Differentiation of dementia with Lewy bodies from Alzheimer's disease using a dopaminergic presynaptic ligand

Background: Dementia with Lewy bodies (DLB) is one of the main differential diagnoses of Alzheimer's disease (AD). Key pathological features of patients with DLB are not only the presence of cerebral cortical neuronal loss, with Lewy bodies in surviving neurones, but also loss of nigrostriatal dopaminergic neurones, similar to that of Parkinson's disease (PD). In DLB there is 40-70% loss of striatal dopamine.Objective: To determine if detection of this dopaminergic degeneration can help to distinguish DLB from AD during life.Methods: The integrity of the nigrostriatal metabolism in 27 patients with DLB, 17 with AD, 19 drug naive patients with PD, and 16 controls was assessed using a dopaminergic presynaptic ligand, I-123-labelled 2beta-carbomethoxy-3beta-(4-iodophenyl)-N-(3-fluoropropyl)nortropane (FP-CIT), and single photon emission tomography (SPET). A SPET scan was carried out with a single slice, brain dedicated tomograph (SME 810) 3.5 hours after intravenous injection of 185 MBq FP-CIT. With occipital cortex used as a radioactivity uptake reference, ratios for the caudate nucleus and the anterior and posterior putamen of both hemispheres were calculated. All scans were also rated by a simple visual method.Results: Both DLB and PD patients had significantly lower uptake of radioactivity than patients with (p<0.01) and controls (p<0.001) in the caudate nucleus and the anterior and posterior Putamen.Conclusion: FP-CIT SPET provides a means of distinguishing DLB from AD during life

Semantic Web-based Software Product Line for Building Intelligent Tutoring Systems

Intelligent Tutoring Systems (ITS) have been assumed as an important learning resource to be added as a module in e-learning systems. However, the construction of such systems is still a hard and complex task that involves, for instance, representation and manipulation of different knowledge source. To alleviate these issues, this paper proposes a new approach for building ITS by integrating Software Product Line and Semantic Web technologies focusing on two software engineering aspects: large-scale production and customization for different learners, and how to allow these knowledge to be automatically shared between software and authors in both reuse and knowledge evolution points of view. This paper shows a modeling for the proposed product line, as well as how the Semantic Web technologies was used to achieve the effective shared knowledge

pGQL: A probabilistic graphical query language for gene expression time courses

<p>Abstract</p> <p>Background</p> <p>Timeboxes are graphical user interface widgets that were proposed to specify queries on time course data. As queries can be very easily defined, an exploratory analysis of time course data is greatly facilitated. While timeboxes are effective, they have no provisions for dealing with noisy data or data with fluctuations along the time axis, which is very common in many applications. In particular, this is true for the analysis of gene expression time courses, which are mostly derived from noisy microarray measurements at few unevenly sampled time points. From a data mining point of view the robust handling of data through a sound statistical model is of great importance.</p> <p>Results</p> <p>We propose probabilistic timeboxes, which correspond to a specific class of Hidden Markov Models, that constitutes an established method in data mining. Since HMMs are a particular class of probabilistic graphical models we call our method Probabilistic Graphical Query Language. Its implementation was realized in the free software package pGQL. We evaluate its effectiveness in exploratory analysis on a yeast sporulation data set.</p> <p>Conclusions</p> <p>We introduce a new approach to define dynamic, statistical queries on time course data. It supports an interactive exploration of reasonably large amounts of data and enables users without expert knowledge to specify fairly complex statistical models with ease. The expressivity of our approach is by its statistical nature greater and more robust with respect to amplitude and frequency fluctuation than the prior, deterministic timeboxes.</p

The cyclic interaction between daytime behavior and the sleep behavior of laboratory dogs

Sleep deprivation has been found to negatively affect an individual´s physical and psychological health. Sleep loss affects activity patterns, increases anxiety-like behaviors, decreases cognitive performance and is associated with depressive states. The activity/rest cycle of dogs has been investigated before, but little is known about the effects of sleep loss on the behavior of the species. Dogs are polyphasic sleepers, meaning the behavior is most observed at night, but bouts are also present during the day. However, sleep can vary with ecological and biological factors, such as age, sex, fitness, and even human presence. In this study, kennelled laboratory adult dogs’ sleep and diurnal behavior were recorded during 24-h, five-day assessment periods to investigate sleep quality and its effect on daily behavior. In total, 1560 h of data were analyzed, and sleep metrics and diurnal behavior were quantified. The relationship between sleeping patterns and behavior and the effect of age and sex were evaluated using non-parametric statistical tests and GLMM modelling. Dogs in our study slept substantially less than previously reported and presented a modified sleep architecture with fewer awakenings during the night and almost no sleep during the day. Sleep loss increased inactivity, decreased play and alert behaviors, while increased time spent eating during the day. Males appeared to be more affected by sleep fragmentation than females. Different age groups also experienced different effects of sleep loss. Overall, dogs appear to compensate for the lack of sleep during the night by remaining inactive during the day. With further investigations, the relationship between sleep loss and behavior has the potential to be used as a measure of animal welfare

Advancing school dropout early warning systems: the IAFREE relational model for identifying at-risk students

IntroductionThere is a global effort to address the school dropout phenomenon. The urgency to act on it comes from the harmful evidence that school dropout has on societal and individual levels. Early Warning Systems (EWS) for school dropout at-risk student identification have been developed to anticipate and help schools have a better chance of acting on it. However, several studies point to a doubt that Correct EWS may come too late because they use only publicly available and general student and school information. We hypothesize that having a tool to assess more subjective and inter-relational factors would help anticipate where and when to act to prevent school dropout. This study aimed to develop a multidimensional measure for assessing relational factors for predicting school dropout (SD) risk in the Brazilian context.MethodsWe performed several procedures, including (a) the specialized literature review, (b) the item development of the Relational Factors for the Risk of School Dropout Scale (IAFREE in Portuguese), (c) the content validity analysis, (d) a pilot study, and (e) the administration of the IAFREE to a large Brazilian sample of high school and middle school students (N = 15,924).ResultsAfter the theoretical steps, we found content validity for five relational dimensions for SD (Student-School, Student-School Professionals, Student-Family, Student-Community, and Student–Student) that include 12 facets of risk factors. At the empirical stage, confirmatory analysis corroborated the proposed theoretical model with 12 first-order risk factors and 5 s-order dimensions (36 items). Further, through the Item Response Theory analysis, we assessed the individual item parameters of the items, providing a brief measure without losing psychometric quality (IAFREE-12).DiscussionWe discuss how this model may fill gaps in Correct EWS models and how to advance it. The IAFREE is a good measure for scholars investigating the risk of SD. These results are important for implementing an early warning system for SD that looks into the complexity of the school dropout phenomenon