Tailoring β-ketoenamine covalent organic framework with azo for blue light-driven selective oxidation of amines with oxygen

Covalent organic frameworks (COFs) present bright prospects in visible light photocatalysis with abundant active sites and exceptional stability. Tailoring an established COF with photoactive group is a prudent strategy to extend visible light absorption toward broad photocatalysis. Here, a β-ketoenamine COF, TpBD-COF, constructed with 1,3,5-triformylphloroglucinol (Tp) and 4,4'-biphenyldiamine (BD), is tailored with azo to validate this strategy. The insertion of azo into BD affords 4,4'-azodianiline (Azo); TpAzo-COF is successfully constructed with Tp and Azo. Intriguingly, the insertion of azo enhances π-conjugation, thereby facilitating visible light absorption and intramolecular electron transfer. Moreover, TpAzo-COF, with an appropriate electronic structure and impressive specific surface area of 1855 m2 g-1, offers substantial active sites conducive to the reduction of oxygen (O2) to superoxide. Compared with TpBD-COF, TpAzo-COF exhibits superior performance for blue light-driven oxidation of amines with O2. Superoxide controls the selective formation of product imines. This work foreshadows the remarkable capacity of tailoring COFs with photoactive group toward broad visible light photocatalysis.

Single-Image-Based Deep Learning for Segmentation of Early Esophageal Cancer Lesions

Accurate segmentation of lesions is crucial for diagnosis and treatment of early esophageal cancer (EEC). However, neither traditional nor deep learning-based methods up to today can meet the clinical requirements, with the mean Dice score - the most important metric in medical image analysis - hardly exceeding 0.75. In this paper, we present a novel deep learning approach for segmenting EEC lesions. Our method stands out for its uniqueness, as it relies solely on a single input image from a patient, forming the so-called "You-Only-Have-One" (YOHO) framework. On one hand, this "one-image-one-network" learning ensures complete patient privacy as it does not use any images from other patients as the training data. On the other hand, it avoids nearly all generalization-related problems since each trained network is applied only to the same input image itself. In particular, we can push the training to "over-fitting" as much as possible to increase the segmentation accuracy. Our technical details include an interaction with clinical doctors to utilize their expertise, a geometry-based data augmentation over a single lesion image to generate the training dataset (the biggest novelty), and an edge-enhanced UNet. We have evaluated YOHO over an EEC dataset collected by ourselves and achieved a mean Dice score of 0.888, which is much higher as compared to the existing deep-learning methods, thus representing a significant advance toward clinical applications. The code and dataset are available at: https://github.com/lhaippp/YOHO.

Morphological and molecular response mechanisms of the root system of different Hemarthria compressa species to submergence stress

Introduction

The severity of flood disasters is increasing due to climate change, resulting in a significant reduction in the yield and quality of forage crops worldwide. This poses a serious threat to the development of agriculture and livestock. Hemarthria compressa is an important high-quality forage grass in southern China. In recent years, frequent flooding has caused varying degrees of impacts on H. compressa and their ecological environment.

Methods

In this study, we evaluated differences in flooding tolerance between the root systems of the experimental materials GY (Guang Yi, flood-tolerant) and N1291 (N201801291, flood-sensitive). We measured their morphological indexes after 7 d, 14 d, and 21 d of submergence stress and sequenced their transcriptomes at 8 h and 24 h, with 0 h as the control.

Results

During submergence stress, the number of adventitious roots and root length of both GY and N1291 tended to increase, but the overall growth of GY was significantly higher than that of N1291. RNA-seq analysis revealed that 6046 and 7493 DEGs were identified in GY-8h and GY-24h, respectively, and 9198 and 4236 DEGs in N1291-8h and N1291-24h, respectively, compared with the control. The GO and KEGG enrichment analysis results indicated the GO terms mainly enriched among the DEGs were oxidation-reduction process, obsolete peroxidase reaction, and other antioxidant-related terms. The KEGG pathways that were most significantly enriched were phenylpropanoid biosynthesis, plant hormone signal transduction etc. The genes of transcription factor families, such as C2H2, bHLH and bZIP, were highly expressed in the H. compressa after submergence, which might be closely related to the submergence adaptive response mechanisms of H. compressa.

Discussion

This study provides basic data for analyzing the molecular and morphological mechanisms of H. compressa in response to submergence stress, and also provides theoretical support for the subsequent improvement of submergence tolerance traits of H. compressa.

Targeting ZIP8 mediated ferroptosis as a novel strategy to protect against the retinal pigment epithelial degeneration

The degeneration of retinal pigment epithelium (RPE) plays an important role in the development of age-related macular degeneration (AMD). However, the underlying mechanism remains elusive. In this study, we identified that ZIP8, a metal-ion transporter, plays a crucial role in the degeneration of RPE cells mediated by ferroptosis. ZIP8 was found to be upregulated in patients with AMD through transcriptome analysis. Upregulated ZIP8 was also observed in both oxidative-stressed RPE cells and AMD mouse model. Importantly, knockdown of ZIP8 significantly inhibited ferroptosis in RPE cells induced by sodium iodate-induced oxidative stress. Blocking ZIP8 with specific antibodies reversed RPE degeneration and restored retinal function, improving visual loss in a mouse model of NaIO3-induced. Interestingly, the modification of the N-glycosylation sites N40, N72 and N88, but not N273, was essential for the intracellular iron accumulation mediated by ZIP8, which further led to increased lipid peroxidation and RPE death. These findings highlight the critical role of ZIP8 in RPE ferroptosis and provide a potential target for the treatment of diseases associated with retinal degeneration, including AMD.

PdmIRD: missense variants pathogenicity prediction for inherited retinal diseases in a disease-specific manner

Accurate discrimination of pathogenic and nonpathogenic variation remains an enormous challenge in clinical genetic testing of inherited retinal diseases (IRDs) patients. Computational methods for predicting variant pathogenicity are the main solutions for this dilemma. The majority of the state-of-the-art variant pathogenicity prediction tools disregard the differences in characteristics among different genes and treat all types of mutations equally. Since missense variants are the most common type of variation in the coding region of the human genome, we developed a novel missense mutation pathogenicity prediction tool, named Prediction of Deleterious Missense Mutation for IRDs (PdmIRD) in this study. PdmIRD was tailored for IRDs-related genes and constructed with the conditional random forest model. Population frequencies and a newly available prediction tool were incorporated into PdmIRD to improve the performance of the model. The evaluation of PdmIRD demonstrated its superior performance over nonspecific tools (areas under the curves, 0.984 and 0.910) and an existing eye abnormalities-specific tool (areas under the curves, 0.975 and 0.891). We also demonstrated the submodel that used a smaller gene panel further slightly improved performance. Our study provides evidence that a disease-specific model can enhance the prediction of missense mutation pathogenicity, especially when new and important features are considered. Additionally, this study provides guidance for exploring the characteristics and functions of the mutated proteins in a greater number of Mendelian disorders.

DVSRNet: Deep Video Super-Resolution Based on Progressive Deformable Alignment and Temporal-Sparse Enhancement

Video super-resolution (VSR) is used to compose high-resolution (HR) video from low-resolution video. Recently, the deformable alignment-based VSR methods are becoming increasingly popular. In these methods, the features extracted from video are aligned to eliminate the motion error targeting high super-resolution (SR) quality. However, these methods often suffer from misalignment and the lack of enough temporal information to compose HR frames, which accordingly induce artifacts in the SR result. In this article, we design a deep VSR network (DVSRNet) based on the proposed progressive deformable alignment (PDA) module and temporal-sparse enhancement (TSE) module. Specifically, the PDA module is designed to accurately align features and to eliminate artifacts via the bidirectional information propagation. The TSE module is constructed to further eliminate artifacts and to generate clear details for the HR frame. In addition, we construct a lightweight deep optical flow network (OFNet) to obtain the bidirectional optical flows for the implementation of the PDA module. Moreover, two new loss functions are designed for our proposed method. The first one is adopted in OFNet and the second one is constructed to guarantee the generation of sharp and clear details for the HR frames. The experimental results demonstrate that our method performs better than the state-of-the-art methods.

Red light photocatalysis of conjugated microporous polymers based on fused thiophenes for selective oxidation of amines

By virtue of tailorable building blocks, the band gaps and electronic structures of conjugated microporous polymers (CMPs) can be readily adjusted at the molecular level. Generally, the building blocks possessing extended π-conjugations result in exceptional photocatalytic performances. In this work, the direct CH arylation of fused thiophenes, thieno[3,2-b]thiophene (TT) and dithieno[3,2-b:2',3'-d]thiophene (DTT), with 1,3,6,8-tetrabromopyrene affords two CMPs, namely TT-Py-CMP and DTT-Py-CMP. The expansion of π-conjugations of the fused thiophenes from TT to DTT gives rise to a bathochromic shift about 30 nm from TT-Py-CMP to DTT-Py-CMP. Besides, systematic characterizations suggest the optoelectronic properties of DTT-Py-CMP are better than that of TT-Py-CMP. Furthermore, DTT-Py-CMP drives better red light photocatalysis than TT-Py-CMP for the selective oxidation of amines with molecular oxygen. The selective oxidation of benzyl amines by red light photocatalysis of DTT-Py-CMP progresses via an electron transfer pathway with high selectivities for imines. This work provides new insights that fused thiophenes could be the stepping stone in designing CMPs for expansive visible light photocatalysis.

RAP80 phase separation at DNA double-strand break promotes BRCA1 recruitment

RAP80 has been characterized as a component of the BRCA1-A complex and is responsible for the recruitment of BRCA1 to DNA double-strand breaks (DSBs). However, we and others found that the recruitment of RAP80 and BRCA1 were not absolutely temporally synchronized, indicating that other mechanisms, apart from physical interaction, might be implicated. Recently, liquid-liquid phase separation (LLPS) has been characterized as a novel mechanism for the organization of key signaling molecules to drive their particular cellular functions. Here, we characterized that RAP80 LLPS at DSB was required for RAP80-mediated BRCA1 recruitment. Both cellular and in vitro experiments showed that RAP80 phase separated at DSB, which was ascribed to a highly disordered region (IDR) at its N-terminal. Meanwhile, the Lys63-linked poly-ubiquitin chains that quickly formed after DSBs occur, strongly enhanced RAP80 phase separation and were responsible for the induction of RAP80 condensation at the DSB site. Most importantly, abolishing the condensation of RAP80 significantly suppressed the formation of BRCA1 foci, encovering a pivotal role of RAP80 condensates in BRCA1 recruitment and radiosensitivity. Together, our study disclosed a new mechanism underlying RAP80-mediated BRCA1 recruitment, which provided new insight into the role of phase separation in DSB repair.

Unsupervised Global and Local Homography Estimation With Motion Basis Learning

In this paper, we introduce a new framework for unsupervised deep homography estimation. Our contributions are 3 folds. First, unlike previous methods that regress 4 offsets for a homography, we propose a homography flow representation, which can be estimated by a weighted sum of 8 pre-defined homography flow bases. Second, considering a homography contains 8 Degree-of-Freedoms (DOFs) that is much less than the rank of the network features, we propose a Low Rank Representation (LRR) block that reduces the feature rank, so that features corresponding to the dominant motions are retained while others are rejected. Last, we propose a Feature Identity Loss (FIL) to enforce the learned image feature warp-equivariant, meaning that the result should be identical if the order of warp operation and feature extraction is swapped. With this constraint, the unsupervised optimization can be more effective and the learned features are more stable. With global-to-local homography flow refinement, we also naturally generalize the proposed method to local mesh-grid homography estimation, which can go beyond the constraint of a single homography. Extensive experiments are conducted to demonstrate the effectiveness of all the newly proposed components, and results show that our approach outperforms the state-of-the-art on the homography benchmark dataset both qualitatively and quantitatively. Code is available at https://github.com/megvii-research/BasesHomo.

Selective oxidation of amines powered with green light and oxygen over an anthraquinone covalent organic framework

The exploration of emerging photocatalysts like covalent organic frameworks (COFs) is an essential but challenging endeavor to find sustainable solutions for selective organic transformations. Anthraquinones are envisaged to construct COFs for visible light photocatalysis because their derivatives are employed industrially as oxidation catalysts or organic dyes. Herein, an anthraquinone COF, TpAQ-COF, is successfully constructed with 1,3,5-triformylphloroglucinol (Tp) and 2,6-diaminoanthraquinone (AQ). Then, the selective oxidation of amines over TpAQ-COF is implemented. Amines can be effectively converted into corresponding imines over TpAQ-COF powered with green light and oxygen, during which superoxide radical anion is discerned as the pivotal reactive oxygen species. This work suggests that COFs could inherit the advantages of molecular building blocks for selective reactions powered with broad visible light.

Early gastric cancer segmentation in gastroscopic images using a co-spatial attention and channel attention based triple-branch ResUnet

BACKGROUND AND OBJECTIVE

The artificial segmentation of early gastric cancer (EGC) lesions in gastroscopic images remains a challenging task due to reasons including the diversity of mucosal features, irregular edges of EGC lesions and nuances between EGC lesions and healthy background mucosa. Hence, this study proposed an automatic segmentation framework: co-spatial attention and channel attention based triple-branch ResUnet (CSA-CA-TB-ResUnet) to achieve accurate segmentation of EGC lesions for aiding clinical diagnosis and treatment.

METHODS

The input gastroscopic image sequences of the triple-branch segmentation network CSA-CA-TB-ResUnet is firstly generated by the designed multi-branch input preprocessing (MBIP) module in order to fully utilize massive correlation information among multiple gastroscopic images of the same a lesion. Then, the proposed CSA-CA-TB-ResUnet performs the segmentation of EGC lesion, in which the co-spatial attention (CSA) mechanism is designed to activate the spatial location of EGC lesions by leveraging on the correlations among multiple gastroscopic images of the same EGC lesion, and the channel attention (CA) mechanism is introduced to extract subtle discriminative features of EGC lesions by capturing the interdependencies between channel features. Finally, two gastroscopic images datasets from different digestive endoscopic centers in the southwest and northeast regions of China respectively were collected to validate the performances of proposed segmentation method.

RESULTS

The correlation information among gastroscopic images was confirmed to be able to improve the accuracy of EGC segmentation. On another unseen dataset, our EGC segmentation method achieves Jaccard similarity index (JSI) of 84.54% (95% confidence interval (CI), 83.49%-85.56%), threshold Jaccard index (TJI) of 81.73% (95% CI, 79.70%-83.61%), Dice similarity coefficient (DSC) of 91.08% (95% CI, 90.40%-91.76%) and pixel-wise accuracy (PA) of 91.18% (95% CI, 90.43%-91.87%), which is superior to other state-of-the-art methods. Even on the challenging small lesions, the segmentation results of our CSA-CA-TB-ResUnet-based method are consistently and significantly better than other state-of-the-art methods. We also compared the segmentation result of our model with the diagnostic accuracy with junior/senior expert. The comparison results indicated that our model performed better than the junior expert.

CONCLUSIONS

This study proposed a novel CSA-CA-TB-ResUnet-based EGC segmentation method and it has a potential for real-time application in improving EGC clinical diagnosis and minimally invasive surgery.

Comparative transcriptomic analysis of the gene expression and underlying molecular mechanism of submergence stress response in orchardgrass roots

INTRODUCTION

Submergence stress creates a hypoxic environment. Roots are the first plant organ to face these low-oxygen conditions, which causes damage and affects the plant growth and yield. Orchardgrass (Dactylis glomerata L.) is one of the most important cold-season forage grasses globally. However, their submergence stress-induced gene expression and the underlying molecular mechanisms of orchardgrass roots are still unknown.

METHODS

Using the submergence-tolerant 'Dianbei' and submergence-sensitive 'Anba', the transcriptomic analysis of orchardgrass roots at different time points of submergence stress (0 h, 8 h, and 24 h) was performed.

RESULTS

We obtained 118.82Gb clean data by RNA-Seq. As compared with the control, a total of 6663 and 9857 differentially expressed genes (DEGs) were detected in Dianbei, while 7894 and 11215 DEGs were detected in Anba at 8 h and 24 h post-submergence-stress, respectively. Gene Ontology (GO) enrichment analysis obtained 986 terms, while Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis obtained 123 pathways. Among them, the DEGs in plant hormones, mitogen-activated protein kinase (MAPK) and Ca²⁺ signal transduction were significantly differentially expressed in Dianbei, but not in Anba.

DISCUSSION

This study was the first to molecularly elucidate the submergence stress tolerance in the roots of two orchardgrass cultivars. These findings not only enhanced our understanding of the orchardgrass submergence tolerance, but also provided a theoretical basis 36 for the cultivation of submergence-tolerant forage varieties.

Cuproptosis-related long non-coding RNAs model that effectively predicts prognosis in hepatocellular carcinoma

BACKGROUND

Cuproptosis has recently been considered a novel form of programmed cell death. To date, long-chain non-coding RNAs (lncRNAs) crucial to the regulation of this process remain unelucidated.

AIM

To identify lncRNAs linked to cuproptosis in order to estimate patients' prognoses for hepatocellular carcinoma (HCC).

METHODS

Using RNA sequence data from The Cancer Genome Atlas Live Hepatocellular Carcinoma (TCGA-LIHC), a co-expression network of cuproptosis-related genes and lncRNAs was constructed. For HCC prognosis, we developed a cuproptosis-related lncRNA signature (CupRLSig) using univariate Cox, lasso, and multivariate Cox regression analyses. Kaplan-Meier analysis was used to compare overall survival among high- and low-risk groups stratified by median CupRLSig risk score. Furthermore, comparisons of functional annotation, immune infiltration, somatic mutation, tumor mutation burden (TMB), and pharmacologic options were made between high- and low-risk groups.

RESULTS

Three hundred and forty-three patients with complete follow-up data were recruited in the analysis. Pearson correlation analysis identified 157 cuproptosis-related lncRNAs related to 14 cuproptosis genes. Next, we divided the TCGA-LIHC sample into a training set and a validation set. In univariate Cox regression analysis, 27 LncRNAs with prognostic value were identified in the training set. After lasso regression, the multivariate Cox regression model determined the identified risk equation as follows: Risk score = (0.2659 × PICSAR expression) + (0.4374 × FOXD2-AS1 expression) + (-0.3467 × AP001065.1 expression). The CupRLSig high-risk group was associated with poor overall survival (hazard ratio = 1.162, 95%CI = 1.063-1.270; P < 0.001) after the patients were divided into two groups depending upon their median risk score. Model accuracy was further supported by receiver operating characteristic and principal component analysis as well as the validation set. The area under the curve of 0.741 was found to be a better predictor of HCC prognosis as compared to other clinicopathological variables. Mutation analysis revealed that high-risk combinations with high TMB carried worse prognoses (median survival of 30 mo vs 102 mo of low-risk combinations with low TMB group). The low-risk group had more activated natural killer cells (NK cells, P = 0.032 by Wilcoxon rank sum test) and fewer regulatory T cells (Tregs, P = 0.021) infiltration than the high-risk group. This finding could explain why the low-risk group has a better prognosis. Interestingly, when checkpoint gene expression (CD276, CTLA-4, and PDCD-1) and tumor immune dysfunction and rejection (TIDE) scores are considered, high-risk patients may respond better to immunotherapy. Finally, most drugs commonly used in preclinical and clinical systemic therapy for HCC, such as 5-fluorouracil, gemcitabine, paclitaxel, imatinib, sunitinib, rapamycin, and XL-184 (cabozantinib), were found to be more efficacious in the low-risk group; erlotinib, an exception, was more efficacious in the high-risk group.

CONCLUSION

The lncRNA signature, CupRLSig, constructed in this study is valuable in prognostic estimation of HCC. Importantly, CupRLSig also predicts the level of immune infiltration and potential efficacy of tumor immunotherapy.

Experimental and Numerical Study of Static Behavior of Precast Segmental Hollow Bridge Piers

To investigate the static performance of precast segmental hollow piers, two precast segmental hollow pier specimens were designed for static loading tests on the top of piers. The finite element model of precast segmental hollow piers was established by the finite element software Abaqus and verified based on the test results. Based on the experimental and finite element models, three optimal design solutions were proposed, and the calculation results of each solution were analyzed. The results show that precast segmental hollow pier mechanical behavior is similar to that of cantilevered bending members. The specimens present brittle damage characteristics after the destruction of the structure at the bottom of the pier pressure edge as the axis of the rigid body rotation. Following the test loading process, the bonding between the segments is good, except for the pier bottom damage surface of the rest of the bonding surface, which has no relative displacement. The calculation results of the finite element model are in good agreement with the test results and can effectively predict the load-displacement response of precast piers. Three optimized design solutions are proposed. The finite element simulation proves all three optimized design solutions show better overall ductility than the original solution and can effectively improve the performance of segmental precast hollow piers.

Instance-Level Contrastive Learning for Weakly Supervised Object Detection

Weakly supervised object detection (WSOD) has received increasing attention in object detection field, because it only requires image-level annotations to indicate the presence or absence of target objects, which greatly reduces the labeling costs. Existing methods usually focus on the current individual image to learn object instance representations, while ignoring instance correlations between different images. To address this problem, we propose an instance-level contrastive learning (ICL) framework to mine reliable instance representations from all learned images, and use the contrastive loss to guide instance representation learning for the current image. Due to the diversity of instances, with different appearances, sizes or shapes, we propose an instance-diverse memory updating (IMU) algorithm to mine different instance representations and store them in a memory bank with multiple representation vectors per class, which also considers background information to enhance foreground representations. With the help of memory bank, we further propose a memory-aware instance mining (MIM) algorithm that combines proposal confidence and instance similarity across images to mine more reliable object instances. In addition, we also propose a memory-aware proposal sampling (MPS) algorithm to sample more positive proposals and remove some negative proposals to balance the learning of positive-negative samples. We conduct extensive experiments on the PASCAL VOC2007 and VOC2012 datasets, which are widely used in WSOD, to demonstrate the effectiveness of our method. Compared to our baseline, our method brings 14.2% mAP and 13.4% CorLoc gains on PASCAL VOC2007 dataset, and 12.2% mAP and 8.3% CorLoc gains on PASCAL VOC2012 dataset.

DNA damage-induced paraspeckle formation enhances DNA repair and tumor radioresistance by recruiting ribosomal protein P0

Paraspeckles are mammal-specific membraneless nuclear bodies that participate in various biological processes. NONO, a central paraspeckle component, has been shown to play pivotal roles in DNA double-strand breaks (DSB) repair, whereas its underlying mechanism needs to be further disclosed. Here, using co-immunoprecipitation and mass spectrum, we identified ribosomal protein P0 (RPLP0) as a DSB-induced NONO-binding protein; RPLP0 binds to the RRM1 and RRM2 domains of NONO. Similar to NONO, RPLP0 enhances non-homologous end joining-mediated DSB repair, which was ascribed to a ribosome-independent manner. Interestingly, paraspeckles were induced as early as 15 min after irradiation; it further recruited nuclear RPLP0 to enhance its interaction with NONO. Radiation-induced NONO/RPLP0 complex subsequently anchored at the damaged DNA and increased the autophosphorylation of DNA-PK at Thr2609, thereby enhancing DSB repair. Consistently, in vivo and in vitro experiments showed that depletion of NONO sensitizes tumor cells to radiation. For patients with locally advanced rectal cancer, NONO expression was remarkably increased in tumor tissues and correlated with a poor response to radiochemotherapy. Our findings suggest a pivotal role of radiation-induced paraspeckles in DNA repair and tumor radioresistance, and provide a new insight into the ribosome-independent function of ribosomal proteins.

A novel senescence-associated LncRNA signature predicts the prognosis and tumor microenvironment of patients with colorectal cancer: a bioinformatics analysis

Background

Accumulating evidence suggests that cellular senescence promotes tumor formation and that long non-coding RNAs (lncRNAs) expression predicts tumor prognosis. However, senescence-related variables, particularly lncRNAs, are still largely unknown. Therefore, the present study developed a novel senescence-associated lncRNA signature to predict colorectal cancer (CRC) prognosis.

Methods

A co-expression network of senescence-associated mRNAs and lncRNAs was built using RNA-sequence data from The Cancer Genome Atlas (TCGA). By using the prognosis outcomes data of overall survival (OS) and disease-free survival (DFS) from TCGA, we constructed a prognostic senescence-associated lncRNA signature (SenALSig). The OS and DFS were compared between the low- and high- risk groups defined by SenALSig. A single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT algorithm were used to investigate the relationship between the predictive signature and immune status. Finally, the relationship between SenALSig and drug treatment options was investigated. An independent CRC cohort and three CRC cell lines were recruited to perform real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) analysis to validate the results discovered in silico.

Results

A prognostic risk model consisting of six senescence-associated lncRNAs was constructed, including SNHG16, AL590483.1, ZEB1-AS1, AC107375.1, AC068580.3, and AC147067.1. High-risk scores according to the SenALSig were significantly associated with poor OS (hazard ratio =1.218, 95% confidence interval: 1.140–1.301; P<0.001). The model’s accuracy was further supported by receiver operating characteristic (ROC) curves (the area under the curve is 0.714) and a principal component analysis (PCA). In univariate and multivariate Cox regression analyses, SenALSig was further found to be a prognostic factor independent of other clinical factors. Furthermore, we discovered that immune checkpoint expression and response to chemotherapy and targeted therapy differed significantly between the SenALSig-stratified high- and low-risk groups. Finally, the qPCR results revealed that the expression levels of the six senescence-associated lncRNAs differed significantly between tumor and normal tissues and between the CRC cell lines and a normal human colon mucosal epithelial cell line.

Conclusions

SenALSig can better predict survival and risk in CRC patients, as well as help develop new anti-cancer treatment strategies for CRC.

The development and validation of a novel senescence-related long-chain non-coding RNA (lncRNA) signature that predicts prognosis and the tumor microenvironment of patients with hepatocellular carcinoma

Background

The epigenetic regulators of cellular senescence, especially long non-coding RNAs (lncRNAs), remain unclear. The expression levels of lncRNA were previously known to be prognostic indicators for tumors. We hypothesized that lncRNAs regulating cellular senescence could also predict prognosis in patients with hepatocellular carcinoma (HCC) and developed a novel lncRNA predictive signature.

Methods

Using RNA sequencing data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) database, a co-expression network of senescence-related messenger RNAs (mRNAs) and lncRNAs was constructed. Using univariate Cox regression analysis and a stepwise multiple Cox regression analysis, we constructed a prognostic HCC senescence-related lncRNA signature (HCCSenLncSig). Kaplan-Meier analysis was used to compare the overall survival (OS) of high- and low-risk groups stratified by the HCCSenLncSig. Furthermore, the HCCSenLncSig risk score and other clinical characteristics were included to develop an HCC prognostic nomogram. The accuracy of the model was evaluated by the time dependent receiver operating characteristic (ROC) and calibration curves, respectively.

Results

We obtained a prognostic risk model consisting of 8 senescence-related lncRNAs: AL117336.3, AC103760.1, FOXD2-AS1, AC009283.1, AC026401.3, AC021491.4, AC124067.4, and RHPN1-AS1. The HCCSenLncSig high-risk group was associated with poor OS [hazard ratio (HR) =1.125, 95% confidence interval (CI): 1.082-1.169; P<0.001]. The accuracy of the model was further supported by ROC curves (the area under the curve is 0.783, sensitivity of 0.600, and specificity of 0.896 at the cut-off value of 1.447). The HCCSenLncSig was found to be an independent prognostic factor from other clinical factors in both univariate and multivariate Cox regression analyses. The prognostic nomogram shows HCCSenLncSig has a good prognostic effect for survival risk stratification. Finally, we found that a higher number of immunosuppressed Treg cells infiltrate in high-risk patients (P<0.001 compared to low-risk patients), possibly explaining why these patients have a poor prognosis. On the other hand, the expression of immunotherapy markers, such as CD276, PDCD1, and CTLA4, was also up-regulated in the high-risk patients, indicating potential immunotherapy response in these patients.

Conclusions

The development of HCCSenLncSig allows us to better predict HCC patients' survival outcomes and disease risk, as well as contribute to the development of novel HCC anti-cancer therapeutic strategies.

Separation and determination of basic orange II, acid orange II and auramine O in soybean products based on ionic liquid reverse micelle microextraction and ultra-high-performance liquid chromatography

A new analytical method was developed for the separation and determination of basic orange II, acid orange II and auramine O in soybean products. The technique was focused on ionic liquid reverse micelle microextraction (IL-RMME) followed by analysis and determination by ultra-high performance liquid chromatography (UHPLC) with photodiode array detector of three chemical dyes. In this method, IL-RMME solution consisting of ionic liquid [Omim]BF6 and surfactant GenapolX-080 was used as extractant. Important parameters affecting IL-RMME efficiency, such as extraction solvent type and volume, sample solution pH, salt effect, centrifugation speed and time were investigated. Under the optimal condition, the linearity of the method was in the range of 0.1-10 ng mL-1with correlation coefficient above 0.9994 and the limits of detection below 0.03 ng mL-1. At the same time, relative standard deviations of the developed procedure for intra- (n = 5) and inter-day (n = 5) precision were in the range of 5.04-8.50%. The results demonstrated that a simple fast environmentally friendly efficient method was successfully applied in the separation and determination of three chemical dyes in soybean products.

JigsawGAN: Auxiliary Learning for Solving Jigsaw Puzzles With Generative Adversarial Networks

The paper proposes a solution based on Generative Adversarial Network (GAN) for solving jigsaw puzzles. The problem assumes that an image is divided into equal square pieces, and asks to recover the image according to information provided by the pieces. Conventional jigsaw puzzle solvers often determine the relationships based on the boundaries of pieces, which ignore the important semantic information. In this paper, we propose JigsawGAN, a GAN-based auxiliary learning method for solving jigsaw puzzles with unpaired images (with no prior knowledge of the initial images). We design a multi-task pipeline that includes, (1) a classification branch to classify jigsaw permutations, and (2) a GAN branch to recover features to images in correct orders. The classification branch is constrained by the pseudo-labels generated according to the shuffled pieces. The GAN branch concentrates on the image semantic information, where the generator produces the natural images to fool the discriminator, while the discriminator distinguishes whether a given image belongs to the synthesized or the real target domain. These two branches are connected by a flow-based warp module that is applied to warp features to correct the order according to the classification results. The proposed method can solve jigsaw puzzles more efficiently by utilizing both semantic information and boundary information simultaneously. Qualitative and quantitative comparisons against several representative jigsaw puzzle solvers demonstrate the superiority of our method.

Improving the Classification Performance of Esophageal Disease on Small Dataset by Semi-supervised Efficient Contrastive Learning

The classification of esophageal disease based on gastroscopic images is important in the clinical treatment, and is also helpful in providing patients with follow-up treatment plans and preventing lesion deterioration. In recent years, deep learning has achieved many satisfactory results in gastroscopic image classification tasks. However, most of them need a training set that consists of large numbers of images labeled by experienced experts. To reduce the image annotation burdens and improve the classification ability on small labeled gastroscopic image datasets, this study proposed a novel semi-supervised efficient contrastive learning (SSECL) classification method for esophageal disease. First, an efficient contrastive pair generation (ECPG) module was proposed to generate efficient contrastive pairs (ECPs), which took advantage of the high similarity features of images from the same lesion. Then, an unsupervised visual feature representation containing the general feature of esophageal gastroscopic images is learned by unsupervised efficient contrastive learning (UECL). At last, the feature representation will be transferred to the down-stream esophageal disease classification task. The experimental results have demonstrated that the classification accuracy of SSECL is 92.57%, which is better than that of the other state-of-the-art semi-supervised methods and is also higher than the classification method based on transfer learning (TL) by 2.28%. Thus, SSECL has solved the challenging problem of improving the classification result on small gastroscopic image dataset by fully utilizing the unlabeled gastroscopic images and the high similarity information among images from the same lesion. It also brings new insights into medical image classification tasks.

The whole-genome and expression profile analysis of WRKY and RGAs in Dactylis glomerata showed that DG6C02319.1 and DgWRKYs may cooperate in the immunity against rust

Orchardgrass (Dactylis glomerata) is one of the top four perennial forages worldwide and, despite its large economic advantages, often threatened by various environmental stresses. WRKY transcription factors (TFs) can regulate a variety of plant processes, widely participate in plant responses to biotic and abiotic stresses, and are one of the largest gene families in plants. WRKYs can usually bind W-box elements specifically. In this study, we identified a total of 93 DgWRKY genes and 281 RGAs, including 65, 169 and 47 nucleotide-binding site-leucine-rich repeats (NBS-LRRs), leucine-rich repeats receptor-like protein kinases (LRR-RLKs), and leucine-rich repeats receptor-like proteins (LRR-RLPs), respectively. Through analyzing the expression of DgWRKY genes in orchardgrass under different environmental stresses, it was found that many DgWRKY genes were differentially expressed under heat, drought, submergence, and rust stress. In particular, it was found that the greatest number of genes were differentially expressed under rust infection. Consistently, GO and KEGG enrichment analysis of all genes showed that 78 DgWRKY TFs were identified in the plant–pathogen interaction pathway, with 59 of them differentially expressed. Through cis-acting element prediction, 154 RGAs were found to contain W-box elements. Among them, DG6C02319.1 (a member of the LRR-RLK family) was identified as likely to interact with 14 DGWRKYs. Moreover, their expression levels in susceptible plants after rust inoculation were first up-regulated and then down-regulated, while those in the resistant plants were always up-regulated. In general, DgWRKYs responded to both biotic stress and abiotic stress. DgWRKYs and RGAs may synergistically respond to the response of orchardgrass to rust. This study provides meaningful insight into the molecular mechanisms of WRKY proteins in orchardgrass.

OIFlow: Occlusion-Inpainting Optical Flow Estimation by Unsupervised Learning

Occlusion is an inevitable and critical problem in unsupervised optical flow learning. Existing methods either treat occlusions equally as non-occluded regions or simply remove them to avoid incorrectness. However, the occlusion regions can provide effective information for optical flow learning. In this paper, we present OIFlow, an occlusion-inpainting framework to make full use of occlusion regions. Specifically, a new appearance-flow network is proposed to inpaint occluded flows based on the image content. Moreover, a boundary dilated warp is proposed to deal with occlusions caused by displacement beyond the image border. We conduct experiments on multiple leading flow benchmark datasets such as Flying Chairs, KITTI and MPI-Sintel, which demonstrate that the performance is significantly improved by our proposed occlusion handling framework.

SlimConv: Reducing Channel Redundancy in Convolutional Neural Networks by Features Recombining

The channel redundancy of convolutional neural networks (CNNs) results in the large consumption of memories and computational resources. In this work, we design a novel Slim Convolution (SlimConv) module to boost the performance of CNNs by reducing channel redundancies. Our SlimConv consists of three main steps: Reconstruct, Transform, and Fuse. It aims to reorganize and fuse the learned features more efficiently, such that the method can compress the model effectively. Our SlimConv is a plug-and-play architectural unit that can be used to replace convolutional layers in CNNs directly. We validate the effectiveness of SlimConv by conducting comprehensive experiments on various leading benchmarks, such as ImageNet, MS COCO2014, Pascal VOC2012 segmentation, and Pascal VOC2007 detection datasets. The experiments show that SlimConv-equipped models can achieve better performances consistently, less consumption of memory and computation resources than non-equipped counterparts. For example, the ResNet-101 fitted with SlimConv achieves 77.84% top-1 classification accuracy with 4.87 GFLOPs and 27.96M parameters on ImageNet, which shows almost 0.5% better performance with about 3 GFLOPs and 38% parameters reduced.

[Inhibitory effect of Xinhui citrus fermentation liquor on liver fibrosis in mice]

OBJECTIVE

To investigate the inhibitory effect of Xinhui citrus fermentation liquor on liver fibrosis in mice.

OBJECTIVE

Mouse models of liver fibrosis were established by intraperitoneal injection of CCl₄ in 105 male C57BL/6 mice, followed by gavage of 0.1 mL 40% CCl₄ olive oil 3 times a week (model group, n=49) or daily gavage of citrus liquor at the dose of 0.26 mL (citrus liquor group, n=56) for 8 weeks. Seven mice receiving only olive oil treatment (0.1 mL, 3 times a week) and another 7 treated with citrus liquor served as the control group. Liver tissues and serum samples were collected from 7 mice in the citrus liquor group and model group each week and from the mice in the two control groups at the 8th week for pathological examination of the liver tissues using HE staining and Sirius red staining and for determination of the biochemical indexes of liver function.

OBJECTIVE

The mice in the model group showed progressively worsened liver fibrosis with obvious hepatic steatosis, necrosis and inflammatory cell infiltration. These liver pathologies were much ameliorated in citrus liquor group, which showed significantly reduced vacuolation, inflammatory cell infiltration, collagen deposition and the Ishak score of the liver tissue (P < 0.05). Serum levels of cholyglycine, alanine aminotransferase, transglutaminase and alanine aminotransferase were all significantly lower in citrus liquor group than in the model group (P < 0.05).

OBJECTIVE

Xinhui citrus fermentation liquor has protective effect on the liver and can significantly ameliorate liver fibrosis in mice.

Automatic classification of esophageal disease in gastroscopic images using an efficient channel attention deep dense convolutional neural network

The accurate diagnosis of various esophageal diseases at different stages is crucial for providing precision therapy planning and improving 5-year survival rate of esophageal cancer patients. Automatic classification of various esophageal diseases in gastroscopic images can assist doctors to improve the diagnosis efficiency and accuracy. The existing deep learning-based classification method can only classify very few categories of esophageal diseases at the same time. Hence, we proposed a novel efficient channel attention deep dense convolutional neural network (ECA-DDCNN), which can classify the esophageal gastroscopic images into four main categories including normal esophagus (NE), precancerous esophageal diseases (PEDs), early esophageal cancer (EEC) and advanced esophageal cancer (AEC), covering six common sub-categories of esophageal diseases and one normal esophagus (seven sub-categories). In total, 20,965 gastroscopic images were collected from 4,077 patients and used to train and test our proposed method. Extensive experiments results have demonstrated convincingly that our proposed ECA-DDCNN outperforms the other state-of-art methods. The classification accuracy (Acc) of our method is 90.63% and the averaged area under curve (AUC) is 0.9877. Compared with other state-of-art methods, our method shows better performance in the classification of various esophageal disease. Particularly for these esophageal diseases with similar mucosal features, our method also achieves higher true positive (TP) rates. In conclusion, our proposed classification method has confirmed its potential ability in a wide variety of esophageal disease diagnosis.

Comprehensive Study of Germline Mutations and Double-Hit Events in Esophageal Squamous Cell Cancer

Esophageal squamous cell cancer (ESCC) is the eighth most common cancer around the world. Several reports have focused on somatic mutations and common germline mutations in ESCC. However, the contributions of pathogenic germline alterations in cancer susceptibility genes (CSGs), highly frequently mutated CSGs, and pathogenically mutated CSG-related pathways in ESCC remain unclear. We obtained data on 571 ESCC cases from public databases and East Asian from the 1000 Genomes Project database and the China Metabolic Analytics Project database to characterize pathogenic mutations. We detected 157 mutations in 75 CSGs, accounting for 25.0% (143/571) of ESCC cases. Six genes had more than five mutations: TP53 (n = 15 mutations), GJB2 (n = 8), BRCA2 (n = 6), RECQL4 (n = 6), MUTYH (n = 6), and PMS2 (n = 5). Our results identified significant differences in pathogenic germline mutations of TP53, BRCA2, and RECQL4 between the ESCC and control cohorts. Moreover, we identified 84 double-hit events (16 germline/somatic double-hit events and 68 somatic/somatic double-hit events) occurring in 18 tumor suppressor genes from 83 patients. Patients who had ESCC with germline/somatic double-hit events were diagnosed at younger ages than patients with the somatic/somatic double-hit events, though the correlation was not significant. Fanconi anemia was the most enriched pathway of pathogenically mutated CSGs, and it appeared to be a primary pathway for ESCC predisposition. The results of this study identified the underlying roles that pathogenic germline mutations in CSGs play in ESCC pathogenesis, increased our awareness about the genetic basis of ESCC, and provided suggestions for using highly mutated CSGs and double-hit features in the early discovery, prevention, and genetic counseling of ESCC.

SDP-GAN: Saliency Detail Preservation Generative Adversarial Networks for High Perceptual Quality Style Transfer

The paper proposes a solution to effectively handle salient regions for style transfer between unpaired datasets. Recently, Generative Adversarial Networks (GAN) have demonstrated their potentials of translating images from source domain X to target domain Y in the absence of paired examples. However, such a translation cannot guarantee to generate high perceptual quality results. Existing style transfer methods work well with relatively uniform content, they often fail to capture geometric or structural patterns that always belong to salient regions. Detail losses in structured regions and undesired artifacts in smooth regions are unavoidable even if each individual region is correctly transferred into the target style. In this paper, we propose SDP-GAN, a GAN-based network for solving such problems while generating enjoyable style transfer results. We introduce a saliency network, which is trained with the generator simultaneously. The saliency network has two functions: (1) providing constraints for content loss to increase punishment for salient regions, and (2) supplying saliency features to generator to produce coherent results. Moreover, two novel losses are proposed to optimize the generator and saliency networks. The proposed method preserves the details on important salient regions and improves the total image perceptual quality. Qualitative and quantitative comparisons against several leading prior methods demonstrates the superiority of our method.

Development of quantitative HPTLC methods for dolutegravir, lamivudine, and tenofovir disproxil fumarate in a combination pharmaceutical product using a model process published earlier for transfer of minilab TLC screening methods to HPTLC-densitometry

High-performance thin-layer chromatography (HPLTC)–densitometry methods are described for the analysis of the anti(retro)virals dolutegravir (D), lamivudine (L), and tenofovir disoproxil fumarate (TDF) in a pharmaceutical tablet product. To the best of our knowledge, no previous quantitative planar chromatography method has been reported in the literature for this combination formulation. The method for L was transferred from a thin-layer chromatography (TLC) screening method published in the Global Pharma Health Fund (GPHF) Minilab Manual designed for identification of counterfeit and substandard drug products using a model process published earlier. D and TDF are not included in the list of drugs for which TLC screening methods are published for the Minilab, but HPTLC–densitometry procedures were developed for them using the transfer process guidelines. L was analyzed simultaneously with TDF on Merck Premium Purity silica gel 60 F plates using the mobile phase ethyl acetate–methanol–acetone–concentrated ammonium hydroxide (30:7:3:1) and densitometric scanning at 254 nm. D was analyzed on a second plate by scanning at 366 nm after chromatography with the chloroform–methanol–formic acid (32:8:2) mobile phase. Data for all three drugs are shown to meet the requirements of the model transfer process for calibration curve r values, assay of tablets relative to their label values, peak purity/peak identity tests, and validation by standard addition analysis of samples spiked at 50%, 100%, and 150% of the label value of active ingredients. A TLC screening method for TDF in the combination product was developed and published online with open access.

Transcriptome profiling of two Dactylis glomerata L. cultivars with different tolerance in response to submergence stress

Submergence is one of the environmental stresses that limit plant growth and development. Dactylis glomerata L. is an important cool-season forage grass globally. To investigate the genes related to submergence response and the molecular mechanism associated with submergence tolerance, the transcriptome of D. glomerata in response to waterlogging treatment was analyzed. RNA-sequencing was performed in two D. glomerata cultivars, submergence tolerant 'Dianbei' and submergence sensitive 'Anba'. A total of 50,045 unique genes matched the known proteins in the NCBI nr database by BLAST searches and 60.8% (30,418) of these genes were annotated with GO terms. Among these, 1395 genes only differentially expressed in 'Dianbei' and 18 genes shown different expression all the time were detected between the submergence tolerant 'Dianbei' and sensitive 'Anba'. Gene ontology (GO) and KEGG pathway enrichment analyses demonstrated that the DEGs were mainly implicated in oxidation-reduction system, nucleic acid binding transcription factor activity, and glycerol kinase activity. The D. glomerata assembled transcriptome provided substantial molecular resource for further genomic analysis of forage grasses in response to submergence stress. The significant difference in expression of specific unigenes may account for waterlogging tolerance or acclimation in the two different D. glomerata cultivars. This study provided new insights into the molecular basis of submergence tolerance in D. glomerata.

Association of genetic and environmental factors with dental caries among adolescents in south China: A cross-sectional study

AIM

The objective of this study was to investigative genetic and environmental factors that contribute to caries susceptibility among adolescents in south China.

MATERIALS AND METHODS

A cross-sectional study was conducted among 1055 adolescents aged 13-14 years old in south China. The International Caries Detection and Assessment System (ICDAS) was used to identify caries. Environmental variables were analysed by regression models. Twenty-three single nucleotide polymorphisms (SNPs) in 14 genes were identified from saliva samples. Regression analysis was used for the evaluation of effects of SNP markers using the minor allele as the effect allele.

RESULTS

Our results suggest that gender, Cariostat score and Plaque Index were associated with dental caries. After the adjustment by age and gender, the G allele in AMBN (rs13115627) nominally was a protective factor for caries under additive model (P=0.028; OR=0.782; 95% CI, 0.627-0.974). However, the association did not meet the Bonferroni correction significance cut-off for multiple testing.

CONCLUSION

Gender, Cariostat score and Plaque Index were associated with dental caries in this population. No selected SNPs showed a significant association with dental caries under either additive model or dominance model.

Identification and Distribution of NBS-Encoding Resistance Genes of Dactylis glomerata L. and Its Expression Under Abiotic and Biotic Stress

Orchardgrass (Dactylis glomerata L.) is drought resistant and tolerant to barren landscapes, making it one of the most important forages for animal husbandry, as well as ecological restoration of rocky landscapes that are undergoing desertification. However, orchardgrass is susceptible to rust, which can significantly reduce its yield and quality. Therefore, understanding the genes that underlie resistance against rust in orchardgrass is critical. The evolution, cloning of plant disease resistance genes, and the analysis of pathogenic bacteria induced expression patterns are important contents in the study of interaction between microorganisms and plants. Genes with nucleotide binding site (NBS) structure are disease-resistant genes ubiquitous in plants and play an important role in plant attacks against various pathogens. Using sequence analysis and re-annotation, we identified 413 NBS resistance genes in orchardgrass. Similar to previous studies, NBS resistance genes containing TIR (toll/interleukin-1 receptor) domain were not found in orchardgrass. The NBS resistance genes can be divided into four types: NBS (up to 264 homologous genes, accounting for 64% of the total number of NBS genes in orchardgrass), NBS-LRR, CC-NBS, and CC-NBS-LRR (minimum of 26 homologous genes, only 6% of the total number of NBS genes in orchardgrass). These 413 NBS resistance genes were unevenly distributed across seven chromosomes where chromosome 5 had up to 99 NBS resistance genes. There were 224 (54%) NBS resistance genes expressed in different tissues (roots, stems, leaves, flowers, and spikes), and we did not detect expression for 45 genes (11%). The remaining 145 (35%) were expressed in some tissues. And we found that 11 NBS resistance genes were differentially expressed under waterlogging stress, 5 NBS resistance genes were differentially expressed under waterlogging and drought stress, and 1 NBS resistance was is differentially expressed under waterlogging and heat stress. Most importantly, we found that 65 NBS resistance genes were significantly expressed in different control groups. On the 7th day of inoculation, 23 NBS resistance genes were differentially expressed in high resistance materials alone, of which 7 NBS resistance genes regulate the "plant-pathogen interaction" pathway by encoding RPM1. At the same time, 2 NBS resistance genes that were differentially expressed in the high resistance material after inoculation were also differentially expressed in abiotic stress. In summary, the NBS resistance gene plays a crucial role in the resistance of orchardgrass to rust.

Transcriptome analysis on responses of orchardgrass (Dactylis glomerata L.) leaves to a short term flooding

BACKGROUND

Orchardgrass (Dactylis glomerata L.) is a popular cool-season perennial grass with a high production value, and orchardgrass seed is the fourth top-selling forage grass seed in the world. However, its yield and quality are often affected by flooding. To date, the molecular responses of orchardgrass to flooding were poorly understood.

RESULTS

Here, we performed mRNA-seq to explore the transcriptomic responses of orchardgrass to a short term flooding (8 h and 24 h). There were 1454 and 565 differentially expressed genes identified in the 8 h and 24 h of flooding, respectively, compared to well control. GO functional enrichment analysis showed that oxidoreductase activity and oxidation-reduction process were highly present, suggesting that flooding induced the response to oxygen stress. Pathways enrichment analysis highlights the importance of glutathione metabolism, peroxidase, glycolysis and plant hormone signal transduction in response to flooding acclimation. Besides, the ROS clearance system is activated by significantly expressed glutathione S-transferase and genes encoding SOD and CAT (CAT1 and CDS2). The significant positive correlation between RNA sequencing data and a qPCR analysis indicated that the identified genes were credible.

CONCLUSION

In the process of orchardgrass response to flooding stress, multiple differential genes and biological processes have participated in its acclimation to flooding, especially the biological processes involved in the removal of ROS. These results provide a basis for further research on the adaptation mechanism of orchardgrass to flood tolerance.

PBR-Net: Imitating Physically Based Rendering using Deep Neural Network

Physically based rendering has been widely used to generate photo-realistic images, which greatly impacts industry by providing appealing rendering, such as for entertainment and augmented reality, and academia by serving large scale high-fidelity synthetic training data for data hungry methods like deep learning. However, physically based rendering heavily relies on ray-tracing, which can be computational expensive in complicated environment and hard to parallelize. In this paper, we propose an end-to-end deep learning based approach to generate physically based rendering efficiently. Our system consists of two stacked neural networks, which effectively simulates the physical behavior of the rendering process and produces photo-realistic images. The first network, namely shading network, is designed to predict the optimal shading image from surface normal, depth and illumination; the second network, namely composition network, learns to combine the predicted shading image with the reflectance to generate the final result. Our approach is inspired by intrinsic image decomposition, and thus it is more physically reasonable to have shading as intermediate supervision. Extensive experiments show that our approach is robust to noise thanks to a modified perceptual loss and even outperforms the physically based rendering systems in complex scenes given a reasonable time budget.

A New Polyphase Down-Sampling Based Multiple Description Image Coding

Multiple description coding (MDC) is an efficient source coding technique for error-prone transmission over multiple channels. In this paper, we focus on the design of a new polyphase down-sampling based MDC (NPDS-MDC) for image signals. The encoding of our proposed NPDS-MDC consists of three steps. First, we perform down-sampling on each N×N image block according to the quincunx down-sampling pattern. Second, we propose a new transform and apply it to the down-sampled pixels to produce the side descriptions. Third, we develop an error compensation algorithm to reduce the compression distortion occurring on the down-sampled pixels. In our scheme, the side decoding is performed posterior to image interpolation with reference to the down-sampled compressed pixels. Moreover, the central decoding is achieved by interlacing the side descriptions. We also propose a compression-constrained central deblocking algorithm to further improve the efficiency of the central decoding. The experimental results indicate that our proposed MDC scheme offers clearly superior performance, especially at high bit rates, as compared to the state-of-the-art methods for various types of images.

Diagnostic value of intraoperative bone marrow assessment for bone margins in patients with head and neck squamous cell carcinoma: a systematic review and meta-analysis

A clear bone margin is essential for complete resection of the bone-involved tumour, but the evaluation of hard tissue takes time and is impractical intraoperatively. Bone marrow assessment remains controversial. The aim of this study was to investigate the diagnostic value of intraoperative bone marrow assessment for bone margins. PubMed and Web of Science were searched for studies published between 1990 and 2017. A systematic review was conducted. After quality assessment, 10 articles with 11 cohorts and 404 patients were identified. Sensitivity, specificity, and other measures were pooled for meta-analysis; the estimates for intraoperative bone marrow assessment were as follows: sensitivity 0.82 (95% confidence interval (CI) 0.62-0.93), specificity 0.99 (95% CI 0.96-1.00), positive likelihood ratio 109.79 (95% CI 22.99-524.34), negative likelihood ratio 0.18 (95% CI 0.08-0.42), and diagnostic odds ratio 241.82 (95% CI 90.33-647.38). Furthermore, sensitivity and specificity at the summary operating point of the summary receiver operating characteristic curve were 0.82 and 0.99, respectively, and the area under the curve was 0.99. Intraoperative bone marrow assessment was investigated by meta-analysis and shown to have a high level of overall accuracy for the diagnosis of bone margins.

A pilot trial of Convolution Neural Network for automatic retention-monitoring of capsule endoscopes in the stomach and duodenal bulb

The retention of a capsule endoscope (CE) in the stomach and the duodenal bulb during the examination is a troublesome problem, which can make the medical staff spend several hours observing whether the CE enters the descending segment of the duodenum (DSD). This paper investigated and evaluated the Convolution Neural Network (CNN) for automatic retention-monitoring of the CE in the stomach or the duodenal bulb. A trained CNN system based on 180,000 CE images of the DSD, stomach, and duodenal bulb was used to assess its recognition of the accuracy by calculating the area under the receiver operating characteristic curve (ROC-AUC), sensitivity and specificity. The AUC for distinguishing the DSD was 0.984. The sensitivity, specificity, positive predictive value, and negative predictive value of the CNN were 97.8%, 96.0%, 96.1% and 97.8%, respectively, at a cut-off value of 0.42 for the probability score. The deviated rate of the time into the DSD marked by the CNN at less than ±8 min was 95.7% (P < 0.01). These results indicate that the CNN for automatic retention-monitoring of the CE in the stomach or the duodenal bulb can be used as an efficient auxiliary measure in the clinical practice.

miR-1245a promotes the proliferation and invasion of colon adenocarcinoma by targeting BRCA2

Background

Colon adenocarcinoma (CA) is the most common one with poor survival in colon cancer. This study aims to investigate the effect of miR-1245a on the process of CA cells and its target gene BRCA2.

Methods

The expression of CA tissues and cells were evaluated by q RT-PCR. Then we explore the association between expression of miR-1245a and prognosis in the CA patients from the TCGA database. CCK8 assays, colony formation assays were performed to explore the effect of miR-1245a in CA cell proliferation. The invasion ability of CA cells was evaluated by Transwell assays. Western blot was performed to assess the BRCA2 expression. Luciferase reporter assay was employed to scrutinize the relationship between miR-1245a and BRCA2. Finally, rescue experiments were performed through BRCA2 downregulation and miR-1245a inhibitors by using colony formation assay and Transwell invasion assay.

Results

miR-1245a is upregulated in CA cells and tissues. Additionally, the high expression of miR-1245a was related to poor survival. CCK8 assays, colony formation assays and Transwell assays showed that miR-1245a promotes the proliferation and invasion of CA cells. The luciferase reporter assay indicated that miR-1245a targeted BRCA2 and inhibited its expression. The rescue experiment further showed that miR-1245a could restore the effect of BRCA2 on CA.

Conclusions

miR-1245a promotes the proliferation and invasion of CA by targeting BRCA2.Our results suggested that miR-1245a could be a potential biomarker for CA progression.

The Differential Effect of Treadmill Exercise Intensity on Hippocampal Soluble Aβ and Lipid Metabolism in APP/PS1 Mice

Alzheimer's disease (AD) is characterized clinically by progressive impairments in learning and memory. Accumulating evidence suggests that regular exercise plays a neuroprotective role in aging-associated memory loss. Our previous study has confirmed that long-term treadmill exercise initiated either before or during the onset of β-amyloid (Aβ) pathology, was beneficial for reducing the levels of soluble Aβ and further improved cognition. In this study, in APP/PS1 mice, we assessed changes in soluble Aβ, and various blood biochemistry and molecular biological indices to assess whether exercise modulated lipid metabolism and thereby decelerated AD progression. Our results show that long-term treadmill exercise reduced the total cholesterol, triglyceride, and low-density lipoprotein cholesterol levels, and increased the level of high-density lipoprotein cholesterol. Exercise also decreased the levels of soluble Aβ1-40 and Aβ1-42, down-regulated retinoid X receptor expression, and up-regulated liver X receptor, Apolipoprotein E, Low density lipoprotein receptor, Low density lipoprotein receptor-related protein 1, and ATP-binding cassette transporter A1 expression. This indicates that long-term treadmill exercise alters the lipoprotein content, increases lipid metabolism and cholesterol transportation, reduces the soluble Aβ, and therein plays an important neuroprotective role and delays AD progression. We further show that medium exercise intensity (60%-70% of maximal oxygen uptake) was more efficacious in increasing lipid metabolism and reducing blood lipid levels and soluble Aβ levels, than low-intensity exercise (45-55% of maximal oxygen uptake). This research has broad prospects and implications, and offers a theoretical basis for the prevention of AD.