Rationale and design of a randomized, double-blind, parallel-group study of terutroban 30 mg/day versus aspirin 100 mg/day in stroke patients: the prevention of cerebrovascular and cardiovascular events of ischemic origin with terutroban in patients with a history of ischemic stroke or transient ischemic attack (PERFORM) study.

BACKGROUND: Ischemic stroke is the leading cause of mortality worldwide and a major contributor to neurological disability and dementia. Terutroban is a specific TP receptor antagonist with antithrombotic, antivasoconstrictive, and antiatherosclerotic properties, which may be of interest for the secondary prevention of ischemic stroke. This article describes the rationale and design of the Prevention of cerebrovascular and cardiovascular Events of ischemic origin with teRutroban in patients with a history oF ischemic strOke or tRansient ischeMic Attack (PERFORM) Study, which aims to demonstrate the superiority of the efficacy of terutroban versus aspirin in secondary prevention of cerebrovascular and cardiovascular events. METHODS AND RESULTS: The PERFORM Study is a multicenter, randomized, double-blind, parallel-group study being carried out in 802 centers in 46 countries. The study population includes patients aged > or =55 years, having suffered an ischemic stroke (< or =3 months) or a transient ischemic attack (< or =8 days). Participants are randomly allocated to terutroban (30 mg/day) or aspirin (100 mg/day). The primary efficacy endpoint is a composite of ischemic stroke (fatal or nonfatal), myocardial infarction (fatal or nonfatal), or other vascular death (excluding hemorrhagic death of any origin). Safety is being evaluated by assessing hemorrhagic events. Follow-up is expected to last for 2-4 years. Assuming a relative risk reduction of 13%, the expected number of primary events is 2,340. To obtain statistical power of 90%, this requires inclusion of at least 18,000 patients in this event-driven trial. The first patient was randomized in February 2006. CONCLUSIONS: The PERFORM Study will explore the benefits and safety of terutroban in secondary cardiovascular prevention after a cerebral ischemic event.

Improved SNR efficiency in gradient echo coronary MRA with high temporal resolution using parallel imaging.

Contemporary coronary magnetic resonance angiography techniques suffer from signal-to-noise ratio (SNR) constraints. We propose a method to enhance SNR in gradient echo coronary magnetic resonance angiography by using sensitivity encoding (SENSE). While the use of sensitivity encoding to improve SNR seems counterintuitive, it can be exploited by reducing the number of radiofrequency excitations during the acquisition window while lowering the signal readout bandwidth, therefore improving the radiofrequency receive to radiofrequency transmit duty cycle. Under certain conditions, this leads to improved SNR. The use of sensitivity encoding for improved SNR in three-dimensional coronary magnetic resonance angiography is investigated using numerical simulations and an in vitro and an in vivo study. A maximum 55% SNR enhancement for coronary magnetic resonance angiography was found both in vitro and in vivo, which is well consistent with the numerical simulations. This method is most suitable for spoiled gradient echo coronary magnetic resonance angiography in which a high temporal and spatial resolution is required.

Identification of cancer/testis-antigen genes by massively parallel signature sequencing.

Massively parallel signature sequencing (MPSS) generates millions of short sequence tags corresponding to transcripts from a single RNA preparation. Most MPSS tags can be unambiguously assigned to genes, thereby generating a comprehensive expression profile of the tissue of origin. From the comparison of MPSS data from 32 normal human tissues, we identified 1,056 genes that are predominantly expressed in the testis. Further evaluation by using MPSS tags from cancer cell lines and EST data from a wide variety of tumors identified 202 of these genes as candidates for encoding cancer/testis (CT) antigens. Of these genes, the expression in normal tissues was assessed by RT-PCR in a subset of 166 intron-containing genes, and those with confirmed testis-predominant expression were further evaluated for their expression in 21 cancer cell lines. Thus, 20 CT or CT-like genes were identified, with several exhibiting expression in five or more of the cancer cell lines examined. One of these genes is a member of a CT gene family that we designated as CT45. The CT45 family comprises six highly similar (>98% cDNA identity) genes that are clustered in tandem within a 125-kb region on Xq26.3. CT45 was found to be frequently expressed in both cancer cell lines and lung cancer specimens. Thus, MPSS analysis has resulted in a significant extension of our knowledge of CT antigens, leading to the discovery of a distinctive X-linked CT-antigen gene family.

Towards Machine Consciousness: Grounding Abstract Models as pi-Processes

We present a two-level model of concurrent communicating systems (CCS) to serve as a basis formachine consciousness. A language implementing threads within logic programming is ¯rstintroduced. This high-level framework allows for the de¯nition of abstract processes that can beexecuted on a virtual machine. We then look for a possible grounding of these processes into thebrain. Towards this end, we map abstract de¯nitions (including logical expressions representingcompiled knowledge) into a variant of the pi-calculus. We illustrate this approach through aseries of examples extending from a purely reactive behavior to patterns of consciousness.

Environmental data mining and modeling based on machine learning algorithms and geostatistics

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. NIL algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.

Shallow-transfer rule-based machine translation for Czech to Polish

This article describes the developmentof an Open Source shallow-transfer machine translation system from Czech to Polish in theApertium platform. It gives details ofthe methods and resources used in contructingthe system. Although the resulting system has quite a high error rate, it is still competitive with other systems.

Automatic acquisition of Named Entities for Rule-Based Machine Translation

This paper proposes to enrich RBMTdictionaries with Named Entities(NEs) automatically acquired fromWikipedia. The method is appliedto the Apertium English-Spanishsystem and its performance comparedto that of Apertium with and withouthandtagged NEs. The system withautomatic NEs outperforms the onewithout NEs, while results vary whencompared to a system with handtaggedNEs (results are comparable forSpanish to English but slightly worstfor English to Spanish). Apart fromthat, adding automatic NEs contributesto decreasing the amount of unknownterms by more than 10%.

Taking on new challenges in multi-word unit processing for Machine Translation

This paper discusses the qualitativecomparative evaluation performed on theresults of two machine translation systemswith different approaches to the processing ofmulti-word units. It proposes a solution forovercoming the difficulties multi-word unitspresent to machine translation by adopting amethodology that combines the lexicongrammar approach with OpenLogos ontologyand semantico-syntactic rules. The paper alsodiscusses the importance of a qualitativeevaluation metrics to correctly evaluate theperformance of machine translation engineswith regards to multi-word units.

A Widely Used Machine Translation Service and its Migration to a Free/Open-Source Solution : the Case of Softcatalà

Softcatalà is a non-profit associationcreated more than 10 years ago to fightthe marginalisation of the Catalan languagein information and communicationtechnologies. It has led the localisationof many applications and thecreation of a website which allows itsusers to translate texts between Spanishand Catalan using an external closed-sourcetranslation engine. Recently,the closed-source translation back-endhas been replaced by a free/open-sourcesolution completely managed by Softcatalà: the Apertium machine translationplatform and the ScaleMT web serviceframework. Thanks to the opennessof the new solution, it is possibleto take advantage of the huge amount ofusers of the Softcatalà translation serviceto improve it, using a series ofmethods presented in this paper. In addition,a study of the translations requestedby the users has been carriedout, and it shows that the translationback-end change has not affected theusage patterns.

C4 Photosynthesis evolved in grasses via parallel adaptive genetic changes.

Phenotypic convergence is a widespread and well-recognized evolutionary phenomenon. However, the responsible molecular mechanisms remain often unknown mainly because the genes involved are not identified. A well-known example of physiological convergence is the C4 photosynthetic pathway, which evolved independently more than 45 times [1]. Here, we address the question of the molecular bases of the C4 convergent phenotypes in grasses (Poaceae) by reconstructing the evolutionary history of genes encoding a C4 key enzyme, the phosphoenolpyruvate carboxylase (PEPC). PEPC genes belong to a multigene family encoding distinct isoforms of which only one is involved in C4 photosynthesis [2]. By using phylogenetic analyses, we showed that grass C4 PEPCs appeared at least eight times independently from the same non-C4 PEPC. Twenty-one amino acids evolved under positive selection and converged to similar or identical amino acids in most of the grass C4 PEPC lineages. This is the first record of such a high level of molecular convergent evolution, illustrating the repeatability of evolution. These amino acids were responsible for a strong phylogenetic bias grouping all C4 PEPCs together. The C4-specific amino acids detected must be essential for C4 PEPC enzymatic characteristics, and their identification opens new avenues for the engineering of the C4 pathway in crops.

Hybrid model of a double-fed induction machine and back-to-back converter

This report details the port interconnection of two subsystems: a power electronics subsystem (a back-to-back AC/AC converter (B2B), coupled to a phase of the power grid), and an electromechanical subsystem (a doubly-fed induction machine (DFIM), coupled mechanically to a flywheel and electrically to the power grid and to a local varying load). Both subsystems have been essentially described in previous reports (deliverables D 0.5 and D 4.3.1), although some previously unpublished details are presented here. The B2B is a variable structure system (VSS), due to the presence of control-actuated switches: however from a modelling and simulation, as well as a control-design, point of view, it is sensible to consider modulated transformers (MTF in the bond-graph language) instead of the pairs of complementary switches. The port-Hamiltonian models of both subsystems are presents and coupled through a power-preserving interconnection, and the Hamiltonian description of the whole system is obtained; detailed bond-graphs of all the subsystems and the complete system are provided.

Energy-based modelling and simulation of the interconnection of a back-to-back converter and a doubly-fed induction machine

This paper describes the port interconnection of two subsystems: a power electronics subsystem (a back-to-back AC/CA converter (B2B), coupled to a phase of the power grid), and an electromechanical subsystem (a doubly-fed induction machine (DFIM). The B2B is a variable structure system (VSS), due to presence of control-actuated switches: however, from a modelling simulation, as well as a control-design, point of view, it is sensible to consider modulated transformers (MTF in the bond graph language) instead of the pairs of complementary switches. The port-Hamiltonian models of both subsystems are presented and, using a power-preserving interconnection, the Hamiltonian description of the whole system is obtained; detailed bond graphs of all subsystems and the complete system are also provided. Using passivity-based controllers computed in the Hamiltonian formalism for both subsystems, the whole model is simulated; simulations are run to rest the correctness and efficiency of the Hamiltonian network modelling approach used in this work.

Impingement jet cooling of end windings in a high-speed electric machine

The rotational speed of high-speed electric machines is over 15 000 rpm. These machines are compact in size when compared to the power rate. As a consequence, the heat fluxes are at a high level and the adequacy of cooling becomes an important design criterion. In the high-speed machines, the air gap between the stator and rotor is a narrow flow channel. The cooling air is produced with a fan and the flow is then directed to the air gap. The flow in the gap does not provide sufficient cooling for the stator end windings, and therefore additional cooling is required. This study investigates the heat transfer and flow fields around the coil end windings when cooling jets are used. As a result, an innovative and new assembly is introduced for the cooling jets, with the benefits of a reduced amount of hot spots, a lower pressure drop, and hence a lower power need for the cooling fan. The gained information can also be applied to improve the cooling of electric machines through geometry modifications. The objective of the research is to determine the locations of the hot spots and to find out induced pressure losses with different jet alternatives. Several possibilities to arrange the extra cooling are considered. In the suggested approach cooling is provided by using a row of air jets. The air jets have three main tasks: to cool the coils effectively by direct impingement jets, to increase and cool down the flow that enters the coil end space through the air gap, and to ensure the correct distribution of the flow by forming an air curtain with additional jets. One important aim of this study is the arrangement of cooling jets in such manner that hot spots can be avoided to wide extent. This enables higher power density in high-speed motors. This cooling system can also be applied to the ordinary electric machines when efficient cooling is needed. The numerical calculations have been performed using a commercial Computational Fluid Dynamics software. Two geometries have been generated: cylindrical for the studied machine and Cartesian for the experimental model. The main parameters include the positions, arrangements and number of jets, the jet diameters, and the jet velocities. The investigated cases have been tested with two widely used turbulence models and using a computational grid of over 500 000 cells. The experimental tests have been made by using a simplified model for the end winding space with cooling jets. In the experiments, an emphasis has been given to flow visualisation. The computational analysis shows good agreement with the experimental results. Modelling of the cooling jet arrangement enables also a better understanding of the complex system of heat transfer at end winding space.

Personalized drug administrations using Support Vector Machine

The decision-making process regarding drug dose, regularly used in everyday medical practice, is critical to patients' health and recovery. It is a challenging process, especially for a drug with narrow therapeutic ranges, in which a medical doctor decides the quantity (dose amount) and frequency (dose interval) on the basis of a set of available patient features and doctor's clinical experience (a priori adaptation). Computer support in drug dose administration makes the prescription procedure faster, more accurate, objective, and less expensive, with a tendency to reduce the number of invasive procedures. This paper presents an advanced integrated Drug Administration Decision Support System (DADSS) to help clinicians/patients with the dose computing. Based on a support vector machine (SVM) algorithm, enhanced with the random sample consensus technique, this system is able to predict the drug concentration values and computes the ideal dose amount and dose interval for a new patient. With an extension to combine the SVM method and the explicit analytical model, the advanced integrated DADSS system is able to compute drug concentration-to-time curves for a patient under different conditions. A feedback loop is enabled to update the curve with a new measured concentration value to make it more personalized (a posteriori adaptation).

Pulp Fractionation in a Pilot Paper Machine

Tämän diplomityön tarkoituksena on tutkia paperimassan jakautumiseen vaikuttavia tekijöitä paperinvalmistusprosessissa. Työn empiirisen osan tavoitteena on analysoida perälaatikon hienoainepitoisuuden ja paperimassan virtausnopeuden vaikutusta paperimassan jakautumiseen pilottipaperikoneessa, sekä selvittää voidaanko näitä prosessiparametreja optimoimalla saavuttaa merkittävää retention, vedenpoiston ja kaksipuolisuuden parantumista. Työn teoreettinen osa sisältää kirjallisuuskatsauksen märänpään kemiasta ja yhteenvedon aikaisemmasta tutkimuksesta koskien paperimassan jakautumista paperinvalmistusprosessissa. Työn empiirisessä osassa on tutkittu perälaatikon hienoainepitoisuuden ja paperimassan virtausnopeuden vaikutusta retentioon, vedenpoistoon ja paperimassan jakautumiseen Papricanin pilottipaperikoneessa. Analyysissä on käytetty yhteensovitettua dataa, joka on saatu kattavien pilottipaperikonekokeiden ja taulukkolaskentaohjelmalla toteutettujen staattisten simulointimallien avulla. Simulointimalleissa perälaatikon hienoainepitoisuus on 30-55%, sekä paperimassan virtausnopeudet ovat 2470 L/min, 3870 L/min ja 5230 L/min. Muut prosessiparametrit on vakioitu, eikä retentioainetta käytetty. Retentio pilottipaperikoneessa oli 55-82% riippuen perälaatikon hienoainepitoisuudesta ja paperimassan virtausnopeudesta. Perälaatikon hienoainepitoisuuden ja retention välillä oli voimakas negatiivinen korrelaatio. Myös paperimassan virtausnopeuden ja retention välillä oli negatiivinen korrelaatio. Mitä alhaisempi retentio, sitä enemmän hienoainesta kerääntyi systeemiin. Hienoaineen huuhtoutuminen paperirainasta korreloi vedenpoistoon: pienemmällä paperimassan virtausnopeudella enemmän sekä vettä että hienoainetta poistui viirapuolelta, ja suuremmalla paperimassan virtausnopeudella saman verran sekä vettä että hienoainetta poistui rainan molemmilta puolilta. Paras paperirainan kaksipuolisuus saavutettiin korkeilla perälaatikon hienoainepitoisuuksilla (50% ja 55%) suurilla paperimassan virtausnopeuksilla (3870 L/min ja 5230 L/min).