リュツォ・ホルム湾，プリンスオラフ海岸，及び，エンダビーランド地質調査隊報告2016-2017（JARE-58）

第58次日本南極地域観測隊（JARE-58）では，2016−2017の夏期期間にリュツォ・ホルム湾，プリンスオラフ海岸，及び，エンダビーランドにおいて地質調査をおこなった．調査隊のメンバーは，日本人地質研究者4名とアジア地域（タイ，インドネシア，モンゴル）の交換科学者3名で構成され，本吉隊長が一部期間の調査に加わった．第58次夏期観測では，「しらせ」搭載の2機の大型ヘリコプター（CH101）とともに観測隊チャーターの小型ヘリコプター（AS350）1機による野外調査の支援がなされた．本稿では，観測計画を実施するための，主に設営面での計画，準備，そして行動経過について報告する．The 58th Japanese Antarctic Research Expedition (JARE-58) conducted geological field surveys in the regions of Lützow-Holm Bay, Prince Olav Coast, and Enderby Land during the 2016−2017 austral summer season. The field party consisted of four Japanese geologists and three Asian geologists (Thai, Indonesian, Mongolian), and was joined periodically by JARE-58 expedition leader, Prof. Motoyoshi. Field parties were supported throughout the summer season by a smaller secondary helicopter (AS350) in addition to two main helicopters (CH101) stationed on the icebreaker Shirase. This report summarizes field preparations and the geological work undertaken, and highlights several key points for future planning and research

Data_Sheet_1_Altered metabolites in the periaqueductal gray of COVID-19 patients experiencing headaches: a longitudinal MRS study.pdf

BackgroundHeadache is one of the most common symptoms of acute COVID-19 infection. However, its mechanisms remain poorly understood, and there is a lack of studies investigating changes in the periaqueductal gray (PAG) in COVID-19 patients exhibiting headaches.PurposeThe study aimed to explore the alterations in metabolites of the PAG pre- and post-COVID-19 infection in individuals who suffered from headaches during the acute phase of the disease using proton magnetic resonance spectroscopy (1H-MRS).MethodsFifteen participants who experienced headaches during the acute phase of COVID-19 were recruited. All subjects underwent two proton magnetic resonance spectroscopy (1H-MRS) examinations focusing on the PAG before and after they were infected. Metabolite changes were assessed between the pre- and post-infection groups.ResultsThe combined glutamine and glutamate/total creatine ratio (Glx/tCr) was increased in the PAG following COVID-19 infection. The total choline/total creatine ratio (tCho/tCr) in the pre-infection group was negatively correlated with the duration of headache during the COVID-19 acute phase.ConclusionThe present study indicates that PAG plays a pivotal role in COVID-19 headaches, thereby supporting the involvement of trigeminovascular system activation in the pathophysiology of COVID-19 headaches.</p

ADHD-200, personal characteristic data with functional images.

<p>ADHD-200, personal characteristic data with functional images.</p

Using Functional or Structural Magnetic Resonance Images and Personal Characteristic Data to Identify ADHD and Autism

<div><p>A clinical tool that can diagnose psychiatric illness using functional or structural magnetic resonance (MR) brain images has the potential to greatly assist physicians and improve treatment efficacy. Working toward the goal of automated diagnosis, we propose an approach for automated classification of ADHD and autism based on histogram of oriented gradients (HOG) features extracted from MR brain images, as well as personal characteristic data features. We describe a learning algorithm that can produce effective classifiers for ADHD and autism when run on two large public datasets. The algorithm is able to distinguish ADHD from control with hold-out accuracy of 69.6% (over baseline 55.0%) using personal characteristics and structural brain scan features when trained on the ADHD-200 dataset (769 participants in training set, 171 in test set). It is able to distinguish autism from control with hold-out accuracy of 65.0% (over baseline 51.6%) using functional images with personal characteristic data when trained on the Autism Brain Imaging Data Exchange (ABIDE) dataset (889 participants in training set, 222 in test set). These results outperform all previously presented methods on both datasets. To our knowledge, this is the first demonstration of a single automated learning process that can produce classifiers for distinguishing patients vs. controls from brain imaging data with above-chance accuracy on large datasets for two different psychiatric illnesses (ADHD and autism). Working toward clinical applications requires robustness against real-world conditions, including the substantial variability that often exists among data collected at different institutions. It is therefore important that our algorithm was successful with the large ADHD-200 and ABIDE datasets, which include data from hundreds of participants collected at multiple institutions. While the resulting classifiers are not yet clinically relevant, this work shows that there is a signal in the (f)MRI data that a learning algorithm is able to find. We anticipate this will lead to yet more accurate classifiers, over these and other psychiatric disorders, working toward the goal of a clinical tool for high accuracy differential diagnosis.</p></div

Details of Holdout Dataset participants, ABIDE.

<p>Details of Holdout Dataset participants, ABIDE.</p

ADHD-200, personal characteristic data.

<p>ADHD-200, personal characteristic data.</p

Extracted block regions from ADHD-200 structural MRI dataset.

<p>See text for details.</p

ABIDE, personal characteristic data with structural images.

<p>ABIDE, personal characteristic data with structural images.</p

ADHD-200 results summary.

<p>ADHD-200 results summary.</p

Gradient vector of a sample pixel.

<p>For illustration, we describe the 2D HOG feature computation process. Here, we consider a single pixel, the one shown in red, whose neighbors have intensities 56, 93, 94, and 55. The blue arrow is the sample gradient, computed as described below. (This figure is best viewed in color.)</p