Naringenin inhibits the microsomal triglyceridetransfer protein/apolipoprotein B axis to inhibit intestinal metaplasia progression.

Intestinal metaplasia (IM) is a premalignant condition that increases the risk for subsequent gastric cancer (GC). Traditional Chinese medicine generally plays a role in the treatment of IM, and the phytochemical naringenin used in Chinese herbal medicine has shown therapeutic potential for the treatment of gastric diseases. However, naringenin's specific effect on IM is not yet clearly understood. Therefore, this study identified potential gene targets for the treatment of IM through bioinformatics analysis and experiment validation. Two genes (MTTP and APOB) were selected as potential targets after a comparison of RNA-seq results of clinical samples, the GEO dataset (GSE78523), and naringenin-related genes from the GeneCards database. The results of both cell and animal experiments suggested that naringenin can improve the changes in the intestinal epithelial metaplasia model via MTTP/APOB expression. In summary, naringenin likely inhibits the MTTP/APOB axis and therefore inhibits IM progression. These results support the development of naringenin as an anti-IM agent and may contribute to the discovery of novel IM therapeutic targets.

Variable Rate Point Cloud Geometry Compression Method.

With the development of 3D sensors technology, 3D point cloud is widely used in industrial scenes due to their high accuracy, which promotes the development of point cloud compression technology. Learned point cloud compression has attracted much attention for its excellent rate distortion performance. However, there is a one-to-one correspondence between the model and the compression rate in these methods. To achieve compression at different rates, a large number of models need to be trained, which increases the training time and storage space. To address this problem, a variable rate point cloud compression method is proposed, which enables the adjustment of the compression rate by the hyperparameter in a single model. To address the narrow rate range problem that occurs when the traditional rate distortion loss is jointly optimized for variable rate models, a rate expansion method based on contrastive learning is proposed to expands the bit rate range of the model. To improve the visualization effect of the reconstructed point cloud, a boundary learning method is introduced to improve the classification ability of the boundary points through boundary optimization and enhance the overall model performance. The experimental results show that the proposed method achieves variable rate compression with a large bit rate range while ensuring the model performance. The proposed method outperforms G-PCC, achieving more than 70% BD-Rate against G-PCC, and performs about, as well as the learned methods at high bit rates.

Effect of bushen tongluo decoction on the ERK/c-Fos signal transduction pathway in rats with bone cancer pain.

Bone cancer pain could lead to pain sensitization. Traditional Chinese Medicine could relieve bone cancer pain (BCP). This study aimed to investigate the analgesic effect of Bushen Tongluo decoction on rats with BCP and its impact on ERK/c-fos pathway in spinal dorsal horn. Cancer cells were injected to induce bone cancer pain rats. Inflammatory factors in serum were determined using enzyme-linked immunosorbent. ERK/c-Fos in the spinal dorsal horn were detected using western blotting and RT-qPCR. Thermal hyperalgesia and mechanical allodynia were observed in BCP rats. The ERK/c-Fos pathway activation was observed in the spinal dorsal horn and the expression of inflammatory cytokines increased in the serum. Bushen Tongluo decoction alleviated inflammatory cytokines and reduced the ERK/c-Fos pathway. We provided evidence that Bushen Tongluo decoction exhibits a potential and beneficial effect on inflammatory cytokines, effectively alleviating allodynia and hyperalgesia in rats with bone cancer. This effect may be attributed to down-regulation of the ERK/c-Fos pathway in spinal dorsal horn and serum inflammatory cytokines.

Unsupervised Monocular Visual Odometry for Fast-Moving Scenes Based on Optical Flow Network with Feature Point Matching Constraint.

Robust and accurate visual feature tracking is essential for good pose estimation in visual odometry. However, in fast-moving scenes, feature point extraction and matching are unstable because of blurred images and large image disparity. In this paper, we propose an unsupervised monocular visual odometry framework based on a fusion of features extracted from two sources, that is, the optical flow network and the traditional point feature extractor. In the training process, point features are generated for scene images and the outliers of matched point pairs are filtered by FlannMatch. Meanwhile, the optical flow network constrained by the principle of forward-backward flow consistency is used to select another group of corresponding point pairs. The Euclidean distance between the matching points found by FlannMatch and the corresponding point pairs by the flow network is added to the loss function of the flow network. Compared with SURF, the trained flow network shows more robust performance in complicated fast-motion scenarios. Furthermore, we propose the AvgFlow estimation module, which selects one group of the matched point pairs generated by the two methods according to the scene motion. The camera pose is then recovered by Perspective-n-Point (PnP) or the epipolar geometry. Experiments conducted on the KITTI Odometry dataset verify the effectiveness of the trajectory estimation of our approach, especially in fast-moving scenarios.

In Vivo Visualization of γ-Glutamyl Transpeptidase Activity with an Activatable Self-Immobilizing Near-Infrared Probe.

γ-Glutamyl transpeptidase (GGT), a type of cell membrane-bound enzyme, is closely involved in a wide range of physiological and pathological processes, and a large number of fluorogenic probes have been developed to detect the activity of GGT. However, the use of these imaging reagents to visualize GGT activity in vivo is largely limited because of rapid diffusion and clearance of activated fluorophores. Herein, by merging quinone methide and a fluorogenic enzyme substrate, we report an activatable self-immobilizing near-infrared probe for the in vitro and in vivo imaging of GGT activity. This probe is initially fluorescently silent, but the selective activation by GGT is able to significantly increase its fluorescence intensity at 714 nm and covalently anchor activated fluorophores at the site of interest. We have shown that this probe induced a much stronger fluorescence on live GGT-overexpressing cells compared to regular fluorogenic probes and allowed wash-free and real-time imaging of enzyme activity. More importantly, the use of this probe in the imaging of GGT activity in U87MG tumor-bearing mice by i.v. administration indicates that this self-immobilizing reagent is capable of efficiently enhancing its retention at the detection target and thus leads to much improved detection sensitivity compared to regular fluorogenic probes. This study demonstrates the advantage of fluorogenic probes with activatable anchors in the noninvasive imaging of enzyme activity in highly dynamic in vivo systems.

Real-Time Vehicle Make and Model Recognition with the Residual SqueezeNet Architecture.

Make and model recognition (MMR) of vehicles plays an important role in automatic vision-based systems. This paper proposes a novel deep learning approach for MMR using the SqueezeNet architecture. The frontal views of vehicle images are first extracted and fed into a deep network for training and testing. The SqueezeNet architecture with bypass connections between the Fire modules, a variant of the vanilla SqueezeNet, is employed for this study, which makes our MMR system more efficient. The experimental results on our collected large-scale vehicle datasets indicate that the proposed model achieves 96.3% recognition rate at the rank-1 level with an economical time slice of 108.8 ms. For inference tasks, the deployed deep model requires less than 5 MB of space and thus has a great viability in real-time applications.

PD806: a novel oral vascular disrupting agent shows antitumor and antivascular effects in vitro and in vivo.

The aim of this study was to investigate the antitumor and antivascular effects of PD806, a new oral prodrug of AVE8063 in vitro and in vivo. The cytotoxicity of PD806 was determined against H22, Walker 256, A549, MCF-7, and BEL-7402 cells using MTT assays. Plasma pharmacokinetic analysis of AVE8063 generated in rats after a single oral administration of PD806 was carried out using the high-performance liquid chromatography method. H22 tumor-bearing mice models were used to show the antitumor activity. Antivascular responses were monitored by in vivo MRI and immunohistochemistry (CD31) in W256 tumor-bearing rats. A blood test and histopathology were performed to evaluate the toxicity of PD806. PD806 showed cytotoxicity against five types of tumor cell lines with the IC50 values in the micromolar concentration. A pharmacokinetic study indicated that PD806 converted into the active form, AVE8063, which showed a half-life of 5.24±0.70 h in rats. Daily oral administration of PD806 inhibited the growth of subcutaneously implanted H22 tumors in a dose-dependent manner. The tumor volume in the 300 mg/kg PD806 group was obviously smaller than that of the vehicle control group from day 6 onward (P<0.05), with inhibition rates of 62% on day 30. PD806 in the three-dose group significantly prolonged the survival of the H22 tumor-bearing mice (P<0.05). At 24 h after PD806 (150 and 200 mg/kg) was administered orally, tumor vascular shutdown was found on CE-T1WI with the presence of extended necrosis and tumor residue at the periphery. The enhancement ratio decreased significantly from 1.00±0.00 at baseline to 0.26±0.08 and 0.17±0.06, respectively (P<0.01). The necrosis ratio measured from CE-T1WI increased significantly from 34% in average at baseline to 52.96 and 60.30%, respectively (P<0.05). Immunohistochemical staining of tumor sections showed a marked reduction in CD31 staining vessels, with microvessel density reduced significantly to 8.71±1.76 and 3.33±1.04, respectively, compared with the vehicle control group (P<0.01). The results of hematology and histopathology showed that PD806 exerted no obvious toxicity during the treatment period. In conclusion, our results indicate that PD806 is an effective and safe vascular disrupting agent.

G-K BertDTA: A graph representation learning and semantic embedding-based framework for drug-target affinity prediction.

Developing new drugs is costly, time-consuming, and risky. Drug-target affinity (DTA), indicating the binding capability between drugs and target proteins, is a crucial indicator for drug development. Accurately predicting interaction strength between new drug-target pairs by analyzing previous experiments aids in screening potential drug molecules, repurposing them, and developing safe and effective medicines. Existing computational models for DTA prediction rely on strings or single-graph neural networks, lacking consideration of protein structure and molecular semantic information, leading to limited accuracy. Our experiments demonstrate that string-based methods may overlook protein conformations, causing a high root mean square error (RMSE) of 3.584 in affinity due to a lack of spatial context. Single graph networks also underperform on topology features, with a 6% lower confidence interval (CI) for activity classification. Absent semantic information also limits generalization across diverse compounds, resulting in 18% increment in RMSE and 5% in misclassifications within quantifications study, restricting potential drug discovery. To address these limitations, we propose G-K BertDTA, a novel framework for accurate DTA prediction incorporating protein features, molecular semantic features, and molecular structural information. In this proposed model, we represent drugs as graphs, with a GIN employed to learn the molecular topological information. For the extraction of protein structural features, we utilize a DenseNet architecture. A knowledge-based BERT semantic model is incorporated to obtain rich pre-trained semantic embeddings, thereby enhancing the feature information. We extensively evaluated our proposed approach on the publicly available benchmark datasets (i.e., KIBA and Davis), and experimental results demonstrate the promising performance of our method, which consistently outperforms previous state-of-the-art approaches. Code is available at https://github.com/AmbitYuki/G-K-BertDTA.

Research on fabric surface defect detection algorithm based on improved Yolo_v4.

In industry, the task of defect classification and defect localization is an important part of defect detection system. However, existing studies only focus on one task and it is difficult to ensure the accuracy of both tasks. This paper proposes a defect detection system based on improved Yolo_v4, which greatly improves the detection ability of minor defects. For K_Means algorithm clustering prianchors question with strong subjectivity, the paper proposes the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to determine the number of Anchors. To solve the problem of low detection rate of small targets caused by insufficient reuse rate of low-level features in CSPDarknet53 feature extraction network, this paper proposes an ECA-DenseNet-BC-121 feature extraction network to improve it. And the Dual Channel Feature Enhancement (DCFE) module is proposed to improve the local information loss and gradient propagation obstruction caused by quad chain convolution in PANet networks to improve the robustness of the model. The experimental results on the fabric surface defect detection datasets show that the mAP of the improved Yolo_v4 is 98.97%, which is 7.67% higher than SSD, 3.75% higher than Faster_RCNN, 10.82% higher than Yolo_v4 tiny, and 5.35% higher than Yolo_v4, and the detection speed reaches 39.4 fps. It can meet the real-time monitoring needs of industrial sites.

G-MBRMD: Lightweight liver segmentation model based on guided teaching with multi-head boundary reconstruction mapping distillation.

BACKGROUND AND OBJECTIVES: Liver segmentation is pivotal for the quantitative analysis of liver cancer. Although current deep learning methods have garnered remarkable achievements for medical image segmentation, they come with high computational costs, significantly limiting their practical application in the medical field. Therefore, the development of an efficient and lightweight liver segmentation model becomes particularly important. METHODS: In our paper, we propose a real-time, lightweight liver segmentation model named G-MBRMD. Specifically, we employ a Transformer-based complex model as the teacher and a convolution-based lightweight model as the student. By introducing proposed multi-head mapping and boundary reconstruction strategies during the knowledge distillation process, Our method effectively guides the student model to gradually comprehend and master the global boundary processing capabilities of the complex teacher model, significantly enhancing the student model's segmentation performance without adding any computational complexity. RESULTS: On the LITS dataset, we conducted rigorous comparative and ablation experiments, four key metrics were used for evaluation, including model size, inference speed, Dice coefficient, and HD95. Compared to other methods, our proposed model achieved an average Dice coefficient of 90.14±16.78%, with only 0.6 MB memory and 0.095 s inference speed for a single image on a standard CPU. Importantly, this approach improved the average Dice coefficient of the baseline student model by 1.64% without increasing computational complexity. CONCLUSION: The results demonstrate that our method successfully realizes the unification of segmentation precision and lightness, and greatly enhances its potential for widespread application in practical settings.

Preparation, characterization, oral bioavailability, and pharmacodynamic study of eugenol-porous silica solidified powder.

Eugenol possesses anti-inflammatory and antioxidant properties, and may serve as a potential therapeutic agent for hepatic fibrosis. However, the development of solid eugenol formulations is challenging due to its volatility. To address this issue, this study employed porous silica to adsorb solidified eugenol. The solidified powder was characterized using Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), and scanning electron microscopy (SEM). In addition, the differences in in vitro release and oral bioavailability between eugenol and solidified eugenol powder were investigated. The effectiveness of eugenol and eugenol powder in treating liver fibrosis was investigated using enzyme-linked immunosorbent assay (ELISA), polymerase chain reaction (PCR), and histopathological observations. Our results indicate that porous silica can effectively solidify eugenol into powder at a lower dosage. Furthermore, we observed that porous silica accelerates eugenol release in vitro and in vivo. The pharmacodynamic results indicated that eugenol has a positive therapeutic effect against hepatic fibrosis and that porous silica does not affect its efficacy. In conclusion, porous silica was able to solidify eugenol, which may facilitate the preparation and storage of solid formulations.

Cucurbitacin IIa promotes the immunogenic cell death­inducing effect of doxorubicin and modulates immune microenvironment in liver cancer.

The immunogenic cell death (ICD) has aroused great interest in cancer immunotherapy. Doxorubicin (DOX), which can induce ICD, is a widely used chemotherapeutic drug in liver cancer. However, DOX­induced ICD is not potent enough to initiate a satisfactory immune response. Cucurbitacin IIa (CUIIa), a tetracyclic triterpene, is a biologically active compound present in the Cucurbitaceae family. The present study assessed the effects of the combination of DOX and CUIIa on the viability, colony formation, apoptosis and cell cycle of HepG2 cells. In vivo anticancer effect was performed in mice bearing H22 tumor xenografts. The hallmark expression of ICD was tested using immunofluorescence and an ATP assay kit. The immune microenvironment was analyzed using flow cytometry. The combination of CUIIa and DOX displayed potent apoptosis inducing, cell cycle arresting and in vivo anticancer effects, along with attenuated cardiotoxicity in H22 mice. The combination of DOX and CUIIa also facilitated ICD as manifested by elevated high­mobility group box 1, calreticulin and ATP secretion. This combination provoked a stronger immune response in H22 mice, including dendritic cell activation, increment of cytotoxic T cells and T helper 1 cells. Moreover, the proportion of immunosuppressive cells including myeloid­derived suppressor cells, T regulatory cells and M2­polarized macrophages, decreased. These data suggested that CUIIa is a promising combination partner with DOX for liver cancer treatment, probably via triggering ICD and remolding the immune microenvironment.

Non-fragile sliding mode control for one-sided Lipschitz chaotic systems.

This paper deals with the sliding mode stabilization for chaotic systems. In the system under consideration, the nonlinear function is one-sided Lipschitz with quadratic inner-boundedness. Specifically, a non-fragile sliding mode surface is constructed, and the sufficient condition for the convergence is derived. Then, a new feedback law is proposed to enable the state trajectories of the closed-loop system to reach the sliding mode surface in finite time. Finally, an example in the background of the unified chaos system is simulated to show the validation of the designed controller.

A dual-targeting anticancer approach: soil and seed principle.

PURPOSE: To test the hypothesis that targeting the microenvironment (soil) may effectively kill cancer cells (seeds) through a small-molecular weight sequential dual-targeting theragnostic strategy, or dual-targeting approach. MATERIALS AND METHODS: With approval from the institutional animal care and use committee, 24 rats were implanted with 48 liver rhabdomyosarcomas (R1). First, the vascular-disrupting agent combretastatin A4 phosphate (CA4P) was injected at a dose of 10 mg/kg to cause tumor necrosis, which became a secondary target. Then, the necrosis-avid agent hypericin was radiolabeled with iodine 131 to form (131)I-hypericin, which was injected at 300 MBq/kg 24 hours after injection of CA4P. Both molecules have small molecular weight, are naturally or synthetically derivable, are intravenously injectable, and are of unique targetablities. The tumor response in the dual-targeting group was compared with that in vehicle-control and single-targeting (CA4P or (131)I-hypericin) groups with in vivo magnetic resonance imaging and scintigrams and ex vivo gamma counting, autoradiography, and histologic analysis. Tumor volumes, tumor doubling time (TDT), and radiobiodistribution were analyzed with statistical software. P values below .05 were considered to indicate a significant difference. RESULTS: Eight days after treatment, the tumor volume of rhabdomyosarcoma in the vehicle-control group was double that in both single-targeting groups (P < .001) and was five times that in the dual-targeting group (P < .0001), without treatment-related animal death. The TDT was significantly longer in the dual-targeting group (P < .0001). Necrosis appeared as hot spots on scintigrams, corresponding to 3.13% of the injected dose of (131)I-hypericin per gram of tissue (interquartile range, 2.92%-3.97%) and a target-to-liver ratio of 20. The dose was estimated to be 100 times the cumulative dose of 50 Gy needed for radiotherapeutic response. Thus, accumulated (131)I-hypericin from CA4P-induced necrosis killed residual cancer cells with ionizing radiation and inhibited tumor regrowth. CONCLUSION: This dual-targeting approach may be a simple and workable solution for cancer treatment and deserves further exploitation.

Efficacy of ginseng and its ingredients as adjuvants to chemotherapy in non-small cell lung cancer.

Chemotherapy is applied to treat non-small cell lung cancer (NSCLC), but often limited due to its unstable therapeutic effects and adverse reactions (ADRs). Ginseng and its main ingredients (ginsenosides and polysaccharides) have been clinically used as adjuvants to chemotherapy. However, their efficacies were based on individual trials with relatively small sample sizes, and it is difficult to draw a valid conclusion. In this study, eligible randomized controlled trials (RCTs) were searched in six international and Chinese databases (PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, Chinese VIP Information and Wanfang). The outcomes of the objective response rate (ORR), disease control rate (DCR), ADRs, quality of life (QOL), survival rates and immunity were extracted using standard data extraction forms. The efficacies of ginseng and its ingredients as adjuvants to chemotherapy in NSCLC were investigated and compared by meta-analysis and subgroup meta-analysis, respectively. A total of 28 RCTs including 2503 subjects were enrolled, and most of the eligible studies were of low-to-moderate quality. For the evaluation of ginseng and its ingredients as adjuvants to chemotherapy, the risk ratio (RR) or standardized mean difference (SMD) and 95% confidence intervals (CI) of the ORR, DCR, leucopenia, thrombocytopenia, myelosuppression, hepatotoxicity, nausea and vomiting, diarrhea, CD4+/CD8+ and one- and two-year survival rates, and QOL were 1.35 (1.21,1.50), 1.20 (1.14,1.28), 0.59 (0.50, 0.70), 0.53 (0.37, 0.76), 0.30 (0.17, 0.53), 0.67 (0.52, 0.87), 0.67 (0.53, 0.86), 0.42 (0.19, 0.96), 1.39 (0.63, 2.16), 1.35 (1.13, 1.60), 3.21 (1.51, 6.81) and 1.31 (1.22, 1.41) with significant differences. Subgroup analysis showed that ginseng enhanced nausea and vomiting and QOL, ginsenosides increased ORR, DCR, QOL, leucopenia, thrombocytopenia, myelosuppression, hepatotoxicity, diarrhea, CD4+/CD8+, and one- and two-year survival rates, while polysaccharides improved ORR, DCR, leucopenia, thrombocytopenia, myelosuppression, hepatotoxicity and nausea and vomiting during chemotherapy. In conclusion, ginseng and its ingredients facilitated the therapeutic effects of chemotherapy on NSCLC patients. Ginseng had beneficial effects on alleviating ADRs and enhancing QOL, ginsenosides demonstrated beneficial effects on enhancing therapeutic effects, reducing ADRs, improving immunity, prolonging survival rates and promoting QOL, while polysaccharides showed beneficial effects on promoting therapeutic effects and reducing ADRs.

Segmentation of coronary arteries images using global feature embedded network with active contour loss.

Coronary heart disease (CHD) is a serious disease that endangers human health and life. In recent years, the morbidity and mortality of CHD are increasing significantly. Because of the particularity and complexity of medical image, it is challenging to segment coronary artery accurately and efficiently. This paper proposes a novel global feature embedded network for better coronary arteries segmentation in 3D coronary computed tomography angiography (CTA) data. The global feature combines multi-level layers from various stages of the network, which contains semantic information and detailed features, aiming to accurately segment target with precise boundary. In addition, we integrate a group of improved noisy activating functions with parameters into our network to eliminate the impact of noise in CTA data. And we improve the learning active contour model, which obtains a refined segmentation result with smooth boundary based on the high-quality score map produced by the networks. The experimental results show that the proposed framework achieved the state-of-the-art performance intuitively and quantitively.

A self-immobilizing near-infrared fluorogenic probe for sensitive imaging of extracellular enzyme activity in vivo.

Reported herein is a self-immobilizing near-infrared fluorogenic probe that can be used to image extracellular enzyme activity in vivo. Using a fluorophore as a quinone methide precursor, this probe covalently anchors at sites of activation and greatly enhances the fluorescence intensity at 710 nm upon enzymatic stimulus, significantly boosting detection sensitivity in a highly dynamic in vivo system.

ADR-Net: Context extraction network based on M-Net for medical image segmentation.

PURPOSE: Medical image segmentation is an essential component of medical image analysis. Accurate segmentation can assist doctors in diagnosis and relieve their fatigue. Although several image segmentation methods based on U-Net have been proposed, their performances have been observed to be suboptimal in the case of small-sized objects. To address this shortcoming, a novel network architecture is proposed in this study to enhance segmentation performance on small medical targets. METHODS: In this paper, we propose a joint multi-scale context attention network architecture to simultaneously capture higher level semantic information and spatial information. In order to obtain a greater number of feature maps during decoding, the network concatenates the images of side inputs by down-sampling during the encoding phase. In the bottleneck layer of the network, dense atrous convolution (DAC) and multi-scale residual pyramid pooling (RMP) modules are exploited to better capture high-level semantic information and spatial information. To improve the segmentation performance on small targets, the attention gate (AG) block is used to effectively suppress feature activation in uncorrelated regions and highlight the target area. RESULTS: The proposed model is first evaluated on the public dataset DRIVE, on which it performs significantly better than the basic framework in terms of sensitivity (SE), intersection-over-union (IOU), and area under the receiver operating characteristic curve (AUC). In particular, the SE and IOU are observed to increase by 7.46% and 5.97%, respectively. Further, the evaluation indices exhibit improvements compared to those of state-of-the-art methods as well, with SE and IOU increasing by 3.58% and 3.26%, respectively. Additionally, in order to demonstrate the generalizability of the proposed architecture, we evaluate our model on three other challenging datasets. The respective performances are observed to be better than those of state-of-the-art network architectures on the same datasets. Moreover, we use lung segmentation as a comparative experiment to demonstrate the transferability of the advantageous properties of the proposed approach in the context of small target segmentation to the segmentation of large targets. Finally, an ablation study is conducted to investigate the individual contributions of the AG block, the DAC block, and the RMP block to the performance of the network. CONCLUSIONS: The proposed method is evaluated on various datasets. Experimental results demonstrate that the proposed model performs better than state-of-the-art methods in medical image segmentation of small targets.

Adversarial Learning for Joint Optimization of Depth and Ego-Motion.

In recent years, supervised deep learning methods have shown a great promise in dense depth estimation. However, massive high-quality training data are expensive and impractical to acquire. Alternatively, self-supervised learning-based depth estimators can learn the latent transformation from monocular or binocular video sequences by minimizing the photometric warp error between consecutive frames, but they suffer from the scale ambiguity problem or have difficulty in estimating precise pose changes between frames. In this paper, we propose a joint self-supervised deep learning pipeline for depth and ego-motion estimation by employing the advantages of adversarial learning and joint optimization with spatial-temporal geometrical constraints. The stereo reconstruction error provides the spatial geometric constraint to estimate the absolute scale depth. Meanwhile, the depth map with an absolute scale and a pre-trained pose network serves as a good starting point for direct visual odometry (DVO). DVO optimization based on spatial geometric constraints can result in a fine-grained ego-motion estimation with the additional backpropagation signals provided to the depth estimation network. Finally, the spatial and temporal domain-based reconstructed views are concatenated, and the iterative coupling optimization process is implemented in combination with the adversarial learning for accurate depth and precise ego-motion estimation. The experimental results show superior performance compared with state-of-the-art methods for monocular depth and ego-motion estimation on the KITTI dataset and a great generalization ability of the proposed approach.

[Medical devices adverse events and its preventive measures].

The text analyzes the reasons of medical devices adverse events occurrence from manufacturers, medical institutions and patients. To guard against the events, the medical institutions should put emphasis on check of purchasing, informed consent, technical permission, SOP performing, the safe of medical and enhance monitoring etc.