Double level selection in a constitutional dynamic library of coordination driven supramolecular polygons.

PubMed

Rancan, Marzio; Tessarolo, Jacopo; Casarin, Maurizio; Zanonato, Pier Luigi; Quici, Silvio; Armelao, Lidia

2014-07-21

A constitutional dynamic library (CDL) of Cu(II) metallo-supramolecular polygons has been studied as a bench test to examine an interesting selection case based on molecular recognition. Sorting of the CDL polygons is achieved through a proper guest that is hosted into the triangular metallo-macrocycle constituent. Two selection mechanisms are observed, a guest induced path and a guest templated self-assembly (virtual library approach). Remarkably, the triangular host can accommodate several guests with a degree of selectivity ranging from ∼1 to ∼10(4) for all possible guest pairs. A double level selection operates: guests drive the CDL toward the triangular polygon, and, at the same time, this is able to pick a specific guest from a set of competitive molecules, according to a selectivity-affinity correlation. Association constants of the host-guest systems have been determined. Guest competition and exchange studies have been analyzed through variable temperature UV-Vis absorption spectroscopy and single crystal X-ray diffraction studies. Molecular structures and electronic properties of the triangular polygon and of the host-guest systems also have been studied by means of all electrons density functional theory (DFT) and time-dependent density functional theory (TDDFT) calculations including dispersive contributions. DFT outcomes ultimately indicate the dispersive nature of the host-guest interactions, while TDDFT results allow a thorough assignment of the host and host-guests spectral features.

Experimental evolution of recombination and crossover interference in Drosophila caused by directional selection for stress-related traits.

PubMed

Aggarwal, Dau Dayal; Rashkovetsky, Eugenia; Michalak, Pawel; Cohen, Irit; Ronin, Yefim; Zhou, Dan; Haddad, Gabriel G; Korol, Abraham B

2015-11-27

Population genetics predicts that tight linkage between new and/or pre-existing beneficial and deleterious alleles should decrease the efficiency of natural selection in finite populations. By decoupling beneficial and deleterious alleles and facilitating the combination of beneficial alleles, recombination accelerates the formation of high-fitness genotypes. This may impose indirect selection for increased recombination. Despite the progress in theoretical understanding, interplay between recombination and selection remains a controversial issue in evolutionary biology. Even less satisfactory is the situation with crossover interference, which is a deviation of double-crossover frequency in a pair of adjacent intervals from the product of recombination rates in the two intervals expected on the assumption of crossover independence. Here, we report substantial changes in recombination and interference in three long-term directional selection experiments with Drosophila melanogaster: for desiccation (~50 generations), hypoxia, and hyperoxia tolerance (>200 generations each). For all three experiments, we found a high interval-specific increase of recombination frequencies in selection lines (up to 40-50% per interval) compared to the control lines. We also discovered a profound effect of selection on interference as expressed by an increased frequency of double crossovers in selection lines. Our results show that changes in interference are not necessarily coupled with increased recombination. Our results support the theoretical predictions that adaptation to a new environment can promote evolution toward higher recombination. Moreover, this is the first evidence of selection for different recombination-unrelated traits potentially leading, not only to evolution toward increased crossover rates, but also to changes in crossover interference, one of the fundamental features of recombination.

An Adaptive Genetic Association Test Using Double Kernel Machines

PubMed Central

Zhan, Xiang; Epstein, Michael P.; Ghosh, Debashis

2014-01-01

Recently, gene set-based approaches have become very popular in gene expression profiling studies for assessing how genetic variants are related to disease outcomes. Since most genes are not differentially expressed, existing pathway tests considering all genes within a pathway suffer from considerable noise and power loss. Moreover, for a differentially expressed pathway, it is of interest to select important genes that drive the effect of the pathway. In this article, we propose an adaptive association test using double kernel machines (DKM), which can both select important genes within the pathway as well as test for the overall genetic pathway effect. This DKM procedure first uses the garrote kernel machines (GKM) test for the purposes of subset selection and then the least squares kernel machine (LSKM) test for testing the effect of the subset of genes. An appealing feature of the kernel machine framework is that it can provide a flexible and unified method for multi-dimensional modeling of the genetic pathway effect allowing for both parametric and nonparametric components. This DKM approach is illustrated with application to simulated data as well as to data from a neuroimaging genetics study. PMID:26640602

Three-level cobblestone-like TiO2 micro/nanocones for dual-responsive water/oil reversible wetting without fluorination

NASA Astrophysics Data System (ADS)

Zhou, Chen; Li, Guoqiang; Li, Chuanzong; Zhang, Zhen; Zhang, Yachao; Wu, Sizhu; Hu, Yanlei; Zhu, Wulin; Li, Jiawen; Chu, Jiaru; Hu, Zhijia; Wu, Dong; Yu, Liandong

2017-10-01

In this work, a kind of three-level cobblestone-like anatase TiO2 microcone array was fabricated on titanium sheets by femtosecond laser-induced self-assembly. This three level structure consisted of cobblestone-like features (15-25 μm in height and 20-35 μm in diameter), ˜460 nm ripple-like features, and smaller particles (10-500 nm). The formation of microcone arrays can be ascribed to the interaction of alternant laser beam ablation. TiO2 surfaces display dual-responsive water/oil reversible wetting via heat treatment and selective UV irradiation without fluorination. It is indicated that three-level scale surface roughness can amplify the wetting character of the Ti surface, and the mechanism for reversible switching between extreme wettabilities is caused by the conversion between Ti-OH and Ti-O. Moreover, the double-faced superhydrophobic and double-faced superhydrophilic Ti samples were constructed, which exhibited stable superhydrophobicity and underwater superoleophobicity in water-oil solution, respectively, even when strongly shaken. Finally, we present the hybrid-patterned TiO2 surface and realized reversible switching pattern wettability.

Joint seismic data denoising and interpolation with double-sparsity dictionary learning

NASA Astrophysics Data System (ADS)

Zhu, Lingchen; Liu, Entao; McClellan, James H.

2017-08-01

Seismic data quality is vital to geophysical applications, so that methods of data recovery, including denoising and interpolation, are common initial steps in the seismic data processing flow. We present a method to perform simultaneous interpolation and denoising, which is based on double-sparsity dictionary learning. This extends previous work that was for denoising only. The original double-sparsity dictionary learning algorithm is modified to track the traces with missing data by defining a masking operator that is integrated into the sparse representation of the dictionary. A weighted low-rank approximation algorithm is adopted to handle the dictionary updating as a sparse recovery optimization problem constrained by the masking operator. Compared to traditional sparse transforms with fixed dictionaries that lack the ability to adapt to complex data structures, the double-sparsity dictionary learning method learns the signal adaptively from selected patches of the corrupted seismic data, while preserving compact forward and inverse transform operators. Numerical experiments on synthetic seismic data indicate that this new method preserves more subtle features in the data set without introducing pseudo-Gibbs artifacts when compared to other directional multi-scale transform methods such as curvelets.

Lesion-induced DNA weak structural changes detected by pulsed EPR spectroscopy combined with site-directed spin labelling.

PubMed

Sicoli, Giuseppe; Mathis, Gérald; Aci-Sèche, Samia; Saint-Pierre, Christine; Boulard, Yves; Gasparutto, Didier; Gambarelli, Serge

2009-06-01

Double electron-electron resonance (DEER) was applied to determine nanometre spin-spin distances on DNA duplexes that contain selected structural alterations. The present approach to evaluate the structural features of DNA damages is thus related to the interspin distance changes, as well as to the flexibility of the overall structure deduced from the distance distribution. A set of site-directed nitroxide-labelled double-stranded DNA fragments containing defined lesions, namely an 8-oxoguanine, an abasic site or abasic site analogues, a nick, a gap and a bulge structure were prepared and then analysed by the DEER spectroscopic technique. New insights into the application of 4-pulse DEER sequence are also provided, in particular with respect to the spin probes' positions and the rigidity of selected systems. The lesion-induced conformational changes observed, which were supported by molecular dynamics studies, confirm the results obtained by other, more conventional, spectroscopic techniques. Thus, the experimental approaches described herein provide an efficient method for probing lesion-induced structural changes of nucleic acids.

Dynamical features and electric field strengths of double layers driven by currents. [in auroras

NASA Technical Reports Server (NTRS)

Singh, N.; Thiemann, H.; Schunk, R. W.

1985-01-01

In recent years, a number of papers have been concerned with 'ion-acoustic' double layers. In the present investigation, results from numerical simulations are presented to show that the shapes and forms of current-driven double layers evolve dynamically with the fluctuations in the current through the plasma. It is shown that double layers with a potential dip can form even without the excitation of ion-acoustic modes. Double layers in two-and one-half-dimensional simulations are discussed, taking into account the simulation technique, the spatial and temporal features of plasma, and the dynamical behavior of the parallel potential distribution. Attention is also given to double layers in one-dimensional simulations, and electrical field strengths predicted by two-and one-half-dimensional simulations.

Intelligent feature selection techniques for pattern classification of Lamb wave signals

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hinders, Mark K.; Miller, Corey A.

2014-02-18

Lamb wave interaction with flaws is a complex, three-dimensional phenomenon, which often frustrates signal interpretation schemes based on mode arrival time shifts predicted by dispersion curves. As the flaw severity increases, scattering and mode conversion effects will often dominate the time-domain signals, obscuring available information about flaws because multiple modes may arrive on top of each other. Even for idealized flaw geometries the scattering and mode conversion behavior of Lamb waves is very complex. Here, multi-mode Lamb waves in a metal plate are propagated across a rectangular flat-bottom hole in a sequence of pitch-catch measurements corresponding to the double crossholemore » tomography geometry. The flaw is sequentially deepened, with the Lamb wave measurements repeated at each flaw depth. Lamb wave tomography reconstructions are used to identify which waveforms have interacted with the flaw and thereby carry information about its depth. Multiple features are extracted from each of the Lamb wave signals using wavelets, which are then fed to statistical pattern classification algorithms that identify flaw severity. In order to achieve the highest classification accuracy, an optimal feature space is required but it’s never known a priori which features are going to be best. For structural health monitoring we make use of the fact that physical flaws, such as corrosion, will only increase over time. This allows us to identify feature vectors which are topologically well-behaved by requiring that sequential classes “line up” in feature vector space. An intelligent feature selection routine is illustrated that identifies favorable class distributions in multi-dimensional feature spaces using computational homology theory. Betti numbers and formal classification accuracies are calculated for each feature space subset to establish a correlation between the topology of the class distribution and the corresponding classification accuracy.« less

Helium in double-detonation models of type Ia supernovae

NASA Astrophysics Data System (ADS)

Boyle, Aoife; Sim, Stuart A.; Hachinger, Stephan; Kerzendorf, Wolfgang

2017-03-01

The double-detonation explosion model has been considered a candidate for explaining astrophysical transients with a wide range of luminosities. In this model, a carbon-oxygen white dwarf star explodes following detonation of a surface layer of helium. One potential signature of this explosion mechanism is the presence of unburned helium in the outer ejecta, left over from the surface helium layer. In this paper we present simple approximations to estimate the optical depths of important He I lines in the ejecta of double-detonation models. We use these approximations to compute synthetic spectra, including the He I lines, for double-detonation models obtained from hydrodynamical explosion simulations. Specifically, we focus on photospheric-phase predictions for the near-infrared 10 830 Å and 2 μm lines of He I. We first consider a double detonation model with a luminosity corresponding roughly to normal SNe Ia. This model has a post-explosion unburned He mass of 0.03 M⊙ and our calculations suggest that the 2 μm feature is expected to be very weak but that the 10 830 Å feature may have modest opacity in the outer ejecta. Consequently, we suggest that a moderate-to-weak He I 10 830 Å feature may be expected to form in double-detonation explosions at epochs around maximum light. However, the high velocities of unburned helium predicted by the model ( 19 000 km s-1) mean that the He I 10 830 Å feature may be confused or blended with the C I 10 690 Å line forming at lower velocities. We also present calculations for the He I 10 830 Å and 2 μm lines for a lower mass (low luminosity) double detonation model, which has a post-explosion He mass of 0.077 M⊙. In this case, both the He I features we consider are strong and can provide a clear observational signature of the double-detonation mechanism.

How do the effects of mutations add up?

NASA Astrophysics Data System (ADS)

Velenich, Andrea; Dai, Mingjie; Gore, Jeff

2011-03-01

Genetic mutations affect the fitness of any organism and provide the variability necessary for natural selection to occur. Given the fitness of a wild type organism and the fitness of mutants A and B which differ from the wild type by a single mutation, predicting the fitness of the double mutant AB is a fundamental problem with broad implications in many fields, from evolutionary theory to medicine. Analysis of millions of double gene knockouts in yeast reveals that, on average, the fitness of AB is the product of the fitness of A and the fitness of B. However, most pairs of mutations deviate from this mean behavior in a way that challenges existing theoretical models. We propose a natural generalization of the geometric Fisher's model which accommodates the experimentally observed features and allows us to characterize the fitness landscape of yeast.

Analysis and Design of a Double-Divert Spiral Groove Seal

NASA Technical Reports Server (NTRS)

Zheng, Xiaoqing; Berard, Gerald

2007-01-01

This viewgraph presentation describes the design and analysis of a double spiral groove seal. The contents include: 1) Double Spiral Design Features; 2) Double Spiral Operational Features; 3) Mating Ring/Rotor Assembly; 4) Seal Ring Assembly; 5) Insert Segment Joints; 6) Rotor Assembly Completed Prototype Parts; 7) Seal Assembly Completed Prototype Parts; 8) Finite Element Analysis; 9) Computational Fluid Dynamics (CFD) Analysis; 10) Restrictive Orifice Design; 11) Orifice CFD Model; 12) Orifice Results; 13) Restrictive Orifice; 14) Seal Face Coning; 15) Permanent Magnet Analysis; 16) Magnetic Repulsive Force; 17) Magnetic Repulsive Test Results; 18) Spin Testing; and 19) Testing and Validation.

Crystal structures of the structure-selective nuclease Mus81-Eme1 bound to flap DNA substrates

PubMed Central

Gwon, Gwang Hyeon; Jo, Aera; Baek, Kyuwon; Jin, Kyeong Sik; Fu, Yaoyao; Lee, Jong-Bong; Kim, YoungChang; Cho, Yunje

2014-01-01

The Mus81-Eme1 complex is a structure-selective endonuclease with a critical role in the resolution of recombination intermediates during DNA repair after interstrand cross-links, replication fork collapse, or double-strand breaks. To explain the molecular basis of 3′ flap substrate recognition and cleavage mechanism by Mus81-Eme1, we determined crystal structures of human Mus81-Eme1 bound to various flap DNA substrates. Mus81-Eme1 undergoes gross substrate-induced conformational changes that reveal two key features: (i) a hydrophobic wedge of Mus81 that separates pre- and post-nick duplex DNA and (ii) a “5′ end binding pocket” that hosts the 5′ nicked end of post-nick DNA. These features are crucial for comprehensive protein-DNA interaction, sharp bending of the 3′ flap DNA substrate, and incision strand placement at the active site. While Mus81-Eme1 unexpectedly shares several common features with members of the 5′ flap nuclease family, the combined structural, biochemical, and biophysical analyses explain why Mus81-Eme1 preferentially cleaves 3′ flap DNA substrates with 5′ nicked ends. PMID:24733841

The double slit experiment and the time reversed fire alarm

NASA Astrophysics Data System (ADS)

Halabi, Tarek

2011-03-01

When both slits of the double slit experiment are open, closing one paradoxically increases the detection rate at some points on the detection screen. Feynman famously warned that temptation to "understand" such a puzzling feature only draws us into blind alleys. Nevertheless, we gain insight into this feature by drawing an analogy between the double slit experiment and a time reversed fire alarm. Much as closing the slit increases probability of a future detection, ruling out fire drill scenarios, having heard the fire alarm, increases probability of a past fire (using Bayesian inference). Classically, Bayesian inference is associated with computing probabilities of past events. We therefore identify this feature of the double slit experiment with a time reversed thermodynamic arrow. We believe that much of the enigma of quantum mechanics is simply due to some variation of time's arrow.

Effects of early focal brain injury on memory for visuospatial patterns: selective deficits of global-local processing.

PubMed

Stiles, Joan; Stern, Catherine; Appelbaum, Mark; Nass, Ruth; Trauner, Doris; Hesselink, John

2008-01-01

Selective deficits in visuospatial processing are present early in development among children with perinatal focal brain lesions (PL). Children with right hemisphere PL (RPL) are impaired in configural processing, while children with left hemisphere PL (LPL) are impaired in featural processing. Deficits associated with LPL are less pervasive than those observed with RPL, but this difference may reflect the structure of the tasks used for assessment. Many of the tasks used to date may place greater demands on configural processing, thus highlighting this deficit in the RPL group. This study employed a task designed to place comparable demands on configural and featural processing, providing the opportunity to obtain within-task evidence of differential deficit. Sixty-two 5- to 14-year-old children (19 RPL, 19 LPL, and 24 matched controls) reproduced from memory a series of hierarchical forms (large forms composed of small forms). Global- and local-level reproduction accuracy was scored. Controls were equally accurate on global- and local-level reproduction. Children with RPL were selectively impaired on global accuracy, and children with LPL on local accuracy, thus documenting a double dissociation in global-local processing.

Switching auditory attention using spatial and non-spatial features recruits different cortical networks.

PubMed

Larson, Eric; Lee, Adrian K C

2014-01-01

Switching attention between different stimuli of interest based on particular task demands is important in many everyday settings. In audition in particular, switching attention between different speakers of interest that are talking concurrently is often necessary for effective communication. Recently, it has been shown by multiple studies that auditory selective attention suppresses the representation of unwanted streams in auditory cortical areas in favor of the target stream of interest. However, the neural processing that guides this selective attention process is not well understood. Here we investigated the cortical mechanisms involved in switching attention based on two different types of auditory features. By combining magneto- and electro-encephalography (M-EEG) with an anatomical MRI constraint, we examined the cortical dynamics involved in switching auditory attention based on either spatial or pitch features. We designed a paradigm where listeners were cued in the beginning of each trial to switch or maintain attention halfway through the presentation of concurrent target and masker streams. By allowing listeners time to switch during a gap in the continuous target and masker stimuli, we were able to isolate the mechanisms involved in endogenous, top-down attention switching. Our results show a double dissociation between the involvement of right temporoparietal junction (RTPJ) and the left inferior parietal supramarginal part (LIPSP) in tasks requiring listeners to switch attention based on space and pitch features, respectively, suggesting that switching attention based on these features involves at least partially separate processes or behavioral strategies. © 2013 Elsevier Inc. All rights reserved.

Glycosyl-Nucleolipids as new bioinspired amphiphiles.

PubMed

Latxague, Laurent; Patwa, Amit; Amigues, Eric; Barthélémy, Philippe

2013-09-30

Four new Glycosyl-NucleoLipid (GNL) analogs featuring either a single fluorocarbon or double hydrocarbon chains were synthesized in good yields from azido thymidine as starting material. Physicochemical studies (surface tension measurements, differential scanning calorimetry) indicate that hydroxybutanamide-based GNLs feature endothermic phase transition temperatures like the previously reported double chain glycerol-based GNLs. The second generation of GNFs featuring a free nucleobase reported here presents a better surface activity (lower glim) compared to the first generation of GNFs.

Detection of shifted double JPEG compression by an adaptive DCT coefficient model

NASA Astrophysics Data System (ADS)

Wang, Shi-Lin; Liew, Alan Wee-Chung; Li, Sheng-Hong; Zhang, Yu-Jin; Li, Jian-Hua

2014-12-01

In many JPEG image splicing forgeries, the tampered image patch has been JPEG-compressed twice with different block alignments. Such phenomenon in JPEG image forgeries is called the shifted double JPEG (SDJPEG) compression effect. Detection of SDJPEG-compressed patches could help in detecting and locating the tampered region. However, the current SDJPEG detection methods do not provide satisfactory results especially when the tampered region is small. In this paper, we propose a new SDJPEG detection method based on an adaptive discrete cosine transform (DCT) coefficient model. DCT coefficient distributions for SDJPEG and non-SDJPEG patches have been analyzed and a discriminative feature has been proposed to perform the two-class classification. An adaptive approach is employed to select the most discriminative DCT modes for SDJPEG detection. The experimental results show that the proposed approach can achieve much better results compared with some existing approaches in SDJPEG patch detection especially when the patch size is small.

Influences of the molecular fuel structure on combustion reactions towards soot precursors in selected alkane and alkene flames.

PubMed

Ruwe, Lena; Moshammer, Kai; Hansen, Nils; Kohse-Höinghaus, Katharina

2018-04-25

In this study, we experimentally investigate the high-temperature oxidation kinetics of n-pentane, 1-pentene and 2-methyl-2-butene (2M2B) in a combustion environment using flame-sampling molecular beam mass spectrometry. The selected C5 fuels are prototypes for linear and branched, saturated and unsaturated fuel components, featuring different C-C and C-H bond structures. It is shown that the formation tendency of species, such as polycyclic aromatic hydrocarbons (PAHs), yielded through mass growth reactions increases drastically in the sequence n-pentane < 1-pentene < 2M2B. This comparative study enables valuable insights into fuel-dependent reaction sequences of the gas-phase combustion mechanism that provide explanations for the observed difference in the PAH formation tendency. First, we investigate the fuel-structure-dependent formation of small hydrocarbon species that are yielded as intermediate species during the fuel decomposition, because these species are at the origin of the subsequent mass growth reaction pathways. Second, we review typical PAH formation reactions inspecting repetitive growth sequences in dependence of the molecular fuel structure. Third, we discuss how differences in the intermediate species pool influence the formation reactions of key aromatic ring species that are important for the PAH growth process underlying soot formation. As a main result it was found that for the fuels featuring a C[double bond, length as m-dash]C double bond, the chemistry of their allylic fuel radicals and their decomposition products strongly influences the combination reactions to the initially formed aromatic ring species and as a consequence, the PAH formation tendency.

Hollow Pd/MOF Nanosphere with Double Shells as Multifunctional Catalyst for Hydrogenation Reaction.

PubMed

Wan, Mingming; Zhang, Xinlu; Li, Meiyan; Chen, Bo; Yin, Jie; Jin, Haichao; Lin, Lin; Chen, Chao; Zhang, Ning

2017-10-01

A new type of hollow nanostructure featured double metal-organic frameworks shells with metal nanoparticles (MNPs) is designed and fabricated by the methods of ship in a bottle and bottle around the ship. The nanostructure material, hereinafter denoted as Void@HKUST-1/Pd@ZIF-8, is confirmed by the analyses of photograph, transmission electron microscopy, scanning electron microscopy, powder X-ray diffraction, inductively coupled plasma, and N 2 sorption. It possesses various multifunctionally structural characteristics such as hollow cavity which can improve mass transfer, the adjacent of the inner HKUST-1 shell to the void which enables the matrix of the shell to host and well disperse MNPs, and an outer ZIF-8 shell which acts as protective layer against the leaching of MNPs and a sieve to guarantee molecular-size selectivity. This makes the material eligible candidates for the heterogeneous catalyst. As a proof of concept, the liquid-phase hydrogenation of olefins with different molecular sizes as a model reaction is employed. It demonstrates the efficient catalytic activity and size-selectivity of Void@HKUST-1/Pd@ZIF-8. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The Anti-Methicillin-Resistant Staphylococcus aureus Quinolone WCK 771 Has Potent Activity against Sequentially Selected Mutants, Has a Narrow Mutant Selection Window against Quinolone-Resistant Staphylococcus aureus, and Preferentially Targets DNA Gyrase▿ †

PubMed Central

Bhagwat, Sachin S.; Mundkur, Lakshmi A.; Gupte, Shrikant V.; Patel, Mahesh V.; Khorakiwala, Habil F.

2006-01-01

WCK 771 is a broad-spectrum fluoroquinolone with enhanced activity against quinolone-resistant staphylococci. To understand the impact of the target-level interactions of WCK 771 on its antistaphylococcal pharmacodynamic properties, we determined the MICs for genetically defined mutants and studied the mutant prevention concentrations (MPCs), the frequency of mutation, and the cidality against the wild type and double mutants. There was a twofold increase in the MICs of WCK 771 for single gyrA mutants, indicating that DNA gyrase is its primary target. All first- and second-step mutants selected by WCK 771 revealed gyrA and grlA mutations, respectively. The MICs of WCK 771 and clinafloxacin were found to be superior to those of other quinolones against strains with double and triple mutations. WCK 771 was also cidal for high-density double mutants at low concentrations. WCK 771 and clinafloxacin showed narrow mutant selection windows compared to those of the other quinolones. Against a panel of 50 high-level quinolone-resistant clinical isolates of staphylococci (ciprofloxacin MIC ≥ 16 μg/ml), the WCK 771 MPCs were ≤2 μg/ml for 68% of the strains and ≤4 μg/ml for 28% of the strains. Our results demonstrate that gyrA is the primary target of WCK 771 and that it has pharmacodynamic properties remarkably different from those of quinolones with dual targets (garenoxacin and moxifloxacin) and topoisomerase IV-specific quinolones (trovafloxacin). WCK 771 displayed an activity profile comparable to that of clinafloxacin, a dual-acting quinolone with a high affinity to DNA gyrase. Overall, the findings signify the key role of DNA gyrase in determining the optimal antistaphylococcal features of quinolones. PMID:16940059

Statistical Analysis of the Skaion Network Security Dataset

DTIC Science & Technology

2012-09-01

DataType :=xlDelimited, _ TextQualifier:=xlDoubleQuote, ConsecutiveDelimiter:=True, Tab:=False, _ Semicolon:=False, Comma:=False, Space...Selection.TextToColumns Destination:=Range(“E1”), DataType :=xlDelimited, _ TextQualifier:=xlDoubleQuote, ConsecutiveDelimiter:=True, Tab:=False...True Columns(“F:F”).Select Selection.TextToColumns Destination:=Range(“F1”), DataType :=xlDelimited, _ TextQualifier:=xlDoubleQuote

Double trisomy 48,XXX,+18 with multiple dysmorphic features.

PubMed

Jiang, Zi-Yan; Wu, Xiao-Hui; Zou, Chao-Chun

2015-02-01

Chromosomal abnormality is a common cause of congenital anomalies, psychiatric disorders, and mental retardation. However, the double trisomy 48,XXX,+18 is a rare chromosome abnormality. Case report and literature review. A 7-hour-old girl presented to our unit because of poor response after birth. She presented with multiple dysmorphic features, including small for gestational age infant, flat nasal bridge, widely-spaced eyes, the left thumb deformities, flat facial profile, raised sternum, ventricular septal defect, the third lateral brain ventricle enlargement, and small liver. This case expands the spectrum of malformations reported in association with the double trisomy 48,XXX,+18. The literature on 16 fetuses or infants with the 48,XXX,+18 were also reviewed. These data suggested that in patients with clinical features similar to trisomy 18, especially with anomalies of the ears and/or reproductive malformations, double trisomy (48,XXX,+18) should be considered and karyotyping should be performed although it is a rare disease.

MYC/BCL2/BCL6 triple hit lymphoma: a study of 40 patients with a comparison to MYC/BCL2 and MYC/BCL6 double hit lymphomas.

PubMed

Huang, Wenting; Medeiros, L Jeffrey; Lin, Pei; Wang, Wei; Tang, Guilin; Khoury, Joseph; Konoplev, Sergej; Yin, C Cameron; Xu, Jie; Oki, Yasuhiro; Li, Shaoying

2018-05-21

High-grade B-cell lymphomas with MYC, BCL2, and BCL6 rearrangements (triple hit lymphoma) are uncommon. We studied the clinicopathologic features of 40 patients with triple hit lymphoma and compared them to 157 patients with MYC/BCL2 double hit lymphoma and 13 patients with MYC/BCL6 double hit lymphoma. The triple hit lymphoma group included 25 men and 15 women with a median age of 61 years (range, 34-85). Nine patients had a history of B-cell lymphoma. Histologically, 23 (58%) cases were diffuse large B-cell lymphoma and 17 cases had features of B-cell lymphoma, unclassifiable, with features intermediate between diffuse large B-cell lymphoma and Burkitt lymphoma. Most cases of triple hit lymphoma were positive for CD10 (100%), BCL2 (95%), BCL6 (82%), MYC (74%), and 71% with MYC and BCL2 coexpression. P53 was overexpressed in 29% of triple hit lymphoma cases. The clinicopathological features of triple hit lymphoma patients were similar to patients with MYC/BCL2 and MYC/BCL6 double hit lymphoma, except that triple hit lymphoma cases were more often CD10 positive compared with MYC/BCL6 double hit lymphoma (p < 0.05). Induction chemotherapy used was similar for patients with triple hit lymphoma and double hit lymphoma and overall survival in triple hit lymphoma patients was 17.6 months, similar to the overall survival of patients with double hit lymphoma (p = 0.67). Patients with triple hit lymphoma showing P53 overexpression had significantly worse overall survival compared with those without P53 overexpression (p = 0.04). On the other hand, double expressor status and prior history of B-cell lymphoma did not correlate with overall survival. In conclusion, most patients with triple hit lymphoma have an aggressive clinical course and poor prognosis and these tumors have a germinal center B-cell immunophenotype, similar to patients with double hit lymphomas. P53 expression is a poor prognostic factor in patients with triple hit lymphoma.

Characteristic Features of Double Layers in Rotating, Magnetized Plasma Contaminated with Dust Grains with Varying Charges

NASA Astrophysics Data System (ADS)

Paul, Jaydeep; Nag, Apratim; Devi, Karabi; Das, Himadri Sekhar

2018-03-01

The evolution and the characteristic features of double layers in a plasma under slow rotation and contaminated with dust grains with varying charges under the effect of an external magnetic field are studied. The Coriolis force resulting from the slow rotation is responsible for the generation of an equivalent magnetic field. A comparatively new pseudopotential approach has been used to derive the small amplitude double layers. The effect of the relative electron-ion concentration, as well as the temperature ratio, on the formation of the double layers has also been investigated. The study reveals that compressive, as well as rarefactive, double layers can be made to co-exist in plasma by controlling the dust charge fluctuation effect supplemented by variations of the plasma constituents. The effectiveness of slow rotation in causing double layers to exist has also emanated from the study. The results obtained could be of interest because of their possible applications in both laboratories and space.

Analysis of Biological Features Associated with Meiotic Recombination Hot and Cold Spots in Saccharomyces cerevisiae

PubMed Central

Hansen, Loren; Kim, Nak-Kyeong; Mariño-Ramírez, Leonardo; Landsman, David

2011-01-01

Meiotic recombination is not distributed uniformly throughout the genome. There are regions of high and low recombination rates called hot and cold spots, respectively. The recombination rate parallels the frequency of DNA double-strand breaks (DSBs) that initiate meiotic recombination. The aim is to identify biological features associated with DSB frequency. We constructed vectors representing various chromatin and sequence-based features for 1179 DSB hot spots and 1028 DSB cold spots. Using a feature selection approach, we have identified five features that distinguish hot from cold spots in Saccharomyces cerevisiae with high accuracy, namely the histone marks H3K4me3, H3K14ac, H3K36me3, and H3K79me3; and GC content. Previous studies have associated H3K4me3, H3K36me3, and GC content with areas of mitotic recombination. H3K14ac and H3K79me3 are novel predictions and thus represent good candidates for further experimental study. We also show nucleosome occupancy maps produced using next generation sequencing exhibit a bias at DSB hot spots and this bias is strong enough to obscure biologically relevant information. A computational approach using feature selection can productively be used to identify promising biological associations. H3K14ac and H3K79me3 are novel predictions of chromatin marks associated with meiotic DSBs. Next generation sequencing can exhibit a bias that is strong enough to lead to incorrect conclusions. Care must be taken when interpreting high throughput sequencing data where systematic biases have been documented. PMID:22242140

Double Emulsion Generation Using a Polydimethylsiloxane (PDMS) Co-axial Flow Focus Device.

PubMed

Cole, Russell H; Tran, Tuan M; Abate, Adam R

2015-12-25

Double emulsions are useful in a number of biological and industrial applications in which it is important to have an aqueous carrier fluid. This paper presents a polydimethylsiloxane (PDMS) microfluidic device capable of generating water/oil/water double emulsions using a coaxial flow focusing geometry that can be fabricated entirely using soft lithography. Similar to emulsion devices using glass capillaries, double emulsions can be formed in channels with uniform wettability and with dimensions much smaller than the channel sizes. Three dimensional flow focusing geometry is achieved by casting a pair of PDMS slabs using two layer soft lithography, then mating the slabs together in a clamshell configuration. Complementary locking features molded into the PDMS slabs enable the accurate registration of features on each of the slab surfaces. Device testing demonstrates formation of double emulsions from 14 µm to 50 µm in diameter while using large channels that are robust against fouling and clogging.

Double Emulsion Generation Using a Polydimethylsiloxane (PDMS) Co-axial Flow Focus Device

PubMed Central

Cole, Russell H.; Tran, Tuan M.; Abate, Adam R.

2015-01-01

Double emulsions are useful in a number of biological and industrial applications in which it is important to have an aqueous carrier fluid. This paper presents a polydimethylsiloxane (PDMS) microfluidic device capable of generating water/oil/water double emulsions using a coaxial flow focusing geometry that can be fabricated entirely using soft lithography. Similar to emulsion devices using glass capillaries, double emulsions can be formed in channels with uniform wettability and with dimensions much smaller than the channel sizes. Three dimensional flow focusing geometry is achieved by casting a pair of PDMS slabs using two layer soft lithography, then mating the slabs together in a clamshell configuration. Complementary locking features molded into the PDMS slabs enable the accurate registration of features on each of the slab surfaces. Device testing demonstrates formation of double emulsions from 14 µm to 50 µm in diameter while using large channels that are robust against fouling and clogging. PMID:26780079

Building dynamical models from data and prior knowledge: the case of the first period-doubling bifurcation.

PubMed

Aguirre, Luis Antonio; Furtado, Edgar Campos

2007-10-01

This paper reviews some aspects of nonlinear model building from data with (gray box) and without (black box) prior knowledge. The model class is very important because it determines two aspects of the final model, namely (i) the type of nonlinearity that can be accurately approximated and (ii) the type of prior knowledge that can be taken into account. Such features are usually in conflict when it comes to choosing the model class. The problem of model structure selection is also reviewed. It is argued that such a problem is philosophically different depending on the model class and it is suggested that the choice of model class should be performed based on the type of a priori available. A procedure is proposed to build polynomial models from data on a Poincaré section and prior knowledge about the first period-doubling bifurcation, for which the normal form is also polynomial. The final models approximate dynamical data in a least-squares sense and, by design, present the first period-doubling bifurcation at a specified value of parameters. The procedure is illustrated by means of simulated examples.

Structural characterization of ultrathin Cr-doped ITO layers deposited by double-target pulsed laser ablation

NASA Astrophysics Data System (ADS)

Cesaria, Maura; Caricato, Anna Paola; Leggieri, Gilberto; Luches, Armando; Martino, Maurizio; Maruccio, Giuseppe; Catalano, Massimo; Grazia Manera, Maria; Rella, Roberto; Taurino, Antonietta

2011-09-01

In this paper we report on the growth and structural characterization of very thin (20 nm) Cr-doped ITO films, deposited at room temperature by double-target pulsed laser ablation on amorphous silica substrates. The role of Cr atoms in the ITO matrix is carefully investigated with increasing doping content by transmission electron microscopy (TEM). Selected-area electron diffraction, conventional bright field and dark field as well as high-resolution TEM analyses, and energy dispersive x-ray spectroscopy demonstrate that (i) crystallization features occur despite the low growth temperature and small thickness, (ii) no chromium or chromium oxide secondary phases are detectable, regardless of the film doping levels, (iii) the films crystallize as crystalline flakes forming large-angle grain boundaries; (iv) the observed flakes consist of crystalline planes with local bending of the crystal lattice. Thickness and compositional information about the films are obtained by Rutherford back-scattering spectrometry. Results are discussed by considering the combined effects of growth temperature, smaller ionic radius of the Cr cation compared with the trivalent In ion, doping level, film thickness, the double-target doping technique and peculiarities of the pulsed laser deposition method.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Descartes, M.; Longshore, J.W.; Crawford, E.

We report an infant with double trisomy 48,XXX,+18, who also displayed features of Roberts syndrome. All previously published cases with similar double trisomy have presented with features of trisomy 18 syndrome. The chromosome analysis done at birth revealed the double trisomy; parental chromosomes were normal. The proband presented with microbrachycephaly, unilateral cleft lip and palate, choanal atresia, midfacial capillary hemanioma, thin nares, shallow orbits, malformed ears, sparse hair, hypomelia of the upper limbs, rocker-bottom feet, auricular septal defect and agenesis of the corpus callosum. Characteristic features of Roberts syndrome included hypomelia, midfacial defects, and severe growth deficiency. Among the manymore » different features reported in the literature for patients with trisomy 18 syndrome, the most consistent were growth deficiency, clenched fingers and congenital heart defects (e.g. VSD, ASD, PDA). Although some of our patient`s features such as cleft lip and cleft palate, low-set malformed ears, ASD, defects of the corpus callosum, choanal atresia, radial aplasia could also be seen in trisomy 18 syndrome (in 10-50% of the cases), her phenotype was more typical of Roberts syndrome because of symmetrical hypomelia and midfacial defects. Our patient`s chromosomes did not show premature separation of centromeric heterochromatin, a feature reported to occur in approximately one-half of individuals with Roberts syndrome. Sporadic aneuploidy involving different chromosomes has been found in lymphocyte cultures from some Roberts syndrome patients and is considered by some authors as a mitotic mutant. This aneuploidy is most likely to be chromosome gain. The simultaneous occurrence of trisomy X and 18 is extremely rare with only 11 cases having been reported in the literature. Our patient is unique since she has the double trisomy in addition to the characteristic features of Roberts syndrome.« less

The nature of pulsar radio emission

NASA Astrophysics Data System (ADS)

Dyks, J.; Rudak, B.; Demorest, P.

2010-01-01

High-quality averaged radio profiles of some pulsars exhibit double, highly symmetric features both in emission and in absorption. It is shown that both types of feature are produced by a split fan beam of extraordinary-mode curvature radiation that is emitted/absorbed by radially extended streams of magnetospheric plasma. With no emissivity in the plane of the stream, such a beam produces bifurcated emission components (BFCs) when our line of sight passes through the plane. An example of a double component created in this way is present in the averaged profile of the 5-ms pulsar J1012+5307. We show that the component can indeed be very well fitted by the textbook formula for the non-coherent beam of curvature radiation in the polarization state that is orthogonal to the plane of electron trajectory. The observed width of the BFC decreases with increasing frequency at a rate that confirms the curvature origin. Likewise, the double absorption features (double notches) are produced by the same beam of the extraordinary-mode curvature radiation, when it is eclipsed by thin plasma streams. The intrinsic property of curvature radiation to create bifurcated fan beams explains the double features in terms of a very natural geometry and implies the curvature origin of pulsar radio emission. Similarly, the `double conal' profiles of class D result from a cut through a wider stream with finite extent in magnetic azimuth. Therefore, their width reacts very slowly to changes of viewing geometry resulting from geodetic precession. The stream-cut interpretation implies a highly non-orthodox origin of both the famous S-swing of polarization angle and the low-frequency pulse broadening in D profiles. The azimuthal structure of polarization modes in the curvature radiation beam provides an explanation for the polarized `multiple imaging' and the edge depolarization of pulsar profiles.

Tunnelling in asymmetric double-well potentials: varying initial states

NASA Astrophysics Data System (ADS)

Cordes, J. G.; Das, A. K.

2001-02-01

Tunnelling in a double-well potential has features which are not derivable through a mere extension of the concepts used in the context of a single potential barrier with no confining walls on either side. Furthermore, an asymmetric double-well potential, relevant in many contemporary areas of physics and chemistry, possesses certain distinctive aspects in contrast to the relatively simple case of a symmetric double-well potential. In this paper a self-contained numerical and analytical study of these features is reported, and a theoretical model is presented with special attention being given to a unified treatment of both the symmetric and asymmetric cases. The popularly used pair-state model is critically examined, and the important role of the initial state (which is rarely discussed in the literature) is highlighted with specific examples.

Multiplicity

DTIC Science & Technology

1991-04-01

that it has not adopted that test. 1 3 5 The Court did, however, recognize that the double Jeopardy protection includes a collateral estoppel feature... estoppel feature. 1 4 1 The Court articulated this new test in the following terms: The Double Jeopardy Clause bars any subsequent prosecution in which...126 Ashe v. Swenson, 397 U.S. at 452 (Brennan, J., concurring). 127 "We defined collateral estoppel as providing that ’when an issue of ultimate

M&A For Lithography Of Sparse Arrays Of Sub-Micrometer Features

DOEpatents

Brueck, Steven R.J.; Chen, Xiaolan; Zaidi, Saleem; Devine, Daniel J.

1998-06-02

Methods and apparatuses are disclosed for the exposure of sparse hole and/or mesa arrays with line:space ratios of 1:3 or greater and sub-micrometer hole and/or mesa diameters in a layer of photosensitive material atop a layered material. Methods disclosed include: double exposure interferometric lithography pairs in which only those areas near the overlapping maxima of each single-period exposure pair receive a clearing exposure dose; double interferometric lithography exposure pairs with additional processing steps to transfer the array from a first single-period interferometric lithography exposure pair into an intermediate mask layer and a second single-period interferometric lithography exposure to further select a subset of the first array of holes; a double exposure of a single period interferometric lithography exposure pair to define a dense array of sub-micrometer holes and an optical lithography exposure in which only those holes near maxima of both exposures receive a clearing exposure dose; combination of a single-period interferometric exposure pair, processing to transfer resulting dense array of sub-micrometer holes into an intermediate etch mask, and an optical lithography exposure to select a subset of initial array to form a sparse array; combination of an optical exposure, transfer of exposure pattern into an intermediate mask layer, and a single-period interferometric lithography exposure pair; three-beam interferometric exposure pairs to form sparse arrays of sub-micrometer holes; five- and four-beam interferometric exposures to form a sparse array of sub-micrometer holes in a single exposure. Apparatuses disclosed include arrangements for the three-beam, five-beam and four-beam interferometric exposures.

Comparing Pattern Recognition Feature Sets for Sorting Triples in the FIRST Database

NASA Astrophysics Data System (ADS)

Proctor, D. D.

2006-07-01

Pattern recognition techniques have been used with increasing success for coping with the tremendous amounts of data being generated by automated surveys. Usually this process involves construction of training sets, the typical examples of data with known classifications. Given a feature set, along with the training set, statistical methods can be employed to generate a classifier. The classifier is then applied to process the remaining data. Feature set selection, however, is still an issue. This paper presents techniques developed for accommodating data for which a substantive portion of the training set cannot be classified unambiguously, a typical case for low-resolution data. Significance tests on the sort-ordered, sample-size-normalized vote distribution of an ensemble of decision trees is introduced as a method of evaluating relative quality of feature sets. The technique is applied to comparing feature sets for sorting a particular radio galaxy morphology, bent-doubles, from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) database. Also examined are alternative functional forms for feature sets. Associated standard deviations provide the means to evaluate the effect of the number of folds, the number of classifiers per fold, and the sample size on the resulting classifications. The technique also may be applied to situations for which, although accurate classifications are available, the feature set is clearly inadequate, but is desired nonetheless to make the best of available information.

First Spectroscopic Identification of Massive Young Stellar Objects in the Galactic Center

NASA Technical Reports Server (NTRS)

An, Deokkeun; Ramirez, V.; Sellgren, Kris; Arendt, Richard G.; Boogert, A. C.; Schultheis, Mathias; Stolovy, Susan R.; Cotera, Angela S.; Robitaille, Thomas P.; Smith, Howard A.

2009-01-01

We report the detection of several molecular gas-phase and ice absorption features in three photometrically-selected young stellar object (YSO) candidates in the central 280 pc of the Milky Way. Our spectra, obtained with the Infrared Spectrograph (IRS) onboard the Spitzer Space Telescope, reveal gas-phase absorption from CO2 (15.0 microns), C2H2 (13.7 microns) and HCN (14.0 microns). We attribute this absorption to warm, dense gas in massive YSOs. We also detect strong and broad 15 microns CO2 ice absorption features, with a remarkable double-peaked structure. The prominent long-wavelength peak is due to CH3OH-rich ice grains, and is similar to those found in other known massive YSOs. Our IRS observa.tions demonstra.te the youth of these objects, and provide the first spectroscopic identification of massive YSOs in the Galactic Center.

Astragalar Morphology of Selected Giraffidae.

PubMed

Solounias, Nikos; Danowitz, Melinda

2016-01-01

The artiodactyl astragalus has been modified to exhibit two trochleae, creating a double pullied structure allowing for significant dorso-plantar motion, and limited mediolateral motion. The astragalus structure is partly influenced by environmental substrates, and correspondingly, morphometric studies can yield paleohabitat information. The present study establishes terminology and describes detailed morphological features on giraffid astragali. Each giraffid astragalus exhibits a unique combination of anatomical characteristics. The giraffid astragalar morphologies reinforce previously established phylogenetic relationships. We find that the enlargement of the navicular head is a feature shared by all giraffids, and that the primitive giraffids possess exceptionally tall astragalar heads in relation to the total astragalar height. The sivatheres and the okapi share a reduced notch on the lateral edge of the astragalus. We find that Samotherium is more primitive in astragalar morphologies than Palaeotragus, which is reinforced by tooth characteristics and ossicone position. Diagnostic anatomical characters on the astragalus allow for giraffid species identifications and a better understanding of Giraffidae.

Main features of detectors and isotopes to investigate double beta decay with increased sensitivity

NASA Astrophysics Data System (ADS)

Barabash, A. S.

2018-03-01

The current situation in double beta decay experiments, the characteristics of modern detectors and the possibility of increasing the sensitivity to neutrino mass in future experiments are discussed. The issue of the production and use of enriched isotopes in double beta decay experiments is discussed in addition.

Detecting double compressed MPEG videos with the same quantization matrix and synchronized group of pictures structure

NASA Astrophysics Data System (ADS)

Aghamaleki, Javad Abbasi; Behrad, Alireza

2018-01-01

Double compression detection is a crucial stage in digital image and video forensics. However, the detection of double compressed videos is challenging when the video forger uses the same quantization matrix and synchronized group of pictures (GOP) structure during the recompression history to conceal tampering effects. A passive approach is proposed for detecting double compressed MPEG videos with the same quantization matrix and synchronized GOP structure. To devise the proposed algorithm, the effects of recompression on P frames are mathematically studied. Then, based on the obtained guidelines, a feature vector is proposed to detect double compressed frames on the GOP level. Subsequently, sparse representations of the feature vectors are used for dimensionality reduction and enrich the traces of recompression. Finally, a support vector machine classifier is employed to detect and localize double compression in temporal domain. The experimental results show that the proposed algorithm achieves the accuracy of more than 95%. In addition, the comparisons of the results of the proposed method with those of other methods reveal the efficiency of the proposed algorithm.

The microgravity environment of the D1 mission

NASA Technical Reports Server (NTRS)

Hamacher, H.; Merbold, U.; Jilg, R.

1990-01-01

Some characteristic features and results of D1 microgravity measurements are discussed as performed in the Material Science Double Rack (MSDR) and the Materials Science Double Rack for Experiment Modules and Apparatus (MEDEA). Starting with a brief review of the main potential disturbances, the payload aspects of interest to the analysis and the accelerometer measuring systems are described. The microgravity data are analyzed with respect to selected mission events such as thruster firings for attitude control, operations of Spacelab experiment facilities, vestibular experiments and crew activities. The origins are divided into orbit, vehicle, and experiment induced perturbations. It has been found that the microgravity-environment is dictated mainly by payload-induced perturbations. To reduce the microgravity-level, the design of some experiment facilities has to be improved by minimizing the number of moving parts, decoupling of disturbing units from experiment facilities, by taking damping measures, etc. In addition, strongly disturbing experiments and very sensitive investigations should be performed in separate mission phases.

Resonant-phonon-assisted THz quantum cascade lasers with metal-metal waveguides.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Callebaut, Hans; Kohen, Stephen; Kumar, Sushil

2004-06-01

We report our development of terahertz (THz) quantum-cascade lasers (QCLs) based on two novel features. First, the depopulation of the lower radiative level is achieved through resonant longitudinal optical (LO-)phonon scattering. This depopulation mechanism is robust at high temperatures and high injection levels. In contrast to infrared QCLs that also use LO-phonon scattering for depopulation, in our THz lasers the selectivity of the depopulation scattering is achieved through a combination of resonant tunneling and LO-phonon scattering, hence the term resonant phonon. This resonant-phonon scheme allows a highly selective depopulation of the lower radiative level with a sub-picosecond lifetime, while maintainingmore » a relatively long upper level lifetime (>5 ps) that is due to upper-to-ground-state scattering. The second feature of our lasers is that mode confinement is achieved by using a novel double-sided metal-metal waveguide, which yields an essentially unity mode confinement factor and therefore a low total cavity loss at THz frequencies. Based on these two unique features, we have achieved some record performance, including, but not limited to, the highest pulsed operating temperature of 137 K, the highest continuous-wave operating temperature of 97 K, and the longest wavelength of 141 {micro}m (corresponding to 2.1 THz) without the assistance of a magnetic field.« less

Re-engineering of CYP2C9 to probe acid-base substrate selectivity.

PubMed

Tai, Guoying; Dickmann, Leslie J; Matovic, Nicholas; DeVoss, James J; Gillam, Elizabeth M J; Rettie, Allan E

2008-10-01

A common feature of many CYP2C9 ligands is their weak acidity. As revealed by crystallography, the structural basis for this behavior involves a charge-pairing interaction between an anionic moiety on the substrate and an active site R108 residue. In the present study we attempted to re-engineer CYP2C9 to better accept basic ligands by charge reversal at this key residue. We expressed and purified the R108E and R108E/D293N mutants and compared their ability with that of native CYP2C9 to interact with (S)-warfarin, diclofenac, pyrene, propranolol, and ibuprofen amine. As expected, the R108E mutant maintained all the native enzyme's pyrene 1-hydroxylation activity, but catalytic activity toward diclofenac and (S)-warfarin was abrogated. In contrast, the double mutant displayed much less selectivity in its behavior toward these control ligands. Neither of the mutants displayed significant enhancement of propranolol metabolism, and all three preparations exhibited a type II (inhibitor) rather than type I (substrate) spectrum with ibuprofen amine, although binding became progressively weaker with the single and double mutants. Collectively, these data underscore the importance of the amino acid at position 108 in the acid substrate selectivity of CYP2C9, highlight the accommodating nature of the CYP2C9 active site, and provide a cautionary note regarding facile re-engineering of these complex cytochrome P450 active sites.

Re-engineering of CYP2C9 to Probe Acid-Base Substrate Selectivity

PubMed Central

Tai, Guoying; Dickmann, Leslie J.; Matovic, Nicholas; DeVoss, James J.; Gillam, Elizabeth M. J.; Rettie, Allan E.

2009-01-01

A common feature of many CYP2C9 ligands is their weak acidity. As revealed by crystallography, the structural basis for this behavior involves a charge-pairing interaction between an anionic moiety on the substrate and an active site R108 residue. In the present study we attempted to re-engineer CYP2C9 to better accept basic ligands by charge reversal at this key residue. We expressed and purified the R108E and R108E/D293N mutants and compared their ability with that of native CYP2C9 to interact with (S)-warfarin, diclofenac, pyrene, propranolol, and ibuprofen amine. As expected, the R108E mutant maintained all the native enzyme's pyrene 1-hydroxylation activity, but catalytic activity toward diclofenac and (S)-warfarin was abrogated. In contrast, the double mutant displayed much less selectivity in its behavior toward these control ligands. Neither of the mutants displayed significant enhancement of propranolol metabolism, and all three preparations exhibited a type II (inhibitor) rather than type I (substrate) spectrum with ibuprofen amine, although binding became progressively weaker with the single and double mutants. Collectively, these data underscore the importance of the amino acid at position 108 in the acid substrate selectivity of CYP2C9, highlight the accommodating nature of the CYP2C9 active site, and provide a cautionary note regarding facile re-engineering of these complex cytochrome P450 active sites. PMID:18606741

A high speed CCSDS encoder for space applications

NASA Technical Reports Server (NTRS)

Whitaker, S.; Liu, K.

1990-01-01

This paper reports a VLSI implementation of the CCSDS standard Reed Solomon encoder circuit for the Space Station. The 1.0 micron double metal CMOS chip is 5.9 mm by 3.6 mm, contains 48,000 transistors, operates at a sustained data rate of 320 Mbits/s, and executes 2,560 Mops. The chip features a pin selectable interleave depth of 1 to 8. Block lengths of up to 255 bytes, as well as shortened codes, are supported. The control circuitry uses register cells which are immune to Single Event Upset. In addition, the CMOS process used is reported to be tolerant of over 1 Mrad total dose radiation.

Independent processing of changes in auditory single features and feature conjunctions in humans as indexed by the mismatch negativity.

PubMed

Takegata, R; Paavilainen, P; Näätänen, R; Winkler, I

1999-05-07

The mismatch negativity (MMN), an event-related potential component of the EEG, is elicited by violations of auditory regularities In the present study, the stimulus blocks contained two types of standard tones, differing from each other in frequency and intensity. MMNs were recorded to three different types of deviant stimuli: (a) feature deviants, differing from standards in their perceived locus of origin; (b) conjunction deviants, having the frequency of one of the standards and the intensity of the other; (c) double deviants, differing from standards in both (a) and (b). The MMN to double deviants was similar to the sum of the MMNs to feature and conjunction deviants. This result indicates that changes in simple stimulus features and conjunction of features are processed independently by the automatic sound change detection system indexed by MMN.

The autoinhibitory CARD2-Hel2i Interface of RIG-I governs RNA selection.

PubMed

Ramanathan, Anand; Devarkar, Swapnil C; Jiang, Fuguo; Miller, Matthew T; Khan, Abdul G; Marcotrigiano, Joseph; Patel, Smita S

2016-01-29

RIG-I (Retinoic Acid Inducible Gene-I) is a cytosolic innate immune receptor that detects atypical features in viral RNAs as foreign to initiate a Type I interferon signaling response. RIG-I is present in an autoinhibited state in the cytoplasm and activated by blunt-ended double-stranded (ds)RNAs carrying a 5' triphosphate (ppp) moiety. These features found in many pathogenic RNAs are absent in cellular RNAs due to post-transcriptional modifications of RNA ends. Although RIG-I is structurally well characterized, the mechanistic basis for RIG-I's remarkable ability to discriminate between cellular and pathogenic RNAs is not completely understood. We show that RIG-I's selectivity for blunt-ended 5'-ppp dsRNAs is ≈3000 times higher than non-blunt ended dsRNAs commonly found in cellular RNAs. Discrimination occurs at multiple stages and signaling RNAs have high affinity and ATPase turnover rate and thus a high katpase/Kd. We show that RIG-I uses its autoinhibitory CARD2-Hel2i (second CARD-helicase insertion domain) interface as a barrier to select against non-blunt ended dsRNAs. Accordingly, deletion of CARDs or point mutations in the CARD2-Hel2i interface decreases the selectivity from ≈3000 to 150 and 750, respectively. We propose that the CARD2-Hel2i interface is a 'gate' that prevents cellular RNAs from generating productive complexes that can signal. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Music and words in the visual cortex: The impact of musical expertise.

PubMed

Mongelli, Valeria; Dehaene, Stanislas; Vinckier, Fabien; Peretz, Isabelle; Bartolomeo, Paolo; Cohen, Laurent

2017-01-01

How does the human visual system accommodate expertise for two simultaneously acquired symbolic systems? We used fMRI to compare activations induced in the visual cortex by musical notation, written words and other classes of objects, in professional musicians and in musically naïve controls. First, irrespective of expertise, selective activations for music were posterior and lateral to activations for words in the left occipitotemporal cortex. This indicates that symbols characterized by different visual features engage distinct cortical areas. Second, musical expertise increased the volume of activations for music and led to an anterolateral displacement of word-related activations. In musicians, there was also a dramatic increase of the brain-scale networks connected to the music-selective visual areas. Those findings reveal that acquiring a double visual expertise involves an expansion of category-selective areas, the development of novel long-distance functional connectivity, and possibly some competition between categories for the colonization of cortical space. Copyright © 2016 Elsevier Ltd. All rights reserved.

Generation of an annotated reference standard for vaccine adverse event reports.

PubMed

Foster, Matthew; Pandey, Abhishek; Kreimeyer, Kory; Botsis, Taxiarchis

2018-07-05

As part of a collaborative project between the US Food and Drug Administration (FDA) and the Centers for Disease Control and Prevention for the development of a web-based natural language processing (NLP) workbench, we created a corpus of 1000 Vaccine Adverse Event Reporting System (VAERS) reports annotated for 36,726 clinical features, 13,365 temporal features, and 22,395 clinical-temporal links. This paper describes the final corpus, as well as the methodology used to create it, so that clinical NLP researchers outside FDA can evaluate the utility of the corpus to aid their own work. The creation of this standard went through four phases: pre-training, pre-production, production-clinical feature annotation, and production-temporal annotation. The pre-production phase used a double annotation followed by adjudication strategy to refine and finalize the annotation model while the production phases followed a single annotation strategy to maximize the number of reports in the corpus. An analysis of 30 reports randomly selected as part of a quality control assessment yielded accuracies of 0.97, 0.96, and 0.83 for clinical features, temporal features, and clinical-temporal associations, respectively and speaks to the quality of the corpus. Copyright © 2018 Elsevier Ltd. All rights reserved.

A competitive interaction theory of attentional selection and decision making in brief, multielement displays.

PubMed

Smith, Philip L; Sewell, David K

2013-07-01

We generalize the integrated system model of Smith and Ratcliff (2009) to obtain a new theory of attentional selection in brief, multielement visual displays. The theory proposes that attentional selection occurs via competitive interactions among detectors that signal the presence of task-relevant features at particular display locations. The outcome of the competition, together with attention, determines which stimuli are selected into visual short-term memory (VSTM). Decisions about the contents of VSTM are made by a diffusion-process decision stage. The selection process is modeled by coupled systems of shunting equations, which perform gated where-on-what pathway VSTM selection. The theory provides a computational account of key findings from attention tasks with near-threshold stimuli. These are (a) the success of the MAX model of visual search and spatial cuing, (b) the distractor homogeneity effect, (c) the double-target detection deficit, (d) redundancy costs in the post-stimulus probe task, (e) the joint item and information capacity limits of VSTM, and (f) the object-based nature of attentional selection. We argue that these phenomena are all manifestations of an underlying competitive VSTM selection process, which arise as a natural consequence of our theory. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Tinkering with the C-Function: A Molecular Frame for the Selection of Double Flowers in Cultivated Roses

PubMed Central

Dubois, Annick; Raymond, Olivier; Maene, Marion; Baudino, Sylvie; Langlade, Nicolas B.; Boltz, Véronique; Vergne, Philippe; Bendahmane, Mohammed

2010-01-01

Background Roses have been cultivated for centuries and a number of varieties have been selected based on flower traits such as petal form, color, and number. Wild-type roses have five petals (simple flowers), whereas high numbers of petals (double flowers) are typical attributes of most of the cultivated roses. Here, we investigated the molecular mechanisms that could have been selected to control petal number in roses. Methodology/Principal Findings We have analyzed the expression of several candidate genes known to be involved in floral organ identity determination in roses from similar genetic backgrounds but exhibiting contrasting petal numbers per flower. We show that the rose ortholog of AGAMOUS (RhAG) is differentially expressed in double flowers as compared to simple flowers. In situ hybridization experiments confirm the differential expression of RhAG and demonstrate that in the double-flower roses, the expression domain of RhAG is restricted toward the center of the flower. Conversely, in simple-flower roses, RhAG expression domain is wider. We further show that the border of RhAG expression domain is labile, which allows the selection of rose flowers with increased petal number. Double-flower roses were selected independently in the two major regions for domestication, China and the peri-Mediterranean areas. Comparison of RhAG expression in the wild-type ancestors of cultivated roses and their descendants both in the European and Chinese lineages corroborates the correlation between the degree of restriction of RhAG expression domain and the number of petals. Our data suggests that a restriction of RhAG expression domain is the basis for selection of double flowers in both the Chinese and peri-Mediterranean centers of domestication. Conclusions/Significance We demonstrate that a shift in RhAG expression domain boundary occurred in rose hybrids, causing double-flower phenotype. This molecular event was selected independently during rose domestication in Europe/Middle East and in China. PMID:20174587

Real-Time Tracking by Double Templates Matching Based on Timed Motion History Image with HSV Feature

PubMed Central

Li, Zhiyong; Li, Pengfei; Yu, Xiaoping; Hashem, Mervat

2014-01-01

It is a challenge to represent the target appearance model for moving object tracking under complex environment. This study presents a novel method with appearance model described by double templates based on timed motion history image with HSV color histogram feature (tMHI-HSV). The main components include offline template and online template initialization, tMHI-HSV-based candidate patches feature histograms calculation, double templates matching (DTM) for object location, and templates updating. Firstly, we initialize the target object region and calculate its HSV color histogram feature as offline template and online template. Secondly, the tMHI-HSV is used to segment the motion region and calculate these candidate object patches' color histograms to represent their appearance models. Finally, we utilize the DTM method to trace the target and update the offline template and online template real-timely. The experimental results show that the proposed method can efficiently handle the scale variation and pose change of the rigid and nonrigid objects, even in illumination change and occlusion visual environment. PMID:24592185

Double-loop frequency selective surfaces for multi frequency division multiplexing in a dual reflector antenna

NASA Technical Reports Server (NTRS)

Wu, Te-Kao (Inventor)

1994-01-01

A multireflector antenna utilizes a frequency-selective surface (FSS) in a subreflector to allow signals in two different RF bands to be selectively reflected back into a main reflector and to allow signals in other RF bands to be transmitted through it to the main reflector for primary focus transmission. A first approach requires only one FSS at the subreflector which may be an array of double-square-loop conductive elements. A second approach uses two FSS's at the subreflector which may be an array of either double-square-loop (DSL) or double-ring (DR). In the case of DR elements, they may be advantageously arranged in a triangular array instead of the rectangular array for the DSL elements.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Gen; Lee, Martin A., E-mail: gjk44@wildcats.unh.edu

The effects of scatter-dominated interplanetary transport on the spectral properties of the differential fluence of large gradual solar energetic particle (SEP) events are investigated analytically. The model assumes for simplicity radial constant solar wind and radial magnetic field. The radial diffusion coefficient is calculated with quasilinear theory by assuming a spectrum of Alfvén waves propagating parallel to the magnetic field. Cross-field transport is neglected. The model takes into consideration several essential features of gradual event transport: nearly isotropic ion distributions, adiabatic deceleration in a divergent solar wind, and particle radial scattering mean free paths increasing with energy. Assuming an impulsivemore » and spherically symmetric injection of SEPs with a power-law spectrum near the Sun, the predicted differential fluence spectrum exhibits at 1 AU three distinctive power laws for different energy domains. The model naturally reproduces the spectral features of the double power-law proton differential fluence spectra that tend to be observed in extremely large SEP events. We select nine western ground-level events (GLEs) out of the 16 GLEs during Solar Cycle 23 and fit the observed double power-law spectra to the analytical predictions. The compression ratio of the accelerating shock wave, the power-law index of the ambient wave intensity, and the proton radial scattering mean free path are determined for the nine GLEs. The derived parameters are generally in agreement with the characteristic values expected for large gradual SEP events.« less

ERP evidence for different strategies in the processing of case markers in native speakers and non-native learners

PubMed Central

Mueller, Jutta L; Hirotani, Masako; Friederici, Angela D

2007-01-01

Background The present experiments were designed to test how the linguistic feature of case is processed in Japanese by native and non-native listeners. We used a miniature version of Japanese as a model to compare sentence comprehension mechanisms in native speakers and non-native learners who had received training until they had mastered the system. In the first experiment we auditorily presented native Japanese speakers with sentences containing incorrect double nominatives and incorrect double accusatives, and with correct sentences. In the second experiment we tested trained non-natives with the same material. Based on previous research in German we expected an N400-P600 biphasic ERP response with specific modulations depending on the violated case and whether the listeners were native or non-native. Results For native Japanese participants the general ERP response to the case violations was an N400-P600 pattern. Double accusatives led to an additional enhancement of the P600 amplitude. For the learners a native-like P600 was present for double accusatives and for double nominatives. The additional negativity, however, was present in learners only for double nominative violations, and it was characterized by a different topographical distribution. Conclusion The results indicate that native listeners use case markers for thematic as well as syntactic structure building during incremental sentence interpretation. The modulation of the P600 component for double accusatives possibly reflects case specific syntactic restrictions in Japanese. For adult language learners later processes, as reflected in the P600, seem to be more native-like compared to earlier processes. The anterior distribution of the negativity and its selective emergence for canonical sentences were taken to suggest that the non-native learners resorted to a rather formal processing strategy whereby they relied to a large degree on the phonologically salient nominative case marker. PMID:17331265

Origin of the 900 cm{sup −1} broad double-hump OH vibrational feature of strongly hydrogen-bonded carboxylic acids

DOE Office of Scientific and Technical Information (OSTI.GOV)

Van Hoozen, Brian L.; Petersen, Poul B.

2015-03-14

Medium and strong hydrogen bonds are common in biological systems. Here, they provide structural support and can act as proton transfer relays to drive electron and/or energy transfer. Infrared spectroscopy is a sensitive probe of molecular structure and hydrogen bond strength but strongly hydrogen-bonded structures often exhibit very broad and complex vibrational bands. As an example, strong hydrogen bonds between carboxylic acids and nitrogen-containing aromatic bases commonly display a 900 cm{sup −1} broad feature with a remarkable double-hump structure. Although previous studies have assigned this feature to the OH, the exact origin of the shape and width of this unusualmore » feature is not well understood. In this study, we present ab initio calculations of the contributions of the OH stretch and bend vibrational modes to the vibrational spectrum of strongly hydrogen-bonded heterodimers of carboxylic acids and nitrogen-containing aromatic bases, taking the 7-azaindole—acetic acid and pyridine—acetic acid dimers as examples. Our calculations take into account coupling between the OH stretch and bend modes as well as how both of these modes are affected by lower frequency dimer stretch modes, which modulate the distance between the monomers. Our calculations reproduce the broadness and the double-hump structure of the OH vibrational feature. Where the spectral broadness is primarily caused by the dimer stretch modes strongly modulating the frequency of the OH stretch mode, the double-hump structure results from a Fermi resonance between the out of the plane OH bend and the OH stretch modes.« less

Optimal Bandwidth Selection in Observed-Score Kernel Equating

ERIC Educational Resources Information Center

Häggström, Jenny; Wiberg, Marie

2014-01-01

The selection of bandwidth in kernel equating is important because it has a direct impact on the equated test scores. The aim of this article is to examine the use of double smoothing when selecting bandwidths in kernel equating and to compare double smoothing with the commonly used penalty method. This comparison was made using both an equivalent…

Double ErrP Detection for Automatic Error Correction in an ERP-Based BCI Speller.

PubMed

Cruz, Aniana; Pires, Gabriel; Nunes, Urbano J

2018-01-01

Brain-computer interface (BCI) is a useful device for people with severe motor disabilities. However, due to its low speed and low reliability, BCI still has a very limited application in daily real-world tasks. This paper proposes a P300-based BCI speller combined with a double error-related potential (ErrP) detection to automatically correct erroneous decisions. This novel approach introduces a second error detection to infer whether wrong automatic correction also elicits a second ErrP. Thus, two single-trial responses, instead of one, contribute to the final selection, improving the reliability of error detection. Moreover, to increase error detection, the evoked potential detected as target by the P300 classifier is combined with the evoked error potential at a feature-level. Discriminable error and positive potentials (response to correct feedback) were clearly identified. The proposed approach was tested on nine healthy participants and one tetraplegic participant. The online average accuracy for the first and second ErrPs were 88.4% and 84.8%, respectively. With automatic correction, we achieved an improvement around 5% achieving 89.9% in spelling accuracy for an effective 2.92 symbols/min. The proposed approach revealed that double ErrP detection can improve the reliability and speed of BCI systems.

Double exposure using 193nm negative tone photoresist

NASA Astrophysics Data System (ADS)

Kim, Ryoung-han; Wallow, Tom; Kye, Jongwook; Levinson, Harry J.; White, Dave

2007-03-01

Double exposure is one of the promising methods for extending lithographic patterning into the low k I regime. In this paper, we demonstrate double patterning of k 1-effective=0.25 with improved process window using a negative resist. Negative resist (TOK N- series) in combination with a bright field mask is proven to provide a large process window in generating 1:3 = trench:line resist features. By incorporating two etch transfer steps into the hard mask material, frequency doubled patterns could be obtained.

Neutrino Mixing and the Double Tetrahedral Group

NASA Astrophysics Data System (ADS)

Bentov, Yoni; Zee, A.

2013-11-01

In the spirit of a previous study of the tetrahedral group T ≃A4, we discuss a minimalist scheme to derive the neutrino mixing matrix using the double tetrahedral group T‧, the double cover of T. The new features are three distinct two-dimensional representations and complex Clebsch-Gordan coefficients, which can result in a geometric source of CP violation in the neutrino mass matrix. In an appendix, we derive explicitly the relevant group theory for the tetrahedral group T and its double cover T‧.

Shifted one-parameter supersymmetric family of quartic asymmetric double-well potentials

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rosu, Haret C., E-mail: hcr@ipicyt.edu.mx; Mancas, Stefan C., E-mail: mancass@erau.edu; Chen, Pisin, E-mail: pisinchen@phys.ntu.edu.tw

2014-10-15

Extending our previous work (Rosu, 2014), we define supersymmetric partner potentials through a particular Riccati solution of the form F(x)=(x−c){sup 2}−1, where c is a real shift parameter, and work out the quartic double-well family of one-parameter isospectral potentials obtained by using the corresponding general Riccati solution. For these parametric double well potentials, we study how the localization properties of the two wells depend on the parameter of the potentials for various values of the shifting parameter. We also consider the supersymmetric parametric family of the first double-well potential in the Razavy chain of double well potentials corresponding to F(x)=1/2more » sinh2x−2((1+√(2))sinh2x)/((1+√(2))cosh2x+1) , both unshifted and shifted, to test and compare the localization properties. - Highlights: • Quartic one-parameter DWs with an additional shift parameter are introduced. • Anomalous localization feature of their zero modes is confirmed at different shifts. • Razavy one-parameter DWs are also introduced and shown not to have this feature.« less

Hepatitis Delta Antigen Requires a Flexible Quasi-Double-Stranded RNA Structure To Bind and Condense Hepatitis Delta Virus RNA in a Ribonucleoprotein Complex

PubMed Central

Griffin, Brittany L.; Chasovskikh, Sergey; Dritschilo, Anatoly

2014-01-01

ABSTRACT The circular genome and antigenome RNAs of hepatitis delta virus (HDV) form characteristic unbranched, quasi-double-stranded RNA secondary structures in which short double-stranded helical segments are interspersed with internal loops and bulges. The ribonucleoprotein complexes (RNPs) formed by these RNAs with the virus-encoded protein hepatitis delta antigen (HDAg) perform essential roles in the viral life cycle, including viral replication and virion formation. Little is understood about the formation and structure of these complexes and how they function in these key processes. Here, the specific RNA features required for HDAg binding and the topology of the complexes formed were investigated. Selective 2′OH acylation analyzed by primer extension (SHAPE) applied to free and HDAg-bound HDV RNAs indicated that the characteristic secondary structure of the RNA is preserved when bound to HDAg. Notably, the analysis indicated that predicted unpaired positions in the RNA remained dynamic in the RNP. Analysis of the in vitro binding activity of RNAs in which internal loops and bulges were mutated and of synthetically designed RNAs demonstrated that the distinctive secondary structure, not the primary RNA sequence, is the major determinant of HDAg RNA binding specificity. Atomic force microscopy analysis of RNPs formed in vitro revealed complexes in which the HDV RNA is substantially condensed by bending or wrapping. Our results support a model in which the internal loops and bulges in HDV RNA contribute flexibility to the quasi-double-stranded structure that allows RNA bending and condensing by HDAg. IMPORTANCE RNA-protein complexes (RNPs) formed by the hepatitis delta virus RNAs and protein, HDAg, perform critical roles in virus replication. Neither the structures of these RNPs nor the RNA features required to form them have been characterized. HDV RNA is unusual in that it forms an unbranched quasi-double-stranded structure in which short base-paired segments are interspersed with internal loops and bulges. We analyzed the role of the HDV RNA sequence and secondary structure in the formation of a minimal RNP and visualized the structure of this RNP using atomic force microscopy. Our results indicate that HDAg does not recognize the primary sequence of the RNA; rather, the principle contribution of unpaired bases in HDV RNA to HDAg binding is to allow flexibility in the unbranched quasi-double-stranded RNA structure. Visualization of RNPs by atomic force microscopy indicated that the RNA is significantly bent or condensed in the complex. PMID:24741096

Two-parameter double-oscillator model of Mathews-Lakshmanan type: Series solutions and supersymmetric partners

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schulze-Halberg, Axel, E-mail: axgeschu@iun.edu, E-mail: xbataxel@gmail.com; Wang, Jie, E-mail: wangjie@iun.edu

2015-07-15

We obtain series solutions, the discrete spectrum, and supersymmetric partners for a quantum double-oscillator system. Its potential features a superposition of the one-parameter Mathews-Lakshmanan interaction and a one-parameter harmonic or inverse harmonic oscillator contribution. Furthermore, our results are transferred to a generalized Pöschl-Teller model that is isospectral to the double-oscillator system.

Definition study for variable cycle engine testbed engine and associated test program

NASA Technical Reports Server (NTRS)

Vdoviak, J. W.

1978-01-01

The product/study double bypass variable cycle engine (VCE) was updated to incorporate recent improvements. The effect of these improvements on mission range and noise levels was determined. This engine design was then compared with current existing high-technology core engines in order to define a subscale testbed configuration that simulated many of the critical technology features of the product/study VCE. Detailed preliminary program plans were then developed for the design, fabrication, and static test of the selected testbed engine configuration. These plans included estimated costs and schedules for the detail design, fabrication and test of the testbed engine and the definition of a test program, test plan, schedule, instrumentation, and test stand requirements.

Molecular Genetic Characterization of Mutagenesis Using a Highly Sensitive Single-Stranded DNA Reporter System in Budding Yeast.

PubMed

Chan, Kin

2018-01-01

Mutations are permanent alterations to the coding content of DNA. They are starting material for the Darwinian evolution of species by natural selection, which has yielded an amazing diversity of life on Earth. Mutations can also be the fundamental basis of serious human maladies, most notably cancers. In this chapter, I describe a highly sensitive reporter system for the molecular genetic analysis of mutagenesis, featuring controlled generation of long stretches of single-stranded DNA in budding yeast cells. This system is ~100- to ~1000-fold more susceptible to mutation than conventional double-stranded DNA reporters, and is well suited for generating large mutational datasets to investigate the properties of mutagens.

Factors affecting nucleolytic efficiency of some ternary metal complexes with DNA binding and recognition domains. Crystal and molecular structure of Zn(phen)(edda).

PubMed

Seng, Hoi-Ling; Ong, Han-Kiat Alan; Rahman, Raja Noor Zaliha Raja Abd; Yamin, Bohari M; Tiekink, Edward R T; Tan, Kong Wai; Maah, Mohd Jamil; Caracelli, Ignez; Ng, Chew Hee

2008-11-01

The binding selectivity of the M(phen)(edda) (M=Cu, Co, Ni, Zn; phen=1,10-phenanthroline, edda=ethylenediaminediacetic acid) complexes towards ds(CG)(6), ds(AT)(6) and ds(CGCGAATTCGCG) B-form oligonucleotide duplexes were studied by CD spectroscopy and molecular modeling. The binding mode is intercalation and there is selectivity towards AT-sequence and stacking preference for A/A parallel or diagonal adjacent base steps in their intercalation. The nucleolytic properties of these complexes were investigated and the factors affecting the extent of cleavage were determined to be: concentration of complex, the nature of metal(II) ion, type of buffer, pH of buffer, incubation time, incubation temperature, and the presence of hydrogen peroxide or ascorbic acid as exogenous reagents. The fluorescence property of these complexes and its origin were also investigated. The crystal structure of the Zn(phen)(edda) complex is reported in which the zinc atom displays a distorted trans-N(4)O(2) octahedral geometry; the crystal packing features double layers of complex molecules held together by extensive hydrogen bonding that inter-digitate with adjacent double layers via pi...pi interactions between 1,10-phenanthroline residues. The structure is compared with that of the recently described copper(II) analogue and, with the latter, included in molecular modeling.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhao, Lulu; Zhang, Ming; Rassoul, Hamid K., E-mail: lzhao@fit.edu

Previous investigations on the energy spectra of solar energetic particle (SEP) events revealed that the energy spectra observed at 1 au often show double power laws with break energies from one to tens of MeV/nuc. In order to determine whether the double power-law features result from the SEP source or the interplanetary transport process from the Sun to 1 au, we separately analyze the SEP spectra in the decay phase, during which the transport effect is minimum. In this paper, we reported three events observed by the Interplanetary Monitory Platform 8 spacecraft, which occurred on 1977 September 19, November 22,more » and 1979 March 1. For the first two events, the event-integrated spectra of protons possess double power-law profiles with break energies in a range of several MeV to tens of MeV, while the spectra integrated in the decay (reservoir) phase yield single power laws. Moreover, a general trend from a double power law at the rising phase to a single power law at the decay phase is observed. For the third event, both the event-integrated and the reservoir spectra show double power-law features. However, the difference between the low- and high-energy power-law indices is smaller for the reservoir spectrum than the event-integrated spectrum. These features were reproduced by solving the 1D diffusion equation analytically and we suggest that the transport process, especially the diffusion process, plays an important role in breaking the energy spectra.« less

Self-Calibration and Laser Energy Monitor Validations for a Double-Pulsed 2-Micron CO2 Integrated Path Differential Absorption Lidar Application

NASA Technical Reports Server (NTRS)

Refaat, Tamer F.; Singh, Upendra N.; Petros, Mulugeta; Remus, Ruben; Yu, Jirong

2015-01-01

Double-pulsed 2-micron integrated path differential absorption (IPDA) lidar is well suited for atmospheric CO2 remote sensing. The IPDA lidar technique relies on wavelength differentiation between strong and weak absorbing features of the gas normalized to the transmitted energy. In the double-pulse case, each shot of the transmitter produces two successive laser pulses separated by a short interval. Calibration of the transmitted pulse energies is required for accurate CO2 measurement. Design and calibration of a 2-micron double-pulse laser energy monitor is presented. The design is based on an InGaAs pin quantum detector. A high-speed photo-electromagnetic quantum detector was used for laser-pulse profile verification. Both quantum detectors were calibrated using a reference pyroelectric thermal detector. Calibration included comparing the three detection technologies in the single-pulsed mode, then comparing the quantum detectors in the double-pulsed mode. In addition, a self-calibration feature of the 2-micron IPDA lidar is presented. This feature allows one to monitor the transmitted laser energy, through residual scattering, with a single detection channel. This reduces the CO2 measurement uncertainty. IPDA lidar ground validation for CO2 measurement is presented for both calibrated energy monitor and self-calibration options. The calibrated energy monitor resulted in a lower CO2 measurement bias, while self-calibration resulted in a better CO2 temporal profiling when compared to the in situ sensor.

Double separate versus contiguous pituitary adenomas: MRI features and endocrinological follow up.

PubMed

Roberts, Sammie; Borges, Manuel Thomas; Lillehei, Kevin O; Kleinschmidt-DeMasters, B K

2016-10-01

Double pituitary adenomas are defined as two adenomas within a gland. These have distinct light microscopic and immunohistochemical features and may be clearly-separate or contiguous. Most reports have focused on the various hormonal combinations in double tumors rather than on any potential increased risk for residual mass or endocrinopathy. Departmental files were searched to identify all double adenomas from 1/1/2000 to 3/1/2016, with review of magnetic resonance imaging (MRI) to determine if the dual nature of the lesions could be discerned retrospectively after histologic diagnosis of double adenoma. All cases were immunostained for standard anterior pituitary hormones. Eight cases were identified: 2 follicle-stimulating hormone (FSH)/alpha subunit (ASU) + prolactinoma (PRL); 1 PRL + corticotroph (ACTH); 1 hormone-negative + PRL; 1 ACTH + ASU/growth hormone (GH)/PRL; 1 GH/PR + PRL; 1 FSH/ASU, + ACTH; 1 GH + luteinizing hormone (LH). One patient had clearly-separate lesions identified preoperatively and required two surgical procedures for gross total resection. A second patient had 2 lesions recognized at surgery and afterwards on retrospective neuroimaging. The remaining 6 patients had double adenomas discovered at the time of histologic examination that were not resolvable at surgery or on retrospective neuroimaging. Four patients, 2 with clearly-separate and 2 with contiguous double adenomas, had persistent MRI abnormalities, and one had continued endocrine abnormalities. Double contiguous pituitary adenomas are difficult to anticipate preoperatively or to resolve intraoperatively. Although double contiguous adenomas are much more common than double separate lesions, both have a risk for subtotal resection and, thus, residual mass and/or endocrinopathy may ensue.

Quantum-enhanced feature selection with forward selection and backward elimination

NASA Astrophysics Data System (ADS)

He, Zhimin; Li, Lvzhou; Huang, Zhiming; Situ, Haozhen

2018-07-01

Feature selection is a well-known preprocessing technique in machine learning, which can remove irrelevant features to improve the generalization capability of a classifier and reduce training and inference time. However, feature selection is time-consuming, particularly for the applications those have thousands of features, such as image retrieval, text mining and microarray data analysis. It is crucial to accelerate the feature selection process. We propose a quantum version of wrapper-based feature selection, which converts a classical feature selection to its quantum counterpart. It is valuable for machine learning on quantum computer. In this paper, we focus on two popular kinds of feature selection methods, i.e., wrapper-based forward selection and backward elimination. The proposed feature selection algorithm can quadratically accelerate the classical one.

Role of the Substrate Specificity-Defining Residues of Human SIRT5 in Modulating the Structural Stability and Inhibitory Features of the Enzyme.

PubMed

Yu, Junru; Haldar, Manas; Mallik, Sanku; Srivastava, D K

2016-01-01

Sirtuins are emerging as the key regulators of metabolism and aging, and their potential activators and inhibitors are being explored as therapeutics for improving health and treating associated diseases. Despite the global structural similarity among all seven isoforms of sirtuins (of which most of them catalyze the deacetylation reaction), SIRT5 is the only isoform that catalyzes the cleavage of negatively charged acylated substrates, and the latter feature appears to be encoded by the presence of Tyr102 and Arg105 residues at the active site pocket of the enzyme. To determine the contributions of the above residues in SIRT5 (vis a vis the corresponding residues of SIRT1) on substrate selectivity, inhibition by EX527 and nicotinamide, secondary structural features and thermal stability of the enzymes, we created single and double mutations (viz. Y102A, R105l, and Y102A/R105I) in SIRT5. The kinetic data revealed that while Y102A mutant enzyme catalyzed both deacetylation and desuccinylation reactions with comparable efficiencies, R105I and Y102A/R105I mutant enzymes favored the deacetylase reaction. Like SIRT1, the nicotinamide inhibition of SIRT5 double mutant (Y102A/R105I) exhibited the mixed non-competitive behavior. On the other hand, the desuccinylation reaction of both wild-type and Y102A mutant enzymes conformed to the competitive inhibition model. The inhibitory potency of EX527 progressively increased from Y102A, R105I, to Y102A/R105 mutant enzymes in SIRT5, but it did not reach to the level obtained with SIRT1. The CD spectroscopic data for the wild-type and mutant enzymes revealed changes in the secondary structural features of the enzymes, and such changes were more pronounced on examining their thermal denaturation patterns. A cumulative account of our experimental data reveal mutual cooperation between Y102 and R105 residues in promoting the desuccinylation versus deacetylation reaction in SIRT5, and the overall catalytic feature of the enzyme is manifested via the mutation induced modulation in the protein structure.

Role of the Substrate Specificity-Defining Residues of Human SIRT5 in Modulating the Structural Stability and Inhibitory Features of the Enzyme

PubMed Central

Yu, Junru; Haldar, Manas; Mallik, Sanku; Srivastava, D. K.

2016-01-01

Sirtuins are emerging as the key regulators of metabolism and aging, and their potential activators and inhibitors are being explored as therapeutics for improving health and treating associated diseases. Despite the global structural similarity among all seven isoforms of sirtuins (of which most of them catalyze the deacetylation reaction), SIRT5 is the only isoform that catalyzes the cleavage of negatively charged acylated substrates, and the latter feature appears to be encoded by the presence of Tyr102 and Arg105 residues at the active site pocket of the enzyme. To determine the contributions of the above residues in SIRT5 (vis a vis the corresponding residues of SIRT1) on substrate selectivity, inhibition by EX527 and nicotinamide, secondary structural features and thermal stability of the enzymes, we created single and double mutations (viz. Y102A, R105l, and Y102A/R105I) in SIRT5. The kinetic data revealed that while Y102A mutant enzyme catalyzed both deacetylation and desuccinylation reactions with comparable efficiencies, R105I and Y102A/R105I mutant enzymes favored the deacetylase reaction. Like SIRT1, the nicotinamide inhibition of SIRT5 double mutant (Y102A/R105I) exhibited the mixed non-competitive behavior. On the other hand, the desuccinylation reaction of both wild-type and Y102A mutant enzymes conformed to the competitive inhibition model. The inhibitory potency of EX527 progressively increased from Y102A, R105I, to Y102A/R105 mutant enzymes in SIRT5, but it did not reach to the level obtained with SIRT1. The CD spectroscopic data for the wild-type and mutant enzymes revealed changes in the secondary structural features of the enzymes, and such changes were more pronounced on examining their thermal denaturation patterns. A cumulative account of our experimental data reveal mutual cooperation between Y102 and R105 residues in promoting the desuccinylation versus deacetylation reaction in SIRT5, and the overall catalytic feature of the enzyme is manifested via the mutation induced modulation in the protein structure. PMID:27023330

A Weekend Workshop on Double Stars for Students

NASA Astrophysics Data System (ADS)

Brewer, Mark; Estrada, Chris; Estrada, Reed; Gillette, Sean

2016-01-01

A weekend double star workshop was held by Vanguard Preparatory for selected eighth grade students with the purpose of introducing them to astrometric observational science. The students were selected based on an essay provided by their language arts class. Collaboration with local visiting astronomers was established to provide telescopes equipped with an astrometric eyepiece, observational supervision, and expertise. During the workshop students learned how to determine the scale constant of an astrometric eyepiece, and the procedure for measuring separations and position angles of double stars. The students compared their data to past measurements reported in the Washington Double Star Catalog. Three goals were set for the student's outcome: 1) observe, record, and report observations of double stars, 2) write a scientific paper for publication in the Journal of Double Star Observations, and 3) present a PowerPoint presentation to their peers. This paper chronicles the planning, preparation, funding, and execution required to complete a double star workshop at a public middle school.

A Model-Based Analysis of Semi-Automated Data Discovery and Entry Using Automated Content Extraction

DTIC Science & Technology

2011-02-01

Accomplish Goal) to (a) visually search the contents of a file folder until the icon corresponding to the desired file is located (Choose...Item_from_set), and (b) move the mouse to that icon and double click to open it (Double_select Object). Note that Choose Item_from_set and Double_select...argument, which Open File fills with <found_item>, a working memory pointer to the file icon that Choose_item_from Set finds. Look_at, Point_to

Machine learning bandgaps of double perovskites

PubMed Central

Pilania, G.; Mannodi-Kanakkithodi, A.; Uberuaga, B. P.; Ramprasad, R.; Gubernatis, J. E.; Lookman, T.

2016-01-01

The ability to make rapid and accurate predictions on bandgaps of double perovskites is of much practical interest for a range of applications. While quantum mechanical computations for high-fidelity bandgaps are enormously computation-time intensive and thus impractical in high throughput studies, informatics-based statistical learning approaches can be a promising alternative. Here we demonstrate a systematic feature-engineering approach and a robust learning framework for efficient and accurate predictions of electronic bandgaps of double perovskites. After evaluating a set of more than 1.2 million features, we identify lowest occupied Kohn-Sham levels and elemental electronegativities of the constituent atomic species as the most crucial and relevant predictors. The developed models are validated and tested using the best practices of data science and further analyzed to rationalize their prediction performance. PMID:26783247

The doubling of stellar black hole nuclei

NASA Astrophysics Data System (ADS)

Kazandjian, Mher V.; Touma, J. R.

2013-04-01

It is strongly believed that Andromeda's double nucleus signals a disc of stars revolving around its central supermassive black hole on eccentric Keplerian orbits with nearly aligned apsides. A self-consistent stellar dynamical origin for such apparently long-lived alignment has so far been lacking, with indications that cluster self-gravity is capable of sustaining such lopsided configurations if and when stimulated by external perturbations. Here, we present results of N-body simulations which show unstable counter-rotating stellar clusters around supermassive black holes saturating into uniformly precessing lopsided nuclei. The double nucleus in our featured experiment decomposes naturally into a thick eccentric disc of apo-apse aligned stars which is embedded in a lighter triaxial cluster. The eccentric disc reproduces key features of Keplerian disc models of Andromeda's double nucleus; the triaxial cluster has a distinctive kinematic signature which is evident in Hubble Space Telescope observations of Andromeda's double nucleus, and has been difficult to reproduce with Keplerian discs alone. Our simulations demonstrate how the combination of an eccentric disc and a triaxial cluster arises naturally when a star cluster accreted over a preexisting and counter-rotating disc of stars drives disc and cluster into a mutually destabilizing dance. Such accretion events are inherent to standard galaxy formation scenarios. They are here shown to double stellar black hole nuclei as they feed them.

Astragalar Morphology of Selected Giraffidae

PubMed Central

2016-01-01

The artiodactyl astragalus has been modified to exhibit two trochleae, creating a double pullied structure allowing for significant dorso-plantar motion, and limited mediolateral motion. The astragalus structure is partly influenced by environmental substrates, and correspondingly, morphometric studies can yield paleohabitat information. The present study establishes terminology and describes detailed morphological features on giraffid astragali. Each giraffid astragalus exhibits a unique combination of anatomical characteristics. The giraffid astragalar morphologies reinforce previously established phylogenetic relationships. We find that the enlargement of the navicular head is a feature shared by all giraffids, and that the primitive giraffids possess exceptionally tall astragalar heads in relation to the total astragalar height. The sivatheres and the okapi share a reduced notch on the lateral edge of the astragalus. We find that Samotherium is more primitive in astragalar morphologies than Palaeotragus, which is reinforced by tooth characteristics and ossicone position. Diagnostic anatomical characters on the astragalus allow for giraffid species identifications and a better understanding of Giraffidae. PMID:27028515

pyGFC - A Python Extension to the C++ Geodesy Foundation Classes

DTIC Science & Technology

2008-09-01

imperative for a successful emulation of a dynamic MANET as intended. To achieve this objective, the same algorithm and its implementation for...solution has two options: (1) selecting, implementing, and integrating an appropriate algorithm into the tool and in the MANE system, or (2) using an...GFCCoord *estimate_southeast_coords(double, double); double lat; double lon; double alt; private: CEarth *earth; char * cstr

Feature Selection for Chemical Sensor Arrays Using Mutual Information

PubMed Central

Wang, X. Rosalind; Lizier, Joseph T.; Nowotny, Thomas; Berna, Amalia Z.; Prokopenko, Mikhail; Trowell, Stephen C.

2014-01-01

We address the problem of feature selection for classifying a diverse set of chemicals using an array of metal oxide sensors. Our aim is to evaluate a filter approach to feature selection with reference to previous work, which used a wrapper approach on the same data set, and established best features and upper bounds on classification performance. We selected feature sets that exhibit the maximal mutual information with the identity of the chemicals. The selected features closely match those found to perform well in the previous study using a wrapper approach to conduct an exhaustive search of all permitted feature combinations. By comparing the classification performance of support vector machines (using features selected by mutual information) with the performance observed in the previous study, we found that while our approach does not always give the maximum possible classification performance, it always selects features that achieve classification performance approaching the optimum obtained by exhaustive search. We performed further classification using the selected feature set with some common classifiers and found that, for the selected features, Bayesian Networks gave the best performance. Finally, we compared the observed classification performances with the performance of classifiers using randomly selected features. We found that the selected features consistently outperformed randomly selected features for all tested classifiers. The mutual information filter approach is therefore a computationally efficient method for selecting near optimal features for chemical sensor arrays. PMID:24595058

Rough sets and Laplacian score based cost-sensitive feature selection

PubMed Central

Yu, Shenglong

2018-01-01

Cost-sensitive feature selection learning is an important preprocessing step in machine learning and data mining. Recently, most existing cost-sensitive feature selection algorithms are heuristic algorithms, which evaluate the importance of each feature individually and select features one by one. Obviously, these algorithms do not consider the relationship among features. In this paper, we propose a new algorithm for minimal cost feature selection called the rough sets and Laplacian score based cost-sensitive feature selection. The importance of each feature is evaluated by both rough sets and Laplacian score. Compared with heuristic algorithms, the proposed algorithm takes into consideration the relationship among features with locality preservation of Laplacian score. We select a feature subset with maximal feature importance and minimal cost when cost is undertaken in parallel, where the cost is given by three different distributions to simulate different applications. Different from existing cost-sensitive feature selection algorithms, our algorithm simultaneously selects out a predetermined number of “good” features. Extensive experimental results show that the approach is efficient and able to effectively obtain the minimum cost subset. In addition, the results of our method are more promising than the results of other cost-sensitive feature selection algorithms. PMID:29912884

Rough sets and Laplacian score based cost-sensitive feature selection.

PubMed

Yu, Shenglong; Zhao, Hong

2018-01-01

Cost-sensitive feature selection learning is an important preprocessing step in machine learning and data mining. Recently, most existing cost-sensitive feature selection algorithms are heuristic algorithms, which evaluate the importance of each feature individually and select features one by one. Obviously, these algorithms do not consider the relationship among features. In this paper, we propose a new algorithm for minimal cost feature selection called the rough sets and Laplacian score based cost-sensitive feature selection. The importance of each feature is evaluated by both rough sets and Laplacian score. Compared with heuristic algorithms, the proposed algorithm takes into consideration the relationship among features with locality preservation of Laplacian score. We select a feature subset with maximal feature importance and minimal cost when cost is undertaken in parallel, where the cost is given by three different distributions to simulate different applications. Different from existing cost-sensitive feature selection algorithms, our algorithm simultaneously selects out a predetermined number of "good" features. Extensive experimental results show that the approach is efficient and able to effectively obtain the minimum cost subset. In addition, the results of our method are more promising than the results of other cost-sensitive feature selection algorithms.

A triangle voting algorithm based on double feature constraints for star sensors

NASA Astrophysics Data System (ADS)

Fan, Qiaoyun; Zhong, Xuyang

2018-02-01

A novel autonomous star identification algorithm is presented in this study. In the proposed algorithm, each sensor star constructs multi-triangle with its bright neighbor stars and obtains its candidates by triangle voting process, in which the triangle is considered as the basic voting element. In order to accelerate the speed of this algorithm and reduce the required memory for star database, feature extraction is carried out to reduce the dimension of triangles and each triangle is described by its base and height. During the identification period, the voting scheme based on double feature constraints is proposed to implement triangle voting. This scheme guarantees that only the catalog star satisfying two features can vote for the sensor star, which improves the robustness towards false stars. The simulation and real star image test demonstrate that compared with the other two algorithms, the proposed algorithm is more robust towards position noise, magnitude noise and false stars.

Measurements of Neglected Double Stars: February 2018 Report

NASA Astrophysics Data System (ADS)

Carro, Joseph M.

2018-07-01

This article presents measurements of 53 neglected double stars. The stars were selected from the Washington Double Star Catalog published by the United States Naval Observatory. The photographs were taken by remote telescopes. The measurements were done by the author.

STT Doubles with Large DM - Part IV: Ophiuchus and Hercules

NASA Astrophysics Data System (ADS)

Knapp, Wilfried; Nanson, John

2016-04-01

The results of visual double star observing sessions suggested a pattern for STT doubles with large DM of being harder to resolve than would be expected based on the WDS catalog data. It was felt this might be a problem with expectations on one hand, and on the other might be an indication of a need for new precise measurements, so we decided to take a closer look at a selected sample of STT doubles and do some research. We found that like in the other constellations covered so far (Gem, Leo, UMa, etc.) at least several of the selected objects in Ophiuchus and Hercules show parameters quite different from the current WDS data.

STT Doubles with Large DM - Part V: Aquila, Delphinus, Cygnus, Aquarius

NASA Astrophysics Data System (ADS)

Knapp, Wilfried; Nanson, John

2016-07-01

The results of visual double star observing sessions suggested a pattern for STT doubles with large DM of being harder to resolve than would be expected based on the WDS catalog data. It was felt this might be a problem with expectations on one hand, and on the other might be an indication of a need for new precise measurements, so we decided to take a closer look at a selected sample of STT doubles and do some research. We found that, as in the other constellations covered so far (Gem, Leo, UMa etc.), at least several of the selected objects in Aql, Del, Cyg and Aqr show parameters quite different from the current WDS data

Characterization of six patients who are double heterozygotes for familial hypercholesterolemia and familial defective apo B-100.

PubMed

Rubinsztein, D C; Raal, F J; Seftel, H C; Pilcher, G; Coetzee, G A; van der Westhuyzen, D R

1993-07-01

Familial defective apolipoprotein B-100 (FDB) and familial hypercholesterolemia (FH) are the common causes of monogenic primary hypercholesterolemia. An individual of mixed English and Afrikaner descent with both FDB and the FH Afrikaner-1 low-density lipoprotein receptor mutation was identified in our laboratory. Subsequent analysis of her extended family revealed the presence of heterozygotes for either FH Afrikaner-1, FH Afrikaner-2, or FDB as well as five additional double heterozygotes for FH Afrikaner-1 and FDB and one "complex" heterozygote with all three mutations. The hypercholesterolemic and clinical features of the pure FDB subjects were similar to those of the pure FH heterozygotes. The double heterozygotes with both FH and FDB have lipid levels and clinical features that are intermediate in severity between heterozygous and homozygous FH.

Machine learning bandgaps of double perovskites

DOE PAGES

Pilania, G.; Mannodi-Kanakkithodi, A.; Uberuaga, B. P.; ...

2016-01-19

The ability to make rapid and accurate predictions on bandgaps of double perovskites is of much practical interest for a range of applications. While quantum mechanical computations for high-fidelity bandgaps are enormously computation-time intensive and thus impractical in high throughput studies, informatics-based statistical learning approaches can be a promising alternative. Here we demonstrate a systematic feature-engineering approach and a robust learning framework for efficient and accurate predictions of electronic bandgaps of double perovskites. After evaluating a set of more than 1.2 million features, we identify lowest occupied Kohn-Sham levels and elemental electronegativities of the constituent atomic species as the mostmore » crucial and relevant predictors. As a result, the developed models are validated and tested using the best practices of data science and further analyzed to rationalize their prediction performance.« less

No evidence for intervention-dependent influence of methodological features on treatment effect.

PubMed

Jacobs, Wilco C H; Kruyt, Moyo C; Moojen, Wouter A; Verbout, Ab J; Oner, F Cumhur

2013-12-01

The goal of this systematic review was to evaluate if the influence of methodological features on treatment effect differs between types of intervention. MEDLINE, Embase, Web of Science, Cochrane methodology register, and reference lists were searched for meta-epidemiologic studies on the influence of methodological features on treatment effect. Studies analyzing influence of methodological features related to internal validity were included. We made a distinction among surgical, pharmaceutical, and therapeutical as separate types of intervention. Heterogeneity was calculated to identify differences among these types. Fourteen meta-epidemiologic studies were found with 51 estimates of influence of methodological features on treatment effect. Heterogeneity was observed among the intervention types for randomization. Surgical intervention studies showed a larger treatment effect when randomized; this was in contrast to pharmaceutical studies that found the opposite. For allocation concealment and double blinding, the influence of methodological features on the treatment effect was comparable across different types of intervention. For the remaining methodological features, there were insufficient observations. The influence of allocation concealment and double blinding on the treatment effect is consistent across studies of different interventional types. The influence of randomization although, may be different between surgical and nonsurgical studies. Copyright © 2013 Elsevier Inc. All rights reserved.

First principles study of the electronic and magnetic structures and bonding properties of UCoC2 ternary, characteristic of C-C units

NASA Astrophysics Data System (ADS)

Matar, Samir F.

2013-03-01

The electronic structure of UCoC2, a di-carbide with the C-C units is examined from ab initio with an assessment of the properties of chemical bonding. The energy-volume equation of state shows large anisotropy effects due to C-C alignment along tetragonal c-axis leading to high linear incompressibility. Relevant features of selective bonding of uranium and cobalt with carbon at two different Wyckoff sites and strong C-C interactions are remarkable. The vibrational frequencies for C⋯C stretching modes indicate closer behavior to aliphatic C-C rather than Cdbnd C double bond. A ferromagnetic ground state is proposed from the calculations.

Coherent Synchrotron Radiation in Laboratory Accelerators and the Double-Spectral Feature in Solar Flares

NASA Astrophysics Data System (ADS)

Cruz, Wellington; Szpigel, Sérgio; Kaufmann, Pierre; Raulin, Jean-Pierre; Klopf, Michael

2017-10-01

Recent observations of solar flares at high-frequencies have provided evidence of a new spectral component with fluxes increasing with frequency in the sub-THz to THz range. This new component occurs simultaneously but is separated from the well-known microwave spectral component that maximizes at frequencies of a few to tens of GHz. The aim of this work is to study in detail a mechanism recently suggested to describe the double-spectrum feature observed in solar flares based on the physical process known as microbunching instability, which occurs with high-energy electron beams in laboratory accelerators.

Noninvasive Classification of Hepatic Fibrosis Based on Texture Parameters From Double Contrast-Enhanced Magnetic Resonance Images

PubMed Central

Bahl, Gautam; Cruite, Irene; Wolfson, Tanya; Gamst, Anthony C.; Collins, Julie M.; Chavez, Alyssa D.; Barakat, Fatma; Hassanein, Tarek; Sirlin, Claude B.

2016-01-01

Purpose To demonstrate a proof of concept that quantitative texture feature analysis of double contrast-enhanced magnetic resonance imaging (MRI) can classify fibrosis noninvasively, using histology as a reference standard. Materials and Methods A Health Insurance Portability and Accountability Act (HIPAA)-compliant Institutional Review Board (IRB)-approved retrospective study of 68 patients with diffuse liver disease was performed at a tertiary liver center. All patients underwent double contrast-enhanced MRI, with histopathology-based staging of fibrosis obtained within 12 months of imaging. The MaZda software program was used to compute 279 texture parameters for each image. A statistical regularization technique, generalized linear model (GLM)-path, was used to develop a model based on texture features for dichotomous classification of fibrosis category (F ≤2 vs. F ≥3) of the 68 patients, with histology as the reference standard. The model's performance was assessed and cross-validated. There was no additional validation performed on an independent cohort. Results Cross-validated sensitivity, specificity, and total accuracy of the texture feature model in classifying fibrosis were 91.9%, 83.9%, and 88.2%, respectively. Conclusion This study shows proof of concept that accurate, noninvasive classification of liver fibrosis is possible by applying quantitative texture analysis to double contrast-enhanced MRI. Further studies are needed in independent cohorts of subjects. PMID:22851409

Photocatalytic organic transformation by layered double hydroxides: highly efficient and selective oxidation of primary aromatic amines to their imines under ambient aerobic conditions.

PubMed

Yang, Xiu-Jie; Chen, Bin; Li, Xu-Bing; Zheng, Li-Qiang; Wu, Li-Zhu; Tung, Chen-Ho

2014-06-25

We report the first application of layered double hydroxide as a photocatalyst in the transformation of primary aromatic amines to their corresponding imines with high efficiency and selectivity by using oxygen in an air atmosphere as a terminal oxidant under light irradiation.

Added value of double reading in diagnostic radiology,a systematic review.

PubMed

Geijer, Håkan; Geijer, Mats

2018-06-01

Double reading in diagnostic radiology can find discrepancies in the original report, but a systematic program of double reading is resource consuming. There are conflicting opinions on the value of double reading. The purpose of the current study was to perform a systematic review on the value of double reading. A systematic review was performed to find studies calculating the rate of misses and overcalls with the aim of establishing the added value of double reading by human observers. The literature search resulted in 1610 hits. After abstract and full-text reading, 46 articles were selected for analysis. The rate of discrepancy varied from 0.4 to 22% depending on study setting. Double reading by a sub-specialist, in general, led to high rates of changed reports. The systematic review found rather low discrepancy rates. The benefit of double reading must be balanced by the considerable number of working hours a systematic double-reading scheme requires. A more profitable scheme might be to use systematic double reading for selected, high-risk examination types. A second conclusion is that there seems to be a value of sub-specialisation for increased report quality. A consequent implementation of this would have far-reaching organisational effects. • In double reading, two or more radiologists read the same images. • A systematic literature review was performed. • The discrepancy rates varied from 0.4 to 22% in various studies. • Double reading by sub-specialists found high discrepancy rates.

Switch-on fluorescence scheme for antibiotics based on a magnetic composite probe with aptamer and hemin/G-quadruplex coimmobilized nano-Pt-luminol as signal tracer.

PubMed

Miao, Yang-Bao; Gan, Ning; Ren, Hong-Xia; Li, Tianhua; Cao, Yuting; Hu, Futao; Chen, Yinji

2016-01-15

A selective and facile fluorescence "switch-on" scheme is developed to detect antibiotics residues in food, using chloramphenicol (CAP) as model, based on a novel magnetic aptamer probe (aptamer-Pt-luminol nanocomposite labeled with hemin/G-quadruplex). Firstly, the composite probe is prepared through the immuno-reactions between the capture beads (anti-dsDNA antibody labeled on magnetic Dynabeads) and the nanotracer (nano-Pt-luminol labeled with double-strand aptamer, as ds-Apt, and hemin/G-quadruplex). When the composite probe is mixed with CAP, the aptamer preferentially reacted with CAP to decompose the double-strand aptamer to ssDNA, which cannot be recognized by the anti-dsDNA antibody on the capture probes. Thus, after magnetic separation, the nanotracer can be released into the supernatant. Because the hemin/G-quadruplex and PtNPs in nanotracer can catalyze luminol-H2O2 system to emit fluorescence. Thus a dual-amplified "switch-on" signal appeared, of which intensity is proportional to the concentration of CAP between 0.001 and 100ng mL(-1) with detection limit of 0.0005ng mL(-1) (S/N=3). Besides, our method has good selectivity and was employed for CAP detection in real milk samples. The results agree well with those from conventional gas chromatograph-mass spectrometer (GC-MS). The switch-on signal is produced by one-step substitution reaction between aptamer in nanotracer and target. When the analyte is changed, the probe can be refabricated only by changing the corresponding aptamer. Thus, all features above prove our strategy to be a facile, feasible and selective method in antibiotics screening for food safety. Copyright © 2015 Elsevier B.V. All rights reserved.

Online feature selection with streaming features.

PubMed

Wu, Xindong; Yu, Kui; Ding, Wei; Wang, Hao; Zhu, Xingquan

2013-05-01

We propose a new online feature selection framework for applications with streaming features where the knowledge of the full feature space is unknown in advance. We define streaming features as features that flow in one by one over time whereas the number of training examples remains fixed. This is in contrast with traditional online learning methods that only deal with sequentially added observations, with little attention being paid to streaming features. The critical challenges for Online Streaming Feature Selection (OSFS) include 1) the continuous growth of feature volumes over time, 2) a large feature space, possibly of unknown or infinite size, and 3) the unavailability of the entire feature set before learning starts. In the paper, we present a novel Online Streaming Feature Selection method to select strongly relevant and nonredundant features on the fly. An efficient Fast-OSFS algorithm is proposed to improve feature selection performance. The proposed algorithms are evaluated extensively on high-dimensional datasets and also with a real-world case study on impact crater detection. Experimental results demonstrate that the algorithms achieve better compactness and higher prediction accuracy than existing streaming feature selection algorithms.

Coronary heart disease in the diabetic African: frequency clinical and angiographic features.

PubMed

Touze, J E; Ekra, A; Darracq, R; Mardelle, T; Adoh, A; Ake, E; Chauvet, J; Bertrand, E

1987-01-01

The frequency and clinical and coronarographic features of coronary heart disease (CHD) in black African diabetic patients were assessed in a two-part study. The aim of part I was to determine the frequency of CHD in 50 diabetic patients selected by the following criteria: male, age between 40 and 60 years, diabetes history less than 20 years, no history of CHD and normal E.K.G. All 50 of these patients underwent a stress test and those who failed or for whom results were inconclusive were submitted to coronary arteriography. Part II was a retrospective study of 104 patients with CHD. Its aim was to compare the clinical and coronarographic features of CHD patients with (27 cases) and without (77 cases) diabetes mellitus. The frequency of CHD in the 50 diabetics selected for this study was 10% (31 negative exercise tests, 19 inconclusive exercise tests, 5 coronary arteriographies with significant narrowing). Of these 5 diabetics with CHD, 3 had single vessel involvement (left descending artery: 2 cases, circumflex artery: 1 case), 1 patient had double vessel involvement (right coronary circumflex artery) and 1 had triple vessel involvement (left descending, circumflex, and right coronary artery). In the retrospective study the clinical profile of the diabetic and non-diabetic CHD patients was the same with respect to sex, age, angina, myocardial infarction, and death rate. As regard the risk factors, blood cholesterol level was higher in diabetics while cigarette smoking was higher in non-diabetics. The frequency of hypertension was the same in both groups.(ABSTRACT TRUNCATED AT 250 WORDS)

Dry etch challenges for CD shrinkage in memory process

NASA Astrophysics Data System (ADS)

Matsushita, Takaya; Matsumoto, Takanori; Mukai, Hidefumi; Kyoh, Suigen; Hashimoto, Kohji

2015-03-01

Line pattern collapse attracts attention as a new problem of the L&S formation in sub-20nm H.P feature. Line pattern collapse that occurs in a slight non-uniformity of adjacent CD (Critical dimension) space using double patterning process has been studied with focus on micro-loading effect in Si etching. Bias RF pulsing plasma etching process using low duty cycle helped increase of selectivity Si to SiO2. In addition to the effect of Bias RF pulsing process, the thin mask obtained from improvement of selectivity has greatly suppressed micro-loading in Si etching. However it was found that micro-loading effect worsen again in sub-20nm space width. It has been confirmed that by using cycle etch process to remove deposition with CFx based etching micro-loading effect could be suppressed. Finally, Si etching process condition using combination of results above could provide finer line and space without "line pattern collapse" in sub-20nm.

Enhancing the Discrimination Ability of a Gas Sensor Array Based on a Novel Feature Selection and Fusion Framework.

PubMed

Deng, Changjian; Lv, Kun; Shi, Debo; Yang, Bo; Yu, Song; He, Zhiyi; Yan, Jia

2018-06-12

In this paper, a novel feature selection and fusion framework is proposed to enhance the discrimination ability of gas sensor arrays for odor identification. Firstly, we put forward an efficient feature selection method based on the separability and the dissimilarity to determine the feature selection order for each type of feature when increasing the dimension of selected feature subsets. Secondly, the K-nearest neighbor (KNN) classifier is applied to determine the dimensions of the optimal feature subsets for different types of features. Finally, in the process of establishing features fusion, we come up with a classification dominance feature fusion strategy which conducts an effective basic feature. Experimental results on two datasets show that the recognition rates of Database I and Database II achieve 97.5% and 80.11%, respectively, when k = 1 for KNN classifier and the distance metric is correlation distance (COR), which demonstrates the superiority of the proposed feature selection and fusion framework in representing signal features. The novel feature selection method proposed in this paper can effectively select feature subsets that are conducive to the classification, while the feature fusion framework can fuse various features which describe the different characteristics of sensor signals, for enhancing the discrimination ability of gas sensors and, to a certain extent, suppressing drift effect.

Halo and Pseudohalo Cu(I)-Pyridinato Double Chains with Tunable Physical Properties.

PubMed

Hassanein, K; Amo-Ochoa, P; Gómez-García, C J; Delgado, S; Castillo, O; Ocón, P; Martínez, J I; Perles, J; Zamora, F

2015-11-16

The properties recently reported on the Cu(I)-iodide pyrimidine nonporous 1D-coordination polymer [CuI(ANP)]n (ANP = 2-amino-5-nitropyridine) showing reversible physically and chemically driven electrical response have prompted us to carry a comparative study with the series of [CuX(ANP)]n (X = Cl (1), X = Br (2), X = CN (4), and X = SCN (5)) in order to understand the potential influence of the halide and pseudohalide bridging ligands on the physical properties and their electrical response to vapors of these materials. The structural characterization of the series shows a common feature, the presence of -X-Cu(ANP)-X- (X = Cl, Br, I, SCN) double chain structure. Complex [Cu(ANP)(CN)]n (4) presents a helical single chain. Additionally, the chains show supramolecular interlinked interactions via hydrogen bonding giving rise to the formation of extended networks. Their luminescent and electrical properties have been studied. The results obtained have been correlated with structural changes. Furthermore, the experimental and theoretical results have been compared using the density functional theory (DFT). The electrical response of the materials has been evaluated in the presence of vapors of diethyl ether, dimethyl methylphosphonate (DMMP), CH2Cl2, HAcO, MeOH, and EtOH, to build up simple prototype devices for gas detectors. Selectivity toward gases consisting of molecules with H-bonding donor or acceptor groups is clearly observed. This selective molecular recognition is likely due to the 2-amino-5-nitropyridine terminal ligand.

CALUTRON

DOEpatents

Brobeck, W.M.; Lofgren, E.J.; Thornton, R.L.

1959-06-01

A calutron improved in liner and capacity is offered. The liner is a hollow insulated structure at high negative potential with respect to the vessel. The liner has delimiting vanes to prevent ions from one beam scattering into the receiver from another beam. The double beam-double receiver feature is thus made possible, increasing the capacity of the calutron. (T.R.H.)

Double-dictionary matching pursuit for fault extent evaluation of rolling bearing based on the Lempel-Ziv complexity

NASA Astrophysics Data System (ADS)

Cui, Lingli; Gong, Xiangyang; Zhang, Jianyu; Wang, Huaqing

2016-12-01

The quantitative diagnosis of rolling bearing fault severity is particularly crucial to realize a proper maintenance decision. Aiming at the fault feature of rolling bearing, a novel double-dictionary matching pursuit (DDMP) for fault extent evaluation of rolling bearing based on the Lempel-Ziv complexity (LZC) index is proposed in this paper. In order to match the features of rolling bearing fault, the impulse time-frequency dictionary and modulation dictionary are constructed to form the double-dictionary by using the method of parameterized function model. Then a novel matching pursuit method is proposed based on the new double-dictionary. For rolling bearing vibration signals with different fault sizes, the signals are decomposed and reconstructed by the DDMP. After the noise reduced and signals reconstructed, the LZC index is introduced to realize the fault extent evaluation. The applications of this method to the fault experimental signals of bearing outer race and inner race with different degree of injury have shown that the proposed method can effectively realize the fault extent evaluation.

X-ray topographic studies and measurement of lattice parameter differences within synthetic diamonds grown by the reconstitution technique

NASA Astrophysics Data System (ADS)

Wierzchowski, W.; Moore, M.; Makepeace, A. P. W.; Yacoot, A.

1991-10-01

A 4 x 4 x 1.5 cu mm cuboctahedral diamond and two 0.7 mm thick slabs cut from a truncated octahedral diamond grown by the reconstitution technique were studied in different double-crystal arrangements with both conventional and synchrotron X-ray sources. The back-reflection double crystal topographs of large polished 001-plane-oriented faces intersecting different growth sectors, together with cathodoluminescence patterns, allowed identification of these sectors. A double-crystal arrangement, employing the -3 2 5 quartz reflection matching the symmetrical 004 diamond reflection in CuK(alpha 1) radiation, was used for measurement of lattice parameter differences with an accuracy of one and a half parts per million. The simultaneous investigation by means of Lang projection and section topography provided complementary information about the crystallographic defects and internal structures of growth sectors. Observation of the cuboctahedral diamond with a filter of peak transmittance at 430 nm revealed a 'Maltese cross' growth feature in the central (001) growth sector, which also affected the birefringence pattern. However, this feature only very slightly affected the double-crystal topographs.

Combined measurement system for double shield tunnel boring machine guidance based on optical and visual methods.

PubMed

Lin, Jiarui; Gao, Kai; Gao, Yang; Wang, Zheng

2017-10-01

In order to detect the position of the cutting shield at the head of a double shield tunnel boring machine (TBM) during the excavation, this paper develops a combined measurement system which is mainly composed of several optical feature points, a monocular vision sensor, a laser target sensor, and a total station. The different elements of the combined system are mounted on the TBM in suitable sequence, and the position of the cutting shield in the reference total station frame is determined by coordinate transformations. Subsequently, the structure of the feature points and matching technique for them are expounded, the position measurement method based on monocular vision is presented, and the calibration methods for the unknown relationships among different parts of the system are proposed. Finally, a set of experimental platforms to simulate the double shield TBM is established, and accuracy verification experiments are conducted. Experimental results show that the mean deviation of the system is 6.8 mm, which satisfies the requirements of double shield TBM guidance.

Selective Audiovisual Semantic Integration Enabled by Feature-Selective Attention.

PubMed

Li, Yuanqing; Long, Jinyi; Huang, Biao; Yu, Tianyou; Wu, Wei; Li, Peijun; Fang, Fang; Sun, Pei

2016-01-13

An audiovisual object may contain multiple semantic features, such as the gender and emotional features of the speaker. Feature-selective attention and audiovisual semantic integration are two brain functions involved in the recognition of audiovisual objects. Humans often selectively attend to one or several features while ignoring the other features of an audiovisual object. Meanwhile, the human brain integrates semantic information from the visual and auditory modalities. However, how these two brain functions correlate with each other remains to be elucidated. In this functional magnetic resonance imaging (fMRI) study, we explored the neural mechanism by which feature-selective attention modulates audiovisual semantic integration. During the fMRI experiment, the subjects were presented with visual-only, auditory-only, or audiovisual dynamical facial stimuli and performed several feature-selective attention tasks. Our results revealed that a distribution of areas, including heteromodal areas and brain areas encoding attended features, may be involved in audiovisual semantic integration. Through feature-selective attention, the human brain may selectively integrate audiovisual semantic information from attended features by enhancing functional connectivity and thus regulating information flows from heteromodal areas to brain areas encoding the attended features.

Patients double-seropositive for ANCA and anti-GBM antibodies have varied renal survival, frequency of relapse, and outcomes compared to single-seropositive patients.

PubMed

McAdoo, Stephen P; Tanna, Anisha; Hrušková, Zdenka; Holm, Lisa; Weiner, Maria; Arulkumaran, Nishkantha; Kang, Amy; Satrapová, Veronika; Levy, Jeremy; Ohlsson, Sophie; Tesar, Vladimir; Segelmark, Mårten; Pusey, Charles D

2017-09-01

Co-presentation with both ANCA and anti-GBM antibodies is thought to be relatively rare. Current studies of such 'double-positive' cases report small numbers and variable outcomes. To study this further we retrospectively analyzed clinical features and long-term outcomes of a large cohort of 568 contemporary patients with ANCA-associated vasculitis, 41 patients with anti-GBM disease, and 37 double-positive patients with ANCA and anti-GBM disease from four European centers. Double-positive patients shared characteristics of ANCA-associated vasculitis (AAV), such as older age distribution and longer symptom duration before diagnosis, and features of anti-GBM disease, such as severe renal disease and high frequency of lung hemorrhage at presentation. Despite having more evidence of chronic injury on renal biopsy compared to patients with anti-GBM disease, double-positive patients had a greater tendency to recover from being dialysis-dependent after treatment and had intermediate long-term renal survival compared to the single-positive patients. However, overall patient survival was similar in all three groups. Predictors of poor patient survival included advanced age, severe renal failure, and lung hemorrhage at presentation. No single-positive anti-GBM patients experienced disease relapse, whereas approximately half of surviving patients with AAV and double-positive patients had recurrent disease during a median follow-up of 4.8 years. Thus, double-positive patients have a truly hybrid disease phenotype, requiring aggressive early treatment for anti-GBM disease, and careful long-term follow-up and consideration for maintenance immunosuppression for AAV. Since double-positivity appears common, further work is required to define the underlying mechanisms of this association and define optimum treatment strategies. Copyright © 2017 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.

Modeling selected emulsions and double emulsions as memristive systems.

PubMed

Spasic, Aleksandar M; Jovanovic, Jovan M; Jovanovic, Mica

2012-06-15

The recent development in basic and applied science and engineering of finely dispersed systems is presented in general, but more attention has been paid to the liquid-liquid finely dispersed systems or to the particular emulsions and double emulsions. The selected systems for theoretical and experimental research were emulsions and double emulsions that appeared in the pilot plant for extraction of uranium from wet phosphoric acid. The objective of this research was to try to provide a new or different approach to elaborate the complex phenomena that occur at developed liquid-liquid interfaces. New concepts were introduced, the first is a concept of an entity, and the corresponding classification of finely dispersed systems and the second concept consider the introduction of an almost forgotten basic electrodynamics element memristor, and the corresponding memristive systems. Based on these concepts a theory of electroviscoelasticity was proposed and experimentally corroborated using the selected representative liquid-liquid system. Also, it is shown that the droplet, and/or droplet-film structure, that is, selected emulsion and/or double emulsion may be considered as the particular example of memristive systems. Copyright © 2012 Elsevier B.V. All rights reserved.

Diode-laser-based RIMS measurements of strontium-90

NASA Astrophysics Data System (ADS)

Bushaw, B. A.; Cannon, B. D.

1998-12-01

Double- and triple-resonance excitation schemes for the ionization of strontium are presented. Use of single-mode diode lasers for the resonance excitations provides a high degree of optical isotopic selectivity: with double-resonance, selectivity of >104 for 90Sr against the stable Sr isotopes has been demonstrated. Measurement of lineshapes and stable isotope shifts in the triple-resonance process indicate that optical selectivity should increase to ˜109. When combined with mass spectrometer selectivity this is sufficient for measurement of 90Sr at background environmental levels. Additionally, autoionizing resonances have been investigated for improving ionization efficiency with lower power lasers.

High-rate lithium/manganese dioxide batteries; the double cell concept

NASA Astrophysics Data System (ADS)

Drews, Jürgen; Wolf, Rüdiger; Fehrmann, Gerd; Staub, Roland

An implantable defibrillator battery has to provide pulse-power capabilities as well as high energy density. Low self-discharge rates are mandatory and an ability to check the state of charge is required. To accomplish these requirements, a lithium/manganese dioxide battery with a modified active cathode mass has been developed. Usage of a double cell design increases significantly the battery performance within an implantable defibrillator. The design features of a high-rate, pulse-power, manganese dioxide double cell are described.

Double Bragg Interferometry

NASA Astrophysics Data System (ADS)

Ahlers, H.; Müntinga, H.; Wenzlawski, A.; Krutzik, M.; Tackmann, G.; Abend, S.; Gaaloul, N.; Giese, E.; Roura, A.; Kuhl, R.; Lämmerzahl, C.; Peters, A.; Windpassinger, P.; Sengstock, K.; Schleich, W. P.; Ertmer, W.; Rasel, E. M.

2016-04-01

We employ light-induced double Bragg diffraction of delta-kick collimated Bose-Einstein condensates to create three symmetric Mach-Zehnder interferometers. They rely on (i) first-order, (ii) two successive first-order, and (iii) second-order processes which demonstrate the scalability of the corresponding momentum transfer. With respect to devices based on conventional Bragg scattering, these symmetric interferometers double the scale factor and feature a better suppression of noise and systematic uncertainties intrinsic to the diffraction process. Moreover, we utilize these interferometers as tiltmeters for monitoring their inclination with respect to gravity.

EEG feature selection method based on decision tree.

PubMed

Duan, Lijuan; Ge, Hui; Ma, Wei; Miao, Jun

2015-01-01

This paper aims to solve automated feature selection problem in brain computer interface (BCI). In order to automate feature selection process, we proposed a novel EEG feature selection method based on decision tree (DT). During the electroencephalogram (EEG) signal processing, a feature extraction method based on principle component analysis (PCA) was used, and the selection process based on decision tree was performed by searching the feature space and automatically selecting optimal features. Considering that EEG signals are a series of non-linear signals, a generalized linear classifier named support vector machine (SVM) was chosen. In order to test the validity of the proposed method, we applied the EEG feature selection method based on decision tree to BCI Competition II datasets Ia, and the experiment showed encouraging results.

Compensatory selection for roads over natural linear features by wolves in northern Ontario: Implications for caribou conservation

PubMed Central

Patterson, Brent R.; Anderson, Morgan L.; Rodgers, Arthur R.; Vander Vennen, Lucas M.; Fryxell, John M.

2017-01-01

Woodland caribou (Rangifer tarandus caribou) in Ontario are a threatened species that have experienced a substantial retraction of their historic range. Part of their decline has been attributed to increasing densities of anthropogenic linear features such as trails, roads, railways, and hydro lines. These features have been shown to increase the search efficiency and kill rate of wolves. However, it is unclear whether selection for anthropogenic linear features is additive or compensatory to selection for natural (water) linear features which may also be used for travel. We studied the selection of water and anthropogenic linear features by 52 resident wolves (Canis lupus x lycaon) over four years across three study areas in northern Ontario that varied in degrees of forestry activity and human disturbance. We used Euclidean distance-based resource selection functions (mixed-effects logistic regression) at the seasonal range scale with random coefficients for distance to water linear features, primary/secondary roads/railways, and hydro lines, and tertiary roads to estimate the strength of selection for each linear feature and for several habitat types, while accounting for availability of each feature. Next, we investigated the trade-off between selection for anthropogenic and water linear features. Wolves selected both anthropogenic and water linear features; selection for anthropogenic features was stronger than for water during the rendezvous season. Selection for anthropogenic linear features increased with increasing density of these features on the landscape, while selection for natural linear features declined, indicating compensatory selection of anthropogenic linear features. These results have implications for woodland caribou conservation. Prey encounter rates between wolves and caribou seem to be strongly influenced by increasing linear feature densities. This behavioral mechanism–a compensatory functional response to anthropogenic linear feature density resulting in decreased use of natural travel corridors–has negative consequences for the viability of woodland caribou. PMID:29117234

Compensatory selection for roads over natural linear features by wolves in northern Ontario: Implications for caribou conservation.

PubMed

Newton, Erica J; Patterson, Brent R; Anderson, Morgan L; Rodgers, Arthur R; Vander Vennen, Lucas M; Fryxell, John M

2017-01-01

Woodland caribou (Rangifer tarandus caribou) in Ontario are a threatened species that have experienced a substantial retraction of their historic range. Part of their decline has been attributed to increasing densities of anthropogenic linear features such as trails, roads, railways, and hydro lines. These features have been shown to increase the search efficiency and kill rate of wolves. However, it is unclear whether selection for anthropogenic linear features is additive or compensatory to selection for natural (water) linear features which may also be used for travel. We studied the selection of water and anthropogenic linear features by 52 resident wolves (Canis lupus x lycaon) over four years across three study areas in northern Ontario that varied in degrees of forestry activity and human disturbance. We used Euclidean distance-based resource selection functions (mixed-effects logistic regression) at the seasonal range scale with random coefficients for distance to water linear features, primary/secondary roads/railways, and hydro lines, and tertiary roads to estimate the strength of selection for each linear feature and for several habitat types, while accounting for availability of each feature. Next, we investigated the trade-off between selection for anthropogenic and water linear features. Wolves selected both anthropogenic and water linear features; selection for anthropogenic features was stronger than for water during the rendezvous season. Selection for anthropogenic linear features increased with increasing density of these features on the landscape, while selection for natural linear features declined, indicating compensatory selection of anthropogenic linear features. These results have implications for woodland caribou conservation. Prey encounter rates between wolves and caribou seem to be strongly influenced by increasing linear feature densities. This behavioral mechanism-a compensatory functional response to anthropogenic linear feature density resulting in decreased use of natural travel corridors-has negative consequences for the viability of woodland caribou.

McTwo: a two-step feature selection algorithm based on maximal information coefficient.

PubMed

Ge, Ruiquan; Zhou, Manli; Luo, Youxi; Meng, Qinghan; Mai, Guoqin; Ma, Dongli; Wang, Guoqing; Zhou, Fengfeng

2016-03-23

High-throughput bio-OMIC technologies are producing high-dimension data from bio-samples at an ever increasing rate, whereas the training sample number in a traditional experiment remains small due to various difficulties. This "large p, small n" paradigm in the area of biomedical "big data" may be at least partly solved by feature selection algorithms, which select only features significantly associated with phenotypes. Feature selection is an NP-hard problem. Due to the exponentially increased time requirement for finding the globally optimal solution, all the existing feature selection algorithms employ heuristic rules to find locally optimal solutions, and their solutions achieve different performances on different datasets. This work describes a feature selection algorithm based on a recently published correlation measurement, Maximal Information Coefficient (MIC). The proposed algorithm, McTwo, aims to select features associated with phenotypes, independently of each other, and achieving high classification performance of the nearest neighbor algorithm. Based on the comparative study of 17 datasets, McTwo performs about as well as or better than existing algorithms, with significantly reduced numbers of selected features. The features selected by McTwo also appear to have particular biomedical relevance to the phenotypes from the literature. McTwo selects a feature subset with very good classification performance, as well as a small feature number. So McTwo may represent a complementary feature selection algorithm for the high-dimensional biomedical datasets.

A Double-Coil TMS Method to Assess Corticospinal Excitability Changes at a Near-Simultaneous Time in the Two Hands during Movement Preparation

PubMed Central

Wilhelm, Emmanuelle; Quoilin, Caroline; Petitjean, Charlotte; Duque, Julie

2016-01-01

Background: Many previous transcranial magnetic stimulation (TMS) studies have investigated corticospinal excitability changes occurring when choosing which hand to use for an action, one of the most frequent decision people make in daily life. So far, these studies have applied single-pulse TMS eliciting motor-evoked potential (MEP) in one hand when this hand is either selected or non-selected. Using such method, hand choices were shown to entail the operation of two inhibitory mechanisms, suppressing MEPs in the targeted hand either when it is non-selected (competition resolution, CR) or selected (impulse control, IC). However, an important limitation of this “Single-Coil” method is that MEPs are elicited in selected and non-selected conditions during separate trials and thus those two settings may not be completely comparable. Moreover, a more important problem is that MEPs are computed in relation to the movement of different hands. The goal of the present study was to test a “Double-Coil” method to evaluate IC and CR preceding the same hand responses by applying Double-Coil TMS over the two primary motor cortices (M1) at a near-simultaneous time (1 ms inter-pulse interval). Methods: MEPs were obtained in the left (MEPLEFT) and right (MEPRIGHT) hands while subjects chose between left and right hand key-presses in blocks using a Single-Coil or a Double-Coil method; in the latter blocks, TMS was either applied over left M1 first (TMSLRM1 group, n = 12) or right M1 first (TMSRLM1 group, n = 12). Results: MEPLEFT were suppressed preceding both left (IC) and right (CR) hand responses whereas MEPRIGHT were only suppressed preceding left (CR) but not right (IC) hand responses. This result was observed regardless of whether Single-Coil or Double-Coil TMS was applied in the two subject groups. However, in the TMSLRM1 group, the MEP suppression was attenuated in Double-Coil compared to Single-Coil blocks for both IC and CR, when probed with MEPLEFT (elicited by the second pulse). Conclusions: Although Double-Coil TMS may be a reliable method to assess bilateral motor excitability provided that a RM1-LM1 pulse order is used, further experiments are required to understand the reduced MEPLEFT changes in Double-Coil blocks when the LM1-RM1 pulse order was used. PMID:27014020

Line-frequency doubling of directed self-assembly patterns for single-digit bit pattern media lithography

NASA Astrophysics Data System (ADS)

Patel, K. C.; Ruiz, R.; Lille, J.; Wan, L.; Dobiz, E.; Gao, H.; Robertson, N.; Albrecht, T. R.

2012-03-01

Directed self-assembly is emerging as a promising technology to define sub-20nm features. However, a straightforward path to scale block copolymer lithography to single-digit fabrication remains challenging given the diverse material properties found in the wide spectrum of self-assembling materials. A vast amount of block copolymer research for industrial applications has been dedicated to polystyrene-b-methyl methacrylate (PS-b-PMMA), a model system that displays multiple properties making it ideal for lithography, but that is limited by a weak interaction parameter that prevents it from scaling to single-digit lithography. Other block copolymer materials have shown scalability to much smaller dimensions, but at the expense of other material properties that could delay their insertion into industrial lithographic processes. We report on a line doubling process applied to block copolymer patterns to double the frequency of PS-b-PMMA line/space features, demonstrating the potential of this technique to reach single-digit lithography. We demonstrate a line-doubling process that starts with directed self-assembly of PS-b-PMMA to define line/space features. This pattern is transferred into an underlying sacrificial hard-mask layer followed by a growth of self-aligned spacers which subsequently serve as hard-masks for transferring the 2x frequency doubled pattern to the underlying substrate. We applied this process to two different block copolymer materials to demonstrate line-space patterns with a half pitch of 11nm and 7nm underscoring the potential to reach single-digit critical dimensions. A subsequent patterning step with perpendicular lines can be used to cut the fine line patterns into a 2-D array of islands suitable for bit patterned media. Several integration challenges such as line width control and line roughness are addressed.

Attentional Selection Can Be Predicted by Reinforcement Learning of Task-relevant Stimulus Features Weighted by Value-independent Stickiness.

PubMed

Balcarras, Matthew; Ardid, Salva; Kaping, Daniel; Everling, Stefan; Womelsdorf, Thilo

2016-02-01

Attention includes processes that evaluate stimuli relevance, select the most relevant stimulus against less relevant stimuli, and bias choice behavior toward the selected information. It is not clear how these processes interact. Here, we captured these processes in a reinforcement learning framework applied to a feature-based attention task that required macaques to learn and update the value of stimulus features while ignoring nonrelevant sensory features, locations, and action plans. We found that value-based reinforcement learning mechanisms could account for feature-based attentional selection and choice behavior but required a value-independent stickiness selection process to explain selection errors while at asymptotic behavior. By comparing different reinforcement learning schemes, we found that trial-by-trial selections were best predicted by a model that only represents expected values for the task-relevant feature dimension, with nonrelevant stimulus features and action plans having only a marginal influence on covert selections. These findings show that attentional control subprocesses can be described by (1) the reinforcement learning of feature values within a restricted feature space that excludes irrelevant feature dimensions, (2) a stochastic selection process on feature-specific value representations, and (3) value-independent stickiness toward previous feature selections akin to perseveration in the motor domain. We speculate that these three mechanisms are implemented by distinct but interacting brain circuits and that the proposed formal account of feature-based stimulus selection will be important to understand how attentional subprocesses are implemented in primate brain networks.

Bootstrap-after-bootstrap model averaging for reducing model uncertainty in model selection for air pollution mortality studies.

PubMed

Roberts, Steven; Martin, Michael A

2010-01-01

Concerns have been raised about findings of associations between particulate matter (PM) air pollution and mortality that have been based on a single "best" model arising from a model selection procedure, because such a strategy may ignore model uncertainty inherently involved in searching through a set of candidate models to find the best model. Model averaging has been proposed as a method of allowing for model uncertainty in this context. To propose an extension (double BOOT) to a previously described bootstrap model-averaging procedure (BOOT) for use in time series studies of the association between PM and mortality. We compared double BOOT and BOOT with Bayesian model averaging (BMA) and a standard method of model selection [standard Akaike's information criterion (AIC)]. Actual time series data from the United States are used to conduct a simulation study to compare and contrast the performance of double BOOT, BOOT, BMA, and standard AIC. Double BOOT produced estimates of the effect of PM on mortality that have had smaller root mean squared error than did those produced by BOOT, BMA, and standard AIC. This performance boost resulted from estimates produced by double BOOT having smaller variance than those produced by BOOT and BMA. Double BOOT is a viable alternative to BOOT and BMA for producing estimates of the mortality effect of PM.

Selectivity Enhancement by Using Double-Layer MOX-Based Gas Sensors Prepared by Flame Spray Pyrolysis (FSP).

PubMed

Rebholz, Julia; Grossmann, Katharina; Pham, David; Pokhrel, Suman; Mädler, Lutz; Weimar, Udo; Barsan, Nicolae

2016-09-06

Here we present a novel concept for the selective recognition of different target gases with a multilayer semiconducting metal oxide (SMOX)-based sensor device. Direct current (DC) electrical resistance measurements were performed during exposure to CO and ethanol as single gases and mixtures of highly porous metal oxide double- and single-layer sensors obtained by flame spray pyrolysis. The results show that the calculated resistance ratios of the single- and double-layer sensors are a good indicator for the presence of specific gases in the atmosphere, and can constitute some building blocks for the development of chemical logic devices. Due to the inherent lack of selectivity of SMOX-based gas sensors, such devices could be especially relevant for domestic applications.

Selectivity Enhancement by Using Double-Layer MOX-Based Gas Sensors Prepared by Flame Spray Pyrolysis (FSP)

PubMed Central

Rebholz, Julia; Grossmann, Katharina; Pham, David; Pokhrel, Suman; Mädler, Lutz; Weimar, Udo; Barsan, Nicolae

2016-01-01

Here we present a novel concept for the selective recognition of different target gases with a multilayer semiconducting metal oxide (SMOX)-based sensor device. Direct current (DC) electrical resistance measurements were performed during exposure to CO and ethanol as single gases and mixtures of highly porous metal oxide double- and single-layer sensors obtained by flame spray pyrolysis. The results show that the calculated resistance ratios of the single- and double-layer sensors are a good indicator for the presence of specific gases in the atmosphere, and can constitute some building blocks for the development of chemical logic devices. Due to the inherent lack of selectivity of SMOX-based gas sensors, such devices could be especially relevant for domestic applications. PMID:27608028

The effect of feature selection methods on computer-aided detection of masses in mammograms

NASA Astrophysics Data System (ADS)

Hupse, Rianne; Karssemeijer, Nico

2010-05-01

In computer-aided diagnosis (CAD) research, feature selection methods are often used to improve generalization performance of classifiers and shorten computation times. In an application that detects malignant masses in mammograms, we investigated the effect of using a selection criterion that is similar to the final performance measure we are optimizing, namely the mean sensitivity of the system in a predefined range of the free-response receiver operating characteristics (FROC). To obtain the generalization performance of the selected feature subsets, a cross validation procedure was performed on a dataset containing 351 abnormal and 7879 normal regions, each region providing a set of 71 mass features. The same number of noise features, not containing any information, were added to investigate the ability of the feature selection algorithms to distinguish between useful and non-useful features. It was found that significantly higher performances were obtained using feature sets selected by the general test statistic Wilks' lambda than using feature sets selected by the more specific FROC measure. Feature selection leads to better performance when compared to a system in which all features were used.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moroz, P.E.

A new stellarator configuration, the Double-Helix Stellarator (DHS), is introduced. This novel configuration features a double-helix center post as the only helical element of the stellarator coil system. The DHS configuration has many unique characteristics. One of them is the extreme low plasma aspect ratio, A {approx} 1--1.2. Other advantages include a high enclosed volume, appreciable rotational transform, and a possibility of extreme-high-{beta} MHD equilibria. Moreover, the DHS features improved transport characteristics caused by the absence of the magnetic field ripple on the outboard of the torus. Compactness, simplicity and modularity of the coil system add to the DHS advantagesmore » for fusion applications.« less

Double photoionization of tropone and cyclooctatetraene

NASA Astrophysics Data System (ADS)

Hartman, Tim; Wehlitz, Ralf

2017-05-01

We have studied the double-photoionization process of tropone (C7H6O) and cyclooctatetraene (C8H8) as a function of photon energy using monochromatized synchrotron radiation between 18 and 270 eV. We compare our results with previously published data for partially deuterated benzene (C6H3D3), which exhibits three distinct features in the ratio of doubly to singly charged parent ions, whereas pyrrole (C4H4N) exhibits only two of these features. The question that we address in this paper is how molecules with different molecular structures (pentagonal, hexagonal, heptagonal, and octagonal carbon rings) affect the photon-energy dependence of this ratio.

Measurement of fluid properties using rapid-double-exposure and time-average holographic interferometry

NASA Technical Reports Server (NTRS)

Decker, A. J.

1984-01-01

The holographic recording of the time history of a flow feature in three dimensions is discussed. The use of diffuse illumination holographic interferometry or the three dimensional visualization of flow features such as shock waves and turbulent eddies is described. The double-exposure and time-average methods are compared using the characteristic function and the results from a flow simulator. A time history requires a large hologram recording rate. Results of holographic cinematography of the shock waves in a flutter cascade are presented as an example. Future directions of this effort, including the availability and development of suitable lasers, are discussed.

Scopolamine Reduces Electrophysiological Indices of Distractor Suppression: Evidence from a Contingent Capture Task

PubMed Central

Laube, Inga; Matthews, Natasha; Dean, Angela J.; O’Connell, Redmond G.; Mattingley, Jason B.; Bellgrove, Mark A.

2017-01-01

Limited resources for the in-depth processing of external stimuli make it necessary to select only relevant information from our surroundings and to ignore irrelevant stimuli. Attentional mechanisms facilitate this selection via top-down modulation of stimulus representations in the brain. Previous research has indicated that acetylcholine (ACh) modulates this influence of attention on stimulus processing. However, the role of muscarinic receptors as well as the specific mechanism of cholinergic modulation remains unclear. Here we investigated the influence of ACh on feature-based, top-down control of stimulus processing via muscarinic receptors by using a contingent capture paradigm which specifically tests attentional shifts toward uninformative cue stimuli which display one of the target defining features In a double-blind, placebo controlled study we measured the impact of the muscarinic receptor antagonist scopolamine on behavioral and electrophysiological measures of contingent attentional capture. The results demonstrated all the signs of functional contingent capture, i.e., attentional shifts toward cued locations reflected in increased amplitudes of N1 and N2Pc components, under placebo conditions. However, scopolamine did not affect behavioral or electrophysiological measures of contingent capture. Instead, scopolamine reduced the amplitude of the distractor-evoked Pd component which has recently been associated with active suppression of irrelevant distractor information. The findings suggest a general cholinergic modulation of top-down control during distractor processing. PMID:29270112

Dynamic updating atlas for heart segmentation with a nonlinear field-based model.

PubMed

Cai, Ken; Yang, Rongqian; Yue, Hongwei; Li, Lihua; Ou, Shanxing; Liu, Feng

2017-09-01

Segmentation of cardiac computed tomography (CT) images is an effective method for assessing the dynamic function of the heart and lungs. In the atlas-based heart segmentation approach, the quality of segmentation usually relies upon atlas images, and the selection of those reference images is a key step. The optimal goal in this selection process is to have the reference images as close to the target image as possible. This study proposes an atlas dynamic update algorithm using a scheme of nonlinear deformation field. The proposed method is based on the features among double-source CT (DSCT) slices. The extraction of these features will form a base to construct an average model and the created reference atlas image is updated during the registration process. A nonlinear field-based model was used to effectively implement a 4D cardiac segmentation. The proposed segmentation framework was validated with 14 4D cardiac CT sequences. The algorithm achieved an acceptable accuracy (1.0-2.8 mm). Our proposed method that combines a nonlinear field-based model and dynamic updating atlas strategies can provide an effective and accurate way for whole heart segmentation. The success of the proposed method largely relies on the effective use of the prior knowledge of the atlas and the similarity explored among the to-be-segmented DSCT sequences. Copyright © 2016 John Wiley & Sons, Ltd.

Speech Emotion Feature Selection Method Based on Contribution Analysis Algorithm of Neural Network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang Xiaojia; Mao Qirong; Zhan Yongzhao

There are many emotion features. If all these features are employed to recognize emotions, redundant features may be existed. Furthermore, recognition result is unsatisfying and the cost of feature extraction is high. In this paper, a method to select speech emotion features based on contribution analysis algorithm of NN is presented. The emotion features are selected by using contribution analysis algorithm of NN from the 95 extracted features. Cluster analysis is applied to analyze the effectiveness for the features selected, and the time of feature extraction is evaluated. Finally, 24 emotion features selected are used to recognize six speech emotions.more » The experiments show that this method can improve the recognition rate and the time of feature extraction.« less

Feature Selection Method Based on Neighborhood Relationships: Applications in EEG Signal Identification and Chinese Character Recognition

PubMed Central

Zhao, Yu-Xiang; Chou, Chien-Hsing

2016-01-01

In this study, a new feature selection algorithm, the neighborhood-relationship feature selection (NRFS) algorithm, is proposed for identifying rat electroencephalogram signals and recognizing Chinese characters. In these two applications, dependent relationships exist among the feature vectors and their neighboring feature vectors. Therefore, the proposed NRFS algorithm was designed for solving this problem. By applying the NRFS algorithm, unselected feature vectors have a high priority of being added into the feature subset if the neighboring feature vectors have been selected. In addition, selected feature vectors have a high priority of being eliminated if the neighboring feature vectors are not selected. In the experiments conducted in this study, the NRFS algorithm was compared with two feature algorithms. The experimental results indicated that the NRFS algorithm can extract the crucial frequency bands for identifying rat vigilance states and identifying crucial character regions for recognizing Chinese characters. PMID:27314346

Selective Audiovisual Semantic Integration Enabled by Feature-Selective Attention

PubMed Central

Li, Yuanqing; Long, Jinyi; Huang, Biao; Yu, Tianyou; Wu, Wei; Li, Peijun; Fang, Fang; Sun, Pei

2016-01-01

An audiovisual object may contain multiple semantic features, such as the gender and emotional features of the speaker. Feature-selective attention and audiovisual semantic integration are two brain functions involved in the recognition of audiovisual objects. Humans often selectively attend to one or several features while ignoring the other features of an audiovisual object. Meanwhile, the human brain integrates semantic information from the visual and auditory modalities. However, how these two brain functions correlate with each other remains to be elucidated. In this functional magnetic resonance imaging (fMRI) study, we explored the neural mechanism by which feature-selective attention modulates audiovisual semantic integration. During the fMRI experiment, the subjects were presented with visual-only, auditory-only, or audiovisual dynamical facial stimuli and performed several feature-selective attention tasks. Our results revealed that a distribution of areas, including heteromodal areas and brain areas encoding attended features, may be involved in audiovisual semantic integration. Through feature-selective attention, the human brain may selectively integrate audiovisual semantic information from attended features by enhancing functional connectivity and thus regulating information flows from heteromodal areas to brain areas encoding the attended features. PMID:26759193

Deorientation of PolSAR coherency matrix for volume scattering retrieval

NASA Astrophysics Data System (ADS)

Kumar, Shashi; Garg, R. D.; Kushwaha, S. P. S.

2016-05-01

Polarimetric SAR data has proven its potential to extract scattering information for different features appearing in single resolution cell. Several decomposition modelling approaches have been developed to retrieve scattering information from PolSAR data. During scattering power decomposition based on physical scattering models it becomes very difficult to distinguish volume scattering as a result from randomly oriented vegetation from scattering nature of oblique structures which are responsible for double-bounce and volume scattering , because both are decomposed in same scattering mechanism. The polarization orientation angle (POA) of an electromagnetic wave is one of the most important character which gets changed due to scattering from geometrical structure of topographic slopes, oriented urban area and randomly oriented features like vegetation cover. The shift in POA affects the polarimetric radar signatures. So, for accurate estimation of scattering nature of feature compensation in polarization orientation shift becomes an essential procedure. The prime objective of this work was to investigate the effect of shift in POA in scattering information retrieval and to explore the effect of deorientation on regression between field-estimated aboveground biomass (AGB) and volume scattering. For this study Dudhwa National Park, U.P., India was selected as study area and fully polarimetric ALOS PALSAR data was used to retrieve scattering information from the forest area of Dudhwa National Park. Field data for DBH and tree height was collect for AGB estimation using stratified random sampling. AGB was estimated for 170 plots for different locations of the forest area. Yamaguchi four component decomposition modelling approach was utilized to retrieve surface, double-bounce, helix and volume scattering information. Shift in polarization orientation angle was estimated and deorientation of coherency matrix for compensation of POA shift was performed. Effect of deorientation on RGB color composite for the forest area can be easily seen. Overestimation of volume scattering and under estimation of double bounce scattering was recorded for PolSAR decomposition without deorientation and increase in double bounce scattering and decrease in volume scattering was noticed after deorientation. This study was mainly focused on volume scattering retrieval and its relation with field estimated AGB. Change in volume scattering after POA compensation of PolSAR data was recorded and a comparison was performed on volume scattering values for all the 170 forest plots for which field data were collected. Decrease in volume scattering after deorientation was noted for all the plots. Regression between PolSAR decomposition based volume scattering and AGB was performed. Before deorientation, coefficient determination (R2) between volume scattering and AGB was 0.225. After deorientation an improvement in coefficient of determination was found and the obtained value was 0.613. This study recommends deorientation of PolSAR data for decomposition modelling to retrieve reliable volume scattering information from forest area.

Natural image classification driven by human brain activity

NASA Astrophysics Data System (ADS)

Zhang, Dai; Peng, Hanyang; Wang, Jinqiao; Tang, Ming; Xue, Rong; Zuo, Zhentao

2016-03-01

Natural image classification has been a hot topic in computer vision and pattern recognition research field. Since the performance of an image classification system can be improved by feature selection, many image feature selection methods have been developed. However, the existing supervised feature selection methods are typically driven by the class label information that are identical for different samples from the same class, ignoring with-in class image variability and therefore degrading the feature selection performance. In this study, we propose a novel feature selection method, driven by human brain activity signals collected using fMRI technique when human subjects were viewing natural images of different categories. The fMRI signals associated with subjects viewing different images encode the human perception of natural images, and therefore may capture image variability within- and cross- categories. We then select image features with the guidance of fMRI signals from brain regions with active response to image viewing. Particularly, bag of words features based on GIST descriptor are extracted from natural images for classification, and a sparse regression base feature selection method is adapted to select image features that can best predict fMRI signals. Finally, a classification model is built on the select image features to classify images without fMRI signals. The validation experiments for classifying images from 4 categories of two subjects have demonstrated that our method could achieve much better classification performance than the classifiers built on image feature selected by traditional feature selection methods.

EFS: an ensemble feature selection tool implemented as R-package and web-application.

PubMed

Neumann, Ursula; Genze, Nikita; Heider, Dominik

2017-01-01

Feature selection methods aim at identifying a subset of features that improve the prediction performance of subsequent classification models and thereby also simplify their interpretability. Preceding studies demonstrated that single feature selection methods can have specific biases, whereas an ensemble feature selection has the advantage to alleviate and compensate for these biases. The software EFS (Ensemble Feature Selection) makes use of multiple feature selection methods and combines their normalized outputs to a quantitative ensemble importance. Currently, eight different feature selection methods have been integrated in EFS, which can be used separately or combined in an ensemble. EFS identifies relevant features while compensating specific biases of single methods due to an ensemble approach. Thereby, EFS can improve the prediction accuracy and interpretability in subsequent binary classification models. EFS can be downloaded as an R-package from CRAN or used via a web application at http://EFS.heiderlab.de.

A compact "water-window" microscope with 60-nm spatial resolution based on a double stream gas-puff target and Fresnel zone plate optics

NASA Astrophysics Data System (ADS)

Wachulak, Przemyslaw; Torrisi, Alfio; Nawaz, Muhammad F.; Adjei, Daniel; Bartnik, Andrzej; Kostecki, Jerzy; Wegrzynski, Łukasz; Vondrová, Šárka; Turňová, Jana; Fok, Tomasz; Jančarek, Alexandr; Fiedorowicz, Henryk

2015-05-01

Radiation with shorter illumination wavelength allows for extension of the diffraction limit towards nanometer scale, which is a straightforward way to significantly improve a spatial resolution in photon based microscopes. Soft X-ray (SXR) radiation, from the so called "water window" spectral range, λ=2.3-4.4 nm, which is particularly suitable for biological imaging due to natural optical contrast, providing much better spatial resolution than one obtained with visible light microscopes. The high contrast is obtained because of selective absorption of radiation by carbon and water, being constituents of the biological samples. We present a desk-top system, capable of resolving 60 nm features in few seconds exposure time. We exploit the advantages of a compact, laser-plasma SXR source, based on a double stream nitrogen gas puff target, developed at the Institute of Optoelectronics, Military University of Technology. The source, emitting quasi-monochromatic, incoherent radiation, in the "water widow" spectral range at λ = 2.88 nm, is coupled with ellipsoidal, grazing incidence condenser and Fresnel zone plate objective. The construction of the microscope with some recent images of test and real samples will be presented and discussed.

Double-outlet right ventricle revisited.

PubMed

Ebadi, Ameneh; Spicer, Diane E; Backer, Carl L; Fricker, F Jay; Anderson, Robert H

2017-08-01

Double-outlet right ventricle is a form of ventriculoarterial connection. The definition formulated by the International Society for Nomenclature of Paediatric and Congenital Heart Disease is based on hearts with both arterial trunks supported in their greater part by a morphologically right ventricle. Bilateral infundibula and ventricular septal defects are highly debated criteria. This study examines the anatomic controversies surrounding double-outlet right ventricle. We show that hearts with double-outlet right ventricle can have atrioventricular-to-arterial valvular continuity. We emphasize the difference between the interventricular communication and the zone of deficient ventricular septation. The hearts examined were from the University of Florida in Gainesville; Johns Hopkins All Children's Hospital, St Petersburg, Fla; and Lurie Children's Hospital, Chicago, Ill. Each specimen had at least 75% of both arterial roots supported by the morphologically right ventricle, with a total of 100 hearts examined. The morphologic method was used to assess anatomic features, including arterial-atrioventricular valvular continuity, subarterial infundibular musculature, and the location of the hole between the ventricles. Most hearts had fibrous continuity between one of the arterial valves and an atrioventricular valve, with bilateral infundibula in 23%, and intact ventricular septum in 5%. Bilateral infundibula are not a defining feature of double-outlet right ventricle, representing only 23% of the specimens in our sample. The interventricular communication can have a posteroinferior muscular rim or extend to become perimembranous (58%). Double-outlet right ventricle can exist with an intact ventricular septum. Copyright © 2017 The American Association for Thoracic Surgery. All rights reserved.

Feature selection methods for big data bioinformatics: A survey from the search perspective.

PubMed

Wang, Lipo; Wang, Yaoli; Chang, Qing

2016-12-01

This paper surveys main principles of feature selection and their recent applications in big data bioinformatics. Instead of the commonly used categorization into filter, wrapper, and embedded approaches to feature selection, we formulate feature selection as a combinatorial optimization or search problem and categorize feature selection methods into exhaustive search, heuristic search, and hybrid methods, where heuristic search methods may further be categorized into those with or without data-distilled feature ranking measures. Copyright © 2016 Elsevier Inc. All rights reserved.

Feature selection method based on multi-fractal dimension and harmony search algorithm and its application

NASA Astrophysics Data System (ADS)

Zhang, Chen; Ni, Zhiwei; Ni, Liping; Tang, Na

2016-10-01

Feature selection is an important method of data preprocessing in data mining. In this paper, a novel feature selection method based on multi-fractal dimension and harmony search algorithm is proposed. Multi-fractal dimension is adopted as the evaluation criterion of feature subset, which can determine the number of selected features. An improved harmony search algorithm is used as the search strategy to improve the efficiency of feature selection. The performance of the proposed method is compared with that of other feature selection algorithms on UCI data-sets. Besides, the proposed method is also used to predict the daily average concentration of PM2.5 in China. Experimental results show that the proposed method can obtain competitive results in terms of both prediction accuracy and the number of selected features.

Double MITs and magnetoresistance: an intrinsic feature of Ru substituted La0.67Ca0.33MnO3

NASA Astrophysics Data System (ADS)

Seetha Lakshmi, L.; Sridharan, V.; Sukumar, A. A.; Kamruddin, M.; Sastry, V. S.; Raju, V. S.

2006-05-01

In this paper, we examine the possible influence of extrinsic factors on the electrical and magnetotransport of La0.67Ca0.33Mn1-xRuxO3 (x<=0.10). Ru substitution results in double metal-insulator transitions (MITs) at TMI1 and TMI2, both exhibiting magnetoresistance (MR). No additional magnetic signal corresponding to a second low-temperature maximum (LTM) at TMI2 could be observed, either in ac susceptibility (χ') or in specific heat (Cp). Typical grain sizes of ~18 000-20 000 nm, as estimated from the scanning electron microscope (SEM) micrographs, are not so small as to warrant an LTM. The absence of additional peaks in the high statistics powder x-ray diffraction (XRD), a linear systematic increase of the unit cell parameters, close matching of the transition temperatures in resistivity, χ' and Cp and their linear systematic decrease with x, and an homogeneous distribution of Mn, Ru and O at arbitrarily selected regions within and across the grains exclude chemical inhomogeneity in the samples. The insensitivity of grain boundary MR at 5 K to Ru composition indicates that the grain boundary is not altered to result in an LTM. Oxygen stoichiometry of all the compounds is close to the nominal value of 3. These results not only exclude the extrinsic factors, but also establish that double MITs, both exhibiting MR, are intrinsic to Ru substituted La0.67Ca0.33MnO3.

A modification of the Hammett equation for predicting ionisation constants of p-vinyl phenols.

PubMed

Sipilä, Julius; Nurmi, Harri; Kaukonen, Ann Marie; Hirvonen, Jouni; Taskinen, Jyrki; Yli-Kauhaluoma, Jari

2005-01-01

Currently there are several compounds used as drugs or studied as new chemical entities, which have an electron withdrawing group connected to a vinylic double bond in a phenolic or catecholic core structure. These compounds share a common feature--current computational methods utilizing the Hammett type equation for the prediction of ionisation constants fail to give accurate prediction of pK(a)'s for compounds containing the vinylic moiety. The hypothesis was that the effect of electron-withdrawing substituents on the pK(a) of p-vinyl phenols is due to the delocalized electronic structure of these compounds. Thus, this effect should be additive for multiple substituents attached to the vinylic double bond and quantifiable by LFER-based methods. The aim of this study was to produce an improved equation with a reduced tendency to underestimate the effect of the double bond on the ionisation of the phenolic hydroxyl. To this end a set of 19 para-substituted vinyl phenols was used. The ionisation constants were measured potentiometrically, and a training set of 10 compounds was selected to build a regression model (r2 = 0.987 and S.E. = 0.09). The average error with an external test set of six compounds was 0.19 for our model and 1.27 for the ACD-labs 7.0. Thus, we have been able to significantly improve the existing model for prediction of the ionisation constants of substituted p-vinyl phenols.

Double-Targeting Explosible Nanofirework for Tumor Ignition to Guide Tumor-Depth Photothermal Therapy.

PubMed

Zhang, Ming-Kang; Wang, Xiao-Gang; Zhu, Jing-Yi; Liu, Miao-Deng; Li, Chu-Xin; Feng, Jun; Zhang, Xian-Zheng

2018-04-17

This study reports a double-targeting "nanofirework" for tumor-ignited imaging to guide effective tumor-depth photothermal therapy (PTT). Typically, ≈30 nm upconversion nanoparticles (UCNP) are enveloped with a hybrid corona composed of ≈4 nm CuS tethered hyaluronic acid (CuS-HA). The HA corona provides active tumor-targeted functionality together with excellent stability and improved biocompatibility. The dimension of UCNP@CuS-HA is specifically set within the optimal size window for passive tumor-targeting effect, demonstrating significant contributions to both the in vivo prolonged circulation duration and the enhanced size-dependent tumor accumulation compared with ultrasmall CuS nanoparticles. The tumors featuring hyaluronidase (HAase) overexpression could induce the escape of CuS away from UCNP@CuS-HA due to HAase-catalyzed HA degradation, in turn activating the recovery of initially CuS-quenched luminescence of UCNP and also driving the tumor-depth infiltration of ultrasmall CuS for effective PTT. This in vivo transition has proven to be highly dependent on tumor occurrence like a tumor-ignited explosible firework. Together with the double-targeting functionality, the pathology-selective tumor ignition permits precise tumor detection and imaging-guided spatiotemporal control over PTT operation, leading to complete tumor ablation under near infrared (NIR) irradiation. This study offers a new paradigm of utilizing pathological characteristics to design nanotheranostics for precise detection and personalized therapy of tumors. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

[Selective attention and schizophrenia before the administration of neuroleptics].

PubMed

Lussier, I; Stip, E

1999-01-01

In recent years, the presence of attention deficits has been recognized as a key feature of schizophrenia. Past studies reveal that selective attention, or the ability to select relevant information while ignoring simultaneously irrelevant information, is disturbed in schizophrenic patients. According to Treisman feature-integration theory of selective attention, visual search for conjunctive targets (e.g., shape and color) requires controlled processes, that necessitate attention and operate in a serial manner. Reaction times (RTs) are therefore function of the number of stimuli in the display. When subjects are asked to detect the presence or absence of a target in an array of a variable number of stimuli, different performance patterns are expected for positive (present target) and negative trials (absent target). For positive trials, a self-terminating search is triggered, that is, the search is ended when the target is encountered. For negative trials, an exhaustive search strategy is displayed, where each stimulus is examined before the search can end; the RT slope pattern is thus double that of the positive trials. To assess the integrity of these processes, thirteen drug naive schizophrenic patients were compared to twenty normal control subjects. Neuroleptic naive patients were chosen as subjects to avoid the potential influence of medication and chronicity-related factors on performance. The subjects had to specify as fast as possible the presence or absence of the target in an array of a variable number of stimuli presented in a circular display, and comprising or not the target. Results showed that the patients can use self-terminating search strategies as well as normal control subjects. However, their ability to trigger exhaustive search strategies is impaired. Not only were patients slower than controls, but their pattern of RT results was different. These results argue in favor of an early impairment in selective attention capacities in schizophrenia, which appears before the introduction of neuroleptics. The attention performance was also shown to present some association to clinical symptoms.

Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson’s Disease

NASA Astrophysics Data System (ADS)

Adeli, Ehsan; Wu, Guorong; Saghafi, Behrouz; An, Le; Shi, Feng; Shen, Dinggang

2017-01-01

Feature selection methods usually select the most compact and relevant set of features based on their contribution to a linear regression model. Thus, these features might not be the best for a non-linear classifier. This is especially crucial for the tasks, in which the performance is heavily dependent on the feature selection techniques, like the diagnosis of neurodegenerative diseases. Parkinson’s disease (PD) is one of the most common neurodegenerative disorders, which progresses slowly while affects the quality of life dramatically. In this paper, we use the data acquired from multi-modal neuroimaging data to diagnose PD by investigating the brain regions, known to be affected at the early stages. We propose a joint kernel-based feature selection and classification framework. Unlike conventional feature selection techniques that select features based on their performance in the original input feature space, we select features that best benefit the classification scheme in the kernel space. We further propose kernel functions, specifically designed for our non-negative feature types. We use MRI and SPECT data of 538 subjects from the PPMI database, and obtain a diagnosis accuracy of 97.5%, which outperforms all baseline and state-of-the-art methods.

Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson’s Disease

PubMed Central

Adeli, Ehsan; Wu, Guorong; Saghafi, Behrouz; An, Le; Shi, Feng; Shen, Dinggang

2017-01-01

Feature selection methods usually select the most compact and relevant set of features based on their contribution to a linear regression model. Thus, these features might not be the best for a non-linear classifier. This is especially crucial for the tasks, in which the performance is heavily dependent on the feature selection techniques, like the diagnosis of neurodegenerative diseases. Parkinson’s disease (PD) is one of the most common neurodegenerative disorders, which progresses slowly while affects the quality of life dramatically. In this paper, we use the data acquired from multi-modal neuroimaging data to diagnose PD by investigating the brain regions, known to be affected at the early stages. We propose a joint kernel-based feature selection and classification framework. Unlike conventional feature selection techniques that select features based on their performance in the original input feature space, we select features that best benefit the classification scheme in the kernel space. We further propose kernel functions, specifically designed for our non-negative feature types. We use MRI and SPECT data of 538 subjects from the PPMI database, and obtain a diagnosis accuracy of 97.5%, which outperforms all baseline and state-of-the-art methods. PMID:28120883

Two-dimensional spectroscopy: An approach to distinguish Förster and Dexter transfer processes in coupled nanostructures

NASA Astrophysics Data System (ADS)

Specht, Judith F.; Knorr, Andreas; Richter, Marten

2015-04-01

The linear and two-dimensional coherent optical spectra of Coulomb-coupled quantum emitters are discussed with respect to the underlying coupling processes. We present a theoretical analysis of the two different resonance energy transfer mechanisms between coupled nanostructures: Förster and Dexter interaction. Our investigation shows that the features visible in optical spectra of coupled quantum dots can be traced back to the nature of the underlying coupling mechanism (Förster or Dexter). Therefore, we discuss how the excitation transfer pathways can be controlled by choosing particular laser polarizations and mutual orientations of the quantum emitters in coherent two-dimensional spectroscopy. In this context, we analyze to what extent the delocalized double-excitonic states are bound to the optical selection rules of the uncoupled system.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Chi; Arvapally, Ravi K.; Tekarli, Sammer M.

The trinuclear triangle-shaped system [tris{3,5-bis(heptafluoropropyl)-1,2,4-triazolatosilver(I)}] (1) and the multi-armed square-shaped metalloporphyrin PtOEP or the free porphyrin base H2OEP serve as excellent octopus hosts (OEP=2,3,7,8,12,13,17,18-octaethyl-21H,23H-porphine). Coupling of the fluorous/organic molecular octopi 1 and H2OEP or PtOEP by strong quadrupole-quadrupole and metal- interactions affords the supramolecular assemblies [1PtOEP] or [1H(2)OEP] (2a), which feature nanoscopic cavities surrounding the upper triangular and lower square cores. The fluorous/organic biphasic configuration of [1PtOEP] leads to an increase in the phosphorescence of PtOEP under ambient conditions. Guest molecules can be included in the biphasic double-octopus assembly in three different site-selective modes.

An Interoperability Consideration in Selecting Domain Parameters for Elliptic Curve Cryptography

NASA Technical Reports Server (NTRS)

Ivancic, Will (Technical Monitor); Eddy, Wesley M.

2005-01-01

Elliptic curve cryptography (ECC) will be an important technology for electronic privacy and authentication in the near future. There are many published specifications for elliptic curve cryptosystems, most of which contain detailed descriptions of the process for the selection of domain parameters. Selecting strong domain parameters ensures that the cryptosystem is robust to attacks. Due to a limitation in several published algorithms for doubling points on elliptic curves, some ECC implementations may produce incorrect, inconsistent, and incompatible results if domain parameters are not carefully chosen under a criterion that we describe. Few documents specify the addition or doubling of points in such a manner as to avoid this problematic situation. The safety criterion we present is not listed in any ECC specification we are aware of, although several other guidelines for domain selection are discussed in the literature. We provide a simple example of how a set of domain parameters not meeting this criterion can produce catastrophic results, and outline a simple means of testing curve parameters for interoperable safety over doubling.

MARRIAGE, BMI, AND WAGES: A DOUBLE SELECTION APPROACH

PubMed Central

Brown, Heather

2011-01-01

Obesity rates have been rising over the past decade. As more people become obese, the social stigma of obesity may be reduced. Marriage has typically been used as a positive signal to employers. If obese individuals possess other characteristics that are valued in the labour market they may no longer face a wage penalty for their physical appearance. This paper investigates the relationship between marital status, body mass index (BMI), and wages by estimating a double selection model that controls for selection into the labour and marriage markets using waves 14 and 16 (2004 and 2006) of the British Household Panel Survey. Results suggest that unobserved characteristics related to marriage and labour market participation are causing an upward bias onthe BMI coefficients. The BMI coefficient is positive and significant for married men only in the double selection model. The findings provide evidence that unobserved characteristics related to success in the marriage and labour market may influence the relationship between BMI and wages. PMID:21910281

Integrated feature extraction and selection for neuroimage classification

NASA Astrophysics Data System (ADS)

Fan, Yong; Shen, Dinggang

2009-02-01

Feature extraction and selection are of great importance in neuroimage classification for identifying informative features and reducing feature dimensionality, which are generally implemented as two separate steps. This paper presents an integrated feature extraction and selection algorithm with two iterative steps: constrained subspace learning based feature extraction and support vector machine (SVM) based feature selection. The subspace learning based feature extraction focuses on the brain regions with higher possibility of being affected by the disease under study, while the possibility of brain regions being affected by disease is estimated by the SVM based feature selection, in conjunction with SVM classification. This algorithm can not only take into account the inter-correlation among different brain regions, but also overcome the limitation of traditional subspace learning based feature extraction methods. To achieve robust performance and optimal selection of parameters involved in feature extraction, selection, and classification, a bootstrapping strategy is used to generate multiple versions of training and testing sets for parameter optimization, according to the classification performance measured by the area under the ROC (receiver operating characteristic) curve. The integrated feature extraction and selection method is applied to a structural MR image based Alzheimer's disease (AD) study with 98 non-demented and 100 demented subjects. Cross-validation results indicate that the proposed algorithm can improve performance of the traditional subspace learning based classification.

Compact cancer biomarkers discovery using a swarm intelligence feature selection algorithm.

PubMed

Martinez, Emmanuel; Alvarez, Mario Moises; Trevino, Victor

2010-08-01

Biomarker discovery is a typical application from functional genomics. Due to the large number of genes studied simultaneously in microarray data, feature selection is a key step. Swarm intelligence has emerged as a solution for the feature selection problem. However, swarm intelligence settings for feature selection fail to select small features subsets. We have proposed a swarm intelligence feature selection algorithm based on the initialization and update of only a subset of particles in the swarm. In this study, we tested our algorithm in 11 microarray datasets for brain, leukemia, lung, prostate, and others. We show that the proposed swarm intelligence algorithm successfully increase the classification accuracy and decrease the number of selected features compared to other swarm intelligence methods. Copyright © 2010 Elsevier Ltd. All rights reserved.

Frequency and zero-point vibrational energy scale factors for double-hybrid density functionals (and other selected methods): can anharmonic force fields be avoided?

PubMed

Kesharwani, Manoj K; Brauer, Brina; Martin, Jan M L

2015-03-05

We have obtained uniform frequency scaling factors λ(harm) (for harmonic frequencies), λ(fund) (for fundamentals), and λ(ZPVE) (for zero-point vibrational energies (ZPVEs)) for the Weigend-Ahlrichs and other selected basis sets for MP2, SCS-MP2, and a variety of DFT functionals including double hybrids. For selected levels of theory, we have also obtained scaling factors for true anharmonic fundamentals and ZPVEs obtained from quartic force fields. For harmonic frequencies, the double hybrids B2PLYP, B2GP-PLYP, and DSD-PBEP86 clearly yield the best performance at RMSD = 10-12 cm(-1) for def2-TZVP and larger basis sets, compared to 5 cm(-1) at the CCSD(T) basis set limit. For ZPVEs, again, the double hybrids are the best performers, reaching root-mean-square deviations (RMSDs) as low as 0.05 kcal/mol, but even mainstream functionals like B3LYP can get down to 0.10 kcal/mol. Explicitly anharmonic ZPVEs only are marginally more accurate. For fundamentals, however, simple uniform scaling is clearly inadequate.

Equilibration and nonclassicality of a double-well potential

NASA Astrophysics Data System (ADS)

Campbell, Steve; de Chiara, Gabriele; Paternostro, Mauro

2016-01-01

A double well loaded with bosonic atoms represents an ideal candidate to simulate some of the most interesting aspects in the phenomenology of thermalisation and equilibration. Here we report an exhaustive analysis of the dynamics and steady state properties of such a system locally in contact with different temperature reservoirs. We show that thermalisation only occurs ‘accidentally’. We further examine the nonclassical features and energy fluxes implied by the dynamics of the double-well system, thus exploring its finite-time thermodynamics in relation to the settlement of nonclassical correlations between the wells.

The double quasar 0957+561: examination of the gravitational lens hypothesis using the very large array.

PubMed

Greenfield, P E; Roberts, D H; Burke, B F

1980-05-02

A full 12-hour synthesis at 6-centimeter wavelength with the Very Large Array confirms the major features previously reported for the double quasar 0957+561. In addition, the existence of radio jets apparently associated with both quasars is demonstrated. Gravitational lens models are now favored on the basis of recent optical observations, and the radio jets place severe constraints on such models. Further radio observations of the double quasar are needed to establish the expected relative time delay in variations between the images.

Multi-task feature selection in microarray data by binary integer programming.

PubMed

Lan, Liang; Vucetic, Slobodan

2013-12-20

A major challenge in microarray classification is that the number of features is typically orders of magnitude larger than the number of examples. In this paper, we propose a novel feature filter algorithm to select the feature subset with maximal discriminative power and minimal redundancy by solving a quadratic objective function with binary integer constraints. To improve the computational efficiency, the binary integer constraints are relaxed and a low-rank approximation to the quadratic term is applied. The proposed feature selection algorithm was extended to solve multi-task microarray classification problems. We compared the single-task version of the proposed feature selection algorithm with 9 existing feature selection methods on 4 benchmark microarray data sets. The empirical results show that the proposed method achieved the most accurate predictions overall. We also evaluated the multi-task version of the proposed algorithm on 8 multi-task microarray datasets. The multi-task feature selection algorithm resulted in significantly higher accuracy than when using the single-task feature selection methods.

WISE J233237.05-505643.5: A Double-Peaked Broad-Lined AGN with Spiral-Shaped Radio Morphology

NASA Technical Reports Server (NTRS)

Tsai, Chao Wei; Jarrett, Thomas H.; Stern, Daniel; Emonts, Bjorn; Barrows, R. Scott; Assef, Roberto J.; Norris, Ray P.; Eisenhardt, Peter R. M.; Lonsdale, Carol; Blain, Andrew W.;

7 CFR 43.104 - Master table of single and double sampling plans.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 7 Agriculture 2 2012-01-01 2012-01-01 false Master table of single and double sampling plans. 43... STANDARD CONTAINER REGULATIONS STANDARDS FOR SAMPLING PLANS Sampling Plans § 43.104 Master table of single and double sampling plans. (a) In the master table, a sampling plan is selected by first determining...

7 CFR 43.104 - Master table of single and double sampling plans.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 7 Agriculture 2 2013-01-01 2013-01-01 false Master table of single and double sampling plans. 43... STANDARD CONTAINER REGULATIONS STANDARDS FOR SAMPLING PLANS Sampling Plans § 43.104 Master table of single and double sampling plans. (a) In the master table, a sampling plan is selected by first determining...

7 CFR 43.104 - Master table of single and double sampling plans.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 7 Agriculture 2 2014-01-01 2014-01-01 false Master table of single and double sampling plans. 43... STANDARD CONTAINER REGULATIONS STANDARDS FOR SAMPLING PLANS Sampling Plans § 43.104 Master table of single and double sampling plans. (a) In the master table, a sampling plan is selected by first determining...

7 CFR 43.104 - Master table of single and double sampling plans.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 2 2011-01-01 2011-01-01 false Master table of single and double sampling plans. 43... STANDARD CONTAINER REGULATIONS STANDARDS FOR SAMPLING PLANS Sampling Plans § 43.104 Master table of single and double sampling plans. (a) In the master table, a sampling plan is selected by first determining...

Attentional Selection of Feature Conjunctions Is Accomplished by Parallel and Independent Selection of Single Features.

PubMed

Andersen, Søren K; Müller, Matthias M; Hillyard, Steven A

2015-07-08

Experiments that study feature-based attention have often examined situations in which selection is based on a single feature (e.g., the color red). However, in more complex situations relevant stimuli may not be set apart from other stimuli by a single defining property but by a specific combination of features. Here, we examined sustained attentional selection of stimuli defined by conjunctions of color and orientation. Human observers attended to one out of four concurrently presented superimposed fields of randomly moving horizontal or vertical bars of red or blue color to detect brief intervals of coherent motion. Selective stimulus processing in early visual cortex was assessed by recordings of steady-state visual evoked potentials (SSVEPs) elicited by each of the flickering fields of stimuli. We directly contrasted attentional selection of single features and feature conjunctions and found that SSVEP amplitudes on conditions in which selection was based on a single feature only (color or orientation) exactly predicted the magnitude of attentional enhancement of SSVEPs when attending to a conjunction of both features. Furthermore, enhanced SSVEP amplitudes elicited by attended stimuli were accompanied by equivalent reductions of SSVEP amplitudes elicited by unattended stimuli in all cases. We conclude that attentional selection of a feature-conjunction stimulus is accomplished by the parallel and independent facilitation of its constituent feature dimensions in early visual cortex. The ability to perceive the world is limited by the brain's processing capacity. Attention affords adaptive behavior by selectively prioritizing processing of relevant stimuli based on their features (location, color, orientation, etc.). We found that attentional mechanisms for selection of different features belonging to the same object operate independently and in parallel: concurrent attentional selection of two stimulus features is simply the sum of attending to each of those features separately. This result is key to understanding attentional selection in complex (natural) scenes, where relevant stimuli are likely to be defined by a combination of stimulus features. Copyright © 2015 the authors 0270-6474/15/359912-08$15.00/0.

Collective feature selection to identify crucial epistatic variants.

PubMed

Verma, Shefali S; Lucas, Anastasia; Zhang, Xinyuan; Veturi, Yogasudha; Dudek, Scott; Li, Binglan; Li, Ruowang; Urbanowicz, Ryan; Moore, Jason H; Kim, Dokyoon; Ritchie, Marylyn D

2018-01-01

Machine learning methods have gained popularity and practicality in identifying linear and non-linear effects of variants associated with complex disease/traits. Detection of epistatic interactions still remains a challenge due to the large number of features and relatively small sample size as input, thus leading to the so-called "short fat data" problem. The efficiency of machine learning methods can be increased by limiting the number of input features. Thus, it is very important to perform variable selection before searching for epistasis. Many methods have been evaluated and proposed to perform feature selection, but no single method works best in all scenarios. We demonstrate this by conducting two separate simulation analyses to evaluate the proposed collective feature selection approach. Through our simulation study we propose a collective feature selection approach to select features that are in the "union" of the best performing methods. We explored various parametric, non-parametric, and data mining approaches to perform feature selection. We choose our top performing methods to select the union of the resulting variables based on a user-defined percentage of variants selected from each method to take to downstream analysis. Our simulation analysis shows that non-parametric data mining approaches, such as MDR, may work best under one simulation criteria for the high effect size (penetrance) datasets, while non-parametric methods designed for feature selection, such as Ranger and Gradient boosting, work best under other simulation criteria. Thus, using a collective approach proves to be more beneficial for selecting variables with epistatic effects also in low effect size datasets and different genetic architectures. Following this, we applied our proposed collective feature selection approach to select the top 1% of variables to identify potential interacting variables associated with Body Mass Index (BMI) in ~ 44,000 samples obtained from Geisinger's MyCode Community Health Initiative (on behalf of DiscovEHR collaboration). In this study, we were able to show that selecting variables using a collective feature selection approach could help in selecting true positive epistatic variables more frequently than applying any single method for feature selection via simulation studies. We were able to demonstrate the effectiveness of collective feature selection along with a comparison of many methods in our simulation analysis. We also applied our method to identify non-linear networks associated with obesity.

AVC: Selecting discriminative features on basis of AUC by maximizing variable complementarity.

PubMed

Sun, Lei; Wang, Jun; Wei, Jinmao

2017-03-14

The Receiver Operator Characteristic (ROC) curve is well-known in evaluating classification performance in biomedical field. Owing to its superiority in dealing with imbalanced and cost-sensitive data, the ROC curve has been exploited as a popular metric to evaluate and find out disease-related genes (features). The existing ROC-based feature selection approaches are simple and effective in evaluating individual features. However, these approaches may fail to find real target feature subset due to their lack of effective means to reduce the redundancy between features, which is essential in machine learning. In this paper, we propose to assess feature complementarity by a trick of measuring the distances between the misclassified instances and their nearest misses on the dimensions of pairwise features. If a misclassified instance and its nearest miss on one feature dimension are far apart on another feature dimension, the two features are regarded as complementary to each other. Subsequently, we propose a novel filter feature selection approach on the basis of the ROC analysis. The new approach employs an efficient heuristic search strategy to select optimal features with highest complementarities. The experimental results on a broad range of microarray data sets validate that the classifiers built on the feature subset selected by our approach can get the minimal balanced error rate with a small amount of significant features. Compared with other ROC-based feature selection approaches, our new approach can select fewer features and effectively improve the classification performance.

Unveiling of novel regio-selective fatty acid double bond hydratases from Lactobacillus acidophilus involved in the selective oxyfunctionalization of mono- and di-hydroxy fatty acids.

PubMed

Kim, Kyoung-Rok; Oh, Hye-Jin; Park, Chul-Soon; Hong, Seung-Hye; Park, Ji-Young; Oh, Deok-Kun

2015-11-01

The aim of this study is the first time demonstration of cis-12 regio-selective linoleate double-bond hydratase. Hydroxylation of fatty acids, abundant feedstock in nature, is an emerging alternative route for many petroleum replaceable products thorough hydroxy fatty acids, carboxylic acids, and lactones. However, chemical route for selective hydroxylation is still quite challenging owing to low selectivity and many environmental concerns. Hydroxylation of fatty acids by hydroxy fatty acid forming enzymes is an important route for selective biocatalytic oxyfunctionalization of fatty acids. Therefore, novel fatty acid hydroxylation enzymes should be discovered. The two hydratase genes of Lactobacillus acidophilus were identified by genomic analysis, and the expressed two recombinant hydratases were identified as cis-9 and cis-12 double-bond selective linoleate hydratases by in vitro functional validation, including the identification of products and the determination of regio-selectivity, substrate specificity, and kinetic parameters. The two different linoleate hydratases were the involved enzymes in the 10,13-dihydroxyoctadecanoic acid biosynthesis. Linoleate 13-hydratase (LHT-13) selectively converted 10 mM linoleic acid to 13S-hydroxy-9(Z)-octadecenoic acid with high titer (8.1 mM) and yield (81%). Our study will expand knowledge for microbial fatty acid-hydroxylation enzymes and facilitate the designed production of the regio-selective hydroxy fatty acids for useful chemicals from polyunsaturated fatty acid feedstocks. © 2015 Wiley Periodicals, Inc.

A Feature and Algorithm Selection Method for Improving the Prediction of Protein Structural Class.

PubMed

Ni, Qianwu; Chen, Lei

2017-01-01

Correct prediction of protein structural class is beneficial to investigation on protein functions, regulations and interactions. In recent years, several computational methods have been proposed in this regard. However, based on various features, it is still a great challenge to select proper classification algorithm and extract essential features to participate in classification. In this study, a feature and algorithm selection method was presented for improving the accuracy of protein structural class prediction. The amino acid compositions and physiochemical features were adopted to represent features and thirty-eight machine learning algorithms collected in Weka were employed. All features were first analyzed by a feature selection method, minimum redundancy maximum relevance (mRMR), producing a feature list. Then, several feature sets were constructed by adding features in the list one by one. For each feature set, thirtyeight algorithms were executed on a dataset, in which proteins were represented by features in the set. The predicted classes yielded by these algorithms and true class of each protein were collected to construct a dataset, which were analyzed by mRMR method, yielding an algorithm list. From the algorithm list, the algorithm was taken one by one to build an ensemble prediction model. Finally, we selected the ensemble prediction model with the best performance as the optimal ensemble prediction model. Experimental results indicate that the constructed model is much superior to models using single algorithm and other models that only adopt feature selection procedure or algorithm selection procedure. The feature selection procedure or algorithm selection procedure are really helpful for building an ensemble prediction model that can yield a better performance. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Measurement of fluid properties using rapid-double-exposure and time-average holographic interferometry

NASA Technical Reports Server (NTRS)

Decker, A. J.

1984-01-01

The holographic recording of the time history of a flow feature in three dimensions is discussed. The use of diffuse illumination holographic interferometry or the three-dimensional visualization of flow features such as shock waves and turbulent eddies is described. The double-exposure and time-average methods are compared using the characteristic function and the results from a flow simulator. A time history requires a large hologram recording rate. Results of holographic cinematography of the shock waves in a flutter cascade are presented as an example. Future directions of this effort, including the availability and development of suitable lasers, are discussed. Previously announced in STAR as N84-21849

Non-negative matrix factorization in texture feature for classification of dementia with MRI data

NASA Astrophysics Data System (ADS)

Sarwinda, D.; Bustamam, A.; Ardaneswari, G.

2017-07-01

This paper investigates applications of non-negative matrix factorization as feature selection method to select the features from gray level co-occurrence matrix. The proposed approach is used to classify dementia using MRI data. In this study, texture analysis using gray level co-occurrence matrix is done to feature extraction. In the feature extraction process of MRI data, we found seven features from gray level co-occurrence matrix. Non-negative matrix factorization selected three features that influence of all features produced by feature extractions. A Naïve Bayes classifier is adapted to classify dementia, i.e. Alzheimer's disease, Mild Cognitive Impairment (MCI) and normal control. The experimental results show that non-negative factorization as feature selection method able to achieve an accuracy of 96.4% for classification of Alzheimer's and normal control. The proposed method also compared with other features selection methods i.e. Principal Component Analysis (PCA).

Comparison of Genetic Algorithm, Particle Swarm Optimization and Biogeography-based Optimization for Feature Selection to Classify Clusters of Microcalcifications

NASA Astrophysics Data System (ADS)

Khehra, Baljit Singh; Pharwaha, Amar Partap Singh

2017-04-01

Ductal carcinoma in situ (DCIS) is one type of breast cancer. Clusters of microcalcifications (MCCs) are symptoms of DCIS that are recognized by mammography. Selection of robust features vector is the process of selecting an optimal subset of features from a large number of available features in a given problem domain after the feature extraction and before any classification scheme. Feature selection reduces the feature space that improves the performance of classifier and decreases the computational burden imposed by using many features on classifier. Selection of an optimal subset of features from a large number of available features in a given problem domain is a difficult search problem. For n features, the total numbers of possible subsets of features are 2n. Thus, selection of an optimal subset of features problem belongs to the category of NP-hard problems. In this paper, an attempt is made to find the optimal subset of MCCs features from all possible subsets of features using genetic algorithm (GA), particle swarm optimization (PSO) and biogeography-based optimization (BBO). For simulation, a total of 380 benign and malignant MCCs samples have been selected from mammogram images of DDSM database. A total of 50 features extracted from benign and malignant MCCs samples are used in this study. In these algorithms, fitness function is correct classification rate of classifier. Support vector machine is used as a classifier. From experimental results, it is also observed that the performance of PSO-based and BBO-based algorithms to select an optimal subset of features for classifying MCCs as benign or malignant is better as compared to GA-based algorithm.

Feature Selection for Classification of Polar Regions Using a Fuzzy Expert System

NASA Technical Reports Server (NTRS)

Penaloza, Mauel A.; Welch, Ronald M.

1996-01-01

Labeling, feature selection, and the choice of classifier are critical elements for classification of scenes and for image understanding. This study examines several methods for feature selection in polar regions, including the list, of a fuzzy logic-based expert system for further refinement of a set of selected features. Six Advanced Very High Resolution Radiometer (AVHRR) Local Area Coverage (LAC) arctic scenes are classified into nine classes: water, snow / ice, ice cloud, land, thin stratus, stratus over water, cumulus over water, textured snow over water, and snow-covered mountains. Sixty-seven spectral and textural features are computed and analyzed by the feature selection algorithms. The divergence, histogram analysis, and discriminant analysis approaches are intercompared for their effectiveness in feature selection. The fuzzy expert system method is used not only to determine the effectiveness of each approach in classifying polar scenes, but also to further reduce the features into a more optimal set. For each selection method,features are ranked from best to worst, and the best half of the features are selected. Then, rules using these selected features are defined. The results of running the fuzzy expert system with these rules show that the divergence method produces the best set features, not only does it produce the highest classification accuracy, but also it has the lowest computation requirements. A reduction of the set of features produced by the divergence method using the fuzzy expert system results in an overall classification accuracy of over 95 %. However, this increase of accuracy has a high computation cost.

Foraging patterns of Caspian terns and double-crested cormorants in the Columbia River estuary

USGS Publications Warehouse

Lyons, Donald E.; Roby, D.D.; Collis, K.

2007-01-01

We examined spatial and temporal foraging patterns of Caspian terns and double-crested cormorants nesting in the Columbia River estuary, to potentially identify circumstances where juvenile salmonids listed under the U.S. Endangered Species Act might be more vulnerable to predation by these avian piscivores. Data were collected during the 1998 and 1999 breeding seasons, using point count surveys of foraging birds at 40 sites along the river's banks, and using aerial strip transect counts throughout the estuary for terns. In 1998, terns selected tidal flats and sites with roosting beaches nearby for foraging, making greater use of the marine/mixing zone of the estuary later in the season, particularly areas near the ocean jetties. In 1999, cormorants selected foraging sites in freshwater along the main channel with pile dikes present, particularly early in the season. Foraging trends in the other year for each species were generally similar to the above but usually not significant. During aerial surveys we observed 50% of foraging and commuting terns within 8 km of the Rice Island colony, and ??? 5% of activity occurred ??? 27 km from this colony in both years. Disproportionately greater cormorant foraging activity at pile dikes may indicate greater vulnerability of salmonids to predation at those features. Colony relocations to sites at sufficient distance from areas of relatively high salmonid abundance may be a straightforward means of reducing impacts of avian predation on salmonids than habitat alterations within the Columbia River estuary, at least for terns. ?? 2007 by the Northwest Scientific Association. All rights reserved.

pyGrav, a Python-based program for handling and processing relative gravity data

NASA Astrophysics Data System (ADS)

Hector, Basile; Hinderer, Jacques

2016-06-01

pyGrav is a Python-based open-source software dedicated to the complete processing of relative-gravity data. It is particularly suited for time-lapse gravity surveys where high precision is sought. Its purpose is to bind together single-task processing codes in a user-friendly interface for handy and fast treatment of raw gravity data from many stations of a network. The intuitive object-based implementation allows to easily integrate additional functions (reading/writing routines, processing schemes, data plots) related to the appropriate object (a station, a loop, or a survey). This makes pyGrav an evolving tool. Raw data can be corrected for tides and air pressure effects. The data selection step features a double table-plot graphical window with either manual or automatic selection according to specific thresholds on data channels (tilts, gravity values, gravity standard deviation, duration of measurements, etc.). Instrumental drifts and gravity residuals are obtained by least square analysis of the dataset. This first step leads to the gravity simple differences between a reference point and any point of the network. When different repetitions of the network are done, the software computes then the gravity double differences and associated errors. The program has been tested on two specific case studies: a large dataset acquired for the study of water storage changes on a small catchment in West Africa, and a dataset operated and processed by several different users for geothermal studies in northern Alsace, France. In both cases, pyGrav proved to be an efficient and easy-to-use solution for the effective processing of relative-gravity data.

The Effect of Beta Adrenergic Blockade on Ratings of Perceived Exertion.

DTIC Science & Technology

1984-01-01

exrcis is uvo Hughson, et al. (47) investigated the effect of beta blockade using a single, 100-mg oral dose of metoprolol or matched placebo on 12...administered either placebo, propranolol (80 mug) or metoprolol (100 mug) in a double- blind, randomised manner. Before the muscle-strength tests were...The non-selective BABA propranolol and the selective agent metoprolol were compared with a placebo in a double blind cross-over design. Measurements

Unbiased feature selection in learning random forests for high-dimensional data.

PubMed

Nguyen, Thanh-Tung; Huang, Joshua Zhexue; Nguyen, Thuy Thi

2015-01-01

Random forests (RFs) have been widely used as a powerful classification method. However, with the randomization in both bagging samples and feature selection, the trees in the forest tend to select uninformative features for node splitting. This makes RFs have poor accuracy when working with high-dimensional data. Besides that, RFs have bias in the feature selection process where multivalued features are favored. Aiming at debiasing feature selection in RFs, we propose a new RF algorithm, called xRF, to select good features in learning RFs for high-dimensional data. We first remove the uninformative features using p-value assessment, and the subset of unbiased features is then selected based on some statistical measures. This feature subset is then partitioned into two subsets. A feature weighting sampling technique is used to sample features from these two subsets for building trees. This approach enables one to generate more accurate trees, while allowing one to reduce dimensionality and the amount of data needed for learning RFs. An extensive set of experiments has been conducted on 47 high-dimensional real-world datasets including image datasets. The experimental results have shown that RFs with the proposed approach outperformed the existing random forests in increasing the accuracy and the AUC measures.

Train axle bearing fault detection using a feature selection scheme based multi-scale morphological filter

NASA Astrophysics Data System (ADS)

Li, Yifan; Liang, Xihui; Lin, Jianhui; Chen, Yuejian; Liu, Jianxin

2018-02-01

This paper presents a novel signal processing scheme, feature selection based multi-scale morphological filter (MMF), for train axle bearing fault detection. In this scheme, more than 30 feature indicators of vibration signals are calculated for axle bearings with different conditions and the features which can reflect fault characteristics more effectively and representatively are selected using the max-relevance and min-redundancy principle. Then, a filtering scale selection approach for MMF based on feature selection and grey relational analysis is proposed. The feature selection based MMF method is tested on diagnosis of artificially created damages of rolling bearings of railway trains. Experimental results show that the proposed method has a superior performance in extracting fault features of defective train axle bearings. In addition, comparisons are performed with the kurtosis criterion based MMF and the spectral kurtosis criterion based MMF. The proposed feature selection based MMF method outperforms these two methods in detection of train axle bearing faults.

Features of the incorporation of single and double based powders within emulsion explosives

NASA Astrophysics Data System (ADS)

Ribeiro, J. B.; Mendes, R.; Tavares, B.; Louro, C.

2014-05-01

In this work, features of the thermal and detonation behaviour of compositions resulting from the mixture of single and double based powders within ammonium nitrate based emulsion explosives are shown. Those features are portrayed through results of thermodynamic-equilibrium calculations of the detonation velocity, the chemical compatibility assessment through differential thermal analysis [DTA] and thermo gravimetric analysis [TGA], the experimental determination of the detonation velocity and a comparative evaluation of the shock sensitivity using a modified version of the "gap-test". DTA/TGA results for the compositions and for the individual components overlap until the beginning of the thermal decomposition which is an indication of the absence of formation of any new chemical species and so of the compatibility of the components of the compositions. After the beginning of the thermal decomposition it can be seen that the rate of mass loss is much higher for the compositions with powder than for the one with sole emulsion explosive. Both, theoretical and experimental, values of the detonation velocity have been shown to be higher for the powdered compositions than for the sole emulsion explosive. Shock sensitivity assessments have ended-up with a slightly bigger sensitivity for the compositions with double based powder when compared to the single based compositions or to the sole emulsion.

Voluntary saccadic eye movements in humans studied with a double-cue paradigm.

PubMed

Sheliga, B M; Brown, V J; Miles, F A

2002-07-01

In the classic double-step paradigm, subjects are required to make a saccade to a visual target that is briefly presented at one location and then shifted to a new location before the subject has responded. The saccades in this situation are "reflexive" in that they are made in response to the appearance of the target itself. In the present experiments we adapted the double-step paradigm to study "voluntary" saccades. For this, several identical targets were always visible and subjects were given a cue to indicate that they should make a saccade to one of them. This cue was then changed to indicate another of the targets before the subject had responded: double-cue (DC) paradigm. The saccadic eye movements in our DC paradigm had many features in common with those in the double-step paradigm and we show that apparent differences can be attributed to the spatio-temporal arrangements of the cues/targets rather than to any intrinsic differences in the programming of these two kinds of eye movements. For example, a feature of our DC paradigm that is not seen in the usual double-step paradigm is that the second cue could cause transient delays of the initial saccade, and these delays still occurred when the second cue was reflexive--provided that it was at the fovea (as in our DC paradigm) and not in the periphery (as in the usual double-step paradigm). Thus, the critical factor for the delay was the retinal (foveal) location of the second cue/target--not whether it was cognitive or reflexive--and we argue that the second cue/target is here acting as a distractor. We conclude that the DC paradigm can be used to study the programming of voluntary saccades in the same way that the double-step paradigm can be used to study reflexive saccades.

Genetic Particle Swarm Optimization-Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection.

PubMed

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-07-30

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm.

Genetic Programming and Frequent Itemset Mining to Identify Feature Selection Patterns of iEEG and fMRI Epilepsy Data

PubMed Central

Smart, Otis; Burrell, Lauren

2014-01-01

Pattern classification for intracranial electroencephalogram (iEEG) and functional magnetic resonance imaging (fMRI) signals has furthered epilepsy research toward understanding the origin of epileptic seizures and localizing dysfunctional brain tissue for treatment. Prior research has demonstrated that implicitly selecting features with a genetic programming (GP) algorithm more effectively determined the proper features to discern biomarker and non-biomarker interictal iEEG and fMRI activity than conventional feature selection approaches. However for each the iEEG and fMRI modalities, it is still uncertain whether the stochastic properties of indirect feature selection with a GP yield (a) consistent results within a patient data set and (b) features that are specific or universal across multiple patient data sets. We examined the reproducibility of implicitly selecting features to classify interictal activity using a GP algorithm by performing several selection trials and subsequent frequent itemset mining (FIM) for separate iEEG and fMRI epilepsy patient data. We observed within-subject consistency and across-subject variability with some small similarity for selected features, indicating a clear need for patient-specific features and possible need for patient-specific feature selection or/and classification. For the fMRI, using nearest-neighbor classification and 30 GP generations, we obtained over 60% median sensitivity and over 60% median selectivity. For the iEEG, using nearest-neighbor classification and 30 GP generations, we obtained over 65% median sensitivity and over 65% median selectivity except one patient. PMID:25580059

Modeling and characterization of double resonant tunneling diodes for application as energy selective contacts in hot carrier solar cells

NASA Astrophysics Data System (ADS)

Jehl, Zacharie; Suchet, Daniel; Julian, Anatole; Bernard, Cyril; Miyashita, Naoya; Gibelli, Francois; Okada, Yoshitaka; Guillemolles, Jean-Francois

2017-02-01

Double resonant tunneling barriers are considered for an application as energy selective contacts in hot carrier solar cells. Experimental symmetric and asymmetric double resonant tunneling barriers are realized by molecular beam epitaxy and characterized by temperature dependent current-voltage measurements. The negative differential resistance signal is enhanced for asymmetric heterostructures, and remains unchanged between low- and room-temperatures. Within Tsu-Esaki description of the tunnel current, this observation can be explained by the voltage dependence of the tunnel transmission amplitude, which presents a resonance under finite bias for asymmetric structures. This effect is notably discussed with respect to series resistance. Different parameters related to the electronic transmission of the structure and the influence of these parameters on the current voltage characteristic are investigated, bringing insights on critical processes to optimize in double resonant tunneling barriers applied to hot carrier solar cells.

Recombinant S. cerevisiae expressing Old Yellow Enzymes from non-conventional yeasts: an easy system for selective reduction of activated alkenes

PubMed Central

2014-01-01

Background Old Yellow Enzymes (OYEs) are flavin-dependent enoate reductases (EC 1.6.99.1) that catalyze the stereoselective hydrogenation of electron-poor alkenes. Their ability to generate up to two stereocenters by the trans-hydrogenation of the C = C double bond is highly demanded in asymmetric synthesis. Isolated redox enzymes utilization require the addition of cofactors and systems for their regeneration. Microbial whole-cells may represent a valid alternative combining desired enzymatic activity and efficient cofactor regeneration. Considerable efforts were addressed at developing novel whole-cell OYE biocatalysts, based on recombinant Saccharomyces cerevisiae expressing OYE genes. Results Recombinant S. cerevisiae BY4741∆Oye2 strains, lacking endogenous OYE and expressing nine separate OYE genes from non-conventional yeasts, were used as whole-cell biocatalysts to reduce substrates with an electron-poor double bond activated by different electron-withdrawing groups. Ketoisophorone, α-methyl-trans-cinnamaldehyde, and trans-β-methyl-β-nitrostyrene were successfully reduced with high rates and selectivity. A series of four alkyl-substituted cyclohex-2-enones was tested to check the versatility and efficiency of the biocatalysts. Reduction of double bond occurred with high rates and enantioselectivity, except for 3,5,5-trimethyl-2-cyclohexenone. DFT (density functional theory) computational studies were performed to investigate whether the steric hindrance and/or the electronic properties of the substrates were crucial for reactivity. The three-dimensional structure of enoate reductases from Kluyveromyces lodderae and Candida castellii, predicted through comparative modeling, resulted similar to that of S. cerevisiae OYE2 and revealed the key role of Trp116 both in substrate specificity and stereocontrol. All the modeling studies indicate that steric hindrance was a major determinant in the enzyme reactivity. Conclusions The OYE biocatalysts, based on recombinant S. cerevisiae expressing OYE genes from non-conventional yeasts, were able to differently reduce the activated double bond of enones, enals and nitro-olefins, exhibiting a wide range of substrate specificity. Moreover whole-cells biocatalysts bypassed the necessity of the cofactor recycling and, tuning reaction parameters, allowed the synthetic exploitation of endogenous carbonyl reductases. Molecular modeling studies highlighted key structural features for further improvement of catalytic properties of OYE enzymes. PMID:24767246

Method of generating features optimal to a dataset and classifier

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bruillard, Paul J.; Gosink, Luke J.; Jarman, Kenneth D.

A method of generating features optimal to a particular dataset and classifier is disclosed. A dataset of messages is inputted and a classifier is selected. An algebra of features is encoded. Computable features that are capable of describing the dataset from the algebra of features are selected. Irredundant features that are optimal for the classifier and the dataset are selected.

Searching for Dual AGNs in Galaxy Mergers: Understanding Double-Peaked [O III] and Ultra Hard X-rays as Selection Method

NASA Astrophysics Data System (ADS)

McGurk, Rosalie C.; Max, Claire E.; Medling, Anne; Shields, Gregory A.

2015-01-01

When galaxies merge, gas accretes onto both central supermassive black holes. Thus, one expects to see close pairs of active galactic nuclei (AGNs), or dual AGNs, in a fraction of galaxy mergers. However, finding them remains a challenge. The presence of double-peaked [O III] or of ultra hard X-rays have been proposed as techniques to select dual AGNs efficiently. We studied a sample of double-peaked narrow [O III] emitting AGNs from SDSS DR7. By obtaining new and archival high spatial resolution images taken with the Keck 2 Laser Guide Star Adaptive Optics system and the near-infrared (IR) camera NIRC2, we showed that 30% of double-peaked [O III] emission line SDSS AGNs have two spatial components within a 3' radius. However, spatially resolved spectroscopy or X-ray observations are needed to confirm these galaxy pairs as systems containing two AGNs. We followed up these spatially-double candidate dual AGNs with integral field spectroscopy from Keck OSIRIS and Gemini GMOS and with long-slit spectroscopy from Keck NIRSPEC and Shane Kast Double Spectrograph. We find double-peaked emitters are caused sometimes by dual AGN and sometimes by outflows or narrow line kinematics. We also performed Chandra X-ray ACIS-S observations on 12 double-peaked candidate dual AGNs. Using our observations and 8 archival observations, we compare the distribution of X-ray photons to our spatially double near-IR images, measure X-ray luminosities and hardness ratios, and estimate column densities. By assessing what fraction of double-peaked emission line SDSS AGNs are true dual AGNs, we can better determine whether double-peaked [O III] is an efficient dual AGN indicator and constrain the statistics of dual AGNs. A second technique to find dual AGN is the detection of ultra hard X-rays by the Swift Burst Alert Telescope. We use CARMA observations to measure and map the CO(1-0) present in nearby ultra-hard X-ray Active Galactic Nuclei (AGNs) merging with either a quiescent companion galaxy or a companion galaxy hosting a second AGN, in order to understand the role molecular gas plays in feeding this unusual population of ultra-hard X-ray AGNs and to understand ultra-hard X-rays as a dual AGN selection method.

Temporal Correlation Mechanisms and Their Role in Feature Selection: A Single-Unit Study in Primate Somatosensory Cortex

PubMed Central

Gomez-Ramirez, Manuel; Trzcinski, Natalie K.; Mihalas, Stefan; Niebur, Ernst

2014-01-01

Studies in vision show that attention enhances the firing rates of cells when it is directed towards their preferred stimulus feature. However, it is unknown whether other sensory systems employ this mechanism to mediate feature selection within their modalities. Moreover, whether feature-based attention modulates the correlated activity of a population is unclear. Indeed, temporal correlation codes such as spike-synchrony and spike-count correlations (rsc) are believed to play a role in stimulus selection by increasing the signal and reducing the noise in a population, respectively. Here, we investigate (1) whether feature-based attention biases the correlated activity between neurons when attention is directed towards their common preferred feature, (2) the interplay between spike-synchrony and rsc during feature selection, and (3) whether feature attention effects are common across the visual and tactile systems. Single-unit recordings were made in secondary somatosensory cortex of three non-human primates while animals engaged in tactile feature (orientation and frequency) and visual discrimination tasks. We found that both firing rate and spike-synchrony between neurons with similar feature selectivity were enhanced when attention was directed towards their preferred feature. However, attention effects on spike-synchrony were twice as large as those on firing rate, and had a tighter relationship with behavioral performance. Further, we observed increased rsc when attention was directed towards the visual modality (i.e., away from touch). These data suggest that similar feature selection mechanisms are employed in vision and touch, and that temporal correlation codes such as spike-synchrony play a role in mediating feature selection. We posit that feature-based selection operates by implementing multiple mechanisms that reduce the overall noise levels in the neural population and synchronize activity across subpopulations that encode the relevant features of sensory stimuli. PMID:25423284

Fluorometric determination of nucleic acids based on the use of polydopamine nanotubes and target-induced strand displacement amplification.

PubMed

Ge, Jia; Bai, Dong-Mei; -Geng, Xin; Hu, Ya-Lei; Cai, Qi-Yong; Xing, Ke; Zhang, Lin; Li, Zhao-Hui

2018-01-10

The authors describe a fluorometric method for the quantitation of nucleic acids by combining (a) cycled strand displacement amplification, (b) the unique features of the DNA probe SYBR Green, and (c) polydopamine nanotubes. SYBR Green undergoes strong fluorescence enhancement upon intercalation into double-stranded DNA (dsDNA). The polydopamine nanotubes selectively adsorb single-stranded DNA (ssDNA) and molecular beacons. In the absence of target DNA, the molecular beacon, primer and SYBR Green are adsorbed on the surface of polydopamine nanotubes. This results in quenching of the fluorescence of SYBR Green, typically measured at excitation/emission wavelengths of 488/518 nm. Upon addition of analyte (target DNA) and polymerase, the stem of the molecular beacon is opened so that it can bind to the primer. This triggers target strand displacement polymerization, during which dsDNA is synthesized. The hybridized target is then displaced due to the strand displacement activity of the polymerase. The displaced target hybridizes with another molecular beacon. This triggers the next round of polymerization. Consequently, a large amount of dsDNA is formed which is detected by addition of SYBR Green. Thus, sensitive and selective fluorometric detection is realized. The fluorescent sensing strategy shows very good analytical performances towards DNA detection, such as a wide linear range from 0.05 to 25 nM with a low limit of detection of 20 pM. Graphical abstract Schematic of a fluorometric strategy for highly sensitive and selective determination of nucleic acids by combining strand displacement amplification and the unique features of SYBR Green I (SG) and polydopamine nanotubes.

Feature Selection Methods for Zero-Shot Learning of Neural Activity.

PubMed

Caceres, Carlos A; Roos, Matthew J; Rupp, Kyle M; Milsap, Griffin; Crone, Nathan E; Wolmetz, Michael E; Ratto, Christopher R

2017-01-01

Dimensionality poses a serious challenge when making predictions from human neuroimaging data. Across imaging modalities, large pools of potential neural features (e.g., responses from particular voxels, electrodes, and temporal windows) have to be related to typically limited sets of stimuli and samples. In recent years, zero-shot prediction models have been introduced for mapping between neural signals and semantic attributes, which allows for classification of stimulus classes not explicitly included in the training set. While choices about feature selection can have a substantial impact when closed-set accuracy, open-set robustness, and runtime are competing design objectives, no systematic study of feature selection for these models has been reported. Instead, a relatively straightforward feature stability approach has been adopted and successfully applied across models and imaging modalities. To characterize the tradeoffs in feature selection for zero-shot learning, we compared correlation-based stability to several other feature selection techniques on comparable data sets from two distinct imaging modalities: functional Magnetic Resonance Imaging and Electrocorticography. While most of the feature selection methods resulted in similar zero-shot prediction accuracies and spatial/spectral patterns of selected features, there was one exception; A novel feature/attribute correlation approach was able to achieve those accuracies with far fewer features, suggesting the potential for simpler prediction models that yield high zero-shot classification accuracy.

Selective plane illumination microscopy (SPIM) with time-domain fluorescence lifetime imaging microscopy (FLIM) for volumetric measurement of cleared mouse brain samples

NASA Astrophysics Data System (ADS)

Funane, Tsukasa; Hou, Steven S.; Zoltowska, Katarzyna Marta; van Veluw, Susanne J.; Berezovska, Oksana; Kumar, Anand T. N.; Bacskai, Brian J.

2018-05-01

We have developed an imaging technique which combines selective plane illumination microscopy with time-domain fluorescence lifetime imaging microscopy (SPIM-FLIM) for three-dimensional volumetric imaging of cleared mouse brains with micro- to mesoscopic resolution. The main features of the microscope include a wavelength-adjustable pulsed laser source (Ti:sapphire) (near-infrared) laser, a BiBO frequency-doubling photonic crystal, a liquid chamber, an electrically focus-tunable lens, a cuvette based sample holder, and an air (dry) objective lens. The performance of the system was evaluated with a lifetime reference dye and micro-bead phantom measurements. Intensity and lifetime maps of three-dimensional human embryonic kidney (HEK) cell culture samples and cleared mouse brain samples expressing green fluorescent protein (GFP) (donor only) and green and red fluorescent protein [positive Förster (fluorescence) resonance energy transfer] were acquired. The results show that the SPIM-FLIM system can be used for sample sizes ranging from single cells to whole mouse organs and can serve as a powerful tool for medical and biological research.

Organization and roles of nucleosomes at mouse meiotic recombination hotspots

PubMed Central

Getun, Irina V.; Wu, Zhen K.; Bois, Philippe R.J.

2012-01-01

Meiotic double strand breaks (DSBs) occur at discrete regions in the genome coined hotspots. Precisely what directs site selection of these DSBs is hotly debated and in particular it is unclear which chromatin features, and regulatory factors are necessary for a genomic region to initiate and resolve DSBs as a crossover (CO) event. In human and mouse, one layer of hotspot selection control is a recognition sequence element present at these sites that is bound by the Prdm9 zinc-finger protein. Furthermore, an overall open chromatin structure is thought to be required to allow access of the recombination machinery, and this is often dictated by the packaging of DNA around nucleosomes. We recently defined the nucleosome occupancy maps of four mouse recombination hotspots throughout meiosis. These analyses revealed no obvious dynamic changes in nucleosome occupancy, suggesting an intrinsic nature of recombinogenic sites, yet they also revealed that nucleosomes define zones of exclusion for CO resolution. Here, we discuss new evidence implicating nucleosome occupancy in recombinogenic repair and its potential roles in controlling chromatin structure at mouse meiotic hotspots. PMID:22572955

Recognition of Double Stranded RNA by Guanidine-Modified Peptide Nucleic Acids (GPNA)

PubMed Central

Gupta, Pankaj; Muse, Oluwatoyosi; Rozners, Eriks

2011-01-01

Double helical RNA has become an attractive target for molecular recognition because many non-coding RNAs play important roles in control of gene expression. Recently, we discovered that short peptide nucleic acids (PNA) bind strongly and sequence selectively to a homopurine tract of double helical RNA via triple helix formation. Herein we tested if the molecular recognition of RNA can be enhanced by α-guanidine modification of PNA. Our study was motivated by the discovery of Ly and co-workers that the guanidine modification greatly enhances the cellular delivery of PNA. Isothermal titration calorimetry showed that the guanidine-modified PNA (GPNA) had reduced affinity and sequence selectivity for triple helical recognition of RNA. The data suggested that in contrast to unmodified PNA, which formed a 1:1 PNA-RNA triple helix, GPNA preferred a 2:1 GPNA-RNA triplex-invasion complex. Nevertheless, promising results were obtained for recognition of biologically relevant double helical RNA. Consistent with enhanced strand invasion ability, GPNA derived from D-arginine recognized the transactivation response element (TAR) of HIV-1 with high affinity and sequence selectivity, presumably via Watson-Crick duplex formation. On the other hand, strong and sequence selective triple helices were formed by unmodified and nucelobase-modified PNAs and the purine rich strand of bacterial A-site. These results suggest that appropriate chemical modifications of PNA may enhance molecular recognition of complex non-coding RNAs. PMID:22146072

Selectivity of Electronic Coherence and Attosecond Ionization Delays in Strong-Field Double Ionization

NASA Astrophysics Data System (ADS)

Kobayashi, Yuki; Reduzzi, Maurizio; Chang, Kristina F.; Timmers, Henry; Neumark, Daniel M.; Leone, Stephen R.

2018-06-01

Experiments are presented on real-time probing of coherent electron dynamics in xenon initiated by strong-field double ionization. Attosecond transient absorption measurements allow for characterization of electronic coherences as well as relative ionization timings in multiple electronic states of Xe+ and Xe2 + . A high degree of coherence g =0.4 is observed between P3 2 0-P3 0 0 of Xe2 + , whereas for other possible pairs of states the coherences are below the detection limits of the experiments. A comparison of the experimental results with numerical simulations based on an uncorrelated electron-emission model shows that the coherences produced by strong-field double ionization are more selective than predicted. Surprisingly short ionization time delays, 0.85 fs, 0.64 fs, and 0.75 fs relative to Xe+ formation, are also measured for the P2 3 , P0 3 , and P1 3 states of Xe2 + , respectively. Both the unpredicted selectivity in the formation of coherence and the subfemtosecond time delays of specific states provide new insight into correlated electron dynamics in strong-field double ionization.

Selective ablation of dental calculus with a frequency-doubled Alexandrite laser

NASA Astrophysics Data System (ADS)

Rechmann, Peter; Hennig, Thomas

1996-01-01

The aim of the study was the selective removal of dental calculus by means of pulsed lasers. In a first approach the optical characteristics of subgingival calculus were calculated using fluorescence emission spectroscopy (excitation laser: N2-laser, wavelength 337 nm, pulse duration 4 ns). Subgingival calculus seems to absorb highly in the ultraviolet spectral region up to 420 nm. According to these measurements a frequency doubled Alexandrite-laser (wavelength 377 nm, pulse duration 100 ns, repetition rate 110 Hz) was used to irradiate calculus located on enamel, at the cementum enamel junction and on the root surface (located on dentin or on cementum). Irradiation was performed perpendicular to the root surface with a laser fluence of 1 Jcm-2. During the irradiation procedure an effective water cooling-system was engaged. Histological investigations were done on undecalcified sections. As a result, engaging low fluences allows a fast and strictly selective removal of subgingival calculus. Even more the investigations revealed that supragingival calculus can be removed in a strictly selective manner engaging a frequency doubled Alexandrite-laser. No adverse side effects to the surrounding tissues could be found.

Double Reformatsky reaction: divergent synthesis of δ-hydroxy-β-ketoesters.

PubMed

Mineno, Masahiro; Sawai, Yasuhiro; Kanno, Kazuaki; Sawada, Naotaka; Mizufune, Hideya

2013-06-21

The double Reformatsky reaction, tandem addition of two molecules of zinc alkanoate to a carbonyl compound, and its synthetic application to a series of δ-hydroxy-β-ketoesters has been developed. The key to accelerate the double Reformatsky reaction is considered to be a complex-induced proximity effect of the in situ generated zinc alkoxide coordinated with the pyridyl group of the substrate or bidentate amines. A noteworthy feature of the reaction system is its high tolerance of functional groups due to the moderate nucleophilicity of organozinc reagents and the mild reaction conditions. Moreover, spectroscopic and crystallographic analyses of the zinc complex of the double Reformatsky product support the proposed mechanism of reaction site discrimination for ketones, aldehydes, nitriles, carboxylic acid anhydrides, and esters.

Max-AUC Feature Selection in Computer-Aided Detection of Polyps in CT Colonography

PubMed Central

Xu, Jian-Wu; Suzuki, Kenji

2014-01-01

We propose a feature selection method based on a sequential forward floating selection (SFFS) procedure to improve the performance of a classifier in computerized detection of polyps in CT colonography (CTC). The feature selection method is coupled with a nonlinear support vector machine (SVM) classifier. Unlike the conventional linear method based on Wilks' lambda, the proposed method selected the most relevant features that would maximize the area under the receiver operating characteristic curve (AUC), which directly maximizes classification performance, evaluated based on AUC value, in the computer-aided detection (CADe) scheme. We presented two variants of the proposed method with different stopping criteria used in the SFFS procedure. The first variant searched all feature combinations allowed in the SFFS procedure and selected the subsets that maximize the AUC values. The second variant performed a statistical test at each step during the SFFS procedure, and it was terminated if the increase in the AUC value was not statistically significant. The advantage of the second variant is its lower computational cost. To test the performance of the proposed method, we compared it against the popular stepwise feature selection method based on Wilks' lambda for a colonic-polyp database (25 polyps and 2624 nonpolyps). We extracted 75 morphologic, gray-level-based, and texture features from the segmented lesion candidate regions. The two variants of the proposed feature selection method chose 29 and 7 features, respectively. Two SVM classifiers trained with these selected features yielded a 96% by-polyp sensitivity at false-positive (FP) rates of 4.1 and 6.5 per patient, respectively. Experiments showed a significant improvement in the performance of the classifier with the proposed feature selection method over that with the popular stepwise feature selection based on Wilks' lambda that yielded 18.0 FPs per patient at the same sensitivity level. PMID:24608058

Max-AUC feature selection in computer-aided detection of polyps in CT colonography.

PubMed

Xu, Jian-Wu; Suzuki, Kenji

2014-03-01

We propose a feature selection method based on a sequential forward floating selection (SFFS) procedure to improve the performance of a classifier in computerized detection of polyps in CT colonography (CTC). The feature selection method is coupled with a nonlinear support vector machine (SVM) classifier. Unlike the conventional linear method based on Wilks' lambda, the proposed method selected the most relevant features that would maximize the area under the receiver operating characteristic curve (AUC), which directly maximizes classification performance, evaluated based on AUC value, in the computer-aided detection (CADe) scheme. We presented two variants of the proposed method with different stopping criteria used in the SFFS procedure. The first variant searched all feature combinations allowed in the SFFS procedure and selected the subsets that maximize the AUC values. The second variant performed a statistical test at each step during the SFFS procedure, and it was terminated if the increase in the AUC value was not statistically significant. The advantage of the second variant is its lower computational cost. To test the performance of the proposed method, we compared it against the popular stepwise feature selection method based on Wilks' lambda for a colonic-polyp database (25 polyps and 2624 nonpolyps). We extracted 75 morphologic, gray-level-based, and texture features from the segmented lesion candidate regions. The two variants of the proposed feature selection method chose 29 and 7 features, respectively. Two SVM classifiers trained with these selected features yielded a 96% by-polyp sensitivity at false-positive (FP) rates of 4.1 and 6.5 per patient, respectively. Experiments showed a significant improvement in the performance of the classifier with the proposed feature selection method over that with the popular stepwise feature selection based on Wilks' lambda that yielded 18.0 FPs per patient at the same sensitivity level.

Feature engineering for drug name recognition in biomedical texts: feature conjunction and feature selection.

PubMed

Liu, Shengyu; Tang, Buzhou; Chen, Qingcai; Wang, Xiaolong; Fan, Xiaoming

2015-01-01

Drug name recognition (DNR) is a critical step for drug information extraction. Machine learning-based methods have been widely used for DNR with various types of features such as part-of-speech, word shape, and dictionary feature. Features used in current machine learning-based methods are usually singleton features which may be due to explosive features and a large number of noisy features when singleton features are combined into conjunction features. However, singleton features that can only capture one linguistic characteristic of a word are not sufficient to describe the information for DNR when multiple characteristics should be considered. In this study, we explore feature conjunction and feature selection for DNR, which have never been reported. We intuitively select 8 types of singleton features and combine them into conjunction features in two ways. Then, Chi-square, mutual information, and information gain are used to mine effective features. Experimental results show that feature conjunction and feature selection can improve the performance of the DNR system with a moderate number of features and our DNR system significantly outperforms the best system in the DDIExtraction 2013 challenge.

Effect of feature-selective attention on neuronal responses in macaque area MT

PubMed Central

Chen, X.; Hoffmann, K.-P.; Albright, T. D.

2012-01-01

Attention influences visual processing in striate and extrastriate cortex, which has been extensively studied for spatial-, object-, and feature-based attention. Most studies exploring neural signatures of feature-based attention have trained animals to attend to an object identified by a certain feature and ignore objects/displays identified by a different feature. Little is known about the effects of feature-selective attention, where subjects attend to one stimulus feature domain (e.g., color) of an object while features from different domains (e.g., direction of motion) of the same object are ignored. To study this type of feature-selective attention in area MT in the middle temporal sulcus, we trained macaque monkeys to either attend to and report the direction of motion of a moving sine wave grating (a feature for which MT neurons display strong selectivity) or attend to and report its color (a feature for which MT neurons have very limited selectivity). We hypothesized that neurons would upregulate their firing rate during attend-direction conditions compared with attend-color conditions. We found that feature-selective attention significantly affected 22% of MT neurons. Contrary to our hypothesis, these neurons did not necessarily increase firing rate when animals attended to direction of motion but fell into one of two classes. In one class, attention to color increased the gain of stimulus-induced responses compared with attend-direction conditions. The other class displayed the opposite effects. Feature-selective activity modulations occurred earlier in neurons modulated by attention to color compared with neurons modulated by attention to motion direction. Thus feature-selective attention influences neuronal processing in macaque area MT but often exhibited a mismatch between the preferred stimulus dimension (direction of motion) and the preferred attention dimension (attention to color). PMID:22170961

Effect of feature-selective attention on neuronal responses in macaque area MT.

PubMed

Chen, X; Hoffmann, K-P; Albright, T D; Thiele, A

2012-03-01

Attention influences visual processing in striate and extrastriate cortex, which has been extensively studied for spatial-, object-, and feature-based attention. Most studies exploring neural signatures of feature-based attention have trained animals to attend to an object identified by a certain feature and ignore objects/displays identified by a different feature. Little is known about the effects of feature-selective attention, where subjects attend to one stimulus feature domain (e.g., color) of an object while features from different domains (e.g., direction of motion) of the same object are ignored. To study this type of feature-selective attention in area MT in the middle temporal sulcus, we trained macaque monkeys to either attend to and report the direction of motion of a moving sine wave grating (a feature for which MT neurons display strong selectivity) or attend to and report its color (a feature for which MT neurons have very limited selectivity). We hypothesized that neurons would upregulate their firing rate during attend-direction conditions compared with attend-color conditions. We found that feature-selective attention significantly affected 22% of MT neurons. Contrary to our hypothesis, these neurons did not necessarily increase firing rate when animals attended to direction of motion but fell into one of two classes. In one class, attention to color increased the gain of stimulus-induced responses compared with attend-direction conditions. The other class displayed the opposite effects. Feature-selective activity modulations occurred earlier in neurons modulated by attention to color compared with neurons modulated by attention to motion direction. Thus feature-selective attention influences neuronal processing in macaque area MT but often exhibited a mismatch between the preferred stimulus dimension (direction of motion) and the preferred attention dimension (attention to color).

Comparison of Feature Selection Techniques in Machine Learning for Anatomical Brain MRI in Dementia.

PubMed

Tohka, Jussi; Moradi, Elaheh; Huttunen, Heikki

2016-07-01

We present a comparative split-half resampling analysis of various data driven feature selection and classification methods for the whole brain voxel-based classification analysis of anatomical magnetic resonance images. We compared support vector machines (SVMs), with or without filter based feature selection, several embedded feature selection methods and stability selection. While comparisons of the accuracy of various classification methods have been reported previously, the variability of the out-of-training sample classification accuracy and the set of selected features due to independent training and test sets have not been previously addressed in a brain imaging context. We studied two classification problems: 1) Alzheimer's disease (AD) vs. normal control (NC) and 2) mild cognitive impairment (MCI) vs. NC classification. In AD vs. NC classification, the variability in the test accuracy due to the subject sample did not vary between different methods and exceeded the variability due to different classifiers. In MCI vs. NC classification, particularly with a large training set, embedded feature selection methods outperformed SVM-based ones with the difference in the test accuracy exceeding the test accuracy variability due to the subject sample. The filter and embedded methods produced divergent feature patterns for MCI vs. NC classification that suggests the utility of the embedded feature selection for this problem when linked with the good generalization performance. The stability of the feature sets was strongly correlated with the number of features selected, weakly correlated with the stability of classification accuracy, and uncorrelated with the average classification accuracy.

Methylphenidate alters selective attention by amplifying salience.

PubMed

ter Huurne, Niels; Fallon, Sean James; van Schouwenburg, Martine; van der Schaaf, Marieke; Buitelaar, Jan; Jensen, Ole; Cools, Roshan

2015-12-01

Methylphenidate, the most common treatment of attention deficit hyperactivity disorder (ADHD), is increasingly used by healthy individuals as a "smart drug" to enhance cognitive abilities like attention. A key feature of (selective) attention is the ability to ignore irrelevant but salient information in the environment (distractors). Although crucial for cognitive performance, until now, it is not known how the use of methylphenidate affects resistance to attentional capture by distractors. The present study aims to clarify how methylphenidate affects distractor suppression in healthy individuals. The effect of methylphenidate (20 mg) on distractor suppression was assessed in healthy subjects (N = 20), in a within-subject double-blind placebo-controlled crossover design. We used a visuospatial attention task with target faces flanked by strong (faces) or weak distractors (scrambled faces). Methylphenidate increased accuracy on trials that required gender identification of target face stimuli (methylphenidate 88.9 ± 1.4 [mean ± SEM], placebo 86.0 ± 1.2 %; p = .003), suggesting increased processing of the faces. At the same time, however, methylphenidate increased reaction time when the target face was flanked by a face distractor relative to a scrambled face distractor (methylphenidate 34.9 ± 3.73, placebo 26.7 ± 2.84 ms; p = .027), suggesting enhanced attentional capture by distractors with task-relevant features. We conclude that methylphenidate amplifies salience of task-relevant information at the level of the stimulus category. This leads to enhanced processing of the target (faces) but also increased attentional capture by distractors drawn from the same category as the target.

Modified Bat Algorithm for Feature Selection with the Wisconsin Diagnosis Breast Cancer (WDBC) Dataset

PubMed

Jeyasingh, Suganthi; Veluchamy, Malathi

2017-05-01

Early diagnosis of breast cancer is essential to save lives of patients. Usually, medical datasets include a large variety of data that can lead to confusion during diagnosis. The Knowledge Discovery on Database (KDD) process helps to improve efficiency. It requires elimination of inappropriate and repeated data from the dataset before final diagnosis. This can be done using any of the feature selection algorithms available in data mining. Feature selection is considered as a vital step to increase the classification accuracy. This paper proposes a Modified Bat Algorithm (MBA) for feature selection to eliminate irrelevant features from an original dataset. The Bat algorithm was modified using simple random sampling to select the random instances from the dataset. Ranking was with the global best features to recognize the predominant features available in the dataset. The selected features are used to train a Random Forest (RF) classification algorithm. The MBA feature selection algorithm enhanced the classification accuracy of RF in identifying the occurrence of breast cancer. The Wisconsin Diagnosis Breast Cancer Dataset (WDBC) was used for estimating the performance analysis of the proposed MBA feature selection algorithm. The proposed algorithm achieved better performance in terms of Kappa statistic, Mathew’s Correlation Coefficient, Precision, F-measure, Recall, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE) and Root Relative Squared Error (RRSE). Creative Commons Attribution License

Genetic Particle Swarm Optimization–Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection

PubMed Central

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-01-01

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm. PMID:27483285

Toward optimal feature and time segment selection by divergence method for EEG signals classification.

PubMed

Wang, Jie; Feng, Zuren; Lu, Na; Luo, Jing

2018-06-01

Feature selection plays an important role in the field of EEG signals based motor imagery pattern classification. It is a process that aims to select an optimal feature subset from the original set. Two significant advantages involved are: lowering the computational burden so as to speed up the learning procedure and removing redundant and irrelevant features so as to improve the classification performance. Therefore, feature selection is widely employed in the classification of EEG signals in practical brain-computer interface systems. In this paper, we present a novel statistical model to select the optimal feature subset based on the Kullback-Leibler divergence measure, and automatically select the optimal subject-specific time segment. The proposed method comprises four successive stages: a broad frequency band filtering and common spatial pattern enhancement as preprocessing, features extraction by autoregressive model and log-variance, the Kullback-Leibler divergence based optimal feature and time segment selection and linear discriminate analysis classification. More importantly, this paper provides a potential framework for combining other feature extraction models and classification algorithms with the proposed method for EEG signals classification. Experiments on single-trial EEG signals from two public competition datasets not only demonstrate that the proposed method is effective in selecting discriminative features and time segment, but also show that the proposed method yields relatively better classification results in comparison with other competitive methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

Sentiment analysis of feature ranking methods for classification accuracy

NASA Astrophysics Data System (ADS)

Joseph, Shashank; Mugauri, Calvin; Sumathy, S.

2017-11-01

Text pre-processing and feature selection are important and critical steps in text mining. Text pre-processing of large volumes of datasets is a difficult task as unstructured raw data is converted into structured format. Traditional methods of processing and weighing took much time and were less accurate. To overcome this challenge, feature ranking techniques have been devised. A feature set from text preprocessing is fed as input for feature selection. Feature selection helps improve text classification accuracy. Of the three feature selection categories available, the filter category will be the focus. Five feature ranking methods namely: document frequency, standard deviation information gain, CHI-SQUARE, and weighted-log likelihood -ratio is analyzed.

Cesium Platinide Hydride 4Cs 2 Pt-CsH: An Intermetallic Double Salt Featuring Metal Anions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Smetana, Volodymyr; Mudring, Anja-Verena

2016-10-24

With Cs9Pt4H a new representative of ionic compounds featuring metal anions can be added to this rare-membered family. Cs 9Pt 4H exhibits a complex crystal structure containing Cs + cations, Pt 2- and H - anions. Being a red, transparent compound its band gap is in the visible range of the electromagnetic spectrum and the ionic type of bonding is confirmed by quantum chemical calculations. This cesium platinide hydride can formally be considered as a double salt of the “alloy” cesium–platinum, or better cesium platinide, Cs2Pt, and the salt cesium hydride CsH according to Cs 9Pt 4H≡4 Cs 2Pt∙CsH.

Double MITs and magnetoresistance: an intrinsic feature of Ru substituted La(0.67)Ca(0.33)MnO(3).

PubMed

Seetha Lakshmi, L; Sridharan, V; Sukumar, A A; Kamruddin, M; Sastry, V S; Raju, V S

2006-05-03

In this paper, we examine the possible influence of extrinsic factors on the electrical and magnetotransport of La(0.67)Ca(0.33)Mn(1-x)Ru(x)O(3) (x≤0.10). Ru substitution results in double metal-insulator transitions (MITs) at T(MI1) and T(MI2), both exhibiting magnetoresistance (MR). No additional magnetic signal corresponding to a second low-temperature maximum (LTM) at T(MI2) could be observed, either in ac susceptibility (χ(')) or in specific heat (C(p)). Typical grain sizes of ∼18 000-20 000 nm, as estimated from the scanning electron microscope (SEM) micrographs, are not so small as to warrant an LTM. The absence of additional peaks in the high statistics powder x-ray diffraction (XRD), a linear systematic increase of the unit cell parameters, close matching of the transition temperatures in resistivity, χ(') and C(p) and their linear systematic decrease with x, and an homogeneous distribution of Mn, Ru and O at arbitrarily selected regions within and across the grains exclude chemical inhomogeneity in the samples. The insensitivity of grain boundary MR at 5 K to Ru composition indicates that the grain boundary is not altered to result in an LTM. Oxygen stoichiometry of all the compounds is close to the nominal value of 3. These results not only exclude the extrinsic factors, but also establish that double MITs, both exhibiting MR, are intrinsic to Ru substituted La(0.67)Ca(0.33)MnO(3).

Morphologies, Preparations and Applications of Layered Double Hydroxide Micro-/Nanostructures

PubMed Central

Kuang, Ye; Zhao, Lina; Zhang, Shuai; Zhang, Fazhi; Dong, Mingdong; Xu, Sailong

2010-01-01

Layered double hydroxides (LDHs), also well-known as hydrotalcite-like layered clays, have been widely investigated in the fields of catalysts and catalyst support, anion exchanger, electrical and optical functional materials, flame retardants and nanoadditives. This feature article focuses on the progress in micro-/nanostructured LDHs in terms of morphology, and also on the preparations, applications, and perspectives of the LDHs with different morphologies. PMID:28883378

Mutual information criterion for feature selection with application to classification of breast microcalcifications

NASA Astrophysics Data System (ADS)

Diamant, Idit; Shalhon, Moran; Goldberger, Jacob; Greenspan, Hayit

2016-03-01

Classification of clustered breast microcalcifications into benign and malignant categories is an extremely challenging task for computerized algorithms and expert radiologists alike. In this paper we present a novel method for feature selection based on mutual information (MI) criterion for automatic classification of microcalcifications. We explored the MI based feature selection for various texture features. The proposed method was evaluated on a standardized digital database for screening mammography (DDSM). Experimental results demonstrate the effectiveness and the advantage of using the MI-based feature selection to obtain the most relevant features for the task and thus to provide for improved performance as compared to using all features.

Did Shakespeare write double falsehood? Identifying individuals by creating psychological signatures with text analysis.

PubMed

Boyd, Ryan L; Pennebaker, James W

2015-05-01

More than 100 years after Shakespeare's death, Lewis Theobald published Double Falsehood, a play supposedly sourced from a lost play by Shakespeare and John Fletcher. Since its release, scholars have attempted to determine its true authorship. Using new approaches to language and psychological analysis, we examined Double Falsehood and the works of Theobald, Shakespeare, and Fletcher. Specifically, we created a psychological signature from each author's language and statistically compared the features of each signature with those of Double Falsehood's signature. Multiple analytic approaches converged in suggesting that Double Falsehood's psychological style and content architecture predominantly resemble those of Shakespeare, showing some similarity with Fletcher's signature and only traces of Theobald's. Closer inspection revealed that Shakespeare's influence is most apparent early in the play, whereas Fletcher's is most apparent in later acts. Double Falsehood has a psychological signature consistent with that expected to be present in the long-lost play The History of Cardenio, cowritten by Shakespeare and Fletcher. © The Author(s) 2015.

Feature Selection Methods for Zero-Shot Learning of Neural Activity

PubMed Central

Caceres, Carlos A.; Roos, Matthew J.; Rupp, Kyle M.; Milsap, Griffin; Crone, Nathan E.; Wolmetz, Michael E.; Ratto, Christopher R.

2017-01-01

Dimensionality poses a serious challenge when making predictions from human neuroimaging data. Across imaging modalities, large pools of potential neural features (e.g., responses from particular voxels, electrodes, and temporal windows) have to be related to typically limited sets of stimuli and samples. In recent years, zero-shot prediction models have been introduced for mapping between neural signals and semantic attributes, which allows for classification of stimulus classes not explicitly included in the training set. While choices about feature selection can have a substantial impact when closed-set accuracy, open-set robustness, and runtime are competing design objectives, no systematic study of feature selection for these models has been reported. Instead, a relatively straightforward feature stability approach has been adopted and successfully applied across models and imaging modalities. To characterize the tradeoffs in feature selection for zero-shot learning, we compared correlation-based stability to several other feature selection techniques on comparable data sets from two distinct imaging modalities: functional Magnetic Resonance Imaging and Electrocorticography. While most of the feature selection methods resulted in similar zero-shot prediction accuracies and spatial/spectral patterns of selected features, there was one exception; A novel feature/attribute correlation approach was able to achieve those accuracies with far fewer features, suggesting the potential for simpler prediction models that yield high zero-shot classification accuracy. PMID:28690513

Copper foils with gradient structure in thickness direction and different roughnesses on two surfaces fabricated by double rolling

NASA Astrophysics Data System (ADS)

Wang, Xi-yong; Liu, Xue-feng; Zou, Wen-jiang; Xie, Jian-xin

2013-12-01

Copper foils with gradient structure in thickness direction and different roughnesses on two surfaces were fabricated by double rolling. The two surface morphologies of double-rolled copper foils are quite different, and the surface roughness values are 61 and 1095 nm, respectively. The roughness value of matt surface can meet the requirement for bonding the resin matrix with copper foils used for flexible printed circuit boards, thus may omit traditional roughening treatment; the microstructure of double-rolled copper foils demonstrates an obviously asymmetric gradient feature. From bright surface to matt surface in thickness direction, the average grain size first increases from 2.3 to 7.4 μm and then decreases to 3.6 μm; compared with conventional rolled copper foils, the double-rolled copper foils exhibit a remarkably increased bending fatigue life, and the increased range is about 16.2%.

Coulomb-repulsion-assisted double ionization from doubly excited states of argon

NASA Astrophysics Data System (ADS)

Liao, Qing; Winney, Alexander H.; Lee, Suk Kyoung; Lin, Yun Fei; Adhikari, Pradip; Li, Wen

2017-08-01

We report a combined experimental and theoretical study to elucidate nonsequential double-ionization dynamics of argon atoms at laser intensities near and below the recollision-induced ionization threshold. Three-dimensional momentum measurements of two electrons arising from strong-field nonsequential double ionization are achieved with a custom-built electron-electron-ion coincidence apparatus, showing laser intensity-dependent Coulomb repulsion effect between the two outgoing electrons. Furthermore, a previously predicted feature of double ionization from doubly excited states is confirmed in the distributions of sum of two-electron momenta. A classical ensemble simulation suggests that Coulomb-repulsion-assisted double ionization from doubly excited states is at play at low laser intensity. This mechanism can explain the dependence of Coulomb repulsion effect on the laser intensity, as well as the transition from side-by-side to back-to-back dominant emission along the laser polarization direction.

High-Pressure Band-Gap Engineering in Lead-Free Cs 2 AgBiBr 6 Double Perovskite

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Qian; Wang, Yonggang; Pan, Weicheng

Novel inorganic lead-free double perovskites with improved stability are regarded as alternatives to state-of-art hybrid lead halide perovskites in photovoltaic devices. The recently discovered Cs2AgBiBr6 double perovskite exhibits attractive optical and electronic features, making it promising for various optoelectronic applications. However, its practical performance is hampered by the large band gap. In this work, remarkable band gap narrowing of Cs2AgBiBr6 is, for the first time, achieved on inorganic photovoltaic double perovskites through high pressure treatments. Moreover, the narrowed band gap is partially retainable after releasing pressure, promoting its optoelectronic applications. This work not only provides novel insights into the structure–propertymore » relationship in lead-free double perovskites, but also offers new strategies for further development of advanced perovskite devices.« less

Double-well chimeras in 2D lattice of chaotic bistable elements

NASA Astrophysics Data System (ADS)

Shepelev, I. A.; Bukh, A. V.; Vadivasova, T. E.; Anishchenko, V. S.; Zakharova, A.

2018-01-01

We investigate spatio-temporal dynamics of a 2D ensemble of nonlocally coupled chaotic cubic maps in a bistability regime. In particular, we perform a detailed study on the transition ;coherence - incoherence; for varying coupling strength for a fixed interaction radius. For the 2D ensemble we show the appearance of amplitude and phase chimera states previously reported for 1D ensembles of nonlocally coupled chaotic systems. Moreover, we uncover a novel type of chimera state, double-well chimera, which occurs due to the interplay of the bistability of the local dynamics and the 2D ensemble structure. Additionally, we find double-well chimera behavior for steady states which we call double-well chimera death. A distinguishing feature of chimera patterns observed in the lattice is that they mainly combine clusters of different chimera types: phase, amplitude and double-well chimeras.

Probabilistic simple sticker systems

NASA Astrophysics Data System (ADS)

Selvarajoo, Mathuri; Heng, Fong Wan; Sarmin, Nor Haniza; Turaev, Sherzod

2017-04-01

A model for DNA computing using the recombination behavior of DNA molecules, known as a sticker system, was introduced by by L. Kari, G. Paun, G. Rozenberg, A. Salomaa, and S. Yu in the paper entitled DNA computing, sticker systems and universality from the journal of Acta Informatica vol. 35, pp. 401-420 in the year 1998. A sticker system uses the Watson-Crick complementary feature of DNA molecules: starting from the incomplete double stranded sequences, and iteratively using sticking operations until a complete double stranded sequence is obtained. It is known that sticker systems with finite sets of axioms and sticker rules generate only regular languages. Hence, different types of restrictions have been considered to increase the computational power of sticker systems. Recently, a variant of restricted sticker systems, called probabilistic sticker systems, has been introduced [4]. In this variant, the probabilities are initially associated with the axioms, and the probability of a generated string is computed by multiplying the probabilities of all occurrences of the initial strings in the computation of the string. Strings for the language are selected according to some probabilistic requirements. In this paper, we study fundamental properties of probabilistic simple sticker systems. We prove that the probabilistic enhancement increases the computational power of simple sticker systems.

Effects of preparation steps on the physical parameters and electromechanical properties of IPMC actuators

NASA Astrophysics Data System (ADS)

Wang, Yanjie; Zhu, Zicai; Chen, Hualing; Luo, Bin; Chang, Longfei; Wang, Yongquan; Li, Dichen

2014-12-01

The electromechanical properties of ionic polymer-metal composites (IPMC) are affected by many factors, including resistivity of surface electrodes, bending stiffness and dielectric modulus, etc, which are closely related to physical and chemical preparation steps. This paper focuses on the effects of preparation steps on these physical parameters and electromechanical properties of IPMC actuators. The mechanisms of electrode formation in the preparation steps are also clarified and investigated. To obtain samples with different features, one or more of the crucial process steps, including pretreatment, impregnation-reduction and chemical plating, were selected to fabricate IPMC. The experimental observations revealed that the physical parameters of IPMC strongly depend on their electrode morphologies caused by different steps, which were reasonable from the standpoint of physics. IPMC with the characteristics of low surface resistance and low bending stiffness, and a large area of interface electrode exhibits a perfect performance. The improvements were considered to be attributed to the double-layer electrostatic effect, induced by the broad dispersion of penetrated electrode nanoparticles. An electrical component, consisting of an equivalent circuit of a parallel combination of the serial circuit of the resistance and the electric double-layer capacitance, is introduced to qualitatively explain the deformation behaviors of IPMC. This research helps to improve the preparation steps and promote the understanding of IPMC.

DOUBLE ENDOR with a linearly and a circularly polarized radiofrequency field

NASA Astrophysics Data System (ADS)

Schweiger, A.; Rudin, M.; Forrer, J.; Günthard, Hs. H.

The combination of the two spectroscopical techniques, DOUBLE ENDOR and ENDOR with a circularly polarized radiofrequency field (CP-ENDOR), is described. with this new method, termed by the acronym CP-DOUBLE ENDOR, the selective induction of transitions of different types of nuclei and of different paramagnetic species allows a drastic reduction of the number of observed ENDOR lines. With this technique, analysis of hitherto not interpretable ENDOR spectra is often made possible. The experimental setup of the CP-DOUBLE ENDOR spectrometer is described. The advantage of using circularly polarized rf fields in DOUBLE ENDOR spectroscopy is illustrated by two applications on transition metal complexes in single crystals.

Enhancing the Performance of LibSVM Classifier by Kernel F-Score Feature Selection

NASA Astrophysics Data System (ADS)

Sarojini, Balakrishnan; Ramaraj, Narayanasamy; Nickolas, Savarimuthu

Medical Data mining is the search for relationships and patterns within the medical datasets that could provide useful knowledge for effective clinical decisions. The inclusion of irrelevant, redundant and noisy features in the process model results in poor predictive accuracy. Much research work in data mining has gone into improving the predictive accuracy of the classifiers by applying the techniques of feature selection. Feature selection in medical data mining is appreciable as the diagnosis of the disease could be done in this patient-care activity with minimum number of significant features. The objective of this work is to show that selecting the more significant features would improve the performance of the classifier. We empirically evaluate the classification effectiveness of LibSVM classifier on the reduced feature subset of diabetes dataset. The evaluations suggest that the feature subset selected improves the predictive accuracy of the classifier and reduce false negatives and false positives.

The fate of task-irrelevant visual motion: perceptual load versus feature-based attention.

PubMed

Taya, Shuichiro; Adams, Wendy J; Graf, Erich W; Lavie, Nilli

2009-11-18

We tested contrasting predictions derived from perceptual load theory and from recent feature-based selection accounts. Observers viewed moving, colored stimuli and performed low or high load tasks associated with one stimulus feature, either color or motion. The resultant motion aftereffect (MAE) was used to evaluate attentional allocation. We found that task-irrelevant visual features received less attention than co-localized task-relevant features of the same objects. Moreover, when color and motion features were co-localized yet perceived to belong to two distinct surfaces, feature-based selection was further increased at the expense of object-based co-selection. Load theory predicts that the MAE for task-irrelevant motion would be reduced with a higher load color task. However, this was not seen for co-localized features; perceptual load only modulated the MAE for task-irrelevant motion when this was spatially separated from the attended color location. Our results suggest that perceptual load effects are mediated by spatial selection and do not generalize to the feature domain. Feature-based selection operates to suppress processing of task-irrelevant, co-localized features, irrespective of perceptual load.

Classification Influence of Features on Given Emotions and Its Application in Feature Selection

NASA Astrophysics Data System (ADS)

Xing, Yin; Chen, Chuang; Liu, Li-Long

2018-04-01

In order to solve the problem that there is a large amount of redundant data in high-dimensional speech emotion features, we analyze deeply the extracted speech emotion features and select better features. Firstly, a given emotion is classified by each feature. Secondly, the recognition rate is ranked in descending order. Then, the optimal threshold of features is determined by rate criterion. Finally, the better features are obtained. When applied in Berlin and Chinese emotional data set, the experimental results show that the feature selection method outperforms the other traditional methods.

Feature Selection in Classification of Eye Movements Using Electrooculography for Activity Recognition

PubMed Central

Mala, S.; Latha, K.

2014-01-01

Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition. PMID:25574185

Feature selection in classification of eye movements using electrooculography for activity recognition.

PubMed

Mala, S; Latha, K

2014-01-01

Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition.

Sharing One Biographical Detail Elicits Priming between Famous Names: Empirical and Computational Approaches

PubMed Central

Ihrke, Matthias; Brennen, Tim

2011-01-01

In this paper three experiments and corresponding model simulations are reported that investigate the priming of famous name recognition in order to explore the structure of the part of the semantic system dealing with people. Consistent with empirical findings, novel computational simulations using Burton et al.’s interactive activation and competition model point to a conceptual distinction between how priming is initiated in single- and double-familiarity tasks, indicating that priming should be weaker or non-existent for the single-familiarity task. Experiment 1 demonstrates that, within a double-familiarity framework using famous names, categorical, and associative priming are reliable effects. Pushing the model to the limit, it predicts that pairs of celebrities who are neither associatively nor categorically related but who share single biographical features, both died in a car crash for example, should prime each other. Experiment 2 investigated this in a double-familiarity task but the effect was not observed. We therefore simulated and realized a pairwise learning task that was conceptually similar to the double-familiarity-decision task but allowed to strengthen the underlying connections. Priming based on a single biographical feature could be found both in simulations and the experiment. The effect was not due to visual or name similarity which were controlled for and participants did not report using the biographical links between the people to learn the pairs. The results are interpreted to lend further support to structural models of the memory for persons. Furthermore, the results are consistent with the idea that episodic features known about people are stored in semantic memory and are automatically activated when encountering that person. PMID:21687446

Application of the Double-Tangent Construction of Coexisting Phases to Any Type of Phase Equilibrium for Binary Systems Modeled with the Gamma-Phi Approach

ERIC Educational Resources Information Center

Jaubert, Jean-Noël; Privat, Romain

2014-01-01

The double-tangent construction of coexisting phases is an elegant approach to visualize all the multiphase binary systems that satisfy the equality of chemical potentials and to select the stable state. In this paper, we show how to perform the double-tangent construction of coexisting phases for binary systems modeled with the gamma-phi…

Scattering property based contextual PolSAR speckle filter

NASA Astrophysics Data System (ADS)

Mullissa, Adugna G.; Tolpekin, Valentyn; Stein, Alfred

2017-12-01

Reliability of the scattering model based polarimetric SAR (PolSAR) speckle filter depends upon the accurate decomposition and classification of the scattering mechanisms. This paper presents an improved scattering property based contextual speckle filter based upon an iterative classification of the scattering mechanisms. It applies a Cloude-Pottier eigenvalue-eigenvector decomposition and a fuzzy H/α classification to determine the scattering mechanisms on a pre-estimate of the coherency matrix. The H/α classification identifies pixels with homogeneous scattering properties. A coarse pixel selection rule groups pixels that are either single bounce, double bounce or volume scatterers. A fine pixel selection rule is applied to pixels within each canonical scattering mechanism. We filter the PolSAR data and depending on the type of image scene (urban or rural) use either the coarse or fine pixel selection rule. Iterative refinement of the Wishart H/α classification reduces the speckle in the PolSAR data. Effectiveness of this new filter is demonstrated by using both simulated and real PolSAR data. It is compared with the refined Lee filter, the scattering model based filter and the non-local means filter. The study concludes that the proposed filter compares favorably with other polarimetric speckle filters in preserving polarimetric information, point scatterers and subtle features in PolSAR data.

MCTs and IGBTs - A comparison of performance in power electronic circuits

NASA Technical Reports Server (NTRS)

Sul, S. K.; Profumo, F.; Cho, G. H.; Lipo, T. A.

1989-01-01

There is a continuous demand for improvements in the quality of switching power devices, such as higher switching frequency, higher withstand voltage capability, larger current-handling capability, and lower conduction losses. However, for single-conduction-mechanism devices (SCRs, GTOs, BJTs, FETs), possessing all these features is probably unrealizable for physical reasons. An attractive solution appears to be double-mechanism devices, in which the features of both a minority carrier device (BJT or SCR) and a majority carrier device (MOSFET) are embedded. Both IGBTs (insulated-gate bipolar transistors) and MCTs (MOS-controlled thyristors) belong to this family of double-mechanism devices and promise to have a major impact on converter circuit signs. The authors deal with the major features of these two devices, pointing out those that are most critical to the design of converter topologies. In particular, the two devices have been tested both in a chopper and in two resonant link converter topologies, and the experimental results are reported.

Perceptual quality estimation of H.264/AVC videos using reduced-reference and no-reference models

NASA Astrophysics Data System (ADS)

Shahid, Muhammad; Pandremmenou, Katerina; Kondi, Lisimachos P.; Rossholm, Andreas; Lövström, Benny

2016-09-01

Reduced-reference (RR) and no-reference (NR) models for video quality estimation, using features that account for the impact of coding artifacts, spatio-temporal complexity, and packet losses, are proposed. The purpose of this study is to analyze a number of potentially quality-relevant features in order to select the most suitable set of features for building the desired models. The proposed sets of features have not been used in the literature and some of the features are used for the first time in this study. The features are employed by the least absolute shrinkage and selection operator (LASSO), which selects only the most influential of them toward perceptual quality. For comparison, we apply feature selection in the complete feature sets and ridge regression on the reduced sets. The models are validated using a database of H.264/AVC encoded videos that were subjectively assessed for quality in an ITU-T compliant laboratory. We infer that just two features selected by RR LASSO and two bitstream-based features selected by NR LASSO are able to estimate perceptual quality with high accuracy, higher than that of ridge, which uses more features. The comparisons with competing works and two full-reference metrics also verify the superiority of our models.

Feature Grouping and Selection Over an Undirected Graph.

PubMed

Yang, Sen; Yuan, Lei; Lai, Ying-Cheng; Shen, Xiaotong; Wonka, Peter; Ye, Jieping

2012-01-01

High-dimensional regression/classification continues to be an important and challenging problem, especially when features are highly correlated. Feature selection, combined with additional structure information on the features has been considered to be promising in promoting regression/classification performance. Graph-guided fused lasso (GFlasso) has recently been proposed to facilitate feature selection and graph structure exploitation, when features exhibit certain graph structures. However, the formulation in GFlasso relies on pairwise sample correlations to perform feature grouping, which could introduce additional estimation bias. In this paper, we propose three new feature grouping and selection methods to resolve this issue. The first method employs a convex function to penalize the pairwise l ∞ norm of connected regression/classification coefficients, achieving simultaneous feature grouping and selection. The second method improves the first one by utilizing a non-convex function to reduce the estimation bias. The third one is the extension of the second method using a truncated l 1 regularization to further reduce the estimation bias. The proposed methods combine feature grouping and feature selection to enhance estimation accuracy. We employ the alternating direction method of multipliers (ADMM) and difference of convex functions (DC) programming to solve the proposed formulations. Our experimental results on synthetic data and two real datasets demonstrate the effectiveness of the proposed methods.

CO Sensing Performance of a Micro Thermoelectric Gas Sensor with AuPtPd/SnO₂ Catalyst and Effects of a Double Catalyst Structure with Pt/α-Al₂O₃.

PubMed

Goto, Tomoyo; Itoh, Toshio; Akamatsu, Takafumi; Shin, Woosuck

2015-12-15

The CO sensing properties of a micro thermoelectric gas sensor (micro-TGS) with a double AuPtPd/SnO₂ and Pt/α-Al₂O₃ catalyst were investigated. While several nanometer sized Pt and Pd particles were uniformly dispersed on SnO₂, the Au particles were aggregated as particles measuring >10 nm in diameter. In situ diffuse reflectance Fourier transform Infrared spectroscopy (DRIFT) analysis of the catalyst showed a CO adsorption peak on Pt and Pd, but no clear peak corresponding to the interaction between CO and Au was detected. Up to 200 °C, CO combustion was more temperature dependent than that of H₂, while H₂ combustion was activated by repeated exposure to H₂ gas during the periodic gas test. Selective CO sensing of the micro-TGS against H₂ was attempted using a double catalyst structure with 0.3-30 wt% Pt/α-Al₂O₃ as a counterpart combustion catalyst. The sensor output of the micro-TGS decreased with increasing Pt content in the Pt/α-Al₂O₃ catalyst, by cancelling out the combustion heat from the AuPtPd/SnO₂ catalyst. In addition, the AuPtPd/SnO₂ and 0.3 wt% Pt/α-Al₂O₃ double catalyst sensor showed good and selective CO detection. We therefore demonstrated that our micro-TGS with double catalyst structure is useful for controlling the gas selectivity of CO against H₂.

Natural image statistics and low-complexity feature selection.

PubMed

Vasconcelos, Manuela; Vasconcelos, Nuno

2009-02-01

Low-complexity feature selection is analyzed in the context of visual recognition. It is hypothesized that high-order dependences of bandpass features contain little information for discrimination of natural images. This hypothesis is characterized formally by the introduction of the concepts of conjunctive interference and decomposability order of a feature set. Necessary and sufficient conditions for the feasibility of low-complexity feature selection are then derived in terms of these concepts. It is shown that the intrinsic complexity of feature selection is determined by the decomposability order of the feature set and not its dimension. Feature selection algorithms are then derived for all levels of complexity and are shown to be approximated by existing information-theoretic methods, which they consistently outperform. The new algorithms are also used to objectively test the hypothesis of low decomposability order through comparison of classification performance. It is shown that, for image classification, the gain of modeling feature dependencies has strongly diminishing returns: best results are obtained under the assumption of decomposability order 1. This suggests a generic law for bandpass features extracted from natural images: that the effect, on the dependence of any two features, of observing any other feature is constant across image classes.

Study for Updated Gout Classification Criteria (SUGAR): identification of features to classify gout

PubMed Central

Taylor, William J.; Fransen, Jaap; Jansen, Tim L.; Dalbeth, Nicola; Schumacher, H. Ralph; Brown, Melanie; Louthrenoo, Worawit; Vazquez-Mellado, Janitzia; Eliseev, Maxim; McCarthy, Geraldine; Stamp, Lisa K.; Perez-Ruiz, Fernando; Sivera, Francisca; Ea, Hang-Korng; Gerritsen, Martijn; Scire, Carlo; Cavagna, Lorenzo; Lin, Chingtsai; Chou, Yin-Yi; Tausche, Anne-Kathrin; Vargas-Santos, Ana Beatriz; Janssen, Matthijs; Chen, Jiunn-Horng; Slot, Ole; Cimmino, Marco A.; Uhlig, Till; Neogi, Tuhina

2015-01-01

Objective To determine which clinical, laboratory and imaging features most accurately distinguished gout from non-gout. Methods A cross-sectional study of consecutive rheumatology clinic patients with at least one swollen joint or subcutaneous tophus. Gout was defined by synovial fluid or tophus aspirate microscopy by certified examiners in all patients. The sample was randomly divided into a model development (2/3) and test sample (1/3). Univariate and multivariate association between clinical features and MSU-defined gout was determined using logistic regression modelling. Shrinkage of regression weights was performed to prevent over-fitting of the final model. Latent class analysis was conducted to identify patterns of joint involvement. Results In total, 983 patients were included. Gout was present in 509 (52%). In the development sample (n=653), these features were selected for the final model (multivariate OR) joint erythema (2.13), difficulty walking (7.34), time to maximal pain < 24 hours (1.32), resolution by 2 weeks (3.58), tophus (7.29), MTP1 ever involved (2.30), location of currently tender joints: Other foot/ankle (2.28), MTP1 (2.82), serum urate level > 6 mg/dl (0.36 mmol/l) (3.35), ultrasound double contour sign (7.23), Xray erosion or cyst (2.49). The final model performed adequately in the test set with no evidence of misfit, high discrimination and predictive ability. MTP1 involvement was the most common joint pattern (39.4%) in gout cases. Conclusion Ten key discriminating features have been identified for further evaluation for new gout classification criteria. Ultrasound findings and degree of uricemia add discriminating value, and will significantly contribute to more accurate classification criteria. PMID:25777045

High Resolution SAR Imaging Employing Geometric Features for Extracting Seismic Damage of Buildings

NASA Astrophysics Data System (ADS)

Cui, L. P.; Wang, X. P.; Dou, A. X.; Ding, X.

2018-04-01

Synthetic Aperture Radar (SAR) image is relatively easy to acquire but difficult for interpretation. This paper probes how to identify seismic damage of building using geometric features of SAR. The SAR imaging geometric features of buildings, such as the high intensity layover, bright line induced by double bounce backscattering and dark shadow is analysed, and show obvious differences texture features of homogeneity, similarity and entropy in combinatorial imaging geometric regions between the un-collapsed and collapsed buildings in airborne SAR images acquired in Yushu city damaged by 2010 Ms7.1 Yushu, Qinghai, China earthquake, which implicates a potential capability to discriminate collapsed and un-collapsed buildings from SAR image. Study also shows that the proportion of highlight (layover & bright line) area (HA) is related to the seismic damage degree, thus a SAR image damage index (SARDI), which related to the ratio of HA to the building occupation are of building in a street block (SA), is proposed. While HA is identified through feature extraction with high-pass and low-pass filtering of SAR image in frequency domain. A partial region with 58 natural street blocks in the Yushu City are selected as study area. Then according to the above method, HA is extracted, SARDI is then calculated and further classified into 3 classes. The results show effective through validation check with seismic damage classes interpreted artificially from post-earthquake airborne high resolution optical image, which shows total classification accuracy 89.3 %, Kappa coefficient 0.79 and identical to the practical seismic damage distribution. The results are also compared and discussed with the building damage identified from SAR image available by other authors.

Study of Double-Weighted Graph Model and Optimal Path Planning for Tourist Scenic Area Oriented Intelligent Tour Guide

NASA Astrophysics Data System (ADS)

Shi, Y.; Long, Y.; Wi, X. L.

2014-04-01

When tourists visiting multiple tourist scenic spots, the travel line is usually the most effective road network according to the actual tour process, and maybe the travel line is different from planned travel line. For in the field of navigation, a proposed travel line is normally generated automatically by path planning algorithm, considering the scenic spots' positions and road networks. But when a scenic spot have a certain area and have multiple entrances or exits, the traditional described mechanism of single point coordinates is difficult to reflect these own structural features. In order to solve this problem, this paper focuses on the influence on the process of path planning caused by scenic spots' own structural features such as multiple entrances or exits, and then proposes a doubleweighted Graph Model, for the weight of both vertexes and edges of proposed Model can be selected dynamically. And then discusses the model building method, and the optimal path planning algorithm based on Dijkstra algorithm and Prim algorithm. Experimental results show that the optimal planned travel line derived from the proposed model and algorithm is more reasonable, and the travelling order and distance would be further optimized.

Dynamic conductivity modified by impurity resonant states in doping three-dimensional Dirac semimetals

NASA Astrophysics Data System (ADS)

Li, Shuai; Wang, Chen; Zheng, Shi-Han; Wang, Rui-Qiang; Li, Jun; Yang, Mou

2018-04-01

The impurity effect is studied in three-dimensional Dirac semimetals in the framework of a T-matrix method to consider the multiple scattering events of Dirac electrons off impurities. It has been found that a strong impurity potential can significantly restructure the energy dispersion and the density of states of Dirac electrons. An impurity-induced resonant state emerges and significantly modifies the pristine optical response. It is shown that the impurity state disturbs the common longitudinal optical conductivity by creating either an optical conductivity peak or double absorption jumps, depending on the relative position of the impurity band and the Fermi level. More importantly, these conductivity features appear in the forbidden region between the Drude and interband transition, completely or partially filling the Pauli block region of optical response. The underlying physics is that the appearance of resonance states as well as the broadening of the bands leads to a more complicated selection rule for the optical transitions, making it possible to excite new electron-hole pairs in the forbidden region. These features in optical conductivity provide valuable information to understand the impurity behaviors in 3D Dirac materials.

Transient studies of low temperature catalysts for methane conversion. Final report, [September 1992--March 1996

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wolf, E.E.

1996-09-30

The objective of this project is to use transient techniques to study gas surface interactions during the oxidative conversion of methane. Two groups of catalysts were studied: a double oxide of vanadium and phosphate or VPO, and double oxides of Ni, Co and Rh and lanthana. The objective of the studies involving the VPO catalyst was to understand gas-surface interactions leading to the formation of formaldehyde. In the second group of catalysts, involving metallo-oxides, the main objective was to study the gas-surface interactions that determine the selectivity to C{sub 2} hydrocarbons or synthesis gas. Transient techniques were used to studymore » the methane-surface interactions and the role of lattice oxygen. The selection of the double oxides was made on the hypothesis that the metal oxide would provide an increase interaction with methane whereas the phosphate or lanthanide would provide the sites for oxygen adsorption. The hypothesis behind this selection of catalysts was that increasing the methane interaction with the catalysts would lower the reaction temperature and thus increase the selectivity to the desired products over the total oxidation reaction. In both groups of catalysts the role of Li as a modifier of the selectivity was also studied in detail.« less

A Double-function Digital Watermarking Algorithm Based on Chaotic System and LWT

NASA Astrophysics Data System (ADS)

Yuxia, Zhao; Jingbo, Fan

A double- function digital watermarking technology is studied and a double-function digital watermarking algorithm of colored image is presented based on chaotic system and the lifting wavelet transformation (LWT).The algorithm has realized the double aims of the copyright protection and the integrity authentication of image content. Making use of feature of human visual system (HVS), the watermark image is embedded into the color image's low frequency component and middle frequency components by different means. The algorithm has great security by using two kinds chaotic mappings and Arnold to scramble the watermark image at the same time. The algorithm has good efficiency by using LWT. The emulation experiment indicates the algorithm has great efficiency and security, and the effect of concealing is really good.

Effective traffic features selection algorithm for cyber-attacks samples

NASA Astrophysics Data System (ADS)

Li, Yihong; Liu, Fangzheng; Du, Zhenyu

2018-05-01

By studying the defense scheme of Network attacks, this paper propose an effective traffic features selection algorithm based on k-means++ clustering to deal with the problem of high dimensionality of traffic features which extracted from cyber-attacks samples. Firstly, this algorithm divide the original feature set into attack traffic feature set and background traffic feature set by the clustering. Then, we calculates the variation of clustering performance after removing a certain feature. Finally, evaluating the degree of distinctiveness of the feature vector according to the result. Among them, the effective feature vector is whose degree of distinctiveness exceeds the set threshold. The purpose of this paper is to select out the effective features from the extracted original feature set. In this way, it can reduce the dimensionality of the features so as to reduce the space-time overhead of subsequent detection. The experimental results show that the proposed algorithm is feasible and it has some advantages over other selection algorithms.

Double beta decay: yesterday, today, tomorrow

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fiorini, Ettore

2011-12-16

After a brief introduction on the main features of Double Beta Decay (DBD) and on its origin, its importance is stressed in view of the recent results of experiments on neutrino oscillations. The present experimental situation is reported with special reference to direct experiments and to the comparison of their results with theory. The expectations of the future experiments aiming to reach the sensitivity indicated by neutrino oscillations in the inverse hierarchy hypothesis are discussed.

Asymmetry of bifurcated features in radio pulsar profiles

NASA Astrophysics Data System (ADS)

Dyks, J.; Rudak, B.

2012-03-01

High-quality integrated radio profiles of some pulsars contain bifurcated, highly symmetric emission components (BECs). They are observed when our line of sight traverses through a split-fan shaped emission beam. It is shown that for oblique cuts through such a beam, the features appear asymmetric at nearly all frequencies, except for a single 'frequency of symmetry'νsym, at which both peaks in the BEC have the same height. Around νsym, the ratio of flux in the two peaks of a BEC evolves in a way resembling the multifrequency behaviour of J1012+5307. Because of the inherent asymmetry resulting from the oblique traverse of the sightline, each minimum in double notches can be modelled independently. Such a composed model reproduces the double notches of B1929+10 if the fitted function is the microscopic beam of curvature radiation in the orthogonal polarization mode. These results confirm our view that some of the double components in radio pulsar profiles directly reveal the microscopic nature of the emitted radiation beam as the microbeam of the curvature radiation polarized orthogonally to the trajectory of electrons.

Spectral features of the tunneling-induced transparency and the Autler-Townes doublet and triplet in a triple quantum dot.

PubMed

Luo, Xiao-Qing; Li, Zeng-Zhao; Jing, Jun; Xiong, Wei; Li, Tie-Fu; Yu, Ting

2018-02-15

We theoretically investigate the spectral features of tunneling-induced transparency (TIT) and Autler-Townes (AT) doublet and triplet in a triple-quantum-dot system. By analyzing the eigenenergy spectrum of the system Hamiltonian, we can discriminate TIT and double TIT from AT doublet and triplet, respectively. For the resonant case, the presence of the TIT does not exhibit distinguishable anticrossing in the eigenenergy spectrum in the weak-tunneling regime, while the occurrence of double anticrossings in the strong-tunneling regime shows that the TIT evolves to the AT doublet. For the off-resonance case, the appearance of a new detuning-dependent dip in the absorption spectrum leads to double TIT behavior in the weak-tunneling regime due to no distinguished anticrossing occurring in the eigenenergy spectrum. However, in the strong-tunneling regime, a new detuning-dependent dip in the absorption spectrum results in AT triplet owing to the presence of triple anticrossings in the eigenenergy spectrum. Our results can be applied to quantum measurement and quantum-optics devices in solid systems.

Relevance popularity: A term event model based feature selection scheme for text classification.

PubMed

Feng, Guozhong; An, Baiguo; Yang, Fengqin; Wang, Han; Zhang, Libiao

2017-01-01

Feature selection is a practical approach for improving the performance of text classification methods by optimizing the feature subsets input to classifiers. In traditional feature selection methods such as information gain and chi-square, the number of documents that contain a particular term (i.e. the document frequency) is often used. However, the frequency of a given term appearing in each document has not been fully investigated, even though it is a promising feature to produce accurate classifications. In this paper, we propose a new feature selection scheme based on a term event Multinomial naive Bayes probabilistic model. According to the model assumptions, the matching score function, which is based on the prediction probability ratio, can be factorized. Finally, we derive a feature selection measurement for each term after replacing inner parameters by their estimators. On a benchmark English text datasets (20 Newsgroups) and a Chinese text dataset (MPH-20), our numerical experiment results obtained from using two widely used text classifiers (naive Bayes and support vector machine) demonstrate that our method outperformed the representative feature selection methods.

Hybrid feature selection for supporting lightweight intrusion detection systems

NASA Astrophysics Data System (ADS)

Song, Jianglong; Zhao, Wentao; Liu, Qiang; Wang, Xin

2017-08-01

Redundant and irrelevant features not only cause high resource consumption but also degrade the performance of Intrusion Detection Systems (IDS), especially when coping with big data. These features slow down the process of training and testing in network traffic classification. Therefore, a hybrid feature selection approach in combination with wrapper and filter selection is designed in this paper to build a lightweight intrusion detection system. Two main phases are involved in this method. The first phase conducts a preliminary search for an optimal subset of features, in which the chi-square feature selection is utilized. The selected set of features from the previous phase is further refined in the second phase in a wrapper manner, in which the Random Forest(RF) is used to guide the selection process and retain an optimized set of features. After that, we build an RF-based detection model and make a fair comparison with other approaches. The experimental results on NSL-KDD datasets show that our approach results are in higher detection accuracy as well as faster training and testing processes.

Classification of epileptic EEG signals based on simple random sampling and sequential feature selection.

PubMed

Ghayab, Hadi Ratham Al; Li, Yan; Abdulla, Shahab; Diykh, Mohammed; Wan, Xiangkui

2016-06-01

Electroencephalogram (EEG) signals are used broadly in the medical fields. The main applications of EEG signals are the diagnosis and treatment of diseases such as epilepsy, Alzheimer, sleep problems and so on. This paper presents a new method which extracts and selects features from multi-channel EEG signals. This research focuses on three main points. Firstly, simple random sampling (SRS) technique is used to extract features from the time domain of EEG signals. Secondly, the sequential feature selection (SFS) algorithm is applied to select the key features and to reduce the dimensionality of the data. Finally, the selected features are forwarded to a least square support vector machine (LS_SVM) classifier to classify the EEG signals. The LS_SVM classifier classified the features which are extracted and selected from the SRS and the SFS. The experimental results show that the method achieves 99.90, 99.80 and 100 % for classification accuracy, sensitivity and specificity, respectively.

Probabilistic double guarantee kidnapping detection in SLAM.

PubMed

Tian, Yang; Ma, Shugen

2016-01-01

For determining whether kidnapping has happened and which type of kidnapping it is while a robot performs autonomous tasks in an unknown environment, a double guarantee kidnapping detection (DGKD) method has been proposed. The good performance of DGKD in a relative small environment is shown. However, a limitation of DGKD is found in a large-scale environment by our recent work. In order to increase the adaptability of DGKD in a large-scale environment, an improved method called probabilistic double guarantee kidnapping detection is proposed in this paper to combine probability of features' positions and the robot's posture. Simulation results demonstrate the validity and accuracy of the proposed method.

Joint L2,1 Norm and Fisher Discrimination Constrained Feature Selection for Rational Synthesis of Microporous Aluminophosphates.

PubMed

Qi, Miao; Wang, Ting; Yi, Yugen; Gao, Na; Kong, Jun; Wang, Jianzhong

2017-04-01

Feature selection has been regarded as an effective tool to help researchers understand the generating process of data. For mining the synthesis mechanism of microporous AlPOs, this paper proposes a novel feature selection method by joint l 2,1 norm and Fisher discrimination constraints (JNFDC). In order to obtain more effective feature subset, the proposed method can be achieved in two steps. The first step is to rank the features according to sparse and discriminative constraints. The second step is to establish predictive model with the ranked features, and select the most significant features in the light of the contribution of improving the predictive accuracy. To the best of our knowledge, JNFDC is the first work which employs the sparse representation theory to explore the synthesis mechanism of six kinds of pore rings. Numerical simulations demonstrate that our proposed method can select significant features affecting the specified structural property and improve the predictive accuracy. Moreover, comparison results show that JNFDC can obtain better predictive performances than some other state-of-the-art feature selection methods. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Investigating a memory-based account of negative priming: support for selection-feature mismatch.

PubMed

MacDonald, P A; Joordens, S

2000-08-01

Using typical and modified negative priming tasks, the selection-feature mismatch account of negative priming was tested. In the modified task, participants performed selections on the basis of a semantic feature (e.g., referent size). This procedure has been shown to enhance negative priming (P. A. MacDonald, S. Joordens, & K. N. Seergobin, 1999). Across 3 experiments, negative priming occurred only when the repeated item mismatched in terms of the feature used as the basis for selections. When the repeated item was congruent on the selection feature across the prime and probe displays, positive priming arose. This pattern of results appeared in both the ignored- and the attended-repetition conditions. Negative priming does not result from previously ignoring an item. These findings strongly support the selection-feature mismatch account of negative priming and refute both the distractor inhibition and the episodic-retrieval explanations.

Plasma Radiation Source Development Program

DTIC Science & Technology

2006-03-01

shell mass distributions perform belter than thin shells. The dual plenum, double shell load has unique diagnostic features that enhance our...as implosion time increases. 13. SUBJECT TERMS Zpinch x-ray diagnostics Rayleigh-Taylor instability pulsed-power x-ray spectroscopy supersonic...feature permits some very useful diagnostics that shed light on critical details of the implosion process. See Section 3 for details. We have

Life Span as the Measure of Performance and Learning in a Business Gaming Simulation

ERIC Educational Resources Information Center

Thavikulwat, Precha

2012-01-01

This study applies the learning curve method of measuring learning to participants of a computer-assisted business gaming simulation that includes a multiple-life-cycle feature. The study involved 249 participants. It verified the workability of the feature and estimated the participants' rate of learning at 17.4% for every doubling of experience.…

Model-based assist feature insertion for sub-40nm memory device

NASA Astrophysics Data System (ADS)

Suh, Sungsoo; Lee, Suk-joo; Choi, Seong-woon; Lee, Sung-Woo; Park, Chan-hoon

2009-04-01

Many issues need to be resolved for a production-worthy model based assist feature insertion flow for single and double exposure patterning process to extend low k1 process at 193 nm immersion technology. Model based assist feature insertion is not trivial to implement either for single and double exposure patterning compared to rule based methods. As shown in Fig. 1, pixel based mask inversion technology in itself has difficulties in mask writing and inspection although it presents as one of key technology to extend single exposure for contact layer. Thus far, inversion technology is tried as a cooptimization of target mask to simultaneously generate optimized main and sub-resolution assists features for a desired process window. Alternatively, its technology can also be used to optimize for a target feature after an assist feature types are inserted in order to simplify the mask complexity. Simplification of inversion mask is one of major issue with applying inversion technology to device development even if a smaller mask feature can be fabricated since the mask writing time is also a major factor. As shown in Figure 2, mask writing time may be a limiting factor in determining whether or not an inversion solution is viable. It can be reasoned that increased number of shot counts relates to increase in margin for inversion methodology. On the other hand, there is a limit on how complex a mask can be in order to be production worthy. There is also source and mask co-optimization which influences the final mask patterns and assist feature sizes and positions for a given target. In this study, we will discuss assist feature insertion methods for sub 40-nm technology.

Parallel object-oriented data mining system

DOEpatents

Kamath, Chandrika; Cantu-Paz, Erick

2004-01-06

A data mining system uncovers patterns, associations, anomalies and other statistically significant structures in data. Data files are read and displayed. Objects in the data files are identified. Relevant features for the objects are extracted. Patterns among the objects are recognized based upon the features. Data from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) sky survey was used to search for bent doubles. This test was conducted on data from the Very Large Array in New Mexico which seeks to locate a special type of quasar (radio-emitting stellar object) called bent doubles. The FIRST survey has generated more than 32,000 images of the sky to date. Each image is 7.1 megabytes, yielding more than 100 gigabytes of image data in the entire data set.

Ordinal feature selection for iris and palmprint recognition.

PubMed

Sun, Zhenan; Wang, Libin; Tan, Tieniu

2014-09-01

Ordinal measures have been demonstrated as an effective feature representation model for iris and palmprint recognition. However, ordinal measures are a general concept of image analysis and numerous variants with different parameter settings, such as location, scale, orientation, and so on, can be derived to construct a huge feature space. This paper proposes a novel optimization formulation for ordinal feature selection with successful applications to both iris and palmprint recognition. The objective function of the proposed feature selection method has two parts, i.e., misclassification error of intra and interclass matching samples and weighted sparsity of ordinal feature descriptors. Therefore, the feature selection aims to achieve an accurate and sparse representation of ordinal measures. And, the optimization subjects to a number of linear inequality constraints, which require that all intra and interclass matching pairs are well separated with a large margin. Ordinal feature selection is formulated as a linear programming (LP) problem so that a solution can be efficiently obtained even on a large-scale feature pool and training database. Extensive experimental results demonstrate that the proposed LP formulation is advantageous over existing feature selection methods, such as mRMR, ReliefF, Boosting, and Lasso for biometric recognition, reporting state-of-the-art accuracy on CASIA and PolyU databases.

Economic indicators selection for crime rates forecasting using cooperative feature selection

NASA Astrophysics Data System (ADS)

Alwee, Razana; Shamsuddin, Siti Mariyam Hj; Salleh Sallehuddin, Roselina

2013-04-01

Features selection in multivariate forecasting model is very important to ensure that the model is accurate. The purpose of this study is to apply the Cooperative Feature Selection method for features selection. The features are economic indicators that will be used in crime rate forecasting model. The Cooperative Feature Selection combines grey relational analysis and artificial neural network to establish a cooperative model that can rank and select the significant economic indicators. Grey relational analysis is used to select the best data series to represent each economic indicator and is also used to rank the economic indicators according to its importance to the crime rate. After that, the artificial neural network is used to select the significant economic indicators for forecasting the crime rates. In this study, we used economic indicators of unemployment rate, consumer price index, gross domestic product and consumer sentiment index, as well as data rates of property crime and violent crime for the United States. Levenberg-Marquardt neural network is used in this study. From our experiments, we found that consumer price index is an important economic indicator that has a significant influence on the violent crime rate. While for property crime rate, the gross domestic product, unemployment rate and consumer price index are the influential economic indicators. The Cooperative Feature Selection is also found to produce smaller errors as compared to Multiple Linear Regression in forecasting property and violent crime rates.

Feature Selection for Ridge Regression with Provable Guarantees.

PubMed

Paul, Saurabh; Drineas, Petros

2016-04-01

We introduce single-set spectral sparsification as a deterministic sampling-based feature selection technique for regularized least-squares classification, which is the classification analog to ridge regression. The method is unsupervised and gives worst-case guarantees of the generalization power of the classification function after feature selection with respect to the classification function obtained using all features. We also introduce leverage-score sampling as an unsupervised randomized feature selection method for ridge regression. We provide risk bounds for both single-set spectral sparsification and leverage-score sampling on ridge regression in the fixed design setting and show that the risk in the sampled space is comparable to the risk in the full-feature space. We perform experiments on synthetic and real-world data sets; a subset of TechTC-300 data sets, to support our theory. Experimental results indicate that the proposed methods perform better than the existing feature selection methods.

Improving Classification of Protein Interaction Articles Using Context Similarity-Based Feature Selection.

PubMed

Chen, Yifei; Sun, Yuxing; Han, Bing-Qing

2015-01-01

Protein interaction article classification is a text classification task in the biological domain to determine which articles describe protein-protein interactions. Since the feature space in text classification is high-dimensional, feature selection is widely used for reducing the dimensionality of features to speed up computation without sacrificing classification performance. Many existing feature selection methods are based on the statistical measure of document frequency and term frequency. One potential drawback of these methods is that they treat features separately. Hence, first we design a similarity measure between the context information to take word cooccurrences and phrase chunks around the features into account. Then we introduce the similarity of context information to the importance measure of the features to substitute the document and term frequency. Hence we propose new context similarity-based feature selection methods. Their performance is evaluated on two protein interaction article collections and compared against the frequency-based methods. The experimental results reveal that the context similarity-based methods perform better in terms of the F1 measure and the dimension reduction rate. Benefiting from the context information surrounding the features, the proposed methods can select distinctive features effectively for protein interaction article classification.

Feature Selection Using Information Gain for Improved Structural-Based Alert Correlation

PubMed Central

Siraj, Maheyzah Md; Zainal, Anazida; Elshoush, Huwaida Tagelsir; Elhaj, Fatin

2016-01-01

Grouping and clustering alerts for intrusion detection based on the similarity of features is referred to as structurally base alert correlation and can discover a list of attack steps. Previous researchers selected different features and data sources manually based on their knowledge and experience, which lead to the less accurate identification of attack steps and inconsistent performance of clustering accuracy. Furthermore, the existing alert correlation systems deal with a huge amount of data that contains null values, incomplete information, and irrelevant features causing the analysis of the alerts to be tedious, time-consuming and error-prone. Therefore, this paper focuses on selecting accurate and significant features of alerts that are appropriate to represent the attack steps, thus, enhancing the structural-based alert correlation model. A two-tier feature selection method is proposed to obtain the significant features. The first tier aims at ranking the subset of features based on high information gain entropy in decreasing order. The‏ second tier extends additional features with a better discriminative ability than the initially ranked features. Performance analysis results show the significance of the selected features in terms of the clustering accuracy using 2000 DARPA intrusion detection scenario-specific dataset. PMID:27893821

Frequency-doubled Alexandrite laser for use in periodontology: a scanning electron microscopic investigation

NASA Astrophysics Data System (ADS)

Rechmann, Peter; Hennig, Thomas

1996-12-01

During prior studies it could be demonstrated that engaging a frequency double Alexandrite-laser allows a fast and strictly selective ablation of supra- and subgingival calculus. Furthermore, the removal of unstained microbial plaque was observed. First conclusions were drawn following light microscopic investigations on undecalcified sections of irradiated teeth. In the present study the cementum surface after irradiation with a frequency doubled Alexandrite-laser was observed by means of a scanning electron microscope. After irradiation sections of teeth were dried in alcohol and sputtered with gold. In comparison irradiated cementum surfaces of unerupted operatively removed wisdom teeth and tooth surfaces after the selective removal of calculus were investigated. A complete removal of calculus was observed as well as a remaining smooth surface of irradiated cementum.

A New Direction of Cancer Classification: Positive Effect of Low-Ranking MicroRNAs.

PubMed

Li, Feifei; Piao, Minghao; Piao, Yongjun; Li, Meijing; Ryu, Keun Ho

2014-10-01

Many studies based on microRNA (miRNA) expression profiles showed a new aspect of cancer classification. Because one characteristic of miRNA expression data is the high dimensionality, feature selection methods have been used to facilitate dimensionality reduction. The feature selection methods have one shortcoming thus far: they just consider the problem of where feature to class is 1:1 or n:1. However, because one miRNA may influence more than one type of cancer, human miRNA is considered to be ranked low in traditional feature selection methods and are removed most of the time. In view of the limitation of the miRNA number, low-ranking miRNAs are also important to cancer classification. We considered both high- and low-ranking features to cover all problems (1:1, n:1, 1:n, and m:n) in cancer classification. First, we used the correlation-based feature selection method to select the high-ranking miRNAs, and chose the support vector machine, Bayes network, decision tree, k-nearest-neighbor, and logistic classifier to construct cancer classification. Then, we chose Chi-square test, information gain, gain ratio, and Pearson's correlation feature selection methods to build the m:n feature subset, and used the selected miRNAs to determine cancer classification. The low-ranking miRNA expression profiles achieved higher classification accuracy compared with just using high-ranking miRNAs in traditional feature selection methods. Our results demonstrate that the m:n feature subset made a positive impression of low-ranking miRNAs in cancer classification.

Artificial bee colony algorithm for single-trial electroencephalogram analysis.

PubMed

Hsu, Wei-Yen; Hu, Ya-Ping

2015-04-01

In this study, we propose an analysis system combined with feature selection to further improve the classification accuracy of single-trial electroencephalogram (EEG) data. Acquiring event-related brain potential data from the sensorimotor cortices, the system comprises artifact and background noise removal, feature extraction, feature selection, and feature classification. First, the artifacts and background noise are removed automatically by means of independent component analysis and surface Laplacian filter, respectively. Several potential features, such as band power, autoregressive model, and coherence and phase-locking value, are then extracted for subsequent classification. Next, artificial bee colony (ABC) algorithm is used to select features from the aforementioned feature combination. Finally, selected subfeatures are classified by support vector machine. Comparing with and without artifact removal and feature selection, using a genetic algorithm on single-trial EEG data for 6 subjects, the results indicate that the proposed system is promising and suitable for brain-computer interface applications. © EEG and Clinical Neuroscience Society (ECNS) 2014.

Application of quantum-behaved particle swarm optimization to motor imagery EEG classification.

PubMed

Hsu, Wei-Yen

2013-12-01

In this study, we propose a recognition system for single-trial analysis of motor imagery (MI) electroencephalogram (EEG) data. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the system chiefly consists of automatic artifact elimination, feature extraction, feature selection and classification. In addition to the use of independent component analysis, a similarity measure is proposed to further remove the electrooculographic (EOG) artifacts automatically. Several potential features, such as wavelet-fractal features, are then extracted for subsequent classification. Next, quantum-behaved particle swarm optimization (QPSO) is used to select features from the feature combination. Finally, selected sub-features are classified by support vector machine (SVM). Compared with without artifact elimination, feature selection using a genetic algorithm (GA) and feature classification with Fisher's linear discriminant (FLD) on MI data from two data sets for eight subjects, the results indicate that the proposed method is promising in brain-computer interface (BCI) applications.

Optimum location of external markers using feature selection algorithms for real‐time tumor tracking in external‐beam radiotherapy: a virtual phantom study

PubMed Central

Nankali, Saber; Miandoab, Payam Samadi; Baghizadeh, Amin

2016-01-01

In external‐beam radiotherapy, using external markers is one of the most reliable tools to predict tumor position, in clinical applications. The main challenge in this approach is tumor motion tracking with highest accuracy that depends heavily on external markers location, and this issue is the objective of this study. Four commercially available feature selection algorithms entitled 1) Correlation‐based Feature Selection, 2) Classifier, 3) Principal Components, and 4) Relief were proposed to find optimum location of external markers in combination with two “Genetic” and “Ranker” searching procedures. The performance of these algorithms has been evaluated using four‐dimensional extended cardiac‐torso anthropomorphic phantom. Six tumors in lung, three tumors in liver, and 49 points on the thorax surface were taken into account to simulate internal and external motions, respectively. The root mean square error of an adaptive neuro‐fuzzy inference system (ANFIS) as prediction model was considered as metric for quantitatively evaluating the performance of proposed feature selection algorithms. To do this, the thorax surface region was divided into nine smaller segments and predefined tumors motion was predicted by ANFIS using external motion data of given markers at each small segment, separately. Our comparative results showed that all feature selection algorithms can reasonably select specific external markers from those segments where the root mean square error of the ANFIS model is minimum. Moreover, the performance accuracy of proposed feature selection algorithms was compared, separately. For this, each tumor motion was predicted using motion data of those external markers selected by each feature selection algorithm. Duncan statistical test, followed by F‐test, on final results reflected that all proposed feature selection algorithms have the same performance accuracy for lung tumors. But for liver tumors, a correlation‐based feature selection algorithm, in combination with a genetic search algorithm, proved to yield best performance accuracy for selecting optimum markers. PACS numbers: 87.55.km, 87.56.Fc PMID:26894358

Optimum location of external markers using feature selection algorithms for real-time tumor tracking in external-beam radiotherapy: a virtual phantom study.

PubMed

Nankali, Saber; Torshabi, Ahmad Esmaili; Miandoab, Payam Samadi; Baghizadeh, Amin

2016-01-08

In external-beam radiotherapy, using external markers is one of the most reliable tools to predict tumor position, in clinical applications. The main challenge in this approach is tumor motion tracking with highest accuracy that depends heavily on external markers location, and this issue is the objective of this study. Four commercially available feature selection algorithms entitled 1) Correlation-based Feature Selection, 2) Classifier, 3) Principal Components, and 4) Relief were proposed to find optimum location of external markers in combination with two "Genetic" and "Ranker" searching procedures. The performance of these algorithms has been evaluated using four-dimensional extended cardiac-torso anthropomorphic phantom. Six tumors in lung, three tumors in liver, and 49 points on the thorax surface were taken into account to simulate internal and external motions, respectively. The root mean square error of an adaptive neuro-fuzzy inference system (ANFIS) as prediction model was considered as metric for quantitatively evaluating the performance of proposed feature selection algorithms. To do this, the thorax surface region was divided into nine smaller segments and predefined tumors motion was predicted by ANFIS using external motion data of given markers at each small segment, separately. Our comparative results showed that all feature selection algorithms can reasonably select specific external markers from those segments where the root mean square error of the ANFIS model is minimum. Moreover, the performance accuracy of proposed feature selection algorithms was compared, separately. For this, each tumor motion was predicted using motion data of those external markers selected by each feature selection algorithm. Duncan statistical test, followed by F-test, on final results reflected that all proposed feature selection algorithms have the same performance accuracy for lung tumors. But for liver tumors, a correlation-based feature selection algorithm, in combination with a genetic search algorithm, proved to yield best performance accuracy for selecting optimum markers.

Automatic migraine classification via feature selection committee and machine learning techniques over imaging and questionnaire data.

PubMed

Garcia-Chimeno, Yolanda; Garcia-Zapirain, Begonya; Gomez-Beldarrain, Marian; Fernandez-Ruanova, Begonya; Garcia-Monco, Juan Carlos

2017-04-13

Feature selection methods are commonly used to identify subsets of relevant features to facilitate the construction of models for classification, yet little is known about how feature selection methods perform in diffusion tensor images (DTIs). In this study, feature selection and machine learning classification methods were tested for the purpose of automating diagnosis of migraines using both DTIs and questionnaire answers related to emotion and cognition - factors that influence of pain perceptions. We select 52 adult subjects for the study divided into three groups: control group (15), subjects with sporadic migraine (19) and subjects with chronic migraine and medication overuse (18). These subjects underwent magnetic resonance with diffusion tensor to see white matter pathway integrity of the regions of interest involved in pain and emotion. The tests also gather data about pathology. The DTI images and test results were then introduced into feature selection algorithms (Gradient Tree Boosting, L1-based, Random Forest and Univariate) to reduce features of the first dataset and classification algorithms (SVM (Support Vector Machine), Boosting (Adaboost) and Naive Bayes) to perform a classification of migraine group. Moreover we implement a committee method to improve the classification accuracy based on feature selection algorithms. When classifying the migraine group, the greatest improvements in accuracy were made using the proposed committee-based feature selection method. Using this approach, the accuracy of classification into three types improved from 67 to 93% when using the Naive Bayes classifier, from 90 to 95% with the support vector machine classifier, 93 to 94% in boosting. The features that were determined to be most useful for classification included are related with the pain, analgesics and left uncinate brain (connected with the pain and emotions). The proposed feature selection committee method improved the performance of migraine diagnosis classifiers compared to individual feature selection methods, producing a robust system that achieved over 90% accuracy in all classifiers. The results suggest that the proposed methods can be used to support specialists in the classification of migraines in patients undergoing magnetic resonance imaging.

LabVIEW Task Manager v. 1.10.0

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vargo, Timothy D.

LabVIEW Task Manager is a debugging tool for use during code development in the National Instruments (NI) LabVIEW® IDE. While providing a dynamic & big-picture view of running code, an expandable/collapsible tree diagram displays detailed information (both static and dynamic) on all VIs in memory, belonging to a selected project/target. It allows for interacting with single or multiple selected VIs at a time, providing significant benefits while troubleshooting, and has the following features: Look & Feel similar to Windows® Task Manager; Selection of project/target; Lists all VIs in memory, grouped by class/library; Searches for and enumerates clones in memory; DropInmore » VI for including dynamically referenced clones (Clone Beacon); 'Refresh Now' (F5) re-reads all VIs in memory and adds new ones to the tree; Displays VI name, owning class/library, state, path, data size & code size; Displays VI FP Behavior, Reentrant?, Reentrancy Type, Paused? & Highlight?; Sort by any column, including by library name; Filter by item types vi, ctl, and vit/ctt; Filter out vi.lib and global VIs; Tracking of, and ability to toggle, execution highlighting on multiple selected VIs; Tracking of paused VIs with ability to Pause/Resume/TogglePause multiple selected VIs; DropIn VI for pausing on a condition; If a clone initiates a pause, a different pause symbol is used for all clones of that same reentrant original VI; Select multiple VIs and open or close their FPs or BDs; Double Click a VI from the tree to bring the BD (first choice) or FP to front, if already open; and Select multiple top-level VIs and Abort them.« less

Joint Feature Selection and Classification for Multilabel Learning.

PubMed

Huang, Jun; Li, Guorong; Huang, Qingming; Wu, Xindong

2018-03-01

Multilabel learning deals with examples having multiple class labels simultaneously. It has been applied to a variety of applications, such as text categorization and image annotation. A large number of algorithms have been proposed for multilabel learning, most of which concentrate on multilabel classification problems and only a few of them are feature selection algorithms. Current multilabel classification models are mainly built on a single data representation composed of all the features which are shared by all the class labels. Since each class label might be decided by some specific features of its own, and the problems of classification and feature selection are often addressed independently, in this paper, we propose a novel method which can perform joint feature selection and classification for multilabel learning, named JFSC. Different from many existing methods, JFSC learns both shared features and label-specific features by considering pairwise label correlations, and builds the multilabel classifier on the learned low-dimensional data representations simultaneously. A comparative study with state-of-the-art approaches manifests a competitive performance of our proposed method both in classification and feature selection for multilabel learning.

A hybrid feature selection method using multiclass SVM for diagnosis of erythemato-squamous disease

NASA Astrophysics Data System (ADS)

Maryam, Setiawan, Noor Akhmad; Wahyunggoro, Oyas

2017-08-01

The diagnosis of erythemato-squamous disease is a complex problem and difficult to detect in dermatology. Besides that, it is a major cause of skin cancer. Data mining implementation in the medical field helps expert to diagnose precisely, accurately, and inexpensively. In this research, we use data mining technique to developed a diagnosis model based on multiclass SVM with a novel hybrid feature selection method to diagnose erythemato-squamous disease. Our hybrid feature selection method, named ChiGA (Chi Square and Genetic Algorithm), uses the advantages from filter and wrapper methods to select the optimal feature subset from original feature. Chi square used as filter method to remove redundant features and GA as wrapper method to select the ideal feature subset with SVM used as classifier. Experiment performed with 10 fold cross validation on erythemato-squamous diseases dataset taken from University of California Irvine (UCI) machine learning database. The experimental result shows that the proposed model based multiclass SVM with Chi Square and GA can give an optimum feature subset. There are 18 optimum features with 99.18% accuracy.

STT Doubles with Large Delta M - Part VII: Andromeda, Pisces, Auriga

NASA Astrophysics Data System (ADS)

Knapp, Wilfried; Nanson, John

2017-01-01

The results of visual double star observing sessions suggested a pattern for STT doubles with large DM of being harder to resolve than would be expected based on the WDS catalog data. It was felt this might be a problem with expectations on one hand, and on the other might be an indication of a need for new precise measurements, so we decided to take a closer look at a selected sample of STT doubles and do some research. Similar to the other objects covered so far several of the components show parameters quite different from the current WDS data.

Selective attention to temporal features on nested time scales.

PubMed

Henry, Molly J; Herrmann, Björn; Obleser, Jonas

2015-02-01

Meaningful auditory stimuli such as speech and music often vary simultaneously along multiple time scales. Thus, listeners must selectively attend to, and selectively ignore, separate but intertwined temporal features. The current study aimed to identify and characterize the neural network specifically involved in this feature-selective attention to time. We used a novel paradigm where listeners judged either the duration or modulation rate of auditory stimuli, and in which the stimulation, working memory demands, response requirements, and task difficulty were held constant. A first analysis identified all brain regions where individual brain activation patterns were correlated with individual behavioral performance patterns, which thus supported temporal judgments generically. A second analysis then isolated those brain regions that specifically regulated selective attention to temporal features: Neural responses in a bilateral fronto-parietal network including insular cortex and basal ganglia decreased with degree of change of the attended temporal feature. Critically, response patterns in these regions were inverted when the task required selectively ignoring this feature. The results demonstrate how the neural analysis of complex acoustic stimuli with multiple temporal features depends on a fronto-parietal network that simultaneously regulates the selective gain for attended and ignored temporal features. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A combinatorial feature selection approach to describe the QSAR of dual site inhibitors of acetylcholinesterase.

PubMed

Asadabadi, Ebrahim Barzegari; Abdolmaleki, Parviz; Barkooie, Seyyed Mohsen Hosseini; Jahandideh, Samad; Rezaei, Mohammad Ali

2009-12-01

Regarding the great potential of dual binding site inhibitors of acetylcholinesterase as the future potent drugs of Alzheimer's disease, this study was devoted to extraction of the most effective structural features of these inhibitors from among a large number of quantitative descriptors. To do this, we adopted a unique approach in quantitative structure-activity relationships. An efficient feature selection method was emphasized in such an approach, using the confirmative results of different routine and novel feature selection methods. The proposed methods generated quite consistent results ensuring the effectiveness of the selected structural features.

Design study and performance analysis of a high-speed multistage variable-geometry fan for a variable cycle engine

NASA Technical Reports Server (NTRS)

Sullivan, T. J.; Parker, D. E.

1979-01-01

A design technology study was performed to identify a high speed, multistage, variable geometry fan configuration capable of achieving wide flow modulation with near optimum efficiency at the important operating condition. A parametric screening study of the front and rear block fans was conducted in which the influence of major fan design features on weight and efficiency was determined. Key design parameters were varied systematically to determine the fan configuration most suited for a double bypass, variable cycle engine. Two and three stage fans were considered for the front block. A single stage, core driven fan was studied for the rear block. Variable geometry concepts were evaluated to provide near optimum off design performance. A detailed aerodynamic design and a preliminary mechanical design were carried out for the selected fan configuration. Performance predictions were made for the front and rear block fans.

A luminescent Cd(II)-based metal-organic framework for detection of Fe(III) ions in aqueous solution

NASA Astrophysics Data System (ADS)

Li, Fen-Fang; Zhu, Miao-Li; Lu, Li-Ping

2018-05-01

A novel Cd((II)-organic framework [Cd(Hcbic)]n (H3cbic = 1-(4-carboxybenz-yl)-1H-benzoim-idazole-5, 6-dicarboxylic acid) was assembled and characterized by X-ray single crystal analysis. The Cd-MOF features one-dimensional left and right-handed double helical chains with screw-pitch of about 4.727 Å and the 4-methyl benzoic acid groups of Hcbic2- ligands in MOF-1 play many ribbons distributing in the two sides of the 2D networks. It is found that MOF-1 shows high selectivity (KSV = 1.8 × 105 L / mol) for Fe3+ ions in water solution with luminescent quenching because of the existence of uncoordinated carboxyl groups within open frameworks, which indicates that MOF-1 is a simple and reliable detection sensing reagent for Fe3+ in practical applications.

Formation of a Fluorous/Organic Biphasic Supramolecular Octopus Assembly for Enhanced Porphyrin Phosphorescence in Air

DOE PAGES

Yang, Chi; Arvapally, Ravi K.; Tekarli, Sammer M.; ...

2015-03-03

The trinuclear triangle-shaped system [tris{3,5-bis(heptafluoropropyl)-1,2,4-triazolatosilver(I)}] (1) and the multi-armed square-shaped metalloporphyrin PtOEP or the free porphyrin base H2OEP serve as excellent octopus hosts (OEP=2,3,7,8,12,13,17,18-octaethyl-21H,23H-porphine). Coupling of the fluorous/organic molecular octopi 1 and H2OEP or PtOEP by strong quadrupole-quadrupole and metal- interactions affords the supramolecular assemblies [1PtOEP] or [1H(2)OEP] (2a), which feature nanoscopic cavities surrounding the upper triangular and lower square cores. The fluorous/organic biphasic configuration of [1PtOEP] leads to an increase in the phosphorescence of PtOEP under ambient conditions. Guest molecules can be included in the biphasic double-octopus assembly in three different site-selective modes.

The Laser MicroJet (LMJ): a multi-solution technology for high quality micro-machining

NASA Astrophysics Data System (ADS)

Mai, Tuan Anh; Richerzhagen, Bernold; Snowdon, Paul C.; Wood, David; Maropoulos, Paul G.

2007-02-01

The field of laser micromachining is highly diverse. There are many different types of lasers available in the market. Due to their differences in irradiating wavelength, output power and pulse characteristic they can be selected for different applications depending on material and feature size [1]. The main issues by using these lasers are heat damages, contamination and low ablation rates. This report examines on the application of the Laser MicroJet(R) (LMJ), a unique combination of a laser beam with a hair-thin water jet as a universal tool for micro-machining of MEMS substrates, as well as ferrous and non-ferrous materials. The materials include gallium arsenide (GaAs) & silicon wafers, steel, tantalum and alumina ceramic. A Nd:YAG laser operating at 1064 nm (infra red) and frequency doubled 532 nm (green) were employed for the micro-machining of these materials.

Integrating gender and number information in Spanish word pairs: an ERP study.

PubMed

Barber, Horacio; Carreiras, Manuel

2003-06-01

The aim of the current study was to explore the integration processes of gender and number morphological features, since it has been proposed that grammatical gender and number features might be associated with different strength with the word stem in lexical representation. Event related potentials (ERPs) were recorded using a 128-channel sensor net while twenty-four volunteers read Spanish word pairs and performed a syntactic judgment task. The word pairs which could agree or disagree in gender or number or in gender and number at the same time, were formed by a noun and an adjective (e.g. faro-alto [lighthouse-high]). A negativity around 400 msec with posterior distribution, which has been related to lexical integration processes, was found in response to both gender and number violations. No differences were found between gender disagreement, number disagreement and the double disagreement. Therefore, ERPs suggest that integration of gender and number features may not be different, and that the detection of disagreement may work under a binary state, since the double disagreement condition did not differ from the others. In addition, a subsequent component (identified as P3) showed delayed latencies in the gender disagreement condition as compared to the number disagreement condition, while the double disagreement conditions showed a shorter peak latency than the other two disagreement conditions and similar to the agreement condition. The variations in the latency of the P3 component, which has been related to categorization processes, suggest that these processes are quickly triggered by the accumulation of two incongruent as compared to one disagreement features, and that reanalysis is costlier in the case of gender disagreement as compared to the number disagreement.

A combined Fisher and Laplacian score for feature selection in QSAR based drug design using compounds with known and unknown activities.

PubMed

Valizade Hasanloei, Mohammad Amin; Sheikhpour, Razieh; Sarram, Mehdi Agha; Sheikhpour, Elnaz; Sharifi, Hamdollah

2018-02-01

Quantitative structure-activity relationship (QSAR) is an effective computational technique for drug design that relates the chemical structures of compounds to their biological activities. Feature selection is an important step in QSAR based drug design to select the most relevant descriptors. One of the most popular feature selection methods for classification problems is Fisher score which aim is to minimize the within-class distance and maximize the between-class distance. In this study, the properties of Fisher criterion were extended for QSAR models to define the new distance metrics based on the continuous activity values of compounds with known activities. Then, a semi-supervised feature selection method was proposed based on the combination of Fisher and Laplacian criteria which exploits both compounds with known and unknown activities to select the relevant descriptors. To demonstrate the efficiency of the proposed semi-supervised feature selection method in selecting the relevant descriptors, we applied the method and other feature selection methods on three QSAR data sets such as serine/threonine-protein kinase PLK3 inhibitors, ROCK inhibitors and phenol compounds. The results demonstrated that the QSAR models built on the selected descriptors by the proposed semi-supervised method have better performance than other models. This indicates the efficiency of the proposed method in selecting the relevant descriptors using the compounds with known and unknown activities. The results of this study showed that the compounds with known and unknown activities can be helpful to improve the performance of the combined Fisher and Laplacian based feature selection methods.

A combined Fisher and Laplacian score for feature selection in QSAR based drug design using compounds with known and unknown activities

NASA Astrophysics Data System (ADS)

Valizade Hasanloei, Mohammad Amin; Sheikhpour, Razieh; Sarram, Mehdi Agha; Sheikhpour, Elnaz; Sharifi, Hamdollah

2018-02-01

Quantitative structure-activity relationship (QSAR) is an effective computational technique for drug design that relates the chemical structures of compounds to their biological activities. Feature selection is an important step in QSAR based drug design to select the most relevant descriptors. One of the most popular feature selection methods for classification problems is Fisher score which aim is to minimize the within-class distance and maximize the between-class distance. In this study, the properties of Fisher criterion were extended for QSAR models to define the new distance metrics based on the continuous activity values of compounds with known activities. Then, a semi-supervised feature selection method was proposed based on the combination of Fisher and Laplacian criteria which exploits both compounds with known and unknown activities to select the relevant descriptors. To demonstrate the efficiency of the proposed semi-supervised feature selection method in selecting the relevant descriptors, we applied the method and other feature selection methods on three QSAR data sets such as serine/threonine-protein kinase PLK3 inhibitors, ROCK inhibitors and phenol compounds. The results demonstrated that the QSAR models built on the selected descriptors by the proposed semi-supervised method have better performance than other models. This indicates the efficiency of the proposed method in selecting the relevant descriptors using the compounds with known and unknown activities. The results of this study showed that the compounds with known and unknown activities can be helpful to improve the performance of the combined Fisher and Laplacian based feature selection methods.

Irrelevant reward and selection histories have different influences on task-relevant attentional selection.

PubMed

MacLean, Mary H; Giesbrecht, Barry

2015-07-01

Task-relevant and physically salient features influence visual selective attention. In the present study, we investigated the influence of task-irrelevant and physically nonsalient reward-associated features on visual selective attention. Two hypotheses were tested: One predicts that the effects of target-defining task-relevant and task-irrelevant features interact to modulate visual selection; the other predicts that visual selection is determined by the independent combination of relevant and irrelevant feature effects. These alternatives were tested using a visual search task that contained multiple targets, placing a high demand on the need for selectivity, and that was data-limited and required unspeeded responses, emphasizing early perceptual selection processes. One week prior to the visual search task, participants completed a training task in which they learned to associate particular colors with a specific reward value. In the search task, the reward-associated colors were presented surrounding targets and distractors, but were neither physically salient nor task-relevant. In two experiments, the irrelevant reward-associated features influenced performance, but only when they were presented in a task-relevant location. The costs induced by the irrelevant reward-associated features were greater when they oriented attention to a target than to a distractor. In a third experiment, we examined the effects of selection history in the absence of reward history and found that the interaction between task relevance and selection history differed, relative to when the features had previously been associated with reward. The results indicate that under conditions that demand highly efficient perceptual selection, physically nonsalient task-irrelevant and task-relevant factors interact to influence visual selective attention.

Double-frequency microwave ionization of Na

NASA Astrophysics Data System (ADS)

Ruff, G. A.; Dietrick, K. M.; Gallagher, T. F.

1990-11-01

We report the ionization of Na atoms by the simultaneous application of microwave fields of two different frequencies. We conclude that the salient features of double-frequency ionization can be readily understood. Both the hydrogenlike ||m||=2 states and the nonhydrogenic ||m||=0 and 1 states ionize when the sum of the field amplitudes, the peak field, reaches the field required for ionization by a single microwave frequency, E=1/9n4 and E=1/3n5, respectively.

Multi-level gene/MiRNA feature selection using deep belief nets and active learning.

PubMed

Ibrahim, Rania; Yousri, Noha A; Ismail, Mohamed A; El-Makky, Nagwa M

2014-01-01

Selecting the most discriminative genes/miRNAs has been raised as an important task in bioinformatics to enhance disease classifiers and to mitigate the dimensionality curse problem. Original feature selection methods choose genes/miRNAs based on their individual features regardless of how they perform together. Considering group features instead of individual ones provides a better view for selecting the most informative genes/miRNAs. Recently, deep learning has proven its ability in representing the data in multiple levels of abstraction, allowing for better discrimination between different classes. However, the idea of using deep learning for feature selection is not widely used in the bioinformatics field yet. In this paper, a novel multi-level feature selection approach named MLFS is proposed for selecting genes/miRNAs based on expression profiles. The approach is based on both deep and active learning. Moreover, an extension to use the technique for miRNAs is presented by considering the biological relation between miRNAs and genes. Experimental results show that the approach was able to outperform classical feature selection methods in hepatocellular carcinoma (HCC) by 9%, lung cancer by 6% and breast cancer by around 10% in F1-measure. Results also show the enhancement in F1-measure of our approach over recently related work in [1] and [2].

N- vs. C-Domain Selectivity of Catalytic Inactivation of Human Angiotensin Converting Enzyme by Lisinopril-Coupled Transition Metal Chelates

PubMed Central

Hocharoen, Lalintip; Joyner, Jeff C.; Cowan, J. A.

2014-01-01

The N- and C-terminal domains of human somatic Angiotensin I Converting Enzyme (sACE-1) demonstrate distinct physiological functions, with resulting interest in the development of domain-selective inhibitors for specific therapeutic applications. Herein, the activity of lisinopril-coupled transition metal chelates were tested for both reversible binding and irreversible catalytic inactivation of sACE-1. C/N domain binding selectivity ratios ranged from 1 to 350, while rates of irreversible catalytic inactivation of the N- and C-domains were found to be significantly greater for the N-domain, suggesting a more optimal orientation of the M-chelate-lisinopril complexes within the active site of the N-domain of sACE-1. Finally, the combined effect of binding selectivity and inactivation selectivity was assessed for each catalyst (double-filter selectivity factors), and several catalysts were found to cause domain-selective catalytic inactivation. The results of this study demonstrate the ability to optimize the target selectivity of catalytic metallopeptides through both binding and orientation factors (double-filter effect). PMID:24228790

N- versus C-domain selectivity of catalytic inactivation of human angiotensin converting enzyme by lisinopril-coupled transition metal chelates.

PubMed

Hocharoen, Lalintip; Joyner, Jeff C; Cowan, J A

2013-12-27

The N- and C-terminal domains of human somatic angiotensin I converting enzyme (sACE-1) demonstrate distinct physiological functions, with resulting interest in the development of domain-selective inhibitors for specific therapeutic applications. Herein, the activity of lisinopril-coupled transition metal chelates was tested for both reversible binding and irreversible catalytic inactivation of each domain of sACE-1. C/N domain binding selectivity ratios ranged from 1 to 350, while rates of irreversible catalytic inactivation of the N- and C-domains were found to be significantly greater for the N-domain, suggesting a more optimal orientation of M-chelate-lisinopril complexes within the active site of the N-domain of sACE-1. Finally, the combined effect of binding selectivity and inactivation selectivity was assessed for each catalyst (double-filter selectivity factors), and several catalysts were found to cause domain-selective catalytic inactivation. The results of this study demonstrate the ability to optimize the target selectivity of catalytic metallopeptides through both binding and catalytic factors (double-filter effect).

A Hierarchical Feature and Sample Selection Framework and Its Application for Alzheimer’s Disease Diagnosis

NASA Astrophysics Data System (ADS)

An, Le; Adeli, Ehsan; Liu, Mingxia; Zhang, Jun; Lee, Seong-Whan; Shen, Dinggang

2017-03-01

Classification is one of the most important tasks in machine learning. Due to feature redundancy or outliers in samples, using all available data for training a classifier may be suboptimal. For example, the Alzheimer’s disease (AD) is correlated with certain brain regions or single nucleotide polymorphisms (SNPs), and identification of relevant features is critical for computer-aided diagnosis. Many existing methods first select features from structural magnetic resonance imaging (MRI) or SNPs and then use those features to build the classifier. However, with the presence of many redundant features, the most discriminative features are difficult to be identified in a single step. Thus, we formulate a hierarchical feature and sample selection framework to gradually select informative features and discard ambiguous samples in multiple steps for improved classifier learning. To positively guide the data manifold preservation process, we utilize both labeled and unlabeled data during training, making our method semi-supervised. For validation, we conduct experiments on AD diagnosis by selecting mutually informative features from both MRI and SNP, and using the most discriminative samples for training. The superior classification results demonstrate the effectiveness of our approach, as compared with the rivals.

An improved wrapper-based feature selection method for machinery fault diagnosis

PubMed Central

2017-01-01

A major issue of machinery fault diagnosis using vibration signals is that it is over-reliant on personnel knowledge and experience in interpreting the signal. Thus, machine learning has been adapted for machinery fault diagnosis. The quantity and quality of the input features, however, influence the fault classification performance. Feature selection plays a vital role in selecting the most representative feature subset for the machine learning algorithm. In contrast, the trade-off relationship between capability when selecting the best feature subset and computational effort is inevitable in the wrapper-based feature selection (WFS) method. This paper proposes an improved WFS technique before integration with a support vector machine (SVM) model classifier as a complete fault diagnosis system for a rolling element bearing case study. The bearing vibration dataset made available by the Case Western Reserve University Bearing Data Centre was executed using the proposed WFS and its performance has been analysed and discussed. The results reveal that the proposed WFS secures the best feature subset with a lower computational effort by eliminating the redundancy of re-evaluation. The proposed WFS has therefore been found to be capable and efficient to carry out feature selection tasks. PMID:29261689

Asymmetric bagging and feature selection for activities prediction of drug molecules.

PubMed

Li, Guo-Zheng; Meng, Hao-Hua; Lu, Wen-Cong; Yang, Jack Y; Yang, Mary Qu

2008-05-28

Activities of drug molecules can be predicted by QSAR (quantitative structure activity relationship) models, which overcomes the disadvantages of high cost and long cycle by employing the traditional experimental method. With the fact that the number of drug molecules with positive activity is rather fewer than that of negatives, it is important to predict molecular activities considering such an unbalanced situation. Here, asymmetric bagging and feature selection are introduced into the problem and asymmetric bagging of support vector machines (asBagging) is proposed on predicting drug activities to treat the unbalanced problem. At the same time, the features extracted from the structures of drug molecules affect prediction accuracy of QSAR models. Therefore, a novel algorithm named PRIFEAB is proposed, which applies an embedded feature selection method to remove redundant and irrelevant features for asBagging. Numerical experimental results on a data set of molecular activities show that asBagging improve the AUC and sensitivity values of molecular activities and PRIFEAB with feature selection further helps to improve the prediction ability. Asymmetric bagging can help to improve prediction accuracy of activities of drug molecules, which can be furthermore improved by performing feature selection to select relevant features from the drug molecules data sets.

Microwave photonic filter using multiwavelength Brillouin-erbium fiber laser with double-Brillouin-frequency shift

DOE Office of Scientific and Technical Information (OSTI.GOV)

Loh, K. K.; Yeo, K. S.; Shee, Y. G.

2015-04-24

A microwave photonic filter based on double-Brillouin-frequency spaced multiwavelength Brillouin-erbium fiber laser (BEFL) is experimentally demonstrated. The filter selectivity can be easily adjusted by tuning and apodizing the optical taps generated from the multiwavelength BEFL. Reconfiguration of different frequency responses are demonstrated.

Reading Disorders in Primary Progressive Aphasia: A Behavioral and Neuroimaging Study

ERIC Educational Resources Information Center

Brambati, S. M.; Ogar, J.; Neuhaus, J.; Miller, B. L.; Gorno-Tempini, M. L.

2009-01-01

Previous neuropsychological studies on acquired dyslexia revealed a double dissociation in reading impairments. Patients with phonological dyslexia have selective difficulty in reading pseudo-words, while those with surface dyslexia misread exception words. This double dissociation in reading abilities has often been reported in brain-damaged…

System Complexity Reduction via Feature Selection

ERIC Educational Resources Information Center

Deng, Houtao

2011-01-01

This dissertation transforms a set of system complexity reduction problems to feature selection problems. Three systems are considered: classification based on association rules, network structure learning, and time series classification. Furthermore, two variable importance measures are proposed to reduce the feature selection bias in tree…

Classification of motor imagery tasks for BCI with multiresolution analysis and multiobjective feature selection.

PubMed

Ortega, Julio; Asensio-Cubero, Javier; Gan, John Q; Ortiz, Andrés

2016-07-15

Brain-computer interfacing (BCI) applications based on the classification of electroencephalographic (EEG) signals require solving high-dimensional pattern classification problems with such a relatively small number of training patterns that curse of dimensionality problems usually arise. Multiresolution analysis (MRA) has useful properties for signal analysis in both temporal and spectral analysis, and has been broadly used in the BCI field. However, MRA usually increases the dimensionality of the input data. Therefore, some approaches to feature selection or feature dimensionality reduction should be considered for improving the performance of the MRA based BCI. This paper investigates feature selection in the MRA-based frameworks for BCI. Several wrapper approaches to evolutionary multiobjective feature selection are proposed with different structures of classifiers. They are evaluated by comparing with baseline methods using sparse representation of features or without feature selection. The statistical analysis, by applying the Kolmogorov-Smirnoff and Kruskal-Wallis tests to the means of the Kappa values evaluated by using the test patterns in each approach, has demonstrated some advantages of the proposed approaches. In comparison with the baseline MRA approach used in previous studies, the proposed evolutionary multiobjective feature selection approaches provide similar or even better classification performances, with significant reduction in the number of features that need to be computed.

Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

PubMed Central

Arruti, Andoni; Cearreta, Idoia; Álvarez, Aitor; Lazkano, Elena; Sierra, Basilio

2014-01-01

Study of emotions in human–computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested. PMID:25279686

The Double ITCZ Syndrome in GCMs: A Coupled Problem among Convection, Atmospheric and Ocean Circulations

NASA Astrophysics Data System (ADS)

Zhang, G. J.; Song, X.

2017-12-01

The double ITCZ bias has been a long-standing problem in coupled atmosphere-ocean models. A previous study indicates that uncertainty in the projection of global warming due to doubling of CO2 is closely related to the double ITCZ biases in global climate models. Thus, reducing the double ITCZ biases is not only important to getting the current climate features right, but also important to narrowing the uncertainty in future climate projection. In this work, we will first review the possible factors contributing to the ITCZ problem. Then, we will focus on atmospheric convection, presenting recent progress in alleviating the double ITCZ problem and its sensitivity to details of convective parameterization, including trigger conditions for convection onset, convective memory, entrainment rate, updraft model and closure in the NCAR CESM1. These changes together can result in dramatic improvements in the simulation of ITCZ. Results based on both atmospheric only and coupled simulations with incremental changes of convection scheme will be shown to demonstrate the roles of convection parameterization and coupled interaction between convection, atmospheric circulation and ocean circulation in the simulation of ITCZ.

QED in a time-dependent double cavity and creation of entanglement between noninteracting atoms via quantum eraser technique

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cirone, Markus A.; Rzazewski, Kazimierz; Centrum Fizyki Teoretycznej, Polska Akademia Nauk, and College of Science, Al. Lotnikow 32/46, 02-668 Warsaw

1999-03-11

We discuss two striking features of quantum mechanics: The concepts of vacuum and of entanglement. We first study the radiation field inside a double cavity (a cavity which contains a reflecting mirror). If the mirror is rapidly removed, peculiar quantum phenomena, such as photon creation from vacuum and squeezing, occur. We discuss then a gedanken experiment which employs the double cavity to create entanglement between two atoms. The atoms cross the double cavity and interact with its two independent radiation fields. After the atoms leave the cavity, the mirror is suddenly removed. Measurement of the radiation field inside the cavitymore » can give rise to entanglement between the atoms. The method can be extended to an arbitrary number of atoms, providing thus an N-particle GHZ state.« less

The impact of feature selection on one and two-class classification performance for plant microRNAs.

PubMed

Khalifa, Waleed; Yousef, Malik; Saçar Demirci, Müşerref Duygu; Allmer, Jens

2016-01-01

MicroRNAs (miRNAs) are short nucleotide sequences that form a typical hairpin structure which is recognized by a complex enzyme machinery. It ultimately leads to the incorporation of 18-24 nt long mature miRNAs into RISC where they act as recognition keys to aid in regulation of target mRNAs. It is involved to determine miRNAs experimentally and, therefore, machine learning is used to complement such endeavors. The success of machine learning mostly depends on proper input data and appropriate features for parameterization of the data. Although, in general, two-class classification (TCC) is used in the field; because negative examples are hard to come by, one-class classification (OCC) has been tried for pre-miRNA detection. Since both positive and negative examples are currently somewhat limited, feature selection can prove to be vital for furthering the field of pre-miRNA detection. In this study, we compare the performance of OCC and TCC using eight feature selection methods and seven different plant species providing positive pre-miRNA examples. Feature selection was very successful for OCC where the best feature selection method achieved an average accuracy of 95.6%, thereby being ∼29% better than the worst method which achieved 66.9% accuracy. While the performance is comparable to TCC, which performs up to 3% better than OCC, TCC is much less affected by feature selection and its largest performance gap is ∼13% which only occurs for two of the feature selection methodologies. We conclude that feature selection is crucially important for OCC and that it can perform on par with TCC given the proper set of features.

Facile preparation of magnetic graphene double-sided mesoporous composites for the selective enrichment and analysis of endogenous peptides.

PubMed

Yin, Peng; Sun, Nianrong; Deng, Chunhui; Li, Yan; Zhang, Xiangmin; Yang, Pengyuan

2013-08-01

In this work, magnetic graphene double-sided mesoporous nanocomposites (mag-graphene@mSiO₂) were synthesized by coating a layer of mesoporous silica materials on each side of magnetic grapheme. The surfactant (CTAB) mediated sol-gel coating was performed using tetraethyl orthosilicate as the silica source. The as-made magnetic graphene double-sided mesoporous silica composites were treated with high-temperature calcination to remove the hydroxyl on the surface. The novel double-sided materials possess high surface area (167.8 cm²/g) and large pore volume (0.2 cm³/g). The highly open pore structure presents uniform pore size (3.2 nm) and structural stability. The hydrophobic interior pore walls could ensure an efficient adsorption of target molecules through hydrophobic-hydrophobic interaction. At the same time, the magnetic Fe₃O₄ particles on both sides of the materials could simplify the process of enrichment, which plays an important role in the treatment of complex biological samples. The magnetic graphene double-sided nanocomposites were successfully applied to size-selective and specific enrichment of peptides in standard peptide mixtures, protein digest solutions, and human urine samples. Finally, the novel material was applied to selective enrichment of endogenous peptides in mouse brain tissue. The enriched endogenous peptides were then analyzed by LC-MS/MS, and 409 endogenous peptides were detected and identified. The results demonstrate that the as-made mag-graphene@mSiO₂ have powerful potential for peptidome research. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Double-barreled and Concentric Microelectrodes for Measurement of Extracellular Ion Signals in Brain Tissue

PubMed Central

Haack, Nicole; Durry, Simone; Kafitz, Karl W.; Chesler, Mitchell; Rose, Christine R.

2015-01-01

Electrical activity in the brain is accompanied by significant ion fluxes across membranes, resulting in complex changes in the extracellular concentration of all major ions. As these ion shifts bear significant functional consequences, their quantitative determination is often required to understand the function and dysfunction of neural networks under physiological and pathophysiological conditions. In the present study, we demonstrate the fabrication and calibration of double-barreled ion-selective microelectrodes, which have proven to be excellent tools for such measurements in brain tissue. Moreover, so-called “concentric” ion-selective microelectrodes are also described, which, based on their different design, offer a far better temporal resolution of fast ion changes. We then show how these electrodes can be employed in acute brain slice preparations of the mouse hippocampus. Using double-barreled, potassium-selective microelectrodes, changes in the extracellular potassium concentration ([K+]o) in response to exogenous application of glutamate receptor agonists or during epileptiform activity are demonstrated. Furthermore, we illustrate the response characteristics of sodium-sensitive, double-barreled and concentric electrodes and compare their detection of changes in the extracellular sodium concentration ([Na+]o) evoked by bath or pressure application of drugs. These measurements show that while response amplitudes are similar, the concentric sodium microelectrodes display a superior signal-to-noise ratio and response time as compared to the double-barreled design. Generally, the demonstrated procedures will be easily transferable to measurement of other ions species, including pH or calcium, and will also be applicable to other preparations. PMID:26381747

FSR: feature set reduction for scalable and accurate multi-class cancer subtype classification based on copy number.

PubMed

Wong, Gerard; Leckie, Christopher; Kowalczyk, Adam

2012-01-15

Feature selection is a key concept in machine learning for microarray datasets, where features represented by probesets are typically several orders of magnitude larger than the available sample size. Computational tractability is a key challenge for feature selection algorithms in handling very high-dimensional datasets beyond a hundred thousand features, such as in datasets produced on single nucleotide polymorphism microarrays. In this article, we present a novel feature set reduction approach that enables scalable feature selection on datasets with hundreds of thousands of features and beyond. Our approach enables more efficient handling of higher resolution datasets to achieve better disease subtype classification of samples for potentially more accurate diagnosis and prognosis, which allows clinicians to make more informed decisions in regards to patient treatment options. We applied our feature set reduction approach to several publicly available cancer single nucleotide polymorphism (SNP) array datasets and evaluated its performance in terms of its multiclass predictive classification accuracy over different cancer subtypes, its speedup in execution as well as its scalability with respect to sample size and array resolution. Feature Set Reduction (FSR) was able to reduce the dimensions of an SNP array dataset by more than two orders of magnitude while achieving at least equal, and in most cases superior predictive classification performance over that achieved on features selected by existing feature selection methods alone. An examination of the biological relevance of frequently selected features from FSR-reduced feature sets revealed strong enrichment in association with cancer. FSR was implemented in MATLAB R2010b and is available at http://ww2.cs.mu.oz.au/~gwong/FSR.

Selective processing of multiple features in the human brain: effects of feature type and salience.

PubMed

McGinnis, E Menton; Keil, Andreas

2011-02-09

Identifying targets in a stream of items at a given constant spatial location relies on selection of aspects such as color, shape, or texture. Such attended (target) features of a stimulus elicit a negative-going event-related brain potential (ERP), termed Selection Negativity (SN), which has been used as an index of selective feature processing. In two experiments, participants viewed a series of Gabor patches in which targets were defined as a specific combination of color, orientation, and shape. Distracters were composed of different combinations of color, orientation, and shape of the target stimulus. This design allows comparisons of items with and without specific target features. Consistent with previous ERP research, SN deflections extended between 160-300 ms. Data from the subsequent P3 component (300-450 ms post-stimulus) were also examined, and were regarded as an index of target processing. In Experiment A, predominant effects of target color on SN and P3 amplitudes were found, along with smaller ERP differences in response to variations of orientation and shape. Manipulating color to be less salient while enhancing the saliency of the orientation of the Gabor patch (Experiment B) led to delayed color selection and enhanced orientation selection. Topographical analyses suggested that the location of SN on the scalp reliably varies with the nature of the to-be-attended feature. No interference of non-target features on the SN was observed. These results suggest that target feature selection operates by means of electrocortical facilitation of feature-specific sensory processes, and that selective electrocortical facilitation is more effective when stimulus saliency is heightened.

Feature selection for the classification of traced neurons.

PubMed

López-Cabrera, José D; Lorenzo-Ginori, Juan V

2018-06-01

The great availability of computational tools to calculate the properties of traced neurons leads to the existence of many descriptors which allow the automated classification of neurons from these reconstructions. This situation determines the necessity to eliminate irrelevant features as well as making a selection of the most appropriate among them, in order to improve the quality of the classification obtained. The dataset used contains a total of 318 traced neurons, classified by human experts in 192 GABAergic interneurons and 126 pyramidal cells. The features were extracted by means of the L-measure software, which is one of the most used computational tools in neuroinformatics to quantify traced neurons. We review some current feature selection techniques as filter, wrapper, embedded and ensemble methods. The stability of the feature selection methods was measured. For the ensemble methods, several aggregation methods based on different metrics were applied to combine the subsets obtained during the feature selection process. The subsets obtained applying feature selection methods were evaluated using supervised classifiers, among which Random Forest, C4.5, SVM, Naïve Bayes, Knn, Decision Table and the Logistic classifier were used as classification algorithms. Feature selection methods of types filter, embedded, wrappers and ensembles were compared and the subsets returned were tested in classification tasks for different classification algorithms. L-measure features EucDistanceSD, PathDistanceSD, Branch_pathlengthAve, Branch_pathlengthSD and EucDistanceAve were present in more than 60% of the selected subsets which provides evidence about their importance in the classification of this neurons. Copyright © 2018 Elsevier B.V. All rights reserved.

Mosaic Double Aneuploidy of X and G Chromosomes

ERIC Educational Resources Information Center

And Others; Singh, D. N.

1975-01-01

Reported are case histories of three severely retarded adolescents with typical Down's Syndrome features but whose cytogenetic analysis revealed a rare chromosomal anomaly of mosaicism of Down's and Turner's syndromes. (CL)

Color-selective attention need not be mediated by spatial attention.

PubMed

Andersen, Søren K; Müller, Matthias M; Hillyard, Steven A

2009-06-08

It is well-established that attention can select stimuli for preferential processing on the basis of non-spatial features such as color, orientation, or direction of motion. Evidence is mixed, however, as to whether feature-selective attention acts by increasing the signal strength of to-be-attended features irrespective of their spatial locations or whether it acts by guiding the spotlight of spatial attention to locations containing the relevant feature. To address this question, we designed a task in which feature-selective attention could not be mediated by spatial selection. Participants observed a display of intermingled dots of two colors, which rapidly and unpredictably changed positions, with the task of detecting brief intervals of reduced luminance of 20% of the dots of one or the other color. Both behavioral indices and electrophysiological measures of steady-state visual evoked potentials showed selectively enhanced processing of the attended-color items. The results demonstrate that feature-selective attention produces a sensory gain enhancement at early levels of the visual cortex that occurs without mediation by spatial attention.

RESIDENTIAL RADON RESISTANT CONSTRUCTION FEATURE SELECTION SYSTEM

EPA Science Inventory

The report describes a proposed residential radon resistant construction feature selection system. The features consist of engineered barriers to reduce radon entry and accumulation indoors. The proposed Florida standards require radon resistant features in proportion to regional...

Object-based selection from spatially-invariant representations: evidence from a feature-report task.

PubMed

Matsukura, Michi; Vecera, Shaun P

2011-02-01

Attention selects objects as well as locations. When attention selects an object's features, observers identify two features from a single object more accurately than two features from two different objects (object-based effect of attention; e.g., Duncan, Journal of Experimental Psychology: General, 113, 501-517, 1984). Several studies have demonstrated that object-based attention can operate at a late visual processing stage that is independent of objects' spatial information (Awh, Dhaliwal, Christensen, & Matsukura, Psychological Science, 12, 329-334, 2001; Matsukura & Vecera, Psychonomic Bulletin & Review, 16, 529-536, 2009; Vecera, Journal of Experimental Psychology: General, 126, 14-18, 1997; Vecera & Farah, Journal of Experimental Psychology: General, 123, 146-160, 1994). In the present study, we asked two questions regarding this late object-based selection mechanism. In Part I, we investigated how observers' foreknowledge of to-be-reported features allows attention to select objects, as opposed to individual features. Using a feature-report task, a significant object-based effect was observed when to-be-reported features were known in advance but not when this advance knowledge was absent. In Part II, we examined what drives attention to select objects rather than individual features in the absence of observers' foreknowledge of to-be-reported features. Results suggested that, when there was no opportunity for observers to direct their attention to objects that possess to-be-reported features at the time of stimulus presentation, these stimuli must retain strong perceptual cues to establish themselves as separate objects.

Importance of double-pole CFS-PML for broad-band seismic wave simulation and optimal parameters selection

NASA Astrophysics Data System (ADS)

Feng, Haike; Zhang, Wei; Zhang, Jie; Chen, Xiaofei

2017-05-01

The perfectly matched layer (PML) is an efficient absorbing technique for numerical wave simulation. The complex frequency-shifted PML (CFS-PML) introduces two additional parameters in the stretching function to make the absorption frequency dependent. This can help to suppress converted evanescent waves from near grazing incident waves, but does not efficiently absorb low-frequency waves below the cut-off frequency. To absorb both the evanescent wave and the low-frequency wave, the double-pole CFS-PML having two poles in the coordinate stretching function was developed in computational electromagnetism. Several studies have investigated the performance of the double-pole CFS-PML for seismic wave simulations in the case of a narrowband seismic wavelet and did not find significant difference comparing to the CFS-PML. Another difficulty to apply the double-pole CFS-PML for real problems is that a practical strategy to set optimal parameter values has not been established. In this work, we study the performance of the double-pole CFS-PML for broad-band seismic wave simulation. We find that when the maximum to minimum frequency ratio is larger than 16, the CFS-PML will either fail to suppress the converted evanescent waves for grazing incident waves, or produce visible low-frequency reflection, depending on the value of α. In contrast, the double-pole CFS-PML can simultaneously suppress the converted evanescent waves and avoid low-frequency reflections with proper parameter values. We analyse the different roles of the double-pole CFS-PML parameters and propose optimal selections of these parameters. Numerical tests show that the double-pole CFS-PML with the optimal parameters can generate satisfactory results for broad-band seismic wave simulations.

High-resolution spectroscopy of extremely metal-poor stars from SDSS/Segue. II. Binary fraction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aoki, Wako; Suda, Takuma; Beers, Timothy C.

2015-02-01

The fraction of binary systems in various stellar populations of the Galaxy and the distribution of their orbital parameters are important but not well-determined factors in studies of star formation, stellar evolution, and Galactic chemical evolution. While observational studies have been carried out for a large sample of nearby stars, including some metal-poor Population II stars, almost no constraints on the binary nature for extremely metal-poor (EMP; [Fe/H] <−3.0) stars have yet been obtained. Here we investigate the fraction of double-lined spectroscopic binaries and carbon-enhanced metal-poor (CEMP) stars, many of which could have formed as pairs of low-mass and intermediate-massmore » stars, to estimate the lower limit of the fraction of binary systems having short periods. The estimate is based on a sample of very metal-poor stars selected from the Sloan Digital Sky Survey and observed at high spectral resolution in a previous study by Aoki et al. That survey reported 3 double-lined spectroscopic binaries and 11 CEMP stars, which we consider along with a sample of EMP stars from the literature compiled in the SAGA database. We have conducted measurements of the velocity components for stacked absorption features of different spectral lines for each double-lined spectroscopic binary. Our estimate indicates that the fraction of binary stars having orbital periods shorter than 1000 days is at least 10%, and possibly as high as 20% if the majority of CEMP stars are formed in such short-period binaries. This result suggests that the period distribution of EMP binary systems is biased toward short periods, unless the binary fraction of low-mass EMP stars is significantly higher than that of other nearby stars.« less

Single and double multiphoton ionization of Li and Be atoms by strong laser fields

NASA Astrophysics Data System (ADS)

Telnov, Dmitry; Heslar, John; Chu, Shih-I.

2011-05-01

The time-dependent density functional theory with self-interaction correction and proper asymptotic long-range potential is extended for nonperturbative treatment of multiphoton single and double ionization of Li and Be atoms by strong 800 nm laser fields. We make use of the time-dependent Krieger-Li-Iafrate (TDKLI) exchange-correlation potential with the integer discontinuity which improves the description of the double ionization process. However, we have found that the discontinuity of the TDKLI potential is not sufficient to reproduce the characteristic feature of double ionization. This may happen because the discontinuity of the TDKLI potential is related to the spin particle numbers only and not to the total particle number. Introducing a discontinuity with respect to the total particle number to the exchange-correlation potential, we were able to obtain the knee structure in the intensity dependence of the double ionization probability of Be. This work was partially supported by DOE and NSF and by NSC-Taiwan.

Constraint programming based biomarker optimization.

PubMed

Zhou, Manli; Luo, Youxi; Sun, Guoquan; Mai, Guoqin; Zhou, Fengfeng

2015-01-01

Efficient and intuitive characterization of biological big data is becoming a major challenge for modern bio-OMIC based scientists. Interactive visualization and exploration of big data is proven to be one of the successful solutions. Most of the existing feature selection algorithms do not allow the interactive inputs from users in the optimizing process of feature selection. This study investigates this question as fixing a few user-input features in the finally selected feature subset and formulates these user-input features as constraints for a programming model. The proposed algorithm, fsCoP (feature selection based on constrained programming), performs well similar to or much better than the existing feature selection algorithms, even with the constraints from both literature and the existing algorithms. An fsCoP biomarker may be intriguing for further wet lab validation, since it satisfies both the classification optimization function and the biomedical knowledge. fsCoP may also be used for the interactive exploration of bio-OMIC big data by interactively adding user-defined constraints for modeling.

Predication of different stages of Alzheimer's disease using neighborhood component analysis and ensemble decision tree.

PubMed

Jin, Mingwu; Deng, Weishu

2018-05-15

There is a spectrum of the progression from healthy control (HC) to mild cognitive impairment (MCI) without conversion to Alzheimer's disease (AD), to MCI with conversion to AD (cMCI), and to AD. This study aims to predict the different disease stages using brain structural information provided by magnetic resonance imaging (MRI) data. The neighborhood component analysis (NCA) is applied to select most powerful features for prediction. The ensemble decision tree classifier is built to predict which group the subject belongs to. The best features and model parameters are determined by cross validation of the training data. Our results show that 16 out of a total of 429 features were selected by NCA using 240 training subjects, including MMSE score and structural measures in memory-related regions. The boosting tree model with NCA features can achieve prediction accuracy of 56.25% on 160 test subjects. Principal component analysis (PCA) and sequential feature selection (SFS) are used for feature selection, while support vector machine (SVM) is used for classification. The boosting tree model with NCA features outperforms all other combinations of feature selection and classification methods. The results suggest that NCA be a better feature selection strategy than PCA and SFS for the data used in this study. Ensemble tree classifier with boosting is more powerful than SVM to predict the subject group. However, more advanced feature selection and classification methods or additional measures besides structural MRI may be needed to improve the prediction performance. Copyright © 2018 Elsevier B.V. All rights reserved.

Method of Fabricating Double Sided Si(Ge)/Sapphire/III-Nitride Hybrid Structure

NASA Technical Reports Server (NTRS)

Choi, Sang Hyouk (Inventor); Park, Yeonjoon (Inventor)

2017-01-01

One aspect of the present invention is a double sided hybrid crystal structure including a trigonal Sapphire wafer containing a (0001) C-plane and having front and rear sides. The Sapphire wafer is substantially transparent to light in the visible and infrared spectra, and also provides insulation with respect to electromagnetic radio frequency noise. A layer of crystalline Si material having a cubic diamond structure aligned with the cubic <111> direction on the (0001) C-plane and strained as rhombohedron to thereby enable continuous integration of a selected (SiGe) device onto the rear side of the Sapphire wafer. The double sided hybrid crystal structure further includes an integrated III-Nitride crystalline layer on the front side of the Sapphire wafer that enables continuous integration of a selected III-Nitride device on the front side of the Sapphire wafer.

Double Sided Si(Ge)/Sapphire/III-Nitride Hybrid Structure

NASA Technical Reports Server (NTRS)

Park, Yeonjoon (Inventor); Choi, Sang Hyouk (Inventor)

2016-01-01

One aspect of the present invention is a double sided hybrid crystal structure including a trigonal Sapphire wafer containing a (0001) C-plane and having front and rear sides. The Sapphire wafer is substantially transparent to light in the visible and infrared spectra, and also provides insulation with respect to electromagnetic radio frequency noise. A layer of crystalline Si material having a cubic diamond structure aligned with the cubic <111> direction on the (0001) C-plane and strained as rhombohedron to thereby enable continuous integration of a selected (SiGe) device onto the rear side of the Sapphire wafer. The double sided hybrid crystal structure further includes an integrated III-Nitride crystalline layer on the front side of the Sapphire wafer that enables continuous integration of a selected III-Nitride device on the front side of the Sapphire wafer.

Interlayer tunneling in double-layer quantum hall pseudoferromagnets.

PubMed

Balents, L; Radzihovsky, L

2001-02-26

We show that the interlayer tunneling I-V in double-layer quantum Hall states displays a rich behavior which depends on the relative magnitude of sample size, voltage length scale, current screening, disorder, and thermal lengths. For weak tunneling, we predict a negative differential conductance of a power-law shape crossing over to a sharp zero-bias peak. An in-plane magnetic field splits this zero-bias peak, leading instead to a "derivative" feature at V(B)(B(parallel)) = 2 pi Planck's over 2 pi upsilon B(parallel)d/e phi(0), which gives a direct measurement of the dispersion of the Goldstone mode corresponding to the spontaneous symmetry breaking of the double-layer Hall state.

[Feature extraction for breast cancer data based on geometric algebra theory and feature selection using differential evolution].

PubMed

Li, Jing; Hong, Wenxue

2014-12-01

The feature extraction and feature selection are the important issues in pattern recognition. Based on the geometric algebra representation of vector, a new feature extraction method using blade coefficient of geometric algebra was proposed in this study. At the same time, an improved differential evolution (DE) feature selection method was proposed to solve the elevated high dimension issue. The simple linear discriminant analysis was used as the classifier. The result of the 10-fold cross-validation (10 CV) classification of public breast cancer biomedical dataset was more than 96% and proved superior to that of the original features and traditional feature extraction method.

Application of machine learning on brain cancer multiclass classification

NASA Astrophysics Data System (ADS)

Panca, V.; Rustam, Z.

2017-07-01

Classification of brain cancer is a problem of multiclass classification. One approach to solve this problem is by first transforming it into several binary problems. The microarray gene expression dataset has the two main characteristics of medical data: extremely many features (genes) and only a few number of samples. The application of machine learning on microarray gene expression dataset mainly consists of two steps: feature selection and classification. In this paper, the features are selected using a method based on support vector machine recursive feature elimination (SVM-RFE) principle which is improved to solve multiclass classification, called multiple multiclass SVM-RFE. Instead of using only the selected features on a single classifier, this method combines the result of multiple classifiers. The features are divided into subsets and SVM-RFE is used on each subset. Then, the selected features on each subset are put on separate classifiers. This method enhances the feature selection ability of each single SVM-RFE. Twin support vector machine (TWSVM) is used as the method of the classifier to reduce computational complexity. While ordinary SVM finds single optimum hyperplane, the main objective Twin SVM is to find two non-parallel optimum hyperplanes. The experiment on the brain cancer microarray gene expression dataset shows this method could classify 71,4% of the overall test data correctly, using 100 and 1000 genes selected from multiple multiclass SVM-RFE feature selection method. Furthermore, the per class results show that this method could classify data of normal and MD class with 100% accuracy.

Local Feature Selection for Data Classification.

PubMed

Armanfard, Narges; Reilly, James P; Komeili, Majid

2016-06-01

Typical feature selection methods choose an optimal global feature subset that is applied over all regions of the sample space. In contrast, in this paper we propose a novel localized feature selection (LFS) approach whereby each region of the sample space is associated with its own distinct optimized feature set, which may vary both in membership and size across the sample space. This allows the feature set to optimally adapt to local variations in the sample space. An associated method for measuring the similarities of a query datum to each of the respective classes is also proposed. The proposed method makes no assumptions about the underlying structure of the samples; hence the method is insensitive to the distribution of the data over the sample space. The method is efficiently formulated as a linear programming optimization problem. Furthermore, we demonstrate the method is robust against the over-fitting problem. Experimental results on eleven synthetic and real-world data sets demonstrate the viability of the formulation and the effectiveness of the proposed algorithm. In addition we show several examples where localized feature selection produces better results than a global feature selection method.

Decision Variants for the Automatic Determination of Optimal Feature Subset in RF-RFE.

PubMed

Chen, Qi; Meng, Zhaopeng; Liu, Xinyi; Jin, Qianguo; Su, Ran

2018-06-15

Feature selection, which identifies a set of most informative features from the original feature space, has been widely used to simplify the predictor. Recursive feature elimination (RFE), as one of the most popular feature selection approaches, is effective in data dimension reduction and efficiency increase. A ranking of features, as well as candidate subsets with the corresponding accuracy, is produced through RFE. The subset with highest accuracy (HA) or a preset number of features (PreNum) are often used as the final subset. However, this may lead to a large number of features being selected, or if there is no prior knowledge about this preset number, it is often ambiguous and subjective regarding final subset selection. A proper decision variant is in high demand to automatically determine the optimal subset. In this study, we conduct pioneering work to explore the decision variant after obtaining a list of candidate subsets from RFE. We provide a detailed analysis and comparison of several decision variants to automatically select the optimal feature subset. Random forest (RF)-recursive feature elimination (RF-RFE) algorithm and a voting strategy are introduced. We validated the variants on two totally different molecular biology datasets, one for a toxicogenomic study and the other one for protein sequence analysis. The study provides an automated way to determine the optimal feature subset when using RF-RFE.

Nucleic acid nanomaterials: Silver-wired DNA

NASA Astrophysics Data System (ADS)

Auffinger, Pascal; Ennifar, Eric

2017-10-01

DNA double helical structures are supramolecular assemblies that are typically held together by classical Watson-Crick pairing. Now, nucleotide chelation of silver ions supports an extended silver-DNA hybrid duplex featuring an uninterrupted silver array.

Selection in action: dissecting the molecular underpinnings of the increasing muscle mass of Belgian Blue Cattle.

PubMed

Druet, Tom; Ahariz, Naima; Cambisano, Nadine; Tamma, Nico; Michaux, Charles; Coppieters, Wouter; Charlier, Carole; Georges, Michel

2014-09-17

Belgian Blue cattle are famous for their exceptional muscular development or "double-muscling". This defining feature emerged following the fixation of a loss-of-function variant in the myostatin gene in the eighties. Since then, sustained selection has further increased muscle mass of Belgian Blue animals to a comparable extent. In the present paper, we study the genetic determinants of this second wave of muscle growth. A scan for selective sweeps did not reveal the recent fixation of another allele with major effect on muscularity. However, a genome-wide association study identified two genome-wide significant and three suggestive quantitative trait loci (QTL) affecting specific muscle groups and jointly explaining 8-21% of the heritability. The top two QTL are caused by presumably recent mutations on unique haplotypes that have rapidly risen in frequency in the population. While one appears on its way to fixation, the ascent of the other is compromised as the likely underlying MRC2 mutation causes crooked tail syndrome in homozygotes. Genomic prediction models indicate that the residual additive variance is largely polygenic. Contrary to complex traits in humans which have a near-exclusive polygenic architecture, muscle mass in beef cattle (as other production traits under directional selection), appears to be controlled by (i) a handful of recent mutations with large effect that rapidly sweep through the population, and (ii) a large number of presumably older variants with very small effects that rise slowly in the population (polygenic adaptation).

Intertwined electron-nuclear motion in frustrated double ionization in driven heteronuclear molecules

NASA Astrophysics Data System (ADS)

Vilà, A.; Zhu, J.; Scrinzi, A.; Emmanouilidou, A.

2018-03-01

We study frustrated double ionization (FDI) in a strongly-driven heteronuclear molecule HeH+ and compare with H2. We compute the probability distribution of the sum of the final kinetic energies of the nuclei for strongly-driven HeH+. We find that this distribution has more than one peak for strongly-driven HeH+, a feature we do not find to be present for strongly-driven H2. Moreover, we compute the probability distribution of the principal quantum number n of FDI. We find that this distribution has several peaks for strongly-driven HeH+, while the respective distribution has one main peak and a ‘shoulder’ at lower principal quantum numbers n for strongly-driven H2. Surprisingly, we find this feature to be a clear signature of the intertwined electron-nuclear motion.

Three-Dimensional Finite Element Analysis on Stress Distribution of Internal Implant-Abutment Engagement Features.

PubMed

Cho, Sung-Yong; Huh, Yun-Hyuk; Park, Chan-Jin; Cho, Lee-Ra

To investigate the stress distribution in an implant-abutment complex with a preloaded abutment screw by comparing implant-abutment engagement features using three-dimensional finite element analysis (FEA). For FEA modeling, two implants-one with a single (S) engagement system and the other with a double (D) engagement system-were placed in the human mandibular molar region. Two types of abutments (hexagonal, conical) were connected to the implants. Different implant models (a single implant, two parallel implants, and mesial and tilted distal implants with 1-mm bone loss) were assumed. A static axial force and a 45-degree oblique force of 200 N were applied as the sum of vectors to the top of the prosthetic occlusal surface with a preload of 30 Ncm in the abutment screw. The von Mises stresses at the implant-abutment and abutment-screw interfaces were measured. In the single implant model, the S-conical abutment type exhibited broader stress distribution than the S-hexagonal abutment. In the double engagement system, the stress concentration was high in the lower contact area of the implant-abutment engagement. In the tilted implant model, the stress concentration point was different from that in the parallel implant model because of the difference in the bone level. The double engagement system demonstrated a high stress concentration at the lower contact area of the implant-abutment interface. To decrease the stress concentration, the type of engagement features of the implant-abutment connection should be carefully considered.

Enabling Disabled Persons to Gain Access to Digital Media

NASA Technical Reports Server (NTRS)

Beach, Glenn; OGrady, Ryan

2011-01-01

A report describes the first phase in an effort to enhance the NaviGaze software to enable profoundly disabled persons to operate computers. (Running on a Windows-based computer equipped with a video camera aimed at the user s head, the original NaviGaze software processes the user's head movements and eye blinks into cursor movements and mouse clicks to enable hands-free control of the computer.) To accommodate large variations in movement capabilities among disabled individuals, one of the enhancements was the addition of a graphical user interface for selection of parameters that affect the way the software interacts with the computer and tracks the user s movements. Tracking algorithms were improved to reduce sensitivity to rotations and reduce the likelihood of tracking the wrong features. Visual feedback to the user was improved to provide an indication of the state of the computer system. It was found that users can quickly learn to use the enhanced software, performing single clicks, double clicks, and drags within minutes of first use. Available programs that could increase the usability of NaviGaze were identified. One of these enables entry of text by using NaviGaze as a mouse to select keys on a virtual keyboard.

Isolation of site-specific anharmonicities of individual water molecules in the I-·(H2O)2 complex using tag-free, isotopomer selective IR-IR double resonance

NASA Astrophysics Data System (ADS)

Yang, Nan; Duong, Chinh H.; Kelleher, Patrick J.; Johnson, Mark A.; McCoy, Anne B.

2017-12-01

We reveal the microscopic mechanics of iodide ion microhydration by recording the isotopomer-selective vibrational spectra of the I-·(H2O)·(D2O), I-·(HOD)·(D2O), and I-·(DOH)·(H2O) isotopologues using a new class of ion spectrometer that is optimized to carry out two-color, IR-IR photodissociation in a variety of pump-probe schemes. Using one of these, we record the linear absorption spectrum of a cryogenically cooled cluster without the use of a messenger ;tag;. In another protocol, we reveal the spectra of individual H2O and D2O molecules embedded in each of the two possible binding sites in the iodide dihydrate, as well as the bands due to individual OH and OD groups in each of the four local binding environments. Finally, we demonstrate how temperature dependent isotopic scrambling among the spectral features can be used to monitor the onset of large amplitude motion, heretofore inferred from changes in the envelope of the OH stretching vibrational manifold.

Discovery of melanocortin ligands via a double simultaneous substitution strategy based on the Ac-His-DPhe-Arg-Trp-NH2 template.

PubMed

Todorovic, Aleksandar; Lensing, Cody J; Holder, Jerry Ryan; Scott, Joseph W; Sorensen, Nicholas B; Haskell-Luevano, Carrie

2018-05-21

The melanocortin system regulates an array of diverse physiological functions including pigmentation, feeding behavior, energy homeostasis, cardiovascular regulation, sexual function, and steroidogenesis. Endogenous melanocortin agonist ligands all possess the minimal messaging tetrapeptide sequence His-Phe-Arg-Trp. Based on this endogenous sequence, the Ac-His1-DPhe2-Arg3-Trp4-NH 2 tetrapeptide has previously been shown to be a useful scaffold when utilizing traditional positional scanning approaches to modify activity at the various melanocortin receptors (MC1-5R). The study reported herein was undertaken to evaluate a double simultaneous substitution strategy as an approach to further diversify the Ac-His1-DPhe2-Arg3-Trp4-NH 2 tetrapeptide with concurrent introduction of natural and unnatural amino acids at positions 1, 2, or 4 as well as an octanoyl residue at the N-terminus. The designed library includes the following combinations: (A) double simultaneous substitution at capping group position (Ac) together with position 1, 2, or 4, (B) double simultaneous substitution at position 1 and 2, (C) double simultaneous substitution at position 1 and 4, and (D) double simultaneous substitution at position 2 and 4. Several lead ligands with unique pharmacologies were discovered in the current study including antagonists targeting the neuronal mMC3R with minimal agonist activity and ligands with selective profiles for the various melanocortin subtypes. The results suggest that the double simultaneous substitution strategy is a suitable approach in altering melanocortin receptor potency, selectivity, or converting agonists into antagonists and vice versa.

Spatial redistribution of radiation in flip-chip photodiodes based on InAsSbP/InAs double heterostructures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zakgeim, A. L.; Il’inskaya, N. D.; Karandashev, S. A.

2017-02-15

The spatial distribution of equilibrium and nonequilibrium (including luminescent) IR (infrared) radiation in flip-chip photodiodes based on InAsSbP/InAs double heterostructures (λ{sub max} = 3.4 μm) is measured and analyzed; the structural features of the photodiodes, including the reflective properties of the ohmic contacts, are taken into account. Optical area enhancement due to multiple internal reflection in photodiodes with different geometric characteristics is estimated.

Influence of time and length size feature selections for human activity sequences recognition.

PubMed

Fang, Hongqing; Chen, Long; Srinivasan, Raghavendiran

2014-01-01

In this paper, Viterbi algorithm based on a hidden Markov model is applied to recognize activity sequences from observed sensors events. Alternative features selections of time feature values of sensors events and activity length size feature values are tested, respectively, and then the results of activity sequences recognition performances of Viterbi algorithm are evaluated. The results show that the selection of larger time feature values of sensor events and/or smaller activity length size feature values will generate relatively better results on the activity sequences recognition performances. © 2013 ISA Published by ISA All rights reserved.

Adaptive runtime for a multiprocessing API

DOEpatents

Antao, Samuel F.; Bertolli, Carlo; Eichenberger, Alexandre E.; O'Brien, John K.

2016-11-15

A computer-implemented method includes selecting a runtime for executing a program. The runtime includes a first combination of feature implementations, where each feature implementation implements a feature of an application programming interface (API). Execution of the program is monitored, and the execution uses the runtime. Monitor data is generated based on the monitoring. A second combination of feature implementations are selected, by a computer processor, where the selection is based at least in part on the monitor data. The runtime is modified by activating the second combination of feature implementations to replace the first combination of feature implementations.

Adaptive runtime for a multiprocessing API

DOEpatents

Antao, Samuel F.; Bertolli, Carlo; Eichenberger, Alexandre E.; O'Brien, John K.

2016-10-11

A computer-implemented method includes selecting a runtime for executing a program. The runtime includes a first combination of feature implementations, where each feature implementation implements a feature of an application programming interface (API). Execution of the program is monitored, and the execution uses the runtime. Monitor data is generated based on the monitoring. A second combination of feature implementations are selected, by a computer processor, where the selection is based at least in part on the monitor data. The runtime is modified by activating the second combination of feature implementations to replace the first combination of feature implementations.

Informative Feature Selection for Object Recognition via Sparse PCA

DTIC Science & Technology

2011-04-07

constraint on images collected from low-power camera net- works instead of high-end photography is that establishing wide-baseline feature correspondence of...variable selection tool for selecting informative features in the object images captured from low-resolution cam- era sensor networks. Firstly, we...More examples can be found in Figure 4 later. 3. Identifying Informative Features Classical PCA is a well established tool for the analysis of high

Grading A-Level Double Subject Mathematicians and the Implications for Selection.

ERIC Educational Resources Information Center

Newbould, Charles A.

1981-01-01

Test data were used to compare the grading of two forms of double mathematics: pure and applied math, and regular and advanced math. Results confirm expectations that in the former system, the grading is comparable, and in the latter, it is not necessarily comparable. Implications for student admission are discussed. (MSE)

STT Doubles with Large Delta_M - Part VIII: Tau Per Ori Cam Mon Cnc Peg

NASA Astrophysics Data System (ADS)

Knapp, Wilfried; Nanson, John

2017-04-01

The results of visual double star observing sessions suggested a pattern for STT doubles with large delta_M of being harder to resolve than would be expected based on the WDS catalog data. It was felt this might be a problem with expectations on one hand, and on the other might be an indication of a need for new precise measurements, so we decided to take a closer look at a selected sample of STT doubles and do some research. Again like for the other STT objects covered so far several of the components show parameters quite different from the current WDS data.

Emotional textile image classification based on cross-domain convolutional sparse autoencoders with feature selection

NASA Astrophysics Data System (ADS)

Li, Zuhe; Fan, Yangyu; Liu, Weihua; Yu, Zeqi; Wang, Fengqin

2017-01-01

We aim to apply sparse autoencoder-based unsupervised feature learning to emotional semantic analysis for textile images. To tackle the problem of limited training data, we present a cross-domain feature learning scheme for emotional textile image classification using convolutional autoencoders. We further propose a correlation-analysis-based feature selection method for the weights learned by sparse autoencoders to reduce the number of features extracted from large size images. First, we randomly collect image patches on an unlabeled image dataset in the source domain and learn local features with a sparse autoencoder. We then conduct feature selection according to the correlation between different weight vectors corresponding to the autoencoder's hidden units. We finally adopt a convolutional neural network including a pooling layer to obtain global feature activations of textile images in the target domain and send these global feature vectors into logistic regression models for emotional image classification. The cross-domain unsupervised feature learning method achieves 65% to 78% average accuracy in the cross-validation experiments corresponding to eight emotional categories and performs better than conventional methods. Feature selection can reduce the computational cost of global feature extraction by about 50% while improving classification performance.

Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers

NASA Astrophysics Data System (ADS)

Weinmann, Martin; Jutzi, Boris; Hinz, Stefan; Mallet, Clément

2015-07-01

3D scene analysis in terms of automatically assigning 3D points a respective semantic label has become a topic of great importance in photogrammetry, remote sensing, computer vision and robotics. In this paper, we address the issue of how to increase the distinctiveness of geometric features and select the most relevant ones among these for 3D scene analysis. We present a new, fully automated and versatile framework composed of four components: (i) neighborhood selection, (ii) feature extraction, (iii) feature selection and (iv) classification. For each component, we consider a variety of approaches which allow applicability in terms of simplicity, efficiency and reproducibility, so that end-users can easily apply the different components and do not require expert knowledge in the respective domains. In a detailed evaluation involving 7 neighborhood definitions, 21 geometric features, 7 approaches for feature selection, 10 classifiers and 2 benchmark datasets, we demonstrate that the selection of optimal neighborhoods for individual 3D points significantly improves the results of 3D scene analysis. Additionally, we show that the selection of adequate feature subsets may even further increase the quality of the derived results while significantly reducing both processing time and memory consumption.

Non-steroidal anti-inflammatory drug-induced small bowel injuries identified by double-balloon endoscopy

PubMed Central

Hayashi, Yoshikazu; Yamamoto, Hironori; Kita, Hiroto; Sunada, Keijiro; Sato, Hiroyuki; Yano, Tomonori; Iwamoto, Michiko; Sekine, Yutaka; Miyata, Tomohiko; Kuno, Akiko; Iwaki, Takaaki; Kawamura, Yoshiyuki; Ajibe, Hironari; Ido, Kenichi; Sugano, Kentaro

2005-01-01

AIM: To clarify clinical features of the NSAID-induced small bowel lesions using a new method of endoscopy. METHODS: This is a retrospective study and we analyzed seven patients with small bowel lesions while taking NSAIDs among 61 patients who had undergone double-balloon endoscopy because of gastro-intestinal bleeding or anemia between September 2000 and March 2004, at Jichi Medical School Hospital in Japan. Neither conventional EGD nor colonoscopy revealed any lesions of potential bleeding sources including ulcerations. Double-balloon endoscopy was carried out from oral approach in three patients, from anal approach in three patients, and from both approaches in one patient. RESULTS: Ulcers or erosions were observed in the ileum in six patients and in the jejunum in one patient, respectively. The ulcers were multiple in all the patients with different features from tiny punched out ulcers to deep ulcerations with oozing hemorrhage or scar. All the patients recovered uneventfully and had full resolution of symptoms after suspension of the drug. CONCLUSION: NSAIDs can induce injuries in the small bowel even in patients without any lesions in both the stomach and colon. PMID:16097059

Efficient feature selection using a hybrid algorithm for the task of epileptic seizure detection

NASA Astrophysics Data System (ADS)

Lai, Kee Huong; Zainuddin, Zarita; Ong, Pauline

2014-07-01

Feature selection is a very important aspect in the field of machine learning. It entails the search of an optimal subset from a very large data set with high dimensional feature space. Apart from eliminating redundant features and reducing computational cost, a good selection of feature also leads to higher prediction and classification accuracy. In this paper, an efficient feature selection technique is introduced in the task of epileptic seizure detection. The raw data are electroencephalography (EEG) signals. Using discrete wavelet transform, the biomedical signals were decomposed into several sets of wavelet coefficients. To reduce the dimension of these wavelet coefficients, a feature selection method that combines the strength of both filter and wrapper methods is proposed. Principal component analysis (PCA) is used as part of the filter method. As for wrapper method, the evolutionary harmony search (HS) algorithm is employed. This metaheuristic method aims at finding the best discriminating set of features from the original data. The obtained features were then used as input for an automated classifier, namely wavelet neural networks (WNNs). The WNNs model was trained to perform a binary classification task, that is, to determine whether a given EEG signal was normal or epileptic. For comparison purposes, different sets of features were also used as input. Simulation results showed that the WNNs that used the features chosen by the hybrid algorithm achieved the highest overall classification accuracy.

A study of metaheuristic algorithms for high dimensional feature selection on microarray data

NASA Astrophysics Data System (ADS)

Dankolo, Muhammad Nasiru; Radzi, Nor Haizan Mohamed; Sallehuddin, Roselina; Mustaffa, Noorfa Haszlinna

2017-11-01

Microarray systems enable experts to examine gene profile at molecular level using machine learning algorithms. It increases the potentials of classification and diagnosis of many diseases at gene expression level. Though, numerous difficulties may affect the efficiency of machine learning algorithms which includes vast number of genes features comprised in the original data. Many of these features may be unrelated to the intended analysis. Therefore, feature selection is necessary to be performed in the data pre-processing. Many feature selection algorithms are developed and applied on microarray which including the metaheuristic optimization algorithms. This paper discusses the application of the metaheuristics algorithms for feature selection in microarray dataset. This study reveals that, the algorithms have yield an interesting result with limited resources thereby saving computational expenses of machine learning algorithms.

Functional nucleic acid-based hydrogels for bioanalytical and biomedical applications

PubMed Central

Mo, Liuting; Lu, Chun-Hua; Fu, Ting

2016-01-01

Hydrogels are crosslinked hydrophilic polymers that can absorb a large amount of water. By their hydrophilic, biocompatible and highly tunable nature, hydrogels can be tailored for applications in bioanalysis and biomedicine. Of particular interest are DNA-based hydrogels owing to the unique features of nucleic acids. Since the discovery of DNA double helical structure, interest in DNA has expanded beyond its genetic role to applications in nanotechnology and materials science. In particular, DNA-based hydrogels present such remarkable features as stability, flexibility, precise programmability, stimuli-responsive DNA conformations, facile synthesis and modification. Moreover, functional nucleic acids (FNAs) have allowed the construction of hydrogels based on aptamers, DNAzymes, i-motif nanostructures, siRNAs and CpG oligodeoxynucleotides to provide additional molecular recognition, catalytic activities and therapeutic potential, making them key players in biological analysis and biomedical applications. To date, a variety of applications have been demonstrated with FNA-based hydrogels, including biosensing, environmental analysis, controlled drug release, cell adhesion and targeted cancer therapy. In this review, we focus on advances in the development of FNA-based hydrogels, which have fully incorporated both the unique features of FNAs and DNA-based hydrogels. We first introduce different strategies for constructing DNA-based hydrogels. Subsequently, various types of FNAs and the most recent developments of FNA-based hydrogels for bioanalytical and biomedical applications are described with some selected examples. Finally, the review provides an insight into the remaining challenges and future perspectives of FNA-based hydrogels. PMID:26758955

Improved sparse decomposition based on a smoothed L0 norm using a Laplacian kernel to select features from fMRI data.

PubMed

Zhang, Chuncheng; Song, Sutao; Wen, Xiaotong; Yao, Li; Long, Zhiying

2015-04-30

Feature selection plays an important role in improving the classification accuracy of multivariate classification techniques in the context of fMRI-based decoding due to the "few samples and large features" nature of functional magnetic resonance imaging (fMRI) data. Recently, several sparse representation methods have been applied to the voxel selection of fMRI data. Despite the low computational efficiency of the sparse representation methods, they still displayed promise for applications that select features from fMRI data. In this study, we proposed the Laplacian smoothed L0 norm (LSL0) approach for feature selection of fMRI data. Based on the fast sparse decomposition using smoothed L0 norm (SL0) (Mohimani, 2007), the LSL0 method used the Laplacian function to approximate the L0 norm of sources. Results of the simulated and real fMRI data demonstrated the feasibility and robustness of LSL0 for the sparse source estimation and feature selection. Simulated results indicated that LSL0 produced more accurate source estimation than SL0 at high noise levels. The classification accuracy using voxels that were selected by LSL0 was higher than that by SL0 in both simulated and real fMRI experiment. Moreover, both LSL0 and SL0 showed higher classification accuracy and required less time than ICA and t-test for the fMRI decoding. LSL0 outperformed SL0 in sparse source estimation at high noise level and in feature selection. Moreover, LSL0 and SL0 showed better performance than ICA and t-test for feature selection. Copyright © 2015 Elsevier B.V. All rights reserved.

Neurological soft signs feature a double dissociation within the language system in Williams syndrome.

PubMed

Tavano, Alessandro; Gagliardi, Chiara; Martelli, Sara; Borgatti, Renato

2010-09-01

The neurocognitive profile of Williams-Beuren syndrome (WBS) is characterized by visuospatial deficits, apparently fluent language, motor soft signs, and hypersociability. We investigated the association between neuromotor soft signs and visuospatial, executive-attentive, mnestic and linguistic functions in a group of 26 children and young adults with WBS. We hypothesized that neurological soft signs could be an index of subtle neurofunctional deficits and thus provide a behavioural window into the processes underlying neurocognition in Williams-Beuren syndrome. Dysmetria and dystonic movements were selected as grouping neurological variables, indexing cerebellar and basal ganglia dysfunction, respectively. No detrimental effects on visuospatial/visuoconstructive skills were evident following the presence of either neurological variable. As for language skills, participants with dysmetria showed markedly reduced expressive syntactic and lexico-semantic skills as compared to non-affected individuals, while no difference in chronological age was evident. Participants with dystonic movements showed reduced receptive syntax and increased lexical comprehension skills as compared to non-affected individuals, the age factor being significant. In both instances, the effect size was greater for syntactic measures. We take these novel findings as suggestive of a double dissociation between expressive and receptive skills at sentence level within the WBS linguistic phenotype. The investigation of neuromotor soft signs and neuropsychological functions may provide a key to new non-cortico-centric genotype/phenotype relationships. Copyright 2010 Elsevier Ltd. All rights reserved.

The double-stranded RNA binding protein RDE-4 can act cell autonomously during feeding RNAi in C. elegans

PubMed Central

Raman, Pravrutha; Zaghab, Soriayah M.; Traver, Edward C.

2017-01-01

Abstract Long double-stranded RNA (dsRNA) can silence genes of matching sequence upon ingestion in many invertebrates and is therefore being developed as a pesticide. Such feeding RNA interference (RNAi) is best understood in the worm Caenorhabditis elegans, where the dsRNA-binding protein RDE-4 initiates silencing by recruiting an endonuclease to process long dsRNA into short dsRNA. These short dsRNAs are thought to move between cells because muscle-specific rescue of rde-4 using repetitive transgenes enables silencing in other tissues. Here, we extend this observation using additional promoters, report an inhibitory effect of repetitive transgenes, and discover conditions for cell-autonomous silencing in animals with tissue-specific rescue of rde-4. While expression of rde-4(+) in intestine, hypodermis, or neurons using a repetitive transgene can enable silencing also in unrescued tissues, silencing can be inhibited wihin tissues that express a repetitive transgene. Single-copy transgenes that express rde-4(+) in body-wall muscles or hypodermis, however, enable silencing selectively in the rescued tissue but not in other tissues. These results suggest that silencing by the movement of short dsRNA between cells is not an obligatory feature of feeding RNAi in C. elegans. We speculate that similar control of dsRNA movement could modulate tissue-specific silencing by feeding RNAi in other invertebrates. PMID:28541563

Characterization of calmodulin binding domains in TRPV2 and TRPV5 C-tails.

PubMed

Holakovska, Blanka; Grycova, Lenka; Bily, Jan; Teisinger, Jan

2011-02-01

The transient receptor potential channels TRPV2 and TRPV5 belong to the vanilloid TRP subfamily. TRPV2 is highly similar to TRPV1 and shares many common properties with it. TRPV5 (and also its homolog TRPV6) is a rather distinct member of the TRPV subfamily. It is distant for being strictly Ca(2+)-selective and features quite different properties from the rest of the TRPV subfamily. It is known that TRP channels are regulated by calmodulin in a calcium-dependent manner. In our study we identified a calmodulin binding site on the C-termini of TRPV2 (654-683) and TRPV5 (587-616) corresponding to the consensus CaM binding motif 1-5-10. The R679 and K681 single mutants of TRPV2 caused a 50% decrease in binding affinity and a double mutation of K661/K664 of the same peptide lowered the binding affinity by up to 75%. A double mutation of R606/K607 and triple mutation of R594/R606/R610 in TRPV5 C-terminal peptide resulted in the total loss of binding affinity to calmodulin. These results demonstrate that the TRPV2 C-tail and TRPV5 C-tail contain calmodulin binding sites and that the basic residues are strongly involved in TRP channel binding to calmodulin.

Double Dissociation of Conditioning and Declarative Knowledge Relative to the Amygdala and Hippocampus in Humans

NASA Astrophysics Data System (ADS)

Bechara, Antoine; Tranel, Daniel; Damasio, Hanna; Adolphs, Ralph; Rockland, Charles; Damasio, Antonio R.

1995-08-01

A patient with selective bilateral damage to the amygdala did not acquire conditioned autonomic responses to visual or auditory stimuli but did acquire the declarative facts about which visual or auditory stimuli were paired with the unconditioned stimulus. By contrast, a patient with selective bilateral damage to the hippocampus failed to acquire the facts but did acquire the conditioning. Finally, a patient with bilateral damage to both amygdala and hippocampal formation acquired neither the conditioning nor the facts. These findings demonstrate a double dissociation of conditioning and declarative knowledge relative to the human amygdala and hippocampus.

Features of plastic strain localization at the yield plateau in Hadfield steel single crystals

NASA Astrophysics Data System (ADS)

Barannikova, S. A.; Zuev, L. B.

2008-07-01

Spatiotemporal distributions of local components of the plastic distortion tensor in Hadfield steel single crystals oriented for single twinning have been studied under active tensile straining conditions using the double-exposure speckle photography technique. Features of the macroscopically inhomogeneous strain localization at the yield plateau are considered. Relations between local components of the plastic distortion tensor in the zone of strain localization are analyzed.

Feature-Selective Attention Adaptively Shifts Noise Correlations in Primary Auditory Cortex.

PubMed

Downer, Joshua D; Rapone, Brittany; Verhein, Jessica; O'Connor, Kevin N; Sutter, Mitchell L

2017-05-24

Sensory environments often contain an overwhelming amount of information, with both relevant and irrelevant information competing for neural resources. Feature attention mediates this competition by selecting the sensory features needed to form a coherent percept. How attention affects the activity of populations of neurons to support this process is poorly understood because population coding is typically studied through simulations in which one sensory feature is encoded without competition. Therefore, to study the effects of feature attention on population-based neural coding, investigations must be extended to include stimuli with both relevant and irrelevant features. We measured noise correlations ( r noise ) within small neural populations in primary auditory cortex while rhesus macaques performed a novel feature-selective attention task. We found that the effect of feature-selective attention on r noise depended not only on the population tuning to the attended feature, but also on the tuning to the distractor feature. To attempt to explain how these observed effects might support enhanced perceptual performance, we propose an extension of a simple and influential model in which shifts in r noise can simultaneously enhance the representation of the attended feature while suppressing the distractor. These findings present a novel mechanism by which attention modulates neural populations to support sensory processing in cluttered environments. SIGNIFICANCE STATEMENT Although feature-selective attention constitutes one of the building blocks of listening in natural environments, its neural bases remain obscure. To address this, we developed a novel auditory feature-selective attention task and measured noise correlations ( r noise ) in rhesus macaque A1 during task performance. Unlike previous studies showing that the effect of attention on r noise depends on population tuning to the attended feature, we show that the effect of attention depends on the tuning to the distractor feature as well. We suggest that these effects represent an efficient process by which sensory cortex simultaneously enhances relevant information and suppresses irrelevant information. Copyright © 2017 the authors 0270-6474/17/375378-15$15.00/0.

Feature-Selective Attention Adaptively Shifts Noise Correlations in Primary Auditory Cortex

PubMed Central

2017-01-01

Sensory environments often contain an overwhelming amount of information, with both relevant and irrelevant information competing for neural resources. Feature attention mediates this competition by selecting the sensory features needed to form a coherent percept. How attention affects the activity of populations of neurons to support this process is poorly understood because population coding is typically studied through simulations in which one sensory feature is encoded without competition. Therefore, to study the effects of feature attention on population-based neural coding, investigations must be extended to include stimuli with both relevant and irrelevant features. We measured noise correlations (rnoise) within small neural populations in primary auditory cortex while rhesus macaques performed a novel feature-selective attention task. We found that the effect of feature-selective attention on rnoise depended not only on the population tuning to the attended feature, but also on the tuning to the distractor feature. To attempt to explain how these observed effects might support enhanced perceptual performance, we propose an extension of a simple and influential model in which shifts in rnoise can simultaneously enhance the representation of the attended feature while suppressing the distractor. These findings present a novel mechanism by which attention modulates neural populations to support sensory processing in cluttered environments. SIGNIFICANCE STATEMENT Although feature-selective attention constitutes one of the building blocks of listening in natural environments, its neural bases remain obscure. To address this, we developed a novel auditory feature-selective attention task and measured noise correlations (rnoise) in rhesus macaque A1 during task performance. Unlike previous studies showing that the effect of attention on rnoise depends on population tuning to the attended feature, we show that the effect of attention depends on the tuning to the distractor feature as well. We suggest that these effects represent an efficient process by which sensory cortex simultaneously enhances relevant information and suppresses irrelevant information. PMID:28432139

Scene of Multiple Explosions

NASA Image and Video Library

2007-03-07

This composite image NASA Galaxy Evolution Explorer shows Z Camelopardalis, or Z Cam, a double-star system featuring a collapsed, dead star, called a white dwarf, and a companion star, as well as a ghostly shell around the system.

Higher criticism thresholding: Optimal feature selection when useful features are rare and weak.

PubMed

Donoho, David; Jin, Jiashun

2008-09-30

In important application fields today-genomics and proteomics are examples-selecting a small subset of useful features is crucial for success of Linear Classification Analysis. We study feature selection by thresholding of feature Z-scores and introduce a principle of threshold selection, based on the notion of higher criticism (HC). For i = 1, 2, ..., p, let pi(i) denote the two-sided P-value associated with the ith feature Z-score and pi((i)) denote the ith order statistic of the collection of P-values. The HC threshold is the absolute Z-score corresponding to the P-value maximizing the HC objective (i/p - pi((i)))/sqrt{i/p(1-i/p)}. We consider a rare/weak (RW) feature model, where the fraction of useful features is small and the useful features are each too weak to be of much use on their own. HC thresholding (HCT) has interesting behavior in this setting, with an intimate link between maximizing the HC objective and minimizing the error rate of the designed classifier, and very different behavior from popular threshold selection procedures such as false discovery rate thresholding (FDRT). In the most challenging RW settings, HCT uses an unconventionally low threshold; this keeps the missed-feature detection rate under better control than FDRT and yields a classifier with improved misclassification performance. Replacing cross-validated threshold selection in the popular Shrunken Centroid classifier with the computationally less expensive and simpler HCT reduces the variance of the selected threshold and the error rate of the constructed classifier. Results on standard real datasets and in asymptotic theory confirm the advantages of HCT.

Higher criticism thresholding: Optimal feature selection when useful features are rare and weak

PubMed Central

Donoho, David; Jin, Jiashun

2008-01-01

In important application fields today—genomics and proteomics are examples—selecting a small subset of useful features is crucial for success of Linear Classification Analysis. We study feature selection by thresholding of feature Z-scores and introduce a principle of threshold selection, based on the notion of higher criticism (HC). For i = 1, 2, …, p, let πi denote the two-sided P-value associated with the ith feature Z-score and π(i) denote the ith order statistic of the collection of P-values. The HC threshold is the absolute Z-score corresponding to the P-value maximizing the HC objective (i/p − π(i))/i/p(1−i/p). We consider a rare/weak (RW) feature model, where the fraction of useful features is small and the useful features are each too weak to be of much use on their own. HC thresholding (HCT) has interesting behavior in this setting, with an intimate link between maximizing the HC objective and minimizing the error rate of the designed classifier, and very different behavior from popular threshold selection procedures such as false discovery rate thresholding (FDRT). In the most challenging RW settings, HCT uses an unconventionally low threshold; this keeps the missed-feature detection rate under better control than FDRT and yields a classifier with improved misclassification performance. Replacing cross-validated threshold selection in the popular Shrunken Centroid classifier with the computationally less expensive and simpler HCT reduces the variance of the selected threshold and the error rate of the constructed classifier. Results on standard real datasets and in asymptotic theory confirm the advantages of HCT. PMID:18815365

Application-Dedicated Selection of Filters (ADSF) using covariance maximization and orthogonal projection.

PubMed

Hadoux, Xavier; Kumar, Dinesh Kant; Sarossy, Marc G; Roger, Jean-Michel; Gorretta, Nathalie

2016-05-19

Visible and near-infrared (Vis-NIR) spectra are generated by the combination of numerous low resolution features. Spectral variables are thus highly correlated, which can cause problems for selecting the most appropriate ones for a given application. Some decomposition bases such as Fourier or wavelet generally help highlighting spectral features that are important, but are by nature constraint to have both positive and negative components. Thus, in addition to complicating the selected features interpretability, it impedes their use for application-dedicated sensors. In this paper we have proposed a new method for feature selection: Application-Dedicated Selection of Filters (ADSF). This method relaxes the shape constraint by enabling the selection of any type of user defined custom features. By considering only relevant features, based on the underlying nature of the data, high regularization of the final model can be obtained, even in the small sample size context often encountered in spectroscopic applications. For larger scale deployment of application-dedicated sensors, these predefined feature constraints can lead to application specific optical filters, e.g., lowpass, highpass, bandpass or bandstop filters with positive only coefficients. In a similar fashion to Partial Least Squares, ADSF successively selects features using covariance maximization and deflates their influences using orthogonal projection in order to optimally tune the selection to the data with limited redundancy. ADSF is well suited for spectroscopic data as it can deal with large numbers of highly correlated variables in supervised learning, even with many correlated responses. Copyright © 2016 Elsevier B.V. All rights reserved.

Double-shell CuS nanocages as advanced supercapacitor electrode materials

NASA Astrophysics Data System (ADS)

Guo, Jinxue; Zhang, Xinqun; Sun, Yanfang; Zhang, Xiaohong; Tang, Lin; Zhang, Xiao

2017-07-01

Metal sulfides hollow structures are advanced materials for energy storage applications of lithium-ion batteries and supercapacitors. However, constructing hollow metal sulfides with specific features, such as multi-shell and non-spherical shape, still remains great challenge. In this work, we firstly demonstrate the synthesis of CuS double-shell hollow nanocages using Cu2O nanocubes as precursors. The synthesis processes involve the repeated anion exchange reaction with Na2S and the controllable etching using hydrochloric acid. The whole synthesis processes are well revealed and the obtained double-shell CuS is tested as pseudocapacitive electrode material for supercapacitors. As expected, the CuS double-shell hollow nanocages deliver high specific capacitance, good rate performance and excellent cycling stability due to their unique nano-architecture. The present work contributes greatly to the exploration of hollow metal sulfides with complex architecture and non-spherical shape, as well as their promising application in high-performance electrochemical supercapacitors.

Double heterozygosity for Hb New York [beta 113 GTG-->GAG; VAL-->GLU] and beta degrees-thalassemia mutations manifests as a thalassemia trait.

PubMed

Lee, Anselm C W; Ma, Edmond S K; Chan, Amy Y Y; Szeto, S C; Chan, L C

2008-01-01

An extended family with three individuals affected by two different forms of double heterozygosity for beta-thalassemia and Hb New York is reported. Double heterozygosity of Hb New York [beta 113 GTG-->GAG; VAL-->GLU] and beta degrees codon 17 was detected in a fetus following prenatal screening for thalassemia. The father and a paternal aunt were also found to be heterozygous for Hb New York and beta degrees IVSII-654. Both adults had clinical and hematological features consistent with beta-thalassemia trait. The affected child was followed up after birth and manifested the typical course of a thalassemia trait, with no signs of organomegaly or overt hemolysis. Observations strongly suggest that double heterozygosity of Hb New York and beta degrees thalassemia has mild, if any, clinical symptoms, and is not an indication of therapeutic abortion when detected antenatally.

A fully redundant double difference algorithm for obtaining minimum variance estimates from GPS observations

NASA Technical Reports Server (NTRS)

Melbourne, William G.

1986-01-01

In double differencing a regression system obtained from concurrent Global Positioning System (GPS) observation sequences, one either undersamples the system to avoid introducing colored measurement statistics, or one fully samples the system incurring the resulting non-diagonal covariance matrix for the differenced measurement errors. A suboptimal estimation result will be obtained in the undersampling case and will also be obtained in the fully sampled case unless the color noise statistics are taken into account. The latter approach requires a least squares weighting matrix derived from inversion of a non-diagonal covariance matrix for the differenced measurement errors instead of inversion of the customary diagonal one associated with white noise processes. Presented is the so-called fully redundant double differencing algorithm for generating a weighted double differenced regression system that yields equivalent estimation results, but features for certain cases a diagonal weighting matrix even though the differenced measurement error statistics are highly colored.

Specific features of low-frequency vibrational dynamics and low-temperature heat capacity of double-walled carbon nanotubes

NASA Astrophysics Data System (ADS)

Avramenko, M. V.; Roshal, S. B.

2016-05-01

A continuous model has been constructed for low-frequency dynamics of a double-walled carbon nanotube. The formation of the low-frequency part of the phonon spectrum of a double-walled nanotube from phonon spectra of its constituent single-walled nanotubes has been considered in the framework of the proposed approach. The influence of the environment on the phonon spectrum of a single double-walled carbon nanotube has been analyzed. A combined method has been proposed for estimating the coefficients of the van der Waals interaction between the walls of the nanotube from the spectroscopic data and the known values of the elastic moduli of graphite. The low-temperature specific heat has been calculated for doublewalled carbon nanotubes, which in the field of applicability of the model ( T < 35 K) is substantially less than the sum of specific heats of two individual single-walled nanotubes forming it.

Multisensor-based real-time quality monitoring by means of feature extraction, selection and modeling for Al alloy in arc welding

NASA Astrophysics Data System (ADS)

Zhang, Zhifen; Chen, Huabin; Xu, Yanling; Zhong, Jiyong; Lv, Na; Chen, Shanben

2015-08-01

Multisensory data fusion-based online welding quality monitoring has gained increasing attention in intelligent welding process. This paper mainly focuses on the automatic detection of typical welding defect for Al alloy in gas tungsten arc welding (GTAW) by means of analzing arc spectrum, sound and voltage signal. Based on the developed algorithms in time and frequency domain, 41 feature parameters were successively extracted from these signals to characterize the welding process and seam quality. Then, the proposed feature selection approach, i.e., hybrid fisher-based filter and wrapper was successfully utilized to evaluate the sensitivity of each feature and reduce the feature dimensions. Finally, the optimal feature subset with 19 features was selected to obtain the highest accuracy, i.e., 94.72% using established classification model. This study provides a guideline for feature extraction, selection and dynamic modeling based on heterogeneous multisensory data to achieve a reliable online defect detection system in arc welding.

SVM-RFE based feature selection and Taguchi parameters optimization for multiclass SVM classifier.

PubMed

Huang, Mei-Ling; Hung, Yung-Hsiang; Lee, W M; Li, R K; Jiang, Bo-Ru

2014-01-01

Recently, support vector machine (SVM) has excellent performance on classification and prediction and is widely used on disease diagnosis or medical assistance. However, SVM only functions well on two-group classification problems. This study combines feature selection and SVM recursive feature elimination (SVM-RFE) to investigate the classification accuracy of multiclass problems for Dermatology and Zoo databases. Dermatology dataset contains 33 feature variables, 1 class variable, and 366 testing instances; and the Zoo dataset contains 16 feature variables, 1 class variable, and 101 testing instances. The feature variables in the two datasets were sorted in descending order by explanatory power, and different feature sets were selected by SVM-RFE to explore classification accuracy. Meanwhile, Taguchi method was jointly combined with SVM classifier in order to optimize parameters C and γ to increase classification accuracy for multiclass classification. The experimental results show that the classification accuracy can be more than 95% after SVM-RFE feature selection and Taguchi parameter optimization for Dermatology and Zoo databases.

SVM-RFE Based Feature Selection and Taguchi Parameters Optimization for Multiclass SVM Classifier

PubMed Central

Huang, Mei-Ling; Hung, Yung-Hsiang; Lee, W. M.; Li, R. K.; Jiang, Bo-Ru

2014-01-01

Recently, support vector machine (SVM) has excellent performance on classification and prediction and is widely used on disease diagnosis or medical assistance. However, SVM only functions well on two-group classification problems. This study combines feature selection and SVM recursive feature elimination (SVM-RFE) to investigate the classification accuracy of multiclass problems for Dermatology and Zoo databases. Dermatology dataset contains 33 feature variables, 1 class variable, and 366 testing instances; and the Zoo dataset contains 16 feature variables, 1 class variable, and 101 testing instances. The feature variables in the two datasets were sorted in descending order by explanatory power, and different feature sets were selected by SVM-RFE to explore classification accuracy. Meanwhile, Taguchi method was jointly combined with SVM classifier in order to optimize parameters C and γ to increase classification accuracy for multiclass classification. The experimental results show that the classification accuracy can be more than 95% after SVM-RFE feature selection and Taguchi parameter optimization for Dermatology and Zoo databases. PMID:25295306

Feature selection in feature network models: finding predictive subsets of features with the Positive Lasso.

PubMed

Frank, Laurence E; Heiser, Willem J

2008-05-01

A set of features is the basis for the network representation of proximity data achieved by feature network models (FNMs). Features are binary variables that characterize the objects in an experiment, with some measure of proximity as response variable. Sometimes features are provided by theory and play an important role in the construction of the experimental conditions. In some research settings, the features are not known a priori. This paper shows how to generate features in this situation and how to select an adequate subset of features that takes into account a good compromise between model fit and model complexity, using a new version of least angle regression that restricts coefficients to be non-negative, called the Positive Lasso. It will be shown that features can be generated efficiently with Gray codes that are naturally linked to the FNMs. The model selection strategy makes use of the fact that FNM can be considered as univariate multiple regression model. A simulation study shows that the proposed strategy leads to satisfactory results if the number of objects is less than or equal to 22. If the number of objects is larger than 22, the number of features selected by our method exceeds the true number of features in some conditions.

Self-assembly of a double-helical complex of sodium.

PubMed

Bell, T W; Jousselin, H

1994-02-03

Spontaneous self-organization of helical and multiple-helical molecular structures occurs on several levels in living organisms. Key examples are alpha-helical polypeptides, double-helical nucleic acids and helical protein structures, including F-actin, microtubules and the protein sheath of the tobacco mosaic virus. Although the self-assembly of double-helical transition-metal complexes bears some resemblance to the molecular organization of double-stranded DNA, selection between monohelical, double-helical and triple-helical structures is determined largely by the size and geometrical preference of the tightly bound metal. Here we present an example of double-helical assembly induced by the weaker and non-directional interactions of an alkali-metal ion with an organic ligand that is pre-organized into a coil. We have characterized the resulting complex by two-dimensional NMR and fast-atom-bombardment mass spectrometry. These results provide a step toward the creation of molecular tubes or ion channels consisting of intertwined coils.

A Filter Feature Selection Method Based on MFA Score and Redundancy Excluding and It's Application to Tumor Gene Expression Data Analysis.

PubMed

Li, Jiangeng; Su, Lei; Pang, Zenan

2015-12-01

Feature selection techniques have been widely applied to tumor gene expression data analysis in recent years. A filter feature selection method named marginal Fisher analysis score (MFA score) which is based on graph embedding has been proposed, and it has been widely used mainly because it is superior to Fisher score. Considering the heavy redundancy in gene expression data, we proposed a new filter feature selection technique in this paper. It is named MFA score+ and is based on MFA score and redundancy excluding. We applied it to an artificial dataset and eight tumor gene expression datasets to select important features and then used support vector machine as the classifier to classify the samples. Compared with MFA score, t test and Fisher score, it achieved higher classification accuracy.

An ant colony optimization based feature selection for web page classification.

PubMed

Saraç, Esra; Özel, Selma Ayşe

2014-01-01

The increased popularity of the web has caused the inclusion of huge amount of information to the web, and as a result of this explosive information growth, automated web page classification systems are needed to improve search engines' performance. Web pages have a large number of features such as HTML/XML tags, URLs, hyperlinks, and text contents that should be considered during an automated classification process. The aim of this study is to reduce the number of features to be used to improve runtime and accuracy of the classification of web pages. In this study, we used an ant colony optimization (ACO) algorithm to select the best features, and then we applied the well-known C4.5, naive Bayes, and k nearest neighbor classifiers to assign class labels to web pages. We used the WebKB and Conference datasets in our experiments, and we showed that using the ACO for feature selection improves both accuracy and runtime performance of classification. We also showed that the proposed ACO based algorithm can select better features with respect to the well-known information gain and chi square feature selection methods.

Highly Stable Double-Stranded DNA Containing Sequential Silver(I)-Mediated 7-Deazaadenine/Thymine Watson-Crick Base Pairs.

PubMed

Santamaría-Díaz, Noelia; Méndez-Arriaga, José M; Salas, Juan M; Galindo, Miguel A

2016-05-17

The oligonucleotide d(TX)9 , which consists of an octadecamer sequence with alternating non-canonical 7-deazaadenine (X) and canonical thymine (T) as the nucleobases, was synthesized and shown to hybridize into double-stranded DNA through the formation of hydrogen-bonded Watson-Crick base pairs. dsDNA with metal-mediated base pairs was then obtained by selectively replacing W-C hydrogen bonds by coordination bonds to central silver(I) ions. The oligonucleotide I adopts a duplex structure in the absence of Ag(+) ions, and its stability is significantly enhanced in the presence of Ag(+) ions while its double-helix structure is retained. Temperature-dependent UV spectroscopy, circular dichroism spectroscopy, and ESI mass spectrometry were used to confirm the selective formation of the silver(I)-mediated base pairs. This strategy could become useful for preparing stable metallo-DNA-based nanostructures. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

SEM investigations of the cementum surface after irradiation with a frequency-doubled Alexandrite laser

NASA Astrophysics Data System (ADS)

Rechmann, Peter; Hennig, Thomas

1996-04-01

During prior studies it could be demonstrated while engaging a frequency doubled Alexandrite-laser (wavelength 380 nm, pulse duration 100 ns, fluence 1 J/cm2, pulse repetition rate 110 Hz) a fast and strictly selective ablation of supra- and subgingival calculus is possible. Even the removal of unstained microbial plaque was observed. First conclusions were drawn after light microscopical investigations on undecalcified sections of irradiated teeth. In the present study the cementum surface after irradiation with a frequency doubled Alexandrite-laser was observed by means of a Scanning Electron Microscope. After irradiation sections of teeth were dried in alcohol and sputtered with gold. In comparison irradiated cementum surfaces of unerupted operatively removed wisdom teeth and tooth surfaces after the selective removal of calculus were investigated. A complete removal of calculus was observed as well as a remaining smooth surface of irradiated cementum.

Generation of astaxanthin mutants in Xanthophyllomyces dendrorhous using a double recombination method based on hygromycin resistance.

PubMed

Niklitschek, Mauricio; Baeza, Marcelo; Fernández-Lobato, María; Cifuentes, Víctor

2012-01-01

Generally two selection markers are required to obtain homozygous mutations in a diploid background, one for each gene copy that is interrupted. In this chapter is described a method that allows the double gene deletions of the two copies of a gene from a diploid organism, a wild-type strain of the Xanthophyllomyces dendrorhous yeast, using hygromycin B resistance as the only selection marker. To accomplish this, in a first step, a heterozygous hygromycin B-resistant strain is obtained by a single process of transformation (carrying the inserted hph gene). Following, the heterozygous mutant is grown in media with increasing concentrations of the antibiotic. In this way, the strains that became homozygous (by mitotic recombination) for the antibiotic marker would able to growth at higher concentration of the antibiotic than the heterozygous. The method can be potentially applied for obtaining double mutants of other diploid organisms.

Genome-enabled selection doubles the accuracy of predicted breeding values for bacterial cold water disease resistance compared to traditional family-based selection in rainbow trout aquaculture

USDA-ARS?s Scientific Manuscript database

We have shown previously that bacterial cold water disease (BCWD) resistance in rainbow trout can be improved using traditional family-based selection, but progress has been limited to exploiting only between-family genetic variation. Genomic selection (GS) is a new alternative enabling exploitation...

CO Sensing Performance of a Micro Thermoelectric Gas Sensor with AuPtPd/SnO2 Catalyst and Effects of a Double Catalyst Structure with Pt/α-Al2O3

PubMed Central

Goto, Tomoyo; Itoh, Toshio; Akamatsu, Takafumi; Shin, Woosuck

2015-01-01

The CO sensing properties of a micro thermoelectric gas sensor (micro-TGS) with a double AuPtPd/SnO2 and Pt/α-Al2O3 catalyst were investigated. While several nanometer sized Pt and Pd particles were uniformly dispersed on SnO2, the Au particles were aggregated as particles measuring >10 nm in diameter. In situ diffuse reflectance Fourier transform Infrared spectroscopy (DRIFT) analysis of the catalyst showed a CO adsorption peak on Pt and Pd, but no clear peak corresponding to the interaction between CO and Au was detected. Up to 200 °C, CO combustion was more temperature dependent than that of H2, while H2 combustion was activated by repeated exposure to H2 gas during the periodic gas test. Selective CO sensing of the micro-TGS against H2 was attempted using a double catalyst structure with 0.3–30 wt% Pt/α-Al2O3 as a counterpart combustion catalyst. The sensor output of the micro-TGS decreased with increasing Pt content in the Pt/α-Al2O3 catalyst, by cancelling out the combustion heat from the AuPtPd/SnO2 catalyst. In addition, the AuPtPd/SnO2 and 0.3 wt% Pt/α-Al2O3 double catalyst sensor showed good and selective CO detection. We therefore demonstrated that our micro-TGS with double catalyst structure is useful for controlling the gas selectivity of CO against H2. PMID:26694397

Wide variation of prostate-specific antigen doubling time of untreated, clinically localized, low-to-intermediate grade, prostate carcinoma.

PubMed

Choo, Richard; Klotz, Laurence; Deboer, Gerrit; Danjoux, Cyril; Morton, Gerard C

2004-08-01

To assess the prostate specific antigen (PSA) doubling time of untreated, clinically localized, low-to-intermediate grade prostate carcinoma. A prospective single-arm cohort study has been in progress since November 1995 to assess the feasibility of a watchful-observation protocol with selective delayed intervention for clinically localized, low-to-intermediate grade prostate adenocarcinoma. The PSA doubling time was estimated from a linear regression of ln(PSA) against time, assuming a simple exponential growth model. As of March 2003, 231 patients had at least 6 months of follow-up (median 45) and at least three PSA measurements (median 8, range 3-21). The distribution of the doubling time was: < 2 years, 26 patients; 2-5 years, 65; 5-10 years, 42; 10-20 years, 26; 20-50 years, 16; >50 years, 56. The median doubling time was 7.0 years; 42% of men had a doubling time of >10 years. The doubling time of untreated clinically localized, low-to-intermediate grade prostate cancer varies widely.

Vessel Classification in Cosmo-Skymed SAR Data Using Hierarchical Feature Selection

NASA Astrophysics Data System (ADS)

Makedonas, A.; Theoharatos, C.; Tsagaris, V.; Anastasopoulos, V.; Costicoglou, S.

2015-04-01

SAR based ship detection and classification are important elements of maritime monitoring applications. Recently, high-resolution SAR data have opened new possibilities to researchers for achieving improved classification results. In this work, a hierarchical vessel classification procedure is presented based on a robust feature extraction and selection scheme that utilizes scale, shape and texture features in a hierarchical way. Initially, different types of feature extraction algorithms are implemented in order to form the utilized feature pool, able to represent the structure, material, orientation and other vessel type characteristics. A two-stage hierarchical feature selection algorithm is utilized next in order to be able to discriminate effectively civilian vessels into three distinct types, in COSMO-SkyMed SAR images: cargos, small ships and tankers. In our analysis, scale and shape features are utilized in order to discriminate smaller types of vessels present in the available SAR data, or shape specific vessels. Then, the most informative texture and intensity features are incorporated in order to be able to better distinguish the civilian types with high accuracy. A feature selection procedure that utilizes heuristic measures based on features' statistical characteristics, followed by an exhaustive research with feature sets formed by the most qualified features is carried out, in order to discriminate the most appropriate combination of features for the final classification. In our analysis, five COSMO-SkyMed SAR data with 2.2m x 2.2m resolution were used to analyse the detailed characteristics of these types of ships. A total of 111 ships with available AIS data were used in the classification process. The experimental results show that this method has good performance in ship classification, with an overall accuracy reaching 83%. Further investigation of additional features and proper feature selection is currently in progress.

Features selection and classification to estimate elbow movements

NASA Astrophysics Data System (ADS)

Rubiano, A.; Ramírez, J. L.; El Korso, M. N.; Jouandeau, N.; Gallimard, L.; Polit, O.

2015-11-01

In this paper, we propose a novel method to estimate the elbow motion, through the features extracted from electromyography (EMG) signals. The features values are normalized and then compared to identify potential relationships between the EMG signal and the kinematic information as angle and angular velocity. We propose and implement a method to select the best set of features, maximizing the distance between the features that correspond to flexion and extension movements. Finally, we test the selected features as inputs to a non-linear support vector machine in the presence of non-idealistic conditions, obtaining an accuracy of 99.79% in the motion estimation results.

Efficient feature subset selection with probabilistic distance criteria. [pattern recognition

NASA Technical Reports Server (NTRS)

Chittineni, C. B.

1979-01-01

Recursive expressions are derived for efficiently computing the commonly used probabilistic distance measures as a change in the criteria both when a feature is added to and when a feature is deleted from the current feature subset. A combinatorial algorithm for generating all possible r feature combinations from a given set of s features in (s/r) steps with a change of a single feature at each step is presented. These expressions can also be used for both forward and backward sequential feature selection.

FSMRank: feature selection algorithm for learning to rank.

PubMed

Lai, Han-Jiang; Pan, Yan; Tang, Yong; Yu, Rong

2013-06-01

In recent years, there has been growing interest in learning to rank. The introduction of feature selection into different learning problems has been proven effective. These facts motivate us to investigate the problem of feature selection for learning to rank. We propose a joint convex optimization formulation which minimizes ranking errors while simultaneously conducting feature selection. This optimization formulation provides a flexible framework in which we can easily incorporate various importance measures and similarity measures of the features. To solve this optimization problem, we use the Nesterov's approach to derive an accelerated gradient algorithm with a fast convergence rate O(1/T(2)). We further develop a generalization bound for the proposed optimization problem using the Rademacher complexities. Extensive experimental evaluations are conducted on the public LETOR benchmark datasets. The results demonstrate that the proposed method shows: 1) significant ranking performance gain compared to several feature selection baselines for ranking, and 2) very competitive performance compared to several state-of-the-art learning-to-rank algorithms.

Computational Prediction of Protein Epsilon Lysine Acetylation Sites Based on a Feature Selection Method.

PubMed

Gao, JianZhao; Tao, Xue-Wen; Zhao, Jia; Feng, Yuan-Ming; Cai, Yu-Dong; Zhang, Ning

2017-01-01

Lysine acetylation, as one type of post-translational modifications (PTM), plays key roles in cellular regulations and can be involved in a variety of human diseases. However, it is often high-cost and time-consuming to use traditional experimental approaches to identify the lysine acetylation sites. Therefore, effective computational methods should be developed to predict the acetylation sites. In this study, we developed a position-specific method for epsilon lysine acetylation site prediction. Sequences of acetylated proteins were retrieved from the UniProt database. Various kinds of features such as position specific scoring matrix (PSSM), amino acid factors (AAF), and disorders were incorporated. A feature selection method based on mRMR (Maximum Relevance Minimum Redundancy) and IFS (Incremental Feature Selection) was employed. Finally, 319 optimal features were selected from total 541 features. Using the 319 optimal features to encode peptides, a predictor was constructed based on dagging. As a result, an accuracy of 69.56% with MCC of 0.2792 was achieved. We analyzed the optimal features, which suggested some important factors determining the lysine acetylation sites. We developed a position-specific method for epsilon lysine acetylation site prediction. A set of optimal features was selected. Analysis of the optimal features provided insights into the mechanism of lysine acetylation sites, providing guidance of experimental validation. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Skin lesion computational diagnosis of dermoscopic images: Ensemble models based on input feature manipulation.

PubMed

Oliveira, Roberta B; Pereira, Aledir S; Tavares, João Manuel R S

2017-10-01

The number of deaths worldwide due to melanoma has risen in recent times, in part because melanoma is the most aggressive type of skin cancer. Computational systems have been developed to assist dermatologists in early diagnosis of skin cancer, or even to monitor skin lesions. However, there still remains a challenge to improve classifiers for the diagnosis of such skin lesions. The main objective of this article is to evaluate different ensemble classification models based on input feature manipulation to diagnose skin lesions. Input feature manipulation processes are based on feature subset selections from shape properties, colour variation and texture analysis to generate diversity for the ensemble models. Three subset selection models are presented here: (1) a subset selection model based on specific feature groups, (2) a correlation-based subset selection model, and (3) a subset selection model based on feature selection algorithms. Each ensemble classification model is generated using an optimum-path forest classifier and integrated with a majority voting strategy. The proposed models were applied on a set of 1104 dermoscopic images using a cross-validation procedure. The best results were obtained by the first ensemble classification model that generates a feature subset ensemble based on specific feature groups. The skin lesion diagnosis computational system achieved 94.3% accuracy, 91.8% sensitivity and 96.7% specificity. The input feature manipulation process based on specific feature subsets generated the greatest diversity for the ensemble classification model with very promising results. Copyright © 2017 Elsevier B.V. All rights reserved.

(E)-1,3-Bis(2,3,4,5,6-penta­fluoro­phen­yl)prop-2-en-1-one

PubMed Central

Schwarzer, Anke; Weber, Edwin

2010-01-01

In the title compound, C15H2F10O, the two perfluorinated arene rings are tilted at an angle of 66.08 (5)° with respect to each other. The olefinic double bond adopts an E configuration and the single bond between the olefinic and carbonyl double bonds has an s-trans conformation. The carbonyl group is not in a coplanar alignment with respect to the neighbouring arene ring (0.963 Å from aryl plane) while being coplanar with regard to the olefinic double bond (0.0805 Å from olefinic bond). The crystal packing does not feature significant hydrogen-bond-type or stacking inter­actions. PMID:21588260

A NON-PRE DOUBLE-PEAKED BURST FROM 4U 1636-536: EVIDENCE FOR BURNING FRONT PROPAGATION

NASA Technical Reports Server (NTRS)

Bhattacharyya, Sudip; Strohmayer, Tod E.

2005-01-01

We analyse Rossi X-ray Timing Explorer (RXTE) Proportional Counter Array (PCA) data of a double-peaked burst from the low mass X-ray binary (LMXB) 4U 1636-536 that shows no evidence for photospheric radius expansion (PRE). We find that the X-ray emitting area on the star increases with time as the burst progresses, even though the photosphere does not expand. We argue that this is a strong indication of thermonuclear flame spreading on the stellar surface during such bursts. We propose a model for such double-peaked bursts, based on thermonuclear flame spreading, that can qualitatively explain their essential features, as well as the rarity of these bursts.

A Double-Blind, Randomized, Placebo-Controlled Trial of Escitalopram in the Treatment of Pediatric Depression

ERIC Educational Resources Information Center

Wagner, Karen Dineen; Jonas, Jeffrey; Findling, Robert L.; Ventura, Daniel; Saikali, Khalil

2006-01-01

Objective: Escitalopram is a selective serotonin reuptake inhibitor antidepressant indicated for use in adults. This trial examined the efficacy and safety of escitalopram in pediatric depression. Method: Patients (6-17 years old) with major depressive disorder were randomized to receive 8 weeks of double-blind flexibly dosed treatment with…

Psychoactive Medication and Learning Disabilities

ERIC Educational Resources Information Center

Eaton, Marie; And Others

1977-01-01

A seven-year-old emotionally disturbed boy with some features of the hyperkinetic syndrome was placed on a double-blind placebo control program to assess the effects of psychoactive medications (Ritalin and Dexedrine) on academic and social behaviors. (Author)

Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources.

PubMed

Yu, Sheng; Liao, Katherine P; Shaw, Stanley Y; Gainer, Vivian S; Churchill, Susanne E; Szolovits, Peter; Murphy, Shawn N; Kohane, Isaac S; Cai, Tianxi

2015-09-01

Analysis of narrative (text) data from electronic health records (EHRs) can improve population-scale phenotyping for clinical and genetic research. Currently, selection of text features for phenotyping algorithms is slow and laborious, requiring extensive and iterative involvement by domain experts. This paper introduces a method to develop phenotyping algorithms in an unbiased manner by automatically extracting and selecting informative features, which can be comparable to expert-curated ones in classification accuracy. Comprehensive medical concepts were collected from publicly available knowledge sources in an automated, unbiased fashion. Natural language processing (NLP) revealed the occurrence patterns of these concepts in EHR narrative notes, which enabled selection of informative features for phenotype classification. When combined with additional codified features, a penalized logistic regression model was trained to classify the target phenotype. The authors applied our method to develop algorithms to identify patients with rheumatoid arthritis and coronary artery disease cases among those with rheumatoid arthritis from a large multi-institutional EHR. The area under the receiver operating characteristic curves (AUC) for classifying RA and CAD using models trained with automated features were 0.951 and 0.929, respectively, compared to the AUCs of 0.938 and 0.929 by models trained with expert-curated features. Models trained with NLP text features selected through an unbiased, automated procedure achieved comparable or slightly higher accuracy than those trained with expert-curated features. The majority of the selected model features were interpretable. The proposed automated feature extraction method, generating highly accurate phenotyping algorithms with improved efficiency, is a significant step toward high-throughput phenotyping. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Turkey habitat use and nesting characteristics in ponderosa pine

Treesearch

Mark A. Rumble; Stanley H. Anderson

1987-01-01

Turkeys (Meleagris gallapovo) selected nest sites that provided good horizontal concealment. Rock or rock outcrops were selected most frequently for nest concealment on first-nest attempts. Renest attempts showed a selection preference for shrubs as nest cover; most of these were located in meadows. Nesting success doubled for renests versus first...

The unusual and dynamic character of PX-DNA

DOE PAGES

Niu, Dong; Jiang, Hualin; Sha, Ruojie; ...

2015-07-15

PX-DNA is a four-stranded DNA structure that has been implicated in the recognition of homology, either continuously, or in an every-other-half-turn fashion. Some of the structural features of the molecule have been noted previously, but the structure requires further characterization. Here, we report atomic force microscopic characterization of PX molecules that contain periodically placed biotin groups, enabling the molecule to be labeled by streptavidin molecules at these sites. In comparison with conventional double stranded DNA and with antiparallel DNA double crossover molecules, it is clear that PX-DNA is a more dynamic structure. Moreover, the spacing between the nucleotide pairs alongmore » the helix axis is shorter, suggesting a mixed B/A structure. Circular dichroism spectroscopy indicates unusual features in the PX molecule that are absent in both the molecules to which it is compared.« less

An improved method for constructing and selectively silanizing double-barreled, neutral liquid-carrier, ion-selective microelectrodes

PubMed Central

Deveau, Jason S.T.; Grodzinski, Bernard

2005-01-01

We describe an improved, efficient and reliable method for the vapour-phase silanization of multi-barreled, ion-selective microelectrodes of which the silanized barrel(s) are to be filled with neutral liquid ion-exchanger (LIX). The technique employs a metal manifold to exclusively and simultaneously deliver dimethyldichlorosilane to only the ion-selective barrels of several multi-barreled microelectrodes. Compared to previously published methods the technique requires fewer procedural steps, less handling of individual microelectrodes, improved reproducibility of silanization of the selected microelectrode barrels and employs standard borosilicate tubing rather than the less-conventional theta-type glass. The electrodes remain stable for up to 3 weeks after the silanization procedure. The efficacy of a double-barreled electrode containing a proton ionophore in the ion-selective barrel is demonstrated in situ in the leaf apoplasm of pea (Pisum) and sunflower (Helianthus). Individual leaves were penetrated to depth of ~150 μm through the abaxial surface. Microelectrode readings remained stable after multiple impalements without the need for a stabilizing PVC matrix. PMID:16136222

Compact Representation of High-Dimensional Feature Vectors for Large-Scale Image Recognition and Retrieval.

PubMed

Zhang, Yu; Wu, Jianxin; Cai, Jianfei

2016-05-01

In large-scale visual recognition and image retrieval tasks, feature vectors, such as Fisher vector (FV) or the vector of locally aggregated descriptors (VLAD), have achieved state-of-the-art results. However, the combination of the large numbers of examples and high-dimensional vectors necessitates dimensionality reduction, in order to reduce its storage and CPU costs to a reasonable range. In spite of the popularity of various feature compression methods, this paper shows that the feature (dimension) selection is a better choice for high-dimensional FV/VLAD than the feature (dimension) compression methods, e.g., product quantization. We show that strong correlation among the feature dimensions in the FV and the VLAD may not exist, which renders feature selection a natural choice. We also show that, many dimensions in FV/VLAD are noise. Throwing them away using feature selection is better than compressing them and useful dimensions altogether using feature compression methods. To choose features, we propose an efficient importance sorting algorithm considering both the supervised and unsupervised cases, for visual recognition and image retrieval, respectively. Combining with the 1-bit quantization, feature selection has achieved both higher accuracy and less computational cost than feature compression methods, such as product quantization, on the FV and the VLAD image representations.

Analysis of energetically biased transcripts of viruses and transposable elements

PubMed Central

Secolin, Rodrigo; Pascoal, Vinícius D’Ávila Bitencourt; Lopes-Cendes, Iscia; Pereira, Tiago Campos

2012-01-01

RNA interference (RNAi) is a natural endogenous process by which double-stranded RNA molecules trigger potent and specific gene silencing in eukaryotic cells and is characterized by target RNA cleavage. In mammals, small interfering RNAs (siRNAs) are the trigger molecules of choice and constitute a new class of RNA-based antiviral agents. In an efficient RNAi response, the antisense strand of siRNAs must enter the RNA-induced silencing complex (RISC) in a process mediated by thermodynamic features. In this report, we hypothesize that silent mutations capable of inverting thermodynamic properties can promote resistance to siRNAs. Extensive computational analyses were used to assess whether continuous selective pressure that promotes such mutations could lead to the emergence of viral strains completely resistant to RNAi (i.e., prone to transfer only the sense strands to RISC). Based on our findings, we propose that, although synonymous mutations may produce functional resistance, this strategy cannot be systematically adopted by viruses since the longest RNAi-refractory sequence is only 10 nt long. This finding also suggests that all mRNAs display fluctuating thermodynamic landscapes and that, in terms of thermodynamic features, RNAi is a very efficient antiviral system since there will always be sites susceptible to siRNAs. PMID:23271949

Automatic MeSH term assignment and quality assessment.

PubMed Central

Kim, W.; Aronson, A. R.; Wilbur, W. J.

2001-01-01

For computational purposes documents or other objects are most often represented by a collection of individual attributes that may be strings or numbers. Such attributes are often called features and success in solving a given problem can depend critically on the nature of the features selected to represent documents. Feature selection has received considerable attention in the machine learning literature. In the area of document retrieval we refer to feature selection as indexing. Indexing has not traditionally been evaluated by the same methods used in machine learning feature selection. Here we show how indexing quality may be evaluated in a machine learning setting and apply this methodology to results of the Indexing Initiative at the National Library of Medicine. PMID:11825203

Optimization of breast mass classification using sequential forward floating selection (SFFS) and a support vector machine (SVM) model

PubMed Central

Tan, Maxine; Pu, Jiantao; Zheng, Bin

2014-01-01

Purpose: Improving radiologists’ performance in classification between malignant and benign breast lesions is important to increase cancer detection sensitivity and reduce false-positive recalls. For this purpose, developing computer-aided diagnosis (CAD) schemes has been attracting research interest in recent years. In this study, we investigated a new feature selection method for the task of breast mass classification. Methods: We initially computed 181 image features based on mass shape, spiculation, contrast, presence of fat or calcifications, texture, isodensity, and other morphological features. From this large image feature pool, we used a sequential forward floating selection (SFFS)-based feature selection method to select relevant features, and analyzed their performance using a support vector machine (SVM) model trained for the classification task. On a database of 600 benign and 600 malignant mass regions of interest (ROIs), we performed the study using a ten-fold cross-validation method. Feature selection and optimization of the SVM parameters were conducted on the training subsets only. Results: The area under the receiver operating characteristic curve (AUC) = 0.805±0.012 was obtained for the classification task. The results also showed that the most frequently-selected features by the SFFS-based algorithm in 10-fold iterations were those related to mass shape, isodensity and presence of fat, which are consistent with the image features frequently used by radiologists in the clinical environment for mass classification. The study also indicated that accurately computing mass spiculation features from the projection mammograms was difficult, and failed to perform well for the mass classification task due to tissue overlap within the benign mass regions. Conclusions: In conclusion, this comprehensive feature analysis study provided new and valuable information for optimizing computerized mass classification schemes that may have potential to be useful as a “second reader” in future clinical practice. PMID:24664267

Checklist/Guide to Selecting a Small Computer.

ERIC Educational Resources Information Center

Bennett, Wilma E.

This 322-point checklist was designed to help executives make an intelligent choice when selecting a small computer for a business. For ease of use the questions have been divided into ten categories: Display Features, Keyboard Features, Printer Features, Controller Features, Software, Word Processing, Service, Training, Miscellaneous, and Costs.…

Feature selection methods for object-based classification of sub-decimeter resolution digital aerial imagery

USDA-ARS?s Scientific Manuscript database

Due to the availability of numerous spectral, spatial, and contextual features, the determination of optimal features and class separabilities can be a time consuming process in object-based image analysis (OBIA). While several feature selection methods have been developed to assist OBIA, a robust c...

News video story segmentation method using fusion of audio-visual features

NASA Astrophysics Data System (ADS)

Wen, Jun; Wu, Ling-da; Zeng, Pu; Luan, Xi-dao; Xie, Yu-xiang

2007-11-01

News story segmentation is an important aspect for news video analysis. This paper presents a method for news video story segmentation. Different form prior works, which base on visual features transform, the proposed technique uses audio features as baseline and fuses visual features with it to refine the results. At first, it selects silence clips as audio features candidate points, and selects shot boundaries and anchor shots as two kinds of visual features candidate points. Then this paper selects audio feature candidates as cues and develops different fusion method, which effectively using diverse type visual candidates to refine audio candidates, to get story boundaries. Experiment results show that this method has high efficiency and adaptability to different kinds of news video.

Hybrid maize breeding with doubled haploids: II. Optimum type and number of testers in two-stage selection for general combining ability.

PubMed

Longin, C Friedrich H; Utz, H Friedrich; Melchinger, Albrecht E; Reif, Jochen C

2007-02-01

Optimum allocation of test resources is of crucial importance for the efficiency of breeding programs. Our objectives were to (1) determine the optimum allocation of the number of lines, test locations, as well as number and type of testers in hybrid maize breeding using doubled haploids with two breeding strategies for improvement of general combining ability (GCA), (2) compare the maximum selection gain (DeltaG) achievable under both strategies, and (3) give recommendations for the optimum implementation of doubled haploids in commercial hybrid maize breeding. We calculated DeltaG by numerical integration for two two-stage selection strategies with evaluation of (1) testcross performance in both stages (BS1) or (2) line per se performance in the first stage followed by testcross performance in the second stage (BS2). Different assumptions were made regarding the budget, variance components (VCs), and the correlation between line per se performance and GCA. Selection gain for GCA increased with a broader genetic base of the tester. Hence, testers combining a large number of divergent lines are advantageous. However, in applied breeding programs, the use of single- or double-cross testers in the first and inbred testers in the second selection stage may be a good compromise between theoretical and practical requirements. With a correlation between line per se performance and GCA of 0.50, DeltaG for BS1 is about 5% higher than for BS2, if an economic weight of line per se performance is neglected. With increasing economic weight of line per se performance, relative efficiency of BS2 increased rapidly resulting in a superiority of BS2 over BS1 already for an economic weight for line per se performance larger than 0.1. Considering the importance of an economic seed production, an economic weight larger than 0.1 seems realistic indicating the necessity of separate breeding strategies for seed and pollen parent heterotic groups.

Gene Transfer Efficiency in Gonococcal Biofilms: Role of Biofilm Age, Architecture, and Pilin Antigenic Variation.

PubMed

Kouzel, Nadzeya; Oldewurtel, Enno R; Maier, Berenike

2015-07-01

Extracellular DNA is an important structural component of many bacterial biofilms. It is unknown, however, to which extent external DNA is used to transfer genes by means of transformation. Here, we quantified the acquisition of multidrug resistance and visualized its spread under selective and nonselective conditions in biofilms formed by Neisseria gonorrhoeae. The density and architecture of the biofilms were controlled by microstructuring the substratum for bacterial adhesion. Horizontal transfer of antibiotic resistance genes between cocultured strains, each carrying a single resistance, occurred efficiently in early biofilms. The efficiency of gene transfer was higher in early biofilms than between planktonic cells. It was strongly reduced after 24 h and independent of biofilm density. Pilin antigenic variation caused a high fraction of nonpiliated bacteria but was not responsible for the reduced gene transfer at later stages. When selective pressure was applied to dense biofilms using antibiotics at their MIC, the double-resistant bacteria did not show a significant growth advantage. In loosely connected biofilms, the spreading of double-resistant clones was prominent. We conclude that multidrug resistance readily develops in early gonococcal biofilms through horizontal gene transfer. However, selection and spreading of the multiresistant clones are heavily suppressed in dense biofilms. Biofilms are considered ideal reaction chambers for horizontal gene transfer and development of multidrug resistances. The rate at which genes are exchanged within biofilms is unknown. Here, we quantified the acquisition of double-drug resistance by gene transfer between gonococci with single resistances. At early biofilm stages, the transfer efficiency was higher than for planktonic cells but then decreased with biofilm age. The surface topography affected the architecture of the biofilm. While the efficiency of gene transfer was independent of the architecture, spreading of double-resistant bacteria under selective conditions was strongly enhanced in loose biofilms. We propose that while biofilms help generating multiresistant strains, selection takes place mostly after dispersal from the biofilm. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

A comparative analysis of swarm intelligence techniques for feature selection in cancer classification.

PubMed

Gunavathi, Chellamuthu; Premalatha, Kandasamy

2014-01-01

Feature selection in cancer classification is a central area of research in the field of bioinformatics and used to select the informative genes from thousands of genes of the microarray. The genes are ranked based on T-statistics, signal-to-noise ratio (SNR), and F-test values. The swarm intelligence (SI) technique finds the informative genes from the top-m ranked genes. These selected genes are used for classification. In this paper the shuffled frog leaping with Lévy flight (SFLLF) is proposed for feature selection. In SFLLF, the Lévy flight is included to avoid premature convergence of shuffled frog leaping (SFL) algorithm. The SI techniques such as particle swarm optimization (PSO), cuckoo search (CS), SFL, and SFLLF are used for feature selection which identifies informative genes for classification. The k-nearest neighbour (k-NN) technique is used to classify the samples. The proposed work is applied on 10 different benchmark datasets and examined with SI techniques. The experimental results show that the results obtained from k-NN classifier through SFLLF feature selection method outperform PSO, CS, and SFL.

Feature selection for elderly faller classification based on wearable sensors.

PubMed

Howcroft, Jennifer; Kofman, Jonathan; Lemaire, Edward D

2017-05-30

Wearable sensors can be used to derive numerous gait pattern features for elderly fall risk and faller classification; however, an appropriate feature set is required to avoid high computational costs and the inclusion of irrelevant features. The objectives of this study were to identify and evaluate smaller feature sets for faller classification from large feature sets derived from wearable accelerometer and pressure-sensing insole gait data. A convenience sample of 100 older adults (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence) walked 7.62 m while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, left and right shanks. Feature selection was performed using correlation-based feature selection (CFS), fast correlation based filter (FCBF), and Relief-F algorithms. Faller classification was performed using multi-layer perceptron neural network, naïve Bayesian, and support vector machine classifiers, with 75:25 single stratified holdout and repeated random sampling. The best performing model was a support vector machine with 78% accuracy, 26% sensitivity, 95% specificity, 0.36 F1 score, and 0.31 MCC and one posterior pelvis accelerometer input feature (left acceleration standard deviation). The second best model achieved better sensitivity (44%) and used a support vector machine with 74% accuracy, 83% specificity, 0.44 F1 score, and 0.29 MCC. This model had ten input features: maximum, mean and standard deviation posterior acceleration; maximum, mean and standard deviation anterior acceleration; mean superior acceleration; and three impulse features. The best multi-sensor model sensitivity (56%) was achieved using posterior pelvis and both shank accelerometers and a naïve Bayesian classifier. The best single-sensor model sensitivity (41%) was achieved using the posterior pelvis accelerometer and a naïve Bayesian classifier. Feature selection provided models with smaller feature sets and improved faller classification compared to faller classification without feature selection. CFS and FCBF provided the best feature subset (one posterior pelvis accelerometer feature) for faller classification. However, better sensitivity was achieved by the second best model based on a Relief-F feature subset with three pressure-sensing insole features and seven head accelerometer features. Feature selection should be considered as an important step in faller classification using wearable sensors.

Select Features in "Finale 2011" for Music Educators

ERIC Educational Resources Information Center

Thompson, Douglas Earl

2011-01-01

A feature-laden software program such as "Finale" is an overwhelming tool to master--if one hopes to master many features in a short amount of time. Believing that working with a fewer number of features can be a helpful approach, this article looks at a select number of features in "Finale 2011" of obvious use to music educators. These features…

Effects of Spatial and Feature Attention on Disparity-Rendered Structure-From-Motion Stimuli in the Human Visual Cortex

PubMed Central

Ip, Ifan Betina; Bridge, Holly; Parker, Andrew J.

2014-01-01

An important advance in the study of visual attention has been the identification of a non-spatial component of attention that enhances the response to similar features or objects across the visual field. Here we test whether this non-spatial component can co-select individual features that are perceptually bound into a coherent object. We combined human psychophysics and functional magnetic resonance imaging (fMRI) to demonstrate the ability to co-select individual features from perceptually coherent objects. Our study used binocular disparity and visual motion to define disparity structure-from-motion (dSFM) stimuli. Although the spatial attention system induced strong modulations of the fMRI response in visual regions, the non-spatial system’s ability to co-select features of the dSFM stimulus was less pronounced and variable across subjects. Our results demonstrate that feature and global feature attention effects are variable across participants, suggesting that the feature attention system may be limited in its ability to automatically select features within the attended object. Careful comparison of the task design suggests that even minor differences in the perceptual task may be critical in revealing the presence of global feature attention. PMID:24936974

IMMAN: free software for information theory-based chemometric analysis.

PubMed

Urias, Ricardo W Pino; Barigye, Stephen J; Marrero-Ponce, Yovani; García-Jacas, César R; Valdes-Martiní, José R; Perez-Gimenez, Facundo

2015-05-01

The features and theoretical background of a new and free computational program for chemometric analysis denominated IMMAN (acronym for Information theory-based CheMoMetrics ANalysis) are presented. This is multi-platform software developed in the Java programming language, designed with a remarkably user-friendly graphical interface for the computation of a collection of information-theoretic functions adapted for rank-based unsupervised and supervised feature selection tasks. A total of 20 feature selection parameters are presented, with the unsupervised and supervised frameworks represented by 10 approaches in each case. Several information-theoretic parameters traditionally used as molecular descriptors (MDs) are adapted for use as unsupervised rank-based feature selection methods. On the other hand, a generalization scheme for the previously defined differential Shannon's entropy is discussed, as well as the introduction of Jeffreys information measure for supervised feature selection. Moreover, well-known information-theoretic feature selection parameters, such as information gain, gain ratio, and symmetrical uncertainty are incorporated to the IMMAN software ( http://mobiosd-hub.com/imman-soft/ ), following an equal-interval discretization approach. IMMAN offers data pre-processing functionalities, such as missing values processing, dataset partitioning, and browsing. Moreover, single parameter or ensemble (multi-criteria) ranking options are provided. Consequently, this software is suitable for tasks like dimensionality reduction, feature ranking, as well as comparative diversity analysis of data matrices. Simple examples of applications performed with this program are presented. A comparative study between IMMAN and WEKA feature selection tools using the Arcene dataset was performed, demonstrating similar behavior. In addition, it is revealed that the use of IMMAN unsupervised feature selection methods improves the performance of both IMMAN and WEKA supervised algorithms. Graphic representation for Shannon's distribution of MD calculating software.

A Wortmannin-Cetuximab As A Double Drug

PubMed Central

Smith, R. Adam; Yuan, Hushan; Weissleder, Ralph; Cantley, Lewis C.; Josephson, Lee

2012-01-01

Double drugs are obtained when two pharmacologically active entities are covalently joined to improve potency. We conjugated the viridin Wm with a self-activating linkage to cetuximab and demonstrated the retention of immunoreactivity by the conjugate. Though cetuximab lacked a growth inhibitory activity against A549 cells, the Wmcetuximab conjugate had an anti-proliferative IC50 of 155 nM in vitro. The chemistry of attaching a self-releasing Wm to clinically approved antibodies is general and, in selected instances, may yield antibody-based double drugs with improved efficacy. PMID:19883074

The Molecular Detection and Clinical Significance of ALK Rearrangement in Selected Advanced Non-Small Cell Lung Cancer: ALK Expression Provides Insights into ALK Targeted Therapy

PubMed Central

Zhang, Ning-Ning; Liu, Yu-Tao; Ma, Li; Wang, Lin; Hao, Xue-Zhi; Yuan, Zheng; Lin, Dong-Mei; Li, Dan; Zhou, Yu-Jie; Lin, Hua; Han, Xiao-Hong; Sun, Yan; Shi, Yuankai

2014-01-01

Background This study aimed to elucidate clinical significance of anaplastic lymphoma kinase (ALK) rearrangement in selected advanced non-small cell lung cancer (NSCLC), to compare the application of different ALK detection methods, and especially evaluate a possible association between ALK expression and clinical outcomes in crizotinib-treated patients. Methods ALK status was assessed by fluorescent in situ hybridization (FISH), immunohistochemistry (IHC) and quantitative RT-PCR (qRT-PCR) in 173 selected advanced NSCLC patients. Clinicopathologic data, genotype status and survival outcomes were analyzed. Moreover, the association of ALK expression with clinical outcomes was evaluated in ALK FISH-positive crizotinib-treated patients including two patients with concurrent epidermal growth factor receptor (EGFR) mutation. Results The positivity detection rate of ALK rearrangement by FISH, IHC and qRT-PCR was 35.5% (59/166), 35.7% (61/171), and 27.9% (34/122), respectively. ALK rearrangement was observed predominantly in young patients, never or light smokers, and adenocarcinomas, especially with signet ring cell features and poor differentiation. Median progression-free survival (PFS) of crizotinib-treated patients was 7.6 months. The overall survival (OS) of these patients was longer compared with that of crizotinib-naive or wild-type cohorts, but there was no significant difference in OS compared with patients with EGFR mutation. ALK expression did not associate with PFS; but, when ALK expression was analyzed as a dichotomous variable, moderate and strong ALK expression had a decreased risk of death (P = 0.026). The two patients with concomitant EGFR and ALK alterations showed difference in ALK expression, response to EGFR and ALK inhibitors, and overall survival. Conclusions Selective enrichment according to clinicopathologic features in NSCLC patients could highly improve the positivity detection rate of ALK rearrangement for ALK-targeted therapy. IHC could provide more clues for clinical trial design and therapeutic strategies for ALK-positive NSCLC patients including patients with double genetic aberration of ALK and EGFR. PMID:24404167

Oculomotor selection underlies feature retention in visual working memory.

PubMed

Hanning, Nina M; Jonikaitis, Donatas; Deubel, Heiner; Szinte, Martin

2016-02-01

Oculomotor selection, spatial task relevance, and visual working memory (WM) are described as three processes highly intertwined and sustained by similar cortical structures. However, because task-relevant locations always constitute potential saccade targets, no study so far has been able to distinguish between oculomotor selection and spatial task relevance. We designed an experiment that allowed us to dissociate in humans the contribution of task relevance, oculomotor selection, and oculomotor execution to the retention of feature representations in WM. We report that task relevance and oculomotor selection lead to dissociable effects on feature WM maintenance. In a first task, in which an object's location was encoded as a saccade target, its feature representations were successfully maintained in WM, whereas they declined at nonsaccade target locations. Likewise, we observed a similar WM benefit at the target of saccades that were prepared but never executed. In a second task, when an object's location was marked as task relevant but constituted a nonsaccade target (a location to avoid), feature representations maintained at that location did not benefit. Combined, our results demonstrate that oculomotor selection is consistently associated with WM, whereas task relevance is not. This provides evidence for an overlapping circuitry serving saccade target selection and feature-based WM that can be dissociated from processes encoding task-relevant locations. Copyright © 2016 the American Physiological Society.

JCDSA: a joint covariate detection tool for survival analysis on tumor expression profiles.

PubMed

Wu, Yiming; Liu, Yanan; Wang, Yueming; Shi, Yan; Zhao, Xudong

2018-05-29

Survival analysis on tumor expression profiles has always been a key issue for subsequent biological experimental validation. It is crucial how to select features which closely correspond to survival time. Furthermore, it is important how to select features which best discriminate between low-risk and high-risk group of patients. Common features derived from the two aspects may provide variable candidates for prognosis of cancer. Based on the provided two-step feature selection strategy, we develop a joint covariate detection tool for survival analysis on tumor expression profiles. Significant features, which are not only consistent with survival time but also associated with the categories of patients with different survival risks, are chosen. Using the miRNA expression data (Level 3) of 548 patients with glioblastoma multiforme (GBM) as an example, miRNA candidates for prognosis of cancer are selected. The reliability of selected miRNAs using this tool is demonstrated by 100 simulations. Furthermore, It is discovered that significant covariates are not directly composed of individually significant variables. Joint covariate detection provides a viewpoint for selecting variables which are not individually but jointly significant. Besides, it helps to select features which are not only consistent with survival time but also associated with prognosis risk. The software is available at http://bio-nefu.com/resource/jcdsa .

Adaptive feature selection using v-shaped binary particle swarm optimization.

PubMed

Teng, Xuyang; Dong, Hongbin; Zhou, Xiurong

2017-01-01

Feature selection is an important preprocessing method in machine learning and data mining. This process can be used not only to reduce the amount of data to be analyzed but also to build models with stronger interpretability based on fewer features. Traditional feature selection methods evaluate the dependency and redundancy of features separately, which leads to a lack of measurement of their combined effect. Moreover, a greedy search considers only the optimization of the current round and thus cannot be a global search. To evaluate the combined effect of different subsets in the entire feature space, an adaptive feature selection method based on V-shaped binary particle swarm optimization is proposed. In this method, the fitness function is constructed using the correlation information entropy. Feature subsets are regarded as individuals in a population, and the feature space is searched using V-shaped binary particle swarm optimization. The above procedure overcomes the hard constraint on the number of features, enables the combined evaluation of each subset as a whole, and improves the search ability of conventional binary particle swarm optimization. The proposed algorithm is an adaptive method with respect to the number of feature subsets. The experimental results show the advantages of optimizing the feature subsets using the V-shaped transfer function and confirm the effectiveness and efficiency of the feature subsets obtained under different classifiers.

Adaptive feature selection using v-shaped binary particle swarm optimization

PubMed Central

Dong, Hongbin; Zhou, Xiurong

2017-01-01

Feature selection is an important preprocessing method in machine learning and data mining. This process can be used not only to reduce the amount of data to be analyzed but also to build models with stronger interpretability based on fewer features. Traditional feature selection methods evaluate the dependency and redundancy of features separately, which leads to a lack of measurement of their combined effect. Moreover, a greedy search considers only the optimization of the current round and thus cannot be a global search. To evaluate the combined effect of different subsets in the entire feature space, an adaptive feature selection method based on V-shaped binary particle swarm optimization is proposed. In this method, the fitness function is constructed using the correlation information entropy. Feature subsets are regarded as individuals in a population, and the feature space is searched using V-shaped binary particle swarm optimization. The above procedure overcomes the hard constraint on the number of features, enables the combined evaluation of each subset as a whole, and improves the search ability of conventional binary particle swarm optimization. The proposed algorithm is an adaptive method with respect to the number of feature subsets. The experimental results show the advantages of optimizing the feature subsets using the V-shaped transfer function and confirm the effectiveness and efficiency of the feature subsets obtained under different classifiers. PMID:28358850

Long-term clinical results of double-pursestring stapled hemorrhoidopexy in a selected group of patients for the treatment of chronic hemorrhoids.

PubMed

Arroyo, Antonio; Pérez-Legaz, Juan; Miranda, Elena; Moya, Pedro; Ruiz-Tovar, Jaime; Lacueva, Francisco-Javier; Candela, Fernando; Calpena, Rafael

2011-05-01

The aim of this prospective controlled trial was to evaluate the long-term clinical and manometric results of stapled hemorrhoidopexy performed by expert surgeons in a selected group of patients for the treatment of chronic hemorrhoids. This study took place in the outpatient clinic and at the Day Surgery Unit attached to the University Hospital of Elche. From March 2003 to May 2005, 200 consecutive patients with third-degree hemorrhoids and treated with double-pursestring stapled hemorrhoidopexy with a PPH33-03 stapler were included in the study. Demographic, manometric, and clinical features were analyzed, as well as the variables related to surgery, postoperative course, and follow-up. Manometry was repeated at the 6-month, 1-year, and 5-year follow-up. Median follow-up was 110 months. Four patients (2%) reported daily rectal bleeding. One patient with active rectal bleeding was taken for reoperation within the first 12 postoperative hours. Seventy percent of patients reported pain ≤ 2 on the first postoperative day, 85% on the fourth postoperative day, and 95% on the seventh postoperative day. Pain was measured with a linear analog scale from 0 to 10 (0 = no pain; 10 = unbearable pain). Seventeen patients (8.5%) reported tenesmus during the first week. Eight patients (4%) reported persistent pain: in 5 patients, the pain resolved within the next 6 months; 2 patients presented with anal fissure; and 1 patient required the removal of the staples. Two patients (1%) reported residual soiling at the 5-year revision. Fourteen patients (7%) experienced recurrence with symptomatic prolapse. Six (3%) underwent further surgery: stapled hemorrhoidopexy was indicated again in 2 patients, and 4 patients underwent a Milligan-Morgan open hemorrhoidectomy, because they did not have a uniform prolapse. Six patients required treatment with rubber band ligation. There were no statistically significant differences between preoperative and postoperative manometric values. The new PPH33-03 stapler, the learning process of the modified surgical procedure, and the correct selection of patients will overcome the main objections to stapled hemorrhoidopexy.

Multiclass feature selection for improved pediatric brain tumor segmentation

NASA Astrophysics Data System (ADS)

Ahmed, Shaheen; Iftekharuddin, Khan M.

2012-03-01

In our previous work, we showed that fractal-based texture features are effective in detection, segmentation and classification of posterior-fossa (PF) pediatric brain tumor in multimodality MRI. We exploited an information theoretic approach such as Kullback-Leibler Divergence (KLD) for feature selection and ranking different texture features. We further incorporated the feature selection technique with segmentation method such as Expectation Maximization (EM) for segmentation of tumor T and non tumor (NT) tissues. In this work, we extend the two class KLD technique to multiclass for effectively selecting the best features for brain tumor (T), cyst (C) and non tumor (NT). We further obtain segmentation robustness for each tissue types by computing Bay's posterior probabilities and corresponding number of pixels for each tissue segments in MRI patient images. We evaluate improved tumor segmentation robustness using different similarity metric for 5 patients in T1, T2 and FLAIR modalities.

Comparison of Different EHG Feature Selection Methods for the Detection of Preterm Labor

PubMed Central

Alamedine, D.; Khalil, M.; Marque, C.

2013-01-01

Numerous types of linear and nonlinear features have been extracted from the electrohysterogram (EHG) in order to classify labor and pregnancy contractions. As a result, the number of available features is now very large. The goal of this study is to reduce the number of features by selecting only the relevant ones which are useful for solving the classification problem. This paper presents three methods for feature subset selection that can be applied to choose the best subsets for classifying labor and pregnancy contractions: an algorithm using the Jeffrey divergence (JD) distance, a sequential forward selection (SFS) algorithm, and a binary particle swarm optimization (BPSO) algorithm. The two last methods are based on a classifier and were tested with three types of classifiers. These methods have allowed us to identify common features which are relevant for contraction classification. PMID:24454536

HIV-1 protease cleavage site prediction based on two-stage feature selection method.

PubMed

Niu, Bing; Yuan, Xiao-Cheng; Roeper, Preston; Su, Qiang; Peng, Chun-Rong; Yin, Jing-Yuan; Ding, Juan; Li, HaiPeng; Lu, Wen-Cong

2013-03-01

Knowledge of the mechanism of HIV protease cleavage specificity is critical to the design of specific and effective HIV inhibitors. Searching for an accurate, robust, and rapid method to correctly predict the cleavage sites in proteins is crucial when searching for possible HIV inhibitors. In this article, HIV-1 protease specificity was studied using the correlation-based feature subset (CfsSubset) selection method combined with Genetic Algorithms method. Thirty important biochemical features were found based on a jackknife test from the original data set containing 4,248 features. By using the AdaBoost method with the thirty selected features the prediction model yields an accuracy of 96.7% for the jackknife test and 92.1% for an independent set test, with increased accuracy over the original dataset by 6.7% and 77.4%, respectively. Our feature selection scheme could be a useful technique for finding effective competitive inhibitors of HIV protease.

A comparative evaluation of treatments with 17β-estradiol and its brain-selective prodrug in a double-transgenic mouse model of Alzheimer's disease.

PubMed

Tschiffely, Anna E; Schuh, Rosemary A; Prokai-Tatrai, Katalin; Prokai, Laszlo; Ottinger, Mary Ann

2016-07-01

Estrogens are neuroprotective and, thus, potentially useful for the therapy of Alzheimer's disease; however, clinical use of hormone therapy remains controversial due to adverse peripheral effects. The goal of this study was to investigate the benefits of treatment with 10β,17β-dihydroxyestra-1,4-dien-3-one (DHED), a brain-selective prodrug of 17β-estradiol, in comparison with the parent hormone using APPswe/PS1dE9 double transgenic mice to model the pathology of the disease. Ovariectomized and intact females were continuously treated with vehicle, 17β-estradiol, or DHED via subcutaneous osmotic pumps from 6 to 8months of age. We confirmed that this prolonged treatment with DHED did not stimulate uterine tissue, whereas 17β-estradiol treatment increased uterine weight. Amyloid precursor protein decreased in both treatment groups of intact, but not in ovariectomized double transgenic females in which ovariectomy already decreased the expression of this protein significantly. However, reduced brain amyloid-β peptide levels could be observed for both treatments. Consequently, double-transgenic ovariectomized and intact mice had higher cognitive performance compared to untreated control animals in response to both estradiol and DHED administrations. Overall, the tested brain-selective 17β-estradiol prodrug proved to be an effective early-stage intervention in an Alzheimer's disease-relevant mouse model without showing systemic impact and, thus, warrants further evaluation as a potential therapeutic candidate. Copyright © 2016 Elsevier Inc. All rights reserved.

Controlling chaos-assisted directed transport via quantum resonance.

PubMed

Tan, Jintao; Zou, Mingliang; Luo, Yunrong; Hai, Wenhua

2016-06-01

We report on the first demonstration of chaos-assisted directed transport of a quantum particle held in an amplitude-modulated and tilted optical lattice, through a resonance-induced double-mean displacement relating to the true classically chaotic orbits. The transport velocity is controlled by the driving amplitude and the sign of tilt, and also depends on the phase of the initial state. The chaos-assisted transport feature can be verified experimentally by using a source of single atoms to detect the double-mean displacement one by one, and can be extended to different scientific fields.

Quiet Clean Short-haul Experimental Engine (QCSEE). Double-annular clean combustor technology development report

NASA Technical Reports Server (NTRS)

Bahr, D. W.; Burrus, D. L.; Sabla, P. E.

1979-01-01

A sector combustor technology development program was conducted to define an advanced double annular dome combustor sized for use in the quiet clean short haul experimental engine (QCSEE). A design which meets the emission goals, and combustor performance goals of the QCSEE engine program was developed. Key design features were identified which resulted in substantial reduction in carbon monoxide and unburned hydrocarbon emission levels at ground idle operating conditions, in addition to very low nitric oxide emission levels at high power operating conditions. Their significant results are reported.

Double axis, two-crystal x-ray spectrometer.

PubMed

Erez, G; Kimhi, D; Livnat, A

1978-05-01

A two-crystal double axis x-ray spectrometer, capable of goniometric accuracy on the order of 0.1", has been developed. Some of its unique design features are presented. These include (1) a modified commercial thrust bearing which furnishes a precise, full circle theta:2theta coupling, (2) a new tangent drive system design in which a considerable reduction of the lead screw effective pitch is achieved, and (3) an automatic step scanning control which eliminates most of the mechanical deficiencies of the tangent drive by directly reading the tangent arm displacement.

Controlling chaos-assisted directed transport via quantum resonance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tan, Jintao; Zou, Mingliang; Luo, Yunrong

2016-06-15

We report on the first demonstration of chaos-assisted directed transport of a quantum particle held in an amplitude-modulated and tilted optical lattice, through a resonance-induced double-mean displacement relating to the true classically chaotic orbits. The transport velocity is controlled by the driving amplitude and the sign of tilt, and also depends on the phase of the initial state. The chaos-assisted transport feature can be verified experimentally by using a source of single atoms to detect the double-mean displacement one by one, and can be extended to different scientific fields.

Systematics of the low-energy pionic double charge exchange in nuclei

NASA Astrophysics Data System (ADS)

Draeger, J.; Bilger, R.; Clement, H.; Cröni, M.; Denz, H.; Gräter, J.; Meier, R.; Pätzold, J.; Schapler, D.; Wagner, G. J.; Wilhelm, O.; Föhl, K.; Schepkin, M.

2000-12-01

The experimental results for the (π+,π-) reaction on nuclei obtained in recent years reveal clear systematic features of this reaction. New data on 7Li, 12C, 16O, and 56Fe supplementing the existing data base are presented. The data on 12C are partly at variance with previous results. The dependence of the cross sections on incident energy, scattering angle, and on the target mass is discussed for transitions leading to the ground state of the final nucleus or to the double isobaric analog state.

Interplay of coupling and superradiant emission in the optical response of a double quantum dot

NASA Astrophysics Data System (ADS)

Sitek, Anna; Machnikowski, Paweł

2009-09-01

We study theoretically the optical response of a double quantum dot structure to an ultrafast optical excitation. We show that the interplay of a specific type of coupling between the dots and their collective interaction with the radiative environment leads to very characteristic features in the time-resolved luminescence as well as in the absorption spectrum of the system. For a sufficiently strong coupling, these effects survive even if the transition energy mismatch between the two dots exceeds by far the emission linewidth.

Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients.

PubMed

Capela, Nicole A; Lemaire, Edward D; Baddour, Natalie

2015-01-01

Human activity recognition (HAR), using wearable sensors, is a growing area with the potential to provide valuable information on patient mobility to rehabilitation specialists. Smartphones with accelerometer and gyroscope sensors are a convenient, minimally invasive, and low cost approach for mobility monitoring. HAR systems typically pre-process raw signals, segment the signals, and then extract features to be used in a classifier. Feature selection is a crucial step in the process to reduce potentially large data dimensionality and provide viable parameters to enable activity classification. Most HAR systems are customized to an individual research group, including a unique data set, classes, algorithms, and signal features. These data sets are obtained predominantly from able-bodied participants. In this paper, smartphone accelerometer and gyroscope sensor data were collected from populations that can benefit from human activity recognition: able-bodied, elderly, and stroke patients. Data from a consecutive sequence of 41 mobility tasks (18 different tasks) were collected for a total of 44 participants. Seventy-six signal features were calculated and subsets of these features were selected using three filter-based, classifier-independent, feature selection methods (Relief-F, Correlation-based Feature Selection, Fast Correlation Based Filter). The feature subsets were then evaluated using three generic classifiers (Naïve Bayes, Support Vector Machine, j48 Decision Tree). Common features were identified for all three populations, although the stroke population subset had some differences from both able-bodied and elderly sets. Evaluation with the three classifiers showed that the feature subsets produced similar or better accuracies than classification with the entire feature set. Therefore, since these feature subsets are classifier-independent, they should be useful for developing and improving HAR systems across and within populations.

Feature Selection for Wearable Smartphone-Based Human Activity Recognition with Able bodied, Elderly, and Stroke Patients

PubMed Central

2015-01-01

Human activity recognition (HAR), using wearable sensors, is a growing area with the potential to provide valuable information on patient mobility to rehabilitation specialists. Smartphones with accelerometer and gyroscope sensors are a convenient, minimally invasive, and low cost approach for mobility monitoring. HAR systems typically pre-process raw signals, segment the signals, and then extract features to be used in a classifier. Feature selection is a crucial step in the process to reduce potentially large data dimensionality and provide viable parameters to enable activity classification. Most HAR systems are customized to an individual research group, including a unique data set, classes, algorithms, and signal features. These data sets are obtained predominantly from able-bodied participants. In this paper, smartphone accelerometer and gyroscope sensor data were collected from populations that can benefit from human activity recognition: able-bodied, elderly, and stroke patients. Data from a consecutive sequence of 41 mobility tasks (18 different tasks) were collected for a total of 44 participants. Seventy-six signal features were calculated and subsets of these features were selected using three filter-based, classifier-independent, feature selection methods (Relief-F, Correlation-based Feature Selection, Fast Correlation Based Filter). The feature subsets were then evaluated using three generic classifiers (Naïve Bayes, Support Vector Machine, j48 Decision Tree). Common features were identified for all three populations, although the stroke population subset had some differences from both able-bodied and elderly sets. Evaluation with the three classifiers showed that the feature subsets produced similar or better accuracies than classification with the entire feature set. Therefore, since these feature subsets are classifier-independent, they should be useful for developing and improving HAR systems across and within populations. PMID:25885272

Gene features selection for three-class disease classification via multiple orthogonal partial least square discriminant analysis and S-plot using microarray data.

PubMed

Yang, Mingxing; Li, Xiumin; Li, Zhibin; Ou, Zhimin; Liu, Ming; Liu, Suhuan; Li, Xuejun; Yang, Shuyu

2013-01-01

DNA microarray analysis is characterized by obtaining a large number of gene variables from a small number of observations. Cluster analysis is widely used to analyze DNA microarray data to make classification and diagnosis of disease. Because there are so many irrelevant and insignificant genes in a dataset, a feature selection approach must be employed in data analysis. The performance of cluster analysis of this high-throughput data depends on whether the feature selection approach chooses the most relevant genes associated with disease classes. Here we proposed a new method using multiple Orthogonal Partial Least Squares-Discriminant Analysis (mOPLS-DA) models and S-plots to select the most relevant genes to conduct three-class disease classification and prediction. We tested our method using Golub's leukemia microarray data. For three classes with subtypes, we proposed hierarchical orthogonal partial least squares-discriminant analysis (OPLS-DA) models and S-plots to select features for two main classes and their subtypes. For three classes in parallel, we employed three OPLS-DA models and S-plots to choose marker genes for each class. The power of feature selection to classify and predict three-class disease was evaluated using cluster analysis. Further, the general performance of our method was tested using four public datasets and compared with those of four other feature selection methods. The results revealed that our method effectively selected the most relevant features for disease classification and prediction, and its performance was better than that of the other methods.

A feature selection approach towards progressive vector transmission over the Internet

NASA Astrophysics Data System (ADS)

Miao, Ru; Song, Jia; Feng, Min

2017-09-01

WebGIS has been applied for visualizing and sharing geospatial information popularly over the Internet. In order to improve the efficiency of the client applications, the web-based progressive vector transmission approach is proposed. Important features should be selected and transferred firstly, and the methods for measuring the importance of features should be further considered in the progressive transmission. However, studies on progressive transmission for large-volume vector data have mostly focused on map generalization in the field of cartography, but rarely discussed on the selection of geographic features quantitatively. This paper applies information theory for measuring the feature importance of vector maps. A measurement model for the amount of information of vector features is defined based upon the amount of information for dealing with feature selection issues. The measurement model involves geometry factor, spatial distribution factor and thematic attribute factor. Moreover, a real-time transport protocol (RTP)-based progressive transmission method is then presented to improve the transmission of vector data. To clearly demonstrate the essential methodology and key techniques, a prototype for web-based progressive vector transmission is presented, and an experiment of progressive selection and transmission for vector features is conducted. The experimental results indicate that our approach clearly improves the performance and end-user experience of delivering and manipulating large vector data over the Internet.

A new approach to modeling the influence of image features on fixation selection in scenes

PubMed Central

Nuthmann, Antje; Einhäuser, Wolfgang

2015-01-01

Which image characteristics predict where people fixate when memorizing natural images? To answer this question, we introduce a new analysis approach that combines a novel scene-patch analysis with generalized linear mixed models (GLMMs). Our method allows for (1) directly describing the relationship between continuous feature value and fixation probability, and (2) assessing each feature's unique contribution to fixation selection. To demonstrate this method, we estimated the relative contribution of various image features to fixation selection: luminance and luminance contrast (low-level features); edge density (a mid-level feature); visual clutter and image segmentation to approximate local object density in the scene (higher-level features). An additional predictor captured the central bias of fixation. The GLMM results revealed that edge density, clutter, and the number of homogenous segments in a patch can independently predict whether image patches are fixated or not. Importantly, neither luminance nor contrast had an independent effect above and beyond what could be accounted for by the other predictors. Since the parcellation of the scene and the selection of features can be tailored to the specific research question, our approach allows for assessing the interplay of various factors relevant for fixation selection in scenes in a powerful and flexible manner. PMID:25752239

A comparison of three feature selection methods for object-based classification of sub-decimeter resolution UltraCam-L imagery

USDA-ARS?s Scientific Manuscript database

The availability of numerous spectral, spatial, and contextual features with object-based image analysis (OBIA) renders the selection of optimal features a time consuming and subjective process. While several feature election methods have been used in conjunction with OBIA, a robust comparison of th...

Processing Dynamic Image Sequences from a Moving Sensor.

DTIC Science & Technology

1984-02-01

65 Roadsign Image Sequence ..... ................ ... 70 Roadsign Sequence with Redundant Features .. ........ . 79 Roadsign Subimage...Selected Feature Error Values .. ........ 66 2c. Industrial Image Selected Feature Local Search Values. .. .... 67 3ab. Roadsign Image Error Values...72 3c. Roadsign Image Local Search Values ............. 73 4ab. Roadsign Redundant Feature Error Values. ............ 8 4c. Roadsign

Mammogram classification scheme using 2D-discrete wavelet and local binary pattern for detection of breast cancer

NASA Astrophysics Data System (ADS)

Adi Putra, Januar

2018-04-01

In this paper, we propose a new mammogram classification scheme to classify the breast tissues as normal or abnormal. Feature matrix is generated using Local Binary Pattern to all the detailed coefficients from 2D-DWT of the region of interest (ROI) of a mammogram. Feature selection is done by selecting the relevant features that affect the classification. Feature selection is used to reduce the dimensionality of data and features that are not relevant, in this paper the F-test and Ttest will be performed to the results of the feature extraction dataset to reduce and select the relevant feature. The best features are used in a Neural Network classifier for classification. In this research we use MIAS and DDSM database. In addition to the suggested scheme, the competent schemes are also simulated for comparative analysis. It is observed that the proposed scheme has a better say with respect to accuracy, specificity and sensitivity. Based on experiments, the performance of the proposed scheme can produce high accuracy that is 92.71%, while the lowest accuracy obtained is 77.08%.

A Generalization Strategy for Discrete Area Feature by Using Stroke Grouping and Polarization Transportation Selection

NASA Astrophysics Data System (ADS)

Wang, Xiao; Burghardt, Dirk

2018-05-01

This paper presents a new strategy for the generalization of discrete area features by using stroke grouping method and polarization transportation selection. The mentioned stroke is constructed on derive of the refined proximity graph of area features, and the refinement is under the control of four constraints to meet different grouping requirements. The area features which belong to the same stroke are detected into the same group. The stroke-based strategy decomposes the generalization process into two sub-processes by judging whether the area features related to strokes or not. For the area features which belong to the same one stroke, they normally present a linear like pat-tern, and in order to preserve this kind of pattern, typification is chosen as the operator to implement the generalization work. For the remaining area features which are not related by strokes, they are still distributed randomly and discretely, and the selection is chosen to conduct the generalization operation. For the purpose of retaining their original distribution characteristic, a Polarization Transportation (PT) method is introduced to implement the selection operation. Buildings and lakes are selected as the representatives of artificial area feature and natural area feature respectively to take the experiments. The generalized results indicate that by adopting this proposed strategy, the original distribution characteristics of building and lake data can be preserved, and the visual perception is pre-served as before.

Over-under double-pass interferometer

NASA Technical Reports Server (NTRS)

Schindler, R. A. (Inventor)

1977-01-01

An over-under double pass interferometer in which the beamsplitter area and thickness can be reduced to conform only with optical flatness considerations was achieved by offsetting the optical center line of one cat's-eye retroreflector relative to the optical center line of the other in order that one split beam be folded into a plane distinct from the other folded split beam. The beamsplitter is made transparent in one area for a first folded beam to be passed to a mirror for doubling back and is made totally reflective in another area for the second folded beam to be reflected to a mirror for doubling back. The two beams thus doubled back are combined in the central, beamsplitting area of the beamsplitting and passed to a detector. This makes the beamsplitter insensitive to minimum thickness requirements and selection of material.

Over-under double-pass interferometer

NASA Technical Reports Server (NTRS)

Schindler, Rudolf A. (Inventor)

1980-01-01

An over-under double-pass interferometer in which the beamsplitter area and thickness can be reduced to conform only with optical flatness considerations is achieved by offsetting the optical center line of one cat's-eye retroreflector relative to the optical center line of the other in order that one split beam be folded into a plane distinct from the other folded split beam. The beamsplitter is made transparent in one area for a first folded beam to be passed to a mirror for doubling back and is made totally reflective in another area for the second folded beam to be reflected to a mirror for doubling back. The two beams thus doubled back are combined in the central, beam-splitting area of the beamsplitter and passed to a detector. This makes the beamsplitter insensitive to minimum-thickness requirements and selection of material.

Utility of the Anterior Oblique-Viewing Endoscope and the Double-Balloon Enteroscope for Endoscopic Retrograde Cholangiopancreatography in Patients with Billroth II Gastrectomy

PubMed Central

Sen-yo, Manabu; Kaino, Seiji; Suenaga, Shigeyuki; Uekitani, Toshiyuki; Yoshida, Kanako; Harano, Megumi; Sakaida, Isao

2012-01-01

Background/Purpose. The difficulties of endoscopic retrograde cholangiopancreatography in patients with Billroth II gastrectomy have been reported. We evaluated the usefulness of an anterior oblique-viewing endoscope and a double-balloon enteroscope for endoscopic retrograde cholangiopancreatography in such patients. Methods. From January 2003 to December 2011, 65 patients with Billroth II gastrectomy were enrolled in this study. An anterior oblique-viewing endoscope was used for all patients. From February 2007, a double-balloon enteroscope was used for the failed cases. The success rate of procedures was compared with those in 20 patients with Billroth II gastrectomy using forward-viewing endoscope or side-viewing endoscope from March 1996 to July 2002 as historical controls. Results. In all patients in whom the papilla was reached (60/65), selective cannulation was achieved. The success rate of selective cannulation and accomplishment of planned procedures in the anterior oblique-viewing endoscope group were both significantly higher than that in the control group (100% versus 70.1%, 100 versus 58.8%, resp.). A double-balloon enteroscope was used in 2 patients, and the papilla could be reached and the planned procedures completed. Conclusions. An anterior oblique-viewing endoscope and double-balloon enteroscope appear to be useful in performing endoscopic retrograde cholangiopancreatography in patients with Billroth II gastrectomy. PMID:23056039

An Ant Colony Optimization Based Feature Selection for Web Page Classification

PubMed Central

2014-01-01

The increased popularity of the web has caused the inclusion of huge amount of information to the web, and as a result of this explosive information growth, automated web page classification systems are needed to improve search engines' performance. Web pages have a large number of features such as HTML/XML tags, URLs, hyperlinks, and text contents that should be considered during an automated classification process. The aim of this study is to reduce the number of features to be used to improve runtime and accuracy of the classification of web pages. In this study, we used an ant colony optimization (ACO) algorithm to select the best features, and then we applied the well-known C4.5, naive Bayes, and k nearest neighbor classifiers to assign class labels to web pages. We used the WebKB and Conference datasets in our experiments, and we showed that using the ACO for feature selection improves both accuracy and runtime performance of classification. We also showed that the proposed ACO based algorithm can select better features with respect to the well-known information gain and chi square feature selection methods. PMID:25136678

Hypergraph Based Feature Selection Technique for Medical Diagnosis.

PubMed

Somu, Nivethitha; Raman, M R Gauthama; Kirthivasan, Kannan; Sriram, V S Shankar

2016-11-01

The impact of internet and information systems across various domains have resulted in substantial generation of multidimensional datasets. The use of data mining and knowledge discovery techniques to extract the original information contained in the multidimensional datasets play a significant role in the exploitation of complete benefit provided by them. The presence of large number of features in the high dimensional datasets incurs high computational cost in terms of computing power and time. Hence, feature selection technique has been commonly used to build robust machine learning models to select a subset of relevant features which projects the maximal information content of the original dataset. In this paper, a novel Rough Set based K - Helly feature selection technique (RSKHT) which hybridize Rough Set Theory (RST) and K - Helly property of hypergraph representation had been designed to identify the optimal feature subset or reduct for medical diagnostic applications. Experiments carried out using the medical datasets from the UCI repository proves the dominance of the RSKHT over other feature selection techniques with respect to the reduct size, classification accuracy and time complexity. The performance of the RSKHT had been validated using WEKA tool, which shows that RSKHT had been computationally attractive and flexible over massive datasets.

Reducing Sweeping Frequencies in Microwave NDT Employing Machine Learning Feature Selection

PubMed Central

Moomen, Abdelniser; Ali, Abdulbaset; Ramahi, Omar M.

2016-01-01

Nondestructive Testing (NDT) assessment of materials’ health condition is useful for classifying healthy from unhealthy structures or detecting flaws in metallic or dielectric structures. Performing structural health testing for coated/uncoated metallic or dielectric materials with the same testing equipment requires a testing method that can work on metallics and dielectrics such as microwave testing. Reducing complexity and expenses associated with current diagnostic practices of microwave NDT of structural health requires an effective and intelligent approach based on feature selection and classification techniques of machine learning. Current microwave NDT methods in general based on measuring variation in the S-matrix over the entire operating frequency ranges of the sensors. For instance, assessing the health of metallic structures using a microwave sensor depends on the reflection or/and transmission coefficient measurements as a function of the sweeping frequencies of the operating band. The aim of this work is reducing sweeping frequencies using machine learning feature selection techniques. By treating sweeping frequencies as features, the number of top important features can be identified, then only the most influential features (frequencies) are considered when building the microwave NDT equipment. The proposed method of reducing sweeping frequencies was validated experimentally using a waveguide sensor and a metallic plate with different cracks. Among the investigated feature selection techniques are information gain, gain ratio, relief, chi-squared. The effectiveness of the selected features were validated through performance evaluations of various classification models; namely, Nearest Neighbor, Neural Networks, Random Forest, and Support Vector Machine. Results showed good crack classification accuracy rates after employing feature selection algorithms. PMID:27104533

Stabilizing l1-norm prediction models by supervised feature grouping.

PubMed

Kamkar, Iman; Gupta, Sunil Kumar; Phung, Dinh; Venkatesh, Svetha

2016-02-01

Emerging Electronic Medical Records (EMRs) have reformed the modern healthcare. These records have great potential to be used for building clinical prediction models. However, a problem in using them is their high dimensionality. Since a lot of information may not be relevant for prediction, the underlying complexity of the prediction models may not be high. A popular way to deal with this problem is to employ feature selection. Lasso and l1-norm based feature selection methods have shown promising results. But, in presence of correlated features, these methods select features that change considerably with small changes in data. This prevents clinicians to obtain a stable feature set, which is crucial for clinical decision making. Grouping correlated variables together can improve the stability of feature selection, however, such grouping is usually not known and needs to be estimated for optimal performance. Addressing this problem, we propose a new model that can simultaneously learn the grouping of correlated features and perform stable feature selection. We formulate the model as a constrained optimization problem and provide an efficient solution with guaranteed convergence. Our experiments with both synthetic and real-world datasets show that the proposed model is significantly more stable than Lasso and many existing state-of-the-art shrinkage and classification methods. We further show that in terms of prediction performance, the proposed method consistently outperforms Lasso and other baselines. Our model can be used for selecting stable risk factors for a variety of healthcare problems, so it can assist clinicians toward accurate decision making. Copyright © 2015 Elsevier Inc. All rights reserved.

On the use of feature selection to improve the detection of sea oil spills in SAR images

NASA Astrophysics Data System (ADS)

Mera, David; Bolon-Canedo, Veronica; Cotos, J. M.; Alonso-Betanzos, Amparo

2017-03-01

Fast and effective oil spill detection systems are crucial to ensure a proper response to environmental emergencies caused by hydrocarbon pollution on the ocean's surface. Typically, these systems uncover not only oil spills, but also a high number of look-alikes. The feature extraction is a critical and computationally intensive phase where each detected dark spot is independently examined. Traditionally, detection systems use an arbitrary set of features to discriminate between oil spills and look-alikes phenomena. However, Feature Selection (FS) methods based on Machine Learning (ML) have proved to be very useful in real domains for enhancing the generalization capabilities of the classifiers, while discarding the existing irrelevant features. In this work, we present a generic and systematic approach, based on FS methods, for choosing a concise and relevant set of features to improve the oil spill detection systems. We have compared five FS methods: Correlation-based feature selection (CFS), Consistency-based filter, Information Gain, ReliefF and Recursive Feature Elimination for Support Vector Machine (SVM-RFE). They were applied on a 141-input vector composed of features from a collection of outstanding studies. Selected features were validated via a Support Vector Machine (SVM) classifier and the results were compared with previous works. Test experiments revealed that the classifier trained with the 6-input feature vector proposed by SVM-RFE achieved the best accuracy and Cohen's kappa coefficient (87.1% and 74.06% respectively). This is a smaller feature combination with similar or even better classification accuracy than previous works. The presented finding allows to speed up the feature extraction phase without reducing the classifier accuracy. Experiments also confirmed the significance of the geometrical features since 75.0% of the different features selected by the applied FS methods as well as 66.67% of the proposed 6-input feature vector belong to this category.

Intrathecal Baclofen in Children with Spastic Cerebral Palsy: A Double-Blind, Randomized, Placebo-Controlled, Dose-Finding Study

ERIC Educational Resources Information Center

Hoving, Marjanke A.; van Raak, Elisabeth P. M.; Spincemaille, Geert H. J. J.; Palmans, Liesbeth J.; Sleypen, Frans A. M.; Vles, Johan S. H.

2007-01-01

Intrathecal baclofen (ITB) therapy can be very effective in the treatment of intractable spasticity, but its effectiveness and safety have not yet been thoroughly studied in children with cerebral palsy (CP). The aims of this double-blind, randomized, placebo-controlled, dose-finding study were to select children eligible for continuous ITB…

Selecting Feature Subsets Based on SVM-RFE and the Overlapping Ratio with Applications in Bioinformatics.

PubMed

Lin, Xiaohui; Li, Chao; Zhang, Yanhui; Su, Benzhe; Fan, Meng; Wei, Hai

2017-12-26

Feature selection is an important topic in bioinformatics. Defining informative features from complex high dimensional biological data is critical in disease study, drug development, etc. Support vector machine-recursive feature elimination (SVM-RFE) is an efficient feature selection technique that has shown its power in many applications. It ranks the features according to the recursive feature deletion sequence based on SVM. In this study, we propose a method, SVM-RFE-OA, which combines the classification accuracy rate and the average overlapping ratio of the samples to determine the number of features to be selected from the feature rank of SVM-RFE. Meanwhile, to measure the feature weights more accurately, we propose a modified SVM-RFE-OA (M-SVM-RFE-OA) algorithm that temporally screens out the samples lying in a heavy overlapping area in each iteration. The experiments on the eight public biological datasets show that the discriminative ability of the feature subset could be measured more accurately by combining the classification accuracy rate with the average overlapping degree of the samples compared with using the classification accuracy rate alone, and shielding the samples in the overlapping area made the calculation of the feature weights more stable and accurate. The methods proposed in this study can also be used with other RFE techniques to define potential biomarkers from big biological data.

Hadoop neural network for parallel and distributed feature selection.

PubMed

Hodge, Victoria J; O'Keefe, Simon; Austin, Jim

2016-06-01

In this paper, we introduce a theoretical basis for a Hadoop-based neural network for parallel and distributed feature selection in Big Data sets. It is underpinned by an associative memory (binary) neural network which is highly amenable to parallel and distributed processing and fits with the Hadoop paradigm. There are many feature selectors described in the literature which all have various strengths and weaknesses. We present the implementation details of five feature selection algorithms constructed using our artificial neural network framework embedded in Hadoop YARN. Hadoop allows parallel and distributed processing. Each feature selector can be divided into subtasks and the subtasks can then be processed in parallel. Multiple feature selectors can also be processed simultaneously (in parallel) allowing multiple feature selectors to be compared. We identify commonalities among the five features selectors. All can be processed in the framework using a single representation and the overall processing can also be greatly reduced by only processing the common aspects of the feature selectors once and propagating these aspects across all five feature selectors as necessary. This allows the best feature selector and the actual features to select to be identified for large and high dimensional data sets through exploiting the efficiency and flexibility of embedding the binary associative-memory neural network in Hadoop. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Prediction of protein-protein interactions based on PseAA composition and hybrid feature selection.

PubMed

Liu, Liang; Cai, Yudong; Lu, Wencong; Feng, Kaiyan; Peng, Chunrong; Niu, Bing

2009-03-06

Based on pseudo amino acid (PseAA) composition and a novel hybrid feature selection frame, this paper presents a computational system to predict the PPIs (protein-protein interactions) using 8796 protein pairs. These pairs are coded by PseAA composition, resulting in 114 features. A hybrid feature selection system, mRMR-KNNs-wrapper, is applied to obtain an optimized feature set by excluding poor-performed and/or redundant features, resulting in 103 remaining features. Using the optimized 103-feature subset, a prediction model is trained and tested in the k-nearest neighbors (KNNs) learning system. This prediction model achieves an overall accurate prediction rate of 76.18%, evaluated by 10-fold cross-validation test, which is 1.46% higher than using the initial 114 features and is 6.51% higher than the 20 features, coded by amino acid compositions. The PPIs predictor, developed for this research, is available for public use at http://chemdata.shu.edu.cn/ppi.

Comparison of Naive Bayes and Decision Tree on Feature Selection Using Genetic Algorithm for Classification Problem

NASA Astrophysics Data System (ADS)

Rahmadani, S.; Dongoran, A.; Zarlis, M.; Zakarias

2018-03-01

This paper discusses the problem of feature selection using genetic algorithms on a dataset for classification problems. The classification model used is the decicion tree (DT), and Naive Bayes. In this paper we will discuss how the Naive Bayes and Decision Tree models to overcome the classification problem in the dataset, where the dataset feature is selectively selected using GA. Then both models compared their performance, whether there is an increase in accuracy or not. From the results obtained shows an increase in accuracy if the feature selection using GA. The proposed model is referred to as GADT (GA-Decision Tree) and GANB (GA-Naive Bayes). The data sets tested in this paper are taken from the UCI Machine Learning repository.

Fuzzy feature selection based on interval type-2 fuzzy sets

NASA Astrophysics Data System (ADS)

Cherif, Sahar; Baklouti, Nesrine; Alimi, Adel; Snasel, Vaclav

2017-03-01

When dealing with real world data; noise, complexity, dimensionality, uncertainty and irrelevance can lead to low performance and insignificant judgment. Fuzzy logic is a powerful tool for controlling conflicting attributes which can have similar effects and close meanings. In this paper, an interval type-2 fuzzy feature selection is presented as a new approach for removing irrelevant features and reducing complexity. We demonstrate how can Feature Selection be joined with Interval Type-2 Fuzzy Logic for keeping significant features and hence reducing time complexity. The proposed method is compared with some other approaches. The results show that the number of attributes is proportionally small.

Neural evidence reveals the rapid effects of reward history on selective attention.

PubMed

MacLean, Mary H; Giesbrecht, Barry

2015-05-05

Selective attention is often framed as being primarily driven by two factors: task-relevance and physical salience. However, factors like selection and reward history, which are neither currently task-relevant nor physically salient, can reliably and persistently influence visual selective attention. The current study investigated the nature of the persistent effects of irrelevant, physically non-salient, reward-associated features. These features affected one of the earliest reliable neural indicators of visual selective attention in humans, the P1 event-related potential, measured one week after the reward associations were learned. However, the effects of reward history were moderated by current task demands. The modulation of visually evoked activity supports the hypothesis that reward history influences the innate salience of reward associated features, such that even when no longer relevant, nor physically salient, these features have a rapid, persistent, and robust effect on early visual selective attention. Copyright © 2015 Elsevier B.V. All rights reserved.

Systematic interrogation of diverse Omic data reveals interpretable, robust, and generalizable transcriptomic features of clinically successful therapeutic targets.

PubMed

Rouillard, Andrew D; Hurle, Mark R; Agarwal, Pankaj

2018-05-01

Target selection is the first and pivotal step in drug discovery. An incorrect choice may not manifest itself for many years after hundreds of millions of research dollars have been spent. We collected a set of 332 targets that succeeded or failed in phase III clinical trials, and explored whether Omic features describing the target genes could predict clinical success. We obtained features from the recently published comprehensive resource: Harmonizome. Nineteen features appeared to be significantly correlated with phase III clinical trial outcomes, but only 4 passed validation schemes that used bootstrapping or modified permutation tests to assess feature robustness and generalizability while accounting for target class selection bias. We also used classifiers to perform multivariate feature selection and found that classifiers with a single feature performed as well in cross-validation as classifiers with more features (AUROC = 0.57 and AUPR = 0.81). The two predominantly selected features were mean mRNA expression across tissues and standard deviation of expression across tissues, where successful targets tended to have lower mean expression and higher expression variance than failed targets. This finding supports the conventional wisdom that it is favorable for a target to be present in the tissue(s) affected by a disease and absent from other tissues. Overall, our results suggest that it is feasible to construct a model integrating interpretable target features to inform target selection. We anticipate deeper insights and better models in the future, as researchers can reuse the data we have provided to improve methods for handling sample biases and learn more informative features. Code, documentation, and data for this study have been deposited on GitHub at https://github.com/arouillard/omic-features-successful-targets.

Offshore submarine storage facility for highly chilled liquified gases

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cook, S.F.

1982-12-28

Improvements in an offshore platform and submarine storage facility for highly chilled liquified gas, such as liquified natural gas, are disclosed. The improved facility includes an elongated, vertically oriented submerged anchoring frame to which one or more insulated storage tanks are moveably mounted so they can be positioned at a selected depth in the water. The double piston tank is constructed with improved seals to transfer ambient water pressure of the selected depth to the cryogenic liquified gas without intermixture. This transferred pressure at the depth selected aids in maintaining the liquified state of the stored liquified gas. Structural improvementsmore » to the tank facilitating ballasting, locking the double piston cylinders together and further facilitating surface access to the tank for inspection, repairs and removal, and structural improvements to the platform are disclosed.« less

Feature selection using probabilistic prediction of support vector regression.

PubMed

Yang, Jian-Bo; Ong, Chong-Jin

2011-06-01

This paper presents a new wrapper-based feature selection method for support vector regression (SVR) using its probabilistic predictions. The method computes the importance of a feature by aggregating the difference, over the feature space, of the conditional density functions of the SVR prediction with and without the feature. As the exact computation of this importance measure is expensive, two approximations are proposed. The effectiveness of the measure using these approximations, in comparison to several other existing feature selection methods for SVR, is evaluated on both artificial and real-world problems. The result of the experiments show that the proposed method generally performs better than, or at least as well as, the existing methods, with notable advantage when the dataset is sparse.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jing, Yaqi; Meng, Qinghao, E-mail: qh-meng@tju.edu.cn; Qi, Peifeng

An electronic nose (e-nose) was designed to classify Chinese liquors of the same aroma style. A new method of feature reduction which combined feature selection with feature extraction was proposed. Feature selection method used 8 feature-selection algorithms based on information theory and reduced the dimension of the feature space to 41. Kernel entropy component analysis was introduced into the e-nose system as a feature extraction method and the dimension of feature space was reduced to 12. Classification of Chinese liquors was performed by using back propagation artificial neural network (BP-ANN), linear discrimination analysis (LDA), and a multi-linear classifier. The classificationmore » rate of the multi-linear classifier was 97.22%, which was higher than LDA and BP-ANN. Finally the classification of Chinese liquors according to their raw materials and geographical origins was performed using the proposed multi-linear classifier and classification rate was 98.75% and 100%, respectively.« less

Features of the Valorization of Single and Double Based Powders for Codetonation in Emulsion Explosives

NASA Astrophysics Data System (ADS)

Ribeiro, Jose; Mendes, Ricardo; Tavares, Bruno; Louro, Cristina

2013-06-01

In this work, features of the thermal and detonation behavior of compositions resulting from the mixture of single and double based gun powder within ammonium nitrate (AN) based emulsion explosives are shown. That includes results of thermodynamic-equilibrium calculations of the detonation velocity, the chemical compatibility assessment through differential scanning calorimetry [DSC] and thermo gravimetric analysis [TGA], the experimental determination of the detonation velocity and a comparative evaluation of the shock sensitivity using a modified version of the ``gap-test''. DSC/TGA results for the compositions and for the individual components overlap until the beginning of the thermal decomposition which is an indication of the absence of formation of any new chemical specimens and so of the capability of the composition components. After the beginning of the thermal decomposition it can be seen that the rate of mass loss is much higher for the compositions with gun powder than for the sole emulsion explosive. Both, theoretical and experimental, values of the detonation velocity have shown to be higher for the powdered compositions than for the pure emulsion explosive. Shock sensitivity assessment have ended-up with a slightly bigger sensitivity for the compositions with double based gun powder when compared to the single based compositions or to the pure emulsion.

Double-u double-u double-u dot APIC dot org: a review of the APIC World Wide Web site.

PubMed

Harr, J

1996-12-01

The widespread use of the Internet and the development of the World Wide Web have led to a revolution in electronic communication and information access. The Association for Professional in Infection Control and Epidemiology (APIC) has developed a site on the World Wide Web to provide mechanisms for international on-line information access and exchange on issues related to the practice of infection control and the application of epidemiology. From the home page of the APIC Web site, users can access information on professional resources, publications, educational offering, governmental affairs, the APIC organization, and the infection control profession. Among the chief features of the site is a discussion forum for posing questions and sharing information about infection control and epidemiology. The site also contains a searchable database of practice-related abstracts and descriptions and order forms for APIC publications. Users will find continuing education course descriptions and registration forms, legislative and regulatory action alerts and a congressional mailer, chapter and committee information, and infection control information of interest to the general public. APIC is considering several potential future enhancements to their Web site and will continue to review the site's content and features to provide current and useful information to infection control professionals.

Ghostly Remnant of an Explosive Past

NASA Image and Video Library

2007-03-07

This enhanced image from the far-ultraviolet detector on NASA Galaxy Evolution shows a ghostly shell of ionized gas around Z Camelopardalis, a binary, or double-star system featuring a collapsed, dead star known as a white dwarf, and a companion star.

Double-outlet right ventricle: Pathology and angiocardiography.

PubMed

Freedom, Robert M.; Yoo, Shi-Joon

2000-01-01

Double-outlet right ventricle is but one form of abnormal ventriculoarterial connection. The definition that more than half of each great artery originates above the morphologically right ventricle is arbitrary. As pointed out by Lecompte, those features that should be defined in hearts with the ventriculoarterial connection of double-outlet right ventricle (and indeed other forms of abnormal ventriculoarterial connection) include the nature of the infundibular septum, ventriculoinfundibular fold, trabeculoseptomargin-alis, attachments of infundibular septum to anterior or posterior limb of trabeculoseptomargin-alis, the size and position of the ventricular septal defect, the spatial relation of great artery(s) to the ventricular septal defect, the spatial relationship between the great, and the distance between the tricuspid and pulmonary valves and the semilunar valves. Copyright 2000 by W.B. Saunders Company

Double-clad photonic crystal fiber coupler for compact nonlinear optical microscopy imaging.

PubMed

Fu, Ling; Gu, Min

2006-05-15

A 1 x 2 double-clad photonic crystal fiber coupler is fabricated by the fused tapered method, showing a low excess loss of 1.1 dB and a splitting ratio of 97/3 over the entire visible and near-infrared wavelength range. In addition to the property of splitting the laser power, the double-clad feature of the coupler facilitates the separation of a near-infrared single-mode beam from a visible multimode beam, which is ideal for nonlinear optical microscopy imaging. In conjunction with a gradient-index lens, this coupler is used to construct a miniaturized microscope based on two-photon fluorescence and second-harmonic generation. Three-dimensional nonlinear optical images demonstrate potential applications of the coupler to compact all-fiber and nonlinear optical microscopy and endoscopy.

Ultrafast Molecular Three-Electron Auger Decay.

PubMed

Feifel, Raimund; Eland, John H D; Squibb, Richard J; Mucke, Melanie; Zagorodskikh, Sergey; Linusson, Per; Tarantelli, Francesco; Kolorenč, Přemysl; Averbukh, Vitali

2016-02-19

Three-electron Auger decay is an exotic and elusive process, in which two outer-shell electrons simultaneously refill an inner-shell double vacancy with emission of a single Auger electron. Such transitions are forbidden by the many-electron selection rules, normally making their decay lifetimes orders of magnitude longer than the few-femtosecond lifetimes of normal (two-electron) Auger decay. Here we present theoretical predictions and direct experimental evidence for a few-femtosecond three-electron Auger decay of a double inner-valence-hole state in CH_{3}F. Our analysis shows that in contrast to double core holes, double inner-valence vacancies in molecules can decay exclusively by this ultrafast three-electron Auger process, and we predict that this phenomenon occurs widely.

New bandwidth selection criterion for Kernel PCA: approach to dimensionality reduction and classification problems.

PubMed

Thomas, Minta; De Brabanter, Kris; De Moor, Bart

2014-05-10

DNA microarrays are potentially powerful technology for improving diagnostic classification, treatment selection, and prognostic assessment. The use of this technology to predict cancer outcome has a history of almost a decade. Disease class predictors can be designed for known disease cases and provide diagnostic confirmation or clarify abnormal cases. The main input to this class predictors are high dimensional data with many variables and few observations. Dimensionality reduction of these features set significantly speeds up the prediction task. Feature selection and feature transformation methods are well known preprocessing steps in the field of bioinformatics. Several prediction tools are available based on these techniques. Studies show that a well tuned Kernel PCA (KPCA) is an efficient preprocessing step for dimensionality reduction, but the available bandwidth selection method for KPCA was computationally expensive. In this paper, we propose a new data-driven bandwidth selection criterion for KPCA, which is related to least squares cross-validation for kernel density estimation. We propose a new prediction model with a well tuned KPCA and Least Squares Support Vector Machine (LS-SVM). We estimate the accuracy of the newly proposed model based on 9 case studies. Then, we compare its performances (in terms of test set Area Under the ROC Curve (AUC) and computational time) with other well known techniques such as whole data set + LS-SVM, PCA + LS-SVM, t-test + LS-SVM, Prediction Analysis of Microarrays (PAM) and Least Absolute Shrinkage and Selection Operator (Lasso). Finally, we assess the performance of the proposed strategy with an existing KPCA parameter tuning algorithm by means of two additional case studies. We propose, evaluate, and compare several mathematical/statistical techniques, which apply feature transformation/selection for subsequent classification, and consider its application in medical diagnostics. Both feature selection and feature transformation perform well on classification tasks. Due to the dynamic selection property of feature selection, it is hard to define significant features for the classifier, which predicts classes of future samples. Moreover, the proposed strategy enjoys a distinctive advantage with its relatively lesser time complexity.

Tumor recognition in wireless capsule endoscopy images using textural features and SVM-based feature selection.

PubMed

Li, Baopu; Meng, Max Q-H

2012-05-01

Tumor in digestive tract is a common disease and wireless capsule endoscopy (WCE) is a relatively new technology to examine diseases for digestive tract especially for small intestine. This paper addresses the problem of automatic recognition of tumor for WCE images. Candidate color texture feature that integrates uniform local binary pattern and wavelet is proposed to characterize WCE images. The proposed features are invariant to illumination change and describe multiresolution characteristics of WCE images. Two feature selection approaches based on support vector machine, sequential forward floating selection and recursive feature elimination, are further employed to refine the proposed features for improving the detection accuracy. Extensive experiments validate that the proposed computer-aided diagnosis system achieves a promising tumor recognition accuracy of 92.4% in WCE images on our collected data.

Efficacy of texture, shape, and intensity feature fusion for posterior-fossa tumor segmentation in MRI.

PubMed

Ahmed, Shaheen; Iftekharuddin, Khan M; Vossough, Arastoo

2011-03-01

Our previous works suggest that fractal texture feature is useful to detect pediatric brain tumor in multimodal MRI. In this study, we systematically investigate efficacy of using several different image features such as intensity, fractal texture, and level-set shape in segmentation of posterior-fossa (PF) tumor for pediatric patients. We explore effectiveness of using four different feature selection and three different segmentation techniques, respectively, to discriminate tumor regions from normal tissue in multimodal brain MRI. We further study the selective fusion of these features for improved PF tumor segmentation. Our result suggests that Kullback-Leibler divergence measure for feature ranking and selection and the expectation maximization algorithm for feature fusion and tumor segmentation offer the best results for the patient data in this study. We show that for T1 and fluid attenuation inversion recovery (FLAIR) MRI modalities, the best PF tumor segmentation is obtained using the texture feature such as multifractional Brownian motion (mBm) while that for T2 MRI is obtained by fusing level-set shape with intensity features. In multimodality fused MRI (T1, T2, and FLAIR), mBm feature offers the best PF tumor segmentation performance. We use different similarity metrics to evaluate quality and robustness of these selected features for PF tumor segmentation in MRI for ten pediatric patients.

Discriminative least squares regression for multiclass classification and feature selection.

PubMed

Xiang, Shiming; Nie, Feiping; Meng, Gaofeng; Pan, Chunhong; Zhang, Changshui

2012-11-01

This paper presents a framework of discriminative least squares regression (LSR) for multiclass classification and feature selection. The core idea is to enlarge the distance between different classes under the conceptual framework of LSR. First, a technique called ε-dragging is introduced to force the regression targets of different classes moving along opposite directions such that the distances between classes can be enlarged. Then, the ε-draggings are integrated into the LSR model for multiclass classification. Our learning framework, referred to as discriminative LSR, has a compact model form, where there is no need to train two-class machines that are independent of each other. With its compact form, this model can be naturally extended for feature selection. This goal is achieved in terms of L2,1 norm of matrix, generating a sparse learning model for feature selection. The model for multiclass classification and its extension for feature selection are finally solved elegantly and efficiently. Experimental evaluation over a range of benchmark datasets indicates the validity of our method.

A Quantum Hybrid PSO Combined with Fuzzy k-NN Approach to Feature Selection and Cell Classification in Cervical Cancer Detection.

PubMed

Iliyasu, Abdullah M; Fatichah, Chastine

2017-12-19

A quantum hybrid (QH) intelligent approach that blends the adaptive search capability of the quantum-behaved particle swarm optimisation (QPSO) method with the intuitionistic rationality of traditional fuzzy k -nearest neighbours (Fuzzy k -NN) algorithm (known simply as the Q-Fuzzy approach) is proposed for efficient feature selection and classification of cells in cervical smeared (CS) images. From an initial multitude of 17 features describing the geometry, colour, and texture of the CS images, the QPSO stage of our proposed technique is used to select the best subset features (i.e., global best particles) that represent a pruned down collection of seven features. Using a dataset of almost 1000 images, performance evaluation of our proposed Q-Fuzzy approach assesses the impact of our feature selection on classification accuracy by way of three experimental scenarios that are compared alongside two other approaches: the All-features (i.e., classification without prior feature selection) and another hybrid technique combining the standard PSO algorithm with the Fuzzy k -NN technique (P-Fuzzy approach). In the first and second scenarios, we further divided the assessment criteria in terms of classification accuracy based on the choice of best features and those in terms of the different categories of the cervical cells. In the third scenario, we introduced new QH hybrid techniques, i.e., QPSO combined with other supervised learning methods, and compared the classification accuracy alongside our proposed Q-Fuzzy approach. Furthermore, we employed statistical approaches to establish qualitative agreement with regards to the feature selection in the experimental scenarios 1 and 3. The synergy between the QPSO and Fuzzy k -NN in the proposed Q-Fuzzy approach improves classification accuracy as manifest in the reduction in number cell features, which is crucial for effective cervical cancer detection and diagnosis.

Mechanical shear and tensile characteristics of selected biomass stems

USDA-ARS?s Scientific Manuscript database

Mechanical characteristics (stress and energy of tensile and shear modes) of selected biomass stems, such as big bluestem, bromegrass, and Barlow wheat were determined. A high capacity MTI-100K universal testing machine attached with standard tensile clamps and designed fabricated double-shear devic...

Isotopologue-selective excitation studied via optical-optical double resonance using the E3 Σ1+(63S1) ←A3Π0+(53P1) ←X1Σ0+(51S0) transitions in CdAr and CdKr van der Waals complexes

NASA Astrophysics Data System (ADS)

Urbańczyk, T.; Dudek, J.; Koperski, J.

2018-06-01

A method of experimental selection of molecular isotopologues using optical-optical double resonance (OODR) scheme and supersonic beam source of van der Waals (vdW) complexes is presented. Due to an appropriately large isotopic shift, the proper choice of a wavenumber of a sufficiently narrowband laser in the first transition of OODR scheme can lead to a selective isotopologue excitation to the intermediate state. Thanks to this approach, it is possible to select some of the isotopologues which subsequently give a contribution to laser induced fluorescence (LIF) signal originated from the final state of OODR. In this article, results of tests of the proposed method that employs the E3 Σ1+ ←A3Π0+ ←X1Σ0+ transitions in two vdW complexes, CdKr and CdAr, are presented and analysed.

Minimizing the semantic gap in biomedical content-based image retrieval

NASA Astrophysics Data System (ADS)

Guan, Haiying; Antani, Sameer; Long, L. Rodney; Thoma, George R.

2010-03-01

A major challenge in biomedical Content-Based Image Retrieval (CBIR) is to achieve meaningful mappings that minimize the semantic gap between the high-level biomedical semantic concepts and the low-level visual features in images. This paper presents a comprehensive learning-based scheme toward meeting this challenge and improving retrieval quality. The article presents two algorithms: a learning-based feature selection and fusion algorithm and the Ranking Support Vector Machine (Ranking SVM) algorithm. The feature selection algorithm aims to select 'good' features and fuse them using different similarity measurements to provide a better representation of the high-level concepts with the low-level image features. Ranking SVM is applied to learn the retrieval rank function and associate the selected low-level features with query concepts, given the ground-truth ranking of the training samples. The proposed scheme addresses four major issues in CBIR to improve the retrieval accuracy: image feature extraction, selection and fusion, similarity measurements, the association of the low-level features with high-level concepts, and the generation of the rank function to support high-level semantic image retrieval. It models the relationship between semantic concepts and image features, and enables retrieval at the semantic level. We apply it to the problem of vertebra shape retrieval from a digitized spine x-ray image set collected by the second National Health and Nutrition Examination Survey (NHANES II). The experimental results show an improvement of up to 41.92% in the mean average precision (MAP) over conventional image similarity computation methods.

Action detection by double hierarchical multi-structure space-time statistical matching model

NASA Astrophysics Data System (ADS)

Han, Jing; Zhu, Junwei; Cui, Yiyin; Bai, Lianfa; Yue, Jiang

2018-03-01

Aimed at the complex information in videos and low detection efficiency, an actions detection model based on neighboring Gaussian structure and 3D LARK features is put forward. We exploit a double hierarchical multi-structure space-time statistical matching model (DMSM) in temporal action localization. First, a neighboring Gaussian structure is presented to describe the multi-scale structural relationship. Then, a space-time statistical matching method is proposed to achieve two similarity matrices on both large and small scales, which combines double hierarchical structural constraints in model by both the neighboring Gaussian structure and the 3D LARK local structure. Finally, the double hierarchical similarity is fused and analyzed to detect actions. Besides, the multi-scale composite template extends the model application into multi-view. Experimental results of DMSM on the complex visual tracker benchmark data sets and THUMOS 2014 data sets show the promising performance. Compared with other state-of-the-art algorithm, DMSM achieves superior performances.

Multiple parton interactions and forward double pion production in pp and dA scattering

DOE Office of Scientific and Technical Information (OSTI.GOV)

Strikman, M.; Vogelsang, W.

2011-02-01

We estimate the contributions by double-parton interactions to the cross sections for pp{yields}{pi}{sup 0}{pi}{sup 0}X and dA{yields}{pi}{sup 0}{pi}{sup 0}X at the Relativistic Heavy Ion Collider (RHIC). We find that such contributions become important at large forward rapidities of the produced pions. This is, in particular, the case for dA scattering, where they strongly enhance the azimuthal-angular independent pedestal component of the cross section, providing a natural explanation of this feature of the RHIC dA data. We argue that the discussed processes open a window to studies of double quark distributions in nucleons. We also briefly address the roles of shadowingmore » and energy loss in dA scattering, which we show to affect the double-inclusive pion cross section much more strongly than the single-inclusive one. We discuss the implications of our results for the interpretation of pion azimuthal correlations.« less

Double hull grounding experiments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rodd, J.L.; Sikora, J.P.

1995-12-31

In the last few years the public and governments of many nations have become increasingly aware of the need for improving oil tanker safety. The requirements for double hull tankers are an attempt to address this need through legislation. Even though a number of investigations on the mechanics of collisions have been done in the past, until recently very little research supported the development of structural improvements to reduce oil tanker damage during grounding and stranding accidents. An aggressive evaluation of double hull tanker crashworthiness in stranding and grounding accidents is underway at CD/NSWC (formerly the David Taylor Research Center).more » The ability to predict damage from grounding accidents accurately is not currently available. The objective of this paper is to present qualitatively the structural failure mechanisms associated with stranding and grounding events for candidate double hull tanker structures and to present some simple methods for comparing damage scenarios. A comparison of the structural performance of key features in several very different designs will provide useful information toward this understanding.« less

New technique of skin embedded wire double-sided laser beam welding

NASA Astrophysics Data System (ADS)

Han, Bing; Tao, Wang; Chen, Yanbin

2017-06-01

In the aircraft industry, double-sided laser beam welding is an approved method for producing skin-stringer T-joints on aircraft fuselage panels. As for the welding of new generation aluminum-lithium alloys, however, this technique is limited because of high hot cracking susceptibility and strengthening elements' uneven distributions within weld. In the present study, a new technique of skin embedded wire double-sided laser beam welding (LBW) has been developed to fabricate T-joints consisting of 2.0 mm thick 2060-T8/2099-T83 aluminum-lithium alloys using eutectic alloy AA4047 filler wire. Necessary dimension parameters of the novel groove were reasonably designed for achieving crack-free welds. Comparisons were made between the new technique welded T-joint and conventional T-joint mainly on microstructure, hot crack, elements distribution features and mechanical properties within weld. Excellent crack-free microstructure, uniform distribution of silicon and superior tensile properties within weld were found in the new skin embedded wire double-sided LBW T-joints.

Action detection by double hierarchical multi-structure space–time statistical matching model

NASA Astrophysics Data System (ADS)

Han, Jing; Zhu, Junwei; Cui, Yiyin; Bai, Lianfa; Yue, Jiang

2018-06-01

Aimed at the complex information in videos and low detection efficiency, an actions detection model based on neighboring Gaussian structure and 3D LARK features is put forward. We exploit a double hierarchical multi-structure space-time statistical matching model (DMSM) in temporal action localization. First, a neighboring Gaussian structure is presented to describe the multi-scale structural relationship. Then, a space-time statistical matching method is proposed to achieve two similarity matrices on both large and small scales, which combines double hierarchical structural constraints in model by both the neighboring Gaussian structure and the 3D LARK local structure. Finally, the double hierarchical similarity is fused and analyzed to detect actions. Besides, the multi-scale composite template extends the model application into multi-view. Experimental results of DMSM on the complex visual tracker benchmark data sets and THUMOS 2014 data sets show the promising performance. Compared with other state-of-the-art algorithm, DMSM achieves superior performances.

Double shell planar experiments on OMEGA

NASA Astrophysics Data System (ADS)

Dodd, E. S.; Merritt, E. C.; Palaniyappan, S.; Montgomery, D. S.; Daughton, W. S.; Schmidt, D. W.; Cardenas, T.; Wilson, D. C.; Loomis, E. N.; Batha, S. H.; Ping, Y.; Smalyuk, V. A.; Amendt, P. A.

2017-10-01

The double shell project is aimed at fielding neutron-producing capsules at the National Ignition Facility (NIF), in which an outer low-Z ablator collides with an inner high-Z shell to compress the fuel. However, understanding these targets experimentally can be challenging when compared with conventional single shell targets. Halfraum-driven planar targets at OMEGA are being used to study physics issues important to double shell implosions outside of a convergent geometry. Both VISAR and radiography through a tube have advantages over imaging through the hohlraum and double-shell capsule at NIF. A number physics issues are being studied with this platform that include 1-d and higher dimensional effects such as defect-driven hydrodynamic instabilities from engineering features. Additionally, the use of novel materials with controlled density gradients require study in easily diagnosed 1-d systems. This work ultimately feeds back into the NIF capsule platform through manufacturing tolerances set using data from OMEGA. Supported under the US DOE by the LANS, LLC under contract DE-AC52-06NA25396. LA-UR-17-25386.

Accurate double many-body expansion potential energy surface for the 2(1)A' state of N2O.

PubMed

Li, Jing; Varandas, António J C

2014-08-28

An accurate double many-body expansion potential energy surface is reported for the 2(1)A' state of N2O. The new double many-body expansion (DMBE) form has been fitted to a wealth of ab initio points that have been calculated at the multi-reference configuration interaction level using the full-valence-complete-active-space wave function as reference and the cc-pVQZ basis set, and subsequently corrected semiempirically via double many-body expansion-scaled external correlation method to extrapolate the calculated energies to the limit of a complete basis set and, most importantly, the limit of an infinite configuration interaction expansion. The topographical features of the novel potential energy surface are then examined in detail and compared with corresponding attributes of other potential functions available in the literature. Exploratory trajectories have also been run on this DMBE form with the quasiclassical trajectory method, with the thermal rate constant so determined at room temperature significantly enhancing agreement with experimental data.

What Top-Down Task Sets Do for Us: An ERP Study on the Benefits of Advance Preparation in Visual Search

ERIC Educational Resources Information Center

Eimer, Martin; Kiss, Monika; Nicholas, Susan

2011-01-01

When target-defining features are specified in advance, attentional target selection in visual search is controlled by preparatory top-down task sets. We used ERP measures to study voluntary target selection in the absence of such feature-specific task sets, and to compare it to selection that is guided by advance knowledge about target features.…

An Ensemble Method with Integration of Feature Selection and Classifier Selection to Detect the Landslides

NASA Astrophysics Data System (ADS)

Zhongqin, G.; Chen, Y.

2017-12-01

Abstract Quickly identify the spatial distribution of landslides automatically is essential for the prevention, mitigation and assessment of the landslide hazard. It's still a challenging job owing to the complicated characteristics and vague boundary of the landslide areas on the image. The high resolution remote sensing image has multi-scales, complex spatial distribution and abundant features, the object-oriented image classification methods can make full use of the above information and thus effectively detect the landslides after the hazard happened. In this research we present a new semi-supervised workflow, taking advantages of recent object-oriented image analysis and machine learning algorithms to quick locate the different origins of landslides of some areas on the southwest part of China. Besides a sequence of image segmentation, feature selection, object classification and error test, this workflow ensemble the feature selection and classifier selection. The feature this study utilized were normalized difference vegetation index (NDVI) change, textural feature derived from the gray level co-occurrence matrices (GLCM), spectral feature and etc. The improvement of this study shows this algorithm significantly removes some redundant feature and the classifiers get fully used. All these improvements lead to a higher accuracy on the determination of the shape of landslides on the high resolution remote sensing image, in particular the flexibility aimed at different kinds of landslides.

Energy Efficient Engine (E3) combustion system component technology performance report

NASA Technical Reports Server (NTRS)

Burrus, D. L.; Chahrour, C. A.; Foltz, H. L.; Sabla, P. E.; Seto, S. P.; Taylor, J. R.

1984-01-01

The Energy Efficient Engine (E3) combustor effort was conducted as part of the overall NASA/GE E3 Program. This effort included the selection of an advanced double-annular combustion system design. The primary intent of this effort was to evolve a design that meets the stringent emissions and life goals of the E3, as well as all of the usual performance requirements of combustion systems for modern turbofan engines. Numerous detailed design studies were conducted to define the features of the combustion system design. Development test hardware was fabricated, and an extensive testing effort was undertaken to evaluate the combustion system subcomponents in order to verify and refine the design. Technology derived from this effort was incorporated into the engine combustion hardware design. The advanced engine combustion system was then evaluated in component testing to verify the design intent. What evolved from this effort was an advanced combustion system capable of satisfying all of the combustion system design objectives and requirements of the E3.

Metal-Metal Interactions in Heterobimetallic Complexes with Dinucleating Redox-Active Ligands.

PubMed

Broere, Daniël L J; Modder, Dieuwertje K; Blokker, Eva; Siegler, Maxime A; van der Vlugt, Jarl Ivar

2016-02-12

The tuning of metal-metal interactions in multinuclear assemblies is a challenge. Selective P coordination of a redox-active PNO ligand to Au(I) followed by homoleptic metalation of the NO pocket with Ni(II) affords a unique trinuclear Au-Ni-Au complex. This species features two antiferromagnetically coupled ligand-centered radicals and a double intramolecular d(8)-d(10) interaction, as supported by spectroscopic, single-crystal X-ray diffraction, and computational data. A corresponding cationic dinuclear Au-Ni analogue with a stronger d(8)-d(10) interaction is also reported. Although both heterobimetallic structures display rich electrochemistry, only the trinuclear Au-Ni-Au complex facilitates electrocatalytic C-X bond activation of alkyl halides in its doubly reduced state. Hence, the presence of a redox-active ligand framework, an available coordination site at gold, and the nature of the nickel-gold interaction appear to be essential for this reactivity. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Mode-locking evolution in ring fiber lasers with tunable repetition rate.

PubMed

Korobko, D A; Fotiadi, A A; Zolotovskii, I O

2017-09-04

We have applied a simple approach to analyze behavior of the harmonically mode-locked fiber laser incorporating an adjustable Mach-Zehnder interferometer (MZI). Our model is able to describe key features of the laser outputs and explore limitations of physical mechanisms responsible for laser operation at different pulse repetition rates tuned over a whole GHz range. At low repetition rates the laser operates as a harmonically mode-locked soliton laser triggered by a fast saturable absorber. At high repetition rates the laser mode-locking occurs due to dissipative four-wave mixing seeded by MZI and gain spectrum filtering. However, the laser stability in this regime is rather low due to poor mode selectivity provided by MZI that is able to support the desired laser operation just near the lasing threshold. The use of a double MZI instead of a single MZI could improve the laser stability and extends the range of the laser tunability. The model predicts a gap between two repetitive rate ranges where pulse train generation is not supported.

Macrocyclic Prodrugs of a Selective Nonpeptidic Direct Thrombin Inhibitor Display High Permeability, Efficient Bioconversion but Low Bioavailability.

PubMed

Andersson, Vincent; Bergström, Fredrik; Brånalt, Jonas; Grönberg, Gunnar; Gustafsson, David; Karlsson, Staffan; Polla, Magnus; Bergman, Joakim; Kihlberg, Jan

2016-07-28

The only oral direct thrombin inhibitors that have reached the market, ximelagatran and dabigatran etexilat, are double prodrugs with low bioavailability in humans. We have evaluated an alternative strategy: the preparation of a nonpeptidic, polar direct thrombin inhibitor as a single, macrocyclic esterase-cleavable (acyloxy)alkoxy prodrug. Two homologous prodrugs were synthesized and displayed high solubilities and Caco-2 cell permeabilities, suggesting high absorption from the intestine. In addition, they were rapidly and completely converted to the active zwitterionic thrombin inhibitor in human hepatocytes. Unexpectedly, the most promising prodrug displayed only moderately higher oral bioavailability in rat than the polar direct thrombin inhibitor, most likely due to rapid metabolism in the intestine or the intestinal wall. To the best of our knowledge, this is the first in vivo ADME study of macrocyclic (acyloxy)alkoxy prodrugs, and it remains to be established if the modest increase in bioavailability is a general feature of this category of prodrugs or not.

Coherence parameter measurements for neon and hydrogen

NASA Astrophysics Data System (ADS)

Wright, Robert; Hargreaves, Leigh; Khakoo, Murtadha; Zatsarinny, Oleg; Bartschat, Klaus; Stauffer, Al

2015-09-01

We present recent coherence parameter measurements for excitation of neon and hydrogen by 50 eV electrons. The measurements were made using a crossed electron/gas beam spectrometer, featuring a hemispherically selected electron energy analyzer for detecting scattered electrons and double-reflection VUV polarization analyzer to register fluorescence photons. Time-coincidence counting methods on the electron and photon signals were employed to determine Stokes Parameters at each scattering angle, with data measured at angles between 20 - 115 degrees. The data are compared with calculated results using the B-Spline R-Matrix (BSR) and Relativistic Distorted Wave (RDW) approaches. Measurements were made of both the linear (Plin and γ) and circular (Lperp) parameters for the lowest lying excited states in these two targets. We particularly focus on results in the Lperp parameter, which shows unusual behavior in these particular targets, including strong sign changes implying reversal of the angular momentum transfer. In the case of neon, the unusual behavior is well captured by the BSR, but not by other models.

An anionic phthalocyanine decreases NRAS expression by breaking down its RNA G-quadruplex.

PubMed

Kawauchi, Keiko; Sugimoto, Wataru; Yasui, Takatoshi; Murata, Kohei; Itoh, Katsuhiko; Takagi, Kazuki; Tsuruoka, Takaaki; Akamatsu, Kensuke; Tateishi-Karimata, Hisae; Sugimoto, Naoki; Miyoshi, Daisuke

2018-06-11

Aberrant activation of RAS signalling pathways contributes to aggressive phenotypes of cancer cells. The RAS-targeted therapies for cancer, therefore, have been recognised to be effective; however, current developments on targeting RAS have not advanced due to structural features of the RAS protein. Here, we show that expression of NRAS, a major isoform of RAS, can be controlled by photo-irradiation with an anionic phthalocyanine, ZnAPC, targeting NRAS mRNA. In vitro experiments reveal that ZnAPC binds to a G-quadruplex-forming oligonucleotide derived from the 5'-untranslated region of NRAS mRNA even in the presence of excess double-stranded RNA, which is abundant in cells, resulting in selective cleavage of the target RNA's G-quadruplex upon photo-irradiation. In line with these results, upon photo-irradiation, ZnAPC decreases NRAS mRNA and NRAS expression and thus viability of cancer cells. These results indicate that ZnAPC may be a prominent photosensitiser for a molecularly targeted photodynamic therapy for cancer.

Attention improves encoding of task-relevant features in the human visual cortex

PubMed Central

Jehee, Janneke F.M.; Brady, Devin K.; Tong, Frank

2011-01-01

When spatial attention is directed towards a particular stimulus, increased activity is commonly observed in corresponding locations of the visual cortex. Does this attentional increase in activity indicate improved processing of all features contained within the attended stimulus, or might spatial attention selectively enhance the features relevant to the observer’s task? We used fMRI decoding methods to measure the strength of orientation-selective activity patterns in the human visual cortex while subjects performed either an orientation or contrast discrimination task, involving one of two laterally presented gratings. Greater overall BOLD activation with spatial attention was observed in areas V1-V4 for both tasks. However, multivariate pattern analysis revealed that orientation-selective responses were enhanced by attention only when orientation was the task-relevant feature, and not when the grating’s contrast had to be attended. In a second experiment, observers discriminated the orientation or color of a specific lateral grating. Here, orientation-selective responses were enhanced in both tasks but color-selective responses were enhanced only when color was task-relevant. In both experiments, task-specific enhancement of feature-selective activity was not confined to the attended stimulus location, but instead spread to other locations in the visual field, suggesting the concurrent involvement of a global feature-based attentional mechanism. These results suggest that attention can be remarkably selective in its ability to enhance particular task-relevant features, and further reveal that increases in overall BOLD amplitude are not necessarily accompanied by improved processing of stimulus information. PMID:21632942

Attention improves encoding of task-relevant features in the human visual cortex.

PubMed

Jehee, Janneke F M; Brady, Devin K; Tong, Frank

2011-06-01

When spatial attention is directed toward a particular stimulus, increased activity is commonly observed in corresponding locations of the visual cortex. Does this attentional increase in activity indicate improved processing of all features contained within the attended stimulus, or might spatial attention selectively enhance the features relevant to the observer's task? We used fMRI decoding methods to measure the strength of orientation-selective activity patterns in the human visual cortex while subjects performed either an orientation or contrast discrimination task, involving one of two laterally presented gratings. Greater overall BOLD activation with spatial attention was observed in visual cortical areas V1-V4 for both tasks. However, multivariate pattern analysis revealed that orientation-selective responses were enhanced by attention only when orientation was the task-relevant feature and not when the contrast of the grating had to be attended. In a second experiment, observers discriminated the orientation or color of a specific lateral grating. Here, orientation-selective responses were enhanced in both tasks, but color-selective responses were enhanced only when color was task relevant. In both experiments, task-specific enhancement of feature-selective activity was not confined to the attended stimulus location but instead spread to other locations in the visual field, suggesting the concurrent involvement of a global feature-based attentional mechanism. These results suggest that attention can be remarkably selective in its ability to enhance particular task-relevant features and further reveal that increases in overall BOLD amplitude are not necessarily accompanied by improved processing of stimulus information.

Effective and extensible feature extraction method using genetic algorithm-based frequency-domain feature search for epileptic EEG multiclassification

PubMed Central

Wen, Tingxi; Zhang, Zhongnan

2017-01-01

Abstract In this paper, genetic algorithm-based frequency-domain feature search (GAFDS) method is proposed for the electroencephalogram (EEG) analysis of epilepsy. In this method, frequency-domain features are first searched and then combined with nonlinear features. Subsequently, these features are selected and optimized to classify EEG signals. The extracted features are analyzed experimentally. The features extracted by GAFDS show remarkable independence, and they are superior to the nonlinear features in terms of the ratio of interclass distance and intraclass distance. Moreover, the proposed feature search method can search for features of instantaneous frequency in a signal after Hilbert transformation. The classification results achieved using these features are reasonable; thus, GAFDS exhibits good extensibility. Multiple classical classifiers (i.e., k-nearest neighbor, linear discriminant analysis, decision tree, AdaBoost, multilayer perceptron, and Naïve Bayes) achieve satisfactory classification accuracies by using the features generated by the GAFDS method and the optimized feature selection. The accuracies for 2-classification and 3-classification problems may reach up to 99% and 97%, respectively. Results of several cross-validation experiments illustrate that GAFDS is effective in the extraction of effective features for EEG classification. Therefore, the proposed feature selection and optimization model can improve classification accuracy. PMID:28489789

Effective and extensible feature extraction method using genetic algorithm-based frequency-domain feature search for epileptic EEG multiclassification.

PubMed

Wen, Tingxi; Zhang, Zhongnan

2017-05-01

In this paper, genetic algorithm-based frequency-domain feature search (GAFDS) method is proposed for the electroencephalogram (EEG) analysis of epilepsy. In this method, frequency-domain features are first searched and then combined with nonlinear features. Subsequently, these features are selected and optimized to classify EEG signals. The extracted features are analyzed experimentally. The features extracted by GAFDS show remarkable independence, and they are superior to the nonlinear features in terms of the ratio of interclass distance and intraclass distance. Moreover, the proposed feature search method can search for features of instantaneous frequency in a signal after Hilbert transformation. The classification results achieved using these features are reasonable; thus, GAFDS exhibits good extensibility. Multiple classical classifiers (i.e., k-nearest neighbor, linear discriminant analysis, decision tree, AdaBoost, multilayer perceptron, and Naïve Bayes) achieve satisfactory classification accuracies by using the features generated by the GAFDS method and the optimized feature selection. The accuracies for 2-classification and 3-classification problems may reach up to 99% and 97%, respectively. Results of several cross-validation experiments illustrate that GAFDS is effective in the extraction of effective features for EEG classification. Therefore, the proposed feature selection and optimization model can improve classification accuracy.

Selection and response bias as determinants of priming of pop-out search: Revelations from diffusion modeling.

PubMed

Burnham, Bryan R

2018-05-03

During visual search, both top-down factors and bottom-up properties contribute to the guidance of visual attention, but selection history can influence attention independent of bottom-up and top-down factors. For example, priming of pop-out (PoP) is the finding that search for a singleton target is faster when the target and distractor features repeat than when those features trade roles between trials. Studies have suggested that such priming (selection history) effects on pop-out search manifest either early, by biasing the selection of the preceding target feature, or later in processing, by facilitating response and target retrieval processes. The present study was designed to examine the influence of selection history on pop-out search by introducing a speed-accuracy trade-off manipulation in a pop-out search task. Ratcliff diffusion modeling (RDM) was used to examine how selection history influenced both attentional bias and response execution processes. The results support the hypothesis that selection history biases attention toward the preceding target's features on the current trial and also influences selection of the response to the target.

Intermodel spread of the double-ITCZ bias in coupled GCMs tied to land surface temperature in AMIP GCMs

NASA Astrophysics Data System (ADS)

Zhou, Wenyu; Xie, Shang-Ping

2017-08-01

Global climate models (GCMs) have long suffered from biases of excessive tropical precipitation in the Southern Hemisphere (SH). The severity of the double-Intertropical Convergence Zone (ITCZ) bias, defined here as the interhemispheric difference in zonal mean tropical precipitation, varies strongly among models in the Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble. Models with a more severe double-ITCZ bias feature warmer tropical sea surface temperature (SST) in the SH, coupled with weaker southeast trades. While previous studies focus on coupled ocean-atmosphere interactions, here we show that the intermodel spread in the severity of the double-ITCZ bias is closely related to land surface temperature biases, which can be further traced back to those in the Atmosphere Model Intercomparison Project (AMIP) simulations. By perturbing land temperature in models, we demonstrate that cooler land can indeed lead to a more severe double-ITCZ bias by inducing the above coupled SST-trade wind pattern in the tropics. The response to land temperature can be consistently explained from both the dynamic and energetic perspectives. Although this intermodel spread from the land temperature variation does not account for the ensemble model mean double-ITCZ bias, identifying the land temperature effect provides insights into simulating a realistic ITCZ for the right reasons.

Double-Layer Gadolinium Zirconate/Yttria-Stabilized Zirconia Thermal Barrier Coatings Deposited by the Solution Precursor Plasma Spray Process

NASA Astrophysics Data System (ADS)

Jiang, Chen; Jordan, Eric H.; Harris, Alan B.; Gell, Maurice; Roth, Jeffrey

2015-08-01

Advanced thermal barrier coatings (TBCs) with lower thermal conductivity, increased resistance to calcium-magnesium-aluminosilicate (CMAS), and improved high-temperature capability, compared to traditional yttria-stabilized zirconia (YSZ) TBCs, are essential to higher efficiency in next generation gas turbine engines. Double-layer rare-earth zirconate/YSZ TBCs are a promising solution. From a processing perspective, solution precursor plasma spray (SPPS) process with its unique and beneficial microstructural features can be an effective approach to obtaining the double-layer microstructure. Previously durable low-thermal-conductivity YSZ TBCs with optimized layered porosity, called the inter-pass boundaries (IPBs) were produced using the SPPS process. In this study, an SPPS gadolinium zirconate (GZO) protective surface layer was successfully added. These SPPS double-layer TBCs not only retained good cyclic durability and low thermal conductivity, but also demonstrated favorable phase stability and increased surface temperature capabilities. The CMAS resistance was evaluated with both accumulative and single applications of simulated CMAS in isothermal furnaces. The double-layer YSZ/GZO exhibited dramatic improvement in the single application, but not in the continuous one. In addition, to explore their potential application in integrated gasification combined cycle environments, double-layer TBCs were tested under high-temperature humidity and encouraging performance was recorded.

Isolated double adrenocorticotropic hormone-secreting pituitary adenomas: A case report and review of the literature

PubMed Central

PU, JIUJUN; WANG, ZHIMING; ZHOU, HUI; ZHONG, AILING; JIN, KAI; RUAN, LUNLIANG; YANG, GANG

2016-01-01

Only a few cases of double or multiple pituitary adenomas have previously been reported in the literature; however, isolated double adrenocorticotropic hormone (ACTH)-secreting pituitary adenomas are even more rare. The present study reports a rare case of a 50-year-old female patient who presented with typical clinical features of Cushing's disease and was diagnosed with isolated double ACTH-secreting pituitary adenomas. Endocrinological examination revealed an ACTH-producing pituitary adenoma, and preoperative magnetic resonance imaging (MRI) demonstrated a microadenoma with a lower intensity on the right side of the pituitary gland. The patient underwent endoscopic endonasal transsphenoidal surgery, which revealed another pituitary tumor in the left side of the pituitary gland. The two, clearly separated, pituitary adenomas identified in the same gland were completely resected. Immunohistochemistry and pathology revealed that the clearly separated double pituitary adenomas were positive for ACTH, thyroid-stimulating, growth and prolactin hormones. Postoperatively, the levels of ACTH and cortisol hormone decreased rapidly. The case reported in the present study is considerably rare, due to the presence of a second pituitary adenoma in the same gland, which was not detected by preoperative MRI scan, but was noticed during surgery. Intraoperative evaluation may be important in the identification of double or multiple pituitary adenomas. PMID:27347184

Laser Energy Monitor for Double-Pulsed 2-Micrometer IPDA Lidar Application

NASA Technical Reports Server (NTRS)

Refaat, Tamer F.; Petros, Mulugeta; Remus, Ruben; Yu, Jirong; Singh, Upendra N.

2014-01-01

Integrated path differential absorption (IPDA) lidar is a remote sensing technique for monitoring different atmospheric species. The technique relies on wavelength differentiation between strong and weak absorbing features normalized to the transmitted energy. 2-micron double-pulsed IPDA lidar is best suited for atmospheric carbon dioxide measurements. In such case, the transmitter produces two successive laser pulses separated by short interval (200 microseconds), with low repetition rate (10Hz). Conventional laser energy monitors, based on thermal detectors, are suitable for low repetition rate single pulse lasers. Due to the short pulse interval in double-pulsed lasers, thermal energy monitors underestimate the total transmitted energy. This leads to measurement biases and errors in double-pulsed IPDA technique. The design and calibration of a 2-micron double-pulse laser energy monitor is presented. The design is based on a high-speed, extended range InGaAs pin quantum detectors suitable for separating the two pulse events. Pulse integration is applied for converting the detected pulse power into energy. Results are compared to a photo-electro-magnetic (PEM) detector for impulse response verification. Calibration included comparing the three detection technologies in single-pulsed mode, then comparing the pin and PEM detectors in double-pulsed mode. Energy monitor linearity will be addressed.

A Randomized, Double-Blind, Crossover Comparison of MK-0929 and Placebo in the Treatment of Adults with ADHD

ERIC Educational Resources Information Center

Rivkin, Anna; Alexander, Robert C.; Knighton, Jennifer; Hutson, Pete H.; Wang, Xiaojing J.; Snavely, Duane B.; Rosah, Thomas; Watt, Alan P.; Reimherr, Fred W.; Adler, Lenard A.

2012-01-01

Objective: Preclinical models, receptor localization, and genetic linkage data support the role of D4 receptors in the etiology of ADHD. This proof-of-concept study was designed to evaluate MK-0929, a selective D4 receptor antagonist as treatment for adult ADHD. Method: A randomized, double-blind, placebo-controlled, crossover study was conducted…

Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection.

PubMed

Attallah, Omneya; Karthikesalingam, Alan; Holt, Peter Je; Thompson, Matthew M; Sayers, Rob; Bown, Matthew J; Choke, Eddie C; Ma, Xianghong

2017-11-01

Feature selection is essential in medical area; however, its process becomes complicated with the presence of censoring which is the unique character of survival analysis. Most survival feature selection methods are based on Cox's proportional hazard model, though machine learning classifiers are preferred. They are less employed in survival analysis due to censoring which prevents them from directly being used to survival data. Among the few work that employed machine learning classifiers, partial logistic artificial neural network with auto-relevance determination is a well-known method that deals with censoring and perform feature selection for survival data. However, it depends on data replication to handle censoring which leads to unbalanced and biased prediction results especially in highly censored data. Other methods cannot deal with high censoring. Therefore, in this article, a new hybrid feature selection method is proposed which presents a solution to high level censoring. It combines support vector machine, neural network, and K-nearest neighbor classifiers using simple majority voting and a new weighted majority voting method based on survival metric to construct a multiple classifier system. The new hybrid feature selection process uses multiple classifier system as a wrapper method and merges it with iterated feature ranking filter method to further reduce features. Two endovascular aortic repair datasets containing 91% censored patients collected from two centers were used to construct a multicenter study to evaluate the performance of the proposed approach. The results showed the proposed technique outperformed individual classifiers and variable selection methods based on Cox's model such as Akaike and Bayesian information criterions and least absolute shrinkage and selector operator in p values of the log-rank test, sensitivity, and concordance index. This indicates that the proposed classifier is more powerful in correctly predicting the risk of re-intervention enabling doctor in selecting patients' future follow-up plan.

Genomic selection models double the accuracy of predicted breeding values for bacterial cold water disease resistance compared to a traditional pedigree-based model in rainbow trout aquaculture

USDA-ARS?s Scientific Manuscript database

Previously we have shown that bacterial cold water disease (BCWD) resistance in rainbow trout can be improved using traditional family-based selection, but progress has been limited to exploiting only between-family genetic variation. Genomic selection (GS) is a new alternative enabling exploitation...

Universal state-selective corrections to multireference coupled-cluster theories with single and double excitations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brabec, Jiri; van Dam, Hubertus JJ; Pittner, Jiri

2012-03-28

The recently proposed Universal State-Selective (USS) corrections [K. Kowalski, J. Chem. Phys. 134, 194107 (2011)] to approximate Multi-Reference Coupled Cluster (MRCC) energies can be commonly applied to any type of MRCC theory based on the Jeziorski-Monkhorst [B. Jeziorski, H.J. Monkhorst, Phys. Rev. A 24, 1668 (1981)] exponential Ansatz. In this letter we report on the performance of a simple USS correction to the Brillouin-Wigner MRCC (BW-MRCC) formalism employing single and double excitations (BW-MRCCSD). It is shown that the resulting formalism (USS-BW-MRCCSD), which uses the manifold of single and double excitations to construct the correction, can be related to a posteriorimore » corrections utilized in routine BW-MRCCSD calculations. In several benchmark calculations we compare the results of the USS-BW-MRCCSD method with results of the BW-MRCCSD approach employing a posteriori corrections and with results obtained with the Full Configuration Interaction (FCI) method.« less

Robust Feature Selection Technique using Rank Aggregation.

PubMed

Sarkar, Chandrima; Cooley, Sarah; Srivastava, Jaideep

2014-01-01

Although feature selection is a well-developed research area, there is an ongoing need to develop methods to make classifiers more efficient. One important challenge is the lack of a universal feature selection technique which produces similar outcomes with all types of classifiers. This is because all feature selection techniques have individual statistical biases while classifiers exploit different statistical properties of data for evaluation. In numerous situations this can put researchers into dilemma as to which feature selection method and a classifiers to choose from a vast range of choices. In this paper, we propose a technique that aggregates the consensus properties of various feature selection methods to develop a more optimal solution. The ensemble nature of our technique makes it more robust across various classifiers. In other words, it is stable towards achieving similar and ideally higher classification accuracy across a wide variety of classifiers. We quantify this concept of robustness with a measure known as the Robustness Index (RI). We perform an extensive empirical evaluation of our technique on eight data sets with different dimensions including Arrythmia, Lung Cancer, Madelon, mfeat-fourier, internet-ads, Leukemia-3c and Embryonal Tumor and a real world data set namely Acute Myeloid Leukemia (AML). We demonstrate not only that our algorithm is more robust, but also that compared to other techniques our algorithm improves the classification accuracy by approximately 3-4% (in data set with less than 500 features) and by more than 5% (in data set with more than 500 features), across a wide range of classifiers.

A machine learning heuristic to identify biologically relevant and minimal biomarker panels from omics data

PubMed Central

2015-01-01

Background Investigations into novel biomarkers using omics techniques generate large amounts of data. Due to their size and numbers of attributes, these data are suitable for analysis with machine learning methods. A key component of typical machine learning pipelines for omics data is feature selection, which is used to reduce the raw high-dimensional data into a tractable number of features. Feature selection needs to balance the objective of using as few features as possible, while maintaining high predictive power. This balance is crucial when the goal of data analysis is the identification of highly accurate but small panels of biomarkers with potential clinical utility. In this paper we propose a heuristic for the selection of very small feature subsets, via an iterative feature elimination process that is guided by rule-based machine learning, called RGIFE (Rule-guided Iterative Feature Elimination). We use this heuristic to identify putative biomarkers of osteoarthritis (OA), articular cartilage degradation and synovial inflammation, using both proteomic and transcriptomic datasets. Results and discussion Our RGIFE heuristic increased the classification accuracies achieved for all datasets when no feature selection is used, and performed well in a comparison with other feature selection methods. Using this method the datasets were reduced to a smaller number of genes or proteins, including those known to be relevant to OA, cartilage degradation and joint inflammation. The results have shown the RGIFE feature reduction method to be suitable for analysing both proteomic and transcriptomics data. Methods that generate large ‘omics’ datasets are increasingly being used in the area of rheumatology. Conclusions Feature reduction methods are advantageous for the analysis of omics data in the field of rheumatology, as the applications of such techniques are likely to result in improvements in diagnosis, treatment and drug discovery. PMID:25923811

Sensor feature fusion for detecting buried objects

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clark, G.A.; Sengupta, S.K.; Sherwood, R.J.

1993-04-01

Given multiple registered images of the earth`s surface from dual-band sensors, our system fuses information from the sensors to reduce the effects of clutter and improve the ability to detect buried or surface target sites. The sensor suite currently includes two sensors (5 micron and 10 micron wavelengths) and one ground penetrating radar (GPR) of the wide-band pulsed synthetic aperture type. We use a supervised teaming pattern recognition approach to detect metal and plastic land mines buried in soil. The overall process consists of four main parts: Preprocessing, feature extraction, feature selection, and classification. These parts are used in amore » two step process to classify a subimage. Thee first step, referred to as feature selection, determines the features of sub-images which result in the greatest separability among the classes. The second step, image labeling, uses the selected features and the decisions from a pattern classifier to label the regions in the image which are likely to correspond to buried mines. We extract features from the images, and use feature selection algorithms to select only the most important features according to their contribution to correct detections. This allows us to save computational complexity and determine which of the sensors add value to the detection system. The most important features from the various sensors are fused using supervised teaming pattern classifiers (including neural networks). We present results of experiments to detect buried land mines from real data, and evaluate the usefulness of fusing feature information from multiple sensor types, including dual-band infrared and ground penetrating radar. The novelty of the work lies mostly in the combination of the algorithms and their application to the very important and currently unsolved operational problem of detecting buried land mines from an airborne standoff platform.« less

[Combining speech sample and feature bilateral selection algorithm for classification of Parkinson's disease].

PubMed

Zhang, Xiaoheng; Wang, Lirui; Cao, Yao; Wang, Pin; Zhang, Cheng; Yang, Liuyang; Li, Yongming; Zhang, Yanling; Cheng, Oumei

2018-02-01

Diagnosis of Parkinson's disease (PD) based on speech data has been proved to be an effective way in recent years. However, current researches just care about the feature extraction and classifier design, and do not consider the instance selection. Former research by authors showed that the instance selection can lead to improvement on classification accuracy. However, no attention is paid on the relationship between speech sample and feature until now. Therefore, a new diagnosis algorithm of PD is proposed in this paper by simultaneously selecting speech sample and feature based on relevant feature weighting algorithm and multiple kernel method, so as to find their synergy effects, thereby improving classification accuracy. Experimental results showed that this proposed algorithm obtained apparent improvement on classification accuracy. It can obtain mean classification accuracy of 82.5%, which was 30.5% higher than the relevant algorithm. Besides, the proposed algorithm detected the synergy effects of speech sample and feature, which is valuable for speech marker extraction.

Structural basis of the 3′-end recognition of a leading strand in stalled replication forks by PriA

PubMed Central

Sasaki, Kaori; Ose, Toyoyuki; Okamoto, Naoaki; Maenaka, Katsumi; Tanaka, Taku; Masai, Hisao; Saito, Mihoko; Shirai, Tsuyoshi; Kohda, Daisuke

2007-01-01

In eubacteria, PriA helicase detects the stalled DNA replication forks. This critical role of PriA is ascribed to its ability to bind to the 3′ end of a nascent leading DNA strand in the stalled replication forks. The crystal structures in complexes with oligonucleotides and the combination of fluorescence correlation spectroscopy and mutagenesis reveal that the N-terminal domain of PriA possesses a binding pocket for the 3′-terminal nucleotide residue of DNA. The interaction with the deoxyribose 3′-OH is essential for the 3′-terminal recognition. In contrast, the direct interaction with 3′-end nucleobase is unexpected, considering the same affinity for oligonucleotides carrying the four bases at the 3′ end. Thus, the N-terminal domain of PriA recognizes the 3′-end base in a base-non-selective manner, in addition to the deoxyribose and 5′-side phosphodiester group, of the 3′-terminal nucleotide to acquire both sufficient affinity and non-selectivity to find all of the stalled replication forks generated during DNA duplication. This unique feature is prerequisite for the proper positioning of the helicase domain of PriA on the unreplicated double-stranded DNA. PMID:17464287

Axial compressor blade design for desensitization of aerodynamic performance and stability to tip clearance

NASA Astrophysics Data System (ADS)

Erler, Engin

Tip clearance flow is the flow through the clearance between the rotor blade tip and the shroud of a turbomachine, such as compressors and turbines. This flow is driven by the pressure difference across the blade (aerodynamic loading) in the tip region and is a major source of loss in performance and aerodynamic stability in axial compressors of modern aircraft engines. An increase in tip clearance, either temporary due to differential radial expansion between the blade and the shroud during transient operation or permanent due to engine wear or manufacturing tolerances on small blades, increases tip clearance flow and results in higher fuel consumption and higher risk of engine surge. A compressor design that can reduce the sensitivity of its performance and aerodynamic stability to tip clearance increase would have a major impact on short and long-term engine performance and operating envelope. While much research has been carried out on improving nominal compressor performance, little had been done on desensitization to tip clearance increase beyond isolated observations that certain blade designs such as forward chordwise sweep, seem to be less sensitive to tip clearance size increase. The current project aims to identify through a computational study the flow features and associated mechanisms that reduces sensitivity of axial compressor rotors to tip clearance size and propose blade design strategies that can exploit these results. The methodology starts with the design of a reference conventional axial compressor rotor followed by a parametric study with variations of this reference design through modification of the camber line and of the stacking line of blade profiles along the span. It is noted that a simple desensitization method would be to reduce the aerodynamic loading of the blade tip which would reduce the tip clearance flow and its proportional contribution to performance loss. However, with the larger part of the work on the flow done in this region, this approach would entail a nominal performance penalty. Therefore, the chosen rotor design philosophy aims to keep the spanwise loading constant to avoid trading performance for desensitization. The rotor designs that resulted from this exercise are simulated in ANSYS CFX at different tip clearance sizes. The change in their performance with respect to tip clearance size (sensitivity) is compared both on an integral level in terms of pressure ratio and adiabatic efficiency, as well as on a detailed level in terms of aerodynamic losses and blockage associated with tip clearance flow. The sensitivity of aerodynamic stability is evaluated either directly through the simulations of the rotor characteristics up to the stall point (expensive in time and resources) for a few designs or indirectly through the position of the interface between the incoming and tip clearance flow with respect to the rotor leading edge plane. The latter approach is based on a generally observed stall criteria in modern axial compressors. The rotor designs are then assessed according to their sensitivity in comparison to that of the reference rotor design to detect features that can explain the trend in sensitivity to tip clearance size. These features can then be validated and the associated flow mechanisms explained through numerical simulations and modelling. Analysis of the database from the rotor parametric study shows that the observed trend in sensitivity cannot be explained by the shifting of the aerodynamic loading along the blade chord, as initially hypothesized based on the literature review. Instead, two flow features are found to reduce sensitivity of performance and stability to tip clearance, namely an increase in incoming meridional momentum in the tip region and a reduction/elimination of double leakage flow. Double leakage flow is the flow that exits the tip clearance of one blade and proceeds into the clearance of the adjacent blade rather than convecting downstream out of the local blade passage. These flow features are isolated and validated based on the reference rotor design through changes in the inlet total pressure condition to alter incoming flow momentum and blade number count to change double leakage rate. In terms of flow mechanism, double leakage is shown to be detrimental to performance and stability, and its proportional increase with tip clearance size explains the sensitivity increase in the presence of double leakage and, conversely, the desensitization effect of reducing or eliminating double leakage. The increase in incoming meridional momentum in the tip region reduces sensitivity to tip clearance through its reduction of double leakage as well as through improved mixing with tip clearance flow, as demonstrated by an analytical model without double leakage flow. The above results imply that any blade design strategy that exploits the two desensitizing flow features would reduce the performance and stability sensitivity to tip clearance size. The increase of the incoming meridional momentum can be achieved through forward chordwise sweep of the blade. The reduction of double leakage without changing blade pitch can be obtained by decreasing the blade stagger angle in the tip region. Examples of blade designs associated with these strategies are shown through CFX simulations to be successful in reducing sensitivity to tip clearance size.

Value of ultrasonography in the diagnosis of gout in patients presenting with acute arthritis.

PubMed

Pattamapaspong, Nuttaya; Vuthiwong, Withawat; Kanthawang, Thanat; Louthrenoo, Worawit

2017-06-01

To evaluate the value of ultrasonographic features of crystal deposition for diagnosing gout in patients presenting with acute arthritis. Ultrasound scanning of the most inflamed joint was performed on 89 consecutively enrolled patients with acute arthritis. Two radiologists independently reviewed the ultrasound images, and a consensus was achieved with a third radiologist when the interpretations of four key ultrasound features of gout differed. Arthrocentesis and crystal analysis using compensated polarized light microscopy of aspirates are considered the gold standards for gout diagnosis. Fifty-three (60%) patients had gout, whereas the remaining 36 (40%) had non-gout arthritis. The mean serum uric acid level was 7.1 mg/dl in patients with gout and 4.7 mg/dl in patients with non-gout arthritis. Three US features differed significantly (p < 0.001) between patients with gout and non-gout arthritis: the double contour sign (42 vs. 8%, respectively), intra-articular aggregates (58 vs. 8%), and tophi (40 vs. 0%). No statistically significant differences in detecting intra-tendinous aggregates (32 vs. 17%, p = 0.14) were observed. The sensitivity and specificity of the double contour sign were 42 and 92%, respectively; those of the intra-articular aggregates were 58 and 92%; and those of tophi were 40 and 100%. The positive predictive values for these three features ranged from 88 to 100%, whereas the negative predictive values ranged from 52 to 60%. When the prevalence is high, these three ultrasound features may be a useful adjunct in the diagnosis of acute gout, particularly when specialized microscopic techniques are not available.

Sparse Zero-Sum Games as Stable Functional Feature Selection

PubMed Central

Sokolovska, Nataliya; Teytaud, Olivier; Rizkalla, Salwa; Clément, Karine; Zucker, Jean-Daniel

2015-01-01

In large-scale systems biology applications, features are structured in hidden functional categories whose predictive power is identical. Feature selection, therefore, can lead not only to a problem with a reduced dimensionality, but also reveal some knowledge on functional classes of variables. In this contribution, we propose a framework based on a sparse zero-sum game which performs a stable functional feature selection. In particular, the approach is based on feature subsets ranking by a thresholding stochastic bandit. We provide a theoretical analysis of the introduced algorithm. We illustrate by experiments on both synthetic and real complex data that the proposed method is competitive from the predictive and stability viewpoints. PMID:26325268

Selective sodium intercalation into sodium nickel-manganese sulfate for dual Na-Li-ion batteries.

PubMed

Marinova, Delyana M; Kukeva, Rosica R; Zhecheva, Ekaterina N; Stoyanova, Radostina K

2018-05-09

Double sodium transition metal sulfates combine in themselves unique intercalation properties with eco-compatible compositions - a specific feature that makes them attractive electrode materials for lithium and sodium ion batteries. Herein, we examine the intercalation properties of novel double sodium nickel-manganese sulfate, Na2Ni1/2Mn1/2(SO4)2, having a large monoclinic unit cell, through electrochemical and ex situ diffraction and spectroscopic methods. The sulfate salt Na2Ni1/2Mn1/2(SO4)2 is prepared by thermal dehydration of the corresponding hydrate salt Na2Ni1/2Mn1/2(SO4)2·4H2O having a blödite structure. The intercalation reactions on Na2Ni1-xMnx(SO4)2 are studied in two model cells: half-ion cell versus Li metal anode and full-ion cell versus Li4Ti5O12 anode by using lithium (LiPF6 dissolved in EC/DMC) and sodium electrolytes (NaPF6 dissolved in EC:DEC). Based on ex situ XRD and TEM analysis, it is found that sodium intercalation into Na2Ni1/2Mn1/2(SO4)2 takes place via phase separation into the Ni-rich monoclinic phase and Mn-rich alluaudite phase. The redox reactions involving participation of manganese and titanium ions are monitored by ex situ EPR spectroscopy. It has been demonstrated that manganese ions from the sulfate salt are participating in the electrochemical reaction, while the nickel ions remain intact. As a result, a reversible capacity of about 65 mA h g-1 is reached. The selective intercalation properties determine sodium nickel-manganese sulfate as a new electrode material for hybrid lithium-sodium ion batteries that is thought to combine the advantages of individual lithium and sodium batteries.

Machine learning approach for automated screening of malaria parasite using light microscopic images.

PubMed

Das, Dev Kumar; Ghosh, Madhumala; Pal, Mallika; Maiti, Asok K; Chakraborty, Chandan

2013-02-01

The aim of this paper is to address the development of computer assisted malaria parasite characterization and classification using machine learning approach based on light microscopic images of peripheral blood smears. In doing this, microscopic image acquisition from stained slides, illumination correction and noise reduction, erythrocyte segmentation, feature extraction, feature selection and finally classification of different stages of malaria (Plasmodium vivax and Plasmodium falciparum) have been investigated. The erythrocytes are segmented using marker controlled watershed transformation and subsequently total ninety six features describing shape-size and texture of erythrocytes are extracted in respect to the parasitemia infected versus non-infected cells. Ninety four features are found to be statistically significant in discriminating six classes. Here a feature selection-cum-classification scheme has been devised by combining F-statistic, statistical learning techniques i.e., Bayesian learning and support vector machine (SVM) in order to provide the higher classification accuracy using best set of discriminating features. Results show that Bayesian approach provides the highest accuracy i.e., 84% for malaria classification by selecting 19 most significant features while SVM provides highest accuracy i.e., 83.5% with 9 most significant features. Finally, the performance of these two classifiers under feature selection framework has been compared toward malaria parasite classification. Copyright © 2012 Elsevier Ltd. All rights reserved.

FOUR DUAL AGN CANDIDATES OBSERVED WITH THE VLBA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gabányi, K. É.; Frey, S.; An, T.

According to hierarchical structure formation models, merging galaxies are expected to be seen in different stages of coalescence. However, there are currently no straightforward observational methods to either select or to confirm a large number of dual active galactic nucleus (AGN) candidates. Most attempts involve obtaining a better understanding of double-peaked narrow emission line sources, in order to distinguish the objects for which the emission lines originate from narrow-line kinematics or jet-driven outflows, from those which might harbor dual AGNs. We observed four such candidate sources with the Very Long Baseline Array (VLBA), at 1.5 GHz with a ∼10 masmore » angular resolution, for which the spectral profiles of AGN optical emission suggested the existence of dual AGNs. In SDSS J210449.13–000919.1 and SDSS J23044.82–093345.3 the radio structures are aligned with the optical emission features, thus the double-peaked emission lines might be the results of jet-driven outflows. In the third detected source SDSS J115523.74+150756.9, the radio structure is less extended and is oriented nearly perpendicular to the position angle derived from optical spectroscopy. The fourth source remained undetected with the VLBA, but it was imaged with the Very Large Array at arcsec resolution a few months before our observations, suggesting the existence of an extended radio structure. We did not detect two radio-emitting cores in any of the four sources, a convincing signature of duality.« less