Double gene targeting PCR assay for the detection of Crocodylus porosus in commercial products

The demand for crocodile meat is quickly growing because of its exotic and organoleptic appeal and also the low content of cholesterol and lipids. Moreover, crocodile oil and blood have been used in alternative medicines for treating asthma and several other ailments since ancient times. Furthermore, crocodile hides have great demand in leather industries. All of these have collectively contributed to the extensive hunting, illegal trading and consequent decline of crocodiles in most parts of the world. To keep space with the growing demands, some crocodile species such as Crocodylus porosus have been raised in farms and its commercial trades have been legalised. However, demand for wild crocodiles in foods and medicines has continued in high gear. Recently, several DNA-based methods have been proposed for crocodile detection, but those assays are based on single gene and longer-sized amplicon targets that break down during extensive processing. To address this gap, here we developed and validated a highly stable double gene targeted multiplex PCR assay for the identification of C. porosus materials in commercial products. The assay involved two short sites from C. porosus atp6 (77 bp) and cytb (127 bp) genes and a universal internal control (99 bp) for eukaryotes. The PCR primers were cross-tested against 18 species and validated under pure and mixed matrices under extensive boiling, autoclaving and microwave cooking conditions. Finally, it was used to identify five crocodile-based commercial products. The lower limits of detection for atp6 and cytb genes were 0.001 ng and 0.01 ng DNA, respectively, in pure meat and 1% under mixed matrices. Some inherent features, such as 77–127 bp amplicon sizes, exceptional stability and superior sensitivity, suggested the assay could be used for the identification of C. porosus in any forensic specimen.     

Correction to: Photonic crystal fiber sensor for the detection of hazardous gases

Microsystem Technologies -     

High-temperature 434 Mhz surface acoustic wave devices based on GaPO4

Research into surface acoustic wave (SAW) devices began in the early 1970s and led to the development of high performance, small size, and high reproducibility devices. Much research has now been done on the application of such devices to consumer electronics, process monitoring, and communication systems. The use of novel materials, such as gallium phosphate (GaPO4), extends the operating temperature of the elements. SAW devices based on this material operating at 434 MHz and up 800 degrees C, can be used for passive wireless sensor applications. Interdigital transducer (IDT) devices with platinum/zirconium metallization and 1.4 microm finger-gap ratio of 1:1 have been fabricated using direct write e-beam lithography and a lift-off process. The performance and long-term stability of these devices has been studied, and the results are reported in this paper.     

Multi-stage production cost optimization of semi-rigid steel frames using genetic algorithms

The response of a steel structure is closely related to the behavior of its joints. This means that it is necessary to take explicit account of joint properties in order to ensure a consistent approach to design optimization of steel frames. Semi-rigid design has been introduced into steel construction standards such as Eurocode 3 and AISC. However, in the absence of appropriate guidelines, engineers encounter difficulties when bringing in semi-rigid design to everyday engineering practice. Moreover, connection design significantly affects the production cost of steel frame structures. Thus, a realistic optimization of frame design should take into account the effective costs of different stages of production including manufacturing and erection activities. This paper presents a Genetic Algorithm based method for multi-stage cost optimization of steel structures. In the objective function, the total cost of different production stages is minimized. A new cost model is presented that itemizes costs of all stages of production (material supply, manufacturing, erection and foundation). Design examples are used to validate the proposed methodology. Numerical validation shows that the multi-stage design optimization results in substantial cost benefits between 10% and 25% compared to traditional design of steel frames. Furthermore, the developed methodology is shown to be capable of measuring the possible impact of design choices in the early design stage thus assisting designers to make better design decisions.     

Cross Layer QoS Guarantees in Multiuser WLAN Systems

The use of real-time delay-sensitive applications in wireless systems has significantly increased during the last years. Consequently, the demand to guarantee certain Quality of Service (QoS) is a challenging issue for the system’s designers. A cross-layer based dynamically tuned queue length scheduler is presented in this paper, for the Downlink of multiuser WLAN systems with heterogeneous traffic requirements. An opportunistic scheduling algorithm is applied, while users from higher priority traffic classes are prioritized. A trade-off between the throughput maximization of the system and the guarantee of the users QoS requirements is obtained. Therefore the length of the queue is dynamically adjusted to select the appropriate conditions based on the operator requirements.     

A hybrid SEM algorithm for high-dimensional unsupervised learning using a finite generalized Dirichlet mixture.

This paper applies a robust statistical scheme to the problem of unsupervised learning of high-dimensional data. We develop, analyze, and apply a new finite mixture model based on a generalization of the Dirichlet distribution. The generalized Dirichlet distribution has a more general covariance structure than the Dirichlet distribution and offers high flexibility and ease of use for the approximation of both symmetric and asymmetric distributions. We show that the mathematical properties of this distribution allow high-dimensional modeling without requiring dimensionality reduction and, thus, without a loss of information. This makes the generalized Dirichlet distribution more practical and useful. We propose a hybrid stochastic expectation maximization algorithm (HSEM) to estimate the parameters of the generalized Dirichlet mixture. The algorithm is called stochastic because it contains a step in which the data elements are assigned randomly to components in order to avoid convergence to a saddle point. The adjective "hybrid" is justified by the introduction of a Newton-Raphson step. Moreover, the HSEM algorithm autonomously selects the number of components by the introduction of an agglomerative term. The performance of our method is tested by the classification of several pattern-recognition data sets. The generalized Dirichlet mixture is also applied to the problems of image restoration, image object recognition and texture image database summarization for efficient retrieval. For the texture image summarization problem, results are reported for the Vistex texture image database from the MIT Media Lab.     

Nonparametric variational learning of multivariate beta mixture models in medical applications

Clustering as an essential technique has matured into a capable solution to address the gap between the growing availability of data and deriving the knowledge from them. In this paper, we propose a novel clustering method “variational learning of infinite multivariate Beta mixture models.” The motivation behind proposing this technique is the flexibility of mixture models to fit the data. This approach has the capability to infer the model complexity and estimate model parameters from the observed data automatically. Moreover, as a label‐free method, it could also address the problem of high costs of medical data labeling, which can be undertaken just by medical experts. The performance of the model is evaluated on real medical applications and compared with other similar alternatives. We demonstrate the ability of our proposed method to outperform widely used methods in the field as it has been shown in experimental results.     

On multivariate binary data clustering and feature weighting

This paper presents an approach that partitions data sets of unlabeled binary vectors without a priori information about the number of clusters or the saliency of the features. The unsupervised binary feature selection problem is approached using finite mixture models of multivariate Bernoulli distributions. Using stochastic complexity, the proposed model determines simultaneously the number of clusters in a given data set composed of binary vectors and the saliency of the features used. We conduct different applications involving real data, document classification and images categorization to show the merits of the proposed approach.     

Infinite Liouville mixture models with application to text and texture categorization

This paper addresses the problem of proportional data modeling and clustering using mixture models, a problem of great interest and of importance for many practical pattern recognition, image processing, data mining and computer vision applications. Finite mixture models are broadly applicable to clustering problems. But, they involve the challenging problem of the selection of the number of clusters which requires a certain trade-off. The number of clusters must be sufficient to provide the discriminating capability between clusters required for a given application. Indeed, if too many clusters are employed overfitting problems may occur and if few are used we have a problem of underfitting. Here we approach the problem of modeling and clustering proportional data using infinite mixtures which have been shown to be an efficient alternative to finite mixtures by overcoming the concern regarding the selection of the optimal number of mixture components. In particular, we propose and discuss the consideration of infinite Liouville mixture model whose parameter values are fitted to the data through a principled Bayesian algorithm that we have developed and which allows uncertainty in the number of mixture components. Our experimental evaluation involves two challenging applications namely text classification and texture discrimination, and suggests that the proposed approach can be an excellent choice for proportional data modeling.