The time-dependent expression of FPR2 and ANXA1 in murine deep vein thrombosis model and its relation to thrombus age.

Thrombus age determination in fatal venous thromboembolism cases is an important task for forensic pathologists. In this study, we investigated the time-dependent expressions of formyl peptide receptor 2 (FPR2) and Annexin A1 (ANXA1) in a stasis-induced deep vein thrombosis (DVT) murine model, with the aim of obtaining useful information for thrombus age timing. A total of 75 ICR mice were randomly classified into thrombosis group and control group. In thrombosis group, a DVT model was established by ligating the inferior vena cava (IVC) of mice, and thrombosed IVCs were harvested at 1, 3, 5, 7, 10, 14, and 21 days after modeling. In control group, IVCs without thrombosis were taken as control samples. The expressions of FPR2 and ANXA1 during thrombosis were detected using immunohistochemistry and double immunofluorescence staining. Their protein and mRNA levels in the samples were determined by Western blotting and quantitative real-time PCR. The results reveal that FPR2 was predominantly expressed by intrathrombotic neutrophils and macrophages. ANXA1 expression in the thrombi was mainly distributed in neutrophils, endothelial cells of neovessels, and fibroblastic cells. After thrombosis, the expressions of FPR2 and ANXA1 were time-dependently up-regulated. The percentage of FPR2-positive cells and the level of FPR2 protein significantly elevated at 1, 3, 5 and 7 days after IVC ligation as compared to those at 10, 14 and 21 days after ligation (p < 0.05). Moreover, the mRNA level of FPR2 were significantly higher at 5 days than that at the other post-ligation intervals (p < 0.05). Besides, the levels of ANXA1 mRNA and protein peaked at 10 and 14 days after ligation, respectively. A significant increase in the mRNA level of ANXA1 was found at 10 and 14 days as compared with that at the other post-ligation intervals (p < 0.01). Our findings suggest that FPR2 and ANXA1 are promising as useful markers for age estimation of venous thrombi.

Eugenol Possesses Colitis Protective Effects: Impacts on the TLR4/MyD88/NF-[Formula: see text]B Pathway, Intestinal Epithelial Barrier, and Macrophage Polarization.

Eugenol (EU) has been shown to ameliorate experimental colitis due to its anti-oxidant and anti-inflammatory bioactivities. In this study, DSS-induced acute colitis was established and applied to clarify the regulation efficacy of EU on intestinal barrier impairment and macrophage polarization imbalance along with the inflammatory response. Besides, the adjusting effect of EU on macrophages was further investigated in vitro. The results confirmed that EU intervention alleviated DSS-induced colitis through methods such as restraining weight loss and colonic shortening and decreasing DAI scores. Microscopic observation manifested that EU maintained the intestinal barrier integrity in line with the mucus barrier and tight junction protection. Furthermore, EU intervention significantly suppressed the activation of TLR4/MyD88/NF-[Formula: see text]B signaling pathways and pro-inflammatory cytokines gene expressions, while enhancing the expressions of anti-inflammatory cytokines. Simultaneously, WB and FCM analyses of the CD86 and CD206 showed that EU could regulate the DSS-induced macrophage polarization imbalance. Overall, our data further elucidated the mechanism of EU's defensive effect on experimental colitis, which is relevant to the protective efficacy of intestinal barriers, inhibition of oxidative stress and excessive inflammatory response, and reprogramming of macrophage polarization. Hence, this study may facilitate a better understanding of the protective action of the EU against UC.

Wetting and Spreading Behavior of Axisymmetric Compound Droplets on Curved Solid Walls Using Conservative Phase Field Lattice Boltzmann Method.

Compound droplets have received increasing attention due to their applications in many several areas, including medicine and materials. Previous works mostly focused on compound droplets on planar surfaces and, as such, the effects of curved walls have not been studied thoroughly. In this paper, the influence of the properties of curved solid wall (including the shape, curvature, and contact angle) on the wetting behavior of compound droplets is explored. The axisymmetric lattice Boltzmann method, based on the conservative phase field formulation for ternary fluids, was used to numerically study the wetting and spreading of a compound droplet of the Janus type on various curved solid walls at large density ratios, focusing on whether the separation of compound droplets occurs. Several types of wall geometries were considered, including a planar wall, a concave wall with constant curvature, and a convex wall with fixed or variable curvature (specifically, a prolate or oblate spheroid). The effects of surface wettability, interfacial angles, and the density ratio (of droplet to ambient fluid) on the wetting process were also explored. In general, it was found that, under otherwise identical conditions, droplet separation tends to happen more likely on more hydrophilic walls, under larger interfacial angles (measured inside the droplet), and at larger density ratios. On convex walls, a larger radius of curvature of the surface near the droplet was found to be helpful to split the Janus droplet. On concave walls, as the radius of curvature increases from a small value, the possibility to observe droplet separation first increases and then decreases. Several phase diagrams on whether droplet separation occurs during the spreading process were produced for different kinds of walls to illustrate the influences of various factors.

Arg177 and Asp159 from dog prion protein slow liquid-liquid phase separation and inhibit amyloid formation of human prion protein.

Prion diseases are a group of transmissible neurodegenerative diseases primarily caused by the conformational conversion of prion protein (PrP) from α-helix-dominant cellular prion protein (PrPC) to ß-sheet-rich pathological aggregated form of PrPSc in many mammalian species. Dogs exhibit resistance to prion diseases, but the mechanism behind the phenomenon remains poorly understood. Compared with human PrP and mouse PrP, dog PrP has two unique amino acid residues, Arg177 and Asp159. Because PrPC contains a low-complexity and intrinsically disordered region in its N-terminal domain, it undergoes liquid-liquid phase separation (LLPS) in vitro and forms protein condensates. However, little is known about whether these two unique residues modulate the formation of PrPC condensates. Here, using confocal microscopy, fluorescence recovery after photobleaching assays, thioflavin T binding assays, and transmission electron microscopy, we report that Arg177 and Asp159 from the dog PrP slow the LLPS of full-length human PrPC, shifting the equilibrium phase boundary to higher protein concentrations and inhibit amyloid formation of the human protein. In sharp contrast, His177 and Asn159 from the human PrP enhance the LLPS of full-length dog PrPC, shifting the equilibrium phase boundary to lower protein concentrations, and promote fibril formation of the canid protein. Collectively, these results demonstrate how LLPS and amyloid formation of PrP are inhibited by a single residue Arg177 or Asp159 associated with prion disease resistance, and how LLPS and fibril formation of PrP are promoted by a single residue His177 or Asn159. Therefore, Arg177/His177 and Asp159/Asn159 are key residues in modulating PrPC liquid-phase condensation.

Construction of ecological security pattern in Ningbo based on remote sensing ecological index and graph theory knowledge.

Constructing ecological security pattern and identifying ecological important areas are the focus of current research on regional ecological security. With Ningbo City as a case study area, we identified ecological sources by remote sensing ecological index, the ecological corridors and pinch point by circuit theory model, and the minimum spanning tree and cuts by graph theory algorithm. The results showed that there were 203 ecological sources in Ningbo, and that the main type of land cover was forest, including a small amount of paddy fields and flooded vegetation. There were 368 ecological corridors with a total length of 573.42 km, being dense in the southwest and sparse in the northeast. There were 91 ecological pinch points, which mainly distributed between coastal areas and closely related ecological sources. According to current situation, we put forward the optimization strategy with 187 primary corridors, 181 secondary corridors, 50 ecological restoration priority areas and 59 long-term ecological restoration areas. The optimization strategy combined with graph theory and circuit theory model would provide a refe-rence for the constructing of ecological security pattern.

Multi-view subspace clustering via adaptive graph learning and late fusion alignment.

Multi-view subspace clustering has attracted great attention due to its ability to explore data structure by utilizing complementary information from different views. Most of existing methods learn a sample representation coefficient matrix or an affinity graph for each single view, then the final clustering result is obtained from the spectral embedding of a consensus graph using certain traditional clustering techniques, such as k-means. However, clustering performance will be degenerated if the early fusion of partitions cannot fully exploit relationships between all samples. Different from existing methods, we propose a multi-view subspace clustering method via adaptive graph learning and late fusion alignment (AGLLFA). For each view, AGLLFA learns an affinity graph adaptively to capture the similarity relationship among samples. Moreover, a spectral embedding learning term is designed to exploit the latent feature space of different views. Furthermore, we design a late fusion alignment mechanism to generate an optimal clustering partition by fusing view-specific partitions obtained from multiple views. An alternate updating algorithm with validated convergence is developed to solve the resultant optimization problem. Extensive experiments on several benchmark datasets are conducted to illustrate the effectiveness of the proposed method when compared with other state-of-the-art methods. The demo code of this work is publicly available at https://github.com/tangchuan2000/AGLLFA.

Modulatory effect of n-3 polyunsaturated fatty acids on depressive-like behaviors in rats with chronic sleep deprivation: potential involvement of melatonin receptor pathway and brain lipidome.

Clinical evidence suggests that a bidirectional relationship is present between sleep loss and psychiatric disorders. Both melatonin receptor agonist ramelteon (RMT) and n-3 polyunsaturated fatty acids (n-3 PUFAs) exhibit antidepressant effects, while their underlying molecular mechanisms might be different. Thus, the present study aims to investigate the add-on effects and possible mechanisms of how RMT and different n-3 PUFAs modulate the melatonin receptor pathway as well as brain lipidome to ameliorate the neuropsychiatric behaviors displayed in rats under chronic sleep deprivation. Thirty-one 6-week-old male Wistar rats were divided into five groups: control (C), sleep deprivation (S), sleep deprivation treated with RMT (SR), sleep deprivation treated with RMT and eicosapentaenoic acid (C20:5n-3, EPA) (SRE), and sleep deprivation treated with RMT and docosahexaenoic acid (C22:6n-3, DHA) (SRD) groups. The results reveal that RMT plus EPA alleviated depressive-like behavior when the rats were subjected to the forced swimming test, whereas RMT plus DHA alleviated anxiety-like behavior when the rats were subjected to the elevated plus maze test. The results of a western blot analysis further revealed that compared with the rats in the S group, those in the SRE and SRD groups exhibited a significantly increased expression of MT2 in the prefrontal cortex, with greater benefits observed in the SRE group. In addition, decreased BDNF and TrkB expression levels were upregulated only in the SRE group. Lipidomic analysis further revealed possible involvement of aberrant lipid metabolism and neuropsychiatric behaviors. RMT plus EPA demonstrated promise as having the effects of reversing the levels of the potential biomarkers of depressive-like behaviors. RMT plus EPA or DHA could ameliorate depressive- and anxiety-like behaviors in sleep-deprived rats through the alteration of the lipidome and MT2 receptor pathway in the brain, whereas EPA and DHA exerted a differential effect.

Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter.

Inadequate bioavailability is one of the most critical reasons for the failure of oral drug development. However, the way that substructures affect bioavailability remains largely unknown. Serotonin transporter (SERT) inhibitors are first-line drugs for major depression disorder, and improving their bioavailability may be able to decrease side-effects by reducing daily dose. Thus, it is an excellent model to probe the relationship between substructures and bioavailability. Here, we proposed the concept of "nonbioavailable substructures", referring to substructures that are unfavorable to bioavailability. A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. A more potent SERT inhibitor DH4 was discovered with a bioavailability of 83.28% in rats by replacing the nonbioavailable substructure of approved drug vilazodone. DH4 exhibits promising anti-depression efficacy in animal experiments. The concept of nonbioavailable substructures may open up a new venue for the improvement of drug bioavailability.

Metalearning-Based Alternating Minimization Algorithm for Nonconvex Optimization.

In this article, we propose a novel solution for nonconvex problems of multiple variables, especially for those typically solved by an alternating minimization (AM) strategy that splits the original optimization problem into a set of subproblems corresponding to each variable and then iteratively optimizes each subproblem using a fixed updating rule. However, due to the intrinsic nonconvexity of the original optimization problem, the optimization can be trapped into a spurious local minimum even when each subproblem can be optimally solved at each iteration. Meanwhile, learning-based approaches, such as deep unfolding algorithms, have gained popularity for nonconvex optimization; however, they are highly limited by the availability of labeled data and insufficient explainability. To tackle these issues, we propose a meta-learning based alternating minimization (MLAM) method that aims to minimize a part of the global losses over iterations instead of carrying minimization on each subproblem, and it tends to learn an adaptive strategy to replace the handcrafted counterpart resulting in advance on superior performance. The proposed MLAM maintains the original algorithmic principle, providing certain interpretability. We evaluate the proposed method on two representative problems, namely, bilinear inverse problem: matrix completion and nonlinear problem: Gaussian mixture models. The experimental results validate the proposed approach outperforms AM-based methods.

Research on the emissions from industrial products exported from Guangdong Province-an input-output model analysis.

This study uses an input-output model to analyze the wastewater, waste gas, and solid waste emissions in Guangdong's industrial exports from 2004 to 2015; the Logarithmic Mean Divisia Index (LMDI) is used to analyze the factors influencing such pollution. The results reveal that embodied emissions of waste gas and solid waste in Guangdong's export trade are increasing, while the increase in wastewater emissions is not apparent. The Logarithmic Mean Divisia Index (LMDI) is used to analyze the influencing factors of pollution, specifically, the structural, scale, and technical effects. We discovered that emissions of the top five industries account for about 80% of total emissions and the wastewater emissions' technical effect has more impact; however, it is difficult for this technical effect in terms of embodied waste gas and solid waste to offset the scale and structural effects' impacts. Moreover, the trends and factors influencing various industries' pollution emissions differ. This study proposes that when the government carries out environmental pollution control measures, they should consider the embodied pollution caused by products from foreign trade and focus on treating industries with severe pollution. Simultaneously, the pollution controlling measures of different industries should also vary.

Tough complex hydrogels transformed from highly swollen polyelectrolyte hydrogels based on Cu2+ coordination with anti-bacterial properties.

The development of broad-spectrum anti-bacterial tough hydrogels without antibiotics remains a challenge in biomedical applications. In this study, we have synthesized a novel tough anti-bacterial complex hydrogel based on Cu2+ coordination. A swollen and weak poly(acrylamide-co-4-vinylbenzyl-(trihydroxymethyl-phosphonium)chloride) (P(AAm-co-VBzTHPC)) hydrogel was prepared by the radical copolymerization of AAm and VBzTHPC monomer solutions, followed by immersion in CuSO4 solution to coordinate with Cu2+ to form a strong and tough hydrogel. Fourier transform infrared (FTIR) spectra and X-ray photoelectron spectra (XPS) were used to characterize the coordination structure between phosphorus and oxygen atoms in the VBzTHPC monomer and copper ions. The water content and mechanical properties of the obtained hydrogel varied with gel composition. The prepared toughened hydrogel exhibited excellent anti-bacterial performance because of the introduction of copper ion coordination and the slow release of copper ions, with bacterial viability of 5.1% when the mole fraction of VBzTHPC was 10 mol%. Cell viability when cocultured with the toughened hydrogel was above 85% using the Cell Counting Kit-8 (CCK-8) method, indicating the good biocompatibility of the hydrogel. Compared with the control group experiment in vivo, this tough hydrogel can also promote wound healing, making it a promising candidate for wound dressing.

WINNet: Wavelet-Inspired Invertible Network for Image Denoising.

Image denoising aims to restore a clean image from an observed noisy one. Model-based image denoising approaches can achieve good generalization ability over different noise levels and are with high interpretability. Learning-based approaches are able to achieve better results, but usually with weaker generalization ability and interpretability. In this paper, we propose a wavelet-inspired invertible network (WINNet) to combine the merits of the wavelet-based approaches and learning-based approaches. The proposed WINNet consists of K -scale of lifting inspired invertible neural networks (LINNs) and sparsity-driven denoising networks together with a noise estimation network. The network architecture of LINNs is inspired by the lifting scheme in wavelets. LINNs are used to learn a non-linear redundant transform with perfect reconstruction property to facilitate noise removal. The denoising network implements a sparse coding process for denoising. The noise estimation network estimates the noise level from the input image which will be used to adaptively adjust the soft-thresholds in LINNs. The forward transform of LINNs produces a redundant multi-scale representation for denoising. The denoised image is reconstructed using the inverse transform of LINNs with the denoised detail channels and the original coarse channel. The simulation results show that the proposed WINNet method is highly interpretable and has strong generalization ability to unseen noise levels. It also achieves competitive results in the non-blind/blind image denoising and in image deblurring.

Mixed X-Ray Image Separation for Artworks With Concealed Designs.

In this paper, we focus on X-ray images (X-radiographs) of paintings with concealed sub-surface designs (e.g., deriving from reuse of the painting support or revision of a composition by the artist), which therefore include contributions from both the surface painting and the concealed features. In particular, we propose a self-supervised deep learning-based image separation approach that can be applied to the X-ray images from such paintings to separate them into two hypothetical X-ray images. One of these reconstructed images is related to the X-ray image of the concealed painting, while the second one contains only information related to the X-ray image of the visible painting. The proposed separation network consists of two components: the analysis and the synthesis sub-networks. The analysis sub-network is based on learned coupled iterative shrinkage thresholding algorithms (LCISTA) designed using algorithm unrolling techniques, and the synthesis sub-network consists of several linear mappings. The learning algorithm operates in a totally self-supervised fashion without requiring a sample set that contains both the mixed X-ray images and the separated ones. The proposed method is demonstrated on a real painting with concealed content, Do na Isabel de Porcel by Francisco de Goya, to show its effectiveness.

Development and Validation of Benzophenone Derivatives in Packaged Cereal-Based Foods by Solid-Liquid Extraction and Ultrahigh-Performance Liquid Chromatography-Tandem Mass Spectrometry.

We established and validated a sensitive multi-residue analytical method for identifying benzophenone (BP) and nine BP derivatives (2,4-dihydroxybenzophenone [BP-1], 2,2',4,4'-tetrahydroxydroxybenzophenone, 2-hydroxy-4-methoxy benzophenone, 2,2'-dihydroxy 4-methoxy benzophenone, 2-hydroxybenzophenone [2-OHBP], 4-hydroxybenzophenone, 4-methylbenzophenone [4-MBP], methyl-2-benzoylbenzoate, and 4-benzoylbiphenyl). Solid−liquid extraction pretreatment and ultra-high-performance liquid chromatography−tandem mass spectrometry (UHPLC−MS/MS) were employed in an analysis of 85 packaged cereal-based food samples (25 pastry, 50 rice, and 10 noodle samples). The method had satisfactory linearity (R2 ≥ 0.995), low limits of detection (pastry: 0.02−4.2 ng/g; rice and noodle: 0.02−2 ng/g), and favorable precision, with within-run and between-run coefficient of variation ranges of 1−29% and 1−28%, respectively. BP and 4-MBP were detected in 100% of the pastry samples, and BP-1 and 2-OHBP were found in 76% and 56% of the pastry samples, respectively. BP and 2-OHBP were found in 92% and 38% of the rice samples, respectively. BP was found in 50% of the noodle samples. BP contributed the most to the total level of BPs in pastries, with significantly higher mean ± standard deviation (range) levels for pastries (26.8 ± 32.6 [1.8−115.4] ng/g) than rice (1.2 ± 2.0 [0.4−13.4] ng/g) and noodles (0.7 ± 0.7 [0.4−1.9] ng/g); p < 0.0001). The trace levels of 4-MBP identified in the samples demonstrate the need for the development of analytical methods with high sensitivity and specificity; the proposed method satisfies this need.

Identification of Benzophenone Analogs in Rice Cereal through Fast Pesticide Extraction and Ultrahigh-Performance Liquid Chromatography-Tandem Mass Spectrometry.

A fast, robust, and sensitive analytical method was developed and validated for the simultaneous identification of benzophenone (BP) and nine BP analogs (BP-1, BP-2, BP-3, BP-8, 2-hydroxybenzophenone, 4-hydroxybenzophenone, 4-methylbenzophenone [4-MBP], methyl-2-benzoylbenzoate, and 4-benzoylbiphenyl) in 25 samples of rice cereal. Fast pesticide extraction (FaPEx) coupled with ultrahigh-performance liquid chromatography-tandem mass spectrometry was applied. The developed method exhibited satisfactory linearity (r > 0.997), favorable recoveries between 71% and 119%, and a limit of detection ranging from 0.001 to 0.5 ng/g. The detection frequencies of BP, 4-MBP, and BP-3 were 100%, 88%, and 52%, respectively. BP had higher geometric levels, with a mean of 39.8 (19.1-108.9) ng/g, and 4-MBP had low levels, with a mean of 1.9 (1.3-3.3) ng/g. The method can be applied to routine rice cereal analysis at the nanogram-per-gram level. For infants aged 0-3 years, the hazard quotients of BP and 4-MBP were lower than one, and the margin of exposure for BP was higher than 10,000, suggesting that rice cereal consumption poses no health concern for Taiwanese infants.

Video Summarization Through Reinforcement Learning With a 3D Spatio-Temporal U-Net.

Intelligent video summarization algorithms allow to quickly convey the most relevant information in videos through the identification of the most essential and explanatory content while removing redundant video frames. In this paper, we introduce the 3DST-UNet-RL framework for video summarization. A 3D spatio-temporal U-Net is used to efficiently encode spatio-temporal information of the input videos for downstream reinforcement learning (RL). An RL agent learns from spatio-temporal latent scores and predicts actions for keeping or rejecting a video frame in a video summary. We investigate if real/inflated 3D spatio-temporal CNN features are better suited to learn representations from videos than commonly used 2D image features. Our framework can operate in both, a fully unsupervised mode and a supervised training mode. We analyse the impact of prescribed summary lengths and show experimental evidence for the effectiveness of 3DST-UNet-RL on two commonly used general video summarization benchmarks. We also applied our method on a medical video summarization task. The proposed video summarization method has the potential to save storage costs of ultrasound screening videos as well as to increase efficiency when browsing patient video data during retrospective analysis or audit without loosing essential information.

Ecology and Evolution of Marine Fungi With Their Adaptation to Climate Change.

Climate change agitates interactions between organisms and the environment and forces them to adapt, migrate, get replaced by others, or extinct. Marine environments are extremely sensitive to climate change that influences their ecological functions and microbial community including fungi. Fungi from marine habitats are engaged and adapted to perform diverse ecological functions in marine environments. Several studies focus on how complex interactions with the surrounding environment affect fungal evolution and their adaptation. However, a review addressing the adaptation of marine fungi to climate change is still lacking. Here we have discussed the adaptations of fungi in the marine environment with an example of Hortaea werneckii and Aspergillus terreus which may help to reduce the risk of climate change impacts on marine environments and organisms. We address the ecology and evolution of marine fungi and the effects of climate change on them to explain the adaptation mechanism. A review of marine fungal adaptations will show widespread effects on evolutionary biology and the mechanism responsible for it.

In Silico Resources of Drug-Likeness as a Mirror: What Are We Lacking in Pesticide-Likeness?

Unfavorable bioavailability is an important aspect underlying the failure of drug candidates. Computational approaches for evaluating drug-likeness can minimize these risks. Over the past decades, computational approaches for evaluating drug-likeness have sped up the process of drug development and were also quickly derived to pesticide-likeness. As a result of many critical differences between drugs and pesticides, many kinds of methods for drug-likeness cannot be used for pesticide-likeness. Therefore, it is crucial to comprehensively compare and analyze the differences between drug-likeness and pesticide-likeness, which may provide a basis for solving the problems encountered during the evaluation of pesticide-likeness. Here, we systematically collected the recent advances of drug-likeness and pesticide-likeness and compared their characteristics. We also evaluated the current lack of studies on pesticide-likeness, the molecular descriptors and parameters adopted, the pesticide-likeness model on pesticide target organisms, and comprehensive analysis tools. This work may guide researchers to use appropriate methods for developing pesticide-likeness models. It may also aid non-specialists to understand some important concepts in drug-likeness and pesticide-likeness.

Simplified method for simulation of incompressible viscous flows inspired by the lattice Boltzmann method.

The lattice Boltzmann method (LBM) has gained increasing popularity in incompressible viscous flow simulations, but it uses many distribution functions (far more than the flow variables) and is often memory demanding. This disadvantage was overcome by a recent approach that solves the more actual macroscopic equations obtained through Taylor series expansion analysis of the lattice Boltzmann equations [Lu et al., J. Comput. Phys. 415, 109546 (2020)JCTPAH0021-999110.1016/j.jcp.2020.109546]. The key is to keep some small additional terms (SATs) to stabilize the numerical solution of the weakly compressible Navier-Stokes equations. However, there are many SATs that complicate the implementation of their method. Based on some analyses and numerous tests, we ultimately pinpoint two essential ingredients for stable simulations: (1) suitable density (pressure) diffusion added to the continuity equation and (2) proper numerical dissipation related to the velocity divergence added to the momentum equations. Then we propose a simplified method that is not only easier to implement but noticeably faster than the original method and the LBM. It contains much simpler SATs that only involve the density (pressure) derivatives, and it requires no intermediate steps or variables. As well, it is extended for thermal flows with small temperature variations and for two-phase flows with uniform density and viscosity. Several test cases, including some two-phase problems under two-dimensional, axisymmetric, and three-dimensional geometries, are presented to demonstrate its capability. This work may help pave the way for the simplest simulation of incompressible viscous flows on collocated grids based on the artificial compressibility methodology.