μ-Succinato-bis­[aqua­(2,2′:6′,2′′-terpyridine)copper(II)] dinitrate dihydrate

The title compound, [Cu2(C4H4O4)(C15H11N3)2(H2O)2](NO3)2·2H2O, was synthesized under hydro­thermal conditions. The dinuclear copper complex is located on a crystallographic inversion centre. The CuII ion is penta­coordinated in a tetra­gonal–pyramidal geometry, with one O atom of a succinate dianion and three N atoms of a 2,2′:6′,2′′-terpyridine ligand occupying the basal plane, and a water O atom located at the apical site. In the crystal structure, O—H⋯O hydrogen bonding links the mol­ecules into a chain parallel to the a axis

1-(4-Bromo-2-fluoro­benz­yl)pyridinium bis­(2-thioxo-1,3-dithiole-4,5-dithiol­ato)nickelate(III)

The title compound, (C12H10BrFN)[Ni(C3S5)2], is an ion-pair complex consisting of N-(2-fluoro-4-bromo­benz­yl)pyridinium cations and [Ni(dmit)2]− anions (dmit = 2-thioxo-1,3-dithiole-4,5-dithiol­ate). In the anion, the NiIII ion exhibits a square-planar coordination involving four S atoms from two dmit ligands. In the crystal structure, weak S⋯S [3.474 (3), 3.478 (3) and 3.547 (3) Å] and S⋯π [S⋯centroid distances = 3.360 (3), 3.378 (2), 3.537 (2) and 3.681 (3) Å] inter­actions and C—H⋯F hydrogen bonds lead to a three-dimensional supra­molecular network

Triaqua­bis(1H-imidazole)bis­[μ2-2-(oxalo­amino)benzoato(3−)]dicopper(II)calcium(II) hepta­hydrate

In the title heterotrinuclear coordination compound, [CaCu2(C9H4NO5)2(C3H4N2)2(H2O)3]·7H2O, the Ca2+ cation is in a penta­gonal–bipyramidal geometry and bridges two (1H-imidazole)[2-(oxaloamino)benzoato(3−)]copper(II) units in its equatorial plane. Each CuII atom has a normal square-planar geometry. The mol­ecule has approximate local (non-crystallographic) mirror symmetry and 23 classical hydrogen bonds are found in the crystal structure

Network pharma cology and GEO chip based elucidation of mechanisms underlying the use of Yi Tieqing for prevention and treatment of postoperative nausea and vomiting

Purpose: To investigate the mechanism(s) involved in the use of Yi Tieqing for the prevention and treatment of postoperative nausea and vomiting (PONV), using network pharmacology and GEO chip. Methods: The chemical constituents and functional targets of five traditional Chinese medicines in Yi Tieqing were obtained by searching TCMSP database. The PONV disease targets were identified through DisGeNET, GeneCards and DrugBank databases, and differential expression genes of the GEO database chip (GSE7762) were mined. From the intersections of the component targets and disease targets, the core targets of drugs and diseases were obtained. The core targets were investigated in R language using GO-biological process and KEGG enrichment analyses, and their biological activities were verified via molecular docking. Finally, the severity and incidence of PONV in control and treatment groups were determined and compared. Results: A total of 254 bioactive components and 301 related potential targets were obtained from the TCMSP database. There were 2092 related targets in PONV, and 6 intersecting targets were obtained from Venn diagram. The results of GO biological process and KEGG enrichment analysis showed that the incidence of PONV was strongly correlated with the negative regulation of response to wounding and nervous system. Clinical results showed that from 24 – 48 h (T2) after operation, the severity and incidence of PONV in the treatment group were significantly lower than those in the control group (p < 0.05). Conclusion: Yi Tieqing alleviates PONV through multi-components, multi-targets, and multi-pathways

Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey

Autonomous systems possess the features of inferring their own state, understanding their surroundings, and performing autonomous navigation. With the applications of learning systems, like deep learning and reinforcement learning, the visual-based self-state estimation, environment perception and navigation capabilities of autonomous systems have been efficiently addressed, and many new learning-based algorithms have surfaced with respect to autonomous visual perception and navigation. In this review, we focus on the applications of learning-based monocular approaches in ego-motion perception, environment perception and navigation in autonomous systems, which is different from previous reviews that discussed traditional methods. First, we delineate the shortcomings of existing classical visual simultaneous localization and mapping (vSLAM) solutions, which demonstrate the necessity to integrate deep learning techniques. Second, we review the visual-based environmental perception and understanding methods based on deep learning, including deep learning-based monocular depth estimation, monocular ego-motion prediction, image enhancement, object detection, semantic segmentation, and their combinations with traditional vSLAM frameworks. Then, we focus on the visual navigation based on learning systems, mainly including reinforcement learning and deep reinforcement learning. Finally, we examine several challenges and promising directions discussed and concluded in related research of learning systems in the era of computer science and robotics.Comment: This paper has been accepted by IEEE TNNL