can be reconstructed using a back propagation technique to give an image of the object. The second type of system is a tomographic scanner, consisting of a multiplexed 64-element circular array of waveguides. The waveguides are electronically scanned, alternately as sources and receivers, to give a complete scan of the object with no mechanical movement. A tomographic ‘slice’ of the object is reconstructed using spectral domain interpolation. Both systems work at 2.45 GHz with an incident power < 1 mW cm⁻² at the object and require a coupling medium (usually water) between the object and the source/receiver. Imaging parameters are appropriate for clinical use: a spatial resolution of 1 cm, measurement time of a few seconds and contrast resolution of around 1%. The effects of changes in perfusion on images of isolated animal organs are presented. Images have also been obtained, with both systems, of the internal dielectric structure of the forearm and of variations in dielectric properties due to changes of tissue blood content effected by application and release of tourniquets to the upper arm. Results s how that these changes are well demonstrated by microwave imaging, and possible clinical applications are discussed.     

Development of a cloud-based platform for reproducible science: A case study of an IUCN Red List of Ecosystems Assessment

One of the challenges of computational-centric research is to make the research undertaken reproducible in a form that others can repeat and re-use with minimal effort. In addition to the data and tools necessary to re-run analyses, execution environments play crucial roles because of the dependencies of the operating system and software version used. However, some of the challenges of reproducible science can be addressed using appropriate computational tools and cloud computing to provide an execution environment.Here, we demonstrate the use of a Kepler scientific workflow for reproducible science that is sharable, reusable, and re-executable. These workflows reduce barriers to sharing and will save researchers time when undertaking similar research in the future.To provide infrastructure that enables reproducible science, we have developed cloud-based Collaborative Environment for Ecosystem Science Research and Analysis (CoESRA) infrastructure to build, execute and share sophisticated computation-centric research. The CoESRA provides users with a storage and computational platform that is accessible from a web-browser in the form of a virtual desktop. Any registered user can access the virtual desktop to build, execute and share the Kepler workflows. This approach will enable computational scientists to share complete workflows in a pre-configured environment so that others can reproduce the computational research with minimal effort.As a case study, we developed and shared a complete IUCN Red List of Ecosystems Assessment workflow that reproduces the assessments undertaken by Burns et al. (2015) on Mountain Ash forests in the Central Highlands of Victoria, Australia. This workflow provides an opportunity for other researchers and stakeholders to run this assessment with minimal supervision. The workflow also enables researchers to re-evaluate the assessment when additional data becomes available. The assessment can be run in a CoESRA virtual desktop by opening a workflow in a Kepler user interface and pressing a “start” button. The workflow is pre-configured with all the open access datasets and writes results to a pre-configured folder.