It is fundamental to cut multi-domain proteins into individual domains, for precise domain-based structural and functional studies. In the past, sequence-based and structure-based domain parsing was carried out independently with different methodologies. The recent progress in deep learning-based protein structure prediction provides the opportunity to unify sequence-based and structure-based domain parsing.Based on the inter-residue distance matrix, which can be either derived from the input structure or predicted by trRosettaX, we can decode the domain boundaries under a unified framework. We name the proposed method UniDoc. The principle of UniDoc is based on the well-accepted physical concept of maximizing intra-domain interaction while minimizing inter-domain interaction. Comprehensive tests on five benchmark datasets indicate that UniDoc outperforms other state-of-the-art methods in terms of both accuracy and speed, for both sequence-based and structure-based domain parsing. The major contribution of UniDoc is providing a unified framework for structure-based and sequence-based domain parsing. We hope that UniDoc would be a convenient tool for protein domain analysis.https://yanglab.nankai.edu.cn/UniDoc/.Bioinformatics online. The protein domain is defined as the basic unit of proteins, which can fold and carry out biological functions independently . DecompoA few automated algorithms were developed to decompose protein structures into domains. PDP  is one oAs many proteins do not have experimental structures, another closely related research is the sequence-based prediction of the domain boundaries. Many methods have been developed for this purpose, including ThreaDom , FIEFDOMWith the advance in deep learning-based structure prediction methods  are used in this work, which are summarized in Each dataset contains single-domain (denoted by Single) and multi-domain (denoted by Multi) proteins. For the multi-domain proteins, we give a more detailed classification  based onβC–βC (αC–αC for Glycine) distance matrix D. The distance is transformed into contact probability between 0 and 1 using the following transform:ijd is the distance between the ith residue and the jth residue. The values for 0d and δ are 8.0 and 1.5 Å, respectively (The division of a protein structure into domains is based on the intuition that the interaction inside the domains (i.e. intra-domain interaction) is stronger than that between domains (inter-domain interaction). To quantitively measure the domain interactions, we define domain interaction score (DIS) based on the inter-residue interaction as follows. It can be characterized by the D1 and D2 are two domains with sizes l1 and l2, respectively; ijp is defined by α is 0.43 according to The inter-domain interaction score is defined as:D is a domain of l amino acids; the parameter β is 0.95 empirically. In The intra-domain interaction score is defined as:The overall architecture of UniDoc is shown in With the distance map and the secondary structure, a two-step approach is used to predict domain boundaries. The first step splits a protein into fragments . We use two approaches to decompose a protein into domains, including continuous and discontinuous splits (The principle of splitting a protein into domains is to minimize the interaction between domains. Two constraints are required in the procedure of split. The first one is the size of a fragment should be longer than 30. The second one is a cutting position should not be from regular secondary structures (s splits . The oneContinuous split. Given a candidate fragment (D), a continuous split is defined as the split that results in two continuous sub-fragments (D1 and D2). A single cutting position is needed for such a split  and one continuous fragment (D2). Two cutting positions are needed for such a split  to ensure that the resulting continuous fragment is not too small. Second, the two cutting points should not be too far away from each other in the 3D structure to ensure that the sequentially distant sites are spatially close. a split . The optintra) as follows. First, we select the optimal split with the smallest interaction score (DISinter) from the continuous and the discontinuous splits. Second, this split is accepted only when DISinter is less than half of DISintra; otherwise, no split is performed on this fragment to avoid over-splitting.To partly address the issue of over-splitting (i.e. splitting more number of domains than the native definition), the interaction score between the sub-fragments after splitting is compared with the interaction score of the original fragment  are selected from all fragment pairs to maximize the score defined by the following formula:i and j are between 1 and the total number of fragments from the top-down splitting. The fragments ID and JD are merged, if S is positive, meaning that the interaction between these fragments is stronger than within one of the individual fragments; otherwise, the merging step is done.A bottom-up algorithm is applied iteratively to merge the fragments from the top-down splitting into domains to maximize the interactions inside each domain as follows . Two fraintra <1.0) will be merged with another fragment that has the strongest interaction with it .A post-processing step is further applied to merge less compact fragments. A fragment with weak intra-domain contact (DISThe performance of classifying single and multi-domain proteins is measured mainly based on the metric Matthew’s correlation coefficient (MCC). In addition, the precision and the accuracy are also calculated.t). Previously, this threshold was simply set to 85%  score is used to measure the performance of a method . A domaiFor sequence-based domain boundary prediction, besides the above metrics, NDO  score . When this threshold is changed between 0 and 1, we calculate the corresponding CDO score on each dataset for each method. A higher/lower value of t means a more/less stringent requirement for an assignment to be regarded as correct, which thus results in a lower/higher CDO score. The results are shown in When the domain boundary for a protein is blurred, the domain annotations by CATH and SCOP are not always consistent. CATH tends to cut proteins into smaller domains; while SCOP uses evolutionary and structural relationships to define domains, making the SCOP domains larger. This inconsistent domain definition may affect the performance assessment. A few case studies are given to illustrate this issue.APO-liver alcohol dehydrogenase  and the segments (residues 73–95 and 352–394), CATH assigns multiple discontinuous fragments in both domains; while SCOP keeps continuous fragments into one of the domains. The predictions by UniDoc and PDP are consistent with the SCOP definition while the SWORD* prediction is different from both CATH and SCOP definitions. None of the solutions by SWORD are similar to the definition by SCOP or CATH.For the protein multifunctional methyltransferase (PDB ID: 1PJQ, chain A, The last example is the protein We compare the time complexity of structure-based domain parsing by UniDoc, SWORD and PDP on the proteins from the dataset Broad-consensus. All three methods were run locally and the running time for all proteins was recorded. As the inter-residue distance prediction becomes much more accurate than before, we can apply the above structure-based domain parsing algorithm to the problem of sequence-based domain boundary prediction. The only difference is the inter-residue distance is predicted by trRosettaX rather than derived from the input structure.Though many sequence-based domain boundary prediction methods are available, we only compare UniDoc with FUpred (First, we compare the performances in classifying single-domain and multi-domain proteins by both methods. As shown in Second, the performances on parsing 849 multi-domain proteins by UniDoc and FUpred are compared based on CDO and NDO scores . SupplemSimilar to the previous analysis, we compute the CDO scores at different overlap thresholds in In addition, we compare the time complexity of UniDoc and FUpred on 849 multi-domain proteins in Since UniDoc decomposes protein domains based on distance matrix, whether accurate distance prediction can be transferred into a correct prediction of domain boundaries. First, we compute the accuracy of the predicted distance [i.e. distance precision defined in crystal structure of the Arg-specific cysteine proteinase gingipain R (PDB ID: 1CVR, chain A, According to SCOP, the protein solution structure of HNF-6 (PDB ID: 1S7E, chain A, The opposite of this example is the We divide the distance precision from 0 to 1 into 10 bins and calculate the distribution of NDO scores in each bin. The numbers of proteins with distance precision in the range from 0 to 0.6 are very small , which hDecomposition of multi-domain proteins into domains is of fundamental meaning for precise domain-based structure prediction and functional characterizations. The recent breakthroughs in deep learning-based protein structure prediction make it possible to unify structure-based and sequence-based domain parsing. We introduce one of such approaches, UniDoc, for unified domain parsing using either structure-derived or predicted inter-residue distance matrix. Comprehensive tests on five benchmark datasets show that UniDoc works very well, outperforming other peering methods, in terms of both accuracy and speed. Though with a high prediction accuracy, UniDoc still has a limit. Only one domain decomposition is given even if multiple alternative decompositions exist. More efforts are needed to deal with the proteins with ambiguous definition of domain boundaries. We anticipate that the release of UniDoc could contribute to the community for protein domain analysis.btad070_Supplementary_DataClick here for additional data file.


Bovine viral diarrhoea virus (BVDV) causes substantial economic losses to the cattle industry; however, control and eradication can be achieved by identifying and removing persistently infected cattle from the herd. Each UK nation has separate control programmes. The English scheme, BVDFree, started in 2016 and is voluntary.We analysed the test results submitted to BVDFree from 5847 herds between 2016 and 2020.In 2020, 13.5% of beef breeders and 20.0% of dairy herds that submitted tests had at least one positive (virus/antibody) test result. Although lower than in previous years, there was no clear trend in the proportion of positive tests over time. In virus testing herds, 0.4% of individual tests were positive in 2020, and 1.5% of individual tests were positive in BVDV‐positive virus testing herds. Dairy herds and larger herds were more likely to join BVDFree, and dairy herds were also more likely to virus test than beef breeder herds. Larger herds, herds that used virus testing and herds that had BVDV‐positive test results were more likely to continue submitting tests to BVDFree.The findings provide a benchmark for the status of BVDV control in England; continued analysis of test results will be important to assess progress towards eradication. Bovine viral diarrhoea virus (BVDV) is a pestivirus that affects cattle and is endemic in the UK. BVDV infections in a herd cause diverse production losses, such as reduced fertility, decreased milk yield, abortions, increased susceptibility to other diseases, poor growth rates and mucosal disease.6 There are two strategies commonly used for BVDV detection in herds: virus or antibody testing.In order to control and eradicate BVDV, PI animals must be identified and removed from the herd.10 BVDV control in the UK is conducted by schemes in each of the home nations, which are diverse in terms of testing used, mandatory participation and progress.A number of countries have had success in BVDV control and eradication using both virus and antibody testing regimens.19 Members of BVDFree are required to upload the results of all testing for BVD to the BVDFree database. Two years of testing is required in order to achieve a negative status, which can be achieved through either an antigen/virus test (via ear tag) of every newborn calf or blood antibody tests of a minimum of five cattle between 9 and 18 months old from each management group.BVDFree England (hereafter referred to as BVDFree) is the voluntary BVD eradication programme for England that started in 2016.Assessment of test results and demographics of farmers participating in BVDFree are needed to assess the scheme's progress and inform decisions on further development towards eradication. In this study, we present a descriptive analysis of participation and test results submitted to the BVDFree database between 2016 and 2020. We also investigate whether there is evidence for specific factors impacting the likelihood of herds not continuing to submit tests to the BVDFree scheme, which may offer insights to inform strategies to enhance engagement.We conducted all data preparation and analysis using R statistical software (version 4.0.5).BVDFree provided data that included all test results  from samples submitted between 2016 and 2020 (correct as of 14 April 2021). All herd‐level information associated with each test was collected at the time the herd signed up to BVDFree and included the County Parish Holding number, postcode, type of herd , herd size (number of breeding cows) and veterinary practice. We removed the variable ‘herd size’ for 49 (of 5611) herds that gave either implausible herd sizes or a range of sizes. Test‐level information was complete for all tests and included the dates the sample was taken, tested and uploaded, the type of sample (tissue/blood/milk), whether the sample was pooled from multiple animals, the type of test , and the laboratory and the test result. We omitted 89  tests that had a later sample date than the automatically generated test result upload date, resulting in a dataset with 535,243 tests from 5611 herds. Herd‐level details contained missing data: five (<0.01%) tests had a missing County Parish Holding number, 49,402 (9.2%) tests had a missing postcode, 58,678 (11.0%) did not give the type of cattle on the farm, 64,284 (12.0%) tests did not give a herd size and 49,529 (9.3%) tests did not state a veterinary practice.20 we categorised herds according to the test regimen employed in any one calendar year: virus  or antibody . We then classified herds as virus positive if at least one individual antigen or virus test was positive and antibody positive if at least one individual antibody test was positive. We investigated the testing behaviour of herds that we categorised as using either testing regimen to verify that their testing behaviour appeared compliant with the required test regimen. We also investigated the proportion of herds with a positive virus test for different numbers of individual virus tests as a proportion of their herd size to assess the impact of using different cutoffs in our definition.To enable the identification of tests in the database that were likely to be from herds conforming to BVDFree testing requirements,Summary statistics were calculated by test regimen (virus or antibody), herd type (beef breeder only or dairy only) and calendar year. Assessment of the spatial distribution of the herds in the dataset by year was conducted by plotting herd locations by postcode. Regional variation in the prevalence of herds with positive virus or antibody test results was assessed by calculating the proportion of the virus or antibody testing herds with a positive test result in each region of England and plotting heatmaps of the resulting prevalence.p‐value less than 0.05 were selected in the final model. We assessed the final model fit using decile plots of the observed and predicted data.Factors associated with the test regimen used by each herd (antibody or virus) were explored using a generalised logistic mixed‐effects model to account for herds testing for multiple years. The model was built using the lme4 package (v1.1‐26).p‐value < 0.05 were omitted from the final model. Assessment of the final model fit was carried out using decile plots of the observed and predicted data.Factors associated with the herds in the scheme each year participating again the following year were investigated using a generalised logistic mixed‐effects model to account for herds testing for multiple years. The model was built using the lme4 package (v1.1‐26)A total of 5611 herds submitted 535,243 tests to BVDFree between 2016 and 2020  and 45 (IQR = 24–76) breeding cows for dairy and beef breeding herds, respectively, which is higher than the national averages.Visual inspection of herd locations plotted by postcode in each calendar year showed a similar distribution to the distribution of herds nationally across all years Figure , which wThe number of new herds recruited to the scheme each year increased each year until 2019 (2507 herds) . There was a bimodal distribution to the number of tests carried out as a proportion of the number of breeding cows , with peaks at both 0.1 and 0.8 Figure . The higThere were 3162 herds that were tested using blood antibody tests for at least 1 year (4519 herd‐years for 2016–2020). Antibody testing herds had a median of 1 test visit/year (range = 1–4 for beef breeders and 1–3 for dairy herds). Thirty percent of antibody testing herds with a positive result also carried out individual virus tests in the same year, and of these, 12.4% had a positive test result with the individual virus testing.The generalised logistic mixed‐effects model of variables associated with virus versus antibody testing indicated that herds were more likely to antibody test in 2018–2020 than in 2016 Table .In 2020, 13.5% (120/891) of beef breeding herds had at least one positive individual virus or individual antibody test (8.3% [30/362] of virus testing herds and 17.0% [90/529] of antibody testing herds) compared to 20.0% (190/950) of dairy herds (20.6% 114/554] of virus testing herds and 19.2% [76/396] of antibody testing herds). There were fluctuations in the prevalence of positive herds between years with no clear trend There was no clear trend in the prevalence of virus‐ or antibody‐positive herds, which fluctuated by year. The fluctuations could result from the large number of farmers recruited to the scheme in 2019 who had not engaged with the scheme previously and therefore may have been less likely to be controlling BVD Figure 30, 31There were fewer virus‐positive cattle in a herd in 2020 than in the previous 4 years, with 0.4% of cattle in virus testing herds positive for BVD. There was also a lower proportion of cattle positive for BVD virus in virus‐positive herds in 2020 (1.5%) than in the previous 4 years. This test positive rate is similar to other European countries, which typically have a BVD prevalence of less than 0.8% of animals,Dairy herds were more likely to use virus testing than beef herds, and all herds were more likely to use antibody testing in 2018–2020 than in 2016. ‘Stamp It Out’ funding was available to veterinarians to carry out blood antibody testing from 2018 to 2021, which could have encouraged farmers to use antibody testing during those years. Dairy herds, being larger, are both the most likely to have sufficient youngstock numbers for blood antibody cohort testing and likely to find antibody testing more cost effective.Herds that join the scheme do not always continue submitting tests in the following years. Continued submission of tests to the scheme was associated with years in which funding was available for testing, using virus testing, large herds and being virus or antibody positive in a previous year. An increase in participation coinciding with available funding is common to other BVD control schemes,A limitation of this study is that participating farmers are unlikely to be a representative sample of English cattle farmers, which is partially evidenced in the differences in herd sizes and types recruited to the scheme each year—herds in the scheme were 1.5–2 times larger than average.In conclusion, we have presented an estimate of the prevalence of BVD in England from a national dataset and find that although the prevalence of BVD‐positive tests in herds submitting test results to BVDFree was lower in 2020 than in previous years, there was not a clear decreasing trend in either the proportion of positive tests or positive herds over that time frame. Not all herds remained engaged in the scheme; herds with positive BVD tests, those using virus testing and larger herds were more likely to submit tests the following year, and continued engagement was also associated with available funding. More widespread farmer engagement appears crucial for the effective control of BVD in England, and research to stimulate this may be worthwhile.Derek Armstrong and Lorna Gow are directly involved in the delivery of the BVDFree programme.Ethical approval was granted by the University of Warwick Biomedical and Scientific Research Ethics Committee (BSREC 100/19‐20) prior to commencement of the study.Martin Green, Michael Tildesley, Jasmeet Kaler, Eamonn Ferguson and Matt Keeling were responsible for funding acquisition and supervision. Derek Armstrong, Lorna Gow and Martin Green were involved in conceptualisation. Naomi Prosser, Martin Green and Edward Hill were involved in the formal analysis, investigation and methodology. Naomi Prosser curated the data and wrote the original draft with input from Edward Hill, and all authors contributed to the review and editing of the paper.Supporting InformationClick here for additional data file.


Mucins are large glycoproteins that cover and protect epithelial surface of the body. Mucin domains of gel-forming mucins are rich in proline, threonine, and serine that are heavily glycosylated. These domains show great complexity with tandem repeats, thus make it difficult to study the sequences. With the coming of single molecule real-time (SMRT) sequencing technologies, we manage to present sequence structure of mucin domains via SMRT long reads for gel-forming mucins MUC2, MUC5AC, MUC5B and MUC6. Our study shows that for different individuals, single nucleotide polymorphisms could be found in mucin domains of MUC2, MUC5AC, MUC5B and MUC6, while different number of tandem repeats could be found in mucin domains of MUC2 and MUC6. Furthermore, we get the sequence of MUC2, MUC5AC, and MUC5B mucin domain in a Chinese individual for each nucleotide at accuracy of possibly 99.98–99.99%, 99.93–99.99%, and 99.76–99.99%, respectively. We report a new method to obtain DNA sequence of gel-forming mucin domains. This method will provided new insights on getting the sequence for Tandem Repeat parts which locate in coding region. With the sequences we obtained through this method, we can give more information for people to study the sequences of gel-forming mucin domains. The gel-like mucus could protect against harmful microorganisms2. There are five gel-forming mucins in mammals1. They are MUC2, MUC5AC, MUC5B, MUC6 and MUC19. Each protein contains a mucin domain which is rich in amino acids proline (P), threonine (T) and serine (S). The mucin domain, also called PTS domain, is heavily glycosylated and therefore has a stiff conformation which could give the mucin protein the function of protection3.The gel-forming mucins are large glycosylated proteins essential for mucus layer to cover epithelial cells7. Both the cysteine number and their positions are extremely conserved in those domains, which play an essential role in forming dimers and trimers7.For each gel-forming mucin, the sequence before the mucin domain is called N-terminal sequence and the sequence afterwards is called C-terminal sequence. All the N-terminal sequences of gel-forming mucins share a similar structure which contains three VWD domains, and all the C-terminal sequences have Cysteine knot (CK) domains9. Computational and phylogenetic analyses suggested an evolutionary history of the four human mucin genes from an ancestor gene common to the human von Willebrand factor gene10.In human, the genes of MUC2, MUC5AC, MUC5B and MUC6 are clustered in a complex of 400 kb very rich in CpG islands on chromosome 11 in region p15.511. Therefore, it is very difficult to get the exact sequence of this part.In human, DNA sequence of mucin domain in MUC2/MUC5AC/MUC5B/MUC6 is only one exon. This exon is organized in tandem repeats. These tandem repeats are very large, and the length of each repeat could vary12. This length could span the whole mucin domain. Therefore, the sequence of mucin domain could be assembled with SMRT reads13. However, this technology has high error rate14. Thus a high coverage of the reads is needed to obtain the sequence of mucin domain15.SMRT DNA sequencing technology could generate reads up to 10–20 k bases16. Moreover, a whole human genome SMRT reads of a Chinese individual (HX1) are available on the website http//:hx1.wglab.org/, and this could be used to make the assembly of mucin domains13. In our work, by combining with a new method we constructed, we take advantage of these data in obtaining the exact full sequences of MUC2, MUC5AC, and MUC5B mucin domains in HX1 which are extremely difficult to get due to their tandem repeats structure. Meanwhile, SNP and CNV information of mucin domains of MUC2, MUC5AC, MUC5B and MUC6 is also demonstrated in HX1.Several human reference genomes are available, and these could be used to locate mucin domainsFrom NCBI, we downloaded whole chromosome 11 of human genome of Refseq, a Korean individual (BioProject PRJNA294231), and an American individual (BioProject PRJNA294231). Then we extracted whole chromosome 11 from 1 to 1.4 M.In Refseq, this region includes six genes. They are APA2 , MUC6 , MUC2 , MUC5AC , MUC5B , and TOLLIP .We downloaded whole human genome SRA data of a Chinese individual (HX1) from NCBI. There are two types of SRA data. One is PACBIO_SMRT data from PacBio RS II, and the other is ILLUMINA data from Illumina Hiseq 2000. HX1 human genome assembly was made with Illumina Hiseq data which has 2.8 billion reads (N50 length 151) as well as PacBio SMRT data which has 44.2 million reads (N50 length 12.1 k). We only downloaded PACBIO_SMRT data and the size is 2773.6Gbp.For the genome data of HX1 downloaded, we used perl and python scripts to convert SRA data to fasta sequence data , MUC5AC (Accession number NM_001304359.1), MUC5B (Accession number NM_002458.2), and MUC6 (Accession number NM_005961.2). Next we used the sequence found as query sequences doing similarity search into genome assembly of Refseq in Chr11p15.5, Korean individual in Chr11 and American individual.First we found the DNA sequence of the exons of mucin domains in NCBI MUC2 (Accession number NM_002457.4), MUC5AC (Accession number NM_001304359.1), MUC5B (Accession number NM_002458.2) and MUC6 (Accession number NM_005961.2). Next for each mucin, we took introns up-stream and down-stream of the mucin domain exon. Then we used the combination of the three fragments as query sequences doing similarity search into SMRT reads of HX1.Due to the TR character, it is hard for CLUSTALW to make a good alignment if only PTS exon was used. Therefore, it is essential to include the introns up-stream and down-stream.For MUC2, in all the SMRT reads downloaded, 4 reads could be found to cover introns both up-stream and down-stream of mucin domain exon. In human MUC2 mucin domain, there is a CysD domain in the middle of a domain which is full of proline, threonine and serine (PTS). Although TR structure of PTS domain makes it impossible to be identified accurately with similarity search, CysD domain could be precisely found. Therefore, we took use of the CysD domain in the middle of PTS exon. We regarded the last “cysteine” in CysD domain as delimiter. The first part could contain the intron up-stream, PTS TRs, and the CysD island, and the second part could contain the CysD island, PTS TRs, and the intron down-stream. Thus we searched for two types of read: I. intron up-stream mucin domain exon + up-stream part of mucin domain exon; II. down-stream part of mucin domain exon + intron down-stream mucin domain exon. We found 18 type I and 9 type II reads. Combining with the 4 reads which could be found to cover introns both up-stream and down-stream, 22 reads could be used to build up-stream part and 13 reads could be used to build down-stream part.For MUC5AC, MUC5B and MUC6, we could not split the mucin domain exons and had to find the reads which could cover introns both up-stream and down-stream of mucin domain exon. In all the SMRT reads downloaded, 10, 9, and 3 reads could be found for MUC5AC, MUC5B and MUC6, respectively.17. For multiple sequence alignment we used CLUSTALW18. Genetic codon table for human was used for translation. HMMER was used for identifying protein domains19. Perl and python scripts were used for other tasks .For similarity search we used BLASTFor CLUSTALW, we set gap_opening_penalty and gap_extension_penalty to 0. The reason is that if you consider to align two sequences with different TRs numbers, the default numbers of gap_opening_penalty and gap_extension_penalty in CLUSTALW will cause the interruption of one complete repeat unit. However, the alignment of two common sequences (none TR) has no such problem.The length of mucin domains of MUC2 of Refseq, Korean and American assembly are 5884 bases, 4471 bases and 8773 bases, respectively. In NCBI there is a nucleotide entry of human MUC2 DNA sequence with Accession number NM_002457.4. The mucin domain of this entry is 9270 bases and only a part of it could be found in Refseq. We regard the mucin domain of NM_002457.4 as the most complete assembly among all available MUC2 mucin domain assemblies by similarity search.The length of mucin domains of MUC5AC of Refseq, Korean and American assembly are 10,371 bases, 10,576 bases and 11,196 bases, respectively. We regard the mucin domain of MUC5AC in Refseq as the most accurate assembly among all the three assemblies by similarity search.The length of mucin domains of MUC5B of Refseq, Korean and American assembly are 10,893 bases, 11,589 bases and 10,772 bases, respectively. We regard the mucin domain of MUC5B in Refseq as the most accurate assembly among all the three assemblies by similarity search.The lengths of mucin domains in MUC6 of Refseq, Korean and American assembly are 3009 bases, 13,299 bases and 8727 bases, respectively. For the American mucin domain of MUC6, the beginning sequence goes into gap region. We regard the mucin domain of MUC6 in Korean individual as the most complete assembly among the three assemblies by similarity search.We could see that even by length, these domains in each of the MUC2, MUC5AC, MUC5B and MUC6 make some differences Table .Table 1LThe PacBio SMRT data of HX1 human genome was used to get the mucin domains. Since the whole data set has 44.2 million reads, the data with a size of several tens of Terabytes was dealt with. Therefore, it is essential to make an efficient pipeline to obtain the reads which could be used to assemble the mucin domains.17. Then we used multiple alignment method CLUSTALW to get an alignment18  which appears maximum number of times. Then we removed all the insertions and got the consensus sequence  has 105 TRs. Comparing with that in HX1, it has 4 more TR CNVs directly after 89th repeat and 18 SNPs at 15th, 18th, 25th, 25th, 38th, 38th, 39th, 39th, 39th, 40th, 40th, 40th, 41st, 41st, 42nd, 42nd, 60th, and 104th repeat, respectively  groups, 4 Short Tandem Repeat (STR) groups and 1 unique short piece  has same length and TR structure as that in HX1. There are only 3 SNPs. One is in 99th repeat in 1st STR group; another two are in 3rd and 4th CysD, respectively  has same length and TR structure as that in HX1. There are only 7 SNPs. Three are in PTS tail of 3rd PTS domain, one is in 11th repeat of 5th PTS domain, one is in 4th repeat of 6th PTS domain, and three are in 7th, 8th and 9th repeats of 7th PTS domain, respectively  only has the head, 1st TR, first 33 amino acids of 2nd TR, last 117 amino acids of 24th TR, 25th TR, 26th TR, 27th TR, and the tail , 10,893 bases (same as Refseq), and 13,470 bases, respectively (Table In HX1 own assembly, mucin domains of MUC2, MUC5AC, MUC5B and MUC6 could not be found in one clusterly Table –S9. Ther21. With this we could reveal the exact number of TRs in gel-forming mucin domains in a rather quick way. Therefore, people could try to seek the link between mucin domain TR numbers and the corresponding human traits in large populations, and this will be helpful to study genetic associations with many mucus related diseases such as pancreatic cancer, breast cancer, lung cancer, egg groove cancer, colon cancer, asthma, bronchitis, chronic obstructive pulmonary disease, pulmonary cyst fibrosis, gastric ulcer, edema, etc.Through the method we developed, we managed to generate full and reliable sequences which have verified to be extremely hard to obtain in the pastSupplementary Information.


Persistent left superior vena cava (PLSVC) is a relatively rare congenital anomaly in the general population. It plays an important role in initiating and maintaining atrial fibrillation (AF) in some patients. Radiofrequency catheter ablation is the major treatment for patients with AF and PLSVC in most publications. Here, we reported a case of thoracoscopic ablation for a patient with atrial fibrillation and persistent left superior vena cava. After preprocedural simulation using virtual reality, we successfully completed box-lesion, ablation line from superior vena cava to inferior vena cava, left atrial appendage (LAA) excision, and PLSVC ablation. It provides a new perspective on surgical treatment for patients with AF and PLSVC. During fetal development, the failure of obliteration of the left anterior cardinal vein and the left Cuvier's canal of the left superior vena cava in time would result in their continued existence after birth and the formation of PLSVC . MeanwhiA 61-year-old male patient was admitted to our hospital due to drug-refractory recurrent palpitations for 5 years. The electrocardiogram showed AF at 66 beats per minute. Echocardiography revealed the presence of PLSVC with an enlarged left atrium (57 mm) and right atrium (61 mm). Further computed tomography (CT) confirmed the identification of PLSVC . Then thDetails of the procedure have been reported previously . The lesPLSVC is a relatively rare congenital anomaly, but it is one of the most common systemic venous anomalies in the thoracic cavity and has been reported to occur in 0.3–2% of the otherwise normal population and up to 10% of patients with congenital heart disease , 10. TheIn this case of thoracoscopic ablation for a patient with AF and PLSVC, better visualization and surgical simulation were achieved by three-dimensional reconstruction and virtual reality technique. However, the first dilemma was the feasibility of thoracoscopic surgical ablation. The PLSVC sterically blocked our insulator from reaching the pulmonary veins. By measuring the diameter of the PLSVC and observing its relationship with surrounding structures, the diagnosis of a type 2B PLSVC was reconfirmed, and we concluded that the PLSVC could be bypassed without incisional enlargement or cardiopulmonary bypass. During the surgical simulation, we assumed the operation hole was set at the 4th rib, and the virtual visual field of thoracoscopy showed that both the PLSVC and the left atrial appendage were exposed within the operating range. Lu et al.  had a siIn addition, when clamping the PLSVC for ablation, the venous volume return to the heart might suddenly decrease, leading to a dipping in blood pressure. In such cases, cooperation between the surgeon and the anesthetist is required. The PLSVC should be relieved till to the hemodynamics return stable. Volume management and vasoactive agents were required when necessary. To achieve PLSVC ablation, we preferred performing more times instead of each long-time scale. There might be an extremely rare condition which only the PLSVC is complicated by atrial fibrillation while the right superior vena cava (RSVC) is absent. In such a scenario, decision to ablate the LSVC depends on the adjacent structures and the diameter of the left superior cavity. On the one hand, if the cavity of LSVC is too small, it should not be ablated because the risk of LSVC stenosis is too high. On the contrary, if the lumen of LSVC was large enough and easy to separate from adjacent tissue, we thought it would be reasonable to perform ablation.In this case, we confirmed that thoracoscopic ablation can be a feasible surgical option for patients with AF and PLSVC.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.The studies involving human participants were reviewed and approved by Ethics Committee of Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.HL and TT contributed to the study design and manuscript drafting. HQ contributed to data acquisition. JC, PW, and HG contributed greatly to the revision of the manuscript. JL approved the submission of the final version. All authors contributed to the article and approved the submitted version.


As blood–brain barrier (BBB) disruption emerges as a common problem in the early stages of neurodegenerative diseases, the crucial roles of barrier-type brain endothelial cells (BECs), the primary part of the BBB, have been reported in the pathophysiology of neurodegenerative diseases. The mechanisms of how early vascular dysfunction contributes to the progress of neurodegeneration are still unclear, and understanding BEC functions is a promising start. Our understanding of the BBB has gone through different stages, from a passive diffusion barrier to a mediator of central-peripheral interactions. BECs serve two seemingly paradoxical roles: as a barrier to protect the delicate brain from toxins and as an interface to constantly receive and release signals, thus maintaining and regulating the homeostasis of the brain. Most previous studies about neurodegenerative diseases focus on the loss of barrier functions, and far too little attention has been paid to the active regulations of BECs. In this review, we present the current evidence of BEC dysfunction in neurodegenerative diseases and explore how BEC signals participate in the pathogenesis of neurodegenerative diseases. Neurodegenerative diseases, such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS), are defined as disorders characterized by progressive nervous system dysfunction . AlthougThe BBB was originally regarded as a barrier for preventing blood cells, neurotoxic substances, and pathogens into the central nervous system (CNS). Specialized brain endothelial cells form the main part of the barrier and mediate the transport between cerebral capillaries and brain tissue . In contBarrier-type brain endothelial cell dysfunction or degeneration in BBB has been reported in neurodegenerative diseases for a long time . The disAlzheimer’s disease is a neurodegenerative disorder with prominent amnestic cognitive impairment. One of the pathology hallmarks of AD is the aggregation of pathological protein: β-amyloid (Aβ) extracellular plaques and tau neurofibrillary tangles. A classical hypothesis is the amyloid cascade hypothesis, in which the aggregation of Aβ precedes cortical tau pathology and contributes to neurodegeneration in AD .In vitro, different aggregation properties and heterogeneous compositions of Aβ peptides have distinct effects on endothelial cell viability, BBB integrity, and angiogenesis  in AD patients . Based oIndeed, the critical role of Aβ in AD pathogenesis has not escaped criticism for a long time. in vitro models have been used  have uncovered plenty of disease-associated genomic loci in AD . BECs areen used . Among teen used . Howevercurately ,b.Parkinson’s disease is the second most common neurodegenerative disorder with more than 6 million patients in the world. Bradykinesia, rest tremor, rigidity, and alterations in posture/gait are typical symptoms of PD. Loss of nigrostriatal dopamine neurons and the following imbalance between anti-kinetic and prokinetic activity contribute to these typical symptoms . VasculaThe aggregation of pathological protein α-synuclein in the form of intraneural Lewy bodies is the key pathological hallmark in PD. BEC alterations are observed mostly in the substantia nigra, locus coeruleus, and caudate putamen, where the aberrant α-synuclein aggregation is prominent. A 2.5-fold increase in the number of BECs is identified in the substantia nigra pars compacta of PD brain tissue, where the proangiogenic molecule vascular endothelial growth factors (VEGF) correspondingly upregulated because of the astrocytes activated by oligomeric α-synuclein . The newAmyotrophic lateral sclerosis, the third most common neurodegenerative disease, is characterized by the loss of motor neurons in the cortex, brainstem, and spinal anterior horn. Biomarkers for the diagnosis of ALS and the assessment of upper motor neuron abnormality in the early phase are limited, but neuroimaging techniques such as advanced magnetic resonance imaging (MRI) provide increasing insights into the pathophysiology of ALS . A correMultiple sclerosis (MS) is an inflammatory-mediated demyelinating and neurodegenerative disease of the CNS. Many therapies for inflammation are available, while neurodegeneration and corresponding brain function impairments remain untargeted in MS. In progressive MS, the pathophysiological mechanisms of the demyelination and neurodegeneration are still unclear, which is important for new therapy and drugs development .In the pathogenesis of MS, peripheral immune cells cross the BBB into the brain and attack myelin membranes and neurons. BEC activation mediates leukocyte recruitment into CNS by the upregulation of adhesion molecules on them, which can be regarded as one of the biomarkers for the activity of MS . The expHuntington’s disease (HD) is a monogenic neurodegenerative disorder characterized by a CAG repeat expansion in the Huntingtin (HTT) gene. In HD patients, similar BBB disruption and increase in blood vessel density have been identified in all major components of the NVU . HiPSC-din vitro  in neurodegenerative diseases , increasing microglial reactivity and impairing NPC activity . UpregulActivated BECs can also recruit microglia and macrophages and induce a chronic proinflammatory condition in the perivascular area. A recent study has shown an unidentified role of BECs in the phagocytosis of myelin sheaths in the MS mouse model. The uptake of myelin debris activates BECs and promotes the secretion of pro-inflammatory mediators from them, causing the clustering of macrophages and microglia and other sequential events in the progression of MS . SimilarOther than recruiting immune cells, BECs themselves modulate the immune response. Single-nucleus transcriptome analysis of prefrontal cortical samples from AD patients and healthy controls points out that some of the dysregulated pathways in BECs are related to antigen presentation . InterfeThe occurrence of adult hippocampal neurogenesis in humans was first reported by in vivo. In these areas, BECs are in a specialized planar morphology to increase the contact area with the endfeet of adult NSCs  generate new neurons. Adult neurogenesis takes place in the dentate gyrus (DG) hippocampus subfield and the subventricular zone (SVZ) in a continuous manner throughout aging. The extracellular microenvironment in DG and SVZ, namely the neurogenic niche, is a crucial regulator of stem cell behaviors ult NSCs . Secretiult NSCs . The conult NSCs . Remarkault NSCs . This ouInterestingly, there is a shared signaling “language” between NSCs and BECs. Some factors were originally identified by neurotrophic effects and later shown to regulate angiogenesis, and some angiogenesis factors were discovered to have effects on NSCs. A growing body of literature describes the neurotrophic effects of these factors in normal brain functions, which may be important targets in the pathogenesis of neurodegenerative diseases. For instance, a shared semaphorin pathway exists in neuronal and vascular morphogenesis . BEC-derOther than the well-accepted neurovascular niche, some studies raise the concept of “oligovascular niche,” indicating the close bidirectional crosstalk between oligodendrocyte precursor cells (OPCs) and BECs in white matter. Oligodendrocytes are the myelinating cells around axons of neurons in the CNS, providing metabolic support and maintaining the rapid conduction function in axons. OPCs are stem cells that persist into adulthood and generate oligodendrocytes throughout life. They regenerate the damaged myelin during the process of remyelination in demyelination diseases, such as MS. Effective remyelination by OPCs can restore metabolic support to the axon, thus limiting the axon degeneration and neurodegeneration . Recent So far, vascular dysfunction has emerged as a common problem in the early stages of all human neurodegenerative diseases. More studies challenge the traditional opinion that BECs are indolent and passive barrier cells in BBB Recent technical advances such as single-cell sequencing have helped us notice the crucial roles of BECs in neurodegenerative diseases.In the pathogenesis of neurodegenerative diseases, pathological proteins are crucially linked to BEC dysfunction in most neurodegenerative disorders, and damaged BEC transport accelerates the deposition of pathological proteins. Moreover, recent studies uncover more pathological-protein-independent neurodegeneration, especially neuroinflammation induced by activated BECs. The relative contributions of interactions between microglia and BECs to inflammation in neurodegenerative diseases are needed to be examined. In addition, a thorough knowledge of the link between BEC signals and disease resilience is required. Although neurogenesis failure in neurodegenerative diseases is still controversial, we ought to attend to the roles of stem cells in the process of diseases. The behaviors of OPC have emerged as an important area in the field of demyelinating diseases such as MS. Considering the regulations of BECs in the stem cell behaviors and the vascular niche created by BEC secretions, we need to understand whether BEC dysfunction is the reason for the regeneration failure of neurons. In summary, the molecular basis of BEC dysfunction will be crucial for the understanding of the pathological roles of vascular dysfunction and the development of new therapies during the early phase of neurodegenerative diseases.QD, MC, JS, and YY researched data for the review, wrote and revised the manuscript, contributed substantially to discussions of its content, and undertook review and editing of the manuscript before submission. All authors read, revised, and approved the final manuscript.


Intelligent fault diagnosis is of great significance to guarantee the safe operation of mechanical equipment. However, the widely used diagnosis models rely on sufficient independent and homogeneously distributed monitoring data to train the model. In practice, the available data of mechanical equipment faults are insufficient and the data distribution varies greatly under different working conditions, which leads to the low accuracy of the trained diagnostic model and restricts it, making it difficult to apply to other working conditions. To address these problems, a novel fault diagnosis method combining a generative adversarial network and transfer learning is proposed in this paper. Dummy samples with similar fault characteristics to the actual engineering monitoring data are generated by the generative adversarial network to expand the dataset. The transfer fault characteristics of monitoring data under different working conditions are extracted by a deep residual network. Domain-adapted regular term constraints are formulated to the training process of the deep residual network to form a deep transfer fault diagnosis model. The bearing fault data are used as the original dataset to validate the effectiveness of the proposed method. The experimental results show that the proposed method can reduce the influence of insufficient original monitoring data and enable the migration of fault diagnosis knowledge under different working conditions. Intelligent fault diagnosis is used to intelligently identify the health status of mechanical equipment by automatically extracting the hidden fault characteristic information from the monitoring data of the mechanical equipment, which is the current research hotspot in the field of fault diagnosis ,2,3. An These studies were mainly based on the two assumptions of machine learning, i.e., the samples have rich label information and are independent and identically distributed ,11. HoweTransfer learning (TL) is a new machine learning method that uses existing knowledge to solve problems in different but related fields . TL can Zhang et al. combined deep CNN with TL to reduce the dependence of the model on training data and accurately identify different fault types . Chen etA generative adversarial network (GAN) is composed of a generative network and a discriminator network, wherein the generative network and the discriminator network dynamically iterative optimization so that the images generated by the generative network are as close to the real samples as possible . RecentlThe rest of this article is organized as follows. In TL is a method of using existing knowledge to solve problems in different but related fields. Under variable conditions, TL plays a vital role. The operating conditions of mechanical equipment will change facing different fault diagnosis fields. The fault signal data distribution is different under various working conditions, which are defined as different domains. The case where the data are labeled is defined as the source domain The GAN is a generative model based on adversarial theory. Specifically, the network contains two parts, i.e., a generator and a discriminator. The purpose of the generator is to learn the distribution law of real data samples and generate new data samples. The purpose of the discriminator is to determine whether the data come from the generator or the real data sample. These two pairs are continuously optimized in the confrontation and finally reach a balance. The basic composition framework of GAN is shown in The input of the generator is random noise The objective function According to Equation (4), when GAN can be used for the extension of a fault signal dataset. Based on its advantages in feature extraction and distribution learning, GAN is considered for the extension of the target domain fault signal dataset to solve the problem of the scarcity of available data for mechanical equipment faults in practical engineering.Aiming at the cross-condition problem of the scarcity of available data in fault diagnosis in the actual engineering environment and the low generalization ability of the single-condition model, a deep transfer fault diagnosis method is proposed. This method is composed of three parts, target domain data enhancement, transfer fault feature extraction, and distribution adaptation.Firstly, the measured one-dimensional fault vibration signal is converted into a two-dimensional image signal through the short-time Fourier transform (STFT). Once the dataset is established, a deep convolutional generative adversarial network (DCGAN) is used to extract the fault characteristic information of the target domain and generate similar fake samples to expand the target domain dataset. Then, transfer fault features in the source domain and target domain data are extracted by Resnet-50. Finally, the training process of Resnet-50 is constrained by domain adaptation regularization to form a deep transfer diagnosis model of mechanical faults, which reduces the distribution difference between the transfer fault features in the extracted source domain and target domain data. The fault diagnosis knowledge of the source domain equipment can be used to identify the health status of the target domain equipment. The overall block diagram of the proposed method is shown in The generator and discriminator of the original GAN are both composed of a multilayer perceptron (MLP), which cannot show better ability of image feature extraction. To make the extracted image feature more representative, a CNN is employed as a network model of the generator and discriminator, which is called DCGAN . In DCGAIn the discriminator, the CNN is used to extract the fault features; in the generator, the transposed convolution structure is used to restore the information in the image, which is equivalent to the inverse operation of convolution.DCGAN uses the powerful feature extraction capabilities of the CNN to model generators and discriminators. Compared with the original GAN using MLP for feature extraction, DCGAN extracts more features, and the extraction effect of image features is more prominent, which can more accurately restore the fault characteristics of the sample.In In general, neural network models with complex structures have superior learning performance, and shallow neural networks have poor feature expression capabilities and generalization capabilities due to their simple model structure and are prone to underfitting. As the number of hidden layers increases, in the process of gradient back propagation, the continuous multiplication of gradients causes the training of deep neural network models to face the problem of gradient disappearance. The parameters of the model cannot be updated effectively, and the model finally shows poor prediction performance.To deal with the problem of the disappearance of the gradient during the back propagation of the gradient in the deep neural network, a structure called identity mapping in Resnet-50 is processed. Specifically, the input of each residual block as a part of the output of the residual block can be directly used. This process can be expressed as:The derivation of the above formula with respect to the input gives:By repeatedly cascading multiple residual blocks, a network model with a deep structure can be built. Due to the structure of cascading multiple identity maps in the model, the chain derivation rule is adopted in the process of gradient backpropagation. In this way, the gradient of the deeper parameters in the model can be directly transferred to the previous layer. As a result, the parameters of each layer can be updated in time and the problem of gradient disappearance is well solved.Therefore, Resnet-50 is used to process mechanical fault signals in this paper, which can establish a domain-sharing deep Resnet and extract transfer fault features in samples from the source and target domains. By adding domain adaptation regular terms, the difference in feature distribution between the source domain and the target domain is calculated, network parameters are optimized in backpropagation, and the health status of the samples in the source domain and the target domain is accurately classified.Maximum mean discrepancy (MMD) is a non-parametric distance indicator that measures the difference in the distribution of two datasets. After the monitoring sample data of the source domain and the target domain extract the deep fault features through Resnet, the distribution difference of the transfer fault features stays in the fully connected layer. Assuming that the transfer failure feature set is It can be seen from Equation (7) that there is always a mapping essed as :(8)D2Collect one-dimensional fault vibration signals from mechanical equipment, segment the signals and perform short-time Fourier transform on them to convert them into two-dimensional fault characteristic spectrograms, and construct source and target domain datasets.(2)Input the target domain data into DCGAN for training. Specifically, the fault feature information is extracted through the CNN in the discriminator, while the generator will transpose and convolve the input noise signal to restore the fault feature spectrogram. Meanwhile, the generator and the discriminator keep fighting against the training, and finally a fake sample similar to the target domain sample is obtained.(3)Input the source domain and target domain samples into Resnet-50, extract the transfer fault features in the samples layer by layer, obtain the predicted label of the source domain sample and the pseudo label of the target domain, and combine the estimated value of MMD calculated by Equation (8), Furthermore, the objective function value of the transfer diagnosis model is calculated by Equation (9).(4)Using the SGD optimization algorithm, the network parameters of Resnet-50 are updated layer by layer in the reverse direction. Return to step (3) until the output diagnostic accuracy rate reaches the termination condition.(5)Save the diagnosis model network parameters, input the target domain test dataset, and the output result is the predicted probability distribution of the sample health label. The health label corresponding to the maximum value of the element in the probability distribution represents the predicted health status of the target domain sample.The optimization of the objective function can be summarized as the following steps:To verify the effectiveness of the proposed method, experiments were carried out on a Case Western Reserve University (CWRU) dataset and a Paderborn University (PU) dataset. The program of the formulated model was developed in python 3.6.7 with Pytorch deep learning library and implemented on Linux with an RTX 2080 Ti GPU.The CWRU bearing dataset was collected from the experimental platform provided by Case Western Reserve University . As showThe datasets collected under four different load conditions are defined as domains A, B, C, and D, respectively. There are 2000 samples in each domain, including 10 different health states, 200 samples for each state, and each sample contains 1024 sampling points. The configuration information of the samples in each domain is shown in DCGAN was used to extend the target domain dataset. Both the generator and discriminator use the Adam optimization algorithm and the learning rate is set to 0.0002. First, the discriminator is trained and the parameters of the generator are fixed so that the discriminator can judge the true sample as 1 and the false sample as 0. Then, the parameters of the discriminator are fixed and the generator is trained so that the discriminator judges the false sample generated by the generator as 1. The generator and the discriminator continue the adversarial training, update the parameters by the back propagation method, the epoch time of the network is set to 500, and finally the false samples with similar fault characteristics as the input samples are generated.The input of the network is 2000 samples of each domain, and data enhancement is performed on each domain separately. In particular, the label information is not used in the training process, and the output is an unlabeled fault feature spectrogram. To evaluate the robustness of the proposed method more comprehensively, 12 transfer tasks were set: A→B, A→C, A→D, B→A, B→C, B→D, C→A, C→B, C→D, D→A, D→B, D→C. Taking A→B as an example, it means transfer from domain A to domain B. In other words, domain A is the source domain and domain B is the target domain. The source domain consists of 2000 labeled samples, and the target domain consists of 2000 unlabeled samples, including 1000 real samples and 1000 fake samples generated by DCGAN. The test set is the remaining 1000 labeled samples in the target domain. The purpose of the A→B transfer task is to use the fault diagnosis knowledge learned by the source domain (domain A) in the fault diagnosis task of the target domain (domain B) to assist the target domain in identifying the health status of the sample.In the training process of the transfer diagnosis model, the main parameters of the proposed method are set as follows: use the SGD optimization algorithm to train the network with a dynamic learning rate. The learning rate is set to 0.01, dynamic attenuation. The batch size is set to 32 and the epoch is set to 100.This experiment also used other methods for comparison to verify the superiority of the proposed method. All the methods involved in the experiment are listed as follows.(1)CNN(2)CNN + MMD(3)Resnet-50In contrast method CNN, CNN + MMD both use the same neural network model with seven layers. The structural information of the model is shown in (1)It can be seen from (2)The traditional machine learning method CNN has the worst performance among all models because it does not consider how to reduce the distribution difference between the source domain and the target domain. The diagnosis accuracy of CNN + MMD has some improvement, which indicates that it is necessary to reduce the distribution difference between the source domain and the target domain when dealing with fault data under different working conditions. However, the diagnosis accuracy of CNN + MMD is lower than that of CNN in individual cases, such as the transfer task A→C, which indicates that the method cannot handle the fault diagnosis task under complicated and variable working conditions well, and the simple structure of CNN cannot effectively extract the common features in both the source domain and target domain.(3)Resnet-50 has a higher diagnosis accuracy than the first two methods, which indicates its strong feature extraction capability. Compared with the proposed method, the difference in feature distribution between the source domain and target domain is not considered to be reduced in Resnet-50, so the diagnosis effect of Resnet-50 is still a little bit worse for the fault diagnosis tasks under different working conditions. The proposed method reduces the distribution difference between the two domains by minimizing the MMD and reduces the distance between the same labeled samples in the source domain and target domain to achieve better diagnosis results.Feature visualization is used to further evaluate the superiority of the proposed method. The confusion matrix is used to display the diagnosis result of task D→A, as shown in In order to analyze the effect of the proposed method more visually, the t-distributed random neighborhood embedding (t-SNE) algorithm was introduced to reduce the dimensionality of the extracted fault features to a two-dimensional plane and present them in the form of a scatter plot. Taking task D→C as an example, by extracting the intermediate feature maps of several models and reducing the dimensionality by t-SNE, the visualization effect graph is shown in This subsection uses the Paderborn University (PU) dataset  to perfoThe datasets collected under three different working conditions are defined as domains A, B, and C. There are 2250 samples in each domain, including sample data from nine different categories. There are 250 samples under each category, and each sample contains 1024 sampling points. The one-dimensional vibration signals in the dataset are converted into two-dimensional spectrograms by STFT to facilitate feature extraction by the diagnostic model. The experiments in this subsection also use four methods  to conduct comparison experiments to verify the effectiveness of the proposed method through six migration tasks. In each migration task, the source domain consists of 2250 labeled samples, and the target domain training set consists of 1250 real samples and 1000 fake samples, for a total of 2250 samples. The target domain test set consists of the remaining 1000 labeled samples. Again, the label information of the target domain is not used in the training process, but only in the calculation of the classification accuracy of the test set. The diagnostic accuracy of the four methods is shown in The comparative experimental analysis shows that the diagnostic accuracy of the proposed method is still better than other diagnostic models. The diagnostic accuracy of CNN is lower because it does not consider the domain feature differences, and the clustering effect of feature mapping is poor. With the addition of MMD for domain adaptation, the diagnostic accuracy of the model has been improved, and some fault states can be accurately identified. Since the network layers are shallow and do not learn deep fault features, there will still be some categories that are difficult to classify correctly, such as categories 5 and 6 and categories 7 and 8, which are all fault categories with the same damage location and the same damage type but different damage levels. Compared with the proposed method, ResNet does not add the domain adaptation regular term constraint, so there are still problems that the feature points of some categories are sticky and the inter-class distance is too small. Thus, the experimental validation in the PU dataset further illustrates the superiority and robustness of the proposed method for the task of bearing fault diagnosis under variable operating conditions.(1)Utilizing DCGAN to expand the target domain dataset to solve the problems of overfitting and negative transformation caused by insufficient data of the target domain dataset.(2)Adopting the MMD regularization term to the feature extractor to minimize the difference in domain distribution.Intelligent fault diagnosis plays an important role in improving the availability of mechanical equipment. Developing mechanical fault diagnosis models under different working conditions is the key to applying fault diagnosis technology in practice. This paper proposes a novel fault diagnosis model combining a generative confrontation network and TL to realize mechanical fault diagnosis under insufficient data volume and variable working conditions. The main contributions of the proposed model can be summarized as:Multiple sets of fault diagnosis experiments under variable work conditions were set up in two bearing fault datasets, respectively, to verify the effectiveness of the proposed method. The analyzed results demonstrate that the diagnostic accuracy of the proposed method is superior to other state-of-the-art fault diagnosis algorithms.The intelligent fault diagnosis model proposed in this paper can still be improved. Specifically, MMD is only used as a strategy to measure distribution differences, and the metric is relatively singular. Future work will focus on adopting a variety of metrics to quantify the difference in feature distribution and formulating metrics with high computational efficiency. Future work will also focus on simplifying the formulated network models to improve computational efficiency. In addition, for the noisy data case, combining the sparsity-based denoising feature extraction method with the intelligent fault diagnosis model developed in this paper still remains to be conducted in future work.


Res) and holistically nested edge detection (Hed), and ensembled to minimize the out‐of‐distribution drift effects. The DCNN is implemented for real‐time operations on SPM, converting the data stream into the semantically segmented image of domain walls and the corresponding uncertainty. Further the pre‐defined experimental workflows perform piezoresponse spectroscopy measurement on thus discovered domain walls, and alternating high‐ and low‐polarization dynamic (out‐of‐plane) ferroelastic domain walls in a PbTiO3 (PTO) thin film and high polarization dynamic (out‐of‐plane) at short ferroelastic walls  in a lead zirconate titanate (PZT) thin film is reported. This work establishes the framework for real‐time DCNN analysis of data streams in scanning probe and other microscopies and highlights the role of out‐of‐distribution effects and strategies to ameliorate them in real time analytics.The functionality of ferroelastic domain walls in ferroelectric materials is explored in real‐time via the in situ implementation of computer vision algorithms in scanning probe microscopy (SPM) experiment. The robust deep convolutional neural network (DCNN) is implemented based on a deep residual learning framework ( This work reports the implementation of the computer‐vision‐based workflow in scanning probe microscopy, enabling real‐time segmentation of the data stream from the microscope and direct exploration of a priori known objects of interest. This approach allows thorough investigation of objects of interest. This approach is universal to other microscopy modalities. Finally, recent advanced in materials characterization have opened for exploration the device concepts enabled by the functionality of individual topological elements such as domain walls and vortices.Ferroelectric materials are one of the most exciting material systems explored over the last century. Traditionally, these materials have been explored in the context of bulk applications, including piezoelectric transducers and actuators and electrooptical devices.1 The  scandium, boron, and aluminum nitrides; and zinc‐magnesium oxides. These discoveries have both changed the extant paradigms on mechanisms underpinning ferroelectricity, and open the pathway to a broad range of information technology device applications. Similarly, ferroelectric phenomena were discovered in low‐dimensional materials such as twisted double layers graphene, boron nitride, molybdenum disulfide, tungsten ditelluride, etc.Over the last decade, the field of ferroelectric materials has been significantly broadened by the discovery of ferroelectric phenomena in new materials classes, most notably binary oxides such as hafnia and zirconia and their solid solutions;12 sca In classical ferroelectric, polarization dynamics, including domain nucleation and 180° ferroelectric wall pinning is strongly affected by the presence of the ferroelastic domain walls or structural defects. In low dimensional ferroelectrics, the structural defects, stacking ordering, and heterostructure are coupled to the polarization dynamics. Similarly, coupling between polarization discontinuities and electronic systems gives rise to a broad set of wall‐coupled transport phenomena.Both in classical ferroelectric materials and new low dimensional materials systems, the phenomena of interest, including polarization switching, conductivity and conductance switching, etc. are strongly coupled to the structural and topological defects.25 In  and conductive Atomic Force Microscopy (cAFM). In these measurements, the sharp SPM tip serves as a mobile electrode and sensor, applying a bias to the material's surface and detecting associated electromechanical responses and currents. In the imaging modes, these responses are collected in the form of dense 2D maps. However, spectroscopic applications such as hysteresis loop measurements or current–voltage curve mapping required the development of hyperspectral imaging modes, collecting the spectra over a dense rectangular grid. The subsequent physics‐based or unsupervised machine learning‐based analysis of the resultant multidimensional data sets yields the 2D images highlighting the ferroelectric or conductivity functionality of individual microstructural elements. Similarly, unsupervised ML methods can be used to build structure–property relationships in ferroelectric materials.Many of these advances in the physics of low‐dimensional ferroelectrics have been enabled by scanning probe microscopy (SPM) techniques, most notably Piezoresponse Force Microscopy (PFM)34 and Consequently, the position of the probing tip with respect to the wall can vary strongly, limiting quantitative studies. Second, the polarization distributions in the material can change during the measurements, e.g., polarization switching can shift both 180° and ferroelastic walls. In this case, establishing the relationship between the domain and hyperspectral image is complicated.However, exploration of domain wall physics via the combination of the PFM and spectroscopic images suffers from a number of significant limitations. First, the typical size of domain structures in materials is fairly large (>100 nm), whereas the thickness of domain walls is typically small.42 ConHere we explore the intrinsic functionality of the ferroelastic domain walls via an automated experiment in PFM. We develop a workflow for real‐time data analytics based on the ensembled ResHedNet architecture that minimizes out‐of‐distribution drift effects and yields domain wall structure and reconstruction uncertainty. We further extend this approach to investigate the properties at domain walls, i.e., perform band excitation piezoresponse spectroscopy measurement at domain walls to study the properties encoded in piezoresponse–voltage hysteresis loops. We observed that adjacent ferroelastic domain walls exhibit alternating strong and weak out‐of‐plane polarization, attributed to the wall tilting effects. However, we also observe more subtle variations in domain wall switching behavior associated with proximity to large in‐plane domains.23 (PTO) thin film grown by metalorganic chemical vapor deposition method on a SrRuO3 conducting layer and (001) KTaO3 substrates. Previously, we have explored the relationship between local domain structures and the ferroelectric properties encoded in the piezoresponse–voltage hysteresis in this sample implementing an active learning network on operational PFM. That work demonstrated different exploration pathways (sequence of sampled points) by the active learning workflow for the acquisition functions based on on‐field and off‐field hysteresis loops, suggesting different dominating properties of the hysteresis loops under on‐field and off‐field conditions. The nonlinearity of this material was also investigated by the active learning network in a follow‐on work. In addition, our analysis of causal physical mechanism among different band excitation PFM channels revealed a mutual interaction among the channels associated with different properties. These previous studies aimed at discovering microstructural elements that maximize certain functional response of interest, i.e., find the domain structure  that corresponds to the largest hysteresis loop opening or nonlinear response.As a model system, we have chosen a PbTiObstrates.47 PreHere, we demonstrate an alternative approach for an automated experiment—implemented with ensembled ResHedNet on operational PFM—based on the identification of a prior known object of interest  in real time and further explore its functionality.Figure a‐domains show as clearly visible stripes , c‐domain (cyan), c+/c– 180° domain wall (blue), and a–c domain wall (red). The difference between these loops suggests the roles of local domain/wall structure in material's functionality.The typical domain structure of the PTO film is shown in band excitation piezoresponse force microscopy (BEPFM) results in s Figure , correspe Figure . Note ths Figure ; here th and scanning probe microscopies. However, while these techniques have amply demonstrated their potential for post‐acquisition data analysis, their implementation as a part of real time experiment is highly non‐trivial. The reason for this is the out of distribution drift effect, manifested as network trained for one set of imaging conditions will perform poorly when the image conditions change. This out of distribution effect has been broadly recognized in the machine learning (ML) community, and are directly responsible for dearth of practical applications of ML methods in medicine or autonomous driving.Here, we aim to explore the physics of specific domain walls, e.g., explore the polarization switching at selected types of ferroelastic walls. The ferroelastic walls can be determined on the image directly via human eye; however, finding these automatically is a challenge. Over the last 5 years, deep convolutional neural networks (DCNN) have been broadly adopted in electron51 and do not work well on thin objects such as domain walls. We demonstrated that augmenting the modified version of holistically nested edge detector with deep residue learning framework modules (ResHedNet), as shown in Figure  However, the previous work was limited to the already collected data where training and test/validation data came from the same distribution . Application of the network trained on data from previous experiments to a new experiment under slightly different conditions often leads to the unreliable performance, the behavior generally referred to as the out‐of‐distribution shift. Very generally, it can be understood as the same object visualized under slightly different conditions will be perceived by the neural network differently, and once imaging conditions are sufficiently different the identification becomes impossible. While this problem has been recognized only few years ago (and is implicitly responsible for the lack of the fully autonomous driving to date), it is crucial for any real‐time applications of ML methods during the experiments.We chose a deep learning‐based semantic segmentation for converting raw microscopy data into the domain walls maps. Earlier, we found that standard neural network architectures for semantic segmentation such as U‐Net63 do —where neural network weights are substituted by probability distributions—is considered to be the current state‐of‐the‐art for these purposes. In this approach, the predictive mean and uncertainty on the newly observed data x* given the training set D are expressed aswn ≈ P(w|D) are neural network weights drawn from the posterior. However, the fully Bayesian approach to deep learning is computationally expensive and is not suitable for real‐time implementation even on modern graphics processing unit (GPU) accelerators.The two most important requirements for working with data characterized by a distribution shift are robust generalization and reliable uncertainty estimates. The fully Bayesian approach to deep learning based on Hamiltonian Monte Carlo sampling67—whe In the deep ensembles method, one trains a collection of neural networks via a stochastic gradient descent using different random initialization of weights and different shuffling of training samples. The deep ensembles can be viewed as approximating the posterior predictive distribution with the set of point masses at different modes such that the posterior samples wn in Equation  and low‐loop‐width walls  are alternatingly distributed. We ascribe this behavior to the tilting of ferroelastic domain walls as has originally been explored by Ganpule for polarization switching. Here, switching of the wall oriented away from the tip and toward the tip involves dissimilar number of in‐plane domains, leading to dissimilar responses.Shown in Figure 71 “ske Figure . Accordie Figure  at each Figure The analyses of hysteresis loops are also extended to coercive field, loop area, and nucleation biases, as show in Figure a‐domains.This approach is further used to study a PZT thin film, results shown in Figure  and domain wall  in the PTO film, the BEPS measurement locations are shown in Figure  and larger loop area on the a‐domain side near domain wall . Loop height results in Figure  and larger loop height around domain wall , this result is consistent with our observation in Figure  , and a National Instruments DAQ card and chassis operated with a LabView framework. In piezoresponse force microscopy image measurement, the scan speed is 1 Hz and the scan size is 256*256.The piezoresponse force microscopy and spectroscopy measurements are performed on an Oxford Instrument, Asylum Research Cypher microscope with a Budget Sensor Multi75E‐G Cr/Pt coated AFM probes (≈3 N mThe authors declare no conflict of interest.S.V.K. and M.Z. conceived the project. M.Z. realized the ensemble‐ResHedNet. Y.L. performed analyses. Y.L. deployed the ResHedNet to PFM measurement and obtained results. K.K. helped with the deployment. H.F. provided the PTO and PZT sample. All authors contributed to discussions and the final manuscript.


The multi-process manufacturing of steel rolling products requires the cooperation of complicated and variable rolling conditions. Such conditions pose challenges to the fault diagnosis of the key equipment of the rolling mill. The development of transfer learning has alleviated the problem of fault diagnosis under variable working conditions to a certain extent. However, existing diagnosis methods based on transfer learning only consider the distribution alignment from a single representation, which may only transfer part of the state knowledge and generate fuzzy decision boundaries. Therefore, this paper proposes a multi-representation domain adaptation network with duplex adversarial learning for hot rolling mill fault diagnosis. First, a multi-representation network structure is designed to extract rolling mill equipment status information from multiple perspectives. Then, the domain adversarial strategy is adopted to match the source and target domains of each pair of representations for learning domain-invariant features from multiple representation networks. In addition, the maximum classifier discrepancy adversarial algorithm is adopted to generate target features that are close to the source support, thereby forming a robust decision boundary. Finally, the average value of the predicted probabilities of the two classifiers is used as the final diagnostic result. Extensive experiments are conducted on an experimental platform of a four-high hot rolling mill to collect the fault state data of the reduction gearbox and roll bearing. The experimental results reveal that the method can effectively realize the fault diagnosis of rolling mill equipment under variable working conditions and can achieve average diagnostic rates of up to 99.15% and 99.40% on the data sets of the rolling mill gearbox and bearing, which are respectively 2.19% and 1.93% higher than the rates achieved by the most competitive method. The rolling mill is indispensable in the production of steel products, and its safe and reliable operation is an effective premise to ensure product quality ,2. As moWith the development of artificial intelligence and sensing technology, fault diagnosis has shifted from traditional methods based on expert experience and signal analysis to data-driven fault diagnosis . The supAs a branch of machine learning, deep learning can overcome the lack of nonlinear mapping ability of shallow machine learning algorithms and adaptively learn fault-sensitive features from multiple hidden layers. In recent years, deep learning has been widely reported in the field of fault diagnosis ,9. Shao The change in data distribution caused by the change in mechanical equipment working conditions is called domain shift , as show(1)A multi-representation network structure is designed to fully extract the status information of rolling mill equipment from multiple perspectives.(2)Domain confrontation and maximum classifier difference discrepancy confrontation training are simultaneously applied to express the transfer of diagnostic knowledge from multiple features and divide the classification boundary of specific tasks.(3)Extensive experiments are performed to collect the fault state data of the reduction gearbox and roll bearing from a four-high (4-H) hot rolling mill experimental platform. Thus, the effectiveness of the proposed method for rolling mill equipment fault diagnosis under variable working conditions is verified.Although various domain-adaptive and improved transfer learning methods have alleviated the domain offset problem caused by varying working conditions to a certain extent, the existing domain-adaptive methods only express the transfer diagnosis knowledge from a single piece of information; that is, only part of the mechanical state information is concerned, and the important information related to the machine health may be lost. Thus, the diagnostic performance is unsatisfactory. Literature  shows thThe remainder of this paper is organized as follows.In this study, the general definition of the domain-adaptive fault diagnosis method is followed. Specifically, it is assumed that a tagged source domain dataset F and a domain classifier D; usually, a classifier C is also included. For a pattern distinguishability problem, their parameters are represented by Fθ, Dθ, and Cθ, respectively. In the training process, feature extractor F and domain classifier D are two players in a minimax game, that is, domain classifier D attempts to identify whether the representation learned by feature extractor F originates from the source domain or the target domain, and feature extractor F generates cross-domain-invariant characteristics as far as possible to fool domain classifier D. In this adversarial training process, the distribution difference between the source domain and the target domain gradually reduces. At the same time, under the supervision of the source domain, classifier C is trained to distinguish the categories of different samples. By adding a gradient reverse layer (GRL) to feature extractor F and domain classifier D, the model optimization of this process can be simultaneously realized.iy is the corresponding class label of the source domain sample, id is the domain label, and λ is the trade-off parameter. Fθ, Dθ, Cθ, respectively.Domain adversarial training is a typical domain-adaptive method; Ganin et al.  first inF and two predictive classifiers C1 and C2 are included in the maximum mean discrepancy network architecture. In the training process, the discrepancy between classifiers C1 and C2 is maximized to detect the target samples close to the decision boundary. These samples are very easy to be mis predicted by the classifiers trained under source supervision. At the same time, feature extractor F is trained to generate a target representation that is far from the classification boundary and close to the source domain. This optimized process can be formulated as follows:Considering that the general domain-adaptive method ignores the relationship between the characteristics of target samples and the task-specific decision boundary, Saito et al.  proposediG is shown in The integrated architecture of the proposed multi-representation network is shown in After the model is built, the specific optimization function should be designed to update the model parameters to achieve the expected diagnostic performance. Specifically, the loss objective function of the proposed model can be divided into three parts: supervised source domain classification loss, domain distinguishability loss in the multi-representation branch structure, and discrepancy loss of two classifiers. Under the supervised training of source domain samples, the two classifiers can independently learn to divide the decision boundary of fault classification. The loss function of the two classifiers can be expressed as follows:In each multi-representation network branch, a domain classifier is used to perform adversarial training to realize the edge distribution matching represented by this feature; thus, the domain-invariant feature can be learned in this process. The domain adversarial loss of the multi-representation structure can be formulated as follows:In addition to domain adversarial training, the second adversarial strategy of the proposed model is maximum classifier difference confrontation, which aims to use the predicted difference between the two classifiers to establish the relationship between the target sample and the task specific decision boundary. The two classifiers aim to detect the target samples far away from the source support, and the feature extractor is used to generate the target representation close to the source support. In this adversarial training process, more distinguished domain-invariant features can be learned. The maximum classifier discrepancy loss function of this model is given as follows:λ changes gradually according to the formula γ being set to 10. In this study, By adding a GRL, the two adversarial training processes and source supervision training can be carried out synchronously, and the parameters of each module of the model can be updated synchronously. The total loss function of the proposed model is as follows:According to the total loss function formula, the proposed parameter optimization problem of each module of the model can be expressed by the following formula:η0 = 0.01, α = 10, and β = 0.75. This learning rate attenuation method helps the model rapidly converge to the optimal value [Through the random gradient descent algorithm, the parameter update process for each network module is as follows:al value .The overall training process of the proposed method is shown in In this section, by collecting the operating data of the bearing and reducer under different working conditions on a 4-H hot rolling mill test bench, an extensive experimental scheme was designed to verify the performance of the proposed method. The diagnostic results of several typical diagnostic models and the proposed method under the same experimental conditions are compared and analyzed.The overall structure of the 4-H hot rolling mill experimental platform is shown in g, and three different load pressures were applied in turn to simulate different working conditions. The vibration signals collected under each load were used as a data source. The sampling frequency was set to 5120 Hz.The gearbox data were collected on the reduction gearbox of the 4-H hot rolling mill experimental platform. As shown in g to simulate different working conditions. The vibration signal collected at each speed was used as a data source. The sampling frequency of the acquisition card was set to 10,240 Hz.The bearing data were collected by monitoring the outer bearing of the working roll on the rolling mill, and the acceleration sensor was placed in the horizontal direction of the bearing seat. Four different bearing states were simulated: normal, inner ring fault (IRF), outer ring fault (ORF), and rolling element fault (REF). These faults were introduced in different parts of the rolling bearing through EDM, as shown in (1)Convolutional neural network (CNN): CNN implements the traditional supervised learning paradigm without adding a domain-adaptive algorithm. It uses the source domain for training and directly tests in the target domain.(2)Deep adaptation network (DAN) : DAN is (3)Deep CORAL (D-CORAL) : D-CORAL(4)Domain adversarial neural network (DANN) : DANN is(5)Joint adaptation network (JAN) : JAN is (6)Multi-adversarial domain adaptation (MADA) : MADA isFor the data of each failure mode under different working conditions, we used a sliding window with a size of 1024 to intercept samples. In the gearbox dataset, 300 samples were obtained for each fault class; there were 1800 samples under each working condition. In the bearing dataset, 200 samples were obtained for each fault class, including a total of 800 samples under each working condition. In each diagnostic task, 50% of the samples were randomly selected as the training set and the remaining 50% as the test set. Because the data under each working condition was used as a source, in the experiment, a data source was randomly selected as the source domain in cross-domain fault diagnosis, and the remaining data sources were selected in turn as the target domain to be diagnosed. In this study, a total of 12 diagnostic tasks were set, and the detailed information is shown in For a fair comparison, all comparison methods used the same network parameters as the proposed model. To avoid the effect of random factors, each trial was repeated 10 times, and the average diagnostic results were adopted.2, the DAN and D-CORAL showed a negative transfer phenomenon, and their diagnostic accuracy was lower than that of the CNN. The DANN still achieved 82.88% diagnostic accuracy, indicating that the DANN with a domain adversarial strategy can better mitigate the effect of negative transfer than the DAN and D-CORAL. The JAN and MADA consider the conditional distribution domain adaptation; therefore, their diagnostic performance is significantly improved compared with that of the global distribution domain-adaptive method. Their average diagnostic accuracy in the six diagnostic tasks reached 93.50% and 96.96%, respectively; however, when using a single-feature representation for domain adaptation, the JAN and MADA may lose some important diagnostic information, and the diagnostic performance degrades. The proposed method migrates information from the perspective of multi-feature representation and considers the decision boundary division of the target task. The joint distribution difference between the source domain and the target domain can be well compensated. Therefore, the proposed method obtained the highest average diagnostic accuracy in this diagnostic task, which is 2.19% higher than the most competitive MADA, and showed the best model stability.In this section, the diagnostic results of the proposed method and other comparative methods on different diagnostic tasks are presented and discussed. The diagnostic results of different methods from the gearbox dataset are shown in The diagnostic results of different methods on the diagnostic task of the rolling mill bearing dataset are shown in 3 and B3.To compare the diagnostic performance of the proposed method with that of several typical methods more clearly, this section presents several visualization results on diagnostic tasks A3. It can be seen that category-level distribution differences of varying degrees exist in the high-level characteristics of the CNN, DAN, and DANN. Specifically, the characteristic distributions of category 2 samples in the source and target domains are not well matched. This is because the same fault mode exists for large gear tooth breakage and small gear wear, which easily causes feature confusion. Multi-representation feature learning and duplex adversarial strategies are used to extract features from multiple perspectives and clarify the clear division of the target decision boundary, as shown in 3, the proposed method still achieved the best migration effect. As shown in First, the t-distributed stochastic neighbor embedding (t-SNE) algorithm  was appl3, as shown in According to the visual characteristics of learning, the confusion matrixes of the proposed method and the three comparison methods are further displayed. As shown in 3 and B3 with the proposed methods, CNN, DAN, and DANN. Clearly, the area under the curve (AUC) of the proposed method for each fault category was basically close to 1, and the CNN obtained the minimum AUC, followed by the DAN and DANN. This confirms that the introduction of the domain-adaptive algorithm can improve the diagnosis performance to a certain extent in the fault diagnosis task under variable conditions; however, only considering the edge distribution matching is not enough. The multi-representation feature extraction mechanism and dual adversarial strategy of the proposed method realize more comprehensive learning of diagnostic knowledge and accurate transfer, and thus the proposed method has high sensitivity and stability.Finally, the model sensitivity and stability of different methods are analyzed, as shown in This study developed a multi-representation domain adaptation network with duplex adversarial learning for rolling mill fault diagnosis under varying working conditions. The proposed method can extract comprehensive features and perform accurate knowledge transfer to realize high-performance fault diagnosis of key components of the hot rolling mill. Specifically, a multi-representation network structure was designed to extract rolling mill equipment status information from multiple perspectives. Then, the domain adversarial strategy was adopted to match the source and target domains of each pair of representations for learning the domain-invariant features from multiple representations. In addition, maximum classifier diversity was adopted to generate target features that are close to the source support, thus forming a robust decision boundary. Extensive experiments were carried out on the reducer and roll bearing fault state data set of a four-high rolling mill experimental platform. The average diagnostic rates of the proposed method on different diagnostic tasks reached 99.15% and 99.40%, which were 2.19% and 1.93% higher than the rates of the most competitive method, respectively. Furthermore, t-SNE feature visualization, the confusion matrix, and the ROC curve were applied to intuitively display the implementation results of the proposed method. The experimental results showed that the proposed duplex adaptive multi-representation domain adaptation method can effectively diagnose knowledge transfer from multiple perspectives and divide clear fault category decision boundaries. The proposed method is superior to other domain-adaptive methods in model stability and fault identification accuracy and can realize effective fault diagnosis of rolling mill equipment under variable working conditions.Although the experiments for this method showed good diagnostic accuracy achieved by the proposed method, only four different representation subnets were used for multi-view feature extraction. To gain a better understanding of feature representation from more perspectives, more network branches at different scales are needed. However, that will inevitably increase the computational complexity of the network as well as require more sample training to avoid overfitting. Therefore, further research is necessary to learn representation features from more perspectives and to design lightweight networks.