dataesr/scientific-paragraphs-categorization huggingface dataset:
The apparent calibration thus seems to be preserved. Hazard ratios between groups at risk. HRs between groups at risk are presented in Fig. 3. They were not significantly different. Calibration. The Kaplan Meier curves (green, Fig. 3 , see "calibration") seem to underestimate survival. The predicted survival curves in the two datasets (red and black) are superimposed for the first risk group, very close for the second risk group and more separated for the third group. Then, there is some similarity in the PI distribution in each risk group in the derivation and validation datasets. The empirical cumulative distribution functions of the PI by dataset and risk group, on Fig. 3S  (see Supplementary Figures), further support this.
Discussion A DQI, which qualifies the liver graft, is one of the key factors for a better matching between donor and recipient. As the existing DRIs were not discriminant (i. e. slope on the PI: 0. 57 (SE 0. 15) and 0. 64 (SE 0. 16) for the DRI and ET-DRI respectively, P-value < 0. 001) and miscalibrated (e. g. survival, according to DRIs categories, was not consistent between the construction and validation datasets) according to our dataset  4, 5  , because of population differences. We thus decided to create a DQI, which characterizes the graft in view of transplantation. It is based on Cox model adjusted on the recipients' characteristics. Three different ways to elaborate the model were tested. We explored new covariates that were not taken into account in the DRI and ET-DRI, defining the donor such as MDRD clearance or adjustment covariates such as MELD exception, decompensated cirrhosis or HCC.
2 ). The third group will  correspond to donors at higher risk that we may define as "extended criteria donors". As shown by Collett et al. 11  , the average MELD scores for recipient in the three groups at risk (20. 6, 20. 8 and 19. 3, respectively) were not different (p = 0. 06). The performance of the model remained satisfactory even after correction by the optimism. Finally, an external validation  4, 6  was performed; discrimination and apparent calibration were preserved in the validation dataset. Our study has some limitations. First, it is very difficult to evaluate the intrinsic quality of a graft since the allocation procedure associates to each donor a recipient. The quality of the graft is a function of the graft/recipient survival. We then assume that the allocation was appropriate.
The graft/recipient survival provides an indirect measure of the donor quality and thus can be considered as a surrogate variable. We assumed that this variable was highly correlated to the target variable, i. e. the graft quality, and was thus able to be extrapolated as an outcome. In prognostic models, discrimination measures are generally not high  12  which the case in our study. This occurred even when models were built in large datasets  2, 13. A selection bias has been explored as 858 recipient/donor pairs were removed from the model due to missing data. We compared the distributions of data of this subset to the data used in the analysis either for covariates belonging to the recipient (age, sex, cancer, decompensated cirrhosis, waiting time, medical condition before LT) or donor (age, sex, COD, liver type).
After Bonferroni correction, only the presence of a cancer and medical condition before LT were significant (χ 2 tests). Of note, only the covariate "presence of a liver cancer" was not part of the score. However, the frequency of liver cancer was higher in the analysis data set, which limits the occurrence of a potential bias. As the size was part of the DRI and the weight of donors is often difficult to establish due to fluctuations in hydration during the ICU stay, we gave more importance to the size and we did not include the weight in our study. Nevertheless, we tested the weight in the final model. The weight did not provide a pertinent information to be added in the model (p = 0. 82). This result is consistent with the DRI and ET-DRI models  1, 2. Macro/micro-steatosis was not included in the model since this information was not available in the French database.
e. increase in Akaiké criterion (AIC) and decrease in c-index, respectively). For example, in case-control studies, when a model is forced to include non-retrained covariates, the model often becomes overfitted. An overfitted model has a weak validity and the model would have insufficient discriminative power in new patients. Donor age was different between the Organ Procurement and Transplantation Network (OPTN) database 1 and the French database  4, 5. Since age below 70 did not exert a significant effect on recipient survival in our  database, the first three quartiles were grouped together. Age over 70 represented 25% in the French database compared to 4. 3% in the OPTN database. For COD, 60% of patients presented a CVA, 12% an anoxia and 25% a trauma. This distribution was quite different from that observed in the OPTN database  1  , which was 44%, 9% and 45%, respectively (p < 0.
However, Cuende et al. 18  included 3429 LT and showed that an ICU stay of more than six days represented a moderate risk (RR: 1. 21 [1. 1-1. 4]). Finally, concerning the external validation, the survival among the different DQI groups was not significantly different (HRs with large confidence interval). This may be due to a lack of power given that the follow up was shorter than in the derivation dataset. We were thus truncated the derivation data follow-up. A longer follow-up period would have been beneficial to obtain a better accuracy. Nevertheless, Kaplan Meier curves were well separated (Fig. 3 ). Then, the risk groups were discriminative. For the calibration step, an under-estimation was observed. This may be due to the specific structure of the DQI. Indeed, this score includes only a part of the PI of the original model (the β adjustment for recipient covariates were not included).
But, baseline survival was calculated on the original model, which includes all the covariates; both donor and recipient. The weights of the recipients' covariates then may play an important role in the estimation of S t ( ) deriv 0 , i. e. the baseline survival in the derivation dataset. We checked whether this hypothesis was consistent by computing the baseline survival on the derivation dataset, without the recipients' covariates (Fig. 4S , Supplementary Figures). Compared to those in Fig. 3 , the predicted survival was under-estimated. It confirms that baseline survival is affected by recipients' covariates. This result therefore impacts the seventh step of the external validation. Nevertheless, according to Kaplan Meier curves (Fig. 3 ), the apparent calibration and the discrimination were preserved in the validation dataset.
In a recent review, Flores and Asrani 14 suggested that the donor score might benefit from being updated. We aimed to create a flexible DQI model and adaptable. Indeed, if calibration is lacking with a validation dataset comprising a longer follow-up, and if the discrimination is preserved, a re-calibration of the DQI will be possible. This type of procedure is dedicated to be repeated yearly using LT data from subsequent years. This adaptive procedure will enable to gradually take into account the modifications of the graft allocation system. This approach may be of interest for other countries. Indeed, French allocation system is quite similar to most European systems. Moreover, in France, the MELD and MELD exceptions are also used such as in the United States of America. For an international-reader, our model is a tool for decision support in countries with a high activity of harvest of brain dead organ donors, with a significant increase of elderly donors, such as in Italy, Spain or Portugal thanks to a political proactive organ census and collection of old or very old donors.
We encourage these countries to make an external validation of the DQI on their own datasets. Furthermore, recently, another national index was introduced, the DLI (Donor Liver Index, which has been calculated in the French dataset (Table  1 ))  11. This score is an example that a national index gives complementary information to bring an aid to improving the evaluation of the quality of grafts. This DQI allows considering a next step to explore the optimal matching between donor and recipient and the "extended criteria donor" graft attribution, to improve the success of LTs, through sequential stratification method  3. The DQI will be used to qualify the graft. We also aim to create a score based on the survival benefit, which combines two models: a pre-transplantation model and a post-transplantation model.
Definitions of covariates. In France, all donors are cared for in intensive care units (ICU). The ICU length of stay is based on the number of nights spent, rather than the number of days. Therefore, a stay of 0 day is observable. For recipient, decompensated cirrhosis was identified in the database using the MELD score and the Child-Pugh score. All recipients presenting with cirrhosis of the liver, a MELD score ≥16 and a Child-Pugh B or C were considered as decompensated cirrhosis. Patients first-transplanted, without cancer and cirrhosis, were considered as having a non-cirrhotic liver disease. The estimated distance between donor and recipient centers was calculated on the basis of a geographic model taking into account road distances in minutes. MELD exceptions were identified and resulted in extra points while on the waiting list  19.
Score creation. In the derivation dataset, as in Feng's 1 and Braat's 2 studies we used a Cox model with adjustment on recipient characteristics to create the score (Table  1 ). We tested three different ways to elaborate this model: (1) A complete model with a covariate selection according to AIC; (2) An analysis using a two-step procedure. First, performing log rank tests with a threshold of 20% for the type I error for each covariate, according to the outcome. Then, followed by a multivariate model that included the selected covariates on the basis of the previous step with a selection threshold set at 20% for the type I error. (3) An analysis using the same first step of the procedure above was performed but followed by two multivariate models for donor and recipient, respectively. These models included the selected covariates on the basis of the first step with a selection threshold set at 20% for the type I error.
Internal validation is a necessary part of model development  25  , although according to Moons  20  , internal validation quantifies the predictive ability of a model on the derivation data (often called apparent performance  [26] [27] [28]  ). Internal validation was assessed using a bootstrapping procedure in order to quantify the optimism associated with the performance of the model. A seven-step procedure was performed according to the method described in Moons  20. The confidence intervals of the 3 indices were calculated using 200 bootstrap estimates. External validation. We performed an external validation of the DQI according to Royston and Altman  6, 24  ; the method described in a previous study  4. The external validation of a model explores the assessment of its performance on an independent database  6.
Model performance was evaluated considering two fundamental aspects: discrimination and calibration. Discrimination, known as "separation", allows differentiation of patients' prognoses through risk estimates from the model. Calibration reflects the prediction accuracy; if well-calibrated, a score assigns the appropriate probability at each level of the predicted risk  6. Comparison of the datasets. A comparison of the construction dataset with the validation dataset was performed using χ 2 tests or ANOVA, when appropriate. Regression on the prognostic index (PI) in the validation data. In order to obtain the prognostic index (PI), we used ln(DQI). The model fitted was as follows: β β + X ln(DQI) (2) DRI a djustment adjustment with X adjustment the covariates used in the DQI and β adjustment fixed at the estimated value in the original model.
Measures of discrimination. As in the construction step we calculated three discrimination indexes: Harrell C-index  22  , Gönen and Heller K statistic  23  , and Royston and Sauerbrei R D 2 24. Kaplan-Meier curves for groups at risk. We plotted the survival curves according to the groups at risk using the Kaplan Meier estimates. We also did a visual comparison of these curves with those of the construction dataset to evaluate the calibration. Indeed, if the survival curves of the construction and validation data for each group at risk were superimposable, then the visual calibration was considered as preserved. Discrimination can also be evaluated from the curves; the more the survival curves are separate the better the discrimination. Hazard ratios (HR) between groups at risk. HR were estimated for each group at risk using a Cox model.
The higher the discrimination, the larger the HR. Calibration. In this step, we estimated the baseline survival in the derivation: S t ( ) deriv 0 data, using Breslow's estimator  21. We then calculated the estimated survival function in the validation dataset as: = S t DQI S t ( , ) () val i deriv DQI 0 i. For each group at risk, we averaged S t ( ) i val , at each time-point, to obtain the expected survival curves. The same procedure was performed for S t DQI ( , ) deriv i. We then superimposed the predicted survival curves for each risk group in the derivation and validation dataset, and the Kaplan Meier curves observed for each group in the validation dataset. All analyses were performed using R software, version 3. 3. 0 (R Development Core Team, A Language and Environment for Statistical Computing, Vienna, Austria, 2016.
Acknowledgements The authors warmly thank  Sarah Kabani  and  Nova Smedley  for their expert editing of the manuscript. We also thank all the professionals who contributed to collect the LT Cristal database and all the contributed members of the  Optimatch research group. The present study was supported by the  Optimatch research program  funded by the  French Ministry of Health  through the  national Clinical Research Hospital Program. The views expressed are those of the authors.
to the difficulty in collecting such data over a time-period that allows for coral decline-recovery cycles to occur, few studies have examined the interplay between early recruitment, dynamics of juvenile and adult populations, and disturbances at a relevant time-scale  15. Consequently, there is still a critical knowledge gap in the ecological drivers of coral community trajectory, particularly during recovery processes that follow disturbances  16, 17. Here, we analyzed a unique long-term dataset to examine the interannual variability in cover (1991-2014; at a reference site) and abundance (2003-2014; across different locations and depths) of the three benthic stages of corals (recruits, juveniles, and adults)  18  on the outer reef slope of Moorea Island (17°30′S, 149°50′W) in French Polynesia (Fig.
1. 0 41 complemented by the package glmmADMB  42. Ab fij = β 0i + ε ij Level 2: β 0i = β 0 + u 0i Model outputs were then used to determine coral recovery using pairwise comparisons where the first year of survey was considered as reference. Post-hoc analyses were conducted at the level of stations, depths (all locations pooled), locations (all depths pooled), and for all stations pooled. The multcomp package  43  was used to perform these tests. Finally, we used non-metric multidimensional scaling (nMDS) to examine interannual variation in the composition of the coral assemblage based on the Bray-Curtis dissimilarity index of the cover of coral genera recorded annually. This analysis was encoded in R version 3. 1. 0 41.
Except for demonstrations of the large number of centronucleated fibers in muscle from aged Gaa -/-mice suggesting that degenerative and regenerative events have occurred  [14, 88, 89] , studies investigating the myopathic abnormalities occurring with the progression of the disease have rarely been performed. Therefore, the understanding of the skeletal muscle pathology is incomplete. In the present work, we considered two skeletal muscles from anterior and posterior limbs and four ages ranging from 1. 5 to 9 months and compared Gaa -/-mice to their wild-type (WT) littermates. Specifically, we histologically investigated the glycogen overload and cytoplasmic accumulation in both lysosome and autophagic vesicles over the course of the disease. We combined these data with biochemical and infrared microspectroscopy approaches.
Histological and immunofluorescence analyses The Tibialis anterior (TA) and Triceps brachii (TB) muscles were dissected, positioned on tragacanth gum, snap-frozen in isopentane prechilled in liquid nitrogen and stored at -80 °C until sectioning. Transverse muscle sections (10-μm thickness) were stained with hematoxylin-eosin-saffron (HES) or periodic-acid-Schiff (PAS). The microscope images were viewed under a Nikon Eclipse 90i® microscope (Nikon France, Champigny sur Marne) using NIS-Elements software (Nikon, Champigny sur Marne, France). To study the CTX-injected muscles, four transverse frozen sections (10-μm thickness, each separated by 100 μm intervals) were considered to ensure that the entire injected area was included. The muscle sections were blindly analyzed by three veterinary pathologists certified by the European College of Veterinary Pathologists (TL, FF and MAC).
In the CTX-injected muscles, macrophage infiltration was expressed as the percentage of the muscle section area occupied by F4/80 immunolabeling since the vast infiltration did not allow for counting each cell separately. For that, the same threshold was applied to each picture using Fiji software. To investigate anisocytosis, the minimum Feret diameter (MinFeret; shortest distance between two parallel tangents of the muscle fiber edges) of the dystrophin-immunolabeled muscle fibers was used and determined semi-automatically. The recognition of the muscle fiber perimeter was manually controlled to prevent potential errors. The regenerative activity was measured by counting the number of fibers expressing the developmental isoform of the myosin heavy chain (dMyHC) relative to the total number of muscle fibers in the field.
Infrared microspectroscopy Transverse sections (10-μm thickness) of TA muscle from 1. 5-and 9-mo-old mice (n = 3 Gaa -/-and WT mice per age) were placed on Zinc Sulfate windows (Crystran Limited, Poole, UK). An infrared (IR) spectrometer Tensor 27 coupled to a Hyperion 2000 microscope (Bruker, Billerica, MA, USA) was used to collect spectra in the range of 4000-700 cm -1 at a spectral resolution of 8 cm -1 and a spatial resolution of 20 μm × 20 μm. In total, 100 muscle fibers were selected from each sample, and an average of 300 scans per fiber resulted in a collection of 100 spectra per sample. Using Unscrambler® software, the spectral data were first baseline corrected and unit vector normalized. The second derivatives of the spectral data were assessed (9-point Savitzky-Golay filter) to enhance the spectral resolution of the absorption bands.
The second derivative IR spectra were analyzed by applying a principal component analysis (PCA). The computation of the principal components was based on the non-linear iterative projections by the alternating least-squares (NIPALS) algorithm. While the score plots allowed for a comparison of the IR spectra, the corresponding loading plots revealed the main characteristic absorption bands. The three major peaks at 1180-950 cm -1 , 1770-1720 cm -1 and 3100-2800 cm -1 were assigned to glycogen, esters and lipids, respectively, according to the literature  [43]. The comparisons among the samples were performed by using OPUS® software to measure the area under each assigned peak with normalization against the protein amides peak area (1720-1480 cm -1 ).
Statistical analysis The data are expressed as the average ± standard error of the mean (SEM). One-way analysis of variance (ANOVA) was performed, followed by a Sidak multiple comparison post hoc test as appropriate, to reveal the influence of the age of the Gaa -/-mice on the variables of interest. To compare the impact of both the status (Gaa -/-versus WT) and age (1. 5, 4, 6 and 9 mo) of the animals on the variables of interest, a two-way ANOVA was used, followed by the application of Sidak multiple comparison post hoc tests to both factors. The Sidak test is designed to compensate for the inflation of first-type risks, which are well-known phenomena occurring in multiple comparison procedures. These statistical analyses were performed using GraphPad Prism v. 6. 0 (GraphPad Software, La Jolla, CA, USA).
5-and 9-mo-old Gaa -/-and WT mice using the infrared IR microspectroscopy approach. As shown in Fig. 2a , the bands assigned to the carbohydrates of glycogen (IR absorption bands 1152, 1080 and 1025 cm - 1 ) were elevated in the Gaa -/-mice in a comparison of the IR spectra obtained from muscle fibers from Gaa -/- and WT mice at each age considered. A multivariate analysis was implemented to identify the possible trends in the changes observed in the glycogen spectral data set. A PCA was performed on the second-derivative spectra calculated from all spectra acquired considering the spectral region 1400 cm -1 to 950 cm -1 (Fig. 2b ). The score plot revealed the following two independent clusters using the first two components, i. e. , PC1 and PC2, which represent 80% and 4%, respectively, of the total variance: the first cluster grouped the Gaa -/-mice, and the second cluster grouped the WT mice without clearly separating the old and young mice in each group.
Acknowledgements We thank  Fabien Le Grand  ( Centre de Recherche en Myologie, Faculté de Médecine de la Pitié Salpétrière, Paris, France ) for his contribution to the establishment of the cardiotoxin induced-muscle injury protocol. We thank  Ganna Panasyuk  (Inserm  U1151/CNRS UMR 8253 ,  Necker Enfants Malades Institute (INEM) ,  Faculté de Médecine Paris Descartes, Paris, France)  for helpful discussion and improving the manuscript. Additionally, we thank the technical staff of  Oniris rodent facility (Nantes, France ) for the animal care. The authors also thank  Chantal Thorin  ( NP3 unit, Nutrition, Physiopathologie et Pharmacologie, Oniris, Nantes, France ) for assisting with our statistical analysis.
Funding This work was supported by grants from the  Région Pays de la Loire  and the  National French Academy of Medicine  and " Investissement d'Avenir- ANR-11-INBS-0011 " -" NeurATRIS: A Translational Research Infrastructure for Biotherapies in Neurosciences ". The academic theses of  Lydie Lagalice  and  Julien Pichon  were financed by  Oniris and the "Ministère de l'Enseignement Supérieur et de la Recherche ", respectively.
All images were analyzed by the two independent readers using the outer retina to choriocapillaris (ORCC) slab, which allows a good visualization of type 1 and type 2 CNV important features  [30]. Quantitative analysis of CNV size was performed on the same slab, using free image analysis software (ImageJ, open-source imaging processing software, 2. 0. 0-rc-43/1,51 K). Quantitative and qualitative changes on OCTA were then compared between the two groups and correlated with bestcorrected visual acuity (BCVA) and exudation signs on structural spectral domain (SD-OCT).