Investigate the plasmonic enhanced solar photothermal effect of gold nanorod nanofilm

Gold nanospheres (Au NSs) and gold nanorods (Au NRs) are traditional noble metal plasmonic nanomaterials. Particularly, Au NRs with tunable longitudinal plasmon resonance from visible to the near infrared (NIR) range were suitable for high efficient photothermal applications due to extended light receiving range. In this work, we synthesized Au NRs and Au NSs of similar volume, and subsequently developed them into Au NR/PVDF and Au NS/PVDF nanofilm, both of which exhibited excellent solar photothermal performance evaluated by solar photothermal experiments. We found that Au NR/PVDF nanofilm showed higher solar photothermal performance than Au NS/PVDF nanofilm. Through detailed analysis, such as morphological characterization, optical measurement, and finite element method (FEM) modeling, we found that the plasmonic coupling effects inside the aggregated Au NRs nanoclusters contributed to the spectral blue-shifts and intensified photothermal performance. Compare to Au NS/PVDF nanofilms, Au NR/PVDF nanofilm exhibited higher efficient light-to-heat conversion rate, because of the extended light receiving range and high absorbance, as the result of strong plasmonic interactions inside nanoclusters, which was further validated by monochromatic laser photothermal experiments and FEM simulations. Our work proved that the Au NRs have huge potential for plasmonic solar photothermal applications, and are envisioned for novel plasmonic applications

Simultaneous Extraction and Identification of Phenolic Compounds in Anoectochilus roxburghii Using Microwave-Assisted Extraction Combined with UPLC-Q-TOF-MS/MS and Their Antioxidant Activities

This study used MAE and RSM to extract phenolic compounds from Anoectochilus roxburghii, and the optimum conditions defined by the model to give an optimum yield of 1.31%. The antioxidant activity in vitro showed when the concentration of phenolic compounds was reached 1 mg mL-1, the clearance rates were 82.58% for DPPH and 97.62% for ABTS+. In vivo antioxidant experiments used D-galactose to build oxidative damage in healthy Kunming mice. The result showed that the extractions of A. roxburghii can improve the antioxidant ability and the medium and low dose groups had better ability to scavenge free radicals. The UPLC-Q-TOF-MS/MS was developed to identify 21 kinds of phenolic compounds by molecular mass, ms/ms fragmentation, as well as retention time. The result showed that the phenolic compounds of A. roxburghii had significant potential as a natural antioxidant to promote health and to reduce the risk of disease

Transfer Learning for Music Genre Classification

Part 3: Big Data Analysis and Machine LearningInternational audienceModern music information retrieval system provides high-level features (genre, instrument, mood and so on) for searching and recommending conveniently. Among these music tags, genre is the most widely used in practice. Machine learning technique has the ability of cataloguing different genres from raw music. A disadvantage of it is that the final performance heavily depends on the used features. As a powerful learning algorithm, deep neural network can extract useful features automatically and effectively instead of time-consuming feature engineering. But deeper architecture means larger data are needed to train the neural network. In many cases, we may not have enough data to train a deep network. Transfer learning solves the problem by pre-training the network in a similar task which has enough data, then fine-tuning the parameters of the pre-trained network using the target dataset. Magnatagatune dataset is used for pre-training the proposed five-layer Recurrent Neural Network (RNN) with Gated Recurrent Unit (GRU). And in order to reduce the input of the network, scattering transform is used in this paper. Then GTZAN dataset is used as the target dataset of genre classification. Experimental results show the transfer learning way can achieve a higher average classification accuracy (95.8%) than the same deep RNN which initials the parameters randomly (93.5%). In addition, the deep RNN using transfer learning converges to the final accuracy faster than using random initialization

Woven Fabric Pattern Recognition and Classification Based on Deep Convolutional Neural Networks

The weave pattern (texture) of woven fabric is considered to be an important factor of the design and production of high-quality fabric. Traditionally, the recognition of woven fabric has a lot of challenges due to its manual visual inspection. Moreover, the approaches based on early machine learning algorithms directly depend on handcrafted features, which are time-consuming and error-prone processes. Hence, an automated system is needed for classification of woven fabric to improve productivity. In this paper, we propose a deep learning model based on data augmentation and transfer learning approach for the classification and recognition of woven fabrics. The model uses the residual network (ResNet), where the fabric texture features are extracted and classified automatically in an end-to-end fashion. We evaluated the results of our model using evaluation metrics such as accuracy, balanced accuracy, and F1-score. The experimental results show that the proposed model is robust and achieves state-of-the-art accuracy even when the physical properties of the fabric are changed. We compared our results with other baseline approaches and a pretrained VGGNet deep learning model which showed that the proposed method achieved higher accuracy when rotational orientations in fabric and proper lighting effects were considered

GPC3 Promotes Lung Squamous Cell Carcinoma Progression and HLA-A2-Restricted GPC3 Antigenic Peptide-Modified Dendritic Cell-Induced Cytotoxic T Lymphocytes to Kill Lung Squamous Cell Carcinoma Cells

Lung squamous cell carcinoma (LUSC) is associated with poor clinical prognosis and lacks available targeted agents. GPC3 is upregulated in LUSC. Our study aimed to explore the roles of GPC3 in LUSC and the antitumor effects of HLA-A2-restricted GPC3 antigenic peptide-sensitized dendritic cell (DC)-induced cytotoxic T lymphocytes (CTLs) on LUSC. LUSC cells with GPC3 knockdown and overexpression were built using lentivirus packaging, and cell viability, clone formation, apoptosis, cycle, migration, and invasion were determined. Western blotting was used to detect the expression of cell cycle-related proteins and PI3K-AKT pathway-associated proteins. Subsequently, HLA-A2-restricted GPC3 antigenic peptides were predicted and synthesized by bioinformatic databases, and DCs were induced and cultured in vitro. Finally, HLA-A2-restricted GPC3 antigenic peptide-modified DCs were co-cultured with T cells to generate specific CTLs, and the killing effects of different CTLs on LUSC cells were studied. A series of cell function experiments showed that GPC3 overexpression promoted the proliferation, migration, and invasion of LUSC cells, inhibited their apoptosis, increased the number of cells in S phase, and reduced the cells in G2/M phase. GPC3 knockdown downregulated cyclin A, c-Myc, and PI3K, upregulated E2F1, and decreased the pAKT/AKT level. Three HLA-A2-restricted GPC3 antigenic peptides were synthesized, with GPC3522-530 FLAELAYDL and GPC3102-110 FLIIQNAAV antigenic peptide-modified DCs inducing CTL production, and exhibiting strong targeted killing ability in LUSC cells at 80 : 1 multiplicity of infection. GPC3 may advance the onset and progression of LUSC, and GPC3522-530 FLAELAYDL and GPC3102-110 FLIIQNAAV antigenic peptide-loaded DC-induced CTLs have a superior killing ability against LUSC cells

Characteristics and outcomes of antiretroviral-treated HIV-HBV co-infected patients in Canada?

Abstract Background Hepatitis B (HBV) and Human Immunodeficiency Virus (HIV) share common risk factors for exposure. Co-infected patients have an increased liver-related mortality risk and may have accelerated HIV progression. The epidemiology and demographic characteristics of HIV-HBV co-infection in Canada remain poorly defined. We compared the demographic and clinical characteristics and factors associated with advanced hepatic fibrosis between HIV and HIV-HBV co-infected patients. Methods A retrospective cohort analysis was conducted using data from the Canadian Observational Cohort (CANOC) Collaboration, including eight sites from British Columbia, Quebec, and Ontario. Eligible participants were HIV-infected patients who initiated combination ARV between January 1, 2000 and December 14, 2014. Demographic and clinical characteristics were compared between HIV-HBV co-infected and HIV-infected groups using chi-square or Fisher exact tests for categorical variables, and Wilcoxon’s Rank Sum test for continuous variables. Liver fibrosis was estimated by the AST to Platelet Ratio Index (APRI). Results HBV status and APRI values were available for 2419 cohort participants. 199 (8%) were HBV co-infected. Compared to HIV-infected participants, HIV-HBV co-infected participants were more likely to use injection drugs (28% vs. 21%, p = 0.03) and be HCV-positive (31%, vs. 23%, p = 0.02). HIV-HBV co-infected participants had lower baseline CD4 T cell counts (188 cells/mm₃, IQR: 120–360) compared to 235 cells/mm₃ in HIV-infected participants (IQR: 85–294) (p = 0.0002) and higher baseline median APRI scores (0.50 vs. 0.37, p < 0.0001). This difference in APRI was no longer clinically significant at follow-up (0.32 vs. 0.30, p = 0.03). HIV-HBV co-infected participants had a higher mortality rate compared to HIV-infected participants (11% vs. 7%, p = 0.02). Conclusion The prevalence, demographic and clinical characteristics of the HIV-HBV co-infected population in Canada is described. HIV-HBV co-infected patients have higher mortality, more advanced CD4 T cell depletion, and liver fibrosis that improves in conjunction with ARV therapy. The high prevalence of unknown HBV status demonstrates a need for increased screening among HIV-infected patients in Canada.Other UBCNon UBCReviewedFacult