dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analysis with R We employed the statistical programming environment R (R Core Team, 2020) and the package Luminescence  (Kreutzer et al. , 2012 (Kreutzer et al. , , 2020) )  for processing the IR-RF singlegrain data. Therefore, we developed two new functions for a seamless data import and processing of *. rf files: read_RF2R() and plot_ROI() (Luminescence ≥ v0. 9. 8). Both functions work in conjunction with the already available function analyse_IRSAR. RF(). See below for an application example. Advanced users can also deploy our experimental R package dedicated to spatially resolved luminescence data analysis called RLumSTARR  (Kreutzer and Mittelstrass, 2020a). The sole relevance of RLumSTARR for this contribution is the function run_ImageJ(). We used this function to run ImageJ in a batch mode and autoprocess our image data.
, 2017 , their supplement, proposed at least 40 channels for a resolution of 15 s/channel). We used the comparable solar simulator settings as in  Frouin et al. (2017)   2  : 365 nm: 20 mW cm -2 , 462 nm: , 525 nm: 53 mW cm -2 , 590 nm: 37 mW cm -2 , 623 nm: 112 mW cm -2 , 850 nm: 94 mW cm -2. In our bleaching protocol, the UV power settings are doubled in comparison to the recommendations by  Frouin et al. (2015). This setting may lead to an unwanted temperature increase in the sample. However, we carefully monitored the temperature as recorded by the thermocouple in the reader's sample arm. We found the temperature stable at 70 • C for all measurements (data not shown), indicating that the temperature in the samples was stable.
Select a slow readout rate, but not too slow The CCD chip readout process induces another source of image noise called read noise or readout noise (cf. standard textbooks for both notations). Longer exposure times lead to better SNR because more signal is gathered while the readout noise remains constant. Another way to reduce readout noise is to choose a slow CCD readout rate. In our system, the slowest available readout rate is 100 kHz. At this rate, a full-resolution (512 × 512 px) CCD readout takes 2. 13 s. If the readout process lasts longer than the RF measurement channel, either images are lost or the camera runs asynchronous to the measurement sequence. In our systems, the readout process started after the preset time interval for the image exposure ended. The camera is then locked until the image data are transferred to the computer.
Thus, the user has to incorporate a camera dead time when parameterising channel width and exposure time in the camera's sequence settings; see Table  3  and Ap-pendix A. However, all modern scientific CCD cameras, including our ProEM camera, can read out the last image while already gathering signal light for the next image. The camera dead time is a setting particular to the LexStudio 2 software solution we used. Later software iterations or more advanced systems might set exposure time and channel width equal by default.
Consider hardware pixel binning The most straightforward approach to improving the SNR and the signal sensitivity is pixel binning performed by the CCD camera image-processing software like ImageJ. This software pixel binning, however, is less effective than potential hardware binning by the camera. With applied hardware pixel binning, multiple pixels are considered as one pixel and read out together. This feature reduces the readout noise per imaged area and reduces the readout time and therefore the camera dead time (if applicable). As a side effect, the image stacks' file size is also reduced, positively impacting image processing time. We applied 2 × 2 pixel binning as a default setting and deactivated it only if we had sufficiently bright samples. On the downside, pixel binning lowers the camera resolution to 256 × 256 pixel, corresponding to a decreased spatial resolution of 54 µm (before 27 µm).
Step 4: extract single RF curves We used the ImageJ ROI manager to obtain the arithmetic mean of the pixel values in each ROI for each image in the RF nat stack and RF reg stack. Thus, the consecutive average signal in one ROI forms the IR-RF curve of one sample grain. These single-grain IR-RF measurements and the lateral position of each ROI are saved into one ASCII text file (table. rf) to be further analysed with other software than ImageJ.
Single-grain data analysis in R We analysed the single-grain IR-RF data the same way that we would analyse conventional PMT IR-RF measurements. A simple R script to analyse the table. rf file of one aliquot reads as follows (R package Luminescence ≥ v0. 9. 8 needed): #load R package 'Luminescence' library(Luminescence) #import data file <-file. choose() RF_data <-read_RF2R(file) #get ROI locations (optional) ROI_data <-plot_ROI(RF_data) #determine equivalent doses equivalent_doses <-analyse_IRSAR. RF( object = RF_data, method = "SLIDE", method. control = list( vslide_range = "auto", correct_onset = FALSE))   3.
We aimed at 30 to 50 grains per aliquot. We prepared at least three cups per sample. Irradiation times were equal to values reported in  Frouin et al. (2017)  and Kreutzer et al. 2015 ) after a dose of ca. 100 Gy. The violet shaded area approximates the grain (not in height but width). Please note that the data by  Gribenski et al. (2015)  were only added to provide a rough qualitative comparison. Please note that the metric distances (sub-labels) refer to the chip surface. (2018): 3600 s (RF nat ) and 30 000 s (RF reg ) for sample TH0; 3600 s and 10 000 s for sample BDX16651. Figure  5  shows typical IR-RF curves from one ROI (in our case one grain) for TH0 (Fig. 5a ) and BDX16651 (Fig. 5b ). To obtain the D e s, we applied the vertical and horizontal sliding technique  (Murari et al. , 2018)  to TH0.
BDX16651 We counted ca. 40 grains on the analysed cup, and 35 emitted light and were analysed. We discarded three grains because the R analysis indicated a bad match of RF nat and RF reg. The sample shows a large D e scatter with an average D e of 148. 6 ± 6. 7 Gy (average dose and associated standard error (SE)). This value is significantly larger than the mean D e of ca. 96 Gy reported by  Kreutzer et al. (2018). However, in contrast to the study by  Kreutzer et al. (2018) , the single-grain data allow further statistical treatment of the results. We applied the finite mixture model (FMM, cf. Galbraith and Roberts, 2012) using the function calc_FiniteMixture() with an assumed sigmab value of 0. 05 (Fig. 7 ). The Bayesian information criterion indicated the statistically significant number of components.
This would increase grain separation and decrease any influence of signal cross-talk. Finally, while the presented software toolchain is opensource, hence freely available and open to inspections and improvements, we acknowledge that the combination of three different software tools adds an additional layer of complexity. However, in particular, the image processing through ImageJ has the advantage that the data processing is transparent and available on all platforms and independent of the particular measurement system. Furthermore, users can tap into an extensive repository of available functions and plug-ins to record their own macros and thus adjust the image analysis with ImageJ without a need for programming skills. D. Mittelstraß and S. Kreutzer: Spatially resolved infrared radiofluorescence.
Does this leave the question open of whether to expect hidden malign effects in the results of samples TH0 and BDX16551? Our observations indicated that cooling system problems were always clearly visible in the IR-RF curves, manifesting in vastly overestimated unrealistic results -an observation we did not make for the presented results. In the absence of such technical issues, given that our method can be tested successfully at more extensive data sets, the next logical step would be to link SR IR-RF with spectral measurements. Trautmann et al. (2000)  performed spectrally resolved radiofluorescence measurements of single feldspar grains. They demonstrated that the radioluminescence emission spectra could significantly differ from grain to grain. They also showed that plagioclase grains might also emit IR-RF signals and mentioned that the separation of Kfeldspar grains from other feldspar grains could not be taken for granted.
Nevertheless, we are confident that more measurements using fully functional systems can exploit the presented method's full potential. As rule of thumb, reducing the CCD chip temperature by 10 K quarters the dark current signal and halves the dark noise. The following estimation formula is derived from the Certificate of Performance dark charge calibration value of our camera (PI ProEM512B @L2, IRAMAT-CRP2A) and multiple specifications sheets of similar cameras: φ dark ∼ = 210e 0. 144T CCD. ( A4 ) See Fig. S1  in the Supplement for a plot of that equation. The dark current signal itself dissolves in the data analysis process. However, the contribution of the dark shot noise in Eq. (A2) grows with the binning factor, the exposure time and especially the CCD temperature. The dark shot noise of the camera used in this contribution is estimated in Table  A3.
Code and data availability. The SR-RF macro developed for this paper and used for image processing and the software toolchain overview and tutorials are available at http://doi. org/10. 5281/ zenodo. 4745491 (Mittelstraß and Kreutzer, 2021). The R package RLumSTARR used for an automated data processing is available at https://github. com/R-Lum/RLumSTARR (Kreutzer and Mittelstrass, 2020a). The SR-RF macro and RLumSTARR are distributed under GPL-3 licence conditions. The data sets presented in this paper as well as the used measurement sequences and analysis scripts are available at https://doi. org/10. 5281/zenodo. 4395968 (Kreutzer  and Mittelstrass, 2020b)  and make use of the Creative Commons License (CC-BY-NC). Please contact Dirk Mittelstraß (dirk. mittelstrass@luminescence. de) for technical questions and Sebastian Kreutzer (sebastian.
Secondly, emerging multiplexing techniques present new opportunities for systems-level studies of cell types and their changing states at unprecedented resolution in whole organisms. The Clytia medusa offers an appealing platform for pioneering such studies. It is small, transparent, and has simple tissues and organs, stem cell populations actively replenishing many cell types in mature animals, and remarkable tissue plasticity and regenerative capacity  19, 22, [24] [25] [26] [27]. Furthermore, the 1cmdiameter adult medusae used in this study contain on the order of 10 5 cells (see Methods), making it possible to sample cells comprehensively across a whole animal in a cost-effective manner (Supplementary Table  1 ) using current scRNA-seq technology. In this study, we generate a cell atlas for the Clytia medusa while simultaneously performing a whole organism perturbation study, providing the first medusa single-cell dataset and an examination of changing cell states across the organism.
All labeled suspensions were pooled and processed with the 10X Genomics V2. 0 workflow and Illumina sequencing, allowing construction of a combined dataset across organisms and treatments, without the necessity of batch correction (Fig. 1 ; Supplementary Table  1 ; Supplementary Fig. 2 ). A total of 13,673 single-cell profiles derived from 10 individuals (5 control, 5 starved) passed quality control, with high concordance in cell type abundance and gene expression among animals in the same treatment condition (see Methods, Supplementary Fig. 3 ). From this gene expression matrix, we 1) derived a Clytia medusa cell atlas, and 2) performed a high-resolution analysis of the transcriptional impact of starvation across all observed cell types. To assess the utility of this approach, we performed a separate experiment examining Clytia medusae responses to multiple transient stimuli over short timescales (Supplementary Fig.
The gland cell types may also be generated from i-cells, for instance from the pools at the base of the manubrium and in the gonad, as well as by self-renewal and position-dependent transdifferentiation as reported in Hydra 8. Within our dataset, we expected to be able to capture dynamic information relating to the development of i-cell derived cell types, similar to that extracted from Hydra polyp single cell transcriptome data  8. Indeed, our cell atlas embedding revealed neuronal and nematocyte populations connected to the i-cell population  3, 8  (Fig. 2a ; Supplementary Fig. 14, 15 ), likely corresponding to differentiation trajectories  34, 35. To identify known and novel markers for nematocyte and neuronal development, we assigned pseudotime values to the cells and ranked genes in each trajectory (Fig.
3e ). For example, the pp7+ subpopulation is located around the rim of the mouth and the pp9+ population is located in the tentacle bulbs and tentacles, while the pp5+ population is widely distributed across the tentacles, bulbs, nerve rings, subumbrella, and mouth (Fig. 3e ). In comparison to neuropeptides, genes involved in classical chemical neurotransmission were not obvious markers for subpopulations in our dataset. There were, however, a number of interesting subtype-specific genes potentially involved in neurotransmission, for example, a possible nitric oxide synthase, and a member of the Slc6 family of transporters (Supplementary Table  5 ).
The techniques for multiplexed experimentation that underlie this study are also well suited to large-scale perturbation studies in other marine organisms given the sea-water compatible workflow. As sequencing costs drop and cell throughput in scRNA-seq grows, WHAM-seq should also become tractable for larger, more complex systems. Moreover, the lack of library-induced batch effects demonstrates how largescale experiments can be conducted without introduction (or minimizing introduction) of confounding factors from multiple experiments, which can be highly non-linear and difficult to account for  13. The second perturbation dataset also demonstrates how batch effects are reduced within multiplexed experiments (see Methods; Supplementary Fig. 10 ). The fully reproducible and usable framework we have presented, which includes code that can be run on a laptop or for free in the cloud (see Code Availability), will further assist in extending this expression-based analysis to other organisms.
Generation of Reference Transcriptome Assignments to PANTHER database entries (version 11) were made using the 'pantherScore2. 0. pl' script available from the database website (www. pantherdb. org) (ref: PMID: 30804569). Human -Clytia orthologs were assigned using the OMA program (ref: PMID: 31235654) as described in PMID: 30858591, and taken from the pairwise human / Clytia orthologs output, rather than the orthologous groups. To ensure highly sensitive transcriptome alignment, a new transcriptome assembly for Clytia Hemisphaerica was generated from bulk RNA-seq data produced from Clytia medusae (organisms at the same life stage as in the single-cell experiments) (link). We used the Trinity 59 de novo assembler, with default parameters, to generate a transcriptome (link) and the Cufflinks Cuffcompare utility  52  to merge the Trinity assembled transcripts with any XLOC annotations from the MARIMBA v.
Pre-Processing and Clustering of Sequencing Data Initial Cell Ranger Demultiplexing for ClickTags Initial demultiplexing of ClickTag libraries was done using output from with the 10X Cell Ranger pipeline, using Cell Ranger 3. 0 count and aggr functions with the MiSeq ClickTag fastqs as input, and combining the counts from the two lanes using the denoted sample IDs. A ClickTag count matrix (ClickTag x cell) was generated by counting ClickTag barcodes that had high sequence similarity to the designed sequences using the Python fuzzywuzzy package to identify targets within Levenshtein distance 1. [Code]   We additionally quantified gene expression with the kallisto-bustools workflow  61  to create a more integrated workflow, which reproduced concordant results, described below.
We also followed similar pre-processing to standard cell-by-gene workflows, using the inflection point in rank-UMI vs cell barcodes knee plots to filter cell barcodes based on their tag UMI counts. [Code]   For the stimulation experiment we concatenated sequencing data from the MiSeq and HiSeq as input to the previously described kallisto-kITE workflow. With the same clustering procedure we selected cell barcodes in clusters with strong overlapping expression of both individual and condition ClickTags. [Code]   To standardize the cDNA analysis workflow, we re-processed the starvation data using the kallisto-bustools workflow to generate gene count matrices for each lane, which were then concatenated. Cells were also filtered based on the ClickTag analysis. Values were log1p normalized, mean-centered and scaled, and filtered for highly variable genes using the same procedure described above for downstream dimensionality reduction and visualization (e.
The L 1 distances were then calculated for all possible pairs of these cell type centroids using (1). The distributions of the inter-and intra-cluster distances are shown in (Fig. 4 ). We chose to use the L 1 distance metric as it tends to better retain relative distances in high dimensions, particularly in comparison to the commonly used Euclidean distance or other higher Lnorms  64, 65. We then used the stimulation dataset to assess the validity of the clusters/cell types generated with the starvation data. We created a joint representation of the two datasets by using a concatenated cell-by-gene matrix including only the genes highly variable in both the control and starved datasets,, and used 70% of the starvation data to train a k-nearest neighbor (KNN) classifier (using sklearn's KNNClassifier with k = 15) to assign cluster labels to the remaining starvation cells and to the stimulation dataset.
This showed that the stimulation cells' labels from their neighbors in starvation data were assigned at the same accuracy as the test starvation data, meaning that clusters from the starvation data are applicable to the stimulation dataset and capture the main features of the stimulation dataset to the same extent (Supplementary Fig. 5a ). We also examined batch effects in the stimulation experiment. As with the starvation experiment, the L 1 metric was utilized to visualize the magnitude of batch effect within the multiplexed experiments compared to between experiments (Supplementary Fig. 10 ). We used the merged representation (used for the KNN assignment) between both experiments to find the average pairwise distances between cell types of control condition individuals within the starvation experiment, within the stimulation experiment, and across both experiments.
RNA-seq Analysis & Clustering with MARIMBA Annotation To make our dataset more easily searchable with the MARIMBA v. 1 (created on 05/30/2016) transcriptome annotation  21  , we generated single-cell gene count matrices with respect to transcript sequences distributed via the MARIMBA website (link). The gene count matrices were generated using the same kallisto-bustools workflow, and compared the application of the previous clustering/cell type assignment (including the overlap in differentially expressed genes delineating these clusters) to validate the quantification derived from the Trinity/Cuffcompare assembled transcriptome (Supplementary Fig. 18 ). [Code]   We also produced a notebook for visualization of gene expression in this dataset to facilitate its use in future studies. This establishes a code base for rapidly and transparently processing and comparing single-cell datasets with future transcriptome annotations.
Pseudotime Analysis [Code] We selected cells from cell types of interest (stem cells, nematocytes, and neuronal cells) and used diffusion mapping  67  to create a reduced dimension representation of cells, along with Scanpy's dpt function which uses geodesic distance along the graph of cells (in the determined 'diffusion component' space). We then computed a PAGA-based embedding to visualize the cells in the context of the different trajectories with a ForceAtlas2 layout. To determine which genes constituted the important features in a given pseudotime trajectory, we implemented a method based on the random forest method used in the dynverse R package for extracting 'important' genes  68. A random forest regression model implemented with sklearn's random_forest_regressor was used to identify genes that were good predictors of the generated pseudotime values (grouped into quantiles).
This was run for each of the two inferred trajectories separately (stem cells to nematocytes, and stem cells to neurons). The training set consisted of 80% of the genes' expression data, within which 80% was used for optimizing the model, and 20% was used to evaluate the mean squared error (MSE). Both trajectory models had an  2 of 0. 85 or greater. The remaining 20% of data from the full dataset was used to calculate the gene-wise permutation importance scores, providing a ranking of each feature (gene) in terms of its contribution to the model's predictive capabilities using sklearn's permutation_importance. In the ranking, positive scores indicate importance, with the importance   for gene j being the difference between the original score s (MSE) calculated from the original model, and the average score across K=5 random permutations of the feature columns in the validation dataset: 𝑖𝑖 𝑗𝑗 = 𝑠𝑠 - 1 𝐾𝐾 ∑ 𝑠𝑠 𝑘𝑘,𝑗𝑗 𝐾𝐾 𝑘𝑘=1.
Acknowledgements We thank  Xiaolin Da  and  Xiao Wang  for their technical assistance,  Tsuyoshi Momose  for his assistance with the single-cell experimentation, the  Caltech Single-Cell Profiling and Engineering Center  for use of their single-cell and sequencing tools, and the  Caltech Bioinformatics Resource Center  for transcriptome assembly and annotation analysis. We thank  A. Sina Booeshaghi  for help with kallisto, bustools, the kITE demultiplexing of the ClickTag reads, and for rescuing the stimulation experiment sequencing data. We thank  Jocelyn Malamy  for helping to establish Clytia work at  Caltech. We thank  Sophie Peron  for initial characterization of some of the cell type marker genes,  Pascal Lapébie  for identification of novel neuropeptide sequences, and  Muriel Jager  for valuable advice on the in situ protocol.
J. G. , M. H. and L. P. were supported in part by a seed grant from the  Chen Institute at the California Institute of Technology. T. C. , J. G. and L. P. were supported in part by  NIH   U19MH114830  and  NIH RF1AG062324A. We thank the  Marine Resources Centre (CRBM and PIV imaging platform) of Institut de la Mer de Villefranche (IMEV) , supported by  EMBRC-France. The French state funds of EMBRC-France are managed by the  ANR  within the investments of the  Future program. L. L. was supported by the  Agence Nationale de la Recherche  ( ANR-19-CE13-0003 ) A. F. , R. R. C, and E. H. , were supported by the  H2020 / Marie Skłodowska-Curie ITN "EvoCell" Grant  agreement  N° 766053. B. W. was supported in part by a  Howard Hughes Medical Institute Fellowship of the Life Sciences Research Foundation  and in part by  NIH   K99NS119749.
In the present study, the genome of Betula pendula was analyzed to identify the aquaporin encoding genes (named BpeAQPs). The genomical, structural, and biochemical features were inferred based on present knowledge for the entire set of BpeAQPs published here from raw and unannotated sequences from NCBI databases, therefore classifying each sequence in the general plant AQP system. Furthermore, the expression profiling of BpeAQPs were analyzed and compared using transcriptome sequencing data available for B. pendula from different organs (flower, leaf, xylem, and root), specifically in leaves under cold stress. The knowledge obtained from this study is expected to provide the first comprehensive classification of AQPs in the B. pendula species in particular, and in the Betulaceae clade in general.