Germination of Ocotea pulchella (Nees) Mez (Lauraceae) seeds in laboratory and natural restinga environment conditions

The germination response of Ocotea pulchella (Nees) Mez seeds to light, temperature, water level and pulp presence is introduced. The laboratory assays were carried out in germination chambers and thermal-gradient apparatus, whereas the field assays were performed in environments with distinct light, temperature and soil moisture conditions within a permanent parcel of Restinga forest of the Parque Estadual da Ilha do Cardoso, Cananéia, São Paulo. The seeds do not exhibit dormancy, they are non photoblastic, and a loss of viability in dry stored seeds can be related to a decrease in water content of the seed. The presence of the pulp and the flooded substratum influenced negatively the germination of O. pulchella seeds tested in the laboratory. Otherwise, light and temperature probably are not limiting factors of the germination of O. pulchella seeds in the natural environment of Restinga. The optimum temperature range for germination of Ocotea pulchella seeds was 20 to 32 ºC, the minimum or base temperature estimated was 11 ºC and the maximum ranged between 33 and 42 ºC. The isotherms exhibited a sigmoidal pattern well described by the Weibull model in the sub-optimal temperature range. The germinability of O. pulchella seeds in the understorey, both in wet and dry soil, was higher than in gaps. Germination was not affected by fluctuations in soil moisture content in the understorey environment, whereas in gaps, germination was higher in wet soils. Thus, the germination of this species involves the interaction of two or more factors and it cannot be explained by a single factor

Performance and carcass characteristics of free-range broiler chickens fed diets containing alternative feedstuffs

The present study was carried out to evaluate the effects of alternative feedstuffs as partial substitutes of corn and soybean in free-range broiler diets on performance, carcass yield and technical-economic viability. A total of 400 Pescoço Pelado broilers were distributed in a completely randomized experimental design (CRD), with four treatments (treatment 1:Control; treatment 2: 10% rice bran inclusion; treatment 3: 10% ground cassava leaves; and treatment 4: 10% ground lead tree hay) with four replicates per treatment. Each replicate consisted of a group of 25 birds per paddock, separated per sex. Initial weight (IW), final weight (FW), body weight (BW), daily weight gain (DWG), feed intake (FI) and feed conversion ratio (FCR) were evaluated. Carcass, cuts (breast, thigh, drumstick, back, neck, leg and wings), abdominal fat and giblets (gizzard, heart and liver) yields were determined. The technical-economic viability of each treatment was assessed by determining the cost of feed per kg body weight, economic efficiency index and cost. The highest final weights were obtained with the use of rice bran. Rice bran and cassava leaves promoted higher carcass yield, as well as lower back and abdominal fat yields. The use of cassava leaves showed better economic efficiency among the treatments with alternative feedstuffs. The use of alternative feedstuffs at 10% inclusion in substitution of corn and soybean meal did not not result in major changes in performance and carcass parameters, and economic efficiency, and therefore, their use is recommended when the availability or the price of key ingredients, such as soybean meal and corn, increase

Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset