Redshifting galaxies from DESI to JWST CEERS: Correction of biases and uncertainties in quantifying morphology

Observations of high-redshift galaxies with unprecedented detail have now been rendered possible with JWST. However, accurately quantifying their morphology remains uncertain due to potential biases and uncertainties. To address this issue, we used a sample of 1816 nearby DESI galaxies, with a mass range of $10^{9.75-11.25}M_{\odot}$, to compute artificial images of galaxies of the same mass located at $0.75\leq z\leq 3$ and observed at rest-frame optical wavelength in CEERS. We analyzed the effects of cosmological redshift on the measurements of Petrosian radius ($R_p$), half-light radius ($R_{50}$), asymmetry ($A$), concentration ($C$), axis ratio ($q$), and S\'ersic index ($n$). Our results show that $R_p$ and $R_{50}$, calculated using non-parametric methods, are slightly overestimated due to PSF smoothing, while $R_{50}$, $q$, and $n$ obtained through model fitting does not exhibit significant biases. We improve the computation of $A$ by incorporating a more accurate noise effect removal procedure. Due to PSF asymmetry, there is a minor overestimation of $A$ for intrinsically symmetric galaxies. However, for intrinsically asymmetric galaxies, PSF smoothing dominates and results in an underestimation of $A$, an effect that becomes more significant with higher intrinsic $A$ or at lower resolutions. Moreover, PSF smoothing also leads to an underestimation of $C$, which is notably more pronounced in galaxies with higher intrinsic $C$ or at lower resolutions. We developed functions based on resolution level, defined as $R_p/$FWHM, for correcting these biases and the associated statistical uncertainties. Applying these corrections, we measured the bias-corrected morphology for the simulated CEERS images and we find that the derived quantities are in good agreement with their intrinsic values -- except for $A$, which is robust only for angularly large galaxies where $R_p/{\rm FWHM}\geq 5$.Comment: 21 pages, 17 figures; A&A in pres

Angiogenesis and Vasculogenesis at 7-Day of Reperfused Acute Myocardial Infarction

Objectives 
This study is to investigate the angiogenesis and vasculogenesis at the first week of reperfused acute myocardial infarction (AMI).
Methods 
16 of mini-swines (20 to 30 Kg) were randomly assigned to the sham-operated group and the AMI group. The acute myocardial infarction and reperfusion model was created and the pig tail catheter was performed to monitor hemodynamics before left anterior descending coronary artery (LAD) occlusion, 90 min of LAD occlusion and 120 min of LAD reperfusion. Pathologic myocardial tissue was collected at 7-day of LAD reperfusion and further assessed by immunochemistry, dual immunochemistry, in-situ hybridization, real-time quantitative polymerase chain reaction and western blot. 
Results 
The infarcted area had higher FLK1 mRNA expression than sham-operated area and the normal area (all P<0.05), and the infarcted and marginal areas showed higher CD146 protein expression than the sham-operated area (all P<0.05), but the microvessel density (CD31 positive expression of microvessels/HP) was not significantly different between the infarcted area and the sham-operated area (8.92±3.05 vs 6.43±1.54) at 7-day of reperfused acute myocardial infarction (P>0.05). 
Conclusions 
FLK1 and CD146 expression significantly increase in the infarcted and marginal areas, and the microvessel density is not significantly different between the infarcted area and the sham-operated area, suggesting that angiogenesis and vasculogenesis in the infarcted area appear to high frequency of increase in 7-day of reperfused myocardial infarction. 
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Positive and unlabeled learning for user behavior analysis based on mobile internet traffic data

With the rapid development of wireless communication and mobile Internet, mobile phone becomes ubiquitous and functions as a versatile and smart system, on which people frequently interact with various mobile applications (Apps). Understanding human behaviors using mobile phone is significant for mobile system developers, for human-centered system optimization and better service provisioning. In this paper, we focus on mobile user behavior analysis and prediction based on mobile Internet traffic data. Real traffic flow data is collected from the public network of Internet Service Providers (ISPs), by high-performance network traffic monitors.We construct User-App bipartite network to represent the traffic interaction pattern between users and App servers. After mining the explicit and implicit features from User-App bipartite network, we propose two positive and unlabeled learning (PU learning) methods, including Spy-based PU learning and K-means-based PU learning, for App usage prediction and mobile video traffic identification. We firstly use the traffic flow data of QQ, a very famous messaging and social media application possessing high market share in China, as the experimental dataset for App usage prediction task. Then we use the traffic flow data from six popular Apps, including video intensive Apps (Youku, Baofeng, LeTV, Tudou) and other Apps (Meituan, Apple), as the experimental dataset for mobile video traffic identification task. Experimental results show that our proposed PU learning methods perform well in both tasks

Insulin resistance, autophagy and apoptosis in patients with polycystic ovary syndrome: Association with PI3K signaling pathway

Polycystic ovary syndrome (PCOS) is a disease in which endocrine metabolic abnormalities coexist with reproductive system abnormalities, with the main clinical manifestations including abnormal menstruation, hirsutism, acne, infertility, and obesity, and it is also a high risk for the development of many pregnancy complications, gynecological malignancies and other diseases. Therefore, timely intervention to prevent the progression of PCOS is of great significance for improving the quality of life of most female patients. Insulin resistance (IR) is one of the most common endocrine disorders in PCOS patients, with approximately 75% of PCOS patients experiencing varying degrees of IR. It is now believed that it is mainly related to the PI3K signaling pathway. The role of autophagy and apoptosis of ovarian granulosa cells (GCs) in the pathogenesis of PCOS has also been gradually verified in recent years. Coincidentally, it also seems to be associated with the PI3K signaling pathway. Our aim is to review these relevant studies, to explore the association between the IR, cellular autophagy and apoptosis in PCOS patients and the PI3K pathway. We summarize some of the drug studies that have improved PCOS as well. We have also found that proteomics holds great promise in exploring the pathogenesis of PCOS, and we have published our views on this