Thematic and Country-Specific Characteristics of Research on the Great East Japan Earthquake: An Analysis Using Data Science Methods

The Great East Japan Earthquake of 2011 had profound impacts in various ways because it was a complex disaster. In addition to the earthquake itself, the tsunami and nuclear accident were even more severe for human lives, health, economy, and the environment. Researchers around the world responded to the disaster. The study topics spanned from natural sciences to social sciences. In this study, we analyzed over 20, 000 academic records concerning the Great East Japan Earthquake from a data science perspective. As a result of text mining, the characteristics of many research fields were elucidated. By collecting the studies in terms of country and research subject, we found characteristics of countries that conducted studies on the disaster. We found that countries in the same Asian region as Japan and countries prone to frequent earthquakes and tsunamis have a high research interest. With the possibility of such a catastrophe in the future in mind, we should prepare ourselves by learning from previous studies to take better countermeasures next time

Automatic detection of alien plant species in action camera images using the chopped picture method and the potential of citizen science

Monitoring and detection of invasive alien plant species are necessary for effective management and control measures. Although efforts have been made to detect alien trees using satellite images, the detection of alien herbaceous species has been difficult. In this study, we examined the possibility of detecting non-native plants using deep learning on images captured by two action cameras. We created a model for each camera using the chopped picture method. The models were able to detect the alien plant Solidago altissima (tall goldenrod) and obtained an average accuracy of 89%. This study proved that it is possible to automatically detect exotic plants using inexpensive action cameras through deep learning. This advancement suggests that, in the future, citizen science may be useful for conducting distribution surveys of alien plants in a wide area at a low cost

Unmanned aerial vehicles and deep learning for assessment of anthropogenic marine debris on beaches on an island in a semi-enclosed sea in Japan

The increasing prevalence of marine debris is a global problem, and urgent action for amelioration is needed. Identifying hotspots where marine debris accumulates will enable effective control; however, knowledge on the location of accumulation hotspots remains incomplete. In particular, marine debris accumulation on beaches is a concern. Surveys of beaches require intensive human effort, and survey methods are not standardized. If marine debris monitoring is conducted using a standardized method, data from different regions can be compared. With an unmanned aerial vehicle (UAV) and deep learning computational methods, monitoring a wide area at a low cost in a standardized way may be possible. In this study, we aimed to identify marine debris on beaches through deep learning using high-resolution UAV images by conducting a survey on Narugashima Island in the Seto Inland Sea of Japan. The flight altitude relative to the ground was set to 5 m, and images of a 0.81-ha area were obtained. Flight was conducted twice: before and after the beach cleaning. The combination of UAVs equipped with a zoom lens and operation at a low altitude allows for the acquisition of high resolution images of 1.1 mm/pixel. The training dataset (2970 images) was annotated by using VoTT, categorizing them into two classes: 'anthropogenic marine debris' and 'natural objects.' Using RetinaNet, marine debris was identified with an average sensitivity of 51% and a precision of 76%. In addition, the abundance and area of marine debris coverage were estimated. In this study, it was revealed that the combination of UAVs and deep learning enables the effective identification of marine debris. The effects of cleanup activities by citizens were able to be quantified. This method can widely be used to evaluate the effectiveness of citizen efforts toward beach cleaning and low-cost long-term monitoring

The transcription factor Foxo1 controls germinal center B cell proliferation in response to T cell help

Germinal center (GC) B cells cycle between two states, the light zone (LZ) and the dark zone (DZ), and in the latter they proliferate and hypermutate their immunoglobulin genes. How this functional transition takes place is still controversial. In this study, we demonstrate that ablation of Foxo1 after GC development led to the loss of the DZ GC B cells and disruption of the GC architecture, which is consistent with recent studies. Mechanistically, even upon provision of adequate T cell help, Foxo1-deficient GC B cells showed less proliferative expansion than controls. Moreover, we found that the transcription factor BATF was transiently induced in LZ GC B cells in a Foxo1-dependent manner and that deletion of BATF similarly led to GC disruption. Thus, our results are consistent with a model where the switch from the LZ to the DZ is triggered after receipt of T cell help, and suggest that Foxo1-mediated BATF up-regulation is at least partly involved in this switch

3Dマッピングシステムを用いた両心房Stimulus-V mapによる順行性速伝導路入口部の解剖学的位置並び特徴の検討

Purpose Previous studies examined the right atrial (RA) input site of the antegrade fast pathway (AFp) (AFpI). However, the left atrial (LA) input to the atrioventricular (AV) node has not been extensively evaluated. In this study, we created three-dimensional (3-D) bi-atrial stimulus-ventricle (St-V) maps and analyzed the input site and characteristics of the AFp in both the RA and LA. Methods Forty-four patients diagnosed with atrial fibrillation or WPW syndrome were included in this study. Three-dimensional bi-atrial St-V mapping was performed using an electroanatomical mapping system. Sites exhibiting the minimal St-V interval (MinSt-V) were defined as AFpIs and were classified into seven segments, four in the RA (F, S, M, and I) and three in the LA (M1, M2, and M3). By combining the MinSt-V in the RA and LA, the AFpIs were classified into three types: RA, LA, and bi-atrial (BA) types. The clinical and electrophysiological characteristics were compared. Results AFpIs were most frequently observed at site S in the RA (34%) and M2 in the LA (50%), and the BA type was the most common (57%). AFpIs in the LA were recognized in 75% of the patients. There were no clinical or electrophysiological indicators for predicting AFpI sites. Conclusions Three-dimensional bi-atrial St-V maps could classify AFpIs in both the RA and LA. AFpIs in the LA were frequently recognized. There were no significant clinical or electrophysiological indicators for predicting AFpI sites, and 3-D bi-atrial St-V mapping was the only method to reveal the precise AFp input site