Word2vec-based latent semantic analysis (W2V-LSA) for topic modeling: A study on blockchain technology trend analysis

Blockchain has become one of the core technologies in Industry 4.0. To help decision-makers establish action plans based on blockchain, it is an urgent task to analyze trends in blockchain technology. However, most of existing studies on blockchain trend analysis are based on effort demanding full-text investigation or traditional bibliometric methods whose study scope is limited to a frequency-based statistical analysis. Therefore, in this paper, we propose a new topic modeling method called Word2vec-based Latent Semantic Analysis (W2V-LSA), which is based on Word2vec and Spherical k-means clustering to better capture and represent the context of a corpus. We then used W2V-LSA to perform an annual trend analysis of blockchain research by country and time for 231 abstracts of blockchain-related papers published over the past five years. The performance of the proposed algorithm was compared to Probabilistic LSA, one of the common topic modeling techniques. The experimental results confirmed the usefulness of W2V-LSA in terms of the accuracy and diversity of topics by quantitative and qualitative evaluation. The proposed method can be a competitive alternative for better topic modeling to provide direction for future research in technology trend analysis and it is applicable to various expert systems related to text mining. (C) 2020 The Authors. Published by Elsevier Ltd

Four Human Cases of Diphyllobothrium latum Infection

Diphyllobothrium latum infections in 4 young Korean men detected from 2008 to 2012 are presented. Three were diagnosed based on spontaneously discharged strobila of the adult worm in their feces, and 1 case was diagnosed by finding the worm at colonoscopy examination in a local clinic. The morphologic characteristics of the gravid proglottid and eggs were consistent with D. latum. All patients were treated with praziquantel 15 mg/kg, and follow-up stool examinations were done at 2 months after the medication. The main clinical complaints were intermittent gastrointestinal troubles such as indigestion, abdominal distension, and spontaneous discharge of tapeworm's segments in their feces. The most probable source of infection was the flesh of salmon or trout according to a patient's past history. These are the 45th to 48th recorded cases diagnosed by the adult worm in the Republic of Korea since 1971

Patient-specific molecular response dynamics can predict the possibility of relapse during the second treatment-free remission attempt in chronic myelogenous leukemia

In chronic myelogenous leukemia (CML), treatment-free remission (TFR) is defined as maintaining a major molecular response (MMR) without a tyrosine kinase inhibitor (TM), such as imatinib (IM). Several studies have investigated the safety of the first TFR (TFR1) attempt and suggested recommendation guidelines for such an attempt. However, the plausibility and predictive factors for a second TFR (TFR2) have yet to be reported. The present study included 21 patients in chronic myeloid leukemia who participated in twice repeated treatment stop attempts. We develop a mathematical model to analyze and explain the outcomes of TFR2. Our mathematical model framework can explain patient-specific molecular response dynamics. Fitting the model to longitudinal BCR ABL1 transcripts from the patients generated patient-specific parameters. Binary tree decision analyses of the model parameters suggested a model based predictive binary classification factor that separated patients into low- and high-risk groups of TFR2 attempts with an overall accuracy of 76.2% (sensitivity of 81.1% and specificity of 69.9%). The low-risk group maintained a median TFR2 of 28.2 months, while the high-risk group relapsed at a median time of 3.25 months. Further, our model predicted a patient-specific optimal IM treatment duration before the second IM stop that could achieve the desired TFR 2 (e.g., 5 years)

Prediction of type 2 diabetes using genome-wide polygenic risk score and metabolic profiles: A machine learning analysis of population-based 10-year prospective cohort study

Background: Previous work on predicting type 2 diabetes by integrating clinical and genetic factors has mostly focused on the Western population. In this study, we use genome-wide polygenic risk score (gPRS) and serum metabolite data for type 2 diabetes risk prediction in the Asian population. Methods: Data of 1425 participants from the Korean Genome and Epidemiology Study (KoGES) Ansan-Ansung cohort were used in this study. For gPRS analysis, genotypic and clinical information from KoGES health examinee (n = 58,701) and KoGES cardiovascular disease association (n = 8105) sub-cohorts were included. Linkage disequilibrium analysis identified 239,062 genetic variants that were used to determine the gPRS, while the metabolites were selected using the Boruta algorithm. We used bootstrapped cross-validation to evaluate logistic regression and random forest (RF)-based machine learning models. Finally, associations of gPRS and selected metabolites with the values of homeostatic model assessment of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) were further estimated. Findings: During the follow-up period (8.3 ?? 2.8 years), 331 participants (23.2%) were diagnosed with type 2 diabetes. The areas under the curves of the RF-based models were 0.844, 0.876, and 0.883 for the model using only demographic and clinical factors, model including the gPRS, and model with both gPRS and metabolites, respectively. Incorporation of additional parameters in the latter two models improved the classification by 11.7% and 4.2% respectively. While gPRS was significantly associated with HOMA-B value, most metabolites had a significant association with HOMA-IR value. Interpretation: Incorporating both gPRS and metabolite data led to enhanced type 2 diabetes risk prediction by capturing distinct etiologies of type 2 diabetes development. An RF-based model using clinical factors, gPRS, and metabolites predicted type 2 diabetes risk more accurately than the logistic regression-based model

Deep learning-based monitoring of overshooting cloud tops from geostationary satellite data

Overshooting tops can cause a variety of severe weather conditions, such as cloud-to-ground lightning, strong winds, and heavy rainfall, which can affect flight and ground operations. Many previous studies have developed overshooting top (OT) detection models. However, rather than identifying individual pixels in satellite images as OTs or non-OTs, we typically find OTs through visual inspection of the contextual information of pixels (i.e., dome-like shape). Such an approach is more intuitive, accurate, and generalizable regardless of the OT characteristics that are used in the existing OT detection algorithms. In this paper, a new approach is proposed for OT detection using deep learning, more specifically a convolutional neural network (CNN), which can mimic the human process by convolution operation. Himawari-8 satellite images were used as input data, which were chopped into patches (i.e., grids) with a 31 ?? 31 window size and binary detection (OT or non-OT) for each patch was the output of the model. The validation results show that CNN can be successfully applied for the detection of OTs over the tropical regions, showing a mean probability of detection (POD) of 79.68% and a mean false alarm ratio (FAR) of 9.78%

DGU-HAO: A Dataset With Daily Life Objects for Comprehensive 3D Human Action Analysis

The importance of a high-quality dataset availability in 3D human action analysis research cannot be overstated. This paper introduces DGU-HAO (Human Action analysis dataset with daily life Objects). This novel 3D human action multi-modality dataset encompasses four distinct data modalities accompanied by annotation data, including motion capture, RGB video, image, and 3D object modeling data. It features 63 action classes involving interactions with 60 common furniture and electronic devices. Each action class comprises approximately 1,000 motion capture data representing 3D skeleton data and corresponding RGB video and 3D object modeling data, resulting in 67,505 motion capture data samples. It offers comprehensive 3D structural information of the human, RGB images and videos, and point cloud data for 60 objects, collected through the participation of 126 subjects to ensure inclusivity and account for diverse human body types. To validate our dataset, we leveraged MMNet, a 3D human action recognition model, achieving Top-1 accuracy of 91.51% and 92.29% using the skeleton joint and bone methods, respectively. Beyond human action recognition, our versatile dataset is valuable for various 3D human action analysis research endeavors

Technology Opportunity Discovery using Deep Learning-based Text Mining and a Knowledge Graph

To capture emerging technologies in the fast-changing technology market, use of information concerning new technology-based firms (NTBFs) is strongly encouraged, in addition to the information about the technology itself. Especially, NTBFs rapidly respond to technological change, and their investment information is a significant criterion of technology valuation. Therefore, this study proposes a new technology opportunity discovery (TOD) framework that exploits text mining by deep learning and a knowledge graph (KG) by using three data sources: technology, NTBF, and investor data. First, a technology-classification model was developed using technical text data acquired using Doc2vec and logistic regression, and then this model assigned highly-relevant technology fields to NTBFs using NTBFs' investor relation text data. Next, a KG that considers technology, NTBF, and NTBF's investor was constructed to represent their relations for TOD by using the results of previous steps. Lastly, considering inter-connectivities of such factors, a TOD index that measures the potential of technologies was proposed. The accuracy and validity of the methods were demonstrated empirically, and an evaluation of emerging technologies identified by the analysis was provided. Our framework will be of great significance as a useful alternative to provide new insights for emerging technologies in the industry and market