Biosynthesis of SeNPs by Mycobacterium bovis and their enhancing effect on the immune response against HBs antigens: an in vivo study

Conventional hepatitis B vaccine contains alum but is less effective to induce Th1 response. Selenium nanoparticles and Bacillus Calmette–Guerin were reported as immune modulators. In this study, SeNPs were extracted from Mycobacterium bovis and characterised. SeNPs were mixed with HBs‐Ag and administered to the mice to investigate he immune response pattern. With an addition of Se ions at a sub‐inhibitory concentration to the Sauton medium broth after 24 h, SeNPs were extracted from M. bovis and characterised by Fourier transform infrared spectroscopy, dynamic light scattering, atomic forcemicroscopy, energy dispersive X‐ray spectrum, transmission electron microscopy, and thermogravimetric analysis. Furthermore, female inbred BALB/c mice were injected subcutaneously on the first, 14th, 28th day with 100 and 200 µg doses of that SeNPs supplemented with HBs‐Ag vaccine. Later, the total antibody, isotypes of Immunoglobulin G1, Interlukin 4, and interferon‐γ were measured by enzye‐linked immunosorbent assay. The size of the SeNPs was <150 nm. Level of total antibody and immunoglobulin G2a increased significantly in the group that received 200 µg/ml nano selenium extracted from M. bovis. SeNPs in dose of 200 µg coated with organic materials of M. bovis could induce an influential immune response in relation to the conventional HBs‐Ag vaccine.Inspec keywords: selenium, nanoparticles, nanofabrication, microorganisms, Fourier transform spectra, infrared spectra, light scattering, atomic force microscopy, X‐ray chemical analysis, transmission electron microscopy, thermal analysis, nanomedicineOther keywords: biosynthesis, selenium nanoparticles, Mycobacterium bovis, enhnced immune response, HBs antigens, Bacillus Calmette‐Guerin, immune modulators, biogenic SeNPs, immune response pattern, Sauton medium broth, Fourier transform infrared spectroscopy, dynamic light scattering, atomic force microscopy, energy dispersive X‐ray spectrum, transmission electron microscopy, thermogravimetric analysis, female inbred BALB/c mice, antibody, IgG1 isotype, IgG2a isotype, IL₄ isotype, interferon‐γ, enzyme‐linked immunosorbent assay, bacterium, time 24 h, time 14 day, time 28 day, Se     

Analysis of a clock synchronization protocol for wireless sensor networks

The Dutch company Chess develops a wireless sensor network (WSN) platform using an epidemic communication model. One of the greatest challenges in the design is to find suitable mechanisms for clock synchronization. In this paper, we study a proposed clock synchronization protocol for the Chess platform. First, we model the protocol as a network of timed automata and verify various instances using the Uppaal model checker. Next, we present a full parametric analysis of the protocol for the special case of cliques (networks with full connectivity), that is, we give constraints on the parameters that are both necessary and sufficient for correctness. These results have been checked using the proof assistant Isabelle. We report on the exhaustive analysis of the protocol for networks with four nodes, and we present a negative result for the special case of line topologies: for any instantiation of the parameters, the protocol will eventually fail if the network grows.     

Remineralization effects of self-assembling peptide P11-4 associated with three materials on early enamel carious lesions: An in vitro study

The aim of this study was to assess the remineralization of enamel caries lesions using the self-assembling peptide P₁₁-4 associated with different materials. Artificial early enamel lesions were prepared on 154 primary teeth. The samples were randomly divided into eight groups: (1) control, (2) P₁₁-4, (3) fluoridate toothpaste (FT), (4) P₁₁-4 + FT, (5) casein phosphopeptide–amorphous calcium phosphate (CPP–ACP), (6) P₁₁-4 + CPP–ACP, (7) fluoridate bioactive glass toothpaste (BT), and (8) P₁₁-4 + BT. The surface enamel microhardness (EMH) and energy-dispersive X-ray spectroscopy (EDS) of the teeth were then measured at the baseline, after demineralization, and after 28 days of remineralization. The enamel surfaces were assessed by field emission scanning electron microscopy (FESEM) and atomic force microscopy (AFM). The data were analyzed with one-way analysis of variance (ANOVA) (p < .05). EMH after demineralization was significantly lower than the baseline value (p < .001). The interventions led to an enhanced percentage of EMH recovery (%REMH), which was higher in Groups 6 and 7. There was no significant difference between Groups 3 and 4. Groups 1 and 2 had the lowest %REMH. The mean calcium/phosphate weight percentage ratio of P₁₁-4 was significantly lower than the others (p < .001). The FESEM and AFM images revealed mineral deposition on the eroded enamel and reductions in surface roughness in Groups 5 and 7.     

Understanding holistic human pose using class-specific convolutional neural network

This paper presents a method to capture human pose from individual real-world RGB images using a deep learning technique. The current works on estimating human pose by deep learning are designed in a detection or a regression framework, and in a part-based manner. As a new perspective, we introduce a classification scheme for this problem, which reasons the pose holistically. To the best of our knowledge, this is the first work for holistic human pose classification task that owes its feasibility to the great power of convolutional neural networks in feature learning. After training a convolutional neural network to classify the input image to one of the KeyPoses, the final pose is computed as a linear combination of several KeyPoses. In this new holistic classification attitude, the vast and high degree of freedom human pose space is divided into a finite number of subspaces and the convolutional neural network shows promising results in learning the features of each subspace. Empirical results (PCP and PCK rates) demonstrate that the proposed scheme is successfully able to understand human pose (i.e., predict a valid, true and coarse pose) in real-world unconstrained images with challenges like severe occlusion, high articulation, low quality and cluttered background. Furthermore, using the proposed method, the need for defining a complex model (such as appearance model or joints pairwise relations) is relieved. We have also verified a potential application of our proposed method in semantic image retrieval based on human pose.     

Fusing the information in visible light and near-infrared images for iris recognition

Automated human identification is a significant issue in real and virtual societies. Iris is a suitable choice for meeting this goal. In this paper, we present an iris recognition system that uses images acquired in both near-infrared and visible lights. These two types of images reveal different textural information of the iris tissue. We demonstrated the necessity to process both VL and NIR images to recognize irides. The proposed system exploits two feature extraction algorithms: one is based on 1D log-Gabor wavelet which gives a detailed representation of the iris region and the other is based on 1D Haar wavelet which represents a coarse model of iris. The Haar wavelet algorithm is proposed in this paper. It makes smaller iris templates than the 1D log-Gabor approach and yet achieves an appropriate recognition rate. We performed the fusion at the match score level and examined the performance of the system in both verification and identification modes. UTIRIS database was used to evaluate the method. The results were compared with other approaches and proved to have better recognition accuracy, while no image enhancement technique is utilized prior to the feature extraction stage. Furthermore, we demonstrated that fusion can compensate the lack of input image information, which can be beneficial in reducing the computation complexity and handling non-cooperative iris images.     

Annual variation of deoxynivalenol in Danish wheat flour 1998-2003 and estimated daily intake by the Danish population

The occurrence of deoxynivalenol (DON) in Danish wheat flour was studied during the period 1998-2003 by either capillary gas chromatography with electron capture detection and liquid chromatography coupled to an ion trap mass spectrophotometer. A total of 151 samples were collected from mills and the retail market in Denmark. Contamination levels varied considerably from year-to-year with the highest concentrations occurring in samples from the 2002 harvest with mean and median concentrations of 255 and 300 µg kg^-1, respectively. Compared to other harvest years, 2002 had the highest amount of precipitation around flowering time, i.e. from the end of June to the beginning of July covering weeks 25-27. The lowest average levels were found in samples from the 2001 harvest, where weeks 25-27 were dry compared with other harvest years. The highest value (705 µg kg^-1) was obtained in a flour sample from the 2002 harvest, but none of the tested samples exceeded the maximum limit of 750 µg kg^-1, which has been recently introduced by the European Commission for DON in flour used as raw materials in food products. Calculation of chronic or usual intake by a deterministic approach showed that intake did not exceed the TDI of 1 µg kg^-1 bw day^-1 either for the whole population or for children. A probabilistic approach also showed that intake in general was below the TDI, but intake for children in the 99% percentile amounted to more than 75% of the TDI. The highest intake is calculated to be 2.5 µg kg^-1 bw day^-1.     

Continuum-molecular modelling of graphene

In the present contribution we address the modeling of graphene membranes using a hierarchical modeling strategy to bridge the scales required to describe and understand the material. Quantum Mechanical (QM) and optimized Molecular Mechanical (MM) models are used to describe details on the nanoscale, while a multiscale continuum mechanical method is used to model the graphene response at the device or micrometer scale. The complete method is obtained on the basis of the Cauchy Born Rule (CBR), where the continuum model is coupled to the atomic field via the CBR and a local discrete fluctuation field. The MM method, often used to model carbon structures, involves the Tersoff–Brenner (TB) potential; however, when applying this potential to graphene with standard parameters one obtains material stress behavior much weaker than experiments. On the other hand, the more fundamental Hartree Fock and Density Functional Theory (DFT) methods are computationally too expensive and very limited in terms of their applicability to model the geometric scale at the device level. In this contribution a simple calibration of some of the TB parameters is proposed in order to reproduce the results obtained from QM calculations. Subsequently, the fine-tuned TB-potential is used for the multiscale modeling of a nano indentation sample, where experimental data are available. Effects of the mechanical response after the calibration are demonstrated.