dataesr/scientific-paragraphs-categorization huggingface dataset:
In order to determine themselves whether or not to eat the various food products, it is useful to know how much radioactivity they take when they eat those products, the degree of internal exposure, and also the quantity of radioactivity discharged from the body. In this perspective a tool which provides information about all these aspects was considered as very helpful. After the Chernobyl accident, the ETHOS project and the CORE program  [7, 8]  in the contaminated areas of Belarus provided education for local residents about radiation protection to favor their involvement in their own protection. A software for internal exposure assessment called CORPORE has been developed and used in this program in cooperation with Norwegian radiation protection experts. With this software, it is possible to describe and analyze the results of whole body counters (WBC) measurements.
For this reason, it was decided that the support of multi-nuclide is also required. Original CORPORE was basically intended for use by radiation protection and medical personnel. In order to be used by residents, the user management system and intake history management system have been developed. Original CORPORE also has statistical functions about the intake status for assessment in order to display the position of the resident in the group. All these features have been kept. In order to adapt the original CORPORE to the Japanese situation, it is developed as a Web application for widespread use. A database structure allows managing ingestion of food products as dish. It hence supports to various nuclide types. Applying user management function, users can manage their own intake history respectively.
System Local residents can enter their own dish to evaluate their contamination level, so they can make the exposure management of themselves by browsing the radioactivity intake information and internal exposure information. They are also able to access it and use it from personal computers or smart phones through the Internet, as it is developed as a Web application. To operate in typical server configurations, MySQL 5. 5 database system, most popular open source database system in the world [9], was used, and it was implemented in PHP 5. 5, Hypertext Preprocessor which is a serverside scripting language  [10] , to work on typical web server applications such as Apache 2. 4 [11], etc. Also, it is available from the general viewing environment, and is successfully displayed in Internet Explorer 9 or higher, Google Chrome version 10.
Database Structure In order to input food intake situations to match the Japanese food environment, we considered the modification of the food database structure of the original CORPORE. It was initially limited to the food database, and we enlarged it to the ingredients database. Also, the original CORPORE allowed only Cs-137 input, while in the modified version the database structure corresponds to the multi-radionuclide species. Presented in the survey results by the MHLW released I-131, Cs-134, Sr-90 and plutonium (Pu-238, Pu-239, Pu-240) became a possible input.
Form of Use Original CORPORE was used in Belarus as an assessment tool (1) to determine the amount of radioactivity in the body using the WBC measurements as inputs, (2) to engage a dialogue with the residents on their diet in case the internal contamination levels were significantly higher than the average level, (3) to analyze the estimated exposure dose using the graph to display it; thus, it is the reverse direction to explore the cause from the result. Displaying the radiation dose that is expected by ingesting the food is not only the opposite direction to determine whether to eat or not. Also, the ingested radioactivity substance amount is recorded, because it is assumed that a possible use of its dose can happen in the future again. This data is saved in history of the daily diet, and it can be displayed in a graph to see the remaining radioactivity quantity in the body.
3). Furthermore, the product database is updated periodically through the access to the MHLW website which is obtained automatically, to see the results of the radioactivity tests. In April 2015, about 1. 2 million cases of food radioactivity test results were published and are available as contamination information. Although it was confirmed that the database allows entering not only Cs-137 information but also other radionuclides, however, currently this system implemented only Cs-137 biological model, hence the result graphs of such other nuclides are not shown.
Verification of the Usability As a method of using this system, before cooking any meal or buying any ingredients, one can enter the system from a personal computer or a smart phone, enter the meal ingredients one has access to. By doing this, one will estimate the intake. This way allows obtaining the history of radioactivity intake and estimates the information about the intake of internal exposure dose, so one can make the decision to reduce the amount to eat/not to eat something if it is not suitable/eat everyday or occasionally/not eat at all/so from that previously ingested data the user can decide what and how much to eat or not. When the user ate it, the information is kept in history and becomes the basic judgment criteria for the meals eaten after. Thus, this system can be considered to contribute to the "recovery of the right to selfdetermination" and is expected to help residents to regain confidence by managing their day-to-day intakes.
" Also, it became possible to handle the natural origins of radioactivity information from the previous self, and to perform the management of global radioactivity uptake. The number of food products is 1,199,241, which is integrated in the product database. The levels of 1,113,931 products of them (92. 8 %) are under the detection limit value. Among them the number of food products added from January to March 2015 is 67,401, among them 65,117 (96. 6 %) are about the detection limit value or less, equal to or greater than the limit value (Table  20. 1) of the food makes 145 (0. 2 %) of cases. For the 145 ingredients of the limit value or more see Table  20. 2. The percentage of foods with the limit value or more is very little, mostly it is wild boar meat, but because black bear meat and Japanese deer meat is not common in a general household it is hardly applicable as a food ingredient for Japan.
Although inputting cooking information is available and there are cooking information templates to be obtained from a recipe site to make the input easier, it is still a hard work to manage each meal every day. We want to consider the ways to make this process even easier. For example, we consider making it possible to identify food from a photograph of the cooking or the supermarket receipt and POS information of shopping to guess the food. For determining the amount of the radioactivity ingested, the food eaten by the user might not be available in the food database, and does not correspond to any food measurement results; even if it is unknown it still may be sufficient. We do not consider the radiation level to be 0 but wait a month and build a graph of the uptake potential calculating it from the maximum value and the minimum value of radioactivity in food of the predetermined period on the food database width, or ingenuity, such as displaying the value of the radioactivity survey conducted before the accident  [14].
In this regard we think it is necessary to pursue the reflection on the best way to proceed. As mentioned earlier, the results for Cs-137 and other nuclide species are not currently integrated into the database structure to correspond to the multinuclide variety. This not only concerns nuclides which are listed in the radioactivity measurement results of the MHLW, but also the internal exposure control is possible by ingestion of natural radionuclides by incorporating models such as potassium 40 and polonium 210. Also, due to the use of a computational model of the original CORPORE calculation system, used in the European context, we would like to consider the comparison method and use of the computational model with accordance to the Japanese data only. Then, from the original CORPORE development team we receive an opinion that "since the genre of diet became different we want to be able to compare the exposure situations.
Acknowledgement This work was partly funded by the 2012 fiscal year of  Education, Culture, Sports, Science and Scientific Research. The results of this study are part of Fukushima Prefecture radiation medicine research and development business subsidy. Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
A three-channel charge amplifier is built-in the dynamometer. Therefore, the output signal at the dynamometer is of low impedance. The integrated cable is connected to the control unit Type 5233A1. The control unit can select the four measuring ranges in two groups (F x and F y resp. F z ). The control unit is easy to operate and contains power pack and keyboard with status displays together with a connector for signal input. The output voltages are proportional to the forces occurring. The cutting forces were acquired and recorded using Dynoware software.
The output signals from the AE sensor were amplified to a level of 60 dB gain by using an amplifier. The amplifier has two series-connected second order filters designed as plug-in elements. A band pass filter is obtained by the series connection of one high-pass and one low-pass filter to eliminate noise and signals caused by phenomena such as collisions and twining of chips that are not directly related to the cutting phenomenon  [33]. The data acquisition system was used to acquire signals from the coupler. The AE signals were mainly evaluated by using an analog output signals parameter AE Out (Filter) mean value in volts (which is one of the outputs of AE signal from the coupler like AE rms signal. AE signal obtained from the signal processing corresponding to fluctuations in the amplitude of the AE signals  [37].
Regression Modeling The regression equations were developed using Minitab software with the measured set of experimental data to predict the cutting force values at any combination of spindle speed and feed. The resultant cutting force equation for the tool T1 was obtained as Resultant cutting force for T1 = 2. 70 + (0. 00105 × spindle speed) + (83. 0 × feed) (2) The value of the correlation coefficient R 2 = 82. 5%. The resultant cutting force equation for the tool T2 was obtained as Resultant cutting force for T2 = 32. 3 + (0. 00150 × spindle speed) + (51. 6 × feed) The value of the correlation coefficient R 2 = 88. 7%. The resultant cutting force equation for the tool T3 was obtained as Resultant cutting force for T3 = 35. 9 + (0. 00151 × spindle speed) + (44. 5 × feed) (4) The value of the correlation coefficient R 2 = 93.
Regression Modeling The regression equations were developed using Minitab software with the measured set of experimental data to predict the cutting force values at any combination of spindle speed and feed. The resultant cutting force equation for the tool T1 was obtained as Resultant cutting force for T1 = 2. 70 + (0. 00105 × spindle speed) + (83. 0 × feed) The value of the correlation coefficient R 2 = 82. 5%. The resultant cutting force equation for the tool T2 was obtained as Resultant cutting force for T2 = 32. 3 + (0. 00150 × spindle speed) + (51. 6 × feed) The value of the correlation coefficient R 2 = 88. 7%. The resultant cutting force equation for the tool T3 was obtained as Resultant cutting force for T3 = 35. 9 + (0. 00151 × spindle speed) + (44. 5 × feed) The value of the correlation coefficient R 2 = 93.
The links possess associated weights which are multiplied with the input signal to produce output. There are three layers such as input, hidden, and output as shown in Figure  10. A neural network model has been generated using MATLAB 2013, and it is trained to predict the velocity with the given input parameters. In a feed forward neural network, the units do not form a directed cycle. The information moves in only one direction-forward-from the input nodes, through the hidden nodes and to the output nodes. The training process adjusts the connection weight and bias of network to minimize the error function. The adjustment of connection weight is conducted by back propagating the errors to the network. The neural network has been developed using neural network toolbox in MATLAB 2013, parameters for developing ANN model is shown in Table  7.
Comparison between Measured Values with ANN and Regression Modeling The predicted values of cutting forces for different cutting conditions for the different tool geometries were found and compared between the regression method and ANN. From the results shown in Tables 8-10, it was observed that prediction of cutting forces through Artificial Neural Network model yielded better results when compared to regression model. A neural network model has been generated using MATLAB 2013, and it is trained to predict the velocity with the given input parameters. In a feed forward neural network, the units do not form a directed cycle. The information moves in only one direction-forward-from the input nodes, through the hidden nodes and to the output nodes. The training process adjusts the connection weight and bias of network to minimize the error function.
The adjustment of connection weight is conducted by back propagating the errors to the network. The neural network has been developed using neural network toolbox in MATLAB 2013, parameters for developing ANN model is shown in Table  7. The spindle speed and feed were taken as input vectors, and resultant forces were taken as target vectors. The neurons were trained by the incremental value of 10. The number of neurons was selected such that the value of R (correlation between outputs and targets) is almost one.
The nine roo insertions add the same cis-regulatory sequences We analyzed multiple sequence alignments of all the roo insertions located nearby CG18446. We identified TFBSs using the JASPAR database (see Material and Methods). We also specifically looked for conservation of the regulatory regions previously described in the roo family  [8, 30] , and for conserved core promoter motifs  [1]   (Fig 3A and S2A Table). Overall, there was very little diversity among the nine solo-LTRs (S3A Fig). The five TFBSs and the Inr sequence previously identified in the consensus sequence of roo LTRs are conserved in all the roo copies located in the proximal promoter of CG18446  [8]. Additionally, we found another four TFBSs that are also highly conserved in all the copies (Fig  3A.
Analysis of the population frequencies of the nine roo solo-LTR insertions using Tlex2 We estimated the frequencies of the nine roo solo-LTR insertions described in this work using T-lex2 software  [34]. Because T-lex2 works only for annotated TEs, we constructed eight new reference sequences including each one of the newly described roo solo-LTR insertions. The new reference sequences included 500 bp at each side of the TE and the TSD of each insertion. We run T-lex2 in strains from three different populations: 50 strains from North Carolina (DGRP  [31, 32] ), 27 strains from a population collected in Stockholm, Sweden  [33] , and 67 strains from a population collected in Siavonga, Zambia  [60]. As a control, we also run T-lex2 in the strains for which we have PCR results (S1 Table ). We obtained results for 21 out of 50 DGRP strains, 26 out of 27 Swedish strains and 42 out of 67 Zambian strains.
Detection of piRNA reads We used three piRNA libraries  [44] [45] [46]  to map piRNA reads to a 1. 4 kb region including FBti0019985 and to all the roo insertions described in this work following the methodology described in Ullastres et al (2015)  [33]. Briefly, we used BWA-MEM package version 0. 7. 5 a-r405  [65]  to align the reads and then we used SamTools and BamTools  [66]  to index and filter by sense/antisense reads. The total read density was obtained using R (Rstudio v0. 98. 507)  [67].
We have used vcftools to calculate the number of segregating sites, and Tajima´s D using parameters -maf 1/(2n), where n is the sample size, and -remove-indels. We have obtained iHS, nSL, and XP-EHH using the selscan software with default parameters  [73]. Finally, we have calculated H 12 with ad hoc scripts. The four latter statistics require phased data. Thus, chromosome 2R of the 205 DGRP strains were phased together using ShapeIt  [74]. To calculate the significance for the number of segregating sites, we resampled at random the same number of strains from the 205 DGRP strains available and calculated the distribution of segregating sites in the same 2 kb region. To calculate the significance of Tajima´s D, iHS, nSL and XP-EHH, we have used the empirical distributions of these statistics obtained from chromosome 2R.
This work was supported by the  Ministerio de Economía y Competitividad  (MINECO: http://www. idi. mineco. gob. es) BFU-2011-24397  and  RYC-2010-07306  to JG, and  BES-2012-052999  to  AU, Ministerio de Economía y Competitividad  and  Fondo Europeo de Desarrollo Regional (MINECO/FEDER :http://www. idi. mineco. gob. es) BFU2014-57779-P , the  European Commission  (https://ec. europa. eu) FP7-PEOPLE-2011-CIG-293860 , the  Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement -SGR-201  to JG, and by the  Agence Nationale de la Recherche  (http://www. agencenationale-recherche. fr) ANR-14-CE02-0003  to MARdC. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Classification and features In March 2014 a blood culture was performed on a 16-monthold child presenting at the Hôpital Principal de Dakar, Senegal. Strain FF9 T (Table  1 ) was isolated from this blood culture by culture on 5% sheep's blood-enriched Columbia agar (bio-Mérieux, Marcy l'Etoile, France). Identification was not obtained using MALDI-TOF because the scores obtained by this strain were low  [23]. Moreover, strain FF9 exhibited 98. 18% 16S rRNA sequence similarity with Paenibacillus uliginis  [24]  (GenBank accession no. FN556467), the phylogenetically closest bacterial species with standing in nomenclature (Fig. 1 ). These values were lower than the 98. 7% 16S rRNA sequence threshold recommended by Meier-Kolthoff et al. [25]  in 2013 to delineate a new species within the Firmicutes phylum without carrying out DNA-DNA hybridization.
Strain FF9 T is susceptible to amoxicillin/clavulanic acid, ticarcillin, ceftriaxone, cefalotin, imipenem, gentamicin and doxycycline but is resistant to penicillin, metronidazole and trimethoprim/ sulfamethoxazole. The minimum inhibitory concentrations (MICs) for some antibiotics tested by Paenibacillus dakarensis strain FF9 T sp. nov. are listed in Table  2. A comparison of phenotypic characteristics with Paenibacillus polymyxa  [1] , Paenibacillus massiliensis and Paenibacillus sanguinis  [26]  is summarized in Table  3. MALDI-TOF protein analysis was performed using a Microflex LT (Bruker Daltonics, Leipzig, Germany), as previously reported  [27, 28]. The scores previously established by Bruker to identify or validate species compared to the instrument's database were applied. In short, a score 2.
NMNI were then imported into the MALDI BioTyper 2. 0 software (Bruker) and analysed by standard pattern matching (with default parameter settings) against the main spectra of 6252 bacteria. Scores ranging from 1. 225 to 1. 456 were obtained for the FF9 T , suggesting that this strain was not a member of any known species. The reference mass spectrum from strain FF9 T was incremented in our database (Fig. 4 ). The gel view highlighted spectrum differences with other Paenibacillaceae species (Fig. 5 ).
Genome sequencing information Genome project history The organism was selected for sequencing on the basis of its phylogenetic position, 16S rRNA similarity and phenotypic differences with other members of the Paenibacillaceae family. There are more than 15 genomes for the Paenibacillus genus available in public genomic collections. Here we present the first Paenibacillus dakarensis sp. nov. genome. The GenBank accession number is CDSE01000001, and it consists of 102 contigs. Table  4  shows the project information and its association with minimum information about a genome sequence (MIGS) 2. 0 compliance  [29].
Genome annotation Open reading frames (ORFs) were predicted using Prodigal  [30]  with default parameters, but the predicted ORFs were excluded if they spanned a sequencing gap region. The predicted bacterial protein sequences were searched against the GenBank database  [31]  and the Clusters of Orthologous Groups (COGs) database using BLASTP. The tRNAScanSE tool  [32]  was used to find tRNA genes, while ribosomal RNAs were found using RNAmmer  [33]  and BLASTn against the GenBank database. Lipoprotein signal peptides and the number of transmembrane helices were predicted using SignalP  [34]  and TMHMM  [35]  respectively. ORFans were identified if their BLASTP E value was lower than 1e-03 for alignment length greater than 80 amino acids. If alignment lengths were smaller than 80 amino acids, we used an E value of 1e-05.
Such parameter thresholds have been used in previous works to define ORFans. Artemis  [36]  was used for data management and DNA Plotter  [37]  for visualization of genomic features. The Mauve alignment tool (version 2. 3. 1) was used for multiple genomic sequence alignment  [38]. To estimate the mean level of nucleotide sequence similarity at the genome level, we used the MAGI homemade software to calculate the average genomic identity of gene sequences (AGIOS) among compared genomes. Briefly, this software combines the Proteinortho software  [39]  for detecting orthologous proteins in pairwise genomic comparisons, then retrieves the corresponding genes and determines the mean percentage of nucleotide sequence identity among orthologous ORFs using the Needleman-Wunsch global alignment algorithm.
Genomes from the Paenibacillus genus and closely related genera were used for the calculation of AGIOS values. The script created to calculate AGIOS values was named MAGi (Marseille Average genomic identity) and is written in Perl and Bioperl modules. Genometo-Genome Distance Calculator (GGDC) analysis was also performed using the GGDC Web server (http://ggdc. dsmz. de) as previously reported  [40, 41]. Here, we compared the genome sequences of P. dakarensis strain FF9 T (GenBank accession no. CDSE01000001) with those of Paenibacillus lactis strain 154 (AGIP00000000), Paenibacillus polymyxa strain ATCC 842 T (AFOX00000000), Paenibacillus massiliensis strain 2301065 T (ARIL00000000), Paenibacillus sabinae strain T27 T (CP004078), Paenibacillus borealis strain DSM 13188 T (CP009285) and Paenibacillus forsythiae strain T98 T (ASSC00000000).
Paenibacillus dakarensis strain FF9 T is susceptible to amoxicillin/clavulanic acid, ticarcillin, ceftriaxone, cefalotin, imipenem, gentamicin and doxycycline but resistant to metronidazole, penicillin and trimethoprim/sulfamethoxazole. The G+C content of the  genome is 45. 7%. The 16S rRNA and genome sequences are deposited in GenBank under accession numbers LM652718 and CDSE01000001, respectively. The type strain FF9 T (= CSUR P1429 = DSM 29777) was isolated from a blood sample taken from a 16-month-old child presenting at the Hôpital Principal de Dakar, Senegal.