dataesr/scientific-paragraphs-categorization huggingface dataset:
immune status across vaccination, infection, autoimmunity, transplant rejection, transfusion reactions, and cancer. AIRRseq makes it possible to read this record. The past few years have seen an explosion of proofs of principle in the research literature. For example, in patients who have had influenza or received an influenza vaccine, AIRR-seq has demonstrated an increase in influenza-specific antibody and TR genes in the blood and in tissues  (15) (16) (17) (18) (19) (20). Similar results have been demonstrated in viral infections as diverse as dengue and SARS-CoV-2  (21) (22) (23) (24) (25) (26) (27) (28). Noteworthy in this regard is the current effort to discern a T cell fingerprint for SARS-CoV-2 exposure, immune status and possibly even immunopathology in the ImmuneCODE project, a collaboration between Adaptive Biotechnologies and Microsoft, which leverages a rapidly growing and publicly accessible dataset of over 1,400 TR immunomes from individuals who were exposed to SARS-CoV-2  (28, 29).
Each query parses the data for immunoglobulin or TR sequences, motifs, or clusters specific for the given condition. In this manner, AIRR-seq testing can reveal unusual or unexpected patterns in a series of patients with a shared condition. Immunomes and specific sequences can be made easily available through existing resources that have been developed in part in anticipation of diagnostic purposes; these resources include the AIRR Data Commons, which can be accessed via the iReceptor Plus portal (45) and using the AIRR-C API  (46). Reference sequences for specific variable genes, alleles and haplotypes can be found at IMGT  (47) , the Open Germline Reference Database (OGRDB)  (48) , and the VDJdb database  (49)  and antigen-specific antibodies and TRs can be queried in the Immune Epitope Database (IEDB) using a suite of tools  (50).
Immunome-based diagnostics also align well with the vision of so-called learning health systems, in which the experience of each patient and physician contribute to an improved understanding of disease prevention and management for the benefit of all  (51) (52) (53) (54) (55) (56) (57). With enhanced data collection in the electronic medical record, secure data sharing, and advances in machine learning, this vision is gradually moving closer to becoming a reality. Just as the growth of repositories such as the UK Biobank increases the power for finding patterns across many conditions, each newly sequenced immunome adds statistical power for finding immunome patterns across the broader population. Data from AIRR-seq based tests could be used for continuing improvement of their diagnostic power.
All of the stages of this process will need to be rigorously validated to achieve regulatory approval. Fourth, sequencing and computational resources will have to be in place to guarantee clinically viable price points and turnaround times. Fifth, samples will have to be clinically annotated, and this and indeed every process will have to be carefully vetted for adherence to patient privacy directives and related legislation. Sixth, systems for storing and sharing the new clinical data must be set up, to avoid silos and instead maximize the statistical power of widespread testing (as in the vision for learning health systems). And seventh, the signatures, signals, and patterns on which the tests are based must be robust and interpretable, which will likely require the development of "reference ranges" that are relatable to age, gender, genetic background (e.
This low number reflects the high percentage of regional professors, especially in law. Theologians were much more mobile. Abstracting from the number for the period 1450-1526 (based on 2 observations), the median distance has a tendency to decrease over time. Finally, 14. 2% of the scholars have a Wikipedia page (in some language), and 19. 2% of them have left a footprint in the catalogues of the libraries of the world, Worldcat, either by having published some work, or by having been the subject of published books and articles. These two percentages are to be seen as the upper bound, as the database is probably missing some of the obscure teachers of the University of Aix.
Human capital of scholars and literati For each person in the database, we compute a heuristic human capital index, identified by combining information from Worldcat and Wikipedia using principal component analysis. We also compute the notability of the university at each date by averaging the human capital of the scholars active at the University of Aix 25 years before that date. The details are given in the Appendix. Figure  3  shows the names of all the scholars with a positive human capital index. The orange line plots the notability of the university. The notability of the university was very low until 1600; this is partly due to missing sources, but also to the slow start of the university. Then, in the 17th century, there was a golden age for a few decades, followed by a decline. The notability rose again in the 18th century, giving the impression that a revival was cut short by the Revolution in 1793.
, 1999) , that NIR and SWIR measurements are also quite sensitive to the presence of scattering particles on the optical path, which can then substantially perturb X CO 2 retrievals if unaccounted for  (Houweling et al. , 2005). As exact multiple scattering calculations are too time consuming for operational X CO 2 retrievals, all the previously mentioned retrieval algorithms have radiative transfer models that implement various approximations to speed up forward modelling. Finally, radiative transfer fundamentally depends on spectroscopic databases that contain the parameters enabling to compute atmospheric gas absorption. The HITRAN spectroscopic database (latest version from 2016;  Gordon et al. , 2017)  is widely used for greenhouse gas concentration retrievals, as are the absorption coefficient (ABSCO) atmospheric absorption tables for the ACOS algorithm  (Drouin et al.
In this paper, we present the Adaptable 4A Inversion (5AI) that implements the optimal estimation inverse method  (Rodgers, 2000). 5AI relies on the OPerational version of the Automatized Atmospheric Absorption Atlas (4A/OP) radiative transfer model  (Scott and Chédin, 1981; Tournier, 1995; Cheruy et al. , 1995;  https://4aop. aeris-data. fr, last access: 25 May 2021) and the GEISA (Gestion et Étude des Informations Spectroscopiques Atmosphériques: Management and Study of Spectroscopic Information) spectroscopic database  (Jacquinet-Husson et al. , 2016;  http://cds-espri. ipsl. fr/etherTypo/?id=950, last access: 25 May 2021). The 5AI scheme is applied to retrieve X CO 2 from a sample of OCO-2 measurements that compromises between coverage and the lowest possible values of ACOS retrieved aerosol optical depth in order to avoid possible singular biases due to strong aerosol events.
This sample selection comprises the (1) OCO-2 best flag target mode soundings between 2014 and 2018 and (2) a sample of 2 years of OCO-2 nadir measurements with a global land coverage. First, for computational time purposes, retrievals are performed without taking into account scattering particles. We then discuss how considering them and accounting for differences in the radiative transfer modelling and retrieval setups impact the 5AI results, which are compared to ACOS and TCCON reference data over identical sets of soundings. This paper is organised as follows: Sect. 2 describes the 5AI retrieval scheme and its current features, as well as the 4A/OP radiative transfer model, the GEISA spectroscopic database, and the empirically corrected O 2 A-band absorption continuum on which it relies.
The 5AI retrieval scheme As for any other retrieval scheme, 5AI aims at finding the estimate of atmospheric and surface parameters (for example, trace gas concentration, temperature profile, surface albedo, or scattering particle optical depth) that best fits hyperspec-tral measurements made from space. This inverse problem can be expressed with the following equation: y = F(x) + ε, (1) where y is the measurement vector containing the radiances measured by the space instrument, x is the state vector containing the geophysical parameters to be retrieved, ε is the measurement noise, and, finally, F is the forward radiative transfer model that describes the physics linking the geophysical parameters to be retrieved to the measured infrared radiances. 2. 1 Forward modelling -4A/OP and GEISA spectroscopic database The 5AI retrieval scheme uses the OPerational version of the Automatized Atmospheric Absorption Atlas (4A/OP).
, 2010). The absorption lines of CO 2 that we use in this work are, thus, identical to those included in  HITRAN 2008. (2) The solar spectrum is a flexible input, and the Doppler shift of its lines is computed. (3) The radiative transfer model is now coupled with the LIDORT model  (Spurr, 2002)  for scalar multiple-scattering simulation performed with the discrete ordinates method and with VLIDORT  (Spurr, 2006)   The 4A/OP radiative transfer model can be used with monochromatic optical thickness atlases computed from any spectroscopic database. For this present work, the atlases are computed using the GEISA 2015 (Gestion et Étude des Informations Spectroscopiques Atmosphériques: Management and Study of Spectroscopic Information) spectroscopic database. Being the base of many projects since the beginning in astronomical and astrophysical communities, GEISA has also been used since the 2000s for the preparation of several current and future spatial missions and has been chosen by CNES as the reference spectroscopic database for the definition of IASI-NG, MicroCarb, and MERLIN.
Data description The OCO-2 satellite has three distinct observation modes. The nadir and glint modes are the nominal science observation modes; they constitute the vast majority of OCO-2 measurements. In addition, the target mode of the OCO-2 mission provides data for the validation of the retrievals. In target mode, the satellite tilts and aims at a validation target (most of them are TCCON stations) and scans its whereabouts several times during the overpass. These sessions thus provide OCO-2 data points closely collocated with validation targets (over areas that can be as small as 0. 2 • longitude × 0. 2 • latitude) and registered over a few minutes  (Wunch et al. , 2017). OCO-2 high-resolution spectra are analysed by the ACOS team in order to retrieve X CO 2 and other geophysical parameters from them.
The ACOS team provides two different X CO 2 values, i. e. raw and posterior bias-corrected X CO 2. Raw X CO 2 is the direct output of the ACOS algorithm following the full physics retrieval; B8 retrospective (B8r) ACOS data release is used here  (O'Dell et al. , 2018). Posterior bias-corrected X CO 2 is an empirically corrected X CO 2 that has reduced averaged bias with regard to different truth proxies  (O'Dell et al. , 2018). In this work, 5AI results are compared with raw X CO 2 as we do not perform any empirical bias correction. In addition, we compare X CO 2 retrieved from OCO-2 spectra to TCCON data. The TCCON network uses groundbased high-resolution Fourier transform spectrometers to measure NIR and SWIR spectra that enable the retrieval of the column-averaged dry air mole fractions of greenhouse gases.
Data selection We intend to compare 5AI results with regard to TCCON against ACOS results for corresponding sets of soundings. First, we select all the OCO-2 target soundings between 2015 and 2018 with the best ACOS cloud, sounding quality, and outcome flags values. As a compromise between scattering particle content and coverage, we set an upper limit of 0. 5 for the ACOS retrieved total aerosol optical depth (AOD). This sample set of OCO-2 target soundings includes 16 414 soundings, with a median ACOS retrieved total AOD of 0. 05 and a 75 % percentile of 0. 1. For this study, we select the TCCON official products measured ±2 h compared to OCO-2 overpass time and only keep the target sessions where there are at least five OCO-2 measurements passing 5AI posterior filters and five TCCON data points are available.
This shows that taking scattering particle parameters into account can indeed explain much of the differences between 5AI-NS results and ACOS. Regarding the retrieved optical depths, Fig. 10  shows 5AI-AER and ACOS retrieved total AOD compared to AERONET reference data interpolated at 755 nm. 5AI-AER exhibits a higher average difference to AERONET than ACOS, but both retrieval algorithms exhibit a considerable scatter of their results compared to AERONET. Efforts to optimise 4A/OP coupling with (V)LIDORT are underway so that more OCO-2 data can be processed. Once those are completed, a dedicated study will help to tune the 5AI scattering particle setup better (varying aerosol types, impact of cirrus clouds, varying layer altitudes, etc. ).
). In order to compare 5AI retrievals with ACOS products while attenuating the impact of the forward and inverse modelling differences, the obtained averaged calc-obs residual is added to every OCO-2 measurements within the complementary half of the selected nadir soundings from 2016 (6799 in total). We then apply the 5AI inverse scheme on this new data set. Fig. 12  compares the distributions of 5AI-ACOS retrieval results obtained with and without the calc-obs adjustment. The systematic differences between 5AI and ACOS results for X H 2 O , X CO 2 , surface pressure, and global temperature profile shift are fully removed when adding the spectral residual adjustment to OCO-2 measurements (remaining differences are negligible compared to standard deviations). This X CO 2 shows that 5AI can, on average, reproduce ACOS results when all their respective differences are compensated with a calc-obs adjustment.
Conclusions In this work, we have introduced the 5AI inverse scheme; it implements the optimal estimation algorithm and uses the 4A/OP radiative transfer model with the GEISA spectroscopic database and an empirically corrected absorption continuum in the O 2 A band. We have applied the 5AI inverse scheme to retrieve X CO 2 from a sample of ∼ 44k OCO-2 soundings that compromises between coverage and the lowest ACOS-retrieved total AOD. We neglected the impact of scattering particles for computational time purposes and obtained a global averaged uncorrected bias compared to TC-CON of 1. 30 ppm, with a standard deviation of 1. 32 ppm for air masses below 3. 0. These results are comparable in standard deviation with those obtained by ACOS on the correhttps://doi. org/10. 5194/amt-14-4689-2021 Atmos.
Acknowledgements. The authors would like to thank the  ACOS team  and  NASA  for OCO-2 data availability. We thank the  AERONET team  for the whole database availability and especially  Jochen Stutz ,  Brent Holben ,  Toshihiko Takemura ,  Rigel Kivi ,  Veijo Aaltonen ,  Francois Ravetta , and  Jacques Pelon  for their effort in establishing and maintaining  Caltech, Ascension Island, Saint-Denis, Réunion, Fukuoka, Sodankylä , and Paris AERONET sites. Finally, we would like to thank the two anonymous referees whose comments and advice significantly improved the quality of this paper. The TCCON site at Réunion is operated by the  Royal Belgian Institute for Space Aeronomy , with financial support, since 2014, from the  EU  project  ICOS-INWIRE  and the  ministerial decree for ICOS  (grant nos.
Nicholas M. Deutscher  is funded by an  ARC   Future Fellowship  (grant no. FT180100327 ). Darwin and Wollongong TCCON stations are supported by  ARC  (grant nos. DP160100598 ,  LE0668470 ,  DP140101552 ,  DP110103118 , and  DP0879468 ) and  NASA  (grant nos. NAG5-12247  and  NNG05-GD07G ). Financial support. This work has received funding from  CNES  and  CNRS. Matthieu Dogniaux  has been funded by  Airbus Defence and Space  in the framework of a scientific collaboration with École polytechnique. Review statement. This paper was edited by  Abhishek Chatterjee  and reviewed by two anonymous referees.
Data availability. For this work, we use the B8r of OCO-2 data that were produced by the OCO-2 project at the Jet Propulsion Laboratory, California Institute of Technology, and obtained from the OCO-2 data archive maintained at the NASA Goddard Earth Science Data and Information Services Center (NASA GES-DISC). All TCCON references are given in Table  2. AERONET data are available from the AERONET website (https://aeronet. gsfc. nasa. gov/new_web/download_all_v3_aod. html, last access: 25 May 2021,  Stutz et al. , 2021). 5AI retrieval results presented in this work are available, upon emailed request, from Matthieu Dogniaux (matthieu. dogniaux@lmd. ipsl. fr).
Participants Two hundred and seventy-seven healthy participants were recruited from the University of York. Written informed consent was obtained for all participants and the study was approved by the York Neuroimaging Centre Ethics Committee. The participants were right-handed, native English speakers with normal/corrected vision. None of them had a history of psychiatric or neurological illness, severe claustrophobia, drug use that could alter cognitive functioning, or pregnancy. Twenty-four participants were excluded from fMRI analyses; two due to technical issues during the neuroimaging data acquisition, one due to a data processing error and twenty-one for excessive movement during the scan  (Power et al. 2014 ; mean framewise displacement > 0. 3 mm and/or more than 15% of their data affected by motion), resulting in a final cohort of N = 253 (169 females, mean ± SD age = 20.
7 ± 2. 4 years). A subset of 175 of these participants also completed a semantic relatedness judgement task, a digit span task and Raven's Progressive Matrices (along with other behavioural tasks outside the scope of this study), in a separate session. While the current analysis of hemispheric gradient differences is novel, this data has been used in previous studies to examine the neural basis of memory and mind-wandering, including region-ofinterest based connectivity analysis and cortical thickness investigations  (Evans et al. 2020; Gonzalez Alam et al. 2018 , 2019 , 2021; Karapanagiotidis et al. 2017; Poerio et al. 2017; Sormaz et al. 2018; Turnbull et al. 2018; Vatansever et al. 2017; Wang et al. 2018a, b).
Procedure All participants underwent a 9 min resting-state fMRI scan. During the scan, they were instructed to passively view a fixation cross and not to think of anything in particular. Immediately following the scan, they completed a 25-item experience-sampling questionnaire while still in the scanner; these data have been reported in  Karapanagiotidis et al. (2020)  and  Mckeown et al. (2020). In two subsequent sessions, participants completed a battery of semantic and other cognitive tasks. These sessions included personality and wellbeing scales, and measures of perception, episodic memory, cognitive control and mind-wandering, which were beyond the scope of this investigation.
The Supplementary Materials describe assessments of the suitability of the data for response efficiency analysis (see Supplementary Analysis: assumption check for efficiency scores). The stimuli employed in the tasks were selected from a larger set of words and photographs used in previous experiments  (Davey et al. 2015; Krieger-Redwood et al. 2015). The pictures were coloured photographs collected from the internet and re-sized to fit the trial structure (200 pixels, 72 dpi). All the coloured pictures and words were rated for familiarity using 7-point Likert scales, and imageability (> 500) from the MRC psycholinguistic database  (Coltheart 1981; Wilson 1988). Lexical frequency for the words was obtained by the SUBTLEX-UK database  (van Heuven et al. 2014)  to allow matching on psycholinguistic properties.
Neuroimaging The MRI data acquisition and pre-processing steps reported in this paper are identical to the steps reported in  Karapanagiotidis et al. (2020) , and the dimension reduction steps are identical to the ones reported in  Mckeown et al. (2020) , as reproduced in the sections below. MRI data acquisition MRI data were acquired on a GE 3T Signa Excite HDx MRI scanner, equipped with an eightchannel phase-array head coil at York Neuroimaging Centre, University of York. For each participant, we acquired a sagittal isotropic 3D fast spoiled gradient-recalled echo T1-weighted structural scan (TR = 7. 8 ms, TE = minimum full, flip angle = 20°, matrix = 256 × 256, voxel size = 1. 13 × 1. 13 × 1 mm 3 , FOV = 289 × 289 mm 2 ). Resting-state fMRI data based on blood oxygen level-dependent contrast images with fat saturation were acquired using a gradient single-shot echo-planar imaging sequence (TE = minimum full (≈ 19 ms), flip angle = 90°, matrix = 64 × 64, FOV = 192 × 192 mm 2 , voxel size = 3 × 3 × 3 mm 3 , TR = 3000 ms, 60 axial slices with no gap and slice thickness of 3 mm).
Scan duration was 9 min which allowed us to collect 180 whole-brain volumes. MRI data pre-processing fMRI data pre-processing was performed using SPM12 (http:// www. fil. ion. ucl. ac. uk/ spm) and the CONN toolbox (v. 18b) (https:// www. nitrc. org/ proje cts/ conn) (Whitfield-Gabrieli and Nieto-Castanon 2012) implemented in Matlab (R2018a) (https:// uk. mathw orks. com/ produ cts/ matlab). Pre-processing steps followed CONN's default pipeline and included motion estimation and correction by volume realignment using a six-parameter rigid body transformation, slice-time correction, and simultaneous grey matter (GM), white matter and cerebrospinal fluid (CSF) segmentation and normalisation to MNI152 stereotactic space (2 mm isotropic) of both functional and structural data. Following pre-processing, the following potential confounders were statistically controlled for: 6 motion parameters calculated at the previous step and their 1st and 2nd order derivatives, volumes with excessive movement (motion greater than 0.
9). This study was primarily focussed on the first gradient, which has well-described functional associations relevant to previous lateralisation findings; however, we extracted ten gradients to maximize the degree of fit between the groupaveraged gradients and the individual-level first gradient (this method is justified by the analysis in Supplementary Table  S2 , which shows a higher degree of fit with the canonical group-level gradients established by  Margulies et al. (2016)  when extracting ten gradients compared with three). The variance explained by each group-averaged gradient is provided in Supplementary Fig. 6. The group-level gradient solutions were aligned using Procrustes rotation to a subsample of the HCP dataset (N = 217, 122 women, mean ± SD age = 28. 5 ± 3. 7 y; for further details about subject selection and the benefits of this gradient alignment step, see Vos De  Wael et al.
We re-analysed an fMRI dataset  (Gao et al. 2021 ) examining the effects of semantic control demands (via a parametric manipulation of strength of association) and verbal working memory load (using a parametric manipulation of the number of items to be maintained). In this new analysis, we extracted the effect of these parametric regressors on the BOLD response in left-and right-hemisphere components of large-scale networks that showed behavioural associations with lateralisation effects on the principal gradient in Study 1. Previous research has shown that retrieving semantic links between more weakly-related words elicits strong engagement of the left-lateralised semantic control network (Jackson 2021;  Noonan et al. 2013). In contrast, higher loads in WM tasks are associated with greater responses within the Multiple Demand Network, particularly within the left hemisphere for verbal materials  (Emch et al.