EnzML: multi-label prediction of enzyme classes using InterPro signatures

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De Ferrari Luna

2012-04-01

Full Text Available Abstract Background Manual annotation of enzymatic functions cannot keep up with automatic genome sequencing. In this work we explore the capacity of InterPro sequence signatures to automatically predict enzymatic function. Results We present EnzML, a multi-label classification method that can efficiently account also for proteins with multiple enzymatic functions: 50,000 in UniProt. EnzML was evaluated using a standard set of 300,747 proteins for which the manually curated Swiss-Prot and KEGG databases have agreeing Enzyme Commission (EC annotations. EnzML achieved more than 98% subset accuracy (exact match of all correct Enzyme Commission classes of a protein for the entire dataset and between 87 and 97% subset accuracy in reannotating eight entire proteomes: human, mouse, rat, mouse-ear cress, fruit fly, the S. pombe yeast, the E. coli bacterium and the M. jannaschii archaebacterium. To understand the role played by the dataset size, we compared the cross-evaluation results of smaller datasets, either constructed at random or from specific taxonomic domains such as archaea, bacteria, fungi, invertebrates, plants and vertebrates. The results were confirmed even when the redundancy in the dataset was reduced using UniRef100, UniRef90 or UniRef50 clusters. Conclusions InterPro signatures are a compact and powerful attribute space for the prediction of enzymatic function. This representation makes multi-label machine learning feasible in reasonable time (30 minutes to train on 300,747 instances with 10,852 attributes and 2,201 class values using the Mulan Binary Relevance Nearest Neighbours algorithm implementation (BR-kNN.

Seminal Quality Prediction Using Clustering-Based Decision Forests

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Hong Wang

2014-08-01

Full Text Available Prediction of seminal quality with statistical learning tools is an emerging methodology in decision support systems in biomedical engineering and is very useful in early diagnosis of seminal patients and selection of semen donors candidates. However, as is common in medical diagnosis, seminal quality prediction faces the class imbalance problem. In this paper, we propose a novel supervised ensemble learning approach, namely Clustering-Based Decision Forests, to tackle unbalanced class learning problem in seminal quality prediction. Experiment results on real fertility diagnosis dataset have shown that Clustering-Based Decision Forests outperforms decision tree, Support Vector Machines, random forests, multilayer perceptron neural networks and logistic regression by a noticeable margin. Clustering-Based Decision Forests can also be used to evaluate variables’ importance and the top five important factors that may affect semen concentration obtained in this study are age, serious trauma, sitting time, the season when the semen sample is produced, and high fevers in the last year. The findings could be helpful in explaining seminal concentration problems in infertile males or pre-screening semen donor candidates.

Impulsivity facets’ predictive relations with DSM-5 PTSD symptom clusters

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Roley, Michelle E.; Contractor, Ateka A.; Weiss, Nicole H.; Armour, Cherie; Elhai, Jon D.

2017-01-01

Objective Posttraumatic Stress Disorder (PTSD) has a well-established theoretical and empirical relation with impulsivity. Prior research has not used a multidimensional approach for measuring both PTSD and impulsivity constructs when assessing their relationship. Method The current study assessed the unique relationship of impulsivity facets on PTSD symptom clusters among a non-clinical sample of 412 trauma-exposed adults. Results Linear regression analyses revealed that impulsivity facets best accounted for PTSD’s arousal symptoms. The negative urgency facet of impulsivity was most predictive, as it was associated with all of PTSD’s symptom clusters. Sensation seeking did not predict PTSD’s intrusion symptoms, but did predict the other symptom clusters of PTSD. Lack of perseverance only predicted intrusion symptoms, while lack of premeditation only predicted PTSD’s mood/cognition symptoms. Conclusions Results extend theoretical and empirical research on the impulsivity-PTSD relationship, suggesting that impulsivity facets may serve as both risk and protective factors for PTSD symptoms. PMID:27243571

Scalable Prediction of Energy Consumption using Incremental Time Series Clustering

Energy Technology Data Exchange (ETDEWEB)

Simmhan, Yogesh; Noor, Muhammad Usman

2013-10-09

Time series datasets are a canonical form of high velocity Big Data, and often generated by pervasive sensors, such as found in smart infrastructure. Performing predictive analytics on time series data can be computationally complex, and requires approximation techniques. In this paper, we motivate this problem using a real application from the smart grid domain. We propose an incremental clustering technique, along with a novel affinity score for determining cluster similarity, which help reduce the prediction error for cumulative time series within a cluster. We evaluate this technique, along with optimizations, using real datasets from smart meters, totaling ~700,000 data points, and show the efficacy of our techniques in improving the prediction error of time series data within polynomial time.

Hybrid Clustering-GWO-NARX neural network technique in predicting stock price

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Das, Debashish; Safa Sadiq, Ali; Mirjalili, Seyedali; Noraziah, A.

2017-09-01

Prediction of stock price is one of the most challenging tasks due to nonlinear nature of the stock data. Though numerous attempts have been made to predict the stock price by applying various techniques, yet the predicted price is not always accurate and even the error rate is high to some extent. Consequently, this paper endeavours to determine an efficient stock prediction strategy by implementing a combinatorial method of Grey Wolf Optimizer (GWO), Clustering and Non Linear Autoregressive Exogenous (NARX) Technique. The study uses stock data from prominent stock market i.e. New York Stock Exchange (NYSE), NASDAQ and emerging stock market i.e. Malaysian Stock Market (Bursa Malaysia), Dhaka Stock Exchange (DSE). It applies K-means clustering algorithm to determine the most promising cluster, then MGWO is used to determine the classification rate and finally the stock price is predicted by applying NARX neural network algorithm. The prediction performance gained through experimentation is compared and assessed to guide the investors in making investment decision. The result through this technique is indeed promising as it has shown almost precise prediction and improved error rate. We have applied the hybrid Clustering-GWO-NARX neural network technique in predicting stock price. We intend to work with the effect of various factors in stock price movement and selection of parameters. We will further investigate the influence of company news either positive or negative in stock price movement. We would be also interested to predict the Stock indices.

PINGU: PredIction of eNzyme catalytic residues usinG seqUence information.

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Priyadarshini P Pai

Full Text Available Identification of catalytic residues can help unveil interesting attributes of enzyme function for various therapeutic and industrial applications. Based on their biochemical roles, the number of catalytic residues and sequence lengths of enzymes vary. This article describes a prediction approach (PINGU for such a scenario. It uses models trained using physicochemical properties and evolutionary information of 650 non-redundant enzymes (2136 catalytic residues in a support vector machines architecture. Independent testing on 200 non-redundant enzymes (683 catalytic residues in predefined prediction settings, i.e., with non-catalytic per catalytic residue ranging from 1 to 30, suggested that the prediction approach was highly sensitive and specific, i.e., 80% or above, over the incremental challenges. To learn more about the discriminatory power of PINGU in real scenarios, where the prediction challenge is variable and susceptible to high false positives, the best model from independent testing was used on 60 diverse enzymes. Results suggested that PINGU was able to identify most catalytic residues and non-catalytic residues properly with 80% or above accuracy, sensitivity and specificity. The effect of false positives on precision was addressed in this study by application of predicted ligand-binding residue information as a post-processing filter. An overall improvement of 20% in F-measure and 0.138 in Correlation Coefficient with 16% enhanced precision could be achieved. On account of its encouraging performance, PINGU is hoped to have eventual applications in boosting enzyme engineering and novel drug discovery.

Sequence-based Screening for Rare Enzymes: New Insights into the World of AMDases Reveal a Conserved Motif and 58 Novel Enzymes Clustering in Eight Distinct Families.

Directory of Open Access Journals (Sweden)

Janine Maimanakos

2016-08-01

Full Text Available Arylmalonate-Decarboxylases (AMDases, EC 4.1.1.76 are very rare and mostly underexplored enzymes. Currently only four known and biochemically characterized representatives exist. However, their ability to decarboxylate α-disubstituted malonic acid derivatives to optically pure products without cofactors makes them attractive and promising candidates for the use as biocatalysts in industrial processes. Until now, AMDases could not be separated from other members of the aspartate/glutamate racemase superfamily based on their gene sequences. Within this work, a search algorithm was developed that enables a reliable prediction of AMDase activity for potential candidates. Based on specific sequence patterns and screening methods 58 novel AMDase candidate genes could be identified in this work. Thereby, AMDases with the conserved sequence pattern of Bordetella bronchiseptica’s prototype appeared to be limited to the classes of Alpha-, Beta- and Gammaproteobacteria. Amino acid homologies and comparison of gene surrounding sequences enabled the classification of eight enzyme clusters. Particularly striking is the accumulation of genes coding for different transporters of the TTT family, TRAP transporters and ABC transporters as well as genes coding for mandelate racemases/muconate lactonizing enzymes that might be involved in substrate uptake or degradation of AMDase products. Further, three novel AMDases were characterized which showed a high enantiomeric excess (>99% of the (R-enantiomer of flurbiprofen. These are the recombinant AmdA and AmdV from Variovorax sp. strains HH01 and HH02, originated from soil, and AmdP from Polymorphum gilvum found by a data base search. Altogether our findings give new insights into the class of AMDases and reveal many previously unknown enzyme candidates with high potential for bioindustrial processes.

Which clustering algorithm is better for predicting protein complexes?

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Moschopoulos Charalampos N

2011-12-01

Full Text Available Abstract Background Protein-Protein interactions (PPI play a key role in determining the outcome of most cellular processes. The correct identification and characterization of protein interactions and the networks, which they comprise, is critical for understanding the molecular mechanisms within the cell. Large-scale techniques such as pull down assays and tandem affinity purification are used in order to detect protein interactions in an organism. Today, relatively new high-throughput methods like yeast two hybrid, mass spectrometry, microarrays, and phage display are also used to reveal protein interaction networks. Results In this paper we evaluated four different clustering algorithms using six different interaction datasets. We parameterized the MCL, Spectral, RNSC and Affinity Propagation algorithms and applied them to six PPI datasets produced experimentally by Yeast 2 Hybrid (Y2H and Tandem Affinity Purification (TAP methods. The predicted clusters, so called protein complexes, were then compared and benchmarked with already known complexes stored in published databases. Conclusions While results may differ upon parameterization, the MCL and RNSC algorithms seem to be more promising and more accurate at predicting PPI complexes. Moreover, they predict more complexes than other reviewed algorithms in absolute numbers. On the other hand the spectral clustering algorithm achieves the highest valid prediction rate in our experiments. However, it is nearly always outperformed by both RNSC and MCL in terms of the geometrical accuracy while it generates the fewest valid clusters than any other reviewed algorithm. This article demonstrates various metrics to evaluate the accuracy of such predictions as they are presented in the text below. Supplementary material can be found at: http://www.bioacademy.gr/bioinformatics/projects/ppireview.htm

Comparison of Cluster Lensing Profiles with Lambda CDM Predictions

Energy Technology Data Exchange (ETDEWEB)

Broadhurst, Tom; /Tel Aviv U.; Umetsu, Keiichi; /Taipei, Inst. Astron. Astrophys.; Medezinski, Elinor; /Tel Aviv U.; Oguri, Masamune; /KIPAC, Menlo Park; Rephaeli, Yoel; /Tel Aviv U. /San Diego, CASS

2008-05-21

We derive lens distortion and magnification profiles of four well known clusters observed with Subaru. Each cluster is very well fitted by the general form predicted for Cold Dark Matter (CDM) dominated halos, with good consistency found between the independent distortion and magnification measurements. The inferred level of mass concentration is surprisingly high, 8 < c{sub vir} < 15 ( = 10.39 {+-} 0.91), compared to the relatively shallow profiles predicted by the {Lambda}CDM model, c{sub vir} = 5.06 {+-} 1.10 (for = 1.25 x 10{sup 15} M{sub {circle_dot}}/h). This represents a 4{sigma} discrepancy, and includes the relatively modest effects of projection bias and profile evolution derived from N-body simulations, which oppose each other with little residual effect. In the context of CDM based cosmologies, this discrepancy implies some modification of the widely assumed spectrum of initial density perturbations, so clusters collapse earlier (z {ge} 1) than predicted (z < 0.5) when the Universe was correspondingly denser.

Link-Prediction Enhanced Consensus Clustering for Complex Networks (Open Access)

Science.gov (United States)

2016-05-20

RESEARCH ARTICLE Link-Prediction Enhanced Consensus Clustering for Complex Networks Matthew Burgess1*, Eytan Adar1,2, Michael Cafarella1 1Computer...consensus clustering algorithm to enhance community detection on incomplete networks. Our framework utilizes existing community detection algorithms that...types of complex networks exhibit community structure: groups of highly connected nodes. Communities or clusters often reflect nodes that share similar

A cluster expansion model for predicting activation barrier of atomic processes

International Nuclear Information System (INIS)

Rehman, Tafizur; Jaipal, M.; Chatterjee, Abhijit

2013-01-01

We introduce a procedure based on cluster expansion models for predicting the activation barrier of atomic processes encountered while studying the dynamics of a material system using the kinetic Monte Carlo (KMC) method. Starting with an interatomic potential description, a mathematical derivation is presented to show that the local environment dependence of the activation barrier can be captured using cluster interaction models. Next, we develop a systematic procedure for training the cluster interaction model on-the-fly, which involves: (i) obtaining activation barriers for handful local environments using nudged elastic band (NEB) calculations, (ii) identifying the local environment by analyzing the NEB results, and (iii) estimating the cluster interaction model parameters from the activation barrier data. Once a cluster expansion model has been trained, it is used to predict activation barriers without requiring any additional NEB calculations. Numerical studies are performed to validate the cluster expansion model by studying hop processes in Ag/Ag(100). We show that the use of cluster expansion model with KMC enables efficient generation of an accurate process rate catalog

Crius: A Novel Fragment-Based Algorithm of De Novo Substrate Prediction for Enzymes.

Science.gov (United States)

Yao, Zhiqiang; Jiang, Shuiqin; Zhang, Lujia; Gao, Bei; He, Xiao; Zhang, John Z H; Wei, Dongzhi

2018-05-03

The study of enzyme substrate specificity is vital for developing potential applications of enzymes. However, the routine experimental procedures require lot of resources in the discovery of novel substrates. This article reports an in silico structure-based algorithm called Crius, which predicts substrates for enzyme. The results of this fragment-based algorithm show good agreements between the simulated and experimental substrate specificities, using a lipase from Candida antarctica (CALB), a nitrilase from Cyanobacterium syechocystis sp. PCC6803 (Nit6803), and an aldo-keto reductase from Gluconobacter oxydans (Gox0644). This opens new prospects of developing computer algorithms that can effectively predict substrates for an enzyme. This article is protected by copyright. All rights reserved. © 2018 The Protein Society.

Prediction of Detailed Enzyme Functions and Identification of Specificity Determining Residues by Random Forests

Science.gov (United States)

Nagao, Chioko; Nagano, Nozomi; Mizuguchi, Kenji

2014-01-01

Determining enzyme functions is essential for a thorough understanding of cellular processes. Although many prediction methods have been developed, it remains a significant challenge to predict enzyme functions at the fourth-digit level of the Enzyme Commission numbers. Functional specificity of enzymes often changes drastically by mutations of a small number of residues and therefore, information about these critical residues can potentially help discriminate detailed functions. However, because these residues must be identified by mutagenesis experiments, the available information is limited, and the lack of experimentally verified specificity determining residues (SDRs) has hindered the development of detailed function prediction methods and computational identification of SDRs. Here we present a novel method for predicting enzyme functions by random forests, EFPrf, along with a set of putative SDRs, the random forests derived SDRs (rf-SDRs). EFPrf consists of a set of binary predictors for enzymes in each CATH superfamily and the rf-SDRs are the residue positions corresponding to the most highly contributing attributes obtained from each predictor. EFPrf showed a precision of 0.98 and a recall of 0.89 in a cross-validated benchmark assessment. The rf-SDRs included many residues, whose importance for specificity had been validated experimentally. The analysis of the rf-SDRs revealed both a general tendency that functionally diverged superfamilies tend to include more active site residues in their rf-SDRs than in less diverged superfamilies, and superfamily-specific conservation patterns of each functional residue. EFPrf and the rf-SDRs will be an effective tool for annotating enzyme functions and for understanding how enzyme functions have diverged within each superfamily. PMID:24416252

Ensemble Architecture for Prediction of Enzyme-ligand Binding Residues Using Evolutionary Information.

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Pai, Priyadarshini P; Dattatreya, Rohit Kadam; Mondal, Sukanta

2017-11-01

Enzyme interactions with ligands are crucial for various biochemical reactions governing life. Over many years attempts to identify these residues for biotechnological manipulations have been made using experimental and computational techniques. The computational approaches have gathered impetus with the accruing availability of sequence and structure information, broadly classified into template-based and de novo methods. One of the predominant de novo methods using sequence information involves application of biological properties for supervised machine learning. Here, we propose a support vector machines-based ensemble for prediction of protein-ligand interacting residues using one of the most important discriminative contributing properties in the interacting residue neighbourhood, i. e., evolutionary information in the form of position-specific- scoring matrix (PSSM). The study has been performed on a non-redundant dataset comprising of 9269 interacting and 91773 non-interacting residues for prediction model generation and further evaluation. Of the various PSSM-based models explored, the proposed method named ROBBY (pRediction Of Biologically relevant small molecule Binding residues on enzYmes) shows an accuracy of 84.0 %, Matthews Correlation Coefficient of 0.343 and F-measure of 39.0 % on 78 test enzymes. Further, scope of adding domain knowledge such as pocket information has also been investigated; results showed significant enhancement in method precision. Findings are hoped to boost the reliability of small-molecule ligand interaction prediction for enzyme applications and drug design. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

How Do Social Capital and HIV/AIDS Outcomes Geographically Cluster and Which Sociocontextual Mechanisms Predict Differences Across Clusters?

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Ransome, Yusuf; Dean, Lorraine T; Crawford, Natalie D; Metzger, David S; Blank, Michael B; Nunn, Amy S

2017-09-01

Place of residence has been associated with HIV transmission risks. Social capital, defined as features of social organization that improve efficiency of society by facilitating coordinated actions, often varies by neighborhood, and hypothesized to have protective effects on HIV care continuum outcomes. We examined whether the association between social capital and 2 HIV care continuum outcomes clustered geographically and whether sociocontextual mechanisms predict differences across clusters. Bivariate Local Moran's I evaluated geographical clustering in the association between social capital (participation in civic and social organizations, 2006, 2008, 2010) and [5-year (2007-2011) prevalence of late HIV diagnosis and linkage to HIV care] across Philadelphia, PA, census tracts (N = 378). Maps documented the clusters and multinomial regression assessed which sociocontextual mechanisms (eg, racial composition) predict differences across clusters. We identified 4 significant clusters (high social capital-high HIV/AIDS, low social capital-low HIV/AIDS, low social capital-high HIV/AIDS, and high social capital-low HIV/AIDS). Moran's I between social capital and late HIV diagnosis was (I = 0.19, z = 9.54, P social capital was lowest and HIV burden the highest, compared with clusters with high social capital and lowest HIV burden. The association between social participation and HIV care continuum outcomes cluster geographically in Philadelphia, PA. HIV prevention interventions should account for this phenomenon. Reducing geographic disparities will require interventions tailored to each continuum step and that address socioeconomic factors such as neighborhood median income.

piRNA analysis framework from small RNA-Seq data by a novel cluster prediction tool - PILFER.

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Ray, Rishav; Pandey, Priyanka

2017-12-19

With the increasing number of studies focusing on PIWI-interacting RNA (piRNAs), it is now pertinent to develop efficient tools dedicated towards piRNA analysis. We have developed a novel cluster prediction tool called PILFER (PIrna cLuster FindER), which can accurately predict piRNA clusters from small RNA sequencing data. PILFER is an open source, easy to use tool, and can be executed even on a personal computer with minimum resources. It uses a sliding-window mechanism by integrating the expression of the reads along with the spatial information to predict the piRNA clusters. We have additionally defined a piRNA analysis pipeline incorporating PILFER to detect and annotate piRNAs and their clusters from raw small RNA sequencing data and implemented it on publicly available data from healthy germline and somatic tissues. We compared PILFER with other existing piRNA cluster prediction tools and found it to be statistically more accurate and superior in many aspects such as the robustness of PILFER clusters is higher and memory efficiency is more. Overall, PILFER provides a fast and accurate solution to piRNA cluster prediction. Copyright © 2017 Elsevier Inc. All rights reserved.

Discovery of new enzymes and metabolic pathways using structure and genome context

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Zhao, Suwen; Kumar, Ritesh; Sakai, Ayano; Vetting, Matthew W.; Wood, B. McKay; Brown, Shoshana; Bonanno, Jeffery B.; Hillerich, Brandan S.; Seidel, Ronald D.; Babbitt, Patricia C.; Almo, Steven C.; Sweedler, Jonathan V.; Gerlt, John A.; Cronan, John E.; Jacobson, Matthew P.

2014-01-01

Assigning valid functions to proteins identified in genome projects is challenging, with over-prediction and database annotation errors major concerns1. We, and others2, are developing computation-guided strategies for functional discovery using “metabolite docking” to experimentally derived3 or homology-based4 three-dimensional structures. Bacterial metabolic pathways often are encoded by “genome neighborhoods” (gene clusters and/or operons), which can provide important clues for functional assignment. We recently demonstrated the synergy of docking and pathway context by “predicting” the intermediates in the glycolytic pathway in E. coli5. Metabolite docking to multiple binding proteins/enzymes in the same pathway increases the reliability of in silico predictions of substrate specificities because the pathway intermediates are structurally similar. We report that structure-guided approaches for predicting the substrate specificities of several enzymes encoded by a bacterial gene cluster allowed i) the correct prediction of the in vitro activity of a structurally characterized enzyme of unknown function (PDB 2PMQ), 2-epimerization of trans-4-hydroxy-L-proline betaine (tHyp-B) and cis-4-hydroxy-D-proline betaine (cHyp-B), and ii) the correct identification of the catabolic pathway in which Hyp-B 2-epimerase participates. The substrate-liganded pose predicted by virtual library screening (docking) was confirmed experimentally. The enzymatic activities in the predicted pathway were confirmed by in vitro assays and genetic analyses; the intermediates were identified by metabolomics; and repression of the genes encoding the pathway by high salt was established by transcriptomics, confirming the osmolyte role of tHyp-B. This study establishes the utility of structure-guide functional predictions to enable the discovery of new metabolic pathways. PMID:24056934

antiSMASH 3.0-a comprehensive resource for the genome mining of biosynthetic gene clusters.

Science.gov (United States)

Weber, Tilmann; Blin, Kai; Duddela, Srikanth; Krug, Daniel; Kim, Hyun Uk; Bruccoleri, Robert; Lee, Sang Yup; Fischbach, Michael A; Müller, Rolf; Wohlleben, Wolfgang; Breitling, Rainer; Takano, Eriko; Medema, Marnix H

2015-07-01

Microbial secondary metabolism constitutes a rich source of antibiotics, chemotherapeutics, insecticides and other high-value chemicals. Genome mining of gene clusters that encode the biosynthetic pathways for these metabolites has become a key methodology for novel compound discovery. In 2011, we introduced antiSMASH, a web server and stand-alone tool for the automatic genomic identification and analysis of biosynthetic gene clusters, available at http://antismash.secondarymetabolites.org. Here, we present version 3.0 of antiSMASH, which has undergone major improvements. A full integration of the recently published ClusterFinder algorithm now allows using this probabilistic algorithm to detect putative gene clusters of unknown types. Also, a new dereplication variant of the ClusterBlast module now identifies similarities of identified clusters to any of 1172 clusters with known end products. At the enzyme level, active sites of key biosynthetic enzymes are now pinpointed through a curated pattern-matching procedure and Enzyme Commission numbers are assigned to functionally classify all enzyme-coding genes. Additionally, chemical structure prediction has been improved by incorporating polyketide reduction states. Finally, in order for users to be able to organize and analyze multiple antiSMASH outputs in a private setting, a new XML output module allows offline editing of antiSMASH annotations within the Geneious software. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Cluster analysis as a prediction tool for pregnancy outcomes.

Science.gov (United States)

Banjari, Ines; Kenjerić, Daniela; Šolić, Krešimir; Mandić, Milena L

2015-03-01

Considering specific physiology changes during gestation and thinking of pregnancy as a "critical window", classification of pregnant women at early pregnancy can be considered as crucial. The paper demonstrates the use of a method based on an approach from intelligent data mining, cluster analysis. Cluster analysis method is a statistical method which makes possible to group individuals based on sets of identifying variables. The method was chosen in order to determine possibility for classification of pregnant women at early pregnancy to analyze unknown correlations between different variables so that the certain outcomes could be predicted. 222 pregnant women from two general obstetric offices' were recruited. The main orient was set on characteristics of these pregnant women: their age, pre-pregnancy body mass index (BMI) and haemoglobin value. Cluster analysis gained a 94.1% classification accuracy rate with three branch- es or groups of pregnant women showing statistically significant correlations with pregnancy outcomes. The results are showing that pregnant women both of older age and higher pre-pregnancy BMI have a significantly higher incidence of delivering baby of higher birth weight but they gain significantly less weight during pregnancy. Their babies are also longer, and these women have significantly higher probability for complications during pregnancy (gestosis) and higher probability of induced or caesarean delivery. We can conclude that the cluster analysis method can appropriately classify pregnant women at early pregnancy to predict certain outcomes.

Clustering gene expression data based on predicted differential effects of GV interaction.

Science.gov (United States)

Pan, Hai-Yan; Zhu, Jun; Han, Dan-Fu

2005-02-01

Microarray has become a popular biotechnology in biological and medical research. However, systematic and stochastic variabilities in microarray data are expected and unavoidable, resulting in the problem that the raw measurements have inherent "noise" within microarray experiments. Currently, logarithmic ratios are usually analyzed by various clustering methods directly, which may introduce bias interpretation in identifying groups of genes or samples. In this paper, a statistical method based on mixed model approaches was proposed for microarray data cluster analysis. The underlying rationale of this method is to partition the observed total gene expression level into various variations caused by different factors using an ANOVA model, and to predict the differential effects of GV (gene by variety) interaction using the adjusted unbiased prediction (AUP) method. The predicted GV interaction effects can then be used as the inputs of cluster analysis. We illustrated the application of our method with a gene expression dataset and elucidated the utility of our approach using an external validation.

FLOCK cluster analysis of mast cell event clustering by high-sensitivity flow cytometry predicts systemic mastocytosis.

Science.gov (United States)

Dorfman, David M; LaPlante, Charlotte D; Pozdnyakova, Olga; Li, Betty

2015-11-01

In our high-sensitivity flow cytometric approach for systemic mastocytosis (SM), we identified mast cell event clustering as a new diagnostic criterion for the disease. To objectively characterize mast cell gated event distributions, we performed cluster analysis using FLOCK, a computational approach to identify cell subsets in multidimensional flow cytometry data in an unbiased, automated fashion. FLOCK identified discrete mast cell populations in most cases of SM (56/75 [75%]) but only a minority of non-SM cases (17/124 [14%]). FLOCK-identified mast cell populations accounted for 2.46% of total cells on average in SM cases and 0.09% of total cells on average in non-SM cases (P < .0001) and were predictive of SM, with a sensitivity of 75%, a specificity of 86%, a positive predictive value of 76%, and a negative predictive value of 85%. FLOCK analysis provides useful diagnostic information for evaluating patients with suspected SM, and may be useful for the analysis of other hematopoietic neoplasms. Copyright© by the American Society for Clinical Pathology.

Development and optimization of SPECT gated blood pool cluster analysis for the prediction of CRT outcome

Energy Technology Data Exchange (ETDEWEB)

Lalonde, Michel, E-mail: mlalonde15@rogers.com; Wassenaar, Richard [Department of Physics, Carleton University, Ottawa, Ontario K1S 5B6 (Canada); Wells, R. Glenn; Birnie, David; Ruddy, Terrence D. [Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario K1Y 4W7 (Canada)

2014-07-15

Purpose: Phase analysis of single photon emission computed tomography (SPECT) radionuclide angiography (RNA) has been investigated for its potential to predict the outcome of cardiac resynchronization therapy (CRT). However, phase analysis may be limited in its potential at predicting CRT outcome as valuable information may be lost by assuming that time-activity curves (TAC) follow a simple sinusoidal shape. A new method, cluster analysis, is proposed which directly evaluates the TACs and may lead to a better understanding of dyssynchrony patterns and CRT outcome. Cluster analysis algorithms were developed and optimized to maximize their ability to predict CRT response. Methods: About 49 patients (N = 27 ischemic etiology) received a SPECT RNA scan as well as positron emission tomography (PET) perfusion and viability scans prior to undergoing CRT. A semiautomated algorithm sampled the left ventricle wall to produce 568 TACs from SPECT RNA data. The TACs were then subjected to two different cluster analysis techniques, K-means, and normal average, where several input metrics were also varied to determine the optimal settings for the prediction of CRT outcome. Each TAC was assigned to a cluster group based on the comparison criteria and global and segmental cluster size and scores were used as measures of dyssynchrony and used to predict response to CRT. A repeated random twofold cross-validation technique was used to train and validate the cluster algorithm. Receiver operating characteristic (ROC) analysis was used to calculate the area under the curve (AUC) and compare results to those obtained for SPECT RNA phase analysis and PET scar size analysis methods. Results: Using the normal average cluster analysis approach, the septal wall produced statistically significant results for predicting CRT results in the ischemic population (ROC AUC = 0.73;p < 0.05 vs. equal chance ROC AUC = 0.50) with an optimal operating point of 71% sensitivity and 60% specificity. Cluster

Development and optimization of SPECT gated blood pool cluster analysis for the prediction of CRT outcome

International Nuclear Information System (INIS)

Lalonde, Michel; Wassenaar, Richard; Wells, R. Glenn; Birnie, David; Ruddy, Terrence D.

2014-01-01

Purpose: Phase analysis of single photon emission computed tomography (SPECT) radionuclide angiography (RNA) has been investigated for its potential to predict the outcome of cardiac resynchronization therapy (CRT). However, phase analysis may be limited in its potential at predicting CRT outcome as valuable information may be lost by assuming that time-activity curves (TAC) follow a simple sinusoidal shape. A new method, cluster analysis, is proposed which directly evaluates the TACs and may lead to a better understanding of dyssynchrony patterns and CRT outcome. Cluster analysis algorithms were developed and optimized to maximize their ability to predict CRT response. Methods: About 49 patients (N = 27 ischemic etiology) received a SPECT RNA scan as well as positron emission tomography (PET) perfusion and viability scans prior to undergoing CRT. A semiautomated algorithm sampled the left ventricle wall to produce 568 TACs from SPECT RNA data. The TACs were then subjected to two different cluster analysis techniques, K-means, and normal average, where several input metrics were also varied to determine the optimal settings for the prediction of CRT outcome. Each TAC was assigned to a cluster group based on the comparison criteria and global and segmental cluster size and scores were used as measures of dyssynchrony and used to predict response to CRT. A repeated random twofold cross-validation technique was used to train and validate the cluster algorithm. Receiver operating characteristic (ROC) analysis was used to calculate the area under the curve (AUC) and compare results to those obtained for SPECT RNA phase analysis and PET scar size analysis methods. Results: Using the normal average cluster analysis approach, the septal wall produced statistically significant results for predicting CRT results in the ischemic population (ROC AUC = 0.73;p < 0.05 vs. equal chance ROC AUC = 0.50) with an optimal operating point of 71% sensitivity and 60% specificity. Cluster

Performance prediction model for distributed applications on multicore clusters

CSIR Research Space (South Africa)

Khanyile, NP

2012-07-01

Full Text Available discusses some of the short comings of this law in the current age. We propose a theoretical model for predicting the behavior of a distributed algorithm given the network restrictions of the cluster used. The paper focuses on the impact of latency...

K-Line Patterns’ Predictive Power Analysis Using the Methods of Similarity Match and Clustering

Directory of Open Access Journals (Sweden)

Lv Tao

2017-01-01

Full Text Available Stock price prediction based on K-line patterns is the essence of candlestick technical analysis. However, there are some disputes on whether the K-line patterns have predictive power in academia. To help resolve the debate, this paper uses the data mining methods of pattern recognition, pattern clustering, and pattern knowledge mining to research the predictive power of K-line patterns. The similarity match model and nearest neighbor-clustering algorithm are proposed for solving the problem of similarity match and clustering of K-line series, respectively. The experiment includes testing the predictive power of the Three Inside Up pattern and Three Inside Down pattern with the testing dataset of the K-line series data of Shanghai 180 index component stocks over the latest 10 years. Experimental results show that (1 the predictive power of a pattern varies a great deal for different shapes and (2 each of the existing K-line patterns requires further classification based on the shape feature for improving the prediction performance.

Predicting healthcare outcomes in prematurely born infants using cluster analysis.

Science.gov (United States)

MacBean, Victoria; Lunt, Alan; Drysdale, Simon B; Yarzi, Muska N; Rafferty, Gerrard F; Greenough, Anne

2018-05-23

Prematurely born infants are at high risk of respiratory morbidity following neonatal unit discharge, though prediction of outcomes is challenging. We have tested the hypothesis that cluster analysis would identify discrete groups of prematurely born infants with differing respiratory outcomes during infancy. A total of 168 infants (median (IQR) gestational age 33 (31-34) weeks) were recruited in the neonatal period from consecutive births in a tertiary neonatal unit. The baseline characteristics of the infants were used to classify them into hierarchical agglomerative clusters. Rates of viral lower respiratory tract infections (LRTIs) were recorded for 151 infants in the first year after birth. Infants could be classified according to birth weight and duration of neonatal invasive mechanical ventilation (MV) into three clusters. Cluster one (MV ≤5 days) had few LRTIs. Clusters two and three (both MV ≥6 days, but BW ≥or <882 g respectively), had significantly higher LRTI rates. Cluster two had a higher proportion of infants experiencing respiratory syncytial virus LRTIs (P = 0.01) and cluster three a higher proportion of rhinovirus LRTIs (P < 0.001) CONCLUSIONS: Readily available clinical data allowed classification of prematurely born infants into one of three distinct groups with differing subsequent respiratory morbidity in infancy. © 2018 Wiley Periodicals, Inc.

High endothelin-converting enzyme-1 expression independently predicts poor survival of patients with esophageal squamous cell carcinoma.

Science.gov (United States)

Wu, Ching-Fang; Lee, Ching-Tai; Kuo, Yao-Hung; Chen, Tzu-Haw; Chang, Chi-Yang; Chang, I-Wei; Wang, Wen-Lun

2017-09-01

Patients with esophageal squamous cell carcinoma have poor survival and high recurrence rate, thus an effective prognostic biomarker is needed. Endothelin-converting enzyme-1 is responsible for biosynthesis of endothelin-1, which promotes growth and invasion of human cancers. The role of endothelin-converting enzyme-1 in esophageal squamous cell carcinoma is still unknown. Therefore, this study investigated the significance of endothelin-converting enzyme-1 expression in esophageal squamous cell carcinoma clinically. We enrolled patients with esophageal squamous cell carcinoma who provided pretreated tumor tissues. Tumor endothelin-converting enzyme-1 expression was evaluated by immunohistochemistry and was defined as either low or high expression. Then we evaluated whether tumor endothelin-converting enzyme-1 expression had any association with clinicopathological findings or predicted survival of patients with esophageal squamous cell carcinoma. Overall, 54 of 99 patients with esophageal squamous cell carcinoma had high tumor endothelin-converting enzyme-1 expression, which was significantly associated with lymph node metastasis ( p = 0.04). In addition, tumor endothelin-converting enzyme-1 expression independently predicted survival of patients with esophageal squamous cell carcinoma, and the 5-year survival was poorer in patients with high tumor endothelin-converting enzyme-1 expression ( p = 0.016). Among patients with locally advanced and potentially resectable esophageal squamous cell carcinoma (stage II and III), 5-year survival was poorer with high tumor endothelin-converting enzyme-1 expression ( p = 0.003). High tumor endothelin-converting enzyme-1 expression also significantly predicted poorer survival of patients in this population. In patients with esophageal squamous cell carcinoma, high tumor endothelin-converting enzyme-1 expression might indicate high tumor invasive property. Therefore, tumor endothelin-converting enzyme-1 expression

Framework for Optimizing Cluster Selection using Geo-assisted Movement Prediction

DEFF Research Database (Denmark)

Kristensen, Thomas Sander; Madsen, Jacob Theilgaard; Pedersen, Michael Sølvkjær

2012-01-01

Due to the availability of satellite- and radio-based location systems in most new devices, it is possible to use geographical location of a node for network management and communication protocol optimization. It is a common belief that usage of location information can bring performance benefits...... movement prediction and inaccurate location estimation on its performance. The proposed algorithm is compared with two reference algorithms: when a considered node associates with either the first discovered cluster or the nearest cluster. Evaluation shows significant performance benefits in terms...

Number of Clusters and the Quality of Hybrid Predictive Models in Analytical CRM

Directory of Open Access Journals (Sweden)

Łapczyński Mariusz

2014-08-01

Full Text Available Making more accurate marketing decisions by managers requires building effective predictive models. Typically, these models specify the probability of customer belonging to a particular category, group or segment. The analytical CRM categories refer to customers interested in starting cooperation with the company (acquisition models, customers who purchase additional products (cross- and up-sell models or customers intending to resign from the cooperation (churn models. During building predictive models researchers use analytical tools from various disciplines with an emphasis on their best performance. This article attempts to build a hybrid predictive model combining decision trees (C&RT algorithm and cluster analysis (k-means. During experiments five different cluster validity indices and eight datasets were used. The performance of models was evaluated by using popular measures such as: accuracy, precision, recall, G-mean, F-measure and lift in the first and in the second decile. The authors tried to find a connection between the number of clusters and models' quality.

A Deep Learning Prediction Model Based on Extreme-Point Symmetric Mode Decomposition and Cluster Analysis

OpenAIRE

Li, Guohui; Zhang, Songling; Yang, Hong

2017-01-01

Aiming at the irregularity of nonlinear signal and its predicting difficulty, a deep learning prediction model based on extreme-point symmetric mode decomposition (ESMD) and clustering analysis is proposed. Firstly, the original data is decomposed by ESMD to obtain the finite number of intrinsic mode functions (IMFs) and residuals. Secondly, the fuzzy c-means is used to cluster the decomposed components, and then the deep belief network (DBN) is used to predict it. Finally, the reconstructed ...

Prediction, Detection, and Validation of Isotope Clusters in Mass Spectrometry Data

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Hendrik Treutler

2016-10-01

Full Text Available Mass spectrometry is a key analytical platform for metabolomics. The precise quantification and identification of small molecules is a prerequisite for elucidating the metabolism and the detection, validation, and evaluation of isotope clusters in LC-MS data is important for this task. Here, we present an approach for the improved detection of isotope clusters using chemical prior knowledge and the validation of detected isotope clusters depending on the substance mass using database statistics. We find remarkable improvements regarding the number of detected isotope clusters and are able to predict the correct molecular formula in the top three ranks in 92 % of the cases. We make our methodology freely available as part of the Bioconductor packages xcms version 1.50.0 and CAMERA version 1.30.0.

Unmatter Entities inside Nuclei, Predicted by the Brightsen Nucleon Cluster Model

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Smarandache F.

2006-01-01

Full Text Available Applying the R. A. Brightsen Nucleon Cluster Model of the atomic nucleus we discuss how unmatter entities (the conjugations of matter and antimatter may be formed as clusters inside a nucleus. The model supports a hypothesis that antimatter nucleon clusters are present as a parton (sensu Feynman superposition within the spatial confinement of the proton (1H1, the neutron, and the deuteron (1H2. If model predictions can be confirmed both mathematically and experimentally, a new physics is suggested. A proposed experiment is connected to othopositronium annihilation anomalies, which, being related to one of known unmatter entity, orthopositronium (built on electron and positron, opens a way to expand the Standard Model.

Influence of nutrient restriction and melatonin supplementation of pregnant ewes on maternal and fetal pancreatic digestive enzymes and insulin-containing clusters.

Science.gov (United States)

Keomanivong, F E; Lemley, C O; Camacho, L E; Yunusova, R; Borowicz, P P; Caton, J S; Meyer, A M; Vonnahme, K A; Swanson, K C

2016-03-01

Primiparous ewes (n=32) were assigned to dietary treatments in a 2×2 factorial arrangement to determine effects of nutrient restriction and melatonin supplementation on maternal and fetal pancreatic weight, digestive enzyme activity, concentration of insulin-containing clusters and plasma insulin concentrations. Treatments consisted of nutrient intake with 60% (RES) or 100% (ADQ) of requirements and melatonin supplementation at 0 (CON) or 5 mg/day (MEL). Treatments began on day 50 of gestation and continued until day 130. On day 130, blood was collected under general anesthesia from the uterine artery, uterine vein, umbilical artery and umbilical vein for plasma insulin analysis. Ewes were then euthanized and the pancreas removed from the ewe and fetus, trimmed of mesentery and fat, weighed and snap-frozen until enzyme analysis. In addition, samples of pancreatic tissue were fixed in 10% formalin solution for histological examination including quantitative characterization of size and distribution of insulin-containing cell clusters. Nutrient restriction decreased (P⩽0.001) maternal pancreatic mass (g) and α-amylase activity (U/g, kU/pancreas, U/kg BW). Ewes supplemented with melatonin had increased pancreatic mass (P=0.03) and α-amylase content (kU/pancreas and U/kg BW). Melatonin supplementation decreased (P=0.002) maternal pancreatic insulin-positive tissue area (relative to section of tissue), and size of the largest insulin-containing cell cluster (P=0.04). Nutrient restriction decreased pancreatic insulin-positive tissue area (P=0.03) and percent of large (32 001 to 512 000 µm2) and giant (⩾512 001 µm2) insulin-containing cell clusters (P=0.04) in the fetus. Insulin concentrations in plasma from the uterine vein, umbilical artery and umbilical vein were greater (P⩽0.01) in animals receiving 100% requirements. When comparing ewes to fetuses, ewes had a greater percentage of medium insulin-containing cell clusters (2001 to 32 000 µm2) while fetuses

Application of clustering analysis in the prediction of photovoltaic power generation based on neural network

Science.gov (United States)

Cheng, K.; Guo, L. M.; Wang, Y. K.; Zafar, M. T.

2017-11-01

In order to select effective samples in the large number of data of PV power generation years and improve the accuracy of PV power generation forecasting model, this paper studies the application of clustering analysis in this field and establishes forecasting model based on neural network. Based on three different types of weather on sunny, cloudy and rainy days, this research screens samples of historical data by the clustering analysis method. After screening, it establishes BP neural network prediction models using screened data as training data. Then, compare the six types of photovoltaic power generation prediction models before and after the data screening. Results show that the prediction model combining with clustering analysis and BP neural networks is an effective method to improve the precision of photovoltaic power generation.

Measurement of circulating transcripts and gene cluster analysis predicts and defines therapeutic efficacy of peptide receptor radionuclide therapy (PRRT) in neuroendocrine tumors

International Nuclear Information System (INIS)

Bodei, L.; Kidd, M.; Modlin, I.M.; Severi, S.; Nicolini, S.; Paganelli, G.; Drozdov, I.; Kwekkeboom, D.J.; Krenning, E.P.; Baum, R.P.

2016-01-01

Peptide receptor radionuclide therapy (PRRT) is an effective method for treating neuroendocrine tumors (NETs). It is limited, however, in the prediction of individual tumor response and the precise and early identification of changes in tumor size. Currently, response prediction is based on somatostatin receptor expression and efficacy by morphological imaging and/or chromogranin A (CgA) measurement. The aim of this study was to assess the accuracy of circulating NET transcripts as a measure of PRRT efficacy, and moreover to identify prognostic gene clusters in pretreatment blood that could be interpolated with relevant clinical features in order to define a biological index for the tumor and a predictive quotient for PRRT efficacy. NET patients (n = 54), M: F 37:17, median age 66, bronchial: n = 13, GEP-NET: n = 35, CUP: n = 6 were treated with 177 Lu-based-PRRT (cumulative activity: 6.5-27.8 GBq, median 18.5). At baseline: 47/54 low-grade (G1/G2; bronchial typical/atypical), 31/49 18 FDG positive and 39/54 progressive. Disease status was assessed by RECIST1.1. Transcripts were measured by real-time quantitative reverse transcription PCR (qRT-PCR) and multianalyte algorithmic analysis (NETest); CgA by enzyme-linked immunosorbent assay (ELISA). Gene cluster (GC) derivations: regulatory network, protein:protein interactome analyses. Statistical analyses: chi-square, non-parametric measurements, multiple regression, receiver operating characteristic and Kaplan-Meier survival. The disease control rate was 72 %. Median PFS was not achieved (follow-up: 1-33 months, median: 16). Only grading was associated with response (p < 0.01). At baseline, 94 % of patients were NETest-positive, while CgA was elevated in 59 %. NETest accurately (89 %, χ 2 = 27.4; p = 1.2 x 10 -7 ) correlated with treatment response, while CgA was 24 % accurate. Gene cluster expression (growth-factor signalome and metabolome) had an AUC of 0.74 ± 0.08 (z-statistic = 2.92, p < 0.004) for predicting

Measurement of circulating transcripts and gene cluster analysis predicts and defines therapeutic efficacy of peptide receptor radionuclide therapy (PRRT) in neuroendocrine tumors

Energy Technology Data Exchange (ETDEWEB)

Bodei, L. [European Institute of Oncology, Division of Nuclear Medicine, Milan (Italy); LuGenIum Consortium, Milan, Rotterdam, Bad Berka, London, Italy, Netherlands, Germany (Country Unknown); Kidd, M. [Wren Laboratories, Branford, CT (United States); Modlin, I.M. [LuGenIum Consortium, Milan, Rotterdam, Bad Berka, London, Italy, Netherlands, Germany (Country Unknown); Yale School of Medicine, New Haven, CT (United States); Severi, S.; Nicolini, S.; Paganelli, G. [Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Nuclear Medicine and Radiometabolic Units, Meldola (Italy); Drozdov, I. [Bering Limited, London (United Kingdom); Kwekkeboom, D.J.; Krenning, E.P. [LuGenIum Consortium, Milan, Rotterdam, Bad Berka, London, Italy, Netherlands, Germany (Country Unknown); Erasmus Medical Center, Nuclear Medicine Department, Rotterdam (Netherlands); Baum, R.P. [LuGenIum Consortium, Milan, Rotterdam, Bad Berka, London, Italy, Netherlands, Germany (Country Unknown); Zentralklinik Bad Berka, Theranostics Center for Molecular Radiotherapy and Imaging, Bad Berka (Germany)

2016-05-15

Peptide receptor radionuclide therapy (PRRT) is an effective method for treating neuroendocrine tumors (NETs). It is limited, however, in the prediction of individual tumor response and the precise and early identification of changes in tumor size. Currently, response prediction is based on somatostatin receptor expression and efficacy by morphological imaging and/or chromogranin A (CgA) measurement. The aim of this study was to assess the accuracy of circulating NET transcripts as a measure of PRRT efficacy, and moreover to identify prognostic gene clusters in pretreatment blood that could be interpolated with relevant clinical features in order to define a biological index for the tumor and a predictive quotient for PRRT efficacy. NET patients (n = 54), M: F 37:17, median age 66, bronchial: n = 13, GEP-NET: n = 35, CUP: n = 6 were treated with {sup 177}Lu-based-PRRT (cumulative activity: 6.5-27.8 GBq, median 18.5). At baseline: 47/54 low-grade (G1/G2; bronchial typical/atypical), 31/49 {sup 18}FDG positive and 39/54 progressive. Disease status was assessed by RECIST1.1. Transcripts were measured by real-time quantitative reverse transcription PCR (qRT-PCR) and multianalyte algorithmic analysis (NETest); CgA by enzyme-linked immunosorbent assay (ELISA). Gene cluster (GC) derivations: regulatory network, protein:protein interactome analyses. Statistical analyses: chi-square, non-parametric measurements, multiple regression, receiver operating characteristic and Kaplan-Meier survival. The disease control rate was 72 %. Median PFS was not achieved (follow-up: 1-33 months, median: 16). Only grading was associated with response (p < 0.01). At baseline, 94 % of patients were NETest-positive, while CgA was elevated in 59 %. NETest accurately (89 %, χ{sup 2} = 27.4; p = 1.2 x 10{sup -7}) correlated with treatment response, while CgA was 24 % accurate. Gene cluster expression (growth-factor signalome and metabolome) had an AUC of 0.74 ± 0.08 (z-statistic = 2.92, p < 0

Monovalent Cation Activation of the Radical SAM Enzyme Pyruvate Formate-Lyase Activating Enzyme.

Science.gov (United States)

Shisler, Krista A; Hutcheson, Rachel U; Horitani, Masaki; Duschene, Kaitlin S; Crain, Adam V; Byer, Amanda S; Shepard, Eric M; Rasmussen, Ashley; Yang, Jian; Broderick, William E; Vey, Jessica L; Drennan, Catherine L; Hoffman, Brian M; Broderick, Joan B

2017-08-30

Pyruvate formate-lyase activating enzyme (PFL-AE) is a radical S-adenosyl-l-methionine (SAM) enzyme that installs a catalytically essential glycyl radical on pyruvate formate-lyase. We show that PFL-AE binds a catalytically essential monovalent cation at its active site, yet another parallel with B 12 enzymes, and we characterize this cation site by a combination of structural, biochemical, and spectroscopic approaches. Refinement of the PFL-AE crystal structure reveals Na + as the most likely ion present in the solved structures, and pulsed electron nuclear double resonance (ENDOR) demonstrates that the same cation site is occupied by 23 Na in the solution state of the as-isolated enzyme. A SAM carboxylate-oxygen is an M + ligand, and EPR and circular dichroism spectroscopies reveal that both the site occupancy and the identity of the cation perturb the electronic properties of the SAM-chelated iron-sulfur cluster. ENDOR studies of the PFL-AE/[ 13 C-methyl]-SAM complex show that the target sulfonium positioning varies with the cation, while the observation of an isotropic hyperfine coupling to the cation by ENDOR measurements establishes its intimate, SAM-mediated interaction with the cluster. This monovalent cation site controls enzyme activity: (i) PFL-AE in the absence of any simple monovalent cations has little-no activity; and (ii) among monocations, going down Group 1 of the periodic table from Li + to Cs + , PFL-AE activity sharply maximizes at K + , with NH 4 + closely matching the efficacy of K + . PFL-AE is thus a type I M + -activated enzyme whose M + controls reactivity by interactions with the cosubstrate, SAM, which is bound to the catalytic iron-sulfur cluster.

Symptom clusters predict mortality among dialysis patients in Norway: a prospective observational cohort study.

Science.gov (United States)

Amro, Amin; Waldum, Bård; von der Lippe, Nanna; Brekke, Fredrik Barth; Dammen, Toril; Miaskowski, Christine; Os, Ingrid

2015-01-01

Patients with end-stage renal disease on dialysis have reduced survival rates compared with the general population. Symptoms are frequent in dialysis patients, and a symptom cluster is defined as two or more related co-occurring symptoms. The aim of this study was to explore the associations between symptom clusters and mortality in dialysis patients. In a prospective observational cohort study of dialysis patients (n = 301), Kidney Disease and Quality of Life Short Form and Beck Depression Inventory questionnaires were administered. To generate symptom clusters, principal component analysis with varimax rotation was used on 11 kidney-specific self-reported physical symptoms. A Beck Depression Inventory score of 16 or greater was defined as clinically significant depressive symptoms. Physical and mental component summary scores were generated from Short Form-36. Multivariate Cox regression analysis was used for the survival analysis, Kaplan-Meier curves and log-rank statistics were applied to compare survival rates between the groups. Three different symptom clusters were identified; one included loading of several uremic symptoms. In multivariate analyses and after adjustment for health-related quality of life and depressive symptoms, the worst perceived quartile of the "uremic" symptom cluster independently predicted all-cause mortality (hazard ratio 2.47, 95% CI 1.44-4.22, P = 0.001) compared with the other quartiles during a follow-up period that ranged from four to 52 months. The two other symptom clusters ("neuromuscular" and "skin") or the individual symptoms did not predict mortality. Clustering of uremic symptoms predicted mortality. Assessing co-occurring symptoms rather than single symptoms may help to identify dialysis patients at high risk for mortality. Copyright © 2015 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

Data characterizing the energetics of enzyme-catalyzed hydrolysis and transglycosylation reactions by DFT cluster model calculations

Directory of Open Access Journals (Sweden)

Jitrayut Jitonnom

2018-04-01

Full Text Available The data presented in this paper are related to the research article entitled “QM/MM modeling of the hydrolysis and transfructosylation reactions of fructosyltransferase from Aspergillus japonicas, an enzyme that produces prebiotic fructooligosaccharide” (Jitonnom et al., 2018 [1]. This paper presents the procedure and data for characterizing the whole relative energy profiles of hydrolysis and transglycosylation reactions whose elementary steps differ in chemical composition. The data also reflects the choices of the QM cluster model, the functional/basis set method and the equations in determining the reaction energetics.

Variable selection based on clustering analysis for improvement of polyphenols prediction in green tea using synchronous fluorescence spectra

Science.gov (United States)

Shan, Jiajia; Wang, Xue; Zhou, Hao; Han, Shuqing; Riza, Dimas Firmanda Al; Kondo, Naoshi

2018-04-01

Synchronous fluorescence spectra, combined with multivariate analysis were used to predict flavonoids content in green tea rapidly and nondestructively. This paper presented a new and efficient spectral intervals selection method called clustering based partial least square (CL-PLS), which selected informative wavelengths by combining clustering concept and partial least square (PLS) methods to improve models’ performance by synchronous fluorescence spectra. The fluorescence spectra of tea samples were obtained and k-means and kohonen-self organizing map clustering algorithms were carried out to cluster full spectra into several clusters, and sub-PLS regression model was developed on each cluster. Finally, CL-PLS models consisting of gradually selected clusters were built. Correlation coefficient (R) was used to evaluate the effect on prediction performance of PLS models. In addition, variable influence on projection partial least square (VIP-PLS), selectivity ratio partial least square (SR-PLS), interval partial least square (iPLS) models and full spectra PLS model were investigated and the results were compared. The results showed that CL-PLS presented the best result for flavonoids prediction using synchronous fluorescence spectra.

Predicting novel substrates for enzymes with minimal experimental effort with active learning.

Science.gov (United States)

Pertusi, Dante A; Moura, Matthew E; Jeffryes, James G; Prabhu, Siddhant; Walters Biggs, Bradley; Tyo, Keith E J

2017-11-01

Enzymatic substrate promiscuity is more ubiquitous than previously thought, with significant consequences for understanding metabolism and its application to biocatalysis. This realization has given rise to the need for efficient characterization of enzyme promiscuity. Enzyme promiscuity is currently characterized with a limited number of human-selected compounds that may not be representative of the enzyme's versatility. While testing large numbers of compounds may be impractical, computational approaches can exploit existing data to determine the most informative substrates to test next, thereby more thoroughly exploring an enzyme's versatility. To demonstrate this, we used existing studies and tested compounds for four different enzymes, developed support vector machine (SVM) models using these datasets, and selected additional compounds for experiments using an active learning approach. SVMs trained on a chemically diverse set of compounds were discovered to achieve maximum accuracies of ~80% using ~33% fewer compounds than datasets based on all compounds tested in existing studies. Active learning-selected compounds for testing resolved apparent conflicts in the existing training data, while adding diversity to the dataset. The application of these algorithms to wide arrays of metabolic enzymes would result in a library of SVMs that can predict high-probability promiscuous enzymatic reactions and could prove a valuable resource for the design of novel metabolic pathways. Copyright © 2017 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

Predicting novel substrates for enzymes with minimal experimental effort with active learning

Energy Technology Data Exchange (ETDEWEB)

Pertusi, Dante A.; Moura, Matthew E.; Jeffryes, James G.; Prabhu, Siddhant; Walters Biggs, Bradley; Tyo, Keith E. J.

2017-11-01

Enzymatic substrate promiscuity is more ubiquitous than previously thought, with significant consequences for understanding metabolism and its application to biocatalysis. This realization has given rise to the need for efficient characterization of enzyme promiscuity. Enzyme promiscuity is currently characterized with a limited number of human-selected compounds that may not be representative of the enzyme's versatility. While testing large numbers of compounds may be impractical, computational approaches can exploit existing data to determine the most informative substrates to test next, thereby more thoroughly exploring an enzyme's versatility. To demonstrate this, we used existing studies and tested compounds for four different enzymes, developed support vector machine (SVM) models using these datasets, and selected additional compounds for experiments using an active learning approach. SVMs trained on a chemically diverse set of compounds were discovered to achieve maximum accuracies of similar to 80% using similar to 33% fewer compounds than datasets based on all compounds tested in existing studies. Active learning-selected compounds for testing resolved apparent conflicts in the existing training data, while adding diversity to the dataset. The application of these algorithms to wide arrays of metabolic enzymes would result in a library of SVMs that can predict high-probability promiscuous enzymatic reactions and could prove a valuable resource for the design of novel metabolic pathways.

HotRegion: a database of predicted hot spot clusters.

Science.gov (United States)

Cukuroglu, Engin; Gursoy, Attila; Keskin, Ozlem

2012-01-01

Hot spots are energetically important residues at protein interfaces and they are not randomly distributed across the interface but rather clustered. These clustered hot spots form hot regions. Hot regions are important for the stability of protein complexes, as well as providing specificity to binding sites. We propose a database called HotRegion, which provides the hot region information of the interfaces by using predicted hot spot residues, and structural properties of these interface residues such as pair potentials of interface residues, accessible surface area (ASA) and relative ASA values of interface residues of both monomer and complex forms of proteins. Also, the 3D visualization of the interface and interactions among hot spot residues are provided. HotRegion is accessible at http://prism.ccbb.ku.edu.tr/hotregion.

Gravitational redshift of galaxies in clusters as predicted by general relativity.

Science.gov (United States)

Wojtak, Radosław; Hansen, Steen H; Hjorth, Jens

2011-09-28

The theoretical framework of cosmology is mainly defined by gravity, of which general relativity is the current model. Recent tests of general relativity within the Lambda Cold Dark Matter (ΛCDM) model have found a concordance between predictions and the observations of the growth rate and clustering of the cosmic web. General relativity has not hitherto been tested on cosmological scales independently of the assumptions of the ΛCDM model. Here we report an observation of the gravitational redshift of light coming from galaxies in clusters at the 99 per cent confidence level, based on archival data. Our measurement agrees with the predictions of general relativity and its modification created to explain cosmic acceleration without the need for dark energy (the f(R) theory), but is inconsistent with alternative models designed to avoid the presence of dark matter. © 2011 Macmillan Publishers Limited. All rights reserved

A Model Study on the Possible Effects of an External Electrical Field on Enzymes Having Dinuclear Iron Cluster [2Fe-2S

Directory of Open Access Journals (Sweden)

Lemi Türker

2012-01-01

Full Text Available Hydrogenases which catalyze the H2 ↔ 2H+ + 2e− reaction are metalloenzymes that can be divided into two classes, the NiFe and Fe enzymes, on the basis of their metal content. Iron-sulfur clusters [2Fe-2S] and [4Fe-4S] are common in ironhydrogenases. In the present model study, [2Fe-2S] cluster has been considered to visualize the effect of external electric field on various quantum chemical properties of it. In the model, all the cysteinyl residues are in the amide form. The PM3 type semiempirical calculations have been performed for the geometry optimization of the model structure in the absence and presence of the external field. Then, single point DFT calculations (B3LYP/6-31+G(d have been carried out. Depending on the direction of the field, the chemical reactivity of the model enzyme varies which suggests that an external electric field could, under proper conditions, improve the enzymatic hydrogen production.

Tuberculosis outbreaks predicted by characteristics of first patients in a DNA fingerprint cluster.

Science.gov (United States)

Kik, Sandra V; Verver, Suzanne; van Soolingen, Dick; de Haas, Petra E W; Cobelens, Frank G; Kremer, Kristin; van Deutekom, Henk; Borgdorff, Martien W

2008-07-01

Some clusters of patients who have Mycobacterium tuberculosis isolates with identical DNA fingerprint patterns grow faster than others. It is unclear what predictors determine cluster growth. To assess whether the development of a tuberculosis (TB) outbreak can be predicted by the characteristics of its first two patients. Demographic and clinical data of all culture-confirmed patients with TB in the Netherlands from 1993 through 2004 were combined with DNA fingerprint data. Clusters were restricted to cluster episodes of 2 years to only detect newly arising clusters. Characteristics of the first two patients were compared between small (2-4 cases) and large (5 or more cases) cluster episodes. Of 5,454 clustered cases, 1,756 (32%) were part of a cluster episode of 2 years. Of 622 cluster episodes, 54 (9%) were large and 568 (91%) were small episodes. Independent predictors for large cluster episodes were as follows: less than 3 months' time between the diagnosis of the first two patients, one or both patients were young (<35 yr), both patients lived in an urban area, and both patients came from sub-Saharan Africa. In the Netherlands, patients in new cluster episodes should be screened for these risk factors. When the risk pattern applies, targeted interventions (e.g., intensified contact investigation) should be considered to prevent further cluster expansion.

Theoretical predictions for vehicular headways and their clusters

Science.gov (United States)

Krbálek, Milan

2013-11-01

This paper presents a derivation of analytical predictions for steady-state distributions of netto time gaps among clusters of vehicles moving inside a traffic stream. Using the thermodynamic socio-physical traffic model with short-ranged repulsion between particles (originally introduced in Krbálek and Helbing 2004 Physica A 333 370) we first derive the time-clearance distribution in the model and confront it with relation to the theoretical criteria for the acceptability of analytical clearance distributions. Consecutively, the approximating statistical distributions for the so-called time multi-clearances are calculated by means of the theory of functional convolutions. Moreover, all the theoretical surmises used during the above-mentioned calculations are evaluated by the statistical analysis of traffic data. The mathematical predictions acquired in this paper are thoroughly compared with relevant empirical quantities and discussed in the context of traffic theory.

Assessment of structures and stabilities of defect clusters and surface energies predicted by nine interatomic potentials for UO{sub 2}

Energy Technology Data Exchange (ETDEWEB)

Taller, Stephen A. [School of Nuclear Engineering, Purdue University, West Lafayette, IN 47907 (United States); Bai, Xian-Ming, E-mail: xianming.bai@inl.gov [Fuels Modeling and Simulation Department, Idaho National Laboratory, Idaho Falls, ID 83415 (United States)

2013-11-15

The irradiation in nuclear reactors creates many point defects and defect clusters in uranium dioxide (UO{sub 2}) and their evolution severely degrades the thermal and mechanical properties of the nuclear fuels. Previously many empirical interatomic potentials have been developed for modeling defect production and evolution in UO{sub 2}. However, the properties of defect clusters and extended defects are usually not fitted into these potentials. In this work nine interatomic potentials for UO{sub 2} are examined by using molecular statics and molecular dynamics to assess their applicability in predicting the properties of various types of defect clusters in UO{sub 2}. The binding energies and structures for these defect clusters have been evaluated for each potential. In addition, the surface energies of voids of different radii and (1 1 0) flat surfaces predicted by these potentials are also evaluated. It is found that both good agreement and significant discrepancies exist for these potentials in predicting these properties. For oxygen interstitial clusters, these potentials predict significantly different defect cluster structures and stabilities; For defect clusters consisting of both uranium and oxygen defects, the prediction is in better agreement; The surface energies predicted by these potentials have significant discrepancies, and some of them are much higher than the experimentally measured values. The results from this work can provide insight on interpreting the outcome of atomistic modeling of defect production using these potentials and may provide guidelines for choosing appropriate potential models to study problems of interest in UO{sub 2}.

4-Demethylwyosine Synthase from Pyrococcus abyssi Is a Radical-S-adenosyl-l-methionine Enzyme with an Additional [4Fe-4S]+2 Cluster That Interacts with the Pyruvate Co-substrate*

Science.gov (United States)

Perche-Letuvée, Phanélie; Kathirvelu, Velavan; Berggren, Gustav; Clemancey, Martin; Latour, Jean-Marc; Maurel, Vincent; Douki, Thierry; Armengaud, Jean; Mulliez, Etienne; Fontecave, Marc; Garcia-Serres, Ricardo; Gambarelli, Serge; Atta, Mohamed

2012-01-01

Wybutosine and its derivatives are found in position 37 of tRNA encoding Phe in eukaryotes and archaea. They are believed to play a key role in the decoding function of the ribosome. The second step in the biosynthesis of wybutosine is catalyzed by TYW1 protein, which is a member of the well established class of metalloenzymes called “Radical-SAM.” These enzymes use a [4Fe-4S] cluster, chelated by three cysteines in a CX3CX2C motif, and S-adenosyl-l-methionine (SAM) to generate a 5′-deoxyadenosyl radical that initiates various chemically challenging reactions. Sequence analysis of TYW1 proteins revealed, in the N-terminal half of the enzyme beside the Radical-SAM cysteine triad, an additional highly conserved cysteine motif. In this study we show by combining analytical and spectroscopic methods including UV-visible absorption, Mössbauer, EPR, and HYSCORE spectroscopies that these additional cysteines are involved in the coordination of a second [4Fe-4S] cluster displaying a free coordination site that interacts with pyruvate, the second substrate of the reaction. The presence of two distinct iron-sulfur clusters on TYW1 is reminiscent of MiaB, another tRNA-modifying metalloenzyme whose active form was shown to bind two iron-sulfur clusters. A possible role for the second [4Fe-4S] cluster in the enzyme activity is discussed. PMID:23043105

Does objective cluster analysis serve as a useful precursor to seasonal precipitation prediction at local scale? Application to western Ethiopia

Science.gov (United States)

Zhang, Ying; Moges, Semu; Block, Paul

2018-01-01

Prediction of seasonal precipitation can provide actionable information to guide management of various sectoral activities. For instance, it is often translated into hydrological forecasts for better water resources management. However, many studies assume homogeneity in precipitation across an entire study region, which may prove ineffective for operational and local-level decisions, particularly for locations with high spatial variability. This study proposes advancing local-level seasonal precipitation predictions by first conditioning on regional-level predictions, as defined through objective cluster analysis, for western Ethiopia. To our knowledge, this is the first study predicting seasonal precipitation at high resolution in this region, where lives and livelihoods are vulnerable to precipitation variability given the high reliance on rain-fed agriculture and limited water resources infrastructure. The combination of objective cluster analysis, spatially high-resolution prediction of seasonal precipitation, and a modeling structure spanning statistical and dynamical approaches makes clear advances in prediction skill and resolution, as compared with previous studies. The statistical model improves versus the non-clustered case or dynamical models for a number of specific clusters in northwestern Ethiopia, with clusters having regional average correlation and ranked probability skill score (RPSS) values of up to 0.5 and 33 %, respectively. The general skill (after bias correction) of the two best-performing dynamical models over the entire study region is superior to that of the statistical models, although the dynamical models issue predictions at a lower resolution and the raw predictions require bias correction to guarantee comparable skills.

Clustering Dycom

KAUST Repository

Minku, Leandro L.

2017-10-06

Background: Software Effort Estimation (SEE) can be formulated as an online learning problem, where new projects are completed over time and may become available for training. In this scenario, a Cross-Company (CC) SEE approach called Dycom can drastically reduce the number of Within-Company (WC) projects needed for training, saving the high cost of collecting such training projects. However, Dycom relies on splitting CC projects into different subsets in order to create its CC models. Such splitting can have a significant impact on Dycom\\'s predictive performance. Aims: This paper investigates whether clustering methods can be used to help finding good CC splits for Dycom. Method: Dycom is extended to use clustering methods for creating the CC subsets. Three different clustering methods are investigated, namely Hierarchical Clustering, K-Means, and Expectation-Maximisation. Clustering Dycom is compared against the original Dycom with CC subsets of different sizes, based on four SEE databases. A baseline WC model is also included in the analysis. Results: Clustering Dycom with K-Means can potentially help to split the CC projects, managing to achieve similar or better predictive performance than Dycom. However, K-Means still requires the number of CC subsets to be pre-defined, and a poor choice can negatively affect predictive performance. EM enables Dycom to automatically set the number of CC subsets while still maintaining or improving predictive performance with respect to the baseline WC model. Clustering Dycom with Hierarchical Clustering did not offer significant advantage in terms of predictive performance. Conclusion: Clustering methods can be an effective way to automatically generate Dycom\\'s CC subsets.

Does objective cluster analysis serve as a useful precursor to seasonal precipitation prediction at local scale? Application to western Ethiopia

Directory of Open Access Journals (Sweden)

Y. Zhang

2018-01-01

Full Text Available Prediction of seasonal precipitation can provide actionable information to guide management of various sectoral activities. For instance, it is often translated into hydrological forecasts for better water resources management. However, many studies assume homogeneity in precipitation across an entire study region, which may prove ineffective for operational and local-level decisions, particularly for locations with high spatial variability. This study proposes advancing local-level seasonal precipitation predictions by first conditioning on regional-level predictions, as defined through objective cluster analysis, for western Ethiopia. To our knowledge, this is the first study predicting seasonal precipitation at high resolution in this region, where lives and livelihoods are vulnerable to precipitation variability given the high reliance on rain-fed agriculture and limited water resources infrastructure. The combination of objective cluster analysis, spatially high-resolution prediction of seasonal precipitation, and a modeling structure spanning statistical and dynamical approaches makes clear advances in prediction skill and resolution, as compared with previous studies. The statistical model improves versus the non-clustered case or dynamical models for a number of specific clusters in northwestern Ethiopia, with clusters having regional average correlation and ranked probability skill score (RPSS values of up to 0.5 and 33 %, respectively. The general skill (after bias correction of the two best-performing dynamical models over the entire study region is superior to that of the statistical models, although the dynamical models issue predictions at a lower resolution and the raw predictions require bias correction to guarantee comparable skills.

Prediction of chemotherapeutic response in bladder cancer using K-means clustering of dynamic contrast-enhanced (DCE)-MRI pharmacokinetic parameters.

Science.gov (United States)

Nguyen, Huyen T; Jia, Guang; Shah, Zarine K; Pohar, Kamal; Mortazavi, Amir; Zynger, Debra L; Wei, Lai; Yang, Xiangyu; Clark, Daniel; Knopp, Michael V

2015-05-01

To apply k-means clustering of two pharmacokinetic parameters derived from 3T dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict the chemotherapeutic response in bladder cancer at the mid-cycle timepoint. With the predetermined number of three clusters, k-means clustering was performed on nondimensionalized Amp and kep estimates of each bladder tumor. Three cluster volume fractions (VFs) were calculated for each tumor at baseline and mid-cycle. The changes of three cluster VFs from baseline to mid-cycle were correlated with the tumor's chemotherapeutic response. Receiver-operating-characteristics curve analysis was used to evaluate the performance of each cluster VF change as a biomarker of chemotherapeutic response in bladder cancer. The k-means clustering partitioned each bladder tumor into cluster 1 (low kep and low Amp), cluster 2 (low kep and high Amp), cluster 3 (high kep and low Amp). The changes of all three cluster VFs were found to be associated with bladder tumor response to chemotherapy. The VF change of cluster 2 presented with the highest area-under-the-curve value (0.96) and the highest sensitivity/specificity/accuracy (96%/100%/97%) with a selected cutoff value. The k-means clustering of the two DCE-MRI pharmacokinetic parameters can characterize the complex microcirculatory changes within a bladder tumor to enable early prediction of the tumor's chemotherapeutic response. © 2014 Wiley Periodicals, Inc.

Cluster evolution

International Nuclear Information System (INIS)

Schaeffer, R.

1987-01-01

The galaxy and cluster luminosity functions are constructed from a model of the mass distribution based on hierarchical clustering at an epoch where the matter distribution is non-linear. These luminosity functions are seen to reproduce the present distribution of objects as can be inferred from the observations. They can be used to deduce the redshift dependence of the cluster distribution and to extrapolate the observations towards the past. The predicted evolution of the cluster distribution is quite strong, although somewhat less rapid than predicted by the linear theory

An enhanced deterministic K-Means clustering algorithm for cancer subtype prediction from gene expression data.

Science.gov (United States)

Nidheesh, N; Abdul Nazeer, K A; Ameer, P M

2017-12-01

Clustering algorithms with steps involving randomness usually give different results on different executions for the same dataset. This non-deterministic nature of algorithms such as the K-Means clustering algorithm limits their applicability in areas such as cancer subtype prediction using gene expression data. It is hard to sensibly compare the results of such algorithms with those of other algorithms. The non-deterministic nature of K-Means is due to its random selection of data points as initial centroids. We propose an improved, density based version of K-Means, which involves a novel and systematic method for selecting initial centroids. The key idea of the algorithm is to select data points which belong to dense regions and which are adequately separated in feature space as the initial centroids. We compared the proposed algorithm to a set of eleven widely used single clustering algorithms and a prominent ensemble clustering algorithm which is being used for cancer data classification, based on the performances on a set of datasets comprising ten cancer gene expression datasets. The proposed algorithm has shown better overall performance than the others. There is a pressing need in the Biomedical domain for simple, easy-to-use and more accurate Machine Learning tools for cancer subtype prediction. The proposed algorithm is simple, easy-to-use and gives stable results. Moreover, it provides comparatively better predictions of cancer subtypes from gene expression data. Copyright © 2017 Elsevier Ltd. All rights reserved.

Prediction of in vitro and in vivo oestrogen receptor activity using hierarchical clustering

Science.gov (United States)

In this study, hierarchical clustering classification models were developed to predict in vitro and in vivo oestrogen receptor (ER) activity. Classification models were developed for binding, agonist, and antagonist in vitro ER activity and for mouse in vivo uterotrophic ER bindi...

Data-driven modeling and predictive control for boiler-turbine unit using fuzzy clustering and subspace methods.

Science.gov (United States)

Wu, Xiao; Shen, Jiong; Li, Yiguo; Lee, Kwang Y

2014-05-01

This paper develops a novel data-driven fuzzy modeling strategy and predictive controller for boiler-turbine unit using fuzzy clustering and subspace identification (SID) methods. To deal with the nonlinear behavior of boiler-turbine unit, fuzzy clustering is used to provide an appropriate division of the operation region and develop the structure of the fuzzy model. Then by combining the input data with the corresponding fuzzy membership functions, the SID method is extended to extract the local state-space model parameters. Owing to the advantages of the both methods, the resulting fuzzy model can represent the boiler-turbine unit very closely, and a fuzzy model predictive controller is designed based on this model. As an alternative approach, a direct data-driven fuzzy predictive control is also developed following the same clustering and subspace methods, where intermediate subspace matrices developed during the identification procedure are utilized directly as the predictor. Simulation results show the advantages and effectiveness of the proposed approach. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Muscle enzyme release does not predict muscle function impairment after triathlon.

Science.gov (United States)

Margaritis, I; Tessier, F; Verdera, F; Bermon, S; Marconnet, P

1999-06-01

We sought to determine the effects of a long distance triathlon (4 km swim, 120 km bike-ride, and 30 km run) on the four-day kinetics of the biochemical markers of muscle damage, and whether they were quantitatively linked with muscle function impairment and soreness. Data were collected from 2 days before until 4 days after the completion of the race. Twelve triathletes performed the triathlon and five did not. Maximal voluntary contraction (MVC), muscle soreness (DOMS) and total serum CK, CK-MB, LDH, AST and ALT activities were assessed. Significant changes after triathlon completion were found for all muscle damage indirect markers over time (p triathlon. Long distance triathlon race caused muscle damage, but extent, as well as muscle recovery cannot be evaluated by the magnitude of changes in serum enzyme activities. Muscle enzyme release cannot be used to predict the magnitude of the muscle function impairment caused by muscle damage.

Prediction of operon-like gene clusters in the Arabidopsis thaliana genome based on co-expression analysis of neighboring genes.

Science.gov (United States)

Wada, Masayoshi; Takahashi, Hiroki; Altaf-Ul-Amin, Md; Nakamura, Kensuke; Hirai, Masami Y; Ohta, Daisaku; Kanaya, Shigehiko

2012-07-15

Operon-like arrangements of genes occur in eukaryotes ranging from yeasts and filamentous fungi to nematodes, plants, and mammals. In plants, several examples of operon-like gene clusters involved in metabolic pathways have recently been characterized, e.g. the cyclic hydroxamic acid pathways in maize, the avenacin biosynthesis gene clusters in oat, the thalianol pathway in Arabidopsis thaliana, and the diterpenoid momilactone cluster in rice. Such operon-like gene clusters are defined by their co-regulation or neighboring positions within immediate vicinity of chromosomal regions. A comprehensive analysis of the expression of neighboring genes therefore accounts a crucial step to reveal the complete set of operon-like gene clusters within a genome. Genome-wide prediction of operon-like gene clusters should contribute to functional annotation efforts and provide novel insight into evolutionary aspects acquiring certain biological functions as well. We predicted co-expressed gene clusters by comparing the Pearson correlation coefficient of neighboring genes and randomly selected gene pairs, based on a statistical method that takes false discovery rate (FDR) into consideration for 1469 microarray gene expression datasets of A. thaliana. We estimated that A. thaliana contains 100 operon-like gene clusters in total. We predicted 34 statistically significant gene clusters consisting of 3 to 22 genes each, based on a stringent FDR threshold of 0.1. Functional relationships among genes in individual clusters were estimated by sequence similarity and functional annotation of genes. Duplicated gene pairs (determined based on BLAST with a cutoff of EOperon-like clusters tend to include genes encoding bio-machinery associated with ribosomes, the ubiquitin/proteasome system, secondary metabolic pathways, lipid and fatty-acid metabolism, and the lipid transfer system. Copyright © 2012 Elsevier B.V. All rights reserved.

PSPP: a protein structure prediction pipeline for computing clusters.

Directory of Open Access Journals (Sweden)

Michael S Lee

2009-07-01

Full Text Available Protein structures are critical for understanding the mechanisms of biological systems and, subsequently, for drug and vaccine design. Unfortunately, protein sequence data exceed structural data by a factor of more than 200 to 1. This gap can be partially filled by using computational protein structure prediction. While structure prediction Web servers are a notable option, they often restrict the number of sequence queries and/or provide a limited set of prediction methodologies. Therefore, we present a standalone protein structure prediction software package suitable for high-throughput structural genomic applications that performs all three classes of prediction methodologies: comparative modeling, fold recognition, and ab initio. This software can be deployed on a user's own high-performance computing cluster.The pipeline consists of a Perl core that integrates more than 20 individual software packages and databases, most of which are freely available from other research laboratories. The query protein sequences are first divided into domains either by domain boundary recognition or Bayesian statistics. The structures of the individual domains are then predicted using template-based modeling or ab initio modeling. The predicted models are scored with a statistical potential and an all-atom force field. The top-scoring ab initio models are annotated by structural comparison against the Structural Classification of Proteins (SCOP fold database. Furthermore, secondary structure, solvent accessibility, transmembrane helices, and structural disorder are predicted. The results are generated in text, tab-delimited, and hypertext markup language (HTML formats. So far, the pipeline has been used to study viral and bacterial proteomes.The standalone pipeline that we introduce here, unlike protein structure prediction Web servers, allows users to devote their own computing assets to process a potentially unlimited number of queries as well as perform

Do Staphylococcus epidermidis Genetic Clusters Predict Isolation Sources?

Science.gov (United States)

Tolo, Isaiah; Thomas, Jonathan C.; Fischer, Rebecca S. B.; Brown, Eric L.; Gray, Barry M.

2016-01-01

Staphylococcus epidermidis is a ubiquitous colonizer of human skin and a common cause of medical device-associated infections. The extent to which the population genetic structure of S. epidermidis distinguishes commensal from pathogenic isolates is unclear. Previously, Bayesian clustering of 437 multilocus sequence types (STs) in the international database revealed a population structure of six genetic clusters (GCs) that may reflect the species' ecology. Here, we first verified the presence of six GCs, including two (GC3 and GC5) with significant admixture, in an updated database of 578 STs. Next, a single nucleotide polymorphism (SNP) assay was developed that accurately assigned 545 (94%) of 578 STs to GCs. Finally, the hypothesis that GCs could distinguish isolation sources was tested by SNP typing and GC assignment of 154 isolates from hospital patients with bacteremia and those with blood culture contaminants and from nonhospital carriage. GC5 was isolated almost exclusively from hospital sources. GC1 and GC6 were isolated from all sources but were overrepresented in isolates from nonhospital and infection sources, respectively. GC2, GC3, and GC4 were relatively rare in this collection. No association was detected between fdh-positive isolates (GC2 and GC4) and nonhospital sources. Using a machine learning algorithm, GCs predicted hospital and nonhospital sources with 80% accuracy and predicted infection and contaminant sources with 45% accuracy, which was comparable to the results seen with a combination of five genetic markers (icaA, IS256, sesD [bhp], mecA, and arginine catabolic mobile element [ACME]). Thus, analysis of population structure with subgenomic data shows the distinction of hospital and nonhospital sources and the near-inseparability of sources within a hospital. PMID:27076664

Prediction of strontium bromide laser efficiency using cluster and decision tree analysis

Directory of Open Access Journals (Sweden)

Iliev Iliycho

2018-01-01

Full Text Available Subject of investigation is a new high-powered strontium bromide (SrBr2 vapor laser emitting in multiline region of wavelengths. The laser is an alternative to the atom strontium lasers and electron free lasers, especially at the line 6.45 μm which line is used in surgery for medical processing of biological tissues and bones with minimal damage. In this paper the experimental data from measurements of operational and output characteristics of the laser are statistically processed by means of cluster analysis and tree-based regression techniques. The aim is to extract the more important relationships and dependences from the available data which influence the increase of the overall laser efficiency. There are constructed and analyzed a set of cluster models. It is shown by using different cluster methods that the seven investigated operational characteristics (laser tube diameter, length, supplied electrical power, and others and laser efficiency are combined in 2 clusters. By the built regression tree models using Classification and Regression Trees (CART technique there are obtained dependences to predict the values of efficiency, and especially the maximum efficiency with over 95% accuracy.

A SOM clustering pattern sequence-based next symbol prediction method for day-ahead direct electricity load and price forecasting

International Nuclear Information System (INIS)

Jin, Cheng Hao; Pok, Gouchol; Lee, Yongmi; Park, Hyun-Woo; Kim, Kwang Deuk; Yun, Unil; Ryu, Keun Ho

2015-01-01

Highlights: • A novel pattern sequence-based direct time series forecasting method was proposed. • Due to the use of SOM’s topology preserving property, only SOM can be applied. • SCPSNSP only deals with the cluster patterns not each specific time series value. • SCPSNSP performs better than recently developed forecasting algorithms. - Abstract: In this paper, we propose a new day-ahead direct time series forecasting method for competitive electricity markets based on clustering and next symbol prediction. In the clustering step, pattern sequence and their topology relations are obtained from self organizing map time series clustering. In the next symbol prediction step, with each cluster label in the pattern sequence represented as a pair of its topologically identical coordinates, artificial neural network is used to predict the topological coordinates of next day by training the relationship between previous daily pattern sequence and its next day pattern. According to the obtained topology relations, the nearest nonzero hits pattern is assigned to next day so that the whole time series values can be directly forecasted from the assigned cluster pattern. The proposed method was evaluated on Spanish, Australian and New York electricity markets and compared with PSF and some of the most recently published forecasting methods. Experimental results show that the proposed method outperforms the best forecasting methods at least 3.64%

Predictive coupled-cluster isomer orderings for some SinCm (m, n ≤ 12) clusters: A pragmatic comparison between DFT and complete basis limit coupled-cluster benchmarks

International Nuclear Information System (INIS)

Byrd, Jason N.; Lutz, Jesse J.; Jin, Yifan; Ranasinghe, Duminda S.; Perera, Ajith; Bartlett, Rodney J.; Montgomery, John A.; Duan, Xiaofeng F.; Burggraf, Larry W.; Sanders, Beverly A.

2016-01-01

The accurate determination of the preferred Si 12 C 12 isomer is important to guide experimental efforts directed towards synthesizing SiC nano-wires and related polymer structures which are anticipated to be highly efficient exciton materials for the opto-electronic devices. In order to definitively identify preferred isomeric structures for silicon carbon nano-clusters, highly accurate geometries, energies, and harmonic zero point energies have been computed using coupled-cluster theory with systematic extrapolation to the complete basis limit for set of silicon carbon clusters ranging in size from SiC 3 to Si 12 C 12 . It is found that post-MBPT(2) correlation energy plays a significant role in obtaining converged relative isomer energies, suggesting that predictions using low rung density functional methods will not have adequate accuracy. Utilizing the best composite coupled-cluster energy that is still computationally feasible, entailing a 3-4 SCF and coupled-cluster theory with singles and doubles extrapolation with triple-ζ (T) correlation, the closo Si 12 C 12 isomer is identified to be the preferred isomer in the support of previous calculations [X. F. Duan and L. W. Burggraf, J. Chem. Phys. 142, 034303 (2015)]. Additionally we have investigated more pragmatic approaches to obtaining accurate silicon carbide isomer energies, including the use of frozen natural orbital coupled-cluster theory and several rungs of standard and double-hybrid density functional theory. Frozen natural orbitals as a way to compute post-MBPT(2) correlation energy are found to be an excellent balance between efficiency and accuracy.

PREDICTED SIZES OF PRESSURE-SUPPORTED HI CLOUDS IN THE OUTSKIRTS OF THE VIRGO CLUSTER

Energy Technology Data Exchange (ETDEWEB)

Burkhart, Blakesley; Loeb, Abraham [Harvard-Smithsonian Center for Astrophysics, 60 Garden St. Cambridge, MA (United States)

2016-06-10

Using data from the ALFALFA AGES Arecibo HI survey of galaxies and the Virgo cluster X-ray pressure profiles from XMM-Newton , we investigate the possibility that starless dark HI clumps, also known as “dark galaxies,” are supported by external pressure in the surrounding intercluster medium. We find that the starless HI clump masses, velocity dispersions, and positions allow these clumps to be in pressure equilibrium with the X-ray gas near the virial radius of the Virgo cluster. We predict the sizes of these clumps to range from 1 to 10 kpc, in agreement with the range of sizes found for spatially resolved HI starless clumps outside of Virgo. Based on the predicted HI surface density of the Virgo sources, as well as a sample of other similar resolved ALFALFA HI dark clumps with follow-up optical/radio observations, we predict that most of the HI dark clumps are on the cusp of forming stars. These HI sources therefore mark the transition between starless HI clouds and dwarf galaxies with stars.

Spectromicroscopy of self-assembled protein clusters

Energy Technology Data Exchange (ETDEWEB)

Schonschek, O.; Hormes, J.; Herzog, V. [Univ. of Bonn (Germany)

1997-04-01

The aim of this project is to use synchrotron radiation as a tool to study biomedical questions concerned with the thyroid glands. The biological background is outlined in a recent paper. In short, Thyroglobulin (TG), the precursor protein of the hormone thyroxine, forms large (20 - 500 microns in diameter) clusters in the extracellular lumen of thyrocytes. The process of the cluster formation is still not well understood but is thought to be a main storage mechanism of TG and therefore thyroxine inside the thyroid glands. For human thyroids, the interconnections of the proteins inside the clusters are mainly disulfide bondings. Normally, sulfur bridges are catalyzed by an enzyme called Protein Disulfide Bridge Isomerase (PDI). While this enzyme is supposed to be not present in any extracellular space, the cluster formation of TG takes place in the lumen between the thyrocytes. A possible explanation is the autocatalysis of TG.

QSAR and docking studies of anthraquinone derivatives by similarity cluster prediction.

Science.gov (United States)

Harsa, Alexandra M; Harsa, Teodora E; Diudea, Mircea V

2016-01-01

Forty anthraquinone derivatives have been downloaded from PubChem database and investigated in a quantitative structure-activity relationships (QSAR) study. The models describing log P and LD50 of this set were built up on the hypermolecule scheme that mimics the investigated receptor space; the models were validated by the leave-one-out procedure, in the external test set and in a new version of prediction by using similarity clusters. Molecular docking approach using Lamarckian Genetic Algorithm was made on this class of anthraquinones with respect to 3Q3B receptor. The best scored molecules in the docking assay were used as leaders in the similarity clustering procedure. It is demonstrated that the LD50 data of this set of anthraquinones are related to the binding energies of anthraquinone ligands to the 3Q3B receptor.

Several genes encoding enzymes with the same activity are necessary for aerobic fungal degradation of cellulose in nature.

Directory of Open Access Journals (Sweden)

Peter K Busk

Full Text Available The cellulose-degrading fungal enzymes are glycoside hydrolases of the GH families and lytic polysaccharide monooxygenases. The entanglement of glycoside hydrolase families and functions makes it difficult to predict the enzymatic activity of glycoside hydrolases based on their sequence. In the present study we further developed the method Peptide Pattern Recognition to an automatic approach not only to find all genes encoding glycoside hydrolases and lytic polysaccharide monooxygenases in fungal genomes but also to predict the function of the genes. The functional annotation is an important feature as it provides a direct route to predict function from primary sequence. Furthermore, we used Peptide Pattern Recognition to compare the cellulose-degrading enzyme activities encoded by 39 fungal genomes. The results indicated that cellobiohydrolases and AA9 lytic polysaccharide monooxygenases are hallmarks of cellulose-degrading fungi except brown rot fungi. Furthermore, a high number of AA9, endocellulase and β-glucosidase genes were identified, not in what are known to be the strongest, specialized lignocellulose degraders but in saprophytic fungi that can use a wide variety of substrates whereas only few of these genes were found in fungi that have a limited number of natural, lignocellulotic substrates. This correlation suggests that enzymes with different properties are necessary for degradation of cellulose in different complex substrates. Interestingly, clustering of the fungi based on their predicted enzymes indicated that Ascomycota and Basidiomycota use the same enzymatic activities to degrade plant cell walls.

A Bayesian method for identifying missing enzymes in predicted metabolic pathway databases

Directory of Open Access Journals (Sweden)

Karp Peter D

2004-06-01

Full Text Available Abstract Background The PathoLogic program constructs Pathway/Genome databases by using a genome's annotation to predict the set of metabolic pathways present in an organism. PathoLogic determines the set of reactions composing those pathways from the enzymes annotated in the organism's genome. Most annotation efforts fail to assign function to 40–60% of sequences. In addition, large numbers of sequences may have non-specific annotations (e.g., thiolase family protein. Pathway holes occur when a genome appears to lack the enzymes needed to catalyze reactions in a pathway. If a protein has not been assigned a specific function during the annotation process, any reaction catalyzed by that protein will appear as a missing enzyme or pathway hole in a Pathway/Genome database. Results We have developed a method that efficiently combines homology and pathway-based evidence to identify candidates for filling pathway holes in Pathway/Genome databases. Our program not only identifies potential candidate sequences for pathway holes, but combines data from multiple, heterogeneous sources to assess the likelihood that a candidate has the required function. Our algorithm emulates the manual sequence annotation process, considering not only evidence from homology searches, but also considering evidence from genomic context (i.e., is the gene part of an operon? and functional context (e.g., are there functionally-related genes nearby in the genome? to determine the posterior belief that a candidate has the required function. The method can be applied across an entire metabolic pathway network and is generally applicable to any pathway database. The program uses a set of sequences encoding the required activity in other genomes to identify candidate proteins in the genome of interest, and then evaluates each candidate by using a simple Bayes classifier to determine the probability that the candidate has the desired function. We achieved 71% precision at a

Ternary alloy material prediction using genetic algorithm and cluster expansion

Energy Technology Data Exchange (ETDEWEB)

Chen, Chong [Iowa State Univ., Ames, IA (United States)

2015-12-01

This thesis summarizes our study on the crystal structures prediction of Fe-V-Si system using genetic algorithm and cluster expansion. Our goal is to explore and look for new stable compounds. We started from the current ten known experimental phases, and calculated formation energies of those compounds using density functional theory (DFT) package, namely, VASP. The convex hull was generated based on the DFT calculations of the experimental known phases. Then we did random search on some metal rich (Fe and V) compositions and found that the lowest energy structures were body centered cube (bcc) underlying lattice, under which we did our computational systematic searches using genetic algorithm and cluster expansion. Among hundreds of the searched compositions, thirteen were selected and DFT formation energies were obtained by VASP. The stability checking of those thirteen compounds was done in reference to the experimental convex hull. We found that the composition, 24-8-16, i.e., Fe₃VSi₂ is a new stable phase and it can be very inspiring to the future experiments.

Classification via Clustering for Predicting Final Marks Based on Student Participation in Forums

Science.gov (United States)

Lopez, M. I.; Luna, J. M.; Romero, C.; Ventura, S.

2012-01-01

This paper proposes a classification via clustering approach to predict the final marks in a university course on the basis of forum data. The objective is twofold: to determine if student participation in the course forum can be a good predictor of the final marks for the course and to examine whether the proposed classification via clustering…

The clustering-based case-based reasoning for imbalanced business failure prediction: a hybrid approach through integrating unsupervised process with supervised process

Science.gov (United States)

Li, Hui; Yu, Jun-Ling; Yu, Le-An; Sun, Jie

2014-05-01

Case-based reasoning (CBR) is one of the main forecasting methods in business forecasting, which performs well in prediction and holds the ability of giving explanations for the results. In business failure prediction (BFP), the number of failed enterprises is relatively small, compared with the number of non-failed ones. However, the loss is huge when an enterprise fails. Therefore, it is necessary to develop methods (trained on imbalanced samples) which forecast well for this small proportion of failed enterprises and performs accurately on total accuracy meanwhile. Commonly used methods constructed on the assumption of balanced samples do not perform well in predicting minority samples on imbalanced samples consisting of the minority/failed enterprises and the majority/non-failed ones. This article develops a new method called clustering-based CBR (CBCBR), which integrates clustering analysis, an unsupervised process, with CBR, a supervised process, to enhance the efficiency of retrieving information from both minority and majority in CBR. In CBCBR, various case classes are firstly generated through hierarchical clustering inside stored experienced cases, and class centres are calculated out by integrating cases information in the same clustered class. When predicting the label of a target case, its nearest clustered case class is firstly retrieved by ranking similarities between the target case and each clustered case class centre. Then, nearest neighbours of the target case in the determined clustered case class are retrieved. Finally, labels of the nearest experienced cases are used in prediction. In the empirical experiment with two imbalanced samples from China, the performance of CBCBR was compared with the classical CBR, a support vector machine, a logistic regression and a multi-variant discriminate analysis. The results show that compared with the other four methods, CBCBR performed significantly better in terms of sensitivity for identifying the

A Novel Method to Predict Genomic Islands Based on Mean Shift Clustering Algorithm.

Directory of Open Access Journals (Sweden)

Daniel M de Brito

Full Text Available Genomic Islands (GIs are regions of bacterial genomes that are acquired from other organisms by the phenomenon of horizontal transfer. These regions are often responsible for many important acquired adaptations of the bacteria, with great impact on their evolution and behavior. Nevertheless, these adaptations are usually associated with pathogenicity, antibiotic resistance, degradation and metabolism. Identification of such regions is of medical and industrial interest. For this reason, different approaches for genomic islands prediction have been proposed. However, none of them are capable of predicting precisely the complete repertory of GIs in a genome. The difficulties arise due to the changes in performance of different algorithms in the face of the variety of nucleotide distribution in different species. In this paper, we present a novel method to predict GIs that is built upon mean shift clustering algorithm. It does not require any information regarding the number of clusters, and the bandwidth parameter is automatically calculated based on a heuristic approach. The method was implemented in a new user-friendly tool named MSGIP--Mean Shift Genomic Island Predictor. Genomes of bacteria with GIs discussed in other papers were used to evaluate the proposed method. The application of this tool revealed the same GIs predicted by other methods and also different novel unpredicted islands. A detailed investigation of the different features related to typical GI elements inserted in these new regions confirmed its effectiveness. Stand-alone and user-friendly versions for this new methodology are available at http://msgip.integrativebioinformatics.me.

BioGPS descriptors for rational engineering of enzyme promiscuity and structure based bioinformatic analysis.

Directory of Open Access Journals (Sweden)

Valerio Ferrario

Full Text Available A new bioinformatic methodology was developed founded on the Unsupervised Pattern Cognition Analysis of GRID-based BioGPS descriptors (Global Positioning System in Biological Space. The procedure relies entirely on three-dimensional structure analysis of enzymes and does not stem from sequence or structure alignment. The BioGPS descriptors account for chemical, geometrical and physical-chemical features of enzymes and are able to describe comprehensively the active site of enzymes in terms of "pre-organized environment" able to stabilize the transition state of a given reaction. The efficiency of this new bioinformatic strategy was demonstrated by the consistent clustering of four different Ser hydrolases classes, which are characterized by the same active site organization but able to catalyze different reactions. The method was validated by considering, as a case study, the engineering of amidase activity into the scaffold of a lipase. The BioGPS tool predicted correctly the properties of lipase variants, as demonstrated by the projection of mutants inside the BioGPS "roadmap".

Prediction (early recognition) of emerging flu strain clusters

Science.gov (United States)

Li, X.; Phillips, J. C.

2017-08-01

Early detection of incipient dominant influenza strains is one of the key steps in the design and manufacture of an effective annual influenza vaccine. Here we report the most current results for pandemic H3N2 flu vaccine design. A 2006 model of dimensional reduction (compaction) of viral mutational complexity derives two-dimensional Cartesian mutational maps (2DMM) that exhibit an emergent dominant strain as a small and distinct cluster of as few as 10 strains. We show that recent extensions of this model can detect incipient strains one year or more in advance of their dominance in the human population. Our structural interpretation of our unexpectedly rich 2DMM involves sialic acid, and is based on nearly 6000 strains in a series of recent 3-year time windows. Vaccine effectiveness is predicted best by analyzing dominant mutational epitopes.

Cross-linked glucose oxidase clusters for biofuel cell anode catalysts

International Nuclear Information System (INIS)

Dudzik, Jonathan; Audette, Gerald F; Chang, Wen-Chi; Kannan, A M; Filipek, Slawomir; Viswanathan, Sowmya; Li, Pingzuo; Renugopalakrishnan, V

2013-01-01

The efficient localization of increased levels of active enzymes onto conducting scaffolds is important for the development of enzyme-based biofuel cells. Cross-linked enzyme clusters (CEC) of glucose oxidase (GOx) constrained to functionalized carbon nanotubes (CEC-CNTs) were generated in order to evaluate the potential of using CECs for developing GOx-based bioanodes functioning via direct electron transfer from the GOx active site to the CNT scaffold. CEC-CNTs generated from several weight-to-weight ratios of GOx:CNT were examined for comparable catalytic activity to free GOx into the solution, with CEC-CNTs generated from a 100% GOx solution displaying the greatest enzymatic activity. Scanning transmission electron microscopic analysis of CEC-CNTs generated from 100% GOx to CNT (wt/wt) ratios revealed that CEC clusters of ∼78 µm 2 localized to the CNT surface. Electrochemical analysis indicates that the enzyme is engaged in direct electron transfer, and biofuel cells generated using GOx CEC-CNT bioanodes were observed to have a peak power density of ∼180 µW cm −2 . These data indicate that the generation of nano-to-micro-sized active enzyme clusters is an attractive option for the design of enzyme-specific biofuel cell powered implantable devices. (paper)

Mass distribution and multiple fragmentation events in high energy cluster-cluster collisions: evidence for a predicted phase transition

International Nuclear Information System (INIS)

Farizon, B.; Farizon, M.; Gaillard, M.J.; Genre, R.; Louc, S.; Martin, J.; Senn, G.; Scheier, P.; Maerk, T.D.

1996-09-01

Fragment size distributions including multiple fragmentation events have been measured for high energy H 25 + cluster ions (60 keV/amu) colliding with a neutral C 60 target. In contrast to earlier collision experiments with a helium target the present studies do not show a U-shaped fragment mass distribution, but a single power-law falloff with increasing fragment mass. This behaviour is similar to what is known for the intermediate regime in nuclear collision physics and thus confirms a recently predicted scaling from nuclear to molecular collisions

CONSTRAINING CLUSTER PHYSICS WITH THE SHAPE OF X-RAY CLUSTERS: COMPARISON OF LOCAL X-RAY CLUSTERS VERSUS ΛCDM CLUSTERS

International Nuclear Information System (INIS)

Lau, Erwin T.; Nagai, Daisuke; Kravtsov, Andrey V.; Vikhlinin, Alexey; Zentner, Andrew R.

2012-01-01

Recent simulations of cluster formation have demonstrated that condensation of baryons into central galaxies during cluster formation can drive the shape of the gas distribution in galaxy clusters significantly rounder out to their virial radius. These simulations generally predict stellar fractions within cluster virial radii that are ∼2-3 times larger than the stellar masses deduced from observations. In this paper, we compare ellipticity profiles of simulated clusters performed with varying input physics (radiative cooling, star formation, and supernova feedback) to the cluster ellipticity profiles derived from Chandra and ROSAT observations, in an effort to constrain the fraction of gas that cools and condenses into the central galaxies within clusters. We find that local relaxed clusters have an average ellipticity of ε = 0.18 ± 0.05 in the radial range of 0.04 ≤ r/r 500 ≤ 1. At larger radii r > 0.1r 500 , the observed ellipticity profiles agree well with the predictions of non-radiative simulations. In contrast, the ellipticity profiles of simulated clusters that include dissipative gas physics deviate significantly from the observed ellipticity profiles at all radii. The dissipative simulations overpredict (underpredict) ellipticity in the inner (outer) regions of galaxy clusters. By comparing simulations with and without dissipative gas physics, we show that gas cooling causes the gas distribution to be more oblate in the central regions, but makes the outer gas distribution more spherical. We find that late-time gas cooling and star formation are responsible for the significantly oblate gas distributions in cluster cores, but the gas shapes outside of cluster cores are set primarily by baryon dissipation at high redshift (z ≥ 2). Our results indicate that the shapes of X-ray emitting gas in galaxy clusters, especially at large radii, can be used to place constraints on cluster gas physics, making it potential probes of the history of baryonic

Descriptive and predictive assessment of enzyme activity and enzyme related processes in biorefinery using IR spectroscopy and chemometrics

DEFF Research Database (Denmark)

Baum, Andreas

the understanding of the structural properties of the extracted pectin. Secondly, enzyme kinetics of biomass converting enzymes was examined in terms of measuring enzyme activity by spectral evolution profiling utilizing FTIR. Chemometric multiway methods were used to analyze the tensor datasets enabling the second......-order calibration advantage (reference Theory of Analytical chemistry). As PAPER 3 illustrates the method is universally applicable without the need of any external standards and was exemplified by performing quantitative enzyme activity determinations for glucose oxidase, pectin lyase and a cellolytic enzyme blend...... (Celluclast 1.5L). In PAPER 4, the concept is extended to quantify enzyme activity of two simultaneously acting enzymes, namely pectin lyase and pectin methyl esterase. By doing so the multiway methods PARAFAC, TUCKER3 and NPLS were compared and evaluated towards accuracy and precision....

FUZZY CLUSTERING BASED BAYESIAN FRAMEWORK TO PREDICT MENTAL HEALTH PROBLEMS AMONG CHILDREN

Directory of Open Access Journals (Sweden)

M R Sumathi

2017-04-01

Full Text Available According to World Health Organization, 10-20% of children and adolescents all over the world are experiencing mental disorders. Correct diagnosis of mental disorders at an early stage improves the quality of life of children and avoids complicated problems. Various expert systems using artificial intelligence techniques have been developed for diagnosing mental disorders like Schizophrenia, Depression, Dementia, etc. This study focuses on predicting basic mental health problems of children, like Attention problem, Anxiety problem, Developmental delay, Attention Deficit Hyperactivity Disorder (ADHD, Pervasive Developmental Disorder(PDD, etc. using the machine learning techniques, Bayesian Networks and Fuzzy clustering. The focus of the article is on learning the Bayesian network structure using a novel Fuzzy Clustering Based Bayesian network structure learning framework. The performance of the proposed framework was compared with the other existing algorithms and the experimental results have shown that the proposed framework performs better than the earlier algorithms.

NRSA enzyme decomposition model data

Data.gov (United States)

U.S. Environmental Protection Agency — Microbial enzyme activities measured at more than 2000 US streams and rivers. These enzyme data were then used to predict organic matter decomposition and microbial...

A novel polyketide biosynthesis gene cluster is involved in fruiting body morphogenesis in the filamentous fungi Sordaria macrospora and Neurospora crassa.

Science.gov (United States)

Nowrousian, Minou

2009-04-01

During fungal fruiting body development, hyphae aggregate to form multicellular structures that protect and disperse the sexual spores. Analysis of microarray data revealed a gene cluster strongly upregulated during fruiting body development in the ascomycete Sordaria macrospora. Real time PCR analysis showed that the genes from the orthologous cluster in Neurospora crassa are also upregulated during development. The cluster encodes putative polyketide biosynthesis enzymes, including a reducing polyketide synthase. Analysis of knockout strains of a predicted dehydrogenase gene from the cluster showed that mutants in N. crassa and S. macrospora are delayed in fruiting body formation. In addition to the upregulated cluster, the N. crassa genome comprises another cluster containing a polyketide synthase gene, and five additional reducing polyketide synthase (rpks) genes that are not part of clusters. To study the role of these genes in sexual development, expression of the predicted rpks genes in S. macrospora (five genes) and N. crassa (six genes) was analyzed; all but one are upregulated during sexual development. Analysis of knockout strains for the N. crassa rpks genes showed that one of them is essential for fruiting body formation. These data indicate that polyketides produced by RPKSs are involved in sexual development in filamentous ascomycetes.

Multigene families encode the major enzymes of antioxidant metabolism in Eucalyptus grandis L

Directory of Open Access Journals (Sweden)

Felipe Karam Teixeira

2005-01-01

Full Text Available Antioxidant metabolism protects cells from oxidative damage caused by reactive oxygen species (ROS. In plants, several enzymes act jointly to maintain redox homeostasis. Moreover, isoform diversity contributes to the fine tuning necessary for plant responses to both exogenous and endogenous signals influencing antioxidant metabolism. This study aimed to provide a comprehensive view of the major classes of antioxidant enzymes in the woody species Eucalyptus grandis. A careful survey of the FORESTs data bank revealed 36 clusters as encoding antioxidant enzymes: six clusters encoding ascorbate peroxidase (APx isozymes, three catalase (CAT proteins, three dehydroascorbate reductase (DHAR, two glutathione reductase (GR isozymes, four monodehydroascorbate reductase (MDHAR, six phospholipid hydroperoxide glutathione peroxidases (PhGPx, and 12 encoding superoxide dismutases (SOD isozymes. Phylogenetic analysis demonstrated that all clusters (identified herein grouped with previously characterized antioxidant enzymes, corroborating the analysis performed. With respect to enzymes involved in the ascorbate-glutathione cycle, both cytosolic and chloroplastic isoforms were putatively identified. These sequences were widely distributed among the different ESTs libraries indicating a broad gene expression pattern. Overall, the data indicate the importance of antioxidant metabolism in eucalyptus.

Random-walk enzymes

Science.gov (United States)

Mak, Chi H.; Pham, Phuong; Afif, Samir A.; Goodman, Myron F.

2015-09-01

Enzymes that rely on random walk to search for substrate targets in a heterogeneously dispersed medium can leave behind complex spatial profiles of their catalyzed conversions. The catalytic signatures of these random-walk enzymes are the result of two coupled stochastic processes: scanning and catalysis. Here we develop analytical models to understand the conversion profiles produced by these enzymes, comparing an intrusive model, in which scanning and catalysis are tightly coupled, against a loosely coupled passive model. Diagrammatic theory and path-integral solutions of these models revealed clearly distinct predictions. Comparison to experimental data from catalyzed deaminations deposited on single-stranded DNA by the enzyme activation-induced deoxycytidine deaminase (AID) demonstrates that catalysis and diffusion are strongly intertwined, where the chemical conversions give rise to new stochastic trajectories that were absent if the substrate DNA was homogeneous. The C →U deamination profiles in both analytical predictions and experiments exhibit a strong contextual dependence, where the conversion rate of each target site is strongly contingent on the identities of other surrounding targets, with the intrusive model showing an excellent fit to the data. These methods can be applied to deduce sequence-dependent catalytic signatures of other DNA modification enzymes, with potential applications to cancer, gene regulation, and epigenetics.

Exploring functionally related enzymes using radially distributed properties of active sites around the reacting points of bound ligands

Directory of Open Access Journals (Sweden)

Ueno Keisuke

2012-04-01

Full Text Available Abstract Background Structural genomics approaches, particularly those solving the 3D structures of many proteins with unknown functions, have increased the desire for structure-based function predictions. However, prediction of enzyme function is difficult because one member of a superfamily may catalyze a different reaction than other members, whereas members of different superfamilies can catalyze the same reaction. In addition, conformational changes, mutations or the absence of a particular catalytic residue can prevent inference of the mechanism by which catalytic residues stabilize and promote the elementary reaction. A major hurdle for alignment-based methods for prediction of function is the absence (despite its importance of a measure of similarity of the physicochemical properties of catalytic sites. To solve this problem, the physicochemical features radially distributed around catalytic sites should be considered in addition to structural and sequence similarities. Results We showed that radial distribution functions (RDFs, which are associated with the local structural and physicochemical properties of catalytic active sites, are capable of clustering oxidoreductases and transferases by function. The catalytic sites of these enzymes were also characterized using the RDFs. The RDFs provided a measure of the similarity among the catalytic sites, detecting conformational changes caused by mutation of catalytic residues. Furthermore, the RDFs reinforced the classification of enzyme functions based on conventional sequence and structural alignments. Conclusions Our results demonstrate that the application of RDFs provides advantages in the functional classification of enzymes by providing information about catalytic sites.

Predictive coupled-cluster isomer orderings for some Si{sub n}C{sub m} (m, n ≤ 12) clusters: A pragmatic comparison between DFT and complete basis limit coupled-cluster benchmarks

Energy Technology Data Exchange (ETDEWEB)

Byrd, Jason N., E-mail: byrd.jason@ensco.com [Quantum Theory Project, University of Florida, Gainesville, Florida 32611 (United States); ENSCO, Inc., 4849 North Wickham Road, Melbourne, Florida 32940 (United States); Lutz, Jesse J., E-mail: jesse.lutz.ctr@afit.edu; Jin, Yifan; Ranasinghe, Duminda S.; Perera, Ajith; Bartlett, Rodney J., E-mail: rodbartl@ufl.edu [Quantum Theory Project, University of Florida, Gainesville, Florida 32611 (United States); Montgomery, John A. [Department of Physics, University of Connecticut, Storrs, Connecticut 06269 (United States); Duan, Xiaofeng F. [Air Force Institute of Technology, Wright-Patterson Air Force Base, Ohio 45433 (United States); Air Force Research Laboratory DoD Supercomputing Resource Center, Wright-Patterson Air Force Base, Ohio 45433 (United States); Burggraf, Larry W. [Air Force Institute of Technology, Wright-Patterson Air Force Base, Ohio 45433 (United States); Sanders, Beverly A. [Quantum Theory Project, University of Florida, Gainesville, Florida 32611 (United States); Department of Computer and Information Science and Engineering, University of Florida, Gainesville, Florida 32611 (United States)

2016-07-14

The accurate determination of the preferred Si{sub 12}C{sub 12} isomer is important to guide experimental efforts directed towards synthesizing SiC nano-wires and related polymer structures which are anticipated to be highly efficient exciton materials for the opto-electronic devices. In order to definitively identify preferred isomeric structures for silicon carbon nano-clusters, highly accurate geometries, energies, and harmonic zero point energies have been computed using coupled-cluster theory with systematic extrapolation to the complete basis limit for set of silicon carbon clusters ranging in size from SiC{sub 3} to Si{sub 12}C{sub 12}. It is found that post-MBPT(2) correlation energy plays a significant role in obtaining converged relative isomer energies, suggesting that predictions using low rung density functional methods will not have adequate accuracy. Utilizing the best composite coupled-cluster energy that is still computationally feasible, entailing a 3-4 SCF and coupled-cluster theory with singles and doubles extrapolation with triple-ζ (T) correlation, the closo Si{sub 12}C{sub 12} isomer is identified to be the preferred isomer in the support of previous calculations [X. F. Duan and L. W. Burggraf, J. Chem. Phys. 142, 034303 (2015)]. Additionally we have investigated more pragmatic approaches to obtaining accurate silicon carbide isomer energies, including the use of frozen natural orbital coupled-cluster theory and several rungs of standard and double-hybrid density functional theory. Frozen natural orbitals as a way to compute post-MBPT(2) correlation energy are found to be an excellent balance between efficiency and accuracy.

Efficacy of GPS cluster analysis for predicting carnivory sites of a wide-ranging omnivore: the American black bear

Science.gov (United States)

Kindschuh, Sarah R.; Cain, James W.; Daniel, David; Peyton, Mark A.

2016-01-01

The capacity to describe and quantify predation by large carnivores expanded considerably with the advent of GPS technology. Analyzing clusters of GPS locations formed by carnivores facilitates the detection of predation events by identifying characteristics which distinguish predation sites. We present a performance assessment of GPS cluster analysis as applied to the predation and scavenging of an omnivore, the American black bear (Ursus americanus), on ungulate prey and carrion. Through field investigations of 6854 GPS locations from 24 individual bears, we identified 54 sites where black bears formed a cluster of locations while predating or scavenging elk (Cervus elaphus), mule deer (Odocoileus hemionus), or cattle (Bos spp.). We developed models for three data sets to predict whether a GPS cluster was formed at a carnivory site vs. a non-carnivory site (e.g., bed sites or non-ungulate foraging sites). Two full-season data sets contained GPS locations logged at either 3-h or 30-min intervals from April to November, and a third data set contained 30-min interval data from April through July corresponding to the calving period for elk. Longer fix intervals resulted in the detection of fewer carnivory sites. Clusters were more likely to be carnivory sites if they occurred in open or edge habitats, if they occurred in the early season, if the mean distance between all pairs of GPS locations within the cluster was less, and if the cluster endured for a longer period of time. Clusters were less likely to be carnivory sites if they were initiated in the morning or night compared to the day. The top models for each data set performed well and successfully predicted 71–96% of field-verified carnivory events, 55–75% of non–carnivory events, and 58–76% of clusters overall. Refinement of this method will benefit from further application across species and ecological systems.

Operational Numerical Weather Prediction systems based on Linux cluster architectures

International Nuclear Information System (INIS)

Pasqui, M.; Baldi, M.; Gozzini, B.; Maracchi, G.; Giuliani, G.; Montagnani, S.

2005-01-01

The progress in weather forecast and atmospheric science has been always closely linked to the improvement of computing technology. In order to have more accurate weather forecasts and climate predictions, more powerful computing resources are needed, in addition to more complex and better-performing numerical models. To overcome such a large computing request, powerful workstations or massive parallel systems have been used. In the last few years, parallel architectures, based on the Linux operating system, have been introduced and became popular, representing real high performance-low cost systems. In this work the Linux cluster experience achieved at the Laboratory far Meteorology and Environmental Analysis (LaMMA-CNR-IBIMET) is described and tips and performances analysed

A hybrid clustering and classification approach for predicting crash injury severity on rural roads.

Science.gov (United States)

Hasheminejad, Seyed Hessam-Allah; Zahedi, Mohsen; Hasheminejad, Seyed Mohammad Hossein

2018-03-01

As a threat for transportation system, traffic crashes have a wide range of social consequences for governments. Traffic crashes are increasing in developing countries and Iran as a developing country is not immune from this risk. There are several researches in the literature to predict traffic crash severity based on artificial neural networks (ANNs), support vector machines and decision trees. This paper attempts to investigate the crash injury severity of rural roads by using a hybrid clustering and classification approach to compare the performance of classification algorithms before and after applying the clustering. In this paper, a novel rule-based genetic algorithm (GA) is proposed to predict crash injury severity, which is evaluated by performance criteria in comparison with classification algorithms like ANN. The results obtained from analysis of 13,673 crashes (5600 property damage, 778 fatal crashes, 4690 slight injuries and 2605 severe injuries) on rural roads in Tehran Province of Iran during 2011-2013 revealed that the proposed GA method outperforms other classification algorithms based on classification metrics like precision (86%), recall (88%) and accuracy (87%). Moreover, the proposed GA method has the highest level of interpretation, is easy to understand and provides feedback to analysts.

BrEPS: a flexible and automatic protocol to compute enzyme-specific sequence profiles for functional annotation

Directory of Open Access Journals (Sweden)

Schomburg D

2010-12-01

Full Text Available Abstract Background Models for the simulation of metabolic networks require the accurate prediction of enzyme function. Based on a genomic sequence, enzymatic functions of gene products are today mainly predicted by sequence database searching and operon analysis. Other methods can support these techniques: We have developed an automatic method "BrEPS" that creates highly specific sequence patterns for the functional annotation of enzymes. Results The enzymes in the UniprotKB are identified and their sequences compared against each other with BLAST. The enzymes are then clustered into a number of trees, where each tree node is associated with a set of EC-numbers. The enzyme sequences in the tree nodes are aligned with ClustalW. The conserved columns of the resulting multiple alignments are used to construct sequence patterns. In the last step, we verify the quality of the patterns by computing their specificity. Patterns with low specificity are omitted and recomputed further down in the tree. The final high-quality patterns can be used for functional annotation. We ran our protocol on a recent Swiss-Prot release and show statistics, as well as a comparison to PRIAM, a probabilistic method that is also specialized on the functional annotation of enzymes. We determine the amount of true positive annotations for five common microorganisms with data from BRENDA and AMENDA serving as standard of truth. BrEPS is almost on par with PRIAM, a fact which we discuss in the context of five manually investigated cases. Conclusions Our protocol computes highly specific sequence patterns that can be used to support the functional annotation of enzymes. The main advantages of our method are that it is automatic and unsupervised, and quite fast once the patterns are evaluated. The results show that BrEPS can be a valuable addition to the reconstruction of metabolic networks.

Non-homologous isofunctional enzymes: a systematic analysis of alternative solutions in enzyme evolution.

Science.gov (United States)

Omelchenko, Marina V; Galperin, Michael Y; Wolf, Yuri I; Koonin, Eugene V

2010-04-30

Evolutionarily unrelated proteins that catalyze the same biochemical reactions are often referred to as analogous - as opposed to homologous - enzymes. The existence of numerous alternative, non-homologous enzyme isoforms presents an interesting evolutionary problem; it also complicates genome-based reconstruction of the metabolic pathways in a variety of organisms. In 1998, a systematic search for analogous enzymes resulted in the identification of 105 Enzyme Commission (EC) numbers that included two or more proteins without detectable sequence similarity to each other, including 34 EC nodes where proteins were known (or predicted) to have distinct structural folds, indicating independent evolutionary origins. In the past 12 years, many putative non-homologous isofunctional enzymes were identified in newly sequenced genomes. In addition, efforts in structural genomics resulted in a vastly improved structural coverage of proteomes, providing for definitive assessment of (non)homologous relationships between proteins. We report the results of a comprehensive search for non-homologous isofunctional enzymes (NISE) that yielded 185 EC nodes with two or more experimentally characterized - or predicted - structurally unrelated proteins. Of these NISE sets, only 74 were from the original 1998 list. Structural assignments of the NISE show over-representation of proteins with the TIM barrel fold and the nucleotide-binding Rossmann fold. From the functional perspective, the set of NISE is enriched in hydrolases, particularly carbohydrate hydrolases, and in enzymes involved in defense against oxidative stress. These results indicate that at least some of the non-homologous isofunctional enzymes were recruited relatively recently from enzyme families that are active against related substrates and are sufficiently flexible to accommodate changes in substrate specificity.

Short-Term Predictive Validity of Cluster Analytic and Dimensional Classification of Child Behavioral Adjustment in School

Science.gov (United States)

Kim, Sangwon; Kamphaus, Randy W.; Baker, Jean A.

2006-01-01

A constructive debate over the classification of child psychopathology can be stimulated by investigating the validity of different classification approaches. We examined and compared the short-term predictive validity of cluster analytic and dimensional classifications of child behavioral adjustment in school using the Behavior Assessment System…

Prediction of drop time and impact velocity of rod cluster control assembly

International Nuclear Information System (INIS)

Choi, Kee Sung; Yim, Jeong Sik; Kim, Il Kon; Kim, Kyu Tae

1992-01-01

This paper deals with the drop modelling of rod cluster control assembly(RCCA) and the prediction of drop time and impact velocity of RCCA at scram event. On the scram, RCCA, dropping into the guide thimble of fuel assembly by the gravity, is subject to retarding forces such as hydraulic resistance, mechanical friction and buoyancy. Considering these retarding forces RCCA dynamic equation is derived and computerized it to solve the equation in conjunction with fluid equation which is coupled with the motion of the RCCA. Because the equation is nonlinear, coupled with fluid equations, the program is written in FORTRAN using numerical method in order to calculate the drop distance and velocity with time increment. To verify the program, its results are compared with those of other fuel vendors. Predicting identical tendency as other fuel vendors and the deviation is insignificant in values this program is expected to be used for predicting the drop time and impact velocity of RCCA when the parameters affecting the control rod drop time and impact velocity changes are occurred

Effect of mitochondrial complex I inhibition on Fe-S cluster protein activity

Energy Technology Data Exchange (ETDEWEB)

Mena, Natalia P. [Department of Biology, Faculty of Sciences, Universidad de Chile, Las Palmeras 3425, Santiago (Chile); Millennium Institute of Cell Dynamics and Biotechnology, Santiago (Chile); Bulteau, Anne Laure [UPMC Univ Paris 06, UMRS 975 - UMR 7725, Centre de Recherche en Neurosciences, ICM, Therapeutique Experimentale de la Neurodegenerescence, Hopital de la Salpetriere, F-75005 Paris (France); Inserm, U 975, Centre de Recherche en Neurosciences, ICM, Therapeutique Experimentale de la Neurodegenerescence, Hopital de la Salpetriere, F-75005 Paris (France); CNRS, UMR 7225, Centre de Recherche en Neurosciences, ICM, Therapeutique Experimentale de la Neurodegenerescence, Hopital de la Salpetriere, F-75005 Paris (France); ICM, Therapeutique Experimentale de la Neurodegenerescence, Hopital de la Salpetriere, Paris 75013 (France); Salazar, Julio [Millennium Institute of Cell Dynamics and Biotechnology, Santiago (Chile); Hirsch, Etienne C. [UPMC Univ Paris 06, UMRS 975 - UMR 7725, Centre de Recherche en Neurosciences, ICM, Therapeutique Experimentale de la Neurodegenerescence, Hopital de la Salpetriere, F-75005 Paris (France); Inserm, U 975, Centre de Recherche en Neurosciences, ICM, Therapeutique Experimentale de la Neurodegenerescence, Hopital de la Salpetriere, F-75005 Paris (France); CNRS, UMR 7225, Centre de Recherche en Neurosciences, ICM, Therapeutique Experimentale de la Neurodegenerescence, Hopital de la Salpetriere, F-75005 Paris (France); ICM, Therapeutique Experimentale de la Neurodegenerescence, Hopital de la Salpetriere, Paris 75013 (France); Nunez, Marco T., E-mail: mnunez@uchile.cl [Department of Biology, Faculty of Sciences, Universidad de Chile, Las Palmeras 3425, Santiago (Chile); Millennium Institute of Cell Dynamics and Biotechnology, Santiago (Chile)

2011-06-03

Highlights: {yields} Mitochondrial complex I inhibition resulted in decreased activity of Fe-S containing enzymes mitochondrial aconitase and cytoplasmic aconitase and xanthine oxidase. {yields} Complex I inhibition resulted in the loss of Fe-S clusters in cytoplasmic aconitase and of glutamine phosphoribosyl pyrophosphate amidotransferase. {yields} Consistent with loss of cytoplasmic aconitase activity, an increase in iron regulatory protein 1 activity was found. {yields} Complex I inhibition resulted in an increase in the labile cytoplasmic iron pool. -- Abstract: Iron-sulfur (Fe-S) clusters are small inorganic cofactors formed by tetrahedral coordination of iron atoms with sulfur groups. Present in numerous proteins, these clusters are involved in key biological processes such as electron transfer, metabolic and regulatory processes, DNA synthesis and repair and protein structure stabilization. Fe-S clusters are synthesized mainly in the mitochondrion, where they are directly incorporated into mitochondrial Fe-S cluster-containing proteins or exported for cytoplasmic and nuclear cluster-protein assembly. In this study, we tested the hypothesis that inhibition of mitochondrial complex I by rotenone decreases Fe-S cluster synthesis and cluster content and activity of Fe-S cluster-containing enzymes. Inhibition of complex I resulted in decreased activity of three Fe-S cluster-containing enzymes: mitochondrial and cytosolic aconitases and xanthine oxidase. In addition, the Fe-S cluster content of glutamine phosphoribosyl pyrophosphate amidotransferase and mitochondrial aconitase was dramatically decreased. The reduction in cytosolic aconitase activity was associated with an increase in iron regulatory protein (IRP) mRNA binding activity and with an increase in the cytoplasmic labile iron pool. Since IRP activity post-transcriptionally regulates the expression of iron import proteins, Fe-S cluster inhibition may result in a false iron deficiency signal. Given that

Effect of mitochondrial complex I inhibition on Fe-S cluster protein activity

International Nuclear Information System (INIS)

Mena, Natalia P.; Bulteau, Anne Laure; Salazar, Julio; Hirsch, Etienne C.; Nunez, Marco T.

2011-01-01

Highlights: → Mitochondrial complex I inhibition resulted in decreased activity of Fe-S containing enzymes mitochondrial aconitase and cytoplasmic aconitase and xanthine oxidase. → Complex I inhibition resulted in the loss of Fe-S clusters in cytoplasmic aconitase and of glutamine phosphoribosyl pyrophosphate amidotransferase. → Consistent with loss of cytoplasmic aconitase activity, an increase in iron regulatory protein 1 activity was found. → Complex I inhibition resulted in an increase in the labile cytoplasmic iron pool. -- Abstract: Iron-sulfur (Fe-S) clusters are small inorganic cofactors formed by tetrahedral coordination of iron atoms with sulfur groups. Present in numerous proteins, these clusters are involved in key biological processes such as electron transfer, metabolic and regulatory processes, DNA synthesis and repair and protein structure stabilization. Fe-S clusters are synthesized mainly in the mitochondrion, where they are directly incorporated into mitochondrial Fe-S cluster-containing proteins or exported for cytoplasmic and nuclear cluster-protein assembly. In this study, we tested the hypothesis that inhibition of mitochondrial complex I by rotenone decreases Fe-S cluster synthesis and cluster content and activity of Fe-S cluster-containing enzymes. Inhibition of complex I resulted in decreased activity of three Fe-S cluster-containing enzymes: mitochondrial and cytosolic aconitases and xanthine oxidase. In addition, the Fe-S cluster content of glutamine phosphoribosyl pyrophosphate amidotransferase and mitochondrial aconitase was dramatically decreased. The reduction in cytosolic aconitase activity was associated with an increase in iron regulatory protein (IRP) mRNA binding activity and with an increase in the cytoplasmic labile iron pool. Since IRP activity post-transcriptionally regulates the expression of iron import proteins, Fe-S cluster inhibition may result in a false iron deficiency signal. Given that inhibition of complex

[Predicting Incidence of Hepatitis E in Chinausing Fuzzy Time Series Based on Fuzzy C-Means Clustering Analysis].

Science.gov (United States)

Luo, Yi; Zhang, Tao; Li, Xiao-song

2016-05-01

To explore the application of fuzzy time series model based on fuzzy c-means clustering in forecasting monthly incidence of Hepatitis E in mainland China. Apredictive model (fuzzy time series method based on fuzzy c-means clustering) was developed using Hepatitis E incidence data in mainland China between January 2004 and July 2014. The incidence datafrom August 2014 to November 2014 were used to test the fitness of the predictive model. The forecasting results were compared with those resulted from traditional fuzzy time series models. The fuzzy time series model based on fuzzy c-means clustering had 0.001 1 mean squared error (MSE) of fitting and 6.977 5 x 10⁻⁴ MSE of forecasting, compared with 0.0017 and 0.0014 from the traditional forecasting model. The results indicate that the fuzzy time series model based on fuzzy c-means clustering has a better performance in forecasting incidence of Hepatitis E.

The surface science of enzymes

DEFF Research Database (Denmark)

Rod, Thomas Holm; Nørskov, Jens Kehlet

2002-01-01

One of the largest challenges to science in the coming years is to find the relation between enzyme structure and function. Can we predict which reactions an enzyme catalyzes from knowledge of its structure-or from its amino acid sequence? Can we use that knowledge to modify enzyme function......? To solve these problems we must understand in some detail how enzymes interact with reactants from its surroundings. These interactions take place at the surface of the enzyme and the question of enzyme function can be viewed as the surface science of enzymes. In this article we discuss how to describe...... catalysis by enzymes, and in particular the analogies between enzyme catalyzed reactions and surface catalyzed reactions. We do this by discussing two concrete examples of reactions catalyzed both in nature (by enzymes) and in industrial reactors (by inorganic materials), and show that although analogies...

Clustering-based classification of road traffic accidents using hierarchical clustering and artificial neural networks.

Science.gov (United States)

Taamneh, Madhar; Taamneh, Salah; Alkheder, Sharaf

2017-09-01

Artificial neural networks (ANNs) have been widely used in predicting the severity of road traffic crashes. All available information about previously occurred accidents is typically used for building a single prediction model (i.e., classifier). Too little attention has been paid to the differences between these accidents, leading, in most cases, to build less accurate predictors. Hierarchical clustering is a well-known clustering method that seeks to group data by creating a hierarchy of clusters. Using hierarchical clustering and ANNs, a clustering-based classification approach for predicting the injury severity of road traffic accidents was proposed. About 6000 road accidents occurred over a six-year period from 2008 to 2013 in Abu Dhabi were used throughout this study. In order to reduce the amount of variation in data, hierarchical clustering was applied on the data set to organize it into six different forms, each with different number of clusters (i.e., clusters from 1 to 6). Two ANN models were subsequently built for each cluster of accidents in each generated form. The first model was built and validated using all accidents (training set), whereas only 66% of the accidents were used to build the second model, and the remaining 34% were used to test it (percentage split). Finally, the weighted average accuracy was computed for each type of models in each from of data. The results show that when testing the models using the training set, clustering prior to classification achieves (11%-16%) more accuracy than without using clustering, while the percentage split achieves (2%-5%) more accuracy. The results also suggest that partitioning the accidents into six clusters achieves the best accuracy if both types of models are taken into account.

The clustering of z > 7 galaxies: predictions from the BLUETIDES simulation

Science.gov (United States)

Bhowmick, Aklant K.; Di Matteo, Tiziana; Feng, Yu; Lanusse, Francois

2018-03-01

We study the clustering of the highest z galaxies (from ˜0.1 to a few tens Mpc scales) using the BLUETIDES simulation and compare it to current observational constraints from Hubble legacy and Hyper Suprime Cam (HSC) fields (at z = 6-7.2). With a box length of 400 Mpc h-1 on each side and 0.7 trillion particles, BLUETIDES is the largest volume high-resolution cosmological hydrodynamic simulation to date ideally suited for studies of high-z galaxies. We find that galaxies with magnitude mUV < 27.7 have a bias (bg) of 8.1 ± 1.2 at z = 8, and typical halo masses MH ≳ 6 × 1010 M⊙. Given the redshift evolution between z = 8 and z = 10 [bg ∝ (1 + z)1.6], our inferred values of the bias and halo masses are consistent with measured angular clustering at z ˜ 6.8 from these brighter samples. The bias of fainter galaxies (in the Hubble legacy field at H160 ≲ 29.5) is 5.9 ± 0.9 at z = 8 corresponding to halo masses MH ≳ 1010 M⊙. We investigate directly the 1-halo term in the clustering and show that it dominates on scales r ≲ 0.1 Mpc h-1 (Θ ≲ 3 arcsec) with non-linear effect at transition scales between the one-halo and two-halo term affecting scales 0.1 Mpc h-1≲ r ≲ 20 Mpc h-1 (3 arcsec ≲ Θ ≲ 90 arcsec). Current clustering measurements probe down to the scales in the transition between one-halo and two-halo regime where non-linear effects are important. The amplitude of the one-halo term implies that occupation numbers for satellites in BLUETIDES are somewhat higher than standard halo occupation distributions adopted in these analyses (which predict amplitudes in the one-halo regime suppressed by a factor 2-3). That possibly implies a higher number of galaxies detected by JWST (at small scales and even fainter magnitudes) observing these fields.

Statistical Issues in Galaxy Cluster Cosmology

Science.gov (United States)

Mantz, Adam

2013-01-01

The number and growth of massive galaxy clusters are sensitive probes of cosmological structure formation. Surveys at various wavelengths can detect clusters to high redshift, but the fact that cluster mass is not directly observable complicates matters, requiring us to simultaneously constrain scaling relations of observable signals with mass. The problem can be cast as one of regression, in which the data set is truncated, the (cosmology-dependent) underlying population must be modeled, and strong, complex correlations between measurements often exist. Simulations of cosmological structure formation provide a robust prediction for the number of clusters in the Universe as a function of mass and redshift (the mass function), but they cannot reliably predict the observables used to detect clusters in sky surveys (e.g. X-ray luminosity). Consequently, observers must constrain observable-mass scaling relations using additional data, and use the scaling relation model in conjunction with the mass function to predict the number of clusters as a function of redshift and luminosity.

ClubSub-P: Cluster-Based Subcellular Localization Prediction for Gram-Negative Bacteria and Archaea

Science.gov (United States)

Paramasivam, Nagarajan; Linke, Dirk

2011-01-01

The subcellular localization (SCL) of proteins provides important clues to their function in a cell. In our efforts to predict useful vaccine targets against Gram-negative bacteria, we noticed that misannotated start codons frequently lead to wrongly assigned SCLs. This and other problems in SCL prediction, such as the relatively high false-positive and false-negative rates of some tools, can be avoided by applying multiple prediction tools to groups of homologous proteins. Here we present ClubSub-P, an online database that combines existing SCL prediction tools into a consensus pipeline from more than 600 proteomes of fully sequenced microorganisms. On top of the consensus prediction at the level of single sequences, the tool uses clusters of homologous proteins from Gram-negative bacteria and from Archaea to eliminate false-positive and false-negative predictions. ClubSub-P can assign the SCL of proteins from Gram-negative bacteria and Archaea with high precision. The database is searchable, and can easily be expanded using either new bacterial genomes or new prediction tools as they become available. This will further improve the performance of the SCL prediction, as well as the detection of misannotated start codons and other annotation errors. ClubSub-P is available online at http://toolkit.tuebingen.mpg.de/clubsubp/ PMID:22073040

Galectin-4 and small intestinal brush border enzymes form clusters

DEFF Research Database (Denmark)

Danielsen, E M; van Deurs, B

1997-01-01

that galectin-4 is indeed an intestinal brush border protein; we also localized galectin-4 throughout the cell, mainly associated with membraneous structures, including small vesicles, and to the rootlets of microvillar actin filaments. This was confirmed by subcellular fractionation, showing about half...... by a nonclassical pathway, and the brush border enzymes represent a novel class of natural ligands for a member of the galectin family. Newly synthesized galectin-4 is rapidly "trapped" by association with intracellular structures prior to its apical secretion, but once externalized, association with brush border......Detergent-insoluble complexes prepared from pig small intestine are highly enriched in several transmembrane brush border enzymes including aminopeptidase N and sucrase-isomaltase, indicating that they reside in a glycolipid-rich environment in vivo. In the present work galectin-4, an animal lectin...

The Enzyme Function Initiative†

Science.gov (United States)

Gerlt, John A.; Allen, Karen N.; Almo, Steven C.; Armstrong, Richard N.; Babbitt, Patricia C.; Cronan, John E.; Dunaway-Mariano, Debra; Imker, Heidi J.; Jacobson, Matthew P.; Minor, Wladek; Poulter, C. Dale; Raushel, Frank M.; Sali, Andrej; Shoichet, Brian K.; Sweedler, Jonathan V.

2011-01-01

The Enzyme Function Initiative (EFI) was recently established to address the challenge of assigning reliable functions to enzymes discovered in bacterial genome projects; in this Current Topic we review the structure and operations of the EFI. The EFI includes the Superfamily/Genome, Protein, Structure, Computation, and Data/Dissemination Cores that provide the infrastructure for reliably predicting the in vitro functions of unknown enzymes. The initial targets for functional assignment are selected from five functionally diverse superfamilies (amidohydrolase, enolase, glutathione transferase, haloalkanoic acid dehalogenase, and isoprenoid synthase), with five superfamily-specific Bridging Projects experimentally testing the predicted in vitro enzymatic activities. The EFI also includes the Microbiology Core that evaluates the in vivo context of in vitro enzymatic functions and confirms the functional predictions of the EFI. The deliverables of the EFI to the scientific community include: 1) development of a large-scale, multidisciplinary sequence/structure-based strategy for functional assignment of unknown enzymes discovered in genome projects (target selection, protein production, structure determination, computation, experimental enzymology, microbiology, and structure-based annotation); 2) dissemination of the strategy to the community via publications, collaborations, workshops, and symposia; 3) computational and bioinformatic tools for using the strategy; 4) provision of experimental protocols and/or reagents for enzyme production and characterization; and 5) dissemination of data via the EFI’s website, enzymefunction.org. The realization of multidisciplinary strategies for functional assignment will begin to define the full metabolic diversity that exists in nature and will impact basic biochemical and evolutionary understanding, as well as a wide range of applications of central importance to industrial, medicinal and pharmaceutical efforts. PMID

The Enzyme Function Initiative.

Science.gov (United States)

Gerlt, John A; Allen, Karen N; Almo, Steven C; Armstrong, Richard N; Babbitt, Patricia C; Cronan, John E; Dunaway-Mariano, Debra; Imker, Heidi J; Jacobson, Matthew P; Minor, Wladek; Poulter, C Dale; Raushel, Frank M; Sali, Andrej; Shoichet, Brian K; Sweedler, Jonathan V

2011-11-22

The Enzyme Function Initiative (EFI) was recently established to address the challenge of assigning reliable functions to enzymes discovered in bacterial genome projects; in this Current Topic, we review the structure and operations of the EFI. The EFI includes the Superfamily/Genome, Protein, Structure, Computation, and Data/Dissemination Cores that provide the infrastructure for reliably predicting the in vitro functions of unknown enzymes. The initial targets for functional assignment are selected from five functionally diverse superfamilies (amidohydrolase, enolase, glutathione transferase, haloalkanoic acid dehalogenase, and isoprenoid synthase), with five superfamily specific Bridging Projects experimentally testing the predicted in vitro enzymatic activities. The EFI also includes the Microbiology Core that evaluates the in vivo context of in vitro enzymatic functions and confirms the functional predictions of the EFI. The deliverables of the EFI to the scientific community include (1) development of a large-scale, multidisciplinary sequence/structure-based strategy for functional assignment of unknown enzymes discovered in genome projects (target selection, protein production, structure determination, computation, experimental enzymology, microbiology, and structure-based annotation), (2) dissemination of the strategy to the community via publications, collaborations, workshops, and symposia, (3) computational and bioinformatic tools for using the strategy, (4) provision of experimental protocols and/or reagents for enzyme production and characterization, and (5) dissemination of data via the EFI's Website, http://enzymefunction.org. The realization of multidisciplinary strategies for functional assignment will begin to define the full metabolic diversity that exists in nature and will impact basic biochemical and evolutionary understanding, as well as a wide range of applications of central importance to industrial, medicinal, and pharmaceutical efforts. �

A Knowledge-Based System for Display and Prediction of O-Glycosylation Network Behaviour in Response to Enzyme Knockouts.

Directory of Open Access Journals (Sweden)

Andrew G McDonald

2016-04-01

Full Text Available O-linked glycosylation is an important post-translational modification of mucin-type protein, changes to which are important biomarkers of cancer. For this study of the enzymes of O-glycosylation, we developed a shorthand notation for representing GalNAc-linked oligosaccharides, a method for their graphical interpretation, and a pattern-matching algorithm that generates networks of enzyme-catalysed reactions. Software for generating glycans from the enzyme activities is presented, and is also available online. The degree distributions of the resulting enzyme-reaction networks were found to be Poisson in nature. Simple graph-theoretic measures were used to characterise the resulting reaction networks. From a study of in-silico single-enzyme knockouts of each of 25 enzymes known to be involved in mucin O-glycan biosynthesis, six of them, β-1,4-galactosyltransferase (β4Gal-T4, four glycosyltransferases and one sulfotransferase, play the dominant role in determining O-glycan heterogeneity. In the absence of β4Gal-T4, all Lewis X, sialyl-Lewis X, Lewis Y and Sda/Cad glycoforms were eliminated, in contrast to knockouts of the N-acetylglucosaminyltransferases, which did not affect the relative abundances of O-glycans expressing these epitopes. A set of 244 experimentally determined mucin-type O-glycans obtained from the literature was used to validate the method, which was able to predict up to 98% of the most common structures obtained from human and engineered CHO cell glycoforms.

Redox Behavior of the S-Adenosylmethionine (SAM)-Binding Fe-S Cluster in Methylthiotransferase RimO, toward Understanding Dual SAM Activity.

Science.gov (United States)

Molle, Thibaut; Moreau, Yohann; Clemancey, Martin; Forouhar, Farhad; Ravanat, Jean-Luc; Duraffourg, Nicolas; Fourmond, Vincent; Latour, Jean-Marc; Gambarelli, Serge; Mulliez, Etienne; Atta, Mohamed

2016-10-18

RimO, a radical-S-adenosylmethionine (SAM) enzyme, catalyzes the specific C 3 methylthiolation of the D89 residue in the ribosomal S 12 protein. Two intact iron-sulfur clusters and two SAM cofactors both are required for catalysis. By using electron paramagnetic resonance, Mössbauer spectroscopies, and site-directed mutagenesis, we show how two SAM molecules sequentially bind to the unique iron site of the radical-SAM cluster for two distinct chemical reactions in RimO. Our data establish that the two SAM molecules bind the radical-SAM cluster to the unique iron site, and spectroscopic evidence obtained under strongly reducing conditions supports a mechanism in which the first molecule of SAM causes the reoxidation of the reduced radical-SAM cluster, impeding reductive cleavage of SAM to occur and allowing SAM to methylate a HS - ligand bound to the additional cluster. Furthermore, by using density functional theory-based methods, we provide a description of the reaction mechanism that predicts the attack of the carbon radical substrate on the methylthio group attached to the additional [4Fe-4S] cluster.

Transformations of the FeS Clusters of the Methylthiotransferases MiaB and RimO, Detected by Direct Electrochemistry

Science.gov (United States)

2016-01-01

The methylthiotransferases (MTTases) represent a subfamily of the S-adenosylmethionine (AdoMet) radical superfamily of enzymes that catalyze the attachment of a methylthioether (-SCH3) moiety on unactivated carbon centers. These enzymes contain two [4Fe-4S] clusters, one of which participates in the reductive fragmentation of AdoMet to generate a 5′-deoxyadenosyl 5′-radical and the other of which, termed the auxiliary cluster, is believed to play a central role in constructing the methylthio group and attaching it to the substrate. Because the redox properties of the bound cofactors within the AdoMet radical superfamily are so poorly understood, we have examined two MTTases in parallel, MiaB and RimO, using protein electrochemistry. We resolve the redox potentials of each [4Fe-4S] cluster, show that the auxiliary cluster has a potential higher than that of the AdoMet-binding cluster, and demonstrate that upon incubation of either enzyme with AdoMet, a unique low-potential state of the enzyme emerges. Our results are consistent with a mechanism whereby the auxiliary cluster is transiently methylated during substrate methylthiolation. PMID:27598886

The role of extended Fe4S4 cluster ligands in mediating sulfite reductase hemoprotein activity.

Science.gov (United States)

Cepeda, Marisa R; McGarry, Lauren; Pennington, Joseph M; Krzystek, J; Elizabeth Stroupe, M

2018-05-28

The siroheme-containing subunit from the multimeric hemoflavoprotein NADPH-dependent sulfite reductase (SiR/SiRHP) catalyzes the six electron-reduction of SO 3 2- to S 2- . Siroheme is an iron-containing isobacteriochlorin that is found in sulfite and homologous siroheme-containing nitrite reductases. Siroheme does not work alone but is covalently coupled to a Fe 4 S 4 cluster through one of the cluster's ligands. One long-standing hypothesis predicted from this observation is that the environment of one iron-containing cofactor influences the properties of the other. We tested this hypothesis by identifying three amino acids (F437, M444, and T477) that interact with the Fe 4 S 4 cluster and probing the effect of altering them to alanine on the function and structure of the resulting enzymes by use of activity assays, X-ray crystallographic analysis, and EPR spectroscopy. We showed that F437 and M444 gate access for electron transfer to the siroheme-cluster assembly and the direct hydrogen bond between T477 and one of the cluster sulfides is important for determining the geometry of the siroheme active site. Copyright © 2018. Published by Elsevier B.V.

Post-traumatic stress symptom clusters in acute whiplash associated disorder and their prediction of chronic pain-related disability

Directory of Open Access Journals (Sweden)

Annick Maujean

2017-12-01

Conclusion:. Given that only the hyperarousal/numbing symptom cluster predicted long-term neck pain-related disability, this finding may have implications in terms of diagnosis, assessment, and management of the psychological impact of whiplash-injured individuals following a MVC.

Diametrical clustering for identifying anti-correlated gene clusters.

Science.gov (United States)

Dhillon, Inderjit S; Marcotte, Edward M; Roshan, Usman

2003-09-01

Clustering genes based upon their expression patterns allows us to predict gene function. Most existing clustering algorithms cluster genes together when their expression patterns show high positive correlation. However, it has been observed that genes whose expression patterns are strongly anti-correlated can also be functionally similar. Biologically, this is not unintuitive-genes responding to the same stimuli, regardless of the nature of the response, are more likely to operate in the same pathways. We present a new diametrical clustering algorithm that explicitly identifies anti-correlated clusters of genes. Our algorithm proceeds by iteratively (i). re-partitioning the genes and (ii). computing the dominant singular vector of each gene cluster; each singular vector serving as the prototype of a 'diametric' cluster. We empirically show the effectiveness of the algorithm in identifying diametrical or anti-correlated clusters. Testing the algorithm on yeast cell cycle data, fibroblast gene expression data, and DNA microarray data from yeast mutants reveals that opposed cellular pathways can be discovered with this method. We present systems whose mRNA expression patterns, and likely their functions, oppose the yeast ribosome and proteosome, along with evidence for the inverse transcriptional regulation of a number of cellular systems.

ClubSub-P: Cluster-based subcellular localization prediction for Gram-negative bacteria and Archaea.

Directory of Open Access Journals (Sweden)

Nagarajan eParamasivam

2011-11-01

Full Text Available The subcellular localization of proteins provides important clues to their function in a cell. In our efforts to predict useful vaccine targets against Gram-negative bacteria, we noticed that misannotated start codons frequently lead to wrongly assigned subcellular localizations. This and other problems in subcellular localization prediction, such as the relatively high false positive and false negative rates of some tools, can be avoided by applying multiple prediction tools to groups of homologous proteins. Here we present ClubSub-P, an online database that combines existing subcellular localization prediction tools into a consensus pipeline from more than 600 proteomes of fully sequenced microorganisms. On top of the consensus prediction at the level of single sequences, the tool uses clusters of homologous proteins from Gram-negative bacteria and from Archaea to eliminate false positive and false negative predictions. ClubSub-P can assign the subcellular localization of proteins from Gram-negative bacteria and Archaea with high precision. The database is searchable, and can easily be expanded using either new bacterial genomes or new prediction tools as they become available. This will further improve the performance of the subcellular localization prediction, as well as the detection of misannotated start codons and other annotation errors. ClubSub-P is available online at http://toolkit.tuebingen.mpg.de/clubsubp/

Operational mesoscale atmospheric dispersion prediction using high performance parallel computing cluster for emergency response

International Nuclear Information System (INIS)

Srinivas, C.V.; Venkatesan, R.; Muralidharan, N.V.; Das, Someshwar; Dass, Hari; Eswara Kumar, P.

2005-08-01

An operational atmospheric dispersion prediction system is implemented on a cluster super computer for 'Online Emergency Response' for Kalpakkam nuclear site. The numerical system constitutes a parallel version of a nested grid meso-scale meteorological model MM5 coupled to a random walk particle dispersion model FLEXPART. The system provides 48 hour forecast of the local weather and radioactive plume dispersion due to hypothetical air borne releases in a range of 100 km around the site. The parallel code was implemented on different cluster configurations like distributed and shared memory systems. Results of MM5 run time performance for 1-day prediction are reported on all the machines available for testing. A reduction of 5 times in runtime is achieved using 9 dual Xeon nodes (18 physical/36 logical processors) compared to a single node sequential run. Based on the above run time results a cluster computer facility with 9-node Dual Xeon is commissioned at IGCAR for model operation. The run time of a triple nested domain MM5 is about 4 h for 24 h forecast. The system has been operated continuously for a few months and results were ported on the IMSc home page. Initial and periodic boundary condition data for MM5 are provided by NCMRWF, New Delhi. An alternative source is found to be NCEP, USA. These two sources provide the input data to the operational models at different spatial and temporal resolutions and using different assimilation methods. A comparative study on the results of forecast is presented using these two data sources for present operational use. Slight improvement is noticed in rainfall, winds, geopotential heights and the vertical atmospheric structure while using NCEP data probably because of its high spatial and temporal resolution. (author)

Enzyme clusters during the metamorphic period of Ambystoma mexicanum: role of thyroid hormone

NARCIS (Netherlands)

Lamers, W. H.; Mooren, P. G.; de Graaf, A.

1982-01-01

Enzyme activities and DNA content have been measure in axolotl liver during the metamorphic period (4-8 months after spawning). Three different types of enzyme activity profiles were observed. In the type I profile (carbamoyl-phosphate synthase, arginase, ornithine transcarbamoylase, and glutamate

clusterMaker: a multi-algorithm clustering plugin for Cytoscape

Directory of Open Access Journals (Sweden)

Morris John H

2011-11-01

Full Text Available Abstract Background In the post-genomic era, the rapid increase in high-throughput data calls for computational tools capable of integrating data of diverse types and facilitating recognition of biologically meaningful patterns within them. For example, protein-protein interaction data sets have been clustered to identify stable complexes, but scientists lack easily accessible tools to facilitate combined analyses of multiple data sets from different types of experiments. Here we present clusterMaker, a Cytoscape plugin that implements several clustering algorithms and provides network, dendrogram, and heat map views of the results. The Cytoscape network is linked to all of the other views, so that a selection in one is immediately reflected in the others. clusterMaker is the first Cytoscape plugin to implement such a wide variety of clustering algorithms and visualizations, including the only implementations of hierarchical clustering, dendrogram plus heat map visualization (tree view, k-means, k-medoid, SCPS, AutoSOME, and native (Java MCL. Results Results are presented in the form of three scenarios of use: analysis of protein expression data using a recently published mouse interactome and a mouse microarray data set of nearly one hundred diverse cell/tissue types; the identification of protein complexes in the yeast Saccharomyces cerevisiae; and the cluster analysis of the vicinal oxygen chelate (VOC enzyme superfamily. For scenario one, we explore functionally enriched mouse interactomes specific to particular cellular phenotypes and apply fuzzy clustering. For scenario two, we explore the prefoldin complex in detail using both physical and genetic interaction clusters. For scenario three, we explore the possible annotation of a protein as a methylmalonyl-CoA epimerase within the VOC superfamily. Cytoscape session files for all three scenarios are provided in the Additional Files section. Conclusions The Cytoscape plugin cluster

Optical response of small magnesium clusters

DEFF Research Database (Denmark)

Solov'yov, Ilia; Solov'yov, Andrey V.; Greiner, Walter

2004-01-01

We predict strong enhancement in the photoabsorption of small Mg clusters in the region of 4–5 eV due to the resonant excitation of the plasmon oscillations of cluster electrons. Photoabsorption spectra for neutral Mg clusters consisting of up to N = 11 atoms have been calculated using an ab initio...... framework based on the time-dependent density functional theory (TDDFT). The nature of predicted resonances has been elucidated by comparison of the results of the an ab initio calculations with the results of the classical Mie theory. The splitting of the plasmon resonances caused by the cluster...

Recent development of antiSMASH and other computational approaches to mine secondary metabolite biosynthetic gene clusters

DEFF Research Database (Denmark)

Blin, Kai; Kim, Hyun Uk; Medema, Marnix H.

2017-01-01

Many drugs are derived from small molecules produced by microorganisms and plants, so-called natural products. Natural products have diverse chemical structures, but the biosynthetic pathways producing those compounds are often organized as biosynthetic gene clusters (BGCs) and follow a highly...... conserved biosynthetic logic. This allows for the identification of core biosynthetic enzymes using genome mining strategies that are based on the sequence similarity of the involved enzymes/genes. However, mining for a variety of BGCs quickly approaches a complexity level where manual analyses...... are no longer possible and require the use of automated genome mining pipelines, such as the antiSMASH software. In this review, we discuss the principles underlying the predictions of antiSMASH and other tools and provide practical advice for their application. Furthermore, we discuss important caveats...

DYNAMIC MODELLING AND ADVANCED PREDICTIVE CONTROL OF A CONTINUOUS PROCESS OF ENZYME PURIFICATION

Directory of Open Access Journals (Sweden)

Dechechi E.C.

1997-01-01

Full Text Available A dynamic mathematical model, simulation and computer control of a Continuous Affinity Recycle Extraction (CARE process, a protein purification technique based on protein adsorption on solid-phase adsorbents is described in this work. This process, consisting of three reactors, is a multivariable process with considerable time delay in the on-line analyses of the controlled variable. An advanced predictive control configuration, specifically the Dynamic Matrix Control (DMC, was applied. The DMC algorithm was applied in process schemes where the aim was to maintain constant the enzyme concentration in the outlet of the third reactor. The performance of the DMC controller was analyzed in the feed-flow disturbances and the results are presented.

The implementation of two stages clustering (k-means clustering and adaptive neuro fuzzy inference system) for prediction of medicine need based on medical data

Science.gov (United States)

Husein, A. M.; Harahap, M.; Aisyah, S.; Purba, W.; Muhazir, A.

2018-03-01

Medication planning aim to get types, amount of medicine according to needs, and avoid the emptiness medicine based on patterns of disease. In making the medicine planning is still rely on ability and leadership experience, this is due to take a long time, skill, difficult to obtain a definite disease data, need a good record keeping and reporting, and the dependence of the budget resulted in planning is not going well, and lead to frequent lack and excess of medicines. In this research, we propose Adaptive Neuro Fuzzy Inference System (ANFIS) method to predict medication needs in 2016 and 2017 based on medical data in 2015 and 2016 from two source of hospital. The framework of analysis using two approaches. The first phase is implementing ANFIS to a data source, while the second approach we keep using ANFIS, but after the process of clustering from K-Means algorithm, both approaches are calculated values of Root Mean Square Error (RMSE) for training and testing. From the testing result, the proposed method with better prediction rates based on the evaluation analysis of quantitative and qualitative compared with existing systems, however the implementation of K-Means Algorithm against ANFIS have an effect on the timing of the training process and provide a classification accuracy significantly better without clustering.

Prediction of interindividual variation in drug plasma levels in vivo from individual enzyme kinetic data and physiologically based pharmacokinetic modeling

NARCIS (Netherlands)

Bogaards, J.J.P.; Hissink, E.M.; Briggs, M.; Weaver, R.; Jochemsen, R.; Jackson, P.; Bertrand, M.; Bladeren, P. van

2000-01-01

A strategy is presented to predict interindividual variation in drug plasma levels in vivo by the use of physiologically based pharmacokinetic modeling and human in vitro metabolic parameters, obtained through the combined use of microsomes containing single cytochrome P450 enzymes and a human liver

Drug repositioning for enzyme modulator based on human metabolite-likeness.

Science.gov (United States)

Lee, Yoon Hyeok; Choi, Hojae; Park, Seongyong; Lee, Boah; Yi, Gwan-Su

2017-05-31

Recently, the metabolite-likeness of the drug space has emerged and has opened a new possibility for exploring human metabolite-like candidates in drug discovery. However, the applicability of metabolite-likeness in drug discovery has been largely unexplored. Moreover, there are no reports on its applications for the repositioning of drugs to possible enzyme modulators, although enzyme-drug relations could be directly inferred from the similarity relationships between enzyme's metabolites and drugs. We constructed a drug-metabolite structural similarity matrix, which contains 1,861 FDA-approved drugs and 1,110 human intermediary metabolites scored with the Tanimoto similarity. To verify the metabolite-likeness measure for drug repositioning, we analyzed 17 known antimetabolite drugs that resemble the innate metabolites of their eleven target enzymes as the gold standard positives. Highly scored drugs were selected as possible modulators of enzymes for their corresponding metabolites. Then, we assessed the performance of metabolite-likeness with a receiver operating characteristic analysis and compared it with other drug-target prediction methods. We set the similarity threshold for drug repositioning candidates of new enzyme modulators based on maximization of the Youden's index. We also carried out literature surveys for supporting the drug repositioning results based on the metabolite-likeness. In this paper, we applied metabolite-likeness to repurpose FDA-approved drugs to disease-associated enzyme modulators that resemble human innate metabolites. All antimetabolite drugs were mapped with their known 11 target enzymes with statistically significant similarity values to the corresponding metabolites. The comparison with other drug-target prediction methods showed the higher performance of metabolite-likeness for predicting enzyme modulators. After that, the drugs scored higher than similarity score of 0.654 were selected as possible modulators of enzymes for

SRMDAP: SimRank and Density-Based Clustering Recommender Model for miRNA-Disease Association Prediction

Directory of Open Access Journals (Sweden)

Xiaoying Li

2018-01-01

Full Text Available Aberrant expression of microRNAs (miRNAs can be applied for the diagnosis, prognosis, and treatment of human diseases. Identifying the relationship between miRNA and human disease is important to further investigate the pathogenesis of human diseases. However, experimental identification of the associations between diseases and miRNAs is time-consuming and expensive. Computational methods are efficient approaches to determine the potential associations between diseases and miRNAs. This paper presents a new computational method based on the SimRank and density-based clustering recommender model for miRNA-disease associations prediction (SRMDAP. The AUC of 0.8838 based on leave-one-out cross-validation and case studies suggested the excellent performance of the SRMDAP in predicting miRNA-disease associations. SRMDAP could also predict diseases without any related miRNAs and miRNAs without any related diseases.

A hybrid Genetic Algorithm and Monte Carlo simulation approach to predict hourly energy consumption and generation by a cluster of Net Zero Energy Buildings

International Nuclear Information System (INIS)

Garshasbi, Samira; Kurnitski, Jarek; Mohammadi, Yousef

2016-01-01

Graphical abstract: The energy consumption and renewable generation in a cluster of NZEBs are modeled by a novel hybrid Genetic Algorithm and Monte Carlo simulation approach and used for the prediction of instantaneous and cumulative net energy balances and hourly amount of energy taken from and supplied to the central energy grid. - Highlights: • Hourly energy consumption and generation by a cluster of NZEBs was simulated. • Genetic Algorithm and Monte Carlo simulation approach were employed. • Dampening effect of energy used by a cluster of buildings was demonstrated. • Hourly amount of energy taken from and supplied to the grid was simulated. • Results showed that NZEB cluster was 63.5% grid dependant on annual bases. - Abstract: Employing a hybrid Genetic Algorithm (GA) and Monte Carlo (MC) simulation approach, energy consumption and renewable energy generation in a cluster of Net Zero Energy Buildings (NZEBs) was thoroughly investigated with hourly simulation. Moreover, the cumulative energy consumption and generation of the whole cluster and each individual building within the simulation space were accurately monitored and reported. The results indicate that the developed simulation algorithm is able to predict the total instantaneous and cumulative amount of energy taken from and supplied to the central energy grid over any time period. During the course of simulation, about 60–100% of total daily generated renewable energy was consumed by NZEBs and up to 40% of that was fed back into the central energy grid as surplus energy. The minimum grid dependency of the cluster was observed in June and July where 11.2% and 9.9% of the required electricity was supplied from the central energy grid, respectively. On the other hand, the NZEB cluster was strongly grid dependant in January and December by importing 70.7% and 76.1% of its required energy demand via the central energy grid, in the order given. Simulation results revealed that the cluster was 63

Incremental Criterion Validity of the WJ-III COG Clinical Clusters: Marginal Predictive Effects beyond the General Factor

Science.gov (United States)

McGill, Ryan J.

2015-01-01

The current study examined the incremental validity of the clinical clusters from the Woodcock-Johnson III Tests of Cognitive Abilities (WJ-III COG) for predicting scores on the Woodcock-Johnson III Tests of Achievement (WJ-III ACH). All participants were children and adolescents (N = 4,722) drawn from the nationally representative WJ-III…

The cluster analysis based on non-teacher artificial neural network for the danger prediction of coal spontaneous fire

Energy Technology Data Exchange (ETDEWEB)

Wang, D.; Wang, J. [China University of Mining and Technology (China)

1999-04-01

This paper focuses on the problem of predicting the danger level of spontaneous fire in coal mines. Firstly, the inadequacy of the present artificial neural networks prediction model is analysed. Then a new cluster model based on non-teacher neural network is constructed according to the danger judgement standards given by experts. On this basis, by adopting the error square sum criterion and its algorithm, the corresponding prediction software is developed and applied in two working faces of Chaili Coal Mine. The forecasting result is importantly significant for the prevention of spontaneous fire. 4 refs., 1 fig., 1 tab.

Prediction of line failure fault based on weighted fuzzy dynamic clustering and improved relational analysis

Science.gov (United States)

Meng, Xiaocheng; Che, Renfei; Gao, Shi; He, Juntao

2018-04-01

With the advent of large data age, power system research has entered a new stage. At present, the main application of large data in the power system is the early warning analysis of the power equipment, that is, by collecting the relevant historical fault data information, the system security is improved by predicting the early warning and failure rate of different kinds of equipment under certain relational factors. In this paper, a method of line failure rate warning is proposed. Firstly, fuzzy dynamic clustering is carried out based on the collected historical information. Considering the imbalance between the attributes, the coefficient of variation is given to the corresponding weights. And then use the weighted fuzzy clustering to deal with the data more effectively. Then, by analyzing the basic idea and basic properties of the relational analysis model theory, the gray relational model is improved by combining the slope and the Deng model. And the incremental composition and composition of the two sequences are also considered to the gray relational model to obtain the gray relational degree between the various samples. The failure rate is predicted according to the principle of weighting. Finally, the concrete process is expounded by an example, and the validity and superiority of the proposed method are verified.

AR-V7 in circulating tumor cells cluster as a predictive biomarker of abiraterone acetate and enzalutamide treatment in castration-resistant prostate cancer patients.

Science.gov (United States)

Okegawa, Takatsugu; Ninomiya, Naoki; Masuda, Kazuki; Nakamura, Yu; Tambo, Mitsuhiro; Nutahara, Kikuo

2018-06-01

We examined whether androgen receptor splice variant 7 (AR-V7) in circulating tumor cell(CTC)clusters can be used to predict survival in patients with bone metastatic castration resistant-prostate cancer (mCRPC) treated with abiraterone or enzalutamide. We retrospectively enrolled 98 patients with CRPC on abiraterone or enzalutamide, and investigated the prognostic value of CTC cluster detection (+ v -) and AR-V7 detection (+ v -) using a CTC cluster detection - based AR-V7 mRNA assay. We examined ≤50% prostate-specific antigen (PSA) responses, PSA progression-free survival (PSA-PFS), clinical and radiological progression-free survival (radiologic PSF), and overall survival (OS). We then assessed whether AR-V7 expression in CTC clusters identified after On-chip multi-imaging flow cytometry was related to disease progression and survival after first-line systemic therapy. All abiraterone-treated or enzalutamide-treated patients received prior docetaxel. The median follow-up was 20.7 (range: 3.0-37.0) months in the abiraterone and enzalutamide cohorts, respectively. Forty-nine of the 98 men (50.0%) were CTC cluster (-), 23 of the 98 men (23.5%) were CTC cluster(+)/AR-V7(-), and 26 of the 98 men (26.5%) were CTC cluster(+)/AR-V7(+). CTC cluster(+)/AR-V7(+) patients were more likely to have EOD ≥3 at diagnosis (P = 0.003), pain (P = 0.023), higher alkaline phosphatase levels (P cluster(+), CTC cluster(+)/AR-V7(-), and ALP >UNL were independently associated with a poor PSA-PFS, radiographic PFS, and OS in abiraterone-treated patients and enzalutamide-treated patients. The CTC clusters and AR-V7-positive CTC clusters detected were important for assessing the response to abiraterone or enzalutamide therapy and for predicting disease outcome. © 2018 Wiley Periodicals, Inc.

Predicting protein complexes from weighted protein-protein interaction graphs with a novel unsupervised methodology: Evolutionary enhanced Markov clustering.

Science.gov (United States)

Theofilatos, Konstantinos; Pavlopoulou, Niki; Papasavvas, Christoforos; Likothanassis, Spiros; Dimitrakopoulos, Christos; Georgopoulos, Efstratios; Moschopoulos, Charalampos; Mavroudi, Seferina

2015-03-01

Proteins are considered to be the most important individual components of biological systems and they combine to form physical protein complexes which are responsible for certain molecular functions. Despite the large availability of protein-protein interaction (PPI) information, not much information is available about protein complexes. Experimental methods are limited in terms of time, efficiency, cost and performance constraints. Existing computational methods have provided encouraging preliminary results, but they phase certain disadvantages as they require parameter tuning, some of them cannot handle weighted PPI data and others do not allow a protein to participate in more than one protein complex. In the present paper, we propose a new fully unsupervised methodology for predicting protein complexes from weighted PPI graphs. The proposed methodology is called evolutionary enhanced Markov clustering (EE-MC) and it is a hybrid combination of an adaptive evolutionary algorithm and a state-of-the-art clustering algorithm named enhanced Markov clustering. EE-MC was compared with state-of-the-art methodologies when applied to datasets from the human and the yeast Saccharomyces cerevisiae organisms. Using public available datasets, EE-MC outperformed existing methodologies (in some datasets the separation metric was increased by 10-20%). Moreover, when applied to new human datasets its performance was encouraging in the prediction of protein complexes which consist of proteins with high functional similarity. In specific, 5737 protein complexes were predicted and 72.58% of them are enriched for at least one gene ontology (GO) function term. EE-MC is by design able to overcome intrinsic limitations of existing methodologies such as their inability to handle weighted PPI networks, their constraint to assign every protein in exactly one cluster and the difficulties they face concerning the parameter tuning. This fact was experimentally validated and moreover, new

bcl::Cluster : A method for clustering biological molecules coupled with visualization in the Pymol Molecular Graphics System.

Science.gov (United States)

Alexander, Nathan; Woetzel, Nils; Meiler, Jens

2011-02-01

Clustering algorithms are used as data analysis tools in a wide variety of applications in Biology. Clustering has become especially important in protein structure prediction and virtual high throughput screening methods. In protein structure prediction, clustering is used to structure the conformational space of thousands of protein models. In virtual high throughput screening, databases with millions of drug-like molecules are organized by structural similarity, e.g. common scaffolds. The tree-like dendrogram structure obtained from hierarchical clustering can provide a qualitative overview of the results, which is important for focusing detailed analysis. However, in practice it is difficult to relate specific components of the dendrogram directly back to the objects of which it is comprised and to display all desired information within the two dimensions of the dendrogram. The current work presents a hierarchical agglomerative clustering method termed bcl::Cluster. bcl::Cluster utilizes the Pymol Molecular Graphics System to graphically depict dendrograms in three dimensions. This allows simultaneous display of relevant biological molecules as well as additional information about the clusters and the members comprising them.

Structure and Mobility of Metal Clusters in MOFs: Au, Pd, and AuPd Clusters in MOF-74

DEFF Research Database (Denmark)

Vilhelmsen, Lasse; Walton, Krista S.; Sholl, David S.

2012-01-01

is just as important for nanocluster adsorption as open Zn or Mg metal sites. Using the large number of clusters generated by the GA, we developed a systematic method for predicting the mobility of adsorbed clusters. Through the investigation of diffusion paths a relationship between the cluster......Understanding the adsorption and mobility of metal–organic framework (MOF)-supported metal nanoclusters is critical to the development of these catalytic materials. We present the first theoretical investigation of Au-, Pd-, and AuPd-supported clusters in a MOF, namely MOF-74. We combine density...... functional theory (DFT) calculations with a genetic algorithm (GA) to reliably predict the structure of the adsorbed clusters. This approach allows comparison of hundreds of adsorbed configurations for each cluster. From the investigation of Au8, Pd8, and Au4Pd4 we find that the organic part of the MOF...

Comparison of loline alkaloid gene clusters across fungal endophytes: predicting the co-regulatory sequence motifs and the evolutionary history.

Science.gov (United States)

Kutil, Brandi L; Greenwald, Charles; Liu, Gang; Spiering, Martin J; Schardl, Christopher L; Wilkinson, Heather H

2007-10-01

LOL, a fungal secondary metabolite gene cluster found in Epichloë and Neotyphodium species, is responsible for production of insecticidal loline alkaloids. To analyze the genetic architecture and to predict the evolutionary history of LOL, we compared five clusters from four fungal species (single clusters from Epichloë festucae, Neotyphodium sp. PauTG-1, Neotyphodium coenophialum, and two clusters we previously characterized in Neotyphodium uncinatum). Using PhyloCon to compare putative lol gene promoter regions, we have identified four motifs conserved across the lol genes in all five clusters. Each motif has significant similarity to known fungal transcription factor binding sites in the TRANSFAC database. Conservation of these motifs is further support for the hypothesis that the lol genes are co-regulated. Interestingly, the history of asexual Neotyphodium spp. includes multiple interspecific hybridization events. Comparing clusters from three Neotyphodium species and E. festucae allowed us to determine which Epichloë ancestors are the most likely contributors of LOL in these asexual species. For example, while no present day Epichloë typhina isolates are known to produce lolines, our data support the hypothesis that the E. typhina ancestor(s) of three asexual endophyte species contained a LOL gene cluster. Thus, these data support a model of evolution in which the polymorphism in loline alkaloid production phenotypes among endophyte species is likely due to the loss of the trait over time.

Predicting the mean cycle time as a function of throughput and product mix for cluster tool workstations using EPT-based aggregate modeling

NARCIS (Netherlands)

Veeger, C.P.L.; Etman, L.F.P.; Herk, van J.; Rooda, J.E.

2009-01-01

Predicting the mean cycle time as a function of throughput and product mix is helpful in making the production planning for cluster tools. To predict the mean cycle time, detailed simulation models may be used. However, detailed models require much development time, and it may not be possible to

Consumer attitudes to enzymes in food production

DEFF Research Database (Denmark)

Søndergaard, Helle Alsted; Grunert, Klaus G.; Scholderer, Joachim

2005-01-01

The use of enzymes in food production has potential benefits for both food manufacturers and consumers. A central question is how consumers react to new ways of producing foods with enzymes. This study investigates the formation of consumer attitudes to different enzyme production methods in three...... European countries. Results show that consumers are most positive towards non-GM enzyme production methods. The enzyme production method is by far the most important factor for the formation of buying intentions compared to price and benefits. Results also show that environmental concern and attitudes...... to technological progress are the socio-political attitudes that have the highest predictive value regarding attitudes to enzyme production methods....

Photoabsorption of small sodium and magnesium clusters

DEFF Research Database (Denmark)

Solov'yov, Ilia; Solov'yov, Andrey V.; Greiner, Walter

2004-01-01

We predict the strong enhancement in the photoabsorption of small Mg clusters in the region of 4-5 eV due to the resonant excitation of the plasmon oscillations of cluster electrons. The photoabsorption spectra for neutral Mg clusters consisting of up to N=11 atoms have been calculated using it ab...... initio framework based on the time dependent density functional theory (TDDFT). The nature of predicted resonances has been elucidated by comparison of the results of the it ab initio calculations with the results of the classical Mie theory. The splitting of the plasmon resonances caused by the cluster...

Prediction of novel families of enzymes involved in oxidative and other complex modifications of bases in nucleic acids.

Science.gov (United States)

Iyer, Lakshminarayan M; Tahiliani, Mamta; Rao, Anjana; Aravind, L

2009-06-01

Modified bases in nucleic acids present a layer of information that directs biological function over and beyond the coding capacity of the conventional bases. While a large number of modified bases have been identified, many of the enzymes generating them still remain to be discovered. Recently, members of the 2-oxoglutarate- and iron(II)-dependent dioxygenase super-family, which modify diverse substrates from small molecules to biopolymers, were predicted and subsequently confirmed to catalyze oxidative modification of bases in nucleic acids. Of these, two distinct families, namely the AlkB and the kinetoplastid base J binding proteins (JBP) catalyze in situ hydroxylation of bases in nucleic acids. Using sensitive computational analysis of sequences, structures and contextual information from genomic structure and protein domain architectures, we report five distinct families of 2-oxoglutarate- and iron(II)-dependent dioxygenase that we predict to be involved in nucleic acid modifications. Among the DNA-modifying families, we show that the dioxygenase domains of the kinetoplastid base J-binding proteins belong to a larger family that includes the Tet proteins, prototyped by the human oncogene Tet1, and proteins from basidiomycete fungi, chlorophyte algae, heterolobosean amoeboflagellates and bacteriophages. We present evidence that some of these proteins are likely to be involved in oxidative modification of the 5-methyl group of cytosine leading to the formation of 5-hydroxymethylcytosine. The Tet/JBP homologs from basidiomycete fungi such as Laccaria and Coprinopsis show large lineage-specific expansions and a tight linkage with genes encoding a novel and distinct family of predicted transposases, and a member of the Maelstrom-like HMG family. We propose that these fungal members are part of a mobile transposon. To the best of our knowledge, this is the first report of a eukaryotic transposable element that encodes its own DNA-modification enzyme with a

Improved pan-specific prediction of MHC class I peptide binding using a novel receptor clustering data partitioning strategy

DEFF Research Database (Denmark)

Mattsson, Andreas Holm; Kringelum, Jens Vindahl; Garde, C.

2016-01-01

Pan-specific prediction of receptor-ligand interaction is conventionally done using machine-learning methods that integrates information about both receptor and ligand primary sequences. To achieve optimal performance using machine learning, dealing with overfitting and data redundancy is critical....... Most often so-called ligand clustering methods have been used to deal with these issues in the context of pan-specific receptor-ligand predictions, and the MHC system the approach has proven highly effective for extrapolating information from a limited set of receptors with well characterized binding...

EnzDP: improved enzyme annotation for metabolic network reconstruction based on domain composition profiles.

Science.gov (United States)

Nguyen, Nam-Ninh; Srihari, Sriganesh; Leong, Hon Wai; Chong, Ket-Fah

2015-10-01

Determining the entire complement of enzymes and their enzymatic functions is a fundamental step for reconstructing the metabolic network of cells. High quality enzyme annotation helps in enhancing metabolic networks reconstructed from the genome, especially by reducing gaps and increasing the enzyme coverage. Currently, structure-based and network-based approaches can only cover a limited number of enzyme families, and the accuracy of homology-based approaches can be further improved. Bottom-up homology-based approach improves the coverage by rebuilding Hidden Markov Model (HMM) profiles for all known enzymes. However, its clustering procedure relies firmly on BLAST similarity score, ignoring protein domains/patterns, and is sensitive to changes in cut-off thresholds. Here, we use functional domain architecture to score the association between domain families and enzyme families (Domain-Enzyme Association Scoring, DEAS). The DEAS score is used to calculate the similarity between proteins, which is then used in clustering procedure, instead of using sequence similarity score. We improve the enzyme annotation protocol using a stringent classification procedure, and by choosing optimal threshold settings and checking for active sites. Our analysis shows that our stringent protocol EnzDP can cover up to 90% of enzyme families available in Swiss-Prot. It achieves a high accuracy of 94.5% based on five-fold cross-validation. EnzDP outperforms existing methods across several testing scenarios. Thus, EnzDP serves as a reliable automated tool for enzyme annotation and metabolic network reconstruction. Available at: www.comp.nus.edu.sg/~nguyennn/EnzDP .

Eucalyptus ESTs involved in the production of 9-cis epoxycarotenoid dioxygenase, a regulatory enzyme of abscisic acid production

Directory of Open Access Journals (Sweden)

Iraê A. Guerrini

2005-01-01

Full Text Available Abscisic acid (ABA regulates stress responses in plants, and genomic tools can help us to understand the mechanisms involved in that process. FAPESP, a Brazilian research foundation, in association with four private forestry companies, has established the FORESTs database (https://forests.esalq.usp.br. A search was carried out in the Eucalyptus expressed sequence tag database to find ESTs involved with 9-cis epoxycarotenoid dioxygenase (NCED, the regulatory enzyme for ABA biosynthesis, using the basic local BLAST alignment tool. We found four clusters (EGEZLV2206B11.g, EGJMWD2252H08.g, EGBFRT3107F10.g, and EGEQFB1200H10.g, which represent similar sequences of the gene that produces NCED. Data showed that the EGBFRT3107F10.g cluster was similar to the maize (Zea mays NCED enzyme, while EGEZLV2206B11.g and EGJMWD2252H08.g clusters were similar to the avocado (Persea americana NCED enzyme. All Eucalyptus clusters were expressed in several tissues, especially in flower buds, where ABA has a special participation during the floral development process.

Are clusters of dietary patterns and cluster membership stable over time? Results of a longitudinal cluster analysis study.

Science.gov (United States)

Walthouwer, Michel Jean Louis; Oenema, Anke; Soetens, Katja; Lechner, Lilian; de Vries, Hein

2014-11-01

Developing nutrition education interventions based on clusters of dietary patterns can only be done adequately when it is clear if distinctive clusters of dietary patterns can be derived and reproduced over time, if cluster membership is stable, and if it is predictable which type of people belong to a certain cluster. Hence, this study aimed to: (1) identify clusters of dietary patterns among Dutch adults, (2) test the reproducibility of these clusters and stability of cluster membership over time, and (3) identify sociodemographic predictors of cluster membership and cluster transition. This study had a longitudinal design with online measurements at baseline (N=483) and 6 months follow-up (N=379). Dietary intake was assessed with a validated food frequency questionnaire. A hierarchical cluster analysis was performed, followed by a K-means cluster analysis. Multinomial logistic regression analyses were conducted to identify the sociodemographic predictors of cluster membership and cluster transition. At baseline and follow-up, a comparable three-cluster solution was derived, distinguishing a healthy, moderately healthy, and unhealthy dietary pattern. Male and lower educated participants were significantly more likely to have a less healthy dietary pattern. Further, 251 (66.2%) participants remained in the same cluster, 45 (11.9%) participants changed to an unhealthier cluster, and 83 (21.9%) participants shifted to a healthier cluster. Men and people living alone were significantly more likely to shift toward a less healthy dietary pattern. Distinctive clusters of dietary patterns can be derived. Yet, cluster membership is unstable and only few sociodemographic factors were associated with cluster membership and cluster transition. These findings imply that clusters based on dietary intake may not be suitable as a basis for nutrition education interventions. Copyright © 2014 Elsevier Ltd. All rights reserved.

Biocatalyst including porous enzyme cluster composite immobilized by two-step crosslinking and its utilization as enzymatic biofuel cell

Science.gov (United States)

Chung, Yongjin; Christwardana, Marcelinus; Tannia, Daniel Chris; Kim, Ki Jae; Kwon, Yongchai

2017-08-01

An enzyme cluster composite (TPA/GOx) formed from glucose oxidase (GOx) and terephthalaldehyde (TPA) that is coated onto polyethyleneimine (PEI) and carbon nanotubes (CNTs) is suggested as a new catalyst ([(TPA/GOx)/PEI]/CNT). In this catalyst, TPA promotes inter-GOx links by crosslinking to form a large and porous structure, and the TPA/GOx composite is again crosslinked with PEI/CNT to increase the amount of immobilized GOx. Such a two-step crosslinking (i) increases electron transfer because of electron delocalization by π conjugation and (ii) reduces GOx denaturation because of the formation of strong chemical bonds while its porosity facilitates mass transfer. With these features, an enzymatic biofuel cell (EBC) employing the new catalyst is fabricated and induces an excellent maximum power density (1.62 ± 0.08 mW cm-2), while the catalytic activity of the [(TPA/GOx)/PEI]/CNT catalyst is outstanding. This is clear evidence that the two-step crosslinking and porous structure caused by adoption of the TPA/GOx composite affect the performance enhancement of EBC.

An indigoidine biosynthetic gene cluster from Streptomyces chromofuscus ATCC 49982 contains an unusual IndB homologue.

Science.gov (United States)

Yu, Dayu; Xu, Fuchao; Valiente, Jonathan; Wang, Siyuan; Zhan, Jixun

2013-01-01

A putative indigoidine biosynthetic gene cluster was located in the genome of Streptomyces chromofuscus ATCC 49982. The silent 9.4-kb gene cluster consists of five open reading frames, named orf1, Sc-indC, Sc-indA, Sc-indB, and orf2, respectively. Sc-IndC was functionally characterized as an indigoidine synthase through heterologous expression of the enzyme in both Streptomyces coelicolor CH999 and Escherichia coli BAP1. The yield of indigoidine in E. coli BAP1 reached 2.78 g/l under the optimized conditions. The predicted protein product of Sc-indB is unusual and much larger than any other reported IndB-like protein. The N-terminal portion of this enzyme resembles IdgB and the C-terminal portion is a hypothetical protein. Sc-IndA and/or Sc-IndB were co-expressed with Sc-IndC in E. coli BAP1, which demonstrated the involvement of Sc-IndB, but not Sc-IndA, in the biosynthetic pathway of indigoidine. The yield of indigoidine was dramatically increased by 41.4 % (3.93 g/l) when Sc-IndB was co-expressed with Sc-IndC in E. coli BAP1. Indigoidine is more stable at low temperatures.

MEASURING THE ULTIMATE HALO MASS OF GALAXY CLUSTERS: REDSHIFTS AND MASS PROFILES FROM THE HECTOSPEC CLUSTER SURVEY (HeCS)

International Nuclear Information System (INIS)

Rines, Kenneth; Geller, Margaret J.; Kurtz, Michael J.; Diaferio, Antonaldo

2013-01-01

The infall regions of galaxy clusters represent the largest gravitationally bound structures in a ΛCDM universe. Measuring cluster mass profiles into the infall regions provides an estimate of the ultimate mass of these halos. We use the caustic technique to measure cluster mass profiles from galaxy redshifts obtained with the Hectospec Cluster Survey (HeCS), an extensive spectroscopic survey of galaxy clusters with MMT/Hectospec. We survey 58 clusters selected by X-ray flux at 0.1 200 , a new observational cosmological test in essential agreement with simulations. Summed profiles binned in M 200 and in L X demonstrate that the predicted Navarro-Frenk-White form of the density profile is a remarkably good representation of the data in agreement with weak lensing results extending to large radius. The concentration of these summed profiles is also consistent with theoretical predictions.

Psychological Factors Predict Local and Referred Experimental Muscle Pain: A Cluster Analysis in Healthy Adults

Science.gov (United States)

Lee, Jennifer E.; Watson, David; Frey-Law, Laura A.

2012-01-01

Background Recent studies suggest an underlying three- or four-factor structure explains the conceptual overlap and distinctiveness of several negative emotionality and pain-related constructs. However, the validity of these latent factors for predicting pain has not been examined. Methods A cohort of 189 (99F; 90M) healthy volunteers completed eight self-report negative emotionality and pain-related measures (Eysenck Personality Questionnaire-Revised; Positive and Negative Affect Schedule; State-Trait Anxiety Inventory; Pain Catastrophizing Scale; Fear of Pain Questionnaire; Somatosensory Amplification Scale; Anxiety Sensitivity Index; Whiteley Index). Using principal axis factoring, three primary latent factors were extracted: General Distress; Catastrophic Thinking; and Pain-Related Fear. Using these factors, individuals clustered into three subgroups of high, moderate, and low negative emotionality responses. Experimental pain was induced via intramuscular acidic infusion into the anterior tibialis muscle, producing local (infusion site) and/or referred (anterior ankle) pain and hyperalgesia. Results Pain outcomes differed between clusters (multivariate analysis of variance and multinomial regression), with individuals in the highest negative emotionality cluster reporting the greatest local pain (p = 0.05), mechanical hyperalgesia (pressure pain thresholds; p = 0.009) and greater odds (2.21 OR) of experiencing referred pain compared to the lowest negative emotionality cluster. Conclusion Our results provide support for three latent psychological factors explaining the majority of the variance between several pain-related psychological measures, and that individuals in the high negative emotionality subgroup are at increased risk for (1) acute local muscle pain; (2) local hyperalgesia; and (3) referred pain using a standardized nociceptive input. PMID:23165778

Effect of deletion polymorphism of angiotensin converting enzyme gene on progression of diabetic nephropathy during inhibition of angiotensin converting enzyme

DEFF Research Database (Denmark)

Parving, H H; Jacobsen, P; Tarnow, L

1996-01-01

OBJECTIVE: To evaluate the concept that an insertion/deletion polymorphism of the angiotensin converting enzyme gene predicts the therapeutic efficacy of inhibition of angiotensin converting enzyme on progression of diabetic nephropathy. DESIGN: Observational follow up study of patients with insu...

Multiparticle production through isoscalar clusters

International Nuclear Information System (INIS)

Armburst, W.T.; Scott, D.M.

1975-01-01

The isoscalar cluster model for multiparticle production was extended to include clusters of A 2 meson pairs in addition to previously studied rho-rho and sigma clusters. The production of each type of cluster is given by an energy dependent Poisson distribution. The Poisson parameters determined from the charged particle multiplicity distributions indicate that the inclusion of A 2 -A 2 clusters does not improve the fit to the data. The predictions of the model for n 0 n/sub -/, f/sup 2//sub -,-/, and f/sup 2//sub 0,0/ compare favorably to the experimental values. (U.S.)

A climate-based prediction model in the high-risk clusters of the Mekong Delta region, Vietnam: towards improving dengue prevention and control.

Science.gov (United States)

Phung, Dung; Talukder, Mohammad Radwanur Rahman; Rutherford, Shannon; Chu, Cordia

2016-10-01

To develop a prediction score scheme useful for prevention practitioners and authorities to implement dengue preparedness and controls in the Mekong Delta region (MDR). We applied a spatial scan statistic to identify high-risk dengue clusters in the MDR and used generalised linear-distributed lag models to examine climate-dengue associations using dengue case records and meteorological data from 2003 to 2013. The significant predictors were collapsed into categorical scales, and the β-coefficients of predictors were converted to prediction scores. The score scheme was validated for predicting dengue outbreaks using ROC analysis. The north-eastern MDR was identified as the high-risk cluster. A 1 °C increase in temperature at lag 1-4 and 5-8 weeks increased the dengue risk 11% (95% CI, 9-13) and 7% (95% CI, 6-8), respectively. A 1% rise in humidity increased dengue risk 0.9% (95% CI, 0.2-1.4) at lag 1-4 and 0.8% (95% CI, 0.2-1.4) at lag 5-8 weeks. Similarly, a 1-mm increase in rainfall increased dengue risk 0.1% (95% CI, 0.05-0.16) at lag 1-4 and 0.11% (95% CI, 0.07-0.16) at lag 5-8 weeks. The predicted scores performed with high accuracy in diagnosing the dengue outbreaks (96.3%). This study demonstrates the potential usefulness of a dengue prediction score scheme derived from complex statistical models for high-risk dengue clusters. We recommend a further study to examine the possibility of incorporating such a score scheme into the dengue early warning system in similar climate settings. © 2016 John Wiley & Sons Ltd.

In silico analysis highlights the frequency and diversity of type 1 lantibiotic gene clusters in genome sequenced bacteria

LENUS (Irish Health Repository)

Marsh, Alan J

2010-11-30

Abstract Background Lantibiotics are lanthionine-containing, post-translationally modified antimicrobial peptides. These peptides have significant, but largely untapped, potential as preservatives and chemotherapeutic agents. Type 1 lantibiotics are those in which lanthionine residues are introduced into the structural peptide (LanA) through the activity of separate lanthionine dehydratase (LanB) and lanthionine synthetase (LanC) enzymes. Here we take advantage of the conserved nature of LanC enzymes to devise an in silico approach to identify potential lantibiotic-encoding gene clusters in genome sequenced bacteria. Results In total 49 novel type 1 lantibiotic clusters were identified which unexpectedly were associated with species, genera and even phyla of bacteria which have not previously been associated with lantibiotic production. Conclusions Multiple type 1 lantibiotic gene clusters were identified at a frequency that suggests that these antimicrobials are much more widespread than previously thought. These clusters represent a rich repository which can yield a large number of valuable novel antimicrobials and biosynthetic enzymes.

Evolution of Chemical Diversity in a Group of Non-Reduced Polyketide Gene Clusters: Using Phylogenetics to Inform the Search for Novel Fungal Natural Products

Directory of Open Access Journals (Sweden)

Kurt Throckmorton

2015-09-01

Full Text Available Fungal polyketides are a diverse class of natural products, or secondary metabolites (SMs, with a wide range of bioactivities often associated with toxicity. Here, we focus on a group of non-reducing polyketide synthases (NR-PKSs in the fungal phylum Ascomycota that lack a thioesterase domain for product release, group V. Although widespread in ascomycete taxa, this group of NR-PKSs is notably absent in the mycotoxigenic genus Fusarium and, surprisingly, found in genera not known for their secondary metabolite production (e.g., the mycorrhizal genus Oidiodendron, the powdery mildew genus Blumeria, and the causative agent of white-nose syndrome in bats, Pseudogymnoascus destructans. This group of NR-PKSs, in association with the other enzymes encoded by their gene clusters, produces a variety of different chemical classes including naphthacenediones, anthraquinones, benzophenones, grisandienes, and diphenyl ethers. We discuss the modification of and transitions between these chemical classes, the requisite enzymes, and the evolution of the SM gene clusters that encode them. Integrating this information, we predict the likely products of related but uncharacterized SM clusters, and we speculate upon the utility of these classes of SMs as virulence factors or chemical defenses to various plant, animal, and insect pathogens, as well as mutualistic fungi.

Clustering Dycom

KAUST Repository

Minku, Leandro L.; Hou, Siqing

2017-01-01

baseline WC model is also included in the analysis. Results: Clustering Dycom with K-Means can potentially help to split the CC projects, managing to achieve similar or better predictive performance than Dycom. However, K-Means still requires the number

Assembling Fe/S-clusters and modifying tRNAs: ancient co-factors meet ancient adaptors.

Science.gov (United States)

Alfonzo, Juan D; Lukeš, Julius

2011-06-01

Trypanosoma brucei undergoes two clearly distinct develomental stages: in the insect vector (procyclic stage) the cells generate the bulk of their energy through respiration, whereas in the bloodstream of the mammalian host (bloodstream stage) they grow mostly glycolytically. Several mitochondrial respiratory proteins require iron-sulfur clusters for activity, and their activation coincides with developmental changes. Likewise some tRNA modification enzymes either require iron-sulfur clusters or use components of the iron-sulfur cluster assembly pathway for activity. These enzymes affect the anticodon loop of various tRNAs and can impact protein synthesis. Herein, the possibility of these pathways being integrated and exploited by T. brucei to carefully coordinate energy demands to translational rates in response to enviromental changes is examined.

Measurement Error Correction Formula for Cluster-Level Group Differences in Cluster Randomized and Observational Studies

Science.gov (United States)

Cho, Sun-Joo; Preacher, Kristopher J.

2016-01-01

Multilevel modeling (MLM) is frequently used to detect cluster-level group differences in cluster randomized trial and observational studies. Group differences on the outcomes (posttest scores) are detected by controlling for the covariate (pretest scores) as a proxy variable for unobserved factors that predict future attributes. The pretest and…

Differential Retention of Gene Functions in a Secondary Metabolite Cluster.

Science.gov (United States)

Reynolds, Hannah T; Slot, Jason C; Divon, Hege H; Lysøe, Erik; Proctor, Robert H; Brown, Daren W

2017-08-01

In fungi, distribution of secondary metabolite (SM) gene clusters is often associated with host- or environment-specific benefits provided by SMs. In the plant pathogen Alternaria brassicicola (Dothideomycetes), the DEP cluster confers an ability to synthesize the SM depudecin, a histone deacetylase inhibitor that contributes weakly to virulence. The DEP cluster includes genes encoding enzymes, a transporter, and a transcription regulator. We investigated the distribution and evolution of the DEP cluster in 585 fungal genomes and found a wide but sporadic distribution among Dothideomycetes, Sordariomycetes, and Eurotiomycetes. We confirmed DEP gene expression and depudecin production in one fungus, Fusarium langsethiae. Phylogenetic analyses suggested 6-10 horizontal gene transfers (HGTs) of the cluster, including a transfer that led to the presence of closely related cluster homologs in Alternaria and Fusarium. The analyses also indicated that HGTs were frequently followed by loss/pseudogenization of one or more DEP genes. Independent cluster inactivation was inferred in at least four fungal classes. Analyses of transitions among functional, pseudogenized, and absent states of DEP genes among Fusarium species suggest enzyme-encoding genes are lost at higher rates than the transporter (DEP3) and regulatory (DEP6) genes. The phenotype of an experimentally-induced DEP3 mutant of Fusarium did not support the hypothesis that selective retention of DEP3 and DEP6 protects fungi from exogenous depudecin. Together, the results suggest that HGT and gene loss have contributed significantly to DEP cluster distribution, and that some DEP genes provide a greater fitness benefit possibly due to a differential tendency to form network connections. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution 2017. This work is written by US Government employees and is in the public domain in the US.

Profiling the orphan enzymes

Science.gov (United States)

2014-01-01

The emergence of Next Generation Sequencing generates an incredible amount of sequence and great potential for new enzyme discovery. Despite this huge amount of data and the profusion of bioinformatic methods for function prediction, a large part of known enzyme activities is still lacking an associated protein sequence. These particular activities are called “orphan enzymes”. The present review proposes an update of previous surveys on orphan enzymes by mining the current content of public databases. While the percentage of orphan enzyme activities has decreased from 38% to 22% in ten years, there are still more than 1,000 orphans among the 5,000 entries of the Enzyme Commission (EC) classification. Taking into account all the reactions present in metabolic databases, this proportion dramatically increases to reach nearly 50% of orphans and many of them are not associated to a known pathway. We extended our survey to “local orphan enzymes” that are activities which have no representative sequence in a given clade, but have at least one in organisms belonging to other clades. We observe an important bias in Archaea and find that in general more than 30% of the EC activities have incomplete sequence information in at least one superkingdom. To estimate if candidate proteins for local orphans could be retrieved by homology search, we applied a simple strategy based on the PRIAM software and noticed that candidates may be proposed for an important fraction of local orphan enzymes. Finally, by studying relation between protein domains and catalyzed activities, it appears that newly discovered enzymes are mostly associated with already known enzyme domains. Thus, the exploration of the promiscuity and the multifunctional aspect of known enzyme families may solve part of the orphan enzyme issue. We conclude this review with a presentation of recent initiatives in finding proteins for orphan enzymes and in extending the enzyme world by the discovery of new

Enzymes of industrial purpose - review of the market of enzyme preparations and prospects for its development

Directory of Open Access Journals (Sweden)

A. A. Tolkacheva

2017-01-01

Full Text Available Microbial enzyme preparations are increasingly replacing conventional chemical catalysts in a number of industrial processes. Such drugs, in addition to environmental friendliness and high activity, have a number of advantages over enzyme preparations of vegetable and animal origin, namely: the production of microbial enzymes in bioreactors is easily controlled and predictable; excreted microbiological enzymes are more stable than intracellular animals and plant enzymes; the genetic diversity of microorganisms makes it possible to produce enzyme preparations with a wide range of specificity; microbiological enzymes can be synthesized year-round, in contrast to the production of plant enzymes, which is often seasonal. The leaders of the world market of enzymes are proteases and amylases, which account for 25% and 15%, respectively. Over the past five years, the world market for carbohydrases, including mainly amylases, cellulases and xylanases, has been the fastest growing segment of the enzyme market with an aggregate annual growth rate of more than 7.0%. Another major product of the industrial enzyme market, which has a great potential for growth, is lipases. From the point of view of designation, the main part is represented by food and food enzymes. The Russian market continues to be unsaturated - the current supply is not able to meet the needs of the Russian feed and food industry in enzyme preparations. Enzyme preparations of domestic producers are in demand in forage production, while food industrial enterprises prefer imported products. The most significant enterprises in the enzymatic industry in Russia at the moment are Sibbiofarm, AgroSistema, Agroferment. In the light of the Russian policy of increasing food security, the development of the domestic enzyme industry is an extremely topical task.

CytoCluster: A Cytoscape Plugin for Cluster Analysis and Visualization of Biological Networks.

Science.gov (United States)

Li, Min; Li, Dongyan; Tang, Yu; Wu, Fangxiang; Wang, Jianxin

2017-08-31

Nowadays, cluster analysis of biological networks has become one of the most important approaches to identifying functional modules as well as predicting protein complexes and network biomarkers. Furthermore, the visualization of clustering results is crucial to display the structure of biological networks. Here we present CytoCluster, a cytoscape plugin integrating six clustering algorithms, HC-PIN (Hierarchical Clustering algorithm in Protein Interaction Networks), OH-PIN (identifying Overlapping and Hierarchical modules in Protein Interaction Networks), IPCA (Identifying Protein Complex Algorithm), ClusterONE (Clustering with Overlapping Neighborhood Expansion), DCU (Detecting Complexes based on Uncertain graph model), IPC-MCE (Identifying Protein Complexes based on Maximal Complex Extension), and BinGO (the Biological networks Gene Ontology) function. Users can select different clustering algorithms according to their requirements. The main function of these six clustering algorithms is to detect protein complexes or functional modules. In addition, BinGO is used to determine which Gene Ontology (GO) categories are statistically overrepresented in a set of genes or a subgraph of a biological network. CytoCluster can be easily expanded, so that more clustering algorithms and functions can be added to this plugin. Since it was created in July 2013, CytoCluster has been downloaded more than 9700 times in the Cytoscape App store and has already been applied to the analysis of different biological networks. CytoCluster is available from http://apps.cytoscape.org/apps/cytocluster.

Stability cluster links hydrofobic gate to K873 in ATP8A2

DEFF Research Database (Denmark)

Mikkelsen, Stine; Vestergaard, Anna Lindeløv; Coleman, Jonathan Allan

ATPases, though it catalyzes the transport of a much larger substrate, an enigma referred to as the “giant substrate problem”. Recently, based on mutational analysis and molecular dynamics we have identified a hydrophobic gate in a groove surrounded by M1, M2, M4 and M6 (1). A plausible water filled...... harboring key residues in the center of the enzyme important for linking M5 through K873 to M4 and M6. This stability cluster supposedly allows M4 to act as a pumping rod during enzyme reaction cycle. We find that mutation of residues in this stability cluster affects the substrate affinity, as previously...

ComPath: comparative enzyme analysis and annotation in pathway/subsystem contexts

Directory of Open Access Journals (Sweden)

Kim Sun

2008-03-01

Full Text Available Abstract Background Once a new genome is sequenced, one of the important questions is to determine the presence and absence of biological pathways. Analysis of biological pathways in a genome is a complicated task since a number of biological entities are involved in pathways and biological pathways in different organisms are not identical. Computational pathway identification and analysis thus involves a number of computational tools and databases and typically done in comparison with pathways in other organisms. This computational requirement is much beyond the capability of biologists, so information systems for reconstructing, annotating, and analyzing biological pathways are much needed. We introduce a new comparative pathway analysis workbench, ComPath, which integrates various resources and computational tools using an interactive spreadsheet-style web interface for reliable pathway analyses. Results ComPath allows users to compare biological pathways in multiple genomes using a spreadsheet style web interface where various sequence-based analysis can be performed either to compare enzymes (e.g. sequence clustering and pathways (e.g. pathway hole identification, to search a genome for de novo prediction of enzymes, or to annotate a genome in comparison with reference genomes of choice. To fill in pathway holes or make de novo enzyme predictions, multiple computational methods such as FASTA, Whole-HMM, CSR-HMM (a method of our own introduced in this paper, and PDB-domain search are integrated in ComPath. Our experiments show that FASTA and CSR-HMM search methods generally outperform Whole-HMM and PDB-domain search methods in terms of sensitivity, but FASTA search performs poorly in terms of specificity, detecting more false positive as E-value cutoff increases. Overall, CSR-HMM search method performs best in terms of both sensitivity and specificity. Gene neighborhood and pathway neighborhood (global network visualization tools can be used

Transcriptome wide identification and characterization of starch branching enzyme in finger millet.

Science.gov (United States)

Tyagi, Rajhans; Tiwari, Apoorv; Garg, Vijay Kumar; Gupta, Sanjay

2017-01-01

Starch-branching enzymes (SBEs) are one of the four major enzyme classes involved in starch biosynthesis in plants and play an important role in determining the structure and physical properties of starch granules. Multiple SBEs are involved in starch biosynthesis in plants. Finger millet is calcium rich important serial crop belongs to grass family and the transcriptome data of developing spikes is available on NCBI. In this study it was try to find out the gene sequence of starch branching enzyme and annotate the sequence and submit the sequence for further use. Rice SBE sequence was taken as reference and for characterization of the sequence different in silico tools were used. Four domains were found in the finger millet Starch branching enzyme like alpha amylase catalytic domain from 925 to2172 with E value 0, N-terminal Early set domain from 634 to 915 with E value 1.62 e-42, Alpha amylase, C-terminal all-beta domain from 2224 to 2511 with E value 5.80e-24 and 1,4-alpha-glucan-branching enzyme from 421 to 2517 with E value 0. Major binding interactions with the GLC (alpha-d-glucose), CA (calcium ion), GOL (glycerol), TRS (2-amino-2-hydroxymethylpropane- 1, 3-diol), MG (magnesium ion) and FLC (citrate anion) are fond with different residues. It was found in the phylogenetic study of the finger millet SBE with the 6 species of grass family that two clusters were form A and B. In cluster A, finger millet showed closeness with Oryzasativa and Setariaitalica, Sorghum bicolour and Zea mays while cluster B was formed with Triticumaestivum and Brachypodium distachyon. The nucleotide sequence of Finger millet SBE was submitted to NCBI with the accession no KY648913 and protein structure of SBE of finger millet was also submitted in PMDB with the PMDB id - PM0080938. This research presents a comparative overview of Finger millet SBE and includes their properties, structural and functional characteristics, and recent developments on their post-translational regulation.

Clustering and visualizing similarity networks of membrane proteins.

Science.gov (United States)

Hu, Geng-Ming; Mai, Te-Lun; Chen, Chi-Ming

2015-08-01

We proposed a fast and unsupervised clustering method, minimum span clustering (MSC), for analyzing the sequence-structure-function relationship of biological networks, and demonstrated its validity in clustering the sequence/structure similarity networks (SSN) of 682 membrane protein (MP) chains. The MSC clustering of MPs based on their sequence information was found to be consistent with their tertiary structures and functions. For the largest seven clusters predicted by MSC, the consistency in chain function within the same cluster is found to be 100%. From analyzing the edge distribution of SSN for MPs, we found a characteristic threshold distance for the boundary between clusters, over which SSN of MPs could be properly clustered by an unsupervised sparsification of the network distance matrix. The clustering results of MPs from both MSC and the unsupervised sparsification methods are consistent with each other, and have high intracluster similarity and low intercluster similarity in sequence, structure, and function. Our study showed a strong sequence-structure-function relationship of MPs. We discussed evidence of convergent evolution of MPs and suggested applications in finding structural similarities and predicting biological functions of MP chains based on their sequence information. © 2015 Wiley Periodicals, Inc.

Cosmological constraints from Chandra observations of galaxy clusters.

Science.gov (United States)

Allen, Steven W

2002-09-15

Chandra observations of rich, relaxed galaxy clusters allow the properties of the X-ray gas and the total gravitating mass to be determined precisely. Here, we present results for a sample of the most X-ray luminous, dynamically relaxed clusters known. We show that the Chandra data and independent gravitational lensing studies provide consistent answers on the mass distributions in the clusters. The mass profiles exhibit a form in good agreement with the predictions from numerical simulations. Combining Chandra results on the X-ray gas mass fractions in the clusters with independent measurements of the Hubble constant and the mean baryonic matter density in the Universe, we obtain a tight constraint on the mean total matter density of the Universe, Omega(m), and an interesting constraint on the cosmological constant, Omega(Lambda). We also describe the 'virial relations' linking the masses, X-ray temperatures and luminosities of galaxy clusters. These relations provide a key step in linking the observed number density and spatial distribution of clusters to the predictions from cosmological models. The Chandra data confirm the presence of a systematic offset of ca. 40% between the normalization of the observed mass-temperature relation and the predictions from standard simulations. This finding leads to a significant revision of the best-fit value of sigma(8) inferred from the observed temperature and luminosity functions of clusters.

Characterization and prediction of the backscattered form function of an immersed cylindrical shell using hybrid fuzzy clustering and bio-inspired algorithms.

Science.gov (United States)

Agounad, Said; Aassif, El Houcein; Khandouch, Younes; Maze, Gérard; Décultot, Dominique

2018-02-01

The acoustic scattering of a plane wave by an elastic cylindrical shell is studied. A new approach is developed to predict the form function of an immersed cylindrical shell of the radius ratio b/a ('b' is the inner radius and 'a' is the outer radius). The prediction of the backscattered form function is investigated by a combined approach between fuzzy clustering algorithms and bio-inspired algorithms. Four famous fuzzy clustering algorithms: the fuzzy c-means (FCM), the Gustafson-Kessel algorithm (GK), the fuzzy c-regression model (FCRM) and the Gath-Geva algorithm (GG) are combined with particle swarm optimization and genetic algorithm. The symmetric and antisymmetric circumferential waves A, S 0 , A 1 , S 1 and S 2 are investigated in a reduced frequency (k 1 a) range extends over 0.1predicted and calculated acoustic backscattered form functions. This representation is used as a comparison criterion between the calculated form function by the analytical method and that predicted by the proposed approach on the one hand and is used to extract the predicted cut-off frequencies on the other hand. Moreover, the transverse velocity of the material constituting the cylindrical shell is extracted. The computational results show that the proposed approach is very efficient to predict the form function and consequently, for acoustic characterization purposes. Copyright © 2017 Elsevier B.V. All rights reserved.

Subaru Weak Lensing Measurements of Four Strong Lensing Clusters: Are Lensing Clusters Over-Concentrated?

Energy Technology Data Exchange (ETDEWEB)

Oguri, Masamune; Hennawi, Joseph F.; Gladders, Michael D.; Dahle, Haakon; Natarajan, Priyamvada; Dalal, Neal; Koester, Benjamin P.; Sharon, Keren; Bayliss, Matthew

2009-01-29

We derive radial mass profiles of four strong lensing selected clusters which show prominent giant arcs (Abell 1703, SDSS J1446+3032, SDSS J1531+3414, and SDSS J2111-0115), by combining detailed strong lens modeling with weak lensing shear measured from deep Subaru Suprime-cam images. Weak lensing signals are detected at high significance for all four clusters, whose redshifts range from z = 0.28 to 0.64. We demonstrate that adding strong lensing information with known arc redshifts significantly improves constraints on the mass density profile, compared to those obtained from weak lensing alone. While the mass profiles are well fitted by the universal form predicted in N-body simulations of the {Lambda}-dominated cold dark matter model, all four clusters appear to be slightly more centrally concentrated (the concentration parameters c{sub vir} {approx} 8) than theoretical predictions, even after accounting for the bias toward higher concentrations inherent in lensing selected samples. Our results are consistent with previous studies which similarly detected a concentration excess, and increases the total number of clusters studied with the combined strong and weak lensing technique to ten. Combining our sample with previous work, we find that clusters with larger Einstein radii are more anomalously concentrated. We also present a detailed model of the lensing cluster Abell 1703 with constraints from multiple image families, and find the dark matter inner density profile to be cuspy with the slope consistent with -1, in agreement with expectations.

MOLECULAR MODELLING OF HUMAN ALDEHYDE OXIDASE AND IDENTIFICATION OF THE KEY INTERACTIONS IN THE ENZYME-SUBSTRATE COMPLEX

Directory of Open Access Journals (Sweden)

Siavoush Dastmalchi

2005-05-01

Full Text Available Aldehyde oxidase (EC 1.2.3.1, a cytosolic enzyme containing FAD, molybdenum and iron-sulphur cluster, is a member of non-cytochrome P-450 enzymes called molybdenum hydroxylases which is involved in the metabolism of a wide range of endogenous compounds and many drug substances. Drug metabolism is one of the important characteristics which influences many aspects of a therapeutic agent such as routes of administration, drug interaction and toxicity and therefore, characterisation of the key interactions between enzymes and substrates is very important from drug development point of view. The aim of this study was to generate a three-dimensional model of human aldehyde oxidase (AO in order to assist us to identify the mode of interaction between enzyme and a set of phethalazine/quinazoline derivatives. Both sequence-based (BLAST and inverse protein fold recognition methods (THREADER were used to identify the crystal structure of bovine xanthine dehydrogenase (pdb code of 1FO4 as the suitable template for comparative modelling of human AO. Model structure was generated by aligning and then threading the sequence of human AO onto the template structure, incorporating the associated cofactors, and molecular dynamics simulations and energy minimization using GROMACS program. Different criteria which were measured by the PROCHECK, QPACK, VERIFY-3D were indicative of a proper fold for the predicted structural model of human AO. For example, 97.9 percentages of phi and psi angles were in the favoured and most favoured regions in the ramachandran plot, and all residues in the model are assigned environmentally positive compatibility scores. Further evaluation on the model quality was performed by investigation of AO-mediated oxidation of a set of phthalazine/quinazoline derivatives to develop QSAR model capable of describing the extent of the oxidation. Substrates were aligned by docking onto the active site of the enzyme using GOLD technology and then

A 3.55 keV line from DM →a→γ: predictions for cool-core and non-cool-core clusters

Energy Technology Data Exchange (ETDEWEB)

Conlon, Joseph P.; Powell, Andrew J. [Rudolf Peierls Centre for Theoretical Physics, University of Oxford, 1 Keble Road, Oxford, OX1 3NP (United Kingdom)

2015-01-13

We further study a scenario in which a 3.55 keV X-ray line arises from decay of dark matter to an axion-like particle (ALP), that subsequently converts to a photon in astrophysical magnetic fields. We perform numerical simulations of Gaussian random magnetic fields with radial scaling of the magnetic field magnitude with the electron density, for both cool-core 'Perseus' and non-cool-core 'Coma' electron density profiles. Using these, we quantitatively study the resulting signal strength and morphology for cool-core and non-cool-core clusters. Our study includes the effects of fields of view that cover only the central part of the cluster, the effects of offset pointings on the radial decline of signal strength and the effects of dividing clusters into annuli. We find good agreement with current data and make predictions for future analyses and observations.

Prediction of difficult mask ventilation using a systematic assessment of risk factors vs. existing practice - a cluster randomised clinical trial in 94,006 patients

DEFF Research Database (Denmark)

Nørskov, A K; Wetterslev, J; Rosenstock, C V

2017-01-01

We compared implementation of systematic airway assessment with existing practice of airway assessment on prediction of difficult mask ventilation. Twenty-six departments were cluster-randomised to assess eleven risk factors for difficult airway management (intervention) or to continue with their......We compared implementation of systematic airway assessment with existing practice of airway assessment on prediction of difficult mask ventilation. Twenty-six departments were cluster-randomised to assess eleven risk factors for difficult airway management (intervention) or to continue...... with their existing airway assessment (control). In both groups, patients predicted as a difficult mask ventilation and/or difficult intubation were registered in the Danish Anaesthesia Database, with a notational summary of airway management. The trial's primary outcome was the respective incidence of unpredicted...... difficult and easy mask ventilation in the two groups. Among 94,006 patients undergoing mask ventilation, the incidence of unpredicted difficult mask ventilation in the intervention group was 0.91% and 0.88% in the control group; (OR) 0.98 (95% CI 0.66-1.44), p = 0.90. The incidence of patients predicted...

Motif-independent prediction of a secondary metabolism gene cluster using comparative genomics: application to sequenced genomes of Aspergillus and ten other filamentous fungal species.

Science.gov (United States)

Takeda, Itaru; Umemura, Myco; Koike, Hideaki; Asai, Kiyoshi; Machida, Masayuki

2014-08-01

Despite their biological importance, a significant number of genes for secondary metabolite biosynthesis (SMB) remain undetected due largely to the fact that they are highly diverse and are not expressed under a variety of cultivation conditions. Several software tools including SMURF and antiSMASH have been developed to predict fungal SMB gene clusters by finding core genes encoding polyketide synthase, nonribosomal peptide synthetase and dimethylallyltryptophan synthase as well as several others typically present in the cluster. In this work, we have devised a novel comparative genomics method to identify SMB gene clusters that is independent of motif information of the known SMB genes. The method detects SMB gene clusters by searching for a similar order of genes and their presence in nonsyntenic blocks. With this method, we were able to identify many known SMB gene clusters with the core genes in the genomic sequences of 10 filamentous fungi. Furthermore, we have also detected SMB gene clusters without core genes, including the kojic acid biosynthesis gene cluster of Aspergillus oryzae. By varying the detection parameters of the method, a significant difference in the sequence characteristics was detected between the genes residing inside the clusters and those outside the clusters. © The Author 2014. Published by Oxford University Press on behalf of Kazusa DNA Research Institute.

Improved predictive mapping of indoor radon concentrations using ensemble regression trees based on automatic clustering of geological units

International Nuclear Information System (INIS)

Kropat, Georg; Bochud, Francois; Jaboyedoff, Michel; Laedermann, Jean-Pascal; Murith, Christophe; Palacios, Martha; Baechler, Sébastien

2015-01-01

Purpose: According to estimations around 230 people die as a result of radon exposure in Switzerland. This public health concern makes reliable indoor radon prediction and mapping methods necessary in order to improve risk communication to the public. The aim of this study was to develop an automated method to classify lithological units according to their radon characteristics and to develop mapping and predictive tools in order to improve local radon prediction. Method: About 240 000 indoor radon concentration (IRC) measurements in about 150 000 buildings were available for our analysis. The automated classification of lithological units was based on k-medoids clustering via pair-wise Kolmogorov distances between IRC distributions of lithological units. For IRC mapping and prediction we used random forests and Bayesian additive regression trees (BART). Results: The automated classification groups lithological units well in terms of their IRC characteristics. Especially the IRC differences in metamorphic rocks like gneiss are well revealed by this method. The maps produced by random forests soundly represent the regional difference of IRCs in Switzerland and improve the spatial detail compared to existing approaches. We could explain 33% of the variations in IRC data with random forests. Additionally, the influence of a variable evaluated by random forests shows that building characteristics are less important predictors for IRCs than spatial/geological influences. BART could explain 29% of IRC variability and produced maps that indicate the prediction uncertainty. Conclusion: Ensemble regression trees are a powerful tool to model and understand the multidimensional influences on IRCs. Automatic clustering of lithological units complements this method by facilitating the interpretation of radon properties of rock types. This study provides an important element for radon risk communication. Future approaches should consider taking into account further variables

Warming rays in cluster cool cores

Science.gov (United States)

Colafrancesco, S.; Marchegiani, P.

2008-06-01

Context: Cosmic rays are confined in the atmospheres of galaxy clusters and, therefore, they can play a crucial role in the heating of their cool cores. Aims: We discuss here the thermal and non-thermal features of a model of cosmic ray heating of cluster cores that can provide a solution to the cooling-flow problems. To this aim, we generalize a model originally proposed by Colafrancesco, Dar & DeRujula (2004) and we show that our model predicts specific correlations between the thermal and non-thermal properties of galaxy clusters and enables various observational tests. Methods: The model reproduces the observed temperature distribution in clusters by using an energy balance condition in which the X-ray energy emitted by clusters is supplied, in a quasi-steady state, by the hadronic cosmic rays, which act as “warming rays” (WRs). The temperature profile of the intracluster (IC) gas is strictly correlated with the pressure distribution of the WRs and, consequently, with the non-thermal emission (radio, hard X-ray and gamma-ray) induced by the interaction of the WRs with the IC gas and the IC magnetic field. Results: The temperature distribution of the IC gas in both cool-core and non cool-core clusters is successfully predicted from the measured IC plasma density distribution. Under this contraint, the WR model is also able to reproduce the thermal and non-thermal pressure distribution in clusters, as well as their radial entropy distribution, as shown by the analysis of three clusters studied in detail: Perseus, A2199 and Hydra. The WR model provides other observable features of galaxy clusters: a correlation of the pressure ratio (WRs to thermal IC gas) with the inner cluster temperature (P_WR/P_th) ˜ (kT_inner)-2/3, a correlation of the gamma-ray luminosity with the inner cluster temperature Lγ ˜ (kT_inner)4/3, a substantial number of cool-core clusters observable with the GLAST-LAT experiment, a surface brightness of radio halos in cool-core clusters

Alpha chymotrypsin coated clusters of Fe3O4 nanoparticles for biocatalysis in low water media

Directory of Open Access Journals (Sweden)

Mukherjee Joyeeta

2012-11-01

Full Text Available Abstract Background Enzymes in low water containing non aqueous media are useful for organic synthesis. For example, hydrolases in such media can be used for synthetic purposes. Initial work in this area was carried out with lyophilized powders of enzymes. These were found to have poor activity. Drying (removing bulk water by precipitation turned out to be a better approach. As enzymes in such media are heterogeneous catalysts, spreading these precipitates over a large surface gave even better results. In this context, nanoparticles with their better surface to volume ratio provide obvious advantage. Magnetic nanoparticles have an added advantage of easy separation after the reaction. Keeping this in view, alpha chymotrypsin solution in water was precipitated over a stirred population of Fe3O4 nanoparticles in n-propanol. This led to alpha chymotrypsin activity coated over clusters of Fe3O4 nanoparticles. These preparations were found to have quite high transesterification activity in low water containing n-octane. Results Precipitation of alpha chymotrypsin over a stirred suspension of Fe3O4 nanoparticles (3.6 nm diameter led to the formation of enzyme coated clusters of nanoparticles (ECCNs. These clusters were also magnetic and their hydrodynamic diameter ranged from 1.2- 2.6 microns (as measured by dynamic light scattering. Transmission electron microscopy (TEM, showed that these clusters had highly irregular shapes. Transesterification assay of various clusters in anhydrous n-octane led to optimization of concentration of nanoparticles in suspension during precipitation. Optimized design of enzyme coated magnetic clusters of nanoparticles (ECCN 3 showed the highest initial rate of 465 nmol min-1 mg-1protein which was about 9 times higher as compared to the simple precipitates with an initial rate of 52 nmol min-1 mg-1 protein. Circular Dichroism (CD(with a spinning cell accessory showed that secondary structure content of the alpha

Diphthamide biosynthesis requires an organic radical generated by an iron-sulphur enzyme

Energy Technology Data Exchange (ETDEWEB)

Zhang, Yang; Zhu, Xuling; Torelli, Andrew T; Lee, Michael; Dzikovski, Boris; Koralewski, Rachel M; Wang, Eileen; Freed, Jack; Krebs, Carsten; Ealick, Steve E; Lin, Hening [Cornell; (Penn)

2010-08-30

Archaeal and eukaryotic translation elongation factor 2 contain a unique post-translationally modified histidine residue called diphthamide, which is the target of diphtheria toxin. The biosynthesis of diphthamide was proposed to involve three steps, with the first being the formation of a C-C bond between the histidine residue and the 3-amino-3-carboxypropyl group of S-adenosyl-l-methionine (SAM). However, further details of the biosynthesis remain unknown. Here we present structural and biochemical evidence showing that the first step of diphthamide biosynthesis in the archaeon Pyrococcus horikoshii uses a novel iron-sulphur-cluster enzyme, Dph2. Dph2 is a homodimer and each of its monomers can bind a [4Fe-4S] cluster. Biochemical data suggest that unlike the enzymes in the radical SAM superfamily, Dph2 does not form the canonical 5'-deoxyadenosyl radical. Instead, it breaks the C_γ,Met-S bond of SAM and generates a 3-amino-3-carboxypropyl radical. Our results suggest that P. horikoshii Dph2 represents a previously unknown, SAM-dependent, [4Fe-4S]-containing enzyme that catalyses unprecedented chemistry.

CLUMP-3D: Testing ΛCDM with Galaxy Cluster Shapes

Science.gov (United States)

Sereno, Mauro; Umetsu, Keiichi; Ettori, Stefano; Sayers, Jack; Chiu, I.-Non; Meneghetti, Massimo; Vega-Ferrero, Jesús; Zitrin, Adi

2018-06-01

The ΛCDM model of structure formation makes strong predictions on the concentration and shape of dark matter (DM) halos, which are determined by mass accretion processes. Comparison between predicted shapes and observations provides a geometric test of the ΛCDM model. Accurate and precise measurements needs a full three-dimensional (3D) analysis of the cluster mass distribution. We accomplish this with a multi-probe 3D analysis of the X-ray regular Cluster Lensing and Supernova survey with Hubble (CLASH) clusters combining strong and weak lensing, X-ray photometry and spectroscopy, and the Sunyaev–Zel’dovich effect (SZe). The cluster shapes and concentrations are consistent with ΛCDM predictions. The CLASH clusters are randomly oriented, as expected given the sample selection criteria. Shapes agree with numerical results for DM-only halos, which hints at baryonic physics being less effective in making halos rounder.

Mining the enzymes involved in the detoxification of reactive oxygen species (ROS) in sugarcane.

Science.gov (United States)

Kurama, Eiko E; Fenille, Roseli C; Rosa, Vicente E; Rosa, Daniel D; Ulian, Eugenio C

2002-07-01

Summary Adopting the sequencing of expressed sequence tags (ESTs) of a sugarcane database derived from libraries induced and not induced by pathogens, we identified EST clusters homologous to genes corresponding to enzymes involved in the detoxification of reactive oxygen species. The predicted amino acids of these enzymes are superoxide dismutases (SODs), glutathione-S-transferase (GST), glutathione peroxidase (GPX), and catalases. Three MnSOD mitochondrial precursors and 10 CuZnSOD were identified in sugarcane: the MnSOD mitochondrial precursor is 96% similar to the maize MnSOD mitochondrial precursor and, of the 10 CuZnSOD identified, seven were 98% identical to maize cytosolic CuZnSOD4 and one was 67% identical to putative peroxisomal CuZnSOD from Arabidopsis. Three homologues to class Phi GST were 87-88% identical to GST III from maize. Five GPX homologues were identified: three were homologous to cytosolic GPX from barley, one was 88% identical to phospholipid hydroperoxide glutathione peroxidase (PHGPX) from rice, and the last was 71% identical to GPX from A. thaliana. Three enzymes similar to maize catalase were identified in sugarcane: two were similar to catalase isozyme 3 and catalase chain 3 from maize, which are mitochondrial, and one was similar to catalase isozyme 1 from maize, whose location is peroxisomal subcellular. All enzymes were induced in all sugarcane libraries (flower, seed, root, callus, leaves) and also in the pathogen-induced libraries, except for CuZnSOD whose cDNA was detected in none of the libraries induced by pathogens (Acetobacter diazotroficans and Herbaspirillum rubrisubalbicans). The expression of the enzymes SOD, GST, GPX, and catalases involved in the detoxification was examined using reverse transcriptase-polymerase chain reaction in cDNA from leaves of sugarcane under biotic stress conditions, inoculated with Puccinia melanocephala, the causal agent of sugarcane rust disease.

Vacancy-indium clusters in implanted germanium

KAUST Repository

Chroneos, Alexander I.

2010-04-01

Secondary ion mass spectroscopy measurements of heavily indium doped germanium samples revealed that a significant proportion of the indium dose is immobile. Using electronic structure calculations we address the possibility of indium clustering with point defects by predicting the stability of indium-vacancy clusters, InnVm. We find that the formation of large clusters is energetically favorable, which can explain the immobility of the indium ions. © 2010 Elsevier B.V. All rights reserved.

Vacancy-indium clusters in implanted germanium

KAUST Repository

Chroneos, Alexander I.; Kube, R.; Bracht, Hartmut A.; Grimes, Robin W.; Schwingenschlö gl, Udo

2010-01-01

Secondary ion mass spectroscopy measurements of heavily indium doped germanium samples revealed that a significant proportion of the indium dose is immobile. Using electronic structure calculations we address the possibility of indium clustering with point defects by predicting the stability of indium-vacancy clusters, InnVm. We find that the formation of large clusters is energetically favorable, which can explain the immobility of the indium ions. © 2010 Elsevier B.V. All rights reserved.

Nonthermal emission from clusters of galaxies

International Nuclear Information System (INIS)

Kushnir, Doron; Waxman, Eli

2009-01-01

We show that the spectral and radial distribution of the nonthermal emission of massive, M ∼> 10 14.5 M ☉ , galaxy clusters may be approximately described by simple analytic expressions, which depend on the cluster thermal X-ray properties and on two model parameter, β core and η e . β core is the ratio of the cosmic-ray (CR) energy density (within a logarithmic CR energy interval) and the thermal energy density at the cluster core, and η e(p) is the fraction of the thermal energy generated in strong collisionless shocks, which is deposited in CR electrons (protons). Using a simple analytic model for the evolution of intra-cluster medium CRs, which are produced by accretion shocks, we find that β core ≅ η p /200, nearly independent of cluster mass and with a scatter Δln β core ≅ 1 between clusters of given mass. We show that the hard X-ray (HXR) and γ-ray luminosities produced by inverse Compton scattering of CMB photons by electrons accelerated in accretion shocks (primary electrons) exceed the luminosities produced by secondary particles (generated in hadronic interactions within the cluster) by factors ≅ 500(η e /η p )(T/10 keV) −1/2 and ≅ 150(η e /η p )(T/10 keV) −1/2 respectively, where T is the cluster temperature. Secondary particle emission may dominate at the radio and very high energy (∼> 1 TeV) γ-ray bands. Our model predicts, in contrast with some earlier work, that the HXR and γ-ray emission from clusters of galaxies are extended, since the emission is dominated at these energies by primary (rather than by secondary) electrons. Our predictions are consistent with the observed nonthermal emission of the Coma cluster for η p ∼ η e ∼ 0.1. The implications of our predictions to future HXR observations (e.g. by NuStar, Simbol-X) and to (space/ground based) γ-ray observations (e.g. by Fermi, HESS, MAGIC, VERITAS) are discussed. In particular, we identify the clusters which are the best candidates for detection in �

Nonthermal emission from clusters of galaxies

Science.gov (United States)

Kushnir, Doron; Waxman, Eli

2009-08-01

We show that the spectral and radial distribution of the nonthermal emission of massive, M gtrsim 1014.5Msun, galaxy clusters may be approximately described by simple analytic expressions, which depend on the cluster thermal X-ray properties and on two model parameter, βcore and ηe. βcore is the ratio of the cosmic-ray (CR) energy density (within a logarithmic CR energy interval) and the thermal energy density at the cluster core, and ηe(p) is the fraction of the thermal energy generated in strong collisionless shocks, which is deposited in CR electrons (protons). Using a simple analytic model for the evolution of intra-cluster medium CRs, which are produced by accretion shocks, we find that βcore simeq ηp/200, nearly independent of cluster mass and with a scatter Δln βcore simeq 1 between clusters of given mass. We show that the hard X-ray (HXR) and γ-ray luminosities produced by inverse Compton scattering of CMB photons by electrons accelerated in accretion shocks (primary electrons) exceed the luminosities produced by secondary particles (generated in hadronic interactions within the cluster) by factors simeq 500(ηe/ηp)(T/10 keV)-1/2 and simeq 150(ηe/ηp)(T/10 keV)-1/2 respectively, where T is the cluster temperature. Secondary particle emission may dominate at the radio and very high energy (gtrsim 1 TeV) γ-ray bands. Our model predicts, in contrast with some earlier work, that the HXR and γ-ray emission from clusters of galaxies are extended, since the emission is dominated at these energies by primary (rather than by secondary) electrons. Our predictions are consistent with the observed nonthermal emission of the Coma cluster for ηp ~ ηe ~ 0.1. The implications of our predictions to future HXR observations (e.g. by NuStar, Simbol-X) and to (space/ground based) γ-ray observations (e.g. by Fermi, HESS, MAGIC, VERITAS) are discussed. In particular, we identify the clusters which are the best candidates for detection in γ-rays. Finally, we show

Gas phase reactivity of thermal metal clusters

Science.gov (United States)

Castleman, A. W., Jr.; Harms, A. C.; Leuchtner, R. E.

1991-03-01

Reaction kinetics of metal cluster ions under well defined thermal conditions were studied using a flow tube reactor in combination with laser vaporization. Aluminum anions and cations were reacted with oxygen, and several species which are predicted jellium shell closings, were found to have special stability. Metal alloy cluster anions comprised of Al, V and Nb were also seen to react with oxygen. Alloy clusters with an even number of electrons reacted more slowly than odd electron species, and certain clusters appeared to be exceptionally unreactive. Copper cation clusters were observed to associate with carbon monoxide with reactivities that approach bulk behavior at surprisingly small cluster size. These reactions demonstrate how the rate of reaction changes with cluster size.

Evaluation of B3LYP, X3LYP, and M06-class density functionals for predicting the binding energies of neutral, protonated, and deprotonated water clusters

OpenAIRE

Bryantsev, Vyacheslav S.; Diallo, Mamadou S.; van Duin, Adri C. T.; Goddard, William A., III

2009-01-01

In this paper we assess the accuracy of the B3LYP, X3LYP, and newly developed M06-L, M06-2X, and M06 functionals to predict the binding energies of neutral and charged water clusters including (H_2O)_n, n = 2−8, 20), H_3O+(H_2O_)n, n = 1−6, and OH−(H_2O)_n, n = 1−6. We also compare the predicted energies of two ion hydration and neutralization reactions on the basis of the calculated binding energies. In all cases, we use as benchmarks calculated binding energies of water clusters extrapolate...

Effects of the number of markers per haplotype and clustering of haplotypes on the accuracy of QTL mapping and prediction of genomic breeding values

Directory of Open Access Journals (Sweden)

Schrooten Chris

2009-01-01

Full Text Available Abstract The aim of this paper was to compare the effect of haplotype definition on the precision of QTL-mapping and on the accuracy of predicted genomic breeding values. In a multiple QTL model using identity-by-descent (IBD probabilities between haplotypes, various haplotype definitions were tested i.e. including 2, 6, 12 or 20 marker alleles and clustering base haplotypes related with an IBD probability of > 0.55, 0.75 or 0.95. Simulated data contained 1100 animals with known genotypes and phenotypes and 1000 animals with known genotypes and unknown phenotypes. Genomes comprising 3 Morgan were simulated and contained 74 polymorphic QTL and 383 polymorphic SNP markers with an average r2 value of 0.14 between adjacent markers. The total number of haplotypes decreased up to 50% when the window size was increased from two to 20 markers and decreased by at least 50% when haplotypes related with an IBD probability of > 0.55 instead of > 0.95 were clustered. An intermediate window size led to more precise QTL mapping. Window size and clustering had a limited effect on the accuracy of predicted total breeding values, ranging from 0.79 to 0.81. Our conclusion is that different optimal window sizes should be used in QTL-mapping versus genome-wide breeding value prediction.

Modified Newtonian dynamics and the Coma cluster

International Nuclear Information System (INIS)

The, L.S.; White, S.D.M.

1988-01-01

The consistency of Milgrom's theory of modified Newtonian dynamics is checked against optical and X-ray data for the Coma cluster of galaxies. It is found that viable models for the cluster containing no dark matter can be constructed. They require an extensive gaseous atmosphere through which galaxies move on near-radial orbits. The gas temperature is predicted to have a shallow minimum near the cluster center; this structure may conflict with the best X-ray spectra of the cluster. 18 references

On the Structural Context and Identification of Enzyme Catalytic Residues

Directory of Open Access Journals (Sweden)

Yu-Tung Chien

2013-01-01

Full Text Available Enzymes play important roles in most of the biological processes. Although only a small fraction of residues are directly involved in catalytic reactions, these catalytic residues are the most crucial parts in enzymes. The study of the fundamental and unique features of catalytic residues benefits the understanding of enzyme functions and catalytic mechanisms. In this work, we analyze the structural context of catalytic residues based on theoretical and experimental structure flexibility. The results show that catalytic residues have distinct structural features and context. Their neighboring residues, whether sequence or structure neighbors within specific range, are usually structurally more rigid than those of noncatalytic residues. The structural context feature is combined with support vector machine to identify catalytic residues from enzyme structure. The prediction results are better or comparable to those of recent structure-based prediction methods.

Two Gene Clusters Coordinate Galactose and Lactose Metabolism in Streptococcus gordonii

Science.gov (United States)

Zeng, Lin; Martino, Nicole C.

2012-01-01

Streptococcus gordonii is an early colonizer of the human oral cavity and an abundant constituent of oral biofilms. Two tandemly arranged gene clusters, designated lac and gal, were identified in the S. gordonii DL1 genome, which encode genes of the tagatose pathway (lacABCD) and sugar phosphotransferase system (PTS) enzyme II permeases. Genes encoding a predicted phospho-β-galactosidase (LacG), a DeoR family transcriptional regulator (LacR), and a transcriptional antiterminator (LacT) were also present in the clusters. Growth and PTS assays supported that the permease designated EIILac transports lactose and galactose, whereas EIIGal transports galactose. The expression of the gene for EIIGal was markedly upregulated in cells growing on galactose. Using promoter-cat fusions, a role for LacR in the regulation of the expressions of both gene clusters was demonstrated, and the gal cluster was also shown to be sensitive to repression by CcpA. The deletion of lacT caused an inability to grow on lactose, apparently because of its role in the regulation of the expression of the genes for EIILac, but had little effect on galactose utilization. S. gordonii maintained a selective advantage over Streptococcus mutans in a mixed-species competition assay, associated with its possession of a high-affinity galactose PTS, although S. mutans could persist better at low pHs. Collectively, these results support the concept that the galactose and lactose systems of S. gordonii are subject to complex regulation and that a high-affinity galactose PTS may be advantageous when S. gordonii is competing against the caries pathogen S. mutans in oral biofilms. PMID:22660715

Activity of an enzyme immobilized on superparamagnetic particles in a rotational magnetic field

Energy Technology Data Exchange (ETDEWEB)

Mizuki, Toru; Watanabe, Noriyuki; Nagaoka, Yutaka [Bio-Nano Electronics Research Centre, Toyo University, Saitama 350-8585 (Japan); Fukushima, Tadamasa [Shimadzu GLC Ltd., Phenomenex Support Centre, Tokyo 110-0016 (Japan); Morimoto, Hisao; Usami, Ron [Bio-Nano Electronics Research Centre, Toyo University, Saitama 350-8585 (Japan); Maekawa, Toru, E-mail: maekawa@toyonet.toyo.ac.jp [Bio-Nano Electronics Research Centre, Toyo University, Saitama 350-8585 (Japan)

2010-03-19

We immobilize {alpha}-amylase extracted from Bacillus Iicheniformis on the surfaces of superparamagnetic particles and investigate the effect of a rotational magnetic field on the enzyme's activity. We find that the activity of the enzyme molecules immobilized on superparamagnetic particles increases in the rotational magnetic field and reaches maximum at a certain frequency. We clarify the effect of the cluster structures formed by the superparamagnetic particles on the activity. Enzyme reactions are enhanced even in a tiny volume of solution using the present method, which is very important for the development of efficient micro reactors and micro total analysis systems ({mu}-TAS).

Iron Sulfur and Molybdenum Cofactor Enzymes Regulate the Drosophila Life Cycle by Controlling Cell Metabolism

Science.gov (United States)

Marelja, Zvonimir; Leimkühler, Silke; Missirlis, Fanis

2018-01-01

Iron sulfur (Fe-S) clusters and the molybdenum cofactor (Moco) are present at enzyme sites, where the active metal facilitates electron transfer. Such enzyme systems are soluble in the mitochondrial matrix, cytosol and nucleus, or embedded in the inner mitochondrial membrane, but virtually absent from the cell secretory pathway. They are of ancient evolutionary origin supporting respiration, DNA replication, transcription, translation, the biosynthesis of steroids, heme, catabolism of purines, hydroxylation of xenobiotics, and cellular sulfur metabolism. Here, Fe-S cluster and Moco biosynthesis in Drosophila melanogaster is reviewed and the multiple biochemical and physiological functions of known Fe-S and Moco enzymes are described. We show that RNA interference of Mocs3 disrupts Moco biosynthesis and the circadian clock. Fe-S-dependent mitochondrial respiration is discussed in the context of germ line and somatic development, stem cell differentiation and aging. The subcellular compartmentalization of the Fe-S and Moco assembly machinery components and their connections to iron sensing mechanisms and intermediary metabolism are emphasized. A biochemically active Fe-S core complex of heterologously expressed fly Nfs1, Isd11, IscU, and human frataxin is presented. Based on the recent demonstration that copper displaces the Fe-S cluster of yeast and human ferredoxin, an explanation for why high dietary copper leads to cytoplasmic iron deficiency in flies is proposed. Another proposal that exosomes contribute to the transport of xanthine dehydrogenase from peripheral tissues to the eye pigment cells is put forward, where the Vps16a subunit of the HOPS complex may have a specialized role in concentrating this enzyme within pigment granules. Finally, we formulate a hypothesis that (i) mitochondrial superoxide mobilizes iron from the Fe-S clusters in aconitase and succinate dehydrogenase; (ii) increased iron transiently displaces manganese on superoxide dismutase, which

Iron Sulfur and Molybdenum Cofactor Enzymes Regulate the Drosophila Life Cycle by Controlling Cell Metabolism

Directory of Open Access Journals (Sweden)

Zvonimir Marelja

2018-02-01

Full Text Available Iron sulfur (Fe-S clusters and the molybdenum cofactor (Moco are present at enzyme sites, where the active metal facilitates electron transfer. Such enzyme systems are soluble in the mitochondrial matrix, cytosol and nucleus, or embedded in the inner mitochondrial membrane, but virtually absent from the cell secretory pathway. They are of ancient evolutionary origin supporting respiration, DNA replication, transcription, translation, the biosynthesis of steroids, heme, catabolism of purines, hydroxylation of xenobiotics, and cellular sulfur metabolism. Here, Fe-S cluster and Moco biosynthesis in Drosophila melanogaster is reviewed and the multiple biochemical and physiological functions of known Fe-S and Moco enzymes are described. We show that RNA interference of Mocs3 disrupts Moco biosynthesis and the circadian clock. Fe-S-dependent mitochondrial respiration is discussed in the context of germ line and somatic development, stem cell differentiation and aging. The subcellular compartmentalization of the Fe-S and Moco assembly machinery components and their connections to iron sensing mechanisms and intermediary metabolism are emphasized. A biochemically active Fe-S core complex of heterologously expressed fly Nfs1, Isd11, IscU, and human frataxin is presented. Based on the recent demonstration that copper displaces the Fe-S cluster of yeast and human ferredoxin, an explanation for why high dietary copper leads to cytoplasmic iron deficiency in flies is proposed. Another proposal that exosomes contribute to the transport of xanthine dehydrogenase from peripheral tissues to the eye pigment cells is put forward, where the Vps16a subunit of the HOPS complex may have a specialized role in concentrating this enzyme within pigment granules. Finally, we formulate a hypothesis that (i mitochondrial superoxide mobilizes iron from the Fe-S clusters in aconitase and succinate dehydrogenase; (ii increased iron transiently displaces manganese on superoxide

Cluster approach to the prediction of thermodynamic and transport properties of ionic liquids

Science.gov (United States)

Seeger, Zoe L.; Kobayashi, Rika; Izgorodina, Ekaterina I.

2018-05-01

The prediction of physicochemical properties of ionic liquids such as conductivity and melting point would substantially aid the targeted design of ionic liquids for specific applications ranging from solvents for extraction of valuable chemicals to biowaste to electrolytes in alternative energy devices. The previously published study connecting the interaction energies of single ion pairs (1 IP) of ionic liquids to their thermodynamic and transport properties has been extended to larger systems consisting of two ion pairs (2 IPs), in which many-body and same-ion interactions are included. Routinely used cations, of the imidazolium and pyrrolidinium families, were selected in the study coupled with chloride, tetrafluoroborate, and dicyanamide. Their two ion pair clusters were subjected to extensive configuration screening to establish most stable structures. Interaction energies of these clusters were calculated at the spin-ratio scaled MP2 (SRS-MP2) level for the correlation interaction energy, and a newly developed scaled Hartree-Fock method for the rest of energetic contributions to interaction energy. A full geometry screening for each cation-anion combination resulted in 192 unique structures, whose stability was assessed using two criteria—widely used interaction energy and total electronic energy. Furthermore, the ratio of interaction energy to its dispersion component was correlated with experimentally observed melting points in 64 energetically favourable structures. These systems were also used to test the correlation of the dispersion contribution to interaction energy with measured conductivity.

Gas phase reactivity of thermal metal clusters

International Nuclear Information System (INIS)

Castleman, A.W. Jr.; Harms, A.C.; Leuchtner, R.E.

1991-01-01

Reaction kinetics of metal cluster ions under well defined thermal conditions were studied using a flow tube reactor in combination with laser vaporization. Aluminum anions and cations were reacted with oxygen, and several species which are predicted jellium shell closings, were found to have special stability. Metal alloy cluster anions comprised of Al, V and Nb were also seen to react with oxygen. Alloy clusters with an even number of electrons reacted more slowly than odd electron species, and certain clusters appeared to be exceptionally unreactive. Copper cation clusters were observed to associate with carbon monoxide with reactivities that approach bulk behavior at surprisingly small cluster size. These reactions demonstrate how the rate of reaction changes with cluster size. (orig.)

Clustering of near clusters versus cluster compactness

International Nuclear Information System (INIS)

Yu Gao; Yipeng Jing

1989-01-01

The clustering properties of near Zwicky clusters are studied by using the two-point angular correlation function. The angular correlation functions for compact and medium compact clusters, for open clusters, and for all near Zwicky clusters are estimated. The results show much stronger clustering for compact and medium compact clusters than for open clusters, and that open clusters have nearly the same clustering strength as galaxies. A detailed study of the compactness-dependence of correlation function strength is worth investigating. (author)

Comparative genomics of Cluster O mycobacteriophages.

Science.gov (United States)

Cresawn, Steven G; Pope, Welkin H; Jacobs-Sera, Deborah; Bowman, Charles A; Russell, Daniel A; Dedrick, Rebekah M; Adair, Tamarah; Anders, Kirk R; Ball, Sarah; Bollivar, David; Breitenberger, Caroline; Burnett, Sandra H; Butela, Kristen; Byrnes, Deanna; Carzo, Sarah; Cornely, Kathleen A; Cross, Trevor; Daniels, Richard L; Dunbar, David; Findley, Ann M; Gissendanner, Chris R; Golebiewska, Urszula P; Hartzog, Grant A; Hatherill, J Robert; Hughes, Lee E; Jalloh, Chernoh S; De Los Santos, Carla; Ekanem, Kevin; Khambule, Sphindile L; King, Rodney A; King-Smith, Christina; Klyczek, Karen; Krukonis, Greg P; Laing, Christian; Lapin, Jonathan S; Lopez, A Javier; Mkhwanazi, Sipho M; Molloy, Sally D; Moran, Deborah; Munsamy, Vanisha; Pacey, Eddie; Plymale, Ruth; Poxleitner, Marianne; Reyna, Nathan; Schildbach, Joel F; Stukey, Joseph; Taylor, Sarah E; Ware, Vassie C; Wellmann, Amanda L; Westholm, Daniel; Wodarski, Donna; Zajko, Michelle; Zikalala, Thabiso S; Hendrix, Roger W; Hatfull, Graham F

2015-01-01

Mycobacteriophages--viruses of mycobacterial hosts--are genetically diverse but morphologically are all classified in the Caudovirales with double-stranded DNA and tails. We describe here a group of five closely related mycobacteriophages--Corndog, Catdawg, Dylan, Firecracker, and YungJamal--designated as Cluster O with long flexible tails but with unusual prolate capsids. Proteomic analysis of phage Corndog particles, Catdawg particles, and Corndog-infected cells confirms expression of half of the predicted gene products and indicates a non-canonical mechanism for translation of the Corndog tape measure protein. Bioinformatic analysis identifies 8-9 strongly predicted SigA promoters and all five Cluster O genomes contain more than 30 copies of a 17 bp repeat sequence with dyad symmetry located throughout the genomes. Comparison of the Cluster O phages provides insights into phage genome evolution including the processes of gene flux by horizontal genetic exchange.

A Performance-Prediction Model for PIC Applications on Clusters of Symmetric MultiProcessors: Validation with Hierarchical HPF+OpenMP Implementation

Directory of Open Access Journals (Sweden)

Sergio Briguglio

2003-01-01

Full Text Available A performance-prediction model is presented, which describes different hierarchical workload decomposition strategies for particle in cell (PIC codes on Clusters of Symmetric MultiProcessors. The devised workload decomposition is hierarchically structured: a higher-level decomposition among the computational nodes, and a lower-level one among the processors of each computational node. Several decomposition strategies are evaluated by means of the prediction model, with respect to the memory occupancy, the parallelization efficiency and the required programming effort. Such strategies have been implemented by integrating the high-level languages High Performance Fortran (at the inter-node stage and OpenMP (at the intra-node one. The details of these implementations are presented, and the experimental values of parallelization efficiency are compared with the predicted results.

Hard X-ray emission from accretion shocks around galaxy clusters

Science.gov (United States)

Kushnir, Doron; Waxman, Eli

2010-02-01

We show that the hard X-ray (HXR) emission observed from several galaxy clusters is consistent with a simple model, in which the nonthermal emission is produced by inverse Compton scattering of cosmic microwave background photons by electrons accelerated in cluster accretion shocks: The dependence of HXR surface brightness on cluster temperature is consistent with that predicted by the model, and the observed HXR luminosity is consistent with the fraction of shock thermal energy deposited in relativistic electrons being lesssim0.1. Alternative models, where the HXR emission is predicted to be correlated with the cluster thermal emission, are disfavored by the data. The implications of our predictions to future HXR observations (e.g. by NuStar, Simbol-X) and to (space/ground based) γ-ray observations (e.g. by Fermi, HESS, MAGIC, VERITAS) are discussed.

Hard X-ray emission from accretion shocks around galaxy clusters

Energy Technology Data Exchange (ETDEWEB)

Kushnir, Doron; Waxman, Eli, E-mail: doron.kushnir@weizmann.ac.il, E-mail: eli.waxman@weizmann.ac.il [Physics Faculty, Weizmann Institute of Science, PO Box 26, Rehovot (Israel)

2010-02-01

We show that the hard X-ray (HXR) emission observed from several galaxy clusters is consistent with a simple model, in which the nonthermal emission is produced by inverse Compton scattering of cosmic microwave background photons by electrons accelerated in cluster accretion shocks: The dependence of HXR surface brightness on cluster temperature is consistent with that predicted by the model, and the observed HXR luminosity is consistent with the fraction of shock thermal energy deposited in relativistic electrons being ∼<0.1. Alternative models, where the HXR emission is predicted to be correlated with the cluster thermal emission, are disfavored by the data. The implications of our predictions to future HXR observations (e.g. by NuStar, Simbol-X) and to (space/ground based) γ-ray observations (e.g. by Fermi, HESS, MAGIC, VERITAS) are discussed.

Hard X-ray emission from accretion shocks around galaxy clusters

International Nuclear Information System (INIS)

Kushnir, Doron; Waxman, Eli

2010-01-01

We show that the hard X-ray (HXR) emission observed from several galaxy clusters is consistent with a simple model, in which the nonthermal emission is produced by inverse Compton scattering of cosmic microwave background photons by electrons accelerated in cluster accretion shocks: The dependence of HXR surface brightness on cluster temperature is consistent with that predicted by the model, and the observed HXR luminosity is consistent with the fraction of shock thermal energy deposited in relativistic electrons being ∼<0.1. Alternative models, where the HXR emission is predicted to be correlated with the cluster thermal emission, are disfavored by the data. The implications of our predictions to future HXR observations (e.g. by NuStar, Simbol-X) and to (space/ground based) γ-ray observations (e.g. by Fermi, HESS, MAGIC, VERITAS) are discussed

Improvement in Saccharification Yield of Mixed Rumen Enzymes by Identification of Recalcitrant Cell Wall Constituents Using Enzyme Fingerprinting.

Science.gov (United States)

Badhan, Ajay; Wang, Yu-Xi; Gruninger, Robert; Patton, Donald; Powlowski, Justin; Tsang, Adrian; McAllister, Tim A

2015-01-01

Identification of recalcitrant factors that limit digestion of forages and the development of enzymatic approaches that improve hydrolysis could play a key role in improving the efficiency of meat and milk production in ruminants. Enzyme fingerprinting of barley silage fed to heifers and total tract indigestible fibre residue (TIFR) collected from feces was used to identify cell wall components resistant to total tract digestion. Enzyme fingerprinting results identified acetyl xylan esterases as key to the enhanced ruminal digestion. FTIR analysis also suggested cross-link cell wall polymers as principal components of indigested fiber residues in feces. Based on structural information from enzymatic fingerprinting and FTIR, enzyme pretreatment to enhance glucose yield from barley straw and alfalfa hay upon exposure to mixed rumen-enzymes was developed. Prehydrolysis effects of recombinant fungal fibrolytic hydrolases were analyzed using microassay in combination with statistical experimental design. Recombinant hemicellulases and auxiliary enzymes initiated degradation of plant structural polysaccharides upon application and improved the in vitro saccharification of alfalfa and barley straw by mixed rumen enzymes. The validation results showed that microassay in combination with statistical experimental design can be successfully used to predict effective enzyme pretreatments that can enhance plant cell wall digestion by mixed rumen enzymes.

Cluster-cluster clustering

International Nuclear Information System (INIS)

Barnes, J.; Dekel, A.; Efstathiou, G.; Frenk, C.S.; Yale Univ., New Haven, CT; California Univ., Santa Barbara; Cambridge Univ., England; Sussex Univ., Brighton, England)

1985-01-01

The cluster correlation function xi sub c(r) is compared with the particle correlation function, xi(r) in cosmological N-body simulations with a wide range of initial conditions. The experiments include scale-free initial conditions, pancake models with a coherence length in the initial density field, and hybrid models. Three N-body techniques and two cluster-finding algorithms are used. In scale-free models with white noise initial conditions, xi sub c and xi are essentially identical. In scale-free models with more power on large scales, it is found that the amplitude of xi sub c increases with cluster richness; in this case the clusters give a biased estimate of the particle correlations. In the pancake and hybrid models (with n = 0 or 1), xi sub c is steeper than xi, but the cluster correlation length exceeds that of the points by less than a factor of 2, independent of cluster richness. Thus the high amplitude of xi sub c found in studies of rich clusters of galaxies is inconsistent with white noise and pancake models and may indicate a primordial fluctuation spectrum with substantial power on large scales. 30 references

Ortholog prediction of the Aspergillus genus applicable for synthetic biology

DEFF Research Database (Denmark)

Rasmussen, Jane Lind Nybo; Vesth, Tammi Camilla; Theobald, Sebastian

of genotype-to-phenotype. To achieve this, we have developed orthologous protein prediction software that utilizes genus-wide genetic diversity. The approach is optimized for large data sets, based on BLASTp considering protein identity and alignment coverage, and clustering using single linkage of bi......The Aspergillus genus contains leading industrial microorganisms, excelling in producing bioactive compounds and enzymes. Using synthetic biology and bioinformatics, we aim to re-engineer these organisms for applications within human health, pharmaceuticals, environmental engineering, and food......-directional hits. The result is orthologous protein families describing the genomic and functional features of individual species, clades and the core/pan genome of Aspergillus; and applicable to genotype-to-phenotype analyses in other microbial genera....

Acute intermittent porphyria: A single-base deletion and a nonsense mutation in the human hydroxymethylbilane synthase gene, predicting truncations of the enzyme polypeptide

Energy Technology Data Exchange (ETDEWEB)

Lee, G.L.; Astrin, K.H.; Desnick, R.J. [Mount Sinai School of Medicine, New York, NY (United States)

1995-08-28

Acute intermittent porphyria (AIP) is an autosomal-dominant inborn error of metabolism that results from the half-normal activity of the third enzyme in the heme biosynthetic pathway, hydroxymethylbilane synthase (HMB-synthase). AIP is an ecogenetic condition, since the life-threatening acute attacks are precipitated by various factors, including drugs, alcohol, fasting, and certain hormones. Biochemical diagnosis is problematic, and the identification of mutations in the HMB-synthase gene provides accurate detection of presymptomatic heterozygotes, permitting avoidance of the acute precipitating factors. By direct solid-phase sequencing, two mutations causing AIP were identified, an adenine deletion at position 629 in exon 11(629delA), which alters the reading frame and predicts premature truncation of the enzyme protein after amino acid 255, and a nonsense mutation in exon 12 (R225X). These mutations were confirmed by either restriction enzyme analysis or family studies of symptomatic patients, permitting accurate presymptomatic diagnosis of affected relatives. 29 refs., 2 figs.

Clustering of Emerging Flux

Science.gov (United States)

Ruzmaikin, A.

1997-01-01

Observations show that newly emerging flux tends to appear on the Solar surface at sites where there is flux already. This results in clustering of solar activity. Standard dynamo theories do not predict this effect.

Chemical reaction vector embeddings: towards predicting drug metabolism in the human gut microbiome.

Science.gov (United States)

Mallory, Emily K; Acharya, Ambika; Rensi, Stefano E; Turnbaugh, Peter J; Bright, Roselie A; Altman, Russ B

2018-01-01

Bacteria in the human gut have the ability to activate, inactivate, and reactivate drugs with both intended and unintended effects. For example, the drug digoxin is reduced to the inactive metabolite dihydrodigoxin by the gut Actinobacterium E. lenta, and patients colonized with high levels of drug metabolizing strains may have limited response to the drug. Understanding the complete space of drugs that are metabolized by the human gut microbiome is critical for predicting bacteria-drug relationships and their effects on individual patient response. Discovery and validation of drug metabolism via bacterial enzymes has yielded >50 drugs after nearly a century of experimental research. However, there are limited computational tools for screening drugs for potential metabolism by the gut microbiome. We developed a pipeline for comparing and characterizing chemical transformations using continuous vector representations of molecular structure learned using unsupervised representation learning. We applied this pipeline to chemical reaction data from MetaCyc to characterize the utility of vector representations for chemical reaction transformations. After clustering molecular and reaction vectors, we performed enrichment analyses and queries to characterize the space. We detected enriched enzyme names, Gene Ontology terms, and Enzyme Consortium (EC) classes within reaction clusters. In addition, we queried reactions against drug-metabolite transformations known to be metabolized by the human gut microbiome. The top results for these known drug transformations contained similar substructure modifications to the original drug pair. This work enables high throughput screening of drugs and their resulting metabolites against chemical reactions common to gut bacteria.

Computational Biochemistry-Enzyme Mechanisms Explored.

Science.gov (United States)

Culka, Martin; Gisdon, Florian J; Ullmann, G Matthias

2017-01-01

Understanding enzyme mechanisms is a major task to achieve in order to comprehend how living cells work. Recent advances in biomolecular research provide huge amount of data on enzyme kinetics and structure. The analysis of diverse experimental results and their combination into an overall picture is, however, often challenging. Microscopic details of the enzymatic processes are often anticipated based on several hints from macroscopic experimental data. Computational biochemistry aims at creation of a computational model of an enzyme in order to explain microscopic details of the catalytic process and reproduce or predict macroscopic experimental findings. Results of such computations are in part complementary to experimental data and provide an explanation of a biochemical process at the microscopic level. In order to evaluate the mechanism of an enzyme, a structural model is constructed which can be analyzed by several theoretical approaches. Several simulation methods can and should be combined to get a reliable picture of the process of interest. Furthermore, abstract models of biological systems can be constructed combining computational and experimental data. In this review, we discuss structural computational models of enzymatic systems. We first discuss various models to simulate enzyme catalysis. Furthermore, we review various approaches how to characterize the enzyme mechanism both qualitatively and quantitatively using different modeling approaches. © 2017 Elsevier Inc. All rights reserved.

Bioethanol from lignocellulose - pretreatment, enzyme immobilization and hydrolysis kinetics

DEFF Research Database (Denmark)

Tsai, Chien Tai

, the cost of enzyme is still the bottle neck, re-using the enzyme is apossible way to reduce the input of enzyme in the process. In the point view of engineering, the prediction of enzymatic hydrolysis kinetics under different substrate loading, enzyme combination is usful for process design. Therefore...... lignocellulose is the required high cellulase enzyme dosages that increase the processing costs. One method to decrease the enzyme dosage is to re-use BG, which hydrolyze the soluble substrate cellobiose. Based on the hypothesis that immobilized BG can be re-used, how many times the enzyme could be recycled...... liquid and pretreatment time can be reduced, the influence of substrate concentration, pretreatment time and temperature were investigated and optimized. Pretreatment of barley straw by [EMIM]Ac, correlative models were constructed using 3 different pretreatment parameters (temperature, time...

Real-time dynamics of RNA Polymerase II clustering in live human cells

Science.gov (United States)

Cisse, Ibrahim

2014-03-01

Transcription is the first step in the central dogma of molecular biology, when genetic information encoded on DNA is made into messenger RNA. How this fundamental process occurs within living cells (in vivo) is poorly understood,[1] despite extensive biochemical characterizations with isolated biomolecules (in vitro). For high-order organisms, like humans, transcription is reported to be spatially compartmentalized in nuclear foci consisting of clusters of RNA Polymerase II, the enzyme responsible for synthesizing all messenger RNAs. However, little is known of when these foci assemble or their relative stability. We developed an approach based on photo-activation localization microscopy (PALM) combined with a temporal correlation analysis, which we refer to as tcPALM. The tcPALM method enables the real-time characterization of biomolecular spatiotemporal organization, with single-molecule sensitivity, directly in living cells.[2] Using tcPALM, we observed that RNA Polymerase II clusters form transiently, with an average lifetime of 5.1 (+/- 0.4) seconds. Stimuli affecting transcription regulation yielded orders of magnitude changes in the dynamics of the polymerase clusters, implying that clustering is regulated and plays a role in the cells ability to effect rapid response to external signals. Our results suggest that the transient crowding of enzymes may aid in rate-limiting steps of genome regulation.

Unanticipated coordination of tris buffer to the Radical SAM cluster of the RimO methylthiotransferase.

Science.gov (United States)

Molle, Thibaut; Clémancey, Martin; Latour, Jean-Marc; Kathirvelu, Velavan; Sicoli, Giuseppe; Forouhar, Farhad; Mulliez, Etienne; Gambarelli, Serge; Atta, Mohamed

2016-07-01

Radical SAM enzymes generally contain a [4Fe-4S](2+/1+) (RS cluster) cluster bound to the protein via the three cysteines of a canonical motif CxxxCxxC. The non-cysteinyl iron is used to coordinate SAM via its amino-carboxylate moiety. The coordination-induced proximity between the cluster acting as an electron donor and the adenosyl-sulfonium bond of SAM allows for the homolytic cleavage of the latter leading to the formation of the reactive 5'-deoxyadenosyl radical used for substrate activation. Most of the structures of Radical SAM enzymes have been obtained in the presence of SAM, and therefore, little is known about the situation when SAM is not present. In this report, we show that RimO, a methylthiotransferase belonging to the radical SAM superfamily, binds a Tris molecule in the absence of SAM leading to specific spectroscopic signatures both in Mössbauer and pulsed EPR spectroscopies. These data provide a cautionary note for researchers who work with coordinative unsaturated iron sulfur clusters.

Nonthermal emission from clusters of galaxies

Energy Technology Data Exchange (ETDEWEB)

Kushnir, Doron; Waxman, Eli, E-mail: doron.kushnir@weizmann.ac.il, E-mail: eli.waxman@weizmann.ac.il [Physics Faculty, Weizmann Institute of Science, PO Box 26, Rehovot (Israel)

2009-08-01

We show that the spectral and radial distribution of the nonthermal emission of massive, M ∼> 10{sup 14.5}M{sub ☉}, galaxy clusters may be approximately described by simple analytic expressions, which depend on the cluster thermal X-ray properties and on two model parameter, β{sub core} and η{sub e}. β{sub core} is the ratio of the cosmic-ray (CR) energy density (within a logarithmic CR energy interval) and the thermal energy density at the cluster core, and η{sub e(p)} is the fraction of the thermal energy generated in strong collisionless shocks, which is deposited in CR electrons (protons). Using a simple analytic model for the evolution of intra-cluster medium CRs, which are produced by accretion shocks, we find that β{sub core} ≅ η{sub p}/200, nearly independent of cluster mass and with a scatter Δln β{sub core} ≅ 1 between clusters of given mass. We show that the hard X-ray (HXR) and γ-ray luminosities produced by inverse Compton scattering of CMB photons by electrons accelerated in accretion shocks (primary electrons) exceed the luminosities produced by secondary particles (generated in hadronic interactions within the cluster) by factors ≅ 500(η{sub e}/η{sub p})(T/10 keV){sup −1/2} and ≅ 150(η{sub e}/η{sub p})(T/10 keV){sup −1/2} respectively, where T is the cluster temperature. Secondary particle emission may dominate at the radio and very high energy (∼> 1 TeV) γ-ray bands. Our model predicts, in contrast with some earlier work, that the HXR and γ-ray emission from clusters of galaxies are extended, since the emission is dominated at these energies by primary (rather than by secondary) electrons. Our predictions are consistent with the observed nonthermal emission of the Coma cluster for η{sub p} ∼ η{sub e} ∼ 0.1. The implications of our predictions to future HXR observations (e.g. by NuStar, Simbol-X) and to (space/ground based) γ-ray observations (e.g. by Fermi, HESS, MAGIC, VERITAS) are discussed. In particular

Evaluating Functional Annotations of Enzymes Using the Gene Ontology.

Science.gov (United States)

Holliday, Gemma L; Davidson, Rebecca; Akiva, Eyal; Babbitt, Patricia C

2017-01-01

The Gene Ontology (GO) (Ashburner et al., Nat Genet 25(1):25-29, 2000) is a powerful tool in the informatics arsenal of methods for evaluating annotations in a protein dataset. From identifying the nearest well annotated homologue of a protein of interest to predicting where misannotation has occurred to knowing how confident you can be in the annotations assigned to those proteins is critical. In this chapter we explore what makes an enzyme unique and how we can use GO to infer aspects of protein function based on sequence similarity. These can range from identification of misannotation or other errors in a predicted function to accurate function prediction for an enzyme of entirely unknown function. Although GO annotation applies to any gene products, we focus here a describing our approach for hierarchical classification of enzymes in the Structure-Function Linkage Database (SFLD) (Akiva et al., Nucleic Acids Res 42(Database issue):D521-530, 2014) as a guide for informed utilisation of annotation transfer based on GO terms.

2008 GRC Iron Sulfur Enzymes-Conference to be held June 8-13, 2008

Energy Technology Data Exchange (ETDEWEB)

Cramer, Stephen [Univ. of California, Davis, CA (United States); Gray, Nancy Ryan [Gordon Research Conferences, West Kingston, RI (United States)

2009-01-01

Iron-sulfur proteins are among the most common and ancient enzymes and electron-transfer agents in nature. They play key roles in photosynthesis, respiration, and the metabolism of small molecules such as H2, CO, and N2. The Iron Sulfur Enzyme Gordon Research Conference evolved from an earlier GRC on Nitrogen Fixation that began in 1994. The scope of the current meeting has broadened to include all enzymes or metalloproteins in which Fe-S bonds play a key role. This year's meeting will focus on the biosynthesis of Fe-S clusters, as well as the structure and mechanism of key Fe-S enzymes such as hydrogenase, nitrogenase and its homologues, radical SAM enzymes, and aconitase-related enzymes. Recent progress on the role of Fe-S enzymes in health, disease, DNA/RNA-processing, and alternative bio-energy systems will also be highlighted. This conference will assemble a broad, diverse, and international group of biologists and chemists who are investigating fundamental issues related to Fe-S enzymes, on atomic, molecular, organism, and environmental scales. The topics to be addressed will include: Biosynthesis & Genomics of Fe-S Enzymes; Fundamental Fe-S Chemistry; Hydrogen and Fe-S Enzymes; Nitrogenase & Homologous Fe-S Enzymes; Fe-S Enzymes in Health & Disease; Radical SAM and Aconitase-Related Fe-S Enzymes; Fe-S Enzymes and Synthetic Analogues in BioEnergy; and Fe-S Enzymes in Geochemistry and the Origin of Life.

Modeling metabolic response to changes of enzyme amount in ...

African Journals Online (AJOL)

Based on the work of Hynne et al. (2001), in an in silico model of glycolysis, Saccharomyces cerevisiae is established by introducing an enzyme amount multiple factor (.) into the kinetic equations. The model is aimed to predict the metabolic response to the change of enzyme amount. With the help of .α, the amounts of ...

Comparative genomics of Cluster O mycobacteriophages.

Directory of Open Access Journals (Sweden)

Steven G Cresawn

Full Text Available Mycobacteriophages--viruses of mycobacterial hosts--are genetically diverse but morphologically are all classified in the Caudovirales with double-stranded DNA and tails. We describe here a group of five closely related mycobacteriophages--Corndog, Catdawg, Dylan, Firecracker, and YungJamal--designated as Cluster O with long flexible tails but with unusual prolate capsids. Proteomic analysis of phage Corndog particles, Catdawg particles, and Corndog-infected cells confirms expression of half of the predicted gene products and indicates a non-canonical mechanism for translation of the Corndog tape measure protein. Bioinformatic analysis identifies 8-9 strongly predicted SigA promoters and all five Cluster O genomes contain more than 30 copies of a 17 bp repeat sequence with dyad symmetry located throughout the genomes. Comparison of the Cluster O phages provides insights into phage genome evolution including the processes of gene flux by horizontal genetic exchange.

A High-Throughput (HTS) Assay for Enzyme Reaction Phenotyping in Human Recombinant P450 Enzymes Using LC-MS/MS.

Science.gov (United States)

Li, Xiaofeng; Suhar, Tom; Glass, Lateca; Rajaraman, Ganesh

2014-03-03

Enzyme reaction phenotyping is employed extensively during the early stages of drug discovery to identify the enzymes responsible for the metabolism of new chemical entities (NCEs). Early identification of metabolic pathways facilitates prediction of potential drug-drug interactions associated with enzyme polymorphism, induction, or inhibition, and aids in the design of clinical trials. Incubation of NCEs with human recombinant enzymes is a popular method for such work because of the specificity, simplicity, and high-throughput nature of this approach for phenotyping studies. The availability of a relative abundance factor and calculated intersystem extrapolation factor for the expressed recombinant enzymes facilitates easy scaling of in vitro data, enabling in vitro-in vivo extrapolation. Described in this unit is a high-throughput screen for identifying enzymes involved in the metabolism of NCEs. Emphasis is placed on the analysis of the human recombinant enzymes CYP1A2, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2B6, and CYP3A4, including the calculation of the intrinsic clearance for each. Copyright © 2014 John Wiley & Sons, Inc. All rights reserved.

Clustering of two genes putatively involved in cyanate detoxification evolved recently and independently in multiple fungal lineages

Science.gov (United States)

Fungi that have the enzymes cyanase and carbonic anhydrase show a limited capacity to detoxify cyanate, a fungicide employed by both plants and humans. Here, we describe a novel two-gene cluster that comprises duplicated cyanase and carbonic anhydrase copies, which we name the CCA gene cluster, trac...

Ligand Access Channels in Cytochrome P450 Enzymes: A Review

Directory of Open Access Journals (Sweden)

Philippe Urban

2018-05-01

Full Text Available Quantitative structure-activity relationships may bring invaluable information on structural elements of both enzymes and substrates that, together, govern substrate specificity. Buried active sites in cytochrome P450 enzymes are connected to the solvent by a network of channels exiting at the distal surface of the protein. This review presents different in silico tools that were developed to uncover such channels in P450 crystal structures. It also lists some of the experimental evidence that actually suggest that these predicted channels might indeed play a critical role in modulating P450 functions. Amino acid residues at the entrance of the channels may participate to a first global ligand recognition of ligands by P450 enzymes before they reach the buried active site. Moreover, different P450 enzymes show different networks of predicted channels. The plasticity of P450 structures is also important to take into account when looking at how channels might play their role.

PRAMANA Cluster radioactivity in xenon isotopes

Indian Academy of Sciences (India)

exotic decay or cluster radioactivity was first predicted by sandulescu et al [1] in. 1980 on the basis of ... separator by 58Ni(58Ni, 2n) reaction and carbon clusters were searched for by means of solid state nuclear ..... Lett. 55, 582 (1985). [22] D N Poenaru, W Greiner, K Depta, M Ivascu, D Mazilu and A Sandulescu, At. Data.

Time-resolved explosion of intense-laser-heated clusters.

Science.gov (United States)

Kim, K Y; Alexeev, I; Parra, E; Milchberg, H M

2003-01-17

We investigate the femtosecond explosive dynamics of intense laser-heated argon clusters by measuring the cluster complex transient polarizability. The time evolution of the polarizability is characteristic of competition in the optical response between supercritical and subcritical density regions of the expanding cluster. The results are consistent with time-resolved Rayleigh scattering measurements, and bear out the predictions of a recent laser-cluster interaction model [H. M. Milchberg, S. J. McNaught, and E. Parra, Phys. Rev. E 64, 056402 (2001)

Time-resolved explosion of intense-laser-heated clusters

International Nuclear Information System (INIS)

Kim, K.Y.; Alexeev, I.; Parra, E.; Milchberg, H.M.

2003-01-01

We investigate the femtosecond explosive dynamics of intense laser-heated argon clusters by measuring the cluster complex transient polarizability. The time evolution of the polarizability is characteristic of competition in the optical response between supercritical and subcritical density regions of the expanding cluster. The results are consistent with time-resolved Rayleigh scattering measurements, and bear out the predictions of a recent laser-cluster interaction model [H. M. Milchberg, S. J. McNaught, and E. Parra, Phys. Rev. E 64, 056402 (2001)

2D Dust Clusters in Theory and Experiments

International Nuclear Information System (INIS)

Tsytovich, V.N.; Gousein-zade, N.G.; Morfill, G.E.

2005-01-01

The theory is applied for more detail analysis of existing experiments of 2D dust clusters with parabolic confinement. It is shown that the equilibrium condition and the frequency of one of the modes of the cluster determines all dimensionless parameters of the cluster allowing to predict the value of other modes and compare them with existing experimental data. This comparison depends on the shielding model, the calculations starting with N = 4 cluster breathing mode predict for Debye shielding model without attraction the frequency of the antisymmetric mode in disagreement with the observed value about 6 standard deviations, while the same calculations for the non-linear screening model gives disagreement about 1 standard deviation. Including the attraction provides an agrement with observations only for non-linear screening model showing the sensitivity of cluster structure to dust attraction. The value of the obtained attractions coefficient is in reasonable agreement with the theoretically expected value. It is shown theoretically that in absence of external parabolic confinement a weak shadow attraction can provide an existence of equilibria for 2D clusters. The equilibrium radius is rapidly decreasing with an increase of the attraction coefficient and with number of grains N in a cluster. The energies of one shell clusters with different N and the energies of N - 1 grain clusters with additional grain in the center of the shell are calculated as functions of attraction coefficient. It is demonstrated that a dissociation of cluster in several smaller clusters needs less energy than a removal of one grain from the cluster. The calculations were performed for Yukawa screening and for non-linear screening and demonstrate the sensitivity of cluster structures to the screening. Frequencies of all modes are calculated up to N = 7 for one shell structure. Stable and unstable modes as well as universal magic numbers are found

Prediction of hourly solar radiation with multi-model framework

International Nuclear Information System (INIS)

Wu, Ji; Chan, Chee Keong

2013-01-01

Highlights: • A novel approach to predict solar radiation through the use of clustering paradigms. • Development of prediction models based on the intrinsic pattern observed in each cluster. • Prediction based on proper clustering and selection of model on current time provides better results than other methods. • Experiments were conducted on actual solar radiation data obtained from a weather station in Singapore. - Abstract: In this paper, a novel multi-model prediction framework for prediction of solar radiation is proposed. The framework started with the assumption that there are several patterns embedded in the solar radiation series. To extract the underlying pattern, the solar radiation series is first segmented into smaller subsequences, and the subsequences are further grouped into different clusters. For each cluster, an appropriate prediction model is trained. Hence a procedure for pattern identification is developed to identify the proper pattern that fits the current period. Based on this pattern, the corresponding prediction model is applied to obtain the prediction value. The prediction result of the proposed framework is then compared to other techniques. It is shown that the proposed framework provides superior performance as compared to others

Probable cluster decays from 298-336126 superheavy nuclei

International Nuclear Information System (INIS)

Priyanka, B.; Santhosh, K.P.

2015-01-01

The present paper deals with an investigation on the cluster decay of even clusters 4 He, 8,10 Be, 14 C, 18,20,22 O , 22,24,26 Ne, 28,30 Mg and odd clusters 15 N, 23 F, 25 Ne, 29 Mg from both the even-even and even-odd isotopes of Z=126, which has helped in predicting the neutron magicity beyond N=126

Clustering of European winter storms: A multi-model perspective

Science.gov (United States)

Renggli, Dominik; Buettner, Annemarie; Scherb, Anke; Straub, Daniel; Zimmerli, Peter

2016-04-01

The storm series over Europe in 1990 (Daria, Vivian, Wiebke, Herta) and 1999 (Anatol, Lothar, Martin) are very well known. Such clusters of severe events strongly affect the seasonally accumulated damage statistics. The (re)insurance industry has quantified clustering by using distribution assumptions deduced from the historical storm activity of the last 30 to 40 years. The use of storm series simulated by climate models has only started recently. Climate model runs can potentially represent 100s to 1000s of years, allowing a more detailed quantification of clustering than the history of the last few decades. However, it is unknown how sensitive the representation of clustering is to systematic biases. Using a multi-model ensemble allows quantifying that uncertainty. This work uses CMIP5 decadal ensemble hindcasts to study clustering of European winter storms from a multi-model perspective. An objective identification algorithm extracts winter storms (September to April) in the gridded 6-hourly wind data. Since the skill of European storm predictions is very limited on the decadal scale, the different hindcast runs are interpreted as independent realizations. As a consequence, the available hindcast ensemble represents several 1000 simulated storm seasons. The seasonal clustering of winter storms is quantified using the dispersion coefficient. The benchmark for the decadal prediction models is the 20th Century Reanalysis. The decadal prediction models are able to reproduce typical features of the clustering characteristics observed in the reanalysis data. Clustering occurs in all analyzed models over the North Atlantic and European region, in particular over Great Britain and Scandinavia as well as over Iberia (i.e. the exit regions of the North Atlantic storm track). Clustering is generally weaker in the models compared to reanalysis, although the differences between different models are substantial. In contrast to existing studies, clustering is driven by weak

A simulation study of sample size demonstrated the importance of the number of events per variable to develop prediction models in clustered data

NARCIS (Netherlands)

Wynants, L.; Bouwmeester, W.; Moons, K. G. M.; Moerbeek, M.; Timmerman, D.; Van Huffel, S.; Van Calster, B.; Vergouwe, Y.

2015-01-01

Objectives: This study aims to investigate the influence of the amount of clustering [intraclass correlation (ICC) = 0%, 5%, or 20%], the number of events per variable (EPV) or candidate predictor (EPV = 5, 10, 20, or 50), and backward variable selection on the performance of prediction models.

A grand unified model for liganded gold clusters

Science.gov (United States)

Xu, Wen Wu; Zhu, Beien; Zeng, Xiao Cheng; Gao, Yi

2016-12-01

A grand unified model (GUM) is developed to achieve fundamental understanding of rich structures of all 71 liganded gold clusters reported to date. Inspired by the quark model by which composite particles (for example, protons and neutrons) are formed by combining three quarks (or flavours), here gold atoms are assigned three `flavours' (namely, bottom, middle and top) to represent three possible valence states. The `composite particles' in GUM are categorized into two groups: variants of triangular elementary block Au3(2e) and tetrahedral elementary block Au4(2e), all satisfying the duet rule (2e) of the valence shell, akin to the octet rule in general chemistry. The elementary blocks, when packed together, form the cores of liganded gold clusters. With the GUM, structures of 71 liganded gold clusters and their growth mechanism can be deciphered altogether. Although GUM is a predictive heuristic and may not be necessarily reflective of the actual electronic structure, several highly stable liganded gold clusters are predicted, thereby offering GUM-guided synthesis of liganded gold clusters by design.

Dynamical Competition of IC-Industry Clustering from Taiwan to China

Science.gov (United States)

Tsai, Bi-Huei; Tsai, Kuo-Hui

2009-08-01

Most studies employ qualitative approach to explore the industrial clusters; however, few research has objectively quantified the evolutions of industry clustering. The purpose of this paper is to quantitatively analyze clustering among IC design, IC manufacturing as well as IC packaging and testing industries by using the foreign direct investment (FDI) data. The Lotka-Volterra system equations are first adopted here to capture the competition or cooperation among such three industries, thus explaining their clustering inclinations. The results indicate that the evolution of FDI into China for IC design industry significantly inspire the subsequent FDI of IC manufacturing as well as IC packaging and testing industries. Since IC design industry lie in the upstream stage of IC production, the middle-stream IC manufacturing and downstream IC packing and testing enterprises tend to cluster together with IC design firms, in order to sustain a steady business. Finally, Taiwan IC industry's FDI amount into China is predicted to cumulatively increase, which supports the industrial clustering tendency for Taiwan IC industry. Particularly, the FDI prediction of Lotka-Volterra model performs superior to that of the conventional Bass model after the forecast accuracy of these two models are compared. The prediction ability is dramatically improved as the industrial mutualism among each IC production stage is taken into account.

Interplay of drug metabolizing enzymes with cellular transporters.

Science.gov (United States)

Böhmdorfer, Michaela; Maier-Salamon, Alexandra; Riha, Juliane; Brenner, Stefan; Höferl, Martina; Jäger, Walter

2014-11-01

Many endogenous and xenobiotic substances and their metabolites are substrates for drug metabolizing enzymes and cellular transporters. These proteins may not only contribute to bioavailability of molecules but also to uptake into organs and, consequently, to overall elimination. The coordinated action of uptake transporters, metabolizing enzymes, and efflux pumps, therefore, is a precondition for detoxification and elimination of drugs. As the understanding of the underlying mechanisms is important to predict alterations in drug disposal, adverse drug reactions and, finally, drug-drug interactions, this review illustrates the interplay between selected uptake/efflux transporters and phase I/II metabolizing enzymes.

Genetic polymorphisms in 5-Fluorouracil-related enzymes predict pathologic response after neoadjuvant chemoradiation for rectal cancer.

Science.gov (United States)

Nelson, Bailey; Carter, Jane V; Eichenberger, Maurice R; Netz, Uri; Galandiuk, Susan

2016-11-01

Many patients with rectal cancer undergo preoperative neoadjuvant chemoradiation, with approximately 70% exhibiting pathologic downstaging in response to treatment. Currently, there is no accurate test to predict patients who are likely to be complete responders to therapy. 5-Fluorouracil is used regularly in the neoadjuvant treatment of rectal cancer. Genetic polymorphisms affect the activity of thymidylate synthase, an enzyme involved in 5-Fluorouracil metabolism, which may account for observed differences in response to neoadjuvant treatment between patients. Detection of genetic polymorphisms might identify patients who are likely to have a complete response to neoadjuvant therapy and perhaps allow them to avoid operation. DNA was isolated from whole blood taken from patients with newly diagnosed rectal cancer who received neoadjuvant therapy (n = 50). Response to therapy was calculated with a tumor regression score based on histology from the time of operation. Polymerase chain reaction was performed targeting the promoter region of thymidylate synthase. Polymerase chain reaction products were separated using electrophoresis to determine whether patients were homozygous for a double-tandem repeat (2R), a triple-tandem repeat (3R), or were heterozygous (2R/3R). A single nucleotide polymorphism, 3G or 3C, also may be present in the second repeat unit of the triple-tandem repeat allele. Restriction fragment length polymorphism assays were performed in patients with at least one 3R allele using HaeIII. Patients with at least 1 thymidylate synthase 3G allele were more likely to have a complete or partial pathologic response to 5-Fluorouracil neoadjuvant therapy (odds ratio 10.4; 95% confidence interval, 1.3-81.6; P = .01) than those without at least one 3G allele. Identification of rectal cancer patients with specific genetic polymorphisms in enzymes involved in 5-Fluorouracil metabolism seems to predict the likelihood of complete or partial pathologic response

Post-traumatic stress symptom clusters in acute whiplash associated disorder and their prediction of chronic pain-related disability.

Science.gov (United States)

Maujean, Annick; Gullo, Matthew J; Andersen, Tonny Elmose; Ravn, Sophie Lykkegaard; Sterling, Michele

2017-11-01

The presence of post-traumatic stress disorder (PTSD) symptoms has been found to be associated with an increased risk of persisting neck pain and disability in motor vehicle crash (MVC) survivors with whiplash injuries. The findings are mixed as to which PTSD symptom(s) best predicts recovery in this population. The aims were (1) to explore the factor structure of the Post-traumatic Stress Diagnostic Scale (PDS) in a sample of acute whiplash-injured individuals following a MVC and (2) to identify the PTSD-symptom clusters that best predict long-term neck pain-related disability in this population as measured by the Neck Pain Disability Index (NDI). A sample (N = 146) of whiplash-injured individuals completed the NDI and the PDS at baseline (whiplash-injured individuals following a MVC.

Distant Galaxy Clusters Hosting Extreme Central Galaxies

Science.gov (United States)

McDonald, Michael

2014-09-01

The recently-discovered Phoenix cluster harbors the most star-forming central cluster galaxy of any cluster in the known Universe, by nearly a factor of 10. This extreme system appears to be fulfilling early cooling flow predictions, although the lack of similar systems makes any interpretation difficult. In an attempt to find other "Phoenix-like" clusters, we have cross-correlated archival all-sky surveys (in which Phoenix was detected) and isolated 4 similarly-extreme systems which are also coincident in position and redshift with an overdensity of red galaxies. We propose here to obtain Chandra observations of these extreme, Phoenix-like systems, in order to confirm them as relaxed, rapidly-cooling galaxy clusters.

Orbital magnetism and dynamics in alkali metal clusters

International Nuclear Information System (INIS)

Nesterenko, V.O.; Kleinig, W.; Souza Cruz, FF. de; Marinelli, J.R.

2000-01-01

Two remarkable orbital magnetic resonances, M1 scissor mode and M2 twist mode, are predicted in deformed and spherical metal clusters, respectively. We show that these resonances provide a valuable information about many cluster properties (quadrupole deformation, magnetic susceptibility, single-particle spectrum, etc.)

Chemical and protein structural basis for biological crosstalk between PPAR α and COX enzymes

Science.gov (United States)

Cleves, Ann E.; Jain, Ajay N.

2015-02-01

We have previously validated a probabilistic framework that combined computational approaches for predicting the biological activities of small molecule drugs. Molecule comparison methods included molecular structural similarity metrics and similarity computed from lexical analysis of text in drug package inserts. Here we present an analysis of novel drug/target predictions, focusing on those that were not obvious based on known pharmacological crosstalk. Considering those cases where the predicted target was an enzyme with known 3D structure allowed incorporation of information from molecular docking and protein binding pocket similarity in addition to ligand-based comparisons. Taken together, the combination of orthogonal information sources led to investigation of a surprising predicted relationship between a transcription factor and an enzyme, specifically, PPAR α and the cyclooxygenase enzymes. These predictions were confirmed by direct biochemical experiments which validate the approach and show for the first time that PPAR α agonists are cyclooxygenase inhibitors.

EPR spectroscopic evidence for a tetranuclear manganese cluster as the site for photosynthetic oxygen evolution

Energy Technology Data Exchange (ETDEWEB)

Dismukes, G C; Ferris, K; Watnick, P

1982-01-01

It has been shown that EPR observations of a polynuclear Mn cluster in spinach chloroplasts can be interpreted in terms of a cluster containing three Mn(III) ions and one Mn(IV) ion within a tetranuclear complex. Both ferromagnetic and antiferromagnetic interactions appear to exist between the Mn ions, which exhibit deeply trapped discrete oxidation states, at least in this EPR active state. These results are discussed in terms of what is currently known about the polypeptide composition of the enzyme. A model of the oxidation state changes in the enzyme is proposed which is consistent with the EPR and protein isolation studies. Finally, a comparison between the electron-transporting metalloenzymes and the electron-storing metalloenzymes shows that the facile electron transfer kinetics observed in the former class and the slow kinetics observed in the latter class are consistent with the distinctly different electronic structures of these enzymes and their functional roles.

DomSign: a top-down annotation pipeline to enlarge enzyme space in the protein universe.

Science.gov (United States)

Wang, Tianmin; Mori, Hiroshi; Zhang, Chong; Kurokawa, Ken; Xing, Xin-Hui; Yamada, Takuji

2015-03-21

Computational predictions of catalytic function are vital for in-depth understanding of enzymes. Because several novel approaches performing better than the common BLAST tool are rarely applied in research, we hypothesized that there is a large gap between the number of known annotated enzymes and the actual number in the protein universe, which significantly limits our ability to extract additional biologically relevant functional information from the available sequencing data. To reliably expand the enzyme space, we developed DomSign, a highly accurate domain signature-based enzyme functional prediction tool to assign Enzyme Commission (EC) digits. DomSign is a top-down prediction engine that yields results comparable, or superior, to those from many benchmark EC number prediction tools, including BLASTP, when a homolog with an identity >30% is not available in the database. Performance tests showed that DomSign is a highly reliable enzyme EC number annotation tool. After multiple tests, the accuracy is thought to be greater than 90%. Thus, DomSign can be applied to large-scale datasets, with the goal of expanding the enzyme space with high fidelity. Using DomSign, we successfully increased the percentage of EC-tagged enzymes from 12% to 30% in UniProt-TrEMBL. In the Kyoto Encyclopedia of Genes and Genomes bacterial database, the percentage of EC-tagged enzymes for each bacterial genome could be increased from 26.0% to 33.2% on average. Metagenomic mining was also efficient, as exemplified by the application of DomSign to the Human Microbiome Project dataset, recovering nearly one million new EC-labeled enzymes. Our results offer preliminarily confirmation of the existence of the hypothesized huge number of "hidden enzymes" in the protein universe, the identification of which could substantially further our understanding of the metabolisms of diverse organisms and also facilitate bioengineering by providing a richer enzyme resource. Furthermore, our results

Enzyme phylogenies as markers for the oxidation state of the environment: the case of respiratory arsenate reductase and related enzymes.

Science.gov (United States)

Duval, Simon; Ducluzeau, Anne-Lise; Nitschke, Wolfgang; Schoepp-Cothenet, Barbara

2008-07-16

Phylogenies of certain bioenergetic enzymes have proved to be useful tools for deducing evolutionary ancestry of bioenergetic pathways and their relationship to geochemical parameters of the environment. Our previous phylogenetic analysis of arsenite oxidase, the molybdopterin enzyme responsible for the biological oxidation of arsenite to arsenate, indicated its probable emergence prior to the Archaea/Bacteria split more than 3 billion years ago, in line with the geochemical fact that arsenite was present in biological habitats on the early Earth. Respiratory arsenate reductase (Arr), another molybdopterin enzyme involved in microbial arsenic metabolism, serves as terminal oxidase, and is thus situated at the opposite end of bioenergetic electron transfer chains as compared to arsenite oxidase. The evolutionary history of the Arr-enzyme has not been studied in detail so far. We performed a genomic search of genes related to arrA coding for the molybdopterin subunit. The multiple alignment of the retrieved sequences served to reconstruct a neighbor-joining phylogeny of Arr and closely related enzymes. Our analysis confirmed the previously proposed proximity of Arr to the cluster of polysulfide/thiosulfate reductases but also unravels a hitherto unrecognized clade even more closely related to Arr. The obtained phylogeny strongly suggests that Arr originated after the Bacteria/Archaea divergence in the domain Bacteria, and was subsequently laterally distributed within this domain. It further more indicates that, as a result of accumulation of arsenate in the environment, an enzyme related to polysulfide reductase and not to arsenite oxidase has evolved into Arr. These findings are paleogeochemically rationalized by the fact that the accumulation of arsenate over arsenite required the increase in oxidation state of the environment brought about by oxygenic photosynthesis.

Collective excitations in deformed alkali metal clusters

International Nuclear Information System (INIS)

Lipparini, E.; Stringari, S.; Istituto Nazionale di Fisica Nucleare, Povo

1991-01-01

A theoretical study of collective excitations in deformed metal clusters is presented. Sum rules are used to study the splittings of the dipole surface plasma resonance originating from the cluster deformation. The vibrating potential model is developed and used to predict the occurrence of a low lying collective mode of orbital magnetic nature. (orig.)

Evolutionarily conserved substrate substructures for automated annotation of enzyme superfamilies.

Directory of Open Access Journals (Sweden)

Ranyee A Chiang

2008-08-01

Full Text Available The evolution of enzymes affects how well a species can adapt to new environmental conditions. During enzyme evolution, certain aspects of molecular function are conserved while other aspects can vary. Aspects of function that are more difficult to change or that need to be reused in multiple contexts are often conserved, while those that vary may indicate functions that are more easily changed or that are no longer required. In analogy to the study of conservation patterns in enzyme sequences and structures, we have examined the patterns of conservation and variation in enzyme function by analyzing graph isomorphisms among enzyme substrates of a large number of enzyme superfamilies. This systematic analysis of substrate substructures establishes the conservation patterns that typify individual superfamilies. Specifically, we determined the chemical substructures that are conserved among all known substrates of a superfamily and the substructures that are reacting in these substrates and then examined the relationship between the two. Across the 42 superfamilies that were analyzed, substantial variation was found in how much of the conserved substructure is reacting, suggesting that superfamilies may not be easily grouped into discrete and separable categories. Instead, our results suggest that many superfamilies may need to be treated individually for analyses of evolution, function prediction, and guiding enzyme engineering strategies. Annotating superfamilies with these conserved and reacting substructure patterns provides information that is orthogonal to information provided by studies of conservation in superfamily sequences and structures, thereby improving the precision with which we can predict the functions of enzymes of unknown function and direct studies in enzyme engineering. Because the method is automated, it is suitable for large-scale characterization and comparison of fundamental functional capabilities of both characterized

Evolutionarily conserved substrate substructures for automated annotation of enzyme superfamilies.

Science.gov (United States)

Chiang, Ranyee A; Sali, Andrej; Babbitt, Patricia C

2008-08-01

The evolution of enzymes affects how well a species can adapt to new environmental conditions. During enzyme evolution, certain aspects of molecular function are conserved while other aspects can vary. Aspects of function that are more difficult to change or that need to be reused in multiple contexts are often conserved, while those that vary may indicate functions that are more easily changed or that are no longer required. In analogy to the study of conservation patterns in enzyme sequences and structures, we have examined the patterns of conservation and variation in enzyme function by analyzing graph isomorphisms among enzyme substrates of a large number of enzyme superfamilies. This systematic analysis of substrate substructures establishes the conservation patterns that typify individual superfamilies. Specifically, we determined the chemical substructures that are conserved among all known substrates of a superfamily and the substructures that are reacting in these substrates and then examined the relationship between the two. Across the 42 superfamilies that were analyzed, substantial variation was found in how much of the conserved substructure is reacting, suggesting that superfamilies may not be easily grouped into discrete and separable categories. Instead, our results suggest that many superfamilies may need to be treated individually for analyses of evolution, function prediction, and guiding enzyme engineering strategies. Annotating superfamilies with these conserved and reacting substructure patterns provides information that is orthogonal to information provided by studies of conservation in superfamily sequences and structures, thereby improving the precision with which we can predict the functions of enzymes of unknown function and direct studies in enzyme engineering. Because the method is automated, it is suitable for large-scale characterization and comparison of fundamental functional capabilities of both characterized and uncharacterized

A Hierarchical Clustering Methodology for the Estimation of Toxicity

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A Quantitative Structure Activity Relationship (QSAR) methodology based on hierarchical clustering was developed to predict toxicological endpoints. This methodology utilizes Ward's method to divide a training set into a series of structurally similar clusters. The structural sim...

Application of a hierarchical enzyme classification method reveals the role of gut microbiome in human metabolism.

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Mohammed, Akram; Guda, Chittibabu

2015-01-01

Enzymes are known as the molecular machines that drive the metabolism of an organism; hence identification of the full enzyme complement of an organism is essential to build the metabolic blueprint of that species as well as to understand the interplay of multiple species in an ecosystem. Experimental characterization of the enzymatic reactions of all enzymes in a genome is a tedious and expensive task. The problem is more pronounced in the metagenomic samples where even the species are not adequately cultured or characterized. Enzymes encoded by the gut microbiota play an essential role in the host metabolism; thus, warranting the need to accurately identify and annotate the full enzyme complements of species in the genomic and metagenomic projects. To fulfill this need, we develop and apply a method called ECemble, an ensemble approach to identify enzymes and enzyme classes and study the human gut metabolic pathways. ECemble method uses an ensemble of machine-learning methods to accurately model and predict enzymes from protein sequences and also identifies the enzyme classes and subclasses at the finest resolution. A tenfold cross-validation result shows accuracy between 97 and 99% at different levels in the hierarchy of enzyme classification, which is superior to comparable methods. We applied ECemble to predict the entire complements of enzymes from ten sequenced proteomes including the human proteome. We also applied this method to predict enzymes encoded by the human gut microbiome from gut metagenomic samples, and to study the role played by the microbe-derived enzymes in the human metabolism. After mapping the known and predicted enzymes to canonical human pathways, we identified 48 pathways that have at least one bacteria-encoded enzyme, which demonstrates the complementary role of gut microbiome in human gut metabolism. These pathways are primarily involved in metabolizing dietary nutrients such as carbohydrates, amino acids, lipids, cofactors and

Application of a hierarchical enzyme classification method reveals the role of gut microbiome in human metabolism

Science.gov (United States)

2015-01-01

Background Enzymes are known as the molecular machines that drive the metabolism of an organism; hence identification of the full enzyme complement of an organism is essential to build the metabolic blueprint of that species as well as to understand the interplay of multiple species in an ecosystem. Experimental characterization of the enzymatic reactions of all enzymes in a genome is a tedious and expensive task. The problem is more pronounced in the metagenomic samples where even the species are not adequately cultured or characterized. Enzymes encoded by the gut microbiota play an essential role in the host metabolism; thus, warranting the need to accurately identify and annotate the full enzyme complements of species in the genomic and metagenomic projects. To fulfill this need, we develop and apply a method called ECemble, an ensemble approach to identify enzymes and enzyme classes and study the human gut metabolic pathways. Results ECemble method uses an ensemble of machine-learning methods to accurately model and predict enzymes from protein sequences and also identifies the enzyme classes and subclasses at the finest resolution. A tenfold cross-validation result shows accuracy between 97 and 99% at different levels in the hierarchy of enzyme classification, which is superior to comparable methods. We applied ECemble to predict the entire complements of enzymes from ten sequenced proteomes including the human proteome. We also applied this method to predict enzymes encoded by the human gut microbiome from gut metagenomic samples, and to study the role played by the microbe-derived enzymes in the human metabolism. After mapping the known and predicted enzymes to canonical human pathways, we identified 48 pathways that have at least one bacteria-encoded enzyme, which demonstrates the complementary role of gut microbiome in human gut metabolism. These pathways are primarily involved in metabolizing dietary nutrients such as carbohydrates, amino acids, lipids

Characterization and identification of enzyme-producing microflora isolated from the gut of sea cucumber Apostichopus japonicus

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Li, Fenghui; Gao, Fei; Tan, Jie; Fan, Chaojing; Sun, Huiling; Yan, Jingping; Chen, Siqing; Wang, Xiaojun

2016-01-01

Gut microorganisms play an important role in the digestion of their host animals. The purpose of this research was to isolate and assess the enzyme-producing microbes from the Apostichopus japonicus gut. Thirty-nine strains that can produce at least one of the three digestive enzymes (protease, amylase, and cellulase) were qualitatively screened based on their extracellular enzyme-producing abilities. The enzyme-producing strains clustered into eight groups at the genetic similarity level of 100% by analyzing the restriction patterns of 16S rDNA amplified with Mbo I. Phylogenetic analysis revealed that 37 strains belonged to the genus Bacillus and two were members of the genus Virgibacillus. Enzyme-producing capability results indicate that the main enzyme-producing microflora in the A. japonicus gut was Bacillus, which can produce protease, amylase, and cellulase. Virgibacillus, however, can only produce protease. The high enzyme-producing capability of the isolates suggests that the gut microbiota play an important role in the sea cucumber digestive process.

Variable selection in multivariate calibration based on clustering of variable concept.

Science.gov (United States)

Farrokhnia, Maryam; Karimi, Sadegh

2016-01-01

Recently we have proposed a new variable selection algorithm, based on clustering of variable concept (CLoVA) in classification problem. With the same idea, this new concept has been applied to a regression problem and then the obtained results have been compared with conventional variable selection strategies for PLS. The basic idea behind the clustering of variable is that, the instrument channels are clustered into different clusters via clustering algorithms. Then, the spectral data of each cluster are subjected to PLS regression. Different real data sets (Cargill corn, Biscuit dough, ACE QSAR, Soy, and Tablet) have been used to evaluate the influence of the clustering of variables on the prediction performances of PLS. Almost in the all cases, the statistical parameter especially in prediction error shows the superiority of CLoVA-PLS respect to other variable selection strategies. Finally the synergy clustering of variable (sCLoVA-PLS), which is used the combination of cluster, has been proposed as an efficient and modification of CLoVA algorithm. The obtained statistical parameter indicates that variable clustering can split useful part from redundant ones, and then based on informative cluster; stable model can be reached. Copyright © 2015 Elsevier B.V. All rights reserved.

The cell wall and cell division gene cluster in the Mra operon of Pseudomonas aeruginosa: cloning, production, and purification of active enzymes.

Science.gov (United States)

Azzolina, B A; Yuan, X; Anderson, M S; El-Sherbeini, M

2001-04-01

We have cloned the Pseudomonas aeruginosa cell wall biosynthesis and cell division gene cluster that corresponds to the mra operon in the 2-min region of the Escherichia coli chromosome. The organization of the two chromosomal regions in P. aeruginosa and E. coli is remarkably similar with the following gene order: pbp3/pbpB, murE, murF, mraY, murD, ftsW, murG, murC, ddlB, ftsQ, ftsA, ftsZ, and envA/LpxC. All of the above P. aeruginosa genes are transcribed from the same strand of DNA with very small, if any, intragenic regions, indicating that these genes may constitute a single operon. All five amino acid ligases, MurC, MurD, MurE, MurF, and DdlB, in addition to MurG and MraY were cloned in expression vectors. The four recombinant P. aeruginosa Mur ligases, MurC, MurD, MurE, and MurF were overproduced in E. coli and purified as active enzymes. Copyright 2001 Academic Press.

Halophiles and their enzymes: Negativity put to good use

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DasSarma, Shiladitya; DasSarma, Priya

2015-01-01

Halophilic microorganisms possess stable enzymes that function in very high salinity, an extreme condition that leads to denaturation, aggregation, and precipitation of most other proteins. Genomic and structural analyses have established that the enzymes of halophilic Archaea and many halophilic Bacteria are negatively charged due to an excess of acidic over basic residues, and altered hydrophobicity, which enhance solubility and promote function in low water activity conditions. Here, we provide an update on recent bioinformatic analysis of predicted halophilic proteomes as well as experimental molecular studies on individual halophilic enzymes. On-going efforts on discovery and utilization of halophiles and their enzymes for biotechnology, including biofuel applications are also considered. PMID:26066288

Halophiles and their enzymes: negativity put to good use.

Science.gov (United States)

DasSarma, Shiladitya; DasSarma, Priya

2015-06-01

Halophilic microorganisms possess stable enzymes that function in very high salinity, an extreme condition that leads to denaturation, aggregation, and precipitation of most other proteins. Genomic and structural analyses have established that the enzymes of halophilic Archaea and many halophilic Bacteria are negatively charged due to an excess of acidic over basic residues, and altered hydrophobicity, which enhance solubility and promote function in low water activity conditions. Here, we provide an update on recent bioinformatic analysis of predicted halophilic proteomes as well as experimental molecular studies on individual halophilic enzymes. Recent efforts on discovery and utilization of halophiles and their enzymes for biotechnology, including biofuel applications are also considered. Copyright © 2015 Elsevier Ltd. All rights reserved.

THE REST-FRAME OPTICAL LUMINOSITY FUNCTION OF CLUSTER GALAXIES AT z < 0.8 AND THE ASSEMBLY OF THE CLUSTER RED SEQUENCE

International Nuclear Information System (INIS)

Rudnick, Gregory; Von der Linden, Anja; De Lucia, Gabriella; White, Simon; Pello, Roser; Aragon-Salamanca, Alfonso; Marchesini, Danilo; Clowe, Douglas; Halliday, Claire; Jablonka, Pascale; Milvang-Jensen, Bo; Poggianti, Bianca; Saglia, Roberto; Simard, Luc; Zaritsky, Dennis

2009-01-01

We present the rest-frame optical luminosity function (LF) of red-sequence galaxies in 16 clusters at 0.4 < z < 0.8 drawn from the ESO Distant Cluster Survey (EDisCS). We compare our clusters to an analogous sample from the Sloan Digital Sky Survey (SDSS) and match the EDisCS clusters to their most likely descendants. We measure all LFs down to M ∼ M * + (2.5-3.5). At z < 0.8, the bright end of the LF is consistent with passive evolution but there is a significant buildup of the faint end of the red sequence toward lower redshift. There is a weak dependence of the LF on cluster velocity dispersion for EDisCS but no such dependence for the SDSS clusters. We find tentative evidence that red-sequence galaxies brighter than a threshold magnitude are already in place, and that this threshold evolves to fainter magnitudes toward lower redshifts. We compare the EDisCS LFs with the LF of coeval red-sequence galaxies in the field and find that the bright end of the LFs agree. However, relative to the number of bright red galaxies, the field has more faint red galaxies than clusters at 0.6 < z < 0.8 but fewer at 0.4 < z < 0.6, implying differential evolution. We compare the total light in the EDisCS cluster red sequences to the total red-sequence light in our SDSS cluster sample. Clusters at 0.4 < z < 0.8 must increase their luminosity on the red sequence (and therefore stellar mass in red galaxies) by a factor of 1-3 by z = 0. The necessary processes that add mass to the red sequence in clusters predict local clusters that are overluminous as compared to those observed in the SDSS. The predicted cluster luminosities can be reconciled with observed local cluster luminosities by combining multiple previously known effects.

Co-Clustering by Bipartite Spectral Graph Partitioning for Out-of-Tutor Prediction

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Trivedi, Shubhendu; Pardos, Zachary A.; Sarkozy, Gabor N.; Heffernan, Neil T.

2012-01-01

Learning a more distributed representation of the input feature space is a powerful method to boost the performance of a given predictor. Often this is accomplished by partitioning the data into homogeneous groups by clustering so that separate models could be trained on each cluster. Intuitively each such predictor is a better representative of…

Small traveling clusters in attractive and repulsive Hamiltonian mean-field models.

Science.gov (United States)

Barré, Julien; Yamaguchi, Yoshiyuki Y

2009-03-01

Long-lasting small traveling clusters are studied in the Hamiltonian mean-field model by comparing between attractive and repulsive interactions. Nonlinear Landau damping theory predicts that a Gaussian momentum distribution on a spatially homogeneous background permits the existence of traveling clusters in the repulsive case, as in plasma systems, but not in the attractive case. Nevertheless, extending the analysis to a two-parameter family of momentum distributions of Fermi-Dirac type, we theoretically predict the existence of traveling clusters in the attractive case; these findings are confirmed by direct N -body numerical simulations. The parameter region with the traveling clusters is much reduced in the attractive case with respect to the repulsive case.

Optimizing Cofactor Specificity of Oxidoreductase Enzymes for the Generation of Microbial Production Strains—OptSwap

DEFF Research Database (Denmark)

King, Zachary A.; Feist, Adam

2013-01-01

Central oxidoreductase enzymes (eg, dehydrogenases, reductases) in microbial metabolism often have preferential binding specificity for one of the two major currency metabolites NAD(H) and NADP(H). These enzyme specificities result in a division of the metabolic functionality of the currency...... specificities of oxidoreductase enzyme and complementary reaction knockouts. Using the Escherichia coli genome-scale metabolic model iJO1366, OptSwap predicted eight growth-coupled production designs with significantly greater product yields or substrate-specific productivities than designs predicted with gene...

Evaluation of B3LYP, X3LYP, and M06-Class Density Functionals for Predicting the Binding Energies of Neutral, Protonated, and Deprotonated Water Clusters.

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Bryantsev, Vyacheslav S; Diallo, Mamadou S; van Duin, Adri C T; Goddard, William A

2009-04-14

In this paper we assess the accuracy of the B3LYP, X3LYP, and newly developed M06-L, M06-2X, and M06 functionals to predict the binding energies of neutral and charged water clusters including (H2O)n, n = 2-8, 20), H3O(+)(H2O)n, n = 1-6, and OH(-)(H2O)n, n = 1-6. We also compare the predicted energies of two ion hydration and neutralization reactions on the basis of the calculated binding energies. In all cases, we use as benchmarks calculated binding energies of water clusters extrapolated to the complete basis set limit of the second-order Møller-Plesset perturbation theory with the effects of higher order correlation estimated at the coupled-cluster theory with single, double, and perturbative triple excitations in the aug-cc-pVDZ basis set. We rank the accuracy of the functionals on the basis of the mean unsigned error (MUE) between calculated benchmark and density functional theory energies. The corresponding MUE (kcal/mol) for each functional is listed in parentheses. We find that M06-L (0.73) and M06 (0.84) give the most accurate binding energies using very extended basis sets such as aug-cc-pV5Z. For more affordable basis sets, the best methods for predicting the binding energies of water clusters are M06-L/aug-cc-pVTZ (1.24), B3LYP/6-311++G(2d,2p) (1.29), and M06/aug-cc-PVTZ (1.33). M06-L/aug-cc-pVTZ also gives more accurate energies for the neutralization reactions (1.38), whereas B3LYP/6-311++G(2d,2p) gives more accurate energies for the ion hydration reactions (1.69).

Fuzzy Modeled K-Cluster Quality Mining of Hidden Knowledge for Decision Support

OpenAIRE

S. Parkash  Kumar; K. S. Ramaswami

2011-01-01

Problem statement: The work presented Fuzzy Modeled K-means Cluster Quality Mining of hidden knowledge for Decision Support. Based on the number of clusters, number of objects in each cluster and its cohesiveness, precision and recall values, the cluster quality metrics is measured. The fuzzy k-means is adapted approach by using heuristic method which iterates the cluster to form an efficient valid cluster. With the obtained data clusters, quality assessment is made by predictive mining using...

Mechanism of electron attachment to van der Waals clusters: Application to carbon dioxide clusters

International Nuclear Information System (INIS)

Tsukada, M.; Shima, N.; Tsuneyuki, S.; Kageshima, H.; Kondow, T.

1987-01-01

A theory on the attachment of very slow electrons to van der Waals clusters was developed on the basis of the electronic structure theory, and was applied to clarify the mechanism of the collisional electron transfer from a high-Rydberg atom to a CO 2 cluster. The strong coupled electron--phonon model is found to afford a reasonable mechanism of the attachment. The equilibrium geometry of (CO 2 )/sub N/ (2≤N≤13) clusters are determined and their vertical affinity levels are obtained by the DV-X α-transition state method. Using this information, as well as some plausible assumptions on the values of the coupling constants, the attachment cross section σ is evaluated as a function of the energy of the incident electron. The theory predicts the existence of the threshold cluster size for the attachment and a sharp decrease of σ with the energy, which are consistent with the experimental results

Predicting DMS-IV cluster B personality disorder criteria from MMPI-2 and Rorschach data: a test of incremental validity.

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Blais, M A; Hilsenroth, M J; Castlebury, F; Fowler, J C; Baity, M R

2001-02-01

Despite their frequent conjoint clinical use, the incremental validity of Rorschach (Rorschach, 1921/1942) and MMPI (Hathaway & McKinley, 1943) data has not been adequately established, nor has any study to date explored the incremental validity of these tests for predicting Diagnostic and Statistical Manual of Mental Disorders (4th ed. [DSM-IV]; American Psychiatric Association, 1994) personality disorders (PDs). In a reanalysis of existing data, we used select Rorschach variables and the MMPI PD scales to predict DSM-IV antisocial, borderline, histrionic, and narcissistic PD criteria in a sample of treatment-seeking outpatients. The correlational findings revealed alimited relation between Rorschach and MMPI-2 (Butcher, Dahlstrom, Graham, Tellegen, & Kaemmer, 1989) variables, with only 5 of 30 correlations reaching significance (p psychological characteristics of the DSM-IV Cluster B PDs.

MEASUREMENT OF THE HALO BIAS FROM STACKED SHEAR PROFILES OF GALAXY CLUSTERS

Energy Technology Data Exchange (ETDEWEB)

Covone, Giovanni [Dipartimento di Fisica, Università di Napoli " Federico II," Via Cinthia, I-80126 Napoli (Italy); Sereno, Mauro [Dipartimento di Fisica e Astronomia, Università di Bologna, Viale Berti Pichat 6/2, I-40127 Bologna (Italy); Kilbinger, Martin [CEA/Irfu/SAp Saclay, Laboratoire AIM, F-91191 Gif-sur-Yvette (France); Cardone, Vincenzo F. [I.N.A.F.-Osservatorio Astronomico di Roma, Via Frascati 33, I-00040 Monteporzio Catone (Roma) (Italy)

2014-04-01

We present observational evidence of the two-halo term in the stacked shear profile of a sample of ∼1200 optically selected galaxy clusters based on imaging data and the public shear catalog from the CFHTLenS. We find that the halo bias, a measure of the correlated distribution of matter around galaxy clusters, has amplitude and correlation with galaxy cluster mass in very good agreement with the predictions based on the LCDM standard cosmological model. The mass-concentration relation is flat but higher than theoretical predictions. We also confirm the close scaling relation between the optical richness of galaxy clusters and their mass.

The growth of Fe clusters over graphene/Cu(111)

International Nuclear Information System (INIS)

Takahashi, Keisuke

2015-01-01

The growth of Fe clusters up to nine atoms over graphene/Cu(111) is investigated within the density functional theory. Graphene is weakly physisorbed on Cu(111) through van der Waals force. The structures of Fe clusters over graphene/Cu(111) grow differently compared to gas-phase Fe clusters where Fe clusters are predicted to form towards a pyramid-like structure on graphene/Cu(111). The graphene is negatively charged upon the adsorption of Fe clusters as a result of charge transfer from Fe to graphene. Despite the fact that the electronic structure of graphene is affected by Fe clusters, magnetic moment of Fe clusters over graphene/Cu(111) remains relatively high. This suggests that graphene can be a potential substrate for supporting Fe clusters towards applications in magnetism and catalysis. (paper)

Prediction and identification of sequences coding for orphan enzymes using genomic and metagenomic neighbours

DEFF Research Database (Denmark)

Yamada, Takuji; Waller, Alison S.; Raes, Jeroen

2012-01-01

Despite the current wealth of sequencing data, one-third of all biochemically characterized metabolic enzymes lack a corresponding gene or protein sequence, and as such can be considered orphan enzymes. They represent a major gap between our molecular and biochemical knowledge, and consequently a...... Systems Biology 8: 581; published online 8 May 2012; doi:10.1038/msb.2012.13...

A computational linguistic measure of clustering behavior on semantic verbal fluency task predicts risk of future dementia in the nun study.

Science.gov (United States)

Pakhomov, Serguei V S; Hemmy, Laura S

2014-06-01

Generative semantic verbal fluency (SVF) tests show early and disproportionate decline relative to other abilities in individuals developing Alzheimer's disease. Optimal performance on SVF tests depends on the efficiency of using clustered organization of semantically related items and the ability to switch between clusters. Traditional approaches to clustering and switching have relied on manual determination of clusters. We evaluated a novel automated computational linguistic approach for quantifying clustering behavior. Our approach is based on Latent Semantic Analysis (LSA) for computing strength of semantic relatedness between pairs of words produced in response to SVF test. The mean size of semantic clusters (MCS) and semantic chains (MChS) are calculated based on pairwise relatedness values between words. We evaluated the predictive validity of these measures on a set of 239 participants in the Nun Study, a longitudinal study of aging. All were cognitively intact at baseline assessment, measured with the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) battery, and were followed in 18-month waves for up to 20 years. The onset of either dementia or memory impairment were used as outcomes in Cox proportional hazards models adjusted for age and education and censored at follow-up waves 5 (6.3 years) and 13 (16.96 years). Higher MCS was associated with 38% reduction in dementia risk at wave 5 and 26% reduction at wave 13, but not with the onset of memory impairment. Higher [+1 standard deviation (SD)] MChS was associated with 39% dementia risk reduction at wave 5 but not wave 13, and association with memory impairment was not significant. Higher traditional SVF scores were associated with 22-29% memory impairment and 35-40% dementia risk reduction. SVF scores were not correlated with either MCS or MChS. Our study suggests that an automated approach to measuring clustering behavior can be used to estimate dementia risk in cognitively normal

Millisecond radio pulsars in globular clusters

Science.gov (United States)

Verbunt, Frank; Lewin, Walter H. G.; Van Paradijs, Jan

1989-01-01

It is shown that the number of millisecond radio pulsars, in globular clusters, should be larger than 100, applying the standard scenario that all the pulsars descend from low-mass X-ray binaries. Moreover, most of the pulsars are located in a small number of clusters. The prediction that Teran 5 and Liller 1 contain at least about a dozen millisecond radio pulsars each is made. The observations of millisecond radio pulsars in globular clusters to date, in particular the discovery of two millisecond radio pulsars in 47 Tuc, are in agreement with the standard scenario, in which the neutron star is spun up during the mass transfer phase.

Particle Simulation of Oxidation Induced Band 3 Clustering in Human Erythrocytes.

Directory of Open Access Journals (Sweden)

Hanae Shimo

2015-06-01

Full Text Available Oxidative stress mediated clustering of membrane protein band 3 plays an essential role in the clearance of damaged and aged red blood cells (RBCs from the circulation. While a number of previous experimental studies have observed changes in band 3 distribution after oxidative treatment, the details of how these clusters are formed and how their properties change under different conditions have remained poorly understood. To address these issues, a framework that enables the simultaneous monitoring of the temporal and spatial changes following oxidation is needed. In this study, we established a novel simulation strategy that incorporates deterministic and stochastic reactions with particle reaction-diffusion processes, to model band 3 cluster formation at single molecule resolution. By integrating a kinetic model of RBC antioxidant metabolism with a model of band 3 diffusion, we developed a model that reproduces the time-dependent changes of glutathione and clustered band 3 levels, as well as band 3 distribution during oxidative treatment, observed in prior studies. We predicted that cluster formation is largely dependent on fast reverse reaction rates, strong affinity between clustering molecules, and irreversible hemichrome binding. We further predicted that under repeated oxidative perturbations, clusters tended to progressively grow and shift towards an irreversible state. Application of our model to simulate oxidation in RBCs with cytoskeletal deficiency also suggested that oxidation leads to more enhanced clustering compared to healthy RBCs. Taken together, our model enables the prediction of band 3 spatio-temporal profiles under various situations, thus providing valuable insights to potentially aid understanding mechanisms for removing senescent and premature RBCs.

Particle Simulation of Oxidation Induced Band 3 Clustering in Human Erythrocytes.

Science.gov (United States)

Shimo, Hanae; Arjunan, Satya Nanda Vel; Machiyama, Hiroaki; Nishino, Taiko; Suematsu, Makoto; Fujita, Hideaki; Tomita, Masaru; Takahashi, Koichi

2015-06-01

Oxidative stress mediated clustering of membrane protein band 3 plays an essential role in the clearance of damaged and aged red blood cells (RBCs) from the circulation. While a number of previous experimental studies have observed changes in band 3 distribution after oxidative treatment, the details of how these clusters are formed and how their properties change under different conditions have remained poorly understood. To address these issues, a framework that enables the simultaneous monitoring of the temporal and spatial changes following oxidation is needed. In this study, we established a novel simulation strategy that incorporates deterministic and stochastic reactions with particle reaction-diffusion processes, to model band 3 cluster formation at single molecule resolution. By integrating a kinetic model of RBC antioxidant metabolism with a model of band 3 diffusion, we developed a model that reproduces the time-dependent changes of glutathione and clustered band 3 levels, as well as band 3 distribution during oxidative treatment, observed in prior studies. We predicted that cluster formation is largely dependent on fast reverse reaction rates, strong affinity between clustering molecules, and irreversible hemichrome binding. We further predicted that under repeated oxidative perturbations, clusters tended to progressively grow and shift towards an irreversible state. Application of our model to simulate oxidation in RBCs with cytoskeletal deficiency also suggested that oxidation leads to more enhanced clustering compared to healthy RBCs. Taken together, our model enables the prediction of band 3 spatio-temporal profiles under various situations, thus providing valuable insights to potentially aid understanding mechanisms for removing senescent and premature RBCs.

Geographic prediction of tuberculosis clusters in Fukuoka, Japan, using the space-time scan statistic

Energy Technology Data Exchange (ETDEWEB)

Daisuke Onozuka; Akihito Hagihara [Fukuoka Institute of Health and Environmental Sciences, Fukuoka (Japan). Department of Information Science

2007-07-01

Tuberculosis (TB) has reemerged as a global public health epidemic in recent years. Although evaluating local disease clusters leads to effective prevention and control of TB, there are few, if any, spatiotemporal comparisons for epidemic diseases. TB cases among residents in Fukuoka Prefecture between 1999 and 2004 (n = 9,119) were geocoded at the census tract level (n = 109) based on residence at the time of diagnosis. The spatial and space-time scan statistics were then used to identify clusters of census tracts with elevated proportions of TB cases. In the purely spatial analyses, the most likely clusters were in the Chikuho coal mining area (in 1999, 2002, 2003, 2004), the Kita-Kyushu industrial area (in 2000), and the Fukuoka urban area (in 2001). In the space-time analysis, the most likely cluster was the Kita-Kyushu industrial area (in 2000). The north part of Fukuoka Prefecture was the most likely to have a cluster with a significantly high occurrence of TB. The spatial and space-time scan statistics are effective ways of describing circular disease clusters. Since, in reality, infectious diseases might form other cluster types, the effectiveness of the method may be limited under actual practice. The sophistication of the analytical methodology, however, is a topic for future study. 48 refs., 3 figs., 3 tabs.

Geographic prediction of tuberculosis clusters in Fukuoka, Japan, using the space-time scan statistic

Directory of Open Access Journals (Sweden)

Onozuka Daisuke

2007-04-01

Full Text Available Abstract Background Tuberculosis (TB has reemerged as a global public health epidemic in recent years. Although evaluating local disease clusters leads to effective prevention and control of TB, there are few, if any, spatiotemporal comparisons for epidemic diseases. Methods TB cases among residents in Fukuoka Prefecture between 1999 and 2004 (n = 9,119 were geocoded at the census tract level (n = 109 based on residence at the time of diagnosis. The spatial and space-time scan statistics were then used to identify clusters of census tracts with elevated proportions of TB cases. Results In the purely spatial analyses, the most likely clusters were in the Chikuho coal mining area (in 1999, 2002, 2003, 2004, the Kita-Kyushu industrial area (in 2000, and the Fukuoka urban area (in 2001. In the space-time analysis, the most likely cluster was the Kita-Kyushu industrial area (in 2000. The north part of Fukuoka Prefecture was the most likely to have a cluster with a significantly high occurrence of TB. Conclusion The spatial and space-time scan statistics are effective ways of describing circular disease clusters. Since, in reality, infectious diseases might form other cluster types, the effectiveness of the method may be limited under actual practice. The sophistication of the analytical methodology, however, is a topic for future study.

Improved Density Based Spatial Clustering of Applications of Noise Clustering Algorithm for Knowledge Discovery in Spatial Data

Directory of Open Access Journals (Sweden)

Arvind Sharma

2016-01-01

Full Text Available There are many techniques available in the field of data mining and its subfield spatial data mining is to understand relationships between data objects. Data objects related with spatial features are called spatial databases. These relationships can be used for prediction and trend detection between spatial and nonspatial objects for social and scientific reasons. A huge data set may be collected from different sources as satellite images, X-rays, medical images, traffic cameras, and GIS system. To handle this large amount of data and set relationship between them in a certain manner with certain results is our primary purpose of this paper. This paper gives a complete process to understand how spatial data is different from other kinds of data sets and how it is refined to apply to get useful results and set trends to predict geographic information system and spatial data mining process. In this paper a new improved algorithm for clustering is designed because role of clustering is very indispensable in spatial data mining process. Clustering methods are useful in various fields of human life such as GIS (Geographic Information System, GPS (Global Positioning System, weather forecasting, air traffic controller, water treatment, area selection, cost estimation, planning of rural and urban areas, remote sensing, and VLSI designing. This paper presents study of various clustering methods and algorithms and an improved algorithm of DBSCAN as IDBSCAN (Improved Density Based Spatial Clustering of Application of Noise. The algorithm is designed by addition of some important attributes which are responsible for generation of better clusters from existing data sets in comparison of other methods.

Simultaneous Co-Clustering and Classification in Customers Insight

Science.gov (United States)

Anggistia, M.; Saefuddin, A.; Sartono, B.

2017-04-01

Building predictive model based on the heterogeneous dataset may yield many problems, such as less precise in parameter and prediction accuracy. Such problem can be solved by segmenting the data into relatively homogeneous groups and then build a predictive model for each cluster. The advantage of using this strategy usually gives result in simpler models, more interpretable, and more actionable without any loss in accuracy and reliability. This work concerns on marketing data set which recorded a customer behaviour across products. There are some variables describing customer and product as attributes. The basic idea of this approach is to combine co-clustering and classification simultaneously. The objective of this research is to analyse the customer across product characteristics, so the marketing strategy implemented precisely.

Power-Law Template for IR Point Source Clustering

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Addison, Graeme E.; Dunkley, Joanna; Hajian, Amir; Viero, Marco; Bond, J. Richard; Das, Sudeep; Devlin, Mark; Halpern, Mark; Hincks, Adam; Hlozek, Renee;

Power-Law Template for Infrared Point-Source Clustering

Science.gov (United States)

Addison, Graeme E; Dunkley, Joanna; Hajian, Amir; Viero, Marco; Bond, J. Richard; Das, Sudeep; Devlin, Mark J.; Halpern, Mark; Hincks, Adam D; Hlozek, Renee;

Discovery and structure determination of the orphan enzyme isoxanthopterin deaminase .

Science.gov (United States)

Hall, Richard S; Agarwal, Rakhi; Hitchcock, Daniel; Sauder, J Michael; Burley, Stephen K; Swaminathan, Subramanyam; Raushel, Frank M

2010-05-25

Two previously uncharacterized proteins have been identified that efficiently catalyze the deamination of isoxanthopterin and pterin 6-carboxylate. The genes encoding these two enzymes, NYSGXRC-9339a ( gi|44585104 ) and NYSGXRC-9236b ( gi|44611670 ), were first identified from DNA isolated from the Sargasso Sea as part of the Global Ocean Sampling Project. The genes were synthesized, and the proteins were subsequently expressed and purified. The X-ray structure of Sgx9339a was determined at 2.7 A resolution (Protein Data Bank entry 2PAJ ). This protein folds as a distorted (beta/alpha)(8) barrel and contains a single zinc ion in the active site. These enzymes are members of the amidohydrolase superfamily and belong to cog0402 within the clusters of orthologous groups (COG). Enzymes in cog0402 have previously been shown to catalyze the deamination of guanine, cytosine, S-adenosylhomocysteine, and 8-oxoguanine. A small compound library of pteridines, purines, and pyrimidines was used to probe catalytic activity. The only substrates identified in this search were isoxanthopterin and pterin 6-carboxylate. The kinetic constants for the deamination of isoxanthopterin with Sgx9339a were determined to be 1.0 s(-1), 8.0 muM, and 1.3 x 10(5) M(-1) s(-1) (k(cat), K(m), and k(cat)/K(m), respectively). The active site of Sgx9339a most closely resembles the active site for 8-oxoguanine deaminase (Protein Data Bank entry 2UZ9 ). A model for substrate recognition of isoxanthopterin by Sgx9339a was proposed on the basis of the binding of guanine and xanthine in the active site of guanine deaminase. Residues critical for substrate binding appear to be conserved glutamine and tyrosine residues that form hydrogen bonds with the carbonyl oxygen at C4, a conserved threonine residue that forms hydrogen bonds with N5, and another conserved threonine residue that forms hydrogen bonds with the carbonyl group at C7. These conserved active site residues were used to identify 24 other genes

Galaxy clusters in the cosmic web

Science.gov (United States)

Acebrón, A.; Durret, F.; Martinet, N.; Adami, C.; Guennou, L.

2014-12-01

Simulations of large scale structure formation in the universe predict that matter is essentially distributed along filaments at the intersection of which lie galaxy clusters. We have analysed 9 clusters in the redshift range 0.4DAFT/FADA survey, which combines deep large field multi-band imaging and spectroscopic data, in order to detect filaments and/or structures around these clusters. Based on colour-magnitude diagrams, we have selected the galaxies likely to be in the cluster redshift range and studied their spatial distribution. We detect a number of structures and filaments around several clusters, proving that colour-magnitude diagrams are a reliable method to detect filaments around galaxy clusters. Since this method excludes blue (spiral) galaxies at the cluster redshift, we also apply the LePhare software to compute photometric redshifts from BVRIZ images to select galaxy cluster members and study their spatial distribution. We then find that, if only galaxies classified as early-type by LePhare are considered, we obtain the same distribution than with a red sequence selection, while taking into account late-type galaxies just pollutes the background level and deteriorates our detections. The photometric redshift based method therefore does not provide any additional information.

Coordinated control of active and reactive power of distribution network with distributed PV cluster via model predictive control

Science.gov (United States)

Ji, Yu; Sheng, Wanxing; Jin, Wei; Wu, Ming; Liu, Haitao; Chen, Feng

2018-02-01

A coordinated optimal control method of active and reactive power of distribution network with distributed PV cluster based on model predictive control is proposed in this paper. The method divides the control process into long-time scale optimal control and short-time scale optimal control with multi-step optimization. The models are transformed into a second-order cone programming problem due to the non-convex and nonlinear of the optimal models which are hard to be solved. An improved IEEE 33-bus distribution network system is used to analyse the feasibility and the effectiveness of the proposed control method

THE EXTENDED MAIN-SEQUENCE TURNOFF CLUSTERS OF THE LARGE MAGELLANIC CLOUD-MISSING LINKS IN GLOBULAR CLUSTER EVOLUTION

International Nuclear Information System (INIS)

Keller, Stefan C.; Mackey, A. Dougal; Da Costa, Gary S.

2011-01-01

Recent observations of intermediate-age (1-3 Gyr) massive star clusters in the Large Magellanic Cloud have revealed that the majority possess bifurcated or extended main-sequence turnoff (EMSTO) morphologies. This effect can be understood to arise from subsequent star formation among the stellar population with age differences between constituent stars amounting to 50-300 Myr. Age spreads of this order are similarly invoked to explain the light-element abundance variations witnessed in ancient globular clusters (GCs). In this paper, we explore the proposition that the clusters exhibiting the EMSTO phenomenon are a general phase in the evolution of massive clusters, one that naturally leads to the particular chemical properties of the ancient GC population. We show that the isolation of EMSTO clusters to intermediate ages is the consequence of observational selection effects. In our proposed scenario, the EMSTO phenomenon is identical to that which establishes the light-element abundance variations that are ubiquitous in the ancient GC population. Our scenario makes a strong prediction: EMSTO clusters will exhibit abundance variations in the light-elements characteristic of the ancient GC population.

Individualization as driving force of clustering phenomena in humans.

Directory of Open Access Journals (Sweden)

Michael Mäs

Full Text Available One of the most intriguing dynamics in biological systems is the emergence of clustering, in the sense that individuals self-organize into separate agglomerations in physical or behavioral space. Several theories have been developed to explain clustering in, for instance, multi-cellular organisms, ant colonies, bee hives, flocks of birds, schools of fish, and animal herds. A persistent puzzle, however, is the clustering of opinions in human populations, particularly when opinions vary continuously, such as the degree to which citizens are in favor of or against a vaccination program. Existing continuous opinion formation models predict "monoculture" in the long run, unless subsets of the population are perfectly separated from each other. Yet, social diversity is a robust empirical phenomenon, although perfect separation is hardly possible in an increasingly connected world. Considering randomness has not overcome the theoretical shortcomings so far. Small perturbations of individual opinions trigger social influence cascades that inevitably lead to monoculture, while larger noise disrupts opinion clusters and results in rampant individualism without any social structure. Our solution to the puzzle builds on recent empirical research, combining the integrative tendencies of social influence with the disintegrative effects of individualization. A key element of the new computational model is an adaptive kind of noise. We conduct computer simulation experiments demonstrating that with this kind of noise a third phase besides individualism and monoculture becomes possible, characterized by the formation of metastable clusters with diversity between and consensus within clusters. When clusters are small, individualization tendencies are too weak to prohibit a fusion of clusters. When clusters grow too large, however, individualization increases in strength, which promotes their splitting. In summary, the new model can explain cultural clustering in

Prediction of Solvent Physical Properties using the Hierarchical Clustering Method

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Recently a QSAR (Quantitative Structure Activity Relationship) method, the hierarchical clustering method, was developed to estimate acute toxicity values for large, diverse datasets. This methodology has now been applied to the estimate solvent physical properties including sur...

Improvement of activity and stability of Chondroitinase ABC I by introducing an aromatic cluster at the surface of protein.

Science.gov (United States)

Shahaboddin, Mohammad Esmaeil; Khajeh, Khosro; Maleki, Monireh; Golestani, Abolfazl

2017-10-01

Chondroitinase ABC I (ChABC I) has been shown to depolymerize a variety of glycosaminoglycan substrates and promote regeneration of damaged spinal cord. However, to date, intrathecal delivery methods have been suboptimal largely due to enzyme instability which necessitates repeated administration to the injured loci. Among the aromatic amino acids, tyrosine has been shown to be more effective in creation of stable clusters and further stabilize of the proteins. Bioinformatics approaches have been used to examine the effect of an extra aromatic cluster at the surface of ChABC I. In this study two amino acids i.e., Asn 806 and Gln 810 were mutated to tyrosine and to alanine as negative control. In this way, four variants i.e., N806Y/Q810Y, N806A/Q810Y, N806Y/Q810A and N806A/Q810A were created. The results showed that N806Y/Q810Y mutation improved both activity and thermal stability of the enzyme while Ala substitution reduced the enzyme activity and destabilized it. Structural analysis of mutants showed an increase in intrinsic fluorescence intensity and secondary structure content of N806Y/Q810Y mutant when compared to the wild type enzyme indicating a more rigid structure of this variant. Moreover, the N806Y/Q810Y enzyme displayed a remarkable resistance against trypsin degradation with a half-life (t 1/2 ) of 45.0min versus 32.5min of wild-type. In conclusion, the data revealed that structural features and activity of ChABC I can be improved by introducing appropriate aromatic clusters at the surface of the enzyme. Copyright © 2017 Elsevier Inc. All rights reserved.

Relativistic Binaries in Globular Clusters

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Matthew J. Benacquista

2013-03-01

Full Text Available Galactic globular clusters are old, dense star systems typically containing 10^4 – 10^6 stars. As an old population of stars, globular clusters contain many collapsed and degenerate objects. As a dense population of stars, globular clusters are the scene of many interesting close dynamical interactions between stars. These dynamical interactions can alter the evolution of individual stars and can produce tight binary systems containing one or two compact objects. In this review, we discuss theoretical models of globular cluster evolution and binary evolution, techniques for simulating this evolution that leads to relativistic binaries, and current and possible future observational evidence for this population. Our discussion of globular cluster evolution will focus on the processes that boost the production of tight binary systems and the subsequent interaction of these binaries that can alter the properties of both bodies and can lead to exotic objects. Direct N-body integrations and Fokker–Planck simulations of the evolution of globular clusters that incorporate tidal interactions and lead to predictions of relativistic binary populations are also discussed. We discuss the current observational evidence for cataclysmic variables, millisecond pulsars, and low-mass X-ray binaries as well as possible future detection of relativistic binaries with gravitational radiation.

Network analysis of metabolic enzyme evolution in Escherichia coli

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Kraulis Per

2004-02-01

Full Text Available Abstract Background The two most common models for the evolution of metabolism are the patchwork evolution model, where enzymes are thought to diverge from broad to narrow substrate specificity, and the retrograde evolution model, according to which enzymes evolve in response to substrate depletion. Analysis of the distribution of homologous enzyme pairs in the metabolic network can shed light on the respective importance of the two models. We here investigate the evolution of the metabolism in E. coli viewed as a single network using EcoCyc. Results Sequence comparison between all enzyme pairs was performed and the minimal path length (MPL between all enzyme pairs was determined. We find a strong over-representation of homologous enzymes at MPL 1. We show that the functionally similar and functionally undetermined enzyme pairs are responsible for most of the over-representation of homologous enzyme pairs at MPL 1. Conclusions The retrograde evolution model predicts that homologous enzymes pairs are at short metabolic distances from each other. In general agreement with previous studies we find that homologous enzymes occur close to each other in the network more often than expected by chance, which lends some support to the retrograde evolution model. However, we show that the homologous enzyme pairs which may have evolved through retrograde evolution, namely the pairs that are functionally dissimilar, show a weaker over-representation at MPL 1 than the functionally similar enzyme pairs. Our study indicates that, while the retrograde evolution model may have played a small part, the patchwork evolution model is the predominant process of metabolic enzyme evolution.

Enzyme based soil stabilization for unpaved road construction

Directory of Open Access Journals (Sweden)

Renjith Rintu

2017-01-01

Full Text Available Enzymes as soil stabilizers have been successfully used in road construction in several countries for the past 30 years. However, research has shown that the successful application of these enzymes is case specific, emphasizing that enzyme performance is dependent on subgrade soil type, condition and the type of enzyme used as the stabilizer. A universal standard or a tool for road engineers to assess the performance of stabilized unbound pavements using well-established enzymes is not available to date. The research aims to produce a validated assessment tool which can be used to predict strength enhancement within a generalized statistical framework. The objective of the present study is to identify new materials for developing the assessment tool which supports enzyme based stabilization, as well as to identify the correct construction sequence for such new materials. A series of characterization tests were conducted on several soil types obtained from proposed construction sites. Having identified the suitable soil type to mix with the enzyme, a trial road construction has been performed to investigate the efficiency of the enzyme stabilization along with the correct construction sequence. The enzyme stabilization has showed significant improvement of the road performance as was evidenced from the test results which were based on site soil obtained before and after stabilization. The research will substantially benefit the road construction industry by not only replacing traditional construction methods with economical/reliable approaches, but also eliminating site specific tests required in current practice of enzyme based road construction.

Fragmentation of percolation cluster perimeters

Science.gov (United States)

Debierre, Jean-Marc; Bradley, R. Mark

1996-05-01

We introduce a model for the fragmentation of porous random solids under the action of an external agent. In our model, the solid is represented by a bond percolation cluster on the square lattice and bonds are removed only at the external perimeter (or `hull') of the cluster. This model is shown to be related to the self-avoiding walk on the Manhattan lattice and to the disconnection events at a diffusion front. These correspondences are used to predict the leading and the first correction-to-scaling exponents for several quantities defined for hull fragmentation. Our numerical results support these predictions. In addition, the algorithm used to construct the perimeters reveals itself to be a very efficient tool for detecting subtle correlations in the pseudo-random number generator used. We present a quantitative test of two generators which supports recent results reported in more systematic studies.

Expression and characterization of thermostable glycogen branching enzyme from Geobacillus mahadia Geo-05

Directory of Open Access Journals (Sweden)

Nur Syazwani Mohtar

2016-12-01

Full Text Available The glycogen branching enzyme (EC 2.4.1.18, which catalyses the formation of α-1,6-glycosidic branch points in glycogen structure, is often used to enhance the nutritional value and quality of food and beverages. In order to be applicable in industries, enzymes that are stable and active at high temperature are much desired. Using genome mining, the nucleotide sequence of the branching enzyme gene (glgB was extracted from the Geobacillus mahadia Geo-05 genome sequence provided by the Malaysia Genome Institute. The size of the gene is 2013 bp, and the theoretical molecular weight of the protein is 78.43 kDa. The gene sequence was then used to predict the thermostability, function and the three dimensional structure of the enzyme. The gene was cloned and overexpressed in E. coli to verify the predicted result experimentally. The purified enzyme was used to study the effect of temperature and pH on enzyme activity and stability, and the inhibitory effect by metal ion on enzyme activity. This thermostable glycogen branching enzyme was found to be most active at 55 °C, and the half-life at 60 °C and 70 °C was 24 h and 5 h, respectively. From this research, a thermostable glycogen branching enzyme was successfully isolated from Geobacillus mahadia Geo-05 by genome mining together with molecular biology technique.

Enzymes Involved in AMPylation and deAMPylation.

Science.gov (United States)

Casey, Amanda K; Orth, Kim

2018-02-14

Posttranslational modifications are covalent changes made to proteins that typically alter the function or location of the protein. AMPylation is an emerging posttranslational modification that involves the addition of adenosine monophosphate (AMP) to a protein. Like other, more well-studied posttranslational modifications, AMPylation is predicted to regulate the activity of the modified target proteins. However, the scope of this modification both in bacteria and in eukaryotes remains to be fully determined. In this review, we provide an up to date overview of the known AMPylating enzymes, the regulation of these enzymes, and the effect of this modification on target proteins.

Towards Automated Binding Affinity Prediction Using an Iterative Linear Interaction Energy Approach

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C. Ruben Vosmeer

2014-01-01

Full Text Available Binding affinity prediction of potential drugs to target and off-target proteins is an essential asset in drug development. These predictions require the calculation of binding free energies. In such calculations, it is a major challenge to properly account for both the dynamic nature of the protein and the possible variety of ligand-binding orientations, while keeping computational costs tractable. Recently, an iterative Linear Interaction Energy (LIE approach was introduced, in which results from multiple simulations of a protein-ligand complex are combined into a single binding free energy using a Boltzmann weighting-based scheme. This method was shown to reach experimental accuracy for flexible proteins while retaining the computational efficiency of the general LIE approach. Here, we show that the iterative LIE approach can be used to predict binding affinities in an automated way. A workflow was designed using preselected protein conformations, automated ligand docking and clustering, and a (semi-automated molecular dynamics simulation setup. We show that using this workflow, binding affinities of aryloxypropanolamines to the malleable Cytochrome P450 2D6 enzyme can be predicted without a priori knowledge of dominant protein-ligand conformations. In addition, we provide an outlook for an approach to assess the quality of the LIE predictions, based on simulation outcomes only.

Clustered DPCM with removing noise spectra for the lossless compression of hyperspectral images

Science.gov (United States)

Wu, Jiaji; Xu, Jianglei

2013-10-01

The clustered DPCM (C-DPCM) lossless compression method by Jarno et al. for hyperspectral images achieved a good compression effect. It can be divided into three components: clustering, prediction, and coding. In the prediction part, it solves a multiple linear regression model for each of the clusters in every band. Without considering the effect of noise spectra, there is still room for improvement. This paper proposes a C-DPCM method with Removing Noise Spectra (C-DPCM-RNS) for the lossless compression of hyperspectral images. C-DPCM-RNS's prediction part consists of two-times trainings. The prediction coefficients obtained from the first training will be used in the linear predictor to compute all the predicted values and then the difference between original and predicted values in current band of current class. Only the non-noise spectra are used in the second training. The resulting prediction coefficients from the second training will be used for prediction and sent to the decoder. The two-times trainings remove part of the interference of noise spectra, and reaches a better compression effect than other methods based on regression prediction.

Prediction of settled water turbidity and optimal coagulant dosage in drinking water treatment plant using a hybrid model of k-means clustering and adaptive neuro-fuzzy inference system

Science.gov (United States)

Kim, Chan Moon; Parnichkun, Manukid

2017-11-01

Coagulation is an important process in drinking water treatment to attain acceptable treated water quality. However, the determination of coagulant dosage is still a challenging task for operators, because coagulation is nonlinear and complicated process. Feedback control to achieve the desired treated water quality is difficult due to lengthy process time. In this research, a hybrid of k-means clustering and adaptive neuro-fuzzy inference system ( k-means-ANFIS) is proposed for the settled water turbidity prediction and the optimal coagulant dosage determination using full-scale historical data. To build a well-adaptive model to different process states from influent water, raw water quality data are classified into four clusters according to its properties by a k-means clustering technique. The sub-models are developed individually on the basis of each clustered data set. Results reveal that the sub-models constructed by a hybrid k-means-ANFIS perform better than not only a single ANFIS model, but also seasonal models by artificial neural network (ANN). The finally completed model consisting of sub-models shows more accurate and consistent prediction ability than a single model of ANFIS and a single model of ANN based on all five evaluation indices. Therefore, the hybrid model of k-means-ANFIS can be employed as a robust tool for managing both treated water quality and production costs simultaneously.

Enzyme

Science.gov (United States)

Enzymes are complex proteins that cause a specific chemical change in all parts of the body. For ... use them. Blood clotting is another example of enzymes at work. Enzymes are needed for all body ...

Cluster analysis for portfolio optimization

OpenAIRE

Vincenzo Tola; Fabrizio Lillo; Mauro Gallegati; Rosario N. Mantegna

2005-01-01

We consider the problem of the statistical uncertainty of the correlation matrix in the optimization of a financial portfolio. We show that the use of clustering algorithms can improve the reliability of the portfolio in terms of the ratio between predicted and realized risk. Bootstrap analysis indicates that this improvement is obtained in a wide range of the parameters N (number of assets) and T (investment horizon). The predicted and realized risk level and the relative portfolio compositi...

Possible ionization ''ignition'' in laser-driven clusters

International Nuclear Information System (INIS)

Rose-Petruck, C.; Schafer, K.J.; Barty, C.P.J.

1995-01-01

The authors use Classical Trajectory Monte Carlo (CTMC) simulations to study the ionization of small rare gas clusters in short pulse, high intensity laser fields. They calculate, for a cluster of 25 neon atoms, the ionization stage reached and the average kinetic energy of the ionized electrons as functions of time and peak laser intensity. The CTMC calculations mimic the results of the much simpler barrier suppression model in the limit of isolated atoms. At solid density the results give much more ionization in the cluster than that predicted by the barrier suppression model. They find that when the laser intensity reaches a threshold value such that on average one electron is ionized from each atom, the cluster atoms rapidly move to higher ionization stages, approaching Ne +8 in a few femtoseconds. This ignition process creates an ultrafast pulse of energetic electrons in the cluster at quite modest laser intensities

Structural profiles of human miRNA families from pairwise clustering

DEFF Research Database (Denmark)

Kaczkowski, Bogumil; Þórarinsson, Elfar; Reiche, Kristin

2009-01-01

secondary structure already predicted, little is known about the patterns of structural conservation among pre-miRNAs. We address this issue by clustering the human pre-miRNA sequences based on pairwise, sequence and secondary structure alignment using FOLDALIGN, followed by global multiple alignment...... of obtained clusters by WAR. As a result, the common secondary structure was successfully determined for four FOLDALIGN clusters: the RF00027 structural family of the Rfam database and three clusters with previously undescribed consensus structures. Availability: http://genome.ku.dk/resources/mirclust...

Gravitational clustering of galaxies in the CfA slice

International Nuclear Information System (INIS)

Crane, P.; Saslaw, W.C.

1988-01-01

The clustering properties of the Galaxies in the CfA slice have been analyzed by comparing the properties of the neighbor distributions to the predictions of gravitational clustering theory. The agreement is excellent and implies that the observed structures can be explained by gravitational effects alone and do not require exotic explanations

A theoretical study of cluster radioactivity in platinum isotopes

Energy Technology Data Exchange (ETDEWEB)

Joseph, Deepthy Maria; Ashok, Nithu; Joseph, Antony [University of Calicut, Department of Physics, Malappuram, Kerala (India)

2018-01-15

The probable cluster decay modes in platinum isotopes are predicted with the help of effective liquid drop model. The calculated half-lives are compared with those of universal decay law model and with the experimental data. The investigation affirms the decisive role of neutron magicity in the phenomenon of cluster radioactivity. It is found that the probability of cluster emission decreases with the increase in the neutron number of parent nucleus. Geiger-Nuttall plots of the probable decay modes show linear behaviour, which in turn leads to the equation for logarithmic half-life for the clusters emitted from Pt isotopes. (orig.)

Reliability of windstorm predictions in the ECMWF ensemble prediction system

Science.gov (United States)

Becker, Nico; Ulbrich, Uwe

2016-04-01

Windstorms caused by extratropical cyclones are one of the most dangerous natural hazards in the European region. Therefore, reliable predictions of such storm events are needed. Case studies have shown that ensemble prediction systems (EPS) are able to provide useful information about windstorms between two and five days prior to the event. In this work, ensemble predictions with the European Centre for Medium-Range Weather Forecasts (ECMWF) EPS are evaluated in a four year period. Within the 50 ensemble members, which are initialized every 12 hours and are run for 10 days, windstorms are identified and tracked in time and space. By using a clustering approach, different predictions of the same storm are identified in the different ensemble members and compared to reanalysis data. The occurrence probability of the predicted storms is estimated by fitting a bivariate normal distribution to the storm track positions. Our results show, for example, that predicted storm clusters with occurrence probabilities of more than 50% have a matching observed storm in 80% of all cases at a lead time of two days. The predicted occurrence probabilities are reliable up to 3 days lead time. At longer lead times the occurrence probabilities are overestimated by the EPS.

Dynamical Mass Measurements of Contaminated Galaxy Clusters Using Support Distribution Machines

Science.gov (United States)

Ntampaka, Michelle; Trac, Hy; Sutherland, Dougal; Fromenteau, Sebastien; Poczos, Barnabas; Schneider, Jeff

2018-01-01

We study dynamical mass measurements of galaxy clusters contaminated by interlopers and show that a modern machine learning (ML) algorithm can predict masses by better than a factor of two compared to a standard scaling relation approach. We create two mock catalogs from Multidark’s publicly available N-body MDPL1 simulation, one with perfect galaxy cluster membership infor- mation and the other where a simple cylindrical cut around the cluster center allows interlopers to contaminate the clusters. In the standard approach, we use a power-law scaling relation to infer cluster mass from galaxy line-of-sight (LOS) velocity dispersion. Assuming perfect membership knowledge, this unrealistic case produces a wide fractional mass error distribution, with a width E=0.87. Interlopers introduce additional scatter, significantly widening the error distribution further (E=2.13). We employ the support distribution machine (SDM) class of algorithms to learn from distributions of data to predict single values. Applied to distributions of galaxy observables such as LOS velocity and projected distance from the cluster center, SDM yields better than a factor-of-two improvement (E=0.67) for the contaminated case. Remarkably, SDM applied to contaminated clusters is better able to recover masses than even the scaling relation approach applied to uncon- taminated clusters. We show that the SDM method more accurately reproduces the cluster mass function, making it a valuable tool for employing cluster observations to evaluate cosmological models.

Hydration dynamics in water clusters via quantum molecular dynamics simulations

Energy Technology Data Exchange (ETDEWEB)

Turi, László, E-mail: turi@chem.elte.hu [Department of Physical Chemistry, Eötvös Loránd University, Budapest 112, P. O. Box 32, H-1518 (Hungary)

2014-05-28

We have investigated the hydration dynamics in size selected water clusters with n = 66, 104, 200, 500, and 1000 water molecules using molecular dynamics simulations. To study the most fundamental aspects of relaxation phenomena in clusters, we choose one of the simplest, still realistic, quantum mechanically treated test solute, an excess electron. The project focuses on the time evolution of the clusters following two processes, electron attachment to neutral equilibrated water clusters and electron detachment from an equilibrated water cluster anion. The relaxation dynamics is significantly different in the two processes, most notably restoring the equilibrium final state is less effective after electron attachment. Nevertheless, in both scenarios only minor cluster size dependence is observed. Significantly different relaxation patterns characterize electron detachment for interior and surface state clusters, interior state clusters relaxing significantly faster. This observation may indicate a potential way to distinguish surface state and interior state water cluster anion isomers experimentally. A comparison of equilibrium and non-equilibrium trajectories suggests that linear response theory breaks down for electron attachment at 200 K, but the results converge to reasonable agreement at higher temperatures. Relaxation following electron detachment clearly belongs to the linear regime. Cluster relaxation was also investigated using two different computational models, one preferring cavity type interior states for the excess electron in bulk water, while the other simulating non-cavity structure. While the cavity model predicts appearance of several different hydrated electron isomers in agreement with experiment, the non-cavity model locates only cluster anions with interior excess electron distribution. The present simulations show that surface isomers computed with the cavity predicting potential show similar dynamical behavior to the interior clusters of

Combining cluster number counts and galaxy clustering

Energy Technology Data Exchange (ETDEWEB)

Lacasa, Fabien; Rosenfeld, Rogerio, E-mail: fabien@ift.unesp.br, E-mail: rosenfel@ift.unesp.br [ICTP South American Institute for Fundamental Research, Instituto de Física Teórica, Universidade Estadual Paulista, São Paulo (Brazil)

2016-08-01

The abundance of clusters and the clustering of galaxies are two of the important cosmological probes for current and future large scale surveys of galaxies, such as the Dark Energy Survey. In order to combine them one has to account for the fact that they are not independent quantities, since they probe the same density field. It is important to develop a good understanding of their correlation in order to extract parameter constraints. We present a detailed modelling of the joint covariance matrix between cluster number counts and the galaxy angular power spectrum. We employ the framework of the halo model complemented by a Halo Occupation Distribution model (HOD). We demonstrate the importance of accounting for non-Gaussianity to produce accurate covariance predictions. Indeed, we show that the non-Gaussian covariance becomes dominant at small scales, low redshifts or high cluster masses. We discuss in particular the case of the super-sample covariance (SSC), including the effects of galaxy shot-noise, halo second order bias and non-local bias. We demonstrate that the SSC obeys mathematical inequalities and positivity. Using the joint covariance matrix and a Fisher matrix methodology, we examine the prospects of combining these two probes to constrain cosmological and HOD parameters. We find that the combination indeed results in noticeably better constraints, with improvements of order 20% on cosmological parameters compared to the best single probe, and even greater improvement on HOD parameters, with reduction of error bars by a factor 1.4-4.8. This happens in particular because the cross-covariance introduces a synergy between the probes on small scales. We conclude that accounting for non-Gaussian effects is required for the joint analysis of these observables in galaxy surveys.

The Observational and Theoretical Tidal Radii of Globular Clusters in M87

Science.gov (United States)

Webb, Jeremy J.; Sills, Alison; Harris, William E.

2012-02-01

Globular clusters have linear sizes (tidal radii) which theory tells us are determined by their masses and by the gravitational potential of their host galaxy. To explore the relationship between observed and expected radii, we utilize the globular cluster population of the Virgo giant M87. Unusually deep, high signal-to-noise images of M87 are used to measure the effective and limiting radii of approximately 2000 globular clusters. To compare with these observations, we simulate a globular cluster population that has the same characteristics as the observed M87 cluster population. Placing these simulated clusters in the well-studied tidal field of M87, the orbit of each cluster is solved and the theoretical tidal radius of each cluster is determined. We compare the predicted relationship between cluster size and projected galactocentric distance to observations. We find that for an isotropic distribution of cluster velocities, theoretical tidal radii are approximately equal to observed limiting radii for R gc < 10 kpc. However, the isotropic simulation predicts a steep increase in cluster size at larger radii, which is not observed in large galaxies beyond the Milky Way. To minimize the discrepancy between theory and observations, we explore the effects of orbital anisotropy on cluster sizes, and suggest a possible orbital anisotropy profile for M87 which yields a better match between theory and observations. Finally, we suggest future studies which will establish a stronger link between theoretical tidal radii and observed radii.

THE OBSERVATIONAL AND THEORETICAL TIDAL RADII OF GLOBULAR CLUSTERS IN M87

International Nuclear Information System (INIS)

Webb, Jeremy J.; Sills, Alison; Harris, William E.

2012-01-01

Globular clusters have linear sizes (tidal radii) which theory tells us are determined by their masses and by the gravitational potential of their host galaxy. To explore the relationship between observed and expected radii, we utilize the globular cluster population of the Virgo giant M87. Unusually deep, high signal-to-noise images of M87 are used to measure the effective and limiting radii of approximately 2000 globular clusters. To compare with these observations, we simulate a globular cluster population that has the same characteristics as the observed M87 cluster population. Placing these simulated clusters in the well-studied tidal field of M87, the orbit of each cluster is solved and the theoretical tidal radius of each cluster is determined. We compare the predicted relationship between cluster size and projected galactocentric distance to observations. We find that for an isotropic distribution of cluster velocities, theoretical tidal radii are approximately equal to observed limiting radii for R gc < 10 kpc. However, the isotropic simulation predicts a steep increase in cluster size at larger radii, which is not observed in large galaxies beyond the Milky Way. To minimize the discrepancy between theory and observations, we explore the effects of orbital anisotropy on cluster sizes, and suggest a possible orbital anisotropy profile for M87 which yields a better match between theory and observations. Finally, we suggest future studies which will establish a stronger link between theoretical tidal radii and observed radii.

Two stellar-mass black holes in the globular cluster M22.

Science.gov (United States)

Strader, Jay; Chomiuk, Laura; Maccarone, Thomas J; Miller-Jones, James C A; Seth, Anil C

2012-10-04

Hundreds of stellar-mass black holes probably form in a typical globular star cluster, with all but one predicted to be ejected through dynamical interactions. Some observational support for this idea is provided by the lack of X-ray-emitting binary stars comprising one black hole and one other star ('black-hole/X-ray binaries') in Milky Way globular clusters, even though many neutron-star/X-ray binaries are known. Although a few black holes have been seen in globular clusters around other galaxies, the masses of these cannot be determined, and some may be intermediate-mass black holes that form through exotic mechanisms. Here we report the presence of two flat-spectrum radio sources in the Milky Way globular cluster M22, and we argue that these objects are black holes of stellar mass (each ∼10-20 times more massive than the Sun) that are accreting matter. We find a high ratio of radio-to-X-ray flux for these black holes, consistent with the larger predicted masses of black holes in globular clusters compared to those outside. The identification of two black holes in one cluster shows that ejection of black holes is not as efficient as predicted by most models, and we argue that M22 may contain a total population of ∼5-100 black holes. The large core radius of M22 could arise from heating produced by the black holes.

Elucidation of a carotenoid biosynthesis gene cluster encoding a novel enzyme, 2,2'-beta-hydroxylase, from Brevundimonas sp. strain SD212 and combinatorial biosynthesis of new or rare xanthophylls.

Science.gov (United States)

Nishida, Yasuhiro; Adachi, Kyoko; Kasai, Hiroaki; Shizuri, Yoshikazu; Shindo, Kazutoshi; Sawabe, Akiyoshi; Komemushi, Sadao; Miki, Wataru; Misawa, Norihiko

2005-08-01

A carotenoid biosynthesis gene cluster mediating the production of 2-hydroxyastaxanthin was isolated from the marine bacterium Brevundimonas sp. strain SD212 by using a common crtI sequence as the probe DNA. A sequence analysis revealed this cluster to contain 12 open reading frames (ORFs), including the 7 known genes, crtW, crtY, crtI, crtB, crtE, idi, and crtZ. The individual ORFs were functionally analyzed by complementation studies using Escherichia coli that accumulated various carotenoid precursors due to the presence of other bacterial crt genes. In addition to functionally identifying the known crt genes, we found that one (ORF11, named crtG) coded for a novel enzyme, carotenoid 2,2'-beta-hydroxylase, which showed intriguingly partial homology with animal sterol-C5-desaturase. When this crtG gene was introduced into E. coli accumulating zeaxanthin and canthaxanthin, the resulting transformants produced their 2-hydroxylated and 2,2'-dihydroxylated products which were structurally novel or rare xanthophylls, as determined by their nuclear magnetic resonance and high-performance liquid chromatography/photodiode array detector/atmospheric pressure chemical ionization mass spectrometry spectral data. The new carotenoid produced was suggested to have a strong inhibitory effect on lipid peroxidation.

Mechanism of Shiga Toxin Clustering on Membranes

DEFF Research Database (Denmark)

Pezeshkian, Weria; Gao, Haifei; Arumugam, Senthil

2017-01-01

between them. The precise mechanism by which this clustering occurs remains poorly defined. Here, we used vesicle and cell systems and computer simulations to show that line tension due to curvature, height, or compositional mismatch, and lipid or solvent depletion cannot drive the clustering of Shiga...... toxin molecules. By contrast, in coarse-grained computer simulations, a correlation was found between clustering and toxin nanoparticle-driven suppression of membrane fluctuations, and experimentally we observed that clustering required the toxin molecules to be tightly bound to the membrane surface...... molecules (several nanometers), and persist even beyond. This force is predicted to operate between manufactured nanoparticles providing they are sufficiently rigid and tightly bound to the plasma membrane, thereby suggesting a route for the targeting of nanoparticles to cells for biomedical applications....

Characterization and detection of a widely distributed gene cluster that predicts anaerobic choline utilization by human gut bacteria.

Science.gov (United States)

Martínez-del Campo, Ana; Bodea, Smaranda; Hamer, Hilary A; Marks, Jonathan A; Haiser, Henry J; Turnbaugh, Peter J; Balskus, Emily P

2015-04-14

Elucidation of the molecular mechanisms underlying the human gut microbiota's effects on health and disease has been complicated by difficulties in linking metabolic functions associated with the gut community as a whole to individual microorganisms and activities. Anaerobic microbial choline metabolism, a disease-associated metabolic pathway, exemplifies this challenge, as the specific human gut microorganisms responsible for this transformation have not yet been clearly identified. In this study, we established the link between a bacterial gene cluster, the choline utilization (cut) cluster, and anaerobic choline metabolism in human gut isolates by combining transcriptional, biochemical, bioinformatic, and cultivation-based approaches. Quantitative reverse transcription-PCR analysis and in vitro biochemical characterization of two cut gene products linked the entire cluster to growth on choline and supported a model for this pathway. Analyses of sequenced bacterial genomes revealed that the cut cluster is present in many human gut bacteria, is predictive of choline utilization in sequenced isolates, and is widely but discontinuously distributed across multiple bacterial phyla. Given that bacterial phylogeny is a poor marker for choline utilization, we were prompted to develop a degenerate PCR-based method for detecting the key functional gene choline TMA-lyase (cutC) in genomic and metagenomic DNA. Using this tool, we found that new choline-metabolizing gut isolates universally possessed cutC. We also demonstrated that this gene is widespread in stool metagenomic data sets. Overall, this work represents a crucial step toward understanding anaerobic choline metabolism in the human gut microbiota and underscores the importance of examining this microbial community from a function-oriented perspective. Anaerobic choline utilization is a bacterial metabolic activity that occurs in the human gut and is linked to multiple diseases. While bacterial genes responsible for

PROXIMAL: a method for Prediction of Xenobiotic Metabolism.

Science.gov (United States)

Yousofshahi, Mona; Manteiga, Sara; Wu, Charmian; Lee, Kyongbum; Hassoun, Soha

2015-12-22

Contamination of the environment with bioactive chemicals has emerged as a potential public health risk. These substances that may cause distress or disease in humans can be found in air, water and food supplies. An open question is whether these chemicals transform into potentially more active or toxic derivatives via xenobiotic metabolizing enzymes expressed in the body. We present a new prediction tool, which we call PROXIMAL (Prediction of Xenobiotic Metabolism) for identifying possible transformation products of xenobiotic chemicals in the liver. Using reaction data from DrugBank and KEGG, PROXIMAL builds look-up tables that catalog the sites and types of structural modifications performed by Phase I and Phase II enzymes. Given a compound of interest, PROXIMAL searches for substructures that match the sites cataloged in the look-up tables, applies the corresponding modifications to generate a panel of possible transformation products, and ranks the products based on the activity and abundance of the enzymes involved. PROXIMAL generates transformations that are specific for the chemical of interest by analyzing the chemical's substructures. We evaluate the accuracy of PROXIMAL's predictions through case studies on two environmental chemicals with suspected endocrine disrupting activity, bisphenol A (BPA) and 4-chlorobiphenyl (PCB3). Comparisons with published reports confirm 5 out of 7 and 17 out of 26 of the predicted derivatives for BPA and PCB3, respectively. We also compare biotransformation predictions generated by PROXIMAL with those generated by METEOR and Metaprint2D-react, two other prediction tools. PROXIMAL can predict transformations of chemicals that contain substructures recognizable by human liver enzymes. It also has the ability to rank the predicted metabolites based on the activity and abundance of enzymes involved in xenobiotic transformation.

Protein-DNA binding dynamics predict transcriptional response to nutrients in archaea.

Science.gov (United States)

Todor, Horia; Sharma, Kriti; Pittman, Adrianne M C; Schmid, Amy K

2013-10-01

Organisms across all three domains of life use gene regulatory networks (GRNs) to integrate varied stimuli into coherent transcriptional responses to environmental pressures. However, inferring GRN topology and regulatory causality remains a central challenge in systems biology. Previous work characterized TrmB as a global metabolic transcription factor in archaeal extremophiles. However, it remains unclear how TrmB dynamically regulates its ∼100 metabolic enzyme-coding gene targets. Using a dynamic perturbation approach, we elucidate the topology of the TrmB metabolic GRN in the model archaeon Halobacterium salinarum. Clustering of dynamic gene expression patterns reveals that TrmB functions alone to regulate central metabolic enzyme-coding genes but cooperates with various regulators to control peripheral metabolic pathways. Using a dynamical model, we predict gene expression patterns for some TrmB-dependent promoters and infer secondary regulators for others. Our data suggest feed-forward gene regulatory topology for cobalamin biosynthesis. In contrast, purine biosynthesis appears to require TrmB-independent regulators. We conclude that TrmB is an important component for mediating metabolic modularity, integrating nutrient status and regulating gene expression dynamics alone and in concert with secondary regulators.

Temperament clusters associate with anxiety disorder comorbidity in depression.

Science.gov (United States)

Paavonen, Vesa; Luoto, Kaisa; Lassila, Antero; Leinonen, Esa; Kampman, Olli

2018-08-15

Individual temperament is associated with psychiatric morbidity and could explain differences in psychiatric comorbidities. We investigated the association of temperament profile clusters with anxiety disorder comorbidity in patients with depression. We assessed the temperament of 204 specialized care-treated depressed patients with the Temperament and Character Inventory (TCI-R) and their diagnoses with the Mini International Neuropsychiatric Interview. Two-step cluster analysis was used for defining patients' temperament profiles and logistic regression analysis was used for predicting different anxiety disorders for various temperament profiles. Four temperament clusters were found: 1) Novelty seekers with highest Novelty Seeking scores (n = 56),2) Persistent with highest Persistence scores (n = 36), 3) Reserved with lowest Novelty Seeking scores (n = 66) and 4) Wearied with highest Harm avoidance, lowest Reward Dependence and lowest Persistence scores (n = 58). After adjusting for clinical variables, panic disorder and/or agoraphobia were predicted by Novelty seekers' temperament profile with odds ratio [OR] = 3.5 (95% confidence interval [CI] = 1.8 - 6.9, p < 0.001), social anxiety disorder was predicted by Wearied temperament profile with OR = 3.4 (95% CI = 1.6 - 7.5, p = 0.002), and generalized anxiety disorder was predicted by Reserved temperament profile with OR = 2.6 (95% CI = 1.2 - 5.3, p = 0.01). The patients' temperament profiles were assessed while displaying depressive symptoms, which may have affected results. Temperament clusters with unique dimensional profiles were specifically associated with different anxiety disorders in this study. These results suggest that TCI-R could offer a valuable dimensional method for predicting the risk of anxiety disorders in diverse depressed patients. Copyright © 2018 Elsevier B.V. All rights reserved.

Poisson Mixture Regression Models for Heart Disease Prediction.

Science.gov (United States)

Mufudza, Chipo; Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.

Poisson Mixture Regression Models for Heart Disease Prediction

Science.gov (United States)

Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611

Modeling the formation of globular cluster systems in the Virgo cluster

International Nuclear Information System (INIS)

Li, Hui; Gnedin, Oleg Y.

2014-01-01

The mass distribution and chemical composition of globular cluster (GC) systems preserve fossil record of the early stages of galaxy formation. The observed distribution of GC colors within massive early-type galaxies in the ACS Virgo Cluster Survey (ACSVCS) reveals a multi-modal shape, which likely corresponds to a multi-modal metallicity distribution. We present a simple model for the formation and disruption of GCs that aims to match the ACSVCS data. This model tests the hypothesis that GCs are formed during major mergers of gas-rich galaxies and inherit the metallicity of their hosts. To trace merger events, we use halo merger trees extracted from a large cosmological N-body simulation. We select 20 halos in the mass range of 2 × 10 12 to 7 × 10 13 M ☉ and match them to 19 Virgo galaxies with K-band luminosity between 3 × 10 10 and 3 × 10 11 L ☉ . To set the [Fe/H] abundances, we use an empirical galaxy mass-metallicity relation. We find that a minimal merger ratio of 1:3 best matches the observed cluster metallicity distribution. A characteristic bimodal shape appears because metal-rich GCs are produced by late mergers between massive halos, while metal-poor GCs are produced by collective merger activities of less massive hosts at early times. The model outcome is robust to alternative prescriptions for cluster formation rate throughout cosmic time, but a gradual evolution of the mass-metallicity relation with redshift appears to be necessary to match the observed cluster metallicities. We also affirm the age-metallicity relation, predicted by an earlier model, in which metal-rich clusters are systematically several billion younger than their metal-poor counterparts.

THE INFRARED PROPERTIES OF EMBEDDED SUPER STAR CLUSTERS: PREDICTIONS FROM THREE-DIMENSIONAL RADIATIVE TRANSFER MODELS

International Nuclear Information System (INIS)

Whelan, David G.; Johnson, Kelsey E.; Indebetouw, Remy; Whitney, Barbara A.; Wood, Kenneth

2011-01-01

With high-resolution infrared data becoming available that can probe the formation of high-mass stellar clusters for the first time, appropriate models that make testable predictions of these objects are necessary. We utilize a three-dimensional radiative transfer code, including a hierarchically clumped dusty envelope, to study the earliest stages of super star cluster (SSC) evolution. We explore a range of parameter space in geometric sequences that mimic the hypothesized evolution of an embedded SSC. The inclusion of a hierarchically clumped medium can make the envelope porous, in accordance with previous models and supporting observational evidence. The infrared luminosity inferred from observations can differ by a factor of two from the true value in the clumpiest envelopes depending on the viewing angle. The infrared spectral energy distribution also varies with viewing angle for clumpy envelopes, creating a range in possible observable infrared colors and magnitudes, silicate feature depths, and dust continua. General observable features of cluster evolution differ between envelopes that are relatively opaque or transparent to mid-infrared photons. For optically thick envelopes, evolution is marked by a gradual decline of the 9.8 μm silicate absorption feature depth and a corresponding increase in the visual/ultraviolet flux. For the optically thin envelopes, clusters typically begin with a strong hot dust component and silicates in emission, and these features gradually fade until the mid-infrared polycyclic aromatic hydrocarbon features are predominant. For the models with a smooth dust distribution, the Spitzer MIPS or Herschel PACS [70]-[160] color is a good probe of the stellar mass relative to the total mass or star formation efficiency (SFE). Likewise, the IRAC/MIPS [3.6]-[24] color can be used to constrain the R in and R out values of the envelope. However, clumpiness confuses the general trends seen in the smooth dust distribution models, making it

Stabilization of dendritic spine clusters and hyperactive Ras-MAPK signaling predict enhanced motor learning in an autistic savant mouse model

Directory of Open Access Journals (Sweden)

Ryan Thomas Ash

2014-03-01

Full Text Available That both prominent behavioral inflexibility and exceptional learning abilities are seen occasionally in autistic patients is a mystery. We hypothesize that these altered patterns of learning and memory can arise from a pathological imbalance between the stability and plasticity of internal neural representations. We evaluated this hypothesis in the mouse model of MECP2 duplication syndrome, which demonstrates enhanced motor learning, stereotyped behaviors, and social avoidance. Learning-associated structural plasticity was measured in the motor cortex of MECP2 duplication mice by 2-photon imaging (Fig. 1A. An increased stabilization rate of learning-associated dendritic spines was observed in mutants, and this correlated with rotarod performance. Analysis of the spatial distribution of stabilized spines revealed that the mutant’s increased spine stabilization was due to a specific increase in the stability of spines jointly formed in ~9-micron clusters. Clustered spine stabilization but not isolated spine stabilization predicted enhanced motor performance in MECP2 duplication mice (Fig. 1B. Biochemical assays of Ras-MAPK and mTOR pathway activation demonstrated profound hyperphosphorylation of MAPK in the motor cortex of MECP2 duplication mice with motor training (Fig. 1C. Taken together these data suggest that a pathological bias towards hyperstability of learning-associated dendritic spine clusters driven by hyperactive Ras-MAPK signaling could contribute to neurobehavioral phenotypes in this form of syndromic autism.

Using Blue Stragglers to Predict Retained Black Hole Population in Globular Clusters

Science.gov (United States)

Hermanek, Keith; Chatterjee, Sourav; Rasio, Frederic

2018-01-01

Large numbers of black holes (BHs) are expected to form in massive star clusters typical of the globular clusters (GCs). Sophisticated theoretical models suggest that many of these BHs can be retained in present-day GCs. Observations have also identified several BH candidates in Galactic and extragalactic GCs (e.g., Macarone et al. 2007; Irwin et al. 2010; Strader et al. 2012; Chomiuk et al. 2013; Miller-Jones et al. 2014). It has also been shown that high-mass and high-density clusters such as GCs are efficient factories of merging binary BHs similar to those observed by the LIGO observatories (Abbott et al. 2016a,b,c,d,e; Rodriguez et al. 2016). Understanding the formation rate and properties of binary BHs are dependent on a detailed understanding of how the BHs dynamically evolve within GCs. Nevertheless, directly detecting BHs in GCs is extremely challenging; BHs only in binaries with limited configurations can be directly detected by the detection of gravitational wave, X-ray, or radio emissions. We propose an indirect of inferring the number of undetected retained BHs in a GC by investigating the dynamical effects of a large number of BHs on the production of other tracer populations such as Blue Straggler Stars (BSS). Using a large grid of detailed GC models we show that there is a clear anti-correlation between the number of BSS in a cluster and the number of retained BHs. Being the most massive species, large numbers of retained BHs will dominate the core of the cluster as a result of mass-segregation driving away other low-mass species such as main-sequence stars from central high-density regions. BSS are expected to form from physical collisions between main-sequence (MS) stars mediated by binary encounters (e.g., Chatterjee et al. 2013) in cores of GCs. Production of BSS by collisions or mass transfer channels are suppressed if a large number of retained BHs in a cluster restrict the number of MS stars in the core. Extensive observational data exist on

Infrared dust emission from globular clusters

International Nuclear Information System (INIS)

Angeletti, L.; Capuzzo-Dolcetta, R.; Giannone, P.; Blanco, A.; Bussoletti, E.

1982-01-01

The implications of the presence of a central cloud in the cores of globular clusters were investigated recently. A possible mechanism of confinement of dust in the central region of our cluster models was also explored. The grain temperature and infrared emission have now been computed for rather realistic grain compositions. The grain components were assumed to be graphite and/or silicates. The central clouds turned out to be roughly isothermal. The wavelengths of maximum emission came out to be larger than 20 μm in all studied cases. An application of the theoretical results to five globular clusters showed that the predictable infrared emission for 47 Tuc, M4 and M22 should be detectable by means of present instrumentation aboard flying platforms. (author)

Infrared dust emission from globular clusters

Energy Technology Data Exchange (ETDEWEB)

Angeletti, L; Capuzzo-Dolcetta, R; Giannone, P. (Rome Univ. (Italy). Osservatorio Astronomico); Blanco, A; Bussoletti, E [Lecce Univ. (Italy). Ist. di Fisica

1982-05-01

The implications of the presence of a central cloud in the cores of globular clusters were investigated recently. A possible mechanism of confinement of dust in the central region of our cluster models was also explored. The grain temperature and infrared emission have now been computed for rather realistic grain compositions. The grain components were assumed to be graphite and/or silicates. The central clouds turned out to be roughly isothermal. The wavelengths of maximum emission came out to be larger than 20 ..mu..m in all studied cases. An application of the theoretical results to five globular clusters showed that the predictable infrared emission for 47 Tuc, M4 and M22 should be detectable by means of present instrumentation aboard flying platforms.

Nova-driven winds in globular clusters

International Nuclear Information System (INIS)

Scott, E.H.; Durisen, R.H.

1978-01-01

Recent sensitive searches for Hα emission from ionized intracluster gas in globular clusters have set upper limits that conflict with theoretical predictions. We suggest that nova outbursts heat the gas, producing winds that resolve this discrepancy. The incidence of novae in globular clusters, the conversion of kinetic energy of the nova shell to thermal energy of the intracluster gas, and the characteristics of the resultant winds are discussed. Calculated emission from the nova-driven models does not conflict with any observations to date. Some suggestions are made concerning the most promising approaches for future detection of intracluster gas on the basis of these models. The possible relationship of nova-driven winds of globular cluster X-ray sources is also considered

Investigations of Galaxy Clusters Using Gravitational Lensing

Energy Technology Data Exchange (ETDEWEB)

Wiesner, Matthew P. [Northern Illinois Univ., DeKalb, IL (United States)

2014-08-01

In this dissertation, we discuss the properties of galaxy clusters that have been determined using strong and weak gravitational lensing. A galaxy cluster is a collection of galaxies that are bound together by the force of gravity, while gravitational lensing is the bending of light by gravity. Strong lensing is the formation of arcs or rings of light surrounding clusters and weak lensing is a change in the apparent shapes of many galaxies. In this work we examine the properties of several samples of galaxy clusters using gravitational lensing. In Chapter 1 we introduce astrophysical theory of galaxy clusters and gravitational lensing. In Chapter 2 we examine evidence from our data that galaxy clusters are more concentrated than cosmology would predict. In Chapter 3 we investigate whether our assumptions about the number of galaxies in our clusters was valid by examining new data. In Chapter 4 we describe a determination of a relationship between mass and number of galaxies in a cluster at higher redshift than has been found before. In Chapter 5 we describe a model of the mass distribution in one of the ten lensing systems discovered by our group at Fermilab. Finally in Chapter 6 we summarize our conclusions.

Search for C+ C clustering in Mg ground state

Indian Academy of Sciences (India)

2017-01-04

Jan 4, 2017 ... Finite-range knockout theory predictions were much larger for (12C,212C) reaction, indicating a very small 12C−12C clustering in 24Mg. (g.s.) . Our present results contradict most of the proposed heavy cluster (12C+12C) structure models for the ground state of 24Mg. Keywords. Direct nuclear reactions ...

Participant intimacy: A cluster analysis of the intranuclear cascade

International Nuclear Information System (INIS)

Cugnon, J.; Knoll, J.; Randrup, J.

1981-01-01

The intranuclear cascade for relativistic nuclear collisions is analyzed in terms of clusters consisting of groups of nucleons which are dynamically linked to each other by violent interactions. The formation cross sections for the different cluster types as well as their intrinsic dynamics are studied and compared with the predictions of the linear cascade model ( rows-on-rows ). (orig.)

Puzzle of magnetic moments of Ni clusters revisited using quantum Monte Carlo method.

Science.gov (United States)

Lee, Hung-Wen; Chang, Chun-Ming; Hsing, Cheng-Rong

2017-02-28

The puzzle of the magnetic moments of small nickel clusters arises from the discrepancy between values predicted using density functional theory (DFT) and experimental measurements. Traditional DFT approaches underestimate the magnetic moments of nickel clusters. Two fundamental problems are associated with this puzzle, namely, calculating the exchange-correlation interaction accurately and determining the global minimum structures of the clusters. Theoretically, the two problems can be solved using quantum Monte Carlo (QMC) calculations and the ab initio random structure searching (AIRSS) method correspondingly. Therefore, we combined the fixed-moment AIRSS and QMC methods to investigate the magnetic properties of Ni n (n = 5-9) clusters. The spin moments of the diffusion Monte Carlo (DMC) ground states are higher than those of the Perdew-Burke-Ernzerhof ground states and, in the case of Ni 8-9 , two new ground-state structures have been discovered using the DMC calculations. The predicted results are closer to the experimental findings, unlike the results predicted in previous standard DFT studies.

Study on distributed re-clustering algorithm for moblie wireless sensor networks

Directory of Open Access Journals (Sweden)

XU Chaojie

2016-04-01

Full Text Available In mobile wireless sensor networks,node mobility influences the topology of the hierarchically clustered network,thus affects packet delivery ratio and energy consumption of communications in clusters.To reduce the influence of node mobility,a distributed re-clustering algorithm is proposed in this paper.In this algorithm,basing on the clustered network,nodes estimate their current locations with particle algorithm and predict the most possible locations of next time basing on the mobility model.Each boundary node of a cluster periodically estimates the need for re-clustering and re-cluster itself to the optimal cluster through communicating with the cluster headers when needed.The simulation results indicate that,with small re-clustering periods,the proposed algorithm can be effective to keep appropriate communication distance and outperforms existing schemes on packet delivery ratio and energy consumption.

COSMOLOGICAL CONSTRAINTS FROM GALAXY CLUSTERING AND THE MASS-TO-NUMBER RATIO OF GALAXY CLUSTERS

International Nuclear Information System (INIS)

Tinker, Jeremy L.; Blanton, Michael R.; Sheldon, Erin S.; Wechsler, Risa H.; Becker, Matthew R.; Rozo, Eduardo; Zu, Ying; Weinberg, David H.; Zehavi, Idit; Busha, Michael T.; Koester, Benjamin P.

2012-01-01

We place constraints on the average density (Ω m ) and clustering amplitude (σ 8 ) of matter using a combination of two measurements from the Sloan Digital Sky Survey: the galaxy two-point correlation function, w p (r p ), and the mass-to-galaxy-number ratio within galaxy clusters, M/N, analogous to cluster M/L ratios. Our w p (r p ) measurements are obtained from DR7 while the sample of clusters is the maxBCG sample, with cluster masses derived from weak gravitational lensing. We construct nonlinear galaxy bias models using the Halo Occupation Distribution (HOD) to fit both w p (r p ) and M/N for different cosmological parameters. HOD models that match the same two-point clustering predict different numbers of galaxies in massive halos when Ω m or σ 8 is varied, thereby breaking the degeneracy between cosmology and bias. We demonstrate that this technique yields constraints that are consistent and competitive with current results from cluster abundance studies, without the use of abundance information. Using w p (r p ) and M/N alone, we find Ω 0.5 m σ 8 = 0.465 ± 0.026, with individual constraints of Ω m = 0.29 ± 0.03 and σ 8 = 0.85 ± 0.06. Combined with current cosmic microwave background data, these constraints are Ω m = 0.290 ± 0.016 and σ 8 = 0.826 ± 0.020. All errors are 1σ. The systematic uncertainties that the M/N technique are most sensitive to are the amplitude of the bias function of dark matter halos and the possibility of redshift evolution between the SDSS Main sample and the maxBCG cluster sample. Our derived constraints are insensitive to the current level of uncertainties in the halo mass function and in the mass-richness relation of clusters and its scatter, making the M/N technique complementary to cluster abundances as a method for constraining cosmology with future galaxy surveys.

COSMOLOGICAL CONSTRAINTS FROM GALAXY CLUSTERING AND THE MASS-TO-NUMBER RATIO OF GALAXY CLUSTERS

Energy Technology Data Exchange (ETDEWEB)

Tinker, Jeremy L.; Blanton, Michael R. [Center for Cosmology and Particle Physics, Department of Physics, New York University, New York, NY 10013 (United States); Sheldon, Erin S. [Brookhaven National Laboratory, Upton, NY 11973 (United States); Wechsler, Risa H. [Kavli Institute for Particle Astrophysics and Cosmology, Physics Department, and SLAC National Accelerator Laboratory, Stanford University, Stanford, CA 94305 (United States); Becker, Matthew R.; Rozo, Eduardo [Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL 60637 (United States); Zu, Ying; Weinberg, David H. [Department of Astronomy, Ohio State University, Columbus, OH 43210 (United States); Zehavi, Idit [Department of Astronomy and CERCA, Case Western Reserve University, Cleveland, OH 44106 (United States); Busha, Michael T. [Institute for Theoretical Physics, Department of Physics, University of Zurich, CH-8057 Zurich (Switzerland); Koester, Benjamin P. [Department of Astronomy and Astrophysics, University of Chicago, Chicago, IL 6037 (United States)

2012-01-20

We place constraints on the average density ({Omega}{sub m}) and clustering amplitude ({sigma}{sub 8}) of matter using a combination of two measurements from the Sloan Digital Sky Survey: the galaxy two-point correlation function, w{sub p} (r{sub p} ), and the mass-to-galaxy-number ratio within galaxy clusters, M/N, analogous to cluster M/L ratios. Our w{sub p} (r{sub p} ) measurements are obtained from DR7 while the sample of clusters is the maxBCG sample, with cluster masses derived from weak gravitational lensing. We construct nonlinear galaxy bias models using the Halo Occupation Distribution (HOD) to fit both w{sub p} (r{sub p} ) and M/N for different cosmological parameters. HOD models that match the same two-point clustering predict different numbers of galaxies in massive halos when {Omega}{sub m} or {sigma}{sub 8} is varied, thereby breaking the degeneracy between cosmology and bias. We demonstrate that this technique yields constraints that are consistent and competitive with current results from cluster abundance studies, without the use of abundance information. Using w{sub p} (r{sub p} ) and M/N alone, we find {Omega}{sup 0.5}{sub m}{sigma}{sub 8} = 0.465 {+-} 0.026, with individual constraints of {Omega}{sub m} = 0.29 {+-} 0.03 and {sigma}{sub 8} = 0.85 {+-} 0.06. Combined with current cosmic microwave background data, these constraints are {Omega}{sub m} = 0.290 {+-} 0.016 and {sigma}{sub 8} = 0.826 {+-} 0.020. All errors are 1{sigma}. The systematic uncertainties that the M/N technique are most sensitive to are the amplitude of the bias function of dark matter halos and the possibility of redshift evolution between the SDSS Main sample and the maxBCG cluster sample. Our derived constraints are insensitive to the current level of uncertainties in the halo mass function and in the mass-richness relation of clusters and its scatter, making the M/N technique complementary to cluster abundances as a method for constraining cosmology with future galaxy

Hot-spot analysis to dissect the functional protein-protein interface of a tRNA-modifying enzyme.

Science.gov (United States)

Jakobi, Stephan; Nguyen, Tran Xuan Phong; Debaene, François; Metz, Alexander; Sanglier-Cianférani, Sarah; Reuter, Klaus; Klebe, Gerhard

2014-10-01

Interference with protein-protein interactions of interfaces larger than 1500 Ų by small drug-like molecules is notoriously difficult, particularly if targeting homodimers. The tRNA modifying enzyme Tgt is only functionally active as a homodimer. Thus, blocking Tgt dimerization is a promising strategy for drug therapy as this protein is key to the development of Shigellosis. Our goal was to identify hot-spot residues which, upon mutation, result in a predominantly monomeric state of Tgt. The detailed understanding of the spatial location and stability contribution of the individual interaction hot-spot residues and the plasticity of motifs involved in the interface formation is a crucial prerequisite for the rational identification of drug-like inhibitors addressing the respective dimerization interface. Using computational analyses, we identified hot-spot residues that contribute particularly to dimer stability: a cluster of hydrophobic and aromatic residues as well as several salt bridges. This in silico prediction led to the identification of a promising double mutant, which was validated experimentally. Native nano-ESI mass spectrometry showed that the dimerization of the suggested mutant is largely prevented resulting in a predominantly monomeric state. Crystal structure analysis and enzyme kinetics of the mutant variant further support the evidence for enhanced monomerization and provide first insights into the structural consequences of the dimer destabilization. © 2014 Wiley Periodicals, Inc.

Dynamics and stability of charged clusters and droplets

International Nuclear Information System (INIS)

Manil, B.; Lebius, H.; Chandezon, F.; Huber, B.A.; Duft, D.; Leisner, T.; Guet, C.

2002-01-01

Lord Raleigh predicted (Phil. Mag. 14, 184(1982) ) that a charged, incompressible liquid droplet becomes unstable as soon as the cohesive forces, which create the surface tension and which try to keep the droplet in its spherical form, are equal to the Coulomb forces, which try to destabilise it. This means that that the Coulomb energy E c corresponds to twice the surface energy E s . The ratio X = E c / 2 E s (feasibility), thus characterising the Raleigh limit by X = 1. In order to test its validity, metal clusters were ionized in collisions with highly charged ions, allowing for the first time to prepare charged systems with a feasibility greater than 1. Multiply charged sodium clusters were produced through collisions of Ar 11+ or Xe 28+ with neutral sodium clusters. It was observed, with increasing cluster charge and consequently cluster size the detected system indeed approach the Raleigh limit (for q = 10 X = 0.85). However, it was not reached due to the initial cluster temperature and the energy transfer in the collision. Subsequent, the stability and the explosion of highly charge microdroplets which were injected into a Paul trap levitator were studied, specifically, glycol was irradiated with a HeNe laser. It was observed that a resonance phenomena appeared just before each explosion. As the resonance is linked to X ∼ 1, this is the first proof that the Coulomb instability of charge glycol microdroplets occurs at X ∼ 1, as predicted by Lord Raleigh. (nevyjel)

SU-D-204-01: A Methodology Based On Machine Learning and Quantum Clustering to Predict Lung SBRT Dosimetric Endpoints From Patient Specific Anatomic Features

Energy Technology Data Exchange (ETDEWEB)

Lafata, K; Ren, L; Wu, Q; Kelsey, C; Hong, J; Cai, J; Yin, F [Duke University Medical Center, Durham, NC (United States)

2016-06-15

Purpose: To develop a data-mining methodology based on quantum clustering and machine learning to predict expected dosimetric endpoints for lung SBRT applications based on patient-specific anatomic features. Methods: Ninety-three patients who received lung SBRT at our clinic from 2011–2013 were retrospectively identified. Planning information was acquired for each patient, from which various features were extracted using in-house semi-automatic software. Anatomic features included tumor-to-OAR distances, tumor location, total-lung-volume, GTV and ITV. Dosimetric endpoints were adopted from RTOG-0195 recommendations, and consisted of various OAR-specific partial-volume doses and maximum point-doses. First, PCA analysis and unsupervised quantum-clustering was used to explore the feature-space to identify potentially strong classifiers. Secondly, a multi-class logistic regression algorithm was developed and trained to predict dose-volume endpoints based on patient-specific anatomic features. Classes were defined by discretizing the dose-volume data, and the feature-space was zero-mean normalized. Fitting parameters were determined by minimizing a regularized cost function, and optimization was performed via gradient descent. As a pilot study, the model was tested on two esophageal dosimetric planning endpoints (maximum point-dose, dose-to-5cc), and its generalizability was evaluated with leave-one-out cross-validation. Results: Quantum-Clustering demonstrated a strong separation of feature-space at 15Gy across the first-and-second Principle Components of the data when the dosimetric endpoints were retrospectively identified. Maximum point dose prediction to the esophagus demonstrated a cross-validation accuracy of 87%, and the maximum dose to 5cc demonstrated a respective value of 79%. The largest optimized weighting factor was placed on GTV-to-esophagus distance (a factor of 10 greater than the second largest weighting factor), indicating an intuitively strong

On the incidence of close binary stars in globular clusters and the nature of the cluster X-ray sources

International Nuclear Information System (INIS)

Trimble, V.

1977-01-01

Recent calculations suggest that the globular clusters could not have formed with more than 20 per cent of the normal Population I fraction of their stars in binary systems. The fact that the clusters have more than their fair share of novae and U Geminorum stars (three each out of approximately 200 of each known, while the clusters contain only about 10 -4 of the mass and 10 -3 of the luminosity of the galaxy) therefore becomes surprising. The hypothesis of binary capture within cluster cores suggested to account for the clusters' high X-ray luminosity provides a few extra systems, but neither it nor any of the similar encounter or capture mechanisms suggested can account for the novae and U Gen stars, which remain puzzling. The number of Algol-type and W UMa eclipsing binaries predicted by these hypotheses do not conflict with data presently available, but careful searches for them would constitute a critical test of the theories. (author)

Evaluation of the energy efficiency of enzyme fermentation by mechanistic modeling.

Science.gov (United States)

Albaek, Mads O; Gernaey, Krist V; Hansen, Morten S; Stocks, Stuart M

2012-04-01

Modeling biotechnological processes is key to obtaining increased productivity and efficiency. Particularly crucial to successful modeling of such systems is the coupling of the physical transport phenomena and the biological activity in one model. We have applied a model for the expression of cellulosic enzymes by the filamentous fungus Trichoderma reesei and found excellent agreement with experimental data. The most influential factor was demonstrated to be viscosity and its influence on mass transfer. Not surprisingly, the biological model is also shown to have high influence on the model prediction. At different rates of agitation and aeration as well as headspace pressure, we can predict the energy efficiency of oxygen transfer, a key process parameter for economical production of industrial enzymes. An inverse relationship between the productivity and energy efficiency of the process was found. This modeling approach can be used by manufacturers to evaluate the enzyme fermentation process for a range of different process conditions with regard to energy efficiency. Copyright © 2011 Wiley Periodicals, Inc.

EMACSS: Evolve Me A Cluster of StarS

Science.gov (United States)

Alexander, Poul E. R.; Gieles, Mark

2012-03-01

The star cluster evolution code Evolve Me A Cluster of StarS (EMACSS) is a simple yet physically motivated computational model that describes the evolution of some fundamental properties of star clusters in static tidal fields. The prescription is based upon the flow of energy within the cluster, which is a constant fraction of the total energy per half-mass relaxation time. According to Henon's predictions, this flow is independent of the precise mechanisms for energy production within the core, and therefore does not require a complete description of the many-body interactions therein. Dynamical theory and analytic descriptions of escape mechanisms is used to construct a series of coupled differential equations expressing the time evolution of cluster mass and radius for a cluster of equal-mass stars. These equations are numerically solved using a fourth-order Runge-Kutta integration kernel; the results were benchmarked against a data base of direct N-body simulations. EMACSS is publicly available and reproduces the N-body results to within 10 per cent accuracy for the entire post-collapse evolution of star clusters.

Theoretical study of the electron-cluster elastic scattering

International Nuclear Information System (INIS)

Descourt, P.; Guet, C.; Farine, M.

1997-01-01

The properties of the clusters consisting of some tens to several hundreds of alkali atoms are generally quite well described in the jellium approximation. This approximation treats the cluster as a charged Fermi liquid of finite size. The optical response predicted by this approximation and taking into account the electron-electron correlations of the Hartree-Fock mean field agrees rather well with the experiment. The objective of this work was to obtain a quantal many-body formalism, within jellium approximation, applicable to elastic scattering of electrons from an alkali-metal-cluster. Influence of correlations on the phase shifts was also taken into account

Supernova blast wave within a stellar cluster outflow

Science.gov (United States)

Rodríguez-Ramírez, J. C.; Raga, A. C.; Velázquez, P. F.; Rodríguez-González, A.; Toledo-Roy, J. C.

2014-11-01

In this paper, we develop a semi-analytic model of a supernova which goes off in the centre of a stellar cluster. The supernova remnant interacts with a stratified, pre-existent outflow produced by the winds of the cluster stars. We compare our semi-analytic model with numerical simulations using the spherically symmetric Euler equations with appropriate mass and energy source terms. We find good agreement between these two approaches, and we find that for typical parameters the blast wave is likely to reach the Taylor-Sedov regime outside the cluster radius. We also calculate the predicted X-ray luminosity of the flow as a function of time, and we obtain its dependence on the outer radius and the number of stars of the cluster.

Automatic single- and multi-label enzymatic function prediction by machine learning

Directory of Open Access Journals (Sweden)

Shervine Amidi

2017-03-01

Full Text Available The number of protein structures in the PDB database has been increasing more than 15-fold since 1999. The creation of computational models predicting enzymatic function is of major importance since such models provide the means to better understand the behavior of newly discovered enzymes when catalyzing chemical reactions. Until now, single-label classification has been widely performed for predicting enzymatic function limiting the application to enzymes performing unique reactions and introducing errors when multi-functional enzymes are examined. Indeed, some enzymes may be performing different reactions and can hence be directly associated with multiple enzymatic functions. In the present work, we propose a multi-label enzymatic function classification scheme that combines structural and amino acid sequence information. We investigate two fusion approaches (in the feature level and decision level and assess the methodology for general enzymatic function prediction indicated by the first digit of the enzyme commission (EC code (six main classes on 40,034 enzymes from the PDB database. The proposed single-label and multi-label models predict correctly the actual functional activities in 97.8% and 95.5% (based on Hamming-loss of the cases, respectively. Also the multi-label model predicts all possible enzymatic reactions in 85.4% of the multi-labeled enzymes when the number of reactions is unknown. Code and datasets are available at https://figshare.com/s/a63e0bafa9b71fc7cbd7.

Asteroid clusters similar to asteroid pairs

Science.gov (United States)

Pravec, P.; Fatka, P.; Vokrouhlický, D.; Scheeres, D. J.; Kušnirák, P.; Hornoch, K.; Galád, A.; Vraštil, J.; Pray, D. P.; Krugly, Yu. N.; Gaftonyuk, N. M.; Inasaridze, R. Ya.; Ayvazian, V. R.; Kvaratskhelia, O. I.; Zhuzhunadze, V. T.; Husárik, M.; Cooney, W. R.; Gross, J.; Terrell, D.; Világi, J.; Kornoš, L.; Gajdoš, Š.; Burkhonov, O.; Ehgamberdiev, Sh. A.; Donchev, Z.; Borisov, G.; Bonev, T.; Rumyantsev, V. V.; Molotov, I. E.

2018-04-01

We studied the membership, size ratio and rotational properties of 13 asteroid clusters consisting of between 3 and 19 known members that are on similar heliocentric orbits. By backward integrations of their orbits, we confirmed their cluster membership and estimated times elapsed since separation of the secondaries (the smaller cluster members) from the primary (i.e., cluster age) that are between 105 and a few 106 years. We ran photometric observations for all the cluster primaries and a sample of secondaries and we derived their accurate absolute magnitudes and rotation periods. We found that 11 of the 13 clusters follow the same trend of primary rotation period vs mass ratio as asteroid pairs that was revealed by Pravec et al. (2010). We generalized the model of the post-fission system for asteroid pairs by Pravec et al. (2010) to a system of N components formed by rotational fission and we found excellent agreement between the data for the 11 asteroid clusters and the prediction from the theory of their formation by rotational fission. The two exceptions are the high-mass ratio (q > 0.7) clusters of (18777) Hobson and (22280) Mandragora for which a different formation mechanism is needed. Two candidate mechanisms for formation of more than one secondary by rotational fission were published: the secondary fission process proposed by Jacobson and Scheeres (2011) and a cratering collision event onto a nearly critically rotating primary proposed by Vokrouhlický et al. (2017). It will have to be revealed from future studies which of the clusters were formed by one or the other process. To that point, we found certain further interesting properties and features of the asteroid clusters that place constraints on the theories of their formation, among them the most intriguing being the possibility of a cascade disruption for some of the clusters.

The anionic biosurfactant rhamnolipid does not denature industrial enzymes

Directory of Open Access Journals (Sweden)

Jens Kvist Madsen

2015-04-01

Full Text Available Biosurfactants (BS are surface-active molecules produced by microorganisms. Their combination of useful properties and sustainable production make them promising industrial alternatives to petrochemical and oleochemical surfactants. Here we compare the impact of the anionic BS rhamnolipid (RL and the conventional/synthetic anionic surfactant sodium dodecyl sulfate (SDS on the structure and stability of three different commercially used enzymes, namely the cellulase Carezyme® (CZ, the phospholipase Lecitase Ultra® (LT and the α-amylase Stainzyme® (SZ. Our data reveal a fundamental difference in their mode of interaction. SDS shows great diversity of interaction towards the different enzymes. It efficiently unfolds both LT and CZ, but LT is unfolded by SDS through formation of SDS clusters on the protein well below the cmc, while CZ is only unfolded by bulk micelles and on average binds significantly less SDS than LT. SDS binds with even lower stoichiometry to SZ and leads to an increase in thermal stability. In contrast, RL does not affect the tertiary or secondary structure of any enzyme at room temperature, has little impact on thermal stability and only binds detectably (but at low stoichiometries to SZ. Furthermore all enzymes maintain activity at both monomeric and micellar concentrations of RL. We conclude that RL, despite its anionic charge, is a surfactant that does not compromise the structural integrity of industrially relevant proteins. This makes RL a promising alternative to current synthetic anionic surfactants in a wide range of commercial applications.

Enzyme Informatics

Science.gov (United States)

Alderson, Rosanna G.; Ferrari, Luna De; Mavridis, Lazaros; McDonagh, James L.; Mitchell, John B. O.; Nath, Neetika

2012-01-01

Over the last 50 years, sequencing, structural biology and bioinformatics have completely revolutionised biomolecular science, with millions of sequences and tens of thousands of three dimensional structures becoming available. The bioinformatics of enzymes is well served by, mostly free, online databases. BRENDA describes the chemistry, substrate specificity, kinetics, preparation and biological sources of enzymes, while KEGG is valuable for understanding enzymes and metabolic pathways. EzCatDB, SFLD and MACiE are key repositories for data on the chemical mechanisms by which enzymes operate. At the current rate of genome sequencing and manual annotation, human curation will never finish the functional annotation of the ever-expanding list of known enzymes. Hence there is an increasing need for automated annotation, though it is not yet widespread for enzyme data. In contrast, functional ontologies such as the Gene Ontology already profit from automation. Despite our growing understanding of enzyme structure and dynamics, we are only beginning to be able to design novel enzymes. One can now begin to trace the functional evolution of enzymes using phylogenetics. The ability of enzymes to perform secondary functions, albeit relatively inefficiently, gives clues as to how enzyme function evolves. Substrate promiscuity in enzymes is one example of imperfect specificity in protein-ligand interactions. Similarly, most drugs bind to more than one protein target. This may sometimes result in helpful polypharmacology as a drug modulates plural targets, but also often leads to adverse side-effects. Many cheminformatics approaches can be used to model the interactions between druglike molecules and proteins in silico. We can even use quantum chemical techniques like DFT and QM/MM to compute the structural and energetic course of enzyme catalysed chemical reaction mechanisms, including a full description of bond making and breaking. PMID:23116471

RSQRT: AN HEURISTIC FOR ESTIMATING THE NUMBER OF CLUSTERS TO REPORT

Science.gov (United States)

Bruso, Kelsey

2012-01-01

Clustering can be a valuable tool for analyzing large datasets, such as in e-commerce applications. Anyone who clusters must choose how many item clusters, K, to report. Unfortunately, one must guess at K or some related parameter. Elsewhere we introduced a strongly-supported heuristic, RSQRT, which predicts K as a function of the attribute or item count, depending on attribute scales. We conducted a second analysis where we sought confirmation of the heuristic, analyzing data sets from theUCImachine learning benchmark repository. For the 25 studies where sufficient detail was available, we again found strong support. Also, in a side-by-side comparison of 28 studies, RSQRT best-predicted K and the Bayesian information criterion (BIC) predicted K are the same. RSQRT has a lower cost of O(log log n) versus O(n2) for BIC, and is more widely applicable. Using RSQRT prospectively could be much better than merely guessing. PMID:22773923

RSQRT: AN HEURISTIC FOR ESTIMATING THE NUMBER OF CLUSTERS TO REPORT.

Science.gov (United States)

Carlis, John; Bruso, Kelsey

2012-03-01

Clustering can be a valuable tool for analyzing large datasets, such as in e-commerce applications. Anyone who clusters must choose how many item clusters, K, to report. Unfortunately, one must guess at K or some related parameter. Elsewhere we introduced a strongly-supported heuristic, RSQRT, which predicts K as a function of the attribute or item count, depending on attribute scales. We conducted a second analysis where we sought confirmation of the heuristic, analyzing data sets from theUCImachine learning benchmark repository. For the 25 studies where sufficient detail was available, we again found strong support. Also, in a side-by-side comparison of 28 studies, RSQRT best-predicted K and the Bayesian information criterion (BIC) predicted K are the same. RSQRT has a lower cost of O(log log n) versus O(n(2)) for BIC, and is more widely applicable. Using RSQRT prospectively could be much better than merely guessing.

Enzyme-Powered Pumps: From Fundamentals to Applications

Science.gov (United States)

Ortiz-Rivera, Isamar

, covering also the effect of the thermodynamics of the enzymatic reaction in the pumping behavior, and (3) the applicability of enzyme pumps as fluid flow-based inhibitor assays and as drug delivery devices. Our findings in each of these areas, gets us closer to our ultimate goal, where we aim to identify the optimal conditions needed for enzyme micropump operation, and construct a general model that could accurately predict enzyme micropump behavior for any enzyme-substrate combination. The information aforementioned has been divided in four chapters. Chapter 1 gives a quick glance into the development of enzyme-powered micropumps: from the systems and observed behaviors inspiring this work, to the first systems that were developed. The stability, duration, and extent of fluid pumping of enzyme pumps in general, are also discussed, along with the optimization of the enzyme-pump design. This chapter aims to provide a general idea of the motivation behind the concept of "enzyme-powered pumps", what are "enzyme-powered pumps", and which are the key features that characterize these systems. Chapter 2 is an extensive analysis of the mechanisms of actuation proposed for enzyme-powered micropumps. This chapter not only covers the first attempts to understand how enzyme pumps work, but also explores further the behavior of urease-powered pumps, which fluid flow patterns cannot be completely predicted only by considering thermal or solutal gradients. The findings of these studies could allow us to rationally control fluid flow for the directed delivery of payloads at designated locations. In Chapters 3 and 4, our focus was to highlight the potential application of enzyme-powered pumps for sensing and delivery. Chapter 3 explores the use of enzyme pumps as fluid flow-based inhibitor assays. At fixed concentrations of an enzyme and its substrate, the presence of an inhibitor can be detected by monitoring the decrease in fluid flow speed. Using this principle, sensors for toxic

Extragalactic globular clusters. I. The metallicity calibration

International Nuclear Information System (INIS)

Brodie, J.P.; Huchra, J.P.

1990-01-01

The ability of absorption-line strength indices, measured from integrated globular cluster spectra, to predict mean cluster metallicity is explored. Statistical criteria, are used to identify the six best indices out of about 20 measured in a large sample of Galactic and M31 cluster spectra. Linear relations between index and metallicity have been derived along with new calibrations of infrared colors (V - K, J - K, and CO) versus Fe/H. Estimates of metallicity from the six spectroscopic index-metallicity relations have been combined in three different ways to identify the most efficient estimator and the minimum bias estimator of Fe/H - the weighted mean. This provides an estimate of Fe/H accurate to about 15 percent. 37 refs

Elucidation of a Carotenoid Biosynthesis Gene Cluster Encoding a Novel Enzyme, 2,2′-β-Hydroxylase, from Brevundimonas sp. Strain SD212 and Combinatorial Biosynthesis of New or Rare Xanthophylls

Science.gov (United States)

Nishida, Yasuhiro; Adachi, Kyoko; Kasai, Hiroaki; Shizuri, Yoshikazu; Shindo, Kazutoshi; Sawabe, Akiyoshi; Komemushi, Sadao; Miki, Wataru; Misawa, Norihiko

2005-01-01

A carotenoid biosynthesis gene cluster mediating the production of 2-hydroxyastaxanthin was isolated from the marine bacterium Brevundimonas sp. strain SD212 by using a common crtI sequence as the probe DNA. A sequence analysis revealed this cluster to contain 12 open reading frames (ORFs), including the 7 known genes, crtW, crtY, crtI, crtB, crtE, idi, and crtZ. The individual ORFs were functionally analyzed by complementation studies using Escherichia coli that accumulated various carotenoid precursors due to the presence of other bacterial crt genes. In addition to functionally identifying the known crt genes, we found that one (ORF11, named crtG) coded for a novel enzyme, carotenoid 2,2′-β-hydroxylase, which showed intriguingly partial homology with animal sterol-C5-desaturase. When this crtG gene was introduced into E. coli accumulating zeaxanthin and canthaxanthin, the resulting transformants produced their 2-hydroxylated and 2,2′-dihydroxylated products which were structurally novel or rare xanthophylls, as determined by their nuclear magnetic resonance and high-performance liquid chromatography/photodiode array detector/atmospheric pressure chemical ionization mass spectrometry spectral data. The new carotenoid produced was suggested to have a strong inhibitory effect on lipid peroxidation. PMID:16085816

What drives the evolution of Luminous Compact Blue Galaxies in Clusters vs. the Field?

Science.gov (United States)

Wirth, Gregory D.; Bershady, Matthew A.; Crawford, Steven M.; Hunt, Lucas; Pisano, Daniel J.; Randriamampandry, Solohery M.

2018-06-01

Low-mass dwarf ellipticals are the most numerous members of present-day galaxy clusters, but the progenitors of this dominant population remain unclear. A prime candidate is the class of objects known as Luminous Compact Blue Galaxies (LCBGs), common in intermediate-redshift clusters but virtually extinct today. Recent cosmological simulations suggest that present-day dwarf galaxies begin as irregular field galaxies, undergo an environmentally-driven starburst phase as they enter the cluster, and stop forming stars earlier than their counterparts in the field. This model predicts that cluster dwarfs should have lower stellar mass per unit dynamical mass than their counterparts in the field. We are undertaking a two-pronged archival research program to test this key prediction using the combination of precision photometry from space and high-quality spectroscopy. First, we are combining optical HST/ACS imaging of five z=0.55 clusters (including two HST Frontier Fields) with Spitzer IR imaging and publicly-released Keck/DEIMOS spectroscopy to measure stellar-to-dynamical-mass ratios for a large sample of cluster LCBGs. Second, we are exploiting a new catalog of LCBGs in the COSMOS field to gather corresponding data for a significant sample of field LCBGs. By comparing mass ratios from these datasets, we aim to test theoretical predictions and determine the primary physical driver of cluster dwarf-galaxy evolution.

The impact of mobile point defect clusters in a kinetic model of pressure vessel embrittlement

International Nuclear Information System (INIS)

Stoller, R.E.

1998-05-01

The results of recent molecular dynamics simulations of displacement cascades in iron indicate that small interstitial clusters may have a very low activation energy for migration, and that their migration is 1-dimensional, rather than 3-dimensional. The mobility of these clusters can have a significant impact on the predictions of radiation damage models, particularly at the relatively low temperatures typical of commercial, light water reactor pressure vessels (RPV) and other out-of-core components. A previously-developed kinetic model used to investigate RPV embrittlement has been modified to permit an evaluation of the mobile interstitial clusters. Sink strengths appropriate to both 1- and 3-dimensional motion of the clusters were evaluated. High cluster mobility leads to a reduction in the amount of predicted embrittlement due to interstitial clusters since they are lost to sinks rather than building up in the microstructure. The sensitivity of the predictions to displacement rate also increases. The magnitude of this effect is somewhat reduced if the migration is 1-dimensional since the corresponding sink strengths are lower than those for 3-dimensional diffusion. The cluster mobility can also affect the evolution of copper-rich precipitates in the model since the radiation-enhanced diffusion coefficient increases due to the lower interstitial cluster sink strength. The overall impact of the modifications to the model is discussed in terms of the major irradiation variables and material parameter uncertainties

Fission approach to cluster radioactivity

Indian Academy of Sciences (India)

2015-08-04

Aug 4, 2015 ... Also, the analytical superasymmetric fission (ASAF) model is successfully employed to make a systematic search and to predict, with other models, cluster ... those of the staff, the journals, various programmes, and Current Science, has changed from 'ias.ernet.in' (or 'academy.ias.ernet.in') to 'ias.ac.in'. Thus ...

Thermodynamics and proton activities of protic ionic liquids with quantum cluster equilibrium theory

Science.gov (United States)

Ingenmey, Johannes; von Domaros, Michael; Perlt, Eva; Verevkin, Sergey P.; Kirchner, Barbara

2018-05-01

We applied the binary Quantum Cluster Equilibrium (bQCE) method to a number of alkylammonium-based protic ionic liquids in order to predict boiling points, vaporization enthalpies, and proton activities. The theory combines statistical thermodynamics of van-der-Waals-type clusters with ab initio quantum chemistry and yields the partition functions (and associated thermodynamic potentials) of binary mixtures over a wide range of thermodynamic phase points. Unlike conventional cluster approaches that are limited to the prediction of thermodynamic properties, dissociation reactions can be effortlessly included into the bQCE formalism, giving access to ionicities, as well. The method is open to quantum chemical methods at any level of theory, but combination with low-cost composite density functional theory methods and the proposed systematic approach to generate cluster sets provides a computationally inexpensive and mostly parameter-free way to predict such properties at good-to-excellent accuracy. Boiling points can be predicted within an accuracy of 50 K, reaching excellent accuracy for ethylammonium nitrate. Vaporization enthalpies are predicted within an accuracy of 20 kJ mol-1 and can be systematically interpreted on a molecular level. We present the first theoretical approach to predict proton activities in protic ionic liquids, with results fitting well into the experimentally observed correlation. Furthermore, enthalpies of vaporization were measured experimentally for some alkylammonium nitrates and an excellent linear correlation with vaporization enthalpies of their respective parent amines is observed.

The influence of posttraumatic stress disorder numbing and hyperarousal symptom clusters in the prediction of physical health status in veterans with chronic tobacco dependence and posttraumatic stress disorder.

Science.gov (United States)

Harder, Laura H; Chen, Shuo; Baker, Dewleen G; Chow, Bruce; McFall, Miles; Saxon, Andrew; Smith, Mark W

2011-12-01

Smoking and PTSD are predictors of poor physical health status. This study examined the unique contribution of PTSD symptoms in the prediction of the SF-36 physical health status subscales accounting for cigarette smoking, chronic medical conditions, alcohol and drug use disorders, and depression. This study examined baseline interview and self-report data from a national tobacco cessation randomized, controlled trial (Veterans Affairs Cooperative Study 519) that enrolled tobacco-dependent veterans with chronic PTSD (N = 943). A series of blockwise multiple regression analyses indicated that PTSD numbing and hyperarousal symptom clusters explained a significant proportion of the variance across all physical health domains except for the Physical Functioning subscale, which measures impairments in specific physical activities. Our findings further explain the impact of PTSD on health status by exploring the way PTSD symptom clusters predict self-perceptions of health, role limitations, pain, and vitality.

[Current seroprevalence, vaccination and predictive value of liver enzymes for hepatitis B among refugees in Germany].

Science.gov (United States)

Hampel, Annika; Solbach, Philipp; Cornberg, Markus; Schmidt, Reinhold E; Behrens, Georg M N; Jablonka, Alexandra

2016-05-01

Currently only vague estimates exist for the seroprevalence and vaccination status for viral hepatitis B (HBV) in refugees arriving in Germany during the current refugee crisis. To assess the prevalence of hepatitis B in refugees arriving in northern Germany in 2015. In a cross-sectional study in 793 patients from all age groups tests for serological markers of hepatitis B virus infection (HBsAg, anti-HBc) and liver enzymes (ALT, AST, bilirubin, γGT, alkaline phosphatase) were performed in August 2015 at six reception centers in northern Germany. In 258 patients anti-HBs antibodies were assessed additionally. Of the tested refugees, 76.7 % were male, the median age was 28.8 ± 11.4 years, and 7.8 % were children under the age of 18. The overall prevalence of HBsAg and total anti-HBc was 2.3 % and 14.0 % respectively (2.5 % and 14.5 % in men; 1.2 % and 13.5 % in women). Prevalence was highest in 35 to 49-year-old patients for HBsAg (3.1 %) and for refugees over 50 years for anti-HBc (38 %). No immunity to Hepatitis B was found in 62 %, 18.6 % had been vaccinated against Hepatitis B, while 50 % of children aged up to 15 years (n = 12) had been vaccinated. Positive predictive values of elevated AST and ALT for detection of HBsAg was 0 and 0.016, respectively. Only two patients with a positive HBsAg had elevated transaminases. This study showed a high prevalence of HBsAg in a German refugee sample in comparison to the general German population. Liver enzymes are not an appropriate tool for screening for hepatitis B virus infection.

Antisymmetrized molecular dynamics studies for exotic clustering phenomena in neutron-rich nuclei

Energy Technology Data Exchange (ETDEWEB)

Kimura, M. [Hokkaido University, Department of Physics, Sapporo (Japan); Hokkaido University, Nuclear Reaction Data Centre, Faculty of Science, Sapporo (Japan); Suhara, T. [Matsue College of Technology, Matsue (Japan); Kanada-En' yo, Y. [Kyoto University, Department of Physics, Kyoto (Japan)

2016-12-15

We present a review of recent works on clustering phenomena in unstable nuclei studied by antisymmetrized molecular dynamics (AMD). The AMD studies in these decades have uncovered novel types of clustering phenomena brought about by the excess neutrons. Among them, this review focuses on the molecule-like structure of unstable nuclei. One of the earliest discussions on the clustering in unstable nuclei was made for neutron-rich Be and B isotopes. AMD calculations predicted that the ground state clustering is enhanced or reduced depending on the number of excess neutrons. Today, the experiments are confirming this prediction as the change of the proton radii. Behind this enhancement and reduction of the clustering, there are underlying shell effects called molecular and atomic orbits. These orbits form covalent and ionic bonding of the clusters analogous to the atomic molecules. It was found that this ''molecular-orbit picture'' reasonably explains the low-lying spectra of Be isotopes. The molecular-orbit picture is extended to other systems having parity asymmetric cluster cores and to the three cluster systems. O and Ne isotopes are the candidates of the former, while the 3α linear chains in C isotopes are the latter. For both subjects, many intensive studies are now in progress. We also pay a special attention to the observables which are the fingerprint of the clustering. In particular, we focus on the monopole and dipole transitions which are recently regarded as good probe for the clustering. We discuss how they have and will reveal the exotic clustering. (orig.)

Gamma-Ray Emission from Galaxy Clusters : DARK MATTER AND COSMIC-RAYS

Science.gov (United States)

Pinzke, Anders

The quest for the first detection of a galaxy cluster in the high energy gamma-ray regime is ongoing, and even though clusters are observed in several other wave-bands, there is still no firm detection in gamma-rays. To complement the observational efforts we estimate the gamma-ray contributions from both annihilating dark matter and cosmic-ray (CR) proton as well as CR electron induced emission. Using high-resolution simulations of galaxy clusters, we find a universal concave shaped CR proton spectrum independent of the simulated galaxy cluster. Specifically, the gamma-ray spectra from decaying neutral pions, which are produced by CR protons, dominate the cluster emission. Furthermore, based on our derived flux and luminosity functions, we identify the galaxy clusters with the brightest galaxy clusters in gamma-rays. While this emission is challenging to detect using the Fermi satellite, major observations with Cherenkov telescopes in the near future may put important constraints on the CR physics in clusters. To extend these predictions, we use a dark matter model that fits the recent electron and positron data from Fermi, PAMELA, and H.E.S.S. with remarkable precision, and make predictions about the expected gamma-ray flux from nearby clusters. In order to remain consistent with the EGRET upper limit on the gamma-ray emission from Virgo, we constrain the minimum mass of substructures for cold dark matter halos. In addition, we find comparable levels of gamma-ray emission from CR interactions and dark matter annihilations without Sommerfeld enhancement.

Applying neural networks as software sensors for enzyme engineering.

Science.gov (United States)

Linko, S; Zhu, Y H; Linko, P

1999-04-01

The on-line control of enzyme-production processes is difficult, owing to the uncertainties typical of biological systems and to the lack of suitable on-line sensors for key process variables. For example, intelligent methods to predict the end point of fermentation could be of great economic value. Computer-assisted control based on artificial-neural-network models offers a novel solution in such situations. Well-trained feedforward-backpropagation neural networks can be used as software sensors in enzyme-process control; their performance can be affected by a number of factors.

An appraisal of the enzyme stability-activity trade-off.

Science.gov (United States)

Miller, Scott R

2017-07-01

A longstanding idea in evolutionary physiology is that an enzyme cannot jointly optimize performance at both high and low temperatures due to a trade-off between stability and activity. Although a stability-activity trade-off has been observed for well-characterized examples, such a trade-off is not imposed by any physical chemical constraint. To better understand the pervasiveness of this trade-off, I investigated the stability-activity relationship for comparative biochemical studies of purified orthologous enzymes identified by a literature search. The nature of this relationship varied greatly among studies. Notably, studies of enzymes with low mean synonymous nucleotide sequence divergence were less likely to exhibit the predicted negative correlation between stability and activity. Similarly, a survey of directed evolution investigations of the stability-activity relationship indicated that these traits are often uncoupled among nearly identical yet phenotypically divergent enzymes. This suggests that the presumptive trade-off often reported for investigations of enzymes with high mean sequence divergence may in some cases instead be a consequence of the degeneration over time of enzyme function in unselected environments, rather than a direct effect of thermal adaptation. The results caution against the general assertion of a stability-activity trade-off during enzyme adaptation. © 2017 The Author(s). Evolution © 2017 The Society for the Study of Evolution.

Dust Evolution in Galaxy Cluster Simulations

Science.gov (United States)

Gjergo, Eda; Granato, Gian Luigi; Murante, Giuseppe; Ragone-Figueroa, Cinthia; Tornatore, Luca; Borgani, Stefano

2018-06-01

We implement a state-of-the-art treatment of the processes affecting the production and Interstellar Medium (ISM) evolution of carbonaceous and silicate dust grains within SPH simulations. We trace the dust grain size distribution by means of a two-size approximation. We test our method on zoom-in simulations of four massive (M200 ≥ 3 × 1014M⊙) galaxy clusters. We predict that during the early stages of assembly of the cluster at z ≳ 3, where the star formation activity is at its maximum in our simulations, the proto-cluster regions are rich in dusty gas. Compared to the case in which only dust production in stellar ejecta is active, if we include processes occurring in the cold ISM,the dust content is enhanced by a factor 2 - 3. However, the dust properties in this stage turn out to be significantly different from those observationally derived for the average Milky Way dust, and commonly adopted in calculations of dust reprocessing. We show that these differences may have a strong impact on the predicted spectral energy distributions. At low redshift in star forming regions our model reproduces reasonably well the trend of dust abundances over metallicity as observed in local galaxies. However we under-produce by a factor of 2 to 3 the total dust content of clusters estimated observationally at low redshift, z ≲ 0.5 using IRAS, Planck and Herschel satellites data. This discrepancy does not subsist by assuming a lower sputtering efficiency, which erodes dust grains in the hot Intracluster Medium (ICM).

Reactions probing effects of quark clusters in nuclei

International Nuclear Information System (INIS)

Lassila, K.E.; Sukhatme, U.P.

1988-01-01

We study signatures of quark clusters in reactions which probe quarks in nuclei. We examine the EMC effect and use physical arguments to establish features of valence and ocean parton distributions in multiquark clusters. We predict from these distributions ratios of structure functions and cross sections measured with neutrino, antineutrinos and proton beams. It appears that a unique determination of the source of the EMC effect will be possible. 6 refs., 4 figs

Quantization State of Baryonic Mass in Clusters of Galaxies

Directory of Open Access Journals (Sweden)

Potter F.

2007-01-01

Full Text Available The rotational velocity curves for clusters of galaxies cannot be explained by Newtonian gravitation using the baryonic mass nor does MOND succeed in reducing this discrepancy to acceptable differences. The dark matter hypothesis appears to offer a solution; however, non-baryonic dark matter has never been detected. As an alternative approach, quantum celestial mechanics (QCM predicts that galactic clusters are in quantization states determined solely by the total baryonic mass of the cluster and its total angular momentum. We find excellent agreement with QCM for ten galactic clusters, demonstrating that dark matter is not needed to explain the rotation velocities and providing further support to the hypothesis that all gravitationally bound systems have QCM quantization states.

POWER-LAW TEMPLATE FOR INFRARED POINT-SOURCE CLUSTERING

Energy Technology Data Exchange (ETDEWEB)

Addison, Graeme E.; Dunkley, Joanna [Sub-department of Astrophysics, University of Oxford, Denys Wilkinson Building, Keble Road, Oxford OX1 3RH (United Kingdom); Hajian, Amir; Das, Sudeep; Hincks, Adam D.; Page, Lyman A.; Staggs, Suzanne T. [Joseph Henry Laboratories of Physics, Jadwin Hall, Princeton University, Princeton, NJ 08544 (United States); Viero, Marco [Department of Astronomy, California Institute of Technology, 1200 East California Boulevard, Pasadena, CA 91125 (United States); Bond, J. Richard [Canadian Institute for Theoretical Astrophysics, University of Toronto, Toronto, ON M5S 3H8 (Canada); Devlin, Mark J.; Reese, Erik D. [Department of Physics and Astronomy, University of Pennsylvania, 209 South 33rd Street, Philadelphia, PA 19104 (United States); Halpern, Mark; Scott, Douglas [Department of Physics and Astronomy, University of British Columbia, Vancouver, BC V6T 1Z4 (Canada); Hlozek, Renee; Marriage, Tobias A.; Spergel, David N. [Department of Astrophysical Sciences, Peyton Hall, Princeton University, Princeton, NJ 08544 (United States); Moodley, Kavilan [Astrophysics and Cosmology Research Unit, School of Mathematical Sciences, University of KwaZulu-Natal, Durban 4041 (South Africa); Wollack, Edward [NASA/Goddard Space Flight Center, Code 665, Greenbelt, MD 20771 (United States)

2012-06-20

We perform a combined fit to angular power spectra of unresolved infrared (IR) point sources from the Planck satellite (at 217, 353, 545, and 857 GHz, over angular scales 100 {approx}< l {approx}< 2200), the Balloon-borne Large-Aperture Submillimeter Telescope (BLAST; 250, 350, and 500 {mu}m; 1000 {approx}< l {approx}< 9000), and from correlating BLAST and Atacama Cosmology Telescope (ACT; 148 and 218 GHz) maps. We find that the clustered power over the range of angular scales and frequencies considered is well fitted by a simple power law of the form C{sup clust}{sub l}{proportional_to}l{sup -n} with n = 1.25 {+-} 0.06. While the IR sources are understood to lie at a range of redshifts, with a variety of dust properties, we find that the frequency dependence of the clustering power can be described by the square of a modified blackbody, {nu}{sup {beta}} B({nu}, T{sub eff}), with a single emissivity index {beta} = 2.20 {+-} 0.07 and effective temperature T{sub eff} = 9.7 K. Our predictions for the clustering amplitude are consistent with existing ACT and South Pole Telescope results at around 150 and 220 GHz, as is our prediction for the effective dust spectral index, which we find to be {alpha}{sub 150-220} = 3.68 {+-} 0.07 between 150 and 220 GHz. Our constraints on the clustering shape and frequency dependence can be used to model the IR clustering as a contaminant in cosmic microwave background anisotropy measurements. The combined Planck and BLAST data also rule out a linear bias clustering model.

Experimental Determination and Prediction of the Fitness Effects of Random Point Mutations in the Biosynthetic Enzyme HisA

Science.gov (United States)

Lundin, Erik; Tang, Po-Cheng; Guy, Lionel; Näsvall, Joakim; Andersson, Dan I

2018-01-01

Abstract The distribution of fitness effects of mutations is a factor of fundamental importance in evolutionary biology. We determined the distribution of fitness effects of 510 mutants that each carried between 1 and 10 mutations (synonymous and nonsynonymous) in the hisA gene, encoding an essential enzyme in the l-histidine biosynthesis pathway of Salmonella enterica. For the full set of mutants, the distribution was bimodal with many apparently neutral mutations and many lethal mutations. For a subset of 81 single, nonsynonymous mutants most mutations appeared neutral at high expression levels, whereas at low expression levels only a few mutations were neutral. Furthermore, we examined how the magnitude of the observed fitness effects was correlated to several measures of biophysical properties and phylogenetic conservation.We conclude that for HisA: (i) The effect of mutations can be masked by high expression levels, such that mutations that are deleterious to the function of the protein can still be neutral with regard to organism fitness if the protein is expressed at a sufficiently high level; (ii) the shape of the fitness distribution is dependent on the extent to which the protein is rate-limiting for growth; (iii) negative epistatic interactions, on an average, amplified the combined effect of nonsynonymous mutations; and (iv) no single sequence-based predictor could confidently predict the fitness effects of mutations in HisA, but a combination of multiple predictors could predict the effect with a SD of 0.04 resulting in 80% of the mutations predicted within 12% of their observed selection coefficients. PMID:29294020

The Post-polyketide Synthase Steps in iso-Migrastatin Biosynthesis Featuring Tailoring Enzymes with Broad Substrate Specificity

Science.gov (United States)

Ma, Ming; Kwong, Thomas; Lim, Si-Kyu; Ju, Jianhua; Lohman, Jeremy R.; Shen, Ben

2013-01-01

The iso-migrastatin (iso-MGS) biosynthetic gene cluster from Streptomyces platensis NRRL 18993 consists of 11 genes, featuring an acyltransferase (AT)-less type I polyketide synthase (PKS) and three tailoring enzymes MgsIJK. Systematic inactivation of mgsIJK in S. platensis enabled us to (i) identify two nascent products (10 and 13) of the iso-MGS AT-less type I PKS, establishing an unprecedented novel feature for AT-less type I PKSs, and (ii) account for the formation of all known post-PKS biosynthetic intermediates (10-17) generated by the three tailoring enzymes MgsIJK, which possessed significant substrate promiscuities. PMID:23394593

Discovery and Structure Determination of the Orphan Enzyme Isoxanthopterin Deaminase

Energy Technology Data Exchange (ETDEWEB)

Hall, R.S.; Swaminathan, S.; Agarwal, R.; Hitchcock, D.; Sauder, J. M.; Burley, S. K.; Raushel, F. M.

2010-05-25

Two previously uncharacterized proteins have been identified that efficiently catalyze the deamination of isoxanthopterin and pterin 6-carboxylate. The genes encoding these two enzymes, NYSGXRC-9339a (gi|44585104) and NYSGXRC-9236b (gi|44611670), were first identified from DNA isolated from the Sargasso Sea as part of the Global Ocean Sampling Project. The genes were synthesized, and the proteins were subsequently expressed and purified. The X-ray structure of Sgx9339a was determined at 2.7 {angstrom} resolution (Protein Data Bank entry 2PAJ). This protein folds as a distorted ({beta}/{alpha}){sub 8} barrel and contains a single zinc ion in the active site. These enzymes are members of the amidohydrolase superfamily and belong to cog0402 within the clusters of orthologous groups (COG). Enzymes in cog0402 have previously been shown to catalyze the deamination of guanine, cytosine, S-adenosylhomocysteine, and 8-oxoguanine. A small compound library of pteridines, purines, and pyrimidines was used to probe catalytic activity. The only substrates identified in this search were isoxanthopterin and pterin 6-carboxylate. The kinetic constants for the deamination of isoxanthopterin with Sgx9339a were determined to be 1.0 s{sup -1}, 8.0 {micro}M, and 1.3 x 10{sup 5} M{sup -1} s{sup -1} (k{sub cat}, K{sub m}, and k{sub cat}/K{sub m}, respectively). The active site of Sgx9339a most closely resembles the active site for 8-oxoguanine deaminase (Protein Data Bank entry 2UZ9). A model for substrate recognition of isoxanthopterin by Sgx9339a was proposed on the basis of the binding of guanine and xanthine in the active site of guanine deaminase. Residues critical for substrate binding appear to be conserved glutamine and tyrosine residues that form hydrogen bonds with the carbonyl oxygen at C4, a conserved threonine residue that forms hydrogen bonds with N5, and another conserved threonine residue that forms hydrogen bonds with the carbonyl group at C7. These conserved active site

Outcome-Driven Cluster Analysis with Application to Microarray Data.

Directory of Open Access Journals (Sweden)

Jessie J Hsu

Full Text Available One goal of cluster analysis is to sort characteristics into groups (clusters so that those in the same group are more highly correlated to each other than they are to those in other groups. An example is the search for groups of genes whose expression of RNA is correlated in a population of patients. These genes would be of greater interest if their common level of RNA expression were additionally predictive of the clinical outcome. This issue arose in the context of a study of trauma patients on whom RNA samples were available. The question of interest was whether there were groups of genes that were behaving similarly, and whether each gene in the cluster would have a similar effect on who would recover. For this, we develop an algorithm to simultaneously assign characteristics (genes into groups of highly correlated genes that have the same effect on the outcome (recovery. We propose a random effects model where the genes within each group (cluster equal the sum of a random effect, specific to the observation and cluster, and an independent error term. The outcome variable is a linear combination of the random effects of each cluster. To fit the model, we implement a Markov chain Monte Carlo algorithm based on the likelihood of the observed data. We evaluate the effect of including outcome in the model through simulation studies and describe a strategy for prediction. These methods are applied to trauma data from the Inflammation and Host Response to Injury research program, revealing a clustering of the genes that are informed by the recovery outcome.

Salmonella enterica pulsed-field gel electrophoresis clusters, Minnesota, USA, 2001-2007.

Science.gov (United States)

Rounds, Joshua M; Hedberg, Craig W; Meyer, Stephanie; Boxrud, David J; Smith, Kirk E

2010-11-01

We determined characteristics of Salmonella enterica pulsed-field gel electrophoresis clusters that predict their being solved (i.e., that result in identification of a confirmed outbreak). Clusters were investigated by the Minnesota Department of Health by using a dynamic iterative model. During 2001-2007, a total of 43 (12.5%) of 344 clusters were solved. Clusters of ≥4 isolates were more likely to be solved than clusters of 2 isolates. Clusters in which the first 3 case isolates were received at the Minnesota Department of Health within 7 days were more likely to be solved than were clusters in which the first 3 case isolates were received over a period >14 days. If resources do not permit investigation of all S. enterica pulsed-field gel electrophoresis clusters, investigation of clusters of ≥4 cases and clusters in which the first 3 case isolates were received at a public health laboratory within 7 days may improve outbreak investigations.

Evaluation of commercial a-amylase enzyme-linked immunosorbent assy (ELISA) test kits for wheat

Science.gov (United States)

a-Amylase enzyme is associated with preharvest sprouting (PHS) and late-maturity a amylase (LMA) in wheat, and reduces wheat and flour quality. Various means have been developed to measure the presence of a-amylase, thereby predicting end-use quality; most are based on enzyme activity. An alternativ...

THE XMM CLUSTER SURVEY: THE BUILD-UP OF STELLAR MASS IN BRIGHTEST CLUSTER GALAXIES AT HIGH REDSHIFT

International Nuclear Information System (INIS)

Stott, J. P.; Collins, C. A.; Hilton, M.; Capozzi, D.; Sahlen, M.; Lloyd-Davies, E.; Hosmer, M.; Liddle, A. R.; Mehrtens, N.; Romer, A. K.; Miller, C. J.; Stanford, S. A.; Viana, P. T. P.; Davidson, M.; Hoyle, B.; Kay, S. T.; Nichol, R. C.

2010-01-01

We present deep J- and K s -band photometry of 20 high redshift galaxy clusters between z = 0.8 and1.5, 19 of which are observed with the MOIRCS instrument on the Subaru telescope. By using near-infrared light as a proxy for stellar mass we find the surprising result that the average stellar mass of Brightest Cluster Galaxies (BCGs) has remained constant at ∼9 x 10 11 M sun since z ∼ 1.5. We investigate the effect on this result of differing star formation histories generated by three well-known and independent stellar population codes and find it to be robust for reasonable, physically motivated choices of age and metallicity. By performing Monte Carlo simulations we find that the result is unaffected by any correlation between BCG mass and cluster mass in either the observed or model clusters. The large stellar masses imply that the assemblage of these galaxies took place at the same time as the initial burst of star formation. This result leads us to conclude that dry merging has had little effect on the average stellar mass of BCGs over the last 9-10 Gyr in stark contrast to the predictions of semi-analytic models, based on the hierarchical merging of dark matter halos, which predict a more protracted mass build-up over a Hubble time. However, we discuss that there is potential for reconciliation between observation and theory if there is a significant growth of material in the intracluster light over the same period.

Cluster radioactivity of Z=125 super heavy nuclei

International Nuclear Information System (INIS)

Manjunatha, H.C.; Seenappa, L.

2015-01-01

For atomic numbers larger than 121 cluster decay and spontaneous fission may compete with α decay. Hence there is a need to make reliable calculations for the cluster decay half-lives of superheavy nuclei to predict the possible isotopes super heavy nuclei. So, in the present work, we have studied the decay of clusters such as 8 Be, 10 Be, 12 C, 14 C, 16 C, 18 O, 20 O, 22 Ne, 24 Ne, 25 Ne, 26 Ne, 28 Mg, 30 Mg, 32 Si, 34 Si, 36 Si, 40 S, 48 Ca, 50 Ca and 52 Ti from the super heavy nuclei Z=125

BRIGHTEST CLUSTER GALAXIES AT THE PRESENT EPOCH

Energy Technology Data Exchange (ETDEWEB)

Lauer, Tod R. [National Optical Astronomy Observatory, P.O. Box 26732, Tucson, AZ 85726 (United States); Postman, Marc [Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218 (United States); Strauss, Michael A.; Graves, Genevieve J.; Chisari, Nora E. [Department of Astrophysical Sciences, Princeton University, Princeton, NJ 08544 (United States)

2014-12-20

We have obtained photometry and spectroscopy of 433 z ≤ 0.08 brightest cluster galaxies (BCGs) in a full-sky survey of Abell clusters to construct a BCG sample suitable for probing deviations from the local Hubble flow. The BCG Hubble diagram over 0 < z < 0.08 is consistent to within 2% of the Hubble relation specified by a Ω {sub m} = 0.3, Λ = 0.7 cosmology. This sample allows us to explore the structural and photometric properties of BCGs at the present epoch, their location in their hosting galaxy clusters, and the effects of the cluster environment on their structure and evolution. We revisit the L{sub m} -α relation for BCGs, which uses α, the log-slope of the BCG photometric curve of growth, to predict the metric luminosity in an aperture with 14.3 kpc radius, L{sub m} , for use as a distance indicator. Residuals in the relation are 0.27 mag rms. We measure central stellar velocity dispersions, σ, of the BCGs, finding the Faber-Jackson relation to flatten as the metric aperture grows to include an increasing fraction of the total BCG luminosity. A three-parameter ''metric plane'' relation using α and σ together gives the best prediction of L{sub m} , with 0.21 mag residuals. The distribution of projected spatial offsets, r{sub x} of BCGs from the X-ray-defined cluster center is a steep γ = –2.33 power law over 1 < r{sub x} < 10{sup 3} kpc. The median offset is ∼10 kpc, but ∼15% of the BCGs have r{sub x} > 100 kpc. The absolute cluster-dispersion normalized BCG peculiar velocity |ΔV {sub 1}|/σ {sub c} follows an exponential distribution with scale length 0.39 ± 0.03. Both L{sub m} and α increase with σ {sub c}. The α parameter is further moderated by both the spatial and velocity offset from the cluster center, with larger α correlated with the proximity of the BCG to the cluster mean velocity or potential center. At the same time, position in the cluster has little effect on L{sub m} . Likewise, residuals from

The Connection between Radio Halos and Cluster Mergers and the ...

Indian Academy of Sciences (India)

(2008) suggested that the behavior of clusters in the. P1.4 − LX diagram is connected with their dynamical state. 2.2 The dynamical state of GMRT clusters ..... (e.g., Cassano et al. 2008) and were used to derive expectations for the planned. LOFAR surveys. Accordingly to these predictions the Tier 1 'Large Area Survey' at.

Measurement of initial clustering on the radon decay product 218Po

International Nuclear Information System (INIS)

Strydom, R.

1989-01-01

The formation of water clusters on 218 Po ions is studied. The formation of the water clusters is discussed in the light of the classical theory of clustering, the clustering theory of Hawrynski and a kinetic model of clustering. The design of a specialized electric mobility spectrometer to measure the electric mobilities of the water clusters at various humidity levels is discussed. From the mobilities the radii of, and a number of water molecules in, the clusters are calculated using kinetic gas theory. The determinations were done for humidity levels between 0,16 and 96% relative humidity, and the results compared with the theoretical predictions. It was found that the classical theory underestimates the sizes of the clusters and the theory of Hawrynski overestimates the cluster sizes. It is concluded that the spectrometer is capable of high resolution measurement of the electric mobility of the small clusters. The underlying result of the clustering theories is that stable clusters with particular radii are formed at each humidity level. 91 refs., 70 figs., 11 tabs

Structures of 38-atom gold-platinum nanoalloy clusters

Energy Technology Data Exchange (ETDEWEB)

Ong, Yee Pin; Yoon, Tiem Leong [School of Physics, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia); Lim, Thong Leng [Faculty of Engineering and Technology, Multimedia University, Melaka Campus, 75450 Melaka (Malaysia)

2015-04-24

Bimetallic nanoclusters, such as gold-platinum nanoclusters, are nanomaterials promising wide range of applications. We perform a numerical study of 38-atom gold-platinum nanoalloy clusters, Au{sub n}Pt{sub 38−n} (0 ≤ n ≤ 38), to elucidate the geometrical structures of these clusters. The lowest-energy structures of these bimetallic nanoclusters at the semi-empirical level are obtained via a global-minimum search algorithm known as parallel tempering multi-canonical basin hopping plus genetic algorithm (PTMBHGA), in which empirical Gupta many-body potential is used to describe the inter-atomic interactions among the constituent atoms. The structures of gold-platinum nanoalloy clusters are predicted to be core-shell segregated nanoclusters. Gold atoms are observed to preferentially occupy the surface of the clusters, while platinum atoms tend to occupy the core due to the slightly smaller atomic radius of platinum as compared to gold’s. The evolution of the geometrical structure of 38-atom Au-Pt clusters displays striking similarity with that of 38-atom Au-Cu nanoalloy clusters as reported in the literature.

Perspective: Size selected clusters for catalysis and electrochemistry

Science.gov (United States)

Halder, Avik; Curtiss, Larry A.; Fortunelli, Alessandro; Vajda, Stefan

2018-03-01

Size-selected clusters containing a handful of atoms may possess noble catalytic properties different from nano-sized or bulk catalysts. Size- and composition-selected clusters can also serve as models of the catalytic active site, where an addition or removal of a single atom can have a dramatic effect on their activity and selectivity. In this perspective, we provide an overview of studies performed under both ultra-high vacuum and realistic reaction conditions aimed at the interrogation, characterization, and understanding of the performance of supported size-selected clusters in heterogeneous and electrochemical reactions, which address the effects of cluster size, cluster composition, cluster-support interactions, and reaction conditions, the key parameters for the understanding and control of catalyst functionality. Computational modeling based on density functional theory sampling of local minima and energy barriers or ab initio molecular dynamics simulations is an integral part of this research by providing fundamental understanding of the catalytic processes at the atomic level, as well as by predicting new materials compositions which can be validated in experiments. Finally, we discuss approaches which aim at the scale up of the production of well-defined clusters for use in real world applications.

Computational cluster validation for microarray data analysis: experimental assessment of Clest, Consensus Clustering, Figure of Merit, Gap Statistics and Model Explorer

Directory of Open Access Journals (Sweden)

Utro Filippo

2008-10-01

Full Text Available Abstract Background Inferring cluster structure in microarray datasets is a fundamental task for the so-called -omic sciences. It is also a fundamental question in Statistics, Data Analysis and Classification, in particular with regard to the prediction of the number of clusters in a dataset, usually established via internal validation measures. Despite the wealth of internal measures available in the literature, new ones have been recently proposed, some of them specifically for microarray data. Results We consider five such measures: Clest, Consensus (Consensus Clustering, FOM (Figure of Merit, Gap (Gap Statistics and ME (Model Explorer, in addition to the classic WCSS (Within Cluster Sum-of-Squares and KL (Krzanowski and Lai index. We perform extensive experiments on six benchmark microarray datasets, using both Hierarchical and K-means clustering algorithms, and we provide an analysis assessing both the intrinsic ability of a measure to predict the correct number of clusters in a dataset and its merit relative to the other measures. We pay particular attention both to precision and speed. Moreover, we also provide various fast approximation algorithms for the computation of Gap, FOM and WCSS. The main result is a hierarchy of those measures in terms of precision and speed, highlighting some of their merits and limitations not reported before in the literature. Conclusion Based on our analysis, we draw several conclusions for the use of those internal measures on microarray data. We report the main ones. Consensus is by far the best performer in terms of predictive power and remarkably algorithm-independent. Unfortunately, on large datasets, it may be of no use because of its non-trivial computer time demand (weeks on a state of the art PC. FOM is the second best performer although, quite surprisingly, it may not be competitive in this scenario: it has essentially the same predictive power of WCSS but it is from 6 to 100 times slower in time

Glycosylation Helps Cellulase Enzymes Bind to Plant Cell Walls (Fact Sheet)

Energy Technology Data Exchange (ETDEWEB)

2012-06-01

Computer simulations suggest a new strategy to design enhanced enzymes for biofuels production. Large-scale computer simulations predict that the addition of glycosylation on carbohydrate-binding modules can dramatically improve the binding affinity of these protein domains over amino acid mutations alone. These simulations suggest that glycosylation can be used as a protein engineering tool to enhance the activity of cellulase enzymes, which are a key component in the conversion of cellulose to soluble sugars in the production of biofuels. Glycosylation is the covalent attachment of carbohydrate molecules to protein side chains, and is present in many proteins across all kingdoms of life. Moreover, glycosylation is known to serve a wide variety of functions in biological recognition, cell signaling, and metabolism. Cellulase enzymes, which are responsible for deconstructing cellulose found in plant cell walls to glucose, contain glycosylation that when modified can affect enzymatic activity-often in an unpredictable manner. To gain insight into the role of glycosylation on cellulase activity, scientists at the National Renewable Energy Laboratory (NREL) used computer simulation to predict that adding glycosylation on the carbohydrate-binding module of a cellulase enzyme dramatically boosts the binding affinity to cellulose-more than standard protein engineering approaches in which amino acids are mutated. Because it is known that higher binding affinity in cellulases leads to higher activity, this work suggests a new route to designing enhanced enzymes for biofuels production. More generally, this work suggests that tuning glycosylation in cellulase enzymes is a key factor to consider when engineering biochemical conversion processes, and that more work is needed to understand how glycosylation affects cellulase activity at the molecular level.

Pharmacokinetic analysis and k-means clustering of DCEMR images for radiotherapy outcome prediction of advanced cervical cancers.

Science.gov (United States)

Andersen, Erlend K F; Kristensen, Gunnar B; Lyng, Heidi; Malinen, Eirik

2011-08-01

Pharmacokinetic analysis of dynamic contrast enhanced magnetic resonance images (DCEMRI) allows for quantitative characterization of vascular properties of tumors. The aim of this study is twofold, first to determine if tumor regions with similar vascularization could be labeled by clustering methods, second to determine if the identified regions can be associated with local cancer relapse. Eighty-one patients with locally advanced cervical cancer treated with chemoradiotherapy underwent DCEMRI with Gd-DTPA prior to external beam radiotherapy. The median follow-up time after treatment was four years, in which nine patients had primary tumor relapse. By fitting a pharmacokinetic two-compartment model function to the temporal contrast enhancement in the tumor, two pharmacokinetic parameters, K(trans) and ύ(e), were estimated voxel by voxel from the DCEMR-images. Intratumoral regions with similar vascularization were identified by k-means clustering of the two pharmacokinetic parameter estimates over all patients. The volume fraction of each cluster was used to evaluate the prognostic value of the clusters. Three clusters provided a sufficient reduction of the cluster variance to label different vascular properties within the tumors. The corresponding median volume fraction of each cluster was 38%, 46% and 10%. The second cluster was significantly associated with primary tumor control in a log-rank survival test (p-value: 0.042), showing a decreased risk of treatment failure for patients with high volume fraction of voxels. Intratumoral regions showing similar vascular properties could successfully be labeled in three distinct clusters and the volume fraction of one cluster region was associated with primary tumor control.

Pharmacokinetic analysis and k-means clustering of DCEMR images for radiotherapy outcome prediction of advanced cervical cancers

International Nuclear Information System (INIS)

Andersen, Erlend K. F.; Kristensen, Gunnar B.; Lyng, Heidi; Malinen, Eirik

2011-01-01

Introduction. Pharmacokinetic analysis of dynamic contrast enhanced magnetic resonance images (DCEMRI) allows for quantitative characterization of vascular properties of tumors. The aim of this study is twofold, first to determine if tumor regions with similar vascularization could be labeled by clustering methods, second to determine if the identified regions can be associated with local cancer relapse. Materials and methods. Eighty-one patients with locally advanced cervical cancer treated with chemoradiotherapy underwent DCEMRI with Gd-DTPA prior to external beam radiotherapy. The median follow-up time after treatment was four years, in which nine patients had primary tumor relapse. By fitting a pharmacokinetic two-compartment model function to the temporal contrast enhancement in the tumor, two pharmacokinetic parameters, K trans and u e , were estimated voxel by voxel from the DCEMR-images. Intratumoral regions with similar vascularization were identified by k-means clustering of the two pharmacokinetic parameter estimates over all patients. The volume fraction of each cluster was used to evaluate the prognostic value of the clusters. Results. Three clusters provided a sufficient reduction of the cluster variance to label different vascular properties within the tumors. The corresponding median volume fraction of each cluster was 38%, 46% and 10%. The second cluster was significantly associated with primary tumor control in a log-rank survival test (p-value: 0.042), showing a decreased risk of treatment failure for patients with high volume fraction of voxels. Conclusions. Intratumoral regions showing similar vascular properties could successfully be labeled in three distinct clusters and the volume fraction of one cluster region was associated with primary tumor control

Pharmacokinetic analysis and k-means clustering of DCEMR images for radiotherapy outcome prediction of advanced cervical cancers

Energy Technology Data Exchange (ETDEWEB)

Andersen, Erlend K. F. (Dept. of Medical Physics, The Norwegian Radium Hospital, Oslo Univ. Hospital, Oslo (Norway)), e-mail: eirik.malinen@fys.uio.no; Kristensen, Gunnar B. (Section for Gynaecological Oncology, The Norwegian Radium Hospital, Oslo Univ. Hospital, Oslo (Norway)); Lyng, Heidi (Dept. of Radiation Biology, The Norwegian Radium Hospital, Oslo Univ. Hospital, Oslo (Norway)); Malinen, Eirik (Dept. of Medical Physics, The Norwegian Radium Hospital, Oslo Univ. Hospital, Oslo (Norway); Dept. of Physics, Univ. of Oslo, Oslo (Norway))

2011-08-15

Introduction. Pharmacokinetic analysis of dynamic contrast enhanced magnetic resonance images (DCEMRI) allows for quantitative characterization of vascular properties of tumors. The aim of this study is twofold, first to determine if tumor regions with similar vascularization could be labeled by clustering methods, second to determine if the identified regions can be associated with local cancer relapse. Materials and methods. Eighty-one patients with locally advanced cervical cancer treated with chemoradiotherapy underwent DCEMRI with Gd-DTPA prior to external beam radiotherapy. The median follow-up time after treatment was four years, in which nine patients had primary tumor relapse. By fitting a pharmacokinetic two-compartment model function to the temporal contrast enhancement in the tumor, two pharmacokinetic parameters, Ktrans and u{sub e}, were estimated voxel by voxel from the DCEMR-images. Intratumoral regions with similar vascularization were identified by k-means clustering of the two pharmacokinetic parameter estimates over all patients. The volume fraction of each cluster was used to evaluate the prognostic value of the clusters. Results. Three clusters provided a sufficient reduction of the cluster variance to label different vascular properties within the tumors. The corresponding median volume fraction of each cluster was 38%, 46% and 10%. The second cluster was significantly associated with primary tumor control in a log-rank survival test (p-value: 0.042), showing a decreased risk of treatment failure for patients with high volume fraction of voxels. Conclusions. Intratumoral regions showing similar vascular properties could successfully be labeled in three distinct clusters and the volume fraction of one cluster region was associated with primary tumor control

Some Unsolved Problems, Questions, and Applications of the Brightsen Nucleon Cluster Model

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Smarandache F.

2010-04-01

Full Text Available Brightsen Model is opposite to the Standard Model, and it was build on John Weeler's Resonating Group Structure Model and on Linus Pauling's Close-Packed Spheron Model. Among Brightsen Model's predictions and applications we cite the fact that it derives the average number of prompt neutrons per fission event, it provides a theoretical way for understanding the low temperature / low energy reactions and for approaching the artificially induced fission, it predicts that forces within nucleon clusters are stronger than forces between such clusters within isotopes; it predicts the unmatter entities inside nuclei that result from stable and neutral union of matter and antimatter, and so on. But these predictions have to be tested in the future at the new CERN laboratory.

Surface processing with ionized cluster beams: computer simulation

International Nuclear Information System (INIS)

Insepov, Z.; Yamada, I.

1999-01-01

Molecular Dynamics (MD) and Monte Carlo (MC) models of energetic gas cluster irradiation of a solid surface have been developed to investigate the phenomena of crater formation, sputtering, surface treatment, and the material hardness evaluation by irradiation with cluster ions. Theoretical estimation of crater dimensions formed with Ar gas cluster ion irradiation of different substrates, based on hydrodynamics and MD simulation, are presented. The atomic scale shock waves arising from cluster impact were obtained by calculating the pressure, temperature and mass-velocity of the target atoms. The crater depth is given as a unique 1/3 dependence on the cluster energy and on the cold material Brinell hardness number (BHN). A new 'true material hardness' scale which can be very useful for example for thin film coatings deposited on a soft substrate, is defined. This finding could be used as a new technique for measuring of a material hardness. Evolution of surface morphology under cluster ion irradiation was described by the surface relaxation equation which contains a term of crater formation at cluster impact. The formation of ripples on a surface irradiated with oblique cluster ion beams was predicted. MD and MC models of Decaborane ion (B 10 H 14 ) implantation into Si and the following rapid thermal annealing (RTA) have been developed

Observations on small anionic clusters in an electrostatic ion beam trap

International Nuclear Information System (INIS)

Eritt, Markus

2008-01-01

The term atomic cluster relates to compounds of at least two or three atoms. Thereby the physical properties are size dependent and the property transitions between single atoms and bulk material are not always smooth. Ion traps allow it to observe internal cluster properties independent from the influence of external forces. In this work the electron induced decay of singly negatively charged atomic clusters was observed. The dissociation cross section of the clusters is dominated by detachment of the only weakly bound outer electrons. For simple atoms at low electron energies a simple scaling law can be obtained that includes only the binding energies of the valence electrons. Nevertheless for larger sizes theoretical calculations predict so called ''giant resonances'' as dominant decay process in metal clusters. Due to mass limitations in storage rings exist so far only cross section measurements for simple anions and small negative molecules. In this work the electron detachment cross sections of small negatively charged carbon (C n - n=2-12), aluminium (Al n - n=2-7) and silver clusters (Ag n - n=1-11) were measured in an electrostatic ion beam trap. The classical scaling law, including only the binding energies of the valence electrons, turned out to be not sufficient, especially for larger clusters. In order to improve the correlation between measured and predicted values it was proposed to involve the influence of the cluster volume and the specific polarisability induced by long range coulomb interaction. For silver clusters the best agreement was obtained using a combination of the projected area reduced by the polarisability. The existence of ''giant resonances'' could not be confirmed. According to theory for clusters with a broad internal energy distribution, a power-law decay close to 1/time is expected. For some clusters the lifetime behaviour would be strongly quenched by photon emission. The thermionic evaporative decay of anionic aluminium and

A novel grain cluster-based homogenization scheme

International Nuclear Information System (INIS)

Tjahjanto, D D; Eisenlohr, P; Roters, F

2010-01-01

An efficient homogenization scheme, termed the relaxed grain cluster (RGC), for elasto-plastic deformations of polycrystals is presented. The scheme is based on a generalization of the grain cluster concept. A volume element consisting of eight (= 2 × 2 × 2) hexahedral grains is considered. The kinematics of the RGC scheme is formulated within a finite deformation framework, where the relaxation of the local deformation gradient of each individual grain is connected to the overall deformation gradient by the, so-called, interface relaxation vectors. The set of relaxation vectors is determined by the minimization of the constitutive energy (or work) density of the overall cluster. An additional energy density associated with the mismatch at the grain boundaries due to relaxations is incorporated as a penalty term into the energy minimization formulation. Effectively, this penalty term represents the kinematical condition of deformation compatibility at the grain boundaries. Simulations have been performed for a dual-phase grain cluster loaded in uniaxial tension. The results of the simulations are presented and discussed in terms of the effective stress–strain response and the overall deformation anisotropy as functions of the penalty energy parameters. In addition, the prediction of the RGC scheme is compared with predictions using other averaging schemes, as well as to the result of direct finite element (FE) simulation. The comparison indicates that the present RGC scheme is able to approximate FE simulation results of relatively fine discretization at about three orders of magnitude lower computational cost

Flavonoids as modulators of metabolic enzymes and drug transporters.

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Miron, Anca; Aprotosoaie, Ana Clara; Trifan, Adriana; Xiao, Jianbo

2017-06-01

Flavonoids, natural compounds found in plants and in plant-derived foods and beverages, have been extensively studied with regard to their capacity to modulate metabolic enzymes and drug transporters. In vitro, flavonoids predominantly inhibit the major phase I drug-metabolizing enzyme CYP450 3A4 and the enzymes responsible for the bioactivation of procarcinogens (CYP1 enzymes) and upregulate the enzymes involved in carcinogen detoxification (UDP-glucuronosyltransferases, glutathione S-transferases (GSTs)). Flavonoids have been reported to inhibit ATP-binding cassette (ABC) transporters (multidrug resistance (MDR)-associated proteins, breast cancer-resistance protein) that contribute to the development of MDR. P-glycoprotein, an ABC transporter that limits drug bioavailability and also induces MDR, was differently modulated by flavonoids. Flavonoids and their phase II metabolites (sulfates, glucuronides) inhibit organic anion transporters involved in the tubular uptake of nephrotoxic compounds. In vivo studies have partially confirmed in vitro findings, suggesting that the mechanisms underlying the modulatory effects of flavonoids are complex and difficult to predict in vivo. Data summarized in this review strongly support the view that flavonoids are promising candidates for the enhancement of oral drug bioavailability, chemoprevention, and reversal of MDR. © 2017 New York Academy of Sciences.

Clustering impact regime with shocks in freely evolving granular gas

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Isobe, Masaharu

2017-06-01

A freely cooling granular gas without any external force evolves from the initial homogeneous state to the inhomogeneous clustering state, at which the energy decay deviates from the Haff's law. The asymptotic behavior of energy in the inelastic hard sphere model have been predicted by several theories, which are based on the mode coupling theory or extension of inelastic hard rods gas. In this study, we revisited the clustering regime of freely evolving granular gas via large-scale molecular dynamics simulation with up to 16.7 million inelastic hard disks. We found novel regime regarding on collisions between "clusters" spontaneously appearing after clustering regime, which can only be identified more than a few million particles system. The volumetric dilatation pattern of semicircular shape originated from density shock propagation are well characterized on the appearing of "cluster impact" during the aggregation process of clusters.

Assessment of clusters of transcription factor binding sites in relationship to human promoter, CpG islands and gene expression

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Sakaki Yoshiyuki

2004-02-01

Full Text Available Abstract Background Gene expression is regulated mainly by transcription factors (TFs that interact with regulatory cis-elements on DNA sequences. To identify functional regulatory elements, computer searching can predict TF binding sites (TFBS using position weight matrices (PWMs that represent positional base frequencies of collected experimentally determined TFBS. A disadvantage of this approach is the large output of results for genomic DNA. One strategy to identify genuine TFBS is to utilize local concentrations of predicted TFBS. It is unclear whether there is a general tendency for TFBS to cluster at promoter regions, although this is the case for certain TFBS. Also unclear is the identification of TFs that have TFBS concentrated in promoters and to what level this occurs. This study hopes to answer some of these questions. Results We developed the cluster score measure to evaluate the correlation between predicted TFBS clusters and promoter sequences for each PWM. Non-promoter sequences were used as a control. Using the cluster score, we identified a PWM group called PWM-PCP, in which TFBS clusters positively correlate with promoters, and another PWM group called PWM-NCP, in which TFBS clusters negatively correlate with promoters. The PWM-PCP group comprises 47% of the 199 vertebrate PWMs, while the PWM-NCP group occupied 11 percent. After reducing the effect of CpG islands (CGI against the clusters using partial correlation coefficients among three properties (promoter, CGI and predicted TFBS cluster, we identified two PWM groups including those strongly correlated with CGI and those not correlated with CGI. Conclusion Not all PWMs predict TFBS correlated with human promoter sequences. Two main PWM groups were identified: (1 those that show TFBS clustered in promoters associated with CGI, and (2 those that show TFBS clustered in promoters independent of CGI. Assessment of PWM matches will allow more positive interpretation of TFBS in

Pancreatic Enzymes

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... Contact Us DONATE NOW GENERAL DONATION PURPLESTRIDE Pancreatic enzymes Home Facing Pancreatic Cancer Living with Pancreatic Cancer ... and see a registered dietitian. What are pancreatic enzymes? Pancreatic enzymes help break down fats, proteins and ...

Major depressive disorder subtypes to predict long-term course

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van Loo, Hanna M.; Cai, Tianxi; Gruber, Michael J.; Li, Junlong; de Jonge, Peter; Petukhova, Maria; Rose, Sherri; Sampson, Nancy A.; Schoevers, Robert A.; Wardenaar, Klaas J.; Wilcox, Marsha A.; Al-Hamzawi, Ali Obaid; Andrade, Laura Helena; Bromet, Evelyn J.; Bunting, Brendan; Fayyad, John; Florescu, Silvia E.; Gureje, Oye; Hu, Chiyi; Huang, Yueqin; Levinson, Daphna; Medina-Mora, Maria Elena; Nakane, Yoshibumi; Posada-Villa, Jose; Scott, Kate M.; Xavier, Miguel; Zarkov, Zahari; Kessler, Ronald C.

2016-01-01

Background Variation in course of major depressive disorder (MDD) is not strongly predicted by existing subtype distinctions. A new subtyping approach is considered here. Methods Two data mining techniques, ensemble recursive partitioning and Lasso generalized linear models (GLMs) followed by k-means cluster analysis, are used to search for subtypes based on index episode symptoms predicting subsequent MDD course in the World Mental Health (WMH) Surveys. The WMH surveys are community surveys in 16 countries. Lifetime DSM-IV MDD was reported by 8,261 respondents. Retrospectively reported outcomes included measures of persistence (number of years with an episode; number of with an episode lasting most of the year) and severity (hospitalization for MDD; disability due to MDD). Results Recursive partitioning found significant clusters defined by the conjunctions of early onset, suicidality, and anxiety (irritability, panic, nervousness-worry-anxiety) during the index episode. GLMs found additional associations involving a number of individual symptoms. Predicted values of the four outcomes were strongly correlated. Cluster analysis of these predicted values found three clusters having consistently high, intermediate, or low predicted scores across all outcomes. The high-risk cluster (30.0% of respondents) accounted for 52.9-69.7% of high persistence and severity and was most strongly predicted by index episode severe dysphoria, suicidality, anxiety, and early onset. A total symptom count, in comparison, was not a significant predictor. Conclusions Despite being based on retrospective reports, results suggest that useful MDD subtyping distinctions can be made using data mining methods. Further studies are needed to test and expand these results with prospective data. PMID:24425049

Balancing computation and communication power in power constrained clusters

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Piga, Leonardo; Paul, Indrani; Huang, Wei

2018-05-29

Systems, apparatuses, and methods for balancing computation and communication power in power constrained environments. A data processing cluster with a plurality of compute nodes may perform parallel processing of a workload in a power constrained environment. Nodes that finish tasks early may be power-gated based on one or more conditions. In some scenarios, a node may predict a wait duration and go into a reduced power consumption state if the wait duration is predicted to be greater than a threshold. The power saved by power-gating one or more nodes may be reassigned for use by other nodes. A cluster agent may be configured to reassign the unused power to the active nodes to expedite workload processing.

Simulation-based marginal likelihood for cluster strong lensing cosmology

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Killedar, M.; Borgani, S.; Fabjan, D.; Dolag, K.; Granato, G.; Meneghetti, M.; Planelles, S.; Ragone-Figueroa, C.

2018-01-01

Comparisons between observed and predicted strong lensing properties of galaxy clusters have been routinely used to claim either tension or consistency with Λ cold dark matter cosmology. However, standard approaches to such cosmological tests are unable to quantify the preference for one cosmology over another. We advocate approximating the relevant Bayes factor using a marginal likelihood that is based on the following summary statistic: the posterior probability distribution function for the parameters of the scaling relation between Einstein radii and cluster mass, α and β. We demonstrate, for the first time, a method of estimating the marginal likelihood using the X-ray selected z > 0.5 Massive Cluster Survey clusters as a case in point and employing both N-body and hydrodynamic simulations of clusters. We investigate the uncertainty in this estimate and consequential ability to compare competing cosmologies, which arises from incomplete descriptions of baryonic processes, discrepancies in cluster selection criteria, redshift distribution and dynamical state. The relation between triaxial cluster masses at various overdensities provides a promising alternative to the strong lensing test.

A phylogenetic analysis of normal modes evolution in enzymes and its relationship to enzyme function.

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Lai, Jason; Jin, Jing; Kubelka, Jan; Liberles, David A

2012-09-21

Since the dynamic nature of protein structures is essential for enzymatic function, it is expected that functional evolution can be inferred from the changes in protein dynamics. However, dynamics can also diverge neutrally with sequence substitution between enzymes without changes of function. In this study, a phylogenetic approach is implemented to explore the relationship between enzyme dynamics and function through evolutionary history. Protein dynamics are described by normal mode analysis based on a simplified harmonic potential force field applied to the reduced C(α) representation of the protein structure while enzymatic function is described by Enzyme Commission numbers. Similarity of the binding pocket dynamics at each branch of the protein family's phylogeny was analyzed in two ways: (1) explicitly by quantifying the normal mode overlap calculated for the reconstructed ancestral proteins at each end and (2) implicitly using a diffusion model to obtain the reconstructed lineage-specific changes in the normal modes. Both explicit and implicit ancestral reconstruction identified generally faster rates of change in dynamics compared with the expected change from neutral evolution at the branches of potential functional divergences for the α-amylase, D-isomer-specific 2-hydroxyacid dehydrogenase, and copper-containing amine oxidase protein families. Normal mode analysis added additional information over just comparing the RMSD of static structures. However, the branch-specific changes were not statistically significant compared to background function-independent neutral rates of change of dynamic properties and blind application of the analysis would not enable prediction of changes in enzyme specificity. Copyright © 2012 Elsevier Ltd. All rights reserved.

Intergalactic stellar populations in intermediate redshift clusters

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Melnick, J.; Giraud, E.; Toledo, I.; Selman, F.; Quintana, H.

2012-11-01

A substantial fraction of the total stellar mass in rich clusters of galaxies resides in a diffuse intergalactic component usually referred to as the intracluster light (ICL). Theoretical models indicate that these intergalactic stars originate mostly from the tidal interaction of the cluster galaxies during the assembly history of the cluster, and that a significant fraction of these stars could have formed in situ from the late infall of cold metal-poor gas clouds on to the cluster. However, these models also overpredict the fraction of stellar mass in the ICL by a substantial margin, something that is still not well understood. The models also make predictions about the age distribution of the ICL stars, which may provide additional observational constraints. Here we present population synthesis models for the ICL of an intermediate redshift (z = 0.29) X-ray cluster that we have extensively studied in previous papers. The advantage of observing intermediate redshift clusters rather than nearby ones is that the former fit the field of view of multi-object spectrographs in 8-m telescopes and therefore permit us to encompass most of the ICL with only a few well-placed slits. In this paper we show that by stacking spectra at different locations within the ICL it is possible to reach sufficiently high signal-to-noise ratios to fit population synthesis models and derive meaningful results. The models provide ages and metallicities for the dominant populations at several different locations within the ICL and the brightest cluster galaxies (BCG) halo, as well as measures of the kinematics of the stars as a function of distance from the BCG. We thus find that the ICL in our cluster is dominated by old metal-rich stars, at odds with what has been found in nearby clusters where the stars that dominate the ICL are old and metal poor. While we see weak evidence of a young, metal-poor component, if real, these young stars would amount to less than 1 per cent of the total ICL

Clustering-based approaches to SAGE data mining

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Wang Haiying

2008-07-01

Full Text Available Abstract Serial analysis of gene expression (SAGE is one of the most powerful tools for global gene expression profiling. It has led to several biological discoveries and biomedical applications, such as the prediction of new gene functions and the identification of biomarkers in human cancer research. Clustering techniques have become fundamental approaches in these applications. This paper reviews relevant clustering techniques specifically designed for this type of data. It places an emphasis on current limitations and opportunities in this area for supporting biologically-meaningful data mining and visualisation.

Three-body cluster state in 11B

International Nuclear Information System (INIS)

Kawabata, T.; Akimune, H.; Fujita, H.; Fujita, Y.; Fujiwara, M.; Hara, K.; Hatanaka, K.; Itoh, M.; Kanada-En'yo, Y.; Kishi, S.; Nakanishi, K.; Sakaguchi, H.; Shimbara, Y.; Tamii, A.; Terashima, S.; Uchida, M.; Wakasa, T.; Yasuda, Y.; Yoshida, H.P.; Yosoi, M.

2007-01-01

The cluster structures of the excited states in 11 B are studied by analyzing the isoscalar monopole and quadrupole strengths in the 11 B(d,d ' ) reaction at E d =200 MeV. The excitation strengths are compared with the predictions by the shell-model and antisymmetrized molecular-dynamics (AMD) calculations. It is found that the large monopole strength for the 3/2 3 - state at E x =8.56 MeV is well described by the AMD calculation and is an evidence for a developed three-body 2α+t cluster structure

Homology models guide discovery of diverse enzyme specificities among dipeptide epimerases in the enolase superfamily

Science.gov (United States)

Lukk, Tiit; Sakai, Ayano; Kalyanaraman, Chakrapani; Brown, Shoshana D.; Imker, Heidi J.; Song, Ling; Fedorov, Alexander A.; Fedorov, Elena V.; Toro, Rafael; Hillerich, Brandan; Seidel, Ronald; Patskovsky, Yury; Vetting, Matthew W.; Nair, Satish K.; Babbitt, Patricia C.; Almo, Steven C.; Gerlt, John A.; Jacobson, Matthew P.

2012-01-01

The rapid advance in genome sequencing presents substantial challenges for protein functional assignment, with half or more of new protein sequences inferred from these genomes having uncertain assignments. The assignment of enzyme function in functionally diverse superfamilies represents a particular challenge, which we address through a combination of computational predictions, enzymology, and structural biology. Here we describe the results of a focused investigation of a group of enzymes in the enolase superfamily that are involved in epimerizing dipeptides. The first members of this group to be functionally characterized were Ala-Glu epimerases in Eschericiha coli and Bacillus subtilis, based on the operon context and enzymological studies; these enzymes are presumed to be involved in peptidoglycan recycling. We have subsequently studied more than 65 related enzymes by computational methods, including homology modeling and metabolite docking, which suggested that many would have divergent specificities;, i.e., they are likely to have different (unknown) biological roles. In addition to the Ala-Phe epimerase specificity reported previously, we describe the prediction and experimental verification of: (i) a new group of presumed Ala-Glu epimerases; (ii) several enzymes with specificity for hydrophobic dipeptides, including one from Cytophaga hutchinsonii that epimerizes D-Ala-D-Ala; and (iii) a small group of enzymes that epimerize cationic dipeptides. Crystal structures for certain of these enzymes further elucidate the structural basis of the specificities. The results highlight the potential of computational methods to guide experimental characterization of enzymes in an automated, large-scale fashion. PMID:22392983

Extended Traffic Crash Modelling through Precision and Response Time Using Fuzzy Clustering Algorithms Compared with Multi-layer Perceptron

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Iman Aghayan

2012-11-01

Full Text Available This paper compares two fuzzy clustering algorithms – fuzzy subtractive clustering and fuzzy C-means clustering – to a multi-layer perceptron neural network for their ability to predict the severity of crash injuries and to estimate the response time on the traffic crash data. Four clustering algorithms – hierarchical, K-means, subtractive clustering, and fuzzy C-means clustering – were used to obtain the optimum number of clusters based on the mean silhouette coefficient and R-value before applying the fuzzy clustering algorithms. The best-fit algorithms were selected according to two criteria: precision (root mean square, R-value, mean absolute errors, and sum of square error and response time (t. The highest R-value was obtained for the multi-layer perceptron (0.89, demonstrating that the multi-layer perceptron had a high precision in traffic crash prediction among the prediction models, and that it was stable even in the presence of outliers and overlapping data. Meanwhile, in comparison with other prediction models, fuzzy subtractive clustering provided the lowest value for response time (0.284 second, 9.28 times faster than the time of multi-layer perceptron, meaning that it could lead to developing an on-line system for processing data from detectors and/or a real-time traffic database. The model can be extended through improvements based on additional data through induction procedure.

Comparison of illness representations dimensions and illness representation clusters in predicting outcomes in the first year following diagnosis of type 2 diabetes

DEFF Research Database (Denmark)

Skinner, T. C.; Carey, M. E.; Cradock, S.

2011-01-01

trial of a self-management education intervention for people with type 2 diabetes, completed measures of illness beliefs (coherence, timeline, impact, seriousness, personal responsibility) and depression along with HbA1c and body mass index (BMI), at baseline 4, 8 and 12 months. The results......This article explores the utility of cluster analysis of illness representations, in comparison to analysing each dimension of the individual's illness representation, to predict an individual's response to diagnosis of type 2 diabetes. Participants in a large multi-centre randomised controlled...

Brightest Cluster Galaxies in REXCESS Clusters

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Haarsma, Deborah B.; Leisman, L.; Bruch, S.; Donahue, M.

2009-01-01

Most galaxy clusters contain a Brightest Cluster Galaxy (BCG) which is larger than the other cluster ellipticals and has a more extended profile. In the hierarchical model, the BCG forms through many galaxy mergers in the crowded center of the cluster, and thus its properties give insight into the assembly of the cluster as a whole. In this project, we are working with the Representative XMM-Newton Cluster Structure Survey (REXCESS) team (Boehringer et al 2007) to study BCGs in 33 X-ray luminous galaxy clusters, 0.055 < z < 0.183. We are imaging the BCGs in R band at the Southern Observatory for Astrophysical Research (SOAR) in Chile. In this poster, we discuss our methods and give preliminary measurements of the BCG magnitudes, morphology, and stellar mass. We compare these BCG properties with the properties of their host clusters, particularly of the X-ray emitting gas.

Classification and prediction of the critical heat flux using fuzzy theory and artificial neural networks

International Nuclear Information System (INIS)

Moon, Sang Ki; Chang, Soon Heung

1994-01-01

A new method to predict the critical heat flux (CHF) is proposed, based on the fuzzy clustering and artificial neural network. The fuzzy clustering classifies the experimental CHF data into a few data clusters (data groups) according to the data characteristics. After classification of the experimental data, the characteristics of the resulting clusters are discussed with emphasis on the distribution of the experimental conditions and physical mechanism. The CHF data in each group are trained in an artificial neural network to predict the CHF. The artificial neural network adjusts the weight so as to minimize the prediction error within the corresponding cluster. Application of the proposed method to the KAIST CHF data bank shows good prediction capability of the CHF, better than other existing methods. ((orig.))

Performance Evaluation of Hadoop-based Large-scale Network Traffic Analysis Cluster