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The expansion of off-/onshore wind farms plays a key role in the transformation of energy production from burning of fossil fuels and nuclear energy to sustainable and safe power generation. However, the wind energy sector is permanently under strong cost pressure and the maintenance of the turbines is currently still carried out quite expensively with human industrial climbers. In this article, we present the results of an interdisciplinary research project on the automation of various image-based inspection steps. Since the use of unmanned aerial vehicles (UAV) is a problem especially offshore, we present here a simple, cost-effective method to obtain a three-dimensional model of a wind energy plant using solely a digital camera equipped with a sensor array to use it for the detection and management of damages and abnormalities. A first approach to detect abnormalities on the surface with deep learning methods achieved an F1-score of about 95%.

In recent years, an increasing number of cabled Fixed Underwater Observatories (FUOs) have been deployed, many of them equipped with digital cameras recording high-resolution digital image time series for a given period. The manual extraction of quantitative information from these data regarding resident species is necessary to link the image time series information to data from other sensors but requires computational support to overcome the bottleneck problem in manual analysis. As a priori knowledge about the objects of interest in the images is almost never available, computational methods are required that are not dependent on the posterior availability of a large training data set of annotated images. In this paper, we propose a new strategy for collecting and using training data for machine learning-based observatory image interpretation much more efficiently. The method combines the training efficiency of a special active learning procedure with the advantages of deep learning feature representations. The method is tested on two highly disparate data sets. In our experiments, we can show that the proposed method ALMI achieves on one data set a classification accuracy A &gt, 90% with less than N = 258 data samples and A &gt, 80% after N = 150 iterations, i.e., training samples, on the other data set outperforming the reference method regarding accuracy and training data required.

Mass Spectrometry Imaging (MSI) is an established and still evolving technique for the spatial analysis of molecular co-location in biological samples. Nowadays, MSI is expanding into new domains such as clinical pathology. In order to increase the value of MSI data, software for visual analysis is required that is intuitive and technique independent. Here, we present QUIMBI (QUIck exploration tool for Multivariate BioImages) a new tool for the visual analysis of MSI data. QUIMBI is an interactive visual exploration tool that provides the user with a convenient and straightforward visual exploration of morphological and spectral features of MSI data. To improve the overall quality of MSI data by reducing non-tissue specific signals and to ensure optimal compatibility with QUIMBI, the tool is combined with the new pre-processing tool ProViM (Processing for Visualization and multivariate analysis of MSI Data), presented in this work. The features of the proposed visual analysis approach for MSI data analysis are demonstrated with two use cases. The results show that the use of ProViM and QUIMBI not only provides a new fast and intuitive visual analysis, but also allows the detection of new co-location patterns in MSI data that are difficult to find with other methods.

The application of protective coating systems is the major measure against the corrosion of steel for tower sections of wind turbines. The inspection, condition monitoring and maintenance of protective coating system is a demanding and time-consuming procedure and requires high human effort. The paper introduces for the first time a Digital Twin concept for the condition monitoring and prescriptive maintenance planning for surface protection systems on wind turbine towers. The initial point of the concept is an in-situ Virtual Twin for the generation of reference areas for condition monitoring. The paper describes the integration of an online image annotation and processing tool, a maintenance model, corrosive resistance parameters, structural load parameters, and sensor data into the Digital Twin concept. The prospects of the concept and its practical relevance are shown for tower structures of large onshore wind turbines.

The paper describes the concept of a fully data-based, mainly automated, procedure for the condition monitoring of surface protection systems on steel structures. Acquisition, processing and evaluation of relevant data as well as their implementation into a mathematical-empirical condition monitoring model are discussed. The monitoring model is based on the definition and assessment of a local deterioration degree, which is characteristic for pre-defined reference areas at the structure. The creation of the reference areas by means of In-situ Virtual Twin Computation is introduced. The data-based assessment procedure is practically exemplified on a steel tower of an onshore wind power structure in a rural corrosive environment. The procedure can be applied to steel structures with similar geometries (storage tanks, silos, chimneys) as well as to offshore conditions.

Natural history collections are leading successful large-scale projects of specimen digitization (images, metadata, DNA barcodes), thereby transforming taxonomy into a big data science. Yet, little effort has been directed towards safeguarding and subsequently mobilizing the considerable amount of original data generated during the process of naming 15,000–20,000 species every year. From the perspective of alpha-taxonomists, we provide a review of the properties and diversity of taxonomic data, assess their volume and use, and establish criteria for optimizing data repositories. We surveyed 4113 alpha-taxonomic studies in representative journals for 2002, 2010, and 2018, and found an increasing yet comparatively limited use of molecular data in species diagnosis and description. In 2018, of the 2661 papers published in specialized taxonomic journals, molecular data were widely used in mycology (94%), regularly in vertebrates (53%), but rarely in botany (15%) and entomology (10%). Images play an important role in taxonomic research on all taxa, with photographs used in >80% and drawings in 58% of the surveyed papers. The use of omics (high-throughput) approaches or 3D documentation is still rare. Improved archiving strategies for metabarcoding consensus reads, genome and transcriptome assemblies, and chemical and metabolomic data could help to mobilize the wealth of high-throughput data for alpha-taxonomy. Because long-term—ideally perpetual—data storage is of particular importance for taxonomy, energy footprint reduction via less storage-demanding formats is a priority if their information content suffices for the purpose of taxonomic studies. Whereas taxonomic assignments are quasifacts for most biological disciplines, they remain hypotheses pertaining to evolutionary relatedness of individuals for alpha-taxonomy. For this reason, an improved reuse of taxonomic data, including machine-learning-based species identification and delimitation pipelines, requires a cyberspecimen approach—linking data via unique specimen identifiers, and thereby making them findable, accessible, interoperable, and reusable for taxonomic research. This poses both qualitative challenges to adapt the existing infrastructure of data centers to a specimen-centered concept and quantitative challenges to host and connect an estimated \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$ \le $\end{document}2 million images produced per year by alpha-taxonomic studies, plus many millions of images from digitization campaigns. Of the 30,000–40,000 taxonomists globally, many are thought to be nonprofessionals, and capturing the data for online storage and reuse therefore requires low-complexity submission workflows and cost-free repository use. Expert taxonomists are the main stakeholders able to identify and formalize the needs of the discipline; their expertise is needed to implement the envisioned virtual collections of cyberspecimens. [Big data; cyberspecimen; new species; omics; repositories; specimen identifier; taxonomy; taxonomic data.]

The number of offshore windmills keeps growing around the world since they are an essential part of any strategy to produce energy without using nuclear power plants or burning fossil resources. Due to the growing number of installations new monitoring and inspection strategies and routines need to be developed that are not only effective but efficient. In this work we will present first results from a joint project of industry partners and academia that aims at the development of new approaches making use of modern imaging technologies and new methods from artificial intelligence and computer vision research. We investigate three different strategies for collecting digital photos or video and posterior computational analysis using Convolutional Neural Networks (CNN). Our results indicate, that patterns of interest like rust or coating damage can be detected and classified with F 1 scores between 0.80 and 0.94 in photos collected by inspectors. In videos collected with unmanned aerial vehicles (UAV) rust patterns, oils spills and coating damage were detected with F 1 = 0.91. To support users in the time-consuming task of visual data exploration, we present a new visualization method referred to as “virtual twin”. Using hand - recorded image collections, a data-driven 3D model of a windmill is generated, that can be used to investigate damage patterns in the full context of the windmill and in full detail. Altogether our results indicate, that although each single approach has its limitations, a combination of different imaging methods, deep learning computer vision algorithms and sophisticated visual data exploration by experienced users appears to have potential to overcome the bottleneck in data analysis, interpretation and decision making in the context of the future inspections of offshore windmills, platforms or other constructions.

Wind energy is a critical part of overcoming the use of fossil or nuclear energy usage. The price pressure on the renewable industry sector demands to cut the costs for costly regular inspections carried out by industrial climbers. Drone-based video-inspection reduces costs as well as increases the safety of inspection personal. To further increase the throughput, automatic or semi-automatic solutions to analyze these videos are needed. However, modern machine learning architectures need a lot of data to work reliably. This is by design a problem, as structural damage is rather rare in industrial infrastructure. Our proposed approach uses Generative Adversarial Networks to generate synthetic unmanned aerial vehicle imagery. This allows us to create a large enough training dataset (> 103) from a dataset, which is at least an order of magnitude smaller (approx. 102). We show that we can increase the classification accuracy of up to 6 percentage points.

Deep convolutional neural networks are emerging as the state of the art method for supervised classification of images also in the context of taxonomic identification. Different morphologies and imaging technologies applied across organismal groups lead to highly specific image domains, which need customization of deep learning solutions. Here we provide an example using deep convolutional neural networks (CNNs) for taxonomic identification of the morphologically diverse microalgal group of diatoms. Using a combination of high-resolution slide scanning microscopy, web-based collaborative image annotation and diatom-tailored image analysis, we assembled a diatom image database from two Southern Ocean expeditions. We use these data to investigate the effect of CNN architecture, background masking, data set size and possible concept drift upon image classification performance. Surprisingly, VGG16, a relatively old network architecture, showed the best performance and generalizing ability on our images. Different from a previous study, we found that background masking slightly improved performance. In general, training only a classifier on top of convolutional layers pre-trained on extensive, but not domain-specific image data showed surprisingly high performance (F1 scores around 97%) with already relatively few (100–300) examples per class, indicating that domain adaptation to a novel taxonomic group can be feasible with a limited investment of effort.

The ability to rapidly generate and share molecular, visual, and acoustic data, and to compare them with existing information, and thereby to detect and name biological entities is fundamentally changing our understanding of evolutionary relationships among organisms and is also impacting taxonomy. Harnessing taxonomic data for rapid, automated species identification by machine learning tools or DNA metabarcoding techniques has great potential but will require their review, accessible storage, comprehensive comparison, and integration with prior knowledge and information. Currently, data production, management, and sharing in taxonomic studies are not keeping pace with these needs. Indeed, a survey of recent taxonomic publications provides evidence that few species descriptions in zoology and botany incorporate DNA sequence data. The use of modern high-throughput (-omics) data is so far the exception in alpha-taxonomy, although they are easily stored in GenBank and similar databases. By contrast, for the more routinely used image data, the problem is that they are rarely made available in openly accessible repositories. Improved sharing and re-using of both types of data requires institutions that maintain long-term data storage and capacity with workable, user-friendly but highly automated pipelines. Top priority should be given to standardization and pipeline development for the easy submission and storage of machine-readable data (e.g., images, audio files, videos, tables of measurements). The taxonomic community in Germany and the German Federation for Biological Data are researching options for a higher level of automation, improved linking among data submission and storage platforms, and for making existing taxonomic information more readily accessible.

Given the need to describe, analyze and index large quantities of marine imagery data for exploration and monitoring activities, a range of specialized image annotation tools have been developed worldwide. Image annotation - the process of transposing objects or events represented in a video or still image to the semantic level, may involve human interactions and computer-assisted solutions. Marine image annotation software (MIAS) have enabled over 500 publications to date. We review the functioning, application trends and developments, by comparing general and advanced features of 23 different tools utilized in underwater image analysis. \{MIAS\} requiring human input are basically a graphical user interface, with a video player or image browser that recognizes a specific time code or image code, allowing to log events in a time-stamped (and/or geo-referenced) manner. \{MIAS\} differ from similar software by the capability of integrating data associated to video collection, the most simple being the position coordinates of the video recording platform. \{MIAS\} have three main characteristics: annotating events in real time, in posteriorly to annotation and interact with a database. These range from simple annotation interfaces, to full onboard data management systems, with a variety of toolboxes. Advanced packages allow to input and display of data from multiple sensors or multiple annotators via intranet or internet. Posterior human-mediated annotation often include tools for data display and image analysis, e.g. length, area, image segmentation, point count; and in a few cases the possibility of browsing and editing previous dive logs or to analyze annotation data. The interaction with a database allows the automatic integration of annotations from different surveys, repeated annotation and collaborative annotation of shared datasets, browsing and querying of data. Progress in the field of automated annotation is mostly in post processing, for stable platforms or still images. Integration into available \{MIAS\} is currently limited to semi-automated processes of pixel recognition through computer-vision modules that compile expert-based knowledge. Important topics aiding the choice of a specific software are outlined, the ideal software is discussed and future trends are presented.

Digital imaging is gaining more and more attention in marine environmental monitoring and exploration. Nowadays, mobile platforms such as autonomous underwater vehicles (AUVs) are equipped with high-resolution cameras that can collect gigabytes of digital images in a single dive. To extract quantitative and qualitative information from the accumulating image collections, annotation tools such as BIIGLE 2.0 have been proposed recently and have been established in the data analysis workflow. These tools run as web applications on a central server and can be accessed worldwide via the Internet. However, marine science and engineering are naturally associated with a high degree of mobility and, in some cases, limited resources or Internet access. Here we present a new application architecture for BIIGLE 2.0, which is particularly suitable for offshore deployment on a variety of platforms such as a server, workstation, laptop or even a small single-board computer such as a Raspberry Pi. We refer to the application architecture in combination with a mobile hardware platform as "BIIGLE2Go", which addresses the need for more flexibility and mobility in image annotation. We present and evaluate a first prototype for BIIGLE2Go, which runs as a mobile annotation system on a low-cost Raspberry Pi 3B.

Highlights • The proposed method automatically assesses the abundance of poly-metallic nodules on the seafloor. • No manually created feature reference set is required. • Large collections of benthic images from a range of acquisition gear can be analysed efficiently. Abstract Underwater image analysis is a new field for computational pattern recognition. In academia as well as in the industry, it is more and more common to use camera-equipped stationary landers, autonomous underwater vehicles, ocean floor observatory systems or remotely operated vehicles for image based monitoring and exploration. The resulting image collections create a bottleneck for manual data interpretation owing to their size. In this paper, the problem of measuring size and abundance of poly-metallic nodules in benthic images is considered. A foreground/background separation (i.e. separating the nodules from the surrounding sediment) is required to determine the targeted quantities. Poly-metallic nodules are compact (convex), but vary in size and appear as composites with different visual features (color, texture, etc.). Methods for automating nodule segmentation have so far relied on manual training data. However, a hand-drawn, ground-truthed segmentation of nodules and sediment is difficult (or even impossible) to achieve for a sufficient number of images. The new ES4C algorithm (Evolutionary tuned Segmentation using Cluster Co-occurrence and a Convexity Criterion) is presented that can be applied to a segmentation task without a reference ground truth. First, a learning vector quantization groups the visual features in the images into clusters. Secondly, a segmentation function is constructed by assigning the clusters to classes automatically according to defined heuristics. Using evolutionary algorithms, a quality criterion is maximized to assign cluster prototypes to classes. This criterion integrates the morphological compactness of the nodules as well as feature similarity in different parts of nodules. To assess its applicability, the ES4C algorithm is tested with two real-world data sets. For one of these data sets, a reference gold standard is available and we report a sensitivity of 0.88 and a specificity of 0.65. Our results show that the applied heuristics, which combine patterns in the feature domain with patterns in the spatial domain, lead to good segmentation results and allow full automation of the resource-abundance assessment for benthic poly-metallic nodules.

Motivation Live cell imaging plays a pivotal role in understanding cell growth. Yet, there is a lack of visualization alternatives for quick qualitative characterization of colonies. Results SeeVis is a Python workflow for automated and qualitative visualization of time-lapse microscopy data. It automatically pre-processes the movie frames, finds particles, traces their trajectories and visualizes them in a space-time cube offering three different color mappings to highlight different features. It supports the user in developing a mental model for the data. SeeVis completes these steps in 1.15 s/frame and creates a visualization with a selected color mapping. Availability and implementation https://github.com/ghattab/seevis/ Supplementary information Supplementary data are available at Bioinformatics online.

Research of deep learning algorithms, especially in the field of convolutional neural networks (CNN), has shown significant progress. The application of CNNs in image analysis and pattern recognition has earned a lot of attention in this regard and few applications to classify a small number of common taxa in marine image collections have been reported yet. In this paper, we address the problem of class imbalance in marine image data, i.e. the common observation that 80%-90% of the data belong to a small subset of L′ classes among the total number of L observed classes, with L′ << L. A small number of methods to compensate for the class imbalance problem in the training step have been proposed for the common computer vision benchmark datasets. But marine image collections (showing for instance megafauna as considered in this study) pose a greater challenge as the observed imbalance is more extreme as habitats can feature a high biodiversity but a low species density. In this paper, we investigate the potential of various over-/undersampling methods to compensate for the class imbalance problem in marine imag- ing. In addition, five different balancing rules are proposed and analyzed to examine the extent to which sampling should be used, i.e. how many samples should be created or removed to gain the most out of the sam- pling algorithms. We evaluate these methods with AlexNet trained for classifying benthic image data recorded at the Porcupine Abyssal Plain (PAP) and use a Support Vector Machine as baseline classifier. We can report that the best of our proposed strategies in combination with data augmentation applied to AlexNet results in an increase of thirteen basis points compared to AlexNet without sampling. Furthermore, examples are presented, which show that the combination of oversampling and augmentation leads to a better generalization than pure augmentation.

Vision based wildlife monitoring is an important task in the field of environmental monitoring. Wildlife monitoring activities often create large collections of data needing computational approaches to (semi-) automated detection and annotation of objects in the images/video. In this work, we consider the special case of marine wildlife monitoring using camera equipped fixed observatories. In such cases where a-priori knowledge about which species to find is limited, a standard computer vision approach, employing supervised learning, will not be applicable for detecting and classifying species (or events) in the images. In a recently proposed unsupervised learning method, image patches are extracted from a time series of underwater images that feature moving species (like starfish, etc). The patches are automatically grouped into clusters with similar morphology and a so called relevance score is assigned to each of the clusters describing the likeliness that it contains patches showing unusual changes. However, due to the unsupervised fashion (i) the categories don't have labels and (ii) do not reflect the species distribution satisfactory. In this paper, we propose an active learning method that builds upon these results and can be used to assign taxonomic categories to single patches based on a set of human expert annotations making use of the cluster structure and relevance scores. The evaluation shows that compared to traditional sampling strategies our approach uses significantly less manual labels to train a classifier. We are confident that the results are relevant for non-marine contexts as well.

Vision based environmental monitoring using fixed cameras generates large image collections, creating a bottleneck in data analysis. In areas with limited background knowledge of the monitored habitat, this bottleneck can often not be overcome by traditional pattern recognition methods. A new change detection method to identify interesting events such as presence and behavior of different species is proposed. The change detection method uses the new Bi-Domain Feature Clustering (BDFC). BDFC integrates the location of a feature vector in the feature space as well as the location in the image into the clustering. Firstly, BDFC is applied to a time dependent representation of the image stream to identify regions of similar change. Secondly it is applied to a time independent representation to group these changes into categories. These categories can rapidly be assessed by a human observer to bypass the time consuming inspection of the whole data set. To make the posterior browsing of detected changes more efficient, a relevance factor computed for each category is proposed. The approach is demonstrated with experimental runs, using images from the Lofoten Vesteralen ocean observatory, showing the potential to harvest changes of interest and novelties in large image collections.

Fixed underwater observatories (FUOs) equipped with a variety of sensors including cameras, allow long-term monitoring with a high temporal resolution of a limited area of interest. FUOs equipped with HD cameras enable in situ monitoring of biological activity, such as live cold-water corals on a level of detail down to individual polyps. We present a workflow which allows monitoring the activity of cold water coral polyps automatically from photos recorded at the FUO LoVe (Lofoten - Vesterålen). The workflow consists of three steps: First the manual polyp activity-level identification, carried out by three observers on a region of interest in 13 images to generate a gold standard. Second, the training of a convolutional neural network (CNN) on the gold standard to automate the polyp activity classification. Third, the computational activity classification is integrated into an algorithmic estimation of polyp activity in a region of interest. We present results obtained for an image series from April to November 2015 that shows interesting temporal behavior patterns correlating with other posterior measurements.

In recent years, a number of fixed long-term underwater observatories (FUO) have been deployed to monitor marine habitats over time. HD cameras deployed on FUOs enable vision based studies of long-term processes in the monitored habitats. However, in many marine environments there is often only little a-priori knowledge about potential changes that can be expected or where such changes are likely to occur. Therefore, we propose a method to detect regions of potentially relevant changes and to group them into categories. Wavelet analysis is employed to extract features that describe the approximate progression of pixel values over time. Clustering the features using the recently proposed Bi-Domain Feature Clustering (BDFC) achieves feature grouping and a data-driven definition of change categories. Moreover, a relevance score is computed for each change category, to find regions with relevant changes and to illustrate different relevant change categories simultaneously in one image. Our experiments with images from the Lofoten Vesteralen (LoVe) ocean observatory demonstrate the effectiveness of the method to find relevant change patterns and associate them to different regions or biota.

Objective: The degeneration of articular cartilage is part of the clinical syndrome of osteoarthritis (OA) and one of the most common causes of pain and disability in middle-aged and older people(1). However, the objective detection of an initial state of OA is still challenging. In order to categorize cartilage into states of OA, an algorithm is presented which offers objective categorization on the basis of two-photon laser-scanning microscopy (TPLSM) images. Methods: The algorithm is based on morphological characteristics of the images and results in a topographical visualization. This paper describes the algorithm and shows the result of a categorization of human cartilage samples. Results: The resulting map of the analysis of TPLSM images can be divided into areas which correspond to the grades of the Outerbridge-Categorization. The algorithm is able to differentiate the samples in coincidence with the macroscopic impression. Conclusion: The method is promising for early OA detection and categorization. In order to achieve a higher benefit for the physician the method must be transferred to an endoscopic setup for an application in surgery. (C) 2013 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

Fixed underwater observatories (FUOs) equipped with a variety of sensors including HD cameras, allow long-term monitoring with a high temporal resolution of a limited area of interest. FUOs enable in situ monitoring of visual features like size and color of for instance live cold-water corals using imaging techniques. We present a computational workflow to extract coral features from the huge collection of images recorded by the FUO LoVe (Lofoten - Vesteralen)1. The presented approach allows to represent the image-time-series as numerical values of the features. This enables the images to be subject to statistical analysis to gain insight into the short- and long-term relationships between the corals and the environment. The presented automatic extraction of features from digital photos includes a customized pre-processing of the images (spatial and signal alignment), automated segmentation of the living parts of the corals using an unsupervised learning algorithm and extraction of coral specific numerical features which compensate color shifts due to changes of the in-optical properties affecting the whole image. In this initial study, unexpected temporal change patterns could be revealed, by comparing change patterns of live coral areas with change patterns of coral rubble areas using the CIELab color space.

Multiple investigators often generate data from seabed images within a single image set to reduce the time burden, particularly with the large photographic surveys now available to ecological studies. These data (annotations) are known to vary as a result of differences in investigator opinion on specimen classification, and human factors such as fatigue and cognition. These variations are rarely recorded or quantified, nor are their impacts on derived ecological metrics (density, diversity, composition). We compared the annotations of three investigators of 73 megafaunal morphotypes in ~28,000 images, including 650 common images. Successful annotation was defined as both detecting and correctly classifying a specimen. Estimated specimen detection success was 77%, and classification success was 95%, giving an annotation success rate of 73%. Specimen detection success varied substantially by morphotype (12-100%). Variation in the detection of common taxa resulted in significant differences in apparent faunal density and community composition among investigators. Such bias has the potential to produce spurious ecological interpretations if not appropriately controlled or accounted for. We recommend that photographic studies document the use of multiple annotators, and quantify potential inter-investigator bias. Randomisation of the sampling unit (photograph or video clip) is clearly critical to the effective removal of human annotation bias in multiple annotator studies (and indeed single annotator works).

Within the recent years clock rates of modern processors stagnated while the demand for computing power continued to grow. This applied particularly for the fields of life sciences and bioinformatics, where new technologies keep on creating rapidly growing piles of raw data with increasing speed. The number of cores per processor increased in an attempt to compensate for slight increments of clock rates. This technological shift demands changes in software development, especially in the field of high performance computing where parallelization techniques are gaining in importance due to the pressing issue of large sized datasets generated by e.g., modern genomics. This paper presents an overview of state-of-the-art manual and automatic acceleration techniques and lists some applications employing these in different areas of sequence informatics. Furthermore, we provide examples for automatic acceleration of two use cases to show typical problems and gains of transforming a serial application to a parallel one. The paper should aid the reader in deciding for a certain techniques for the problem at hand. We compare four different state-of-the-art automatic acceleration approaches (OpenMP, PluTo-SICA, PPCG, and OpenACC). Their performance as well as their applicability for selected use cases is discussed. While optimizations targeting the CPU worked better in the complex k-mer use case, optimizers for Graphics Processing Units (GPUs) performed better in the matrix multiplication example. But performance is only superior at a certain problem size due to data migration overhead. We show that automatic code parallelization is feasible with current compiler software and yields significant increases in execution speed. Automatic optimizers for CPU are mature and usually no additional manual adjustment is required. In contrast, some automatic parallelizers targeting GPUs still lack maturity and are limited to simple statements and structures.

Motivation: Bioimaging techniques rapidly develop toward higher resolution and dimension. The increase in dimension is achieved by different techniques such as multitag fluorescence imaging, Matrix Assisted Laser Desorption / Ionization (MALDI) imaging or Raman imaging, which record for each pixel an N-dimensional intensity array, representing local abundances of molecules, residues or interaction patterns. The analysis of such multivariate bioimages (MBIs) calls for new approaches to support users in the analysis of both feature domains: space (i.e. sample morphology) and molecular colocation or interaction. In this article, we present our approach WHIDE (Web-based Hyperbolic Image Data Explorer) that combines principles from computational learning, dimension reduction and visualization in a free web application. Results: We applied WHIDE to a set of MBI recorded using the multitag fluorescence imaging Toponome Imaging System. The MBI show field of view in tissue sections from a colon cancer study and we compare tissue from normal/healthy colon with tissue classified as tumor. Our results show, that WHIDE efficiently reduces the complexity of the data by mapping each of the pixels to a cluster, referred to as Molecular Co-Expression Phenotypes and provides a structural basis for a sophisticated multimodal visualization, which combines topology preserving pseudocoloring with information visualization. The wide range of WHIDE's applicability is demonstrated with examples from toponome imaging, high content screens and MALDI imaging (shown in the Supplementary Material). Availability and implementation: The WHIDE tool can be accessed via the BioIMAX website http://ani.cebitec.uni-bielefeld.de/BioIMAX/; Login: whidetestuser; Password: whidetest. Supplementary information: Supplementary data are available at Bioinformatics online. Contact: tim.nattkemper@uni-bielefeld.de

Megafauna plays an important role in benthic ecosystems and contributes significantly to benthic biomass in the Arctic. The distribution is mostly studied using towed cameras. Here, we compare the megafauna from two sites located at different distances from the Kongsfjord: one station at the entrance to the fjord, another on the outer shelf. Although they are only located 25 km apart and at comparable depth, there were significant differences in their species composition. While the inshore station was characterized by shrimps (2.57±2.18 ind. m−2) and brittlestars (3.21± 3.21 ind. m−2), the offshore site harboured even higher brittlestar densities (15.23±9.32 ind. m−2) and high numbers of the sea urchin Strongylocentrotus pallidus (1.23±1.09 ind. m−2). Phytodetrital concentrations of the upper sediment centimetres were significantly higher inshore compared with offshore. At a smaller scale, there were also differences in the composition of different transect sections. Several taxa were characteriz...

Listeria monocytogenes causes a life-threatening food-borne disease known as Listeriosis. Elderly,immunocompromised, and pregnant women are primarily the victims of this facultative intracellular Gram-positive pathogen. Since the bacteria survive intracellularly within the human host cells they are protected against the immune system and poorly accessed by many antibiotics. In order to screen pharmaceutical substances for their ability to interfere with the infection, persistence and release of L. monocytogenes a high content as say is required. We established a high content screen (HCS) using the RAW 264.7 mouse macrophage cell line seeded into 96-well glass bottom microplates. Cells were infected with GFP-expressing L. monocytogenes and stained thereafter with Hoechst 33342.Automated image acquisition was carried out by the Scan(R) screening station. We have developed an algorithm that automatically grades cells in microscopy images of fluorescent-tagged Listeria for the severity of infection. The grading accuracy of this newly developed algorithm is 97.1% as compared to a 74.3%grading accuracy we obtained using the commercial Olympus Scan(R) software.

Because of recent advances in sensor technology and a rapid increase in storage capacities, a growing number of intensity values can be recorded and associated with pixel coordinates using new imaging technologies. This growth in dimension can be observed in different scientific areas and this new category of images is referred to as multivariate images (MVIs). In these images, an almost arbitrary number of variables is associated with each pixel that represent, for instance, signal values at different time points or for different spectral bands or for different imaging parameters or modalities. Thus, these images can no longer be interpreted as gray value images or red, green, blue color images, and new information technologies are needed. In this review article, we summarize the different imaging technologies and recently published approaches to MVI mining with a special focus on biomedical applications. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 2-13 DOI: 10.1002/widm.4

Cold-water coral reefs are recognised as important biodiversity hotspots on the conti- nental margin. The location of terrain features likely to be associated with living reef has been made easier by recent developments in acoustic sensing technology. For accurate assessment and fine- scale mapping of these newly identified coral habitats, analysis of video data is still required. In the present study we explore the potential of manual and automatic abundance estimation of cold-water corals and sponges from still image frames extracted from video footage from Tisler Reef (Skagerrak, Norway). The results and processing times from 3 standard visual assessment methods (15-point quadrat, 100-point quadrat and frame mapping) are compared with those produced by a new com- puter vision system. This system uses machine-learning algorithms to detect species within frames automatically. Cold-water coral density estimates obtained from the automated method were similar to those gained by the other methods. The automated method slightly underestimated (by 10 to 20%) coral coverage in frames which lacked a uniform seabed illumination. However, it did much better in the detection of small live coral fragments than the 15-point method. For assessing sponge coverage, the automated system did not perform as satisfactorily. It mistook a percentage of the seabed for sponge (0.1 to 2% of most frames) and underestimated sponge coverage in frames that contained many sponges. Results indicate that the machine-learning approach is appropriate for estimating live cold-water coral density, but further work is required before the system can be applied to sponges within the reef environment.

In this contribution we investigate the applicability of different methods from the field of independent component analysis (ICA) for the examination of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data from breast cancer research. DCE-MRI has evolved in recent years as a powerful complement to X-ray based mammography for breast cancer diagnosis and monitoring. In DCE-MRI the time related development of the signal intensity after the administration of a contrast agent can provide valuable information about tissue states and characteristics. To this end, techniques related to ICA, offer promising options for data integration and feature extraction at voxel level. In order to evaluate the applicability of ICA, topographic ICA and tree-dependent component analysis (TCA), these methods are applied to twelve clinical cases from breast cancer research with a histopathologically confirmed diagnosis. For ICA these experiments are complemented by a reliability analysis of the estimated components. The outcome of all algorithms is quantitatively evaluated by means of receiver operating characteristics (ROC) statistics whereas the results for specific data sets are discussed exemplarily in terms of reification, score-plots and score images. (C) 2009 Elsevier Ltd. All rights reserved.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has become an important tool in breast cancer diagnosis, but evaluation of multitemporal 3D image data holds new challenges for human observers. To aid the image analysis process, we apply supervised and unsupervised pattern recognition techniques for computing enhanced visualizations of suspicious lesions in breast MRI data. These techniques represent art important component of future sophisticated computer-aided diagnosis (CAD) systems and support the visual exploration of spatial and temporal features of DCE-MRI data stemming from patients with confirmed lesion diagnosis. By taking into account the heterogeneity of cancerous tissue, these techniques reveal signals with malignant, benign and normal kinetics. They also provide a regional subclassification of pathological breast tissue, which is the basis for pseudo-color presentations of the image data. Intelligent medical systems Lire expected to have substantial implications in healthcare politics by contributing to the diagnosis of by contributing to the diagnosis of indeterminate breast lesions by non-invasive imaging. (c) 2007 Elsevier Ltd. All rights reserved.

study of mapping and interaction of co-localized proteins at a sub-cellular level is important for understanding complex biological phenomena. One of the recent techniques to map co-localized proteins is to use the standard immuno-fluorescence microscopy in a cyclic manner (Nat Biotechnol 24:1270–8, 2006; Proc Natl Acad Sci 110:11982–7, 2013). Unfortunately, these techniques suffer from variability in intensity and positioning of signals from protein markers within a run and across different runs. Therefore, it is necessary to standardize protocols for preprocessing of the multiplexed bioimaging (MBI) data from multiple runs to a comparable scale before any further analysis can be performed on the data. In this paper, we compare various normalization protocols and propose on the basis of the obtained results, a robust normalization technique that produces consistent results on the MBI data collected from different runs using the Toponome Imaging System (TIS). Normalization results produced by the proposed method on a sample TIS data set for colorectal cancer patients were ranked favorably by two pathologists and two biologists. We show that the proposed method produces higher between class Kullback-Leibler (KL) divergence and lower within class KL divergence on a distribution of cell phenotypes from colorectal cancer and histologically normal samples. Electronic supplementary material The online version of this article (doi:10.1186/s13040-016-0088-2) contains supplementary material, which is available to authorized users.

Computational underwater image analysis is developing into a mature field of research, with an increasing number of companies, academic groups and researchers showing interest in it. While on the one hand, the basic question is addressed by many groups, how algorithms can be applied to automatically detect and classify objects of interest (OOI) in underwater image footage, on the other hand the questions for efficiency and performance, i.e. the time a computer (or a compute cluster) needs to perform this task, has received much attention yet. In this paper we will show, how nowadays methods for high performance computing like parallelization and GPU computing via CUDA (Compute Unified Device Architecture) can be used to achieve both, image enhancement and segmentation in less than 0.2 sec per image (4224 × 2376 pixel) on average, which paves the way to real time online applications.

We present the novel prokaryotic gene finder GISMO, which combines searches for protein family domains with composition-based classification based on a support vector machine. GISMO is highly accurate; exhibiting high sensitivity and specificity in gene identification. We found that it performs well for complete prokaryotic chromosomes, irrespective of their GC content, and also for plasmids as short as 10 kb, short genes and for genes with atypical sequence composition. Using GISMO, we found several thousand new predictions for the published genomes that are supported by extrinsic evidence, which strongly suggest that these are very likely biologically active genes. The source code for GISMO is freely available under the GPL license.

We propose an extension of the algorithm for nonlinear dimensional reduction locally linear embedding (LLE) based on the usage of the geodesic distance (ISOLLE). In LLE, each data point is reconstructed from a linear combination of its n nearest neighbors, which are typically found using the Euclidean distance. We show that the search for the neighbors performed with respect to the geodesic distance can lead to a more accurate preservation of the data structure. This is confirmed by experiments on both real-world and synthetic data. (c) 2006 Elsevier B.V. All rights reserved.

Sinorhizobium meliloti genome sequence determination has provided the basis for different approaches of functional genomics for this symbiotic nitrogen-fixing alpha-proteobacterium. One of these approaches is gene disruption with subsequent analysis of mutant phenotypes. This method is efficient for single genes; however, it is laborious and time-consuming if it is used on a large scale. Here, we used a signature-tagged transposon mutagenesis method that allowed analysis of the survival and competitiveness of many mutants in a single experiment. A novel set of signature tags characterized by similar melting temperatures and G+C contents of the tag sequences was developed. The efficiencies of amplification of all tags were expected to be similar. Thus, no preselection of the tags was necessary to create a library of 412 signature-tagged transposons. To achieve high specificity of tag detection, each transposon was bar coded by two signature tags. In order to generate defined, nonredundant sets of signature-tagged S. meliloti mutants for subsequent experiments, 12,000 mutants were constructed, and insertion sites for more than 5,000 mutants were determined. One set consisting of 378 mutants was used in a validation experiment to identify mutants showing altered growth patterns.

Adduct formation, fragmentation events and matrix effects impose special challenges to the identification and quantitation of metabolites in LC-ESI-MS datasets. An important step in compound identification is the deconvolution of mass signals. During this processing step, peaks representing adducts, fragments, and isotopologues of the same analyte are allocated to a distinct group, in order to separate peaks from coeluting compounds. From these peak groups, neutral masses and pseudo spectra are derived and used for metabolite identification via mass decomposition and database matching. Quantitation of metabolites is hampered by matrix effects and nonlinear responses in LC-ESI-MS measurements. A common approach to correct for these effects is the addition of a U-13C-labeled internal standard and the calculation of mass isotopomer ratios for each metabolite. Here we present a new web-platform for the analysis of LC-ESI-MS experiments. ALLocator covers the workflow from raw data processing to metabolite identification and mass isotopomer ratio analysis. The integrated processing pipeline for spectra deconvolution “ALLocatorSD” generates pseudo spectra and automatically identifies peaks emerging from the U-13C-labeled internal standard. Information from the latter improves mass decomposition and annotation of neutral losses. ALLocator provides an interactive and dynamic interface to explore and enhance the results in depth. Pseudo spectra of identified metabolites can be stored in user- and method-specific reference lists that can be applied on succeeding datasets. The potential of the software is exemplified in an experiment, in which abundance fold-changes of metabolites of the l-arginine biosynthesis in C. glutamicum type strain ATCC 13032 and l-arginine producing strain ATCC 21831 are compared. Furthermore, the capability for detection and annotation of uncommon large neutral losses is shown by the identification of (γ-)glutamyl dipeptides in the same strains. ALLocator is available online at: https://allocator.cebitec.uni-bielefeld.​de. A login is required, but freely available.

Due to their induction characteristics stationary-phase promoters have a great potential in biotechnological processes for the production of heterologous proteins on a large-scale. In order to broaden the utility of stationary-phase promoters in bacterial expression systems and to create novel promoters induced by metabolic conditions, a library of synthetic stationary-phase/stress promoters for Escherichia coli was constructed. For designing the promoters the known -10 consensus sequence as well as the extended -10 region and an A/T-rich region downstream of the - 10 region were kept constant, while sequences from -37 to -14 were partially or completely randomized. For detection and selection of stationary-phase promoters GFP with enhanced fluorescence was used. The expression pattern of the GFP reporter system was compared with that of the LacZ reporter system. To screen and characterize colonies containing stationary-phase/stress promoters a bioinformatic approach was developed. In total, 33 promoters were selected which cover a broad range of promoter activities and induction times indicating that the strength of promoters can be modulated by partially randomizing the sequence upstream of the -10 region. The induction ratio of synthetic promoters at the transition from exponential to stationary-phase was from 4 to over 6000 and the induction time relative to the entrance into stationary-phase from - 1.4 to 2.7 h. Ninety-one percentage of the promoters had no or only low background activity during exponential growth. The broad variability of the promoters offers good possibilities for fine-tuning of gene expression and for applications in industrial bioprocesses. (c) 2005 Elsevier B.V All rights reserved.

Objective: In this work, methods utilizing supervised and unsupervised machine learning are applied to analyze radiologically derived morphological and calculated kinetic tumour features. The features are extracted from dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) time-course data. Material: The DCE-MRI data of the female breast are obtained within the UK Multicenter Breast Screening Study. The group of patients imaged in this study is selected on the basis of an increased genetic risk for developing breast cancer. Methods: The k-means clustering and self-organizing maps (SOM) are applied to analyze the signal structure in terms of visualization. We employ k-nearest neighbor classifiers (k-nn), support vector machines (SVM) and decision trees (DT) to classify features using a computer aided diagnosis (CAD) approach. Results: Regarding the unsupervised techniques, clustering according to features indicating benign and malignant characteristics is observed to a limited extend. The supervised approaches classified the data with 74% accuracy (DT) and providing an area under the receiver-operator-characteristics (ROC) curve (AUC) of 0.88 (SVM). Conclusion: It was found that contour and wash-out type (WOT) features determined by the radiologists lead to the best SVM classification results. Although a fast signal uptake in early time-point measurements is an important feature for malignant/benign classification of tumours, our results indicate that the wash-out characteristics might be considered as important. © 2004 Elsevier B.V. All rights reserved.

A system for the automatic segmentation of fluorescence micrographs is presented. In the first step, positions of fluorescent cells are detected by a fast learning neural network, which acquires the visual knowledge from a set of training cell-image patches selected by the user. Guided by the detected cell positions the system extracts in the second step the contours of the cells. For contour extraction, a recurrent neural network model is used to approximate the cell shapes. Even though the micrographs are noisy and the fluorescent cells vary in shape and size, the system detects at minimum 95% of the cells. (C) 2002 Elsevier Science B.V. All rights reserved.

In the field of underwater imaging it is often necessary to pre-process images to homogenize image quality across a whole image transect and to compensate variations in imaging conditions. A variety of pre-processing methods have been developed in the recent years to overcome different problems occurring in underwater imaging, hence performing different on different image sets. Protocols for an objective comparison and scoring of those methods are needed. Here we show how to use cluster indices to rank the different methods regarding their performances on different image sets. Our results show different ranking for four pre-processing methods for two chosen sets of benthic images from the deep seafloor.

The increasing scientific and economic interest in the visual exploration and monitoring of marine areas is creating huge amounts of new underwater image and video data and approaches to computationally assisted analysis are desperately needed. In this paper we propose an image patch feature representation concept, the Bag of Prototypes (BoP), to cope with the individual problems in underwater image analysis. We consider the case of seafloor classification, which is relevant in many contexts such as habitat mapping or the exploration of mineral resources and show, that the BoP concept allows an efficient and accurate tile-wise estimation of poly-metallic nodule coverage in relation to two differently acquired gold standards.