Potentiation of tumour necrosis factor-mediated cell killing by VP16 on human ovarian cancer cell lines. In vitro results and clinical implications

Synergism between recombinant human tumour necrosis factor (rHuTNF) and DNA topoisomerase II inhibitor VP16 during the killing of cells has been studied in six human ovarian cancer cell lines (A2774, A2780, SW626, IGROV-1, SKOV3, Pa1) and a cervical carcinoma cell line (Me180). Studies were performed using an assay of colony formation inhibition (drug treatment for 1 h) and a growth inhibition assay (continuous exposure for 20 h). Concomitant treatment of cells with VP16+rHuTNF enhanced cell killing in all the cell lines tested--an effect observed in both short- and long-term cytotoxicity assays. This study suggests that the activity of VP16 in ovarian cancer cell lines might be enhanced by rHuTNF in in vitro models.     

Analysis of thin walled structures using the boundary element method

In this article the direct boundary element formulations of stretching and bending plate problems are combined together to solve thin walled structures and other similar frames. The final system is obtained by assuming each individual plane structural element as a subregion. After the necessary transforms of these equations they can be combined together taking into account the displacement compatibility and equilibrium conditions.     

De nouveaux stannures ternaires de rhodium et d'elements des terres rares: TR1+xRh2Sn4−x, 0 ⩽ x ⩽ 0,5, TR = La - SmNew ternary stannides: TR1+xRh2Sn4−x, 0≦x≦0.5, TR = La - Sm

New ternary stannides are reported: RE_1+xRh₂Sn_4?x, 0 ? x ? 0,5 (RE = La ? Sm). NdRh₂Sn₄ has been characterized by single - crystal X-ray diffraction analysis. Its structure is of a new type with space group Pnma and Z = 4: a = 18,535(3), b = 4,463(1), $\text{c}\text{= 7,229(1)}\text{A}\text{?}$, D_x = 9,16 g.cm^?1, μ(AgKα) = 13,6 mm^?1, F(000) = 1300, R = 0,040 for 496 independent reflexions (R_w = 0,047). One of the tin sites can be partly occupied by rare earths atoms. The structure types of NdRh₂Sn₄ and Y₂Rh₃Sn₅ are closely related. LaRh₂Sn₄ does not exhibit superconductivity above 1.4 K.     

Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography

Neural Computing and Applications - In this paper, we present the novel Deep-MEG approach in which image-based representations of magnetoencephalography (MEG) data are combined with ensemble...     

Slicing the metric space to provide quick indexing of complex data in the main memory

Searching in a dataset for elements that are similar to a given query element is a core problem in applications that manage complex data, and has been aided by metric access methods (MAMs). A growing number of applications require indices that must be built faster and repeatedly, also providing faster response for similarity queries. The increase in the main memory capacity and its lowering costs also motivate using memory-based MAMs. In this paper, we propose the Onion-tree, a new and robust dynamic memory-based MAM that slices the metric space into disjoint subspaces to provide quick indexing of complex data. It introduces three major characteristics: (i) a partitioning method that controls the number of disjoint subspaces generated at each node; (ii) a replacement technique that can change the leaf node pivots in insertion operations; and (iii) range and k-NN extended query algorithms to support the new partitioning method, including a new visit order of the subspaces in k-NN queries. Performance tests with both real-world and synthetic datasets showed that the Onion-tree is very compact. Comparisons of the Onion-tree with the MM-tree and a memory-based version of the Slim-tree showed that the Onion-tree was always faster to build the index. The experiments also showed that the Onion-tree significantly improved range and k-NN query processing performance and was the most efficient MAM, followed by the MM-tree, which in turn outperformed the Slim-tree in almost all the tests.     

A new approach of data clustering using a flock of agents

This paper presents a new bio-inspired algorithm (FClust) that dynamically creates and visualizes groups of data. This algorithm uses the concepts of a flock of agents that move together in a complex manner with simple local rules. Each agent represents one data. The agents move together in a 2D environment with the aim of creating homogeneous groups of data. These groups are visualized in real time, and help the domain expert to understand the underlying structure of the data set, like for example a realistic number of classes, clusters of similar data, isolated data. We also present several extensions of this algorithm, which reduce its computational cost, and make use of a 3D display. This algorithm is then tested on artificial and real-world data, and a heuristic algorithm is used to evaluate the relevance of the obtained partitioning.     

Methodological design and comparative evaluation of a MAS providing AmI

Researches on Ambient Intelligent and Ubiquitous Computing using wireless technologies have increased in the last years. In this work, we review several scenarios to define a multi-agent architecture that support the information needs of these new technologies, for heterogeneous domain. Our contribution consists of designing in a methodological way a Context Aware System (involving location services) using agents that can be used in very different domains. We describe all the steps followed in the design of the agent system. We apply a hybridizing methodology between GAIA and AUML. Additionally we propose a way to compare different agent architectures for Context Aware System using agent interactions. So, in this paper, we describe the assignment of weight values to agents interaction in two different MAS architectures for Context Aware problems solving different scenarios inspired in FIPA standard negotiation protocols.     

Flow,Thermal and Microstructural Analysis during Cast Rolling of Steel in In‐line Strip Production Process

A mathematical model was developed to describe the in‐line hot rolling deformation and recrystallization behaviour of austenite after the solidification on a thin slab casting plant. The most significant features of the cast rolling process were taken into consideration: through‐thickness thermal gradients, inhomogeneous stress and strain, temperature discontinuity between the strip and the rolls. A HSLA (High Strength Low Alloy) steel has been chosen to perform the experiments of cast rolling. The characteristic constants ruling the microstructural evolution of that steel were computed and integrated into the computational module which manages the structural stress‐strain and strain rate computation. The developed approach is based on the Navier‐Stokes’ equations which were used to compute the speed field in the strip during the deformation. Then a model providing a proper constitutive equation was structured on the basis of the Yada's model based on evolution of the dislocation populations. The use of the Navier‐Stokes’ formalism allows to reach the resolution of the structural problem from the data measured easily during the industrial practice (i.e. speed of the rolled product at the entry and at the exit of a stand, the temperature of the rolled material). The validation of this computational approach was obtained by a comparison between the prior austenite grain size of the strip in different positions of the hot rolling process, as well as by a comparison between the computed deformation power and the measured one provided by the engines moving the rolls.