The Five Factor Model of personality and evaluation of drug consumption risk

The problem of evaluating an individual's risk of drug consumption and misuse is highly important. An online survey methodology was employed to collect data including Big Five personality traits (NEO-FFI-R), impulsivity (BIS-11), sensation seeking (ImpSS), and demographic information. The data set contained information on the consumption of 18 central nervous system psychoactive drugs. Correlation analysis demonstrated the existence of groups of drugs with strongly correlated consumption patterns. Three correlation pleiades were identified, named by the central drug in the pleiade: ecstasy, heroin, and benzodiazepines pleiades. An exhaustive search was performed to select the most effective subset of input features and data mining methods to classify users and non-users for each drug and pleiad. A number of classification methods were employed (decision tree, random forest, $k$-nearest neighbors, linear discriminant analysis, Gaussian mixture, probability density function estimation, logistic regression and na{\"i}ve Bayes) and the most effective classifier was selected for each drug. The quality of classification was surprisingly high with sensitivity and specificity (evaluated by leave-one-out cross-validation) being greater than 70\% for almost all classification tasks. The best results with sensitivity and specificity being greater than 75\% were achieved for cannabis, crack, ecstasy, legal highs, LSD, and volatile substance abuse (VSA).Comment: Significantly extended report with 67 pages, 27 tables, 21 figure

Searching for the Complex Decision Reducts - The case study of the survival analysis

Generalization of the fundamental rough set discernibility tools aiming at searching for relevant patterns for complex decisions is discussed. As an example of application, there is considered the postsurgery survival analysis problem for the head and neck cancer cases

Semi-Supervised Regression and System Identification

System Identification and Machine Learning are developing mostly as independent subjects, although the underlying problem is the same: To be able to associate “outputs” with “inputs”. Particular areas in machine learning of substantial current interest are manifold learning and unsupervised and semi-supervised regression. We outline a general approach to semi-supervised regression, describe its links to Local Linear Embedding, and illustrate its use for various problems. In particular, we discuss how these techniques have a potential interest for the system identification world

Seroprevalence and risk factors associated with Chlamydophila abortus infection in dairy goats in the Northeast of Brazil

Few data are available on the prevalence and risk factors of Chlamydophila abortus infection in goats in Brazil. A cross-sectional study was carried out to determine the flock-level prevalence of C. abortus infection in goats from the semiarid region of the Paraíba State, Northeast region of Brazil, as well as to identify risk factors associated with the infection. Flocks were randomly selected and a pre-established number of female goats > 12 mo old were sampled in each of these flocks. A total of 975 serum samples from 110 flocks were collected, and structured questionnaire focusing on risk factors for C. abortus infection was given to each farmer at the time of blood collection. For the serological diagnosis the complement fixation test (CFT) using C. abortus S26/3 strain as antigen was performed. The flock-level factors for C. abortus prevalence were tested using multivariate logistic regression model. Fifty-five flocks out of 110 presented at least one seropositive animal with an overall prevalence of 50.0% (95%; CI: 40.3%, 59.7%). Ninety-one out of 975 dairy goats examined were seropositive with titers >32, resulting in a frequency of 9.3%. Lend buck for breeding (odds ratio = 2.35; 95% CI: 1.04-5.33) and history of abortions (odds ratio = 3.06; 95% CI: 1.37-6.80) were associated with increased flock prevalence

Relation between beta-lactamase producing bacteria and patient characteristics in chronic obstructive pulmonary disease (COPD).

BACKGROUND--In addition to bronchodilator and anti-inflammatory therapy, exacerbations in patients with chronic obstructive pulmonary disease (COPD) are often treated with antibiotics. Haemophilus influenzae and Moraxella (Branhamella) catarrhalis, two important respiratory pathogens, may produce beta-lactamase which makes them resistant to ampicillin. Surveillance studies conducted in various countries have shown an increasing incidence of these beta-lactamase producing bacteria. Although this may simply be a consequence of the increasing use of antibiotics, it is possible that other factors are important. A study was undertaken to investigate whether clinical factors are related to the presence of beta-lactamase forming bacteria in the sputum of patients with COPD. METHODS--One hundred patients with COPD aged over 40 years were sequentially selected from an outpatient clinic on the basis of sputum culture results. Fifty had beta-lactamase positive (beta L+) and 50 had beta-lactamase negative (beta L-) bacteria in their sputum. Patients were included only if sputum culture results yielded one pathogen. The files of these patients were investigated for possible causative factors present during the two preceding years. RESULTS--Both groups were almost identical in terms of lung function, maintenance medication, and smoking history. The total number of antibiotic courses in the beta L+ group was higher, as were individual courses of cephalosporins, tetracyclines, and macrolides. The number of patients admitted to hospital was higher in the beta L+ group, but admissions were of equal duration in both groups. Patients admitted to hospital had poorer lung function. Risk factors for beta-lactamase producing bacteria were identified by logistic regression analysis which revealed an odds ratio for one course of antibiotics of 1.15 (95% CI 1.04 to 1.28). CONCLUSIONS--An increased number of antibiotic courses is related to a higher incidence of beta-lactamase producing bacteria and more patients had hospital admissions in the beta L+ group. beta-lactamase stable antibiotics were used more frequently in the beta L+ group, probably because prescribing was adapted to the presence of beta-lactamase producing bacteria. No other differences were found between the beta L+ and beta L- groups