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Abstract The roadway space of coal mine is narrow, and the illumination is low and uneven. Dynamic mining is accompanied by dust and water mist. The accuracy and reliability of roadway data collected by vision and laser sensors are poor. Based on this, a digital modeling method of coal mine roadway based on millimeter-wave radar array is proposed. Firstly, aiming at the problem of complex environmental interference, combined with the characteristics of small amount of single frame data of millimeter-wave point cloud, a multi-layer filtering noise reduction processing and dynamic subgraph registration method of millimeter-wave point cloud is proposed to filter out interference points and realize single radar point cloud registration. Secondly, aiming at the problem that a single millimeter-wave radar cannot scan the complete roadway information at one time, combined with the characteristics of small elevation field of view of millimeter-wave radar, a millimeter-wave radar array acquisition system is built, and an improved iterative closest point (ICP) registration algorithm based on point cloud features is established to construct the roadway point cloud fusion model. Finally, aiming at the problem of uneven and sparse point cloud after array fusion, a Poisson surface reconstruction method based on point cloud density weighted interpolation is proposed to refine the roadway structure and realize the accurate reconstruction of digital roadway model. The experimental results show that the digital modeling method of millimeter-wave radar array can accurately obtain the information of roadway surrounding rock, the density of roadway point cloud is increased by 22.4%, and the average absolute error percentage of the width and height of the reconstructed roadway model is only 0.82% and 0.72%, which provides a new research method for the reconstruction of underground roadway and an important basis for the digital modeling method of coal mine roadway.

AimsThis cohort study aimed to explore the effect of a one-day online continuing medical education (CME) on the improvement of physicians’ knowledge and clinical practice on functional dyspepsia (FD).MethodsPhysicians were invited to participate in this CME via medical education applications. FD training videos made in advance were sent to participants via a weblink. Before and after training, participants were required to finish the FD knowledge test and provide case information of FD patients. McNemar test, Wilcoxon rank-sum test, Freidman test, Chi-square test, quantile regression, and generalized estimating equations (GEE) were used to perform statistical analysis.ResultsThere were 397 of 430 (92.33%) physicians finished this CME program. The total score of the FD knowledge test after training was significantly higher compared with before training [488.3 (468.3–510.0) vs. 391.7 (341.7–450.0), p

In response to the challenges of multiple personnel, heavy support tasks, and high labor intensity in coal mine tunnel drilling and anchoring operations, this study proposes a novel tracked drilling and anchoring robot. The robot is required to maintain alignment with the centerline of the tunnel during operation. However, owing to the effects of skidding and slipping between the track mechanism and the floor, the precise control of a drilling and anchoring robot in tunnel environments is difficult to achieve. Through an analysis of the body and track mechanisms of the drilling and anchoring robot, a kinematic model reflecting the pose, steering radius, steering curvature, and angular velocity of the drive wheel of the drilling and anchoring robot was established. This facilitated the determination of speed control requirements for the track mechanism under varying driving conditions. Mathematical models were developed to describe the relationships between a tracked drilling and anchoring robot and several key factors in tunnel environments, including the minimum steering space required by the robot, the minimum relative steering radius, the steering angle, and the lateral distance to the sidewalls. Based on these models, deviation-correction control strategies were formulated for the robot, and deviation-correction path planning was completed. In addition, a PID motion controller was developed for the robot, and trajectory-tracking control simulation experiments were conducted. The experimental results indicate that the tracked drilling and anchoring robot achieves precise control of trajectory tracking, with a tracking error of less than 0.004 m in the x-direction from the tunnel centerline and less than 0.001 m in the y-direction. Considering the influence of skidding, the deviation correction control performance test experiments of the tracked drilling and anchoring robot at dy = 0.5 m away from the tunnel centerline were completed. In the experiments, the tracked drilling and anchoring robot exhibited a significant difference in speed between the two sides of the tracks with a track skid rate of 0.22. Although the real-time tracking maximum error in the y-direction from the tunnel centerline was 0.13 m, the final error was 0.003 m, meeting the requirements for position deviation control of the drilling and anchoring robot in tunnel environments. These research findings provide a theoretical basis and technical support for the intelligent control of tracked mobile devices in coal mine tunnels, with significant theoretical and engineering implications.

Abstract Background Accurate identification of protein complexes in protein-protein interaction (PPI) networks is crucial for understanding the principles of cellular organization. Most computational methods ignore the fact that proteins in a protein complex have a functional similarity and are co-localized and co-expressed at the same place and time, respectively. Meanwhile, the parameters of the current methods are specified by users, so these methods cannot effectively deal with different input PPI networks. Result To address these issues, this study proposes a new method called MP-AHSA to detect protein complexes with Multiple Properties (MP), and an Adaptation Harmony Search Algorithm is developed to optimize the parameters of the MP algorithm. First, a weighted PPI network is constructed using functional annotations, and multiple biological properties and the Markov cluster algorithm (MCL) are used to mine protein complex cores. Then, a fitness function is defined, and a protein complex forming strategy is designed to detect attachment proteins and form protein complexes. Next, a protein complex filtering strategy is formulated to filter out the protein complexes. Finally, an adaptation harmony search algorithm is developed to determine the MP algorithm’s parameters automatically. Conclusions Experimental results show that the proposed MP-AHSA method outperforms 14 state-of-the-art methods for identifying protein complexes. Also, the functional enrichment analyses reveal that the protein complexes identified by the MP-AHSA algorithm have significant biological relevance.

Abstract Based on the concept of cost sharing, an oscillating buoy wave energy converter‐ floating breakwater integrated system is proposed, in which the surge or pitch motion of the front‐pontoon equipped with Power Take‐off (PTO) system is allowed. In this study, the methods of eigenfunction matching and variables separation are applied to establish a theoretical model on hydrodynamic performance of the proposed integrated system. Under the condition of optimal PTO damping, the effects of the front‐pontoon width, draft and pontoon spacing on the capture width ratio, reflection coefficient and transmission coefficient are investigated, respectively. It is found that the integrated system with surging front‐pontoon performs better on the wave energy conversion than that with pitching one, especially in the high frequency region. The peaks of capture width ratio Cw are associated with the gap resonance and the increase of front‐pontoon motion induced by the ‘hydrodynamic constructive effect’.

Detecting protein complexes is one of the keys to understanding cellular organization and processes principles. With high-throughput experiments and computing science development, it has become possible to detect protein complexes by computational methods. However, most computational methods are based on either unsupervised learning or supervised learning. Unsupervised learning-based methods do not need training datasets, but they can only detect one or several topological protein complexes. Supervised learning-based methods can detect protein complexes with different topological structures. However, they are usually based on a type of training model, and the generalization of a single model is poor. Therefore, we propose an Ensemble Learning Framework for Detecting Protein Complexes (ELF-DPC) within protein-protein interaction (PPI) networks to address these challenges. The ELF-DPC first constructs the weighted PPI network by combining topological and biological information. Second, it mines protein complex cores using the protein complex core mining strategy we designed. Third, it obtains an ensemble learning model by integrating structural modularity and a trained voting regressor model. Finally, it extends the protein complex cores and forms protein complexes by a graph heuristic search strategy. The experimental results demonstrate that ELF-DPC performs better than the twelve state-of-the-art approaches. Moreover, functional enrichment analysis illustrated that ELF-DPC could detect biologically meaningful protein complexes. The code/dataset is available for free download from https://github.com/RongquanWang/ELF-DPC.

The Chinese lake gudgeon is a small benthopelagic freshwater fish. It is presently threatened by human activities and environmental factors in China. Understanding the genetic diversity and population structure is fundamental for implementation of appropriate conservation measures and a sustainable management program. However, little is reported about the current genetic diversity and population structure. Here, we used ten microsatellite markers to genotype 175 individuals from six populations. Low levels of genetic diversity were found in all six tested populations. The Xiang river population showed the highest level of genetic diversity. Genetic differentiation was very low but significant among the Changyang, Changqidang and Mayang populations, but the Qiandao lake, Gan river, Xiang river populations all showed significant and strong differentiation from the other three populations. Contemporary gene flow was observed in among Changyang, Changqidang and Mayang populations and between Gan and Xiang river populations, respectively. This is the first genetic study to report the genetic diversity and population structure of S. sinensis and the results will be used to develop management and conservation strategies. Keywords: Sarcocheilichthys sinensis, Genetic diversity, Population structure, Microsatellites

Detecting epistatic interactions in GWAS (genome-wide association studies) data is of great significance in studying common and complex diseases; however, the ability to detect high-order epistatic interactions in GWAS data is still insufficient. Existing methods are usually used to identify two-order interactions, and they cannot detect a large number of interactions. In this article, we propose a novel stochastic approach named SHEIB-AGM (stochastic approach for detecting high-order epistatic interactions using bioinformation with automatic gene matrix). SHEIB-AGM utilizes bioinformation to construct a gene matrix. In each iteration, it randomly generate a high-order SNP combination based on the gene matrix. SHEIB-AGM utilizes k2 (the Bayesian network scoring criterion) and G-test to detect epistasis in the generated combination and automatically update the gene matrix. We have compared SHEIB-AGM with six other methods, i.e., DECMDR, SNPHarvester, MACOED, AntEpiSeeker, HS-MMGKG and SEE, on simulated data including 108 epistatic models and 17,600 files. The results demonstrate that SHEIB-AGM greatly outperforms the above methods in terms of F-measure and power. We utilized SHEIB-AGM (with and without bioinformation) to analyze a real GWAS dataset from the Wellcome Trust Case Control Consortium. The results indicate that SHEIB-AGM with bioinformation can detect 33.94~3069.40-times more epistatic interactions. We have found numerous genes and gene pairs that may play an important role in seven complex diseases. Some of them have been found in the CTD database (the Comparative Toxicogenomics Database).

Accurately identifying protein-ATP (Adenosine-5'-triphosphate) binding sites is significant for protein function annotation and new drug invention. Previous studies often utilize classical machine learning classification algorithms to predict protein-ATP binding sites based on protein primary sequence. However, deep learning as a newly developed technique has shown outstanding performance in various fields. In this work, we introduce the deep convolutional neural network for protein-ATP binding sites prediction based on sequence information. Two classification networks are developed including a residual-inception-based predictor and a multi-inception-based predictor, then the ensemble learning is applied by giving optimized weights to each network architecture to combine them for more superior performance. We examine the performance of our proposed method on two groups of independent testing sets including a classic ATP-binding benchmark dataset and a newly proposed ATP-binding dataset. As a result, our proposed method outperforms other state-of-art sequence-based predictors with the AUC of 0.922 and 0.896 respectively which illustrates the efficacy of deep learning technique in protein-ATP binding sites prediction. The source code and benchmark datasets can be downloaded at https://github.com/tlsjz/ATPbinding.

Identifying the protein complexes in protein-protein interaction (PPI) networks is essential for understanding cellular organization and biological processes. To address the high false positive/negative rates of PPI networks and detect protein complexes with multiple topological structures, we developed a novel improved memetic algorithm (IMA). IMA first combines the topological and biological properties to obtain a weighted PPI network with reduced noise. Next, it integrates various clustering results to construct the initial populations. Furthermore, a fitness function is designed based on the five topological properties of the protein complexes. Finally, we describe the rest of our IMA method, which primarily consists of four steps: selection operator, recombination operator, local optimization strategy, and updating the population operator. In particular, IMA is a combination of genetic algorithm and a local optimization strategy, which has a strong global search ability, and searches for local optimal solutions effectively. The experimental results demonstrate that IMA performs much better than the base methods and existing state-of-the-art techniques. The source code and datasets of the IMA can be found at https://github.com/RongquanWang/IMA.

Abstract Background Protein complex identification from protein-protein interaction (PPI) networks is crucial for understanding cellular organization principles and functional mechanisms. In recent decades, numerous computational methods have been proposed to identify protein complexes. However, most of the current state-of-the-art studies still have some challenges to resolve, including their high false-positives rates, incapability of identifying overlapping complexes, lack of consideration for the inherent organization within protein complexes, and absence of some biological attachment proteins. Results In this paper, to overcome these limitations, we present a protein complex identification method based on an edge weight method and core-attachment structure (EWCA) which consists of a complex core and some sparse attachment proteins. First, we propose a new weighting method to assess the reliability of interactions. Second, we identify protein complex cores by using the structural similarity between a seed and its direct neighbors. Third, we introduce a new method to detect attachment proteins that is able to distinguish and identify peripheral proteins and overlapping proteins. Finally, we bind attachment proteins to their corresponding complex cores to form protein complexes and discard redundant protein complexes. The experimental results indicate that EWCA outperforms existing state-of-the-art methods in terms of both accuracy and p-value. Furthermore, EWCA could identify many more protein complexes with statistical significance. Additionally, EWCA could have better balance accuracy and efficiency than some state-of-the-art methods with high accuracy. Conclusions In summary, EWCA has better performance for protein complex identification by a comprehensive comparison with twelve algorithms in terms of different evaluation metrics. The datasets and software are freely available for academic research at https://github.com/RongquanWang/EWCA.

Abstract Background The detection of protein complexes is of great significance for researching mechanisms underlying complex diseases and developing new drugs. Thus, various computational algorithms have been proposed for protein complex detection. However, most of these methods are based on only topological information and are sensitive to the reliability of interactions. As a result, their performance is affected by false-positive interactions in PPINs. Moreover, these methods consider only density and modularity and ignore protein complexes with various densities and modularities. Results To address these challenges, we propose an algorithm to exploit protein complexes in PPINs by a Seed-Extended algorithm based on Density and Modularity with Topological structure and GO annotations, named SE-DMTG to improve the accuracy of protein complex detection. First, we use common neighbors and GO annotations to construct a weighted PPIN. Second, we define a new seed selection strategy to select seed nodes. Third, we design a new fitness function to detect protein complexes with various densities and modularities. We compare the performance of SE-DMTG with that of thirteen state-of-the-art algorithms on several real datasets. Conclusion The experimental results show that SE-DMTG not only outperforms some classical algorithms in yeast PPINs in terms of the F-measure and Jaccard but also achieves an ideal performance in terms of functional enrichment. Furthermore, we apply SE-DMTG to PPINs of several other species and demonstrate the outstanding accuracy and matching ratio in detecting protein complexes compared with other algorithms.

RNA-binding proteins (RBPs) play a significant role in many cellular processes and regulation of gene expression, therefore, accurately identifying the RNA-interacting residues in protein sequences is crucial to detect the structure of RBPs and infer their function for new drug design. The protein sequence as basic information has been widely used in many protein researches with the combination of machine learning techniques. Here, we propose a sequence-based method to predict the RNA-protein interacting residues in protein sequences. The prediction method is composed of two predictors including a feature-based predictor and a sequence template-based predictor. The feature-based predictor applies the random forest (RF) classifier with the protein sequence information. After getting the classification probability, an adjustment procedure is used in consideration of neighbouring correlation between RNA interacting residues. The sequence template-based predictor selects the optimal template of the query sequence by multiple sequence alignment and matches the interacting residues in template sequence into the query sequence. With the combination of two predictors, the coverage and prediction performance of our method have been greatly improved, the MCC value increases from 0.467 and 0.352 to 0.499 in our validation set. In order to evaluate our proposed method, an independent testing set is utilized to compare with other two hybrid methods. As a result, our method achieves better performance than the other two methods with an overall accuracy of 0.817, an MCC value of 0.511 and an F-score of 0.605, which demonstrates that our method can reliably predict the RNA interacting residues in protein sequences. Moreover, the effectiveness of our newly proposed adjustment procedure in the feature-based predictor is examined and analyzed in detail.

Although genome-wide association studies play an increasingly important role in identifying causes of complex diseases, detecting SNP epistasis in these studies is a computational challenge. The existing methods are usually based on a single-correlation model between SNP combinations and phenotype and their performance is often unsatisfactory. The highest average power of the existing methods is 0.58 on DME models and 0.97 on DNME models. The highest average F-measure of the existing methods is 0.44 on DME models and 0.90 on DNME models. The lowest average computation time (second) of the existing methods is 2.12 on DME models and 2.09 on DNME models. In this work, a novel multi-objective evolutionary algorithm named SEE is presented for identifying SNP epistasis. In SEE, eight evolution objectives are successfully integrated to measure the association between SNP combinations and phenotype. SEE uses a novel evolutionary strategy based on sort, exploration and exploitation. SEE was compared with other existing methods using 72 simulated datasets. The average power of SEE is 0.71 with DME models and 0.99 with DNME models. The average F-measure of SEE is 0.68 with DME models and 0.99 with DNME models. The average computation time of SEE is 0.21 with DME models and 0.40 with DNME models. It is indicated that SEE outperforms other algorithms in both F-measure and computation time. It was then utilized to analyze real data obtained from the Wellcome Trust Case Control Consortium. Availability and Implementation: SEE is freely available at https://github.com/sunliyan0000/SEE.

Grass carp is one of the most important cultured fishes all over the world. The genetic diversity of grass carp plays an important role whatever in selective breeding progress or ecological conservation purposes. However, some genetic diversity researches were not accuracy and cannot be compared with each other due to different molecular markers, sample size and detection methods. In this study, we constructed five multiplex PCR assays contained 20 microsatellites with highly polymorphism and heterozygosity for evaluating genetic information of grass carp. We used nine cultured populations consisting of 507 individuals to detect stability of the five multiplex PCR assays. The results showed that the number of alleles (Na), effective number of alleles (Ne), observed heterozygosity (Ho), expected heterozygosity (He) and polymorphism information content (PIC) of the 20 microsatellite loci were relative high compared with the genetic diversity parameters of microsatellite loci developed by other researchers. Six loci were significantly deviated from Hardy-Weinberg equilibrium (P

As one of the most critical pathogens, Aeromonas hydrophila (AH) can cause motile aeromonad septicemia (MAS) in freshwater fish. In recent decades, a myriad of studies had been done for bacterial infect fish. However, the mechanism of bacterial infects fish especially freshwater fish was scanty. This study was conducted for investigating the invasion pathway of A. hydrophila in vivo of black carp. We have performed A. hydrophila 4332 transformed with a plasmid encoding the green fluorescent protein (pGFPuv) (AH4332GFPuv) in black carp. The AH4332GFPuv had similar growth properties and virulence as the wild‐type strains under the simulated natural condition. In this study, black carp were divided into five groups: IM (challenged via immersion), IBD (increased stocking density), SAW (skin artificially wounded by scalpel), MR (mucus removed from the body surface), and C0 (control group). The number of AH4332GFPuv in gill, liver, spleen, intestine, mid kidney, head kidney, muscle, eye, brain, heart, and blood were examined after 72 h post-infection from all groups. Significantly high bacterial numbers were observed in the gills and intestine. The number of bacteria was significantly higher in IBD group than IM group. In conclusion, the gill, intestines, and injured skin are likely to be the primary infection routes.

The hydrodynamic response of a land-based oscillating water column (OWC) wave energy converter under various irregular wave conditions is investigated numerically. Based on the potential flow theory, a two-dimensional fully nonlinear numerical wave flume (NWF) is developed to model the hydrodynamic characteristics using the time-domain higher-order boundary element method (HOBEM). The inner-domain sources method and JONSWAP energy spectrum is used to generate the irregular incident waves, and a linear pneumatic model is used to determine the pneumatic pressure inside the chamber. The free surface elevations at the chamber centre and the oscillatory pneumatic pressures induced by the vertical motion of the water column are recorded. The influence of irregular waves on the hydrodynamic characteristics of the OWC device is carried out by comparison with the regular waves, and a number of significant wave heights and peak wave periods are considered. The hydrodynamic efficiency of the OWC device in irregular wave conditions is observed to be lower than that in regular waves for most wave frequencies, especially near the resonant frequency.

Abstract Background In recent decades, detecting protein complexes (PCs) from protein-protein interaction networks (PPINs) has been an active area of research. There are a large number of excellent graph clustering methods that work very well for identifying PCs. However, most of existing methods usually overlook the inherent core-attachment organization of PCs. Therefore, these methods have three major limitations we should concern. Firstly, many methods have ignored the importance of selecting seed, especially without considering the impact of overlapping nodes as seed nodes. Thus, there may be false predictions. Secondly, PCs are generally supposed to be dense subgraphs. However, the subgraphs with high local modularity structure usually correspond to PCs. Thirdly, a number of available methods lack handling noise mechanism, and miss some peripheral proteins. In summary, all these challenging issues are very important for predicting more biological overlapping PCs. Results In this paper, to overcome these weaknesses, we propose a clustering method by core-attachment and local modularity structure, named CALM, to detect overlapping PCs from weighted PPINs with noises. Firstly, we identify overlapping nodes and seed nodes. Secondly, for a node, we calculate the support function between a node and a cluster. In CALM, a cluster which initially consists of only a seed node, is extended by adding its direct neighboring nodes recursively according to the support function, until this cluster forms a locally optimal modularity subgraph. Thirdly, we repeat this process for the remaining seed nodes. Finally, merging and removing procedures are carried out to obtain final predicted clusters. The experimental results show that CALM outperforms other classical methods, and achieves ideal overall performance. Furthermore, CALM can match more complexes with a higher accuracy and provide a better one-to-one mapping with reference complexes in all test datasets. Additionally, CALM is robust against the high rate of noise PPIN. Conclusions By considering core-attachment and local modularity structure, CALM could detect PCs much more effectively than some representative methods. In short, CALM could potentially identify previous undiscovered overlapping PCs with various density and high modularity.

BACKGROUND:Achaete scute-like 2 (Ascl2), a basic helix-loop-helix (bHLH) transcription factor, controls the fate of intestinal stem cells. However, the role of Ascl2 in colon cancer progenitor cells remains unknown. The cell line HT-29 (47.5-95% of CD133(+) population) and LS174T (0.45% of CD133(+) population) were chosen for functional evaluation of Ascl2 in colon cancer progenitor cells after gene knockdown by RNA interference. METHODOLOGY/PRINCIPAL FINDINGS:Immunohistochemistry demonstrated that Ascl2 was significantly increased in colorectal adenocarcinomas. Downregulation of Ascl2 using RNA interference in cultured colonic adenocarcinoma HT-29 and LS174T cells reduced cellular proliferation, colony-forming ability, invasion and migration in vitro, and resulted in the growth arrest of tumor xenografts in vivo. The Ascl2 protein level in CD133(+) HT-29 cells was significantly higher than in CD133(-) HT-29 cells. Ascl2 blockade via shRNA interference in HT-29 cells (shRNA-Ascl2/HT-29 cells) resulted in 26.2% of cells staining CD133(+) compared with 54.7% in control shRNA-Ctr/HT-29 cells. The levels of 'stemness' associated genes, such as CD133, Sox2, Oct4, Lgr5, Bmi1, and C-myc, were significantly decreased in shRNA-Ascl2/HT-29 and shRNA-Ascl2/LS174T cells in vitro as well as in the corresponding tumor xenograft (CD133 was not performed in shRNA-Ascl2/LS174T cells). The shRNA-Ascl2/HT-29 cells had inhibited abilities to form tumorspheres compared with control. The microRNA (miRNAs) microarrays, identified 26 up-regulated miRNAs and 58 down-regulated miRNAs in shRNA-Ascl2/HT-29 cells. Expression levels of let-7b, miRNA-124, miRNA-125b, miRNA-17, miRNA-20a and miRNA-302b, involved in the regulation of 'stemness', were quantified with qPCR, which confirmed their identities. Restoration of miRNA-302b, via its mimic, led to the restoration of shRNA-Ascl2/HT-29 'stemness' characteristics, including tumorsphere formation and 'stemness' associated genes levels, and the recovery of cellular behaviors, including colony-forming ability, invasion and migration in vitro. CONCLUSIONS/SIGNIFICANCE:Ascl2 may be a potential target for the inhibition of colon cancer progenitor cells, and functions through a miR-302b-related mechanism.

The secreted proteins from goblet cells compose the intestinal mucus. The aims of this study were to determine how they exist in two intestinal mucus layers.The intestinal mucosa was fixed with Carnoy solution and immunostained. Mucus from the loose layer, the firm layer was gently suctioned or scraped, respectively, lysed in SDS sample buffer with or without DTT, then subjected to the western blotting of rTFF3, rIgGFcγBP or rMuc2. The non-reduced or reduced soluble mucus samples in RIPA buffer were co-immunoprecipitated to investigate their possible interactions. Polyclonal antibodies for rTFF3, the rIgGFcγBP C-terminal domain and the rMuc2 C-terminal domain confirmed their localization in the mucus layer and in the mucus collected from the rat intestinal loose layer or firm layer in both western blot and immunoprecipitation experiments. A complex of rTFF3, which was approximately 250 kDa, and a monomer of 6 kDa were present in both layers of the intestinal mucus; rIgGFcγBP was present in the complex (250-280 kDa) under non-reducing conditions, but shifted to 164 kDa under reducing conditions in both of the layers. rMuc2 was found mainly in a complex of 214-270 kDa under non-reducing conditions, but it shifted to 140 kDa under reducing conditions. The co-immunoprecipitation experiments showed that binding occurs among rTFF3, rIgGFcγBP and rMuc2 in the RIPA buffer soluble intestinal mucus. Blocking the covalent interaction by 100 mM DTT in the RIPA buffer soluble intestinal mucus disassociated their binding.Rat goblet cell-secreted TFF3, IgGFcγBP and Muc2, existing in the two intestinal mucus layers, are bound together by covalent interactions in the soluble fraction of intestinal mucus and form heteropolymers to be one of the biochemical mechanisms of composing the net-like structure of mucus.