Missae Caput

1964 ◽  
Keyword(s):  
Large Scale ◽  
Structural Similarity ◽  
Formal Organization

The final melisma on the word “caput” in the antiphon “Venit ad Petrum” for the ceremony of the washing of the feet on Maundy Thursday in the Sarum rite is the source of the cantus firmus for masses by Dufay, Ockeghem, and Obrecht. Guillaume Dufay's Missa Caput, probably composed between 1440 and 1450, is unified by motto beginnings, a structural similarity between the movements, and cyclic return of the cantus firmus. Johannes Ockeghem's mass, composed between 1460 and 1465, is closely modeled on Dufay. The mass by Jacob Obrecht was probably written between 1483 and 1485, when he was magister puerorum at Cambrai. Instead of the large-scale formal organization found in the Dufay mass, Obrecht's mass establishes a progression from movement to movement governed only by the successive shifts of the cantus firmus.

The Subordinate Theme in the First Movement of Schubert's "Unfinished" Symphony

2019 ◽  
Vol 6 (2) ◽  
pp. 223-230
Author(s):  
Steven Vande Moortele
Keyword(s):  
Large Scale ◽  
Music History ◽  
Formal Organization ◽  
Sonata Theory

This analytical vignette explores the internal formal organization of the subordinate theme in the first movement of Schubert's "Unfinished" Symphony, D. 759 (1822). Drawing both on Hepokoski and Darcy's sonata theory and on Caplin's theory of formal functions, it shows how the entire theme can be understood as a single large-scale sentence. It is further argued that the theme's specific formal organization, as well as the extent to which it does or does not open up to theories originally designed for the analysis of music from the classical era, is characteristic of works from this period in music history.

scholarly journals Multi-dimensional computational pipeline for large-scale deep screening of compound effect assessment: an in silico case study on ageing-related compounds

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Vipul Gupta ◽  
Alina Crudu ◽  
Yukiko Matsuoka ◽  
Samik Ghosh ◽  
Roger Rozot ◽  
...  
Keyword(s):  
Large Scale ◽  
Network Effects ◽  
Chemical Compounds ◽  
Structural Similarity ◽  
Computational Pipeline ◽  
In Vivo Experiments ◽  
Cell Vitality ◽  
Alternative Means ◽  
Related Compounds

AbstractDesigning alternative approaches to efficiently screen chemicals on the efficacy landscape is a challenging yet indispensable task in the current compound profiling methods. Particularly, increasing regulatory restrictions underscore the need to develop advanced computational pipelines for efficacy assessment of chemical compounds as alternative means to reduce and/or replace in vivo experiments. Here, we present an innovative computational pipeline for large-scale assessment of chemical compounds by analysing and clustering chemical compounds on the basis of multiple dimensions—structural similarity, binding profiles and their network effects across pathways and molecular interaction maps—to generate testable hypotheses on the pharmacological landscapes as well as identify potential mechanisms of efficacy on phenomenological processes. Further, we elucidate the application of the pipeline on a screen of anti-ageing-related compounds to cluster the candidates based on their structure, docking profile and network effects on fundamental metabolic/molecular pathways associated with the cell vitality, highlighting emergent insights on compounds activities based on the multi-dimensional deep screen pipeline.

scholarly journals Detecting Gene Ontology misannotations using taxon-specific rate ratio comparisons

2020 ◽  
Vol 36 (16) ◽  
pp. 4383-4388 ◽  
Author(s):  
Xiaoqiong Wei ◽  
Chengxin Zhang ◽  
Peter L Freddolino ◽  
Yang Zhang
Keyword(s):  
Gene Ontology ◽  
Protein Function ◽  
Large Scale ◽  
Structural Similarity ◽  
Supplementary Information ◽  
Specific Rate ◽  
New Developments ◽  
Detection Approach ◽  
Taxonomic Groups ◽  
Annotation Errors

Abstract Motivation Many protein function databases are built on automated or semi-automated curations and can contain various annotation errors. The correction of such misannotations is critical to improving the accuracy and reliability of the databases. Results We proposed a new approach to detect potentially incorrect Gene Ontology (GO) annotations by comparing the ratio of annotation rates (RAR) for the same GO term across different taxonomic groups, where those with a relatively low RAR usually correspond to incorrect annotations. As an illustration, we applied the approach to 20 commonly studied species in two recent UniProt-GOA releases and identified 250 potential misannotations in the 2018-11-6 release, where only 25% of them were corrected in the 2019-6-3 release. Importantly, 56% of the misannotations are ‘Inferred from Biological aspect of Ancestor (IBA)’ which is in contradiction with previous observations that attributed misannotations mainly to ‘Inferred from Sequence or structural Similarity (ISS)’, probably reflecting an error source shift due to the new developments of function annotation databases. The results demonstrated a simple but efficient misannotation detection approach that is useful for large-scale comparative protein function studies. Availability and implementation https://zhanglab.ccmb.med.umich.edu/RAR. Supplementary information Supplementary data are available at Bioinformatics online.

Exploiting structural similarity in network reliability analysis using graph learning

2021 ◽  
pp. 1748006X2110093
Author(s):  
Ping Zhang ◽  
Min Xie ◽  
Xiaoyan Zhu
Keyword(s):  
Large Scale ◽  
System Analysis ◽  
Network Reliability ◽  
Computational Cost ◽  
Structural Similarity ◽  
Learning Approach ◽  
Network System ◽  
Network Systems ◽  
Graph Learning ◽  
Significance Levels

Considering the large-scale networks that can represent construction of components in a unit, a transportation system, a supply chain, a social network system, and so on, some nodes have similar topological structures and thus play similar roles in the network and system analysis, usually complicating the analysis and resulting in considerable duplicated computations. In this paper, we present a graph learning approach to define and identify structural similarity between the nodes in a network or the components in a network system. Based on the structural similarity, we investigate component clustering at various significance levels that represent different extents of similarity. We further specify a spectral-graph-wavelet based graph learning method to measure the structural similarity and present its application in easing computation load of evaluating system survival signature and system reliability. The numerical examples and the application show the insights of structural similarity and effectiveness of the graph learning approach. Finally, we discuss potential applications of the graph-learning based structural similarity and conclude that the proposed structural similarity, component clustering, and graph learning approach are effective in simplifying the complexity of the network systems and reducing the computational cost for complex network analysis.

scholarly journals Matching Large Scale Ontologies Based on Filter and Verification

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yingxin Li ◽  
Zhou Jianhui ◽  
Jihong Liu ◽  
Yongzhu Hou
Keyword(s):  
Semantic Similarity ◽  
Large Scale ◽  
Heterogeneous Systems ◽  
Structural Similarity ◽  
Recall Rate ◽  
Experimental Result ◽  
Ontology Matching ◽  
Accuracy Rate ◽  
String Similarity ◽  
Innovative Method

Ontology matching is an effective method to realize intercommunication and interoperability between heterogeneous systems. The essence of ontology matching is to discover the similar entity pairs between source ontology and target ontology, which is a process calculating the similarity between entities in ontologies. The similarity can be calculated utilizing various features between entity pairs, such as string similarity, structural similarity, and semantic similarity. The larger the ontology scale, the lower the efficiency and accuracy rate of ontology matching. As the ontology scale increases, the amount of entities in ontologies will be larger and the ontologies will become more heterogeneous. This paper proposes an innovative method of matching large scale ontologies based on filter and verification, which firstly reduces the heterogeneous of large scale ontologies in the filter phase and then matches the reduced ontologies in the verification phase. Large scale ontologies will be partitioned into several subontologies to get a proper scale before matching. The benchmark of Anatomy and Food in OAEI is adopted to evaluate the proposed method, and the experimental result illuminates that the recall rate is improved in the situation of retaining efficiency and accuracy rate using the proposed method.

Starch-processing enzymes – emphasis on thermostable 4-α-glucanotransferases

Biologia ◽  
2015 ◽  
Vol 70 (6) ◽  
Author(s):  
Nasir Ahmad ◽  
Sumaira Mehboob ◽  
Naeem Rashid
Keyword(s):  
Large Scale ◽  
Structural Similarity ◽  
Cane Sugar ◽  
Processing Industry ◽  
Enzymatic Production ◽  
Processing Enzymes ◽  
High Fructose ◽  
The World ◽  
Day By Day

AbstractStarch is a major storage product of several economically important crops and the most common carbohydrate in human diets. A variety of enzymes are capable of starch hydrolysis and a large-scale starch processing industry has emerged in the last century. Enzymatic production of dextrose/glucose, maltose and high fructose syrups is increasing day by day as we have seen a shift from the use of traditional cane sugar to these sweeteners all over the world. The best known starch-processing enzymes are α-amylase, β-amylase and glucoamylase. Among starch-processing enzymes, a group whose functions are comparatively less well understood, are 4-α-glucanotransferases. In this review, we report on the classification of starch-processing enzymes based on the amino acid sequence and structural similarity as well as substrate specificity and reaction mechanism with emphasis on 4-α-glucanotransferases. Furthermore, applications of thermostable starch-processing enzymes are discussed.

scholarly journals Bioinstructive Layer-by-Layer-Coated Customizable 3D Printed Perfusable Microchannels Embedded in Photocrosslinkable Hydrogels for Vascular Tissue Engineering

Biomolecules ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 863
Author(s):  
Cristiana F. V. Sousa ◽  
Catarina A. Saraiva ◽  
Tiago R. Correia ◽  
Tamagno Pesqueira ◽  
Sónia G. Patrício ◽  
...  
Keyword(s):  
Tissue Engineering ◽  
Large Scale ◽  
Vascular Tissue ◽  
Structural Similarity ◽  
Great Promise ◽  
Layer By Layer ◽  
Vascular Networks ◽  
Tissue Constructs ◽  
Assembly Technology ◽  
Arginine Glycine Aspartic Acid

The development of complex and large 3D vascularized tissue constructs remains the major goal of tissue engineering and regenerative medicine (TERM). To date, several strategies have been proposed to build functional and perfusable vascular networks in 3D tissue-engineered constructs to ensure the long-term cell survival and the functionality of the assembled tissues after implantation. However, none of them have been entirely successful in attaining a fully functional vascular network. Herein, we report an alternative approach to bioengineer 3D vascularized constructs by embedding bioinstructive 3D multilayered microchannels, developed by combining 3D printing with the layer-by-layer (LbL) assembly technology, in photopolymerizable hydrogels. Alginate (ALG) was chosen as the ink to produce customizable 3D sacrificial microstructures owing to its biocompatibility and structural similarity to the extracellular matrices of native tissues. ALG structures were further LbL coated with bioinstructive chitosan and arginine–glycine–aspartic acid-coupled ALG multilayers, embedded in shear-thinning photocrosslinkable xanthan gum hydrogels and exposed to a calcium-chelating solution to form perfusable multilayered microchannels, mimicking the biological barriers, such as the basement membrane, in which the endothelial cells were seeded, denoting an enhanced cell adhesion. The 3D constructs hold great promise for engineering a wide array of large-scale 3D vascularized tissue constructs for modular TERM strategies.

scholarly journals Veiling glare removal: synthetic dataset generation, metrics and neural network architecture

2021 ◽  
Vol 45 (4) ◽  
pp. 615-626
Author(s):  
A.V. Shoshin ◽  
E.A. Shvets
Keyword(s):  
Neural Network ◽  
Image Enhancement ◽  
Light Source ◽  
Network Architecture ◽  
Large Scale ◽  
Bright Light ◽  
Structural Similarity ◽  
Network Architectures ◽  
Training Procedure ◽  
Underwater Image

In photography, the presence of a bright light source often reduces the quality and readability of the resulting image. Light rays reflect and bounce off camera elements, sensor or diaphragm causing unwanted artifacts. These artifacts are generally known as "lens flare" and may have different influences on the photo: reduce contrast of the image (veiling glare), add circular or circular-like effects (ghosting flare), appear as bright rays spreading from light source (starburst pattern), or cause aberrations. All these effects are generally undesirable, as they reduce legibility and aesthetics of the image. In this paper we address the problem of removing or reducing the effect of veiling glare on the image. There are no available large-scale datasets for this problem and no established metrics, so we start by (i) proposing a simple and fast algorithm of generating synthetic veiling glare images necessary for training and (ii) studying metrics used in related image enhancement tasks (dehazing and underwater image enhancement). We select three such no-reference metrics (UCIQE, UIQM and CCF) and show that their improvement indicates better veil removal. Finally, we experiment on neural network architectures and propose a two-branched architecture and a training procedure utilizing structural similarity measure.

scholarly journals A comparative study of aldolase from human muscle and liver

1973 ◽  
Vol 133 (3) ◽  
pp. 429-439 ◽  
Author(s):  
Peter A. M. Eagles ◽  
Muzaffar Iqbal
Keyword(s):  
Human Liver ◽  
Large Scale ◽  
Human Skeletal Muscle ◽  
Tryptic Peptide ◽  
Peptide Mapping ◽  
Structural Similarity ◽  
Human Muscle ◽  
Rabbit Muscle ◽  
X Ray ◽  
Crystalline Forms

Aldolase was purified from human skeletal muscle and human liver by techniques capable of processing large quantities (10–20kg) of tissue. The methods used also proved convenient for isolating aldolase on a large scale from other mammalian and avian sources. Aldolase from both human liver and muscle was crystallized; each gave two crystalline forms, depending on the conditions of crystallization. X-ray studies on the muscle aldolase crystals suggest a close structural similarity between human and rabbit muscle aldolase. Aldolases from human muscle and liver have similar pH optima and pH stability but their stability to heat treatment differs. The effect of heat on the enzymes may therefore provide an easy means of distinguishing them. The kinetic constants Km and kcat. for these aldolases are similar to other mammalian aldolases. Amino acid analyses and tryptic peptide ‘mapping’ show that the primary structures of the two aldolases differ greatly.

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