dataesr/scientific-paragraphs-categorization huggingface dataset:
These misclassification examples can likely be explained by class overlap in feature space as there are fewer CNN inputs to provide the necessary discriminatory high-level features. The smaller number of inputs for the Corona classification may have also caused a degree of network overfitting  (Webb, 2011; Cogswell et al. , 2016; Baduma and Locascio, 2017). The CNN employed to classify the Corona imagery had the same architecture used in the Sentinel-2 classification. The removal of inputs can significantly affect network performance. The Corona classification was performed with seven inputs instead of the twenty-one inputs it was optimised for; therefore, the CNN architecture may have been too complex for the mapping task, leading to overfitting and subsequent misclassifications. The performance of a CNN fine-tuned to handle the smaller number of input data would most likely exceed the performance of the CNN architecture employed in this study.
The commission of lateral moraines was the primary reason for the low precision accuracy in the Khumbu region, resulting in an additional 17. 3% (15. 4 km 2 ) of supraglacial debris area coverage. The results of the transfer learning experiment illustrate that transfer learning applied to out-of-sample imagery was not as successful as CNNs trained on in-sample imagery in their respective regions. These results indicate that high-level features derived from a training dataset in one training region may not fully represent the debris-covered surface properties in another region. Therefore, training data from the region to be mapped is an essential prerequisite on regional scales. This reflects that the surfaces of debris-covered tongues are influenced by varying climate forcing, dynamics, topographies, lithologies and surface processes resulting in spatial variation across High Mountain Asia.
The rockfall deposits have a different spectral signature to the debris transported by other processes to the surface, reflecting less light in each multispectral band. Since these rockfall deposits are not present in the Hunza and Khumbu domains, their respective CNNs were not trained with the sample heterogeneity required to recognise rockfall deposits as features of the debris-covered environment causing rockfall deposits to not be mapped by the transfer CNNs. However, transfer learning worked better when applied to the Hunza domain using a CNNs trained in the Khumbu and Manaslu domains. This highlights the importance of intra-class variation in training datasets and suggests it is beneficial to generate samples in regions with diverse debris-covered surface characteristics, or to accumulate training samples from multiple regions, to provide the intra-class variance required to avoid significant omission errors in out-of-sample imagery.
Summary and further work We developed a simple, transferable CNN-OBIA approach that was able to classify supraglacial debris with F-score accuracies up to 93. 7% across the three domains based on freely available multispectral, geomorphometric, thermal, and coherence datasets. Despite encountering common debris-covered glacier mapping omission errors mentioned above, the integration of OBIA following CNN classification into one seamless approach allowed most errors to be rectified during the primary classification process, reducing the amount of analyst input required to improve classification accuracies. CNN-OBIA also showed promise for mapping debriscovered glaciers in historical Corona satellite imagery, producing F-score accuracies up to 84. 7% over the entire debris-covered glacier surface.
We believe the approach holds the potential to streamline the creation of historical glacier inventories across the wider HMA region. To the best of the authors' knowledge, our study is the first to automatically map debris-covered glaciers in Corona imagery and to further test its ability for multitemporal inventory creation. The approach also holds the potential to automate the detection of other geomorphological entities such as landslides, lava flows, permafrost, and quaternary landforms in historical panchromatic imagery. However, due to the limited area covered by the Corona data, further work is needed to assess the suitability of the approach to classify debris-covered glaciers in historical datasets. We suggest that the inclusion of other Corona-derived datasets, such as texture, will help improve the ability of the CNN to distinguish different landforms from panchromatic imagery, and this can be explored in a subsequent study.
With respect to transfer learning applied to out-of-sample imagery, we found that it was heavily dependent on the distribution of debris-covered surface characteristics across sample space and the amount of intra-class variation present in the training dataset. Our experiments showed that the extreme variations of high mountain glacial environments and debriscovered glacier surface characteristics pose significant challenges for the application of transfer learning to out-of-sample imagery for streamlining regional glacier inventory creation. Further work is needed to improve the application of out-of-sample transfer learning to debris-covered glacier mapping. This includes generating extensive training datasets with samples gathered from diverse geographical regions, which capture the complexities of debris-covered tongue surface characteristics.
This also illustrates the necessity for future studies to continue to establish the optimal network architecture and parameters, training dataset qualities, and a consensus on the best remote sensing datasets to employ for mapping. Furthermore, to greatly enhance our ability to produce highly accurate debris-covered and clean ice glacier outlines over large spatial scales, we suggest the addition of OBIA following the classification of more complex network architectures, such as DeepLabV3+. These integrated CNN and OBIA classification approaches can be used for more complex land-cover classification tasks in both contemporary and historical satellite imagery, not just for debris-covered glacier mapping. Lastly, based on the ability of the network used in this study to distinguish between active and stagnant ice, we suggest the inclusion of interferometric coherence imagery as an input to all future networks to address the limitations encountered by previous deep learning methods (e.
Data availability statement Publicly available datasets were analyzed in this study. This data can be found here: AW3D30 was obtained from the JAXA Earth Observation Research Center (EORC) (http://www. eorc. jaxa. jp/ ALOS/en/aw3d30/index. htm). Landsat and Sentinel-2 imagery was obtained from USGS Global Visualisation Viewer (GloVis) (https:// glovis. usgs. gov/). Sentinel-1 imagery was obtained from the ASF Data Search Vertex (https://search. asf. alaska. edu/). The GAMDAM glacier inventory is available from PANGAEA (https://doi. pangaea. de/10. 1594/PANGAEA. 891423). The Himalayan glacial lake inventory is available from NSIDC (https://nsidc. org/data/hma_gli/versions/1).
08. 2018 Sentinel-1 S1A_IW_SLC__1SDV_20180817T010636_20180817T010703_023279_0287FF_59B3 5 × 20 Coherence generation 04. 08. 2018 Landsat-8 LC08_L1TP_149035_20180804_20180815_02_T1 30 (15 Pan, 100 TIR) Classification 11. 08. 2018 Landsat-8 LC08_L1TP_150034_20180811_20180815_02_T1 30 (15 Pan, 100 TIR) Classification 11. 08. 2018 Landsat-8 LC08_L1TP_150035_20180811_20180815_02_T1 30 (15 Pan, 100 TIR) Classification 05. 2016 AW3D30 ALPSMLC30_N036E074 30 Elevation, GMD generation Acronyms: NIR, Near-infrared; SWIR, Shortwave infrared; Pan, Panchromatic; TIRS, Thermal infrared; GMD, Geomorphometric Dataset.
Study sample Facial images and rating data were secured in five locations-Guangzhou (China), Tokyo (Japan), Lyon (France), New Delhi (India) and Cape Town (South Africa)-using experimental equipment and protocols that were part of a larger project on the perception of female physical appearance  [14] [15] [16]. Five-hundred and twenty-six women ('participants') representing five ethnic groups were recruited through local agencies and consented to have their faces imaged: Chinese (n = 106), Japanese (n = 100), French (n = 105), Indian (n = 100) and South African (n = 115). Each sample included participants aged 20-69 years, equally distributed around the mean ages of 10-year cohorts. The facial skin tone of women varied from darkly pigmented to lightly pigmented. According to the Fitzpatrick scale, a widely used photo-type classification tool for UV light sensitivity  [38] [39] [40] ; with type I = lightest pigmentation, and VI = darkest pigmentation, participants corresponded to the following types (as assessed by skin experts at the study centres): Chinese II-IV, Japanese II-IV, French II-III, Indian IV-V and South African V-VI.
Procedure Assessors judged the images for age, health and attractiveness in a monadic presentation design (i. e. one after the other). Each assessor judged 90 randomly selected facial images per attribute, balanced across cohorts (i. e. 270 images, in total). Thus, each image was assessed ~300 times in total, 10 times per subgroup (cohort, ethnicity and gender). Assessments of the three attributes were made in three separate blocks, using web-based software (PhotoScale; Newtone Technologies). The continuous scales ranged from 0 to 100, with age assessment provided in years, and attractiveness and health assessments ranging from 'not attractive/not healthy' (0) to 'attractive/healthy' (100). The order of blocks was randomized across participants, as was the order of images within a block. The time for assessment was limited to 3-5 s.
To explore this, we used HADDOCK 2. 2  28, 29  to model the binding of SHARP SPOC to 8-mer SMRT pS2522, CTD pS5, and CTD pS5pS7 peptides (Fig. 3g ). Based on the HADDOCK score, the strongest interaction was predicted between SHARP SPOC and phosphorylated SMRT. The predicted binding strength for double phosphorylated pS5pS7 CTD was similar to that for single phosphorylated pS5CTD, indicating that pS7 cannot compensate for the absence of acidic residues in the CTD. Similarly, RBM15 SPOC was predicted to bind CTD pS5 and CTD pS5pS7 peptides with comparable affinity (Fig. 3g ). Collectively, our findings establish SHARP SPOC as a phosphoserine binding module that preferentially binds phosphorylated LSD Fig. 1  | Conserved surfaces on SPOC mediate phosphoserine binding. a Multiple sequence alignment of human SPOC domains based on PROMALSD3 using SPOC structures from human SHARP (2RT5), human PHF3 (6Q2V) and sequences from human RBM15, RBM15B, DIDO and SPOCD1.
6  and Supplementary Data 3), box plots showing the genome-wide distribution of log2 fold changes in TT chem -seq signal in the KO and ΔSPOC cell lines relative to WT to visualize alterations in nascent transcript levels at TSS (transcription start sites) and gene bodies (Fig. 7a , b and Supplementary Data 4), we determined the stalling index as TT chem -seq TSS/gene body reads (Fig. 7c  and Supplementary Data 4), and we calculated differences in log2 fold changes KO/WT or ΔSPOC/WT between RNAseq and TT chem -seq to assess changes in mRNA stability (Fig. 7d ). For PHF3, we used our previously published RNA-seq dataset  5  , which showed the upregulation of mature transcripts in PHF3 KO or ΔSPOC cells relative to WT (Fig. 6a-c ). TT chem -seq showed reduced nascent transcript levels and slightly increased Pol II stalling in PHF3 KO and ΔSPOC cells (Fig.
Fluorescence anisotropy Fluorescence anisotropy measurements were performed on a Perkin Elmer LS50 B fluorescence spectrometer operated by Perkin Elmar FL WinLab software (version 3. 00). The excitation wavelength was set to 500 nm (excitation slit 10 nm), the emission wavelength to 518 nm (emission slit 6 nm), and the integration time to 1 s. Measurements were conducted at 20 °C in 25 mM Tris-Cl pH 7. 4, 25 mM NaCl, and 1 mM DTT. The grating factor was determined using unconjugated Atto-488 dye. Totally, 90 nM peptides were titrated with increasing concentrations of SPOC domains in a final volume of 110 µL. Each individual data point is an average of 20 measurements. For each binding curve, data points were acquired from three independent SPOC dilution series. Data are presented as mean anisotropy ± standard deviation from the three independent replicates.
The data set of SHARP SPOC:1×S5P CTD was collected at the MASSIF beamline ID30a1 (ESRF, Grenoble) at 100 K using a wavelength of 0. 966 Å. The data set of RBM15 was collected at the I04 (DLS, Didcot Oxfordshire) 100 K using a wavelength of 0. 9795 Å. The data frames were processed using the XDS package  56  and converted to mtz format with the program AIMLESS  57  with the help of autoprocessing pipelines at the synchrotrons. The structures were determined using the molecular replacement program PHASER with atomic coordinates of SPOC SHARP (PDB code 1OW1) as a search model in the case of SHARP SPOC:1×S5P structure and our SHARP SPOC structure as a search model for RBM15. The structures were then refined with Phenix Refine v 1. 20. 1-4487  58 nd rebuilt using Coot v 0. 9. 6  59. Structural depictions were generated using UCSF Chimera (version 1.
Size exclusion chromatography and multiangle laser light scattering (SEC-MALLS) SEC-MALLS was performed with a Superdex 75 Increase 10/300 GL column (Cytiva) on an Agilent Technologies 1260 Infinity HPLC equipped with a miniDawn Treos detector (Wyatt Technology) using a laser emitting at 690 nm. Experiments were performed at RT with a flow rate of 0. 5 mL/min in 25 mM Tris pH 7. 4, 25 mM NaCl, and 1 mM DTT. 80 µL protein samples at a concentration of 2. 5-6 mg/mL were injected. The HPLC was operated with OpenLAB CDS software (Agilent Technologies, Rev C. 01. 07 SR3 [465]), MALLS data acquisition and analysis were performed with ASTRA software (Wyatt Technology, version 7. 3. 2. 19).
Peptide mass spectrometry data analysis Raw data were processed using the MaxQuant software package  63  (version 1. 6. 16. 0 respect. 1. 6. 14. 0) searching against the Uniprot human reference proteome (January 2020, www. uniprot. org) as well as a database of most common contaminants. The search was performed with full trypsin specificity and a maximum of two missed cleavages. Carbamidomethylation of cysteine residues was set as fixed, oxidation of methionine, and acetylation of protein N-termini as variable modifications. For the FLAG-IP samples, acetylation of lysines and phosphorylation of serine, threonine, and tyrosine were additionally defined as variable modifications. For label-free quantification, the "match between runs" feature and the LFQ function were activated-all other parameters were left at default.
Results were filtered at a false discovery rate of 1% at protein and peptide spectrum match levels. MaxQuant output tables were further processed in R 64 (version 4. 0. 2). Reverse database identifications, contaminant proteins, protein groups identified only by a modified peptide, protein groups with less than two quantitative values in one experimental group, and protein groups with less than 2 razor peptides were removed for further analysis. Missing values were replaced by randomly drawing data points from a normal distribution model on the whole dataset (data mean shifted by -1. 8 standard deviations, the width of the distribution of 0. 3 standard deviations). Differences between groups were statistically evaluated using the LIMMA package  65  at 5% FDR (Benjamini-Hochberg). Mass spectrometry data have been deposited to the jPOST repository  66  under the accession numbers JPST001505 (anti-FLAG IP) and JPST001502 (anti-GFP IP).
RNA-seq data analysis RNA-seq data from HEK293 cells from different genetic backgrounds were processed using the PiGx-RNA-seq 68 pipeline. In short, the data was quantified using the GRCh38/hg38, and the dm6 versions of the human and drosophila spike-in transcriptome (downloaded from the ENSEMBL database  69  ) using SALMON  70  with default parameters. For visualization purposes, the data was mapped to the GRCh38/hg38, and dm6 versions of the human, and drosophila genomes using STAR, with the following parameters: --limitOutSJcollapsed 20000000 --limitIObufferSize=1500000000 --outFilterMultimapNmax 10 --seed-PerWindowNmax 5. The quantified data was processed using tximport  71  , and the differential expression analysis was done using DESeq2  72. Genes with less than 5 reads in all biological replicates of one condition were filtered out before the differential analysis.
The data was normalized by taking the ratio of reads mapping to the human and the drosophila transcriptome. Genes were defined as differentially expressed if they had a minimum absolute log2 fold change of 1, and a BH adjusted p value less than 0. 05. RNA-seq data were deposited under the accession number E-MTAB-12358. TT chem -seq data analysis TT chem -seq data from HEK293T cells from different genetic backgrounds were processed using the PiGx-RNA-seq 68 pipeline. In short, the data was quantified using the GRCh38/hg38, and the dm6 versions of the human, and labeled and unlabeled spike-in sequences (downloaded from the ENSEMBL database  69  ) using SALMON  70  with default parameters. The data were mapped to the GRCh38/hg38 version of the human genome using STAR, with the following parameters: --limit-OutSJcollapsed 20000000 --limitIObufferSize = 1500000000.
Genes were defined as differentially expressed if they had a minimum absolute log2 fold change of 1, and a BH adjusted p value less than 0. 05. TT chem -seq data was deposited under the accession number E-MTAB-12359. RNA isolation and mRNA enrichment for m  6  A mass spectrometry analysis mRNA samples were prepared in three biological replicates. Cells were grown in 15 cm dishes to 70-80% confluency. Cell pellets were resuspended in 1. 5 mL TRI reagent (Sigma). 300 µL Chloroform (Applichem) was added, and samples were mixed and centrifuged at 4 °C, max. speed for 15 min. The upper phase was transferred to a fresh tube and subjected to isopropanol precipitation. Totally, 120 µg of RNA were treated with 100 U DNase I (Roche) at 37 °C for 30 min and purified by phenol-chloroform extraction and Ethanol precipitation.
All coverslips were washed two times, stained with DAPI (1:10,000) for 5 min at RT, washed 1× with PBS and 1× with ddH 2 O, and mounted onto slides with Prolong Diamond (Invitrogen, P36961). Immunofluorescence images were acquired using an inverse point scanning confocal Zeiss LSM980 Microscope equipped with a Zeiss Plan-Apochromat 63x/1. 4 Oil DIC M27 (WD 0. 19 mm) running with Zeiss ZEN Blue 3. 3 software (version 3. 3. 89. 0008). Sequential acquisitions of 2 channels were performed with a 405 nm laser diode (30 mW) and a 561 nm DPSS laser (25 mW) set to 1. 5-1. 8% excitation power for AF568 channel and 0. 9% for DAPI, together with detector gain set to 750-850 V. Secondary beam splitters (SBS LP 570 for AF568 and SBS SP 550) were used to constrain emission wavelengths and pixel dwell time set to 1.
ChIP-seq library preparation and sequencing ChIP-seq libraries were prepared using NEBNext Ultra II DNA library prep kit for Illumina (New England Biolabs) and NEBNext Multiplex Oligos for Illumina (New England Biolabs) according to the manufacturer's instructions. Totally, 5-10 ng ChIP-DNA was used as input for library prep. Sequencing was performed on an Illumina NextSeq 550 instrument in readmode SR75 by the Next Generation Sequencing facility at Vienna BioCenter Core Facilities (VBCF). qPCR qPCR analysis of input and ChIP-DNA was performed on a BioRad CFX Touch cycler operated by CFX Maestro software (version 2. 2) using Takyon No Rox SYBR MasterMix dTTP Blue (Eurogentec). qPCR primer sequences are indicated in Supplementary Table  4. Input Cq values were adjusted to 100%, % input values were calculated as follows: % input = 100*2 adjustedCq input ÀCq ChIP qPCR data were analyzed and plotted using Microsoft Excel 365 (version 16.
ChIP-seq analysis ChIP seq data were processed using the PiGX-ChIPSeq pipeline  68. In short, the data was mapped to the hg38 version of the human genome using Bowtie2  74  , with the k = 1 parameter. The data were quantified over TSS and gene bodies as described for the TTseq data. Genes shorter than 2 kb were filtered out from the analysis. Genes with less than 5 reads in the TSS region or the gene body, in all biological replicates of one condition, were filtered out of the analysis. The data were normalized using DESeq2-derived size factors. Region width was used as an additional normalization factor. Differentially bound regions were defined using DESeq2  72  , with the default parameters. ChIP-seq data were deposited under the accession number E-MTAB-11506.
Acknowledgements We thank  Anton Meinhart  and  Renato Arnese  for RBM15 SPOC X-ray data collection;  Martin Puchinger  for help with labeling CTD peptides and advice regarding fluorescence anisotropy data analysis;  Felix Fischer ,  Kathrin Nagl , and  Alexander Athanasiadis  for help with purifying SPOC domains and FA assays;  Dirk Eick  for sharing ZNF768 antibody; the  VBCF Next Generation Sequencing facility  for sequencing; the  Max Perutz Labs Mass Spectrometry Facility ,  Markus Hartl ,  Dorothea Anrather  and  Natascha Hartl  for mass spectrometry sample processing, data acquisition, and analysis; the staff of the X-ray beamlines at  ESRF Grenoble and DSL Didcot  for their support. L. A. was a member of the  Integrative Structural Biology Ph. D. program  funded by the  Austrian Science Fund  ( W1258  " DK: Integrative Structural Biology ") from  2018-2021.
K. D. C. research was supported by the  Wellcome Trust   Collaborative Award  ( 201543/Z/16 );  COST action   BM1405 -Non-globular proteins-from sequence to structure, function and application in molecular physiopathology (NGP-NET);  WWTF (Vienna Science and Technology Fund) Chemical Biology  project  LS17-008 ;  Christian Doppler Laboratory for High-Content Structural Biology and Biotechnology ; the  Austrian-Slovak Interreg  Project  B301   StruBioMol ,  University of Vienna Research Platform Comammox  and by the  University of Vienna. B. Z. is supported by the  Austrian Science Fund  ( P30550 ). This work was funded by the  Austrian Science Fund  ( P31546  and  W1258  " DK: Integrative Structural Biology " to D. S. ).
Data availability The data that support this study are available from the corresponding author upon reasonable request. The atomic coordinates have been deposited in the Protein Data Bank under accession codes: 7Z27 [https://doi. org/10. 2210/pdb7Z27/pdb] for RBM15 SPOC and 7Z1K [https://doi. org/10. 2210/pdb7Z1K/pdb] for SHARP SPOC:1xS5P CTD. The sequencing data generated in this study have been deposited in ArrayExpress under accession codes: E-MTAB-12358 (RNA-seq), E-MTAB-12359 (TT chem -seq), E-MTAB-11506 (ChIP-seq). The processed sequencing data are provided in Supplementary Data 3-5. Mass spectrometry data have been deposited to the jPOST repository  66  under the accession numbers JPST001505 (anti-FLAG IP) and JPST001502 (anti-GFP IP). These data are also available via Pro-teomXchange under accession codes PXD031938 and PXD031917.
The processed mass spectrometry data are provided in Supplementary Data 1 and 2. Atomic coordinates used in this study are available in the Protein Data Bank under accession codes 2RT5, 1OW1, 6QV2, 6IC8, and 5KXF and in the Alpha Fold Protein Structure Database under accession codes Q9BTC0, Q6ZMY3, and Q96T37. Source data are provided in this paper.