High-throughput sequencing has provided the capacity of broad virus detection for both known and unknown viruses in a variety of hosts and habitats. It has been successfully applied for novel virus discovery in many agricultural crops, leading to the current drive to apply this technology routinely for plant health diagnostics. For this, efficient and precise methods for sequencing-based virus detection and discovery are essential. However, both existing alignment-based methods relying on reference databases and even more recent machine learning approaches are not efficient enough in detecting unknown viruses in RNAseq datasets of plant viromes. We present VirHunter, a deep learning convolutional neural network approach, to detect novel and known viruses in assemblies of sequencing datasets. While our method is generally applicable to a variety of viruses, here, we trained and evaluated it specifically for RNA viruses by reinforcing the coding sequences’ content in the training dataset. Trained on the NCBI plant viruses data for three different host species , VirHunter outperformed the state-of-the-art method, DeepVirFinder, for the detection of novel viruses, both in the synthetic leave-out setting and on the 12 newly acquired RNAseq datasets. Compared with the traditional tBLASTx approach, VirHunter has consistently exhibited better results in the majority of leave-out experiments. In conclusion, we have shown that VirHunter can be used to streamline the analyses of plant HTS-acquired viromes and is particularly well suited for the detection of novel viral contigs, in RNAseq datasets. Study of viromes at an unprecedented scale has been enabled by the adoption of high-throughput sequencing (HTS) technologies and is now frequently undertaken across an increasing range of host species. In particular, sequencing of plant viromes has become quite common, partly due to its relevance to the agricultural sector. The acquired datasets help to elucidate important questions such as virus spread among host reservoirs and effects of agriculture on the ecosystems and their biodiversity as well as the identification of novel viruses in crops and natural environments . These dUsing an efficient virus detection method, including for the identification of novel viruses, is essential for efficient disease management. Standard diagnostic tests (ELISA assays and PCR-based assays) depend on specific antibodies or primers and thus require prior knowledge of the virus and of its phylogenetic neighbors. Precise identification of viruses is further complexified by the large diversity encountered in the majority of viral species which is linked to the high mutation rate of these agents. This is particularly true for plant viruses, the majority of which are RNA viruses whose mutation rate is very high . MoreoveViruses do not have a universal gene marker that could be used for their identification, contrary to the conserved regions of the 16S rRNA and ITS genes, commonly used to classify bacteria and fungi at the genus or species level . MoreoveWhen interested in known viruses or potentially novel viruses but from a known family, bioinformatics methods that compare the sequenced reads to genomes in public databases are very efficient for virus detection and identification . Read-bade novo assembly to recover novel viral contigs from sequencing data is an essential step in order to overcome the incompleteness of virus reference databases, annotation errors and, importantly, the limited homology between novel viral sequences and reference genomes . Moreover, we generated 12 RNAseq datasets for a range of host species and have shown that VirHunter was not only able to uncover the viruses that were previously identified but also to streamline the analyses by considerably reducing the need for manual curation.In this work, we present VirHunter, a deep learning method that uses convolutional neural networks (CNNs), classifies previously assembled contigs to identify potential viral, host, and bacterial (contamination) sequences in RNAseq samples. The hybrid architecture of VirHunter combines a multi-network CNN-based module covering different k-mer sizes with a downstream random forest classifier module. We have trained VirHunter models for three different plant host species: peach, grapevine, and sugar beet. Importantly, we have shown that VirHunter is especially performant for the task of completely novel virus discovery by building 31 leave-out datasets, in which each viral family is excluded from the training dataset, and comparing the results with a standard BLAST-based solution on one side and a state-of-the-art deep learning method, DeepVirFinder, the other side. VirHunter not only systematically outperformed DeepVirFinder in terms of virus detection but also has considerably reduced the False Positive rate. Cross-evaluation has shown that host detection accuracy remained high and decreased slightly when test sequences originated from the plant species were further phylogenetically removed from that used to train the model. We have further evaluated the detection capacity of VirHunter on Viridiplantae on 26/10/2021, which yielded 122,832 sequences. Plant sequences have been downloaded for Prunus persica (peach), Vitis vinifera (grapevine), Beta vulgaris (sugar beet), and Oryza sativa (rice) from the NCBI RefSeq genomes database. On one hand, they consist of the latest available assemblies, GCF_000346465.2, GCF_000003745.3, GCF_000511025.2, and GCF_001433935.1 for peach, grapevine, sugar beet, and rice, respectively, and of the coding region sequences (CDSs), on the other hand. In the absence of the plastid sequence in the reference assembly of the sugar beet, we used the separately available sugar beet plastid sequence (NC_059012.1). All complete representative bacterial genomes have been downloaded from the NCBI RefSeq database on 28/10/2021 using the genome_updater.sh script.We downloaded all complete and incomplete viral sequences from the NCBI virus database for which the host’s taxonomic id belongs to Pospiviroidae and the Avsunviroidae families of viroids as they have very small genomes . All families with the number of available sequences small families. Finally, all sequences without family labels constituted the unclassified dataset. This resulted in 31 leave-out datasets.To simulate the discovery of completely unknown viruses that do not have expected similarities with the available data, we constructed virus family leave-out datasets by excluding in turns all the sequences of a given plant viral family from the downloaded virus dataset. The NCBI taxonomy contains 45 viral families. We excluded the Sample Preparation and Sequencing). Description of these datasets and presence of viruses identified by aligning assembled contigs against the NCBI GenBank database  are listed in the Moreover, we generated 12 novel virome-sequencing RNAseq datasets, sampled from peach, grapevine, and sugar beet  were processed with the QIAGEN CLC Genomics Workbench (v.21.0.5). Briefly, reads were first quality-controlled and trimmed using default parameters and then assembled using de novo assembly . To identify viral contigs present in these datasets, we followed a standard three step BLAST-based approach, see e.g.,  All Annotation results together with the counts of thus identified viral contigs are listed in the N of fragments of size To prepare the data for processing by the neural network module, datasets were preprocessed by creating representative one-hot encoded fragments see . SpecifiIncluding plastids in relatively high proportion into the plant dataset bacteria . Moreovebacteria . Enrichietc., while gaps are encoded by . Moreover, the encoded dataset is augmented by adding the reverse complement of each original fragment. Indeed, it has been shown by Fragments were further transformed from length VirHunter architecture was defined with two main components the first component is a multi-path neural network shown in 1. Neural network component. The neural network module follows a k-mer-based approach. To alleviate a potential difficulty related to the choice of Data Preprocessing. A convolution layer with leaky rectified linear unit activation function  and for fragment sizes Data Preprocessing. Training batches with size 512 were prepared in a balanced manner across the three classes  from the training dataset and are split into training and validation with the ratio of 9:1. Each of the three individual networks was trained for The training dataset for the CNN module was built as presented in Data Preprocessing. Predictions for random viral fragments were further subsetted in the following manner. We split the test dataset viral fragments into those having good quality predictions  with Classification results for the viral fragments by VirHunter in this family leave-out experiment are shown in Reoviridae, Mayoviridae, Phycodnaviridae, and small families, out of which Reoviridae presented a considerable performance gap. After inspection, it appeared that VirHunter’s false negatives for these four families mostly corresponded to viral fragments being classified as bacteria. This is possibly due to the fact that Mayoviridae are bacteriophages, Reoviridae concern a very wide range of hosts and present characteristics of bacteriophages [likely evolutionary relationship to the Cystovirus family of bacteriophage . This is to be counterbalanced by the fact that being trained only on plant and virus sequences due to the 2-class approach, DeepVirFinder systematically erroneously considers the majority of bacterial fragments as being viral . In seven cases, tBLASTx outperformed VirHunter, but this difference was mostly marginal (5.8% difference in TP rate on average), the outlier being Tolecusatellitidae and Tymoviridae, where the gain in favor of tBLASTx was the strongest. For the “moderately difficult to classify” families, VirHunter had a higher TP rate than tBLASTx in all cases. For the three “difficult to classify” families, even if VirHunter’s performance was globally low, it still outperformed tBLASTx, with the notable exception of Tospoviridae. Altogether, VirHunter has shown consistently better results than that of tBLASTx, for which the TP rate was frequently below the threshold 0.5 (16 families out of 31).As shown in As for the capacity to correctly classify bacterial fragments, VirHunter has shown a systematically high TP rate, ranging from 0.958 to 0.983, across all the leave-out experiments. As for plant fragments, the TP rate was also satisfactory, sugar beet TP from 0.950 to 0.961, grapevine TP from 0.983 to 0.991, and peach TP from 0.983 to 0.989 , plant fragments, coming from the same plant that the models were trained on, are consistently well classified for all the three models with the TP rate ranging between 0.95 (“sugar beet” model tested on random fragments from the sugar beet genome) and 0.99 (the “peach” model tested on random fragments from the peach genome) as shown in As previously described , we trained a model on the rice (Oryza sativa) dataset that belongs to monocots higher clade. As shown in the To evaluate how strongly the performance would be affected if the host of RNAseq dataset was to be from an even further phylogenetically removed plant  VirHunter and DeepVirFinder have correctly identified contigs that were previously annotated as viral. For four datasets , VirHunter was able to discover additional 4, 3, 5, and 1 contigs, respectively. However, for two cases (G5 and S1), DeepVirFinder identified one more annotated contig relative to VirHunter. While VirSorter2 exhibited lower overprediction comparted to VirHunter and DeepVirFinder, its ability to correctly identify viral contigs was low, as it detected at best 60% of the expected viral contigs.Remember that contigs annotated by experts were all removed from the virus dataset used for the training of VirHunter and DeepVirFinder, aset see . Accordiaset see , discoveAssembly of RNAseq Datasets and Annotation of Viral Contigs, given the numbers in the “# detected” column, where VirHunter has shown improvement over DeepVirFinder in eight out of 12 datasets.Moreover, it is possible that at least some potentially novel viruses were missed during expert annotation and that the overprediction in columns “# detected” and “tBLASTx hits”  is capable of broad virus detection for both known and unknown viruses in a variety of hosts and habitats. It has been successfully applied for novel virus discovery in many agricultural crops, leading to the current drive to apply this technology routinely for plant health diagnostics. For this, efficient and precise methods for HTS-based virus detection and discovery are essential.RNA viruses are the most abundant pathogens infecting plants. However, RNA plant virus detection using HTS presents a number of challenges due to their genetic diversity, lack of conserved regions across viral species, short genome lengths, high mutation rate, and incomplete knowledge present in reference databases. To address this challenge, we developed a novel deep learning method, VirHunter, to detect novel and known plant viruses in RNAseq datasets.Alphasatellitidae viruses carry high sequence similarity to Geminiviridae (which was confirmed by the majority of tBLASTx hits).VirHunter is particularly well-suited for the discovery of novel viruses as it was exemplified on 31 synthetic leave-out family datasets, where VirHunter systematically outperformed DeepVirFinder and VirSorter2, reference machine learning tools for virus detection. When compared with the standard tBLASTx approach, we have shown that for most (21 out of 31) leave-out families, VirHunter obtained a higher TP rate. In six cases, tBLASTx was slightly better (5.8% on average). However, there remained four cases where we have seen a much worse performance in VirHunter results. For these specific families, it can be noted that they are particularly well-suited to the alignment-based virus identification, for example, We have shown that the 3-class classification design of VirHunter, accounting for possible bacterial contamination, was justified by evaluating how such contaminating contigs would be classified. Not surprisingly, VirHunter efficiently dealt with bacterial contamination, while DeepVirFinder classified bacteria mostly (65%) as viruses, which should have been “plants” if the goal is to identify viruses. We have also demonstrated that VirHunter is also perfectly suited for the detection of known divergent viruses, by evaluating classification accuracy on contigs with progressively increasing the mutation rate.rosids higher clade resulted in better mutual predictions, while sugar beet as an outgroup belonging to the caryophyllids higher clade has shown a relative drop in performance. All these three plants are eudicots  when we performed a cross-evaluation by classifying sequences coming from three phylogenetically distant plants  by each of the three models. As expected, VirHunter performed better, when the plants it was trained and tested on were phylogenetically closer: grapevine and peach belong to the same eudicots . When thlearning , to enabFinally, we validated VirHunter’s capacity to detect novel viruses on 12 newly acquired RNAseq datasets for peach, grapevine, and sugar beet. In these datasets, VirHunter detected at least 90% (73% for DeepVirFinder and 26% for VirSorter2) of all expert-annotated viral contigs, and in seven datasets it was 100%. Another contribution is the low rate of false positives generated by VirHunter, leaving from 19 to 277 contigs depending on the dataset to be inspected by an expert. These results indicate that VirHunter efficiently reduces the number of contigs requiring manual expert curation.In conclusion, we have shown that VirHunter can be used to streamline the analyses of plant HTS-acquired viromes and is particularly well suited for the detection of novel viral contigs, in RNAseq datasets.


Deferribacter desulfuricans and Sulfurihydrogenibium sp. in apo and nucleotide bound form. We show that the N-terminal part is important for ligand binding. The full-length Pif1 helicase was predicted based on the Alphafold algorithm and the nucleic acid binding on the Pif1 helicase core and the WYL domain was modelled based on known crystallographic structures. The model predicts that amino acids in the domains 1A, WYL, and linker between the Helicase core and WYL are important for nucleic acid binding. Therefore, the N-terminal and C-terminal extensions may be necessary to strengthen the binding of nucleic acid on these Pif1 helicases. This may be an adaptation to thermophilic conditions.Pif1 proteins are DNA helicases belonging to Superfamily 1, with 5′ to 3′ directionality. They are conserved from bacteria to human and have been shown to be particularly important in eukaryotes for replication and nuclear and mitochondrial genome stability. However, Pif1 functions in bacteria are less known. While most Pif1 from mesophilic bacteria consist of the helicase core with limited N-terminal and C-terminal extensions, some Pif1 from thermophilic bacteria exhibit a C-terminal WYL domain. We solved the crystal structures of Pif1 helicase cores from thermophilic bacteria  S. cerevisiae as a protein with multiple functions. Pif1 is involved in DNA repair and is necessary for the maintenance of mitochondrial and nuclear genomes integrity, in the regulation of telomerase activity and in the replication fork progression (reviewed in [Helicases are nucleic acids (NA) dependent nucleotide triphosphate (NTP) hydrolases. The chemical energy of NTP hydrolysis is converted into mechanical force to unwind double stranded NA (dsNA) into single stranded NA (ssNA) . Helicasiewed in ,4,5,6). iewed in . Althougiewed in , Pif1 exiewed in . Pif1 isiewed in  and scisiewed in  but funciewed in  and exhiiewed in . Howeveriewed in ,15,16,17iewed in  and thesiewed in .Bacteroides sp. and Thermus oshimai were solved in complex with different ligand, such as nucleotides and ssDNA [Escherichia coli have a RecD copy but lack Pif1. Most thermophilic bacteria have one copy of Pif1 and it has been shown that some thermophilic bacteria, such as Thermotoga elfii, have an extra WYL domain at the C-terminal end of Pif1 [Thermus oshimai, do not have one [Intriguingly, Pif1 helicases exhibit a high conservation in the helicase core (HC) domain from bacteria to humans. Since Pif1 from yeast and from humans have longer sequences and were difficult to purify, bacterial Pif1 helicases have been used as models to understand structure-function relationships for the whole family. Structures of Pif1 from nd ssDNA ,21,22, rnd ssDNA , and G4 nd ssDNA . Human Pnd ssDNA , but budnd ssDNA ,27. Littnd ssDNA . Pif1 isnd ssDNA . Pif1 an of Pif1 , while oDeferribacter desulfuricans, and Sulfurihydrogenibium sp. which are thermophilic bacteria living at 65 °C [In this work, we have studied Pif1 helicases from at 65 °C ,32. BothDeferribacter desulfuricans Pif1  and Sulfurihydrogenibium sp. Pif1  were cloned into plasmid pET15b-SUMO and then transformed into the C2566H E. coli strain . The expression and purification procedures are the same for both proteins. Briefly, when the culture reached the early stationary phase (OD600 = 0.55–0.6) at 37 °C, expression was induced at 18 °C over 16 h by the addition of 0.3 mM IPTG. Bacteria were harvested by centrifugation  and pellets were resuspended in lysis buffer ). Bacteria were broken with a French press and then further sonicated 2–3 times to shear DNA. After centrifugation at 12,000 rpm for 40 min, the supernatants were filtered through a 0.45-μm filter and loaded onto a Ni2+ charged IMAC column (Cytiva). After washing twice, the SUMO-Pif1 was then eluted from the Ni2+ affinity column with elution buffer ) at 4 °C. The eluted protein was treated with SUMO protease  and the digested protein was further purified by a HiTrap Heparin column (Cytiva) to remove the SUMO-tag and other protein impurities. The eluted fraction containing Pif1 was collected and concentrated. The final purified protein was dialyzed against the storage buffer  and concentrated to approximately 10 mg/mL for crystallization trials. Purity was assessed by SDS-PAGE and was determined as 95%.The helicase core domain from genes encoding 4)3Cit, 15% (w/v) PEG MME 2000 (polyethylene glycol monomethyl ether 2000). This condition was optimized by a grid search by using 24-well Linbro plates at 20 °C where 1 μL of protein sample and 1 μL of precipitant were mixed together and equilibrated with 0.4 mL of precipitant. Crystals of the DdPif1-AMPPNP complex were obtained from the precipitant containing 0.1M Bis-Tris propane (pH 6.5), 0.1 M calcium acetate, and 10–15% PEG4000, and crystals of the SsPif1-ADP complex were obtained from the precipitant containing 0.1M Hepes (pH 7.5), 3.5–3.6 M sodium formate, 5% glycerol, and 0.01 M spermidine.Crystallization trials for DdPif1 apo, DdPif1-AMPPNP, and SsPif1-ADP were performed at 20 °C by the sitting-drop vapor diffusion method. Proteins were mixed with the nucleotide (AMPPNP or ADP) prior to crystallization at the molar ratio of 1:1.5. The initial DdPif1 apo crystals were obtained from the precipitant solution containing 0.12 M (NHX-ray diffraction data were collected at beamline BL17U1 at Shanghai Synchrotron Radiation Facility (SSRF). Data were processed with AUTOPROC software  with XDS2 was adjusted to pass through the centers of the capillaries for each experiment. The data were processed with BioXtasRAW [The scattering data of apo DdPif1 were collected at beamline BL19U2 of SSRF in batch mode at 25 °C and at protein concentrations 1.0, 3.0, 5.0, 7.0, and 9.0 mg/mL in buffer containing 25 mM Hepes (pH 7.5), 100 mM NaCl, and 5% Glycerol. The wavelength (λ) of X-ray radiation was set as 1.033 Å, and a sample to detector distance was set to 2.5 m. The X-ray beam with a size of 0.40 × 0.15 (H × V) mmoXtasRAW . Then, 2oXtasRAW . Ab initoXtasRAW , with exoXtasRAW  where floXtasRAW . A compaC. crescentus DriD C-domain (PDB 7U02). The ATP nucleotide was modelled using DdPif1-AMPPNP complex information. The DdPif1-ATP-ssDNA was then subjected to a molecular dynamics simulation. The inputs were prepared using CHARMM-GUI [As DdPif1 structure is not available in the Alphafold database, the full-length DdPif1 structure (residues 1-510) was modelled using Alphafold  and RoseARMM-GUI  with theARMM-GUI . The NVTARMM-GUI , which sHomo sapiens or Saccharomyces cerevisiae, we screened for crystallization assays several recombinant Pif1 produced from different bacterial species, including thermophilic bacteria. We were able to crystallize the helicase core of Pif1 from the thermophilic bacteria Deferribacter desulfuricans and Sulfurihydrogenibium sp., which are moderate thermophilic bacteria, living at an average temperature of 65 °C. Pif1 from these bacteria are named DdPif1 and SsPif1, respectively. DdPif1 and SsPif1 consist of a helicase core (HC) domain, with 45.0 and 37.6 % identity with Bacteroides sp. Pif1 (BsPif1), respectively, and a WYL domain in the C-terminal position (Homo sapiens Pif1 (HsPif1).In an initial effort to crystallize Pif1 from position A. For cr1 with one molecule in the asymmetric unit and DdPif1 in the complex with the nucleotide analogue AMPPNP crystallized in spacegroup P21212 with two molecules in the asymmetric unit (The structures were solved by molecular replacement (MR) using the apo structure of BsPif1 (PDB 5FTD). DdPif1 apo crystallized in spacegroup C222ric unit . Though ric unit . However1, with unit cell a = 141.28 Å b = 178.892 Å c = 129.827 Å α = β = γ = 90 °C containing one molecule but no solution was found with MR using PDB 5FTD as a search model. By testing different subgroups, it was found that indexing in spacegroup P21 with unit cell a = 113.138 Å b = 129.29Å c = 113.68 Å α = 90 °C β = 103.4 °C γ = 90 °C gave an acceptable MR solution. This spacegroup harbors a strong translational non-crystallographic symmetry (tNCS) and twinning with twin law l, -k, h. An unambiguous solution was found in MR with four molecules in the asymmetric unit. The four molecules are arranged in two dimers related by tNCS. In the dimer, the molecules are related by a non-crystallographic two-fold symmetry, interacting by a β-strand in the 2B domain (SsPif1 in complex with ADP has an apparent spacegroup C222B domain .Deinococcus radiodurans (PDB ID 3GPL) and Dda from T4 bacteriophage (PDB ID 3UPU), with a long β-hairpin protruding, named loop-3 in BsPif1 [The helicase core of Pif1 from thermophilic bacteria is very similar to other bacterial Pif1 structures already solved . The HC n BsPif1 . Domain When DdPif1 apo is superimposed on BsPif1 apo (PDB 5FTD) F, BsPif1Only DdPif1 was solved in two different states, apo and in complex with the ATP analogue AMPPNP A,B. Stru2+ and water molecules, could not be built. However, the comparison with the previous structure of Pif1 solved with a nucleotide at a higher resolution shows that the nucleotide binding site is conserved  and the linker between 1A and 2A domains. The fitted structure has a reduced χ2 of 1.22, which exhibits a good fit with the SAXS data . The molecular weight of DdPif1, estimated from I (0), Porod volume, and Volume of correlation (Vc) is 50 kD, which is consistent with a monomer of DdPif1 in solution. If the N-terminal part is added to the model of DdPif1 apo with the conformation of the DdPif1-AMPPNP complex and fitted to the SAXS data, the χAXS data A. The KrAXS data B. The DdAXS data C of DENSAXS data D algoritC. crescentus DriD C-domain bound to ssDNA (PDB 7U02) (The full length DdPi1 structure (residues 1-510) was predicted with no ligand by Alphafold and RosettaFold algorithms, both giving very similar results with differences in domains positions (rmsd 5.7 Å). In order to get a model of DdPif1 bound to ssDNA, we manually moved the domains 2A and 2B in order to fit ssDNA as in known structures of Pif1-ssDNA complexes, such as in BsPif1-ssDNA complex (PDB 5FHD). Alphafold predicts the fold of a WYL domain in the C-terminal part of DdPif1. The WYL domain has been shown in TePif1 to be crucial for helicase activity and was shown to bind to the 5′ end of ssDNA . In the DB 7U02) . We usedIn the DdPif1-ssDNA model, ssDNA is extended from the WYL domain (5′ end) to the end of nucleic acid binding channel of HC (3′ end). The DNA duplex, which could be unwound, would be attached to the 3′ end and would be near the 1B and 2B domains, which have been shown to be necessary for helicase activity . The mosCrystal structures of DdPif1 and SsPif1 confirm the high level of conservation among Pif1 helicase family of the HC domain and the specific features of the 2B domain fold with its extended β-hairpin, named loop-3 in BsPif1 . The nucDdPif1 and SsPif1 share a high homology with TePif1, in particular, by the presence of the C-terminal WYL domain. WYL domains were initially identified in prokaryotic proteins involved in the defense system, in particular, in the CRISPR-Cas loci , but werRecently, the conformations of ssDNA outside of the Pif1 HC tunnel have been explored by molecular dynamics and magnetic tweezers . A stretSulfurihydrogenibium sp. belongs to the phyla Aquificae which does not exhibit a genome with high GC% content and has a low frequency of G4. The situation is the same for Thermogoga elfii, which belongs to phyla Thermotogae and which has lower G4 frequency than expected by chance [However, the WYL domain is not always present in Pif1, even in thermophilic bacteria, since ToPif1 for instance does not contain one. Furthermore, the role of Pif1 helicase in thermophilic bacteria is still obscure. Pif1 has an important activity towards G4 but the frequency of predicted G4 presence is not correlated with high GC% content ,60. For y chance .Deferribacter desulfuricans and Sulfurihydrogenibium sp. is around 65 °C, Pif1 activity should be tested at these temperature but is experimentally difficult, as discussed for TePif1 [How does Pif1 helicase in thermophilic bacteria achieve thermostability? Several observations concerning protein thermostability have been done, regarding salt bridges, amino acid composition, and entropic stabilization ,62,63. Ir TePif1 . Therefo


The quality and safety of wheat flour are of public concern since they are related to the quality of flour products and human health. Therefore, efficient and convenient analytical techniques are needed for the quality and safety controls of wheat flour. Near-infrared (NIR) spectroscopy has become an ideal technique for assessing the quality and safety of wheat flour, as it is a rapid, efficient and nondestructive method. The application of NIR spectroscopy in the quality and safety analysis of wheat flour is addressed in this review. First, we briefly summarize the basic knowledge of NIR spectroscopy and chemometrics. Then, recent advances in the application of NIR spectroscopy for chemical composition, technological parameters, and safety analysis are presented. Finally, the potential of NIR spectroscopy is discussed. Combined with chemometric methods, NIR spectroscopy has been used to detect chemical composition, technological parameters, deoxynivalenol, adulterants and additives of wheat flour. Furthermore, NIR spectroscopy has shown great potential for the rapid and online analysis of the quality and safety of wheat flour. It is anticipated that the current review will serve as a reference for the future analysis of wheat flour by NIR spectroscopy to ensure the quality and safety of flour products.  •NIR spectroscopy is an ideal technique for analysis of wheat flour due to its rapid and nondestructive nature.•Use of NIR spectroscopy for chemical composition, technological parameters, and safety analysis.•Online and handheld NIR spectrometers for wheat flour detection are the future trends. As one The main quality parameters of wheat flour include its chemical composition, which is related to the moisture, protein, ash, and wet gluten contents of the flour, and technological parameters, such as the sedimentation value, falling number and rheological properties of wheat flour dough. Conventional methods available for the quality and safety assessment of wheat flour are listed in Recently, near-infrared (NIR) spectroscopy, as a reliable tool in agricultural and food industry analysis, has been widely used in the daily inspection of wheat flour . NIR spe2As defined by the American Society for Testing and Materials (ASTM), near-infrared light is part of the electromagnetic spectrum in the range of 780–2500 nm, which is between the visible light and mid-infrared light spectrum . NIR speThe NIR spectrometer is the hardware for near-infrared analysis and is mainly composed of a light source, a beam splitting system, a sample detector, an optical detector, and a data processing and analysis system . Based oPCA), artificial neural networks (ANNs), partial least squares (PLS), partial least squares discriminant analysis, linear discriminant analysis (LDA), multiple linear regression, support vector machines (SVMs), radial basis functions (RBFs), back propagation (BP), random forests (RFs), extreme learning machine (ELM), soft independent modeling of class analogy, and cluster analysis (CA) (2) and correlation coefficient for prediction (Rp) are often used to evaluate the performance of built models. The best models typically have the highest R and R2 or sometimes Rp while having a lower root mean square error of prediction (RMSEP) and root mean square error of cross-validation (RMSECV)  . To obta(RMSECV) . Additio(RMSECV) .3The main components of wheat flour analyzed include moisture, protein, ash, and some functional substances, which are closely related to its nutritional quality and processing properties. The hydrogen-containing groups  in each component of wheat flour have characteristic absorption peaks in the near-infrared spectral region, which is the foundation for the detection of chemical constituents of wheat flour by NIR spectroscopy . NIR spe3.12, RMSEP, RMSECV and RPD were 0.92, 0.85, 0.27, 0.47, and 2.43, respectively , and applied PLSR to establish the model, which showed Rp and RMSEP of 0.99 and 0.088, respectively.Moisture is an important quality parameter for wheat flour storage and processing, and moisture content is typically less than 14.5% . PLS regectively . The dev2 of 0.8367, and the RMSEP and RMSECV were 0.32 and 0.35, respectively. In a study by 2, RMSEP and RMSECV were 0.929, 0.154, and 0.125, respectively. In addition, 2 of the moisture content was 0.920.The handheld NIR spectrometer is also suitable for fast and quantitative determination of moisture in wheat flour. The moisture content in wheat flour samples was evaluated quickly and quantitatively using a linear variable filter-based Viavi MicroNIR 1700 spectrometer by 3.22 of 0.81 and an RMSEP of 0.58. 2 of 0.937 and an SEP of 0.492 were obtained. A PLS model of the protein content was established by2 of 0.9624 and an RMSEP of 0.22. Generally, support vector regression (SVR) is superior to the PLS and ANN methods in modeling NIR data and the synergy interval has a strong ability to select appropriate variables during model building  of wheat flour protein affect wheat flour dough properties and the quality of the final products . Kahrımabuilding . In the 3.3−1 and 6708-7304 cm−1. The predictive ability of ash content models was improved with an Rp of 0.911 and an RMSEP of 0.019 using the characteristic bands. A PLS calibration model of ash content was established after pretreatment by the first derivative and SNV within the wavelength range of 908–1676 nm; R2 and RMSEP values of 0.9431 and 0.06, respectively, were achieved  by 2 values of the best models for calcium, phosphorus and potassium were 0.7907, 0.9777, and 0.9777, respectively; the RMSEP values were 5.35, 15.3, and 18.9, respectively; and the RPD were 2.19, 6.71, and 6.84, respectively  contents in wheat flour samples. The Rectively . Althoug42 values of 0.917 and 0.832, respectively.It is known that NIR spectroscopy can be used to predict wheat flour technological parameters since abundant changes in the technological parameters of wheat flour are related to the chemical variation of its components . Zeleny In the study conducted by 52p and RMSEP values of 0.987 and 0.373%, respectively. However, there are few studies in this area at present, and this research direction is worthy of attention.Wheat flour safety issues typically involve chemical additives and undesired contaminants, as well as the adulteration of wheat flour. Generally, the addition of inexpensive substitutes, such as rice, corn or potatoes, can reduce the processing performance and commercial value of wheat flour . And una5.1Chemical additives are used to standardize the quality and process performance of wheat flour and chemical additives used in wheat flour include benzoyl peroxide (BPO), talcum powder, azodicarbonamide, ascorbic acid, emulsifiers, enzymes, etc . Some of2cal, RMSEC, R2pred, and RMSEP of the PLS model were 0.8901, 40.85 mg/kg, 0.8865, and 44.69 mg/kg, respectively. Another study on detecting the concentration of benzoyl peroxide in wheat flour was conducted by 2p was 0.998 and the RMSEP was 0.282%, and the detection limit of the model reached 0.5%. Furthermore, the results of the study by The BPO content in pure wheat flour was determined based on NIR diffuse reflectance spectroscopy by 2 values of lime and calcium carbonate using the PLS algorithm were 99.80% and 96.98%, the RMSEC were 0.19 and 0.34, the RMSECV were 0.26 and 0.75, the RMSEP were 0.63 and 0.44, and the RPD were 8.57 and 5.24, respectively. In terms of azodicarbonamide (ADA) detection, In addition, a feasibility study was conducted by 5.2Biological contamination of wheat flour mainly includes deoxynivalenol (DON) and insects. DON also known as vomitoxin, is a major mycotoxin detected in wheat . DON con5.3p and RPD values of 0.99 and 19.3, respectively, and low SECV and SEP values of 1.12 and 1.39%, respectively. Furthermore, NIR spectroscopy was adopted to detect the adulteration of spelt flour with inexpensive bread wheat flour, and the resulting PLSR model achieved an R2 of 0.966 and an RMSEC of 5.2%  will broaden its application in wheat flour.Shun Zhang: Conceptualization, and, Writing – original draft. Shuliang Liu: Writing – original draft, and, Writing – review & editing. Li Shen: Writing – original draft, and, Writing – review & editing. Shujuan Chen: Writing – original draft. Li He: Writing – original draft. Aiping Liu: Funding acquisition, Writing – review & editing, and, Supervision, All authors contributed to the article and approved the submitted version.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


The motion stability of the compliant mechanism based on this type of FLS is high. When this type of FLS is loaded along the width direction, the shear deformation needs to be characterized. Nevertheless, currently available compliance modeling methods for FLS are established based on Euler–Bernoulli beam model and cannot be used to characterize shear models. Therefore, these methods are not applicable in this case. In this paper, a new six-DOF compliance model for FLSs with large w/L is established under large deformation. The shear deformation along the width direction model is characterized based on the Timoshenko beam theory. The new constraint model and differential equations are established to obtain a high-precision compliance model expression for this type of FLS. The effects of structural parameters on the compliance of the FLS are analyzed. Finally, the accuracy of the model is verified both experimentally and by finite element simulation. The relative error between theoretical result and experiment result is less than 5%.Flexure leaf spring (FLS) with large deformation is the basic unit of compliant mechanisms with large stroke. The stiffness along the non-working directions of FLSs with large width-to-length ratio ( Compliant mechanisms are mechanical mechanisms that rely on the elastic deformation of materials and, among other uses, have been widely applied in medical devices ,2,3, preInitially, nonlinear compliance models concerned FLSs with 2D planar deformation. The methods used include the beam constraint method , chain mThe 2D nonlinear compliance models of FLS are only suitable for planar distributed compliant mechanisms. Therefore, 3D compliance models are needed for the modelling of spatial multi-DOF distributed compliant mechanism. Irschik et al.  proposedw/L) of this type of FLSs is large. Current 3D compliance models all can be used in FLSs with small width-to-length ratio and thickness-to-length ratio. When establishing a compliance model for this type of FLS, the constraint model based on Euler–Bernoulli beam theory and the spatial coordinate system conversion relationship are used to construct a differential equation system without considering shear deformation. Therefore, current 3D compliance models cannot be used to accurately predict the compliance of such FLSs. To this end, this paper proposes a spatial compliance model for FLSs with large w/L. In this model, the shear deformation along the width direction is characterized based on the Timoshenko beam theory and a new differential equation system is established by combining the spatial coordinate transformation relations. The shear deformation along the width direction of the FLS with large w/L and the spatial geometric nonlinearity under large deformation can be accurately described by this model. Furthermore, an experimental platform is set to verify the accuracy of the proposed compliance model.Highly deformable FLSs with low stiffness along the working direction and high stiffness in the non-working directions have been widely used, e.g., in large-stroke compliant linear guiding mechanisms. In general, the width-to-length ratio (L) is established based on the spatial constraint model and the relationship between deformation and load is derived. In The rest of the paper is organized as follows. In X, 0, 0) when the FLS is not deformed and as P1′  after deformation. It is defined by the cross-section where the point P1 (point P1′ after deformation) is located perpendicular to the tangential direction of the FLS axis at P1′ after deformation. A coordinate system is established on the cross-section, where the point P1′ is located after deformation. The Xd1 axis of the coordinate system coincides with the tangent of the axis at P1′, and when the FLS is not deformed, the directions of the Yd1 and Zd1 axes are the same as those of the Y and Z axes of the initial coordinate system. The transformation relationship between the initial coordinate system  and the one on the cross-section where the point P1′ is located after deformation  is depicted in The deformation of the FLS under load and the definition of the structural parameters are given in L, the following approximate relationship can be satisfied ;The range of L is ;The range of t is .The range of The range of each parameter is as follows:E = 206 GPa and Poisson’s ratio of v = 0.29. The effects of the different parameters are presented in L, w, and t, respectively, on compliance.The material of the FLS was set as spring steel (60Si2Mn) with a Young’s modulus of cy decreased with the increase of the FLS deformation along the thickness direction. Moreover, the value of cy increased with the increase of L, decreased with the increase of w, and increased with the increase of t. The variation range of cy increased with increasing L, decreased with increasing w, and decreased with increasing t over the deformation range of the FLS.In cz increased with the increase of the FLS deformation along the thickness direction. The value of cz increased with increasing L, decreased with increasing w, and increased with increasing t. The variation range of cz increased with increasing L, decreased with increasing w, and decreased with increasing t over the deformation range of the FLS.As can be seen in cx increased with the increase of the FLS deformation along the thickness direction. The value of cx increased with increasing L, decreased with increasing w, and decreased with increasing t. The variation range of cx increased with increasing L, decreased with increasing w, and decreased with increasing t over the deformation range of the FLS.According to crx decreased with the increase of the FLS deformation along the thickness direction. More specifically, the value of crx increased with increasing L, decreased with increasing w, and decreased with increasing t. The variation range of crx did not change with the variation of L and that of t but decreased with increasing w over the deformation range of the FLS.In The ratio of the compliance along the working direction to that along the non-working directions is an important indicator that can reflect the motion stability of the FLS. The compliance ratio indicates whether the performance of the FLS can ensure the smoothness of motion in the working direction and resist the disturbance forces in the non-working directions. To this end, the following compliance ratios were analyzed:w is ;The range of L is ;The range of t is .The range of The range of each parameter is as follows:E = 206 GPa and Poisson’s ratio of v = 0.29. The effects of the different parameters are exhibited in L, w, and t, respectively, on compliance ratio.The material of the FLS was set as spring steel (60Si2Mn) with a Young’s modulus of cz/cy increased with the increase of the FLS deformation along the thickness direction. The value of cz/cy did not change with the variation of L, decreased with increasing w and increased with increasing t. The variation range of cz/cy did not change with the variation of L, decreased with increasing w, and increased with increasing t over the deformation range of the FLS.As can be seen in cx/cy increased with the increase of the FLS deformation along the thickness direction. The value of cx/cy decreased with increasing L, remained unchanged with the variation of w, and increased with increasing t. The variation range of cx/cy decreased with increasing L, did not change with the variation of w, and increased with increasing t over the deformation range of the FLS.In crx/cy decreased first and then increased with the increase of the FLS deformation along the thickness direction. The value of crx/cy decreased with increasing L, remained unchanged with the variation of w, and increased with increasing t. The variation range of crx/cy decreased with increasing L, did not change with the variation of w, and increased with increasing t over the deformation range of the FLS.According to P1, P2, P3) on the discrete boundary where nodes 3, 8, and 13 are located were compared with the calculation results obtained by the theoretical model. In order to analyze the rotation angle of the FLS in the simulation and experimental results, the deformations of the points  on the boundary line on the FLS where nodes 3, 8, and 13 are located were extracted. The distance between the different points is given in In this paper, the compliance model of the spatial large deformation FLS is verified both experimentally and through finite element simulations. The structure of the FLS, the nodes of the chain model, and the positions of the applied load are depicted in P11, P12, P21, P22, P31, P32 were recorded from the simulation results. The displacement and rotation angle of each point  could be calculated based on the simulation results of points . The theoretical calculation and simulation results of points  are listed in The mesh of the FLS model is depicted in P11, P12, P21, P22, P31, P32) on the FLS after deformation was obtained by processing the spatial contour data. Subsequently, the deformation and rotation angle of points  on the FLS were calculated. The FLS used in the experiment is depicted in P11, P12, P21, P22, P31, P32) in order to facilitate the subsequent data processing. Due to that the scanning area of the structured light sensor was small and could not fully cover the FLS, the FLS needed to be marked at reference points to implement the splicing algorithm of spatial contour. Then, the scanned contour data of the entire FLS could be obtained. The FLS deformed under load is displayed in m = 100 g and the gravitational acceleration g = 9.8066 N/kg. The spatial contours of the deformed FLS are presented in P1, P2, P3) were obtained after processing the spatial contour data and are listed in The experimental platform used to test the FLS is depicted in w/L under large deformation has been proposed. A new differential equation system is established based on Timoshenko beam theory along the width direction. The shear deformation along the width direction of the FLS with large w/L and the spatial geometric nonlinearity can be accurately described by this method. In L) has been proposed. Then, the chain model of the spatial FLS was established in In this paper, a 6-DOF compliance model for FLSs with large


An established way of realising topologically protected states in a two-dimensional electron gas is by applying a perpendicular magnetic field thus creating quantum Hall edge channels. In electrostatically gapped bilayer graphene intriguingly, even in the absence of a magnetic field, topologically protected electronic states can emerge at naturally occurring stacking domain walls. While individually both types of topologically protected states have been investigated, their intriguing interplay remains poorly understood. Here, we focus on the interplay between topological domain wall states and quantum Hall edge transport within the eight-fold degenerate zeroth Landau level of high-quality suspended bilayer graphene. We find that the two-terminal conductance remains approximately constant for low magnetic fields throughout the distinct quantum Hall states since the conduction channels are traded between domain wall and device edges. For high magnetic fields, however, we observe evidence of transport suppression at the domain wall, which can be attributed to the emergence of spectral minigaps. This indicates that stacking domain walls potentially do not correspond to a topological domain wall in the order parameter. In electrostatically-gapped bilayer graphene, topologically-protected states can emerge at naturally occurring stacking domain walls even in the absence of a magnetic field. Here, the authors describe the interplay between such domain wall states and quantum Hall edge transport within the eight-fold degenerate zeroth Landau level of suspended bilayer graphene. The most direct way to induce non-trivial valley response is by breaking sublattice symmetry. This occurs naturally in boron nitride, which makes it a quantum valley Hall insulator3. In Bernal-stacked bilayer graphene, the same effect is achieved by applying an interlayer bias4. Moreover, by spatially varying its sign, topological domain walls can be created, which exhibit one-dimensional (1D) electronic channels with quantised conductance4, resilient to backscattering5. These electronic domain-wall states provide a flexible platform to study 1D transport8 and correlated physics11. However, creating them by electrostatic gating is technically challenging. Fortunately, similar physics transpire at stacking domain walls (DWs) in bilayer graphene, where the stacking arrangement of graphene layers changes from AB to BA12. Such domain walls are common in naturally Bernal-stacked bilayer graphene15 and even ubiquitous in twisted bilayer graphene17, which is known for hosting superconductivity at a certain twist angle18. When a uniform electric field is applied to a bilayer graphene flake with a DW, topologically protected valley-helical states emerge along the dislocation, surrounded by insulating bulk19. Critically for the present work, stacking domain walls can have much richer interplay with spontaneous symmetry breaking in bilayer graphene27 compared to artificially created ones, as not being forced by applied bias to have charge imbalance between layers. The interplay between stacking domain walls and spontaneous symmetry breaking is of peculiar interest in the presence of a quantising magnetic field, since bilayer graphene exhibits a very rich phase diagram owing to the eightfold degeneracy of the zero-energy Landau levels30 . Interactions lift the degeneracy by generating orderings, leading to quantum Hall plateaus at all integer filling fractions between −4 and 432. This complex and intriguing regime shows a large variety of ways the internal symmetry can break spontaneously in the absence of externally induced layer polarisation. Within this manifold, the valley, sublattice, and layer index are rigidly locked. Since at the stacking domain wall the roles of the layers are exchanged, any ordering that is not a valley singlet is guaranteed to be affected.Electrons near the Fermi surface of two-dimensional hexagonal materials typically occupy two or more distinct electronic valleys. The valley index adds to the carrier’s charge and spin, enabling additional channels for spontaneous symmetry breaking at low temperatures, whereby valleys are polarised independently or in combination with charge and spin degrees of freedomIn this work, the goal is to study this interplay by means of transport measurements. It cannot be fully explored in the artificial electrostatic domain walls as a matter of principle. We chose freestanding dually gated bilayer graphene devices as an ideal and versatile platform, since on the one side—as indicated by our measurements below—DWs remain stable during processing and suspension, and, on the other side, suspending enables the investigation of quantum transport unaffected by surroundings.33. Even though flakes show a smooth surface in the topography  of the latter type. Data from additional domain-wall containing devices are shown in the At first, suitable bilayer graphene flakes were preselected using optical microscopy and subsequently investigated with scattering scanning near-field optical microscopyphy Fig. , the corphy Fig.  can reve24 and a phase transition to the insulating fully layer polarised state for increasing electric field24. The resistance in device D1-DW  in tapping mode to detect any stacking domain walls. Hereby, an infrared CO2 laser beam (with a wavelength of 10.5 μm) was focused onto a metal-coated atomic force microscopy tips , which was oscillating with a frequency and amplitude of 250–270 kHz and 50–80 nm, respectively. With this method, we were able to obtain topographic and infrared nano-images simultaneously. Electrodes  in two distinct configurations, a top gate  as well as a spacer  were fabricated using several steps of standard lithography techniques and electron beam evaporation. Subsequently, the devices were submersed in hydrofluoric acid to etch about 150–200 nm of the SiO2 and consequently suspend both the top gates and bilayer graphene flakes. After loading the freestanding dually gated bilayer graphene devices into a dilution refrigerator current annealing was performed at 1.6 K. In devices without domain wall best results were obtained when using a current of about 0.35 mA μm−1 per layer. In devices with domain wall 150–250% more current was needed to achieve a current saturation due to their lower resistance and shorter channels. All quantum transport measurements were conducted at the base temperature of the cryostat (T < 10 mK), if not noted differently. Moreover, an excitation a.c. bias current of 0.1–10 nA at 78 Hz and Stanford Research Systems SR865A and SR830 lock-in amplifiers were used for the measurements, as well as Keithley 2450 SourceMeters to apply the gate voltages. Low-pass filters were used in series to reduce high frequency noise.Bilayer graphene was exfoliated from a highly ordered pyrolytic graphite (HOPG) block onto Si/SiOSupplementary InformationPeer Review File


The camelid-derived single chain antibody (sdAb), also termed VHH or nanobody, is a unique, functional heavy (H)-chain antibody (HCAb). In contrast to conventional antibodies, sdAb is a unique antibody fragment consisting of a heavy-chain variable domain. It lacks light chains and a first constant domain (CH1). With a small molecular weight of only 12~15 kDa, sdAb has a similar antigen-binding affinity to conventional Abs but a higher solubility, which exerts unique advantages for the recognition and binding of functional, versatile, target-specific antigen fragments. In recent decades, with their unique structural and functional features, nanobodies have been considered promising agents and alternatives to traditional monoclonal antibodies. As a new generation of nano-biological tools, natural and synthetic nanobodies have been used in many fields of biomedicine, including biomolecular materials, biological research, medical diagnosis and immune therapies. This article briefly overviews the biomolecular structure, biochemical properties, immune acquisition and phage library construction of nanobodies and comprehensively reviews their applications in medical research. It is expected that this review will provide a reference for the further exploration and unveiling of nanobody properties and function, as well as a bright future for the development of drugs and therapeutic methods based on nanobodies. Antibodies, also called immunoglobulins (Ig), are at the core of an adaptive immune system. They have the ability to recognize and eliminate specific foreign substances from the body. Due to a natural ability to bind to target antigens, antibodies demonstrate great value in biomedicine and drug development ,2. To imA natural, small, functional antibody known as a heavy-chain antibody (HCAb) was first reported in camelid serum in 1993 . Unlike In this review, we comprehensively summarize the biomolecular structure of the HCAb and its VHH domain, mainly with respect to their biochemical properties, immune acquisition and phage library construction. We review some distinctive and interesting research and applications in the medical field, and expect that this review will provide a reference for the further exploration and unveiling of the properties and function of nanobodies, as well as providing a perspective for the development of drugs and therapeutic methods based on nanobodies.Conventional IgGs are tetramers consisting of two heavy-chains and two light chains A. The heHomodimeric antibodies are different from the conventional, heterotetrametric antibodies that naturally exist in the serum of camelids, including camels, alpacas and vicugnas. The homodimeric antibody has only two heavy-chains and is devoid of light chains; it is named HCAb B 21]. B. B21]. BMuyldermans’s group constructed the VH/VHH dromedary germline database and compared the VH and VHH sequences of dromedary serum samples by PCR amplification. They found that the number of genes coding for VH and VHH of similar structures was almost the same in the camel genome, and that the VHHs evolved within a VH subgroup III . NeverthThe typical variable domain for the VHH structure of folded immunoglobulin is organized as nine β-strands, termed A-B-C-C’-C”-D-E-F-G and arranged in two sheets. The A, B, E, and D β-strands of one sheet lay one side and the C, C’, C”, G, and F strands lay on another. Three hypervariable (HV) regions cluster at one end of the domain: namely H1, H2, and H3. These regions are located in the loops that connect the B-C, C’-C”, and F-G strands, respectively. The three hypervariable regions are wrapped by the relatively conserved structure of the sheets, termed FR, and form a continuous surface with the capacity to bind to epitope, called the complementarity determining region (CDR) . These tThe longer H3 loop in the VHH domain of the HCAb may be due to the selection of multifunctional VHHs after the V-D-J rearrangement. With the expansion of H3, the entire HV loop also becomes longer, which can provide a sufficiently large antigen-interacting surface to better identify the epitope groove of the protein to combine with the antigen and antibody . HoweverAnother distinction existing between the VHH domain of the sdAbs and the VH domain of the conventional antibody is the FR2 fragment. Researchers analyzed the VHH protein–antigen complex using X-ray crystallography and found that the hydrophobic amino acids in the VHH FR2 structure were replaced by the hydrophilic amino acids in the FR2 structure that cross-linked the VH-VL domain of conventional antibodies, which are Phe-42-Val, Glu-49-Gly, Arg-50-Leu, and Gly-52-Trp ,30,31,32Chemical inductions, such as a high concentration of guanidine hydrochloride (GuHcl) and urea, can lead to the reversible unfolding of antigens specifically bound to the CDR region and labile, refolded protein molecules. These can be achieved using far ultraviolet CD spectroscopy and surface plasmon resonance (SPR) for monitoring. In addition, the synergistic unfolding of the nanobody fragments was also observed by pressure induction. The above studies illustrate that the VHH fragment of HCAbs in camelid serum has a remarkably high conformational stability ,36.SdAbs can perform functions under high temperature exposure due to their ability to refold following heat denaturation. The VHH fragment of the HCAb in camel serum has a distinct thermo-reversible stability profile ,38. MostRecently, molecular dynamics (MD) simulations were established to predict mutations affecting the thermal stabilization of sdAbs . This apThe hydrophilicity of an antibody’s light-chain surface , which aIn the VHH of an HCAb, the CDR-H3 region has a larger frequency variation, ranging from 10 to 24 amino acids , while tIn recent decades, bispecific antibodies (BsAbs), as intermediate-sized biomolecules, have attracted extensive attention as therapeutic tools in the field of medicine ,58. AlthThe VH and VL domains of the conventional antibody IgG were covered via two independently functioning VHH domains of the HCAb, assembled into bispecific VHH-IgG and combined with a heavy-chain heterodimerization technology to produce monovalent, bispecific, IgG-like antibodies . Pekar’sAntigen-specific nanobodies are obtained by animal immunization and yeast antibody coupling agent display technology. The exogenous VHH is non-immunogenic , and humThe VHH repertoire of the dromedary HCAb is a biological protein molecule, produced through the stimulation of the B cells of immune animals with an antigen. The high-affinity VHH library generated via specifically combining two cognate antigens is also called the immune library. A unique advantage of maturated, in vivo immune libraries is the relatively high quality of VHH sequences to avoid the immunogenicity of antibodies ,67. In aSome researchers immunized camelids with an anti-DNA mouse mAb, which can cause an anti-idiotypic response and activate the humoral immune system, generating VHHs by constructing phage libraries . HoweverE. coli . W. W104]. Laeremans’s group produced an anti-EGFR nanobody via the immunization of camels with EGFR-containing cell preparations, the isolation of mRNA from the camel serum and the transcription into cDNA, and separating the variable domain fragments of the camel HCAb by gel electrophoresis, and displaying it in a bacteriophage system . AdditioCXC chemokine receptors (CXCRs) 4 and 7 are both members of the seven-transmembrane domain G protein-coupled receptor (GPCR) subfamily that can bind to the extracellular chemokine ligand, CXCL12, and elicit complex immune responses in vitro or in vivo . There iThe CXCR7 nanobody, also called the VHH-derived llama immunoglobulin single variable domain, is a small-molecule ligand with a selective microaffinity characteristic that has a significant action in immune diseases and tumor-intervention therapeutics. This VHH-derived nanobody can compete with the ligand CXCL12 to bind the chemokine receptor CXCR7, antagonize the CXCL12 chemoattractant effect and inhibit the CXCR4/7-mediated signaling, hereby inhibiting the proliferation of tumors  115,116116.Maussang’s team adopted two strategies to immunize llamas to ensure that highly selective nanobody-biologicals were produced that would compete for extracellular-membrane-binding receptors and could correctly target chemokine receptor CXCR7: namely, the CXCR7-targeting nanobody . CXCR7 wMany GPCRs have extremely complex and conserved conformational changes across species . This isThe nanobody is the variable domain of heavy-chain-only antibodies, also called the sdAb. Since the discovery of the nanobody, due to its natural advantages, it has attracted the extensive attention of researchers and is considered to have a bright future in many fields, such as biomedicine and bioengineering. There have been many breakthroughs in nanobody research ,123,124.The nanobody present in camel B lymphocytes was obtained after antigen immunization. In addition to the homologous dimer antibody containing the nanobody, there are also a large number of homologous tetramer antibodies in camel serum after immunization. Although phages are a mature, recombinant antibody fragment technology, after two PCR amplifications, VHH libraries with a high stability and high affinity were obtained. However, due to their small size, which resulted in a short blood retention time and low immunogenicity , the VHHNanobodies are the smallest antigen-binding functional fragments, based on a small molecular weight, ease of identification, and the database knowing the VHH gene fragments. At the CDR region site that binds to the ligands of a specific structure, a binding-mutated site was built with loops that is easily varied and flexible. Based on the nanobodies’ physical properties of affinity and stability, the recombinant nanobodies are able to better pass through the blood–brain barrier, meaning that the nanobodies’ biologics can be targeted to the target lesions and achieve clinical, therapeutic effects. Compared with traditional antibodies, it is longer that the CDR3 region of the variable domain of the single, heavy-chain antibody present in camel serum, and substituted amino acid residues have been reported . However


The functionalized applications of nanoporous metals place clear requirements on their basic mechanical properties, yet there is a lack of research on the mechanical response under multiaxial loading conditions. In this work, the mechanical behaviors of nanoporous gold under multiaxial tension are investigated via molecular dynamics simulations. The mechanical properties under different loading conditions are compared and the microstructure evolution is analyzed to clarify the deformation mechanisms of nanoporous gold in biaxial and triaxial tension. It is found that the modulus of nanoporous gold in multiaxial tension is strengthened and the strength is softened compared to uniaxial tension. The failure of nanoporous gold in multiaxial tension is dominated by the progressive yielding, necking, and rupture of ligaments along the multiple uniaxial loading directions. The dislocation activity under multiaxial loads is more intense and more prone to plastic deformation, ultimately resulting in lower strength and smaller failure strain. The findings provide more insight into the understanding of the deformation mechanisms of nanoporous metals under complex stress states. Nanoporous metals (NPMs), which combine the structural optimization characteristics of porous materials and the superior properties of nanomaterials and metals, show excellent performances ,2,3, sucSince the proposal of “dealloying”  in 2004,The mechanical responses of NPMs in tension ,17, compt is the evolution time, The previous study  has showlable in ,19,21. IAll MD simulations are performed on the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) . Embedde studies ,15,19, tn 3.7.10 , and theTo quantitatively compare the mechanical properties of the specimen under different loading conditions, the ultimate strength and Young’s modulus are derived and illustrated in 10−3 s−1 . HoweverFurthermore, we analyze the deformation behaviors and microstructure evolution of NPG under different loading conditions. Referring to the previous study , the relnd twins .The deformation behaviors and physical mechanism of NPG under uniaxial tension have been analyzed widely and are available in our previous study . In thisIn the first stage (AB), the specimen undergoes elastic deformation, and the stress increases with the applied strain in linearity. The deformation is expected to be reversible after unloading, and the deformation of the ligament surface is negligible. There is no dislocation activity in the elastic deformation stage, as shown in After the yielding point (B), the deformation enters the second stage (BC). In this stage, the stress further increases with strain until the ultimate strength but in nonlinearity. The ligaments undergo plastic-yielding deformation, and new surfaces are generated, resulting in an increase in igaments d, leadinAfter the ultimate strength, the stress decreases with the applied strain. In the third stage (CD), the specimen undergoes large plastic deformation, and some ligaments exhibit necking, rupture, and even failure, as circled in In the last stage (DE), the specimen approaches its final breaking point, and the material fails, as depicted in Further, we summarize the maximum increments of HCP atom fraction and surface atom fraction during the whole deformation. The maximum increments of surface atom fraction in biaxial tension and triaxial tension are 17.2% and 25.3%, respectively, which is about twice and three times as much as that in uniaxial tension (8.6%). The increase in surface atoms is mainly caused by the necking and fracture of ligaments in the NPG specimen, and the deformation of the specimen in uniaxial tension is dominated by the deformation of ligaments along the loading direction. In terms of the deformation degree alone, the deformation of the specimen under multiaxial loading conditions can be simplified as the superposition of deformation under uniaxial loads in multiple directions. The average increments of the HCP atom fraction of NPG in uniaxial tension, biaxial tension, and triaxial tension are 5.8%, 9.8%, and 11.5%, respectively. Under multiaxial loads, the specimen is subjected to greater and more complex loads, and the dislocation activity is more intense. The plastic deformation of the specimen is more intense, resulting in lower strength and smaller fracture strain.The stress–strain responses and microstructure evolution of nanoporous gold in biaxial and triaxial tension are investigated, and the deformation mechanisms and mechanical properties are discussed. It is found that the modulus of NPG in multiaxial tension is strengthened and strength is softened compared to uniaxial tension. Under uniaxial load, NPG fails by the yielding, necking, and rupture of ligaments along the loading direction. Under multiaxial loads, the deformation of the specimen can be regarded as the superposition of the deformation of the specimen under multiple uniaxial loads. Under multiaxial loading, the specimen is subjected to greater and more complex loads, and the dislocation activities and plastic deformation are more intense, ultimately resulting in lower strength and smaller fracture strain. The findings further deepen the understanding of mechanical behavior and behind deformation mechanisms of nanoporous metals under complex stress states.


Although no significant correlation was found between the protein content of the flours and lactic acid-SRC, the mechanical weakening of the protein was significantly correlated with lactic acid-SRC for both tested WA levels. The doughs with WA1 had higher starch gelatinization and hot gel stability values compared to the corresponding dough systems with a higher water amount. Moreover, lower starch retrogradation and setback torques were obtained in the case of the dough prepared with higher amounts of water.Seven commercial gluten-free  flours were investigated in this study from the point of view of thermo-mechanical properties and solvent retention capacity (SRC). Each flour was used to prepare doughs with specific water absorption (WA) to get a consistency of 1.1 Nm (WA1) and doughs with WA2 levels higher than 85% to ensure a sufficient amount of water in the system for allowing the hydration of all components of the flours. Different correlations were established between proteins, ash, pentosans, damaged starch, and amylose contents on the one hand, and the capacity of the flour samples to retain different solvents such as sucrose, sodium carbonate and CaCl The most suitable flour for obtaining gluten free baked products is rice flour. Rice is widely recognized as a hypoallergenic cereal with high nutritional value . The proGenerally, rice flour is blended with other grain flours to improve the functionality and nutritional properties of the final products .In addition to rice, a large number of grains can be used to obtain gluten-free flour, such as the group of cereals named coarse cereals, which includes oat, sorghum, millet, and buckwheat, and the minor grain-like cereals, such as quinoa and amaranth. Beyond the gluten free property, every grain mentioned above is characterized by particular nutritional constituents.Oats are recommended for their high content of β-glucans, which are components that have all the properties that are specific to water-soluble dietary fibers and are used as a healthy food ingredient ,4. Yue eQuinoa has high-quality proteins with a balanced amino acid content  and highAs well, amaranth is a good source of high-quality proteins , having Buckwheat has proteins with a well-balanced amino acid composition and large amounts of flavonoids, rutin, and quercetin with good antioxidant activity . The maiSorghum is rich in polyphenols and phytosterols , and theFoxtail millet contains high levels of protein and fiber . The maiIn order to obtain gluten-free baked products with high overall quality, it is necessary to know the functionality of gluten-free flour constituents and the rheological properties of the dough. Starting from this, the objective of the study was to compare the physicochemical and functional properties of seven gluten-free flours as well as the thermo-mechanical properties of the dough systems.Seven commercial gluten-free grain flours purchased from the Galati market (Romania) were used: rice flour , oat flour , sorghum flour , foxtail millet flour , amaranth flour , quinoa flour , and buckwheat flour .The chemical composition of the flour samples was determined as follows: moisture content by SR ISO 712:2005 , proteinThe damaged starch content was quantified using the AACC Method 76-31.01  and the The fineness module was determined by sieving the flour samples through a 400, 315, 160, and 125 μm mesh .g. The following solvents were tested: water (W-SRC), 5% sodium carbonate (SC-SRC), 50% sucrose (S-SRC), 5% lactic acid (LA-SRC) , and 1 M CaCl2 (Ca-SRC) [The solvent retention capacity (SRC) profile was determined to be in agreement with the AACC Method 56-11.02  when the(Ca-SRC) . The SRCThe thermo-mechanical properties of the flours were determined through the Chopin+ protocol using the Mixolab device . To better understand the evolution of dough during thermal and mixing constraints, two water absorption (WA) values were considered when running the Chopin+ protocol on each of the tested flours. WA1 was needed to obtain dough with a maximum torque C1 of 1.1 Nm, except for sorghum and buckwheat, for which it was not possible to reach the targeted dough consistency and WA2 of 85%. Oat flour was the only sample for which the C1 of 1.1 Nm was achieved for WA1 of 85%. The following torques were registered in the case of the Mixolab tests performed at both WA levels: maximum C1 torque at initial mixing, consistency of the dough after 8 min of mixing at constant temperature of 30 °C (CS), C2 showing dough changes at heating caused by protein weakening, C3 associated to starch gelatinization, C4 provided information on the stability of the hot gel, and C5 registered the cooling phase when starch retrogradation occurred . Furtherp values lower than 0.05 were indicated by ANOVA analysis. The Pearson’s correlation was calculated to identify the potential relationships between the SRC of the flour and dough characteristics.Triplicate measurements were performed, and the results are presented as the average ± standard deviation values. The significant differences among samples were assessed using the Minitab 19 software  through the one-way ANOVA with a 95% confidence interval, after assessing the normality and variance equality conditions. The Tukey method was selected for the post-hoc analysis when The proximate composition of the gluten-free flours is shown in The highest contents of starch were registered in rice, sorghum, and oat flour, while millet flour had the lowest starch content. The fiber content ranged from 16.33% in millet flour to 3.72% in sorghum flour. However, oat flour had the highest pentosans content of 5.13%, while rice and amaranth flour had the lowest values of 1.41–1.48%. Amaranth, millet, and quinoa flour had the highest fat (6–5.30%) and ash (2.94–2.41%) content, while rice flour had the lowest fat (2.14%) and ash (1.51%) content.The damaged starch content ranged between 1.58%, in the case of buckwheat flour, and 5.29%, in the case of oat flour. The starch damage is the result of ripping, rubbing, shearing, and cutting forces acting on the grains during the milling process , but theSRC was initially developed to define the functional profile of wheat flour to allow for the prediction of the baking performance of flour . In the The SRC values of the gluten-free flours investigated in the present study are reported in 2CO3 solution with a pH above the pKa of the starch hydroxyl groups. Indeed, oat flour, having the highest level of damaged starch (5.29%), exhibited the highest SC-SRC value, while buckwheat flour, with the lowest value of damaged starch (1.58%), presented the lowest SC-SRC value (2 of 0.938 and p < 0.01) with SC-SRC, confirming the high swelling ability of the damaged starch when placed in contact with Na2CO3 solution. In addition, a significant correlation (R2 of 0.758 and p < 0.05) was found between the ash content  was found between pentosans content and S-SRC.Oat flour had the highest S-SRC value of 145.41% among all investigated gluten-free flours. In addition, as indicated in 2 of 0.308 and p < 0.05) between the protein content and LA-SRC was not significant.LA-SRC provides information on gluten functionality, being particularly related to glutenin characteristics. Anyway, the presence of high amounts of bran particles, which have good swelling ability in lactic acid solution, might interfere with the accurate interpretation of the LA-SRC of whole flours . If in t2 of 0.758 and p < 0.05) was found between the ash content of gluten-free flours and Ca-SRC. Additional correlations were registered between the protein contents and Ca-SRC (R2 of 0.852 and p < 0.05), and between the amylose content and Ca-SRC (R2 of 0.920 and p < 0.01). On the other hand, in the case of oat flour, different authors reported significant correlations between Ca-SRC and β-glucan, with an R2 of 0.615 (p < 0.01) [2 of 0.82 (p < 0.01), respectively [2 of 0.366 and p < 0.05). As indicated by Guo et al. [2+ in a solution with pH that is regulated from neutral to acidic.A significant correlation (R < 0.01)  and R2 oectively . Additioectively  reportedo et al.  and Yamao et al. , the watThe Mixolab device was designed for the investigation of wheat dough properties during the dual constraints of kneading and temperature. If in the case of the wheat flour bread the dough consistency of 1.1 ± 0.05 Nm is a benchmark, in the case of gluten-free flours, this dough consistency is no longer a necessary target, given the different technology for preparing bread in the absence of gluten in the system. Cappa et al.  appreciaAs can be seen from In order to compare the strength of the dough during kneading, MWP values were calculated . The lowp < 0.05). Amaranth flour presented a C2 value that was about two times higher than quinoa flour, while the lowest C2 value was registered for millet flour, suggesting the weakness of the protein network during heating. In addition to the higher protein contents in quinoa and amaranth flour compared to millet flour, the protein fractions prevailing in quinoa and amaranth flour, consisting of albumins and globulins, have good solubility in water and dilute salt solutions [As the temperature rises, the dough consistency decreases until it reaches the C2 value. The rice and oat doughs had (C1-C2) values close to those of wheat dough, while for the amaranth, quinoa, and millet doughs, the (C1-C2) values were much higher. The same C2 value of 0.67 Nm was registered for the oat flour at both tested WA levels, whereas for rice flour, C2 decreased from 0.68 to 0.09 Nm when WA increased from 66% to 85% . Even ifolutions —unlike polutions .When comparing the C2 of rice and amaranth flour-based doughs, Hadnadev et al.  noted thDoughs prepared with WA 85% can be distributed in three groups based on the behavior during kneading at a constant temperature (30 °C) and while heating up to 50–55 °C: (i) oat dough characterized by a Mixolab curve b was sim2 of 0.891, p < 0.05) and WA2 of 85% (R2 of 0.719 and p < 0.05).MWP was significantly correlated with the LA-SRC for both tested WA levels: WA1 needed to obtain C1 of 1.1 ± 0.05 Nm (R2 of 0.941 and p < 0.01), (C2-C1) (R2 of 0.943 and p < 0.01), and MWP (R2 of 0.776 and p < 0.05) were significantly correlated with SC-SRC. Moreover, for WA1, the Ca-SRC was significantly correlated with (C1-C2) (R2 of 0.968 and p < 0.01), while for WA2, Ca-SRC was significantly correlated with MWP (R2 of 0.629 and p < 0.05). According to Codină et al. [2+ was reported to improve protein solubility and to favor the overall hydration capacity [2+ is facilitated, thereby favoring the increase of the water absorption values of the doughs.Additional correlations were established between the various solvent retention capacities of the gluten-free flours and the thermo-mechanical properties of the corresponding doughs, which depended on the WA level. For instance, for WA1, the C2  when the WA2 of 85% was used, suggesting that the ability of the gluten-free flours to retain the lactic acid solution might provide information on the weakening behavior of the proteins.Finally, (C1-C2) was correlated with LA-SRC  and hot gel stability (C4) values were observed for the doughs prepared using WA1, which needed to obtain C1 of 1.1 ± 0.05 Nm, compared to WA2 of 85% . The useIn the case of quinoa and amaranth flour, the dough with WA2 had a very low maximum consistency C3 and starch retrogradation (C5-C4)  comparedStarch behavior is influenced by the particularities of the starch structure; more specifically, it is influenced by the length of the amylose and amylopectin chains and the ratio between the two macromolecules . Quinoa The dough systems prepared with a higher amount of water (WA2) presented a lower starch retrogradation (C5) compared to the corresponding doughs with WA1. A better bread-making performance of the flours with a low C5 value was suggested by Ekpa et al. , who alsSignificant correlations were found between the solvent retention capacity of gluten-free flours and the thermo-mechanical properties of the doughs prepared at two different hydration levels. A significant correlation was found at lower hydration levels between the mechanical weakening of the proteins and the LA-SRC. At a high water absorption of 85%, the strength of the protein network while heating the dough was correlated with LA-SRC, but no correlation could be established at a lower water absorption level when the dough had C1 of 1.1 ± 0.05 Nm. The starch behavior at high temperatures highly depended on the amount of water used to prepare the dough systems. The gluten-free dough systems with a higher water absorption presented higher values of starch gelatinization and hot gel stability. The high amount of water in the dough system also resulted in the decrease of starch retrogradation and setback torques, a situation that can be associated with the increase of bread shelf life.