Role of AGAP2 in the profibrogenic effects induced by TGFβ in LX-2 hepatic stellate cells

Liver damage induces hepatic stellate cells (HSC) activation, characterised by a fibrogenic, proliferative and migratory phenotype. Activated HSC are mainly regulated by transforming growth factor β 1 (TGFβ1), which increases the production of extracellular matrix proteins (e.g. collagen-I) promoting the progression of hepatic fibrosis. AGAP2 (ArfGAP with GTPase domain, ankyrin repeat and PH domain 2) is a GTPase/GTP-activating protein involved in the actin remodelling system and receptor recycling. In the present work the role of AGAP2 in human HSC in response to TGFβ1 was investigated. LX-2 HSC were transfected with AGAP2 siRNA and treated with TGFβ1. AGAP2 knockdown prevented to some extent the proliferative and migratory TGFβ1-induced capacities of LX-2 cells. An array focused on human fibrosis revealed that AGAP2 knockdown partially prevented TGFβ1-mediated gene expression of the fibrogenic genes ACTA2, COL1A2, EDN1, INHBE, LOX, PDGFB, TGFΒ12, while favored the expression of CXCR4, IL1A, MMP1, MMP3 and MMP9 genes. Furthermore, TGFβ1 induced AGAP2 promoter activation and its protein expression in LX-2. In addition, AGAP2 silencing affected TGFβ1-receptor 2 (TGFR2) trafficking in U2OS cells, blocking its effective recycling to the membrane. AGAP2 silencing in LX-2 cells prevented the TGFβ1-induced increase of collagen-I protein levels, while its over-expression enhanced collagen I protein expression in the presence or absence of the cytokine. AGAP2 overexpression also increased focal adhesion kinase (FAK) phosphorylated levels in LX-2 cells. FAK and MEK1 inhibitors prevented the increase of collagen-I expression caused by TGFβ1 in LX-2 overexpressing AGAP2. In summary, the present work shows for the first time, that AGAP2 is a potential new target involved in TGFβ1 signalling, contributing to the progression of hepatic fibrosis

Multiscale superpixelwise prophet model for noise-robust feature extraction in hyperspectral images

Despite of various approaches proposed to smooth the hyperspectral images (HSIs) before feature extraction, the efficacy is still affected by the noise, even using the corrected dataset with the noisy and water absorption bands discarded. In this study, a novel spectral-spatial feature mining framework, Multiscale Superpixelwise Prophet Model (MSPM), is proposed for noise-robust feature extraction and effective classification of the HSI. The prophet model is highly noise-robust for deeply digging into the complex structured features thus enlarging interclass diversity and improving intraclass similarity. First, the superpixelwise segmentation is produced from the first three principal components of an HSI to group pixels into regions with adaptively determined sizes and shapes. A multiscale prophet model is utilized to extract the multiscale informative trend components from the average spectrum of each superpixel. Taking the multiscale trend signal as the input feature, the HSI data are classified superpixelwisely, which is further refined by a majority vote based decision fusion. Comprehensive experiments on three publicly available datasets have fully validated the efficacy and robustness of our MSPM model when benchmarked with eleven state-of-the-art algorithms, including six spectral-spatial methods and five deep learning ones. Besides, MSPM also shows superiority under limited training samples, due to the combined strategies of superpixelwise fusion and multiscale fusion. Our model has provided a useful solution for noise-robust feature extraction as it achieves superior HSI classification even from the uncorrected dataset without prefiltering the water absorption and noisy bands

Hyperspectral band selection using crossover based gravitational search algorithm

Band selection is an important data dimensionality reduction tool in hyperspectral images (HSIs). To identify the most informative subset band from the hundreds of highly corrected bands in HSIs, a novel hyperspectral band selection method using a crossover based gravitational search algorithm (CGSA) is presented in this paper. In this method, the discriminative capability of each band subset is evaluated by a combined optimization criterion, which is constructed based on the overall classification accuracy and the size of the band subset. As the evolution of the criterion, the subset is updated using the V-shaped transfer function based CGSA. Ultimately, the band subset with the best fitness value is selected. Experiments on two public hyperspectral datasets, i.e. the Indian Pines dataset and the Pavia University dataset, have been conducted to test the performance of the proposed method. Comparing experimental results against the basic GSA and the PSOGSA (hybrid PSO and GSA) revealed that all of the three GSA variants can considerably reduce the band dimensionality of HSIs without damaging their classification accuracy. Moreover, the CGSA shows superiority on both the effectiveness and efficiency compared to the other two GSA variants

Passive gamma-ray analysis of UO2 fuel rods using SrI2(Eu) scintillators in multi-detector arrangements

The use of passive gamma analysis (PGA) to interrogate nuclear fuel rods for quality assurance and non-proliferation purposes has been demonstrated to be as efficient and safer than the more common neutron activation analysis (NAA) for fresh fuel assessment. In this work we will build on existing experimental measurements using a single probe SrI2(Eu), to simulate the response of a multi-probe SrI2(Eu) detector array in the analysis of nuclear fuel rods, containing sintered UO2. The work compares the results to literature studies using bismuth germinate and annulus CsI(Tl) multi-probes to evaluate light water reactor fuel rods. The enhanced scintillation performance of SrI2(Eu) over CsI(Tl) and BGO make it ideally suited for nuclear fuel inspection

Passive, non-destructive enrichment measurement of sintered UO2 pellets

The measurement of uranium dioxide (UO2) enrichment in simulated pressurized water reactor fuel pellets has been performed using the infinite thickness technique. The use of this technique is well established on large volumes of uranium compounds, however new advanced reactor fuels require the enrichment assessment of individual UO2 fuel pellets. Monte Carlo GEANT4 simulations and experimental measurements were performed to collect energy and count data for sintered UO2 sim fuel pellet slices. Results indicate that the infinite thickness technique can be used on individual fuel pellets for determination of enrichment, by confirming the infinite thickness of sintered UO2 (4.9 mm), and that the count rate form the characteristic 185.6 keV 235U photopeak increases linearly with fuel pellet enrichment. Working is ongoing to refine the simulated datasets and improve experimental measurements

Hyperspectral Imaging based Characterization and Identification of Sintered UO2 Fuel Pellets

Hyperspectral Imaging (HSI) is a well-established technology able to capture the same spatial scene or image in hundreds of different wavelengths across the electromagnetic spectrum, covering not only the visible but also the shortwave infrared range (1000-2500nm), potentially revealing information otherwise invisible to the human eye. In this work, we explored whether HIS technology can be used for the fast-non-invasive characterization and identification of sintered UO2 fuel pellets. Preliminary experiments included the imaging of the pellets, revealing their spectral responses. These show promising features that could be used for their identification, where two different groups of pellets, pure and doped, seem to be easily recognized based on their spectral response. The experiments included a pixel-wise classification map generated via the Spectral Angle Mapper (SAM) technique in which the pure and doped pellets are successfully identified

A Novel Intelligent Computational Approach to Model Epidemiological Trends and Assess the Impact of Non-Pharmacological Interventions for COVID-19

The novel coronavirus disease 2019 (COVID-19) pandemic has led to a worldwide crisis in public health. It is crucial we understand the epidemiological trends and impact of non-pharmacological interventions (NPIs), such as lockdowns for effective management of the disease and control of its spread. We develop and validate a novel intelligent computational model to predict epidemiological trends of COVID-19, with the model parameters enabling an evaluation of the impact of NPIs. By representing the number of daily confirmed cases (NDCC) as a time-series, we assume that, with or without NPIs, the pattern of the pandemic satisfies a series of Gaussian distributions according to the central limit theorem. The underlying pandemic trend is first extracted using a singular spectral analysis (SSA) technique, which decomposes the NDCC time series into the sum of a small number of independent and interpretable components such as a slow varying trend, oscillatory components and structureless noise. We then use a mixture of Gaussian fitting (GF) to derive a novel predictive model for the SSA extracted NDCC incidence trend, with the overall model termed SSA-GF. Our proposed model is shown to accurately predict the NDCC trend, peak daily cases, the length of the pandemic period, the total confirmed cases and the associated dates of the turning points on the cumulated NDCC curve. Further, the three key model parameters, specifically, the amplitude ( alpha ), mean ( mu ), and standard deviation ( sigma ) are linked to the underlying pandemic patterns, and enable a directly interpretable evaluation of the impact of NPIs, such as strict lockdowns and travel restrictions. The predictive model is validated using available data from China and South Korea, and new predictions are made, partially requiring future validation, for the cases of Italy, Spain, the UK and the USA. Comparative results demonstrate that the introduction of consistent control measures across countries can lead to development of similar parametric models, reflected in particular by relative variations in their underlying sigma , alpha and mu values. The paper concludes with a number of open questions and outlines future research directions

Pixel-wise segmentation of SAR imagery using encoder-decoder network and fully-connected CRF

Synthetic Aperture Radar (SAR) image segmentation is an important step in SAR image interpretation. Common Patch-based methods treat all the pixels within the patch as a single category and do not take the label consistency between neighbor patches into consideration, which makes the segmentation results less accurate. In this paper, we use an encoder-decoder network to conduct pixel-wise segmentation. Then, in order to make full use of the contextual information between patches, we use fully-connected conditional random field to optimize the combined probability map output from encoder-decoder network. The testing results on our SAR data set shows that our method can effectively maintain contextual information of pixels and achieve better segmentation results