Effect of double-fibre reinforcement on localized bulging of an inflated cylindrical tube of arbitrary thickness

We consider localized bulging of an inflated cylindrical hyperelastic tube of arbitrary thickness that is helically reinforced by two families of fibres. It is shown that localized bulging may become impossible, irrespective of the end conditions, when the tube wall becomes thick enough. This is in sharp contrast with an isotropic hyperelastic tube without fibre reinforcement for which localized bulging has previously been shown to be possible no matter how thick the tube wall is and for which the membrane theory provides a very good approximation for the ratio of wall-thickness/radius as large as 0.67. Our findings provide a feasible explanation on why aneurysms cannot occur in healthy arteries but become possible following pathological changes. They can also be used to guide the design of tubular structures where localized bulging should be prevented

Experimental study of non-Newtonian fluid flow in microchannels

This paper was presented at the 3rd Micro and Nano Flows Conference (MNF2011), which was held at the Makedonia Palace Hotel, Thessaloniki in Greece. The conference was organised by Brunel University and supported by the Italian Union of Thermofluiddynamics, Aristotle University of Thessaloniki, University of Thessaly, IPEM, the Process Intensification Network, the Institution of Mechanical Engineers, the Heat Transfer Society, HEXAG - the Heat Exchange Action Group, and the Energy Institute.Non-Newtonian fluid flow in microchannels has significant applications in science and engineering. The effects of temperature and PAM solution concentrations on rheological parameters are analyzed by measuring them with rotating cylinder viscometer. Flow characteristics for deionized water and PAM solutions in fused silica microtubes with diameters ranging from 50 to 320μm, fused silica square microchannels with diameters 75 and 100μm, and stainless steel microtubes with diameters from 120 to 362μm, are studied experimentally. The test results for deionized water in microchannels are in good agreement with theoretical predictions for conventional-size channels. Friction factors of PAM solutions are much higher than theoretical predictions. With the PAM concentration reduced, the deviation is more, which is possibly caused by the significant electroviscous effect on PAM solutions flow in microchannels

Microcystin-leucine arginine causes cytotoxic effects in sertoli cells resulting in reproductive dysfunction in male mice

2016-2017 > Academic research: refereed > Publication in refereed journal201804_a bcmaVersion of RecordPublishe

Polymorphisms of two neuroendocrine–correlated genes associated with body weight and reproductive traits in Jinghai yellow chicken

In this study, insulin-like growth factor binding protein 2 (IGFBP-2) and signal transducers activators of transcription 5b (STAT5b) gene were studied as candidate gene associated with body weight and reproductive traits of the Jinghai Yellow chicken. Single nucleotide polymorphisms (SNPs) of the IGFBP-2 and STAT5b genes were examined in both Jinghai Yellow chicken and three reference chicken populations by PCR-SSCP. Two SNPs (T3746TG and CC3753TT) were detected in the IGFBP-2 gene. One SNP (C8066T) was observed in the STAT5b gene. For primer 1, the general linear model analysis showed that Jinghai yellow chickens with FF genotypes had significant effect on hatch weight, egg weight at 300 days and body weight at 300 days than those of the EF genotype and had significant effect on body weight at 8 weeks of age than those of the EE genotype (P < 0.05). For primer 2, Jinghai yellow chickens with CT genotype had significant effect on hatch weight and age at first egg than CC genotype and TT genotype respectively (P < 0.05). SNPs in both IGFBP-2 and STAT5b genes had significant effect on body weight and reproductive traits of the Jinghai yellow chicken than those with either SNP alone. These SNPs may be served as a potential genetic marker for growth and reproduction traits evaluation of the Jinghai yellow chicken.Key words: Jinghai Yellow chicken, IGFBP-2 gene, STAT5b gene, economic traits, polymorphism

Optimization of fermentation medium for nisin production from Lactococcus lactis subsp. lactis using response surface methodology (RSM) combined with artificial neural network-genetic algorithm (ANN-GA)

Nisin is a bacteriocin approved in more than 50 countries as a safe natural food preservative. Response surface methodology (RSM) combined with artificial neural network-genetic algorithm (ANN-GA) was employed to optimize the fermentation medium for nisin production. Plackett-Burman design (PBD) was used for identifying the significant components in the fermentation medium. After that, the path of steepest ascent method (PSA) was employed to approach their optimal concentrations. Sequentially, Box-Behnken design experiments were implemented for further optimization. RSM combined with ANNGA were used for analysis of data. Specially, a RSM model was used for determining the individual effect and mutual interaction effect of tested variables on nisin titer (NT), an ANN model was used for NT prediction, and GA was employed to search for the optimum solutions based on the ANN model. As the optimal medium obtained by ANN-GA was located at the verge of the test region, a further Box- Behnken design based on the RSM statistical analysis results was implemented. ANN-GA was implemented using the further Box-Behnken design data to locate the optimum solution which was as follow (g/l): Glucose (GLU) 15.92, peptone (PEP) 30.57, yeast extraction powder (YEP) 39.07, NaCl 5.25, KH2PO4 10.00, and MgSO4·7H2O 0.20, with expected NT of 22216 IU/ml. The validation experiments with the optimum solution were implemented in triplicate and the average NT was 21423 IU/ml, which was 2.13 times higher than that without ANN-GA methods and 8.34 times higher than that without optimization.Key words: Response surface methodology, artificial neural network, genetic algorithm, nisin titer

Density functional theory studies of hydrogen bonding vibrations in sI gas hydrates

Abstract To analyze the vibrational modes of water and methane in structure I gas hydrates, we constructed a 178-atom supercell with two small cages of type 512 and six large cages of type 51262. We applied the density functional theory method to simulate the vibrational spectrum and normal modes of methane hydrates. In accord with our previous studies, we confirmed that two groups of hydrogen bond (H-bond) peaks (at around 291 and 210 cm−1) in the translational bands come from two kinds of intermolecular H-bond vibrational modes. This is the first investigation of H-bond vibrations in methane hydrates. The partial modes of CH4 were extracted. We found that the CH4 phonons in the translational region are below 180 cm−1 so that the influence of methane on the H-bond is insignificant. We proposed a new method to decompose gas hydrates via direct application of terahertz radiation to the H-bonds. Herein, we confirmed that CH4 molecules do not absorb this energy.</jats:p

Weakly supervised segmentation from extreme points

Annotation of medical images has been a major bottleneck for the development of accurate and robust machine learning models. Annotation is costly and time-consuming and typically requires expert knowledge, especially in the medical domain. Here, we propose to use minimal user interaction in the form of extreme point clicks in order to train a segmentation model that can, in turn, be used to speed up the annotation of medical images. We use extreme points in each dimension of a 3D medical image to constrain an initial segmentation based on the random walker algorithm. This segmentation is then used as a weak supervisory signal to train a fully convolutional network that can segment the organ of interest based on the provided user clicks. We show that the network's predictions can be refined through several iterations of training and prediction using the same weakly annotated data. Ultimately, our method has the potential to speed up the generation process of new training datasets for the development of new machine learning and deep learning-based models for, but not exclusively, medical image analysis.Comment: Accepted at the MICCAI Workshop for Large-scale Annotation of Biomedical data and Expert Label Synthesis, Shenzen, China, 201