Multiparametric brainstem segmentation using a modified multivariate mixture of Gaussians

The human brainstem is a densely packed, complex but highly organised structure. It not only serves as a conduit for long projecting axons conveying motor and sensory information, but also is the location of multiple primary nuclei that control or modulate a vast array of functions, including homeostasis, consciousness, locomotion, and reflexive and emotive behaviours. Despite its importance, both in understanding normal brain function as well as neurodegenerative processes, it remains a sparsely studied structure in the neuroimaging literature. In part, this is due to the difficulties in imaging the internal architecture of the brainstem in vivo in a reliable and repeatable fashion. A modified multivariate mixture of Gaussians (mmMoG) was applied to the problem of multichannel tissue segmentation. By using quantitative magnetisation transfer and proton density maps acquired at 3 T with 0.8 mm isotropic resolution, tissue probability maps for four distinct tissue classes within the human brainstem were created. These were compared against an ex vivo fixated human brain, imaged at 0.5 mm, with excellent anatomical correspondence. These probability maps were used within SPM8 to create accurate individual subject segmentations, which were then used for further quantitative analysis. As an example, brainstem asymmetries were assessed across 34 right-handed individuals using voxel based morphometry (VBM) and tensor based morphometry (TBM), demonstrating highly significant differences within localised regions that corresponded to motor and vocalisation networks. This method may have important implications for future research into MRI biomarkers of pre-clinical neurodegenerative diseases such as Parkinson's disease.

The role of role uncertainty in modified dictator games

We compare behavior in modified dictator games with and without role uncertainty. Subjectschoose between a selfish action, a costly surplus creating action (altruistic behavior) and acostly surplus destroying action (spiteful behavior). While costly surplus creating actions are themost frequent under role uncertainty (64%), selfish actions become the most frequent withoutrole uncertainty (69%). Also, the frequency of surplus destroying choices is negligible with roleuncertainty (1%) but not so without it (11%). A classification of subjects into four differenttypes of interdependent preferences (Selfish, Social Welfare maximizing, Inequity Averse andCompetitive) shows that the use of role uncertainty overestimates the prevalence of SocialWelfare maximizing preferences in the subject population (from 74% with role uncertainty to21% without it) and underestimates Selfish and Inequity Averse preferences. An additionaltreatment, in which subjects undertake an understanding test before participating in theexperiment with role uncertainty, shows that the vast majority of subjects (93%) correctlyunderstand the payoff mechanism with role uncertainty, but yet surplus creating actions weremost frequent. Our results warn against the use of role uncertainty in experiments that aim tomeasure the prevalence of interdependent preferences.

On the efficiency and sensitivity of a pyramidal classification algorithm

In this paper we propose a Pyramidal Classification Algorithm,which together with an appropriate aggregation index producesan indexed pseudo-hierarchy (in the strict sense) withoutinversions nor crossings. The computer implementation of thealgorithm makes it possible to carry out some simulation testsby Monte Carlo methods in order to study the efficiency andsensitivity of the pyramidal methods of the Maximum, Minimumand UPGMA. The results shown in this paper may help to choosebetween the three classification methods proposed, in order toobtain the classification that best fits the original structureof the population, provided we have an a priori informationconcerning this structure.

Elicited beliefs and social information in modified dictator games: What do dictators believe other dictators do?

We use subjects actions in modified dictator games to perform a within-subject classification ofindividuals into four different types of interdependent preferences: Selfish, Social Welfaremaximizers, Inequity Averse and Competitive. We elicit beliefs about other subjects actions inthe same modified dictator games to test how much of the existent heterogeneity in others actions is known by subjects. We find that subjects with different interdependent preferences infact have different beliefs about others actions. In particular, Selfish individuals cannotconceive others being non-Selfish while Social Welfare maximizers are closest to the actualdistribution of others actions. We finally provide subjects with information on other subjects actions and re-classify individuals according to their (new) actions in the same modified dictatorgames. We find that social information does not affect Selfish individuals, but that individualswith interdependent preferences are more likely to change their behavior and tend to behavemore selfishly.

Comparison of the diagnostic performance of the original and modified Wells score in inpatients and outpatients with suspected deep vein thrombosis.

Introduction: The original and modified Wells score are widely used prediction rules for pre-test probability assessment of deep vein thrombosis (DVT). The objective of this study was to compare the predictive performance of both Wells scores in unselected patients with clinical suspicion of DVT.Methods: Consecutive inpatients and outpatients with a clinical suspicion of DVT were prospectively enrolled. Pre-test DVT probability (low/intermediate/high) was determined using both scores. Patients with a non-high probability based on the original Wells score underwent D-dimers measurement. Patients with D-dimers <500 mu g/L did not undergo further testing, and treatment was withheld. All others underwent complete lower limb compression ultrasound, and those diagnosed with DVT were anticoagulated. The primary study outcome was objectively confirmed symptomatic venous thromboembolism within 3 months of enrollment.Results: 298 patients with suspected DVT were included. Of these, 82 (27.5%) had DVT, and 46 of them were proximal. Compared to the modified score, the original Wells score classified a higher proportion of patients as low-risk (53 vs 48%; p<0.01) and a lower proportion as high-risk (17 vs 15%; p=0.02); the prevalence of proximal DVT in each category was similar with both scores (7-8% low, 16-19% intermediate, 36-37% high). The area under the receiver operating characteristic curve regarding proximal DVT detection was similar for both scores, but they both performed poorly in predicting isolated distal DVT and DVT in inpatients.Conclusion: The study demonstrates that both Wells scores perform equally well in proximal DVT pre-test probability prediction. Neither score appears to be particularly useful in hospitalized patients and those with isolated distal DVT. (C) 2011 Elsevier Ltd. All rights reserved.

A simple randomized algorithm for consistent sequential prediction of ergodic time series

We present a simple randomized procedure for the prediction of a binary sequence. The algorithm uses ideas from recent developments of the theory of the prediction of individual sequences. We show that if thesequence is a realization of a stationary and ergodic random process then the average number of mistakes converges, almost surely, to that of the optimum, given by the Bayes predictor.

Nyssomyia intermedia (Lutz & Neiva) and Nyssomyia neivai (Pinto) (Diptera, Psychodidae, Phlebotominae) in a sympatric area: seasonal and nocturnal hourly rhythm in black and white modified Shannon traps

Captures with black and white Shannon traps were undertaken to identify aspects of the behavior of the two cryptic and sympatric species implicated as vectors of cutaneous leishmaniasis, Nyssomyia intermedia (Lutz & Neiva, 1912) and Nyssomyia neivai (Pinto, 1926). The traps were installed side by side, monthly, from July 2001 to June 2002, from 18 to 07 hours, in a peridomicile of Iporanga municipality, state of São Paulo, Brazil. A total of 2,142 specimens were captured, Ny. intermedia (47.4%) and Ny. neivai (50.5%). The white trap was more attractive to both sexes of both species. Males of Ny. neivai predominated (70%) over those of Ny. intermedia on the two traps; on the black trap, the females of Ny. intermedia predominated (63.3%) over those of Ny. neivai (36.7%). Seventy percent of the specimens of both species were captured between 18 and 24 h. Females of Ny. intermedia presented the highest peak at 19-20 h, and those of Ny. neivai at 20-21 h. The highest hourly average for females of Ny. intermedia on the black trap occurred in the winter and that for males in the summer. For Ny. neivai, both sexes predominated in the summer. The two species probably transmit the cutaneous leishmaniasis in the area due to their great predominance.

MSISpIC: A Probabilistic Scan Matching Algorithm Using a Mechanical Scanned Imaging Sonar

This paper compares two well known scan matching algorithms: the MbICP and the pIC. As a result of the study, it is proposed the MSISpIC, a probabilistic scan matching algorithm for the localization of an Autonomous Underwater Vehicle (AUV). The technique uses range scans gathered with a Mechanical Scanning Imaging Sonar (MSIS), and the robot displacement estimated through dead-reckoning with the help of a Doppler Velocity Log (DVL) and a Motion Reference Unit (MRU). The proposed method is an extension of the pIC algorithm. Its major contribution consists in: 1) using an EKF to estimate the local path traveled by the robot while grabbing the scan as well as its uncertainty and 2) proposing a method to group into a unique scan, with a convenient uncertainty model, all the data grabbed along the path described by the robot. The algorithm has been tested on an AUV guided along a 600m path within a marina environment with satisfactory results

An Efficient Nominal Unification Algorithm

Nominal Unification is an extension of first-order unification where terms can contain binders and unification is performed modulo α equivalence. Here we prove that the existence of nominal unifiers can be decided in quadratic time. First, we linearly-reduce nominal unification problems to a sequence of freshness and equalities between atoms, modulo a permutation, using ideas as Paterson and Wegman for first-order unification. Second, we prove that solvability of these reduced problems may be checked in quadràtic time. Finally, we point out how using ideas of Brown and Tarjan for unbalanced merging, we could solve these reduced problems more efficiently

Validation of a clinical algorithm to identify low-risk patients with pulmonary embolism.

Summary Background: We previously derived a clinical prognostic algorithm to identify patients with pulmonary embolism (PE) who are at low-risk of short-term mortality who could be safely discharged early or treated entirely in an outpatient setting. Objectives: To externally validate the clinical prognostic algorithm in an independent patient sample. Methods: We validated the algorithm in 983 consecutive patients prospectively diagnosed with PE at an emergency department of a university hospital. Patients with none of the algorithm's 10 prognostic variables (age >/= 70 years, cancer, heart failure, chronic lung disease, chronic renal disease, cerebrovascular disease, pulse >/= 110/min., systolic blood pressure < 100 mm Hg, oxygen saturation < 90%, and altered mental status) at baseline were defined as low-risk. We compared 30-day overall mortality among low-risk patients based on the algorithm between the validation and the original derivation sample. We also assessed the rate of PE-related and bleeding-related mortality among low-risk patients. Results: Overall, the algorithm classified 16.3% of patients with PE as low-risk. Mortality at 30 days was 1.9% among low-risk patients and did not differ between the validation and the original derivation sample. Among low-risk patients, only 0.6% died from definite or possible PE, and 0% died from bleeding. Conclusions: This study validates an easy-to-use, clinical prognostic algorithm for PE that accurately identifies patients with PE who are at low-risk of short-term mortality. Low-risk patients based on our algorithm are potential candidates for less costly outpatient treatment.

Programació d'una aplicació gràfica per l'anàlisi de senyals usant LDB (Local Discriminant Bases) i MLDB (Modified LDB)

Tot seguit presentem un entorn per analitzar senyals de tot tipus amb LDB (Local Discriminant Bases) i MLDB (Modified Local Discriminant Bases). Aquest entorn utilitza funcions desenvolupades en el marc d’una tesi en fase de desenvolupament. Per entendre part d’aquestes funcions es requereix un nivell de coneixement avançat de processament de senyals. S’han extret dels treballs realitzats per Naoki Saito [3], que s’han agafat com a punt de partida per la realització de l’algorisme de la tesi doctoral no finalitzada de Jose Antonio Soria. Aquesta interfície desenvolupada accepta la incorporació de nous paquets i funcions. Hem deixat un menú preparat per integrar Sinus IV packet transform i Cosine IV packet transform, tot i que també podem incorporar-n’hi altres. L’aplicació consta de dues interfícies, un Assistent i una interfície principal. També hem creat una finestra per importar i exportar les variables desitjades a diferents entorns. Per fer aquesta aplicació s’han programat tots els elements de les finestres, en lloc d’utilitzar el GUIDE (Graphical User Interface Development Enviroment) de MATLAB, per tal que sigui compatible entre les diferents versions d’aquest programa. En total hem fet 73 funcions en la interfície principal (d’aquestes, 10 pertanyen a la finestra d’importar i exportar) i 23 en la de l’Assistent. En aquest treball només explicarem 6 funcions i les 3 de creació d’aquestes interfícies per no fer-lo excessivament extens. Les funcions que explicarem són les més importants, ja sigui perquè s’utilitzen sovint, perquè, segons la complexitat McCabe, són les més complicades o perquè són necessàries pel processament del senyal. Passem cada entrada de dades per part de l’usuari per funcions que ens detectaran errors en aquesta entrada, com eliminació de zeros o de caràcters que no siguin números, com comprovar que són enters o que estan dins dels límits màxims i mínims que li pertoquen.