Factors Associated with Height Catch-Up and Catch-Down Growth Among Schoolchildren

In developed countries, children with intrauterine growth restriction (IUGR) or born preterm (PT) tend to achieve catch-up growth. There is little information about height catch-up in developing countries and about height catch-down in both developed and developing countries. We studied the effect of IUGR and PT birth on height catch-up and catch-down growth of children from two cohorts of liveborn singletons. Data from 1,463 children was collected at birth and at school age in Ribeirao Preto (RP), a more developed city, and in Sao Luis (SL), a less developed city. A change in z-score between schoolchild height z-score and birth length z-score >= 0.67 was considered catch-up; a change in z-score <=-0.67 indicated catch-down growth. The explanatory variables were: appropriate weight for gestational age/PT birth in four categories: term children without IUGR (normal), IUGR only (term with IUGR), PT only ( preterm without IUGR) and preterm with IUGR; infant's sex; maternal parity, age, schooling and marital status; occupation of family head; family income and neonatal ponderal index (PI). The risk ratio for catch-up and catch-down was estimated by multinomial logistic regression for each city. In RP, preterms without IUGR (RR = 4.13) and thin children (PI<10th percentile, RR = 14.39) had a higher risk of catch-down; catch-up was higher among terms with IUGR (RR = 5.53), preterms with IUGR (RR = 5.36) and children born to primiparous mothers (RR = 1.83). In SL, catch-down was higher among preterms without IUGR (RR = 5.19), girls (RR = 1.52) and children from low-income families ( RR = 2.74); the lowest risk of catch-down (RR = 0.27) and the highest risk of catch-up (RR = 3.77) were observed among terms with IUGR. In both cities, terms with IUGR presented height catch-up growth whereas preterms with IUGR only had height catch-up growth in the more affluent setting. Preterms without IUGR presented height catch-down growth, suggesting that a better socioeconomic situation facilitates height catch-up and prevents height catch-down growth.

Catch-up growth in children after repair of Tetralogy of Fallot

Purpose: To evaluate the growth of children after repair of Tetralogy of Fallot, as well as the influence of residual lesions and socio-economic status. Methods: A total of 17 children, including 10 boys with a median age of 16 months at surgery, were enrolled in a retrospective cohort, in a tertiary care university hospital. Anthropometric (as z-scores), clinical, nutritional, and social data were collected. Results: Weight-for-age and weight-for-height z-scores decreased pre-operatively and recovered post-operatively in almost all patients, most markedly weight for age. Weight-for-height z-scores improved, but were still lower than birth values in the long term. Long-term height-for-age z-scores were higher than those at birth, surgery, and 3 months post-operatively. Most patients showed catch-up growth for height for age (70%), weight for age (82%), and weight for height (70%). Post-operative residual lesions (76%) influenced weight-for-age z-scores. Despite the fact that most patients (70%) were from low-income families, energy intake was above the estimated requirement for age and gender in all but one patient. There was no influence of socio-economic status on pre- and post-operative growth. Bone age was delayed and long-term-predicted height was within mid-parental height limits in 16 children (93%). Conclusion: Children submitted to Tetralogy of Fallot repair had pre-operative acute growth restriction and showed post-operative catch-up growth for weight and height. Acute growth restriction could still be present in the long term.

Catch rates of artisanal fisheries targeting Dasyatis guttata (Bloch & Schneider, 1801) in Todos os Santos Bay, northeastern Brazil

Dasyatis guttata has been target of artisanal fisheries in the coast of Bahia (Northeast Brazil) mainly by “arraieira” (gillnet) and “grozeira” (bottom long-line), but until now there is no stock assessment study. One of the important data for this knowledge is reliable indices of abundance. The aims of the present work are to: (1) estimate the best predictor for relative abundance (catch-per-unit-of-effort, CPUE), examining whether catch (production – kg) was related to: soak time of the gear, size of the gillnet or number of hooks, applying generalized linear model (GLM); (2) estimate the annual CPUE (kg/hooks and kg/m) averaged by gear; and (3) assess the temporal CPUE variance. Based on monthly sampling between January 2012 and January 2013, 222 landings by grozeira and 76 by arraiaiera were recorded in the two landing sites in Todos os Santos Bay, Bahia. A total of 14,550 kg (average = 44 kg/month) of D. guttata was captured. Models for both gears were highly significant (P < 0.0001). The analysis indicated that the most appropriate variable for CPUE analysis was the size of the gillnet (P < 0.001) and the number of hooks (P < 0.0001). Soak time of the gear was not significant for both gears (P = 0.4). High residual deviance expresses the complexity of the relations between ecosystem factors and other fisheries factors affecting relative abundance, which were not considered in this study. The average CPUE by grozeira was 6.39 kg/100 hooks ± 8.89 and by arraieira, 1.47 kg/100 m ± 1.66 over the year. Kruskal-Wallis test showed effect of the month on the mean grozeira CPUE (P = <0.001), but no effect (P = 0.096) on the mean arraieira CPUE. Grozeira CPUE values were highest in December and March, and lowest between May to August

QuMinS: fast and scalable querying, mining and summarizing multi-modal databases

Given a large image set, in which very few images have labels, how to guess labels for the remaining majority? How to spot images that need brand new labels different from the predefined ones? How to summarize these data to route the user’s attention to what really matters? Here we answer all these questions. Specifically, we propose QuMinS, a fast, scalable solution to two problems: (i) Low-labor labeling (LLL) – given an image set, very few images have labels, find the most appropriate labels for the rest; and (ii) Mining and attention routing – in the same setting, find clusters, the top-'N IND.O' outlier images, and the 'N IND.R' images that best represent the data. Experiments on satellite images spanning up to 2.25 GB show that, contrasting to the state-of-the-art labeling techniques, QuMinS scales linearly on the data size, being up to 40 times faster than top competitors (GCap), still achieving better or equal accuracy, it spots images that potentially require unpredicted labels, and it works even with tiny initial label sets, i.e., nearly five examples. We also report a case study of our method’s practical usage to show that QuMinS is a viable tool for automatic coffee crop detection from remote sensing images.