dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion In telecare, most sensors require an action from the patient, e. g. , it needs to put the hand inside the cuff of a sphygmomanometer for measuring blood pressure. Moreover, for some of the critical information, there are no sensors; for example, no sensor can measure the pain's location. However, the more sensing actions, the more inconvenient for the user, and patients will eventually opt out if a system asks too many questions. On the other hand, holistic treatment is only possible with all the information. Hence, in Extended Hapicare, we have applied BN for diagnosis, which resulted in semi-holistic treatment with minimal data. The evaluation results support the selection of the BN; they show the performance of diagnosis with minimum information; for both datasets, the results are hugely in favor of the BN.
Albeit having more than half of the data, BN and random forest performed similarly. On the other hand, one of the common pitfalls is depending too much on specific causes for diagnosis, which results in a biased prediction. In medical diagnosis, the causes only affect the general probability of a medical condition, and a diagnosis is only possible using the effects, also known as symptoms. The robustness analysis shows that the BN's training is efficient and learning about all the causes is not enough for the diagnosis of CKD in the first dataset. However, for the second dataset, there are only two causes, and they have similar values for most of the records; hence the BN training is not robust. The scenarios presented in Section 4. 2 demonstrate how Extended Hapicare can help patients and doctors with screening and monitoring of medical conditions/episodes.
These constraints indicate that Titan is internally differentiated and has a rigid outer ice shell or crust, a subsurface ocean of liquid water and a high-pressure layer of water ice underneath the ocean  (Iess et al. , 2012). The solid core is decoupled from the other layers and comprises a rock or rock-ice mix  (Zebker et al. , 2009; Hemingway et al. , 2013; Mitri et al. , 2014; Tobie et al. , 2014). The outer ice shell is mainly composed of Ih water ice, likely mixed with methane hydrate, particularly at shallow depths  (Tobie et al. 2005; Lunine and Lorenz, 2009). Estimates of the current thickness of the ice shells were carried out using Cassini data and they vary according to the methods employed. Nimmo and Bills (2010)  suggested that Titan has a floating, isostatically compensated ice shell with a mean thickness of 100 km.
In such places, the aqueous organic phase could have remained liquid for periods of up to some hundreds of years, long enough for hydrolysis to convert Titan's atmospheric organics into oxygen-containing prebiotic molecules such as amino acids. In a sequel paper,  Artemieva and Lunine (2005)  simulated the fraction of liquid water produced by hypervelocity impacts over the entire Titan's history, suggesting that transient melting associated with newly formed craters existed over fractions (ca. 10%) of the surface. They also pointed out that the largest impactors would be able to reach an ammonia-water mantle layer below the ice shell, creating a circular albedo contrast and not a crater. However, these models were developed before the Cassini data and before more recent models of Titan's structure were developed.
The selection of Menrva crater was based on the assumption that a crater as large as this would likely have more chances of breaching the ice shell, assuming an ice shell thickness similar to current day. For this purpose, we used the iSALE-2D shock physics code  (Melosh et al. , 1992; Ivanov et al. , 1997; Collins et al. , 2004; Wünnemann et al. , 2006). We also employed radial structure profiles, i. e. , profiles starting at the center of the crater and extending up to the rim, spanning the minimum and maximum estimated values for Titan's ice thickness of 50 and 125 km, which follows the estimates made by several authors  (Nimmo and Bills, 2010; Béghin et al. , 2012; Mitri et al. , 2014; Hemingway et al. , 2013; Vance et al. , 2018).
Impact record on Titan Crater mapping on Titan is challenging due, first, to the restrictions in terms of available methane atmospheric windows in Titan's dense atmosphere that can be used for optical and infrared remote sensing measurements; second, by the spatially limited and relatively low spatial resolution radar data acquired by the Cassini mission. These challenges are amplified by the combination of erosional and depositional processes that alter crater morphology leading, in the long term, to the partial, and likely considerable, degradation of the impact record  (Lorenz et al. , 2007; Jaumann et al. , 2009; Wood et al. , 2010; Neish and Lorenz, 2012; Neish et al. , 2013 Neish et al. , , 2016;; Werynski et al. , 2019; Hedgepeth et al. , 2020). The number of craters on Titan is notably small in comparison with the expected impactor flux in the Saturnian system and with the crater density of other Saturnian moons  (Zahnle et al.
1 )  (Lopes et al. , 2010; Wood et al. , 2010; Williams et al. , 2011). The western portion of the crater seems to be more eroded and exhibits channels cutting through the outer rim with transport from the western ejecta blanket into the crater. Dune like features can be seen to the south of the central peak area but inside the main ring. The main morpho-structural features of Menrva crater can be analyzed using the available remote sensing data acquired by the Cassini mission during its flybys of Titan, through three of its imaging sensors: Imaging Science Subsystem (ISS), Visible and Infrared Mapping Spectrometer (VIMS), and RADAR. These morpho-structural features are indicated in Figs. 2  and 3 ; in Fig. 3  the topographic transect represents the topographic lows, such as the annular troughsin cool colors (shades of blue and cyan) and the topographic highs, e.
g. the central peak-ring and ring crests in hot colors (orange and red). Fig. 2  is from the near-infrared atmospheric window at 0. 938 μm, which can see through Titan's haze but it is still affected by atmospheric interference  (Brown et al. , 2004; Porco et al. , 2004). The original map from which the Menrva ISS image was taken was produced in June 2015, using data collected through Cassini's T100 flyby of Titan. The spatial resolution of this image is 4 km/pixel. A topographic transect of the Menrva crater, based on Cassini RADAR SARTopo data (Beams 2 and 3,  Stiles et al. , 2009 , Corlies et al. , 2017)  and on data acquired byRADAR is shown in Figs. 3  and 4. In addition to the crater's main morpho-structural features, there is also an indication of a subtle outer ring at 273 km from the center of Menrva, which may or not be related to the crater formation process.
The location of the SARTopo  (Stiles et al. , 2009)  data stripe over the SAR image of the Menrva crater and its surrounding areas is shown in Fig. 3. The SAR image swath was acquired across the entire crater, passing through its center. The topography along with the profile, represented in color in this figure, is shown in Fig. 4. Recent studies analyzed the superficial and the near subsurface layer of Menrva, from the first few microns of the surface, with Visual and Infrared Mapping Spectrometer (VIMS) data, down to 1 m using RADAR in particular radiometry data  (Werynski et al. , 2019; Solomonidou et al. , 2020a ) (Fig. 5 ). Menrva's surface has been analyzed using a radiative transfer code on VIMS data (e. g. ,  Solomonidou et al. , 2018)  from T114 (11/2015) and found that it is significantly different in values and spectral responses from those of the dunes and plains crater groups.
Modeling methods To simulate the formation of a Menrva-like crater, we used iSALE-2D, a multi-material, multi-rheology shock physics code  (Melosh et al. , 1992; Ivanov et al. , 1997; Collins et al. , 2004; Wünnemann et al. , 2006) , which is based on the SALE hydrocode solution algorithm  (Amsden et al. , 1980). Our model inputs are consistent with the earlier modeling study that examined the formation of impact craters on Europa and in terrestrial ice sheets  (Silber and Johnson, 2017; Silber et al. , 2021) , with a few minor adaptations, such as a surface gravity appropriate for Titan. Here we briefly describe the model setup, with further details provided in the Supplemental Material. Due to the axial symmetry of the model, in the simulation runs an icy projectile strikes vertically. The target is composed of an ice shell with the conductive-convective layer structure, overlying an ocean (Fig.
Introduction: Vital status is of central importance to hospital clinical research. However, hospital information systems record only in-hospital death information. Recently, the French government released a publicly available dataset containing death-certificate data for over 25 million individuals. The objective of this study was to link French death certificates to the Bordeaux University Hospital records to complete the vital status information. Materials and Methods: Our linkage strategy was composed of a search engine to reduce the number of comparisons and machine-learning algorithms. The overall pipeline was evaluated by assembling a file containing 3,565 in-hospital deaths and 15,000 alive persons.
INTRODUCTION Ascertainment of the vital status of individuals is of central importance to epidemiological studies that monitor mortality as an end point. 1 Death-related information is recorded in the hospital information system only if the death occurred at the hospital; otherwise, such information is often missing. 2 n December 5, 2019, the French task force for Open Data made publicly available the death certificates established since 1970. This dataset, referred to as the French Death Master File (DMF), enables completion of patient vital status information in the internal registration system of hospitals. In this context, Bordeaux University Hospital launched an initiative aiming to complete vital status information by identifying extrahospital deaths with the French DMF dataset. We report the approach used and the results achieved.
Description of the Dataset The Bordeaux hospital registration system contains administrative information on 2. 2 million patients since 2005 and a total of 54,892 in-hospital deaths. The French DMF contains mortality data for over 25 million deceased individuals since 1970. Approximately 8. 8 million death certificates were produced since 2005. Table  1  shows the common attributes of the two data sources. The hospital registers two last names, the birth name and the used surname, whereas death certificates contain only one last name. There are also differences concerning the first name. French people can have one or more first names; one is used in daily life and the others solely for official documents. 12 The hospital registers only the first name used in daily life whereas death certificates contain all of the first names.
Overview of the Record-Linkage Strategy Step 1: Blocking strategy Elasticsearch TM is a distributed, document-oriented search engine, built on top of Apache Lucene 1. Elasticsearch TM was chosen for its scalability, its speed performance and its scoring metric based on the popular TF-IDF (term frequency-inverse document frequency). The French DMF dataset was indexed in Elasticsearch TM version 7. 6. 1. A query was sent to Elasticsearch TM to find candidate death certificates for a given hospital record. The blocking strategy consisted in selecting only the first N search results and submitting them to the pairwise comparison in step 2. The number of Elasticsearch TM results, N, was set with the gold standard created in step 2. Step 2: Machine learning.
Gold Standard The goal of the ML step is to compare one hospital record with one candidate death certificate and to output a match probability. To train a ML algorithm, a gold standard containing both true and false matches must be created. We used a deterministic approach to find true matches. To identify them correctly, we leveraged the fact that in-hospital deaths have two more matching variables: the date and the department of death. Seven common fields were available: last name, first name, date of birth, zip code, gender, date of death, and department of death. Keeping the date and department of death as blocking variables, the matching strategy was relaxed to capture differences in one of the remaining fields. Clerical review when only one variable mismatched made us confident that differences were due to errors in one of the two data sources and no records were falsely linked.
These pairs of true matches were next used to identify incorrect matches. The difficulty in building a gold standard is to find examples in the gray zone,  13  where pairs of records do not have an exact match agreement, for the algorithm to learn the importance of each variable. For each true match, a search query was sent to Elasticsearch TM and the incorrect match with the highest score was retained. By doing so, we generated a balanced dataset, that is, there were equal numbers of samples from the positive and negative classes. The optimal number of results N in step 1 was estimated by recording the rank of the correct death certificate for each true match.
Evaluation of the Pipeline The overall pipeline was evaluated by assembling a file in which the death status of the individuals included was known beforehand. Inhospital deaths in 2019 were included to evaluate the sensitivity. Table  1. Shared attributes of the hospital and French DMF datasets. Missing values (percentages) are indicated in parentheses. ML features were calculated by comparing the attribute values of hospital records and those of death certificates. String distance methods based on edits (Damerau-Levenshtein, Hamming, Levenshtein, optimal string alignment), qgrams (q-gram, cosine, Jaccard distance), phonetics (soundex), and heuristic metrics (Jaro, Jaro-Winkler) were calculated for the first name and the last name  In the first step, a query is sent to Elasticsearch for each hospital record to retrieve a limited number of N candidate death certificates.
In the second step, ML models predict the match probability of a hospital record and a candidate certificate. In the third step, the pair is classified as a nonlink, undetermined, or link according to the upper and lower thresholds. Hospital N ¼ 2. 2M (percent The date of death was used to validate automatically if two linked records were true or false positives. If there was a discrepancy between the two dates of death, a manual review was performed to check whether it was a classification error or a data source error. Specificity was evaluated by randomly selecting 15,000 patients who attended the hospital in 2020, that is, patients known not to have died in 2019.
Estimation of Outpatient Deaths By applying the overall pipeline to the 2. 2 million hospital records, 207,507 records were linked to a death certificate with a probability over the upper threshold and 29,152 had probabilities between the two thresholds, thus requiring manual validation. In comparison, an exact match query in a relational database on the last name, first name, date of birth, and gender matched only 200,824 pairs of records. In terms of performance, it took approximately 4 minutes and 30 seconds to search and classify 1,000 hospital records on a virtual machine with Intel Core i7-8650U @1. 90GH x 8 CPUs without parallelization. The source code, including the feature matrix and the trained models, is available  2.
DISCUSSION In this study, we demonstrated the feasibility of accurately linking French hospital records with an open dataset to complete the vitalstatus information. The mortality information in the Bordeaux University Hospital information system was found to be incomplete. This is unsurprising because only in-patient deaths were recorded. We found a 1:4 ratio of in-patient and out-patient deaths. We proposed a reproducible pipeline based on a search engine and ML. Elasticsearch TM is horizontally scalable and fast. The blocking strategy based on its relevancy score gave satisfactory results-a death certificate, if it existed, appeared in 99. 8% of the cases in the top 10 results. A recent Brazilian study also applied a blocking strategy with Apache Lucene to reduce the number of comparisons in the subsequent steps.
Limitations This study has various limitations. First, the gold standard was created automatically by a deterministic approach and the maximum difference between two records was limited to one field only. A better-quality gold standard could have been obtained by using a common identifier between the two data sources, such as the social security number. This number is not publicly available and requires authorizations that are difficult to obtain. 16 econd, we didn't compare the performance of our approach with other linkage tools. However, not every tool was designed to cope with huge datasets which complicates comparisons. 14 hird, traditional counterparts of given names in various languages were not taken into account, which decreased recall. Because Elasticsearch TM offers the functionality of adding synonyms, it would be feasible to broaden the search queries by providing a resource of equivalent first names.
Reactive oxygen species (ROS) test. Stock solutions (at 1 mM) of dihydrorhodamine 123 (DHR123) in DMSO were diluted in milli-Q ® water to about 200 nM. Twenty µL of stock solutions (at 1 mg/mL) of the GO materials were added to 2 mL DHR123 test solution and the fluorescence was collected under excitation at 485 nm via a Fluorolog FL3-22(Horiba Jobin Yvon) spectrometer using a swig xenon 450W lamp. A Fenton reaction was performed as a positive control. Twenty µL stock solutions (at 0. 1 mM) of the FeSO47H2O was added to 2 mL DHR 123 test solution followed by 200 µL of H2O2. The fluorescence was collected immediately (<1 min) and 10 min after adding H2O2. All materials were analyzed for endotoxin presence before the assays with immune cells (data not shown), as we previously reported within the Graphene Flagship EU project  [33].
Cytobank allowed the realization of the heat map visualization comparing marker signal intensity of the treated populations with mean marker intensity vs. the untreated control. viSNE tool was exploited. viSNE, a cytometry analysis tool implemented in Cytobank, uses t-stochastic neighbor embedding (t-SNE) portraying single cells in a two-or threedimensional plot, on the basis of their relationships. Nine cell surface markers were used to draw the viSNE map for the Maxar PBMCs staining: CD3, CD4, CD8a, CD11c, CD14, CD16, CD19, CD20, CD123, and HLADR. The specific ViSNE on myeloid populations were performed on the gated CD3-CD19/CD20-CD11c+ HLADR+ events; to construct the ViSNE map CD14, CD16, HLADR and CD11c were applied. The cytokine data analysis was performed exploiting viSNE tool. Plots showing the expression intensity of the analyzed cytokines (IFN, IL-2, IL-4, IL-5, IL17a, IL-6, MIP1 and TNF) and heat maps of mean marker expression ratio for all cytokines were realized.
Statistical analysis Data analyses were performed using Prism GraphPad software. Statistics for experiments were performed using a One-Way and Two-way ANOVA with Dunnet's multiple comparison test. Test T Student were used when appropriate. In all cases, * was used for p < 0. 05, ** for p < 0. 01, *** for p < 0. 001, and **** for p < 0. 0001. Values were expressed as mean ± SD. Flow cytometry data were analyzed with FACS Diva software (BD-Bioscience Mountain View, CA, USA) and FlowJo software, (BD-Bioscience Mountain View, CA, USA).
The corona formation can exert different effects on the materials, e. g. changing their later dimension and moderating their cytotoxicity. Coronation can also disturb the colloidal stability of the formulations because of the formation of large agglomerates, consequently influencing the cell uptake  [36]. However, not significant changes and little aggregation was observed in our dispersions. The colloidal stability was then assessed by the measurement of zeta potential in water. Zeta potential data confirmed the presence of amino groups onto GO surface (Figure  S7 ). GO 1. 32 µm and GO 0. 13 µm possess a typical value around -50 mV. After the reaction with TEG-diamine, both GONH2 1. 32 µm and GONH2 0. 13 µm showed a higher zeta potential compared with GO precursors, confirming also the success of the functionalization.
High-dimensional single-cell immune profiling of GO with different lateral size and functionalization on thirteen cell populations We next performed CyTOF high-dimensional screening on human PBMCs to specifically characterize the effects of GOs with different lateral size and functionalization on 13 distinct immune cell subpopulations at the single-cell level. To this end, PBMCs were incubated with 50 µg/mL of GO 1. 32 µm, GO 0. 13 µm, GONH2 1. 32 µm or GONH2 0. 13 µm for 24 h, or left untreated (Ctrl). As previously reported, we used viSNE to reduce the dimensionality of the data set. [37] CD45 + events were analyzed by viSNE to cluster all the single-cell events into 9 populations according to 8 protein expression readouts used in the analysis (CD3, CD4, CD8, CD14, CD16, CD20, CD19, and CD123).
To better highlight the different modulation played by the materials on monocyte subpopulations we report the histograms data analyzed from three donors (Figure  4B ) showing the release of monocyte specific cytokines (IL-6, MIP-1β, and TNF-α). While the largest material (GO 1. 32 µm) induced IL-6 in all monocyte subpopulations, MIP-1β in C. and NC. monocytes, and TNF-α in Int. monocytes, the smallest GO (GO 0. 13 µm) and both amino functionalized GOs (GONH2 1. 32 µm and GONH2 0. 13 µm) did not show any significant change in the production of the three cytokines analyzed. Further activation analyses were performed measuring CD25 (IL-2 receptor alpha, also known as late activation marker), CD80 and HLA-DR expression on all monocyte subpopulations (C. , Int. , and NC monocytes). As shown in Figure  4C , all the materials were able to change the expression of CD25 and CD80 with different potency in Int.
Acknowledgments This work was partly supported by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement no. 734381  ( CARBO-IMmap ), and  FLAGERA JTC Graphene 2015 (G-IMMUNOMICS  project), and the  Agence Nationale de la Recherche (ANR)  through the  LabEx  project  Chemistry of Complex Systems  ( ANR-10-LABX-0026_CSC ). The authors gratefully acknowledge the financial support from the  EU GRAPHENE   Flagship  project (no. 785219 ). The authors wish to thank  T. Wang  for his help on zeta potential measurements,  I. A. Vacchi  for the contribution to XPS analyses,  C. Royer  and  V. Demais  for TEM analyses at the  Plateforme Imagerie in Vitro at the INCI (Strasbourg, France ).
), as well as software tools dedicated to interface design. 8 n terms of molecular simulations, interactivity has two facets: interactive visualization, which studies how the user can explore biological data, and interactive simulation (or computational steering),  1  which conceptualizes the interactive temporal influence of the user on the ongoing simulation. From a biologist's perspective, this family of approaches might be called in silico experiments on biological phenomena by probing the simulation, and two concepts are depicted in Figure  1. Figure  2  shows that research in interactive molecular dynamics (IMD) began in the late 1990s, with one of the pioneering research contributions being the development of a steered molecular simulation tool called steered molecular dynamics (SMD),  9  which was designed as a digital twin simulation of atomic force microscopy (AFM) and used the popular molecular visualization software VMD in its system.