Intelligent data analysis to interpret major risk factors for diabetic patients with and without ischemic stroke in a small population

This study proposes an intelligent data analysis approach to investigate and interpret the distinctive factors of diabetes mellitus patients with and without ischemic (non-embolic type) stroke in a small population. The database consists of a total of 16 features collected from 44 diabetic patients. Features include age, gender, duration of diabetes, cholesterol, high density lipoprotein, triglyceride levels, neuropathy, nephropathy, retinopathy, peripheral vascular disease, myocardial infarction rate, glucose level, medication and blood pressure. Metric and non-metric features are distinguished. First, the mean and covariance of the data are estimated and the correlated components are observed. Second, major components are extracted by principal component analysis. Finally, as common examples of local and global classification approach, a k-nearest neighbor and a high-degree polynomial classifier such as multilayer perceptron are employed for classification with all the components and major components case. Macrovascular changes emerged as the principal distinctive factors of ischemic-stroke in diabetes mellitus. Microvascular changes were generally ineffective discriminators. Recommendations were made according to the rules of evidence-based medicine. Briefly, this case study, based on a small population, supports theories of stroke in diabetes mellitus patients and also concludes that the use of intelligent data analysis improves personalized preventive intervention

Mortality from lung cancer in workers exposed to sulfur dioxide in the pulp and paper industry.

Our objective in this study was to evaluate the mortality of workers exposed to sulfur dioxide in the pulp and paper industry. The cohort included 57,613 workers employed for at least 1 year in the pulp and paper industry in 12 countries. We assessed exposure to SO(2) at the level of mill and department, using industrial hygiene measurement data and information from company questionnaires; 40,704 workers were classified as exposed to SO(2). We conducted a standardized mortality ratio (SMR) analysis based on age-specific and calendar period-specific national mortality rates. We also conducted a Poisson regression analysis to determine the dose-response relations between SO(2) exposure and cancer mortality risks and to explore the effect of potential confounding factors. The SMR analysis showed a moderate deficit of all causes of death [SMR = 0.89; 95% confidence interval (CI), 0.87-0.96] among exposed workers. Lung cancer mortality was marginally increased among exposed workers (SMR = 1.08; 95% CI, 0.98-1.18). After adjustment for occupational coexposures, the lung cancer risk was increased compared with unexposed workers (rate ratio = 1.49; 95% CI, 1.14-1.96). There was a suggestion of a positive relationship between weighted cumulative SO(2) exposure and lung cancer mortality (p-value of test for linear trend = 0.009 among all exposed workers; p = 0.3 among workers with high exposure). Neither duration of exposure nor time since first exposure was associated with lung cancer mortality. Mortality from non-Hodgkin lymphoma and from leukemia was increased among workers with high SO(2) exposure; a dose-response relationship with cumulative SO(2) exposure was suggested for non-Hodgkin lymphoma. For the other causes of death, there was no evidence of increased mortality associated with exposure to SO(2). Although residual confounding may have occurred, our results suggest that occupational exposure to SO(2) in the pulp and paper industry may be associated with an increased risk of lung cancer

Principal component and factor analytic models in international sire evaluation

<p>Abstract</p> <p>Background</p> <p>Interbull is a non-profit organization that provides internationally comparable breeding values for globalized dairy cattle breeding programmes. Due to different trait definitions and models for genetic evaluation between countries, each biological trait is treated as a different trait in each of the participating countries. This yields a genetic covariance matrix of dimension equal to the number of countries which typically involves high genetic correlations between countries. This gives rise to several problems such as over-parameterized models and increased sampling variances, if genetic (co)variance matrices are considered to be unstructured.</p> <p>Methods</p> <p>Principal component (PC) and factor analytic (FA) models allow highly parsimonious representations of the (co)variance matrix compared to the standard multi-trait model and have, therefore, attracted considerable interest for their potential to ease the burden of the estimation process for multiple-trait across country evaluation (MACE). This study evaluated the utility of PC and FA models to estimate variance components and to predict breeding values for MACE for protein yield. This was tested using a dataset comprising Holstein bull evaluations obtained in 2007 from 25 countries.</p> <p>Results</p> <p>In total, 19 principal components or nine factors were needed to explain the genetic variation in the test dataset. Estimates of the genetic parameters under the optimal fit were almost identical for the two approaches. Furthermore, the results were in a good agreement with those obtained from the full rank model and with those provided by Interbull. The estimation time was shortest for models fitting the optimal number of parameters and prolonged when under- or over-parameterized models were applied. Correlations between estimated breeding values (EBV) from the PC19 and PC25 were unity. With few exceptions, correlations between EBV obtained using FA and PC approaches under the optimal fit were ≥ 0.99. For both approaches, EBV correlations decreased when the optimal model and models fitting too few parameters were compared.</p> <p>Conclusions</p> <p>Genetic parameters from the PC and FA approaches were very similar when the optimal number of principal components or factors was fitted. Over-fitting increased estimation time and standard errors of the estimates but did not affect the estimates of genetic correlations or the predictions of breeding values, whereas fitting too few parameters affected bull rankings in different countries.</p

Effects of experiment start time and duration on measurement of standard physicological variables

Duration and start time of respirometry experiments have significant effects on the measurement of basal values for several commonly measured physiological variables (metabolic rate, evaporative water loss and body temperature). A longer measurement duration reduced values for all variables for all start times, and this was an effect of reduced animal activity rather than random sampling. However, there was also an effect of circadian rhythm on the timing of minimal physiological values. Experiment start time had a significant effect on time taken to reach minimal values for all variables, ranging from 4:00 h ± 38 min (body temperature, start time 23:00 h) to 8:54 h ± 52 min (evaporative water loss, start time 17:00 h). It also influenced the time of day that minimal values were obtained, ranging from 22:24 h ± 40 min (carbon dioxide production, start time 15:00 h) to 06:00 h ± 57 min (oxygen consumption, start time 23:00 h), and the minimum values measured. Consequently both measurement duration and experiment start time should be considered in experimental design to account for both a handling and a circadian effect on the animal’s physiology. We suggest that experiments to measure standard physiological variables for small diurnal birds should commence between 17:00 h and 21:00 h, and measurement duration should be at least 9 h

Climatic Variability Leads to Later Seasonal Flowering of Floridian Plants

Understanding species responses to global change will help predict shifts in species distributions as well as aid in conservation. Changes in the timing of seasonal activities of organisms over time may be the most responsive and easily observable indicator of environmental changes associated with global climate change. It is unknown how global climate change will affect species distributions and developmental events in subtropical ecosystems or if climate change will differentially favor nonnative species. Contrary to previously observed trends for earlier flowering onset of plant species with increasing spring temperatures from mid and higher latitudes, we document a trend for delayed seasonal flowering among plants in Florida. Additionally, there were few differences in reproductive responses by native and nonnative species to climatic changes. We argue that plants in Florida have different reproductive cues than those from more northern climates. With global change, minimum temperatures have become more variable within the temperate-subtropical zone that occurs across the peninsula and this variation is strongly associated with delayed flowering among Florida plants. Our data suggest that climate change varies by region and season and is not a simple case of species responding to consistently increasing temperatures across the region. Research on climate change impacts need to be extended outside of the heavily studied higher latitudes to include subtropical and tropical systems in order to properly understand the complexity of regional and seasonal differences of climate change on species responses

Spatial patterns and temporal variability of drought in Western Iran

An analysis of drought in western Iran from 1966 to 2000 is presented using monthly precipitation data observed at 140 gauges uniformly distributed over the area. Drought conditions have been assessed by means of the Standardized Precipitation Index (SPI). To study the long-term drought variability the principal component analysis was applied to the SPI field computed on 12-month time scale. The analysis shows that applying an orthogonal rotation to the first two principal component patterns, two distinct sub-regions having different climatic variability may be identified. Results have been compared to those obtained for the largescale using re-analysis data suggesting a satisfactory agreement. Furthermore, the extension of the large-scale analysis to a longer period (1948–2007) shows that the spatial patterns and the associated time variability of drought are subjected to noticeable changes. Finally, the relationship between hydrological droughts in the two sub-regions and El Niño Southern Oscillation events has been investigated finding that there is not clear evidence for a link between the two phenomen

Clustering patients on the basis of their individual course of low back pain over a six month period

<p>Abstract</p> <p>Background</p> <p>Several researchers have searched for subgroups in the heterogeneous population of patients with non-specific low back pain (LBP). To date, subgroups have been identified based on psychological profiles and the variation of pain.</p> <p>Methods</p> <p>This multicentre prospective observational study explored the 6- month clinical course with measurements of bothersomeness that were collected from weekly text messages that were sent by 176 patients with LBP. A hierarchical cluster analysis, Ward's method, was used to cluster patients according to the development of their pain.</p> <p>Results</p> <p>Four clusters with distinctly different clinical courses were described and further validated against clinical baseline variables and outcomes. Cluster 1, a "stable" cluster, where the course was relatively unchanged over time, contained young patients with good self- rated health. Cluster 2, a group of "fast improvers" who were very bothered initially but rapidly improved, consisted of patients who rated their health as relatively poor but experienced the fewest number of days with bothersome pain of all the clusters. Cluster 3 was the "typical patient" group, with medium bothersomeness at baseline and an average improvement over the first 4-5 weeks. Finally, cluster 4 contained the "slow improvers", a group of patients who improved over 12 weeks. This group contained older individuals who had more LBP the previous year and who also experienced most days with bothersome pain of all the clusters.</p> <p>Conclusions</p> <p>It is possible to define clinically meaningful clusters of patients based on their individual course of LBP over time. Future research should aim to reproduce these clusters in different populations, add further clinical variables to distinguish the clusters and test different treatment strategies for them.</p

Fungal diversity associated to the olive moth, prays oleae Bernard : a survey for potential entomopathogenic fungi

Olive production is one of the main agricultural activities in Portugal. In the region of Trás-os-Montes this crop has been considerably affected by Prays oleae. In order to evaluate the diversity of fungi on P. oleae population of Trás-os-Montes olive orchards, larvae and pupae of the three annual generations (phyllophagous, antophagous and carpophagous) were collected and evaluated for fungal growth on their surface. From the 3828 larvae and pupae, a high percentage of individuals exhibited growth of a fungal agent (40.6%), particularly those from the phyllophagous generation. From all the moth generations, a total of 43 species from 24 genera were identified, but the diversity and abundance of fungal species differed between the three generations. Higher diversity was found in the carpophagous generation, followed by the antophagous and phyllophagous generations. The presence of fungi displaying entomopathogenic features was highest in the phyllophagous larvae and pupae, being B. bassiana the most abundant taxa. The first report of B. bassiana presence on P. oleae could open new strategies for the biocontrol of this major pest in olive groves, since the use of an already adapted species increases the guarantee of success of a biocontrol approach. The identification of antagonistic fungi able to control agents that cause major olive diseases, such as Verticillium dahliae, will benefit future biological control approaches for limiting this increasingly spreading pathogen.This work was supported by Science and Technology Foundation (Fundação para a Ciência e Tecnologia – FCT) project PTDC/AGR-AAM/102600/2008 “Entomopathogenic fungi associated to olive pests: isolation, characterization and selection for biological control”. The first author is grateful to the Science and Technology Foundation for the PhD grant SFRH/BD/44265/2008

An Ensemble Analysis of Electromyographic Activity during Whole Body Pointing with the Use of Support Vector Machines

We explored the use of support vector machines (SVM) in order to analyze the ensemble activities of 24 postural and focal muscles recorded during a whole body pointing task. Because of the large number of variables involved in motor control studies, such multivariate methods have much to offer over the standard univariate techniques that are currently employed in the field to detect modifications. The SVM was used to uncover the principle differences underlying several variations of the task. Five variants of the task were used. An unconstrained reaching, two constrained at the focal level and two at the postural level. Using the electromyographic (EMG) data, the SVM proved capable of distinguishing all the unconstrained from the constrained conditions with a success of approximately 80% or above. In all cases, including those with focal constraints, the collective postural muscle EMGs were as good as or better than those from focal muscles for discriminating between conditions. This was unexpected especially in the case with focal constraints. In trying to rank the importance of particular features of the postural EMGs we found the maximum amplitude rather than the moment at which it occurred to be more discriminative. A classification using the muscles one at a time permitted us to identify some of the postural muscles that are significantly altered between conditions. In this case, the use of a multivariate method also permitted the use of the entire muscle EMG waveform rather than the difficult process of defining and extracting any particular variable. The best accuracy was obtained from muscles of the leg rather than from the trunk. By identifying the features that are important in discrimination, the use of the SVM permitted us to identify some of the features that are adapted when constraints are placed on a complex motor task