Modulating drug release from poly(lactic-co-glycolic) acid microparticles by the addition of alginate and pectin

The aim of the present work is the characterization of PLGA microparticles including biopolymers for the controlled release of tilmicosin, a broad-spectrum antibiotic. Microparticles were prepared using the double-emulsion solvent evaporation technique. The effect of alginate and pectin incorporation over particle size and porosity, encapsulation efficiency (EE) and pH-responsive drug release was evaluated. Formulations presented a mean particle size of 5.5 μm approximately and a drug EE ranged from 22%–57%. PLGA-Alginate particles showed an increased porosity. Tilmicosin release profiles from PLGA and PLGA-biopolymer microparticles were affected by the particular combination of polymers and the pH of the release medium. The experimental data was simulated using a mathematical model, which takes into account the autocatalytic polymer degradation and the different mechanisms of drug transport. The combination of PLGA and biopolymers strongly influenced the morphology of the particles, offering the possibility of controlling the drug release profiles according to the therapy.     

Synergistic Extraction of Rare Earth Ions by 1-phenyl-3-methyl-4-benzoyl-5-pyrazolone and Trioctylphousphine Oxide in Paraffin Wax

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Use of scanning probe microscopy to study the evolution of nanometer sized liquid structures

The evolution of the profile of nanometer sized water drops on a mica surface has been studied through hydration scanning probe microscopy. A time range from a few seconds down to a fraction of millisecond after the formation of the drop has been explored. This high time resolution has been obtained by sampling a series of statistically equivalent drops. This approach also avoids any probe interference during the drop evolution process.     

A constant-force bistable mechanism for force regulation and overload protection

A novel constant-force bistable mechanism (CFBM) allowing constant contact force and overload protection is developed. When a device equipped with a CFBM is loaded by an unknown force exceeding a critical force of the CFBM, the CFBM can snap to its other stable equilibrium state to safeguard the device. The bistability of the mechanism originates from combined compression and bending of the beam structures. Finite element analyses are used to characterize the constant-force behavior and bistability of the mechanism under static loading. A design formulation is proposed to find the CFBM shape for a specified displacement range with constant output force of the mechanism. Prototypes of the CFBM are fabricated and tested. The characteristics of the CFBM predicted by theory are verified by experiments. Using the CFBM, sophisticated sensors and control system for force regulation of machining systems can be eliminated.     

Multi-mode of vortex-induced vibration of a flexible circular cylinder

The vortex-induced vibration of a flexible circular cylinder is investigated at a constant Reynolds number of 1 000.The finite-volume method on moving meshes is applied for the fluid flow, and the Euler-Bernoulli beam theory is used to model the dynamic response of a flexible cylinder.The relationship between the reduced velocity and the amplitude response agrees well with the experimental results.Moreover, five different vibrating modes appear in the simulation.From the comparisons of their vortex structures, the strength of the wake flow is related to the exciting vibrating mode and different vortex patterns arise for different vibrating modes.Only 2P pattern appears in the first vibrating mode while 2S-2P patterns occur in the other vibrating modes if monitoring at different sections along the length of the cylinder.The vibration of the flexible cylinder can also greatly alter the three-dimensionality in the wake, which needs further studies in our future work, especially in the transition region for the Reynolds number from 170 to 300.     

Dual watermark for image tamper detection and recovery

An effective dual watermark scheme for image tamper detection and recovery is proposed in this paper. In our algorithm, each block in the image contains watermark of other two blocks. That is to say, there are two copies of watermark for each non-overlapping block in the image. Therefore, we maintain two copies of watermark of the whole image and provide second chance for block recovery in case one copy is destroyed. A secret key, which is transmitted along with the watermarked image, and a public chaotic mixing algorithm are used to extract the watermark for tamper recovery. By using our algorithm, a 90% tampered image can be recovered to a dim yet still recognizable condition (PSNR ). Experimental results demonstrate that our algorithm is superior to the compared techniques, especially when the tampered area is large.     

Fast online graph clustering via Erd?s-Rényi mixture

In the context of graph clustering, we consider the problem of simultaneously estimating both the partition of the graph nodes and the parameters of an underlying mixture of affiliation networks. In numerous applications the rapid increase of data size over time makes classical clustering algorithms too slow because of the high computational cost. In such situations online clustering algorithms are an efficient alternative to classical batch algorithms. We present an original online algorithm for graph clustering based on a Erd?s-Rényi graph mixture. The relevance of the algorithm is illustrated, using both simulated and real data sets. The real data set is a network extracted from the French political blogosphere and presents an interesting community organization.     

Who is LB1? Discriminant analysis for the classification of specimens

Many problems in paleontology reduce to finding those features that best discriminate among a set of classes. A clear example is the classification of new specimens. However, these classifications are generally challenging because the number of discriminant features and the number of samples are limited. This has been the fate of LB1, a new specimen found in the Liang Bua Cave of Flores. Several authors have attributed LB1 to a new species of Homo, H. floresiensis. According to this hypothesis, LB1 is either a member of the early Homo group or a descendent of an ancestor of the Asian H. erectus. Detractors have put forward an alternate hypothesis, which stipulates that LB1 is in fact a microcephalic modern human. In this paper, we show how we can employ a new Bayes optimal discriminant feature extraction technique to help resolve this type of issues. In this process, we present three types of experiments. First, we use this Bayes optimal discriminant technique to develop a model of morphological (shape) evolution from Australopiths to H. sapiens. LB1 fits perfectly in this model as a member of the early Homo group. Second, we build a classifier based on the available cranial and mandibular data appropriately normalized for size and volume. Again, LB1 is most similar to early Homo. Third, we build a brain endocast classifier to show that LB1 is not within the normal range of variation in H. sapiens. These results combined support the hypothesis of a very early shared ancestor for LB1 and H. erectus, and illustrate how discriminant analysis approaches can be successfully used to help classify newly discovered specimens.     

Type-2 fuzzy Gaussian mixture models

This paper presents a new extension of Gaussian mixture models (GMMs) based on type-2 fuzzy sets (T2 FSs) referred to as T2 FGMMs. The estimated parameters of the GMM may not accurately reflect the underlying distributions of the observations because of insufficient and noisy data in real-world problems. By three-dimensional membership functions of T2 FSs, T2 FGMMs use footprint of uncertainty (FOU) as well as interval secondary membership functions to handle GMMs uncertain mean vector or uncertain covariance matrix, and thus GMMs parameters vary anywhere in an interval with uniform possibilities. As a result, the likelihood of the T2 FGMM becomes an interval rather than a precise real number to account for GMMs uncertainty. These interval likelihoods are then processed by the generalized linear model (GLM) for classification decision-making. In this paper we focus on the role of the FOU in pattern classification. Multi-category classification on different data sets from UCI repository shows that T2 FGMMs are consistently as good as or better than GMMs in case of insufficient training data, and are also insensitive to different areas of the FOU. Based on T2 FGMMs, we extend hidden Markov models (HMMs) to type-2 fuzzy HMMs (T2 FHMMs). Phoneme classification in the babble noise shows that T2 FHMMs outperform classical HMMs in terms of the robustness and classification rate. We also find that the larger area of the FOU in T2 FHMMs with uncertain mean vectors performs better in classification when the signal-to-noise ratio is lower.