High throughput profile-profile based fold recognition for the entire human proteome.

PubMed

McGuffin, Liam J; Smith, Richard T; Bryson, Kevin; Sørensen, Søren-Aksel; Jones, David T

2006-06-07

In order to maintain the most comprehensive structural annotation databases we must carry out regular updates for each proteome using the latest profile-profile fold recognition methods. The ability to carry out these updates on demand is necessary to keep pace with the regular updates of sequence and structure databases. Providing the highest quality structural models requires the most intensive profile-profile fold recognition methods running with the very latest available sequence databases and fold libraries. However, running these methods on such a regular basis for every sequenced proteome requires large amounts of processing power. In this paper we describe and benchmark the JYDE (Job Yield Distribution Environment) system, which is a meta-scheduler designed to work above cluster schedulers, such as Sun Grid Engine (SGE) or Condor. We demonstrate the ability of JYDE to distribute the load of genomic-scale fold recognition across multiple independent Grid domains. We use the most recent profile-profile version of our mGenTHREADER software in order to annotate the latest version of the Human proteome against the latest sequence and structure databases in as short a time as possible. We show that our JYDE system is able to scale to large numbers of intensive fold recognition jobs running across several independent computer clusters. Using our JYDE system we have been able to annotate 99.9% of the protein sequences within the Human proteome in less than 24 hours, by harnessing over 500 CPUs from 3 independent Grid domains. This study clearly demonstrates the feasibility of carrying out on demand high quality structural annotations for the proteomes of major eukaryotic organisms. Specifically, we have shown that it is now possible to provide complete regular updates of profile-profile based fold recognition models for entire eukaryotic proteomes, through the use of Grid middleware such as JYDE.

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

Middleton, Sarah A.; Illuminati, Joseph; Kim, Junhyong

Recognition of protein structural fold is the starting point for many structure prediction tools and protein function inference. Fold prediction is computationally demanding and recognizing novel folds is difficult such that the majority of proteins have not been annotated for fold classification. Here we describe a new machine learning approach using a novel feature space that can be used for accurate recognition of all 1,221 currently known folds and inference of unknown novel folds. We show that our method achieves better than 94% accuracy even when many folds have only one training example. We demonstrate the utility of this methodmore » by predicting the folds of 34,330 human protein domains and showing that these predictions can yield useful insights into potential biological function, such as prediction of RNA-binding ability. Finally, our method can be applied to de novo fold prediction of entire proteomes and identify candidate novel fold families.« less

Linear Classifier with Reject Option for the Detection of Vocal Fold Paralysis and Vocal Fold Edema

NASA Astrophysics Data System (ADS)

Kotropoulos, Constantine; Arce, Gonzalo R.

2009-12-01

Two distinct two-class pattern recognition problems are studied, namely, the detection of male subjects who are diagnosed with vocal fold paralysis against male subjects who are diagnosed as normal and the detection of female subjects who are suffering from vocal fold edema against female subjects who do not suffer from any voice pathology. To do so, utterances of the sustained vowel "ah" are employed from the Massachusetts Eye and Ear Infirmary database of disordered speech. Linear prediction coefficients extracted from the aforementioned utterances are used as features. The receiver operating characteristic curve of the linear classifier, that stems from the Bayes classifier when Gaussian class conditional probability density functions with equal covariance matrices are assumed, is derived. The optimal operating point of the linear classifier is specified with and without reject option. First results using utterances of the "rainbow passage" are also reported for completeness. The reject option is shown to yield statistically significant improvements in the accuracy of detecting the voice pathologies under study.

Specificity and Affinity Quantification of Flexible Recognition from Underlying Energy Landscape Topography

PubMed Central

Chu, Xiakun; Wang, Jin

2014-01-01

Flexibility in biomolecular recognition is essential and critical for many cellular activities. Flexible recognition often leads to moderate affinity but high specificity, in contradiction with the conventional wisdom that high affinity and high specificity are coupled. Furthermore, quantitative understanding of the role of flexibility in biomolecular recognition is still challenging. Here, we meet the challenge by quantifying the intrinsic biomolecular recognition energy landscapes with and without flexibility through the underlying density of states. We quantified the thermodynamic intrinsic specificity by the topography of the intrinsic binding energy landscape and the kinetic specificity by association rate. We found that the thermodynamic and kinetic specificity are strongly correlated. Furthermore, we found that flexibility decreases binding affinity on one hand, but increases binding specificity on the other hand, and the decreasing or increasing proportion of affinity and specificity are strongly correlated with the degree of flexibility. This shows more (less) flexibility leads to weaker (stronger) coupling between affinity and specificity. Our work provides a theoretical foundation and quantitative explanation of the previous qualitative studies on the relationship among flexibility, affinity and specificity. In addition, we found that the folding energy landscapes are more funneled with binding, indicating that binding helps folding during the recognition. Finally, we demonstrated that the whole binding-folding energy landscapes can be integrated by the rigid binding and isolated folding energy landscapes under weak flexibility. Our results provide a novel way to quantify the affinity and specificity in flexible biomolecular recognition. PMID:25144525

Specificity and affinity quantification of flexible recognition from underlying energy landscape topography.

PubMed

Chu, Xiakun; Wang, Jin

2014-08-01

Flexibility in biomolecular recognition is essential and critical for many cellular activities. Flexible recognition often leads to moderate affinity but high specificity, in contradiction with the conventional wisdom that high affinity and high specificity are coupled. Furthermore, quantitative understanding of the role of flexibility in biomolecular recognition is still challenging. Here, we meet the challenge by quantifying the intrinsic biomolecular recognition energy landscapes with and without flexibility through the underlying density of states. We quantified the thermodynamic intrinsic specificity by the topography of the intrinsic binding energy landscape and the kinetic specificity by association rate. We found that the thermodynamic and kinetic specificity are strongly correlated. Furthermore, we found that flexibility decreases binding affinity on one hand, but increases binding specificity on the other hand, and the decreasing or increasing proportion of affinity and specificity are strongly correlated with the degree of flexibility. This shows more (less) flexibility leads to weaker (stronger) coupling between affinity and specificity. Our work provides a theoretical foundation and quantitative explanation of the previous qualitative studies on the relationship among flexibility, affinity and specificity. In addition, we found that the folding energy landscapes are more funneled with binding, indicating that binding helps folding during the recognition. Finally, we demonstrated that the whole binding-folding energy landscapes can be integrated by the rigid binding and isolated folding energy landscapes under weak flexibility. Our results provide a novel way to quantify the affinity and specificity in flexible biomolecular recognition.

The Proteome Folding Project: Proteome-scale prediction of structure and function

PubMed Central

Drew, Kevin; Winters, Patrick; Butterfoss, Glenn L.; Berstis, Viktors; Uplinger, Keith; Armstrong, Jonathan; Riffle, Michael; Schweighofer, Erik; Bovermann, Bill; Goodlett, David R.; Davis, Trisha N.; Shasha, Dennis; Malmström, Lars; Bonneau, Richard

2011-01-01

The incompleteness of proteome structure and function annotation is a critical problem for biologists and, in particular, severely limits interpretation of high-throughput and next-generation experiments. We have developed a proteome annotation pipeline based on structure prediction, where function and structure annotations are generated using an integration of sequence comparison, fold recognition, and grid-computing-enabled de novo structure prediction. We predict protein domain boundaries and three-dimensional (3D) structures for protein domains from 94 genomes (including human, Arabidopsis, rice, mouse, fly, yeast, Escherichia coli, and worm). De novo structure predictions were distributed on a grid of more than 1.5 million CPUs worldwide (World Community Grid). We generated significant numbers of new confident fold annotations (9% of domains that are otherwise unannotated in these genomes). We demonstrate that predicted structures can be combined with annotations from the Gene Ontology database to predict new and more specific molecular functions. PMID:21824995

Complete fold annotation of the human proteome using a novel structural feature space

DOE PAGES

Middleton, Sarah A.; Illuminati, Joseph; Kim, Junhyong

2017-04-13

Recognition of protein structural fold is the starting point for many structure prediction tools and protein function inference. Fold prediction is computationally demanding and recognizing novel folds is difficult such that the majority of proteins have not been annotated for fold classification. Here we describe a new machine learning approach using a novel feature space that can be used for accurate recognition of all 1,221 currently known folds and inference of unknown novel folds. We show that our method achieves better than 94% accuracy even when many folds have only one training example. We demonstrate the utility of this methodmore » by predicting the folds of 34,330 human protein domains and showing that these predictions can yield useful insights into potential biological function, such as prediction of RNA-binding ability. Finally, our method can be applied to de novo fold prediction of entire proteomes and identify candidate novel fold families.« less

Optimizing Restriction Site Placement for Synthetic Genomes

NASA Astrophysics Data System (ADS)

Montes, Pablo; Memelli, Heraldo; Ward, Charles; Kim, Joondong; Mitchell, Joseph S. B.; Skiena, Steven

Restriction enzymes are the workhorses of molecular biology. We introduce a new problem that arises in the course of our project to design virus variants to serve as potential vaccines: we wish to modify virus-length genomes to introduce large numbers of unique restriction enzyme recognition sites while preserving wild-type function by substitution of synonymous codons. We show that the resulting problem is NP-Complete, give an exponential-time algorithm, and propose effective heuristics, which we show give excellent results for five sample viral genomes. Our resulting modified genomes have several times more unique restriction sites and reduce the maximum gap between adjacent sites by three to nine-fold.

Metal cofactor modulated folding and target recognition of HIV-1 NCp7.

PubMed

Ren, Weitong; Ji, Dongqing; Xu, Xiulian

2018-01-01

The HIV-1 nucleocapsid 7 (NCp7) plays crucial roles in multiple stages of HIV-1 life cycle, and its biological functions rely on the binding of zinc ions. Understanding the molecular mechanism of how the zinc ions modulate the conformational dynamics and functions of the NCp7 is essential for the drug development and HIV-1 treatment. In this work, using a structure-based coarse-grained model, we studied the effects of zinc cofactors on the folding and target RNA(SL3) recognition of the NCp7 by molecular dynamics simulations. After reproducing some key properties of the zinc binding and folding of the NCp7 observed in previous experiments, our simulations revealed several interesting features in the metal ion modulated folding and target recognition. Firstly, we showed that the zinc binding makes the folding transition states of the two zinc fingers less structured, which is in line with the Hammond effect observed typically in mutation, temperature or denaturant induced perturbations to protein structure and stability. Secondly, We showed that there exists mutual interplay between the zinc ion binding and NCp7-target recognition. Binding of zinc ions enhances the affinity between the NCp7 and the target RNA, whereas the formation of the NCp7-RNA complex reshapes the intrinsic energy landscape of the NCp7 and increases the stability and zinc affinity of the two zinc fingers. Thirdly, by characterizing the effects of salt concentrations on the target RNA recognition, we showed that the NCp7 achieves optimal balance between the affinity and binding kinetics near the physiologically relevant salt concentrations. In addition, the effects of zinc binding on the inter-domain conformational flexibility and folding cooperativity of the NCp7 were also discussed.

Improving strand pairing prediction through exploring folding cooperativity

PubMed Central

Jeong, Jieun; Berman, Piotr; Przytycka, Teresa M.

2008-01-01

The topology of β-sheets is defined by the pattern of hydrogen-bonded strand pairing. Therefore, predicting hydrogen bonded strand partners is a fundamental step towards predicting β-sheet topology. At the same time, finding the correct partners is very difficult due to long range interactions involved in strand pairing. Additionally, patterns of aminoacids observed in β-sheet formations are very general and therefore difficult to use for computational recognition of specific contacts between strands. In this work, we report a new strand pairing algorithm. To address above mentioned difficulties, our algorithm attempts to mimic elements of the folding process. Namely, in addition to ensuring that the predicted hydrogen bonded strand pairs satisfy basic global consistency constraints, it takes into account hypothetical folding pathways. Consistently with this view, introducing hydrogen bonds between a pair of strands changes the probabilities of forming hydrogen bonds between other pairs of strand. We demonstrate that this approach provides an improvement over previously proposed algorithms. We also compare the performance of this method to that of a global optimization algorithm that poses the problem as integer linear programming optimization problem and solves it using ILOG CPLEX™ package. PMID:18989036

Recognizing Whispered Speech Produced by an Individual with Surgically Reconstructed Larynx Using Articulatory Movement Data

PubMed Central

Cao, Beiming; Kim, Myungjong; Mau, Ted; Wang, Jun

2017-01-01

Individuals with larynx (vocal folds) impaired have problems in controlling their glottal vibration, producing whispered speech with extreme hoarseness. Standard automatic speech recognition using only acoustic cues is typically ineffective for whispered speech because the corresponding spectral characteristics are distorted. Articulatory cues such as the tongue and lip motion may help in recognizing whispered speech since articulatory motion patterns are generally not affected. In this paper, we investigated whispered speech recognition for patients with reconstructed larynx using articulatory movement data. A data set with both acoustic and articulatory motion data was collected from a patient with surgically reconstructed larynx using an electromagnetic articulograph. Two speech recognition systems, Gaussian mixture model-hidden Markov model (GMM-HMM) and deep neural network-HMM (DNN-HMM), were used in the experiments. Experimental results showed adding either tongue or lip motion data to acoustic features such as mel-frequency cepstral coefficient (MFCC) significantly reduced the phone error rates on both speech recognition systems. Adding both tongue and lip data achieved the best performance. PMID:29423453

Protein folding optimization based on 3D off-lattice model via an improved artificial bee colony algorithm.

PubMed

Li, Bai; Lin, Mu; Liu, Qiao; Li, Ya; Zhou, Changjun

2015-10-01

Protein folding is a fundamental topic in molecular biology. Conventional experimental techniques for protein structure identification or protein folding recognition require strict laboratory requirements and heavy operating burdens, which have largely limited their applications. Alternatively, computer-aided techniques have been developed to optimize protein structures or to predict the protein folding process. In this paper, we utilize a 3D off-lattice model to describe the original protein folding scheme as a simplified energy-optimal numerical problem, where all types of amino acid residues are binarized into hydrophobic and hydrophilic ones. We apply a balance-evolution artificial bee colony (BE-ABC) algorithm as the minimization solver, which is featured by the adaptive adjustment of search intensity to cater for the varying needs during the entire optimization process. In this work, we establish a benchmark case set with 13 real protein sequences from the Protein Data Bank database and evaluate the convergence performance of BE-ABC algorithm through strict comparisons with several state-of-the-art ABC variants in short-term numerical experiments. Besides that, our obtained best-so-far protein structures are compared to the ones in comprehensive previous literature. This study also provides preliminary insights into how artificial intelligence techniques can be applied to reveal the dynamics of protein folding. Graphical Abstract Protein folding optimization using 3D off-lattice model and advanced optimization techniques.

A molecular mechanism of chaperone-client recognition

PubMed Central

He, Lichun; Sharpe, Timothy; Mazur, Adam; Hiller, Sebastian

2016-01-01

Molecular chaperones are essential in aiding client proteins to fold into their native structure and in maintaining cellular protein homeostasis. However, mechanistic aspects of chaperone function are still not well understood at the atomic level. We use nuclear magnetic resonance spectroscopy to elucidate the mechanism underlying client recognition by the adenosine triphosphate-independent chaperone Spy at the atomic level and derive a structural model for the chaperone-client complex. Spy interacts with its partially folded client Im7 by selective recognition of flexible, locally frustrated regions in a dynamic fashion. The interaction with Spy destabilizes a partially folded client but spatially compacts an unfolded client conformational ensemble. By increasing client backbone dynamics, the chaperone facilitates the search for the native structure. A comparison of the interaction of Im7 with two other chaperones suggests that the underlying principle of recognizing frustrated segments is of a fundamental nature. PMID:28138538

Interplay of I-TASSER and QUARK for template-based and ab initio protein structure prediction in CASP10

PubMed Central

Zhang, Yang

2014-01-01

We develop and test a new pipeline in CASP10 to predict protein structures based on an interplay of I-TASSER and QUARK for both free-modeling (FM) and template-based modeling (TBM) targets. The most noteworthy observation is that sorting through the threading template pool using the QUARK-based ab initio models as probes allows the detection of distant-homology templates which might be ignored by the traditional sequence profile-based threading alignment algorithms. Further template assembly refinement by I-TASSER resulted in successful folding of two medium-sized FM targets with >150 residues. For TBM, the multiple threading alignments from LOMETS are, for the first time, incorporated into the ab initio QUARK simulations, which were further refined by I-TASSER assembly refinement. Compared with the traditional threading assembly refinement procedures, the inclusion of the threading-constrained ab initio folding models can consistently improve the quality of the full-length models as assessed by the GDT-HA and hydrogen-bonding scores. Despite the success, significant challenges still exist in domain boundary prediction and consistent folding of medium-size proteins (especially beta-proteins) for nonhomologous targets. Further developments of sensitive fold-recognition and ab initio folding methods are critical for solving these problems. PMID:23760925

Interplay of I-TASSER and QUARK for template-based and ab initio protein structure prediction in CASP10.

PubMed

Zhang, Yang

2014-02-01

We develop and test a new pipeline in CASP10 to predict protein structures based on an interplay of I-TASSER and QUARK for both free-modeling (FM) and template-based modeling (TBM) targets. The most noteworthy observation is that sorting through the threading template pool using the QUARK-based ab initio models as probes allows the detection of distant-homology templates which might be ignored by the traditional sequence profile-based threading alignment algorithms. Further template assembly refinement by I-TASSER resulted in successful folding of two medium-sized FM targets with >150 residues. For TBM, the multiple threading alignments from LOMETS are, for the first time, incorporated into the ab initio QUARK simulations, which were further refined by I-TASSER assembly refinement. Compared with the traditional threading assembly refinement procedures, the inclusion of the threading-constrained ab initio folding models can consistently improve the quality of the full-length models as assessed by the GDT-HA and hydrogen-bonding scores. Despite the success, significant challenges still exist in domain boundary prediction and consistent folding of medium-size proteins (especially beta-proteins) for nonhomologous targets. Further developments of sensitive fold-recognition and ab initio folding methods are critical for solving these problems. Copyright © 2013 Wiley Periodicals, Inc.

Diagnosis of diabetes diseases using an Artificial Immune Recognition System2 (AIRS2) with fuzzy K-nearest neighbor.

PubMed

Chikh, Mohamed Amine; Saidi, Meryem; Settouti, Nesma

2012-10-01

The use of expert systems and artificial intelligence techniques in disease diagnosis has been increasing gradually. Artificial Immune Recognition System (AIRS) is one of the methods used in medical classification problems. AIRS2 is a more efficient version of the AIRS algorithm. In this paper, we used a modified AIRS2 called MAIRS2 where we replace the K- nearest neighbors algorithm with the fuzzy K-nearest neighbors to improve the diagnostic accuracy of diabetes diseases. The diabetes disease dataset used in our work is retrieved from UCI machine learning repository. The performances of the AIRS2 and MAIRS2 are evaluated regarding classification accuracy, sensitivity and specificity values. The highest classification accuracy obtained when applying the AIRS2 and MAIRS2 using 10-fold cross-validation was, respectively 82.69% and 89.10%.

Vocal fold nodules in adult singers: regional opinions about etiologic factors, career impact, and treatment. A survey of otolaryngologists, speech pathologists, and teachers of singing.

PubMed

Hogikyan, N D; Appel, S; Guinn, L W; Haxer, M J

1999-03-01

This study was undertaken to better understand current regional opinions regarding vocal fold nodules in adult singers. A questionnaire was sent to 298 persons representing the 3 professional groups most involved with the care of singers with vocal nodules: otolaryngologists, speech pathologists, and teachers of singing. The questionnaire queried respondents about their level of experience with this problem, and their beliefs about causative factors, career impact, and optimum treatment. Responses within and between groups were similar, with differences between groups primarily in the magnitude of positive or negative responses, rather than in the polarity of the responses. Prevailing opinions included: recognition of causative factors in both singing and speaking voice practices, optimism about responsiveness to appropriate treatment, enthusiasm for coordinated voice therapy and voice training as first-line treatment, and acceptance of microsurgical management as appropriate treatment if behavioral management fails.

Protein Structure Prediction by Protein Threading

NASA Astrophysics Data System (ADS)

Xu, Ying; Liu, Zhijie; Cai, Liming; Xu, Dong

The seminal work of Bowie, Lüthy, and Eisenberg (Bowie et al., 1991) on "the inverse protein folding problem" laid the foundation of protein structure prediction by protein threading. By using simple measures for fitness of different amino acid types to local structural environments defined in terms of solvent accessibility and protein secondary structure, the authors derived a simple and yet profoundly novel approach to assessing if a protein sequence fits well with a given protein structural fold. Their follow-up work (Elofsson et al., 1996; Fischer and Eisenberg, 1996; Fischer et al., 1996a,b) and the work by Jones, Taylor, and Thornton (Jones et al., 1992) on protein fold recognition led to the development of a new brand of powerful tools for protein structure prediction, which we now term "protein threading." These computational tools have played a key role in extending the utility of all the experimentally solved structures by X-ray crystallography and nuclear magnetic resonance (NMR), providing structural models and functional predictions for many of the proteins encoded in the hundreds of genomes that have been sequenced up to now.

Influence of Child Factors on Health-Care Professionals' Recognition of Common Childhood Mental-Health Problems.

PubMed

Burke, Delia A; Koot, Hans M; de Wilde, Amber; Begeer, Sander

Early recognition of childhood mental-health problems can help minimise long-term negative outcomes. Recognition of mental-health problems, needed for referral and diagnostic evaluation, is largely dependent on health-care professionals' (HCPs) judgement of symptoms presented by the child. This study aimed to establish whether HCPs recognition of mental-health problems varies as a function of three child-related factors (type of problem, number of symptoms, and demographic characteristics). In an online survey, HCPs ( n  = 431) evaluated a series of vignettes describing children with symptoms of mental-health problems. Vignettes varied by problem type (Attention-Deficit/Hyperactivity Disorder (ADHD), Generalised Anxiety Disorder (GAD), Autism Spectrum Disorder (ASD), Conduct Disorder (CD) and Major Depressive Disorder), number of symptoms presented (few and many), and child demographic characteristics (ethnicity, gender, age and socio-economic status (SES)). Results show that recognition of mental-health problems varies by problem type, with ADHD best recognised and GAD worst. Furthermore, recognition varies by the number of symptoms presented. Unexpectedly, a child's gender, ethnicity and family SES did not influence likelihood of problem recognition. These results are the first to reveal differences in HCPs' recognition of various common childhood mental-health problems. HCPs in practice should be advised about poor recognition of GAD, and superior recognition of ADHD, if recognition of all childhood mental-health problems is to be equal.

Conduct symptoms and emotion recognition in adolescent boys with externalization problems.

PubMed

Aspan, Nikoletta; Vida, Peter; Gadoros, Julia; Halasz, Jozsef

2013-01-01

In adults with antisocial personality disorder, marked alterations in the recognition of facial affect were described. Less consistent data are available on the emotion recognition in adolescents with externalization problems. The aim of the present study was to assess the relation between the recognition of emotions and conduct symptoms in adolescent boys with externalization problems. Adolescent boys with externalization problems referred to Vadaskert Child Psychiatry Hospital participated in the study after informed consent (N = 114, 11-17 years, mean = 13.4). The conduct problems scale of the strengths and difficulties questionnaire (parent and self-report) was used. The performance in a facial emotion recognition test was assessed. Conduct problems score (parent and self-report) was inversely correlated with the overall emotion recognition. In the self-report, conduct problems score was inversely correlated with the recognition of anger, fear, and sadness. Adolescents with high conduct problems scores were significantly worse in the recognition of fear, sadness, and overall recognition than adolescents with low conduct scores, irrespective of age and IQ. Our results suggest that impaired emotion recognition is dimensionally related to conduct problems and might have importance in the development of antisocial behavior.

The Multiple-Minima Problem in Protein Folding

NASA Astrophysics Data System (ADS)

Scheraga, Harold A.

1991-10-01

The conformational energy surface of a polypeptide or protein has many local minima, and conventional energy minimization procedures reach only a local minimum (near the starting point of the optimization algorithm) instead of the global minimum (the multiple-minima problem). Several procedures have been developed to surmount this problem, the most promising of which are: (a) build up procedure, (b) optimization of electrostatics, (c) Monte Carlo-plus-energy minimization, (d) electrostatically-driven Monte Carlo, (e) inclusion of distance restraints, (f) adaptive importance-sampling Monte Carlo, (g) relaxation of dimensionality, (h) pattern-recognition, and (i) diffusion equation method. These procedures have been applied to a variety of polypeptide structural problems, and the results of such computations are presented. These include the computation of the structures of open-chain and cyclic peptides, fibrous proteins and globular proteins. Present efforts are being devoted to scaling up these procedures from small polypeptides to proteins, to try to compute the three-dimensional structure of a protein from its amino sequence.

Hydrophobic folding units derived from dissimilar monomer structures and their interactions.

PubMed

Tsai, C J; Nussinov, R

1997-01-01

We have designed an automated procedure to cut a protein into compact hydrophobic folding units. The hydrophobic units are large enough to contain tertiary non-local interactions, reflecting potential nucleation sites during protein folding. The quality of a hydrophobic folding unit is evaluated by four criteria. The first two correspond to visual characterization of a structural domain, namely, compactness and extent of isolation. We use the definition of Zehfus and Rose (Zehfus MH, Rose GD, 1986, Biochemistry 25:35-340) to calculate the compactness of a cut protein unit. The isolation of a unit is based on the solvent accessible surface area (ASA) originally buried in the interior and exposed to the solvent after cutting. The third quantity is the hydrophobicity, equivalent to the fraction of the buried non-polar ASA with respect to the total non-polar ASA. The last criterion in the evaluation of a folding unit is the number of segments it includes. To conform with the rationale of obtaining hydrophobic units, which may relate to early folding events, the hydrophobic interactions are implicitly and explicitly applied in their generation and assessment. We follow Holm and Sander (Holm L, Sander C, 1994, Proteins 19:256-268) to reduce the multiple cutting-point problem to a one-dimensional search for all reasonable trial cuts. However, as here we focus on the hydrophobic cores, the contact matrix used to obtain the first non-trivial eigenvector contains only hydrophobic contracts, rather than all, hydrophobic and hydrophilic, interactions. This dataset of hydrophobic folding units, derived from structurally dissimilar single chain monomers, is particularly useful for investigations of the mechanism of protein folding. For cases where there are kinetic data, the one or more hydrophobic folding units generated for a protein correlate with the two or with the three-state folding process observed. We carry out extensive amino acid sequence order independent structural comparisons to generate a structurally non-redundant set of hydrophobic folding units for fold recognition and for statistical purposes.

FRAN and RBF-PSO as two components of a hyper framework to recognize protein folds.

PubMed

Abbasi, Elham; Ghatee, Mehdi; Shiri, M E

2013-09-01

In this paper, an intelligent hyper framework is proposed to recognize protein folds from its amino acid sequence which is a fundamental problem in bioinformatics. This framework includes some statistical and intelligent algorithms for proteins classification. The main components of the proposed framework are the Fuzzy Resource-Allocating Network (FRAN) and the Radial Bases Function based on Particle Swarm Optimization (RBF-PSO). FRAN applies a dynamic method to tune up the RBF network parameters. Due to the patterns complexity captured in protein dataset, FRAN classifies the proteins under fuzzy conditions. Also, RBF-PSO applies PSO to tune up the RBF classifier. Experimental results demonstrate that FRAN improves prediction accuracy up to 51% and achieves acceptable multi-class results for protein fold prediction. Although RBF-PSO provides reasonable results for protein fold recognition up to 48%, it is weaker than FRAN in some cases. However the proposed hyper framework provides an opportunity to use a great range of intelligent methods and can learn from previous experiences. Thus it can avoid the weakness of some intelligent methods in terms of memory, computational time and static structure. Furthermore, the performance of this system can be enhanced throughout the system life-cycle. Copyright © 2013 Elsevier Ltd. All rights reserved.

Fold independent structural comparisons of protein-ligand binding sites for exploring functional relationships.

PubMed

Gold, Nicola D; Jackson, Richard M

2006-02-03

The rapid growth in protein structural data and the emergence of structural genomics projects have increased the need for automatic structure analysis and tools for function prediction. Small molecule recognition is critical to the function of many proteins; therefore, determination of ligand binding site similarity is important for understanding ligand interactions and may allow their functional classification. Here, we present a binding sites database (SitesBase) that given a known protein-ligand binding site allows rapid retrieval of other binding sites with similar structure independent of overall sequence or fold similarity. However, each match is also annotated with sequence similarity and fold information to aid interpretation of structure and functional similarity. Similarity in ligand binding sites can indicate common binding modes and recognition of similar molecules, allowing potential inference of function for an uncharacterised protein or providing additional evidence of common function where sequence or fold similarity is already known. Alternatively, the resource can provide valuable information for detailed studies of molecular recognition including structure-based ligand design and in understanding ligand cross-reactivity. Here, we show examples of atomic similarity between superfamily or more distant fold relatives as well as between seemingly unrelated proteins. Assignment of unclassified proteins to structural superfamiles is also undertaken and in most cases substantiates assignments made using sequence similarity. Correct assignment is also possible where sequence similarity fails to find significant matches, illustrating the potential use of binding site comparisons for newly determined proteins.

H-Bond Self-Assembly: Folding versus Duplex Formation.

PubMed

Núñez-Villanueva, Diego; Iadevaia, Giulia; Stross, Alexander E; Jinks, Michael A; Swain, Jonathan A; Hunter, Christopher A

2017-05-17

Linear oligomers equipped with complementary H-bond donor (D) and acceptor (A) sites can interact via intermolecular H-bonds to form duplexes or fold via intramolecular H-bonds. These competing equilibria have been quantified using NMR titration and dilution experiments for seven systems featuring different recognition sites and backbones. For all seven architectures, duplex formation is observed for homo-sequence 2-mers (AA·DD) where there are no competing folding equilibria. The corresponding hetero-sequence AD 2-mers also form duplexes, but the observed self-association constants are strongly affected by folding equilibria in the monomeric states. When the backbone is flexible (five or more rotatable bonds separating the recognition sites), intramolecular H-bonding is favored, and the folded state is highly populated. For these systems, the stability of the AD·AD duplex is 1-2 orders of magnitude lower than that of the corresponding AA·DD duplex. However, for three architectures which have more rigid backbones (fewer than five rotatable bonds), intramolecular interactions are not observed, and folding does not compete with duplex formation. These systems are promising candidates for the development of longer, mixed-sequence synthetic information molecules that show sequence-selective duplex formation.

Landscapes with megabasins: Polyamorphism in liquids and biopolymers and the role of nucleation in folding and folding diseases

NASA Astrophysics Data System (ADS)

Angell, C. A.

1997-02-01

We show how energy landscape concepts can rationalize the observations on glassforming liquids over the whole range of behavior, strong to fragile. In particular, we show how the existence of landscapes with both strong and fragile megabasins can provide a basis for understanding the nature of quasi-first-order transitions between amorphous states such as those observed to occur in the glassy states of “strong” glassformers. We show how this propensity originates in the liquid state and then emphasize the analogy provided, at the mesoscopic level, by the folding transition in proteins. Recognition that the folding transition is an equilibrium first-order transition between polyamorphic forms of a complex system implies recognition of the need for a nucleation step in the process. When nucleated phase transitions are kinetically retarded, their probability can be influenced by time-temperature history and by the presence of nucleating agents. Nucleation events are statistically rare in mesoscopic systems, hence the ability to fold rapidly should require special features in the folding molecular structure or the presence of nucleating agents. We propose that the unwanted folding events leading to pathogenic forms of certain proteins (prions) can be stimulated by nucleating agents, which thus may be the unidentified infectious agents in “mad cow” disease and related maladies.

Protein structure recognition: From eigenvector analysis to structural threading method

NASA Astrophysics Data System (ADS)

Cao, Haibo

In this work, we try to understand the protein folding problem using pair-wise hydrophobic interaction as the dominant interaction for the protein folding process. We found a strong correlation between amino acid sequence and the corresponding native structure of the protein. Some applications of this correlation were discussed in this dissertation include the domain partition and a new structural threading method as well as the performance of this method in the CASP5 competition. In the first part, we give a brief introduction to the protein folding problem. Some essential knowledge and progress from other research groups was discussed. This part include discussions of interactions among amino acids residues, lattice HP model, and the designablity principle. In the second part, we try to establish the correlation between amino acid sequence and the corresponding native structure of the protein. This correlation was observed in our eigenvector study of protein contact matrix. We believe the correlation is universal, thus it can be used in automatic partition of protein structures into folding domains. In the third part, we discuss a threading method based on the correlation between amino acid sequence and ominant eigenvector of the structure contact-matrix. A mathematically straightforward iteration scheme provides a self-consistent optimum global sequence-structure alignment. The computational efficiency of this method makes it possible to search whole protein structure databases for structural homology without relying on sequence similarity. The sensitivity and specificity of this method is discussed, along with a case of blind test prediction. In the appendix, we list the overall performance of this threading method in CASP5 blind test in comparison with other existing approaches.

Predicting helix orientation for coiled-coil dimers

PubMed Central

Apgar, James R.; Gutwin, Karl N.; Keating, Amy E.

2008-01-01

The alpha-helical coiled coil is a structurally simple protein oligomerization or interaction motif consisting of two or more alpha helices twisted into a supercoiled bundle. Coiled coils can differ in their stoichiometry, helix orientation and axial alignment. Because of the near degeneracy of many of these variants, coiled coils pose a challenge to fold recognition methods for structure prediction. Whereas distinctions between some protein folds can be discriminated on the basis of hydrophobic/polar patterning or secondary structure propensities, the sequence differences that encode important details of coiled-coil structure can be subtle. This is emblematic of a larger problem in the field of protein structure and interaction prediction: that of establishing specificity between closely similar structures. We tested the behavior of different computational models on the problem of recognizing the correct orientation - parallel vs. antiparallel - of pairs of alpha helices that can form a dimeric coiled coil. For each of 131 examples of known structure, we constructed a large number of both parallel and antiparallel structural models and used these to asses the ability of five energy functions to recognize the correct fold. We also developed and tested three sequenced-based approaches that make use of varying degrees of implicit structural information. The best structural methods performed similarly to the best sequence methods, correctly categorizing ∼81% of dimers. Steric compatibility with the fold was important for some coiled coils we investigated. For many examples, the correct orientation was determined by smaller energy differences between parallel and antiparallel structures distributed over many residues and energy components. Prediction methods that used structure but incorporated varying approximations and assumptions showed quite different behaviors when used to investigate energetic contributions to orientation preference. Sequence based methods were sensitive to the choice of residue-pair interactions scored. PMID:18506779

Specificity in substrate binding by protein folding catalysts: tyrosine and tryptophan residues are the recognition motifs for the binding of peptides to the pancreas-specific protein disulfide isomerase PDIp.

PubMed Central

Ruddock, L. W.; Freedman, R. B.; Klappa, P.

2000-01-01

Using a cross-linking approach, we recently demonstrated that radiolabeled peptides or misfolded proteins specifically interact in vitro with two luminal proteins in crude extracts from pancreas microsomes. The proteins were the folding catalysts protein disulfide isomerase (PDI) and PDIp, a glycosylated, PDI-related protein, expressed exclusively in the pancreas. In this study, we explore the specificity of these proteins in binding peptides and related ligands and show that tyrosine and tryptophan residues in peptides are the recognition motifs for their binding by PDIp. This peptide-binding specificity may reflect the selectivity of PDIp in binding regions of unfolded polypeptide during catalysis of protein folding. PMID:10794419

Multiscaled exploration of coupled folding and binding of an intrinsically disordered molecular recognition element in measles virus nucleoprotein

PubMed Central

Wang, Yong; Chu, Xiakun; Longhi, Sonia; Roche, Philippe; Han, Wei; Wang, Erkang; Wang, Jin

2013-01-01

Numerous relatively short regions within intrinsically disordered proteins (IDPs) serve as molecular recognition elements (MoREs). They fold into ordered structures upon binding to their partner molecules. Currently, there is still a lack of in-depth understanding of how coupled binding and folding occurs in MoREs. Here, we quantified the unbound ensembles of the α-MoRE within the intrinsically disordered C-terminal domain of the measles virus nucleoprotein. We developed a multiscaled approach by combining a physics-based and an atomic hybrid model to decipher the mechanism by which the α-MoRE interacts with the X domain of the measles virus phosphoprotein. Our multiscaled approach led to remarkable qualitative and quantitative agreements between the theoretical predictions and experimental results (e.g., chemical shifts). We found that the free α-MoRE rapidly interconverts between multiple discrete partially helical conformations and the unfolded state, in accordance with the experimental observations. We quantified the underlying global folding–binding landscape. This leads to a synergistic mechanism in which the recognition event proceeds via (minor) conformational selection, followed by (major) induced folding. We also provided evidence that the α-MoRE is a compact molten globule-like IDP and behaves as a downhill folder in the induced folding process. We further provided a theoretical explanation for the inherent connections between “downhill folding,” “molten globule,” and “intrinsic disorder” in IDP-related systems. Particularly, we proposed that binding and unbinding of IDPs proceed in a stepwise way through a “kinetic divide-and-conquer” strategy that confers them high specificity without high affinity. PMID:24043820

Influences to ADHD Problem Recognition: Mixed-Method Investigation and Recommendations to Reduce Disparities for Latino Youth.

PubMed

Haack, Lauren M; Meza, Jocelyn; Jiang, Yuanyuan; Araujo, Eva Jimenez; Pfiffner, Linda

2018-05-16

ADHD problem recognition serves as the first step of help seeking for ethnic minority families, such as Latinos, who underutilize ADHD services. The current mixed-method study explores underlying factors influencing recognition of ADHD problems in a sample of 159 school-aged youth. Parent-teacher informant discrepancy results suggest that parent ethnicity, problem domain, and child age influence ADHD problem recognition. Emerging themes from semi-structured qualitative interviews/focus groups conducted with eighteen Spanish-speaking Latino parents receiving school-based services for attention and behavior concerns support a range of recognized ADHD problems, beliefs about causes, and reactions to ADHD identification. Findings provide recommendations for reducing disparities in ADHD problem recognition and subsequent help seeking.

Recognition vs Reverse Engineering in Boolean Concepts Learning

ERIC Educational Resources Information Center

Shafat, Gabriel; Levin, Ilya

2012-01-01

This paper deals with two types of logical problems--recognition problems and reverse engineering problems, and with the interrelations between these types of problems. The recognition problems are modeled in the form of a visual representation of various objects in a common pattern, with a composition of represented objects in the pattern.…

A protein block based fold recognition method for the annotation of twilight zone sequences.

PubMed

Suresh, V; Ganesan, K; Parthasarathy, S

2013-03-01

The description of protein backbone was recently improved with a group of structural fragments called Structural Alphabets instead of the regular three states (Helix, Sheet and Coil) secondary structure description. Protein Blocks is one of the Structural Alphabets used to describe each and every region of protein backbone including the coil. According to de Brevern (2000) the Protein Blocks has 16 structural fragments and each one has 5 residues in length. Protein Blocks fragments are highly informative among the available Structural Alphabets and it has been used for many applications. Here, we present a protein fold recognition method based on Protein Blocks for the annotation of twilight zone sequences. In our method, we align the predicted Protein Blocks of a query amino acid sequence with a library of assigned Protein Blocks of 953 known folds using the local pair-wise alignment. The alignment results with z-value ≥ 2.5 and P-value ≤ 0.08 are predicted as possible folds. Our method is able to recognize the possible folds for nearly 35.5% of the twilight zone sequences with their predicted Protein Block sequence obtained by pb_prediction, which is available at Protein Block Export server.

SIR-A imagery in geologic studies of the Sierra Madre Oriental, northeastern Mexico. Part 1 (Regional stratigraphy): The use of morphostratigraphic units in remote sensing mapping

NASA Technical Reports Server (NTRS)

Longoria, J. F.; Jimenez, O. H.

1985-01-01

SIR-A imaging was used in geological studies of sedimentary terrains in the Sierra Madre Oriental, northeastern Mexico. Geological features such as regional strike and dip, bedding, folding and faulting were readily detected on the image. The recognition of morphostructural units in the imagery, coupled with field verification, enabled geological mapping of the region at the scale of 1:250 000. Structural profiling lead to the elaboration of a morphostructural map allowing the recognition of an echelon folds and field trends which were used to postulate the ectonic setting of the region.

A new method to improve network topological similarity search: applied to fold recognition

PubMed Central

Lhota, John; Hauptman, Ruth; Hart, Thomas; Ng, Clara; Xie, Lei

2015-01-01

Motivation: Similarity search is the foundation of bioinformatics. It plays a key role in establishing structural, functional and evolutionary relationships between biological sequences. Although the power of the similarity search has increased steadily in recent years, a high percentage of sequences remain uncharacterized in the protein universe. Thus, new similarity search strategies are needed to efficiently and reliably infer the structure and function of new sequences. The existing paradigm for studying protein sequence, structure, function and evolution has been established based on the assumption that the protein universe is discrete and hierarchical. Cumulative evidence suggests that the protein universe is continuous. As a result, conventional sequence homology search methods may be not able to detect novel structural, functional and evolutionary relationships between proteins from weak and noisy sequence signals. To overcome the limitations in existing similarity search methods, we propose a new algorithmic framework—Enrichment of Network Topological Similarity (ENTS)—to improve the performance of large scale similarity searches in bioinformatics. Results: We apply ENTS to a challenging unsolved problem: protein fold recognition. Our rigorous benchmark studies demonstrate that ENTS considerably outperforms state-of-the-art methods. As the concept of ENTS can be applied to any similarity metric, it may provide a general framework for similarity search on any set of biological entities, given their representation as a network. Availability and implementation: Source code freely available upon request Contact: lxie@iscb.org PMID:25717198

Predicting beta-turns in proteins using support vector machines with fractional polynomials

PubMed Central

2013-01-01

Background β-turns are secondary structure type that have essential role in molecular recognition, protein folding, and stability. They are found to be the most common type of non-repetitive structures since 25% of amino acids in protein structures are situated on them. Their prediction is considered to be one of the crucial problems in bioinformatics and molecular biology, which can provide valuable insights and inputs for the fold recognition and drug design. Results We propose an approach that combines support vector machines (SVMs) and logistic regression (LR) in a hybrid prediction method, which we call (H-SVM-LR) to predict β-turns in proteins. Fractional polynomials are used for LR modeling. We utilize position specific scoring matrices (PSSMs) and predicted secondary structure (PSS) as features. Our simulation studies show that H-SVM-LR achieves Qtotal of 82.87%, 82.84%, and 82.32% on the BT426, BT547, and BT823 datasets respectively. These values are the highest among other β-turns prediction methods that are based on PSSMs and secondary structure information. H-SVM-LR also achieves favorable performance in predicting β-turns as measured by the Matthew's correlation coefficient (MCC) on these datasets. Furthermore, H-SVM-LR shows good performance when considering shape strings as additional features. Conclusions In this paper, we present a comprehensive approach for β-turns prediction. Experiments show that our proposed approach achieves better performance compared to other competing prediction methods. PMID:24565438

Predicting beta-turns in proteins using support vector machines with fractional polynomials.

PubMed

Elbashir, Murtada; Wang, Jianxin; Wu, Fang-Xiang; Wang, Lusheng

2013-11-07

β-turns are secondary structure type that have essential role in molecular recognition, protein folding, and stability. They are found to be the most common type of non-repetitive structures since 25% of amino acids in protein structures are situated on them. Their prediction is considered to be one of the crucial problems in bioinformatics and molecular biology, which can provide valuable insights and inputs for the fold recognition and drug design. We propose an approach that combines support vector machines (SVMs) and logistic regression (LR) in a hybrid prediction method, which we call (H-SVM-LR) to predict β-turns in proteins. Fractional polynomials are used for LR modeling. We utilize position specific scoring matrices (PSSMs) and predicted secondary structure (PSS) as features. Our simulation studies show that H-SVM-LR achieves Qtotal of 82.87%, 82.84%, and 82.32% on the BT426, BT547, and BT823 datasets respectively. These values are the highest among other β-turns prediction methods that are based on PSSMs and secondary structure information. H-SVM-LR also achieves favorable performance in predicting β-turns as measured by the Matthew's correlation coefficient (MCC) on these datasets. Furthermore, H-SVM-LR shows good performance when considering shape strings as additional features. In this paper, we present a comprehensive approach for β-turns prediction. Experiments show that our proposed approach achieves better performance compared to other competing prediction methods.

The protein structure prediction problem could be solved using the current PDB library

PubMed Central

Zhang, Yang; Skolnick, Jeffrey

2005-01-01

For single-domain proteins, we examine the completeness of the structures in the current Protein Data Bank (PDB) library for use in full-length model construction of unknown sequences. To address this issue, we employ a comprehensive benchmark set of 1,489 medium-size proteins that cover the PDB at the level of 35% sequence identity and identify templates by structure alignment. With homologous proteins excluded, we can always find similar folds to native with an average rms deviation (RMSD) from native of 2.5 Å with ≈82% alignment coverage. These template structures often contain a significant number of insertions/deletions. The tasser algorithm was applied to build full-length models, where continuous fragments are excised from the top-scoring templates and reassembled under the guide of an optimized force field, which includes consensus restraints taken from the templates and knowledge-based statistical potentials. For almost all targets (except for 2/1,489), the resultant full-length models have an RMSD to native below 6 Å (97% of them below 4 Å). On average, the RMSD of full-length models is 2.25 Å, with aligned regions improved from 2.5 Å to 1.88 Å, comparable with the accuracy of low-resolution experimental structures. Furthermore, starting from state-of-the-art structural alignments, we demonstrate a methodology that can consistently bring template-based alignments closer to native. These results are highly suggestive that the protein-folding problem can in principle be solved based on the current PDB library by developing efficient fold recognition algorithms that can recover such initial alignments. PMID:15653774

Global RNA Fold and Molecular Recognition for a pfl Riboswitch Bound to ZMP, a Master Regulator of One-Carbon Metabolism

DOE PAGES

Ren, Aiming; Rajashankar, Kanagalaghatta R.; Patel, Dinshaw J.

2015-06-25

ZTP, the pyrophosphorylated analog of ZMP (5- amino-4-imidazole carboxamide ribose-5'-monophosphate), was identified as an alarmone that senses 10-formyl-tetrahydroflate deficiency in bacteria. Recently, a pfl riboswitch was identified that selectively binds ZMP and regulates genes associated with purine biosynthesis and one-carbon metabolism. Here we report on the structure of the ZMP-bound Thermosinus carboxydivorans pfl riboswitch sensing domain, thereby defining the pseudoknot-based tertiary RNA fold, the binding-pocket architecture, and principles underlying ligand recognition specificity. Molecular recognition involves shape complementarity, with the ZMP 5-amino and carboxamide groups paired with the Watson-Crick edge of an invariant uracil, and the imidazole ring sandwiched between guanines,more » while the sugar hydroxyls form intermolecular hydrogen bond contacts. The burial of the ZMP base and ribose moieties, together with unanticipated coordination of the carboxamide by Mg 2+, contrasts with exposure of the 5'-phosphate to solvent. Lastly, our studies highlight the principles underlying RNA-based recognition of ZMP, a master regulator of one-carbon metabolism.« less

Global RNA Fold and Molecular Recognition for a pfl Riboswitch Bound to ZMP, a Master Regulator of One-Carbon Metabolism

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

Ren, Aiming; Rajashankar, Kanagalaghatta R.; Patel, Dinshaw J.

ZTP, the pyrophosphorylated analog of ZMP (5- amino-4-imidazole carboxamide ribose-5'-monophosphate), was identified as an alarmone that senses 10-formyl-tetrahydroflate deficiency in bacteria. Recently, a pfl riboswitch was identified that selectively binds ZMP and regulates genes associated with purine biosynthesis and one-carbon metabolism. Here we report on the structure of the ZMP-bound Thermosinus carboxydivorans pfl riboswitch sensing domain, thereby defining the pseudoknot-based tertiary RNA fold, the binding-pocket architecture, and principles underlying ligand recognition specificity. Molecular recognition involves shape complementarity, with the ZMP 5-amino and carboxamide groups paired with the Watson-Crick edge of an invariant uracil, and the imidazole ring sandwiched between guanines,more » while the sugar hydroxyls form intermolecular hydrogen bond contacts. The burial of the ZMP base and ribose moieties, together with unanticipated coordination of the carboxamide by Mg 2+, contrasts with exposure of the 5'-phosphate to solvent. Lastly, our studies highlight the principles underlying RNA-based recognition of ZMP, a master regulator of one-carbon metabolism.« less

Structural insight into RNA recognition motifs: versatile molecular Lego building blocks for biological systems.

PubMed

Muto, Yutaka; Yokoyama, Shigeyuki

2012-01-01

'RNA recognition motifs (RRMs)' are common domain-folds composed of 80-90 amino-acid residues in eukaryotes, and have been identified in many cellular proteins. At first they were known as RNA binding domains. Through discoveries over the past 20 years, however, the RRMs have been shown to exhibit versatile molecular recognition activities and to behave as molecular Lego building blocks to construct biological systems. Novel RNA/protein recognition modes by RRMs are being identified, and more information about the molecular recognition by RRMs is becoming available. These RNA/protein recognition modes are strongly correlated with their biological significance. In this review, we would like to survey the recent progress on these versatile molecular recognition modules. Copyright © 2012 John Wiley & Sons, Ltd.

Electrostatically Accelerated Coupled Binding and Folding of Intrinsically Disordered Proteins

PubMed Central

Ganguly, Debabani; Otieno, Steve; Waddell, Brett; Iconaru, Luigi; Kriwacki, Richard W.; Chen, Jianhan

2012-01-01

Intrinsically disordered proteins (IDPs) are now recognized to be prevalent in biology, and many potential functional benefits have been discussed. However, the frequent requirement of peptide folding in specific interactions of IDPs could impose a kinetic bottleneck, which could be overcome only by efficient folding upon encounter. Intriguingly, existing kinetic data suggest that specific binding of IDPs is generally no slower than that of globular proteins. Here, we exploited the cell cycle regulator p27Kip1 (p27) as a model system to understand how IDPs might achieve efficient folding upon encounter for facile recognition. Combining experiments and coarse-grained modeling, we demonstrate that long-range electrostatic interactions between enriched charges on p27 and near its binding site on cyclin A not only enhance the encounter rate (i.e., electrostatic steering), but also promote folding-competent topologies in the encounter complexes, allowing rapid subsequent formation of short-range native interactions en route to the specific complex. In contrast, nonspecific hydrophobic interactions, while hardly affecting the encounter rate, can significantly reduce the efficiency of folding upon encounter and lead to slower binding kinetics. Further analysis of charge distributions in a set of known IDP complexes reveals that, although IDP binding sites tend to be more hydrophobic compared to the rest of the target surface, their vicinities are frequently enriched with charges to complement those on IDPs. This observation suggests that electrostatically accelerated encounter and induced folding might represent a prevalent mechanism for promoting facile IDP recognition. PMID:22721951

Two-dimensional shape classification using generalized Fourier representation and neural networks

NASA Astrophysics Data System (ADS)

Chodorowski, Artur; Gustavsson, Tomas; Mattsson, Ulf

2000-04-01

A shape-based classification method is developed based upon the Generalized Fourier Representation (GFR). GFR can be regarded as an extension of traditional polar Fourier descriptors, suitable for description of closed objects, both convex and concave, with or without holes. Explicit relations of GFR coefficients to regular moments, moment invariants and affine moment invariants are given in the paper. The dual linear relation between GFR coefficients and regular moments was used to compare shape features derive from GFR descriptors and Hu's moment invariants. the GFR was then applied to a clinical problem within oral medicine and used to represent the contours of the lesions in the oral cavity. The lesions studied were leukoplakia and different forms of lichenoid reactions. Shape features were extracted from GFR coefficients in order to classify potentially cancerous oral lesions. Alternative classifiers were investigated based on a multilayer perceptron with different architectures and extensions. The overall classification accuracy for recognition of potentially cancerous oral lesions when using neural network classifier was 85%, while the classification between leukoplakia and reticular lichenoid reactions gave 96% (5-fold cross-validated) recognition rate.

TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions

PubMed Central

2017-01-01

Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the geometric and biological complexity. To address this problem we introduce the element-specific persistent homology (ESPH) method. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains important biological information via a multichannel image-like representation. This representation reveals hidden structure-function relationships in biomolecules. We further integrate ESPH and deep convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the deep learning limitations from small and noisy training sets, we propose a multi-task multichannel topological convolutional neural network (MM-TCNN). We demonstrate that TopologyNet outperforms the latest methods in the prediction of protein-ligand binding affinities, mutation induced globular protein folding free energy changes, and mutation induced membrane protein folding free energy changes. Availability: weilab.math.msu.edu/TDL/ PMID:28749969

Topology-based modeling of intrinsically disordered proteins: balancing intrinsic folding and intermolecular interactions.

PubMed

Ganguly, Debabani; Chen, Jianhan

2011-04-01

Coupled binding and folding is frequently involved in specific recognition of so-called intrinsically disordered proteins (IDPs), a newly recognized class of proteins that rely on a lack of stable tertiary fold for function. Here, we exploit topology-based Gō-like modeling as an effective tool for the mechanism of IDP recognition within the theoretical framework of minimally frustrated energy landscape. Importantly, substantial differences exist between IDPs and globular proteins in both amino acid sequence and binding interface characteristics. We demonstrate that established Gō-like models designed for folded proteins tend to over-estimate the level of residual structures in unbound IDPs, whereas under-estimating the strength of intermolecular interactions. Such systematic biases have important consequences in the predicted mechanism of interaction. A strategy is proposed to recalibrate topology-derived models to balance intrinsic folding propensities and intermolecular interactions, based on experimental knowledge of the overall residual structure level and binding affinity. Applied to pKID/KIX, the calibrated Gō-like model predicts a dominant multistep sequential pathway for binding-induced folding of pKID that is initiated by KIX binding via the C-terminus in disordered conformations, followed by binding and folding of the rest of C-terminal helix and finally the N-terminal helix. This novel mechanism is consistent with key observations derived from a recent NMR titration and relaxation dispersion study and provides a molecular-level interpretation of kinetic rates derived from dispersion curve analysis. These case studies provide important insight into the applicability and potential pitfalls of topology-based modeling for studying IDP folding and interaction in general. Copyright © 2011 Wiley-Liss, Inc.

PSS-3D1D: an improved 3D1D profile method of protein fold recognition for the annotation of twilight zone sequences.

PubMed

Ganesan, K; Parthasarathy, S

2011-12-01

Annotation of any newly determined protein sequence depends on the pairwise sequence identity with known sequences. However, for the twilight zone sequences which have only 15-25% identity, the pair-wise comparison methods are inadequate and the annotation becomes a challenging task. Such sequences can be annotated by using methods that recognize their fold. Bowie et al. described a 3D1D profile method in which the amino acid sequences that fold into a known 3D structure are identified by their compatibility to that known 3D structure. We have improved the above method by using the predicted secondary structure information and employ it for fold recognition from the twilight zone sequences. In our Protein Secondary Structure 3D1D (PSS-3D1D) method, a score (w) for the predicted secondary structure of the query sequence is included in finding the compatibility of the query sequence to the known fold 3D structures. In the benchmarks, the PSS-3D1D method shows a maximum of 21% improvement in predicting correctly the α + β class of folds from the sequences with twilight zone level of identity, when compared with the 3D1D profile method. Hence, the PSS-3D1D method could offer more clues than the 3D1D method for the annotation of twilight zone sequences. The web based PSS-3D1D method is freely available in the PredictFold server at http://bioinfo.bdu.ac.in/servers/ .

Mexican sign language recognition using normalized moments and artificial neural networks

NASA Astrophysics Data System (ADS)

Solís-V., J.-Francisco; Toxqui-Quitl, Carina; Martínez-Martínez, David; H.-G., Margarita

2014-09-01

This work presents a framework designed for the Mexican Sign Language (MSL) recognition. A data set was recorded with 24 static signs from the MSL using 5 different versions, this MSL dataset was captured using a digital camera in incoherent light conditions. Digital Image Processing was used to segment hand gestures, a uniform background was selected to avoid using gloved hands or some special markers. Feature extraction was performed by calculating normalized geometric moments of gray scaled signs, then an Artificial Neural Network performs the recognition using a 10-fold cross validation tested in weka, the best result achieved 95.83% of recognition rate.

Ubiquitin-dependent Protein Degradation at the Yeast Endoplasmic Reticulum and Nuclear Envelope

PubMed Central

Zattas, Dimitrios; Hochstrasser, Mark

2014-01-01

The endoplasmic reticulum (ER) is the primary organelle in eukaryotic cells where membrane and secreted proteins are inserted into or across cell membranes. Its membrane bilayer and luminal compartments provide a favorable environment for the folding and assembly of thousands of newly synthesized proteins. However, protein folding is intrinsically error-prone, and various stress conditions can further increase levels of protein misfolding and damage, particularly in the ER, which can lead to cellular dysfunction and disease. The ubiquitin-proteasome system (UPS) is responsible for the selective destruction of a vast array of protein substrates, either for protein quality control or to allow rapid changes in the levels of specific regulatory proteins. In this review, we will focus on the components and mechanisms of ER-associated protein degradation (ERAD), an important branch of the UPS. ER membranes extend from subcortical regions of the cell to the nuclear envelope, with its continuous outer and inner membranes; the nuclear envelope is a specialized subdomain of the ER. ERAD presents additional challenges to the UPS beyond those faced with soluble substrates of the cytoplasm and nucleus. These include recognition of sugar modifications that occur in the ER, retrotranslocation of proteins across the membrane bilayer, and transfer of substrates from the ER extraction machinery to the proteasome. Here we review characteristics of ERAD substrate degradation signals (degrons), mechanisms underlying substrate recognition and processing by the ERAD machinery, and ideas on the still unresolved problem of how substrate proteins are moved across and extracted from the ER membrane. PMID:25231236

Molecular Basis of the Behavior of Hepatitis A Virus Exposed to High Hydrostatic Pressure

PubMed Central

D'Andrea, Lucía; Pérez-Rodríguez, Francisco J.; Costafreda, M. Isabel; Beguiristain, Nerea; Fuentes, Cristina; Aymerich, Teresa; Guix, Susana; Bosch, Albert

2014-01-01

Food-borne hepatitis A outbreaks may be prevented by subjecting foods at risk of virus contamination to moderate treatments of high hydrostatic pressure (HHP). A pretreatment promoting hepatitis A virus (HAV) capsid-folding changes enhances the virucidal effect of HHP, indicating that its efficacy depends on capsid conformation. HAV populations enriched in immature capsids (125S provirions) are more resistant to HHP, suggesting that mature capsids (150S virions) are more susceptible to this treatment. In addition, the monoclonal antibody (MAb) K24F2 epitope contained in the immunodominant site is a key factor for the resistance to HHP. Changes in capsid folding inducing a loss of recognition by MAb K24F2 render more susceptible conformations independently of the origin of such changes. Accordingly, codon usage-associated folding changes and changes stimulated by pH-dependent breathings, provided they confer a loss of recognition by MAb K24F2, induce a higher susceptibility to HHP. In conclusion, the resistance of HAV to HHP treatments may be explained by a low proportion of 150S particles combined with a good accessibility of the epitope contained in the immunodominant site close to the 5-fold axis. PMID:25107980

Frnakenstein: multiple target inverse RNA folding.

PubMed

Lyngsø, Rune B; Anderson, James W J; Sizikova, Elena; Badugu, Amarendra; Hyland, Tomas; Hein, Jotun

2012-10-09

RNA secondary structure prediction, or folding, is a classic problem in bioinformatics: given a sequence of nucleotides, the aim is to predict the base pairs formed in its three dimensional conformation. The inverse problem of designing a sequence folding into a particular target structure has only more recently received notable interest. With a growing appreciation and understanding of the functional and structural properties of RNA motifs, and a growing interest in utilising biomolecules in nano-scale designs, the interest in the inverse RNA folding problem is bound to increase. However, whereas the RNA folding problem from an algorithmic viewpoint has an elegant and efficient solution, the inverse RNA folding problem appears to be hard. In this paper we present a genetic algorithm approach to solve the inverse folding problem. The main aims of the development was to address the hitherto mostly ignored extension of solving the inverse folding problem, the multi-target inverse folding problem, while simultaneously designing a method with superior performance when measured on the quality of designed sequences. The genetic algorithm has been implemented as a Python program called Frnakenstein. It was benchmarked against four existing methods and several data sets totalling 769 real and predicted single structure targets, and on 292 two structure targets. It performed as well as or better at finding sequences which folded in silico into the target structure than all existing methods, without the heavy bias towards CG base pairs that was observed for all other top performing methods. On the two structure targets it also performed well, generating a perfect design for about 80% of the targets. Our method illustrates that successful designs for the inverse RNA folding problem does not necessarily have to rely on heavy biases in base pair and unpaired base distributions. The design problem seems to become more difficult on larger structures when the target structures are real structures, while no deterioration was observed for predicted structures. Design for two structure targets is considerably more difficult, but far from impossible, demonstrating the feasibility of automated design of artificial riboswitches. The Python implementation is available at http://www.stats.ox.ac.uk/research/genome/software/frnakenstein.

Frnakenstein: multiple target inverse RNA folding

PubMed Central

2012-01-01

Background RNA secondary structure prediction, or folding, is a classic problem in bioinformatics: given a sequence of nucleotides, the aim is to predict the base pairs formed in its three dimensional conformation. The inverse problem of designing a sequence folding into a particular target structure has only more recently received notable interest. With a growing appreciation and understanding of the functional and structural properties of RNA motifs, and a growing interest in utilising biomolecules in nano-scale designs, the interest in the inverse RNA folding problem is bound to increase. However, whereas the RNA folding problem from an algorithmic viewpoint has an elegant and efficient solution, the inverse RNA folding problem appears to be hard. Results In this paper we present a genetic algorithm approach to solve the inverse folding problem. The main aims of the development was to address the hitherto mostly ignored extension of solving the inverse folding problem, the multi-target inverse folding problem, while simultaneously designing a method with superior performance when measured on the quality of designed sequences. The genetic algorithm has been implemented as a Python program called Frnakenstein. It was benchmarked against four existing methods and several data sets totalling 769 real and predicted single structure targets, and on 292 two structure targets. It performed as well as or better at finding sequences which folded in silico into the target structure than all existing methods, without the heavy bias towards CG base pairs that was observed for all other top performing methods. On the two structure targets it also performed well, generating a perfect design for about 80% of the targets. Conclusions Our method illustrates that successful designs for the inverse RNA folding problem does not necessarily have to rely on heavy biases in base pair and unpaired base distributions. The design problem seems to become more difficult on larger structures when the target structures are real structures, while no deterioration was observed for predicted structures. Design for two structure targets is considerably more difficult, but far from impossible, demonstrating the feasibility of automated design of artificial riboswitches. The Python implementation is available at http://www.stats.ox.ac.uk/research/genome/software/frnakenstein. PMID:23043260

The rational design of recognitive polymeric networks for sensing applications

NASA Astrophysics Data System (ADS)

Noss, Kimberly Ryanne Dial

Testosterone recognitive networks were synthesized with varying feed crosslinking percentages and length of the bi-functional crosslinking agent to analyze the effect of changing structural parameters on template binding properties such as affinity, selectivity, capacity, and diffusional transport. The crosslinking percentage of the crosslinking monomer ethylene glycol dimethacrylate was varied from 50% to 90% and associated networks experienced a 2 fold increase in capacity and a 4 fold increase in affinity with the equilibrium association constants, Ka, ranging from 0.32 +/- 0.02 x 10 4 M-1 to 1.3 +/- 0.1 x 104 M -1, respectively. The higher concentration of crosslinking monomer increased the crosslinking points available for inter-chain stabilization creating an increased number of stable cavities for template association. However, by increasing the length of the crosslinking agent and increasing the feed crosslinking percentage from 77% crosslinked poly(methacrylic acid- co-ethylene glycol dimethacrylate) (poly(MAA-co-EGDMA)) to 50% crosslinked poly(methacrylic acid-co-poly(ethylene glycol)200 dimethacrylate) (poly(MAA-co-PEG200DMA)), the mesh size of the network increased resulting in an increased template diffusion coefficient from (2.83 +/- 0.06) x 109 cm2/s to (4.3 +/- 0.06) x 109 cm2/s, respectively, which is approximately a 40% faster template diffussional transport. A 77% crosslinked poly (MAA-co-PEG200DMA) recognitive network had an association constant of (0.20 +/- 0.05) x 104 M -1 and bound (0.72 +/- 0.04) x 10-2 mmol testosterone/g dry polymer, which was less by 6 and 3 fold, respectively, compared to a similarly crosslinked poly(MAA-co-EGDMA) recognitive network. Structural manipulation of the macromolecular architecture illustrates the programmability of recognitive networks for specific template binding parameters and diffusional transport, which may lead to enhanced imprinted sensor materials and successful integration onto sensor platforms.

Problem Solving through Paper Folding

ERIC Educational Resources Information Center

Wares, Arsalan

2014-01-01

The purpose of this article is to describe a couple of challenging mathematical problems that involve paper folding. These problem-solving tasks can be used to foster geometric and algebraic thinking among students. The context of paper folding makes some of the abstract mathematical ideas involved relatively concrete. When implemented…

Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology.

PubMed

Hsu, Yu-Liang; Chou, Po-Huan; Chang, Hsing-Cheng; Lin, Shyan-Lung; Yang, Shih-Chin; Su, Heng-Yi; Chang, Chih-Chien; Cheng, Yuan-Sheng; Kuo, Yu-Chen

2017-07-15

This paper aims to develop a multisensor data fusion technology-based smart home system by integrating wearable intelligent technology, artificial intelligence, and sensor fusion technology. We have developed the following three systems to create an intelligent smart home environment: (1) a wearable motion sensing device to be placed on residents' wrists and its corresponding 3D gesture recognition algorithm to implement a convenient automated household appliance control system; (2) a wearable motion sensing device mounted on a resident's feet and its indoor positioning algorithm to realize an effective indoor pedestrian navigation system for smart energy management; (3) a multisensor circuit module and an intelligent fire detection and alarm algorithm to realize a home safety and fire detection system. In addition, an intelligent monitoring interface is developed to provide in real-time information about the smart home system, such as environmental temperatures, CO concentrations, communicative environmental alarms, household appliance status, human motion signals, and the results of gesture recognition and indoor positioning. Furthermore, an experimental testbed for validating the effectiveness and feasibility of the smart home system was built and verified experimentally. The results showed that the 3D gesture recognition algorithm could achieve recognition rates for automated household appliance control of 92.0%, 94.8%, 95.3%, and 87.7% by the 2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, and leave-one-subject-out cross-validation strategies. For indoor positioning and smart energy management, the distance accuracy and positioning accuracy were around 0.22% and 3.36% of the total traveled distance in the indoor environment. For home safety and fire detection, the classification rate achieved 98.81% accuracy for determining the conditions of the indoor living environment.

Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology

PubMed Central

Chou, Po-Huan; Chang, Hsing-Cheng; Lin, Shyan-Lung; Yang, Shih-Chin; Su, Heng-Yi; Chang, Chih-Chien; Cheng, Yuan-Sheng; Kuo, Yu-Chen

2017-01-01

This paper aims to develop a multisensor data fusion technology-based smart home system by integrating wearable intelligent technology, artificial intelligence, and sensor fusion technology. We have developed the following three systems to create an intelligent smart home environment: (1) a wearable motion sensing device to be placed on residents’ wrists and its corresponding 3D gesture recognition algorithm to implement a convenient automated household appliance control system; (2) a wearable motion sensing device mounted on a resident’s feet and its indoor positioning algorithm to realize an effective indoor pedestrian navigation system for smart energy management; (3) a multisensor circuit module and an intelligent fire detection and alarm algorithm to realize a home safety and fire detection system. In addition, an intelligent monitoring interface is developed to provide in real-time information about the smart home system, such as environmental temperatures, CO concentrations, communicative environmental alarms, household appliance status, human motion signals, and the results of gesture recognition and indoor positioning. Furthermore, an experimental testbed for validating the effectiveness and feasibility of the smart home system was built and verified experimentally. The results showed that the 3D gesture recognition algorithm could achieve recognition rates for automated household appliance control of 92.0%, 94.8%, 95.3%, and 87.7% by the 2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, and leave-one-subject-out cross-validation strategies. For indoor positioning and smart energy management, the distance accuracy and positioning accuracy were around 0.22% and 3.36% of the total traveled distance in the indoor environment. For home safety and fire detection, the classification rate achieved 98.81% accuracy for determining the conditions of the indoor living environment. PMID:28714884

Hidden Markov models for character recognition.

PubMed

Vlontzos, J A; Kung, S Y

1992-01-01

A hierarchical system for character recognition with hidden Markov model knowledge sources which solve both the context sensitivity problem and the character instantiation problem is presented. The system achieves 97-99% accuracy using a two-level architecture and has been implemented using a systolic array, thus permitting real-time (1 ms per character) multifont and multisize printed character recognition as well as handwriting recognition.

Rotation-invariant neural pattern recognition system with application to coin recognition.

PubMed

Fukumi, M; Omatu, S; Takeda, F; Kosaka, T

1992-01-01

In pattern recognition, it is often necessary to deal with problems to classify a transformed pattern. A neural pattern recognition system which is insensitive to rotation of input pattern by various degrees is proposed. The system consists of a fixed invariance network with many slabs and a trainable multilayered network. The system was used in a rotation-invariant coin recognition problem to distinguish between a 500 yen coin and a 500 won coin. The results show that the approach works well for variable rotation pattern recognition.

Conic section function neural network circuitry for offline signature recognition.

PubMed

Erkmen, Burcu; Kahraman, Nihan; Vural, Revna A; Yildirim, Tulay

2010-04-01

In this brief, conic section function neural network (CSFNN) circuitry was designed for offline signature recognition. CSFNN is a unified framework for multilayer perceptron (MLP) and radial basis function (RBF) networks to make simultaneous use of advantages of both. The CSFNN circuitry architecture was developed using a mixed mode circuit implementation. The designed circuit system is problem independent. Hence, the general purpose neural network circuit system could be applied to various pattern recognition problems with different network sizes on condition with the maximum network size of 16-16-8. In this brief, CSFNN circuitry system has been applied to two different signature recognition problems. CSFNN circuitry was trained with chip-in-the-loop learning technique in order to compensate typical analog process variations. CSFNN hardware achieved highly comparable computational performances with CSFNN software for nonlinear signature recognition problems.

Two-step interrogation then recognition of DNA binding site by Integration Host Factor: an architectural DNA-bending protein.

PubMed

Velmurugu, Yogambigai; Vivas, Paula; Connolly, Mitchell; Kuznetsov, Serguei V; Rice, Phoebe A; Ansari, Anjum

2018-02-28

The dynamics and mechanism of how site-specific DNA-bending proteins initially interrogate potential binding sites prior to recognition have remained elusive for most systems. Here we present these dynamics for Integration Host factor (IHF), a nucleoid-associated architectural protein, using a μs-resolved T-jump approach. Our studies show two distinct DNA-bending steps during site recognition by IHF. While the faster (∼100 μs) step is unaffected by changes in DNA or protein sequence that alter affinity by >100-fold, the slower (1-10 ms) step is accelerated ∼5-fold when mismatches are introduced at DNA sites that are sharply kinked in the specific complex. The amplitudes of the fast phase increase when the specific complex is destabilized and decrease with increasing [salt], which increases specificity. Taken together, these results indicate that the fast phase is non-specific DNA bending while the slow phase, which responds only to changes in DNA flexibility at the kink sites, is specific DNA kinking during site recognition. Notably, the timescales for the fast phase overlap with one-dimensional diffusion times measured for several proteins on DNA, suggesting that these dynamics reflect partial DNA bending during interrogation of potential binding sites by IHF as it scans DNA.

Structured and Unstructured Binding of an Intrinsically Disordered Protein as Revealed by Atomistic Simulations.

PubMed

Ithuralde, Raúl Esteban; Roitberg, Adrián Enrique; Turjanski, Adrián Gustavo

2016-07-20

Intrinsically disordered proteins (IDPs) are a set of proteins that lack a definite secondary structure in solution. IDPs can acquire tertiary structure when bound to their partners; therefore, the recognition process must also involve protein folding. The nature of the transition state (TS), structured or unstructured, determines the binding mechanism. The characterization of the TS has become a major challenge for experimental techniques and molecular simulations approaches since diffusion, recognition, and binding is coupled to folding. In this work we present atomistic molecular dynamics (MD) simulations that sample the free energy surface of the coupled folding and binding of the transcription factor c-myb to the cotranscription factor CREB binding protein (CBP). This process has been recently studied and became a model to study IDPs. Despite the plethora of available information, we still do not know how c-myb binds to CBP. We performed a set of atomistic biased MD simulations running a total of 15.6 μs. Our results show that c-myb folds very fast upon binding to CBP with no unique pathway for binding. The process can proceed through both structured or unstructured TS's with similar probabilities. This finding reconciles previous seemingly different experimental results. We also performed Go-type coarse-grained MD of several structured and unstructured models that indicate that coupled folding and binding follows a native contact mechanism. To the best of our knowledge, this is the first atomistic MD simulation that samples the free energy surface of the coupled folding and binding processes of IDPs.

Bio-recognitive photonics of a DNA-guided organic semiconductor

PubMed Central

Back, Seung Hyuk; Park, Jin Hyuk; Cui, Chunzhi; Ahn, Dong June

2016-01-01

Incorporation of duplex DNA with higher molecular weights has attracted attention for a new opportunity towards a better organic light-emitting diode (OLED) capability. However, biological recognition by OLED materials is yet to be addressed. In this study, specific oligomeric DNA–DNA recognition is successfully achieved by tri (8-hydroxyquinoline) aluminium (Alq3), an organic semiconductor. Alq3 rods crystallized with guidance from single-strand DNA molecules show, strikingly, a unique distribution of the DNA molecules with a shape of an ‘inverted' hourglass. The crystal's luminescent intensity is enhanced by 1.6-fold upon recognition of the perfect-matched target DNA sequence, but not in the case of a single-base mismatched one. The DNA–DNA recognition forming double-helix structure is identified to occur only in the rod's outer periphery. This study opens up new opportunities of Alq3, one of the most widely used OLED materials, enabling biological recognition. PMID:26725969

Bio-recognitive photonics of a DNA-guided organic semiconductor.

PubMed

Back, Seung Hyuk; Park, Jin Hyuk; Cui, Chunzhi; Ahn, Dong June

2016-01-04

Incorporation of duplex DNA with higher molecular weights has attracted attention for a new opportunity towards a better organic light-emitting diode (OLED) capability. However, biological recognition by OLED materials is yet to be addressed. In this study, specific oligomeric DNA-DNA recognition is successfully achieved by tri (8-hydroxyquinoline) aluminium (Alq3), an organic semiconductor. Alq3 rods crystallized with guidance from single-strand DNA molecules show, strikingly, a unique distribution of the DNA molecules with a shape of an 'inverted' hourglass. The crystal's luminescent intensity is enhanced by 1.6-fold upon recognition of the perfect-matched target DNA sequence, but not in the case of a single-base mismatched one. The DNA-DNA recognition forming double-helix structure is identified to occur only in the rod's outer periphery. This study opens up new opportunities of Alq3, one of the most widely used OLED materials, enabling biological recognition.

Bio-recognitive photonics of a DNA-guided organic semiconductor

NASA Astrophysics Data System (ADS)

Back, Seung Hyuk; Park, Jin Hyuk; Cui, Chunzhi; Ahn, Dong June

2016-01-01

Incorporation of duplex DNA with higher molecular weights has attracted attention for a new opportunity towards a better organic light-emitting diode (OLED) capability. However, biological recognition by OLED materials is yet to be addressed. In this study, specific oligomeric DNA-DNA recognition is successfully achieved by tri (8-hydroxyquinoline) aluminium (Alq3), an organic semiconductor. Alq3 rods crystallized with guidance from single-strand DNA molecules show, strikingly, a unique distribution of the DNA molecules with a shape of an `inverted' hourglass. The crystal's luminescent intensity is enhanced by 1.6-fold upon recognition of the perfect-matched target DNA sequence, but not in the case of a single-base mismatched one. The DNA-DNA recognition forming double-helix structure is identified to occur only in the rod's outer periphery. This study opens up new opportunities of Alq3, one of the most widely used OLED materials, enabling biological recognition.

Approximate Solutions for a Self-Folding Problem of Carbon Nanotubes

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

Y Mikata

2006-08-22

This paper treats approximate solutions for a self-folding problem of carbon nanotubes. It has been observed in the molecular dynamics calculations [1] that a carbon nanotube with a large aspect ratio can self-fold due to van der Waals force between the parts of the same carbon nanotube. The main issue in the self-folding problem is to determine the minimum threshold length of the carbon nanotube at which it becomes possible for the carbon nanotube to self-fold due to the van der Waals force. An approximate mathematical model based on the force method is constructed for the self-folding problem of carbonmore » nanotubes, and it is solved exactly as an elastica problem using elliptic functions. Additionally, three other mathematical models are constructed based on the energy method. As a particular example, the lower and upper estimates for the critical threshold (minimum) length are determined based on both methods for the (5,5) armchair carbon nanotube.« less

Recognition of skin cancer and sun protective behaviors in skin of color.

PubMed

Wheat, Chikoti M; Wesley, Naissan O; Jackson, Brooke A

2013-09-01

Sun protective behaviors are not as frequently practiced in skin of color as they are amongst Caucasians.1 Thus providing a reasonable assumption this behavior, or lack thereof, increases the risk of skin cancer in this skin of color populations. The aim of this study was two-fold-- the first was to understand whether patients with skin of color, when categorized by ethnicity or skin type, are able to recognize skin cancer lesions. The second was to examine the correlation between ethnicity and/or skin type and practice of sun protective behaviors. We surveyed 105 respondents presenting for various skin problems in a dermatology office in Chicago, IL. Topics covered in the survey included recognition of skin cancer appearance and choice of sun protective behaviors. We show that there is a tendency for patients to potentially recognize atypical pigmented lesions when they are "dark moles with irregular borders" or "new moles". In contrast, there is a reduced ability among darkly pigmented skin types IV to VI, to recognize non-melanoma skin cancers. We also show that in addition to ethnicity, skin type within ethnic groups may also play an influential role on the decision to protect or not protect oneself from the sun.

How can professionals carry out recognition towards children of parents with alcohol problems? A qualitative interview study.

PubMed

Werner, Anne; Malterud, Kirsti

2017-02-01

The aim of this study was to explore informal adult support experienced by children with parental alcohol problems to understand how professionals can show recognition in a similar way. We conducted a qualitative interview study with retrospective accounts from nine adults growing up with problem-drinking parents. Data were analysed with systematic text condensation. Goffman's concept "frame" offered a lens to study how supportive situations were defined and to understand opportunities and limitations for translation of recognition acts and attitudes to professional contexts. Analysis demonstrated frames of commonplace interaction where children experienced that adults recognised and responded to their needs. However, the silent support from an adult who recognised the problems without responding was an ambiguous frame. The child sometimes felt betrayed. Concentrating on frames of recognition which could be passed over to professional interactions, we noticed that participants called for a safe harbour, providing a sense of normality. Being with friends and their families, escaping difficulties at home without having to tell, was emphasised as important. Recognition was experienced when an adult with respect and dignity offered an open opportunity to address the problems, without pushing towards further communication. Our study indicates some specific lessons to be learnt about recognition for professional service providers from everyday situations. Frames of recognition, communicating availability and normality, and also unconditional confidentiality and safety when sharing problems may also be offered by professionals in public healthcare within their current frames of competency and time.

Paradigms and progress in vocal fold restoration.

PubMed

Ford, Charles N

2008-09-01

Science advances occur through orderly steps, puzzle-solving leaps, or divergences from the accepted disciplinary matrix that occasionally result in a revolutionary paradigm shift. Key advances must overcome bias, criticism, and rejection. Examples in biological science include use of embryonic stem cells, recognition of Helicobacter pylori in the etiology of ulcer disease, and the evolution of species. Our work in vocal fold restoration reflects these patterns. We progressed through phases of tissue replacement with fillers and biological implants, to current efforts at vocal fold regeneration through tissue engineering, and face challenges of a new "systems biology" paradigm embracing genomics and proteomics.

A Survey on Sentiment Classification in Face Recognition

NASA Astrophysics Data System (ADS)

Qian, Jingyu

2018-01-01

Face recognition has been an important topic for both industry and academia for a long time. K-means clustering, autoencoder, and convolutional neural network, each representing a design idea for face recognition method, are three popular algorithms to deal with face recognition problems. It is worthwhile to summarize and compare these three different algorithms. This paper will focus on one specific face recognition problem-sentiment classification from images. Three different algorithms for sentiment classification problems will be summarized, including k-means clustering, autoencoder, and convolutional neural network. An experiment with the application of these algorithms on a specific dataset of human faces will be conducted to illustrate how these algorithms are applied and their accuracy. Finally, the three algorithms are compared based on the accuracy result.

Adults' strategies for simple addition and multiplication: verbal self-reports and the operand recognition paradigm.

PubMed

Metcalfe, Arron W S; Campbell, Jamie I D

2011-05-01

Accurate measurement of cognitive strategies is important in diverse areas of psychological research. Strategy self-reports are a common measure, but C. Thevenot, M. Fanget, and M. Fayol (2007) proposed a more objective method to distinguish different strategies in the context of mental arithmetic. In their operand recognition paradigm, speed of recognition memory for problem operands after solving a problem indexes strategy (e.g., direct memory retrieval vs. a procedural strategy). Here, in 2 experiments, operand recognition time was the same following simple addition or multiplication, but, consistent with a wide variety of previous research, strategy reports indicated much greater use of procedures (e.g., counting) for addition than multiplication. Operation, problem size (e.g., 2 + 3 vs. 8 + 9), and operand format (digits vs. words) had interactive effects on reported procedure use that were not reflected in recognition performance. Regression analyses suggested that recognition time was influenced at least as much by the relative difficulty of the preceding problem as by the strategy used. The findings indicate that the operand recognition paradigm is not a reliable substitute for strategy reports and highlight the potential impact of difficulty-related carryover effects in sequential cognitive tasks.

Structural correlates of carrier protein recognition in tetanus toxoid-conjugated bacterial polysaccharide vaccines.

PubMed

Lockyer, Kay; Gao, Fang; Derrick, Jeremy P; Bolgiano, Barbara

2015-03-10

An analysis of structure-antibody recognition relationships in nine licenced polysaccharide-tetanus toxoid (TT) conjugate vaccines was performed. The panel of conjugates used included vaccine components to protect against disease caused by Haemophilus influenzae type b, Neisseria meningitidis groups A, C, W and Y and Streptococcus pneumoniae serotype 18C. Conformation and structural analysis included size exclusion chromatography with multi-angle light scattering to determine size, and intrinsic fluorescence spectroscopy and fluorescence quenching to evaluate the protein folding and exposure of Trp residues. A capture ELISA measured the recognition of TT epitopes in the conjugates, using four rat monoclonal antibodies: 2 localised to the HC domain, and 2 of which were holotoxoid conformation-dependent. The conjugates had a wide range of average molecular masses ranging from 1.8×10(6) g/mol to larger than 20×10(6) g/mol. The panel of conjugates were found to be well folded, and did not have spectral features typical of aggregated TT. A partial correlation was found between molecular mass and epitope recognition. Recognition of the epitopes either on the HC domain or the whole toxoid was not necessarily hampered by the size of the molecule. Correlation was also found between the accessibility of Trp side chains and polysaccharide loading, suggesting also that a higher level of conjugated PS does not necessarily interfere with toxoid accessibility. There were different levels of carrier protein Trp side-chain and epitope accessibility that were localised to the HC domain; these were related to the saccharide type, despite the conjugates being independently manufactured. These findings extend our understanding of the molecular basis for carrier protein recognition in TT conjugate vaccines. Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.

Structural correlates of carrier protein recognition in tetanus toxoid-conjugated bacterial polysaccharide vaccines

PubMed Central

Lockyer, Kay; Gao, Fang; Derrick, Jeremy P.; Bolgiano, Barbara

2015-01-01

An analysis of structure-antibody recognition relationships in nine licenced polysaccharide-tetanus toxoid (TT) conjugate vaccines was performed. The panel of conjugates used included vaccine components to protect against disease caused by Haemophilus influenzae type b, Neisseria meningitidis groups A, C, W and Y and Streptococcus pneumoniae serotype 18C. Conformation and structural analysis included size exclusion chromatography with multi-angle light scattering to determine size, and intrinsic fluorescence spectroscopy and fluorescence quenching to evaluate the protein folding and exposure of Trp residues. A capture ELISA measured the recognition of TT epitopes in the conjugates, using four rat monoclonal antibodies: 2 localised to the HC domain, and 2 of which were holotoxoid conformation-dependent. The conjugates had a wide range of average molecular masses ranging from 1.8 × 106 g/mol to larger than 20 × 106 g/mol. The panel of conjugates were found to be well folded, and did not have spectral features typical of aggregated TT. A partial correlation was found between molecular mass and epitope recognition. Recognition of the epitopes either on the HC domain or the whole toxoid was not necessarily hampered by the size of the molecule. Correlation was also found between the accessibility of Trp side chains and polysaccharide loading, suggesting also that a higher level of conjugated PS does not necessarily interfere with toxoid accessibility. There were different levels of carrier protein Trp side-chain and epitope accessibility that were localised to the HC domain; these were related to the saccharide type, despite the conjugates being independently manufactured. These findings extend our understanding of the molecular basis for carrier protein recognition in TT conjugate vaccines. PMID:25640334

Use of designed sequences in protein structure recognition.

PubMed

Kumar, Gayatri; Mudgal, Richa; Srinivasan, Narayanaswamy; Sandhya, Sankaran

2018-05-09

Knowledge of the protein structure is a pre-requisite for improved understanding of molecular function. The gap in the sequence-structure space has increased in the post-genomic era. Grouping related protein sequences into families can aid in narrowing the gap. In the Pfam database, structure description is provided for part or full-length proteins of 7726 families. For the remaining 52% of the families, information on 3-D structure is not yet available. We use the computationally designed sequences that are intermediately related to two protein domain families, which are already known to share the same fold. These strategically designed sequences enable detection of distant relationships and here, we have employed them for the purpose of structure recognition of protein families of yet unknown structure. We first measured the success rate of our approach using a dataset of protein families of known fold and achieved a success rate of 88%. Next, for 1392 families of yet unknown structure, we made structural assignments for part/full length of the proteins. Fold association for 423 domains of unknown function (DUFs) are provided as a step towards functional annotation. The results indicate that knowledge-based filling of gaps in protein sequence space is a lucrative approach for structure recognition. Such sequences assist in traversal through protein sequence space and effectively function as 'linkers', where natural linkers between distant proteins are unavailable. This article was reviewed by Oliviero Carugo, Christine Orengo and Srikrishna Subramanian.

Permutation coding technique for image recognition systems.

PubMed

Kussul, Ernst M; Baidyk, Tatiana N; Wunsch, Donald C; Makeyev, Oleksandr; Martín, Anabel

2006-11-01

A feature extractor and neural classifier for image recognition systems are proposed. The proposed feature extractor is based on the concept of random local descriptors (RLDs). It is followed by the encoder that is based on the permutation coding technique that allows to take into account not only detected features but also the position of each feature on the image and to make the recognition process invariant to small displacements. The combination of RLDs and permutation coding permits us to obtain a sufficiently general description of the image to be recognized. The code generated by the encoder is used as an input data for the neural classifier. Different types of images were used to test the proposed image recognition system. It was tested in the handwritten digit recognition problem, the face recognition problem, and the microobject shape recognition problem. The results of testing are very promising. The error rate for the Modified National Institute of Standards and Technology (MNIST) database is 0.44% and for the Olivetti Research Laboratory (ORL) database it is 0.1%.

Behavioral health needs and problem recognition by older adults receiving home-based aging services.

PubMed

Gum, Amber M; Petkus, Andrew; McDougal, Sarah J; Present, Melanie; King-Kallimanis, Bellinda; Schonfeld, Lawrence

2009-04-01

Older adults' recognition of a behavioral health need is one of the strongest predictors of their use of behavioral health services. Thus, study aims were to examine behavioral health problems in a sample of older adults receiving home-based aging services, their recognition of behavioral health problems, and covariates of problem recognition. The study design was cross-sectional. Older adults (n = 141) receiving home-based aging services completed interviews that included: Structured Clinical Interview for DSM-IV; Brief Symptom Inventory-18; attitudinal scales of stigma, expectations regarding aging, and thought suppression; behavioral health treatment experience; and questions about recognition of behavioral health problems. Thirty (21.9%) participants received an Axis I diagnosis (depressive, anxiety, or substance); another 17 (12.1%) were diagnosed with an adjustment disorder. Participants were more likely to recognize having a problem if they had an Axis I diagnosis, more distress on the BSI-18, family member or friend with a behavioral health problem, and greater thought suppression. In logistic regression, participants who identified a family member or friend with a behavioral health problem were more likely to identify having a behavioral health problem themselves. Findings suggest that older adults receiving home-based aging services who recognize behavioral health problems are more likely to have a psychiatric diagnosis or be experiencing significant distress, and they are more familiar with behavioral health problems in others. This familiarity may facilitate treatment planning; thus, older adults with behavioral health problems who do not report familiarity of problems in others likely require additional education. (c) 2008 John Wiley & Sons, Ltd.

Marketing/Sales Students' Understanding of What Counts as Sales

ERIC Educational Resources Information Center

Hoshower, Leon; Gupta, Ashok K.

2009-01-01

Improper sales revenue recognition is the single largest issue contributing to financial restatements. Understanding and applying the rules of sales revenue recognition is not just an accounting problem; it is a marketing problem, too. Thus, it is important that the sales force has a basic understanding of the rules of sales recognition and be…

Distorted Character Recognition Via An Associative Neural Network

NASA Astrophysics Data System (ADS)

Messner, Richard A.; Szu, Harold H.

1987-03-01

The purpose of this paper is two-fold. First, it is intended to provide some preliminary results of a character recognition scheme which has foundations in on-going neural network architecture modeling, and secondly, to apply some of the neural network results in a real application area where thirty years of effort has had little effect on providing the machine an ability to recognize distorted objects within the same object class. It is the author's belief that the time is ripe to start applying in ernest the results of over twenty years of effort in neural modeling to some of the more difficult problems which seem so hard to solve by conventional means. The character recognition scheme proposed utilizes a preprocessing stage which performs a 2-dimensional Walsh transform of an input cartesian image field, then sequency filters this spectrum into three feature bands. Various features are then extracted and organized into three sets of feature vectors. These vector patterns that are stored and recalled associatively. Two possible associative neural memory models are proposed for further investigation. The first being an outer-product linear matrix associative memory with a threshold function controlling the strength of the output pattern (similar to Kohonen's crosscorrelation approach [1]). The second approach is based upon a modified version of Grossberg's neural architecture [2] which provides better self-organizing properties due to its adaptive nature. Preliminary results of the sequency filtering and feature extraction preprocessing stage and discussion about the use of the proposed neural architectures is included.

Interprofessional, simulation-based technology-enhanced learning to improve physical health care in psychiatry: The recognition and assessment of medical problems in psychiatric settings course.

PubMed

Akroyd, Mike; Jordan, Gary; Rowlands, Paul

2016-06-01

People with serious mental illness have reduced life expectancy compared with a control population, much of which is accounted for by significant physical comorbidity. Frontline clinical staff in mental health often lack confidence in recognition, assessment and management of such 'medical' problems. Simulation provides one way for staff to practise these skills in a safe setting. We produced a multidisciplinary simulation course around recognition and assessment of medical problems in psychiatric settings. We describe an audit of strategic and design aspects of the recognition and assessment of medical problems in psychiatric settings course, using the Department of Health's 'Framework for Technology Enhanced Learning' as our audit standards. At the same time as highlighting areas where recognition and assessment of medical problems in psychiatric settings adheres to these identified principles, such as the strategic underpinning of the approach, and the means by which information is collected, reviewed and shared, it also helps us to identify areas where we can improve. © The Author(s) 2014.

Three-dimensional fingerprint recognition by using convolution neural network

NASA Astrophysics Data System (ADS)

Tian, Qianyu; Gao, Nan; Zhang, Zonghua

2018-01-01

With the development of science and technology and the improvement of social information, fingerprint recognition technology has become a hot research direction and been widely applied in many actual fields because of its feasibility and reliability. The traditional two-dimensional (2D) fingerprint recognition method relies on matching feature points. This method is not only time-consuming, but also lost three-dimensional (3D) information of fingerprint, with the fingerprint rotation, scaling, damage and other issues, a serious decline in robustness. To solve these problems, 3D fingerprint has been used to recognize human being. Because it is a new research field, there are still lots of challenging problems in 3D fingerprint recognition. This paper presents a new 3D fingerprint recognition method by using a convolution neural network (CNN). By combining 2D fingerprint and fingerprint depth map into CNN, and then through another CNN feature fusion, the characteristics of the fusion complete 3D fingerprint recognition after classification. This method not only can preserve 3D information of fingerprints, but also solves the problem of CNN input. Moreover, the recognition process is simpler than traditional feature point matching algorithm. 3D fingerprint recognition rate by using CNN is compared with other fingerprint recognition algorithms. The experimental results show that the proposed 3D fingerprint recognition method has good recognition rate and robustness.

Engineering solid-state materials. Strategies for modeling and packing control of molecular assemblies into 3-D networks

NASA Astrophysics Data System (ADS)

Videnova-Adrabinska, V.; Etter, M. C.; Ward, M. D.

1993-04-01

The crystal structure and properties of a number of urea cocrystals are studied with regard to symmetry of the hydrogen-bonded molecular assemblies. The logical consequences of hydrogen bonding interactions are followed step-by-step. The problems of aggregate formation, nucleation, and crystal growth are also elucidated. Endeavor is made to envisage the 2-D and 3-D hydrogen bond network in a manageable way by exploiting graph set short hand. Strategies of how to control the symmetry of molecular packing are still to be elaborated. In our strategy, the programmed self-assembly has been based on the principle of molecular recognition of self- and hetero-complementary functional groups. However, the main focus for pre-organizational control has been put on the two-fold axis estimator of the urea molecule.

Competition between protein folding and aggregation: A three-dimensional lattice-model simulation

NASA Astrophysics Data System (ADS)

Bratko, D.; Blanch, H. W.

2001-01-01

Aggregation of protein molecules resulting in the loss of biological activity and the formation of insoluble deposits represents a serious problem for the biotechnology and pharmaceutical industries and in medicine. Considerable experimental and theoretical efforts are being made in order to improve our understanding of, and ability to control, the process. In the present work, we describe a Monte Carlo study of a multichain system of coarse-grained model proteins akin to lattice models developed for simulations of protein folding. The model is designed to examine the competition between intramolecular interactions leading to the native protein structure, and intermolecular association, resulting in the formation of aggregates of misfolded chains. Interactions between the segments are described by a variation of the Go potential [N. Go and H. Abe, Biopolymers 20, 1013 (1981)] that extends the recognition between attracting types of segments to pairs on distinct chains. For the particular model we adopt, the global free energy minimum of a pair of protein molecules corresponds to a dimer of native proteins. When three or more molecules interact, clusters of misfolded chains can be more stable than aggregates of native folds. A considerable fraction of native structure, however, is preserved in these cases. Rates of conformational changes rapidly decrease with the size of the protein cluster. Within the timescale accessible to computer simulations, the folding-aggregation balance is strongly affected by kinetic considerations. Both the native form and aggregates can persist in metastable states, even if conditions such as temperature or concentration favor a transition to an alternative form. Refolding yield can be affected by the presence of an additional polymer species mimicking the function of a molecular chaperone.

Coupling ligand recognition to protein folding in an engineered variant of rabbit ileal lipid binding protein.

PubMed

Kouvatsos, Nikolaos; Meldrum, Jill K; Searle, Mark S; Thomas, Neil R

2006-11-28

We have engineered a variant of the beta-clam shell protein ILBP which lacks the alpha-helical motif that caps the central binding cavity; the mutant protein is sufficiently destabilised that it is unfolded under physiological conditions, however, it unexpectedly binds its natural bile acid substrates with high affinity forming a native-like beta-sheet rich structure and demonstrating strong thermodynamic coupling between ligand binding and protein folding.

Coenzyme Recognition and Gene Regulation by a Flavin Mononucleotide Riboswitch

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

Serganov, A.; Huang, L; Patel, D

2009-01-01

The biosynthesis of several protein cofactors is subject to feedback regulation by riboswitches. Flavin mononucleotide (FMN)-specific riboswitches also known as RFN elements, direct expression of bacterial genes involved in the biosynthesis and transport of riboflavin (vitamin B2) and related compounds. Here we present the crystal structures of the Fusobacterium nucleatum riboswitch bound to FMN, riboflavin and antibiotic roseoflavin. The FMN riboswitch structure, centred on an FMN-bound six-stem junction, does not fold by collinear stacking of adjacent helices, typical for folding of large RNAs. Rather, it adopts a butterfly-like scaffold, stapled together by opposingly directed but nearly identically folded peripheral domains.more » FMN is positioned asymmetrically within the junctional site and is specifically bound to RNA through interactions with the isoalloxazine ring chromophore and direct and Mg{sup 2+}-mediated contacts with the phosphate moiety. Our structural data, complemented by binding and footprinting experiments, imply a largely pre-folded tertiary RNA architecture and FMN recognition mediated by conformational transitions within the junctional binding pocket. The inherent plasticity of the FMN-binding pocket and the availability of large openings make the riboswitch an attractive target for structure-based design of FMN-like antimicrobial compounds. Our studies also explain the effects of spontaneous and antibiotic-induced deregulatory mutations and provided molecular insights into FMN-based control of gene expression in normal and riboflavin-overproducing bacterial strains.« less

Deficits in facial emotion recognition indicate behavioral changes and impaired self-awareness after moderate to severe traumatic brain injury.

PubMed

Spikman, Jacoba M; Milders, Maarten V; Visser-Keizer, Annemarie C; Westerhof-Evers, Herma J; Herben-Dekker, Meike; van der Naalt, Joukje

2013-01-01

Traumatic brain injury (TBI) is a leading cause of disability, specifically among younger adults. Behavioral changes are common after moderate to severe TBI and have adverse consequences for social and vocational functioning. It is hypothesized that deficits in social cognition, including facial affect recognition, might underlie these behavioral changes. Measurement of behavioral deficits is complicated, because the rating scales used rely on subjective judgement, often lack specificity and many patients provide unrealistically positive reports of their functioning due to impaired self-awareness. Accordingly, it is important to find performance based tests that allow objective and early identification of these problems. In the present study 51 moderate to severe TBI patients in the sub-acute and chronic stage were assessed with a test for emotion recognition (FEEST) and a questionnaire for behavioral problems (DEX) with a self and proxy rated version. Patients performed worse on the total score and on the negative emotion subscores of the FEEST than a matched group of 31 healthy controls. Patients also exhibited significantly more behavioral problems on both the DEX self and proxy rated version, but proxy ratings revealed more severe problems. No significant correlation was found between FEEST scores and DEX self ratings. However, impaired emotion recognition in the patients, and in particular of Sadness and Anger, was significantly correlated with behavioral problems as rated by proxies and with impaired self-awareness. This is the first study to find these associations, strengthening the proposed recognition of social signals as a condition for adequate social functioning. Hence, deficits in emotion recognition can be conceived as markers for behavioral problems and lack of insight in TBI patients. This finding is also of clinical importance since, unlike behavioral problems, emotion recognition can be objectively measured early after injury, allowing for early detection and treatment of these problems.

Comparing ensemble learning methods based on decision tree classifiers for protein fold recognition.

PubMed

Bardsiri, Mahshid Khatibi; Eftekhari, Mahdi

2014-01-01

In this paper, some methods for ensemble learning of protein fold recognition based on a decision tree (DT) are compared and contrasted against each other over three datasets taken from the literature. According to previously reported studies, the features of the datasets are divided into some groups. Then, for each of these groups, three ensemble classifiers, namely, random forest, rotation forest and AdaBoost.M1 are employed. Also, some fusion methods are introduced for combining the ensemble classifiers obtained in the previous step. After this step, three classifiers are produced based on the combination of classifiers of types random forest, rotation forest and AdaBoost.M1. Finally, the three different classifiers achieved are combined to make an overall classifier. Experimental results show that the overall classifier obtained by the genetic algorithm (GA) weighting fusion method, is the best one in comparison to previously applied methods in terms of classification accuracy.

Adolescents' ability to read different emotional faces relates to their history of maltreatment and type of psychopathology.

PubMed

Leist, Tatyana; Dadds, Mark R

2009-04-01

Emotional processing styles appear to characterize various forms of psychopathology and environmental adversity in children. For example, autistic, anxious, high- and low-emotion conduct problem children, and children who have been maltreated, all appear to show specific deficits and strengths in recognizing the facial expressions of emotions. Until now, the relationships between emotion recognition, antisocial behaviour, emotional problems, callous-unemotional (CU) traits and early maltreatment have never been assessed simultaneously in one study, and the specific associations of emotion recognition to maltreatment and child characteristics are therefore unknown. We examined facial-emotion processing in a sample of 23 adolescents selected for high-risk status on the variables of interest. As expected, maltreatment and child characteristics showed unique associations. CU traits were uniquely related to impairments in fear recognition. Antisocial behaviour was uniquely associated with better fear recognition, but impaired anger recognition. Emotional problems were associated with better recognition of anger and sadness, but lower recognition of neutral faces. Maltreatment was predictive of superior recognition of fear and sadness. The findings are considered in terms of social information-processing theories of psychopathology. Implications for clinical interventions are discussed.

A combination of feature extraction methods with an ensemble of different classifiers for protein structural class prediction problem.

PubMed

Dehzangi, Abdollah; Paliwal, Kuldip; Sharma, Alok; Dehzangi, Omid; Sattar, Abdul

2013-01-01

Better understanding of structural class of a given protein reveals important information about its overall folding type and its domain. It can also be directly used to provide critical information on general tertiary structure of a protein which has a profound impact on protein function determination and drug design. Despite tremendous enhancements made by pattern recognition-based approaches to solve this problem, it still remains as an unsolved issue for bioinformatics that demands more attention and exploration. In this study, we propose a novel feature extraction model that incorporates physicochemical and evolutionary-based information simultaneously. We also propose overlapped segmented distribution and autocorrelation-based feature extraction methods to provide more local and global discriminatory information. The proposed feature extraction methods are explored for 15 most promising attributes that are selected from a wide range of physicochemical-based attributes. Finally, by applying an ensemble of different classifiers namely, Adaboost.M1, LogitBoost, naive Bayes, multilayer perceptron (MLP), and support vector machine (SVM) we show enhancement of the protein structural class prediction accuracy for four popular benchmarks.

Protein structure-structure alignment with discrete Fréchet distance.

PubMed

Jiang, Minghui; Xu, Ying; Zhu, Binhai

2008-02-01

Matching two geometric objects in two-dimensional (2D) and three-dimensional (3D) spaces is a central problem in computer vision, pattern recognition, and protein structure prediction. In particular, the problem of aligning two polygonal chains under translation and rotation to minimize their distance has been studied using various distance measures. It is well known that the Hausdorff distance is useful for matching two point sets, and that the Fréchet distance is a superior measure for matching two polygonal chains. The discrete Fréchet distance closely approximates the (continuous) Fréchet distance, and is a natural measure for the geometric similarity of the folded 3D structures of biomolecules such as proteins. In this paper, we present new algorithms for matching two polygonal chains in two dimensions to minimize their discrete Fréchet distance under translation and rotation, and an effective heuristic for matching two polygonal chains in three dimensions. We also describe our empirical results on the application of the discrete Fréchet distance to protein structure-structure alignment.

Dissociative identity disorder (DID) in clinical practice - what you don't see may hurt you.

PubMed

Leonard, David; Tiller, John

2016-02-01

To identify problems that interfere with the recognition, diagnosis and management of people with dissociative identity disorder (DID) presenting to psychiatric outpatient and inpatient services and suggest solutions. Problems and suggested solutions associated with clinical presentations and management of people with DID are outlined with references to relevant literature. Problems in the recognition and management of DID are described. These lead to delays in diagnosis and costly, inappropriate management, destructive to services, staff and patients alike. Problems include lack of understanding and experience and scepticism about the disorder, resulting in failure to provide appropriate treatment.Some suggestions to improve recognition and management are included. Better recognition, diagnosis and management of DID will lead to better and more cost effective outcomes. © The Royal Australian and New Zealand College of Psychiatrists 2015.

Diversity in recognition of glycans by F-type lectins and galectins: molecular, structural, and biophysical aspects

PubMed Central

Vasta, Gerardo R.; Ahmed, Hafiz; Bianchet, Mario A.; Fernández-Robledo, José A.; Amzel, L. Mario

2013-01-01

Although lectins are “hard-wired” in the germline, the presence of tandemly arrayed carbohydrate recognition domains (CRDs), of chimeric structures displaying distinct CRDs, of polymorphic genes resulting in multiple isoforms, and in some cases, of a considerable recognition plasticity of their carbohydrate binding sites, significantly expand the lectin ligand-recognition spectrum and lectin functional diversification. Analysis of structural/functional aspects of galectins and F-lectins—the most recently identified lectin family characterized by a unique CRD sequence motif (a distinctive structural fold) and nominal specificity for l-Fuc—has led to a greater understanding of self/nonself recognition by proteins with tandemly arrayed CRDs. For lectins with a single CRD, however, recognition of self and nonself glycans can only be rationalized in terms of protein oligomerization and ligand clustering and presentation. Spatial and temporal changes in lectin expression, secretion, and local concentrations in extracellular microenvironments, as well as structural diversity and spatial display of their carbohydrate ligands on the host or microbial cell surface, are suggestive of a dynamic interplay of their recognition and effector functions in development and immunity. PMID:22973821

The relationship between conduct symptoms and the recognition of emotions in non-clinical adolescents.

PubMed

Halász, József; Áspán, Nikoletta; Bozsik, Csilla; Gádoros, Júlia; Inántsy-Pap, Judit

2013-01-01

In adult individuals with antisocial personality disorder, impairment in the recognition of fear seems established. In adolescents with conduct disorder (antecedent of antisocial personality disorder), only sporadic data were assessed, but literature data indicate alterations in the recognition of emotions. The aim of the present study was to assess the relationship between emotion recognition and conduct symptoms in non-clinical adolescents. 53 adolescents participated in the study (13-16 years, boys, n=29, age 14.7±0.2 years; girls, n=24, age=14.7±0.2 years) after informed consent. The parent version of the Strengths and Difficulties Questionnaire was used to assess behavioral problems. The recognition of six basic emotions was established by the "Facial expressions of emotion-stimuli and tests", while Raven IQ measures were also performed. Compared to boys, girls showed significantly better performance in the recognition of disgust (p<0.035), while no significant difference occurred in the recognition of other emotions. In boys, Conduct Problems score was inversely correlated with the recognition of fear (Spearman R=-0.40, p<0.031) and overall emotion recognition (Spearman R=-0.44, p<0.015), while similar correlation was not present in girls. The relationship between the recognition of emotions and conduct problems might indicate an important mechanism in the development of antisocial behavior.

tRNA acceptor stem and anticodon bases form independent codes related to protein folding

PubMed Central

Carter, Charles W.; Wolfenden, Richard

2015-01-01

Aminoacyl-tRNA synthetases recognize tRNA anticodon and 3′ acceptor stem bases. Synthetase Urzymes acylate cognate tRNAs even without anticodon-binding domains, in keeping with the possibility that acceptor stem recognition preceded anticodon recognition. Representing tRNA identity elements with two bits per base, we show that the anticodon encodes the hydrophobicity of each amino acid side-chain as represented by its water-to-cyclohexane distribution coefficient, and this relationship holds true over the entire temperature range of liquid water. The acceptor stem codes preferentially for the surface area or size of each side-chain, as represented by its vapor-to-cyclohexane distribution coefficient. These orthogonal experimental properties are both necessary to account satisfactorily for the exposed surface area of amino acids in folded proteins. Moreover, the acceptor stem codes correctly for β-branched and carboxylic acid side-chains, whereas the anticodon codes for a wider range of such properties, but not for size or β-branching. These and other results suggest that genetic coding of 3D protein structures evolved in distinct stages, based initially on the size of the amino acid and later on its compatibility with globular folding in water. PMID:26034281

A Complete OCR System for Tamil Magazine Documents

NASA Astrophysics Data System (ADS)

Kokku, Aparna; Chakravarthy, Srinivasa

We present a complete optical character recognition (OCR) system for Tamil magazines/documents. All the standard elements of OCR process like de-skewing, preprocessing, segmentation, character recognition, and reconstruction are implemented. Experience with OCR problems teaches that for most subtasks of OCR, there is no single technique that gives perfect results for every type of document image. We exploit the ability of neural networks to learn from experience in solving the problems of segmentation and character recognition. Text segmentation of Tamil newsprint poses a new challenge owing to its italic-like font type; problems that arise in recognition of touching and close characters are discussed. Character recognition efficiency varied from 94 to 97% for this type of font. The grouping of blocks into logical units and the determination of reading order within each logical unit helped us in reconstructing automatically the document image in an editable format.

Bridge Health Monitoring Using a Machine Learning Strategy

DOT National Transportation Integrated Search

2017-01-01

The goal of this project was to cast the SHM problem within a statistical pattern recognition framework. Techniques borrowed from speaker recognition, particularly speaker verification, were used as this discipline deals with problems very similar to...

RNA folding: structure prediction, folding kinetics and ion electrostatics.

PubMed

Tan, Zhijie; Zhang, Wenbing; Shi, Yazhou; Wang, Fenghua

2015-01-01

Beyond the "traditional" functions such as gene storage, transport and protein synthesis, recent discoveries reveal that RNAs have important "new" biological functions including the RNA silence and gene regulation of riboswitch. Such functions of noncoding RNAs are strongly coupled to the RNA structures and proper structure change, which naturally leads to the RNA folding problem including structure prediction and folding kinetics. Due to the polyanionic nature of RNAs, RNA folding structure, stability and kinetics are strongly coupled to the ion condition of solution. The main focus of this chapter is to review the recent progress in the three major aspects in RNA folding problem: structure prediction, folding kinetics and ion electrostatics. This chapter will introduce both the recent experimental and theoretical progress, while emphasize the theoretical modelling on the three aspects in RNA folding.

Gesture recognition for smart home applications using portable radar sensors.

PubMed

Wan, Qian; Li, Yiran; Li, Changzhi; Pal, Ranadip

2014-01-01

In this article, we consider the design of a human gesture recognition system based on pattern recognition of signatures from a portable smart radar sensor. Powered by AAA batteries, the smart radar sensor operates in the 2.4 GHz industrial, scientific and medical (ISM) band. We analyzed the feature space using principle components and application-specific time and frequency domain features extracted from radar signals for two different sets of gestures. We illustrate that a nearest neighbor based classifier can achieve greater than 95% accuracy for multi class classification using 10 fold cross validation when features are extracted based on magnitude differences and Doppler shifts as compared to features extracted through orthogonal transformations. The reported results illustrate the potential of intelligent radars integrated with a pattern recognition system for high accuracy smart home and health monitoring purposes.

Toward open set recognition.

PubMed

Scheirer, Walter J; de Rezende Rocha, Anderson; Sapkota, Archana; Boult, Terrance E

2013-07-01

To date, almost all experimental evaluations of machine learning-based recognition algorithms in computer vision have taken the form of "closed set" recognition, whereby all testing classes are known at training time. A more realistic scenario for vision applications is "open set" recognition, where incomplete knowledge of the world is present at training time, and unknown classes can be submitted to an algorithm during testing. This paper explores the nature of open set recognition and formalizes its definition as a constrained minimization problem. The open set recognition problem is not well addressed by existing algorithms because it requires strong generalization. As a step toward a solution, we introduce a novel "1-vs-set machine," which sculpts a decision space from the marginal distances of a 1-class or binary SVM with a linear kernel. This methodology applies to several different applications in computer vision where open set recognition is a challenging problem, including object recognition and face verification. We consider both in this work, with large scale cross-dataset experiments performed over the Caltech 256 and ImageNet sets, as well as face matching experiments performed over the Labeled Faces in the Wild set. The experiments highlight the effectiveness of machines adapted for open set evaluation compared to existing 1-class and binary SVMs for the same tasks.

RNAslider: a faster engine for consecutive windows folding and its application to the analysis of genomic folding asymmetry.

PubMed

Horesh, Yair; Wexler, Ydo; Lebenthal, Ilana; Ziv-Ukelson, Michal; Unger, Ron

2009-03-04

Scanning large genomes with a sliding window in search of locally stable RNA structures is a well motivated problem in bioinformatics. Given a predefined window size L and an RNA sequence S of size N (L < N), the consecutive windows folding problem is to compute the minimal free energy (MFE) for the folding of each of the L-sized substrings of S. The consecutive windows folding problem can be naively solved in O(NL3) by applying any of the classical cubic-time RNA folding algorithms to each of the N-L windows of size L. Recently an O(NL2) solution for this problem has been described. Here, we describe and implement an O(NLpsi(L)) engine for the consecutive windows folding problem, where psi(L) is shown to converge to O(1) under the assumption of a standard probabilistic polymer folding model, yielding an O(L) speedup which is experimentally confirmed. Using this tool, we note an intriguing directionality (5'-3' vs. 3'-5') folding bias, i.e. that the minimal free energy (MFE) of folding is higher in the native direction of the DNA than in the reverse direction of various genomic regions in several organisms including regions of the genomes that do not encode proteins or ncRNA. This bias largely emerges from the genomic dinucleotide bias which affects the MFE, however we see some variations in the folding bias in the different genomic regions when normalized to the dinucleotide bias. We also present results from calculating the MFE landscape of a mouse chromosome 1, characterizing the MFE of the long ncRNA molecules that reside in this chromosome. The efficient consecutive windows folding engine described in this paper allows for genome wide scans for ncRNA molecules as well as large-scale statistics. This is implemented here as a software tool, called RNAslider, and applied to the scanning of long chromosomes, leading to the observation of features that are visible only on a large scale.

The IntFOLD server: an integrated web resource for protein fold recognition, 3D model quality assessment, intrinsic disorder prediction, domain prediction and ligand binding site prediction.

PubMed

Roche, Daniel B; Buenavista, Maria T; Tetchner, Stuart J; McGuffin, Liam J

2011-07-01

The IntFOLD server is a novel independent server that integrates several cutting edge methods for the prediction of structure and function from sequence. Our guiding principles behind the server development were as follows: (i) to provide a simple unified resource that makes our prediction software accessible to all and (ii) to produce integrated output for predictions that can be easily interpreted. The output for predictions is presented as a simple table that summarizes all results graphically via plots and annotated 3D models. The raw machine readable data files for each set of predictions are also provided for developers, which comply with the Critical Assessment of Methods for Protein Structure Prediction (CASP) data standards. The server comprises an integrated suite of five novel methods: nFOLD4, for tertiary structure prediction; ModFOLD 3.0, for model quality assessment; DISOclust 2.0, for disorder prediction; DomFOLD 2.0 for domain prediction; and FunFOLD 1.0, for ligand binding site prediction. Predictions from the IntFOLD server were found to be competitive in several categories in the recent CASP9 experiment. The IntFOLD server is available at the following web site: http://www.reading.ac.uk/bioinf/IntFOLD/.

Crystal structure and novel recognition motif of rho ADP-ribosylating C3 exoenzyme from Clostridium botulinum: structural insights for recognition specificity and catalysis.

PubMed

Han, S; Arvai, A S; Clancy, S B; Tainer, J A

2001-01-05

Clostridium botulinum C3 exoenzyme inactivates the small GTP-binding protein family Rho by ADP-ribosylating asparagine 41, which depolymerizes the actin cytoskeleton. C3 thus represents a major family of the bacterial toxins that transfer the ADP-ribose moiety of NAD to specific amino acids in acceptor proteins to modify key biological activities in eukaryotic cells, including protein synthesis, differentiation, transformation, and intracellular signaling. The 1.7 A resolution C3 exoenzyme structure establishes the conserved features of the core NAD-binding beta-sandwich fold with other ADP-ribosylating toxins despite little sequence conservation. Importantly, the central core of the C3 exoenzyme structure is distinguished by the absence of an active site loop observed in many other ADP-ribosylating toxins. Unlike the ADP-ribosylating toxins that possess the active site loop near the central core, the C3 exoenzyme replaces the active site loop with an alpha-helix, alpha3. Moreover, structural and sequence similarities with the catalytic domain of vegetative insecticidal protein 2 (VIP2), an actin ADP-ribosyltransferase, unexpectedly implicates two adjacent, protruding turns, which join beta5 and beta6 of the toxin core fold, as a novel recognition specificity motif for this newly defined toxin family. Turn 1 evidently positions the solvent-exposed, aromatic side-chain of Phe209 to interact with the hydrophobic region of Rho adjacent to its GTP-binding site. Turn 2 evidently both places the Gln212 side-chain for hydrogen bonding to recognize Rho Asn41 for nucleophilic attack on the anomeric carbon of NAD ribose and holds the key Glu214 catalytic side-chain in the adjacent catalytic pocket. This proposed bipartite ADP-ribosylating toxin turn-turn (ARTT) motif places the VIP2 and C3 toxin classes into a single ARTT family characterized by analogous target protein recognition via turn 1 aromatic and turn 2 hydrogen-bonding side-chain moieties. Turn 2 centrally anchors the catalytic Glu214 within the ARTT motif, and furthermore distinguishes the C3 toxin class by a conserved turn 2 Gln and the VIP2 binary toxin class by a conserved turn 2 Glu for appropriate target side-chain hydrogen-bonding recognition. Taken together, these structural results provide a molecular basis for understanding the coupled activity and recognition specificity for C3 and for the newly defined ARTT toxin family, which acts in the depolymerization of the actin cytoskeleton. This beta5 to beta6 region of the toxin fold represents an experimentally testable and potentially general recognition motif region for other ADP-ribosylating toxins that have a similar beta-structure framework. Copyright 2001 Academic Press.

Impact of the Columbia Supercomputer on NASA Space and Exploration Mission

NASA Technical Reports Server (NTRS)

Biswas, Rupak; Kwak, Dochan; Kiris, Cetin; Lawrence, Scott

2006-01-01

NASA's 10,240-processor Columbia supercomputer gained worldwide recognition in 2004 for increasing the space agency's computing capability ten-fold, and enabling U.S. scientists and engineers to perform significant, breakthrough simulations. Columbia has amply demonstrated its capability to accelerate NASA's key missions, including space operations, exploration systems, science, and aeronautics. Columbia is part of an integrated high-end computing (HEC) environment comprised of massive storage and archive systems, high-speed networking, high-fidelity modeling and simulation tools, application performance optimization, and advanced data analysis and visualization. In this paper, we illustrate the impact Columbia is having on NASA's numerous space and exploration applications, such as the development of the Crew Exploration and Launch Vehicles (CEV/CLV), effects of long-duration human presence in space, and damage assessment and repair recommendations for remaining shuttle flights. We conclude by discussing HEC challenges that must be overcome to solve space-related science problems in the future.

QUASAR--scoring and ranking of sequence-structure alignments.

PubMed

Birzele, Fabian; Gewehr, Jan E; Zimmer, Ralf

2005-12-15

Sequence-structure alignments are a common means for protein structure prediction in the fields of fold recognition and homology modeling, and there is a broad variety of programs that provide such alignments based on sequence similarity, secondary structure or contact potentials. Nevertheless, finding the best sequence-structure alignment in a pool of alignments remains a difficult problem. QUASAR (quality of sequence-structure alignments ranking) provides a unifying framework for scoring sequence-structure alignments that aids finding well-performing combinations of well-known and custom-made scoring schemes. Those scoring functions can be benchmarked against widely accepted quality scores like MaxSub, TMScore, Touch and APDB, thus enabling users to test their own alignment scores against 'standard-of-truth' structure-based scores. Furthermore, individual score combinations can be optimized with respect to benchmark sets based on known structural relationships using QUASAR's in-built optimization routines.

The Complexity of Folding Self-Folding Origami

NASA Astrophysics Data System (ADS)

Stern, Menachem; Pinson, Matthew B.; Murugan, Arvind

2017-10-01

Why is it difficult to refold a previously folded sheet of paper? We show that even crease patterns with only one designed folding motion inevitably contain an exponential number of "distractor" folding branches accessible from a bifurcation at the flat state. Consequently, refolding a sheet requires finding the ground state in a glassy energy landscape with an exponential number of other attractors of higher energy, much like in models of protein folding (Levinthal's paradox) and other NP-hard satisfiability (SAT) problems. As in these problems, we find that refolding a sheet requires actuation at multiple carefully chosen creases. We show that seeding successful folding in this way can be understood in terms of subpatterns that fold when cut out ("folding islands"). Besides providing guidelines for the placement of active hinges in origami applications, our results point to fundamental limits on the programmability of energy landscapes in sheets.

Rational design of p53, an intrinsically unstructured protein, for the fabrication of novel molecular sensors.

PubMed

Geddie, Melissa L; O'Loughlin, Taryn L; Woods, Kristen K; Matsumura, Ichiro

2005-10-21

The dominant paradigm of protein engineering is structure-based site-directed mutagenesis. This rational approach is generally more effective for the engineering of local properties, such as substrate specificity, than global ones such as allostery. Previous workers have modified normally unregulated reporter enzymes, including beta-galactosidase, alkaline phosphatase, and beta-lactamase, so that the engineered versions are activated (up to 4-fold) by monoclonal antibodies. A reporter that could easily be "reprogrammed" for the facile detection of novel effectors (binding or modifying activities) would be useful in high throughput screens for directed evolution or drug discovery. Here we describe a straightforward and general solution to this potentially difficult design problem. The transcription factor p53 is normally regulated by a variety of post-translational modifications. The insertion of peptides into intrinsically unstructured domains of p53 generated variants that were activated up to 100-fold by novel effectors (proteases or antibodies). An engineered p53 was incorporated into an existing high throughput screen for the detection of human immunodeficiency virus protease, an arbitrarily chosen novel effector. These results suggest that the molecular recognition properties of intrinsically unstructured proteins are relatively easy to engineer and that the absence of crystal structures should not deter the rational engineering of this class of proteins.

How Robust Is the Mechanism of Folding-Upon-Binding for an Intrinsically Disordered Protein?

PubMed

Bonetti, Daniela; Troilo, Francesca; Brunori, Maurizio; Longhi, Sonia; Gianni, Stefano

2018-04-24

The mechanism of interaction of an intrinsically disordered protein (IDP) with its physiological partner is characterized by a disorder-to-order transition in which a recognition and a binding step take place. Even if the mechanism is quite complex, IDPs tend to bind their partner in a cooperative manner such that it is generally possible to detect experimentally only the disordered unbound state and the structured complex. The interaction between the disordered C-terminal domain of the measles virus nucleoprotein (N TAIL ) and the X domain (XD) of the viral phosphoprotein allows us to detect and quantify the two distinct steps of the overall reaction. Here, we analyze the robustness of the folding of N TAIL upon binding to XD by measuring the effect on both the folding and binding steps of N TAIL when the structure of XD is modified. Because it has been shown that wild-type XD is structurally heterogeneous, populating an on-pathway intermediate under native conditions, we investigated the binding to 11 different site-directed variants of N TAIL of one particular variant of XD (I504A XD) that populates only the native state. Data reveal that the recognition and the folding steps are both affected by the structure of XD, indicating a highly malleable pathway. The experimental results are briefly discussed in the light of previous experiments on other IDPs. Copyright © 2018 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Computational protein design: validation and possible relevance as a tool for homology searching and fold recognition.

PubMed

Schmidt Am Busch, Marcel; Sedano, Audrey; Simonson, Thomas

2010-05-05

Protein fold recognition usually relies on a statistical model of each fold; each model is constructed from an ensemble of natural sequences belonging to that fold. A complementary strategy may be to employ sequence ensembles produced by computational protein design. Designed sequences can be more diverse than natural sequences, possibly avoiding some limitations of experimental databases. WE EXPLORE THIS STRATEGY FOR FOUR SCOP FAMILIES: Small Kunitz-type inhibitors (SKIs), Interleukin-8 chemokines, PDZ domains, and large Caspase catalytic subunits, represented by 43 structures. An automated procedure is used to redesign the 43 proteins. We use the experimental backbones as fixed templates in the folded state and a molecular mechanics model to compute the interaction energies between sidechain and backbone groups. Calculations are done with the Proteins@Home volunteer computing platform. A heuristic algorithm is used to scan the sequence and conformational space, yielding 200,000-300,000 sequences per backbone template. The results confirm and generalize our earlier study of SH2 and SH3 domains. The designed sequences ressemble moderately-distant, natural homologues of the initial templates; e.g., the SUPERFAMILY, profile Hidden-Markov Model library recognizes 85% of the low-energy sequences as native-like. Conversely, Position Specific Scoring Matrices derived from the sequences can be used to detect natural homologues within the SwissProt database: 60% of known PDZ domains are detected and around 90% of known SKIs and chemokines. Energy components and inter-residue correlations are analyzed and ways to improve the method are discussed. For some families, designed sequences can be a useful complement to experimental ones for homologue searching. However, improved tools are needed to extract more information from the designed profiles before the method can be of general use.

PREFACE Protein folding: lessons learned and new frontiers Protein folding: lessons learned and new frontiers

NASA Astrophysics Data System (ADS)

Pappu, Rohit V.; Nussinov, Ruth

2009-03-01

In appropriate physiological milieux proteins spontaneously fold into their functional three-dimensional structures. The amino acid sequences of functional proteins contain all the information necessary to specify the folds. This remarkable observation has spawned research aimed at answering two major questions. (1) Of all the conceivable structures that a protein can adopt, why is the ensemble of native-like structures the most favorable? (2) What are the paths by which proteins manage to robustly and reproducibly fold into their native structures? Anfinsen's thermodynamic hypothesis has guided the pursuit of answers to the first question whereas Levinthal's paradox has influenced the development of models for protein folding dynamics. Decades of work have led to significant advances in the folding problem. Mean-field models have been developed to capture our current, coarse grain understanding of the driving forces for protein folding. These models are being used to predict three-dimensional protein structures from sequence and stability profiles as a function of thermodynamic and chemical perturbations. Impressive strides have also been made in the field of protein design, also known as the inverse folding problem, thereby testing our understanding of the determinants of the fold specificities of different sequences. Early work on protein folding pathways focused on the specific sequence of events that could lead to a simplification of the search process. However, unifying principles proved to be elusive. Proteins that show reversible two-state folding-unfolding transitions turned out to be a gift of natural selection. Focusing on these simple systems helped researchers to uncover general principles regarding the origins of cooperativity in protein folding thermodynamics and kinetics. On the theoretical front, concepts borrowed from polymer physics and the physics of spin glasses led to the development of a framework based on energy landscape theories. These theories predict that evolved sequences (functional proteins as opposed to random sequences) find their native folds by minimizing geometric (topological) frustration (i.e. avoiding entropic bottlenecks/kinetic traps). In some cases, following a dominant pathway is the optimal way to minimize frustration, whereas in extreme cases, proteins may fold without encountering bottlenecks. Experimental studies of two-state proteins led in turn to the development of quantitative descriptors that have allowed specific testing of theoretical predictions. These include methods such as phi value analysis to characterize transition state ensembles and descriptors that measure the effects of geometry/topology on folding rates. Interestingly, there exists a striking inverse correlation between the relative contact order (the distance in sequence space between spatially proximal contacts made in the native state) and the folding rates of several two-state proteins. The relative contact order provides a rough estimate of the net entropic cost associated with realizing the folded state, and theories have been developed to explain the observed correlation between the contact order and folding rates. Despite its maturity as a field, there are several areas that come under the rubric of protein folding that are just beginning to receive attention. For example, how do complications in vivo such as macromolecular crowding, confinement, the presence of cosolutes, membrane anchoring, and tethering to surfaces influence protein stabilities and folding dynamics? While we are accustomed to studying proteins at concentrations that are amenable to investigation via probes whose signal intensities grow with protein concentration, this does not make these readouts relevant to the in vivo setting. In cells, protein concentrations are tightly regulated and are likely to be orders of magnitude lower than what we are accustomed to using within in vitro experimental setups. Protein folding in vivo is a complex multi-scale dynamical problem when one considers the synergies between protein expression, spontaneous folding, chaperonin-assisted folding, protein targeting, the kinetics of post-translational modifications, protein degradation, and of course the drive to avoid aggregation. Further, there is growing recognition that cells not only tolerate but select for proteins that are intrinsically disordered. These proteins are essential for many crucial activities, and yet their inability to fold in isolation makes them prone to proteolytic processing and aggregation. In the series of papers that make up this special focus on protein folding in physical biology, leading researchers provide insights into diverse cross-sections of problems in protein folding. Barrick provides a concise review of what we have learned from the study of two-state folders and draws attention to how several unanswered questions are being approached using studies on large repeat proteins. Dissecting the contribution of hydration-mediated interactions to driving forces for protein folding and assembly has been extremely challenging. There is renewed interest in using hydrostatic pressure as a tool to access folding intermediates and decipher the role of partially hydrated states in folding, misfolding, and aggregation. Silva and Foguel review many of the nuances that have been uncovered by perturbing hydrostatic pressure as a thermodynamic parameter. As noted above, protein folding in vivo is expected to be considerably more complex than the folding of two-state proteins in dilute solutions. Lucent et al review the state-of-the-art in the development of quantitative theories to explain chaperonin-assisted folding in vivo. Additionally, they highlight unanswered questions pertaining to the processing of unfolded/misfolded proteins by the chaperone machinery. Zhuang et al present results that focus on the effects of surface tethering on transition state ensembles and folding mechanisms of a model two-state protein. Their results are important because several proteins in vivo fold while being anchored to membranes. Finally, several neurodegenerative and systemic diseases are associated with the aggregation of intrinsically disordered polypeptides. The search for cures in these debilitating and fatal diseases has focused attention on shared attributes in aggregation mechanisms of different proteins and the possibility of identifying druggable targets from mechanistic studies. Abedini and Raleigh review common features gleaned from mechanistic studies of the aggregation of several intrinsically disordered proteins. They propose that the population of helical intermediates and their stabilization via interactions with membranes might be an important route by which the process of aggregation leads to toxicity. The five papers that form this protein folding focus cover specific sub-topics within the larger field of protein folding. They address current questions and emphasize the importance of the growing and productive interface between the physical sciences and biology. We hope that these papers will stimulate much discussion and more importantly advances in the areas highlighted by the contributors.

Theoretical Aspects of the Patterns Recognition Statistical Theory Used for Developing the Diagnosis Algorithms for Complicated Technical Systems

NASA Astrophysics Data System (ADS)

Obozov, A. A.; Serpik, I. N.; Mihalchenko, G. S.; Fedyaeva, G. A.

2017-01-01

In the article, the problem of application of the pattern recognition (a relatively young area of engineering cybernetics) for analysis of complicated technical systems is examined. It is shown that the application of a statistical approach for hard distinguishable situations could be the most effective. The different recognition algorithms are based on Bayes approach, which estimates posteriori probabilities of a certain event and an assumed error. Application of the statistical approach to pattern recognition is possible for solving the problem of technical diagnosis complicated systems and particularly big powered marine diesel engines.

Accurate secondary structure prediction and fold recognition for circular dichroism spectroscopy

PubMed Central

Micsonai, András; Wien, Frank; Kernya, Linda; Lee, Young-Ho; Goto, Yuji; Réfrégiers, Matthieu; Kardos, József

2015-01-01

Circular dichroism (CD) spectroscopy is a widely used technique for the study of protein structure. Numerous algorithms have been developed for the estimation of the secondary structure composition from the CD spectra. These methods often fail to provide acceptable results on α/β-mixed or β-structure–rich proteins. The problem arises from the spectral diversity of β-structures, which has hitherto been considered as an intrinsic limitation of the technique. The predictions are less reliable for proteins of unusual β-structures such as membrane proteins, protein aggregates, and amyloid fibrils. Here, we show that the parallel/antiparallel orientation and the twisting of the β-sheets account for the observed spectral diversity. We have developed a method called β-structure selection (BeStSel) for the secondary structure estimation that takes into account the twist of β-structures. This method can reliably distinguish parallel and antiparallel β-sheets and accurately estimates the secondary structure for a broad range of proteins. Moreover, the secondary structure components applied by the method are characteristic to the protein fold, and thus the fold can be predicted to the level of topology in the CATH classification from a single CD spectrum. By constructing a web server, we offer a general tool for a quick and reliable structure analysis using conventional CD or synchrotron radiation CD (SRCD) spectroscopy for the protein science research community. The method is especially useful when X-ray or NMR techniques fail. Using BeStSel on data collected by SRCD spectroscopy, we investigated the structure of amyloid fibrils of various disease-related proteins and peptides. PMID:26038575

Effects of Training Program on Recognition and Management of Depression and Suicide Risk Evaluation for Slovenian Primary-care Physicians: Follow-up Study

PubMed Central

Roškar, Saška; Podlesek, Anja; Zorko, Maja; Tavčar, Rok; Dernovšek, Mojca Zvezdana; Groleger, Urban; Mirjanič, Milan; Konec, Nuša; Janet, Evgen; Marušič, Andrej

2010-01-01

Aim To implement and evaluate an educational program for primary care physicians on recognition and treatment of depression and suicide prevention. Method The study was conducted in 3 Slovenian neighboring regions (Celje, Ravne na Koroškem, and Podravska) with similar suicide rates and other health indicators. All primary care physicians from Celje (N = 155) and Ravne na Koroškem (N = 35) were invited to participate in the educational program on depression treatment and suicide risk recognition. From January to March 2003, approximately half of them (82 out of 190; educational group) attended the program, whereas the other half (108 out of 190; control group 1) and physicians from the Podravska region (N = 164; control group 2) did not attend the program. The prescription rates of antidepressants and anxiolytics before and after the intervention were compared between the studied regions. Also, suicide rates three-years before and after the intervention were compared. Results From 2002 to 2003, there was a 2.33-fold increase in the rate of antidepressant prescriptions in the educational group (P < 0.05) and only 1.28-fold (P < 0.05) and 1.34-fold (P < 0.05) increase in control groups 1 and 2, respectively. However, the 12% decrease in suicide rate in the intervention regions was not significantly greater than the 4% decrease in the non-intervention region (P > 0.05). Conclusion Our training program was beneficial for primary care physicians’ ability to recognize and manage depression. However, there was no significant decrease in local suicide rates. PMID:20564767

Crystal Structure of the PAC1R Extracellular Domain Unifies a Consensus Fold for Hormone Recognition by Class B G-Protein Coupled Receptors

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

Kumar, Shiva; Pioszak, Augen; Zhang, Chenghai

2012-02-21

Pituitary adenylate cyclase activating polypeptide (PACAP) is a member of the PACAP/glucagon family of peptide hormones, which controls many physiological functions in the immune, nervous, endocrine, and muscular systems. It activates adenylate cyclase by binding to its receptor, PAC1R, a member of class B G-protein coupled receptors (GPCR). Crystal structures of a number of Class B GPCR extracellular domains (ECD) bound to their respective peptide hormones have revealed a consensus mechanism of hormone binding. However, the mechanism of how PACAP binds to its receptor remains controversial as an NMR structure of the PAC1R ECD/PACAP complex reveals a different topology ofmore » the ECD and a distinct mode of ligand recognition. Here we report a 1.9 {angstrom} crystal structure of the PAC1R ECD, which adopts the same fold as commonly observed for other members of Class B GPCR. Binding studies and cell-based assays with alanine-scanned peptides and mutated receptor support a model that PAC1R uses the same conserved fold of Class B GPCR ECD for PACAP binding, thus unifying the consensus mechanism of hormone binding for this family of receptors.« less

Evolution, Energy Landscapes and the Paradoxes of Protein Folding

PubMed Central

Wolynes, Peter G.

2014-01-01

Protein folding has been viewed as a difficult problem of molecular self-organization. The search problem involved in folding however has been simplified through the evolution of folding energy landscapes that are funneled. The funnel hypothesis can be quantified using energy landscape theory based on the minimal frustration principle. Strong quantitative predictions that follow from energy landscape theory have been widely confirmed both through laboratory folding experiments and from detailed simulations. Energy landscape ideas also have allowed successful protein structure prediction algorithms to be developed. The selection constraint of having funneled folding landscapes has left its imprint on the sequences of existing protein structural families. Quantitative analysis of co-evolution patterns allows us to infer the statistical characteristics of the folding landscape. These turn out to be consistent with what has been obtained from laboratory physicochemical folding experiments signalling a beautiful confluence of genomics and chemical physics. PMID:25530262

Relevance feedback-based building recognition

NASA Astrophysics Data System (ADS)

Li, Jing; Allinson, Nigel M.

2010-07-01

Building recognition is a nontrivial task in computer vision research which can be utilized in robot localization, mobile navigation, etc. However, existing building recognition systems usually encounter the following two problems: 1) extracted low level features cannot reveal the true semantic concepts; and 2) they usually involve high dimensional data which require heavy computational costs and memory. Relevance feedback (RF), widely applied in multimedia information retrieval, is able to bridge the gap between the low level visual features and high level concepts; while dimensionality reduction methods can mitigate the high-dimensional problem. In this paper, we propose a building recognition scheme which integrates the RF and subspace learning algorithms. Experimental results undertaken on our own building database show that the newly proposed scheme appreciably enhances the recognition accuracy.

Interprofessional, simulation-based technology-enhanced learning to improve physical healthcare in psychiatry: The RAMPPS course.

PubMed

Akroyd, Mike; Jordan, Gary; Rowlands, Paul

2016-06-01

People with serious mental illness have reduced life expectancy compared with a control population, much of which is accounted for by significant physical comorbidity. Frontline clinical staff in mental health often lack confidence in recognition, assessment and management of such 'medical' problems. Simulation provides one way for staff to practise these skills in a safe setting. We produced a multidisciplinary simulation course around recognition and assessment of medical problems in psychiatric settings. We describe an audit of strategic and design aspects of the recognition and assessment of medical problems in psychiatric settings, using the Department of Health's 'Framework for Technology Enhanced Learning' as our audit standards. At the same time, as highlighting areas where recognition and assessment of medical problems in psychiatric settings adheres to these identified principles, such as the strategic underpinning of the approach, and the means by which information is collected, reviewed and shared, it also helps us to identify areas where we can improve. © The Author(s) 2014.

Role of water mediated interactions in protein-protein recognition landscapes.

PubMed

Papoian, Garegin A; Ulander, Johan; Wolynes, Peter G

2003-07-30

The energy landscape picture of protein folding and binding is employed to optimize a number of pair potentials for direct and water-mediated interactions in protein complex interfaces. We find that water-mediated interactions greatly complement direct interactions in discriminating against various types of trap interactions that model those present in the cell. We highlight the context dependent nature of knowledge-based binding potentials, as contrasted with the situation for autonomous folding. By performing a Principal Component Analysis (PCA) of the corresponding interaction matrixes, we rationalize the strength of the recognition signal for each combination of the contact type and reference trap states using the differential in the idealized "canonical" amino acid compositions of native and trap layers. The comparison of direct and water-mediated contact potential matrixes emphasizes the importance of partial solvation in stabilizing charged groups in the protein interfaces. Specific water-mediated interresidue interactions are expected to influence significantly the kinetics as well as thermodynamics of protein association.

A light-up probe targeting for Bcl-2 2345 G-quadruplex DNA with carbazole TO

NASA Astrophysics Data System (ADS)

Gu, Yingchun; Lin, Dayong; Tang, Yalin; Fei, Xuening; Wang, Cuihong; Zhang, Baolian; Zhou, Jianguo

2018-02-01

As its significant role, the selective recognition of G-quadruplex with specific structures and functions is important in biological and medicinal chemistry. Carbazole derivatives have been reported as a kind of fluorescent probe with many excellent optical properties. In the present study, the fluorescence of the dye (carbazole TO) increased almost 70 fold in the presence of bcl-2 2345 G4 compared to that alone in aqueous buffer condition with almost no fluorescence and 10-30 fold than those in the presence of other DNAs. The binding study results by activity inhibition of G4/Hemin peroxidase experiment, NMR titration and molecular docking simulation showed the high affinity and selectivity to bcl-2 2345 G4 arises from its end-stacking interaction with G-quartet. It is said that a facile approach with excellent sensitive, good selectivity and quick response for bcl-2 2345 G-quadruplex was developed and may be used for antitumor recognition or antitumor agents.

Binding-induced Folding of Prokaryotic Ubiquitin-like Protein on the Mycobacterium Proteasomal ATPase Targets Substrates for Degradation

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

T Wang; K Heran Darwin; H Li

2011-12-31

Mycobacterium tuberculosis uses a proteasome system that is analogous to the eukaryotic ubiquitin-proteasome pathway and is required for pathogenesis. However, the bacterial analog of ubiquitin, prokaryotic ubiquitin-like protein (Pup), is an intrinsically disordered protein that bears little sequence or structural resemblance to the highly structured ubiquitin. Thus, it was unknown how pupylated proteins were recruited to the proteasome. Here, we show that the Mycobacterium proteasomal ATPase (Mpa) has three pairs of tentacle-like coiled coils that recognize Pup. Mpa bound unstructured Pup through hydrophobic interactions and a network of hydrogen bonds, leading to the formation of an {alpha}-helix in Pup. Ourmore » work describes a binding-induced folding recognition mechanism in the Pup-proteasome system that differs mechanistically from substrate recognition in the ubiquitin-proteasome system. This key difference between the prokaryotic and eukaryotic systems could be exploited for the development of a small molecule-based treatment for tuberculosis.« less

Binding-induced folding of prokaryotic ubiquitin-like protein on the mycobacterium proteasomal ATPase targets substrates for degradation

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

Wang, T.; Li, H.; Darwin, K. H.

2010-11-01

Mycobacterium tuberculosis uses a proteasome system that is analogous to the eukaryotic ubiquitin-proteasome pathway and is required for pathogenesis. However, the bacterial analog of ubiquitin, prokaryotic ubiquitin-like protein (Pup), is an intrinsically disordered protein that bears little sequence or structural resemblance to the highly structured ubiquitin. Thus, it was unknown how pupylated proteins were recruited to the proteasome. Here, we show that the Mycobacterium proteasomal ATPase (Mpa) has three pairs of tentacle-like coiled coils that recognize Pup. Mpa bound unstructured Pup through hydrophobic interactions and a network of hydrogen bonds, leading to the formation of an {alpha}-helix in Pup. Ourmore » work describes a binding-induced folding recognition mechanism in the Pup-proteasome system that differs mechanistically from substrate recognition in the ubiquitin-proteasome system. This key difference between the prokaryotic and eukaryotic systems could be exploited for the development of a small molecule-based treatment for tuberculosis.« less

Deep crustal deformation by sheath folding in the Adirondack Mountains, USA

NASA Technical Reports Server (NTRS)

Mclelland, J. M.

1988-01-01

As described by McLelland and Isachsen, the southern half of the Adirondacks are underlain by major isoclinal (F sub 1) and open-upright (F sub 2) folds whose axes are parallel, trend approximately E-W, and plunge gently about the horizontal. These large structures are themselves folded by open upright folds trending NNE (F sub 3). It is pointed out that elongation lineations in these rocks are parallel to X of the finite strain ellipsoid developed during progressive rotational strain. The parallelism between F sub 1 and F sub 2 fold axes and elongation lineations led to the hypothesis that progressive rotational strain, with a west-directed tectonic transport, rotated earlier F sub 1-folds into parallelism with the evolving elongation lineation. Rotation is accomplished by ductile, passive flow of F sub 1-axes into extremely arcuate, E-W hinges. In order to test these hypotheses a number of large folds were mapped in the eastern Adirondacks. Other evidence supporting the existence of sheath folds in the Adirondacks is the presence, on a map scale, of synforms whose limbs pass through the vertical and into antiforms. This type of outcrop pattern is best explained by intersecting a horizontal plane with the double curvature of sheath folds. It is proposed that sheath folding is a common response of hot, ductile rocks to rotational strain at deep crustal levels. The recognition of sheath folds in the Adirondacks reconciles the E-W orientation of fold axes with an E-W elongation lineation.

Common folds and transport mechanisms of secondary active transporters.

PubMed

Shi, Yigong

2013-01-01

Secondary active transporters exploit the electrochemical potential of solutes to shuttle specific substrate molecules across biological membranes, usually against their concentration gradient. Transporters of different functional families with little sequence similarity have repeatedly been found to exhibit similar folds, exemplified by the MFS, LeuT, and NhaA folds. Observations of multiple conformational states of the same transporter, represented by the LeuT superfamily members Mhp1, AdiC, vSGLT, and LeuT, led to proposals that structural changes are associated with substrate binding and transport. Despite recent biochemical and structural advances, our understanding of substrate recognition and energy coupling is rather preliminary. This review focuses on the common folds and shared transport mechanisms of secondary active transporters. Available structural information generally supports the alternating access model for substrate transport, with variations and extensions made by emerging structural, biochemical, and computational evidence.

Research and Implementation of Tibetan Word Segmentation Based on Syllable Methods

NASA Astrophysics Data System (ADS)

Jiang, Jing; Li, Yachao; Jiang, Tao; Yu, Hongzhi

2018-03-01

Tibetan word segmentation (TWS) is an important problem in Tibetan information processing, while abbreviated word recognition is one of the key and most difficult problems in TWS. Most of the existing methods of Tibetan abbreviated word recognition are rule-based approaches, which need vocabulary support. In this paper, we propose a method based on sequence tagging model for abbreviated word recognition, and then implement in TWS systems with sequence labeling models. The experimental results show that our abbreviated word recognition method is fast and effective and can be combined easily with the segmentation model. This significantly increases the effect of the Tibetan word segmentation.

Recognition and classification of oscillatory patterns of electric brain activity using artificial neural network approach

NASA Astrophysics Data System (ADS)

Pchelintseva, Svetlana V.; Runnova, Anastasia E.; Musatov, Vyacheslav Yu.; Hramov, Alexander E.

2017-03-01

In the paper we study the problem of recognition type of the observed object, depending on the generated pattern and the registered EEG data. EEG recorded at the time of displaying cube Necker characterizes appropriate state of brain activity. As an image we use bistable image Necker cube. Subject selects the type of cube and interpret it either as aleft cube or as the right cube. To solve the problem of recognition, we use artificial neural networks. In our paper to create a classifier we have considered a multilayer perceptron. We examine the structure of the artificial neural network and define cubes recognition accuracy.

Indonesian Sign Language Number Recognition using SIFT Algorithm

NASA Astrophysics Data System (ADS)

Mahfudi, Isa; Sarosa, Moechammad; Andrie Asmara, Rosa; Azrino Gustalika, M.

2018-04-01

Indonesian sign language (ISL) is generally used for deaf individuals and poor people communication in communicating. They use sign language as their primary language which consists of 2 types of action: sign and finger spelling. However, not all people understand their sign language so that this becomes a problem for them to communicate with normal people. this problem also becomes a factor they are isolated feel from the social life. It needs a solution that can help them to be able to interacting with normal people. Many research that offers a variety of methods in solving the problem of sign language recognition based on image processing. SIFT (Scale Invariant Feature Transform) algorithm is one of the methods that can be used to identify an object. SIFT is claimed very resistant to scaling, rotation, illumination and noise. Using SIFT algorithm for Indonesian sign language recognition number result rate recognition to 82% with the use of a total of 100 samples image dataset consisting 50 sample for training data and 50 sample images for testing data. Change threshold value get affect the result of the recognition. The best value threshold is 0.45 with rate recognition of 94%.

Image pattern recognition supporting interactive analysis and graphical visualization

NASA Technical Reports Server (NTRS)

Coggins, James M.

1992-01-01

Image Pattern Recognition attempts to infer properties of the world from image data. Such capabilities are crucial for making measurements from satellite or telescope images related to Earth and space science problems. Such measurements can be the required product itself, or the measurements can be used as input to a computer graphics system for visualization purposes. At present, the field of image pattern recognition lacks a unified scientific structure for developing and evaluating image pattern recognition applications. The overall goal of this project is to begin developing such a structure. This report summarizes results of a 3-year research effort in image pattern recognition addressing the following three principal aims: (1) to create a software foundation for the research and identify image pattern recognition problems in Earth and space science; (2) to develop image measurement operations based on Artificial Visual Systems; and (3) to develop multiscale image descriptions for use in interactive image analysis.

Summary of 1971 pattern recognition program development

NASA Technical Reports Server (NTRS)

Whitley, S. L.

1972-01-01

Eight areas related to pattern recognition analysis at the Earth Resources Laboratory are discussed: (1) background; (2) Earth Resources Laboratory goals; (3) software problems/limitations; (4) operational problems/limitations; (5) immediate future capabilities; (6) Earth Resources Laboratory data analysis system; (7) general program needs and recommendations; and (8) schedule and milestones.

Control and Alcohol-Problem Recognition among College Students

ERIC Educational Resources Information Center

Simons, Raluca M.; Hahn, Austin M.; Simons, Jeffrey S.; Gaster, Sam

2015-01-01

Objective: This study examined negative control (ie, perceived lack of control over life outcomes) and need for control as predictors of alcohol-problem recognition, evaluations (good/bad), and expectancies (likely/unlikely) among college students. The study also explored the interaction between the need for control and alcohol consumption in…

Bayesian Analysis of Recognition Memory: The Case of the List-Length Effect

ERIC Educational Resources Information Center

Dennis, Simon; Lee, Michael D.; Kinnell, Angela

2008-01-01

Recognition memory experiments are an important source of empirical constraints for theories of memory. Unfortunately, standard methods for analyzing recognition memory data have problems that are often severe enough to prevent clear answers being obtained. A key example is whether longer lists lead to poorer recognition performance. The presence…

Pose-Invariant Face Recognition via RGB-D Images.

PubMed

Sang, Gaoli; Li, Jing; Zhao, Qijun

2016-01-01

Three-dimensional (3D) face models can intrinsically handle large pose face recognition problem. In this paper, we propose a novel pose-invariant face recognition method via RGB-D images. By employing depth, our method is able to handle self-occlusion and deformation, both of which are challenging problems in two-dimensional (2D) face recognition. Texture images in the gallery can be rendered to the same view as the probe via depth. Meanwhile, depth is also used for similarity measure via frontalization and symmetric filling. Finally, both texture and depth contribute to the final identity estimation. Experiments on Bosphorus, CurtinFaces, Eurecom, and Kiwi databases demonstrate that the additional depth information has improved the performance of face recognition with large pose variations and under even more challenging conditions.

Computational intelligence techniques for biological data mining: An overview

NASA Astrophysics Data System (ADS)

Faye, Ibrahima; Iqbal, Muhammad Javed; Said, Abas Md; Samir, Brahim Belhaouari

2014-10-01

Computational techniques have been successfully utilized for a highly accurate analysis and modeling of multifaceted and raw biological data gathered from various genome sequencing projects. These techniques are proving much more effective to overcome the limitations of the traditional in-vitro experiments on the constantly increasing sequence data. However, most critical problems that caught the attention of the researchers may include, but not limited to these: accurate structure and function prediction of unknown proteins, protein subcellular localization prediction, finding protein-protein interactions, protein fold recognition, analysis of microarray gene expression data, etc. To solve these problems, various classification and clustering techniques using machine learning have been extensively used in the published literature. These techniques include neural network algorithms, genetic algorithms, fuzzy ARTMAP, K-Means, K-NN, SVM, Rough set classifiers, decision tree and HMM based algorithms. Major difficulties in applying the above algorithms include the limitations found in the previous feature encoding and selection methods while extracting the best features, increasing classification accuracy and decreasing the running time overheads of the learning algorithms. The application of this research would be potentially useful in the drug design and in the diagnosis of some diseases. This paper presents a concise overview of the well-known protein classification techniques.

Precision is essential for efficient catalysis in an evolved Kemp eliminase.

PubMed

Blomberg, Rebecca; Kries, Hajo; Pinkas, Daniel M; Mittl, Peer R E; Grütter, Markus G; Privett, Heidi K; Mayo, Stephen L; Hilvert, Donald

2013-11-21

Linus Pauling established the conceptual framework for understanding and mimicking enzymes more than six decades ago. The notion that enzymes selectively stabilize the rate-limiting transition state of the catalysed reaction relative to the bound ground state reduces the problem of design to one of molecular recognition. Nevertheless, past attempts to capitalize on this idea, for example by using transition state analogues to elicit antibodies with catalytic activities, have generally failed to deliver true enzymatic rates. The advent of computational design approaches, combined with directed evolution, has provided an opportunity to revisit this problem. Starting from a computationally designed catalyst for the Kemp elimination--a well-studied model system for proton transfer from carbon--we show that an artificial enzyme can be evolved that accelerates an elementary chemical reaction 6 × 10(8)-fold, approaching the exceptional efficiency of highly optimized natural enzymes such as triosephosphate isomerase. A 1.09 Å resolution crystal structure of the evolved enzyme indicates that familiar catalytic strategies such as shape complementarity and precisely placed catalytic groups can be successfully harnessed to afford such high rate accelerations, making us optimistic about the prospects of designing more sophisticated catalysts.

Chondroitin sulphate-mediated fusion of brain neural folds in rat embryos.

PubMed

Alonso, M I; Moro, J A; Martín, C; de la Mano, A; Carnicero, E; Martínez-Alvarez, C; Navarro, N; Cordero, J; Gato, A

2009-01-01

Previous studies have demonstrated that during neural fold fusion in different species, an apical extracellular material rich in glycoconjugates is involved. However, the composition and the biological role of this material remain undetermined. In this paper, we show that this extracellular matrix in rat increases notably prior to contact between the neural folds, suggesting the dynamic behaviour of the secretory process. Immunostaining has allowed us to demonstrate that this extracellular matrix contains chondroitin sulphate proteoglycan (CSPG), with a spatio-temporal distribution pattern, suggesting a direct relationship with the process of adhesion. The degree of CSPG involvement in cephalic neural fold fusion in rat embryos was determined by treatment with specific glycosidases.In vitro rat embryo culture and microinjection techniques were employed to carry out selective digestion, with chondroitinase AC, of the CSPG on the apical surface of the neural folds; this was done immediately prior to the bonding of the cephalic neural folds. In all the treated embryos, cephalic defects of neural fold fusion could be detected. These results show that CSPG plays an important role in the fusion of the cephalic neural folds in rat embryos, which implies that this proteoglycan could be involved in cellular recognition and adhesion. (c) 2008 S. Karger AG, Basel.

Impairment of Novel Object Recognition Memory and Brain Insulin Signaling in Fructose- but Not Glucose-Drinking Female Rats.

PubMed

Sangüesa, Gemma; Cascales, Mar; Griñán, Christian; Sánchez, Rosa María; Roglans, Núria; Pallàs, Mercè; Laguna, Juan Carlos; Alegret, Marta

2018-01-26

Excessive sugar intake has been related to cognitive alterations, but it remains unclear whether these effects are related exclusively to increased energy intake, and the molecular mechanisms involved are not fully understood. We supplemented Sprague-Dawley female rats with 10% w/v fructose in drinking water or with isocaloric glucose solution for 7 months. Cognitive function was assessed through the Morris water maze (MWM) and the novel object recognition (NOR) tests. Plasma parameters and protein/mRNA expression in the frontal cortex and hippocampus were determined. Results showed that only fructose-supplemented rats displayed postprandial and fasting hypertriglyceridemia (1.4 and 1.9-fold, p < 0.05) and a significant reduction in the discrimination index in the NOR test, whereas the results of the MWM test showed no differences between groups. Fructose-drinking rats displayed an abnormal glucose tolerance test and impaired insulin signaling in the frontal cortex, as revealed by significant reductions in insulin receptor substrate-2 protein levels (0.77-fold, p < 0.05) and Akt phosphorylation (0.72-fold, p < 0.05), and increased insulin-degrading enzyme levels (1.86-fold, p < 0.001). Fructose supplementation reduced the expression of antioxidant enzymes and altered the amount of proteins involved in mitochondrial fusion/fission in the frontal cortex. In conclusion, cognitive deficits induced by chronic liquid fructose consumption are not exclusively related to increased caloric intake and are correlated with hypertriglyceridemia, impaired insulin signaling, increased oxidative stress and altered mitochondrial dynamics, especially in the frontal cortex.

Linear Programming and Its Application to Pattern Recognition Problems

NASA Technical Reports Server (NTRS)

Omalley, M. J.

1973-01-01

Linear programming and linear programming like techniques as applied to pattern recognition problems are discussed. Three relatively recent research articles on such applications are summarized. The main results of each paper are described, indicating the theoretical tools needed to obtain them. A synopsis of the author's comments is presented with regard to the applicability or non-applicability of his methods to particular problems, including computational results wherever given.

Training Spiking Neural Models Using Artificial Bee Colony

PubMed Central

Vazquez, Roberto A.; Garro, Beatriz A.

2015-01-01

Spiking neurons are models designed to simulate, in a realistic manner, the behavior of biological neurons. Recently, it has been proven that this type of neurons can be applied to solve pattern recognition problems with great efficiency. However, the lack of learning strategies for training these models do not allow to use them in several pattern recognition problems. On the other hand, several bioinspired algorithms have been proposed in the last years for solving a broad range of optimization problems, including those related to the field of artificial neural networks (ANNs). Artificial bee colony (ABC) is a novel algorithm based on the behavior of bees in the task of exploring their environment to find a food source. In this paper, we describe how the ABC algorithm can be used as a learning strategy to train a spiking neuron aiming to solve pattern recognition problems. Finally, the proposed approach is tested on several pattern recognition problems. It is important to remark that to realize the powerfulness of this type of model only one neuron will be used. In addition, we analyze how the performance of these models is improved using this kind of learning strategy. PMID:25709644

Reduction of the dimension of neural network models in problems of pattern recognition and forecasting

NASA Astrophysics Data System (ADS)

Nasertdinova, A. D.; Bochkarev, V. V.

2017-11-01

Deep neural networks with a large number of parameters are a powerful tool for solving problems of pattern recognition, prediction and classification. Nevertheless, overfitting remains a serious problem in the use of such networks. A method of solving the problem of overfitting is proposed in this article. This method is based on reducing the number of independent parameters of a neural network model using the principal component analysis, and can be implemented using existing libraries of neural computing. The algorithm was tested on the problem of recognition of handwritten symbols from the MNIST database, as well as on the task of predicting time series (rows of the average monthly number of sunspots and series of the Lorentz system were used). It is shown that the application of the principal component analysis enables reducing the number of parameters of the neural network model when the results are good. The average error rate for the recognition of handwritten figures from the MNIST database was 1.12% (which is comparable to the results obtained using the "Deep training" methods), while the number of parameters of the neural network can be reduced to 130 times.

My 65 years in protein chemistry.

PubMed

Scheraga, Harold A

2015-05-01

This is a tour of a physical chemist through 65 years of protein chemistry from the time when emphasis was placed on the determination of the size and shape of the protein molecule as a colloidal particle, with an early breakthrough by James Sumner, followed by Linus Pauling and Fred Sanger, that a protein was a real molecule, albeit a macromolecule. It deals with the recognition of the nature and importance of hydrogen bonds and hydrophobic interactions in determining the structure, properties, and biological function of proteins until the present acquisition of an understanding of the structure, thermodynamics, and folding pathways from a linear array of amino acids to a biological entity. Along the way, with a combination of experiment and theoretical interpretation, a mechanism was elucidated for the thrombin-induced conversion of fibrinogen to a fibrin blood clot and for the oxidative-folding pathways of ribonuclease A. Before the atomic structure of a protein molecule was determined by x-ray diffraction or nuclear magnetic resonance spectroscopy, experimental studies of the fundamental interactions underlying protein structure led to several distance constraints which motivated the theoretical approach to determine protein structure, and culminated in the Empirical Conformational Energy Program for Peptides (ECEPP), an all-atom force field, with which the structures of fibrous collagen-like proteins and the 46-residue globular staphylococcal protein A were determined. To undertake the study of larger globular proteins, a physics-based coarse-grained UNited-RESidue (UNRES) force field was developed, and applied to the protein-folding problem in terms of structure, thermodynamics, dynamics, and folding pathways. Initially, single-chain and, ultimately, multiple-chain proteins were examined, and the methodology was extended to protein-protein interactions and to nucleic acids and to protein-nucleic acid interactions. The ultimate results led to an understanding of a variety of biological processes underlying natural and disease phenomena.

My 65 years in protein chemistry

PubMed Central

Scheraga, Harold A.

2015-01-01

This is a tour of a physical chemist through 65 years of protein chemistry from the time when emphasis was placed on the determination of the size and shape of the protein molecule as a colloidal particle, with an early breakthrough by James Sumner, followed by Linus Pauling and Fred Sanger, that a protein was a real molecule, albeit a macromolecule. It deals with the recognition of the nature and importance of hydrogen bonds and hydrophobic interactions in determining the structure, properties, and biological function of proteins until the present acquisition of an understanding of the structure, thermodynamics, and folding pathways from a linear array of amino acids to a biological entity. Along the way, with a combination of experiment and theoretical interpretation, a mechanism was elucidated for the thrombin-induced conversion of fibrinogen to a fibrin blood clot and for the oxidative-folding pathways of ribonuclease A. Before the atomic structure of a protein molecule was determined by x-ray diffraction or nuclear magnetic resonance spectroscopy, experimental studies of the fundamental interactions underlying protein structure led to several distance constraints which motivated the theoretical approach to determine protein structure, and culminated in the Empirical Conformational Energy Program for Peptides (ECEPP), an all-atom force field, with which the structures of fibrous collagen-like proteins and the 46-residue globular staphylococcal protein A were determined. To undertake the study of larger globular proteins, a physics-based coarse-grained UNited-RESidue (UNRES) force field was developed, and applied to the protein-folding problem in terms of structure, thermodynamics, dynamics, and folding pathways. Initially, single-chain and, ultimately, multiple-chain proteins were examined, and the methodology was extended to protein–protein interactions and to nucleic acids and to protein–nucleic acid interactions. The ultimate results led to an understanding of a variety of biological processes underlying natural and disease phenomena. PMID:25850343

Facial-affect recognition deficit as a predictor of different aspects of social-communication impairment in traumatic brain injury.

PubMed

Rigon, Arianna; Turkstra, Lyn S; Mutlu, Bilge; Duff, Melissa C

2018-05-01

To examine the relationship between facial-affect recognition and different aspects of self- and proxy-reported social-communication impairment following moderate-severe traumatic brain injury (TBI). Forty-six adults with chronic TBI (>6 months postinjury) and 42 healthy comparison (HC) adults were administered the La Trobe Communication Questionnaire (LCQ) Self and Other forms to assess different aspects of communication competence and the Emotion Recognition Test (ERT) to measure their ability to recognize facial affects. Individuals with TBI underperformed HC adults in the ERT and self-reported, as well as were reported by close others, as having more communication problems than did HC adults. TBI group ERT scores were significantly and negatively correlated with LCQ-Other (but not LCQ-Self) scores (i.e., participants with lower emotion-recognition scores were rated by close others as having more communication problems). Multivariate regression analysis revealed that adults with higher ERT scores self-reported more problems with disinhibition-impulsivity and partner sensitivity and had fewer other-reported problems with disinhibition-impulsivity and conversational effectiveness. Our findings support growing evidence that emotion-recognition deficits play a role in specific aspects of social-communication outcomes after TBI and should be considered in treatment planning. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Coordinate Transformations in Object Recognition

ERIC Educational Resources Information Center

Graf, Markus

2006-01-01

A basic problem of visual perception is how human beings recognize objects after spatial transformations. Three central classes of findings have to be accounted for: (a) Recognition performance varies systematically with orientation, size, and position; (b) recognition latencies are sequentially additive, suggesting analogue transformation…

Global Conformational Selection and Local Induced Fit for the Recognition between Intrinsic Disordered p53 and CBP

PubMed Central

Yu, Qingfen; Ye, Wei; Wang, Wei; Chen, Hai-Feng

2013-01-01

The transactivation domain (TAD) of tumor suppressor p53 can bind with the nuclear coactivator binding domain (NCBD) of cyclic-AMP response element binding protein (CBP) and activate transcription. NMR experiments demonstrate that both apo-NCBD and TAD are intrinsic disordered and bound NCBD/TAD undergoes a transition to well folded. The recognition mechanism between intrinsic disordered proteins is still hotly debated. Molecular dynamics (MD) simulations in explicit solvent are used to study the recognition mechanism between intrinsic disordered TAD and NCBD. The average RMSD values between bound and corresponding apo states and Kolmogorov-Smirnov P test analysis indicate that TAD and NCBD may follow an induced fit mechanism. Quantitative analysis indicates there is also a global conformational selection. In summary, the recognition of TAD and NCBD might obey a local induced fit and global conformational selection. These conclusions are further supported by high-temperature unbinding kinetics and room temperature landscape analysis. These methods can be used to study the recognition mechanism of other intrinsic disordered proteins. PMID:23555731

Document Form and Character Recognition using SVM

NASA Astrophysics Data System (ADS)

Park, Sang-Sung; Shin, Young-Geun; Jung, Won-Kyo; Ahn, Dong-Kyu; Jang, Dong-Sik

2009-08-01

Because of development of computer and information communication, EDI (Electronic Data Interchange) has been developing. There is OCR (Optical Character Recognition) of Pattern recognition technology for EDI. OCR contributed to changing many manual in the past into automation. But for the more perfect database of document, much manual is needed for excluding unnecessary recognition. To resolve this problem, we propose document form based character recognition method in this study. Proposed method is divided into document form recognition part and character recognition part. Especially, in character recognition, change character into binarization by using SVM algorithm and extract more correct feature value.

Effects of Problem-Based Learning on Recognition Learning and Transfer Accounting for GPA and Goal Orientation

ERIC Educational Resources Information Center

Bergstrom, Cassendra M.; Pugh, Kevin J.; Phillips, Michael M.; Machlev, Moshe

2016-01-01

Conflicting research results have stirred controversy over the effectiveness of problem-based learning (PBL) compared to direct instruction at fostering content learning, particularly for novices. We addressed this by investigating effectiveness with respect to recognition learning and transfer and conducting an aptitude-treatment interaction…

Basics of identification measurement technology

NASA Astrophysics Data System (ADS)

Klikushin, Yu N.; Kobenko, V. Yu; Stepanov, P. P.

2018-01-01

All available algorithms and suitable for pattern recognition do not give 100% guarantee, therefore there is a field of scientific night activity in this direction, studies are relevant. It is proposed to develop existing technologies for pattern recognition in the form of application of identification measurements. The purpose of the study is to identify the possibility of recognizing images using identification measurement technologies. In solving problems of pattern recognition, neural networks and hidden Markov models are mainly used. A fundamentally new approach to the solution of problems of pattern recognition based on the technology of identification signal measurements (IIS) is proposed. The essence of IIS technology is the quantitative evaluation of the shape of images using special tools and algorithms.

Speech Recognition in Noise by Children with and without Dyslexia: How is it Related to Reading?

PubMed

Nittrouer, Susan; Krieg, Letitia M; Lowenstein, Joanna H

2018-06-01

Developmental dyslexia is commonly viewed as a phonological deficit that makes it difficult to decode written language. But children with dyslexia typically exhibit other problems, as well, including poor speech recognition in noise. The purpose of this study was to examine whether the speech-in-noise problems of children with dyslexia are related to their reading problems, and if so, if a common underlying factor might explain both. The specific hypothesis examined was that a spectral processing disorder results in these children receiving smeared signals, which could explain both the diminished sensitivity to phonological structure - leading to reading problems - and the speech recognition in noise difficulties. The alternative hypothesis tested in this study was that children with dyslexia simply have broadly based language deficits. Ninety-seven children between the ages of 7 years; 10 months and 12 years; 9 months participated: 46 with dyslexia and 51 without dyslexia. Children were tested on two dependent measures: word reading and recognition in noise with two types of sentence materials: as unprocessed (UP) signals, and as spectrally smeared (SM) signals. Data were collected for four predictor variables: phonological awareness, vocabulary, grammatical knowledge, and digit span. Children with dyslexia showed deficits on both dependent and all predictor variables. Their scores for speech recognition in noise were poorer than those of children without dyslexia for both the UP and SM signals, but by equivalent amounts across signal conditions indicating that they were not disproportionately hindered by spectral distortion. Correlation analyses on scores from children with dyslexia showed that reading ability and speech-in-noise recognition were only mildly correlated, and each skill was related to different underlying abilities. No substantial evidence was found to support the suggestion that the reading and speech recognition in noise problems of children with dyslexia arise from a single factor that could be defined as a spectral processing disorder. The reading and speech recognition in noise deficits of these children appeared to be largely independent. Copyright © 2018 Elsevier Ltd. All rights reserved.

What is the evidence for retrieval problems in the elderly?

PubMed

White, N; Cunningham, W R

1982-01-01

To determine whether older adults experience particular problems with retrieval, groups of young and elderly adults were given free recall and recognition tests of supraspan lists of unrelated words. Analysis of number of words correctly recalled and recognized yielded a significant age by retention test interaction: greater age differences were observed for recall than for recognition. In a second analysis of words recalled and recognized, corrected for guessing, the interaction disappeared. It was concluded that previous interpretations that age by retention test interactions are indicative of retrieval problems of the elderly may have been confounded by methodological problems. Furthermore, it was suggested that researchers in aging and memory need to be explicit in identifying their underlying models of error processes when analyzing recognition scores: different error models may lead to different results and interpretations.

Development of Collaborative Research Initiatives to Advance the Aerospace Sciences-via the Communications, Electronics, Information Systems Focus Group

NASA Technical Reports Server (NTRS)

Knasel, T. Michael

1996-01-01

The primary goal of the Adaptive Vision Laboratory Research project was to develop advanced computer vision systems for automatic target recognition. The approach used in this effort combined several machine learning paradigms including evolutionary learning algorithms, neural networks, and adaptive clustering techniques to develop the E-MOR.PH system. This system is capable of generating pattern recognition systems to solve a wide variety of complex recognition tasks. A series of simulation experiments were conducted using E-MORPH to solve problems in OCR, military target recognition, industrial inspection, and medical image analysis. The bulk of the funds provided through this grant were used to purchase computer hardware and software to support these computationally intensive simulations. The payoff from this effort is the reduced need for human involvement in the design and implementation of recognition systems. We have shown that the techniques used in E-MORPH are generic and readily transition to other problem domains. Specifically, E-MORPH is multi-phase evolutionary leaming system that evolves cooperative sets of features detectors and combines their response using an adaptive classifier to form a complete pattern recognition system. The system can operate on binary or grayscale images. In our most recent experiments, we used multi-resolution images that are formed by applying a Gabor wavelet transform to a set of grayscale input images. To begin the leaming process, candidate chips are extracted from the multi-resolution images to form a training set and a test set. A population of detector sets is randomly initialized to start the evolutionary process. Using a combination of evolutionary programming and genetic algorithms, the feature detectors are enhanced to solve a recognition problem. The design of E-MORPH and recognition results for a complex problem in medical image analysis are described at the end of this report. The specific task involves the identification of vertebrae in x-ray images of human spinal columns. This problem is extremely challenging because the individual vertebra exhibit variation in shape, scale, orientation, and contrast. E-MORPH generated several accurate recognition systems to solve this task. This dual use of this ATR technology clearly demonstrates the flexibility and power of our approach.

Reduced amino acids in the bovine uterine lumen of cloned versus in vitro fertilized pregnancies prior to implantation.

PubMed

Groebner, Anna E; Zakhartchenko, Valeri; Bauersachs, Stefan; Rubio-Aliaga, Isabel; Daniel, Hannelore; Büttner, Mathias; Reichenbach, Horst D; Meyer, Heinrich H D; Wolf, Eckhard; Ulbrich, Susanne E

2011-10-01

Fetal overgrowth and placental abnormalities frequently occur in pregnancies following somatic cell nuclear transfer (SCNT). An optimal intrauterine supply of amino acids (AA) is of specific importance for the development of the bovine preimplantation embryo, and a defective regulation of AA supply might contribute to pregnancy failures. Thus, we analyzed 41 AA and derivatives by liquid chromatography-tandem mass spectrometry in uterine flushings of day 18 pregnant heifers carrying in vitro fertilized (IVF) or SCNT embryos, which were cultured under identical conditions until transfer to recipients. The concentrations of several AA were reduced in samples from SCNT pregnancies: L-leucine (1.8-fold), L-valine (1.6-fold), L-isoleucine (1.9-fold), L-phenylalanine (1.5-fold), L-glutamic acid (3.9-fold), L-aspartic acid (4.0-fold), L-proline (2.6-fold), L-alanine (2.0-fold), L-arginine (2.5-fold), and L-lysine (1.9-fold). The endometrial transcript abundance for the AA transporter solute carrier family 7 (amino acid transporter, L-type), member 8 (SLC7A8) was also 2.4-fold lower in SCNT pregnancies. O-phosphoethanolamine (PetN) was 11-fold (p=0.0001) reduced in the uterine fluid of animals carrying an SCNT conceptus, pointing toward changes of the phospholipid metabolism. We provide evidence for disturbed embryo-maternal interactions in the preimplantation period after transfer of SCNT embryos, which may contribute to developmental abnormalities. These are unlikely related to the major embryonic pregnancy recognition signal interferon-tau, because similar activities were detected in uterine flushings of the SCNT and IVF groups.

Congenital hypothyroidism mutations affect common folding and trafficking in the α/β-hydrolase fold proteins

PubMed Central

De Jaco, Antonella; Dubi, Noga; Camp, Shelley; Taylor, Palmer

2017-01-01

The α/β-hydrolase fold superfamily of proteins is composed of structurally related members that, despite great diversity in their catalytic, recognition, adhesion and chaperone functions, share a common fold governed by homologous residues and conserved disulfide bridges. Non-synonymous single nucleotide polymorphisms within the α/β-hydrolase fold domain in various family members have been found for congenital endocrine, metabolic and nervous system disorders. By examining the amino acid sequence from the various proteins, mutations were found to be prevalent in conserved residues within the α/β-hydrolase fold of the homologous proteins. This is the case for the thyroglobulin mutations linked to congenital hypothyroidism. To address whether correct folding of the common domain is required for protein export, we inserted the thyroglobulin mutations at homologous positions in two correlated but simpler α/β-hydrolase fold proteins known to be exported to the cell surface: neuroligin3 and acetylcholinesterase. Here we show that these mutations in the cholinesterase homologous region alter the folding properties of the α/β-hydrolase fold domain, which are reflected in defects in protein trafficking, folding and function, and ultimately result in retention of the partially processed proteins in the endoplasmic reticulum. Accordingly, mutations at conserved residues may be transferred amongst homologous proteins to produce common processing defects despite disparate functions, protein complexity and tissue-specific expression of the homologous proteins. More importantly, a similar assembly of the α/β-hydrolase fold domain tertiary structure among homologous members of the superfamily is required for correct trafficking of the proteins to their final destination. PMID:23035660

Recognition of upper airway and surrounding structures at MRI in pediatric PCOS and OSAS

NASA Astrophysics Data System (ADS)

Tong, Yubing; Udupa, J. K.; Odhner, D.; Sin, Sanghun; Arens, Raanan

2013-03-01

Obstructive Sleep Apnea Syndrome (OSAS) is common in obese children with risk being 4.5 fold compared to normal control subjects. Polycystic Ovary Syndrome (PCOS) has recently been shown to be associated with OSAS that may further lead to significant cardiovascular and neuro-cognitive deficits. We are investigating image-based biomarkers to understand the architectural and dynamic changes in the upper airway and the surrounding hard and soft tissue structures via MRI in obese teenage children to study OSAS. At the previous SPIE conferences, we presented methods underlying Fuzzy Object Models (FOMs) for Automatic Anatomy Recognition (AAR) based on CT images of the thorax and the abdomen. The purpose of this paper is to demonstrate that the AAR approach is applicable to a different body region and image modality combination, namely in the study of upper airway structures via MRI. FOMs were built hierarchically, the smaller sub-objects forming the offspring of larger parent objects. FOMs encode the uncertainty and variability present in the form and relationships among the objects over a study population. Totally 11 basic objects (17 including composite) were modeled. Automatic recognition for the best pose of FOMs in a given image was implemented by using four methods - a one-shot method that does not require search, another three searching methods that include Fisher Linear Discriminate (FLD), a b-scale energy optimization strategy, and optimum threshold recognition method. In all, 30 multi-fold cross validation experiments based on 15 patient MRI data sets were carried out to assess the accuracy of recognition. The results indicate that the objects can be recognized with an average location error of less than 5 mm or 2-3 voxels. Then the iterative relative fuzzy connectedness (IRFC) algorithm was adopted for delineation of the target organs based on the recognized results. The delineation results showed an overall FP and TP volume fraction of 0.02 and 0.93.

Manipulation of a DNA aptamer-protein binding site through arylation of internal guanine residues.

PubMed

Van Riesen, Abigail J; Fadock, Kaila L; Deore, Prashant S; Desoky, Ahmed; Manderville, Richard A; Sowlati-Hashjin, Shahin; Wetmore, Stacey D

2018-05-23

Chemically modified aptamers have the opportunity to increase aptamer target binding affinity and provide structure-activity relationships to enhance our understanding of molecular target recognition by the aptamer fold. In the current study, 8-aryl-2'-deoxyguanosine nucleobases have been inserted into the G-tetrad and central TGT loop of the thrombin binding aptamer (TBA) to determine their impact on antiparallel G-quadruplex (GQ) folding and thrombin binding affinity. The aryl groups attached to the dG nucleobase vary greatly in aryl ring size and impact on GQ stability (∼20 °C change in GQ thermal melting (Tm) values) and thrombin binding affinity (17-fold variation in dissociation constant (Kd)). At G8 of the central TGT loop that is distal from the aptamer recognition site, the probes producing the most stable GQ structure exhibited the strongest thrombin binding affinity. However, within the G-tetrad, changes to the electron density of the dG component within the modified nucleobase can diminish thrombin binding affinity. Detailed molecular dynamics (MD) simulations on the modified TBA (mTBA) and mTBA-protein complexes demonstrate how the internal 8-aryl-dG modification can manipulate the interactions between the DNA nucleobases and the amino acid residues of thrombin. These results highlight the potential of internal fluorescent nuclobase analogs (FBAs) to broaden design options for aptasensor development.

FRankenstein becomes a cyborg: the automatic recombination and realignment of fold recognition models in CASP6.

PubMed

Kosinski, Jan; Gajda, Michal J; Cymerman, Iwona A; Kurowski, Michal A; Pawlowski, Marcin; Boniecki, Michal; Obarska, Agnieszka; Papaj, Grzegorz; Sroczynska-Obuchowicz, Paulina; Tkaczuk, Karolina L; Sniezynska, Paulina; Sasin, Joanna M; Augustyn, Anna; Bujnicki, Janusz M; Feder, Marcin

2005-01-01

In the course of CASP6, we generated models for all targets using a new version of the "FRankenstein's monster approach." Previously (in CASP5) we were able to build many very accurate full-atom models by selection and recombination of well-folded fragments obtained from crude fold recognition (FR) results, followed by optimization of the sequence-structure fit and assessment of alternative alignments on the structural level. This procedure was however very arduous, as most of the steps required extensive visual and manual input from the human modeler. Now, we have automated the most tedious steps, such as superposition of alternative models, extraction of best-scoring fragments, and construction of a hybrid "monster" structure, as well as generation of alternative alignments in the regions that remain poorly scored in the refined hybrid model. We have also included the ROSETTA method to construct those parts of the target for which no reasonable structures were generated by FR methods (such as long insertions and terminal extensions). The analysis of successes and failures of the current version of the FRankenstein approach in modeling of CASP6 targets reveals that the considerably streamlined and automated method performs almost as well as the initial, mostly manual version, which suggests that it may be a useful tool for accurate protein structure prediction even in the hands of nonexperts. 2005 Wiley-Liss, Inc.

Paleomagnetic and structural evidence for oblique slip in a fault-related fold, Grayback monocline, Colorado

USGS Publications Warehouse

Tetreault, J.; Jones, C.H.; Erslev, E.; Larson, S.; Hudson, M.; Holdaway, S.

2008-01-01

Significant fold-axis-parallel slip is accommodated in the folded strata of the Grayback monocline, northeastern Front Range, Colorado, without visible large strike-slip displacement on the fold surface. In many cases, oblique-slip deformation is partitioned; fold-axis-normal slip is accommodated within folds, and fold-axis-parallel slip is resolved onto adjacent strike-slip faults. Unlike partitioning strike-parallel slip onto adjacent strike-slip faults, fold-axis-parallel slip has deformed the forelimb of the Grayback monocline. Mean compressive paleostress orientations in the forelimb are deflected 15??-37?? clockwise from the regional paleostress orientation of the northeastern Front Range. Paleomagnetic directions from the Permian Ingleside Formation in the forelimb are rotated 16??-42?? clockwise about a bedding-normal axis relative to the North American Permian reference direction. The paleostress and paleomagnetic rotations increase with the bedding dip angle and decrease along strike toward the fold tip. These measurements allow for 50-120 m of fold-axis-parallel slip within the forelimb, depending on the kinematics of strike-slip shear. This resolved horizontal slip is nearly equal in magnitude to the ???180 m vertical throw across the fold. For 200 m of oblique-slip displacement (120 m of strike slip and 180 m of reverse slip), the true shortening direction across the fold is N90??E, indistinguishable from the regionally inferred direction of N90??E and quite different from the S53??E fold-normal direction. Recognition of this deformational style means that significant amounts of strike slip can be accommodated within folds without axis-parallel surficial faulting. ?? 2008 Geological Society of America.

Free energy profile of RNA hairpins: a molecular dynamics simulation study.

PubMed

Deng, Nan-Jie; Cieplak, Piotr

2010-02-17

RNA hairpin loops are one of the most abundant secondary structure elements and participate in RNA folding and protein-RNA recognition. To characterize the free energy surface of RNA hairpin folding at an atomic level, we calculated the potential of mean force (PMF) as a function of the end-to-end distance, by using umbrella sampling simulations in explicit solvent. Two RNA hairpins containing tetraloop cUUCGg and cUUUUg are studied with AMBER ff99 and CHARMM27 force fields. Experimentally, the UUCG hairpin is known to be significantly more stable than UUUU. In this study, the calculations using AMBER force field give a qualitatively correct description for the folding of two RNA hairpins, as the calculated PMF confirms the global stability of the folded structures and the resulting relative folding free energy is in quantitative agreement with the experimental result. The hairpin stabilities are also correctly differentiated by the more rapid molecular mechanics-Poisson Boltzmann-surface area approach, but the relative free energy estimated from this method is overestimated. The free energy profile shows that the native state basin and the unfolded state plateau are separated by a wide shoulder region, which samples a variety of native-like structures with frayed terminal basepair. The calculated PMF lacks major barriers that are expected near the transition regions, and this is attributed to the limitation of the 1-D reaction coordinate. The PMF results are compared with other studies of small RNA hairpins using kinetics method and coarse grained models. The two RNA hairpins described by CHARMM27 are significantly more deformable than those represented by AMBER. Compared with the AMBER results, the CHARMM27 calculated DeltaG(fold) for the UUUU tetraloop is in better agreement with the experimental results. However, the CHARMM27 calculation does not confirm the global stability of the experimental UUCG structure; instead, the extended conformations are predicted to be thermodynamically stable in solution. This finding is further supported by separate unrestrained CHARMM27 simulations, in which the UUCG hairpin unfolds spontaneously within 10 ns. The instability of the UUCG hairpin originates from the loop region, and propagates to the stem. The results of this study provide a molecular picture of RNA hairpin unfolding and reveal problems in the force field descriptions for the conformational energy of certain RNA hairpin. Copyright 2010 Biophysical Society. Published by Elsevier Inc. All rights reserved.

A new hybrid method based on fuzzy-artificial immune system and k-nn algorithm for breast cancer diagnosis.

PubMed

Sahan, Seral; Polat, Kemal; Kodaz, Halife; Güneş, Salih

2007-03-01

The use of machine learning tools in medical diagnosis is increasing gradually. This is mainly because the effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing diseases. Such a disease is breast cancer, which is a very common type of cancer among woman. As the incidence of this disease has increased significantly in the recent years, machine learning applications to this problem have also took a great attention as well as medical consideration. This study aims at diagnosing breast cancer with a new hybrid machine learning method. By hybridizing a fuzzy-artificial immune system with k-nearest neighbour algorithm, a method was obtained to solve this diagnosis problem via classifying Wisconsin Breast Cancer Dataset (WBCD). This data set is a very commonly used data set in the literature relating the use of classification systems for breast cancer diagnosis and it was used in this study to compare the classification performance of our proposed method with regard to other studies. We obtained a classification accuracy of 99.14%, which is the highest one reached so far. The classification accuracy was obtained via 10-fold cross validation. This result is for WBCD but it states that this method can be used confidently for other breast cancer diagnosis problems, too.

Iris recognition in the presence of ocular disease

PubMed Central

Aslam, Tariq Mehmood; Tan, Shi Zhuan; Dhillon, Baljean

2009-01-01

Iris recognition systems are among the most accurate of all biometric technologies with immense potential for use in worldwide security applications. This study examined the effect of eye pathology on iris recognition and in particular whether eye disease could cause iris recognition systems to fail. The experiment involved a prospective cohort of 54 patients with anterior segment eye disease who were seen at the acute referral unit of the Princess Alexandra Eye Pavilion in Edinburgh. Iris camera images were obtained from patients before treatment was commenced and again at follow-up appointments after treatment had been given. The principal outcome measure was that of mathematical difference in the iris recognition templates obtained from patients' eyes before and after treatment of the eye disease. Results showed that the performance of iris recognition was remarkably resilient to most ophthalmic disease states, including corneal oedema, iridotomies (laser puncture of iris) and conjunctivitis. Problems were, however, encountered in some patients with acute inflammation of the iris (iritis/anterior uveitis). The effects of a subject developing anterior uveitis may cause current recognition systems to fail. Those developing and deploying iris recognition should be aware of the potential problems that this could cause to this key biometric technology. PMID:19324690

Iris recognition in the presence of ocular disease.

PubMed

Aslam, Tariq Mehmood; Tan, Shi Zhuan; Dhillon, Baljean

2009-05-06

Iris recognition systems are among the most accurate of all biometric technologies with immense potential for use in worldwide security applications. This study examined the effect of eye pathology on iris recognition and in particular whether eye disease could cause iris recognition systems to fail. The experiment involved a prospective cohort of 54 patients with anterior segment eye disease who were seen at the acute referral unit of the Princess Alexandra Eye Pavilion in Edinburgh. Iris camera images were obtained from patients before treatment was commenced and again at follow-up appointments after treatment had been given. The principal outcome measure was that of mathematical difference in the iris recognition templates obtained from patients' eyes before and after treatment of the eye disease. Results showed that the performance of iris recognition was remarkably resilient to most ophthalmic disease states, including corneal oedema, iridotomies (laser puncture of iris) and conjunctivitis. Problems were, however, encountered in some patients with acute inflammation of the iris (iritis/anterior uveitis). The effects of a subject developing anterior uveitis may cause current recognition systems to fail. Those developing and deploying iris recognition should be aware of the potential problems that this could cause to this key biometric technology.

Introduction and Overview of the Vicens-Reddy Speech Recognition System.

ERIC Educational Resources Information Center

Kameny, Iris; Ritea, H.

The Vicens-Reddy System is unique in the sense that it approaches the problem of speech recognition as a whole, rather than treating particular aspects of the problems as in previous attempts. For example, where earlier systems treated only segmentation of speech into phoneme groups, or detected phonemes in a given context, the Vicens-Reddy System…

Correlates of Problem Recognition and Intentions to Change among Caregivers of Abused and Neglected Children

ERIC Educational Resources Information Center

Littell, Julia H.; Girvin, Heather

2006-01-01

Objective: To identify individual, family, and caseworker characteristics associated with problem recognition (PR) and intentions to change (ITC) in a sample of caregivers who received in-home child welfare services following substantiated reports of child abuse or neglect. Methods: Caregivers were interviewed at 4 weeks, 16 weeks, and 1 year…

Semisupervised kernel marginal Fisher analysis for face recognition.

PubMed

Wang, Ziqiang; Sun, Xia; Sun, Lijun; Huang, Yuchun

2013-01-01

Dimensionality reduction is a key problem in face recognition due to the high-dimensionality of face image. To effectively cope with this problem, a novel dimensionality reduction algorithm called semisupervised kernel marginal Fisher analysis (SKMFA) for face recognition is proposed in this paper. SKMFA can make use of both labelled and unlabeled samples to learn the projection matrix for nonlinear dimensionality reduction. Meanwhile, it can successfully avoid the singularity problem by not calculating the matrix inverse. In addition, in order to make the nonlinear structure captured by the data-dependent kernel consistent with the intrinsic manifold structure, a manifold adaptive nonparameter kernel is incorporated into the learning process of SKMFA. Experimental results on three face image databases demonstrate the effectiveness of our proposed algorithm.

IQGAP Proteins Reveal an Atypical Phosphoinositide (aPI) Binding Domain with a Pseudo C2 Domain Fold*

PubMed Central

Dixon, Miles J.; Gray, Alexander; Schenning, Martijn; Agacan, Mark; Tempel, Wolfram; Tong, Yufeng; Nedyalkova, Lyudmila; Park, Hee-Won; Leslie, Nicholas R.; van Aalten, Daan M. F.; Downes, C. Peter; Batty, Ian H.

2012-01-01

Class I phosphoinositide (PI) 3-kinases act through effector proteins whose 3-PI selectivity is mediated by a limited repertoire of structurally defined, lipid recognition domains. We describe here the lipid preferences and crystal structure of a new class of PI binding modules exemplified by select IQGAPs (IQ motif containing GTPase-activating proteins) known to coordinate cellular signaling events and cytoskeletal dynamics. This module is defined by a C-terminal 105–107 amino acid region of which IQGAP1 and -2, but not IQGAP3, binds preferentially to phosphatidylinositol 3,4,5-trisphosphate (PtdInsP3). The binding affinity for PtdInsP3, together with other, secondary target-recognition characteristics, are comparable with those of the pleckstrin homology domain of cytohesin-3 (general receptor for phosphoinositides 1), an established PtdInsP3 effector protein. Importantly, the IQGAP1 C-terminal domain and the cytohesin-3 pleckstrin homology domain, each tagged with enhanced green fluorescent protein, were both re-localized from the cytosol to the cell periphery following the activation of PI 3-kinase in Swiss 3T3 fibroblasts, consistent with their common, selective recognition of endogenous 3-PI(s). The crystal structure of the C-terminal IQGAP2 PI binding module reveals unexpected topological similarity to an integral fold of C2 domains, including a putative basic binding pocket. We propose that this module integrates select IQGAP proteins with PI 3-kinase signaling and constitutes a novel, atypical phosphoinositide binding domain that may represent the first of a larger group, each perhaps structurally unique but collectively dissimilar from the known PI recognition modules. PMID:22493426

IQGAP Proteins Reveal an Atypical Phosphoinositide (aPI) Binding Domain with a Pseudo C2 Domain Fold

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

Dixon, Miles J.; Gray, Alexander; Schenning, Martijn

2012-10-16

Class I phosphoinositide (PI) 3-kinases act through effector proteins whose 3-PI selectivity is mediated by a limited repertoire of structurally defined, lipid recognition domains. We describe here the lipid preferences and crystal structure of a new class of PI binding modules exemplified by select IQGAPs (IQ motif containing GTPase-activating proteins) known to coordinate cellular signaling events and cytoskeletal dynamics. This module is defined by a C-terminal 105-107 amino acid region of which IQGAP1 and -2, but not IQGAP3, binds preferentially to phosphatidylinositol 3,4,5-trisphosphate (PtdInsP3). The binding affinity for PtdInsP3, together with other, secondary target-recognition characteristics, are comparable with those ofmore » the pleckstrin homology domain of cytohesin-3 (general receptor for phosphoinositides 1), an established PtdInsP3 effector protein. Importantly, the IQGAP1 C-terminal domain and the cytohesin-3 pleckstrin homology domain, each tagged with enhanced green fluorescent protein, were both re-localized from the cytosol to the cell periphery following the activation of PI 3-kinase in Swiss 3T3 fibroblasts, consistent with their common, selective recognition of endogenous 3-PI(s). The crystal structure of the C-terminal IQGAP2 PI binding module reveals unexpected topological similarity to an integral fold of C2 domains, including a putative basic binding pocket. We propose that this module integrates select IQGAP proteins with PI 3-kinase signaling and constitutes a novel, atypical phosphoinositide binding domain that may represent the first of a larger group, each perhaps structurally unique but collectively dissimilar from the known PI recognition modules.« less

Conceptual Transformation and Cognitive Processes in Origami Paper Folding

ERIC Educational Resources Information Center

Tenbrink, Thora; Taylor, Holly A.

2015-01-01

Research on problem solving typically does not address tasks that involve following detailed and/or illustrated step-by-step instructions. Such tasks are not seen as cognitively challenging problems to be solved. In this paper, we challenge this assumption by analyzing verbal protocols collected during an Origami folding task. Participants…

Cold rescue of the thermolabile tailspike intermediate at the junction between productive folding and off-pathway aggregation.

PubMed Central

Betts, S. D.; King, J.

1998-01-01

Off-pathway intermolecular interactions between partially folded polypeptide chains often compete with correct intramolecular interactions, resulting in self-association of folding intermediates into the inclusion body state. Intermediates for both productive folding and off-pathway aggregation of the parallel beta-coil tailspike trimer of phage P22 have been identified in vivo and in vitro using native gel electrophoresis in the cold. Aggregation of folding intermediates was suppressed when refolding was initiated and allowed to proceed for a short period at 0 degrees C prior to warming to 20 degrees C. Yields of refolded tailspike trimers exceeding 80% were obtained using this temperature-shift procedure, first described by Xie and Wetlaufer (1996, Protein Sci 5:517-523). We interpret this as due to stabilization of the thermolabile monomeric intermediate at the junction between productive folding and off-pathway aggregation. Partially folded monomers, a newly identified dimer, and the protrimer folding intermediates were populated in the cold. These species were electrophoretically distinguished from the multimeric intermediates populated on the aggregation pathway. The productive protrimer intermediate is disulfide bonded (Robinson AS, King J, 1997, Nat Struct Biol 4:450-455), while the multimeric aggregation intermediates are not disulfide bonded. The partially folded dimer appears to be a precursor to the disulfide-bonded protrimer. The results support a model in which the junctional partially folded monomeric intermediate acquires resistance to aggregation in the cold by folding further to a conformation that is activated for correct recognition and subunit assembly. PMID:9684883

The Quaternary thrust system of the northern Alaska Range

USGS Publications Warehouse

Bemis, Sean P.; Carver, Gary A.; Koehler, Richard D.

2012-01-01

The framework of Quaternary faults in Alaska remains poorly constrained. Recent studies in the Alaska Range north of the Denali fault add significantly to the recognition of Quaternary deformation in this active orogen. Faults and folds active during the Quaternary occur over a length of ∼500 km along the northern flank of the Alaska Range, extending from Mount McKinley (Denali) eastward to the Tok River valley. These faults exist as a continuous system of active structures, but we divide the system into four regions based on east-west changes in structural style. At the western end, the Kantishna Hills have only two known faults but the highest rate of shallow crustal seismicity. The western northern foothills fold-thrust belt consists of a 50-km-wide zone of subparallel thrust and reverse faults. This broad zone of deformation narrows to the east in a transition zone where the range-bounding fault of the western northern foothills fold-thrust belt terminates and displacement occurs on thrust and/or reverse faults closer to the Denali fault. The eastern northern foothills fold-thrust belt is characterized by ∼40-km-long thrust fault segments separated across left-steps by NNE-trending left-lateral faults. Altogether, these faults accommodate much of the topographic growth of the northern flank of the Alaska Range.Recognition of this thrust fault system represents a significant concern in addition to the Denali fault for infrastructure adjacent to and transecting the Alaska Range. Although additional work is required to characterize these faults sufficiently for seismic hazard analysis, the regional extent and structural character should require the consideration of the northern Alaska Range thrust system in regional tectonic models.

LiveBench-1: continuous benchmarking of protein structure prediction servers.

PubMed

Bujnicki, J M; Elofsson, A; Fischer, D; Rychlewski, L

2001-02-01

We present a novel, continuous approach aimed at the large-scale assessment of the performance of available fold-recognition servers. Six popular servers were investigated: PDB-Blast, FFAS, T98-lib, GenTHREADER, 3D-PSSM, and INBGU. The assessment was conducted using as prediction targets a large number of selected protein structures released from October 1999 to April 2000. A target was selected if its sequence showed no significant similarity to any of the proteins previously available in the structural database. Overall, the servers were able to produce structurally similar models for one-half of the targets, but significantly accurate sequence-structure alignments were produced for only one-third of the targets. We further classified the targets into two sets: easy and hard. We found that all servers were able to find the correct answer for the vast majority of the easy targets if a structurally similar fold was present in the server's fold libraries. However, among the hard targets--where standard methods such as PSI-BLAST fail--the most sensitive fold-recognition servers were able to produce similar models for only 40% of the cases, half of which had a significantly accurate sequence-structure alignment. Among the hard targets, the presence of updated libraries appeared to be less critical for the ranking. An "ideally combined consensus" prediction, where the results of all servers are considered, would increase the percentage of correct assignments by 50%. Each server had a number of cases with a correct assignment, where the assignments of all the other servers were wrong. This emphasizes the benefits of considering more than one server in difficult prediction tasks. The LiveBench program (http://BioInfo.PL/LiveBench) is being continued, and all interested developers are cordially invited to join.

Emotion Knowledge and Attentional Differences in Preschoolers Showing Context-Inappropriate Anger.

PubMed

Locke, Robin L; Lang, Nichole J

2016-08-01

Some children show anger inappropriate for the situation based on the predominant incentives, which is called context-inappropriate anger. Children need to attend to and interpret situational incentives for appropriate emotional responses. We examined associations of context-inappropriate anger with emotion recognition and attention problems in 43 preschoolers (42% male; M age = 55.1 months, SD = 4.1). Parents rated context-inappropriate anger across situations. Teachers rated attention problems using the Child Behavior Checklist-Teacher Report Form. Emotion recognition was ability to recognize emotional faces using the Emotion Matching Test. Anger perception bias was indicated by anger to non-anger situations using an adapted Affect Knowledge Test. 28% of children showed context-inappropriate anger, which correlated with lower emotion recognition (β = -.28) and higher attention problems (β = .36). Higher attention problems correlated with more anger perception bias (β = .32). This cross-sectional, correlational study provides preliminary findings that children with context-inappropriate anger showed more attention problems, which suggests that both "problems" tend to covary and associate with deficits or biases in emotion knowledge. © The Author(s) 2016.

Post processing for offline Chinese handwritten character string recognition

NASA Astrophysics Data System (ADS)

Wang, YanWei; Ding, XiaoQing; Liu, ChangSong

2012-01-01

Offline Chinese handwritten character string recognition is one of the most important research fields in pattern recognition. Due to the free writing style, large variability in character shapes and different geometric characteristics, Chinese handwritten character string recognition is a challenging problem to deal with. However, among the current methods over-segmentation and merging method which integrates geometric information, character recognition information and contextual information, shows a promising result. It is found experimentally that a large part of errors are segmentation error and mainly occur around non-Chinese characters. In a Chinese character string, there are not only wide characters namely Chinese characters, but also narrow characters like digits and letters of the alphabet. The segmentation error is mainly caused by uniform geometric model imposed on all segmented candidate characters. To solve this problem, post processing is employed to improve recognition accuracy of narrow characters. On one hand, multi-geometric models are established for wide characters and narrow characters respectively. Under multi-geometric models narrow characters are not prone to be merged. On the other hand, top rank recognition results of candidate paths are integrated to boost final recognition of narrow characters. The post processing method is investigated on two datasets, in total 1405 handwritten address strings. The wide character recognition accuracy has been improved lightly and narrow character recognition accuracy has been increased up by 10.41% and 10.03% respectively. It indicates that the post processing method is effective to improve recognition accuracy of narrow characters.

Synergistic cooperation of PDI family members in peroxiredoxin 4-driven oxidative protein folding.

PubMed

Sato, Yoshimi; Kojima, Rieko; Okumura, Masaki; Hagiwara, Masatoshi; Masui, Shoji; Maegawa, Ken-ichi; Saiki, Masatoshi; Horibe, Tomohisa; Suzuki, Mamoru; Inaba, Kenji

2013-01-01

The mammalian endoplasmic reticulum (ER) harbors disulfide bond-generating enzymes, including Ero1α and peroxiredoxin 4 (Prx4), and nearly 20 members of the protein disulfide isomerase family (PDIs), which together constitute a suitable environment for oxidative protein folding. Here, we clarified the Prx4 preferential recognition of two PDI family proteins, P5 and ERp46, and the mode of interaction between Prx4 and P5 thioredoxin domain. Detailed analyses of oxidative folding catalyzed by the reconstituted Prx4-PDIs pathways demonstrated that, while P5 and ERp46 are dedicated to rapid, but promiscuous, disulfide introduction, PDI is an efficient proofreader of non-native disulfides. Remarkably, the Prx4-dependent formation of native disulfide bonds was accelerated when PDI was combined with ERp46 or P5, suggesting that PDIs work synergistically to increase the rate and fidelity of oxidative protein folding. Thus, the mammalian ER seems to contain highly systematized oxidative networks for the efficient production of large quantities of secretory proteins.

Quantitative methods and detection techniques in hyperspectral imaging involving medical and other applications

NASA Astrophysics Data System (ADS)

Roy, Ankita

2007-12-01

This research using Hyperspectral imaging involves recognizing targets through spatial and spectral matching and spectral un-mixing of data ranging from remote sensing to medical imaging kernels for clinical studies based on Hyperspectral data-sets generated using the VFTHSI [Visible Fourier Transform Hyperspectral Imager], whose high resolution Si detector makes the analysis achievable. The research may be broadly classified into (I) A Physically Motivated Correlation Formalism (PMCF), which places both spatial and spectral data on an equivalent mathematical footing in the context of a specific Kernel and (II) An application in RF plasma specie detection during carbon nanotube growing process. (III) Hyperspectral analysis for assessing density and distribution of retinopathies like age related macular degeneration (ARMD) and error estimation enabling the early recognition of ARMD, which is treated as an ill-conditioned inverse imaging problem. The broad statistical scopes of this research are two fold-target recognition problems and spectral unmixing problems. All processes involve experimental and computational analysis of Hyperspectral data sets is presented, which is based on the principle of a Sagnac Interferometer, calibrated to obtain high SNR levels. PMCF computes spectral/spatial/cross moments and answers the question of how optimally the entire hypercube should be sampled and finds how many spatial-spectral pixels are required precisely for a particular target recognition. Spectral analysis of RF plasma radicals, typically Methane plasma and Argon plasma using VFTHSI has enabled better process monitoring during growth of vertically aligned multi-walled carbon nanotubes by instant registration of the chemical composition or density changes temporally, which is key since a significant correlation can be found between plasma state and structural properties. A vital focus of this dissertation is towards medical Hyperspectral imaging applied to retinopathies like age related macular degeneration targets taken with a Fundus imager, which is akin to the VFTHSI. Detection of the constituent components in the diseased hyper-pigmentation area is also computed. The target or reflectance matrix is treated as a highly ill-conditioned spectral un-mixing problem, to which methodologies like inverse techniques, principal component analysis (PCA) and receiver operating curves (ROC) for precise spectral recognition of infected area. The region containing ARMD was easily distinguishable from the spectral mesh plots over the entire band-pass area. Once the location was detected the PMCF coefficients were calculated by cross correlating a target of normal oxygenated retina with the deoxygenated one. The ROCs generated using PMCF shows 30% higher detection probability with improved accuracy than ROCs based on Spectral Angle Mapper (SAM). By spectral unmixing methods, the important endmembers/carotenoids of the MD pigment were found to be Xanthophyl and lutein, while beta-carotene which showed a negative correlation in the unconstrained inverse problem is a supplement given to ARMD patients to prevent the disease and does not occur in the eye. Literature also shows degeneration of meso-zeaxanthin. Ophthalmologists may assert the presence of ARMD and commence the diagnosis process if the Xanthophyl pigment have degenerated 89.9%, while the lutein has decayed almost 80%, as found deduced computationally. This piece of current research takes it to the next level of precise investigation in the continuing process of improved clinical findings by correlating the microanatomy of the diseased fovea and shows promise of an early detection of this disease.

The Recognition of Web Pages' Hyperlinks by People with Intellectual Disabilities: An Evaluation Study

ERIC Educational Resources Information Center

Rocha, Tania; Bessa, Maximino; Goncalves, Martinho; Cabral, Luciana; Godinho, Francisco; Peres, Emanuel; Reis, Manuel C.; Magalhaes, Luis; Chalmers, Alan

2012-01-01

Background: One of the most mentioned problems of web accessibility, as recognized in several different studies, is related to the difficulty regarding the perception of what is or is not clickable in a web page. In particular, a key problem is the recognition of hyperlinks by a specific group of people, namely those with intellectual…

Image ratio features for facial expression recognition application.

PubMed

Song, Mingli; Tao, Dacheng; Liu, Zicheng; Li, Xuelong; Zhou, Mengchu

2010-06-01

Video-based facial expression recognition is a challenging problem in computer vision and human-computer interaction. To target this problem, texture features have been extracted and widely used, because they can capture image intensity changes raised by skin deformation. However, existing texture features encounter problems with albedo and lighting variations. To solve both problems, we propose a new texture feature called image ratio features. Compared with previously proposed texture features, e.g., high gradient component features, image ratio features are more robust to albedo and lighting variations. In addition, to further improve facial expression recognition accuracy based on image ratio features, we combine image ratio features with facial animation parameters (FAPs), which describe the geometric motions of facial feature points. The performance evaluation is based on the Carnegie Mellon University Cohn-Kanade database, our own database, and the Japanese Female Facial Expression database. Experimental results show that the proposed image ratio feature is more robust to albedo and lighting variations, and the combination of image ratio features and FAPs outperforms each feature alone. In addition, we study asymmetric facial expressions based on our own facial expression database and demonstrate the superior performance of our combined expression recognition system.

Structural Basis for Methylarginine-dependent Recognition of Aubergine by Tudor

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

Liu, H.; Wang, J; Huang, Y

2010-01-01

Piwi proteins are modified by symmetric dimethylation of arginine (sDMA), and the methylarginine-dependent interaction with Tudor domain proteins is critical for their functions in germline development. Cocrystal structures of an extended Tudor domain (eTud) of Drosophila Tudor with methylated peptides of Aubergine, a Piwi family protein, reveal that sDMA is recognized by an asparagine-gated aromatic cage. Furthermore, the unexpected Tudor-SN/p100 fold of eTud is important for sensing the position of sDMA. The structural information provides mechanistic insights into sDMA-dependent Piwi-Tudor interaction, and the recognition of sDMA by Tudor domains in general.

Hybrid generative-discriminative approach to age-invariant face recognition

NASA Astrophysics Data System (ADS)

Sajid, Muhammad; Shafique, Tamoor

2018-03-01

Age-invariant face recognition is still a challenging research problem due to the complex aging process involving types of facial tissues, skin, fat, muscles, and bones. Most of the related studies that have addressed the aging problem are focused on generative representation (aging simulation) or discriminative representation (feature-based approaches). Designing an appropriate hybrid approach taking into account both the generative and discriminative representations for age-invariant face recognition remains an open problem. We perform a hybrid matching to achieve robustness to aging variations. This approach automatically segments the eyes, nose-bridge, and mouth regions, which are relatively less sensitive to aging variations compared with the rest of the facial regions that are age-sensitive. The aging variations of age-sensitive facial parts are compensated using a demographic-aware generative model based on a bridged denoising autoencoder. The age-insensitive facial parts are represented by pixel average vector-based local binary patterns. Deep convolutional neural networks are used to extract relative features of age-sensitive and age-insensitive facial parts. Finally, the feature vectors of age-sensitive and age-insensitive facial parts are fused to achieve the recognition results. Extensive experimental results on morphological face database II (MORPH II), face and gesture recognition network (FG-NET), and Verification Subset of cross-age celebrity dataset (CACD-VS) demonstrate the effectiveness of the proposed method for age-invariant face recognition well.

STRONG ORACLE OPTIMALITY OF FOLDED CONCAVE PENALIZED ESTIMATION.

PubMed

Fan, Jianqing; Xue, Lingzhou; Zou, Hui

2014-06-01

Folded concave penalization methods have been shown to enjoy the strong oracle property for high-dimensional sparse estimation. However, a folded concave penalization problem usually has multiple local solutions and the oracle property is established only for one of the unknown local solutions. A challenging fundamental issue still remains that it is not clear whether the local optimum computed by a given optimization algorithm possesses those nice theoretical properties. To close this important theoretical gap in over a decade, we provide a unified theory to show explicitly how to obtain the oracle solution via the local linear approximation algorithm. For a folded concave penalized estimation problem, we show that as long as the problem is localizable and the oracle estimator is well behaved, we can obtain the oracle estimator by using the one-step local linear approximation. In addition, once the oracle estimator is obtained, the local linear approximation algorithm converges, namely it produces the same estimator in the next iteration. The general theory is demonstrated by using four classical sparse estimation problems, i.e., sparse linear regression, sparse logistic regression, sparse precision matrix estimation and sparse quantile regression.

STRONG ORACLE OPTIMALITY OF FOLDED CONCAVE PENALIZED ESTIMATION

PubMed Central

Fan, Jianqing; Xue, Lingzhou; Zou, Hui

2014-01-01

Folded concave penalization methods have been shown to enjoy the strong oracle property for high-dimensional sparse estimation. However, a folded concave penalization problem usually has multiple local solutions and the oracle property is established only for one of the unknown local solutions. A challenging fundamental issue still remains that it is not clear whether the local optimum computed by a given optimization algorithm possesses those nice theoretical properties. To close this important theoretical gap in over a decade, we provide a unified theory to show explicitly how to obtain the oracle solution via the local linear approximation algorithm. For a folded concave penalized estimation problem, we show that as long as the problem is localizable and the oracle estimator is well behaved, we can obtain the oracle estimator by using the one-step local linear approximation. In addition, once the oracle estimator is obtained, the local linear approximation algorithm converges, namely it produces the same estimator in the next iteration. The general theory is demonstrated by using four classical sparse estimation problems, i.e., sparse linear regression, sparse logistic regression, sparse precision matrix estimation and sparse quantile regression. PMID:25598560

Navigating ligand protein binding free energy landscapes: universality and diversity of protein folding and molecular recognition mechanisms

NASA Astrophysics Data System (ADS)

Verkhivker, Gennady M.; Rejto, Paul A.; Bouzida, Djamal; Arthurs, Sandra; Colson, Anthony B.; Freer, Stephan T.; Gehlhaar, Daniel K.; Larson, Veda; Luty, Brock A.; Marrone, Tami; Rose, Peter W.

2001-03-01

Thermodynamic and kinetic aspects of ligand-protein binding are studied for the methotrexate-dihydrofolate reductase system from the binding free energy profile constructed as a function of the order parameter. Thermodynamic stability of the native complex and a cooperative transition to the unique native structure suggest the nucleation kinetic mechanism at the equilibrium transition temperature. Structural properties of the transition state ensemble and the ensemble of nucleation conformations are determined by kinetic simulations of the transmission coefficient and ligand-protein association pathways. Structural analysis of the transition states and the nucleation conformations reconciles different views on the nucleation mechanism in protein folding.

Constructive autoassociative neural network for facial recognition.

PubMed

Fernandes, Bruno J T; Cavalcanti, George D C; Ren, Tsang I

2014-01-01

Autoassociative artificial neural networks have been used in many different computer vision applications. However, it is difficult to define the most suitable neural network architecture because this definition is based on previous knowledge and depends on the problem domain. To address this problem, we propose a constructive autoassociative neural network called CANet (Constructive Autoassociative Neural Network). CANet integrates the concepts of receptive fields and autoassociative memory in a dynamic architecture that changes the configuration of the receptive fields by adding new neurons in the hidden layer, while a pruning algorithm removes neurons from the output layer. Neurons in the CANet output layer present lateral inhibitory connections that improve the recognition rate. Experiments in face recognition and facial expression recognition show that the CANet outperforms other methods presented in the literature.

Automated Field-of-View, Illumination, and Recognition Algorithm Design of a Vision System for Pick-and-Place Considering Colour Information in Illumination and Images

PubMed Central

Chen, Yibing; Ogata, Taiki; Ueyama, Tsuyoshi; Takada, Toshiyuki; Ota, Jun

2018-01-01

Machine vision is playing an increasingly important role in industrial applications, and the automated design of image recognition systems has been a subject of intense research. This study has proposed a system for automatically designing the field-of-view (FOV) of a camera, the illumination strength and the parameters in a recognition algorithm. We formulated the design problem as an optimisation problem and used an experiment based on a hierarchical algorithm to solve it. The evaluation experiments using translucent plastics objects showed that the use of the proposed system resulted in an effective solution with a wide FOV, recognition of all objects and 0.32 mm and 0.4° maximal positional and angular errors when all the RGB (red, green and blue) for illumination and R channel image for recognition were used. Though all the RGB illumination and grey scale images also provided recognition of all the objects, only a narrow FOV was selected. Moreover, full recognition was not achieved by using only G illumination and a grey-scale image. The results showed that the proposed method can automatically design the FOV, illumination and parameters in the recognition algorithm and that tuning all the RGB illumination is desirable even when single-channel or grey-scale images are used for recognition. PMID:29786665

Automated Field-of-View, Illumination, and Recognition Algorithm Design of a Vision System for Pick-and-Place Considering Colour Information in Illumination and Images.

PubMed

Chen, Yibing; Ogata, Taiki; Ueyama, Tsuyoshi; Takada, Toshiyuki; Ota, Jun

2018-05-22

Machine vision is playing an increasingly important role in industrial applications, and the automated design of image recognition systems has been a subject of intense research. This study has proposed a system for automatically designing the field-of-view (FOV) of a camera, the illumination strength and the parameters in a recognition algorithm. We formulated the design problem as an optimisation problem and used an experiment based on a hierarchical algorithm to solve it. The evaluation experiments using translucent plastics objects showed that the use of the proposed system resulted in an effective solution with a wide FOV, recognition of all objects and 0.32 mm and 0.4° maximal positional and angular errors when all the RGB (red, green and blue) for illumination and R channel image for recognition were used. Though all the RGB illumination and grey scale images also provided recognition of all the objects, only a narrow FOV was selected. Moreover, full recognition was not achieved by using only G illumination and a grey-scale image. The results showed that the proposed method can automatically design the FOV, illumination and parameters in the recognition algorithm and that tuning all the RGB illumination is desirable even when single-channel or grey-scale images are used for recognition.

Iris Recognition Using Feature Extraction of Box Counting Fractal Dimension

NASA Astrophysics Data System (ADS)

Khotimah, C.; Juniati, D.

2018-01-01

Biometrics is a science that is now growing rapidly. Iris recognition is a biometric modality which captures a photo of the eye pattern. The markings of the iris are distinctive that it has been proposed to use as a means of identification, instead of fingerprints. Iris recognition was chosen for identification in this research because every human has a special feature that each individual is different and the iris is protected by the cornea so that it will have a fixed shape. This iris recognition consists of three step: pre-processing of data, feature extraction, and feature matching. Hough transformation is used in the process of pre-processing to locate the iris area and Daugman’s rubber sheet model to normalize the iris data set into rectangular blocks. To find the characteristics of the iris, it was used box counting method to get the fractal dimension value of the iris. Tests carried out by used k-fold cross method with k = 5. In each test used 10 different grade K of K-Nearest Neighbor (KNN). The result of iris recognition was obtained with the best accuracy was 92,63 % for K = 3 value on K-Nearest Neighbor (KNN) method.

Identification of cloud fields by the nonparametric algorithm of pattern recognition from normalized video data recorded with the AVHRR instrument

NASA Astrophysics Data System (ADS)

Protasov, Konstantin T.; Pushkareva, Tatyana Y.; Artamonov, Evgeny S.

2002-02-01

The problem of cloud field recognition from the NOAA satellite data is urgent for solving not only meteorological problems but also for resource-ecological monitoring of the Earth's underlying surface associated with the detection of thunderstorm clouds, estimation of the liquid water content of clouds and the moisture of the soil, the degree of fire hazard, etc. To solve these problems, we used the AVHRR/NOAA video data that regularly displayed the situation in the territory. The complexity and extremely nonstationary character of problems to be solved call for the use of information of all spectral channels, mathematical apparatus of testing statistical hypotheses, and methods of pattern recognition and identification of the informative parameters. For a class of detection and pattern recognition problems, the average risk functional is a natural criterion for the quality and the information content of the synthesized decision rules. In this case, to solve efficiently the problem of identifying cloud field types, the informative parameters must be determined by minimization of this functional. Since the conditional probability density functions, representing mathematical models of stochastic patterns, are unknown, the problem of nonparametric reconstruction of distributions from the leaning samples arises. To this end, we used nonparametric estimates of distributions with the modified Epanechnikov kernel. The unknown parameters of these distributions were determined by minimization of the risk functional, which for the learning sample was substituted by the empirical risk. After the conditional probability density functions had been reconstructed for the examined hypotheses, a cloudiness type was identified using the Bayes decision rule.

Joint association of sleep problems and psychosocial working conditions with registered long-term sickness absence. A Danish cohort study.

PubMed

Madsen, Ida Eh; Larsen, Ann D; Thorsen, Sannie V; Pejtersen, Jan H; Rugulies, Reiner; Sivertsen, Børge

2016-07-01

Sleep problems and adverse psychosocial working conditions are associated with increased risk of long-term sickness absence. Because sleep problems affect role functioning they may also exacerbate any effects of psychosocial working conditions and vice versa. We examined whether sleep problems and psychosocial working conditions interact in their associations with long-term sickness absence. We linked questionnaire data from participants to two surveys of random samples of the Danish working population (N=10 752) with registries on long-term sick leave during five years after questionnaire response. We defined sleep problems by self-reported symptoms and/or register data on hypnotics purchases of hypnotics. Psychosocial working conditions included quantitative and emotional demands, influence, supervisor recognition and social support, leadership quality, and social support from colleagues. Using time-to-event models, we calculated hazard ratios (HR) and differences and examined interaction as departure from multiplicativity and additivity. During 40 165 person-years of follow-up, we identified 2313 episodes of long-terms sickness absence. Sleep problems predicted risk of long-term sickness absence [HR 1.54, 95% confidence interval (95% CI) 1.38-1.73]. This association was statistically significantly stronger among participants with high quantitative demands and weaker among those with high supervisor recognition (P<0.0001). High quantitative demands exacerbated the association of sleep problems with risk of long-term sickness absence whereas high supervisor recognition buffered this association. To prevent long-term sickness absence among employees with sleep problems, workplace modifications focusing on quantitative demands and supervisor recognition may be considered. Workplace interventions for these factors may more effectively prevent sickness absence when targeted at this group. The efficacy and effectiveness of such interventions needs to be established in future studies.

The Relative Success of Recognition-Based Inference in Multichoice Decisions

ERIC Educational Resources Information Center

McCloy, Rachel; Beaman, C. Philip; Smith, Philip T.

2008-01-01

The utility of an "ecologically rational" recognition-based decision rule in multichoice decision problems is analyzed, varying the type of judgment required (greater or lesser). The maximum size and range of a counterintuitive advantage associated with recognition-based judgment (the "less-is-more effect") is identified for a range of cue…

Recognition of Acyl Carrier Proteins by Ketoreductases in Assembly Line Polyketide Synthases

PubMed Central

Ostrowski, Matthew P.; Cane, David E.; Khosla, Chaitan

2016-01-01

Ketoreductases (KRs) are the most widespread tailoring domains found in individual modules of assembly line polyketide synthases (PKSs), and are responsible for controlling the configurations of both the α-methyl and β-hydroxyl stereogenic centers in the growing polyketide chain. Because they recognize substrates that are covalently bound to acyl carrier proteins (ACPs) within the same PKS module, we sought to quantify the extent to which protein-protein recognition contributes to the turnover of these oxidoreductive enzymes using stand-alone domains from the 6-deoxyerythronolide B synthase (DEBS). Reduced 2-methyl-3-hydroxyacyl-ACP substrates derived from two enantiomeric acyl chains and four distinct ACP domains were synthesized and presented to four distinct KR domains. Two KRs, from DEBS modules 2 and 5, displayed little preference for oxidation of substrates tethered to their cognate ACP domains over those attached to the other ACP domains tested. In contrast, the KR from DEBS module 1 showed a ca. 10-50-fold preference for substrate attached to its native ACP domain, whereas the KR from DEBS module 6 actually displayed a ca. 10-fold preference for the ACP from DEBS module 5. Our findings suggest that recognition of the ACP by a KR domain is unlikely to affect the rate of native assembly line polyketide biosynthesis. In some cases, however, unfavorable KR-ACP interactions may suppress the rate of substrate processing when KR domains are swapped to construct hybrid PKS modules. PMID:27118242

Multi-layer sparse representation for weighted LBP-patches based facial expression recognition.

PubMed

Jia, Qi; Gao, Xinkai; Guo, He; Luo, Zhongxuan; Wang, Yi

2015-03-19

In this paper, a novel facial expression recognition method based on sparse representation is proposed. Most contemporary facial expression recognition systems suffer from limited ability to handle image nuisances such as low resolution and noise. Especially for low intensity expression, most of the existing training methods have quite low recognition rates. Motivated by sparse representation, the problem can be solved by finding sparse coefficients of the test image by the whole training set. Deriving an effective facial representation from original face images is a vital step for successful facial expression recognition. We evaluate facial representation based on weighted local binary patterns, and Fisher separation criterion is used to calculate the weighs of patches. A multi-layer sparse representation framework is proposed for multi-intensity facial expression recognition, especially for low-intensity expressions and noisy expressions in reality, which is a critical problem but seldom addressed in the existing works. To this end, several experiments based on low-resolution and multi-intensity expressions are carried out. Promising results on publicly available databases demonstrate the potential of the proposed approach.

State Decision-Makers Guide for Hazardous Waste Management: Defining Hazardous Wastes, Problem Recognition, Land Use, Facility Operations, Conceptual Framework, Policy Issues, Transportation.

ERIC Educational Resources Information Center

Corson, Alan; And Others

Presented are key issues to be addressed by state, regional, and local governments and agencies in creating effective hazardous waste management programs. Eight chapters broadly frame the topics which state-level decision makers should consider. These chapters include: (1) definition of hazardous waste; (2) problem definition and recognition; (3)…

Bridging the Gaps between Learning and Teaching through Recognition of Students' Learning Approaches: A Case Study

ERIC Educational Resources Information Center

Malie, Senian; Akir, Oriah

2012-01-01

Learning approaches, learning methods and learning environments have different effects on students? academic performance. However, they are not the sole factors that impact students? academic achievement. The aims of this research are three-fold: to determine the learning approaches preferred by most students and the impact of the learning…

Sleep promotes analogical transfer in problem solving.

PubMed

Monaghan, Padraic; Sio, Ut Na; Lau, Sum Wai; Woo, Hoi Kei; Linkenauger, Sally A; Ormerod, Thomas C

2015-10-01

Analogical problem solving requires using a known solution from one problem to apply to a related problem. Sleep is known to have profound effects on memory and information restructuring, and so we tested whether sleep promoted such analogical transfer, determining whether improvement was due to subjective memory for problems, subjective recognition of similarity across related problems, or by abstract generalisation of structure. In Experiment 1, participants were exposed to a set of source problems. Then, after a 12-h period involving sleep or wake, they attempted target problems structurally related to the source problems but with different surface features. Experiment 2 controlled for time of day effects by testing participants either in the morning or the evening. Sleep improved analogical transfer, but effects were not due to improvements in subjective memory or similarity recognition, but rather effects of structural generalisation across problems. Copyright © 2015 Elsevier B.V. All rights reserved.

Kinetic Dissection of the Pre-existing Conformational Equilibrium in the Trypsin Fold*

PubMed Central

Vogt, Austin D.; Chakraborty, Pradipta; Di Cera, Enrico

2015-01-01

Structural biology has recently documented the conformational plasticity of the trypsin fold for both the protease and zymogen in terms of a pre-existing equilibrium between closed (E*) and open (E) forms of the active site region. How such plasticity is manifested in solution and affects ligand recognition by the protease and zymogen is poorly understood in quantitative terms. Here we dissect the E*-E equilibrium with stopped-flow kinetics in the presence of excess ligand or macromolecule. Using the clotting protease thrombin and its zymogen precursor prethrombin-2 as relevant models we resolve the relative distribution of the E* and E forms and the underlying kinetic rates for their interconversion. In the case of thrombin, the E* and E forms are distributed in a 1:4 ratio and interconvert on a time scale of 45 ms. In the case of prethrombin-2, the equilibrium is shifted strongly (10:1 ratio) in favor of the closed E* form and unfolds over a faster time scale of 4.5 ms. The distribution of E* and E forms observed for thrombin and prethrombin-2 indicates that zymogen activation is linked to a significant shift in the pre-existing equilibrium between closed and open conformations that facilitates ligand binding to the active site. These findings broaden our mechanistic understanding of how conformational transitions control ligand recognition by thrombin and its zymogen precursor prethrombin-2 and have direct relevance to other members of the trypsin fold. PMID:26216877

Enhancement of cell recognition in vitro by dual-ligand cancer targeting gold naoparticles

PubMed Central

Li, Xi; Zhou, Hongyu; Yang, Lei; Du, Guoqing; Pai-Panandiker, Atmaram; Huang, Xuefei; Yan, Bing

2011-01-01

A dual-ligand gold nanoparticle (DLGNP) was designed and synthesized to explore the therapeutic benefits of multivalent interactions between gold nanoparticles (GNPs) and cancer cells. DLGNP was tested on human epidermal cancer cells (KB), which had high expression of folate receptor. The cellular uptake of DLGNP was increased by 3.9 and 12.7 folds compared with GNP-folate or GNP-glucose. The enhanced cell recognition was due to multivalent interactions between both ligands on GNPs and cancer cells as shown by the ligand competition experiments. Furthermore, the multivalent interactions increased contrast between cells with high and low expression of folate receptors. The enhanced cell recognition enabled DLGNP to kill KB cells under X-ray irradiation at a dose that was safe to folate receptor low-expression (such as normal) cells. Thus DLGP has the potential to be a cancer-specific nano-theranostic agent. PMID:21232787

Analysis of MHC class I folding: novel insights into intermediate forms

PubMed Central

Simone, Laura C.; Tuli, Amit; Simone, Peter D.; Wang, Xiaojian; Solheim, Joyce C.

2012-01-01

Folding around a peptide ligand is integral to the antigen presentation function of major histocompatibility complex (MHC) class I molecules. Several lines of evidence indicate that the broadly cross-reactive 34-1-2 antibody is sensitive to folding of the MHC class I peptide-binding groove. Here, we show that peptide-loading complex proteins associated with the murine MHC class I molecule Kd are found primarily in association with the 34-1-2+ form. This led us to hypothesize that the 34-1-2 antibody may recognize intermediately, as well as fully, folded MHC class I molecules. In order to further characterize the form(s) of MHC class I molecules recognized by 34-1-2, we took advantage of its cross-reactivity with Ld. Recognition of the open and folded forms of Ld by the 64-3-7 and 30-5-7 antibodies, respectively, has been extensively characterized, providing us with parameters against which to compare 34-1-2 reactivity. We found that the 34-1-2+ Ld molecules displayed characteristics indicative of incomplete folding, including increased tapasin association, endoplasmic reticulum retention, and instability at the cell surface. Moreover, we demonstrate that an Ld-specific peptide induced folding of the 34-1-2+ Ld intermediate. Altogether, these results yield novel insights into the nature of MHC class I molecules recognized by the 34-1-2 antibody. PMID:22329842

Engineered fluorescent proteins illuminate the bacterial periplasm

PubMed Central

Dammeyer, Thorben; Tinnefeld, Philip

2012-01-01

The bacterial periplasm is of special interest whenever cell factories are designed and engineered. Recombinantely produced proteins are targeted to the periplasmic space of Gram negative bacteria to take advantage of the authentic N-termini, disulfide bridge formation and easy accessibility for purification with less contaminating cellular proteins. The oxidizing environment of the periplasm promotes disulfide bridge formation - a prerequisite for proper folding of many proteins into their active conformation. In contrast, the most popular reporter protein in all of cell biology, Green Fluorescent Protein (GFP), remains inactive if translocated to the periplasmic space prior to folding. Here, the self-catalyzed chromophore maturation is blocked by formation of covalent oligomers via interchain disulfide bonds in the oxidizing environment. However, different protein engineering approaches addressing folding and stability of GFP resulted in improved proteins with enhanced folding properties. Recent studies describe GFP variants that are not only active if translocated in their folded form via the twin-arginine translocation (Tat) pathway, but actively fold in the periplasm following general secretory pathway (Sec) and signal recognition particle (SRP) mediated secretion. This mini-review highlights the progress that enables new insights into bacterial export and periplasmic protein organization, as well as new biotechnological applications combining the advantages of the periplasmic production and the Aequorea-based fluorescent reporter proteins. PMID:24688673

Representations of the language recognition problem for a theorem prover

NASA Technical Reports Server (NTRS)

Minker, J.; Vanderbrug, G. J.

1972-01-01

Two representations of the language recognition problem for a theorem prover in first order logic are presented and contrasted. One of the representations is based on the familiar method of generating sentential forms of the language, and the other is based on the Cocke parsing algorithm. An augmented theorem prover is described which permits recognition of recursive languages. The state-transformation method developed by Cordell Green to construct problem solutions in resolution-based systems can be used to obtain the parse tree. In particular, the end-order traversal of the parse tree is derived in one of the representations. An inference system, termed the cycle inference system, is defined which makes it possible for the theorem prover to model the method on which the representation is based. The general applicability of the cycle inference system to state space problems is discussed. Given an unsatisfiable set S, where each clause has at most one positive literal, it is shown that there exists an input proof. The clauses for the two representations satisfy these conditions, as do many state space problems.

Pre-folding IkappaBalpha alters control of NF-kappaB signaling.

PubMed

Truhlar, Stephanie M E; Mathes, Erika; Cervantes, Carla F; Ghosh, Gourisankar; Komives, Elizabeth A

2008-06-27

Transcription complex components frequently show coupled folding and binding but the functional significance of this mode of molecular recognition is unclear. IkappaBalpha binds to and inhibits the transcriptional activity of NF-kappaB via its ankyrin repeat (AR) domain. The beta-hairpins in ARs 5-6 in IkappaBalpha are weakly-folded in the free protein, and their folding is coupled to NF-kappaB binding. Here, we show that introduction of two stabilizing mutations in IkappaBalpha AR 6 causes ARs 5-6 to fold cooperatively to a conformation similar to that in NF-kappaB-bound IkappaBalpha. Free IkappaBalpha is degraded by a proteasome-dependent but ubiquitin-independent mechanism, and this process is slower for the pre-folded mutants both in vitro and in cells. Interestingly, the pre-folded mutants bind NF-kappaB more weakly, as shown by both surface plasmon resonance and isothermal titration calorimetry in vitro and immunoprecipitation experiments from cells. One consequence of the weaker binding is that resting cells containing these mutants show incomplete inhibition of NF-kappaB activation; they have significant amounts of nuclear NF-kappaB. Additionally, the weaker binding combined with the slower rate of degradation of the free protein results in reduced levels of nuclear NF-kappaB upon stimulation. These data demonstrate clearly that the coupled folding and binding of IkappaBalpha is critical for its precise control of NF-kappaB transcriptional activity.

Automatic detection of wheezes by evaluation of multiple acoustic feature extraction methods and C-weighted SVM

NASA Astrophysics Data System (ADS)

Sosa, Germán. D.; Cruz-Roa, Angel; González, Fabio A.

2015-01-01

This work addresses the problem of lung sound classification, in particular, the problem of distinguishing between wheeze and normal sounds. Wheezing sound detection is an important step to associate lung sounds with an abnormal state of the respiratory system, usually associated with tuberculosis or another chronic obstructive pulmonary diseases (COPD). The paper presents an approach for automatic lung sound classification, which uses different state-of-the-art sound features in combination with a C-weighted support vector machine (SVM) classifier that works better for unbalanced data. Feature extraction methods used here are commonly applied in speech recognition and related problems thanks to the fact that they capture the most informative spectral content from the original signals. The evaluated methods were: Fourier transform (FT), wavelet decomposition using Wavelet Packet Transform bank of filters (WPT) and Mel Frequency Cepstral Coefficients (MFCC). For comparison, we evaluated and contrasted the proposed approach against previous works using different combination of features and/or classifiers. The different methods were evaluated on a set of lung sounds including normal and wheezing sounds. A leave-two-out per-case cross-validation approach was used, which, in each fold, chooses as validation set a couple of cases, one including normal sounds and the other including wheezing sounds. Experimental results were reported in terms of traditional classification performance measures: sensitivity, specificity and balanced accuracy. Our best results using the suggested approach, C-weighted SVM and MFCC, achieve a 82.1% of balanced accuracy obtaining the best result for this problem until now. These results suggest that supervised classifiers based on kernel methods are able to learn better models for this challenging classification problem even using the same feature extraction methods.

Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments.

PubMed

Baldominos, Alejandro; Saez, Yago; Isasi, Pedro

2018-04-23

Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures.

Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments

PubMed Central

2018-01-01

Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures. PMID:29690587

Soft Computing Techniques for the Protein Folding Problem on High Performance Computing Architectures.

PubMed

Llanes, Antonio; Muñoz, Andrés; Bueno-Crespo, Andrés; García-Valverde, Teresa; Sánchez, Antonia; Arcas-Túnez, Francisco; Pérez-Sánchez, Horacio; Cecilia, José M

2016-01-01

The protein-folding problem has been extensively studied during the last fifty years. The understanding of the dynamics of global shape of a protein and the influence on its biological function can help us to discover new and more effective drugs to deal with diseases of pharmacological relevance. Different computational approaches have been developed by different researchers in order to foresee the threedimensional arrangement of atoms of proteins from their sequences. However, the computational complexity of this problem makes mandatory the search for new models, novel algorithmic strategies and hardware platforms that provide solutions in a reasonable time frame. We present in this revision work the past and last tendencies regarding protein folding simulations from both perspectives; hardware and software. Of particular interest to us are both the use of inexact solutions to this computationally hard problem as well as which hardware platforms have been used for running this kind of Soft Computing techniques.

Word Spotting and Recognition with Embedded Attributes.

PubMed

Almazán, Jon; Gordo, Albert; Fornés, Alicia; Valveny, Ernest

2014-12-01

This paper addresses the problems of word spotting and word recognition on images. In word spotting, the goal is to find all instances of a query word in a dataset of images. In recognition, the goal is to recognize the content of the word image, usually aided by a dictionary or lexicon. We describe an approach in which both word images and text strings are embedded in a common vectorial subspace. This is achieved by a combination of label embedding and attributes learning, and a common subspace regression. In this subspace, images and strings that represent the same word are close together, allowing one to cast recognition and retrieval tasks as a nearest neighbor problem. Contrary to most other existing methods, our representation has a fixed length, is low dimensional, and is very fast to compute and, especially, to compare. We test our approach on four public datasets of both handwritten documents and natural images showing results comparable or better than the state-of-the-art on spotting and recognition tasks.

An application of viola jones method for face recognition for absence process efficiency

NASA Astrophysics Data System (ADS)

Rizki Damanik, Rudolfo; Sitanggang, Delima; Pasaribu, Hendra; Siagian, Hendrik; Gulo, Frisman

2018-04-01

Absence was a list of documents that the company used to record the attendance time of each employee. The most common problem in a fingerprint machine is the identification of a slow sensor or a sensor not recognizing a finger. The employees late to work because they get difficulties at fingerprint system, they need about 3 – 5 minutes to absence when the condition of finger is wet or not fit. To overcome this problem, this research tried to utilize facial recognition for attendance process. The method used for facial recognition was Viola Jones. Through the processing phase of the RGB face image was converted into a histogram equalization face image for the next stage of recognition. The result of this research was the absence process could be done less than 1 second with a maximum slope of ± 700 and a distance of 20-200 cm. After implement facial recognition the process of absence is more efficient, just take less 1 minute to absence.

Multi-template image matching using alpha-rooted biquaternion phase correlation with application to logo recognition

NASA Astrophysics Data System (ADS)

DelMarco, Stephen

2011-06-01

Hypercomplex approaches are seeing increased application to signal and image processing problems. The use of multicomponent hypercomplex numbers, such as quaternions, enables the simultaneous co-processing of multiple signal or image components. This joint processing capability can provide improved exploitation of the information contained in the data, thereby leading to improved performance in detection and recognition problems. In this paper, we apply hypercomplex processing techniques to the logo image recognition problem. Specifically, we develop an image matcher by generalizing classical phase correlation to the biquaternion case. We further incorporate biquaternion Fourier domain alpha-rooting enhancement to create Alpha-Rooted Biquaternion Phase Correlation (ARBPC). We present the mathematical properties which justify use of ARBPC as an image matcher. We present numerical performance results of a logo verification problem using real-world logo data, demonstrating the performance improvement obtained using the hypercomplex approach. We compare results of the hypercomplex approach to standard multi-template matching approaches.

Robust Face Recognition via Multi-Scale Patch-Based Matrix Regression.

PubMed

Gao, Guangwei; Yang, Jian; Jing, Xiaoyuan; Huang, Pu; Hua, Juliang; Yue, Dong

2016-01-01

In many real-world applications such as smart card solutions, law enforcement, surveillance and access control, the limited training sample size is the most fundamental problem. By making use of the low-rank structural information of the reconstructed error image, the so-called nuclear norm-based matrix regression has been demonstrated to be effective for robust face recognition with continuous occlusions. However, the recognition performance of nuclear norm-based matrix regression degrades greatly in the face of the small sample size problem. An alternative solution to tackle this problem is performing matrix regression on each patch and then integrating the outputs from all patches. However, it is difficult to set an optimal patch size across different databases. To fully utilize the complementary information from different patch scales for the final decision, we propose a multi-scale patch-based matrix regression scheme based on which the ensemble of multi-scale outputs can be achieved optimally. Extensive experiments on benchmark face databases validate the effectiveness and robustness of our method, which outperforms several state-of-the-art patch-based face recognition algorithms.

Common diagnoses and treatments in professional voice users.

PubMed

Franco, Ramon A; Andrus, Jennifer G

2007-10-01

Common problems among all patients seen by the laryngologist are also common among professional voice users. These include laryngopharyngeal reflux, muscle tension dysphonia, fibrovascular vocal fold lesions (eg, nodules and polyps), cysts, vocal fold scarring, changes in vocal fold mobility, and age-related changes. Microvascular lesions and their associated sequelae of vocal fold hemorrhage and laryngitis due to voice overuse are more common among professional voice users. Much more common among professional voice users is the negative impact that voice problems have on their ability to work, on their overall sense of well-being, and sometimes on their very sense of self. This article reviews the diagnosis and treatment options for these and other problems among professional voice users, describing the relevant roles of medical treatment, voice therapy, and surgery. The common scenario of multiple concomitant entities contributing to a symptom complex is underscored. Emphasis is placed on gaining insight into the "whole" patient so that individualized management plans can be developed. Videos of select diagnoses accompany this content online.

Leveraging Automatic Speech Recognition Errors to Detect Challenging Speech Segments in TED Talks

ERIC Educational Resources Information Center

Mirzaei, Maryam Sadat; Meshgi, Kourosh; Kawahara, Tatsuya

2016-01-01

This study investigates the use of Automatic Speech Recognition (ASR) systems to epitomize second language (L2) listeners' problems in perception of TED talks. ASR-generated transcripts of videos often involve recognition errors, which may indicate difficult segments for L2 listeners. This paper aims to discover the root-causes of the ASR errors…

Recognition of the folded conformation of plant hormone (auxin, IAA) conjugates with glutamic and aspartic acids and their amides

NASA Astrophysics Data System (ADS)

Antolić, S.; Kveder, M.; Klaić, B.; Magnus, V.; Kojić-Prodić, B.

2001-01-01

The molecular structure of the endogenous plant hormone (auxin) conjugate, N-(indol-3-ylacetyl)-L-glutamic acid, is deduced by comparison with N2-(indol-3-ylacetyl)glutamine (IAA-Gln), N2-(indol-3-ylacetyl)asparagine (IAA-Asn) and N-(indol-3-ylacetyl)-L-aspartic acid using X-ray structure analysis, 1H-NMR spectroscopy (NOE measurements) and molecular modelling. The significance of the overall molecular shape, and of the resulting amphiphilic properties, of the compounds studied are discussed in terms of possible implications for trafficking between cell compartments. Both in the solid state and in solution, the molecules are in the hair-pin (folded) conformation in which the side chain is folded over the indole ring. While extended conformations can be detected by molecular dynamics simulations, they are so short-lived that any major influence on the biological properties of the compounds studied is unlikely.

A new model for approximating RNA folding trajectories and population kinetics

NASA Astrophysics Data System (ADS)

Kirkpatrick, Bonnie; Hajiaghayi, Monir; Condon, Anne

2013-01-01

RNA participates both in functional aspects of the cell and in gene regulation. The interactions of these molecules are mediated by their secondary structure which can be viewed as a planar circle graph with arcs for all the chemical bonds between pairs of bases in the RNA sequence. The problem of predicting RNA secondary structure, specifically the chemically most probable structure, has many useful and efficient algorithms. This leaves RNA folding, the problem of predicting the dynamic behavior of RNA structure over time, as the main open problem. RNA folding is important for functional understanding because some RNA molecules change secondary structure in response to interactions with the environment. The full RNA folding model on at most O(3n) secondary structures is the gold standard. We present a new subset approximation model for the full model, give methods to analyze its accuracy and discuss the relative merits of our model as compared with a pre-existing subset approximation. The main advantage of our model is that it generates Monte Carlo folding pathways with the same probabilities with which they are generated under the full model. The pre-existing subset approximation does not have this property.

Dynamics of protein folding: probing the kinetic network of folding-unfolding transitions with experiment and theory.

PubMed

Buchner, Ginka S; Murphy, Ronan D; Buchete, Nicolae-Viorel; Kubelka, Jan

2011-08-01

The problem of spontaneous folding of amino acid chains into highly organized, biologically functional three-dimensional protein structures continues to challenge the modern science. Understanding how proteins fold requires characterization of the underlying energy landscapes as well as the dynamics of the polypeptide chains in all stages of the folding process. In recent years, important advances toward these goals have been achieved owing to the rapidly growing interdisciplinary interest and significant progress in both experimental techniques and theoretical methods. Improvements in the experimental time resolution led to determination of the timescales of the important elementary events in folding, such as formation of secondary structure and tertiary contacts. Sensitive single molecule methods made possible probing the distributions of the unfolded and folded states and following the folding reaction of individual protein molecules. Discovery of proteins that fold in microseconds opened the possibility of atomic-level theoretical simulations of folding and their direct comparisons with experimental data, as well as of direct experimental observation of the barrier-less folding transition. The ultra-fast folding also brought new questions, concerning the intrinsic limits of the folding rates and experimental signatures of barrier-less "downhill" folding. These problems will require novel approaches for even more detailed experimental investigations of the folding dynamics as well as for the analysis of the folding kinetic data. For theoretical simulations of folding, a main challenge is how to extract the relevant information from overwhelmingly detailed atomistic trajectories. New theoretical methods have been devised to allow a systematic approach towards a quantitative analysis of the kinetic network of folding-unfolding transitions between various configuration states of a protein, revealing the transition states and the associated folding pathways at multiple levels, from atomistic to coarse-grained representations. This article is part of a Special Issue entitled: Protein Dynamics: Experimental and Computational Approaches. Copyright © 2010 Elsevier B.V. All rights reserved.

Extreme Folding

NASA Astrophysics Data System (ADS)

Demaine, Erik

2012-02-01

Our understanding of the mathematics and algorithms behind paper folding, and geometric folding in general, has increased dramatically over the past several years. These developments have found a surprisingly broad range of applications. In the art of origami, it has helped spur the technical origami revolution. In engineering and science, it has helped solve problems in areas such as manufacturing, robotics, graphics, and protein folding. On the recreational side, it has led to new kinds of folding puzzles and magic. I will give an overview of the mathematics and algorithms of folding, with a focus on new mathematics and sculpture.

Probabilistic Open Set Recognition

NASA Astrophysics Data System (ADS)

Jain, Lalit Prithviraj

Real-world tasks in computer vision, pattern recognition and machine learning often touch upon the open set recognition problem: multi-class recognition with incomplete knowledge of the world and many unknown inputs. An obvious way to approach such problems is to develop a recognition system that thresholds probabilities to reject unknown classes. Traditional rejection techniques are not about the unknown; they are about the uncertain boundary and rejection around that boundary. Thus traditional techniques only represent the "known unknowns". However, a proper open set recognition algorithm is needed to reduce the risk from the "unknown unknowns". This dissertation examines this concept and finds existing probabilistic multi-class recognition approaches are ineffective for true open set recognition. We hypothesize the cause is due to weak adhoc assumptions combined with closed-world assumptions made by existing calibration techniques. Intuitively, if we could accurately model just the positive data for any known class without overfitting, we could reject the large set of unknown classes even under this assumption of incomplete class knowledge. For this, we formulate the problem as one of modeling positive training data by invoking statistical extreme value theory (EVT) near the decision boundary of positive data with respect to negative data. We provide a new algorithm called the PI-SVM for estimating the unnormalized posterior probability of class inclusion. This dissertation also introduces a new open set recognition model called Compact Abating Probability (CAP), where the probability of class membership decreases in value (abates) as points move from known data toward open space. We show that CAP models improve open set recognition for multiple algorithms. Leveraging the CAP formulation, we go on to describe the novel Weibull-calibrated SVM (W-SVM) algorithm, which combines the useful properties of statistical EVT for score calibration with one-class and binary support vector machines. Building from the success of statistical EVT based recognition methods such as PI-SVM and W-SVM on the open set problem, we present a new general supervised learning algorithm for multi-class classification and multi-class open set recognition called the Extreme Value Local Basis (EVLB). The design of this algorithm is motivated by the observation that extrema from known negative class distributions are the closest negative points to any positive sample during training, and thus should be used to define the parameters of a probabilistic decision model. In the EVLB, the kernel distribution for each positive training sample is estimated via an EVT distribution fit over the distances to the separating hyperplane between positive training sample and closest negative samples, with a subset of the overall positive training data retained to form a probabilistic decision boundary. Using this subset as a frame of reference, the probability of a sample at test time decreases as it moves away from the positive class. Possessing this property, the EVLB is well-suited to open set recognition problems where samples from unknown or novel classes are encountered at test. Our experimental evaluation shows that the EVLB provides a substantial improvement in scalability compared to standard radial basis function kernel machines, as well as P I-SVM and W-SVM, with improved accuracy in many cases. We evaluate our algorithm on open set variations of the standard visual learning benchmarks, as well as with an open subset of classes from Caltech 256 and ImageNet. Our experiments show that PI-SVM, WSVM and EVLB provide significant advances over the previous state-of-the-art solutions for the same tasks.

Protein folding, protein structure and the origin of life: Theoretical methods and solutions of dynamical problems

NASA Technical Reports Server (NTRS)

Weaver, D. L.

1982-01-01

Theoretical methods and solutions of the dynamics of protein folding, protein aggregation, protein structure, and the origin of life are discussed. The elements of a dynamic model representing the initial stages of protein folding are presented. The calculation and experimental determination of the model parameters are discussed. The use of computer simulation for modeling protein folding is considered.

Δ98Δ, a minimalist model of antiparallel β-sheet proteins based on intestinal fatty acid binding protein

PubMed Central

Curto, Lucrecia María; Caramelo, Julio Javier; Franchini, Gisela Raquel; Delfino, José María

2009-01-01

The design of β-barrels has always been a formidable challenge for de novo protein design. For instance, a persistent problem is posed by the intrinsic tendency to associate given by free edges. From the opposite standpoint provided by the redesign of natural motifs, we believe that the intestinal fatty acid binding protein (IFABP) framework allows room for intervention, giving rise to abridged forms from which lessons on β-barrel architecture and stability could be learned. In this context, Δ98Δ (encompassing residues 29–126 of IFABP) emerges as a monomeric variant that folds properly, retaining functional activity, despite lacking extensive stretches involved in the closure of the β-barrel. Spectroscopic probes (fluorescence and circular dichroism) support the existence of a form preserving the essential determinants of the parent structure, albeit endowed with enhanced flexibility. Chemical and physical perturbants reveal cooperative unfolding transitions, with evidence of significant population of intermediate species in equilibrium, structurally akin to those transiently observed in IFABP. The recognition by the natural ligand oleic acid exerts a mild stabilizing effect, being of a greater magnitude than that found for IFABP. In summary, Δ98Δ adopts a monomeric state with a compact core and a loose periphery, thus pointing to the nonintuitive notion that the integrity of the β-barrel can indeed be compromised with no consequence on the ability to attain a native-like and functional fold. PMID:19309727

Involvement of the oxytocin system in the bed nucleus of the stria terminalis in the sex-specific regulation of social recognition.

PubMed

Dumais, Kelly M; Alonso, Andrea G; Immormino, Marisa A; Bredewold, Remco; Veenema, Alexa H

2016-02-01

Sex differences in the oxytocin (OT) system in the brain may explain why OT often regulates social behaviors in sex-specific ways. However, a link between sex differences in the OT system and sex-specific regulation of social behavior has not been tested. Here, we determined whether sex differences in the OT receptor (OTR) or in OT release in the posterior bed nucleus of the stria terminalis (pBNST) mediates sex-specific regulation of social recognition in rats. We recently showed that, compared to female rats, male rats have a three-fold higher OTR binding density in the pBNST, a sexually dimorphic area implicated in the regulation of social behaviors. We now demonstrate that OTR antagonist (5 ng/0.5 μl/side) administration into the pBNST impairs social recognition in both sexes, while OT (100 pg/0.5 μl/side) administration into the pBNST prolongs the duration of social recognition in males only. These effects seem specific to social recognition, as neither treatment altered total social investigation time in either sex. Moreover, baseline OT release in the pBNST, as measured with in vivo microdialysis, did not differ between the sexes. However, males showed higher OT release in the pBNST during social recognition compared to females. These findings suggest a sex-specific role of the OT system in the pBNST in the regulation of social recognition. Copyright © 2015 Elsevier Ltd. All rights reserved.

Cross-modal face recognition using multi-matcher face scores

NASA Astrophysics Data System (ADS)

Zheng, Yufeng; Blasch, Erik

2015-05-01

The performance of face recognition can be improved using information fusion of multimodal images and/or multiple algorithms. When multimodal face images are available, cross-modal recognition is meaningful for security and surveillance applications. For example, a probe face is a thermal image (especially at nighttime), while only visible face images are available in the gallery database. Matching a thermal probe face onto the visible gallery faces requires crossmodal matching approaches. A few such studies were implemented in facial feature space with medium recognition performance. In this paper, we propose a cross-modal recognition approach, where multimodal faces are cross-matched in feature space and the recognition performance is enhanced with stereo fusion at image, feature and/or score level. In the proposed scenario, there are two cameras for stereo imaging, two face imagers (visible and thermal images) in each camera, and three recognition algorithms (circular Gaussian filter, face pattern byte, linear discriminant analysis). A score vector is formed with three cross-matched face scores from the aforementioned three algorithms. A classifier (e.g., k-nearest neighbor, support vector machine, binomial logical regression [BLR]) is trained then tested with the score vectors by using 10-fold cross validations. The proposed approach was validated with a multispectral stereo face dataset from 105 subjects. Our experiments show very promising results: ACR (accuracy rate) = 97.84%, FAR (false accept rate) = 0.84% when cross-matching the fused thermal faces onto the fused visible faces by using three face scores and the BLR classifier.

Involvement of the oxytocin system in the bed nucleus of the stria terminalis in the sex-specific regulation of social recognition

PubMed Central

Dumais, Kelly M.; Alonso, Andrea G.; Immormino, Marisa A.; Bredewold, Remco; Veenema, Alexa H.

2015-01-01

Sex differences in the oxytocin (OT) system in the brain may explain why OT often regulates social behaviors in sex-specific ways. However, a link between sex differences in the OT system and sex-specific regulation of social behavior has not been tested. Here, we determined whether sex differences in the OT receptor (OTR) or in OT release in the posterior bed nucleus of the stria terminalis (pBNST) mediates sex-specific regulation of social recognition in rats. We recently showed that, compared to female rats, male rats have a three-fold higher OTR binding density in the pBNST, a sexually dimorphic area implicated in the regulation of social behaviors. We now demonstrate that OTR antagonist (5 ng/0.5 μl/side) administration into the pBNST impairs social recognition in both sexes, while OT (100 pg/0.5 μl/side) administration into the pBNST prolongs the duration of social recognition in males only. These effects seem specific to social recognition, as neither treatment altered total social investigation time in either sex. Moreover, baseline OT release in the pBNST, as measured with in vivo microdialysis, did not differ between the sexes. However, males showed higher OT release in the pBNST during social recognition compared to females. These findings suggest a sex-specific role of the OT system in the pBNST in the regulation of social recognition. PMID:26630388

Computational Modeling of Proteins based on Cellular Automata: A Method of HP Folding Approximation.

PubMed

Madain, Alia; Abu Dalhoum, Abdel Latif; Sleit, Azzam

2018-06-01

The design of a protein folding approximation algorithm is not straightforward even when a simplified model is used. The folding problem is a combinatorial problem, where approximation and heuristic algorithms are usually used to find near optimal folds of proteins primary structures. Approximation algorithms provide guarantees on the distance to the optimal solution. The folding approximation approach proposed here depends on two-dimensional cellular automata to fold proteins presented in a well-studied simplified model called the hydrophobic-hydrophilic model. Cellular automata are discrete computational models that rely on local rules to produce some overall global behavior. One-third and one-fourth approximation algorithms choose a subset of the hydrophobic amino acids to form H-H contacts. Those algorithms start with finding a point to fold the protein sequence into two sides where one side ignores H's at even positions and the other side ignores H's at odd positions. In addition, blocks or groups of amino acids fold the same way according to a predefined normal form. We intend to improve approximation algorithms by considering all hydrophobic amino acids and folding based on the local neighborhood instead of using normal forms. The CA does not assume a fixed folding point. The proposed approach guarantees one half approximation minus the H-H endpoints. This lower bound guaranteed applies to short sequences only. This is proved as the core and the folds of the protein will have two identical sides for all short sequences.

Social emotion recognition, social functioning, and attempted suicide in late-life depression.

PubMed

Szanto, Katalin; Dombrovski, Alexandre Y; Sahakian, Barbara J; Mulsant, Benoit H; Houck, Patricia R; Reynolds, Charles F; Clark, Luke

2012-03-01

: Lack of feeling connected and poor social problem solving have been described in suicide attempters. However, cognitive substrates of this apparent social impairment in suicide attempters remain unknown. One possible deficit, the inability to recognize others' complex emotional states has been observed not only in disorders characterized by prominent social deficits (autism-spectrum disorders and frontotemporal dementia) but also in depression and normal aging. This study assessed the relationship between social emotion recognition, problem solving, social functioning, and attempted suicide in late-life depression. : There were 90 participants: 24 older depressed suicide attempters, 38 nonsuicidal depressed elders, and 28 comparison subjects with no psychiatric history. We compared performance on the Reading the Mind in the Eyes test and measures of social networks, social support, social problem solving, and chronic interpersonal difficulties in these three groups. : Suicide attempters committed significantly more errors in social emotion recognition and showed poorer global cognitive performance than elders with no psychiatric history. Attempters had restricted social networks: they were less likely to talk to their children, had fewer close friends, and did not engage in volunteer activities, compared to nonsuicidal depressed elders and those with no psychiatric history. They also reported a pattern of struggle against others and hostility in relationships, felt a lack of social support, perceived social problems as impossible to resolve, and displayed a careless/impulsive approach to problems. : Suicide attempts in depressed elders were associated with poor social problem solving, constricted social networks, and disruptive interpersonal relationships. Impaired social emotion recognition in the suicide attempter group was related.

A Small Aptamer with Strong and Specific Recognition of the Triphosphate of ATP

PubMed Central

Sazani, Peter L.; Larralde, Rosa

2004-01-01

We report the in vitro selection of an RNA-based ATP aptamer with the ability to discriminate between adenosine ligands based on their 5‘ phosphorylation state. Previous selection of ATP aptamers yielded molecules that do not significantly discriminate between ligands at the 5‘ position. By applying a selective pressure that demands recognition of the 5‘ triphosphate, we obtained an aptamer that binds to ATP with a Kd of approximately 5 μM, and to AMP with a Kd of approximately 5.5 mM, a difference of 1100-fold. This aptamer demonstrates the ability of small RNAs to interact with negatively charged moieties. PMID:15237981

Chemical Posttranslational Modification with Designed Rhodium(II) Catalysts.

PubMed

Martin, S C; Minus, M B; Ball, Z T

2016-01-01

Natural enzymes use molecular recognition to perform exquisitely selective transformations on nucleic acids, proteins, and natural products. Rhodium(II) catalysts mimic this selectivity, using molecular recognition to allow selective modification of proteins with a variety of functionalized diazo reagents. The rhodium catalysts and the diazo reactivity have been successfully applied to a variety of protein folds, the chemistry succeeds in complex environments such as cell lysate, and a simple protein blot method accurately assesses modification efficiency. The studies with rhodium catalysts provide a new tool to study and probe protein-binding events, as well as a new synthetic approach to protein conjugates for medical, biochemical, or materials applications. © 2016 Elsevier Inc. All rights reserved.

Timing of isoclinal folds in multiply deformed high metamorphic grade region using FIA succession

NASA Astrophysics Data System (ADS)

Cao, Hui; Cai, Zhihui

2013-04-01

Multiply deformed and isoclinally folded interlayered high metamorphic grade gneisses and schists can be very difficult rocks for resolving early formed stratigraphic and structural relationships. When such rocks contain porphyroblasts a new approach is possible because of the way in which porphyroblast growth is affected by crenulation versus reactivation of compositional layering. The asymmetries of the overprinting foliations preserved as inclusion trails that define the FIAs can be used to investigate whether an enigmatic isoclinal fold is an antiform or synform. This approach also reveals when the fold first formed during the tectonic history of the region. Isoclinally folded rocks in the Arkansas River region of Central Colorado contain relics of fold hinges that have been very difficult to ascertain whether they are antiforms or synforms because of younger refolding effects and the locally truncated nature of coarse compositional layering. With the realization that rocks with a schistosity parallel to bedding (S0 parallel S1) have undergone lengthy histories of deformation that predate the obvious first deformation came recognition that large scale regional folds can form early during this process and be preserved throughout orogenesis. This extensive history is lost within the matrix because of reactivational shear on the compositional layering. However, it can be extracted by measuring FIAs. Recent work using this approach has revealed that the trends of axial planes of all map scale folds, when plotted on a rose diagram, strikingly reflect the FIA trends. That is, although it was demonstrated that the largest scale regional folds commonly form early in the total history, other folds can form and be preserved from subsequent destruction in the strain shadows of plutons or through the partitioning of deformation due to heterogeneities at depth.

In silico prediction of Tetrahymena pyriformis toxicity for diverse industrial chemicals with substructure pattern recognition and machine learning methods.

PubMed

Cheng, Feixiong; Shen, Jie; Yu, Yue; Li, Weihua; Liu, Guixia; Lee, Philip W; Tang, Yun

2011-03-01

There is an increasing need for the rapid safety assessment of chemicals by both industries and regulatory agencies throughout the world. In silico techniques are practical alternatives in the environmental hazard assessment. It is especially true to address the persistence, bioaccumulative and toxicity potentials of organic chemicals. Tetrahymena pyriformis toxicity is often used as a toxic endpoint. In this study, 1571 diverse unique chemicals were collected from the literature and composed of the largest diverse data set for T. pyriformis toxicity. Classification predictive models of T. pyriformis toxicity were developed by substructure pattern recognition and different machine learning methods, including support vector machine (SVM), C4.5 decision tree, k-nearest neighbors and random forest. The results of a 5-fold cross-validation showed that the SVM method performed better than other algorithms. The overall predictive accuracies of the SVM classification model with radial basis functions kernel was 92.2% for the 5-fold cross-validation and 92.6% for the external validation set, respectively. Furthermore, several representative substructure patterns for characterizing T. pyriformis toxicity were also identified via the information gain analysis methods. Copyright © 2010 Elsevier Ltd. All rights reserved.

Enantioselective Fluorescent Recognition of Chiral Acids by Cyclohexane-1,2-diamine-Based Bisbinaphthyl Molecules

PubMed Central

Li, Zi-Bo; Lin, Jing; Sabat, Michal; Hyacinth, Marilise; Pu, Lin

2008-01-01

The cyclohexane-1,2-diamine-based bisbinaphthyl macrocycles (S)-/(R)-5 and their cyclic and acyclic analogs are synthesized. The interactions of these compounds with various chiral acids are studied. Compounds (S)-/(R)-5 exhibit highly enantioselective fluorescent responses and high fluorescent sensitivity toward α-hydroxycarboxylic acids and N-protected amino acids. Among these interactions, (S)-mandelic acid (10−3 M) led to over 20 fold fluorescence enhancement of (S)-5 (1.0 × 10−5 M in benzene/0.05% DME) at the monomer emission and (S)-hexahydromandelic acid (10−3 M) led to over 80 fold fluorescence enhancement. These results demonstrate that (S)-5 is useful as an enantioselective fluorescent sensor for the recognition of the chiral acids. On the basis of the study of the structures of (S)-5 and the previously reported 1,2-diphenylethylenediamine-based bisbinaphthyl macrocycle (S)-4, the large fluorescence enhancement of (S)-5 with achirality-matched α-hydroxycarboxylic acid is attributed to the formation of a structurally rigidified host-guest complex and the further interaction of this complex with the acid to suppress the photo-induced electron transfer fluorescent quenching caused by the nitrogens in (S)-5. PMID:17530897

[Temperament of children with vocal fold nodules].

PubMed

Wei, Youhua; Wang, Zhinan; Xu, Zhongqiang; Chen, Ping; Hao, Lili

2009-11-01

To examine the temperament of children with vocal fold nodules. To compare the temperament dimension and temperamental types of 42 children with vocal fold nodules with 46 vocally normal children, using Chinese children's Temperament Problem Screening system (CCTPSs). The children with vocal fold nodules differed significantly from the comparison group in their temperament dimension's adaptability, intensity of reaction, mood value, persistency and temperamental types. There are more difficult and slow-to-warm-up children in patients with vocal fold nodules than vocally normal children.

Poor self-recognition of disordered eating among girls with bulimic-type eating disorders: cause for concern?

PubMed

Gratwick-Sarll, Kassandra; Bentley, Caroline; Harrison, Carmel; Mond, Jonathan

2016-08-01

Bulimic-type eating disorders are common among young women and associated with high levels of distress and disability and low uptake of mental health care. We examined self-recognition of disordered eating and factors associated with this among female adolescents with bulimic-type eating disorders (n = 139) recruited from a large, population-based sample. A vignette of a fictional character with bulimia nervosa was presented, followed by a series of questions addressing the nature and treatment of the problem described. One of these questions required participants to indicate whether they currently had a problem such as the one described. Self-report measures of eating disorder symptoms, general psychological distress and quality of life were also completed. More than half of participants (58%) did not believe that they currently had a problem with their eating. In multivariable analysis, impairment in emotional well-being and self-induced vomiting were the only variables independently associated with self-recognition. Participants who recognized a problem with their eating were more likely to have sought treatment for an eating problem than those who did not. Recognition of disordered eating among adolescents with bulimic-type eating disorders may be poor and this may be a factor in low uptake of mental health care. Health promotion efforts may need to address the misconception that only bulimic-type disorders involving self-induced vomiting are pathological. © 2014 Wiley Publishing Asia Pty Ltd.

Expression, purification, crystallization and X-ray diffraction studies of the molecular chaperone prefoldin from Homo sapiens.

PubMed

Aikawa, Yoshiki; Kida, Hiroshi; Nishitani, Yuichi; Miki, Kunio

2015-09-01

Proper protein folding is an essential process for all organisms. Prefoldin (PFD) is a molecular chaperone that assists protein folding by delivering non-native proteins to group II chaperonin. A heterohexamer of eukaryotic PFD has been shown to specifically recognize and deliver non-native actin and tubulin to chaperonin-containing TCP-1 (CCT), but the mechanism of specific recognition is still unclear. To determine its crystal structure, recombinant human PFD was reconstituted, purified and crystallized. X-ray diffraction data were collected to 4.7 Å resolution. The crystals belonged to space group P21212, with unit-cell parameters a = 123.2, b = 152.4, c = 105.9 Å.

Assessing the Potential of Folded Globular Polyproteins As Hydrogel Building Blocks

PubMed Central

2016-01-01

The native states of proteins generally have stable well-defined folded structures endowing these biomolecules with specific functionality and molecular recognition abilities. Here we explore the potential of using folded globular polyproteins as building blocks for hydrogels. Photochemically cross-linked hydrogels were produced from polyproteins containing either five domains of I27 ((I27)5), protein L ((pL)5), or a 1:1 blend of these proteins. SAXS analysis showed that (I27)5 exists as a single rod-like structure, while (pL)5 shows signatures of self-aggregation in solution. SANS measurements showed that both polyprotein hydrogels have a similar nanoscopic structure, with protein L hydrogels being formed from smaller and more compact clusters. The polyprotein hydrogels showed small energy dissipation in a load/unload cycle, which significantly increased when the hydrogels were formed in the unfolded state. This study demonstrates the use of folded proteins as building blocks in hydrogels, and highlights the potential versatility that can be offered in tuning the mechanical, structural, and functional properties of polyproteins. PMID:28006103

Overview of depression: epidemiology and implications for community nursing practice.

PubMed

Lazarou, Chrystalleni; Kouta, Christiana; Kapsou, Margarita; Kaite, Charis

2011-01-01

Depressive disorders are among the most common psychological conditions currently affecting individuals living in the Westernized world. Yet, available data indicate that fewer than one third of adults with depression obtain appropriate professional treatment. This is attributed, among other reasons, to the under-recognition of the problem by health professionals, including district nurses. In order to improve recognition of the problem, it is imperative for nurses and especially those working in community settings, to appreciate the importance of prompt diagnosis which presumes both an understanding and knowledge of basic aspects of the problem and, an understanding of their role in dealing with depression. This overview presents epidemiological data and identifies the potential consequences of depression on daily functioning and other aspects of life among adults in Westernized countries, aiming to raise awareness and sensitize district nurses about the issue The article discusses how the role of district nurses can be enhanced to improve recognition rates.

A modified active appearance model based on an adaptive artificial bee colony.

PubMed

Abdulameer, Mohammed Hasan; Sheikh Abdullah, Siti Norul Huda; Othman, Zulaiha Ali

2014-01-01

Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition.

Human Activity Recognition from Body Sensor Data using Deep Learning.

PubMed

Hassan, Mohammad Mehedi; Huda, Shamsul; Uddin, Md Zia; Almogren, Ahmad; Alrubaian, Majed

2018-04-16

In recent years, human activity recognition from body sensor data or wearable sensor data has become a considerable research attention from academia and health industry. This research can be useful for various e-health applications such as monitoring elderly and physical impaired people at Smart home to improve their rehabilitation processes. However, it is not easy to accurately and automatically recognize physical human activity through wearable sensors due to the complexity and variety of body activities. In this paper, we address the human activity recognition problem as a classification problem using wearable body sensor data. In particular, we propose to utilize a Deep Belief Network (DBN) model for successful human activity recognition. First, we extract the important initial features from the raw body sensor data. Then, a kernel principal component analysis (KPCA) and linear discriminant analysis (LDA) are performed to further process the features and make them more robust to be useful for fast activity recognition. Finally, the DBN is trained by these features. Various experiments were performed on a real-world wearable sensor dataset to verify the effectiveness of the deep learning algorithm. The results show that the proposed DBN outperformed other algorithms and achieves satisfactory activity recognition performance.

Face sketch recognition based on edge enhancement via deep learning

NASA Astrophysics Data System (ADS)

Xie, Zhenzhu; Yang, Fumeng; Zhang, Yuming; Wu, Congzhong

2017-11-01

In this paper,we address the face sketch recognition problem. Firstly, we utilize the eigenface algorithm to convert a sketch image into a synthesized sketch face image. Subsequently, considering the low-level vision problem in synthesized face sketch image .Super resolution reconstruction algorithm based on CNN(convolutional neural network) is employed to improve the visual effect. To be specific, we uses a lightweight super-resolution structure to learn a residual mapping instead of directly mapping the feature maps from the low-level space to high-level patch representations, which making the networks are easier to optimize and have lower computational complexity. Finally, we adopt LDA(Linear Discriminant Analysis) algorithm to realize face sketch recognition on synthesized face image before super resolution and after respectively. Extensive experiments on the face sketch database(CUFS) from CUHK demonstrate that the recognition rate of SVM(Support Vector Machine) algorithm improves from 65% to 69% and the recognition rate of LDA(Linear Discriminant Analysis) algorithm improves from 69% to 75%.What'more,the synthesized face image after super resolution can not only better describer image details such as hair ,nose and mouth etc, but also improve the recognition accuracy effectively.

Parallel and distributed computation for fault-tolerant object recognition

NASA Technical Reports Server (NTRS)

Wechsler, Harry

1988-01-01

The distributed associative memory (DAM) model is suggested for distributed and fault-tolerant computation as it relates to object recognition tasks. The fault-tolerance is with respect to geometrical distortions (scale and rotation), noisy inputs, occulsion/overlap, and memory faults. An experimental system was developed for fault-tolerant structure recognition which shows the feasibility of such an approach. The approach is futher extended to the problem of multisensory data integration and applied successfully to the recognition of colored polyhedral objects.

Deep kernel learning method for SAR image target recognition

NASA Astrophysics Data System (ADS)

Chen, Xiuyuan; Peng, Xiyuan; Duan, Ran; Li, Junbao

2017-10-01

With the development of deep learning, research on image target recognition has made great progress in recent years. Remote sensing detection urgently requires target recognition for military, geographic, and other scientific research. This paper aims to solve the synthetic aperture radar image target recognition problem by combining deep and kernel learning. The model, which has a multilayer multiple kernel structure, is optimized layer by layer with the parameters of Support Vector Machine and a gradient descent algorithm. This new deep kernel learning method improves accuracy and achieves competitive recognition results compared with other learning methods.

Infrared Ship Classification Using A New Moment Pattern Recognition Concept

NASA Astrophysics Data System (ADS)

Casasent, David; Pauly, John; Fetterly, Donald

1982-03-01

An analysis of the statistics of the moments and the conventional invariant moments shows that the variance of the latter become quite large as the order of the moments and the degree of invariance increases. Moreso, the need to whiten the error volume increases with the order and degree, but so does the computational load associated with computing the whitening operator. We thus advance a new estimation approach to the use of moments in pattern recog-nition that overcomes these problems. This work is supported by experimental verification and demonstration on an infrared ship pattern recognition problem. The computational load associated with our new algorithm is also shown to be very low.

A Taxonomy of 3D Occluded Objects Recognition Techniques

NASA Astrophysics Data System (ADS)

Soleimanizadeh, Shiva; Mohamad, Dzulkifli; Saba, Tanzila; Al-ghamdi, Jarallah Saleh

2016-03-01

The overall performances of object recognition techniques under different condition (e.g., occlusion, viewpoint, and illumination) have been improved significantly in recent years. New applications and hardware are shifted towards digital photography, and digital media. This faces an increase in Internet usage requiring object recognition for certain applications; particularly occulded objects. However occlusion is still an issue unhandled, interlacing the relations between extracted feature points through image, research is going on to develop efficient techniques and easy to use algorithms that would help users to source images; this need to overcome problems and issues regarding occlusion. The aim of this research is to review recognition occluded objects algorithms and figure out their pros and cons to solve the occlusion problem features, which are extracted from occluded object to distinguish objects from other co-existing objects by determining the new techniques, which could differentiate the occluded fragment and sections inside an image.

Interfacial metal and antibody recognition.

PubMed

Zhou, Tongqing; Hamer, Dean H; Hendrickson, Wayne A; Sattentau, Quentin J; Kwong, Peter D

2005-10-11

The unique ligation properties of metal ions are widely exploited by proteins, with approximately one-third of all proteins estimated to be metalloproteins. Although antibodies use various mechanisms for recognition, to our knowledge, none has ever been characterized that uses an interfacial metal. We previously described a family of CD4-reactive antibodies, the archetype being Q425. CD4:Q425 engagement does not interfere with CD4:HIV-1 gp120 envelope glycoprotein binding, but it blocks subsequent steps required for viral entry. Here, we use surface-plasmon resonance to show that Q425 requires calcium for recognition of CD4. Specifically, Q425 binding of calcium resulted in a 55,000-fold enhancement in affinity for CD4. X-ray crystallographic analyses of Q425 in the presence of Ca(2+), Ba(2+), or EDTA revealed an exposed metal-binding site, partially coordinated by five atoms contributed from four antibody complementarity-determining regions. The results suggest that Q425 recognition of CD4 involves direct ligation of antigen by the Q425-held calcium, with calcium binding each ligating atom of CD4 with approximately 1.5 kcal/mol of binding energy. This energetic contribution, which is greater than that from a typical protein atom, demonstrates how interfacial metal ligation can play a unique role in antigen recognition.

Interfacial metal and antibody recognition

PubMed Central

Zhou, Tongqing; Hamer, Dean H.; Hendrickson, Wayne A.; Sattentau, Quentin J.; Kwong, Peter D.

2005-01-01

The unique ligation properties of metal ions are widely exploited by proteins, with approximately one-third of all proteins estimated to be metalloproteins. Although antibodies use various mechanisms for recognition, to our knowledge, none has ever been characterized that uses an interfacial metal. We previously described a family of CD4-reactive antibodies, the archetype being Q425. CD4:Q425 engagement does not interfere with CD4:HIV-1 gp120 envelope glycoprotein binding, but it blocks subsequent steps required for viral entry. Here, we use surface-plasmon resonance to show that Q425 requires calcium for recognition of CD4. Specifically, Q425 binding of calcium resulted in a 55,000-fold enhancement in affinity for CD4. X-ray crystallographic analyses of Q425 in the presence of Ca2+, Ba2+, or EDTA revealed an exposed metal-binding site, partially coordinated by five atoms contributed from four antibody complementarity-determining regions. The results suggest that Q425 recognition of CD4 involves direct ligation of antigen by the Q425-held calcium, with calcium binding each ligating atom of CD4 with ≈1.5 kcal/mol of binding energy. This energetic contribution, which is greater than that from a typical protein atom, demonstrates how interfacial metal ligation can play a unique role in antigen recognition. PMID:16195378

3D face recognition based on multiple keypoint descriptors and sparse representation.

PubMed

Zhang, Lin; Ding, Zhixuan; Li, Hongyu; Shen, Ying; Lu, Jianwei

2014-01-01

Recent years have witnessed a growing interest in developing methods for 3D face recognition. However, 3D scans often suffer from the problems of missing parts, large facial expressions, and occlusions. To be useful in real-world applications, a 3D face recognition approach should be able to handle these challenges. In this paper, we propose a novel general approach to deal with the 3D face recognition problem by making use of multiple keypoint descriptors (MKD) and the sparse representation-based classification (SRC). We call the proposed method 3DMKDSRC for short. Specifically, with 3DMKDSRC, each 3D face scan is represented as a set of descriptor vectors extracted from keypoints by meshSIFT. Descriptor vectors of gallery samples form the gallery dictionary. Given a probe 3D face scan, its descriptors are extracted at first and then its identity can be determined by using a multitask SRC. The proposed 3DMKDSRC approach does not require the pre-alignment between two face scans and is quite robust to the problems of missing data, occlusions and expressions. Its superiority over the other leading 3D face recognition schemes has been corroborated by extensive experiments conducted on three benchmark databases, Bosphorus, GavabDB, and FRGC2.0. The Matlab source code for 3DMKDSRC and the related evaluation results are publicly available at http://sse.tongji.edu.cn/linzhang/3dmkdsrcface/3dmkdsrc.htm.

Folding of viscous sheets and filaments

NASA Astrophysics Data System (ADS)

Skorobogatiy, M.; Mahadevan, L.

2000-12-01

We consider the nonlinear folding behavior of a viscous filament or a sheet under the influence of an external force such as gravity. Everyday examples of this phenomenon are provided by the periodic folding of a sheet of honey as it impinges on toast, or the folding of a stream of shampoo as it falls on one's hand. To understand the evolution of a fold, we formulate and solve a free-boundary problem for the phenomenon, give scaling laws for the size of the folds and the frequency with which they are laid out, and verify these experimentally.

Enriching the annotation of Mycobacterium tuberculosis H37Rv proteome using remote homology detection approaches: insights into structure and function.

PubMed

Ramakrishnan, Gayatri; Ochoa-Montaño, Bernardo; Raghavender, Upadhyayula S; Mudgal, Richa; Joshi, Adwait G; Chandra, Nagasuma R; Sowdhamini, Ramanathan; Blundell, Tom L; Srinivasan, Narayanaswamy

2015-01-01

The availability of the genome sequence of Mycobacterium tuberculosis H37Rv has encouraged determination of large numbers of protein structures and detailed definition of the biological information encoded therein; yet, the functions of many proteins in M. tuberculosis remain unknown. The emergence of multidrug resistant strains makes it a priority to exploit recent advances in homology recognition and structure prediction to re-analyse its gene products. Here we report the structural and functional characterization of gene products encoded in the M. tuberculosis genome, with the help of sensitive profile-based remote homology search and fold recognition algorithms resulting in an enhanced annotation of the proteome where 95% of the M. tuberculosis proteins were identified wholly or partly with information on structure or function. New information includes association of 244 proteins with 205 domain families and a separate set of new association of folds to 64 proteins. Extending structural information across uncharacterized protein families represented in the M. tuberculosis proteome, by determining superfamily relationships between families of known and unknown structures, has contributed to an enhancement in the knowledge of structural content. In retrospect, such superfamily relationships have facilitated recognition of probable structure and/or function for several uncharacterized protein families, eventually aiding recognition of probable functions for homologous proteins corresponding to such families. Gene products unique to mycobacteria for which no functions could be identified are 183. Of these 18 were determined to be M. tuberculosis specific. Such pathogen-specific proteins are speculated to harbour virulence factors required for pathogenesis. A re-annotated proteome of M. tuberculosis, with greater completeness of annotated proteins and domain assigned regions, provides a valuable basis for experimental endeavours designed to obtain a better understanding of pathogenesis and to accelerate the process of drug target discovery. Copyright © 2014 Elsevier Ltd. All rights reserved.

Molecular recognition features (MoRFs) in three domains of life.

PubMed

Yan, Jing; Dunker, A Keith; Uversky, Vladimir N; Kurgan, Lukasz

2016-03-01

Intrinsically disordered proteins and protein regions offer numerous advantages in the context of protein-protein interactions when compared to the structured proteins and domains. These advantages include ability to interact with multiple partners, to fold into different conformations when bound to different partners, and to undergo disorder-to-order transitions concomitant with their functional activity. Molecular recognition features (MoRFs) are widespread elements located in disordered regions that undergo disorder-to-order transition upon binding to their protein partners. We characterize abundance, composition, and functions of MoRFs and their association with the disordered regions across 868 species spread across Eukaryota, Bacteria and Archaea. We found that although disorder is substantially elevated in Eukaryota, MoRFs have similar abundance and amino acid composition across the three domains of life. The abundance of MoRFs is highly correlated with the amount of intrinsic disorder in Bacteria and Archaea but only modestly correlated in Eukaryota. Proteins with MoRFs have significantly more disorder and MoRFs are present in many disordered regions, with Eukaryota having more MoRF-free disordered regions. MoRF-containing proteins are enriched in the ribosome, nucleus, nucleolus and microtubule and are involved in translation, protein transport, protein folding, and interactions with DNAs. Our insights into the nature and function of MoRFs enhance our understanding of the mechanisms underlying the disorder-to-order transition and protein-protein recognition and interactions. The fMoRFpred method that we used to annotate MoRFs is available at http://biomine.ece.ualberta.ca/fMoRFpred/.

Natural triple beta-stranded fibrous folds.

PubMed

Mitraki, Anna; Papanikolopoulou, Katerina; Van Raaij, Mark J

2006-01-01

A distinctive family of beta-structured folds has recently been described for fibrous proteins from viruses. Virus fibers are usually involved in specific host-cell recognition. They are asymmetric homotrimeric proteins consisting of an N-terminal virus-binding tail, a central shaft or stalk domain, and a C-terminal globular receptor-binding domain. Often they are entirely or nearly entirely composed of beta-structure. Apart from their biological relevance and possible gene therapy applications, their shape, stability, and rigidity suggest they may be useful as blueprints for biomechanical design. Folding and unfolding studies suggest their globular C-terminal domain may fold first, followed by a "zipping-up" of the shaft domains. The C-terminal domains appear to be important for registration because peptides corresponding to shaft domains alone aggregate into nonnative fibers and/or amyloid structures. C-terminal domains can be exchanged between different fibers and the resulting chimeric proteins are useful as a way to solve structures of unknown parts of the shaft domains. The following natural triple beta-stranded fibrous folds have been discovered by X-ray crystallography: the triple beta-spiral, triple beta-helix, and T4 short tail fiber fold. All have a central longitudinal hydrophobic core and extensive intermonomer polar and nonpolar interactions. Now that a reasonable body of structural and folding knowledge has been assembled about these fibrous proteins, the next challenge and opportunity is to start using this information in medical and industrial applications such as gene therapy and nanotechnology.

Recent developments in the theory of protein folding: searching for the global energy minimum.

PubMed

Scheraga, H A

1996-04-16

Statistical mechanical theories and computer simulation are being used to gain an understanding of the fundamental features of protein folding. A major obstacle in the computation of protein structures is the multiple-minima problem arising from the existence of many local minima in the multidimensional energy landscape of the protein. This problem has been surmounted for small open-chain and cyclic peptides, and for regular-repeating sequences of models of fibrous proteins. Progress is being made in resolving this problem for globular proteins.

Effects of health and safety problem recognition on small business facility investment

PubMed Central

2013-01-01

Objectives This study involved a survey of the facility investment experiences, which was designed to recognize the importance of health and safety problems, and industrial accident prevention. Ultimately, we hope that small scale industries will create effective industrial accident prevention programs and facility investments. Methods An individual survey of businesses’ present physical conditions, recognition of the importance of the health and safety problems, and facility investment experiences for preventing industrial accidents was conducted. The survey involved 1,145 business operators or management workers in small business places with fewer than 50 workers in six industrial complexes. Results Regarding the importance of occupational health and safety problems (OHS), 54.1% said it was “very important”. Received technical and financial support, and industrial accidents that occurred during the past three years were recognized as highly important for OHS. In an investigation regarding facility investment experiences for industrial accident prevention, the largest factors were business size, greater numbers of industrial accidents, greater technical and financial support received, and greater recognition of the importance of the OHS. The related variables that decided facility investment for industry accident prevention in a logistic regression analysis were the experiences of business facilities where industrial accidents occurred during the past three years, received technical and financial support, and recognition of the OHS. Those considered very important were shown to be highly significant. Conclusions Recognition of health and safety issues was higher when small businesses had experienced industrial accidents or received financial support. The investment in industrial accidents was greater when health and safety issues were recognized as important. Therefore, the goal of small business health and safety projects is to prioritize health and safety issues in terms of business management and recognition of importance. Therefore, currently various support projects are being conducted. However, there are issues regarding the limitations of the target businesses and inadequacies in maintenance and follow-up. Overall, it is necessary to provide various incentives for onsite participation that can lead to increased recognition of health and safety issues and practical investments, while perfecting maintenance and follow up measures by thoroughly revising existing operating systems. PMID:24472180

Effects of health and safety problem recognition on small business facility investment.

PubMed

Park, Jisu; Jeong, Harin; Hong, Sujin; Park, Jong-Tae; Kim, Dae-Sung; Kim, Jongseo; Kim, Hae-Joon

2013-10-23

This study involved a survey of the facility investment experiences, which was designed to recognize the importance of health and safety problems, and industrial accident prevention. Ultimately, we hope that small scale industries will create effective industrial accident prevention programs and facility investments. An individual survey of businesses' present physical conditions, recognition of the importance of the health and safety problems, and facility investment experiences for preventing industrial accidents was conducted. The survey involved 1,145 business operators or management workers in small business places with fewer than 50 workers in six industrial complexes. Regarding the importance of occupational health and safety problems (OHS), 54.1% said it was "very important". Received technical and financial support, and industrial accidents that occurred during the past three years were recognized as highly important for OHS. In an investigation regarding facility investment experiences for industrial accident prevention, the largest factors were business size, greater numbers of industrial accidents, greater technical and financial support received, and greater recognition of the importance of the OHS. The related variables that decided facility investment for industry accident prevention in a logistic regression analysis were the experiences of business facilities where industrial accidents occurred during the past three years, received technical and financial support, and recognition of the OHS. Those considered very important were shown to be highly significant. Recognition of health and safety issues was higher when small businesses had experienced industrial accidents or received financial support. The investment in industrial accidents was greater when health and safety issues were recognized as important. Therefore, the goal of small business health and safety projects is to prioritize health and safety issues in terms of business management and recognition of importance. Therefore, currently various support projects are being conducted. However, there are issues regarding the limitations of the target businesses and inadequacies in maintenance and follow-up. Overall, it is necessary to provide various incentives for onsite participation that can lead to increased recognition of health and safety issues and practical investments, while perfecting maintenance and follow up measures by thoroughly revising existing operating systems.

The PYRIN domain: A member of the death domain-fold superfamily

PubMed Central

Fairbrother, Wayne J.; Gordon, Nathaniel C.; Humke, Eric W.; O'Rourke, Karen M.; Starovasnik, Melissa A.; Yin, Jian-Ping; Dixit, Vishva M.

2001-01-01

PYRIN domains were identified recently as putative protein–protein interaction domains at the N-termini of several proteins thought to function in apoptotic and inflammatory signaling pathways. The ∼95 residue PYRIN domains have no statistically significant sequence homology to proteins with known three-dimensional structure. Using secondary structure prediction and potential-based fold recognition methods, however, the PYRIN domain is predicted to be a member of the six-helix bundle death domain-fold superfamily that includes death domains (DDs), death effector domains (DEDs), and caspase recruitment domains (CARDs). Members of the death domain-fold superfamily are well established mediators of protein–protein interactions found in many proteins involved in apoptosis and inflammation, indicating further that the PYRIN domains serve a similar function. An homology model of the PYRIN domain of CARD7/DEFCAP/NAC/NALP1, a member of the Apaf-1/Ced-4 family of proteins, was constructed using the three-dimensional structures of the FADD and p75 neurotrophin receptor DDs, and of the Apaf-1 and caspase-9 CARDs, as templates. Validation of the model using a variety of computational techniques indicates that the fold prediction is consistent with the sequence. Comparison of a circular dichroism spectrum of the PYRIN domain of CARD7/DEFCAP/NAC/NALP1 with spectra of several proteins known to adopt the death domain-fold provides experimental support for the structure prediction. PMID:11514682

Recognition of handprinted characters for automated cartography A progress report

NASA Technical Reports Server (NTRS)

Lybanon, M.; Brown, R. M.; Gronmeyer, L. K.

1980-01-01

A research program for developing handwritten character recognition techniques is reported. The generation of cartographic/hydrographic manuscripts is overviewed. The performance of hardware/software systems is discussed, along with future research problem areas and planned approaches.

Recognition of medication information from discharge summaries using ensembles of classifiers.

PubMed

Doan, Son; Collier, Nigel; Xu, Hua; Pham, Hoang Duy; Tu, Minh Phuong

2012-05-07

Extraction of clinical information such as medications or problems from clinical text is an important task of clinical natural language processing (NLP). Rule-based methods are often used in clinical NLP systems because they are easy to adapt and customize. Recently, supervised machine learning methods have proven to be effective in clinical NLP as well. However, combining different classifiers to further improve the performance of clinical entity recognition systems has not been investigated extensively. Combining classifiers into an ensemble classifier presents both challenges and opportunities to improve performance in such NLP tasks. We investigated ensemble classifiers that used different voting strategies to combine outputs from three individual classifiers: a rule-based system, a support vector machine (SVM) based system, and a conditional random field (CRF) based system. Three voting methods were proposed and evaluated using the annotated data sets from the 2009 i2b2 NLP challenge: simple majority, local SVM-based voting, and local CRF-based voting. Evaluation on 268 manually annotated discharge summaries from the i2b2 challenge showed that the local CRF-based voting method achieved the best F-score of 90.84% (94.11% Precision, 87.81% Recall) for 10-fold cross-validation. We then compared our systems with the first-ranked system in the challenge by using the same training and test sets. Our system based on majority voting achieved a better F-score of 89.65% (93.91% Precision, 85.76% Recall) than the previously reported F-score of 89.19% (93.78% Precision, 85.03% Recall) by the first-ranked system in the challenge. Our experimental results using the 2009 i2b2 challenge datasets showed that ensemble classifiers that combine individual classifiers into a voting system could achieve better performance than a single classifier in recognizing medication information from clinical text. It suggests that simple strategies that can be easily implemented such as majority voting could have the potential to significantly improve clinical entity recognition.

Folding of the RNA Recognition Motif (RRM) Domains of the Amyotrophic Lateral Sclerosis (ALS)-linked Protein TDP-43 Reveals an Intermediate State*

PubMed Central

Mackness, Brian C.; Tran, Meme T.; McClain, Shannan P.; Matthews, C. Robert; Zitzewitz, Jill A.

2014-01-01

Pathological alteration of TDP-43 (TAR DNA-binding protein-43), a protein involved in various RNA-mediated processes, is a hallmark feature of the neurodegenerative diseases amyotrophic lateral sclerosis and frontotemporal lobar degeneration. Fragments of TDP-43, composed of the second RNA recognition motif (RRM2) and the disordered C terminus, have been observed in cytoplasmic inclusions in sporadic amyotrophic lateral sclerosis cases, suggesting that conformational changes involving RRM2 together with the disordered C terminus play a role in aggregation and toxicity. The biophysical data collected by CD and fluorescence spectroscopies reveal a three-state equilibrium unfolding model for RRM2, with a partially folded intermediate state that is not observed in RRM1. Strikingly, a portion of RRM2 beginning at position 208, which mimics a cleavage site observed in patient tissues, increases the population of this intermediate state. Mutually stabilizing interactions between the domains in the tethered RRM1 and RRM2 construct reduce the population of the intermediate state and enhance DNA/RNA binding. Despite the high sequence homology of the two domains, a network of large hydrophobic residues in RRM2 provides a possible explanation for the increased stability of RRM2 compared with RRM1. The cluster analysis suggests that the intermediate state may play a functional role by enhancing access to the nuclear export signal contained within its sequence. The intermediate state may also serve as a molecular hazard linking productive folding and function with pathological misfolding and aggregation that may contribute to disease. PMID:24497641

Single molecule RNA folding studied with optical trapping

NASA Astrophysics Data System (ADS)

Vieregg, Jeffrey Robert

The RNA folding problem (predicting the equilibrium structure and folding pathway of an RNA molecule from its sequence) is one of the classic problems of biophysics. Recent discoveries of many new functions for RNA have increased its importance, and new instrumental techniques have provided new ways to characterize molecular behavior. In particular, optical trapping (optical tweezers) allows controlled mechanical force to be applied to single RNA molecules while their end-to-end extension is monitored in real time. This enables characterization of RNA folding dynamics at a level unreachable by traditional bulk methods. Furthermore, recent advances in statistical mechanics make it possible to recover equilibrium quantities such as free energy from reactions which occur away from equilibrium. This dissertation describes the application of optical trapping and non-equilibrium statistical mechanics to quantitatively characterize folding of RNA secondary structures. By measuring the folding free energy of several specially designed hairpins in solutions containing various amounts of sodium and potassium, we were able to determine that RNA secondary structure thermodynamics depends not only on monovalent cation concentration but also surprisingly, on species. We also investigated the temperature dependence of hairpin folding thermodynamics and kinetics, which provided a direct measurement of enthalpy and entropy for RNA folding at physiological temperatures. We found that the folding pathway was quite sensitive to both salt and temperature, as measured by the folding success rate of a biologically important hairpin from the HIV-1 viral genome. Finally, I discuss modeling of force-induced RNA folding and unfolding, as well as a series of efforts which have dramatically improved the performance of our optical trapping instrument.

Content analysis in information flows

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

Grusho, Alexander A.; Faculty of Computational Mathematics and Cybernetics, Moscow State University, Moscow; Grusho, Nick A.

The paper deals with architecture of content recognition system. To analyze the problem the stochastic model of content recognition in information flows was built. We proved that under certain conditions it is possible to solve correctly a part of the problem with probability 1, viewing a finite section of the information flow. That means that good architecture consists of two steps. The first step determines correctly certain subsets of contents, while the second step may demand much more time for true decision.

Domain Regeneration for Cross-Database Micro-Expression Recognition

NASA Astrophysics Data System (ADS)

Zong, Yuan; Zheng, Wenming; Huang, Xiaohua; Shi, Jingang; Cui, Zhen; Zhao, Guoying

2018-05-01

In this paper, we investigate the cross-database micro-expression recognition problem, where the training and testing samples are from two different micro-expression databases. Under this setting, the training and testing samples would have different feature distributions and hence the performance of most existing micro-expression recognition methods may decrease greatly. To solve this problem, we propose a simple yet effective method called Target Sample Re-Generator (TSRG) in this paper. By using TSRG, we are able to re-generate the samples from target micro-expression database and the re-generated target samples would share same or similar feature distributions with the original source samples. For this reason, we can then use the classifier learned based on the labeled source samples to accurately predict the micro-expression categories of the unlabeled target samples. To evaluate the performance of the proposed TSRG method, extensive cross-database micro-expression recognition experiments designed based on SMIC and CASME II databases are conducted. Compared with recent state-of-the-art cross-database emotion recognition methods, the proposed TSRG achieves more promising results.

Emotional System for Military Target Identification

DTIC Science & Technology

2009-10-01

algorithm [23], and used it to solve a facial recognition problem. In other works [24,25], we explored the potential of using emotional neural...other application areas, such as security ( facial recognition ) and medical (blood cell identification), can be also efficiently used in military...Application of an emotional neural network to facial recognition . Neural Computing and Applications, 18(4), 309-320. [25] Khashman, A. (2009). Blood cell

Interactive object recognition assistance: an approach to recognition starting from target objects

NASA Astrophysics Data System (ADS)

Geisler, Juergen; Littfass, Michael

1999-07-01

Recognition of target objects in remotely sensed imagery required detailed knowledge about the target object domain as well as about mapping properties of the sensing system. The art of object recognition is to combine both worlds appropriately and to provide models of target appearance with respect to sensor characteristics. Common approaches to support interactive object recognition are either driven from the sensor point of view and address the problem of displaying images in a manner adequate to the sensing system. Or they focus on target objects and provide exhaustive encyclopedic information about this domain. Our paper discusses an approach to assist interactive object recognition based on knowledge about target objects and taking into account the significance of object features with respect to characteristics of the sensed imagery, e.g. spatial and spectral resolution. An `interactive recognition assistant' takes the image analyst through the interpretation process by indicating step-by-step the respectively most significant features of objects in an actual set of candidates. The significance of object features is expressed by pregenerated trees of significance, and by the dynamic computation of decision relevance for every feature at each step of the recognition process. In the context of this approach we discuss the question of modeling and storing the multisensorial/multispectral appearances of target objects and object classes as well as the problem of an adequate dynamic human-machine-interface that takes into account various mental models of human image interpretation.

Emotion recognition in girls with conduct problems.

PubMed

Schwenck, Christina; Gensthaler, Angelika; Romanos, Marcel; Freitag, Christine M; Schneider, Wolfgang; Taurines, Regina

2014-01-01

A deficit in emotion recognition has been suggested to underlie conduct problems. Although several studies have been conducted on this topic so far, most concentrated on male participants. The aim of the current study was to compare recognition of morphed emotional faces in girls with conduct problems (CP) with elevated or low callous-unemotional (CU+ vs. CU-) traits and a matched healthy developing control group (CG). Sixteen girls with CP-CU+, 16 girls with CP-CU- and 32 controls (mean age: 13.23 years, SD=2.33 years) were included. Video clips with morphed faces were presented in two runs to assess emotion recognition. Multivariate analysis of variance with the factors group and run was performed. Girls with CP-CU- needed more time than the CG to encode sad, fearful, and happy faces and they correctly identified sadness less often. Girls with CP-CU+ outperformed the other groups in the identification of fear. Learning effects throughout runs were the same for all groups except that girls with CP-CU- correctly identified fear less often in the second run compared to the first run. Results need to be replicated with comparable tasks, which might result in subgroup-specific therapeutic recommendations.

An immersed-boundary method for flow–structure interaction in biological systems with application to phonation

PubMed Central

Luo, Haoxiang; Mittal, Rajat; Zheng, Xudong; Bielamowicz, Steven A.; Walsh, Raymond J.; Hahn, James K.

2008-01-01

A new numerical approach for modeling a class of flow–structure interaction problems typically encountered in biological systems is presented. In this approach, a previously developed, sharp-interface, immersed-boundary method for incompressible flows is used to model the fluid flow and a new, sharp-interface Cartesian grid, immersed boundary method is devised to solve the equations of linear viscoelasticity that governs the solid. The two solvers are coupled to model flow–structure interaction. This coupled solver has the advantage of simple grid generation and efficient computation on simple, single-block structured grids. The accuracy of the solid-mechanics solver is examined by applying it to a canonical problem. The solution methodology is then applied to the problem of laryngeal aerodynamics and vocal fold vibration during human phonation. This includes a three-dimensional eigen analysis for a multi-layered vocal fold prototype as well as two-dimensional, flow-induced vocal fold vibration in a modeled larynx. Several salient features of the aerodynamics as well as vocal-fold dynamics are presented. PMID:19936017

Folding and Stabilization of Native-Sequence-Reversed Proteins

PubMed Central

Zhang, Yuanzhao; Weber, Jeffrey K; Zhou, Ruhong

2016-01-01

Though the problem of sequence-reversed protein folding is largely unexplored, one might speculate that reversed native protein sequences should be significantly more foldable than purely random heteropolymer sequences. In this article, we investigate how the reverse-sequences of native proteins might fold by examining a series of small proteins of increasing structural complexity (α-helix, β-hairpin, α-helix bundle, and α/β-protein). Employing a tandem protein structure prediction algorithmic and molecular dynamics simulation approach, we find that the ability of reverse sequences to adopt native-like folds is strongly influenced by protein size and the flexibility of the native hydrophobic core. For β-hairpins with reverse-sequences that fail to fold, we employ a simple mutational strategy for guiding stable hairpin formation that involves the insertion of amino acids into the β-turn region. This systematic look at reverse sequence duality sheds new light on the problem of protein sequence-structure mapping and may serve to inspire new protein design and protein structure prediction protocols. PMID:27113844

Folding and Stabilization of Native-Sequence-Reversed Proteins

NASA Astrophysics Data System (ADS)

Zhang, Yuanzhao; Weber, Jeffrey K.; Zhou, Ruhong

2016-04-01

Though the problem of sequence-reversed protein folding is largely unexplored, one might speculate that reversed native protein sequences should be significantly more foldable than purely random heteropolymer sequences. In this article, we investigate how the reverse-sequences of native proteins might fold by examining a series of small proteins of increasing structural complexity (α-helix, β-hairpin, α-helix bundle, and α/β-protein). Employing a tandem protein structure prediction algorithmic and molecular dynamics simulation approach, we find that the ability of reverse sequences to adopt native-like folds is strongly influenced by protein size and the flexibility of the native hydrophobic core. For β-hairpins with reverse-sequences that fail to fold, we employ a simple mutational strategy for guiding stable hairpin formation that involves the insertion of amino acids into the β-turn region. This systematic look at reverse sequence duality sheds new light on the problem of protein sequence-structure mapping and may serve to inspire new protein design and protein structure prediction protocols.

Folding mechanism of β-hairpin trpzip2: heterogeneity, transition state and folding pathways.

PubMed

Xiao, Yi; Chen, Changjun; He, Yi

2009-06-22

We review the studies on the folding mechanism of the beta-hairpin tryptophan zipper 2 (trpzip2) and present some additional computational results to refine the picture of folding heterogeneity and pathways. We show that trpzip2 can have a two-state or a multi-state folding pattern, depending on whether it folds within the native basin or through local state basins on the high-dimensional free energy surface; Trpzip2 can fold along different pathways according to the packing order of tryptophan pairs. We also point out some important problems related to the folding mechanism of trpzip2 that still need clarification, e.g., a wide distribution of the computed conformations for the transition state ensemble.

Naringin Improves Neuronal Insulin Signaling, Brain Mitochondrial Function, and Cognitive Function in High-Fat Diet-Induced Obese Mice.

PubMed

Wang, Dongmei; Yan, Junqiang; Chen, Jing; Wu, Wenlan; Zhu, Xiaoying; Wang, Yong

2015-10-01

The epidemic and experimental studies have confirmed that the obesity induced by high-fat diet not only caused neuronal insulin resistance, but also induced brain mitochondrial dysfunction as well as learning impairment in mice. Naringin has been reported to posses biological functions which are beneficial to human cognitions, but its protective effects on HFD-induced cognitive deficits and underlying mechanisms have not been well characterized. In the present study Male C57BL/6 J mice were fed either a control or high-fat diet for 20 weeks and then randomized into four groups treated with their respective diets including control diet, control diet + naringin, high-fat diet (HFD), and high-fat diet + naringin (HFDN). The behavioral performance was assessed by using novel object recognition test and Morris water maze test. Hippocampal mitochondrial parameters were analyzed. Then the protein levels of insulin signaling pathway and the AMP-activated protein kinase (AMPK) in the hippocampus were detected by Western blot method. Our results showed that oral administration of naringin significantly improved the learning and memory abilities as evidenced by increasing recognition index by 52.5% in the novel object recognition test and inducing a 1.05-fold increase in the crossing-target number in the probe test, and ameliorated mitochondrial dysfunction in mice caused by HFD consumption. Moreover, naringin significantly enhanced insulin signaling pathway as indicated by a 34.5% increase in the expression levels of IRS-1, a 47.8% decrease in the p-IRS-1, a 1.43-fold increase in the p-Akt, and a 1.89-fold increase in the p-GSK-3β in the hippocampus of the HFDN mice versus HFD mice. Furthermore, the AMPK activity significantly increased in the naringin-treated (100 mg kg(-1) d(-1)) group. These findings suggest that an enhancement in insulin signaling and a decrease in mitochondrial dysfunction through the activation of AMPK may be one of the mechanisms that naringin improves cognitive functions in HFD-induced obese mice.

A model of traffic signs recognition with convolutional neural network

NASA Astrophysics Data System (ADS)

Hu, Haihe; Li, Yujian; Zhang, Ting; Huo, Yi; Kuang, Wenqing

2016-10-01

In real traffic scenes, the quality of captured images are generally low due to some factors such as lighting conditions, and occlusion on. All of these factors are challengeable for automated recognition algorithms of traffic signs. Deep learning has provided a new way to solve this kind of problems recently. The deep network can automatically learn features from a large number of data samples and obtain an excellent recognition performance. We therefore approach this task of recognition of traffic signs as a general vision problem, with few assumptions related to road signs. We propose a model of Convolutional Neural Network (CNN) and apply the model to the task of traffic signs recognition. The proposed model adopts deep CNN as the supervised learning model, directly takes the collected traffic signs image as the input, alternates the convolutional layer and subsampling layer, and automatically extracts the features for the recognition of the traffic signs images. The proposed model includes an input layer, three convolutional layers, three subsampling layers, a fully-connected layer, and an output layer. To validate the proposed model, the experiments are implemented using the public dataset of China competition of fuzzy image processing. Experimental results show that the proposed model produces a recognition accuracy of 99.01 % on the training dataset, and yield a record of 92% on the preliminary contest within the fourth best.

Can we match ultraviolet face images against their visible counterparts?

NASA Astrophysics Data System (ADS)

Narang, Neeru; Bourlai, Thirimachos; Hornak, Lawrence A.

2015-05-01

In law enforcement and security applications, the acquisition of face images is critical in producing key trace evidence for the successful identification of potential threats. However, face recognition (FR) for face images captured using different camera sensors, and under variable illumination conditions, and expressions is very challenging. In this paper, we investigate the advantages and limitations of the heterogeneous problem of matching ultra violet (from 100 nm to 400 nm in wavelength) or UV, face images against their visible (VIS) counterparts, when all face images are captured under controlled conditions. The contributions of our work are three-fold; (i) We used a camera sensor designed with the capability to acquire UV images at short-ranges, and generated a dual-band (VIS and UV) database that is composed of multiple, full frontal, face images of 50 subjects. Two sessions were collected that span over the period of 2 months. (ii) For each dataset, we determined which set of face image pre-processing algorithms are more suitable for face matching, and, finally, (iii) we determined which FR algorithm better matches cross-band face images, resulting in high rank-1 identification rates. Experimental results show that our cross spectral matching (the heterogeneous problem, where gallery and probe sets consist of face images acquired in different spectral bands) algorithms achieve sufficient identification performance. However, we also conclude that the problem under study, is very challenging, and it requires further investigation to address real-world law enforcement or military applications. To the best of our knowledge, this is first time in the open literature the problem of cross-spectral matching of UV against VIS band face images is being investigated.

Data-driven indexing mechanism for the recognition of polyhedral objects

NASA Astrophysics Data System (ADS)

McLean, Stewart; Horan, Peter; Caelli, Terry M.

1992-02-01

This paper is concerned with the problem of searching large model databases. To date, most object recognition systems have concentrated on the problem of matching using simple searching algorithms. This is quite acceptable when the number of object models is small. However, in the future, general purpose computer vision systems will be required to recognize hundreds or perhaps thousands of objects and, in such circumstances, efficient searching algorithms will be needed. The problem of searching a large model database is one which must be addressed if future computer vision systems are to be at all effective. In this paper we present a method we call data-driven feature-indexed hypothesis generation as one solution to the problem of searching large model databases.

Object recognition of real targets using modelled SAR images

NASA Astrophysics Data System (ADS)

Zherdev, D. A.

2017-12-01

In this work the problem of recognition is studied using SAR images. The algorithm of recognition is based on the computation of conjugation indices with vectors of class. The support subspaces for each class are constructed by exception of the most and the less correlated vectors in a class. In the study we examine the ability of a significant feature vector size reduce that leads to recognition time decrease. The images of targets form the feature vectors that are transformed using pre-trained convolutional neural network (CNN).

Gait recognition based on integral outline

NASA Astrophysics Data System (ADS)

Ming, Guan; Fang, Lv

2017-02-01

Biometric identification technology replaces traditional security technology, which has become a trend, and gait recognition also has become a hot spot of research because its feature is difficult to imitate and theft. This paper presents a gait recognition system based on integral outline of human body. The system has three important aspects: the preprocessing of gait image, feature extraction and classification. Finally, using a method of polling to evaluate the performance of the system, and summarizing the problems existing in the gait recognition and the direction of development in the future.

Sediment problems in urban areas

USGS Publications Warehouse

Guy, Harold P.

1970-01-01

One obstacle to a scientific recognition and an engineering solution to sediment-related environmental problems is that such problems are bound in conflicting and generally undefinable political and institutional restraints. Also, some of the difficulty may involve the fact that the scientist or engineer, because of his relatively narrow field of investigation, cannot always completely envision the less desirable effects of his work and communicate alternative solutions to the public. For example, the highway and motor-vehicle engineers have learned how to provide the means by which one can transport himself from one point to another with such great efficiency that a person's employment in this country is now commonly more than 5 miles from his residence. However, providing such efficient personal transport has created numerous serious environmental problems. Obstacles to recognition of and action to control sediment problems in and around urban areas are akin to other environmental problems with respect to the many scientific, engineering, economic, and social aspects.

Crosslinking transcription factors to their recognition sequences with PtII complexes

NASA Technical Reports Server (NTRS)

Chu, B. C.; Orgel, L. E.

1992-01-01

We have prepared phosphorothioate-containing cyclic oligodeoxynucleotides that fold into 'dumbbells' containing CRE and TRE sequences, the binding sequences for the CREB and JUN proteins, respectively. Six phosphorothioate residues were introduced into each of the recognition sequences. K2PtCl4 crosslinks CRE to CREB and TRE to JUN. The extent of crosslinking is about eight times greater than that observed with standard oligodeoxynucleotides and amounts to 30-50% of the efficiency of non-covalent association as estimated by gel-shift assays. Crosslinking is reversed by incubation with NaCN. The crosslinking reaction is specific--a dumbbell oligonucleotide with six phosphorothioate groups introduced into the Sp1 recognition sequence could not be crosslinked efficiently to CREB or JUN proteins with K2PtCl4. The binding of TRE to CREB is not strong enough for effective detection by gel-shift assays, but the TRE-CREB complex is crosslinked efficiently by K2PtCl4 and can then readily be detected.

Evolutionary plasticity of the NHL domain underlies distinct solutions to RNA recognition.

PubMed

Kumari, Pooja; Aeschimann, Florian; Gaidatzis, Dimos; Keusch, Jeremy J; Ghosh, Pritha; Neagu, Anca; Pachulska-Wieczorek, Katarzyna; Bujnicki, Janusz M; Gut, Heinz; Großhans, Helge; Ciosk, Rafal

2018-04-19

RNA-binding proteins regulate all aspects of RNA metabolism. Their association with RNA is mediated by RNA-binding domains, of which many remain uncharacterized. A recently reported example is the NHL domain, found in prominent regulators of cellular plasticity like the C. elegans LIN-41. Here we employ an integrative approach to dissect the RNA specificity of LIN-41. Using computational analysis, structural biology, and in vivo studies in worms and human cells, we find that a positively charged pocket, specific to the NHL domain of LIN-41 and its homologs (collectively LIN41), recognizes a stem-loop RNA element, whose shape determines the binding specificity. Surprisingly, the mechanism of RNA recognition by LIN41 is drastically different from that of its more distant relative, the fly Brat. Our phylogenetic analysis suggests that this reflects a rapid evolution of the domain, presenting an interesting example of a conserved protein fold that acquired completely different solutions to RNA recognition.

Robust Radio Broadcast Monitoring Using a Multi-Band Spectral Entropy Signature

NASA Astrophysics Data System (ADS)

Camarena-Ibarrola, Antonio; Chávez, Edgar; Tellez, Eric Sadit

Monitoring media broadcast content has deserved a lot of attention lately from both academy and industry due to the technical challenge involved and its economic importance (e.g. in advertising). The problem pose a unique challenge from the pattern recognition point of view because a very high recognition rate is needed under non ideal conditions. The problem consist in comparing a small audio sequence (the commercial ad) with a large audio stream (the broadcast) searching for matches.

Degraded character recognition based on gradient pattern

NASA Astrophysics Data System (ADS)

Babu, D. R. Ramesh; Ravishankar, M.; Kumar, Manish; Wadera, Kevin; Raj, Aakash

2010-02-01

Degraded character recognition is a challenging problem in the field of Optical Character Recognition (OCR). The performance of an optical character recognition depends upon printed quality of the input documents. Many OCRs have been designed which correctly identifies the fine printed documents. But, very few reported work has been found on the recognition of the degraded documents. The efficiency of the OCRs system decreases if the input image is degraded. In this paper, a novel approach based on gradient pattern for recognizing degraded printed character is proposed. The approach makes use of gradient pattern of an individual character for recognition. Experiments were conducted on character image that is either digitally written or a degraded character extracted from historical documents and the results are found to be satisfactory.

Combinatorial approaches to gene recognition.

PubMed

Roytberg, M A; Astakhova, T V; Gelfand, M S

1997-01-01

Recognition of genes via exon assembly approaches leads naturally to the use of dynamic programming. We consider the general graph-theoretical formulation of the exon assembly problem and analyze in detail some specific variants: multicriterial optimization in the case of non-linear gene-scoring functions; context-dependent schemes for scoring exons and related procedures for exon filtering; and highly specific recognition of arbitrary gene segments, oligonucleotide probes and polymerase chain reaction (PCR) primers.

Methods and means of diagnostics of oncological diseases on the basis of pattern recognition: intelligent morphological systems - problems and solutions

NASA Astrophysics Data System (ADS)

Nikitaev, V. G.

2017-01-01

The development of methods of pattern recognition in modern intelligent systems of clinical cancer diagnosis are discussed. The histological (morphological) diagnosis - primary diagnosis for medical setting with cancer are investigated. There are proposed: interactive methods of recognition and structure of intellectual morphological complexes based on expert training-diagnostic and telemedicine systems. The proposed approach successfully implemented in clinical practice.

Does the cost function matter in Bayes decision rule?

PubMed

Schlü ter, Ralf; Nussbaum-Thom, Markus; Ney, Hermann

2012-02-01

In many tasks in pattern recognition, such as automatic speech recognition (ASR), optical character recognition (OCR), part-of-speech (POS) tagging, and other string recognition tasks, we are faced with a well-known inconsistency: The Bayes decision rule is usually used to minimize string (symbol sequence) error, whereas, in practice, we want to minimize symbol (word, character, tag, etc.) error. When comparing different recognition systems, we do indeed use symbol error rate as an evaluation measure. The topic of this work is to analyze the relation between string (i.e., 0-1) and symbol error (i.e., metric, integer valued) cost functions in the Bayes decision rule, for which fundamental analytic results are derived. Simple conditions are derived for which the Bayes decision rule with integer-valued metric cost function and with 0-1 cost gives the same decisions or leads to classes with limited cost. The corresponding conditions can be tested with complexity linear in the number of classes. The results obtained do not make any assumption w.r.t. the structure of the underlying distributions or the classification problem. Nevertheless, the general analytic results are analyzed via simulations of string recognition problems with Levenshtein (edit) distance cost function. The results support earlier findings that considerable improvements are to be expected when initial error rates are high.

Activity Augmentation of Amphioxus Peptidoglycan Recognition Protein BbtPGRP3 via Fusion with a Chitin Binding Domain

PubMed Central

Wang, Wen-Jie; Cheng, Wang; Luo, Ming; Yan, Qingyu; Yu, Hong-Mei; Li, Qiong; Cao, Dong-Dong; Huang, Shengfeng; Xu, Anlong; Mariuzza, Roy A.; Chen, Yuxing; Zhou, Cong-Zhao

2015-01-01

Peptidoglycan recognition proteins (PGRPs), which have been identified in most animals, are pattern recognition molecules that involve antimicrobial defense. Resulting from extraordinary expansion of innate immune genes, the amphioxus encodes many PGRPs of diverse functions. For instance, three isoforms of PGRP encoded by Branchiostoma belcheri tsingtauense, termed BbtPGRP1~3, are fused with a chitin binding domain (CBD) at the N-terminus. Here we report the 2.7 Å crystal structure of BbtPGRP3, revealing an overall structure of an N-terminal hevein-like CBD followed by a catalytic PGRP domain. Activity assays combined with site-directed mutagenesis indicated that the individual PGRP domain exhibits amidase activity towards both DAP-type and Lys-type peptidoglycans (PGNs), the former of which is favored. The N-terminal CBD not only has the chitin-binding activity, but also enables BbtPGRP3 to gain a five-fold increase of amidase activity towards the Lys-type PGNs, leading to a significantly broadened substrate spectrum. Together, we propose that modular evolution via domain shuffling combined with gene horizontal transfer makes BbtPGRP1~3 novel PGRPs of augmented catalytic activity and broad recognition spectrum. PMID:26479246

Addressing the issue of insufficient information in data-based bridge health monitoring : final report.

DOT National Transportation Integrated Search

2015-11-01

One of the most efficient ways to solve the damage detection problem using the statistical pattern recognition : approach is that of exploiting the methods of outlier analysis. Cast within the pattern recognition framework, : damage detection assesse...

Microcomputers and Preschoolers.

ERIC Educational Resources Information Center

Evans, Dina

Preschool children can benefit by working with microcomputers. Thinking skills are enhanced by software games that focus on logic, memory, problem solving, and pattern recognition. Counting, sequencing, and matching games develop mathematics skills, and word games focusing on basic letter symbol and word recognition develop language skills.…

Expression, purification, crystallization and X-ray diffraction studies of the molecular chaperone prefoldin from Homo sapiens

PubMed Central

Aikawa, Yoshiki; Kida, Hiroshi; Nishitani, Yuichi; Miki, Kunio

2015-01-01

Proper protein folding is an essential process for all organisms. Prefoldin (PFD) is a molecular chaperone that assists protein folding by delivering non-native proteins to group II chaperonin. A heterohexamer of eukaryotic PFD has been shown to specifically recognize and deliver non-native actin and tubulin to chaperonin-containing TCP-1 (CCT), but the mechanism of specific recognition is still unclear. To determine its crystal structure, recombinant human PFD was reconstituted, purified and crystallized. X-ray diffraction data were collected to 4.7 Å resolution. The crystals belonged to space group P21212, with unit-cell parameters a = 123.2, b = 152.4, c = 105.9 Å. PMID:26323306

Mental health problems of undocumented migrants in the Netherlands: A qualitative exploration of recognition, recording, and treatment by general practitioners.

PubMed

Teunissen, Erik; Van Bavel, Eric; Van Den Driessen Mareeuw, Francine; Macfarlane, Anne; Van Weel-Baumgarten, Evelyn; Van Den Muijsenbergh, Maria; Van Weel, Chris

2015-06-01

To explore the views and experiences of general practitioners (GPs) in relation to recognition, recording, and treatment of mental health problems of undocumented migrants (UMs), and to gain insight in the reasons for under-registration of mental health problems in the electronic medical records. Qualitative study design with semi-structured interviews using a topic guide. Sixteen GPs in the Netherlands with clinical expertise in the care of UMs. GPs recognized many mental health problems in UMs. Barriers that prevented them from recording these problems and from delivering appropriate care were their low consultation rates, physical presentation of mental health problems, high number of other problems, the UM's lack of trust towards health care professionals, and cultural differences in health beliefs and language barriers. Referrals to mental health care organizations were often seen as problematic by GPs. To overcome these barriers, GPs provided personalized care as far as possible, referred to other primary care professionals such as social workers or mental health care nurses in their practice, and were a little less restrictive in prescribing psychotropics than guidelines recommended. GPs experienced a variety of barriers in engaging with UMs when identifying or suspecting mental health problems. This explains why there is a gap between the high recognition of mental health problems and the low recording of these problems in general practice files. It is recommended that GPs address mental health problems more actively, strive for continuity of care in order to gain trust of the UMs, and look for opportunities to provide mental care that is accessible and acceptable for UMs.

Interactive machine learning for health informatics: when do we need the human-in-the-loop?

PubMed

Holzinger, Andreas

2016-06-01

Machine learning (ML) is the fastest growing field in computer science, and health informatics is among the greatest challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Most ML researchers concentrate on automatic machine learning (aML), where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from big data with many training sets. However, in the health domain, sometimes we are confronted with a small number of data sets or rare events, where aML-approaches suffer of insufficient training samples. Here interactive machine learning (iML) may be of help, having its roots in reinforcement learning, preference learning, and active learning. The term iML is not yet well used, so we define it as "algorithms that can interact with agents and can optimize their learning behavior through these interactions, where the agents can also be human." This "human-in-the-loop" can be beneficial in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization of health data, where human expertise can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise be an NP-hard problem, reduces greatly in complexity through the input and the assistance of a human agent involved in the learning phase.

A stoichiometry driven universal spatial organization of backbones of folded proteins: are there Chargaff's rules for protein folding?

PubMed

Mittal, A; Jayaram, B; Shenoy, Sandhya; Bawa, Tejdeep Singh

2010-10-01

Protein folding is at least a six decade old problem, since the times of Pauling and Anfinsen. However, rules of protein folding remain elusive till date. In this work, rigorous analyses of several thousand crystal structures of folded proteins reveal a surprisingly simple unifying principle of backbone organization in protein folding. We find that protein folding is a direct consequence of a narrow band of stoichiometric occurrences of amino-acids in primary sequences, regardless of the size and the fold of a protein. We observe that "preferential interactions" between amino-acids do not drive protein folding, contrary to all prevalent views. We dedicate our discovery to the seminal contribution of Chargaff which was one of the major keys to elucidation of the stoichiometry-driven spatially organized double helical structure of DNA.

Chinese Herbal Medicine Image Recognition and Retrieval by Convolutional Neural Network

PubMed Central

Sun, Xin; Qian, Huinan

2016-01-01

Chinese herbal medicine image recognition and retrieval have great potential of practical applications. Several previous studies have focused on the recognition with hand-crafted image features, but there are two limitations in them. Firstly, most of these hand-crafted features are low-level image representation, which is easily affected by noise and background. Secondly, the medicine images are very clean without any backgrounds, which makes it difficult to use in practical applications. Therefore, designing high-level image representation for recognition and retrieval in real world medicine images is facing a great challenge. Inspired by the recent progress of deep learning in computer vision, we realize that deep learning methods may provide robust medicine image representation. In this paper, we propose to use the Convolutional Neural Network (CNN) for Chinese herbal medicine image recognition and retrieval. For the recognition problem, we use the softmax loss to optimize the recognition network; then for the retrieval problem, we fine-tune the recognition network by adding a triplet loss to search for the most similar medicine images. To evaluate our method, we construct a public database of herbal medicine images with cluttered backgrounds, which has in total 5523 images with 95 popular Chinese medicine categories. Experimental results show that our method can achieve the average recognition precision of 71% and the average retrieval precision of 53% over all the 95 medicine categories, which are quite promising given the fact that the real world images have multiple pieces of occluded herbal and cluttered backgrounds. Besides, our proposed method achieves the state-of-the-art performance by improving previous studies with a large margin. PMID:27258404

Relating dynamic brain states to dynamic machine states: Human and machine solutions to the speech recognition problem

PubMed Central

Liu, Xunying; Zhang, Chao; Woodland, Phil; Fonteneau, Elisabeth

2017-01-01

There is widespread interest in the relationship between the neurobiological systems supporting human cognition and emerging computational systems capable of emulating these capacities. Human speech comprehension, poorly understood as a neurobiological process, is an important case in point. Automatic Speech Recognition (ASR) systems with near-human levels of performance are now available, which provide a computationally explicit solution for the recognition of words in continuous speech. This research aims to bridge the gap between speech recognition processes in humans and machines, using novel multivariate techniques to compare incremental ‘machine states’, generated as the ASR analysis progresses over time, to the incremental ‘brain states’, measured using combined electro- and magneto-encephalography (EMEG), generated as the same inputs are heard by human listeners. This direct comparison of dynamic human and machine internal states, as they respond to the same incrementally delivered sensory input, revealed a significant correspondence between neural response patterns in human superior temporal cortex and the structural properties of ASR-derived phonetic models. Spatially coherent patches in human temporal cortex responded selectively to individual phonetic features defined on the basis of machine-extracted regularities in the speech to lexicon mapping process. These results demonstrate the feasibility of relating human and ASR solutions to the problem of speech recognition, and suggest the potential for further studies relating complex neural computations in human speech comprehension to the rapidly evolving ASR systems that address the same problem domain. PMID:28945744

Visual Recognition Software for Binary Classification and Its Application to Spruce Pollen Identification

PubMed Central

Tcheng, David K.; Nayak, Ashwin K.; Fowlkes, Charless C.; Punyasena, Surangi W.

2016-01-01

Discriminating between black and white spruce (Picea mariana and Picea glauca) is a difficult palynological classification problem that, if solved, would provide valuable data for paleoclimate reconstructions. We developed an open-source visual recognition software (ARLO, Automated Recognition with Layered Optimization) capable of differentiating between these two species at an accuracy on par with human experts. The system applies pattern recognition and machine learning to the analysis of pollen images and discovers general-purpose image features, defined by simple features of lines and grids of pixels taken at different dimensions, size, spacing, and resolution. It adapts to a given problem by searching for the most effective combination of both feature representation and learning strategy. This results in a powerful and flexible framework for image classification. We worked with images acquired using an automated slide scanner. We first applied a hash-based “pollen spotting” model to segment pollen grains from the slide background. We next tested ARLO’s ability to reconstruct black to white spruce pollen ratios using artificially constructed slides of known ratios. We then developed a more scalable hash-based method of image analysis that was able to distinguish between the pollen of black and white spruce with an estimated accuracy of 83.61%, comparable to human expert performance. Our results demonstrate the capability of machine learning systems to automate challenging taxonomic classifications in pollen analysis, and our success with simple image representations suggests that our approach is generalizable to many other object recognition problems. PMID:26867017

3D Face Recognition Based on Multiple Keypoint Descriptors and Sparse Representation

PubMed Central

Zhang, Lin; Ding, Zhixuan; Li, Hongyu; Shen, Ying; Lu, Jianwei

2014-01-01

Recent years have witnessed a growing interest in developing methods for 3D face recognition. However, 3D scans often suffer from the problems of missing parts, large facial expressions, and occlusions. To be useful in real-world applications, a 3D face recognition approach should be able to handle these challenges. In this paper, we propose a novel general approach to deal with the 3D face recognition problem by making use of multiple keypoint descriptors (MKD) and the sparse representation-based classification (SRC). We call the proposed method 3DMKDSRC for short. Specifically, with 3DMKDSRC, each 3D face scan is represented as a set of descriptor vectors extracted from keypoints by meshSIFT. Descriptor vectors of gallery samples form the gallery dictionary. Given a probe 3D face scan, its descriptors are extracted at first and then its identity can be determined by using a multitask SRC. The proposed 3DMKDSRC approach does not require the pre-alignment between two face scans and is quite robust to the problems of missing data, occlusions and expressions. Its superiority over the other leading 3D face recognition schemes has been corroborated by extensive experiments conducted on three benchmark databases, Bosphorus, GavabDB, and FRGC2.0. The Matlab source code for 3DMKDSRC and the related evaluation results are publicly available at http://sse.tongji.edu.cn/linzhang/3dmkdsrcface/3dmkdsrc.htm. PMID:24940876

Discretized torsional dynamics and the folding of an RNA chain.

PubMed

Fernández, A; Salthú, R; Cendra, H

1999-08-01

The aim of this work is to implement a discrete coarse codification of local torsional states of the RNA chain backbone in order to explore the long-time limit dynamics and ultimately obtain a coarse solution to the RNA folding problem. A discrete representation of the soft-mode dynamics is turned into an algorithm for a rough structure prediction. The algorithm itself is inherently parallel, as it evaluates concurrent folding possibilities by pattern recognition, but it may be implemented in a personal computer as a chain of perturbation-translation-renormalization cycles performed on a binary matrix of local topological constraints. This requires suitable representational tools and a periodic quenching of the dynamics for system renormalization. A binary coding of local topological constraints associated with each structural motif is introduced, with each local topological constraint corresponding to a local torsional state. This treatment enables us to adopt a computation time step far larger than hydrodynamic drag time scales. Accordingly, the solvent is no longer treated as a hydrodynamic drag medium. Instead we incorporate its capacity for forming local conformation-dependent dielectric domains. Each translation of the matrix of local topological constraints (LTM's) depends on the conformation-dependent local dielectric created by a confined solvent. Folding pathways are resolved as transitions between patterns of locally encoded structural signals which change within the 1 ns-100 ms time scale range. These coarse folding pathways are generated by a search at regular intervals for structural patterns in the LTM. Each pattern is recorded as a base-pairing pattern (BPP) matrix, a consensus-evaluation operation subject to a renormalization feedback loop. Since several mutually conflicting consensus evaluations might occur at a given time, the need arises for a probabilistic approach appropriate for an ensemble of RNA molecules. Thus, a statistical dynamics of consensus formation is determined by the time evolution of the base pairing probability matrix. These dynamics are generated for a functional RNA molecule, a representative of the so-called group I ribozymes, in order to test the model. The resulting ensemble of conformations is sharply peaked and the most probable structure features the predominance of all phylogenetically conserved intrachain helices tantamount to ribozyme function. Furthermore, the magnesium-aided cooperativity that leads to the shaping of the catalytic core is elucidated. Once the predictive folding algorithm has been implemented, the validity of the so-called "adiabatic approximation" is tested. This approximation requires that conformational microstates be lumped up into BPP's which are treated as quasiequilibrium states, while folding pathways are coarsely represented as sequences of BPP transitions. To test the validity of this adiabatic ansatz, a computation of the coarse Shannon information entropy sigma associated to the specific partition of conformation space into BPP's is performed taking into account the LTM evolution and contrasted with the adiabatic computation. The results reveal a subordination of torsional microstate dynamics to BPP transitions within time scales relevant to folding. This adiabatic entrainment in the long-time limit is thus identified as responsible for the expediency of the folding process.

Diagnosing plant problems

Treesearch

Cheryl A. Smith

2008-01-01

Diagnosing Christmas tree problems can be a challenge, requiring a basic knowledge of plant culture and physiology, the effect of environmental influences on plant health, and the ability to identify the possible causes of plant problems. Developing a solution or remedy to the problem depends on a proper diagnosis, a process that requires recognition of a problem and...

A Modified Active Appearance Model Based on an Adaptive Artificial Bee Colony

PubMed Central

Othman, Zulaiha Ali

2014-01-01

Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition. PMID:25165748

Structure of the human TRiC/CCT Subunit 5 associated with hereditary sensory neuropathy

DOE PAGES

Pereira, Jose H.; McAndrew, Ryan P.; Sergeeva, Oksana A.; ...

2017-06-16

The human chaperonin TRiC consists of eight non-identical subunits, and its protein-folding activity is critical for cellular health. Misfolded proteins are associated with many human diseases, such as amyloid diseases, cancer, and neuropathies, making TRiC a potential therapeutic target. A detailed structural understanding of its ATP-dependent folding mechanism and substrate recognition is therefore of great importance. Of particular health-related interest is the mutation Histidine 147 to Arginine (H147R) in human TRiC subunit 5 (CCT5), which has been associated with hereditary sensory neuropathy. In this paper, we describe the crystal structures of CCT5 and the CCT5-H147R mutant, which provide important structuralmore » information for this vital protein-folding machine in humans. This first X-ray crystallographic study of a single human CCT subunit in the context of a hexadecameric complex can be expanded in the future to the other 7 subunits that form the TRiC complex.« less

Structure of the human TRiC/CCT Subunit 5 associated with hereditary sensory neuropathy.

PubMed

Pereira, Jose H; McAndrew, Ryan P; Sergeeva, Oksana A; Ralston, Corie Y; King, Jonathan A; Adams, Paul D

2017-06-16

The human chaperonin TRiC consists of eight non-identical subunits, and its protein-folding activity is critical for cellular health. Misfolded proteins are associated with many human diseases, such as amyloid diseases, cancer, and neuropathies, making TRiC a potential therapeutic target. A detailed structural understanding of its ATP-dependent folding mechanism and substrate recognition is therefore of great importance. Of particular health-related interest is the mutation Histidine 147 to Arginine (H147R) in human TRiC subunit 5 (CCT5), which has been associated with hereditary sensory neuropathy. In this paper, we describe the crystal structures of CCT5 and the CCT5-H147R mutant, which provide important structural information for this vital protein-folding machine in humans. This first X-ray crystallographic study of a single human CCT subunit in the context of a hexadecameric complex can be expanded in the future to the other 7 subunits that form the TRiC complex.

Structure of the human TRiC/CCT Subunit 5 associated with hereditary sensory neuropathy

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

Pereira, Jose H.; McAndrew, Ryan P.; Sergeeva, Oksana A.

The human chaperonin TRiC consists of eight non-identical subunits, and its protein-folding activity is critical for cellular health. Misfolded proteins are associated with many human diseases, such as amyloid diseases, cancer, and neuropathies, making TRiC a potential therapeutic target. A detailed structural understanding of its ATP-dependent folding mechanism and substrate recognition is therefore of great importance. Of particular health-related interest is the mutation Histidine 147 to Arginine (H147R) in human TRiC subunit 5 (CCT5), which has been associated with hereditary sensory neuropathy. In this paper, we describe the crystal structures of CCT5 and the CCT5-H147R mutant, which provide important structuralmore » information for this vital protein-folding machine in humans. This first X-ray crystallographic study of a single human CCT subunit in the context of a hexadecameric complex can be expanded in the future to the other 7 subunits that form the TRiC complex.« less

UFO (UnFold Operator) computer program abstract

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

Kissel, L.; Biggs, F.

UFO (UnFold Operator) is an interactive user-oriented computer program designed to solve a wide range of problems commonly encountered in physical measurements. This document provides a summary of the capabilities of version 3A of UFO.

Precursory signatures of protein folding/unfolding: From time series correlation analysis to atomistic mechanisms

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

Hsu, P. J.; Lai, S. K., E-mail: sklai@coll.phy.ncu.edu.tw; Molecular Science and Technology Program, Taiwan International Graduate Program, Academia Sinica, Taipei 115, Taiwan

Folded conformations of proteins in thermodynamically stable states have long lifetimes. Before it folds into a stable conformation, or after unfolding from a stable conformation, the protein will generally stray from one random conformation to another leading thus to rapid fluctuations. Brief structural changes therefore occur before folding and unfolding events. These short-lived movements are easily overlooked in studies of folding/unfolding for they represent momentary excursions of the protein to explore conformations in the neighborhood of the stable conformation. The present study looks for precursory signatures of protein folding/unfolding within these rapid fluctuations through a combination of three techniques: (1)more » ultrafast shape recognition, (2) time series segmentation, and (3) time series correlation analysis. The first procedure measures the differences between statistical distance distributions of atoms in different conformations by calculating shape similarity indices from molecular dynamics simulation trajectories. The second procedure is used to discover the times at which the protein makes transitions from one conformation to another. Finally, we employ the third technique to exploit spatial fingerprints of the stable conformations; this procedure is to map out the sequences of changes preceding the actual folding and unfolding events, since strongly correlated atoms in different conformations are different due to bond and steric constraints. The aforementioned high-frequency fluctuations are therefore characterized by distinct correlational and structural changes that are associated with rate-limiting precursors that translate into brief segments. Guided by these technical procedures, we choose a model system, a fragment of the protein transthyretin, for identifying in this system not only the precursory signatures of transitions associated with α helix and β hairpin, but also the important role played by weaker correlations in such protein folding dynamics.« less

Precursory signatures of protein folding/unfolding: From time series correlation analysis to atomistic mechanisms

NASA Astrophysics Data System (ADS)

Hsu, P. J.; Cheong, S. A.; Lai, S. K.

2014-05-01

Folded conformations of proteins in thermodynamically stable states have long lifetimes. Before it folds into a stable conformation, or after unfolding from a stable conformation, the protein will generally stray from one random conformation to another leading thus to rapid fluctuations. Brief structural changes therefore occur before folding and unfolding events. These short-lived movements are easily overlooked in studies of folding/unfolding for they represent momentary excursions of the protein to explore conformations in the neighborhood of the stable conformation. The present study looks for precursory signatures of protein folding/unfolding within these rapid fluctuations through a combination of three techniques: (1) ultrafast shape recognition, (2) time series segmentation, and (3) time series correlation analysis. The first procedure measures the differences between statistical distance distributions of atoms in different conformations by calculating shape similarity indices from molecular dynamics simulation trajectories. The second procedure is used to discover the times at which the protein makes transitions from one conformation to another. Finally, we employ the third technique to exploit spatial fingerprints of the stable conformations; this procedure is to map out the sequences of changes preceding the actual folding and unfolding events, since strongly correlated atoms in different conformations are different due to bond and steric constraints. The aforementioned high-frequency fluctuations are therefore characterized by distinct correlational and structural changes that are associated with rate-limiting precursors that translate into brief segments. Guided by these technical procedures, we choose a model system, a fragment of the protein transthyretin, for identifying in this system not only the precursory signatures of transitions associated with α helix and β hairpin, but also the important role played by weaker correlations in such protein folding dynamics.

Gender recognition from vocal source

NASA Astrophysics Data System (ADS)

Sorokin, V. N.; Makarov, I. S.

2008-07-01

Efficiency of automatic recognition of male and female voices based on solving the inverse problem for glottis area dynamics and for waveform of the glottal airflow volume velocity pulse is studied. The inverse problem is regularized through the use of analytical models of the voice excitation pulse and of the dynamics of the glottis area, as well as the model of one-dimensional glottal airflow. Parameters of these models and spectral parameters of the volume velocity pulse are considered. The following parameters are found to be most promising: the instant of maximum glottis area, the maximum derivative of the area, the slope of the spectrum of the glottal airflow volume velocity pulse, the amplitude ratios of harmonics of this spectrum, and the pitch. On the plane of the first two main components in the space of these parameters, an almost twofold decrease in the classification error relative to that for the pitch alone is attained. The male voice recognition probability is found to be 94.7%, and the female voice recognition probability is 95.9%.

Prevalence and Recognition of Depressive Disorder in Three Medical Outpatient Departments of General Hospitals in Beijing, China.

PubMed

Liu, Chang; Liu, Meiyan; Jiang, Ronghuan; Ma, Hong; Wu, Xiamin; Luan, Shuxin; He, Yanling; Wei, Jing; Bai, Wenpei

2016-07-01

This purpose of this study was to explore the prevalence and recognition of depressive disorders in cardiology, gastroenterology, and neurology outpatient departments of general hospitals. Patients screened with a Hospital Anxiety and Depression Scale score of 8 or higher were interviewed by psychiatrists using Mini-International Neuropsychiatric Interview (MINI). Prevalence of depressive disorders within the cohort was determined, sociodemographic data were analyzed for correlations to a depression diagnosis, and comparisons between the surveys and the clinical diagnosis were done to assess recognition of depressive disorders by physicians. Of the patients screened for this study (1552 cases), 12.8% were diagnosed with depressive disorders by MINI, with major depressive disorder, depression due to general medical conditions, and dysthymia having prevalence values of 10.8%, 1.4%, and 0.6%, respectively. As compared with MINI, physicians only recognized 27.6% of any of the depressive disorders. Among the complaints examined, both mood problems and sleeping problems predicted the probability of recognition.

An evolution based biosensor receptor DNA sequence generation algorithm.

PubMed

Kim, Eungyeong; Lee, Malrey; Gatton, Thomas M; Lee, Jaewan; Zang, Yupeng

2010-01-01

A biosensor is composed of a bioreceptor, an associated recognition molecule, and a signal transducer that can selectively detect target substances for analysis. DNA based biosensors utilize receptor molecules that allow hybridization with the target analyte. However, most DNA biosensor research uses oligonucleotides as the target analytes and does not address the potential problems of real samples. The identification of recognition molecules suitable for real target analyte samples is an important step towards further development of DNA biosensors. This study examines the characteristics of DNA used as bioreceptors and proposes a hybrid evolution-based DNA sequence generating algorithm, based on DNA computing, to identify suitable DNA bioreceptor recognition molecules for stable hybridization with real target substances. The Traveling Salesman Problem (TSP) approach is applied in the proposed algorithm to evaluate the safety and fitness of the generated DNA sequences. This approach improves efficiency and stability for enhanced and variable-length DNA sequence generation and allows extension to generation of variable-length DNA sequences with diverse receptor recognition requirements.

Chemical exchange in biomacromolecules: Past, present, and future

PubMed Central

Palmer, Arthur G.

2014-01-01

The perspective reviews quantitative investigations of chemical exchange phenomena in proteins and other biological macromolecules using NMR spectroscopy, particularly relaxation dispersion methods. The emphasis is on techniques and applications that quantify the populations, interconversion kinetics, and structural features of sparsely populated conformational states in equilibrium with a highly populated ground state. Applications to folding, mol ecular recognition, catalysis, and allostery by proteins and nucleic acids are highlighted. PMID:24656076

Exploring the repeat protein universe through computational protein design

DOE PAGES

Brunette, TJ; Parmeggiani, Fabio; Huang, Po-Ssu; ...

2015-12-16

A central question in protein evolution is the extent to which naturally occurring proteins sample the space of folded structures accessible to the polypeptide chain. Repeat proteins composed of multiple tandem copies of a modular structure unit are widespread in nature and have critical roles in molecular recognition, signalling, and other essential biological processes. Naturally occurring repeat proteins have been re-engineered for molecular recognition and modular scaffolding applications. In this paper, we use computational protein design to investigate the space of folded structures that can be generated by tandem repeating a simple helix–loop–helix–loop structural motif. Eighty-three designs with sequences unrelatedmore » to known repeat proteins were experimentally characterized. Of these, 53 are monomeric and stable at 95 °C, and 43 have solution X-ray scattering spectra consistent with the design models. Crystal structures of 15 designs spanning a broad range of curvatures are in close agreement with the design models with root mean square deviations ranging from 0.7 to 2.5 Å. Finally, our results show that existing repeat proteins occupy only a small fraction of the possible repeat protein sequence and structure space and that it is possible to design novel repeat proteins with precisely specified geometries, opening up a wide array of new possibilities for biomolecular engineering.« less

GeneSilico protein structure prediction meta-server.

PubMed

Kurowski, Michal A; Bujnicki, Janusz M

2003-07-01

Rigorous assessments of protein structure prediction have demonstrated that fold recognition methods can identify remote similarities between proteins when standard sequence search methods fail. It has been shown that the accuracy of predictions is improved when refined multiple sequence alignments are used instead of single sequences and if different methods are combined to generate a consensus model. There are several meta-servers available that integrate protein structure predictions performed by various methods, but they do not allow for submission of user-defined multiple sequence alignments and they seldom offer confidentiality of the results. We developed a novel WWW gateway for protein structure prediction, which combines the useful features of other meta-servers available, but with much greater flexibility of the input. The user may submit an amino acid sequence or a multiple sequence alignment to a set of methods for primary, secondary and tertiary structure prediction. Fold-recognition results (target-template alignments) are converted into full-atom 3D models and the quality of these models is uniformly assessed. A consensus between different FR methods is also inferred. The results are conveniently presented on-line on a single web page over a secure, password-protected connection. The GeneSilico protein structure prediction meta-server is freely available for academic users at http://genesilico.pl/meta.

GeneSilico protein structure prediction meta-server

PubMed Central

Kurowski, Michal A.; Bujnicki, Janusz M.

2003-01-01

Rigorous assessments of protein structure prediction have demonstrated that fold recognition methods can identify remote similarities between proteins when standard sequence search methods fail. It has been shown that the accuracy of predictions is improved when refined multiple sequence alignments are used instead of single sequences and if different methods are combined to generate a consensus model. There are several meta-servers available that integrate protein structure predictions performed by various methods, but they do not allow for submission of user-defined multiple sequence alignments and they seldom offer confidentiality of the results. We developed a novel WWW gateway for protein structure prediction, which combines the useful features of other meta-servers available, but with much greater flexibility of the input. The user may submit an amino acid sequence or a multiple sequence alignment to a set of methods for primary, secondary and tertiary structure prediction. Fold-recognition results (target-template alignments) are converted into full-atom 3D models and the quality of these models is uniformly assessed. A consensus between different FR methods is also inferred. The results are conveniently presented on-line on a single web page over a secure, password-protected connection. The GeneSilico protein structure prediction meta-server is freely available for academic users at http://genesilico.pl/meta. PMID:12824313

Crystal Structure of the Eukaryotic Origin Recognition Complex

PubMed Central

Bleichert, Franziska; Botchan, Michael R.; Berger, James M.

2015-01-01

Initiation of cellular DNA replication is tightly controlled to sustain genomic integrity. In eukaryotes, the heterohexameric origin recognition complex (ORC) is essential for coordinating replication onset. The 3.5 Å resolution crystal structure of Drosophila ORC reveals that the 270 kDa initiator core complex comprises a two-layered notched ring in which a collar of winged-helix domains from the Orc1-5 subunits sits atop a layer of AAA+ ATPase folds. Although canonical inter-AAA+ domain interactions exist between four of the six ORC subunits, unanticipated features are also evident, including highly interdigitated domain-swapping interactions between the winged-helix folds and AAA+ modules of neighboring protomers, and a quasi-spiral arrangement of DNA binding elements that circumnavigate a ~20 Å wide channel in the center of the complex. Comparative analyses indicate that ORC encircles DNA, using its winged-helix domain face to engage the MCM2-7 complex during replicative helicase loading; however, an observed >90° out-of-plane rotation for the Orc1 AAA+ domain disrupts interactions with catalytic amino acids in Orc4, narrowing and sealing off entry into the central channel. Prima facie, our data indicate that Drosophila ORC can switch between active and autoinhibited conformations, suggesting a novel means for cell cycle and/or developmental control of ORC functions. PMID:25762138

The Fanconi Anemia DNA Repair Pathway Is Regulated by an Interaction between Ubiquitin and the E2-like Fold Domain of FANCL*

PubMed Central

Miles, Jennifer A.; Frost, Mark G.; Carroll, Eilis; Rowe, Michelle L.; Howard, Mark J.; Sidhu, Ateesh; Chaugule, Viduth K.; Alpi, Arno F.; Walden, Helen

2015-01-01

The Fanconi Anemia (FA) DNA repair pathway is essential for the recognition and repair of DNA interstrand crosslinks (ICL). Inefficient repair of these ICL can lead to leukemia and bone marrow failure. A critical step in the pathway is the monoubiquitination of FANCD2 by the RING E3 ligase FANCL. FANCL comprises 3 domains, a RING domain that interacts with E2 conjugating enzymes, a central domain required for substrate interaction, and an N-terminal E2-like fold (ELF) domain. The ELF domain is found in all FANCL homologues, yet the function of the domain remains unknown. We report here that the ELF domain of FANCL is required to mediate a non-covalent interaction between FANCL and ubiquitin. The interaction involves the canonical Ile44 patch on ubiquitin, and a functionally conserved patch on FANCL. We show that the interaction is not necessary for the recognition of the core complex, it does not enhance the interaction between FANCL and Ube2T, and is not required for FANCD2 monoubiquitination in vitro. However, we demonstrate that the ELF domain is required to promote efficient DNA damage-induced FANCD2 monoubiquitination in vertebrate cells, suggesting an important function of ubiquitin binding by FANCL in vivo. PMID:26149689

Excimer-based peptide beacons: a convenient experimental approach for monitoring polypeptide-protein and polypeptide-oligonucleotide interactions.

PubMed

Oh, Kenneth J; Cash, Kevin J; Plaxco, Kevin W

2006-11-01

While protein-polypeptide and nucleic acid-polypeptide interactions are of significant experimental interest, quantitative methods for the characterization of such interactions are often cumbersome. Here we described a relatively simple means of optically monitoring such interactions using excimer-based peptide beacons (PBs). The design of PBs is based on the observation that, whereas short peptides are almost invariably unfolded and highly dynamic, they become rigid when complexed with macromolecular targets. Using this binding-induced folding to segregate two pyrene moieties and therefore inhibit excimer formation, we have produced PBs directed against both anti-HIV antibodies and the retroviral transactive response (TAR) RNA hairpin. For both polypeptides, target recognition is accompanied by a roughly 2-fold decrease in excimer emission, thus allowing the detection of their respective targets at concentrations of a few nanomolar. Because excimer emission requires the formation of a tight, precisely oriented pyrene dimer, even relatively trivial binding-induced segregation reduces fluorescence significantly. This suggests that the PB approach will be suitable for monitoring a wide range of peptide-macromolecule recognition events. Moreover, the synthesis of excimer-based PBs utilizes commercially available modified pyrenes in a simple and well-established protocol, making the approach well suited for routine laboratory applications.

Development of an Autonomous Face Recognition Machine.

DTIC Science & Technology

1986-12-08

This approach, like Baron’s, would be a very time consuming task. The problem of locating a face in Bromley’s work was the least complex of the three...top level design and the development and design decisions that were made in developing the Autonomous Face Recognition Machine (AFRM). The chapter is...images within a digital image. The second sectio examines the algorithm used in performing face recognition. The decision to divide the development

Functional Studies and Homology Modeling of Msh2-Msh3 Predict that Mispair Recognition Involves DNA Bending and Strand Separation▿ †

PubMed Central

Dowen, Jill M.; Putnam, Christopher D.; Kolodner, Richard D.

2010-01-01

The Msh2-Msh3 heterodimer recognizes various DNA mispairs, including loops of DNA ranging from 1 to 14 nucleotides and some base-base mispairs. Homology modeling of the mispair-binding domain (MBD) of Msh3 using the related Msh6 MBD revealed that mismatch recognition must be different, even though the MBD folds must be similar. Model-based point mutation alleles of Saccharomyces cerevisiae msh3 designed to disrupt mispair recognition fell into two classes. One class caused defects in repair of both small and large insertion/deletion mispairs, whereas the second class caused defects only in the repair of small insertion/deletion mispairs; mutations of the first class also caused defects in the removal of nonhomologous tails present at the ends of double-strand breaks (DSBs) during DSB repair, whereas mutations of the second class did not cause defects in the removal of nonhomologous tails during DSB repair. Thus, recognition of small insertion/deletion mispairs by Msh3 appears to require a greater degree of interactions with the DNA conformations induced by small insertion/deletion mispairs than with those induced by large insertion/deletions that are intrinsically bent and strand separated. Mapping of the two classes of mutations onto the Msh3 MBD model appears to distinguish mispair recognition regions from DNA stabilization regions. PMID:20421420

Functional studies and homology modeling of Msh2-Msh3 predict that mispair recognition involves DNA bending and strand separation.

PubMed

Dowen, Jill M; Putnam, Christopher D; Kolodner, Richard D

2010-07-01

The Msh2-Msh3 heterodimer recognizes various DNA mispairs, including loops of DNA ranging from 1 to 14 nucleotides and some base-base mispairs. Homology modeling of the mispair-binding domain (MBD) of Msh3 using the related Msh6 MBD revealed that mismatch recognition must be different, even though the MBD folds must be similar. Model-based point mutation alleles of Saccharomyces cerevisiae msh3 designed to disrupt mispair recognition fell into two classes. One class caused defects in repair of both small and large insertion/deletion mispairs, whereas the second class caused defects only in the repair of small insertion/deletion mispairs; mutations of the first class also caused defects in the removal of nonhomologous tails present at the ends of double-strand breaks (DSBs) during DSB repair, whereas mutations of the second class did not cause defects in the removal of nonhomologous tails during DSB repair. Thus, recognition of small insertion/deletion mispairs by Msh3 appears to require a greater degree of interactions with the DNA conformations induced by small insertion/deletion mispairs than with those induced by large insertion/deletions that are intrinsically bent and strand separated. Mapping of the two classes of mutations onto the Msh3 MBD model appears to distinguish mispair recognition regions from DNA stabilization regions.

Structure of human POFUT2: insights into thrombospondin type 1 repeat fold and O-fucosylation

PubMed Central

Chen, Chun-I; Keusch, Jeremy J; Klein, Dominique; Hess, Daniel; Hofsteenge, Jan; Gut, Heinz

2012-01-01

Protein O-fucosylation is a post-translational modification found on serine/threonine residues of thrombospondin type 1 repeats (TSR). The fucose transfer is catalysed by the enzyme protein O-fucosyltransferase 2 (POFUT2) and >40 human proteins contain the TSR consensus sequence for POFUT2-dependent fucosylation. To better understand O-fucosylation on TSR, we carried out a structural and functional analysis of human POFUT2 and its TSR substrate. Crystal structures of POFUT2 reveal a variation of the classical GT-B fold and identify sugar donor and TSR acceptor binding sites. Structural findings are correlated with steady-state kinetic measurements of wild-type and mutant POFUT2 and TSR and give insight into the catalytic mechanism and substrate specificity. By using an artificial mini-TSR substrate, we show that specificity is not primarily encoded in the TSR protein sequence but rather in the unusual 3D structure of a small part of the TSR. Our findings uncover that recognition of distinct conserved 3D fold motifs can be used as a mechanism to achieve substrate specificity by enzymes modifying completely folded proteins of very wide sequence diversity and biological function. PMID:22588082

Studies of recognition with multitemporal remote sensor data

NASA Technical Reports Server (NTRS)

Malila, W. A.; Hieber, R. H.; Cicone, R. C.

1975-01-01

Characteristics of multitemporal data and their use in recognition processing were investigated. Principal emphasis was on satellite data collected by the LANDSAT multispectral scanner and on temporal changes throughout a growing season. The effects of spatial misregistration on recognition performance with multitemporal data were examined. A capability to compute probabilities of detection and false alarm was developed and used with simulated distributions for misregistered pixels. Wheat detection was found to be degraded and false alarms increased by misregistration effects. Multitemporal signature characteristics and multitemporal recognition processing were studied to gain insights into problems associated with this approach and possible improvements. Recognition performance with one multitemporal data set displayed marked improvements over results from single-time data.

Iris recognition based on robust principal component analysis

NASA Astrophysics Data System (ADS)

Karn, Pradeep; He, Xiao Hai; Yang, Shuai; Wu, Xiao Hong

2014-11-01

Iris images acquired under different conditions often suffer from blur, occlusion due to eyelids and eyelashes, specular reflection, and other artifacts. Existing iris recognition systems do not perform well on these types of images. To overcome these problems, we propose an iris recognition method based on robust principal component analysis. The proposed method decomposes all training images into a low-rank matrix and a sparse error matrix, where the low-rank matrix is used for feature extraction. The sparsity concentration index approach is then applied to validate the recognition result. Experimental results using CASIA V4 and IIT Delhi V1iris image databases showed that the proposed method achieved competitive performances in both recognition accuracy and computational efficiency.

Recognition of facial emotion and affective prosody in children with ASD (+ADHD) and their unaffected siblings.

PubMed

Oerlemans, Anoek M; van der Meer, Jolanda M J; van Steijn, Daphne J; de Ruiter, Saskia W; de Bruijn, Yvette G E; de Sonneville, Leo M J; Buitelaar, Jan K; Rommelse, Nanda N J

2014-05-01

Autism is a highly heritable and clinically heterogeneous neuropsychiatric disorder that frequently co-occurs with other psychopathologies, such as attention-deficit/hyperactivity disorder (ADHD). An approach to parse heterogeneity is by forming more homogeneous subgroups of autism spectrum disorder (ASD) patients based on their underlying, heritable cognitive vulnerabilities (endophenotypes). Emotion recognition is a likely endophenotypic candidate for ASD and possibly for ADHD. Therefore, this study aimed to examine whether emotion recognition is a viable endophenotypic candidate for ASD and to assess the impact of comorbid ADHD in this context. A total of 90 children with ASD (43 with and 47 without ADHD), 79 ASD unaffected siblings, and 139 controls aged 6-13 years, were included to test recognition of facial emotion and affective prosody. Our results revealed that the recognition of both facial emotion and affective prosody was impaired in children with ASD and aggravated by the presence of ADHD. The latter could only be partly explained by typical ADHD cognitive deficits, such as inhibitory and attentional problems. The performance of unaffected siblings could overall be considered at an intermediate level, performing somewhat worse than the controls and better than the ASD probands. Our findings suggest that emotion recognition might be a viable endophenotype in ASD and a fruitful target in future family studies of the genetic contribution to ASD and comorbid ADHD. Furthermore, our results suggest that children with comorbid ASD and ADHD are at highest risk for emotion recognition problems.

Discovery of a novel calcium-sensitive fluorescent probe for α-ketoglutarate.

PubMed

Gan, Lin-Lin; Chen, Lin-Hai; Nan, Fa-Jun

2017-12-01

α-Ketoglutarate (α-KG), a pivotal metabolite in energy metabolism, has been implicated in nonalcoholic fatty liver disease (NAFLD) and several cancers. It is recently proposed that plasma α-KG is a surrogate biomarker of NAFLD. Here, we report the development of a novel "turn-on" chemosensor for α-KG that contains a coumarin moiety as a fluorophore. Using benzothiazole-coumarin (BTC) as inspiration, we designed a probe for calcium ion recognition that possesses a unique fluorophore compared with previously reported probes for α-KG measurement. This chemosensor is based on the specific Schiff base reaction and the calcium ion recognition property of the widely used calcium indicator BTC. The probe was synthesized, and a series of parallel experiments were conducted to optimize the chemical recognition process. Compared to the initial weak fluorescence, a remarkable 7.6-fold enhancement in fluorescence intensity (I/I 0 at 495 nm) was observed for the conditions in which the probe (1 μmol/L), α-KG (50 μmol/L), and Ca 2+ (100 μmol/L) were incubated at 30 °C in EtOH. The probe displayed good selectivity for α-KG even in an environment with an abundance of amino acids and other interfering species such as glutaric acid. We determined that the quantitative detection range of α-KG in EtOH was between 5 and 50 μmol/L. Finally, probe in serum loaded with α-KG (10 mmol/L) showed a 7.4-fold fluorescence enhancement. In summary, a novel probe for detecting the biomarker α-KG through a typical Schiff base reaction has been discovered. With further optimization, this probe may be a good alternative for detecting the physiological metabolite α-KG.

KM+, a mannose-binding lectin from Artocarpus integrifolia: amino acid sequence, predicted tertiary structure, carbohydrate recognition, and analysis of the beta-prism fold.

PubMed Central

Rosa, J. C.; De Oliveira, P. S.; Garratt, R.; Beltramini, L.; Resing, K.; Roque-Barreira, M. C.; Greene, L. J.

1999-01-01

The complete amino acid sequence of the lectin KM+ from Artocarpus integrifolia (jackfruit), which contains 149 residues/mol, is reported and compared to those of other members of the Moraceae family, particularly that of jacalin, also from jackfruit, with which it shares 52% sequence identity. KM+ presents an acetyl-blocked N-terminus and is not posttranslationally modified by proteolytic cleavage as is the case for jacalin. Rather, it possesses a short, glycine-rich linker that unites the regions homologous to the alpha- and beta-chains of jacalin. The results of homology modeling implicate the linker sequence in sterically impeding rotation of the side chain of Asp141 within the binding site pocket. As a consequence, the aspartic acid is locked into a conformation adequate only for the recognition of equatorial hydroxyl groups on the C4 epimeric center (alpha-D-mannose, alpha-D-glucose, and their derivatives). In contrast, the internal cleavage of the jacalin chain permits free rotation of the homologous aspartic acid, rendering it capable of accepting hydrogen bonds from both possible hydroxyl configurations on C4. We suggest that, together with direct recognition of epimeric hydroxyls and the steric exclusion of disfavored ligands, conformational restriction of the lectin should be considered to be a new mechanism by which selectivity may be built into carbohydrate binding sites. Jacalin and KM+ adopt the beta-prism fold already observed in two unrelated protein families. Despite presenting little or no sequence similarity, an analysis of the beta-prism reveals a canonical feature repeatedly present in all such structures, which is based on six largely hydrophobic residues within a beta-hairpin containing two classic-type beta-bulges. We suggest the term beta-prism motif to describe this feature. PMID:10210179

Comparison of a preQ1 riboswitch aptamer in metabolite-bound and free states with implications for gene regulation.

PubMed

Jenkins, Jermaine L; Krucinska, Jolanta; McCarty, Reid M; Bandarian, Vahe; Wedekind, Joseph E

2011-07-15

Riboswitches are RNA regulatory elements that govern gene expression by recognition of small molecule ligands via a high affinity aptamer domain. Molecular recognition can lead to active or attenuated gene expression states by controlling accessibility to mRNA signals necessary for transcription or translation. Key areas of inquiry focus on how an aptamer attains specificity for its effector, the extent to which the aptamer folds prior to encountering its ligand, and how ligand binding alters expression signal accessibility. Here we present crystal structures of the preQ(1) riboswitch from Thermoanaerobacter tengcongensis in the preQ(1)-bound and free states. Although the mode of preQ(1) recognition is similar to that observed for preQ(0), surface plasmon resonance revealed an apparent K(D) of 2.1 ± 0.3 nm for preQ(1) but a value of 35.1 ± 6.1 nm for preQ(0). This difference can be accounted for by interactions between the preQ(1) methylamine and base G5 of the aptamer. To explore conformational states in the absence of metabolite, the free-state aptamer structure was determined. A14 from the ceiling of the ligand pocket shifts into the preQ(1)-binding site, resulting in "closed" access to the metabolite while simultaneously increasing exposure of the ribosome-binding site. Solution scattering data suggest that the free-state aptamer is compact, but the "closed" free-state crystal structure is inadequate to describe the solution scattering data. These observations are distinct from transcriptional preQ(1) riboswitches of the same class that exhibit strictly ligand-dependent folding. Implications for gene regulation are discussed.

Operator for object recognition and scene analysis by estimation of set occupancy with noisy and incomplete data sets

NASA Astrophysics Data System (ADS)

Rees, S. J.; Jones, Bryan F.

1992-11-01

Once feature extraction has occurred in a processed image, the recognition problem becomes one of defining a set of features which maps sufficiently well onto one of the defined shape/object models to permit a claimed recognition. This process is usually handled by aggregating features until a large enough weighting is obtained to claim membership, or an adequate number of located features are matched to the reference set. A requirement has existed for an operator or measure capable of a more direct assessment of membership/occupancy between feature sets, particularly where the feature sets may be defective representations. Such feature set errors may be caused by noise, by overlapping of objects, and by partial obscuration of features. These problems occur at the point of acquisition: repairing the data would then assume a priori knowledge of the solution. The technique described in this paper offers a set theoretical measure for partial occupancy defined in terms of the set of minimum additions to permit full occupancy and the set of locations of occupancy if such additions are made. As is shown, this technique permits recognition of partial feature sets with quantifiable degrees of uncertainty. A solution to the problems of obscuration and overlapping is therefore available.

Tensor manifold-based extreme learning machine for 2.5-D face recognition

NASA Astrophysics Data System (ADS)

Chong, Lee Ying; Ong, Thian Song; Teoh, Andrew Beng Jin

2018-01-01

We explore the use of the Gabor regional covariance matrix (GRCM), a flexible matrix-based descriptor that embeds the Gabor features in the covariance matrix, as a 2.5-D facial descriptor and an effective means of feature fusion for 2.5-D face recognition problems. Despite its promise, matching is not a trivial problem for GRCM since it is a special instance of a symmetric positive definite (SPD) matrix that resides in non-Euclidean space as a tensor manifold. This implies that GRCM is incompatible with the existing vector-based classifiers and distance matchers. Therefore, we bridge the gap of the GRCM and extreme learning machine (ELM), a vector-based classifier for the 2.5-D face recognition problem. We put forward a tensor manifold-compliant ELM and its two variants by embedding the SPD matrix randomly into reproducing kernel Hilbert space (RKHS) via tensor kernel functions. To preserve the pair-wise distance of the embedded data, we orthogonalize the random-embedded SPD matrix. Hence, classification can be done using a simple ridge regressor, an integrated component of ELM, on the random orthogonal RKHS. Experimental results show that our proposed method is able to improve the recognition performance and further enhance the computational efficiency.

Text recognition and correction for automated data collection by mobile devices

NASA Astrophysics Data System (ADS)

Ozarslan, Suleyman; Eren, P. Erhan

2014-03-01

Participatory sensing is an approach which allows mobile devices such as mobile phones to be used for data collection, analysis and sharing processes by individuals. Data collection is the first and most important part of a participatory sensing system, but it is time consuming for the participants. In this paper, we discuss automatic data collection approaches for reducing the time required for collection, and increasing the amount of collected data. In this context, we explore automated text recognition on images of store receipts which are captured by mobile phone cameras, and the correction of the recognized text. Accordingly, our first goal is to evaluate the performance of the Optical Character Recognition (OCR) method with respect to data collection from store receipt images. Images captured by mobile phones exhibit some typical problems, and common image processing methods cannot handle some of them. Consequently, the second goal is to address these types of problems through our proposed Knowledge Based Correction (KBC) method used in support of the OCR, and also to evaluate the KBC method with respect to the improvement on the accurate recognition rate. Results of the experiments show that the KBC method improves the accurate data recognition rate noticeably.

Development of coffee maker service robot using speech and face recognition systems using POMDP

NASA Astrophysics Data System (ADS)

Budiharto, Widodo; Meiliana; Santoso Gunawan, Alexander Agung

2016-07-01

There are many development of intelligent service robot in order to interact with user naturally. This purpose can be done by embedding speech and face recognition ability on specific tasks to the robot. In this research, we would like to propose Intelligent Coffee Maker Robot which the speech recognition is based on Indonesian language and powered by statistical dialogue systems. This kind of robot can be used in the office, supermarket or restaurant. In our scenario, robot will recognize user's face and then accept commands from the user to do an action, specifically in making a coffee. Based on our previous work, the accuracy for speech recognition is about 86% and face recognition is about 93% in laboratory experiments. The main problem in here is to know the intention of user about how sweetness of the coffee. The intelligent coffee maker robot should conclude the user intention through conversation under unreliable automatic speech in noisy environment. In this paper, this spoken dialog problem is treated as a partially observable Markov decision process (POMDP). We describe how this formulation establish a promising framework by empirical results. The dialog simulations are presented which demonstrate significant quantitative outcome.

Bilevel Model-Based Discriminative Dictionary Learning for Recognition.

PubMed

Zhou, Pan; Zhang, Chao; Lin, Zhouchen

2017-03-01

Most supervised dictionary learning methods optimize the combinations of reconstruction error, sparsity prior, and discriminative terms. Thus, the learnt dictionaries may not be optimal for recognition tasks. Also, the sparse codes learning models in the training and the testing phases are inconsistent. Besides, without utilizing the intrinsic data structure, many dictionary learning methods only employ the l 0 or l 1 norm to encode each datum independently, limiting the performance of the learnt dictionaries. We present a novel bilevel model-based discriminative dictionary learning method for recognition tasks. The upper level directly minimizes the classification error, while the lower level uses the sparsity term and the Laplacian term to characterize the intrinsic data structure. The lower level is subordinate to the upper level. Therefore, our model achieves an overall optimality for recognition in that the learnt dictionary is directly tailored for recognition. Moreover, the sparse codes learning models in the training and the testing phases can be the same. We further propose a novel method to solve our bilevel optimization problem. It first replaces the lower level with its Karush-Kuhn-Tucker conditions and then applies the alternating direction method of multipliers to solve the equivalent problem. Extensive experiments demonstrate the effectiveness and robustness of our method.

A Grassmann graph embedding framework for gait analysis

NASA Astrophysics Data System (ADS)

Connie, Tee; Goh, Michael Kah Ong; Teoh, Andrew Beng Jin

2014-12-01

Gait recognition is important in a wide range of monitoring and surveillance applications. Gait information has often been used as evidence when other biometrics is indiscernible in the surveillance footage. Building on recent advances of the subspace-based approaches, we consider the problem of gait recognition on the Grassmann manifold. We show that by embedding the manifold into reproducing kernel Hilbert space and applying the mechanics of graph embedding on such manifold, significant performance improvement can be obtained. In this work, the gait recognition problem is studied in a unified way applicable for both supervised and unsupervised configurations. Sparse representation is further incorporated in the learning mechanism to adaptively harness the local structure of the data. Experiments demonstrate that the proposed method can tolerate variations in appearance for gait identification effectively.

HWDA: A coherence recognition and resolution algorithm for hybrid web data aggregation

NASA Astrophysics Data System (ADS)

Guo, Shuhang; Wang, Jian; Wang, Tong

2017-09-01

Aiming at the object confliction recognition and resolution problem for hybrid distributed data stream aggregation, a distributed data stream object coherence solution technology is proposed. Firstly, the framework was defined for the object coherence conflict recognition and resolution, named HWDA. Secondly, an object coherence recognition technology was proposed based on formal language description logic and hierarchical dependency relationship between logic rules. Thirdly, a conflict traversal recognition algorithm was proposed based on the defined dependency graph. Next, the conflict resolution technology was prompted based on resolution pattern matching including the definition of the three types of conflict, conflict resolution matching pattern and arbitration resolution method. At last, the experiment use two kinds of web test data sets to validate the effect of application utilizing the conflict recognition and resolution technology of HWDA.

Numerical Simulation of Interaction of Human Vocal Folds and Fluid Flow

NASA Astrophysics Data System (ADS)

Kosík, A.; Feistauer, M.; Horáček, J.; Sváček, P.

Our goal is to simulate airflow in human vocal folds and their flow-induced vibrations. We consider two-dimensional viscous incompressible flow in a time-dependent domain. The fluid flow is described by the Navier-Stokes equations in the arbitrary Lagrangian-Eulerian formulation. The flow problem is coupled with the elastic behaviour of the solid bodies. The developed solution of the coupled problem based on the finite element method is demonstrated by numerical experiments.

N-fold Darboux transformation and double-Wronskian-typed solitonic structures for a variable-coefficient modified Kortweg-de Vries equation

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

Wang, Lei, E-mail: wanglei2239@126.com; Gao, Yi-Tian; State Key Laboratory of Software Development Environment, Beijing University of Aeronautics and Astronautics, Beijing 100191

2012-08-15

Under investigation in this paper is a variable-coefficient modified Kortweg-de Vries (vc-mKdV) model describing certain situations from the fluid mechanics, ocean dynamics and plasma physics. N-fold Darboux transformation (DT) of a variable-coefficient Ablowitz-Kaup-Newell-Segur spectral problem is constructed via a gauge transformation. Multi-solitonic solutions in terms of the double Wronskian for the vc-mKdV model are derived by the reduction of the N-fold DT. Three types of the solitonic interactions are discussed through figures: (1) Overtaking collision; (2) Head-on collision; (3) Parallel solitons. Nonlinear, dispersive and dissipative terms have the effects on the velocities of the solitonic waves while the amplitudes ofmore » the waves depend on the perturbation term. - Highlights: Black-Right-Pointing-Pointer N-fold DT is firstly applied to a vc-AKNS spectral problem. Black-Right-Pointing-Pointer Seeking a double Wronskian solution is changed into solving two systems. Black-Right-Pointing-Pointer Effects of the variable coefficients on the multi-solitonic waves are discussed in detail. Black-Right-Pointing-Pointer This work solves the problem from Yi Zhang [Ann. Phys. 323 (2008) 3059].« less

Analysis of chemical signals in red fire ants by gas chromatography and pattern recognition techniques

USDA-ARS?s Scientific Manuscript database

The combination of gas chromatography and pattern recognition (GC/PR) analysis is a powerful tool for investigating complicated biological problems. Clustering, mapping, discriminant development, etc. are necessary to analyze realistically large chromatographic data sets and to seek meaningful relat...

Enantioselective recognition of mandelic acid by a 3,6-dithiophen-2-yl-9H-carbazole-based chiral fluorescent bisboronic acid sensor.

PubMed

Wu, Yubo; Guo, Huimin; James, Tony D; Zhao, Jianzhang

2011-07-15

We have prepared chiral fluorescent bisboronic acid sensors with 3,6-dithiophen-2-yl-9H-carbazole as the fluorophore. The thiophene moiety was used to extend the π-conjugation framework of the fluorophore in order to red-shift the fluorescence emission and, at the same time, to enhance the novel process where the fluorophore serves as the electron donor of the photoinduced electron transfer process (d-PET) of the boronic acid sensors; i.e., the background fluorescence of the sensor 1 at acidic pH is weaker compared to that at neutral or basic pH, in stark contrast to the typical a-PET boronic acid sensors (where the fluorophore serves as the electron acceptor of the photoinduced electron transfer process). The benefit of the d-PET boronic acid sensors is that the recognition of the hydroxylic acids can be achieved at acidic pH. We found that the thiophene moiety is an efficient π-conjugation linker and electron donor; as a result, the d-PET contrast ratio of the sensors upon variation of the pH is improved 10-fold when compared to the previously reported d-PET sensors without the thiophene moiety. Enantioselective recognition of tartaric acid was achieved at acid pH, and the enantioselectivity (total response K(D)I(F)(D)/K(L)I(F)(L)) is 3.3. The fluorescence enhancement (I(F)(Sample)/I(F)(Blank)) of sensor 1 upon binding with tartaric acid is 3.5-fold at pH 3.0. With the fluorescent bisboronic acid sensor 1, enantioselective recognition of mandelic acid was achieved for the first time. To the best of our knowledge, this is the first time that the mandelic acid has been enantioselectively recognized using a chiral fluorescent boronic acid sensor. Chiral monoboronic acid sensor 2 and bisboronic acid sensor 3 without the thiophene moiety failed to enantioselectively recognize mandelic acid. Our findings with the thiophene-incorporated boronic acid sensors will be important for the design of d-PET fluorescent sensors for the enantioselective recognition of α-hydroxylic acids such as mandelic acid, given that it is currently a challenge to recognize these analytes with boronic acid fluorescent molecular sensors.

Black and White Parents' Willingness to Seek Help for Children's Internalizing and Externalizing Symptoms.

PubMed

Thurston, Idia B; Hardin, Robin; Decker, Kristina; Arnold, Trisha; Howell, Kathryn H; Phares, Vicky

2018-01-01

Understanding social and environmental factors that contribute to parental help-seeking intentions is an important step in addressing service underutilization for children in need of treatment. This study examined factors that contribute to parents' intentions to seek formal and informal help for child psychopathology (anxiety and attention-deficit/hyperactivity disorder [ADHD]). A total of 251 parents (N = 128 mothers, N = 123 fathers; 49% Black, 51% White) read 3 vignettes describing children with anxiety, ADHD, and no diagnosis. Measures of problem recognition, perceived barriers, and formal (pediatricians, psychologists, teachers) and informal (religious leaders, family/friends, self-help) help seeking were completed. Four separate hierarchical logistic regression models were used to examine parental help-seeking likelihood from formal and informal sources for internalizing and externalizing symptoms. Predictors were socioeconomic status, parent race, age, and sex, parent problem recognition (via study vignettes), and perceived barriers to mental health service utilization. Mothers were more likely than fathers to seek help from pediatricians, psychologists, teachers, and religious leaders for child anxiety and pediatricians, religious leaders, and self-help resources for child ADHD. Black parents were more likely to seek help from religious leaders and White parents were more likely to use self-help resources. Problem recognition was associated with greater intentions to seek help from almost all formal and informal sources (except from friends/family). Understanding factors that contribute to parental help seeking for child psychopathology is critical for increasing service utilization and reducing the negative effects of mental health problems. This study highlights the importance of decreasing help-seeking barriers and increasing problem recognition to improve health equity. © 2017 Wiley Periodicals, Inc.

Optimal pattern synthesis for speech recognition based on principal component analysis

NASA Astrophysics Data System (ADS)

Korsun, O. N.; Poliyev, A. V.

2018-02-01

The algorithm for building an optimal pattern for the purpose of automatic speech recognition, which increases the probability of correct recognition, is developed and presented in this work. The optimal pattern forming is based on the decomposition of an initial pattern to principal components, which enables to reduce the dimension of multi-parameter optimization problem. At the next step the training samples are introduced and the optimal estimates for principal components decomposition coefficients are obtained by a numeric parameter optimization algorithm. Finally, we consider the experiment results that show the improvement in speech recognition introduced by the proposed optimization algorithm.

Race, Ethnicity, and Eating Disorder Recognition by Peers

PubMed Central

Sala, Margarita; Reyes-Rodríguez, Mae Lynn; Bulik, Cynthia M.; Bardone-Cone, Anna

2013-01-01

We investigated racial/ethnic stereotyping in the recognition and referral of eating disorders with 663 university students. We explored responses to problem and eating disorder recognition, and health care referral after reading a vignette concerning a patient of different race/ethnic background presenting with eating disorders. A series of three 4 × 3 ANOVAs revealed significant main effects for eating disorder across all three outcome variables. There were no significant main effects across the four different race/ethnicity conditions and no significant race by condition interactions. Lack of general eating disorder recognition and health care referral by student participants were found. PMID:24044598

FaceIt: face recognition from static and live video for law enforcement

NASA Astrophysics Data System (ADS)

Atick, Joseph J.; Griffin, Paul M.; Redlich, A. N.

1997-01-01

Recent advances in image and pattern recognition technology- -especially face recognition--are leading to the development of a new generation of information systems of great value to the law enforcement community. With these systems it is now possible to pool and manage vast amounts of biometric intelligence such as face and finger print records and conduct computerized searches on them. We review one of the enabling technologies underlying these systems: the FaceIt face recognition engine; and discuss three applications that illustrate its benefits as a problem-solving technology and an efficient and cost effective investigative tool.

Statistical mechanics of simple models of protein folding and design.

PubMed Central

Pande, V S; Grosberg, A Y; Tanaka, T

1997-01-01

It is now believed that the primary equilibrium aspects of simple models of protein folding are understood theoretically. However, current theories often resort to rather heavy mathematics to overcome some technical difficulties inherent in the problem or start from a phenomenological model. To this end, we take a new approach in this pedagogical review of the statistical mechanics of protein folding. The benefit of our approach is a drastic mathematical simplification of the theory, without resort to any new approximations or phenomenological prescriptions. Indeed, the results we obtain agree precisely with previous calculations. Because of this simplification, we are able to present here a thorough and self contained treatment of the problem. Topics discussed include the statistical mechanics of the random energy model (REM), tests of the validity of REM as a model for heteropolymer freezing, freezing transition of random sequences, phase diagram of designed ("minimally frustrated") sequences, and the degree to which errors in the interactions employed in simulations of either folding and design can still lead to correct folding behavior. Images FIGURE 2 FIGURE 3 FIGURE 4 FIGURE 6 PMID:9414231

Collapse kinetics and chevron plots from simulations of denaturant-dependent folding of globular proteins

PubMed Central

Liu, Zhenxing; Reddy, Govardhan; O’Brien, Edward P.; Thirumalai, D.

2011-01-01

Quantitative description of how proteins fold under experimental conditions remains a challenging problem. Experiments often use urea and guanidinium chloride to study folding whereas the natural variable in simulations is temperature. To bridge the gap, we use the molecular transfer model that combines measured denaturant-dependent transfer free energies for the peptide group and amino acid residues, and a coarse-grained Cα-side chain model for polypeptide chains to simulate the folding of src SH3 domain. Stability of the native state decreases linearly as [C] (the concentration of guanidinium chloride) increases with the slope, m, that is in excellent agreement with experiments. Remarkably, the calculated folding rate at [C] = 0 is only 16-fold larger than the measured value. Most importantly ln kobs (kobs is the sum of folding and unfolding rates) as a function of [C] has the characteristic V (chevron) shape. In every folding trajectory, the times for reaching the native state, interactions stabilizing all the substructures, and global collapse coincide. The value of (mf is the slope of the folding arm of the chevron plot) is identical to the fraction of buried solvent accessible surface area in the structures of the transition state ensemble. In the dominant transition state, which does not vary significantly at low [C], the core of the protein and certain loops are structured. Besides solving the long-standing problem of computing the chevron plot, our work lays the foundation for incorporating denaturant effects in a physically transparent manner either in all-atom or coarse-grained simulations. PMID:21512127

Collapse kinetics and chevron plots from simulations of denaturant-dependent folding of globular proteins.

PubMed

Liu, Zhenxing; Reddy, Govardhan; O'Brien, Edward P; Thirumalai, D

2011-05-10

Quantitative description of how proteins fold under experimental conditions remains a challenging problem. Experiments often use urea and guanidinium chloride to study folding whereas the natural variable in simulations is temperature. To bridge the gap, we use the molecular transfer model that combines measured denaturant-dependent transfer free energies for the peptide group and amino acid residues, and a coarse-grained C(α)-side chain model for polypeptide chains to simulate the folding of src SH(3) domain. Stability of the native state decreases linearly as [C] (the concentration of guanidinium chloride) increases with the slope, m, that is in excellent agreement with experiments. Remarkably, the calculated folding rate at [C] = 0 is only 16-fold larger than the measured value. Most importantly ln k(obs) (k(obs) is the sum of folding and unfolding rates) as a function of [C] has the characteristic V (chevron) shape. In every folding trajectory, the times for reaching the native state, interactions stabilizing all the substructures, and global collapse coincide. The value of (m(f) is the slope of the folding arm of the chevron plot) is identical to the fraction of buried solvent accessible surface area in the structures of the transition state ensemble. In the dominant transition state, which does not vary significantly at low [C], the core of the protein and certain loops are structured. Besides solving the long-standing problem of computing the chevron plot, our work lays the foundation for incorporating denaturant effects in a physically transparent manner either in all-atom or coarse-grained simulations.

Deterministic folding: The role of entropic forces and steric specificities

NASA Astrophysics Data System (ADS)

da Silva, Roosevelt A.; da Silva, M. A. A.; Caliri, A.

2001-03-01

The inverse folding problem of proteinlike macromolecules is studied by using a lattice Monte Carlo (MC) model in which steric specificities (nearest-neighbors constraints) are included and the hydrophobic effect is treated explicitly by considering interactions between the chain and solvent molecules. Chemical attributes and steric peculiarities of the residues are encoded in a 10-letter alphabet and a correspondent "syntax" is provided in order to write suitable sequences for the specified target structures; twenty-four target configurations, chosen in order to cover all possible values of the average contact order χ (0.2381⩽χ⩽0.4947 for this system), were encoded and analyzed. The results, obtained by MC simulations, are strongly influenced by geometrical properties of the native configuration, namely χ and the relative number φ of crankshafts-type structures: For χ<0.35 the folding is deterministic, that is, the syntax is able to encode successful sequences: The system presents larger encodability, minimum sequence-target degeneracies and smaller characteristic folding time τf. For χ⩾0.35 the above results are not reproduced any more: The folding success is severely reduced, showing strong correlation with φ. Additionally, the existence of distinct characteristic folding times suggests that different mechanisms are acting at the same time in the folding process. The results (all obtained from the same single model, under the same "physiological conditions") resemble some general features of the folding problem, supporting the premise that the steric specificities, in association with the entropic forces (hydrophobic effect), are basic ingredients in the protein folding process.

A study of fuzzy logic ensemble system performance on face recognition problem

NASA Astrophysics Data System (ADS)

Polyakova, A.; Lipinskiy, L.

2017-02-01

Some problems are difficult to solve by using a single intelligent information technology (IIT). The ensemble of the various data mining (DM) techniques is a set of models which are able to solve the problem by itself, but the combination of which allows increasing the efficiency of the system as a whole. Using the IIT ensembles can improve the reliability and efficiency of the final decision, since it emphasizes on the diversity of its components. The new method of the intellectual informational technology ensemble design is considered in this paper. It is based on the fuzzy logic and is designed to solve the classification and regression problems. The ensemble consists of several data mining algorithms: artificial neural network, support vector machine and decision trees. These algorithms and their ensemble have been tested by solving the face recognition problems. Principal components analysis (PCA) is used for feature selection.

Handwritten digits recognition based on immune network

NASA Astrophysics Data System (ADS)

Li, Yangyang; Wu, Yunhui; Jiao, Lc; Wu, Jianshe

2011-11-01

With the development of society, handwritten digits recognition technique has been widely applied to production and daily life. It is a very difficult task to solve these problems in the field of pattern recognition. In this paper, a new method is presented for handwritten digit recognition. The digit samples firstly are processed and features extraction. Based on these features, a novel immune network classification algorithm is designed and implemented to the handwritten digits recognition. The proposed algorithm is developed by Jerne's immune network model for feature selection and KNN method for classification. Its characteristic is the novel network with parallel commutating and learning. The performance of the proposed method is experimented to the handwritten number datasets MNIST and compared with some other recognition algorithms-KNN, ANN and SVM algorithm. The result shows that the novel classification algorithm based on immune network gives promising performance and stable behavior for handwritten digits recognition.

Transfer Learning for Activity Recognition: A Survey

PubMed Central

Cook, Diane; Feuz, Kyle D.; Krishnan, Narayanan C.

2013-01-01

Many intelligent systems that focus on the needs of a human require information about the activities being performed by the human. At the core of this capability is activity recognition, which is a challenging and well-researched problem. Activity recognition algorithms require substantial amounts of labeled training data yet need to perform well under very diverse circumstances. As a result, researchers have been designing methods to identify and utilize subtle connections between activity recognition datasets, or to perform transfer-based activity recognition. In this paper we survey the literature to highlight recent advances in transfer learning for activity recognition. We characterize existing approaches to transfer-based activity recognition by sensor modality, by differences between source and target environments, by data availability, and by type of information that is transferred. Finally, we present some grand challenges for the community to consider as this field is further developed. PMID:24039326

Image registration under translation and rotation in two-dimensional planes using Fourier slice theorem.

PubMed

Pohit, M; Sharma, J

2015-05-10

Image recognition in the presence of both rotation and translation is a longstanding problem in correlation pattern recognition. Use of log polar transform gives a solution to this problem, but at a cost of losing the vital phase information from the image. The main objective of this paper is to develop an algorithm based on Fourier slice theorem for measuring the simultaneous rotation and translation of an object in a 2D plane. The algorithm is applicable for any arbitrary object shift for full 180° rotation.

Structure of the SH3 Domain of Rat Endophilin A2

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

Loll,P.; Swain, E.; Chen, Y.

2008-01-01

The crystal structure of the SH3 domain of rat endophilin A2 has been determined by the multiwavelength anomalous dispersion method and refined at a resolution of 1.70 Angstroms to R and Rfree values of 0.196 and 0.217, respectively. The structure adheres to the canonical SH3-domain fold and is highly similar to those of the corresponding domains of endophilins A1 and A3. An intermolecular packing interaction between two molecules in the lattice exploits features that are commonly observed in SH3-domain ligand recognition, including the insertion of a proline side chain into the ligand-binding groove of the protein and the recognition ofmore » a basic residue by a cluster of acidic side chains on the RT loop.« less

Assessment of biological, psychological and adherence factors in the prediction of step-down treatment for patients with well-controlled asthma.

PubMed

Saito, N; Kamata, A; Itoga, M; Tamaki, M; Kayaba, H; Ritz, T

2017-04-01

Inhaled corticosteroids (ICS) and inhaled corticosteroids combined with long-acting beta2-agonist (ICS/LABA) are standard treatments for asthma. However, factors that might help reduce medication in well-controlled asthma are unknown. We classified problems of asthma patients into biological, psychological and adherence factors, and investigated factors associated with the indication and failure of a medication step-down treatment. Two hundred twenty two well-controlled asthma patients receiving ICS or ICS/LABA were assessed for physical and psychiatric problems and followed up for one year from adjustment of their treatment step. Factor B was defined as a presence of chronic upper airway complications. Factor P was defined as presence of psychiatric complications such as sleep disorder, depression, anxiety and somatoform disorders. Factor A was defined as poor adherence to ICS or ICS/LABA inhaler of 75% or less. Success in step-down treatment was defined as maintenance of well-controlled status for over one year after step-down. Factor B was the most important single negative predictive factor for indication for step-down treatment (Odds ratio; 0.19). Factor A increased the risk of failure to maintain step-down treatment most significantly by 23-fold, and factor B increased it by 11-fold. The combination of factors B and A increased failure by 24-fold, factors P and A by 21-fold, all three factors by 36-fold. Factor P only interacted with the other factors to reduce chances of stepping down, but did not constitute a problem factor when present alone. The evaluation of biological, psychological and adherence problems may lead to a more proactive and targeted approach to step-down treatment for patients with well-controlled asthma. © 2017 John Wiley & Sons Ltd.

Parent Recognition and Responses to Developmental Concerns in Young Children

ERIC Educational Resources Information Center

Marshall, Jennifer; Coulter, Martha L.; Gorski, Peter A.; Ewing, Aldenise

2016-01-01

This mixed-methods study examined influences, factors, and processes associated with parental recognition and appraisal of developmental concerns among 23 English- and Spanish-speaking parents of young children with signs of developmental or behavioral problems. Participants shared their experiences through in-depth interviews or focus groups and…

Pattern Recognition by Retina-Like Devices.

ERIC Educational Resources Information Center

Weiman, Carl F. R.; Rothstein, Jerome

This study has investigated some pattern recognition capabilities of devices consisting of arrays of cooperating elements acting in parallel. The problem of recognizing straight lines in general position on the quadratic lattice has been completely solved by applying parallel acting algorithms to a special code for lines on the lattice. The…

Concept Recognition in an Automatic Text-Processing System for the Life Sciences.

ERIC Educational Resources Information Center

Vleduts-Stokolov, Natasha

1987-01-01

Describes a system developed for the automatic recognition of biological concepts in titles of scientific articles; reports results of several pilot experiments which tested the system's performance; analyzes typical ambiguity problems encountered by the system; describes a disambiguation technique that was developed; and discusses future plans…

The Army word recognition system

NASA Technical Reports Server (NTRS)

Hadden, David R.; Haratz, David

1977-01-01

The application of speech recognition technology in the Army command and control area is presented. The problems associated with this program are described as well as as its relevance in terms of the man/machine interactions, voice inflexions, and the amount of training needed to interact with and utilize the automated system.

Ovulation, In Vivo Emotion Regulation Problems, and Sexual Risk Recognition Deficits

ERIC Educational Resources Information Center

Walsh, Kate; DiLillo, David

2013-01-01

Objective: To examine associations between menstrual cycle phase, negative mood, sexual risk recognition deficits (assessed via an analogue risk vignette), and in vivo emotion dysregulation. Participants: Participants were 714 college women recruited between February 2007 and December 2009. Methods: Participants were randomly assigned to a…

A "Situational" and "Coorientational" Measure of Specialized Magazine Editors' Perceptions of Readers.

ERIC Educational Resources Information Center

Jeffers, Dennis W.

A study was undertaken of specialized magazine editors' perceptions of audience characteristics as well as the perceived role of their publications. Specifically, the study examines the relationship between the editors' perceptions of reader problem recognition, level of involvement, constraint recognition, and possession of reference criteria and…

Cross-domain expression recognition based on sparse coding and transfer learning

NASA Astrophysics Data System (ADS)

Yang, Yong; Zhang, Weiyi; Huang, Yong

2017-05-01

Traditional facial expression recognition methods usually assume that the training set and the test set are independent and identically distributed. However, in actual expression recognition applications, the conditions of independent and identical distribution are hardly satisfied for the training set and test set because of the difference of light, shade, race and so on. In order to solve this problem and improve the performance of expression recognition in the actual applications, a novel method based on transfer learning and sparse coding is applied to facial expression recognition. First of all, a common primitive model, that is, the dictionary is learnt. Then, based on the idea of transfer learning, the learned primitive pattern is transferred to facial expression and the corresponding feature representation is obtained by sparse coding. The experimental results in CK +, JAFFE and NVIE database shows that the transfer learning based on sparse coding method can effectively improve the expression recognition rate in the cross-domain expression recognition task and is suitable for the practical facial expression recognition applications.

From Flapping Birds to Space Telescopes: The Modern Science of Origami (BNL Women in Science Lecture)

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

Lang, Robert J

2010-06-24

During the 1990s, the development and application of mathematical techniques to origami revolutionized this centuries-old Japanese art of paper folding. In his talk, Lang will describe how geometric concepts led to the solution of a broad class of origami-folding problems. Conversely, algorithms and theorems of origami design have shed light on long-standing mathematical questions and have solved practical engineering problems. Lang will discuss how origami has led to huge space telescopes, safer airbags, and more.

Bridging the gap: from biometrics to forensics.

PubMed

Jain, Anil K; Ross, Arun

2015-08-05

Biometric recognition, or simply biometrics, refers to automated recognition of individuals based on their behavioural and biological characteristics. The success of fingerprints in forensic science and law enforcement applications, coupled with growing concerns related to border control, financial fraud and cyber security, has generated a huge interest in using fingerprints, as well as other biological traits, for automated person recognition. It is, therefore, not surprising to see biometrics permeating various segments of our society. Applications include smartphone security, mobile payment, border crossing, national civil registry and access to restricted facilities. Despite these successful deployments in various fields, there are several existing challenges and new opportunities for person recognition using biometrics. In particular, when biometric data is acquired in an unconstrained environment or if the subject is uncooperative, the quality of the ensuing biometric data may not be amenable for automated person recognition. This is particularly true in crime-scene investigations, where the biological evidence gleaned from a scene may be of poor quality. In this article, we first discuss how biometrics evolved from forensic science and how its focus is shifting back to its origin in order to address some challenging problems. Next, we enumerate the similarities and differences between biometrics and forensics. We then present some applications where the principles of biometrics are being successfully leveraged into forensics in order to solve critical problems in the law enforcement domain. Finally, we discuss new collaborative opportunities for researchers in biometrics and forensics, in order to address hitherto unsolved problems that can benefit society at large. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Bridging the gap: from biometrics to forensics

PubMed Central

Jain, Anil K.; Ross, Arun

2015-01-01

Biometric recognition, or simply biometrics, refers to automated recognition of individuals based on their behavioural and biological characteristics. The success of fingerprints in forensic science and law enforcement applications, coupled with growing concerns related to border control, financial fraud and cyber security, has generated a huge interest in using fingerprints, as well as other biological traits, for automated person recognition. It is, therefore, not surprising to see biometrics permeating various segments of our society. Applications include smartphone security, mobile payment, border crossing, national civil registry and access to restricted facilities. Despite these successful deployments in various fields, there are several existing challenges and new opportunities for person recognition using biometrics. In particular, when biometric data is acquired in an unconstrained environment or if the subject is uncooperative, the quality of the ensuing biometric data may not be amenable for automated person recognition. This is particularly true in crime-scene investigations, where the biological evidence gleaned from a scene may be of poor quality. In this article, we first discuss how biometrics evolved from forensic science and how its focus is shifting back to its origin in order to address some challenging problems. Next, we enumerate the similarities and differences between biometrics and forensics. We then present some applications where the principles of biometrics are being successfully leveraged into forensics in order to solve critical problems in the law enforcement domain. Finally, we discuss new collaborative opportunities for researchers in biometrics and forensics, in order to address hitherto unsolved problems that can benefit society at large. PMID:26101280

Building Multiclass Classifiers for Remote Homology Detection and Fold Recognition

DTIC Science & Technology

2006-04-05

classes. In this study we evaluate the effectiveness of one of these formulations that was developed by Crammer and Singer [9], which leads to...significantly more complex model can be learned by directly applying the Crammer -Singer multiclass formulation on the outputs of the binary classifiers...will refer to this as the Crammer -Singer (CS) model. Comparing the scaling approach to the Crammer -Singer approach we can see that the Crammer -Singer

Biometric identification

NASA Astrophysics Data System (ADS)

Syryamkim, V. I.; Kuznetsov, D. N.; Kuznetsova, A. S.

2018-05-01

Image recognition is an information process implemented by some information converter (intelligent information channel, recognition system) having input and output. The input of the system is fed with information about the characteristics of the objects being presented. The output of the system displays information about which classes (generalized images) the recognized objects are assigned to. When creating and operating an automated system for pattern recognition, a number of problems are solved, while for different authors the formulations of these tasks, and the set itself, do not coincide, since it depends to a certain extent on the specific mathematical model on which this or that recognition system is based. This is the task of formalizing the domain, forming a training sample, learning the recognition system, reducing the dimensionality of space.

Performance improvement of multi-class detection using greedy algorithm for Viola-Jones cascade selection

NASA Astrophysics Data System (ADS)

Tereshin, Alexander A.; Usilin, Sergey A.; Arlazarov, Vladimir V.

2018-04-01

This paper aims to study the problem of multi-class object detection in video stream with Viola-Jones cascades. An adaptive algorithm for selecting Viola-Jones cascade based on greedy choice strategy in solution of the N-armed bandit problem is proposed. The efficiency of the algorithm on the problem of detection and recognition of the bank card logos in the video stream is shown. The proposed algorithm can be effectively used in documents localization and identification, recognition of road scene elements, localization and tracking of the lengthy objects , and for solving other problems of rigid object detection in a heterogeneous data flows. The computational efficiency of the algorithm makes it possible to use it both on personal computers and on mobile devices based on processors with low power consumption.

Mechanical development of folded chert beds in Monterey Formation, California

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

Crowther, D.; Snyder, W.S.

1988-03-01

Small-scale folds in the upper siliceous facies of the Miocene Monterey Formation, at Lions Head, California (Santa Maria basin) are of tectonic origin. Folding is well developed in the chert-dominated zones and dies out rapidly in the adjacent siliceous mudstones. A tectonic origin is evidenced by the dominantly brittle deformation of the competent chert layers. Mechanically, the folds formed through a complex interrelationship between fracture and flexural slip. Opal-CT and quartz-chert layers display brittle fractures and rotated fracture blocks that responded to shortening. Thrusting of the chert layers is common in folds where fold propagation was impeded. Dilation breccia andmore » void space occur in the hinges and reflect room problems during development of these disharmonic folds. Subsequent diagenesis has partially healed the fractures and slip surfaces, creating the erroneous appearance that ductile deformation was an important factor in the formation of the folds.« less

Scattering features for lung cancer detection in fibered confocal fluorescence microscopy images.

PubMed

Rakotomamonjy, Alain; Petitjean, Caroline; Salaün, Mathieu; Thiberville, Luc

2014-06-01

To assess the feasibility of lung cancer diagnosis using fibered confocal fluorescence microscopy (FCFM) imaging technique and scattering features for pattern recognition. FCFM imaging technique is a new medical imaging technique for which interest has yet to be established for diagnosis. This paper addresses the problem of lung cancer detection using FCFM images and, as a first contribution, assesses the feasibility of computer-aided diagnosis through these images. Towards this aim, we have built a pattern recognition scheme which involves a feature extraction stage and a classification stage. The second contribution relies on the features used for discrimination. Indeed, we have employed the so-called scattering transform for extracting discriminative features, which are robust to small deformations in the images. We have also compared and combined these features with classical yet powerful features like local binary patterns (LBP) and their variants denoted as local quinary patterns (LQP). We show that scattering features yielded to better recognition performances than classical features like LBP and their LQP variants for the FCFM image classification problems. Another finding is that LBP-based and scattering-based features provide complementary discriminative information and, in some situations, we empirically establish that performance can be improved when jointly using LBP, LQP and scattering features. In this work we analyze the joint capability of FCFM images and scattering features for lung cancer diagnosis. The proposed method achieves a good recognition rate for such a diagnosis problem. It also performs well when used in conjunction with other features for other classical medical imaging classification problems. Copyright © 2014 Elsevier B.V. All rights reserved.

Combining heterogenous features for 3D hand-held object recognition

NASA Astrophysics Data System (ADS)

Lv, Xiong; Wang, Shuang; Li, Xiangyang; Jiang, Shuqiang

2014-10-01

Object recognition has wide applications in the area of human-machine interaction and multimedia retrieval. However, due to the problem of visual polysemous and concept polymorphism, it is still a great challenge to obtain reliable recognition result for the 2D images. Recently, with the emergence and easy availability of RGB-D equipment such as Kinect, this challenge could be relieved because the depth channel could bring more information. A very special and important case of object recognition is hand-held object recognition, as hand is a straight and natural way for both human-human interaction and human-machine interaction. In this paper, we study the problem of 3D object recognition by combining heterogenous features with different modalities and extraction techniques. For hand-craft feature, although it reserves the low-level information such as shape and color, it has shown weakness in representing hiconvolutionalgh-level semantic information compared with the automatic learned feature, especially deep feature. Deep feature has shown its great advantages in large scale dataset recognition but is not always robust to rotation or scale variance compared with hand-craft feature. In this paper, we propose a method to combine hand-craft point cloud features and deep learned features in RGB and depth channle. First, hand-held object segmentation is implemented by using depth cues and human skeleton information. Second, we combine the extracted hetegerogenous 3D features in different stages using linear concatenation and multiple kernel learning (MKL). Then a training model is used to recognize 3D handheld objects. Experimental results validate the effectiveness and gerneralization ability of the proposed method.

Gait Recognition Based on Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Sokolova, A.; Konushin, A.

2017-05-01

In this work we investigate the problem of people recognition by their gait. For this task, we implement deep learning approach using the optical flow as the main source of motion information and combine neural feature extraction with the additional embedding of descriptors for representation improvement. In order to find the best heuristics, we compare several deep neural network architectures, learning and classification strategies. The experiments were made on two popular datasets for gait recognition, so we investigate their advantages and disadvantages and the transferability of considered methods.

Evolution of I-SceI Homing Endonucleases with Increased DNA Recognition Site Specificity

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

Joshi, Rakesh; Ho, Kwok Ki; Tenney, Kristen

2013-09-18

Elucidating how homing endonucleases undergo changes in recognition site specificity will facilitate efforts to engineer proteins for gene therapy applications. I-SceI is a monomeric homing endonuclease that recognizes and cleaves within an 18-bp target. It tolerates limited degeneracy in its target sequence, including substitution of a C:G{sub +4} base pair for the wild-type A:T{sub +4} base pair. Libraries encoding randomized amino acids at I-SceI residue positions that contact or are proximal to A:T{sub +4} were used in conjunction with a bacterial one-hybrid system to select I-SceI derivatives that bind to recognition sites containing either the A:T{sub +4} or the C:G{submore » +4} base pairs. As expected, isolates encoding wild-type residues at the randomized positions were selected using either target sequence. All I-SceI proteins isolated using the C:G{sub +4} recognition site included small side-chain substitutions at G100 and either contained (K86R/G100T, K86R/G100S and K86R/G100C) or lacked (G100A, G100T) a K86R substitution. Interestingly, the binding affinities of the selected variants for the wild-type A:T{sub +4} target are 4- to 11-fold lower than that of wild-type I-SceI, whereas those for the C:G{sub +4} target are similar. The increased specificity of the mutant proteins is also evident in binding experiments in vivo. These differences in binding affinities account for the observed -36-fold difference in target preference between the K86R/G100T and wild-type proteins in DNA cleavage assays. An X-ray crystal structure of the K86R/G100T mutant protein bound to a DNA duplex containing the C:G{sub +4} substitution suggests how sequence specificity of a homing enzyme can increase. This biochemical and structural analysis defines one pathway by which site specificity is augmented for a homing endonuclease.« less

TALE-PvuII fusion proteins--novel tools for gene targeting.

PubMed

Yanik, Mert; Alzubi, Jamal; Lahaye, Thomas; Cathomen, Toni; Pingoud, Alfred; Wende, Wolfgang

2013-01-01

Zinc finger nucleases (ZFNs) consist of zinc fingers as DNA-binding module and the non-specific DNA-cleavage domain of the restriction endonuclease FokI as DNA-cleavage module. This architecture is also used by TALE nucleases (TALENs), in which the DNA-binding modules of the ZFNs have been replaced by DNA-binding domains based on transcription activator like effector (TALE) proteins. Both TALENs and ZFNs are programmable nucleases which rely on the dimerization of FokI to induce double-strand DNA cleavage at the target site after recognition of the target DNA by the respective DNA-binding module. TALENs seem to have an advantage over ZFNs, as the assembly of TALE proteins is easier than that of ZFNs. Here, we present evidence that variant TALENs can be produced by replacing the catalytic domain of FokI with the restriction endonuclease PvuII. These fusion proteins recognize only the composite recognition site consisting of the target site of the TALE protein and the PvuII recognition sequence (addressed site), but not isolated TALE or PvuII recognition sites (unaddressed sites), even at high excess of protein over DNA and long incubation times. In vitro, their preference for an addressed over an unaddressed site is > 34,000-fold. Moreover, TALE-PvuII fusion proteins are active in cellula with minimal cytotoxicity.

OPTICAL INFORMATION PROCESSING: Synthesis of an object recognition system based on the profile of the envelope of a laser pulse in pulsed lidars

NASA Astrophysics Data System (ADS)

Buryi, E. V.

1998-05-01

The main problems in the synthesis of an object recognition system, based on the principles of operation of neuron networks, are considered. Advantages are demonstrated of a hierarchical structure of the recognition algorithm. The use of reading of the amplitude spectrum of signals as information tags is justified and a method is developed for determination of the dimensionality of the tag space. Methods are suggested for ensuring the stability of object recognition in the optical range. It is concluded that it should be possible to recognise perspectives of complex objects.

Optical and digital pattern recognition; Proceedings of the Meeting, Los Angeles, CA, Jan. 13-15, 1987

NASA Technical Reports Server (NTRS)

Liu, Hua-Kuang (Editor); Schenker, Paul (Editor)

1987-01-01

The papers presented in this volume provide an overview of current research in both optical and digital pattern recognition, with a theme of identifying overlapping research problems and methodologies. Topics discussed include image analysis and low-level vision, optical system design, object analysis and recognition, real-time hybrid architectures and algorithms, high-level image understanding, and optical matched filter design. Papers are presented on synthetic estimation filters for a control system; white-light correlator character recognition; optical AI architectures for intelligent sensors; interpreting aerial photographs by segmentation and search; and optical information processing using a new photopolymer.

Speech Recognition Technology for Disabilities Education

ERIC Educational Resources Information Center

Tang, K. Wendy; Kamoua, Ridha; Sutan, Victor; Farooq, Omer; Eng, Gilbert; Chu, Wei Chern; Hou, Guofeng

2005-01-01

Speech recognition is an alternative to traditional methods of interacting with a computer, such as textual input through a keyboard. An effective system can replace or reduce the reliability on standard keyboard and mouse input. This can especially assist dyslexic students who have problems with character or word use and manipulation in a textual…

A Multimodal Approach to Emotion Recognition Ability in Autism Spectrum Disorders

ERIC Educational Resources Information Center

Jones, Catherine R. G.; Pickles, Andrew; Falcaro, Milena; Marsden, Anita J. S.; Happe, Francesca; Scott, Sophie K.; Sauter, Disa; Tregay, Jenifer; Phillips, Rebecca J.; Baird, Gillian; Simonoff, Emily; Charman, Tony

2011-01-01

Background: Autism spectrum disorders (ASD) are characterised by social and communication difficulties in day-to-day life, including problems in recognising emotions. However, experimental investigations of emotion recognition ability in ASD have been equivocal, hampered by small sample sizes, narrow IQ range and over-focus on the visual modality.…

Computing with Connections in Visual Recognition of Origami Objects.

ERIC Educational Resources Information Center

Sabbah, Daniel

1985-01-01

Summarizes an initial foray in tackling artificial intelligence problems using a connectionist approach. The task chosen is visual recognition of Origami objects, and the questions answered are how to construct a connectionist network to represent and recognize projected Origami line drawings and the advantages such an approach would have. (30…

Prediction of Word Recognition in the First Half of Grade 1

ERIC Educational Resources Information Center

Snel, M. J.; Aarnoutse, C. A. J.; Terwel, J.; van Leeuwe, J. F. J.; van der Veld, W. M.

2016-01-01

Early detection of reading problems is important to prevent an enduring lag in reading skills. We studied the relationship between speed of word recognition (after six months of grade 1 education) and four kindergarten pre-literacy skills: letter knowledge, phonological awareness and naming speed for both digits and letters. Our sample consisted…

Appearance-based representative samples refining method for palmprint recognition

NASA Astrophysics Data System (ADS)

Wen, Jiajun; Chen, Yan

2012-07-01

The sparse representation can deal with the lack of sample problem due to utilizing of all the training samples. However, the discrimination ability will degrade when more training samples are used for representation. We propose a novel appearance-based palmprint recognition method. We aim to find a compromise between the discrimination ability and the lack of sample problem so as to obtain a proper representation scheme. Under the assumption that the test sample can be well represented by a linear combination of a certain number of training samples, we first select the representative training samples according to the contributions of the samples. Then we further refine the training samples by an iteration procedure, excluding the training sample with the least contribution to the test sample for each time. Experiments on PolyU multispectral palmprint database and two-dimensional and three-dimensional palmprint database show that the proposed method outperforms the conventional appearance-based palmprint recognition methods. Moreover, we also explore and find out the principle of the usage for the key parameters in the proposed algorithm, which facilitates to obtain high-recognition accuracy.

An improved finger-vein recognition algorithm based on template matching

NASA Astrophysics Data System (ADS)

Liu, Yueyue; Di, Si; Jin, Jian; Huang, Daoping

2016-10-01

Finger-vein recognition has became the most popular biometric identify methods. The investigation on the recognition algorithms always is the key point in this field. So far, there are many applicable algorithms have been developed. However, there are still some problems in practice, such as the variance of the finger position which may lead to the image distortion and shifting; during the identification process, some matching parameters determined according to experience may also reduce the adaptability of algorithm. Focus on above mentioned problems, this paper proposes an improved finger-vein recognition algorithm based on template matching. In order to enhance the robustness of the algorithm for the image distortion, the least squares error method is adopted to correct the oblique finger. During the feature extraction, local adaptive threshold method is adopted. As regard as the matching scores, we optimized the translation preferences as well as matching distance between the input images and register images on the basis of Naoto Miura algorithm. Experimental results indicate that the proposed method can improve the robustness effectively under the finger shifting and rotation conditions.

Syntactic/semantic techniques for feature description and character recognition

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

Gonzalez, R.C.

1983-01-01

The Pattern Analysis Branch, Mapping, Charting and Geodesy (MC/G) Division, of the Naval Ocean Research and Development Activity (NORDA) has been involved over the past several years in the development of algorithms and techniques for computer recognition of free-form handprinted symbols as they appear on the Defense Mapping Agency (DMA) maps and charts. NORDA has made significant contributions to the automation of MC/G through advancing the state of the art in such information extraction techniques. In particular, new concepts in character (symbol) skeletonization, rugged feature measurements, and expert system-oriented decision logic have allowed the development of a very high performancemore » Handprinted Symbol Recognition (HSR) system for identifying depth soundings from naval smooth sheets (accuracies greater than 99.5%). The study reported in this technical note is part of NORDA's continuing research and development in pattern and shape analysis as it applies to Navy and DMA ocean/environment problems. The issue addressed in this technical note deals with emerging areas of syntactic and semantic techniques in pattern recognition as they might apply to the free-form symbol problem.« less

Pose Invariant Face Recognition Based on Hybrid Dominant Frequency Features

NASA Astrophysics Data System (ADS)

Wijaya, I. Gede Pasek Suta; Uchimura, Keiichi; Hu, Zhencheng

Face recognition is one of the most active research areas in pattern recognition, not only because the face is a human biometric characteristics of human being but also because there are many potential applications of the face recognition which range from human-computer interactions to authentication, security, and surveillance. This paper presents an approach to pose invariant human face image recognition. The proposed scheme is based on the analysis of discrete cosine transforms (DCT) and discrete wavelet transforms (DWT) of face images. From both the DCT and DWT domain coefficients, which describe the facial information, we build compact and meaningful features vector, using simple statistical measures and quantization. This feature vector is called as the hybrid dominant frequency features. Then, we apply a combination of the L2 and Lq metric to classify the hybrid dominant frequency features to a person's class. The aim of the proposed system is to overcome the high memory space requirement, the high computational load, and the retraining problems of previous methods. The proposed system is tested using several face databases and the experimental results are compared to a well-known Eigenface method. The proposed method shows good performance, robustness, stability, and accuracy without requiring geometrical normalization. Furthermore, the purposed method has low computational cost, requires little memory space, and can overcome retraining problem.

Face recognition using total margin-based adaptive fuzzy support vector machines.

PubMed

Liu, Yi-Hung; Chen, Yen-Ting

2007-01-01

This paper presents a new classifier called total margin-based adaptive fuzzy support vector machines (TAF-SVM) that deals with several problems that may occur in support vector machines (SVMs) when applied to the face recognition. The proposed TAF-SVM not only solves the overfitting problem resulted from the outlier with the approach of fuzzification of the penalty, but also corrects the skew of the optimal separating hyperplane due to the very imbalanced data sets by using different cost algorithm. In addition, by introducing the total margin algorithm to replace the conventional soft margin algorithm, a lower generalization error bound can be obtained. Those three functions are embodied into the traditional SVM so that the TAF-SVM is proposed and reformulated in both linear and nonlinear cases. By using two databases, the Chung Yuan Christian University (CYCU) multiview and the facial recognition technology (FERET) face databases, and using the kernel Fisher's discriminant analysis (KFDA) algorithm to extract discriminating face features, experimental results show that the proposed TAF-SVM is superior to SVM in terms of the face-recognition accuracy. The results also indicate that the proposed TAF-SVM can achieve smaller error variances than SVM over a number of tests such that better recognition stability can be obtained.

Ear recognition from one sample per person.

PubMed

Chen, Long; Mu, Zhichun; Zhang, Baoqing; Zhang, Yi

2015-01-01

Biometrics has the advantages of efficiency and convenience in identity authentication. As one of the most promising biometric-based methods, ear recognition has received broad attention and research. Previous studies have achieved remarkable performance with multiple samples per person (MSPP) in the gallery. However, most conventional methods are insufficient when there is only one sample per person (OSPP) available in the gallery. To solve the OSPP problem by maximizing the use of a single sample, this paper proposes a hybrid multi-keypoint descriptor sparse representation-based classification (MKD-SRC) ear recognition approach based on 2D and 3D information. Because most 3D sensors capture 3D data accessorizing the corresponding 2D data, it is sensible to use both types of information. First, the ear region is extracted from the profile. Second, keypoints are detected and described for both the 2D texture image and 3D range image. Then, the hybrid MKD-SRC algorithm is used to complete the recognition with only OSPP in the gallery. Experimental results on a benchmark dataset have demonstrated the feasibility and effectiveness of the proposed method in resolving the OSPP problem. A Rank-one recognition rate of 96.4% is achieved for a gallery of 415 subjects, and the time involved in the computation is satisfactory compared to conventional methods.

Intelligent data processing of an ultrasonic sensor system for pattern recognition improvements

NASA Astrophysics Data System (ADS)

Na, Seung You; Park, Min-Sang; Hwang, Won-Gul; Kee, Chang-Doo

1999-05-01

Though conventional time-of-flight ultrasonic sensor systems are popular due to the advantages of low cost and simplicity, the usage of the sensors is rather narrowly restricted within object detection and distance readings. There is a strong need to enlarge the amount of environmental information for mobile applications to provide intelligent autonomy. Wide sectors of such neighboring object recognition problems can be satisfactorily handled with coarse vision data such as sonar maps instead of accurate laser or optic measurements. For the usage of object pattern recognition, ultrasonic senors have inherent shortcomings of poor directionality and specularity which result in low spatial resolution and indistinctiveness of object patterns. To resolve these problems an array of increased number of sensor elements has been used for large objects. In this paper we propose a method of sensor array system with improved recognition capability using electronic circuits accompanying the sensor array and neuro-fuzzy processing of data fusion. The circuit changes transmitter output voltages of array elements in several steps. Relying upon the known sensor characteristics, a set of different return signals from neighboring senors is manipulated to provide an enhanced pattern recognition in the aspects of inclination angle, size and shift as well as distance of objects. The results show improved resolution of the measurements for smaller targets.

Social behavior following traumatic brain injury and its association with emotion recognition, understanding of intentions, and cognitive flexibility.

PubMed

Milders, Maarten; Ietswaart, Magdalena; Crawford, John R; Currie, David

2008-03-01

Although the adverse consequences of changes in social behavior following traumatic brain injury (TBI) are well documented, relatively little is known about possible underlying neuropsychological deficits. Following a model originally developed for social behavior deficits in schizophrenia, we investigated whether impairments in emotion recognition, understanding of other people's intentions ("theory of mind"), and cognitive flexibility soon after first TBI or 1 year later were associated with self and proxy ratings of behavior following TBI. Each of the three functions was assessed with two separate tests, and ratings of behavior were collected on three questionnaires. Patients with TBI (n = 33) were impaired in emotion recognition, "theory of mind," and cognitive flexibility compared with matched orthopedic controls (n = 34). Proxy ratings showed increases in behavioral problems 1 year following injury in the TBI group but not in the control group. However, test performance was not associated with questionnaire data. Severity of the impairments in emotion recognition, understanding intention, and flexibility were unrelated to the severity of behavioral problems following TBI. These findings failed to confirm the used model for social behavior deficits and may cast doubt on the alleged link between deficits in emotion recognition or theory of mind and social functioning.

A framework for the recognition of 3D faces and expressions

NASA Astrophysics Data System (ADS)

Li, Chao; Barreto, Armando

2006-04-01

Face recognition technology has been a focus both in academia and industry for the last couple of years because of its wide potential applications and its importance to meet the security needs of today's world. Most of the systems developed are based on 2D face recognition technology, which uses pictures for data processing. With the development of 3D imaging technology, 3D face recognition emerges as an alternative to overcome the difficulties inherent with 2D face recognition, i.e. sensitivity to illumination conditions and orientation positioning of the subject. But 3D face recognition still needs to tackle the problem of deformation of facial geometry that results from the expression changes of a subject. To deal with this issue, a 3D face recognition framework is proposed in this paper. It is composed of three subsystems: an expression recognition system, a system for the identification of faces with expression, and neutral face recognition system. A system for the recognition of faces with one type of expression (happiness) and neutral faces was implemented and tested on a database of 30 subjects. The results proved the feasibility of this framework.

Fast neuromimetic object recognition using FPGA outperforms GPU implementations.

PubMed

Orchard, Garrick; Martin, Jacob G; Vogelstein, R Jacob; Etienne-Cummings, Ralph

2013-08-01

Recognition of objects in still images has traditionally been regarded as a difficult computational problem. Although modern automated methods for visual object recognition have achieved steadily increasing recognition accuracy, even the most advanced computational vision approaches are unable to obtain performance equal to that of humans. This has led to the creation of many biologically inspired models of visual object recognition, among them the hierarchical model and X (HMAX) model. HMAX is traditionally known to achieve high accuracy in visual object recognition tasks at the expense of significant computational complexity. Increasing complexity, in turn, increases computation time, reducing the number of images that can be processed per unit time. In this paper we describe how the computationally intensive and biologically inspired HMAX model for visual object recognition can be modified for implementation on a commercial field-programmable aate Array, specifically the Xilinx Virtex 6 ML605 evaluation board with XC6VLX240T FPGA. We show that with minor modifications to the traditional HMAX model we can perform recognition on images of size 128 × 128 pixels at a rate of 190 images per second with a less than 1% loss in recognition accuracy in both binary and multiclass visual object recognition tasks.

General tensor discriminant analysis and gabor features for gait recognition.

PubMed

Tao, Dacheng; Li, Xuelong; Wu, Xindong; Maybank, Stephen J

2007-10-01

The traditional image representations are not suited to conventional classification methods, such as the linear discriminant analysis (LDA), because of the under sample problem (USP): the dimensionality of the feature space is much higher than the number of training samples. Motivated by the successes of the two dimensional LDA (2DLDA) for face recognition, we develop a general tensor discriminant analysis (GTDA) as a preprocessing step for LDA. The benefits of GTDA compared with existing preprocessing methods, e.g., principal component analysis (PCA) and 2DLDA, include 1) the USP is reduced in subsequent classification by, for example, LDA; 2) the discriminative information in the training tensors is preserved; and 3) GTDA provides stable recognition rates because the alternating projection optimization algorithm to obtain a solution of GTDA converges, while that of 2DLDA does not. We use human gait recognition to validate the proposed GTDA. The averaged gait images are utilized for gait representation. Given the popularity of Gabor function based image decompositions for image understanding and object recognition, we develop three different Gabor function based image representations: 1) the GaborD representation is the sum of Gabor filter responses over directions, 2) GaborS is the sum of Gabor filter responses over scales, and 3) GaborSD is the sum of Gabor filter responses over scales and directions. The GaborD, GaborS and GaborSD representations are applied to the problem of recognizing people from their averaged gait images.A large number of experiments were carried out to evaluate the effectiveness (recognition rate) of gait recognition based on first obtaining a Gabor, GaborD, GaborS or GaborSD image representation, then using GDTA to extract features and finally using LDA for classification. The proposed methods achieved good performance for gait recognition based on image sequences from the USF HumanID Database. Experimental comparisons are made with nine state of the art classification methods in gait recognition.

Free Energy Landscape and Multiple Folding Pathways of an H-Type RNA Pseudoknot

PubMed Central

Bian, Yunqiang; Zhang, Jian; Wang, Jun; Wang, Jihua; Wang, Wei

2015-01-01

How RNA sequences fold to specific tertiary structures is one of the key problems for understanding their dynamics and functions. Here, we study the folding process of an H-type RNA pseudoknot by performing a large-scale all-atom MD simulation and bias-exchange metadynamics. The folding free energy landscapes are obtained and several folding intermediates are identified. It is suggested that the folding occurs via multiple mechanisms, including a step-wise mechanism starting either from the first helix or the second, and a cooperative mechanism with both helices forming simultaneously. Despite of the multiple mechanism nature, the ensemble folding kinetics estimated from a Markov state model is single-exponential. It is also found that the correlation between folding and binding of metal ions is significant, and the bound ions mediate long-range interactions in the intermediate structures. Non-native interactions are found to be dominant in the unfolded state and also present in some intermediates, possibly hinder the folding process of the RNA. PMID:26030098

γδ T cell receptors recognize the non-classical major histocompatibility complex (MHC) molecule T22 via conserved anchor residues in a MHC peptide-like fashion.

PubMed

Sandstrom, Andrew; Scharf, Louise; McRae, Gabrielle; Hawk, Andrew J; Meredith, Stephen C; Adams, Erin J

2012-02-17

The molecular mechanisms by which γδ T cells recognize ligand remain a mystery. The non-classical MHC molecule T22 represents the best characterized ligand for murine γδ T cells, with a motif (W … EGYEL) present in the γδ T cell receptor complementary-determining region 3δ (CDR3δ) loop mediating γδ T cell recognition of this molecule. Produced through V(D)J recombination, this loop is quite diverse, with different numbers and chemical types of amino acids between Trp and EGYEL, which have unknown functional consequences for T22 recognition. We have investigated the biophysical and structural effects of CDR3δ loop diversity, revealing a range of affinities for T22 but a common thermodynamic pattern. Mutagenesis of these CDR3δ loops defines the key anchor residues involved in T22 recognition as W … EGYEL, similar to those found for the G8 CDR3δ loop, and demonstrates that spacer residues modulate but are not required for T22 recognition. Comparison of the location of these residues in the T22 interface reveals a striking similarity to peptide anchor residues in classically presented MHC peptides, with the key Trp residue of the CDR3δ motif completing the deficient peptide-binding groove of T22. This suggests that γδ T cell recognition of T22 utilizes the conserved ligand-presenting nature of the MHC fold.

INFO-RNA--a fast approach to inverse RNA folding.

PubMed

Busch, Anke; Backofen, Rolf

2006-08-01

The structure of RNA molecules is often crucial for their function. Therefore, secondary structure prediction has gained much interest. Here, we consider the inverse RNA folding problem, which means designing RNA sequences that fold into a given structure. We introduce a new algorithm for the inverse folding problem (INFO-RNA) that consists of two parts; a dynamic programming method for good initial sequences and a following improved stochastic local search that uses an effective neighbor selection method. During the initialization, we design a sequence that among all sequences adopts the given structure with the lowest possible energy. For the selection of neighbors during the search, we use a kind of look-ahead of one selection step applying an additional energy-based criterion. Afterwards, the pre-ordered neighbors are tested using the actual optimization criterion of minimizing the structure distance between the target structure and the mfe structure of the considered neighbor. We compared our algorithm to RNAinverse and RNA-SSD for artificial and biological test sets. Using INFO-RNA, we performed better than RNAinverse and in most cases, we gained better results than RNA-SSD, the probably best inverse RNA folding tool on the market. www.bioinf.uni-freiburg.de?Subpages/software.html.

Cost-sensitive learning for emotion robust speaker recognition.

PubMed

Li, Dongdong; Yang, Yingchun; Dai, Weihui

2014-01-01

In the field of information security, voice is one of the most important parts in biometrics. Especially, with the development of voice communication through the Internet or telephone system, huge voice data resources are accessed. In speaker recognition, voiceprint can be applied as the unique password for the user to prove his/her identity. However, speech with various emotions can cause an unacceptably high error rate and aggravate the performance of speaker recognition system. This paper deals with this problem by introducing a cost-sensitive learning technology to reweight the probability of test affective utterances in the pitch envelop level, which can enhance the robustness in emotion-dependent speaker recognition effectively. Based on that technology, a new architecture of recognition system as well as its components is proposed in this paper. The experiment conducted on the Mandarin Affective Speech Corpus shows that an improvement of 8% identification rate over the traditional speaker recognition is achieved.

Cost-Sensitive Learning for Emotion Robust Speaker Recognition

PubMed Central

Li, Dongdong; Yang, Yingchun

2014-01-01

In the field of information security, voice is one of the most important parts in biometrics. Especially, with the development of voice communication through the Internet or telephone system, huge voice data resources are accessed. In speaker recognition, voiceprint can be applied as the unique password for the user to prove his/her identity. However, speech with various emotions can cause an unacceptably high error rate and aggravate the performance of speaker recognition system. This paper deals with this problem by introducing a cost-sensitive learning technology to reweight the probability of test affective utterances in the pitch envelop level, which can enhance the robustness in emotion-dependent speaker recognition effectively. Based on that technology, a new architecture of recognition system as well as its components is proposed in this paper. The experiment conducted on the Mandarin Affective Speech Corpus shows that an improvement of 8% identification rate over the traditional speaker recognition is achieved. PMID:24999492

Fingerprint recognition system by use of graph matching

NASA Astrophysics Data System (ADS)

Shen, Wei; Shen, Jun; Zheng, Huicheng

2001-09-01

Fingerprint recognition is an important subject in biometrics to identify or verify persons by physiological characteristics, and has found wide applications in different domains. In the present paper, we present a finger recognition system that combines singular points and structures. The principal steps of processing in our system are: preprocessing and ridge segmentation, singular point extraction and selection, graph representation, and finger recognition by graphs matching. Our fingerprint recognition system is implemented and tested for many fingerprint images and the experimental result are satisfactory. Different techniques are used in our system, such as fast calculation of orientation field, local fuzzy dynamical thresholding, algebraic analysis of connections and fingerprints representation and matching by graphs. Wed find that for fingerprint database that is not very large, the recognition rate is very high even without using a prior coarse category classification. This system works well for both one-to-few and one-to-many problems.

Review of chart recognition in document images

NASA Astrophysics Data System (ADS)

Liu, Yan; Lu, Xiaoqing; Qin, Yeyang; Tang, Zhi; Xu, Jianbo

2013-01-01

As an effective information transmitting way, chart is widely used to represent scientific statistics datum in books, research papers, newspapers etc. Though textual information is still the major source of data, there has been an increasing trend of introducing graphs, pictures, and figures into the information pool. Text recognition techniques for documents have been accomplished using optical character recognition (OCR) software. Chart recognition techniques as a necessary supplement of OCR for document images are still an unsolved problem due to the great subjectiveness and variety of charts styles. This paper reviews the development process of chart recognition techniques in the past decades and presents the focuses of current researches. The whole process of chart recognition is presented systematically, which mainly includes three parts: chart segmentation, chart classification, and chart Interpretation. In each part, the latest research work is introduced. In the last, the paper concludes with a summary and promising future research direction.

Face Recognition Using Local Quantized Patterns and Gabor Filters

NASA Astrophysics Data System (ADS)

Khryashchev, V.; Priorov, A.; Stepanova, O.; Nikitin, A.

2015-05-01

The problem of face recognition in a natural or artificial environment has received a great deal of researchers' attention over the last few years. A lot of methods for accurate face recognition have been proposed. Nevertheless, these methods often fail to accurately recognize the person in difficult scenarios, e.g. low resolution, low contrast, pose variations, etc. We therefore propose an approach for accurate and robust face recognition by using local quantized patterns and Gabor filters. The estimation of the eye centers is used as a preprocessing stage. The evaluation of our algorithm on different samples from a standardized FERET database shows that our method is invariant to the general variations of lighting, expression, occlusion and aging. The proposed approach allows about 20% correct recognition accuracy increase compared with the known face recognition algorithms from the OpenCV library. The additional use of Gabor filters can significantly improve the robustness to changes in lighting conditions.

Detection and recognition of targets by using signal polarization properties

NASA Astrophysics Data System (ADS)

Ponomaryov, Volodymyr I.; Peralta-Fabi, Ricardo; Popov, Anatoly V.; Babakov, Mikhail F.

1999-08-01

The quality of radar target recognition can be enhanced by exploiting its polarization signatures. A specialized X-band polarimetric radar was used for target recognition in experimental investigations. The following polarization characteristics connected to the object geometrical properties were investigated: the amplitudes of the polarization matrix elements; an anisotropy coefficient; depolarization coefficient; asymmetry coefficient; the energy of a backscattering signal; object shape factor. A large quantity of polarimetric radar data was measured and processed to form a database of different object and different weather conditions. The histograms of polarization signatures were approximated by a Nakagami distribution, then used for real- time target recognition. The Neyman-Pearson criterion was used for the target detection, and the criterion of the maximum of a posterior probability was used for recognition problem. Some results of experimental verification of pattern recognition and detection of objects with different electrophysical and geometrical characteristics urban in clutter are presented in this paper.

New approach for logo recognition

NASA Astrophysics Data System (ADS)

Chen, Jingying; Leung, Maylor K. H.; Gao, Yongsheng

2000-03-01

The problem of logo recognition is of great interest in the document domain, especially for document database. By recognizing the logo we obtain semantic information about the document which may be useful in deciding whether or not to analyze the textual components. In order to develop a logo recognition method that is efficient to compute and product intuitively reasonable results, we investigate the Line Segment Hausdorff Distance on logo recognition. Researchers apply Hausdorff Distance to measure the dissimilarity of two point sets. It has been extended to match two sets of line segments. The new approach has the advantage to incorporate structural and spatial information to compute the dissimilarity. The added information can conceptually provide more and better distinctive capability for recognition. The proposed technique has been applied on line segments of logos with encouraging results that support the concept experimentally. This might imply a new way for logo recognition.

Examination of soldier target recognition with direct view optics

NASA Astrophysics Data System (ADS)

Long, Frederick H.; Larkin, Gabriella; Bisordi, Danielle; Dorsey, Shauna; Marianucci, Damien; Goss, Lashawnta; Bastawros, Michael; Misiuda, Paul; Rodgers, Glenn; Mazz, John P.

2017-10-01

Target recognition and identification is a problem of great military and scientific importance. To examine the correlation between target recognition and optical magnification, ten U.S. Army soldiers were tasked with identifying letters on targets at 800 and 1300 meters away. Letters were used since they are a standard method for measuring visual acuity. The letters were approximately 90 cm high, which is the size of a well-known rifle. Four direct view optics with angular magnifications of 1.5x, 4x, 6x, and 9x were used. The goal of this approach was to measure actual probabilities for correct target identification. Previous scientific literature suggests that target recognition can be modeled as a linear response problem in angular frequency space using the established values for the contrast sensitivity function for a healthy human eye and the experimentally measured modulation transfer function of the optic. At the 9x magnification, the soldiers could identify the letters with almost no errors (i.e., 97% probability of correct identification). At lower magnification, errors in letter identification were more frequent. The identification errors were not random but occurred most frequently with a few pairs of letters (e.g., O and Q), which is consistent with the literature for letter recognition. In addition, in the small subject sample of ten soldiers, there was considerable variation in the observer recognition capability at 1.5x and a range of 800 meters. This can be directly attributed to the variation in the observer visual acuity.

A multi-view face recognition system based on cascade face detector and improved Dlib

NASA Astrophysics Data System (ADS)

Zhou, Hongjun; Chen, Pei; Shen, Wei

2018-03-01

In this research, we present a framework for multi-view face detect and recognition system based on cascade face detector and improved Dlib. This method is aimed to solve the problems of low efficiency and low accuracy in multi-view face recognition, to build a multi-view face recognition system, and to discover a suitable monitoring scheme. For face detection, the cascade face detector is used to extracted the Haar-like feature from the training samples, and Haar-like feature is used to train a cascade classifier by combining Adaboost algorithm. Next, for face recognition, we proposed an improved distance model based on Dlib to improve the accuracy of multiview face recognition. Furthermore, we applied this proposed method into recognizing face images taken from different viewing directions, including horizontal view, overlooks view, and looking-up view, and researched a suitable monitoring scheme. This method works well for multi-view face recognition, and it is also simulated and tested, showing satisfactory experimental results.

Protein-ligand interfaces are polarized: discovery of a strong trend for intermolecular hydrogen bonds to favor donors on the protein side with implications for predicting and designing ligand complexes.

PubMed

Raschka, Sebastian; Wolf, Alex J; Bemister-Buffington, Joseph; Kuhn, Leslie A

2018-04-01

Understanding how proteins encode ligand specificity is fascinating and similar in importance to deciphering the genetic code. For protein-ligand recognition, the combination of an almost infinite variety of interfacial shapes and patterns of chemical groups makes the problem especially challenging. Here we analyze data across non-homologous proteins in complex with small biological ligands to address observations made in our inhibitor discovery projects: that proteins favor donating H-bonds to ligands and avoid using groups with both H-bond donor and acceptor capacity. The resulting clear and significant chemical group matching preferences elucidate the code for protein-native ligand binding, similar to the dominant patterns found in nucleic acid base-pairing. On average, 90% of the keto and carboxylate oxygens occurring in the biological ligands formed direct H-bonds to the protein. A two-fold preference was found for protein atoms to act as H-bond donors and ligand atoms to act as acceptors, and 76% of all intermolecular H-bonds involved an amine donor. Together, the tight chemical and geometric constraints associated with satisfying donor groups generate a hydrogen-bonding lock that can be matched only by ligands bearing the right acceptor-rich key. Measuring an index of H-bond preference based on the observed chemical trends proved sufficient to predict other protein-ligand complexes and can be used to guide molecular design. The resulting Hbind and Protein Recognition Index software packages are being made available for rigorously defining intermolecular H-bonds and measuring the extent to which H-bonding patterns in a given complex match the preference key.

A deep learning pipeline for Indian dance style classification

NASA Astrophysics Data System (ADS)

Dewan, Swati; Agarwal, Shubham; Singh, Navjyoti

2018-04-01

In this paper, we address the problem of dance style classification to classify Indian dance or any dance in general. We propose a 3-step deep learning pipeline. First, we extract 14 essential joint locations of the dancer from each video frame, this helps us to derive any body region location within the frame, we use this in the second step which forms the main part of our pipeline. Here, we divide the dancer into regions of important motion in each video frame. We then extract patches centered at these regions. Main discriminative motion is captured in these patches. We stack the features from all such patches of a frame into a single vector and form our hierarchical dance pose descriptor. Finally, in the third step, we build a high level representation of the dance video using the hierarchical descriptors and train it using a Recurrent Neural Network (RNN) for classification. Our novelty also lies in the way we use multiple representations for a single video. This helps us to: (1) Overcome the RNN limitation of learning small sequences over big sequences such as dance; (2) Extract more data from the available dataset for effective deep learning by training multiple representations. Our contributions in this paper are three-folds: (1) We provide a deep learning pipeline for classification of any form of dance; (2) We prove that a segmented representation of a dance video works well with sequence learning techniques for recognition purposes; (3) We extend and refine the ICD dataset and provide a new dataset for evaluation of dance. Our model performs comparable or better in some cases than the state-of-the-art on action recognition benchmarks.

Protein-ligand interfaces are polarized: discovery of a strong trend for intermolecular hydrogen bonds to favor donors on the protein side with implications for predicting and designing ligand complexes

NASA Astrophysics Data System (ADS)

Raschka, Sebastian; Wolf, Alex J.; Bemister-Buffington, Joseph; Kuhn, Leslie A.

2018-02-01

Understanding how proteins encode ligand specificity is fascinating and similar in importance to deciphering the genetic code. For protein-ligand recognition, the combination of an almost infinite variety of interfacial shapes and patterns of chemical groups makes the problem especially challenging. Here we analyze data across non-homologous proteins in complex with small biological ligands to address observations made in our inhibitor discovery projects: that proteins favor donating H-bonds to ligands and avoid using groups with both H-bond donor and acceptor capacity. The resulting clear and significant chemical group matching preferences elucidate the code for protein-native ligand binding, similar to the dominant patterns found in nucleic acid base-pairing. On average, 90% of the keto and carboxylate oxygens occurring in the biological ligands formed direct H-bonds to the protein. A two-fold preference was found for protein atoms to act as H-bond donors and ligand atoms to act as acceptors, and 76% of all intermolecular H-bonds involved an amine donor. Together, the tight chemical and geometric constraints associated with satisfying donor groups generate a hydrogen-bonding lock that can be matched only by ligands bearing the right acceptor-rich key. Measuring an index of H-bond preference based on the observed chemical trends proved sufficient to predict other protein-ligand complexes and can be used to guide molecular design. The resulting Hbind and Protein Recognition Index software packages are being made available for rigorously defining intermolecular H-bonds and measuring the extent to which H-bonding patterns in a given complex match the preference key.

Caulobacter crescentus Cell Cycle-Regulated DNA Methyltransferase Uses a Novel Mechanism for Substrate Recognition.

PubMed

Woodcock, Clayton B; Yakubov, Aziz B; Reich, Norbert O

2017-08-01

Caulobacter crescentus relies on DNA methylation by the cell cycle-regulated methyltransferase (CcrM) in addition to key transcription factors to control the cell cycle and direct cellular differentiation. CcrM is shown here to efficiently methylate its cognate recognition site 5'-GANTC-3' in single-stranded and hemimethylated double-stranded DNA. We report the K m , k cat , k methylation , and K d for single-stranded and hemimethylated substrates, revealing discrimination of 10 7 -fold for noncognate sequences. The enzyme also shows a similar discrimination against single-stranded RNA. Two independent assays clearly show that CcrM is highly processive with single-stranded and hemimethylated DNA. Collectively, the data provide evidence that CcrM and other DNA-modifying enzymes may use a new mechanism to recognize DNA in a key epigenetic process.

Functional architecture of the retromer cargo-recognition complex

PubMed Central

Hierro, Aitor; Rojas, Adriana L.; Rojas, Raul; Murthy, Namita; Effantin, Grégory; Kajava, Andrey V.; Steven, Alasdair C.; Bonifacino, Juan S.; Hurley, James H.

2008-01-01

The retromer complex 1, 2 is required for the sorting of acid hydrolases to lysosomes 3-7, transcytosis of the polymeric Ig receptor 8, Wnt gradient formation 9, 10, iron transporter recycling 11, and processing of the amyloid precursor protein 12. Human retromer consists of two smaller complexes, the cargo recognition Vps26:Vps29:Vps35 heterotrimer, and a membrane-targeting heterodimer or homodimer of SNX1 and/or SNX2 13. The crystal structure of a Vps29:Vps35 subcomplex shows how the metallophosphoesterase-fold subunit Vps29 14, 15 acts as a scaffold for the C-terminal half of Vps35. Vps35 forms a horseshoe-shaped right-handed α-helical solenoid whose concave face completely covers the metal-binding site of Vps29 and whose convex face exposes a series of hydrophobic interhelical grooves. Electron microscopy shows that the intact Vps26:Vps29:Vps35 complex is a stick-shaped, somewhat flexible, structure, ∼ 21 nm long. A hybrid structural model derived from crystal structures, electron microscopy, interaction studies, and bioinformatics shows that the α-solenoid fold extends the full length of Vps35, and that Vps26 is bound at the opposite end from Vps29. This extended structure presents multiple binding sites for the SNX complex and receptor cargo, and appears capable of flexing to conform to curved vesicular membranes. PMID:17891154

The Fanconi Anemia DNA Repair Pathway Is Regulated by an Interaction between Ubiquitin and the E2-like Fold Domain of FANCL.

PubMed

Miles, Jennifer A; Frost, Mark G; Carroll, Eilis; Rowe, Michelle L; Howard, Mark J; Sidhu, Ateesh; Chaugule, Viduth K; Alpi, Arno F; Walden, Helen

2015-08-21

The Fanconi Anemia (FA) DNA repair pathway is essential for the recognition and repair of DNA interstrand crosslinks (ICL). Inefficient repair of these ICL can lead to leukemia and bone marrow failure. A critical step in the pathway is the monoubiquitination of FANCD2 by the RING E3 ligase FANCL. FANCL comprises 3 domains, a RING domain that interacts with E2 conjugating enzymes, a central domain required for substrate interaction, and an N-terminal E2-like fold (ELF) domain. The ELF domain is found in all FANCL homologues, yet the function of the domain remains unknown. We report here that the ELF domain of FANCL is required to mediate a non-covalent interaction between FANCL and ubiquitin. The interaction involves the canonical Ile44 patch on ubiquitin, and a functionally conserved patch on FANCL. We show that the interaction is not necessary for the recognition of the core complex, it does not enhance the interaction between FANCL and Ube2T, and is not required for FANCD2 monoubiquitination in vitro. However, we demonstrate that the ELF domain is required to promote efficient DNA damage-induced FANCD2 monoubiquitination in vertebrate cells, suggesting an important function of ubiquitin binding by FANCL in vivo. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

dsRNA-protein interactions studied by molecular dynamics techniques. Unravelling dsRNA recognition by DCL1.

PubMed

Drusin, Salvador I; Suarez, Irina P; Gauto, Diego F; Rasia, Rodolfo M; Moreno, Diego M

2016-04-15

Double stranded RNA (dsRNA) participates in several biological processes, where RNA molecules acquire secondary structure inside the cell through base complementarity. The double stranded RNA binding domain (dsRBD) is one of the main protein folds that is able to recognize and bind to dsRNA regions. The N-terminal dsRBD of DCL1 in Arabidopsis thaliana (DCL1-1), in contrast to other studied dsRBDs, lacks a stable structure, behaving as an intrinsically disordered protein. DCL1-1 does however recognize dsRNA by acquiring a canonical fold in the presence of its substrate. Here we present a detailed modeling and molecular dynamics study of dsRNA recognition by DCL1-1. We found that DCL1-1 forms stable complexes with different RNAs and we characterized the residues involved in binding. Although the domain shows a binding loop substantially shorter than other homologs, it can still interact with the dsRNA and results in bending of the dsRNA A-type helix. Furthermore, we found that R8, a non-conserved residue located in the first dsRNA binding region, recognizes preferentially mismatched base pairs. We discuss our findings in the context of the function of DCL1-1 within the microRNA processing complex. Copyright © 2016 Elsevier Inc. All rights reserved.

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.

Computers Are for Kids: Designing Software Programs to Avoid Problems of Learning.

ERIC Educational Resources Information Center

Grimes, Lynn

1981-01-01

Procedures for programing computers to deal with handicapped students, problems in selective attention, visual discrimination, reaction time differences, short term memory, transfer and generalization, recognition of mistakes, and social skills are discussed. (CL)

Comparing mental health literacy and physical health literacy: an exploratory study.

PubMed

Wickstead, Robert; Furnham, Adrian

2017-10-01

This study compared mental health and physical health literacy using five health problems from each area. The aim was to determine whether the same group had better physical than mental health literacy Method: A sample of 263 participants completed an online questionnaire requiring them to name a problem/illness described in 10 vignettes and suggest treatment options. Five vignettes described mental health problems (anxiety, bipolar-disorder, depression, OCPD and schizophrenia) and five physical problems (angina, COPD, diabetes, a heart attack, and sinusitis). Participants were also asked to rate their sympathy and estimates of prevalence for each disorder. Recognition of the mental health disorders was superior compared recognition of the physical disorders. Analysis of treatment beliefs, sympathy and prevalence ratings also showed significant differences between disorders. Results highlight the importance of education and the lack of public knowledge regarding major physical health conditions.

Combining convolutional neural networks and Hough Transform for classification of images containing lines

NASA Astrophysics Data System (ADS)

Sheshkus, Alexander; Limonova, Elena; Nikolaev, Dmitry; Krivtsov, Valeriy

2017-03-01

In this paper, we propose an expansion of convolutional neural network (CNN) input features based on Hough Transform. We perform morphological contrasting of source image followed by Hough Transform, and then use it as input for some convolutional filters. Thus, CNNs computational complexity and the number of units are not affected. Morphological contrasting and Hough Transform are the only additional computational expenses of introduced CNN input features expansion. Proposed approach was demonstrated on the example of CNN with very simple structure. We considered two image recognition problems, that were object classification on CIFAR-10 and printed character recognition on private dataset with symbols taken from Russian passports. Our approach allowed to reach noticeable accuracy improvement without taking much computational effort, which can be extremely important in industrial recognition systems or difficult problems utilising CNNs, like pressure ridge analysis and classification.

Cysteine-Rich Atrial Secretory Protein from the Snail Achatina achatina: Purification and Structural Characterization

PubMed Central

Shabelnikov, Sergey; Kiselev, Artem

2015-01-01

Despite extensive studies of cardiac bioactive peptides and their functions in molluscs, soluble proteins expressed in the heart and secreted into the circulation have not yet been reported. In this study, we describe an 18.1-kDa, cysteine-rich atrial secretory protein (CRASP) isolated from the terrestrial snail Achatina achatina that has no detectable sequence similarity to any known protein or nucleotide sequence. CRASP is an acidic, 158-residue, N-glycosylated protein composed of eight alpha-helical segments stabilized with five disulphide bonds. A combination of fold recognition algorithms and ab initio folding predicted that CRASP adopts an all-alpha, right-handed superhelical fold. CRASP is most strongly expressed in the atrium in secretory atrial granular cells, and substantial amounts of CRASP are released from the heart upon nerve stimulation. CRASP is detected in the haemolymph of intact animals at nanomolar concentrations. CRASP is the first secretory protein expressed in molluscan atrium to be reported. We propose that CRASP is an example of a taxonomically restricted gene that might be responsible for adaptations specific for terrestrial pulmonates. PMID:26444993

Structural Analysis of the Synaptic Protein Neuroligin and Its β-Neurexin Complex: Determinants for Folding and Cell Adhesion

PubMed Central

Fabrichny, Igor P.; Leone, Philippe; Sulzenbacher, Gerlind; Comoletti, Davide; Miller, Meghan T.; Taylor, Palmer; Bourne, Yves; Marchot, Pascale

2009-01-01

SUMMARY The neuroligins are postsynaptic cell adhesion proteins whose associations with presynaptic neurexins participate in synaptogenesis. Mutations in the neuroligin and neurexin genes appear to be associated with autism and mental retardation. The crystal structure of a neuroligin reveals features not found in its catalytically active relatives, such as the fully hydrophobic interface forming the functional neuroligin dimer; the conformations of surface loops surrounding the vestigial active center; the location of determinants that are critical for folding and processing; and the absence of a macromolecular dipole and presence of an electronegative, hydrophilic surface for neurexin binding. The structure of a β-neurexin-neuroligin complex reveals the precise orientation of the bound neurexin and, despite a limited resolution, provides substantial information on the Ca2+-dependent interactions network involved in trans-synaptic neurexin-neuroligin association. These structures exemplify how an α/β-hydrolase fold varies in surface topography to confer adhesion properties and provide templates for analyzing abnormal processing or recognition events associated with autism. PMID:18093521

Face recognition via edge-based Gabor feature representation for plastic surgery-altered images

NASA Astrophysics Data System (ADS)

Chude-Olisah, Chollette C.; Sulong, Ghazali; Chude-Okonkwo, Uche A. K.; Hashim, Siti Z. M.

2014-12-01

Plastic surgery procedures on the face introduce skin texture variations between images of the same person (intra-subject), thereby making the task of face recognition more difficult than in normal scenario. Usually, in contemporary face recognition systems, the original gray-level face image is used as input to the Gabor descriptor, which translates to encoding some texture properties of the face image. The texture-encoding process significantly degrades the performance of such systems in the case of plastic surgery due to the presence of surgically induced intra-subject variations. Based on the proposition that the shape of significant facial components such as eyes, nose, eyebrow, and mouth remains unchanged after plastic surgery, this paper employs an edge-based Gabor feature representation approach for the recognition of surgically altered face images. We use the edge information, which is dependent on the shapes of the significant facial components, to address the plastic surgery-induced texture variation problems. To ensure that the significant facial components represent useful edge information with little or no false edges, a simple illumination normalization technique is proposed for preprocessing. Gabor wavelet is applied to the edge image to accentuate on the uniqueness of the significant facial components for discriminating among different subjects. The performance of the proposed method is evaluated on the Georgia Tech (GT) and the Labeled Faces in the Wild (LFW) databases with illumination and expression problems, and the plastic surgery database with texture changes. Results show that the proposed edge-based Gabor feature representation approach is robust against plastic surgery-induced face variations amidst expression and illumination problems and outperforms the existing plastic surgery face recognition methods reported in the literature.

Thermal-to-visible face recognition using partial least squares.

PubMed

Hu, Shuowen; Choi, Jonghyun; Chan, Alex L; Schwartz, William Robson

2015-03-01

Although visible face recognition has been an active area of research for several decades, cross-modal face recognition has only been explored by the biometrics community relatively recently. Thermal-to-visible face recognition is one of the most difficult cross-modal face recognition challenges, because of the difference in phenomenology between the thermal and visible imaging modalities. We address the cross-modal recognition problem using a partial least squares (PLS) regression-based approach consisting of preprocessing, feature extraction, and PLS model building. The preprocessing and feature extraction stages are designed to reduce the modality gap between the thermal and visible facial signatures, and facilitate the subsequent one-vs-all PLS-based model building. We incorporate multi-modal information into the PLS model building stage to enhance cross-modal recognition. The performance of the proposed recognition algorithm is evaluated on three challenging datasets containing visible and thermal imagery acquired under different experimental scenarios: time-lapse, physical tasks, mental tasks, and subject-to-camera range. These scenarios represent difficult challenges relevant to real-world applications. We demonstrate that the proposed method performs robustly for the examined scenarios.

Design and simulation of origami structures with smooth folds

PubMed Central

Peraza Hernandez, E. A.; Lagoudas, D. C.

2017-01-01

Origami has enabled new approaches to the fabrication and functionality of multiple structures. Current methods for origami design are restricted to the idealization of folds as creases of zeroth-order geometric continuity. Such an idealization is not proper for origami structures of non-negligible fold thickness or maximum curvature at the folds restricted by material limitations. For such structures, folds are not properly represented as creases but rather as bent regions of higher-order geometric continuity. Such fold regions of arbitrary order of continuity are termed as smooth folds. This paper presents a method for solving the following origami design problem: given a goal shape represented as a polygonal mesh (termed as the goal mesh), find the geometry of a single planar sheet, its pattern of smooth folds, and the history of folding motion allowing the sheet to approximate the goal mesh. The parametrization of the planar sheet and the constraints that allow for a valid pattern of smooth folds are presented. The method is tested against various goal meshes having diverse geometries. The results show that every determined sheet approximates its corresponding goal mesh in a known folded configuration having fold angles obtained from the geometry of the goal mesh. PMID:28484322

Design and simulation of origami structures with smooth folds.

PubMed

Peraza Hernandez, E A; Hartl, D J; Lagoudas, D C

2017-04-01

Origami has enabled new approaches to the fabrication and functionality of multiple structures. Current methods for origami design are restricted to the idealization of folds as creases of zeroth-order geometric continuity. Such an idealization is not proper for origami structures of non-negligible fold thickness or maximum curvature at the folds restricted by material limitations. For such structures, folds are not properly represented as creases but rather as bent regions of higher-order geometric continuity. Such fold regions of arbitrary order of continuity are termed as smooth folds . This paper presents a method for solving the following origami design problem: given a goal shape represented as a polygonal mesh (termed as the goal mesh ), find the geometry of a single planar sheet, its pattern of smooth folds, and the history of folding motion allowing the sheet to approximate the goal mesh. The parametrization of the planar sheet and the constraints that allow for a valid pattern of smooth folds are presented. The method is tested against various goal meshes having diverse geometries. The results show that every determined sheet approximates its corresponding goal mesh in a known folded configuration having fold angles obtained from the geometry of the goal mesh.

Identification of related gene/protein names based on an HMM of name variations.

PubMed

Yeganova, L; Smith, L; Wilbur, W J

2004-04-01

Gene and protein names follow few, if any, true naming conventions and are subject to great variation in different occurrences of the same name. This gives rise to two important problems in natural language processing. First, can one locate the names of genes or proteins in free text, and second, can one determine when two names denote the same gene or protein? The first of these problems is a special case of the problem of named entity recognition, while the second is a special case of the problem of automatic term recognition (ATR). We study the second problem, that of gene or protein name variation. Here we describe a system which, given a query gene or protein name, identifies related gene or protein names in a large list. The system is based on a dynamic programming algorithm for sequence alignment in which the mutation matrix is allowed to vary under the control of a fully trainable hidden Markov model.

Pathways to help-seeking in bulimia nervosa and binge eating problems: a concept mapping approach.

PubMed

Hepworth, Natasha; Paxton, Susan J

2007-09-01

To conduct an in-depth study, using concept mapping, of three factors related to help-seeking for bulimia nervosa and binge eating: problem recognition, barriers to help-seeking, and prompts to help-seeking. Semistructured interviews were conducted to elicit information about help-seeking with 63 women (18-62 years) with past or present bulimic behaviors. Using Leximancer software, factors identified as associated with problem recognition were Changes in Behavior, Interference with Life Roles, Comments about Changes and Psychological Problems. Salient barriers to help-seeking were Fear of Stigma, Low Mental Health Literacy/Perception of Need, Shame, Fear of Change and Cost. Prompts to help-seeking were increased Symptom Severity, Psychological Distress, Interference with Life Roles, Health Problems, and Desire to Get Better. Results highlighted the need for awareness campaigns to reduce both self and perceived stigma by others towards bulimic behaviors, and the need to enhance awareness of available interventions for people ready to engage in treatment, to increase help-seeking. (c) 2007 by Wiley Periodicals, Inc.

Nanobodies: site-specific labeling for super-resolution imaging, rapid epitope-mapping and native protein complex isolation

PubMed Central

Pleiner, Tino; Bates, Mark; Trakhanov, Sergei; Lee, Chung-Tien; Schliep, Jan Erik; Chug, Hema; Böhning, Marc; Stark, Holger; Urlaub, Henning; Görlich, Dirk

2015-01-01

Nanobodies are single-domain antibodies of camelid origin. We generated nanobodies against the vertebrate nuclear pore complex (NPC) and used them in STORM imaging to locate individual NPC proteins with <2 nm epitope-label displacement. For this, we introduced cysteines at specific positions in the nanobody sequence and labeled the resulting proteins with fluorophore-maleimides. As nanobodies are normally stabilized by disulfide-bonded cysteines, this appears counterintuitive. Yet, our analysis showed that this caused no folding problems. Compared to traditional NHS ester-labeling of lysines, the cysteine-maleimide strategy resulted in far less background in fluorescence imaging, it better preserved epitope recognition and it is site-specific. We also devised a rapid epitope-mapping strategy, which relies on crosslinking mass spectrometry and the introduced ectopic cysteines. Finally, we used different anti-nucleoporin nanobodies to purify the major NPC building blocks – each in a single step, with native elution and, as demonstrated, in excellent quality for structural analysis by electron microscopy. The presented strategies are applicable to any nanobody and nanobody-target. DOI: http://dx.doi.org/10.7554/eLife.11349.001 PMID:26633879

Recognition of plant parts with problem-specific algorithms

NASA Astrophysics Data System (ADS)

Schwanke, Joerg; Brendel, Thorsten; Jensch, Peter F.; Megnet, Roland

1994-06-01

Automatic micropropagation is necessary to produce cost-effective high amounts of biomass. Juvenile plants are dissected in clean- room environment on particular points on the stem or the leaves. A vision-system detects possible cutting points and controls a specialized robot. This contribution is directed to the pattern- recognition algorithms to detect structural parts of the plant.

Development of a Curriculum for Long-Term Care Nurses to Improve Recognition of Depression in Dementia

ERIC Educational Resources Information Center

Williams, Christine L.; Molinari, Victor; Bond, Jennifer; Smith, Michael; Hyer, Kathryn; Malphurs, Julie

2006-01-01

There is increasing recognition of the severe consequences of depression in long-term care residents with dementia. Most health care providers are unprepared to recognize and to manage the complexity of depression in dementia. Targeted educational initiatives in nursing homes are needed to address this growing problem. This paper describes the…

The Application of Recognition-Primed Decision Theory to Decisions Made in an Outdoor Education Context

ERIC Educational Resources Information Center

Boyes, Mike; Potter, Tom

2015-01-01

This research examined the decisions that highly experienced outdoor leaders made on backpacking expeditions conducted by a tertiary institution in the Southern Alps of New Zealand. The purpose of the research was to document decision problems and explore them as Recognition-Primed Decisions (RPD) within naturalistic decision making (NDM)…

Common and Unique Impairments in Facial-Expression Recognition in Pervasive Developmental Disorder-Not Otherwise Specified and Asperger's Disorder

ERIC Educational Resources Information Center

Uono, Shota; Sato, Wataru; Toichi, Motomi

2013-01-01

This study was designed to identify specific difficulties and associated features related to the problems with social interaction experienced by individuals with pervasive developmental disorder-not otherwise specified (PDD-NOS) using an emotion-recognition task. We compared individuals with PDD-NOS or Asperger's disorder (ASP) and typically…

Leadership Styles of Principals in Successful Title I Elementary Schools

ERIC Educational Resources Information Center

Gonzales, Cameron

2016-01-01

The problem addressed in the dissertation is the relationship between high poverty and low academic achievement that persists in spite of efforts to change it. In one Western state, a small proportion of the schools that are eligible for Title I funds, a measure of poverty, have achieved recognition for high student achievement. The recognition,…

Machine Learning Through Signature Trees. Applications to Human Speech.

ERIC Educational Resources Information Center

White, George M.

A signature tree is a binary decision tree used to classify unknown patterns. An attempt was made to develop a computer program for manipulating signature trees as a general research tool for exploring machine learning and pattern recognition. The program was applied to the problem of speech recognition to test its effectiveness for a specific…

Overview of radar intra-pulse modulation recognition

NASA Astrophysics Data System (ADS)

Zang, Hanlin; Li, Yanling

2018-05-01

This paper introduces the current radar intra-pulse modulation method, describes the status quo and development direction of the intentional modulation and unintentional modulation in the pulse, and summarizes the existing problems and prospects for the future. Looking forward to the future, and providing a reference direction for the research on radar signal recognition in the next step.

New Protein Mimetics: The Zinc Finger Motif as a Locked-In Tertiary Fold.

PubMed

Tuchscherer, Gabriele; Lehmann, Christian; Mathieu, Marc

1998-11-16

The principle of a molecular kit is used for the covalent assembly of secondary structure forming peptide blocks to predetermined packing topologies. The resulting locked-in folds (LIFs; depicted schematically) are readily accessible and bypass the intriguing folding problem of linear peptide chains. This strategy allows, for example, mimicking of the essential structural and functional features of zinc finger proteins. © 1998 WILEY-VCH Verlag GmbH, Weinheim, Fed. Rep. of Germany.

Molecular recognition of pre-tRNA by Arabidopsis protein-only Ribonuclease P.

PubMed

Klemm, Bradley P; Karasik, Agnes; Kaitany, Kipchumba J; Shanmuganathan, Aranganathan; Henley, Matthew J; Thelen, Adam Z; Dewar, Allison J L; Jackson, Nathaniel D; Koutmos, Markos; Fierke, Carol A

2017-12-01

Protein-only ribonuclease P (PRORP) is an enzyme responsible for catalyzing the 5' end maturation of precursor transfer ribonucleic acids (pre-tRNAs) encoded by various cellular compartments in many eukaryotes. PRORPs from plants act as single-subunit enzymes and have been used as a model system for analyzing the function of the metazoan PRORP nuclease subunit, which requires two additional proteins for efficient catalysis. There are currently few molecular details known about the PRORP-pre-tRNA complex. Here, we characterize the determinants of substrate recognition by the single subunit Arabidopsis thaliana PRORP1 and PRORP2 using kinetic and thermodynamic experiments. The salt dependence of binding affinity suggests 4-5 contacts with backbone phosphodiester bonds on substrates, including a single phosphodiester contact with the pre-tRNA 5' leader, consistent with prior reports of short leader requirements. PRORPs contain an N-terminal pentatricopeptide repeat (PPR) domain, truncation of which results in a >30-fold decrease in substrate affinity. While most PPR-containing proteins have been implicated in single-stranded sequence-specific RNA recognition, we find that the PPR motifs of PRORPs recognize pre-tRNA substrates differently. Notably, the PPR domain residues most important for substrate binding in PRORPs do not correspond to positions involved in base recognition in other PPR proteins. Several of these residues are highly conserved in PRORPs from algae, plants, and metazoans, suggesting a conserved strategy for substrate recognition by the PRORP PPR domain. Furthermore, there is no evidence for sequence-specific interactions. This work clarifies molecular determinants of PRORP-substrate recognition and provides a new predictive model for the PRORP-substrate complex. © 2017 Klemm et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.

Interpreting whether isoclinal folds are antiforms or synforms using FIA succession

NASA Astrophysics Data System (ADS)

Cao, H.

2012-12-01

Using the asymmetries of the overprinting foliations preserved as inclusion trails that define the FIAs to investigate whether an enigmatic isoclinal fold in the region is an antiform or synform. This approach also reveals when the fold first formed during the tectonic history of the region. Multiply deformed and isoclinally folded interlayered high metamorphic grade gneisses and schists can be very difficult rocks for resolving early formed stratigraphic and structural relationships. When such rocks contain porphyroblasts a new approach is possible because of the way in which porphyroblast growth is affected by crenulation versus reactivation of compositional layering (Bell et al., 2003). Isoclinally folded rocks in the Arkansas River region of South Central Colorado contain relics of fold hinges that have been very difficult to ascertain whether they are antiforms or synforms because of younger refolding effects and the locally truncated nature of coarse compositional layering. With the realization that rocks with a schistosity parallel to bedding (S0 parallel S1) have undergone lengthy histories of deformation that predate the obvious first deformation (e.g. Bell et al., 2003; Sayab, 2006; Yeh, 2007) came recognition that large scale regional folds can form early during this process and be preserved throughout orogenesis (e.g., Ham and Bell, 2004; Bell and Newman, 2006. This extensive history is lost within the matrix because of reactivational shear on the compositional layering (Bell et al., 1998, 2003, 2004, 2005; Ham and Bell, 2004). However, it can be extracted by measuring FIAs. Recent work using this approach has revealed that the trends of axial planes of all map scale folds, when plotted on a rose diagram, strikingly reflect the FIA trends (e.g., Sanislav, 2009; Shah, 2009). That is, although it was demonstrated by Bell et al. (2003) that the largest scale regional folds commonly form early in the total history, other folds can form and be preserved from subsequent destruction in the strain shadows of plutons or through the partitioning of deformation due to heterogeneities at depth.

Hyperspectral face recognition with spatiospectral information fusion and PLS regression.

PubMed

Uzair, Muhammad; Mahmood, Arif; Mian, Ajmal

2015-03-01

Hyperspectral imaging offers new opportunities for face recognition via improved discrimination along the spectral dimension. However, it poses new challenges, including low signal-to-noise ratio, interband misalignment, and high data dimensionality. Due to these challenges, the literature on hyperspectral face recognition is not only sparse but is limited to ad hoc dimensionality reduction techniques and lacks comprehensive evaluation. We propose a hyperspectral face recognition algorithm using a spatiospectral covariance for band fusion and partial least square regression for classification. Moreover, we extend 13 existing face recognition techniques, for the first time, to perform hyperspectral face recognition.We formulate hyperspectral face recognition as an image-set classification problem and evaluate the performance of seven state-of-the-art image-set classification techniques. We also test six state-of-the-art grayscale and RGB (color) face recognition algorithms after applying fusion techniques on hyperspectral images. Comparison with the 13 extended and five existing hyperspectral face recognition techniques on three standard data sets show that the proposed algorithm outperforms all by a significant margin. Finally, we perform band selection experiments to find the most discriminative bands in the visible and near infrared response spectrum.

Relationships between alexithymia, affect recognition, and empathy after traumatic brain injury.

PubMed

Neumann, Dawn; Zupan, Barbra; Malec, James F; Hammond, Flora

2014-01-01

To determine (1) alexithymia, affect recognition, and empathy differences in participants with and without traumatic brain injury (TBI); (2) the amount of affect recognition variance explained by alexithymia; and (3) the amount of empathy variance explained by alexithymia and affect recognition. Sixty adults with moderate-to-severe TBI; 60 age and gender-matched controls. Participants were evaluated for alexithymia (difficulty identifying feelings, difficulty describing feelings, and externally-oriented thinking); facial and vocal affect recognition; and affective and cognitive empathy (empathic concern and perspective-taking, respectively). Participants with TBI had significantly higher alexithymia; poorer facial and vocal affect recognition; and lower empathy scores. For TBI participants, facial and vocal affect recognition variances were significantly explained by alexithymia (12% and 8%, respectively); however, the majority of the variances were accounted for by externally-oriented thinking alone. Affect recognition and alexithymia significantly accounted for 16.5% of cognitive empathy. Again, the majority of the variance was primarily explained by externally-oriented thinking. Affect recognition and alexithymia did not explain affective empathy. Results suggest that people who have a tendency to avoid thinking about emotions (externally-oriented thinking) are more likely to have problems recognizing others' emotions and assuming others' points of view. Clinical implications are discussed.

Towards discrete wavelet transform-based human activity recognition

NASA Astrophysics Data System (ADS)

Khare, Manish; Jeon, Moongu

2017-06-01

Providing accurate recognition of human activities is a challenging problem for visual surveillance applications. In this paper, we present a simple and efficient algorithm for human activity recognition based on a wavelet transform. We adopt discrete wavelet transform (DWT) coefficients as a feature of human objects to obtain advantages of its multiresolution approach. The proposed method is tested on multiple levels of DWT. Experiments are carried out on different standard action datasets including KTH and i3D Post. The proposed method is compared with other state-of-the-art methods in terms of different quantitative performance measures. The proposed method is found to have better recognition accuracy in comparison to the state-of-the-art methods.

View-Invariant Gait Recognition Through Genetic Template Segmentation

NASA Astrophysics Data System (ADS)

Isaac, Ebenezer R. H. P.; Elias, Susan; Rajagopalan, Srinivasan; Easwarakumar, K. S.

2017-08-01

Template-based model-free approach provides by far the most successful solution to the gait recognition problem in literature. Recent work discusses how isolating the head and leg portion of the template increase the performance of a gait recognition system making it robust against covariates like clothing and carrying conditions. However, most involve a manual definition of the boundaries. The method we propose, the genetic template segmentation (GTS), employs the genetic algorithm to automate the boundary selection process. This method was tested on the GEI, GEnI and AEI templates. GEI seems to exhibit the best result when segmented with our approach. Experimental results depict that our approach significantly outperforms the existing implementations of view-invariant gait recognition.

An algorithm for generating modular hierarchical neural network classifiers: a step toward larger scale applications

NASA Astrophysics Data System (ADS)

Roverso, Davide

2003-08-01

Many-class learning is the problem of training a classifier to discriminate among a large number of target classes. Together with the problem of dealing with high-dimensional patterns (i.e. a high-dimensional input space), the many class problem (i.e. a high-dimensional output space) is a major obstacle to be faced when scaling-up classifier systems and algorithms from small pilot applications to large full-scale applications. The Autonomous Recursive Task Decomposition (ARTD) algorithm is here proposed as a solution to the problem of many-class learning. Example applications of ARTD to neural classifier training are also presented. In these examples, improvements in training time are shown to range from 4-fold to more than 30-fold in pattern classification tasks of both static and dynamic character.

Incorporating Duration Information in Activity Recognition

NASA Astrophysics Data System (ADS)

Chaurasia, Priyanka; Scotney, Bryan; McClean, Sally; Zhang, Shuai; Nugent, Chris

Activity recognition has become a key issue in smart home environments. The problem involves learning high level activities from low level sensor data. Activity recognition can depend on several variables; one such variable is duration of engagement with sensorised items or duration of intervals between sensor activations that can provide useful information about personal behaviour. In this paper a probabilistic learning algorithm is proposed that incorporates episode, time and duration information to determine inhabitant identity and the activity being undertaken from low level sensor data. Our results verify that incorporating duration information consistently improves the accuracy.

Why Organizations Continue to Create Patient Information Leaflets with Readability and Usability Problems: An Exploratory Study

ERIC Educational Resources Information Center

Gal, Iddo; Prigat, Ayelet

2005-01-01

Readability and usability problems with patient information leaflets continue to be reported despite long-standing recognition of their existence and the availability of guidelines for developing health education materials. This exploratory study examined possible causes for such problems, based on interviews with professionals who developed…

Sino-US cooperation in water saving technologies: essential international problems

USDA-ARS?s Scientific Manuscript database

The United States and China share many agricultural problems, but one of great importance is the need to produce more crop yield in the face of water scarcity. Common recognition of this problem led to the development of a joint Sino-US Water Saving Technologies Flagship project within the larger US...

Teaching Problem-Solving Competency in Business Studies at Secondary School Level

ERIC Educational Resources Information Center

Meintjes, Aloe; Henrico, Alfred; Kroon, Japie

2015-01-01

The high unemployment rate in South Africa compels potential entrepreneurs to start their own businesses in order to survive. Often this is with little or no formal training or education in entrepreneurship. Since problem recognition and problem-solving are amongst the most crucial competencies required for a successful entrepreneurial career,…

Coarse-grained sequences for protein folding and design.

PubMed

Brown, Scott; Fawzi, Nicolas J; Head-Gordon, Teresa

2003-09-16

We present the results of sequence design on our off-lattice minimalist model in which no specification of native-state tertiary contacts is needed. We start with a sequence that adopts a target topology and build on it through sequence mutation to produce new sequences that comprise distinct members within a target fold class. In this work, we use the alpha/beta ubiquitin fold class and design two new sequences that, when characterized through folding simulations, reproduce the differences in folding mechanism seen experimentally for proteins L and G. The primary implication of this work is that patterning of hydrophobic and hydrophilic residues is the physical origin for the success of relative contact-order descriptions of folding, and that these physics-based potentials provide a predictive connection between free energy landscapes and amino acid sequence (the original protein folding problem). We present results of the sequence mapping from a 20- to the three-letter code for determining a sequence that folds into the WW domain topology to illustrate future extensions to protein design.

Coarse-grained sequences for protein folding and design

PubMed Central

Brown, Scott; Fawzi, Nicolas J.; Head-Gordon, Teresa

2003-01-01

We present the results of sequence design on our off-lattice minimalist model in which no specification of native-state tertiary contacts is needed. We start with a sequence that adopts a target topology and build on it through sequence mutation to produce new sequences that comprise distinct members within a target fold class. In this work, we use the α/β ubiquitin fold class and design two new sequences that, when characterized through folding simulations, reproduce the differences in folding mechanism seen experimentally for proteins L and G. The primary implication of this work is that patterning of hydrophobic and hydrophilic residues is the physical origin for the success of relative contact-order descriptions of folding, and that these physics-based potentials provide a predictive connection between free energy landscapes and amino acid sequence (the original protein folding problem). We present results of the sequence mapping from a 20- to the three-letter code for determining a sequence that folds into the WW domain topology to illustrate future extensions to protein design. PMID:12963815

The development of cross-cultural recognition of vocal emotion during childhood and adolescence.

PubMed

Chronaki, Georgia; Wigelsworth, Michael; Pell, Marc D; Kotz, Sonja A

2018-06-14

Humans have an innate set of emotions recognised universally. However, emotion recognition also depends on socio-cultural rules. Although adults recognise vocal emotions universally, they identify emotions more accurately in their native language. We examined developmental trajectories of universal vocal emotion recognition in children. Eighty native English speakers completed a vocal emotion recognition task in their native language (English) and foreign languages (Spanish, Chinese, and Arabic) expressing anger, happiness, sadness, fear, and neutrality. Emotion recognition was compared across 8-to-10, 11-to-13-year-olds, and adults. Measures of behavioural and emotional problems were also taken. Results showed that although emotion recognition was above chance for all languages, native English speaking children were more accurate in recognising vocal emotions in their native language. There was a larger improvement in recognising vocal emotion from the native language during adolescence. Vocal anger recognition did not improve with age for the non-native languages. This is the first study to demonstrate universality of vocal emotion recognition in children whilst supporting an "in-group advantage" for more accurate recognition in the native language. Findings highlight the role of experience in emotion recognition, have implications for child development in modern multicultural societies and address important theoretical questions about the nature of emotions.

Fine-grained recognition of plants from images.

PubMed

Šulc, Milan; Matas, Jiří

2017-01-01

Fine-grained recognition of plants from images is a challenging computer vision task, due to the diverse appearance and complex structure of plants, high intra-class variability and small inter-class differences. We review the state-of-the-art and discuss plant recognition tasks, from identification of plants from specific plant organs to general plant recognition "in the wild". We propose texture analysis and deep learning methods for different plant recognition tasks. The methods are evaluated and compared them to the state-of-the-art. Texture analysis is only applied to images with unambiguous segmentation (bark and leaf recognition), whereas CNNs are only applied when sufficiently large datasets are available. The results provide an insight in the complexity of different plant recognition tasks. The proposed methods outperform the state-of-the-art in leaf and bark classification and achieve very competitive results in plant recognition "in the wild". The results suggest that recognition of segmented leaves is practically a solved problem, when high volumes of training data are available. The generality and higher capacity of state-of-the-art CNNs makes them suitable for plant recognition "in the wild" where the views on plant organs or plants vary significantly and the difficulty is increased by occlusions and background clutter.

Recognition of face identity and emotion in expressive specific language impairment.

PubMed

Merkenschlager, A; Amorosa, H; Kiefl, H; Martinius, J

2012-01-01

To study face and emotion recognition in children with mostly expressive specific language impairment (SLI-E). A test movie to study perception and recognition of faces and mimic-gestural expression was applied to 24 children diagnosed as suffering from SLI-E and an age-matched control group of normally developing children. Compared to a normal control group, the SLI-E children scored significantly worse in both the face and expression recognition tasks with a preponderant effect on emotion recognition. The performance of the SLI-E group could not be explained by reduced attention during the test session. We conclude that SLI-E is associated with a deficiency in decoding non-verbal emotional facial and gestural information, which might lead to profound and persistent problems in social interaction and development. Copyright © 2012 S. Karger AG, Basel.

Theoretical aspects of pressure and solute denaturation of proteins: A Kirkwood-buff-theory approach.

PubMed

Ben-Naim, Arieh

2012-12-21

A new approach to the problem of pressure-denaturation (PD) and solute-denaturation (SD) of proteins is presented. The problem is formulated in terms of Le Chatelier principle, and a solution is sought in terms of the Kirkwood-Buff theory of solutions. It is found that both problems have one factor in common; the excluded volumes of the folded and the unfolded forms with respect to the solvent molecules. It is shown that solvent-induced effects operating on hydrophilic groups along the protein are probably the main reason for PD. On the other hand, the SD depends on the preferential solvation of the folded and the unfolded forms with respect to solvent and co-solvent molecules.

Theoretical aspects of pressure and solute denaturation of proteins: A Kirkwood-buff-theory approach

NASA Astrophysics Data System (ADS)

Ben-Naim, Arieh

2012-12-01

A new approach to the problem of pressure-denaturation (PD) and solute-denaturation (SD) of proteins is presented. The problem is formulated in terms of Le Chatelier principle, and a solution is sought in terms of the Kirkwood-Buff theory of solutions. It is found that both problems have one factor in common; the excluded volumes of the folded and the unfolded forms with respect to the solvent molecules. It is shown that solvent-induced effects operating on hydrophilic groups along the protein are probably the main reason for PD. On the other hand, the SD depends on the preferential solvation of the folded and the unfolded forms with respect to solvent and co-solvent molecules.

Numerical solution of fluid-structure interaction represented by human vocal folds in airflow

NASA Astrophysics Data System (ADS)

Valášek, J.; Sváček, P.; Horáček, J.

2016-03-01

The paper deals with the human vocal folds vibration excited by the fluid flow. The vocal fold is modelled as an elastic body assuming small displacements and therefore linear elasticity theory is used. The viscous incompressible fluid flow is considered. For purpose of numerical solution the arbitrary Lagrangian-Euler method (ALE) is used. The whole problem is solved by the finite element method (FEM) based solver. Results of numerical experiments with different boundary conditions are presented.

Selectivity in ligand recognition of G-quadruplex loops.

PubMed

Campbell, Nancy H; Patel, Manisha; Tofa, Amina B; Ghosh, Ragina; Parkinson, Gary N; Neidle, Stephen

2009-03-03

A series of disubstituted acridine ligands have been cocrystallized with a bimolecular DNA G-quadruplex. The ligands have a range of cyclic amino end groups of varying size. The crystal structures show that the diagonal loop in this quadruplex results in a large cavity for these groups, in contrast to the steric constraints imposed by propeller loops in human telomeric quadruplexes. We conclude that the nature of the loop has a significant influence on ligand selectivity for particular quadruplex folds.

An Essential Protein Repair Enzyme: Investigation of the Molecular Recognition Mechanism of Methionine Sulfoxide Reductase A

DTIC Science & Technology

2008-05-01

4 ). The three-dimensional spatial orientation of the atoms for these resolved solution structures (Protein Data Bank accession codes: 2gt3...Crystal structure of the Escherichia coli peptide methionine sulphoxide reductase at 1.9 Å resolution . Struct. Fold. Des. 8: 1167 – 1178. 2 . Brot...sources (8). There is a 67% sequence identity between the E.coli and human MsrA ( 2 ). N-terminus C-terminus Figure 2 . Three-dimensional structure

Deglycosylated Filovirus Glycoproteins as Effective Vaccine Immunogens

DTIC Science & Technology

2015-11-01

pre-fusion 119 EBOV GP1,2 ΔTM structure ( PDB ID: 3CSY) that lacks the MLD was performed as previously 120 described (22, 23). Briefly, the published... structure lacks four NGS in GP1 due to disordered 121 regions missing from the structure (N204 and N296) or mutations that promoted crystallization...122 (N40 and N228) (20, 21). The EBOV GP sequence was submitted to the PHYRE2 protein fold 123 recognition server (16), which provided a structure

Solvent viscosity and friction in protein folding dynamics.

PubMed

Hagen, Stephen J

2010-08-01

The famous Kramers rate theory for diffusion-controlled reactions has been extended in numerous ways and successfully applied to many types of reactions. Its application to protein folding reactions has been of particular interest in recent years, as many researchers have performed experiments and simulations to test whether folding reactions are diffusion-controlled, whether the solvent is the source of the reaction friction, and whether the friction-dependence of folding rates generally can provide insight into folding dynamics. These experiments involve many practical difficulties, however. They have also produced some unexpected results. Here we briefly review the Kramers theory for reactions in the presence of strong friction and summarize some of the subtle problems that arise in the application of the theory to protein folding. We discuss how the results of these experiments ultimately point to a significant role for internal friction in protein folding dynamics. Studies of friction in protein folding, far from revealing any weakness in Kramers theory, may actually lead to new approaches for probing diffusional dynamics and energy landscapes in protein folding.

Transfer learning for visual categorization: a survey.

PubMed

Shao, Ling; Zhu, Fan; Li, Xuelong

2015-05-01

Regular machine learning and data mining techniques study the training data for future inferences under a major assumption that the future data are within the same feature space or have the same distribution as the training data. However, due to the limited availability of human labeled training data, training data that stay in the same feature space or have the same distribution as the future data cannot be guaranteed to be sufficient enough to avoid the over-fitting problem. In real-world applications, apart from data in the target domain, related data in a different domain can also be included to expand the availability of our prior knowledge about the target future data. Transfer learning addresses such cross-domain learning problems by extracting useful information from data in a related domain and transferring them for being used in target tasks. In recent years, with transfer learning being applied to visual categorization, some typical problems, e.g., view divergence in action recognition tasks and concept drifting in image classification tasks, can be efficiently solved. In this paper, we survey state-of-the-art transfer learning algorithms in visual categorization applications, such as object recognition, image classification, and human action recognition.

Neural network for intelligent query of an FBI forensic database

NASA Astrophysics Data System (ADS)

Uvanni, Lee A.; Rainey, Timothy G.; Balasubramanian, Uma; Brettle, Dean W.; Weingard, Fred; Sibert, Robert W.; Birnbaum, Eric

1997-02-01

Examiner is an automated fired cartridge case identification system utilizing a dual-use neural network pattern recognition technology, called the statistical-multiple object detection and location system (S-MODALS) developed by Booz(DOT)Allen & Hamilton, Inc. in conjunction with Rome Laboratory. S-MODALS was originally designed for automatic target recognition (ATR) of tactical and strategic military targets using multisensor fusion [electro-optical (EO), infrared (IR), and synthetic aperture radar (SAR)] sensors. Since S-MODALS is a learning system readily adaptable to problem domains other than automatic target recognition, the pattern matching problem of microscopic marks for firearms evidence was analyzed using S-MODALS. The physics; phenomenology; discrimination and search strategies; robustness requirements; error level and confidence level propagation that apply to the pattern matching problem of military targets were found to be applicable to the ballistic domain as well. The Examiner system uses S-MODALS to rank a set of queried cartridge case images from the most similar to the least similar image in reference to an investigative fired cartridge case image. The paper presents three independent tests and evaluation studies of the Examiner system utilizing the S-MODALS technology for the Federal Bureau of Investigation.

Parental Recognition of Developmental Problems in Toddlers with Autism Spectrum Disorders

ERIC Educational Resources Information Center

Chawarska, Katarzyna; Paul, Rhea; Klin, Ami; Hannigen, Sarah; Dichtel, Laura E.; Volkmar, Fred

2007-01-01

Symptoms of Autism Spectrum Disorders (ASD) begin to manifest during the first 2 years; there is limited evidence regarding type and timing of symptom onset. We examined factors related to parental age of recognition (AOR) of early abnormalities and the association between AOR and diagnosis and levels of functioning at 2 and 4 years in 75 toddlers…

Teaching Emotion Recognition Skills to Young Children with Autism: A Randomised Controlled Trial of an Emotion Training Programme

ERIC Educational Resources Information Center

Williams, Beth T.; Gray, Kylie M.; Tonge, Bruce J.

2012-01-01

Background: Children with autism have difficulties in emotion recognition and a number of interventions have been designed to target these problems. However, few emotion training interventions have been trialled with young children with autism and co-morbid ID. This study aimed to evaluate the efficacy of an emotion training programme for a group…

Word Recognition and Nonword Repetition in Children with Language Disorders: The Effects of Neighborhood Density, Lexical Frequency, and Phonotactic Probability

ERIC Educational Resources Information Center

Rispens, Judith; Baker, Anne; Duinmeijer, Iris

2015-01-01

Purpose: The effects of neighborhood density (ND) and lexical frequency on word recognition and the effects of phonotactic probability (PP) on nonword repetition (NWR) were examined to gain insight into processing at the lexical and sublexical levels in typically developing (TD) children and children with developmental language problems. Method:…

Structural basis of DNA folding and recognition in an AMP-DNA aptamer complex: distinct architectures but common recognition motifs for DNA and RNA aptamers complexed to AMP.

PubMed

Lin, C H; Patel, D J

1997-11-01

Structural studies by nuclear magnetic resonance (NMR) of RNA and DNA aptamer complexes identified through in vitro selection and amplification have provided a wealth of information on RNA and DNA tertiary structure and molecular recognition in solution. The RNA and DNA aptamers that target ATP (and AMP) with micromolar affinity exhibit distinct binding site sequences and secondary structures. We report below on the tertiary structure of the AMP-DNA aptamer complex in solution and compare it with the previously reported tertiary structure of the AMP-RNA aptamer complex in solution. The solution structure of the AMP-DNA aptamer complex shows, surprisingly, that two AMP molecules are intercalated at adjacent sites within a rectangular widened minor groove. Complex formation involves adaptive binding where the asymmetric internal bubble of the free DNA aptamer zippers up through formation of a continuous six-base mismatch segment which includes a pair of adjacent three-base platforms. The AMP molecules pair through their Watson-Crick edges with the minor groove edges of guanine residues. These recognition G.A mismatches are flanked by sheared G.A and reversed Hoogsteen G.G mismatch pairs. The AMP-DNA aptamer and AMP-RNA aptamer complexes have distinct tertiary structures and binding stoichiometries. Nevertheless, both complexes have similar structural features and recognition alignments in their binding pockets. Specifically, AMP targets both DNA and RNA aptamers by intercalating between purine bases and through identical G.A mismatch formation. The recognition G.A mismatch stacks with a reversed Hoogsteen G.G mismatch in one direction and with an adenine base in the other direction in both complexes. It is striking that DNA and RNA aptamers selected independently from libraries of 10(14) molecules in each case utilize identical mismatch alignments for molecular recognition with micromolar affinity within binding-site pockets containing common structural elements.

A Simulated Research Problem for Undergraduate Metamorphic Petrology.

ERIC Educational Resources Information Center

Amenta, Roddy V.

1984-01-01

Presents a laboratory problem in metamorphic petrology designed to simulate a research experience. The problem deals with data on scales ranging from a geologic map to hand specimens to thin sections. Student analysis includes identifying metamorphic index minerals, locating their isograds on the map, and determining the folding sequence. (BC)

Online Feature Transformation Learning for Cross-Domain Object Category Recognition.

PubMed

Zhang, Xuesong; Zhuang, Yan; Wang, Wei; Pedrycz, Witold

2017-06-09

In this paper, we introduce a new research problem termed online feature transformation learning in the context of multiclass object category recognition. The learning of a feature transformation is viewed as learning a global similarity metric function in an online manner. We first consider the problem of online learning a feature transformation matrix expressed in the original feature space and propose an online passive aggressive feature transformation algorithm. Then these original features are mapped to kernel space and an online single kernel feature transformation (OSKFT) algorithm is developed to learn a nonlinear feature transformation. Based on the OSKFT and the existing Hedge algorithm, a novel online multiple kernel feature transformation algorithm is also proposed, which can further improve the performance of online feature transformation learning in large-scale application. The classifier is trained with k nearest neighbor algorithm together with the learned similarity metric function. Finally, we experimentally examined the effect of setting different parameter values in the proposed algorithms and evaluate the model performance on several multiclass object recognition data sets. The experimental results demonstrate the validity and good performance of our methods on cross-domain and multiclass object recognition application.

Multiclassifier information fusion methods for microarray pattern recognition

NASA Astrophysics Data System (ADS)

Braun, Jerome J.; Glina, Yan; Judson, Nicholas; Herzig-Marx, Rachel

2004-04-01

This paper addresses automatic recognition of microarray patterns, a capability that could have a major significance for medical diagnostics, enabling development of diagnostic tools for automatic discrimination of specific diseases. The paper presents multiclassifier information fusion methods for microarray pattern recognition. The input space partitioning approach based on fitness measures that constitute an a-priori gauging of classification efficacy for each subspace is investigated. Methods for generation of fitness measures, generation of input subspaces and their use in the multiclassifier fusion architecture are presented. In particular, two-level quantification of fitness that accounts for the quality of each subspace as well as the quality of individual neighborhoods within the subspace is described. Individual-subspace classifiers are Support Vector Machine based. The decision fusion stage fuses the information from mulitple SVMs along with the multi-level fitness information. Final decision fusion stage techniques, including weighted fusion as well as Dempster-Shafer theory based fusion are investigated. It should be noted that while the above methods are discussed in the context of microarray pattern recognition, they are applicable to a broader range of discrimination problems, in particular to problems involving a large number of information sources irreducible to a low-dimensional feature space.

Research and Development of Target Recognition and Location Crawling Platform based on Binocular Vision

NASA Astrophysics Data System (ADS)

Xu, Weidong; Lei, Zhu; Yuan, Zhang; Gao, Zhenqing

2018-03-01

The application of visual recognition technology in industrial robot crawling and placing operation is one of the key tasks in the field of robot research. In order to improve the efficiency and intelligence of the material sorting in the production line, especially to realize the sorting of the scattered items, the robot target recognition and positioning crawling platform based on binocular vision is researched and developed. The images were collected by binocular camera, and the images were pretreated. Harris operator was used to identify the corners of the images. The Canny operator was used to identify the images. Hough-chain code recognition was used to identify the images. The target image in the image, obtain the coordinates of each vertex of the image, calculate the spatial position and posture of the target item, and determine the information needed to capture the movement and transmit it to the robot control crawling operation. Finally, In this paper, we use this method to experiment the wrapping problem in the express sorting process The experimental results show that the platform can effectively solve the problem of sorting of loose parts, so as to achieve the purpose of efficient and intelligent sorting.

Activity inference for Ambient Intelligence through handling artifacts in a healthcare environment.

PubMed

Martínez-Pérez, Francisco E; González-Fraga, Jose Ángel; Cuevas-Tello, Juan C; Rodríguez, Marcela D

2012-01-01

Human activity inference is not a simple process due to distinct ways of performing it. Our proposal presents the SCAN framework for activity inference. SCAN is divided into three modules: (1) artifact recognition, (2) activity inference, and (3) activity representation, integrating three important elements of Ambient Intelligence (AmI) (artifact-behavior modeling, event interpretation and context extraction). The framework extends the roaming beat (RB) concept by obtaining the representation using three kinds of technologies for activity inference. The RB is based on both analysis and recognition from artifact behavior for activity inference. A practical case is shown in a nursing home where a system affording 91.35% effectiveness was implemented in situ. Three examples are shown using RB representation for activity representation. Framework description, RB description and CALog system overcome distinct problems such as the feasibility to implement AmI systems, and to show the feasibility for accomplishing the challenges related to activity recognition based on artifact recognition. We discuss how the use of RBs might positively impact the problems faced by designers and developers for recovering information in an easier manner and thus they can develop tools focused on the user.

Activity Inference for Ambient Intelligence Through Handling Artifacts in a Healthcare Environment

PubMed Central

Martínez-Pérez, Francisco E.; González-Fraga, Jose Ángel; Cuevas-Tello, Juan C.; Rodríguez, Marcela D.

2012-01-01

Human activity inference is not a simple process due to distinct ways of performing it. Our proposal presents the SCAN framework for activity inference. SCAN is divided into three modules: (1) artifact recognition, (2) activity inference, and (3) activity representation, integrating three important elements of Ambient Intelligence (AmI) (artifact-behavior modeling, event interpretation and context extraction). The framework extends the roaming beat (RB) concept by obtaining the representation using three kinds of technologies for activity inference. The RB is based on both analysis and recognition from artifact behavior for activity inference. A practical case is shown in a nursing home where a system affording 91.35% effectiveness was implemented in situ. Three examples are shown using RB representation for activity representation. Framework description, RB description and CALog system overcome distinct problems such as the feasibility to implement AmI systems, and to show the feasibility for accomplishing the challenges related to activity recognition based on artifact recognition. We discuss how the use of RBs might positively impact the problems faced by designers and developers for recovering information in an easier manner and thus they can develop tools focused on the user. PMID:22368512

In-the-wild facial expression recognition in extreme poses

NASA Astrophysics Data System (ADS)

Yang, Fei; Zhang, Qian; Zheng, Chi; Qiu, Guoping

2018-04-01

In the computer research area, facial expression recognition is a hot research problem. Recent years, the research has moved from the lab environment to in-the-wild circumstances. It is challenging, especially under extreme poses. But current expression detection systems are trying to avoid the pose effects and gain the general applicable ability. In this work, we solve the problem in the opposite approach. We consider the head poses and detect the expressions within special head poses. Our work includes two parts: detect the head pose and group it into one pre-defined head pose class; do facial expression recognize within each pose class. Our experiments show that the recognition results with pose class grouping are much better than that of direct recognition without considering poses. We combine the hand-crafted features, SIFT, LBP and geometric feature, with deep learning feature as the representation of the expressions. The handcrafted features are added into the deep learning framework along with the high level deep learning features. As a comparison, we implement SVM and random forest to as the prediction models. To train and test our methodology, we labeled the face dataset with 6 basic expressions.

Infrared variation reduction by simultaneous background suppression and target contrast enhancement for deep convolutional neural network-based automatic target recognition

NASA Astrophysics Data System (ADS)

Kim, Sungho

2017-06-01

Automatic target recognition (ATR) is a traditionally challenging problem in military applications because of the wide range of infrared (IR) image variations and the limited number of training images. IR variations are caused by various three-dimensional target poses, noncooperative weather conditions (fog and rain), and difficult target acquisition environments. Recently, deep convolutional neural network-based approaches for RGB images (RGB-CNN) showed breakthrough performance in computer vision problems, such as object detection and classification. The direct use of RGB-CNN to the IR ATR problem fails to work because of the IR database problems (limited database size and IR image variations). An IR variation-reduced deep CNN (IVR-CNN) to cope with the problems is presented. The problem of limited IR database size is solved by a commercial thermal simulator (OKTAL-SE). The second problem of IR variations is mitigated by the proposed shifted ramp function-based intensity transformation. This can suppress the background and enhance the target contrast simultaneously. The experimental results on the synthesized IR images generated by the thermal simulator (OKTAL-SE) validated the feasibility of IVR-CNN for military ATR applications.

[Analysis of the factors related to the needs of patients with cancer].

PubMed

Lee, Jung A; Lee, Sun Hee; Park, Jong Hyock; Park, Jae Hyun; Kim, Sung Gyeong; Seo, Ju Hyun

2010-05-01

Limited research has investigated the specific needs of patients with cancer. This study was performed to explore patients needs and the related factors. The data were collected by 1 National Cancer Center and 9 regional cancer centers in Korea. An interview survey was performed with using a structured questionnaire for the subjects (2,661 patients who gave written informed consent to participate) survey 4 months after diagnosis and review of medical records. Data were analyzed using t-test, ANOVA and multiple regression analysis. When comparing the relating factors related with patient needs to the sociodemographic characteristics, the female group showed a higher level of recognition for physical symptoms, social support needs. The younger group showed a significantly higher level of recognition for health care staff, psychological problems, information and education, social support, hospital services needs. In addition, the higher educated group showed a higher level of recognition for health care staff, physical symptoms, social support needs. The higher income and office workers group showed a higher level of recognition for hospital services needs. When comparing the relating factors related with patient needs to the cancer, the breast cancer group showed a higher level of recognition for all needs excluding physical symptoms, accessibility and financial support needs. The combined radiotherapy with surgery and chemotherapy group showed a higher level of recognition for psychological problems, information and education, social support needs. This study showed that needs on patient with cancer was significantly influenced by female, higher education, lower income, having religion, office worker, liver cancer, breast cancer, colon cancer, chemotherapy, and combined therapy.

Multiple template-based image matching using alpha-rooted quaternion phase correlation

NASA Astrophysics Data System (ADS)

DelMarco, Stephen

2010-04-01

In computer vision applications, image matching performed on quality-degraded imagery is difficult due to image content distortion and noise effects. State-of-the art keypoint based matchers, such as SURF and SIFT, work very well on clean imagery. However, performance can degrade significantly in the presence of high noise and clutter levels. Noise and clutter cause the formation of false features which can degrade recognition performance. To address this problem, previously we developed an extension to the classical amplitude and phase correlation forms, which provides improved robustness and tolerance to image geometric misalignments and noise. This extension, called Alpha-Rooted Phase Correlation (ARPC), combines Fourier domain-based alpha-rooting enhancement with classical phase correlation. ARPC provides tunable parameters to control the alpha-rooting enhancement. These parameter values can be optimized to tradeoff between high narrow correlation peaks, and more robust wider, but smaller peaks. Previously, we applied ARPC in the radon transform domain for logo image recognition in the presence of rotational image misalignments. In this paper, we extend ARPC to incorporate quaternion Fourier transforms, thereby creating Alpha-Rooted Quaternion Phase Correlation (ARQPC). We apply ARQPC to the logo image recognition problem. We use ARQPC to perform multiple-reference logo template matching by representing multiple same-class reference templates as quaternion-valued images. We generate recognition performance results on publicly-available logo imagery, and compare recognition results to results generated from standard approaches. We show that small deviations in reference templates of sameclass logos can lead to improved recognition performance using the joint matching inherent in ARQPC.

Mapping monomeric threading to protein-protein structure prediction.

PubMed

Guerler, Aysam; Govindarajoo, Brandon; Zhang, Yang

2013-03-25

The key step of template-based protein-protein structure prediction is the recognition of complexes from experimental structure libraries that have similar quaternary fold. Maintaining two monomer and dimer structure libraries is however laborious, and inappropriate library construction can degrade template recognition coverage. We propose a novel strategy SPRING to identify complexes by mapping monomeric threading alignments to protein-protein interactions based on the original oligomer entries in the PDB, which does not rely on library construction and increases the efficiency and quality of complex template recognitions. SPRING is tested on 1838 nonhomologous protein complexes which can recognize correct quaternary template structures with a TM score >0.5 in 1115 cases after excluding homologous proteins. The average TM score of the first model is 60% and 17% higher than that by HHsearch and COTH, respectively, while the number of targets with an interface RMSD <2.5 Å by SPRING is 134% and 167% higher than these competing methods. SPRING is controlled with ZDOCK on 77 docking benchmark proteins. Although the relative performance of SPRING and ZDOCK depends on the level of homology filters, a combination of the two methods can result in a significantly higher model quality than ZDOCK at all homology thresholds. These data demonstrate a new efficient approach to quaternary structure recognition that is ready to use for genome-scale modeling of protein-protein interactions due to the high speed and accuracy.

TALE-PvuII Fusion Proteins – Novel Tools for Gene Targeting

PubMed Central

Yanik, Mert; Alzubi, Jamal; Lahaye, Thomas; Cathomen, Toni; Pingoud, Alfred; Wende, Wolfgang

2013-01-01

Zinc finger nucleases (ZFNs) consist of zinc fingers as DNA-binding module and the non-specific DNA-cleavage domain of the restriction endonuclease FokI as DNA-cleavage module. This architecture is also used by TALE nucleases (TALENs), in which the DNA-binding modules of the ZFNs have been replaced by DNA-binding domains based on transcription activator like effector (TALE) proteins. Both TALENs and ZFNs are programmable nucleases which rely on the dimerization of FokI to induce double-strand DNA cleavage at the target site after recognition of the target DNA by the respective DNA-binding module. TALENs seem to have an advantage over ZFNs, as the assembly of TALE proteins is easier than that of ZFNs. Here, we present evidence that variant TALENs can be produced by replacing the catalytic domain of FokI with the restriction endonuclease PvuII. These fusion proteins recognize only the composite recognition site consisting of the target site of the TALE protein and the PvuII recognition sequence (addressed site), but not isolated TALE or PvuII recognition sites (unaddressed sites), even at high excess of protein over DNA and long incubation times. In vitro, their preference for an addressed over an unaddressed site is > 34,000-fold. Moreover, TALE-PvuII fusion proteins are active in cellula with minimal cytotoxicity. PMID:24349308

FOLD-EM: automated fold recognition in medium- and low-resolution (4-15 Å) electron density maps.

PubMed

Saha, Mitul; Morais, Marc C

2012-12-15

Owing to the size and complexity of large multi-component biological assemblies, the most tractable approach to determining their atomic structure is often to fit high-resolution radiographic or nuclear magnetic resonance structures of isolated components into lower resolution electron density maps of the larger assembly obtained using cryo-electron microscopy (cryo-EM). This hybrid approach to structure determination requires that an atomic resolution structure of each component, or a suitable homolog, is available. If neither is available, then the amount of structural information regarding that component is limited by the resolution of the cryo-EM map. However, even if a suitable homolog cannot be identified using sequence analysis, a search for structural homologs should still be performed because structural homology often persists throughout evolution even when sequence homology is undetectable, As macromolecules can often be described as a collection of independently folded domains, one way of searching for structural homologs would be to systematically fit representative domain structures from a protein domain database into the medium/low resolution cryo-EM map and return the best fits. Taken together, the best fitting non-overlapping structures would constitute a 'mosaic' backbone model of the assembly that could aid map interpretation and illuminate biological function. Using the computational principles of the Scale-Invariant Feature Transform (SIFT), we have developed FOLD-EM-a computational tool that can identify folded macromolecular domains in medium to low resolution (4-15 Å) electron density maps and return a model of the constituent polypeptides in a fully automated fashion. As a by-product, FOLD-EM can also do flexible multi-domain fitting that may provide insight into conformational changes that occur in macromolecular assemblies.

The pH-dependent tertiary structure of a designed helix-loop-helix dimer.

PubMed

Dolphin, G T; Baltzer, L

1997-01-01

De novo designed helix-loop-helix motifs can fold into well-defined tertiary structures if residues or groups of residues are incorporated at the helix-helix boundary to form helix-recognition sites that restrict the conformational degrees of freedom of the helical segments. Understanding the relationship between structure and function of conformational constraints therefore forms the basis for the engineering of non-natural proteins. This paper describes the design of an interhelical HisH+-Asp- hydrogen-bonded ion pair and the conformational stability of the folded helix-loop-helix motif. GTD-C, a polypeptide with 43 amino acid residues, has been designed to fold into a hairpin helix-loop-helix motif that can dimerise to form a four-helix bundle. The folded motif is in slow conformational exchange on the NMR timescale and has a well-dispersed 1H NMR spectrum, a narrow temperature interval for thermal denaturation and a near-UV CD spectrum with some fine structure. The conformational stability is pH dependent with an optimum that corresponds to the pH for maximum formation of a hydrogen-bonded ion pair between HisH17+ in helix I and Asp27- in helix II. The formation of an interhelical salt bridge is strongly suggested by the pH dependence of a number of spectroscopic probes to generate a well-defined tertiary structure in a designed helix-loop-helix motif. The thermodynamic stability of the folded motif is not increased by the formation of the salt bridge, but neighbouring conformations are destabilised. The use of this novel design principle in combination with hydrophobic interactions that provide sufficient binding energy in the folded structure should be of general use in de novo design of native-like proteins.

Cofactor-binding sites in proteins of deviating sequence: comparative analysis and clustering in torsion angle, cavity, and fold space.

PubMed

Stegemann, Björn; Klebe, Gerhard

2012-02-01

Small molecules are recognized in protein-binding pockets through surface-exposed physicochemical properties. To optimize binding, they have to adopt a conformation corresponding to a local energy minimum within the formed protein-ligand complex. However, their conformational flexibility makes them competent to bind not only to homologous proteins of the same family but also to proteins of remote similarity with respect to the shape of the binding pockets and folding pattern. Considering drug action, such observations can give rise to unexpected and undesired cross reactivity. In this study, datasets of six different cofactors (ADP, ATP, NAD(P)(H), FAD, and acetyl CoA, sharing an adenosine diphosphate moiety as common substructure), observed in multiple crystal structures of protein-cofactor complexes exhibiting sequence identity below 25%, have been analyzed for the conformational properties of the bound ligands, the distribution of physicochemical properties in the accommodating protein-binding pockets, and the local folding patterns next to the cofactor-binding site. State-of-the-art clustering techniques have been applied to group the different protein-cofactor complexes in the different spaces. Interestingly, clustering in cavity (Cavbase) and fold space (DALI) reveals virtually the same data structuring. Remarkable relationships can be found among the different spaces. They provide information on how conformations are conserved across the host proteins and which distinct local cavity and fold motifs recognize the different portions of the cofactors. In those cases, where different cofactors are found to be accommodated in a similar fashion to the same fold motifs, only a commonly shared substructure of the cofactors is used for the recognition process. Copyright © 2011 Wiley Periodicals, Inc.

An adaptive deep Q-learning strategy for handwritten digit recognition.

PubMed

Qiao, Junfei; Wang, Gongming; Li, Wenjing; Chen, Min

2018-02-22

Handwritten digits recognition is a challenging problem in recent years. Although many deep learning-based classification algorithms are studied for handwritten digits recognition, the recognition accuracy and running time still need to be further improved. In this paper, an adaptive deep Q-learning strategy is proposed to improve accuracy and shorten running time for handwritten digit recognition. The adaptive deep Q-learning strategy combines the feature-extracting capability of deep learning and the decision-making of reinforcement learning to form an adaptive Q-learning deep belief network (Q-ADBN). First, Q-ADBN extracts the features of original images using an adaptive deep auto-encoder (ADAE), and the extracted features are considered as the current states of Q-learning algorithm. Second, Q-ADBN receives Q-function (reward signal) during recognition of the current states, and the final handwritten digits recognition is implemented by maximizing the Q-function using Q-learning algorithm. Finally, experimental results from the well-known MNIST dataset show that the proposed Q-ADBN has a superiority to other similar methods in terms of accuracy and running time. Copyright © 2018 Elsevier Ltd. All rights reserved.

Pattern Recognition Using Artificial Neural Network: A Review

NASA Astrophysics Data System (ADS)

Kim, Tai-Hoon

Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, artificial neural network techniques theory have been receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system using ANN and identify research topics and applications which are at the forefront of this exciting and challenging field.

The molecular determinants of CD8 co-receptor function.

PubMed

Cole, David K; Laugel, Bruno; Clement, Mathew; Price, David A; Wooldridge, Linda; Sewell, Andrew K

2012-10-01

CD8(+) T cells respond to signals mediated through a specific interaction between the T-cell receptor (TCR) and a composite antigen in the form of an epitopic peptide bound between the polymorphic α1 and α2 helices of an MHC class I (MHCI) molecule. The CD8 glycoprotein 'co-receives' antigen by binding to an invariant region of the MHCI molecule and can enhance ligand recognition by up to 1 million-fold. In recent years, a number of structural and biophysical investigations have shed light on the role of the CD8 co-receptor during T-cell antigen recognition. Here, we provide a collated resource for these data, and discuss how the structural and biophysical parameters governing CD8 co-receptor function further our understanding of T-cell cross-reactivity and the productive engagement of low-affinity antigenic ligands. © 2012 The Authors. Immunology © 2012 Blackwell Publishing Ltd.

Protein Detection via Direct Enzymatic Amplification of Short DNA Aptamers

PubMed Central

Fischer, Nicholas O.; Tarasow, Theodore M.; Tok, Jeffrey B.-H.

2008-01-01

Aptamers are single-stranded nucleic acids that fold into defined tertiary structures to bind target molecules with high specificities and affinities. DNA aptamers have garnered much interest as recognition elements for biodetection and diagnostic applications due to their small size, ease of discovery and synthesis, and chemical and thermal stability. Herein, we describe the design and application of a short DNA molecule capable of both protein target binding and amplifiable bioreadout processes. As both recognition and readout capabilities are incorporated into a single DNA molecule, tedious conjugation procedures required for protein-DNA hybrids can be omitted. The DNA aptamer is designed to be amplified directly by either the polymerase chain reaction (PCR) or rolling circle amplification (RCA) processes, taking advantage of real-time amplification monitoring techniques for target detection. A combination of both RCA and PCR provides a wide protein target dynamic range (1 μM to 10 pM). PMID:17980857

Spatial, Hysteretic, and Adaptive Host-Guest Chemistry in a Metal-Organic Framework with Open Watson-Crick Sites.

PubMed

Cai, Hong; Li, Mian; Lin, Xiao-Rong; Chen, Wei; Chen, Guang-Hui; Huang, Xiao-Chun; Li, Dan

2015-09-01

Biological and artificial molecules and assemblies capable of supramolecular recognition, especially those with nucleobase pairing, usually rely on autonomous or collective binding to function. Advanced site-specific recognition takes advantage of cooperative spatial effects, as in local folding in protein-DNA binding. Herein, we report a new nucleobase-tagged metal-organic framework (MOF), namely ZnBTCA (BTC=benzene-1,3,5-tricarboxyl, A=adenine), in which the exposed Watson-Crick faces of adenine residues are immobilized periodically on the interior crystalline surface. Systematic control experiments demonstrated the cooperation of the open Watson-Crick sites and spatial effects within the nanopores, and thermodynamic and kinetic studies revealed a hysteretic host-guest interaction attributed to mild chemisorption. We further exploited this behavior for adenine-thymine binding within the constrained pores, and a globally adaptive response of the MOF host was observed. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

RNAiFold2T: Constraint Programming design of thermo-IRES switches.

PubMed

Garcia-Martin, Juan Antonio; Dotu, Ivan; Fernandez-Chamorro, Javier; Lozano, Gloria; Ramajo, Jorge; Martinez-Salas, Encarnacion; Clote, Peter

2016-06-15

RNA thermometers (RNATs) are cis-regulatory elements that change secondary structure upon temperature shift. Often involved in the regulation of heat shock, cold shock and virulence genes, RNATs constitute an interesting potential resource in synthetic biology, where engineered RNATs could prove to be useful tools in biosensors and conditional gene regulation. Solving the 2-temperature inverse folding problem is critical for RNAT engineering. Here we introduce RNAiFold2T, the first Constraint Programming (CP) and Large Neighborhood Search (LNS) algorithms to solve this problem. Benchmarking tests of RNAiFold2T against existent programs (adaptive walk and genetic algorithm) inverse folding show that our software generates two orders of magnitude more solutions, thus allowing ample exploration of the space of solutions. Subsequently, solutions can be prioritized by computing various measures, including probability of target structure in the ensemble, melting temperature, etc. Using this strategy, we rationally designed two thermosensor internal ribosome entry site (thermo-IRES) elements, whose normalized cap-independent translation efficiency is approximately 50% greater at 42 °C than 30 °C, when tested in reticulocyte lysates. Translation efficiency is lower than that of the wild-type IRES element, which on the other hand is fully resistant to temperature shift-up. This appears to be the first purely computational design of functional RNA thermoswitches, and certainly the first purely computational design of functional thermo-IRES elements. RNAiFold2T is publicly available as part of the new release RNAiFold3.0 at https://github.com/clotelab/RNAiFold and http://bioinformatics.bc.edu/clotelab/RNAiFold, which latter has a web server as well. The software is written in C ++ and uses OR-Tools CP search engine. clote@bc.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

The writer independent online handwriting recognition system frog on hand and cluster generative statistical dynamic time warping.

PubMed

Bahlmann, Claus; Burkhardt, Hans

2004-03-01

In this paper, we give a comprehensive description of our writer-independent online handwriting recognition system frog on hand. The focus of this work concerns the presentation of the classification/training approach, which we call cluster generative statistical dynamic time warping (CSDTW). CSDTW is a general, scalable, HMM-based method for variable-sized, sequential data that holistically combines cluster analysis and statistical sequence modeling. It can handle general classification problems that rely on this sequential type of data, e.g., speech recognition, genome processing, robotics, etc. Contrary to previous attempts, clustering and statistical sequence modeling are embedded in a single feature space and use a closely related distance measure. We show character recognition experiments of frog on hand using CSDTW on the UNIPEN online handwriting database. The recognition accuracy is significantly higher than reported results of other handwriting recognition systems. Finally, we describe the real-time implementation of frog on hand on a Linux Compaq iPAQ embedded device.

Current trends in small vocabulary speech recognition for equipment control

NASA Astrophysics Data System (ADS)

Doukas, Nikolaos; Bardis, Nikolaos G.

2017-09-01

Speech recognition systems allow human - machine communication to acquire an intuitive nature that approaches the simplicity of inter - human communication. Small vocabulary speech recognition is a subset of the overall speech recognition problem, where only a small number of words need to be recognized. Speaker independent small vocabulary recognition can find significant applications in field equipment used by military personnel. Such equipment may typically be controlled by a small number of commands that need to be given quickly and accurately, under conditions where delicate manual operations are difficult to achieve. This type of application could hence significantly benefit by the use of robust voice operated control components, as they would facilitate the interaction with their users and render it much more reliable in times of crisis. This paper presents current challenges involved in attaining efficient and robust small vocabulary speech recognition. These challenges concern feature selection, classification techniques, speaker diversity and noise effects. A state machine approach is presented that facilitates the voice guidance of different equipment in a variety of situations.

Utterance independent bimodal emotion recognition in spontaneous communication

NASA Astrophysics Data System (ADS)

Tao, Jianhua; Pan, Shifeng; Yang, Minghao; Li, Ya; Mu, Kaihui; Che, Jianfeng

2011-12-01

Emotion expressions sometimes are mixed with the utterance expression in spontaneous face-to-face communication, which makes difficulties for emotion recognition. This article introduces the methods of reducing the utterance influences in visual parameters for the audio-visual-based emotion recognition. The audio and visual channels are first combined under a Multistream Hidden Markov Model (MHMM). Then, the utterance reduction is finished by finding the residual between the real visual parameters and the outputs of the utterance related visual parameters. This article introduces the Fused Hidden Markov Model Inversion method which is trained in the neutral expressed audio-visual corpus to solve the problem. To reduce the computing complexity the inversion model is further simplified to a Gaussian Mixture Model (GMM) mapping. Compared with traditional bimodal emotion recognition methods (e.g., SVM, CART, Boosting), the utterance reduction method can give better results of emotion recognition. The experiments also show the effectiveness of our emotion recognition system when it was used in a live environment.

A Theoretical Framework for Studying Adolescent Contraceptive Use.

ERIC Educational Resources Information Center

Urberg, Kathryn A.

1982-01-01

Presents a theoretical framework for viewing adolescent contraceptive usage. The problem-solving process is used for developmentally examining the competencies that must be present for effective contraceptive use, including: problem recognition, motivation, generation of alternatives, decision making and implementation. Each aspect is discussed…

How Captain Amerika uses neural networks to fight crime

NASA Technical Reports Server (NTRS)

Rogers, Steven K.; Kabrisky, Matthew; Ruck, Dennis W.; Oxley, Mark E.

1994-01-01

Artificial neural network models can make amazing computations. These models are explained along with their application in problems associated with fighting crime. Specific problems addressed are identification of people using face recognition, speaker identification, and fingerprint and handwriting analysis (biometric authentication).

Computer discrimination procedures applicable to aerial and ERTS multispectral data

NASA Technical Reports Server (NTRS)

Richardson, A. J.; Torline, R. J.; Allen, W. A.

1970-01-01

Two statistical models are compared in the classification of crops recorded on color aerial photographs. A theory of error ellipses is applied to the pattern recognition problem. An elliptical boundary condition classification model (EBC), useful for recognition of candidate patterns, evolves out of error ellipse theory. The EBC model is compared with the minimum distance to the mean (MDM) classification model in terms of pattern recognition ability. The pattern recognition results of both models are interpreted graphically using scatter diagrams to represent measurement space. Measurement space, for this report, is determined by optical density measurements collected from Kodak Ektachrome Infrared Aero Film 8443 (EIR). The EBC model is shown to be a significant improvement over the MDM model.

Image quality assessment for video stream recognition systems

NASA Astrophysics Data System (ADS)

Chernov, Timofey S.; Razumnuy, Nikita P.; Kozharinov, Alexander S.; Nikolaev, Dmitry P.; Arlazarov, Vladimir V.

2018-04-01

Recognition and machine vision systems have long been widely used in many disciplines to automate various processes of life and industry. Input images of optical recognition systems can be subjected to a large number of different distortions, especially in uncontrolled or natural shooting conditions, which leads to unpredictable results of recognition systems, making it impossible to assess their reliability. For this reason, it is necessary to perform quality control of the input data of recognition systems, which is facilitated by modern progress in the field of image quality evaluation. In this paper, we investigate the approach to designing optical recognition systems with built-in input image quality estimation modules and feedback, for which the necessary definitions are introduced and a model for describing such systems is constructed. The efficiency of this approach is illustrated by the example of solving the problem of selecting the best frames for recognition in a video stream for a system with limited resources. Experimental results are presented for the system for identity documents recognition, showing a significant increase in the accuracy and speed of the system under simulated conditions of automatic camera focusing, leading to blurring of frames.

Presentation Attack Detection for Iris Recognition System Using NIR Camera Sensor

PubMed Central

Nguyen, Dat Tien; Baek, Na Rae; Pham, Tuyen Danh; Park, Kang Ryoung

2018-01-01

Among biometric recognition systems such as fingerprint, finger-vein, or face, the iris recognition system has proven to be effective for achieving a high recognition accuracy and security level. However, several recent studies have indicated that an iris recognition system can be fooled by using presentation attack images that are recaptured using high-quality printed images or by contact lenses with printed iris patterns. As a result, this potential threat can reduce the security level of an iris recognition system. In this study, we propose a new presentation attack detection (PAD) method for an iris recognition system (iPAD) using a near infrared light (NIR) camera image. To detect presentation attack images, we first localized the iris region of the input iris image using circular edge detection (CED). Based on the result of iris localization, we extracted the image features using deep learning-based and handcrafted-based methods. The input iris images were then classified into real and presentation attack categories using support vector machines (SVM). Through extensive experiments with two public datasets, we show that our proposed method effectively solves the iris recognition presentation attack detection problem and produces detection accuracy superior to previous studies. PMID:29695113

Presentation Attack Detection for Iris Recognition System Using NIR Camera Sensor.

PubMed

Nguyen, Dat Tien; Baek, Na Rae; Pham, Tuyen Danh; Park, Kang Ryoung

2018-04-24

Among biometric recognition systems such as fingerprint, finger-vein, or face, the iris recognition system has proven to be effective for achieving a high recognition accuracy and security level. However, several recent studies have indicated that an iris recognition system can be fooled by using presentation attack images that are recaptured using high-quality printed images or by contact lenses with printed iris patterns. As a result, this potential threat can reduce the security level of an iris recognition system. In this study, we propose a new presentation attack detection (PAD) method for an iris recognition system (iPAD) using a near infrared light (NIR) camera image. To detect presentation attack images, we first localized the iris region of the input iris image using circular edge detection (CED). Based on the result of iris localization, we extracted the image features using deep learning-based and handcrafted-based methods. The input iris images were then classified into real and presentation attack categories using support vector machines (SVM). Through extensive experiments with two public datasets, we show that our proposed method effectively solves the iris recognition presentation attack detection problem and produces detection accuracy superior to previous studies.

Tectonic evolution of kid metamorphic complex and the recognition of Najd fault system in South East Sinai, Egypt

NASA Astrophysics Data System (ADS)

Sultan, Yasser M.; El-Shafei, Mohamed K.; Arnous, Mohamed O.

2017-03-01

A low-to medium-grade metamorphic belt of a volcano-sedimentary succession occurs in the eastern side of South Sinai as a part of the northernmost extension of the Arabian-Nubian Shield in Egypt. The belt is known as the Kid metamorphic complex. It is considered as one of the major belt among the other exposed metamorphic belts in South Sinai. Here, we detect and investigate the signature of the Najd Fault system in South Sinai based on detailed structural analysis in field and digital image processing. The enhanced satellite image and the geo-spatial distributions confirm that the Kid belt is essentially composed of nine Precambrian units. Field relations and geometrical analysis of the measured structural data revealed that the study area underwent four successive deformational phases (D1-D4). D1 is an upright tight to isoclinal large-scale folds that caused few F1 small-scale folds and a steeply dipping S1 axial plane foliation. The second deformational event D2 produced dominant of sub-horizontal S2 foliation planes accompanied with recumbent isoclinal folds and NW-SE trending L2 lineations. The main sense during D2 was top-to-the-NW with local reversals to the SE. The third folding generations F3 is recorded as axial plane S3-surfaces and is characterized by open concentric folding that overprinting both F1 and F2 folds and has a flexural-slip mechanism. F3 fold hinges plunge to the west-northwest or east-southeast indicate north-northeast-south-southwest shortening during D3. The fourth deformational event D4 is characterized by NE plunging open concentric folding overprint the pre-existing fold generations and formed under flexural slip mechanism reflecting coaxial deformation and indicating change in the stress regime as a result of the change in shortening from NE-SW to NW-SE. This phase is probably accompanied with the final assembly of east and west Gondwana. The dextral NW-SE shear zone that bounded the southwestern portion of the metamorphic belt is probably related to reactivation of the Najd fault system during Oligo-Miocene in South Sinai.

Recognition Using Hybrid Classifiers.

PubMed

Osadchy, Margarita; Keren, Daniel; Raviv, Dolev

2016-04-01

A canonical problem in computer vision is category recognition (e.g., find all instances of human faces, cars etc., in an image). Typically, the input for training a binary classifier is a relatively small sample of positive examples, and a huge sample of negative examples, which can be very diverse, consisting of images from a large number of categories. The difficulty of the problem sharply increases with the dimension and size of the negative example set. We propose to alleviate this problem by applying a "hybrid" classifier, which replaces the negative samples by a prior, and then finds a hyperplane which separates the positive samples from this prior. The method is extended to kernel space and to an ensemble-based approach. The resulting binary classifiers achieve an identical or better classification rate than SVM, while requiring far smaller memory and lower computational complexity to train and apply.

Comparison of the scanned pages of the contractual documents

NASA Astrophysics Data System (ADS)

Andreeva, Elena; Arlazarov, Vladimir V.; Manzhikov, Temudzhin; Slavin, Oleg

2018-04-01

In this paper the problem statement is given to compare the digitized pages of the official papers. Such problem appears during the comparison of two customer copies signed at different times between two parties with a view to find the possible modifications introduced on the one hand. This problem is a practically significant in the banking sector during the conclusion of contracts in a paper format. The method of comparison based on the recognition, which consists in the comparison of two bag-of-words, which are the recognition result of the master and test pages, is suggested. The described experiments were conducted using the OCR Tesseract and the siamese neural network. The advantages of the suggested method are the steady operation of the comparison algorithm and the high exacting precision, and one of the disadvantages is the dependence on the chosen OCR.

Enhanced iris recognition method based on multi-unit iris images

NASA Astrophysics Data System (ADS)

Shin, Kwang Yong; Kim, Yeong Gon; Park, Kang Ryoung

2013-04-01

For the purpose of biometric person identification, iris recognition uses the unique characteristics of the patterns of the iris; that is, the eye region between the pupil and the sclera. When obtaining an iris image, the iris's image is frequently rotated because of the user's head roll toward the left or right shoulder. As the rotation of the iris image leads to circular shifting of the iris features, the accuracy of iris recognition is degraded. To solve this problem, conventional iris recognition methods use shifting of the iris feature codes to perform the matching. However, this increases the computational complexity and level of false acceptance error. To solve these problems, we propose a novel iris recognition method based on multi-unit iris images. Our method is novel in the following five ways compared with previous methods. First, to detect both eyes, we use Adaboost and a rapid eye detector (RED) based on the iris shape feature and integral imaging. Both eyes are detected using RED in the approximate candidate region that consists of the binocular region, which is determined by the Adaboost detector. Second, we classify the detected eyes into the left and right eyes, because the iris patterns in the left and right eyes in the same person are different, and they are therefore considered as different classes. We can improve the accuracy of iris recognition using this pre-classification of the left and right eyes. Third, by measuring the angle of head roll using the two center positions of the left and right pupils, detected by two circular edge detectors, we obtain the information of the iris rotation angle. Fourth, in order to reduce the error and processing time of iris recognition, adaptive bit-shifting based on the measured iris rotation angle is used in feature matching. Fifth, the recognition accuracy is enhanced by the score fusion of the left and right irises. Experimental results on the iris open database of low-resolution images showed that the averaged equal error rate of iris recognition using the proposed method was 4.3006%, which is lower than that of other methods.

Large heat capacity change in a protein-monovalent cation interaction.

PubMed

Guinto, E R; Di Cera, E

1996-07-09

Current views about protein-ligand interactions state that electrostatic forces drive the binding of charged species and that burial of hydrophobic and polar surfaces controls the heat capacity change associated with the reaction. For the interaction of a protein with a monovalent cation the electrostatic components are expected to be significant due to the ionic nature of the ligand, whereas the heat capacity change is expected to be small due to the size of the surface area involved in the recognition event. The physiologically important interaction of Na+ with thrombin was studied over the temperature range from 5 to 45 degrees C and the ionic strength range from 50 to 800 mM. These measurements reveal an unanticipated result that bears quite generally on studies of molecular recognition and protein folding. Binding of Na+ to thrombin is characterized by a modest dependence on ionic strength but a large and negative heat capacity change of -1.1 +/- 0.1 kcal mol-1 K-1. The small electrostatic coupling can be explained in terms of a minimal perturbation of the ionic atmosphere of the protein upon Na+ binding. The large heat capacity change, however, is difficult to reconcile with current views on the origin of this effect from surface area changes or large folding transitions coupled to binding. It is proposed that this change is linked to burial of a large cluster of water molecules in the Na+ binding pocket upon Na+ binding. Due to their reduced mobility and highly ordered structure, water molecules sequestered in the interior of a protein must have a lower heat capacity compared to those on the surface of a protein or in the bulk solvent. Hence, a binding or folding event where water molecules are buried may result in significant heat capacity changes independent of changes in exposed hydrophobic surface or coupled conformational transitions.

Improved method for predicting protein fold patterns with ensemble classifiers.

PubMed

Chen, W; Liu, X; Huang, Y; Jiang, Y; Zou, Q; Lin, C

2012-01-27

Protein folding is recognized as a critical problem in the field of biophysics in the 21st century. Predicting protein-folding patterns is challenging due to the complex structure of proteins. In an attempt to solve this problem, we employed ensemble classifiers to improve prediction accuracy. In our experiments, 188-dimensional features were extracted based on the composition and physical-chemical property of proteins and 20-dimensional features were selected using a coupled position-specific scoring matrix. Compared with traditional prediction methods, these methods were superior in terms of prediction accuracy. The 188-dimensional feature-based method achieved 71.2% accuracy in five cross-validations. The accuracy rose to 77% when we used a 20-dimensional feature vector. These methods were used on recent data, with 54.2% accuracy. Source codes and dataset, together with web server and software tools for prediction, are available at: http://datamining.xmu.edu.cn/main/~cwc/ProteinPredict.html.

WeFold: A Coopetition for Protein Structure Prediction

PubMed Central

Khoury, George A.; Liwo, Adam; Khatib, Firas; Zhou, Hongyi; Chopra, Gaurav; Bacardit, Jaume; Bortot, Leandro O.; Faccioli, Rodrigo A.; Deng, Xin; He, Yi; Krupa, Pawel; Li, Jilong; Mozolewska, Magdalena A.; Sieradzan, Adam K.; Smadbeck, James; Wirecki, Tomasz; Cooper, Seth; Flatten, Jeff; Xu, Kefan; Baker, David; Cheng, Jianlin; Delbem, Alexandre C. B.; Floudas, Christodoulos A.; Keasar, Chen; Levitt, Michael; Popović, Zoran; Scheraga, Harold A.; Skolnick, Jeffrey; Crivelli, Silvia N.; Players, Foldit

2014-01-01

The protein structure prediction problem continues to elude scientists. Despite the introduction of many methods, only modest gains were made over the last decade for certain classes of prediction targets. To address this challenge, a social-media based worldwide collaborative effort, named WeFold, was undertaken by thirteen labs. During the collaboration, the labs were simultaneously competing with each other. Here, we present the first attempt at “coopetition” in scientific research applied to the protein structure prediction and refinement problems. The coopetition was possible by allowing the participating labs to contribute different components of their protein structure prediction pipelines and create new hybrid pipelines that they tested during CASP10. This manuscript describes both successes and areas needing improvement as identified throughout the first WeFold experiment and discusses the efforts that are underway to advance this initiative. A footprint of all contributions and structures are publicly accessible at http://www.wefold.org. PMID:24677212

Empirical Performance of Cross-Validation With Oracle Methods in a Genomics Context.

PubMed

Martinez, Josue G; Carroll, Raymond J; Müller, Samuel; Sampson, Joshua N; Chatterjee, Nilanjan

2011-11-01

When employing model selection methods with oracle properties such as the smoothly clipped absolute deviation (SCAD) and the Adaptive Lasso, it is typical to estimate the smoothing parameter by m-fold cross-validation, for example, m = 10. In problems where the true regression function is sparse and the signals large, such cross-validation typically works well. However, in regression modeling of genomic studies involving Single Nucleotide Polymorphisms (SNP), the true regression functions, while thought to be sparse, do not have large signals. We demonstrate empirically that in such problems, the number of selected variables using SCAD and the Adaptive Lasso, with 10-fold cross-validation, is a random variable that has considerable and surprising variation. Similar remarks apply to non-oracle methods such as the Lasso. Our study strongly questions the suitability of performing only a single run of m-fold cross-validation with any oracle method, and not just the SCAD and Adaptive Lasso.

GLOBAL SOLUTIONS TO FOLDED CONCAVE PENALIZED NONCONVEX LEARNING

PubMed Central

Liu, Hongcheng; Yao, Tao; Li, Runze

2015-01-01

This paper is concerned with solving nonconvex learning problems with folded concave penalty. Despite that their global solutions entail desirable statistical properties, there lack optimization techniques that guarantee global optimality in a general setting. In this paper, we show that a class of nonconvex learning problems are equivalent to general quadratic programs. This equivalence facilitates us in developing mixed integer linear programming reformulations, which admit finite algorithms that find a provably global optimal solution. We refer to this reformulation-based technique as the mixed integer programming-based global optimization (MIPGO). To our knowledge, this is the first global optimization scheme with a theoretical guarantee for folded concave penalized nonconvex learning with the SCAD penalty (Fan and Li, 2001) and the MCP penalty (Zhang, 2010). Numerical results indicate a significant outperformance of MIPGO over the state-of-the-art solution scheme, local linear approximation, and other alternative solution techniques in literature in terms of solution quality. PMID:27141126

Tracking and recognition face in videos with incremental local sparse representation model

NASA Astrophysics Data System (ADS)

Wang, Chao; Wang, Yunhong; Zhang, Zhaoxiang

2013-10-01

This paper addresses the problem of tracking and recognizing faces via incremental local sparse representation. First a robust face tracking algorithm is proposed via employing local sparse appearance and covariance pooling method. In the following face recognition stage, with the employment of a novel template update strategy, which combines incremental subspace learning, our recognition algorithm adapts the template to appearance changes and reduces the influence of occlusion and illumination variation. This leads to a robust video-based face tracking and recognition with desirable performance. In the experiments, we test the quality of face recognition in real-world noisy videos on YouTube database, which includes 47 celebrities. Our proposed method produces a high face recognition rate at 95% of all videos. The proposed face tracking and recognition algorithms are also tested on a set of noisy videos under heavy occlusion and illumination variation. The tracking results on challenging benchmark videos demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods. In the case of the challenging dataset in which faces undergo occlusion and illumination variation, and tracking and recognition experiments under significant pose variation on the University of California, San Diego (Honda/UCSD) database, our proposed method also consistently demonstrates a high recognition rate.