DiskMINT: A Tool to Estimate Disk Masses with CO Isotopologues

CO is one of the most abundant molecules in protoplanetary disks, and optically thin emission from its isotopologues has been detected in many of them. However, several past works have argued that reproducing the relatively low emission of CO isotopologues requires a very low disk mass or significant CO depletion. Here, we present a Python code, DiskMINT, which includes gas density and temperature structures that are both consistent with the thermal pressure gradient, isotope-selective chemistry, and conversion of CO into $\mathrm{CO_2}$ ice on grain-surfaces. The code generates a self-consistent disk structure, where the gas disk distribution is obtained from a Spectral Energy Distribution (SED)-derived dust disk structure with multiple grain sizes. We use DiskMINT to study the disk of RU~Lup, a high-accreting star whose disk was previously inferred to have a gas mass of only $\sim 1.5\times10^{-3}\,M_\odot$ and gas-to-dust mass ratio of $\sim 4$. Our best-fit model to the long-wavelength continuum emission can explain the total $\mathrm{C^{18}O}$ luminosity as well as the $\mathrm{C^{18}O}$ velocity and radial intensity profiles, and obtains a gas mass of $\sim 1.2\times10^{-2}\,M_\odot$, an order of magnitude higher than previous results. A disk model with parametric Gaussian vertical distribution that better matches the IR-SED can also explain the observables above with a similarly high gas mass $\sim 2.1\times10^{-2}\,M_\odot$. We confirm the conclusions of Ruaud et al. (2022) that optically thin $\mathrm{C^{18}O}$ rotational lines provide reasonable estimates of the disk mass and can therefore be used as gas disk tracers.Comment: 15 pages, 7 figures, accepted for publication in the ApJ. Associated code is released, see http://github.com/DingshanDeng/DiskMINT. v2 has updated the reference and included a correction to Fig

Estimation of the flux at 1450MHz of OB stars for FAST and SKA

Radio observation is crucial to understanding the wind mechanism of OB stars but very scarce. This work estimates the flux at 1450MHz ($S_{\rm 1.4GHz}$) of about 5,000 OB stars identified by the LAMOST spectroscopic survey and confirmed by the Gaia astrometric as well as astrophysical measurements. The calculation is performed under the free-free emission mechanism for wind with the mass loss rate derived from stellar parameters. The estimated $S_{\rm 1.4GHz}$ distributes from $10^{-11}$Jy to $10^{-3}$Jy with the peak at about $10^{-8}$Jy. This implies that the complete SKA-II can detect more than half of them, and some tens of objects are detectable by FAST without considering source confusion. An array of FAST would increase the detectable sample by two orders of magnitude.Comment: 15 pages. 8 figure

ACSM6 overexpression indicates a non-inflammatory tumor microenvironment and predicts treatment response in bladder cancer: results from multiple real-world cohorts

Background: ACSMs play critical roles in lipid metabolism; however, their immunological function within the tumor microenvironment (TME) remains unclear, especially that of ACSM6. In this study, we investigate the latent effect of ACSM6 on bladder cancer (BLCA).Methods: Several real-world cohorts, including the Xiangya (in-house), The Cancer Genome Atlas (TCGA-BLCA), and IMvigor210 cohorts, with TCGA-BLCA cohort serving as the discovery cohort were compared. We investigated the potential immunological effects of ACSM6 in regulating the BLCA tumor microenvironment by analyzing its correlation with immunomodulators, anti-cancer immune cycles, immune checkpoints, tumor-infiltrating immune cells, and the T-cell inflamed score (TIS). Additionally, we assessed the precision of ACSM6 in predicting BLCA molecular subtypes and responses to several treatments using ROC analysis. To ensure the robustness of our findings, all results were confirmed in two independent external cohorts: the IMvigor210 and Xiangya cohorts.Results: ACSM6 expression was markedly upregulated in BLCA. Our analysis suggests that ACSM6 might have significant impact to promote the formation of a non-inflamed tumor microenvironment because of its negative correlation with immunomodulators, anticancer immune cycles, immune checkpoints, tumor-infiltrating immune cells, and the T-cell inflamed score (TIS). Additionally, high ACSM6 expression levels in BLCA may predict the luminal subtype, which is typically associated with resistance to chemotherapy, neoadjuvant chemotherapy, and radiotherapy. These findings were consistent across both the IMvigor210 and Xiangya cohorts.Conclusion: ACSM6 has the potential to serve as a valuable predictor of the tumor microenvironment phenotypes and treatment outcomes in BLCA, thereby contributing to more precise treatment

Molecular vasculogenic mimicry–Related signatures predict clinical outcomes and therapeutic responses in bladder cancer: Results from real-world cohorts

Bladder cancer (BLCA) is a heterogeneous disease, and there are many classical molecular subtypes that reflect tumor immune microenvironment (TME) heterogeneity but their clinical utility is limited and correct individual treatment and prognosis cannot be predicted based on them. To find reliable and effective biomarkers and tools for predicting patients’ clinical responses to several therapies, we developed a new systemic indicator of molecular vasculogenic mimicry (VM)–related genes mediated by molecular subtypes based on the Xiangya cohort and additional external BLCA cohorts using a random forest algorithm. A correlation was then done between the VM_Score and classical molecular subtypes, clinical outcomes, immunophenotypes, and treatment options for BLCA. With the VM_Score, it is possible to predict classical molecular subtypes, immunophenotypes, prognosis, and therapeutic potential of BLCA with high accuracy. The VM_Scores of high levels indicate a more anticancer immune response but a worse prognosis due to a more basal and inflammatory phenotype. The VM_Score was also found associated with low sensitivity to antiangiogenic and targeted therapies targeting the FGFR3, β-catenin, and PPAR-γ pathways but with high sensitivity to cancer immunotherapy, neoadjuvant chemotherapy, and radiotherapy. A number of aspects of BLCA biology were reflected in the VM_Score, providing new insights into precision medicine. Additionally, the VM_Score may be used as an indicator of pan-cancer immunotherapy response and prognosis

Multi-population genetic algorithm with ER network for solving flexible job shop scheduling problems.

A genetic algorithm (GA) cannot always avoid premature convergence, and multi-population is usually used to overcome this limitation by dividing the population into several sub-populations (sub-population number) with the same number of individuals (sub-population size). In previous research, the questions of how a network structure composed of sub-populations affects the propagation rate of advantageous genes among sub-populations and how it affects the performance of GA have always been ignored. Therefore, we first propose a multi-population GA with an ER network (MPGA-ER). Then, by using the flexible job shop scheduling problem (FJSP) as an example and considering the total individual number (TIN), we study how the sub-population number and size and the propagation rate of advantageous genes affect the performance of MPGA-ER, wherein the performance is evaluated by the average optimal value and success rate based on TIN. The simulation results indicate the following regarding the performance of MPGA-ER: (i) performance shows considerable improvement compared with that of traditional GA; (ii) for an increase in the sub-population number for a certain TIN, the performance first increases slowly, and then decreases rapidly; (iii) for an increase in the sub-population size for a certain TIN, the performance of MPGA-ER first increases rapidly and then tends to remain stable; and (iv) with an increase in the propagation rate of advantageous genes, the performance first increases rapidly and then decreases slowly. Finally, we use a parameter-optimized MPGA-ER to solve for more FJSP instances and demonstrate its effectiveness by comparing it with that of other algorithms proposed in other studies

DiskMINT: A Tool to Estimate Disk Masses with CO Isotopologues

CO is one of the most abundant molecules in protoplanetary disks, and optically thin emission from its isotopologues has been detected in many of them. However, several past works have argued that reproducing the relatively low emission of CO isotopologues requires a very low disk mass or significant CO depletion. Here, we present a Python code, DiskMINT , which includes gas density and temperature structures that are both consistent with the thermal pressure gradient, isotope-selective chemistry, and conversion of CO into CO _2 ice on grain surfaces. The code generates a self-consistent disk structure, where the gas disk distribution is obtained from a spectral energy distribution (SED)–derived dust disk structure with multiple grain sizes. We use DiskMINT to study the disk of RU Lup, a high-accreting star whose disk was previously inferred to have a gas mass of only ∼1.5 × 10 ^−3 M _⊙ and gas-to-dust mass ratio of ∼4. Our best-fit model to the long-wavelength continuum emission can explain the total C ^18 O luminosity as well as the C ^18 O velocity and radial intensity profiles, and it obtains a gas mass of ∼1.2 × 10 ^−2 M _⊙ , an order of magnitude higher than previous results. A disk model with parametric Gaussian vertical distribution that better matches the IR SED can also explain the observables above with a similarly high gas mass ∼2.1 × 10 ^−2 M _⊙ . We confirm the conclusions of Ruaud et al. that optically thin C ^18 O rotational lines provide reasonable estimates of the disk mass and can therefore be used as gas disk tracers

Estimation of the Flux at 1450 MHz of OB Stars for FAST and SKA

Radio observation is crucial to understanding the wind mechanism of OB stars but very scarce. This work estimates the flux at 1450 MHz ( S _1.4GHz ) of about 5000 OB stars identified by the LAMOST spectroscopic survey and confirmed by the Gaia astrometric as well as astrophysical measurements. The calculation is performed under the free–free emission mechanism for wind with the mass-loss rate derived from stellar parameters. The estimated S _1.4GHz distributes from 10 ^−11 to 10 ^−3 Jy with the peak at about 10 ^−8 Jy. This implies that the complete SKA-II can detect more than half of them, and some tens of objects are detectable by FAST without considering source confusion. An array of FAST would increase the detectable sample by 2 orders of magnitude