Terms of Trade Shocks and Endogenous Search Unemployment: A Two-sector Model with Non-Traded goods

We develop a simple and tractable two-sector search model featuring a non-traded sector and endogenous search unemployment to examine the impact of terms of trade shocks on unemployment. We show that changes in terms of trade will not only lead to employment reallocation across sectors, as in the traditional trade models, but more importantly, impact upon search unemployment within each sector. Specifically, we show that an improvement (deterioration) of terms of trade reduces (increases) unemployment rates in both traded and non-traded sectors.two-sector search model, trade and unemployment, non-traded good

Multispectral Palmprint Recognition Using a Quaternion Matrix

Palmprints have been widely studied for biometric recognition for many years. Traditionally, a white light source is used for illumination. Recently, multispectral imaging has drawn attention because of its high recognition accuracy. Multispectral palmprint systems can provide more discriminant information under different illuminations in a short time, thus they can achieve better recognition accuracy. Previously, multispectral palmprint images were taken as a kind of multi-modal biometrics, and the fusion scheme on the image level or matching score level was used. However, some spectral information will be lost during image level or matching score level fusion. In this study, we propose a new method for multispectral images based on a quaternion model which could fully utilize the multispectral information. Firstly, multispectral palmprint images captured under red, green, blue and near-infrared (NIR) illuminations were represented by a quaternion matrix, then principal component analysis (PCA) and discrete wavelet transform (DWT) were applied respectively on the matrix to extract palmprint features. After that, Euclidean distance was used to measure the dissimilarity between different features. Finally, the sum of two distances and the nearest neighborhood classifier were employed for recognition decision. Experimental results showed that using the quaternion matrix can achieve a higher recognition rate. Given 3000 test samples from 500 palms, the recognition rate can be as high as 98.83%

Expression of DNMT1 and DNMT3a Are Regulated by GLI1 in Human Pancreatic Cancer

BACKGROUND AND AIMS: GLI1, as an indispensable transcriptional factor of Hedgehog signaling pathway, plays an important role in the development of pancreatic cancer (PC). DNA methyltransferases (DNMTs) mediate the methylation of quantity of tumor-related genes. Our study aimed to explore the relationship between GLI1 and DNMTs. METHODS: Expressions of GLI1 and DNMTs were detected in tumor and adjacent normal tissues of PC patients by immunohistochemistry (IHC). PANC-1 cells were treated by cyclopamine and GLI1-siRNA, while BxPC-3 cells were transfected with overexpression-GLI1 lentiviral vector. Then GLI1 and DNMTs expression were analyzed by qRT-PCR and western blot (WB). Then we took chromatin immunoprecipitation (ChIP) to demonstrate GLI1 bind to DNMT1. Finally, nested MSP was taken to valuate the methylation levels of APC and hMLH1, when GLI1 expression altered. RESULTS: IHC result suggested the expressions of GLI1, DNMT1 and DNMT3a in PC tissues were all higher than those in adjacent normal tissues (p<0.05). After GLI1 expression repressed by cyclopamine in mRNA and protein level (down-regulation 88.1±2.2%, 86.4±2.2%, respectively), DNMT1 and DNMT3a mRNA and protein level decreased by 91.6%±2.2% and 83.8±4.8%, 87.4±2.7% and 84.4±1.3%, respectively. When further knocked down the expression of GLI1 by siRNA (mRNA decreased by 88.6±2.1%, protein decreased by 63.5±4.5%), DNMT1 and DNMT3a mRNA decreased by 80.9±2.3% and 78.6±3.8% and protein decreased by 64.8±2.8% and 67.5±5.6%, respectively. Over-expression of GLI1 by GLI1 gene transfection (mRNA increased by 655.5±85.9%, and protein increased by 272.3±14.4%.), DNMT1 and DNMT3a mRNA and protein increased by 293.0±14.8% and 578.3±58.5%, 143.5±17.4% and 214.0±18.9%, respectively. ChIP assays showed GLI1 protein bound to DNMT1 but not to DNMT3a. Results of nested MSP demonstrated GLI1 expression affected the DNA methylation level of APC but not hMLH1 in PC. CONCLUSION: DNMT1 and DNMT3a are regulated by GLI1 in PC, and DNMT1 is its direct target gene

Identification of RegIV as a Novel GLI1 Target Gene in Human Pancreatic Cancer

GLI1 is the key transcriptional factor in the Hedgehog signaling pathway in pancreatic cancer. RegIV is associated with regeneration, and cell growth, survival, adhesion and resistance to apoptosis. We aimed to study RegIV expression in pancreatic cancer and its relationship to GLI1.GLI1 and RegIV expression were evaluated in tumor tissue and adjacent normal tissues of pancreatic cancer patients and 5 pancreatic cancer cell lines by qRT-PCR, Western blot, and immunohistochemistry (IHC), and the correlation between them. The GLI1-shRNA lentiviral vector was constructed and transfected into PANC-1, and lentiviral vector containing the GLI1 expression sequence was constructed and transfected into BxPC-3. GLI1 and RegIV expression were evaluated by qRT-PCR and Western blot. Finally we demonstrated RegIV to be the target of GLI1 by chromatin immunoprecipitation (CHIP) and electrophoretic mobility shift assays (EMSA).The results of IHC and qRT-PCR showed that RegIV and GLI1 expression was higher in pancreatic cancer tissues versus adjacent normal tissues (p<0.001). RegIV expression correlated with GLI1 expression in these tissues (R = 0.795, p<0.0001). These results were verified for protein (R = 0.939, p = 0.018) and mRNA expression (R = 0.959, p = 0.011) in 5 pancreatic cancer cell lines. RegIV mRNA and protein expression was decreased (94.7±0.3%, 84.1±0.5%; respectively) when GLI1 was knocked down (82.1±3.2%, 76.7±2.2%; respectively) by the RNAi technique. GLI1 overexpression in mRNA and protein level (924.5±5.3%, 362.1±3.5%; respectively) induced RegIV overexpression (729.1±4.3%, 339.0±3.7%; respectively). Moreover, CHIP and EMSA assays showed GLI1 protein bound to RegIV promotor regions (GATCATCCA) in pancreatic cancer cells.GLI1 promotes RegIV transcription by binding to the RegIV gene promoter in pancreatic cancer

Tattoos in forensics : retrieval, detection and synthesis

A wide variety of biometric systems have been developed for forensic investigations based on biometric traits like fingerprints, faces, and iris, etc. Using those biometric traits as evidence in forensic investigations can be very challenging because they are not always visible. In the cases that those biometric traits are not available, tattoos provide us another opportunity to identify the criminals or the victims during the forensic investigation. Tattoos have been widely used by law enforcement agencies for forensic investigation, such as criminal and victim identification. Tattoo retrieval and tattoo detection are two important use cases of tattoos in forensic investigation. Many works have been done in these areas. However, there are still some unsolved problems that limit the performance of these works. For example, most tattoo retrieval systems require a query tattoo image which is not always available; tattoo detectors suffer from the lack of a large-scale tattoo dataset and their performance is limited by the architectures of the detection networks. In this research, we proposed some solutions to those problems. First, we developed a novel tattoo retrieval system that uses geometry information of tattoos to perform tattoo searching. This geometry-based tattoo retrieval (GBTR) system only requires a simple tattoo sketch as a query and avoids the requirement of a query tattoo image or a tattoo sketch with rich details. The location and shape information of the tattoo sketch is extracted first. Then, a location mapping procedure based on full-body coordinates finds all the images in the searching database that have tattoos in the corresponding location. The tattoo shapes are extracted via the Snake algorithm. Coherent Point Drift (CPD) algorithm is then employed to match the shape of the tattoo sketch and the shapes of tattoos in the searching database. The retrieved tattoos are sorted based on the matching scores. To evaluate the GBTR system, we established a full-body human image database. All the images in the database contain at least one tattoo. The evaluation results showed that the GBTR system achieves a promising tattoo retrieval accuracy. Second, we designed a prior knowledge-based attention mechanism (PKAM) to improve the performance of text tattoo detection networks. We concentrate on text tattoo detection because text tattoos provide vital clues in forensic investigations such as names and important dates. A text tattoo detection network with double PKAMs (TTDN-DA) was proposed. The first PKAM uses the features of human instances to enhance the generic tattoo images, and the second PKAM uses the features of general tattoos to enhance the text tattoo images. Two variants of TTDN-DA, TTDN-DA-V2, and TTDN-DA-V3, were also proposed for handling different training and deploying scenarios. To train and evaluate the proposed text tattoo detection networks, we established NTU Tattoo V2 dataset and NTU Text Tattoo V1 dataset. The evaluation results show that PKAM can improve text tattoo detection accuracy significantly. A large tattoo dataset is essential for training robust tattoo retrieval systems and tattoo detectors, therefore, we proposed digital tattooing (DT) approach to synthesize tattoo images. We focus on synthesizing portrait tattoo images because portrait photos contain sufficient texture details for us to evaluate the performance of the DT algorithm. DT algorithm takes a portrait photo, a real portrait tattoo image, and a skin image as input. The portrait tattoo image is used as a reference. DT algorithm extracts the facial landmarks of the portrait tattoo image and enhances it by re-weighting the landmark textures and adding shadow effect, etc. Then it calculates a color mapping from reference portrait tattoo image to the enhanced portrait photo and uses a novel tattoo needle model to simulate the physical procedure of tattooing. A set of parameters are provided for controlling the visual style of the synthetic portrait tattoo images. The experimental results showed that compared with other image synthesizing methods, the DT algorithm generates more realistic portrait tattoos. In this research, we provided new solutions to tattoo retrieval and tattoo detection tasks and showed their potential to address the unsolved problems of existing tattoo retrieval systems and tattoo detectors. Furthermore, we also proposed a tattoo synthesizing approach which can be very useful in generating a large number of tattoo images for training tattoo retrieval systems and tattoo detectors.Doctor of Philosoph