dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion Niche models obtained with NHC or with TS had particularly high performance, especially because of the important breadth of the distribution range (Fig  4 )  [47]. Nonetheless, as validated by the sampling records, the prediction with the TS dataset were more adjusted to the real distribution of Monastria in its entire range. Had we used the model with the NHC dataset to predict where to find new species of Monastria, two species would have been unnoticed. The differences between the predictions made with the two datasets were not only in regions under-sampled by the NHC collection dataset, as in the Northeast, but also in regions well sampled in the South and Southwest. This suggested that the problem was not in the geographic, but in the environmental space. This hypothesis was confirmed by the analysis of climate biases, which showed significant differences in representation in different climate bins between the two datasets.
The results of the rarefaction confirmed the conclusions on the importance of sampling biases for explaining the differences in area in ENMs estimated with the two datasets. The increase in estimated suitable area with rarefaction independently of the way data were deleted brought one more argument to the importance of filtering. Some studies have shown that suitable areas also increased when filtered in geographical space  [28, 29] , i. e. by deleting redundant points occurring at an arbitrary distance from each other. However, a recent study comparing the effects of filtering in geographical and environmental space for virtual species showed that the utility of geographic filters was quite unlikely to be generalized to several places. In fact, it could increase climate biases in areas with heterogeneous and repeated environments across different geographic scales  [48].
The second point contributing to this conclusion was that rarefaction did not necessarily imply a decrease in model performance, as shown by variations in AUC. This was contrary to that observed by  [28, 48]  when using spatial filters, and in accordance with the observation of  [48]  when using environmental filter. It indicated that, when environmental bias was reduced, other combination of variables became evident, so leading to robust models with much less data (Fig  5 ). Although debiasing is an important issue, excluding data is a crucial choice when dealing with NHC datasets  [26] , particularly because very often the number of data available is not enough to make good inferences on the species distribution range  [25]. Nevertheless, as shown by the present results, and also by  [28, 29, 48, 49] , if biases are detected it is necessary to find a way to reduce it, otherwise it will mask the reality of the distribution range.
If not, it would indicate some other cases in which the use of ENMs at genus level is worth to apply. However, the results of this analysis indicate strong possibility of making good inferences for the occurrence of all species in the dataset, even in cases for which very few points are available. This makes that the use of ENMs at the genus level opens to the possibility of inferring where other species in a clade may be found. To conclude, NHC is a goldmine of data readily available to be used in biodiversity science. But, as these data do not become from a pre-defined sampling protocol to answer a specific question, studying how samples are distributed and detecting possible biases is very necessary. In this respect, field validation is crucial, as it is the only way to test the predictions  [10, 30].
Acknowledgments Many people helped facilitating fieldwork. We thank  ICMBio  for authorizing the fieldwork (License No. 44118-1 ), and the people directly working on the conservation units we visited;  Jose Wellington de Morais  ( Instituto Nacional de Pesquisas da Amazonia ) for support with fieldwork license; the people working for the environment and agricultural secretaries from  Joinville, Campo Alegre and Florianopolis , for facilitating our access to reserves from these municipalities; and people that gave us access to private reserves: the owner of  Hotel Dona Francisca in Joinville (SC) ,  Sr Egon  and  Sr Gilson , from  Campo Alegre ,  Kevin Flesher  from the reserve of  Society Michelin at Itubera(BA) ,  Dona Iolanda  from  Aurora do Iguacu , and  John Keller  and  Igu in Foz de Iguacu (PR).
12 ere, we report how tuning the chemical composition of Ti1-x-yx+yO2-4(x+y)F4x(OH)4y modifies Mg 2+ intercalation and transport properties. We find that the presence of titanium vacancies enabled reversible Mg 2+ intercalation, and that the local anionic environment modifies the intercalation behaviour. Our work provides a new strategy to promote Mg 2+ intercalation that can serve as a platform in understanding critical local structural features favouring Mg 2+ mobility. We consider the reaction of titanium alkoxide and aqueous HF solution treated at mild temperature to control the composition of Ti1-x-yx+yO2-4(x+y)F4x(OH)4y electrode materials. Two temperatures were selected: 90 °C and 130 °C (see the ESI † for details). 19 F NMR was used to determine the fluorine content, the vacancy concentration was quantified by structural analysis of the PDF data and the OH content was deduced from charge neutrality.
Acknowledgements. D. D. wishes to thank the  French fluorine network  for continuous support. B. J. M. acknowledges support from the  Royal Society  ( UF130329 ) and from the  Faraday Institution  ( FIRG003 ). DFT calculations were performed using the  Balena High Performance Computing Service at the University of Bath , and using the ARCHER supercomputer, with access through membership of the UK's  HPC Materials Chemistry Consortium , funded by  EPSRC  grant  EP/L000202.
Here CYC2008 is a comprehensive catalog of 408 manually curated yeast protein complexes reliably supported by small-scale experiments, and MIPS provides detailed information involving classification schemes for analysis of protein sequences, RNA genes, and other genetic elements  [4] [5] [6]. Several high-throughput methods have supplied us with large datasets of protein-protein interactions (PPIs)  [7, 8] , such as tandem affinity purification (TAP) and yeast twohybrid (Y2H)  [9]. To predict protein complexes, many researchers have proposed to study the structure of the resulting PPI network  [10] [11] [12] [13] [14] [15] , which is an undirected graph with proteins represented as vertices and interactions between them represented as edges. For example, methods such as Markov Cluster (MCL)  [16] , Molecular Complex Detection (MCODE)  [17] , Clustering-based on Maximal Cliques (CMC)  [18] , Protein Complex Prediction (PCP)  [19] , and CFinder  [20]  are mainly based on the topological structures of PPI networks.
The later method has been shown to have better performance in F-measure for prediction of heterodimers than other existing prediction methods, including MCL, MCODE, RRW, and NWE. To improve the prediction accuracy for heterodimers, Ruan et al. [27]  proposed a supervised method with several features based on PPI weights. The weights are obtained from dataset WI-PHI (a Weighted yeast Interactive enriched for direct PHysical Interactions), which includes 49607 interacting protein pairs except self interactions and the weights of interactions between protein pairs. The main idea behind the design of feature space mappings is that the neighboring weights of a heterodimer tend to be smaller than the weight inside of the heterodimer. In addition to features based on weights, they proposed feature space mappings based on the number of protein domains because domains are considered to be functional and structural units in proteins.
We compute phylogenetic profiles for the 5, 497 proteins encoded by the genome from KEGG OC  [32] , a novel database of ortholog clusters. Each protein sequence (P i ) is encoded by 2, 717 genomes, which consist of eukaryotes, bacteria and archaea. Proteins coded by the j-th genome are defined as including a homolog of a protein P i if they align to the protein P i with a score that is deemed statistically significant. In Fig. 2 , the left side are several genomes with their proteins and the right side are phylogenetic profiles for all proteins. We define a feature space mapping φ phylo for protein P i so that the j-th element of φ phylo (P i ) describes whether or not the j-th genome contains P i. For example, in the genomes, P 1 exists in EC and BS but not in SC, so for the phylogenetic profile of protein P 1 , elements of EC and BS are 1, and SC is 0.
Experiments In order to compare our proposed method with the method in  [27] , we used the same dataset WI-PHI. The weights of interactions were calculated in the following way. (  1 )Used the high-throughput yeast two-hybrid data by Ito  [8]  and Uetz  [7]  as well as several databases such as BioGRID  [11] , MINT  [12]  and BIND  [13]  to build the literature-curated physical interaction (LCPH) dataset. (2)Constructed a benchmark dataset to evaluate highthroughput data. The interactions of the dataset were obtained by two independent methods from LCPH-LS, which was a low-throughput dataset in LCPH. (3)Calculated a log-likelihood score (LLS) to each dataset except LCPH-LS. (4)Computed the weight of each interaction by multiplying the socioaffinity (SA) indices  [1]  and the LLSs from different datasets.
Note that SA index is the log-odds score of the number of times that we observed two proteins interact to each other to the expected value in the dataset. Also, we prepared the same dataset from CYC2008  [2]  for training and testing as the previous study. CYC2008 is a set of 408 manually curated yeast complexes. Compared with MIPS catalogue, which consists 215 heteromeric complexes, we believe that CYC2008 represents a more complete and up-to-date description of the stable yeast interactome, and should hence serve as an improved gold standard for the prediction of complexes. CYC2008 catalogue can be downloaded at: http://wodaklab. org/ cyc2008/. We defined a positive example as a pair of proteins included in WI-PHI as well as a heterodimer included in CYC2008. A negative example was defined as a pair of proteins included in WI-PHI, which meanwhile should not be any heterodimer but be a subset of some other complexes in CYC2008.
In addition, we observed that Normalized Min-MLPK kernel and MinMax-MLPK kernel had better performances in most cases. The observation shows that the normalization form has contribution to improving prediction accuracy. Table  1  shows the exact performance of each combination kernel on their best average precision, recall, and F-measure. Normalized Min-MLPK kernel had the best performance on precision (increased from 61. 8 to 71. 7%) and MinMax kernel had the best performance on recall (increased from 64. 4 to 71. 8%). Normalized Min-MLPK kernel achieved the best performance on F-measure (increased from 63. 1 to 68. 6%) and all the proposed methods that exclude TPPK-combined kernels outperform Domain Composition kernel. The last 5 rows are the results of other existing state-of-the-art methods, which were all given the same dataset WI-PHI as ours and executed with their default settings to predict heterodimers, except the option of the minimum size of predicted complexes, which was set to be two.
it will be concluded that there is a possibility of high intrinsic risk, so that one of the presented arguments by Wu et al. 11  is fallacious. On a pure mathematical side, we show that a scaled Weibull function with 3 parameters approximates the 4-parameter mechanistic tLIR model. On an epidemiological data analytical side, this simple 3-parameter model excellently agrees with age-dependent cancer incidence curves among 18 common solid cancers even when variations due to different locations, races, or periods are taken into account. With this model, we study the relationship between cancer risk and stem cell divisions, the high correlation between the two entities reported by previous studies  8, 10  breaks down when considering age stratified data.
However, using age incidence data obtained from the SEER-database  27  we found that the regression lines for most tissue types c for age data of 40, 50, 60, 70, and 80 years of cuminc c (a) plotted against LCSD c (a) in a log-log-scale are much steeper than the ones of the regression lines for different c and cuminc c (80)-using 80 as average life span as was done by Tomasetti and Vogelstein 8 (see Fig. 4 ). This "life time cancer risk" moderately associates with age-dependent stem cell divisions, if one takes a life-time a that is less than 70 years (Fig. 5 ).
Relationship between cancer incidence and stem cell divisions. Tomasetti and Vogelstein Overall, age-dependent stem cell divisions (using ages 40-80 years) is modestly correlated to age-dependent cancer risk for the 31 cancer types considered by Tomasetti and Vogelstein 8 using the SEER database and the estimates of stem cell divisions given therein (Pearson correlation coefficient ρ = 0. 51). Hence, the strong correlation for (life-time) tissue-specific cancer risk with life-time stem cell divisions (LSCD) cannot be explained by the simple causative factor (involving the product of the number of stem cells and the number of divisions of each stem cell) suggested by Tomasetti and Vogelstein  8. A causal explanation on cancer risk should at least shows that the association between cancer risk and risk factor observed at overall level is reproducible on age stratified data.
Then we have that the tLIR incidence approximately equals to the scaled Weibull incidence if the parameters satisfy λ =. -  period, location, histology and ICD (international classification of disease) code. These data were stored in ASCII file, we used the SEERaBomb R package to parse them into sqlite file facilitating data manipulation. Cancer names provided by Tomasetti and Vogelstein  8  can not be directly mapped into those in SEER database. We addressed this difficulty by two steps: first, annotate tumor primary site to (international classification of disease-oncology 3) ICD-O-3 code based on the literal sense of site in Tomasetti and Vogelstein  8  ; second, annotate histology to ICD-O-3 code based on the literal sense of cancer histology by Tomasetti and Vogelstein  8. For instance, primary site of lung adenocarcinoma is lung, corresponding to ICD-O-3 site code: C340, C341, C342, C343, C348, C349; adenocarcinoma of lung cancer corresponds to ICD-O-3 histology code 8140, 8141, 8143, 8147, 8570, 8571, 8572, 8573, 8574, 8575, 8576.
The dictionary needed for mapping step (we call it ICD dictionary) can be found in http://seer. cancer. gov/icd-o-3/. Osteosarcoma definition can be found in ICD dictionary, it is a subtype of malignant bone neoplasm, corresponding ICD-O-3 histology code: 9180-9189. However, the ICD dictionary does not differentiate between osteosarcoma detected in the head, leg, or arm. The ICD9Data database (http://www. icd9data. com/) defines bone cancer using ICD9 code 1700-1709, bone cancer in head, arms, legs, pelvis using ICD9 code 1700, 1704-1705, 1707-1708, 1706 respectively. Head and neck squamous cell carcinoma involves tumors located in many sites, ICD dictionary fails to provide its definition. Liao et al. 36  provided ICD9 site code: 1400-1419, 1430-1499, 1600-1619, we then used ICD-O-3 histology code: 8070-8076, 8078 to select squamous cell carcinoma.
More detailed cancer definitions using ICD code can be found in Table  3. Two hematopoietic cancers: acute myeloid leukemia and chronic lymphocytic leukemia, are defined using site recode ICD-O-3/WHO 2008 definition (http://seer. cancer. gov/siterecode/icdo3_dwhoheme/index. html). S rd P ( ) (10 Although we carefully annotated 25 cancer definitions using ICD code, we can not avoid misclassifications. because annotation needs several data sources of which information confidential levels differ from each other. The Cancer Genome Atlas (TCGA) program  37  is a flag project of cancer research hosted by National Institutes of Health, it provides comprehensive, high-quality molecular and clinical data. Cancer definitions are well annotated using ICD code in TCGA clinic documents. We therefore assume TCGA cancer definitions are precise and extracted definitions of 18 solid tumors (Table  4 ).
With 18 cancer definitions, we selected patients who were diagnosed with cancer after 2000 from SEER-9 registries, SEER-4 registries, SEER-5 registries data to form SEER-18 registries data. As the highest time resolution of SEER data is 1 year, for each year, we took middle age for fitting models, for example, 0 year-old is modified as 0. 5 years-old. For robustness analysis of parameter estimates we classified each cancer into subgroups based on location, period and race, data of subgroups were separately fitted to the mathematical models. Fitting the models to empirical cancer incidence data. As was done in previous work  38   as the metrics for goodness of fit, where x i and y i is empirical and model-derived cancer incidence respectively, x and y respectively denotes mean value of x and y.
We discuss life time stem cell division (LSCD) hypothesis and extrinsic risk factor ) data were downloaded from Surveillance, Epidemiology, and End Results Program (SEER) database27. SEER database covers about 28% USA population, involving more than 100 features such as race, sex, Notice that using a Poisson approximation [12, p. 104] we finally obtain λ 1 (1 Weibull( , )( )) --k a ≈ P λ Weibull( , )( )) k a. ( 11) hypothesis for 25 remained cancers: AML, acute myeloid leukemia; BCC, basal cell carcinoma; CLL, chronic lym- phocytic leukemia; COAD, colorectal adenocarcinoma; DUAD, duodenum adenocarcinoma; ESCA, esophageal squamous cell carcinoma; GBNPAD, gallbladder non papillary adenocarcinoma; GBM, glioblastoma; HNSC, head and neck squamous cell carcinoma; LHCA, hepatocellular carcinoma; LUAD, lung adenocarcinoma; MBM, medulloblastoma; SKCM, melanoma; OSARC, osteosarcoma; OSARCA, osteosarcoma of the arms; OSARCH, osteosarcoma of the head; OSARCL, osteosarcoma of the legs; OSARCP, osteosarcoma of the pelvis; OVGC, ovarian germ cell; PDAD, pancreatic ductal adenocarcinoma; PECA, pancreatic endocrine (islet cell) carcinoma; SIAD, small intestine adenocarcinoma; TGCC, testicular germ cell cancer; TPFC, thyroid papillary or follicular carcinoma; TMCA, thyroid medullary carcinoma.
The EFSA Panel on Genetically Modified Organisms (GMO Panel) requested additional information from the applicant on 23 October 2015 (EURL-JRC), 4 January 2016, 11 February 2016, 26 April 2016,  23 May 2016, 20 July 2016, 29 September 2016, 17 November 2016, 5 December 2016, 22 February  2017, 11 May 2017, 12 July 2017, 2 August 2017, 18 August 2017 and 7 November 2017. The applicant provided the requested information on 18 March 2016, 7 March 2016, 3 May 2016,  30 August 2016, 22 August 2016, 22 September 2016, 3 January 2017, 27 March 2017 (including the partial dataset received on 18 January 2017), 6 February 2017, 24 April 2017, 10 July 2017,  3 October 2017, 13 October 2017, 20 November 2017 and 12 February 2018, respectively. In the frame of the contracts OC/EFSA/GMO/2013/01 and OC/EFSA/GMO/2014/01, contractors performed preparatory work and delivered reports on the methods applied by the applicant in performing bioinformatic and statistical analyses, respectively.
Systematic literature review 7 The GMO Panel assessed the systematic literature searches provided by the applicant on maize MON 87403 according to the guidelines given in EFSA (2010, 2017). A systematic literature review as referred to in the Regulation (EU) No 503/2013 has not been provided in support to the risk assessment of the application EFSA-GMO-BE-2015-125, because of the limited number of relevant publications on maize MON 87403. 8 lthough the overall quality of the performed literature searches is acceptable, the GMO Panel considers that the searches on maize MON 87403 could be improved. The GMO Panel therefore recommends the applicant to: design broader and more sensitive searches in the context of maize MON 87403; ensure that enough search term variation is used (covering possible synonyms, related terms, acronyms, spelling variants, old and new terminology, brand and generic names, lay and scientific terminology, common typos, translation issues); include controlled vocabulary (subject indexing) in the searches when available (in addition to text words); use truncation consistently; use NEAR/5 (or greater) instead of NEAR/3; adapt the search to the size of the retrieved publications (and thus not combine search sets when one of the search sets already yields only a small number of publications); report the number of publications retrieved for each single search set performed (or search lines); and assess the relevance and risk assessment implications of publications retrieved via searches beyond electronic bibliographic databases.
Inheritance and stability of inserted DNA 21 Genetic stability of the MON 87403 insert was assessed by NGS/JSA and Southern blot 7 analyses for five generations. The obtained data from coverage mapping and junction sequence classes (JSCs) were sufficient to conclude that all the plants tested retained the single copy of the insert and flanking regions, which were stably inherited in subsequent generations. 21 Stability was also observed by segregation PCR analysis of the construct that expresses the AtHB17Δ113 protein in plants from three generations of maize MON 87403. The results supported the presence of a single insertion, segregating in a Mendelian fashion.
Choice of comparator and production of material for the comparative assessment 23 Application EFSA-GMO-BE-2015-125 presents data on agronomic and phenotypic characteristics, as well as forage and grain composition, of maize MON 87403 derived from field trials performed at nine sites in the USA in 2012. 24 In addition, the application contains data on characteristics of pollen and seed from maize MON 87403, and data on additional phenotypic endpoints illustrating the effect of the genetic modification, hereafter referred to as physiological characteristics. The latter were derived from complementary field trials and a greenhouse study (Table  2 ). 24 A total of nine sites were used for the agronomic/phenotypic and compositional studies, of which seven were used for both the agronomic/phenotypic and compositional analysis.
Statistical analysis of field trial data The statistical analysis of the agronomic and phenotypic and compositional data from the 2012 field trials followed the recommendations of the GMO Panel (EFSA GMO Panel, 2010b, 2011a) and complied with Regulation (EU) No 503/2013. This included the application of a difference test (between the GM maize and its conventional counterpart) and an equivalence test (between the GM maize and the set of non-GM maize reference varieties). The results of the equivalence test are categorised into four possible outcomes (I-IV, ranging from equivalence to non-equivalence). 30  The agronomic and phenotypic parameters evaluated in the 2012 field trials were: early stand count, days to 50% pollen shed, days to 50% silking, dropped ear count, ear height, plant height, final stand count, root lodged plants, stalk lodged plants, stay green, grain moisture, test weight, yield, visually observable responses to naturally occurring diseases (disease incidence), arthropod damage and abiotic stress responses.