Robustness and reliability evaluations of image annotation

The semantic gap problem in image retrieval has motivated much work focusing on automatic image annotation, aimed at facilitating computers to automatically assign keywords to images. The basic measure for evaluating the annotation performance is usually to examine the annotation accuracy. To do this, the fraction of the relevant images, which have been correctly classified by a specific classifier or image annotation system, is measured. Consequently, the evaluation result can be thought of as a surrogate for the judgment of real users. However, the ability of this kind of quantitative evaluation measure to fully evaluate the performance and value of image annotation systems is limited. This paper introduces two complementary metrics related to the rates of annotation accuracy, which can help to further assess the robustness and stability of image annotation systems. They are: (i) the number of annotated keywords with zero-rate accuracy and (ii) the coefficient of variation of annotation accuracy. The evaluation results based on three datasets show that these two metrics are very useful to make a more reliable conclusion for image annotation systems.     

Annotation of the human adult urinary metabolome and metabolite identification using ultra high performance liquid chromatography coupled to a linear quadrupole ion trap-orbitrap mass spectrometer

Metabolic profiles of biofluids obtained by atmospheric pressure ionization mass spectrometry-based technologies contain hundreds to thousands of features, most of them remaining unknown or at least not characterized in analytical systems. We report here on the annotation of the human adult urinary metabolome and metabolite identification from electrospray ionization mass spectrometry (ESI-MS)-based metabolomics data sets. Features of biological interest were first of all annotated using the ESI-MS database of the laboratory. They were also grouped, thanks to software tools, and annotated using public databases. Metabolite identification was achieved using two complementary approaches: (i) formal identification by matching chromatographic retention times, mass spectra, and also product ion spectra (if required) of metabolites to be characterized in biological data sets to those of reference compounds and (ii) putative identification from biological data thanks to MS/MS experiments for metabolites not available in our chemical library. By these means, 384 metabolites corresponding to 1484 annotated features (659 in negative ion mode and 825 in positive ion mode) were characterized in human urine samples. Of these metabolites, 192 and 66 were formally and putatively identified, respectively, and 54 are reported in human urine for the first time. These lists of features could be used by other laboratories to annotate their ESI-MS metabolomics data sets.     

Biclustering‐based association rule mining approach for predicting cancer‐associated protein interactions

Protein–protein interactions (PPIs) have been widely used to understand different biological processes and cellular functions associated with several diseases like cancer. Although some cancer‐related protein interaction databases are available, lack of experimental data and conflicting PPI data among different available databases have slowed down the cancer research. Therefore, in this study, the authors have focused on various proteins that are directly related to different types of cancer disease. They have prepared a PPI database between cancer‐associated proteins with the rest of the human proteins. They have also incorporated the annotation type and direction of each interaction. Subsequently, a biclustering‐based association rule mining algorithm is applied to predict new interactions with type and direction. This study shows the prediction power of association rule mining algorithm over the traditional classifier model without choosing a negative data set. The time complexity of the biclustering‐based association rule mining is also analysed and compared to traditional association rule mining. The authors are able to discover 38 new PPIs which are not present in the cancer database. The biological relevance of these newly predicted interactions is analysed by published literature. Recognition of such interactions may accelerate a way of developing new drugs to prevent different cancer‐related diseases.Inspec keywords: cancer, medical computing, data mining, proteins, genetics, pattern clusteringOther keywords: biological processes, cancer‐related diseases, cancer research, cancer‐related protein interaction databases, protein–protein interactions, cancer‐associated protein interactions, biclustering‐based association rule mining approach, negative data set, annotation type, human proteins, cancer‐associated proteins, PPI database, cancer disease     

Terahertz sources and detectors and their application to biological sensing

Terahertz spectroscopy has long been used as an important measurement tool in fields such as radio astronomy, physical chemistry, atmospheric studies and plasma research. More recently terahertz technology has been used to develop an exciting new technique to investigate the properties of a wide range of biological materials. Although much research remains before a full understanding of the interaction between biomaterials and terahertz radiation is developed, these initial studies have created a compelling case for further scientific study. Also, the potential development of practical tools to detect and identify biological materials such as biological-warfare agents and food contaminants, or of medical diagnostic tools, is driving the need for improved terahertz technology. In particular, improved terahertz sources and detectors that can be used in practical spectroscopy systems are needed. This paper overviews some of the recent measurements of the terahertz spectra of biomaterials and the ongoing efforts to create an all-solid-state technology suitable not only for improved scientific experiments but also for military and commercial applications.     

A Novel Auto-Annotation Technique for Aspect Level Sentiment Analysis

In machine learning, sentiment analysis is a technique to find and analyze the sentiments hidden in the text. For sentiment analysis, annotated data is a basic requirement. Generally, this data is manually annotated. Manual annotation is time consuming, costly and laborious process. To overcome these resource constraints this research has proposed a fully automated annotation technique for aspect level sentiment analysis. Dataset is created from the reviews of ten most popular songs on YouTube. Reviews of five aspects—voice, video, music, lyrics and song, are extracted. An N-Gram based technique is proposed. Complete dataset consists of 369436 reviews that took 173.53 s to annotate using the proposed technique while this dataset might have taken approximately 2.07 million seconds (575 h) if it was annotated manually. For the validation of the proposed technique, a sub-dataset—Voice, is annotated manually as well as with the proposed technique. Cohen's Kappa statistics is used to evaluate the degree of agreement between the two annotations. The high Kappa value (i.e., 0.9571%) shows the high level of agreement between the two. This validates that the quality of annotation of the proposed technique is as good as manual annotation even with far less computational cost. This research also contributes in consolidating the guidelines for the manual annotation process.     

Construction and investigation of breast‐cancer‐specific ceRNA network based on the mRNA and miRNA expression data

It has been proved and widely acknowledged that messenger RNAs can talk to each other by competing for a limited pool of miRNAs. The competing endogenous RNAs are called as ceRNAs. Although some researchers have recently used ceRNAs to do biological function annotations, few of them have investigated the ceRNA network on specific disease systematically. In this work, using both miRNA expression data and mRNA expression data of breast cancer patient as well as the miRNA target relations, the authors proposed a computational method to construct a breast‐cancer‐specific ceRNA network by checking whether the shared miRNA sponges between the gene pairs are significant. The ceRNA network is shown to be scale‐free, thus the topological characters such as hub nodes and communities may provide important clues for the biological mechanism. Through investigation on the communities (the dense clusters) in the network, it was found that they are related to cancer hallmarks. In addition, through function annotation of the hub genes in the network, it was found that they are related to breast cancer. Moreover, classifiers based on the discriminative hubs can significantly distinguish breast cancer patients’ risks of distant metastasis in all the three independent data sets.Inspec keywords: cancer, genetics, medical computing, molecular biophysics, RNAOther keywords: breast‐cancer specific ceRNA network construction, miRNA expression data, mRNA expression data, gene pairs, computational method, dense clusters, cancer hallmarks, biological mechanism, discriminative hub genes     

Software tools for analysis of mass spectrometric lipidome data

New software tools for quantitative analysis of mass spectrometric lipidome data have been developed. The LIMSA tool finds and integrates peaks in a mass spectrum, matches the peaks with a user-supplied list of expected lipids, corrects for overlap in their isotopic patterns, and quantifies the identified lipid species according to internal standards. Three different algorithms for isotopic correction (deconvolution) were implemented and compared. LIMSA has a convenient user interface and can be applied on any type of MS spectrum. Typically, analysis of one spectrum takes only a few seconds. The SECD tool, designed for analysis of LC-MS data sets, provides an intuitive and informative display of MS chromatograms as two-dimensional "maps" for visual inspection of the data and allows the user to extract mass spectra, to be further analyzed with LIMSA, from arbitrary regions of these maps. More reliable analysis of complex lipidome data with improved signal-to-noise ratio is obtained when compared to standard time-range averaged spectra. The functionality of these tools is demonstrated by analysis of standard mixtures as well as complex biological samples. The tools described here make accurate, high-throughput analysis of extensive sample sets feasible and are made available to the scientific community free of charge.     

Design and control of compliant tensegrity robots through simulation and hardware validation

To better understand the role of tensegrity structures in biological systems and their application to robotics, the Dynamic Tensegrity Robotics Lab at NASA Ames Research Center, Moffett Field, CA, USA, has developed and validated two software environments for the analysis, simulation and design of tensegrity robots. These tools, along with new control methodologies and the modular hardware components developed to validate them, are presented as a system for the design of actuated tensegrity structures. As evidenced from their appearance in many biological systems, tensegrity (‘tensile–integrity’) structures have unique physical properties that make them ideal for interaction with uncertain environments. Yet, these characteristics make design and control of bioinspired tensegrity robots extremely challenging. This work presents the progress our tools have made in tackling the design and control challenges of spherical tensegrity structures. We focus on this shape since it lends itself to rolling locomotion. The results of our analyses include multiple novel control approaches for mobility and terrain interaction of spherical tensegrity structures that have been tested in simulation. A hardware prototype of a spherical six-bar tensegrity, the Reservoir Compliant Tensegrity Robot, is used to empirically validate the accuracy of simulation.     

Algorithms and tools for the preprocessing of LC-MS metabolomics data

Metabolomics encompasses the study of small molecules in a biological sample. Liquid Chromatography coupled with Mass Spectrometry (LC-MS) profiling is an important approach for the identification and quantification of metabolites from complex biological samples. The amount and complexity of data produced in an LC-MS profiling experiment demand automatic tools for the preprocessing, analysis, and extraction of useful biological information. Data preprocessing—a topic that covers noise filtering, peak detection, deisotoping, alignment, identification, and normalization—is thus an active area of metabolomics research. Recent years have witnessed development of many software for data preprocessing, and still there is a need for further improvement of the data preprocessing pipeline. This review presents an overview of selected software tools for preprocessing LC-MS based metabolomics data and tries to provide future directions.     

Prospects and Challenges of Graphene in Biomedical Applications

Graphene materials have entered a phase of maturity in their development that is characterized by their explorative utilization in various types of applications and fields from electronics to biomedicine. Herein, we describe the recent advances made with graphene‐related materials in the biomedical field and the challenges facing these exciting new tools both in terms of biological activity and toxicological profiling in vitro and in vivo. Graphene materials today have mainly been explored as components of biosensors and for construction of matrices in tissue engineering. Their antimicrobial activity and their capacity to act as drug delivery platforms have also been reported, however, not as coherently. This report will attempt to offer some perspective as to which areas of biomedical applications can expect graphene‐related materials to constitute a tool offering improved functionality and previously unavailable options.     

Plenty more room on the glass bottom: Surface functionalization and nanobiotechnology for cell isolation

Surface functionalization is a widely adopted technique for surface modification which allows researchers to customize surfaces to integrate with their research. Surface functionalization has been used recently to adapt surfaces to integrate with biological materials specifically to isolate cells or mimic biological tissues through cell patterning. Cell isolation and cell patterning both can be integrated with extant techniques or surfaces to customize the research to whatever needs to be tested. Substrates such as metals, biologically mimicking surfaces, environmental responsive surfaces, and even three-dimensional surfaces such as hydrogels have all been adapted to allow for functionalization for both patterning and isolation. In this review we have described both the advantages and disadvantages of these techniques and the related chemistries to better understand these tools and how best to apply them in the hope that we can further expand upon the research in the field.     

Annotations to improve the using and the updating of digital technical publications

Technical documentation, be it of a product or of a plant, is more and more widely distributed and used in a digital form. Annotations are traditionally used in process of utilizing, critiquing and updating documents. Many researchers have highlighted the benefit of digital annotation functionalities. In this paper, we present lessons that have been drawn from operational field studies on the use of digital documentation in maintenance processes and we focus on business practices and on the need for annotation functionalities. We examine a document system, its structures and the impact of its digital form on the working methods within a global industrial system. Our theoretical framework allows identification of the specific pace and function for digital annotation beyond the new possibilities brought by the standardization of digital data formats.     

Informetric studies using databases: Opportunities and challenges

Since their arrival in the 1960s, electronic databases have been an invaluable tool for informetricians. Databases and their delivery mechanism have provided both the source of raw data, as well as the analytical tools for many informetric studies. In particular, the citation databases produced by the Institute for Scientific Information have been the key source of data for a whole range of citation-based research. However, there are also many problems and challenges associated with the use of online databases. Most of the problems arise because databases are designed primarily for information retrieval purposes, and informetric studies represent only a secondary use of the systems. The sorts of problems encountered by informetricians include: errors or inconsistency in the data itself; problems with the coverage, overlap and changeability of the databases; as well as problems and limitations in the tools provided by the database hosts such as DIALOG. For some informetric studies, the only viable solution to these problems is to download the data and perform offline correction and data analysis. This revised version was published online in June 2006 with corrections to the Cover Date.     

Development of protein nanotubes from a multi-purpose biological structure

One approach to develop nanosystems that incorporate biological concepts involves the addition of biotic moieties (carbohydrates, DNA, protein) to abiotic scaffolds such as carbon nanotubes. These hybrids have interesting properties but incorporation of specific, site-directed functionalization is challenging and the resulting material is best described in terms of its bulk properties. An alternative approach to the development of bionanosystems is to adapt an existing biological system. This method has several advantages, including access to the powerful tools of protein engineering and ready biological acceptance as these structures themselves are biotic in origin. We have chosen the type IV pilus, a fiber-like structure from the bacteria Pseudomonas aeruginosa, as our model system for the development of a protein-based nanotube. This review highlights the biological characteristics of our model system, presents the novel features of our pilin-derived protein nanotubes, and discusses how these protein nanotubes may contribute to bionanotechnology.     

An integrated system for optical imaging of ice cores

An end-to-end system for optical image acquisition and data processing for ice cores has been developed for the United States National Ice Core Laboratory (NICL). The components of this system include highly integrated, automated methods for image capture in the cold-room environment and subsequent analysis by scientists. These components seamlessly manage the various aspects of physical scanning, metadata capture, image processing tests for data quality assurance, database integration and file management, processing of raw data to standard products, data distribution, and image processing and annotation tools for end-users in the ice core science community. The system has been tested operationally on cores retrieved from the West Antarctic Ice Sheet Divide drilling project during the core processing lines at NICL in 2006 and 2007.     

Coatings made of tungsten carbide and tantalum carbide for machining tools

Continuous evolution of cutting parameters has been a constant requirement for the manufacture of machining tools. This has promoted the development of new technologies to reduce the production time as well as cost without affecting the competitiveness. In the present work, high speed tools have been coated with WC and TaC by means of chemical vapour deposition and tested in a high speed milling machine. Damages on the tips of these cutting tools were analysed by scanning electron microscope. These observations show that the coating is heterogeneous and affected by the type and composition of various phases formed. Results indicate that the coating made from WC is more highly resistant to attrition than that made of TaC. Surfaces that have been machined by the coated tools are evaluated by means of the average roughness parameter     

Estimation of skin impedance models with experimental data and a proposed model for human skin impedance

The skin is a complex biological tissue whose impedance varies with frequency. The properties and structure of skin changes with the location on the body, age, geographical location and other factors. Considering these factors, skin impedance analysis is a sophisticated data analysis. However, despite all these variations, various researchers have always worked to develop an equivalent electrical model of the skin. The two most important categories of electrical models are RC‐based model and CPE‐based model which focus on the physiological stratification and biological properties of skin, respectively. In this work, experimental skin impedance data is acquired from ten sites on the body to find the fitting model. It is observed that a hybrid of fractional‐order CPE‐based model and higher‐order RC layered‐based model can provide the best fitting electrical model of skin. A new model is developed with this hybrid orders. Genetic algorithm is used for the extraction of parameter components. Least error of fitting has been observed for the proposed model as compared with the other models. This model can be used in correlating many skin problems and in the development of diagnostic tools. It will offer an additional supportive tool in‐vitro to the medical specialist.Inspec keywords: genetic algorithms, skin, data analysis, bioelectric phenomena, medical computing, electric impedance, patient diagnosisOther keywords: skin impedance models, human skin impedance, skin impedance analysis, data analysis, electrical models, RC‐based model, biological properties, experimental skin impedance data, fractional‐order CPE‐based model, skin problems, complex biological tissue, higher‐order RC layered‐based model, genetic algorithm, diagnostic tools     

Improving biomolecular pattern discovery and visualization with hybrid self-adaptive networks

There is an increasing need to develop powerful techniques to improve biomedical pattern discovery and visualization. This paper presents an automated approach, based on hybrid self-adaptive neural networks, to pattern identification and visualization for biomolecular data. The methods are tested on two datasets: leukemia expression data and DNA splice-junction sequences. Several supervised and unsupervised models are implemented and compared. A comprehensive evaluation study of some of their intrinsic mechanisms is presented. The results suggest that these tools may be useful to support biological knowledge discovery based on advanced classification and visualization tasks.     

A molecular scanner to automate proteomic research and to display proteome images

Identification and characterization of all proteins expressed by a genome in biological samples represent major challenges in proteomics. Today's commonly used high-throughput approaches combine two-dimensional electrophoresis (2-DE) with peptide mass fingerprinting (PMF) analysis. Although automation is often possible, a number of limitations still adversely affect the rate of protein identification and annotation in 2-DE databases: the sequential excision process of pieces of gel containing protein; the enzymatic digestion step; the interpretation of mass spectra (reliability of identifications); and the manual updating of 2-DE databases. We present a highly automated method that generates a fully annoated 2-DE map. Using a parallel process, all proteins of a 2-DE are first simultaneously digested proteolytically and electro-transferred onto a poly(vinylidene difluoride) membrane. The membrane is then directly scanned by MALDI-TOF MS. After automated protein identification from the obtained peptide mass fingerprints using PeptIdent software (http://www.expasy.ch/tools/peptident.html + ++), a fully annotated 2-D map is created on-line. It is a multidimensional representation of a proteome that contains interpreted PMF data in addition to protein identification results. This "MS-imaging" method represents a major step toward the development of a clinical molecular scanner.