dataesr/scientific-paragraphs-categorization huggingface dataset:
We then further stratified the obtained non-canonical HLAp according to the annotation of their origins as described in the last section (Figure  4B ). Peptides uniquely mapped to protein coding regions formed the largest subcategory, comprising of 1150 HLAp, while 811 peptides remained unmatched. We found overall 1,423 peptides associated with lncRNA origin. In order to validate that the FDR of these identifications was as expected from the Chong et al. dataset, we again selected a set of peptides for spectral validation. We chose 19 peptide candidates that had a MARS score of >89. 7 (region 1 and 2), and we further prioritized peptides if more than one sequence supported the identification of an lncRNA gene. We validated 11/19 spectra (42. 2% FDR), which was in the range of the expected FDR of 39.
5% from previous results for Region 1 and 2 (MARS score >89. 7). For Region 1 only (MARS score >94. 7) we again yielded high accuracy in annotation with 7/8 correct assignments and an FDR of 12. 5% (Figure  4C , Table  1 ). In summary, MARS identifies thousands of peptides in addition to peptides identified with standard database-search approaches at an accuracy of above 85. 7%, as confirmed in this independent primary tumour dataset, paving the way for transcriptomics-independent antigen discovery workflows to inform the development of novel cancer immunotherapies. A main limitation of non-canonical antigen discovery is the requirement of prior knowledge of expected protein sequences translated in a given sample. We here describe how we can rescue identification of non-canonical lncRNA-derived HLA-associated peptides from LC-MS/MS data without the requirement for integration of RNA sequencing data or, in fact, any other sample-specific sequence information other than HLA type, using a discovery engine that improves candidate selection from de novo sequencing results.
Since in immunopeptidomics the lengths of peptides and positions of individual amino acids in the peptides are crucial for the HLA binding and T-cell response, we here used a study point-wise-fullsequence-recall (PSTR) to assess the development of MARS instead of improvements of single tag recall STR which assesses the percentage of individual amino acids being correctly identified  3, 34. We evaluated the increased sensitivity using an external dataset from Wilhelm et al. 17  , and confirmed an overall increase in performance of MARS de novo candidate selection in comparison to the best ALC scoring peptide as reported by Peaks DeNovo by over 7%. Interestingly, we saw outstanding performance for B27, B40 and B41 alleles. B27 has the requirement of an arginine at position two within the peptide sequence, while B40 and B41 are known to require a glutamic acid in this position.
Hence, chemical properties of the peptide ligands are expected to be rather distinct, and we were not able to explain this much increased level of performance. We did not observe a significant improvement for identification of HLAp originating from HLA-C. Since peptide data was acquired in equimolar ratios for the HLAp library datasets, this could not be due to lower expression of HLA-C in comparison to HLA-A and -B as generally observed across different datasets. Since NetMHCpan-4. 1 performs equally well across all three loci, we conclude that most likely we did not evaluate a sufficient number of HLA-C peptide ligands to reach a significantly better performance. We then proceeded to evaluate how many peptide identifications could be rescued from a list of published non-canonical HLAp that had been generated using a proteogenomics approach.
MARS was able to identify 45% of the reported sequences highlighting the power of MARS to identify non-canonical antigens in the absence of transcriptomics and RiboSeq datasets. One limitation remains the high false discovery rate associated with de novo sequencing results. With MARS, the FDR could be further limited by assigning the peptide sequences to a possible origin in the human genome and transcriptome, resulting in a manageable FDR range below 14. 3% for MARS scores above 94. 8, compared to generally anticipated FDR of 65% for de novo sequencing results. Furthermore, considering only those sequences that mapped to a genome origin resulted in a substantial decrease of the FDR (to 0% in our experimental validation), demonstrating that MARS offers identification of noncanonical peptide sequences at low FDR, and can therefore expand the peptide identification beyond sophisticated proteogenomics approaches alone.
e. by increased ion accumulation times. We could not validate any cysteine containing peptide assignments, confirming the known observed underrepresentation of cysteines in immunopeptidomics data  35, 36  , and highlighting the need for specific modification of the otherwise highly reactive amino acid. Finally, we were able to shortlist novel lncRNA-derived peptide candidates from primary human tumour tissue. We shortlisted HLAp if more than one sequence supported the identification of a specific lncRNA gene. We validated the sequence identifications with spectral matching to their synthetic counterpart, and we validated the FDRs measured in our previous evaluation. Note that we did not set a threshold for number of I and L in the peptide sequence, which could theoretically further reduce the FDR.
Finally, we identified and validated 11 peptides originating from lncRNA origins in human cervical cancer tissue. Peptide KVHVFLVKKT, which originated from lncRNA HLA-F-AS1, plays an oncogenic role in both colorectal cancer and breast cancer  37, 38. The identified peptides MTMSTILSK and MTMSTILSKK were derived from lncRNA lnckb. 42318 also known as MIR4458HG (ENSG00000247516), which was reported as highly expressed lncRNA in 407 TCGA evaluated ovarian tumors  39. It has further been reported that, together with CYTOR, and MAPTAS1, MIR4458HG was an independent prognostic factor in breast cancer patients (https://doi. org/10. 21203/rs. 2. 24062/v1, preprint), with high expression associated with lower hazard ratios. We conclude that MARS is a suitable platform for non-canonical antigen discovery in cancer immunopeptidomics datasets in the absence of high quality RNA sequencing data.
Trinity De-Novo RNA Assembly Raw RNA sequencing reads had adaptors removed and low quality bases trimmed using Trim_Galore v0. 6. 2 (http://www. bioinformatics. babraham. ac. uk/projects/trim_galore) and quality control metrics assessed using FastQC v0. 11. 9 (http://www. bioinformatics. babraham. ac. uk/projects/fastqc). A custom STAR (v2. 7. 3a)  48 genome reference was created utilising UCSC hg38 genome sequence. Reads were then mapped to this custom genome reference using STAR with two passes, sorted and indexed by Samtools v1. 10 49. Duplicates were marked using GATK v4. 1. 7. 0  50. Unmapped reads from STAR were used as inputs for Trinity v2. 11. 0 51 for de novo transcript assembly.
ACKNOWLEDGEMENT We thank  Prof. Masafumi Takiguchi ,  Kumamoto University, Japan  for provision of CD4-expressing cell lines transfected with single HLA-I alleles. This work was funded by the  NIH  project # 1R21AI153978-01  (LCE),  The Leona M. and Harry B. Helmsley Charitable Trust (RM) , the  European Association for the Study of Diabetes   FSD/JDRF/Lilly Programme on Type 1 Diabetes Research 2019 (NT) ,  Cancer Research UK Cancer Immunology  project award  C55884/A21045  (NT), and  Cancer Research UK RadNet Centre  Award  C6078/A28736  (NT). Some immunopeptidomic datasets employed for this study were generated as part of studies supported by  Medical Research Council (MRC)  programme grant  MR/K012037 , grants from the  National Institutes of Health  ( UM1 AI 100645  and  R01 AI 118549 ) and by  AMED  grant  17FK0410302H0003  for AIDS research (PB).
Baseline ALC Model, or One-Factor Model Peaks DeNovo sequencing algorithm was performed using Peaks X (Bioinformatics Solutions) on each MS2 spectrum in the studied data to produce peptide-spectrum matches (PSMs). The quality of PSMs are measured by the average local confidence (ALC, represented by symbol A in the formula), which ranged in [0,100], with 100 being the best PSM quality. We selected PSMS with ALC in range [50, 100] to ensure reasonable input quality. The top-ALC peptide of each spectrum was used as its identity. No training was required for this one-factor model. absolute difference between observed and predicted RT: ΔT=| Tpredicted-Tobserved | was computed. Secondly, T was computed as the ΔT s normalised to the interval of [0,100] to match the range of ALC and ELR: T=100(ΔTmax-ΔT)/(ΔTmax-ΔTmin ).
Performance evaluation: Full-Sequence Recall (FSR) FSR is defined as the ratio between the total number of correctly identified peptides and the total number of peptides in the given dataset. It measures the peptide identification sensitivity at the spectra ensemble level. For each factor model, the top-scoring PSM of each MS2 spectrum was used as an identified peptide sequence, whereas the database peptide sequence of the same MS2 spectrum was considered the correct peptide sequence. A peptide was considered correctly identified if the peptide predicted by the given model was identical (up to I/L indistinguishability) to the database peptide. Differences in FSR were evaluated with a paired T test.
Strategy for MARS candidate HLAp origin annotation and database curation We coarsely stratified the HLAp origins into three categories: "human protein", "single amino acid substitution", and "non-canonical". Besides, we put all HLAp not strongly associated with these three categories into another "unmatched" category. Seqkit (Shen et al. 2016) was employed for locating MARS peptides in the curated databases. For these searches, we excluded all database search against SwissProt assigned peptides with a score cut-off of -10lgP>20, and searching the remaining de novo candidates as shortlisted by the MARS identification module. Firstly, we searched Uniprot and Ensembl (only human protein entries) with isoforms as the "human proteome". MARS peptides matching exactly to human proteome were considered "Human Proteome" originated HLAp, matching human protein with 1AA difference were considered "1AA substitution".
These two categories of peptides were considered of canonical origin, and excluded from non-canonical category. To further examine the possible alternative origins of the peptides of non-canonical origin, we used GENCODE v40 six-frame translated reference genome and three-frame translated reference transcriptome to form the protein database. All annotations were combined into Genecode "Biotypes" as follows: "Protein coding": protein_coding; "lncRNA": lncRNA; "other RNAs": miRNA, misc_RNA, Mt_rRNA, Mt_tRNA, ribozyme, rRNA, rRNA_pseudogene, scaRNA, scRNA, snoRNA, snRNA, sRNA, vault_RNA; "pseudogene": all biotypes including "pseudogene" (polymorphic, processed, transcribed_processed, transcribed_unitary, transcribed_unprocessed, translated_processed, translated_unprocessed, unitary, unprocessed); "IG": IG_C_gene, IG_C_pseudogene, IG_D_gene, IG_J_gene, IG_J_pseudogene, IG_pseudogene, IG_V_gene, IG_V_pseudogene; "TR": TR_C_gene, TR_D_gene, TR_J_gene , TR_J_pseudogene, TR_V_gene, TR_V_pseudogened and "TEC": TEC.
LC-MS datasets and data analysis We utilized the following datasets: Data for the C1866 cell line (A*01:01, B*08:01, B*44:02, C*05:01, and C*07:01) and for single allele transfectant cell lines is partially available in PXD015489  40   Peaks vX (Bioinformatics Solutions) was used to identify to search against a database containing the synthetic standard sequences. For database search, we used no enzyme specificity (setting: unspecific). The precursor mass error range was set within 5 ppm. Only oxidation was selected as variable modification, unless otherwise indicated in the main text. For the de novo sequencing, the precursor mass.
MERLIN (MEthane Remote sensing LIdar missioN) is a Franco-German space mission designed to provide weighted columns of atmospheric methane through an inversion of the lidar signal using a priori information on the atmospheric state. Uncertainties about the meteorological parameters of the observed scene used in the ground segment contribute to the error budget on the retrieved methane column. With the LIDSIM (LIDar SIMulator) data simulator and the PROLID (PROcessor LIDar) inversion processor developed for MERLIN, we perform an impact experiment using ECMWF (European Centre for Medium Weather Range Forecast) ensemble forecast data. In addition, we estimate the standard deviation of the error in the methane column due to the meteorological uncertainties to be about 0. 6 ppb. In addition, we innovate by discussing the impact of interpolations both in time and space, focusing on vertical extrapolations under the topography by using state-of-the-art methods to determine from the scatter between these methods the range in which the actual profile should be.
The data processing is described in the MERLIN ATBD (Algorithm Theoretical Basis Document, an internal project document)  [5]. The LMD (Laboratoire de Météorologie Dynamique) has designed two software packages, LIDSIM (LIDar SIMulator) and PROLID (PROcessor LIDar)  [6] , to investigate the sensitivity of MERLIN performance to different geophysical parameters by inverting simulated data orbits from realistic data and noise for the different processes. In this paper, we do not deal with errors in the signal as a function of the state of the atmosphere (clouds, aerosols, turbulence, etc. ) but only consider the impact of uncertainties about the numerical weather prediction model data used in the ground segment. The methane retrieval performance from simulated MERLIN data is sensitive to discrepancies between the available meteorological data and the actual state of the atmosphere.
V i is the interpolated value at the target point at time t i. (b) For the Corsica region, on a representation of the topography from the EarthEnv DEM  [15] , the points where meteorological data are available in the ECMWF database are in red and the target points that are part of the MERLIN ground track are in black. Bilinear interpolations in longitude and latitude are an extremely simple, cheap and efficient implementation for horizontal interpolations. It avoids the discontinuities encountered with the use of the nearest point and requires only four original points. On each direction, f a (x) is the interpolated value at the location x defined with k = x ∆ x by: ∀x∈[k∆ x , (k + 1)∆ x ] f a (x) = k + 1 - x ∆ x f k + x ∆ x -k f k+1 , (4) where ∆ x is the distance between the points and f k is the value of the function at the location k ∆ x.
However, this statistical approach neglects error correlations both between vertical levels and between variables. For this reason, we have chosen another approach based on ensemble forecasting tools  [21]. ECMWF generates every 12 H a set of fifty analyses compatible with the uncertainties of the forecast used as a first guess and those of the observations available  [22]. These fifty perturbed forecasts were used in this study to provide an estimate of the uncertainty of the actual state of the atmosphere with respect to the unperturbed initial-state forecast used as reference. With our LIDSIM software, we have simulated the MERLIN signals from the atmospheric data of the reference forecast. Then, we have used each of the fifty perturbed forecasts in turn to obtain a estimate of the mole fraction of methane with respect to dry air averaged over the atmospheric column with the PROLID software, by computing WF G (p), the weighting functions for each gas, following Equation (  3 ).
Time Interpolation and Tide Waves We have decided to use the weather data available every 3 h in the MARS database and to interpolate them linearly. However, it has been highlighted by work carried out by the CNES  [20, 23]  that using model outputs at the hourly frequency leads to systematic differences between the pressure fields predicted at time H and interpolated between H -2 and H + 1, whether using linear interpolation or a cubic spline. The differences interpreted as being related to the representation of atmospheric tides may induce regional biases up to 3 ppm on the mole fraction of methane with respect to dry air averaged over the atmospheric column (see Figure  5 ). Indeed, time interpolation creates systematic errors for insufficiently sampled periodic components such as atmospheric tides, as it is well documented in other contexts  [24].
Impact on the Methane Retrieval of Vertical Interpolation Methods We used several methods to adapt the atmospheric profiles corresponding to a particular ground altitude to another ground altitude. With LIDSIM, we compute the temperature and humidity profiles from the ECMWF data for a vertical pressure grid defined from the pressure value estimated at the DEM value, using one of the interpolation methods described above and detailed in Appendices B and C. In addition, we use these profiles to simulate the MERLIN data. Then, with PROLID, we invert the simulated signals using profiles constructed with another method. In all cases, except the "last gradient" case, a "zero gradient" method is used to extrapolate data from the DEM value to the SSE. There is no reason to use a more sophisticated method because the difference between the DEM value and the SSE is small except in the cloud cases, but then the boundary layer should not be moved as the SSE does not represent the ground.
The calculation of the IWF (Integrated Weighting Function, see Equation (  3 )) for a particular temperature and humidity profile, even if the surface pressure is fixed, depends on the distribution of the levels over the vertical. As explained in Appendices B and C, the "standard gradient" and APACHE methods change the discretisation of the profiles above the pressure determined at the DEM altitude (they resample atmospheric levels). However, the implementation of the "last gradient" and "zero gradient" methods is performed without level resampling. The deviation of the IWF due to the resampling could be measured. Expressed as a maximum error in the mole fraction of methane to dry air averaged over the atmospheric column, it corresponds to 0. 08 ppb. This value is in agreement with an estimate we made (not shown) on five representative atmospheres from the TIGR database  [25]  and which shows that the difference between the exact calculation of the IWF and its approximation with a discretisation on 137 levels (vertical resolution of the ECMWF data used) is of the order of 0.
Sensitivity to the Analysis Errors As explained above, we simulated the MERLIN data in LIDSIM using the ECMWF description of the atmosphere from the unperturbed initial state and inverted these data in PROLID using the other fifty descriptions of the atmosphere corresponding to forecasts from the perturbed states. We used data from 6 May 2019 between 03 UT and 06 UT. We present the results for the same orbit as above. In LIDSIM, we did not add instrumental noise to see only the impact of the NWP data. In LIDSIM and PROLID, we used the standard method to adjust the data to the DEM value. Figure  8  shows the mean and the standard deviation of the fifty differences between XCH r 4 (the mole fraction of methane to dry air averaged over the atmospheric column) obtained with Equation (1) for a set of meteorological parameters and its reference value  [6].
Therefore, the values established in the CNES study  [20]  and used by Bousquet et al. [1]  seem pessimistic. In 2011, Kiemle et al. [26]  state that the random pressure error is less than 100 Pa in agreement with the Dee et al. [27]  estimate around 70 Pa. The different values can be also explained by the regular improvement of forecast quality which continues up to now. The data available in the ECMWF website indicate an analysis bias of 3 Pa and a random error of less than 50 Pa for these last years. The estimate derived from the ensemble forecast is therefore representative of the state of the art, especially as our single orbit assessment necessarily underestimates the variability somewhat. Given the advances in numerical weather prediction, the random error estimated by Bousquet et al.
Conclusions We would have liked to recommend for the MERLIN ground segment to use the ECMWF operational analysis data available every three hours and to interpolate them linearly in time. However, we cannot do this until a method is validated to deal with atmospheric tides because of the regional bias they may induce. In addition, we need to plan specific work on this issue. There is no specific problem expected with the use of bilinear interpolations on the horizontal surfaces of the model. For the vertical extrapolation, only the "standard gradient" method or the APACHE method should be used in the MERLIN data processing due to the possible large differences between the topography of the meteorological model and the actual topography. The APACHE extrapolation method appears to be physically more consistent, but using simulated data, it is not possible to demonstrate a significant contribution to MERLIN data processing.
lacking. Most acid-base disturbances are mild and easily overseen by the clinicians  [1, 2]  but can be the first sign of a detrimental process starting. No data of the spectrum of acid-base changes occurring in a large cohort consisting of critically ill patients is available, simply because the large number of calculations cannot be carried out manually. We have combined physiological understanding with mathematical processing to create an overall simplified script providing both numerical and graphic output, using a modern mathematical software (MATLAB ® 2020a). In addition, the script should be written in a format that facilitates it to be used in an application for a handheld phone or a computer, as well as to be incorporated in the software of any device used in health care. The script should be able to perform a single calculation with a clinical meaningful output as well as being able to handle several thousand blood gases and deliver its output in a database format.
Our own simplified combined numerical and graphic approach We argue that all theories described above; Boston, Copenhagen and Stewart are not easy to fully understand and use in everyday interpretation of patients' acid-base status, and clinicians must rely on formulas that are often rough simplifications leading to wrong interpretations in especially complex acid-base disturbances. Therefore, we developed a computer script, which uses all available theory and that can interpret an individual blood gas in full, and deliver a detailed linguistic, numerical and graphic output. The script can also handle an infinite number of blood gases and deliver the output to a database. The script has a basic structure of four levels of complex calculation steps and the output from each level is transferred to the next; however, the final level accepts input from all previous levels.
The script estimates a most probable start-scenario for a certain pH/ pCO 2 combination, on which further calculations are carried out in subsequent levels, as shown in Tables  1  and 2. The layers 1-3 are straightforward and follow basic acid-base theory. The fourth and last layer does pattern recognition tasks, compares values to each other and is possible to extend for future versions of the script with machine learning and adaptive loops. After permission was obtained from The Swedish Ethical Review Authority (Dnr 2020-04642) 8875 initial blood gas analyses during years 2011-2021 at Skåne University Hospital, Adult Critical Care Unit, drawn immediately after the admission to ICU, were extracted from the medical records ICCA system (Philips, Vienna, Austria) using an extraction client, and included in the study cohort on which the script was applied.