Based on the analysis of one genome per WHO clade, the epitopes were 100% conserved among the Wuhan-Hu1, Alpha, Beta, Gamma, Delta, Omicron, Mµ, Zeta, Lambda and R1 variants. An extended epitope-conservancy analysis performed using GISAID metadata of 3,630,666 SARS-CoV-2 genomes of these variants and the additional genomes of the Epsilon, Lota, Theta, Eta, Kappa and GH490 R clades, confirmed the high conservancy of the epitopes. All but one of the CD4 peptides showed a level of conservation greater than 97% among all genomes. All but one of the CD8 epitopes showed a level of conservation greater than 96% among all genomes, with the vast majority greater than 99%. A multiepitope and multivariant recombinant vaccine was designed and it was stable, mildly hydrophobic and non-toxic. The vaccine has good molecular docking with TLR4 and promoted, without adjuvant, strong B and Th1 memory immune responses and secretion of high levels of IL-2, IFN-γ, lower levels of IL-12, TGF-β and IL-10, and no IL-6. Experimental in vivo studies should validate the vaccine’s further use as preventive tool with cross-protective properties.COVID-19 caused, as of September, 1rst, 2022, 599,825,400 confirmed cases, including 6,469,458 deaths. Currently used vaccines reduced severity and mortality but not virus transmission or reinfection by different strains. They are based on the Spike protein of the Wuhan reference virus, which although highly antigenic suffered many mutations in SARS-CoV-2 variants, escaping vaccine-generated immune responses. Multiepitope vaccines based on 100% conserved epitopes of multiple proteins of all SARS-CoV-2 variants, rather than a single highly mutating antigen, could offer more long-lasting protection. In this study, a multiepitope multivariant vaccine was designed using immunoinformatics and in silico approaches. It is composed of highly promiscuous and strong HLA binding CD4 The Wuhan Hu1 is the ancestral reference virus, but variants Gamma, Delta, Beta and Omicron emerged, respectively, in Brazil, India and South Africa, which are countries with dense populations, that had at that time a low anti-COVID-19 vaccination coverage. In Brazil, for instance, the Gamma variant was first described on January 21th, 2021, just before the start of the vaccination campaign2.COVID-19 is a severe acute respiratory syndrome caused by the SARS-CoV-2 coronavirus, which arose in December 2019 and it is affecting all the continents ever since. As of September 1rst, 2022, there have been 599,825,400 confirmed cases of COVID-19, including 6,469,458 deaths3. In contrast, mutants that show changes to receptor binding, but not increased hospitalizations and deaths, are Variants of Interest (VOI): Mµ (B.1.621), Zeta or P.2 (B.1.1.28.2)3, Lambda4 and R1 (B.1.427/B.1.429) and others5. While the ancestral Wuhan Hu1 was worldwide predominant until September 2020, new variants emerged since December 2020 at a much higher rate, consistent with the accumulation of two mutations per month, and to a strong selective pressure on the immunologically important SARS-CoV-2 genes6.According to their increased transmissibility, increased hospitalizations and deaths, reduced neutralization by antibodies generated by previous infection or vaccination, reduced response to treatment or diagnostic failures some of the mutants are considered Variants of Concern (VOC): Alpha (B.1.1.7), Beta (B.1.351), Gamma or P1 (B.1.1.28.1), Delta (lineage B.1.617.2) and Omicron (B.1.1.529), etc.7, either expressed by recombinant non-replicating adenovirus10 or by messenger RNA12. Confirming that, nearly 56% of the 10 billion doses of vaccine delivered, correspond to the S spike protein non-replicating adenovirus and mRNA, while the whole virion inactivated (WVI) vaccines15, correspond to 44% of the delivered doses7.Despite the immunological escape of the SARS-CoV-2 through the rapid rise of new mutants, most of the COVID-19 first generation vaccines, are composed only by the highly variable surface Spike antigen6. However, its high mutation rate jeopardized the vaccine cross-efficacy against other SARS-CoV-2 mutants. Vaccination of large populations is time-consuming, laborious and expensive. While a vaccine against a new mutant is under scaling-up, clinical trial and registration, a new variant can emerge. Therefore, vaccines against coronavirus should be focused not only in the S highly variable antigen, but in other conserved antigens instead, that suffer less mutations, are present in all mutants and are thus capable of inducing cross-protection17. Once the virus is inside an APC, all its components, whether structural or not, have the same probability to be digested and presented to lymphocytes in order to generate an immune response. Thus, it is reasonable to suppose, that non-structural genes, which codify for vital enzymes responsible for the virus replication, would be more conserved and less variable than other proteins of the same virus or of the coronavirus family20. Conserved antigens should be therefore, the basis of second generation anti-COVID-19 vaccines20 as they could cross-protect against SARS, MERS, all the prevalent and even future SARS-CoV-2 variants.The S protein is a surface predominant antigen involved in penetration of the virus into the host cell, therefore, it is the target of vaccine neutralizing antibodies25, fewer formulations combine epitopes of different antigens of the Wuhan Hu1 strain28 and, multiepitope multivariant vaccines using conserved epitopes of all variants have been less explored7.Multiepitope protein vaccines optimize vaccine efficacy by joining the most immunogenic sequences of one or several antigens of a pathogen. A multivariant multiepitope vaccine expresses the most immunogenic conserved epitopes present in all variants of a pathogen, aiming to optimize their cross-efficacy. However, while several vaccines contain the Spike antigen+ and CD8+ T cell epitopes of the S, N, E and M structural proteins, and of the ORF1ab, ORF3a, ORF6, ORF7a, ORF7b ORF8 and ORF10 non-structural proteins of ten SARS-CoV-2 virus variants. Among them, we selected the epitopes that were: (1) promiscuous and had the highest binding affinity to HLA Class I and Class II molecules; (2) conserved in the Wuhan Hu1, Alpha, Beta, Gamma, Delta, and Omicron VOCs, and the Mµ, Zeta, Lambda, and R1 VOIs; and (3) that had higher population coverage. With these peptides, we designed a multiepitope universal vaccine potentially capable of generating cross-protection against all the human variants of SARS-CoV-2. In vivo vaccination tests will be needed in order to confirm this hypothesis.In this investigation, we predicted in silico CD4Mutations and deletions of the S, M, N, E, ORF1ab, ORF3a, ORF 6, ORF7a, ORF7b, ORF8 and ORF10 proteins, of the 9 SARS-CoV-2 variants were compared to the Wuhan-Hu1 strain Table . S prote29. In the case of class II, because the binding groove is open, and the ligand can slide to assume optimal conformation, an exact size is not essential31.For class II, the majority of endogenously bound ligands and epitopes are 12–20 residues in length, with 15-mers being generally the most common and most studiedThe majority of class I ligands, to include both known T cell epitopes and endogenously bound peptides, are 9-mers.+ T cell epitopes were predicted from the sequences of the 109 proteins of all variants, using the IEDB Class II 27 alleles, considering only epitopes that bind to at least 10 alleles. The 100% conserved epitopes were then sorted according to a final score, described on “CD4–6) and final score . Final score and promiscuity correlated positively . No conserved promiscuous epitopes were predicted for ORF3a, ORF7a and ORF10. ORF1ab holds 537 epitopes that bind to a maximum of 26 among the 27 alleles . Non-promiscuous epitopes that bind to less than 14 alleles; epitopes that were not conserved with 100% of identity among the 10 virus strains, and epitopes that were duplicated, were excluded from the list . The majority of class I ligands, to include both known T cell epitopes and endogenously bound peptides, are 9-mersved Fig. . Resultsved Fig. . If two + than CD4+ epitopes, and it was followed by the E and N and by the M and ORF8 antigens. Numbers of CD8+ epitopes and of amino acids of each protein  were correlated  is 12.99  and with the number of bound alleles .The coverage value for the whole Class II epitopes is 99.45%. The average number of epitope hits/HLA combinations recognized by the population (average hit) is 27.51 and the minimum number of epitope hits/HLA combinations recognized by the population  of 0.446 classifies it as mildly hydrophobic. The multiepitope vaccine was predicted to be non-toxic by the PSI-BLAST BTXpred Server tool (https://webs.iiitd.edu.in/cgibin/btxpred/btx_main.pl).The protein has 825 amino acids, a molecular weight of 90,840.18 D and is basic (pI = 8.2). Its instability index of 27.72 (below 40) classifies it as stable. It has 100 h of estimated half-life in mammalian reticulocytes in vitro, more than 20 h in yeast in vivo and more than 10 h in PSIPRED tool revealed in the multiepitope vaccine an estimated 63% of α-helix, 9% of β-strand and 27% coil Fig. . One ami2 of the TLR4 and 2106 Å2 of the multiepitope vaccine.Robetta tool provided five 3D models of the vaccine. Model refinement by Swiss Model and PROCHEK-PDBSUM indicated model 4, for its best scores and good agreement with the usual protein models Fig. a. In facEscherichia coli K12 was cloned into a pET28b + plasmid, between the NcoI and XhoI restriction sites with a 6 His-tag at its C-terminal , respectively. The injections were thus given at day 0, 28 and 56, which correspond to time steps of 1, 84 and 168 since one day is equivalent to 3 periods of 8 h. A sequence of 46 Alanine residues was used as a negative control and the sequence of the Full = Probable citrate synthase, mitochondrial; Flags: Precursor UniProtKB/Swiss-Prot: A4H9H8.1 was used a positive control. IgM and IgM + IgG primary and secondary humoral responses with predominant IgG1 and a mild enhance of IgG2 antibodies Fig. a and its18. In this way, the currently used S-protein vaccines, not only did not block the virus transmission and the re-infection by multiple variants, but also possibly contributed to the positive selection of variants such as the Omicron, which holds a completely different S antigen. In fact, most of the currently used COVID-19 vaccines are composed of the S antigen7, in which mutation rate ranged from 4%, in Zeta to 69%, in Omicron variant. These facts confirm the urgent need of development of a universal multivariant vaccine based on conserved multiple antigens of SARS-CoV-2 virus.A vaccine against a highly mutating virus should contain a pool of antigens. If mutations occur in a single antigen, the protection generated against the remaining could circumvent the immune escape and guarantee the control infection, avoiding the disease42. Our study is the first of them to take into consideration ten SARS-CoV-2 human variants including the Omicron VOC. Before the Omicron VOC, Prakash et al.20 in 2021, identified epitopes highly conserved among SARS-CoV-2 genomes, CoVs responsible for epidemics of common cold; SL-CoVs isolated from bats, pangolins, civet cats and MERS isolated from camels. Other multiepitope vaccines used only 81–100% conserved epitopes of the S, M, N and E43, or epitopes of the S, M and E antigens with almost 100% of conservancy42. Furthermore, while most recent investigations used predicted overlapping epitopes with excellent outcome45, in our study, the non-overlapping CD4+ epitope prediction showed more robust results. Finally, while many studies used for predictions a selected group of the human HLA Class II and/or Class I alleles46, we used the full IEDB reference list.Until present, however, the multivariant conserved-epitope approach was less explored24 or of a non-disclosed variant25. Less vaccines used epitopes of the S, N, E27 or of the S, N, E and M proteins, also of the Wuhan Hu1 reference strain43 or of one SARS-CoV-2 and one SARS-CoV strains of China28.While the number of genomes and antigens investigated for retrieving of the epitopes might be a concern, most multiepitope proposed vaccines used only epitopes of the S protein of the Wuhan-Hu1 reference genome20 investigated the conserved epitopes of 10 proteins although retrieved from 81,963 genomes of GISAID bank, with disregard of their respective variant. Since they published their study in April 2021, they could not include any sample of the Omicron variant. Although the conservation approach in that study is very robust, because it is based on the analysis of many isolates, the epitope predictions were only run for 5 alleles of DRB1 for CD4 and 5 alleles for HLA-A. In contrast, in our study, we used the 27 alleles of DR, DQ and DP, and the 27 alleles of the HLA-A and HLA-B genes, which certainly represents a more extensive world populational coverage approach. Furthermore, Al Zamane et al.42 studied only the epitopes of S, M and E proteins that were however, present in 128 isolates from Bangladesh and 110 from other afflicted countries, while Rajput et al.26 retrieved the common epitopes of 92 genomes of SARS-CoV-2.On the other hand, less multiepitope vaccines are composed of epitopes conserved in multiple isolates. Similar to our study, Prakash et al.+ epitopes identified in our study were described before34. However, the epitope MPNMLRIMASLVLARKHT5018-5034 of our list includes the sequence PNMLRIMASLVLARK5019–5033 described as a common CD4+ epitope, conserved in humans, bats, pangolin and camels20. We now showed that this epitope is also present in the ten SARS-CoV-2 human variants, including the Omicron. Furthermore, the epitope FLAFVVFLLVTLAILTAL20–27 of our list, partially overlaps with the epitope LLAFLAFVVFLLVTLA18–32 described by Singh et al.21 and with the epitopes LLVTLAILTALRLCA and LVTLAILTALRLCAY, previously shown in the literature49 with experimental evidence48. However, both have been shown to be very unrepresentative for countries like Brazil, being good binders just for one of the alleles with a frequency over 5%50. Interestingly, the peptide LIVNSVLLFLAFVVFLLVTLAILTALRLCAY described by Tiloca et al.47, was also present in SARS-CoV, Pangolin CoV, Bat CoV, SARS-CoV-2, Dromedarius CoV, Camel CoV, Canine CoV, Bovine, CoV, H- Enteric CoV and Avian CoV.To the best of our knowledge, none of the 16 CD420 analized 81,963 human sequences of SARS-CoV-2 collected from the GISAID and NCBI databanks on 27th August, 2020. They used a conservancy approach for linear epitopes with a sequence identity threshold set at ≥ 50%. The CD8 and CD4 epitopes that showed ≥ 50% conservancy in at least two human and two animals SARS-CoV strains (from bat/civet/pangolin/camel) were selected as candidates. Although conservancy was 100% among human SARS-CoV-2 strains, identity to 4 major common cold Coronavirus, Coronavirus from previous outbreaks and SL-CoV-2 from bats, civet, camels and pangolins was much lower20. In contrast, in our study, we identified 16 CD4 and 39 CD8 epitopes that were 100% conserved in 10 proteins of each one of the Wuhan-Hu1, Alpha, Beta, Gamma, Delta, Omicron, Mµ, Zeta, Lambda and R1 variants. While Prakash et al.20 studied the epitope-conservancy among 81,963 GISAID sequences, our conservancy approach was far more comprehensive and included 3,630,666 genomes from 16 clades: the 10 that were used for the identification of the conserved epitopes  and 6 more clades . These genomes were also collected during a longer period, from 01 November 2019 up to August 2022.Prakash et al.High levels of epitope conservation were detected. Fifteen out of 16 CD4 selected epitopes showed levels of conservation ranging from 97 to 100% among all genomes, and 38 out of 39 of the CD8 epitopes exhibited level of conservation greater than 96% among all genomes, with the vast majority greater than 99%. These findings support the potential efficacy of our multivariant multiepitope vaccine in generation of cross-protection against infections by virus of different human SARS-CoV-2 clades.51, the Delta genome OK570404.1, used for epitope prediction was collected on June 24, 2021 [https://www.ncbi.nlm.nih.gov/nuccore/OK570404.1]. The decline in conservancy of these two epitopes is most certainly a result of the growth and evolution of the Delta clade.The one CD4 epitope with 67% conservation in all genomes, called NSP6: 66–80 (FLCLFLLPSLATVAY) was only 11% conserved among all Delta genomes and 1.5% conserved among all Kappa genomes due to a T77A substitution; all other clades showed greater than 99% conservation at this epitope. Moreover, the one CD8 epitope with lower conservation ratio  was from NSP13 (helicase) position 73–81 (KSHKPPISF), largely due to the presence of the mutation P77L substitution resulting in a 0.6% conservation level in the Delta clade. This epitope was greater than 98% conserved in all other clades. These two epitopes were however, 100% conserved in the sequence of ORF1ab of the Delta genome used for our epitope prediction (NCBI OK570404.1). Interestingly, while the two Delta mutations of interest NSP6: T77A and NSP13: P77L officially became positively selected on July 2021 and May 2021, respectively52. Individuals with COVID-19 who did not require hospitalization had a greater percentage of circulating Tfh (cTfh) and Th1 cells among SARS-CoV-2-specific cells51.Considering the importance of the Spike protein to the humoral response, it would be expected that it would be present more in CD4 than in CD8 T cell epitopes. In fact, the adaptive immune system can provide strong and durable cellular and humoral immunity to SARS-CoV-2, through coordinated T and B cell responses. CD4+ helper T cells are very important because they differentiate into T helper type 1 (Th1) cells, to stimulate phagocytes and cytotoxic CD8+ T cells, and into T follicular helper (Tfh) cells to promote high affinity and long-lived antibody responses by B cells in germinal center (GC) reactions12, in spite of generating strong humoral responses against the spike protein they do not efficient cross-protect against the other SARS-CoV-2 variants. In fact, we showed that the spike protein is one of the most mutating antigens of SARS-CoV-2.In contrast, we showed that the S protein contributed to the multiepitope vaccine with more CD8 than CD4 epitopes. In fact, our prediction did not retrieved any CD4 epitope of the S protein. These results mean that the Spike protein does not have any CD4 epitope 100% conserved among the 10 variants, that has a high final score. This is why probably, the vaccines based on the S protein of Wuhan-Hu1 variant53, and the allele HLA-DRB1*08 to mortality54. In our study, 10 out of 16 Class II epitopes bind to HLA-DRB1*15:01, 8 epitopes to the DQA1*01:02/DQB1*06:02 and 7 epitopes bind to HLA-DRB1*08:02 alleles, with high affinity, indicating that our vaccine could protect more susceptible individuals.In Italian COVID-19 patients, the HLA-DRB1*15:01, DQB1*06:02 alleles have been associated to severity or extreme severity55. In addition, the frequency of the DPB1*04:01 was lower in Chinese COVID-19 patients56 suggesting correlation with resistance to the disease. In our prediction, 10 epitopes bind to DRB1*04:01, 9 to DQA1*01:01/DQB1*05:01, 12 to DRB1*01:01 and 10 to DPA1*01:03/DPB1*04:01, all of them with high affinity, indicating that the vaccine could protect both, the susceptible and the resistant populations.Furthermore, the frequency of HLA-DRB1*04:01 was higher in asymptomatic individuals, suggesting its association with resistance; and the frequency of the haplotype DQA1*01:01-DQB1*05:01-DRB1*01:01 was lower in the asymptomatic group, suggesting its correlation to COVID-19 severity+ epitopes identified in our study are completely novel, while the other 26 were previously confirmed in immunological assays, however, they were not predicted as conserved sequences34. On the other hand, the fact that more than half of the sequences predicted in our investigation has been previously confirmed as Class I restricted epitopes by in-vitro experiments is also encouraging in the sense of validating our approach. Supporting this, 8 of the previously described epitopes of our list are localized among the 12.8% most dominant of the 1,051 epitopes described by Grifoni et al.34. Additionally, seven of the described epitopes and only one of the novel group were weak binders to the most frequent alleles, suggesting that the novel group concentrates higher affinity-binding CD8+ epitopes. We excluded these 8 weak binders from our selection and ended with 39 epitope candidates, 20 of them novel and 19, described before.Moreover, 21 of the CD834 was not done based on the analysis of multiple peptides together. Since this vaccine formulation consists of presenting them all at the same time, the immunodominance may not be the same as the one reported34.However, it is important to highlight that the description of an epitope as dominant by Grifoni et al.29 predicted epitopes for Class I and Class II of the M, S and E proteins of the Wuhan-Hu-1 reference strain only, and further checked the conservancy of these epitopes, disregarding the variants, among: (1) 180 SARS-CoV-2 genome sequences of GISAID; (2) 4390 new variants of SARS-CoV-2 and in (3) the sequences of 1000 spike, 245 M and 81 E proteins of SARS and MERS. They declared that they assessed the conservancy of the Omicron variant by observing the amino acid changes reported in the journals and if the amino acid changes were not located in the specific vaccine region they evaluated as conserved. However, the authors did not disclose any accession number for the Omicron genomes, sourcing information from reliable data bank, nor references of the consulted journals29. This is a very indirect and inappropriate method to assess conservancy of epitopes. The sequence of all variants, from Alpha to Omicron, were not aligned as they were in our investigation. They report a list of 10 Class II epitopes of M, E and S proteins. None of them is present in our list of Class II epitopes, which mostly concentrates sequences of ORF 1ab protein, and only contains two epitopes of E, one of M and none of S proteins. Among the 9 Class I epitopes of their list29, only the epitope YVYSRVKNL is present in our list. However, as previously stated, that epitope was also previously reviewed as biologically tested by Grifoni et al.34.After the submission of this article, Kaderi Kibria et al.57 and to the infected cases per million. HLA-A*01 is more frequent in Europe and America, where COVID-19 was more lethal and less frequent in East Asia, where severity was lower57. In our study, 27 CD8+ epitopes bind to HLA-A*01:01, a highly frequent allele. Furthermore, HLA-A*11 and HLA DQB1*04 were associated to mortality in Spain58. In our study, 14 Class I epitopes bind to HLA-A*11:01, and 10 of the 16 Class II epitopes bind to DQB1*04:02, suggesting that our vaccine could protect against mortality, severity and rate of infections.The number of deaths due to COVID-19 was correlated to the Class I HLA-A*01 phenotype32, while more epitopes tend to bind to the less frequent alleles. However, the novel epitope LMDGSYYQF binds strongly to HLA-A* 02:01, HLA-A*24:02 and HLA-A*01:01 alleles, and 7, 12 and 19 epitopes bind strongly to each one of these alleles, respectively. KVNSTLEQY, VVYRGTTTY and KLFDRYFKY epitopes are also strong binders of HLA-A* 01:01, HLA-A* 03:01 and HLA-A* 11:01 alleles. It might be adaptive for the virus to have conserved epitopes that do not bind to the Class I most frequent alleles, so they do not generate the antiviral CD8+ cytotoxic response. In this way, the SARS-CoV-2 virus could escape from the immune response in the majority of the World population, and the pandemic could continue. Supporting our hypothesis, the less frequent alleles HLA-A*02:02, HLA-B*15:03, and HLA-C*12:03 were the top presenters of conserved SARS-CoV-2 peptides59. On the other hand, the most frequent HLA-A*02:01 allele was associated to increased risk for COVID-19 and showed a lower capacity to present SARS-CoV-2 antigens compared to the less frequent alleles60. In our prediction, 9 epitopes are strong binders of the HLA-A*02:01 suggesting that they can generate good protection in susceptible subjects60. In addition, the HLA-A*11:01 and B*51:01 alleles also correlate with the extreme severity61. Twelve epitopes of our study are strong binders of HLA-A*11:01 and 3 are strong binders of B*51:01 indicating that the CD8+ epitopes of our vaccine might generate protection in the more susceptible HLA-phenotypes. Future in vivo studies could confirm this hypothesis.On the other hand, we identified that, less epitopes bind to the ten most frequent HLA-Class I alleles in the World population It might be argued that that all the alleles shouldn’t be treated equally for an optimal vaccine design because some alleles are more abundant than others. The equation used in this study counts the number of alleles bound regardless of the allele frequency. This ends giving the same weight to high and low abundant alleles. Although it might have been useful for understanding the biology of the virus, this decision might have impact on the selection of the best epitopes for a massive vaccine.C values. The panels not only showed wide coverages but it also offered deep coverages, meaning that not only the individual is covered, but also that he is covered but multiple different epitopes. This is important when an epitope dominant for one specific allele suffers mutations that determine the pathogen immune escape. If the coverage is deep, the immune escape is less probable since it is unlikely that several epitopes that link to one specific allele would suffer simultaneously mutations. Different from other investigations42, in which population coverage was calculated using only selected alleles, in our study we used the IEDB full set of 27 alleles.The efficiency of our prediction was however, confirmed by the World population analysis, which disclosed high Class I and II epitope coverages, with high average hits and Pc 90+ and CD8 T cell human responses against the whole SARS-CoV-2 proteome of individuals infected with the Alpha, Beta and Gamma variants was reported using a cytokine analysis44. Furthermore, the T cell responses of individuals vaccinated against the S or the M proteins of the Wuhan-Hu1 strain, cross recognize the SARS-CoV2 variants from Alpha to Omicron62. However, these epitopes have not yet been explored in vaccine formulations.A map of epitopes recognized by CD450. However, the two methodologies seem to be very different and hard to compare. The population data of IEDB is taken from a publically available repository that collects data from the International Histocompatibility Working Group (IHWG) certified labs from around the world, and its results can indicate an approximation since there is no other database with a more comprehensive data collection63.A criticism has been raised on the IEDB population coverage tool, which comes directly from the Allele Frequencies Net Database. This calculation was considered not representative of worldwide populations, and mainly influenced by the data of USA and Europe, which comprises the largest source of information in this database, while it probably misses a large amount of information of South America50 was based on data of articles available at PubMed and datasets of AFNSD. These datasets were integrated by country calculating weighted allele frequencies. The authors claim to have included in this way, the frequencies of 91 alleles that were previously considered to have frequencies lower than 5% in South American Countries50. Different from the IEDB full list of HLA-A and HLA-B alleles used in our investigation33, the study by Requena et al. also included the HLA-C alleles50. However, in contrast to the HLA-Class II full reference list of IEDB which comprises HLA-DRB1*, HLA-DRB3*, HLA-DRB4* and HLA-DRB5*63, the study by Requena et al., only includes HLA-DRB1* alleles50.In contrast, the work of Requena et al.28 and confirmed the variant identity in the Pangolin site. In contrast, other investigations used multiple genomes instead of one genome for each variant50, however, none of them specified the proportional contribution of each variant to the set, and only one of them described the contribution of each geographic region to the whole set of genomes50.The algorithm that we used in our study, filtered first the peptides that bound at least 14 of the 27 most frequent Class II alleles. From this subset, the epitopes with conserved identity in 100% of the 10 studied variants were further selected. For Class I prediction only epitopes with 100% of conservancy were selected which bound with percentile rank < 1% (weak binders) or with percentile rank < 0.5% (strong binders). We retrieved the protein FASTA sequences from the NCBI Genbank site, as recommended by the literature64. In fact, different from IEDB, MHCFlurry is a Class-I only predictor. It is not possible to make Class-II predictions with it. Another issue is that MHCFlurry does not output scores for all alleles in a prediction, only for the best one. Because of this, it is not possible to calculate promiscuity scores, on which our final_score depends. As such, there is no direct way of making a comparison between our approach and MHCFlurry. This is yet another reason why we chose the IEDB tools for this study and likely why these are the most widely cited and used tools in this area of research. We however ran a prediction on our final selection of 47 Class I to verify whether the MHCflurry scores  correlates to our final score. A Spearman correlation test confirmed that both scores have a positive correlation of R = 0.35 with a significant p value = 0.016. We noted however that our selection of epitopes includes information on all 27 alleles and the promiscuity, whereas the presentation score from MHCFlurry seems to only consider the strongest binding allele alone. We however noted that when comparing the best predicted allele in our final selection, the method we used agrees with MHCFlurry in a vast majority of cases. Namely, out of the 47 epitopes in the final selection, in 24 cases  the strongest binding allele predicted by MHCFlurry is the same predicted by us: . In 18 cases  the strongest binding allele predicted by MHCFlurry is the second strongest predicted by us: . In 3 cases  the strongest binding allele predicted by MHCFlurry is the third strongest predicted by us: . In 1 case  the strongest binding allele predicted by MHCFlurry is the fourth strongest predicted by us: (QALLKTVQF). In 1 case  the strongest binding allele predicted by MHCFlurry is the sixth strongest predicted by us: (VAMPNLYKM). In no cases the difference in predictions was larger than this. These results show the strength of our methodology compared to the MHCFlurry tool64.Furthermore, we compared the prediction obtained in IEDB with the MHCFlurry method, which is a more recent tool65 and the same 27 alleles that we used for prediction with IEDB recommended method. We observed that in 6 out of 16 peptides (38%) the strongest binding allele predicted by us (IEDB lowest percentile rank) is the same predicted by MIXMHCPred2.0 (% Rank Best Allele) for the exact first position . In 3 more cases (19%) the strongest binding allele predicted by us is the second strongest predicted by MixMHCPred2.0 . In 1 case (7%) the strongest binding allele predicted by us is the third strongest predicted by MixMHCPred2.0 (CTERLKLFAAETLKA). In 2 cases (13%) the strongest binding allele predicted by us is the fourth strongest predicted by MixMHCPred2.0.2 . In 1 case (7%) the strongest binding allele predicted by us is the fifth strongest predicted by MixMHCPred2.0 (FAWWTAFVTNVNASS) and in 1 case (17%) the strongest binding allele predicted by us is the sixth strongest predicted by MixMHCPred2.0 (VFLLVTLAILTALRLCAY). Both for IEDB and MixMHCpred2.0 tools give lower PR% or %Ranks, respectively, for epitopes having higher affinity binding to the HLA-allele receptor molecules. The mean ± SD of the PR % values attributed by IEDB to the best alleles (3.03% ± 3.6%) was not different from the mean of % Ranks attributed by MixMHCPred2.0 to the best alleles (3.66% ± 4.99%). However, taking into account the predictions of all the 27 alleles, we observed that the IEDB tool attributed low percentile ranks to many epitope-allele combinations, suggesting that it retrieved high affinity-binding epitopes to multiple alleles (mean ± SD = 7.08 ± 5.47%). In contrast, MixMHCpred2.0 attributed low % Rank to only a few combinations, and high % Rank, to the majority of the epitope-allele combinations (38.87% ± 4.38% Rank). The difference in variability of the ranks suggest differences in the algorithms of the two tools.Moreover, also for comparison, we ran a prediction on the resumed selected 16 Class II epitopes using MixMHCPred2.0https://services.healthtech.dtu.dk/service.php?NetMHCIIpan-4.0). Different from the MHCFlurry and MixMHC Pred2.0 tools, which only give the % Rank values for the best alleles, both the IEDB and NETMHCPan-4.0EL tools give more detailed predictions and they both output scores for all alleles. We compared the Class II epitope predictions retrieved by IEDB and by NETMHCPan-4.0EL. NETMHCPan-4.0EL is an updated version66 that also uses the IEDB benchmark data and retrieves the % Rank EL value, which is the percentile rank of elution ligand prediction score for each epitope. We compared the performance of both methods for detection of the most powerful epitopes that bind with high affinity to MHC Class II DR, DQ and DP alleles. Only the epitopes binding to alleles with PR < 1% and PR < 5%, of the IEDB method, or with % Rank_EL < 1% (strong binders) and % Rank_EL < 5% (weak binders) of the NETMHCpan-4.0EL tool were considered for comparison. IEDB prediction detected as strong affinity-binders with PR < 1% values, 15 out of the 16 peptides (94%), while all the 16 sequences (100%) also bound with lower affinity (PR < 5%) to several alleles. Better than MIXMHCPred. 2.0, but less sensitive than IEDB, NETMHCpan-4.0EL disclosed 11 out of the 16 epitopes (69%) selected by IEDB . Among these 11 epitopes, 5 bind to alleles with % Rank < 1% (46%), while all the 11 bind with % Rank < 5%, revealing that NETMHCPan-4.0EL is more strict or less sensitive than IEDB and detects less strong-binding epitopes. In fact, NETMHCpan-4.0EL agreed with IEDB in the identification of the same five epitopes among the strongest binders, with average % Rank < 1% values: IILASFSASTSAFVET (% R = 0.27%), TERLKLFAAETLKATEE (% R = 0.37%), MPNMLRIMASLVLARKHT (% R = 0.47%), CTERLKLFAAETLKA (% R = 0.49%), FAWWTAFVTNVNASS (% R = 0.83%). In addition to these 5 peptides, more 6 epitopes were also identified by NETMHCpan-4.0EL as weak binders to other alleles : VTLVFLFVAAIFYLITPVHVM (% R = 2.65%), LMWLSYFIASFRLFARTR (% R = 2.82%), NSWLMWLIINLVQMAPISAM (% R = 4.81%), LMIERFVSLAIDAYPLT (% R = 1.84%), HLVDFQVTIAEILLI (% R = 3.70%), FLAFVVFLLVTLAILTAL (% R = 4.81%). Moreover, the promiscuity of the 11 epitopes disclosed by NETMHCPan-4.0EL was lower than that retrieved by IEDB. Only 2 peptides, which share the same core, bind to 19 out of 27 Class II alleles , only one epitope to 10 alleles (MPNMLRIMASLVLARKHT), and the rest of them bind to only 1 to 5 alleles. In contrast, IEDB detected in all the 16 epitopes promiscuity values ranging from 4 to 14 alleles for PR < 5%, and from 1 to 4, for PR < 1%. We concluded that NETMHCPan-4.0EL predicted less strong and weak binders than IEDB, and predicted epitopes of lower promiscuity, being as such more strict or less sensitive than IEDB. However, IEDB and NETMHCPan-4.0EL agreed in the prediction of 69% of the Class II epitopes.In addition, we also ran a prediction for the resumed selected 16 Class II epitopes using the NETMHCPan-4.0EL software  and used as an alternative to the fractionated inactivated Influenza vaccine70. Proteins of less than 110 kDA were reported as good vaccines72 and a mild hydrophobicity was expected for a vaccine composed only of CD4+73 and CD8+ epitopes74. Its 93.24 aliphatic index indicated high termostability75. The vaccine is very stable and has a remarkable high half-life in mammalian, yeast and E. coli cells.The designed multiepitope vaccine contains, at its N-terminal, the 39 CD8E. coli. It has three N residues (asparagine), but since it lacks peptide signals, it might not be glycosylated in vivo even though it contains the potential motifs. Since E. coli does not perform glycosylation it seems that the presence of these N residues would not affect the protein expression. However, if other expression systems will be used for production and testing in the future, its final sequence might be changed from the current one in order to avoid glycosylation sites.The multiepitope protein was planned to be expressed by −/−, TLR4−/− mice have increased susceptibility to SARS‐CoV76. The best tertiary structure model of the vaccine docked with the TLR4 receptor, indicating that the vaccine can efficiently induce the innate immune response. Long-lasting antibodies, B- and T-memory cells and a cytotoxic T cell peak were predicted to be generated during the adaptive immune response, despite the vaccine lack of B cell epitopes. A Th1, IL-2 and IFN-γ pro-inflammatory response and a mild TGF-β and IL-10 regulatory response were also detected, but IL-6, characteristic of the SARS inflammatory syndrom, was absent. The future vaccine formulation with QS21-containing adjuvants77 could strongly potentiate the vaccine antibody and T cell responses. We conclude that predictions of the immune responses generated by the multiepitope vaccine revealed encouraging results. However, in silico simulations are not enough to estimate the potential use of this formulation in the entire world population during this current pandemic. In vivo data about the immunogenicity of the proposed multiepitope vaccine is critical to evaluate the efficacy of this new strategy.In COVID-19, activation of TLRs lead to activation of inflammasome, production of IL1β and IL-6. TLR327.Finally, the evolution of the COVID-19 vaccine immune responses was monitored mainly considering the anti-S antibody titers. Now, 2 years and 6,469,458 deaths after, the need of vaccines based on conserved antigens and on T cell epitopes of SARS-CoV-2 virus has been recognizedhttps://www.ncbi.nlm.nih.gov/nuccore). The genome reference codes used in this investigation were: NC_045512.2 for Wuhan-Hu1 of China, MW059036.1 for Alpha variant of England, MZ376663.1 for Beta variant of South Africa, MZ264787.1 for Gamma variant of Brazil, OK570404.1 for Delta variant of India, MZ710932.1 for Mu variant of Colombia, MZ833438.1 for Zeta variant of Peru, OK399319.1 for R.1 variant of California, OK546825.1 for Lambda variant of Peru and OK546825.1 for Omicron variant of South Africa (https://www.ncbi.nlm.nih.gov/nuccore). The lineage of each of the variants was further confirmed using the Pangolin website tool (https://pangolin.cog-uk.io/).To obtain the Fasta files of the genomes of the Wuhan-Hu-1 reference strain, Alpha, Beta, Gamma, Delta, Mu, Zeta, R.1, Lambda and Omicron variants, a search was performed using the NCBI Nucleotide database site (http://www.ncbi.nlm.nih.gov/protein). Finally, the sequence of each protein of each variant was aligned with the sequence from the respective protein of the Wuhan-Hu1 reference strain using the Blast Alignment Search Tool, (https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastp&PAGE_TY=BlastSearch&_LOC=blasthome). Mutations and deletions were annotated.Furthermore, the ID numbers of the sequences of the S, M, N, E structural and ORF1ab, ORF3a, ORF 6, ORF7a, ORF7b, ORF8 and ORF10 non-structural proteins of each SARS-CoV-2 variant were obtained using the NCBI protein tool (https://www.iedb.org/home_v3.php).Epitope prediction was performed on the following 11 proteins: S spike glycoprotein, E envelope protein, M membrane glycoprotein, N nucleocapsid protein, open reading frames ORF 1ab, ORF 3a, ORF 6, ORF 7a, ORF 7b, ORF 8 and ORF10, of each one of the following 10 SARS-CoV-2 virus strains: Wuhan-Hu-1, Alpha (B.1.1.7), Beta (B.1.351), Gamma or P1 (B.1.1.28.1), Delta (B.1.617.2), Omicron (B.1.1.529), Mµ (B.1.621), Zeta or P2 (B.1.1.28.2), Lambda (C.37 lineage) and R1 (B.1.427/B.1.429), using the Immune Epitope Database and Analysis Resource (IEDB) (https://www.ncbi.nlm.nih.gov/guide/proteins/). Table Protein sequences of each SARS-CoV-2 protein of each strain used in this study are deposited in the NCBI Protein Repository Bank . Peptides were selected only if they bound at least 14 of the 27 most frequent Class II alleles (promiscuity ≥ 14). From this subset, the epitopes with conserved identity in 100% of the 10 studied variants were further selected (corresponding to conservancy = 10).The sequences of the 109 proteins to the MHC-II Binding Predictions of the IEDB platform were submitted using a Python API , combines the values for promiscuity, conservancy and percentile rank into a single quantity.In order to facilitate the selection and comparison of the best candidate peptides for a multiepitope conserved vaccine against SARS-CoV-2, it was necessary to define a quantitative metric corresponding to each peptide. Such a metric, here termed “final score” .As the selected CD4+ epitopes, the final score was calculated according to the equation:Nbound is the number of alleles bound by the epitope, Ntotal is the total number of HLA class II, and pr is the percentile rank. As such, the final score is a combination of the relative promiscuity  and the percentile rank.Thus, in the case of CD4fs, pr is multiplied by a negative coefficient (in this case − 0.1 or − 1/10). By adjusting this coefficient, the pr influence over the final score can be increased or decreased accordingly.However, by design, the relative promiscuity will be a number between 0 and 1. The percentile rank, on the other hand, is a number between 0 and 100, inversely proportional to the quality of a given candidate peptide. That is to say, desirable peptides will have very low values of percentile rank (often around 1 or less). To reflect this in the calculation of fs, compared to a very good candidate with percentile rank 1.As an illustrative example, a very bad candidate, with percentile rank of 100, would greatly decrease the value of The final score was used to identify of the best epitopes in the category plots discussed in the “+ T cell epitopes with overlapping of 10 amino acids45.Furthermore, for comparison, we also obtained a prediction of the 15-mer CD4+ case, a prediction was ran in a Python API (https://github.com/mattfemia/iedb-python). In this case the Class I API was used for the prediction of 9-mer length peptides, using the Full HLA Class I reference list of the IEDB platform33, which contains 27 HLA Class I A and B alleles 80.The world population coverage of the predicted epitopes was determined using the IEDB platform . The precomputed amino acid substitutions were used to compute the extent of sequence conservation within each individual WHO clade and across all high-quality SARS-CoV-2 genomes.To evaluate the amino acid sequence conservation of 16 CD4 and 39 CD8 SARS-CoV-2 epitope regions of the structural and non-structural proteins, the amino acid substitutions found in the GISAIDFor quality control, records were removed from further analysis if: (i) the genome sequence was less than 29,400 nucleotides, (ii) the genome sequence had an N-content proportion greater than 1%, or (iii) the metadata record was missing a geographical region of isolation, collection date, or not annotated with a variant clade. Following this quality control and including data since the earliest collection date of 01 November 2019, 3,630,666 genomes remained for conservation analysis.51. The protein amino acid substitution position obtained from the GISAID metadata file was then compared to the protein start and end positions of each CD4 and CD8 epitope to determine if the substitution was contained within the epitope region. The number of sequence records in which each epitope was affected by an amino acid substitution was determined for the entire collection of high quality genomes and for 14 disjoint WHO clades: Alpha, Beta, Delta, Epsilon, Eta, Gamma, Iota, Kappa, Lambda, Mu, Omicron, Theta, Zeta, and GH/490R . From this analysis we were able to compute a conservation ratio between 0 and 1 quantifying the conservation of each epitope. The conservation of epitope k among all genomes was calculated according to formula  formula .1\documehttps://web.expasy.org/protparam/)21, which predicts the following important parameters: theoretical isolectric point (pI), amino acid composition, molecular weight (MW), in vitro and in vivo half-life, instability and aliphatic index, and grand average of hydropathicity (GRAVY). Prediction of toxicity was addressed using PSI-BLAST BTXpred Server tool (https://webs.iiitd.edu.in/cgibin/btxpred/btx_main.pl).Physicochemical evaluation of the vaccine construct were carried out utilizing the Expasy portal’s ProtParam tool (http://bioinf.cs.ucl.ac.uk/psipred/) which predicts the secondary structure, transmembrane topology, transmembrane helix, fold and domain recognition. The solvent accessibility of the vaccine was assessed using the RaptorX server (http://raptorx.uchicago.edu/), which is a protein structure and function prediction tool powered by deep learning.This was carried out using the PSIPRED tool (https://robetta.bakerlab.org/submit.php)82 which performs accurate predictions of protein structures and interactions using a three-track neural network. Refinement of the models was performed with the Swiss Model (https://swissmodel.expasy.org/) and the PROCHEK tool of the PDBSUM (http://www.ebi.ac.uk/thornton-srv/databases/cgi-bin/pdbsum/GetPage.pl?pdbcode=index.html). Validation of the models was assessed by their Ramachandran plots, their best Q mean Z score, the highest Qmean Disco Global, the highest GMQE, solvation value in the Swiss Model, and the highest G factor that gives an estimate of how unusual the model is in the PROCHEK-PDBSUM tool.Since the multiepitope vaccine includes epitopes of several different proteins of SARS-CoV-2, it was not possible to predict its 3D structure by homology. We used instead the Robetta Fold tool (https://cluspro.bu.edu/login.php?redir=/home.php). The Cluspro 2.0 model, showing the highest cluster sizes and lowest interaction energy scores was considered as the best docked model83. Furthermore, the interactions between the receptors and vaccine were further analyzed by the PDBSUM tool (http://www.ebi.ac.uk/thornton-srv/databases/cgi-bin/pdbsum/GetPage.pl?pdbcode=index.html).In order to describe the interaction of the vaccine with the Toll-like human receptors, the tertiary structure models of TLR4 (2Z63) were obtained from the Protein Data Bank. The docking of the best vaccine model and TLR4 were analyzed using the ClusPro 2.0 (http://www.jcat.de/)84 was used, and the multieptitope protein DNA with optimized codons was obtained. Then, the codon adaptation index (CAI) value and GC content were also obtained. Furthermore, Escherichia coli (strain K12) was selected as the target organism, and XhoI and NcoI as the restriction enzyme cleavage sites. SnapGene (version 6.0.2) (https://www.snapgene.com/) was used to integrate the adapted DNA sequence to pET-28b (+) vector, between the NcoI and XhoI restriction sites with no stop codon. This vector enables enhanced protein yield and purification due to its C-terminally 6× HIS-tag. For determination of the N-glycosylation sites in human protein we used the tool NetNGlyc-1.0 .Codon optimization is a crucial tool to enhance protein expression. For this purpose, the Java codon adaptation index (JCAT) (https://kraken.iac.rm.cnr.it/C-IMMSIM/)86. It determines the pathogen epitopes, using existing prediction methods, and evaluates the molecular interactions of the immune complexes. It combines a mesoscopic stimulator of the immune response with machine learning techniques  for predictions of MHC-peptide binding interactions, linear B-cell epitopes and protein–protein potential estimation60. A sequence of 46 Alanine residues was used as a negative control. The sequence of the Full = Probable citrate synthase, mitochondrial; Flags: Precursor UniProtKB/Swiss-Prot: A4H9H8.1 was used a positive control.For this purpose we used the C-ImmSim software .Correlation analysis between percentile ranks and promiscuity values of CD4Supplementary Information.


Environmental problem is an international problem that transcends national boundaries and develops into regional and global environmental pollution and ecological problems. Facing the increasing environmental pollution, the international community has successively formulated many relevant environmental pollution prevention laws, but the world situation is complicated after all, environmental problems still emerge endlessly, and the protection of environmental rights has become the consensus of the international community. Environmental right is an integral part of human rights, and protecting environmental right is the concrete expression and proper meaning of protecting human rights. Using international criminal law to protect environmental rights will play a positive role in global environmental protection. As with the development of computer technology, the research of machine learning has gradually transferred to the field of social science, especially the judicial field. While sentencing is a crucial part of environmental crime, this paper studies the sentencing of environmental rights cases from the perspective of international criminal law and uses Convolutional Neural Networks (CNN) to determine the sentencing of environmental rights cases. Through the experiment on the Integrated Database (IDB) dataset, the results show that the introduction of CNN improves the effect of the sentencing term prediction model and the fine prediction model significantly. The CNN-based model scored 91.6542 in the sentencing term prediction model and 90.8890 in the fine prediction model. International criminal law is generated and developed in the process of the international community fighting against international crimes, which is an important force in fighting international crime . In receEnvironmental right is generally defined internationally as the right to a good environment. However, this concept is in the process of being developed and there is no exact definition so far . We beliWith the proposal and development of the environmental right theory, many countries and international organizations around the world have started relevant legislative activities. In the process of legislative practice, an inevitable problem is the division of environmental rights , 11. We Although international environmental crime has not been comprehensively stipulated by international criminal law conventions, in view of the strong demand of the international community to bring violations of international environmental law into the international criminal system, international environmental crime as a major type of international crime is also a general trend, and how to its sentencing has become the focus of research . In receWe use convolutional neural networks (CNN) to determine the sentencing term and fine of environmental rights cases from the perspective of international criminal law.We analyze the protection of environmental rights under international criminal law.Our contributions are summarized as follows:The rest of this study is organized as follows. Machine learning is the most common application of Artificial Intelligence (AI), which is the ability of computer systems to learn itself by analyzing large datasets and pattern recognition. Machine learning can draw conclusions and preprocess them based on probability, which is applicable to some data. In the long run, the application of AI in legal practice is unlimited, but currently it is commonly used in contract review, legal research, and results prediction. In , the autMany strategies have been proposed for environmental right and sentencing. In , the autTo the best of our knowledge, there is almost no application of AI to the protection of environmental rights from the perspective of international criminal law. In terms of sentencing, AI is not applied too. Therefore, based on AI, this paper studies the international criminal law protection of environmental rights and its corresponding sentencing issues.This paper mainly considers the study of environmental rights cases in international criminal law and the term and fine in the sentencing system. Machine learning-assisted sentencing refers to predicting the specific value of term and fine in a given case through modeling under the condition of the fact text. Since it is difficult to find the factual case text of international criminal law protecting environmental rights, therefore, we use the Federal Court Cases Integrated Database (IDB) provided by the Federal Judicial Center for experiments. The IDB has case data of criminal, civil, appellate, and bankruptcy cases.At first, data preprocessing is performed on the case text in IDB, and the English text is segmented by identifying space based on the case text data cleaning. Then, the text features of the case text data are analyzed, from which high-frequency words and low-frequency words are obtained. On this basis, the text data after word segmentation are cleaned and filtered, and the text word vectors are constructed based on Word2Vec. Finally, according to the word vector, the corresponding prediction model of sentencing and fine of environmental rights cases is constructed, the evaluation system of the model is constructed to evaluate the model according to the distribution characteristics of sentencing and fine, and some conclusions are obtained from the evaluation results of the model.Generally, when using CNN for text analysis, the input layer inputs the text directly and then the embedding layer constructs the word vector. In this study, the basic CNN model has been modified slightly, and the embedding vector pre-trained by Word2Vec is input directly into the network. However, the application of Word2Vec in a CNN model is different from the averaging of all word segmentation in the machine learning model. It directly converts the text after word segmentation into a numerical matrix, i.e., each case text is expressed into a numerical matrix for later model building.CNN is one of the most widely popular deep learning algorithms, and it can achieve a good learning effect on many tasks. CNN can use its unique structure to analyze the text and discover the hidden information in the text. Especially on the premise of large-scale data, CNN is more suitable for text analysis than traditional machine learning methods . This pak × n obtained by the Word2Vec method at the input layer, where k represents the length of a case text composed of words k1, k2, k3,…, ki, and n represents the word vector dimension of each word. Afterwards, the collection and extraction of vector feature information are mainly carried out in the convolutional and pooling layers, and then the features extracted in the previous steps are output through the fully connected layer. Finally, the prediction effect of the model can be obtained through the output layer. In this way, the corresponding model of CNN for predicting sentencing and fine is built.On the basis of the schematic diagram shown in ∗ 400, and then the vector matrix is input into the network for training.In this paper, the input of CNN is the word vector matrix processed by Word2Vec, and the output is the sentencing and fine of each environmental rights case. Before constructing the word vector, it is necessary to process the dataset filtered by word segmentation. In the IDB dataset, 20000 words are reserved after word segmentation. Considering that each text has a large length gap after word segmentation, the text length is limited to 400. Text length with less than 400 is supplemented with 0, and text length with more than 400 is truncated. After fixing the length of each text to 400, the environmental rights case text after segmentation needs to be transformed into a vector representation. The dimension of word vector in Word2Vec is set as 400. Since each row in the matrix represents a word participle, the case after each word segmentation becomes a matrix of 400 It can be seen from the operation of the input layer that the environmental right case text is input in the convolutional layer in the form of a two-dimensional matrix, and the feature information in the case text can be extracted by the convolution operation in the convolutional layer. In addition, in convolution operation, the selection of an activation function is also very important. In this paper, the Rectified Linear Function (ReLU) function that can accelerate the convergence speed in training the case text word vector data is selected.After obtaining the text features of environmental right cases through convolution operation, if these features are directly used for the regression prediction of sentencing, the calculation amount will be too large due to the large amount of feature data, and the training process will be relatively slow. On the premise of fully retaining useful features, to simplify the number of parameters of CNN and reduce the computational complexity, it is necessary to compress and merge the features of case texts with similar semantics in the pooling layer, which is equivalent to extracting features again and filtering some useless features to meet the requirement of reducing the number of parameters in the model. Common pooling methods are average pooling, maximum pooling, and random pooling, and maximum pooling is used to preserve more features.The pooling structures are aggregated in the fully connected layer. In Keras, the connections between neurons in the fully connected layer are dense, and this layer is defined as the Dense layer in order to represent this relationship . We defiIn the construction of CNN, parameters involved in the model need to be set through analysis, including the text length of the environment right case text input to the CNN, the size of the dictionary constructed by the case text, the dimension of the word vector, the size of the convolution kernel of the CNN, the value of the Dropout parameter, and the number of iterations of training CNN.Earlier in the paper, it has been known that the length of text of environmental rights cases should be set to 400 in terms of sentencing and fine prediction. In the IDB dataset, 20000 words are reserved after word segmentation. For other parameter settings, generally speaking, the more dimensions of word vector constructed by the case text, the better the prediction effect of the corresponding model. However, if the word vector dimension continues to increase, the complexity of the network structure will also continue to increase, which will bring a lot of calculations, greatly extend the training time of the model, resulting in the reliability of the prediction effect being decreased. The smaller the size of the convolution kernel, the less parameters and computation are needed to train the CNN. Therefore, the minimum convolution kernel size should be selected on the premise that the prediction effect can be guaranteed. Generally, a smaller convolution kernel size with a slightly larger number of convolution kernels is recommended with the support of computer performance. When the sample size is large, it can be considered to reduce the value of the batch size. However, if the sample size is blindly reduced, it is likely to be non-convergence. Therefore, selecting an appropriate batch size value is helpful to improve the efficiency of model operation.The settings of parameters in sentencing term and fine prediction of CNN in this paper are shown in di between the predicted value and the true value after sample xi processing. The larger the di, the larger the distance between the predicted value and the real value, and the farther the predicted value is from the real situation, the worse the prediction effect of the model. Therefore, di is used to construct the evaluation metric of the machine learning-assisted sentencing model, i.e., the score of the model. The model can be evaluated according to the score of the model.yi′ is the predicted value of sample xi, and yi is the true value of sample xi.In traditional regression prediction, most of the evaluation metrics consider using mean square error. Due to the particularity of sentencing and fine prediction, to make the model results more clear, we try to score the model results, and the score results can also be used for the comparison between models in the later stage. The mean absolute error after transformation considers the distance s is defined as follows:The model score In terms of sentencing term prediction, the CNN is built to predict the sentencing term in environmental rights cases protected by international criminal law. At the same time, three traditional machine learning methods such as random forest (RF) , artificThe CNN model is built with the IDB dataset for the prediction of fine, and RF, ANN, and XGBoost models also tried, respectively. The constructed scoring system is used for evaluation and comparison, and the results are shown in In terms of feature extraction, every dimension of word vectors is trained as a feature in the traditional machine learning-assisted sentencing model. However, the CNN adopted in this paper can automatically extract feature information of the case text, which can save time and effort. Moreover, the features automatically extracted have stronger discriminant ability for later analysis. At the same time, when CNN is used to model and predict the environmental rights cases, the feature extraction and results prediction are performed in the CNN as a whole, which can maximize the performance of feature extraction and model prediction.When we understand and determine the scope of sentencing circumstances, we can consider the weight of each circumstance in sentencing. The proportion of different sentencing circumstances in sentencing has not yet formed a unified provision in the international criminal court. However, according to the handling of relevant cases, we can know that the role of bad physical condition and confession in sentencing cannot be underestimated, but it is always difficult to judge the proportion of many factors in the final sentencing. In this case, in order to clearly inform the aggravation or reduction of criminal law and avoid paying too much attention to sentencing factors as much as possible, the proportion of sentencing circumstances must be determined.It should be noted that this proportion cannot be accurately determined to a number, only for its relevant provisions and requirements. In determining the proportion of sentencing circumstances, the purpose of punishment should be considered. For international criminal law, it is necessary to always adhere to the purpose of retribution and pay necessary attention to the special purpose of prevention. Therefore, the two purposes of sentencing should be determined in combination with the proportion of circumstances. However, it is impossible to cover all sentencing circumstances, which can only be determined at the general level. Hence, in the determination of sentencing circumstances, the following aspects should be followed: (i) The determination of circumstances of sentencing should be related to the purpose of criminal law. (ii) If it is an aggravating circumstance, because it will directly increase the penalty and cause serious damage to the rights of the defendant, it should be restricted according to the explicit provisions, and the analogy is not allowed to exist. At the same time, aggravating circumstances only include the scope of the implementation of the crime and the cause of a close relationship between the criminal act and the offender. In addition, the requirements for mitigating circumstances need not be too strict, and the scope should be appropriately expanded. As long as there is no direct connection with the criminal act, it can be regarded as the reason for mitigating the circumstances of punishment. (iii) No double evaluation is allowed. Specifically, for the same reasons of the case, the penalty should not be evaluated repeatedly, so as to effectively avoid the excessive increase or reduction of the defendant's punishment.When determining the proportion of sentencing circumstances, on the one hand, the determination of international criminal law to punish international criminal acts should be shown, so it is not allowed to focus on the sentencing circumstances for preventive purposes. On the other hand, if the increase or decrease of the proportion of criminal law for no less than one circumstance exceeds the scope of the established proportion, specific reasons must be elaborated in detail.For international criminal law, the criminal law itself is in the initial stage of development, and thus the problem of sentencing imbalance has not received necessary attention. That is to say, theoretical research related to sentencing imbalance is not mature, and there is no effective system to overcome it in practice. Therefore, we must attach great importance to the study of sentencing imbalance from the perspective of international criminal law.Object of international environmental crime. The object violated by principal crime is the ecological environment (resources) on which human beings depend, including environmental elements such as atmosphere, water, ocean, forest, grassland, and flora and fauna. In the case of pollution in outer space, it should be considered an international environmental crime if it violates the provisions of relevant international conventions.Subject of international environmental crime. The subjects of international environmental crime include natural persons, legal persons, and other organizations and countries, but there is a great controversy on whether a country accepts criminal responsibility for international environmental crime. We believe that with the continuous improvement of international criminal law legislation and international theory of environmental crime, incorporating the country into the subject of international environmental crime fits the needs of current international environmental protection work. Apparently, the way in which a country assumes criminal responsibility for international environmental crime is not clearly defined in the existing documents such as international criminal law. Therefore, theoretical perfection and system innovation are urgently required in future research. At present, it has gradually been accepted by the international community to include the country as the subject of international environmental crime.Objective aspects of international environmental crime. Principal crime is objectively manifested as a violation of the prohibitive provisions of international criminal law, a serious damage to human ecological environment, and a harmful act subject to international criminal responsibility. According to the different behavior modes, harmful behaviors can be divided into actions and omissions. According to the different behavioral means, harmful behaviors can be divided into those that destroy natural resources and those that pollute the environment. If there are specific classifications of harmful behaviors, they can be roughly divided into the following categories: (i) Air pollution; (ii) Marine pollution; (iii) Outer space pollution, such as the test and use of nuclear weapons and weapons of mass destruction; and (iv) Transfer of pollution.Subjective aspects of international environmental crime. The subjective aspect of international environmental crime can be intentional or negligent. Although some scholars disapprove of negligence as the subjective element of the crime, we believe it is against the efforts of the international community to strengthen environmental protection. Generally, the destruction of natural resources is mostly intentional while the pollution of the environment is mostly negligent.Given the above, we analyze the environmental rights sentencing under the protection of international criminal law assisted by machine learning. However, there are more and more provisions in international environmental crime in international treaties, and the theory and practice of using international criminal law to protect environmental rights are becoming more and more mature. Then, as a relatively independent international criminal law norm, we try to analyze what constitutive elements of international environmental crime should include.International criminal law plays an irreplaceable role in the protection of environmental rights. Using international criminal law to protect environmental rights is consistent with the current general promotion of human rights. In this paper, CNN is used to study the sentencing of environmental rights from the perspective of international criminal law, including the prediction of the sentencing term and fine. The corresponding sentencing term or fine is obtained through the description of environmental rights case text. To make the prediction effect of the sentencing term or fine more clearly displayed, the distance between the predicted sentencing term or fine and the true value is considered to construct the score; thus, the evaluation and analysis of the predicting effect of the model can be realized, and the reference basis can be provided for the selection of different assisted sentencing models. The experimental results show that the CNN-based model achieves good prediction effect on the test set. In the computer-assisted sentencing of environmental crime, besides the influence of criminal circumstances on the results of sentencing, there are other impact factors such as charges, judges, regions, etc. We should fully consider all possible factors that may affect the results of sentencing, and adopt more complex machine learning models or integrate more knowledge models to build more effective models, so that computer-assisted sentencing can be more effectively used in the actual case trial.


De novo donor-specific HLA antibodies (dnDSA) are key factors in the diagnosis of antibody-mediated rejection (ABMR) and related to graft loss.® screening. All 400 kidney transplant recipients with 731 dnDSA against the last graft (01/03/2000-31/05/2021) were included.This retrospective study was designed to evaluate the natural course of dnDSA in graft function and kidney allograft survival and to assess the impact of mean fluorescence intensity (MFI) evolution as detected by annual Luminexp < 0.001). Interestingly, dnDSA in 168 (24.3%) cases became negative at some point during follow-up, and 38/400 (9.5%) patients became stable negative, which was associated with better graft survival. Multivariable analysis revealed the importance of MFI evolution and rejection, while class and number of dnDSA were not contributors in this model.During 8.3 years of follow-up, ABMR occurred in 24.8% and graft loss in 33.3% of the cases, especially in patients with class I and II dnDSA, and those with multiple dnDSA. We observed frequent changes in MFI with 5-year allograft survivals post-dnDSA of 74.0% in patients with MFI reduction ≥ 50%, 62.4% with fluctuating MFI (MFI reduction ≥ 50% and doubling), and 52.7% with doubling MFI (log-rank In summary, we provide an in-depth analysis of the natural course of dnDSA after kidney transplantation, first evidence for the impact of MFI evolution on graft outcomes, and describe a relevant number of patients with a stable disappearance of dnDSA, related to better allograft survival. It ity (MFI) , 47, 48.ty (MFI) , 41, 46.ty (MFI)  gave recThe purpose of the current study was to evaluate the natural history and clinical evolution of patients with dnDSA after kidney transplantation. We wanted to specifically address the relationship of dnDSA MFI values with graft failure. Changes in renal function were evaluated to assess the evolution of these analytical parameters after the occurrence of dnDSA.For this retrospective analysis, we included all kidney transplant recipients with dnDSA from 01/03/2000 until 31/05/2021 (end of follow-up) at Charité-Universitätsmedizin Berlin (Germany). All patients with dnDSA against the last graft with complete HLA typing were included, excluding those patients with preformed DSA before transplantation. The primary outcome variable in our study was time to death-censored graft failure, defined as graft loss . Patients who developed dnDSA after graft loss were excluded.1 No institutional review board approval was required for this retrospective analysis.All data including estimated glomerular filtration rate , proteinuria (mg/g creatinine), delayed graft function (DGF), defined as the need for dialysis within 7 days of transplant, and biopsy data were collected from the prospectively maintained database (TBase) . All rej® -based LABScreen® SAB assay . The general MFI positivity threshold in our laboratory was 1,000. Despite this, the first occurrence date in our study was defined as the date of the medical report by the immunology department in which dnDSA was first assigned, considering other factors such as plausibility , and they were also classified according to MFI evolution in the subsequent samples . In dnDSA with ≥ 50% MFI reduction. specific active treatment for ABMR was recorded  was used to compare variables with non-normal distribution. A chi-square test was used to compare the average values of categorical variables. Univariable and multivariable Cox regression analyses were performed to determine which clinical variables were associated with death-censored graft loss, and hazard ratios (HR) were reported with 95% confidence intervals. Missing laboratory values due to graft loss or lack of follow-up after dnDSA appearance were imputed using last observation carried forward (LOCF) analysis and automatic multiple imputation (MI) using five default imputations. Time-to-event outcome data were assessed by Kaplan–Meier plots and log-rank tests. P < 5% defined statistical significance. Statistical analysis was conducted using the SPSS statistical software package .Continuous variables were expressed as mean ± standard deviation (SD) or median and interquartile range (IQR) according to their distribution. Categorical variables were described as relative frequencies. A non-parametric test , as shown in In total, we identified 400 patients with dnDSA , which aRegular annual DSA screening was performed for more than 18 years , with a n = 174, 43.5%), 113 (64.9%) had all dnDSA with the same date of appearance. In the other patients , the median time from the first occurrence to the next first appearance of other dnDSA was 14.4 months (IQR 4.7–43.7)  .n = 260), 64.2% (n = 167) had only DQ-dnDSA, and 35.8% (n = 93) had DQ along with other dnDSA. In the latter group, most of the patients presented DQ at the time or before the appearance of other dnDSA . These 79 patients had additional class I (50.6%), class II (27.8%), and both class I and II (21.5%) dnDSA. In patients with DQ-dnDSA which appeared before other dnDSA (n = 19), the median time from DQ to the occurrence of other dnDSA was 15.1 months (IQR 6.9–20.0). The class of dnDSA was associated with 5-year death-censored allograft survival , and 72.0% of patients had at least one biopsy . About 3survival . Similarsurvival .n = 731) (p = 0.575) (Stratification by dnDSA (n = 731)  revealed= 0.575) .n = 691; n = 255), 25.5% (n = 65) had received some form of treatment (n = 190) had a ‘spontaneous’ reduction. Interestingly, 168 (24.3%) dnDSA became negative at some point during follow-up and 100 (14.5%) dnDSA became stable negative. Altogether, 38/400 (9.5%) patients became stable negative.Analyzing MFI at the time and after the first occurrence in Luminex-defined dnDSA n = 691; , we had reatment , but 74.p < 0.001), and temporary and stable MFI negativity was significantly lower in the graft loss group (p = 0.034 and 0.004).The relationship between MFI evolution and graft loss is shown in n = 363, 49.7% of total dnDSA), the proportion of DQ was significantly lower in the graft loss group . The number of DQ-dnDSA with MFI available at the first occurrence was 346 (n = 108), and 7.2% (n = 25) became stable negative. In 84/108 (77.8%) cases, the MFI reduction occurred without treatment. The MFI evolution was associated with 5-year death-censored allograft survival  before and after dnDSA appearance are shown in p = 0.007), and the presence of both class I and II dnDSAs was significantly associated with graft failure (p < 0.001). Patients with ≥ 4 dnDSA experienced significantly more frequent graft loss (p < 0.001). DGF was associated with graft loss in univariable analysis, and conversely, those patients with a combined transplant experienced significantly less graft failure. Patients with doubling and fluctuating MFI values of dnDSA had significantly more graft loss , while patients with ≥50% MFI reduction (p < 0.001) and stable negative MFI (p = 0.018) of dnDSA were significantly associated with less graft failure. These results were confirmed by multivariable Cox regression analysis . However, doubling and fluctuating MFI are related to graft failure in univariable analysis but are not contributors in the multivariable model .It has been described that changes in MFI values <25%, or in some cases <50%, are not considered clinically important , but untp < 0.001). Temporary and stable MFI negativity are significantly associated with better graft survival. This stable disappearance of dnDSA may have several causes, such as the development of anti-idiotypic antibodies that suppress DSA production  is needed in order to avoid survival bias. In our cohort, adherence to treatment and levels of immunosuppressive drugs are not evaluated at the time of appearance of dnDSA. The analysis of dnDSA by LuminexIn summary, we are providing a large body of evidence for the natural course of dnDSA. We highlight the problem of the MFI positivity threshold, as even low, but plausible, MFI may have a negative impact. We confirm the high frequency of DQ dnDSA, presenting with higher MFI at the time of appearance and being more persistent, but seem less harmful to the graft. For the first time, we describe that MFI evolution is associated with graft survival, demonstrating the positive effect of a ≥50% reduction in MFI values, and we observed that almost 10% of patients became stable negative, which is related to better outcomes. Our large observational study provides important evidence for a better understanding of the evolution of dnDSA in renal allograft recipients. Further studies are needed to distinguish those dnDSA which are harmful from those dnDSA with an uneventful clinical course. A better knowledge of relevant HLA epitopes or the use of novel biomarkers of graft dysfunction, such as cell-free DNA, may provide additional information to identify patients at risk.The original contributions presented in this study are included in the article/Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.CL, NL, and KB conceived and designed the study and wrote the article. MN and DSc provided technical support and acquired the data. CL analyzed and interpreted the data. NL, DSt, and SH performed HLA antibody testing. KW performed and interpreted histopathological examinations. MN, BO, AA, MC, FB, FH, and ES advised on the preparation of the article and provided conceptual advice. KB designed the study and supervised the research. All authors contributed to the article and approved the submitted version.


The aim of this study was to test the applicability of high-throughput polysaccharide profiling for plant and algal cell-wall compounds in dated sediment cores from a coastal marine environment, to examine the preservation of cell-wall polysaccharides and explore their potential as proxies for temporal environmental changes. Preserved compounds and remains of organisms are routinely used as paleoenvironmental proxies as the amount and composition of different compounds that can provide insight into past environmental conditions, and novel means for reporting environmental changes are highly sought.Polysaccharides constitute an important carbon pool in marine systems, but much is still unknown about the fate and degradation of these compounds. They are derived partly from production  We wished to determine the temporal preservation of polysaccharide epitopes and further explore the potential of these compounds as proxies for temporal environmental changes in a coastal marine setting.Dissolved organic carbon (DOC) is by far the largest planetary pool of organic carbon . The pho2, is the main point source of freshwater to the fjord. The strong stratification in the fjord leads to a more or less stagnant bottom water, with periodically occurring hypoxic or anoxic conditions  and Phaeophyceae  . ErikssoRemains of different organisms in such undisturbed sediments can constitute time series of the biological and chemical conditions of a water body, and sediment proxies of the environment can broadly speaking be physical remains of different organisms and molecules from either the organisms or the water itself. Such geochemical proxies can be organic or can constitute isotopes, isotope ratios, or heavy metals. Organic geochemical proxies are molecules, produced by living organisms in the past that are preserved in marine and/or freshwater sediments. Central requirements for a geochemical proxy are that it is well-preserved in aquatic sediments and that it shows patterns of response to environmental forcing. Photosynthetic pigments , sterolsKoljö Fjord has been the target of several paleoenvironmental studies, and patterns in environmental change derived from studies of temporal changes in dinoflagellate cyst and diatom communities were used to compare with the temporal patterns in the polysaccharides.Comprehensive microarray polymer profiling (CoMPP) is a high throughput technique, which allows the analysis of multiple samples with the advantage of not needing complex separation techniques such as chromatography or hydrolysis. Samples undergo a fractionation with different solvents and later are probed using specific monoclonal antibodies and carbohydrate-binding module (CBM) probes. These probes bind to target epitopes in the sample matrix. Revealing the positive reaction requires an enzymatic process, which generates a signal (fluorescent or chemometric) on the array surface that can be interpreted with a dedicated software . This seThe target site is, thus, known to present a good environment for the preservation of biological and geochemical remains and to be subject to documented environmental change over time. Our aim was, in this setting, to test the applicability of high-throughput polysaccharide profiling for detecting polysaccharides in sediment cores, with the final aim to explore the potential of such compounds as proxies for temporal environmental changes.210Pb dating with 137Cs-peaks for verification  constituted the pilot project and was used to test the methodology. The three samples were homogenized and freeze-dried. The first stage was the preparation of alcohol insoluble residue (AIR), which serves to concentrate larger polymers and remove water. To prepare AIR, 1 g of freeze-dried material was suspended in falcon tubes to a final volume of 5 ml of 70% ethanol, stirred for 3 min, and centrifuged at 40,000 rpm for 10 min. The resulting pellet was resuspended to a final volume of 5 ml 96% ethanol, and the previous step was repeated two times more with 1:1 methanol:chloroform and 100% acetone. All the resulting supernatants were kept and freeze-dried for analysis, and the final pellet was also freeze-dried.Since the sediments are marine, a sequential chemical fractionation with the aim to extract FCSP and alginates , but alsSince the results from the pilot project provided more land plant signals than originally expected, for the core KF12/5, CDTA was used instead of HCl to extract more pectins.The supernatants from AIR and the subsequent chemical fractionations were freeze-dried and redissolved up to a 1-ml Arrayjet buffer  and were printed on to nitrocellulose as previously described . The resSamples from core KF12/5 were printed as described above and probed against the same anti-mouse antibodies under the same conditions except for INRA RU1 and INRA RU2, which were excluded this time, the same anti-His antibodies also including CBM6 (at a concentration 10 μg/ml), and all the anti-rat antibodies cited before, including the following as well : LM18, LThe arrays were scanned, and their signals quantified as described by Like previous cores taken at the same site in Koljö Fjord , the ageIn the extractions from the core, KF12/5 signals were detected in all 18 samples by 30 out of 31 antibodies . For somDue to the semiquantitative nature of the analysis, it is not possible to directly translate the strength of the signals to sedimentary concentrations and, therefore, not possible to make quantitative comparisons between the epitopes. However, within epitopes, signal strength indicates relative changes in sedimentary concentrations. Therefore, we plotted signal strength against the age of the samples for the epitopes with the strongest and/or most consistent signals. This revealed that some of the dominant epitopes fluctuated more or less synchronously ; howeverTo our knowledge, there are only a few other studies of the long-term preservation of carbohydrates in sediments. One study by This is the first time the antibody-based CoMPP method has been used to detect polysaccharides in marine sediments, and to our knowledge, this is the first time such a variety of polysaccharide epitopes have been detected in marine sediments. Compared with other traditional methods used to determine polysaccharides, CoMPP could become a strong asset for sediment analysis. Some of these methods can be less specific as it is the case of atomic C to N ratios  or requiWe found a large variety of compounds preserved throughout much of the studied time period. The preservation of many epitopes throughout the core and the patterns of both increase and decrease over time indicate that the epitopes are responding to something other than degradation. A pure degradation pattern would be expected to take the shape of a more or less exponential decline. Although we would expect a pattern of resilience in the order of pectin > hemicelluloses > celluloses > lignin, due to the differences in degradability of these compounds , this isMany of the 30 epitopes for which we found signals in the sediment have been detected in previous studies in a variety of marine and freshwater land plants, seaweeds, and algae; a few have also been found in marine animals. The most dominant epitopes and their possible provenance are discussed below.Among the polysaccharide epitopes detected in the samples, the most straightforward to mention is cellulose, which is detected by CBMs . In the Koljö Fjord samples, particularly CMB30 shows a consistently high signal throughout the core. All plants have cellulose in their cell walls and their content usually accounts for 35–50% of dry weight . MicroalXylans, which are detected by the antibody LM11, are a structurally diverse group of polysaccharides commonly found in land plants . They haD-glucan (MLG), detected by the probe BS-400-3, is the epitope with the highest signal in the heat map and a consistently high signal throughout the core. MLG is restricted to certain taxonomic groups in the plant kingdom. While it is not present in dicotyledonous plants, it is a major glycan component of the cell walls of grasses and cereals of the Poaceae ((1→3)(1→4)-β- Poaceae . It is aD-glucan recognized by antibody BS-400-2 is a polysaccharide that occurs in land plants at certain stages of development, for example, during cell plate formation, and in response to some biotic stresses when it has a role in plant defense —an algal polysaccharide used as a storage glycan that is based on (1→3)-β-D-glucan with (1 →6)-β-D branches -β- defense . Neverthbranches , and lamA recent study  has showEpitopes of fucose-containing sulfated polysaccharides (FCSP) also known as fucans or fucoidans are detected by BAM1 (an unsulfated epitope present in sulfated fucan), BAM2 (a sulfated epitope present in sulfated fucan), and BAM3 (a possibly sulfated epitope present in sulfated fucan) mAbs . Brown aWe would like to remark that regardless of the presence of red seaweeds in the area; at the time when the analysis was performed, we lacked the right probes to determine the presence of sulfated galactans in the samples. However, these complex polysaccharides are known to be present in the cell walls of these aquatic organisms and could be found with the right analytical tools in the sediments. In a previous study, There are clear temporal patterns in the intensity of the signals of many of the detected epitopes. Data on the temporal organic carbon content in a core from Koljö Fjord reported by While there was a reduction in signal strength for some compounds with core depth, we did not, in this study, reach the limit for the preservation of these compounds, as at least four  were present with strong signals to the bottom of the core at 67 cm (estimated to be ca. 200 years old). The maximal preservation limit for these compounds is, therefore, yet to be determined, e.g., by analyzing longer/older core material.Given that this exploratory study is from one single location and a cold-temperate fjord setting, future study should also explore the longevity of these compounds in sediment records of different compositions and under different environmental settings.As some of the compounds appear to be responding positively to the milder and wetter conditions of positive NAO index and as many of them likely are of terrestrial origin, they may have potential as paleoenvironmental proxies of terrestrial influence. As the method is simple and has high throughput, they could constitute a valuable contribution to geochemical proxies.However, the response seen in this study should be confirmed. The preservation potential should be tested in other coastal  locations. We saw from the pilot study that some of the epitopes were present at other sites, but this needs to be confirmed through down-core analyses. To test whether the potential proxies are in fact responding to terrestrial input, it would be relevant to analyze gradients from land, as they should, in this case, decline with distance, or in a river mouth .To shed light on the role of these compounds in marine carbon cycling and their possible role in carbon sequestration, it would be relevant to look at gradients through the water column and into the sediment. Again, the high-throughput nature of this methodology is helpful.At present, we cannot identify the epitopes with certainty, and we suggest elucidating this when we know which of them has the potential to be a robust, broadly applicable proxy and when it has been calibrated by further studies.We have shown that this method with its simple extraction and high-throughput analysis is applicable for analyzing epitopes of polysaccharides in marine sediment core samples. We have documented long-term preservation of a large variety of epitopes, in some cases, for more than 200 years. We further see fluctuations in relative abundance, apparently in some cases driven by environmental fluctuations.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.AS, WW, and ME planned the study. AS and TA carried out the laboratory analyses. AS, ME, TA, and SR analyzed and interpreted the data. All authors contributed to writing the manuscript.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.