(Young Investigator Award) Trabecular Bone Score Helps Classifying Women At Risk Of Fracture Prospectively In The Ofely Study

Introduction: To determine the metabolic effect of teriparatide (TPTD) on bone, 99mTc-MDP skeletal plasma clearance was measured in postmenopausal women with osteoporosis treated with TPTD 20 μg/day. Methods: Ten postmenopausal women with osteoporosis had radionuclide bone scans at baseline, 3, and 18 months after starting TPTD 20 μg/day and after 6 months off therapy. Participants were injected with 600 MBq 99mTc- MDP and whole body bone scans acquired at 10 min, 1, 2, 3, and 4 h. Multiple blood samples were taken between 5 min and 4 h and free 99mTc-MDP measured using ultrafiltration. 99mTc-MDP plasma clearance (Kbone) was evaluated using the Patlak plot method. Regional differences in Kbone were studied by measuring the whole skeleton and subregions. Serum procollagen type I Nterminal propeptide (PINP), bone-specific alkaline phosphatase (BSAP), and urinary N-terminal telopeptide (NTX) were measured at each visit.Discussion: The median increase from baseline in whole skeleton Kbone was 22% (P=0.004) at 3 months and 34% (P= 0.002) at 18 months, decreasing to 0.7% after 6 months off therapy. In subregions, Kbone value increases were statistically significant at 3 months and in all subregions except the pelvis at 18 months. After 6 months off therapy, subregional Kbone values also returned toward baseline. Bone markers increases from baseline were statistically significant at 3 and 18 months (BSAP, 15% and 36%; PINP, 137% and 192%; NTX, 109% and 125%). After 6 months off therapy, PINP and NTX values had declined, though remained above baseline (BSAP, −3%; PINP, 43%; NTX, 56%). Increased Kbone values in the whole body and lower extremities were correlated with increases in most bone markers at 3 and 18 months. Increased skeletal uptake of 99mTc-MDP during treatment with TPTD is indicative of increased bone formation and is supported by increases in bone turnover markers.Conclusion: Changes in Kbone and skeletal uptake measured by radionuclide bone scans in patients taking TPTD are the result of metabolic activity of the drug. These data may provide physicians with useful insights when interpreting bone scan results in this population.

Classifying minimally disabled multiple sclerosis patients from resting state functional connectivity.

Multiple sclerosis (MS), a variable and diffuse disease affecting white and gray matter, is known to cause functional connectivity anomalies in patients. However, related studies published to-date are post hoc; our hypothesis was that such alterations could discriminate between patients and healthy controls in a predictive setting, laying the groundwork for imaging-based prognosis. Using functional magnetic resonance imaging resting state data of 22 minimally disabled MS patients and 14 controls, we developed a predictive model of connectivity alterations in MS: a whole-brain connectivity matrix was built for each subject from the slow oscillations (<0.11Hz) of region-averaged time series, and a pattern recognition technique was used to learn a discriminant function indicating which particular functional connections are most affected by disease. Classification performance using strict cross-validation yielded a sensitivity of 82% (above chance at p<0.005) and specificity of 86% (p<0.01) to distinguish between MS patients and controls. The most discriminative connectivity changes were found in subcortical and temporal regions, and contralateral connections were more discriminative than ipsilateral connections. The pattern of decreased discriminative connections can be summarized post hoc in an index that correlates positively (ρ=0.61) with white matter lesion load, possibly indicating functional reorganisation to cope with increasing lesion load. These results are consistent with a subtle but widespread impact of lesions in white matter and in gray matter structures serving as high-level integrative hubs. These findings suggest that predictive models of resting state fMRI can reveal specific anomalies due to MS with high sensitivity and specificity, potentially leading to new non-invasive markers.

Validation of a computer-assisted system on classifying lower third molars

Objectives: The present study evaluates the reliability of the Radio Memory® software (Radio Memory; Belo Horizonte,Brasil.) on classifying lower third molars, analyzing intra- and interexaminer agreement of the results. Study Design: An observational, descriptive study of 280 lower third molars was made. The corresponding orthopantomographs were analyzed by two examiners using the Radio Memory® software. The exam was repeated 30 days after the first observation by each examiner. Both intra- and interexaminer agreement were determined using the SPSS v 12.0 software package for Windows (SPSS; Chicago, USA). Results: Intra- and interexaminer agreement was shown for both the Pell & Gregory and the Winter classifications, p<0.01, with 99% significant correlation between variables in all the cases. Conclusions: The use of Radio Memory® software for the classification of lower third molars is shown to be a valid alternative to the conventional method (direct evaluation on the orthopantomograph), for both clinical and investigational applications.

Modeling and Classifying Breast Tissue Density in Mammograms

We present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first, we will discover the distribution of the different tissue densities in an unsupervised manner, and second, we will use this tissue distribution to perform the classification. We achieve this using a classifier based on local descriptors and probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature. We studied the influence of different descriptors like texture and SIFT features at the classification stage showing that textons outperform SIFT in all cases. Moreover we demonstrate that pLSA automatically extracts meaningful latent aspects generating a compact tissue representation based on their densities, useful for discriminating on mammogram classification. We show the results of tissue classification over the MIAS and DDSM datasets. We compare our method with approaches that classified these same datasets showing a better performance of our proposal

Efficiency of distinct data mining algorithms for classifying stress level in piglets from their vocalization

Among the challenges of pig farming in today's competitive market, there is factor of the product traceability that ensures, among many points, animal welfare. Vocalization is a valuable tool to identify situations of stress in pigs, and it can be used in welfare records for traceability. The objective of this work was to identify stress in piglets using vocalization, calling this stress on three levels: no stress, moderate stress, and acute stress. An experiment was conducted on a commercial farm in the municipality of Holambra, São Paulo State , where vocalizations of twenty piglets were recorded during the castration procedure, and separated into two groups: without anesthesia and local anesthesia with lidocaine base. For the recording of acoustic signals, a unidirectional microphone was connected to a digital recorder, in which signals were digitized at a frequency of 44,100 Hz. For evaluation of sound signals, Praat® software was used, and different data mining algorithms were applied using Weka® software. The selection of attributes improved model accuracy, and the best attribute selection was used by applying Wrapper method, while the best classification algorithms were the k-NN and Naive Bayes. According to the results, it was possible to classify the level of stress in pigs through their vocalization.

Finding Methods for Recognizing and Classifying Spare Part Criticality – Case Full Deposit Stripping Machines

The aim of this Master’s thesis is to find a method for classifying spare part criticality in the case company. Several approaches exist for criticality classification of spare parts. The practical problem in this thesis is the lack of a generic analysis method for classifying spare parts of proprietary equipment of the case company. In order to find a classification method, a literature review of various analysis methods is required. The requirements of the case company also have to be recognized. This is achieved by consulting professionals in the company. The literature review states that the analytic hierarchy process (AHP) combined with decision tree models is a common method for classifying spare parts in academic literature. Most of the literature discusses spare part criticality in stock holding perspective. This is relevant perspective also for a customer orientated original equipment manufacturer (OEM), as the case company. A decision tree model is developed for classifying spare parts. The decision tree classifies spare parts into five criticality classes according to five criteria. The criteria are: safety risk, availability risk, functional criticality, predictability of failure and probability of failure. The criticality classes describe the level of criticality from non-critical to highly critical. The method is verified for classifying spare parts of a full deposit stripping machine. The classification can be utilized as a generic model for recognizing critical spare parts of other similar equipment, according to which spare part recommendations can be created. Purchase price of an item and equipment criticality were found to have no effect on spare part criticality in this context. Decision tree is recognized as the most suitable method for classifying spare part criticality in the company.

Classifying medication use in clinical research

Les données sur l'utilisation des médicaments sont généralement recueillies dans la recherche clinique. Pourtant, aucune méthode normalisée pour les catégoriser n’existe, que ce soit pour la description des échantillons ou pour l'étude de l'utilisation des médicaments comme une variable. Cette étude a été conçue pour développer un système de classification simple, sur une base empirique, pour la catégorisation d'utilisation des médicaments. Nous avons utilisé l'analyse factorielle pour réduire le nombre de groupements de médicaments possible. Cette analyse a fait émerger un modèle de constellations de consommation de médicaments qui semble caractériser des groupes cliniques spécifiques. Pour illustrer le potentiel de la technique, nous avons appliqué ce système de classification des échantillons où les troubles du sommeil sont importants: syndrome de fatigue chronique et l'apnée du sommeil. Notre méthode de classification a généré 5 facteurs qui semblent adhérer de façon logique. Ils ont été nommés: Médicaments cardiovasculaire/syndrome métabolique, Médicaments pour le soulagement des symptômes, Médicaments psychotropes, Médicaments préventifs et Médicaments hormonaux. Nos résultats démontrent que le profil des médicaments varie selon l'échantillon clinique. Le profil de médicament associé aux participants apnéiques reflète les conditions de comorbidité connues parmi ce groupe clinique, et le profil de médicament associé au Syndrome de fatigue chronique semble refléter la perception commune de cette condition comme étant un trouble psychogène

Inertial oscillation forced by the September 1997 cyclone in the Bay of Bengal

Time-series measurements from a moored data buoy located in the Bay of Bengal captured signals of inertial oscillation forced by the September 1997 cyclone. The progressive vector diagram showed mean northeastward current with well-defined clockwise circulation. Spectral analysis exhibited inertial peak at 0.67 cpd with blue shift and high rotary coefficient of –0.99, which signifies strong circular inertial oscillation. The wind and SST also exhibited spectral peak at inertial band (0.69 cpd) with higher blue shift. The inertial amplitude of 148.8 cm/s corresponding to a wind stress of 0.99 N/m2 and spectral peak near the local inertial frequency (0.653 cpd) indicate that the transfer of momentum was high.

Study of the Oceanic Heat Budget Components over the Arabian Sea during the Formation and Evolution of Super Cyclone, Gonu

Oceans play a vital role in the global climate system. They absorb the incoming solar energy and redistribute the energy through horizontal and vertical transports. In this context it is important to investigate the variation of heat budget components during the formation of a low-pressure system. In 2007, the monsoon onset was on 28th May. A well- marked low-pressure area was formed in the eastern Arabian Sea after the onset and it further developed into a cyclone. We have analysed the heat budget components during different stages of the cyclone. The data used for the computation of heat budget components is Objectively Analyzed air-sea flux data obtained from WHOI (Woods Hole Oceanographic Institution) project. Its horizontal resolution is 1° × 1°. Over the low-pressure area, the latent heat flux was 180 Wm−2. It increased to a maximum value of 210 Wm−2 on 1st June 2007, on which the system was intensified into a cyclone (Gonu) with latent heat flux values ranging from 200 to 250 Wm−2. It sharply decreased after the passage of cyclone. The high value of latent heat flux is attributed to the latent heat release due to the cyclone by the formation of clouds. Long wave radiation flux is decreased sharply from 100 Wm−2 to 30 Wm−2 when the low-pressure system intensified into a cyclone. The decrease in long wave radiation flux is due to the presence of clouds. Net heat flux also decreases sharply to −200 Wm−2 on 1st June 2007. After the passage, the flux value increased to normal value (150 Wm−2) within one day. A sharp increase in the sensible heat flux value (20 Wm−2) is observed on 1st June 2007 and it decreased there- after. Short wave radiation flux decreased from 300 Wm−2 to 90 Wm−2 during the intensification on 1st June 2007. Over this region, short wave radiation flux sharply increased to higher value soon after the passage of the cyclone.

Variations of surface boundary layer parameters associated with Cyclone Gonu over the Arabian Sea using QuikSCAT data

This study attempted to quantify the variations of the surface marine atmospheric boundary layer (MABL) parameters associated with the tropical Cyclone Gonu formed over the Arabian Sea during 30 May–7 June 2007 (just after the monsoon onset). These characteristics were evaluated in terms of surface wind, drag coefficient, wind stress, horizontal divergence, and frictional velocity using 0.5◦ × 0.5◦ resolution Quick Scatterometer (QuikSCAT) wind products. The variation of these different surface boundary layer parameters was studied for three defined cyclone life stages: prior to the formation, during, and after the cyclone passage. Drastic variations of the MABL parameters during the passage of the cyclone were observed. The wind strength increased from 12 to 22 m s−1 in association with different stages of Gonu. Frictional velocity increased from a value of 0.1–0.6 m s−1 during the formative stage of the system to a high value of 0.3–1.4 m s−1 during the mature stage. Drag coefficient varied from 1.5 × 10−3 to 2.5 × 10−3 during the occurrence of Gonu. Wind stress values varied from 0.4 to 1.1 N m−2. Wind stress curl values varied from 10 × 10−7 to 45 × 10−7 N m−3. Generally, convergent winds prevailed with the numerical value of divergence varying from 0 to –4 × 10−5 s−1. Maximum variations of the wind parameters were found in the wall cloud region of the cyclone. The parameters returned to normally observed values in 1–3 days after the cyclone passage

Modeling and Classifying Breast Tissue Density in Mammograms

We present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first, we will discover the distribution of the different tissue densities in an unsupervised manner, and second, we will use this tissue distribution to perform the classification. We achieve this using a classifier based on local descriptors and probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature. We studied the influence of different descriptors like texture and SIFT features at the classification stage showing that textons outperform SIFT in all cases. Moreover we demonstrate that pLSA automatically extracts meaningful latent aspects generating a compact tissue representation based on their densities, useful for discriminating on mammogram classification. We show the results of tissue classification over the MIAS and DDSM datasets. We compare our method with approaches that classified these same datasets showing a better performance of our proposal