Nanjing, China
Nanjing, China

Nanjing Normal University is a normal university in Nanjing, Jiangsu province, China.Nanjing Normal University was originally the Sanjiang Normal Institute, which was established in 1902 by Zhang Zhidong, the Governor-General of Jiangsu and Jiangxi. After that, it was renamed Liangjiang Excellent Normal Institute in 1905, National Nanjing Higher Normal School in 1914, various other names, before being named National Central University and then Nanjing University, and in 1952 Nanjing University Normal College became Nanjing Normal College and then renamed Nanjing Normal University in 1984. Nanjing Normal University became a comprehensive university well known for the educational and scientific research. It was included in the 211 Project.The International Research And Training Centre For Rural Education is affiliated with the university's Education Faculty. Wikipedia.


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Liu H.-K.,Nanjing Normal University | Sadler P.J.,University of Warwick
Accounts of Chemical Research | Year: 2011

DNA has a strong affinity for many heterocyclic aromatic dyes, such as acridine and its derivatives. Lerman in 1961 first proposed intercalation as the source of this affinity, and this mode of DNA binding has since attracted considerable research scrutiny. Organic intercalators can inhibit nucleic acid synthesis in vivo, and they are now common anticancer drugs in clinical therapy.The covalent attachment of organic intercalators to transition metal coordination complexes, yielding metallointercalators, can lead to novel DNA interactions that influence biological activity. Metal complexes with ?-bonded aromatic side arms can act as dual-function complexes: they bind to DNA both by metal coordination and through intercalation of the attached aromatic ligand. These aromatic side arms introduce new modes of DNA binding, involving mutual interactions of functional groups held in close proximity. The biological activity of both cis- and trans-diamine PtII complexes is dramatically enhanced by the addition of ?-bonded intercalators.We have explored a new class of organometallic "piano-stool" RuII and OsII arene anticancer complexes of the type [(?6-arene)Ru/ Os(XY)Cl]+. Here XY is, for example, ethylenediamine (en), and the arene ligand can take many forms, including tetrahydroanthracene, biphenyl, or p-cymene. Arene-nucleobase stacking interactions can have a significant influence on both the kinetics and thermodynamics of DNA binding. In particular, the cytotoxic activity, conformational distortions, recognition by DNA-binding proteins, and repair mechanisms are dependent on the arene. A major difficulty in developing anticancer drugs is cross-resistance, a phenomenon whereby a cell that is resistant to one drug is also resistant to another drug in the same class. These new complexes are non-cross-resistant with cisplatin towards cancer cells: they constitute a new class of anticancer agents, with a mechanism of action that differs from the anticancer drug cisplatin and its analogs. The Ru-arene complexes with dual functions are more potent towards cancer cells than their nonintercalating analogs.In this Account, we focus on recent studies of dual-function organometallic RuII- and OsII-arene complexes and the methods used to detect arene-DNA intercalation. We relate these interactions to the mechanism of anticancer activity and to structure-activity relationships. The interactions between these complexes and DNA show close similarities to those of covalent polycyclic aromatic carcinogens, especially to N7-alkylating intercalation compounds. However, Ru-arene complexes exhibit some new features. Classical intercalation and base extrusion next to the metallated base is observed for {(?6-biphenyl) Ru(ethylenediamine)}2+ adducts of a 14-mer duplex, while penetrating arene intercalation occurs for adducts of the nonaromatic bulky intercalator {(?6-tetrahydroanthracene)Ru(ethylenediamine)}2+ with a 6-mer duplex. The introduction of dual-function Ru-arene complexes introduces new mechanisms of antitumor activity, novel mechanisms for attack on DNA, and new concepts for developing structure- activity relationships. We hope this discussion will stimulate thoughtful and focused research on the design of anticancer chemotherapeutic agents using these unique approaches. © 2011 American Chemical Society.


Du D.-Y.,Northeast Normal University | Qin J.-S.,Northeast Normal University | Li S.-L.,Nanjing Normal University | Su Z.-M.,Northeast Normal University | And 2 more authors.
Chemical Society Reviews | Year: 2014

Polyoxometalate (POM)-based metal-organic framework (MOF) materials contain POM units and generally generate MOF materials with open networks. POM-based MOF materials, which utilize the advantages of both POMs and MOFs, have received increasing attention, and much effort has been devoted to their preparation and relevant applications over the past few decades. They have good prospects in catalysis owing to the electronic and physical properties of POMs that are tunable by varying constituent elements. In this review, we present recent developments in porous POM-based MOF materials, including their classification, synthesis strategies, and applications, especially in the field of catalysis. © 2014 The Royal Society of Chemistry.


Yuan T.-F.,Nanjing Normal University | Slotnick B.M.,American University of Washington
Progress in Neuro-Psychopharmacology and Biological Psychiatry | Year: 2014

The olfactory system is involved in sensory functions, emotional regulation and memory formation. Olfactory bulbectomy in rat has been employed as an animal model of depression for antidepressant discovery studies for many years. Olfaction is impaired in animals suffering from chronic stress, and patients with clinical depression were reported to have decreased olfactory function. It is believed that the neurobiological bases of depression might include dysfunction in the olfactory system. Further, brain stimulation, including nasal based drug delivery could provide novel therapies for management of depression. © 2014 .


Zhu Z.,Nanjing Normal University
Journal of Experimental Botany | Year: 2014

Jasmonate (JA) and ethylene (ET) are considered to be two essential plant hormones helping plants to tolerate infections by necrotrophic fungi. Phenotypic observations and marker gene expression analysis suggest that JA and ET act synergistically and interdependently in these defence responses. However, JA and ET also interact in an antagonistic way. JA represses ET-induced apical hook formation, while ET inhibits JA-controlled wounding responses. Although these physiological observations have been reported for more than a decade, only recently have the underlying molecular mechanisms been uncovered. Here, I review the recent advances in the understanding of these two hormone interactions and further discuss the biological significance of these apparently opposite interactions between these two hormones in orchestrating plant growth and development. © 2014 The Author.


Zhu J.,Nanjing Normal University
Physics Letters, Section A: General, Atomic and Solid State Physics | Year: 2013

In this Letter, Kuramoto model in a high-dimensional linear space is investigated. Some results on the equilibria and synchronization of the classical Kuramoto model are generalized to the high-dimensional Kuramoto model. It is proved that, if the interconnection graph is connected and all the initial states lie in a half part of the state space, the synchronization can be achieved. Finally, numerical simulations are given to validate the obtained theoretical results. © 2013 Elsevier B.V.


Xu J.,Nanjing Normal University
Neurocomputing | Year: 2011

Hybrid strategy, which generalizes a specific single-label algorithm while one or two data decomposition tricks are applied implicitly or explicitly, has become an effective and efficient tool to design and implement various multi-label classification algorithms. In this paper, we extend traditional binary support vector machine by introducing an approximate ranking loss as its empirical loss term to build a novel support vector machine for multi-label classification, resulting into a quadratic programming problem with different upper bounds of variables to characterize label correlation of individual instance. Further, our optimization problem can be solved via combining one-versus-rest data decomposition trick with modified binary support vector machine, which dramatically reduces computational cost. Experimental study on ten multi-label data sets illustrates that our method is a powerful candidate for multi-label classification, compared with four state-of-the-art multi-label classification approaches. © 2011 Elsevier B.V.


Xu J.,Nanjing Normal University
Pattern Recognition | Year: 2013

The existing multi-label support vector machine (Rank-SVM) has an extremely high computational complexity due to a large number of variables in its quadratic programming. When the Frank-Wolfe (FW) method is applied, a large-scale linear programming still needs to be solved at any iteration. Therefore it is highly desirable to design and implement a new efficient SVM-type multi-label algorithm. Binary core vector machine (CVM), as a variant of traditional SVM, is formulated as a quadratic programming with a unit simplex constraint, in which each linear programming in FW has an analytical solution. In this paper, we combine Rank-SVM with CVM to construct a novel SVM-type multi-label classifier (Rank-CVM) which is described as the same optimization form as binary CVM. At its any iteration of FW, there exist analytical solution and step size, and several useful recursive formulae for proxy solution, gradient vector, and objective function value, all of which reduce computational cost greatly. Experimental study on nine benchmark data sets shows that when Rank-CVM performs as statistically well as its rival Rank-SVM according to five performance measures, our method runs averagely about 13 times faster and has less support vectors than Rank-SVM in the training phase under C/C++ environment. © 2012 Elsevier Ltd.


Jin L.-G.,Nanjing Normal University | Tang R.,Nanjing Normal University | Zhang F.,Nanjing Normal University
Physics Letters, Section B: Nuclear, Elementary Particle and High-Energy Physics | Year: 2015

We present a model that generates small neutrino masses at three-loop level due to the existence of Majorana fermionic dark matter, which is stabilized by a Z2 symmetry. The model predicts that the lightest neutrino is massless. We show a prototypical parameter choice allowed by relevant experimental data, which favors the case of normal neutrino mass spectrum and the dark matter with m~50-135 GeV and a sizable Yukawa coupling. It means that new particles can be searched for in future e+e- collisions. © 2014 The Authors.


Wang W.,Nanjing Normal University
Journal of Invertebrate Pathology | Year: 2011

Bacterial diseases of crabs are manifested as bacteremias caused by organisms such as Vibrio, Aeromonas, and a Rhodobacteriales-like organism or tissue and organ tropic organisms such as chitinoclastic bacteria, Rickettsia intracellular organisms, Chlamydia-like organism, and Spiroplasma. This paper provides general information about bacterial diseases of both marine and freshwater crabs. Some bacteria pathogens such as Vibrio cholerae and Vibrio vulnificus occur commonly in blue crab haemolymph and should be paid much attention to because they may represent potential health hazards to human beings because they can cause serious diseases when the crab is consumed as raw sea food. With the development of aquaculture, new diseases associated with novel pathogens such as spiroplasmas and Rhodobacteriales-like organisms have appeared in commercially exploited crab species in recent years. Many potential approaches to control bacterial diseases of crab will be helpful and practicable in aquaculture. © 2010.


Xu J.,Nanjing Normal University
Pattern Recognition | Year: 2014

Multi-label core vector machine (Rank-CVM) is an efficient and effective algorithm for multi-label classification. But there still exist two aspects to be improved: reducing training and testing computational costs further, and detecting relevant labels effectively. In this paper, we extend Rank-CVM via adding a zero label to construct its variant with a zero label, i.e., Rank-CVMz, which is formulated as the same quadratic programming form with a unit simplex constraint and non-negative ones as Rank-CVM, and then is solved by Frank-Wolfe method efficiently. Attractively, our Rank-CVMz has fewer variables to be solved than Rank-CVM, which speeds up training procedure dramatically. Further, the relevant labels are effectively detected by the zero label. Experimental results on 12 benchmark data sets demonstrate that our method achieves a competitive performance, compared with six existing multi-label algorithms according to six indicative instance-based measures. Moreover, on the average, our Rank-CVMz runs 83 times faster and has slightly fewer support vectors than its origin Rank-CVM. © 2014 Elsevier Ltd. All rights reserved.

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