"Quem furta mais e esconde": o roubo de escravos em Pernambuco, 1832-1855

Using police and trial records, the author found that slaves were stolen frequently in Pernambuco during the years I832 1855. The greatest number of thefts occurred in the IS-W\u27s, wncn the international trade to that province was in the decline. Slaves were usually stolen in Recife, the provincial capital, and sold on the sugar plantations. Butsenhores de engenho also bought slaves stolen from other plantations, and in some cases the owners were active participants in the thefts. Despite their legal conditions as chattels, bondsmen were seldom passive objects in these crimes: only their consent could assure success. Therefore slaves had a bargaining power which they could use to improve their situation within the slave regime. Being stolen meant, in effect, choosing another master, for better or worse. In the latter case, the possibility remained of returning to the former owner.Através da an´álise de registros policiais e de casos em tribunais, o autor constatou que os roubos de escravos em Pernambuco foram frequentes no período de 1832 a 1855. O maior número desses crimes ocorreu na década de 1840, época de declínio do tráfico internacional de escravos para essa província. Em geral, roubavam-se os escravos em Recife, capital da província, para vendê-los aos engenhos. Entretanto os senhores de engenho também compravam escravos roubados de outros engenhos e, em alguns casos, participavam ativamente dos roubos. Apesar de sua condição legal igualá-los a um bem móvel, os escravos raramente se mantinham como objetos passives nesses crimes; somente suaconivência podia assegurar o êxito da ação. Portanto, os cativos possuíam urn poder de barganha que podiam utilizar para melhorar sua situação no regime escravista. Ser roubado significava, de fato, escolher outro senhor, que poderia ser melhor ou pior que o anterior. Nesta segunda hipótese, permanecia ainda a possibilidade de voltar para o dono original.

Combined effects of time spent in physical activity, sedentary behaviors and sleep on obesity and cardio-metabolic health markers: a novel compositional data analysis approach

<div><p>The associations between time spent in sleep, sedentary behaviors (SB) and physical activity with health are usually studied without taking into account that time is finite during the day, so time spent in each of these behaviors are codependent. Therefore, little is known about the combined effect of time spent in sleep, SB and physical activity, that together constitute a composite whole, on obesity and cardio-metabolic health markers. Cross-sectional analysis of NHANES 2005–6 cycle on N = 1937 adults, was undertaken using a compositional analysis paradigm, which accounts for this intrinsic codependence. Time spent in SB, light intensity (LIPA) and moderate to vigorous activity (MVPA) was determined from accelerometry and combined with self-reported sleep time to obtain the 24 hour time budget composition. The distribution of time spent in sleep, SB, LIPA and MVPA is significantly associated with BMI, waist circumference, triglycerides, plasma glucose, plasma insulin (all p<0.001), and systolic (p<0.001) and diastolic blood pressure (p<0.003), but not HDL or LDL. Within the composition, the strongest positive effect is found for the proportion of time spent in MVPA. Strikingly, the effects of MVPA replacing another behavior and of MVPA being displaced by another behavior are asymmetric. For example, re-allocating 10 minutes of SB to MVPA was associated with a lower waist circumference by 0.001% but if 10 minutes of MVPA is displaced by SB this was associated with a 0.84% higher waist circumference. The proportion of time spent in LIPA and SB were detrimentally associated with obesity and cardiovascular disease markers, but the association with SB was stronger. For diabetes risk markers, replacing SB with LIPA was associated with more favorable outcomes. Time spent in MVPA is an important target for intervention and preventing transfer of time from LIPA to SB might lessen the negative effects of physical inactivity.</p></div

Algorithms to predict cerebral malaria in murine models using the SHIRPA protocol

<p>Abstract</p> <p>Background</p> <p><it>Plasmodium berghei </it>ANKA infection in C57Bl/6 mice induces cerebral malaria (CM), which reproduces, to a large extent, the pathological features of human CM. However, experimental CM incidence is variable (50-100%) and the period of incidence may present a range as wide as 6-12 days post-infection. The poor predictability of which and when infected mice will develop CM can make it difficult to determine the causal relationship of early pathological changes and outcome. With the purpose of contributing to solving these problems, algorithms for CM prediction were built.</p> <p>Methods</p> <p>Seventy-eight <it>P. berghei</it>-infected mice were daily evaluated using the primary SHIRPA protocol. Mice were classified as CM+ or CM- according to development of neurological signs on days 6-12 post-infection. Logistic regression was used to build predictive models for CM based on the results of SHIRPA tests and parasitaemia.</p> <p>Results</p> <p>The overall CM incidence was 54% occurring on days 6-10. Some algorithms had a very good performance in predicting CM, with the area under the receiver operator characteristic (_auROC) curve ≥ 80% and positive predictive values (PV+) ≥ 95, and correctly predicted time of death due to CM between 24 and 72 hours before development of the neurological syndrome (_auROC = 77-93%; PV+ = 100% using high cut off values). Inclusion of parasitaemia data slightly improved algorithm performance.</p> <p>Conclusion</p> <p>These algorithms work with data from a simple, inexpensive, reproducible and fast protocol. Most importantly, they can predict CM development very early, estimate time of death, and might be a valuable tool for research using CM murine models.</p

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

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