Ischemic spinal cord disorders

Infarcts in the territory of the anterior spinal artery usually are located in the Adamkiewicz artery. Onset is either sudden or progressive with clinical signs of deficit and spinal pain of short duration. The extent and distribution of the deficit depends on the extension and the level of the spinal lesions. A perfect knowledge of the spinal cord vascularization is required to discern the complex and variable clinical expression. Unlike cerebral vascular events, emboli or cardiac origin are rarely involved in spinal cord events. Aortic pathology, including surgery of the aorta, is however of major importance in ischaemic spinal cord syndromes. Several reports have shown that atheroma causing spinal cord ischaemia are not located in the perispinal arterial network but lie on the aorta and its spinal branches. Dissection of the aorta is found responsible in 2 to 8% of the cases. Isthma stenosis can also give spinal cord signs and syphylitic arteritis is exceptionally encountered. Other rare causes are gas emboli and fibrocalcium emboli.     

A new family of sugar-inducible expression vectors for Escherichia coli

A set of 11 expression vectors was constructed, each of themharbouring a cloning cassette under the control of the promoter.Some of these vectors enable expression of foreign proteinsin the cytoplasm, while others include a synthetic sequencecoding for a very efficient secretion signal sequence. Otherfeatures are an fl origin of replication (in plus or minus orientation)and a promoter^up mutation that enhances the already very highlevel of expression from these vectors. With such a versatilevector family, cloning, sequencing and sitedirected mutagenesiscan be performed on the same vector, and the level of expressioncan be defined according to the specific constraints of a givenprotein.     

Cognitive science: Making the whole different from the sum of its parts.

Reviews the book, From models to modules: Studies in cognitive science from the McGill workshops edited by I. Gopnik and M. Gopnik (1986). This book is only moderately successful in conveying the exciting advances that are beginning to appear as a result of the interdisciplinary efforts in cognitive science. The book's emphasis on language processes and linguistics will seriously limit its potential readership, although there are some contributions from outside that domain. Moreover, many of the chapters were not prepared with a general audience in mind, and to appreciate a number of the chapters fully the reader must have a strong linguistic background. A more severe problem is that the chapters are based on a series of workshops held at McGill University in 1982 and 1983. The sad consequences are that (a) the book is badly out of date (few references are more recent than 1983), (b) most of the contributions were not original but were based on ideas and results that were or were about to be in print at the time the workshops were held, and (c) many of the chapters are so short that they do not provide adequate depth of coverage. At this late date, the value of the book may be as a summary of a few of the early issues and methodologies that have captured the attention of cognitive scientists. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

CEVCLUS: evidential clustering with instance-level constraints for relational data

Recent advances in clustering consider incorporating background knowledge in the partitioning algorithm, using, e.g., pairwise constraints between objects. As a matter of fact, prior information, when available, often makes it possible to better retrieve meaningful clusters in data. Here, this approach is investigated in the framework of belief functions, which allows us to handle the imprecision and the uncertainty of the clustering process. In this context, the EVCLUS algorithm was proposed for partitioning objects described by a dissimilarity matrix. It is extended here so as to take pairwise constraints into account, by adding a term to its objective function. This term corresponds to a penalty term that expresses pairwise constraints in the belief function framework. Various synthetic and real datasets are considered to demonstrate the interest of the proposed method, called CEVCLUS, and two applications are presented. The performances of CEVCLUS are also compared to those of other constrained clustering algorithms.     

ECM: An evidential version of the fuzzy c-means algorithm

A new clustering method for object data, called ECM (evidential c-means) is introduced, in the theoretical framework of belief functions. It is based on the concept of credal partition, extending those of hard, fuzzy, and possibilistic ones. To derive such a structure, a suitable objective function is minimized using an FCM-like algorithm. A validity index allowing the determination of the proper number of clusters is also proposed. Experiments with synthetic and real data sets show that the proposed algorithm can be considered as a promising tool in the field of exploratory statistics.     

High Figure of Merit (FOM) of Bragg Modes in Au‐Coated Nanodisk Arrays for Plasmonic Sensing

Gold‐coated nanodisk arrays of nearly micron periodicity are reported that have high figure of merit (FOM) and sensitivity necessary for plasmonic refractometric sensing, with the added benefit of suitability for surface‐enhanced Raman scattering (SERS), large‐scale microfabrication using standard photolithographic techniques and a simple instrumental setup. Gold nanodisk arrays are covered with a gold layer to excite the Bragg modes (BM), which are the propagative surface plasmons localized by the diffraction from the disk array. This generates surface‐guided modes, localized as standing waves, leading to highly confined fields confirmed by a mapping of the SERS intensity and numerical simulations with 3D finite element method. The optimal gold‐coated nanodisk arrays are applied for refractometric sensing in transmission spectroscopy with better performance than nanohole arrays and they are integrated to a 96‐well plate reader for detection of IgY proteins in the nanometer range in PBS. The potential for sensing in biofluids is assessed with IgG detection in 1:1 diluted urine. The structure exhibits a high FOM of up to 46, exceeding the FOM of structures supporting surface plasmon polaritons and comparable to more complex nanostructures, demonstrating that subwavelength features are not necessary for high‐performance plasmonic sensing.     

Design theory at Bauhaus: teaching “splitting” knowledge

Recent advances in design theory help clarify the logic, forms and conditions of generativity. In particular, the formal model of forcing predicts that high-level generativity (so-called generic generativity) can only be reached if the knowledge structure meets the ‘splitting condition’. We test this hypothesis for the case of Bauhaus (1919–1933), where we can expect strong generativity and where we have access to the structures of knowledge provided by teaching. We analyse teaching at Bauhaus by focusing on the courses of Itten and Klee. We show that these courses aimed to increase students’ creative design capabilities by providing the students with methods of building a knowledge base with two critical features: (1) a knowledge structure that is characterized by non-determinism and non-modularity and (2) a design process that helps students progressively ‘superimpose’ languages on the object. From the results of the study, we confirm the hypothesis deduced from design theory; we reveal unexpected conditions on the knowledge structure required for generativity and show that the structure is different from the knowledge structure and design process of engineering systematic design and show that the conditions required for generativity, which can appear as a limit on generativity, can also be positively interpreted. The example of Bauhaus shows that enabling a splitting condition is a powerful way to increase designers’ generativity.     

Hypercube sandwich approach to conferencing

This paper presents a novel cascaded conference network that provides distributed processing and signal transmission among members of disjoint sets of generic send/receive devices called conferees. It assumes an online request model in which idle groups of conferees may request the formation of a conference interconnection. Once a conference is established, all conferees remain connected until the entire conference is dissolved. The Hypercube Sandwich Network (HSN) consists of two components. A bidirectional permutation network is used for routing purposes to and from a hypercube of special processing elements for the purpose of conference formation. The HSN achieves strictly nonblocking performance for N conferees using O(Nlog N) processing elements, and this is shown to be tight to within a log ^1/4 N factor. Previous constructions required a quadratic number of processing elements for strictly nonblocking performance or could only provide wide-sense nonblocking conferencing. If the stronger requirement is made that the communication delay is logarithmic in the conference size, a simple algorithm is presented for wide-sense nonblocking conferencing in an HSN with O(N log N) processing elements.An earlier version of this paper was presented at the 1995 International Conference on Parallel Processing Techniques and Applications.     

Action Recognition Using a Bio-Inspired Feedforward Spiking Network

We propose a bio-inspired feedforward spiking network modeling two brain areas dedicated to motion (V1 and MT), and we show how the spiking output can be exploited in a computer vision application: action recognition. In order to analyze spike trains, we consider two characteristics of the neural code: mean firing rate of each neuron and synchrony between neurons. Interestingly, we show that they carry some relevant information for the action recognition application. We compare our results to Jhuang et al. (Proceedings of the 11th international conference on computer vision, pp. 1–8, 2007) on the Weizmann database. As a conclusion, we are convinced that spiking networks represent a powerful alternative framework for real vision applications that will benefit from recent advances in computational neuroscience.