Unusual Two-Step Assembly of a Minimalistic Dipeptide-Based Functional Hypergelator

Self-assembled peptide hydrogels represent the realization of peptide nanotechnology into biomedical products. There is a continuous quest to identify the simplest building blocks and optimize their critical gelation concentration (CGC). Herein, a minimalistic, de novo dipeptide, Fmoc-Lys(Fmoc)-Asp, as an hydrogelator with the lowest CGC ever reported, almost fourfold lower as compared to that of a large hexadecapeptide previously described, is reported. The dipeptide self-assembles through an unusual and unprecedented two-step process as elucidated by solid-state NMR and molecular dynamics simulation. The hydrogel is cytocompatible and supports 2D/3D cell growth. Conductive composite gels composed of Fmoc-Lys(Fmoc)-Asp and a conductive polymer exhibit excellent DNA binding. Fmoc-Lys(Fmoc)-Asp exhibits the lowest CGC and highest mechanical properties when compared to a library of dipeptide analogues, thus validating the uniqueness of the molecular design which confers useful properties for various potential applications.     

Oxygen-enrichment of YBa2Cu3 YBa2Cu3O7-δ using the fluidization techniqueusing the fluidization technique

The oxygen-deficient phase of the highT _c superconductor, YBa₂Cu₃O₇, was oxygen-enriched using the fluidization technique to give good superconducting properties. The normal method of oxygen treatment at 900°C for 24 h and at 600°C for 24 h has been reduced to just one treatment at 600°C for 12 h by the fluidization technique to achieve almost the same strength of superconducting signal for the YBa₂Cu₃O₇ powder, which establishes the attractiveness of the latter route for the large-scale preparation of superconducting material. The particle sizes were in the range 0–90, 90–180 and 180–420 μm. The fluidized particles were crystalline with orthorhombic distortion.T _c ^onset , estimated using the a.c. magnetic susceptibility method, was 91·3 K. The volume fraction of superconducting material in the product was 83·7–85·3%, one of the highest values reported so far for YBa₂Cu₃O₇.     

Advances in software engineering

Software is the key technology in applications as diverse as accounting, hospital management, aviation, and nuclear power. Application advances in different domains such as these-each with different requirements-have propelled software development from small batch programs to large, real-time programs with multimedia capabilities. To cope, software's enabling technologies have undergone tremendous improvement in hardware, communications, operating systems, compilers, databases, programming languages, and user interfaces, among others. In turn, those improvements have fueled even more advanced applications. Improvements in VLSI technology and multimedia, for example, have resulted in faster, more compact computers that significantly widened the range of software applications. Database and user interface enhancements, on the other hand, have spawned more interactive and collaborative development environments. Such changes have a ripple effect on software development processes as well as on software techniques and tools. In this article, we highlight software development's crucial methods and techniques of the past 30 years     

Simultaneous nonrigid registration of multiple point sets and atlas construction

Group-wise registration of a set of shapes represented by unlabeled point-sets is a challenging problem since, usually this involves solving for point correspondence in a nonrigid motion setting. In this paper, we propose a novel and robust algorithm that is capable of simultaneously computing the mean shape represented by a probability density function from multiple unlabeled point-sets and registering them non-rigidly to this emerging mean shape. This algorithm avoids the correspondence problem by minimizing the Jensen-Shannon (JS) divergence between the point sets. We motivate the use of the JS divergence by pointing out its close relationship to hypothesis testing. We derive the analytic gradient of the cost function in order to efficiently achieve the optimal solution. JS-divergence is symmetric with no bias toward any of the given shapes to be registered and whose mean is being sought. A by product of the registration process is a probabilistic atlas defined as the convex combination of the probability densities of the input point sets being aligned. Our algorithm can be especially useful for creating atlases of various shapes present in images as well as for simultaneously (rigidly or non-rigidly) registering 3D range data sets without having to establish any correspondence. We present experimental results on real and synthetic data.     

Bayesian image reconstruction in SPECT using higher order mechanical models as priors

While the ML-EM algorithm for reconstruction for emission tomography is unstable due to the ill-posed nature of the problem. Bayesian reconstruction methods overcome this instability by introducing prior information, often in the form of a spatial smoothness regularizer. More elaborate forms of smoothness constraints may be used to extend the role of the prior beyond that of a stabilizer in order to capture actual spatial information about the object. Previously proposed forms of such prior distributions were based on the assumption of a piecewise constant source distribution. Here, the authors propose an extension to a piecewise linear model-the weak plate-which is more expressive than the piecewise constant model. The weak plate prior not only preserves edges but also allows for piecewise ramplike regions in the reconstruction. Indeed, for the authors' application in SPECT, such ramplike regions are observed in ground-truth source distributions in the form of primate autoradiographs of rCBF radionuclides. To incorporate the weak plate prior in a MAP approach, the authors model the prior as a Gibbs distribution and use a GEM formulation for the optimization. They compare quantitative performance of the ML-EM algorithm, a GEM algorithm with a prior favoring piecewise constant regions, and a GEM algorithm with their weak plate prior. Pointwise and regional bias and variance of ensemble image reconstructions are used as indications of image quality. The authors' results show that the weak plate and membrane priors exhibit improved bias and variance relative to ML-EM techniques.     

Malware detection using assembly and API call sequences

One of the major problems concerning information assurance is malicious code. To evade detection, malware has also been encrypted or obfuscated to produce variants that continue to plague properly defended and patched networks with zero day exploits. With malware and malware authors using obfuscation techniques to generate automated polymorphic and metamorphic versions, anti-virus software must always keep up with their samples and create a signature that can recognize the new variants. Creating a signature for each variant in a timely fashion is a problem that anti-virus companies face all the time. In this paper we present detection algorithms that can help the anti-virus community to ensure a variant of a known malware can still be detected without the need of creating a signature; a similarity analysis (based on specific quantitative measures) is performed to produce a matrix of similarity scores that can be utilized to determine the likelihood that a piece of code under inspection contains a particular malware. Two general malware detection methods presented in this paper are: Static Analyzer for Vicious Executables (SAVE) and Malware Examiner using Disassembled Code (MEDiC). MEDiC uses assembly calls for analysis and SAVE uses API calls (Static API call sequence and Static API call set) for analysis. We show where Assembly can be superior to API calls in that it allows a more detailed comparison of executables. API calls, on the other hand, can be superior to Assembly for its speed and its smaller signature. Our two proposed techniques are implemented in SAVE) and MEDiC. We present experimental results that indicate that both of our proposed techniques can provide a better detection performance against obfuscated malware. We also found a few false positives, such as those programs that use network functions (e.g. PuTTY) and encrypted programs (no API calls or assembly functions are found in the source code) when the thresholds are set 50% similarity measure. However, these false positives can be minimized, for example by changing the threshold value to 70% that determines whether a program falls in the malicious category or not.     

Estimation of heart rate signals for mental stress assessment using neuro fuzzy technique

This paper presents the evaluation of mental stress assesment using heart-rate variability (HRV). The activity of the autonomic nervous system (ANS) is studied by means of time-frequency analysis (TFA) of the heart-rate variability signal. Spectral decomposition of the heart-rate variability before smoking and after smoking was obtained. Mental stress is accompanied by dynamic changes in ANS activity. HRV analysis is a popular tool for assessing the activities of autonomic nervous system. The approach consists of (1) monitoring of heart rate signals, (2) signal processing using wavelet transform (WT) (different wavelets), (3) neuro fuzzy evaluation techniques to provide robustness in HRV analysis, (4) monitoring the function of ANS under different stress conditions. Our experiment involves 20 physically fit persons under different times (before smoking and after smoking). Nero fuzzy technique have been used to model the experimental data.