A two-stage adaptive thresholding segmentation for noisy low-contrast images

Image recognition is the process of recognizing and classifying objects with machine learning algorithms. Image binarization is the first and most challenging step in image recognition, in which foreground objects are separated from their background. When foreground objects have complex morphological structure and background noise is strong, foreground objects are often being fractured into subcomponents. To address the over-segmentation issue of organisms with complex structures, we propose a 2-stage adaptive binarization approach based on Sauvola's binarization algorithm. We tested the effectiveness of the new approach on a set of underwater images with jellyfish collected in nearshore waters using a shadowgraph underwater plankton imaging system, PlanktonScope, because jellyfish have relatively complex structure and are often over-segemented. The results showed that the 2-stage approach improved the integrity of extracted jellyfish compared to traditional binarization methods, including Sauvola's algorithm. The analysis of local entropy values showed that the first stage effectively suppresses redundant information in the image and reduces the number of Region of Interests (ROIs), and the second stage preserves relatively weak and low-intensity signals to ensure the integrity of the extracted targets. The 2-stage approach improves hardware resource utilization and computational efficiency. It is robust for images acquired in sub-optimal conditions and enhances the accuracy of analytical results in the study of marine organisms using imaging systems.     

Catechol-O-methyltransferase: A method for autoradiographic visualization of isozymes in Cellogel

An electrophoretic procedure for separating the molecular forms of catechol-O-methyltransferase in cellulose acetate gel is described; the zones of enzyme activity were revealed by autoradiography. The electrophoretic patterns of the enzyme in several tissues and cell lines derived from four different species are presented.This investigation was supported by Grant 500.6/Ric. 70/1981 from the Italian Ministry of Health and by the Italian Ministry of Education. We are grateful to Dr. M. Castagnola for useful advice and help with thin-layer chromatography.     

Multi-sensor neural-network processing of noisy speech

In this paper, a novel Artificial Neural-Network (ANN) based multi-sensor multi-band adaptive signal-processing scheme is described for enhancing acoustic-speech corrupted by real noise and reverberation. Numerically robust adaptation-algorithms are employed for the ANN based sub-band filters; and, new simulation experiments are reported using real-reverberant automobile data which demonstrate that the proposed speech-enhancement system is capable of outperforming conventional linear filtering-based wide-band and multi-band noise-cancellation schemes.     

The ImageJ ecosystem: Open‐source software for image visualization,processing, and analysis

For decades, biologists have relied on software to visualize and interpret imaging data. As techniques for acquiring images increase in complexity, resulting in larger multidimensional datasets, imaging software must adapt. ImageJ is an open‐source image analysis software platform that has aided researchers with a variety of image analysis applications, driven mainly by engaged and collaborative user and developer communities. The close collaboration between programmers and users has resulted in adaptations to accommodate new challenges in image analysis that address the needs of ImageJ's diverse user base. ImageJ consists of many components, some relevant primarily for developers and a vast collection of user‐centric plugins. It is available in many forms, including the widely used Fiji distribution. We refer to this entire ImageJ codebase and community as the ImageJ ecosystem. Here we review the core features of this ecosystem and highlight how ImageJ has responded to imaging technology advancements with new plugins and tools in recent years. These plugins and tools have been developed to address user needs in several areas such as visualization, segmentation, and tracking of biological entities in large, complex datasets. Moreover, new capabilities for deep learning are being added to ImageJ, reflecting a shift in the bioimage analysis community towards exploiting artificial intelligence. These new tools have been facilitated by profound architectural changes to the ImageJ core brought about by the ImageJ2 project. Therefore, we also discuss the contributions of ImageJ2 to enhancing multidimensional image processing and interoperability in the ImageJ ecosystem.     

iRASPA: GPU-accelerated visualization software for materials scientists

A new macOS software package, iRASPA, for visualisation and editing of materials is presented. iRASPA is a document-based app that manages multiple documents with each document containing a unique set of data that is stored in a file located either in the application sandbox or in iCloud drive. The latter allows collaboration on a shared document (on High Sierra). A document contains a gallery of projects that show off the main features, a CloudKit-based access to the CoRE MOF database (approximately 8000 structures), and local projects of the user. Each project contains a scene of one or more structures that can initially be read from CIF, PDB or XYZ-files, or made from scratch. Main features of iRASPA are: structure creation and editing, pictures and movies, ambient occlusion and high-dynamic range rendering, collage of structures, (transparent) adsorption surfaces, cell replicas and supercells, symmetry operations like space group and primitive cell detection, screening of structures using user-defined predicates, and GPU-computation of helium void fraction and surface areas in a matter of seconds. Leveraging the latest graphics technologies like Metal, iRASPA can render hundreds of thousands of atoms (including ambient occlusion) with stunning performance. The software is freely available from the Mac App Store.     

JColorGrid: software for the visualization of biological measurements

Background  

Two-dimensional data colourings are an effective medium by which to represent three-dimensional data in two dimensions. Such "color-grid" representations have found increasing use in the biological sciences (e.g. microarray 'heat maps' and bioactivity data) as they are particularly suited to complex data sets and offer an alternative to the graphical representations included in traditional statistical software packages. The effectiveness of color-grids lies in their graphical design, which introduces a standard for customizable data representation. Currently, software applications capable of generating limited color-grid representations can be found only in advanced statistical packages or custom programs (e.g. micro-array analysis tools), often associated with steep learning curves and requiring expert knowledge.     

Decon2LS: An open-source software package for automated processing and visualization of high resolution mass spectrometry data

Background

The majority of ovarian cancer biomarker discovery efforts focus on the identification of proteins that can improve the predictive power of presently available diagnostic tests. We here show that metabolomics, the study of metabolic changes in biological systems, can also provide characteristic small molecule fingerprints related to this disease.

Results

In this work, new approaches to automatic classification of metabolomic data produced from sera of ovarian cancer patients and benign controls are investigated. The performance of support vector machines (SVM) for the classification of liquid chromatography/time-of-flight mass spectrometry (LC/TOF MS) metabolomic data focusing on recognizing combinations or "panels" of potential metabolic diagnostic biomarkers was evaluated. Utilizing LC/TOF MS, sera from 37 ovarian cancer patients and 35 benign controls were studied. Optimum panels of spectral features observed in positive or/and negative ion mode electrospray (ESI) MS with the ability to distinguish between control and ovarian cancer samples were selected using state-of-the-art feature selection methods such as recursive feature elimination and L1-norm SVM.

Conclusion

Three evaluation processes (leave-one-out-cross-validation, 12-fold-cross-validation, 52-20-split-validation) were used to examine the SVM models based on the selected panels in terms of their ability for differentiating control vs. disease serum samples. The statistical significance for these feature selection results were comprehensively investigated. Classification of the serum sample test set was over 90% accurate indicating promise that the above approach may lead to the development of an accurate and reliable metabolomic-based approach for detecting ovarian cancer.     

Decon2LS: An open-source software package for automated processing and visualization of high resolution mass spectrometry data

Background  

Data generated from liquid chromatography coupled to high-resolution mass spectrometry (LC-MS)-based studies of a biological sample can contain large amounts of biologically significant information in the form of proteins, peptides, and metabolites. Interpreting this data involves inferring the masses and abundances of biomolecules injected into the instrument. Because of the inherent complexity of mass spectral patterns produced by these biomolecules, the analysis is significantly enhanced by using visualization capabilities to inspect and confirm results. In this paper we describe Decon2LS, an open-source software package for automated processing and visualization of high-resolution MS data. Drawing extensively on algorithms developed over the last ten years for ICR2LS, Decon2LS packages the algorithms as a rich set of modular, reusable processing classes for performing diverse functions such as reading raw data, routine peak finding, theoretical isotope distribution modelling, and deisotoping. Because the source code is openly available, these functionalities can now be used to build derivative applications in relatively fast manner. In addition, Decon2LS provides an extensive set of visualization tools, such as high performance chart controls.     

BiVisu: software tool for bicluster detection and visualization

BiVisu is an open-source software tool for detecting and visualizing biclusters embedded in a gene expression matrix. Through the use of appropriate coherence relations, BiVisu can detect constant, constant-row, constant-column, additive-related as well as multiplicative-related biclusters. The biclustering results are then visualized under a 2D setting for easy inspection. In particular, parallel coordinate (PC) plots for each bicluster are displayed, from which objective and subjective cluster quality evaluation can be performed. Availability: BiVisu has been developed in Matlab and is available at http://www.eie.polyu.edu.hk/~nflaw/Biclustering/.     

Interactive visualization software for exploring phylogenetic trees and clades

Summary: The Summary Tree Explorer (STE) is a Java applicationfor interactively exploring sets of phylogenetic trees usingtwo coupled representations: a node-and-link diagram and a textuallist of common clades. Selection, pruning, filtering or re-rootingin one representation is immediately reflected in the other.While summary trees are more effective at showing the relationshipamong clades, they can only show a consistent subset of thosethat appear in the textual list. Working with both representationsmitigates the disadvantages of having to choose just one. Availability: STE, along with several sample datasets, is availableat http://cityscape.inf.cs.cmu.edu/phylogeny/ Contact: mad{at}cs.cmu.edu Associate Editor: Martin Bishop     

A hybridization method for autoradiographic visualization of small unique sequence fragments resolved on polyacrylamide gels

A useful method is described for sequence-specific visualization of small nucleic acid fragments on polyacrylamide gels. Excess highly radioactive probe is annealed in solution to smaller fragments. The resulting hybrids are trimmed (under appropriate conditions) with S1 nuclease to yield duplex fragments whose sizes are determined by the strand contributed by the smaller unlabeled fragment, but which also contain a labeled strand from the radioactive probe. These fragments can then be resolved on polyacrylamide gels and visualized by drying the gel and subjecting it to autoradiography. This method is shown to visualize fragments as small as 40 base pairs.     

Information processing in noisy burster models of sensory neurons

Processing of external stimuli by sensory neurons often involves bursting, when epochs of fast firing alternate with intervals of quiescence. In particular, sensory neurons of electroreceptors in paddlefish (Polyodon spathula) undergo bursting when stimulated externally with broad-band noise, but otherwise fire spontaneously in a quasiperiodic tonic manner. We use a simple phenomenological model for noise-induced bursting to quantify analytically, by means of the Kullback entropy and Fisher information, the gain in information transfer and electroreceptor sensitivity for external noisy stimuli. A good agreement between theoretical predictions, numerical simulations and experimental data is shown.     

Recombinant carbazole-degrading strains for enhanced petroleum processing

Biotechnological upgrading of fossil fuels is of increasing interest as remaining stocks of petroleum show increasing levels of contaminants such as heavy metals, sulfur and nitrogen-containing heteroaromatic compounds. Carbazole is of particular interest as a major petroleum component known to reduce refining yields through catalyst poisoning. In this study, the biotransformation of carbazole was successfully demonstrated in a liquid two-phase system, when solubilized in either 1-methylnaphthalene or in diesel fuel. The effects of solvent toxicity were investigated by expressing the carbazole-transformation genes from MB1332, a rifampicin-resistant derivative of Pseudomonas sp. LD2, in a solvent-resistant heterologous host, P. putida Idaho [1]. This solvent-resistant strain successfully degraded carbazole solubilized in 1-methylnaphthalene and in the presence of 10 vol% xylenes similar to the non-recombinant strain Pseudomonas sp. LD2. Identification of a suitable recombinant host, however, was essential for further investigations of partial pathway transformations. Recombinant P. putida Idaho expressing only the initial dioxygenase enzymes transformed carbazole to an intermediate well retained in the oil phase. Partial carbazole transformation converts carbazole to non-aromatic species; their effect is unknown on refinery catalyst poisoning, but would allow almost complete retention of carbon content and fuel value. Electronic Publication     

GGT 2.0: versatile software for visualization and analysis of genetic data

Ever since its first release in 1999, the free software package for visualization of molecular marker data, graphical genotype (GGT), has been constantly adapted and improved. The GGT package was developed in a plant-breeding context and thus focuses on plant genetic data but was not intended to be limited to plants only. The current version has many options for genetic analysis of populations including diversity analyses and simple association studies. A second release of the GGT package, GGT 2.0 (available through http://www.plantbreeding.wur.nl), is therefore presented in this paper. An overview of existing and new features that are available within GGT 2.0, and a case study in which GGT 2.0 is applied to analyze an existing set of plant genetic data, are presented and discussed.     

Libaffy: software for processing Affymetrix GeneChip data

SUMMARY: Affymetrix GeneChip microarrays are increasingly used in gene expression studies and in greater number. A software library was developed that supports Affymetrix file formats and implements two popular summary algorithms (MAS5.0 and RMA). The library is modular in design for integration into larger systems and processing pipelines. Additionally, a graphical interface (GENE) was developed to allow end-user access to the functionality within the library. AVAILABILITY: libaffy is free to use under the GNU GPL license. The source code and Windows binaries can be freely accessed from the website http://src.moffitt.usf.edu/libaffy. Additional API documentation and user manual are available.     

SNPTools: a software tool for visualization and analysis of microarray data

SUMMARY: We have created a software tool, SNPTools, for analysis and visualization of microarray data, mainly SNP array data. The software can analyse and find differences in intensity levels between groups of arrays and identify segments of SNPs (genes, clones), where the intensity levels differ significantly between the groups. In addition, SNPTools can show jointly loss-of-heterozygosity (LOH) data (derived from genotypes) and intensity data for paired samples of tumour and normal arrays. The output graphs can be manipulated in various ways to modify and adjust the layout. A wizard allows options and parameters to be changed easily and graphs replotted. All output can be saved in various formats, and also re-opened in SNPTools for further analysis. For explorative use, SNPTools allows various genome information to be loaded onto the graphs. AVAILABILITY: The software, example data sets and tutorials are freely available from http://www.birc.au.dk/snptools