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

Adaptive Intra-Class Variation Contrastive Learning for Unsupervised Person Re-Identification

The memory dictionary-based contrastive learning method has achieved remarkable results in the field of unsupervised person Re-ID. However, The method of updating memory based on all samples does not fully utilize the hardest sample to improve the generalization ability of the model, and the method based on hardest sample mining will inevitably introduce false-positive samples that are incorrectly clustered in the early stages of the model. Clustering-based methods usually discard a significant number of outliers, leading to the loss of valuable information. In order to address the issues mentioned before, we propose an adaptive intra-class variation contrastive learning algorithm for unsupervised Re-ID, called AdaInCV. And the algorithm quantitatively evaluates the learning ability of the model for each class by considering the intra-class variations after clustering, which helps in selecting appropriate samples during the training process of the model. To be more specific, two new strategies are proposed: Adaptive Sample Mining (AdaSaM) and Adaptive Outlier Filter (AdaOF). The first one gradually creates more reliable clusters to dynamically refine the memory, while the second can identify and filter out valuable outliers as negative samples

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

Damage prediction and long-term cost performance analysis of glass fiber recycled concrete under freeze-thaw cycles

This paper establishes a freeze-thaw cycle damage model by analyzing the changes in mass, relative dynamic elastic modulus and compressive strength of glass fibers (0 %, 0.5 %, 1.0 %, and 1.5 %) recycled concrete after the freeze-thaw cycle (0, 50, 100, and 150) tests. Meanwhile, the antifreeze life of concrete is predicted based on the Weibull distribution model. The study show that glass fiber can reduce the deterioration of recycled concrete specimen surfaces result from frozen-thaw environment. After 150 freeze-thaw cycles, the specimens with 0.5 %, 1.0 %, and 1.5 % glass fiber content showed a reduction in mass loss of 0.405 %, 1.100 %, and 0.725 %, and an increase in compressive strength of 8.19 %, 21.35 %, and 17.79 %, respectively, when compared with the specimens without glass fiber. Fiber can provide tension when recycled concrete is compressed, thus improving compressive strength, and the optimum glass fiber content is 1.0 %. After 150 freeze-thaw cycles, the freeze-thaw damage of recycled concrete specimens with 1.0 % glass fiber content was the smallest. Compared with that before freeze-thaw, the mass of the specimens only decreased by 2.128 %, and the compressive strength decreased by 35.2 %. Finally, the long-term cost-effectiveness of Recycled Aggregate Concrete (RAC) is analyzed based on the predicted life, and the performance optimization and economic benefits are comprehensively considered. Therefore, the appropriate volumetric admixture of glass fiber can be selected according to the actual situation in different regions, considering the cost-effectiveness of glass fiber recycled concrete to provide suggestions for related research.</p

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

Privacy-protected P2P electricity and carbon emission trading markets based on distributionally robust proximal atomic coordination algorithm

As global power systems modernize towards intelligent infrastructures, peer-to-peer (P2P) energy trading is increasingly adopted worldwide as an innovative electricity market mechanism. This paper explores the decision-making behaviors of diverse agents, market mechanisms, and privacy protections in fully decentralized P2P electricity and carbon emission trading (CET), accounting for uncertainties from renewable energy sources. A novel P2P energy trading mechanism is proposed based on asymmetric Nash bargaining theory. The P2P electricity and carbon market models are decomposed into a cooperative alliance operation problem and an asymmetric cost distribution problem. Additionally, a contribution factor calculation method is introduced, considering both P2P electricity trading and CET marginal effect contribution. To manage renewable energy output uncertainties, a distributionally robust model using Kullback–Leibler (KL) divergence is reformulated as a chance-constrained problem. A proximal atomic coordination (PAC) algorithm is implemented to enhance privacy protection within a fully decentralized framework. Case studies demonstrate that P2P energy trading can reduce total costs by 10.29% and carbon quotas by 11.86% for cooperative alliances. Furthermore, the PAC algorithm decreases total computational time by 12.65% compared to the ADMM algorithm, highlighting its effectiveness in improving computational efficiency and safeguarding user privacy

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