32 research outputs found
EMPRESS. XI. SDSS and JWST Search for Local and z~4-5 Extremely Metal-Poor Galaxies (EMPGs): Clustering and Chemical Properties of Local EMPGs
We search for local extremely metal-poor galaxies (EMPGs), selecting
photometric candidates by broadband color excess and machine-learning
techniques with the SDSS photometric data. After removing stellar contaminants
by shallow spectroscopy with Seimei and Nayuta telescopes, we confirm that
three candidates are EMPGs with 0.05--0.1 by deep Magellan/MagE
spectroscopy for faint {\sc[Oiii]}4363 lines. Using a statistical
sample consisting of 105 spectroscopically-confirmed EMPGs taken from our study
and the literature, we calculate cross-correlation function (CCF) of the EMPGs
and all SDSS galaxies to quantify environments of EMPGs. Comparing another CCF
of all SDSS galaxies and comparison SDSS galaxies in the same stellar mass
range (), we find no significant ()
difference between these two CCFs. We also compare mass-metallicity relations
(MZRs) of the EMPGs and those of galaxies at 0--4 with a steady
chemical evolution model and find that the EMPG MZR is comparable with the
model prediction on average. These clustering and chemical properties of EMPGs
are explained by a scenario of stochastic metal-poor gas accretion on
metal-rich galaxies showing metal-poor star formation. Extending the broadband
color-excess technique to a high- EMPG search, we select 17 candidates of
4--5 EMPGs with the deep ( mag) near-infrared JWST/NIRCam
images obtained by ERO and ERS programs. We find galaxy candidates with
negligible {\sc[Oiii]}4959,5007 emission weaker than the local
EMPGs and known high- galaxies, suggesting that some of these candidates may
fall in 0--0.01 , which potentially break the lowest metallicity limit
known to date
EMPRESS. IX. Extremely Metal-Poor Galaxies are Very Gas-Rich Dispersion-Dominated Systems: Will JWST Witness Gaseous Turbulent High-z Primordial Galaxies?
We present kinematics of 6 local extremely metal-poor galaxies (EMPGs) with
low metallicities () and low stellar masses
(). Taking deep medium-high resolution
() integral-field spectra with 8.2-m Subaru, we resolve the small
inner velocity gradients and dispersions of the EMPGs with H emission.
Carefully masking out sub-structures originated by inflow and/or outflow, we
fit 3-dimensional disk models to the observed H flux, velocity, and
velocity-dispersion maps. All the EMPGs show rotational velocities () of 5--23 km s smaller than the velocity dispersions
() of 17--31 km s, indicating dispersion-dominated () systems affected by inflow and/or outflow. Except
for two EMPGs with large uncertainties, we find that the EMPGs have very large
gas-mass fractions of . Comparing our results with
other H kinematics studies, we find that
decreases and increases with decreasing metallicity, decreasing
stellar mass, and increasing specific star-formation rate. We also find that
simulated high- () forming galaxies have gas fractions and dynamics
similar to the observed EMPGs. Our EMPG observations and the simulations
suggest that primordial galaxies are gas-rich dispersion-dominated systems,
which would be identified by the forthcoming James Webb Space Telescope (JWST)
observations at .Comment: Submitted to ApJ; After revisio
学習指導要領における「郷土」から「地域」への変遷に関する考察: 昭和40年代に存在し続けた「郷土」への着目
金沢大学人間社会研究域学校教育系This paper attempts to elucidate about a transition on volume changes of “home province” and “region” in course of study and a meaning of remaining “home province” in the showa 40s that was times changed from “home province” to “region”. First, in elementary school and junior high school, it was only in “social studies” that changed from “home province” to “region”. Secondly, for education about sense of dwelling, “home province” in the showa 40s was not sufficient
Anatomy-aware Self-supervised Learning for Anomaly Detection in Chest Radiographs
Large numbers of labeled medical images are essential for the accurate
detection of anomalies, but manual annotation is labor-intensive and
time-consuming. Self-supervised learning (SSL) is a training method to learn
data-specific features without manual annotation. Several SSL-based models have
been employed in medical image anomaly detection. These SSL methods effectively
learn representations in several field-specific images, such as natural and
industrial product images. However, owing to the requirement of medical
expertise, typical SSL-based models are inefficient in medical image anomaly
detection. We present an SSL-based model that enables anatomical
structure-based unsupervised anomaly detection (UAD). The model employs the
anatomy-aware pasting (AnatPaste) augmentation tool. AnatPaste employs a
threshold-based lung segmentation pretext task to create anomalies in normal
chest radiographs, which are used for model pretraining. These anomalies are
similar to real anomalies and help the model recognize them. We evaluate our
model on three opensource chest radiograph datasets. Our model exhibit area
under curves (AUC) of 92.1%, 78.7%, and 81.9%, which are the highest among
existing UAD models. This is the first SSL model to employ anatomical
information as a pretext task. AnatPaste can be applied in various deep
learning models and downstream tasks. It can be employed for other modalities
by fixing appropriate segmentation. Our code is publicly available at:
https://github.com/jun-sato/AnatPaste
Species Distribution of Candidemia and Their Susceptibility in a Single Japanese University Hospital: Prior Micafungin Use Affects the Appearance of Candida parapsilosis and Elevation of Micafungin MICs in Non-parapsilosis Candida Species
Introduction: Micafungin is a recommended echinocandin antifungal agent for candidemia treatment and prophylaxis. However, overuse of echinocandin antifungals may cause resistance. There is currently no information available regarding the low susceptibility associated with using micafungin. This study investigated the effect of micafungin use on changes in the detected Candida species and low susceptibility. Methods: We conducted a retrospective survey and included records of Candida spp. detected in blood cultures from January 2010 to December 2018 in our hospital. Survey items included clinical outcomes at 30 days after positive cultures, patient characteristics, and drug prescription status. Patient background information included gender, previous hospitalization, stay in the intensive care unit, comorbidities, and history of surgery (within 90 days before candidemia onset) and drug exposure. Species detected and their minimum inhibitory concentrations (MICs) and amount of antifungal prescriptions by department were investigated. Risk factors for detecting C. parapsilosis and for low susceptibility to micafungin were evaluated using multivariate analysis. Results: A total of 153 Candida clinical blood isolates were collected and C. albicans was the most prevalent species, followed by C. parapsilosis and C. glabrata. In the analysis by department, antifungal use and non-albicans Candida species were most frequently detected in the hematology department. Multivariate analysis showed that prior micafungin use increased the risk of C. parapsilosis (odds ratio (OR) 4.22; 95% confidence interval (CI) 1.39–12.79; p = 0.011). MIC90 of micafungin on C. glabrata and C. parapsilosis was 1.0 μg/mL. Prior micafungin use was clarified as a risk factor resulting in MIC > 0.06 μg/mL for micafungin in non-parapsilosis Candida species (OR 13.2; 95% CI 3.23–54.2; p < 0.01). Conclusion: Prior micafungin use increased the risk of C. parapsilosis and the MIC > 0.06 μg/mL of micafungin in non-parapsilosis Candida species. Since there are only a few antifungal options, further antifungal stewardship considering azole antifungal agents use is required