Fishers (Paraty, RJ) and fish manipulation time: a variable associated to the choice for consumption and sale

973975Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

The ethnoecology of Caiçara metapopulations (Atlantic Forest, Brazil): ecological concepts and questions

The Atlantic Forest is represented on the coast of Brazil by approximately 7,5% of remnants, much of these concentrated on the country's SE coast. Within these southeastern remnants, we still find the coastal Caiçaras who descend from Native Indians and Portuguese Colonizers. The maintenance of such populations, and their existence in spite of the deforestation that occurred on the Atlantic Forest coast, deserves especial attention and analysis. In this study, I address, in particular, the Caiçaras who live on the coast of São Paulo and Rio de Janeiro States, illustrating with examples of coastal inhabitants from other areas, such as Bahia State (NE coast) and of other forested areas (riverine caboclos of the Amazon). The major focus of this study, based on previous research, performed since 1986 in several populations or villages of the Atlantic Forest coast, is to understand the resilience of the Caiçaras, which is analyzed using ecological concepts, such as metapopulation, resilience and adaptive cycles. The Caiçara populations are located on islands (Búzios, Comprida, Grande, Ilhabela, Jaguanum, Gipóia) and on the coast (Bertioga, Puruba, Picinguaba, among others). Information gathered about the Caiçaras regarding the economic cycles of the local regions, along with ecological, historical and economic data available, are used to understand such resilience, and are complemented with comparative examples from the Brazilian Amazon and with variables such as the local restrictions imposed by environmental governmental agencies

Energy- and flux-budget (EFB) turbulence closure model for the stably stratified flows. Part I: Steady-state, homogeneous regimes

We propose a new turbulence closure model based on the budget equations for the key second moments: turbulent kinetic and potential energies: TKE and TPE (comprising the turbulent total energy: TTE = TKE + TPE) and vertical turbulent fluxes of momentum and buoyancy (proportional to potential temperature). Besides the concept of TTE, we take into account the non-gradient correction to the traditional buoyancy flux formulation. The proposed model grants the existence of turbulence at any gradient Richardson number, Ri. Instead of its critical value separating - as usually assumed - the turbulent and the laminar regimes, it reveals a transition interval, 0.1< Ri <1, which separates two regimes of essentially different nature but both turbulent: strong turbulence at Ri<<1; and weak turbulence, capable of transporting momentum but much less efficient in transporting heat, at Ri>1. Predictions from this model are consistent with available data from atmospheric and lab experiments, direct numerical simulation (DNS) and large-eddy simulation (LES).Comment: 40 pages, 6 figures, Boundary-layer Meteorology, resubmitted, revised versio

An Electrode Array for Limiting Blood Loss During Liver Resection: Optimization via Mathematical Modeling

Liver resection is the current standard treatment for patients with both primary and metastatic liver cancer. The principal causes of morbidity and mortality after liver resection are related to blood loss (typically between 0.5 and 1 L), especially in cases where transfusion is required. Blood transfusions have been correlated with decreased long-term survival, increased risk of perioperative mortality and complications. The goal of this study was to evaluate different designs of a radiofrequency (RF) electrode array for use during liver resection. The purpose of this electrode array is to coagulate a slice of tissue including large vessels before resecting along that plane, thereby significantly reducing blood loss. Finite Element Method models were created to evaluate monopolar and bipolar power application, needle and blade shaped electrodes, as well as different electrode distances. Electric current density, temperature distribution, and coagulation zone sizes were measured. The best performance was achieved with a design of blade shaped electrodes (5 × 0.1 mm cross section) spaced 1.5 cm apart. The electrodes have power applied in bipolar mode to two adjacent electrodes, then switched sequentially in short intervals between electrode pairs to rapidly heat the tissue slice. This device produces a ~1.5 cm wide coagulation zone, with temperatures over 97 ºC throughout the tissue slice within 3 min, and may facilitate coagulation of large vessels

Disease-Free Survival after Hepatic Resection in Hepatocellular Carcinoma Patients: A Prediction Approach Using Artificial Neural Network

Background: A database for hepatocellular carcinoma (HCC) patients who had received hepatic resection was used to develop prediction models for 1-, 3- and 5-year disease-free survival based on a set of clinical parameters for this patient group. Methods: The three prediction models included an artificial neural network (ANN) model, a logistic regression (LR) model, and a decision tree (DT) model. Data for 427, 354 and 297 HCC patients with histories of 1-, 3- and 5-year disease-free survival after hepatic resection, respectively, were extracted from the HCC patient database. From each of the three groups, 80 % of the cases (342, 283 and 238 cases of 1-, 3- and 5-year disease-free survival, respectively) were selected to provide training data for the prediction models. The remaining 20 % of cases in each group (85, 71 and 59 cases in the three respective groups) were assigned to validation groups for performance comparisons of the three models. Area under receiver operating characteristics curve (AUROC) was used as the performance index for evaluating the three models. Conclusions: The ANN model outperformed the LR and DT models in terms of prediction accuracy. This study demonstrated the feasibility of using ANNs in medical decision support systems for predicting disease-free survival based on clinical databases in HCC patients who have received hepatic resection