dataesr/scientific-paragraphs-categorization huggingface dataset:
| Statistics All the analyses were carried out using Stata software (version 13, StataCorp, College Station, Texas) and R 3. The percentage of PLHIV who considered that the pharmacist dispensing the ARVs ought to offer medication interviews, and the percentage of PLHIV who would have accepted the medication file if it were offered are presented with a 95% confidence interval. A multiple correspondence analysis (MCA) followed by a mixed unsupervised classification (k-means clustering applied to the partition obtained from an ascending hierarchical classification using Ward's distance) was also implemented to (a) study the relations between the modalities of the variables and (b) determine PLHIV profiles (clusters of individuals sharing closely similar characteristics). For this analysis, the variables were chosen according to clinical relevance and to statistical distribution (parameters always present or always absent were not considered)-as age, gender, in work, ARV duration, ARN VIH, comorbidities, etc listed.
The centers and pharmacies involved were probably already interested in the subject. However, the number of centers and pharmacies, their different sizes, and their geographic location throughout the French territory could overcome a part of this recruitment bias. Moreover, the "in a given week" survey methodology with a 94% patient participation rate compensated for the selection bias of PLHIV. Besides, the characteristics of the PLHIV in this study seem to be the same as those of the PLHIV in the French hospital database on HIV infection (FHDH), with a sex ratio of 2/3 men due to the epidemic among MSM in our country. Precisely, in 2016 in France, median age of PLHIV was 49. 9 years old [IQR: 41. 6-56. 9], 65. 4% were male and 61. 2% were born in France, 89. 7% had undetectable VL, and 68% had CD4 cell count> 500 mm 3.
Concerns about medication file were well examined by Gilbert in 2016 and Barnes in 2020, in terms of adherence rate, VL, and CD4 count in clinics-highlighting the impact of HIVtrained pharmacists embedded in clinical services. 23, 24 In our study, the high level of PLHIV with undetectable HIV VL (nearly 90%) is maintained whatever the mode of dispensing. Despite this high level, the large acceptance of the patient medication file (80%) might encourage pharmacists and compel them to offer more clinical activity as patients will age and be prescribed treatments for comorbidities. In some countries, adequate checks for drug interactions are facilitated by an online medication file or a merging of pharmacies' data to a central database. In the same way, it is a pity that only 7% of patients felt well cared for because "medication advice" was offered to them, that only 7% considered that their pharmacist could help their compliance, and only 9.
The bunch is then tracked using the ELEGANT code  [45]  through the rest of the machine up to the DBD screen. Effects of linac wakefields  [46, 47]  and the laser heater, and 1D CSR  [48]  and LSC models  [40]  are included in the tracking. The code computes the impedances of these collective effects based on a binned histogram of the longitudinal density distribution. The effect of the transverse deflector and propagation of the beam to the diagnostics line is also included, providing a complete simulation of the measurement. By comparing the measured and simulated bunching factors, it is possible to determine an optimal setting for the number of bins (and the number of macroparticles) to be used in simulations of the microbunching instability. In our case, since the ratio between the initial density modulation wavelength and the bunch length in the laser heater undulator is known, it is possible for the simulation code to find a balance between underestimating and overestimating the effect of CSR and LSC  [49].
It was found that all three compression schemes resulted in similar maximum bunching factors. In the case of bunches compressed using only the first bunch compressor, the modulations on the bunch did not persist for even moderate settings of the laser heater energy, as a result of increased Landau damping for a bunch that propagated for a longer time with a short bunch length. On the other hand, the BC2-only compression scheme allowed for the manipulated bunches to persist with strong bunching over a wider range of laser heater energies. We used a high-fidelity simulation of the Fermi injector using the GPT code, based on experimentally measured photoinjector laser parameters and including collective effects such as 3D space charge and geometric wakefields in the rf cavities. The simulated injector bunch was then tracked through the remainder of the accelerator lattice using ELEGANT, a code that is well suited to simulating the microbunching instability, as it can both tackle large numbers of particles relatively quickly and model the most pertinent collective effects (LSC and CSR).
Given that the properties of the bunch longitudinal phase space are well defined at the exit of the injector and in the laser heater, we were able to produce a comparison between the simulated and experimental measurements of the bunching at the exit of the linac, analyzing both the modulation depth and the final bunching period. In terms of the latter parameter, the simulation was able to reproduce accurately the measured results, demonstrating that this small-scale structure can persist through a long, complex accelerator lattice. The ELEGANT code employs 1D models for longitudinal space charge and coherent synchrotron radiation impedances, but it is still able to capture the physics of microbunching in its 2D content. This systematic comparison of microbunching in simulated and experimentally measured beams across a wide range of parameters suggests that the results from this code can be relied upon for future accelerator designs in which microbunching is expected to be an issue.
Patients And Methods This observational cross-sectional study was conducted using data from the REIN registry (Nephrology Information and Epidemiological Network) nested in the Alsace region (Northeastern France) and was approved by the Comité consultatif sur le traitement de l'information en matière de recherche dans le domaine de la santé (CCTIRS) and the Commission nationale de l'informatique et des libertés (CNIL). The following demographic data and clinical characteristics were collected in all patients treated by chronic haemodialysis in Alsace (n = 1000) between October 2011 and April 2013: 1. Age, gender 2. Weight, height, waist circumference (WC) and body mass index (BMI). WC was measured in each centre by the same operator according to the method described by the World Health Organisation (WHO)  (27).
20 given its acceptance as a major prognostic factor in the dialysis population. Univariate and multivariate analyses were also repeated for studying the relationship between risk factors and coronary heart disease, PAD stage 3-4, stroke and CHF individually. The relationships between MACE and the various associations of the MetS criterion, but always including MsWC, were studied using the same univariate and multivariate models as described above. The nonparametric Wilcoxon test was used to study the number of cardiovascular events between groups (abnormal distribution of the data). All statistical analyses were performed with the STATA v14 software (Statacorp LLC, Texas, USA).
Unsupervised algorithms mainly gather centroid-based clustering methods  [2, 3] , density-based methods  [4, 5] , biological clustering methods  [6, 7]  and graph-based methods  [8] [9] [10]. Supervised methods can be separated into two groups-methods using metrics and machine learning methods. Methods using metrics, such as Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID)  [11] [12] [13] , aim to match pixel spectra with a spectral database or learning samples. Machine learning algorithms have been studied a lot for hyperspectral classification. Many pixelwise classification methods exploiting only the spectral dimension were proposed, Figure  1. Spectral reflectances of different types of materials acquired from the two different hyperspectral instruments HySpex and HyMap: synthetic tarpaulin (HySpex), synthetic greenhouse (HyMap), dry grass (HySpex) and green tree (HyMap).
• The second objective is that the classification method can be applied on hyperspectral images acquired at different spectral and spatial resolutions by common airborne hyperspectral sensors. Machine learning methods may be quite accurate when the trained model is applied on images acquired with the same sensor and the same spatial resolution as the training samples, but may be less efficient when it is applied on images acquired with other sensors (transfer ability) because they are generally not trained for more than one sensor. CHRIPS method is based on the computation of criteria that are robust to the sensor change. The paper is organized as follows. In Section 2, an overview of classification method CHRIPS is presented. Section 3 presents hyperspectral datasets used to design and assess the CHRIPS method.
This processing is described in Section 6. The full processing chain of CHRIPS is presented in Figure  2. CHRIPS class criteria only use spectral information-spatial dimension is not taken into account. However, the regularization processing takes into account neighboring pixels. Otherwise, spatial features are not used by CHRIPS criteria. Many machine learning methods, notably deep learning methods, exploit spatial features and give quite accurate results. However, in practice, such spatial features are hard to gather in hyperspectral images. Intraclass spatial information is sometimes accessible but interclass spatial information is rarer. Currently, there is no database dedicated to hyperspectral remote sensing which includes a lot of annotated data and then would make possible to learn spatial features, on the contrary to many other types of images where deep learning could be more easily applied: traditional color images, medical images, and so forth.
2 and I 8. 8 were simulated using a dedicated processing chain  [47]. A first atmospheric correction is applied to airborne radiance using the COCHISE code (atmospheric COrrection Code for Hyperspectral Images of remote SEnsing sensors  [48, 49] ). COCHISE retrieves the surface spectral reflectance as well as the water vapor content from a sensor-level radiance image. It removes atmospheric and environment effects. The atmospheric radiative parameters are computed using MODTRAN 5  [50]. Spectral reflectances are resampled to the same spectral resolution (3. 5 nm in VNIR and 7. 5 nm in SWIR). Then, satellite simulated radiance is computed with the Comanche code  [48]. Spatial aggregation, noise and point spread function are applied following the specifications of the future hyperspectral satellite Hypxim  [51].
Selection of Training Samples CHRIPS method sorts the pixels into thirteen different classes as described in Section 2. For each class, criteria are defined from a set S 1 ρ of reflectance spectra that are used as training samples: S 1 ρ gathers around 9500 reflectance spectra. These training samples are extracted from a dataset S I composed of the six following hyperspectral images: Fauga (HySpex) and five Odin images (more details in Section 3. 1) that were interpolated at HySpex spectral bands (spectral resolution of 4-6 nm). These samples aim to cover as much spectral variability as possible for each class and to allow the design of criteria that are as robust as possible. The sample collection has been enriched several times by iterating the following process: design of optimal CHRIPS criteria from the training samples S 1 ρ (see Section 5) -application of CHRIPS on the set of images S I identification of misclassified pixels update of S 1 ρ : addition of new training samples from misclassified pixels Depending on the spectral variability of each class and the discriminating power of CHRIPS criteria, the number of training samples varies from class to class: dark green vegetation (240 spectra), water(400) unidentified dark surface (350), plastic matter (aliphatic and aromatic: 510), carbonate (250), clay (210), dense green vegetation (630), sparse green vegetation (660), stressed vegetation (940), house roof/tile (1230), asphalt (800), vehicle (3030), non-carbonated gravel (250).
Atmospheric Correction Reflectance can be estimated from hyperspectral radiance images by applying atmospheric correction methods based on radiative transfer codes  [48, [52] [53] [54] [55] [56]. In this paper, all HySpex, Odin and AisaFENIX hyperspectral images were corrected with the COCHISE code  [48]. The Garons image (HyMap sensor) was corrected by the DLR. As some noise may be present on reflectance spectra after atmospheric correction, a smoothing processing is proposed in next section to reduce its effects.
Several tests on data led us to use the following values: σ 1 = 0. 01, σ 2 = 2 and σ 3 = 2. Two hyperspectral images are created: an image I B from bilateral filtering and an image I G from Gaussian filtering. CHRIPS criteria are applied to I B for classes associated with materials with specific absorptions because it is crucial to preserve the absorptions. For other classes, the CHRIPS method is applied to I G because the Gaussian filter allows a better attenuation of the noise. In practice, CHRIPS method classifies each pixel p by following the order of the classification tree described in Section 2. For notation, ρ G and ρ B are respectively the spectral reflectances of pixel p in I G and I B. First, criteria of dark green vegetation class are applied to ρ G (no specific absorption for this class).
Methodology Classification performances are assessed on six hyperspectral images, as described in Section 3. 3-Fauga, Mauzac, Garons and suburban Mauzac (three images). Some parts of the Fauga image were used for training, but classification results are still interesting to analyze. No training samples are extracted from Mauzac, Garons and surburban Mauzac images. Three-band false color images of the hyperspectral data sets of these images and corresponding ground truths are illustrated in  Figures 15, 17, 18 and 20. CHRIPS is an expert spectral classification method for which criteria were estimated once for all by a supervised way. In order to assess classification performances, images are classified with CHRIPS method including spatial regularization and three widely used supervised methods that exploit only the spectral dimension-the Convolutional Neural Network proposed by Hu et al.
Garons Image Classification maps are presented in Figure  18. Garons image was not used for training. The scene is very different from Fauga and Mauzac images. Vehicle class is not present in this image but clay class appears (plowed fields, dirt roads). Precision and accuracy are quite high for every class for CHRIPS (0. 81 ≤ F 1 ≤ 0. 96) except for the asphalt class (F 1 = 0. 65). Weak detection of asphalt is due to the fact that pixels associated to roads are often not spectrally pure (spatial resolution of 4 m). Performances of CNN-1D, SVM and RFC are overall much lower than those of CHRIPS for this image. HyMap sensor has fewer spectral bands (125) than HySpex (416) and its spectral resolution is lower (15 nm versus 3-6 nm for HySpex)-the impact of spectral resolution can then be observed in this dataset.
Reflectance spectra are smoother in this image because HyMap has less spectral bands than HySpex and reflectances are possibly smoothed during atmospheric correction. It can have a significant impact on the performances of SVM and RFC. But the main reason for their poor performance is that both methods are very sensitive to training samples. For example, for stressed vegetation, Garons image contains pixels for which reflectance spectra may not be included in spectral variability of this class in the training dataset S ρ. Then, SVM hyperplanes and RFC decision criteria are not adjusted to correctly classify them. This is also the case for CNN-1D because the performances of convolutional networks strongly depend on the representativeness of the training samples. On the opposite, CHRIPS criteria dedicated to stressed vegetation are based on geometric features that are robust and allow an accurate detection.
Indeed, this criterion is opposite to the criterion dedicated to clay detection. In this case, the absorption of clay prevails and then CHRIPS considers the presence of clay. In vineyard 2, the clay content of soil is lower and a local maximum can be observed around 2200 nm, all criteria for vegetation are true and CHRIPS classifies it as green vegetation. Another advantage of CHRIPS is that its criteria have been defined from a reduced set of training samples (19,000 samples, see Section 3. 2) on the contrary to deep learning networks which require huge datasets to be set. In practice, the selection of training samples is not an easy task. Ground truth is often focused on very local areas, and samples from all existing classes are not necessarily available. Moreover, spectral variability (and spatial variability for 2D or 3D networks) of each class is rarely fully characterized with training samples.
In CHRIPS method, classes with specific absorptions do not need exhaustive datasets to be characterized. They just require the spectral location and typical depths of its absorption bands. It is the same for vegetation classes-they are characterized with geometric patterns. Other classes characterized by CHRIPS are described with dedicated indices-these ones need datasets that are as exhaustive as possible to account for spectral variability. Spectral information is sufficient to reach high accuracy (see Table  2 ). Spatial context can be very informative to reduce the number of unclassified pixels using the regularization step but is not essential.
As of May 5, 2020, the number of confirmed coronavirus disease (COVID-19) cases has been more than 3. 5 million with 243,540 deaths. We aimed to determine the associations between ageing population, median age, life expectancy at birth and COVID-19 mortality. The numbers of COVID-19 cases and deaths in the European region were obtained from the World Health Organization database. The data on percentage of the population aged 65 and over, median age and life expectancy at birth were extracted from the World Factbook of Central Intelligence Agency. A total of 56 countries/areas in the Europe reported COVID-19 cases and deaths (1,121,853 cases and 100,938 deaths) on April 20, 2020. The results showed significant positive associations between COVID-19 mortality and ageing population (r =0. 274; P =0.
First, COVID-19 and healthcare resources (e. g. , Germany had to our knowledge, this work was the first to estimate the 29. 2 critical care beds per 100,000 while Italy had 12. 5) association between COVID-19 mortality with ageing in Germany [21, 22]. We hoped that multiple linear population, median age, and life expectancy at birth in regression analysis should be used to adjust for Europe. Second, since our study included 56 European confounding factors. However, it was difficult for us to countries/areas involving 100,938 deaths and 1,121,853 get the data, such as the number of health-care workers, confirmed COVID-19 patients from the WHO physicians' density, hospital beds, hospital beds density, databases, our results were reliably representative. medical care costs and other medical facilities from 56 Third, the subpopulation analyses (included total countries/areas in the European region.
Statistical Analysis Statistical tests and graphic representations were performed with GraphPad Prism 6 (GraphPad Software, San Diego, CA, USA). All the data are presented as mean ± standard deviation (SD) of independent experiments with cells from different donors that were pooled. Significant statistical differences were calculated using one-or two-way ANOVA (analysis of variance). A p-value less than 0. 05 was considered significant for the ANOVA. If significance existed, a post-hoc analysis was performed using the Bonferonni post-tests to evaluate significance for all experiments. For mixed lymphocyte reaction (MLR) results, the Mann-Whitney test was used to determine the significance between two groups. The difference was considered significant when the p-value was below 0. 05.
ICU admission mHLA-DR expression and β-d-glucan (BDG) failed to predict secondary invasive Candida sp infection. Time-dependent causespeci c hazard ratio of ICI was 6. 56[1. 24-34. 61] for mHLA-DR < 5000 Ab/c and 5. 25[0. 47-58. 9] for BDG > 350pg/mL. Predictive negative value of mHLA-DR > 5000 Ab/c and BDG > 350 pg/mL combination at day 7 was 81% [95%CI 70-92]. Conclusion: This proof of concept study suggests that mHLA-DR predicts ICI in high risk patients with septic shock. The added value of BDG and other fungal tests should be regarded according to the host immune function markers. Trial registration: ClinicalTrials. gov identi er: NCT03136081 Take home message: Combination of monocytic HLA-DR expression and beta-D-glucan in patients hospitalized for septic shock failed to predict invasive candida infection but could be useful to rule out infection.
Study setting and patients This prospective observational study (ClinicalTrials. gov identi er: NCT03136081) was performed in two adult ICUs of a University hospital from June 2014 to May 2015. The study was approved by the ethic committee (Comité de Protection des Personnes Sud-Mediterranée III, 2014-A00500-47) and followed the STARD guidelines  [15]. In accordance with French law, informed consent was obtained by the patient or his/her next of kin. Were included all consecutive adult patients admitted to the 2 participating ICUs with non-fungal septic shock or adult patients who developed septic shock during the ICU stay. Non-inclusion criteria were neutropenia (de ned as a total leukocytes count < 500/mm3), immunosuppressive therapy, cancer related chemotherapy in the last year, history of bone marrow or solid-organ transplantation.
Baseline assessment and clinical data collection Clinical data were recorded at ICU admission: demographic characteristics, severity of underlying medical condition according to simpli ed acute physiology score II (SAPS II) [18], Sepsis-related Organ Failure Assessment (SOFA) score  [19] , the presence of comorbidities, reason for admission to the ICU and cause of septic shock. During the ICU stay, the following data were collected at days 0, 2 and 7; SOFA score, length of stay, duration of mechanical ventilation, need for vasopressor and survival at ICU discharge. Antibacterial and antifungal drugs were given according to the recommendation applied in a same way in both ICUs. Immunological data EDTA-anti-coagulated tubes were collected on each visit day (days 0, 2 and 7) for blood cell count, lymphocyte phenotype, CD4 T-cell, CD8 T-cell count and mHLA-DR.
Mycological data Mycological samples were collected in case of infection suspicion according to the physicians in charge of the patient. Candida colonization was screened at days 0 and 7 in at least 3 sites from urine, gastric aspiration, tracheal aspiration and cutaneous swab. Candida Score  [22]  was calculated for each visit day. Colonization was de ned when one nonsterile sample site was positive for Candida sp. Multifocal colonization was de ned when more than one non sterile sample site was positive for Candida sp. Standardized procedures for in vitro identi cation of microorganism were used according to the usual procedures of the local mycology laboratory.