dataesr/scientific-paragraphs-categorization huggingface dataset:
A new customized databased composed of high confidence identifications resulting from the initial search was then constructed for definitive CD44 glycoproteoforms identification in glycoproteogenomics settings. For cell lines, carbamidomethylcysteine (+57. 0215 Da) was set as a fixed modification, while methionine oxidation (+15. 9949 Da), protein N-terminal formylation (+27. 994 Da), N-terminal acetylation (+42. 0106 Da), Asparagine deamidation (+0. 9840 Da), ammonia-loss of Cysteine N-terminal (-17. 0265 Da) and glutamine to pyro-glutamine modification (-17. 0265 Da) were considered as variable modifications. For tumor samples, carbamidomethylcysteine (+57. 0215 Da) was elected as a fixed modification and, based on previous reports concerning the analysis of FFPE tissues  [34] , the following oxidative modifications were also included in the variable modifications list: lysine to aminoadipic semialdehyde (-1.
3598 Da). The presence of sialic acids was included to contemplate the possibility of incomplete de-sialylation. Glycoproteomics data was also analyzed using ByonicTM version 2. 13. 2 (Protein Metrics, Cupertino, CA, USA) using default settings  [36]. Glycopeptide assignment was confirmed by manual spectra interpretation using the Xcalibur TM software (Thermo Scientific).
2 mg/mL, overnight at 37 ºC). CD44 and anti-tag-72 were detected using Novolink Polymer Detection System (Leica) according to manufacturer guidelines. Biotinylated VVA was detected using Streptavidin, Horseradish Peroxidase Conjugate (Thermo Fisher, ready-to-use, 30 min, RT) followed by incubation with ImmPACT® DAB Substrate, Peroxidase (Vector, 30:1000, 5 min, RT). All images were acquired on a Motic BA310E microscope (Motic) using the Motic Images Plus 3. 0 software (Motic).
02 µg/µL; Table  S2 ), or FITC-labeled PNA lectin (T and ST after desialylation with 70 mU α-neuraminidase, 0. 02 µg/µL; Table  S2 ), or anti-tag-72 (STn, 5 µL/well; Table  S2 ) and CD44 (1:100; Table  S2 ). Alexa Fluor 594 anti-rabbit and Alexa Fluor 488 anti-mouse (ThermoFisher Scientific, 1:100) were used to detect CD44 and STn primary antibodies. Nuclear counterstain was performed with 4',6'-diamidino-2phenylindole dihydrochloride (DAPI, 2. 3x10 -3 µg/µL; Thermo Scientific); for 10 min at RT in the dark. Fluorescence images were acquired on a Leica DMI6000 FFW microscope using Las X software (Leica).
Ligation and amplification of the PLA signal were achieved using the Duolink PLA Technology kit (Sigma-Aldrich). All slides were incubated with DAPI and mounted using Duolink Mounting Medium. Sialidase treatment prior to antibody probing was used to confirm the specificity of the PLA signals. STn and CD44 negative bladder tumors were used as negative controls. MCR-STn+ glycoengineered cell lines expressing CD44-STn  [37, 38]  and/or tumor tissues were used as positive controls. The images were acquired on a Leica DMI6000 FFW microscope (Leica Microsystems) using the Las X software (Leica Microsystems). siRNA Silencing Assay siRNA reverse transfection was applied to silence CD44 in 5637, T24, and T24 glycoengineered cells in vitro, using two different Silencer Select siRNAs targeting CD44 (siRNAs ID: s2681 [target exon: 2] and s2682 [target exons: 1 and 2], Invitrogen).
Phospho-Kinase Antibody Array The relative phosphorylation levels of 37 phosphorylation sites by kinases and 49 receptor tyrosine kinases (RTK) (Figures  S7-8 ) were determined with the Human Phospho-Kinase/RTK Array kits (ARY003C and ARY001B, respectively, R&D Systems), according to manufacturer's instructions. Briefly, T24 wild type, glycoengineered cells and corresponding controls were firstly transfected with Silencer Select siRNA targeting CD44 as described above, and then lysed to extract protein from whole cells. For each cell line 300 µg and 600 µg of protein were used for Human Phospho-Kinase and RTK Array kits, respectively. The Amersham ECL Prime Western Blotting Detection Reagent (GE Healthcare Life Sciences) was used as developing reagent. Data analysis was performed through Image Lab Software (Bio-Rad) in a ChemiDoc XRS (Bio-Rad).
CD44s variant is expressed in healthy human tissues Precise targeting requires significant cancer specificity. Therefore, we also evaluated a wide number of healthy tissues that exhibited variable but always detectable CD44 mRNA expression (Figure  1E ). This highlighted the complex mosaicism presented by CD44 in human organs, later confirmed by consultation of the GTEx database (Figure  S3 ). In addition, RT-PCR (Figure  1E ) and whole transcriptome analysis (Figure  S3 ) show that, despite overexpressed in bladder cancer, CD44s may also be strongly expressed by most healthy cells, which significantly challenges its cancer specificity. Therefore, addressing CD44s post-translational modifications, namely glycosylation, poses as the next logical step towards this objective. gene expression significantly decreased in bladder tumors in comparison to the healthy urothelium.
Furthermore, the nature of the variants changed with the severity of disease, with non-muscle invasive bladder cancer (NMIBC), showing higher expression of e5-v2 and e5-v3 generally found in lengthier isoforms (CD44v2-10; CD44v3-10) and a significantly lower abundance of e5-e15 characteristic of CD44s/st in comparison to muscle-invasive bladder cancer (MIBC). MIBC presented an opposite pattern, supporting enrichment for shorter CD44s isoform. The CD44sol isoform was also evaluated but its expression was vestigial and not linked to any tumor type, as such it was not represented in the panel A. B) TCGA analysis of 413 MIBC cases confirmed the association of CD44s with more aggressive late-stage disease. Briefly, CD44s mRNA was elevated in T3/4 tumors in comparison to T2 tumors, supporting its association with invasion, confirming the observations from our patient's dataset.
Alongside, RNAseq analysis also considered probable transcripts containing the following junctions: e5-v6; e5-v7; e5-v9, e3-v8, and e3-v16, to name a few (Figure  3E ), which still warrant definitive confirmation by other methods. Collectively, these findings support the complex nature of the bladder cancer splicing code suggested by the analysis of tissues. Nevertheless, we considered T24 cell line as a suitable model to study the influence of CD44s/st in BC, since the other variants are practically vestigial. Moreover, based on the close similarity between RT4 and 5637, we selected 5637 and T24 cells for downstream proteomics-based studies. In explorative studies using conventional bottom-up proteomics approaches, we concluded that the precise characterization of CD44 glycoproteoforms could only be accomplished by the customization of the databases used for protein annotation and by upfront knowledge of the cellular glycome.
After excluding ambiguous transcripts, a total of 9 sequences were considered plausible to generate proteins and were initially selected to construct the CD44 protein database (Table  S3 ). and 5637 ) are significantly less capable of invading Matrigel in vitro. B) CD44 expression is higher for grade II (5637) and III (T24) cells. Flow cytometry analysis showed significantly higher CD44 levels in 5637 and T24 compared to RT4 cells. C) Western blots show different CD44 expression patterns according to cell grade, with T24 cells showing mostly shorter proteoforms. WB confirmed the overexpression of shorter CD44 proteoforms (at approximately 75 and 50 kDa) in T24 cells, and the presence of heavier proteoforms in the other cell lines (above 150 kDa). D) Characterization of CD44 isoforms by RT-PCR showing isoforms shortening with cell lines aggressiveness and the marked CD44s high phenotype of T24 cells.
087) in the HCD-MS/MS. To render the glycosylation more homogeneous and facilitate downstream assignments by MS/MS, the glycoproteins were desialylated prior to proteolytic digestion. The western blots in Figure  4B  started by highlighting multiple proteoforms across a wide range of molecular weights in BC cell lines (Figures  4C-D ), as suggested by transcripts analysis. Western blots also reflected the abundance of CD44 in the cell lines, previously suggested by RT-PCR and later confirmed by flow cytometry. CD44 molecules bigger than 150 kDa predominated in 5637 cells, whereas T24 cells presented a major band at 75 kDa. Interestingly, both cell lines presented several well defined proteoforms below 37 kDa. In explorative settings, we observed that unambiguous identification of peptides corresponding to exon-exon junctions was not possible without considering O-glycosylation, even when using transcriptome-customized databases.
Briefly, CD44 immunoprecipitated from membrane extracts was separated by gradient SDS-PAGE and bands were excised and analyzed by nanoLC-HCD/CID-MS/MS using RNAseq-customized databases and glycomics data for isoforms annotation. The nature of the isoforms translated by reporter ions for constitute-variable junctions identified in each band and cell line was highlighted. CD44 heterogeneity was evident for the two cell lines. CD44s was found to be the main isoform in T24 cells. C) nano-LC-MS and HCD-MS/MS spectra for reporter ions v2-v3, e5-v3, e5-e15 (CD44s/st), and e2(partial)-e3(partial) CD44sol in T24 cells. For reporter ions we show the MS isotopic envelope, the corresponding HCD product ion spectra highlighting GalNAc oxonium ions, GalNAc cross-ring fragments, other glycan fragments, and y-and b-series peptide backbone ions that support protein annotation.
Acknowledgments The authors wish to acknowledge the  Portuguese Foundation for Science and Technology (FCT)  for the human resources grants: PhD grants  SFRH/BD/127327/2016  (CG),  SFRH/BD/142479/ 2018  (JS),  SFRH/BD/146500/2019  (MRS),  SFRH/BD/111242/2015  (AP), 2020. 08708. BD (RF),  SFRH/BD/105355/2014  (RA), 2020. 09384. BD (DF), and FCT assistant researcher grant  CEECIND/ 03186/2017  (JAF). AP also acknowledges junior researcher position  UIDP/00776/2020-5  funded through  CI-IPOP Programmatic  funding  2020-2023  (reference  UIDP/00776/2020 ) from  FCT. It also received funding  FCT  project  IPOscore   DSAIPA/ DS/0042/2018  and  PTDC/MED-OUT/2512/2021. FCT is co-financed by  European Social Fund (ESF)  under  Human Potential Operation Programme (POPH)  from  National Strategic Reference Framework (NSRF).
The authors also acknowledge  PhD Programs in Biomedical Sciences and Pathology and Molecular Genetics of ICBAS-University of Porto. The authors thank the financial support of the  Portuguese Oncology Institute of Porto -Research Centre  ( CI-IPOP-29-2017-2020 ;  CI-IPOP-58-2017-2021 ). This work was also financed by national funds through  FCT/MCTES  within the scope of the project  UIDB/50006/2020 | UIDP/50006/2020 , and by the  Portuguese Mass Spectrometry Network , integrated in the  National Roadmap of Research Infrastructures of Strategic Relevance  ( ROTEIRO/0028/2013 ;  LISBOA-01-0145-FEDER-022125 ). The authors also wish to acknowledge the  GTEx  Project, supported by the  Common Fund  of the Office of the Director of the  National Institutes of Health , and by  NCI ,  NHGRI ,  NHLBI ,  NIDA ,  NIMH , and  NINDS.
Data Availability Proteomics datasets and tables describing protein assignment glycosylation annotations have been deposited in the Proteomics Identifications Database (PRIDE; https://www. ebi. ac. uk/pride/; Project accession: PXD028307) and RNAseq was deposited in the Gene Expression Omnibus (GEO; https://www. ncbi. nlm. nih. gov/geo/) and are freely available. All other study data are included in the article and/or SI Appendix. Transcriptomics analysis of healthy tissues for CD44 isoforms were obtained from the Genotype-Tissue Expression (GTEx) Portal (https://gtexportal. org) on 03/06/21 and/or dbGaP accession number phs000424. v8. p2.
7. 15 kextend command; contigs < 250 bp in length were filtered out. This assembly approach ensures that contig extension only occurs if there is an unambiguous 1 bp extension possible in the input k-mer data set. Methylobacterium are well documented, common contaminants of reagents used in Illumina sequencing. As such, contigs showing high sequence identity to one of several Methylobacterium genomes (NZ_CP006992. 1, NC_010511. 1, NZ_CP017640. 1, CP001029. 1, AP014813. 1, AP014810. 1) or phiX (NC_001422. 1) were also filtered out. These de novo assembled sequences are referred as "Sonmez-specific contigs" hereafter.
Gabriel Doredo Perez Dep. Bioquímica y Biología Molecular, Campus Rabanales C6-1-E17, Campus de Excelencia Internacional Agroalimentario (ceiA3), Universidad de Córdoba, Córdoba, Spain The manuscript "The complete genome sequence of elite bread wheat cultivar, "Sonmez"", published on F1000Research by Akpinar et al, is an interesting work on structural genomics, covering both re-sequencing of 21 chromosomes of the A, B and D genomes (13. 3 Gbp), as well as de novo sequencing (1. 05 Gbp). Such latter interesting result represents either Sonmez-specific sequences, or sequences that considerably diverged between Sonmez and Chinese Spring (used as reference genome). The de novo assembly identified 35 gene models, of which 11 were annotated with high-confidence, that may contribute to the favorable traits of this variety.
O. M. and A. B. acknowledge support from CNES. J. I. L. was supported by the JPL Distinguished Visiting Scientist Program. Z. M. acknowledges funding from FEDER-Fundo Europeu de Desenvolvimento Regional funds through the COMPETE 2020-Operational Programme for Competitiveness and Internationalisation (POCI) and by Portuguese funds through FCT-Fundação para a Ciência e Tecnologia in the framework of project POCI-01-0145-FEDER-029932 (PTDC/FIS-AST/ 29932/2017). Centro de Química Estrutural acknowledges the financial support of FCT-Fundação para a Ciência e Tecnologia (UIDB/00100/2020 and UIDP/00100/2020), and the Institute of Molecular Sciences acknowledges the financial support of FCT-Fundação para a Ciência e Tecnologia (LA/P/ 0056/2020). Some of this work was conducted at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004).
Clouds are an important component of the climate system, yet our understanding of how they directly and indirectly affect glacier melt in different climates is incomplete. Here we analyse high-quality datasets from 16 mountain glaciers in diverse climates around the globe to better understand how relationships between clouds and near-surface meteorology, radiation, and surface energy balance vary. The seasonal cycle of cloud frequency varies markedly between mountain glacier sites. During the main melt season at each site, an increase in cloud cover is associated with increased vapour pressure and relative humidity but relationships to wind speed are site-specific. At colder sites (average near-surface air temperature in melt season < 0 °C), air temperature generally increases with increasing cloudiness, while for warmer sites (average near-surface air temperature in melt season >> 0 °C) air temperature decreases with increasing cloudiness.
Sites and dataset requirements Datasets of near-surface meteorology and glacier surface energy balance were collated from a diverse set of sites where highquality observations and modelling were available. The sites were required to have a published SEB record calculated from automatic weather station (AWS) data collected over a glacier surface during melt seasons at hourly or smaller timestep. The AWS data needed to include measurements of all four components of the radiation balance, incoming (SWin) and outgoing shortwave (SWout), incoming (LWin) and outgoing longwave (LWout), all in W m -2. In addition, other SEB components needed to be calculated using accepted best-practice methods (e. g. turbulent fluxes calculated using bulk aerodynamic methods) and avoiding potentially inaccurate assumptions (e.
g. surface temperature fixed at 0 °C regardless of SEB). Note that published values of surface melt and SEB fluxes are used in these analyses rather than being recalculated from near-surface meteorology and radiation. Thus, differences in the methods used to calculate SEB may introduce some uncertainty, but the values are congruent with previous studies, and no additional validation is needed. A call for datasets was made on Cryolist in January 2020, and data from over 30 sites was offered. After assessing each dataset against the criteria above, 16 sites were selected for analysis (Figure  1  and Table  1 ). These sites covered many of the mountain glacier regions including continental North America, the European Alps, Norway, Greenland, the Himalaya, tropical glaciers in Africa and the Andes, the arid region of central Chile and the Southern Alps of New Zealand.
It is worth noting that no suitable datasets were made available from some large regions of mountain glaciers including Alaska, Patagonia and Asia outside of the Himalaya. As most AWS sites are in ablation areas, they follow a broad pattern of decreasing altitude with distance from the equator (Figure  2 ). Note that two locations have observations in both the ablation and accumulation area -Conrad Glacier (CABL, CACC) and Mera Summit (MERA) / Naulek (NAUL, an ablation area of Mera Glacier). Records from the same site in different years were also joined into continuous records (CABL and NAUL). Records from CABL, CACC and NORD cover only summer periods and CHHO has three two-month periods throughout the year, otherwise the records span all months of the year and range from 46 to 3231 days in length.
Analysis of cloud effects The relationship between cloudiness, meteorology, SEB and melt is assessed by binning the timeseries of different variables by daily average cloudiness. Five evenly sized bins were used with bin centres at   = 0. 1, 0. 3, 0. 5, 0. 7 and 0. 9, with the top and bottom bins corresponding to clear-sky and overcast conditions, respectively. Data within each bin were then averaged across all days within the main melt season to demonstrate the average relationships between cloudiness and different variables. In sections 3. 2, 3. 3 and 3. 4, we use the term cloud effects to describe the change in a variable during cloudy conditions with respect to clear-sky conditions. In studies of net radiation, the cloud effect (CE) is defined as the difference between average and clear-sky conditions.
Limitations While efforts have been made to homogenise the datasets, it is possible that biases still affect the results. Interannual variability causes uncertainty, particularly for sites with only one or two seasons (e. g. NORD, ZONG). Giesen et al. (2008 Table 4)  show that at MIDT, the contribution of SEB components to melt during clear-sky periods can vary up to 12% between years, while variability in overcast periods is less. The interannual variability is partly influenced by the seasonality of anomalies in cloudiness, with strong anomalies in spring causing the importance of QS to melt to change markedly. Some sites also have discontinuous records (CABL, CACC, NORD, CHHO) that do not include periods with lower melt rate outside the peak melt season. Increased clear-sky solar radiation and Ta as well as decreased albedo during the peak melt season are likely to cause Rnet and QM cloud effects to be larger at these sites compared to those with longer records that include periods of more marginal melt.
Future work should also assess the mechanisms driving the observed covariance between cloudiness and near-surface meteorology, e. g. Do large-scale changes in airmass or local/meso-scale processes drive changes in Ta with cloud? How well are these processes represented in the datasets used to force glacier melt models on regional scales? The derivation of cloudiness from LWin also poses challenges. At some sites (e. g. LANG, and MORT), εcs shows a poor fit at higher vapour pressure, with incoming LWin during clear-sky periods being higher than that expected from the theoretical curves (Figure  4 ). This mismatch between theoretical and observed εcs during periods of higher ea may cause some clear-sky periods to be misclassified as being in first partial cloud bin (Nε ~ 0. 3). Indeed, at both LANG and MORT, the Nε ~0.
However, caution is warranted in making generalisations as the analysis here shows that even in this set of 16 glaciers, we find variability in the links between clouds and melt, and it seems that some processes are site specific even in this small sample. The non-linear relationships between clouds and melt motivates the use of SEB models in regional and global assessments of glacier response to climate change. To aid in the development of globally and regionally applicable SEB models and parameter sets, the research community should investigate creating a central open-source repository for glacier AWS and SEB datasets along with supporting meta data. Such a repository would facilitate the easy transfer of data between researchers, streamline processing by establishing data format and meta data standards, as well as motivating best-practice in data collection and quality control.
Alongside this, careful assessments of SWin and LWin and their relationship to near-surface meteorology from global, regional and meso-scale meteorological models should be undertaken to ensure uncertainties in model input data are reduced and to assess the need for downscaling to account for local-scale processes. As many models rely on empirical relationships between SWin and LWin to account for local-scale changes in topography, globally applicable parameterisations of SWin and LWin should be tested.
Conclusions Sixteen high-quality published datasets of near-surface meteorology, radiation, and surface energy balance from very different climate settings have been homogenised and analysed in a common framework. The analyses sought to assess how the relationships between clouds, near-surface meteorology and surface energy balance vary in different mountain glacier environments. Distinct regional differences in the seasonality of cloudiness are demonstrated between different mountain glacier environments. On average, over the main period of melt at each site: -Near-surface humidity is shown to universally increase in cloudy conditions, whereas a divergent relationship is found between near-surface air temperature and cloudiness; at colder sites (average near-surface air temperature in melt season < 0 °C), air temperature is increased in cloudy conditions, while for warmer sites (average near-surface air temperature in melt season >> 0 °C), air temperature decreases in cloudy conditions.
Acknowledgements JCs contribution to this research was supported by the  Royal Society of New Zealand Marsden Fund. The authors wish to thank the following additional people and organisations for data contributions and funding data collection and processing:  Maxime Litt ,  Hans Oerlemans , GLACIOCLIM, PROMICE, the  Greenland Ecosystem Monitoring Programme, ICIMOD , https://doi. org/10. 5194/tc-2022-24Preprint. Discussion started: 17 February 2022 c Author(s)  2022. CC BY 4. 0 License. Antoine Rabatel  (IGE),  Alvaro Soruco  ( UMSA, Bolivia ),  LMI GREATICE ,  NSERC   Discovery Grant  and  Research Tools and Instruments ,  Canada Foundation for Innovation. MFA acknowledges research grants from  IFCPAR  and  IRD, France.
The only work known to the authors that has considered non-ideal gas EoS in multi-dimensional flow solvers is  [10] ; a front tracking method was employed for resolving an argon bubble interface motion under the influence of a strong incident shock wave with varying pressure of 0. 1-1000 GPa. The authors compared the results with ideal gas and two real gas EoSs namely the quotidian EoS, which utilise the thermodynamic functions from the Helmholtz free energy with the electronic contribution, and the SESAME database  [43, 44] , which considers the formation of plasma and ionization. It was revealed that the real gas EoSs estimate lower collapse temperatures depending on the shock strength; with a temperature difference of 3. 5 × 10 4 K and 1. 5 × 10 7 K for incident shock pressure of 1 and 1000 GPa, respectively.