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Conventional galaxy mass estimation methods suffer from model assumptions and degeneracies. Machine learning (ML), which reduces the reliance on such assumptions, can be used to determine how well present-day observations can yield predictions for the distributions of stellar and dark matter. In this work, we use a general sample of galaxies from the TNG100 simulation to investigate the ability of multibranch convolutional neural network (CNN) based ML methods to predict the central (i.e. within 1−2 effective radii) stellar and total masses, and the stellar mass-to-light ratio (M */ L). These models take galaxy images and spatially resolved mean velocity and velocity dispersion maps as inputs. Such CNN-based models can, in general, break the degeneracy between baryonic and dark matter in the sense that the model can make reliable predictions on the individual contributions of each component. For example, with r -band images and two galaxy kinematic maps as inputs, our model predicting M */ L has a prediction uncertainty of 0.04 dex. Moreover, to investigate which (global) features significantly contribute to the correct predictions of the properties above, we utilize a gradient-boosting machine. We find that galaxy luminosity dominates the prediction of all masses in the central regions, with stellar velocity dispersion coming next. We also investigate the main contributing features when predicting stellar and dark matter mass fractions (f *, f DM) and the dark matter mass M DM, and discuss the underlying astrophysics. [ABSTRACT FROM AUTHOR]

[Display omitted] • An H4R target pathway model for high-throughput screening tests was developed. • Cryptotanshinone was a new H4R non-competitive antagonist. • Cryptotanshinone interacts with H4R at a novel allosteric binding site. Histamine 4 receptor (H4R), the most recently identified subtype of histamine receptor, primarily induces inflammatory reactions upon activation. Several H4R antagonists have been developed for the treatment of inflammatory bowel disease (IBD) and atopic dermatitis (AD), but their use has been limited by adverse side effects, such as a short half-life and toxicity. Natural products, as an important source of anti-inflammatory agents, offer minimal side effects and reduced toxicity. This work aimed to identify novel H4R antagonists from natural products. An H4R target-pathway model deconvoluted downstream G i and MAPK signaling pathways was established utilizing cellular label-free integrative pharmacology (CLIP), on which 148 natural products were screened. Cryptotanshinone was identified as selective H4R antagonist, with an IC 50 value of 11.68 ± 1.30 μM, which was verified with Fluorescence Imaging Plate Reader (FLIPR) and Cellular Thermal Shift (CTS) assays. The kinetic binding profile revealed the noncompetitive antagonistic property of cryptotanshinone. Two allosteric binding sites of H4R were predicted using SiteMap, Fpocket and CavityPlus. Subsequent molecular docking and dynamics simulation indicated that cryptotanshinone interacts with H4R at a pocket formed by the outward interfaces between TM3/4/5, potentially representing a new allosteric binding site for H4R. Overall, this study introduced cryptotanshinone as a novel H4R antagonist, offering promise as a new hit for drug design of H4R antagonist. Additionally, this study provided a novel screening model for the discovery of H4R antagonists. [ABSTRACT FROM AUTHOR]

Tetrahymena piriformis belongs to the ciliated protists (ciliates), causing severe economic losses in aquaculture. Chemical drugs currently used usually have toxic side effects, and there is no specific drug against Tetrahymena. Therefore, it is an urgent need to identify new antiparasitic lead compounds. In the present study, the in vitro parasiticidal activity of ethyl acetate (EtOAc) extracts and water extracts from 22 selected traditional Chinese medicines (TCMs) were evaluated against T. piriformis. The EtOAc extract of P. corylifolia turned out to be the most active with the minimum parasiticidal concentration of 100 mg/L within 3 h. Thus, it was separated into 12 fractions by the first-dimensional (D1) normal phase liquid chromatography (NPLC), meanwhile combining with in vitro antiparasitic tests for activity tracking. Subsequently, 8 flavonoids were identified in the active fractions by the second-dimensional (D2) reverse phase liquid chromatography (RPLC) tandem high-resolution mass spectrometry. According to the results, 5 flavonoids were selected for in vitro antiparasitic test, of which isobavachalcone showed the minimum parasiticidal concentration of 3.125 mg/L in 2 h. Bathing treatment of infected guppies with isobavachalcone could significantly reduce the burden of T. piriformis , obtaining a 24-h median effective concentration (24-h EC 50) value of 1.916 mg/L. And the concentration of isobavachalcone causing guppies to die within 24 h is 39 times than that of 24-h EC 50. The results demonstrated that isobavachalcone has the potential to be developed into a novel commercial fish drug against T. piriformis. [Display omitted] • The EtOAc extract of Psoralea corylifolia has activity against Tetrahymena piriformis. • Bavachinin A, Bavachin D, Corylifol A, Neobavaisoflavone and Isobavachalcone were identified. • The acute toxicity and anthelmintic effect of isobavachalcone on Poecilia reticulate were studied. [ABSTRACT FROM AUTHOR]

Particle migration in oil and gas reservoirs is a common phenomenon in the process of oil and gas development, and is considered to be an important reason for the damage of reservoir permeability and the reduction of oil and gas productivity. The mechanism of this phenomenon includes the desorption, migration, and precipitation of particles, which eventually clogs the throat and causes reservoir damage. Therefore, it is necessary to accurately characterize the complex mechanism of particle migration and identify the main controlling factors of particle migration, which is very important for efficient oilfield development and plugging solution. First, the reservoir types are divided into three types and the pore structure models of different types of reservoirs are established. Then, computational fluid dynamics and discrete element coupling method numerical simulation and microscopic visualization of pore throat structure model were combined, to characterize the rules of particles and migration, and analyze the main controlling factors. Finally, a typical model of particle migration and clogging is established. The results show that particle size/throat and particle concentration are the key factors affecting particle plugging, and particle migration has the least effect on the permeability of Type I reservoir and the greatest damage to Type III reservoir. According to the mechanical and hydrodynamic behavior of particles in porous media, three mechanisms and six modes of particle plugging are proposed. [ABSTRACT FROM AUTHOR]