Electrospun medicated shellac nanofibers for colon-targeted drug delivery

Medicated shellac nanofibers providing colon-specific sustained release were fabricated using coaxial electrospinning. A solution of 7.5 g shellac and 1.5 g of ferulic acid (FA) in 10 mL ethanol was used as the core fluid, and a mixture of ethanol and N,N-dimethylformamide (8/10 v/v) as the shell. The presence of the shell fluid was required to prevent frequent clogging of the spinneret. The diameters of the fibers (D) can be manipulated by varying the ratio of shell to core flow rates (F), according to the equation D = 0.52F−0.19. Scanning electron microscopy images revealed that fibers prepared with F values of 0.1 and 0.25 had linear morphologies with smooth surfaces, but when the shell fluid flow rate was increased to 0.5 the fiber integrity was compromised. FA was found to be amorphously distributed in the fibers on the basis of X-ray diffraction and differential scanning calorimetry results. This can be attributed to good compatibility between the drug and carrier: IR spectra indicated the presence of hydrogen bonds between the two. In vitro dissolution tests demonstrated that there was minimal FA release at pH 2.0, and sustained release in a neutral dissolution medium. The latter occurred through an erosion mechanism. During the dissolution processes, the shellac fibers were gradually converted into nanoparticles as the FA was freed into solution, and ultimately completely dissolved

Face Detection with Effective Feature Extraction

There is an abundant literature on face detection due to its important role in many vision applications. Since Viola and Jones proposed the first real-time AdaBoost based face detector, Haar-like features have been adopted as the method of choice for frontal face detection. In this work, we show that simple features other than Haar-like features can also be applied for training an effective face detector. Since, single feature is not discriminative enough to separate faces from difficult non-faces, we further improve the generalization performance of our simple features by introducing feature co-occurrences. We demonstrate that our proposed features yield a performance improvement compared to Haar-like features. In addition, our findings indicate that features play a crucial role in the ability of the system to generalize.Comment: 7 pages. Conference version published in Asian Conf. Comp. Vision 201

Interactive Visualization of the Largest Radioastronomy Cubes

3D visualization is an important data analysis and knowledge discovery tool, however, interactive visualization of large 3D astronomical datasets poses a challenge for many existing data visualization packages. We present a solution to interactively visualize larger-than-memory 3D astronomical data cubes by utilizing a heterogeneous cluster of CPUs and GPUs. The system partitions the data volume into smaller sub-volumes that are distributed over the rendering workstations. A GPU-based ray casting volume rendering is performed to generate images for each sub-volume, which are composited to generate the whole volume output, and returned to the user. Datasets including the HI Parkes All Sky Survey (HIPASS - 12 GB) southern sky and the Galactic All Sky Survey (GASS - 26 GB) data cubes were used to demonstrate our framework's performance. The framework can render the GASS data cube with a maximum render time < 0.3 second with 1024 x 1024 pixels output resolution using 3 rendering workstations and 8 GPUs. Our framework will scale to visualize larger datasets, even of Terabyte order, if proper hardware infrastructure is available.Comment: 15 pages, 12 figures, Accepted New Astronomy July 201

Nanofibers fabricated using triaxial electrospinning as zero order drug delivery systems

A new strategy for creating functional trilayer nanofibers through triaxial electrospinning is demonstrated. Ethyl cellulose (EC) was used as the filament-forming matrix in the outer, middle, and inner working solutions and was combined with varied contents of the model active ingredient ketoprofen (KET) in the three fluids. Triaxial electrospinning was successfully carried out to generate medicated nanofibers. The resultant nanofibers had diameters of 0.74 ± 0.06 μm, linear morphologies, smooth surfaces, and clear trilayer nanostructures. The KET concentration in each layer gradually increased from the outer to the inner layer. In vitro dissolution tests demonstrated that the nanofibers could provide linear release of KET over 20 h. The protocol reported in this study thus provides a facile approach to creating functional nanofibers with sophisticated structural features

A framework for evaluating automatic image annotation algorithms

Several Automatic Image Annotation (AIA) algorithms have been introduced recently, which have been found to outperform previous models. However, each one of them has been evaluated using either different descriptors, collections or parts of collections, or "easy" settings. This fact renders their results non-comparable, while we show that collection-specific properties are responsible for the high reported performance measures, and not the actual models. In this paper we introduce a framework for the evaluation of image annotation models, which we use to evaluate two state-of-the-art AIA algorithms. Our findings reveal that a simple Support Vector Machine (SVM) approach using Global MPEG-7 Features outperforms state-of-the-art AIA models across several collection settings. It seems that these models heavily depend on the set of features and the data used, while it is easy to exploit collection-specific properties, such as tag popularity especially in the commonly used Corel 5K dataset and still achieve good performance

Bayesian data integration and variable selection for pan‐cancer survival prediction using protein expression data

Accurate prognostic prediction using molecular information is a challenging area of research, which is essential to develop precision medicine. In this paper, we develop translational models to identify major actionable proteins that are associated with clinical outcomes, like the survival time of patients. There are considerable statistical and computational challenges due to the large dimension of the problems. Furthermore, data are available for different tumor types; hence data integration for various tumors is desirable. Having censored survival outcomes escalates one more level of complexity in the inferential procedure. We develop Bayesian hierarchical survival models, which accommodate all the challenges mentioned here. We use the hierarchical Bayesian accelerated failure time model for survival regression. Furthermore, we assume sparse horseshoe prior distribution for the regression coefficients to identify the major proteomic drivers. We borrow strength across tumor groups by introducing a correlation structure among the prior distributions. The proposed methods have been used to analyze data from the recently curated “The Cancer Proteome Atlas” (TCPA), which contains reverse‐phase protein arrays–based high‐quality protein expression data as well as detailed clinical annotation, including survival times. Our simulation and the TCPA data analysis illustrate the efficacy of the proposed integrative model, which links different tumors with the correlated prior structures.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154486/1/biom13132_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154486/2/biom13132.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154486/3/biom13132-sup-0003-supmat.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154486/4/biom13132-sup-0002-supplementary-v6-22Jul2019.pd

Synthesis and Stoichiometry of MgB2

The system MgxB2 has been investigated to investigate possible nonstoichiometry in MgB2. When synthesized at 850oC, MgB2 is a line compound with a possible Mg vacancy content of about 1%. Small changes in lattice constants as a function of starting composition result from grain interaction stresses, whose character is different in the Mg-rich, near-stoichiometric, and Mg-deficient regimes. A small linear decrease of the superconducting transition temperature, Tc, in the Mg-rich regime results from accidental impurity doping.Comment: Accepted for publication in Physica C. 24 pages, 7 figure

Revised standards for reporting interventions in clinical trials of acupuncture (STRICTA) : Extending the CONSORT statement

The Standards for Reporting Interventions in Clinical Trials of Acupuncture (STRICTA) were published in five journals in 2001 and 2002. These guidelines, in the form of a checklist and explanations for use by authors and journal editors, were designed to improve reporting of acupuncture trials, particularly the interventions, thereby facilitating their interpretation and replication. Subsequent reviews of the application and impact of STRICTA have highlighted the value of STRICTA as well as scope for improvements and revision

Block bond-order potential as a convergent moments-based method

The theory of a novel bond-order potential, which is based on the block Lanczos algorithm, is presented within an orthogonal tight-binding representation. The block scheme handles automatically the very different character of sigma and pi bonds by introducing block elements, which produces rapid convergence of the energies and forces within insulators, semiconductors, metals, and molecules. The method gives the first convergent results for vacancies in semiconductors using a moments-based method with a low number of moments. Our use of the Lanczos basis simplifies the calculations of the band energy and forces, which allows the application of the method to the molecular dynamics simulations of large systems. As an illustration of this convergent O(N) method we apply the block bond-order potential to the large scale simulation of the deformation of a carbon nanotube.Comment: revtex, 43 pages, 11 figures, submitted to Phys. Rev.