Characterization of the 6'-N-aminoglycoside acetyltransferase gene aac(6')-Iq from the integron of a natural multiresistance plasmid

The nucleotide sequence of a newly identified amikacin resistance gene, aac(6')-Iq (551 bp), is reported. It has 68.4 and 94.4% homology with the aac(6')-Ia gene and the recently described aac(6')-Ip gene, respectively. Analysis of its flanking sequences indicated that it is in the first cassette of a class I integron and has an attC site (59-base element) 108 bp in length.     

Pharmacokinetics of phenprobamate after oral administration to healthy subjects

Phenprobamate (CAS 673-31-4) is a centrally acting skeletal-muscle relaxant agent. There are only two studies in the literature about the pharmacokinetics of phenoprobamate in man. The inconsistency between the results of these studies can be attributed partly to the different analytical methodologies used. A sensitive, specific and reproducible HPLC-assay, which may increase the reliability of the pharmacokinetic studies of phenprobamate in plasma, has been developed recently. The objective of this investigation was to assess the single-dose kinetics of phenprobamate in human and to determine the pharmacokinetic parameters of clinical and regulatory concern. The plasma pharmacokinetics of phenprobamate have been investigated following single oral administration at a dose of 800 mg in eleven healthy volunteers.     

Cathepsin L2, a novel human cysteine proteinase produced by breast and colorectal carcinomas

We have identified and cloned a new member of the papain family of cysteine proteinases from a human brain cDNA library. The isolated cDNA codes for a polypeptide of 334 amino acids that exhibits all of the structural features characteristic of cysteine proteinases, including the active site cysteine residue essential for peptide hydrolysis. Pairwise comparisons of this amino acid sequence with the remaining human cysteine proteinases identified to date showed a high percentage of identity (78%) with cathepsin L; the percentage of identity with all other members of the family was much lower (<40%). On the basis of these structural characteristics, we have tentatively called this novel protein cathepsin L2. The cDNA encoding the mature cathepsin L2 was expressed in Escherichia coli, and after purification, the recombinant protein was able to degrade the synthetic peptide benzyloxycarbonyl-L-phenylalanyl-L-arginine-7-amido-4-methylcoumarin, which is commonly used as a substrate for cysteine proteinases. Cathepsin L2 proteolytic activity on this substrate was abolished by trans-epoxysuccinyl-L-leucylamido-(4-guanidino)butane, an inhibitor of cysteine proteinases, thus providing additional evidence that the isolated cDNA encodes a functional cysteine proteinase. Northern blot analysis of polyadenylated RNAs isolated from a variety of human tissues demonstrated that cathepsin L2 is predominantly expressed in the thymus and testis. This finding is in marked contrast with the wide tissue distribution of most cysteine proteinases characterized to date, including cathepsin L, and suggests that cathepsin L2 may play a specialized role in the thymus and testis. Expression analysis of cathepsin L2 in human tumors revealed a widespread expression in colorectal and breast carcinomas but not in normal colon or mammary gland or in peritumoral tissues. Cathepsin L2 was also expressed by colorectal and breast cancer cell lines as well as by some tumors of diverse origin, including ovarian and renal carcinomas. These results open the possibility that this novel enzyme may be involved in tumor processes, as already reported for other cysteine proteinases, including cathepsin L.     

Prediction of software vulnerability based deep symbiotic genetic algorithms: Phenotyping of dominant-features

The detection of software vulnerabilities is considered a vital problem in the software security area for a long time. Nowadays, it is challenging to manage software security due to its increased complexity and diversity. So, vulnerability detection applications play a significant part in software development and maintenance. The ability of the forecasting techniques in vulnerability detection is still weak. Thus, one of the efficient defining features methods that have been used to determine the software vulnerabilities is the metaheuristic optimization methods. This paper proposes a novel software vulnerability prediction model based on using a deep learning method and SYMbiotic Genetic algorithm. We are first to apply Diploid Genetic algorithms with deep learning networks on software vulnerability prediction to the best of our knowledge. In this proposed method, a deep SYMbiotic-based genetic algorithm model (DNN-SYMbiotic GAs) is used by learning the phenotyping of dominant-features for software vulnerability prediction problems. The proposed method aimed at increasing the detection abilities of vulnerability patterns with vulnerable components in the software. Comprehensive experiments are conducted on several benchmark datasets; these datasets are taken from Drupal, Moodle, and PHPMyAdmin projects. The obtained results revealed that the proposed method (DNN-SYMbiotic GAs) enhanced vulnerability prediction, which reflects improving software quality prediction.

     

Improving sub-pixel accuracy for long range stereo

Dense stereo algorithms are able to estimate disparities at all pixels including untextured regions. Typically these disparities are evaluated at integer disparity steps. A subsequent sub-pixel interpolation often fails to propagate smoothness constraints on a sub-pixel level.We propose to increase the sub-pixel accuracy in low-textured regions in four possible ways: First, we present an analysis that shows the benefit of evaluating the disparity space at fractional disparities. Second, we introduce a new disparity smoothing algorithm that preserves depth discontinuities and enforces smoothness on a sub-pixel level. Third, we present a novel stereo constraint (gravitational constraint) that assumes sorted disparity values in vertical direction and guides global algorithms to reduce false matches, especially in low-textured regions. Finally, we show how image sequence analysis improves stereo accuracy without explicitly performing tracking. Our goal in this work is to obtain an accurate 3D reconstruction. Large-scale 3D reconstruction will benefit heavily from these sub-pixel refinements.Results based on semi-global matching, obtained with the above mentioned algorithmic extensions are shown for the Middlebury stereo ground truth data sets. The presented improvements, called ImproveSubPix, turn out to be one of the top-performing algorithms when evaluating the set on a sub-pixel level while being computationally efficient. Additional results are presented for urban scenes. The four improvements are independent of the underlying type of stereo algorithm.     

Evaluating the influence of multiplexing schemes and buffer implementation on perceived VoIP conversation quality

This work presents a study of RTP multiplexing schemes, which are compared with the normal use of RTP, in terms of experienced quality. Bandwidth saving, latency and packet loss for different options are studied, and some tests of Voice over IP (VoIP) traffic are carried out in order to compare the quality obtained using different implementations of the router buffer. Voice quality is calculated using ITU R-factor, which is a widely accepted quality estimator. The tests show the bandwidth savings of multiplexing, and also the importance of packet size for certain buffers, as latency and packet loss may be affected. The customer’s experience improvement is measured, showing that the use of multiplexing can be interesting in some scenarios, like an enterprise with different offices connected via the Internet. The system is also tested using different numbers of samples per packet, and the distribution of the flows into different tunnels is found to be an important factor in order to achieve an optimal perceived quality for each kind of buffer. Grouping all the flows into a single tunnel will not always be the best solution, as the increase of the number of flows does not improve bandwidth efficiency indefinitely. If the buffer penalizes big packets, it will be better to group the flows into a number of tunnels. The router processing capacity has to be taken into account too, as the limit of packets per second it can manage must not be exceeded. The obtained results show that multiplexing is a good way to improve customer’s experience of VoIP in scenarios where many RTP flows share the same path.     

Confidence voting method ensemble applied to off-line signature verification

In this paper, a new approximation to off-line signature verification is proposed based on two-class classifiers using an expert decisions ensemble. Different methods to extract sets of local and a global features from the target sample are detailed. Also a normalization by confidence voting method is used in order to decrease the final equal error rate (EER). Each set of features is processed by a single expert, and on the other approach proposed, the decisions of the individual classifiers are combined using weighted votes. Experimental results are given using a subcorpus of the large MCYT signature database for random and skilled forgeries. The results show that the weighted combination outperforms the individual classifiers significantly. The best EER obtained were 6.3 % in the case of skilled forgeries and 2.31 % in the case of random forgeries.