dataesr/scientific-paragraphs-categorization huggingface dataset:
3D Modeling Based on CGA Rules Esri CityEngine is a new member in 3D modeling software, which can create virtual scene quickly based on 2D data. In addition, because of the fully support of ArcGIS， the basis of the existing GIS data can be used without transformation. It allows not only performing 3D modeling quickly and shorten the time for designing virtual citybut it also enables reducing the cost of investment in the system. CGA (Computer Generated Architecture) is a custom modeling rule of CityEngine platform which defines a series of rules to create the 3D models creation automatically. All models in CityEngine software are created by CGA rules. The benefits of rule-based modeling are to define the rules with the repeated optimization, in order to create more detailed data and to save as a rules file for information reuse.
Facade Modeling Based on Images The facade wizard is a handy tool which allows the user to create complex CGA facade rule templates. The great advantage is that no actual CGA code has to be written by the user, but it is automatically produced in the background by the CityEngine. Very complex structures can be generated very efficiently and easily. The beauty of the process is that new CGA rules are resulting, which can adapt to any given facade geometries. With the facade wizard, it is easy to create large pools of facade templates that can always be reused. This model can be utilized in upcoming projects. At this point, it is possible to separate the face by using the Separate Faces tool. You can first start blocking out the main building masses into ground floor, upper floors and roof part.
Discussion In this section we will compare our approach with the previous work, and determine the advantages and characteristics of our procedural approach. a. Combine with the geographic information General building modeling methods often only focus on the architectural outline of the buildings, which neglect the geographic data information of the real buildings, such as geographic spatial coordinate and terrain elevation information. In this paper we proposed the procedural modeling approach that combined with Google Earth to acquire kinds of geographic data information, and processed the data information in ArcGIS. The perfect combination of Geographic data information and building models in CityEngine effectively guarantee the authenticity of building models.
c. Useful for large-scale buildings modeling tasks There are many 3D modeling software for buildings, such as Sketchup, 3DS MAX, AutoCAD, Maya, etc. , the software have a comparative advantage for a single building, but for large-scale complex city buildings, such problems as low efficiency, insufficient data update will appear. So the procedural modeling approach is proposed in this paper. Through repeated use of CGA rules and structural parts (such as door, window and ledge), the approach can effectively implement the rapid modeling of large-scale city buildings, and can guarantee the quality of building models. Taking a 3D virtual campus as an application example to validate the approach of procedural modeling, it shows the approach can take good advantage of existing GIS data, can rapidly modeling based on CGA rule for the large-scale campus, and can facade modeling to create complex building.
Value of the data These data exhibit the behavior of a proton caged compound into 3T3-NIH cells when dosed in association with the membrane cell permeability enhancer DMSO. The infrared spectra of the cells are taken after filtered ultraviolet light irradiation and show characteristic band variations. The band variation is different than in absence of DMSO, also compared to the vectorization with gold nanoparticles, opening up to new pathways of employing proton caged compounds.
The experimental setup was the same as the one used afterwards for the gold coupled PCCs  [3]  (a Bruker IFS66/VS interferometer, in transmission mode with a resolution of 2 cm À 1 ). Infrared spectra were collected in the 3500-1000 cm À 1 range for reference spectra and 3000-1000 cm À 1 range for the cells. A few sequential infrared spectra of the cells were taken to verify their stability. Afterwards the samples were irradiated once for 1 min with near-UV light, by using a deuterium discharge lamp (Acton Research Corporation) equipped with a band-pass FGUV11 filter (Thorlabs) in the 275-375 nm. A few independent measurements were performed and they all provide similar outcome. Here we report two of them in Figs. 1  and 2. The data are treated with the OPUS software for vector normalization and offset correction.
Finally, they are normalized by the first spectrum after irradiation and exported as ASCII files. The spectra are taken at intervals of 2 min. Therefore, the whole datasets are taken in a time span of 30 (1st set) to 40 min (2nd set). In the second data set a saturation level may be appreciated. The datasets are characterized by the arising of two bands which grow upon irradiation, one, rather large, centered at 2510 cm À 1 and the other one narrower centered at 1452 cm À 1. No contribution is found, instead, at 2343 cm À 1 the typical value for the intracellular CO 2. The data, however, are largely reproducible, and are not related to dead cells, since we had previously verified that in our experimental set up they do not provide any spectral variation as a function of irradiation and of time.
The positive peak at 1691 cm À 1 can be associated to the newly formed ketone, whereas the peaks at 1424 cm À 1 , 1378 cm À 1 and 1271 cm À 1 are related to the cis and trans NO stretching  [6]. The protons released in DMSO give rise to two new bands at 1652 cm À 1 and 3320 cm À 1 which can be associated to the bending and stretching of newly formed O-H þ interactions, as already observed for DMSO protonation by HCl  [7]. The region around 2500 cm À 1 is free both for DMSO and for HDNS in DMSO. This rules out that in the datasets of the cells, the increase of features intensity upon irradiation is the direct observation of processes solely related to the molecules themselves. The interpretation is therefore to be found in processes which may affect the cells as effect of the proton release.
Study variables The following data were collected retrospectively from the files at the insurance company: date of birth, profession, gender, the existence of one or more exclusions and the period during which the exclusion was in effect, the nature of the exclusion and information on periods of SA of 30 days or more. Exclusions can be temporary, e. g. when accurate estimation of the risk at application is not possible. Especially when a health complaint has only recently emerged, often more time is needed to evaluate the course of this risk factor or illness. In these cases, the exclusions are combined with a right to re-assessment after a fixed period of time; most often 3 or 5 years. In general, these exclusions can be ended if at re-assessment the risk of SA due to the health complaint is estimated to be no higher than in the general population.
No distinction was made between partial and full SA. Length of SA was calculated from the number of calendar days an insured worker received a benefit. Full return to work in this study was defined as the end of the claim with the insurance company. Maternity leave for normal pregnancies was excluded, however, pregnancy-related SA was included. The following categories of diagnoses were distinguished: mental and behavioural disorders, cardiovascular disorders, musculoskeletal disorders, pregnancy-related disorders and other causes of SA. Data on SA periods were checked for inconsistencies and overlapping and directly consecutive periods were combined. In combined periods of SA, the cause that was the reason for most days of absence was coded as the cause of the total period. Follow-up ended when an insured person ended the insurance policy or on 1 July 2011.
Statistical analyses To describe the sample characteristics, we calculated numbers and percentages for categorical variables and means, percentiles (pctl) and SD's for continuous variables in SPSS 19. The statistical package R3. 01 was used to examine the hazard of SA periods and of recurrence of SA for the four different exclusion groups. 13 In these analyses, the Andersen-Gill extension of the Cox proportional hazards model was used to allow for the recurrent nature of the event studied; here periods of SA. 14  The four groups were then compared regarding the number of sick days using general linear models with resulting 95% confidence intervals (CIs) based on 1000 bootstrap samples. Analyses were conducted for all SA periods regardless of relation to the exclusion and for SA periods that were unrelated to the exclusion separately.
Multimorbidity, the co-occurrence of more than one disease or condition, is common, even among working-age individuals. 15 The elevated risk of SA is probably not just caused by a single condition leading to an exclusion but related to the overall health status including complications of disease and to shared risk factors. We specifically analysed periods of SA that were unrelated to an exclusion present during that period to examine whether the higher risks could be caused by the specific condition that led to the exclusion. In these analyses, it was still found that persons with an exclusion had a higher hazard for experiencing a new period of SA and had on average more sick days. This indicates that the exclusions do not fully neutralize the elevated risk of SA. Data on the two groups whose exclusions ran for part of the follow-up were also analysed (group C and group D).
Sickness allowance Data on sickness allowance were based on the register of the Social Insurance Institution. The register includes all sickness allowance periods that exceed a waiting period which normally consists of 10 working days. For those who have not been engaged in any gainful activities during the preceding 3 months, the waiting period is 55 days. 13 The number of sickness allowance days was examined in 1year (365 days) intervals counting backwards from the start of the pension. The length of each sickness allowance period was calculated as the difference between the last and the first allowance day.
Flow cytometry measurements Flow-cytometric measurements were performed by Partec CyFlow Space flow cytometer (Partec GmbH, Munster, Germany) with 633 nm excitation from a red solid-state laser. In the flow cytometer, optical filters were set up so that cyanine 5 was measured in the FL7 channel. Data was acquired on two-parameter dot plots of forward scatter (FSC) versus sideward scatter (SSC) by gating cell population of interest. For each sample 10 000 events were selected and analyzed. Data was processed with FlowJo 10 software (FlowJo LLC, Ashland, Oregon, USA).
Acknowledgements The authors acknowledge financial support from the  European Union 's  FP7-People Program  ( Marie Curie Actions ) within the  Initial Training Network No. 608163 "ARGENT" , the  French agency of research  ( Agence Nationale de la Recherche ,  ANR 2012 RPIB 0010 ) and the  Région Franche-Comté  (PhD grant for GJS). A part of this work was done at PIMPA Platform, partly funded by the  French program  " Investissement d'Avenir " run by the  "Agence Nationale pour la Recherche"  (grant " Infrastructure d'avenir en Biologie Santé- ANR-11-INBS-0006 "). Authors are thankful to  Oliver Nüsse  for providing access to flow cytometry facilities and to  Sandrine Lécart  for providing the access to level 2 laboratory. Lastly the authors thank  Sylvaine Linget  from  Qualio Laboratory  for ICP-OES analysis and  Melisa Moran-Plaza  for help with illustrations.
Statistical analysis Statistical analysis was performed using GraphPad Prism software (Graphpad software, La Jolla, CA, USA). Categorical data were expressed as percentages and compared with the Fischer exact test. Agostino and Pearson omnibus was used to confirm non-normality of our continuous data. We compared them with the Mann-Whitney test and expressed them as median values and range or mean ± standard deviation. A P value of less than 0. 05 was considered to be significant.
Material and methods (a) Global endemism in Poaceae Species numbers and distribution data on all 11 313 accepted species of Poaceae at the Taxonomic Databases Working Group (TDWG) level 3 were extracted from GrassBase  [31] , described in  [32] , and filtered into total richness and total endemic species numbers. Percentage endemism was calculated for each TDWG level 3 area (figure  2 ). Areas were obtained from TDWG shapefiles and  [33]  http://www. kew. org/science-conservation/research-data/ resources/gis-unit/tdwg-world in QGIS  [34]  using the Eckert VI projection (http://bdtracker. cybertaxonomy. africamuseum. be/ node/641).
(d) Field sampling and quantifying disturbance The grass flora was sampled at 60 sites in Madagascar, from five ecoregions, representing much of the climate range and the whole altitude range. Only the eastern rainforest region was not sampled. We selected sites to represent the widest range of habitats, and to include both undisturbed and highly disturbed locations (figure  1 ; electronic supplementary material, S2 and figure  S3 ). Thirteen Afromontane and mid-elevation open grassland sites in Tanzania were also studied (electronic supplementary material, S2). Each site was placed in a visually uniform vegetation community. Plots were placed along four transects and all Poaceae species were listed and collected. An associated vegetation list, soil, geomorphology and disturbance data were recorded; sampling methodology is described in the electronic supplementary material,  S1 and illustrated in figure  S2.
(e) Phylogeny and phylogenetic diversity The plastid regions rbcL, ndhF and matK were sequenced for each species following the methodology described in  [41]. A phylogenetic tree of all species found in the 73 sites was produced using the maximum-likelihood criteria as implemented in the program RAxML v. 8. 1. 11 [42]  and performed on the Cipres Science Gateway portal (https://www. phylo. org) with a rapid bootstrapping approach (1000 replicates). The tree was made ultrametric using the penalized likelihood criteria  [30]  as implemented in the function chronos and the model correlated of the R package ape  [43 -45]  and assigning the value of 1. 0 to the crown node of the tree (electronic supplementary material, S3). The package picante  [46]  was used to calculate MPD and MNTD  [47]. The observed phylogenetic relatedness was compared to the expected pattern using the functions ses.
mpd and ses. mntd with the 'richness' null model, taking into account species abundances in each plot, and using 9999 randomizations. Phylogenetic beta diversity was calculated using the function comdistnt, the 'among-community equivalent of MPD and MNTD'  [46]  and taking into account species abundance in each plot. Communities were clustered based on their phylogenetic relatedness using the hierarchical cluster analysis implemented in the package stats  [44]  under the function hclust. Phylogenetic structure was assessed by comparing the trees to 1000 randomly generated trees within the Mesquite system for phylogenetic computing  [48].
Figure2. Endemism in Malagasy grass flora compared to island (blue) and continental (grey) regions of the world. Madagascar (red) is included twice, as a separate island (MDG) and again together with the surrounding islands (29) , to demonstrate that endemism is similar in both cases. Numbers indicate regions of the world following the Taxonomic Database Working Group, which are listed in the electronic supplementary material, tableS1. (a) Number of Poaceae endemics plotted against species richness (data from GrassBase [31] ); R 2 ¼ 0. 47. Poaceae endemicity in the Malagasy floristic region is in the line with other subtropical islands. (b) Poaceae endemism plotted against land area; R 2 ¼ 0. 09. Madagascar shows high endemism for its land area, comparable to New Zealand.
Data accessibility. Data on global endemism of the Poaceae are availa- ble for download as part of GrassBase at http://www. kew. org/ data/grasses-syn/download. htm and TDWG shapefiles are available at http://www. kew. org/science-conservation/research-data/ resources/gis-unit/tdwg-world. DNA sequences have been deposited at the European Nucleotide Archive with the following numbers: LN906638-LN906796 (matK), LN907872 -LN908034 (rbcL) and LN908035 -LN908194 (ndhF). Phylogenetic data are available under TreeBase ID 18532.
Splits of the sediment fraction > 150 μm were picked completely to obtain, where possible, at least 30 specimens. A total of 782 T. sacculifer and 760 Gt. tumida were measured. No Gt. tumida was found in the Caribbean sample corresponding to 2. 3 Ma and only one T. sacculifer in the Pacific sample at 5. 3 Ma. Similar to other studies  (Hull and Norris, 2009) , our data does not allow to assess if this rarity is global or a temporary restriction of the species' biogeographic distribution. The foraminifers were oriented with the final chamber at the top, and mounted on slides. Two views of the oriented specimens were digitized to describe their three-dimensional morphology: a face view of the umbilical side and primary aperture, and a side view with aperture facing the viewer (Fig. 2 ). Both species contain sinistrally-and dextrally-coiled forms; therefore, a mirror image was used on left-coiled specimens in order to pool both the rightand left-coiled specimens in the morphometric analyses.
Morphometric analysis The maximum diameter of each specimen and two-dimensional outlines were extracted using the image-processing software OPTIMAS. Each outline was defined by a set of 64 points sampled at equal curvilinear distance. The starting point of the outline analysis was at the suture between the last and penultimate chambers for T. sacculifer, and at the contact between the last chamber and the inner whorl for the face view and the tip of the last chamber for the side view for Gt. plesiotumida and Gt. tumida. The variation of the radius (e. g. the distance of each point to the centre of the outline) was expressed as a function of the cumulative distance along the outline. This function was decomposed into a sum of trigonometric functions of decreasing wavelengths (harmonics), each being weighted by two Fourier coefficients (FC) (see  de Vargas et al.
Shape changes: temperature and vicariance Our temperature-driven changes in size affect shape due to allometric variations. These allometric variations mask subtle changes in shape departing from the allometric relationship  (Girard and Renaud, 2008). The effect of size as a major driver of shape evolution is well known  (Marroig and Cheverud, 2005). We assessed size-free shape variations to investigate how they may relate to environmental changes beyond the signal driven by the sizetemperature relationship. The similarity of our results on raw and size-free data suggests that shape response was not merely a result of allometry. A differentiation of Pacific and Atlantic stocks in the shallow-dweller T. sacculifer occured several millions years after the closure of the Isthmus, concomittant with divergence in the temperature conditions at both locations.
Acknowledgments We would like to thank  NERC  for funding AGMC ( NE/G524228/1 ) and JWBR ( NE/F007345/1 ) via PhD studentships and the  Royal Society  for funding DNS via a  University Research Fellowship  and a  Wolfson Merit Award. The work was supported by  NERC  grant  NE/H006273/1. OS acknowledges the  Ministry of Education, Culture, Sports, Science and Technology for funding via Japan Society of Promotion of Science  (No. 1800410000 ) enabling.
Materiel and Methods The population studied concerned forty Algerian primary invasive breast cancer cases selected from patients diagnosed at 40th or below referred to Anti-Cancer Center of Setif, eastern Algeria during the period 2013-2014, unselected for family history, to assess pathophysiological factors that may influence clinical outcome and prognosis including patient demographics, clinical presentation, treatments, and genetic spectrum. All patient detailed data regarding personal and family history of breast cancer were gathered on individual interview. Clinical and pathological characteristics were collected from medical records. Eligible patients had to be alive, be ≥18 years of age, and consent to genetic testing for BRCA1/2. All selected patients were informed about study objectives and they gave their signed informed consent for inclusion before they participated in the study.
The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of Anti-Cancer Center of Setif (KsBR/S11/2012). Forty blood samples were taken from 40 different families diagnosed with early-onset breast cancer stored in EDTA tubes; most samples were of 5 mL, to investigate the contribution of BRCA1/2 mutations in breast cancer among Algerian young women. Analysis consists of PCRbased, bidirectional Sanger sequencing of all translated exons and immediately adjacent intronic regions of the BRCA1/2 genes, as well as large rearrangement analysis by multiplex ligation-dependent probe amplification (MLPA) if a negative result was found of all BRCA1 (OMIM 113705/GenBank entry U14680) and BRCA2 (OMIM 600185/GenBank entry U43746) exons (Comprehensive BRACAnalysis® of Myriad Genetic Laboratory Munich, Germany).
All mutations and genetic variants are named according to the convention of Beaudet and Tsui  [12]. Nucleotide numbering starts at the first transcribed base of BRCA1 and BRCA2 based on GenBank entries U14680 and U43746, respectively. (Under these conventions, the two mutations commonly referred to as "185delAG" and "5382insC" are named 187delAG and 5385insC, resp. ) Statistical analyses in the present study were performed using SPSS version 20. This is the first published study analyzing complete genes BRCA1/2 using PCR, bidirectional Sanger sequencing, and MLPA techniques among young patients unselected for family history in this region.
4% and 25. 64% as reported by  [14, 15] , respectively. These differences are principally related to inclusion criteria of cases and their ethnicity. In this study, BRCA1 mutation rate is identical to BRCA2 mutation rate; this result may be due to the small number of patients analyzed but if this finding is confirmed in elevated sample, the BRCA1/2 mutations may contribute similarly to early-onset breast cancer in Algeria. To our knowledge, one novel deleterious mutation was detected within the BRCA2 gene, the heterozygous germline BRCA2 c. 6450del mutation not previously reported in other populations; it was found in patient diagnosed at 30 years of age and without family history of breast cancer. The remaining variants identified in the present report were already described in the Breast Cancer Information Core Database (http://research.
GA regulation of nodulation depends on DELLA proteins. As DELLA proteins are central repressors of GA-dependent responses  [33] [34] [35] [36]  , we hypothesized that GAs could control the nodule formation depending on DELLAs as suggested by Floss et al. 32  Three DELLA genes are present in the M. truncatula genome: MtDELLA1 (Medtr3g065980, M. truncatula genome v4. 0 database; http://jcvi. org/medicago/), MtDELLA2 (contig_52215, NCBI database; http://www. ncbi. nlm. nih. gov) and MtDELLA3 (contig_ 55897, NCBI database; http://www. ncbi. nlm. nih. gov). The encoded proteins MtDELLA1 and MtDELLA2 are, respectively, most closely related to the Arabidopsis DELLA proteins AtRGL1-3 and AtRGA/AtGAI, and are the closest relatives of the pea DELLA proteins PsLA and PsCRY 32. The M. truncatula MtDELLA3 protein is more divergent compared with Arabidopsis and pea DELLAs, containing a non-canonical DGLLA domain  32.