Isomorphous replacement of cystine with selenocystine in endothelin: oxidative refolding, biological and conformational properties of [Sec3,Sec11,Nle7]-endothelin-1

Air re-oxidation of fully reduced human endothelin-1 under optimized conditions yields the natural isomer with parallel disulfide bridges and the non-natural isomer with crossed disulfide bridges at a ratio of 3:1. In view of the recently determined highly reducing redox potential of selenocysteine (-381 mV) in peptides, the half-cystine residues Cys3 and Cys11 of the natural isomer of endothelin-1 were replaced by selenocysteine. Taking advantage of the high stability of the diselenide group toward reducing agents for disulfides a regioselective disulfide bridging of the second cysteine pair allowed for straightforward preparation of the [Sec3,Sec11, Nle7]-endothelin-1. NMR structural analysis showed conformational preferences of this endothelin analog that were identical to those of the natural hormone. Similarly, the bioactivity data confirmed that replacement of cysteine residues with selenocysteine was without detectable effect on receptor recognition and signal transduction. Both findings strongly support that the exchange of sulfur against selenium produces a fully isomorphous molecule as recently observed for similar exchanges at the level of methionine residues in proteins. Moreover, oxidative refolding of the fully reduced [Sec3,Sec11,Nle7]-endothelin-1 fulfilled the expectation that the redox potential of the selenocysteines would dictate quantitative formation of the natural isomer. These results suggest that the selenocysteine approach, besides offering an interesting chemical tool for induction of correct oxidative folding of multiple cysteine-containing peptides, should even allow for the preparation of non-natural isomers and thus for studying conformational preferences of folding intermediates in peptides and proteins.     

The ChaLearn gesture dataset (CGD 2011)

This paper describes the data used in the ChaLearn gesture challenges that took place in 2011/2012, whose results were discussed at the CVPR 2012 and ICPR 2012 conferences. The task can be described as: user-dependent, small vocabulary, fixed camera, one-shot-learning. The data include 54,000 hand and arm gestures recorded with an RGB-D \(\hbox {Kinect}^\mathrm{TM}\) camera. The data are organized into batches of 100 gestures pertaining to a small gesture vocabulary of 8–12 gestures, recorded by the same user. Short continuous sequences of 1–5 randomly selected gestures are recorded. We provide man-made annotations (temporal segmentation into individual gestures, alignment of RGB and depth images, and body part location) and a library of function to preprocess and automatically annotate data. We also provide a subset of batches in which the user’s horizontal position is randomly shifted or scaled. We report on the results of the challenge and distribute sample code to facilitate developing new solutions. The data, datacollection software and the gesture vocabularies are downloadable from http://gesture.chalearn.org. We set up a forum for researchers working on these data http://groups.google.com/group/gesturechallenge.     

On vector averaging over the unit hypersphere

Sample averaging is a commonly used way to smooth out irregularities of data and to get rid of random fluctuations in measurements analysis. In adaptive signal processing, where an adaptive system learns its own parameters in order to perform a predefined task, the learnt parameters-pattern may depend on the initial learning state and on the fluctuations of the statistical features of the input signals to the system. In adaptive system learning, averaging may be employed as a method to merge several learnt parameters-patterns in order to get a better representative pattern. Even in the case of scalar parameters, the concept of averaging is not uniquely defined as scalar parameters spaces may exhibit a rich structure to be dealt with. The case of multiple parameter patterns where single parameters are mutually constrained to each other may exhibit an even richer structure. In the present paper, we deal with the case of parameters-patterns belonging to the unit hypersphere and develop an averaging technique based on the differential geometrical structure of such a curved space. Numerical experiments illustrate the behavior of the developed averaging algorithm.     

Principal motion components for one-shot gesture recognition

This paper introduces principal motion components (PMC), a new method for one-shot gesture recognition. In the considered scenario a single training video is available for each gesture to be recognized, which limits the application of traditional techniques (e.g., HMMs). In PMC, a 2D map of motion energy is obtained per each pair of consecutive frames in a video. Motion maps associated to a video are processed to obtain a PCA model, which is used for recognition under a reconstruction-error approach. The main benefits of the proposed approach are its simplicity, easiness of implementation, competitive performance and efficiency. We report experimental results in one-shot gesture recognition using the ChaLearn Gesture Dataset; a benchmark comprising more than 50,000 gestures, recorded as both RGB and depth video with a Kinect?camera. Results obtained with PMC are competitive with alternative methods proposed for the same data set.     

Automated Meta-Control for Adaptable Real-Time Software

The software meta-controller is an online agent responsible for dynamically adapting an application's software configuration, e.g. altering operational modes and migrating tasks, to best accommodate varying runtime circumstances. In distributed real-time applications such adaptations must be carried out in a manner which maintains the schedulability of all critical tasks while maximizing some notion of system value for all other tasks. For large-scale real-time applications, considering all possible adaptations at the task-level is computationally intractable. This paper presents an automated aggregate approach to software meta-control, appropriate for large-scale distributed real-time systems. The aggregate automated meta-control problem is still NP-hard, but it has very practical approximate solutions. Introduced, here, are two very-effective approximation algorithms, QDP and GG, with very reasonable polynomial time complexity. Both algorithms also provide us with upper bounds for optimum system values, useful for deriving absolute, albeit somewhat pessimistic, measures of actual performance. Extensive Monte Carlo analysis is used to illustrate that expected performance for both algorithms is generally suboptimal by no more than a few percent. Our flexible software meta-control model is also shown to be readily applied to a wide range of time-sensitive applications.     

Analogue CMOS prototype vision chip with prewitt edge processing

In this study, the design and realization of an analogue CMOS prototype vision chip with Prewitt edge processing is presented. The chip is designed with voltage and current mode and the main parts are one 16?×?16 pixel array, one pair of absolute value circuits and two transimpedance amplifiers. The technology process is TSMC 0.35???m. The edge processing is performed parallely on pixel level. The performance of the sensor comprises a processing time of 450?ns; optical dynamic range of 53?dB; power consumption at 30 frames per second of 1.5?mW; peak signal to noise ratio of 44?dB.     

Auto-Regressive Moving Average Models on Complex-Valued Matrix Lie Groups

The present contribution aims at extending the classical scalar autoregressive moving average (ARMA) model to generate random (as well as deterministic) paths on complex-valued matrix Lie groups. The numerical properties of the developed ARMA model are studied by recurring to a tailored version of the Z-transform on Lie groups and to statistical indicators tailored to Lie groups, such as correlation functions on tangent bundles. The numerical behavior of the devised ARMA model is also illustrated by numerical simulations.     

Lessons Learned from the Two Major Tailings Dam Accidents in Brazil

Mine Water and the Environment - The failures of the Mariana and Brumadinho tailings dams in Minas Gerais, Brazil, had severe environmental repercussions and caused many fatalities. What should or...     

Identification of unbalance forces by metaheuristic search algorithms

This paper discusses the identification of parameters in rotary systems, namely, the unbalance magnitude, phase and position in the rotor system. These parameters can be identified using the measured orbits in the hydrodynamic bearings. The oil film forces are evaluated in the different positions of the orbit of the journal and are applied to the model of the shaft. The model, integrated in time domain, allows with an assumed unbalance, to simulate the orbits. The objective function is basically the difference between measured and simulated orbits, and its minimum corresponds to the identified unbalance amount, phase and position along the shaft. With respect to traditional model based identification procedures, this approach using oil film forces instead of oil film linearized stiffness and damping coefficients, and unfiltered orbits instead of 1X vibration components is suitable to deal with non-linear behaviour of the system.