Cellular buckling in I-section struts

An analytical model that describes the interactive buckling of a thin-walled I-section strut under pure compression based on variational principles is presented. A formulation combining the Rayleigh--Ritz method and continuous displacement functions is used to derive a system of differential and integral equilibrium equations for the structural component. Numerical continuation reveals progressive cellular buckling (or snaking) arising from the nonlinear interaction between the weakly stable global buckling mode and the strongly stable local buckling mode. The resulting behaviour is highly unstable and when the model is extended to include geometric imperfections it compares excellently with some recently published experiments.Comment: 23 pages, 12 figures. Submitted for special issue of Thin-Walled Structure

Cellular buckling in stiffened plates

An analytical model based on variational principles for a thin-walled stiffened plate subjected to axial compression is presented. A system of nonlinear differential and integral equations is derived and solved using numerical continuation. The results show that the system is susceptible to highly unstable local--global mode interaction after an initial instability is triggered. Moreover, snap-backs in the response showing sequential destabilization and restabilization, known as cellular buckling or snaking, arise. The analytical model is compared to static finite element models for joint conditions between the stiffener and the main plate that have significant rotational restraint. However, it is known from previous studies that the behaviour, where the same joint is insignificantly restrained rotationally, is captured better by an analytical approach than by standard finite element methods; the latter being unable to capture cellular buckling behaviour even though the phenomenon is clearly observed in laboratory experiments.Comment: 22 pages, 9 figures, 1 table, accepted for publication. Proceedings of the Royal Society A, 201

Interaction of Salmonella spp. with the Intestinal Microbiota

Salmonella spp. are major cause of human morbidity and mortality worldwide. Upon entry into the human host, Salmonella spp. must overcome the resistance to colonization mediated by the gut microbiota and the innate immune system. They successfully accomplish this by inducing inflammation and mechanisms of innate immune defense. Many models have been developed to study Salmonella spp. interaction with the microbiota that have helped to identify factors necessary to overcome colonization resistance and to mediate disease. Here we review the current state of studies into this important pathogen/microbiota/host interaction in the mammalian gastrointestinal tract

Geometric modelling of kink banding in laminated structures

An analytical model founded on geometric and potential energy principles for kink band deformation in laminated composite struts is presented. It is adapted from an earlier successful study for confined layered structures which was formulated to model kink band formation in the folding of geological layers. The principal aim is to explore the underlying mechanisms governing the kinking response of flat, laminated components comprising unidirectional composite laminae. A pilot parametric study suggests that the key features of the mechanical response are captured well and that quantitative comparisons with experiments presented in the literature are highly encouraging

O-GlcNAcylation and Regulation of Galectin-3 in Extraembryonic Endoderm Differentiation

The regulation of proteins through the addition and removal of O-linked β-N-acetylglucosamine (O-GlcNAc) plays a role in many signaling events, specifically in stem cell pluripo-tency and the regulation of differentiation. However, these post-translational modifications have not been explored in extraembryonic endoderm (XEN) differentiation. Of the plethora of proteins regulated through O-GlcNAc, we explored galectin-3 as a candidate protein known to have various intracellular and extracellular functions. Based on other studies, we predicted a reduction in global O-GlcNAcylation levels and a distinct galectin expression profile in XEN cells relative to embryonic stem (ES) cells. By conducting dot blot analysis, XEN cells had decreased levels of global O-GlcNAc than ES cells, which reflected a disbalance in the expression of genes encoding O-GlcNAc cycle enzymes. Immunoassays (Western blot and ELISA) revealed that although XEN cells (low O-GlcNAc) had lower concentrations of both intracellular and extracellular galectin-3 than ES cells (high O-GlcNAc), the relative secretion of galectin-3 was significantly increased by XEN cells. Inducing ES cells toward XEN in the presence of an O-GlcNAcase inhibitor was not sufficient to inhibit XEN differentiation. However, global O-GlcNAcylation was found to decrease in differentiated cells and the extracellular localization of galectin-3 accompanies these changes. Inhibiting global O-GlcNAcylation status does not, however, impact pluripotency and the ability of ES cells to differentiate to the XEN lineage

Explainable Machine Learning Techniques in Medical Image Analysis Based on Classification with Feature Extraction

Animals are also afflicted by COVID-19, a virus that is quickly spreading and infects both humans and animals. This fatal viral disease has an impact on people's daily lives, health, and economy of a nation. Most effective machine learning method is deep learning, which offers insightful analysis for examining a significant number of chest x-ray pictures that have a significant bearing on COVID-19 screening. This research proposes novel technique in lung image analysis for detection of lung infection due to COVID using Explainable Machine learning techniques. Here the input has been collected as COVID patient’s lung image dataset and it has been processed for noise removal and smoothening. This processed image features have been extracted using spatio transfer neural network integrated with DenseNet+ architecture. Extracted features has been classified using stacked auto Boltzmann encoder machine with VGG-19Net+. With the transfer learning method integrated into the binary classification process, the suggested algorithm achieves good classification accuracy. The experimental analysis has been carried out for various COVID dataset in terms of accuracy, precision, Recall, F-1score, RMSE, MAP. The proposed technique attained accuracy of 95%, precision of 91%, recall of 85%, F_1 score of 80%, RMSE of 61% and MAP of 51%

E. coli K-12 and EHEC Genes Regulated by SdiA

Background: Escherichia and Salmonella encode SdiA, a transcription factor of the LuxR family that regulates genes in response to N-acyl homoserine lactones (AHLs) produced by other species of bacteria. E. coli genes that change expression in the presence of plasmid-encoded sdiA have been identified by several labs. However, many of these genes were identified by overexpressing sdiA on a plasmid and have not been tested for a response to sdiA produced from its natural position in the chromosome or for a response to AHL. Methodology/Principal Findings: We determined that two important loci reported to respond to plasmid-based sdiA, ftsQAZ and acrAB, do not respond to sdiA expressed from its natural position in the chromosome or to AHLs. To identify genes that are regulated by chromosomal sdiA and/or AHLs, we screened 10,000 random transposon-based luciferase fusions in E. coli K-12 and a further 10,000 in E. coli O157:H7 for a response to AHL and then tested these genes for sdiAdependence. We found that genes encoding the glutamate-dependent acid resistance system are up-regulated, and fliE is down-regulated, by sdiA. Gene regulation by sdiA of E. coli is only partially dependent upon AHL. Conclusions/Significance: The genes of E. coli that respond to plasmid-based expression of sdiA are largely different than those that respond to chromosomal sdiA and/or AHL. This has significant implications for determining the true function o