Machine learning refinement of in situ images acquired by low electron dose LC-TEM

We study a machine learning (ML) technique for refining images acquired during in situ observation using liquid-cell transmission electron microscopy (LC-TEM). Our model is constructed using a U-Net architecture and a ResNet encoder. For training our ML model, we prepared an original image dataset that contained pairs of images of samples acquired with and without a solution present. The former images were used as noisy images and the latter images were used as corresponding ground truth images. The number of pairs of image sets was $1,204$ and the image sets included images acquired at several different magnifications and electron doses. The trained model converted a noisy image into a clear image. The time necessary for the conversion was on the order of 10ms, and we applied the model to in situ observations using the software Gatan DigitalMicrograph (DM). Even if a nanoparticle was not visible in a view window in the DM software because of the low electron dose, it was visible in a successive refined image generated by our ML model.Comment: 33 pages, 9 figure

The effects of caregiving resources on the incidence of depression over one year in family caregivers of disabled elderly

The purpose of the study was to investigate the over-time effects of physical, psychological and social resources on the incidence of depression in family caregivers of the disabled elderly. Data were collected twice at a one-year interval from 1,141 primary caregivers of a disabled older person in an urban area of Japan using a self-reported questionnaire survey. The questionnaire included physical health as an indicator of physical resources, caregiving satisfaction and intention to care as indicators of psychological resources, and instrumental and emotional support network and formal home care service utilization as indicators of social resources. The mental health outcome measure was the General Health Questionnaire 12-item version (GHQ-12). Complete data on 235 non-depressed female caregivers were separated into 3 groups according to the relationship type (wife, daughter and daughter-in-law) and analyzed separately. Multivariate logistic regression models controlling for duration of caregiving, care-recipient's gender, ADL dependency and behavioral problems demonstrated that significant predictors of depression were caregiving satisfaction and intention to care in wives, caregiving satisfaction in daughters, and physical health and emotional support network in daughters-in-law. Noteworthy, intention to care increased the risk of depression in wives, while decreasing the risk of depression in daughters-in-law. The findings indicate that the effects of caregivers' resources on mental health may differ by relationship type.</p

Calcium sparks enhance the tissue fluidity within epithelial layers and promote apical extrusion of transformed cells

In vertebrates, newly emerging transformed cells are often apically extruded from epithelial layers through cell competition with surrounding normal epithelial cells. However, the underlying molecular mechanism remains elusive. Here, using phospho-SILAC screening, we show that phosphorylation of AHNAK2 is elevated in normal cells neighboring RasV12 cells soon after the induction of RasV12 expression, which is mediated by calcium-dependent protein kinase C. In addition, transient upsurges of intracellular calcium, which we call calcium sparks, frequently occur in normal cells neighboring RasV12 cells, which are mediated by mechanosensitive calcium channel TRPC1 upon membrane stretching. Calcium sparks then enhance cell movements of both normal and RasV12 cells through phosphorylation of AHNAK2 and promote apical extrusion. Moreover, comparable calcium sparks positively regulate apical extrusion of RasV12-transformed cells in zebrafish larvae as well. Hence, calcium sparks play a crucial role in the elimination of transformed cells at the early phase of cell competition

Optic Perineuritis Associated with Nivolumab Treatment for Non-Small Cell Lung Cancer

We report the case of a 54-year-old man who was treated with nivolumab for recurrent squamous cell lung cancer. After 7 cycles of nivolumab treatment, the patient presented to our hospital with right eye vision loss. Gadolinium-enhanced magnetic resonance imaging of the brain showed enhancement around the optic nerve sheath. This finding and his symptoms led to the diagnosis of optic perineuritis (OPN). Steroid pulse therapy was administered twice although there was no remarkable improvement in his visual field defect. The relationship between OPN and nivolumab is unclear. However, immune-related adverse events caused by immune checkpoint inhibitors should be considered

Survey of Period Variations of Superhumps in SU UMa-Type Dwarf Novae. VIII: The Eighth Year (2015-2016)

Continuing the project described by Kato et al. (2009, arXiv:0905.1757), we collected times of superhump maxima for 128 SU UMa-type dwarf novae observed mainly during the 2015-2016 season and characterized these objects. The data have improved the distribution of orbital periods, the relation between the orbital period and the variation of superhumps, the relation between period variations and the rebrightening type in WZ Sge-type objects. Coupled with new measurements of mass ratios using growing stages of superhumps, we now have a clearer and statistically greatly improved evolutionary path near the terminal stage of evolution of cataclysmic variables. Three objects (V452 Cas, KK Tel, ASASSN-15cl) appear to have slowly growing superhumps, which is proposed to reflect the slow growth of the 3:1 resonance near the stability border. ASASSN-15sl, ASASSN-15ux, SDSS J074859.55+312512.6 and CRTS J200331.3-284941 are newly identified eclipsing SU UMa-type (or WZ Sge-type) dwarf novae. ASASSN-15cy has a short (~0.050 d) superhump period and appears to belong to EI Psc-type objects with compact secondaries having an evolved core. ASASSN-15gn, ASASSN-15hn, ASASSN-15kh and ASASSN-16bu are candidate period bouncers with superhump periods longer than 0.06 d. We have newly obtained superhump periods for 79 objects and 13 orbital periods, including periods from early superhumps. In order that the future observations will be more astrophysically beneficial and rewarding to observers, we propose guidelines how to organize observations of various superoutbursts.Comment: 123 pages, 162 figures, 119 tables, accepted for publication in PASJ (including supplementary information

Fast Improvement of TEM Images with Low-Dose Electrons by Deep Learning

Low electron dose observation is indispensable for observing various samples using a transmission electron microscope; consequently, image processing has been used to improve transmission electron microscopy (TEM) images. To apply such image processing to in situ observations, we here apply a convolutional neural network to TEM imaging. Using a dataset that includes short-exposure images and long-exposure images, we develop a pipeline for processed short-exposure images, based on end-to-end training. The quality of images acquired with a total dose of approximately 5 e(-) per pixel becomes comparable to that of images acquired with a total dose of approximately 1,000 e(-) per pixel. Because the conversion time is approximately 8 ms, in situ observation at 125 fps is possible. This imaging technique enables in situ observation of electron-beam-sensitive specimens

Machine learning refinement of in situ images acquired by low electron dose LC-TEM

&lt;p&gt;This is a dataset of images acquired in vacuum condition and corresponding images in a solution listed in the text file list_dataset.txt.&nbsp;&lt;/p&gt;&lt;p&gt;&nbsp;&lt;/p&gt;&lt;p&gt;The code is available at &lt;a href="https://github.com/hiroyasukatsuno/Machine-learning-refinement-of-images-acquired-by-LC-TEM"&gt;this website&lt;/a&gt;:&lt;/p&gt;&lt;p&gt;https://github.com/hiroyasukatsuno/Machine-learning-refinement-of-images-acquired-by-LC-TEM/&lt;/p&gt;&lt;p&gt;&lt;br&gt;Equipment of TEM:&lt;/p&gt;&lt;ul&gt;&lt;li&gt;field-emission gun (JEM-2100F, JEOL, Tokyo)&lt;/li&gt;&lt;li&gt;OneView IS (Gatan, Inc., Pleasanton, CA, USA)&lt;/li&gt;&lt;/ul&gt;&lt;p&gt;&nbsp;&lt;/p&gt

Early Detection of Nucleation Events From Solution in LC-TEM by Machine Learning

To support the detection, recording, and analysis of nucleation events during in situ observations, we developed an early detection system for nucleation events observed using a liquid-cell transmission electron microscope. Detectability was achieved using the machine learning equivalent of detection by humans watching a video numerous times. The detection system was applied to the nucleation of sodium chloride crystals from a saturated acetone solution of sodium chlorate. Nanoparticles with a radius of more greater than 150 nm were detected in a viewing area of 12 mu m x 12 mu m by the detection system. The analysis of the change in the size of the growing particles as a function of time suggested that the crystal phase of the particles with a radius smaller than 400 nm differed from that of the crystals larger than 400 nm. Moreover, the use of machine learning enabled the detection of numerous nanometer sized nuclei. The nucleation rate estimated from the machine-learning-based detection was of the same order as that estimated from the detection using manual procedures