dataesr/scientific-paragraphs-categorization huggingface dataset:
The exact version number and release year (if appended) of the product used should always be reported in studies rather than making a generic reference to GLODAP. Creating and interpreting inversions, as well as other checks of the entire dataset needed for full updates, are too demanding in terms of time and resources to be performed every year or every 2 years. The aim is to conduct a full analysis (i. e. , including an inversion) again after the third GO-SHIP survey has been completed. This completion is currently scheduled for 2024, and we anticipate that GLO-DAPv3 will become available a few years thereafter (pending funding). In the interim, the fourth intermediate update is presented here, which adds data from 96 cruises to the last update, GLODAPv2. 2021  (Lauvset et al. , 2021).
Key features of the update GLODAPv2. 2022 contains data from 1085 cruises covering the global ocean from 1972 to 2021, compared to 989 for the period 1972-2020 for the previous GLODAPv2. 2021  (Lauvset et al. , 2021). Information about the 96 cruises added to this version is provided in Table  A1  in the Appendix. Cruise sampling locations are shown alongside those of GLODAPv2. 2021 in Fig. 1 , while the coverage in time is shown in Fig. 2. Not all cruises have data for all the abovementioned 13 core variables. For example, cruises with only seawater CO 2 chemistry or transient tracer data are still included even without accompanying nutrient data due to their value towards the computation of carbon inventories. In a few cases, cruises without any of these properties are included because they do contain data for other carbon-related tracers such as carbon isotopes, with the intention of ensuring their wider availability.
In addition, we are for the first time including the cruises in the GEOTRACES intermediate data product where seawater CO 2 chemistry data are available (https://www. geotraces. org/geotraces-intermediate-data-product-2021/, last access: 23 June 2022). The GEOTRACES mission is "to identify processes and quantify fluxes that control the distributions of key trace elements and isotopes in the ocean, and to establish the sensitivity of these distributions to changing environmental conditions", but several cruises that measure trace elements and isotopes also measure CO 2 chemistry, and these have now been included in GLODAPv2. All new data in GLODAPv2. 2022 include seawater CO 2 chemistry, and additionally, 10 new cruises include halogenated transient tracers. All new cruises were subjected to primary (Sect.
The basin identifiers are unchanged in GLODAPv2. 2022 and added to the product files to make it easier for users to identify which ocean basin an individual cruise belongs to without having to use one of the four regional files. Note that there is no overlap between the regional files or for our basin identifiers, and cruises in the Southern Ocean are placed in the basin where most of the data were collected. As in GLODAPv2. 2021 we include the DOI for each cruise in all product files with the aim of easing access to the original data and metadata, as well as improving the visibility of data providers.
3. 2. 2). Importantly, the interpolation of CO 2 chemistry variables is never performed, and thus a flag value of 0 has a unique interpretation. If no WOCE flags were submitted with the data, then they were assigned by us. Regardless, all incoming files were sub-jected to primary QC to detect questionable or bad data -this was carried out following  Sabine et al. (2005)  and  Tanhua et al. (2010) , primarily by inspecting property-property plots. For this task, the GLODAP primary quality control software  (Velo et al. , 2021)  was used, as it presents a custom predefined schema of property-property plots designed by the consortium to ease the detection of outliers. Outliers showing up in two or more different such plots were generally defined as questionable and flagged. In some cases, outliers were detected during the secondary QC; the consequent flag changes have then also been applied in the GLODAP versions of the original cruise data files in agreement with the data submitter.
In GLODAPv2. 2022 we did not have to rely on the results of the multiple linear regression (MLR) analyses to make decisions about adjustments, and, in general, we are increasingly moving towards only using CANYON-B and CON-TENT estimates (Sect. 3. 2. 4) as additional information when the crossover analysis is insufficient. For the halogenated transient tracers, comparisons of surface saturation levels and the relationships among the tracers were used to assess the data consistency (Sect. 3. 2. 5). For salinity and oxygen, CTD and bottle values were merged into a "hybrid" variable prior to the consistency analyses (Sect. 3. 2. 1).
Crossover analyses The crossover analyses were conducted with the MATLAB toolbox prepared by  Lauvset and Tanhua (2015)  and with GLODAPv2. 2021 as the reference data product. The toolbox implements the "running-cluster" crossover analysis first described by  Tanhua et al. (2010). This analysis compares data from two cruises on a station-by-station basis and calculates a weighted mean offset between the two and its weighted standard deviation. The weighting is based on the scatter in the data such that data that have less scatter have a larger influence on the comparison than data with more scatter. Whether the scatter reflects actual variability or data precision is irrelevant in this context as increased scatter nevertheless decreases the confidence in the comparison. Stations are compared when they are within 2 arcdeg distance (∼ 200 km) of each other.
Data availability The GLODAPv2. 2022 merged and adjusted data product is archived at the OCADS of NOAA NCEI (https://doi. org/10. 25921/1f4w-0t92,  Lauvset et al. , 2022). These data and ancillary information are also available via our web pages and https://www. ncei. noaa. gov/ access/ocean-carbon-acidification-data-system/oceans/ GLODAPv2_2022/ (last access: 15 August 2022). The data are available as comma-separated ascii files (*. csv) and as binary MATLAB files (*. mat) that use the open-source Hierarchical Data Format version 5 (HDF5). The data product is also made available as an Ocean Data View (ODV) file which can be easily explored using the "webODV Explore" online data service (https://explore. webodv. awi. de/, webODV Explore, 2022). Regional subsets are available for the Arctic, Atlantic, Pacific, and Indian oceans.
There are no data overlaps between regional subsets, and each cruise exists in only one basin file even if data from that cruise All material produced during the secondary QC is available via the online GLODAP adjustment table hosted by GE-OMAR, Kiel, Germany, at https://glodapv2-2022. geomar. de/ (GLODAP, 2022a) and can also be accessed through http:// www. glodap. info  (GLODAP, 2022b). This is similar in form and function to the GLODAPv2 adjustment table  (Olsen et al. , 2016)  and includes a brief written justification for any adjustments applied. The original cruise files, with updated flags determined during additional primary GLODAP QC, are available through the GLODAPv2. 2022 cruise summary table (CST) hosted by OCADS: https://www. ncei. noaa. gov/ access/ocean-carbon-acidification-data-system/oceans/ GLODAPv2_2022/cruise_table_v2022.
html  (GLODAP, 2022c). Each of these files has been assigned a DOI, which is included in the data product files but not listed here. The CST also provides brief information on each cruise and access to metadata, cruise reports, and its adjustment table entry. While GLODAPv2. 2022 is made available without any restrictions, users of the data should adhere to the fair data use principles: for investigations that rely on a particular (set of) cruise(s), recognize the contribution of GLODAP data contributors by at least citing both the cruise DOI and any articles where the data are described, as well as, preferably, contacting principal investigators to explore opportunities for collaboration and co-authorship. To this end, DOIs are pro-vided in the product files, as well as relevant articles and principal investigator names in the cruise summary table.
In GLODAPv2. 2022 there are 98 951 records with SF 6 data and 427 913 records with at least one transient tracer or SF 6. A total of 2 % (27 906) of all data records do not have salinity. There are several reasons for this, the main one being the inability to vertically interpolate due to a separation that is too large between measured samples. Other reasons for missing salinity include salinity not being reported and missing depth or pressure. As for previous versions there is a bias toward summertime in the data in both hemispheres; most data are collected during April through November in the Northern Hemisphere, while most data are collected during November through April in the Southern Hemisphere (Fig. 10 ). These tendencies are strongest for the poleward regions and reflect the harsh conditions during winter months which make fieldwork difficult.
The upper 100 m is the best-sampled part of the global ocean, both in terms of number (Fig. 11a ) and density (Fig. 11b ) of observations. The number of observations steadily declines with depth. In part, this is caused by the reduction in ocean volume towards greater depths. Below 1000 m the density of observations stabilizes and even increases between 5000 and 6000 m; the latter is a zone where the volume of each depth surface decreases sharply  (Weatherall et al. , 2015). In the deep trenches, i. e. , areas deeper than ∼ 6000 m, both the number and density of observations are low. Except for salinity and oxygen, the core data were collected exclusively through chemical analyses of collected water samples. The data of the 13 core variables were subjected to primary quality control to identify questionable or bad data points (outliers) and secondary quality control to identify systematic measurement biases.
The data are provided in two ways: as a set of individual exchange-formatted original cruise data files with assigned WOCE flags and as globally and regionally merged data product files with adjustments applied to the data according to the outcome of the consistency analyses. Importantly, no adjustments were ap-plied to data in the individual cruise files, while primary QC changes were applied. The consistency analyses were conducted by comparing the data from the 96 new cruises to the previous data product GLODAPv2. 2021. Adjustments were only applied when the offsets were believed to reflect biases relative to the earlier data product release related to measurement calibration and/or data handling practices and not to natural variability or anthropogenic trends. For GLODAPv2. 2022 a special case is the RV Knorr cruises in 1994-1995 in which the adjustment reflects offsets in CRM measurements that have not previously been corrected for.
The adjustment table at https://glodapv2-2022. geomar. de/ (last access: 15 August 2022) lists all applied adjustments and provides a brief justification for each. The consistency analyses rely on deep ocean data (> 1500 or 2000 dbar depending on region), but supplementary CANYON-B and CONTENT analyses consider data below 500 dbar. Data consistency for cruises with exclusively shallow sampling was not examined. All new pH data for this version were comprehensively reviewed using crossover analysis, and only one required adjustment, while another had to be flagged bad (-777) and removed from the product. Regardless, full reanalysis of all available pH data, particularly in the North Pacific, will be conducted for GLO-DAPv3. Secondary QC flags are included for the 13 core variables in the product files.
These flags indicate whether (1) or not (0) the data successfully received secondary QC. A secondary QC flag of 0 does not by itself imply that the data are of lower quality than those with a flag of 1. It means these data have not been as thoroughly checked. For δ 13 C, the QC results by  Becker et al. (2016)  for the North Atlantic were applied, and a secondary QC flag was therefore added to this variable. The primary WOCE QC flags in the product files are simplified (e. g. , all questionable and bad data were removed). For salinity, oxygen, and the nutrients, any data flagged 0 are interpolated rather than measured. For TCO 2 , TAlk, pH, and f CO 2 any data flags of 0 indicate that the values were calculated from two other measured seawater CO 2 variables. Finally, while questionable (WOCE flag = 3) and bad (WOCE flag = 4) data have been excluded from the product files, some may have gone unnoticed through our analyses.
Users are encouraged to report on any data that appear suspicious. Based on the initial minimum adjustment limits and the improvement in the consistency resulting from the adjustments (Table  7 ), the data subjected to consistency analyses are believed to be consistent to better than 0. 005 in salinity, 1 % in oxygen, 2 % in nitrate, 2 % in silicate, 2 % in phosphate, 4 µmol kg -1 in TCO 2 , 4 µmol kg -1 in TAlk, and 5 % for the halogenated transient tracers and SF 6. For pH, the consistency among all data is estimated as 0. 01-0. 02, depending on the region. As mentioned above, the included f CO 2 data have not been subjected to quality control; therefore no consistency estimate is given for this variable. This should be conducted in future efforts.
As physical effort could strongly affect breathing due to oxygen needs, biking movements were performed at a comfortable rhythm for the participant. Furthermore, arm or leg biking motions may induce the contraction of muscles involved in breathing or antagonist muscles impacting the thoracic cage. We aimed to test whether these motions, with low energy demands and no synchrony constraints, generate F0 and intensity peaks in speech at the highest physical impulse of the motion (the acceleration peak within each movement cycle). If so, this would be supplementary evidence in favor of a biomechanical influence of limb movements on speech via breathing. The data were analyzed using an approach similar to Pouw et al. (13) , investigating the nonlinearity in the speech amplitude envelope and F0 around the acceleration peak in limb movements.
Participants Twenty-five healthy participants took part in this experiment. Because of technical issues, there was a problem with saving the movement signals for three participants. Hence, we ran the analysis on 22 participants (16 females and 6 males) aged 20-29 yr [mean = 23. 5 yr; standard deviation (SD) = 2. 6 yr]. They were registered on the participant database Lingex and replied to an email announcement. All participants spoke German as their mother tongue and reported no respiratory, motor, speech, neurological, or auditory impairments. They signed an informed consent form and received 10e per hour for their participation.
Experimental Setup and Data Acquisition Participants sat on a chair during the whole session. Their speech was recorded using a stand-up microphone (MKH 50 P48, Sennheiser) placed at 50 cm from their mouth at a 44,100 Hz sampling frequency. Participants' movements were recorded using motion capture (OptiTrack, Natural-Point Inc. , cameras prime 13 recording at 200 Hz) and video recordings. The motion capture markers were assembled as rigid bodies (for more reliability in the detection) and placed on the head, the back, the shoulders, and the hands of the participants. A minibike (SportPlus, Hamburg, Germany) was placed either on a table in front of the participant when biking with the hands, or at her/his feet when biking with the legs. A marker was attached to each pedal. Only the motion capture data of the markers on the pedals and the acoustic speech signal are analyzed in this paper.
Procedure On day 1, participants completed forms and performed the baseline for the breathing recordings. They then performed the narrative task (watching and listening to the story and recalling it) in the four experimental conditions. On the following day (day 2), they freely recalled everything they could remember about the stories in the armFree condition and then did the main task again in the same conditions as on day 1. Participants were finally invited to return 10 days later to assess long-term memory of the stories without motion. These data are not relevant for the current analysis as they did not involve biking motion.
Data Processing Processing of biking movements. The time series of the positional coordinates of the motion capture markers were extracted in a csv file format. The marker of the right pedal was detected using a code in Python to parse the file and locate the markers moving circularly. The coordinates of the vertical position were saved in a new csv-file at 200 Hz. The other dimensions were not used because the bike experienced some small displacements, especially on the horizontal plane, through the trials. The extracted coordinate vector was resampled at 100 Hz and filtered with a Butterworth filter at a cutoff frequency of 10 Hz. The signal peaks (one peak for each cycle) were detected using the peak finder function of MATLAB  (36)  with adequate parameters validated for each participant based on signal visualization.
Speech parameters. The fundamental frequency (F0) was extracted from each speech signal using the Praat software (37; autocorrelation method, range = 50-250 Hz for males and 100-300 Hz for females; cf. Fig. 1 , row 3). The values above the third quartile plus three times the inter-quartile space and below the first quartile minus one time the inter-quartile space were discarded. These thresholds were chosen after looking deeply into the data and noticing that most of the values between the third quartile plus one time and three times the interquartile range were not outliers. On average 3% of all the data was discarded. After downsampling the speech signal to 11,025 Hz, the amplitude envelope was extracted and processed using the method of Pouw et al. (13) : the envelope function of MATLAB  (36) , using a Hilbert transformation, was applied to the speech signal.
The upper bound of the output was filtered using a 5-Hz Hanning window, and the signal was smoothed using a Gaussian-weighted moving average over each window of 1,000 points (see Fig. 1 , row 2). The scripts are available on the OSF repository; https://doi. org/10. 17605/ OSF. IO/ZFV75. To temporally align the values of the acoustical parameters (F0 and intensity) with the movement signals, all were re-sampled to 100 values per second (100 Hz). After preprocessing, the movement and synchronous acoustic signals were paired for legMot and armMot conditions. As there was no biking signal in the armBlock condition, a surrogate condition was created. For each speech_armBlock trial, a biking signal was randomly chosen from the legMot or armMot condition of another participant. The longest signal was then cut to align with the shortest one.
Statistical Analyses The scripts and the data sets of the statistical analyses are available in the repositories; https://doi. org/10. 17605/OSF. IO/K8J6T, https://doi. org/10. 17605/OSF. IO/XHK25, https:// doi. org/10. 17605/OSF. IO/8S5BU. Predicting the magnitude of F0 and amplitude envelope peaks based on the magnitude of acceleration peaks. To investigate the effect of limb movements on speech, we first evaluated if the amplitude of the acceleration peaks in the biking data could predict the amplitude of the acoustic peaks in the speech signal (F0 and amplitude envelope) calculated as the average value 50 ms around the time of occurrence of the acceleration peak (T accPeak ). A linear mixed model was applied to F0. The predictors are the acceleration magnitude 50 ms around T accPeak , day, condition, and their interactions.
For both F0 and envelope, the participant variable was included as a nonlinear random effect with a random smooth for participant and condition. Since this analysis focuses on F0 and amplitude envelope as time-series, the correlation from one point of the time-series to the next has to be considered: the value at a given time t is dependent on the value at the time t À 1, and possibly before. A term to consider the potential autocorrelation between the consecutive points within the time series was therefore included. We ran the analysis using the R software  (38)  environment and the mgcv package  (39) , following Martijn Wieling's tutorial  (40) , as well as the nlme package  (41). The nonlinearity of the curves and the difference between the curves across the conditions were tested in two different models (see Ref.
In brief Bonteet al. combine and analyze singlecell and bulk transcriptomic samples with immunopeptidomic datasets to identify glioblastoma HLA-I peptide-coding transposable elements. Some peptides are encoded by single copies of transposable elements from old subfamilies and represent potential tumor-specific targets for cancer immunotherapies. Single-cell RNA-seq-based proteogenomics identifies glioblastoma-specific transposable elements encoding HLA-I-presented peptides INTRODUCTION T cells can control, and sometimes reject, solid tumors, especially after reprogramming by immune checkpoint blockade (ICB)  (Morotti et al. , 2021; Waldman et al. , 2020). The nature of the tumor antigens targeted by these T cells, however, remains unclear. After identification of differentiation and tumor-testis antigens decades ago  (Almeida et al.
Single-cell TE expression resolves all cell populations in tumors We reasoned that a powerful way to identify TEs expressed specifically in tumor cells would be to compare TE expression in tumor and in tumor-infiltrating cells from the same patient. To do so, we used single-cell transcriptomics (single-cell RNA-seq [scRNA-seq]) of all cells present in the tumor microenvironment. We initiated the study on a public dataset including tumor and juxta-tumor samples from four GBM patients analyzed by SMARTseq2 (Figure  S1A ). Consistent with the analysis performed in the original article  (Darmanis et al. , 2017) , dimensionality reduction and t-distributed stochastic neighbor embedding (t-SNE) visualization based on gene expression resolves the seven sorted cell populations from the tumor core and the surrounding tissue (tumor and periphery in Figure  S1A ): immune cells (mostly macrophages), neoplastic cells, and oligodendrocyte precursor cells (OPCs) are the most numerous  (Figures 1A, left panel, and S1B).
TE subfamilies are differentially expressed in neoplastic and immune cells To better understand the nature of these TEs, we performed differential expression (DE) analyses of TEs in each cell population against all others, thus defining population-specific TE signatures (Figure  S1E ). These signatures are selective for neoplastic cells, immune cells (Figure  1B ), and for each of the other cell populations present in the tumor microenvironment (Figure  S1F ). Heatmap representation of the 20 most differentially expressed TEs based on the average log2 fold change shows selective expression in each cell population, including in neoplastic cells (Figure  1C ). To further investigate the nature of the TEs differentially expressed in each cell population, we compared each signature with all TEs expressed in the dataset (130,028).
Gain of chromosome 7 and loss of chromosome 10 are recurrent genomic copy number alterations in GBM  (Kurscheid et al. , 2015). As an internal control for TE mapping to chromosomal loci, we quantified genes and TEs in each cell-type-specific signature to their respective chromosomes. As shown in Fig-  ure 1E , TEs differentially expressed in neoplastic cells, but not in other cell populations, present a clear bias for chromosome 7 (Figures  1E, S2C,  and S2D ). The bias for chromosome 7 in neoplastic cells is even stronger for TEs than for genes, while the loss of chromosome 10 is similar in the TE and gene signatures (Figure  S2C ). A chromosome 7 bias is also observed when considering only the expression of distal TEs, i. e. , TEs located at more than 2 Kb from the nearest protein-coding genes (mostly intergenic), indicating that this bias is not due to high contamination with intron retained TEs in the scRNA-seq datasets (Figure  S2E ).