2,5-Bis[(3-chloro­benz­yl)sulfan­yl]-1,3,4-thia­diazole

The complete mol­ecule of the title compound, C16H12Cl2N2S3, is generated by crystallographic twofold symmetry, with the S atom of the thiadiazole ring lying on the rotation axis. The dihedral angle between the mean planes of the 1,3,4-thia­diazole and benzene rings is 87.19 (7)°. In the crystal, mol­ecules are linked by C—H⋯N inter­actions and short S⋯S contacts [3.3389 (9) Å] occur

Hepcidin and sports anemia

Iron is an important mineral element used by the body in a variety of metabolic and physiologic processes. These processes are highly active when the body is undergoing physical exercises. Prevalence of exercise-induced iron deficiency anemia (also known as sports anemia) is notably high in athletic populations, particularly those with heavy training loads. The pathogenesis of sports anemia is closely related to disorders of iron metabolism, and a more comprehensive understanding of the mechanism of iron metabolism in the course of physical exercises could expand ways of treatment and prevention of sports anemia. In recent years, there have been remarkable research advances regarding the molecular mechanisms underlying changes of iron metabolism in response to physical exercises. This review has covered these advances, including effects of exercise on duodenum iron absorption, serum iron status, iron distribution in organs, erythropoiesis, and hepcidin’s function and its regulation. New methods for the treatment of exercise-induced iron deficiency are also discussed

Mesenchymal stem cell-based Smad7 gene therapy for experimental liver cirrhosis

BackgroundBone mesenchymal stem cells (MSCs) can promote liver regeneration and inhibit inflammation and hepatic fibrosis. MSCs also can serve as a vehicle for gene therapy. Smad7 is an essential negative regulatory gene in the TGF-β1/Smad signalling pathway. Activation of TGF-β1/Smad signalling accelerates liver inflammation and fibrosis; we therefore hypothesized that MSCs overexpressing the Smad7 gene might be a new cell therapy approach for treating liver fibrosis via the inhibition of TGF-β1/Smad signalling.MethodsMSCs were isolated from 6-week-old Wistar rats and transduced with the Smad7 gene using a lentivirus vector. Liver cirrhosis was induced by subcutaneous injection of carbon tetrachloride (CCl4) for 8 weeks. The rats with established liver cirrhosis were treated with Smad7-MSCs by direct injection of cells into the main lobes of the liver. The expression of Smad7, Smad2/3 and fibrosis biomarkers or extracellular matrix proteins and histopathological change were assessed by quantitative PCR, ELISA and Western blotting and staining.ResultsThe mRNA and protein level of Smad7 in the recipient liver and serum were increased after treating with Smad-MSCs for 7 and 21 days (P

Smoothing algorithm for the maximal eigenvalue of non-defective positive matrices

This paper introduced a smoothing algorithm for calculating the maximal eigenvalue of non-defective positive matrices. Two special matrices were constructed to provide monotonically increasing lower-bound estimates and monotonically decreasing upper-bound estimates of the maximal eigenvalue. The monotonicity and convergence of these estimations was also proven. Finally, the effectiveness of the algorithm was demonstrated with numerical examples

Style-Hallucinated Dual Consistency Learning for Domain Generalized Semantic Segmentation

In this paper, we study the task of synthetic-to-real domain generalized semantic segmentation, which aims to learn a model that is robust to unseen real-world scenes using only synthetic data. The large domain shift between synthetic and real-world data, including the limited source environmental variations and the large distribution gap between synthetic and real-world data, significantly hinders the model performance on unseen real-world scenes. In this work, we propose the Style-HAllucinated Dual consistEncy learning (SHADE) framework to handle such domain shift. Specifically, SHADE is constructed based on two consistency constraints, Style Consistency (SC) and Retrospection Consistency (RC). SC enriches the source situations and encourages the model to learn consistent representation across style-diversified samples. RC leverages real-world knowledge to prevent the model from overfitting to synthetic data and thus largely keeps the representation consistent between the synthetic and real-world models. Furthermore, we present a novel style hallucination module (SHM) to generate style-diversified samples that are essential to consistency learning. SHM selects basis styles from the source distribution, enabling the model to dynamically generate diverse and realistic samples during training. Experiments show that our SHADE yields significant improvement and outperforms state-of-the-art methods by 5.07% and 8.35% on the average mIoU of three real-world datasets on single- and multi-source settings respectively

Style-Hallucinated Dual Consistency Learning: A Unified Framework for Visual Domain Generalization

Domain shift widely exists in the visual world, while modern deep neural networks commonly suffer from severe performance degradation under domain shift due to the poor generalization ability, which limits the real-world applications. The domain shift mainly lies in the limited source environmental variations and the large distribution gap between source and unseen target data. To this end, we propose a unified framework, Style-HAllucinated Dual consistEncy learning (SHADE), to handle such domain shift in various visual tasks. Specifically, SHADE is constructed based on two consistency constraints, Style Consistency (SC) and Retrospection Consistency (RC). SC enriches the source situations and encourages the model to learn consistent representation across style-diversified samples. RC leverages general visual knowledge to prevent the model from overfitting to source data and thus largely keeps the representation consistent between the source and general visual models. Furthermore, we present a novel style hallucination module (SHM) to generate style-diversified samples that are essential to consistency learning. SHM selects basis styles from the source distribution, enabling the model to dynamically generate diverse and realistic samples during training. Extensive experiments demonstrate that our versatile SHADE can significantly enhance the generalization in various visual recognition tasks, including image classification, semantic segmentation and object detection, with different models, i.e., ConvNets and Transformer.Comment: Accepted by IJCV. Journal extension of arXiv:2204.02548. Code is available at https://github.com/HeliosZhao/SHADE-VisualD