Pulmonary alveolar type I cell population consists of two distinct subtypes that differ in cell fate.

Pulmonary alveolar type I (AT1) cells cover more than 95% of alveolar surface and are essential for the air-blood barrier function of lungs. AT1 cells have been shown to retain developmental plasticity during alveolar regeneration. However, the development and heterogeneity of AT1 cells remain largely unknown. Here, we conducted a single-cell RNA-seq analysis to characterize postnatal AT1 cell development and identified insulin-like growth factor-binding protein 2 (Igfbp2) as a genetic marker specifically expressed in postnatal AT1 cells. The portion of AT1 cells expressing Igfbp2 increases during alveologenesis and in post pneumonectomy (PNX) newly formed alveoli. We found that the adult AT1 cell population contains both Hopx+Igfbp2+ and Hopx+Igfbp2- AT1 cells, which have distinct cell fates during alveolar regeneration. Using an Igfbp2-CreER mouse model, we demonstrate that Hopx+Igfbp2+ AT1 cells represent terminally differentiated AT1 cells that are not able to transdifferentiate into AT2 cells during post-PNX alveolar regeneration. Our study provides tools and insights that will guide future investigations into the molecular and cellular mechanism or mechanisms underlying AT1 cell fate during lung development and regeneration

Fooling the Image Dehazing Models by First Order Gradient

The research on the single image dehazing task has been widely explored. However, as far as we know, no comprehensive study has been conducted on the robustness of the well-trained dehazing models. Therefore, there is no evidence that the dehazing networks can resist malicious attacks. In this paper, we focus on designing a group of attack methods based on first order gradient to verify the robustness of the existing dehazing algorithms. By analyzing the general purpose of image dehazing task, four attack methods are proposed, which are predicted dehazed image attack, hazy layer mask attack, haze-free image attack and haze-preserved attack. The corresponding experiments are conducted on six datasets with different scales. Further, the defense strategy based on adversarial training is adopted for reducing the negative effects caused by malicious attacks. In summary, this paper defines a new challenging problem for the image dehazing area, which can be called as adversarial attack on dehazing networks (AADN). Code and Supplementary Material are available at https://github.com/Xiaofeng-life/AADN Dehazing.Comment: This paper is accepted by IEEE Transactions on Circuits and Systems for Video Technology (TCSVT

Towards Understanding Third-party Library Dependency in C/C++ Ecosystem

Third-party libraries (TPLs) are frequently reused in software to reduce development cost and the time to market. However, external library dependencies may introduce vulnerabilities into host applications. The issue of library dependency has received considerable critical attention. Many package managers, such as Maven, Pip, and NPM, are proposed to manage TPLs. Moreover, a significant amount of effort has been put into studying dependencies in language ecosystems like Java, Python, and JavaScript except C/C++. Due to the lack of a unified package manager for C/C++, existing research has only few understanding of TPL dependencies in the C/C++ ecosystem, especially at large scale. Towards understanding TPL dependencies in the C/C++ecosystem, we collect existing TPL databases, package management tools, and dependency detection tools, summarize the dependency patterns of C/C++ projects, and construct a comprehensive and precise C/C++ dependency detector. Using our detector, we extract dependencies from a large-scale database containing 24K C/C++ repositories from GitHub. Based on the extracted dependencies, we provide the results and findings of an empirical study, which aims at understanding the characteristics of the TPL dependencies. We further discuss the implications to manage dependency for C/C++ and the future research directions for software engineering researchers and developers in fields of library development, software composition analysis, and C/C++package manager.Comment: ASE 202

A coordinated restoration method of three-phase AC unbalanced distribution network with DC connections and mobile energy storage systems

In the rapidly changing domain of hybrid AC/DC urban distribution networks, this research unveils a groundbreaking method for the restoration of three-phase unbalanced systems by astutely harnessing the unique potential of DC line interconnections. At the heart of this innovation lies the synthesis of symmetrical Second Order Cone Programming (SOCP) with a sophisticated topology search technique, a union that offers a precise depiction of complex three-phase power flow with network restoration while simultaneously accelerating computational processes. Building upon this foundation, our approach places significant emphasis on the utilization of adaptable DC power control, coupled with the optimal deployment of mobile energy storage systems (MESSs), to ensure a harmonized power balance during critical interruptions. These strategies converge to prioritize the restoration of vital loads, especially those with high weighting factors, thereby significantly augmenting the network’s resilience, particularly in contexts vulnerable to disasters. The corroborative numerical results, as delineated in our study, highlight the distinct advantage and effectiveness of our methodology over prevailing practices in fortifying grid resilience against serious adversities

Clinicopathological and Prognostic Characteristics of Hepatoid Adenocarcinoma of the Stomach

The present study was undertaken to clarify the association of the clinicopathological features of hepatoid adenocarcinoma (HAC) in the stomach, a special kind of carcinoma that histologically resembled hepatocellular carcinoma (HCC) and is characterized by large amounts of α-fetoprotein (AFP) in serum, with the clinical prognosis. We collected the data of the clinicopathological features and the follow-up information from a total of 31 HACs from January 2005 to December 2012 in our hospital. High lymphatic (54.8%) and distant (25.8%) metastasis rates before surgery, large proportion of advanced HACs (71.0%) at admission, short median overall survival time (6 months), and low three-year survival rate (22.6%) suggested that HAC in the stomach was an aggressive disease, resulting in a poor prognosis. And pTNM stages, immunohistochemical staining of AFP, CEA, CK7, and CK20 had statistically relation with the survival as the independent risk factors, P<0.05. Therefore, early and clear differentiation of HAC from cancerous or noncancerous conditions with AFP elevation and assessment of high risk patients by histopathology may improve the clinical prognosis

Inhibitory effects of betulinic acid on LPS-induced neuroinflammation involve M2 microglial polarization via CaMKKβ-dependent AMPK activation

In response to the microenvironment, microglia may polarize into either an M1 pro-inflammatory phenotype, exacerbating neurotoxicity, or an M2 anti-inflammatory phenotype, conferring neuroprotection. Betulinic acid (BA) is a naturally pentacyclic triterpenoid with considerable anti-inflammatory properties. Here, we aim to investigate the potential effects of BA on microglial phenotype polarization and to reveal the underlying mechanisms of action. First, we confirmed that BA promoted M2 polarization and inhibited M1 polarization in lipopolysaccharide (LPS)-stimulated BV-2 microglial cells. Then, we demonstrated that the effect of BA on microglial polarization was dependent on AMP-activated protein kinase (AMPK) activation, as evidenced by the fact that both AMPK inhibitor compound C and AMPK siRNA abolished the M2 polarization promoted by BA. Moreover, we found that calmodulin-dependent protein kinase kinase β (CaMKKβ), but not liver kinase B1, was the upstream kinase required for BA-mediated AMPK activation and microglial M2 polarization, via the use of both the CaMKKb inhibitor STO-609 and CaMKKβ siRNA. Finally, BA enhanced AMPK phosphorylation and promoted M2 microglial polarization in the cerebral cortex of LPSinjected mice brains, which was attenuated by pre-administration of the AMPK inhibitor. This study demonstrated that BA promoted M2 polarization of microglia, thus conferring anti-neuroinflammatory effects via CaMKKβ-dependent AMPK activation

The role of Raptor in lymphocytes differentiation and function

Raptor, a key component of mTORC1, is required for recruiting substrates to mTORC1 and contributing to its subcellular localization. Raptor has a highly conserved N-terminus domain and seven WD40 repeats, which interact with mTOR and other mTORC1-related proteins. mTORC1 participates in various cellular events and mediates differentiation and metabolism. Directly or indirectly, many factors mediate the differentiation and function of lymphocytes that is essential for immunity. In this review, we summarize the role of Raptor in lymphocytes differentiation and function, whereby Raptor mediates the secretion of cytokines to induce early lymphocyte metabolism, development, proliferation and migration. Additionally, Raptor regulates the function of lymphocytes by regulating their steady-state maintenance and activation

The Ninth Visual Object Tracking VOT2021 Challenge Results

acceptedVersionPeer reviewe

Discovering Lin-Kernighan-Helsgaun heuristic for routing optimization using self-supervised reinforcement learning

Vehicle routing optimization is a crucial responsibility of transportation service providers, which can significantly reduce operating expenses and improve client satisfaction. Learning to tackle routing optimization problems automatically can be the next significant step forward in optimization technology. Despite recent advancements in automatically learned heuristics for routing optimization problems, state-of-the-art traditional methods such as Lin-Kernighan-Helsgaun (LKH) still outperform machine learning-based approaches. To narrow this gap, we propose a novel technique called self-supervised reinforcement learning (SSRL), which combines self-supervised learning with the LKH heuristic. We provide a node decoder and an edge decoder corresponding to reinforcement learning and self-supervised learning for learning node penalties and edge scores, respectively. The self-supervised part with cross-entropy loss offers strong gradient signals for parameter updates. At the same time, the reinforcement learning component functions as a regularizer to drive the supervised part, which focuses on particular rewards. SSRL learns and replicates all of the LKH’s significant components, improving the original LKH’s generalization and performance. Through experiments on multiple vehicle routing problems, SSRL has demonstrated superior accuracy and efficiency compared to existing methods. Our results provide empirical evidence of SSRL’s effectiveness and potential as a promising solution for optimizing complex routing problems