Aurora and Airglow Observations with an All-Sky Imager on Shirase to Fill the Observation Gap over the Southern Ocean

The Tenth Symposium on Polar Science/Special session: [S] Future plan of Antarctic research: Towards phase X of the Japanese Antarctic Research Project (2022-2028) and beyond, Tue. 3 Dec. / Entrance Hall (1st floor) at National Institute of Polar Research (NIPR

Non-Gaussian Error Contribution to Likelihood Analysis of the Matter Power Spectrum

We study the sample variance of the matter power spectrum for the standard Lambda Cold Dark Matter universe. We use a total of 5000 cosmological N-body simulations to study in detail the distribution of best-fit cosmological parameters and the baryon acoustic peak positions. The obtained distribution is compared with the results from the Fisher matrix analysis with and without including non-Gaussian errors. For the Fisher matrix analysis, we compute the derivatives of the matter power spectrum with respect to cosmological parameters using directly full nonlinear simulations. We show that the non-Gaussian errors increase the unmarginalized errors by up to a factor 5 for k_{max}=0.4h/Mpc if there is only one free parameter provided other parameters are well determined by external information. On the other hand, for multi-parameter fitting, the impact of the non-Gaussian errors is significantly mitigated due to severe parameter degeneracies in the power spectrum. The distribution of the acoustic peak positions is well described by a Gaussian distribution, with its width being consistent with the statistical interval predicted from the Fisher matrix. We also examine systematic bias in the best-fit parameter due to the non-Gaussian errors. The bias is found to be smaller than the 1 sigma statistical error for both the cosmological parameters and the acoustic scale positions.Comment: 12 pages, 10 figures, accepted for publication in ApJ, minor change

Simulations of Baryon Acoustic Oscillations II: Covariance matrix of the matter power spectrum

We use 5000 cosmological N-body simulations of 1(Gpc/h)^3 box for the concordance LCDM model in order to study the sampling variances of nonlinear matter power spectrum. We show that the non-Gaussian errors can be important even on large length scales relevant for baryon acoustic oscillations (BAO). Our findings are (1) the non-Gaussian errors degrade the cumulative signal-to-noise ratios (S/N) for the power spectrum amplitude by up to a factor of 2 and 4 for redshifts z=1 and 0, respectively. (2) There is little information on the power spectrum amplitudes in the quasi-nonlinear regime, confirming the previous results. (3) The distribution of power spectrum estimators at BAO scales, among the realizations, is well approximated by a Gaussian distribution with variance that is given by the diagonal covariance component. (4) For the redshift-space power spectrum, the degradation in S/N by non-Gaussian errors is mitigated due to nonlinear redshift distortions. (5) For an actual galaxy survey, the additional shot noise contamination compromises the cosmological information inherent in the galaxy power spectrum, but also mitigates the impact of non-Gaussian errors. The S/N is degraded by up to 30% for a WFMOS-type survey. (6) The finite survey volume causes additional non-Gaussian errors via the correlations of long-wavelength fluctuations with the fluctuations we want to measure, further degrading the S/N values by about 30% even at high redshift z=3.Comment: submitted to ApJ, 14 pages, 12 figures. The full halo model is included. Minor changes also made, and references adde

Phosphorylated Smad2 in Advanced Stage Gastric Carcinoma

<p>Abstract</p> <p>Background</p> <p>Transforming growth factor β (TGFβ) receptor signaling is closely associated with the invasion ability of gastric cancer cells. Although Smad signal is a critical integrator of TGFβ receptor signaling transduction systems, not much is known about the role of Smad2 expression in gastric carcinoma. The aim of the current study is to clarify the role of phosphorylated Smad2 (p-Smad2) in gastric adenocarcinomas at advanced stages.</p> <p>Methods</p> <p>Immunohistochemical staining with anti-p-Smad2 was performed on paraffin-embedded specimens from 135 patients with advanced gastric adenocarcinomas. We also evaluated the relationship between the expression levels of p-Smad2 and clinicopathologic characteristics of patients with gastric adenocarcinomas.</p> <p>Results</p> <p>The p-Smad2 expression level was high in 63 (47%) of 135 gastric carcinomas. The p-Smad2 expression level was significantly higher in diffuse type carcinoma (p = 0.007), tumours with peritoneal metastasis (p = 0.017), and tumours with lymph node metastasis (p = 0.047). The prognosis for p-Smad2-high patients was significantly (p = 0.035, log-rank) poorer than that of p-Smad2-low patients, while a multivariate analysis revealed that p-Smad2 expression was not an independence prognostic factor.</p> <p>Conclusion</p> <p>The expression of p-Smad2 is associated with malignant phenotype and poor prognosis in patients with advanced gastric carcinoma.</p

Akt1 in Osteoblasts and Osteoclasts Controls Bone Remodeling

Bone mass and turnover are maintained by the coordinated balance between bone formation by osteoblasts and bone resorption by osteoclasts, under regulation of many systemic and local factors. Phosphoinositide-dependent serine-threonine protein kinase Akt is one of the key players in the signaling of potent bone anabolic factors. This study initially showed that the disruption of Akt1, a major Akt in osteoblasts and osteoclasts, in mice led to low-turnover osteopenia through dysfunctions of both cells. Ex vivo cell culture analyses revealed that the osteoblast dysfunction was traced to the increased susceptibility to the mitochondria-dependent apoptosis and the decreased transcriptional activity of runt-related transcription factor 2 (Runx2), a master regulator of osteoblast differentiation. Notably, our findings revealed a novel role of Akt1/forkhead box class O (FoxO) 3a/Bim axis in the apoptosis of osteoblasts: Akt1 phosphorylates the transcription factor FoxO3a to prevent its nuclear localization, leading to impaired transactivation of its target gene Bim which was also shown to be a potent proapoptotic molecule in osteoblasts. The osteoclast dysfunction was attributed to the cell autonomous defects of differentiation and survival in osteoclasts and the decreased expression of receptor activator of nuclear factor-κB ligand (RANKL), a major determinant of osteoclastogenesis, in osteoblasts. Akt1 was established as a crucial regulator of osteoblasts and osteoclasts by promoting their differentiation and survival to maintain bone mass and turnover. The molecular network found in this study will provide a basis for rational therapeutic targets for bone disorders

Classification of Soymilk and Tofu with Diffuse Reflection Light Using a Deep Learning Technique

Tofu is an ancient soybean product that is produced by heating soymilk containing a coagulation agent. Owing to its benefits to human health, it has become popular all over the world. An important index that determines the final product&rsquo;s (tofu&rsquo;s) quality is firmness. Coagulants such as CaSO4 and MgCl2 affect the firmness. With the increasing demand for tofu, a monitoring methodology that ensures high-quality tofu is needed. In our previous paper, an opportunity to monitor changes in the physical properties of soymilk by studying its optical properties during the coagulation process was implied. To ensure this possibility, whether soymilk and tofu can be discriminated via their optical properties should be examined. In this study, a He&ndash;Ne laser (Thorlabs Japan Inc., Tokyo, Japan, 2015) with a wavelength of 633 nm was emitted to soymilk and tofu. The images of the scattered light on their surfaces were discriminated using a type of deep learning technique. As a result, the images were classified with an accuracy of about 99%. We adjusted the network architecture and hyperparameters for the learning, and this contributed to the successful classification. The construction of a network that is specific to our task led to the successful classification result. In addition to this monitoring method of the tofu coagulation process, the classification methodology in this study is worth noting for possible use in many relevant agricultural fields

Estimation of Citrus Maturity with Fluorescence Spectroscopy Using Deep Learning

To produce high-quality citrus, the harvest time of citrus should be determined by considering its maturity. To evaluate citrus maturity, the Brix/acid ratio, which is the ratio of sugar content or soluble solids content to acid content, is one of the most commonly used indicators of fruit maturity. To estimate the Brix/acid ratio, fluorescence spectroscopy, which is a rapid, sensitive, and cheap technique, was adopted. Each citrus peel was extracted, and its fluorescence value was measured. Then, the fluorescent spectrum was analyzed using a convolutional neural network (CNN). In fluorescence spectroscopy, a matrix called excitation and emission matrix (EEM) can be obtained, in which each fluorescence intensity was recorded at each excitation and emission wavelength. Then, by regarding the EEM as an image, the Brix/acid ratio of juice from the flesh was estimated via performing a regression with a CNN (CNN regression). As a result, the Brix/acid ratio absolute error was estimated to be 2.48, which is considerably better than the values obtained by the other methods in previous studies. Hyperparameters, such as depth of layers, learning rate, and the number of filters used for this estimation, could be observed using Bayesian optimization, and the optimization contributed to the high accuracy