Synergistic antitumor activity between HER2 antibody-drug conjugate and chemotherapy for treating advanced colorectal cancer.

Colorectal cancer (CRC) is the third most common cancer associated with a poor prognosis. Effective targeted therapy alone or in combination for treating advanced CRC remains to be a major clinical challenge. Here, we propose the therapeutic efficacy and molecular mechanism underlying RC48, a FDA-approved anti-HER2 antibody conjugate via a cleavable linker to the microtubule inhibitor monomethyl auristatin E (MMAE), either alone or in combination with gemcitabine (GEM) in various models of HER2-positive advanced CRC. Our findings demonstrated that HER2 was widely expressed and located on the plasma membrane of CRC patient specimens, PDX xenograft tumors and cell lines. It confirmed that RC48 alone significantly targeted and eradicated HER2 positive CRC tumor in these models. Moreover, we screened a panel of FDA-approved first-line chemotherapy drugs in vitro. We found that GEM exhibited stronger antiproliferative activity compared to the other first-line anti-cancer agents. Furthermore, combination therapy of RC48 and GEM significantly showed synergetic antitumor activity in vitro and in vivo. To gain further mechanistic insights into the combination therapy, we performed RNA-seq analysis. The results revealed that combination treatment of RC48 and GEM regulated multiple signaling pathways, such as PI3K-AKT, MAPK, p53, Foxo, apoptosis, cell cycle and cell senescence, etc., to exert its antitumor activity in CRC cells. Collectively, these preclinical findings demonstrated that RC48 alone or combinational therapy exerted promising antitumor activity, and meriting the preclinical framework for combinational therapy of anti-HER2 drug conjugate drug and chemotherapy drugs for HER2-positive patients with advanced CRC.

Glimpsing the Future of Animal Welfare through a Bottle of Milk: Insights from Chinese University Students.

The consumption patterns of university students hold the power to significantly influence market trends. This study illuminates the escalating emphasis on animal welfare in these students' purchasing choices, specifically concerning milk products. Utilizing a discrete choice experiment, we identified a pronounced preference among students for milk products with animal welfare certifications. Students were segmented into three categories based on their motivations: "Quality-Oriented" (20.55%), "Emotionally Intuitive" (30.67%), and "Quality-Emotion Balanced" (48.77%). The "Emotionally Intuitive" group manifested the most robust inclination toward such certifications. Based on these findings, we recommend tailored market strategies targeting these distinct segments. Moreover, our findings emphasize the importance of intensifying animal welfare education, shaping a market aligned with animal welfare principles, and fostering a broader societal environment attuned to animal welfare.

Optimizing Dog Rabies Vaccination Services to the Public: A Discrete Choice Experiment in Guangdong, China.

Vaccination for dogs is essential for controlling rabies and achieving the goal of eliminating dog-mediated rabies globally by 2030. This paper aims to investigate the preferences for public services regarding rabies vaccination, in an effort to optimize the existing rabies vaccination and prevention programs in China. The households investigated had significant preferences for dog rabies vaccination service attributes. The households can be classified into three types: resolute executors (52.13%), mischievous rebels (5.85%), and incentivized compliers (42.02%). The residence, the presence of children in the household, perception of the safety risks, and knowledge of rabies may be sources of heterogeneity. Supportive services on dog rabies vaccination should be made available, such as arranging weekend vaccination services, building mobile vaccination stations, providing home vaccination services, and increasing vaccine supply through multiple channels. Furthermore, multiple measures can be taken to increase rabies vaccination awareness among family members and facilitate dog management innovation to further increase the level of rabies prevention and control.

Morphologic variations of the superior intercostal vein: An anatomical study with clinical applications.

This study aimed to validate and compare the anatomical variations of the superior intercostal veins, focusing on their origin, course, anastomoses, and destination. In addition, the results were compared with findings from other relevant studies. Fifty Korean and 16 Chinese adult cadavers were dissected for this study. The superior intercostal veins were dissected and measured. In our study of 66 specimens, the right superior intercostal vein was observed in 92.3% of cases, while the left superior intercostal vein was observed in 50%. The right superior intercostal vein was subdivided into six types based on its composition, which mainly drained the second and third right posterior intercostal veins. Similarly, the left superior intercostal vein was subdivided into eight types, primarily involving the second to fourth left posterior intercostal veins. This detailed anatomical study successfully identified and classified the various morphologic types of the superior intercostal vein and reviewed the clinical significance of this vein. The findings of this study can offer valuable anatomical evidence to physicians, aiding in their understanding and utilization of the superior intercostal vein.

Enantioselective synthesis of α,α-diarylketones by sequential visible light photoactivation and phosphoric acid catalysis.

Chiral ketones and their derivatives are useful synthetic intermediates for the synthesis of biologically active natural products and medicinally relevant molecules. Nevertheless, general and broadly applicable methods for enantioenriched acyclic α,α-disubstituted ketones, especially α,α-diarylketones, remain largely underdeveloped, owing to the easy racemization. Here, we report a visible light photoactivation and phosphoric acid-catalyzed alkyne-carbonyl metathesis/transfer hydrogenation one-pot reaction using arylalkyne, benzoquinone, and Hantzsch ester for the expeditious synthesis of α,α-diarylketones with excellent yields and enantioselectivities. In the reaction, three chemical bonds, including C═O, C─C, and C─H, are formed, providing a de novo synthesis reaction for chiral α,α-diarylketones. Moreover, this protocol provides a convenient and practical method to synthesize or modify complex bioactive molecules, including efficient routes to florylpicoxamid and BRL-15572 analogs. Computational mechanistic studies revealed that C-H/π interactions, π-π interaction, and the substituents of Hantzsch ester all play crucial roles in the stereocontrol of the reaction.

Transforming Complex Problems Into K-Means Solutions.

K-means is a fundamental clustering algorithm widely used in both academic and industrial applications. Its popularity can be attributed to its simplicity and efficiency. Studies show the equivalence of K-means to principal component analysis, non-negative matrix factorization, and spectral clustering. However, these studies focus on standard K-means with squared euclidean distance. In this review paper, we unify the available approaches in generalizing K-means to solve challenging and complex problems. We show that these generalizations can be seen from four aspects: data representation, distance measure, label assignment, and centroid updating. As concrete applications of transforming problems into modified K-means formulation, we review the following applications: iterative subspace projection and clustering, consensus clustering, constrained clustering, domain adaptation, and outlier detection.

Graph-Graph Similarity Network.

Graph learning aims to predict the label for an entire graph. Recently, graph neural network (GNN)-based approaches become an essential strand to learning low-dimensional continuous embeddings of entire graphs for graph label prediction. While GNNs explicitly aggregate the neighborhood information and implicitly capture the topological structure for graph representation, they ignore the relationships among graphs. In this article, we propose a graph-graph (G2G) similarity network to tackle the graph learning problem by constructing a SuperGraph through learning the relationships among graphs. Each node in the SuperGraph represents an input graph, and the weights of edges denote the similarity between graphs. By this means, the graph learning task is then transformed into a classical node label propagation problem. Specifically, we use an adversarial autoencoder to align embeddings of all the graphs to a prior data distribution. After the alignment, we design the G2G similarity network to learn the similarity between graphs, which functions as the adjacency matrix of the SuperGraph. By running node label propagation algorithms on the SuperGraph, we can predict the labels of graphs. Experiments on five widely used classification benchmarks and four public regression benchmarks under a fair setting demonstrate the effectiveness of our method.

Evaluation of lipidomic change in goat sperm after cryopreservation.

The current study aimed to detect the relationship between the spermatozoa cryotolerance and the post-thawed sperm lipidome. Ejaculates from 20 goats, and performed a uniform frozen-thawed procedure in this study. According to the total motility of thawed sperm of goats, semen samples were classified into HF group (High Freezers, n = 8) with >60% total motility and LF group (Low Freezers, n = 8) with < 45% total motility. The lipidomic analysis based on UHPLC-MS/MS was utilized to investigate the relationship between sperm cryotolerance and their lipid metabolites expression. The results showed that the cryotolerance of sperm from different individual goats were in great variation. The total motility of post-thawed sperm in HF group (60.93 ± 2.43%) is significantly higher than that in LF group (34.04 ± 3.41%, P < 0.01). And the post-thawed sperm in HF group exhibited significantly higher plasma membrane (59.06 ± 2.34%) and acrosome integrity (62.93 ± 1.15%) than that in LF group (34.06 ± 4.85%, 44.92 ± 2.19% respectively, P < 0.01). The total of 29 lipid subclasses and 1,133 lipid molecules in the post-thawed goat sperm were identified by lipidomics analysis. The lipid content of thawed sperm in HF group was higher than that in LF group, the lipid profile in HF group was significantly separated from LF group, which indicated that the difference in lipid composition and lipid metabolism mode of sperm between the two groups was existed, especially the expression of phosphatidylcholine and triglyceride molecules. In conclusion, the cryotolerance of sperm from different individual goats were in great variation. Sperm with high cryotolerance may be able to uptake more lipids during cryopreservation. The increase in phosphatidylcholine and triglyceride content of thawed. Sperm may relate to more active lipid anabolic processes.

Reasons and resolution approaches of environmental conflict in grassland areas: An empirical study in contemporary northern China.

Environmental conflicts frequently occur in grassland areas, which form the largest terrestrial ecosystem in China. Grassland conflict not only seriously affects environmental and public governance, but also threatens social stability and economic development. Based on a field study using surveys and interviews with 5,490 respondents and a case study of 30 cases in northern China, this paper explores the reasons for and resolution approaches to environmental conflict in grassland areas and identified three key findings. First, the reasons for conflict included economic, political and administrative, technological, social, cultural, and historical factors. Economic and political and administrative factors were confirmed as the most important causes of such conflict. Although economic reasons were often deemed more important than political and administrative ones, they always played a preconditioning role. Political and administrative causes often directly led to the outbreak and deterioration of conflict. Second, although multiple approaches have been used to resolve conflicts in grassland areas, governmental intervention and mediation remain the most strongly recognized and widely used approach. The importance of judicial settlement has increased significantly in contemporary practices. Third, both the reasons for and approaches adopted to mitigate conflict affected the effectiveness of the conflict resolution, and the reasons also influenced the selection of appropriate conflict resolution approaches. These findings provide a new theoretical direction for future studies as well as practical implications for resolving conflicts in environmental and other types of public governance in contemporary China and elsewhere.

Identification of Genes Related to Hair Follicle Cycle Development in Inner Mongolia Cashmere Goat by WGCNA.

Cashmere goat from Inner Mongolia is an excellent local breed in China, and the related cashmere product is a kind of precious textile raw material with high price. Cashmere is generated from secondary hair follicles, which has obvious annual periodicity and includes three different stages: anagen, catagen, and telogen. Therefore, we investigated skin transcriptome data for 12 months using weighted gene co-expression network analysis (WGCNA) to explore essential modules, pathways, and genes responsible for the periodic growth and development of secondary hair follicles. A total of 17 co-expression modules were discovered by WGCNA, and there is a strong correlation between steelblue module and month (0.65, p = 3E-09), anagen (0.52, p = 1E-05), telogen (-0.6, p = 8E-08). Gene expression was generally high during late anagen to catagen (June to December), while expression was downregulated from telogen to early anagen (January-May), which is similar to the growth rule of hair follicle cycle. KEGG pathway enrichment analyses of the genes of steelblue module indicated that genes are mainly enriched in Cell cycle, Wnt signaling pathway, p53 signaling pathway and other important signal pathways. These genes were also significantly enriched in GO functional annotation of the cell cycle, microtubule movement, microtubule binding, tubulin binding, and so on. Ten genes (WIF1, WNT11, BAMBI, FZD10, NKD1, LEF1, CCND3, E2F3, CDC6, and CDC25A) were selected from these modules, and further identified as candidate biomarkers to regulate periodic development of hair follicles using qRT-PCR. The Wnt signaling pathway and Cell cycle play an important role in the periodic development of hair follicles. Ten genes were identified as essential functional molecules related to periodic development of hair follicle. These findings laid a foundation for understanding molecular mechanisms in biological functions such as hair follicle development and hair growth in cashmere goats.

Deep Learning-Assisted Investigation of Electric Field-Dipole Effects on Catalytic Ammonia Synthesis.

External electric fields can modify binding energies of reactive surface species and enhance catalytic performance of heterogeneously catalyzed reactions. In this work, we used density functional theory (DFT) calculations-assisted and accelerated by a deep learning algorithm-to investigate the extent to which ruthenium-catalyzed ammonia synthesis would benefit from application of such external electric fields. This strategy allows us to determine which electronic properties control a molecule's degree of interaction with external electric fields. Our results show that (1) field-dependent adsorption/reaction energies are closely correlated to the dipole moments of intermediates over the surface, (2) a positive field promotes ammonia synthesis by lowering the overall energetics and decreasing the activation barriers of the potential rate-limiting steps (e.g., NH2 hydrogenation) over Ru, (3) a positive field (>0.6 V/Å) favors the reaction mechanism by avoiding kinetically unfavorable N≡N bond dissociation over Ru(1013), and (4) local adsorption environments (i.e., dipole moments of the intermediates in the gas phase, surface defects, and surface coverage of intermediates) influence the resulting surface adsorbates' dipole moments and further modify field-dependent reaction energetics. The deep learning algorithm developed here accelerates field-dependent energy predictions with acceptable accuracies by five orders of magnitudes compared to DFT alone and has the capacity of transferability, which can predict field-dependent energetics of other catalytic surfaces with high-quality performance using little training data.

TOPK Affects Autophagy of Skin Squamous Cell Carcinoma by Regulating NF-KB Pathway through HDAC1.

Objective: To explore the effect and potential mechanism of T-LAK cell-originated protein kinase (TOPK) on autophagy in cutaneous squamous cell carcinoma (cSCC). Methods: Human cSCC cancer tissue and paracancerous tissue samples were collected clinically; immunohistochemistry was used to detect the expression of TOPK, nuclear factor κB p65 (NF-κB p65), phosphorylated nuclear factor κB inhibitor α (p-IκBα), Beclin-1, and microtubule-associated protein 1 light chain 3 (LC3) in cSCC tissue; and immunofluorescence was adopted to detect the coexpression of NF-κB p65, p-IκBα, and TOPK in cSCC tissue. After TOPK silencing or overexpression, SCL-1 and A431 cells were treated with PDTC and 3-MA, respectively. RT-qPCR and Western Blot were used to detect the mRNA and protein expressions of histone deacetylase 1 (HDAC1) in TOPK-silenced/overexpressing cells. Western Blot was performed to detect the protein expressions of NF-κB p65, p-p65, IκBα, p-IκBα, Beclin-1, and LC3 in each group. Transwell and scratch healing experiments were used to detect the ability of cells to invade and migrate. The formation of autophagosomes in each group was observed by TEM. Results: Compared with adjacent tissues, TOPK, NF-κB p65, p-IκBα, Beclin-1, and LC3 were highly expressed in cSCC cancer tissues; TOPK and NF-κB p65 were coexpressed; and TOPK and p-IκBα were expressed in cSCC cancer tissues both increased. The mRNA and protein levels of TOPK in human cSCC cells were significantly higher than those in human normal skin HaCaT cells. After TOPK knockout, the expression of HDAC1, p-IκBα/IκBα, NF-κB p65, p-p65, Beclin-1, LC3II/I proteins, cell invasion, and migration abilities were significantly reduced, and fewer autophagosomes were observed. Treatment with PDTC and 3-MA significantly downregulated NF-κB pathway protein activity and autophagy level and reduced cell migration and invasion ability. Conclusion: TOPK promotes the malignant progression of cSCC by upregulating HDAC1 to activate the NF-κB pathway and promote autophagy.

Multiplex network disintegration strategy inference based on deep network representation learning.

Multiplex networks have attracted more and more attention because they can model the coupling of network nodes between layers more accurately. The interaction of nodes between layers makes the attack effect on multiplex networks not simply a linear superposition of the attack effect on single-layer networks, and the disintegration of multiplex networks has become a research hotspot and difficult. Traditional multiplex network disintegration methods generally adopt approximate and heuristic strategies. However, these two methods have a number of drawbacks and fail to meet our requirements in terms of effectiveness and timeliness. In this paper, we develop a novel deep learning framework, called MINER (Multiplex network disintegration strategy Inference based on deep NEtwork Representation learning), which transforms the disintegration strategy inference of multiplex networks into the encoding and decoding process based on deep network representation learning. In the encoding process, the attention mechanism encodes the coupling relationship of corresponding nodes between layers, and reinforcement learning is adopted to evaluate the disintegration action in the decoding process. Experiments indicate that the trained MINER model can be directly transferred and applied to the disintegration of multiplex networks with different scales. We extend it to scenarios that consider node attack cost constraints and also achieve excellent performance. This framework provides a new way to understand and employ multiplex networks.

Long-term recurrence-free survival and complications of percutaneous balloon compression and radiofrequency thermocoagulation of Gasserian ganglion for trigeminal neuralgia: A retrospective study of 1313 cases.

OBJECTIVE: To estimate long-term recurrence, complications after percutaneous balloon compression (PBC) and radiofrequency thermocoagulation (RFT) of gasserian ganglion among a large sample of patients with trigeminal neuralgia (TN) during a long-term follow-up. METHODS: A retrospective analysis of 1313 patients undergoing PBC or RFT for the treatment of TN was conducted from 2006 to 2020. Recurrence-free survival (RFS) was assessed by the Kaplan-Meier method. Complications including facial numbness, corneal reflex decrease and masseter weakness were also estimated. RESULTS: For patients who received first initial PBC and RFT, the median RFS was 130.1 months (95% CI: 124.4, 135.9) and 123.3 months (95% CI: 117.6, 128.9) in PBC and RFT group with log-rank p = 0.108. The RFS rate was, respectively, 90.6% (95% CI: 88.1%-93.3%) and 91.4% (95% CI: 89.1%-93.7%) at 1 year, 84.6% (95% CI: 81.4%-87.8%) and 83.3% (95% CI: 80.3%-86.3%) at 3 years, 81.5% (95% CI: 78.1%-85.0%), and 78.6% (95% CI: 75.2%-81.9%) at 5 years, 71.5% (95% CI: 67.5%-75.5%), and 64.8% (95% CI: 61.0%-68.7%) at 10 years in two groups. No significant difference was observed in facial numbness degree between two groups after procedure. Compared with PBC group, ophthalmic complication prevalence was higher in RFT group (9.6%) (p = 0.001). However, masseter weakness incidence was lower (10.7%) than that in PBC group (24.0%) with p < 0.001. CONCLUSIONS: Patients with TN seemed to attain similar long-term benefit from PBC and RFT, especially in elderly. However, in order to reduce postoperative complications, PBC provided a safer and alternative for treating TN involving ophthalmic division, whereas RFT could be employed as a preferred regimen for maxillary and mandibular TN.

Accuracy of Genomic Selection for Important Economic Traits of Cashmere and Meat Goats Assessed by Simulation Study.

Genomic selection in plants and animals has become a standard tool for breeding because of the advantages of high accuracy and short generation intervals. Implementation of this technology is hindered by the high cost of genotyping and other factors. The aim of this study was to determine an optional marker density panel and reference population size for using genomic selection of goats, with speculation on the number of QTLs that affect the important economic traits of goats. In addition, the effect of buck population size in the reference population on the accuracy of genomic estimated breeding value (GEBV) was discussed. Based on the previous genetic evaluation results of Inner Mongolia White Cashmere Goats, live body weight (LBW, h 2 = 0.11) and fiber diameter (FD, h 2 = 0.34) were chosen to perform genomic selection in this study. Reasonable genome parameters and generation transmission processes were set, and phenotypic and genotype data of the two traits were simulated. Then, different sizes of the reference population and validation population were selected from progeny. The GEBVs were obtained by six methods, including GBLUP (Genomic Best Linear Unbiased Prediction), ssGBLUP (Single Step Genomic Best Linear Unbiased Prediction), BayesA, BayesB, Bayesian ridge regression, and Bayesian LASSO. The correlation coefficient between the predicted and realized phenotypes from simulation was calculated and used as a measure of the accuracy of GEBV in each trait. The results showed that the medium marker density Panel (45 K) could be used for genomic selection in goats, which can ensure the accuracy of the GEBV. The reference population size of 1,500 can achieve greater genetic progress in genomic selection for fiber diameter and live body weight in goats by comparing with the population size below this level. The accuracy of the GEBV for live body weight and fiber diameter was better when the number of QTLs was 100 and 50, respectively. Additionally, the accuracy of GEBV was discovered to be good when the buck population size was up to 200. Meanwhile, the accuracy of the GEBV for medium heritability traits (FDs) was found to be higher than the accuracy of the GEBV for low heritability traits (LBWs). These findings will provide theoretical guidance for genomic selection in goats by using real data.

Dipeptidyl peptidase-8 induces sorafenib resistance via binding with c-Rel to mediate NF-κB signaling in hepatocellular carcinoma.

Sorafenib is the important first-standard drug for patients with advanced hepatocellular carcinoma (HCC). A major obstacle to successful treatment is sorafenib resistance. However, the mechanism of sorafenib resistance is unclear. The present study aimed to determine the involvement of dipeptidyl peptidase-8 (DPP8) in sorafenib resistance. DPP8 expression was detected using quantitative real-time PCR (qPCR) and western blot analysis. The effect of DPP8 on sorafenib resistance was examined using terminal deoxynulceotidyl transferase nick-end-labeling (TUNEL), colony formation, flow cytometry, luciferase reporter, immunofluorescence, and immunoprecipitation (IP) assays. We found that DPP8 mRNA and protein levels were dramatically upregulated in HCC. Gene set enrichment analysis (GSEA) illustrated that DPP8 might be involved in apoptosis regulation. Downregulation of DPP8 substantially promoted the sensitivity of HCC cells to sorafenib. Further analysis showed that DPP8 might regulate nuclear factor kappa B (NF-κB) signaling, which was confirmed using a luciferase reporter assay. Downregulation of DPP8 decreased the expression levels of downstream genes of the NF-κB pathway. IP showed that DPP8 can interact with NF-κB subunit c-Rel, an important protein of NF-κB signaling. Finally, a drug combination of sorafenib and Val-boroPro induced higher mortality of HCC cells than sorafenib alone in DPP8-upregulated cells. Our findings indicated that using the inhibitor Val-boroPro might be a promising method to enhance sorafenib sensitivity in advanced HCC.

Self-Guided Deep Multiview Subspace Clustering via Consensus Affinity Regularization.

Multiview subspace clustering (MVSC) leverages the complementary information among different views of multiview data and seeks a consensus subspace clustering result better than that using any individual view. Though proved effective in some cases, existing MVSC methods often obtain unsatisfactory results since they perform subspace analysis with raw features that are often of high dimensions and contain noises. To remedy this, we propose a self-guided deep multiview subspace clustering (SDMSC) model that performs joint deep feature embedding and subspace analysis. SDMSC comprehensively explores multiview data and strives to obtain a consensus data affinity relationship agreed by features from not only all views but also all intermediate embedding spaces. With more constraints being cast, the desirable data affinity relationship is supposed to be more reliably recovered. Besides, to secure effective deep feature embedding without label supervision, we propose to use the data affinity relationship obtained with raw features as the supervision signals to self-guide the embedding process. With this strategy, the risk that our deep clustering model being trapped in bad local minima is reduced, bringing us satisfactory clustering results in a higher possibility. The experiments on seven widely used datasets show the proposed method significantly outperforms the state-of-the-art clustering methods. Our code is available at https://github.com/kailigo/dmvsc.git.

Learnable Subspace Clustering.

This article studies the large-scale subspace clustering (LS2C) problem with millions of data points. Many popular subspace clustering methods cannot directly handle the LS2C problem although they have been considered to be state-of-the-art methods for small-scale data points. A simple reason is that these methods often choose all data points as a large dictionary to build huge coding models, which results in high time and space complexity. In this article, we develop a learnable subspace clustering paradigm to efficiently solve the LS2C problem. The key concept is to learn a parametric function to partition the high-dimensional subspaces into their underlying low-dimensional subspaces instead of the computationally demanding classical coding models. Moreover, we propose a unified, robust, predictive coding machine (RPCM) to learn the parametric function, which can be solved by an alternating minimization algorithm. Besides, we provide a bounded contraction analysis of the parametric function. To the best of our knowledge, this article is the first work to efficiently cluster millions of data points among the subspace clustering methods. Experiments on million-scale data sets verify that our paradigm outperforms the related state-of-the-art methods in both efficiency and effectiveness.

Genome-Wide Association Study of Body Weight Traits in Inner Mongolia Cashmere Goats.

Objective: Body weight is an important economic trait for a goat, which greatly affects animal growth and survival. The purpose of this study was to identify genes associated with birth weight (BW), weaning weight (WW), and yearling weight (YW). Materials and Methods: In this study, a genome-wide association study (GWAS) of BW, WW, and YW was determined using the GGP_Goat_70K single-nucleotide polymorphism (SNP) chip in 1,920 Inner Mongolia cashmere goats. Results: We discovered that 21 SNPs were significantly associated with BW on the genome-wide levels. These SNPs were located in 10 genes, e.g., Mitogen-Activated Protein Kinase 3 (MAPK3), LIM domain binding 2 (LDB2), and low-density lipoprotein receptor-related protein 1B (LRP1B), which may be related to muscle growth and development in Inner Mongolia Cashmere goats. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that these genes were significantly enriched in the regulation of actin cytoskeleton and phospholipase D signaling pathway etc. Conclusion: In summary, this study will improve the marker-assisted breeding of Inner Mongolia cashmere goats and the molecular mechanisms of important economic traits.

Photo-Fenton Process over an Fe-Free 3%-CuO/Sr0.76Ce0.16WO4 Photocatalyst under Simulated Sunlight.

Photo-Fenton is a promising photocatalytic technology that utilizes sunlight. Herein, an Fe-free 3%-CuO/Sr0.76Ce0.16WO4 photocatalyst was synthesized to apply simulated wastewater degradation via a photo-Fenton process under simulated sunlight. The photodegradation efficiency of RhB solution over the 3%-CuO/Sr0.76Ce0.16WO4 photocatalyst is 93.2% in the first 3 h; its photocatalytic efficiency remains at 91.6% even after three cycle experiments. The kinetic constant of the 3%-CuO/Sr0.76Ce0.16WO4 photocatalyst is 0.0127 min-1, which is 2.8-fold that of an intrinsic Sr0.76Ce0.16WO4 sample. The experiment of radical quenching revealed that the photogenerated electrons and holes are transferred to CuO to form hydroxyl radicals. Besides, the photocatalyst was characterized by scanning electron microscopy (SEM), Fourier transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD), diffused reflectance spectroscopy (DRS), and X-ray photoelectron spectroscopy (XPS) measurements. It has some reference significance for the design of iron-free photocatalysts.