UV-A radiation increases biomass yield by enhancing energy flow and carbon assimilation in the edible cyanobacterium Nostoc sphaeroides.

Ultraviolet (UV) A radiation (315-400 nm) is the predominant component of solar UV radiation that reaches the Earth's surface. However, the underlying mechanisms of the positive effects of UV-A on photosynthetic organisms have not yet been elucidated. In this study, we investigated the effects of UV-A radiation on the growth, photosynthetic ability, and metabolome of the edible cyanobacterium Nostoc sphaeroides. Exposures to 5-15 W m-2 (15-46 µmol photons m-2 s-1) UV-A and 4.35 W m-2 (20 µmol photons m-2 s-1) visible light for 16 days significantly increased the growth rate and biomass production of N. sphaeroides cells by 18%-30% and 15%-56%, respectively, compared to the non-UV-A-acclimated cells. Additionally, the UV-A-acclimated cells exhibited a 1.8-fold increase in the cellular nicotinamide adenine dinucleotide phosphate (NADP) pool with an increase in photosynthetic capacity (58%), photosynthetic efficiency (24%), QA re-oxidation, photosystem I abundance, and cyclic electron flow (87%), which further led to an increase in light-induced NADPH generation (31%) and ATP content (83%). Moreover, the UV-A-acclimated cells showed a 2.3-fold increase in ribulose-1,5-bisphosphate carboxylase/oxygenase activity, indicating an increase in their carbon-fixing capacity. Gas chromatography-mass spectrometry-based metabolomics further revealed that UV-A radiation upregulated the energy-storing carbon metabolism, as evidenced by the enhanced accumulation of sugars, fatty acids, and citrate in the UV-A-acclimated cells. Therefore, our results demonstrate that UV-A radiation enhances energy flow and carbon assimilation in the cyanobacterium N. sphaeroides.IMPORTANCEUltraviolet (UV) radiation exerts harmful effects on photo-autotrophs; however, several studies demonstrated the positive effects of UV radiation, especially UV-A radiation (315-400 nm), on primary productivity. Therefore, understanding the underlying mechanisms associated with the promotive effects of UV-A radiation on primary productivity can facilitate the application of UV-A for CO2 sequestration and lead to the advancement of photobiological sciences. In this study, we used the cyanobacterium Nostoc sphaeroides, which has an over 1,700-year history of human use as food and medicine, to explore its photosynthetic acclimation response to UV-A radiation. As per our knowledge, this is the first study to demonstrate that UV-A radiation increases the biomass yield of N. sphaeroides by enhancing energy flow and carbon assimilation. Our findings provide novel insights into UV-A-mediated photosynthetic acclimation and provide a scientific basis for the application of UV-A radiation for optimizing light absorption capacity and enhancing CO2 sequestration in the frame of a future CO2 neutral, circular, and sustainable bioeconomy.

Transcriptional analysis of the expression, prognostic value and immune infiltration activities of the COMMD protein family in hepatocellular carcinoma.

BACKGROUND: The copper metabolism MURR1 domain (COMMD) protein family involved in tumor development and progression in several types of human cancer, but little is known about the function of COMMD proteins in hepatocellular carcinoma (HCC). METHODS: The ONCOMINE and the UALCAN databases were used to evaluate the expression of COMMD1-10 in HCC and the association of this family with individual cancer stage and tumor grade. Kaplan-Meier (K-M) Plotter and Cox analysis hint the prognostic value of COMMDs. A network comprising 50 most similar genes and COMMD1-10 was constructed with the STRING database. Gene set enrichment analysis (GSEA) was performed using LinkedOmics database. The correlations between COMMD expression and the presence of immune infiltrating cells were also analyzed by the tumor immune estimation resource (TIMER) database. GSE14520 dataset and 80 HCC patients were used to validated the expression and survival value of COMMD3. Human HCC cell lines were also used for validating the function of COMMD3. RESULTS: The expression of all COMMD family members showed higher expression in HCC tissues than that in normal tissues, and is associated with clinical cancer stage and pathological tumor grade. In HCC patients, the transcriptional levels of COMMD1/4 are positively correlated with overall survival (OS), while those of COMMD2/3/7/8/9 are negatively correlated with OS. Multivariate analysis indicated that a high level of COMMD3 mRNA is an independent prognostic factor for shorter OS in HCC patients. However, the subset of patients with grade 3 HCC, K-M survival curves revealed that high COMMD3/5/7/8/9 expression and low COMMD4/10 expression were associated with shorter OS. In addition, the expression of COMMD2/3/10 was associated with tumor-induced immune response activation and immune infiltration in HCC. The expression of COMMD3 from GSE14520 dataset and 80 patients are both higher in tumor than that in normal tissue, and a higher level of COMMD3 mRNA is associated with shorter OS. Knockdown of COMMD3 inhibits human HCC cell lines proliferation in vitro. CONCLUSIONS: Our study indicates that COMMD3 is an independent prognostic biomarker for the survival of HCC patients. COMMD3 supports the proliferation of HCC cells and contributes to the poor OS in HCC patients.

Systematic analysis on multiple Gene Expression Omnibus data sets reveals fierce immune response in hepatitis B virus-related acute liver failure.

Acute liver failure (ALF) caused by hepatitis B virus (HBV) is common type of liver failure in the world, with high morbidity and mortality rates. However, the prevalence, genetic background and factors determining the development of HBV-related ALF are rarely studied. In this study, we examined three Gene Expression Omnibus (GEO) data sets by bioinformatics analysis to identify differentially expressed genes (DEGs), key biological processes and pathways. Immune infiltration analysis showed high immune cells infiltration in HBV-related ALF tissue. We then confirmed natural killer cells and macrophages infiltration in clinical samples by immunohistochemistry assay, implying these cells play a significant role in HBV-ALF. We found 1277 genes were co-up-regulated and that 1082 genes were co-down-regulated in the 3 data sets. Inflammation-related pathways were enriched in the co-up-regulated genes and synthetic metabolic pathways were enriched in the co-down-regulated genes. WGCNA also revealed a key module enriching in immune inflammation response and identified 10 hub genes, differentially expressed in an independent data set. In conclusion, we identified fierce immune inflammatory response to elucidate the immune-driven mechanism of HBV-ALF and 10 hub genes based on gene expression profiles.

Future Frame Prediction Network for Video Anomaly Detection.

Video Anomaly detection in videos refers to the identification of events that do not conform to expected behavior. However, almost all existing methods cast this problem as the minimization of reconstruction errors of training data including only normal events, which may lead to self-reconstruction and cannot guarantee a larger reconstruction error for an abnormal event. In this paper, we propose to formulate the video anomaly detection problem within a regime of video prediction. We advocate that not all video prediction networks are suitable for video anomaly detection. Then, we introduce two principles for the design of a video prediction network for video anomaly detection. Based on them, we elaborately design a video prediction network with appearance and motion constraints for video anomaly detection. Further, to promote the generalization of the prediction-based video anomaly detection for novel scenes, we carefully investigate the usage of a meta learning within our framework, where our model can be fast adapted to a new testing scene with only a few starting frames. Extensive experiments on both a toy dataset and three real datasets validate the effectiveness of our method in terms of robustness to the uncertainty in normal events and the sensitivity to abnormal events.

Locating and Counting Heads in Crowds With a Depth Prior.

To simultaneously estimate the number of heads and locate heads with bounding boxes, we resort to detection-based crowd counting by leveraging RGB-D data and design a dual-path guided detection network (DPDNet). Specifically, to improve the performance of detection-based approaches for dense/tiny heads, we propose a density map guided detection module, which leverages density map to improve the head/non-head classification in detection network where the density implies the probability of a pixel being a head, and a depth-adaptive kernel that considers the variances in head sizes is also introduced to generate high-fidelity density map for more robust density map regression. In order to prevent dense heads from being filtered out during post-processing, we utilize such a density map for post-processing of head detection and propose a density map guided NMS strategy. Meanwhile, to improve the ability of detecting small heads, we also propose a depth-guided detection module to generate a dynamic dilated convolution to extract features of heads of different scales, and a depth-aware anchor is further designed for better initialization of anchor sizes in the detection framework. Then we use the bounding boxes whose sizes are generated with depth to train our DPDNet. Considering that existing RGB-D datasets are too small and not suitable for performance evaluation of data-driven based approaches, we collect two large-scale RGB-D crowd counting datasets, which comprise a synthetic dataset and a real-world dataset, respectively. Since the depth value at long-distance positions cannot be obtained in the real-world dataset, we further propose a depth completion method with meta learning, which fully utilizes the synthetic depth data to complete the depth value at long-distance positions. Extensive experiments on our proposed two RGB-D datasets and the MICC RGB-D counting dataset show that our method achieves the best performance for RGB-D crowd counting and localization. Further, our method can be easily extended to RGB image based crowd counting and achieves comparable or even better performance on the RGB datasets for both head counting and localization.

Proxy-Bridged Image Reconstruction Network for Anomaly Detection in Medical Images.

Anomaly detection in medical images refers to the identification of abnormal images with only normal images in the training set. Most existing methods solve this problem with a self-reconstruction framework, which tends to learn an identity mapping and reduces the sensitivity to anomalies. To mitigate this problem, in this paper, we propose a novel Proxy-bridged Image Reconstruction Network (ProxyAno) for anomaly detection in medical images. Specifically, we use an intermediate proxy to bridge the input image and the reconstructed image. We study different proxy types, and we find that the superpixel-image (SI) is the best one. We set all pixels' intensities within each superpixel as their average intensity, and denote this image as SI. The proposed ProxyAno consists of two modules, a Proxy Extraction Module and an Image Reconstruction Module. In the Proxy Extraction Module, a memory is introduced to memorize the feature correspondence for normal image to its corresponding SI, while the memorized correspondence does not apply to the abnormal images, which leads to the information loss for abnormal image and facilitates the anomaly detection. In the Image Reconstruction Module, we map an SI to its reconstructed image. Further, we crop a patch from the image and paste it on the normal SI to mimic the anomalies, and enforce the network to reconstruct the normal image even with the pseudo abnormal SI. In this way, our network enlarges the reconstruction error for anomalies. Extensive experiments on brain MR images, retinal OCT images and retinal fundus images verify the effectiveness of our method for both image-level and pixel-level anomaly detection.

Memorizing Structure-Texture Correspondence for Image Anomaly Detection.

This work focuses on image anomaly detection by leveraging only normal images in the training phase. Most previous methods tackle anomaly detection by reconstructing the input images with an autoencoder (AE)-based model, and an underlying assumption is that the reconstruction errors for the normal images are small, and those for the abnormal images are large. However, these AE-based methods, sometimes, even reconstruct the anomalies well; consequently, they are less sensitive to anomalies. To conquer this issue, we propose to reconstruct the image by leveraging the structure-texture correspondence. Specifically, we observe that, usually, for normal images, the texture can be inferred from its corresponding structure (e.g., the blood vessels in the fundus image and the structured anatomy in optical coherence tomography image), while it is hard to infer the texture from a destroyed structure for the abnormal images. Therefore, a structure-texture correspondence memory (STCM) module is proposed to reconstruct image texture from its structure, where a memory mechanism is used to characterize the mapping from the normal structure to its corresponding normal texture. As the correspondence between destroyed structure and texture cannot be characterized by the memory, the abnormal images would have a larger reconstruction error, facilitating anomaly detection. In this work, we utilize two kinds of complementary structures (i.e., the semantic structure with human-labeled category information and the low-level structure with abundant details), which are extracted by two structure extractors. The reconstructions from the two kinds of structures are fused together by a learned attention weight to get the final reconstructed image. We further feed the reconstructed image into the two aforementioned structure extractors to extract structures. On the one hand, constraining the consistency between the structures extracted from the original input and that from the reconstructed image would regularize the network training; on the other hand, the error between the structures extracted from the original input and that from the reconstructed image can also be used as a supplement measurement to identify the anomaly. Extensive experiments validate the effectiveness of our method for image anomaly detection on both industrial inspection images and medical images.

Bioinformatic Analysis and In Vitro and In Vivo Experiments Reveal That Fibrillarin Participates in the Promotion of Lung Metastasis in Hepatocellular Carcinoma.

Lung metastasis, the most frequent metastatic pattern in hepatocellular carcinoma, is an important contributor to poor prognosis. However, the mechanisms responsible for lung metastasis in hepatocellular carcinoma remain unknown. Aiming to explore these mechanisms, weighted gene coexpression network analysis (WGCNA) was firstly used to find hub genes related to lung metastasis. Then, we obtained 67 genes related to lung metastasis in hepatocellular carcinoma which were mainly related to ribosomal pathways and functions, and a protein interaction network analysis identified that fibrillarin (FBL) might be an important hub gene. Furthermore, we found that FBL is highly expressed in hepatocellular carcinoma and that its high expression increases the rate of lung metastasis and indicates a poor prognosis. Knockdown of FBL could significantly reduce proliferation and stemness as well as inhibiting the migration and invasion of hepatocellular carcinoma cells. Moreover, we found that FBL might be involved in the regulation of MYC and E2F pathways in hepatocellular carcinoma. Finally, we demonstrated that the knockdown of FBL could suppress hepatocellular carcinoma cell growth in vivo. In conclusion, ribosome-biogenesis-related proteins, especially Fibrillarin, play important roles in lung metastasis from hepatocellular carcinoma.

Video Anomaly Detection with Sparse Coding Inspired Deep Neural Networks.

This paper presents an anomaly detection method that is based on a sparse coding inspired Deep Neural Networks (DNN). Specifically, in light of the success of sparse coding based anomaly detection, we propose a Temporally-coherent Sparse Coding (TSC), where a temporally-coherent term is used to preserve the similarity between two similar frames. The optimization of sparse coefficients in TSC with the Sequential Iterative Soft-Thresholding Algorithm (SIATA) is equivalent to a special stacked Recurrent Neural Networks (sRNN) architecture. Further, to reduce the computational cost in alternatively updating the dictionary and sparse coefficients in TSC optimization and to alleviate hyperparameters selection in TSC, we stack one more layer on top of the TSC-inspired sRNN to reconstruct the inputs, and arrive at an sRNN-AE. We further improve sRNN-AE in the following aspects: i) rather than using a predefined similarity measurement between two frames, we propose to learn a data-dependent similarity measurement between neighboring frames in sRNN-AE to make it more suitable for anomaly detection; ii) to reduce computational costs in the inference stage, we reduce the depth of the sRNN in sRNN-AE and, consequently, our framework achieves real-time anomaly detection; iii) to improve computational efficiency, we conduct temporal pooling over the appearance features of several consecutive frames for summarizing information temporally, then we feed appearance features and temporally summarized features into a separate sRNN-AE for more robust anomaly detection. To facilitate anomaly detection evaluation, we also build a large-scale anomaly detection dataset which is even larger than the summation of all existing datasets for anomaly detection in terms of both the volume of data and the diversity of scenes. Extensive experiments on both a toy dataset under controlled settings and real datasets demonstrate that our method significantly outperforms existing methods, which validates the effectiveness of our sRNN-AE method for anomaly detection. Codes and data have been released at https://github.com/StevenLiuWen/sRNN_TSC_Anomaly_Detection.

DTYMK Expression Predicts Prognosis and Chemotherapeutic Response and Correlates with Immune Infiltration in Hepatocellular Carcinoma.

INTRODUCTION: Hepatocellular carcinoma (HCC) is the most common malignant tumor of the liver. Identifying specific molecular markers that can predict HCC prognosis is extremely important. The protein deoxythymidylate kinase (DTYMK) has been reported to contribute to unfavorable prognosis in non-small cell lung cancer patients, but its role in the prediction of HCC patient prognosis has not been clarified. METHODS: Samples from the TCGA and GEO databases were consecutively enrolled for gene expression analysis, clinicopathology analysis, immune microenvironment analysis and chemotherapeutic response prediction. The results were validated using 86 samples from the First Affiliated Hospital of Sun Yat-sen University. Cox regression analysis was used to analyze the effect of DTYMK on progression-free survival (PFS) and overall survival (OS). Functional enrichment analysis was used to describe the marker pathways that were significantly related to DTYMK. TIMER (Tumor Immune Estimation Resource), TISIDB (Tumor and Immune System Interaction DataBase) and CIBERSORT (Cell type Identification By Estimating Relative Subsets Of RNA Transcripts) were used to explore the immune microenvironment. RESULTS: We found that DTYMK expression upregulation is associated with poor prognosis in HCC patients and tightly related to the pathways regulating the cell cycle and acid metabolism. Our findings revealed that hepatocellular carcinoma cell lines with high DTYMK expression were more sensitive to sorafenib and many other chemotherapeutic drugs. We also found an inhibiting effect of DTYMK on the immune microenvironment in the process of tumorigenesis. DISCUSSION: We found that DTYMK has potential as a new prognostic and chemotherapeutic response biomarker for HCC patients and correlates with the immune microenvironment in HCC. However, there are some deficiencies in our study. First, this is a retrospective study that may lead to selection bias. Second, the protein expression of DTYMK was investigated via immunohistochemical analysis. Finally, we did not explore the exact underlying molecular mechanisms of DTYMK in tumorigenesis in this study, which is needed to be clarified in future research.

Investigating the Rheological Properties of Carbon Nanotubes/Polymer Composites Modified Asphalt.

The utilization of nanomaterials in the field of binder materials for road paving has attracted researchers' attention in recent years. This study presented the performance properties of a binder modified with carbon nanotubes (CNT) and polyethylene (PE). The rheological properties, adhesion behavior, morphology, and storage stability of the modified asphalt were investigated. Experimental analysis indicated a positive effect of CNT/PE composites on the performance of the binder. The results indicate that the combined use of CNT and PE shows a significant enhancement on complex modulus, viscosity, and creep recovery of the binder at high temperatures and a great decrease in compliance, indicating great resistance to permanent deformation. Meanwhile, only using CNT to improve the high temperature performance of the binder is limited due to high shear mixing. CNT/PE modifiers enhance the cracking resistance at low temperatures and moisture damage resistance. The CNT/PE melt mixing composites endow asphalt with stronger cracking resistance, better resistance to moisture damage and workability. Asphalt with CNT/PE composites formed an even dispersion system. Notably, CNT bridges on the interface between PE phase and asphalt for the two modified asphalts, which reinforces the cohesion of interface. Asphalt with CNT/PE composites showed improved storage stability in comparison with PE modified asphalt.

Construction of a risk score prognosis model based on hepatocellular carcinoma microenvironment.

BACKGROUND: Hepatocellular carcinoma (HCC) is a common cancer with a poor prognosis. Previous studies revealed that the tumor microenvironment (TME) plays an important role in HCC progression, recurrence, and metastasis, leading to poor prognosis. However, the effects of genes involved in TME on the prognosis of HCC patients remain unclear. Here, we investigated the HCC microenvironment to identify prognostic genes for HCC. AIM: To identify a robust gene signature associated with the HCC microenvironment to improve prognosis prediction of HCC. METHODS: We computed the immune/stromal scores of HCC patients obtained from The Cancer Genome Atlas based on the ESTIMATE algorithm. Additionally, a risk score model was established based on Differentially Expressed Genes (DEGs) between high- and low-immune/stromal score patients. RESULTS: The risk score model consisting of eight genes was constructed and validated in the HCC patients. The patients were divided into high- or low-risk groups. The genes (Disabled homolog 2, Musculin, C-X-C motif chemokine ligand 8, Galectin 3, B-cell-activating transcription factor, Killer cell lectin like receptor B1, Endoglin and adenomatosis polyposis coli tumor suppressor) involved in our risk score model were considered to be potential immunotherapy targets, and they may provide better performance in combination. Functional enrichment analysis showed that the immune response and T cell receptor signaling pathway represented the major function and pathway, respectively, related to the immune-related genes in the DEGs between high- and low-risk groups. The receiver operating characteristic (ROC) curve analysis confirmed the good potency of the risk score prognostic model. Moreover, we validated the risk score model using the International Cancer Genome Consortium and the Gene Expression Omnibus database. A nomogram was established to predict the overall survival of HCC patients. CONCLUSION: The risk score model and the nomogram will benefit HCC patients through personalized immunotherapy.

EZH2 facilitates BMI1-dependent hepatocarcinogenesis through epigenetically silencing microRNA-200c.

EZH2, a histone methyltransferase, has been shown to involve in cancer development and progression via epigenetic regulation of tumor suppressor microRNAs, whereas BMI1, a driver of hepatocellular carcinoma (HCC), is a downstream target of these microRNAs. However, it remains unclear whether EZH2 can epigenetically regulate microRNA expression to modulate BMI1-dependent hepatocarcinogenesis. Here, we established that high EZH2 expression correlated with enhanced tumor size, elevated metastasis, increased relapse, and poor prognosis in HCC patients. Further clinical studies revealed that EZH2 overexpression was positively correlated to its gene copy number gain/amplification in HCC. Mechanistically, EZH2 epigenetically suppressed miR-200c expression both in vitro and in vivo, and more importantly, miR-200c post-transcriptionally regulated BMI1 expression by binding to the 3'-UTR region of its mRNA. Furthermore, miR-200c overexpression inhibits the growth of HCC cells in vivo. Silencing miR-200c rescued the tumorigenicity of EZH2-depleted HCC cells, whereas knocking down BMI1 reduced the promoting effect of miR-200c depletion on HCC cell migration. Finally, combination treatment of EZH2 and BMI1 inhibitors further inhibited the viability of HCC cells compared with the cells treated with EZH2 or BMI1 inhibitor alone. Our findings demonstrated that alteration of EZH2 gene copy number status induced BMI1-mediated hepatocarcinogenesis via epigenetically silencing miR-200c, providing novel therapeutic targets for HCC treatment.

Multiview Multitask Gaze Estimation With Deep Convolutional Neural Networks.

Gaze estimation, which aims to predict gaze points with given eye images, is an important task in computer vision because of its applications in human visual attention understanding. Many existing methods are based on a single camera, and most of them only focus on either the gaze point estimation or gaze direction estimation. In this paper, we propose a novel multitask method for the gaze point estimation using multiview cameras. Specifically, we analyze the close relationship between the gaze point estimation and gaze direction estimation, and we use a partially shared convolutional neural networks architecture to simultaneously estimate the gaze direction and gaze point. Furthermore, we also introduce a new multiview gaze tracking data set that consists of multiview eye images of different subjects. As far as we know, it is the largest multiview gaze tracking data set. Comprehensive experiments on our multiview gaze tracking data set and existing data sets demonstrate that our multiview multitask gaze point estimation solution consistently outperforms existing methods.

Multi-Cell Multi-Task Convolutional Neural Networks for Diabetic Retinopathy Grading.

Diabetic Retinopathy (DR) is a non-negligible eye disease among patients with Diabetes Mellitus, and automatic retinal image analysis algorithm for the DR screening is in high demand. Considering the resolution of retinal image is very high, where small pathological tissues can be detected only with large resolution image and large local receptive field are required to identify those late stage disease, but directly training a neural network with very deep architecture and high resolution image is both time computational expensive and difficult because of gradient vanishing/exploding problem, we propose a Multi-Cell architecture which gradually increases the depth of deep neural network and the resolution of input image, which both boosts the training time but also improves the classification accuracy. Further, considering the different stages of DR actually progress gradually, which means the labels of different stages are related. To considering the relationships of images with different stages, we propose a Multi-Task learning strategy which predicts the label with both classification and regression. Experimental results on the Kaggle dataset show that our method achieves a Kappa of 0.841 on test set which is the 4th rank of all state-of-the-arts methods. Further, our Multi-Cell Multi-Task Convolutional Neural Networks (M2CNN) solution is a general framework, which can be readily integrated with many other deep neural network architectures.