Towards Deep Universal Sketch Perceptual Grouper

Human free-hand sketches provide the useful data for studying human perceptual grouping, where the grouping principles such as the Gestalt laws of grouping are naturally in play during both the perception and sketching stages. In this paper, we make the first attempt to develop a universal sketch perceptual grouper. That is, a grouper that can be applied to sketches of any category created with any drawing style and ability, to group constituent strokes/segments into semantically meaningful object parts. The first obstacle to achieving this goal is the lack of large-scale datasets with grouping annotation. To overcome this, we contribute the largest sketch perceptual grouping dataset to date, consisting of 20 000 unique sketches evenly distributed over 25 object categories. Furthermore, we propose a novel deep perceptual grouping model learned with both generative and discriminative losses. The generative loss improves the generalization ability of the model, while the discriminative loss guarantees both local and global grouping consistency. Extensive experiments demonstrate that the proposed grouper significantly outperforms the state-of-the-art competitors. In addition, we show that our grouper is useful for a number of sketch analysis tasks, including sketch semantic segmentation, synthesis, and fine-grained sketch-based image retrieval. © 1992-2012 IEEE

Apatinib has anti-tumor effects and induces autophagy in colon cancer cells

Objective(s): Apatinib recently has been used to treat patients with gastric cancer, but the function of apatinib in colon cancer remains unclear. This study was conducted to investigate the impacts of apatinib on the biological function and its potential mechanism of colon cancer cells in vitro. Materials and Methods:The effect of apatinib in colon cancer cells were detected by assessing cell viability, migration and invasion capabilities. Apoptosis cells and the cell cycle distribution of colon cancer cells were analyzed by flow cytometry. The potential mechanism was investigated via autophagy related proteins and pathways in vitro. Results: The proliferation, migration and invasion of colon cancer cells were inhibited when they were treated with different concentration of apatinib (20, 40 μM). When HCT116 and SW480 cells were treated with apatinib at the concentration of 20 μM, the apoptosis percentage were 3.7% and 5.8% respectively. As the drug concentration increased to 40μΜ, the the apoptosis percentage increased to 11.9% and 13.5%. Meanwhile, cell cycle was also altered. Furthermore, apatinib inhibited the expression of AKT-mTOR signaling pathway and increased the expression of LC3-Ⅱ. Conclusion: Apatinib can significantly inhibit the malignant phenotype of colon cancer cells, and it was involved in regulation of autophagy

Universal Sketch Perceptual Grouping

In this work we aim to develop a universal sketch grouper. That is, a grouper that can be applied to sketches of any category in any domain to group constituent strokes/segments into semantically meaningful object parts. The first obstacle to this goal is the lack of large-scale datasets with grouping annotation. To overcome this, we contribute the largest sketch perceptual grouping (SPG) dataset to date, consisting of 20, 000 unique sketches evenly distributed over 25 object categories. Furthermore, we propose a novel deep universal perceptual grouping model. The model is learned with both generative and discriminative losses. The generative losses improve the generalisation ability of the model to unseen object categories and datasets. The discriminative losses include a local grouping loss and a novel global grouping loss to enforce global grouping consistency. We show that the proposed model significantly outperforms the state-of-the-art groupers. Further, we show that our grouper is useful for a number of sketch analysis tasks including sketch synthesis and fine-grained sketch-based image retrieval (FG-SBIR). © Springer Nature Switzerland AG 2018

Physicochemical property, volatile flavor quality, and microbial community composition of Jinhua fatty ham and lean ham: A comparative study

The physicochemical property, volatile flavor compounds, and microbial community structure of Jinhua fatty ham (FH) and lean ham (LH) were investigated and compared by high-throughput sequencing and HS-GC-IMS. Results showed that FH had higher pH and slightly lighter and yellower color than LH. Meanwhile, 33 volatile flavor compounds were identified from FH and LH, among which LH showed higher abundance of total alcohols and acids, but FH had generally richer aldehydes, ketones, esters, heterocyclic, and sulfur-containing compounds. Moreover, FH and LH did not have significant difference in α-diversity of bacterial community, but LH presented a much lower α-diversity of fungal community than FH. Besides, the dominant microorganisms (relative abundance >2%) in FH were Ruminococcaceae UCG-005, Staphylococcus, Ruminococcaceae UCG-014, Meyerozyma, and Aspergillus at the genus level, while in LH were Staphylococcus, Psychrobacter, Halomonas, Propionicicella, Ruminococcaceae UCG-005, Meyerozyma, Yamadazyma, and Aspergillus. Furthermore, the analysis of Pearson’s correlation and metabolic network confirmed that the discriminative flavor compounds of FH were mainly β-oxidation and degradation products of fatty acids, while those of LH were mostly derived from the Strecker reaction or microbial metabolism of amino acids. The present study could help understand the potential pathway of characteristic microorganisms affecting flavor formation of fat-deficient dry-cured hams and provide theoretical supports for developing healthier fermented meat products

Immune microenvironment analysis and novel biomarkers of early-stage lung adenocarcinoma evolution

BackgroundLung cancer is the deadliest and most diagnosed type of cancer worldwide. The 5-year survival rate of lung adenocarcinoma (LUAD) dropped significantly when tumor stages advanced. Patients who received surgically resecting at the pre-invasive stage had a 5-year survival rate of nearly 100%. However, the study on the differences in gene expression profiles and immune microenvironment among pre-invasive LUAD patients is still lacking.MethodsIn this study, the gene expression profiles of three pre-invasive LUAD stages were compared using the RNA-sequencing data of 10 adenocarcinoma in situ (AIS) samples, 12 minimally invasive adenocarcinoma (MIA) samples, and 10 invasive adenocarcinoma (IAC) samples.ResultsThe high expression levels of PTGFRN (Hazard Ratio [HR] = 1.45; 95% Confidence Interval [CI]: 1.08-1.94; log-rank P = 0.013) and SPP1 (HR = 1.44; 95% CI: 1.07-1.93; log-rank P = 0.015) were identified to be associated with LUAD prognosis. Moreover, the early LUAD invasion was accompanied by the enhancement of antigen presentation ability, reflected by the increase of myeloid dendritic cells infiltration rate (Cuzick test P < 0.01) and the upregulation of seven important genes participating in the antigen presentation, including HLA-A (Cuzick test P = 0.03), MICA (Cuzick test P = 0.01), MICB (Cuzick test P = 0.01), HLA-DPA1 (Cuzick test P = 0.04), HLA-DQA2 (Cuzick test P < 0.01), HLA-DQB1 (Cuzick test P = 0.03), and HLA-DQB2 (Cuzick test P < 0.01). However, the tumor-killing ability of the immune system was inhibited during this process, as there were no rising cytotoxic T cell activity (Cuzick test P = 0.20) and no increasing expression in genes encoding cytotoxic proteins. ConclusionIn all, our research elucidated the changes in the immune microenvironment during early-stage LUAD evolution and may provide a theoretical basis for developing novel early-stage lung cancer therapeutic targets

The Thermal Infrared Visual Object Tracking VOT-TIR2015 challenge results

The Thermal Infrared Visual Object Tracking challenge 2015, VOT-TIR2015, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance. VOT-TIR2015 is the first benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2015 challenge is based on the VOT2013 challenge, but introduces the following novelties: (i) the newly collected LTIR (Link - ping TIR) dataset is used, (ii) the VOT2013 attributes are adapted to TIR data, (iii) the evaluation is performed using insights gained during VOT2013 and VOT2014 and is similar to VOT2015