A Color Hierarchy for Automatic Target Selection

Visual processing of color starts at the cones in the retina and continues through ventral stream visual areas, called the parvocellular pathway. Motion processing also starts in the retina but continues through dorsal stream visual areas, called the magnocellular system. Color and motion processing are functionally and anatomically discrete. Previously, motion processing areas MT and MST have been shown to have no color selectivity to a moving stimulus; the neurons were colorblind whenever color was presented along with motion. This occurs when the stimuli are luminance-defined versus the background and is considered achromatic motion processing. Is motion processing independent of color processing? We find that motion processing is intrinsically modulated by color. Color modulated smooth pursuit eye movements produced upon saccading to an aperture containing a surface of coherently moving dots upon a black background. Furthermore, when two surfaces that differed in color were present, one surface was automatically selected based upon a color hierarchy. The strength of that selection depended upon the distance between the two colors in color space. A quantifiable color hierarchy for automatic target selection has wide-ranging implications from sports to advertising to human-computer interfaces.     

Automatic Background Knowledge Selection for Matching Biomedical Ontologies

Ontology matching is a growing field of research that is of critical importance for the semantic web initiative. The use of background knowledge for ontology matching is often a key factor for success, particularly in complex and lexically rich domains such as the life sciences. However, in most ontology matching systems, the background knowledge sources are either predefined by the system or have to be provided by the user. In this paper, we present a novel methodology for automatically selecting background knowledge sources for any given ontologies to match. This methodology measures the usefulness of each background knowledge source by assessing the fraction of classes mapped through it over those mapped directly, which we call the mapping gain. We implemented this methodology in the AgreementMakerLight ontology matching framework, and evaluate it using the benchmark biomedical ontology matching tasks from the Ontology Alignment Evaluation Initiative (OAEI) 2013. In each matching problem, our methodology consistently identified the sources of background knowledge that led to the highest improvements over the baseline alignment (i.e., without background knowledge). Furthermore, our proposed mapping gain parameter is strongly correlated with the F-measure of the produced alignments, thus making it a good estimator for ontology matching techniques based on background knowledge.     

DWFS: A Wrapper Feature Selection Tool Based on a Parallel Genetic Algorithm

Many scientific problems can be formulated as classification tasks. Data that harbor relevant information are usually described by a large number of features. Frequently, many of these features are irrelevant for the class prediction. The efficient implementation of classification models requires identification of suitable combinations of features. The smaller number of features reduces the problem’s dimensionality and may result in higher classification performance. We developed DWFS, a web-based tool that allows for efficient selection of features for a variety of problems. DWFS follows the wrapper paradigm and applies a search strategy based on Genetic Algorithms (GAs). A parallel GA implementation examines and evaluates simultaneously large number of candidate collections of features. DWFS also integrates various filtering methods that may be applied as a pre-processing step in the feature selection process. Furthermore, weights and parameters in the fitness function of GA can be adjusted according to the application requirements. Experiments using heterogeneous datasets from different biomedical applications demonstrate that DWFS is fast and leads to a significant reduction of the number of features without sacrificing performance as compared to several widely used existing methods. DWFS can be accessed online at www.cbrc.kaust.edu.sa/dwfs.     

Automatic Combination of Operators in a Genetic Algorithm to Solve the Traveling Salesman Problem

Genetic algorithms are powerful search methods inspired by Darwinian evolution. To date, they have been applied to the solution of many optimization problems because of the easy use of their properties and their robustness in finding good solutions to difficult problems. The good operation of genetic algorithms is due in part to its two main variation operators, namely, crossover and mutation operators. Typically, in the literature, we find the use of a single crossover and mutation operator. However, there are studies that have shown that using multi-operators produces synergy and that the operators are mutually complementary. Using multi-operators is not a simple task because which operators to use and how to combine them must be determined, which in itself is an optimization problem. In this paper, it is proposed that the task of exploring the different combinations of the crossover and mutation operators can be carried out by evolutionary computing. The crossover and mutation operators used are those typically used for solving the traveling salesman problem. The process of searching for good combinations was effective, yielding appropriate and synergic combinations of the crossover and mutation operators. The numerical results show that the use of the combination of operators obtained by evolutionary computing is better than the use of a single operator and the use of multi-operators combined in the standard way. The results were also better than those of the last operators reported in the literature.     

Human Peak Experience Triggered by Encounters with Cetaceans

ABSTRACT

Although wild animals are listed in the literature of the humanistic sciences as a known peak trigger, this phenomenon has not until now been formally explored. In investigating peak experience from the perspective of the human—animal connection, the present study focuses on spontaneous encounters with cetaceans (whales and dolphins) that the human participant considered to be a highly significant personal event. Seven narrative stories were obtained from six human percipients who reported peak experience as a result of interactions with cetaceans which, with one exception, were free-ranging. The species represented are the Atlantic bottlenose dolphin (Tursiops truncatus); the orca (Orcinus orca), also known as killer whale or blackfish; and the beluga or white whale (Delphinapterus leucas). The data were analyzed through the process of phenomenological reduction, yielding five invariant themes: reciprocity of process, intention, connectedness, aliveness and harmony. Other essences of the wild-animal-triggered peak experience also are presented, along with suggestions regarding dynamics that may underlie spontaneous encounters which culminate in an emotional catharsis or other healing.     

A Simulation Study of Truncation Selection for a Quantitative Trait Opposed by Natural Selection

A quantitative character controlled at one locus with two alleles was submitted to artificial (mass) selection and to three modes of opposing natural selection (directional selection, overdominance and underdominance) in a large random-mating population. The selection response and the limits of the selective process were studied by deterministic simulation. The lifetime of the process was generally between 20 and 100 generations and did not appear to depend on the mode of natural selection. However, depending on the values of the parameters (initial gene frequency, selection intensity, ratio of the effect of the gene to the environmental standard deviation, fitness values) the following outcomes of selection were observed: fixation of the allele favored by artificial selection, stable nontrivial equilibrium, unstable equilibrium and loss of the allele favored by artificial selection. Finally, the results of the simulation were compared to the results of selection experiments.     

基于知识的颅内出血（ICH）三维医学图像分割方法

本文描述一种基于知识的三维医学图像自动分割方法,用于进行人体颅内出血(Intracranial Hemorrhage,ICH)的分割和分析。首先,数字化CT胶片,并自动对数字化后的胶片按照有无异常分类。然后,阀值结合模糊C均值聚类算法将图像分类成多个具有统一亮度的区域。最后,在先验知识以及预定义的规则的基础上,借助基于知识的专家系统将各个区域标记为背景、钙化点、血肿、颅骨、脑干。     

Algorithm for Automatic Forced Spirometry Quality Assessment: Technological Developments

We hypothesized that the implementation of automatic real-time assessment of quality of forced spirometry (FS) may significantly enhance the potential for extensive deployment of a FS program in the community. Recent studies have demonstrated that the application of quality criteria defined by the ATS/ERS (American Thoracic Society/European Respiratory Society) in commercially available equipment with automatic quality assessment can be markedly improved. To this end, an algorithm for assessing quality of FS automatically was reported. The current research describes the mathematical developments of the algorithm. An innovative analysis of the shape of the spirometric curve, adding 23 new metrics to the traditional 4 recommended by ATS/ERS, was done. The algorithm was created through a two-step iterative process including: (1) an initial version using the standard FS curves recommended by the ATS; and, (2) a refined version using curves from patients. In each of these steps the results were assessed against one expert''s opinion. Finally, an independent set of FS curves from 291 patients was used for validation purposes. The novel mathematical approach to characterize the FS curves led to appropriate FS classification with high specificity (95%) and sensitivity (96%). The results constitute the basis for a successful transfer of FS testing to non-specialized professionals in the community.     

Automatic ROI Selection in Structural Brain MRI Using SOM 3D Projection

This paper presents a method for selecting Regions of Interest (ROI) in brain Magnetic Resonance Imaging (MRI) for diagnostic purposes, using statistical learning and vector quantization techniques. The proposed method models the distribution of GM and WM tissues grouping the voxels belonging to each tissue in ROIs associated to a specific neurological disorder. Tissue distribution of normal and abnormal images is modelled by a Self-Organizing map (SOM), generating a set of representative prototypes, and the receptive field (RF) of each SOM prototype defines a ROI. Moreover, the proposed method computes the relative importance of each ROI by means of its discriminative power. The devised method has been assessed using 818 images from the Alzheimer''s disease Neuroimaging Initiative (ADNI) which were previously segmented through Statistical Parametric Mapping (SPM). The proposed algorithm was used over these images to parcel ROIs associated to the Alzheimer''s Disease (AD). Additionally, this method can be used to extract a reduced set of discriminative features for classification, since it compresses discriminative information contained in the brain. Voxels marked by ROIs which were computed using the proposed method, yield classification results up to 90% of accuracy for controls (CN) and Alzheimer''s disease (AD) patients, and 84% of accuracy for Mild Cognitive Impairment (MCI) and AD patients.     

Peak High-Frequency HRV and Peak Alpha Frequency Higher in PTSD

Posttraumatic stress disorder (PTSD) is difficult to treat and current PTSD treatments are not effective for all people. Despite limited evidence for its efficacy, some clinicians have implemented biofeedback for PTSD treatment. As a first step in constructing an effective biofeedback treatment program, we assessed respiration, electroencephalography (EEG) and heart rate variability (HRV) as potential biofeedback parameters for a future clinical trial. This cross-sectional study included 86 veterans; 59 with and 27 without PTSD. Data were collected on EEG measures, HRV, and respiration rate during an attentive resting state. Measures were analyzed to assess sensitivity to PTSD status and the relationship to PTSD symptoms. Peak alpha frequency was higher in the PTSD group (F(1,84) = 6.14, p = 0.01). Peak high-frequency HRV was lower in the PTSD group (F(2,78) = 26.5, p < 0.00005) when adjusting for respiration rate. All other EEG and HRV measures and respiration were not different between groups. Peak high-frequency HRV and peak alpha frequency are sensitive to PTSD status and may be potential biofeedback parameters for future PTSD clinical trials.     

Selection of a buried salt bridge by phage display

The α-helical coiled coil is a valuable folding motif for protein design and engineering. By means of phage display technology, we selected a capable binding partner for one strand of a coiled coil bearing a charged amino acid in a central hydrophobic core position. This procedure resulted in a novel coiled coil pair featuring an opposed Glu-Lys pair arranged staggered within the hydrophobic core of a coiled coil structure. Structural investigation of the selected coiled coil dimer by CD spectroscopy and MD simulations suggest that a buried salt bridge within the hydrophobic core enables the specific dimerization of two peptides.     

Clonal Selection Based Memetic Algorithm for Job Shop Scheduling Problems

A clonal selection based memetic algorithm is proposed for solving job shop scheduling problems in this paper. In the proposed algorithm, the clonal selection and the local search mechanism are designed to enhance exploration and exploitation. In the clonal selection mechanism, clonal selection, hypermutation and receptor edit theories are presented to construct an evolutionary searching mechanism which is used for exploration. In the local search mechanism, a simulated annealing local search algorithm based on Nowicki and Smutnicki＇s neighborhood is presented to exploit local optima. The proposed algorithm is examined using some well-known benchmark problems. Numerical results validate the effectiveness of the proposed algorithm.     

Classification Performance-Based Feature Selection Algorithm for Machine Learning: P-Score

Feature selection algorithms are the cornerstone of machine learning. By increasing the properties of the samples and samples, the feature selection algorithm selects the significant features. The general name of the methods that perform this function is the feature selection algorithm. The general purpose of feature selection algorithms is to select the most relevant properties of data classes and to increase the classification performance. Thus, we can select features based on their classification performance. In this study, we have developed a feature selection algorithm based on decision support vectors classification performance. The method can work according to two different selection criteria. We tested the classification performances of the features selected with P-Score with three different classifiers. Besides, we assessed P-Score performance with 13 feature selection algorithms in the literature. According to the results of the study, the P-Score feature selection algorithm has been determined as a method which can be used in the field of machine learning.