Analyzing Changes in Sport Activity Patterns Caused by the COVID-19 Pandemic: Focusing on the Sport Role of Sustainable Development Goals Related to Health and Well-being

PURPOSE The United Nations (UN) has proposed 17 Sustainable Development Goals and has been extending its efforts to achieve them. Sport can be linked closely to the third goal, which is related to health and well-being. Therefore, this study aimed to explore and to analyze individual's changed sport activities during the COVID-19 pandemic, focusing on ways to achieve health and well-being related goals through sport. METHODS A qualitative research method was employed, and in-depth interview methods were used for data collection. For data analysis, categorization and itemization were used along with content analysis. RESULTS Looking at the derived results, in the context of an infectious disease such COVID-19, sport activity patterns have changed due to reasons such as stadiums or facilities, interpersonal reasons, fear, inconvenience, staying healthy, increase in leisure time, and individual preferences. CONCLUSIONS Due to the COVID-19 pandemic, the indicators of health and well-being related SDGs are exhibiting a downward trend. At this point, it is necessary to find a way to achieve the goal through sport that can participate voluntarily for the purpose of pursuing pleasure

Co-occurrence matrix analysis-based semi-supervised training for object detection

One of the most important factors in training object recognition networks using convolutional neural networks (CNNs) is the provision of annotated data accompanying human judgment. Particularly, in object detection or semantic segmentation, the annotation process requires considerable human effort. In this paper, we propose a semi-supervised learning (SSL)-based training methodology for object detection, which makes use of automatic labeling of un-annotated data by applying a network previously trained from an annotated dataset. Because an inferred label by the trained network is dependent on the learned parameters, it is often meaningless for re-training the network. To transfer a valuable inferred label to the unlabeled data, we propose a re-alignment method based on co-occurrence matrix analysis that takes into account one-hot-vector encoding of the estimated label and the correlation between the objects in the image. We used an MS-COCO detection dataset to verify the performance of the proposed SSL method and deformable neural networks (D-ConvNets) as an object detector for basic training. The performance of the existing state-of-the-art detectors (DConvNets, YOLO v2, and single shot multi-box detector (SSD)) can be improved by the proposed SSL method without using the additional model parameter or modifying the network architecture.Comment: Submitted to International Conference on Image Processing (ICIP) 201

Comprehensive molecular characterization of gastric adenocarcinoma

Gastric cancer is a leading cause of cancer deaths, but analysis of its molecular and clinical characteristics has been complicated by histological and aetiological heterogeneity. Here we describe a comprehensive molecular evaluation of 295 primary gastric adenocarcinomas as part of The Cancer Genome Atlas (TCGA) project. We propose a molecular classification dividing gastric cancer into four subtypes: tumours positive for Epstein–Barr virus, which display recurrent PIK3CA mutations, extreme DNA hypermethylation, and amplification of JAK2, CD274 (also known as PD-L1) and PDCD1LG2 (also knownasPD-L2); microsatellite unstable tumours, which show elevated mutation rates, including mutations of genes encoding targetable oncogenic signalling proteins; genomically stable tumours, which are enriched for the diffuse histological variant and mutations of RHOA or fusions involving RHO-family GTPase-activating proteins; and tumours with chromosomal instability, which show marked aneuploidy and focal amplification of receptor tyrosine kinases. Identification of these subtypes provides a roadmap for patient stratification and trials of targeted therapies

Integrated genomic characterization of oesophageal carcinoma

Oesophageal cancers are prominent worldwide; however, there are few targeted therapies and survival rates for these cancers remain dismal. Here we performed a comprehensive molecular analysis of 164 carcinomas of the oesophagus derived from Western and Eastern populations. Beyond known histopathological and epidemiologic distinctions, molecular features differentiated oesophageal squamous cell carcinomas from oesophageal adenocarcinomas. Oesophageal squamous cell carcinomas resembled squamous carcinomas of other organs more than they did oesophageal adenocarcinomas. Our analyses identified three molecular subclasses of oesophageal squamous cell carcinomas, but none showed evidence for an aetiological role of human papillomavirus. Squamous cell carcinomas showed frequent genomic amplifications of CCND1 and SOX2 and/or TP63, whereas ERBB2, VEGFA and GATA4 and GATA6 were more commonly amplified in adenocarcinomas. Oesophageal adenocarcinomas strongly resembled the chromosomally unstable variant of gastric adenocarcinoma, suggesting that these cancers could be considered a single disease entity. However, some molecular features, including DNA hypermethylation, occurred disproportionally in oesophageal adenocarcinomas. These data provide a framework to facilitate more rational categorization of these tumours and a foundation for new therapies

Active delivery of multi-layer drug-loaded microneedle patches using magnetically driven capsule

In this paper, we propose the active delivery of multi-layer drug-loaded microneedle (MN) patches using a capsule that can be driven by an external magnetic field. Firstly, the multi-layer drug-loaded MN patches consist of three delivered MN patches which are composed of a drug-loaded MN patch and polydimethylsiloxane layer. The drug-loaded MN patch is made of a 10% gelatin solution and a drug. The multi-layer MN patches are attached to a permanent magnet in a magnetically driven capsule. Under an external magnetic field generated by an electromagnetic actuation system, the capsule with the multi-layer MN patches can reach the target lesions, and each MN patch can be delivered to the target lesions for medical treatment. The active delivery of the multi-layer MN patches using the proposed magnetically driven capsule was confirmed via phantom experiments. Accordingly, the adhesion of the three separated faces of the multi-layer MN patches and the adhesion between the porcine small intestine and the MN patch were measured using a load cell. We demonstrate the feasibility of the active delivery of the multi-layer MN patches to the target lesions on a porcine small intestine. Consequently, we expect that the active delivery of the multi-layer drug-loaded MN patches using the magnetically driven capsule presented in this study can be a useful method for drug delivery to lesions at various locations in the gastrointestinal tract. © 20201

A Smart Farm DNN Survival Model Considering Tomato Farm Effect

Recently, smart farming research based on artificial intelligence (AI) has been widely applied in the field of agriculture to improve crop cultivation and management. Predicting the harvest time (time-to-harvest) of crops is important in smart farming to solve problems such as planning the production schedule of crops and optimizing the yield and quality. This helps farmers plan their labor and resources more efficiently. In this paper, our concern is to predict the time-to-harvest (i.e., survival time) of tomatoes on a smart farm. For this, it is first necessary to develop a deep learning modeling approach that takes into account the farm effect on the tomato plants, as each farm has multiple tomato plant subjects and outcomes on the same farm can be correlated. In this paper, we propose deep neural network (DNN) survival models to account for the farm effect as a fixed effect using one-hot encoding. The tomato data used in our study were collected on a weekly basis using the Internet of Things (IoT). We compare the predictive performance of our proposed method with that of existing DNN and statistical survival modeling methods. The results show that our proposed DNN method outperforms the existing methods in terms of the root mean squared error (RMSE), concordance index (C-index), and Brier score

Investigation of energy absorption by clustered gold nanoparticles

The utilization of gold nanoparticles (GNPs) as a radiation sensitizer has received broad attention. Although GNPs form clusters in living cells, most previous simulation studies have assumed a homogeneous distribution of GNPs. In this study, a GNP cluster was constructed for simulations and the impact of cluster formation on dose enhancement was examined. Energy absorption by the GNPs was compared between clustered and homogeneous distributions for several different GNP concentrations and diameters under 100 keV X-ray irradiations. Our simulations showed that clusters more efficiently absorbed the secondary electrons and photons produced by GNPs themselves. Furthermore, the impact of cluster formation on dose enhancement was more significant for smaller GNPs and higher concentrations. Our results suggest that previous simulations assuming a homogeneous GNP distribution have overestimated the dose enhancement, especially for smaller GNPs and higher concentrations. These findings should guide the selection of GNP size and concentration for effectively optimizing dose enhancement in future studies