Benzaldehyde acts as a behaviorally active component in brewer's yeast protein powder which attracts B. dorsalis through olfaction.

The Oriental fruit fly, Bactrocera dorsalis, is a significant pest that damages a variety of fruit crops. The effectiveness of chemical pesticides against such pests is limited, raising concerns about pesticide residues and resistance. Proteins naturally attract B. dorsalis and have led to the development of a management strategy known as protein bait attractant technology (BAT). Although the attraction of protein sources to B. dorsalis is well-documented, the biologically active components within these sources are not fully understood. This study employed analytical chemistry, behavioral tests, and electrophysiological techniques to investigate the behaviorally active components of beer yeast protein powder (BYPD), aiming to provide a basis for improving and developing protein baits. An olfactory trap assay confirmed the attractiveness of BYPD, and five components with high abundance were identified from its headspace volatiles using GC-MS. These components included ethanol, isoamyl alcohol, ethyl decanoate, benzaldehyde, and phenylethyl alcohol. Mixtures of these five components demonstrated significant attraction to B. dorsalis adults, with benzaldehyde identified as a potential key component. The attractiveness of benzaldehyde required a relatively large dose, and it was most attractive to adults that had been starved from dusk until the following morning. Attraction of adult flies to benzaldehyde appeared mainly mediated by inputs from olfactory receptors. While EAG data supports that ionotropic receptors could influence the detection of benzaldehyde in female adults, they did not affect female behavior towards benzaldehyde. These findings indicate that benzaldehyde is an important behaviorally active component in BYPD and offer insights for developing novel protein lures to control B. dorsalis in an environmentally friendly manner.

MFN2 suppresses the accumulation of lipid droplets and the progression of clear cell renal cell carcinoma.

Dissolving the lipid droplets in tissue section with alcohol during a hematoxylin and eosin (H&E) stain causes the tumor cells to appear like clear soap bubbles under a microscope, which is a key pathological feature of clear cell renal cell carcinoma (ccRCC). Mitochondrial dynamics have been reported to be closely associated with lipid metabolism and tumor development. However, the relationship between mitochondrial dynamics and lipid metabolism reprogramming in ccRCC remains to be further explored. We conducted bioinformatics analysis to identify key genes regulating mitochondrial dynamics differentially expressed between tumor and normal tissues and immunohistochemistry and Western blot to confirm. After the target was identified, we created stable ccRCC cell lines to test the impact of the target gene on mitochondrial morphology, tumorigenesis in culture cells and xenograft models, and profiles of lipid metabolism. It was found that mitofusin 2 (MFN2) was downregulated in ccRCC tissues and associated with poor prognosis in patients with ccRCC. MFN2 suppressed mitochondrial fragmentation, proliferation, migration, and invasion of ccRCC cells and growth of xenograft tumors. Furthermore, MFN2 impacted lipid metabolism and reduced the accumulation of lipid droplets in ccRCC cells. MFN2 suppressed disease progression and improved prognosis for patients with ccRCC possibly by interrupting cellular lipid metabolism and reducing accumulation of lipid droplets.

Measuring and classifying IP usage scenarios: a continuous neural trees approach.

Understanding user behavior via IP addresses is a crucial measure towards numerous pragmatic IP-based applications, including online content delivery, fraud prevention, marketing intelligence, and others. While profiling IP addresses through methods like IP geolocation and anomaly detection has been thoroughly studied, the function of an IP address-e.g., whether it pertains to a private enterprise network or a home broadband-remains underexplored. In this work, we initiate the first attempt to address the IP usage scenario classification problem. We collect data consisting of IP addresses from four large-scale regions. A novel continuous neural tree-based ensemble model is proposed to learn IP assignment rules and complex feature interactions. We conduct extensive experiments to evaluate our model in terms of classification accuracy and generalizability. Our results demonstrate that the proposed model is capable of efficiently uncovering significant higher-order feature interactions that enhance IP usage scenario classification, while also possessing the ability to generalize from the source region to the target one.

Virtual Reality Utilized for Safety Skills Training for Autistic Individuals: A Review.

In recent years, virtual reality technology, which is able to simulate real-life environments, has been widely used in the field of intervention for individuals with autism and has demonstrated distinct advantages. This review aimed to evaluate the impact of virtual reality technology on safety skills intervention for individuals with autism. After searching and screening three databases, a total of 20 pertinent articles were included. There were six articles dedicated to the VR training of street-crossing skills for individuals with autism, nine articles focusing on the training of driving skills for individuals with ASD, and three studies examining the training of bus riding for individuals with ASD. Furthermore, there were two studies on the training of air travel skills for individuals with ASD. First, we found that training in some complex skills (e.g., driving skills) should be selected for older, high-functioning individuals with ASD, to determine their capacity to participate in the training using scales or questionnaires before the intervention; VR devices with higher levels of immersion are not suitable for younger individuals with ASD. Second, VR is effective in training safety skills for ASD, but there is not enough evidence to determine the relationship between the level of VR immersion and intervention effects. Although the degree of virtual reality involvement has an impact on the ability of ASD to be generalized to the real world, it is important to ensure that future virtual reality settings are realistic and lifelike. Again, adaptive models that provide personalized training to individuals with ASD in VR environments are very promising, and future research should continue in this direction. This paper also discusses the limitations of these studies, as well as potential future research directions.

Anomaly detection using spatial and temporal information in multivariate time series.

Real-world industrial systems contain a large number of interconnected sensors that generate a significant amount of time series data during system operation. Performing anomaly detection on these multivariate time series data can timely find faults, prevent malicious attacks, and ensure these systems safe and reliable operation. However, the rarity of abnormal instances leads to a lack of labeled data, so the supervised machine learning methods are not applicable. Furthermore, most current techniques do not take full advantage of the spatial and temporal dependencies implied among multiple variables to detect anomalies. Hence, we propose STADN, a novel Anomaly Detection Network Using Spatial and Temporal Information. STADN models the relationship graph between variables for a graph attention network to capture the spatial dependency between variables and utilizes a long short-term memory network to mine the temporal dependency of time series to fully use the spatial and temporal information of multivariate time series. STADN predicts the future behavior of each sensor by combining the historical behavior of the sensor and its neighbors, then detects and locates anomalies according to the prediction error. Furthermore, we improve the proposed model's ability to discriminate anomaly and regularity and expand the prediction error gap between normal and abnormal instances by reconstructing the prediction errors. We conduct experiments on two real-world datasets, and the experimental results suggested that STADN achieves state-of-the-art outperformance.

Spermidine suppresses the activation of hepatic stellate cells to cure liver fibrosis through autophagy activator MAP1S.

BACKGROUND AND AIMS: Liver diseases present a wide range of fibrosis, from fatty liver with no inflammation to steatohepatitis with varying degrees of fibrosis, to established cirrhosis leading to HCC. In a multivariate analysis, serum levels of spermidine were chosen as the top metabolite from 237 metabolites and its levels were drastically reduced along with progression to advanced steatohepatitis. Our previous studies that showed spermidine supplementation helps mice prevent liver fibrosis through MAP1S have prompted us to explore the possibility that spermidine can alleviate or cure already developed liver fibrosis. METHODS: We collected tissue samples from patients with liver fibrosis to measure the levels of MAP1S. We treated wild-type and MAP1S knockout mice with CCl4 -induced liver fibrosis with spermidine and isolated HSCs in culture to test the effects of spermidine on HSC activation and liver fibrosis. RESULTS: Patients with increasing degrees of liver fibrosis had reduced levels of MAP1S. Supplementing spermidine in mice that had already developed liver fibrosis after 1 month of CCl4 induction for an additional 3 months resulted in significant reductions in levels of ECM proteins and a remarkable improvement in liver fibrosis through MAP1S. Spermidine also suppressed HSC activation by reducing ECM proteins at both the mRNA and protein levels, and increasing the number of lipid droplets in stellate cells. CONCLUSIONS: Spermidine supplementation is a potentially clinically meaningful approach to treating and curing liver fibrosis, preventing cirrhosis and HCC in patients.

COVID-19 Vaccines and Public Anxiety: Antibody Tests May Be Widely Accepted.

Background: More than 200 countries are experiencing the coronavirus disease (COVID-19) pandemic. COVID-19 vaccination strategies have been implemented worldwide, and repeat COVID-19 outbreaks have been seen. The purpose of this study was to investigate the impact of COVID-19 vaccination on the reduction of perceived anxiety and the association between public anxiety and antibody testing intention during the COVID-19 pandemic. Methods: Chinese adults aged 18 and over were surveyed using an anonymous online questionnaire in April and May 2021. The questionnaire collected sociodemographic characteristics, vaccination characteristics, perceived anxiety due to COVID-19, and attitudes toward future antibody testing after COVID-19 vaccination. Perceived anxiety was assessed on a visual analog scale (VAS). Multivariate logistic regression analysis was used to determine the factors influencing future antibody detection. Results: A total of 3,233 people were investigated, 3,209 valid questionnaires were collected, and the response rate was 99.3%. Of the 3,209 respondents, 2,047 were vaccinated, and 1,162 were unvaccinated. There was a significant difference in anxiety levels between vaccinated and unvaccinated respondents (24.9±25.4 vs. 50.0±33.1, respectively). With the local spread of COVID-19 in mainland China, the public anxiety VAS scores increased by 15.4±25.6 (SMD=120%) and 33.8±31.7 (SMD=49%) among vaccinated and unvaccinated respondents, respectively. Of the 2,047 respondents who were vaccinated, 1,626 (79.4%) thought they would accept antibody testing. Those who displayed more anxiety about acquiring COVID-19 disease were more likely to accept COVID-19 antibody testing. If the antibody test results showed protective antibodies, 1,190 (58.1%) were more likely to arrange travel plans in China, while 526 (25.7%) thought they would feel safer traveling abroad. Conclusion: COVID-19 vaccination strategies help reduce public anxiety. However, public anxiety may be elevated as the local transmission of COVID-19 occurs in mainland China, which is usually caused now by imported cases. Those who display more anxiety choose to have antibody testing. Improving the accessibility of COVID-19 antibody tests can help ease public anxiety and enhance the confidence of some people to participate in social activities.

Willingness of the General Public to Receive A COVID-19 Vaccine Booster - China, April-May 2021.

WHAT IS ALREADY KNOWN ABOUT THIS TOPIC?: A coronavirus disease 2019 (COVID-19) vaccine booster is planned for administration to eligible individuals. Understanding the factors that influence attitudes towards the booster shot will help to identify groups that will most readily accept a booster dose. WHAT IS ADDED BY THIS REPORT?: Of the individuals polled, 75.2% reported they would receive a booster shot. Sociodemographic characteristics influencing booster vaccine acceptance included age, gender, occupation, and education. Moreover, those who had been vaccinated against influenza, who believed herd immunity would be effective against severe acute respiratory syndrome coronavirus 2, and who reported reduced anxiety after vaccination were more likely to accept a booster dose. WHAT ARE THE IMPLICATIONS FOR PUBLIC HEALTH PRACTICE?: A booster shot of the COVID-19 vaccine could be widely accepted. Communicating about the effectiveness of the COVID-19 vaccine and the impact of infection on people's health could help increase public willingness to get a booster dose.

An Information Gain-Based Model and an Attention-Based RNN for Wearable Human Activity Recognition.

Wearable sensor-based HAR (human activity recognition) is a popular human activity perception method. However, due to the lack of a unified human activity model, the number and positions of sensors in the existing wearable HAR systems are not the same, which affects the promotion and application. In this paper, an information gain-based human activity model is established, and an attention-based recurrent neural network (namely Attention-RNN) for human activity recognition is designed. Besides, the attention-RNN, which combines bidirectional long short-term memory (BiLSTM) with attention mechanism, was tested on the UCI opportunity challenge dataset. Experiments prove that the proposed human activity model provides guidance for the deployment location of sensors and provides a basis for the selection of the number of sensors, which can reduce the number of sensors used to achieve the same classification effect. In addition, experiments show that the proposed Attention-RNN achieves F1 scores of 0.898 and 0.911 in the ML (Modes of Locomotion) task and GR (Gesture Recognition) task, respectively.

Risk Scores Based on Six Survival-Related RNAs in a Competing Endogenous Network Composed of Differentially Expressed RNAs Between Clear Cell Renal Cell Carcinoma Patients Carrying Wild-Type or Mutant Von Hippel-Lindau Serve Well to Predict Malignancy and Prognosis.

Clear cell renal cell carcinoma (ccRCC) carrying wild-type Von Hippel-Lindau (VHL) tumor suppressor are more invasive and of high morbidity. Concurrently, competing endogenous RNA (ceRNA) network has been suggested to play an important role in ccRCC malignancy. In order to understand why the patients carrying wild-type VHL gene have high degrees of invasion and morbidity, we applied bioinformatics approaches to identify 861 differentially expressed RNAs (DE-RNAs) between patients carrying wild-type and patients carrying mutant VHL from The Cancer Genome Atlas (TCGA) database, established a ceRNA network including 122 RNAs, and elected six survival-related DE-RNAs including Linc00942, Linc00858, RP13_392I16.1, hsa-miR-182-5p, hsa-miR-183-5p, and PAX3. Examining clinical samples from our hospital revealed that patients carrying wild-type VHL had significantly higher levels of all six RNAs than those carrying mutant VHL. Patients carrying wild-type VHL had significantly higher risk scores, which were calculated based on expression levels of all six RNAs, than those carrying mutant VHL. Patients with higher risk scores had significantly shorter survival times than those with lower risk scores. Therefore, the risk scores serve well to predict malignancy and prognosis.

Survey on security issues of routing and anomaly detection for space information networks.

Space information networks is network systems that can receive, transmit, and process spatial information lively. It uses satellites, stratosphere airships, Unmanned Aerial Vehicles, and other platforms as the carrier. It supports high-dynamic, real-time broadband transmission of earth observations and ultra-long-distance, long-delay reliable transmission of deep space exploration. The deeper the network integration, the higher the system's security concerns and the more likely SINs will be controlled and destroyed in terms of cybersecurity. How to integrate new IT technologies such as artificial intelligence, digital twins, and blockchain to diverse application scenarios of SINs while maintaining SIN cybersecurity will be a long-term critical technical issue. This study is a review of the security issues for space information networks. First, this paper examines space information networks' security issues and figures out the relationship between the main security threats, services, and mechanisms. Then, this article selects secure routing and anomaly detection from many security technologies to conduct a detailed overview from two perspectives of traditional methods and artificial intelligence. Subsequently, this paper investigates anomaly detection schemes for spatial information networks and proposes a deep learning-based anomaly detection scheme. Finally, we suggest the potential research directions and opening problems of space information network security. Overall, this paper aims to give readers an overview of the newly emerging technologies in space information networks' security issues and provide inspiration for future exploration.

Early Screening of Children With Autism Spectrum Disorder Based on Electroencephalogram Signal Feature Selection With L1-Norm Regularization.

Early screening is vital and helpful for implementing intensive intervention and rehabilitation therapy for children with autism spectrum disorder (ASD). Research has shown that electroencephalogram (EEG) signals can reflect abnormal brain function of children with ASD, and screening with EEG signals has the characteristics of good real-time performance and high sensitivity. However, the existing EEG screening algorithms mostly focus on the data analysis in the resting state, and the extracted EEG features have some disadvantages such as weak representation capacity and information redundancy. In this study, we utilized the event-related potential (ERP) technique to acquire the EEG data of the subjects under positive and negative emotional stimulation and proposed an EEG Feature Selection Algorithm based on L1-norm regularization to perform screening of autism. The proposed EEG Feature Selection Algorithm includes the following steps: (1) extracting 20 EEG features from the raw data, (2) classification with support vector machine, (3) selecting appropriate EEG feature with L1-norm regularization according to the classification performance. The experimental results show that the accuracy for screening of children with ASD can reach 93.8% and 87.5% under positive and negative emotional stimulation and the proposed algorithm can effectively eliminate redundant features and improve screening accuracy.

Head Pose Estimation through Keypoints Matching between Reconstructed 3D Face Model and 2D Image.

Mainstream methods treat head pose estimation as a supervised classification/regression problem, whose performance heavily depends on the accuracy of ground-truth labels of training data. However, it is rather difficult to obtain accurate head pose labels in practice, due to the lack of effective equipment and reasonable approaches for head pose labeling. In this paper, we propose a method which does not need to be trained with head pose labels, but matches the keypoints between a reconstructed 3D face model and the 2D input image, for head pose estimation. The proposed head pose estimation method consists of two components: the 3D face reconstruction and the 3D-2D matching keypoints. At the 3D face reconstruction phase, a personalized 3D face model is reconstructed from the input head image using convolutional neural networks, which are jointly optimized by an asymmetric Euclidean loss and a keypoint loss. At the 3D-2D keypoints matching phase, an iterative optimization algorithm is proposed to match the keypoints between the reconstructed 3D face model and the 2D input image efficiently under the constraint of perspective transformation. The proposed method is extensively evaluated on five widely used head pose estimation datasets, including Pointing'04, BIWI, AFLW2000, Multi-PIE, and Pandora. The experimental results demonstrate that the proposed method achieves excellent cross-dataset performance and surpasses most of the existing state-of-the-art approaches, with average MAEs of 4.78∘ on Pointing'04, 6.83∘ on BIWI, 7.05∘ on AFLW2000, 5.47∘ on Multi-PIE, and 5.06∘ on Pandora, although the model of the proposed method is not trained on any of these five datasets.

Self-Difference Convolutional Neural Network for Facial Expression Recognition.

Facial expression recognition (FER) is a challenging problem due to the intra-class variation caused by subject identities. In this paper, a self-difference convolutional network (SD-CNN) is proposed to address the intra-class variation issue in FER. First, the SD-CNN uses a conditional generative adversarial network to generate the six typical facial expressions for the same subject in the testing image. Second, six compact and light-weighted difference-based CNNs, called DiffNets, are designed for classifying facial expressions. Each DiffNet extracts a pair of deep features from the testing image and one of the six synthesized expression images, and compares the difference between the deep feature pair. In this way, any potential facial expression in the testing image has an opportunity to be compared with the synthesized "Self"-an image of the same subject with the same facial expression as the testing image. As most of the self-difference features of the images with the same facial expression gather tightly in the feature space, the intra-class variation issue is significantly alleviated. The proposed SD-CNN is extensively evaluated on two widely-used facial expression datasets: CK+ and Oulu-CASIA. Experimental results demonstrate that the SD-CNN achieves state-of-the-art performance with accuracies of 99.7% on CK+ and 91.3% on Oulu-CASIA, respectively. Moreover, the model size of the online processing part of the SD-CNN is only 9.54 MB (1.59 MB ×6), which enables the SD-CNN to run on low-cost hardware.

Suppression of autophagy facilitates hydrogen gas-mediated lung cancer cell apoptosis.

Our previous study found that hydrogen gas (H2) could efficiently inhibit lung cancer progression; however, the underlying mechanisms still remains to be elucidated. The present study aimed to explore the roles of H2 in lung cancer cell autophagy, and reveal the effects of autophagy on H2-mediated lung cancer cell apoptosis and the underlying mechanisms. The expression levels of proteins associated with cell apoptosis and autophagy were detected using western blot analysis. Cell autophagy was inhibited by 3-methyladenine treatment or Beclin1 downregulation, while rapamycin was used to induce autophagy. Cell growth and apoptosis were detected using the Cell Counting Kit-8 and flow cytometry assays, respectively. The results demonstrated that cell apoptosis and autophagy were significantly enhanced in the A549 and H1975 lung cancer cell lines treated with H2. However, autophagy enhancement weakened H2 roles in promoting cell apoptosis and vice versa. In addition, it was found that H2 treatment induced marked decreases in the protein expression levels of phosphorylated STAT3 and Bcl2, and overexpression of STAT3 abolished H2 roles in promoting cell apoptosis and autophagy. Overall, the present study revealed that H2 can promote lung cancer cell apoptosis and autophagy via inhibiting the activation of STAT3/Bcl2 signaling and suppression of autophagy can enhance H2 roles in promoting lung cancer cell apoptosis.

An Energy-Efficient Fall Detection Method Based on FD-DNN for Elderly People.

A fall detection module is an important component of community-based care for the elderly to reduce their health risk. It requires the accuracy of detections as well as maintains energy saving. In order to meet the above requirements, a sensing module-integrated energy-efficient sensor was developed which can sense and cache the data of human activity in sleep mode, and an interrupt-driven algorithm is proposed to transmit the data to a server integrated with ZigBee. Secondly, a deep neural network for fall detection (FD-DNN) running on the server is carefully designed to detect falls accurately. FD-DNN, which combines the convolutional neural networks (CNN) with long short-term memory (LSTM) algorithms, was tested on both with online and offline datasets. The experimental result shows that it takes advantage of CNN and LSTM, and achieved 99.17% fall detection accuracy, while its specificity and sensitivity are 99.94% and 94.09%, respectively. Meanwhile, it has the characteristics of low power consumption.

p62 promotes proliferation, apoptosis­resistance and invasion of prostate cancer cells through the Keap1/Nrf2/ARE axis.

Prostate cancer poses a public health threat to hundreds of people around the world. p62 has been identified as a tumor suppressor, however, the mechanism by which p62 promotes prostate cancer remains poorly understood. The present study aimed to investigate whether p62 promotes proliferation, apoptosis resistance and invasion of prostate cancer cells via the Kelch­like ECH­associated protein 1/nuclear factor erytheroid­derived 2­like 2/antioxidant response element (Keap1/Nrf2/ARE) axis. Immunohistochemical staining and immunoblotting were performed to determine the protein levels. Rates of proliferation, invasion and apoptosis of prostate cancer cells were assessed using an RTCA system and flow cytometric assays. Levels of reactive oxygen species (ROS) were assessed using Cell ROX Orange reagent and mRNA levels of Nrf2 target genes were detected by qRT­PCR. It was revealed that p62 increased the levels and activities of Nrf2 by suppressing Keap1­mediated proteasomal degradation in prostate cancer cells and tissues, and high levels of p62 promoted growth of prostate cancer through the Keap1/Nrf2/ARE system. Silencing of Nrf2 in DU145 cells overexpressing p62 led to decreases in the rate of cell proliferation and invasion and an increase in the rate of cell apoptosis. p62 activated the Nrf2 pathway, promoted the transcription of Nrf2­mediated target genes and suppressed ROS in prostate cancer. Therefore, p62 promoted the development of prostate cancer by activating the Keap1/Nrf2/ARE pathway and decreasing p62 may provide a new strategy to ameliorate tumor aggressiveness and suppress tumorigenesis to improve clinical outcomes.

Hydrogen gas represses the progression of lung cancer via down-regulating CD47.

Hydrogen gas (H2) has been identified to play an anti-tumor role in several kinds of cancers, but the molecular mechanisms remain largely unknown. In our previous study, our project group found that H2 could decrease the expression of CD47 in lung cancer A549 cells via the next-generation sequencing, indicating that CD47 might be involved in H2-mediated lung cancer repression. Therefore, the present study aimed to explore the effects of CD47 on H2-induced lung cancer repression. Western blotting and real-time PCR (RT-PCR) assays were used to detect the levels of proteins and mRNAs, respectively. Cell proliferation, invasion, migration and apoptosis were detected by using the cell counting kit-8 (CCK-8), Transwell chambers, wound healing and flow cytometry assays, respectively. The results showed that H2 treatment caused decreases in the expression levels of CD47 and cell division control protein 42 (CDC42) in a dose-dependent manner. Up-regulation of CD47 abolished H2 roles in promoting lung cancer cell apoptosis and repressing cell growth, invasion and migration in both A549 and H1975 cell lines. However, knockdown of CD47 enhanced H2 role in lung cancer inhibition. Moreover, we also observed that H2 treatment induced obvious inhibitions in the expression levels of CDC42 and CD47 in mice tumor tissues, as well as reinforced macrophage-mediated phagocytosis in A549 and H1975 cells. In conclusion, the current study reveals that H2 inhibits the progression of lung cancer via down-regulating CD47, which might be a potent method for lung cancer treatment.

LRPPRC sustains Yap-P27-mediated cell ploidy and P62-HDAC6-mediated autophagy maturation and suppresses genome instability and hepatocellular carcinomas.

Mutants in the gene encoding mitochondrion-associated protein LRPPRC were found to be associated with French Canadian Type Leigh syndrome, a human disorder characterized with neurodegeneration and cytochrome c oxidase deficiency. LRPPRC interacts with one of microtubule-associated protein family MAP1S that promotes autophagy initiation and maturation to suppress genomic instability and tumorigenesis. Previously, although various studies have attributed LRPPRC nuclear acid-associated functions, we characterized that LRPPRC acted as an inhibitor of autophagy in human cancer cells. Here we show that liver-specific deletion of LRPPRC causes liver-specific increases of YAP and P27 and decreases of P62, leading to an increase of cell polyploidy and an impairment of autophagy maturation. The blockade of autophagy maturation and promotion of polyploidy caused by LRPPRC depletion synergistically enhances diethylnitrosamine-induced DNA damage, genome instability, and further tumorigenesis so that LRPPRC knockout mice develop more and larger hepatocellular carcinomas and survive a shorter lifespan. Therefore, LRPPRC suppresses genome instability and hepatocellular carcinomas and promotes survivals in mice by sustaining Yap-P27-mediated cell ploidy and P62-HDAC6-controlled autophagy maturation.

Superpixel-Based Temporally Aligned Representation for Video-Based Person Re-Identification.

Most existing person re-identification methods focus on matching still person images across non-overlapping camera views. Despite their excellent performance in some circumstances, these methods still suffer from occlusion and the changes of pose, viewpoint or lighting. Video-based re-id is a natural way to overcome these problems, by exploiting space-time information from videos. One of the most challenging problems in video-based person re-identification is temporal alignment, in addition to spatial alignment. To address the problem, we propose an effective superpixel-based temporally aligned representation for video-based person re-identification, which represents a video sequence only using one walking cycle. Particularly, we first build a candidate set of walking cycles by extracting motion information at superpixel level, which is more robust than that at the pixel level. Then, from the candidate set, we propose an effective criterion to select the walking cycle most matching the intrinsic periodicity property of walking persons. Finally, we propose a temporally aligned pooling scheme to describe the video data in the selected walking cycle. In addition, to characterize the individual still images in the cycle, we propose a superpixel-based representation to improve spatial alignment. Extensive experimental results on three public datasets demonstrate the effectiveness of the proposed method compared with the state-of-the-art approaches.