Готові списки джерел за темами / Structure de motifs

Добірка наукової літератури з теми "Structure de motifs"

Автор: Grafiati
Опубліковано: 10 червня 2023

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  2. Дисертації
  3. Книги
  4. Частини книг
  5. Тези доповідей конференцій
  6. Звіти організацій

Статті в журналах з теми "Structure de motifs":

1

Han, Jiahui, Shijie Jiang, Zhengfu Zhou, Min Lin, and Jin Wang. "Artificial Proteins Designed from G3LEA Contribute to Enhancement of Oxidation Tolerance in E. coli in a Chaperone-like Manner." Antioxidants 12, no. 6 (May 24, 2023): 1147. http://dx.doi.org/10.3390/antiox12061147.

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G3LEA is a family of proteins that exhibit chaperone-like activity when under distinct stress. In previous research, DosH was identified as a G3LEA protein from model extremophile—Deinococcus radiodurans R1 with a crucial core HD domain consisting of eight 11-mer motifs. However, the roles of motifs participating in the process of resistance to stress and their underlying mechanisms remain unclear. Here, eight different proteins with tandem repeats of the same motif were synthesized, named Motif1–8, respectively, whose function and structure were discussed. In this way, the role of each motif in the HD domain can be comprehensively analyzed, which can help in finding possibly crucial amino acid sites. Circular dichroism results showed that all proteins were intrinsically ordered in phosphate buffer, and changed into more α-helical ordered structures with the addition of trifluoroethanol and glycerol. Transformants expressing artificial proteins had significantly higher stress resistance to oxidation, desiccation, salinity and freezing compared with the control group; E. coli with Motif1 and Motif8 had more outstanding performance in particular. Moreover, enzymes and membrane protein protection viability suggested that Motif1 and Motif8 had more positive influences on various molecules, demonstrating a protective role in a chaperone-like manner. Based on these results, the artificial proteins synthesized according to the rule of 11-mer motifs have a similar function to wildtype protein. Regarding the sequence in all motifs, there are more amino acids to produce H bonds and α-helices, and more amino acids to promote interaction between proteins in Motif1 and Motif8; in addition, considering linkers, there are possibly more amino acids forming α-helix and binding substrates in these two proteins, which potentially provides some ideas for us to design potential ideal stress-response elements for synthetic biology. Therefore, the amino acid composition of the 11-mer motif and linker is likely responsible for its biological function.
2

Badr, Ghada, Isra Al-Turaiki, Marcel Turcotte, and Hassan Mathkour. "IncMD: Incremental trie-based structural motif discovery algorithm." Journal of Bioinformatics and Computational Biology 12, no. 05 (October 2014): 1450027. http://dx.doi.org/10.1142/s0219720014500279.

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The discovery of common RNA secondary structure motifs is an important problem in bioinformatics. The presence of such motifs is usually associated with key biological functions. However, the identification of structural motifs is far from easy. Unlike motifs in sequences, which have conserved bases, structural motifs have common structure arrangements even if the underlying sequences are different. Over the past few years, hundreds of algorithms have been published for the discovery of sequential motifs, while less work has been done for the structural motifs case. Current structural motif discovery algorithms are limited in terms of accuracy and scalability. In this paper, we present an incremental and scalable algorithm for discovering RNA secondary structure motifs, namely IncMD. We consider the structural motif discovery as a frequent pattern mining problem and tackle it using a modified a priori algorithm. IncMD uses data structures, trie-based linked lists of prefixes (LLP), to accelerate the search and retrieval of patterns, support counting, and candidate generation. We modify the candidate generation step in order to adapt it to the RNA secondary structure representation. IncMD constructs the frequent patterns incrementally from RNA secondary structure basic elements, using nesting and joining operations. The notion of a motif group is introduced in order to simulate an alignment of motifs that only differ in the number of unpaired bases. In addition, we use a cluster beam approach to select motifs that will survive to the next iterations of the search. Results indicate that IncMD can perform better than some of the available structural motif discovery algorithms in terms of sensitivity (Sn), positive predictive value (PPV), and specificity (Sp). The empirical results also show that the algorithm is scalable and runs faster than all of the compared algorithms.
3

Leontis, Neocles B., and Eric Westhof. "The Annotation of RNA Motifs." Comparative and Functional Genomics 3, no. 6 (2002): 518–24. http://dx.doi.org/10.1002/cfg.213.

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The recent deluge of new RNA structures, including complete atomic-resolution views of both subunits of the ribosome, has on the one hand literally overwhelmed our individual abilities to comprehend the diversity of RNA structure, and on the other hand presented us with new opportunities for comprehensive use of RNA sequences for comparative genetic, evolutionary and phylogenetic studies. Two concepts are key to understanding RNA structure: hierarchical organization of global structure and isostericity of local interactions. Global structure changes extremely slowly, as it relies on conserved long-range tertiary interactions. Tertiary RNA–RNA and quaternary RNA–protein interactions are mediated by RNA motifs, defined as recurrent and ordered arrays of non-Watson–Crick base-pairs. A single RNA motif comprises a family of sequences, all of which can fold into the same three-dimensional structure and can mediate the same interaction(s). The chemistry and geometry of base pairing constrain the evolution of motifs in such a way that random mutations that occur within motifs are accepted or rejected insofar as they can mediate a similar ordered array of interactions. The steps involved in the analysis and annotation of RNA motifs in 3D structures are: (a) decomposition of each motif into non-Watson–Crick base-pairs; (b) geometric classification of each basepair; (c) identification of isosteric substitutions for each basepair by comparison to isostericity matrices; (d) alignment of homologous sequences using the isostericity matrices to identify corresponding positions in the crystal structure; (e) acceptance or rejection of the null hypothesis that the motif is conserved.
4

Shamim, Amen, Maria Razzaq, and Kyeong Kyu Kim. "MD-TSPC4: Computational Method for Predicting the Thermal Stability of I-Motif." International Journal of Molecular Sciences 22, no. 1 (December 23, 2020): 61. http://dx.doi.org/10.3390/ijms22010061.

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I-Motif is a tetrameric cytosine-rich DNA structure with hemi-protonated cytosine: cytosine base pairs. Recent evidence showed that i-motif structures in human cells play regulatory roles in the genome. Therefore, characterization of novel i-motifs and investigation of their functional implication are urgently needed for comprehensive understanding of their roles in gene regulation. However, considering the complications of experimental investigation of i-motifs and the large number of putative i-motifs in the genome, development of an in silico tool for the characterization of i-motifs in the high throughput scale is necessary. We developed a novel computation method, MD-TSPC4, to predict the thermal stability of i-motifs based on molecular modeling and molecular dynamic simulation. By assuming that the flexibility of loops in i-motifs correlated with thermal stability within certain temperature ranges, we evaluated the correlation between the root mean square deviations (RMSDs) of model structures and the thermal stability as the experimentally obtained melting temperature (Tm). Based on this correlation, we propose an equation for Tm prediction from RMSD. We expect this method can be useful for estimating the overall structure and stability of putative i-motifs in the genome, which can be a starting point of further structural and functional studies of i-motifs.
5

Alam, Tanvir, Meshari Alazmi, Xin Gao, and Stefan T. Arold. "How to find a leucine in a haystack? Structure, ligand recognition and regulation of leucine–aspartic acid (LD) motifs." Biochemical Journal 460, no. 3 (May 29, 2014): 317–29. http://dx.doi.org/10.1042/bj20140298.

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LD motifs (leucine–aspartic acid motifs) are short helical protein–protein interaction motifs that have emerged as key players in connecting cell adhesion with cell motility and survival. LD motifs are required for embryogenesis, wound healing and the evolution of multicellularity. LD motifs also play roles in disease, such as in cancer metastasis or viral infection. First described in the paxillin family of scaffolding proteins, LD motifs and similar acidic LXXLL interaction motifs have been discovered in several other proteins, whereas 16 proteins have been reported to contain LDBDs (LD motif-binding domains). Collectively, structural and functional analyses have revealed a surprising multivalency in LD motif interactions and a wide diversity in LDBD architectures. In the present review, we summarize the molecular basis for function, regulation and selectivity of LD motif interactions that has emerged from more than a decade of research. This overview highlights the intricate multi-level regulation and the inherently noisy and heterogeneous nature of signalling through short protein–protein interaction motifs.
6

Tran, Ngoc Tam L., Luke DeLuccia, Aidan F. McDonald, and Chun-Hsi Huang. "Cross-Disciplinary Detection and Analysis of Network Motifs." Bioinformatics and Biology Insights 9 (January 2015): BBI.S23619. http://dx.doi.org/10.4137/bbi.s23619.

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The detection of network motifs has recently become an important part of network analysis across all disciplines. In this work, we detected and analyzed network motifs from undirected and directed networks of several different disciplines, including biological network, social network, ecological network, as well as other networks such as airlines, power grid, and co-purchase of political books networks. Our analysis revealed that undirected networks are similar at the basic three and four nodes, while the analysis of directed networks revealed the distinction between networks of different disciplines. The study showed that larger motifs contained the three-node motif as a subgraph. Topological analysis revealed that similar networks have similar small motifs, but as the motif size increases, differences arise. Pearson correlation coefficient showed strong positive relationship between some undirected networks but inverse relationship between some directed networks. The study suggests that the three-node motif is a building block of larger motifs. It also suggests that undirected networks share similar low-level structures. Moreover, similar networks share similar small motifs, but larger motifs define the unique structure of individuals. Pearson correlation coefficient suggests that protein structure networks, dolphin social network, and co-authorships in network science belong to a superfamily. In addition, yeast protein-protein interaction network, primary school contact network, Zachary's karate club network, and co-purchase of political books network can be classified into a superfamily.
7

Banjade, Huta R., Sandro Hauri, Shanshan Zhang, Francesco Ricci, Weiyi Gong, Geoffroy Hautier, Slobodan Vucetic, and Qimin Yan. "Structure motif–centric learning framework for inorganic crystalline systems." Science Advances 7, no. 17 (April 2021): eabf1754. http://dx.doi.org/10.1126/sciadv.abf1754.

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Incorporation of physical principles in a machine learning (ML) architecture is a fundamental step toward the continued development of artificial intelligence for inorganic materials. As inspired by the Pauling’s rule, we propose that structure motifs in inorganic crystals can serve as a central input to a machine learning framework. We demonstrated that the presence of structure motifs and their connections in a large set of crystalline compounds can be converted into unique vector representations using an unsupervised learning algorithm. To demonstrate the use of structure motif information, a motif-centric learning framework is created by combining motif information with the atom-based graph neural networks to form an atom-motif dual graph network (AMDNet), which is more accurate in predicting the electronic structures of metal oxides such as bandgaps. The work illustrates the route toward fundamental design of graph neural network learning architecture for complex materials by incorporating beyond-atom physical principles.
8

Al-Khafaji, Hussein, and Ghada Kassim. "A New Approach to Motif Templates Analysis via Compilation Technique." Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ), no. 2 (October 15, 2021): 180–208. http://dx.doi.org/10.55562/jrucs.v34i2.289.

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Motif template assertion and analysis is compulsory operation in most of bioinformatics systems such as motif search, sequential pattern miner, and bioinformatics databases analysis. The motif template can be in any length, therefore, the typing errors increased according to the length of motif. Also, when the structure motifs are submitted to bioinformatics systems they require specification of their components, i.e. the simple motifs, gaps, and the limits of the gaps. This research proposed a context free grammar, GFC, to describe the motif structure, and then this CFG is utilized to design an interpreter to detect, debug the errors, and analyze the motif template to its components. All the errors of 100 motifs of length arranged from 100 Base to 10 KBase are detected. These motifs are entered by 10 data entries. The experiments showed high correlation between number of errors and number of gaps, size of simple motifs, and motif template size. The target code of the interpreter is the components of a submitted motif template to be used in bioinformatics systems as next steps
9

Buckett, M. I., L. D. Marks, and D. E. Luzzi. "Correlation analysis of structure images." Proceedings, annual meeting, Electron Microscopy Society of America 45 (August 1987): 752–53. http://dx.doi.org/10.1017/s0424820100128079.

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A typical high resolution structure image contains a large amount of intensity information which is masked by both statistical and amorphous noise. One useful method of quantifying such images is to employ correlation techniques. When one seeks to quantify the atom column positions, correlation techniques can be used to decompose the image into separate motifs (of specific peak amplitudes and positions - each motif corresponding to a single column of atoms), thereby reducing the data to a more manageable form.This problem can be considered as the least squares minimization of the function: where I(r) is the image, and m(r) is the motif, and the unknowns are the positions, rj, of the motifs and their peak heights, αj. The standard approach is to look for peaks in the cross-correlation (equation 2) between the motif and image, to determine rj and αj
10

XING, ERIC P., WEI WU, MICHAEL I. JORDAN, and RICHARD M. KARP. "LOGOS: A MODULAR BAYESIAN MODEL FOR DE NOVO MOTIF DETECTION." Journal of Bioinformatics and Computational Biology 02, no. 01 (March 2004): 127–54. http://dx.doi.org/10.1142/s0219720004000508.

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The complexity of the global organization and internal structure of motifs in higher eukaryotic organisms raises significant challenges for motif detection techniques. To achieve successful de novo motif detection, it is necessary to model the complex dependencies within and among motifs and to incorporate biological prior knowledge. In this paper, we present LOGOS, an integrated LOcal and GlObal motif Sequence model for biopolymer sequences, which provides a principled framework for developing, modularizing, extending and computing expressive motif models for complex biopolymer sequence analysis. LOGOS consists of two interacting submodels: HMDM, a local alignment model capturing biological prior knowledge and positional dependency within the motif local structure; and HMM, a global motif distribution model modeling frequencies and dependencies of motif occurrences. Model parameters can be fit using training motifs within an empirical Bayesian framework. A variational EM algorithm is developed for de novo motif detection. LOGOS improves over existing models that ignore biological priors and dependencies in motif structures and motif occurrences, and demonstrates superior performance on both semi-realistic test data and cis-regulatory sequences from yeast and Drosophila genomes with regard to sensitivity, specificity, flexibility and extensibility.
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Статті в журналах Дисертації Книги

Дисертації з теми "Structure de motifs":

1

Tang, Thomas Cheuk Kai. "Discovering Protein Sequence-Structure Motifs and Two Applications to Structural Prediction." Thesis, University of Waterloo, 2004. http://hdl.handle.net/10012/1188.

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This thesis investigates the correlations between short protein peptide sequences and local tertiary structures. In particular, it introduces a novel algorithm for partitioning short protein segments into clusters of local sequence-structure motifs, and demonstrates that these motif clusters contain useful structural information via two applications to structural prediction. The first application utilizes motif clusters to predict local protein tertiary structures. A novel dynamic programming algorithm that performs comparably with some of the best existing algorithms is described. The second application exploits the capability of motif clusters in recognizing regular secondary structures to improve the performance of secondary structure prediction based on Support Vector Machines. Empirical results show significant improvement in overall prediction accuracy with no performance degradation in any specific aspect being measured. The encouraging results obtained illustrate the great potential of using local sequence-structure motifs to tackle protein structure predictions and possibly other important problems in computational biology.
2

Stombaugh, Jesse. "Predicting the Structure of RNA 3D Motifs." Bowling Green State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1225391806.

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3

Gaillard, Anne-Laure. "Identification de motifs au sein des structures biologiques arborescentes." Phd thesis, Université Sciences et Technologies - Bordeaux I, 2011. http://tel.archives-ouvertes.fr/tel-00652227.

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Avec l'explosion de la quantité de données biologiques disponible, développer de nouvelles méthodes de traitements efficaces est une problématique majeure en bioinformatique. De nombreuses structures biologiques sont modélisées par des structures arborescentes telles que les structures secondaires d'ARN et l'architecture des plantes. Ces structures contiennent des motifs répétés au sein même de leur structure mais également d'une structure à l'autre. Nous proposons d'exploiter cette propriété fondamentale afin d'améliorer le stockage et le traitement de tels objets. En nous inspirant du principe de filtres sur les séquences, nous définissons dans cette thèse une méthode de filtrage sur les arborescences ordonnées, permettant de rechercher efficacement dans une base de données, un ensemble d'arborescences ordonnées proches d'une arborescence requête. La méthode se base sur un découpage de l'arborescence en graines et sur une recherche de graines communes entre les structures. Nous définissons et résolvons le problème de chaînage maximum sur des arborescences. Nous proposons dans le cas des structures secondaires d'ARN une définition de graines (l−d) centrées. Dans un second temps, en nous basant sur des techniques d'instanciations utilisées, par exemple, en infographie et sur la connaissance des propriétés de redondances au sein des structures biologiques, nous présentons une méthode de compression permettant de réduire l'espace mémoire nécessaire pour le stockage d'arborescences non-ordonnées. Après une détermination des redondances, nous utilisons une structure de données plus compacte pour représenter notamment l'architecture de la plante, celle-ci pouvant contenir des informations topologiques mais également géométriques.
4

Dyer, Charlotte Emma. "Investigation of the structure of fibrillin eight-cysteine motifs." Thesis, University of Manchester, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.488146.

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The eight-cysteine motif is a protein repeat sequence that has been identified in extracellular matrix proteins of the fibrillin superfamily which includes isoforms fibrillin-l and fibrillin-2 and the closely related latent transforming growth factor-B binding proteins (LTBPs). These multidomain glycoproteins contain a number of cysteine-rich sequence repeats which resemble precursor epidermal growth factor domains (EGF-like domains) interspersed with eight-cysteine motifs and so-called "hybrid" domains, which exhibit some features of both these repeats. The signature of the eight-cysteine motif is a conserved pattern of eight"cysteine residues, three of which are contiguous. The structure of fibrillin eight-cysteine motifs has not been determined but mutations within fibrillin-l eight-cysteine motifs cause the heritable connective tissue disorder Marfan syndrome, confirming their critical role in fibrillin function and microfibril integrity. This study investigated the structure of fibrillin eight-cysteine motifs. Computer predictions of eight-cysteine motif secondary structure suggest limited amounts of defined secondary structure, most of which is beta-sheet (B-sheet), and a high proportion of loop regions. Several of the cysteine residues were predicted to be involved in disulphide bond formation. Eight-cysteine motif sequences did not fit to any known protein fold structures. Attempts to express a recombinant DNA molecule encoding a polyhistidine tagged human fibrillin-2 hybrid motif in mammalian and yeast cells, using plasmid expression vectors pCR™3 and pCS69 respectively, failed to produce detectable levels of recombinant protein, although a functional transcript and full-length polypeptide were produced using cell-free transcription and translation systems. However, a recombinant protein consisting of human fibrillin-l sixth eight-cysteine motif, flanked on either side by single EGF-like domains, was subsequently expressed using vector pSPEK in COS-I mammalian cells. This recombinant protein was N-glycosylated and exhibited anomalous migration during SDS-P AGE analysis, appearing larger than its predicted molecular weight of 18 kDa. Mass spectrometry analysis of the purified recombinant protein revealed products of masses 18 863 and 20 783 Da.. thought to correspond to non-glycosylated and glycosylated forms of. the protein respectively. Circular dichroism analysis of the recombinant protein revealed a high amount of ~-sheet structure (58% +/- 0.59), a small but significant alpha-helix (a-helix) content (1.0 +/- 0.56) and high amounts of residual structure (41 +/- 1.0), indicating significant amounts of ~-turn or loop regions. This study has provided insights into the secondary structure of eight-cysteine motifs. Computer predictions of secondary structure support data obtained by structural analysis of a recombinant protein containing a fibrillin eight-cysteine motif It is proposed that the eight-cysteine motif assumes a novel protein fold which contains a limited amount of defined secondary structure, most of which is ~-sheet with a small but significant amount of a-helix, the predominant feature of this motif being a high proportion of loop regions, which are held in place by disulphide bonding between pairs of the eight cysteine residues.
5

Sarver, Michael. "STRUCTURE-BASED MULTIPLE RNA SEQUENCE ALIGNMENT AND FINDING RNA MOTIFS." Bowling Green State University / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1151076710.

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6

Roll, James Elwood. "Inferring RNA 3D Motifs from Sequence." Bowling Green State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1557482505513958.

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7

Hébert, David. "Structure de poids : homologique & motivique." Paris 13, 2012. http://www.theses.fr/2012PA132006.

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L’objet de cette thèse est l’étude des structures de poids et leurs applications au monde motivique, en particulier aux motifs de Beilinson, version relative des motifs de Voevodsky, introduits et étudiés par Ayoub, Cisinski et Déglise. Le résultat principal construit une structure de poids sur cette catégorie coïncidant, lorsque la base est un corps, avec la structure précédemment établie par Bondarko sur la catégorie des motifs de Voevodsky. Il est accompagné d’un théorème de fonctorialité et de divers applications : poids des motifs, complexe de poids à la Gillet-Soulé, caractéristique d’Euler motivique. La première partie est entièrement dédiée au formalisme des structures de poids du point de vue de la théorie des catégories
The purpose of this thesis is the study of weight structures and their applications to motivic world, especially to the Beilinson motives, relative version of Voevodsky motives, introduced and studied by Ayoub, Cisinski and Déglise. The main result builds a weight structure on this category which coincide, when the base is a field, with the structure previously established by Bondarko on the category of Voevodsky motives. It isaccompanied by a theorem of functoriality and various applications : weight of motives, weight complex à la Gillet-Soulé, motivic Euler characteristic. The first part is dedicated to the formalism of weight structures in terms of category theory
8

Carpentier, Mathilde. "Méthodes de détection des similarités structurales : caractérisation des motifs conservés dans les familles de structures pour l' annotation des génomes." Paris 6, 2005. http://www.theses.fr/2005PA066571.

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9

Filling, Charlotta. "Short-chain dehydrogenases/reductases : structure, function and motifs of hydroxysteroid dehydrogenases /." Stockholm, 2002. http://diss.kib.ki.se/2002/91-7349-371-6/.

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10

Crowley, Louis J. "Structure-function studies of conserved sequence motifs of cytochrome b5 reductase." [Tampa, Fla] : University of South Florida, 2007. http://purl.fcla.edu/usf/dc/et/SFE0001913.

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Книги з теми "Structure de motifs":

1

Berg, Sandra Beth. The book of Esther: Motifs, themes and structure. Ann Arbor, Mich: UMI, 1986.

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2

Frye, Northrop. [The secular scripture: A study of the structure of romance. [Tokyo]: Hosei University Press, 1999.

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3

Littlewood, Trevor D. Helix-loop-helix transcription factors. 3rd ed. Oxford: Oxford University Press, 1998.

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4

Steeg, Evan W. Automated motif discovery in protein structure prediction. Toronto: University of Toronto, Dept. of Computer Science, 1997.

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5

Cowgill, Linda J. Secrets of screenplay structure: How to recognize and emulate the structural frameworks of great films. Los Angeles, CA: Lone Eagle Pub., 1999.

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6

Hauge, Martin Ravndal. Between sheol and temple: Motif structure and function in the I-Psalms. Sheffield, England: Sheffield Academic Press, 1995.

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7

Şimşek, Esma. Yukarıçukurova masallarında motif ve tip araştırması. Ankara: T.C. Kültür Bakanlığı, 2001.

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8

C, Vassilicos J., and Hunt Julian C. R, eds. Turbulence structure and vortex dynamics. Cambridge, U.K: Cambridge University Press, 2000.

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9

Kallas, Christina. Creative screenwriting: Understanding emotional structure. New York: Palgrave Macmillan, 2010.

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10

Vassilicos, J. C., and Julian C. R. Hunt. Turbulence structure and vortex dynamics. Cambridge: Cambridge University Press, 2011.

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Частини книг з теми "Structure de motifs":

1

Nilmeier, Jerome P., Elaine C. Meng, Benjamin J. Polacco, and Patricia C. Babbitt. "3D Motifs." In From Protein Structure to Function with Bioinformatics, 361–92. Dordrecht: Springer Netherlands, 2017. http://dx.doi.org/10.1007/978-94-024-1069-3_11.

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Rammensee, Hans-Georg, Jutta Bachmann, and Stefan Stevanović. "The Structure." In MHC Ligands and Peptide Motifs, 141–216. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-22162-4_3.

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Novak, Walter R. P. "Tertiary Structure Domains, Folds, and Motifs." In Molecular Life Sciences, 1–5. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6436-5_15-3.

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Novak, Walter R. P. "Tertiary Structure Domains, Folds and Motifs." In Molecular Life Sciences, 1174–78. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-1531-2_15.

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Yesselman, Joseph D., and Rhiju Das. "Modeling Small Noncanonical RNA Motifs with the Rosetta FARFAR Server." In RNA Structure Determination, 187–98. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-6433-8_12.

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Krzyzosiak, W. J., M. Napierala, and M. Drozdz. "RNA Structure Modules with Trinucleotide Repeat Motifs." In RNA Biochemistry and Biotechnology, 303–14. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-011-4485-8_22.

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Unger, Ron. "Short Structural Motifs: Definition, Identification, and Applications." In The Protein Folding Problem and Tertiary Structure Prediction, 339–51. Boston, MA: Birkhäuser Boston, 1994. http://dx.doi.org/10.1007/978-1-4684-6831-1_11.

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Tsigelny, Igor, Takehiko Matsumura, Thomas Südhof, and Palmer Taylor. "Metal Binding Motifs in Cholinesterases and Neuroligins." In Structure and Function of Cholinesterases and Related Proteins, 407–12. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4899-1540-5_112.

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Bernstein, J., and R. E. Davis. "Graph Set Analysis of Hydrogen Bond Motifs." In Implications of Molecular and Materials Structure for New Technologies, 275–90. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-011-4653-1_20.

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Pathak, Sudipta, Vamsi Krishna Kundeti, Martin R. Schiller, and Sanguthevar Rajasekaran. "A Structure Based Algorithm for Improving Motifs Prediction." In Pattern Recognition in Bioinformatics, 242–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39159-0_22.

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Тези доповідей конференцій з теми "Structure de motifs":

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Wang, Xueyi, Jun Huan, Jack S. Snoeyink, and Wei Wang. "Mining RNA Tertiary Motifs with Structure Graphs." In 19th International Conference on Scientific and Statistical Database Management (SSDBM 2007). IEEE, 2007. http://dx.doi.org/10.1109/ssdbm.2007.38.

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"Asymmetry of motifs conservation within composite elements." In Bioinformatics of Genome Regulation and Structure/ Systems Biology. institute of cytology and genetics siberian branch of the russian academy of science, Novosibirsk State University, 2020. http://dx.doi.org/10.18699/bgrs/sb-2020-032.

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Anwar, Mohammad, and Marcel Turcotte. "Evaluation of RNA Secondary Structure Motifs using Regression Analysis." In 2006 Canadian Conference on Electrical and Computer Engineering. IEEE, 2006. http://dx.doi.org/10.1109/ccece.2006.277314.

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Sudarwati, Sudarwati, Anik Cahyaning Rahayu, and Novi Andari. "Motifs of Narrative Structure of Sacred Tombs in Surabaya." In International Conference of Communication Science Research (ICCSR 2018). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/iccsr-18.2018.102.

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AUNG, ZEYAR, and JINYAN LI. "MINING SUPER-SECONDARY STRUCTURE MOTIFS FROM 3D PROTEIN STRUCTURES: A SEQUENCE ORDER INDEPENDENT APPROACH." In Proceedings of the 18th International Conference. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2007. http://dx.doi.org/10.1142/9781860949852_0002.

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TANG, THOMAS, JINBO XU, and MING LI. "DISCOVERING SEQUENCE-STRUCTURE MOTIFS FROM PROTEIN SEGMENTS AND TWO APPLICATIONS." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2004. http://dx.doi.org/10.1142/9789812702456_0035.

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Medina, Andres, Andrew Takayama, Ankita Mohapatra, and Stevan Pecic. "Predicting motifs and secondary structure of steroid aptamers using APTANI." In 2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, 2023. http://dx.doi.org/10.1109/ccwc57344.2023.10099310.

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"Interpreting non-coding genome variation with DNA sequence motifs." In Bioinformatics of Genome Regulation and Structure/Systems Biology (BGRS/SB-2022) :. Institute of Cytology and Genetics, the Siberian Branch of the Russian Academy of Sciences, 2022. http://dx.doi.org/10.18699/bgrs/sb-2022-038.

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"Interpreting non-coding genome variation with DNA sequence motifs." In Bioinformatics of Genome Regulation and Structure/Systems Biology (BGRS/SB-2022) :. Institute of Cytology and Genetics, the Siberian Branch of the Russian Academy of Sciences, 2022. http://dx.doi.org/10.18699/sbb-2022-038.

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Liu, Zhi-Ping. "Systematic identification of local structure binding motifs in protein-RNA recognition." In 2014 8th International Conference on Systems Biology (ISB). IEEE, 2014. http://dx.doi.org/10.1109/isb.2014.6990735.

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Звіти організацій з теми "Structure de motifs":

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Opella, S. J. Structural biology of the sequestration and transport of heavy metal toxins: NMR structure determination of proteins containing the -Cys-X-Y-Cys-metal binding motifs. 1997 annual progress report. Office of Scientific and Technical Information (OSTI), January 1997. http://dx.doi.org/10.2172/13583.

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Opella, S. J. Structural biology of the sequestration and transport of heavy metal toxins: NMR structure determination of roteins containing the -Cys-X-Y-Cys-metal binding motifs. 1998 annual progress report. Office of Scientific and Technical Information (OSTI), June 1998. http://dx.doi.org/10.2172/13584.

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Vultur, Mircea, Lucie Enel, Louis-Pierre Barette, and Simon Viviers. Les travailleurs des plateformes numériques de transport de personnes et de livraison de repas au Québec : profil et motivations. CIRANO, June 2022. http://dx.doi.org/10.54932/xpzk8254.

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Анотація:
Cette étude vise deux objectifs : a) établir un profil des travailleurs qui utilisent une plateforme numérique de transport de personne et/ou de livraison de repas au Québec et b) d’analyser les motifs sous-jacents à leur engagement dans le travail sur une plateforme numérique. La structure des analyses se décline comme suit : dans une première partie, sur la base d’une revue de la littérature, nous présentons les caractéristiques des travailleurs des plateformes et divers types de motivations pour s’engager dans l’emploi sur les plateformes. Nous y exposons également le protocole méthodologique et les données à la source de nos analyses. Dans une deuxième partie, en utilisant des données statistiques secondaires et inédites, nous dressons un portrait des travailleurs engagés sur les plateformes de transport de personnes et de livraison de repas au Québec en faisant ressortir notamment les spécificités de la population des jeunes travailleurs. Dans une troisième partie, en analysant des données d’entrevues, nous exposons une typologie des motivations invoquées par les jeunes québécois de 18 à 34 ans pour travailler sur les plateformes Uber et Uber Eats. En conclusion, nous ferons une synthèse des constats significatifs issus des analyses.
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Thompson, William B. Structure from Motion. Fort Belvoir, VA: Defense Technical Information Center, December 1985. http://dx.doi.org/10.21236/ada175059.

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Ritchie, Elizabeth A. Tropical Cyclone Structure and Motion. Fort Belvoir, VA: Defense Technical Information Center, September 2000. http://dx.doi.org/10.21236/ada610205.

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Ritchie, Elizabeth A., R. L. Elsberry, and P. A. Harr. Tropical Cyclone Structure and Motion. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada630661.

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Ritchie, Elizabeth A. Tropical Cyclone Structure and Motion. Fort Belvoir, VA: Defense Technical Information Center, August 2001. http://dx.doi.org/10.21236/ada625681.

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Mazzoni, Silvia, Nicholas Gregor, Linda Al Atik, Yousef Bozorgnia, David Welch, and Gregory Deierlein. Probabilistic Seismic Hazard Analysis and Selecting and Scaling of Ground-Motion Records (PEER-CEA Project). Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, November 2020. http://dx.doi.org/10.55461/zjdn7385.

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Анотація:
This report is one of a series of reports documenting the methods and findings of a multi-year, multi-disciplinary project coordinated by the Pacific Earthquake Engineering Research Center (PEER) and funded by the California Earthquake Authority (CEA). The overall project is titled “Quantifying the Performance of Retrofit of Cripple Walls and Sill Anchorage in Single-Family Wood-Frame Buildings,” henceforth referred to as the “PEER–CEA Project.” The overall objective of the PEER–CEA Project is to provide scientifically based information (e.g., testing, analysis, and resulting loss models) that measure and assess the effectiveness of seismic retrofit to reduce the risk of damage and associated losses (repair costs) of wood-frame houses with cripple wall and sill anchorage deficiencies as well as retrofitted conditions that address those deficiencies. Tasks that support and inform the loss-modeling effort are: (1) collecting and summarizing existing information and results of previous research on the performance of wood-frame houses; (2) identifying construction features to characterize alternative variants of wood-frame houses; (3) characterizing earthquake hazard and ground motions at representative sites in California; (4) developing cyclic loading protocols and conducting laboratory tests of cripple wall panels, wood-frame wall subassemblies, and sill anchorages to measure and document their response (strength and stiffness) under cyclic loading; and (5) the computer modeling, simulations, and the development of loss models as informed by a workshop with claims adjustors. This report is a product of Working Group 3 (WG3), Task 3.1: Selecting and Scaling Ground-motion records. The objective of Task 3.1 is to provide suites of ground motions to be used by other working groups (WGs), especially Working Group 5: Analytical Modeling (WG5) for Simulation Studies. The ground motions used in the numerical simulations are intended to represent seismic hazard at the building site. The seismic hazard is dependent on the location of the site relative to seismic sources, the characteristics of the seismic sources in the region and the local soil conditions at the site. To achieve a proper representation of hazard across the State of California, ten sites were selected, and a site-specific probabilistic seismic hazard analysis (PSHA) was performed at each of these sites for both a soft soil (Vs30 = 270 m/sec) and a stiff soil (Vs30=760 m/sec). The PSHA used the UCERF3 seismic source model, which represents the latest seismic source model adopted by the USGS [2013] and NGA-West2 ground-motion models. The PSHA was carried out for structural periods ranging from 0.01 to 10 sec. At each site and soil class, the results from the PSHA—hazard curves, hazard deaggregation, and uniform-hazard spectra (UHS)—were extracted for a series of ten return periods, prescribed by WG5 and WG6, ranging from 15.5–2500 years. For each case (site, soil class, and return period), the UHS was used as the target spectrum for selection and modification of a suite of ground motions. Additionally, another set of target spectra based on “Conditional Spectra” (CS), which are more realistic than UHS, was developed [Baker and Lee 2018]. The Conditional Spectra are defined by the median (Conditional Mean Spectrum) and a period-dependent variance. A suite of at least 40 record pairs (horizontal) were selected and modified for each return period and target-spectrum type. Thus, for each ground-motion suite, 40 or more record pairs were selected using the deaggregation of the hazard, resulting in more than 200 record pairs per target-spectrum type at each site. The suites contained more than 40 records in case some were rejected by the modelers due to secondary characteristics; however, none were rejected, and the complete set was used. For the case of UHS as the target spectrum, the selected motions were modified (scaled) such that the average of the median spectrum (RotD50) [Boore 2010] of the ground-motion pairs follow the target spectrum closely within the period range of interest to the analysts. In communications with WG5 researchers, for ground-motion (time histories, or time series) selection and modification, a period range between 0.01–2.0 sec was selected for this specific application for the project. The duration metrics and pulse characteristics of the records were also used in the final selection of ground motions. The damping ratio for the PSHA and ground-motion target spectra was set to 5%, which is standard practice in engineering applications. For the cases where the CS was used as the target spectrum, the ground-motion suites were selected and scaled using a modified version of the conditional spectrum ground-motion selection tool (CS-GMS tool) developed by Baker and Lee [2018]. This tool selects and scales a suite of ground motions to meet both the median and the user-defined variability. This variability is defined by the relationship developed by Baker and Jayaram [2008]. The computation of CS requires a structural period for the conditional model. In collaboration with WG5 researchers, a conditioning period of 0.25 sec was selected as a representative of the fundamental mode of vibration of the buildings of interest in this study. Working Group 5 carried out a sensitivity analysis of using other conditioning periods, and the results and discussion of selection of conditioning period are reported in Section 4 of the WG5 PEER report entitled Technical Background Report for Structural Analysis and Performance Assessment. The WG3.1 report presents a summary of the selected sites, the seismic-source characterization model, and the ground-motion characterization model used in the PSHA, followed by selection and modification of suites of ground motions. The Record Sequence Number (RSN) and the associated scale factors are tabulated in the Appendices of this report, and the actual time-series files can be downloaded from the PEER Ground-motion database Portal (https://ngawest2.berkeley.edu/)(link is external).
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Todd, James T. Visual Perception of Structure from Motion. Fort Belvoir, VA: Defense Technical Information Center, April 1992. http://dx.doi.org/10.21236/ada253235.

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Harr, Patrick A. Tropical Cyclone Formation/Structure/Motion Studies. Fort Belvoir, VA: Defense Technical Information Center, September 2007. http://dx.doi.org/10.21236/ada548344.

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