The identification and quantification of highly stable 'common hairpin' in the dynamic process of co-transcriptional mRNA folding

In our studies, 88 human mRNA samples were collected from the Integrated Sequence-Structure database and then the dynamic process in co-transcriptional mRNA folding was simulated using the RNAstructure version 4.1 program. Through statistical analyses of the frequencies of occurrence of hairpins, a group of special folding structures-the 'common hairpins'-were identified. These 'common hairpins' have lower energies and occur in all the subsequent folding units that formed in the dynamic folding process. By applying the formulas (1)-(4) of the 'common hairpins' statistical model, 163 'common hairpins' were found, to make up about 7% of the total of 2286 hairpins. Classified studies further show that the 'common hairpins' that were studied may oscillate in the dynamic folding process. However, the hairpin loops of the 'common hairpins' and stems proximal to those 'common hairpins' loops maintain topologically stable structures, while other loops and stems distal to the 'common hairpins' loops are shown to be alterable structures. Strikingly, further studies indicate that the stable structures of these 'common hairpins' may have unbeknown effects on controlling the formation of protein structures in the translation process (unpublished results). (c) 2005 Elsevier B.V. All rights reserved.

Bias-free identification of a linear model-predictive steering controller from measured driver steering behavior

Recent developments in modeling driver steering control with preview are reviewed. While some validation with experimental data has been presented, the rigorous application of formal system identification methods has not yet been attempted. This paper describes a steering controller based on linear model-predictive control. An indirect identification method that minimizes steering angle prediction error is developed. Special attention is given to filtering the prediction error so as to avoid identification bias that arises from the closed-loop operation of the driver-vehicle system. The identification procedure is applied to data collected from 14 test drivers performing double lane change maneuvers in an instrumented vehicle. It is found that the identification procedure successfully finds parameter values for the model that give small prediction errors. The procedure is also able to distinguish between the different steering strategies adopted by the test drivers. © 2006 IEEE.

In vivo dynamics of the internal fibrous structure in smooth adhesive pads of insects.

Many insects with smooth adhesive pads can rapidly enlarge their contact area by centripetal pulls on the legs, allowing them to cope with sudden mechanical perturbations such as gusts of wind or raindrops. The short time scale of this reaction excludes any neuromuscular control; it is thus more likely to be caused by mechanical properties of the pad's specialized cuticle. This soft cuticle contains numerous branched fibrils oriented almost perpendicularly to the surface. Assuming a fixed volume of the water-filled cuticle, we hypothesized that pulls could decrease the fibril angle, thereby helping the contact area to expand laterally and longitudinally. Three-dimensional fluorescence microscopy on the cuticle of smooth stick insect pads confirmed that pulls significantly reduced the fibril angle. However, the fibril angle variation appeared insufficient to explain the observed increase in contact area. Direct strain measurements in the contact zone demonstrated that pulls not only expand the cuticle laterally, but also add new contact area at the pad's outer edge.

Identification of Materials from Construction Site Images Using Content Based Image Retrieval Techniques

The capability to automatically identify shapes, objects and materials from the image content through direct and indirect methodologies has enabled the development of several civil engineering related applications that assist in the design, construction and maintenance of construction projects. Examples include surface cracks detection, assessment of fire-damaged mortar, fatigue evaluation of asphalt mixes, aggregate shape measurements, velocimentry, vehicles detection, pore size distribution in geotextiles, damage detection and others. This capability is a product of the technological breakthroughs in the area of Image and Video Processing that has allowed for the development of a large number of digital imaging applications in all industries ranging from the well established medical diagnostic tools (magnetic resonance imaging, spectroscopy and nuclear medical imaging) to image searching mechanisms (image matching, content based image retrieval). Content based image retrieval techniques can also assist in the automated recognition of materials in construction site images and thus enable the development of reliable methods for image classification and retrieval. The amount of original imaging information produced yearly in the construction industry during the last decade has experienced a tremendous growth. Digital cameras and image databases are gradually replacing traditional photography while owners demand complete site photograph logs and engineers store thousands of images for each project to use in a number of construction management tasks. However, construction companies tend to store images without following any standardized indexing protocols, thus making the manual searching and retrieval a tedious and time-consuming effort. Alternatively, material and object identification techniques can be used for the development of automated, content based, construction site image retrieval methodology. These methods can utilize automatic material or object based indexing to remove the user from the time-consuming and tedious manual classification process. In this paper, a novel material identification methodology is presented. This method utilizes content based image retrieval concepts to match known material samples with material clusters within the image content. The results demonstrate the suitability of this methodology for construction site image retrieval purposes and reveal the capability of existing image processing technologies to accurately identify a wealth of materials from construction site images.

Identification of Cj1051c as a Major determinant for the restriction barrier of Campylobacter jejuni StrainNCTC11168

Campylobacter jejuni is a leading cause of human diarrheal illness in the world, and research on it has benefitted greatly by the completion of several genome sequences and the development of molecular biology tools. However, many hurdles remain for a full understanding of this unique bacterial pathogen. One of the most commonly used strains for genetic work with C. jejuni is NCTC11168. While this strain is readily transformable with DNA for genomic recombination, transformation with plasmids is problematic. In this study, we have identified a determinant of this to be cj1051c, predicted to encode a restriction-modification type IIG enzyme. Knockout mutagenesis of this gene resulted in a strain with a 1,000-fold-enhanced transformation efficiency with a plasmid purified from a C. jejuni host. Additionally, this mutation conferred the ability to be transformed by plasmids isolated from an Escherichia coli host. Sequence analysis suggested a high level of variability of the specificity domain between strains and that this gene may be subject to phase variation. We provide evidence that cj1051c is active in NCTC11168 and behaves as expected for a type IIG enzyme. The identification of this determinant provides a greater understanding of the molecular biology of C. jejuni as well as a tool for plasmid work with strain NCTC11168. © 2012, American Society for Microbiology.

Probabilistic soil identification based on cone penetration tests

In geotechnical engineering, soil classification is an essential component in the design process. Field methods such as the cone penetration test (CPT) can be used as less expensive and faster alternatives to sample retrieval and testing. Unfortunately, current soil classification charts based on CPT data and laboratory measurements are too generic, and may not provide an accurate prediction of the soil type. A probabilistic approach is proposed here to update and modify soil identification charts based on site-specific CPT data. The probability that a soil is correctly classified is also estimated. The updated identification chart can be used for a more accurate prediction of the classification of the soil, and can account for prior information available before conducting the tests, site-specific data, and measurement errors. As an illustration, the proposed approach is implemented using CPT data from the Treporti Test Site (TTS) near Venice (Italy) and the National Geotechnical Experimentation Sites (NGES) at Texas A&M University. The applicability of the site-specific chart for other sites in Venice Lagoon is assessed using data from the Malamocco test site, approximately 20 km from TTS.

Average corotation of line segments near a point and vortex identification

An easy-to-interpret kinematic quantity measuring the average corotation of material line segments near a point is introduced and applied to vortex identification. At a given point, the vector of average corotation of line segments is defined as the average of the instantaneous local rigid-body rotation over "all planar cross sections" passing through the examined point. The vortex-identification method based on average corotation is a one-parameter, region-type local method sensitive to the axial stretching rate as well as to the inner configuration of the velocity gradient tensor. The method is derived from a well-defined interpretation of the local flow kinematics to determine the "plane of swirling" and is also applicable to compressible and variable-density flows. Practical application to direct numerical simulation datasets includes a hairpin vortex of boundary-layer transition, the reconnection process of two Burgers vortices, a flow around an inclined flat plate, and a flow around a revolving insect wing. The results agree well with some popular local methods and perform better in regions of strong shearing. Copyright © 2013 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

Identification of the steering control behaviour of five test subjects following a randomly curving path in a driving simulator

The paper is concerned with the identification of theoretical preview steering controllers using data obtained from five test subjects in a fixed-base driving simulator. An understanding of human steering control behaviour is relevant to the design of autonomous and semi-autonomous vehicle controls. The driving task involved steering a linear vehicle along a randomly curving path. The theoretical steering controllers identified from the data were based on optimal linear preview control. A direct-identification method was used, and the steering controllers were identified so that the predicted steering angle matched as closely as possible the measured steering angle of the test subjects. It was found that identification of the driver's time delay and noise is necessary to avoid bias in identification of the controller parameters. Most subjects' steering behaviour was predicted well by a theoretical controller based on the lateral/yaw dynamics of the vehicle. There was some evidence that an inexperienced driver's steering action was better represented by a controller based on a simpler model of the vehicle dynamics, perhaps reflecting incomplete learning by the driver. Copyright © 2014 Inderscience Enterprises Ltd.

Functionally different pads on the same foot allow control of attachment: stick insects have load-sensitive "heel" pads for friction and shear-sensitive "toe" pads for adhesion.

Stick insects (Carausius morosus) have two distinct types of attachment pad per leg, tarsal "heel" pads (euplantulae) and a pre-tarsal "toe" pad (arolium). Here we show that these two pad types are specialised for fundamentally different functions. When standing upright, stick insects rested on their proximal euplantulae, while arolia were the only pads in surface contact when hanging upside down. Single-pad force measurements showed that the adhesion of euplantulae was extremely small, but friction forces strongly increased with normal load and coefficients of friction were [Formula: see text] 1. The pre-tarsal arolium, in contrast, generated adhesion that strongly increased with pulling forces, allowing adhesion to be activated and deactivated by shear forces, which can be produced actively, or passively as a result of the insects' sprawled posture. The shear-sensitivity of the arolium was present even when corrected for contact area, and was independent of normal preloads covering nearly an order of magnitude. Attachment of both heel and toe pads is thus activated partly by the forces that arise passively in the situations in which they are used by the insects, ensuring safe attachment. Our results suggest that stick insect euplantulae are specialised "friction pads" that produce traction when pressed against the substrate, while arolia are "true" adhesive pads that stick to the substrate when activated by pulling forces.

Species identification of Chinese sturgeon using acoustic descriptors and ascertaining their spatial distribution in the spawning ground of Gezhouba Dam

This study has developed an improved subjective approach of classification in conjunction with Step wise DFA analysis to discriminate Chinese sturgeon signals from other targets. The results showed that all together 25 Chinese sturgeon echo-signals were detected in the spawning ground of Gezhouba Dam during the last 3 years, and the identification accuracy reached 90.9%. In Stepwise DFA, 24 out of 67 variables were applied in discrimination and identification. PCA combined with DFA was then used to ensure the significance of the 24 variables and detailed the identification pattern. The results indicated that we can discriminate Chinese sturgeon from other fish species and noise using certain descriptors such as the behaviour variables, echo characteristics and acoustic cross-section characteristics. However, identification of Chinese sturgeon from sediments is more difficult and needs a total of 24 variables. This is due to the limited knowledge about the acoustic-scattering properties of the substrate regions. Based on identified Chinese sturgeon individuals, 18 individuals were distributed in the region between the site of Gezhouba Dam and Miaozui reach, with a surface area of about 3.4 km(2). Seven individuals were distributed in the region between Miaozui and Yanshouba reach, with a surface area of about 13 km(2).

Discovery of a male-biased mutant family and identification of a male-specific SCAR marker in gynogenetic gibel carp Carassius auratus gibelio

Gibel carp ( Carassius auratus gibelio) is a uniquely gynogenetic species with a minor ratio of males in natural habitats, but its male origin and sex determination mechanisms have been unknown. In this study, a male-biased mutant family was discovered from the gynogenetic gibel carp, and a male-specific SCAR marker was identified from the mutant family. Normal spermatogenesis was observed in the male testes by immuno. fluorescence histochemistry. Nearly identical AFLP profiles were observed between males and females, but a male-specific 86 bp AFLP fragment was screened by sex-pool bulked segregant analysis and individual screening. Based on the male-specific AFLP fragment, a total of 579 bp sequences were cloned by genome walking. Subsequently, a male-specific SCAR marker was designed, and the male-specific DNA fragment was confirmed to be steadily transmitted to the next generation and consistently detected only in males. (C) 2009 National Natural Science Foundation of China and Chinese Academy of Sciences. Published by Elsevier Limited and Science in China Press. All rights reserved.

Bayesian parameter identification for vibro-acoustic models with nonparametric uncertainty

Vibration and acoustic analysis at higher frequencies faces two challenges: computing the response without using an excessive number of degrees of freedom, and quantifying its uncertainty due to small spatial variations in geometry, material properties and boundary conditions. Efficient models make use of the observation that when the response of a decoupled vibro-acoustic subsystem is sufficiently sensitive to uncertainty in such spatial variations, the local statistics of its natural frequencies and mode shapes saturate to universal probability distributions. This holds irrespective of the causes that underly these spatial variations and thus leads to a nonparametric description of uncertainty. This work deals with the identification of uncertain parameters in such models by using experimental data. One of the difficulties is that both experimental errors and modeling errors, due to the nonparametric uncertainty that is inherent to the model type, are present. This is tackled by employing a Bayesian inference strategy. The prior probability distribution of the uncertain parameters is constructed using the maximum entropy principle. The likelihood function that is subsequently computed takes the experimental information, the experimental errors and the modeling errors into account. The posterior probability distribution, which is computed with the Markov Chain Monte Carlo method, provides a full uncertainty quantification of the identified parameters, and indicates how well their uncertainty is reduced, with respect to the prior information, by the experimental data. © 2013 Taylor & Francis Group, London.