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The papilla number is one of the most economically important traits of sea cucumber in the China marketing trade. However, the genetic basis for papilla number diversity in holothurians is still scarce. In the present study, we conducted genome-wide association studies (GWAS) for the trait papilla number of sea cucumbers utilizing a set of 400,186 high-quality SNPs derived from 200 sea cucumbers. Two significant trait-associated SNPs that passed Bonferroni correction (P< 1.25E−7) were located in the intergenic region near PATS1and the genic region of EIF4G, which were reported to play a pivotal role in cell growth and proliferation. The fine-mapping regions around the top two lead SNPs provided precise causative loci/genes related to papilla formation and cellular activity, including PPP2R3C, GBP1, and BCAS3. Potential SNPs with P< 1E−4 were acquired for the following GO and KEGG enrichment analysis. Moreover, the two lead SNPs were verified in another population of sea cucumber, and the expressive detection of three potential candidate genes PATS1, PPP2R3C, and EIF4Gthat near or cover the two lead SNPs was conducted in papilla tissue of TG (Top papilla number group) and BG (Bottom papilla number group) by qRT-PCR. We found the significantly higher expression profile of PATS1(3.34-fold), PPP2R3C(4.90-fold), and EIF4G(4.23-fold) in TG, implying their potential function in papilla polymorphism. The present results provide valuable information to decipher the phenotype differences of the papilla trait and will provide a scientific basis for selective breeding in sea cucumbers.

ABSTRACTGlioma is the most common primary malignant brain tumor with poor survival and limited therapeutic options. The non-psychoactive phytocannabinoid cannabidiol (CBD) has been shown to be effective against glioma; however, the molecular target and mechanism of action of CBD in glioma are poorly understood. Here we investigated the molecular mechanisms underlying the antitumor effect of CBD in preclinical models of human glioma. Our results showed that CBD induced autophagic rather than apoptotic cell death in glioma cells. We also showed that CBD induced mitochondrial dysfunction and lethal mitophagy arrest, leading to autophagic cell death. Mechanistically, calcium flux induced by CBD through TRPV4 (transient receptor potential cation channel subfamily V member 4) activation played a key role in mitophagy initiation. We further confirmed TRPV4 levels correlated with both tumor grade and poor survival in glioma patients. Transcriptome analysis and other results demonstrated that ER stress and the ATF4-DDIT3-TRIB3-AKT-MTOR axis downstream of TRPV4 were involved in CBD-induced mitophagy in glioma cells. Lastly, CBD and temozolomide combination therapy in patient-derived neurosphere cultures and mouse orthotopic models showed significant synergistic effect in both controlling tumor size and improving survival. Altogether, these findings showed for the first time that the antitumor effect of CBD in glioma is caused by lethal mitophagy and identified TRPV4 as a molecular target and potential biomarker of CBD in glioma. Given the low toxicity and high tolerability of CBD, we therefore propose CBD should be tested clinically for glioma, both alone and in combination with temozolomide.Abbreviations: 4-PBA: 4-phenylbutyrate; AKT: AKT serine/threonine kinase; ATF4: activating transcription factor 4; Baf-A1: bafilomycin A1; CANX: calnexin; CASP3: caspase 3; CAT: catalase; CBD: cannabidiol; CQ: chloroquine; DDIT3: DNA damage inducible transcript 3; ER: endoplasmic reticulum; GBM: glioblastoma multiforme; GFP: green fluorescent protein; MAP1LC3B/LC3B: microtubule associated protein 1 light chain 3 beta; MTOR: mechanistic target of rapamycin kinase; PARP1: poly(ADP-ribose) polymerase; PINK1: PTEN induced kinase 1; PRKN: parkin RBR E3 ubiquitin protein ligase; SLC8A1: solute carrier family 8 member A1; SQSTM1: sequestosome 1; TCGA: The cancer genome atlas; TEM: transmission electron microscopy; TMZ: temozolomide; TRIB3: tribbles pseudokinase 3; TRPC: transient receptor potential cation channel subfamily C; TRPV4: transient receptor potential cation channel subfamily V member 4

Lipid nanoparticles (LNPs)-based messenger RNA (mRNA) therapeutics have emerged with promising potentials in the fields of infectious diseases, cancer vaccines, and protein replacement therapies; however, their therapeutic efficacy and safety can still be promoted by the optimization of LNPs formulations. Unfortunately, current LNPs suffer from increased production of reactive oxygen species during translation, which leads to a decreased translation efficiency and the onset of inflammation and other side effects. Herein, we synthesize a lipid-modified poly(guanidine thioctic acid) polymer to fabricate novel LNPs for mRNA vaccines. The acquired G-LNPs significantly promote the translation efficiency of loaded mRNA and attenuate inflammation after vaccination through the elimination of reactive oxygen species that are responsible for translational inhibition and inflammatory responses. In vivostudies demonstrate the excellent antitumor efficacy of the G-LNPs@mRNA vaccine, and two-dose vaccination dramatically increases the population and infiltration of cytotoxic T cells due to the intense antitumor immune responses, thus generating superior antitumor outcomes compared with the mRNA vaccine prepared from traditional LNPs. By synergy with immune checkpoint blockade, the tumor inhibition of G-LNPs@mRNA is further boosted, indicating that G-LNPs-based mRNA vaccines will be powerful and versatile platforms to combat cancer.

This brief investigates nonautonomous stochastic reaction–diffusion neural-network models with S-type distributed delays. First, the existence and uniqueness of mild solution are studied under the Lipschitz condition without the linear growth condition. Due to the existence of a nonautonomous reaction–diffusion term and the infinite dimensional Wiener process, the criteria for the well-posedness of the models are established based on the evolution system theory. Then, the S-type distributed delay, which is an infinite delay, is handled by the truncation method, and sufficient conditions for the global exponential stability are obtained by constructing a simple Lyapunov–Krasovskii functional candidate. Finally, neural-network examples and an illustrative example are given to show the applications of the obtained results.

Neural networks (NNs) have emerged as a new tool for genomic selection (GS) in animal breeding. However, the properties of NN used in GS for the prediction of phenotypic outcomes are not well characterized due to the problem of over-parameterization of NN and difficulties in using whole-genome marker sets as high-dimensional NN input. In this note, we have developed an R package called snnR that finds an optimal sparse structure of a NN by minimizing the square error subject to a penalty on the L1-norm of the parameters (weights and biases), therefore solving the problem of over-parameterization in NN. We have also tested some models fitted in the snnR package to demonstrate their feasibility and effectiveness to be used in several cases as examples. In comparison of snnR to the R package brnn (the Bayesian regularized single layer NNs), with both using the entries of a genotype matrix or a genomic relationship matrix as inputs, snnR has greatly improved the computational efficiency and the prediction ability for the GS in animal breeding because snnR implements a sparse NN with many hidden layers.

AbstractThis paper presents a heuristic Learning-based Non-Negativity Constrained Variation (L-NNCV) aiming to search the coefficients of variational model automatically and make the variation adapt different images and problems by supervised-learning strategy. The model includes two terms: a problem-based term that is derived from the prior knowledge, and an image-driven regularization which is learned by some training samples. The model can be solved by classical ε-constraint method. Experimental results show that: the experimental effectiveness of each term in the regularization accords with the corresponding theoretical proof; the proposed method outperforms other PDE-based methods on image denoising and deblurring.

In this paper, we focus on the long time behavior of the mild solution to delayed reaction–diffusion Hopfield neural networks (DRDHNNs) driven by infinite dimensional Wiener processes. We analyze the existence, uniqueness, and stability of this system under the local Lipschitz function by constructing an appropriate Lyapunov–Krasovskii function and utilizing the semigroup theory. Some easy-to-test criteria affecting the well-posedness and stability of the networks, such as infinite dimensional noise and diffusion effect, are obtained. The criteria can be used as theoretic guidance to stabilize DRDHNNs in practical applications when infinite dimensional noise is taken into consideration. Meanwhile, considering the fact that the standard Brownian motion is a special case of infinite dimensional Wiener process, we undertake an analysis of the local Lipschitz condition, which has a wider range than the global Lipschitz condition. Two samples are given to examine the availability of the results in this paper. Simulations are also given using the MATLAB.

•The magnetic field has been applied during the Wire Arc Additive Manufacturing (WAAM) processing of Inconel 625 in this work.•The mechanism that the effect (grain refinement effect and element segregation inhibition) generated by magnetic field on the microstructure was discussed.•The application of magnetic field during WAAM process effectively improves the mechanical properties of Inconel 625 alloy.

Ni-rich NiTi alloys were deposited using the in-situ alloying wire arc additive manufacturing (WAAM) method, with varying deposition currents from 80 A to 120 A. The effects of deposition current on the crystal orientation, precipitation, phase transformation and mechanical properties of the WAAM-deposited NiTi alloys were investigated. The results show that increasing the deposition current during the WAAM process would result in noticeable coarsening of B2 grain and an increased volume fraction of high angle grain boundaries (HAGBs). Also, the texture intensity gradually decreased with increasing deposition current. The fabricated components are dominated by the B2 phase with quantities of Ni4Ti3 precipitates in all samples. When increasing the deposition current during the WAAM process, the size of Ni4Ti3 precipitates generally increased and gradually decomposed into a stable Ni3Ti phase which could be detected in the sample produced at 120 A. Furthermore, all of the characteristic phase transformation temperatures increased with the deposition current while the ultimate tensile strength dropped from 927.9 MPa to 613.8 MPa and elongation reduced from 8.7 % to 5.6 %. The cyclic loading-unloading tests revealed that similar trends for the evolution of irreversible strain (εir), recoverable strain (εre), recovery ratio, and elastic energy storage efficiency (η) during cycling were obtained in all samples processed with different deposition currents. The highest εreof 3.2 % and the highest recovery ratio of 53.9 % were obtained in the sample processed with 80 A at an applied stress of 700 MPa for ten cycles. The change of mechanical properties with varying deposition current is due to a combination of factors including precipitation hardening effect, grain refinement effect, and crystal orientation. These results can be useful for optimizing WAAM process parameters to fabricate NiTi components with acceptable structural properties.

Fish ethology is a prospective discipline for ocean surveys. In this paper, one ROV-based system is established to perform underwater visual tasks with customized optical sensors installed. One image quality enhancement method is first presented in the context of creating underwater imaging models combined with homomorphic filtering and wavelet decomposition. The underwater vision system can further detect and track swimming fish from the resulting images with the strategies developed using curve evolution and particular filtering, in order to obtain a deeper understanding of fish behaviours. The simulation results have shown the excellent performance of the developed scheme, in regard to both robustness and effectiveness.