Rosiglitazone Attenuated Endothelin-1-Induced Vasoconstriction of Pulmonary Arteries in the Rat Model of Pulmonary Arterial Hypertension via Differential Regulation of ET-1 Receptors

Pulmonary arterial hypertension (PAH) is a fatal disease characterized by a progressive increase in pulmonary arterial pressure leading to right ventricular failure and death. Activation of the endothelin (ET)-1 system has been demonstrated in plasma and lung tissue of PAH patients as well as in animal models of PAH. Recently, peroxisome proliferator-activated receptor γ (PPARγ) agonists have been shown to ameliorate PAH. The present study aimed to investigate the mechanism for the antivasoconstrictive effects of rosiglitazone in response to ET-1 in PAH. Sprague-Dawley rats were exposed to chronic hypoxia (10% oxygen) for 3 weeks. Pulmonary arteries from PAH rats showed an enhanced vasoconstriction in response to ET-1. Treatment with PPARγ agonist rosiglitazone (20 mg/kg per day) with oral gavage for 3 days attenuated the vasocontractive effect of ET-1. The effect of rosiglitazone was lost in the presence of L-NAME, indicating a nitric oxide-dependent mechanism. Western blotting revealed that rosiglitazone increased ETBR but decreased ETAR level in pulmonary arteries from PAH rats. ETBR antagonist A192621 diminished the effect of rosiglitazone on ET-1-induced contraction. These results demonstrated that rosiglitazone attenuated ET-1-induced pulmonary vasoconstriction in PAH through differential regulation of the subtypes of ET-1 receptors and, thus, provided a new mechanism for the therapeutic use of PPARγ agonists in PAH

Stroke Extraction of Chinese Character Based on Deep Structure Deformable Image Registration

Stroke extraction of Chinese characters plays an important role in the field of character recognition and generation. The most existing character stroke extraction methods focus on image morphological features. These methods usually lead to errors of cross strokes extraction and stroke matching due to rarely using stroke semantics and prior information. In this paper, we propose a deep learning-based character stroke extraction method that takes semantic features and prior information of strokes into consideration. This method consists of three parts: image registration-based stroke registration that establishes the rough registration of the reference strokes and the target as prior information; image semantic segmentation-based stroke segmentation that preliminarily separates target strokes into seven categories; and high-precision extraction of single strokes. In the stroke registration, we propose a structure deformable image registration network to achieve structure-deformable transformation while maintaining the stable morphology of single strokes for character images with complex structures. In order to verify the effectiveness of the method, we construct two datasets respectively for calligraphy characters and regular handwriting characters. The experimental results show that our method strongly outperforms the baselines. Code is available at https://github.com/MengLi-l1/StrokeExtraction.Comment: 10 pages, 8 figures, published to AAAI-23 (oral

Continual Named Entity Recognition without Catastrophic Forgetting

Continual Named Entity Recognition (CNER) is a burgeoning area, which involves updating an existing model by incorporating new entity types sequentially. Nevertheless, continual learning approaches are often severely afflicted by catastrophic forgetting. This issue is intensified in CNER due to the consolidation of old entity types from previous steps into the non-entity type at each step, leading to what is known as the semantic shift problem of the non-entity type. In this paper, we introduce a pooled feature distillation loss that skillfully navigates the trade-off between retaining knowledge of old entity types and acquiring new ones, thereby more effectively mitigating the problem of catastrophic forgetting. Additionally, we develop a confidence-based pseudo-labeling for the non-entity type, \emph{i.e.,} predicting entity types using the old model to handle the semantic shift of the non-entity type. Following the pseudo-labeling process, we suggest an adaptive re-weighting type-balanced learning strategy to handle the issue of biased type distribution. We carried out comprehensive experiments on ten CNER settings using three different datasets. The results illustrate that our method significantly outperforms prior state-of-the-art approaches, registering an average improvement of $6.3$\% and $8.0$\% in Micro and Macro F1 scores, respectively.Comment: Accepted by EMNLP2023 main conference as a long pape

The iNOS/Src/FAK axis is critical in Toll-like receptor-mediated cell motility in macrophages

AbstractThe Toll-like receptors (TLRs) play a pivotal role in innate immunity for the detection of highly conserved, pathogen-expressed molecules. Previously, we demonstrated that lipopolysaccharide (LPS, TLR4 ligand)-increased macrophage motility required the participation of Src and FAK, which was inducible nitric oxide synthase (iNOS)-dependent. To investigate whether this iNOS/Src/FAK pathway is a general mechanism for macrophages to mobilize in response to engagement of TLRs other than TLR4, peptidoglycan (PGN, TLR2 ligand), polyinosinic–polycytidylic acid (polyI:C, TLR3 ligand) and CpG-oligodeoxynucleotides (CpG, TLR9 ligand) were used to treat macrophages in this study. Like LPS stimulation, simultaneous increase of cell motility and Src (but not Fgr, Hck, and Lyn) was detected in RAW264.7, peritoneal macrophages, and bone marrow-derived macrophages exposed to PGN, polyI:C and CpG. Attenuation of Src suppressed PGN-, polyI:C-, and CpG-elicited movement and the level of FAK Pi-Tyr861, which could be reversed by the reintroduction of siRNA-resistant Src. Besides, knockdown of FAK reduced the mobility of macrophages stimulated with anyone of these TLR ligands. Remarkably, PGN-, polyI:C-, and CpG-induced Src expression, FAK Pi-Tyr861, and cell mobility were inhibited in macrophages devoid of iNOS, indicating the importance of iNOS. These findings corroborate that iNOS/Src/FAK axis occupies a central role in macrophage locomotion in response to engagement of TLRs

Development of Industrial Software for Building Materials Industry

The building materials industry is the foundation of national economy. Intelligent reforms and digital transformations have deepened the application of new technologies and brought about more digital scenarios, which drives the development of industrial software for the building materials industry and facilitates the high-quality development of the building materials industry. Industrial software is a link that connects industrial design and manufacturing processes with informatization, intelligence, and digitization. In this study, we categorize the industrial software for the building materials industry into the following four types based on industrial characteristics, technical processes, and core functions of the software: operation management; research, development and design; production control; and service support software. Subsequently, the development status of industrial software for the building materials industry in China and abroad is reviewed, and the gap between China and the advanced international level regarding industrial software development is analyzed. The key development directions are clarified from the perspectives of improving weak links, promoting replacement, and developing advantages. The research suggests that the industrial software for the building materials industry should be developed from the following technological perspectives: (1) breaking technical barriers in key areas, (2) ensuring the safety and control of key links, (3) promoting the application of information technology innovations, and (4) building an industry public service platform. Moreover, we propose the following development strategies: (1) optimizing the support policies, (2) improving the industrial software standards system, (3) encouraging industry–university–research–application collaboration to address key problems, (4) cultivating industrial software compound talents, and (5) establishing a good software application ecology

Short-Term Evaluation of Woodland Strawberry in Response to Melatonin Treatment under Low Light Environment

The cultivation of strawberries in controlled environments presents challenges related to environmental stressors, especially insufficient light. Melatonin, as a widely investigated plant growth regulator, was considered as a potential candidate to mitigate damage, and enhance photosynthesis stability. However, whether melatonin can improve photosynthesis under light deficiency in woodland strawberry (Fragaria vesca) remains elusive. In this study, we evaluated gas exchange parameters, Chlorophyll fluorescence parameters, photochemical efficiency, and the related genes’ expression levels to decipher the multifaceted impact of melatonin on photosynthesis. We found concentration-dependent effects of melatonin on photosynthetic parameters, with potential benefits at lower concentration and inhibitory effects at higher concentration. Notably, melatonin increased non-photochemical quenching (NPQ), a mechanism for dissipating excess light energy, while leaving photochemical quenching (qP) relatively stable. Further analysis showed that melatonin up-regulated key xanthophyll cycle-related genes (DHAR, VDE, and PsbS), indicating its involvement in energy dissipation processes. In conclusion, our study uncovered the dual and complex role of melatonin in the short-term response of photosynthesis in woodland strawberries under low-light conditions