dataesr/scientific-paragraphs-categorization huggingface dataset:
Tree files of independent runs were then combined with LOGCOMBINER v. 2. 1. 3 [58] , discarding the first 20% and re-sampling at lower frequency of 15,000. The maximum clade credibility (MCC) tree was recovered from a sample of 10,000 posterior trees, and branch support was annotated, using TREEANNOTA-TOR v. 2. 1. 3 [58]. All analyses started with a random starting tree and seed with no root specified. Sequence data from Trisetacus species was used to estimate the root of the mitochondrial gene tree. Additionally, Maximum Likelihood (ML) analysis was performed in PAUP* with starting trees obtained by Neighbor-joining, and Tree bisection-reconnection (TBR) as branch swapping algorithm. Bootstrap proportions  [59]  were obtained to access node robustness, using 1000 bootstrap replications. In order to test the timing of diversification between WCM mitochondrial lineages, the mitochondrial gene tree was time calibrated, with divergence times of nodes being inferred from 95% highest posterior density (HPD) intervals.
DNA sequence-based species delineation We implemented two DNA taxonomy approaches to evaluate the presence of additional putative mitochondrial lineages using the Cox1 dataset. (1) The general mixed Yule coalescent (GMYC) approach  [62, 63]  was applied to the ultrametric tree (produced by BEAST analyses) in R 2. 15. 3  [64]  with the 'splits package'. The GMYC model is a process-based approach for detecting the threshold in a gene tree at which within-species processes (i. e. , coalescence) shift to between-species processes (i. e. , speciation and extinction). (  2 ) We applied the combination of the Poisson Tree Processes model for species delimitation (PTP), and a Bayesian implementation of PTP (bPTP) to infer putative lineages boundaries on a given phylogenetic input tree  [65]. The PTP/bPTP model, unlike the GMYC model, requires a bifurcated phylogenetic tree, not an ultrametric tree.
95). There was also no support for genetic grouping for lineages associated with cereal or wild growing grass hosts, however lineages occurring on cereals were the most polyphagous. Additionally, WCM lineages associated with taxonomically related plant species, namely congeneric species, did not group together (Fig. 1a ). BI reconstructions of WCM interrelationships based on the D2 dataset (Fig. 2 ) supported high level of genetic structuring within WCM, however it failed to support some lineage sorting observed in mtDNA analysis. The D2 tree was only partly coinciding with the mitochondrial tree (ILD tests indicated phylogenetic incongruence between the Cox1 and D2, P = 0. 01): some relationships were similar to those observed in the Cox1 gene tree (e. g. clustering of MT-6 and MT-16; MT-12 and MT-29 with PP > 0.
Interestingly, lineages within MT-26 and MT-18, both from Turkey, were associated with different grass species. Within the globally distributed MT-1 lineage, one distinct lineage was collected in Turkey, and the second one comprised mites from multiple countries (See figure on previous page. ) Fig. 1  a Bayesian inference (BI) tree constructed using the GTR + G model for the cytochrome c oxidase subunit 1 (Cox1) sequences of the wheat curl mite (WCM) species complex and outgroup species. WCM individuals on the tree are colored according to their grass host species; color boxes correspond to cultivated grasses (cereals) and color labels correspond to non-cultivated grasses. Sequence labels correspond to data in Additional file 1: Table  S1 and include information about the host species and geographic locality.
e. A. tulipae  [81] , and this supports notion that the variation seen in WCM is related to their overall genetic divergence, and that WCM is not a single evolutionary unit. We identified some discrepancies in lineage delineation between the mitochondrial and nuclear DNA datasets, a phenomenon frequently reported in many animal taxa  [82] [83] [84] [85] [86]. The uniparental inheritance and smaller effective population size of mitochondrial DNA suggests that lineage sorting will occur faster in mitochondrial DNA than in nuclear DNA, rendering the locus more reliable for characterizing recent lineage divergences  [84].
Funding The study was funded by the  National Science Centre in Poland , grants no. 2011/03/B/NZ8/00129  given to Skoracka A and  2011/01/N/NZ8/04540  given to Szydło W. Lopes LF was funded by the  FCT  fellowship  SFRH/BPD/84478/ 2012. Alves MJ and Lopes LF were financed by  Portuguese National Funds , through  FCT -Fundação para a Ciência e a Tecnologia , within the project  UID/BIA/00329/2013. The work was supported by the  KNOW RNA Research Centre in Poznań  (No. 01/KNOW2/2014 ) (Funding for open access charge:  Ministry of Science and Higher Education of the Republic of Poland , from the quality promoting subsidy, under the  Leading National Research Centre  ( KNOW) program  for the years 2014-2019). The funding bodies provided funds for data collection (1 and 2), analyses, data interpretation (1-4), and writing the manuscript (all 5).
Availability of data and materials The datasets supporting the conclusions of this article are included in this published article and its supplementary information files. The sequence data used in this study have been submitted to the GenBank databases under accession numbers: MG076809-MG076875, MG193928-MG194416, MG208676-MG208850. The frozen DNA isolates and DNA samples (mites stored in ATL buffer) of field samples collected in Poland are available in the Faculty of Biology, Adam Mickiewicz University, Poznań, Poland (unless not exhausted for repeated or other analyses).
Data were corrected for the background (measured prior to the initial Rhodamine B injection) and normalized to T = 0 for each flow rate. c. Influence of the position within the channel on the media switch dynamics. 5 μM Rhodamine B was injected (20 μL/min) for 10 min prior to switching to water (20 μL/min, T = 0). Fluorescence intensity was then measured over time at various distances from the channel border. Data were corrected for the background (measured prior to the initial Rhodamine B injection) and normalized to T = 0 for each position. d. Reproducibility of the media switch dynamics. 50 μM Rhodamine B was injected (20 μL/min) for 10 min. At T = 0 and T = 13. 5 min (asterisk), the perfusion was switched to water (20 μL/min) and back to Rhodamine B, respectively. Fluorescence intensity was then followed over time (Experiment 1).
Microscopy Imaging for the results presented in Figs. 2c-e, 3b-d , 4a-b, 5a-b and 6a was performed using an inverted Zeiss Axio Observer (Carl Zeiss Inc. ) equipped with a Lumencor Spectra X illumination system and a Hamamatsu Orca Flash 4. 0 V2 sCMOS camera. The data for Figs. 1f and 3c were obtained using a similar setup that is equipped with a spinning disc confocal head and a laser bench (Visitron GmbH). For both systems, images were acquired using Visiview (Visitron GmbH). The images displayed in Figs. 1c-e  (bottom panel) and 4c were acquired using a Zeiss Axio Vert. A1 (Carl Zeiss Inc. ) equipped with a Nikon 1 J3 camera and a PlasDIC system. All image analyses were performed using Fiji (National Institutes of Health).
Funding JB was co-funded by a grant to DC from the  Direction Générale des Armées (DGA) , France and by  Cherry Biotech, France. This work was supported by a collaborative grant from the  Région Bretagne, France. The  DGA  and  Région Bretagne  did not participate in any aspect of the research project and preparation of the manuscript. Cherry Biotech  helped with the temperature calibration of the chips.
Methods Sample selection. The study involved the use of human samples, which were collected after written informed consent was obtained. All patients were diagnosed between August 2008 and January 2014 before the revised International Myeloma Working Group diagnostic criteria were published  5. Based on the new criteria, patient PD26424 would have been classified as active MM because of 61% BM plasma cells (Supplementary Table  1 ). However, this patient was considered as SMM based on prevailing criteria at the time and was included in this study. As this patient received treatment prior to obtaining a progression sample, it was not included in the analysis of progression. The protocol was approved by RES Committee East of England-Cambridge Central (WTSI protocol number 15/046). Samples and data were obtained after an informed consent was signed, and managed in accordance with the Declaration of Helsinki.
5. 9)  35. Processing of genomic data. CaVEMan (Cancer Variants Through Expectation Maximization: http://cancerit. github. io/CaVEMan/) was used to call somatic substitutions  36. Indels were called using a modified Pindel version 2. 0. (http://cancerit. github. io/cgpPindel/) on the NCBI37 genome build  37. Structural variants were discovered using a bespoke algorithm, BRASS (BReakpoint AnalySiS) (https:// github. com/cancerit/BRASS) through discordantly mapping paired-end reads  38. Discordantly mapping read pairs that were likely to span breakpoints, as well as a selection of nearby properly paired reads, were grouped for each region of interest. All rearrangements identified by BRASS that failed high-quality mapping of split reads were manually evaluated by IGV. Considering the number of reads supporting each rearrangement breakpoints and adjusting this value for both copy number and ACF, we were able to estimate the adjusted variant allelic frequency (VAF) of each rearrangement VAF.
Acknowledgments N. B. is funded by  AIRC  ( Associazione Italiana per la Ricerca sul Cancro)  through a MFAG (n. 17658). F. M. is supported by  AIL  ( Associazione Italiana Contro le Leucemie-Linfomi e Mieloma ONLUS ) and by  SIES  ( Società Italiana di Ematologia Sperimentale ). This work was supported by  Department of Veterans Affairs Merit Review  Award  I01BX001584-01  (NCM),  NIH  grants  P01-155258  (NCM, HAL, MF, PC, and KCA) and  5P50 CA100707-13  (NCM, HAL, and KCA) and  Leukemia and Lymphoma Society  translational research grant (NCM).
Here, we exploit this technology to reveal the mechanism by which C 3 A hydration is retarded in the presence of gypsum (with a C 3 A-to-gypsum mass ratio = 1: 0. 4, and a water-to-solid mass ratio = 10), aiming to identify features with sizes on the order of tens of nm at the C 3 A dissolution frontier. These data are complemented by three-dimensional (3D) tomograms reconstructed from full-field transmission X-ray microscopy (TXM) data at a voxel resolution ~20 nm 3  [26]. Crystallographic information is also obtained using in-situ wide angle X-ray scattering (WAXS). Small angle scattering (SAS) data is calculated from the X-ray ptychographic images  [27, 28]  and compared with in-situ small angle X-ray scattering (SAXS) results, to obtain quantitative morphological information of the reacting C 3 A particle surface boundaries.
Stacks of absorption contrast images were recorded from 1552 to 1605 eV with step size 0. 1 eV from 1565 to 1594 eV and step size 0. 3 eV over the bordering central energy ranges of this investigation. A dwell time of 3 s was used. The image stack was aligned and analyzed with the aXis2000 software  [31]. As reference minerals, hydrogarnet was dispersed with acetone whereas ettringite and s-AFm were dispersed with nanopure water, drop-cast and measured similarly. Internal calibration was conducted by calibrating the background absorption maximum to 1560 eV.
Acknowledgments This work is funded by the  Singapore-Berkeley Building Efficiency and Sustainability in the Tropics (SinBerBEST) Program. The authors acknowledge the support received from  Ubonwan Khopongpaiboon ,  Panod Viseshchitra  and  Thanakrit Chantra  ( Siam Cement, Thailand ), and from  Siriwat Soontaranon  (SAXS beamline of Synchrotron light research institute, Thailand). The Advanced Light Source was supported by the Director,  Office of Science, Office of Basic Energy Sciences , of the  U. S. Department of Energy  under Contract No. DE-AC02-05CH11231. Use of the Center for Nanoscale Materials  and the  Advanced Photon Source , both Office of Science user facilities, was supported by the  U. S. Department of Energy, Office of Science, Office of Basic Energy Sciences , under Contract No.
The aim of this paper is to investigate the factors influencing the Beck Depression Inventory score, the Beck Hopelessness Scale score and the Rosenberg Self-Esteem score and the relationships among the psychiatric, demographic and socio-economic variables with Bayesian network modeling. The data of 823 university students consist of 21 continuous and discrete relevant psychiatric, demographic and socio-economic variables. After the discretization of the continuous variables by two approaches, two Bayesian networks models are constructed using the bnlearn package in R, and the results are presented via figures and probabilities. One of the most significant results is that in the first Bayesian network model, the gender of the students influences the level of depression, with female students being more depressive.
Participants The participants of this study are the students of Bitlis Eren University, located in Turkey, in the 2016-2017 academic year. In the first week of May 2017, the students were given a four-page form consisting of the Turkish versions of the Beck Depression Inventory, the Beck Hopelessness Inventory, the Rosenberg Self-Esteem Scale and demographic and socio-economic variables. The students were asked to fill the forms out on a voluntary basis. A total of 890 students from five schools of Bitlis Even University were included in the study, and the data of 823 students were obtained after extracting the missing data.
Data Discretization The data discretization is a technique that converts continuous data into discrete data with a finite number of intervals, and it has become remarkably popular in many research areas including data mining, machine learning, artificial intelligence and Bayesian networks. There are many reasons to discretize data, among which the most important ones are (i) reducing and simplifying the dataset, (ii) making modeling fast and easy, (iii) obtaining easily interpretable outputs  [25]  and (iv) the statistical method to be used may operate in discrete data only as in this study. As Figure  1  shows, the psychiatric variables are not normally distributed, which violates the Gaussian Bayesian networks (GBNs) assumption. In this case, we may specify a suitable conditional distribution for every variable and build a hybrid Bayesian network  [24].
Analysis In this section, we analyze the datasets by constructing suitable BNs. All of the analysis is conducted in statistical software R using bnlearn  [34] , lattice  [35] , gRain  [36]  and Rgraphviz  [37]  packages. First of all, to test the reliability of the psychiatric scales, Cronbach's alpha internal consistency coefficients  [38]  need checking. Cronbach's alpha internal consistency coefficients for BDI, BHS and RSES are 0. 89, 0. 87 and 0. 83, respectively, meaning these scales are safe (greater than 0. 70) for use in the analysis. We build two different discrete BNs using the same method as two different approaches of discretization were used. We call the BN whose data were discretized by expert knowledge Bayesian Network 1 (BN1) and call the BN whose data were discretized by Information-Preserving Discretization Bayesian Network 2 (BN2).
Discussion and Conclusions In this paper, the relationship among the 3 psychiatric, 2 demographic and 16 socio-economic variables was investigated through BN modeling in R. The data were obtained from 823 university students via a survey. In the analysis, firstly, the Beck Depression Inventory, the Beck Hopelessness Scale and the Rosenberg Self-Esteem Scale scores were calculated from the answers of students. Then, Cronbach's alpha internal consistency coefficients for the these psychiatric scales were tested and found satisfying. Next, the continuous data were discretized by two approaches, which led to two BN models (BN1 and BN2). Finally, after building the BNs with the tabu search algorithm, inference and queries were made through graphs and conditional probabilities. To summarize the results, only the occupational status of the mother has no relation with the other variables in both models.
1 CaCl 2 , 1. 1 EGTA, 10 HEPES, 4 Mg 2 +ATP, 0. 3 Na + GTP, pH 7. 22, 279 mOsm. To record miniature currents, 1 μM TTX (Tocris, UK) was added in ACSF and recordings were started after network activity had stopped (typically 5-10 min after TTX addition). Cells were clamped at -70 mV for mEPSCs 3 min-long recordings and then at 0 mV to record mIPSCs for another 3 min. Biocytin (0. 5%, Sigma, USA) was added to the pipette solution for post hoc reconstruction. Whole-cell measurements were made using a Multiclamp 700B amplifier (Axon Instruments, Molecular Devices, USA) with a Digidata 1440 A (Molecular Devices) and acquired using pCLAMP 10. 3 software (Molecular Devices, USA). Signals were analyzed off line using MiniAnalysis 6. 0. 7 (Synaptosoft, USA) by the experimenters blind to experimental conditions.
Funding  La Fondation pour la Recherche Médicale  (# FDT20160435216  to F. M. ),  La Fédération pour la Recherche sur le Cerveau/Rotary  (Espoir en tête 2009 to A. R. ), the  French National Agency for Research (Santé Mentale-Addictions  [SAMENTA], # 19012012  to A. R. ) and the  European Community 7th Framework programs (Development and Epilepsy-Strategies for Innovative Research  to improve diagnosis, prevention and treatment in children with difficult to treat Epilepsy [DESIRE], Health-F2-602531-2013 to A. R. ; Deciphering hyperexcitable networks associated with neurodevelopmental lesions [DeCipher], # ANR-15-NEUR-0001-03  to A. R. ). Laminar Position and DCX  Influence Synaptogenesis Martineau  et al. | 2987.
Life Cycle Assessment (LCA) Model Consequential life cycle assessment (cLCA, e. g. , references  [11, 12] ) modeling was used to assess the environmental burdens of producing one extra tonne (the functional unit) of pig feed produced today (present CO 2 scenario) and in a future with 550 µmole CO 2 mole -1 (future CO 2 scenario). The life cycle impact assessment was carried out using the stepwise method, which allows for the full monetarization of environmental impacts (expressed in €2003). This method combines characterization models from IMPACT2002 + v2. 1 and EDIP2003 and is further described in reference  [13]. The assessment was facilitated with the LCA software Simapro ® 7. 3. 3. Translating all of the environmental impact into monetized costs enables the selection and presentation of the most important environmental impact categories causing the socio-economic impacts of compound pig feed (other impact categories are presented in Supplementary).
Biogenic carbon (C), including soil C changes and C sequestration by feed crops, was included in all of the calculations in order to allow for a transparent carbon balance, as described by reference  [14]. Background LCA data were taken from the Ecoinvent v. 2. 2 database [15], while foreground data were essentially related to the cultivation, yield, and chemical composition of the crops used in feed formulation. These are described in Section 2. 4 and Supplementary. The geographical scope considered for the foreground system was Denmark, i. e. , the pig feed was considered to be manufactured for the Danish market. The Danish example is taken as representative for most industrialized countries in a temperate climate.
Formulation of Present and Future Pig Feed To apply a market-based approach, the commercial feed formulation software Bestmix ®  [16]  was used to establish both the present and future feed formulation for piglets, sows, and slaughter pigs (a feed mix consisting of 20% piglet feed, 20% sow feed, and 60% fattening pig feed was considered, because this is what is used in a normal pig life cycle). Through optimization algorithms, Bestmix ® formulated the best compromise between profitability, nutritional value, and animal health, while ensuring that the Danish and European legal requirements of the feed were met. This procedure allowed determining the exact ingredient proportions constituting the present and future pig feed. One important input to Bestmix ® is the chemical composition of crop ingredients, in terms of starch, protein, amino acids, macronutrients, and micronutrients.
Another important input parameter to Bestmix ® is the price of feed ingredients. However, as the aim of this study is primarily to investigate the consequences of altered crop composition, and since no reliable estimate is available on future crop prices, the prices of crop ingredients under a high CO 2 future were taken to be equal to the present prices. The chemical composition of all of the crop ingredients, when grown under today's atmospheric CO 2 , was established based on the Danish Pig Research Centre feed ingredients database  [17]. Data on the yield and chemical composition of the crop ingredients grown at 550 µmole CO 2 mole -1 was established based on data from growth experiments with increased CO 2 exposure taken from the scientific literature. Reliable data was only found for the four main crop ingredients (Table  1 ): namely wheat, barley, soy, and rape, which together make up 89% of the present pig feed by dry weight (Table  2 ).
Since there is a large variability reported in the literature for plant responses to elevated CO 2 , the following principles for selection of the best data were adopted. Results from Free Air Carbon Dioxide Exposure (FACE) systems were assumed to provide the most reliable data, since they represent the most realistic growing conditions, although FACE responses are typically smaller than for other exposure systems, see e. g. , references  [18, 19]. When FACE data were not available, data from open-top chambers (OTC), from outdoor closed-top chambers (CTC), or from climate chambers were used, in that order of ranking. As crops respond differently to elevated atmospheric CO 2 with different climatic or other abiotic or biotic conditions, exposure experiments that were performed near to where the feed ingredients that were considered in this study were grown were preferred over other studies.
Life Cycle Inventory (LCI) Data for Crop and Non-Crop Ingredients Crop Ingredients The LCI of crops grown in Denmark (wheat and barley) was based on a recent Danish consequential LCI dataset  [14] , which comprises all of the processes involved during the cultivation stage, up to harvest. This includes the tillage activities, liming, seed propagation, plant protection, fertilization, sowing, harvest, and transport from farm to field. A sandy soil has been considered for both crops, as well as precipitations of 964 mm y -1 and removal (harvest) of the straw. LCI data from the Ecoinvent (v. 2. 2) database were used to model the cultivation of imported soybean (Brazil), rapeseed (Germany), sunflower (Spain), and palm oil (Malaysia). Other important foreground data for crop ingredients are thoroughly described in the appendices.
Industrial Amino Acids As shown in Table  2 , two types of industrial amino acids are included in the assessed pig feed: industrial amino acids produced by fermentation (lysine, threonine, and tryptophan) and industrial amino acids produced synthetically (dl-methionine). Based on reference  [19] , a generic recipe was considered for 1 kg of amino acids produced by fermentation; 1 kg of sugar (from sugar beet), 0. 5 kg of maize starch, 0. 5 kg of wheat starch, 0. 3 kg of liquid ammonia, and 36 MJ of process energy at the plant. In this study, the recipe was supplemented with 0. 053 kg of sulfuric acid per kg amino acid, and 4. 6 g of phosphorous based on data from the Danish lysine supplier (see Supplementary). Under Danish production conditions, the energy consumption was estimated at only 18 MJ, which was supplied as electricity (50%) and natural gas (50%).