Ghrelin Cells in the Gastrointestinal Tract

Ghrelin is 28-amino-acid peptide that was discovered from the rat and human stomach in 1999. Since the discovery of ghrelin, various functions of ghrelin, including growth hormone release, feeding behavior, glucose metabolism, memory, and also antidepressant effects, have been studied. It has also been reported that ghrelin in the gastrointestinal tract has an important physiological effect on gastric acid secretion and gastrointestinal motility. Ghrelin has a unique structure that is modified by O-acylation with n-octanoic acid at third serine residues, and this modification enzyme has recently been identified and named ghrelin O-acyl transferase (GOAT). Ghrelin is considered to be a gut-brain peptide and is abundantly produced from endocrine cells in the gastrointestinal mucosa. In the gastrointestinal tract, ghrelin cells are most abundant in the stomach and are localized in gastric mucosal layers. Ghrelin cells are also widely distributed throughout the gastrointestinal tract. In addition, abundance of ghrelin cells in the gastric mucosa is evolutionally conserved from mammals to lower vertebrates, indicating that gastric ghrelin plays important roles for fundamental physiological functions. Ghrelin cells in the gastrointestinal tract are a major source of circulating plasma ghrelin, and thus understanding the physiology of these cells would reveal the biological significance of ghrelin

Differentiable Instruction Optimization for Cross-Task Generalization

Instruction tuning has been attracting much attention to achieve generalization ability across a wide variety of tasks. Although various types of instructions have been manually created for instruction tuning, it is still unclear what kind of instruction is optimal to obtain cross-task generalization ability. This work presents instruction optimization, which optimizes training instructions with respect to generalization ability. Rather than manually tuning instructions, we introduce learnable instructions and optimize them with gradient descent by leveraging bilevel optimization. Experimental results show that the learned instruction enhances the diversity of instructions and improves the generalization ability compared to using only manually created instructions.Comment: 14pages, 6 figures, accepted for Findings of ACL202

Identifying Emerging Research Related to Solar Cells Field using a Machine Learning Approach

The number of research papers related to solar cells field is increasing rapidly. It is hard to grasp research trends and to identify emerging research issues because of exponential growth of publications, and the field’s subdivided knowledge structure. Machine learning techniques can be applied to the enormous amounts of data and subdivided research fields to identify emerging researches. This paper proposed a prediction model using a machine learning approach to identify emerging solar cells related academic research, i.e. papers that might be cited very frequently within three years. The proposed model performed well and stable. The model highlighted some articles published in 2015 that will be emerging in the future. Research related to vegetable-based dye-sensitized solar cells was identified as the one of the promising researches by the model. The proposed prediction model is useful to gain foresight into research trends in science and technology, facilitating decision-making processes

SciReviewGen: A Large-scale Dataset for Automatic Literature Review Generation

Automatic literature review generation is one of the most challenging tasks in natural language processing. Although large language models have tackled literature review generation, the absence of large-scale datasets has been a stumbling block to the progress. We release SciReviewGen, consisting of over 10,000 literature reviews and 690,000 papers cited in the reviews. Based on the dataset, we evaluate recent transformer-based summarization models on the literature review generation task, including Fusion-in-Decoder extended for literature review generation. Human evaluation results show that some machine-generated summaries are comparable to human-written reviews, while revealing the challenges of automatic literature review generation such as hallucinations and a lack of detailed information. Our dataset and code are available at https://github.com/tetsu9923/SciReviewGen.Comment: ACL findings 2023 (to be appeared). arXiv admin note: text overlap with arXiv:1810.04020 by other author

A high-throughput direct FRET-based assay for analysing apoptotic proteases using flow cytometry and fluorescence-lifetime measurements

International audienceCytometry is a versatile and powerful method applicable to different fields, particularly pharmacology and biomedical studies. Based on the data obtained, cytometric studies are classified into high-throughput (HTP) or high-content screening (HCS) groups. However, assays combining the advantages of both are required to facilitate research. In this study, we developed a high-throughput system to profile cellular populations in terms of time- or dose-dependent responses to apoptotic stimulations, since apoptotic inducers are potent anti-cancer drugs. We previously established assay systems involving protease to monitor live cells for apoptosis using tuneable FRET-based bioprobes. These assays can be used for microscopic analyses or fluorescence-activated cell sorting. In this study, we developed FRET-based bioprobes to detect the activity of the apoptotic markers caspase-3 and caspase-9 via changes in bioprobe fluorescence lifetimes using a flow cytometer for direct estimation of FRET efficiencies. Different patterns of changes in the fluorescence lifetimes of these markers during apoptosis were observed, indicating a relationship between discrete steps in the apoptosis process. The findings demonstrate the feasibility of evaluating collective cellular dynamics during apoptosis

Unsupervised Abstractive Opinion Summarization by Generating Sentences with Tree-Structured Topic Guidance

This paper presents a novel unsupervised abstractive summarization method for opinionated texts. While the basic variational autoencoder-based models assume a unimodal Gaussian prior for the latent code of sentences, we alternate it with a recursive Gaussian mixture, where each mixture component corresponds to the latent code of a topic sentence and is mixed by a tree-structured topic distribution. By decoding each Gaussian component, we generate sentences with tree-structured topic guidance, where the root sentence conveys generic content, and the leaf sentences describe specific topics. Experimental results demonstrate that the generated topic sentences are appropriate as a summary of opinionated texts, which are more informative and cover more input contents than those generated by the recent unsupervised summarization model (Bra\v{z}inskas et al., 2020). Furthermore, we demonstrate that the variance of latent Gaussians represents the granularity of sentences, analogous to Gaussian word embedding (Vilnis and McCallum, 2015).Comment: accepted to TACL, pre-MIT Press publication versio

Appliance Diffusion Model for Energy Efficiency Standards and Labeling Evaluation in the Capital of Lao PDR

Because of the rapid growth of energy demand in developing countries, policies for energy efficiency are receiving increasing attention. Although Energy Efficiency Standards and Labeling (EES&L) is a standard policy tool in many countries, some developing countries, such as Lao PDR, have not yet implemented them fully. In order to understand the potential impact of EES&L, this paper aims at collecting data that contribute to EES&L and at analysing appliance possessions in Vientiane City, Lao PDR. We conducted an interview survey on 600 households in Vientiane City and performed logistic regression analysis that set possession of appliances as the dependent variable. As a result of the analysis, we identified that the income level and the electricity consumption are the principal independent variables and the relationship of these variables with possession rates depends on appliances. Our model helps identify appliances that are expected to be in high demand associated with either economic growth or human population increase in Vientiane City