Opposite angiogenic outcome of curcumin against ischemia and Lewis lung cancer models: in silico, in vitro and in vivo studies

AbstractThe aim of this study was to investigate the angiogenic effects of curcumin on an ischemia and lung cancer model. To induce ischemia combined with lung cancer models, unilateral femoral arteries of C57BL/6 mice were disconnected on one side of the mouse and Lewis lung carcinoma (LLC) cells were xenografted on the opposite side. Angiogenic effects and underlying mechanisms associated with curcumin were investigated. Molecular target(s), signaling cascades and binding affinities were detected by Western blot, two-dimensional gel electrophoresis (2-DE), computer simulations and surface plasmon resonance (SPR) techniques. Curcumin promoted post-ischemic blood recirculation and suppressed lung cancer progression in inbred C57BL/6 mice via regulation of the HIF1α/mTOR/VEGF/VEGFR cascade oppositely. Inflammatory stimulation induced by neutrophil elastase (NE) promoted angiogenesis in lung cancer tissues, but these changes were reversed by curcumin through directly reducing NE secretion and stimulating α1-antitrypsin (α1-AT) and insulin receptor substrate-1 (IRS-1) production. Meanwhile, curcumin dose-dependently influenced endothelial cells (EC) tube formation and chicken embryo chorioallantoic membrane (CAM) neovascularization. Curcumin had opposite effects on blood vessel regeneration under physiological and pathological angiogenesis, which was effected through negative or positive regulation of the HIF1α/mTOR/VEGF/VEGFR cascade. Curcumin had the promise as a new treatment modality for both ischemic conditions and lung cancer simultaneously in the clinic

Investigating word length effects in Chinese reading

A word’s length in English is fundamental in determining whether readers fixate it, and how long they spend processing it during reading. Chinese is unspaced and most words are two characters long: Is word length an important cue to eye guidance in Chinese reading? Eye movements were recorded as participants read sentences containing a one-, two-, or three-character word matched for frequency. Results showed that longer words took longer to process (primarily driven by refixations). Furthermore, skips were fewer, incoming saccades longer and landing positions further to the right of long than short words. Additional analyses of a three-character region (matched stroke number) showed an incremental processing cost when character(s) belonged to different, rather than the same, word. These results demonstrate that word length affects both lexical identification and saccade target selection in Chinese reading

Point Cloud Upsampling via Cascaded Refinement Network

Point cloud upsampling focuses on generating a dense, uniform and proximity-to-surface point set. Most previous approaches accomplish these objectives by carefully designing a single-stage network, which makes it still challenging to generate a high-fidelity point distribution. Instead, upsampling point cloud in a coarse-to-fine manner is a decent solution. However, existing coarse-to-fine upsampling methods require extra training strategies, which are complicated and time-consuming during the training. In this paper, we propose a simple yet effective cascaded refinement network, consisting of three generation stages that have the same network architecture but achieve different objectives. Specifically, the first two upsampling stages generate the dense but coarse points progressively, while the last refinement stage further adjust the coarse points to a better position. To mitigate the learning conflicts between multiple stages and decrease the difficulty of regressing new points, we encourage each stage to predict the point offsets with respect to the input shape. In this manner, the proposed cascaded refinement network can be easily optimized without extra learning strategies. Moreover, we design a transformer-based feature extraction module to learn the informative global and local shape context. In inference phase, we can dynamically adjust the model efficiency and effectiveness, depending on the available computational resources. Extensive experiments on both synthetic and real-scanned datasets demonstrate that the proposed approach outperforms the existing state-of-the-art methods.Comment: The first two authors contributed equally to this work. The code is publicly available at https://github.com/hikvision-research/3DVision. Accepted to ACCV 2022 as oral presentatio

The influence of foveal lexical processing load on parafoveal preview and saccadic targeting during Chinese reading

Whether increased foveal load causes a reduction of parafoveal processing remains equivocal. The present study examined foveal load effects on parafoveal processing in natural Chinese reading. Parafoveal preview of a single-character parafoveal target word was manipulated by using the boundary paradigm (Rayner, 1975; pseudocharacter or identity previews) under high foveal load (low-frequency pretarget word) compared with low foveal load (high-frequency pretarget word) conditions. Despite an effective manipulation of foveal processing load, we obtained no evidence of any modulatory influence on parafoveal processing in first-pass reading times. However, our results clearly showed that saccadic targeting, in relation to forward saccade length from the pretarget word and in relation to target word skipping, was influenced by foveal load and this influence occurred independent of parafoveal preview. Given the optimal experimental conditions, these results provide very strong evidence that preview benefit is not modulated by foveal lexical load during Chinese reading. (PsycINFO Database Record (c) 2019 APA, all rights reserved

FBNet: Feedback Network for Point Cloud Completion

The rapid development of point cloud learning has driven point cloud completion into a new era. However, the information flows of most existing completion methods are solely feedforward, and high-level information is rarely reused to improve low-level feature learning. To this end, we propose a novel Feedback Network (FBNet) for point cloud completion, in which present features are efficiently refined by rerouting subsequent fine-grained ones. Firstly, partial inputs are fed to a Hierarchical Graph-based Network (HGNet) to generate coarse shapes. Then, we cascade several Feedback-Aware Completion (FBAC) Blocks and unfold them across time recurrently. Feedback connections between two adjacent time steps exploit fine-grained features to improve present shape generations. The main challenge of building feedback connections is the dimension mismatching between present and subsequent features. To address this, the elaborately designed point Cross Transformer exploits efficient information from feedback features via cross attention strategy and then refines present features with the enhanced feedback features. Quantitative and qualitative experiments on several datasets demonstrate the superiority of proposed FBNet compared to state-of-the-art methods on point completion task.Comment: The first two authors contributed equally to this work. The source code and model are available at https://github.com/hikvision-research/3DVision/. Accepted to ECCV 2022 as oral presentatio

The morphosyntactic structure of compound words influences parafoveal processing in Chinese reading

In an eye movement experiment employing the boundary paradigm (Rayner, 1975) we compared parafoveal preview benefit during the reading of Chinese sentences. The target word was a 2-character compound that had either a noun-noun or an adjective-noun structure each sharing an identical noun as the second character. The boundary was located between the two characters of the compound word. Prior to the eyes crossing the boundary the preview of the second character was presented either normally or was replaced by a pseudo-character. Previously, Juhasz, Inhoff and Rayner (2005) observed that inserting a space into a normally unspaced compound in English significantly disrupted processing and that this disruption was larger for adjective-noun compounds than for noun-noun compounds. This finding supports the hypothesis that, at least in English, for adjective-noun compounds, the noun is more important for lexical identification than the adjective, while for noun-noun compounds, both constituents are similar in importance for lexical identification. Our results indicate a similar division of the importance of compounds in reading in Chinese as the pseudo-character preview was more disruptive for the adjective-noun compounds than for the noun-noun compounds. These findings also indicate that parafoveal processing can be influenced by the morphosyntactic structure of the currently fixated character

Epidemiology and associations with climatic conditions of Mycoplasma pneumoniae and Chlamydophila pneumoniae infections among Chinese children hospitalized with acute respiratory infections

BACKGROUND: The incidence of severe acute respiratory tract infections in children caused by Mycoplasma pneumoniae (syn. Schizoplasma pneumoniae) and Chlamydophila pneumoniae (formerly Chlamydia pneumoniae) varies greatly from year to year and place to place around the world. This study investigated the epidemiology of M. pneumoniae and C. pneumoniae infections among children hospitalized with acute respiratory infections in Suzhou, China in the year 2006, and associations between incidence rates and climatic conditions. METHODS: Nasopharyngeal aspirates obtained from 1598 patients (aged 26.4 ± 28.3 months; range, 1 month to 13 years) were analyzed with real-time PCR and ELISA. Meteorological data were obtained from the weather bureau. RESULTS: About 18.5% of patients were infected with M. pneumoniae and, C. pneumoniae, or both. Isolated M. pneumoniae infection was positively correlated with increasing age (χ(2) = 34.76, P < 0.0001). Incidence of M. pneumoniae infection was seasonal with a peak in summer (P < 0.0001) and minimum in winter (P = 0.0001), whereas C. pneumoniae infection was low only in autumn (P = 0.02). Monthly mean temperature was strongly correlated with the incidence of M. pneumoniae infection (r = 0.825, P = 0.001). CONCLUSIONS: M. pneumoniae and C. pneumoniae are important infectious agents in hospitalized children with acute respiratory tract infections. M. pneumoniae infection showed a strong direct correlation with environmental temperature

Improved RBF Network Intrusion Detection Model Based on Edge Computing with Multi-algorithm Fusion

Edge computing is difficult to deploy a complete and reliable security strategy due to its distributed computing architecture and inherent heterogeneity of equipment and limited resources. When malicious attacks occur, the loss will be immeasurable. RBF neural network has strong nonlinear representation ability and fast learning convergence speed, which is suitable for intrusion detection of edge detection industrial control network. In this paper, an improved RBF network intrusion detection model based on multi-algorithm fusion is proposed. kernel principal component analysis (KPCA) is used to extract data dimension and simplify data representation. Then subtractive clustering algorithm(SCM) and grey wolf algorithm(GWO) are used to jointly optimize RBF neural network parameters to avoid falling into local optimum, reduce the calculation of model training and improve the detection accuracy. The algorithm can better adapt to the edge computing platform with weak computing ability and bearing capacity, and realize real-time data analysis.The experimental results of BATADAL data set and Gas data set show that the accuracy of the algorithm is over 99% and the training time of larger samples is shortened by 50 times for BATADAL data set. The results show that the improved RBF network is effective in improving the convergence speed and accuracy in intrusion detection

A Survey on Automated Program Repair Techniques

With the rapid development and large-scale popularity of program software, modern society increasingly relies on software systems. However, the problems exposed by software have also come to the fore. Software defect has become an important factor troubling developers. In this context, Automated Program Repair (APR) techniques have emerged, aiming to automatically fix software defect problems and reduce manual debugging work. In particular, benefiting from the advances in deep learning, numerous learning-based APR techniques have emerged in recent years, which also bring new opportunities for APR research. To give researchers a quick overview of APR techniques' complete development and future opportunities, we revisit the evolution of APR techniques and discuss in depth the latest advances in APR research. In this paper, the development of APR techniques is introduced in terms of four different patch generation schemes: search-based, constraint-based, template-based, and learning-based. Moreover, we propose a uniform set of criteria to review and compare each APR tool, summarize the advantages and disadvantages of APR techniques, and discuss the current state of APR development. Furthermore, we introduce the research on the related technical areas of APR that have also provided a strong motivation to advance APR development. Finally, we analyze current challenges and future directions, especially highlighting the critical opportunities that large language models bring to APR research.Comment: This paper's earlier version was submitted to CSUR in August 202