Automatic landmark annotation and dense correspondence registration for 3D human facial images

Dense surface registration of three-dimensional (3D) human facial images holds great potential for studies of human trait diversity, disease genetics, and forensics. Non-rigid registration is particularly useful for establishing dense anatomical correspondences between faces. Here we describe a novel non-rigid registration method for fully automatic 3D facial image mapping. This method comprises two steps: first, seventeen facial landmarks are automatically annotated, mainly via PCA-based feature recognition following 3D-to-2D data transformation. Second, an efficient thin-plate spline (TPS) protocol is used to establish the dense anatomical correspondence between facial images, under the guidance of the predefined landmarks. We demonstrate that this method is robust and highly accurate, even for different ethnicities. The average face is calculated for individuals of Han Chinese and Uyghur origins. While fully automatic and computationally efficient, this method enables high-throughput analysis of human facial feature variation.Comment: 33 pages, 6 figures, 1 tabl

Construction health and safety: A topic landscape study

We aim to draw in-depth insights into the current literature in construction health and safety and provide perspectives for future research efforts. The existing literature on construction health and safety is not only diverse and rich in sight, but also complex and fragmented in structure. It is essential for the construction industry and research community to understand the overall development and existing challenges of construction health and safety to adapt to future new code of practice and challenges in this field. We mapped the topic landscape followed by identifying the salient development trajectories of this research area over time. We used the topic modeling algorithm to extract 10 distinct topics from 662 abstracts (filtered from a total of 895) of articles published between 1991 and 2020. In addition, we provided the most cited references and the most popular journal per topic as well. The results from a time series analysis suggested that the construction health and safety would maintain its popularity in the next 5 years. Research efforts would be devoted to the topics including “Physical health and disease”, “Migrant and race”, “Vocational ability and training”, and “Smart devices.” Among these topics, “Smart devices” would be the most promising one

Towards East Asian Facial Expression Recognition in the Real World: A New Database and Deep Recognition Baseline

In recent years, the focus of facial expression recognition (FER) has gradually shifted from laboratory settings to challenging natural scenes. This requires a great deal of real-world facial expression data. However, most existing real-world databases are based on European-American cultures, and only one is for Asian cultures. This is mainly because the data on European-American expressions are more readily accessed and publicly available online. Owing to the diversity of huge data, FER in European-American cultures has recently developed rapidly. In contrast, the development of FER in Asian cultures is limited by the data. To narrow this gap, we construct a challenging real-world East Asian facial expression (EAFE) database, which contains 10,000 images collected from 113 Chinese, Japanese, and Korean movies and five search engines. We apply three neural network baselines including VGG-16, ResNet-50, and Inception-V3 to classify the images in EAFE. Then, we conduct two sets of experiments to find the optimal learning rate schedule and loss function. Finally, by training with the cosine learning rate schedule and island loss, ResNet-50 can achieve the best accuracy of 80.53% on the testing set, proving that the database is challenging. In addition, we used the Microsoft Cognitive Face API to extract facial attributes in EAFE, so that the database can also be used for facial recognition and attribute analysis. The release of the EAFE can encourage more research on Asian FER in natural scenes and can also promote the development of FER in cross-cultural domains

VPRNet: Virtual Points Registration Network for Partial-to-Partial Point Cloud Registration

With the development of high-precision and high-frame-rate scanning technology, we can quickly obtain scan data of various large-scale scenes. As a manifestation of information fusion, point cloud registration is of great significance in various fields, such as medical imaging, autonomous driving, and 3D reconstruction. The Iterative Closest Point (ICP) algorithm, as the most classic algorithm, leverages the closest point to search corresponding points, which is the pioneer of correspondences-based approaches. Recently, some deep learning-based algorithms witnessed extracting deep features to compress point cloud information, then calculate corresponding points, and finally output the optimal rigid transformation like Deep Closest Point (DCP) and DeepVCP. However, the partiality of point clouds hinders the acquisition of enough corresponding points when dealing with the partial-to-partial registration problem. To this end, we propose Virtual Points Registration Network (VPRNet) for this intractable problem. We first design a self-supervised virtual point generation network (VPGnet), which utilizes the attention mechanism of Transformer and Self-Attention to fuse the geometric information of two partial point clouds, combined with the Generative Adversarial Network (GAN) structure to produce missing points. Subsequently, the following registration network structure is spliced to the end of VPGnet, thus estimating rich corresponding points. Unlike the existing methods, our network tries to eliminate the side effects of incompleteness on registration. Thus, our method expresses resilience to the initial rotation and sparsity. Various experiments indicate that our proposed algorithm shows advanced performance compared to recent deep learning-based and classical methods

Progress on the Study of PD-L1 Detection Methods in Non-small Cell Lung Cancer

PD-1/PD-L1 inhibitors play an important role in the first-line and second-line treatment of non-small cell lung cancer (NSCLC), indicating a new treatment strategy of NSCLC. Completed clinical trials have shown that effective detection of PD-L1 expression is the key to the use of immunosuppressive agents. However, the gold standard for PD-L1 detection has still lacked. In recent years, immunohistochemistry (IHC) and enzyme-linked immunosorbent assay (ELISA) have been continuously innovated, which accounts for good prospect in PD-L1 detection. The research progress of PD-L1 detection methods in NSCLC is summarized in this review

VPRNet: Virtual Points Registration Network for Partial-to-Partial Point Cloud Registration

With the development of high-precision and high-frame-rate scanning technology, we can quickly obtain scan data of various large-scale scenes. As a manifestation of information fusion, point cloud registration is of great significance in various fields, such as medical imaging, autonomous driving, and 3D reconstruction. The Iterative Closest Point (ICP) algorithm, as the most classic algorithm, leverages the closest point to search corresponding points, which is the pioneer of correspondences-based approaches. Recently, some deep learning-based algorithms witnessed extracting deep features to compress point cloud information, then calculate corresponding points, and finally output the optimal rigid transformation like Deep Closest Point (DCP) and DeepVCP. However, the partiality of point clouds hinders the acquisition of enough corresponding points when dealing with the partial-to-partial registration problem. To this end, we propose Virtual Points Registration Network (VPRNet) for this intractable problem. We first design a self-supervised virtual point generation network (VPGnet), which utilizes the attention mechanism of Transformer and Self-Attention to fuse the geometric information of two partial point clouds, combined with the Generative Adversarial Network (GAN) structure to produce missing points. Subsequently, the following registration network structure is spliced to the end of VPGnet, thus estimating rich corresponding points. Unlike the existing methods, our network tries to eliminate the side effects of incompleteness on registration. Thus, our method expresses resilience to the initial rotation and sparsity. Various experiments indicate that our proposed algorithm shows advanced performance compared to recent deep learning-based and classical methods

Subsampling bias and the best-discrepancy systematic cross validation

Statistical machine learning models should be evaluated and validated before putting to work. Conventional k-fold Monte Carlo Cross-Validation (MCCV) procedure uses a pseudo-random sequence to partition instances into k subsets, which usually causes subsampling bias, inflates generalization errors and jeopardizes the reliability and effectiveness of cross-validation. Based on ordered systematic sampling theory in statistics and low-discrepancy sequence theory in number theory, we propose a new k-fold cross-validation procedure by replacing a pseudo-random sequence with a best-discrepancy sequence, which ensures low subsampling bias and leads to more precise Expected-Prediction-Error (EPE) estimates. Experiments with 156 benchmark datasets and three classifiers (logistic regression, decision tree and na¨ıve bayes) show that in general, our cross-validation procedure can extrude subsampling bias in the MCCV by lowering the EPE around 7.18% and the variances around 26.73%. In comparison, the stratified MCCV can reduce the EPE and variances of the MCCV around 1.58% and 11.85% respectively. The Leave-One-Out (LOO) can lower the EPE around 2.50% but its variances are much higher than the any other CV procedure. The computational time of our cross-validation procedure is just 8.64% of the MCCV, 8.67% of the stratified MCCV and 16.72% of the LOO. Experiments also show that our approach is more beneficial for datasets characterized by relatively small size and large aspect ratio. This makes our approach particularly pertinent when solving bioscience classification problems. Our proposed systematic subsampling technique could be generalized to other machine learning algorithms that involve random subsampling mechanism

Increased expression of the P2Y12 receptor is involved in the failure of autogenous arteriovenous fistula caused by stenosis

AbstractObjective This study investigated the role of the P2Y12 receptor in autogenous arteriovenous fistula (AVF) failure resulting from stenosis.Methods Stenotic venous tissues and blood samples were obtained from patients with end-stage renal disease (ESRD) together with AVF stenosis, while venous tissues and blood samples were collected from patients with ESRD undergoing initial AVF surgery as controls. Immunohistochemistry and/or immunofluorescence techniques were utilized to assess the expression of P2Y12, transforming growth factor-β1 (TGF-β1), monocyte chemotactic protein 1 (MCP-1), and CD68 in the venous tissues. The expression levels of P2Y12, TGFβ1, and MCP-1 were quantified using quantitative reverse transcription–polymerase chain reaction and western blot analyses. Double and triple immunofluorescence staining was performed to precisely localize the cellular localization of P2Y12 expression.Results Expression levels of P2Y12, TGFβ1, MCP-1, and CD68 were significantly higher in stenotic AVF venous tissues than in the control group tissues. Double and triple immunofluorescence staining of stenotic AVF venous tissues indicated that P2Y12 was predominantly expressed in α-SMA-positive vascular smooth muscle cells (VSMCs) and, to a lesser extent, in CD68-positive macrophages, with limited expression in CD31-positive endothelial cells. Moreover, a subset of macrophage-like VSMCs expressing P2Y12 were observed in both stenotic AVF venous tissues and control venous tissues. Additionally, a higher number of P2Y12+/TGF-β1+ double-positive cells were identified in stenotic AVF venous tissues than in the control group tissues.Conclusion Increased expression of P2Y12 in stenotic AVF venous tissues of patients with ESRD suggests its potential involvement in the pathogenesis of venous stenosis within AVFs