Produtividade de espiga verde de milho em diferentes densidades de plantas e doses de nitrogênio sob irrigação.

Suplemento. Edição dos resumos do 42º Congresso Brasileiro de Olericultura e 11º Congresso Latinoamericano de Horticultura, Uberlândia, 2002

Gross motor coordination and weight status of Portuguese children aged 6-14 years

Objectives: To construct age- and gender-specific percentiles for gross motor coordination (MC) tests and to explore differences in gross MC in normal-weight, overweight and obese children. Methods: Data are from the "Healthy Growth of Madeira Study", a cross-sectional study carried out in children, aged 6–14 years. All 1,276 participants, 619 boys and 657 girls, were assessed for gross MC (Korperkoordinations Test fur Kinder, KTK), anthropometry (height and body mass), physical activity (Baecke questionnaire) and socioeconomic status (SES). Centile curves for gross MC were obtained for boys and girls separately using generalized additive models for location, scale and shape. Results: A significant main effect for age was found in walking backwards and moving sideways. Boys performed significantly better than girls on moving sideways. At the upper limit of the distributions, interindividual variability was higher in hopping on one leg (girls) and jumping and moving sideways (boys and girls). One-way ANCOVA, controlling for age, physical activity and SES, indicated that normal-weight children scored significantly better than their obese peers in all gross MC tests. Overweight boys and girls also scored significantly better than their obese colleagues in some MC tests. Conclusions: These centile curves can be used as reference data in Portuguese children and youth, aged 6–14 years. Being overweight or obese was a major limitation in MC tests and, therefore, of the children’s health- and performance related physical fitness

Metabolic Signatures of Lung Cancer in Biofluids: NMR-Based Metabonomics of Blood Plasma

In this work, the variations in the metabolic profile of blood plasma from lung cancer patients and healthy controls were investigated through NMR-based metabonomics, to assess the potential of this approach for lung cancer screening and diagnosis. PLS-DA modeling of CPMG spectra from plasma, subjected to Monte Carlo Cross Validation, allowed cancer patients to be discriminated from controls with sensitivity and specificity levels of about 90%. Relatively lower HDL and higher VLDL + LDL in the patients' plasma, together with increased lactate and pyruvate and decreased levels of glucose, citrate, formate, acetate, several amino acids (alanine, glutamine, histidine, tyrosine, valine), and methanol, could be detected. These changes were found to be present at initial disease stages and could be related to known cancer biochemical hallmarks, such as enhanced glycolysis, glutaminolysis, and gluconeogenesis, together with suppressed Krebs cycle and reduced lipid catabolism, thus supporting the hypothesis of a systemic metabolic signature for lung cancer. Despite the possible confounding influence of age, smoking habits, and other uncontrolled factors, these results indicate that NMR-based metabonomics of blood plasma can be useful as a screening tool to identify suspicious cases for subsequent, more specific radiological tests, thus contributing to improved disease management.ERDF - Competitive Factors Thematic Operational ProgrammeFCT/PTDC/ QUI/68017/2006FCOMP-01-0124-FEDER-007439SFRH/BD/ 63430/2009National UNESCO Committee - L'Oréal Medals of Honor for Women in Science 200Portuguese National NMR Network - RNRM

Interaction networks for the identification of boosted H→bb‾H\to b\overline{b} decays

We develop an algorithm based on an interaction network to identify high-transverse-momentum Higgs bosons decaying to bottom quark-antiquark pairs and distinguish them from ordinary jets that reflect the configurations of quarks and gluons at short distances. The algorithm's inputs are features of the reconstructed charged particles in a jet and the secondary vertices associated with them. Describing the jet shower as a combination of particle-to-particle and particle-to-vertex interactions, the model is trained to learn a jet representation on which the classification problem is optimized. The algorithm is trained on simulated samples of realistic LHC collisions, released by the CMS Collaboration on the CERN Open Data Portal. The interaction network achieves a drastic improvement in the identification performance with respect to state-of-the-art algorithms.Comment: 20 pages, 8 figures, 6 tables, version published in PR