Synchronization analysis of coupled Lienard-type oscillators by averaging

Sufficient conditions for the synchronization of coupled Lienard-type oscillators are investigated via averaging technique. The coupling considered here is fixed, nonsymmetric, and nonlinear. Under the assumption that the interconnection topology defines a connected graph, it is shown that the solutions of oscillators converge arbitrarily close to each other, starting from initial conditions arbitrarily far apart, provided that the frequency of oscillations is large enough and the initial phases of oscillators all lie in an open semicircle. It is also shown that the nearly-synchronized oscillations always take place around some fixed magnitude independent of the initial conditions and the coupling functions.     

Size-induced effects in wet-chemically synthesized CoPt3 nanoparticles

CoPt3 alloy spherical nanoparticles with sizes tuned between 3-7 nm were produced by the simultaneous thermal treatment of proper platinum and cobalt precursors in the presence of surfactants. The final size and composition of the particles are determined by the precursors' ratio, the chemical behavior of Co precursors and the stabilizing efficiency of the surfactants. By employing higher reaction temperatures (approximately 350 degrees C) better alloying is achieved leading to enhancement of macroscopic magnetic features and decrease of the superparamagnetic limit down to 7 nm.     

Link classification with probabilistic graphs

The need to deal with the inherent uncertainty in real-world relational or networked data leads to the proposal of new probabilistic models, such as probabilistic graphs. Every edge in a probabilistic graph is associated with a probability whose value represents the likelihood of its existence, or the strength of the relation between the entities it connects. The aim of this paper is to propose two machine learning techniques for the link classification problem in relational data exploiting the probabilistic graph representation. Both the proposed methods will exploit a language-constrained reachability method to infer the probability of possible hidden relationships that may exists between two nodes in a probabilistic graph. Each hidden relationships between two nodes may be viewed as a feature (or a factor), and its corresponding probability as its weight, while an observed relationship is considered as a positive instance for its corresponding link label. Given a training set of observed links, the first learning approach is to use a propositionalization technique adopting a L2-regularized Logistic Regression to learn a model able to predict unobserved link labels. Since in some cases the edges’ probability may be not known in advance or they could not be precisely defined for a classification task, the second xposed approach is to exploit the inference method and to use a mean squared technique to learn the edges’ probabilities. Both the proposed methods have been evaluated on real world data sets and the corresponding results proved their validity.     

Valproate Targets Mammalian Gastrulation Impairing Neural Tissue Differentiation and Development of the Placental Source In Vitro

The teratogenic activity of valproate (VPA), an antiepileptic and an inhibitor of histone deacetylase (HDACi), is dose-dependent in humans. Previous results showed that VPA impairs in vitro development and neural differentiation of the gastrulating embryo proper. We aimed to investigate the impact of a lower VPA dose in vitro and whether this effect is retained in transplants in vivo. Rat embryos proper (E9.5) and ectoplacental cones were separately cultivated at the air-liquid interface with or without 1 mM VPA. Embryos were additionally cultivated with HDACi Trichostatin A (TSA), while some cultures were syngeneically transplanted under the kidney capsule for 14 days. Embryos were subjected to routine histology, immunohistochemistry, Western blotting and pyrosequencing. The overall growth of VPA-treated embryos in vitro was significantly impaired. However, no differences in the apoptosis or proliferation index were found. Incidence of the neural tissue was lower in VPA-treated embryos than in controls. TSA also impaired growth and neural differentiation in vitro. VPA-treated embryos and their subsequent transplants expressed a marker of undifferentiated neural cells compared to controls where neural differentiation markers were expressed. VPA increased the acetylation of histones. Our results point to gastrulation as a sensitive period for neurodevelopmental impairment caused by VPA.     

Ultra-Short Cyclized β-Boomerang Peptides: Structures,Interactions with Lipopolysaccharide,Antibiotic Potentiator and Wound Healing

Many antibiotics are ineffective in killing Gram-negative bacteria due to the permeability barrier of the outer-membrane LPS. Infections caused by multi-drug-resistant Gram-negative pathogens require new antibiotics, which are often difficult to develop. Antibiotic potentiators disrupt outer-membrane LPS and can assist the entry of large-scaffold antibiotics to the bacterial targets. In this work, we designed a backbone-cyclized ultra-short, six-amino-acid-long (WKRKRY) peptide, termed cWY6 from LPS binding motif of β-boomerang bactericidal peptides. The cWY6 peptide does not exhibit any antimicrobial activity; however, it is able to permeabilize the LPS outer membrane. Our results demonstrate the antibiotic potentiator activity in the designed cWY6 peptide for several conventional antibiotics (vancomycin, rifampicin, erythromycin, novobiocin and azithromycin). Remarkably, the short cWY6 peptide exhibits wound-healing activity in in vitro assays. NMR, computational docking and biophysical studies describe the atomic-resolution structure of the peptide in complex with LPS and mode of action in disrupting the outer membrane. The dual activities of cWY6 peptide hold high promise for further translation to therapeutics.     

Dual Functionalization of Liquid‐Exfoliated Semiconducting 2H‐MoS2 with Lanthanide Complexes Bearing Magnetic and Luminescence Properties

Liquid exfoliated, atomically thin semiconducting transition metal dichalcogenides (TMDs), as inorganic equivalents of graphene, have attracted great interest due to their distinctive physical, optoelectronic, and chemical properties. Functionalization of 2D TMDs brings new prospects for applications in optoelectronics, quantum technologies, catalysis, and medicine. In this report, dual functionalization of 2D semiconducting 2H‐MoS₂ nanosheets through simultaneous incorporation of magnetic and luminescent properties is demonstrated. A facile method is proposed for tuning the properties of the TDM semiconductors and accessing multimodal platforms, consisting in covalent grafting of lanthanide complexes onto the surface of 2D TMDs. Dual functionalization of liquid‐exfoliated MoS₂ nanosheets is demonstrated simultaneously with both europium (III) and gadolinium (III) complexes to form a colloidally stable luminescent (with millisecond lifetimes) and paramagnetic MoS₂‐based nanohybrid material. This work is the first example of transition metal dichalcogenide nanosheets functionalized with preformed lanthanide complexes. These findings open new prospects for covalent functionalization of TMDs with molecular species bearing specific functionalities as a means to tune the optoelectronic properties of the semiconductors, in order to create advanced materials and devices with a wide range of functionalities.     

Transforming paper documents into XML format with WISDOM++

The transformation of scanned paper documents to a form suitable for an Internet browser is a complex process that requires solutions to several problems. The application of an OCR to some parts of the document image is only one of the problems. In fact, the generation of documents in HTML format is easier when the layout structure of a page has been extracted by means of a document analysis process. The adoption of an XML format is even better, since it can facilitate the retrieval of documents in the Web. Nevertheless, an effective transformation of paper documents into this format requires further processing steps, namely document image classification and understanding. WISDOM++ is a document processing system that operates in five steps: document analysis, document classification, document understanding, text recognition with an OCR, and transformation into HTML/XML format. The innovative aspects described in the paper are: the preprocessing algorithm, the adaptive page segmentation, the acquisition of block classification rules using techniques from machine learning, the layout analysis based on general layout principles, and a method that uses document layout information for conversion to HTML/XML formats. A benchmarking of the system components implementing these innovative aspects is reported. Received June 15, 2000 / Revised November 7, 2000