Predicting smear negative pulmonary tuberculosis with classification trees and logistic regression: a cross-sectional study

Abstract Background Smear negative pulmonary tuberculosis (SNPT) accounts for 30% of pulmonary tuberculosis cases reported yearly in Brazil. This study aimed to develop a prediction model for SNPT for outpatients in areas with scarce resources. Methods The study enrolled 551 patients with clinical-radiological suspicion of SNPT, in Rio de Janeiro, Brazil. The original data was divided into two equivalent samples for generation and validation of the prediction models. Symptoms, physical signs and chest X-rays were used for constructing logistic regression and classification and regression tree models. From the logistic regression, we generated a clinical and radiological prediction score. The area under the receiver operator characteristic curve, sensitivity, and specificity were used to evaluate the model's performance in both generation and validation samples. Results It was possible to generate predictive models for SNPT with sensitivity ranging from 64% to 71% and specificity ranging from 58% to 76%. Conclusion The results suggest that those models might be useful as screening tools for estimating the risk of SNPT, optimizing the utilization of more expensive tests, and avoiding costs of unnecessary anti-tuberculosis treatment. Those models might be cost-effective tools in a health care network with hierarchical distribution of scarce resources.

Assessment of SNPs for linkage mapping in Eucalyptus: construction of a consensus SNP/microsatellite map from two unrelated pedigrees

Financial support. Brazilian Ministry of Science and Technology (CNPq Grant 577047-2008-6), FAP-DF NEXTREE Grant 193.000.570/2009 and EMBRAPA Macroprogram 2 project grant 02.07.01.004.

Canine heartworm disease in Porto Velho: first record, distribution map and occurrence of positive mosquitoes

The aim of this study was to make the first report on canine heartworm disease in the state of Rondônia and confirm its transmission in this state. Blood samples were randomly collected from 727 dogs in the city of Porto Velho. The samples were analyzed to search for microfilariae and circulating antigens, using three different techniques: optical microscopy on thick blood smears stained with Giemsa; immunochromatography; and PCR. Mosquitoes were collected inside and outside the homes of all the cases of positive dogs and were tested using PCR to search for DNA of Dirofilaria immitis. Ninety-three blood samples out of 727 (12.8%) were positive according to the immunoassay technique and none according to the thick smear method. Among the 93 positive dogs, 89 (95.7%) were born in Porto Velho. No difference in the frequency of infection was observed between dogs raised indoors and in the yard. PCR on the mosquitoes resulted in only one positive pool. This result shows that the transmission of canine heartworm disease is occurring in the city of Porto Velho and that there is moderate prevalence among the dogs. The techniques of immunochromatography and PCR were more effective for detecting canine heartworm than thick blood smears. The confirmation of canine heartworm disease transmission in Porto Velho places this disease in the ranking for differential diagnosis of pulmonary nodules in humans in Rondônia.

Avaliação hemodinâmica de duas vias de reposição volêmica em um modelo de hemorragia da veia cava caudal em porcos

Introdução: A reposição volêmica em pacientes traumatizados tem sido controvérsa. O A.T.L.S. recomenda a infusão de um grande volume de fluidos na fase inicial de tratamento, enquanto outros autores recomendam a administração somente quando do controle da hemorragia. O acesso venoso femoral é contra indicado em pacientes com trauma abdominal por temor de aumento de hemorragia. A solução hipertônica de NaCl a 7,5% (SH) possui benefícios consideráveis de logística e de recuperação hemodinâmica com pequenos volumes de infusão, semelhante as vantagens das soluções padrões isotônicas na fase pré-hospitalar. Objetivos: Criar um modelo de choque hemorrágico induzido por trauma venoso. Avaliar a hemodinâmica e o volume de hemorragia abdominal nos animais submetidos a choque hemorrágico e tratados com SH via acesso femoral e jugular. Métodos: Em 18 porcos da raça landrace, divididos em 3 grupos de 6 animais (Controle, Jugular e Femoral), foi induzido um choque hipovolêmico não controlado pela ruptura da veia cava caudal. Os animais do grupo controle (GC) foram observados por 40 minutos quanto ao seu padrão hemodinâmico de Pressão de Artéria Pulmonar (PAP), Pressão Artérial Média (PAM), Débito Cardíaco (DC) e Fluxo de Veia Porta (FVP), porém sem reposição volêmica. Os animais dos grupos Femoral (GC) e Jugular (GJ) foram tratados com 4 ml/Kg de solução hipertônica de NaCl a 7,5% (SH) aos 20 minutos de experimento. Ao final do experimento, o volume de hemorragia abdominal foi mensurado.Resultados: O grupo controle (GC) apresentou queda dos valores hemodinâmicos aos 10 minutos e estes permaneceram estáveis até o final do experimento. Os animais dos grupos tratamento (GF e GJ) apresentaram melhora da hemodinâmica aos 30 minutos, sem aumento da hemorragia abdominal. Conclusão: A solução hipertônica de NaCl (SH) permitiu a melhora parcial da hemodinâmica no modelo de choque hipovolêmico, sem aumento da hemorragia, independentemente do acesso utilizada para a infusão.

Dense disparity map estimation respecting image discontinuities

[EN] We present an energy based approach to estimate a dense disparity map from a set of two weakly calibrated stereoscopic images while preserving its discontinuities resulting from image boundaries. We first derive a simplified expression for the disparity that allows us to estimate it from a stereo pair of images using an energy minimization approach. We assume that the epipolar geometry is known, and we include this information in the energy model. Discontinuities are preserved by means of a regularization term based on the Nagel-Enkelmann operator. We investigate the associated Euler-Lagrange equation of the energy functional, and we approach the solution of the underlying partial differential equation (PDE) using a gradient descent method The resulting parabolic problem has a unique solution. In order to reduce the risk to be trapped within some irrelevant local minima during the iterations, we use a focusing strategy based on a linear scalespace. Experimental results on both synthetic and real images arere presented to illustrate the capabilities of this PDE and scale-space based method.

The assessment of change in rural landscape and the analysis of the driving forces. Proposal of a spatial model for the rural built environment

The presented study carried out an analysis on rural landscape changes. In particular the study focuses on the understanding of driving forces acting on the rural built environment using a statistical spatial model implemented through GIS techniques. It is well known that the study of landscape changes is essential for a conscious decision making in land planning. From a bibliography review results a general lack of studies dealing with the modeling of rural built environment and hence a theoretical modelling approach for such purpose is needed. The advancement in technology and modernity in building construction and agriculture have gradually changed the rural built environment. In addition, the phenomenon of urbanization of a determined the construction of new volumes that occurred beside abandoned or derelict rural buildings. Consequently there are two types of transformation dynamics affecting mainly the rural built environment that can be observed: the conversion of rural buildings and the increasing of building numbers. It is the specific aim of the presented study to propose a methodology for the development of a spatial model that allows the identification of driving forces that acted on the behaviours of the building allocation. In fact one of the most concerning dynamic nowadays is related to an irrational expansion of buildings sprawl across landscape. The proposed methodology is composed by some conceptual steps that cover different aspects related to the development of a spatial model: the selection of a response variable that better describe the phenomenon under study, the identification of possible driving forces, the sampling methodology concerning the collection of data, the most suitable algorithm to be adopted in relation to statistical theory and method used, the calibration process and evaluation of the model. A different combination of factors in various parts of the territory generated favourable or less favourable conditions for the building allocation and the existence of buildings represents the evidence of such optimum. Conversely the absence of buildings expresses a combination of agents which is not suitable for building allocation. Presence or absence of buildings can be adopted as indicators of such driving conditions, since they represent the expression of the action of driving forces in the land suitability sorting process. The existence of correlation between site selection and hypothetical driving forces, evaluated by means of modeling techniques, provides an evidence of which driving forces are involved in the allocation dynamic and an insight on their level of influence into the process. GIS software by means of spatial analysis tools allows to associate the concept of presence and absence with point futures generating a point process. Presence or absence of buildings at some site locations represent the expression of these driving factors interaction. In case of presences, points represent locations of real existing buildings, conversely absences represent locations were buildings are not existent and so they are generated by a stochastic mechanism. Possible driving forces are selected and the existence of a causal relationship with building allocations is assessed through a spatial model. The adoption of empirical statistical models provides a mechanism for the explanatory variable analysis and for the identification of key driving variables behind the site selection process for new building allocation. The model developed by following the methodology is applied to a case study to test the validity of the methodology. In particular the study area for the testing of the methodology is represented by the New District of Imola characterized by a prevailing agricultural production vocation and were transformation dynamic intensively occurred. The development of the model involved the identification of predictive variables (related to geomorphologic, socio-economic, structural and infrastructural systems of landscape) capable of representing the driving forces responsible for landscape changes.. The calibration of the model is carried out referring to spatial data regarding the periurban and rural area of the study area within the 1975-2005 time period by means of Generalised linear model. The resulting output from the model fit is continuous grid surface where cells assume values ranged from 0 to 1 of probability of building occurrences along the rural and periurban area of the study area. Hence the response variable assesses the changes in the rural built environment occurred in such time interval and is correlated to the selected explanatory variables by means of a generalized linear model using logistic regression. Comparing the probability map obtained from the model to the actual rural building distribution in 2005, the interpretation capability of the model can be evaluated. The proposed model can be also applied to the interpretation of trends which occurred in other study areas, and also referring to different time intervals, depending on the availability of data. The use of suitable data in terms of time, information, and spatial resolution and the costs related to data acquisition, pre-processing, and survey are among the most critical aspects of model implementation. Future in-depth studies can focus on using the proposed model to predict short/medium-range future scenarios for the rural built environment distribution in the study area. In order to predict future scenarios it is necessary to assume that the driving forces do not change and that their levels of influence within the model are not far from those assessed for the time interval used for the calibration.

Entwicklung von computergestützten Hangstabilitätsmodellen zur Erstellung einer Gefahrenhinweiskarte für die Region Rheinhessen

Das Ziel der Arbeit war die Entwicklung computergestützter Methoden zur Erstellung einer Gefahrenhinweiskarte für die Region Rheinhessen, zur Minimierung der Hangrutschungsgefährdung. Dazu wurde mit Hilfe zweier statistischer Verfahren (Diskriminanzanalyse, Lo­gistische Regression) und einer Methode aus dem Bereich der Künstlichen Intelligenz (Fuzzy Logik) versucht, die potentielle Gefährdung auch solcher Hänge zu klassifizieren, die bis heute noch nicht durch Massenbewegungen aufgefallen sind. Da ingenieurgeologische und geotechnische Hanguntersuchungen aus Zeit und Kostengründen im regionalen Maßstab nicht möglich sind, wurde auf punktuell vorhandene Datenbestände zu einzelnen Rut­schungen des Winters 1981/82, die in einer Rutschungsdatenbank zu­sammengefaßt sind, zurückgegriffen, wobei die daraus gewonnenen Erkenntnisse über Prozeßmechanismen und auslösende Faktoren genutzt und in das jeweilige Modell integriert wurden. Flächenhafte Daten (Lithologie, Hangneigung, Landnutzung, etc.), die für die Berechnung der Hangstabilität notwendig sind, wurden durch Fernerkundungsmethoden, dem Digitalisieren von Karten und der Auswertung von Digitalen Geländemodellen (Reliefanalyse) gewonnen. Für eine weiterführende Untersuchung von einzelnen, als rutschgefährdet klassifi­zierten Bereichen der Gefahrenhinweiskarte, wurde am Beispiel eines Testgebietes, eine auf dem infinite-slope-stability Modell aufbauende Me­thode untersucht, die im Maßstabsbereich von Grundkarten (1:5000) auch geotechnische und hydrogeologische Parameter berücksichtigt und damit eine genauere, der jeweiligen klimatischen Situation angepaßte, Gefahrenabschätzung ermöglicht.

A novel map-matching procedure for low-sampling GPS data with applications to traffic flow analysis

An extensive sample (2%) of private vehicles in Italy are equipped with a GPS device that periodically measures their position and dynamical state for insurance purposes. Having access to this type of data allows to develop theoretical and practical applications of great interest: the real-time reconstruction of traffic state in a certain region, the development of accurate models of vehicle dynamics, the study of the cognitive dynamics of drivers. In order for these applications to be possible, we first need to develop the ability to reconstruct the paths taken by vehicles on the road network from the raw GPS data. In fact, these data are affected by positioning errors and they are often very distanced from each other (~2 Km). For these reasons, the task of path identification is not straightforward. This thesis describes the approach we followed to reliably identify vehicle paths from this kind of low-sampling data. The problem of matching data with roads is solved with a bayesian approach of maximum likelihood. While the identification of the path taken between two consecutive GPS measures is performed with a specifically developed optimal routing algorithm, based on A* algorithm. The procedure was applied on an off-line urban data sample and proved to be robust and accurate. Future developments will extend the procedure to real-time execution and nation-wide coverage.

Recupero della funzione renale in pazienti con acute kidney injury (AKI) sottoposti a terapia sostitutiva renale

L’insufficienza renale acuta(AKI) grave che richiede terapia sostitutiva, è una complicanza frequente nelle unità di terapia intensiva(UTI) e rappresenta un fattore di rischio indipendente di mortalità. Scopo dello studio é stato valutare prospetticamente, in pazienti “critici” sottoposti a terapie sostitutive renali continue(CRRT) per IRA post cardiochirurgia, la prevalenza ed il significato prognostico del recupero della funzione renale(RFR). Pazienti e Metodi:Pazienti(pz) con AKI dopo intervento di cardiochirurgia elettivo o in emergenza con disfunzione di due o più organi trattati con CRRT. Risultati:Dal 1996 al 2011, 266 pz (M 195,F 71, età 65.5±11.3aa) sono stati trattati con CRRT. Tipo di intervento: CABG(27.6%), dissecazione aortica(33%), sostituzione valvolare(21.1%), CABG+sostituzione valvolare(12.6%), altro(5.7%). Parametri all’inizio del trattamento: BUN 86.1±39.4, creatininemia(Cr) 3.96±1.86mg/dL, PAM 72.4±13.6mmHg, APACHE II score 30.7±6.1, SOFAscore 13.7±3. RIFLE: Risk (11%), Injury (31.4%), Failure (57.6%). AKI oligurica (72.2%), ventilazione meccanica (93.2%), inotropi (84.5%). La sopravvivenza a 30 gg ed alla dimissione è stata del 54.2% e del 37.1%. La sopravvivenza per stratificazione APACHE II: <24=85.1 e 66%, 25-29=63.5 e 48.1%, 30-34=51.8 e 31.8%, >34=31.6 e 17.7%. RFR ha consentito l’interruzione della CRRT nel 87.8% (86/98) dei survivors (Cr 1.4±0.6mg/dL) e nel 14.5% (24/166) dei nonsurvivors (Cr 2.2±0.9mg/dL) con un recupero totale del 41.4%. RFR è stato osservato nel 59.5% (44/74) dei pz non oligurici e nel 34.4% dei pz oligurici (66/192). La distribuzione dei pz sulla base dei tempi di RFR è stata:<8=38.2%, 8-14=20.9%, 15-21=11.8%, 22-28=10.9%, >28=18.2%. All’analisi multivariata, l’oliguria, l’età e il CV-SOFA a 7gg dall’inizio della CRRT si sono dimostrati fattori prognostici sfavorevoli su RFR(>21gg). RFR si associa ad una sopravvivenza elevata(78.2%). Conclusioni:RFR significativamente piu frequente nei pz non oligurici si associa ad una sopravvivenza alla dimissione piu elevata. La distribuzione dei pz in rapporto ad APACHE II e SOFAscore dimostra che la sopravvivenza e RFR sono strettamente legati alla gravità della patologia.

Bayesian space-time data fusion for real-time forecasting and map uncertainty

Environmental computer models are deterministic models devoted to predict several environmental phenomena such as air pollution or meteorological events. Numerical model output is given in terms of averages over grid cells, usually at high spatial and temporal resolution. However, these outputs are often biased with unknown calibration and not equipped with any information about the associated uncertainty. Conversely, data collected at monitoring stations is more accurate since they essentially provide the true levels. Due the leading role played by numerical models, it now important to compare model output with observations. Statistical methods developed to combine numerical model output and station data are usually referred to as data fusion. In this work, we first combine ozone monitoring data with ozone predictions from the Eta-CMAQ air quality model in order to forecast real-time current 8-hour average ozone level defined as the average of the previous four hours, current hour, and predictions for the next three hours. We propose a Bayesian downscaler model based on first differences with a flexible coefficient structure and an efficient computational strategy to fit model parameters. Model validation for the eastern United States shows consequential improvement of our fully inferential approach compared with the current real-time forecasting system. Furthermore, we consider the introduction of temperature data from a weather forecast model into the downscaler, showing improved real-time ozone predictions. Finally, we introduce a hierarchical model to obtain spatially varying uncertainty associated with numerical model output. We show how we can learn about such uncertainty through suitable stochastic data fusion modeling using some external validation data. We illustrate our Bayesian model by providing the uncertainty map associated with a temperature output over the northeastern United States.