dataesr/scientific-paragraphs-categorization huggingface dataset:
Results and Discussion In our work, we used three widely known and easy-to-implement SMs: decision tree (DT), support vector machine (SVM), and deep neural network (DNN). Briefly, DT algorithms search which value of which variables split the initial dataset in such a way to separate as much class labels as possible, minimizing in the meantime the associated error. In the case of SVM the basic principle is to find the best hyperplane which discriminates as best as possible the data used for the training. The concept behind DNN is to reproduce the way in which human brain works in terms of neurons and their connections, generating a matrix of coefficients that links the input variables with the output observables. For further details about these algorithms the interested readers are referred to the Supporting Information, Section 1 a, b and c.
The descriptors used for the electrode are its porosity (after drying) and mass loading, i. e. , AM mass per unit area. The choice of un-calendered electrodes was made for the sake of simplifying the analysis, as an extra electrode manufacturing step (calendering) would have increased the size of the needed dataset and complexed the trend analysis discussed in this manuscript. Nevertheless, these un-calendered porosities are very important from the engineering-process point of view as dealing with highly porous electrodes would need of higher calender pressure and/or more time of calendering. On the other side, the choice as descriptor of the AM content was done because this value is directly related to the final capacity of the electrodes. As previously described, to use SM a class label for each dataset point is required.
In our case, three class labels were defined (high, medium and low) for both mass loading and porosity. Their definition is arbitrary and it was done in order to have roughly the same number of data for each class label (Table  1 ). The experimental dataset comprises 82 data points defined by the three descriptors of the slurries and their associated class label for the electrodes. The mass loading and porosity used to define the class label for each condition arise from averaging eight the mass loading and porosity of eight electrodes. For all the detail about the sampling process the readers are referred to the Experimental Section. The complete dataset used is reported in the Supporting Information and it will be also available for download for free from the ARTISTIC project webpage. [32] igure  2  shows a scheme of our SM workflow.
First, the initial dataset is sub-divided into a training and a test set. This splitting is made randomly after the choice of a seed number. The training set is used to create the model using one of the three SMs. To validate it, the prediction accuracy was calculated by counting how many data of the test set are correctly classified by the model. This process was repeated ten times (using ten different seed numbers) for all the SMs, in order to evaluate the statistical accuracy of the used methods. The accuracy of the three models (DT, SVM and DNN) as a function of the dataset size is reported in Figures  3 A  and B , for both mass loading and porosity, respectively. For further information regarding how the risk of under/overfitting of the models was taken into account, readers are referred to Section 1d in the Supporting Information.
It should be stressed that the three studied ML methodologies do not infer accuracy in terms of the standard deviation of the dataset. The accuracy reported in Figure  3  is related to the error of the model in predicting the output associated to certain input dataset that was not used during the training. This offers an estimation of the accuracy of the ML methods in order to verify if, as expected, the accuracy of the model increases by increasing the dataset size. Overall, the models generally respect this trend, even if some oscillations are observed. From Figure  3  it can be noticed that the DT method, in general, outputs the lowest prediction accuracy in terms of the dataset size for both mass loading and porosity. The DT method provides an intuitive 2D scheme, as showed in Figure  S1  in the Supporting Information.
However, this 2D representation makes harder to analyse n-dimensional problems, i. e. , when the observable of interest depends on n parameters, as for the case of manufacturing processes. Because of this, the analysis of ML methods was focused in the other two algorithms: SVM and DNN. The DNN method gives quite accurate results even if the dataset size is orders of magnitudes smaller than the ones generally used in other contexts. [33, 34] This is an indication that, if the quality of the data is high enough, DNN outputs reasonable results even with a relatively small dataset. However, the DNN method does not allow to easily analyze the effect of AM content, viscosity and S-to-L ratio on the mass loading and porosity of the electrodes. The output of the DNN is a coefficients matrix, where each one is associated to a specific neuron in the neural network.
This matrix predicts the output in terms of a given set of inputs, which in our case are   represented by the mass loading or the porosity level and the slurry descriptors, respectively. SVM divides the dataset space in different zones by the best possible hyperplane in order to minimize the error associated to the classification. While in the present study we defined three slurry properties as the model descriptors, it should be underlined that the SVM classification has no limitations in terms of studied parameters. For all the above, we will focus our analysis in the SVM model outputs in order to disclose interdependencies between the manufacturing parameters and the electrode properties. Figure  4  shows the levels classification performed by the SVM algorithm represented in a graphical straightforward 2D plot for the case of the electrodes mass loading.
Concerning the effect of AM amount, it can be noticed that the slope of the curves (mass loading vs. η for a specific S-to-L ratio) tends to increase by increasing the percentage of AM, as it is shown in Figure  6. In this way, the conclusions observed in the plots of Figures  5  and 6  matched perfectly the trends observed by the output coming from the SVM model, confirming its accuracy and its capability to disclose trends without the need to make simultaneous analyses with different kinds of 2D plots. In the case of porosity, the dataset classification performed by the SVM algorithm is displayed in Figure  7 , in terms of η, Sto-L ratio and AM amount. The most distinct feature of this classification is that by increasing the amount of AM the upperright low porosity region (that corresponds to high S-to-L ratio and η) increases at the expense of the left side high porosity one.
In this way, several trends linking the electrode mass loading and porosity to the slurry characteristics were disclosed. All of these trends were explained in terms of the viscoelastic behavior of the slurries. For the best of our knowledge, this is the first time that this scheme designed to find interdependencies between LIB electrode manufacturing parameters is proposed and its validity demonstrated. Furthermore, the versatility of the classification variables that can be used to sort datasets, paves the way towards exciting applications in the electrode engineering field, which is in desperate need of optimization tools for reducing costs and time related to the manufacturing process of LIBs.
[32] For each dried electrode (with a certain composition and at a specific comma-gap), eight small circular disk of 13 mm of diameter where punched, and their mass and thickness measured, in different zones of the coated aluminum. This was done to work with average values of each descriptor to take into account possible coating heterogeneities. In order to make the graphs more readable, Figures 5, 6 and 8 do not present the standard deviation of each mean value. However, for transparency reasons the full dataset used to performed this works will be reported in the Supporting Information and it will be also available in the ARTISTIC project website. [32].
Acknowledgements The authors acknowledge the European Union's  Horizon 2020 research and innovation programme  for the funding support through the  European Research Council  (grant agreement  772873 , " ARTISTIC " project). A. A. F. acknowledges  Institut Universitaire de France  for the support. The authors are also grateful to  Garima Shukla  at  LRCS, Amiens  for providing graphics used for the fabrication scheme. The authors gratefully acknowledge  Raphaëlle Levy  for the realization of the cover feature.
descriptors are automatically computed from other descriptors by using logical operators (boolean operators). Xper3 (http://www. xper3. com) is a web platform that manages descriptive data and provides interactive identification keys. Xper3 and its previous version, Xper2, have been used already for various taxonomic groups. We will focus on fossils in order to reveal how calculated descriptors in Xper3 knowledge bases can solve the multi-context problem. The main source of content is the archaeocyaths knowledge base (http:// archaeocyatha. infosyslab. fr). Archaeocyaths are the first animals to build reefs during the Cambrian. They are calcified sponges without spicules. The archaeocyaths knowledge base is an efficient resource for scientific studies and a useful tool for non-specialists, especially with the support of calculated descriptors.
Acknowledgements We would like to thank  Juan Baztan  from the  Université de Versailles-Saint-Quentin-en-Yvelines (France) ,  Ana Carrasco Martin  from the  Biosphere Reserve of Lanzarote (Spain) ,  Aquilino Miguélez López  from the  Biosphere Observatory of Lanzarote (Spain)  and  Bethany Jorgensen  from  Cornell University (United States ) who helped us improve our understanding of plastic issues at local, medium and global scales.
1980). It became acceptable to transplant patients in early phase of their disease, e. g. , CR1 or first chronic phase (CP1)  (Thomas et al. 1975). The boom of BMT began  (Thomas 2007; Gratwohl et al. 2015a ). The first patient in the EBMT database dates back to 1965. In 1973, at the first informal gathering in St. Moritz, the database comprised 13 patients; 4 transplanted in that year. In 1980, a total of 285 HSCT were performed, increasing to 4025 10 years later. HSCT rapidly diversified in terms of donor type, by including autologous and allogeneic stem cells from related and unrelated donors, and of stem cell source, from bone marrow and peripheral blood to cord blood. Indications expanded from the early congenital immunodeficiency, leukemia, and aplastic anemia to a full variety of severe congenital disorders of the hematopoietic system, to other hematological malignancies such as myeloma and lymphoma, and to non-hematological malignancies, e.
g. , germ cell tumors. The HSCT technology improved to encompass a variety of in vivo and ex vivo GvHD prevention methods and conditioning regimens of varying intensities with or without TBI. HSCT became open to centers with no links to radiobiology institutes and was no longer bound to "sterile units" and to selected countries  (Gratwohl et al. 2015a; Copelan, 2006). The previously informal gatherings and the database no longer sufficed to share the urgently needed information exchange. EBMT became a formal structure, with elections for presidents and working party chairs. It was listed in PubMed for the first time in 1985  (EBMT 1985). The meetings were no longer confined to ski resorts and became open to all involved in patient care and scientific analyses  (Table 2. 1). Obviously, organization of the annual meeting is today a major undertaking and only possible with the support of corporate sponsors.
ebmt. org/Contents/Members-Sponsors/Sponsors/ Listofcorporatesponsors/Pages/List-ofcorporate-sponsors. aspx. Accessed 26 Feb 2018). EBMT is part of the global network of organizations involved in HSCT, the Worldwide Network for Blood and Marrow Transplantation (WBMT), and in close link with national and other international professional organizations involved in HSCT, such as AFBMT, APBMT, CIBMTR, EMBMT, LABMT, or WMDA. The EBMT database now holds information on more than 500,000 transplants. Over 35,000 new patients were treated annually over the last 5 years and more than 40,000 HSCT performed (Fig. 2. 1 ). An estimated number of more than 400,000 patients are currently alive after HSCT in Europe; they reflect the EBMT achievements and the challenges ahead.
Some registries recruit donors themselves, while others have an agreement with blood banks, donor centres or donor recruitment groups. The donor's or cord blood information is provided by the donor centre or CBB to a registry. The registry is responsible for listing the donors on the global database and handling communication with national and international transplant centres (through their national registries) if a potential match for a patient has been found. The search for a suitable stem cell source is based on the HLA-type of the patient. Transplant centres and search coordinators within donor registries have access to the Search & Match Service of WMDA (https://search. wmda. info/login), where they can register patient data and get a match list to see if there is a potential stem cell source in the global database.
Ethnic Diversity and Chance to Find a Donor As of January 2018, more than 32 million potential AUDs and CBUs are listed in the global Search & Match Service of WMDA. Almost 95% of these donors have DNA-based HLA-A, HLA-B and HLA-DRB1 phenotype presented, and more than half are listed with additional information on HLA-C, HLA-DQB1 and HLA-DPB1. Every year, registries across the world add approximately two million new volunteer donors to the worldwide pool, with the vast majority being HLA-typed at high and allelic level resolution. The chance of finding a well-matched donor varies for patients belonging to different ethnic groups. In 2014, the National Marrow Donor Program (NMDP) study demonstrated that whereas approximately 75% of Caucasian patients are likely to identify an 8/8 HLAmatched AUD, the rate is much lower for ethnic minority and mixed-race patients.
Donor Profile WMDA defines an unrelated donor as "a person who is the source of cells or tissue for cellular therapy product. Donors are unrelated to the patient seeking a transplant". Donor centres recruit volunteer donors from 16 to 55 years of age with variations in individual policies. Although donors can remain on the database until they are 60, donor centres try to recruit more young volunteers, as donor age has been proven to be linked to better HSCT outcomes  (Kollman et al. 2016). According to the World Marrow Donor Association (WMDA) data, approximately 50% of donors listed globally are younger than 35 (see  Fig. 3. 3). Medical suitability for donation, gender diversity, behaviour and psychological risks are constantly changing factors in donor recruitment and management. Donor centres align their policies with national and international standards and recommendations, including donor suitability guidelines produced by the WMDA on https://share.
Recruitment, Retention and Data Confidentiality Recruiting volunteer donors is challenging. Registries and donor centres must ensure they are recruiting the preferred donors (usually younger donors) who are appropriately counselled to fully understand their commitment. Registries and donor centres use a combination of methods to recruit potential donors including patient-related drives, targeting special groups, e. g. universities, uniformed services, engaging blood donors or online recruitment. The approach depends on the laws of the country and takes traditions, religion and habits into account. The same factors influence donor retention. Considering several options and alternative donors in urgent cases is a recommended practice. By signing to a donor centre or registry, a potential donor agrees that his/her data are registered in the global database.
Donor Attrition Time to transplant is reported to be a factor of overall survival  (Craddock et al. 2011). Formal search for an unrelated donor on average takes about 2 months. However, more and more urgent search requests are made to the registries, where transplant centres are hoping to get a donor workup in weeks rather than months. Not all potential donors listed on the database will be available for donation due to different reasons, including medical or personal circumstances or loss of contact with the registry. It varies in different countries. According to WMDA annual questionnaire, in 2017, the recommended target for donor availability at verification typing stage was 80% and at work-up stage 95%. Registries and donor centres are working hard to keep in contact with their donors to have updated information to help reach the donor without delays.
Donor Pool HLA Diversity Current trends in HSCT (with high requirements for patient/donor matching, complexity of standard and research protocols and a growing index of indications) present challenges for registries, donor centres and CBBs. Different strategies need to be applied to recruit not only a larger number of potential donors but also increase HLA diversity of the pool. As HLA allele and haplotype frequencies have population-specific patterns, there are limitations to how many different phenotypes can be obtained by adding new donors. In 2016, the WMDA reported no more than 50 different phenotypes per thousand new AUDs and CBUs submitted to the global database. This can be addressed by recruiting among ethnic minority groups or in parts of the world with a wider genetic diversity, e.
-Appropriately trained lab scientist and technicians -Multidisciplinary medical staff (radiology, pathology, ICU, surgery, gastroenterology a , pulmonary a ) Outcome database -Monitor patient demographics, treatment, and outcomes (level I data reporting) Quality management -Written institutional protocols/guidelines -Regular audits of various HSCT procedures and patient treatment outcomes -System to detect errors or adverse events for corrective or preventative actions a Requirements for allogeneic HSCT programs W. Rasheed et al. differential between patient rooms and the hallway, self-closing doors, more than 12 air exchanges per hour, and continuous pressure monitoring. HEPA (high efficiency particulate air) filters have shown efficacy in providing protection against acquisition of fungal infections in immune-compromised hematology patients, including HSCT patients, and during hospital construction or renovation works.
Institutional Database and Data Manager Monitoring patient demographics, treatment details, and outcomes is an essential minimal requirement. Each program should keep complete and accurate patient records, and a database containing relevant patient data should be established and regularly maintained. Appropriate patient consent needs to be obtained for such database. An example of the minimal data required to be obtained on each transplant patient is the information required in the CIBMTR or EBMT mid A forms. Having a data manager in a transplant program to initiate and maintain this institutional transplant database is highly recommended. Often data managers have nursing background with experience in stem cell transplantation. Attending training data management courses during international meetings or through links with other experienced and well-established programs would be valuable.
Impact of Accreditation in Clinical Practice Much literature indicating a better clinical outcome in teaching hospitals and centers of excellence has been available since the 1990s  (Hartz et al. 1989; Birkmeyer et al. 2005; Loberiza et al. 2005). Initial evidence of a positive relationship between the implementation of a quality management system and outcome of HSCT in Europe was published in 2011  (Gratwohl et al. 2011). In this paper, patients' outcome was systematically better when the transplantation center was at a more advanced phase of JACIE accreditation, independent of year of transplantation and other risk factors. Another analysis  (Gratwohl et al. 2014 ) was performed on a large cohort of patients who received either an allogeneic or an autologous transplantation between 1996 and 2006 and reported to the EBMT database.
Methods for Missing Values Missing values in risk predictors are a common problem in clinical studies. The simplest solution is to exclude the patients with missing values from the analysis (complete case analysis). This solution is not optimal, however: firstly, not all information is used (an excluded patient might have other characteristics known), and secondly, this approach can lead to bias if patients with missing values have on average a different outcome from the patients with observed values. If values can be imputed on the basis of observed values in the dataset, these patients can be retained in the analysis to increase precision of estimates and avoid bias. The method most commonly used is called multiple imputation  (White et al. 2011). A major advantage of this method is that it properly takes into account the uncertainty caused by the imputation in the estimates.
The importance of these discoveries was recognized by the Nobel Prices in Physiology and Medicine to  Medawar, Snell, and Dausset in 1960 and 1980, respectively. Since then, the MHC has emerged as the single most polymorphic gene locus in eukaryotes, with 17,695 HLA alleles reported to date in the IMGT/HLA database, Release 3. 31. 0, 2018/01/19  (Robinson et al. 2015). While the main barrier to successful tissue grafting remain the HLA incompatibilities, also non-HLA polymorphisms have been recognized as important players, in particular minor histocompatibility antigens (mHAg), killer immunoglobulin-like receptors (KIR), and other polymorphic gene systems  (Dickinson and Holler 2008; Gam et al. 2017; Heidenreich and Kröger 2017; Spierings 2014).
Overview HLA alleles are the most but not the only polymorphic genes in humans. Overall, interindividual gene variability by single nucleotide polymorphism (SNP) or copy-number variation (CNV) affects 0. 5% of the 3 × 10 9 bp in the human genome. Although most of these polymorphisms are probably nonfunctional, some of them can give rise to polymorphic proteins that can be mHAg as described in Sect. 9. 2. 2, affect the expression of different genes including those encoding immunologically active cytokines, or act themselves as immune ligands or receptors relevant to transplantation biology. Among the latter, the KIR gene locus on the long arm of human chromosome 19 displays considerable polymorphism, with 907 alleles reported to the IPD/KIR database, Release 2. 7. 0, July 2017  (Robinson et al.
Red Blood Cell Concentrates (RBCs) A restrictive RBC transfusion threshold of 7-8 g/ dL hemoglobin is recommended for adult patients who are hemodynamically stable. A restrictive RBC transfusion threshold of 8 g/dL is recommended for patients with existing cardiovascular disease  (Carson et al. 2016 ). These cutoffs are derived from studies on a broad range of indications. Only one randomized clinical trial is available specifically for patients undergoing HSCT (TRIST trial, NCT01237639). It compared a liberal strategy (Hb threshold <90 g/L) with a restrictive strategy (Hb threshold <70 g/L). Health-related quality of life was similar between groups, and no appreciable differences in HSCTassociated outcomes were reported  (Tay et al. 2016). The median number of RBC units transfused was lower in the restrictive strategy compared to the liberal strategy group, but this did not reach statistical significance  (Tay et al.