dataesr/scientific-paragraphs-categorization huggingface dataset:
Code availability The code used to compute the resistivity is available from the corresponding authors upon reasonable request. Letter https://doi. org/10. 1038/s41567-022-01763-0 Extended Data Fig. 2 | Magnetoresistance in different LSCO samples. Magnetic field dependence of the in-plane resistivity ( J∥a) in LSCO at p = 0. 19 (a), from 4 ) compared with b) LSCO sample S1 and c) LSCO sample S2 at p = 0. 24, at temperatures as indicated. In all cases, B∥c.
Compilation of database A comprehensive literature review of gene dispersal, spatial genetic structure, and mating system in forest tree populations from seasonally dry tropical forests was undertaken by searching and accessing peer-reviewed literature from the Scopus database, for articles listed until December 2020. The keywords: "seasonally dry tropical forest" AND "tree species" AND "gene dispersal" were considered. Therefore, this review is drawn from published papers in journals with diverse scopes to provide an up-to-date account of the present status of this active research area. For each study, we considered the following variables on population and species characteristics (see categories and further precisions below): pollination syndrome, body size of pollinators, distances of pollen movement, seed dispersal syndrome, seed dispersers, the morphology of diaspores, distances of seed movement, successional stage, seasons of blooming and fruiting, mating system, population density, and landscape features.
Funding This research was supported by  PIP   N° 112-2015001-00860CO  from  Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)  to MVG and  MEB  and  16/Q1303-TI  from  Universidad Nacional de Misiones (UNaM)  to  ALG. Also , this work has benefited from support of a grant from  Investissement d'Avenir grants  of the  ANR  ( CEBA : ANR-10-LABX-25-01 ) to MH.
Data availability Data are available from the FIGSHARE repository  (Goncalves et al. 2022). Dataset not peer-reviewed. Goncalves et al. (2022). Data from "Spatial genetic structure and mating system in forest tree populations from Seasonally Dry Tropical Forests: a review". V1. figshare. [Dataset]. https:// doi. org/ 10. 6084/ m9. figsh are. 18128 774. v1.
The ELM benefits from a superior learning speed and a good ability to generalize  [7]  hence its use in a variety of learning problems, such as classification, regression… The ELM that overcomes the slow training speed and the over-adjustment problem, however, may cause the classification performance to fluctuate. Based on these two points, as part of this work, we will make a comparison between the two neural networks ELM and MLP in the classification and estimation of precipitation from MSG (Meteosat Second Generation) data. Training and validation of ELM and MLP is performed by comparing MSG data to radar data. The rest of the paper is organized as follows. Section 2 presents the study area and the data used. Section 3 briefly describes the mathematical concept of ELM and MLP. The classification and estimation results are presented in section 3.
Study area and data As part of this work, rainfall was estimated over northern Algeria (see Fig. 1 ) using data from the MSG satellite. We used MSG data for the classification and estimation of precipitation and radar data for learning and validation of the implemented model. MSG data are available as multispectral images (12 channels) at a frequency of 15 minutes with a spatial resolution of 3km for 11 channels and 1km for a high resolution visible channel  [8]. As for the radar data, a radar image is provided every 15 minutes whose pixels can take 16 levels of precipitation intensities in dBZ (04,  12, 18, 22, 26, 30, 34, 38, 42, 46, 50 , 54, 58, 62, 66, 70). The spatial resolution is 1 km. For the spatial correspondence of the two types of data, we transformed the radar resolution from 1x1 km2 to 4x5 km2 by taking the average of the pixels.
Multilayer perceptron (MLP) The MLP type neural network consists of a structure of successive layers including an input layer and an output layer connected one after the other  [12]. Between the input layer and the output layer, the MLP can have one or more hidden layers. A layer is a set of neurons that feed the neurons of the next layer through their outputs. To work, each input xi of a neuron j is multiplied by an adaptive coefficient wij (synaptic weight). Then, the nonlinear activation function is applied at the level of the output neurons. This function usually takes the form of a sigmoid ∅. Thus, the output of the jth neuron, oj, is given by: 𝑂𝑂 𝑗𝑗 = ∅(∑ 𝑤𝑤 𝑖𝑖𝑗𝑗. 𝑥𝑥 𝑖𝑖 𝑛𝑛 𝑖𝑖=0 ) ( 1 ) where n is the number of variables in the dataset. The learning process consists of adjusting the synaptic weights of the MLP.
Precipitation classification In this section, we will give classification results and precipitation estimates by applying ELM and MLP neural networks. Thus, the precipitation scenes collected during the period from October 2008 to March 2009 are classified. The classification accuracy is calculated by comparing the classification results with the reference data from the radar. For both models, we used the same architecture, namely 7 nodes for the input layer, 8 nodes for the hidden layer and 7 nodes for the output layer. We calculated the classification accuracy for the two models for the different classes (see Fig. 3 ).
Conclusion The purpose of this work is, on the one hand, to estimate rainfall from satellite data in northern Algeria, and on the other hand to test the contribution of ELM compared to MLP. Both models were therefore implemented and used for precipitation classification and estimation. Each of the architectures of the two models consists of an input layer, a hidden layer and an output layer. For the implementation of this study, multi-spectral images from MSG satellites and their correspondences in radar images are combined and used. MSG data provide information on the optical and microphysical properties of clouds. From these data, seven input parameters are obtained and at the output seven target classes are selected. The classification results showed the superiority for the MLP against the ELM.
We report the results of follow-up investigations of a possible new thermally emitting isolated neutron star (INS), 4XMM J022141. 5-735632, using observations from XMM-Newton and Spectrum Roentgen Gamma (SRG) eROSITA. The analysis is complemented by Legacy Survey imaging in the optical and near-infrared wavelengths. The X-ray source, the first to be targeted by XMM-Newton in an effort to identify new INS candidates from the fourth generation of the XMM-Newton serendipitous source catalogue Data Release 9 (4XMM-DR9), shows a remarkably soft energy distribution and a lack of catalogued counterparts; the very high X-ray-to-optical flux ratio virtually excludes any other identification than an INS. Within current observational limits, no significant flux variation or change of spectral state is registered over nearly ten years.
XMM-Newton The XMM-Newton Observatory  (Jansen et al. 2001 ) targeted the X-ray source 4XMM J022141. 5-735632 (J0221, for short) for circa 50 ks on July 9-10 2021. We included in the analysis the only other XMM-Newton observation that serendipitously detected the target in 2012 (0674110401). We performed standard data reduction with SAS 20 (xmmsas_20211130_0941) using the most up-to-date calibration files and following the analysis guidelines of each EPIC instrument. We filtered the event lists to exclude "bad" CCD pixels and columns and retain the photon patterns with the highest quality energy calibration. The source centroid and optimal extraction region in each EPIC camera, with typical sizes of 30 -40 , were defined with the SAS task eregionanalyse in the 0. 2-2 keV energy band; the X-ray emission of the source is compatible with the background level at energies above 2 keV.
( ) Likelihood of detection. ( †) Coordinates of the target, corrected for the astrometry with the SAS task eposcorr. ( ‡) Positional offsets in each coordinate determined for the astrometry (see text; for details). catalogue energy bands (see caption of Table  1 , for a definition). Only the combined EPIC results are shown. We used the SAS task eposcorr to refine the astrometry by cross-correlating the list of EPIC X-ray source positions with those of optical Guide Star Catalogue  (Lasker et al. 2021)  objects lying within 15 around the nominal pointing coordinates. The small positional offsets in right ascension and declination listed in Table  3  were consistently detected with the Gaia EDR3 and AllWISE catalogues (Gaia Collaboration 2020;  Cutri et al. 2021). The agreement between epochs is good overall (<3σ).
Stacked source detection on both XMM-Newton observations was performed using the XMM-Newton SAS task edetect_stack  (Traulsen et al. 2019 ) and the 4XMM-DR12s catalogue pipeline. First, the results of the eposcorr astrometric correction of the individual observations were applied to the attitude information in order to shift the events to rectified positions, as described by  Traulsen et al. (2020). We then extracted images, background maps, exposure maps, and detection masks for both observations with reference coordinates centred at the position of J0221. Deviating from the default parameters, we used an image binning of 2 × 2 pixels and a PSF fit radius of 2 about the sources, in order to optimise the sensitivity to faint sources in these fields, which are dominated by point-like sources and do not suffer from source confusion, and in order to achieve optimum positional accuracy of the target.
The eROSITA instrument consists of seven telescope modules (TM 1-7) operating in the 0. 2-10 keV energy band. The detectors of TM 5 and 7 ("TM9") are more sensitive to soft X-rays, as they lack the aluminium on-chip optical light filter the other five cameras ("TM8") carry. The TM9 detectors are known to suffer from time-variable light leaks that affect their performance and calibration at the softest energies (see  Predehl et al. 2021, for details). For the analysis, we retrieved the event lists corresponding to the target's sky tile in each eRASS1-4. We list in Table  2  an overview of these observations. The position of the X-ray source was repeatedly covered in April and October 2020-2021, with exposures of ∼0. 9-1 ks. The data sets were processed with pipeline version c020 and analysed in the 0.
2-10 keV energy band with the eSASS software system, applying up-to-date calibration files (eSASSusers_211214;  Brunner et al. 2022). For all observations, the event files of each individual TM were filtered for periods of high background activity with the eSASS task flaregti in the 2. 2-10 keV energy band. Unless otherwise noted, all valid photon patterns and active telescope modules ("TM0") were considered for optimal sensitivity. The total sum of GTIs accumulated over the four epochs is 2. 3 ks (∼1 ks, corrected for the vignetting). Altogether, 101 ± 12 photons are collected from the X-ray source in the 0. 2-1 keV energy band, considering all seven active TMs. Similarly to the XMM-Newton observations, we analysed the X-ray source content of the observations and the parameters of the target using maximum likelihood PSF fitting  (Brunner et al.
Spectral analysis and long-term variability The analysed data set comprises six epochs, ten spectra 2  , and over 4000 counts (0. 2-2 keV); background noise amounts to up to 15% and 30%, respectively, of the total EPIC and eROSITA events. The spectral analysis was restricted to GTI-filtered photons, single-and double-pattern events for pn, and all valid CCD patterns for the MOS and TM cameras. We regrouped the energy channels to avoid low (<5) counts per spectral bin and kept oversampling of the instrumental resolution capped to a maximum factor of 3. To fit the spectra we used XSPEC 12. 10. 1f and the Cash C statistic  (Arnaud 1996; Cash 1979) ; unless otherwise noted the fit parameters were allowed to vary freely within reasonable ranges. The spectra were fitted simultaneously, allowing for a renormalisation factor to account for cross-calibration uncertainties between epochs and instruments.
7 +1. 0 -0. 4 km. ( †) Parameters of the powerlaw tail: photon index Γ = 4. 2 +1. 6 -1. 3 and observed flux f PL X = 6. 1 +1. 5 -1. 4 × 10 -14 erg s -1 cm -2 (0. 2-2 keV). 4 ). The two EPIC pn and four MOS data sets are colour-coded in dark grey and blue, respectively. The eROSITA spectrum (merged here for display purposes) is shown in pink. analysed here, we employed the web-based tool High-Energy Lightcurve Generator (HILIGT 3 ;  Saxton et al. 2022)  to retrieve upper limits and flux values for the source. These were determined through aperture photometry at J0221's sky position and the Bayesian approach of  Kraft et al. (1991)  on archival observations from Swift XRT, ROSAT, and the XMM-Newton Slew Survey (see  Ruiz et al. 2022 , and references therein). We assumed an absorbed blackbody spectral model consistent with the source's spectral shape and selected a 3σ confidence interval 3 http://xmmuls.
This work was supported by the project XMM2ATHENA, which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement n o 101004168. IT gratefully acknowledges the support of SSC work at AIP by Deutsches Zentrum für Luft-und Raumfahrt (DLR) through grant 50 OX 1901. DT acknowledges support by DLR grants FKZ 50 OR 2203. This research has made use of data obtained from the 4XMM XMM-Newton serendipitous source catalogue compiled by the 10 institutes of the XMM-Newton Survey Science Centre selected by ESA. This work is based on data from eROSITA the soft X-ray instrument aboard SRG, a joint Russian-German science mission supported by the Russian Space Agency (Roskosmos), in the interests of the Russian Academy of Sciences represented by its Space Research Institute (IKI), and the Deutsches Zentrum für Luft-und Raumfahrt (DLR).
NGS data analysis The reference fission yeast S. pombe genome (2018-09-04) and annotation  (2019-11-15)  were downloaded from PomBase (https://www. pombase. org/; [68, 72] ). Read quality was checked using FastQC version 0. 11. 8 (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/), and reads were aligned to the S. pombe genome using HISAT2 2. 1. 0 [73]  and SAMtools 1. 9  [74, 75]. Read coverage tracks (i. e. , target protein occupancy) were then computed and normalized to the respective mapped library sizes using deepTools 3. 5. 1  [76]. The raw ChIPseq data are available from the ArrayExpress (https://www. ebi. ac. uk/) database under the accession number E-MTAB-11081. WT fission yeast RNA-seq data were obtained from the NCBI Sequence Read Archive (https://www. ncbi. nlm. nih.
Microscopy For the Nse4-GFP foci number determination, cells were grown in YES medium overnight, diluted to OD = 0. 4 in the morning, and treated with 0. 03 % MMS or 20 mM HU for 5 h at 30 °C. 2. 5 μl of cell culture was mounted on the slides, and GFP fluorescence was observed. Pictures were taken on the Axio Imager Z1 microscope, using a Plan-Apochromat 63x oil objective, the Axiocam CCD camera, and processed with the AxioVision software (all by Zeiss). A minimum of 500 cells were counted in three independent experiments. For statistical evaluation, p-values were calculated using the χ2 test. Figure legends.
Funding Funding from the  Ministry of Education, Youth, and Sports of the Czech Republic  (project  INTER-COST   LTC20033  to JJP) is gratefully acknowledged. BŠ was supported by the  Masaryk University  grant  MUNI/R/1142/2021. MP, AM, and JP were supported by the  Charles University  grant  PRIMUS/MED/26. We duly acknowledge the assistance of the GeneCore facility ( EMBL Heidelberg ) and  Genomics CF  (CEITEC; project  LM2018132  funded by  MEYS CR)  with the NGS part of our project. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The genes were classified according to the Pombase GO database into Biological processes and Cellular component categories, (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made bioRxiv preprint doi: ; https://doi. org/10. 1101/2022. 09. 24. 508842 this version posted October 12, 2022. The copyright holder for this preprint. CC-BY-NC 4. 0 International license available under a.
Spatially resolved transcriptomics (SRT) provides the opportunity to investigate the gene expression profiles and the spatial context of cells in naive state. Cell type annotation is a crucial task in the spatial transcriptome analysis of cell and tissue biology. In this study, we propose Spatial-ID, a supervision-based cell typing method, for high-throughput cell-level SRT datasets that integrates transfer learning and spatial embedding. Spatial-ID effectively incorporates the existing knowledge of reference scRNA-seq datasets and the spatial information of SRT datasets. A series of quantitative comparison experiments on public available SRT datasets demonstrate the superiority of Spatial-ID compared with other state-of-the-art methods. Besides, the application of Spatial-ID on a SRT dataset with 3D spatial dimension measured by Stereo-seq shows its advancement on the large field tissues with subcellular spatial resolution.
Cell type annotation is a fundamental task for cell and tissue biology 70 that can help characterize the biological process of tissues at single cell level. This task is conventionally performed by single cell transcriptome analysis on the data acquired by scRNA-seq technology. Facing the exponential growth of high-dimensional and noisy data associated with sequencing technologies  18  , it requires high performance annotation methods that can effectively reduce dimensionality and are robust to data noise  19. Annotating cell identities is even more challenging for SRT datasets  20. For example, spot-based protocols such as Visium  9  , Slide-seq  14, 15  , HDST  16  and Stereo-seq  17  capture RNA from areas spanning more (or less) than one cell, without consideration of cell boundaries. In addition, due to inherited experimental design, the low transcript detection sensitivity of spot-based protocol further makes the measured transcriptomic profiles deviated from the real transcript levels of single cells.
The main contribution of the Spatial-ID is the effective incorporation of existing knowledge of reference scRNA-seq datasets and the spatial information of SRT datasets. A series of comparison experiments on public available SRT datasets with different data characteristics (See Table  1 ) demonstrate the superiority of Spatial-ID in cell type annotation compared with other state-of-the-art methods (See Table  2 ), i. e. , Seruat v3, SingleR, Scmap, Cell-ID, ScNym and SciBet. Furthermore, Spatial-ID can effectively perform cell type annotation for 3D SRT datasets. Moreover, the extended experiments of new cell type discovery demonstrate that Spatial-ID has a promising ability to detect new cell types. A group of simulation experiments with different gene dropout rates demonstrates more robustness of Spatial-ID than other state-of-the-art methods.
These results suggest that embedding spatial information can substantially improve cell type annotation on SRT datasets. Besides, the application of Spatial-ID on a Stereo-seq SRT dataset with 3D spatial dimension shows its advancement on the large field tissues (~1cm 2 ) with subcellular spatial resolution. The cell types identified by Spatial-ID present high consistency with previous studies. Besides, by mapping the identified cell types with identified spatial gene patterns, the significant GO terms of the spatial gene patterns further reveal the functions and underlying biological processes of the identified cell types. (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted May 27, 2022.
Results The pipeline of Spatial-ID. The pipeline of our proposed Spatial-ID is shown in Fig. 1. Given a SRT dataset, the DNN model of transfer learning generates the probability distribution of each cell, which is used to construct pseudo-label (Fig. 1b ) through a temperature setting strategy of knowledge transfer (See Stage 1 and Methods). Meanwhile, the autoencoder module (Fig. 1c ) learns encoded gene representations from the gene expression matrix, and a spatial neighbor graph is constructed through calculating the Euclidean distances between cells (Fig. 1d ). Next, the variational graph autoencoder incorporates the encoded gene representations and the spatial neighbor graph to produce spatial embeddings (Fig. 1e ). Then, the encoded gene representations and spatial embeddings are aggregated to generate the final latent representations that provide comprehensive characters of gene expression and spatial information.
Thereafter, the final latent representations are used to reconstruct the gene expression in the autoencoder and the spatial neighbor graph in the variational graph autoencoder. Simultaneously, using the generated pseudo-labels, the self-supervised learning strategy employs final latent representations to train the classifier (See Stage 2). After training, our framework generates the final class probability distribution of each cell in a given SRT dataset (See Stage 3). To verify the advancement of the proposed Spatial-ID, we apply Spatial-ID on 4 representative SRT datasets with different characteristics (See Table  1 ), including two public available FISH-based SRT datasets (i. e. , mouse primary motor cortex  43  and mouse hypothalamic preoptic region  44  ) generated by MERFISH, a public available Slide-seq SRT dataset (i.
e. , mouse spermatogenesis  45  ), and a self-collected Stereo-seq SRT dataset (i. e. , large field mouse brain hemisphere). The two FISH-based datasets measure hundreds of genes, in which the mouse hypothalamic preoptic region dataset is a 3D SRT dataset that provides continuous slices with equal interval. The spot-based Slide-seq SRT dataset and the Stereo-seq SRT dataset measure tens of thousands of genes, and the last one provides 3D spatial dimension, subcellular spatial resolution and large field view on entire mouse brain hemisphere. The two FISH-based SRT datasets and the Slide-seq SRT dataset are employed to quantitatively compare Spatial-ID with other state-of-the-art methods (See Table  2 ). The Stereo-seq SRT dataset is employed to demonstrate the advancement of Spatial-ID on spatial transcriptomic analysis of the large field tissues.
Application to mouse primary motor cortex dataset measured by MERFISH. We first perform a quantitative comparison between the Spatial-ID and the control methods on the mouse primary motor cortex (MOP, See Fig. 2a ) dataset  43  measured by MERFISH. The MOP SRT dataset contains 12 samples, including total 280,186 cells and 254 genes. The snRNA-seq 10x v3 B dataset  46  is used as the trainset of the DNN model in Spatial-ID and the reference dataset in the control methods, which contains 159,738 cells and 31,053 genes. The MOP SRT dataset and the snRNA-seq 10x v3 B dataset are derived from Brain Atlas of BICCN  23  , thus the cell type assignments of them adapt the same MOP cell taxonomy that is a hierarchical organization with reference to the common cell type nomenclature (CCN)  47  found by Allen Institute.