Bixin is a commercially valuable apocarotenoid pigment found in the seed aril of Bixa orellana. The dynamics and regulation of its biosynthesis and accumulation during seed development remain largely unknown. Here, we combined chemical, anatomical, and transcriptomic data to provide stage-specific resolution of the cellular and molecular events occurring during B. orellana seed development. Seeds at five developmental stages (S1–S5) were used for analysis of bixin content and seed anatomy, and three of them (S1, S3 and S4) selected for Illumina HiSeq sequencing. Bixin accumulated sharply during seed development, particularly during the S2 stage, peaking at the S4 stage, and then decreasing slightly in the S5 stage. Anatomical analysis revealed that bixin accumulated in the large central vacuole of specialized cells, which were scattered throughout the developing mesotesta at the S2 stage, but enlarged progressively at later stages, until they occupied most of the parenchyma in the aril. A total of 13 million reads were generated and assembled into 73,381 protein-encoding contigs, from which 312 were identified as containing 1-deoxy-D-xylulose-5-phosphate/2-C-methyl-D-erythritol-4-phosphate (DOXP/MEP), carotenoid, and bixin pathways genes. Differential expression analysis of these genes revealed that 50 of them were differentially expressed between all the seed developmental stages, including seven carotenoid cleavage dioxygenases, eight aldehyde dehydrogenases and 22 methyltransferases. Taken together, these results provide a comprehensive description of the cellular and molecular events related to the dynamics of bixin synthesis and accumulation during seed development in B. orellana.
Abstractachine learning methods can be used to help design energy-efficient buildings reducing energy loads while maintaining the desired internal temperature. They work by estimating a response from a set of inputs such as building geometry, material properties, project costs, local weather conditions, as well as environmental impacts. These methods require a training phase which considers a dataset drawn from selected variables in the problem domain. This paper evaluates the performance of four machine learning methods to predict cooling and heating loads of residential buildings. The dataset consists of 768 samples with eight input variables and two output variables derived from building designs. The methods were selected based on exhaustive research with cross validation. Four statistical measures and one synthesis index were used for the performance assessment and comparison. The proposed framework resulted in accurate prediction models with optimized parameters that can potentially avoid modeling and testing various designs, helping to economize in the initial phase of the project. Keywords: Energy efficiency. Heating and cooling loads. Machine learning.
Resumo
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.