dataesr/scientific-paragraphs-categorization huggingface dataset:
Patients' heterogeneity should be addressed and methodological and practical solutions will have to be found. Moreover, we presently have included half of the maximal required sample size, and recruitment has dramatically decreased over the last weeks. There is a possibility that we will not be able to include the required number of patients, and therefore to finish the study, a situation already observed during the Ebola outbreak  (Kalil 2020). In this case, we will need to take contact with other principal investigators of studies evaluating corticosteroid (17 studies are evaluating corticosteroid in SARS-CoV-2 infected patients, source clinicaltrials. gov May 20, 2020) to propose some meta-analysis of unfinished trials. This is unusual but the clinical question still needs to be answered.
Search strategy We conducted an electronic search of the following databases: Medline (via PubMed), Embase, CINAHL and Cochrane databases, from inception until 12 June 2018. Due to the broad spectrum of the topic, the task force decided to limit the search to the most important/frequent topics, being 'drug therapy', 'exercise', 'nutrition' and 'visits'. We used comprehensive free text and MeSH synonyms for 'adherence to drug therapy', 'adherence to exercise', 'adherence to diet' and 'adherence to visits', plus synonyms of RMDs, with a filter for SR. 'Exercise' in this context refers to any physical activity, exercise or training; 'visits' mean regular medical check-ups with a healthcare provider. Additionally, a search strategy was developed to capture studies of instruments to assess adherence in RMDs.
122 123 A challenge in doing such overview is the risk of including data from individual studies more than once. This could happen if studies are included in two or more reviews. This would result in a misleading estimate. 122 123 To overcome this obstacle, once we selected the reviews we analysed the results of the individual studies, including them only once. The PICOs/clinical questions formulated in the first task-force meeting aimed to cover the entire therapeutic process. The questions focused on prevention, screening, assessment and management of non-adherent behaviour. We only included adults who are independent of caregivers. We believe that the inclusion of caregivers requires a comprehensive extension of the scope and search. Children and older people or people with cognitive limitation who are dependent on a guardian/carer need special attention in terms of non-adherence.
Introduction Precision medicine requires streamlined software pipelines to handle vast amounts of information, yet it faces additional challenges as we embrace new technologies: high-throughput sequencing, improved medical imaging, wearable sensors, etc. Over the past two decades, the fastest technological advance in history has universalised access to genomics, prompting an increase in the number of national genome sequencing programmes  [1]. Managing these data and their interpretation are the biggest challenges alongside safe storage and longterm preservation. Redundant data can clog our warehouses, but "data" is not synonymous of "information". Data scientists are required to curate noisy datasets. All this is ushering us to a new era of "precision healthcare", bringing human biology to centre stage as we integrate information about complex traits and susceptibility to disease in our healthcare systems.
Methods Clinical records transcend their original purpose of keeping a record of disease progression and crucial information to support an optimal therapy. Hospitals have adopted EHR systems to hold mountains of paperwork  [16] [17] [18]. Yet, clinicians favour flexible (unstructured) data entry methods. This requires therefore a careful strategy to capture that critical contextual information as we develop suitable tools. ConSoRe is a tool to query medical notes, pathology reports, diagnoses, hardto-find lifestyle data and structures, all the required information from an EHR system  [19]. Processing unstructured medical notes with accuracy according to a predefined disease model, cancer, is automated  [20]. It combines state-of-the-art text mining natural language processing (NLP) techniques with semantic knowledge graphs.
It provides the necessary flexibility to enable physicians to quickly identify patients matching precise criteria (potentially reducing recruitment time for a clinical trial from years to just days or weeks). A disconnected patchwork of electronic information systems becomes queryable through a unique gateway, not far from the cancer Biomedical Informatics Grid (caBIG ® ) promoted by the NCI  [21]. We are quantifying human health and disease with the help of artificial intelligence (AI) approaches. Large datasets are analysed helping us to discover new drugs and tailored treatments. However these applications in precision medicine can be severely hindered by the scarcity of data available in the training datasets. Indeed, we can find datasets containing nearly as many features as samples. When applied to population-based samples beyond the original clinical setting, these datasets will underperform due to distributional shift  [22].
Another issue AI faces is that it cannot yet replicate the diagnostic process. A physician will order different tests sequentially throughout the period she's following a patient, any given test might be ordered due to the results of a previous result. So, when an algorithm is trained on retrospective corpora, the temporal dimension is removed and therefore the dependency within the dataset is often lost. Any such model subsequently produced will not include the related decisions which ultimately led to the original diagnosis  [23]. The final aspect in this equation is accountability. Algorithms should be able to detect biases and therefore, they require robust and complete data  [24, 25]. When we use mathematical models (e. g. , neural networks) to identify patterns, skewed collections will lead to biased models (data collections may contain inaccuracies and errors which should have been cleaned prior to be used for training models)  [26, 27].
Structuring medical records with ConSoRe can be compared to the work from an experienced team of curators. Understanding how cancer arises requires more than converting biopsies into ones and zeroes, or lists identifying which genes are mutated in certain cancers. Molecular signatures bring us a step closer towards finding interventions to halt disease and enhance health. Patient stratification meets clinical practise, evidence reveals the language of the cell as each subtype may exhibit a predictable clinical phenotype. Machine Learning (ML) is benefiting from robust platforms that enable scalable and reproducible computing on large datasets, however, quality is often the challenge  [33]. Oncology datasets are often unsuited, as we are dealing with noisy and sparse data, various independent resistance mechanisms can operate  [34].
Acknowledgments I wanted to thank the reviewers whose suggestions have improved this contribution. The greatest gratitude to the generosity of all our donors for the support provided to the  Curie Foundation  to beat cancer together. This work was supported in part by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  780495. We also benefited from  SiRIC  (grant  INCa -DGOS-Inserm_12554 ).
Heavy metals concentrations in plant shoots and phytoliths Heavy metals concentrations in plant shoots and in phytoliths are illustrated in Figure  3  and the raw data are listed in Appendix A and B, respectively. Cartagena, and from 33 to 187 mg kg -1 in other polluted sites. On control sites, Mn concentrations vary between 21. 6 and 58. 5 mg kg -1. Lead concentrations range from 5. 6 to 53 mg kg -1 in Cartagena, and from 0. 28 to 3. 2 mg kg -1 on other polluted sites. On control sites, Pb concentrations are lower than 0. 36 mg kg -1. Zinc concentrations range from 21 to 329 mg kg -1 on polluted sites, and from 7. 8 to 18. 8 mg kg - 1 on control sites. 3. 4. 2. Heavy metals in phytoliths. In phytoliths, HM concentrations are significantly higher than those measured in aerial plant tissues for Cu, Mn, Pb and Zn (Kruskal-Wallis test; p-value < 0.
Acknowledgements The authors would like to thank  Rémi Freydier  for his help getting some results for HM concentrations in soils,  Thierry Aigouy  for his help with the SEM-EDX observations and  Alice Gandara  and  Enzo Civallero  for extraction protocol improvements. This work was carried out as part of the  SOIL TAKE CARE   SOE1/P4/F0023  project through the  European Regional Development Fund (ERDF) , under the  Interreg Sudoe Program.
Aims: The present study aims to model grape quality criteria by combining a large number of viticultural practices and soil and climatic variables related to the main determinants. Methods and results: A database has been developed using the Chenin blanc grape variety in a protected designation of origin. A statistical model, namely, a partial least squares (PLS) regression, was performed for each grape quality criterion (sugar content, total acidity, malic acid, tartaric acid, yeast available nitrogen, pH and bunch rot). This statistical analysis identified the main viticultural practices as well as soil and climate variables related to the grape quality at harvest. The results highlight the relationships between the vine pruning length (spur pruning = short pruning or cane pruning = long pruning) and pH and malic acid but also reveal even more significant relationships with tartaric acid, yeast available nitrogen and bunch rot.
Baudrit et al. (2015)  and  Perrot et al. (2015)  proposed a predictive model of grape quality (sugar and acidity concentration) from climate (air temperature, rainfall, sunshine hours, etc. ) and grape maturity criteria (berry size, grape color, phenolic compounds, etc. ). This model is based on a mathematical approach that integrates fuzzy logic inside a dynamic Bayesian network. It also uses a database and experts' statements. Even if this model enables one to follow the grape quality during grape berry maturation, it considers neither viticultural practices nor many other grape quality parameters. Alternatively, the STICS vine model (aims to provide environmental and agricultural variables  (Brisson et al. , 2003; Fraga et al. , 2015). Among the objectives of the model is to simulate phenological stages.
Field sample The statistical analysis was performed on a database of fifteen plots of the Chenin blanc variety in the middle Loire Valley (France) in PDOs, which are described by their soil and climate conditions,  The perennial practices on the vine (VinePerennialPractices) Viticulture practices made on the vine during the studied year (VineAnnualPractices) Viticulture practices made on the working soil during the studied year (S oilAnnualPractices) The perennial practices on the structure of the vineyard (S tructurePerennialPractices)     et al. , 2014). Some details of the features of the plots are presented in Table  1. To account for the variability of natural variables such as climate, data were also been collected for one to four years (according to the data availability for each plot) (Table  1 ).
Some plots were planted before the directive or had obtained an exemption, thus explaining why the planting densities of our database do not always correspond to the PDO specifications. The "perennial practices on the structure" were defined through the plant density (Dens), trunk height (HV), canopy height (CaH), canopy thickness (CaT) and row spacing (RS). Thus, the exposed leaf area (AL) (canopy outer surface) was calculated from the three previous indicators. The winegrower's annual practices were also studied. Annual pruning was defined in this study through the number of buds remaining on the vine per hectare (P) and pruning length (short or long pruning corresponding to, respectively, spur and cane pruning) (LP). The date of pruning was not interpreted, as it was not provided by all the winegrowers for all years studied.
Ground cover could be managed by using chemical herbicides which is measured by the treatment frequency index of chemical weed control (WC), mechanical soil tillage or mowing the grass several times during the year. In the score evaluation, we dissociated those practices applied interrow and under the row. Twenty-three criteria characterized the natural environment (nine for climate and fourteen for soil). In this study, several variables, known to potentially influence the grape quality, were measured for each year studied: temperature, rainfall and sunshine. Raw data were collected from the national weather station (from Météo France) nearest the plot concerned (from 2 to 25 km). The Multicriteria Climatic Classification System (MCC System) was considered through its index calculations  (Conceição and Tonietto, 2005; Tonietto and Carbonneau, 2004).
Statistical analysis The explanatory models were built through partial least squares (PLS) regression  (Wold, 1966). The choice of a PLS regression model was dictated by the nature of the dataset as PLS can address multicollinearity with a large number of variables (even greater than the number of observations) and provides good prediction capacity. PLS regression aims at identifying a small set of orthogonal components from X, here in the Climate, Soil and Practices variables. These components are related to Y in terms of prediction, through the maximization of the covariance between X and Y. These components are linear combinations of X variables: each component is defined by means of vector loadings providing the weights associated with each variable. PLS regression has been widely applied in sensory analysis, for example to The input and output variables are described in Tables 2, 3 and 4.
Criteria selection Out of the 65 input variables related to viticultural practices, soil and climate, 53 were significantly involved (i. e. , with a VIP value greater than 0. 8) in the modeling of at least one of the seven grape quality criteria. Among these variables, 54 % concerned viticultural practices mostly related to Vine Annual and Perennial Practices. The year effect was removed from the analysis for avoidance and to perform the multigroup model. This leads to a relatively poor contribution of the Climate block, indicating strong homogeneity of the plots regarding the Climatic variables (3 % of the variables that constitute the database). However, it is important to consider the effect of soil and climatic variables in the interpretation of the results.
The limitations on the selected criteria 3. 1 Criteria that were not maintained in the database even though there were sufficient data However, some viticultural practices were not included in the models because they did not have a significant effect on at least one of the attributes studied. This is the case for two types of rootstocks (Rupestris du Lot and 5bb), the single Guyot pruning system and the double Guyot pruning system, which had no significant relationships with any of the studied quality criteria (the other types of rootstocks and pruning systems were maintained in the database). In addition, regarding annual practices, chemical weed control in the interrow, mechanical soil tillage under the row and chemical weed control under the row were not significant. The soil was only analyzed from 0-25 cm depth while the vines' rooting depth is 1.
5 m on average. This means that less than 20 % of the soil explored by the root system was considered in the study. The deeper soil (greater than 25 cm) was analyzed as the rooting system extends deeper but given the results associated with a first principal component analysis (PCA), the experts estimated that the sample of data obtained was insufficient for a statistical treatment compared to other data available in the database. However, the deeper soil characteristic influence on vine was accounted for through the vigor, precocity and soil water content variables. The analysis of the first horizon allowed us to determine the soil mineral composition of the soil.
Criteria that were not maintained in the database because of data gaps The input variables that comprise the dataset of the study were chosen for their influence on grape quality at harvest time. They thus had to characterize the entire growing period. Some climatic criteria, such as wind and hail, did not seem relevant because they consider the internal variability. Some of the output variables are rather basic (i. e. , sugar content or total acidity). Other quality parameters, such as aroma, phenolic composition (less important here as we address a white grape variety) or sensorial criteria would have been interesting to include. Although phenological variables (date of flowering, ripening, etc. ) are interesting, they were not considered  (Jones, 2000; van Leeuwen, 2004). For example, aroma and grape color at harvest time are particularly dependent on climate variables during the growth period  (Conceição and Tonietto, 2005; Tonietto and Carbonneau, 2004).
Potential relationships between grape quality criteria and viticultural practices, climate and soil have been quantified through statistical analysis. The importance of each variable in predicting the quality criteria has been measured thanks to their VIP associated with each multigroup multiblock PLS model. There is one explanatory model per grape quality criterion studied; it consists first of VIPs compared to each viticultural practice or soil and climate criterion and then the loadings assigned to each relationship. Consequently, seven explanatory models were developed in this study. The construction of this model (approach, data selection, and statistical model) provides a framework to be transferred to other varieties in other regions. Although different years were analyzed, the aim of this study was not to assess grape quality according to a single year effect -because it is well known that annual climatic variations outperform the other variables related to quality -but rather to measure the effect of viticultural practices and identify the interactions between climate variations, soil, viticultural practices and grape quality.
Overall, the stakeholders can identify direct relationships between each of the seven quality criteria studied and thirty-five criteria for the evaluation of viticultural practices. Of the latter, 54 % concern the evaluation of annual practices on which the winemaker can immediately act. Our models provide stakeholders a first stage in a decision-making tool to manage the quality of the vine by reviewing the practices they would like to change depending on the VIP and loadings results. The results can be applied to other regions for the same variety by adding individuals to the database from the new region. The same method could also be applied to other varieties based on the same criteria. Finally, it would be interesting to add other quality criteria such as sensory attributes to strengthen the grape quality assessment in the future.
ACKNOWLEDGMENTS The authors thank  ADEME (French Environmental Agency) , the  Regional Council of Pays de Loire ,  ESA  and  CASDAR QUALENVIC  for their financial support. We are also grateful to all the winegrowers for their help in this study, the staff of the GRAPPE research unit for their technical support and  Fanny Bessonneau ,  Eloi Ferry ,  Victor Ludeau  from  Oniris and Clément Planchenault  for their help. The authors declare no financial conflict of interest.
Magnetic and photomagnetic measurements: About 10 mg of powder was spread on a piece of cardboard and some drops of nujol oil were added to fixate it. Direct Current (DC) data were recorded using a XL-7 SQUID magnetometer. Field Cooled-Zero Field Cooled (FC-ZFC) curves were recorded over the 2-15 K temperature range under a 50 Oe magnetic field. Alternative Current (AC) data were recorded using a XL7 SQUID magnetometer working with an oscillating field of 3 Oe and frequencies ranging from 0. 1 to 50 Hz. For photomagnetic measurements, the sample was irradiated at 10 K in the SQUID with a laser diode (635 nm, 25 mW/cm 2 ). Samples were irradiated until reaching a photo-stationary state. The temperature dependence of the magnetization was recorded using a XL-7 SQUID magnetometer under a 5000 Oe magnetic field with a sweeping rate of 2 K.
3. Databases As discussed in section 8. 1. 2, the computational cost to generate and store the mean-field configurations and their related quantities can be extremely high in the context of fission applications. At the same time, many applications only require "integral" quantities related to these configurations. For example, computing spontaneous fission lifetimes in the WKB approximation, or fission fragment distributions within the TDGCM+GOA framework, only requires the HFB PES and the collective inertia tensor. Let us assume for the sake of the argument a 3-dimensional collective space with 200×50×50 = 500 000 points. Lifetimes, barrier penetrabilities, charge and mass distributions of the fission fragments can be computed merely from the knowledge of 1 scalar function of 3 variables (the energy) and a rank-2 tensor function of 3 variables (the inertia tensor).
In our example, we would only need a grand total of 3. 5×10 6 function values, which takes a very small amount of storage. By contrast, storing all the information about the HFB solution across the same PES would take up in excess of 1. 6×10 13 function values (assuming a 40×20×20 box discretisation of 2000 HFB spinors). Having a database of such potential energy surfaces for nuclei, different energy functionals (Skyrme, Gogny, relativistic, different pairing functionals, etc. ) and different DFT solver technologies would be very valuable to quantify theoretical uncertainties. Since the cost of generating a PES is high, it would also offer maximum leverage to our small community.
To achieve this goal, this report recommends the development of specific computing capabilities and launching a library of general-purpose fission software based on novel algorithms and programming that can efficiently utilise modern computing infrastructures. To this end, collaborations with computer scientists, applied mathematicians, and data scientists will be needed to (i) develop open-source, modular nuclear solvers and (ii) leverage high-performance computing and statistical machine learning. [8. 1]  Databases Establish databases of microscopic fission output for further processing. This can include various HFB and TDHFB results (PESs, fission pathways, fission fragment yields and properties). Having computed multi-model fission data available will be essential not only for post-processing but also for benchmarking and uncertainty quantification.
Acknowledgements ASU and CS would like to thank  Prof. David Hinde  for useful discussions regarding the neutron clock. This work was partially supported by the  STFC  grant Nos. ST/M006433/1 ,  ST/P003885/1 , and  ST/P005314/1 , by the  Polish National Science Center  under Contract Nos. 2018/31/B/ST2/02220 ,  2018/30/Q/ST2/00185 , and  2017/27/B/ST2/02792 ; by  JSPS KAKENHI  Grant Number  JP19K03861 ; by the  National Natural Science Foundation of China  under grant Nos. 11875225  and  11790325 ; by the  U. S. Department of Energy  under grant Nos. DE-SC0013847 ,  DE-SC0019521 ,  DE-SC0013365 ,  DE-SC0018083 ,  DE-NA0003885 , and  DE-SC0019521 ; by  Spanish Ministry of Economy and Competitiveness (MINECO)  grant No. PGC2018-094583-B-I00 ; by the  Fonds de la Recherche Scientifique (F. R.