Dataset Information

Comparison of Cell Disruption Methods for Improving Lipid Extraction from Thraustochytrid Strains.

ABSTRACT: Lipid extraction is an integral part of biodiesel production, as it facilitates the release of fatty acids from algal cells. To utilise thraustochytrids as a potential source for lipid production. We evaluated the extraction efficiency of various solvents and solvent combinations for lipid extraction from Schizochytrium sp. S31 and Thraustochytrium sp. AMCQS5-5. The maximum lipid extraction yield was 22% using a chloroform:methanol ratio of 2:1. We compared various cell disruption methods to improve lipid extraction yields, including grinding with liquid nitrogen, bead vortexing, osmotic shock, water bath, sonication and shake mill. The highest lipid extraction yields were obtained using osmotic shock and 48.7% from Schizochytrium sp. S31 and 29.1% from Thraustochytrium sp. AMCQS5-5. Saturated and monounsaturated fatty acid contents were more than 60% in Schizochytrium sp. S31 which suggests their suitability for biodiesel production.

SUBMITTER: Byreddy AR

PROVIDER: S-EPMC4557016 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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Comparison of Cell Disruption Methods for Improving Lipid Extraction from Thraustochytrid Strains.

Byreddy Avinesh R AR Gupta Adarsha A Barrow Colin J CJ Puri Munish M

Marine drugs 20150811 8

Lipid extraction is an integral part of biodiesel production, as it facilitates the release of fatty acids from algal cells. To utilise thraustochytrids as a potential source for lipid production. We evaluated the extraction efficiency of various solvents and solvent combinations for lipid extraction from Schizochytrium sp. S31 and Thraustochytrium sp. AMCQS5-5. The maximum lipid extraction yield was 22% using a chloroform:methanol ratio of 2:1. We compared various cell disruption methods to imp ...[more]

PMID: 26270668

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Project description:BackgroundAutomated ICD coding on medical texts via machine learning has been a hot topic. Related studies from medical field heavily relies on conventional bag-of-words (BoW) as the feature extraction method, and do not commonly use more complicated methods, such as word2vec (W2V) and large pretrained models like BERT. This study aimed at uncovering the most effective feature extraction methods for coding models by comparing BoW, W2V and BERT variants.MethodsWe experimented with a Chinese dataset from Fuwai Hospital, which contains 6947 records and 1532 unique ICD codes, and a public Spanish dataset, which contains 1000 records and 2557 unique ICD codes. We designed coding tasks with different code frequency thresholds (denoted as [Formula: see text]), with a lower threshold indicating a more complex task. Using traditional classifiers, we compared BoW, W2V and BERT variants on accomplishing these coding tasks.ResultsWhen [Formula: see text] was equal to or greater than 140 for Fuwai dataset, and 60 for the Spanish dataset, the BERT variants with the whole network fine-tuned was the best method, leading to a Micro-F1 of 93.9% for Fuwai data when [Formula: see text], and a Micro-F1 of 85.41% for the Spanish dataset when [Formula: see text]. When [Formula: see text] fell below 140 for Fuwai dataset, and 60 for the Spanish dataset, BoW turned out to be the best, leading to a Micro-F1 of 83% for Fuwai dataset when [Formula: see text], and a Micro-F1 of 39.1% for the Spanish dataset when [Formula: see text]. Our experiments also showed that both the BERT variants and BoW possessed good interpretability, which is important for medical applications of coding models.ConclusionsThis study shed light on building promising machine learning models for automated ICD coding by revealing the most effective feature extraction methods. Concretely, our results indicated that fine-tuning the whole network of the BERT variants was the optimal method for tasks covering only frequent codes, especially codes that represented unspecified diseases, while BoW was the best for tasks involving both frequent and infrequent codes. The frequency threshold where the best-performing method varied differed between different datasets due to factors like language and codeset.

Dataset Information

Comparison of Cell Disruption Methods for Improving Lipid Extraction from Thraustochytrid Strains.

Publications

Comparison of Cell Disruption Methods for Improving Lipid Extraction from Thraustochytrid Strains.

Similar Datasets

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