On the convergence of EM-like algorithms for image segmentation using Markov random fields.

Inference of Markov random field images segmentation models is usually performed using iterative methods which adapt the well-known expectation-maximization (EM) algorithm for independent mixture models. However, some of these adaptations are ad hoc and may turn out numerically unstable. In this paper, we review three EM-like variants for Markov random field segmentation and compare their convergence properties both at the theoretical and practical levels. We specifically advocate a numerical scheme involving asynchronous voxel updating, for which general convergence results can be established. Our experiments on brain tissue classification in magnetic resonance images provide evidence that this algorithm may achieve significantly faster convergence than its competitors while yielding at least as good segmentation results.

Faculty-librarians collaboration in an e-learning experience : resources management and training in Open University of Catalonia (UOC)

The traditional model of learning based on knowledge transfer doesn't promote the acquisition of information-related competencies and development of autonomous learning. More needs to be done to embrace learner-centred approaches, based on constructivism, collaboration and co-operation. This new learning paradigm is aligned with the European Higher Education Area (EHEA) requirements. In this sense, a learning experience based in faculty' librarian collaboration was seen as the best option for promoting student engagement and also a way to increase information-related competences in Open University of Catalonia (UOC) academic context. This case study outlines the benefits of teacher-librarian collaboration in terms of pedagogy innovation, resources management and introduction of open educational resources (OER) in virtual classrooms, Information literacy (IL) training and use of 2.0 tools in teaching. Our faculty-librarian's collaboration aims to provide an example of technology-enhanced learning and demonstrate how working together improves the quality and relevance of educational resources in UOC's virtual classrooms. Under this new approach, while teachers change their role from instructors to facilitators of the learning process and extend their reach to students, libraries acquire an important presence in the academic learning communities.

Myocardial tolerance to ischemia-reperfusion injury, training intensity and cessation.

Training has been shown to induce cardioprotection. The mechanisms involved remain still poorly understood. Aims of the study were to examine the relevance of training intensity on myocardial protection against ischemia/reperfusion (I/R) injury, and to which extent the beneficial effects persist after training cessation in rats. Sprague-Dawley rats trained at either low (60% [Formula: see text]) or high (80% [Formula: see text]) intensity for 10 weeks. An additional group of highly trained rats was detrained for 4 weeks. Untrained rats served as controls. At the end of treatment, rats of all groups were split into two subgroups. In the former, rats underwent left anterior descending artery (LAD) ligature for 30 min, followed by 90-min reperfusion, with subsequent measurement of the infarct size. In the latter, biopsies were taken to measure heat-shock proteins (HSP) 70/72, vascular endothelial growth factor (VEGF) protein levels, and superoxide dismutase (SOD) activity. Training reduced infarct size proportionally to training intensity. With detraining, infarct size increased compared to highly trained rats, maintaining some cardioprotection with respect to controls. Cardioprotection was proportional to training intensity and related to HSP70/72 upregulation and Mn-SOD activity. The relationship with Mn-SOD was lost with detraining. VEGF protein expression was not affected by either training or detraining. Stress proteins and antioxidant defenses might be involved in the beneficial effects of long-term training as a function of training intensity, while HSP70 may be one of the factors accounting for the partial persistence of myocardial protection against I/R injury in detrained rats.

Computational and Energy Costs of Cryptographic Algorithms on Handheld Devices

Networks are evolving toward a ubiquitous model in which heterogeneousdevices are interconnected. Cryptographic algorithms are required for developing securitysolutions that protect network activity. However, the computational and energy limitationsof network devices jeopardize the actual implementation of such mechanisms. In thispaper, we perform a wide analysis on the expenses of launching symmetric and asymmetriccryptographic algorithms, hash chain functions, elliptic curves cryptography and pairingbased cryptography on personal agendas, and compare them with the costs of basic operatingsystem functions. Results show that although cryptographic power costs are high and suchoperations shall be restricted in time, they are not the main limiting factor of the autonomyof a device.

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.

Evolution of Gait Performance and Fear of Falling after a 10-Week Program of Exercise Training in Community Dwelling Older People

A pilot study was conducted to determine the effect of a 10-week, low intensity, exercise training program on fear of falling and gait in fifty (mean age 78.1 years, 79% women) community-dwelling volunteers. Fear of falling (measured by falls self-efficacy) and gait performance were assessed at baseline and one week after program completion. At follow-up, participants modestly improved their falls self-efficacy and gait speed. To investigate whether this effect differed according to participants' fear of falling, secondary analyses stratified by subject's baseline falls efficacy were performed. Subjects with lower than average falls efficacy improved significantly their falls efficacy and gait performance, while no significant change occurred in the others. Small but significant improvements occurred after this pilot training program, particularly in subjects with low baseline falls efficacy. These results suggest that measures of falls efficacy might be useful for better targeting individuals most likely to benefit from similar training programs.

Robust power allocation algorithms for MIMO OFDM systems with imperfect CSI

This paper presents a Bayesian approach to the design of transmit prefiltering matrices in closed-loop schemes robust to channel estimation errors. The algorithms are derived for a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. Two different optimizationcriteria are analyzed: the minimization of the mean square error and the minimization of the bit error rate. In both cases, the transmitter design is based on the singular value decomposition (SVD) of the conditional mean of the channel response, given the channel estimate. The performance of the proposed algorithms is analyzed,and their relationship with existing algorithms is indicated. As withother previously proposed solutions, the minimum bit error rate algorithmconverges to the open-loop transmission scheme for very poor CSI estimates.

Practical algorithms for a family of waterfilling solutions

Many engineering problems that can be formulatedas constrained optimization problems result in solutionsgiven by a waterfilling structure; the classical example is thecapacity-achieving solution for a frequency-selective channel.For simple waterfilling solutions with a single waterlevel and asingle constraint (typically, a power constraint), some algorithmshave been proposed in the literature to compute the solutionsnumerically. However, some other optimization problems result insignificantly more complicated waterfilling solutions that includemultiple waterlevels and multiple constraints. For such cases, itmay still be possible to obtain practical algorithms to evaluate thesolutions numerically but only after a painstaking inspection ofthe specific waterfilling structure. In addition, a unified view ofthe different types of waterfilling solutions and the correspondingpractical algorithms is missing.The purpose of this paper is twofold. On the one hand, itoverviews the waterfilling results existing in the literature from aunified viewpoint. On the other hand, it bridges the gap betweena wide family of waterfilling solutions and their efficient implementationin practice; to be more precise, it provides a practicalalgorithm to evaluate numerically a general waterfilling solution,which includes the currently existing waterfilling solutions andothers that may possibly appear in future problems.

ML approaches to channel estimation for pilot-aided multi-rate DS/CDMA systems

This paper analyzes the asymptotic performance of maximum likelihood (ML) channel estimation algorithms in wideband code division multiple access (WCDMA) scenarios. We concentrate on systems with periodic spreading sequences (period larger than or equal to the symbol span) where the transmitted signal contains a code division multiplexed pilot for channel estimation purposes. First, the asymptotic covariances of the training-only, semi-blind conditional maximum likelihood (CML) and semi-blind Gaussian maximum likelihood (GML) channelestimators are derived. Then, these formulas are further simplified assuming randomized spreading and training sequences under the approximation of high spreading factors and high number of codes. The results provide a useful tool to describe the performance of the channel estimators as a function of basicsystem parameters such as number of codes, spreading factors, or traffic to training power ratio.

Quantitative modelling of transcription factor binding sites

Abstract One of the most important issues in molecular biology is to understand regulatory mechanisms that control gene expression. Gene expression is often regulated by proteins, called transcription factors which bind to short (5 to 20 base pairs),degenerate segments of DNA. Experimental efforts towards understanding the sequence specificity of transcription factors is laborious and expensive, but can be substantially accelerated with the use of computational predictions. This thesis describes the use of algorithms and resources for transcriptionfactor binding site analysis in addressing quantitative modelling, where probabilitic models are built to represent binding properties of a transcription factor and can be used to find new functional binding sites in genomes. Initially, an open-access database(HTPSELEX) was created, holding high quality binding sequences for two eukaryotic families of transcription factors namely CTF/NF1 and LEFT/TCF. The binding sequences were elucidated using a recently described experimental procedure called HTP-SELEX, that allows generation of large number (> 1000) of binding sites using mass sequencing technology. For each HTP-SELEX experiments we also provide accurate primary experimental information about the protein material used, details of the wet lab protocol, an archive of sequencing trace files, and assembled clone sequences of binding sequences. The database also offers reasonably large SELEX libraries obtained with conventional low-throughput protocols.The database is available at http://wwwisrec.isb-sib.ch/htpselex/ and and ftp://ftp.isrec.isb-sib.ch/pub/databases/htpselex. The Expectation-Maximisation(EM) algorithm is one the frequently used methods to estimate probabilistic models to represent the sequence specificity of transcription factors. We present computer simulations in order to estimate the precision of EM estimated models as a function of data set parameters(like length of initial sequences, number of initial sequences, percentage of nonbinding sequences). We observed a remarkable robustness of the EM algorithm with regard to length of training sequences and the degree of contamination. The HTPSELEX database and the benchmarked results of the EM algorithm formed part of the foundation for the subsequent project, where a statistical framework called hidden Markov model has been developed to represent sequence specificity of the transcription factors CTF/NF1 and LEF1/TCF using the HTP-SELEX experiment data. The hidden Markov model framework is capable of both predicting and classifying CTF/NF1 and LEF1/TCF binding sites. A covariance analysis of the binding sites revealed non-independent base preferences at different nucleotide positions, providing insight into the binding mechanism. We next tested the LEF1/TCF model by computing binding scores for a set of LEF1/TCF binding sequences for which relative affinities were determined experimentally using non-linear regression. The predicted and experimentally determined binding affinities were in good correlation.

Probabilistic algorithms for blind adaptive multiuser detection

In this paper, two probabilistic adaptive algorithmsfor jointly detecting active users in a DS-CDMA system arereported. The first one, which is based on the theory of hiddenMarkov models (HMM’s) and the Baum–Wech (BW) algorithm,is proposed within the CDMA scenario and compared withthe second one, which is a previously developed Viterbi-basedalgorithm. Both techniques are completely blind in the sense thatno knowledge of the signatures, channel state information, ortraining sequences is required for any user. Once convergencehas been achieved, an estimate of the signature of each userconvolved with its physical channel response (CR) and estimateddata sequences are provided. This CR estimate can be used toswitch to any decision-directed (DD) adaptation scheme. Performanceof the algorithms is verified via simulations as well as onexperimental data obtained in an underwater acoustics (UWA)environment. In both cases, performance is found to be highlysatisfactory, showing the near–far resistance of the analyzed algorithms.

Performance of noninvasive ventilation algorithms on ICU ventilators during pressure support: a clinical study.

To evaluate the impact of noninvasive ventilation (NIV) algorithms available on intensive care unit ventilators on the incidence of patient-ventilator asynchrony in patients receiving NIV for acute respiratory failure. Prospective multicenter randomized cross-over study. Intensive care units in three university hospitals. Patients consecutively admitted to the ICU and treated by NIV with an ICU ventilator were included. Airway pressure, flow and surface diaphragmatic electromyography were recorded continuously during two 30-min periods, with the NIV (NIV+) or without the NIV algorithm (NIV0). Asynchrony events, the asynchrony index (AI) and a specific asynchrony index influenced by leaks (AIleaks) were determined from tracing analysis. Sixty-five patients were included. With and without the NIV algorithm, respectively, auto-triggering was present in 14 (22%) and 10 (15%) patients, ineffective breaths in 15 (23%) and 5 (8%) (p = 0.004), late cycling in 11 (17%) and 5 (8%) (p = 0.003), premature cycling in 22 (34%) and 21 (32%), and double triggering in 3 (5%) and 6 (9%). The mean number of asynchronies influenced by leaks was significantly reduced by the NIV algorithm (p < 0.05). A significant correlation was found between the magnitude of leaks and AIleaks when the NIV algorithm was not activated (p = 0.03). The global AI remained unchanged, mainly because on some ventilators with the NIV algorithm premature cycling occurs. In acute respiratory failure, NIV algorithms provided by ICU ventilators can reduce the incidence of asynchronies because of leaks, thus confirming bench test results, but some of these algorithms can generate premature cycling.

Training needs in adolescent medicine of practising doctors: a Swiss national survey of six disciplines.

OBJECTIVE: To assess and compare the training needs in adolescent medicine of doctors within 6 specialties as a basis for the development of pre/postgraduate and continuing medical education (CME) training curricula. DESIGN: Cross-sectional postal survey. SETTING: Switzerland. PARTICIPANTS: National, representative, random sample of 1857 practising doctors in 6 disciplines (general practitioners, paediatricians, gynaecologists, internists, psychiatrists, child psychiatrists) registered with the Swiss Medical Association. MAIN OUTCOME MEASURES: Perceived importance of and training interest in 35 topics related to adolescent medicine listed in a self-administered, anonymous questionnaire. RESULTS: A total of 1367 questionnaires were returned, representing a response rate of 73.9%. Clear interest in adolescent medicine was reported by 62.1% of respondents. Topics perceived to be the most important in everyday practice were functional symptoms (71.4%), acne (67.1%), obesity (64.6%), depression-anxiety (68.1%) and communication with adolescents (61.7%). Differences between disciplines were especially marked for gynaecologists, who expressed interest almost exclusively in medical topics specific to their field. In contrast, other disciplines commonly reported a keen interest in psychosocial problems. Accordingly, interest in further training was expressed mostly for functional symptoms (62.4%), eating disorders (56.3%), depression-anxiety (53.7%) and obesity (52.6%). Issues related to injury prevention, chronic disease and confidentiality were rated as low priorities. CONCLUSIONS: Regardless of discipline, Swiss primary care doctors expressed a strong interest in adolescent medicine. Continuing medical education courses should include both interdisciplinary courses and discipline-specific sessions. Further training should address epidemiological and legal/ethical issues (e.g. injury prevention, confidentiality, impact of chronic conditions).

Meta-analysis of the effects of MI training on clinicians' behavior.

MI-based interventions are widely used with a number of different clinical populations and their efficacy has been well established. However, the clinicians' training has not traditionally been the focus of empirical investigations. We conducted a meta-analytic review of clinicians' MI-training and MI-skills findings. Fifteen studies were included, involving 715 clinicians. Pre-post training effect sizes were calculated (13 studies) as well as group contrast effect sizes (7 studies). Pre-post training comparisons showed medium to large ES of MI training, which are maintained over a short period of time. When compared to a control group, our results also suggested higher MI proficiency in the professionals trained in MI than in nontrained ones (medium ES). However, this estimate of ES may be affected by a publication bias and therefore, should be considered with caution. Methodological limitations and potential sources of heterogeneity of the studies included in this meta-analysis are discussed.

Business competence and training needs in Finnish companies operating in the Leningrad Region

Tässä pro gradu -työssä tutkitaan Leningradin alueella, Venäjällä, toimivien suomalaisyritysten liiketoimintaosaamisen koulutustarpeita. Tavoitteena on ollut tutkia, millaisia yritysten koulutustarpeet ovat, sekä lisäksi selvittää yleisemmällä tasolla, miten liiketoimintaosaaminen määritellään. Useat tutkimusta varten haastatellut johtajat pitävät liiketoimintaosaamista erityisesti markkinoilla toimimiseen liittyvänä osaamisena. Myös johtaminen, sekä tuotteet ja teknologia nähdään liiketoimintaosaamisen tärkeinä osina. Yrityksillä on koulutustarpeita seuraavilla alueilla: johtaminen; myynti, markkinat ja asiakkaat; yrityksen sisäinen yhteistyö; kielet, sekä juridiikka ja laskentatoimi. Haastateltavien mukaan markkinoiden nopea kehitys sekä yrityksen kasvu luovat yrityksille koulutustarpeita. Yllättäen myös Venäjän koulutusjärjestelmää itsessään pidetään koulutustarpeiden syynä. Tutkimuksessa mukana olleiden yritysten koulutuskäytännöt ovat keskenään melko erilaisia: koulutusbudjetti, koulutuspäivien määrä ja koulutusorganisaation valintakriteerit vaihtelevatyrityksestä riippuen. Joka tapauksessa yleisin koulutusmuoto näyttää olevan yrityksen sisäinen koulutus. Monet haastateltavat painottavat suuresti uusien työntekijöiden kouluttamista. Selvästikin rekrytointi ja uusien työntekijöiden koulutus vievät suuren osan tutkimusta varten haastateltujen johtajien ajasta. Tärkeä huomio koulutusmarkkinoihin liittyen on se, että lyhyiden, kaikille avoimien koulutusten kohdalla markkinat ovat Pietarissa täynnä. Suurimpana uhkana nähdään alalla vallitseva kouluttajapula.