dataesr/scientific-paragraphs-categorization huggingface dataset:
Depending on the complexity of the system, for some cases (for example structure validation) a data collection array will only contain one data collection group. However, for others there could be many groups in the array. 2. 1. 3. Example of possible experiments. An example enzyme which in its functional form is comprised of three individual subunits A, B and C is schematically depicted in Fig. 3(a). The subunits can form a dimeric complex (Fig. 3b ) and a trimeric complex (Fig. 3c ). The first part of the experi-  ment, P1, is to determine how the subunits fit together. Thus, the individual macromolecules A, B and C as well as the dimeric complexes AB and BC and finally the trimeric complex will be measured individually under the same buffer conditions. The second part of the experiment, P2, is to understand how the enzyme functions and will comprise data collections for the trimeric complex ABC (in the same conditions as for P1) plus additional data collections for different buffer conditions (Fig.
Integration with the MX data model As BioSAXS experiments necessitate different information to be stored in the database from MX experiments, the data model, its tables and their values have to be modified (Fig. 4 ). However, the overall layout of the extension is similar and as there are parts which are the same or similar, such as shipping, many tables have been reused. Although BioSAXS is the first extension to be added to ISPyB, care was taken to ensure that the data model can be further extended. Additional experiment types can be included in order to anticipate future experiments undertaken at the partner beamlines.
Remote visualization of experimental status The status of a given experiment can be followed directly on the beamline or remotely by users at home. Once a user (or a beamline operator) has logged into the BCM (Beamline Control Module) connected to ISPyBB, data acquisitions are immediately recorded in the database, archived and displayed in the 'Data Acquisition' table (Fig. 6a ). Data acquisitions performed by the same user (i. e. with the same experimental logging) are divided into sessions according to the date when experiments were (or are) performed. There are currently two main types of experiments carried out at synchrotron-based BioSAXS facilities: data acquisition using a sample changer robot or a SEC system, which are denoted as STATIC or HPLC types in the Data Acquisition list, respectively.
ISPyB as an integrated system As ISPyB for BioSAXS is an extension to the existing ISPyB for MX, it inherently contains the possibility of combining information from both techniques. Macromolecules used for MX and SAXS may be the same construct and if so will have the same acronym in the database. This facilitates increased automation of advanced analysis techniques, such as verification of crystal structures under physiological conditions or even rigid-body modelling of complexes when data are available from both techniques. Additionally, it is foreseen to enable users to search the database and provide all relevant data sets (both MX and SAXS), but visualization of this is not currently available in the GUI. 3. 9. 1. Chain of custody. ISPyB integrates all steps of a SAXS experiment from sample preparation to acquisition and analysis in a single database.
Model description ORCHIDEE-iso  (Risi, 2009)  is based on the land surface model ORCHIDEE  (Krinner et al. , 2005)  developed at the Institut Pierre-Simon Laplace (IPSL), in France. The setup used for this study is the natural evolution of the one developed and extensively tested by  Risi (2009)  and has been accordingly evaluated against isotopic water measurement at stations, representative of different climates, belonging to the MIBA (Moisture Isotopes in Biosphere and Atmosphere) network  (Wingate et al. , 2010; Raz-Yaseef et al. , 2010; Knohl et al. , 2007). Differences between the two ORCHIDEE-iso setups solely depend on differences between the version of ORCHIDEE isotopes were originally implemented in and a more recent version of ORCHIDEE this study is based on. ORCHIDEE includes three separate modules: SECHIBA resolves water and energy exchanges between land surface and atmosphere  (de Rosnay, 1999; Ducoudré et al.
, 1993) ; STOMATE accounts for vegetation phenology and growth and for the carbon cycle  (Krinner et al. , 2005) ; LPJ  (Sitch, 2003)   (Choisnel et al. , 1995)  used for this study the soil is 2 m deep and contains a superficial and a bottom water reservoir. When the water content exceeds the total soil holding capacity (300 mm), runoff occurs, partitioned into 95 % drainage and 5 % surface runoff  (Ngo-Duc, 2005). Snow is represented by a single layer reservoir subject to melting and sublimation. ORCHIDEE resolves different fluxes contributing to the evapotranspiration: bare soil evaporation, vegetation transpiration (soil-moisture-limited), and evaporation of water intercepted by the vegetation canopy. Water stable isotopes 18 O and 2 H were implemented in the SECHIBA module, in parallel to the soil water ( 1 H 16 2 O) cycle (Fig.
Numerical experiments In this study ORCHIDEE-iso was run in stand-alone mode, point-wise, at four stations in Labytnangi (cf. 2. 2) using ORCHIDEE's standard time resolution. The model spin-up consisted in running ORCHIDEE iteratively five times over the same meteorological year (2012), at each location. A further year of simulation was then performed using initial state variable values from the spin-up simulations at each location. In the following, we describe the meteorological forcing, the isotopic forcing, and the characterization of the stations in ORCHIDEE-iso based on the available soil and vegetation data at the measurement sites. 20 km southeast of Labytnangi. For the missing variables, ERA-INTERIM reanalysis were used to complete the dataset. Due to the spatial proximity (≤ 10 km) of the stations and to the absence of significant orographic features, we assumed that the meteorological conditions (advection of air masses, frequency, intensity, amount of the precipitation) did not significantly differ from site to site.
2D map projections for visualization and quantitative analysis of 3D fluorescence micrographs G. Hernán Sendra 1 , Christian H. Hoerth 1 , Christian Wunder 2,3 & Holger Lorenz  1  We introduce Map3-2D, a freely available software to accurately project up to five-dimensional (5D) fluorescence microscopy image data onto full-content 2D maps. Similar to the Earth's projection onto cartographic maps, Map3-2D unfolds surface information from a stack of images onto a single, structurally connected map. We demonstrate its applicability for visualization and quantitative analyses of spherical and uneven surfaces in fixed and dynamic live samples by using mammalian and yeast cells, and giant unilamellar vesicles. Map3-2D software is available at http://www. zmbh. uniheidelberg. de//Central_Services/Imaging_Facility/Map3-2D.
Since this is presenting a particular challenge for qualitative and quantitative analyses of structurally connected, multiple images-spanning signals, we sought to overcome such limitations. We developed a new software application in MATLAB (MathWorks), called 'Map3-2D' , to visualize surface data from 3D fluorescence microscopy. We implemented map projection algorithms used in cartography to unfold spatial fluorescence image data onto a single structurally connected map (Fig. 1 ). A similar approach has recently been published in order to reduce data rate in storage-demanding selective-plane illumination microscopy of Zebrafish embryos  2. Here, we extent the application range of map projections to quantitative analyses of commonly used (and often smaller-scale) translucent biological samples like cells and subcellular components.
2 ). In short, all biological specimens were fluorescently labeled, and series of micrographs were acquired along the axial dimension (i. e. Z-stacks) to get the full surface content of the translucent sample. It is worth mentioning that Map3-2D does not require special 3D image acquisition procedures, which means that already existing image stacks can also be analyzed. As file format, Map3-2D software relies on TIFF (tagged image file format) images to perform the unfolding of the surface information from the image series into an interconnected 2D map. For a first evaluation of map projections we decided to use artificially created image stacks with surface objects containing geometrical patterns. As opposed to fluorescence micrographs, artificial images do not suffer from optical limitations like spherical aberrations and diffraction.
Instead, the scars are distinct ring-like structures across the cell wall with signal-free areas between them. Since it was hardly possible to display each individual scar and its distribution in a single quantifiable image by any conventional display method, we tested whether Map3-2D software could provide such information in a single 2D map. We acquired Z-stacks of fluorescently labeled wheat germ agglutinin-treated yeast cells on a widefield microscope and subjected the images to the software. The resulting 2D map projection clearly and accurately showed both the total number and the distribution of all bud scars in one interconnected image (Fig. 2c ). Since the majority of biological samples do not present perfectly smooth surface shapes, we looked for solutions to display membrane undulation on a 2D map.
The generation of map projections relies on reference ellipsoids to perform the unfolding of the 3D surface data onto 2D maps (further explained in Supplementary methods). In addition to intensity value projections (as shown in Fig. 2a-c ), this also offers the possibility to project the surface's height distribution by using the reference ellipsoid as an elevation reference (i. e. baseline). To put it in a cartographical context, the reference ellipsoid can be compared to the geographical mean sea level, and the object's surface represents the (positive, zero or negative) height above, on or below it. Similar to hypsometric tints on cartographic maps the height differences between a surface and its reference ellipsoid can be assessed and color-coded to indicate deviation from the baseline (protrusions and indentations).
The color-coding can be used to describe the shape of a surface and the change of shape over time with respect to a reference ellipsoid. As an example for non-ellipsoid uneven surfaces we chose the outer nuclear membrane of a mammalian cell. Since it is contiguous with the endoplasmic reticulum (ER), we took confocal Z-stacks of NRK cells expressing the ER transmembrane protein CD3δ -GFP  6  , extracted the nuclear surface signal by image processing and subjected the image series to the Map3-2D software (Fig. 2d ). The resulting projection showed the corresponding height map of the nuclear surface, color-coded to display the areas of protrusion (positive elevation) and indentation (negative elevation). Changes of the nuclear shape over time could thus be examined and evaluated by analyzing 2D map projections from image stacks taken at different time points (Supplementary Videos 1-3).
To further explore the usability of map projections for dynamic analyses of biological samples we subjected 4D image stacks (i. e. 3D imaging over time) to the Map3-2D software. The only limiting factor with such experiments is the additional time needed to acquire the whole image stack instead of a single layer. However, since many diffusion rates of proteins (including GFP chimeras) can be sufficiently analyzed within the higher milliseconds to lower seconds range  7, 8  , most motorized widefield setups and many confocal microscopes (for example spinning disk microscopes, Leica's resonant scanner, Zeiss LIVE) allow for such high data acquisition rates. The advantage of image stacks over single frames in combination with the Map3-2D unfolding is that the whole surface data content can be visualized and inspected on a single map projection image.
This way, no biological signal or structure of interest will ever be lost and therefor omitted from the image analysis, simply because it was not located on the single frame acquired, as done in conventional 2D time-lapse imaging experiments. Also, any intensity value-based image analysis will become more complete, and hence more accurate with the Map3-2D software, as the evaluation is based on the entirety of the surface data instead of solely the sub-content from a single layer. Since Map3-2D's map projections cross-reference with the original data and vice versa, regions of interest (ROIs) can conveniently be selected on the map projection image. Thereupon images get precisely analyzed from the corresponding area on the raw data image stack (Fig. 1 ). We tested the Map3-2D software to analyze protein dynamics from photoactivation  9  (Fig.
3  and Supplementary Fig. 5  and Supplementary Video 4), fluorescence loss in photobleaching (FLIP) (Supplementary Video 5) and fluorescence recovery after photobleaching (FRAP)  7  (Supplementary Fig. 6 ) experiments in mammalian and yeast cells. For photoactivation, the ER signal of CD3δ -PAmCherry 6 was locally switched to red fluorescence 10 on the back-lateral side of N2a cells. Its diffusion across the ER was recorded over time by confocal microscopy using a resonant scanner, and the 4D image stacks were subjected to Map3-2D software (Fig. 3a ). Here, the whole CD3δ -PAmCherry ER signal was considered as All mean intensities were measured on the original image stacks by crossreferencing the area information from the corresponding maps. For the single Z-layer images, the whole ER area from that single layer was used for measurements.
Acknowledgements The authors are grateful to  W. Wolf  ( ZMBH, University of Heidelberg ) for sharing yeast samples and  A. Jafar Pour  ( ZMBH, University of Heidelberg ) and  V. Kamvissi  ( University of Dresden ) for critical reading of the manuscript. H. L. acknowledges support from the  Excellence Initiative. All microscopy and image processing were performed at the  ZMBH Imaging Facility.
Background Chimpanzees (Pan troglodytes) are still widely distributed across sub-Saharan Africa, and the species exploits a wide range of habitats including rainforests, ecotones and savannas  [1] [2] [3]. Across this range they exhibit considerable genetic  [4] , behavioral  [5]  and ecological diversity  [3]. Studies consisting mostly of samples from wild-born captive chimpanzees have given great insights regarding the phylogenetic history of this species, but genetic data from wild individuals remain sparse. Recent studies of captive wild-born chimpanzees include datasets such as complete mitochondrial (mt) genomes  [6] , genome-wide single nucleotide polymorphisms (SNPs)  [7] , and complete genome sequences  [8]. The overall picture to emerge from these studies suggests that chimpanzees are divided into two geographically-and genetically-defined groups: a western African group that includes P.
Dataset preparation We calculated pairwise estimates of relatedness for all pairs of genotyped individuals using the software program COANCESTRY  [23]  and removed duplicate individuals and those that were closely related to one another. A total of three individuals with a relatedness index value  [24]  above 0. 75 were determined to be either identical individuals or highly related and were excluded from all further analyses. The analyses presented here include mtDNA sequencing data from 604 sequences from 35 sampling locations, and microsatellite data were included from 187 unrealated individuals from 28 sampling locations (Additional file 1). In order to ensure that all of the genetic markers were selectively neutral, and therefore suitable for further analysis, the data were subjected to various neutrality tests.
05). The  first Principal Component (PC 1) separated chimpanzees north and south of the Sanaga River (P. t. ellioti and P. t. troglodytes), and accounted for 71% of the total extracted variation in the dataset. PC 2 accounted for 21% of the total extracted variation and separated P. t. ellioti chimpanzees into two groups: one cluster of individuals mostly from the forests of western Cameron (further designated as: P. t. ellioti (Rainforest), purple) and another cluster of individuals from the ecotone in central Cameroon (further designated as: P. t. ellioti (Ecotone), green). PC 3 and PC4 accounted for 5% and 3% respectively, and separated small clusters of individuals from the same sampling locations from the rest of the dataset. These final PCs were sample location specific and are possibly the result of sampling bias (i.
e. sampling of related individuals below our relatedness threshold). Results of the cluster analysis in TESS  [30]  are shown in Figures  6a  and b. We used several methods to infer the maximum number of populations, K, represented by this dataset. Deviance Information Criteria (DIC)  [31]  values suggest that K MAX is 2 (Additional file 8a). In contrast, the post hoc statistic, ΔK  [32] , suggests a K MAX of 3 (Additional file 8b). Overall, these results suggest that there is a primary separation along the banks of the Sanaga River. P. t. ellioti, (purple) is found north and west of the river, whereas P. t. troglodytes (orange) occurs south of the Sanaga (Figure  6a ). There is additional evidence of a population subdivision within P. t. ellioti: a population located in the forests of western Cameron (P.
t. ellioti (Rainforest) versus P. t. ellioti (Ecotone)). Additional file 9 shows the estimated number of migrants exchanged between populations per generation (2Nm) as calculated using in the Arlequin version 3. 5 software package  [37]. Between 1 and 2 migrants per generation are exchanged between populations located  Allele richness and number of private alleles by population, corrected for unequal population size, is shown in Additional files 10a and 10b. Allele richness varied considerably between populations, with P. t. troglodytes displaying almost twice as many distinct alleles per locus compared to P. t. ellioti (Rainforest) and P. t. ellioti (Ecotone). However, the mean number of private alleles per locus did not vary considerably between regions, although we observed a similar pattern, with P.
Conclusions This study represents the most comprehensive genetic dataset available for wild chimpanzees from Cameroon, which is important because it is the only known area where the distributions of two chimpanzee subspecies overlap. Microsatellite genotype profiles from 187 unrelated individuals and mtDNA haplotypes of 607 individuals were used to test between a variety of hypotheses for the presence and type of population structure of chimpanzees across the study area, including (i) panmixia, (ii) isolation-by-distance, (iii) population structure with complete isolation, and (iv) population structure with ongoing migration. Overall, the results suggest that P. t. ellioti and P. t. troglodytes represent genetically distinct populations in Cameroon, confirming results from previous studies of captive chimpanzee with inferred origins  [6] [7] [8] 39]  and previous analyses of mtDNA haplotypes of wild individuals  [9, 10].
Dataset preparation We gathered fecal (n = 247) and hair samples (n = 223) from wild-living, non-habituated chimpanzee populations from 36 remote regions spanning eastern Nigeria through southern Cameroon (Figure  2  and Additional file 1). Most samples originating from the regions of Belgique (BQ), Boumba Bek (BB), Diang (DG), Dja Biosphere (DB), Duomo Pierre (DP), Ekom (EK), Lobeke (LB), Mambele (MB) and Minta (MT) were collected between 2004 and 2006 by the team of BHH and MP. Hair and tissue samples from Nigeria and Cameroon were collected during a series of studies from 1994 -2010 by the team of MKG. We recruited a team of ~9 field assistants for each mission and base camps were established in the vicinity of forests were chimpanzee presence was reported by local hunters and villagers.
2. 3 software as an ' Absolute Quantification (standard curve)' assay with the settings recommended in the Quantifiler® Kit User's Manual  [61]. We used primers L15997 (5′-CACCATTAGCACCCA AAGCT-3') and H16498 (5′-CCTGAAGTAGGAACCA GATG-3′) to amplify a 460 -500-bp mtDNA fragment spanning the hypervariable D-loop region (HVRI) in all samples that were newly collected for this study, using methods described in previous studies  [10, 13, 59 ]. We assembled and aligned the resulting sequences with SEQ-MAN DNASTAR software (Lasergene, DNASTAR, Inc. , Madison, WI), along with georeferenced sequences from previous studies  [9, 10, 59, 62]. We deposited all newly generated sequences from this study in GenBank under accession numbers KM401682-KM401815. We used twenty-one autosomal microsatellite loci to produce genotype profiles from both hair and fecal samples.
For samples that failed to produce reliable genotype profiles, we increased the amount of DNA template to up to 8 μL per reaction, regardless of the DNA quantitation results. Many of the hair samples from the Nigerian locations had been typed previously for some of the loci selected, and were adjusted to the differences in base pair sizes due to apparatus and protocol discrepancies  [13, 65]. All PCR reactions included negative control samples for quality assurance. We analyzed each multiplex PCR product on an ABI 3130 capillary array genetic analyzer (Applied Biosystems, Foster City, CA). We determined fragment sizes against a Genescan 600 Liz size standard (Applied Biosystems, Foster City, CA), and allele sizes using the Genemapper ID version 2. 7 software (Applied Biosystems, Foster City, CA).
5 software package  [37]. We tested deviations from Hardy-Weinberg equilibrium for each locus against 1,000,000 random permutations of the data. We calculated pairwise estimates of relatedness for all samples that produced reliable microsatellite genotypes using the software program COANCESTRY  [23]. COAN-CESTRY implements three inbreeding estimators and seven relatedness estimators  [24, [70] [71] [72] [73] [74] [75] [76]  to estimate relatedness between individuals and inbreeding coefficients from multi-locus genotype data. We tested 95% confidence intervals for relatedness and inbreeding estimates for all genotyped individuals against 1000 bootstrap permutations of the data. Samples estimated to come from the same individual or close relatives, with a relatedness index  [24]  above 0. 75, were excluded in order to remove all pairs of individuals down to first cousins from all further data analyses.
mtDNA diversity analysis We generated haplotype networks for HVRI mtDNA sequences via the median-joining algorithm of Network 4. 5 (http://www. fluxus-engineering. com). Because it allows for reticulation, the median-joining approach for inferring haplotype relationships is appropriate for the analyses of mtDNA control region sequences, which exhibits high levels of homoplasy in humans  [77, 78]. We identified hypermutable sites by post-processing using the Steiner maximum parsimony algorithm within Network 4. 5 and were excluded from the network analyses. We performed an AMOVA using Arlequin version 3. 5 software package  [37]. We tested population differentiation values against 10,000 random permutations of the data. For the AMOVA, we grouped individuals according to their origin either north or south of the Sanaga River.
In addition to the AMOVA, we ran a spatial AMOVA (SAMOVA) at k = 2, to confirm the groupings used in the AMOVA, as well as to test how geospatial partitioning of the sampled populations affected population differentiation  [28]. We generated mismatch distributions by computing distribution of the number of pairwise differences between mtDNA haplotypes using Arlequin version 3. 5 software package  [37]. We generated distributions by grouping individuals into either two or three separate groups (as identified by microsatellite cluster analysis), using 100 bootstrap replicates.