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Graphical abstract, Highlights • PLA2G16 is up-regulated in CRC, and high expression of PLA2G16 is associated with the advanced stages. • PLA2G16 promotes the malignant progression of CRC through the Hippo signaling pathway. • GCK exerts its anti-CRC effects by inhibiting the protein expression of PLA2G16. • Provide a new insights towards the development of effective therapeutic strategies for CRC treatment by targeting PLA2G16., Introduction Colorectal cancer (CRC) is a common malignant tumor with a high global incidence, metastasis rate and low cure rate. Changes in lipid metabolism-related genes can affect the occurrence and development of CRC, and may be a potential therapeutic target for CRC. Therefore, starting from lipid metabolism-related genes to find natural medicines for tumor treatment may become a new direction in CRC research. Objectives This study aimed to investigate the effect of PLA2G16, a key gene involved in lipid metabolism, on the biological function of CRC, and whether the anti-CRC effect of GCK is related to PLA2G16. Methods To explore the role of PLA2G16 in CRC in vitro and in vivo, we performed cell proliferation, migration, invasion and nude mice tumorigenesis assays. As for the mechanism, we designed RNA-seq analysis and verified by western blotting and immunofluorescence experiments. Subsequently, we found the anti-CRC effect of GCK is related to PLA2G16 through western blotting and rescue experiments. Results We showed that PLA2G16 was significantly higher in CRC tissues than the adjacent normal appearing tissues, and high PLA2G16 expression correlates with unfavorable prognosis of CRC patients. Further, PLA2G16 promoted the malignant progression of CRC by inhibiting the Hippo signaling pathway determined by RNA-seq analysis, and GCK exerted anti-CRC effects by inhibiting the protein expression of PLA2G16 in vitro and in vivo. Conclusion Our results suggested that PLA2G16 promote the malignant progression of CRC by inhibiting the Hippo signaling pathway and the anti-CRC effect of GCK is through inhibiting the protein expression of PLA2G16.

Background: Colorectal cancer (CRC) represents a third leading cause of cancer-related death worldwide. The reliable diagnostic biomarkers for detecting CRC at early stage is critical for decreasing the mortality.Method: A conjunctive lipidomic approach was employed to investigate the differences in plasma lipid profiles of CRC patients (n = 101) and healthy volunteers (n = 52). Based on UHPLC-Q-TOF MS and UHPLC-QQQ MS platforms, a total of 236 lipids were structurally detected. Multivariate data analysis was conducted for biomarkers discovery.Results: A total of 11 lipid species, including 1 Glycerophosphoethanolamine (PE), 3 ethanolamine plasmalogens (PlsEtn), 1 plasmanyl glycerophosphatidylethanolamine (PE-O), 3 fatty acids (FFA), 1 Fatty acid ester of hydroxyl fatty acid (FAHFA) and 2 Diacylglycerophosphates (PA) were identified to distinguish the CRC patients at early stage from healthy controls. In addition, these potential lipid biomarkers achieved an estimated AUC=0.981 in a validation set for univariate ROC analysis.Conclusion: By combining Q-TOF MS and QQQ MS analysis, the 11 lipids exhibited good performance in differentiating early-stage CRC and healthy control. This study also demonstrated that lipidomics is a powerful tool in discovering new potential biomarkers for cancer diagnosis.

The gastrointestinal microbiota and host co-evolve into a complex 'super-organism,' and this relationship plays a vital role in many physiological processes, such as drug metabolism. Ginseng is an important medicinal resource and the main ingredients are ginsenosides, which are less polar, difficult to absorb, and have low bioavailability. However, studies have shown that the biological activity of ginsenosides such as compound K (CK), ginsenoside Rg3 (Rg3), ginsenoside Rh2 (Rh2), 20(S)-protopanaxatriol (20(S)-PPT), and 20(S)-protopanaxadiol (20(S)-PPD) is closely related to the gastrointestinal microbiota. In this paper, the metabolic pathway of gastrointestinal microbiota-generated ginsenosides and the main pharmacological effects of these metabolites are discussed. Furthermore, our study provides a new insight into the discovery of novel drugs. Specifically, in new drug screening process, candidates with low biological activity and bioavailability should not be excluded. Because their metabolites may exhibit good pharmacological effects due to the involvement of the gastrointestinal microbiota. In addition, in further research studies to develop probiotics, a combination of agents could exert greater efficacy than single agents. Moreover, differences in lifestyle and diet lead to differences in the gastrointestinal microbiota in the human body. Therefore, administration of the same drug dose to different individuals could elicit different therapeutic effects, owing to the involvement of the gastrointestinal microbiota. Thus, treatment accuracy could be achieved by detecting the gastrointestinal microbiota before drug treatment.HighlightsGastrointestinal microbiota plays a decisive role in bioactivities of ginsenosides.The metabolic pathway and main pharmacological effects of ginsenoside metabolites are discussed.It provides new insights into novel drug discovery and further research to find probiotic, combinations to exert greater efficacy.Differences in lifestyle and diet, varies the gastrointestinal microbiota in the human body. However, the same dose of a drug producing different therapeutic effects may involve gastrointestinal microbiota.

In the SAR change detection algorithm based on self-supervised learning, speckle noise reduces the difference image (DI) quality. Therefore, the contrast of the DI is low, and its change area is not significant. Moreover, the preclassification algorithm with the poor robustness makes the classification results of the low-quality DI inaccurate. When the wrong labels are sent into the classification network, the accuracy of the final detection results is reduced. First, to improve the quality of the initial DI, we design an adaptive gamma correction algorithm that adjusts the contrast according to the mean value of the initial DI and the variation coefficient $\beta$ . The contrast of the new DI generated by this algorithm is higher. Furthermore, to suppress the noise, we adopt a new algorithm based on popular ranking to obtain the saliency map of the new DI. Combining the initial DI with this saliency map, a high-quality DI with a low noise level is obtained. After that, we introduce the structure tensor into the fuzzy local information c-means clustering algorithm (FLICM) to classify the DI more accurately. The new clustering algorithm improves the accuracy of preclassification, especially its hierarchical version. Besides, we use the structure tensor to generate the structure maps of the original images. Finally, according to the prior information obtained from the preclassification, we use a convolution wavelet neural network (CWNN) to supervise and train the structure maps of the original images. Experimental results show that the DI generated by us is closer to the ground-truth than other methods. Our preclassification algorithm performs better. Our algorithm shows higher detection accuracy for SAR images with strong noise than some advanced change detection algorithms.

Ridge-furrow plastic film mulching (RFPM) is an efficient planting technique for rainwater harvesting in rainfed regions of the Loess Plateau, China. However, relevant data on root water use patterns under RFPM remain sparse. To clarify the water use pattern of roots under this planting technique and determine the root characteristic parameters related to water absorption, this study conducted a two-year field experiment using spring maize with three treatments: flat planting (FP), and RFPM with two different ridge:furrow ratios (40 cm:70 cm and 70 cm:40 cm). Hydrogen and oxygen stable isotopes (δD and δ18O) of different water sources were measured, and the MixSIAR model was used to quantify the contributions of different water sources to spring maize root water uptake. The results showed that the root characteristic parameters of spring maize under the three treatments decreased with increasing soil depth and were mainly distributed in the shallow (0–20 cm) and middle (20–60 cm) soil layers. Compared to FP, under RFPM the distribution proportions of the root characteristic parameters in shallow soil were higher, which may be related to the increase in shallow soil water content. The main water-absorbing soil layers of spring maize under the three treatments were the same: 0–20 cm, 20–40 cm, 40–60 cm, 40–60 cm, and 0–20 cm in the V6, V12, R1, R3, and R6 growth stages, respectively. However, compared to FP, under RFPM the water use proportion of spring maize of shallow soil increased, especially under the larger ridge to furrow ratio, and correlation analysis showed that this phenomenon might be related to the RFPM increasing the proportion of the spring maize root characteristic parameters in shallow soil. Correlation analysis further showed that the root length density and root surface area density might be the key characteristic parameters for determining root water uptake.

The ridge-furrow rainfall harvesting system (RFRHS) is an in-situ rainfall collection and utilization technique that has been widely adopted in global arid and semiarid farming areas because of its significant benefits to soil water availability and crop yield. However, the integrated effects of different ridge-furrow configurations on rainwater collection and crop performance are still lacking. Using a specialized database drawn from field experiments in the Loess Plateau, we examined the regional effects of three typical ridge-furrow configurations, namely high (>1:1), uniform (=1:1), and low (

This paper is concerned with the error estimation of the fast multipole method (FMM) for scattering problems in 2-D. The FMM error is caused by truncating Graf's addition theorem in each step of the algorithm, including two expansions and three translations. We first give a novel bound on the truncation error of Graf's addition theorem by the limiting forms of Bessel and Neumann functions, and then estimate the error of the FMM. Explicit error bound and its convergence order are derived. The method proposed in this paper can also be used to the FMM for other problems, such as potential problems, elastostatic problems, Stokes flow problems and so on.

We study the approximate message passing algorithm for nonconvex regularization methods. We propose some improved nonconvex iterative thresholding algorithms based on approximate message passing. The new iterative thresholding algorithms are inspired by belief propagation in graphical models. Further, we study the convergence of the new algorithms and provide a series of experiments to assess the performance of the new algorithms. The experiments show that several important nonconvex iterative thresholding algorithms based on approximate message passing have strong reconstruction capabilities and high phase transition for sparse signal recovery.

The fast multipole method (FMM) is an effective approach for accelerating the computation efficiency of the boundary element method (BEM) in solving problems that are computationally intensive. This paper presents two different BEMs, i.e., Kress' and Seydou's methods, for solving two-dimensional (2D) acoustic transmission problems with a multilayered obstacle, along with application of the FMM to solution of the related boundary integral equations. Conventional BEM requires O(MN2) operations to compute the equations for this problem. By using the FMM, both the amount of computation and the memory requirement of the BEM are reduced to order O(MN), where M is the number of layers of the obstacle. The efficiency and accuracy of this approach in dealing with the acoustic transmission problems containing a multilayered obstacle are demonstrated in the numerical examples. It is confirmed that this approach can be applied to solving the acoustic transmission problems for an obstacle with multilayers.