The Multiwavelength Survey by Yale-Chile (MUSYC): Deep Medium-Band optical imaging and high quality 32-band photometric redshifts in the ECDF-S

We present deep optical 18-medium-band photometry from the Subaru telescope over the ~30' x 30' Extended Chandra Deep Field-South (ECDF-S), as part of the Multiwavelength Survey by Yale-Chile (MUSYC). This field has a wealth of ground- and space-based ancillary data, and contains the GOODS-South field and the Hubble Ultra Deep Field. We combine the Subaru imaging with existing UBVRIzJHK and Spitzer IRAC images to create a uniform catalog. Detecting sources in the MUSYC BVR image we find ~40,000 galaxies with R_AB<25.3, the median 5 sigma limit of the 18 medium bands. Photometric redshifts are determined using the EAZY code and compared to ~2000 spectroscopic redshifts in this field. The medium band filters provide very accurate redshifts for the (bright) subset of galaxies with spectroscopic redshifts, particularly at 0.1 < z 3.5. For 0.1 < z < 1.2, we find a 1 sigma scatter in \Delta z/(1+z) of 0.007, similar to results obtained with a similar filter set in the COSMOS field. As a demonstration of the data quality, we show that the red sequence and blue cloud can be cleanly identified in rest-frame color-magnitude diagrams at 0.1 < z < 1.2. We find that ~20% of the red-sequence-galaxies show evidence of dust-emission at longer rest-frame wavelengths. The reduced images, photometric catalog, and photometric redshifts are provided through the public MUSYC website.Comment: 19 pages, 14 image

In Silico Analysis of ACE Inhibitory Peptides from Chloroplast Proteins of Red Alga Grateloupia asiatica

Inhibition of angiotensin I-converting enzyme (ACE) is one of the key factors to repress high blood pressure. Although many studies have been reported that seaweed protein hydrolysates showed the ACE inhibitory activity, the comprehensive understanding of the relationship was still unclear. In this study, we employed chloroplast genome for in silico analysis and compared it with in vitro experiments. We first extracted water-soluble proteins (WSP) from red alga Grateloupia asiatica, which contained mainly PE, PC, APC, and Rbc, and prepared WSP hydrolysate by thermolysin, resulting that the hydrolysate showed ACE inhibitory activity. Then, we determined the complete chloroplast genome of G. asiatica (187,518 bp: 206 protein-coding genes, 29 tRNA, and 3 rRNA) and clarified the amino acid sequences of main WSP, i.e., phycobiliproteins and Rubisco, to perform in silico analysis. Consequently, 190 potential ACE inhibitory peptides existed in the main WSP sequences, and 21 peptides were obtained by in silico thermolysin digestion. By comparing in vitro and in silico analyses, in vitro ACE inhibitory activity was correlated to the IC50 value from in silico digestion. Therefore, in silico approach provides insight into the comprehensive understanding of the potential bioactive peptides from seaweed proteins

Development and external validation of a deep learning-based computed tomography classification system for COVID-19

[BACKGROUND] We aimed to develop and externally validate a novel machine learning model that can classify CT image findings as positive or negative for SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR). [METHODS] We used 2, 928 images from a wide variety of case-control type data sources for the development and internal validation of the machine learning model. A total of 633 COVID-19 cases and 2, 295 non-COVID-19 cases were included in the study. We randomly divided cases into training and tuning sets at a ratio of 8:2. For external validation, we used 893 images from 740 consecutive patients at 11 acute care hospitals suspected of having COVID-19 at the time of diagnosis. The dataset included 343 COVID-19 patients. The reference standard was RT-PCR. [RESULTS] In external validation, the sensitivity and specificity of the model were 0.869 and 0.432, at the low-level cutoff, 0.724 and 0.721, at the high-level cutoff. Area under the receiver operating characteristic was 0.76. [CONCLUSIONS] Our machine learning model exhibited a high sensitivity in external validation datasets and may assist physicians to rule out COVID-19 diagnosis in a timely manner at emergency departments. Further studies are warranted to improve model specificity