Problems such as redundancy of detection model parameters make it difficult to apply to factory embedded device applications. This paper focuses on the analysis of different existing deep learning model compression algorithms and proposes a model pruning algorithm based on geometric median filtering for structured pruning and compression of defect segmentation detection networks on the basis of structured pruning. Through experimental comparisons and optimizations, the proposed optimization algorithm can greatly reduce the network parameters and computational effort to achieve effective pruning of the defect detection algorithm for steel plate surfaces. Applying defect detection segmentation algorithms to real industrial production scenarios, hardware resources are a challenge that must be faced . ComplexModel pruning is a fast and effective way to compress neural networks by cutting out unimportant neurons or filters to obtain a small network model with small storage capacity and fast inference. Model pruning can inherit the weights of the network before pruning, so the model can be pruned to achieve better optimization results.Model pruning is a fast and effective way to compress neural networks by cutting out unimportant neurons or filters to obtain a network model with small storage capacity and fast inference. Model pruning inherits the weights of the network before pruning, so model pruning allows for better mobile deployment and better optimization.For real-time applications such as surface EMG signal processing , gesture1) A model pruning method based on improved geometric median filter pruning is proposed on the basis of structured pruning.2) The pruning method and pruning process are improved by performing model acceleration and fine-tuning in the structured pruning process, and determining whether the pruning end condition is satisfied by the evaluation function to improve the pruning compression efficiency.3) After experimental comparison, the improved geometric median filter model-based pruning method proposed in this paper outperforms other classical pruning methods. And the pruning algorithm has better detection performance and pruning efficiency in steel plate surface defect segmentation detection.The key contributions of this work are:The rest of this paper is organized as follows: In recent years, in order to perform more complex information processing tasks, deep learning-based neural network models have become deeper and deeper, also making them increasingly computationally intensive, making it difficult to deploy neural networks on devices with scarce computational resources or with strict latency requirements . As a reDenseNet  is a denThe above-mentioned deep learning-based model compression methods still have problems such as requiring large hardware resources for acceleration, high redundancy, the stability and robustness of the network after model compression is difficult to be guaranteed in complex environments, and the network model has insufficient self-adaptability.There are two main types of model pruning methods: unstructured pruning and structured pruning. Unstructured pruning prunes the neuron or connection weights, which means that some non-0 elements in the network calculation are set to 0, or the dense connections of the network are turned into sparse connections, turning the original dense matrix operation into a sparse matrix operation, as shown in Structured pruning is a type of pruning at the filter level, which focuses on pruning the filters with smaller contributions in each layer of the network. When the filter van value (the filter's impact factor) is less than a set range, the network is structured to prune redundant filters according to the van value, as shown in Since the convolution kernel obtained after pruning is sparse, and most GPUs today do not provide additional acceleration for sparse matrix operations, this results in a pruned network that is not accelerated in any way compared to the original network, but may be slower.Therefore, structured pruning is now a more general approach, and is relatively more efficient than unstructured pruning methods. For the use of the pruned network does not require the support of specific hardware platforms, computational libraries, effectively avoiding the drawbacks of unstructured pruning and enabling direct deployment on the mainstream deep learning frameworks nowadays .Model-structured pruning requires a criterion to select the filter to be pruned, i.e. the filter’s magnitude value. The most common pruning criterion is that the filter's parametric value is compared to some threshold value and if it is below the threshold, the filter is set to zero, i.e., the filter is pruned and pruned.Unlike the previous methods, geometric median filter-based pruning compresses the convolutional neural network model by removing redundant filters. Geometric median filtering works by calculating the geometric median of the filters within the same layer and, depending on the properties of the geometric median, filters near the geometric median can be represented by the remaining filters. Therefore, pruning the geometric median filter does not have a substantial negative impact on the model performance.In d-dimensional space, given any set of n points: In which, = {1, …, n}; def means that the The geometric median is a classical robust estimator of data centeredness in Euclidean space and is used when pruning the model to obtain common information about all filters within a single layer i as the geometric median for that layer In which, The core idea of geometric median filtering is that if there are filters within layer i that are close to geometric median In The geometric median is a classical robust estimator of data-centricity in Euclidean space. This shows that the information of the selected filter The pruning flow chart based on geometric median structured pruning is shown in The structured pruning process in this paper includes updating the mask, updating the indirect sparsity and updating the module. After the pruning process, the model is accelerated and refined to optimize the model. Finally, an evaluation score is calculated to determine whether the end condition is met. If the end condition is met, the pruned and compressed model is output; if not, the pruning process continues.Geometric median filtering algorithms can effectively improve the compression rate of neural networks and reduce detection model redundancy. The pruned detection model can be deployed to portable devices for faster processing .In this paper, a model pruning algorithm based on geometric median filtering is used to compress the steel plate surface defect detection network and implement a model pruning defect segmentation detection algorithm based on geometric median filtering to reduce the number of parameters and computational effort of the detection model.The Severstal dataset was released open source on the competition platform Kaggle. The Severstal dataset contains 12,568 images from the training set and 1,801 images from the test set. There are 5,902 defect-free images and 6,666 defective images in the training set. The number of defective and non-defective images in the dataset is roughly equal, and most of the images have no defects or contain only one type of defect .All images in the Severstal dataset have a vertical and horizontal resolution of 256 and 1,600 respectively. There are four types of steel surface defects in the Severstal dataset, as shown in The Severstal dataset contains a large variation in morphology between different defects on the surface of steel plates, both large defects such as scratches and scrapes, and very small defects such as pits and edge cracks.The extremely large span of defect scales places high demands on the defect detection segmentation algorithm: it has to focus on the details to achieve fine segmentation; and it has to focus on the global picture and have sufficient sensory field for large scale defects. These factors make feature extraction and detection segmentation of the network difficult and lead to the need for pruning and compression of the defect detection model.The algorithm research and network training in this paper were conducted on a laboratory server. The specific computer systems used and the configuration of the experimental environment are shown in This paper uses relevant open source libraries and toolkits to implement the overall algorithmic procedure based on the good ecology and scalability of the Python language and the open source framework PyTorch .These open source tools greatly save the development time of the defect detection and segmentation procedure in this paper, thus allowing more time and effort to be devoted to the research, improvement and experimentation of the structured pruning algorithm.In order to verify the practical effectiveness of the proposed defect segmentation algorithm based on geometric median filter pruning, experiments with different pruning rates were conducted on different models under the same conditions to test the effect of different pruning rates on the accuracy of the models.Since the main network layer of the pruned model is the convolutional layer, this paper only detects pruning on the convolutional layer of the detection model, and does not perform pruning experiments on the fully connected layers, Batchnorm layers, etc.The input size of the model only affects the computational volume of the model and does not affect the number of model parameters. Therefore, the input size was set to  for the model pruning experiments, i.e., the simulated input image size was 64 × 64 for the 3-channel image.The ResNet50 model has good performance in image recognition and localization tasks . The ResThe FPN-ResNeSt50 model is an improved fusion of the FPN (Feature pyramid networks) and the ResNeSt50 model , with poIn this paper, ResNet50, ResNeXt50 and FPN-ResNeSt50 are used as the detection models for defects on the surface of steel plates, and pruning experiments and validation are performed on them.The effect of different pruning rates on the ResNet50 model is shown in The effect of different pruning rates on the ResNeXt50 (32 × 4d) model is shown in However, the structured pruning effect of the model was not evident at smaller pruning rates in the early stages due to the ResNeXt50 (32 × 4d) model having a 32-component group convolution, resulting in a smaller pruning rate.The effects of different pruning rates on the FPN-ResNeSt50 model are shown in Comparing The model pruning algorithm based on geometric median filtering prunes and compresses the steel plate surface defect segmentation model based on depth feature fusion, and experiments with different pruning rates were conducted on it under the same conditions to test the effect of different pruning rates on the accuracy of the FPN-ResNeSt50 model, and the detection results are shown in At a pruning rate of 40%, the defect detection accuracy starts to gradually decline, so at a pruning rate greater than 40%, it will lead to the loss of important parameters of the model, resulting in a serious decline in accuracy. In contrast, at a pruning rate of 10%–30%, the model accuracy is able to maintain a low loss of accuracy.The test results show that when the pruning rate is small, pruning brings regularization to the network and enhances the generalization performance of the network; when the pruning rate is large, the characterization ability of the network is severely damaged and the performance of the model decreases significantly.In order to solve the problems of large number of model parameters and difficulty in applying the model to actual plant equipment, this paper investigates the defect segmentation detection algorithm based on geometric median filter pruning. Based on the structured pruning, a model pruning algorithm based on geometric median filtering is proposed to prune and compress the defect segmentation detection network, which greatly reduces the network parameters and computational effort and improves the generalization ability of the model. Through experimental comparisons and optimizations, the detection accuracy of steel surface defects is improved. Meanwhile, the parameters and computation of the detection model are reduced. The pruning and compression algorithm proposed in this paper has good prospects for application in the segmentation and detection of defects on steel plate surfaces. Good pruning algorithms can be applied to a variety of factory embedded or portable mobile devices and can meet the demand for real-time scene detection. In the future, there is still a long way to go in model pruning and compression research.


This complex chemistry entails co-ordination of activity across the active sites, allosteric activation of the GATase domain to modulate Gln hydrolysis and channeling of ammonia from the GATase to the acceptor active site. Functional GMPS dimers associate through the dimerization domain. The crystal structure of the Gln-bound complex of Plasmodium falciparum GMPS (PfGMPS) for the first time revealed large-scale domain rotation to be associated with catalysis and leading to the juxtaposition of two otherwise spatially distal cysteinyl (C113/C337) residues. In this manuscript, we report on an unusual structural variation in the crystal structure of the C89A/C113A PfGMPS double mutant, wherein a larger degree of domain rotation has led to the dissociation of the dimeric structure. Furthermore, we report a hitherto overlooked signature motif tightly related to catalysis.Glutamine amidotransferases, enzymes that transfer nitrogen from Gln to various cellular metabolites, are modular, with the amidotransferase (GATase) domain hydrolyzing Gln, generating ammonia and the acceptor domain catalyzing the addition of nitrogen onto its cognate substrate. GMP synthetase (GMPS), an enzyme in the de novo purine nucleotide biosynthetic pathway, is a glutamine amidotransferase that catalyzes the synthesis of GMP from XMP. The reaction involves activation of XMP though adenylation by ATP in the ATP pyrophosphatase (ATPPase) active site, followed by channeling and attack of NH Trypanosoma brucei [Cryptococcus neoformans has been reported to be essential for virulence since, in its absence, the yeast cells become guanine auxotroph, leading to compromise in the production of key virulence factors [Clostridioides difficile strain 630 led to cell death in minimal growth conditions, but not in a rich medium. Importantly, the capacity of a guaA mutant to colonize the mouse gut was significantly reduced. Together, these results demonstrate the importance of de novo GMP biosynthesis in C. difficile during infection [Purine metabolites are essential players in cellular processes and constitute one of the most abundant classes of molecules within mammalian cells. Besides being essential for DNA and RNA synthesis, they play a crucial role in providing cellular energy and intracellular signaling . The syna brucei . For GMPa brucei , while t factors . Finallynfection .Plasmodium falciparum, PfGMPS, and the human homolog, thereby establishing these enzymes as attractive drug targets. An immediate example of these differences is that while the human enzyme has been reported to be a functional monomer [GMP synthetase (EC 6.3.5.2) is a glutamine amidotransferase that catalyzes the final step of de novo GMP biosynthesis, whereby it converts xanthosine 5′-monophosphate (XMP) to guanosine 5′-monophosphate (GMP). GMPS is made up of two domains, one being a glutaminase (GATase) domain that catalyzes the hydrolysis of the amino acid glutamine to glutamate and ammonia. The generated ammonia is used by the ATP pyrophosphatase (ATPPase) domain to catalyze the amination of XMP to form the end product, GMP. It should be noted that whereas in eukaryotes and bacteria, these two domains are located on the same polypeptide chain, the catalytic reactions are carried out by two independent polypeptides in archaea. Hence, GMPS’ found in the first category are referred to as two-domain GMPS’, while those from the second category are named two-subunit GMPS’. Among the two-domain enzymes, substantial differences have been observed between the GMPS from the parasite  monomer , that ofPfGMPS to establish the relations between the structure, function and activity of these enzymes [Plasmodium falciparum GMP synthetase. Upon this dramatic large-angle rotation, Cys113 (GATase domain) and Cys377 (ATPPase domain) are brought together, engendering the formation of a disulfide bridge in the crystal, as seen in the three-dimensional structure of PfGMPS_C89A  in complex with Gln. Moreover, we confirmed that the obligate functional dimer is formed via the ATPPase domain, as also observed in GMPS structures from prokaryotic organisms: Escherichia coli ; Coxiella burnetti ; Acinetobacter baummanii .In an attempt to contribute to the detailed understanding of the mechanistic aspects of GMPS’, we have undertaken a series of in-depth studies of  enzymes ,11. We, ID 1GPM, ); ThermuHomo sapiens GMPS has been reported to be a functional monomer [PfGMPS_C89A_C113A, which sheds further light on domain rotation and its role in the functioning of the enzyme.Compared to their prokaryotic and parasitic counterparts, mammalian GMP synthetases display a 130 amino acid insert, and at least the  monomer . In ordePfGMPS_C89A was constructed using pETPfGMPS [P. falciparum GMPS, PfGMPSwt, was constructed as described earlier [P. falciparum genome database PlasmoDB (http://www.plasmodb.org (accessed on 1 January 2007)). The plasmid pETPfGMPS served as a template for the C113A mutation. Phusion DNA polymerase from Thermo Scientific was employed for the PCRs, and the resulting PCR products were digested with DpnI from New England Biolabs prior to transformation. The clones were verified by DNA sequencing.The three proteins studied herein were fused to an N-terminal 6His tag. The mutant ETPfGMPS  as a temETPfGMPS . Native  earlier  from a nPfGMPSwt was expressed from the pQE30PfGMPS construct in E. coli, and the pETPfGMPS mutant constructs were transformed into BL21-CodonPlus(DE3)-RIL. Hereafter, colonies were inoculated into 5 mL Terrific Broth containing 100 mg × mL−1 ampicillin and 34 mg × mL−1 chloramphenicol and grown overnight at 37 °C. Inoculum (0.5%) of the overnight culture was then added to 50 mL Terrific Broth supplemented with antibiotics, grown at 37 °C for 6 H and thereafter used at 0.5% to inoculate 2.8 L of Terrific Broth supplemented with antibiotics. Induction with 0.5 mM IPTG  was performed after culture growth to an OD600 of 0.6, and the growth continued for 18 H at 18 °C. Following harvesting and storage (−80 °C), the cell pellet was resuspended in 50 mM Tris-HCl, pH 7.4, 10% (v/v) glycerol, 0.1 mM DTT (dithiothreitol), and 0.1 mM PMSF (phenylmethanesulfonylfluoride), which will be referred to as Buffer A. The cell suspension was supplemented with 1 mM PMSF and sonicated on ice, and the cell lysate was centrifuged . The supernatant of this latter was then loaded onto 1 mL HisTrap HP columns  using an ÄKTA HPLC . The column was washed with Buffer A containing 1 M NaCl, followed by step gradients of 20 to 100 mM imidazole in Buffer A. Proteins were eluted with Buffer A containing 1 M imidazole. Protein fractions were pooled and concentrated employing 30 kDa cut-off Amicon Ultra-15 centrifugal filter units from EMD Millipore. A final step of size-exclusion chromatography using a HiLoad 16/600 Superdex 200 pg column  was carried out in 20 mM Tris-HCl, pH 7.4, 10% (v/v) glycerol, 1 mM EDTA and 2 mM DTT, and the aliquots of the purified protein were flash frozen and stored at −80 °C. The yield was 5 mg of pure protein from 1 liter of culture.Protein expression and purification of the three enzymes studied herein were carried out as described earlier ,14. Brie® crystallization robot from STP Labtech was employed using two protein/crystallization agent ratios ). The mutant protein PfGMPS_C89A_C113A was concentrated to 13 mg × mL−1 in 50 mM Tris-HCl pH 8.0, 100 mM NaCl buffer. Crystals grew in a mixture of 16% polyethylene glycol (PEG) 3350, 0.06 M sodium citrate (pH 2.3) and 0.04 M Bis-Tris propane (pH 9.7). Prior to data collection, the crystal was briefly soaked in a precipitant solution to which 15% ethylene glycol  had been added and flash frozen in liquid nitrogen.Crystallization condition screening was carried out at 292 K (vapor-diffusion in sitting drops), using commercially available crystallization kits. For screening, a MosquitoPfGMPSwt three-dimensional structures [X-ray diffraction data were collected on a single crystal  at a wavelength of 0.87290Å. Data processing was done using programs from the XDS package . Phases ructures  of the Cructures  program.ructures . Hereaftructures  and REFMructures /Phenix [ructures .−1. Data were collected on beamline BM29  at a wavelength of 0.9919 Å. The data were analyzed using the ATSAS program package [PfGMPS_C89A_C113A were generated from the experimental data. After averaging the 50 generated models, they were filtered to generate the final model.For SAXS data collection, the samples were concentrated to ~12 mg × mL package . Ab initPfGMPSwt, PfGMPS_C89A/Gln and PfGMPS_C89A_C113A (monomer of each structure). Hereafter, UCSF ChimeraX v1.3 [PfGMPSwt or PfGMPS_C89A/Gln onto PfGMPS_C89A_C113A, holding the ATPPase domain fixed. After superposing the ATPPase domains, the change in orientation of the GATase domains was established using ChimeraX [Two dynamic domains, corresponding to the GATase and ATPPase structural domains, were identified by the program DynDom  from pdbraX v1.3  was usedChimeraX , which gEcGMPS and HsGMPS as query sequences  [http://weblogo.threeplusone.com (accessed on 15 April 2022)) [For the 90 selected sequences used for phylogeny, sequence analyses were done using the Bioinformatics Toolkit resource at il 2022) ,33. Clusil 2022)  was emplil 2022)  employinl 2022)) .Figures with three-dimensional structure representations were rendered using PyMOL  and UCSFPfGMPS mutant in which one of the catalytic residues in the GATase domain, Cys89, was mutated to an alanine, the three-dimensional structure in complex with the substrate Gln revealed that this domain was rotated by 85° as compared to the wild-type enzyme [PfGMPS_C89A/Gln complex.Although the first structural details of a GMPS were published in 1994  and since enzyme . Here, wPfGMPS_C89A/C113A mutant enzyme was crystallized, and the crystals of this double mutant belonged to the monoclinic space group C2. The crystal structure of this mutant solved by molecular replacement was determined to a 2.8 Å resolution. The asymmetric unit contains 1 molecule, and all residues have been modeled with the exception of flexible regions with no electron density . The data collection and refinement statistics are given in The PfGMPS_C89A_C113A) is rotated by ~170° and translated ~1.5 Å compared to the native structure and when holding the ATPPase domain fixed. If compared with the structure of the single mutant, the GATase domain is rotated by ~120° and translated ~4 Å, again when holding the ATPPase domain fixed  and is, as evident from our earlier mutagenesis studies, a key residue in the AMP-XMP intermediate formation. The formation of a disulfide bridge in this position has only been observed in the PfGMPS_C89A/Gln crystal structure among the GMPS structures solved to date.Indeed, PfGMPSwt and the rotated PfGMPS_C89A indicates that upon the 85° rotation of the GATase domain, the distance between these two residues is reduced by approximately 14 Å, from ~19.5 Å to ~5.5 Å. To ensure that only the above-mentioned Trp and Cys were labeled, the second tryptophan and the 10 remaining cysteines in the enzyme were mutated to phenylalanine and alanines, respectively. Unfortunately, this resulted in a mutated enzyme that was insoluble.It should be noted that to measure the rotation of the GATase domain in solution, a FRET-based assay was conceived. For this experiment, Trp167 from the GATase domain was chosen as the FRET donor, and Cys377 from the ATPPase domain was covalently labeled with 5-(2-((2-iodoacetyl)amino)ethylamino)naphthalene-1-sulfonic acid “IAEDANS” as the FRET (Förster Resonance Energy Transfer) acceptor. Inspection of the crystal structures of native PfGMPS_C89A_C113A) can adopt a dimeric form, as concluded from the conservation of the enzyme activity, it is also able to transit to a monomer conformation at the extreme rotation of the GATase domain, as confirmed by the crystal structure and Small Angle X-ray Scattering studies.Altogether, one may conclude that whereas this double mutant  are involved in the highly conserved C-terminal signature motif KPPXTXE(F/W)X. Within this motif, Glu553 has been determined to be critical for XMP binding (mutation to Leu), whereas for Glu555 the Gln and NH4Cl dependent GMP formation by the PfGMPS_E555L mutant was only marginally lower than what was observed for the wild-type enzyme [Considering that all our recent (described above) and earlier ,11 obserhis area , indicathis area  and invoeg. T. thermophilus GMPS which is stabilized by a salt bridge, which is a frequent stabilizing factor in proteins from thermophilic species.A second part of the interface is less conserved  and rathHsGMPS, which is a monomeric enzyme, the overall fold differs from that seen for functional dimeric enzymes as eg. PfGMPS, in that HsGMPS displays an extra-domain (known as the D1 sub-domain) made up of 130 amino acid residues. This subdomain, which is present in mammalian GMPS’, is involved in substrate binding but also in dimerization, as seen in the crystal structure of HsGMPS, which turned out to be a dimer in the crystal [P. falciparum parasite , mimicsparasite . Moreovethetases  and inclBased on these considerations and in order to get a better understanding of the evolution of these multi-domain architectural proteins, for which two different quaternary structures constitute the physiologically active forms, we decided to look at the phylogenetic relationships between these enzymes.E. coli and/or H. sapiens was used in a NCBI blastp search against 42 different proteomes belonging to Bacteria, Archaea and Eukaryotes. The retrieved sequences were then clustered to maintain a representative number of sequences for each group. Nineteen sequences of model organisms were then added along with sequences of bacteria used in an earlier phylogenetic analysis [As a means to start with representative samples of these two groups, the GMPS protein sequence from analysis . DiffereH. sapiens GMPS corresponding to the dimerization domain D1) absent in the protein sequences found in ensemble B.For each tree, a clear separation occurred between a group (ensemble A) of eukaryotic proteins and a group (ensemble B) of both prokaryotic and eukaryotic proteins . EukaryoBased on our above-standing comparative analysis of the three-dimensional structures of GMPS’ available in the Protein Data Bank RCSB , we obseE. coli, 1GPM; T. thermophilus, 2YWC; P. horikoshii, 2DPL; C. burnetti, 3TQI; P. falciparum 3UOW and 4WIM; N. gonorrhea, 5TW7; M. jannashii, 6JP9; A. baumannii, 7SBC) are all present in this second group supporting this hypothesis. Ensemble B is constituted by bacteria, archae and eukaryotes. Among the eukaryotes, we find Plantae , Fungi, Parabasalidea and Alveolata (Dinoflagellata and Apicomplexa). No species possessing either type of protein was found.In ensemble B, the extra-sequence is systematically absent, suggesting that the active form of all these proteins displays a dimeric organization. Organisms for which the GMPS three-dimensional structures have been solved and which form dimers  by a laterally transferred gene. Such a GMPS-encoding lateral gene transfer (LGT) was suggested in an earlier study .PfGMPS_C89A/Gln (3 in front of the adenyl-XMP bond for the attack to form GMP [HsGMPS) or ordered but not present in the ATPPase pocket, as seen for HsGMPS /S332—M355 (Hs) in front of the helix bearing the catalytic residues, as well as the helix, which is the first structural element in the ATPPase domain (H241—Y251 (Pf)/T218—G235 (Hs), With the aim of studying whether these observations are fortuitous, we performed a bioinformatics analysis to establish the extent to which the catalytic lid-loops are conserved within the two identified phylogenetic ensembles. These studies indicated that within each ensemble, there is an important conservation of both primary and secondary structures , whereasPfGMPS_C89A_C113A indicated that a rotation of the GATase domain of approximately 170° and a ~1.5 Å translation has taken place, as compared to its position in the PfGMPSwt structure. Whether this corresponds to a physiologically relevant form seems unlikely and is in any case uncertain. As opposed hereto, the earlier observed crystal structure of the single mutant PfGMPS_C89A in complex with Gln, in which an 85° rotation had taken place upon the binding of glutamine, seems more realistic. Indeed, rotation of the GATase domain upon addition of glutamine has also been observed in a very recent study of A. fumigatus GMPS, for which the rotation was established by mass ion-mobility mass spectrometry, IM-MS [PfGMPS_C89A_C113A, the interactions that are critical for ensuring correct ammonia channeling from the GATase domain to the ATPPase domain are modified. It is tempting to think that there is a relation between this observation and the fact that upon rotation, in the native structure, Cys377 may form a disulfide bridge with Cys113, as earlier observed for the C89A mutant structure [P. falciparum, P. vivax and P. knowlesi, and that the purine nucleotide metabolic pathway constitutes the sole source of purine nucleotides to the rapidly multiplying parasite. As concerns GMPS’ from the phylogenic ensemble containing monomeric enzymes, as revealed in this study, this issue is irrelevant since this group of enzymes with their extra-domain mimic the dimer-interface and therefore do not seem to be concerned by the destabilization of the ATPPase binding pocket. Indeed, in principle, they should always display the same structure.The three-dimensional structure of the double mutant y, IM-MS . By all tructure . Hence, The lid-loop, which has been shown to be essential for all the key steps in the catalytic process, including inter-domain communication, enhancement of the substrate affinity, formation of the ammonia channel, expulsion of the reaction products from the substrate binding pocket, etc. , appears


So, this work may offer a promising platform for the ultrasensitive detection of exosomes.Covalent organic frameworks (COFs) are gaining growing interest owing to their various structures and versatility. Since their specific physical–chemical characteristics endow them great usage potentiality in biosensing, we herein have synthesized spherical COFs with regular shape and good dispersion, which are further used for the design of a novel nanoprobe by modifying Histostar on the surface of the COFs. Moreover, we have applied a nanoprobe for the fabrication of an electrochemical biosensor to detect exosomes. Since Histostar is a special polymer, conjugated with many secondary antibodies (IgG), and HRP can increase the availability of HRP at the antigenic site, the biosensor can have a strong signal amplification ability. Meanwhile, since COFs with high porosity can be loaded with a huge amount of Histostar, the sensitivity of the biosensor can be further improved. With such a design, the proposed biosensor can achieve a low exosomes detection limit of 318 particles/µL, and a wide linear detection range from 10 Covalent organic frameworks (COFs) are regarded as a rising kind of metal-free porous crystalline material, formed by the covalent connection and periodic extension of designed organic structural units ,2,3,4. DExosomes are vesicles with lipid bilayer membrane structures that are released by most cells and can circulate stably in body fluids ,28,29. EHerein, we have synthesized spherical COFs with a regular shape and good dispersion and have used the material to fabricate an electrochemical biosensor for exosome detection. In order to have a better performance of the biosensor, the COFs are designed to be loaded with a large amount of Histostar. Histostar is a special polymer that couples multiple secondary antibodies and HRP on one chain. This structure can increase the availability of HRP at the antigenic sites and the signal amplification ability. Meanwhile, since COFs can load a large number of Histostar due to their high porosity, and the exoskeleton of COFs can maintain the function of Histostar, the sensitivity can be further improved. So, the electrochemical biosensor fabricated in this work by using Histostar-functionalized COFs can be used for the sensitive detection of exosomes.2O2 included) was purchased from Sigma-Aldrich. Anti-CD63 mouse monoclonal antibody was purchased from Sangon Biotech Co., Ltd. . Anti-EpCAM rabbit monoclonal antibody was obtained from Abcam. Histostar was offered by MBL Beijing Biotech Co., Ltd. . All other reagents used were analytical grade. Ultra-pure water was purified by Millipore purification system .The 2,5-divinylterephthalaldehyde (DVA) was purchased from Jilin Chinese Academy of Sciences-Yanshen Technology Co., Ltd. . Acetonitrile (ACN), tris(hydroxymethyl)aminoethane (Tris), glacial acetic acid (HAc), 1,3,5-Tris (4-aminophenyl) benzene (TPB), and tetrahydrofuran (THF) were supplied by Aladdin. The 3,3′,5,5′-tetramethylbenzidine (TMB) substrate  and stirring for 4 h. Then, the product was obtained via centrifugation (9500 rpm) and finally dispersed in PBS .Histostar@COFs was prepared by combining 1 mL of Histostar with COFs dispersion  penicillin-streptomycin and 10% (v/v) FBS under a moist environment with 5% CO2. When the cells grew to 70%, the supernatant was discarded and the cells were transferred into serum-free medium for two days. Finally, the treated supernatant was collected for exosome isolation.The cells were cultured in 1640 medium containing 1%  the collected supernatant to remove cell fragments and larger molecules. Then, centrifuge at 11,000× g for 0.5 h, and then ultrafiltration (0.22 μm pore diameter). The exosomes were obtained by centrifuging the solution at 120,000× g for 2 h, and the obtained exosome was kept in −80 °C for further use. Nanoparticle tracking analysis (NTA) was used to measure the concentration of exosome.Centrifuge (2000× 2) of the Ab1 to introduce sulfhydryl (-SH) groups, thus allowing covalent immobilization of the thiolated antibodies to the gold electrode (the electrode was labeled as Ab1/Au). After washing with PBS, the electrode was treated with 3% BSA for 1 h in order to prevent non-specific adsorption. Next, 5 μL of exosomes at different concentrations were dropped onto the Ab1/Au and incubated at 37 °C for 2 h. After washing with PBS, 5 μL of 0.5 mg mL−1 anti-EpCAM rabbit monoclonal antibody (Ab2) was dripped onto the exosome/Ab1/Au electrode surface and incubated at 37 °C for 1 h. The prepared electrode was labeled as Ab2/exosome/Ab1/Au. After washing PBS for three times, the prepared Histostar@COFs was dripped onto the Ab2/exosome/Ab1/Au and incubated for 1 h, and the electrode was labeled as Histostar@COFs/Ab2/exosome/Ab1/Au.The gold electrodes were pretreated based on the previous literature . The ant6]3−/4− and 0.1 M KCl (pH = 7.4) with a ranging frequency from 0.1 to 105 Hz. The i-t curves were measured in a TMB substrate solution (H2O2 included) at a voltage of −0.1 V.Electrochemical impedance spectroscopy (EIS) and amperometric i-t curves were measured by a CHI660D electrochemical workstation. Gold electrodes, saturated calomel electrodes (SCE), and platinum electrodes were used as working electrodes, reference electrodes, and counter electrodes, respectively. EIS was measured in 0.1 M PBS buffer containing 5.0 mM 3−/4− is hindered by the antibodies and exosomes. When Histostar@COFs (curve e) are modified on the electrode, the Rct further increases, indicating the successful combination between the exosomes and Histostar@COFs. In addition, we have measured the current response of different modified electrodes to confirm the preparation of this biosensor. As shown in The preparation process of this biosensor has been characterized by EIS. As shown in 3 to 108 particles/µL . As shown in 6 particles/μL (To assess the selectivity of this sensor, we have selected some substances that may coexist in biological samples as interferences, such as cysteine, glucose, glutathione, and BSA (the concentration of exosomes is 10icles/μL B. The RSIn this work, we have synthesized spherical COFs with a regular shape at room temperature by the Schiff base reaction between DVA and TPB. Through the reasonable control of the reaction conditions, the obtained COFs present good crystallinity and morphology, a large specific surface area, and good stability. After preparation, the COFs have been further functionalized with Histostar and then applied for the sensitive detection of exosomes. So, this work may open up a new avenue for the application of COFs in biosensing, while it also provides a simple and effective method for exosome detection. The simple and easy preparation method of spherical COFs as well as their functionalization by Histostar may advance the development of biosensors based on metal-free porous crystalline materials.


In emotion recognition based on physiological signals, collecting enough labeled data of a single subject for training is time-consuming and expensive. The physiological signals’ individual differences and the inherent noise will significantly affect emotion recognition accuracy. To overcome the difference in subject physiological signals, we propose a joint probability domain adaptation with the bi-projection matrix algorithm (JPDA-BPM). The bi-projection matrix method fully considers the source and target domain’s different feature distributions. It can better project the source and target domains into the feature space, thereby increasing the algorithm’s performance. We propose a substructure-based joint probability domain adaptation algorithm (SSJPDA) to overcome physiological signals’ noise effect. This method can avoid the shortcomings that the domain level matching is too rough and the sample level matching is susceptible to noise. In order to verify the effectiveness of the proposed transfer learning algorithm in emotion recognition based on physiological signals, we verified it on the database for emotion analysis using physiological signals (DEAP dataset). The experimental results show that the average recognition accuracy of the proposed SSJPDA-BPM algorithm in the multimodal fusion physiological data from the DEAP dataset is 63.6 and 64.4% in valence and arousal, respectively. Compared with joint probability domain adaptation (JPDA), the performance of valence and arousal recognition accuracy increased by 17.6 and 13.4%, respectively. Emotion is a complex expression that integrates people’s psychological and physiological functions. It reflects the subjective response of individuals to external stimuli all the time . Since aNowadays, there are many emotion recognition methods, such as analyzing users’ voices , facial In the aspect of emotion recognition, electroencephalogram (EEG) has been paid more attention by researchers among many physiological signals. The analysis of EEG signals in the field of emotion recognition depends on data preprocessing, feature extraction, and feature classification . Many reHowever, a single EEG signal’s lack of feature information will lead to low emotion recognition accuracy. Some researchers use feature level fusion or signal level fusion to fuse multimodal signals to improve emotion recognition accuracy. The EEG signals are very complex due to the inherent non-stationary, non-linear, and non-Gaussian characteristics . MeanwhiIn order to solve the above problems, some researchers have introduced transfer learning to emotion recognition. Generally, the domain adaptation method usually seeks the alignment between the source and target domains. Different domain adaptation methods often use different alignments. The current alignment methods can be divided into three categories according to distribution matching schemes: domain-level, class-level, and sample-level . Pan et The sample level matching can avoid the problem that domain-level matching ignores intra-domain data structure. Class-level matching can neutralize too rough domain-level matching and too fine sample-level matching. In the matching process of the source and target domains, it is necessary to project the source and target domains into the same feature space through the projection matrix. The TCA, JDA, BDA, and JPDA all uses the single projection matrix for transfer . HoweverThis paper uses EEG, RES, PPG, and GSR signals collected from the DEAP dataset to extract features, and we concatenate four mode features to achieve multimodal feature fusion. Multimodal fusion gives full play to the advantages of each mode and makes up for its inherent limitations, improving the accuracy of emotion recognition. In order to improve the generalization ability of the model, we propose a joint probability domain adaptation method based on the substructure. Substructure-level data is aligned by discriminative joint probability maximum mean discrepancy (DJP-MMD) . SubstruThe main contributions of this study are as follows:We proposed a substructure-based joint probability domain adaptation method (SSJPDA).We proposed the bi-projection matrix (BPM) method and applied it to the SSJPDA algorithm.We validated the SSJPDA algorithm and the SSJPDA-BPM method on DEAP dataset.The rest of this paper is arranged as follows: Section “Materials and methods” introduces the SSJPDA with the BPM algorithm. Section “Results” presents the results verified on the DEAP dataset. Section “Discussion” gives the full discussion above the result.This study adopted the DEAP dataset to inspect our proposed algorithm. DEAP dataset was established by Considering that the subjects are not always in a high emotional activation state if the sliding window is used to divide the data into small segments, many segments will contain useless information . TherefoWe extract their time-domain and frequency-domain features for the peripheral physiological signals PPG, GSR, and RES. The extracted time-domain features and frequency-domain features refer to numerous previous studies . Time-dos𝒟. The target domain t𝒟. The ns and nt are the number of source domain samples and target samples, respectively. x ∈ ℝd×1 is the feature vector, and y ∈ {1, … , C} is its label in the C-class classification problem. s𝒟 and t𝒟 have the same feature space and label space, but the feature distribution is different, i.e., P ≠ P. The task of domain adaptation is to reduce the distribution difference between the source domain and the target domain, so as to predict the label yt of the target domain t𝒟 with the help of the source domain s𝒟  and X to represent all feature data and the Gaussian mixture model (GMM) to fit them. The kth component in GMM is recorded as Xk ∼ 𝒩  where zk represents mean value and σk represents covariance. Our goal is to get mean value zk and covariance σk. These GMM parameters can be obtained using the Expectation Maximum (EM) algorithm. Suppose Ks and Kt are the number of GMM components in the source domain and the target domain, respectively. Ks is determined by the Bayesian Information Criterion (BIC), and Kt is manually set according to the specific data set.We use δ ∼ After obtaining the GMM of the source domain and the target domain, we regard each component of the GMM as a substructure in the feature space, and the information of the cluster center represents the substructure. Specifically, sets and μtare the distribution of source domain and target domain, respectively. z ∈ ℝd×1 is cluster center, and δz is the Dirac function at location z . wis the probability weight associated with z, where where μi and zj in square Euclidean distance can be expressed asThe cost between zws,i is 1. The total cost of POT is ⟨π, C⟩F that is the Frobenius dot product of cost matrix C and coupling matrix π. The C ∈ ℝs×ktk represents the cost of μs and μt distribution, and the π ∈ ℝs×ktk represents the coupling between μs and μtdistribution.Therefore, the problem can be regarded as the partial optimal transmission (POT) problem, and the upper bound The goal is to obtain the optimal transmission, which can be expressed as1 is the super parameter to balance the speed and accuracy calculation.where T1sk = wt is C1, and then it can be solved by the Lagrange method. Thus, we can easily get the optimal π*.The feasible solution set of πdiagrepresents the diagonals. Once the coupling matrix where h. The source domain and target domain are mapped to the same subspace, so that the classifier trained on h(xs)can achieve good classification effect on h(xt). For example, a linear maph(x) = ATxfor the source and the target domains, where A ∈ ℝd× p, p ≤ d.The domain adaptation (DA) method attempts to find a mapping P(Xs) ≠ P(Xt) or the conditional probabilities P(Ys|Xs) ≠ P(Yt|Xt). However, the JPDA algorithm derives from the inequality assumption of joint probabilities P ≠ P. Because JPDA directly considers the difference of joint probability distribution, the performance of JPDA is better than the traditional DA method, which JPDA can improve the between-domain transferability and the between-class discrimination , and C-th, (except the c-th) columns of Fs ∈ ℝs×(C(C−1))k and Fs is fixed, and where Therefore, the objective function of JPDA can be written as follows:Ns, Nt, Ms and Mt are defined aswhere μ > 0 is a trade-off parameter and λ is a regularization parameter. H = I−1k is the centering matrix, in which k = ks + kt and 1k ∈ ℝk×k is a matrix with all elements being where Z = , then we reach the Lagrange function of Eq. 8Let where η is Lagrange multiplier, andRmax and Rmin have dimensionality k × k.A𝒥 = 0, Eq. 17 becomes a generalized eigen-decomposition problem:By setting the derivative ∇A is then formed by the p trailing eigen-vectors. A classifier can then be trained onATZs and applied to ATZt.The pseudocode of SSJPDA for classification is summarized in As described in the previous subsection, the source and target domains have different probability distributions, so applying only a single projection matrix to both domains simultaneously may lack the ability to align their probability distributions well. It is better to make the source domain and the target domain have their own projection matrix to accomplish the distribution alignment task together. On this basis, we take SSJPDA algorithm as an example to explain how to design the projection matrix of source domain and target domain, respectively, and call it SSJPDA-BPM.As and At, respectively. Therefore, the objective function of SSJPDA-BPM can be written as follows:Donate the projection matrices of the source domain and the target domain as Hs = skI − 1sk (or Ht = tkI − 1tk) is the centering matrix, in which 1sk ∈ ℝs×ksk (or 1tk ∈ ℝt×ktk) is a matrix with all elements being where Let s ηt are Lagrange multipliers, andwhere ηsA𝒥 = 0, ∇sA𝒥 = 0, and add a constraint By setting the derivative ∇As and At are then formed by the p trailing eigen-vectors of each problem. A classifier can then be trained on The pseudocode of SSJPDA-BPM for classification is summarized in The DEAP dataset contains 32 subjects, each taking turns as the target domain and the remaining 31 people as the source domain. The number of samples in the source domain is 1,240 (31 subjects × 40 samples), and the number of target domain samples is 40 (1 subject × 40 samples). After dividing the source and target domains, the EEG, GSR, PPG, and RES modes were transferred, respectively, and all the subjects’ valence and arousal dimensions were classified, respectively. In each sample, the feature dimension of EEG is 160, the feature dimension of GSR is 7, the feature dimension of PPG is 8, and the feature dimension of RES is 8. Those four modes were fused through average splicing, where the feature dimension after fusion in each sample is 183. The feature dimension of the modes remains the same dimension before and after the transfer learning. The effects of single-mode transfer and multi-mode transfer are compared to explore whether data fusion can promote the accuracy of the transfer learning algorithm. By comparing SSJPDA with other transfer learning methods and traditional machine learning methods, this paper explores whether SSJPDA can improve recognition accuracy.Hyperparameters of the model will affect the recognition accuracy. We divide the target domain with 40 samples from 1 subject into a verification set and a test set for the specific hyperparameter configuration in the algorithm, which follows similar protocols used in In Experiment 1, JPDA, JPDA (BMP), SSJPDA, and SSJPDA (BMP) algorithms were used to transfer EEG, PPG, GSR, RES, and four-mode fusion data (ALL) of subjects, respectively. In order to present the representations generated by different methods more intuitively, we use the t-SNE algorithm in multimodal data experiments to reduce the dimension and visualize the representations generated by different algorithms. According to The source domain data and target domain data settings of Experiment 2 are the same as Experiment 1, but only fusion data is used for comparison in the different algorithms. Traditional machine learning and transfer learning algorithms are used to classify valence and arousal. Because the TCA, JDA, BDA, and JPDA algorithms all use the 1-Nearest Neighbor (1NN) model in classification, we choose 1NN as the traditional machine learning model to compare the impact of the transfer learning algorithm on recognition results. In the algorithm based on non-substructure, the recognition accuracy using multimodal data is less improved than that using single-mode data. In the substructure-based algorithm, multimodal data can significantly improve recognition performance. Multimodal data in JPDA, the amount of data in the source domain and target domain are very different. The source domain consists of 31 subjects, each of which contains 40 samples. The target domain is 40 samples from one subject, of which 40 samples are also divided into a validation set composed of 10 random samples and a test set consisting of 30 random samples. Therefore, there is an enormous difference in the data volume between the source domain and the target domain. When the source and the target domain are projected to the same feature space, the probability of false matching will increase, which affects transfer recognition’s accuracy.Fusing the features of the four modes will increase the sample dimensions of the source and the target domain. The probability of sample error matching is greater than that of single-mode identification, so the performance of the non-substructure algorithm in multimodal data identification is poor. The transfer learning algorithm based on substructure can avoid error matching caused by sample dimensions increasing and data volume differences between the source and target domains. SSJPDA first generates substructures by clustering in the domain and then matches the substructures. The generation of substructures can dramatically reduce the data volume gap between the source and target domains. This can significantly reduce the probability of false matching. Therefore, the SSJPDA algorithm performs better than JPDA in single-mode emotion recognition. Without the influence of data volume, multimodal fusion data can provide more dimensional information to align the substructures of the source domain and target domains’ substructures. Therefore, using the SSJPDA algorithm to recognize multi-mode emotional data can obtain high recognition accuracy.The application of BPM in SSJPDA can significantly improve recognition performance. Because the emotional labels of subjects in the DEAP dataset are provided by the subjects themselves, this will affect the consistency of the emotional labels of different subjects. At the same time, because the emotional stimulation of the DEAP dataset depends on multimedia clips, some subjects also have the problem of weak emotional stimulation. In this experiment, the source domain contains all the test samples of 31 subjects, so there must be many abnormal samples and noise in the source domain. If no substructure is generated in the source domain and the data is projected directly, the abnormal samples and noise greatly impact the projecting matrix. Therefore, the advantages of the BPM algorithm are not reflected in JPDA. However, the substructure algorithm can cluster the noise or outliers of samples into the substructures of adjacent samples to reduce the impact of noise and outliers. When the source and target domain samples are clustered into substructures, we fully consider the distribution differences between the source and target domain substructures. Projecting the substructure through two different projecting matrices can better project the substructure of the source domain and the target domain to the feature space to improve the algorithm’s recognition performance.When the data distribution of the training set and test set is inconsistent, the traditional machine learning algorithm cannot be competent for classification. Therefore, the 1-NearestNeighbor (1NN) algorithm performs worst in this emotion recognition problem. The purpose of transfer learning is to solve the inconsistency between the data distribution of the training set and test set, that is, the inconsistency between the distribution of the source domain and target domain. Therefore, the transfer learning algorithm performs well in this emotion recognition problem. Among them, transfer component analysis (TCA) assumes that if the marginal distributions of the source domain and the target domain are close, the conditional distributions of the two domains will also be close. Therefore, TCA projects the source and target domain data together into a high-dimensional reproducing kernel Hilbert space. In this space, the data distance between the source and the target is minimized, while their respective internal attributes are preserved to the greatest extent to complete the transfer learning. The joint distribution adaptation (JDA) method simultaneously assumes that the marginal and conditional distribution of the source and target domains are different. Then the two distributions are adapted together to achieve transfer. The goal of JDA is to reduce the distance between the source and target domain’s joint probability distribution to complete the transfer learning. Balanced distribution adaptation (BDA) is improved on the basis of JDA. BDA assumes that marginal distribution adaptation and conditional distribution adaptation are not equally important. BDA adaptively adjusts the importance of marginal and conditional distribution in the distribution adaptation process according to specific data fields to complete the transfer.We proposed the SSJPDA algorithm can better measure the distribution difference between the two domains through the joint probability distribution. This is better than JDA and BDA algorithms, which directly calculate the sum of marginal probability and conditional probability distribution differences between the two domains. In the SSJPDA algorithm, the algorithm’s transferability is achieved by minimizing the difference in joint probability distribution between different domains of the same class, and the algorithm’s discriminability is achieved by maximizing the difference in joint probability distribution between different domains. At the same time, using substructures reduces the difference in data volume between the source domain and the target domain and reduces the impact of noise or outliers. After using the substructure, the SSJPDA-BPM algorithm we proposed fully considers the distribution difference between the substructure of the source domain and the target domain and projects the substructure through two different mapping matrices to improve the performance of the algorithm further. Therefore, this paper’s SSJPDA (BMP) algorithm has the highest recognition performance accuracy.Negative transfer means that the knowledge learned in the source domain has a negative effect on the learning in the target domain. When the source domain data is not similar to the target domain data, or the source domain data is similar to the target domain data, but the transfer learning method is not good enough that no transferable components are found, the negative transfer is likely to occur in those two cases . In thisHowever, if the source domain data used in the transfer learning algorithm contains a lot of noise, it is likely to negatively impact the classification model. The multiple source domain transfer method will further amplify the impact of noise. Regrettably, the four physiological signals, especially EEG signals, in this experiment contain numerous noise and abnormal samples. Therefore, the noise and abnormal samples in the source and target domains will inevitably lead to negative transfer. Therefore, in addition to SSJPDA-BPM algorithm, the classification accuracy of every algorithm in some subjects is lower than the chance level of 50% for two classes.Compared with other algorithms, SSJPDA and SSJPDA-BPM generate substructures in the source domain and target domain. These substructures can properly process the data according to the data’s similarity, which can validly reduce the negative impact of noise and abnormal samples in the source and target domains. It can effectively avoid negative transfer and improve the performance of the transfer learning algorithm. At the same time, as traditional migration learning methods, TCA, JDA, and BDA algorithms have a better effect on the transfer of feature size within a certain threshold. The information redundancy caused by too large feature vectors makes the impact of confusing information greater than that of task-related information, resulting in negative transfer . HoweverMore than that, how to transfer the components found in the source and target domain data also affects the negative transfer. In comparing whether to use the BPM algorithm, if the algorithm finds the correct transferable components, projecting the effective data to the feature space through two different projecting matrices can improve the algorithm’s performance and better avoid the negative transfer. However, suppose there is a lot of noise and outliers in the data. In that case, the BPM algorithm changes from an excellent method that avoids more negative transfers to a lousy method that leads to more negative transfers.This paper proposes SSJPDA and SSJPDA-BPM algorithms to use the labeled physiological data to recognize the emotion of new subjects. We also explored single-mode and multimodal data’s influence on emotion recognition based on physiological signals. The performance of the SSJPDA-BPM algorithm is verified by the comparative experiments of various algorithms on DEAP dataset. The results show that SSJPDA and SSJPDA-BPM algorithms can better deal with noise and outliers in data by clustering substructures. Meanwhile, these algorithms can reduce the quantity of data that better use the multi-dimensional information provided by multimodal fusion data. BPM algorithm can project the substructure through two different projecting matrices, which can better project the source domain and target domain data to the feature space, to improve the algorithm’s recognition performance. The experimental results show that the average recognition accuracy of the proposed SSJPDA-BPM algorithm in the multimodal fusion physiological data is 63.6 and 64.4% in valence and arousal, respectively.http://www.eecs.qmul.ac.uk/mmv/datasets/deap/.Publicly available datasets were analyzed in this study. This data can be found here: ZF, XH, and JH: conceptualization and supervision. BZ, YL, and HW: data curation. ZF, XH, and BZ: methodology, writing – original draft, and review and editing. BZ, HW, and ZF: validation. XH and YL: visualization. All authors contributed to the article and approved the submitted version.


Digital dermatitis (DD) is a contagious bovine foot disease causing painful lesions, lameness, and reduced animal welfare. Previous studies indicate a complex aetiology of the disease. The aim of this study was to compare DD negative and DD positive herds to identify factors associated with DD in Norwegian dairy herds by analysing data obtained in a questionnaire and data recorded in the Norwegian Dairy Herd Recording System (NDHRS). The questionnaire was e-mailed to the owners of all 380 herds recorded with DD in 2019 and to 1530 randomly selected herds with no recordings of DD. Altogether 559 dairy herds were included in the final study population, of which 113 was classified as DD positive (≥ one cow recorded with DD in NDHRS) and 446 as DD negative. When stratified by housing system, the ratio between DD positive and negative herds was 108/281 in free stalls and 5/165 in tie stalls. Multilevel logistic regression was used to model the association between potential risk factors and variables related to the detection and diagnosis of DD, and the outcome in the free-stall population. Geographical area (county) was included in the model as a random effect.In the final study population 108/113 (96%) of the DD positive herds were housed in free stalls versus 5/113 (4%) in tie stalls. The free-stall herds’ DD status was associated with purchase of cattle during the last 5 years . The DD status was also associated with claw trimming frequency , whether the claw trimming was done by a certified professional trimmer or not , cleaning of feet in the chute before trimming , and alley flooring .Digital dermatitis was far more frequent in Norwegian dairy herds housed in free stalls versus those housed in tie stalls. In the free-stall herds purchase of cattle, increasing trimming frequency, use of certified professional trimmer, cleaning of the feet in the chute, and solid flooring in the alleys were associated with increased odds of recorded DD.The online version contains supplementary material available at 10.1186/s13028-022-00635-0. Digital dermatitis (DD) is a contagious bovine foot disease causing ulcerative lesions in the heels, close to the coronary band or the skin in and around the interdigital cleft –3. The lExternal biosecurity is considered essential to control DD, and studies have shown that purchase of live animals is important for the dissemination and introduction of DD to naïve herds , 17. A DImportant risk factors for within-herd spread are housing conditions and managements factors like hygiene, herd size, grazing- and trimming routines . Relun eThe diagnostic method is important for correct recording of DD, and different procedures are introduced. The traditional method is inspection at trimming in chutes. However, the DD lesions have different stages and a dynamic appearance  and the An ongoing Norwegian DD study indicates a mild course of the disease with small lesions, low degree of lameness and relatively low prevalence in DD positive dairy herds . There aThe aim of this study was to compare DD positive (DD+) and DD negative (DD−) herds to identify factors associated with DD in Norwegian dairy herds by analysing data obtained in a questionnaire and data recorded in the Norwegian Dairy Herd Recording System (NDHRS).In this case–control study, the NDHRS was used to select cases and controls for identification of factors associated with DD, both risk factors and factors related to detection and diagnosis. In NDHRS, individual cows are diagnosed and recorded as DD− or DD+ by professional trimmers and veterinarians after trimming and examination of the feet. Their diagnoses are guided by the Nordic Claw Atlas , where DIndividual claw trimming records from 2019 were aggregated to herd level to determine the DD status of each herd. A herd’s DD status became positive when one or more cows are recorded with DD during 2019. No exclusion criteria were set, meaning all members of NDHRS were eligible for inclusion in the study. All herds recorded with DD in 2019, a total of 380, were included in the study population as cases and 1530 DD negative herds were included as controls. Controls were selected from a population of 6870 dairy herds using computer-generated random numbers.The questionnaire consisted of 35 closed questions. The questions addressed issues like housing, management including secondary control measures, biosecurity, claw health, and trimming routines. If necessary, more than one answer could be given, and with room for comments where relevant. A pilot version was tested on five different dairy farmers and their feedback was used to improve the questions. The 4th of November 2019, the project partner TINE SA, e-mailed the questionnaire in Questback to all 1910 farmers included in the study population. The estimated time for completing the questionnaire was 15 min. The questionnaire was available in Questback until the 15th of December 2019 and during this period, reminders were e-mailed once a week. The complete questionnaire, translated into English, is provided in Additional file Additional data was extracted from NDHRS by TINE SA. Herd level data retrieved from the recording system was geographical area (county in Norway), herd size, housing system, milking system, and average 305d milk yield in 2019. Records of claw trimming, infectious claw diseases (including DD), and use of artificial insemination (ai)/bull were retrieved at cow level. Individual animal records were aggregated to herd level, and included information on the frequency of claw trimming, the proportion of different infectious claw diseases , and the proportion of mating performed by bull and ai, respectively. Furthermore, records of purchased animals in the period from Jan 2015 to Dec 2019 were extracted. The number of incoming animals in each age group  and the total number of purchased animals were aggregated at herd level.Data from Questback and NDHRS was separately imported to Stata for cleaning and quality control before being merged into one dataset for further processing and analyses. All data handling and statistical analyses were performed in Stata .Distribution by DD category (DD+ and DD−) stratified on housing system  regarding housing conditions, management characteristics, trimming routines, and biosecurity is in Tables The outcome variable was the herd’s DD status retrieved from the NDHRS. If one cow in the herd was recorded with a diagnosis of DD, the herd was classified as a case (DD+).The explanatory variables were the answers in the questionnaire and data from NDHRS. When possible, open answers with comments were recoded into matching closed alternatives. For some questions with multiple answers, the number of categories was reduced by merging similar or few responses. For example, the answers “sand” and “straw” were merged with “others” in question “Describe the bedding in the cubicles/tie stalls” and the answers “4, 5 and 6 persons” were merged into one group “ ≥ 4 persons” in question “How many persons, including yourself, are working in the barn handling cattle?”.Linearity between the outcome variable and all continuous variables was examined. If no linear association were seen transformation  of the explanatory variable was performed and linearity assessed again. Thereafter continuous variables were recoded into categorical variables. Cut-offs for continuous variables were based on either equally sized groups, percentiles, or previous experience. The number of cows in the herds was according to the free stalls grouped in five approximately equally sized groups as 11.7–30.3, 30.4–39.8, 39.9–46.9, 47.0–56.4, and  ≥ 56.5, and milk yield (kg) as 4872.3–7863.5, 7863.6–8403.9, 8404–8912.5, 8912.6–9534.1, and  > 9534.2 The average milk yield of all Norwegian dairy cows in 2019 was 8119 kg .The continuous variable “Purchased cattle during the last 5 years” was categorized into seven groups: 0 animals, 1–2 animals, 3–4 animals, 5–11 animals, 12–27 animals, 28–52 animals, and  ≥ 53 animals. “Purchased cattle during the last 5 years” includes all registered animals purchased during the period Jan 2015 to Dec 2019, both dairy and beef, at all ages except calves. Calves were excluded based on assumed low risk for transferring DD between herds.The variable “Trimming frequency” was created by dividing number of trimming events registered in NDHRS by the number of registered cows for each herd. One trimming event is equal to one recorded trimming and one cow can have  > 1 trimming event registered. The variables “Certified professional trimmer trims  ≥ 90% of the cows”, “Uncertified professional trimmer trims  ≥ 30% of the cows”, and “Owner trims  ≥ 30% of the cows” were created by dividing the number of registered trimming events by certified professional trimmer, uncertified professional trimmer, and owner respectively, by the total number of trimming events.Multilevel logistic regression was used to model the association between potential risk factors identified by literature studies and experienced causality, and variables related to the detection and diagnosis of DD, and the outcome. Due to few DD positive tie-stall herds, only the free-stall herds were included in the model. Questions with more than 15% missing answers/values were excluded from further analyses. Furthermore, the variables heel horn erosion, interdigital dermatitis, interdigital hyperplasia and interdigital phlegmon were regarded as competing outcome variables and not included in the analyses. Explanatory variables like “Use of disinfectant litter in cubicles/tie stalls”, “Use of disinfectant footbaths”, “Installed claw washing system in the barn”, and “Use of soap or disinfectant in claw washing system” are often not routinely used in Norwegian dairy herds, but frequently introduced as curative and preventive measures after a DD diagnose. Consequently, these secondary control measures were excluded from the multilevel logistic regression analyses. In total, 27 selected explanatory variables (out of 49), passed this initial screening steps.Univariable associations were tested between each selected explanatory variable (one at a time) and the outcome variable (herd DD-status). This was performed using a univariable logistic regression model with a cut-off level for statistical significance of P < 0.2. The explanatory variables categorized as potential risk factors and variables related to the detection and diagnosis of DD were maintained for building the multilevel logistic regression model.Twelve risk factor variables and variables related to the detection and diagnosis of DD with P < 0.2, were offered to the initial multilevel logistic regression model. Geographical area (county) was included in the model as a random effect. Possible correlation between explanatory variables (predictors) were assessed by visual inspection of scatter plots and box plots, but no correlations were found. A manual backwards elimination procedure was conducted to define the final model. In this procedure the variables with highest P-value were eliminated in a descending order until all remaining variables were significantly associated with the outcome (P < 0.05) or were considered as potential confounders. Variables considered to be potential confounders were tested by running the model with and without the variables in question while changes in estimates were monitored. The likelihood ratio test (LRT) was used to evaluate significance of categorical variables.Biological plausible interactions between predictors retained in the final model Table  were tesIn total 685 questionnaires were completed giving a response rate of 36%. Of the 685 respondents 18.3% (126/685) respondents were excluded from further analyses due to missing claw trimmer (122) or housing system 4) recordings in NDHRS, resulting in a final study population of 559 herds. Among these herds 27.8% (108/389) of the free stalls versus 2.9% (5/170) of the tie stalls were DD positive. The distribution of replies to each question and data from NDHRS by DD category (DD+/DD−) stratified on free- and tie-stall herds is presented in Tables  recordinTwenty-six percent (145/559) of all respondents were in the county of Trøndelag, 11% 62/559) in Oppland, 11% (60/559) in Rogaland, 10% (58/559) in Hedmark, and the remaining 42% were from 13 other counties. Of the respondents with DD+ herds, 39% (44/113) were in Trøndelag, 20% (23/113) in Rogaland, and the remaining 41% were from 13 other counties. There were no DD+ respondents from Aust-Agder and Finnmark. Geographic distribution and DD status of the 559 respondents stratified on free- and tie-stall herds are shown in Additional file /559 in OThe average size of all herds included in this study was 50.0 cows (SD ± 17.7) in category DD+ and 34.2 cows (SD ± 17.6) in category DD−. The average herds size in the free-stall herds was 51.0 cows (SD ± 17.4) in category DD+ and 42.3 cows (SD ± 16.5) in category DD−, compared to 28.9 cows (SD ± 5.5) in category DD+ and 20.4 cows (SD ± 8.3) in category DD− in the tie-stall herds.Of the DD+ herds, 96% 108/113) were housed in free stalls compared to 63% 281/446) of the DD− herds. In free stalls with category DD+ automatic milking systems were installed in 89% (96/108) versus 79% (222/281) in category DD−. The mean production of milk yield per cow per year in the free stalls was 8739 kg (SD ± 1001 kg) in category DD+ and 8652 kg (SD ± 1091 kg) in category DD−. The mean production of milk yield per cow per year in the tie stalls was 7822 kg (SD ± 525 kg) in category DD+ and 7675 kg (SD ± 68 kg) in category DD−. The distribution of herds by housing conditions and management characteristics by DD category (DD+/DD−) stratified on housing system is listed in Table 08/113 we81/446 ofIn the free stalls with category DD+, 95% (103/108) of the herds used a certified professional trimmer for  ≥ 90% of the cows, 4% (4/108) used an uncertified professional trimmer for  ≥ 30% of the cows, and in no herds the owner trimmed  ≥ 30% of the cows. In free stalls with category DD−, 85% (239/281) of the herds used a certified professional trimmer for  ≥ 90% of the cows, 8% (22/281) used an uncertified professional trimmer for  ≥ 30% of the cows, and in 7% (21/281) the owner trimmed  ≥ 30% of the cows.Trimming was more frequent in free- versus tie-stall herds. Twenty percent (76/389) of the free-stall herds were trimmed  < 1/year versus 38% (65/170) of the tie-stall herds while 42% (164/389) of the free-stall herds was trimmed  ≥ 2/year versus 13% (22/170) of the tie-stall herds. In the free stalls with category DD+, trimming 2–3 times per year was most frequent in 44% (48/108) of the herds, followed by 1–2 trimmings per year in 31% (33/108) of the herds. In free stalls with category DD−, trimming 1–2 times per year was most frequent in 41% (116/281) of the herds, followed by 2–3 trimmings per year in 30% (85/281) of the herds.Cleaning of the feet in the chute before trimming, was performed in 44% (47/108) of the free-stall herds in category DD+ versus 25% (69/281) of the herds in category DD−. The distribution of trimming routines by DD category (DD+/DD−) stratified on housing system is in Table In DD+ free-stall herds, 79% (85/108) of the farmers had bought cattle during the last 5 years compared to 64% (180/281) of the farmers of DD− free-stall herds. Thirty-five percent (28/79) of the respondents in the DD+ category and 48% (79/166) in the DD− category required claw health documentation before the purchase in these herds. Only 8% (6/80) of the responding farmers with category DD+ and 7% (11/164) of the farmers with category DD− free-stall herds housed the purchased cattle in quarantine after arrival.For all herds including both free- and tie-stall herds, high missing numbers were found for questions regarding requirement of claw health documentation before purchase of cattle (30/113) and 179/446), the type of claw health documentation required (81/113) and (330/446), and if the purchased cattle were placed in quarantine or not (29/113) and (181/446) for category DD+versus DD−. The distribution of biosecurity characteristics by DD category (DD+/DD−) stratified on housing system is in Table 46, the tIn the free-stall herds there was a significant association between digital dermatitis and the use of disinfectant footbaths  for category yes), and installation of claw washing system in the barn  for category yes). The distribution of secondary control measures by DD category (DD+/DD−) in free-stall herds are listed in Table The distribution of infectious claw diseases by DD category (DD+/DD−) in free-stall herds are listed in Table Results from the final model for the free-stall herd population are shown in Table The main purpose of this case–control study was to identify factors associated with DD in Norwegian dairy herds by analysing questionnaire responses from 559 farmers and data from the NDHRS. We found that 96% of the DD positive herds were housed in free stalls versus 4% in tie stalls. In the free-stall herds purchase of cattle during the last 5 years, increased claw trimming frequency, use of certified professional trimmer, cleaning of the feet in the chute, and solid versus slatted flooring in the alleys were associated with higher OR of DD. Based on experience and previous studies, we will discuss our results from different aspects to cover possible causalities.Seventy percent of the producers had free-stall barns and out of these, 27.8% were DD positive. Of the 170 producers with tie-stall barns, only 2.9% were DD positive. This agrees with Cramer et al.  who showTreponema spp. in slurry from herds with DD, and the disease may be transferred by purchased cows with manure on their skin. It is also documented that these treponemes can invade wounds like sole ulcers, hock, udder, and teat injuries [Purchase of cattle in the free-stall herds was associated with increased odds for DD. The risk for positive DD status increased significantly with purchase of 12–27 animals OR = 2.30), 28 to 52 animals (OR = 4.34), and  ≥ 53 animals OR = 5.39) during the last 5 years compared to no purchase. This agrees with several studies, which have shown that introduction of new animals is associated with higher odds of DD , 21. Van during t0, 28 to injuries . The impinjuries . This syClaw trimming frequency in the free-stall herds was associated with DD, trimming  < 1 time per year (OR = 0.41) and trimming  ≥ 3 times per year (OR = 4.09). The relationship between increased trimming frequency and DD might imply a risk for spreading the disease . At the Treponema spp. if not washed and disinfected properly between different herds. Sullivan et al. [Treponema spp. can survive on rubber gloves for 3 days [Use of a certified professional trimmer for more than 90% of the cows versus not in the present free-stall herds was associated with higher odds for a positive DD status (OR = 3.98). The reason for higher odds ratio when using a certified professional trimmer may be that trimmers visiting a lot of herds could be a potential risk factor for introducing DD to naïve herds. During claw trimming session, the chute, grinder, hoof knife, and hoof tester are in close contact with DD lesions and manure, making them potential vectors for transmission of n et al.  found thr 3 days . AnotherFree-stall herds where feet were cleaned before trimming had higher odds for DD compared to free-stall herds where no cleaning was performed OR = 1.98). The most probable reason for this is that cleaning makes it easier to detect small DD lesions compared to when the feet are covered with manure. To characterize the different DD lesions, a M-stage scoring system is developed, where clinical stages of DD are scored based on size and whether the lesions are active, chronic, or healed .98. The  found thIn the free-stall herds, solid versus slatted flooring in the alleys was associated with higher odds for a positive DD status (OR = 2.36). This finding partly agrees with a previous Norwegian study by Fjeldaas et al.  who founIn the present study, considering factors associated with DD in Norwegian dairy herds we chose the professional trimmers recordings in NDHRS for assessment of the DD status. Their NDHRS recordings are based on scored lesions on the cows’ feet at routine trimming in a chute, and are the “gold standard” for clinical diagnosis of DD. This is supported by ICARs global standard for livestock data, Section 7—Guidelines for health, female fertility, udder health, claw health traits and lameness in bovine . HoweverEven though Norwegian legislation require that all dairy cows are housed in free stalls by 2034 , we chosThe questionnaire was sent to the farmer/owner of the herds. In Norway with relatively small dairy herds, the farmers usually know their herds and individual cows well and are competent to answer the questions correctly. No difference in ratio between DD positive and DD negative herds in the total population receiving the questionnaire, 380/1530 = 0.25 versus 113/446 = 0.25 among the respondents, indicates no bias regarding who answered the questions even though one could suspect that farmers with DD+ herds might be more interested in answering the questionnaire versus those with DD− herds. Stratified random sampling of controls on housing system could probably have recruited more DD negative free-stall herds.A possible weakness of the present study is the inclusion of all three categories of claw trimmers: certified professional trimmers, uncertified professional trimmers, and farmers. One could argue that only herds trimmed by certified professional trimmers should have been included, because they are better educated and trained in detection and registration of DD. On the other hand, it was interesting to analyse if they registered differently compared to the other two categories. As described above, uncertified professional trimmers and farmers are not required to register claw diseases, which may have underestimated the actual DD status in the study population.Digital dermatitis was far more frequent in Norwegian dairy herds housed in free stalls versus those housed in tie stalls. Factors associated with increased odds of digital dermatitis within free-stall herds were purchase of cattle, increasing claw trimming frequency, use of certified professional trimmer, cleaning of the feet in the chute before trimming, and solid versus slatted flooring in the alleys.Additional file 1. English version of farmer questionnaire in Questback used to obtain data in this Norwegian case-control study of bovine digital dermatitis.Additional file 2. Geographic distribution of respondents (n) by DD category (DD+/DD−) for each county in two separate diagrams, on free-stall herds (n=389) and tie-stall herds (n=170). N=559.