Characteristics Of Different Classes Research Articles

CloudForest: A Scalable and Efficient Random Forest Implementation for Biological Data.

Random Forest has become a standard data analysis tool in computational biology. However, extensions to existing implementations are often necessary to handle the complexity of biological datasets and their associated research questions. The growing size of these datasets requires high performance implementations. We describe CloudForest, a Random Forest package written in Go, which is particularly well suited for large, heterogeneous, genetic and biomedical datasets. CloudForest includes several extensions, such as dealing with unbalanced classes and missing values. Its flexible design enables users to easily implement additional extensions. CloudForest achieves fast running times by effective use of the CPU cache, optimizing for different classes of features and efficiently multi-threading. https://github.com/ilyalab/CloudForest.

Fourier power, subjective distance, and object categories all provide plausible models of BOLD responses in scene-selective visual areas.

Perception of natural visual scenes activates several functional areas in the human brain, including the Parahippocampal Place Area (PPA), Retrosplenial Complex (RSC), and the Occipital Place Area (OPA). It is currently unclear what specific scene-related features are represented in these areas. Previous studies have suggested that PPA, RSC, and/or OPA might represent at least three qualitatively different classes of features: (1) 2D features related to Fourier power; (2) 3D spatial features such as the distance to objects in a scene; or (3) abstract features such as the categories of objects in a scene. To determine which of these hypotheses best describes the visual representation in scene-selective areas, we applied voxel-wise modeling (VM) to BOLD fMRI responses elicited by a set of 1386 images of natural scenes. VM provides an efficient method for testing competing hypotheses by comparing predictions of brain activity based on encoding models that instantiate each hypothesis. Here we evaluated three different encoding models that instantiate each of the three hypotheses listed above. We used linear regression to fit each encoding model to the fMRI data recorded from each voxel, and we evaluated each fit model by estimating the amount of variance it predicted in a withheld portion of the data set. We found that voxel-wise models based on Fourier power or the subjective distance to objects in each scene predicted much of the variance predicted by a model based on object categories. Furthermore, the response variance explained by these three models is largely shared, and the individual models explain little unique variance in responses. Based on an evaluation of previous studies and the data we present here, we conclude that there is currently no good basis to favor any one of the three alternative hypotheses about visual representation in scene-selective areas. We offer suggestions for further studies that may help resolve this issue.

A METHOD OF DETERMINING THE ABILITY OF THE ARRESTER TO ABSORB ENERGY WITHOUT BREAKING THE HEAT BALANCE

Purpose. The aim of this study is to obtain a method for determining the capacity surge arrester nonlinear absorb energy without breaking the heat balance in modes of long-term application of operating voltage, which allows for analysis of their work in terms of violations as electricity. Methodology. For values of the energy passing through the arrester must be able to determine the current value for the voltage value in the area of leakage current-voltage characteristics. We have carried out calculations of the energy passing everywhere arrester for certain periods of time based on the current-voltage characteristics obtained experimentally. Analysis of the experimental current-voltage characteristics of resistors and literature led to the important conclusion that the dielectric properties of the ceramic varistor affect the value of active power losses in the arrester only when the active component of the leakage current is very small. This is confirmed by the characteristics of different classes of varistor voltage. This property of varistors and surge arresters shows the need to consider how the dielectric and conductive properties of the varistor ceramics in the analysis of work in the area of the arrester leakage current-voltage characteristic. These results demonstrate the need to clarify the mathematical model and the method for determining the energy dissipates in the area of the arrester leakage current CVC with their account. Results. The study, an improved mathematical model for calculating energy affects surge arrester during its working life. The study obtained the method, of evaluation capacity surge arrester, maintains heat balance throughout working life. Based on experimentally obtained current-voltage characteristic of the varistors is defined voltage at which surge arrester starts conducting active current. This allowed to receive specified mathematical model for calculating energy affects surge arrester and develop a method of evaluation capacity surge arrester maintain heat balance throughout working life.

An object-based SVM method incorporating optimal segmentation scale estimation using Bhattacharyya Distance for mapping salt cedar (Tamarisk spp.) with QuickBird imagery

Salt cedar, a predominant arid type of vegetation in Western China, provides invaluable ecosystem services and is important for economic sustainability. Most previous attempts to map salt cedar have been focused at the pixel level and not at the object level. A particular problem for object-based classification is determining the optimal scale for the segmentation. In this study, we proposed a new object-based image analysis method for classifying high resolution satellite image with support vector machine (SVM). Specifically, we set forth three objectives: (1) to choose the optimal multiscale parameters for different cover types with the aid of the Bhattacharyya Distance index; (2) to extract the class specific features for different classes to feed into the SVM classification procedure; and (3) to compare three different classification methods: the integration of object-SVM, SVM at the pixel level and nearest-neighbor at the object level. The result of the case study demonstrated that the multiscale object-SVM method, which employed spectral, texture and shadow features, produced the best overall accuracy (91.6%).

Membrane-active peptides from marine organisms--antimicrobials, cell-penetrating peptides and peptide toxins: applications and prospects.

Marine organisms are known to be a rich and unique source of bioactive compounds as they are exposed to extreme conditions in the oceans. The present study is an attempt to briefly describe some of the important membrane-active peptides (MAPs) such as antimicrobial peptides (AMPs), cell-penetrating peptides (CPPs) and peptide toxins from marine organisms. Since both AMPs and CPPs play a role in membrane perturbation and exhibit interchangeable role, they can speculatively fall under the broad umbrella of MAPs. The study focuses on the structural and functional characteristics of different classes of marine MAPs. Further, AMPs are considered as a potential remedy to antibiotic resistance acquired by several pathogens. Peptides from marine organisms show novel post-translational modifications such as cysteine knots, halogenation and histidino-alanine bridge that enable these peptides to withstand harsh marine environmental conditions. These unusual modifications of AMPs from marine organisms are expected to increase their half-life in living systems, contributing to their increased bioavailability and stability when administered as drug in in vivo systems. Apart from AMPs, marine toxins with membrane-perturbing properties could be essentially investigated for their cytotoxic effect on various pathogens and their cell-penetrating activity across various mammalian cells. The current review will help in identifying the MAPs from marine organisms with crucial post-translational modifications that can be used as template for designing novel therapeutic agents and drug-delivery vehicles for treatment of human diseases.

A New Hybrid PSO-based Genetic Algorithm and its Application to Layout Problems

Layout problems belong to NP-Complete problems theoretically. They are concerned more and more in recent years and arise in a variety of application fields such as the layout design of spacecraft modules, plant equipments, plat- forms of marine drilling well, shipping, vehicle and robots. The algorithms based on swarm intelligence are relatively ef- fective to solve these kind of problems. But usually there still exist two main defects, i.e. premature convergence and slow convergence rate. To overcome them, a new improved hybrid PSO-based genetic algorithm (HPSO-GA) is proposed on the basis of parallel genetic algorithms (PGA). In this algorithm, chaos initialization, hybrid strategy and multi- subpopulation evolution based on improved adaptive crossover and mutation are adopted. The proposed interpolating rank-based selection with pressure can prevent the algorithm from premature in the early stage and benefit accelerating convergence in the late stage as well. And more importantly, in accordance with characteristics of different classes of sub- populations, different modes of PSO update operator are introduced. It aims at making full use of the fast convergence property of particle swarm optimization (PSO). An example of layout problems shows that HPSO-GA is feasible and ef- fective.

High-Fidelity Dispatch Model of Storage Technologies for Production Costing Studies

This paper presents a high-fidelity storage dispatch model for production costing studies. The model captures the relationship between storage reservoir status and storage's participation in energy as well as ancillary markets in order to effectively dispatch such energy-limited technologies. The paper also delineates features of the proposed model that enable capturing unique characteristics of different classes of storage technologies. Production cost studies are used to ascertain the abilities of the proposed storage model to represent bulk as well as short-term storage technologies in the IEEE 24-bus reliability test system and to analyze the various avenues where each technology typically contributes to grid operation. The significance of the modeling features that attribute high fidelity to the dispatch decisions is discussed in the context of simple economic evaluation of these technologies.

On multifractality and synchronization of nonlinear stellar pulsators

Simple models of nonlinear stellar pulsation, whose temporal behavior may reproduce some of the observed features of different classes of variable stars, have been studied. The temporal behavior of dynamical variables of these models exhibits a cascade of period doubling chaos, depending on the specific values of the various control parameters. A multifractal detrended fluctuation analysis (MFDFA) method is further used to identify the scaling behavior of such synthetic time series. The MFDFA of the considered time series, for various models of nonlinear stellar pulsation, shows that the observed multifractal nature is due to long-range correlations. The pulsating star with increased nonadiabaticity and the star with increased convective luminosity, as represented by the simulated data, is shown to possess a strange attractor with noninteger correlation dimension that lies between 2–3. Also the problem of synchronization in coupled nonlinear pulsation models has been investigated using permutation entropy—a complexity measure of the system. The computed order parameter, Γ, representing the correlation of computed permutation entropy for different segments of the simulated time series of displacement of two nonidentical oscillators, has been further used to find the critical coupling parameter for general synchronization of the oscillators.

In Vitro Fermentability of Sugar Beet Pulp Derived Oligosaccharides Using Human and Pig Fecal Inocula

The in vitro fermentation characteristics of different classes of sugar beet pectic oligosaccharides (SBPOS) were studied using human and pig fecal inocula. The SBPOS consisted mainly of partially acetylated rhamnogalacturonan oligosaccharides and partially methyl-esterified/acetylated homogalacturonan oligosaccharides. Some SBPOS contained an unsaturated galacturonic acid residue at their non-reducing end. It was shown that SBPOS could be completely fermented by human and pig fecal microbiota, thereby producing butyrate yet mainly acetate and propionate as metabolites. The degradation of SBPOS by pig fecal microbiota was different and much slower compared to human fecal microbiota. In general, rhamnogalacturonan oligosaccharides were degraded slower than homogalacturonan oligosaccharides. Acetylation of rhamnogalacturonan oligosaccharides lowered the degradation rate by pig fecal microbiota but not by human fecal microbiota. No classic bifidogenic effect was shown for SBPOS using human fecal inoculum. However, several other potentially interesting modifications in the microbiota composition that can be associated with host health were observed, which are discussed.

Methods for identification of isolated carbonate buildups from seismic reflection data

Isolated carbonate buildups (ICBs) are commonly attractive exploration targets. However, identifying ICBs based only on seismic data can be difficult for a variety of reasons. These include poor-quality two-dimensional data and a basic similarity between ICBs and other features such as volcanoes, erosional remnants, and tilted fault blocks. To address these difficulties and develop reliable methods to identify ICBs, 234 seismic images were analyzed. The images included proven ICBs and other features, such as folds, volcanoes, and basement highs, which may appear similar to ICBs when imaged in seismic data. From this analysis, 18 identification criteria were derived to distinguish ICBs from non-ICB features. These criteria can be grouped into four categories: regional constraints, analysis of basic seismic geometries, analysis of geophysical details, and finer-scale seismic geometries. Systematically assessing the criteria is useful because it requires critical evaluation of the evidence present in the available data, working from the large-scale regional geology to the fine details of seismic response. It is also useful to summarize the criteria as a numerical score to facilitate comparison between different examples and different classes of ICBs and non-ICBs. Our analysis of scores of different classes of features suggests that the criteria do have some discriminatory power, but significant challenges remain.

Learning image-to-class distance metric for image classification

Image-To-Class (I2C) distance is a novel distance used for image classification and has successfully handled datasets with large intra-class variances. However, it uses Euclidean distance for measuring the distance between local features in different classes, which may not be the optimal distance metric in real image classification problems. In this article, we propose a distance metric learning method to improve the performance of I2C distance by learning per-class Mahalanobis metrics in a large margin framework. Our I2C distance is adaptive to different classes by combining with the learned metric for each class. These multiple per-class metrics are learned simultaneously by forming a convex optimization problem with the constraints that the I2C distance from each training image to its belonging class should be less than the distances to other classes by a large margin. A subgradient descent method is applied to efficiently solve this optimization problem. For efficiency and scalability to large-scale problems, we also show how to simplify the method to learn a diagonal matrix for each class. We show in experiments that our learned Mahalanobis I2C distance can significantly outperform the original Euclidean I2C distance as well as other distance metric learning methods in several prevalent image datasets, and our simplified diagonal matrices can preserve the performance but significantly speed up the metric learning procedure for large-scale datasets. We also show in experiment that our method is able to correct the class imbalance problem, which usually leads the NN-based methods toward classes containing more training images.

Computer‐aided diagnosis for dynamic contrast‐enhanced breast MRI of mass‐like lesions using a multiparametric model combining a selection of morphological, kinetic, and spatiotemporal features

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a radiological tool for the detection and discrimination of breast lesions. The aim of this study is to evaluate a computer-aided diagnosis (CAD) system for discriminating malignant from benign breast lesions at DCE-MRI by the combined use of morphological, kinetic, and spatiotemporal lesion features. Fifty-four malignant and 19 benign breast lesions in 51 patients were retrospectively evaluated. Images were acquired at two centers at 1.5 T. Mass-like lesions were automatically segmented after image normalization and elastic coregistration of contrast-enhanced frames. For each lesion, a set of 28 3D features were extracted: ten morphological (related to shape, margins, and internal enhancement distribution); nine kinetic (computed from signal-to-time curves); and nine spatiotemporal (related to the variation of the signal between adjacent frames). A support vector machine (SVM) was trained with feature subsets selected by a genetic search. Best subsets were composed of the most frequent features selected by majority rule. The performance was measured by receiver operator characteristics analysis with a stratified tenfold cross-validation and bootstrap method for confidence intervals. SVM training by the three separated classes of features resulted in an area under the curve (AUC) of 0.90 ± 0.04 (mean ± standard deviation), 0.87 ± 0.06, and 0.86 ± 0.06 for morphological, kinetic, and spatiotemporal feature, respectively. Combined training with all 28 features resulted in AUC of 0.96 ± 0.02 obtained with a selected feature subset composed by two morphological, one kinetic, and two spatiotemporal features. Quantitative combination of morphological, kinetic, and spatiotemporal features is feasible and provides a higher discriminating power than using the three different classes of features separately.

Supervised and semi-supervised learning in text classification using enhanced KNN algorithm: a comparative study of supervised and semi-supervised classification in text categorisation

To make efficient decisions, knowledge in terms of experience is needed that can be obtained from the process of learning. The present paper's aim and objective are to explore the learning process in text classification using semi-supervised learning paradigm and compare the results obtained with the supervised learning classifier's accuracy. Semi-supervised learning can be applied when limited amount of training data is available. In traditional K-nearest neighbour algorithm all features are given similar weights in all classes which is not reasonable. Few features may play vital role in some classes and in others there presence has no impact. In the present paper, exploration of assigning different weights to the features in different classes based on the concept of variance is discussed. Finally to gain insight in semi-supervised learning paradigm, supervised and semi-supervised learning paradigm in text classification are compared. Results obtained show that the semi-supervised learning paradigm can be applied in cases where very limited training data is available, but still reasonable classifier accuracy can be obtained.

Network, Anatomical, and Non-Imaging Measures for the Prediction of ADHD Diagnosis in Individual Subjects

Brain imaging methods have long held promise as diagnostic aids for neuropsychiatric conditions with complex behavioral phenotypes such as Attention-Deficit/Hyperactivity Disorder. This promise has largely been unrealized, at least partly due to the heterogeneity of clinical populations and the small sample size of many studies. A large, multi-center dataset provided by the ADHD-200 Consortium affords new opportunities to test methods for individual diagnosis based on MRI-observable structural brain attributes and functional interactions observable from resting-state fMRI. In this study, we systematically calculated a large set of standard and new quantitative markers from individual subject datasets. These features (>12,000 per subject) consisted of local anatomical attributes such as cortical thickness and structure volumes, and both local and global resting-state network measures. Three methods were used to compute graphs representing interdependencies between activations in different brain areas, and a full set of network features was derived from each. Of these, features derived from the inverse of the time series covariance matrix, under an L1-norm regularization penalty, proved most powerful. Anatomical and network feature sets were used individually, and combined with non-imaging phenotypic features from each subject. Machine learning algorithms were used to rank attributes, and performance was assessed under cross-validation and on a separate test set of 168 subjects for a variety of feature set combinations. While non-imaging features gave highest performance in cross-validation, the addition of imaging features in sufficient numbers led to improved generalization to new data. Stratification by gender also proved to be a fruitful strategy to improve classifier performance. We describe the overall approach used, compare the predictive power of different classes of features, and describe the most impactful features in relation to the current literature.

Value of amplitude, phase, and coherence features for a sensorimotor rhythm-based brain–computer interface

Measures that quantify the relationship between two or more brain signals are drawing attention as neuroscientists explore the mechanisms of large-scale integration that enable coherent behavior and cognition. Traditional Fourier-based measures of coherence have been used to quantify frequency-dependent relationships between two signals. More recently, several off-line studies examined phase-locking value (PLV) as a possible feature for use in brain-computer interface (BCI) systems. However, only a few individuals have been studied and full statistical comparisons among the different classes of features and their combinations have not been conducted. The present study examines the relative BCI performance of spectral power, coherence, and PLV, alone and in combination. The results indicate that spectral power produced classification at least as good as PLV, coherence, or any possible combination of these measures. This may be due to the fact that all three measures reflect mainly the activity of a single signal source (i.e., an area of sensorimotor cortex). This possibility is supported by the finding that EEG signals from different channels generally had near-zero phase differences. Coherence, PLV, and other measures of inter-channel relationships may be more valuable for BCIs that use signals from more than one distinct cortical source.

Recent Advances in Nonviral Vectors for Gene Delivery

Gene therapy has long been regarded a promising treatment for many diseases, whether acquired (such as AIDS or cancer) or inherited through a genetic disorder. A drug based on a nucleic acid, however, must be delivered to the interior of the target cell while surviving an array of biological defenses honed by evolution. Successful gene therapy is thus dependent on the development of an efficient delivery vector. Researchers have pursued two major vehicles for gene delivery: viral and nonviral (synthetic) vectors. Although viral vectors currently offer greater efficiency, nonviral vectors, which are typically based on cationic lipids or polymers, are preferred because of safety concerns with viral vectors. So far, nonviral vectors can readily transfect cells in culture, but efficient nanomedicines remain far removed from the clinic. Overcoming the obstacles associated with nonviral vectors to improve the delivery efficiency and therapeutic effect of nucleic acids is thus an active area of current research. The difficulties are manifold, including the strong interaction of cationic delivery vehicles with blood components, uptake by the reticuloendothelial system (RES), toxicity, and managing the targeting ability of the carriers with respect to the cells of interest. Modifying the surface with poly(ethylene glycol), that is, PEGylation, is the predominant method used to reduce the binding of plasma proteins to nonviral vectors and minimize clearance by the RES after intravenous administration. Nanoparticles that are not rapidly cleared from the circulation accumulate in the tumors because of the enhanced permeability and retention effect, and the targeting ligands attached to the distal end of the PEGylated components allow binding to the receptors on the target cell surface. Neutral and anionic liposomes have been also developed for systemic delivery of nucleic acids in experimental animal models. Other approaches include (i) designing and synthesizing novel cationic lipids and polymers, (ii) chemically coupling the nucleic acid to peptides, targeting ligands, polymers, or environmentally sensitive moieties, and (iii) utilizing inorganic nanoparticles in nucleic acid delivery. Recently, the different classes of nonviral vectors appear to be converging, and the ability to combine features of different classes of nonviral vectors in a single strategy has emerged. With the strengths of several approaches working in concert, more hurdles associated with efficient nucleic acid delivery might therefore be overcome. In this Account, we focus on these novel nonviral vectors, which are classified as multifunctional hybrid nucleic acid vectors, novel membrane/core nanoparticles for nucleic acid delivery, and ultrasound-responsive nucleic acid vectors. We highlight systemic delivery studies and consider the future prospects for nucleic acid delivery. A better understanding of the fate of the nanoparticles inside the cell and of the interactions between the parts of hybrid particles should lead to a delivery system suitable for clinical use. We also underscore the value of sustained release of a nucleic acid in this endeavor; making vectors targeted to cells with sustained release in vivo should provide an interesting research challenge.

System co-design and data management for flash devices

Flash devices are emerging as a replacement for disks. How does this evolution impact the design of data management systems? While flash devices have been available for years, this question is still open. In this tutorial, we share two views on the development of data management systems for flash devices. The first view considers that flash devices introduce so much complexity that it is necessary to reconsider the strictly layered approach between storage system, operating system and data management system. The second view considers that data management systems should recognize the complexity of flash devices and leverage the characteristics of different classes of devices for different usage patterns. Throughout the tutorial, we will cover the data management stack: from the fundamentals of flash technology, through storage for database systems and the manipulation of flash-resident data, to query processing.

Giving the ‘Right’ Route Directions: The Requirements for Pedestrian Navigation Systems

AbstractIt is more natural and effective to include a variety of landmarks when providing route directions. Many of the existing solutions, however, behave like car navigation systems and do not include references to such landmarks. This research provides empirical evidence to identify the information requirements for an automated pedestrian navigation system. The two experiments discussed within this article helped identify the criteria that govern the relative saliency of features of interest within an urban environment. There are a large variety of features of interest that can be used as directional aids within route descriptions (buildings, statues, monuments, hills, roads, etc). Different measures are therefore required in order to calculate the saliency of the different classes of features. The research revealed that the most important measures of saliency include name, size, age, and colour. The research confirmed that that relative visibility and the direction of approach to the feature also affect a landmark's saliency. The research also highlighted the role that features of interest play in reorientation, confirmatory, and direction‐giving tasks.

Characterizations of volatile organic compounds during high ozone episodes in Beijing, China

Air samples were collected in Beijing from June through August 2008, and concentrations of volatile organic compounds (VOCs) in those samples are here discussed. This sampling was performed to increase understanding of the distributions of their compositions, illustrate the overall characteristics of different classes of VOCs, assess the ages of air masses, and apportion sources of VOCs using principal compound analysis/absolute principal component scores (PCA/APCS). During the sampling periods, the relative abundance of the four classes of VOCs as determined by the concentration-based method was different from that determined by the reactivity approach. Alkanes were found to be most abundant (44.3-50.1%) by the concentration-based method, but aromatic compounds were most abundant (38.2-44.5%) by the reactivity approach. Aromatics and alkenes contributed most (73-84%) to the ozone formation potential. Toluene was the most abundant compound (11.8-12.7%) during every sampling period. When the maximum incremental reactivity approach was used, propene, toluene, m,p-xylene, 1-butene, and 1,2,4-trimethylbenzene were the five most abundant compounds during two sampling periods. X/B, T/B, and E/B ratios in this study were lower than those found in other cities, possibly due to the aging of the air mass at this site. Four components were extracted from application of PCA to the data. It was found that the contribution of vehicle exhaust to total VOCs accounted for 53% of VOCs, while emissions due to the solvent use contributed 33% of the total VOCs. Industrial sources contributed 3% and biogenic sources contributed 11%. The results showed that vehicle exhausts (i.e., unburned vehicle emissions+ vehicle internal engine combustion) were dominant in VOC emissions during the experimental period. The solvent use made the second most significant contribution to ambient VOCs.

Lymphatic absorption of choline plasmalogen is much higher than that of ethanolamine plasmalogen in rats

Plasmalogen is a subclass of phospholipids widely distributed in animal tissues and ingested as food; however, the absorptive characteristics of different classes of plasmalogen have not been clarified. Our object was to compare the lymphatic output of choline and ethanolamine plasmalogens after an administration of phospholipid preparations containing each class of plasmalogens, and to analyze molecular species of plasmalogen absorbed into the lymph. A duodenal infusion of 1 ml of 10% emulsion of choline phospholipid (PC) containing 50.6% choline plasmalogen (PlsCho) or ethanolamine phospholipid (PE) containing 52.5% ethanolamine plasmalogen (PlsEtn) was administered in the lymph duct-cannulated rats. Molecular species of plasmalogen absorbed into the lymph were measured by LC-MS/MS. Lymph outputs of PlsCho and PlsEtn increased and reached a peak value at 3 h after PC and PE injection, respectively. The peak value of PlsCho was much higher and remained at a high level until 8 h, whereas PlsEtn output fell to half of the peak value at 7 h. Total lymphatic output of PlsCho was 5-times higher than that of PlsEtn. Compositions of sn-1 in lymph plasmalogens roughly reflected those of the injected lipids, whereas sn-2 in both PlsCho and PlsEtn was rich in arachidonic acid (20:4) regardless of the composition of the administered fatty acid. Both plasmalogen and lysoplasmalogen after PE injection were not released into the portal vein. Lymphatic absorption of PlsCho is much higher than that of PlsEtn in rats, and plasmalogens are re-esterified as 20:4-rich forms in the small intestine.