Analysis of the diagnostic presentation profile, parathyroidectomy indication and bone mineral density follow-up of Brazilian patients with primary hyperparathyroidism

Primary hyperparathyroidism is an endocrine disorder with variable clinical expression, frequently presenting as asymptomatic hypercalcemia in Western countries but still predominantly as a symptomatic disease in developing countries. The objective of this retrospective study was to describe the diagnostic presentation profile, parathyroidectomy indication and post-surgical bone mineral density follow-up of patients with primary hyperparathyroidism seen at a university hospital. We found 115 patients (92 women, median age 56 years) with primary hyperparathyroidism diagnosed during the last 20 years. We defined symptomatic patients based on the presence of any classical symptom affecting bone, kidney or the neuromuscular system. Surgical criteria followed the guidelines of the National Institutes of Health regarding asymptomatic primary hyperparathyroidism. Symptomatic patients and patients meeting surgical criteria for parathyroidectomy were 66 and 93% of the sample, respectively. Median calcium and parathyroid hormone values were 11.9 mg/dL and 189 pg/mL, respectively. After surgical treatment, 97% of patients were cured, with increases in bone mineral density of 19.4% in the lumbar spine and 15.7% in the femoral neck 3 years after surgery. Greater bone mass increases were detected in pre-menopausal women, men, and in symptomatic and younger patients, both in the lumbar spine and femoral neck. Our results support the previous findings of a predominantly symptomatic disease with a presentation profile that could be mainly related to a delayed diagnosis. Nevertheless, genetic and racial backgrounds, and nutritional factors such as calcium and vitamin D deficiency may play a role in the clinical presentation of primary hyperparathyroidism of Brazilian patients.

Synergistic control of forearm based on accelerometer data and artificial neural networks

In the present study, we modeled a reaching task as a two-link mechanism. The upper arm and forearm motion trajectories during vertical arm movements were estimated from the measured angular accelerations with dual-axis accelerometers. A data set of reaching synergies from able-bodied individuals was used to train a radial basis function artificial neural network with upper arm/forearm tangential angular accelerations. The trained radial basis function artificial neural network for the specific movements predicted forearm motion from new upper arm trajectories with high correlation (mean, 0.9149-0.941). For all other movements, prediction was low (range, 0.0316-0.8302). Results suggest that the proposed algorithm is successful in generalization over similar motions and subjects. Such networks may be used as a high-level controller that could predict forearm kinematics from voluntary movements of the upper arm. This methodology is suitable for restoring the upper limb functions of individuals with motor disabilities of the forearm, but not of the upper arm. The developed control paradigm is applicable to upper-limb orthotic systems employing functional electrical stimulation. The proposed approach is of great significance particularly for humans with spinal cord injuries in a free-living environment. The implication of a measurement system with dual-axis accelerometers, developed for this study, is further seen in the evaluation of movement during the course of rehabilitation. For this purpose, training-related changes in synergies apparent from movement kinematics during rehabilitation would characterize the extent and the course of recovery. As such, a simple system using this methodology is of particular importance for stroke patients. The results underlie the important issue of upper-limb coordination.

A clinical gamma camera-based pinhole collimated system for high resolution small animal SPECT imaging

The main objective of the present study was to upgrade a clinical gamma camera to obtain high resolution tomographic images of small animal organs. The system is based on a clinical gamma camera to which we have adapted a special-purpose pinhole collimator and a device for positioning and rotating the target based on a computer-controlled step motor. We developed a software tool to reconstruct the target’s three-dimensional distribution of emission from a set of planar projections, based on the maximum likelihood algorithm. We present details on the hardware and software implementation. We imaged phantoms and heart and kidneys of rats. When using pinhole collimators, the spatial resolution and sensitivity of the imaging system depend on parameters such as the detector-to-collimator and detector-to-target distances and pinhole diameter. In this study, we reached an object voxel size of 0.6 mm and spatial resolution better than 2.4 and 1.7 mm full width at half maximum when 1.5- and 1.0-mm diameter pinholes were used, respectively. Appropriate sensitivity to study the target of interest was attained in both cases. Additionally, we show that as few as 12 projections are sufficient to attain good quality reconstructions, a result that implies a significant reduction of acquisition time and opens the possibility for radiotracer dynamic studies. In conclusion, a high resolution single photon emission computed tomography (SPECT) system was developed using a commercial clinical gamma camera, allowing the acquisition of detailed volumetric images of small animal organs. This type of system has important implications for research areas such as Cardiology, Neurology or Oncology.

Prevalence of metabolic syndrome and its association with educational inequalities among Brazilian adults: a population-based study

The present study estimated the prevalence of metabolic syndrome (MS) according to the criteria established by the National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATPIII) and the International Diabetes Federation (IDF) and analyzed the contribution of social factors in an adult urban population in the Southeastern region of Brazil. The sample plan was based on multistage probability sampling according to family head income and educational level. A random sample of 1116 subjects aged 30 to 79 years was studied. Participants answered a questionnaire about socio-demographic variables and medical history. Fasting capillary glucose (FCG), total cholesterol, high-density lipoprotein cholesterol (HDL-C), and triglycerides were determined and all non-diabetic subjects were submitted to the 75-g oral glucose tolerance test. Body mass index (BMI, kg/m²), waist circumference and blood pressure (BP) were determined. Age- and gender-adjusted prevalence of MS was 35.9 and 43.2% according to NCEP-ATPIII and IDF criteria, respectively. Substantial agreement was found between NCEP-ATPIII and IDF definitions. Low HDL-C levels and high BP were the most prevalent MS components according to NCEP-ATPIII criteria (76.3 and 59.2%, respectively). Considering the diagnostic criteria adopted, 13.5% of the subjects had diabetes and 9.7% had FCG ≥100 mg/dL. MS prevalence was significantly associated with age, skin color, BMI, and educational level. This cross-sectional population-based study in the Southeastern region of Brazil indicates that MS is highly prevalent and associated with an important social indicator, i.e., educational level. This result suggests that in developing countries health policy planning to reduce the risk of MS, in particular, should consider improvement in education.

Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases

Personalized medicine will revolutionize our capabilities to combat disease. Working toward this goal, a fundamental task is the deciphering of geneticvariants that are predictive of complex diseases. Modern studies, in the formof genome-wide association studies (GWAS) have afforded researchers with the opportunity to reveal new genotype-phenotype relationships through the extensive scanning of genetic variants. These studies typically contain over half a million genetic features for thousands of individuals. Examining this with methods other than univariate statistics is a challenging task requiring advanced algorithms that are scalable to the genome-wide level. In the future, next-generation sequencing studies (NGS) will contain an even larger number of common and rare variants. Machine learning-based feature selection algorithms have been shown to have the ability to effectively create predictive models for various genotype-phenotype relationships. This work explores the problem of selecting genetic variant subsets that are the most predictive of complex disease phenotypes through various feature selection methodologies, including filter, wrapper and embedded algorithms. The examined machine learning algorithms were demonstrated to not only be effective at predicting the disease phenotypes, but also doing so efficiently through the use of computational shortcuts. While much of the work was able to be run on high-end desktops, some work was further extended so that it could be implemented on parallel computers helping to assure that they will also scale to the NGS data sets. Further, these studies analyzed the relationships between various feature selection methods and demonstrated the need for careful testing when selecting an algorithm. It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model. It was also demonstrated that without proper model validation, for example using nested cross-validation, the models can result in overly-optimistic prediction accuracies and decreased generalization ability. It is through the implementation and application of machine learning methods that one can extract predictive genotype–phenotype relationships and biological insights from genetic data sets.

Striatal and extrastriatal dopamine D2/3 receptors studied with [11C]raclopride and high-resolution PET

The human striatum is a heterogeneous structure representing a major part of the dopamine (DA) system’s basal ganglia input and output. Positron emission tomography (PET) is a powerful tool for imaging DA neurotransmission. However, PET measurements suffer from bias caused by the low spatial resolution, especially when imaging small, D2/3 -rich structures such as the ventral striatum (VST). The brain dedicated high-resolution PET scanner, ECAT HRRT (Siemens Medical Solutions, Knoxville, TN, USA) has superior resolution capabilities than its predecessors. In the quantification of striatal D2/3 binding, the in vivo highly selective D2/3 antagonist [11C] raclopride is recognized as a well-validated tracer. The aim of this thesis was to use a traditional test-retest setting to evaluate the feasibility of utilizing the HRRT scanner for exploring not only small brain regions such as the VST but also low density D2/3 areas such as cortex. It was demonstrated that the measurement of striatal D2/3 binding was very reliable, even when studying small brain structures or prolonging the scanning interval. Furthermore, the cortical test-retest parameters displayed good to moderate reproducibility. For the first time in vivo, it was revealed that there are significant divergent rostrocaudal gradients of [11C]raclopride binding in striatal subregions. These results indicate that high-resolution [11C]raclopride PET is very reliable and its improved sensitivity means that it should be possible to detect the often very subtle changes occurring in DA transmission. Another major advantage is the possibility to measure simultaneously striatal and cortical areas. The divergent gradients of D2/3 binding may have functional significance and the average distribution binding could serve as the basis for a future database. Key words: dopamine, PET, HRRT, [11C]raclopride, striatum, VST, gradients, test-retest.

Phase transitions in biodegradable films based on blends of gelatin and poly (vinyl alcohol)

The aim of this work was to study the effect of the hydrolysis degree (HD) and the concentration (C PVA) of two types of poly (vinyl alcohol) (PVA) and the effect of the type and the concentration of plasticizers on the phase properties of biodegradable films based on blends of gelatin and PVA, using a response-surface methodology. The films were made by casting and the studied properties were their glass (Tg) and melting (Tm) transition temperatures, which were determined by diferential scanning calorimetry (DSC). For the data obtained on the first scan, the fitting of the linear model was statistically significant and predictive only for the second melting temperature. In this case, the most important effect on the second Tm of the first scan was due to the HD of the PVA. In relation to the second scan, the linear model could be fit to Tg data with only two statistically significant parameters. Both the PVA and plasticizer concentrations had an important effect on Tg. Concerning the second Tm of the second scan, the linear model was fit to data with two statistically significant parameters, namely the HD and the plasticizer concentration. But, the most important effect was provoked by the HD of the PVA.

GIS-based approach for optimization of onshore wind park infrastructure alignment in Finland

Wind power is a rapidly developing, low-emission form of energy production. In Fin-land, the official objective is to increase wind power capacity from the current 1 005 MW up to 3 500–4 000 MW by 2025. By the end of April 2015, the total capacity of all wind power project being planned in Finland had surpassed 11 000 MW. As the amount of projects in Finland is record high, an increasing amount of infrastructure is also being planned and constructed. Traditionally, these planning operations are conducted using manual and labor-intensive work methods that are prone to subjectivity. This study introduces a GIS-based methodology for determining optimal paths to sup-port the planning of onshore wind park infrastructure alignment in Nordanå-Lövböle wind park located on the island of Kemiönsaari in Southwest Finland. The presented methodology utilizes a least-cost path (LCP) algorithm for searching of optimal paths within a high resolution real-world terrain dataset derived from airborne lidar scannings. In addition, planning data is used to provide a realistic planning framework for the anal-ysis. In order to produce realistic results, the physiographic and planning datasets are standardized and weighted according to qualitative suitability assessments by utilizing methods and practices offered by multi-criteria evaluation (MCE). The results are pre-sented as scenarios to correspond various different planning objectives. Finally, the methodology is documented by using tools of Business Process Management (BPM). The results show that the presented methodology can be effectively used to search and identify extensive, 20 to 35 kilometers long networks of paths that correspond to certain optimization objectives in the study area. The utilization of high-resolution terrain data produces a more objective and more detailed path alignment plan. This study demon-strates that the presented methodology can be practically applied to support a wind power infrastructure alignment planning process. The six-phase structure of the method-ology allows straightforward incorporation of different optimization objectives. The methodology responds well to combining quantitative and qualitative data. Additional-ly, the careful documentation presents an example of how the methodology can be eval-uated and developed as a business process. This thesis also shows that more emphasis on the research of algorithm-based, more objective methods for the planning of infrastruc-ture alignment is desirable, as technological development has only recently started to realize the potential of these computational methods.

Scanning the future of Single Euro Payment Area – towards cashless payment methods

The future of paying in the age of digitalization is a topic that includes varied visions. This master’s thesis explores images of the future of paying in the Single Euro Payment Area (SEPA) up to 2020 and 2025 through the views of experts specialized in paying. This study was commissioned by a credit management company in order to obtain more detailed information about the future of paying. Specifically, this thesis investigates what could be the most used payment methods in the future, what items could work as a medium of exchange in 2020 and how will they evolve towards the year 2025. Changing consumer behavior, trends connected to payment methods, security and private issues of new cashless payment methods were also part of this study. In the empirical part of the study the experts’ ideas about probable and preferable future images of paying were investigated through a two-round Disaggregative Delphi method. The questionnaire included numeric statements and open questions. Three alternative future images were created with the help of cluster analysis: “Unsurprising Future”, “Technology Driven Future” and “The Age of the Customer”. The plausible images had similarities and differences, which were reflected to the previous studies in the literature review. The study’s findings were formed based on the images of futures’ similarities and to the open questions answers that were received from the questionnaire. The main conclusion of the study was that development of technology will unify and diversify SEPA; the trend in 2020 seems to be towards more cashless payment methods but their usage depends on the countries’ financial possibilities and customer preferences. Mobile payments, cards and cash will be the main payment methods but the banks will have competitors from outside the financial sector. Wearable payment methods and NFC technology are seen as widely growing trends but subcutaneous payment devices will likely keep their niche position until 2025. In the meantime, security and private issues are seen to increase because of identity thefts and various frauds. Simultaneously, privacy will lose its meaning to younger consumers who are used to sharing their transaction and personal data with third parties in order to get access to attractive services. Easier access to consumers’ transaction data will probably open the door for hackers and cause new risks in paying processes. There exist many roads to future, and this study was not an attempt to give any complete answers about it even if some plausible assumptions about the future’s course were provided.

Evaluation of a stage II screening protocol for prostate cancer

In 2003, prostate cancer (PCa) is estimated to be the most commonly diagnosed cancer and third leading cause of cancer death in Canada. During PCa population screening, approximately 25% of patients with a normal digital rectal examination (DRE) and intermediate serum prostate specific antigen (PSA) level have PCa. Since all patients typically undergo biopsy, it is expected that approximately 75% of these procedures are unnecessary. The purpose of this study was to compare the degree of efficacy of clinical tests and algorithms in stage II screening for PCa while preventing unnecessary biopsies from occurring. The sample consisted of 201 consecutive men who were suspected of PCa based on the results of a DRE and serum PSA. These men were referred for venipuncture and transrectal ultrasound (TRUS). Clinical tests included TRUS, agespecific reference range PSA (Age-PSA), prostate specific antigen density (PSAD), and free-to-total prostate specific antigen ratio (%fPSA). Clinical results were evaluated individually and within algorithms. Cutoffs of 0.12 and 0.15 ng/ml/cc were employed for PSAD. Cutoffs that would provide a minimum sensitivity of 0.90 and 0.95, respectively were utilized for %fPSA. Statistical analysis included ROC curve analysis, calculated sensitivity (Sens), specificity (Spec), and positive likelihood ratio (LR), with corresponding confidence intervals (Cl). The %fPSA, at a 23% cutoff ({ Sens=0.92; CI, 0.06}, {Spec=0.4l; CI, 0.09}, {LR=1.56; CI, O.ll}), proved to be the most efficacious independent clinical test. The combination of PSAD (cutoff 0.15 ng/ml/cc) and %fPSA (cutoff 23%) ({Sens=0.93; CI, 0.06}, {Spec=0.38; CI, 0.08}, {LR=1.50; CI, 0.10}) was the most efficacious clinical algorithm. This study advocates the use of %fPSA at a cutoff of 23% when screening patients with an intermediate serum PSA and benign DRE.

The effects of moisture, cation concentration, temperature, density and composition of soils on their electrical resistivity

The effects. of moisture, cation concentration, dens ity , temper~ t ure and grai n si ze on the electrical resistivity of so il s are examined using laboratory prepared soils. An i nexpen si ve method for preparing soils of different compositions was developed by mixing various size fractions i n the laboratory. Moisture and cation c oncentration are related to soil resistivity by powe r functions, whereas soil resistiv ity and temperature, density, Yo gravel, sand , sil t, and clay are related by exponential functions . A total of 1066 cases (8528 data) from all the experiments were used in a step-wise multiple linear r egression to determine the effect of each variable on soil resistivity. Six variables out of the eight variables studied account for 92.57/. of the total variance in so il resistivity with a correlation coefficient of 0.96. The other two variables (silt and gravel) did not increase the · variance. Moisture content was found to be - the most important Yo clay. variable- affecting s oil res istivi ty followed by These two variables account for 90.81Yo of the total variance in soil resistivity with a correlation ~oefficient ·.of 0 . 95. Based on these results an equation to ' ~~ed{ ct soil r esist ivi ty using moisture and Yo clay is developed . To t est the predicted equation, resistivity measurements were made on natural soils both in s i tu a nd i n the laboratory. The data show that field and laboratory measurements are comparable. The predicted regression line c losely coinciqes with resistivity data from area A and area B soils ~clayey and silty~clayey sands). Resistivity data and the predicted regression line in the case of c layey soils (clays> 40%) do not coincide, especially a t l ess than 15% moisture. The regression equation overestimates the resistivity of so i l s from area C and underestimates for area D soils. Laboratory prepared high clay soils give similar trends. The deviations are probably caused by heterogeneous distribution of mo i sture and difference in the type o f cl ays present in these soils.

Age-related changes in ERPs associated with context-based and response-based interference

Age-related differences in information processing have often been explained through deficits in older adults' ability to ignore irrelevant stimuli and suppress inappropriate responses through inhibitory control processes. Functional imaging work on young adults by Nelson and colleagues (2003) has indicated that inferior frontal and anterior cingulate cortex playa key role in resolving interference effects during a delay-to-match memory task. Specifically, inferior frontal cortex appeared to be recruited under conditions of context interference while the anterior cingulate was associated with interference resolution at the stage of response selection. Related work has shown that specific neural activities related to interference resolution are not preserved in older adults, supporting the notion of age-related declines in inhibitory control (Jonides et aI., 2000, West et aI., 2004b). In this study the time course and nature of these inhibition-related processes were investigated in young and old adults using high-density ERPs collected during a modified Sternberg task. Participants were presented with four target letters followed by a probe that either did or did not match one of the target letters held in working memory. Inhibitory processes were evoked by manipulating the nature of cognitive conflict in a particular trial. Conflict in working memory was elicited through the presentation of a probe letter in immediately previous target sets. Response-based conflict was produced by presenting a negative probe that had just been viewed as a positive probe on the previous trial. Younger adults displayed a larger orienting response (P3a and P3b) to positive probes relative to a non-target baseline. Older adults produced the orienting P3a and 3 P3b waveforms but their responses did not differentiate between target and non-target stimuli. This age-related change in response to targetness is discussed in terms of "early selection/late correction" models of cognitive ageing. Younger adults also showed a sensitivity in their N450 response to different levels of interference. Source analysis of the N450 responses to the conflict trials of younger adults indicated an initial dipole in inferior frontal cortex and a subsequent dipole in anterior cingulate cortex, suggesting that inferior prefrontal regions may recruit the anterior cingulate to exert cognitive control functions. Individual older adults did show some evidence of an N450 response to conflict; however, this response was attenuated by a co-occurring positive deflection in the N450 time window. It is suggested that this positivity may reflect a form of compensatory activity in older adults to adapt to their decline in inhibitory control.

Developing a numerical inverse-theory-based extraction of orientation-dependent relaxation rates from partially- relaxed spectra

Second-rank tensor interactions, such as quadrupolar interactions between the spin- 1 deuterium nuclei and the electric field gradients created by chemical bonds, are affected by rapid random molecular motions that modulate the orientation of the molecule with respect to the external magnetic field. In biological and model membrane systems, where a distribution of dynamically averaged anisotropies (quadrupolar splittings, chemical shift anisotropies, etc.) is present and where, in addition, various parts of the sample may undergo a partial magnetic alignment, the numerical analysis of the resulting Nuclear Magnetic Resonance (NMR) spectra is a mathematically ill-posed problem. However, numerical methods (de-Pakeing, Tikhonov regularization) exist that allow for a simultaneous determination of both the anisotropy and orientational distributions. An additional complication arises when relaxation is taken into account. This work presents a method of obtaining the orientation dependence of the relaxation rates that can be used for the analysis of the molecular motions on a broad range of time scales. An arbitrary set of exponential decay rates is described by a three-term truncated Legendre polynomial expansion in the orientation dependence, as appropriate for a second-rank tensor interaction, and a linear approximation to the individual decay rates is made. Thus a severe numerical instability caused by the presence of noise in the experimental data is avoided. At the same time, enough flexibility in the inversion algorithm is retained to achieve a meaningful mapping from raw experimental data to a set of intermediate, model-free

GA approach for finding Rough Set decision rules based on bireducts

Feature selection plays an important role in knowledge discovery and data mining nowadays. In traditional rough set theory, feature selection using reduct - the minimal discerning set of attributes - is an important area. Nevertheless, the original definition of a reduct is restrictive, so in one of the previous research it was proposed to take into account not only the horizontal reduction of information by feature selection, but also a vertical reduction considering suitable subsets of the original set of objects. Following the work mentioned above, a new approach to generate bireducts using a multi--objective genetic algorithm was proposed. Although the genetic algorithms were used to calculate reduct in some previous works, we did not find any work where genetic algorithms were adopted to calculate bireducts. Compared to the works done before in this area, the proposed method has less randomness in generating bireducts. The genetic algorithm system estimated a quality of each bireduct by values of two objective functions as evolution progresses, so consequently a set of bireducts with optimized values of these objectives was obtained. Different fitness evaluation methods and genetic operators, such as crossover and mutation, were applied and the prediction accuracies were compared. Five datasets were used to test the proposed method and two datasets were used to perform a comparison study. Statistical analysis using the one-way ANOVA test was performed to determine the significant difference between the results. The experiment showed that the proposed method was able to reduce the number of bireducts necessary in order to receive a good prediction accuracy. Also, the influence of different genetic operators and fitness evaluation strategies on the prediction accuracy was analyzed. It was shown that the prediction accuracies of the proposed method are comparable with the best results in machine learning literature, and some of them outperformed it.

Bone mineral density measurement and osteoporosis treatment after a fragility fracture in older adults: regional variation and determinants of use in Quebec

Affiliation: Pierre Dagenais : Hôpital Maisonneuve-Rosemont, Faculté de médecine, Université de Montréal