Impact of liver fat on the differential partitioning of hepatic triacylglycerol into VLDL subclasses on high and low sugar diets.

Dietary sugars are linked to the development of non-alcoholic fatty liver disease (NAFLD) and dyslipidaemia, but it is unknown if NAFLD itself influences the effects of sugars on plasma lipoproteins. To study this further, men with NAFLD (n=11) and low liver fat 'controls' (n= 14) were fed two iso-energetic diets, high or low in sugars (26% or 6% total energy) for 12 weeks, in a randomised, cross-over design. Fasting plasma lipid and lipoprotein kinetics were measured after each diet by stable isotope trace-labelling. There were significant differences in the production and catabolic rates of VLDL subclasses between men with NAFLD and controls, in response to the high and low sugar diets. Men with NAFLD had higher plasma concentrations of VLDL1-triacylglycerol (TAG) after the high ( P <0.02) and low sugar ( P <0.0002) diets, a lower VLDL1-TAG fractional catabolic rate after the high sugar diet ( P <0.01), and a higher VLDL1-TAG production rate after the low sugar diet ( P <0.01), relative to controls. An effect of the high sugar diet, was to channel hepatic TAG into a higher production of VLDL1-TAG ( P <0.02) in the controls, but in contrast, a higher production of VLDL2-TAG ( P <0.05) in NAFLD. These dietary effects on VLDL subclass kinetics could be explained, in part, by differences in the contribution of fatty acids from intra-hepatic stores, and de novo lipogenesis. This study provides new evidence that liver fat accumulation leads to a differential partitioning of hepatic TAG into large and small VLDL subclasses, in response to high and low intakes of sugars.The work was supported by a UK government grant from the Biological Biotechnology Scientific Research Council (Grant no. BB/G009899/1); University of Surrey PhD scholarship for AM; Medical Research Council (body composition measurements) and infrastructure support from the National Institute of Health Research at the Cambridge Biomedical Research Centre

Computational Linguistics Based Emotion Detection and Classification Model on Social Networking Data

Computational linguistics (CL) is the application of computer science for analysing and comprehending written and spoken languages. Recently, emotion classification and sentiment analysis (SA) are the two techniques that are mostly utilized in the Natural Language Processing (NLP) field. Emotion analysis refers to the task of recognizing the attitude against a topic or target. The attitude may be polarity (negative or positive) or an emotional state such as sadness, joy, or anger. Therefore, classifying posts and opinion mining manually is a difficult task. Data subjectivity has made this issue an open problem in the domain. Therefore, this article develops a computational linguistics-based emotion detection and a classification model on social networking data (CLBEDC-SND) technique. The presented CLBEDC-SND technique investigates the recognition and classification of emotions in social networking data. To attain this, the presented CLBEDC-SND model performs different stages of data pre-processing to make it compatible for further processing. In addition, the CLBEDC-SND model undergoes vectorization and sentiment scoring process using fuzzy approach. For emotion classification, the presented CLBEDC-SND model employs extreme learning machine (ELM). Finally, the parameters of the ELM model are optimally modified by the use of the shuffled frog leaping optimization (SFLO) algorithm. The performance validation of the CLBEDC-SND model is tested using benchmark datasets. The experimental results demonstrate the better performance of the CLBEDC-SND model over other models

Computational Linguistics with Deep-Learning-Based Intent Detection for Natural Language Understanding

Computational linguistics explores how human language is interpreted automatically and then processed. Research in this area takes the logical and mathematical features of natural language and advances methods and statistical procedures for automated language processing. Slot filling and intent detection are significant modules in task-based dialogue systems. Intent detection is a critical task in any natural language understanding (NLU) system and constitutes the base of a task-based dialogue system. In order to build high-quality, real-time conversational solutions for edge gadgets, there is a demand for deploying intent-detection methods on devices. This mandates an accurate, lightweight, and fast method that effectively operates in a resource-limited environment. Earlier works have explored the usage of several machine-learning (ML) techniques for detecting intent in user queries. In this article, we propose Computational Linguistics with Deep-Learning-Based Intent Detection and Classification (CL-DLBIDC) for natural language understanding. The presented CL-DLBIDC technique receives word embedding as input and learned meaningful features to determine the probable intention of the user query. In addition, the presented CL-DLBIDC technique uses the GloVe approach. In addition, the CL-DLBIDC technique makes use of the deep learning modified neural network (DLMNN) model for intent detection and classification. For the hyperparameter tuning process, the mayfly optimization (MFO) algorithm was used in this study. The experimental analysis of the CL-DLBIDC method took place under a set of simulations, and the results were scrutinized for distinct aspects. The simulation outcomes demonstrate the significant performance of the CL-DLBIDC algorithm over other DL models