Comparing Campus Discipline Rates: A Multivariate Approach for Identifying Schools with Significantly Different than Expected Exclusionary Discipline Rates

Campus behavior management is important for ensuring classroom order and promoting positive academic outcomes. Previous studies have shown the importance of individual student and campus personnel characteristics and campus context for explaining campus discipline rates (e.g., rates of suspension and expulsion). Assessing campus discipline rates, while controlling for these individual and campus characteristics, is important for the monitoring, evaluation, and intervention role of policymakers as well as state and federal level education agencies. Systems or metrics exist that measure other student outcomes (i.e., academic performance) with controls for individual and campus characteristics, but none exist that monitor these differences for discipline rates across campuses. In this paper, we use a multivariate model to analyze a longitudinal, statewide dataset for all secondary students in Texas from 2000 to 2008 in order to examine how campus discipline rates differ across schools with statistically similar students, teachers, and campus characteristics. The findings are important for understanding that some schools with similar characteristics have significantly different exclusionary discipline rates, and they are important for informing policy and agency level decision-making. The methodology described can easily be used by monitoring agencies as well as local school districts.

Influence of Remote Monitoring on Long-Term Cardiovascular Outcomes after Cardioverter-Defibrillator Implantation

AIMS: Device-based remote monitoring (RM) has been linked to improved clinical outcomes at short to medium-term follow-up. Whether this benefit extends to long-term follow-up is unknown. We sought to assess the effect of device-based RM on long-term clinical outcomes in recipients of implantable cardioverter-defibrillators (ICD). METHODS: We performed a retrospective cohort study of consecutive patients who underwent ICD implantation for primary prevention. RM was initiated with patient consent according to availability of RM hardware at implantation. Patients with concomitant cardiac resynchronization therapy were excluded. Data on hospitalizations, mortality and cause of death were systematically assessed using a nationwide healthcare platform. A Cox proportional hazards model was employed to estimate the effect of RM on mortality and a composite endpoint of cardiovascular mortality and hospital admission due to heart failure (HF). RESULTS: 312 patients were included with a median follow-up of 37.7months (range 1 to 146). 121 patients (38.2%) were under RM since the first outpatient visit post-ICD and 191 were in conventional follow-up. No differences were found regarding age, left ventricular ejection fraction, heart failure etiology or NYHA class at implantation. Patients under RM had higher long-term survival (hazard ratio [HR] 0.50, CI 0.27-0.93, p=0.029) and lower incidence of the composite outcome (HR 0.47, CI 0.27-0.82, p=0.008). After multivariate survival analysis, overall survival was independently associated with younger age, higher LVEF, NYHA class lower than 3 and RM. CONCLUSION: RM was independently associated with increased long-term survival and a lower incidence of a composite endpoint of hospitalization for HF or cardiovascular mortality.

Preparation of environmental monitoring plan pattern for surface water quality in Iran (Case study: Gharehsou River)

Using water quality management programs is a necessary and inevitable way for preservation and sustainable use of water resources. One of the important issues in determining the quality of water in rivers is designing effective quality control networks, so that the measured quality variables in these stations are, as far as possible, indicative of overall changes in water quality. One of the methods to achieve this goal is increasing the number of quality monitoring stations and sampling instances. Since this will dramatically increase the annual cost of monitoring, deciding on which stations and parameters are the most important ones, along with increasing the instances of sampling, in a way that shows maximum change in the system under study can affect the future decision-making processes for optimizing the efficacy of extant monitoring network, removing or adding new stations or parameters and decreasing or increasing sampling instances. This end, the efficiency of multivariate statistical procedures was studied in this thesis. Multivariate statistical procedure, with regard to its features, can be used as a practical and useful method in recognizing and analyzing rivers’ pollution and consequently in understanding, reasoning, controlling, and correct decision-making in water quality management. This research was carried out using multivariate statistical techniques for analyzing the quality of water and monitoring the variables affecting its quality in Gharasou river, in Ardabil province in northwest of Iran. During a year, 28 physical and chemical parameters were sampled in 11 stations. The results of these measurements were analyzed by multivariate procedures such as: Cluster Analysis (CA), Principal Component Analysis (PCA), Factor Analysis (FA), and Discriminant Analysis (DA). Based on the findings from cluster analysis, principal component analysis, and factor analysis the stations were divided into three groups of highly polluted (HP), moderately polluted (MP), and less polluted (LP) stations Thus, this study illustrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets, and in water quality assessment, identification of pollution sources/factors and understanding spatial variations in water quality for effective river water quality management. This study also shows the effectiveness of these techniques for getting better information about the water quality and design of monitoring network for effective management of water resources. Therefore, based on the results, Gharasou river water quality monitoring program was developed and presented.

Chronic contamination assessment integrating biomarkers' responses in transplanted mussels-A seasonal monitoring

This study aimed to provide the first biomonitoring integrating biomarkers and bioaccumulation data in São Paulo coast, Brazil and, for this purpose, a battery of biomarkers of defense mechanisms was analyzed and linked to contaminants' body burden in a weigh-of-evidence approach. The brown mussel Perna perna was selected to be transplanted from a farming area (Caraguatatuba) to four possibly polluted sites: Engenho D'Agua, DTCS (Dutos e Terminais do Centro-Oeste de São Paulo) oil terminal (Sao Sebastiao zone), Palmas Island, and Itaipu (It; Santos Bay zone). After 3 months of exposure in each season, mussels were recollected and the cytochrome P4501A (CYP1A)- and CYP3A-like activities, glutathione-S-transferase and antioxidants enzymes (catalase, glutathione peroxidase, and glutathione reductase) were analyzed in gills. The concentrations of polycyclic aromatic hydrocarbons, linear alkylbenzenes, and nonessential metals (Cr, Cd, Pb, and Hg) in whole tissue were also analyzed and data were linked to biomarkers' responses by multivariate analysis (principal component analysisfactor analysis). A representation of estimated factor scores was performed to confirm the factor descriptions and to characterize the studied stations. Biomarkers exhibited most significant alterations all year long in mussels transplanted to It, located at Santos Bay zone, where bioaccumulation of organic and inorganic compounds was detected. This integrated approach using transplanted mussels showed satisfactory results, pointing out differences between sites, seasons, and critical areas, which could be related to land-based contaminants' sources. The influence of natural factors and other contaminants (e.g., pharmaceuticals) on biomarkers' responses are also discussed. (C) 2010 Wiley Periodicals, Inc. Environ Toxicol, 2012.

Machine Learning Unsupervised Methods in the Design of an On-board Health Monitoring System for Satellite Applications

The dissertation starts by providing a description of the phenomena related to the increasing importance recently acquired by satellite applications. The spread of such technology comes with implications, such as an increase in maintenance cost, from which derives the interest in developing advanced techniques that favor an augmented autonomy of spacecrafts in health monitoring. Machine learning techniques are widely employed to lay a foundation for effective systems specialized in fault detection by examining telemetry data. Telemetry consists of a considerable amount of information; therefore, the adopted algorithms must be able to handle multivariate data while facing the limitations imposed by on-board hardware features. In the framework of outlier detection, the dissertation addresses the topic of unsupervised machine learning methods. In the unsupervised scenario, lack of prior knowledge of the data behavior is assumed. In the specific, two models are brought to attention, namely Local Outlier Factor and One-Class Support Vector Machines. Their performances are compared in terms of both the achieved prediction accuracy and the equivalent computational cost. Both models are trained and tested upon the same sets of time series data in a variety of settings, finalized at gaining insights on the effect of the increase in dimensionality. The obtained results allow to claim that both models, combined with a proper tuning of their characteristic parameters, successfully comply with the role of outlier detectors in multivariate time series data. Nevertheless, under this specific context, Local Outlier Factor results to be outperforming One-Class SVM, in that it proves to be more stable over a wider range of input parameter values. This property is especially valuable in unsupervised learning since it suggests that the model is keen to adapting to unforeseen patterns.

Ihwg-μnir: A Miniaturised Near-infrared Gas Sensor Based On Substrate-integrated Hollow Waveguides Coupled To A Micro-nir-spectrophotometer.

A miniaturised gas analyser is described and evaluated based on the use of a substrate-integrated hollow waveguide (iHWG) coupled to a microsized near-infrared spectrophotometer comprising a linear variable filter and an array of InGaAs detectors. This gas sensing system was applied to analyse surrogate samples of natural fuel gas containing methane, ethane, propane and butane, quantified by using multivariate regression models based on partial least square (PLS) algorithms and Savitzky-Golay 1(st) derivative data preprocessing. The external validation of the obtained models reveals root mean square errors of prediction of 0.37, 0.36, 0.67 and 0.37% (v/v), for methane, ethane, propane and butane, respectively. The developed sensing system provides particularly rapid response times upon composition changes of the gaseous sample (approximately 2 s) due the minute volume of the iHWG-based measurement cell. The sensing system developed in this study is fully portable with a hand-held sized analyser footprint, and thus ideally suited for field analysis. Last but not least, the obtained results corroborate the potential of NIR-iHWG analysers for monitoring the quality of natural gas and petrochemical gaseous products.

Censored Linear Regression Models For Irregularly Observed Longitudinal Data Using The Multivariate-t Distribution.

In acquired immunodeficiency syndrome (AIDS) studies it is quite common to observe viral load measurements collected irregularly over time. Moreover, these measurements can be subjected to some upper and/or lower detection limits depending on the quantification assays. A complication arises when these continuous repeated measures have a heavy-tailed behavior. For such data structures, we propose a robust structure for a censored linear model based on the multivariate Student's t-distribution. To compensate for the autocorrelation existing among irregularly observed measures, a damped exponential correlation structure is employed. An efficient expectation maximization type algorithm is developed for computing the maximum likelihood estimates, obtaining as a by-product the standard errors of the fixed effects and the log-likelihood function. The proposed algorithm uses closed-form expressions at the E-step that rely on formulas for the mean and variance of a truncated multivariate Student's t-distribution. The methodology is illustrated through an application to an Human Immunodeficiency Virus-AIDS (HIV-AIDS) study and several simulation studies.

Deregulation Of Adipokines Related To Target Organ Damage On Resistant Hypertension.

Resistant hypertension (RHTN) includes patients with controlled blood pressure (BP) (CRHTN) and uncontrolled BP (UCRHTN). In fact, RHTN patients are more likely to have target organ damage (TOD), and resistin, leptin and adiponectin may affect BP control in these subjects. We assessed the relationship between adipokines levels and arterial stiffness, left ventricular hypertrophy (LVH) and microalbuminuria (MA). This cross-sectional study included CRHTN (n=51) and UCRHTN (n=38) patients for evaluating body mass index, ambulatory blood pressure monitoring, plasma adiponectin, leptin and resistin concentrations, pulse wave velocity (PWV), MA and echocardiography. Leptin and resistin levels were higher in UCRHTN, whereas adiponectin levels were lower in this same subgroup. Similarly, arterial stiffness, LVH and MA were higher in UCRHTN subgroup. Adiponectin levels negatively correlated with PWV (r=-0.42, P<0.01), and MA (r=-0.48, P<0.01) only in UCRHTN. Leptin was positively correlated with PWV (r=0.37, P=0.02) in UCRHTN subgroup, whereas resistin was not correlated with TOD in both subgroups. Adiponectin is associated with arterial stiffness and renal injury in UCRHTN patients, whereas leptin is associated with arterial stiffness in the same subgroup. Taken together, our results showed that those adipokines may contribute to vascular and renal damage in UCRHTN patients.

Comparing Near-infrared Conventional Diffuse Reflectance Spectroscopy And Hyperspectral Imaging For Determination Of The Bulk Properties Of Solid Samples By Multivariate Regression: Determination Of Mooney Viscosity And Plasticity Indices Of Natural Rubber.

Conventional reflectance spectroscopy (NIRS) and hyperspectral imaging (HI) in the near-infrared region (1000-2500 nm) are evaluated and compared, using, as the case study, the determination of relevant properties related to the quality of natural rubber. Mooney viscosity (MV) and plasticity indices (PI) (PI0 - original plasticity, PI30 - plasticity after accelerated aging, and PRI - the plasticity retention index after accelerated aging) of rubber were determined using multivariate regression models. Two hundred and eighty six samples of rubber were measured using conventional and hyperspectral near-infrared imaging reflectance instruments in the range of 1000-2500 nm. The sample set was split into regression (n = 191) and external validation (n = 95) sub-sets. Three instruments were employed for data acquisition: a line scanning hyperspectral camera and two conventional FT-NIR spectrometers. Sample heterogeneity was evaluated using hyperspectral images obtained with a resolution of 150 × 150 μm and principal component analysis. The probed sample area (5 cm(2); 24,000 pixels) to achieve representativeness was found to be equivalent to the average of 6 spectra for a 1 cm diameter probing circular window of one FT-NIR instrument. The other spectrophotometer can probe the whole sample in only one measurement. The results show that the rubber properties can be determined with very similar accuracy and precision by Partial Least Square (PLS) regression models regardless of whether HI-NIR or conventional FT-NIR produce the spectral datasets. The best Root Mean Square Errors of Prediction (RMSEPs) of external validation for MV, PI0, PI30, and PRI were 4.3, 1.8, 3.4, and 5.3%, respectively. Though the quantitative results provided by the three instruments can be considered equivalent, the hyperspectral imaging instrument presents a number of advantages, being about 6 times faster than conventional bulk spectrometers, producing robust spectral data by ensuring sample representativeness, and minimizing the effect of the presence of contaminants.

Monitoramento de parâmetros imunológicos em atletas adolescentes de basquetebol durante e após a temporada esportiva= : Monitoring of immunological parameters in adolescent basketball athletes during and after a sports season

Universidade Estadual de Campinas . Faculdade de Educação Física

Contribution Of Antepartum And Intrapartum Hemorrhage To The Burden Of Maternal Near Miss And Death In A National Surveillance Study

Objective To evaluate the occurrence of severe obstetric complications associated with antepartum and intrapartum hemorrhage among women from the Brazilian Network for Surveillance of Severe Maternal Morbidity.Design Multicenter cross-sectional study.Setting Twenty-seven obstetric referral units in Brazil between July 2009 and June 2010.Population A total of 9555 women categorized as having obstetric complications.Methods The occurrence of potentially life-threatening conditions, maternal near miss and maternal deaths associated with antepartum and intrapartum hemorrhage was evaluated. Sociodemographic and obstetric characteristics and the use of criteria for management of severe bleeding were also assessed in these women.Main outcome measures The prevalence ratios with their respective 95% confidence intervals adjusted for the cluster effect of the design, and multiple logistic regression analysis were performed to identify factors independently associated with the occurrence of severe maternal outcome.Results Antepartum and intrapartum hemorrhage occurred in only 8% (767) of women experiencing any type of obstetric complication. However, it was responsible for 18.2% (140) of maternal near miss and 10% (14) of maternal death cases. On multivariate analysis, maternal age and previous cesarean section were shown to be independently associated with an increased risk of severe maternal outcome (near miss or death).Conclusion Severe maternal outcome due to antepartum and intrapartum hemorrhage was highly prevalent among Brazilian women. Certain risk factors, maternal age and previous cesarean delivery in particular, were associated with the occurrence of bleeding.

Multivariate analysis of the effects of soil parameters and environmental factors on the flavonoid content of leaves of Passiflora incarnata L., Passifloraceae

The aim of the present study was to evaluate the effect of soil characteristics (pH, macro- and micro-nutrients), environmental factors (temperature, humidity, period of the year and time of day of collection) and meteorological conditions (rain, sun, cloud and cloud/rain) on the flavonoid content of leaves of Passiflora incarnata L., Passifloraceae. The total flavonoid contents of leaf samples harvested from plants cultivated or collected under different conditions were quantified by high-performance liquid chromatography with ultraviolet detection (HPLC-UV/PAD). Chemometric treatment of the data by principal component (PCA) and hierarchic cluster analyses (HCA) showed that the samples did not present a specific classification in relation to the environmental and soil variables studied, and that the environmental variables were not significant in describing the data set. However, the levels of the elements Fe, B and Cu present in the soil showed an inverse correlation with the total flavonoid contents of the leaves of P. incarnata.

A feasibility study of cell phone and landline phone interviews for monitoring of risk and protection factors for chronic diseases in Brazil

The study objective was to evaluate the feasibility of interviews by cell phone as a complement to interviews by landline to estimate risk and protection factors for chronic non-communicable diseases. Adult cell phone users were evaluated by random digit dialing. Questions asked were: age, sex, education, race, marital status, ownership of landline and cell phones, health condition, weight and height, medical diagnosis of hypertension and diabetes, physical activity, diet, binge drinking and smoking. The estimates were calculated using post-stratification weights. The cell phone interview system showed a reduced capacity to reach elderly and low educated populations. The estimates of the risk and protection factors for chronic non-communicable diseases in cell phone interviews were equal to the estimates obtained by landline phone. Eligibility, success and refusal rates using the cell phone system were lower than those of the landline system, but loss and cost were much higher, suggesting it is unsatisfactory as a complementary method in such a context.

Monitoring Millennium Development Goals in Brazilian municipalities: challenges to be met in facing up to iniquities

The Healthy Cities and Agenda 21 programs improve living and health conditions and affect social and economic determinants of health. The Millennium Development Goals (MDG) indicators can be used to assess the impact of social agendas. A data search was carried out for the period 1997 to 2006 to obtain 48 indicators proposed by the United Nations and a further 74 proposed by the technical group for the MDGin Brazil. There is a scarcity of studies concerned with assessing the MDG at the municipal level. Data from Brazilian health information systems are not always consistent or accurate for municipalities. The lack of availability and reliable data led to the substitution of some indicators. The information systems did not always provide annual data; national household surveys could not be disaggregated at the municipal level and there were also modifications on conceptual definitions over time. As a result, the project created an alternative list with 29 indicators. MDG monitoring at the local community can be important to measure the performance of actions toward improvements in quality of life and social iniquities.

Epidemiology of leisure, transportation, occupational, and household physical activity: prevalence and associated factors

Background: To estimate the prevalence of and identify factors associated with physical activity in leisure, transportation, occupational, and household settings. Methods: This was a cross-sectional study aimed at investigating living and health conditions among the population of São Paulo, Brazil. Data on 1318 adults aged 18 to 65 years were used. To assess physical activity, the long version of the International Physical Activity Questionnaire was applied. Multivariate analysis was conducted using a hierarchical model. Results: The greatest prevalence of insufficient activity related to transportation (91.7%), followed by leisure (77.5%), occupational (68.9%), and household settings (56.7%). The variables associated with insufficient levels of physical activity in leisure were female sex, older age, low education level, nonwhite skin color, smoking, and self-reported poor health; in occupational settings were female sex, white skin color, high education level, self-reported poor health, nonsmoking, and obesity; in transportation settings were female sex; and in household settings, with male sex, separated, or widowed status and high education level. Conclusion: Physical activity in transportation and leisure settings should be encouraged. This study will serve as a reference point in monitoring different types of physical activities and implementing public physical activity policies in developing countries