dataesr/scientific-paragraphs-categorization huggingface dataset:
Data sources The ComPaRe long covid cohort is an ongoing nationwide e-cohort (ie, a cohort where recruitment and follow-up are performed online) of patients with long covid in France, nested in the ComPaRe research programme (www. compare. aphp. fr), an umbrella e-cohort of patients with chronic conditions. 15 Participation opened in November 2020 and is ongoing. The cohort includes adult patients who have reported a SARS-CoV-2 infection (whether or not confirmed by a positive polymerase chain reaction (PCR) test result or serological assay, or both) and have symptoms persisting for more than three weeks after the original infection. Recruitment took place from calls on social and general media, by partner patient associations, on the official French contact tracing app TousAntiCOVID, and by a snowball sampling method where participants were encouraged to invite people who had covid-19 and persisting symptoms to enrol.
Eligibility criteria Our analyses used data from patients enrolled in the ComPaRe long covid cohort before 1 May 2021. We included adult patients (aged ≥18 years) with a confirmed or suspected SARS-CoV-2 infection, diagnosed by a health professional, whose symptoms persisted for >3 weeks after the original infection, and who reported at least one symptom attributable to long covid at baseline. We excluded patients whose date of first symptoms was <3 months before baseline because at the time of the study the recommendation in France was to delay vaccination for three months for patients who had recently been infected with the SARS-CoV-2 virus. 18 We also excluded patients reporting a history of severe allergy in ComPaRe because of the likelihood of a history of anaphylaxis, a contraindication to vaccination at the time of the study.
Study groups and follow-up To define a vaccinated group and a matched unvaccinated control group in a population where most patients were vaccinated against covid-19, we used the cohort data to emulate a sequence of three trials which were then pooled. 21 In the first trial, we identified all patients who met the eligibility criteria when they were enrolled in the ComPaRe long covid cohort (ie, their first observation point, T0). Patients who received their first covid-19 vaccine injection with the ChAdOx1 (AstraZeneca), BNT162b2 mRNA (Pfizer-BioNTech), Ad26. COV2. S (Johnson & Johnson), or mRNA-1273 (Moderna) vaccines between baseline and 60 days (ie, their second observation point, T1), were classified as the vaccinated group and matched in a 1:1 ratio to patients who did not receive the vaccine in the same period (T0 to T1), classified as the control group.
Secondly, we analysed how our design, based on a sequence of emulated trials, could affect our results by estimating separate treatment effect estimates for each trial and then conducting a meta-analysis with a fixed effect approach. Finally, we conducted a secondary analysis that used standardised mortality ratio weighting as an alternative to propensity score matching. 27 issing baseline and outcome variables were handled by multiple imputation with chained equations that used the other variables available. All statistical analyses were performed with the R statistical package version 4. 0. 3 (The R Foundation for Statistical Computing, www. R-project. org/).
There is strong interest in developing predictive models to better understand individual heterogeneity and disease progression in Alzheimer's disease (AD). We have built upon previous longitudinal AD progression models, using a nonlinear, mixed-effect modeling approach to predict Clinical Dementia Rating Scale -Sum of Boxes (CDR-SB) progression. Data from the Alzheimer's Disease Neuroimaging Initiative (observational study) and placebo arms from four interventional trials (N = 1093) were used for model building. The placebo arms from two additional interventional trials (N = 805) were used for external model validation. In this modeling framework, CDR-SB progression over the disease trajectory timescale was obtained for each participant by estimating disease onset time (DOT). Disease progression following DOT was described by both global progression rate (RATE) and individual progression rate (α).
Earlier models assumed a linear score progression from baseline (start of the trial). 11 Holford and Peace developed a population pharmacodynamics model of five clinical trials and characterized the response to treatment via an offset in disease progression curve. 21, 22 Yang et al. 12  proposed the concept of disease onset time (DOT) by accounting for the fact that participants enrolled in a trial are at different stages of their disease, thus fitting the data to a theoretical curve of disease progression for ADAS-Cog 13. The ADAS-Cog 13 includes all ADAS-Cog 11 items as well as a test of delayed word recall and a number cancellation or maze task. 23 ADAS-Cog 13 scores range from 0-85. 23 Ueckert et al. combined item response theory and a pharmacometrics approach to describe progression of total ADAS-Cog and item level scores.
Model-building dataset The model-building datasets included participants across the following disease stages: prodromal AD, mild AD, and moderate AD. Data were extracted from the placebo arms of clinical trials of crenezumab (ABBY [NCT01343966, n = 144, phase II, mild-tomoderate AD] and BLAZE [NCT01397578, n = 29, phase II, mild-to-moderate AD])  27, 33  and gantenerumab (SR [NCT01224106, n = 266, phase III, prodromal AD] and MR [NCT02051608, n = 195, phase III, mild AD]  30  ; Table  1 ). Participant data from the ADNI database (adni. loni. usc. edu) were also used for model building. 34 The ADNI study was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner. The primary goal of the ADNI has been to test whether biomarkers and clinical and neuropsychological assessments can be combined to measure the progression of MCI and AD.
For up-to-date information, see www. adni-info. org. For ADNI data (data extracted on February 2, 2020), only participants who were amyloid-positive with a baseline diagnosis of late MCI or AD were included in the analysis (N = 459); in order to mirror inclusion criteria for the most recent AD clinical trials (e. g. , SR, MR, BLAZE, CREAD, and CREAD2). Demographics and characteristics for participants included in the analysis are summarized in Table  2. Populations were similar in age and sex ratios. In SR, about 13% of participants were APOEε4-positive; in the other studies, this percentage ranged from 67% to 79%. Participants did not receive any concomitant medications in the SR study, but the use of symptomatic therapies (i. e. , rivastigmine, donepezil, galantamine, and memantine) was permitted in the other studies.
The MR, ABBY, and BLAZE studies, which enrolled only participants with mild or mild-to-moderate AD, had slightly higher baseline CDR-SB scores than other study populations. SR enrolled participants with prodromal AD, which resulted in lower baseline CDR-SB scores. Participants with only one CDR-SB score value or a missing baseline CDR-SB score were excluded from the analysis. The lengths of the treatment duration in interventional trials were generally up to 2 years, and data up to 4 years were included from the ADNI. For the covariate analysis, missing data were imputed to the population median from the study and the corresponding diagnosis group. For baseline scores, we had fewer than 5% missing. We did not have missing categorical covariates.
Base structural model The AD progression model for CDR-SB was developed using a nonlinear, mixed-effect modeling approach. The modeling was performed in the logit domain, resulting in the estimate of a score that was bound between zero and one; this was multiplied by 18, to ensure that the number stays within the bounds of the CDR-SB score (zero and 18). To obtain the CDR-SB score, an estimated term (baseline correction [BLC]; Table  3 ) repositioned the logit at the most probable level (i. e. , 1-4. 11 = -3. 11; Equation  1 ). (1) An equation (Equation  2 ) describing the rate of progression of A (1) was fitted to the model-building dataset and parameters of the equation describing progression were estimated. CDR -SB = (exp (A (1)-BLC)∕(1 + exp (A (1)-BLC))) × 18 Change in A (1) over time was described via a differential equation, similar to Delor et al.
Model evaluation and validation In the first step, parameter estimates and goodnessof-fit plots were carefully examined on the population and individual levels, such as plot of observation (DV) versus population prediction (PRED), DV versus individual prediction (IPRED), individual weighted residuals (IWRES) versus  In producing VPCs, 1000 simulated replications of the dataset were created by random sampling of parameterlevel random error (i. e. , IIV) and observation-level random error (residual error or unexplained differences between predicted and observed values). Simulations to obtain VPCs were conducted in NONMEM version 7. 3 and plotted in R version 3. 6. 1. 37 External validation using VPCs was then performed with data from the placebo arms of the CREAD trials. In addition to VPCs, the PPC including parameter uncertainty was conducted to compare model-predicted and observed mean changes from baseline for each study.
For this analysis, similar to VPCs, 1000 replications of the dataset were simulated across various sources of variability, and predicted mean changes from baseline, and confidence intervals for the mean changes from baseline, were calculated at each timepoint and compared with observed changes. Thus, the model was developed using a dataset with participants ranging in disease severity from prodromal AD to moderate AD, and internal validation was conducted to confirm that the model can predict all different disease stages used for model building.
All parameters were well-estimated (Table  3 ). DOT was estimated as 3. 3 years before entering the trial, or start of study for the ADNI data (with a relative standard error [RSE] of 1. 5%). The estimated IIV of α was large (84. 7% [RSE 9. 3%]). Despite the large range of DOT (up to 6. 94 years before the start of the study in model-building studies), the covariates largely explained the variability in DOT very well, and IIV was estimated at 4% (RSE 6. 4%) (correlation plots of IIV on DOT and α are presented in Figure  S3 ). Shrinkage of IIV on DOT and α were 35% and 32%, respectively. Fewer than 1% of participants had estimated DOTs after the start of the study (DOT <0); this occurred in participants whose score did not progress or decreased over time. Larger values of α were associated with fast progressors (DOTs close to the start of the trial/ study, but scores progressed rapidly).
DISCUSSION In this analysis, we built upon previous modeling by Delor et al. 16  to develop a disease progression model to describe longitudinal, clinical progression in AD (as measured by the CDR-SB) using a nonlinear, mixed-effect population modeling approach. The model was developed using a rich dataset, with participants ranging in disease severity from prodromal AD to moderate AD; internal validation confirmed that the model can predict all different disease stages used in model building. External validation, which included early AD, was also well-predicted by this model. AD is a slowly progressive disease, and trials follow participants over relatively short periods of time (roughly 2 years); disease progression appears linear during trial duration (as seen in progression of scores such CDR-SB over time), whereas there is an exponential trajectory when looking at such scores over a longer period of time (e.
g. , onset of disease). To account for this, in this model, change in CDR-SB score over time was described via a differential equation, similar to Delor et al. 16  (Equation  2 ). We obtained the individual disease progression trajectory for each participant by estimating DOT prior to the start of the trial, thus enabling us to capture the nonlinear time course of the score. CDR-SB score is bounded between zero and 18. In our model-building dataset, fewer than 1% of participants had CDR-SB greater or equal to 10. Due to nature of the score, the participants who start at a very high CDR-SB score may show slower progression of score. A previous model, developed by Delor et al. ,  16  used a mixture model to identify fast and slow progressors in the ADNI. They identified baseline CDR-SB and ADAS-Cog as significant covariates on DOT, baseline MMSE on α, and baseline CDR-SB, FAQ, and RHPNM (hippocampal volumes normalized to that of a healthy subject with the same age and head size) as covariates on the modeled fraction of the population in the first (or second) subpopulation ($MIX).
Our model uses the same base structural model as Delor et al. 16  In addition to data from the ADNI, we used data from the SR, MR, ABBY, and BLAZE trials for model building. We identified baseline CDR-SB and baseline MMSE on DOT, baseline MMSE on α, and baseline CDR-SB on RATE as significant covariates in our model. CDR-SB assesses both cognition and function, whereas MMSE and ADAS-Cog both focus on cognitive assessments. Thus, in both our model and the model by Delor et al. ,  16  we identified baseline CDR-SB and a cognitive score as significant covariates of DOT. Not all covariates used by Delor et al. 16  were available (e. g. , FAQ) or comparable (e. g. , brain volume) for all studies we used in model building. A mixture model (mixture models allow for the estimation of two or more [sets of] parameters for different subpopulations of subjects without a specific variable to assign subjects to relevant groups) was also assessed during our model development; however, in this study, we were able to finalize a model without a mixture model, using data from interventional studies and the ADNI, and, thus, with an easier application in simulation mode.
12 he model was successfully validated (via VPC and PPC) by assessing its ability to predict CDR-SB progression for the model-building dataset. The discrepancy in SR could be attributed to the high proportion of nonprogressors in this study. Assessing model predictive performance on data from independent clinical trials not seen by the model is highly preferable  38  ; to this end, the placebo arms of the two phase III CREAD trials were used for external validation. In addition to cohort-level analysis, the model can be used to make individual-level predictions. Such predictions could be used to prospectively predict progression of patients enrolled in an interventional study based on their score at the start of the trial. We tested the model performance for this individual level prediction, using only baseline score as the input.
The concept of DOT (i. e. , the time at which the score starts increasing before the start of the trial) (a) and observed longitudinal trajectories of CDR-SB scores for 100 randomly selected participants from the model-building dataset on the clinical trial time scale (b1), on the disease trajectory time scale (using DOT) (b2), and corresponding individual predictions from the model for those participants (b3). CDR-SB, Clinical Dementia Rating Scale -Sum of Boxes; DOT, disease onset time.
internal validation of the model in studies used for model development (a-e) and external validation of the model in studies not used for model development (f, g). *ADNI, Alzheimer's Disease Neuroimaging Initiative; CDR-SB, Clinical Dementia Rating Scale -Sum of Boxes; CI, confidence interval; VPC, visual predictive check. Solid and dashed black lines are the observed median and 10th and 90th percentiles, respectively. Three predicted percentiles are shown in color for each of the observed metrics (solid line is median, shaded areas are 10th, and 90th percentiles). *Obtained from 1000 simulated datasets using the final model.
ACKNOWLEDGMENTS The authors would like to acknowledge the following contributions:  Balazs Toth  and  Christina Rabe  of  Genentech, Inc. , and  Tobias Bittner  of  F. Hoffmann-La Roche Ltd. , for reviewing the manuscript and data preparation. Michaela Mertes  and  Kellie Riley  of  F. Hoffmann-La Roche Ltd. , for data preparation. Medical writing was provided by  Adam Ruth, PhD ,  Sarah Engelberth, PhD , of  Medical Expressions (Chicago, Illinois ) and  Ayesha Babar , MSc, of  Chrysalis Medical Communications (United Kingdom ), and funded by  F. Hoffmann-La Roche.
F I G U R E 1 1 The concept of DOT (i. e. , the time at which the score starts increasing before the start of the trial) (a) and observed longitudinal trajectories of CDR-SB scores for 100 randomly selected participants from the model-building dataset on the clinical trial time scale (b1), on the disease trajectory time scale (using DOT) (b2), and corresponding individual predictions from the model for those participants (b3). CDR-SB, Clinical Dementia Rating Scale -Sum of Boxes; DOT, disease onset time.
JHEP08(2023)119 the resulting data with respect to N using LinearModelFit in Mathematica. As a result, we obtain the following numerical 1/N expansion of the ADHM TTI, Re log Z ADHM S 1 ×S 2 (N, N f , ∆, n) = f (lmf) 3/2 (N f , ∆, n)N 3 2 + f (lmf) 1/2 (N f , ∆, n)N 1 2 + f (lmf) log (N f , ∆, n) log N + f (lmf) 0 (N f , ∆, n) + L s=1 f (lmf) -s/2 (N f , ∆, n)N -s 2 , ( 2. 35) with numerical coefficients f (lmf) X (N f , ∆, n) for a given N f and (∆, n)-configuration. The superscript "(lmf)" in the expansion coefficients emphasizes that they are numerical coefficients obtained by LinearModelFit. The upperbound L for fitting is chosen as L = 16 to minimize standard errors in estimating the coefficients f (lmf) X (N f , ∆, n). Next, repeating the LinearModelFit (2. 35) with five different n-configurations satisfying the constraint (2.
3) 1 -g 2(1 -g) KN-AdS (ω+1) 2 2ω 2 BH with spindle horizon 1 8ω 1 n + + 1 n -ω + 2 2 + ω 8 1 n + -1 n - 2 1 n + + 1 n - Table 1. The quantities F and χ for various Euclidean 4d minimal gauged supergravity backgrounds. The spindle black hole is characterized by two integers n ± that specify the topology of the horizon. For n + = n -= 1, we recover the KN-AdS black hole. hole can be found in  [71] , that of the AdS 4 -Taub-NUT space is presented in section 3. 2. 3 of  [10] , and the data for the black hole with spindle horizon can be found in  [72]. 14  The information about the details of the holographic SCFT enters the result in (5. 8) through the (A, B, C) coefficients. For a given SCFT, these coefficients can be deduced from the large N expansion of any two distinct physical observables that can be written as in  (5.
Acknowledgments We are grateful to  Anthony Charles ,  Sunjin Choi ,  Pieter-Jan De Smet ,  Kiril Hristov ,  Sameer Murthy , and  Xuao Zhang  for valuable discussions. This research is supported by the  FWO  projects  G003523N  and  G094523N. NB and JH are also supported in part by the  KU Leuven   C1  grant  ZKD1118 C16/16/005  and by  Odysseus  grant  G0F9516N  from the  FWO. VR is supported by a public grant as part of the  Investissement d'avenir project , reference  ANR-11-LABX-0056-LMH ,  LabEx LMH. NB is grateful to KITP  Santa Barbara  for the warm hospitality during the final stages of this project. Research at KITP is supported in part by the  National Science Foundation  under Grant No. NSF   PHY-1748958 JHEP08(2023 ) 119.
Our results strongly support the use of clumped 30 isotopes as an alternative temperature proxy and indicate that brachiopod Δ47 values can be used with δ 18 O to estimate past δ 18 Osw with a precision of about ±1‰ VSMOW. The obtained Mg/Ca ratios show no relationship with temperatures, indicating that this ratio is a poor recorder of past changes in temperatures, an observation at variance with several previous studies. Brachiopod shell Sr/Ca, Na/Ca and Li/Ca display relatively good and significant correlations with brachiopod living temperature, but data indicate the influence of environmental and 35 biological factors unrelated to temperature. Our proposed revision of marine temperature and water δ 18 O proxies based on brachiopod shell geochemistry is promising to refine the record of these oceanic parameters in the Phanerozoic.
, 2003; Parkinson et al. , 2005; Yamamoto et al. , 2010a, b; Cusack et al. , 2012; Takayanagi et al. , 2012 Takayanagi et al. , , 2013 Takayanagi et al. , , 2015;; Bajnai et al. , 2018 Bajnai et al. , , 2020;; Romanin et al. , 2018; Rollion-Bard et al. , 2019). Based on the conclusions of  Bajnai et al. , (2018) ,  Brand et al. , (2019)  characterise their oxygen isotope fractionation database as representing a "brachiopod based-equilibrium". This term is opposed to the thermodynamic equilibrium, which is best described by very slow 95 growing calcites  (Coplen, 2007; Daëron et al. , 2019) , thus ensuring isotopic equilibration of the DIC species with water  (Watkins et al. , 2013 (Watkins et al. , , 2014)). Trace element incorporation into calcite is also affected by kinetic effects. In synthetic carbonates, the relative abundance of elements such as Sr, Na and Li rise with higher growth rates  (Lorens, 1981; Busenberg and Plummer, 1985; Tesoriero and Pankow, 1996; Gabitov et al.
The dependence of elemental ratios to seawater temperature is discussed at the light of this new available dataset of modern brachiopods. Finally, we highlight kinetic effects registered in the dataset of brachiopod shell calcite and we provide suggestions on how they may be considered for proxy data interpretation. The studied material consists of 37 articulated brachiopod shells that were collected in situ during institutional oceanographic cruises or other scientific missions (see supplementary data for detail). Sampling location was 115 documented with depth and geographic coordinates for most samples. Temperature and salinity, which were not measured in situ for most samples, were estimated independently using the NOAA World Ocean Atlas 2018  (Locarnini et al. , 2018; Zweng et al. , 2018)  or, whenever available, using local records in the literature (Table  1 ).
Stable isotopes The whole dataset of δ 13 C and δ 18 O values from modern brachiopod shells ranges from -2. 24‰ to 3. 17‰ and 210 from -1. 22‰ to 3. 98‰, n=73, respectively. There is a robust negative linear correlation (R² > 0. 73, p-slope < 0. 001) between Δ 18 Oc-w (= δ 18 Oc -δ 18 Ow) and MAT (Figure  1 , Table  3 ). The correlation observed in bulk shell samples is indistinguishable from that in inner layer samples (Figure  1A ). On the contrary, the correlation obtained from outer layer samples shows a significant offset with both inner layers and bulk layer samples. This is in line with the differences between the outer and inner layers observed within the same specimen, which are in most 215 cases above analytical uncertainties and range from -3. 72 to -0. 19‰ (mean = -1. 41±0. 40‰; 2σ; n=17) and -0.
280 We use a published independent modern brachiopod δ 18 O dataset  (Bajnai et al. , 2018)  to test our equation against other previously published equations in their ability to predict environmental temperatures when δ 18 Osw is known (Figure  3 ). This dataset proposes two sets of δ 18 Osw for most data. One derived from direct measurements and one   (York et al. , 2004).
Locally measured δ 18 Osw correct for those effects, however those measurements are often scarce in time can be can be biased at the seasonal or inter-annual scale for brachiopods living above the base of the thermocline. For   2019 ), both derived from very similar datasets, has the only effect of slightly improving the precision of the estimates decreasing the scatter by ~ 0. 6°C around the mean deviation from environmental temperatures. This effect is about one order of magnitude below the remaining dispersal of data.