MicroFilament Analyzer,an image analysis tool for quantifying fibrillar orientation,reveals changes in microtubule organization during gravitropism

Image acquisition is an important step in the study of cytoskeleton organization. As visual interpretations and manual measurements of digital images are prone to errors and require a great amount of time, a freely available software package named MicroFilament Analyzer (MFA) was developed. The goal was to provide a tool that facilitates high‐throughput analysis to determine the orientation of filamentous structures on digital images in a more standardized, objective and repeatable way. Here, the rationale and applicability of the program is demonstrated by analyzing the microtubule patterns in epidermal cells of control and gravi‐stimulated Arabidopsis thaliana roots. Differential expansion of cells on either side of the root results in downward bending of the root tip. As cell expansion depends on the properties of the cell wall, this may imply a differential orientation of cellulose microfibrils. As cellulose deposition is orchestrated by cortical microtubules, the microtubule patterns were analyzed. The MFA program detects the filamentous structures on the image and identifies the main orientation(s) within individual cells. This revealed four distinguishable microtubule patterns in root epidermal cells. The analysis indicated that gravitropic stimulation and developmental age are both significant factors that determine microtubule orientation. Moreover, the data show that an altered microtubule pattern does not precede differential expansion. Other possible applications are also illustrated, including field emission scanning electron micrographs of cellulose microfibrils in plant cell walls and images of fluorescent actin.     

Applying machine learning based on multiscale classifiers to detect remote phenology patterns in Cerrado savanna trees

Plant phenology is one of the most reliable indicators of species responses to global climate change, motivating the development of new technologies for phenological monitoring. Digital cameras or near remote systems have been efficiently applied as multi-channel imaging sensors, where leaf color information is extracted from the RGB (Red, Green, and Blue) color channels, and the changes in green levels are used to infer leafing patterns of plant species. In this scenario, texture information is a great ally for image analysis that has been little used in phenology studies. We monitored leaf-changing patterns of Cerrado savanna vegetation by taking daily digital images. We extract RGB channels from the digital images and correlate them with phenological changes. Additionally, we benefit from the inclusion of textural metrics for quantifying spatial heterogeneity. Our first goals are: (1) to test if color change information is able to characterize the phenological pattern of a group of species; (2) to test if the temporal variation in image texture is useful to distinguish plant species; and (3) to test if individuals from the same species may be automatically identified using digital images. In this paper, we present a machine learning approach based on multiscale classifiers to detect phenological patterns in the digital images. Our results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; (2) different plant species present a different behavior with respect to the color change information; and (3) texture variation along temporal images is promising information for capturing phenological patterns. Based on those results, we suggest that individuals from the same species and functional group might be identified using digital images, and introduce a new tool to help phenology experts in the identification of new individuals from the same species in the image and their location on the ground.     

The global biogeography of semi‐arid periodic vegetation patterns

Aim Vegetation exhibiting landscape‐scale regular spatial patterns has been reported for arid and semi‐arid areas world‐wide. Recent theories state that such structures are bound to low‐productivity environments and result from a self‐organization process. Our objective was to test this relationship between periodic pattern occurrence and environmental factors at a global scale and to parametrize a predictive distribution model. Location Arid and semi‐arid areas world‐wide. Methods We trained an empirical predictive model (Maxent) for the occurrence of periodic vegetation patterns, based on environmental predictors and known occurrences verified on Landsat satellite images. Results This model allowed us to discover previously unreported pattern locations, and to report the first ever examples of spotted patterns in natural systems. Relationships to the main environmental drivers are discussed. Main conclusions These results confirm that periodic patterned vegetations are ubiquitous at the interface between arid and semi‐arid regions. Self‐organized patterning appears therefore to be a biome‐scale response to environmental conditions, including soil and topography. The set of correlations between vegetation patterns and their environmental conditions presented in this study will need to be reproduced in future modelling attempts.     

Design patterns for wildlife‐related camera trap image analysis

This paper describes and explains design patterns for software that supports how analysts can efficiently inspect and classify camera trap images for wildlife‐related ecological attributes. Broadly speaking, a design pattern identifies a commonly occurring problem and a general reusable design approach to solve that problem. A developer can then use that design approach to create a specific software solution appropriate to the particular situation under consideration. In particular, design patterns for camera trap image analysis by wildlife biologists address solutions to commonly occurring problems they face while inspecting a large number of images and entering ecological data describing image attributes. We developed design patterns for image classification based on our understanding of biologists' needs that we acquired over 8 years during development and application of the freely available Timelapse image analysis system. For each design pattern presented, we describe the problem, a design approach that solves that problem, and a concrete example of how Timelapse addresses the design pattern. Our design patterns offer both general and specific solutions related to: maintaining data consistency, efficiencies in image inspection, methods for navigating between images, efficiencies in data entry including highly repetitious data entry, and sorting and filtering image into sequences, episodes, and subsets. These design patterns can inform the design of other camera trap systems and can help biologists assess how competing software products address their project‐specific needs along with determining an efficient workflow.     

A new high‐resolution computed tomography (CT) segmentation method for trabecular bone architectural analysis

In the last decade, high‐resolution computed tomography (CT) and microcomputed tomography (micro‐CT) have been increasingly used in anthropological studies and as a complement to traditional histological techniques. This is due in large part to the ability of CT techniques to nondestructively extract three‐dimensional representations of bone structures. Despite prior studies employing CT techniques, no completely reliable method of bone segmentation has been established. Accurate preprocessing of digital data is crucial for measurement accuracy, especially when subtle structures such as trabecular bone are investigated. The research presented here is a new, reproducible, accurate, and fully automated computerized segmentation method for high‐resolution CT datasets of fossil and recent cancellous bone: the Ray Casting Algorithm (RCA). We compare this technique with commonly used methods of image thresholding (i.e., the half‐maximum height protocol and the automatic, adaptive iterative thresholding procedure). While the quality of the input images is crucial for conventional image segmentation, the RCA method is robust regarding the signal to noise ratio, beam hardening, ring artifacts, and blurriness. Tests with data of extant and fossil material demonstrate the superior quality of RCA compared with conventional thresholding procedures, and emphasize the need for careful consideration of optimal CT scanning parameters. Am J Phys Anthropol 2009. © 2009 Wiley‐Liss, Inc.     

Comprehensive modeling and functional analysis of Toll‐like receptor ligand‐recognition domains

Toll‐like receptors (TLRs) are innate immune pattern‐recognition receptors endowed with the capacity to detect microbial pathogens based on pathogen‐associated molecular patterns. The understanding of the molecular principles of ligand recognition by TLRs has been greatly accelerated by recent structural information, in particular the crystal structures of leucine‐rich repeat‐containing ectodomains of TLR2, 3, and 4 in complex with their cognate ligands. Unfortunately, for other family members such as TLR7, 8, and 9, no experimental structural information is currently available. Methods such as X‐ray crystallography or nuclear magnetic resonance are not applicable to all proteins. Homology modeling in combination with molecular dynamics may provide a straightforward yet powerful alternative to obtain structural information in the absence of experimental (structural) data, provided that the generated three‐dimensional models adequately approximate what is found in nature. Here, we report the development of modeling procedures tailored to the structural analysis of the extracellular domains of TLRs. We comprehensively compared secondary structure, torsion angles, accessibility for glycosylation, surface charge, and solvent accessibility between published crystal structures and independently built TLR2, 3, and 4 homology models. Finding that models and crystal structures were in good agreement, we extended our modeling approach to the remaining members of the TLR family from human and mouse, including TLR7, 8, and 9.     

Structured illumination imaging with quasi‐periodic patterns

Structured illumination microscopy (SIM) is a well‐established method for optical sectioning and super‐resolution. The core of structured illumination is using a periodic pattern to excite image signals. This work reports a method for estimating minor pattern distortions from the raw image data and correcting these distortions during SIM image processing. The method was tested with both simulated and experimental image data from two‐photon Bessel light‐sheet SIM. The results proves the method is effective in challenging situations, where strong scattering background exists, signal‐to‐noise ratio (SNR) is low and the sample structure is sparse. Experimental results demonstrate restoring synaptic structures in deep brain tissue, despite the presence of strong light scattering and tissue‐induced SIM pattern distortion.     

Micro‐CT evaluation of murine fetal skeletal development yields greater morphometric precision over traditional clear‐staining methods

Traditional techniques for quantification of murine fetal skeletal development (gross measurements, clear‐staining) are severely limited by specimen processing, soft tissue presence, diffuse staining, and unclear landmarks between which to make measurements. Nondestructive microcomputed tomography (micro‐CT) imaging is a versatile, well‐documented tool traditionally used to generate high‐resolution 3‐D images and quantify microarchitectural parameters of trabecular bone. Although previously described as a tool for phenotyping fetal murine specimens, micro‐CT has not previously been used to directly measure individual fetal skeletal structures. Imaging murine fetal skeletons using micro‐CT enables the researcher to nondestructively quantify fetal skeletal development parameters including limb length, total bone volume, and average bone mineral density, as well as identify skeletal malformations. Micro‐CT measurement of fetal limb lengths correlates well with traditional clear‐staining methods (83.98% agreement), decreases variability in measurements (average standard errors: 6.28% for micro‐CT and 10.82% for clear‐staining), decreases data acquisition time by eliminating the need for tissue processing, and preserves the intact fixed fetus for further analysis. Use of the rigorous micro‐CT technique to generate 3‐D images for digital measurement enables isolation of skeletal structures based on degree of mineralization (local radiodensity), eliminating the complications of blurred stain boundaries and soft tissue inclusion that accompany clear‐staining and gross measurement techniques. Microcomputed tomography provides a facile, accurate, and nondestructive method for determining the developmental state of the fetal skeleton using not only limb lengths and identification of malformations, but total skeletal bone volume and average skeletal mineral density as well. Birth Defects Res (Part B) 2008. © 2008 Wiley‐Liss, Inc.     

TiLIA: a software package for image analysis of firefly flash patterns

As flash signaling patterns of fireflies are species specific, signal‐pattern analysis is important for understanding this system of communication. Here, we present time‐lapse image analysis (TiLIA), a free open‐source software package for signal and flight pattern analyses of fireflies that uses video‐recorded image data. TiLIA enables flight path tracing of individual fireflies and provides frame‐by‐frame coordinates and light intensity data. As an example of TiLIA capabilities, we demonstrate flash pattern analysis of the fireflies Luciola cruciata and L. lateralis during courtship behavior.     

Structured illumination microscopy with interleaved reconstruction (SIMILR)

Structured illumination microscopy (SIM) is the commonly used super‐resolution (SR) technique for imaging subcellular dynamics. However, due to its need for multiple illumination patterns, the frame rate is just a fraction of that of conventional microscopy and is thus too slow for fast dynamic studies. A new SR image reconstruction method that maximizes the use of each subframe of the acquisition series is proposed for improving the super‐resolved frame rate by N times for N illumination directions. The method requires no changes in raw data and is appropriate for many versions of SIM setup, including those implementing fast illumination pattern generation mechanism based on spatial light modulator or digital micromirror device. The performance of the proposed method is demonstrated through imaging the highly dynamic endoplasmic reticulum where continuous rapid growths or shape changes of tiny structures are observed.      

Preventing Overestimation of Pixels in Computed Tomography Assessment of Visceral Fat

Objective: Computed tomography (CT) is a common research procedure for measuring abdominal fat distribution, but little is written about the software used to analyze images. Our objective was to compare in‐house and commercially available software for quantitative measurement of abdominal fat distribution. In the process, we encountered some unexpected problems. Research Methods and Procedures: A total of 123 volunteers had single‐slice abdominal CT images taken that were used to evaluate various aspects of the commercial image analysis program. Results: The agreement between the commercial and in‐house programs was excellent (r = 0.996, p < 0.00, 001) for both total and intraabdominal fat, and we were able to reduce between‐observer variability in measured fat areas through the use of statistical handling of region of interest information. We also noted that intracolonic contents sometimes had the same Hounsfield units as adipose tissue. We analyzed single‐slice CT images from 50 volunteers to determine the potential impact of this effect on visceral fat area; the overestimate of visceral fat area was 19 ± 22% (maximum, 112% overestimate). The commercial program could prevent this error, whereas our in‐house program could not. Discussion: We concluded that a readily available commercial image analysis program compares well with a previously validated in‐house program and that it offers some advantages with respect to preventing overestimation of pixels as visceral fat.     

Preliminary Study towards the Use of In‐situ Microscopy for the Online Analysis of Microcarrier Cultivations

In order to apply newly developed non‐invasive in‐situ microscope systems for the monitoring of microcarrier‐based cultivations, appropriate image analysis systems must be available. Thus a simple, but effective greyscale distribution scan algorithm was tested for the evaluation of images generated by either a standard phase‐contrast microscope or an in‐situ microscope. The images were analyzed according to their greyscale pattern in order to examine whether the greyscale distribution is a possibility to gain information about the covering ratio. The mouse fibroblast cell line (NIH–3T3) was grown on different microcarrier spheres. At first, different microcarriers were tested with respect to their suitability for microscopic observation. In a second part, the phase‐contrast pictures and in‐situ microscope pictures of the microcarrier were separately analyzed using the histogram function of CorelPhotopaint, which analyzes the greyscale distribution within the chosen area. Due to the low optical density of the polydextrin matrix, the images of Cytodex 1 microcarriers proved to be an ideal model for the image analysis. Significant differences in the greyscale distribution of this microcarrier without cells and with increased cell density were observed. Therefore a relationship between the cell density on the microcarriers and the greyscale pattern can be assumed. After automating this image analysis and calibrating the cell number/greyscale pattern relationship, it should be possible to analyze the plating efficiency/covering ratio on the microcarrier online by in‐situ microscopy.     

Using phenological cameras to track the green up in a cerrado savanna and its on-the-ground validation

Plant phenology has gained new importance in the context of global change research, stimulating the development of novel technologies for phenological observations. Regular digital cameras have been effectively used as three-channel imaging sensors, providing measures of leaf color change or phenological shifts in plants. We monitored a species rich Brazilian cerrado savanna to assess the reliability of digital images to detect leaf-changing patterns. Analysis was conducted by extracting color information from selected parts of the image named regions of interest (ROIs). We aimed to answer the following questions: (i) Do digital cameras capture leaf changes in cerrado savanna vegetation? (ii) Can we detect differences in phenological changes among species crowns and the cerrado community? (iii) Is the greening pattern detected for each species by digital camera validated by our on-the-ground leafing phenology (direct observation of tree leaf changes)? We analyzed daily sequences of five images per hour, taken from 6:00 to 18:00 h, recorded during the cerrado main leaf flushing season. We defined 24 ROIs in the original digital image, including total or partial regions and crowns of six plant species. Our results indicated that: (i) for the studied period, single plant species ROIs were more sensitive to changes in relative green values than the community ROIs, (ii) three leaf strategies could be depicted from the species' ROI patterns of green color change, and (iii) the greening patterns and leaf functional groups were validated by our on-the-ground phenology. We concluded that digital cameras are reliable tools to monitor high diverse tropical seasonal vegetation and it is sensitive to inter-species differences of leafing patterns.     

A red line not to cross: Evaluating the limitation and properness of gel image tuning procedures

Currently, results of gel electrophoresis are commonly documented in digital formats by image acquisition instruments. In this study, gel images tuned by a common image processing software package, Photoshop, were assessed to understand the transforming algorithms and their impacts on quantitative analysis. TotalLab 100, an electrophoresis gel image analysis software package, was applied for image quantitation and evaluation. The three most frequently used image tuning functions—adjustments of the brightness, contrast, and grayscale span (level) of images—were investigated using both data generated from a standard grayscale tablet and an actual electrophoresis gel image. The influences of these procedures were analyzed for the grayscale transformation between the input and output images. Although all three procedures differentially improved the visualization of the input image, adjusting the contrast of images disrupted the quantitative information because of its nonlinear transforming algorithm. Under certain conditions, adjusting the brightness or the level of images could preserve the quantitative information because of the linear transforming algorithms. It was found that when the minimum and maximum grayscales of a gel image were recognized, using a commercial software package to maximally stretch the level may significantly improve the quality of a gel image without jeopardizing quantitative analysis.     

Spatial analysis of oblique photo‐point images for quantifying spatio‐temporal changes in plant communities

Question: Can spatial analytical techniques be used to extract quantitative measurements of vegetation communities from ground‐based permanent photo‐point images? Location: Mount Aspiring National Park, south‐western South Island, New Zealand. Methods: Sets of ground‐based photographs representing two contrasting vegetation types were selected to test two spatial analytical techniques. In the grid technique, a grid was superimposed onto the photographs and the frequency of species presence in each grid‐square was calculated to estimate species abundance/cover over the defined area. In the object‐oriented technique, the photographs were segmented into meaningful objects, based on the colour of the pixels and the textural patterns of the images, and the area occupied by an object in the image was used to derive species abundance/cover over the area. Results: Both techniques allow quick and easy classification of digital elements into ecologically relevant categories of vegetation components. The grid technique appeared more robust, being quick and efficient, accommodating all image types and providing presence/absence matrices for multivariate analysis. Fewer classes were identified using the object‐oriented technique, in particular for the forest interior site and for small individual plants such as Astelia spp. Conclusions: Both techniques showed potential for the objective quantitative analysis of long‐term vegetation monitoring of cover and changes of several component species, using repeat ground‐based photographs more specifically for grassland habitats. However, both rely to various degrees on manual classification. Corrective factors and strict protocols for taking the photographs are necessary to account for variation in view angles and to compute values more representative of absolute species abundance.     

Efficient Decoding of 2D Structured Illumination with Linear Phase Stepping in X-Ray Phase Contrast and Dark-Field Imaging

The ability to map the phase distribution and lateral coherence of an x-ray wavefront offers the potential for imaging the human body through phase contrast, without the need to deposit significant radiation energy. The classic means to achieve this goal is structured illumination, in which a periodic intensity modulation is introduced into the image, and changes in the phase distribution of the wavefront are detected as distortions of the modulation pattern. Two-dimensional periodic patterns are needed to fully characterize a transverse wavefront. Traditionally, the information in a 2D pattern is retrieved at high resolution by acquiring multiple images while shifting the pattern over a 2D matrix of positions. Here we describe a method to decode 2D periodic patterns with single-axis phase stepping, without either a loss of information or increasing the number of sampling steps. The method is created to reduce the instrumentation complexity of high-resolution 2D wavefront sensing in general. It is demonstrated with motionless electromagnetic phase stepping and a flexible processing algorithm in x-ray dark-field and phase contrast imaging.     

MMS2plot: An R Package for Visualizing Multiple MS/MS Spectra for Groups of Modified and Non‐Modified Peptides

A large number of post‐translational modifications (PTMs) in proteins are buried in the unassigned mass spectrometric (MS) spectra in shot‐gun proteomics datasets. Because the modified peptide fragments are low in abundance relative to the corresponding non‐modified versions, it is critical to develop tools that allow facile evaluation of assignment of PTMs based on the MS/MS spectra. Such tools will preferably have the ability to allow comparison of fragment ion spectra and retention time between the modified and unmodified peptide pairs or group. Herein, MMS2plot, an R package for visualizing peptide‐spectrum matches (PSMs) for multiple peptides, is described. MMS2plot features a batch mode and generates the output images in vector graphics file format that facilitate evaluation and publication of the PSM assignment. MMS2plot is expected to play an important role in PTM discovery from large‐scale proteomics datasets generated by liquid chromatography‐MS/MS. The MMS2plot package is freely available at https://github.com/lileir/MMS2plot under the GPL‐3 license.     

Alternative forms for genomic clines

Understanding factors regulating hybrid fitness and gene exchange is a major research challenge for evolutionary biology. Genomic cline analysis has been used to evaluate alternative patterns of introgression, but only two models have been used widely and the approach has generally lacked a hypothesis testing framework for distinguishing effects of selection and drift. I propose two alternative cline models, implement multivariate outlier detection to identify markers associated with hybrid fitness, and simulate hybrid zone dynamics to evaluate the signatures of different modes of selection. Analysis of simulated data shows that previous approaches are prone to false positives (multinomial regression) or relatively insensitive to outlier loci affected by selection (Barton's concordance). The new, theory‐based logit‐logistic cline model is generally best at detecting loci affecting hybrid fitness. Although some generalizations can be made about different modes of selection, there is no one‐to‐one correspondence between pattern and process. These new methods will enhance our ability to extract important information about the genetics of reproductive isolation and hybrid fitness. However, much remains to be done to relate statistical patterns to particular evolutionary processes. The methods described here are implemented in a freely available package “HIest” for the R statistical software (CRAN; http://cran.r-project.org/ ).