Risk prediction for weed infestation using classification rules

This paper proposes a fuzzy classification system for the risk of infestation by weeds in agricultural zones considering the variability of weeds. The inputs of the system are features of the infestation extracted from estimated maps by kriging for the weed seed production and weed coverage, and from the competitiveness, inferred from narrow and broad-leaved weeds. Furthermore, a Bayesian network classifier is used to extract rules from data which are compared to the fuzzy rule set obtained on the base of specialist knowledge. Results for the risk inference in a maize crop field are presented and evaluated by the estimated yield loss. © 2009 IEEE.

Serum inhibin A and angiogenic factor levels in pregnancies with previous preeclampsia and/or chronic hypertension: are they useful markers for prediction of subsequent preeclampsia?

OBJECTIVE: Our objective was to determine whether measurement of placenta growth factor (PLGF), inhibin A, or soluble fms-like tyrosine kinase-1 (sFlt-1) at 2 times during pregnancy would usefully predict subsequent preeclampsia ( PE) in women at high risk. STUDY DESIGN: We analyzed serum obtained at enrollment (12(0/7) to 19(6/7) weeks) and follow-up (24-28 weeks) from 704 patients with previous PE and/or chronic hypertension (CHTN) enrolled in a randomized trial for the prevention of PE. Logistic regression analysis assessed the association of log-transformed markers with subsequent PE; receiver operating characteristic analysis assessed predictive value. RESULTS: One hundred four developed preeclampsia: 27 at 37 weeks or longer and 77 at less than 37 weeks (9 at less than 27 weeks). None of the markers was associated with PE at 37 weeks or longer. Significant associations were observed between PE at less than 37 weeks and reduced PLGF levels at baseline (P =.022) and follow-up (P <.0001) and elevated inhibin A (P <.0001) and sFlt-1 (P =.0002) levels at follow-up; at 75% specificity, sensitivities ranged from 38% to 52%. Using changes in markers from baseline to follow-up, sensitivities were 52-55%. Associations were observed between baseline markers and PE less than 27 weeks (P <=.0004 for all); sensitivities were 67-89%, but positive predictive values (PPVs) were only 3.4-4.5%. CONCLUSION: Inhibin A and circulating angiogenic factors levels obtained at 12(0/7) to 19(6/7) weeks have significant associations with onset of PE at less than 27 weeks, as do levels obtained at 24-28 weeks with onset of PE at less than 37 weeks. However, because the corresponding sensitivities and/or PPVs were low, these markers might not be clinically useful to predict PE in women with previous PE and/or CHTN.

Bayesian binary regression model: an application to in-hospital death after AMI prediction

Um modelo bayesiano de regressão binária é desenvolvido para predizer óbito hospitalar em pacientes acometidos por infarto agudo do miocárdio. Métodos de Monte Carlo via Cadeias de Markov (MCMC) são usados para fazer inferência e validação. Uma estratégia para construção de modelos, baseada no uso do fator de Bayes, é proposta e aspectos de validação são extensivamente discutidos neste artigo, incluindo a distribuição a posteriori para o índice de concordância e análise de resíduos. A determinação de fatores de risco, baseados em variáveis disponíveis na chegada do paciente ao hospital, é muito importante para a tomada de decisão sobre o curso do tratamento. O modelo identificado se revela fortemente confiável e acurado, com uma taxa de classificação correta de 88% e um índice de concordância de 83%.

Weather-based prediction of anthracnose severity using artificial neural network models

Data were collected and analysed from seven field sites in Australia, Brazil and Colombia on weather conditions and the severity of anthracnose disease of the tropical pasture legume Stylosanthes scabra caused by Colletotrichum gloeosporioides. Disease severity and weather data were analysed using artificial neural network (ANN) models developed using data from some or all field sites in Australia and/or South America to predict severity at other sites. Three series of models were developed using different weather summaries. of these, ANN models with weather for the day of disease assessment and the previous 24 h period had the highest prediction success, and models trained on data from all sites within one continent correctly predicted disease severity in the other continent on more than 75% of days; the overall prediction error was 21.9% for the Australian and 22.1% for the South American model. of the six cross-continent ANN models trained on pooled data for five sites from two continents to predict severity for the remaining sixth site, the model developed without data from Planaltina in Brazil was the most accurate, with >85% prediction success, and the model without Carimagua in Colombia was the least accurate, with only 54% success. In common with multiple regression models, moisture-related variables such as rain, leaf surface wetness and variables that influence moisture availability such as radiation and wind on the day of disease severity assessment or the day before assessment were the most important weather variables in all ANN models. A set of weights from the ANN models was used to calculate the overall risk of anthracnose for the various sites. Sites with high and low anthracnose risk are present in both continents, and weather conditions at centres of diversity in Brazil and Colombia do not appear to be more conducive than conditions in Australia to serious anthracnose development.

Pathological and behavioral risk factors for higher serum c-reactive protein concentrations in free-living adults - A Brazilian community-based study

Low-grade chronic systemic inflammation is often associated with chronic non-communicable diseases, and its most frequently used marker, the C-reactive protein (CRP), has become an identifier of such diseases as well as an independent predictor for cardiovascular disorders and mortality. CRP is produced in response to pro-inflammatory signaling and to individual and behavioral factors, leading to pathological states. The aim of this study was to rank the predicting factors of high CRP concentrations in free-living adults from a community-based sample. We evaluated 522 adults (40-84 years old; 381 women) for anthropometric characteristics, dietary intake, clinical and physical tests, and blood analysis. Subjects were assigned to groups, according to CRP concentrations, as normal CRP (G1;<3.0 mg/L; n = 269), high CRP (G2; 3.0-6.0 mg/L; n = 139), and very high CRP (G3; >6.0 mg/dL; n = 116). Statistical comparison between groups used one-way ANOVA or Kruskal-Wallis tests, and prediction of altered values in increasing CRP was evaluated by proportional hazard models (odds ratio). CRP distribution was influenced by gender, body mass index, body and abdominal fatness, blood leukocytes, and neutrophil counts. The higher CRP group was discriminated by the above variables in addition to lower VO2max, serum metabolic syndrome components (triglycerides, glucose, and HDL cholesterol), higher insulin, homeostasis assessment of insulin resistance, uric acid, gamma-GT, and homocysteine. After adjustments, only fatness, blood leukocytes, and hyperglycemia remained as independent predictors for increased serum CRP concentrations. Intervention procedures to treat low-grade chronic inflammation in overweight women would mainly focus on restoring muscle mass and functions in addition to an antioxidant-rich diet. © 2012 Springer Science+Business Media, LLC.

Mild cognitive impairment (part 2): Biological markers for diagnosis and prediction of dementia in Alzheimer's disease

To present a critical review of publications reporting on the rationale and clinical implications of the use of biomarkers for the early diagnosis of Alzheimer's disease (AD). Methods: We conducted a systematic search of the PubMed and Web of Science electronic databases, limited to articles published in English between 1999 and 2012, and based on the following terms: mild cognitive impairment, Alzheimer's disease OR dementia, biomarkers. We retrieved 1,130 articles, of which 175 were reviews. Overall, 955 original articles were eligible. Results: The following points were considered relevant for the present review: a) rationale for biomarkers research in AD and mild cognitive impairment (MCI); b) usefulness of distinct biomarkers for the diagnosis and prediction of AD; c) the role of multimodality biomarkers for the diagnosis and prediction of AD; d) the role of biomarkers in clinical trials of patients with AD and MCI; and e) current limitations to the widespread use of biomarkers in research and clinical settings. Conclusion: Different biomarkers are useful for the early diagnosis and prediction of AD in at-risk subjects. Nonetheless, important methodological limitations need to be overcome for widespread use of biomarkers in research and clinical settings. © 2013 Associação Brasileira de Psiquiatria.

Validation of Relapse Risk Biomarkers for Routine Use in Patients With Juvenile Idiopathic Arthritis

Objective. The myeloid-related proteins 8 and 14 (MRP-8/MRP-14) and neutrophil-derived S100A12 are biomarkers of inflammation. They can be used to determine the relapse risk in patients with juvenile idiopathic arthritis (JIA) after stopping antiinflammatory treatment. In this study, we tested the performance of different enzyme-linked immunosorbent assays (ELISAs) in order to validate systems available for routine use.Methods. MRP-8/MRP-14 and S100A12 serum concentrations of 188 JIA patients in remission were analyzed. Commercially available test systems were compared to experimental ELISAs established in house. The ability of the assays to identify JIA patients at risk for relapse was analyzed.Results. For MRP-8/MRP-14, the PhiCal Calprotectin and Buhlmann MRP8/14 Calprotectin ELISAs revealed hazard ratios of 2.3 and 2.1, respectively. For S100A12, the CircuLex S100A12/EN-RAGE ELISA revealed a hazard ratio of 3.1. The commercial assays allowed a JIA relapse prediction that was at least comparable to the experimental ELISAs.Conclusion. For the prediction of JIA relapse after stopping medication, the biomarkers MRP-8/MRP-14 and S100A12 can be determined by using assays that are available for routine use. The tested commercial MRP-8/MRP-14 and S100A12 ELISAs showed a performance comparable to well-established experimental ELISA protocols when assay-specific cutoffs for the indication of relapse prediction are thoroughly applied.