1,720,999 research outputs found
Protein Gaussian Image (PGI): A protein structural representation based on the spatial attitude of secondary structure
No full textA well-known shape representation usually applied for 3D object
recognition is the Extended Gaussian Image (EGI) which maps the histogram of the
orientations of the object surface on the unitary sphere. We propose to adopt an
analogous “abstract” data-structure named Protein Gaussian Image (PNM) for representing
the orientation of the protein secondary structures (e.g. helices or strands)
which combines the characteristics of the EGI and the ones of needle maps. The
“concrete” data structures is the same as for the EGI, with a hierarchy that starting
with a discretization corresponding to the 20 orientations of the icosahedron facets,
it is iteratively refined with a factor 4 at each new level (80, 320, 1280, . . . ) up
to the maximum precision required. However, in this case to each orientation does
not correspond the area of the patches having that orientation but the features of
the protein secondary structures having that direction. Among the features we may
include the versus (origin versus surface or vice versa), the length of the structure
(e.g. the number of amino acids), biochemical properties, and even the sequence of
the amino acids (stored as a list). We consider this representation very effective for
a preliminary screening when looking in a protein data base for retrieval of a given
structural block, or a domain, or even an entire protein. In fact, on this structure
it is possible to identify the presence of a given motif, or also sheets (note that parallel
or anti-parallel β-sheets are characterized by common or opposite directions of
ladders). Herewith some known proteins are described with common typical motifs
easily marked in the PGI
Feature selection based on composition of rough sets induced by feature granulation
No full textThe term “feature selection” refers to the problem of selecting the most predictive features for a given outcome. Reducing the number of features is important to lower the computation cost of algorithms and also to achieve better generalization capabilities. The rough set theory has rapidly established itself as an effective tool for finding the smallest sets of features without any loss of information. This paper introduces a new approach based on the idea of exploiting rough set theory to compose partitions of the data induced by feature granulation. The partitions are iteratively aggregated in a single representative partition and, at each iteration, the obtained partition is used to evaluate the quality of the subset of features selected, thus reducing the cost of each evaluation. The consequence of using rough set theory is an algorithm that presents good reduction capability with less computational complexity w.r.t. Quickreduct algorithm. The proposed approach, called Roughinement, has been compared with approaches recently appeared in literature yielding comparable to better results
Neural Background Subtraction for Pan-Tilt-Zoom Cameras
No full textWe propose an extension of a neural-based background subtraction approach to moving object detection to the case of image sequences taken from pan-tilt-zoom (PTZ) cameras. The background model automatically adapts in a self-organizing way to changes in the scene background. Background variations arising in a usual stationary camera setting, such as those due to gradual illumination changes, to waving trees, or to shadows cast by moving objects, are accurately handled by the neural self-organizing background model originally proposed for this type of setting. Handling of variations due to the PTZ camera movement is ensured by a novel registration mechanism that allows the neural background model to automatically compensate the eventual ego-motion, estimated at each time instant. Experimental results on several real image sequences and comparisons with seven state-of-the-art methods demonstrate the accuracy of the proposed approach
Integrating rough set principles in the graded possibilistic clustering
No full textApplied to fuzzy clustering, the graded possibilistic model allows the soft transition from probabilistic to possibilistic memberships, constraining the memberships in a region that is narrower the closer to probabilistic the memberships are. The integration of rough sets principles in the graded possibilistic clustering aims to improve the flexibility and the performance of the graded possibilistic model, providing a further option for uncertainty modeling. Through the novel concept of the Rough Feasible Region, the proposed approach differentiates the projection of memberships in the core and in the boundary of each cluster, exploiting the indiscernibility relation typical of rough sets and allowing a more robust and efficient estimation of centroids. Tests on real data confirm its viability
A blockchain-based infection tracing and notification system by non-fungible tokens
No full textSARS-CoV2 pandemic is heavily affecting our lives. Many actions have been undertaken to slow down its expansion and, among the others, contact tracing applications are the less invasive to monitor the spread of the virus. The idea behind contact tracing is to track contacts between people by the exchange of identifiers, not linked to individuals, exploiting the use of Bluetooth Low Energy (BLE) technology to estimate the duration and proximity of contacts. The data collected in this way is used for the sole purpose of notifying a potential contact with an infected person without revealing their identity and location. This paper presents a contact tracing protocol based on blockchain technology that exploits smart contracts for reporting contacts at risk of contagion. The novelty of the proposed solution is the use of Non Fungible Tokens (NFT) to guarantee user privacy through a decentralized approach, equipped with a reliable non-proprietary notification mechanism that allows public access to anonymous infections data
Decoy clustering through graded possibilistic c-medoids
No full textModern methods for ab initio prediction of protein structures
typically explore multiple simulated conformations, called decoys, to
find the best native-like conformations. To limit the search space,
clustering algorithms are routinely used to group similar decoys, based
on the hypothesis that the largest group of similar decoys will be the
closest to the native state. In this paper a novel clustering algorithm,
called Graded Possibilistic c-medoids, is proposed and applied to a
decoy selection problem. As it will be shown, the added flexibility of
the graded possibilistic framework allows an effective selection of the
best decoys with respect to similar methods based on medoids - that is
on the most central points belonging to each cluster. The proposed
algorithm has been compared with other c-medoids algorithms and also
with SPICKER on real data, the large majority of times outperforming
both
Comparison of GHT-Based Approaches to Structural Motif Retrieval
No full textThe structure of a protein gives important information about its function and can be used for understanding the evolutionary relationships among proteins, predicting protein functions, and predicting protein folding. A structural motif is a compact 3D protein block referring to a small specific combination of secondary structural elements which appears in a variety of molecules. In this paper we present a comparison between few approaches for motif retrieval based on the Generalized Hough Transform (GHT). Performance comparisons, in terms of precision and computation time, are presented considering the retrieval of motifs composed by three to five SSs for more than 15 million searches. The approaches object of this study can be easily applied to the retrieval of greater blocks, up to protein domains, or even entire proteins
Structural Analysis of Protein Secondary Structure by GHT
No full textStructural biology is a branch of life science concerned with the study of the structure of biological macromolecules like proteins. The structure of a protein gives much more insight in its functions than that of its amino acid sequence. Protein structure comparison is important for understanding the evolutionary relationships among proteins, predicting
protein functions, and predicting protein structures from the chemical composition. In this paper we propose a new approach for structural block retrieval based on the Generalized Hough Transform (GHT). A first technique uses as primitives the single Secondary Structure (SS), an alternative adopts co-occurrence of SSs couple, the third approach uses SSs triplets, and finally the primitive can be an entire block. In this paper we describe some experiments for the retrieval of elementary structural blocks consisting of four and five SSs
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