Nipah virus (NiV) is a zoonotic virus that causes lethalencephalitisand respiratory disease with the symptom of endothelial cell–cellfusion. Several NiV outbreaks have been reported since 1999 with nearlyannual occurrences in Bangladesh. The outbreaks had high mortalityrates ranging from 40 to 90%. No specific vaccine has yet been reportedagainst NiV. Recently, several vaccine candidates and different designsof vaccines composed of epitopes against NiV were proposed. Most ofthe vaccines target single protein or protein complex subunits ofthe pathogen. The multiepitope vaccines proposed also cover a largelylimited number of epitopes, and hence, their efficiency is still uncertain.To address the urgent need for a specific and effective vaccine againstNiV infection, in the present study, we have utilized the “reverseepitomics” approach (“overlapping-epitope-clusters-to-patches”method) to identify “antigenic patches” (Ag-Patches)and utilize them as immunogenic composition for multipatch vaccine(MPV) design. The designed MPVs were analyzed for immunologicallycrucial parameters, physiochemical properties, and interaction withToll-like receptor 3 ectodomain. In total, 30 CTL (cytotoxic T lymphocyte)and 27 HTL (helper T lymphocyte) antigenic patches were identifiedfrom the entire NiV proteome based on the clusters of overlappingepitopes. These identified Ag-Patches cover a total of discrete 362CTL and 414 HTL epitopes from the entire proteome of NiV. The antigenicpatches were utilized as immunogenic composition for the design oftwo CTL and two HTL multipatch vaccines. The 57 antigenic patchesutilized here cover 776 overlapping epitopes targeting 52 differentHLA class I and II alleles, providing a global ethnically distributedhuman population coverage of 99.71%. Such large number of epitopecoverage resulting in large human population coverage cannot be reachedwith single-protein/subunit or multiepitope based vaccines. The reportedantigenic patches also provide potential immunogenic composition forearly detection diagnostic kits for NiV infection. Further, all theMPVs and Toll-like receptor ectodomain complexes show a stable natureof molecular interaction with numerous hydrogen bonds, salt bridges,and nonbounded contact formation and acceptable root mean square deviationand fluctuation. The cDNA analysis shows a favorable large-scale expressionof the MPV constructs in a human cell line. By utilizing the novel“reverse epitomics” approach, highly immunogenic novel“GaEl antigenic patches” (GaEl Ag-Patches), a synonymterm for “antigenic patches”, were identified and utilizedas immunogenic composition to design four MPVs against NiV. We concludethat the novel multipatch vaccines are potential candidates to combatNiV, with greater effectiveness, high specificity, and large humanpopulation coverage worldwide. Henipavirus and family Paramyxoviridae.1 The firstNiV outbreak was reported among pigs in Malaysia in 1999.2 Later, another NiV outbreak was reported in Meherpur,Bangladesh, in year 2001, this time in humans. The transmission ofNiV infection in Bangladesh and India was associated with both thecontaminated date palm sap and the human-to-human contact.3 Bats were identified as the main reservoir forNiV, and they are responsible for the transmission of the infectionto both humans and animals.4 Since 2001,NiV outbreaks have been reported for Bangladesh almost every year. In India, two NiV outbreakswere reported in the state of West Bengal in 2001 and 2007.5 Afterward, another NiV outbreak was reportedin the state of Kerala in India during the period of May to June in2018. The Kerala outbreak claimed 17 human lives, leaving only 2 survivorsout of 19 confirmed cases.6 Hitherto, noefficient vaccine against NiV has been reported. Vaccines targetingmultiple proteins of NiV might provide efficient protection, and theirpotential needs to be explored in the future.Nipah virus (NiV) is a zoonotic virusof the genus 21 The C protein regulates the early host proinflammatory responseas well as the pathogen virulence; the glycoprotein (G), the matrixprotein (M), and the fusion protein (F) together form a cluster onthe host human cell membrane and facilitate virus particle assemblyformation.23 The NiV polymerase is essentiallyresponsible for the initiation of RNA synthesis, primer extension,and transition to elongation mode. The phosphoprotein and glycoproteinare also essentially involved in viral replication, whereas the Vprotein is responsible for the host interferon signaling evasion.20 The identical N-terminal region of the V and W proteins was foundto be sufficient to exert the IFN-antagonist activity.21 Hence, all the above-mentioned nine NiV proteinsplay an essential role in NiV proliferation and pathogenesis and soprovide important drug and vaccine target candidates.The essentialproteins involved in NiV pathogenesis to humans includeC protein, glycoproteins (G), matrix proteins (M), fusion glycoproteins(F), nucleocapsid protein, phosphoprotein, polymerase, V protein,and W protein.37 However, using a single or a small number of epitopes might be limitingthe vaccine potential. The long-term adaptive immunity essentiallyinvolves the presentation of the antigen as short peptides (epitope)on the surface of antigen presenting cells (APCs). To achieve thispresentation, an intracellular proteolytic chop down process is orchestratedby the proteasome and lysosome. The “transporter associatedwith antigen processing” (TAP) and further the binding HLAallele (human leukocyte antigen) molecules facilitate the epitopepresentation.39In recentstudies, a number of T-cell and B-cell epitope candidatesfrom different NiV proteins have been reported. Additionally, a numberof vaccine design approaches and vaccine candidates including multiepitopevaccines were reported.43 In the present study, we have utilized the “reverseepitomics” approach to identify “GaEl Ag-Patch”from NiV proteins. The reverse epitomics approach applies the novel“overlapping-epitope-clusters-to-patches” method toidentify the “GaEl Ag-Patches”. Here, we first identifythe overlapping epitopes that arise from a particular region of theprotein. This particular region is a consensus peptide sequence ofall the overlapping epitopes, and we identify this region of proteinas “GaEl Ag-Patches”. From the proteome of NiV, herewe report a total of 57 “GaEl Ag-Patches” identifiedfrom overlapping 776 epitopes. Next, we utilized these “GaElAg-Patches” from the NiV proteome as immunogenic compositionto design multipatch vaccines.Here, we introduce theterm “GaEl antigenic patch”(GaEl Ag-Patch) as a synonym for “antigenic patch”.This is to acknowledge the inventors of “reverse epitomics”,and the prefix GaEl is derived from river Ganga and Elna of the inventors’home countries. The novel “reverse epitomics” approachto identify “GaEl Ag-Patch” has been introduced in ourearlier study on SARS-CoV-2.43 All nine proteins of the NiV proteome (https://www.uniprot.org/proteomes/UP000120177) were used in this study, viz., C protein (gi-1859635642), glycoprotein(gi-253559848), fusion protein (gi-13559813), nucleoprotein (gi-1679387250),matrix protein (gi-13559811), phosphoprotein (gi-1802790259), W protein(gi-374256971), V protein (gi-1802790260), and RNA polymerase (gi-15487370).The full-length amino acid sequences of NiV proteins were retrievedfrom the National Center for Biotechnology Information (NCBI). Upto 96 full-length protein sequences belonging to different strainsand origins of NiV were retrieved. The MPVs designed with antigenicpatches as immunogenic composition carry potential discontinuous B-cellepitopes as well as IFN-γ inducing epitopes in their tertiarystructure models. Hence, the designed MPVs carry the potential toelicit cell-mediated as well as humoral immune response. Furthermore,both MPVs were designed with the adjuvants human β defensin2 and human β defensin 3 at N and C termini.45 The β-defensins have considerable immunological adjuvant activityand generate potent humoral immune responses.48 Tertiary structure models ofthe MPVs were generated, refined, and further analyzed for molecularinteraction with the ectodomain of the human Toll-like receptor 3(TLR3) by molecular docking. TLR3 is an essential immunoreceptor andacts as a sentinel to bind and process antigens causing activationof the IFN response against foreign antigens.50 With these functions, TLR3 plays an important role as immune-receptorduring the NiV infection and hence was chosen to examine the candidateMPVs.52 The ability of CTL or HTL MPVs forming acomplex with the ectodomain of human TLR3 was further investigatedby molecular dynamics simulation studies. The cDNA of the designedMPVs was also analyzed for its high expression potential in the human host cell line. The corresponding workflow for the vaccinedesign and utility of “GaEl Ag-Patches” in diagnosticsare shown in Figure S1.In the present study, we have designedtwo CTL and two HTL multipatchvaccines (MPVs). These vaccines are composed of “GaEL antigenicpatches” (GaEl Ag-Patches) identified from the proteome ofNiV. The “GaEl Ag-Patches” were identified by the reverseepitomics approach as introduced in our earlier studies.http://tools.iedb.org/processing/) and “MHC-I Binding Predictions” (http://tools.iedb.org/mhci/).54 The tool provides a “Total Score”that is a combined score including results from the proteasome, MHC,TAP , and processing analysis. A combinationof six different methods, viz., Consensus, NN-align, SMM-align, Combinatoriallibrary, Sturniolo, and the NetMHCIIpan, was applied.55 Further, immunogenicity of all the shortlisted CTL epitopeswas obtained by using the “MHC I Immunogenicity” toolof IEDB (http://tools.iedb.org/immunogenicity/)55 on the basis of physiochemical propertiesof constituting amino acid of the peptide sequence.Identification of cytotoxic T lymphocyte epitopes was performed bythe IEDB (Immune Epitope Database) tool “Proteasomal cleavage/TAPtransport/MHC class I combined predictor” (http://tools.iedb.org/mhcii/) was used to identify helper T lymphocyte (HTL) epitopes from NiVproteins.59 Three different methods, viz., combinatorial library, SMM_align,and Sturniolo, were used; further, a comparison of score of the peptideagainst the scores of other known five million 15-mer peptides ofthe Swiss-Prot database was performed to screen HTL epitopes.59 A percentile rank for each peptide is generated, of which a lowervalue indicates a higher immunogenic potential of the HTL epitope.TheIEDB tool “MHC-II Binding Predictions” (60 The “SVM(Swiss-Prot) based” (support vector machine) method was usedhere. The method utilizes a data set of 1805 sequences as positive(toxic) and 3593 sequences as negative (nontoxic) peptides from Swiss-Protas well as 1805 positive and 12,541 negative peptide sequences fromTrEMBLE.The ToxinPredtool was used to characterize the toxic potential of shortlisted CTLand HTL epitopes. The tool facilitates the identification of the highlytoxic or nontoxic peptides.61 The consensus amino acid sequence of overlappingepitopes was identified as “GaEl Ag-Patches”. This novelapproach of search and identification of “GaEl antigenic patches”from a source protein is named as “reverse epitomics”as introduced and explained in our earlier studies.43 This approach is applicable toproteins/antigens of any pathogen and not only to SARS-CoV-2 or NiV.All the shortlisted high-scoring epitopesfrom NiV proteome were aligned using their amino acid sequences withthe multiple sequence alignment (MSA) tool of Clustal Omega.62 T cells recognize complexes of MHC moleculeswith a given epitope. The epitope can elicit an immune response inindividuals who express the binding MHC molecule.59 The MHC molecules are expressed differentially in the humanpopulation in different ethnically distributed populations. This MHCrestricted epitope binding provides an opportunity to analyze worldwidepopulation coverage by the given epitope.The world population coverage by the overlappingCTL and HTL epitopes that were utilized to identify the GaEl Ag-Patcheswas studied by the “population coverage” tool of IEDB.63The aminoacid sequence conservation of the shortlisted CTL and HTL “GaElantigenic patches” was analyzed with the “Epitope ConservancyAnalysis” tool of IEDB. Epitope conservancy was performed againstup to 96 protein sequences of NiV of different strains and originscollected from NCBI.The identified andshortlisted “GaEl Ag-Patches” from the NiV proteinswere further used as immunogenic composition to design two CTL andtwo HTL multipatch vaccine constructs as explained in the 64 The tool performs an empirical investigationof various physicochemical parameters, viz., amino acid length, theoreticalpI, molecular weight, aliphatic index, expected half-life , grand averageof hydropathicity (GRAVY), and the instability index score. The aliphaticindex and the grand average of hydropathicity (GRAVY) indicate theglobular and hydrophilic nature of proteins. Further, the instabilityindex score indicates the stable nature of proteins.Two CTL and two HTL MPVs were analyzed with the ProtParam tool.66The two CTL and two HTL MPVs were screened for potential interferon-gamma(IFN-γ) inducing epitopes (from CD4+ T cells) utilizing the“IFN epitope” tool implementing the “Motif andSVM hybrid” method, i.e., MERCI: Motif-EmeRging and with Classes-Identification,and the SVM: support vector machine hybrid. The prediction is basedon the IEDB database of 3705 IFN-gamma inducing and 6728 noninducingepitopes.68 The AllergenFP prediction is based on hydrophobicity, their size,their helix-forming propensity, the relative abundance of amino acids,and β-strand forming propensity. The VaxiJen tool utilizes analignment-free approach based on physicochemical properties of queryprotein sequence.The CTLand HTL MPVs were analyzed for allergenicity and antigenicity withthe AllergenFP and VaxiJen tools, respectively.69The tertiary structures of the MPVs were calculated by homology modelingutilizing I-TASSER, which utilizes the sequence-to-structure-to-functionparadigm for protein structure prediction.72 The GalaxyRefine tool refines the input tertiary structure by repeatedstructure perturbation followed by subsequent structural relaxationand molecular dynamics simulation.74The refinement of all calculated structural models (two CTLand two HTL MPVs) was performed with the GalaxyRefine tools.https://zlab.umassmed.edu/bu/rama/index.pl).76 The Ramachandran plots show sterically allowedand disallowed residues along with dihedral psi (ψ) and phi(φ) angles.Both therefined two CTL and two HTL MPV tertiary models were further validatedby the Ramachandran Plot Server  of the discontinuous epitopes.78The IEDB tool Ellipro (ElliPro: Antibody Epitope Predictiontool) was used to screen the linear and discontinuous B-cell epitopesfrom all the CTL and HTL MPV tertiary models. Here, the farthest residueto be considered was limited to 6 Å, the residues lying outsideof an ellipsoid covering 90% of the inner core residues of the proteinscore highest protrusion index (PI) of 0.9, and so on. The discontinuousepitopes were predicted based on the distance “79 The GRAMM-X toolutilizes the GRAMM Fast Fourier Transformation (FFT) methodology byemploying smoothed potentials, refinement stage, and knowledge-basedscoring.The molecular interaction of the CTL and HTL MPVs with the ectodomainof Toll-like receptor 3 (TLR3) was analyzed by molecular docking followedby a molecular dynamics simulation study. The protein–proteinmolecular docking was performed by the GRAMM-X Protein–ProteinDocking v.1.2.0 tool.81 MD simulation studies were carriedout in an explicit water environment in a cubic box as the unit cellsimulation box at a stabilized temperature of 300 K and pressure of1 atm with periodic cell boundary conditions. The solvated systemswere neutralized with counter Cl– ions. Only theions necessary to neutralize the net charge on the protein are addedby gmx genion. The OPLS all-atom force field wasused on the systems during MD simulation.82 The solvated structures were energy minimized by steepest descent.Further, the complexes were equilibrated for a period of 100 ps. Afterequilibration, the MD simulation was run for 150 ns. The RMSD andRMSF values for Cα, backbone, and all the atoms of CTL and HTLMPVs in complex with TLR3 were analyzed.The molecular interactions of MPVs–TLR3(ECD)complexes were further evaluated using molecular dynamics (MD) simulationanalysis. The MD simulation studies were performed for 150 ns usingthe GROMACS tool.84 The CAI indicates the possibilityof expression in the chosen human cell line expression system. Thetandem rare codon frequency indicates the presence of low-frequencycodons in the cDNA construct.A codon-optimized complementary DNA (cDNA) of the two CTL and twoHTL MPVs was generated and analyzed for favored expression in themammalian cell line (human) by the Java Codon Adaptation Tool. ThecDNA of MPVs was further analyzed by the GenScript Rare Codon AnalysisTool for the large-scale expression potential. The tool analyzes theGC content, codon adaptation index (CAI), and the tandem rare codonfrequency.Tables S1 and S2).Thecytotoxic T lymphocyte (CTL) epitopes were screened and shortlistedaccording to their high “Total Score”, low IC(50) (nM)value for epitope-HLA class I allele binding, and larger number ofthe HLA class I allele binders. Additional epitopes were screenedfrom the protein sequences of NiV on the basis of their percentilerank. A smaller value of percentile rank indicates a higher affinityof peptide with its respective HLA class I allele binders. A totalof 1811 (579 + 1232) CD8+ T-cell epitopes/HLA class I allele pairswere screened. These included the top-scoring epitopes and the highest-percentile-rankingepitopes from the NiV proteome. The immunogenicity of the shortlistedCTL epitopes was also determined. All the screened and shortlistedCTL epitopes are assessed as highly immunogenic in humans (Table S3).Thescreening of helper T lymphocyte (HTL) epitopes was performed on thebasis of “percentile rank”. A smaller value of percentilerank suggests a higher affinity of the peptide with its respectiveHLA allele binders. We screened in total 773 potential CD4+ T-cellepitopes/allele pairs showing the highest percentile rank. The epitopeswith the largest number of HLA class II allele binders were also included(Tables S1–S3).All the screenedand shortlisted CTL and HTL epitopes were evaluated as nontoxic  with 100% aminoacid sequence of epitope being present in most of the NiV strainsas shown in 43  candidates are expected to be superior by utilizing “GaElAg-Patches” as immunogenic composition . Because HLA alleles are differentiallyexpressed in the ethnically distributed global population, they provideus with the opportunity to analyze population coverage. The convincing99.71% (average: 85.79 and standard deviation: 20.73) of the globalhuman population is predicted to be covered by the CTL and HTL multipatchvaccine candidates proposed in this study. The countries most affectedby NiV infections showed a significant coverage like India: 97.17%,Malaysia: 91.87%, etc. (Table S6). Theepitope–HLA allele pairs are summarized in Supplementary txt 3.The populationcoverage by the GaEl antigenic patches was analyzed by the overlappingepitopes and their HLA allele binding pairs using the “PopulationCoverage” tool of IEDB. The 57 GaEL Ag-Patches constructedin this study from 362 CTL and 414 HTL overlapping epitopes targeta total of discrete 27 HLA class I and 25 HLA class II alleles , and HTL GaEl Ag-Patches were between 40.54 and 100% conservation(mostly above 90%) 1 and 2. Table S7). Short amino acid linkers EAAAK and GGGGS providea covalent and flexible connection between the constituting peptide,respectively. The EAAAK linker facilitates domain formation, and theGGGGS linker provides conformational flexibility; hence, together,they favor stable protein folding. The EAAAK linker was used to fusethe human β defensin 2 and 3 (hBD-2 and hBD-3) at N and C terminalends of the MPVs, respectively. The constructs include hBD-2 withsequence: GIGDPVTCLKSGAICHPVFCPRRYKQIGTCGLPGTKCCKKP and 3D structuredeposited in the Protein Data Bank (PDB code 1FD3) and hBD-3 withsequence: GIINTLQKYYCRVRGGRCAVLSCLPKEEQIGKCSTRGRKCCRRKK and 3D structure(PDB code 1KJ6). hBD 2 and 3 are utilized here as adjuvants to enhance the immunogenicresponse.89 The multipatch vaccine design and the method thereofare included in the field patents 202011037939, 202011037939, PCT/IN2021/050841,and previous publications.43The GaEl Ag-Patches identifiedfrom the proteome of NiV were utilized as immunogenic compositionfor the design of two CTL and two HTL MULTIPatch vaccines .Further, the MPV candidate molecules were also analyzed for thepresence of potential interferon-gamma (IFN-γ) inducing epitopes.The amino acid sequences of all the MPV candidates were screened forIFN-γ inducing 15-mer peptide epitopes by utilizing the IFNepitopetool. A total of 75 IFN-γ inducing epitopes were screened fromtwo CTL MPVs and two HTL MPVs .Thecharacterization of the immunogenic properties of the suggested MPVis a crucial step in selecting appropriate vaccine candidates. Weinvestigated both the Table S11). The C-score isa confidence score indicating the significance of template alignmentsand the convergence parameters of structure assembly simulations.The C-score typically ranges from −5 to 2, with higher valuesindicating a model with higher confidence and vice versa. The C-scoresof all the four MPV models are acceptable, indicating high confidenceon the generated models. The TM-score indicates structural alignmentbetween the query structure and template structure. The RMSD (rootmean square deviation) is the deviation between the residues thatare structurally aligned  to the template structure.Tertiary structure homology models of MPV candidates were generatedby using the I-TASSER modeling tool 5. All thTable S12). The Rama favored criterion indicates the percentage ofresidues that are in the favored region of the Ramachandran plot,the GDT-HA  indicates the accuracyof the model structure backbone, and the RMSD (root mean square deviation)value indicates the deviation from the initial model. Further, theMolProbity score indicates the log-weighted combination score of theclash score, the percentage of Ramachandran not favored residues,and the percentage of bad side-chain rotamers.All the generated MPV tertiary models were refinedwith the GalaxyRefinetools to improve the sterical conformation parameters of the calculatedstructures. We used for further analysis only the top-scoring models.All selected MPV refinement output models have good Rama favored,GDT-HA, RMSD value, and MolProbity scores . Hence, all the MPVmodels show acceptable sterical conformations.We nextanalyzed the sterical acceptable conformation of all MPV models usingRamachandran plots. The Ramachandran Plot analysis indicates thatthe MPV models (CTL-MPV-1 and CTL-MPV-2) have 95.5 and 94.9% residuesin the favored region. Likewise, the HTL MPV models (HTL-MPV-1 andHTL-MPV-2) were found to have 90.9 and 87.3% residues in the favoredregion (Table S13). Further, the CTL MPVs alsocarry 14 discontinuous B-cell epitopes [CTL-MPV-1 (3 epitopes) andCTL-MPV-2 (11 epitopes)]. Likewise, the HTL MPVs carry 15 potentialdiscontinuous epitopes [HTL-MPV-1 (7 epitopes) and HTL-MPV-2 (8 epitopes)](Table S14). The ElliPro associates itsprediction with a PI (protrusion index) value. The high PI value ofthe linear and discontinuous epitopes suggests MPVs to have high potentialto cause humoral immune response.The tertiary models of MPV candidates were tested forthe presence of potential linear and discontinuous B-cell epitopeswith the ElliPro tool of IEDB. The screening revealed that the CTLMPVs carry a total of 39 linear epitopes [CTL-MPV-1 (21 epitopes)and CTL-MPV-2 (18 epitopes)]. Likewise, the HTL MPVs carry a totalof 26 linear epitopes [HTL-MPV-1 (10 epitopes) and HTL-MPV-2 (16 epitopes)](2A0Z) was performed with the GRAMM-X Protein–Protein Docking toolof the Vakser Lab, Center for Bioinformatics at KU (The innate immune system Toll-like receptor 3 (TLR3) acts as asentinel against foreign antigens. For those reasons, the molecularinteraction of MPVs with the ectodomain (ECD) of the human TLR3 isan important aspect to study for a potential vaccine candidate. Therefore,the MPV candidates were docked with the ectodomain of human TLR3.The molecular docking study for the MPV models using a TLR3-ECD crystalstructure  value of thestructure 6. The B-Figure S3. These complexes form multiple hydrogenbonds in the interaction interface . In summary, all the four simulated complexes were foundto form very stable interactions with TLR3-ECD at constant temperature(∼300 K) and pressure (∼1 atm). Further, the variationin the radius of gyration (Rg) for all the MPV and TLR3-ECD complexesremains in the acceptable range throughout the MD simulation . The stable Rg indicates thepresence of structurally compact complexes throughout the MD simulation.Furthermore, the amino acid residues of the complexes show acceptableroot mean square fluctuation (RMSF) from their respective initialconfirmation  . Overall, the molecular docking and the molecular dynamics simulationstudy of all the MPV and TLR3-ECD complexes indicate stable complexesformed.The MPV/TLR3-ECD complexes were subjected to moleculardynamics (MD) simulation analysis with the program GROMACS version2021.4 to investigate their stability. The complexes have very convincingroot mean square deviation (RMSD) values for the backbone that graduallystabilize toward the end of 150 ns of MD simulation  (complementary DNA (cDNA)of all the MPVs was generated for expression in the human cell lineby the Java Codon Adaptation Tool. The optimized cDNA was furtheranalyzed for its expression feasibility by the GenScript Rare CodonAnalysis Tool. The GC content, the CAI score, and the tandem rarecodon frequency optimized MPV cDNA were observed to be in the acceptablerage (Table S15). The CAI score indicatesa high propensity of cDNA expression in the human cell expressionsystem for PMV constructs. The tandem rare codons that may hinderthe proper expression of the cDNA in the chosen expression systemwere observed to be 0% in all the MPVs. Therefore, as per the GenScriptRare Codon analysis, the cDNA of all the MPVs is predicted to havea high potential for large-scale expression in the human cell line.TheMPV construct was analyzed for expression feasibility in the humancell line. Codon-optimized 85 The recent strategies for the design and developmentof vaccines to combat NiV involve subunit vaccines or multiepitopevaccines. The subunit vaccine involves the use of a single proteinor multiple subunits of NiV proteins. The major limitation with thosevaccine candidates is their limited efficiency being composed of single/limitednumber of protein/subunits. The more recent multiepitope vaccine approachprovides an opportunity to target multiple proteins of NiV. However,the low probability of MEV epitopes to be successfully presented byAPC after the intercellular chop down process limits the efficacyof the MEV candidates. Moreover, the multiepitope vaccines can accommodateonly a limited number of epitopes, causing them to face the challengeof frequent mutations in the proteome of the NiV.Majority of the vaccines designed againstNiV are focused on thesingle protein like G and F proteins, protein subunits, or the epitopesfrom NiV proteins.90 The consensus amino acid sequenceof overlapping epitopes is identified as the GaEl Ag-Patch. This methodis termed as the “overlapping-epitope-clusters-to-patches”method. We have identified potential GaEl Ag-Patches from the entireproteome of NiV. All the identified GaEl Ag-Patches are observed toarise from the surface of the NiV proteins, providing a potentialtarget for immunogenic response. Further, we utilized the GaEl Ag-Patchas immunogenic composition of the multipatch vaccine against NiV.Hence, these GaEl Ag-Patches cover a large number of overlapping epitopesthat they could produce upon the proteolytic intracellular chop downprocess by APC. These shortlisted GaEl Ag-Patches originate from allthe proteins encoded by NiV, thus targeting its entire proteome.89 These advantages of the multipatch strategywould enhance the vaccine efficiency and lead to vaccines that aremore targeted/specific and effective. Because the GaEl Ag-Patchescover a large number of epitopes that in turn target a large numberof different numbers of HLA alleles, the MPV would also provide alarger ethnically distributed human population coverage. Here, thedesigned MPVs cover 99.71% of the world population, targeting 52 differentHLA class I and class II alleles, a coverage that is much higher incomparison to vaccines composed of single/limited number of protein/subunits/epitopes.In addition to potential immunogenic composition for vaccine candidates,the reported GaEl Ag-Patches also provide potential immunogenic compositionfor early detection diagnostic kits against NiV infection. Further,the MPVs show stable binding with TLR3-ECD, which is one of the essentialcriteria for an antigen to be recognized and processed by the humanimmune system. The physicochemical properties and cDNA analysis ofMPVs favor their overexpression in human cell lines.In the presentstudy, we have utilized the novel “reverseepitomics” approach to design multipatch vaccines.In the present study, we have identifiedhighly immunogenic novelGaEl antigenic patches (GaEl Ag-Patches) (30 CTL and 27 HTL) fromthe entire proteome of NiV. We have utilized the novel “reverseepitomics” approach involving the “overlapping-epitope-clusters-to-patches”method. These GaEl Ag-Patches are highly evolutionarily conservedin nature. Further, for the first time, we have identified the GaElAg-Patches and used them as immunogenic composition to design a multipatchvaccine (MPV) against NiV. The MPVs designed against NiV in our studyhave potential to give rise to a total of 776 discrete CTL and HTLepitopes targeting a total of discrete 52 HLA alleles and hence coveringa convincing 99.71% of the world human population. Such large numberof epitopes is not possible to accommodate in multiepitope vaccines.We conclude that the multipatch vaccine designed by the novel “reverseepitomics” approach utilizing GaEl Ag-Patches as immunogeniccomposition could be highly potent with greater effectiveness andhigh specificity with large human population coverage worldwide andprotect the human population against NiV infection in an effectivemanner. The reported GaEl Ag-Patches also provide potential candidatesfor early detection diagnostic kits for NiV infection. The futureprospects would involve production and animal model based validationof our MPVs to be potential vaccine candidates for NiV.


White salted (udon) noodles are one of the major staple foods in Asian countries, particularly in Japan. Noodle manufacturers prefer the Australian noodle wheat (ANW) varieties to produce high-quality udon noodles. However, the production of this variety has reduced significantly in recent years, thus affecting the Japanese noodle market. Noodle manufacturers often add tapioca starch to compensate for the flour scarcity; however, the noodle-eating quality and texture are significantly reduced. This study, therefore, investigated the effect of the addition of porous tapioca starch on the cooking quality and texture of udon noodles. For this, tapioca starch was initially subjected to enzyme treatment, ultrasonication, and a combination of both to produce a porous starch where a combined enzyme –ultrasound treatment (20 kHz) yielded a porous starch with increased specific surface area and better absorbent properties which are ideal for udon noodle manufacturing, Later, udon noodles were prepared using three varieties of ANW, a hard Mace variety, and commercial wheat flour by incorporating the prepared porous tapioca starch at a concentration of 5% and 10% of dry ingredients. Adding this porous starch resulted in a lower cooking time with higher water absorption and desirable lower cooking loss compared to the control sample with 5% of the porous starch chosen as the optimum formulation. Increasing the level of the porous starch reduced the hardness of the noodles whilst maintaining the desired instrumental texture. Additionally, a multivariate analysis indicated a good correlation between responses’ optimum cooking time and water absorption capacity as well as turbidity and cooking loss, and a cluster analysis grouped noodle samples prepared from different varieties into the same clusters based on the porous starch added, indicating the possibility of different market strategies to improve the quality of the udon noodles produced from different wheat varieties. Noodles are traditional cereal-based products popular throughout the globe and highly consumed in Asian countries. The major factors leading to the popularity of Asian noodles are affordability, convenience, and long shelf life . NoodlesTo help maintain the high-quality udon noodle production market in Japan, it would be beneficial to understand the significance of starch quality and its modification on noodle quality. Noodle eating quality and texture are the significant aspects of high-quality udon noodles . These nNative wheat starch shows bimodal size distribution with large A-type and small B-type granules with a lenticular and spherical morphology, respectively . TypicalTapioca starch is known to have numerous industrial and food applications owing to its low cost and high expansion characteristics and is reported to confer an elastic texture to noodles when modified by means of physical or chemical methods ,22. The Hifumiya Udon Noodle House, Perth, WA, supplied the commercial flour for standard udon noodle preparation. The ANW  and hard (Mace) wheat varieties were obtained from Intergrain Pty Ltd., Bibra Lake, WA, and were milled and passed through a sieve to attain a particle size <180 µm in a laboratory facility at the Intergrain. Tapioca starch and salt were purchased from Coles, Waterford Plaza, WA. Alpha-amylase (Sigma A6255 from porcine pancreas) was obtained from Sigma-Aldrich .Three different treatments, viz enzyme treatment, ultrasonication, and a combination of both, were used to prepare the porous tapioca starch, as discussed by Majzoobi, Hedayati, and Farahnaky (2015) , with slg for 20 min at room temperature. The pellet obtained was recovered and washed three times using distilled water and dried in an oven  at 80 °C for 12 h. The dried pellet was ground, sieved to pass 100% through a 125 µm sieve, and stored vacuum-packed for further analysis.The prepared starch slurry (100 mL) was subjected to two concentrations of α-amylase enzyme, viz. 0.4% and 0.6%. The mixtures were placed in a water bath of 45 °C for 24 h for starch hydrolysis and then centrifuged at 3000× g for 20 min at room temperature and then dried in an oven  at 80 °C for 12 h. The dried pellet was ground to pass 100% through a 125 µm sieve and stored vacuum-packed for further analysis.The prepared starch slurry (100 mL) was subjected to ultrasonication  for 40 min using two different frequency settings: high frequency (48 kHz)/low power (60 W) and low frequency (20 kHz)/high power (100 W) at a constant amplitude of 100%. The temperature of the ultrasonication bath was maintained at 40 °C. The pellet was recovered after centrifugation at 3000× For preparing the porous starch by the combined enzymatic–ultrasound treatment, the prepared starch slurry was first treated with α-amylase (0.4% and 0.6%) , and theThe surface morphology of the prepared porous starch samples was imaged under a scanning electron microscope  in high vacuum mode with a 5 kV voltage and 5–10 mm working distance (WD) after platinum coating.The flour from Australian wheat cultivars and the controls  were blended with different proportions of porous tapioca starch (5 and 10% of the dry ingredients) to make the noodles. The dough crumb was made by adding a calculated amount of water and salt solution to the wheat flour–porous tapioca starch blend in a mixer. The water addition was decided based on the flour moisture content of each wheat variety using mass balance equations  . The cruThe optimal cooking time (OCT) was determined in duplicate according to the AACC-approved method 66.50.01 . BrieflyExactly 20 ± 0.05 g of freshly prepared noodles with a strand length of 20 cm were boiled in 300 mL distilled water to their OCT as described above. The cooked noodles were then drained, weighed, and the water absorption upon cooking was determined in duplicate . Water aExactly 20 ± 0.05 g of freshly prepared noodles of a strand length of 20 cm were boiled in 300 mL of distilled water to their OCT as described in The three-strand cutting system was used to determine the force required to cut through the cooked and cooled noodles with a strand length of 4 cm. A texture analyzer TA.XT2i  calibrated using 5 kg of load cell with a cutting probe of 33 mm diameter was used for the test. The equipment was set to pre-test, test, and post-test speeds of 2.0, 2.0, and 1.0 mm/s, respectively. The analysis was carried out in triplicate for each cooked sample .The turbidity of cooked noodles with a strand length of 20 cm was analyzed as per the method described by Jeon et al. . Exactlyp ≤ 0.05 was considered significant. A correlation analysis, principal component analysis (PCA), and cluster analysis were performed on the complete dataset using R software  with corrplot , prcromp , and factoextra packages, respectively. Scripts were written in R and ‘fviz’ and ‘ggplot2′ functions were used to visualize the multivariate analyses outputs.The data were analyzed using SPSS Version 16 software using a two-way analysis of variance (ANOVA), followed by pairwise comparisons based on marginal means and adjustment with the Bonferroni method where a SEM images of porous tapioca starch granules prepared using the different enzyme concentration and ultrasonication settings are presented in Native tapioca starch granules a generalThe quality attribute values obtained for the udon noodles for each variety of wheat used and the amount of porous starch added is depicted in p < 0.05) depending on the wheat variety used and the amount of porous starch added, except for WAC, where only the wheat variety was the significant factor. The interaction effects were also significant for all the responses except turbidity and texture.The results of the two-way ANOVA  conducteFurther, pairwise comparisons were made between the factors to determine the significant difference between the means among the two factors studied, viz., the wheat variety and level of porous starch added .Cooking time is important for determining noodle quality and consumer acceptability as it decides whether the noodle is undercooked or overcooked . Figure p < 0.05) to 10.35 min. At the same level of porous starch, the cooking time for noodles prepared from Zen and Kinsei varieties, which are the ANW varieties, was the shortest. A pairwise comparison among the wheat varieties at a specific concentration of porous starch showed no significant differences between the ANW Kinsei and Zen varieties at both 5 and 10% porous starch  on the WAC values, whereas the wheat variety and the interaction term had a significant influence. The WAC values obtained were then compared for each variety of wheat used for a specific concentration of porous starch added  in the cooking loss values obtained for noodles prepared from Mace, Ninja, and Zen varieties  of both the wheat variety and level of porous starch addition on the turbidity values; however, the interaction effect was insignificant  was employed to obtain a linear and unsupervised visualization of the results by reducing the original data dimension by the orthogonal transformation of the vector space. A variance bar plot and scree plot were generated to visualize the importance of principal components (PCs) created based on the amount of variation they covered . Two PCsIn contrast, cooking loss and turbidity vectors diverged from OCT, indicating a strong negative correlation. The biplot also depicts the scores of each sample studied, and their positions within the four quadrants indicate their possible relation. For instance, N5, N10, K5, K10, and M10 are all closely located to each other, meaning they have similar PC scores.Prior to the cluster analysis for classifying the data samples into groups of similar objects, a distance or dissimilarity matrix was created c using tFor further understanding the similarity among different groups of data, K-means clustering was employed by partitioning the dataset into a set of two and three groups d,e afterThis study demonstrated the new knowledge on adding porous tapioca starch to wheat flour in udon noodle formulation and showed the improved noodle-cooking and texture quality apparent from a reduced optimum cooking time and increased water absorption capacity with cooking loss within acceptable limits. It was evident that wheat varieties with different inherent properties produced noodles of acceptable quality upon adding porous starch at different concentrations. Upon evaluating the quality parameters of noodles, it was concluded that a 5% addition of porous tapioca starch was the most desirable. However, the findings from this study are limited to the most popular wheat classes for udon-making. Still, it allows for exploring other classes, such as APWN or combinations of ANW/APWN, using a similar study design. The current findings could be valuable for noodle manufacturers to produce udon noodles from hard and soft wheat flour by adding starch with varied porosity and achieving desirable features. We suggest further research on the eating quality (noodle sensory properties) of udon noodles with the added porous tapioca starch, and investigations of interactions between wheat flour components and porous starch in the noodle matrix.


Dear readers,As the seasons begin to change (at least in the northern hemisphere), we turn our attention to gathering the fruits of our collective labor and start our planning for the coming year. Since the groundwork and planning began in earnest in the fall of 2017, to our first issue in March of 2018, to the present day, the ‘seeds’ that were the JOR Spine concept have come to fruition, and we can now all share in the bounty that is our JOR Spine journal. Over the last six years, the international spine community has come together to support this new effort, and we have steadily progressed though the benchmarks that signify stability and success of a new journal. Last summer we were delighted to receive our first impact factor, a major milestone. This past July, we were even more delighted to find out that our impact factor had held steady, at a 3.7. While impact factor is only one metric, we are happy that all of your labors, and excellent submissions, have had this result, and firmly establish the JOR Spine in the top echelon of Spine related journals.JOR Spine Early Career Awardee. As you'll recall from past years, this award, developed in collaboration with the ORS Spine Section, is designed to recognize the ‘rising stars’ of our field and highlight their outstanding work published in the JOR Spine. For each of the past four years, awardees have been recognized at the ORS Annual Meeting, and we look forward to continuing this tradition in the coming year. Applications for this highly competitive award are due October 30th, 2023 and information on the award nomination process and eligibility criteria can be found at: https://onlinelibrary.wiley.com/page/journal/25721143/homepage/earlycareeraward. Please consider nominating your colleagues (or yourself) for this Award!As we think to the future, we are excited by plans for the coming year. The December issue is slated to be a ‘special issue’, with a compendium of manuscripts from the 2022 ORS/PSRS meeting. We thank the organizers for that great meeting and for assembling an outstanding lineup of papers that are being finalized. Many of you also just finished a race to the finish line for your ORS 2024 abstract submissions. We are excited to see all of the new spine‐focused work presented at the meeting, and to see it submitted to the JOR Spine soon! We also look forward to celebrating with all of you our next As we move into the fall, we wish you all a bountiful harvest, and look forward to your excellent submissions and suggestions, the seeds that will continue our progress in building JOR Spine into the preeminent spine journal for our community.Rob, Mauro, and Dai.


Revista Brasileira de Terapia Intensiva (RBTI). For years, RBTI hasbeen acknowledged in all Ibero-America, as a reference for clinicians and scientists inthe field of critical care medicine. RBTI is the official journal of both theAssociação de Medicina Intensiva Brasileira (AMIB)and the Sociedade Portuguesa de Cuidados Intensivos (SPCI). Since itsindexation in different platforms, the journal has increased its reach in theinternational scientific community, and, over the last years, about 30% of the submittedmanuscripts came from outside of Brazil.This issue celebrates the first edition of Critical Care Science, previously known asDuring the last years, the editorial board and representatives from AMIB and SPCIdiscussed changing the original Portuguese title acknowledge this increasing diversityof authors. We believe that the change in the journals’ name reflects the trajectory ofa journal that is well consolidated in Brazil and with broad international coverage. Ourefforts are focused on making Critical Care Science a journal that reflects the currentmaturity and needs of the international scientific community. This change will improveits competitiveness and increase the submission of manuscripts, both in number andquality.Despite the new name, Critical Care Science will maintain the same mission and uphold itslongstanding scientific and ethical standards. The support of AMIB and SPCI in thiscontext is essential. We wish to invite all the international critical care community tosubmit both basic and clinical research in this field to our journal. Besides its longhistory as a relevant vehicle in disseminating high-value research in the critical carefield, Critical Care Science will continue the editorial policy of RBTI and will remainfully open access with no article processing charges.This change has been an arduous task, but after years of editorial experience, and thesupport of AMIB and SPCI we are convinced that these changes will improve its visibilityglobally serving better our authors and readers, the supporting societies, and all thecritical care scientific community.Editorial BoardCritical Care Science


At present, the most scalable methods are approximate membership query data structures that combine the ability to query small signatures or variants while being scalable to collections up to 10 000 eukaryotic samples. Results. Here, we present PAC, a novel approximate membership query data structure for querying collections of sequence datasets. PAC index construction works in a streaming fashion without any disk footprint besides the index itself. It shows a 3–6 fold improvement in construction time compared to other compressed methods for comparable index size. A PAC query can need single random access and be performed in constant time in favorable instances. Using limited computation resources, we built PAC for very large collections. They include 32 000 human RNA-seq samples in 5 days, the entire GenBank bacterial genome collection in a single day for an index size of 3.5 TB. The latter is, to our knowledge, the largest sequence collection ever indexed using an approximate membership query structure. We also showed that PAC’s ability to query 500 000 transcript sequences in less than an hour.The Sequence Read Archive public database has reached 45 petabytes of raw sequences and doubles its nucleotide content every 2 years. Although BLAST-like methods can routinely search for a sequence in a small collection of genomes, making searchable immense public resources accessible is beyond the reach of alignment-based strategies. In recent years, abundant literature tackled the task of finding a sequence in extensive sequence collections using https://github.com/Malfoy/PAC.PAC’s open-source software is available at  Public databases, such as the Sequence Read Archive (SRA) or European Nucleotide Archive overflow with sequencing data. The vast amount of sequences, experiments, and species allows, in principle, ubiquitous applications for biologists and clinicians. Such databases are becoming a fundamental shared resource for daily sequence analysis. But concretely, we only witness the onset of their exploitation, while their exponential growth poses serious scalability challenges .k -mer-based strategies.Today, searching for a query sequence in a single genome/dataset or a restricted collection of genomes is considered routine through alignment-based tools, such as BLAST . In our application case, queries are typically alternative splicing events or a short genomic context around a small variant or mutation, for both eukaryotic and microbial species. Our structure is designed to be highly scalable with the number of indexed samples and requires moderate resources.https://github.com/Malfoy/PAC.In this section, we present our PAC. We kept PAC as an acronym that covers the three keywords, including the main novelties compared to previous tree structures. PAC is available at set of sequences R  is a set of finite strings on the alphabet k-mers are extracted .A k-mers, Bloom filters  is an AMQ structure introduced in n Bloom filters, each representing the distinct k-mers of the n datasets in an input database. A SBT is a balanced binary tree that represents separately each Bloom filter in its leaves and the union of all Bloom filters in its root. Therefore, it allows fast membership queries in the whole database using recursive queries along the tree. We call a “sequence Bloom matrix” the matrix of Bloom filters introduced in BIGSI  , for each iR D, we build a Bloom filter BFi and populate BFi with i’Rs k-mers. Thus, each Bloom filter represents the k-mers of a dataset from D. As SBT, we organize our Bloom filters in a tree whose inner nodes result from a merge operation on Bloom filters:Definition 1Bloom filter merge) Let two Bloom filters BF1 and BF2 with the same set of parameters . We define the (Definition 2union Bloom filter) A union Bloom filter is the Bloom filter result of a b, h). We define a series of Bloom filters BF1, BF2, …BFt built using common  parameters. They represent k-mer sets t filters of size b as a matrix M of size t × b. Let V be an integer array of size b. For i of M. V can effectively encode such a series of b using b integers of size S. In this way, we can encode t of such Bloom filters of size b using V encoding for the series of Bloom filters BF1, BF2, …tBF an “Aggregated Bloom filter.” We call a comb tree a binary tree whose each internal node has at least one leaf as a child. When considering an order on the leaves, a “left-comb tree”  has its root connected to the rightmost leaf .(Definition 5Bloom comb tree) We call a Bloom comb tree a comb tree built using the  Unless otherwise specified, we denote by “Aggregated Bloom Comb Tree” a structure composed of a pair of Bloom left and right-comb trees built on the same list of leaves. They are represented using two components. First, two Aggregated Bloom filters lV , rV  (see Definition 3) of size b, representing the branch going through all internal nodes down to the deepest leaf in both left and right-comb tree. Second, n Bloom filters are the leaves of both combs. defines the maximal depth at which 1’s can be encountered at a given position in Bloom filters of the combs, i.e. the search space for a hit. Therefore, intersecting the two k-mer set.Definition 7k-mer[super- (Deorowicz )] From an input string, a super-k-mer is a substring containing all consecutive k-mers that share a minimizer of size m.In PAC, we build a distinct “Aggregated Bloom comb tree” for each minimizer associated with a non-empty k-mers in a sequence largely overlap, blocks of k-mers tend to share their minimizer and yield super-k-mers. By associating super-k-mers to their minimizers (and therefore the k-mers they come from) one can divide a k-mer set into up to As consecutive Definition 8Single query) We call a single query the query of a single k-mer to an AMQ data structure. The single query outputs a bit vector of size n indicating the presence/absence of a k-mer in each dataset.(Definition 9Multiple query) Let s be a string of size k-mers in s through single queries to an AMQ data structure. The results scores are reported in an integer vector of size n.(Definition 10Inverted Bloom filter index) In this framework, we call an inverted Bloom filter index an index that maps each possible hash value h to a bit vector of size n that represents which filters have one at position h.. Finally, we maintain and return a score vector of size n. It is incremented by one at a position j each time a k-mer appears in leaf j. Therefore, PAC is also related to Sequence Bloom matrices because queries are handled using a matrix of Bloom filters.For each Adding new datasets does not require changing the index structure. New datasets can be added by constructing their Bloom filters and updating the values of the Aggregated Bloom filters when needed. We implemented this functionality to allow the user to insert a dataset collection into an existing index. This insertion presents a very similar cost to building an index from the said collection, as both the construction and update algorithms consist of successive insertions. It avoids the need to rebuild the index from scratch due to novel datasets.C if C threads are used to treat the datasets in parallel). A PAC index is distributed into P sections serialized in P different files. Such sections can be handled separately, being mutually exclusive. This strategy provides inherent coarse-grained parallelism, as different threads can operate on different sections without mutual exclusion mechanisms during construction. It also grants low memory usage as only small sections  have to be stored in memory at a given time.Bloom filters are constructed in RAM and serialized on disk to avoid heavy memory usage. In this way, only one Bloom filter is stored in RAM at a given time . This behavior guarantees that each partition will only be read from the disk once and that only one partition will be stored in RAM at a given time. Furthermore, partitions not associated with any query k-mer can be skipped. Both construction and query benefit from improved cache coherence as several successive k-mers are located in the same small structure, generating cache misses for groups of k-mers instead of doing so for nearly each k-mer.Similarly, the query also benefits from partitioning. All https://github.com/Malfoy/PAC/blob/main/listpacpaper.txt.gz )] in order to optimize memory usage.Bloom filters are represented as sparse bit vectors (using the BitMagic library (All experiments were performed on a single cluster node running with Intel(R) Xeon(R) Gold 6130 CPU @ 2.10 GHz with 128 GB of RAM and Ubuntu 22.04.532d545). SeqOthello (commit 68d47e0) uses Jellyfish , resulting in a total of k-mers. The input files are represented as compacted de Bruijn graphs generated by Bcalm2 ] for a total of 30 TB of uncompressed FASTQ data. We created incremental batches of 28, 29, …, up to 215 datasets to document the scalability of methods according to the number of datasets. In this second experiment, the indexes are built directly on sequencing datasets in raw FASTA format, containing redundant k-mers and sequencing errors. Each tool was configured to filter unique k-mers before indexing to remove most sequencing errors (we used k = 31). Since COBS does not provide such an option and produces huge index files, we did not include it in this benchmark. To approximate the order of magnitude of the amount of (non-unique) distinct k-mers, we used ntcard , using 22 GB of RAM and for a total index size of 1.1 TB. PAC’s construction is faster than the state-of-the-art for the whole construction process. It produces slightly bigger indexes, but requires much less external memory. SeqOthello crashed during index construction in the experiment with 229 bits) for low k-mer query false-positive rates (below 1% for five megabases genomes) and a second index with smaller Bloom filters (227 bits) when higher false-positive rates can be allowed (below 7% for five megabases genomes). Both indexes were constructed within 24 h using 21 and 6 GB of RAM, respectively, for a total size of 2.4 and 1.4 TB, respectively.Besides indexing RNA-seq data, AMQ indexes were also used to index large collections of bacterial genomes. To further highlight the scalability of PAC, we built it on two massive bacterial collections. In a first experiment, we fetched the 661 000 bacterial genomes representing more than three terabases, which were previously collected in a database  and built an index with a Bloom filter size of 2We designed a similar experiment to the one presented in HowDeSBT’s paper, with sequence batches of increasing sizes. We repetitively selected random transcripts from human RefSeq [using seqkit , we obseWe performed a separate benchmark to assess the queries wall-clock times. On a batch of 100 transcripts, HowDeSBT lasted 1 h 30 min, COBS lasted 30 min, SeqOthello lasted 10 min, and PAC lasted 5 min. Interestingly, on a larger batch of 10 000 transcripts, SeqOthello and PAC presented very similar results . These results indicate that the query time of these two tools is dominated by index loading. To assess when query time became predominant, we queried a large batch of 100 000 transcripts for which PAC lasted 11 min (two CPU hours) and an even larger batch of 500 000 transcripts handled by PAC in 40 min (6.5 CPU hours).Salmonella enterica genomes from RefSeq (counting 11 993 genomes at the time of the experiment). We report three different query times for each batch, using, respectively, two Aggregated Bloom Comb trees (according to Definition 6), a single one and none (i.e. the structure is a Bloom matrix). Without the use of Aggregated Bloom filters PAC index is conceptually identical to a matrix approach, such as COBS while using one or two Aggregated Bloom filters allow to skip some bitslice of the index. The number of bits that can be skipped depends in practice on the similarity shared between the query and the indexed documents. To highlight this effect, a batch is made up of S.enterica that are highly similar to the indexed genomes, a batch is made up of Escherichia coli that are very dissimilar to the indexed genomes, and a batch is made up of random sequence to show an extreme example of dissimilarity. The first observation is that Aggregated Bloom filters hardly improve the query time of the S.enterica batch. This result is expected because most query k-mers should be found in many indexed genomes and the amount of zeros that could be skipped in such slices should be very low. However, we see that using one Aggregated Bloom filter greatly accelerates the query on the two dissimilar batches, increasing the throughput by several fold. Furthermore, using a second Aggregated Bloom filter accelerates the query time even more but is not as beneficial as the first one.One novelty of PAC is the use of lightweight Aggregated Bloom filters to accelerate the queries by skipping subsections of the bit-slices. To assess the efficiency of this strategy, we report in k-mer set structure to index the entire GenBank bacterial genome collection and reach 32k human RNA-seq datasets.To our knowledge, PAC is the first AMQ PAC combines the simplicity and efficiency of inverted index matrix approaches, such as COBS or BIGSI with a lightweight tree structure.k-mer sets are designed to efficiently skip unrelevant documents, our Aggregated Bloom filters allow us to efficiently prune our query space.The novel tree structure has a minimal resource footprint, yet greatly improves the query time when a query is dissimilar to the index content, a scenario possibly met with microbial databases. As Using several real datasets, we demonstrate that PAC is practically scalable for its construction. In contrast to other approaches, PAC is simultaneously frugal in RAM, disk, and time requirements for building an index.k-mer present in all n datasets.We showed PAC’s ability to query 500 000 human transcripts in less than an hour, being the fastest in wall-clock time, and comparable to SeqOthello in CPU time. The worst-case query complexity remains the same for all methods, k-mers present in a single dataset with no collision or absent everywhere), PAC can answer in We reviewed that inverted indexes methods perform btad225_Supplementary_DataClick here for additional data file.


We propose a broad range triboelectric sensor system employing elastic sponge and shielding layers, which can realize fast stiffness recognition within 1.0 s at a low cost.A novel algorithm is proposed for rapid stiffness identification by extracting signal characteristics, effectively reducing demand of computing resources.The proposed sensor system can identify the multi-layer stiffness structure of objects, enabling effective recognition of variable inclusions in soft objects with an accuracy of 99.7%.The online version contains supplementary material available at 10.1007/s40820-023-01201-7. With the development of artificial intelligence, stiffness sensors are extensively utilized in various fields, and their integration with robots for automated palpation has gained significant attention. This study presents a broad range self-powered stiffness sensor based on the triboelectric nanogenerator (Stiff-TENG) for variable inclusions in soft objects detection. The Stiff-TENG employs a stacked structure comprising an indium tin oxide film, an elastic sponge, a fluorinated ethylene propylene film with a conductive ink electrode, and two acrylic pieces with a shielding layer. Through the decoupling method, the Stiff-TENG achieves stiffness detection of objects within 1.0 s. The output performance and characteristics of the TENG for different stiffness objects under 4 mm displacement are analyzed. The Stiff-TENG is successfully used to detect the heterogeneous stiffness structures, enabling effective recognition of variable inclusions in soft object, reaching a recognition accuracy of 99.7%. Furthermore, its adaptability makes it well-suited for the detection of pathological conditions within the human body, as pathological tissues often exhibit changes in the stiffness of internal organs. This research highlights the innovative applications of TENG and thereby showcases its immense potential in healthcare applications such as palpation which assesses pathological conditions based on organ stiffness.The online version contains supplementary material available at 10.1007/s40820-023-01201-7. The rapid advancement of artificial intelligence paves the way for robots to be extensively applied in a multitude of fields bringing tremendous convenience to humanity –7. For eTo address the flexibility problem, several signal processing techniques and advanced mechanical designs are proposed. Novel stiffness measuring methodology utilizes multi-sensory electronic skins and deep learning algorithms to avoid the limitation of benchtop –17. HoweIn this study, we propose a stiffness sensor based on the triboelectric nanogenerator (Stiff-TENG), overcoming the limitations of previous work –22 in adThe inner structure of Stiff-TENG consists of an ITO film, an FEP film with a conductive ink printed electrode, and an elastic sponge. The FEP film actes as the electronegative triboelectric layer, which has a working area of 10 mm × 10 mm and a thickness of 0.05 mm. Since FEP is an electrically insulating material, a screen-printing device is utilized to print the conductive ink on the backside of the FEP film to transfer electrons between the two triboelectric layers  . All the electric output in the experiments is measured by the electrometer Keithley 6514. The real-time signals measured were pre-processed and displayed on the MATLAB interface in LabVIEW.For standardized experimental characterization, the Stiff-TENG is mounted on the end tip of the push–pull force gage ZhiQu (ZQ-990LB). During each pressing and releasing, the movement of the equipment tip keeps a constant speed and moves in a straight line. In the experiments below, pressing speed of 150 mm minThe detection platform is designed for standard stiffness estimation experiments. The Stiff-TENG is mounted on the end of a vertical push–pull force gage. The Stiff-TENG's two electrodes are connected to a 1 GΩ resistor in series, and linked to the two terminals of the electrometer. When the Stiff-TENG contacts the objects, the mechanical energy is converted into the electrical signal displayed on the computer screen . The output performance is investigated with various connected external load resistance ranging from 100 MΩ to 10 GΩ , and the maximum power occurs when the load resistor is 800 MΩ.The electric output is generated through the electrostatic induction and electrostatic equilibrium. The working mechanism of the presented Stiff-TENG is shown in Fig. Consequently, larger Stiff-TENG deformation results in higher voltage output, which corresponds to the COMSOL simulation result as shown in Fig. Voc, the transferred charge Qsc, and the short-circuit current Isc are measured for Object 9 under 4 mm displacement  are fabricated. They are with different Young’s modulus , and defined as Object No. 1–9, respectively. The cubes are pressed by the Stiff-TENG along with the movement of equipment tip to deform. The voltage signal under various displacement is shown in Fig. It should be noted that the stiffness of the Stiff-TENG remains constant. According to Eq. , under tmentclass2pt{minimThe process to decouple the stiffness of the pressed objects is to extract force and displacement from the Stiff-TENG output Fig. a. The pe}x^ Fig. f, the reTo compare the performance of the Stiff-TENG with the results from the push–pull force gage, the results under 4 mm displacement are taken as one example. Both measurements Fig. g exhibitl and l denote the deformation length and original length. The thinner top layer deforms less than a thicker one under the same touching force. Meanwhile, the bottom layer of different samples deforms to a similar extent, so as the Stiff-TENG's elastic sponge layer. Therefore, the overall deformation which represents the displacement of the equipment tip is less for the sample with the thinner top layer. Thus, the Stiff-TENG deforms larger when press against the object with the thinner upper layer under the same overall displacement, resulting in a larger exhibited force as illustrated in Fig. To characterize the nonuniformly distributed objects, double-layer soft objects with various upper layer thickness  or bottom layer stiffness  are fabricated. The bottom layers’ thickness is the same (7.5 mm), and the upper layers’ stiffness is constant (0.37 MPa). The first scenario aims to explore the effect of varying the thickness of the upper layer while keeping the stiffness of the bottom layer constant. During the TENG pressing process, the device first comes into contact with the softer top layer, and the deformation of these three parts occurs simultaneously. It is known that Young’s modulus can be expressed as  The Stiff-TENG's ability to recognize objects with heterogeneous stiffness allows for its application in identifying materials encapsulated within objects. The Stiff-TENG is placed on the end of the robot arm via a solid rod. The Stiff-TENG can press different objects along with the movement of the robotic arm Fig. b. When tTo identify the complex encapsulated objects inside, the collected data should be processed as shown in Fig. In this work, a self-powered stiffness sensor based on TENG with broad range of measurement is proposed. The Stiff-TENG consists of an ITO film, an FEP film coated with conductive ink printed electrode, and one holed elastic sponge. Two acrylic boards are attached to the FEP and the ITO film, respectively. Furthermore, a shielding film is attached to the bottom and connected to the ground. The Stiff-TENG can decouple the object’s stiffness regardless of the movement displacement in the range of 4 mm without the assistance of other sensors.Due to Stiff-TENG’s ability to respond different pressing process, the characteristics for heterogeneous stiffness objects which are nonuniformly distributed is studied. Furthermore, the Stiff-TENG can be utilized for identifying objects that conceal different materials which are of various shapes, sizes, amounts and stiffness. The reason is that the foreign materials encapsulated within the object, regardless of their shapes, sizes, and amounts, alter the object's overall ability to resist deformation. With the processing pipeline made of data augmentation, normalization, FFT, and machine learning, the recognition accuracy achieves 99.7%. The generated signals can be displayed on the computer interface in real time. As it can recognize the objects whose encapsulated structure exhibits similar characteristics to those found in soft tissue lesions or cancerous growths in the human body, the Stiff-TENG has significant potential for the advancement of self-powered palpation sensing.Supplementary file 1 (MP4 9075 KB)Supplementary file 2 (MP4 9985 KB)Supplementary file 3 (PDF 1556 KB)Below is the link to the electronic supplementary material.


Introduction: Nipah (NiV) is the zoonotic deadly bat-borne virus that causes neurological and respiratory infections which ultimately lead to death. There are 706 infected cases reported up till now especially in Asia, out of which 409 patients died. There is no vaccine and effective treatment available for NiV infections and we have to timely design such strategies as world could not bear another pandemic situation.Methods: In this study, we screened viral proteins of NiV strains based on pangenomics analysis, antigenicity, molecular weight, and sub-cellular localization. The immunoproteomics based approach was used to predict T-cell epitopes of MHC class-I and II as potential vaccine candidates. These epitopes are capable to activate CD4+, CD8+, and T-cell dependent B-lymphocytes.Results: The two surface proteins including fusion glycoprotein (F) and attachment glycoprotein (G) are antigenic with molecular weights of 60 kDa and 67 kDa respectively. Three epitopes of F protein  were ranked and selected based on the binding affinity with MHC class-I, and 3 epitopes  with MHC-II molecules. Similarly, for G protein, 3 epitopes each for MHC-I  and MHC-II  with substantial binding energies were predicted. Based on the physicochemical properties, all these epitopes are non-toxic, hydrophilic, and stable.Conclusion: Our vaccinomics and system-level investigation could help to trigger the host immune system to prevent NiV infection. Pteropus species are the natural host of the NiV  (It is an enveloped non-segmented RNA virus with a genome size of roughly 18.2 kb that belongs to the  (ssRNA)  containi (ssRNA) . The ord (ssRNA) . NiV is  (ssRNA) .+ and CD8+ T-cell epitopes of Nipah virus based on pangenomic and immunoinformatics system level analysis. We used integrative framework to evaluate the antigenicity, molecular modeling and interaction studies, simulation analysis and physicochemical predictions and structural stability analysis of potential NiV epitopes.The risk factors include intimate association with animal reservoirs, contaminated food consumption, and close association with animals infected with NiV . The batThe Virus Pathogen Resource Database (VIPR) was used to obtain the genomic and proteomic sequences  of NiV. The database includes complete genomic and proteomic information of each variant . The intMultiple Sequence Alignment (MSA) was performed to analyze the sequences of NiV variants to observe the differences and similarities among the isolates . ComparaThe ROARY tool was used to carry out pan-genomic analysis . InitialThe Virus-mPLoc tool was used to predict the subcellular localization elaborating the subcellular location of proteins within the host cell . The antMHC class-I and II specific epitopes were predicted by ProPred tool . These TWe determined the physicochemical parameters of the epitopes. CamSol server was applied to analyze the solubility of epitopes that would help to determine how quickly it would dissolve in a solvent . ToxinPrThe conservation evaluation is important to study the conservancy of epitopes among all NiV variants using IEDB tool . The fraProteasomal cleavage is a necessary step for MHC-I epitopes because the process of breaking down proteins into small peptides that can be displayed on the MHC-I surface took place through this process. The NetChop tool was used for proteasomal cleavage analysis . The selE. coli K12 strain was selected as the host organism. It provided the GC content and the value of the Codon Adaptation Index (CAI) (E. coli pET30a (+) and BamHI and HindIII enzymes were used for restriction.Using the Molecular Operating Environment (MOE) software, the binding affinity of the selective epitopes with MHCs molecules was analyzed. Ligands with low binding energies bind to the immune receptors more precisely . The MHCex (CAI) . Codon o+ and Cl-counterions were added to the solvent after solvation to keep the box neutral. The Particle Mesh Ewald (PME) approach with 1.0 nm short-range electrostatic and van der Waals cutoffs was used to calculate the electrostatic interactions. To enable quick exploration of the conformational space, the simulations were run at a temperature of 350 K. To keep the system stable at this temperature, we employed a modified Berendsen thermostat  and the projected score difference for each potential pair of peptides coincide on the sign of the difference. Based on this, we determined whether a peptide, when compared to another, boosts or decreases the activity as a dichotomous response, individually for each scoring function and in a consensus framework. We checked the consensus strategies to see if the prediction matches the experimental ΔΔG between peptides A and B (ΔΔGAB). A linear regression model serves as the foundation of the first consensus strategy. In this instance, the response variable is the anticipated ΔΔG, and the independent variables are the scores for each pair of peptides that differ from one another. When the experimental and anticipated ΔΔG signs coincide, the prediction is said to be accurate. We used a leave-out-one training and testing method to cross-validate the model . In order to apply the bootstrapping method to evaluate the errors, we selected the consensus strategy with the best performance out of the two that we tested. To execute the consensus procedure, we repeatedly employed arbitrary blocks of the mean scores. The final accuracy was calculated by averaging all of the units, and it was then compared to the earlier consensus conclusions.The FASTA sequences of nine proteins with 41 isolates were analyzed. Additionally, the full genomic sequences of all isolates were retrieved . To deteAll NiV isolates were evaluated by the phylogenetic tree. The distance demonstrated the evolutionary distance among various strains. The degree of divergence between isolates increases with increasing distance. Since the virus was originally propagated in Malaysia, the MK673562, KY425655, and KY425646 were the ancestors of all isolates . The litThe sub-cellular localization of NiV core proteins showed that out of the five core proteins, three  were extracellular proteins . To choo+ and CD8+ T-cell epitopes of antigenic proteins (F and G) of NiV were predicted  and the three MHC-II epitopes  of the F proteins, and the three MHC-I  and three MHC-II  epitopes of the G protein were selected as potential epitopes based on the physicochemical properties. Each epitope is non-toxic based on the significant SVM threshold value of 0.5. The substantial SVM indicates a negative value. The half-life is 1.1 h indicating a lower range and 100 h as the highest range demonstrating best for epitopes. These epitopes are stable peptides based on the threshold of stability index (< 40). The aliphatic index and theoretical pI values showed the significant score .All epitopes are conserved indicating more than 70% conservation. The population coverage analysis showed that these epitopes are 17–51 percent conserved in entire population against multiple alleles. To determine the number of cleavage sites, the proteasomal cleavage of F and G protein showed a significant score greater than 0.5 indicating an amino acid’s ability to cleave at a favorable position . The quaThe two NiV proteins G and F perform the activities of attachment and fusion during infection of the host, but they also interact with many other human proteins. Both the protein G and F interact with EFNB3 and EFNB2, which are crucial for viral entry. SELENOF, FBXO2, GPX1, SERINC1, CERS1, and PGRMC2 all interact with the G protein. FKBP10, RUFY3, DNAJC10, GET4, ALDH1L2, ARHGAP21, ALDH3A2, PON2, TES, NSF, UBL 4A, HLA-C, TFRC, and NAP1L1 are interacting with F protein. The functional interaction of these human and NiV proteins are responsible for pathogenesis .The epitopes were docked with MHC-I and II molecules. The most crucial screening criterion is the binding energies of epitopes. The lowest binding affinity value demonstrates a strong interaction with the target. Each MHC-I and II epitope displayed the lowest score of binding energy. The MHC-I epitopes PNFILVRNT, VNYNSEGIA, and IKMIPNVSN of the F protein and ILKPKLISY, GKYDKVMPY, and KNKIWCISL of the G protein, showed binding affinities of −9.19, −10.15, −9.33, −9.43, −10.72, and −9.25 kcal/mol respectively. These epitopes demonstrated interactions with the MHC-I amino acids histidine, isoleucine, alanine, tyrosine, phenylalanine, lysine, valine, arginine, glutamic acid, glycine, asparagine, serine, glutamine, and tryptophan. The MHC-II epitopes ILVRNTLIS, VILNKRYYS, and VKLQETAEK of the F protein and FLIDRINWI, LRNIEKGKY, and FLLKNKIWC of the G protein have binding affinities of −11.10, −12.56, −11.52, −11.28, −11.58, and −11.19 kcal/mol. The amino acids glutamic acid, glycine, phenylalanine, asparagine, leucine, glutamine, arginine, lysine, proline, histidine, threonine, serine, vaine, and isoleucine of MHC-II were discovered to interact with these epitopes .−05, 3.00e−05, 3.06e−05, 2.97e−05, 2.98e−05 and 2.70e−05 respectively. The MHC-II epitopes ILVRNTLIS, VILNKRYYS, and VKLQETAEK of the F protein and FLIDRINWI, LRNIEKGKY, and FLLKNKIWC of the G protein displayed the eigen values of 1.24e−04, 1.23e−04, 1.22e−04, 1.22e−04, 1.234e−04 and 1.237e−04 respectively. The B-factor scores were equivalent to RMS. The coupling between pairs of residues was explained by the covariance matrix, where various pairs displayed correlated, anti-correlated, or uncorrelated motions, which were represented by red, blue, and white colors, respectively. The elastic network model displayed the pair of atoms connected by springs in accordance with the degree of stiffness between them. This stiffness was represented by color, moving from lighter grey with softer strings to darker grey with stiffer strings  (The epitopes expression in the K12 strain of T30a (+) .in silico techniques have passed successful clinical testing. The COVID-19 vaccine was designed with a lot of aid from this innovative procedures  are surface and the remaining are intracellular proteins.In current study, we applied an integrative system-level immunoinformatics based framework to predict the CD4F and G are significant proteins with molecular weight of more than 60 kDa. T-cell epitopes of G and F proteins are antigenic, as evidenced by the primary screening of the epitopes based on substantial cut off parameters. We observed the binding affinity of these peptides with MHCs molecules to analyze the pharmacological activities and the immunological response . All epiThe pan-genome analysis is important to find and provide information for efficient findings on core and accessory genomes of pathogenic microbes. The core proteins could be used for epitopes predictions as conserved antigenic proteins are potential vaccine candidates.In conclusion, we found 12 antigenic T-cell epitopes of F and G proteins of NiV, as they have potential affinity for HLA alleles. Two epitopes of G protein including KNKIWCISL of MHC-I, and FLLKNKIWC of MHC-II, and similarly two epitopes of F protein including PNFILVRNT of class MHC-I and ILNKRYYS of MHC-II showed high antigenicity values of 2.35, 1.76, 0.87 and 1.14 kcal/mol respectively. The immunoinformatics based approach for designing NiV vaccine is an important step to cope against this infection.