MUSIC Algorithm for IRS-Assisted AOA Estimation

Based on the signals received across its antennas, a multi-antenna base station (BS) can apply the classic multiple signal classification (MUSIC) algorithm for estimating the angle of arrivals (AOAs) of its incident signals. This method can be leveraged to localize the users if their line-of-sight (LOS) paths to the BS are available. In this paper, we consider a more challenging AOA estimation setup in the intelligent reflecting surface (IRS) assisted integrated sensing and communication (ISAC) system, where LOS paths do not exist between the BS and the users, while the users' signals can be transmitted to the BS merely via their LOS paths to the IRS as well as the LOS path from the IRS to the BS. Specifically, we treat the IRS as the anchor and are interested in estimating the AOAs of the incident signals from the users to the IRS. Note that we have to achieve the above goal based on the signals received by the BS, because the passive IRS cannot process its received signals. However, the signals received across different antennas of the BS only contain AOA information of its incident signals via the LOS path from the IRS to the BS. To tackle this challenge arising from the spatial-domain received signals, we propose an innovative approach to create temporal-domain multi-dimension received signals for estimating the AOAs of the paths from the users to the IRS. Specifically, via a proper design of the user message pattern and the IRS reflecting pattern, we manage to show that our designed temporal-domain multi-dimension signals can be surprisingly expressed as a function of the virtual steering vectors of the IRS towards the users. This amazing result implies that the classic MUSIC algorithm can be applied to our designed temporal-domain multi-dimension signals for accurately estimating the AOAs of the signals from the users to the IRS.Comment: to appear in IEEE VTC 2023-Fal

Evaluation of the inhibition potential of plumbagin against cytochrome P450 using LC-MS/MS and cocktail approach

Plumbagin (5-hydroxy-2-methyl-1,4-naphthoquinone), a natural naphthoquinone compound isolated from roots of Plumbago zeylanica L., has drawn a lot of attention for its plenty of pharmacological properties including antidiabetes and anti-cancer. The aim of this study was to investigate the effects of plumbagin on CYP1A2, CYP2B1/6, CYP2C9/11, CYP2D1/6, CYP2E1 and CYP3A2/4 activities in human and rat liver and evaluate the potential herb-drug interactions using the cocktail approach. All CYP substrates and their metabolites were analyzed using high-performance liquid chromatography–tandem mass spectrometry (LC-MS/MS). Plumbagin presented non-time-dependent inhibition of CYP activities in both human and rat liver. In humans, plumbagin was not only a mixed inhibitor of CYP2B6, CYP2C9, CYP2D6, CYP2E1 and CYP3A4, but also a non-competitive inhibitor of CYP1A2, with K(i) values no more than 2.16 μM. In rats, the mixed inhibition of CYP1A2 and CYP2D1, and competitive inhibition for CYP2B1, CYP2C11 and CYP2E1 with K(i) values less than 9.93 μM were observed. In general, the relatively low K(i) values of plumbagin in humans would have a high potential to cause the toxicity and drug interactions involving CYP enzymes

Cosmic evolution of radio-excess active galactic nuclei in quiescent and star-forming galaxies across 0 < z < 4

Context. Radio-excess active galactic nuclei (radio-AGNs) are essential to our understanding of both the physics of black hole (BH) accretion and the interaction between BHs and host galaxies. Recent deep and wide radio continuum surveys have made it possible to study radio-AGNs down to lower luminosities and up to higher redshifts than previous studies, and are providing new insights into the abundance and physical origin of radio-AGNs.Aims. Here we focus on the cosmic evolution, physical properties, and AGN-host galaxy connections of radio-AGNs selected from a total sample of ∼400 000 galaxies at 0 < z < 4 in the GOODS-N and COSMOS fields.Methods. Combining the deep radio continuum data with multi-band, de-blended far-infrared, and submillimeter data, we were able to identify 983 radio-AGNs out of the entire galaxy sample through radio excess relative to the far-infrared–radio relation.Results. We studied the cosmic evolution of 1.4 GHz radio luminosity functions (RLFs) for both star-forming galaxies (SFGs) and radio-AGNs, which can be well described by a pure luminosity evolution of L⋆ ∝ (1 + z)−0.34 × z + 3.57 and a pure density evolution of Φ⋆ ∝ (1 + z)−0.77 × z + 2.69, respectively. We derived the turnover luminosity, above which the number density of radio-AGNs surpasses that of SFGs. We show that this crossover luminosity increases with increasing redshifts, from 1022.9 W Hz−1 at z ∼ 0 to 1025.2 W Hz−1 at z ∼ 4. At the full redshift range of 0 < z < 4, we further derive the probability (pradio) of SFGs and quiescent galaxies (QGs) hosting a radio-AGN, as a function of stellar mass (M⋆), radio luminosity (LR), and redshift (z), which yields pradio ∝ (1+z)3.08 M⋆1.06 LR−0.77 for SFGs, and pradio ∝ (1+z)2.47 M⋆1.41 LR−0.60 for QGs, respectively.Conclusions. The quantitative relation for the probabilities of galaxies hosting a radio-AGN indicates that radio-AGNs in QGs prefer to reside in more massive galaxies with higher LR than those in SFGs. The fraction of radio-AGN increases toward higher redshift in both SFGs and QGs, with a more rapid increase in SFGs

Preclinical Absorption, Distribution, Metabolism, and Excretion of Sodium Danshensu, One of the Main Water-Soluble Ingredients in Salvia miltiorrhiza, in Rats

In this study, the absorption, distribution, metabolism and excretion (ADME) of sodium danshensu (Sodium DL-β-(3, 4-dihydroxyphenyl)lactate), one of the main water-soluble active constituents in Salvia miltiorrhiza, were evaluated in rats. Pharmacokinetic study was evaluated in doses of 15, 30, and 60 mg/kg after intravenous administration of sodium danshensu. Bioavailability study was evaluated by comparing between 30 mg/kg (I.V.) and 180 mg/kg (P.O.) of sodium danshensu. Tissue distribution, metabolism, and excretion were evaluated at 30 mg/kg (I.V.) of sodium danshensu. Following intravenous administration, sodium danshensu exhibited linear pharmacokinetics in the dose range of 15–60 mg/kg. Sodium danshensu appeared to be poorly absorbed after oral administration, with an absolute bioavailability of 13.72%. The primary distribution tissue was kidney, but it was also distributed to lung, stomach, muscle, uterus, heart, etc. Within 96 h after intravenous administration, 46.99% was excreted via urine and 1.16% was excreted via feces as the parent drug. Biliary excretion of sodium danshensu was about 0.83% for 24 h. Metabolites in urine were identified as methylation, sulfation, both methylation and sulfation, and acetylation of danshensu. Sodium danshensu can be developed as an injection because of its poor oral bioavailability. In conclusion, sodium danshensu is widely distributed, mainly phase II metabolized and excreted primarily in urine as an unchanged drug in rats

Research progress on deep learning in magnetic resonance imaging–based diagnosis and treatment of prostate cancer: a review on the current status and perspectives

Multiparametric magnetic resonance imaging (mpMRI) has emerged as a first-line screening and diagnostic tool for prostate cancer, aiding in treatment selection and noninvasive radiotherapy guidance. However, the manual interpretation of MRI data is challenging and time-consuming, which may impact sensitivity and specificity. With recent technological advances, artificial intelligence (AI) in the form of computer-aided diagnosis (CAD) based on MRI data has been applied to prostate cancer diagnosis and treatment. Among AI techniques, deep learning involving convolutional neural networks contributes to detection, segmentation, scoring, grading, and prognostic evaluation of prostate cancer. CAD systems have automatic operation, rapid processing, and accuracy, incorporating multiple sequences of multiparametric MRI data of the prostate gland into the deep learning model. Thus, they have become a research direction of great interest, especially in smart healthcare. This review highlights the current progress of deep learning technology in MRI-based diagnosis and treatment of prostate cancer. The key elements of deep learning-based MRI image processing in CAD systems and radiotherapy of prostate cancer are briefly described, making it understandable not only for radiologists but also for general physicians without specialized imaging interpretation training. Deep learning technology enables lesion identification, detection, and segmentation, grading and scoring of prostate cancer, and prediction of postoperative recurrence and prognostic outcomes. The diagnostic accuracy of deep learning can be improved by optimizing models and algorithms, expanding medical database resources, and combining multi-omics data and comprehensive analysis of various morphological data. Deep learning has the potential to become the key diagnostic method in prostate cancer diagnosis and treatment in the future