dataesr/scientific-paragraphs-categorization huggingface dataset:
Instrumental origin We investigated whether the CHEOPS spacecraft and instrument could be at the origin of the variability behaviour described above. We repeated the same analysis as the one described in Sect. 2. 2 but on out-of-eclipse time series where there are no occultations, extracted at the same orbital phase across all visits. We ran an MCMC analysis on that dataset, with the only difference compared with Sect. 2. 2 and Table 2 being a 0. 18-day shift in the T 0 to match the data given as input to the MCMC. This global fit yielded a depth of -1±3 ppm, which points to the instrument being innocuous to the occultation signal previously detected at the expected 55 Cnc e phase. We also investigated any dependence of this analysis's measured depths on time, as done above for the occultation dataset.
2 ppm for 55 Cnc A (see Table  2  of Rodríguez Díaz et al. 2022). To compare this value with our dataset, we binned the occultation time series into 3-min intervals and filtered the periods >27 min (see details in  Sulis et al. 2022). We computed an RMS between 36 and 60 ppm, which is consistent with these 3D model's predictions. 55 Cnc's granulation amplitude and timescale thus appears as a plausible cause of the correlated noise observed in the individual occultation time series. Because of the partial coverage of our occultations, granulation could impact only part of each occultation, biasing the individual retrieved depth by several tens of ppm. One issue for this hypothesis, however, is that the A64, page 6 of 10 B. -O. Demory et al. :  55 Cancri e's reflected light captured with CHEOPS shallow and negative occultation depths are Normal-distributed, as discussed in Sect.
4, while stellar granulation should induce at zeroth order the same level of correlated noise throughout all datasets. We note that a straightforward way to improve the quality of our fit would be to model granulation noise using Gaussian processes  (Aigrain et al. 2015; Barros et al. 2020 ), but the reliability of this extra step would rely on the assumption that the occultation depth is constant across visits, which is challenged by the observed infrared occultation depth variability and the long-term sinusoidal pattern (Sect. 4). Since thermal emission accounts for roughly two-thirds of the signal measured in the CHEOPS bandpass, this approach could induce a bias of up to 8 ppm in the retrieved occultation depth for the thermal contribution alone. We finally note that the sinusoidal model period of 47.
We also argue that granulation is not the cause for the observed variability in the Spitzer bandpass, since only the occultations have been shown to vary, not the transits  (Demory et al. 2016; Tamburo et al. 2018) , and with a significantly larger amplitude than the observed RMS in the CHEOPS observations. Furthermore, granulation noise is of lower amplitude in the infrared than in the visible. In summary, while stellar granulation can explain the shorttimescale noise seen in our data, it cannot impact the observed longer-timescale variations. We cannot identify a process originating from the instrument, nor from the star that could explain the 47-day modulation of the occultation depths detected with CHEOPS. With the data at hand, we are not able to discard a planetary origin for this signal.
Acknowledgements. We thank the referee,  Jacob Bean , and the editor,  Emmanuel Lellouch , for a very careful reading of this manuscript and useful suggestions that improved the paper. CHEOPS is an ESA mission in partnership with Switzerland with important contributions to the payload and the ground segment from  Austria, Belgium, France, Germany, Hungary, Italy, Portugal, Spain, Sweden , and the  United Kingdom. The CHEOPS Consortium would like to gratefully acknowledge the support received by all the agencies, offices, universities, and industries involved. Their flexibility and willingness to explore new approaches were essential to the success of this mission. B. -O. D. acknowledges insightful discussions with  Hannah Diamond-Lowe  and  João Mendonça  on the way to Andechs monastery, a lot of help from Patricio Cubillos on bibtex management (Cubillos  2020 ) as well as support from the  Swiss National Science Foundation  ( PP00P2_190080 )and the  Swiss State Secretariat for Education, Research and Innovation (SERI)  under contract number  MB22.
00046. The Belgian participation to CHEOPS has been supported by the  Belgian Federal Science Policy Office (BELSPO)  in the framework of the  PRODEX Program , and by the  University of Liege  through an  ARC grant for Concerted Research Actions  financed by the  Wallonia-Brussels Federation. L. D. is an F. R. S. -FNRS Postdoctoral Researcher. S. H. gratefully acknowledges CNES funding through the grant  837319. S. G. S. acknowledge support from  FCT  through  FCT  contract nr. CEECIND/00826/2018  and  POPH/FSE (EC). A. Br. was supported by the  SNSA. M. L. acknowledges support of the  Swiss National Science Foundation  under grant number  PCEFP2_194576. Y. A. and M. J. H. acknowledge the support of the  Swiss National Fund  under grant  200020_172746. We acknowledge support from the  Spanish Ministry of Science and Innovation  and the  European Regional Development Fund  through grants  ESP2016-80435 MDM-2017-0737  Unidad de Excelencia Maria de Maeztu-Centro de Astrobiologia (INTA-CSIC) , as well as the support of the  Generalitat de Catalunya/CERCA  programme.
D. S. D. is supported in the form of work contract ( DL 57/2016/CP1364/CT0004 ) funded by national funds through FCT. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (project  FOUR ACES. grant agreement No  724427 ). It has also been carried out in the frame of the  National Centre for Competence in Research PlanetS  supported by the  Swiss National Science Foundation (SNSF). D. E. acknowledges financial support from the  Swiss National Science Foundation  for project  200021_200726. MF and CMP gratefully acknowledge the support of the  Swedish National Space Agency  ( DNR 65/19 ,  174/18 ). D. G. gratefully acknowledges financial support from the  CRT foundation  under Grant No. 2018. 2323"Gaseous or rocky?Unveiling the nature of small worlds".
M. G. is an F. R. S. - FNRS Senior Research Associate. KGI is the  ESA   CHEOPS  Project Scientist and is responsible for the  ESA CHEOPS Guest Observers Programme. She does not participate in, or contribute to, the definition of the  Guaranteed Time Programme  of the CHEOPS mission through which observations described in this paper have been taken, nor to any aspect of target selection for the programme. This work was granted access to the HPC resources of MesoPSL financed by the  Region Ile de France  and the project  Equip@Meso  (reference  ANR-10-EQPX-29-01 ) of the programme Investissements d'Avenir supervised by the  Agence Nationale pour la Recherche. P. M. acknowledges support from  STFC  research grant number  ST/M001040/1. L. Bo. , G. Br. , V. Na. , I. Pa. , G. Pi. ,  R. Ra. , G.
STRENGTHS AND LIMITATIONS OF THIS STUDY ⇒ Our national administrative database, PMSI, is an important tool for assessing quality of care as it collects the characteristics of patients from all hospitals (public and private) in France, with a national recruitment over 15 years. ⇒ Data pertaining to pulmonary resection for lung cancer are reliable enough to count such patients: large size of our sample (157 566 patients operated on for lung cancer in France from 2005 to 2020). ⇒ Characteristics of patients (age, gender, comorbidities), surgery (surgical approach, type of resection, extent of resection) and hospital (type of hospital, hospital volume for pulmonary resections) were retrieved. ⇒ The tumour, node, metastases stage cannot be retrieved in the PMSI, nor can Eastern Cooperative Oncology Group (ECOG) or previous treatment status.
7 8  A national administrative database is an important tool for assessing the quality of care, and provides data relative to all patients and all care centres nationwide. In particular, the French national administrative database for hospital care (PMSI) provides a huge amount of epidemiological information concerning hospitalised French patients. [9] [10] [11]  The main objective of this work was thus to estimate the evolution of quality indicators (30-day mortality and failure-to-rescue) from 2005 to 2020 in patients who underwent lung cancer surgery using this national medico-administrative database. The second objective was to study the different factors that may have influenced the evolution of these quality indicators over time.
Data source and study population For this retrospective cohort study, all data for patients who underwent pulmonary resection for lung cancer in France were collected from January 2005 to December 2020 from the national administrative database. This database, called PMSI for 'Programme de Médicalisation des Systèmes d'Information', was inspired by the US Medicare system. The reliability and validity of PMSI data have already been assessed. [9] [10] [11]  Routinely collected medical information includes the principal diagnosis, secondary diagnoses and procedures performed. Diagnoses identified during the hospital stay are coded according to the International Classification of Diseases, tenth revision (ICD-10). 12 13  All patients undergoing lung cancer surgery in France were included. We thus selected patients for whom a diagnosis of primary lung cancer was coded as the principal discharge diagnosis (all codes C34), associated with a procedure of lung cancer surgery (thoracotomy, VATS or robot-assisted surgery) during the same hospital stay.
Ethical approval Patient consent was not required, and patient-identifying information was not used in the research as this national retrospective study was based on pseudonymised data. In fact, the French national administrative hospital database does not contain any patient-identifying data. The patient's identity is pseudonymised, making it possible to link data from the same patient without knowing his or her identity. Ethics approval for use of this database was obtained from the French National Commission for Data protection (Commission Nationale de l'Informatique et des Libertés: No 1576793), and this study adhered to the tenets of the Declaration of Helsinki.
Patient characteristics Baseline demographics included age and gender. From the national administrative database, we included the following comorbidities: pulmonary disease (chronic bronchitis, emphysema), heart disease (coronary artery disease, cardiac arrhythmia, congestive heart failure, valvular heart disease, pulmonary artery hypertension, pulmonary embolism), peripheral vascular disease, liver disease, cerebrovascular events, neurological diseases (hemiplegia or paraplegia), renal disease, haematological disease (leukaemia, lymphoma), metabolic disease (including obesity), anaemia, other therapies (preoperative chemotherapy including neoadjuvant therapies, steroids) and infectious disease. We also calculated a Open access modified Charlson Comorbidity Index (CCI) as a marker of comorbidity.
bmj. com/ BMJ Open: first published as 10. 1136/bmjopen-2023-075463 on 12 September 2023. Downloaded from Open access 12. 2% in 2005 to 7. 1% in 2020. These results suggest that the new organisation required to obtain the cancer surgery authorisations have had a beneficial effect on patient outcomes. The limitations of our study concern first reliance on ICD-10 codes for the selection of patients and the ascertainment of outcomes. Misclassification or underdetection of related biases is possible, especially for comorbidities. Coding practices vary greatly among institutions, which can increase the number of recorded comorbidities, but the quality of coding in the PMSI has greatly improved since the beginning of hospital data collection in 1990. It is also conceivable that there could be significant interhospital differences in the severity of illness, which would impact mortality and failure-to-rescue analyses.
We used the modified CCI, which has been validated as a preoperative risk score. We are not sure that the ASA Score would carry more weight in the model than the CCI. Moreover, BMI is an important variable, so we may consider obesity as a pejorative factor, which was included in the metabolic disease variable. No details were available in this database concerning the accommodation facilities of the centre (number of beds in the surgical department and intensive care unit, number of days spent in intensive care, number of nurses and medical practitioners) and the organisation (tumour board meeting, application of recommendations, qualification of the surgeons and years of experience and surgery performed by residents), although these criteria could have an influence on the quality of care. However, the factors that we were able to analyse explained half of the observed difference in mortality between the two periods.
Finally, as most ongoing policy initiatives have focused on hospital volume, we did not examine potential changes in surgeon volume over time. Thus, it is possible that trends towards declining operative mortality are attributable in part to increasing surgeon specialisation within hospitals. The main strength of our results is related to the large size of our sample (157 566 patients). Our national administrative database, PMSI, is an important tool for assessing quality of care because it collects the characteristics of patients from all care centres in France, with a national recruitment over 15 years. It provides a huge amount of epidemiological information concerning hospitalised French patients. [9] [10] [11]  Moreover, data pertaining to pulmonary resection for lung cancer are reliable enough to count such patients, as previously shown in other studies.
The membranes with large surfaces were installed in the middle of the pilot-scale forward filtration unit to separate the flowing draw and feed solution. The draw and feed tank were filled with 2 M sucrose solution and 0. 58 wt. % NaCl solution, respectively. A peristaltic pump was utilized to provide a constant flow rate of 3. 75 mL min -1. Two balances were used to monitor the mass changes of draw and feed tank and the recorded data was further used to calculate the flux. The solutions in the tank were refreshed every 20 h. At the same time, the sucrose solution was collected and the salt concentration was analyzed by Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES). According to the increased mass and salt concentration in draw side, the salt rejection could be calculated using equations (5)     and (6).
Molecular dynamics simulations parameter. Molecular dynamics simulations have been performed with the LAMMPS package  46  and the OPLS interatomic potential  47. To describe Mo-S interactions, we use the potential proposed by Blankschtein  48. We developed OPLS parameters (harmonic bonds, harmonic angles and dihedrals) for S-C interactions between fixed MoS2 and various organic functional groups. We used a Monte Carlo procedure (similar to that presented in Ref. 49) to optimize OPLS parameters against a training set of density functional theory calculations, as developed in the Supplementary file. Molecular models consist of 2 layers of T' MoS2 replicated 8 and 14 times in the x and y directions, starting from the rectangular unit cell. Functionalization was achieved by randomly grafting functional groups to S atoms until the fraction of 20% groups/S was reached.
Acknowledgments This research study was supported by the  French National Agency (ANR , program  2D-MEMBA -ANR-21-CE09-0034-01 ). D. V. , K. Q. , and H. W. acknowledge funding from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation programme  (grant agreement no. 804320 ). W. W. ,  J. Liu  and  J. Li  acknowledge PhD scholarship from the  China Scholarship Council (CSC). C. S. acknowledged funding from the  French National Agency  ( ANR ,  JCJC program ,  MONOMEANR-20-CE08-0009 ). K. Q. and Y. Z. acknowledge financial support from the  China Postdoctoral Science Foundation  ( 2018M633127 ) and the  Natural Science.
Chemical Profiles A methanolic maceration of each part of plant was carried out to extract most of the secondary mebabolites. By comparing the yields obtained (Table  1 ), we could conclude that the yields were quite similar for all parts of V. madiensis (between 3 and 6%). For C. febrifuga, the yields were quite high for the leaves, trunk bark and roots (between 10 and 19%), but the yield is lower for fruits (3%). The extract of each part of plants was analyzed by HPLC in order to determine the best conditions to separate most of the compounds present in the extracts. For both plants, the analyses were performed using a method developed in our group to study complex matrices and for which a database of around one hundred compounds was established using commercially available standards. noindent+: presence of the compound in the extract; ++: abundant compound; +++: very abundant compound; -: absence of the compound in the extract; L: leaves; TB: trunk bark; R: root bark; Fr: fruit.
The flow rate was 0. 8 mL/min, and the injection volume was 5 µL. The UHPLC system was connected to an Orbitrap (Thermo Fisher Scientific Inc. , Waltham, MA, USA) mass spectrometer, operated in the positive and negative electrospray ionization mode. Source operating conditions were: 3 kV spray voltage; 320 • C heated capillary temperature; 400 • C auxiliary gas temperature; sheath, sweep and auxiliary gas (nitrogen) flow rate 50, 10 and 2 arbitrary units, respectively; and collision cell voltage between 10 and 50 eV. Full scan data were obtained at a resolution of 70,000, whereas MS 2 data were obtained at a resolution of 17,500. Data were processed using Xcalibur software (Thermo Fisher Scientific Inc. , City, MA, USA). The sample to be analysed was prepared under the following operating conditions: 5 mg of the methanolic extract was diluted in 5 mL of MeOH of HPLC grade quality.
Positron emission tomography (PET) using a fraction of the usual injected dose would reduce the amount of radioligand needed, as well as the radiation dose to patients and staff, but would compromise reconstructed image quality. For performing the same clinical tasks with such images, a clinical (rather than numerical) image quality assessment is essential. This process can be automated with convolutional neural networks (CNNs). However, the scarcity of clinical quality readings is a challenge. We hypothesise that exploiting easily available quantitative information in pretext learning tasks or using established pre-trained networks could improve CNN performance for predicting clinical assessments with limited data. CNNs were pre-trained to predict injected dose from image patches extracted from eight real patient datasets, reconstructed using between 0.
Traditionally, image quality assessment has relied on human raters, but this is not scalable and impractical for large-scale investigations or repeating assessments while optimising reconstruction algorithms. Thus, it is beneficial to determine automatically whether a reconstructed image is of satisfactory diagnostic quality. One approach is to use convolutional neural networks (CNNs), as they are data-driven  [8]  and versatile, being used for medical imaging tasks including image segmentation, lesion detection and image de-noising  [9] -  [14]. CNNs have also shown to outperform radiomic analysis in certain tasks  [15]. A drawback of using CNNs is that generally a large dataset is required for training  [16]. However, this is challenging in medical imaging, as clinically-annotated data are scarce and not scalable  [17].
Associated acquisition and processing costs contribute to the paucity of clinician-labelled data. Pre-training (an architecture trained on a separate image database) and transfer learning (taking features learned on one problem and using them for a new problem) could be exploited to overcome this lack of training data  [18]   [19]. Transfer learning can be approached with either fine tuning or fixed representations  [20]. Fine tuning involves unfreezing some or all of the network parameters in order to update the weights with a very low learning rate and a new dataset  [18]. P Pre-training with natural image databases has been used in the medical imaging field. ImageNet  [21]  is a dataset of more than 15 million high-resolution natural images annotated for 22,000 classes. A subset of ImageNet used for deep learning tasks consists of 1.
2 million images categorised into 1000 classes  [22]. Pre-trained networks via ImageNet have been used for the binary classification of papillary thyroid carcinoma  [23] , where exploiting a VGG16  [24]  network achieved a 97. 66% accuracy when distinguishing between carcinomas and benign thyroid nodules in two-dimensional cytological image patches. Dunnmon et al. [25]  showed that CNNs could be pre-trained on ImageNet to classify chest radiographs into "normal" or "abnormal" with an area under the receiver operating characteristic curve (AUC) of 0. 96. This was built upon by Tang et al. [26] , who investigated a greater number of established architectures, including VGGNets  [24]  and Inception-v3 networks  [27] , and compared pre-trained networks against training from scratch. Their work showed that when using a moderately sized dataset (8500 two-dimensional chest radiographs), pre-training on ImageNet out-performed training the model from scratch.
It has also been shown that a transfer-learned VGG16 network pre-trained via ImageNet outperformed an Inception-v3 network for the task of classifying into 'Normal' or 'Pneumonia present', and matching the complexity of the model to the dataset size along with data augmentation, further improved CNN accuracy  [28]. However, there is debate on the usefulness of transfer learning from a natural image domain to a medical imaging domain  [29] ,  [30]. Recent work on transfer learning questions the validity of using natural images for pre-training in medical imaging tasks due to the high dissimilarity between the domains  [31]. Heker et al. [32]  also showed transfer learning within domain (from one medical image dataset to another) outperformed inter-domain pre-training (from a natural image database to a medical image database) for segmentation and classification tasks.
Pre-training can be used to overcome extensive training data needs. Mustafa et al. [19]  showed network performance could be improved by increasing size of both the model architecture, and the natural image pre-training dataset. ImageNet-21k (the full ImageNet dataset classified into 21000 classes) obtained superior performance over ImageNet (the subset of ImageNet classified into 1000 classes). However, the full dataset is more computationally expensive and labels are not mutually exclusive: a single image may have multiple labels, making classification more difficult  [33]. As pre-trained networks act as feature extractors, layers need to be frozen to prevent the network from retraining too extensively, potentially negating any pre-training benefit. As shown in  [34] , inclusion of multiple layers in the fine tuning process can improve model performance.
In an Alzheimer's disease diagnosis classification task, all convolutional layers of pre-trained networks were frozen, except for the last fully connected layer which was trained on the new dataset  [35]. Kieffer et al. [36]  retrained the last convolutional block and fully connected layer of a VGG16 network for their dissimilar domains. Whilst there have been many image quality assessment investigations in the computer vision field  [37] -  [39] , similar experiments within the medical imaging field have emphasised binary classifications. One such investigation by Sujit et al. [40]  used a CNN to predict image quality of brain MRIs compared to visual evaluation by two experts by scoring the images as '0 (acceptable)' or '1 (unacceptable)', achieving an accuracy of 84%. Oksuz et al. [41]  used cardiac MR images to detect motion-related artefacts, classifying the image as "good" or "poor".