dataesr/scientific-paragraphs-categorization huggingface dataset:
Access to complementary information on the same material is relevant, but usually, it is carried out sequentially and separately. Therefore, acceleration of materials discovery for outperforming LIB/LMB materials design needs a multiscale approach from the material to the characterization level. Developing new correlative characterization workflows (CCWFs) adapted to LIB/LMB with the aim to become generic characterization solutions could yield acceleration in materials design while saving time and cost. The CCWFs include harmonized characterization protocols, and data treatment steps dedicated to data classification. CCWFs will not only assist to gather key characterization techniques but also to establish synergies between techniques Adv. Energy Mater. 2021, 2102678 Figure  9. Schematic of a) out-of-plane configuration and b) in-plane (integrated) configuration for electrical measurements.
The IDE footprint was 1 mm × 1 mm. Reproduced with permission. [117] Copyright 2013, American Chemical Society. to correlate the various types of information and generate a high added-value consolidated set of data. It is therefore necessary to design a HT approach starting from materials over, components and processes to systems to ensure wide transferability of results to industrial level. Therefore, common characterization methodologies go together with a harmonization of experimental protocols as well as data treatment, in terms of classification, types, format, curation, and establishment of infrastructure facilitating management and sharing information. Studying a region of interest, for instance, bulk as well as interfaces and interphases in LIB/LMB with a multidimensional characterization approach faces several challenges from sample preparation to data treatment level.
This step can be repeated at different length scale, combining two or several characterization techniques. • from dynamic (in situ/operando) to postmortem, through combined electrochemical and physicochemical characterization, and • adequate sample preparation to fit with characterization technique constraints (environment, beam size, sample holder, length and time scale acquisition, etc. ). In similar ways, collection of multidimensional data must follow a correlative workflow considering • classification of samples with enhanced quality of reproducibility and traceability, through combinatorial material design or material design by road map,  [20, 138]  • single dataset, often specific for each characterization technique, manipulation, analysis (quantitatively and qualitatively), and reconstruction within multidimensional datasets.
Some software allowed the manipulation of data sets from single to general element, to create multi-output datasets,  [139]  with the assumption of perfect matching of the analyzed area probed by both techniques. • raw and/or treated data storage through harmonized formats. The large volume of data that can be produced through different subsequent workflows needs specifically structured big-data treatment strategies for optimum multiscale data exchange and manipulation within reasonable time scales. • Data manipulation should keep retroactive loops of data processing possible. 142] [143]  Using the example illustrated in Figure  10 , the correlative approach needs new characterization paradigms where every aspect must be designed taking in account the modalities and the time/length scale inside the established workflow.
Moreover, these descriptors also play a central role for the development of ML models as feature variables, although in this particular context also experimental descriptors such as the composition of an electrolyte may be defined. Finally, the correspondence between molecular and material properties established by the descriptors results in a robust way to create material databases which are a fundamental component of the HTVS process. This process also includes automated data job management processes  [198]  ranging from input file generation to final data storage. These software protocols formulate genomic projects. The electrolyte genome project and polymer genome are examples of the pioneering works done for battery applications. [199, 200] Unlike DFT, MD simulations relying on classical force fields have.
MD and coarse grained models can estimate these properties, though the accuracies of these models are not up to DFT/MD, but with the advent of novel descriptors and ML-based interatomic potentials, these properties can be determined with ab initio accuracies. [180, 204] hese models are computationally too demanding to screen millions of molecule compositions. To accelerate the screening process surrogate models can be used, which provide property estimates with low computational expense and with equivalent accuracy to the trained model. Suitable regression models or neural network models are used to achieve the desired chemical accuracies. [191] These surrogate models are then used in the construction of necessary material databases for the material discovery process. D) Computational funnel approach -Next step in the HTVS process is screening of materials with respect to the properties of interest for battery applications.
E) Refinement based on human intuition -Once the pool of desired candidates is established from a large database, it is further screened by chemical intuition and voting from scientists, which is used only to exclude compounds that are not appropriate. The final dataset is again put into experimental validation for suitable material property predictions. Further screening is based on ranking of the down-selected candidates on the basis of their structure and ease of synthesis. Based on this approach, recently, a study based on integrated organic functional material design that incorporates theoretical insight, quantum chemistry, cheminformatics, ML, industrial expertise, organic synthesis, molecular characterization, device fabrication, and optoelectronic testing has been reported. [191] This HTVS process enables a fast screening of more than 1.
Conceptual Framework of Machine Learning-Based Prediction In general, the complete dataset is divided into three parts: 1) training, 2) cross validation, and 3) test. The model is trained on training datasets using optimization methods. For example, fitting a polynomial on training datasets using steepest descent optimization method, where optimization is carried out to obtain values of prefactors of the polynomial. During the optimization procedure one needs to fix a set of hyperparameters, e. g. , the parameters characterizing a steepest descent process. For this set of hyperparameters, the prediction quality is obtained from comparison of the predictions with the information of the cross validation dataset that is not observed by model during training. Subsequently, by minimizing the error, hyperparameters will be updated and optimized.
The relevant final error is then determined from predicting the so-far unused test dataset. If the optimization of hyperparameters is not relevant one may just work with a training and a test set. In this case we will separate the N measured data in M training data and N --M test data. The desired but unknown relation between a feature x and the experimental or numerical outcome y is denoted f(x). In general, the outcome is additionally hampered by noise commonly taken from a normal distribution, thereby incorporating uncertainty so that the outcome is given by η ( ) = + y f x i i i (1) In principle, the feature x can have some uncertainty as well which will be neglected. The N measurements are expressed by the index i. In the limit of infinite N and homogeneous coverage of all feature values, perfect learning of the function f(x) would be possible.
The process of estimation involves a function h(x, θ) with a parameter θ, reflecting, e. g. , the regression coefficients. In general, the surrogate model, as expressed by this function, cannot fully recover the full complexity of the system, i. e. , the function f(x). The determination of θ is via minimization of an appropriately chosen cost function which is often taken as the squared error , 1 2 ∑ θ θ [ ] ( ) ( ) = - = c y h x i M i i ( 2 ) The optimum is denoted θ opt. Naturally, its value depends on the specific training set T M , selected from the available number of data, as well as on the noise realization {η i }. The notation θ opt (T M ,η) expresses this dependence. Now one can conceptually think about the limit of the availability of an infinite dataset, which furthermore covers the whole relevant range of feature values x.
(22 of 30) © 2021 The Authors. Advanced Energy Materials published by Wiley-VCH GmbH well-designed data repository and query system in order to retrieve and reuse the data. However, in many cases software platforms within the HTE systems are still neglected or even underfunded, thus resulting in lost value and opportunity toward reaching the overall goal. The ability to test multiple hypotheses in parallel through the continued evolution and improvement of automation platforms produces an exponential increase in data generation. Due to their size, acquired datasets are "mission impossible" for scientists alone to process and make decisions. This inevitably calls for adapted algorithms and high-performance computing to decipher the large data volume. Here is already a word of caution: if the given data is highly biased it will provide wrong results since the analysis is purely data driven.
To assess, e. g. , interfacial properties on larger scales, approaches based on classical MD or even ML-derived force fields seem to provide another thriving avenue of research. In the end, research approaches, which successfully couple HT acquired datasets with ML, DL, and AI by resolving the compatibility and seamless integration challenges will easily differentiate themselves in the large R&D landscape and accelerate development of advanced electrolytes and their interfaces/ interphases in considered battery chemistries.
Targeted HTS of promising electrolyte compositions/formulations based on corresponding design of experiments hand in hand with critical scientific and mathematical thinking yields success. As depicted by the given examples, HTE enables faster synthesis of new battery materials and systematic characterization of electrolytes performance in given battery chemistries toward identification of lead candidates based on the set of previously established requirements. Welldesigned HTE workflows result in a wealth of experimental datasets representing a solid foundation for enhanced technical decision making. To fully capture acquired datasets in a findable, accessible, interoperable, reusable (FAIR) fashion, an appropriate informatics infrastructure is required with a.
Targeted HTS of promising electrolyte compositions/formulations based on corresponding design of experiments hand in hand with critical scientific and mathematical thinking yields success. As depicted by the given examples, HTE enables faster synthesis of new battery materials and systematic characterization of electrolytes performance in given battery chemistries toward identification of lead candidates based on the set of previously established requirements. Welldesigned HTE workflows result in a wealth of experimental datasets representing a solid foundation for enhanced technical decision making. To fully capture acquired datasets in a findable, accessible, interoperable, reusable (FAIR) fashion, an appropriate informatics infrastructure is required with a Adv. Energy Mater. 2021, 2102678.
IV. Genodiversity of bryophytes Whereas the metabolic diversity of bryophytes is increasingly recognized, the genetics underlying this chemical diversity largely remain to be described. To date, a small number of (draft) genomes (including P. patens, Ceratodon purpureus, Fontinalis antipyretica, Pleurozium schreberi, M. polymorpha, Marchantia inflexa, Sphagnum fallax, Sphagnum magellanicum, A. agrestis, Anthoceros punctatus, Anthocerus angustus, H. plumaeforme) are available (http://phytozome. jgi. doe. gov/)  (Rensing et al. , 2008; Bowman et al. , 2017; Lang et al. , 2018; Weston et al. , 2018; Marks et al. , 2019; Pederson et al. , 2019; Li et al. , 2020; Mao et al. , 2020; Zhang et al. , 2020). However, within the next 5 years this number is expected to increase significantly. The OneKP database encompasses transcriptome resources of 74 species (7 hornworts, 41 mosses, 26 liverworts) (https://sites.
google. com/a/ualberta. ca/ onekp/), which are mostly obtained from gametophores (2019). The NCBI database currently lists a total of 443 transcriptome datasets, some of which also capture the P. patens transcriptome under abiotic stress (https://www. ncbi. nlm. nih. gov, Richardt et al. , 2010; Beike, Lang, et al. , 2015). For P. patens, a comprehensive gene atlas encompassing all developmental stages and the impact of some abiotic stresses, is available  (Ortiz-Ram ırez et al. , 2016; Perroud et al. , 2018). Generally, the advancement of next generation sequencing techniques, especially the long-read sequencing technology, has resulted in an accelerating accumulation of genomic data for bryophytes the last few years, thus paving the road for functional approaches and comparative genomics.
A. Cultivation and scale-up production A range of species have already been established as axenic cultures, generally by being grown photoautotrophically in simple low-cost inorganic media without the supplementation of microelements, vitamins and phytohormones  (Hohe and Reski, 2005; Beike et al. , 2010). As part of the Mosstech. eu project around 50 species have been brought into axenic culture, which shows that many bryophytes can be cultivated in cell cultures (www. mosstech. eu). Out of these, 15 species are at present deposited at the International Moss Stock Center (IMSC, https://www. moss-stock-center. org/) with the following accession numbers: 40096, 40097, 40098  , 40099, 40100, 41101, 41102, 41103, 41104, 41248, 41254, 41255, 41212, 41249, 41246. In vitro cultures of bryophytes can be initiated from surface-sterilized spores, gemmae or vegetative fragments  (Beike et al.
Acknowledgements This project has been funded with support from the National Council for Scientific Research in Lebanon. Appendix A: PSLR, ISLR, and R res of tangentbased NLFM waveform The PSLR, ISLR, and R res of a tangent-based NLFM waveform vary when β varies. In Fig. 29 , the PSLR and the ISLR are presented for different values of β. When β ∈ 0, 0. 9 , the instantaneous frequency given in Fig  1  is close to a linear function of the time. The resulting PSLR and the ISLR tend to evolve in a smooth way. When β becomes larger, the degree of non-linearity is more significant. In this case, the performance in terms of PSLR and ISLR vary much. In Fig. 30 , the range resolution versus β is presented. As β increases, the range resolution increases.
In June 2021, the CDC Advisory Committee on Immunization Practices (ACIP) recommended use of Dengvaxia to prevent dengue in children aged 9-16 years, with laboratory-confirmed previous dengue virus infection and living in areas where dengue is common  [17]. We have developed a model of the dengue vaccination program in the Philippines to provide understanding of the controversy surrounding this vaccination program. We used reported case data and reported mortality data from the Philippines Department of Health, to analyze quantitatively this vaccination program. We then reformulated our model to correspond to the case that no vaccination program had occurred or only previously infected children had been vaccinated. Our results our summarized in Table  4. We varied the vaccine efficacy rate as 60, 70, and 80%.
Acknowledgments We express our gratitude to the accelerator staff at NSCL for their efforts in beam delivery during the experiment. N. K. acknowledges support of the  Grant-in-Aid for JSPS Fellows  ( 18J12542 ) from the  Ministry of Education, Culture, Sports, Science, and Technology (MEXT), Japan. K. W. acknowledges support from the  Ministerio de Ciencia, Innovación y Universidades (Spain)   RYC-2017-22007. A. P. is supported in part by the  Ministerio de Ciencia, Innovación y Universidades (Spain) ,  Severo Ochoa Programme   SEV-2016-0597  and grant  PGC-2018-94583. The SDPF-M calculations were enabled by the  CNS-RIKEN  joint project for large-scale nuclear structure calculations and were performed mainly on the Oakforest-PACS supercomputer. N. S. acknowledges support from " Priority Issue on post-K computer " (hp190160) and " Program for Promoting Researches on the Supercomputer Fugaku " (hp200130) by  JICFuS  and  MEXT, Japan.
J. A. T. acknowledges support from the  U. K. Science and Technology Facilities Council  Grant No. ST/L005743/1. This work was supported by the  U. S. Department of Energy (DOE), Office of Science, Office of Nuclear Physics , under Grant No. DE-SC0020451  and by the  U. S. National Science Foundation (NSF)  under Grant No. PHY-1306297. GRETINA was funded by the  U. S. DOE, Office of Science. Operation of the array at NSCL was supported by the  U. S. NSF  under Cooperative Agreement No. PHY-1102511  (NSCL) and  DOE  under Grant No. DE-AC02-05CH11231  (LBNL).
Acknowledgements The authors sincerely thank  Maria Tavano , curator of the  Museo civico di Storia naturale "G. Doria" , for the loan of the Pavesi material,  Christine Rollard , curator of the  Muséum d'Histoire naturelle de Paris  for the loan of the Lucas and Simon material and  Jason Dunlop , curator of  Zoologisches Museum, Berlin  for the loan of the Karsch material. Pierre Oger  is thanked for the photos. Finally,  Jørgen Lissner ,  Andrei V. Tanasevitch , an anonymous reviewer, and  Theo Blick ,  Tobias Bauer ,  Petr Dolejš ,  Konrad Wiśniewski  are thanked for their most valuable comments.
ORR: Overall response rate; OS: Overall survival; PFS: Progression-free survival. Data taken from [14]. Case 3: patient with retroperitoneal dedifferentiated liposarcoma A 58-year-old man with retroperitoneal dedifferentiated liposarcoma presented with pain and abdominal swelling. A left kidney tumor mass was detected and biopsied in September 2016. A confirmatory CT scan showed a 15 cm tumor and no metastases. The patient refused our suggestion for enrolment into a study which compared surgery with or without preoperative radiotherapy in patients with untreated primary retroperitoneal sarcoma (STRASS)  [12]. Neoadjuvant doxorubicin and ifosfamide (×6 cycles) led to a minor response (stable disease). In March 2017, the tumor was resected (R1) at a reference center. In March 2018, the patient relapsed with appearance of lung nodules.
Evidence review: treatment strategies in retroperitoneal sarcoma In the STRASS study of untreated primary retroperitoneal sarcoma (n = 266), no significant difference in median abdominal recurrence-free survival was found between preoperative radiotherapy plus surgery and surgery alone (4. 5 vs 5 years, respectively; HR: 1. 01; 95% CI: 0. 71-1. 44; log rank p = 0. 95), whereas serious AEs were more common in the radiotherapy plus surgery group (24 vs 10%)  [12]. Although the study itself did not support preoperative radiotherapy for leiomyosarcoma and high-grade retroperitoneal sarcoma, a subgroup analysis indicated that patients with liposarcoma who had received radiotherapy plus surgery had an absolute abdominal recurrence-free survival benefit of 10%. The ongoing STRASS 2 study (ClinicalTrials.
gov: NCT04031677) is comparing surgery with or without neoadjuvant chemotherapy in patients with high-risk retroperitoneal sarcoma  [13]. First-line treatment of advanced well-differentiated/dedifferentiated liposarcoma (WD/DDLPS) has been assessed in a recent retrospective analysis involving 109 patients enrolled in European Organization for Research and Treatment of Cancer-Soft Tissue and Bone Sarcoma Group (EORTC-STBSG) clinical trials between 1990 and 2012  [14]. The activity of cytotoxic chemotherapy was limited, particularly with doxorubicin alone. The best results were found with doxorubicin plus ifosfamide (overall response rate: 22. 2%; median PFS: 11. 6 months; median OS: 31. 4 months) compared with other chemotherapy regimens (Table  2 ). With regard to the second-line setting, a preliminary analysis of a Phase II study performed in patients with advanced retroperitoneal leiomyosarcoma and WD/DDLPS reported that trabectedin was associated with a median PFS of 6.
Acknowledgments The article is based on the proceedings of a live webinar entitled 'Dealing with soft tissue sarcoma: the tireless search for the perfect balance' held on 12 November 2020. Financial & competing interests disclosure  J-Y Blay  has received sponsorship or research funds and remuneration from  AstraZeneca ,  Bayer ,  BMS ,  Deciphera ,  GSK ,  MSD ,  Novartis ,  PharmaMar and Roche ; and is a supervisory board member for  Innate Pharma. The author has no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. Writing assistance for this article was provided by  R Furlong  and K Dechant on behalf of  Content Ed Net (Madrid, Spain)  with funding from  PharmaMar, Madrid, Spain.
ACKNOWLEDGMENTS TEM experiments were performed at the  Plateforme Aquitaine de Caractérisation des Matériaux  ( UAR CNRS 3626 ) of the  University of Bordeaux  and at the  Centre de Microcaractérisation Raimond Castaing  ( UAR CNRS 3623 ) of the  University of Toulouse. We gratefully acknowledge  Teresa Hungria  for her assistance in mapping analysis.
ACKNOWLEDGMENTS This work was performed in the framework of the joint lab between LuMIn and Thales. We thank  Matthieu Goulard ,  Bertrand Morbieu  and  Frédéric Seguineau  ( Thales AVS ) for their help with the fiber cavity. We also thank  Alexia Ravaille  ( Thales Underwater System ),  Ouali Acef  ( Observatoire de Paris ),  Frédéric Du-Burck  ( Université Sorbonne Paris Nord ), and  Romain Stomp  ( Zurich Instruments ) for fruitful discussions, and  Erell Laot  for checking the calculations.
Study design This medicoeconomic modelling study estimated the cost-effectiveness of the 'test-and treat' strategy using UBT in the diagnosis of H. pylori infection and in the subsequent prevention of H. pylori-related complications. The 'test-and treat' strategy using UBT for primary detection of H. pylori was compared with three other diagnostic strategies, namely, 'test-and treat' using FAT, an 'endoscopy-based strategy' and a 'strategy starting directly with empirical symptomatic treatment'. Two therapeutic outcomes were evaluated, relief of symptoms of dyspepsia and prevention of peptic ulcers. Values and costs were estimated from a decision tree model simulating the four strategies. The management pathways modelled correspond to those used in routine clinical practice in the UK, and transition probabilities were based on British data whenever available.
Acknowledgments This work has been carried out within the framework of the  EUROfusion Consortium  and has received funding from the  Euratom  research and training programme 2014-2018 and  2019-2020  under grant agreement No  633053. The views and opinions expressed herein do not necessarily reflect those of the  European Commission. The authors gratefully acknowledge the computing time granted through JARA on the supercomputer JURECA [45] at  Forschungszentrum Jülich.