dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, HBV DNA levels may fluctuate over time, and patients with HBV DNA <20,000 IU/ml at the time of therapy initiation may have had higher levels during previous assessments. Of note, pretreatment ALT levels were not associated with off-treatment outcomes in the overall population or in the subset of patients with low pretreatment HBV DNA levels. Another potential limitation of this dataset is the relatively low number of non-Asian patients. Although we observed consistent results among Asian and Caucasian patients, with superior outcomes observed for patients with a baseline HBV DNA level <20,000 IU/ml, findings were non-significant in the relatively limited number of Caucasian patients, and the group of non-Asian/non-Caucasian patients was too small to analyse separately. Finally, the current cohort lacks detailed information on pretreatment stage of liver disease (information was missing in 309 cases), and we were therefore unable to analyse the association between stage of liver fibrosis and outcomes after therapy cessation.
Financial support The CREATE study was supported by Fujirebio. Materials for HBcrAg testing were provided free of charge to several participating centres. Fujirebio had no influence on CREATE study design, data collection, data ] with pretreatment HBV DNA <20,000 IU/ml). A pretreatment HBV DNA level <20,000 IU/ml was associated with a lower cumulative risk of clinical relapse (p = 0. 001 by log-rank test) and a higher cumulative chance of HBsAg loss (p <0. 001 by log-rank test) among patients with HBsAg >100 IU/ml at the end of treatment; similar results were obtained among patients with HBsAg <100 IU/ml at the end of treatment (p <0. 001 for clinical relapse and p <0. 001 for HBsAg loss).
The carbon, water and energy modules in CANVEG have been validated for various environmental conditions and forest types  (Baldocchi, 1997; Baldocchi et al. , 2002; Baldocchi et al. , 1999). Moreover, CANVEG has previously been applied to an unmanaged beech-dominated forest site only 30 km away from the site of this study  (Knohl and Baldocchi, 2008) , and has recently been used to simulate the isotopic composition of carbon assimilates at Leinefelde  (Braden-Behrens et al. , 2019). We translated the original C code  (Baldocchi, 1997)  to Fortran 90, which was then used for further implementations. Atmospheric O2 mole fraction (O2 atm) as input for the model was deduced from a fixed O2:CO2 mole ratio of -1. 15 mol mol -1 (R 2 = 0. 99) and continuous CO2 mole fraction measurements at the site (Table  1 ).
Acknowledgements This research was funded by the  European Research Council  under the  European Union 's  Horizon 2020 research and innovation program  (grant agreement no. 682512-OXYFLUX ). We acknowledge support by the  Open Access Publication Funds of the Göttingen University. We thank the staff from the  Bioclimatology Group of the University of Göttingen , especially  Dietmar Fellert ,  Frank Tiedemann ,  Edgar Tunsch  and  Marek Peksa , for their continuous support in data acquisition and instrument maintenance. We thank  Penelope A. Pickers ,  Emmanuel Blei ,  Julian Deventer  and  Mattia Bonazza  for building O2 flux instruments and providing atmospheric O2 and CO2 background data. We also thank  Jelka Braden-Behrens  for obtaining leaf area measurements,  Ashehad Ali  for suggestions on model parameter calibrations,  Jan Muhr  for interpretations https://doi.
Survey designs and study populations We used data from two large nationwide representative surveys recently conducted in France using the same four measures of disability (GALI, ADL, IADL, and BI-M2/ WG-SS). First, the Disability Healthcare Household Survey (Enquête Handicap-Santé Ménages, HSM) is a cross-sectional two-stage survey that was conducted in 2008 with a focus on health, disability, and dependency. The participation rate was 80% for the first stage, which involved identifying individuals with potential disabilities. Note that this first stage did not involve screening strictly speaking but rather aimed to overrepresent subjects with disability in the study sample for the subsequent stage of the survey. For the second stage, the participation rate was 77%, leading to 23,348 participants aged ≥ 25 years living in France evaluated in face-to-face interviews and self-administered questionnaires  [37].
1 logit was used as the threshold to indicate meaningful DIF  [42] ); 4) measurement precision and ability to discriminate between different levels of disability assessed by the person separation index (PSI). Fourth, another series of Rasch analyses was performed to address the possibility of defining a single continuum of disability on which disability measures, including GALI, can be located. The Rasch partial credit model, which is appropriate for ordered response categories, was used in the third and fourth steps. Fifth, associations between disability measures and sociodemographic variables were assessed using logistic regression models, odds ratios (OR), and 95% confidence intervals (CI). As the largest and most comprehensive survey, HSM 2008 provided data for the main study, although replications with the other surveys were systematically performed due to the large samples (generating associations of limited value and possibly explained by oversampling).
Rasch analyses of separate BI-M2/WG-SS, ADL, and IADL measures The first Rasch analyses performed using the original four-category coding of BI-M2/WG-SS, ADL, and IADL items revealed almost generalized disordered thresholds (Supplementary Fig. 1 ), which persisted after recoding the items into three categories (grouping "much difficulty" with "unable to do alone"; Supplementary Fig. 2 ). This phenomenon of disordered thresholds was consistently observed across surveys (ESPS surveys: data not shown). Further analyses were then performed using binary items ("any limitation" vs "not limited" for the given activity). Table  2  and Supplementary Tables  4  and 5  present summaries of the Rasch analyses of BI-M2/WG-SS, ADL, and IADL items (recoded as two categories). A strong misfit to the unidimensional Rasch model was found for ADL and IADL items in the three surveys (total-item chi-square test for the item-trait interaction p < 10 -5 ) and in one survey (ESPS 2012) for WG-SS/BI-M2 items.
8. 0_172. Ten cells were used for measurements taking 10 cell wall thickness measurements from each cell. The statistical unpaired two-tailed student t test (p < 0. 05) was performed to compare the cell wall thickness between two concentrations (0-0. 5 M, 0. 5-2. 0 M, 0-2. 0 M). Statistical analysis was done using Microsoft Excel 365 and violin plots were generated using Origin Pro 2019b software.
The 13 C-  13  C intramolecular interactions were probed using a 100 ms dipolarassisted rotational resonance (DARR) scheme. Long-range intermolecular cross-peaks were detected using a 1. 5 s proton-driven spin diffusion (PDSD) experiment. The resolved chemical shifts were compared with the values indexed in the Complex Carbohydrate Magnetic Resonance Database (CCMRD; www. ccmrd. org)  81  to validate the chemical nature of the carbohydrates. The confirmed resonance assignments are listed in Supplementary Table  10. Protein secondary structure was determined by the chemical shift differences between the observed  13  C chemical shifts of Cα and the standard values of random-coil conformation  52. The chemical shifts were obtained using 2D DP refocused J-INADEQUATE spectra for mobile amino acid residues and using 2D 13 C-  13  C DARR spectra for rigid proteins.
Estimation of carbohydrate composition We analyzed the peak volumes in 2D 13 C-13 C spectra measured using 100 ms DARR and DP refocused J-INADEUQTAE schemes to estimate the composition of the rigid and mobile polysaccharides, respectively (Supplementary Table  2 ). The integration function of the Bruker Topspin software was used to get the peak volumes in 2D spectra. To minimize uncertainty caused by spectral crowding, only well-resolved signals were used for compositional analysis. The NMR peaks used for quantification, their resonance assignments, and the corresponding peak volumes, were provided in Source Data file.
Data obtained from the 1D spectra were analyzed by plotting the relative intensities as a function of the square root of the 1 H mixing time, which gave a buildup curve of peak intensity. The data obtained from the 2D scheme were analyzed by comparing the intensities between the water-edited spectrum (S) and the non-edited control spectrum (S 0 ), for each resolved carbon site. These S/S 0 intensity ratios reflect the extent of water retention around different carbon sites, which were documented in Supplementary Tables  4  and 6  for polysaccharides and proteins. 13 C-T 1 relaxation was measured using CP-based Torchia T 1 scheme  83  , with the z-filter duration varying from 0. 1 μs to 8 s to provide complete relaxation curves as shown in Supplementary Fig. C-detected 1 H-T 1ρ relaxation were measured using the Lee-Goldburg spin-lock sequence in which 1 H spin diffusion was suppressed during both the spin-lock period and the CP period to obtain site-specific 1 H relaxation information for protons that are directly bonded to a carbon site.
Acknowledgements This work was supported by the  National Institutes of Health  grant  AI173270  to T. W. and Project-Conacyt -CB-285816  to R. A. B-G. N. G-C. acknowledge the support by the  Slovenian Research Agency to Infrastructural Center Mycosmo (MRIC UL ,  I0-0022 ) and program  P4-0432. The high-field NMR spectra were collected at the  Environmental Molecular Sciences Laboratory  (grid. 436923. 9), a  DOE Office of Science  scientific user facility sponsored by the  Department of Energy's Office of Biological and Environmental Research  and located at  PNNL  under contract  DE-AC05-76RL01830. Y. P-L. and L. M-A. received postdoc and PhD fellowships from CONACyT. R. A. B-G acknowledges a  Sabbatical fellowship  ( CVU 389816 ) from  CONACyT. The NMR experiments of A. fumigatus samples were performed at the  National High Magnetic Field Laboratory , which is supported by  National Science Foundation Cooperative Agreement  No.
Data availability The unprocessed ssNMR data files generated in this study have been deposited in the Zenodo repository: https://doi. org/10. 5281/zenodo. 10001628. All relevant data that support the findings of this study are provided in the article and supplementary Information. The resonance assignment documented in Supplementary Table  10  was confirmed by cross-checking data available at CCMRD (publicly available at www. ccmrd. org). The source data underlying Figs. 3a, d, , and 5c are provided as Source Data file. Source data are provided in this paper.
This eventually leads to the series of reports issued by the Intergovernmental Panel on Climate Change, the latest one being the sixth, where the human footprint on climate and biodiversity is now starkly visible, with fossil fuels being a primary cause. There are by now about 116 models from 44 institutions around the planet building ESMs (according to the ESGF data statistics portal) to understand the climate and work out the implications of various climate policy choices. The construction of these ESMs is generally described in "model documentation" papers, that detail the various scientific and technical choices that went into the building of these models. We take here as examples the documentation of the most recent model versions from two of the world's premier modeling institutions, the Geophysical Fluid Dynamics Laboratory  (Held et al.
The system is integrated using a Runge-Kutta fourth order scheme with a time step of Δt = 0. 001 (note that L96 is fully non-dimensional). We based our implementation of the L96 on the Python code accompanying the paper of  Rasp (2019)  https://github. com/raspstephan/Lorenz-Online, with the difference being in using the original formulation of the L96 as stated in Equations 1 and 2. As in Lorenz' seminal paper, we use K = 36, J = 10, a chaotic behavior is ensured by using the parameters h = 1 and F = c = b = 10  (Lorenz, 1996). In such setting, the temporal-scale ratio c implies that the Y (fast) variables fluctuate 10 times rapidly as the X (slow) variables, and b the spatio-scale ratio implies that their amplitude is 1/10 of the slow variables. As noted above in Section 1, this article intends to reproduce the process of tuning of a system with multiple temporal scales, in order to help us anticipate what might transpire when the HM method is applied to a full coupled climate model.
The Code As stated before, the L96 model is written in Python, special attention was given to parallel computing in order to run several experiments efficiently, the joblib Python library was used for this task. The main HM routine is written in R (LHS, GP-fitting, L1O, visualization). The code is based on the ExEtErUQ_MoGP library that acts as an R interface for the Python written MoGP_EMUlator library for training GPs, also available online. Again, to speed-up the calculation of implausibilities, we make use of the fUtUrE R library for parallel computation. Please note that the code requires minimal R knowledge, which would not constitute a barrier for pure Python practitioners. All the scripts for this paper are preserved at Zenodo https://doi. org/10. 5281/zenodo. 7384270 (Lguensat, 2022).
Acknowledgments The authors would like to thank  Frédéric Hourdin ,  Fleur Couvreux ,  Daniel Williamson ,  Najda Villefranque  and  Martin Vancoppenolle  for the helpful discussions. The authors are also grateful for the constructive comments and valuable suggestions provided by two anonymous reviewers that highly improved the quality of this paper. This research was supported by the  "Agence Nationale de la Recherche " through the  HRMES ANR-17-MPGA-0010  project. This work was granted access to the  HPC/AI resources of IDRIS  under the allocation A0120107451 made by  GENCI.
Data Availability Statement Jupyter notebooks (Python and R) developed for this work are open sourced and can be used to generate the datasets and figures used in this paper. They can also be minimally modified in order to be used for another simulation model for further exploration. They can be accessed from the Github repository https://github. com/HRMES-MOPGA/L96HistoryMatching and are preserved at Zenodo https://doi. org/10. 5281/ zenodo. 7384270 (Lguensat, 2022).
8 mm), the entorhinal cortex (Ent; Bregma between -3. 1 and -3. 2 mm) and the hippocampus (Hip; Bregma between -1. 9 and -2. 2 mm). First, images were manually edited to remove edge artifacts, folds, and blood vessels, then subjectively thresholded. To quantify global amyloid plaque extension, the % positive area covered by all amyloid plaques within the manually outlined regions of interest (Ctx, Hip) was automatically obtained through ImageJ software. Other quantitative morphometric parameters such as plaque number, area and perimeter of every plaque in the analyzed areas, were measured through a Nikon NIS-Elements D software. In the analysis of Nissl staining, at least three somatosensory cortex (sCtx) and Hip slices/mouse (Bregma between -0. 7 and -0. 8 mm and between -1. 9 and -2. 2 mm, respectively) were acquired with 4× objective and layer thickness quantified through the length/thickness function of ImageJ software.
For SYN immunoreactivity (ir) analysis, three slices/mouse from sCtx and Hip (Bregma between -0. 7 and -0. 8 mm and between -1. 9 and -2. 2 mm, respectively) were acquired with 10× objective and the morphometric parameters quantified through the length/thickness and area functions of ImageJ software. For densitometric analysis, acquired images were converted to 32 bit grayscale and specific optical density values were obtained by subtracting the optical density of the sampled region from the optical density of non-specific staining, i. e. , corpus callosum for both cortex and CA3 analysis. For densitometric analysis of GFAP and Iba1 immunostainings, at least three slices/mouse from cingulate cortex (cCtx; Bregma between -0. 5 and -0. 9), sCtx (Bregma between -0. 7 and -0. 8 mm) and Hip (Bregma between -1.
9 and -2. 2 mm) were acquired with 10× objective. Acquired images were converted to 32 bit grayscale and a thresholding procedure was applied (-20 units from the peak mean gray value) and % positive area, i. e. , the area covered by pixels with gray value above the threshold, was automatically recorded by means of analyze particles function of ImageJ software. As specifically concerns the analysis of morphometric parameters related to microglia, firstly Iba1+ cell count was performed manually, in live mode, within the area of interest by using a 40× objective; in the same section, for each Iba1+ cell, cell outline was manually selected and stereological parameters, such as area, perimeter, number of primary processes and total primary process length, were automatically recorded by means of Nikon NIS-Elements D software.
Statistical analysis Values are shown as mean ± standard error of the mean (SEM) or 95% confidence interval as appropriate. Group differences were analyzed by means of two-way ANOVA or Mann-Whitney U-test as appropriate. Proportions were analyzed by means of the Chi square test. Correlations were analyzed by means of the Spearman test. Statistical analyses were performed through SPSS software, with p < 0. 05 as the level for a significant difference and 0. 10 > p > 0. 05 as trend for a significant difference.
Patients Cancer-immunity markers among 72 eligible, consecutive patients with gynecologic solid cancers seen at the University of California San Diego Moores Cancer Center for Personalized Therapy were evaluated at a Clinical Laboratory Improvement Amendments (CLIA)-licensed and College of American Pathologist (CAP)-accredited clinical laboratory, OmniSeq (https://www. omniseq. com/). All investigations followed the Institutional Review Board protocol for data collection (Pro le Related Evidence Determining Individualized Cancer Therapy, NCT02478931) and for any investigational interventions for which the patients consented.
Endpoints and statistical methods Patient characteristics and the pattern of cancer-immunity markers were summarized by descriptive statistics. Proportions were compared using Fisher's Exact test. Means were compared using Student's t test. The correlation between the disease sites and comprehensive expression patterns was evaluated through principal component analysis (PCA) and quanti ed by calculating silhouette scores. Statistical analyses were performed using SPSS version 28. 0 (Chicago, IL, USA), Microsoft Excel version 16. 49, and R 3. 6. 1 (R Foundation for Statistics Computing, Vienna, Austria). A p value <0. 05 was considered signi cant.
Discussion In our report, we characterize the immune pro les of patients with gynecologic cancers using 51 RNA transcript levels associated with the cancer immunity cycle. Notably, we found that no two tumors have an identical immune pro le. This nding highlights the complexities of immune interactions, as well as the need to interrogate each tumor in the context of choosing precision immunotherapeutics. Contemporary work with The Cancer Genome Atlas (TCGA) and other large databases, integrating immunogenomics using powerful computational science have started to describe six distinct clusters of immune subtypes with potential implications for cancer treatment, though the prior work also demonstrated signi cant interpatient immune landscape heterogeneity  30, 31. These unique immune pro les again highlight the opposing immune pressures that affect the tumor microenvironment.
Moreover, patients with cervix cancer have higher RNA expression levels of immune-activating factors compared to many other types of solid tumors including uterine and ovarian cancers, consistent with larger studies utilizing the TCGA databases of diverse solid tumors  29. Patients with uterine and ovarian cancers have a slightly lower than average (<50% expression rank level) RNA level of both immune activating and immune inhibitory markers. These ndings may begin to explain the success of incorporating pembrolizumab (anti-PD-1) in the treatment of metastatic cervical cancer, and the comparatively lackluster response with the incorporation of immune checkpoints for patients with ovarian cancer  17, 35. Upregulation of alternative checkpoints may also explain why some patients do not respond to anti-PD-L1/anti-PD-1 agents  24.
An analysis of immune gene expression in patients with cervix cancer using the TCGA and GEO databases was able to delineate high-versus low-risk immune pro les that reliably predicted survival. The high-risk group was characterized by over-expression of macrophages and mast cells, probably due to their ability to promote lymphangiogenesis and angiogenesis  36. In our study, we found higher expression levels of pro-in ammatory markers in patients with PD-L1 IHC positive tumors across all three disease sites, but lower levels of other immune markers. This may begin to explain why PD-L1 IHC serves as a limited therapeutic biomarker as it likely captures only one aspect of the immune microenvironment. The IHC 22C3 antibody only measures tumor PD-L1 whereas IHC SP142 measures tumoral and immune cell PD-L1.
Of interest, patients with gynecologic cancers had higher IDO1 RNA expression levels compared to other cancer types, with about 44% of gynecologic cancers expressing high IDO1 compared to less than 13% of other cancer types (p<0. 001). IDO1 is the rst and rate-limiting enzyme in the degradation of tryptophan, which is expressed in cancer cells or draining lymph nodes  39. When IDO1 is high, it can potentially be targeted with IDO1 inhibitors such as epacadostat, indoximod, etc. Previously, bioinformatics analysis of IDO1 immune function in gynecologic cancers using databases such as Oncomine, GEPIA etc. also found overexpression of IDO1 RNA as well as protein expression in gynecologic tumors  40. Though phase 1 studies showed promising tolerance and response rates to the IDO inhibitor epacadostat  41  , phase 2 studies comparing the IDO inhibitor epacadostat against tamoxifen in ovarian cancer types did not show signi cantly improved e cacy  42.
IGCS 2022) We also found higher expression of the checkpoint LAG3 in gynecologic cancers as compared to nongynecologic cancers (34. 7% versus 20. 6%; p=0. 008); similar ndings were previously shown in endometrial cancers  44. The anti-LAG3 relatlimab was recently FDA approved for melanoma  45  and other LAG3 inhibitors are in clinical trials. It is plausible that LAG3 may be an alternative checkpoint upregulated in some of the tumors resistant to anti-PD1/PDL1 agents, and LAG3 inhibitors merit investigation in patients with gynecologic cancers, especially those with high LAG3 expression. There were several limitations to our study. First, our sample size was relatively small, especially in regard to cervical cancer, with uneven distribution among gynecologic disease sites. Second, since the database was not clinically annotated, we were not able to associate immune biomarker expression levels with immunotherapeutic responses or other clinical oncologic endpoints.
Though this data was obtained from a CLIA-licensed laboratory, immune marker RNA from normal tissues were not delineated. Finally, while we were able to delineate differences in immune landscape between PDL1-positive versus -negative cancers, the small number of neoplasms with high TMB or MSI-High disease precluded analysis of these     1  and Methods) (N=24) Frequency of high RNA expression (≥75 percentile rank) among immune suppressive markers in patients with cervical cancer (see also Supplemental Figure  1  and Methods) (N=5) Figure  2  a. Frequency of low RNA expression (<25 percentile rank) among cancer-immunity markers across gynecologic disease sites (see also Supplemental Table  1  and (N=72 b. Frequency of low RNA expression (< 25 percentile rank ) among immune activating markers in patients with ovarian cancer (see Supplemental Table  1  and Methods) (N=43) c.