Cryo-EM structures of tetrameric rat ALDH1L1 revealed the architecture and functional domain interactions of this complex enzyme. Highly mobile N-terminal domains, which remove formyl from 10-formyltetrahydrofolate, undergo multiple transient inter-domain interactions. The C-terminal aldehyde dehydrogenase domains, which convert formyl to CO2, form unusually large interfaces with the intermediate domains, homologs of acyl/peptidyl carrier proteins (A/PCPs), which transfer the formyl group between the catalytic domains. The 4′-phosphopantetheine arm of the intermediate domain is fully extended and reaches deep into the catalytic pocket of the C-terminal domain. Remarkably, the tetrameric state of ALDH1L1 is indispensable for catalysis because the intermediate domain transfers formyl between the catalytic domains of different protomers. These findings emphasize the versatility of A/PCPs in complex, highly dynamic enzymatic systems.Putative tumor suppressor ALDH1L1, the product of natural fusion of three unrelated genes, regulates folate metabolism by catalyzing NADP Tsybovsky et. al. report cryo-EM structures of tetrameric rat ALDH1L1 elucidating its architecture and domain interactions important for its functions. These findings emphasize the versatility of acyl/peptidyl carrier proteins in complex, highly dynamic enzymatic systems. The importance of this regulation is emphasized by high abundance of the enzyme in the liver, the main organ of folate metabolism, as well as by tight control of the ALDH1L1 expression during embryonic development and by the role of the protein as a pan-astrocyte marker3. The regulatory role of ALDH1L1 is linked to its catalytic reaction, the NADP+-dependent conversion of 10-formyltetrahydrofolate (10-fTHF) to tetrahydrofolate (THF) and CO2, which irreversibly removes one-carbon groups from the folate pool, thus diminishing the anabolic capacity4 , an enzyme of folate metabolism, regulates the availability of one-carbon groups for folate-dependent biochemical reactionsty4 Fig. . It has ALDH1L1 originated from a natural fusion of three unrelated genes, the phenomenon defining the structural organization of the ALDH1L1 enzyme1. The protein exists as a homotetramer, with each 902 amino acid-long protomer organized in three distinct functional domains  carries the folate-binding site and has sequence and structural similarity to methionyl-tRNAMet-formyltransferase (FMT), the enzyme involved in translation initiation in mitochondria21. FMT formylates the initiator Met-tRNAMet by transferring the formyl group from 10-fTHF, thus using the same substrate as ALDH1L124. The C-terminal domain  belongs to the family of aldehyde dehydrogenases (ALDHs), the group of enzymes catalyzing the conversion of a large variety of aldehydes to corresponding acids using NAD+ or NADP+ as the electron acceptor25. The Ct domain shares up to 50% sequence similarity with members of this family and has a typical ALDH fold, which includes NAD(P)+-binding, catalytic, and oligomerization sub-domains27. The Ct domain contains all critical catalytic residues conserved in ALDHs, including Cys707, which plays the role of the catalytic center nucleophile28. Accordingly, the Ct domain catalyzes the conversion of short-chain aldehydes to corresponding acids in vitro, but it is not known whether ALDH1L1 participates in aldehyde oxidation in vivo1. Finally, the intermediate domain  linking the Nt and Ct domains is a homolog of a group of small, structurally closely related carrier proteins involved in fatty acid, polyketide, and non-ribosomal peptide biosynthesis29. A characteristic feature of these acyl/peptidyl carrier proteins (A/PCPs) is the 4′-phosphopantetheine prosthetic group (4′-PP) covalently attached to a serine residue through a phosphoester bond31. This prosthetic group serves as a flexible arm enabling the transfer of building blocks between subunits of multi-enzyme complexes31.ins Fig. . The N-t41. Overall, in the ALDH1L1 catalysis, the 4′-PP arm of the Int domain transfers the formyl group cleaved from 10-fTHF in the folate-binding Nt domain to the Ct domain, where it is oxidized to carbon dioxide1 , the structure of the full-length enzyme has not been resolved so far. Here, we report the structures of ligand-free and NADP+-bound full-length ALDH1L1 at resolutions of 3.7 Å and 2.9 Å, respectively, obtained by cryo-electron microscopy (cryo-EM). This study provides insights into the ALDH1L1 structure and function by (i) demonstrating high mobility of the Nt domains, which form transient complexes with other structural units; (ii) describing the unusual mode of interaction between the Int and Ct domains, with a large contact interface atypical for A/PCPs, and (iii) revealing unique pairing of the Int and Ct domains, which requires the tetrameric organization for catalysis.Functional studies of ALDH1L1, its numerous mutants and engineered constructs together with structural and functional characterization of the individual domains provided insight into the enzyme catalytic machineryde1 Fig. . To execRattus norvegicus ALDH1L1 produced in insect cells using a baculovirus expression system) as well as of its individually expressed Ct domain, which forms the rigid tetrameric core of the full-length enzyme. The Ct core was clearly visible in the 2D class averages  of the full-length ligand-free protein  forming a loose bundle, with another short helix (III) bridging helices II and IV. Helices I and II are connected by a long 19-residue linker that forms a loop and a helical turn. Serine 354, the site of the 4′-PP group attachment, is located in the beginning of helix II.Both the Int and aps Fig. , which pity Fig. . As expeCPs Fig. 42. AccorCt domain contains the catalytic, NADP+-binding and oligomerization sub-domains  between the protomer of the Ct domain solved by cryo-EM and the protomer of the corresponding X-ray structure (PDB 2o2p or 2o2q27) was 0.46 Å and 0.75 Å, respectively, for ligand-free and NADP+-bound proteins. The r.m.s.d. between the entire tetramers was slightly higher , suggesting slight differences in protomer positions in full-length ALDH1L1, which may be a consequence of the interactions between the Ct and Int domains. It is also possible that the conformation of the protein in the crystal structures was affected by crystal packing.Each ins Fig. . The dee+-bound ALDH1L1 were highly similar (r.m.s.d. = 0.86 Å)  Fig. . A small+-bound ALDH1L1, each Int domain formed contacts primarily with one of the four Ct domains  and C/D (dimer 2). Therefore, each Int domain of ALDH1L1 is paired to a Ct domain of the opposite dimer . The total contact area between the Int domain (excluding the 4′-PP prosthetic group) and the two Ct protomers is 653 Å2. Calculation of electrostatic potentials revealed that the surface of the Int domain is mostly negatively charged, including the contact interface  and the amide of Gln693 (2.8 Å), the guanidinium group of Arg359 (helix II) and the side-chain oxygen of Asn745 (3.2 Å), as well as a side-chain oxygen of Glu366 (helix II) and the guanidinium group of Arg742 (3.3 Å). Alignment of the cryo-EM structures of ALDH1L1 and available X-ray structures of the Ct domain revealed no significant alterations in positions of amino acid side chains in the regions that contact the Int domain. Similarly, structural superposition of the Int domain with the NMR structure of individual human Int without the 4′-PP prosthetic group (PDB 2cq8) revealed no major differences (r.m.s.d. = 0.83 Å). Therefore, complex formation between the Int and Ct domains relies on the shape and charge complementarity.We found that the Int and ace Fig. . The basIII Fig. . This seace Fig. . This isCt domain and making multiple contacts with residues forming the tunnel  and m/z = 730.0 ([M + 3H]3+) that corresponded to a tryptic digestion product of nominal mass 2187 Da, identical to the theoretical mass of 4′-PP-crosslinked peptides 350S-R359 and 704G-R712  or meta-phosphoric (HPO3) acid 45. The presence of a series of b- and y-ions unambiguously confirmed the amino acid sequences and sites of the 4′-PP covalent modification in the crosslinked peptides. While it was not possible to quantify the prevalence of the crosslinked peptide, its presence confirmed that the disulfide bond formed between the 4′-PP prosthetic group and Cys707 in a population of ALDH1L1 molecules. The inclusion of the disulfide bond in the molecular models improved the fit of the two sulfur atoms to the cryo-EM density.Curiously, we found that the sulfur atom of the catalytic nucleophile Cys707 in the ALDH active center was positioned closely to the sulfur atom of the 4′-PP, and the cryo-EM density between the two atoms appeared continuous Fig. . This su712 Fig. . The sub712 Fig. . The mosCt domains, we designed a shorter version of ALDH1L1, containing only the Int and Ct domains (termed int-Ct), and expressed it in E. coli. It was shown previously that ALDH1L1 produced in bacteria lacks the 4′-PP prosthetic group29, and therefore only protein–protein contacts could contribute to the interactions between the Int and Ct domains in int-Ct. NS-EM of int-Ct resolved the Ct core and, in some cases, Int domains adjacent to it  Int domains, we performed local 3D classification within a mask encompassing each Int domain and calculated the total number of particles that contributed to empty versus occupied classes  enzyme. The partial disulfide bond between the 4′-PP arm and Cys707 could serve to support this conformation.During the initial step of the ALDH1L1 catalysis, 4′-PP arms must be accessible to interact with the Nt domain of ALDH1L1, we performed refinement of the cryo-EM dataset of ligand-free ALDH1L1 without enforcing symmetry. The resulting 4.4-Å-resolution map was very similar to its symmetrical counterpart, except that weak density for a single Nt domain became visible  indicated that this interaction did not induce large structural rearrangements in the protein core. A subsequent local 3D classification of the same dataset isolated a conformation with two clearly visible Nt domains in the same orientation, corresponding to 5517 particles. 3D refinement of this smaller dataset resulted in a 6.8-Å map with strong density for the two Nt domains.  of the Ct domain that forms one side of the cleft accommodating the adenine moiety of NADP+ 27. Notably, although most α-helices of the Ct core were well resolved in the map, there was no cryo-EM density for this key helix.In an attempt to resolve the ble Fig. . This Ntns. Fig. . The midNt domain was found to straddle the Ct core between two Int domains, with both ends of Nt in contact with the ALDH1L1 core , the Int (carrier) domains were resolved docked at the substrate entrance tunnels of the Ct core, but the incomplete occupancy of these anchored carriers indicates that they operate as highly mobile units. The complex between the Nt and Int domains was not detected by cryo-EM or NS-EM, which suggests it is transient in nature, a common phenomenon for interactions involving A/PCPs49. Overall, our findings provide strong structural support for the catalytic mechanism wherein (i) the Nt domain transfers the formyl group from 10-fTHF to the 4′-PP arm of the Int domain and (ii) the Int domain delivers the formyl to the Ct domain where (iii) the formyl is oxidized to CO229  to transfer the reaction intermediate between these centers. This carrier protein domain is highly similar to A/PCPs employed in the biosynthesis of fatty acids, non-ribosomal peptides and polyketides, reactions performed by large and complex multi-enzymatic molecular machines50. Such homotetramer is organized as a dimer of homodimers formed by protomers A/B and C/D as schematically presented in Fig. Ct domains of protomers A and B. This pairing scheme can only be realized in a tetrameric enzyme. Furthermore, we found that the length and composition of the linker connecting the Int and Ct domains are preserved in cytosolic (ALDH1L1) and mitochondrial (ALDH1L2) enzymes as well as across multiple species, suggesting that this intricate domain pairing is a universal characteristic of 10-formyltetrahydrofolate dehydrogenases. Of note, although multiple studies analyzed the oligomeric state of ALDH proteins , the physiological significance of oligomerization is unclear for most of these enzymes. One possible exception is tetrameric ALDH from Thermus thermophilus, which has a ~30 amino-acid-long C-terminal extension that interacts with the N-terminal region of a protomer from a different homodimer52. Other ALDHs, including fatty aldehyde dehydrogenase (FALDH) and ALDH7A1, also feature short C-terminal extensions, but they interact with the protomer within the same homodimer54. In contrast, ALDH1L1 is the example of an ALDH with two additional domains spanning 400 amino acids at the N-terminus of the enzyme, with the tetrameric state being a prerequisite for its complex function.Most aldehyde dehydrogenases are known to exist as either homodimers, typified by the members of the ALDH3 family, or homotetramers, represented mainly by ALDH1/2 families, which also include ALDH1L149. Accordingly, the contact area between the A/PCP and the partner protein is usually small, with most interacting residues confined to helix II of the carrier protein and, to a lesser extent, helix III and the part of the linker between helices I and II that is close to helix II57. The small size of the contact interface often requires cross-linking to enable structural investigation59. In contrast, the relatively large contact interface between the Int and Ct domains of ALDH1L1 was resolved in its native, non-cross-linked form 26, suggesting that the proposed effect is likely small. In the second cryo-EM map, the N-terminal moiety of the hydrolase domain rested on the linker connecting the carrier and ALDH domains of a different protomer  grown as monolayer  at 27 °C were infected with a high titer recombinant baculovirus stock produced as previously described69. Five days after infection, the culture medium was collected, and detached cells were removed by centrifugation . To purify ALDH1L1, the cell culture medium was applied to a column containing 5-formyl-THF-Sepharose affinity resin equilibrated with 10 mM Tris-HCl buffer, pH 7.4, containing 10 mM 2-ME and 1 mM NaN3 (buffer A). The column was washed with buffer A and then with the same buffer containing 1.0 M KCl; the enzyme was eluted with buffer A containing 1.0 M KCl and 20 mM folic acid. The eluate was concentrated and excess KCl removed using a spin concentrator. Additional purification was then carried out using FPLC/Mono-Q column (GE) chromatography with a linear KCl gradient (0–0.5 M in buffer A) and Sephacryl S-300 (GE) size-exclusion chromatography in buffer A with 0.2 M NaCl. The individual Ct domain and Int-Ct protein were expressed as 6xHis tagged constructs in E. coli (Invitrogen) from pRSET vectors. Protein expression was carried out at 22 °C, and the soluble cell fraction was separated by sonication and centrifugation. The proteins were purified using Ni-NTA or Co-NTA agarose (Qiagen) using a 5–20 mM imidazole gradient to remove impurities followed by elution with 100 mM imidazole in buffer A supplemented with 100 mM KCl. Additional purification was done by size-exclusion chromatography on Sephacryl S-300. The purity of all proteins was confirmed by SDS-PAGE with Coomassie staining. Purified full-length ALDH1L1 was tested for the 10-formylTHF dehydrogenase activity as we previously described33. Ct domain and Int-Ct protein were tested for the aldehyde dehydrogenase activity using propanal as the substrate and NADP+ as the cofactor essentially as we described26. After purification, all protein preparations used in the present study had specific activities close to previously reported values33 and were stored at −80 °C in the presence of 10 mM 2-ME and 20% glycerol.Full-length rat ALDH1L1 was expressed following a previously developed protocol2O with 0.1% (v/v) formic acid (A) and 2% acetonitrile with 0.1% (v/v) formic acid (B) to 100% B) developed over 20 min. Separation was achieved at a flow rate of 0.3 mL/min using an Agilent Technology 1100 Series HPLC system. The eluent was directed into an LTQ Velos linear trap quadropole mass spectrometer (Thermo Scientific) equipped with an electrospray ionization source operated in positive ion mode. Parameter settings of the mass spectrometer for peptide detection were as follows: activation type, collision-induced dissociation; normalized collision energy, 35 kV; capillary temperature, 370 °C; source voltage, 5 kV; capillary voltage, 43 V; tube lens, 105 V. MS spectra were collected over a 200–2000 m/z range. The raw MS data were analyzed using Qual Browser for Thermo Xcalibur version 2.1.In total, 30 µg of ALDH1L1 (50 µL of protein solution) was combined with 25 µL of 9 M urea and 10 µL of acetonitrile and incubated for 10 min at 42 °C. This mixture was diluted with 250 µL of 100 mM ammonium bicarbonate prior to the addition of 5 µg of sequencing grade trypsin (Promega). The proteolytic digestion was carried out for 4 h at 37 °C. The resulting peptides were loaded onto a reverse-phase C4 (2.1 mm × 50 mm) column (Thermo Scientific). Peptides were resolved and eluted with a gradient of acetonitrile in water  equipped with a LaB6 filament operated at 200 kV and a 2k × 2k FEI Eagle CCD camera. The nominal magnification was 100,000x, which corresponded to a pixel size of 2.2 Å. EMAN271 was used to semi-automatically select 249,416 particles from 3920 micrographs. The selected particles were extracted into 128 × 128-pixel boxes and subjected to reference-free 2D classification into 256 classes using Relion 2.172. For separate visualization of the Nt domains, 30,186 peripheral domains (arms) of negatively stained FDH molecules were selected manually using EMAN2 from 392 micrographs of the same dataset. The selected particles were extracted into 64×64-pixel boxes and classified using Relion 2.1 into 256 classes.Protein samples were diluted with buffer containing 10 mM HEPES, pH 7, and 150 mM NaCl to ~0.02 mg/ml. A 4.7-µl drop of the diluted sample was placed on a freshly glow-discharged carbon-coated copper grid and left for 15 s. Excess liquid was removed using filter paper, and the grid was washed three times with 4.7-µl drops of the same buffer. After the final wash, the buffer drop was removed in the same manner, and the protein was negatively stained by applying a 4.7-µl drop of 0.75% uranyl acetate for 30 s. Excess negative stain was removed using filter paper, and the grid was allowed to dry. Data were collected using SerialEMNt domains could be captured by 2D classification due to their dynamic nature.Full-length ALDH1L1 was diluted to 0.01 mg/ml with buffer containing 10 mM HEPES, pH 7, and 150 mM NaCl, followed by the addition of 0.1% glutaraldehyde. Aliquots were taken before the addition of glutaraldehyde and after 1 min, 5 min, 10 min, 30 min, and 60 min of incubation at 4 °C, and negative staining and NS-EM data collection were performed as described above. All datasets were subjected to 2D classification in Relion. After discarding 2D classes that did not represent intact ALDH1L1 molecules, the final NS-EM datasets contained 57,888 (control), 36,538 (1 min), 32,722 (5 min), 39,150 (10 min), 47,877 (30 min), and 30,449 (60 min) particles. 2D class averages displaying ALDH1L1 molecules with at least one arm that was not in contact with the protein core were identified by visual inspection, and their fractions were calculated based on the total number of particles that contributed to these classes. These fractions were used solely for the purpose of comparing the cross-linking time points because not all mobile + (ALDH1L1-NADP+ complex). Cryo-EM specimens were prepared by plunge-freezing in liquid ethane using Vitrobot Mark IV (FEI) at room temperature and 90% humidity. The grids (Quantifoil R2/2 with gold support) were glow-discharged for 30 s at a pressure of 37 mBar and with the current set to 30 mA. The drop volume was 3 µl. Data were collected at the National Cryo-Electron Microscopy Facility (NCEF) at National Cancer Institute on a Titan Krios electron microscope (FEI) operated at 300 kV and equipped with a K2-Summit direct electron detector (Gatan). The detector was used in the super-resolution mode. For apo-ALDH1L1, 2202 movies were collected with a nominal dose of 40 e−/Å2 equally distributed between 40 frames of a 12-s movie, and the pixel size (super-resolution mode) was 0.66 Å . The defocus range was −1 to −3 µm. For ALDH1L1-NADP+ complex, 2381 movies were collected with a nominal dose of 40 e−/Å2 equally distributed between 40 frames of a 14-s movie, and the pixel size (super-resolution mode) was 0.532 Å . The defocus range was −1 to −2.5 µm  or 40 mM HEPES, pH 7, 1 mM NADP73. For local motion correction, frames were divided into 25 tiles. Images were binned 2x (apo-ALDH1L1) and 1.5x (ALDH1L1-NADP+) during motion correction, resulting in pixel sizes of 1.32 Å and 0.76 Å, respectively. Contrast transfer function parameters were estimated using ctffind 4.174. All other image processing steps were performed in Relion 3.072 unless stated otherwise. Particles were picked automatically using projections of an X-ray structure of the tetrameric C-terminal domain of ALDH1L1 (PDB 2o2p27) low-pass filtered to 40 Å, resulting in datasets containing 1,082,600 (apo-ALDH1L1) and 1,050,740 (ALDH1L1/NADP+) particles. The particles were extracted, with 2x binning, into 80 × 80 (apo- ALDH1L1) or 140 × 140 (ALDH1L1/NADP+) pixel boxes and subjected to reference-free 2D classification into 128 classes with selection of high-resolution classes corresponding to a complete, undistorted tetramer of the Ct domain that appeared symmetrical. This selection reduced the size of the datasets to 424,239 and 640,945 particles, respectively. The corresponding particles were re-extracted, without binning, into 160 × 160 (apo-ALDH1L1) or 280 × 280 (ALDH1L1/NADP+) pixel boxes, and reference-free 2D classification into 128 classes was repeated. Selection of best-looking classes resulted in datasets of 147,837 (apo-FDH) and 594,883 (FDH/NADP+) particles. 3D classification into 10 classes was performed next with the above-mentioned X-ray structure of the tetramer of the Ct domain low-pass filtered to 40 Å serving as the initial model. No symmetry was imposed at this stage. The presence of additional density at the substrate entrance tunnel of the Ct domain was obvious in the resulting 3D classes, and in all 3D classes with sufficiently high resolution this density consisted of four α-helices and an arm protruding deep into the substrate entrance tunnel. Additional density consistent with the size and shape of the Nt domain of ALDH1L1 (PDB 1s3i21) was observed in several 3D classes. High-resolution 3D classes, as well as 3D classes with density for one or more Nt domains, were subjected to 3D auto-refinement with D2 or C2 symmetry imposed as well as without imposing symmetry. Post-processing included automatic B-factor sharpening and detector modulation transfer function correction, and the gold-standard resolution was determined within a soft mask using a 0.143 FSC threshold. Local resolution was estimated using ResMap75. Representative micrographs and 2D class averages, FSC curves, and local resolution data are presented in Supplementary Figs. +.Motion correction and dose weighting were performed using MotionCor227) or in complex with NADP+ (PDB 2o2q27) and four instances of a homology model of the Int domain of rat FDH (residues 306–402) obtained using the SWISS-MODEL server76 were fit into the corresponding cryo-EM density using USCF Chimera77. This was followed by one round of real-space refinement in PHENIX78 and alternating rounds of model building in Coot79 and restrained model refinement in Refmac80. Molprobity81 was used to assess the quality of the atomic models. Map-model correlations were evaluated using phenix.mtriage82.The crystal structure of the tetrameric C-terminal domain of ALDH1L1 (residues 405-902) in the apo form . Figures were prepared in UCSF Chimera, UCSF ChimeraXLC/MS experiments were repeated four times. The cross-linked peptides were detected in all these experiments.Further information on research design is available in the Transparent Peer Review FileSupplemental InformationReporting Summary


Fungi are well equipped to cope with oxidative stress and the reactive oxygen species that are, in the case of phytopathogens, produced mainly by the plant host for defence purposes. Peroxidases represent the major line of evolution for rapid decomposition of harmful peroxides in all aerobically metabolising organisms. In all the sequenced fungal genomes, many divergent genes coding for various peroxidases have been discovered, and Hybrid B heme peroxidases represent a distinctive mode of fungal-gene evolution within a large peroxidase–catalase superfamily that ranges from bacteria to plants.hyBpox genes, mainly within the genomes of Sclerotiniaceae , which is a specifically evolved fungal family of necrotrophic host generalists and saprophytic or biotrophic host specialists. Members of the genus Sclerotium produce only sclerotia and no fruiting bodies or spores. Thus, their physiological role for peroxidases remains open. A representative species, S. cepivorum, is a dangerous plant pathogen causing white rot in Allium species, particularly in onions, leeks, and garlic. On a worldwide basis, the white rot caused by this soil-borne fungus is apparently the most serious threat to Allium-crop production. We have also found very similar peroxidase sequences in the related fungus S. sclerotiorum, although with minor yet important modifications in the architecture of its active centre. The presence of ScephyBpox1-specific mRNA was confirmed by transcriptomic analysis. The presence of Hybrid B peroxidase at the protein level as the sole extracellular peroxidase of this fungus was confirmed in the secretome of S. cepivorum through detailed proteomic analyses. This prompted us to systematically search for all available genes coding for Hybrid B heme peroxidases in the whole fungal family of Sclerotiniaceae. We present here a reconstruction of their molecular phylogeny and analyse the unique aspects of their conserved-sequence features and structural folds in corresponding ancestral sequences.In this study, we focus on a detailed bioinformatics analysis of  During their unique evolution in various environments, fungi were adapted mainly for aerobic metabolism and are mostly well equipped with efficient antioxidants , the mosSclerotium cepivorum Berk is a typical phytopathogenic representative and, more specifically, it is a plant necrotroph [Allium species, particularly onions, leeks, and garlic by producing a characteristic strong colour changes [The filamentous ascomycetous fungus crotroph . It caus changes . This ca changes . Efficie changes .Sclerotium cepivorum was released previously, [The complete genome of viously,  showing Sclerotium cepivorum, Berkeley CBS 276.93 was obtained from Fungal Biodiversity Centre . Only this strain was used throughout all experiments. Fungal culture was routinely grown at 25 °C in potato-dextrose-broth medium  containing 4 g potato infusion and 20 g dextrose, with the addition of fresh, sterilised onion extract, pH 5.1 for 1 L final volume. This fungus was grown either on solid agar medium (with the addition of 15 g/L agar) or in the liquid medium that was used for cultivation in 500 mL Schott flasks. In this case, the two cultures were grown in a final volume of 100 mL, without shaking, at 25 °C for 7 days, as optimised previously [2O2 was added to the first culture at a final concentration of 1 mM, and the second culture was grown in parallel without the addition of peroxide. Growth was continued for 30 min. at the same temperature with agitation of 130 rpm on a rotary shaker. Obtained samples were used for the isolation of DNA or RNA . Total RNA of this fungus from the frozen mycelium of samples with and without the addition of 1 mM H2O2 was purified with RNeasy Plus Mini Kit . From the obtained RNA libraries, corresponding cDNA was prepared with First Strand cDNA synthesis kit  by using the random hexamers approach. Resulting cDNAs were amplified and then sequenced with the hyBpox-specific primers described in Fungal genomic DNA from 100 mg of frozen S. cepivorum CBS 276.93 was investigated from the liquid PDB cultures obtained under conditions described in 2 transformation of the relative protein abundances, Student’s t-tests were calculated and the results were visualised as a volcano plot.The fungal protein secretome of www.psc.edu/biomed/genedoc accessed on 2 February 2022). Putative signal sequences of analysed peroxidases with the detection of Sec/SPI cleavage sites were predicted with SignalP 6.0 server [Complete protein sequences of 170 peroxidases belonging to the peroxidase–catalase superfamily . The minimum probe radius was set to 0.7 to detect even the narrower tunnels present in the ScepHyBpox1 model. The remaining parameters were kept at their default settings. The coordinates of the starting point of the tunnel search were calculated using the Arg69 and His73 residues together with the heme cofactor. The AutoDock Vina plugin [Tunnels in the protein were identified using the Caver 3.0.3  plugin ia plugin  was thenScephyBpox1-mRNA was found in both fungal mRNA libraries, either induced with the addition of 1 mM hydrogen peroxide  or also in the control sample without this addition. After performing cDNA synthesis within both independent mRNA libraries, the same ScephyBpox1 product was obtained from the fungal strain CBS 276.93, with a total length of 1614 base pairs for the open reading frame. The presence of 1 spliced intron with a length of 54 nucleotides was detected by direct comparison of genomic and RT-PCR fragments  and also shared with Family III earlier . A diversification had occurred already within the early steps in the evolution of the distinct HyBpox subfamily; the basal clade of still-putative Hybrid B heme peroxidases is positioned among Chytridiomycetous fungi with high bootstrap support. Based on investigated microfossils, it was reported  that zoo genomes ,35. In sxist cf. , which pAllium species, high bootstrap support is achieved. This particular sequence of Hybrid B peroxidase from Sclerotium cepivorum can thus be considered a typical model protein for all closely related heme peroxidases from the mostly phytopathogenic ascomycetous class of Leotiomycetes; however, the corresponding native peroxidases, other than the newly discovered ScepHyBpox1, still remain unknown at the protein level.By further following the unique history of this subfamily in the robust evolutionary tree presented, the ascomycetous descendants that could have emerged through a lateral gene transfer event from some Basidiomycota predecessor serving as gene donor can be observed in the next clades. This is supported by a high bootstrap value . HoweverNext, we analysed the conserved and unique features of the newly discovered subfamily named Hybrid B heme peroxidases and compared them with already well-known sequence features in corresponding amino-acid positions within the peroxidase–catalase superfamily. The complete multiple sequence alignments of selected Hybrid B heme peroxidases is presented in For an easy overview, only 14 representative protein sequences from all resolved HyBpox subfamily clades of the evolutionary tree shown in R-X-X-F-H-D consensus sequence for all Family II and Family III heme peroxidases [S. sclerotiorum and S. trifoliorum HyBpox as Y-H-D.K-T-A- The catalytic histidine responsible for the correct orientation and heterolytic cleavage of incoming peroxide is strictly conserved on the distal side. Additionally, the importance of the preceding arginine for the heterolytic cleavage of hydrogen peroxide was verified experimentally in distantly related CcP [In the first two panels, the N-terminal heme peroxidase domain is presented, whereas in the last two panels, the C-terminal sugar-binding domain (CBM) that is typical among all known heme peroxidases solely for HyBpox proteins is shown. The highly conserved motifs on the distal a and prooxidases  that shaated CcP . Althougated CcP  and appeL/M-V-A-C-G-H-T/V for all Hybrid B peroxidases and is also located in the α-helix. However, there are some differences also, including a deletion in Family-II and Family-III representatives . To verify the relevance of this motif, we applied PSI-Blast (position-specific iterated) search in a non-redundant protein-sequence database and found that it is frequently present in numerous WSC-containing proteins (water soluble carbohydrate-binding domain) [L/M/V-T-I/V-T/S-A-A-V-R-A-D-R-I that has large differences only in the sequence of basal chytridiomycetous HyBpox and is completely absent in the sequences of Family II and III heme peroxidases. We again used PSI-Blast to reveal the relationships. The output again demonstrated its presence among numerous WSC-containing proteins and many of them without a preceding domain.In contrast to all manganese peroxidases, versatile, and lignin peroxidases, both known Hybrid B peroxidases, possess a unique CBM-containing domain. Details can be found in the panel of  domain) . Some ofS. cepivorum was modelled together with structures of its selected evolutionary ancestors in order to determine whether the typical heme-containing fold allowing peroxidase activity was preserved during the evolution of this subfamily, and how the second, non-heme domain is spatially related with the larger peroxidase domain. For this purpose we used the methodology of AlphaFold 2 [The spatial structure of yet completely unknown Hybrid B heme peroxidase from the phytopathogen haFold 2  because haFold 2 . AlthoughaFold 2  and theyThe larger peroxidase domain that is mainly α-helical, as in the majority of other heme peroxidases from the whole peroxidase., Refs. ,44,45 ca., Refs. , and theThe smaller CBM domain occurs in contrast with a much lower portion of α-helical and more β-strand structural content that can be attributed mostly as β-sandwich. This typical double-domain structure of Hybrid B heme peroxidases represents a strictly conserved fold, observed also for the six reconstructed ancestral sequences in important evolutionary nodes cf. , which aThe details of the reconstructed active centres of all the AlphaFold models were inspected in the presented phytopathogenic peroxidase and in its six ancestors in order to follow the fine details of the specific spatial orientation of the conserved amino acids allowing peroxidase reactivity. The output, again presented in the structural overlay, is shown in In principle, all above presented structural figures clearly show that the N-terminal heme-binding peroxidase domain was highly conserved during its entire evolutionary history, thus allowing essential and efficient peroxidase activity. On the other hand, there is additional variability, which is not involved in peroxidation cycles, in the additional CBM-binding domain on the C-terminus of Hybrid B peroxidases.Finally, two potential substrates were docked in the active centres of all of the models presented here. Hydrogen peroxide is the first and most common substrate reduced in the active centres of all peroxidases. The second potential substrate—guaiacol—is used by a large group of evolutionary-related peroxidases as an electron acceptor for reactive enzyme intermediates. Results are presented in In We demonstrated that in a typical secreted heme-peroxidase representative from a phytopathogenic fungus, namely ScepHyBpox1 and its ancestors, there exists an important novel and still rather undiscovered heme peroxidase subfamily that may have an important function for pathogenic fungal cells in their defence against oxidative bursts caused by the host plant. Corresponding mRNA is produced rather constitutively and is translated into a unique two-domain protein representing the sole secreted peroxidase of this filamentous fungus. This fact underlines its importance for the survival of the described phytopathogen and probably also for related pathogenic fungi from the whole class of Leotiomycetes during infection cycles and when attacking plant tissues. Numerous Hybrid B heme peroxidase sequences obtained from different fungal families of both Ascomycota and Basidiomycota were systematically collected for phylogenetic purposes. Even the presence of corresponding Hybrid B heme peroxidase sequences that are supposed to be an early diverging lineage of the whole fungal kingdom were detected among Chytridiomycota. All representatives of the newly discovered peroxidase subfamily presented here reveal surprisingly high overall sequence similarity which is mainly localised in the larger heme-peroxidase domain on the N-terminus of the protein, irrespective of their trophic levels in various ecosystems. The C-terminal domain that is directly fused with the larger α-helical peroxidase domain is quite unusual for all previously described heme peroxidases, but it is also present in all of the known deep ancestors of this subfamily in nearly the same orientation where the CBM-containing domain is located near the region of the proximal side of heme. The proposed “hybrid“ character of the proteins analysed herien is concealed in this unique double-domain architecture and apparently has deep evolutionary origins among early-diverging aquatic fungal lineages. This observation is in noticeable in constrast to, e.g., lignin peroxidases among early-diverging fungi that have not yet been described. In contrast, the presence of these flexible enzymes among Chytridiomycota before their presence among Dikarya indicates that, in addition to their basic protective function against reactive oxygen species, they may have been involved in the ancient extraction of useful nutrients from early land-colonising plants and their algal relatives . In thisIn this study, a new type of Hybrid B heme peroxidase was detected and investigated on the proteomic level as the sole peroxidase in the secretome of an important phytopathogenic fungus. The reconstruction of its broad phylogenetic relationships in corresponding peroxidase (sub)families reveals its importance as a model protein representing numerous, related heme peroxidases. Its unique two-domain architecture was modelled, and a typical tunnel leading to the distal heme cavity was resolved by the advanced method of structural modelling methodology represented by AlphaFold and CAVER. The performed docking of two potential substrates must be verified in a highly purified sample of this or a related Hybrid B heme peroxidase. The observed Hybrid B heme and CBM-fusion proteins are now attractive targets for future functional and engineering studies.


Many brain disorders, including alcohol use disorder (AUD), are associated with induction of multiple proinflammatory genes. One aspect of proinflammatory signaling is progressive increases in expression across cells and induction of other innate immune genes. High‐mobility group box 1 (HMGB1) heteromers contribute to amplification by potentiating multiple proinflammatory responses, including Toll‐like receptors (TLRs). TLR signaling recruits coupling proteins linked to nuclear transcription factors that induce proinflammatory cytokines and chemokines and their respective receptors. We tested the hypothesis that AUD induction of TLR expression increases levels of proinflammatory genes and cellular signaling cascades in association with neurodegeneration in the orbitofrontal cortex (OFC).n = 10) from males diagnosed with AUD were compared to age‐matched moderate drinking controls (CON). Neuroimmune signaling molecules were assessed using immunohistochemistry for protein and reverse transcription polymerase chain reaction for messenger RNA (mRNA).Postmortem human OFC tissue samples  except TLR1. This was accompanied by increased expression of the TLR adaptor protein myeloid differentiation primary response 88 (MyD88), activation of the proinflammatory nuclear transcription factor nuclear factor kappa‐light‐chain‐enhancer of activated B cells (NFκB), and downstream induction of proinflammatory cytokines, chemokines, and their corresponding receptors. Several of these proinflammatory signaling markers are expressed in glia and neurons. The induction of HMGB1‐TLR‐MyD88‐NFκB proinflammatory signaling pathways correlates with neurodegeneration , lifetime alcohol consumption, and age of drinking onset.These data implicate the induction of HMGB1‐TLR‐MyD88‐NFκB cascades through coordinated glial and neuronal signaling as contributors to the neurodegeneration seen in the postmortem human OFC of individuals with AUD. Many brain disorders, including alcohol use disorder (AUD), are associated with induction of multiple proinflammatory genes and neurodegeneration. We tested the hypothesis that AUD induction of Toll‐like receptor (TLR) expression induces proinflammatory signaling cascades in association with neurodegeneration in the post‐mortem human orbitofrontal cortex (OFC). We report that induction of HMGB1‐TLR‐MyD88‐NFκB cascades through coordinated glial and neuronal signaling contributes to neurodegeneration in the OFC of individuals with AUD. Subject information was collected through personal interviews and next‐of‐kin interviews as well as medical records and is presented in Table Postmortem human OFC paraffin‐embedded and frozen tissue samples from moderate drinking control (CON) and AUD subjects n = 0/group wTotal RNA was extracted from frozen human OFC tissue samples from CON and AUD subjects by homogenization in TRI reagent (Sigma‐Aldrich) following the single‐step method of RNA isolation  for 1 h at 70°C. Following incubation in blocking solution , slides were incubated in a primary antibody solution for 24 h at 4°C. Primary antibodies, dilutions, and validation information are included in Table 2.BioQuant Nova Advanced Image Analysis software (R&M Biometric) was used for image capture and quantification of immunohistochemistry. Representative images were captured using an Olympus BX50 microscope and Sony DXC‐390 video camera linked to a computer. For each measure, the microscope, camera, and software were background corrected and normalized to preset light levels to ensure fidelity of data acquisition. A modified unbiased stereological quantification method was used to quantify TLR9‐, MyD88‐, IKKβ‐, cleaved IL‐1β‐, CCL2‐, and CXCL8‐immunoreactive (+IR) cells in the postmortem human OFC. We previously reported that comparison of traditional unbiased stereological methodology with our modified unbiased stereological approach yielded nearly identical values relative to control subjects  for 1 h at 70°C. Following incubation in blocking solution , slides were incubated for 24 h at 4°C in a primary antibody solution consisting of blocking solution or Dako antibody diluent  in combination with either rabbit anti‐TLR9 (1:70), rabbit anti‐pNF‐κB p65 (phospho S536) (1:150), mouse anti‐MCP‐1 (1:100), or mouse anti‐IL‐8 (1:300) combined with an antibody against neurons , astrocytes , or microglia . Slides were washed in PBS and incubated for 1 h at room temperature with the appropriate secondary antibodies (1:1000). Immunoblotting was visualized using Alexa Fluor 488 or 594 dye. Secondary‐only negative controls were performed without primary antibody incubation. Slides were coverslipped using Prolong Gold Anti‐fade mounting media (Life Technologies). Immunofluorescent images were obtained using a Nikon DS‐Ri2 scope (Nikon Inc.) and colocalization quantified using NIS Elements AR46 (Nikon Inc.).t tests were used to assess human demographics, RT‐PCR, and immunohistochemistry data unless otherwise reported. Levene's Test for Equality of Variances was performed for each analysis. When reported in the Results, Welch's t tests were used to assess data with unequal variances. Two‐tailed Pearson's r was used for all correlative analyses. Since performing multiple comparisons can increase the incidence of Type I Errors, the Benjamini‐Hochberg procedure  for controlling false positives was calculated . All values are reported as mean ± SEM, and significance was defined as p ≤ 0.05.Statistical analysis was performed using SPSS. Sample size determinations were based on previously published studies  relative to age‐matched CONs (n = 10). Individuals with AUD averaged lifetime alcohol consumption levels of 2663 kg of alcohol, whereas CONs averaged 39 kg of lifetime alcohol consumption (Table HMGB1 mRNA increased 1.4‐fold (t(18) = 2.9, p = 0.009) in the AUD OFC relative to CONs  = 2.4, p = 0.029), TLR3  = 2.4, p = 0.038, Welch's t test), TLR4  = 2.3, p = 0.035), TLR5  = 2.6, p = 0.018), TLR6  = 2.4, p = 0.027), TLR7  = 3.6, p = 0.002), TLR8  = 2.7, p = 0.013), and TLR9  = 2.5, p = 0.023) relative to CONs  = 8.3, p = 0.000001, Welch's t test) relative to CONs  as well as the viral‐mediated TLR adaptor interferon regulatory factor 3 (IRF3). We report here that AUD increased MYD88 mRNA expression approximately 2.2‐fold (t(18) = 2.9, p = 0.010), but decreased TICAM1 by 35%  = −2.8, p = 0.013) in the OFC relative to CONs  = 6.7, p = 0.000003) relative to CONs  and TIR‐domain‐containing adapter‐inducing interferon‐β  and regulatory inhibitors . We report an approximate 2.1‐fold increase of IKBKB  = 3.5, p = 0.002) and 2.4‐fold increase of IKBKG (t(18) = 2.6, p = 0.018) that was accompanied by a 1.5‐fold increase of NFKBIB (t(10.2) = 2.6, p = 0.025, Welch's t test) and a 1.9‐fold increase of NFKBIE (t(18) = 2.8, p = 0.012) in AUD relative to CONs  = 5.9, p = 0.00006, Welch's t test) relative to CONs  = 3.6, p = 0.003, Welch's t test) and a 1.7‐fold increase of RELA (t(14.8) = 2.3, p = 0.039; Welch's t test) relative to CONs  = 2.5, p = 0.028) and a 1.8‐fold increase of the IL1β receptor IL1R (t(18) = 2.8, p = 0.012) relative to CONs. This was accompanied by an approximate 2.7‐fold increase in expression of the IL1 receptor antagonist IL1RN (t(18) = 3.5, p = 0.003) in the AUD OFC  = 4.9, p = 0.0001) relative to CONs  = 2.3, p = 0.036) in the AUD OFC relative to moderate drinking CONs, whereas expression of IL6R was unchanged  = 3.0, p = 0.013, Welch's t test), which was accompanied by a 2.1‐fold increase in expression of the TNFα receptor TNFRSF1A (t(14) = 3.2, p = 0.007) in the AUD OFC relative to CONs  = 3.0, p = 0.008), a 1.5‐fold increase of CCL7 (t(18) = 2.7, p = 0.016), a 2.9‐fold increase of CCL13 (t(18) = 3.2, p = 0.005), and a 2.4‐fold increase of CCL5 (t(18) = 3.2, p = 0.005) in the AUD individuals relative to CONs  = 2.0, p = 0.059), whereas CCL3, CCL4, and CCL19 expression was unchanged between groups. Immunohistological assessment of CCL2 cellular protein expression revealed an approximate 2.6‐fold increase in the AUD OFC (t(5.8) = 10.6, p = 0.0001, Welch's t test) relative to CONs  = 2.4, p = 0.026) and CCR2 (t(18) = 2.9, p = 0.010) was increased approximately 1.5‐fold and 2.5‐fold, respectively, in the AUD OFC relative to CONs  = 2.6, p = 0.026, Welch's t test) and a 2.9‐fold increase of CXCL12 (t(14) = 3.1, p = 0.009) in the AUD OFC relative to CONs  = 9.4, p = 0.00000001) relative to CONs  = 3.3, p = 0.004) and CXCR2 (t(11.6) = 3.4, p = 0.006, Welch's t test) that were both significantly increased (~2.5‐fold) in the AUD OFC relative to CONs. We report an approximate 2.1‐fold increase of the CXCL10 receptor CXCR3 (t(13.1) = 4.2, p = 0.001, Welch's t test) and a 2.4‐fold increase of the CXCL12 receptor CXCR4 (t(18) = 2.7, p = 0.014) in the AUD OFC relative to CONs  = −3.4, p = 0.0001; Figure r = −0.59, p = 0.032) with Fluoro‐Jade B expression  and age of drinking onset with expression of Fluoro‐Jade B+IR and HMGB1‐TLR‐MyD88‐NFκB cytokine and chemokine neuroimmune signaling markers. In this cohort, AUD individuals consumed between 613 and 8052 kg of alcohol, providing a broad range of consumption and showing a progressive increase in neurodegeneration  and neuroimmune marker expression with increased lifetime alcohol consumption. Fluoro‐Jade B+IR, on Table . Age of on Table , suggestHMGB1 as well as multiple TLRs , but not TLR1. Toll‐like receptors are well‐characterized pattern recognition receptors in brain implicated in numerous disease states, including AUD and AD  and neurons. Microglia, which are the resident monocyte‐like glia of the brain, express many of these proinflammatory signaling genes, and studies in postmortem human AUD brain  except TLR1. Induction of HMGB1‐TLR signaling is accompanied by increased expression of the TLR adaptor protein MyD88, activation of the proinflammatory nuclear transcription factor NFκB, and downstream induction of proinflammatory cytokines, chemokines, and their corresponding receptors. Expression of several of these proinflammatory signaling markers is observed in glia and neurons. Induction of the HMGB1‐TLR‐MyD88‐NFκB proinflammatory signaling pathways correlates with neurodegeneration  as well as lifetime alcohol consumption and age of drinking onset. These data implicate induction of HMGB1‐TLR‐MyD88‐NFκB cascades through coordinated glial and neuronal signaling as contributing to neurodegeneration in the postmortem human OFC of individuals with AUD.None.Fig S1Click here for additional data file.Supplementary MaterialClick here for additional data file.Table S1Click here for additional data file.Table S2Click here for additional data file.Table S3Click here for additional data file.Table S4Click here for additional data file.Table S5Click here for additional data file.Table S6Click here for additional data file.Table S7Click here for additional data file.Table S8Click here for additional data file.


After HMT, the gelatinization temperature of wheat flour increased and the gelatinization enthalpy reduced. The solubility and swelling power of wheat flour increased after the heat-moisture treatment. The solubility of modified flour after PAW-HMT treatment was lower than that of distilled water (DW)-HMT at the same temperature. The wheat flour with HMT had higher storage modulus (G') and loss modulus (G“), and had better ductility and deformability. Common wheat flour was partially replaced by modified flour to make steamed bread. The results indicated that the volume, height, diameter and specific volume of steamed bread were significantly decreased with the addition of HMT flour. However, the hardness, viscosity and chewiness increased significantly. The resistant starch content of steamed bread with the modified wheat flour increased. The results provide new insights for the development of new functional steamed bread.The study investigated the effects of plasma-activated water (PAW) and heat moisture treatment (HMT) on the rheological properties of wheat flour and the  Wheat is the largest food crop globally, accounting for one-third of the world's total annual production. It is the main food material in China and many countries in the world. Approximately 35% of the world's population eat wheat-based food, such as bread, biscuits, noodles and steamed bread . Wheat fIn order to improve the functional characteristics of wheat flour and broad its application in food, it is necessary to modify wheat flour. Heat moisture treatment (HMT) can be used to improve the undesirable properties of flour or starch. It can promote the change of starch chain structure arrangement in amorphous and crystalline regions of granules, resulting in granule expansion and changes in starch crystallinity, pasting characteristics, thermal stability and other properties . Chen etin vitro digestibility decreased. Misra et al. , which is a liquid obtained by plasma discharge in water or on the surface of water. It has the characteristics of low pH, high conductivity and high REDOX potential . The maia et al.  found tha et al.  found thWheat flour (WF) was obtained from COFCO Grain and Oil Co., Ltd. . Porcine pancreatin  and amyloglucosidase  were purchased from Sigma in the United States. Glucose oxidase peroxidase (GOPOD) glucose kit was purchased from Megazyme Co., Ltd (Ireland). Absolute ethanol was provided by Fuyu Chemical Co., Ltd. . The chemical reagents used were all analytical grade.Atmospheric pressure plasma jet (APPJ) device was used  to prepare plasma-activated water (PAW). PAW can be obtained by treating every 100 mL of distilled water with plasma jetting for 2 min. The plasma jet probe was located directly above the water surface at a distance of 56.25 mm. The high frequency of the power supply was 40 kHz, and the high voltage was 5 kV.Wheat flour  was placed in an air oven (40°C) and dried until the moisture content was about 5%. Then, the moisture content of wheat flour was adjusted to 15 and 25% with plasma-activated water. Distilled water (DW) was regarded as a reference. The flour samples were put into the hydrothermal reactor  to heat at 100°C for 1 h in an air oven after equilibrating at 25°C for 12 h. The treated flour sample was dried at 40°C for 12 h in an air oven, then ground to pass through 100-mesh sieve for further analysis. The samples were denoted as DW-15%, DW-25%, PAW-15% and PAW-25% on the basis of the moisture content of DW and PAW.The flour sample  was accurately weighed into an aluminum pan, and equilibrated at room temperature for 12 h. An empty aluminum pan was used as a control. Heating of each sample was carried out from 20 to 140°C at 10°C/min rate. The onset (To), peak (Tp), conclusion (Tc) transition temperatures and enthalpy changes (ΔH) were determined using the DSC differential scanning calorimeter .Solubility and swelling power were determined and calculated by the method suggested by Colussi et al.  with a sThe rheological behavior of wheat flour was measured by the Discovery rheometer . The flat panel system was used to test, with a probe diameter (40 mm) and a gap (1 mm). Before measurement, the sample was equilibrated at 25°C for 5 min. The sample preparation referred to the method of Solaesa et al. , with apThe oscillation frequency mode was adopted. The temperature was constant at 25°C, and the scanning frequency was set at 1 Hz. The shear strain was from 0.01 to 100%. The relation curves of the storage modulus (G') and loss modulus (G”) with the strain amplitude were measured.The dynamic rheological data of the sample were measured in the frequency sweep range of 0.1–20.0 Hz with the strain of 1% within the linear viscoelastic region at 25°C, and the storage modulus (G'), loss modulus (G“) and loss tangent (tan δ) of the flour gel were measured.The wheat flour (100 g) was mixed with a well-mixed yeast solution . The mixture was put into the dough mixer  for a certain period of time (the mixing time was determined by the stability time of the dough) until the dough was smooth. Then the dough was pressed 15 times in a noodle machine to squeeze out the bubbles. Afterwards, the dough was put in the fermenting box  for 1 h at 35°C. Steamed breads (50 g of each dough) were steamed in a steamer for 30 min, and then cooled at 25°C for 1 h. Steamed bread was made with 20% or 30% (w/w) DW-15% and PAW-15% samples instead of wheat flour, respectively. The samples were denoted as B-DW-20%, B-DW-30%, B-PAW-20% and B-PAW-30% on the basis of the addition amount of HMT flour.The weight and diameter of steamed bread were measured according to GB/T 5991-2018 . The volume of steamed breads was measured according to the method of Shi et al. . SpecifiThe texture analysis of the steamed bread was carried out with the TA-XT plus texture analyzer . According to the method of Gao et al. , the cenin vitro digestibility of the steamed bread was carried out according to the method of Englyst et al. . The graphs were all obtained from Origin 9.0 software.All experiments were repeated three times, and the data were expressed as mean values ± standard deviations. Statistical analysis used SPSS 26.0 software, and analysis of variance used Duncan's multiple comparison method for significance test  is used to represent the elastic characteristics of the gel and reflects the degree of deformation of the sample. The loss modulus (G”) represents the viscosity properties of the gel and reflects the resistance of the sample to flow. As shown in Tan δ is the ratio of viscosity modulus to elastic modulus (G“/G'). The higher the value of tan δ represented the stronger the viscosity and fluidity. The smaller the tan δ value represented the stronger the elasticity and solid properties. It can be seen from The effects of the modified flours on the basic indicators of steamed bread are shown in The effect of modified flour on the steamed bread texture is shown in in vitro digestion of steamed bread is shown in Slowly digestible starch (SDS) is related to slower glucose entering the blood and lower blood sugar response. SDS has a lower rate of complete digestion in the small intestine than rapidly digestible starch (RDS). However, resistant starch (RS) cannot be digested in the small intestine , 28. StuThe physicochemical properties of wheat flour were affected by HMT and PAW. Under the effect of high temperature and PAW, the crystal structure of wheat flour samples was damaged. With the increase of PAW treated wheat flour, the specific volume of steamed bread decreased and the spread ratio increased. The addition of PAW-HMT increased the hardness and chewiness of the steamed bread. On the other hand, the resistant starch content of steamed bread with the addition of PAW-HMT modified flour was significantly increased. However, excessive addition of HMT flour was not conducive to the quality of steamed bread, and excessive addition will reduce the acceptance. Therefore, it was recommended to add 20% PAW-15% flour to the steamed bread.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.MS contributed to conception and design the study. YC and FW performed the experiments and analyzed the data. YC wrote the first draft of the manuscript. XJ, YL, and YY contributed to writing-review and editing and funding. All authors contributed to the article and approved the submitted version.We gratefully acknowledge financial support by Science and Technology Innovation Talents in Universities of Henan Province (20HASTIT037), Science and Technology Basic Research Program of Henan Province .FW was employed by Lanzhou Quality Supervision Center Limited, China Grain Reserves Group Ltd. Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Under low voltages, head-to-tail domain walls are more mobile than other domain walls, while, under high voltages, tail-to-tail domain walls become rather active and possess relatively long average lengths. This is due to the high nucleation energy and relatively low growth energy for charged domain walls. Finally, we demonstrate the manipulation of domain walls through successive electric writings, resulting in well-aligned conduction paths as designed, paving the way for their application in advanced spintronic, memory and communication nano-devices.Ferroelectric domain walls differ from domains not only in their crystalline and discrete symmetry, but also in their electronic, magnetic, and mechanical properties. Although domain walls provide a degree of freedom to regulate the physical properties at the nanoscale, the relatively lower controllability prevents their practical applications in nano-devices. In this work, with the advantages of 3D domain configuration detection based on piezoresponse force microscopy, we find that the mobility of three types of domain walls  in (001) BiFeO Domain walls (DWs) in ferroic materials serve as active elements possessing dramatic mechanical, electronic, optical, and magnetic properties aside from ferroic domains, offering great potential for technological applications in spintronics, memory devices, and communications ,2. As ac3 (BFO) films, which are an attractive platform to study DW dynamics and properties, taking advantages of their regular DW shapes as compared to other improper ferroelectrics, such as YMnO3, ErMnO3 and TbMnO3 [3, PZT and BaTiO3 [Here we study the subject in BiFeOd TbMnO3 . Additiod BaTiO3 ,24,31–33d BaTiO3 . To furtIn this work, using piezoresponse force microscopy (PFM), the dynamics of three types of DWs (head-to-head (H–H), head-to-tail (H–T), and tail-to-tail (T–T)) in (001) epitaxial BFO films are found to vary with applied electric bias. This phenomenon is attributed to the pinning effect of charged defects, partly supported by high-resolution X-ray photoelectron spectroscopy (HR-XPS). Beyond the investigations, a two-step scanning scheme corresponding to low and high voltages, respectively, was designed and carried out in a trial experiment, demonstrating the controllable feature of conductive paths in systems with complex domain configurations. Our results establish better understanding of DW dynamics, shedding light on potential DW nanoelectronics based on ferroelectrics.3 single-crystal substrates using pulsed laser deposition, with a 40 nm-thick bottom electrode of epitaxial SrRuO3 (SRO) for the purpose of electrical contact. The out-of-plane (OP) polarizations are downward-pointing, and most DWs are of 71° in the as-grown BFO films. The 3D polarization configuration is achieved by composing PFM images of three mutually perpendicular directions (x-in-plane (x-IP), y-IP, OP) on a commercial scanning probe microscope (SPM)  -oriented SrTiOx, +x, −y, +y directions are indicated by blue, yellow, orange and green arrows, respectively. The OP polarizations in the whole area of Fig. z to the +z direction, as 3.4 V is applied to the bottom electrode  BFO thin film. This feature remains unchanged after the polarization switching and the domain reconfiguration caused by the electric poling. Notably, 71° DWs can be categorized by the bound charges on them, i.e. H–H, H–T and T–T DWs, which possess different conductivities ,27,35,36Here we distinguish three kinds of DWs with different charge conditions from Fig. As shown in Fig. DW length. This is the sum of DW lengths, classified by DW types , indicating preferred DW types in the system. As shown in Fig. The above statistics reveals the clear difference of DW dynamics among the three types of DWs. Generally speaking, in the voltage range studied, the H–H and T–T DWs are fewer in number Fig. g, while In BFO thin films, negatively charged Bi vacancies and positively charged O vacancies accumulate around H–H and T–T DWs, respectively . They coIn addition to the diverse mobility of the three kinds of DWs, we find an interesting role of IP electric field from the scanning cantilever on the polarization switching and the domain reconfiguration. Although OP electric field dominates the poling process, the IP field is particular for the scanning probe microscopy while non-negligible for the polarization control.x-IP polarization is obviously larger than other domains, possibly due to the substrate and the preparation conditions [x- and +x-IP polarization reverse  starts to exert an influence [x-IP polarization  and the cantilever (+y) results in the constant enlargement of the −x and +y domains  in (001) BFO/SRO/STO films with an applied electric field. Under low voltages, uncharged (H–T) DWs exhibit relatively high mobility, while movements of charged  DWs, which have longer total DW lengths, are hindered by charged defects. Under high voltages, the applied electric fields are large enough to provide nucleation energy for charged DWs, and to overcome the pinning effect from the surrounding charged defects, thus contributing to shorter total DW lengths and longer average DW lengths. It is noteworthy that under high voltages the mobility and average DW lengths of conductive T–T DWs are the largest among the three kinds of DWs. Additionally, with increasing applied voltage, IP electric field from the slow scan direction and the cantilever exhibits a stronger influence on the sample, leading to a preferred IP polarization direction and larger average domain size. Based on the above results, we designed a two-step approach incorporating both low- and high-voltage poling processes, and successfully produced arrays of well-aligned and stable conductive T–T DWs. Our work reveals the remarkable impact of charge accumulation around DWs on DW mobility, providing a generalizable approach for DW dynamic studies in ferroic materials. The methodology proposed here for the advanced tunability of conductive DWs makes significant progress towards their applications in functional nano-devices.nwz176_Supplemental_FileClick here for additional data file.


Sentiment classification for financial texts is of great importance for predicting stock markets and financial crises. At present, with the popularity of applications in the field of natural language processing (NLP) adopting deep learning, the application of automatic text classification and text-based sentiment classification has become more and more extensive. However, in the field of financial text-based sentiment classification, due to a lack of labeled samples, such applications are limited. A domain-adaptation-based financial text sentiment classification method is proposed in this paper, which can adopt source domain (SD) text data with sentiment labels and a large amount of unlabeled target domain (TD) financial text data as training samples for the proposed neural network. The proposed method is a cross-domain transfer-learning-based method. The domain classification subnetwork is added to the original neural network, and the domain classification loss function is also added to the original training loss function. Therefore, the network can simultaneously adapt to the target domain and then accomplish the classification task. The experiment of the proposed sentiment classification transfer learning method is carried out through an open-source dataset. The proposed method in this paper uses the reviews of Amazon Books, DVDs, electronics, and kitchen appliances as the source domain for cross-domain learning, and the classification accuracy rates can reach 65.0%, 61.2%, 61.6%, and 66.3%, respectively. Compared with nontransfer learning, the classification accuracy rate has improved by 11.0%, 7.6%, 11.4%, and 13.4%, respectively. With the rapid development of technologies such as big data, artificial intelligence, and deep learning, how to adopt these technologies to the financial field has become a research hotspot. The sentiment classification of financial texts based on machine learning is of great significance for predicting stock markets and financial crises. Compared with traditional methods based on financial text sentiment classification, it has the following advantages: (1) the influence of human emotional factors can be reduced and (2) a large amount of data samples can be adopted for analysis. As a matter of fact, sentiment prediction of financial text based on machine learning has become a research hotspot. Through the analysis of current financial texts from the Internet, a preliminary judgment can be made about current economic fundamentals.Currently, there has been in-depth research in the field of text classification. In terms of text representation, Collobert  firstly The topic of this paper is sentiment classification for financial texts. Judging whether the financial text is positive through the classification network can be of great significance for judging the current economic situations or investment enthusiasm. It has great significance for predicting the stock market and financial crisis. However, in the field of financial text sentiment classification, the biggest difference from text sentiment classification in other domains is the lack of a large number of labeled samples. Therefore, the application of sentiment classification in the financial text has been greatly restricted. In order to adopt deep learning methods for performing sentiment analysis on financial texts, a straightforward idea is to use cross-domain learning methods to transfer sentiment classification knowledge from other domains to the domain of financial texts. Generally speaking, the methods of transfer learning in this field can be divided into three categories. (1) The first category is the parameter fine-tuning-based method. In , by pretA financial text sentiment classification method based on unsupervised domain adaptation (DA) is proposed in this paper. The proposed method can transfer the sentiment classification knowledge of the source domain to the target domain of the financial text. Although the distribution of the source domain and the target domain are different, the shared features can still be learned for classification. The proposed method has the following advantages: (1) it does not require labeled samples in financial texts, so the method is unsupervised and suitable for the actual situation of lack of sentiment labels in financial texts and (2) the network structure adopted in the proposed method is similar to the original network structure, where the only difference is that the domain classification subnetwork needs to be added. This can make the proposed network structure less complex. The proposed method is a cross-domain neural network transfer learning method. The domain classification subnetwork is added to the traditional sentiment classification network, and the domain classification cost is added to the original training cost so that the network can simultaneously adapt to the target domain as well as the source domain. The experiment of emotion classification migration learning is carried out through the open-source dataset. The experiment of the proposed sentiment classification transfer learning method is carried out through the open-source dataset. The proposed method in this paper uses the reviews of Amazon Books, DVDs, electronics, and kitchen appliances as the source domain for cross-domain learning, and the classification accuracy rates have reached 65.0%, 61.2%, 61.6%, and 66.3%. Compared with nontransfer learning, the classification accuracy rate has improved by 11.0%, 7.6%, 11.4%, and 13.4%, respectively.xi, where i represents the vector representation of the i-th sample, and the corresponding label can be represented as si, where i represents the label of the i-th sample. For the target domain sample, it is represented as yi, and the corresponding domain label is di. In the application of this article, the number of classifications required is 2, which are positive evaluation labels and negative evaluation labels. In this method, the model is trained according to the source domain samples and part of the target domain sample set, and then the target domain sample test set is used to test the accuracy of the model. In this section, the model is introduced according to the following parts. First, the overall model of domain adaptation in this article is introduced. Second, the cost function of the model in this article is introduced. Finally, the training process of the proposed model is introduced.The problem to be studied in this paper can be expressed as follows: after training with labeled text data in the source domain and unlabeled data in the target domain, emotion classification is performed on the target domain, that is, financial text. Among them, the vector of the text sample of the source domain is represented as The proposed model in this paper is shown in (1)vi. The feature extraction network in this paper can have two structures. As shown in the figure, the first one is a common convolutional network structure, including three layers of convolutional network layers. The feature extraction subnetwork is similar to the traditional convolutional network layers, which extracts from shallow to deep features in the samples for classification. The second one is the feature extraction structure from a residual network. Since the residual network has direct links between convolutional layers that are not adjacent, it is usually easier to train. In our implementation, the residual network is adopted as the structure for the feature extraction subnetwork. In traditional convolutional neural networks, since there is only the cost function for classification, the extracted features can be used to distinguish sample labels. However, herein, the sentiment classification cost and the domain classification cost are both adopted. Through training, the feature extraction subnetwork can extract features that meet the following two conditions: firstly, the features can be effectively adopted for sentiment differentiation, and secondly, it is impossible to distinguish which domain the input sample comes from according to the extracted features, for example, the data domain of financial text or ordinary product review text.The detailed structure of the feature extraction subnetwork is shown in (2)N represents the number of categories and ei represents the feature input of the dimension i.The structure of the sentiment classification subnetwork is shown in (3)The structure of the domain classification network is shown in The network structure proposed in this paper is composed of three substructures: feature extraction subnetwork, sentiment prediction subnetwork, and domain classification subnetwork. The details of the three subnetworks are introduced as follows:RG(.) represents the function of the forward propagation of the reverse gradient layer. During the forward propagation, this layer does not have any influence on the propagation of the data. When the gradient is backpropagated, the layer adds a minus sign to the original gradient. Designing the reverse gradient layer in this way can enable the domain classification subnetwork trainable and enable the feature extraction subnetwork to obtain features that cannot distinguish domains.Among them, According to the above descriptions, (1)Cs, which can be written as follows:Cost function for sentiment classification: the cost can be expressed by i denotes the index of the sample, M represents the number of samples, si represents the sentiment label of the corresponding sample, xi represents the sample of the input source domain, and Cs,i(.) represents the corresponding cross-entropy function, which can be written as follows:here Oi represents the output of the softmax layer and si represents the number of one-hot encoding based on the actual sentiment classification label.where (2)Domain classification cost function: the cost can be written as follows:Cd represents the cost function, i represents the index of the sample, M represents the number of samples, di represents the domain label of the corresponding sample, xi represents the sample of the input source or target domain, and Cd,i(.) represents the domain classification cross-entropy:Among them, Oi represents the output of the softmax layer of the domain classification subnetwork and di represents the number of domain sources after one-hot encoding.where According to the above symbols, it can be seen that there are two cost functions in the network structure:Call represents the overall cost function, and the parameter τ denotes the ratio of the two cost functions. Noting that here, a minus sign is added before the domain classification cost function, which is equivalent to the operation of the gradient backpropagation layer mentioned above. After the addition of the minus sign, the training process is equivalent to maximizing the cost of the domain classification subnetwork while minimizing the cost of the sentiment classification subnetwork. In this way, the features extracted by the feature extraction subnetwork can be both effective for distinguishing sentiment labels and at the same time insensitive for distinguishing domains.According to the sentiment classification cost function and domain classification cost function, the overall cost function can be written as follows:According to the overall cost function mentioned above, during training, different subnetwork parameters can be updated as follows:μ indicates the update rate. The value of the learning rate μ in our implementation is set to 0.0005. The value is a typical value for the learning rate.The parameter τp determines the proportion of the two cost functions. During the training process, the hyperparameter τp is set according to the following equation:In actual training process, training is carried out in batches. In order to optimize all cost function items in each batch, the batch adopted here needs to include the samples of the source domain and the target domain. As a result, during optimization, there is a certain confrontational relationship between minimizing the cost of sentiment classification and maximizing the cost of domain classification. Here, the parameter p is close to 0. Setting p close to 0 can make the training network become a pure sentiment classification network without considering the problem of transfer learning. (2) When p increases, τp also increases. It means that with the convergence of the feature extraction and sentiment classification subnetwork during training, the cost of transfer learning should be taken into consideration. The parameter of α determines the conversion speed for the two situations. In one situation, only the recognition rate is considered. In the other, only the domain classification accuracy is considered. In the above formula, the value of α is set to 25 according to subsequent experiments.To set the hyperparameter according to the above formula, the advantages are as follows. (1) At the early stage of training, the recognition rate is low and the value of In order to verify the proposed cross-domain sentiment classification method, the classic Amazon dataset is adopted. The dataset contains more than 340,000 reviews, which cover 22 different products. Because the dataset contains too many types, the size of the set is large, and the positive and negative reviews are uneven; herein, the corresponding reduced Amazon dataset is adopted for experimentation. The basics of the reduced dataset are shown in According to the dataset mentioned above, the proposed method can be verified. The reviews of the four products in the reduced Amazon dataset are used as the source domains, and the Microblog dataset is used as the target domain for training and testing. The test results are shown in Rt and in-domain ratio index Ri are defined. Among them, the transfer ratio index Rt can be expressed as follows:s and t represent the source domain and the target domain respectively; N represents the number of pairs from the source domain to the target domain; e represents the transfer error, which denotes the test error of transfer learning; and eb represents the in-domain error according to the baseline method. The transfer ratio index reflects the transfer learning effect of the corresponding method, and its value is positively correlated with the performance of the transfer learning method. The in-domain ratio index can be expressed as follows:The proposed method is compared with the previously mentioned SVM-based method, the parameter fine-tuning method, and the SDA-based transfer method. Herein, the SVM-based method is used as the baseline method. Note that in the baseline method, transfer learning is not performed. The purpose of adding the baseline method is to evaluate the effect of the transfer learning method according to the following metrics. In order to better compare the transfer effect, inspired by , the traM represents the number of all domains; e represents the in-domain error of the corresponding method, that is, the test error under the condition that both training and testing are in the source domain, and eb represents the in-domain error of the baseline method. The in-domain error can be used to represent the sentiment classification effect of the method under the conditions of the same data domain for training and testing. The smaller the value is, the better the classification performance is. Among them, For financial-text-based sentiment classification, it is often difficult to obtain a high recognition rate due to the lack of labeled samples for financial texts. In order to solve the problem, a transfer learning method based on domain adaptation is proposed in this paper. In the proposed method, a large amount of source domain text samples is adopted for sentiment classification. The proposed method is a cross-domain transfer-learning-based method. The domain classification subnetwork is added to the original sentiment classification network, and the domain classification loss function is also added to the original training loss function. Therefore, the network can simultaneously adapt to the target domain and accomplish the classification task. The experiment of the proposed sentiment classification transfer learning method is carried out through the open-source dataset. The proposed method in this paper uses Amazon books, DVDs, electronics, and kitchen appliances as the source domain for cross-domain learning, and the classification accuracy rates have reached 65.0%, 61.2%, 61.6%, and 66.3%, respectively. Compared with nontransfer learning, the classification accuracy rate has improved by 11.0%, 7.6%, 11.4%, and 13.4%, respectively. Compared with the SVM-based baseline method, the CNN-based parameter fine-tuning transfer learning method, and the SDA-based method, the average recognition accuracy rate has been improved by 11.1%, 5.1%, and 1.8%, respectively.For the adopted dataset, the data volume is not large enough for the GAN-based network. Therefore, it is not compared in our implementation. This also shows the disadvantage of the GAN-based method, which is hard to be trained. The performance of the GAN-based method will be studied in the future.


Deep learning-based object counting models have recently been considered preferable choices for plant counting. However, the performance of these data-driven methods would probably deteriorate when a discrepancy exists between the training and testing data. Such a discrepancy is also known as the domain gap. One way to mitigate the performance drop is to use unlabeled data sampled from the testing environment to correct the model behavior. This problem setting is also called unsupervised domain adaptation (UDA). Despite UDA has been a long-standing topic in machine learning society, UDA methods are less studied for plant counting. In this paper, we first evaluate some frequently-used UDA methods on the plant counting task, including feature-level and image-level methods. By analyzing the failure patterns of these methods, we propose a novel background-aware domain adaptation (BADA) module to address the drawbacks. We show that BADA can easily fit into object counting models to improve the cross-domain plant counting performance, especially on background areas. Benefiting from learning where to count, background counting errors are reduced. We also show that BADA can work with adversarial training strategies to further enhance the robustness of counting models against the domain gap. We evaluated our method on 7 different domain adaptation settings, including different camera views, cultivars, locations, and image acquisition devices. Results demonstrate that our method achieved the lowest Mean Absolute Error on 6 out of the 7 settings. The usefulness of BADA is also supported by controlled ablation studies and visualizations. In plant counting, various factors can contribute to domain gaps, e.g., different camera views, cultivars, object sizes or background. The performance of a counting model trained on one domain (source domain) usually deteriorates when tested on another domain (target domain) due to the domain gap. A straight-forward solution is to annotate additional data, while the consumption of time and labor is expensive. Naturally, one comes to the thought whether the unlabeled data in the target domain can be used to correct the model performance as much as possible. This problem setting is called unsupervised domain adaptation (UDA).Estimating the number of plants accurately and efficiently is an important task in agriculture breeding and plant phenotyping. Counting plants  module. This module can fit into existing plant counting models to enhance their cross-domain performance. Specifically, BADA is implemented as a parallel branch in the CNN model. This branch aims to segment areas which potentially contain counting objects, i.e., the foreground. The predicted foregrounds are merged into the feature maps as useful cues. In this way the network learns where to count. We also found that adding only a background-aware branch was insufficient to yield satisfactory cross-domain performance. Hence, two additional domain discriminators were connected to the input feature maps and the output foreground masks. We use adversarial training strategy to jointly optimize the discriminators and other parts of the model, facilitating to extract domain-invariant features and refine the predicted foreground masks.We evaluated our method on three public datasets: MTC  methods in open literature.Machine Learning. Machine learning is a useful tool for plant science, which can model the relationships and patterns between targets and factors given a set of data. It is widely used in many none-destructive phenotyping tasks, e.g., field estimation  dataset . The UAV took 306 pictures of an experimental field which covered around 1 ha. Images were captured at the height of 12.5 m, and the focal length of the camera was 28 mm. Thus, the ground sampling resolution is about 0.3 cm/pixel.This dataset was adopted to evaluate the UDA performance between different image acquisition devices. This setup is challenging as camera views, perspectives, and object scales in images captured by a UAV are significantly different from those of the images captured by phenopoles.Xs denotes the images in the source domain and Ys stores the point annotations for each image. Unlabeled data is denoted by Assume that we have two domains of data under different distributions: labeled data from the source domain and unlabeled data from the target domain. Labeled data from source domain can be denoted by E, θD, θS, θC, θF and θM, respectively.  denotes the channel-wise concatenation of Ms and Mc.We verified our BADA module on a popular and straight-forward object counting method CSRNet . Each element in Cf (Cm) can be viewed as a 2-d vector, and the first element in the vector denotes the probability of the corresponding 4 × 4 patch in Mf (Ms) being the target domain. Similarly, the second dimension denotes the probability being the source domain.The architectures of Mf and Ms into source and target domains, Mf and Ms. This can be achieved by adding a gradient reversal layer  at the beginning of the training process. We will discuss the updating strategy of λ in section 3.2.6.where x denotes the input of the gradient reverse layer, and 1) Counting lossCest and the ground truth local count maps Cgt. One can obtain Cgt from the ground truth density map Dgt. Supposing the image Ii have n annotated points P ∈ ℝn×2 and the corresponding density map can be defined byThe counting loss Pk and the variance σ2. Then the ground truth local count map Cgt can be obtained bywhere 1h × w denotes the convolution operation using a h × w matrix with all ones as kernel. The horizontal and vertical strides are h and w, respectively. In our method, we set h = 64 and w = 64. Then, we define the counting loss by:*N = H · W, i.e., the number of pixels in the local count map.where 2) Segmentation lossSgt from ground truth density maps. Sgt is obtained byAkin to semantic segmentation  Loss for adversarial trainingAgt can be obtained byThe adversarial training loss function 1 and 0 denote matrices filled with ones and zeros. I ∈ It denotes the image I belongs to the source domain, and I ∈ It means I comes from the target domain.where Cf and Cm be Aest, i.e., the probability that the feature maps (foreground masks) are from the source domain. Then, the adversarial training loss Let the second channel of N = H · W.where 1) Training detailsPyTorch  Parameters updateHere we specify the parameter updating strategy during training. At each epoch, At the beginning of each epoch, λ of the gradient reverse layer was updated byp denotes the ratio of the current epoch to total epochs. And γ denotes a pre-defined parameter that controls the speed when λ ascends. As the training proceeds, λ increases from 0 to 1.where Here we report the experiments results. We first evaluated multiple UDA methods on 7 different domain adaptation settings. The results were compared with our method. The efficiency of each module in our method was verified via ablation study. We also conducted visualizations to show the qualitative results of our method. First, we introduce the evaluation metrics.We used mean absolute error (MAE) and root mean square error (MSE) as the main evaluation metrics, which can be defined by:N denotes the number of samples on the test set. yn denote the estimated count and the ground truth count of the nth sample.where To measure the ratio of counting error to the total count of each sample, we used mean absolute percentage error (MAPE), which can be calculated by:R2:In addition, we measured the correlation between estimated counts and annotations by We also noticed that, the false positive responses in the estimated density maps may compensate for errors from missing targets. This indicated that MAE may not fully reflect the real performance of counting models. Therefore, we designed a decoupled MAE where errors on target areas and background areas are calculated independently and then summed up, instead of directly comparing the total counts. For example, if the model wrongly predicts density responses on background and omits some targets. The density responses on background will not compensate for the error on real targets when calculating metrics. To be specific, DMAE is defined as follows,b,n and ŷf,n denote the estimated object count in the background areas and target areas. Similarly, yb,n and yf,n denote the ground truth count in the background areas and target areas. To obtain ŷb,n and ŷf,n, we used the same pesudo segmentation mask Sgt mentioned in section 3.2.5 to divide the image into background areas B and target areas. This process can be defined as follows,ŷyb,n and yf,n can be obtained likewise.Here we specify the experimental settings, including the split of source domain and the introduction of other tested algorithm.We split the MTC dataset according to cultivars. As Zhengdan No.958 contains more samples while samples of other cultivars are much fewer, we used Zhengdan No.958 as the source domain, and the other 5 cultivars as the target domains. Accordingly, there were 5 different adaptation pairs for MTC dataset. For convenience, we named the adaptation pairs by abbreviation, e.g., adaption from Zhengdan No.958 to Jundan No.20 was marked as Z→Jun. The abbreviations for cultivars Zhengdan No.958, Jundan No.20, Wuyue No.3, Jidan No.32, Tianlong No.9 and Nongda No.108 were Z, Jun, W, Ji, T and N, respectively.We split the RPC dataset into two domains according to different locations. Since only 62 images were captured from Guangxi, we adapted the model from Jiangxi to Guangxi, marking this setting as J→G.The MTC-UAV dataset and the MTC dataset shared the same counting object. We used data from MTC dataset as source domain and data from MTC-UAV dataset as target domain to constitute a domain adaptation setting.As UDA for plant counting has seldom been studied, we first evaluated some frequently-used UDA methods. We trained these methods on the plant counting datasets using official implementations  when available. If no codes are released, we implement the method according to the their papers .1) CSRNetCSRNet  CSRNet_DACSRNet_DA refers to a naïve upgrade of CSRNet. We added a discriminator for CSRNet and applied adversarial training strategy discussed in section 3.2.5. The discriminator receives the features extracted by decoder as input and outputs domain class maps.3) Multi-level feature-aware domain adaptationMulti-level Feature Aware (MFA) domain adaption is a feature-level UDA method purposed by Gao et al. . Multi-l4) PCEDAPCEDA is an image-level unsupervised domain adaptation method based on Cycle GAN framework  of the image. The phase consistency requires the phases of the original and transferred images to be close. Thus, instead of manipulating the shapes or textures, the generator tends to transfer the illuminations, hues or colors to target domain. For different domain adaptation setups, we used the official implementation to transfer source images to the target domain, and used the transferred images to train CSRNet, and directly evaluated the model on target data.5) Fourier domain adaptationFourier Domain Adaptation (FDA) , the results show that adversarial training can help aligning the features for different domains in plant counting datasets. Compared with CSRNet_DA, MFA aligns features at different scales with multiple discriminators. MFA showed marginal improvement on MTC datasets, while significant improvements on the setting MTC→MTC-UAV were obtained. This indicates that multi-scale adversarial training is more suitable when objects in different domains are with different scales. This also inspires us that the proposed BADA module can be further improved with multi-scale adaptation strategy. Our methods aligns the features as well as the foreground segmentation results. The visualizations show that, our method can better distinguish the targets and other background elements and generate more precise density maps comparing with other UDA methods. Therefore, the overall MAE and DMAE can be effectively reduced.For image-level domain adaptaion methods (FDA and PCEDA), domain adaptation is achieved by aligning the image styles. We visualize the transferred images in In this paper, we investigate the influence of domain gap for deep learning-based plant counting method and show how to alleviate the influence with unsupervised domain adaptation methods. We evaluated the performance of several popular UDA methods. We found that these methods only prompted limited cross-domain performance due to the characteristics of domain gaps in plant counting. Particularly, the counting models produced large errors on background areas. To address this, we purpose a flexible background-aware domain adaptation module, which can easily fit into existing object counting methods and enhance the cross-domain performance. We evaluated our methods under 7 different domain adaptation settings. The results showed that our method can obtain better cross-domain accuracy than existing UDA methods on plant counting task.Nowadays, despite the rapid development of deep learning-based plant counting methods, the scale and diversity of plant counting datasets are still limited. When applying data-driven plant counting methods on new scenes, it is necessary to consider the hazard of domain gaps. We hope our work can help more researchers and practitioners noticing this issue and bring more solutions for UDA in plant counting. In the future, we will investigate how to extract more generic features for plant counting.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.PyTorch, conducted the experiments, analyzed the results, drafted, and revised the manuscript. X-YL helped draft the manuscript and organized part of the figures and tables. HL helped refine the idea, organized part of the experiments, and revised the manuscript. Z-GC provided the funding and supervised the study. All authors contributed to the article and approved the submitted version.MS proposed the idea of BADA module, implemented the algorithm in This work was supported in part by the National Natural Science Foundation of China under grant no. 61876211 and in part by the Chinese Fundamental Research Funds for the Central Universities under grant no. 2021XXJS095.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.