The Krüppel-Like Factor 4 as a novel gene therapy target for Hypoxic Pulmonary Hypertension

Persistent low oxygen tension (hypoxia) causes Pulmonary hypertension, resulting in pulmonary vascular inflammation and remodeling and, subsequently, blood flow impairment. Krüppel-like factor 4 (KLF4) is a transcription factor expressed in the pulmonary vasculature endothelium, where it promotes anti-inflammatory and anticoagulant states and increases expression of endothelial nitric oxide synthase, a significant source of vasodilating nitric oxide. Previous studies have also shown that an improved adeno-associated virus (AAV) vector enabled an efficient gene transfer into mice's pulmonary arterial endothelium, thereby allowing therapeutic modulation of gene expression. In this study, we investigated the therapeutic potential of overexpression of the transcription factor KLF4 to prevent pulmonary hypertension. In vitro experiments were performed using AAV-mediated gene transfer of KLF4 into human umbilical vein endothelial cells (HUVECs) under hypoxic conditions. We analyzed inflammatory markers by real-time qPCR and Western blots. The results showed that overexpression of KLF4 had anti-inflammatory properties and contributed to the maintenance of endothelial barrier function. Besides this, overexpression of KLF4 inhibited the transition of endothelial cells to mesenchymal cells, improved mitochondrial function, and reduced the generation of reactive oxygen species (ROS). This study's results enable further investigations on AAV-mediated KLF4 overexpression in pulmonary artery endothelial cells in a murine model of chronic hypoxia-induced pulmonary hypertension

Is the effect of drop limit order policy of the real estate industry in Yue yang city in line with expectations—Empirical analysis based on difference in difference

In recent years, the spread of covid-19 has led to a decline in residential consumption levels and a decrease in demand in the housing market. In order to maintain, some housing enterprises started to reduce prices significantly, this move destroyed the market law, and the local government set up the policy of limit drop order in order to restrict the malicious price reduction without following the market law, and it is not known whether the policy of limit drop order can promote the development of the real estate industry while limiting the malicious downgrade, according to the general rule, through the regulation of the real estate market should help the development of the real estate industry, this paper through This paper analyses Yue yang City, the first city to implement the drop restriction policy, through the double difference method. The results show that the drop restriction policy has a certain suppressive effect on the local real estate industry in the short term, while the impact on the market in the long term is yet to be studied

Multimode optical feedback dynamics in InAs/GaAs quantum dot lasers emitting exclusively on ground or excited states: transition from short- to long-delay regimes

© 2018 Optical Society of America. Users may use, reuse, and build upon the article, or use the article for text or data mining, so long as such uses are for non-commercial purposes and appropriate attribution is maintained. All other rights are reserved.The optical feedback dynamics of two multimode InAs/GaAs quantum dot lasers emitting exclusively on sole ground or excited lasing states is investigated. The transition from long- to short-delay regimes is analyzed, while the boundaries associated to the birth of periodic and chaotic oscillations are unveiled to be a function of the external cavity length. The results show that depending on the initial lasing state, different routes to chaos are observed. These results are of importance for the development of isolator-free transmitters in short-reach networks

Parameter Estimation of Stellar Mass Binary Black Holes under the Network of TianQin and LISA

We present a Bayesian parameter estimation progress to infer the stellar mass binary black hole properties by TianQin, LISA, and TianQin+LISA. Two typical Stellar-mass Black Hole Binary systems, GW150914 and GW190521 are chosen as the fiducial sources. In this work, we establish the ability of TianQin to infer the parameters of those systems and first apply the full frequency response in TianQin's data analysis. We obtain the parameter estimation results and explain the correlation between them. We also find the TianQin+LISA could marginally increase the parameter estimation precision and narrow the $1\sigma$ area compared with TianQin and LISA individual observations. We finally demonstrate the importance of considering the effect of spin when the binaries have a non-zero component spin and great derivation will appear especially on mass, coalescence time and sky location.Comment: 17 pages, 6 figures, comments welcom

Experimental investigation of two-bolt connections for high strength steel members

[EN] This paper presents an experimental research on bearing-type bolted connections consisting of two bolts positioned perpendicular to the loading direction. A total of 24 connections in double shear fabricated from high strength steels with yield stresses of 677MPa and 825MPa are tested. Two failure modes as tearout failure and splitting failure are observed in experiments. The effect of end distance, edge distance, bolt spacing and steel grade on the failure mode and bearing behavior are discussed. For connection design with bolts positioned perpendicular to loading direction, it is further found that combination of edge distance and bolt spacing effectively determines the failure mode and ultimate load. The test results are compared with Eurocode3. An optimal combination of edge distance and bolt spacing as well as related design suggestion is thus recommended.The authors would like to acknowledge the funding support by National Natural Science Foundation of China, Grant No. 51408428.Wang, Y.; Lyu, Y.; Li, G. (2018). Experimental investigation of two-bolt connections for high strength steel members. En Proceedings of the 12th International Conference on Advances in Steel-Concrete Composite Structures. ASCCS 2018. Editorial Universitat Politècnica de València. 595-600. https://doi.org/10.4995/ASCCS2018.2018.7211OCS59560

Light-LOAM: A Lightweight LiDAR Odometry and Mapping based on Graph-Matching

Simultaneous Localization and Mapping (SLAM) plays an important role in robot autonomy. Reliability and efficiency are the two most valued features for applying SLAM in robot applications. In this paper, we consider achieving a reliable LiDAR-based SLAM function in computation-limited platforms, such as quadrotor UAVs based on graph-based point cloud association. First, contrary to most works selecting salient features for point cloud registration, we propose a non-conspicuous feature selection strategy for reliability and robustness purposes. Then a two-stage correspondence selection method is used to register the point cloud, which includes a KD-tree-based coarse matching followed by a graph-based matching method that uses geometric consistency to vote out incorrect correspondences. Additionally, we propose an odometry approach where the weight optimizations are guided by vote results from the aforementioned geometric consistency graph. In this way, the optimization of LiDAR odometry rapidly converges and evaluates a fairly accurate transformation resulting in the back-end module efficiently finishing the mapping task. Finally, we evaluate our proposed framework on the KITTI odometry dataset and real-world environments. Experiments show that our SLAM system achieves a comparative level or higher level of accuracy with more balanced computation efficiency compared with the mainstream LiDAR-based SLAM solutions

Alteration-free and Model-agnostic Origin Attribution of Generated Images

Recently, there has been a growing attention in image generation models. However, concerns have emerged regarding potential misuse and intellectual property (IP) infringement associated with these models. Therefore, it is necessary to analyze the origin of images by inferring if a specific image was generated by a particular model, i.e., origin attribution. Existing methods are limited in their applicability to specific types of generative models and require additional steps during training or generation. This restricts their use with pre-trained models that lack these specific operations and may compromise the quality of image generation. To overcome this problem, we first develop an alteration-free and model-agnostic origin attribution method via input reverse-engineering on image generation models, i.e., inverting the input of a particular model for a specific image. Given a particular model, we first analyze the differences in the hardness of reverse-engineering tasks for the generated images of the given model and other images. Based on our analysis, we propose a method that utilizes the reconstruction loss of reverse-engineering to infer the origin. Our proposed method effectively distinguishes between generated images from a specific generative model and other images, including those generated by different models and real images