How do we Tweet? The Comparative Analysis of Twitter Usage by Message Types, Devices, and Sources

Facing the growing importance of social media in the marketing field, this study is intended to build a better understanding of Twitter usage. A total of 73,192 tweets were examined by message types, devices and platforms used. Instead of relying on the audience’s response (e.g., survey or experiment) or traditional content analysis, this study used a data-mining approach and software that are widely used in the computer science field. Overall findings indicate that individual users prefer mobile devices to desktops and use more official web pages or mobile applications provided by Twitter when they tweet, and their most popular message type was the Singleton, an undirected message with no specific recipient. However, we also found that tweets generated through business sources were different from those through official sources in terms of message type, devices, and the nature. The implications of these findings were discussed

Clinical effectiveness of the sequential 4-channel NMES compared with that of the conventional 2-channel NMES for the treatment of dysphagia in a prospective double-blind randomized controlled study

Background To date, conventional swallowing therapies and 2-channel neuromuscular electrical stimulation (NMES) are standard treatments for dysphagia. The precise mechanism of 2-channel NMES treatment has not been determined, and there are controversies regarding the efficacy of this therapy. The sequential 4-channel NMES was recently developed and its action is based on the normal contractile sequence of swallowing-related muscles. Objective To evaluate and compare the rehabilitative effectiveness of the sequential 4-channel NMES with that of conventional 2-channel NMES. Methods In this prospective randomized case–control study, 26 subjects with dysphagia were enrolled. All participants received 2- or 4-channel NMES for 2–3weeks (minimal session: 7 times, treatment duration: 300–800min). Twelve subjects in the 4-channel NMES group and eleven subjects in the 2-channel NMES group completed the intervention. Initial and follow-up evaluations were performed using the videofluoroscopic dysphagia scale (VDS), the penetration-aspiration scale (PAS), the MD Anderson dysphagia inventory (MDADI), the functional oral intake scale (FOIS), and the Likert scale. Results The sequential 4-channel NMES group experienced significant improvement in their VDS (oral, pharyngeal, and total), PAS, FOIS, and MDADI (emotional, functional, and physical subsets) scores, based on their pretreatment data. VDS (oral, pharyngeal, and total) and MDADI (emotional and physical subsets) scores, but not PAS and FOIS scores, significantly improved in the 2-channel NMES group posttreatment. When the two groups were directly compared, the 4-channel NMES group showed significant improvement in oral and total VDS scores. Conclusions The sequential 4-channel NMES, through its activation of the suprahyoid and thyrohyoid muscles, and other infrahyoid muscles mimicking physiological activation, may be a new effective treatment for dysphagia. Trial registration: clinicaltrial.gov, registration number: NCT03670498, registered 13 September 2018, https://clinicaltrials.gov/ct2/show/NCT03670498?term=NCT03670498&draw=2&rank=1 .This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (Grant Number: HI18C1169). This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Min‑ istry of Science, ICT and Future Planning (NRF- NRF-2016R1D1A1B03935130)

Crystal structure and pyridoxal 5-phosphate binding property of lysine decarboxylase from Selenomonas ruminantium

Lysine decarboxylase (LDC) is a crucial enzyme for acid stress resistance and is also utilized for the biosynthesis of cadaverine, a promising building block for bio-based polyamides. We determined the crystal structure of LDC from Selenomonas ruminantium (SrLDC). SrLDC functions as a dimer and each monomer consists of two distinct domains; a PLPbinding barrel domain and a sheet domain. We also determined the structure of SrLDC in complex with PLP and cadaverine and elucidated the binding mode of cofactor and substrate. Interestingly, compared with the apo-form of SrLDC, the SrLDC in complex with PLP and cadaverine showed a remarkable structural change at the PLP binding site. The PLP binding site of SrLDC contains the highly flexible loops with high b-factors and showed an open-closed conformational change upon the binding of PLP. In fact, SrLDC showed no LDC activity without PLP supplement, and we suggest that highly flexible PLP binding site results in low PLP affinity of SrLDC. In addition, other structurally homologous enzymes also contain the flexible PLP binding site, which indicates that high flexibility at the PLP binding site and low PLP affinity seems to be a common feature of these enzyme family.close0

Uncertainty of Rules Extracted from Artificial Neural Networks

Artificial neural networks evolve into deep learning recently and perform well in various fields, such as image and speech recognition and translation. However, there is a problem that it is difficult for a person to understand what exactly the trained knowledge of an artificial neural network. As one of the methods for solving the problem of the artificial neural network, rule extraction methods had been devised. In this study, rules are extracted from artificial neural networks using ordered-attribute search (OAS) algorithm, which is one of the methods of extracting rules from trained neural networks, and the rules are analyzed to improve the extracted rules. As a result, we found that when the output value of the hidden layer has an intermediate value that is not close to 0 or 1 after passing through the sigmoid function, the problem of rule uncertainty occurs and affects the accuracy of the rules. In order to solve the uncertainty problem of the rules, we applied the hidden unit clarification method and suggested that it is possible to extract the efficient rule by binarizing the hidden layer output value. In addition, we extracted CDRPs (critical data routing paths) from the trained neural networks and used CDRPs to prune the extracted rules, which showed that the uncertainty problem of rules can be improved

Structural insights into the inhibition properties of archaeon citrate synthase from Metallosphaera sedula.

Metallosphaera sedula is a thermoacidophilic archaeon and has an incomplete TCA/glyoxylate cycle that is used for production of biosynthetic precursors of essential metabolites. Citrate synthase from M. sedula (MsCS) is an enzyme involved in the first step of the incomplete TCA/glyoxylate cycle by converting oxaloacetate and acetyl-CoA into citrate and coenzyme A. To elucidate the inhibition properties of MsCS, we determined its crystal structure at 1.7 Å resolution. Like other Type-I CS, MsCS functions as a dimer and each monomer consists of two distinct domains, a large domain and a small domain. The oxaloacetate binding site locates at the cleft between the two domains, and the active site was more closed upon binding of the oxaloacetate substrate than binding of the citrate product. Interestingly, the inhibition kinetic analysis showed that, unlike other Type-I CSs, MsCS is non-competitively inhibited by NADH. Finally, amino acids and structural comparison of MsCS with other Type-II CSs, which were reported to be non-competitively inhibited by NADH, revealed that MsCS has quite unique NADH binding mode for non-competitive inhibition

Unbalanced data, type II error, and nonlinearity in predicting M&A failure

The traditional forecasting methods in the M&A data have three limitations: first, the outcome of M&A deal is an event with a small probability of failure, second, the consequences of misclassifying failure as success are much more severe than those of misclassifying success as failure, and third, the nonlinear and complex nature of the relationship between predictors and M&A outcome could limit the advantage of logistic regression. To overcome these limitations, we develop a forecasting model that combines two complementary approaches: a generalized logit model framework and a context-specific cost-sensitive function. Our empirical results demonstrate that the proposed approach provides excellent forecasts when compared with traditional forecasting methods