dataesr/scientific-paragraphs-categorization huggingface dataset:
The PCR products were purified using PCR purification kit (PCR and Gel clean up, Macherey-Nagel, Duren, Germany) with a provided manual. DNA sequence analysis (n=10) of two population was done at GenoTech Corporation (Dajeon, Korea). Outgroup taxa for each dataset was taken based on references from previous studies  (Sekimoto et al. , 2017; Subbotin et al. , 2001; Vovlas et al. , 2015). The newly obtained sequences of ITS rRNA and COI gene were aligned using ClustalX 1. 83 program with datasets of 'Schachtii' group species, registered in National center for biotechnology information. Bayesian analysis of the sequence datasets was performed using MrBayes 3. 1. 2 (Kang et al. , 2016; Sekimoto et al. , 2017; Vovlas et al. , 2015). The general time reversible substitution model (GTR+I+G) was chosen for this analysis and was run with four chains for 1 × 10 6 generations.
The posterior probability of phylogenetic tree was estimated with MCMC (Markov Chain Monte Carlo) method and consensus tree was generated with a 50% majority rule. The trees were edited and visualized using Dendroscope 3. 5. 7. Intraspecific and interspecific sequence variation was analyzed using Mega 6. 0 program  (Tamura et al. , 2013). Statistical analysis. Morphometric data of both J2s and vulval cones of the two nematode species were subjected to analysis of variance according to the general linear model procedure using the SAS statistical package (SAS Institute Inc. Cary, North Carolina, USA). Arithmetic means were compared using Tukey's HSD (Honest Significant Difference) test at P ≤ 0. 05 to test the differences or similarities between measurements of the different morphological characters used in identification.
glycines (LC208697) and H. medicaginis (AF274391) were 2. 0% and 2. 1%, respectively. In this analysis, GC147 population was identified as H. schachtii. COI sequence dataset included 21 sequences of Heterodera species, containing newly obtained sequences of H. trifolii and H. schachtii in Korea. The phylogenetic tree as inferred from Bayesian analysis of COI gene sequence dataset with GTR+I+G substitution model is shown in Fig. VII. The results showed that YS503 (MG682348) and GC147 (KY775592) population belong to H. trifolii and H. schachtii clades, respectively. Intraspecific COI sequence variation between YS503 (MG682348) population and H. trifolii of previous studies was 0. 0% whereas interspecific COI sequence variation between YS503 (MG682348) population and closely related species, H.
3. These results also suggest that the modified TRI Reagent ® (MRC) manual can be a proper method for molecular identification of nematode species from a single cyst. Contrastingly, there exist different levels of intraspecific variation in the ITS-rDNA sequences within 'Schachtii' group and this has an effect on cluster analysis  (Madani et al. , 2007). Indeed, the phylogenetic relationships of the ITS sequences showed two sub-branches of the H. schachtii populations; the sequence of H. schachtii (EF611118) population was clustered together with closely related 'Schachtii' group species like H. betae (LC208689), H. daverti (KT163232), H. trifolii (LC208683), including the Korean population of H. trifolii (YS503; MG682350). The second sub-branch consisted of four H. schachtii species (LC208693, AY166438, LC208692, EF611107), populations available in the Genbank (https://www.
, see lanes of individual 3), suggesting that individual 1 and 2 are H. trifolii, while individual 3 is H. schachtii. These results further confirm the mixture of the two species of Heterodera in the Chinese cabbage fields of Korea. Phylogeny: ITS rRNA and COI gene. ITS region and COI gene were successfully amplified as indicated in Fig. 4from YS503 and GC147 population and their sequence information was also obtained and submitted to the GenBank data base under accession numbers; MG682350, MG682348 for H. trifolii and MF043911, KY775592 for H. schachtii. ITS sequence dataset included 22 sequences of 'Schachtii' group species of the genus Heterodera, containing newly obtained sequences in Korea. The phylogenetic tree as inferred from Bayesian analysis of the dataset with GTR+I+G substitution model is shown in Fig.
Acknowledgments This research was carried out with the support of the " Research Program for Agriculture Science and Technology Development  (Project No. PJ012640 )",  Rural Development of Administration, Republic of Korea. Thanks to  Kyoung Hwan Moon ,  Faisal Kabir ,  Mungi Jeong ,  Jeongun Kim ,  Hyeon Guk Kim ,  Yoon Suk Cha ,  Heebeen Na  and  Hyeon Jeong Ahn  for the necessary laboratory technical support.
Statistical Analysis Statistical analysis was performed using GraphPad Prism v5. 0 (GraphPad Software, USA). Group comparisons were made considering levels of antibodies, parasite load, and cytokines using the non-parametric Mann Whitney U-test. Hierarchical cluster analysis (Ward's method) with bootstrap was performed to depict the overall expression profile of serum biomarkers in the negative and experimentally infected dogs. Significant statistical differences between more than two groups were evaluated using the Kruskal-Wallis test with Dunn's multiple comparisons. Differences were considered significant when P-values ≤ 0. 05.
Cytokine production was evaluated by ELISA following stimulation with a recombinant protein (LJM17 or LJL143), L. longipalpis SGH (equivalent to 1 pair/mL) or Concanavalin A as a positive control. PBMCs from immunized dogs stimulated with SGH or one of the recombinant proteins exhibited higher IFN-γ production compared to controls. (P < 0. 005) (Figure  1D ; Supplementary Table  2 ). In addition, no IL-10 production was observed in any of the experimental groups, which suggests that a Th1 response was induced by immunization with the salivary proteins (Data not shown). Our evaluation of circulating cytokines in the sera of experimental and control groups after immunization found that the groups immunized with salivary proteins presented higher levels of IFN-γ and IL-10 (Figures  1E, F ; Supplementary Table  2 ).
IgG Anti-Leishmania, Parasite Load Quantification, and Clinical Evaluation After Infection To evaluate the humoral immune response induced by infection under the experimental protocol employing L. infantum promastigotes and L. longipalpis saliva gland homogenate, an ELISA assay was performed to detect anti-Leishmania IgG in canine sera at zero (T0), and every 2 months after the infection. At 2 months after infection, anti-Leishmania antibodies had already been detected in all animals. The groups that received LJM17 presented significantly higher levels of IgG in comparison to controls (P < 0. 0001). However, at 4 months after infection antibody concentrations decreased and all animals exhibited negative serology. Interestingly, 6 months after infection, we found an increase in the serological levels of anti-Leishmania antibodies (Figures  2A-C ; Supplementary Table  3 ), yet no parasites were detected in the skin, spleen, bone marrow or lymph nodes at this time point (Data not shown).
ACKNOWLEDGMENTS The authors would like to thank  Andris K. Walter  for English language revision and manuscript copyediting assistance. The authors also would like to thank  Andrezza Souza  for technical and logistics support. This work was supported by grants from  Fundação de Amparo à Pesquisa do Estado da Bahia-FAPESB  (N •  SUS0036/2013  and  PET0024/2013  to CB); grant from  Conselho Nacional de Desenvolvimento Científico e Tecnológico  ( 402670/2012-4  to CB); and  Instituto Nacional de Ciência e Tecnologia de Investigação em Imunologia (III-INCT)  (to CB). MA received the PhD fellowship from  CAPES  ( Coordenação de Aperfeiçoamento de Pessoal de Nível Superior ). CB, PB, are senior investigators of  CNPq. The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript.
Therefore, subsequent tools are needed to reconstruct full root system architectures from extracted root traits. Root architecture models, such as SimRoot  (Lynch et al. , 1997) , RootBox  (Leitner et al. , 2010) , RootTyp  (Pagès et al. , 2013) , ArchiSimple  (Pagès et al. , 2014) , OpenSimRoot  (Postma et al. , 2017b)  or CRootBox  (Schnepf et al. , 2018) , are designed to simulate root systems from a limited number of traits, given as input parameters. The major interest of root architectural models is to generate a large number of contrasted root system architectures. Root system modeling enables the exploration of several variants for the same mean traits and the simulation of contrasted architectures, even from synthetic datasets. These contrasting architectures can then be tested in different scenarios, to identify traits that would be beneficial in challenging environments.
An example that illustrates how root architecture models can be applied to interpret experimental data of other root zone processes is given by  Schnepf et al. (2016). Those authors developed a 3D model of the development of mycorrhizal root systems. The model was designed to simulate primary and secondary root infection with arbuscular mycorrhizal (AM) fungi as well as growth of external fungal hyphae in soil. It was calibrated using root architectural data obtained from pot experiments of Medicago truncatula, with and without mycorrhizal inoculum of the AM fungus Rhizophagus irregularis BEG 158. In those pots, AM root colonization was determined under a compound microscope and the abundance of R. irregularis hyphal biomass was determined using real-time PCR. The root system architecture, however, could not be parameterised from those pot experiments.
, Couvreur, V. , Meunier, F. , Draye, X. , Javaux, M. , Leitner, D. , Pagès, L. , Schnepf, A. , Vanderborght, J. ,  Lobet, G (2019). Connecting the dots between computational tools to analyse soil-root water relations. Journal of Experimental Botany, 70 (9), 2345 -2357. , DOI : 10. 1093/jxb/ery361 A c c e p t e d M a n u s c r i p t 13 Regarding soil properties, soil water retention curves were fitted on simultaneous soil water content and pressure head measurements  (Cai et al. , 2016)  with the software RETC  (van Genuchten et al. , 1991)  (fig. 5C ). However, the parameters of the soil hydraulic conductivity curve were not experimentally determined (fig. 5D ). The vertical distribution of roots in the field (root length density profiles over time) was extracted from in situ rhizotube pictures, with the software Rootfly  (Zeng et al.
For image analysis tools, we refer the reader to the www. plant-image-analysis. org database  (Lobet et al. , 2013; Lobet, 2017). We created an interactive online visualization of the network, which contains links to the different tools and makes the understanding of Figure  6  easier. We also added a submission form, such as the community could update the network with new (or missing) tools. The online visualisation tool (open-source) is available at https://plantmodelling. shinyapps. io/water_network/.
Limitations and future developments Simulating water fluxes in roots with this collection of tools can either help understanding plant water relations as a main goal or be a tool for further application. These tools could also be used as a side usage of a dataset obtained for other purposes. The advent of imaging in plant sciences Comment citer ce document : Passot, S. , Couvreur, V. , Meunier, F. , Draye, X. , Javaux, M. , Leitner, D. , Pagès, L. , Schnepf, A. , Vanderborght, J. ,  Lobet, G (2019). Connecting the dots between computational tools to analyse soil-root water relations. Journal of Experimental Botany, 70 (9), 2345 -2357. , DOI : 10. 1093/jxb/ery361 A c c e p t e d M a n u s c r i p t 16 and the huge progress made in image analysis allow generating high quality quantitative data of plant structure, suitable for model parameterization.
Many models exist at different scales and we highlighted the many possibilities to combine these tools. This set of tools greatly increases the potential of interpretation of experimental data. Yet many authors still publish rich datasets without using modelling tools to interpret them. Using models is not trivial for a large part of the plant science community. Coupling several tools and models together is still rarely applied. Several requirements seem essential to facilitate the use of this pipeline whenever it may add value to the data. A lot of image analysis tools and models flourish in the water transport domain. Thanks to the wide breadth of scientific literature available (scientific papers, reviews, websites…), developers are usually aware of already existing tools and keep in mind to justify the interest of their tool in this landscape.
However, we suggest that further efforts should be made to render the tools compatible with existing ones. In this context, the existence of several tools at the same place of the network (eg. RootTyp and CRootBox for root architecture simulation) is not conflicting. Each one can best suit one scale or specific situation. In our opinion, special attention should be paid to the data format. Indeed, the output data format of upstream tools must be compatible with the input format required by downstream ones. If this is not the case, easy-to-handle tools must exist to convert these data. The multiplication of formats and the need to convert data from one type to another may discourage the use of some of the models. In this respect, the existence of standard formats, such as the Root System Markup Language  (Lobet et al.
Acknowledgments The authors would like to thank  Jennifer Brophy ,  Heike Lindner  and  Thérèse LaRue  ( Carnegie Institution for Science, Stanford, CA ) for their useful comment on the first version of manuscript. This work was also supported by the  Belgian French community   ARC 16/21-075  project. VC was also supported by the  Interuniversity Attraction Poles Programme-Belgian Science Policy  (grant  IAP7/29 ). During the preparation of this manuscript, FM was supported by the  "Fonds National de la Recherche Scientifique" (FNRS) of Belgium  as a Research Fellow and is grateful to this organization for its financial support.
online at his website and others available through online stores such as Amazon. In a sense, Brownlee is one of us, with a Ph. D. (Swinburne University, Melbourne, Australia) and a thesis and academic publications on modeling of artificial immune systems. Master Machine Learning Algorithms can be purchased online at https://machinelearningmastery. com/master-machine-learning-algorithms/ (accessed on 03. 08. 2017) at modest cost ($USD 37), which also includes 17 Excel spreadsheets to illustrate the main algorithms. His website offers 10 related books (including four at a more advanced level) that are tailored for use with the statistical program such as R or the data mining program Weka (both freely distributed on the internet). So, for very little money, a reader can have a useful basic introduction to the topic together with ready-made software to play around with.
Where it succeeds The book nicely fills the gap between popular oriented, often hyperbolic introductions to machine learning for laypeople, and textbooks for professionals. To a novice entering the field, it is highly educational to use the tools of machine learning as provided in Microsoft Excel spreadsheets and trace their operation step by step. Unlike other introductions to machine learning  [3] [4] [5] [6] , the reader does not need to buy expensive software such as Matlab or grapple with complicated software such as R and Weka which are referenced in other versions of this book. This approach has great merit as an introduction to a challenging subject that requires a low initial investment. It is a bit like introducing elementary school students to music by teaching them to play inexpensive recorders: the lessons can instil a lifelong appreciation of music but nobody pretends to turn the kids into musicians.
com/data-leakage-machine-learning/); an extensive professional literature exists on the subject (e. g. Kaurman 2012). A common novice error is to tune a classifier to obtain the "best" results, but continue to use the same test data-which consequently invalidates its statistical independence and makes it unreliable for validation purposes. There are, of course, many discussions of these problems in the professional literature but these are more advanced sources than this present volume. A different set of problems arise with developing sophisticated statistical methods for use in clinical medicine. These need to work at the technical level that is familiar to engineers, and also meet the needs of doctors and patients. A quick search on Google Scholar will uncover hundreds of papers that use machine learning or data mining to develop methods to diagnose disease, estimate a patient's prognosis from a disease, or another purpose.
Numerous papers are easily located on Google Scholar on these topics, a large fraction of which appeared in engineering or computer science journals. We describe databases that have been used for such purposes. Our goal is not to criticize the studies, but to point to the differences in scale of data needed to develop an algorithm and in establishing its clinical efficacy for real-world medical use. Two datasets, available online, have been used to develop algorithms for diagnosis of coronary artery disease (CAD). One is the "Z-Alizadeh" dataset  [7]  that consists of 55 different clinical parameters, demographic data and results of medical tests measured in 303 patients that were collected from random visitors to a Tehran cardiology center. A second dataset is "heart" (http://www-bcf. usc.
edu/~gareth/ISL/data. html), that has 13 attributes from 303 patients from an unknown medical center. This latter data set has been used in an extensive case study in the James's textbook  [3]. Both datasets raise interesting technical issues. They are both unbalanced (unequal numbers of healthy and diseased subjects) and contain a mix of qualitative and quantitative data. Both datasets have too many attributes relative to the number of subjects and must be pruned (choosing a subset of attributes for the classifier). James et al. [3]  and Alizadehsani  [7]  both give excellent discussions of the pruning process, one from the perspective of a research paper and the second from a didactic perspective. One of the attributes in the "heart" data set is the result of the thallium stress test, which is a diagnostic test for CAD.
Not surprisingly, James et al. [3]  show that this attribute has by far the greatest importance in training a classifier for diagnosis of CAD. The second example is prognosis of breast cancer survival. Several papers use the Haberman Survival dataset (http://archive. ics. uci. edu/ml/datasets/ Haberman's+Survival), that contains the 5-year survival status of 306 patients who had undergone breast cancer surgery. The data set has two classes (alive or dead 5 years after surgery) and three attributes (age of patient at time of operation, year of patient's operation, and the number of positive axilliary nodes detected). This data set is also interesting as a didactic example of machine learning for binary classification, and has been discussed by one expert as a particularly difficult problem in binary classification  [8]  (For comments on that see Appendix and Additional file 1).
However, it lacks information such as grade of the tumor and data about hormone sensitivity and use of any adjuvant therapy (such as chemotherapy after surgery) that would be needed for accurate prognosis. The data set is also unbalanced (most of the patients were still alive after 5 years) and it has too few attributes to benefit from the distinctive benefits of machine learning, which is to discover new parameters or combinations of parameters that would improve diagnosis. (Shelby J. Haberman, who collected the data for a 1976 paper on log-linear models, became a distinguished statistician and spent much of his later career at the Educational Testing Service in Princeton NJ). All three datasets are readily available online, and can be easily imported into statistical programs such as R for use with their built-in machine learning or data mining tools.
These three datasets, among others, have been used in a rich research literature, almost entirely focused on algorithm development. But the authors have not always distinguished clearly between technical goals (developing algorithms for classifiers) and actual medical use, using terms such as "survival prediction" or "diagnosis" without qualification. This distinction is understandably, not discussed in Brownlee's book, or in most other texts on machine learning for that matter. The differences in scale between an engineering study on algorithm development and a developing a classifier or other mathematical model that is suitable for use in medical practice can be very large. For example, in cardiology, physicians would need more than a binary classification of a patient as having or not having CAD.
No machine learning-based models met the inclusion criteria for acceptance in that review A 2017 study by Korley et al. [10]  assessed use of clinical risk factors (such as in the Z-Aldesani database) for diagnosing CAD as a pre-test selection tool. That study derived a classifier used a regularized regression method, based on a derivation set of 679 patents to train and validate a classifier, with additional validation on 1056 patients from a different cohort. The investigators concluded that "clinical risk factors, either individually or in combination, are insufficient for accurately identifying suspected ACS (acute coronary symptom) patients harboring undiagnosed significant coronary artery disease. " The possibility exists, however, that an improved classifier after proper validation might prove more successful.
[12]  noted the potential usefulness of such studies, but also considered areas in which improvement is needed. These include the need for studies to validate their results across different healthcare centers, develop better methods to deal with missing data, and assessing how the algorithms impact clinical decision making. In a recent tutorial  [13]  Goldstein et al. describe the use of machine learning to predict risk of death in patients admitted to an emergency after sudden myocardial infarction, using electronic health records of 1944 patients-a data set that is nearly seven times larger than the Z-Alizadehsani dataset  [7]  but not out of range of many biomedical engineering groups. The authors conclude that machine learning methods "can be employed to help confront issues of multiple and correlated predictors, non-linear relationships, and interactions between predictors and endpoints, in large datasets.
However, it provides a simple test case for discussions of machine learning (e. g. https://www. kaggle. com/gilsousa/haberman-s-survival). For this Appendix, the data set was randomly divided into a learning set comprising 2/3 of the complete data (204 cases) and a test set with the remaining 102 cases. Classifiers were trained and validated using three algorithms in the Matlab Statistical Toolbox (support vector machine (SVM), pruned decision trees and naive Bayes classifier). For each algorithm, this process was repeated 1000 times using randomly chosen training and test sets from the dataset. There was no attempt to address problems arising from the significant imbalance in the data set (66% of the patients were still alive 5 years after surgery). Table  1  summarizes the results for 3 different classifiers, applied repeatedly to the dataset 1000 times.