Evaluation of the aquatic macroinvertebrate community as a tool for monitoring a reservoir in the Pitangui river basin, Parana, Brazil

Evaluation of the aquatic macroinvertebrate community as a tool for monitoring a reservoir in the Pitangui river basin, Parana, Brazil. Benthic and nektonic macroinvertebrates play an important role in the structure and function of aquatic ecosystems and their distribution is influenced by chemical features of the substrate, vegetation composition, and water depth. Knowledge on the fauna contributes to the assessment of water quality and development of biodiversity conservation activities. Different biotic factors affecting the invertebrate community were evaluated in the Alagados reservoir, the main water source of the city of Ponta Grossa, Parana. In five different sampling points, 18,473 specimens of aquatic or semiaquatic macroinvertebrates were collected, belonging to 46 taxa of the phylla Annelida (Hirudinea and Oligochaeta), Mollusca (Gastropoda), Platyhelminthes (Turbellaria), Nematoda and Arthropoda (Arachnida, Crustacea and Insecta). This community was composed mainly of predators (45.7% of the taxa sampled), collectors and/or filterers (23.9%), scrapers (15.2%), shredders (13.0%) and detritivores (2.2%). Diversity (H`) and evenness (J) indices were significantly low for the sites examined, and H` ranged between 0.3301 and 1.0396. Regarding tolerance of organisms to organic pollution, more sensitive taxa were very rare (Plecoptera) or unusual (Trichoptera and Ephemeroptera). Among the more resistant groups are Chironomidae and Hirudinea, both fairly common in the samples. This study corroborates the importance of bioindicators as a tool to assess water quality for human consumption and for the conservation of aquatic environments, integrating physical, chemical and biological factors in monitoring programs.

Improving Alzheimer`s Disease Diagnosis with Machine Learning Techniques

There is not a specific test to diagnose Alzheimer`s disease (AD). Its diagnosis should be based upon clinical history, neuropsychological and laboratory tests, neuroimaging and electroencephalography (EEG). Therefore, new approaches are necessary to enable earlier and more accurate diagnosis and to follow treatment results. In this study we used a Machine Learning (ML) technique, named Support Vector Machine (SVM), to search patterns in EEG epochs to differentiate AD patients from controls. As a result, we developed a quantitative EEG (qEEG) processing method for automatic differentiation of patients with AD from normal individuals, as a complement to the diagnosis of probable dementia. We studied EEGs from 19 normal subjects (14 females/5 males, mean age 71.6 years) and 16 probable mild to moderate symptoms AD patients (14 females/2 males, mean age 73.4 years. The results obtained from analysis of EEG epochs were accuracy 79.9% and sensitivity 83.2%. The analysis considering the diagnosis of each individual patient reached 87.0% accuracy and 91.7% sensitivity.

The interaction between N-category and N-ratio as a new tool to improve lymph node metastasis staging in gastric cancer: Results of a single cancer center in Brazil

Background: Depth of tumor invasion (T-category) and the number of metastatic lymph nodes (N-category) are the most important prognostic factors in patients with gastric cancer. Recently, the ratio between metastatic and dissected lymph nodes (N-ratio) has been established as one. The aim of this study is to evaluate the impact of N-ratio and its interaction with N-category as a prognostic factor in gastric cancer. Methods: This was a retrospective study in which we reviewed clinical and pathological data of 165 patients who had undergone curative surgery at our institution through a 9-year period. The exclusion criteria included metastases, gastric stump tumors and gastrectomy with less than 15 lymph nodes dissected. Results: The median age of the patients was 63 years and most of them were male. Total gastrectomy was the most common procedure and 92.1% of the patients had a D2-lymphadenectomy. Their 5-year overall survival was 57.7%. T-category, N-category, extended gastrectomy, and N-ratio were prognostic factors in overall and disease-free survival in accordance with univariate analysis. In accordance with TNM staging, N1 patients who have had NR1 had 5-year survival in 75.5% whereas in the NR2 group only 33% of the cases had 5-year survival. In the multivariate analysis, the interaction between N-category and N-ratio was an independent prognostic factor. Conclusion: Our findings confirmed the role of N-ratio as prognostic factor of survival in patients with gastric cancer surgically treated with at least 15 lymph nodes dissected. The relationship between N-category and N-ratio is a better predictor than lymph node metastasis staging. (C) 2010 Elsevier Ltd. All rights reserved.

The Finnish Diabetes Risk Score (FINDRISC) as a screening tool for hepatic steatosis

Introduction. Hepatic steatosis due to non-alcoholic fatty liver disease is associated with obesity, dyslipidemia, insulin resistance, and type 2 diabetes. The Finnish Diabetes Risk Score (FINDRISC) is a prognostic screening tool to detect people at risk for type 2 diabetes without the use of any blood test. The objective of this study was to evaluate whether FINDRISC can also be used to screen for the presence of hepatic steatosis. Patients and methods. Steatosis was determined by ultrasound. The study sample consisted of 821 non-diabetic subjects without previous hepatic disease; 81% were men (mean age 45 +/- 9 years) and 19% women (mean age 41 +/- 10 years). Results. Steatosis was present in 44% of men and 10% of women. The odds ratio for one unit increase in the FINDRISC associated with the risk of steatosis was 1.30 (95% CI 1.25-1.35), similar for men and women. The area under the receiver operating characteristics curve for steatosis was 0.80 (95% CI 0.77-0.83); 0.80 in men (95% CI 0.77-0.83) and 0.83 (95% CI 0.73-0.93) in women. Conclusions. Our data suggest that the FINDRISC could be a useful primary screening tool for the presence of steatosis.

An fMRI Normative Database for Connectivity Networks Using One-Class Support Vector Machines

The application of functional magnetic resonance imaging (fMRI) in neuroscience studies has increased enormously in the last decade. Although primarily used to map brain regions activated by specific stimuli, many studies have shown that fMRI can also be useful in identifying interactions between brain regions (functional and effective connectivity). Despite the widespread use of fMRI as a research tool, clinical applications of brain connectivity as studied by fMRI are not well established. One possible explanation is the lack of normal pattern, and intersubject variability-two variables that are still largely uncharacterized in most patient populations of interest. In the current study, we combine the identification of functional connectivity networks extracted by using Spearman partial correlation with the use of a one-class support vector machine in order construct a normative database. An application of this approach is illustrated using an fMRI dataset of 43 healthy Subjects performing a visual working memory task. In addition, the relationships between the results obtained and behavioral data are explored. Hum Brain Mapp 30:1068-1076, 2009. (C) 2008 Wiley-Liss. Inc.

Forecasting regional crop production using SOI phases: an example for the Australian peanut industry

Using peanuts as an example, a generic methodology is presented to forward-estimate regional crop production and associated climatic risks based on phases of the Southern Oscillation Index (SOI). Yield fluctuations caused by a highly variable rainfall environment are of concern to peanut processing and marketing bodies. The industry could profitably use forecasts of likely production to adjust their operations strategically. Significant, physically based lag-relationships exist between an index of ocean/atmosphere El Nino/Southern Oscillation phenomenon and future rainfall in Australia and elsewhere. Combining knowledge of SOI phases in November and December with output from a dynamic simulation model allows the derivation of yield probability distributions based on historic rainfall data. This information is available shortly after planting a crop and at least 3-5 months prior to harvest. The study shows that in years when the November-December SOI phase is positive there is an 80% chance of exceeding average district yields. Conversely, in years when the November-December SOI phase is either negative or rapidly falling there is only a 5% chance of exceeding average district yields, but a 95% chance of below average yields. This information allows the industry to adjust strategically for the expected volume of production. The study shows that simulation models can enhance SOI signals contained in rainfall distributions by discriminating between useful and damaging rainfall events. The methodology can be applied to other industries and regions.

Cybertutor: a teaching tool in Dermatology

It was developed a teaching tool in Dermatology for undergraduate medical students, using an interactive website, the Cybertutor. Clinical cases, lectures and updated bibliography were selected. Photographies of dermatological lesions were taken from ambulatory patients. The topics of the lectures were based on the current curriculum of the Federal University of Rio Grande do Sul. The Cybertutor is a dynamic and modern teaching tool, allowing constant innovation.

Frontal Assessment Battery (FAB) is a simple tool for detecting executive deficits in chronic cannabis users

Background: Cannabis is the most used illicit drug in the world, and its use has been associated with prefrontal cortex (PFC) dysfunction, including deficits in executive functions (EF). Considering that EF may influence treatment outcome, it would be interesting to have a brief neuropsychological battery to assess EF in chronic cannabis users (CCU). In the present study, the Frontal Assessment Battery (FAB), a brief, easy to use neuropsychological instrument aimed to evaluate EF, was used to evaluate cognitive functioning of CCU. Methods: We evaluated 107 abstinent CCU with the FAB and compared with 44 controls matched for age, estimated IQ, and years of education. Results: CCU performed poorly as compared to controls (FAB total score = 16.53 vs. 17.09, p .05). CCU had also a poor performance in the Motor Programming subtest (2.47 vs. 2.73, p .05). Conclusion: This study examined effects of cannabis in executive functioning and showed evidence that the FAB is sensitive to detect EF deficits in early abstinent chronic cannabis users. Clinical significance of these findings remains to be investigated in further longitudinal studies. FAB may be useful as a screening instrument to evaluate the necessity for a complete neuropsychological assessment in this population.