Towards Realistic Facial Expression Recognition

Automatic facial expression recognition has attracted significant attention over the past decades. Although substantial progress has been achieved for certain scenarios (such as frontal faces in strictly controlled laboratory settings), accurate recognition of facial expression in realistic environments remains unsolved for the most part. The main objective of this thesis is to investigate facial expression recognition in unconstrained environments. As one major problem faced by the literature is the lack of realistic training and testing data, this thesis presents a web search based framework to collect realistic facial expression dataset from the Web. By adopting an active learning based method to remove noisy images from text based image search results, the proposed approach minimizes the human efforts during the dataset construction and maximizes the scalability for future research. Various novel facial expression features are then proposed to address the challenges imposed by the newly collected dataset. Finally, a spectral embedding based feature fusion framework is presented to combine the proposed facial expression features to form a more descriptive representation. This thesis also systematically investigates how the number of frames of a facial expression sequence can affect the performance of facial expression recognition algorithms, since facial expression sequences may be captured under different frame rates in realistic scenarios. A facial expression keyframe selection method is proposed based on keypoint based frame representation. Comprehensive experiments have been performed to demonstrate the effectiveness of the presented methods

Mechanisms and Therapeutic Targets of Depression After Intracerebral Hemorrhage

The relationship between depression and intracerebral hemorrhage (ICH) is complicated. One of the most common neuropsychiatric comorbidities of hemorrhagic stroke is Post-ICH depression. Depression, as a neuropsychiatric symptom, also negatively impacts the outcome of ICH by enhancing morbidity, disability, and mortality. However, the ICH outcome can be improved by antidepressants such as the frequently-used selective serotonin reuptake inhibitors. This review therefore presents the mechanisms of post-ICH depression, we grouped the mechanisms according to inflammation, oxidative stress (OS), apoptosis and autophagy, and explained them through their several associated signaling pathways. Inflammation is mainly related to Toll-like receptors (TLRs), the NF-kB mediated signal pathway, the PPAR-γ-dependent pathway, as well as other signaling pathways. OS is associated to nuclear factor erythroid-2 related factor 2 (Nrf2), the PI3K/Akt pathway and the MAPK/P38 pathway. Moreover, autophagy is associated with the mTOR signaling cascade and the NF-kB mediated signal pathway, while apoptosis is correlated with the death receptor-mediated apoptosis pathway, mitochondrial apoptosis pathway, caspase-independent pathways and others. Furthermore, we found that neuroinflammation, oxidative stress, autophagy, and apoptosis experience interactions with one another. Additionally, it may provide several potential therapeutic targets for patients that might suffer from depression after ICH

The Role of lncRNAs in the Distant Metastasis of Breast Cancer

Breast cancer (BC) remains the most frequently diagnosed cancer worldwide. Among breast cancer patients, distant metastasis and invasion is the leading cause of BC related death. Recently, long non-coding RNAs (lncRNAs), which used to be considered a genetic byproduct (owing to their unknown biological function), have been reported to be highly implicated in the development and progression of BC. In this review, we produce a summary of the functions and mechanisms of lncRNAs implicated in the different distant metastases of BC. The functions of lncRNAs have been divided into two types: oncogenic type and tumor suppressor. Furthermore, the majority of them exert their roles through the regulation of invasion, migration, epithelial—mesenchymal transition (EMT), and the metastasis process. In the final part, we briefly addressed future research prospects of lncRNAs, especially the testing methods through which to detect lncRNAs in the clinical work, and introduced several different tools with which to detect lncRNAs more conveniently. Although lncRNA research is still in the initial stages, it is a promising prognosticator and a novel therapeutic target for BC metastasis, which requires more research in the future

Spectral embedding based facial expression recognition with multiple features

Many approaches to facial expression recognition utilize only one type of features at a time. It can be difficult for a single type of features to characterize in a best possible way the variations and complexity of realistic facial expressions. In this paper, we propose a spectral embedding based multi-view dimension reduction method to fuse multiple features for facial expression recognition. Facial expression features extracted from one type of expressions can be assumed to form a manifold embedded in a high dimensional feature space. We construct a neighborhood graph that encodes the structure of the manifold locally. A graph Laplacian matrix is constructed whose spectral decompositions reveal the low dimensional structure of the manifold. In order to obtain discriminative features for classification, we propose to build a neighborhood graph in a supervised manner by utilizing the label information of training data. As a result, multiple features are able to be transformed into a unified low dimensional feature space by combining the Laplacian matrix of each view with the multiview spectral embedding algorithm. A linearization method is utilized to map unseen data to the learned unified subspace. Experiments are conducted on a set of established real-world and benchmark datasets. The experimental results provide a strong support to the effectiveness of the proposed feature fusion framework on realistic facial expressions. 2013

Cloning and Expression of irf7 Gene in Spotted Knifejaw (Oplegnathus punctatus) Under Virus Infection

Interferon regulatory factor (irf7) is an immune regulatory factor that plays an important role in the antiviral process. To explore the role of irf7 in Oplegnathus punctatus (Temminck & Schlegel, 1844) under viral infection, we cloned the coding DNA sequence (CDS) region of irf7 through PCR and analyzed the expression patterns at both tissue and cell levels. The results showed that the CDS region of Opirf7 was 1 332 bp and encoded a peptide with 443 amino acids. The predicted molecular weight was 50.5 kDa and the theoretical isoelectric point was 5.546. Protein structure analysis showed that Opirf7 has three conserved domains: the DNA binding domain (DBD), IRF-associated domain (IAD), and serine-rich domain (SRD). Amino acid similarity analysis showed that OpIRF7 had the highest similarity to the IRF7 of Lates calcarifer, which was 82.92%. The similarity of Opirf7 with the IRF7 of Larimichthys crocea, Paralichthys olivaceus, and Cynoglossus semilaevis were 81.99%, 79.95%, and 73.74%, respectively. Phylogenetic analysis showed that Opirf7 and other fish irf7 genes were clustered into one branch, and irf7s from Gallus gallus, Mus musculus, Macaca mulatta, and Homo sapiens were clustered into another. Tissues from healthy O. punctatus were collected, including the liver, spleen, kidney, head kidney, intestine, gill, skin, muscle, brain, heart, and blood. After RNA extraction and cDNA synthesis, real-time PCR (qPCR) was performed to detect the expression level of Opirf7 using the comparative CT method (2−ΔΔCT method). The results of qPCR showed that Opirf7 was expressed in different tissues of healthy individuals and its expression was highest in the liver, followed by the skin and intestines. The lowest expression was observed in the head kidney. In this study, the expression profiles of Opirf7 before and after viral infection were determined at the tissue and cell levels. For the in vivo challenge study, fish were intraperitoneally injected with spotted knifejaw iridovirus, and the expression level of Opirf7 was tested in the spleen, kidney, and liver. Compared with the control group at 0 h, the expression level of Opirf7 was 15-fold in the spleen and 3-fold in the kidney 4 days after infection, and the expression peak was at 7 days after infection. However, the expression of Opirf7 was not significantly altered in the liver. A poly I: C-infected O. punctatus brain cell model was established, and the expression profiles of Opirf7 mRNA were detected before and after infection. The expression of Opirf7 mRNA in the low and medium concentration groups (50 μg/mL and 100 μg/mL, respectively) increased by 13 to 17 times, and the expression level of Opirf7 mRNA in the high concentration group (200 μg/mL) increased by approximately 8 times. It was speculated that the high concentration of 200 μg/mL caused some damage to the cells and that the expression level in the high concentration group was lower than that in the low and medium groups. In this study, the full-length open reading frame sequence of Opirf7 was cloned and characterized for the first time. The deduced amino acid sequence displayed a structure similar to those of other vertebrates. Further functional analysis showed that Opirf7 has a significant response to viral infection at both tissue and cell levels. This study demonstrated that the Opirf7 gene might play an important role in the antiviral response of O. punctatus and provide a potential molecular marker for antivirus breeding of O. punctatus

Adaptive Interacting Multiple Model Algorithm Based on Information-Weighted Consensus for Maneuvering Target Tracking

Networked multiple sensors are used to solve the problem of maneuvering target tracking. To avoid the linearization of nonlinear dynamic functions, and to obtain more accurate estimates for maneuvering targets, a novel adaptive information-weighted consensus filter for maneuvering target tracking is proposed. The pseudo measurement matrix is computed with unscented transform to utilize the information form of measurements, which is necessary for consensus iterations. To improve the maneuvering target tracking accuracy and get a unified estimation in each sensor node across the entire network, the adaptive current statistical model is exploited to update the estimate, and the information-weighted consensus protocol is applied among neighboring nodes for each dynamic model. Based on posterior probabilities of multiple models, the final estimate of each sensor is acquired with weighted combination of model-conditioned estimates. Experimental results illustrate the superior performance of the proposed algorithm with respect tracking accuracy and agreement of estimates in the whole network

Electro-Acupuncture Promotes Endogenous Multipotential Mesenchymal Stem Cell Mobilization into the Peripheral Blood

Background/Aims: Mobilization of endogenous stem cells is an appealing strategy for cell therapy However, there is little evidence for reproducible, effective methods of mesenchymal stem cell (MSC) mobilization. In the present study, we investigated the mobilizing effect of electro-acupuncture (EA) on endogenous MSCs. Methods: Normal adult rats were randomly divided into six groups, namely, EA for 14 days (EA14d), sham EA14d, EA21d, sham EA21d and matched control groups. MSC mobilization efficiency was determined by colony-forming unit fibroblast (CFU-F) assays. Mobilized peripheral blood (PB)-derived MSCs were identified by immunophenotype and multi-lineage differentiation potential. Results: CFU-F frequency was significantly increased in the PB of EA14d rats compared with the sham EA and control groups. Moreover, the number of CFU-Fs was increased further in the EA21d group. MSCs derived from EA-mobilized PB were positive for CD90 and CD44, but negative for CD45. Additionally, these cells could differentiate into adipocytes, osteoblasts, chondrocytes and neural-like cells in vitro. Finally, stromal cell-derived factor-1α (SDF-1α) was increased in the PB of rats subjected to EA, and the migration of MSCs was improved in response to SDF-1α. Conclusions: MSCs with multi-lineage differentiation potential can be mobilized by EA. Our data provide a promising strategy for MSC mobilization