Sonia Malefaki

Neuropsychological deficits in long-term frequent cannabis users.

The authors examined neuropsychological functioning in 20 long-term (LT), 20 shorter term (ST) heavy frequent cannabis users, and 24 controls after abstinence for ≥24 hours prior to testing. LT users performed significantly worse on verbal memory and psychomotor speed. LT and ST users had a higher proportion of deficits on verbal fluency, verbal memory, attention, and psychomotor speed. Specific cognitive domains appear to deteriorate with increasing years of heavy frequent cannabis use.

Multi-State Reliability Systems Under Discrete Time Semi-Markovian Hypothesis

We consider repairable Multi-state reliability systems with components, the lifetimes and the repair times of which are -independent. The -th component can be either in the complete failure state 0, in the perfect state , or in one of the degradation states . The sojourn time in any of these states is a random variable following a discrete distribution. Thus, the time behavior of each component is described by a discrete-time semi-Markov chain, and the time behavior of the whole system is described by the vector of paired processes of the semi-Markov chain and the corresponding backward recurrence time process. Using recently obtained results concerning the discrete-time semi-Markov chains, we derive basic reliability measures. Finally, we present some numerical results of our proposed approach in specific reliability systems, namely series, parallel, k-out-of-n:F, and consecutive-k-out-of-n:F systems.

Reliability of maintained systems under a semi-Markov setting

A semi-Markov setting is considered in order to study the main dependability measures of a repairable continuous time system under the hypothesis that the evolution in time of its components is described by a continuous time semi-Markov process. Moreover, the main dependability measures of a periodically maintained system are studied. Finally, all the above systems are compared with the corresponding Markov systems where the general repair time distribution is replaced by the exponential distribution with the same mean which is the most commonly used approximation of the original system in practice.

Communicating the diagnosis of multiple sclerosis

In the frame of current treatment options for multiple sclerosis (MS) and recommendations for early intervention, we investigated the practice and attitudes of neurologists towards MS-diagnosis communication in Greece. We constructed and sent out a 22-item questionnaire to neurologists practising in different employment settings and geographic regions in Greece. Overall, 217 (37.41 %) of 580 neurologists replied. The vast majority (94.9 %) informs the patient of a definite MS diagnosis, and 73.6 % do so immediately, but only 41.7 % use the term multiple sclerosis. Furthermore, neurologists strongly agreed that timing of diagnosis communication depends to a large extent on the individual patient’s personality (62.5 %) and mental state (52.3 %). Most neurologists (78.7 %) inform relatives about the diagnosis, but only in the presence of the patient. In cases where disclosure was delayed, 59.5 % noted that they did not observe any changes as regards the trust or confidence of their patients towards them. Most neurologists also noted that education level (72 %) and mental state (51.9 %), at the time of disclosure influenced patients who did not fully understand the meaning of their diagnosis. This survey provided some useful new findings with respect to MS diagnosis communication; however, the questions of how and possibly how much to communicate warrant further cross-cultural investigation.

Optimization of the dependability and performance measures of a generic model for multi-state deteriorating systems under maintenance

In this paper, a general model for multi-state deteriorating systems with condition based preventive maintenance is introduced and analyzed extensively. The system experiences various levels of deterioration and at each stage, an inspection is carried out at constant time intervals in order to identify what kind of preventive maintenance, the system should undergo. When the system fails, despite preventive maintenance, a repair procedure is carried out and the system is restored to its initial fully operational state. The proposed model incorporates also imperfect maintenance, either minimal or major, failed maintenance and sudden failures that may occur mostly due to external factors at any deterioration state as well. Moreover, the sojourn times are assumed to be generally distributed. The main dependability and performance measures of the proposed model are computed while the corresponding transient measures are estimated using Monte Carlo simulation. Our endmost aim is to distinguish inspection and consequently maintenance policies that optimize multi-state deteriorating systems dependability and/or performance. Additionally, multi-objective optimization problems are formulated and solve in order to distinguish preventive maintenance policies that optimize simultaneously both the dependability and performance measures.

Optimizing the Availability and the Operational Cost of a Periodically Inspected Multi-state Deteriorating System with Condition Based Maintenance Policies

In this paper a multi-state deterioration system which experiences several states of performance degradation until it fails is studied extensively and condition-based preventive maintenance policies are examined. The optimal maintenance policy aims at maximizing systems asymptotic availability and at minimizing its total operational cost, with respect to the two different inspection intervals. For the simultaneous optimization of the aforementioned measures, multi-objective optimization methods are employed. In the current work, the inspection times are assumed to be constant, thus the systems evolution in time is modeled by a semi-Markov process. Finally, the proposed system is compared with the corresponding Markov one which is the most commonly used approximation of the original system in practice.

Parameter estimation via stochastic variants of the ECM algorithm with applications to plant growth modeling

Mathematical modeling of plant growth has gained increasing interest in recent years due to its potential applications. A general family of models, known as functional–structural plant models (FSPMs) and formalized as dynamic systems, serves as the basis for the current study. Modeling, parameterization and estimation are very challenging problems due to the complicated mechanisms involved in plant evolution. A specific type of a non-homogeneous hidden Markov model has been proposed as an extension of the GreenLab FSPM to study a certain class of plants with known organogenesis. In such a model, the maximum likelihood estimator cannot be derived explicitly. Thus, a stochastic version of an expectation conditional maximization (ECM) algorithm was adopted, where the E-step was approximated by sequential importance sampling with resampling (SISR). The complexity of the E-step creates the need for the design and the comparison of different simulation methods for its approximation. In this direction, three variants of SISR and a Markov Chain Monte Carlo (MCMC) approach are compared for their efficiency in parameter estimation on simulated and real sugar beet data, where observations are taken by censoring plant’s evolution (destructive measurements). The MCMC approach seems to be more efficient for this particular application context and also for a large variety of crop plants. Moreover, a data-driven automated MCMC–ECM algorithm for finding an appropriate sample size in each ECM step and also an appropriate number of ECM steps is proposed. Based on the available real dataset, some competing models are compared via model selection techniques.

Age and education adjusted normative data and discriminative validity for Rey’s Auditory Verbal Learning Test in the elderly Greek population

ABSTRACT Rey’s Auditory Verbal Learning Test (RAVLT) is a widely used neuropsychological test to assess episodic memory. In the present study we sought to establish normative and discriminative validity data for the RAVLT in the elderly population using previously adapted learning lists for the Greek adult population. We administered the test to 258 cognitively healthy elderly participants, aged 60–89 years, and two patient groups (192 with amnestic mild cognitive impairment, aMCI, and 65 with Alzheimer’s disease, AD). From the statistical analyses, we found that age and education contributed significantly to most trials of the RAVLT, whereas the influence of gender was not significant. Younger elderly participants with higher education outperformed the older elderly with lower education levels. Moreover, both clinical groups performed significantly worse on most RAVLT trials and composite measures than matched cognitively healthy controls. Furthermore, the AD group performed more poorly than the aMCI group on most RAVLT variables. Receiver operating characteristic (ROC) analysis was used to examine the utility of the RAVLT trials to discriminate cognitively healthy controls from aMCI and AD patients. Area under the curve (AUC), an index of effect size, showed that most of the RAVLT measures (individual and composite) included in this study adequately differentiated between the performance of healthy elders and aMCI/AD patients. We also provide cutoff scores in discriminating cognitively healthy controls from aMCI and AD patients, based on the sensitivity and specificity of the prescribed scores. Moreover, we present age- and education-specific normative data for individual and composite scores for the Greek adapted RAVLT in elderly subjects aged between 60 and 89 years for use in clinical and research settings.

Short Communication: Simulating from a multinomial distribution with large number of categories

The multinomial distribution is a key-distribution for several applications. For this reason, many methods have been proposed so far in the literature in order to deal with the problem of simulation from it. A slight modification is suggested which can be used in conjunction with any of the standard schemes. The proposed variation is a two-stage procedure based on the property of the multinomial distribution that for any partition of the set of outcomes the vector of total frequencies of each part follows also a multinomial distribution with parameters adjusted accordingly. It is empirically exhibited that this variation is faster than the original procedures in case the numbers of independent trials and possible outcomes are both large. The time reduction is illustrated via a simulation study for several programming languages such as R, Matlab, and others.

Efficacy of a Computer-Assisted Cognitive Rehabilitation Intervention in Relapsing-Remitting Multiple Sclerosis Patients: A Multicenter Randomized Controlled Trial

Cognitive impairment is frequently encountered in multiple sclerosis (MS) affecting between 40–65% of individuals, irrespective of disease duration and severity of physical disability. In the present multicenter randomized controlled trial, fifty-eight clinically stable RRMS patients with mild to moderate cognitive impairment and relatively low disability status were randomized to receive either computer-assisted (RehaCom) functional cognitive training with an emphasis on episodic memory, information processing speed/attention, and executive functions for 10 weeks (IG; n = 32) or standard clinical care (CG; n = 26). Outcome measures included a flexible comprehensive neuropsychological battery of tests sensitive to MS patient deficits and feedback regarding personal benefit gained from the intervention on four verbal questions. Only the IG group showed significant improvements in verbal and visuospatial episodic memory, processing speed/attention, and executive functioning from pre - to postassessment. Moreover, the improvement obtained on attention was retained over 6 months providing evidence on the long-term benefits of this intervention. Group by time interactions revealed significant improvements in composite cognitive domain scores in the IG relative to the demographically and clinically matched CG for verbal episodic memory, processing speed, verbal fluency, and attention. Treated patients rated the intervention positively and were more confident about their cognitive abilities following treatment.