261 research outputs found
Characterization of DNA\u27s from several Neurospora species
Characterization of DNA\u27s from several Neurospora specie
Neuromorphic In-Memory Computing Framework using Memtransistor Cross-bar based Support Vector Machines
This paper presents a novel framework for designing support vector machines
(SVMs), which does not impose restriction on the SVM kernel to be
positive-definite and allows the user to define memory constraint in terms of
fixed template vectors. This makes the framework scalable and enables its
implementation for low-power, high-density and memory constrained embedded
application. An efficient hardware implementation of the same is also
discussed, which utilizes novel low power memtransistor based cross-bar
architecture, and is robust to device mismatch and randomness. We used
memtransistor measurement data, and showed that the designed SVMs can achieve
classification accuracy comparable to traditional SVMs on both synthetic and
real-world benchmark datasets. This framework would be beneficial for design of
SVM based wake-up systems for internet of things (IoTs) and edge devices where
memtransistors can be used to optimize system's energy-efficiency and perform
in-memory matrix-vector multiplication (MVM).Comment: 4 pages, 5 figures, MWSCAS 201
Influence of the structural modulations and the Chain-ladder interaction in the compounds
We studied the effects of the incommensurate structural modulations on the
ladder subsystem of the family of compounds
using ab-initio explicitly-correlated calculations. From these calculations we
derived model as a function of the fourth crystallographic coordinate
describing the incommensurate modulations. It was found that in the
highly calcium-doped system, the on-site orbital energies are strongly
modulated along the ladder legs. On the contrary the two sites of the ladder
rungs are iso-energetic and the holes are thus expected to be delocalized on
the rungs. Chain-ladder interactions were also evaluated and found to be very
negligible. The ladder superconductivity model for these systems is discussed
in the light of the present results.Comment: 8 octobre 200
Validation of a Novel Sensing Approach for Continuous Pavement Monitoring Using Full-Scale APT Testing
The objective of this paper is to present a novel approach for the continuous monitoring of pavement condition through the use of combined piezoelectric sensing and novel condition-based interpretation methods. The performance of the developed approach is validated for the detection of bottom-up fatigue cracking through full-scale accelerated pavement testing (APT). The innovative piezoelectric sensors are installed at the bottom of a thin 102 mm (4 in.) asphalt layer. The structure is then loaded until failure (up to 1 million loading cycles in this study). The condition-based approach, used in this work, does not rely on stain measurements and allows users to bypass the need for any structural or finite-element models. Instead, the data compression approach relies on variations in strain energy harvested by smart sensors to track changes in material and structural conditions. Falling weight deflectometer (FWD) measurements and visual inspections were used to validate the observations from the sensing system. The results in this paper present a first large-scale validation in pavement structures for a piezopowered sensing system combined with a new response-only based approach for data reduction and interpretation. The proposed data analysis method has demonstrated a very early detection capability compared to classical inspection methods, which unveils a huge potential for improved pavement monitoring
An effective all-atom potential for proteins
We describe and test an implicit solvent all-atom potential for simulations
of protein folding and aggregation. The potential is developed through studies
of structural and thermodynamic properties of 17 peptides with diverse
secondary structure. Results obtained using the final form of the potential are
presented for all these peptides. The same model, with unchanged parameters, is
furthermore applied to a heterodimeric coiled-coil system, a mixed alpha/beta
protein and a three-helix-bundle protein, with very good results. The
computational efficiency of the potential makes it possible to investigate the
free-energy landscape of these 49--67-residue systems with high statistical
accuracy, using only modest computational resources by today's standards
Structure and evolutionary origin of Ca2+-dependent herring type II antifreeze protein
10.1371/journal.pone.0000548PLoS ONE26
Deciphering the Preference and Predicting the Viability of Circular Permutations in Proteins
Circular permutation (CP) refers to situations in which the termini of a protein are relocated to other positions in the structure. CP occurs naturally and has been artificially created to study protein function, stability and folding. Recently CP is increasingly applied to engineer enzyme structure and function, and to create bifunctional fusion proteins unachievable by tandem fusion. CP is a complicated and expensive technique. An intrinsic difficulty in its application lies in the fact that not every position in a protein is amenable for creating a viable permutant. To examine the preferences of CP and develop CP viability prediction methods, we carried out comprehensive analyses of the sequence, structural, and dynamical properties of known CP sites using a variety of statistics and simulation methods, such as the bootstrap aggregating, permutation test and molecular dynamics simulations. CP particularly favors Gly, Pro, Asp and Asn. Positions preferred by CP lie within coils, loops, turns, and at residues that are exposed to solvent, weakly hydrogen-bonded, environmentally unpacked, or flexible. Disfavored positions include Cys, bulky hydrophobic residues, and residues located within helices or near the protein's core. These results fostered the development of an effective viable CP site prediction system, which combined four machine learning methods, e.g., artificial neural networks, the support vector machine, a random forest, and a hierarchical feature integration procedure developed in this work. As assessed by using the hydrofolate reductase dataset as the independent evaluation dataset, this prediction system achieved an AUC of 0.9. Large-scale predictions have been performed for nine thousand representative protein structures; several new potential applications of CP were thus identified. Many unreported preferences of CP are revealed in this study. The developed system is the best CP viability prediction method currently available. This work will facilitate the application of CP in research and biotechnology
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