The PANDB/γ-Al2O3 core–shell nanocomposites were synthesized by in situ polymerization. Pure PANDB and the PANDB/γ-Al2O3 core–shell nanocomposites were characterized using Fourier transform infrared spectroscopy, ultraviolet–visible spectroscopy, transmission electron microscopy, field emission scanning electron microscopy, and measurement of a four-point probe. The conductivity of the PANDB/γ-Al2O3 core–shell nanocomposite was about 0.72 S/cm when the weight ratio of aniline/γ-Al2O3 was 3/1. The results showed that the conductivity of the PANDB/γ-Al2O3 core–shell nanocomposite decreased with increasing amounts of γ-Al2O3 nanoparticles. The transmission electron microscopy results indicated that the γ-Al2O3 nanoparticles were thoroughly coated with PANDB to form a core–shell structure. Transmission electron microscopy and field emission scanning electron microscopy images of the conductive PANDB/γ-Al2O3 core–shell nanocomposites also showed that the thickness of the PANDB layer decreased as the amount of γ-Al2O3 was increased.In this study, electrically conductive PANDB/γ-Al Most polymers are insulators because they are made of covalent bonds without free- moving electrons or ions. Inherently conductive polymers (ICPs) are a special class of synthetic polymers with unique electro-optical characteristics. ICPs possess conjugated chains with alternating single and double bonds . Polyani2O3 [2O4 [2 [2 [The encapsulation of inorganic materials inside a PANI shell has become the most popular and interesting aspect of nanocomposites in recent years. The combination of inorganic components with electrically conductive PANI -type PANI) has attracted considerable attention due to the novel physical and chemical properties of the resulting nanocomposite and potential applications. It has great potential applications in the fields of medication delivery, biosensors, chemical assembly, materials science, etc. These core–shell conductive nanocomposites can provide new synergistic properties that cannot be obtained from individual materials alone. Many groups have reported PANI/inorganic core–shell nanocomposites such as PANI/bagasse fiber (BF) , PANI/Y22O3 , PANI/Ni2O3 [2O4 , PANI/T-2O3 [2O4 , PANI/Ti [2O4 [2 , PANI/MnO4 [2 [2 , PANI/alO4 [2 [2 ,22,23,24O4 [2 [2 ,26.12H25), PANDB-based materials are soluble in common organic solvents. Therefore, PANDB-based materials can have more diverse applications.However, PANI has some processing disadvantages, such as low or no solubility in most common organic solvents and poor processing properties . Many re2O3) has good physical properties, such as thermal stability, corrosion resistance, abrasion resistance, electrical insulation, and high mechanical strength. Therefore, Al2O3 is the material most commonly applied in industrial applications. γ-Al2O3 is one of the metastable polymorphs of transition alumina, which has higher thermal stability compared with other polymorphs of Al2O3.Aluminum oxide (Al2O3 core–shell nanocomposites via in situ polymerization of aniline (AN) in the presence of DBSA. In this study, γ-Al2O3 nanoparticles are embedded by PANDB via in situ polymerization to form conductive PANDB/γ-Al2O3 core–shell nanocomposites. Furthermore, the influences of the weight ratio of AN/γ-Al2O3 on the electrical conductivity, chemical structure, and morphology of the synthesized conductive PANDB/γ-Al2O3 core–shell nanocomposites are systemically examined using a four-point probe, Fourier transform infrared spectroscopy (FTIR), ultraviolet–visible spectroscopy (UV-Vis), transmission electron microscopy (TEM), and field emission scanning electron microscopy (FE-SEM).To our knowledge, no studies have been reported on the preparation of conductive PANDB/γ-Al2O3 nanoparticles were purchased from Degussa Co., Frankfurt, Germany. The diameter of an individual γ-Al2O3 nanoparticle is about 10~30 nm. Aniline (AN), dodecylbenzene sulfonic acid (DBSA), and ammonium persulfate (APS) were purchased from Merck Co., Darmstadt, Germany. The γ-AlPANDB was directly synthesized by chemical oxidative polymerization according to the modified procedure described by Cao et al.  and our 2O3 core–shell nanocomposites were synthesized by in situ chemical oxidative polymerization. Two grams of γ-Al2O3 nanoparticles were dispersed in 100 mL of an aqueous solution containing 1 g of aniline, and the resulting dispersion was stirred at room temperature for 10 min. The weight ratio of AN/γ-Al2O3 was 3/1. Then, 50 mL of aqueous DBSA was added to the solution. After stirring for 10 min, 30 mL of APS aqueous solution was added dropwise to the dispersion with constant stirring. The molar ratio of aniline:DBSA:APS was 1:0.8:1. The resulting mixture was reacted at room temperature for 2 h. Subsequently, the product was washed with deionized water until the filtrate became colorless. Afterwards, the product was dried in a vacuum oven at 60 °C for 24 h. To determine the effect of the weight ratio of AN/γ-Al2O3 on the properties of the PANDB/γ-Al2O3 core–shell nanocomposites, products with different weight ratios were also applied, namely 3/2, 3/3, 3/4, and 3/5. Conductive PANDB/γ-Al5 psi for 2 min at room temperature. Four-point probe measurement  was used to determine the electrical conductivity σ (S/cm) of the sample at room temperature.A sample of 0.1 g was weighed and then pressed at 3.0 × 10−1. The powdered sample was ground together with potassium bromide (KBr) (approximately 1:99 by weight) to a fine powder, and the homogeneous mixture was pressed into a pellet for analysis.The chemical structure of the samples was detected by Fourier transform infrared spectroscopy (FTIR)  at 32 scans/s in the wavenumber range of 4000–400 cmThe synthesized product was dispersed in absolute ethanol with ultrasonic stirring for 1 h at room temperature. A UV-Vis spectrophotometer  was used to measure the absorption of the sample solution in the wavelength range of 300–900 nm.2O3 nanocomposite samples were diluted and dispersed uniformly. They were then cast on carbon-coated copper grids to prepare for transmission electron microscopy (TEM)  analysis. The microscope was operated at an accelerating voltage of 80 kV.The synthesized PANDB and PANDB/γ-AlThe samples were coated with a gold-palladium film. The surface morphology of the samples was observed using a field emission scanning electron microscope .2O3 = 3/1 after a reaction time of 3, 27, and 30 min. The color of the initial reaction solution was milky white. After adding the APS solution, the color of the reaction solution remained unchanged for the first 15 min and then turned light blue, blue, and finally turquoise within 15–27 min. This color change indicated the formation of the pernigraniline oxidation state of PANI [ of PANI . After 22O3 weight ratio decreased. This observation was due to the fact that the probability of collision between AN and initiator  molecules was decreased while the amount of γ-Al2O3 was increased in the reaction solution. Therefore, the AN polymerization rate decreased as the weight of γ-Al2O3 was increased.The color change during the reaction can be used as an indicator of the polymerization rate of aniline. The experimental results show that the time required for color change increased  as the AN/γ-Al2O3, PANDB, and the PANDB/γ-Al2O3 core–shell nanocomposites. Since γ-Al2O3 has good electrical insulation, its electrical conductivity cannot be detected by four-point probe measurement. Meanwhile, the electrical conductivity of pure PANDB was 0.82 S/cm according to the test results. The results in 2O3 core–shell nanocomposites decreased as the weight ratio of AN/γ-Al2O3 was decreased. When the weight ratio of AN/γ-Al2O3 was 3/1, the electrical conductivity of the PANDB/γ-Al2O3 core–shell nanocomposite was about 0.72 S/cm. This result implies that PANDB could be successfully coated onto the surface of γ-Al2O3 nanoparticles, and the conductivity of γ-Al2O3 could be improved by forming electrically conductive PANDB/γ-Al2O3 core–shell nanocomposites. When the weight ratio of AN/γ-Al2O3 was decreased from 3/1 to 3/5, the conductivity of the PANDB/γ-Al2O3 core–shell nanocomposite decreased from 0.72 to 0.53 S/cm. This is due to the fact that the thickness of the conductive PANDB on γ-Al2O3 was relatively decreased with the increasing weight of γ-Al2O3.2O3 nanoparticles  type with good electrical conductivity. Therefore, the color of the conductive PANDB/γ-Al2O3 core–shell nanocomposite was dark green. articles a and therization b. The ap2O3 core–shell nanocomposites synthesized at the weight ratios of AN/γ-Al2O3 = 3/2 and 3/5. Peaks at about 2853 and 2953 cm−1 were observed due to the stretching vibration mode of the -CH (-CH3 or -CH2-) for all samples. For pure PANDB . As these absorption peaks overlapped in the range of 1000 to 1200 cm−1, a broad peak was observed. The peaks at 800–700 cm−1 were attributed to the characteristic feature of the B-NH-Q bond or the B-NH-B bond and the out-of-plane bending vibration of benzenoid or quinonoid -CH and –N–H bonds. These characteristic peaks are in good agreement with those reported in the literature [−1 were associated with γ-Al2O3 nanoparticles. Note that there was no significant interaction between PANDB molecules and γ-Al2O3 nanoparticles from the FTIR results. The good adhesion of PANDB to γ-Al2O3 nanoparticles was assumed to be physicochemical in nature [re PANDB a, the chterature ,33. The n nature .2O3 core–shell nanocomposites. For pure PANDB, three characteristic absorption peaks can be clearly observed in the UV-Vis spectrum at ~340, ~430, and ~830 nm  by decreasing the weight ratio of AN/γ-Al2O3 from 3/1 to 3/5. This result was attributed to less PANDB being coated on the surface of the γ-Al2O3 nanoparticles when the amount of γ-Al2O3 was increased. This result is consistent with the decrease in conductivity. ~830 nm a. The abectively . The anaof PANDB b–f. Howe2O3 nanoparticles (2O3 core–shell nanocomposites synthesized at the weight ratios of AN/γ-Al2O3 = 3/1 and 3/3 (2O3 nanoparticles and aggregates of γ-Al2O3 nanoparticles with irregular shapes due to the high surface energy of the nanoparticles. 2O3 nanoparticles was about 10~30 nm and the diameter of the clusters of γ-Al2O3 nanoparticles ranged from 30 to 100 nm. Comparing 2O3 nanoparticle was coated with less PANDB as the amount of γ-Al2O3 nanoparticle was increased. Therefore, the electrical conductivity of the PANDB/γ-Al2O3 core–shell nanocomposites decreased as the amount of γ-Al2O3 nanoparticle was increased. articles b, and th and 3/3 c,d. Figu2O3 core–shell nanocomposites via an in situ polymerization process is shown in 2O3 nanoparticles were coated with an anilinium–DBSA complex. Then, by adding APS for polymerization, conductive PANDB/γ-Al2O3 core–shell nanocomposites could be obtained. Furthermore, the independent γ-Al2O3 nanoparticles and the aggregates of γ-Al2O3 nanoparticles were simultaneously coated by PANDB to form conductive PANDB/γ-Al2O3 core–shell nanocomposites.Based on the result of the TEM images, the synthesis of conductive PANDB/γ-Al2O3 core–shell nanocomposites. The core is γ-Al2O3, and the shell is PANDB. The electrical conductivity of pure PANDB was 0.82 S/cm. The electrical conductivity of the PANDB/γ-Al2O3 core–shell nanocomposite decreased with increasing the amount of γ-Al2O3. When the weight ratio of AN/γ-Al2O3 was decreased from 3/1 to 3/5, the conductivity of the PANDB/γ-Al2O3 core–shell nanocomposite decreased from 0.72 to 0.53 S/cm. Both the TEM and FE-SEM images showed that PANDB could be successfully coated on the surface of γ-Al2O3 nanoparticles. Therefore, the electrical conductivity of γ-Al2O3 could be improved by forming a conductive PANDB/γ-Al2O3 core–shell nanocomposite.In this study, an in situ polymerization method was used to prepare electrically conductive PANDB/γ-Al


Seasonings play a key role in determining sensory attributes of instant starch noodles. Controlling and improving the quality of seasoning is becoming important. In this study, five different brands along with fifteen instant starch noodles seasonings  were characterized by electronic nose (e-nose) and electronic tongue (e-tongue). Feature-level fusion for the integration of the signals was introduced to integrate the e-nose and e-tongue signals, aiming at improving the performances of identification and prediction models. Principal component analysis (PCA) explained over 85.00% of the total variance in e-nose data and e-tongue data, discriminated all samples. Multilayer perceptron neural networks analysis (MLPN) modeling demonstrated that the identification rate of the combined data was basically 100%. PCA, cluster analysis (CA), and MLPN proved that the classification results acquired from the combined e-nose and e-tongue data were better than individual e-nose and e-tongue result. This work demonstrated that in combination e-nose and e-tongue provided more comprehensive information about the seasonings compared to each individual e-nose and e-tongue. E-nose and e-tongue technologies hold great potential in the production, quality control, and flavor detection of instant starch noodles seasonings. Instant starch noodles are made from various starch , water and other ingredients, and processed into dry, no-cooking, convenience food . It usuaSeasonings play a key role in determining sensory attributes of instant starch noodles. Seasonings are mainly used for improving aroma and taste. Aroma is an important sensory attribute of flavor in food and volatile compounds plays an important role in it . NowadayConsumers are mainly concerned about the flavor of instant starch noodles and the major difference of flavors is the variety of instant starch noodles seasonings. Different brands of instant starch noodles seasonings with the same taste vary greatly in flavor. To a certain extent, brand is an indicator of the product’s quality and flavor. Different brands of instant starch noodles seasonings possessed variant chemical ingredients, aroma, taste, and price . BasicalElectronic nose (e-nose) and electronic tongue (e-tongue) are sensor-based apparatus which imitate the olfactory and gustatory perception of human. They provide fast detection and comprehensive information about the sample, and they can monitor and differentiate samples that are otherwise difficult for human sensory panels to discriminate . The e-nThe main objectives of this study was to establish an easy, rapid and objective identification method for instant starch noodles seasonings based on different flavor profiles. Therefore, we used e-nose and e-tongue independently and in combination to discriminate different brands of instant starch noodles seasonings. Then, recognition of the seasonings was performed by principal components analysis (PCA), cluster analysis (CA), and multilayer perceptron neural networks analysis (MLPN), with the intention of testing whether e-nose and e-tongue can provide a rapid and accurate approach for flavor recognition and quality assurance of instant starch noodles seasonings.Five different brands of instant starch noodles with braised beef were obtained at the commercial market in China. The five brands were marked as C, D, G, L, and Z, respectively. Each brand contained three categories: seasoning powder (P), seasoning mixture sauce (S), and the mixture of powder and sauce (M). The PEN3 portable e-nose  was applied to classify and characterize the instant starch noodles seasonings. The apparatus is composed of a sampling unit and a gas detection system consisting of 10 metal oxide semiconductor (MOS) sensors with varying sensitivity to each volatile compound . Prior tBased on the preliminary experimental results, 7 g of seasoning powder, 4 g of seasoning mixture sauce, and 11 g of the mixture of powder and sauce were put into three 150 mL beakers, respectively. The seasoning solution was prepared after pouring 100 mL boiling water into the beaker and stirring for 2 min. A volume of 10 mL sample solution was put into a 30 mL sealed glass vial and incubated for 30 min at 40°C. The chamber and injection flow rate was 400 mL/min. The measurement time, sample interval time, flush time, and automatic zero adjustment time were 60, 1, 300, and 5 s. Each sample was measured 10 times.Instant starch noodles seasonings were analyzed using ASTREE e-tongue . The instrument consists of an automatic sampler, chemical sensor array, and chemometric software package . The senAccording to preliminary experiments, 6 g of seasoning powder, 8 g of seasoning mixture sauce, and 14 g of the mixture of powder and sauce were put into three 500 mL beakers, respectively. The seasoning solution was prepared after pouring 400 mL boiling water into the beaker and stirring for 2 min. The seasoning solution was first filtered using a piece of gauze and the filtrate was then filtered through a 0.45 μm nylon syringe filters. Then, 80 mL of filtrate of each sample was analyzed using the e-tongue. Before the test begins, 0.01 mol/L HCl, NaCl, and sodium glutamate were applied to activate, calibrate, and diagnose the chemical sensors . The e-tData fusion involves different levels of data abstraction , with loThe individual e-nose and e-tongue data and their fusion were applied to PCA, CA, and MLPN. PCA is the most popular feature extraction method in pattern recognition. It is used to differentiate samples and extract the most important information from data. PCA reduces the dimensionality of datasets while retaining most of the information of the original data . PCA intCluster analysis is an unsupervised classification technology which can cluster samples according to their similarity in multidimensional space . It idenMultilayer perceptron neural networks analysis is a feed-forward supervised classification method consisting of an input layer, several hidden layers, and an output layer . It can Principal component analysis and MLPN were performed using Statistical Product and Service Solutions (SPSS) 20.0. CA was conducted using Minitab 16.2.The typical e-nose response curves of CP, CS, and CM are shown in The radar plots of e-nose sensor responses are presented in The e-tongue sensor responses of CP, CS, and CM are shown in The radar plots of e-tongue sensor responses are presented in The e-nose PCA plots are presented in In multidimensional space, the samples were clustered into five non-overlapping groups. Samples with more similar flavor would appear closer to each other. The CP and DP samples were located very close to each other, suggesting that CP and DP had similar aromas. The characteristic aroma attributes of CS, DS, and GS were also similar. The results showed that principal components analysis of e-nose data can be used to classify different brands of instant starch noodles seasonings.The e-tongue PCA plots are presented in As shown in Combination of e-nose and e-tongue data has been applied in various foods analysis and quality control . PCA andRelationships between samples were visualized with a dendrogram based on a nearest neighbor method that used Euclidean distances. The combined e-nose and e-tongue data were analyzed using CA, for comparison to PCA. As shown in In this paper, e-nose, e-tongue, and the combination of their data were used to discriminate instant starch noodles seasonings from five different brands. PCA explained over 85.00% of the total variance in e-nose and e-tongue data, suggested that both individual e-nose and e-tongue could discriminate different brands of instant starch noodles seasonings. For the combination data of seasoning powder, seasoning mixture sauce, and mixture of powder and sauce, the first three PCs explained 86.96, 98.09, and 94.38% of the total variance and the results of CA were in agreement with those from PCA. PCA and CA results suggested that the combination of e-nose and e-tongue showed much better classification ability than either alone. MLPN modeling demonstrated that the identification rate of the combined data was basically 100%, further indicated that the combination of e-nose and e-tongue produced more optimal discrimination and analysis of samples.In summary, e-nose and e-tongue can provide integrated information of flavors to distinguish among samples. The combination of e-nose and e-tongue an effective method for rapid, easy, and accurate food odor analysis that is objective, highly automated and lower cost. This work may be helpful in providing some valuable information on evaluating and discriminating the quality of instant starch noodles seasoning to consumers.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.RM: conceptualization, investigation, writing – original draft, and data curation. HS: methodology and writing – review and editing. HC: software and formal analysis. GZ: project administration and supervision. JZ: supervision and resources. All authors contributed to the article and approved the submitted version.


Streptococcus pneumoniae (Pneumococcus) has remained a leading cause of fatal infections such as pneumonia, meningitis, and sepsis. Moreover, this pathogen plays a major role in bacterial co-infection in patients with life-threatening respiratory virus diseases such as influenza and COVID-19. High morbidity and mortality in over one million cases, especially in very young children and the elderly, are the main motivations for pneumococcal vaccine development. Due to the limitations of the currently marketed polysaccharide-based vaccines, non-serotype-specific protein-based vaccines have received wide research interest in recent years. One step further is to identify high antigenic regions within multiple highly-conserved proteins in order to develop peptide vaccines that can affect various stages of pneumococcal infection, providing broader serotype coverage and more effective protection. In this study, immunoinformatics tools were used to design an effective multi-epitope vaccine in order to elicit neutralizing antibodies against multiple strains of pneumococcus.E. coli, followed by in silico cloning in the pET28a(+) vector.The B- and T-cell epitopes from highly protective antigens PspA (clades 1–5) and PhtD were predicted and immunodominant peptides were linked to each other with proper linkers. The domain 4 of Ply, as a potential TLR4 agonist adjuvant candidate, was attached to the end of the construct to enhance the immunogenicity of the epitope vaccine. The evaluation of the physicochemical and immunological properties showed that the final construct was stable, soluble, antigenic, and non-allergenic. Furthermore, the protein was found to be acidic and hydrophilic in nature. The protein 3D-structure was built and refined, and the Ramachandran plot, ProSA–web, ERRAT, and Verify3D validated the quality of the final model. Molecular docking analysis showed that the designed construct via Ply domain 4 had a strong interaction with TLR4. The structural stability of the docked complex was confirmed by molecular dynamics. Finally, codon optimization was performed for gene expression in The computational analysis of the construct showed acceptable results, however, the suggested vaccine needs to be experimentally verified in laboratory to ensure its safety and immunogenicity.The online version contains supplementary material available at 10.1186/s12859-023-05175-6. Streptococcus pneumoniae, a commensal Gram-positive bacterium, is one of the leading causes of death worldwide, causing a variety of infections such as pneumonia, meningitis, and bacteremia , WU2 (Clade 2) [AC AAF27710], BG8090 (Clade 3) [AC AAF27713], EF5668 (Clade 4) [AC AAC62252] and ATCC6303 (Clade 5) [AC AAF27715], as well as PhtD from strain R6 [AC AAK99711] and Ply from strain D39 [AC ABJ53672]. The proteins PspA and PhtD were used for prediction of B-cell and helper T-cell epitopes and pneumolysin was applied as a TLR4 agonist.The NCBI protein database (www.cbs.dtu.dk/services/TMHMM-2.0) [The presence of the transmembrane regions of the proteins PspA and PhtD were evaluated using TMHMM 2.0 (HMM-2.0) . TMHMM, https://zhanglab.ccmb.med.umich.edu/I-TASSER/) [http://swissmodel.expasy.org) [https://zhanglab.ccmb.med.umich.edu/ModRefiner/) [http://galaxy.seoklab.org/cgi-bin/submit.cgi?type=REFINE) [https://saves.mbi.ucla.edu/). PROCHECK checks the stereochemical quality of 3D models by plotting the Ramachandran plot [In order to predict the conformational epitopes, the protein tertiary structures were constructed by I-TASSER server (TASSER/)  or SWISSasy.org)  with resefiner/)  and Gala=REFINE) . The 3D =REFINE)  and ERRA=REFINE)  at SAVESran plot , and ERRran plot . BIOVIA http://crdd.osdd.net/raghava/lbtope/) [http://crdd.osdd.net/raghava/abcpred/) [http://tools.iedb.org/bcell/result/) [http://tools.iedb.org/ellipro/) [Linear B-cell epitopes were identified by the servers LBtope (lbtope/) , ABCpredbcpred/) , Emini sresult/)  and Ellillipro/) . LBtope http://tools.iedb.org/discotope/) [Furthermore, conformational epitopes from the modeled structures were predicted by the softwares ElliPro and DiscoTope 2.0 (cotope/) . Elliprohttp://tools.immuneepitope.org/mhcii) [http://www.cbs.dtu.dk/services/NetMHCIIpan/) [Identification of binding peptides to MHC-II molecules was performed by the consensus method of IEDB MHC-II prediction tool (g/mhcii)  and NetMCIIpan/) . The MHCCIIpan/) , and alsThe high-scored B-cell epitopes identified by various servers were analyzed and scanned for an overlapping sequence with the highly immunogenic MHC class II epitopes. The selected peptides were linked to each other by GPGPG linkers which conserve the independent immunological activities of the subunits and facilitate epitope presentation . In addihttp://web.expasy.org/protparam/) [https://scratch.proteomics.ics.uci.edu/) [E. coli. VaxiJen v2.0 (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html) [http://scratch.proteomics.ics.uci.edu/) [https://www.imtech.res.in/raghava/algpred/) [http://www.ddg-pharmfac.net/AllerTOP/) [After designing and construction of the multi-epitope vaccine by positioning the selected amino acid sequences linked with suitable linkers, the most important features of the construct were evaluated using different bioinformatics tools. The physical and chemical properties like amino acid composition, molecular weight, theoretical isoelectric point, in vitro/in vivo half-life, instability index, aliphatic index and grand average of hydropathicity of the designed sequence were computed using the ExPASy ProtParam tool (tparam/) . Solubilci.edu/)  implemenen.html)  and ANTIci.edu/)  were uselgpred/)  and AllelerTOP/)  were emphttp://robetta.bakerlab.org/) [http://galaxy.seoklab.org/cgi-bin/submit.cgi?type=REFINE) [https://saves.mbi.ucla.edu/), and also ProSA-web server (https://prosa.services.came.sbg.ac.at/prosa.php) [The prediction of tertiary structure of the designed protein vaccine was done by the Robetta server (ab.org/) . This se=REFINE)  was appl=REFINE) , PROCHEC=REFINE)  and VERI=REFINE)  at SAVESosa.php) . The VERhttp://crdd.osdd.net/raghava/ifnepitope/) [The peptides having the potential to activate IFN-γ inducing T-helper cells were predicted using online server IFNepitope (pitope/) . This sehttp://tools.iedb.org/ellipro/) [Conformational epitopes are comprised of residues that form the 3D structures recognized by the antibodies and play a key role in the humoral immunity. Therefore, the designed construct should have effective structural epitopes within its protein domains to provide more potent immunity. ElliPro (llipro/)  was usedhttps://cluspro.org/login.php) [www.rcsb.org) [http://www.ebi.ac.uk/thornton-srv/databases/pdbsum/Generate.html [Protein–protein docking was performed through ClusPro web server (gin.php)  to detercsb.org)  as the rate.html  was used+ and Cl− ions, followed by system energy minimization using the steepest descent algorithm. The system equilibration was done under 100 ps NVT at 300 K using Berendsen thermostat algorithm, followed by 100 ps NPT at 300 K and 1 bar using Parrinello Rahman barostat. The root mean square deviation (RMSD) versus time of the vaccine-TLR4 complex was plotted for 20 ns simulation to evaluate the system stability. The root mean square fluctuation (RMSF) per residue plots of the developed vaccine and the receptor were generated to verify the flexibility of the backbone atoms.The MD simulation was performed to confirm the structural stability of developed vaccine-TLR4 complex by the GROMACS 4.6.5 version  using thhttp://www.jcat.de/) [E. coli (strain K12) as the expression host. The additional criteria of JCat were selected such as the avoidance of unwanted sites for Rho-independent transcription termination, prokaryotic ribosome binding and restriction enzymes. Finally, the optimized vaccine sequence, with restriction sites NdeI and XhoI added to the N- and C-terminus, respectively, was cloned in pET-28a(+) plasmid vector using the SnapGene tool (https://www.snapgene.com/try-snapgene/) to confirm the expression of the vaccine.The codon adaptation approach is applied to improve the expression of recombinant proteins. The vaccine protein sequence was submitted to the Java Codon Adaptation Tool (JCat) (cat.de/)  for reveAdditional file 1. Table S1. Protein sequences of PspA1-5 (clades 1 to 5), PhtD and Ply. A region of PspA clade 2, B and C region of PspA clades 1 to 5 are underlined and shown in blue, red and green color, respectively. The C-terminal of PhtD (amino acid 383 to 853) and domain 4 of Ply (amino acid 360 to 471) are shown in color and underlined. In Ply4, the amino acids D385, C428 and W433, which must be replaced with N, G and F, respectively, are presented in blue color. Signal peptide is represented in lowercase italics. Table S2. The predicted B cell epitopes of A region of PspA2. Table S3. The predicted B cell epitopes of B region of PspA1. Table S4. The predicted B cell epitopes of B region of PspA2. Table S5. The predicted B cell epitopes of B region of PspA3. Table S6. The predicted B cell epitopes of B region of PspA4. Table S7. The predicted B cell epitopes of B region of PspA5. Table S8. The experimentally verified B cell epitopes from C region of PspAs. Table S9. The predicted linear B cell epitopes of PhtD-C. Table S10. The predicted discontinuous B cell epitopes of PhtD-C. Table S11. Prediction of MHC-II epitopes of PspA2-A and PspA1-5-B. The peptides with IEDB percentile rank <10.0 and NetMHCIIpan rank value <1.0 were considered for the next analysis. Table S12. Prediction of MHC-II epitopes of PhtD-C. The epitopes with IEDB percentile rank <10.0 and NetMHCIIpan rank value <1.0 were considered for the further analysis. Figure S1. Domain structure of PspA protein. Major domains of PspA are α-helical charged domain (amino acids 1-288) consisting of A, Aˊ and B regions, proline-rich domain , and choline-binding domain (amino acids 371-571). Within the α-HD, region B is a clade-defining region of the PspA molecule, which is represented by the stippled box. Figure S2. The results of transmembrane helices prediction. A and B show the prediction results of transmembrane helices in PspA2 and PhtD-C, respectively. The pink lines indicate the domains facing outside. Figure S3. The 3D models of PspA2 and PhtD-C. The predicted and refined 3D structures were visualized by the Discovery Studio Visualizer. Figure S4. The validation of refined model of PspA2 (A) and PhtD-C (B) with PROCHECK and ERRAT. Ramachandran plot of the structure of PspA2 or PhtD-C represents 82.1% or 87.2% residues in favored regions, respectively. In the ERRAT plot, the overall quality factor of structure of PspA2 or PhtD-C is 88.69% or 85.23%, respectively. Figure S5. The 3D models of B regions of PspA1-5. The homology modeled structures of B regions of PspA clades 1 to 5  visualized by Discovery Studio Visualizer. Figure S6. Codon optimization results of the designed construct. Codon optimization was performed using JCAT and codons were adapted for efficient expression in E.coli K12. The CAI index and GC content of the optimized sequence were 0.99 and 53.27%, respectively.


People with lower limb impairment can participate in activities such as running with the use of a passive-dynamic ankle-foot orthosis (PD-AFO). Specifically, the Intrepid Dynamic Exoskeletal Orthosis (IDEO) is a PD-AFO design that includes a carbon-fiber strut, which attaches posteriorly to a custom-fabricated tibial cuff and foot plate and acts in parallel with the impaired biological ankle joint to control sagittal and mediolateral motion, while allowing elastic energy storage and return during the stance phase of running. The strut stiffness affects the extent to which the orthosis keeps the impaired biological ankle in a neutral position by controling sagittal and mediolateral motion. The struts are currently manufactured to a thickness that corresponds with one of five stiffness categories  and are prescribed to patients based on their body mass and activity level. However, the stiffness values of IDEO carbon-fiber struts have not been systematically determined, and these values can inform dynamic function and biomimetic PD-AFO prescription and design. The PD-AFO strut primarily deflects in the anterior direction (ankle dorsiflexion), and resists deflection in the posterior direction (ankle plantarflexion) during the stance phase of running. Thus, we constructed a custom apparatus and measured strut stiffness for 0.18 radians (10°) of anterior deflection and 0.09 radians (5°) of posterior deflection. We measured the applied moment and strut deflection to compute angular stiffness, the quotient of moment and angle. The strut moment-angle curves for anterior and posterior deflection were well characterized by a linear relationship. The strut stiffness values for categories 1–5 at 0.18 radians (10°) of anterior deflection were 0.73–1.74 kN·m/rad and at 0.09 radians (5°) of posterior deflection were 0.86–2.73 kN·m/rad. Since a PD-AFO strut acts in parallel with the impaired biological ankle, the strut and impaired biological ankle angular stiffness sum to equal total stiffness. Thus, strut stiffness directly affects total ankle joint stiffness, which in turn affects ankle motion and energy storage and return during running. Future research is planned to better understand how use of a running-specific PD-AFO with different strut stiffness affects the biomechanics and metabolic costs of running in people with lower limb impairment. The use of a passive-dynamic ankle-foot orthosis (PD-AFO) enables people with lower limb impairment to participate in dynamic high-impact activities such as running. The Intrepid Dynamic Exoskeletal Orthosis (IDEO) is a PD-AFO specifically designed to enable people with lower limb impairment to return to running . The IDEBecause an IDEO strut acts in parallel with the impaired biological ankle joint, total angular ankle joint stiffness equals the sum of the strut stiffness and impaired biological ankle joint stiffness. However, when people with a lower limb impairment use a more-stiff PD-AFO, they may adjust their knee and hip joint mechanics during running, which could indirectly affect their total ankle joint stiffness through changes in ankle angle or moment during running. Esposito et al.  measuredA person with a lower limb impairment requires control of sagittal and mediolateral motion in the impaired biological ankle joint to decrease pain during running , which cPrevious studies have shown that unimpaired biological ankle joint stiffness is best characterized by a curvilinear moment-angle relationship during running at moderate speeds 2.6–3.8 m/s), where the ankle stiffens as it undergoes dorsiflexion during the first half of the stance phase . Moreove–3.8 m/s,We determined IDEO strut stiffness from strut stiffness categories 1, 2, 3, 4, and 5  using a custom bending apparatus that applied force to deflect the strut in the anterior and posterior directions relative to the user . We defiTo determine strut stiffness, we rigidly secured the distal end of each strut to a steel frame using bolts and nuts and applied force to the proximal end of each strut, two struts from each stiffness category, in three successive loading and unloading cycles using a non-extensible rope and hand cranked winch . The nonkstrut) as the slope of the moment vs. angle linear regressions in the anterior (up to 0.18 rad) and posterior (up to 0.09 rad) directions from three loading cycles of two struts from each stiffness category. We measured the change in angle of the proximal end of the strut, relative to the distal end , and resolved the applied force to be perpendicular to the strut. We calculated the rotational moment at each time point as the product of the perpendicular force applied to the strut, which was down-sampled to 100 Hz, and the moment arm of the strut (0.21 m). We calculated the average coefficients of determination (adjusted R2) for linear and curvilinear characterizations of the moment vs. angle relationships for each loading cycle and then averaged these adjusted R2 across the three loading cycles for each category and then across categories.We calculated the average angular strut stiffness  software.We used a one-way ANOVA and p < 0.001). Contrary to our initial assumption, the stiffness values did not change by the same magnitude between categories with anterior deflection. Stiffness increased by 0.46, 0.25, 0.51, and 0.12 kN·m/rad between categories 1 and 2, 2 and 3, 3 and 4, and 4 and 5, respectively. We found no significant difference between the adjusted R2 values of a 2nd degree polynomial and linear fit with anterior deflection , with the exception of categories 2 and 3, where we detected no significant difference between stiffness values (p = 0.07). Contrary to our initial assumption, the stiffness values did not change by the same magnitude between categories with posterior deflection. Stiffness increased by 0.59, 0.14, 0.44, and 0.75 kN·m/rad between categories 1 and 2, 2 and 3, 3 and 4, and 4 and 5, respectively. We found no significant difference between the adjusted R2 values of a 2nd degree polynomial and linear fit with posterior deflection (p = 0.10). Thus, we characterized the strut moment versus angle relationship with a linear fit due to a greater numerical R2  of dorsiflexion and has a curvilinear moment versus angle relationship, where the ankle becomes stiffer with greater dorsiflexion , 10. Thu° of dorsIn addition to controlling sagittal and mediolateral ankle joint range of motion, the stiffness profile of a PD-AFO strut affects leg stiffness and elastic energy storage and return during the stance phase of running. The stance phase of running is well-characterized by a spring-mass model, where leg stiffness equals the quotient of peak ground reaction force and total leg displacement , and priPD-AFO strut stiffness directly affects total ankle joint stiffness, which in turn affects ankle range of motion, energy storage and total leg stiffness during running. Hewett et al.  found thWe determined the stiffness values of carbon fiber struts that are utilized within a PD-AFO for patients with lower limb impairment. A better understanding of the stiffness values and the effects of different stiffness for PD-AFO struts can inform orthotic prescription, dynamic function, and biomimetic design. This study had several potential limitations. The purpose of this research was to quantify and characterize the stiffness of PD-AFO struts. However, when in use, the PD-AFO strut is attached to a custom-fabricated tibial cuff and foot plate , which mThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.KA and AG contributed to conception and design of the study and wrote sections of the manuscript. KA acquired the data, performed the statistical analysis, and wrote the first draft of the manuscript. Both authors contributed to manuscript revision, read, and approved the submitted version.This work was supported by a grant from the Defense for Health Affairs, Clinical Research Intramural Initiative, CDMRPL-17-0-DMI170709.The views expressed in this publication do not reflect the views or policies of the Department of the Navy, Department of Defense, Defense for Health Affairs, or United States Government.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.


The size of the anterior cruciate ligament (ACL) often varies between individuals, but such variation is not typically considered during ACL reconstruction (ACLR). This study aimed to explore how the size of the ACL affects the selection of a suitable graft diameter. A finite element model of a human knee was implanted with intact ACLs of different dimensions  and with grafts of different diameters, to simulate ACLR (diameter 7.5–12 mm in 0.5 mm increments). The knee models were flexed to 30° and loaded with an anterior tibial load of 103 N, internal tibial moment of 7.5 Nm, and valgus tibial moment of 6.9 Nm. Knee kinematics , internal tibial rotation (ITR) and valgus tibial rotation (VTR)) and ligament forces were recorded and compared among the different groups. The results showed that, compared with the intact knee, a graft diameter of 7.5 mm was found to increase the ATT and VTR, but reduce the graft force. Increasing the graft diameter reduced knee laxity and increased the graft force. A 10% increase in the size of the ACL corresponded to a 3 mm larger graft diameter required to restore knee stability and graft force after ACLR. It was concluded that the graft diameter should be selected according to the dimensions of the native ACL, for better restoration of knee functionality. This study may help to improve the clinical treatment of ACL ruptures. The anterior cruciate ligament (ACL) is a critical stabilizer for the knee, especially for constraining anterior tibial translation (ATT) and internal tibial rotation (ITR) ,2,3. HowPrevious studies have reported that larger graft diameters may improve the outcomes of ACLR. Snaebjornsson et al. reported that each incremental increase of 0.5 mm in graft diameter resulted in an 86% lower likelihood of needing revision surgery . Park etHowever, Fujimaki et al. reported a large variation in the dimensions of the human ACL , which iThis study aimed to explore how the size of the native ACL affects the selection of graft diameter, chosen with the aim of restoring knee stability and graft force. It was hypothesized that a 10% increase in the size of the native ACL would require a considerably larger graft diameter to restore knee stability and graft force. The outcome of this study may elucidate whether or not it is important to choose the graft diameter according to the individual size of the native ACL. This may provide a scientific basis for improving surgical techniques for treating ACL ruptures and improving clinical outcomes.A finite element model of a human cadaveric knee was simulated with different-sized native ACLs  and after ACLR using hamstring tendon grafts with different diameters (7.5–12 mm in 0.5 mm increments). The same loading condition was applied to each model, after which knee kinematics and ligament forces were compared among the groups, to explore the differences in suitable graft diameter for replacing the different-sized ACL.θ = 125 MPa, ER = EZ = 27.5 MPa, GθR = GθZ = 2 MPa, GRZ = 10.34, VθR = VθZ = 0.1 and VRZ = 0.33). Ligaments were defined as isotropic hyperelastic, using strain energy functions. The Veronda-Westmann function was used for the ACL and PCL , while the Mooney–Rivlin material model was used to define the mechanical properties of the MCL and LCL. The coefficients for the LCL were assumed to be identical to those of the MCL (C1 = 30.1 MPa and C2 = −27.1 MPa). The calculation accuracy of the model was validated using biomechanical data from a cadaveric experiment on the same knee sample. The model was able to calculate the ATT, valgus tibial rotation (VTR) and in situ force in the ACL with an accuracy of 0.1 mm, 1° and 1 N, respectively [A previously developed and validated model of a human cadaveric knee  was used in this study . The threctively .To simulate different sizes of the ACL, the original geometry of the cadaveric ACL was proportionally shrunk, and enlarged, by 5% b, using This study simulated anatomical single-bundle ACLR, using different graft diameters . The cenGiven that the diameter of hamstring tendon grafts are approximately 10% larger than the ACL isthmus , the resThe simulations were conducted using finite element analysis in Abaqus/CAE 6.14-2  and all models were subject to the same boundary and loading conditions . First, Joint kinematics under the above loading conditions were recorded to evaluate knee stability, including ATT, ITR and VTR. Greater knee displacement than the intact state indicated knee instability in the corresponding DOFs, which may cause an abnormal stress distribution on the articular cartilage, and consequently induce knee OA in the long-term . The in Knee kinematics and ligament forces from the intact knee and the knee after ACLR are shown in As shown in In general, larger graft diameters were required to restore VTR, while restoring ITR required the smallest graft diameters. Compared with the original-sized ACL group, the required graft diameter for restoring knee stability and graft force in the 0.95 times-sized ACL group decreased by 0.5–1.5 mm, and that for the 1.05 times-sized ACL group increased by 0.5–1.5 mm . CompareThis study found that knees with a large ACL, similarly, required a large diameter graft to restore knee stability and graft force after ACLR. A 10% difference in the size of the ACL resulted in a difference of 1.5–3 mm in the required graft diameter. Considering such a big difference in required graft diameter and a rather narrow range in graft diameters used clinically , this sRecent studies have indicated that the “one-size-fits-all” principle may not be suitable for patients whose physiology is in the tails of the bell curve of human anatomy . However2 [2 and 111–176 mm2 for the femoral and tibial insertion sites, respectively [The results of this study showed a relatively large variation in the required graft diameter when used to replace native ACLs of different sizes. Studies have shown a large individual variation in the dimensions of the native ACL, with the CSA at the isthmus ranging from 35.1 to 103.6 mm2 , and ranectively ,21,22. Aectively , which mectively  have fouIt should be noted that when VTR was restored to a physiological level, both ATT and ITR were over-restrained and the graft force was greater than the value recorded for the intact ACL. Over-restraining of ATT and ITR may present as a stiffness in the knee in these DOFs, which may hinder daily activities that require good flexibility of the knee joint. A larger force in the graft may make it vulnerable to rupture, which would require revision surgery. These concerns warrant further study into improving ACLR techniques, thus reducing the risk of asynchronous restoration of knee kinematics and graft force. This study also showed that even the large graft diameter of 12 mm could not restore VTR in line with the 1.05 times-sized ACL. A 12 mm graft diameter was the largest size in this study because expanding the tibial bone tunnel any further would have damaged the tibial cartilage.This study has some limitations: (1) The smaller and larger sizes of the ACL were modeled by proportionally scaling the ACL geometry without changing the morphology of other joint tissues. In reality, the morphology of the other joint structures, especially the contour of the articular surfaces, may also show individual variation, which might affect knee kinematics and function. However, the method used in this study ensured that the size of the ACL was the only variable from which conclusions were drawn. This is one of the advantages of using finite element analysis over cadaveric or in vivo studies. The strict control of the variables allows for individual differences to be excluded and negates any impact on the characteristic being studied. Therefore, statistical analysis was not considered necessary for this study. (2) All grafts were considered to have isotropic material properties. This does not accurately reflect all graft materials, particularly those from tissues commonly displaying anisotropic properties. Nevertheless, the main function of the ACL is to bear tensile forces and the conditions used in this study adequately simulated physiological loading, which primarily acted to tension the ACL and grafts. Assigning isotropic properties to the ligaments is not expected to affect the main finding of this study. (3) The internal tibial rotation (ITR) was possibly over-constrained in this FE model, in comparison to the native ACL, because the longitudinal fibers of the ligament were not simulated, and thus the rotation of fibers could not be simulated. This may result in a lower simulated ITR. (4) The finite element model was simplified in comparison to the complex human joint, and the model geometry was adopted from a single sample. However, finite element analysis allows the variate to be changed an infinite number of times, without damaging the sample, and the variate can be strictly controlled to one factor when the other factors are kept constant. This permits the basic joint biomechanics to be examined more clearly. This study shows that a minor difference in the size of the ACL can have a considerable effect on the graft diameter required to restore knee stability. Thus, it is necessary to choose the graft diameter according to the size of the native ACL, with the averaged diameter—calculated from the ACL insertion sites and the isthmus—being a good indicator of a suitable graft diameter. The findings of this study may help to improve the clinical treatment of ACL ruptures.


Solid-state NMR is a nondestructive and noninvasive technique used to study the chemical structure and dynamics of starch-based materials and to bridge the gap between structure–function relationships and industrial applications. The study of crystallinity, chemical modification, product blending, molecular packing, amylose–amylopectin ratio, end chain motion, and solvent–matrix interactions is essential for tailoring starch product properties to various applications. This article aims to provide a comprehensive and critical review of research characterizing starch-based materials using solid-state NMR, and to briefly introduce the most advanced and promising NMR strategies and hardware designs used to overcome the sensitivity and resolution issues involved in structure–function relationships. The world population is expected to increase to 10 billion by 2050 .Starch is a highly abundant, biodegradable, and hydrophilic carbohydrate typically found in staple crops such as corn, potatoes, wheat, rice, and green fruits ,5,6,7. I6H10O5)n. A granule of starch typically contains up to 98-99% (dry weight) of two types of polysaccharide components: amylose and amylopectin [Starch consists of long chains of glucose units connected via glycosidic bonds , with a lopectin ,8,13,14.lopectin  polymer lopectin . Amylopelopectin . The remlopectin .Starch can be used in a ‘raw’ or ‘modified’ form . The phyMany of the key properties of starch  can be either desirable or unfavorable, depending on the application. To improve raw starch properties, and to tailor these for specific applications, modification is often necessary ,8,21,25.Chemical modification involves changing the functionalization of the starch either via esterification, etherification, cationization, oxidation or cross-linking ,7,8. PhyDue to starch’s important role within food-based industries, its accurate characterization is critical . The tecDong et al.  see Tab studied Morphological properties of starch can be analyzed by means of microscopy see . In one X-ray techniques can be used to characterize the structure of starch before and after retrogradation see  2,28,29,2928,29.g) and melting point (Tm) of starch-TiO2 nanocomposite films using DSC [2 was shown to increase both Tg and Tm, a beneficial property for packaging systems.Thermal techniques such as differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) are commonly used to determine the glass transition temperature, gelatinization temperature, melting temperature and moisture content of starch and starch composites ,45,46,47sing DSC . In thisSpectroscopic techniques such as infrared (IR) and Raman spectroscopy see  have beeIn all cases in ssNMR is a powerful technique, well known for its use in studying the structure and dynamics of carbohydrate polymers ,49. This13C, 15N and 31P), via the cross-polarization effect, starting from high gyromagnetic ratio nuclei  relying on the strong heteronuclear dipolar coupling in the system. However, the technique is not considered quantitative due to its low efficiency when heteronuclear dipolar coupling is weak, as in the case of mobile and highly mobile systems. Single pulse experiments provide quantitative results when set with sufficient recycle delays (five time the spin-lattice relaxation time T1), granting complete relaxation of the targeted nuclei. CP can be considered a useful sensitivity enhancement technique to discriminate rigid molecules while an improved modified version for rigid and mobile molecules exists: the CPSP. CPSP provides its greatest benefits over the standard CP for the 13C nuclei with short relaxation values and cross-polarize inefficiently [Highly valuable information can be aggregated when experiments are performed with a combination of different ssNMR techniques—such as magic angle spinning (MAS) and cross polarization (CP) . The CP iciently . Differeiciently .13C direct excitation experiment.To obtain deep insights into the carbon skeleton chemical structure for starch-based materials, 2D INADEQUATE  is one of the most powerful NMR techniques available. This technique relies on J(C-C) to provide information about the carbon skeleton. 2D INADEQUATE is not used extensively in characterizing materials due to its extremely low sensitivity when compared to a 1D 13C isotopic labeling can be used on the sample . This iell wall . Alternaell wall . Enhanceell wall ,57.MAS and CP are standard techniques used in ssNMR for the study of organic and polymeric materials. A wealth of information on carbohydrate polymers can be obtained from these techniques including: polymorphism, degree of substitution/crosslinking, grafting position, crystallinity index, solvent–biopolymer interactions, aggregate formation, polymer chain dynamics and lipid–biopolymer interactions ,59,60,61Starch is the most abundant biopolymer in plants, consisting mainly of amylose and amylopectin. These form semicrystalline granules with a wide range of crystallinity between 15 and 45% . Starch’13C CP MAS NMR experiment is a powerful and straightforward analytical tool for studying the molecular organization [6, (II) theregion between 68 and 78 ppm, assigned to the ring of the molecule C2,3 and 5, followed by C4 at 84 ppm, and (III) the region between 90 and 105 ppm, assigned to C1 [The nization , semicrynization ,68,69,70nization ,72,73,74ed to C1 .The starch polymeric structure is assembled of crystalline and amorphous regions differing in the torsion angles of the α-(1 → 4) glycosidic bond. Thus, carbon atoms at opposite ends of the glycosidic bond possess different local electron densities in their different ordering structures . Upon de13C-labeled starches produced from Chlamydomonas reinhardtii microalgae [To obtain higher-resolution spectra, 2D INADEQUATE ssNMR experiments based on NOE (Nuclear Overhauser Effect) and CP were performed on croalgae . The comcroalgae . The latThis NOE-based experiment, represented in Meanwhile, the CP-based experiment represented in The spin correlation between the crystalline domains appeared to be well resolved in the NOE-based experiment, while the amorphous domains were observed better in the CP-based experiment . Overall, the 2D ssNMR experiments were able to characterize the structure of both highly crystalline amylopectin and poorly crystalline B-type amylose. Moreover, new chemical shifts and multiplicities were assigned and an interpretation for the ordered, disordered, chain length, crystallinity and amylose/amylopectin ratio was provided .Several factors have a direct effect on, and bear responsibility for, the structure heterogeneity of starch-based products and food products. These factors include: water content ,78,79,8013C ssNMR spectra based on CP and insensitive nuclei enhanced by polarization transfer (INEPT) experiments are considered an alternative approach. CP-based experiments exhibit better efficiency on rigid crystalline and amorphous structures, while INEPT-based ones depend directly on the mobility of polymeric segments and flexible gelatinized chains [On a molecular level, these heterogeneities appear in the forms of granule swelling, starch gelatinization and granule disintegration ,77,97. Hd chains . The CP The INEPT experiment appears to work better for soaked starch; however, only relatively small peaks were observed from the starch region which could be assigned possibly to mobile fractions of dissolved starch or amylose leakage. The INEPT experiment showed peaks related to lipids and proteins with higher intensities compared to the dry starch pasta .A different approach for measuring and assigning the local mobility of a heterogeneous structured compound is by the comparison of CP and single pulse CP (CPSP) MAS NMR experiments . This apTo obtain a better understanding, spectral deconvolution was applied to the C1 region, which was separated into three different peaks, represented in the insert of Many industrial and food applications of starch are directly related to its physical-chemical properties, such as gelatinization, crystallinity, adhesion, solubility, and viscosity. Starch plasticization, via the addition of water and glycerol in different proportions, can tune the thermoplastic properties and change the phase transition of starch. This, in turn, produces a physically modified starch in a homogeneous polymeric state ,103,104.2 relaxation decays that possess crucial information regarding the dynamics and composition of native and plasticized starch. The CPMG decay curves presented in Carr–Purcell–Meiboom–Gill (CPMG) echo decay train ,106 is aFrom the three peaks observed for native starch, two could be related to the rigid backbone chain. The first peak at 0.2 msec could be associated with the polymeric chain segments with the lowest mobility located close to the branched backbone. The second peak at 1 msec could be associated with a more mobile segment located away from the core branched region. The third peak at 20 msec represents the mobile branches of the amylopectin microstructure and free amylose end chains.2 values was observed. The peak corresponding to the rigid component shifted in the opposite direction, thus indicating the formation of soft matter structures. The addition of glycerol lead to the formation of a semi-mobile region. This semi-mobile region consists of two peaks at 0.6 and 2.5 msec and a mobile one at around 20 and 70 msec. This is related to an increase in the amylose free end chains and amylopectin lateral branches. This suggests an increase in the total mobility of the starch polymeric chain [Upon the addition of water, a shift in the mobile and semi-mobile peaks to higher Tic chain .Starch modification and blending with active compounds have been considered a wide sectional area in the food and product industry. However little information, on a molecular level, is known about the dominant interactions and binding sites between the starch and the integrated active compounds ,115,116.1H single pulse depicted in 13C CP MAS experiments presented in 13C solution state NMR experiment was conducted on cuminaldehyde (C).Conventional solid-state NMR experiments, including the 3 of the phenyl group, where two different peaks appeared in the solid-state spectrum compared to one single peak in solution state.The results obtained from proton and carbon solid-state NMR spectra were consistent with starch loaded with significant amounts of cuminaldehyde. Upon comparing the solution state spectra of cuminaldehyde to the solid-state one for C/PS, it was found that only a single methyl peak appeared in the solution state spectrum, while three peaks between 20 and 30 ppm were detected in the solid-state spectrum. This indicates interactions between cuminaldehyde methyl groups and starch. It also indicates that three different environments of free and adsorbed cuminaldehyde were present. The same phenomena appeared in C1H T1 measurements show a significant difference in the molecular dynamics between cuminaldehyde in its pure form (1H T1 = 1.5 sec) and when adsorbed in the starch structure (1H T1 = 1.92 sec). The increase in the 1H T1 values for cuminaldehyde was attributed to the restricted mobility of the cuminaldehyde molecules in the starch structure, thus resulting in an increase in the longitudinal relaxation [Molecular mobility was investigated by measuring the relaxation properties for cuminaldehyde represented in laxation .Starch is one of the most abundant components in food products. However, it possesses a complicated semicrystalline packing ordered structure, which creates a dilemma for investigators resolving the molecular structure, polymorphism and solvent–matrix interactions. Several analytical techniques have been used to investigate the structure of starch, but none as efficient at providing information about the structure and dynamics in a quantitative, non-destructive way. ssNMR has proven to be a useful technique when it comes to the inspection of the structure and dynamics of food derivatives, since the majority of such compounds have disordered to semicrystalline structures with wide range of polymorphs. Using ssNMR, valuable information can be gathered about the dynamics, crystallinity, water pools, degree of modification, starch blending and structural heterogeneity of starch-based compounds.13C and 2D 13C-13C CP MAS experiments and 3D NMR experiments. It has also seen a wide range of newly developed hardware including, but not limited to, Pulse Field Gradient NMR, comprehensive multiphase NMR, low field NMR, magnetic resonance imaging and microimaging, ultra-high-magnetic-field magnets, CryoProbes, and ultra-fast MAS probes. This opens the way for resolving the full structure of native, gelatinized, plasticized and modified starch, as well as starch-based products.Recently, ssNMR has seen major developments in pulse sequences such as water-edited 1D Advanced hyperpolarization and sensitivity boosting techniques have developed significantly and have been applied to different materials, including carbohydrate and cellulose-based systems ,118,119.


The quality of starch greatly affects the quality of processed products. There are many indexes for quality evaluation of starch. Currently, amylose content is considered the chief index in the quality evaluation of sweet potato starch, which is entirely based on tradition (experience) method. The existing evaluation standards lack quality evaluation standards for sweet potato starch.To screen reasonable evaluation indexes of sweet potato starch, and establish a scientific and systematic evaluation system of sweet potato starch.Twenty-two components and quality indexes of sweet potato starch were measured. The evaluation indexes of sweet potato starch were screened based on a statistical description, correlation analysis, and principal component analysis (PCA), and a quality evaluation model of sweet potato starch for brewing was established based on analytic hierarchy process. The calculated values of the model were verified by linear fitting with standardized sensory evaluation values.L* value, ΔE, water absorption capacity (g/g), and pasting temperature was less than 6%, while the coefficient of variation of other indexes was larger. In addition, there were different degrees of correlation among the indexes. PCA was used to identify interrelated variables, and the first six principal components together account for 82.26% of the total variability. Then, seven core indexes — setback (cp), rate of regression (%), ratio of amylose to amylopectin (%), gel strength (kgf/cm2), a* value, ash content (%), and solubility (%) — were selected from the six principal components according to the load value of the rotation matrix. These seven core indexes replaced the original 22 indexes to simplify the evaluation of sweet potato starch. The quality evaluation model of sweet potato starch was Y = 0.034X2 + 0.321X6 + 0.141X8 + 0.08X17 + 0.023X19 + 0.08X21 + 0.321X22.The coefficient of variation of total starch content (%), amylose content (%), amylopectin content (%), The comprehensive evaluation system of sweet potato starch can accurately predict the quality of sweet potato starch. The development of such a system is of great significance to the post-harvest processing of high-starch sweet potato and the breeding of high-quality and high-starch sweet potato varieties. D-glucose. Amylose is a linear polysaccharide linked by α(1–4)-glycosidic bonds, whereas amylopectin is a multi-branched polymer linked by α(1–4)-glycosidic bonds and α(1–6)-glycosidic bonds , and analytic hierarchy process (AHP), the core evaluation indexes were selected, and the quality evaluation system of brewing sweet potato starch was established. Finally, the effectiveness of the comprehensive quality evaluation model was verified by sensory evaluation results.Eighteen varieties of sweet potato, namely Yanshu 29, Yushu 15, Wanshu 9, Luxuan 1, Jishu 25, Qinshu 9, Jishu 99, Xushu 18, Xushu 37, Sushu 18, Sushu 23, Sushu 28, Luoxushu 9, Luoshu 10, Luoshu 11, Luoshu 12, Luoshu 14, and Luoshu 15, were harvested in the experimental field of the Sweet Potato Research Institute of the Chinese Academy of Agricultural Sciences  on October 21, 2020. After callus treatment at high temperature, sweet potato roots of uniform size with no insect infestation or scars were selected for further treatment.The starch was extracted using the method reported by Vithu et al., with slight modification . Clean sThe moisture content and ash content were determined according to the AOAC method . The conL*, a*, b*, and ΔE, where L* represents the sample brightness; a* represents the red and green value; b* represents the yellow and blue value; and ΔE represents the total chroma.The color of sweet potato starch was determined by a spectrophotometric colorimeter . The measurement indexes were g, 15 min). Then, the water was removed. The centrifuge tube was inverted for 2 min and weighed. WAC (g/g) was calculated using Eq. 1. For oil absorption capacity (OAC), 1 g of starch was weighed in a 10 mL centrifuge tube, and 6 mL of soybean oil was added. The sample mixture was placed in a water bath at 30°C for 30 min and was centrifuged . The oil was removed. The centrifuge tube was inverted for 15 min and weighed. OAC (g/g) was calculated using Eq. 2 (For water absorption capacity (WAC), 0.6 g of starch was weighed in a 50 mL centrifuge tube, and 10 mL of distilled water was added. The sample mixture was placed in a water bath at 30°C for 30 min and was centrifuged  , according to a method reported by Qian et al. . Three gg, 15 min). The supernatant was collected, and the weight of the precipitate after starch expansion was measured. The supernatant was dried to a constant weight in a 105°C oven and weighed  was calculated (Eq. 5) :In a 100 mL beaker, 3 g of starch was weighed, and 50 mL of distilled water was added. The beaker containing the sample was placed in a water bath at 95°C for 30 min with constant agitation. Subsequently, it was cooled to room temperature, wrapped with plastic wrap, and placed in a refrigerator at 4°C for 24 h. After 24 h, the sample was taken out and balanced for 2 h at room temperature to form a stable starch gel. The gel strength (GS) of the starch was measured using a texture analyzer . A cylinder probe with a diameter of 6 mm was selected. The pre-test speed was 2 mm/s, the test speed was 1 mm/s, and the post-test speed was 5 mm/s. The compression ratio was 50%. The triggering force was determined according to the instrument situation. During puncture, the GS was determined as the maximum stress per unit area of the probe .i = i is the initialization value, and x′imax is the maximum initialization value.There were different dimensions and orders of magnitude for the quality comprehensive evaluation indexes of sweet potato starch. Therefore, the normalization method was adopted to eliminate the influence of each index dimension on the results . The oriFi, and the corresponding normalized value was Xi. A judgment matrix A = (aij) was established using the 1–9 scale method, and each element in the matrix was assigned using the following rules Fi and Fj were equally important, aij = 1;When 2)Fi was slightly more important than Fj, aij = 3;When 3)Fi was more important than Fj, aij = 5;When 4)Fi was much more important than Fj, aij = 7;When 5)Fi was extremely more important than Fj, aij = 9; andWhen 6)ij = 2, 4, 6, 8.When between the above judgments, aWi of each quality evaluation index was calculated according to the judgment matrix A (The weight matrix A .1)A: Mi = Calculate the product of each row of the judgment matrix 2)Mi: Calculate the n-root of 3)Wi: Wi = Calculate the 4)A: W = T is the eigenvector, and (AW)i is the ith element of AW;Calculate the maximum eigenvalue of the judgment matrix 5)CI: CI = CR = RI is the mean random consistency index), the judgment matrix meets the consistency requirements; andCalculate the consistency index 6)The synthetic value is obtained by the AHP: Y = i = i, zmax, and zmin are the comprehensive score, highest score, and lowest score of sensory evaluation, respectively.Ten trained sensory evaluators were organized to perform a sensory evaluation on sweet potato starch, and sensory data were normalized. The normalized value of sensory evaluation was ZP < 0.05). Origin 2021 software was used for correlation analysis. Each experiment was analyzed in triplicate, and the experimental data were expressed as mean ± standard deviation (Mean ± SD).SPSS 18.0 software was used for Variance analysis, and PCA. Duncan multiple comparison method was used to test the significance of Variance analysis  of 18 kinds of sweet potato starch was similar and high, ranging from 73.29 to 86.79%, and the coefficient of variation was 4.21%. The amylose content and amylopectin content were significantly different, ranging from 17.78 to 21.65% and 55.50 to 68.69% respectively, and the coefficient of variation was 5.79 and 5.57%, respectively. The results showed that amylopectin was the main component of sweet potato starch, which was consistent with the study of Aina et al. . The amyThe larger the AA is, the higher the ratio of amylose to total starch is. The AA of 18 kinds of sweet potato starch was significantly different, ranging from 26.41 to 35.48% with a variation coefficient of 8.53%. AA of Jishu 99 was the highest, and amylose accounted for a large proportion of total starch.L* value and ΔE are two main indexes to evaluate starch color. As shown in L* value and ΔE of Jishu 99 were the highest among the sweet potato varieties, and a* and b* values were small, indicating that the starch of Jishu 99 had the brightest and highest color quality. In contrast, L* value and ΔE of Xushu 37 were the lowest, and the absolute a* and b* values were the largest, indicating that the hue of the starch was reddish yellow and that its color was dull.Color is an intuitive index to evaluate the quality of starch, and it affects the quality of starch products, such as starch noodles , 28. ThePasting temperature is the lowest temperature of gelatinized starch and reflected energy consumption. PV is the maximum viscosity after the suspension gelatinized with rising temperature, and it reflects the swelling power of starch particles. TV refers to the lowest viscosity value of starch from gel to collosol due to increased spacing between starch molecules. FV is the stable viscosity value after the solution changed from collosol to gel with decreasing temperature. BD reflects the shear resistance of starch, and SB reflects the short-term rearrangement capacity of starch . The pasThe physicochemical properties of all samples are presented in The solubility and SP are basic properties of starch and important properties in food processing, reflecting the interaction between starch and water. As shown in Among the 22 components and processing quality indexes, WAC and OAC, pasting properties, S, SP, RR, and GS reflect the processing quality characteristics of sweet potato starch. As expected, there was collinearity in the measurement indexes; therefore, correlation analysis was used to determine the relationship between the indexes and provide a theoretical basis for the classification and screening of quality indexes in the next step. The heatmap of the correlation coefficient is shown in Principal component analysis can extract a few mutually independent indexes and reduce redundant variables . PCA wasa* value; and the sixth principal component mainly explained ash and solubility. Finally, seven indexes, namely SB, RR, AA, GS, a* value, ash content, and solubility, were selected as the core indexes of the quality evaluation of sweet potato starch.To better explain the relationship between the original evaluation indexes and principal components, the principal component load matrix was constructed by orthogonal rotation method , 37. Thei). The results are shown in a* value was a neutral index, and the average value was the ideal value. Ash content, AA, SB, RR, and GS were inverse indexes, and the minimum value was taken as the ideal value.The original data of the seven sweet potato starch quality indexes were normalized max = 7.228, CI = 0.038.λRI = 1.32, CR = 0.029 < 0.1. This indicates the established judgment matrix had satisfactory consistency. The weight coefficients were 0.034, 0.321, 0.141, 0.080, 0.023, 0.080, and 0.321. The determination of weight coefficients based on the method of establishing judgment matrix ensured the acquisition of reasonable and accurate evaluation results and avoided the limitations of the assignment method . The linear relationship between the two was obtained as follows: y = 0.708x + 0.253, R2 = 0.903 (The comprehensive score of the sensory evaluation was normalized , and the = 0.903 . The resa* value, the variation coefficient of other color parameters was small. The RVA viscograph of 18 kinds of sweet potato starch was different. Both morphology and structural of starch and alkali concentration affected pasting properties. Liu et al. and Cardoso et al. found that the pasting properties of starch were affected by the granule size and shape, amylose and phosphorus contents, amylopectin chain length, size and molecular structure of crystallization region, and also affected the quality of starch and starch products (Sweet potato starch is one of the important processing materials, and its quality greatly affects the properties of starch processing products. There are many factors affecting the quality of sweet potato starch, including sensory quality and processing quality . Some ofproducts , 45. Theproducts , 46. Zhoproducts . In addiproducts . The solproducts . The RR products , 51. In L* value was positively correlated with ΔE. It may be that L* plays a dominant role in the calculation of ΔE, so L* greatly affects ΔE. The PV was positively correlated with TV, BD, FV and setback value. The TV was positively correlated with the FV and setback value. The FV was negatively correlated with the setback value. These results were consistent with the research of Hou et al. and Zhang et al. (There is a relationship between the composition and physicochemical properties of starch. Consistent with the findings of Yu et al. , we foung et al. , 54.a* value, ash content, and solubility, were selected as the core indexes of the quality evaluation of sweet potato starch. The seven indexes are independent of each other. The a* was chosen as the evaluation index because a* value reflects the red and green value of sweet potato starch, which can be used to evaluate the color quality of sweet potato starch (In SPA, seven indexes, namely SB, RR, AA, GS, o starch . The bado starch . The AA o starch . Solubilo starch . When tho starch , 58. Wheo starch . Sweet pR2 of the sweet potato starch model established in this study reached 0.903. The results showed that the starch quality could be predicted well. Compared with the existing evaluation methods of sweet potato starch, the evaluation method established in this study integrated all the core evaluation indexes, and scientifically assigned weights to each index. So far, there is no systematic quality evaluation method for sweet potato starch, however, similar methods have been applied to the evaluation of fresh fruit. Bai et al. took different varieties of apples as the research objects, measured the quality indexes of fresh apples, analyzed the distribution of indexes and the correlation among indexes, screened eight evaluation indexes such as SSC, and finally established the comprehensive quality evaluation method of fresh apples by objective assignment method (Evaluation model of sweet potato starch is the core part of evaluation system. The t method . Nie et t method  establist method . The acca* value, ash content, and solubility, were screened by PCA. The quality evaluation model of sweet potato starch was Y = 0.034X2 + 0.321X6 + 0.141X8 + 0.08X17 + 0.023X19 + 0.08X21 + 0.321X22. The comprehensive quality evaluation system of sweet potato starch established in this experiment is of great significance for the post-harvest processing of high-starch sweet potato. The results provide a new idea for the quality evaluation of sweet potato starch and its products.A quality evaluation system of brewing sweet potato starch was established based on a multivariate statistical analysis method. There were different degrees of correlation among different evaluation indexes. Seven core indexes, namely SB, RR, AA, GS, The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.CM: investigation, formal analysis, writing – original draft, writing – review and editing. WZ: chemical analysis. YZ and RY: data analysis. YL, ZM, and HZ: contributed reagent, analysis tools, and materials. JS and FN: conceptualization, methodology, and supervision. All authors contributed to the article and approved the submitted version.


Based on established metrics, binny outperforms or is highly competitive with commonly used and state-of-the-art binning methods and finds unique genomes that could not be detected by other methods. binny uses k-mer-composition and coverage by metagenomic reads for iterative, nonlinear dimension reduction of genomic signatures as well as subsequent automated contig clustering with cluster assessment using lineage-specific marker gene sets. When compared with seven widely used binning algorithms, binny provides substantial amounts of uniquely identified MAGs and almost always recovers the most near-complete (The reconstruction of genomes is a critical step in genome-resolved metagenomics and for multi-omic data integration from microbial communities. Here, we present  High-throughput shotgun sequencing has become the standard to investigate metagenomes , 2. Metak-mer frequency or abundance profiles or both. Therefore, issues with metagenomic assemblies, such as fragmentation of the assembly because of insufficient sequencing depth, repeat elements within genomes and unresolved ambiguities between closely related genomes, are perpetuated to MAGs. In addition, the features based on which contigs are binned are not generally homogeneous over genomes: for example copy number, and thereby metagenomic coverage, may vary over the replicating genome; certain conserved genomic regions, and also newly acquired genetic material, can deviate in their k-mer frequency from the rest of the genome  washttps://concoct.readthedocs.io/en/latest/usage.html).CONCOCT (1.1.0)  was run https://github.com/RasmussenLab/vamb/blob/master/README.md). Because VAMB is designed to achieve optimal performance through the combination of the data of multiple samples, the samples from each of the six CAMI datasets were concatenated and run together, as described by the authors (README sections Recommended workflow and Snakemake workflow). For the real-world metagenomes, samples sharing a JGI GOLD Study ID were run together. As VAMB could not be successfully run on some of the real-world samples using default values, or when trying with lower values of -m and –minfasta, the number of MAGs recovered was counted as zero for these samples. For a list of these samples see VAMB (3.0.2)  was run single_easy_bin mode with –random-seed 0 and default parameters otherwise. For the single sample binning the global model was used, except for the CAMI 2 Gastrointestinal (GI) tract samples, for which –environment human_gut was used and the CAMI 2 Oral samples, for which –environment human_oral was used. For the real-world benchmark the respective models matching wastewater, ocean and soil samples were employed.SemiBin (1.0.2)  was run coverage, seed and cluster. To use coverage, the assemblies’ respective bam files were converted to sam format using samtools.MetaDecoder (1.0.9)  was run DAS Tool (1.1.2)  was run binny, MetaDecoder, and SemiBin and (ii) the two binning methods except binny, to asses how many MAGs binny contributes in an ensemble approach.MetaWRAP (1.2.2)  was set et al. [To match real-world workflows, all binning outputs were assessed using CheckM (1.0.12)  and filtet al. .Additionally, the MIMAG definition of high-quality draft genomes was employed, requiring at least 18 unique tRNAs and three unique rRNAs to be present in the MAG in addition to a purity of >95% and a completeness of >90% .https://github.com/pysam-developers/pysam).Besides the recall in terms of bps of the assembly recovered, the read recruitment of MAGs was assessed. All reads mapping as primary mappings to contigs of a MAG were counted per sample and divided by the total read count (forward + reverse) using pysam  , a genomSum of base pairs coming from the most abundant genome in each predicted genome bin divided by the sum of base pairs in all predicted bins....’ and overall purity as ‘Sum of base pairs coming from the most abundant genome in each predicted genome MAG divided by the sum of base pairs in all predicted bins....’.To evaluate a MAG, AMBER selects the gold standard genome with the highest share of bps in that MAG as the reference. In contrast to CheckM, where purity and completeness refer to the amount of marker genes present or duplicated, within AMBER and using an available gold standard, purity and completeness refer to the amount of bp of the reference genome recovered for completeness, and the share of bp of a given MAG with a given reference genome, respectively. Additionally, to assess one or multiple datasets taken together, AMBER defines overall completeness as ‘Purity and completeness values are reported as the per dataset average, unless specified otherwise. For the real-world benchmarks, the average proportion of bp recovered or the number of MAGs recovered is reported together with the standard error of the mean (SEM). Another metric used is the adjusted Rand index (ARI), which is a commonly used metric to measure how similar two datasets are. Trying to make the comparisons between different binning methods as realistic, fair and transparent as possible, we report all metrics derived from the CheckM-filtered binning results, unless specified otherwise.matplotlib [Seaborn [UpSetPlot [NumPy and pandas libraries.To assess the intersections of MAGs formed by the different binning methods on multi-sample datasets, genomes were counted separately for each sample. To this end, the gold standard genome name was concatenated with the sample id to yield unique identifiers for each genome in each sample. All other figures were created using the Python libraries tplotlib  and Seab[Seaborn , as wellpSetPlot , settingTo evaluate if the binning methods could recover NC and HQ MAGs from organisms with closely related or highly similar genomes in the same sample, for each of the 54 samples of the six CAMI datasets all versus all Average Nucleotide Identity (ANI) calculations were performed using FastANI (1.33) . Each gebinny’s performance, six datasets from the CAMI initiative were chosen: the high complexity toy dataset of the first CAMI iteration to investigate how binny performs on very large datasets and the five toy human microbiome datasets of the second CAMI iteration to evaluate the performance on a wide range of microbiome sizes and complexities. Generally, a binning tool performs best, if it recovers the most complete MAGs with the highest purity, which corresponds to the highest ARI.To assess binny with default settings recovered 35.5% (SEM 2.8%) of the reference genome lengths in the samples as NC MAGs (n = 1564) and 42.7% (SEM 3.0%) as HQ MAGs (n = 2021), with median recall values of 26.3% and 36.3%, respectively , ectively . In totaectively .binny recovered 30.0% of the total reference genomes with a purity of 97.8% and an ARI of 0.970 ; 48.0%, 98.9% and 0.983 (CAMI 2 Skin); and 33.2%, 98.6% and 0.982  . Notably, a substantial proportion of the reads recruited were mapped to single contig MAGs for the CAMI 2 datasets (on average 60.7%), whereas for the CAMI 1 datasets, only about a fifth of the reads recruited by binned contigs, were mapped to single contig MAGs  in total and 102.5% more NC MAGs (209) of contig sizes larger than 5  as HQ (42.7%) or NC (35.5%) MAGs, followed by MetaDecoder  and SemiBin . Additionally, binny showed the highest median MAG counts with 23.8%, 36.8% more NC and 14.8%, 29.2% more HQ MAGs than MetaDecoder and SemiBin, respectively  and high ARI (0.962) output over all datasets without additional CheckM filtering. Using CheckM filtering, binny’s purity and ARI were increased by 1.1% and 0.015, respectively, whereas the assembly recall was decreased by 3.0%  and HQ (25.3%) MAGs than the second best performing method, SemiBin  and the CAMI 1 High complexity dataset after SemiBin (0.4% less), respectively  than the next best, VAMB for the CAMI datasets. Additionally, the two largest sets of MAGs shared by two binning methods are both binny sharing MAGs with MetaDecoder (140) or SemiBin (57), respectively (binny recovered the second most unique MAGs after VAMB (5.8% less) and was present in all of the intersections with the largest numbers of genomes  than almost all methods (50 NC MAGs). Only CONCOCT recovered more highly fragmented genomes than binny (54), whereas both methods shared the recovery of a large portion of these fragmented genomes. VAMB produced the third most with 27 highly fragmented NC MAGs  and SemiBin . When taking only into account genomes consisting of six or more contigs, binny still outperformed all other methods, followed by SemiBin  and VAMB . At an ANI cut-off of 95% binny recovered 36.8% and 22.6% more NC MAGs than the second highest performing method from genomes consisting of any number or at least six contigs, respectively. Finally, at an ANI of 99.0% binny was able to generate 41.8% and 61.1% more NC MAGs from genomes consisting of any number or at least six contigs, respectively, than the method placing second  as NC (19.8%) bins, after MetaDecoder (20.2%), and the largest amount of HQ (28.8%) MAGs. MetaDecoder in total recovered the most NC MAGs (1647), followed by binny (1523) and SemiBin (1513). Notably, there was a substantial gap in performance to the next best method, MetaBAT2, with 1223 NC MAGs recovered (23.7% less than SemiBin). binny recovered the largest amount of HQ MAGs (3013), followed by MetaDecoder (2969) and SemiBin (2747). As in the CAMI benchmarks, CONCOCT showed the lowest recall for both NC and HQ MAGs, whereas MaxBin2 performed comparatively better with these data than in the CAMI benchmark , with MetaDecoder ranking third with 533  and MetaWRAP (45.0%), respectively, whereas binny had marginally lower recall than DAS Tool with 48.1%  than binny on its own, whereas DAS Tool produced 2.4% more NC MAGs (1605)  . The runs including all three binning methods showed the highest performance overall, with MetaWRAP recovering the most MAGs (binny (When th 48.1% . binny os (1605) . Only Me0% more) . Includiost MAGs . Evaluats  to incorporate also short informative contigs, which would be discarded by most other binning methods due to their applied contig length thresholds, recision . Additiobinny also outperformed all other tested binning methods on the CAMI co-assemblies, where the added information provided by the coverage data from multiple samples substantially increased the overall performance. This is in line with previous studies observing additional discriminatory power of differential coverage depth compared with only sequence-based features [binny’s iterative, supervised strategy seems well suited to the complexity of assemblies that contain highly fragmented genomes.features , 15. Herk-mer profiles. While the difference in performance with and without masking was not substantial, we believe that it reduces noise in the k-mer distributions of contigs from the same genome. One key reason for the small impact in the current setting might be the strong effect of the read coverage depth on the embedding and clustering procedure, which could outweigh the impact of the masked k-mer profile. Masking reads mapping to the disruptive regions, also modifying the depth information, might increase its effectiveness and could be implemented in future versions.We also evaluated the effect of masking potentially disruptive sequence regions for the calculation of binny was shown to be an excellent addition to such approaches, because of its ability to recover large amounts of unique pure and complete MAGs (It is generally advised , 63 to mete MAGs  and 5.binny’s performance translates to real-world scenarios, competing well with the latest methods on the recovery of NC and HQ MAGs, while massively outperforming all other methods on the number of MIMAG-standard MAGs recovered. Still, there are also many samples where all binning methods were unable to recover a sizeable proportion of the assemblies as MAGs of sufficient quality. This might hint at the still limited capabilities of binning methods or could be caused by low quality of these assemblies.Finally, the results of the 105 metagenome benchmark show that binny outperforms or is highly competitive with currently available, state-of-the-art and/or popular binning methods based on established evaluation metrics, recovering unique, complete, and pure MAGs from simple and complex samples alike, while being able to handle contiguous, as well as fragmented genomes. Moreover, we could show that binny adds new MAGs when used in combination with other binning methods and binning refinement approaches, enabling researchers to further improve the recovery of genomes from their metagenomes.In conclusion, we demonstrate that binny outperforms or is highly competitive with commonly used and recently developed genome reconstruction tools.binny is benchmarked using community-standard simulations and a wide range of real-world metagenomes.binny efficiently and iteratively learns using lineage-specific markers and selected genomic features.binny can be found at https://github.com/a-h-b/binny. Scripts used in this study and related data are available at https://github.com/ohickl/binny_manuscript and https://doi.org/10.5281/zenodo.6977322.The latest version of O.H., P.M. and A.H.-B. designed this study. O.H. and A.H.-B. created the application. O.H. performed all experiments. O.H., P.Q., P.M. and A.H.-B. were involved in creating the workflow. O.H., P.M. and A.H.-B. wrote the manuscript; P.Q. and P.W. contributed to the review of the manuscript before submission. All authors read and approved the manuscript.supplementary_tables_p1_bbac431Click here for additional data file.supplementary_tables_p2_bbac431Click here for additional data file.supplementary_figures_bbac431Click here for additional data file.binny_bib_rev2_rev3_tc_bbac431Click here for additional data file.