dataesr/scientific-paragraphs-categorization huggingface dataset:
Clustering analysis Based on the morphological analysis, we present an overview of existing regulations and support schemes. Therefore, the first parameter of each morphological box is selected and translated into an ordinal scale with a 1-of-3 encoding of the elements. The ordinal dataset is then grouped with a clustering algorithm to identify similar policy frameworks between the countries. Ideally, all parameters of the morphological boxes should be used in the clustering, but this is not possible due to a lack of data. Therefore, we used parameters that are commonly discussed in literature and where data was at least partly available. Data gaps were filled with a survey and interviews (see section 2. 3). Each country was evaluated based on the legislation in force during the data collection period, i.
(ii) Clustering Clustering or cluster analyses is a collective term for statistical procedures that make it possible to structure a data set by group assignments. Cluster analyses are based on the consideration of the similarity or distance of the objects to each other. There are a variety of different algorithms that use different distance measures to assign objects to clusters. In general, partitioning (including k-means), hierarchical, density-based, grid-based and combined methods are distinguished (compare e. g. Stein and Vollnhals, 2011). In this paper, we performed hierarchical clustering as well as k-means clustering. The hierarchical clustering was performed using algorithms provided by the Python package SciPy. Further information, including formulas for the linkage types and dissimilarity calculations, can be found in the documentation of SciPy.
5  The k-means clustering was computed using the k-means++ algorithm provided by the Python package Scikit-learn. Further information is available in the documentation and user guide. 6 For the hierarchical clustering algorithms, we use the cophenetic correlation coefficient 7  to compare different linkage types and dissimilarity calculations (see Table  A2 ). This coefficient calculates the cophenetic distances between each observation in the hierarchical clustering defined by the linkage. It measures the height of the dendrogram at the point where two branches merge. The magnitude of this value should be close to one for a high-quality solution.
Acknowledgements Financial support from the  European Commission , within the scope of the project ' Overview of District Heating and Cooling Markets and Regulatory Frameworks under the Revised Renewable Energy Directive' in short 'DHC Trend ' and with the project reference number  N • ENER/ C1/2018-496  is gratefully acknowledged. The authors would like to thank all members of the project, namely  Tilia GmbH ,  TU Wien ,  IREES GmbH ,  Oeko-Institut e. V. and  Fraunhofer ISI. Furthermore, the authors would like to thank all district heating experts who contributed to this project by sharing information about the regulation in their country. Anna Billerbeck  would like to thank  Barbara Koch , Chair of Remote Sensing and  Landscape Information Systems at the University of Freiburg , for her support and guidance.
Nutrient pulses can also naturally occur through mixing events of short-term frontal systems and commonly enhance production in the surface waters of the eastern Fram Strait during summer  [35, 36]. Based on recommendations by Calbet and Saiz  [37] , we added 50 µM NO - 3 , 4. 7 µM PO 3- 4 , and 25 µM Si(OH) 4 , as well as trace metals and vitamins in accordance with the F/2 R medium concentrations. During the first days of the experiment, six bottles per temperature were incubated for a micrograzing experiment (not included in this dataset), with half of them undiluted and the other half diluted 1:5 with 0. 22 µm filtered seawater taken from near the sampling location. After three days, we pooled the diluted and undiluted communities from t0 at each temperature. These pooled communities were then once again diluted (1:5), and nutrients were added as at t0 before they were incubated in triplicates for the last seven days of the experiment.
Dickson (Scripps Institution of Oceanography, San Diego, CA, USA). The full carbonate system was calculated for tfin using the software CO2sys  [38]  with dissociation constants of carbonic acid by Mehrbach et al. [39] , refitted by Dickson and Millero  [40]. Dissolved nutrients were measured colorimetrically at t0 and tfin on a continuous-flow autoanalyzer (Evolution III, Alliance Instruments, Freilassing, Germany) following standard seawater analytical methods for nitrate and nitrite  [41] , phosphate  [42] , silicate  [43] , and ammonium  [44].
Demultiplexing and FASTQ sequence file generation were carried out using the Generate FASTQ workflow of the MiSeq sequencer software. Primers were removed with v2. 8 cutadapt  [48] , and further processing of the sequence data was performed using the v1. 18 DADA2 R package  [49]. In consideration of the read quality, which usually drops towards the 3 -end, the forward reads were trimmed after 240 to 260 base pairs, and the reverse reads were trimmed after 200 to 210 base pairs. For each pool, error rates were learned independently, and sequences were denoised. Paired-end reads were merged with a minimum overlap of 50 base pairs allowing 0 mismatches, and chimeras were predicted and removed. Taxonomic assignment of the resulting amplicon sequence variants (ASVs) was performed using the reference databases PR2 (v4.
For analysis, samples were thawed at room temperature, vortexed, and measured at a fast speed for three minutes using an Accuri C6 flow cytometer Genes 2023, 14, 623 6 of 18 (BD Sciences, Franklin Lakes, NJ, USA) after setting the threshold of the FL-3 channel to 900. Phenotypic diversity (D2) was calculated for each sample based on the flow cytometric fingerprint according to Props et al. [61] , using the values of FSC-H, SSC-H, FL-2, FL-3, and FL-4. To generate Figure  2 , replicate C of the 6 • C treatment had to be removed due to an erroneous measurement of the flow cytometer and, thus, a missing D2 value. However, excluding D2 as a constraint, 6 • C replicate C clustered together with the other replicates, and the overall pattern did not change (Figure  S4 ). Species richness and evenness were calculated from the sequencing data before transformation (Table  S3 ).
Bacterial Diversity and Composition Response to Warming A notable outcome of our study is linking the bacterial responses to temperature, which is equally important in controlling Arctic bacteria as organic matter  [97, 98]. However, we must acknowledge that our study is biased towards particle-associated bacteria (>0. 8 µm), excluding many smaller free-living bacteria in our analysis  [99]. Despite the bias, the dataset allows us to explore details that are usually unavailable for the class Gammaproteobacteria compared to Alphaproteobacteria and Bacteroidia  [100]. The microbial community was dominated by Gammaproteobacteria, particularly Colweilla, with minor contributions by Bacteroidia, particularly Polaribacter (Figure  5 ). Throughout the incubation, we observed an increase in the relative abundance of Colweilla, peaking at 6 • C, and a decrease in the relative abundance of Polaribacter (Figure  5 ).
Acknowledgments: We thank the crew, our colleagues, the cruise leader, and the captain of the RV Polarstern PS126 expedition for their assistance with logistics, scientific input, and equipment during the expedition. Furthermore, we acknowledge  Nancy Kühne  and  Swantje Ziemann  for their help with 16s library preparation and sequencing. We are grateful for the nutrient measurements performed by  Kai-Uwe Ludwichowski  within the scope of the AWI nutrient facility. Finally, we thank  Björn Rost ,  Sylke Wohlrab , and  Katja Metfies  for their valuable opinion on several aspects of the.
Neuroimaging data The dataset that was used for the current analysis was part of the EU-AIMS LEAP project, a multi-site European collaborative effort with its main aim to identify biomarkers associated with ASD. The study design is described extensively in an earlier publication by Loth et al. [22]  along with a comprehensive overview of the clinical characterization of the cohort  [23]  (for data acquisition details, see Supplementary Material). Resting-state functional MRI (rfMRI) scans were available for 656 participants, to which we applied a set of stringent quality control exclusion criteria. Participants with a structural brain abnormality (not clinically relevant; n = 17), an incomplete rfMRI scan (n = 8), excessive head motion during the rfMRI scan (mean frame-wise displacement >.
fMRI preprocessing Preprocessing of both the rfMRI and task fMRI was performed using a standard preprocessing pipeline that included tools from the FMRIB Software Library (FSL version 5. 0. 6; http://www. fmrib. ox. ac. uk/fsl). For the rfMRI data, we initially recombined the three rfMRI scan echoes using echo-time weighted averaging. Preprocessing included removal of the first five volumes to allow for signal equilibration, primary head motion correction via realignment to the middle volume using MCFLIRT  [25] , global 4D mean intensity normalization and spatial smoothing with a 6 mm FWHM Gaussian kernel. Then, ICA-AROMA was used to identify and remove secondary motion-related artifacts  [26, 27]. Next, nuisance regression was applied to remove signal from white matter and CSF, and a 0. 01 Hz high-pass filter was applied to remove very low-frequency drifts in the time-series data.
To preserve the broad-band characteristics of functional connectivity data no low-pass filtering was applied  [28]. The fMRI images of each participant were co-registered to the participants' high-resolution T1 anatomical images via boundary-based registration (BBR) implemented in FSL FLIRT  [25]. The T1 images of each participant were registered to MNI152 standard space with FLIRT 12-dof linear registration  [29] , and further refined using FNIRT non-linear registration (10 mm warp, 2 mm resampling resolution)  [25]. Finally, we brought all participant-level fMRI images to 2 mm MNI152 standard space, in which all further analyses were conducted.
g. , heightened level of tactile seeking)  [33]. The differential availability of the measures led to a limited subgroup of participants examined for each one (Table  1  and Table 2 ). However, repeating the analysis using imputed measures led to similar conclusions. The imputed measures were estimated using Extra Tree multivariate regression, a method that outperformed all other imputation strategies evaluated in the specific dataset (e. g. , K-nearest neighbors, Bayesian Ridge Regression, mean and median variable imputation)  [34].
Connectopic mapping We selected the post-central gyrus based on the Harvard-Oxford atlas (available as part of FSL  [35] ) as our region of interest. Based on this ROI we estimated connection topographies ('connectopies')  [17]  separately for each participant, hemisphere, and imaging measure (rfMRI, Hariri task) using congrads (publicly available at https://github. com/koenhaak/ congrads). This analysis approach involves three main steps and is summarized in Fig. 1  of the Supplementary Material. Briefly though, first we obtain the connectivity fingerprint of each somatosensory voxel computed as the Pearson correlation between the voxel-wise time series from the predefined ROI and the losslessly SVD-transformed time series from all gray-matter voxels outside the ROI. We then insert the fingerprint matrix to a manifold learning algorithm (Laplacian eigenmaps) to obtain a set of connection topographies.
The polynomial degree, i. e. , the number of TSM coefficients, controls the granularity of the representation of the connectopic map and was chosen based on the value that minimized the model's Bayesian information criterion (BIC) while, at the same time, maximizing its explained variance (EV). We further used a reference connectopy derived from the well-known excellent quality rfMRI dataset Human Connectome Project (HCP) in which the somatotopic organization of the primary motor cortex has been mapped before  [17]. We compared the connectopies between the two datasets, so that we get an estimation of the quality of the obtained LEAP connectopies, as well as of the validity of the main axis of connectivity change that we observe. To sensitize our analyses to subtle variations within connectopies, while ensuring that poor-quality connectopies do not unnecessarily inflate the polynomial degree, individuals whose connectopies correlated less than r = 0.
83, (right hemisphere) Pearson's r = 0. 94; HCP-task: (left hemisphere) Pearson's r = 0. 89, (right hemisphere) Pearson's r = 0. 96; p-values <. 001 for all four comparisons). The average S1 connectopy along the dorsoventral axis of each hemisphere was reproduced across both conditions and datasets with similar reconstructions of the connectopies for all (see reconstruction for the average S1 connectopy during rest in Fig. 1d ) and matches the somatotopic organization.
Our findings are consistent with studies suggesting that atypical sensory features in ASD are a consequence of both bottom-up, and top-down processing differences  [38]  and highlight a potential transdiagnostic value of S1 in cases where sensory distress co-occurs with higher-order cognitive atypicalities. Although we provide convincing evidence for the validity of the S1 connectopies using two individual datasets and imaging measures, there is a certain limitation that needs to be highlighted. We use the Harvard-Oxford anatomical atlas, which, although well established in our field, remains a probabilistic atlas that could potentially lead to signal contamination from neighboring brain regions and interfere with the alignment between the actual functional boundaries of the selected region for S1 and the boundaries used in this work.
To understand further what we have studied here, we could benefit from applying connectopic mapping to different sensory modalities, such as auditory or face processing of the somatosensoryin the latter case higher spatial resolution would be required. In addition, an important aspect of sensory atypicalities that we cannot address by looking at a single time point alone is their developmental effect. This could be further investigated by using data from subsequent time points from the LEAP dataset. Assessing inter-individual differences in a paired design may prove more sensitive concerning the effects examined here compared to a cross-sectional analysis. Lastly, because sensory hyper-/hypo-responsivity is not unique to ASD (but is albeit more common in this population than in other developmental disorders), the relationship examined in the current work could be explored in other clinical populations.
ACKNOWLEDGEMENTS This work is primarily supported by the  EU-AIMS consortium  ( European Autism Interventions ), which receives support from  Innovative Medicines Initiative Joint Undertaking  Grant No. 115300 , the resources of which are composed of financial contributions from the  European Union 's  Seventh Framework Programme  (Grant No. FP7/2007-2013 ), from the  European Federation of Pharmaceutical Industries and Associations  companies' in-kind contributions; and by the  AIMS-2-TRIALS  consortium ( Autism Innovative Medicine Studies-2-Trials ), which has received funding from the  Innovative Medicines Initiative 2 Joint Undertaking  under grant agreement No. 777394 , and this Joint Undertaking receives support from the  European Union 's  Horizon 2020 research and innovation programme  and  EFPIA  and  AUTISM SPEAKS, Autistica ,  SFARI.
Among SlSRS genes, tandem duplication events were not found, but 6 segmental duplications were detected in the tomato chromosomes (Fig. 3A ). There are 9 pairs of collinearity relationships among 6 Group I members with segmental duplication. The results mean that those SlSRS Fig. 8  Expression profiles of SlSRS genes under various hormone treatments. The expression level of SlSRS genes under IAA, GA3, 6-BA, ethephon, EBL, SA, MeJA, ABA and GR24 treatments was analyzed by qRT-PCR. Tomato seedlings were treated with different hormones and sampled at 1 h, 2 h, 4 h, 8 and 16 h to test the responsiveness of SlSRS genes to different hormones. The data were converted to log2FC (FC, fold change) and the heat map was used to represent the responsiveness of the SlSRS genes. Blue and red represent downregulated and upregulated genes under different hormone treatments, respectively.
AtLRP1, the first reported SRS gene, is specifically expressed in Arabidopsis thaliana LRs  [7]. It has been reported that auxin and histone deacetylation influence AtLRP1 expression to regulate root development  [12]. The maize homologous Fig. 9  Expression profiles of SlSRS genes under various sterss treatments. The expression level of SlSRS genes under dehydrated (Dehydration), oxidative (MV), salt (NaCl), Droughty (PEG6000), injured (Wound) and osmotic (Mannitol)treatments was analyzed by qRT-PCR. One-month-old tomato plants were treated with different hormones and sampled at 1 h, 3 h, 6 h, 12 and 24 h to test the responsiveness of SlSRS genes to different stresses. The data were converted to log2FC (FC, fold change) and the heat map was used to represent the responsiveness of the SlSRS genes.
Solanum lycopersicum Genomic data were obtained from Phytozome (https:// phytozome-next. jgi. doe. gov/)  [34] , including Solanum lycopersicum ITAG4. 0  [35] , Arabidopsis thaliana TAIR10  [36] , Glycine max Wm82. a4. v1  [37] , Solanum tuberosum v3. 0  [38] , Oryza sativa v7. 0  [39] , Zea mays Zm-B73-REF-ERENCE-NAM-5. 0  [40] , Marchantia polymorpha v3. 1  [41]  and Physcomitrella patens v3  [42]. The hidden Markov model (HHM) of Domain of Unknown Function 702 (DUF702) (PF01542) was downloaded from Pfam database (InterPro, https://www. ebi. ac. uk/interpro/)  [43] , which was used to search for the putative tomato SRS proteins on HMMER web server (http://hmmer. org/)  [44]. Arabidopsis and rice SRS protein sequences were used as queries to carry out BLASTP search against tomato proteins.
The sequences with E-value < 1e 10 were selected for further analysis. Subsequently, all the candidate protein sequences were further examined by Conserved Domain Database (NCBI-CDD) (https://www. ncbi. nlm. nih. gov/Structure/cdd/wrpsb. cgi/)  [45]. In addition, ProtParam tool of Expasy web server (https:// web. expasy. org/protparam/)  [28]  was used to analyze the physical and chemical parameters of the tomato SRS proteins. MUSCLE was used for multiple sequence alignment of the given protein sequences. After being processed by trimAL, the alignment was used to construct the phylogenetic tree by MEGA X  [46]  with the neighborjoining (NJ) method with the bootstrap test replicated 1,000 times, the Poisson model and pairwise deletion. Finally, Interactive Life Tree (iTOL, https://itol. embl.
Data Availability Genomic data were collected from Phytozome (https://phytozome-next. jgi. doe. gov/), including Solanum lycopersicum ITAG4. 0, Arabidopsis thaliana TAIR10, Glycine max Wm82. a4. v1, Solanum tuberosum v3. 0, Oryza sativa v7. 0, Zea mays Zm-B73-REFERENCE-NAM-5. 0. Cis-elements were obtained from PlantCARE database (http://bioinformatics. psb. ugent. be/webtools/plantcare/ html/). The SRS family expression data were generated by qRT-PCR and were available from the corresponding authors when needed. All other data supporting the results are included within the article and its Additional files.
Although less severe, this disease bears strong clinical overlap with the syndrome reported in this study. The exon 12skipping event prompted us to search for the presence of possible TAPT1 intronic mutations. Targeted Sanger sequencing for this exon and its neighboring nucleotides (~300 bps) revealed a deep intronic single nucleotide polymorphism (NM_153365. 3, c. 1237-52 G>A) within intron 11 that entirely segregated with the disease in all available family members  (Figs 2D and 3A). This variant was not reported in the Genome Aggregation Database (gnomAD). While there were similar variants described to occur in its vicinity, none of them were homozygous. Together, these findings indicate that the c. 1237-52 G>A mutation within TAPT1 intron 11 most likely caused the disease for the 6 affected children.
3 (F1) and V. 5 (F1) individuals are shown. The mutation is present in heterozygosis in IV. 3 (F1) (unaffected mother) and in homozygosis in V. 5 (F1) (affected patient). A Schematic representation of TAPT1 and TAPT1-AS1, indicating the causative intronic mutation (c. 1237-52 G>A). The transcription start sites and the direction of transcription are indicated by arrows. Scale bar represents 2 kb. B Diagram showing the branchpoint scores for the target c. 1237-52 position and flanking nucleotides in TAPT1 intron 11 in both WT (+/+) and patient cells (À/À), as obtained from the RNABPS  (Nazari et al, 2018) , LaBranchoR  (Paggi & Bejerano, 2018)  and BPP (Zhang et al, 2017a) softwares. High branchpoint scores were predicted for the G>A transition in the patient cells using the RNABPS and LaBranchoR methods.
Following RNA extraction and cDNA synthesis, RT-PCR was done using vector specific primers (F: SD6 forward; R: SA2 reverse). The 263 bp PCR product in the empty vector showed internal splicing between SDv and SA2 exons. In c. 1237-52 G>A mutant minigene construct, the majority of splicing products had a size of 263 bp due to the aberrant exon 12 skipping while in the rescued construct, most of the transcripts had the expected size of 340 bps. Direct Sanger sequencing confirmed the identity of the normal and exon-12 skipped products. Source data are available online for this figure.
3 (F1)) and homozygous (V. 1 (F1), V. 5 (F1) and IV. 1 (F2)) individuals and two different commercial antibodies (top: Sigma, HPA042567; bottom: Sigma, HPA048658). Results show a complete absence of TAPT1 protein in patient samples. GAPDH was used as a loading control. D qPCR analysis of TAPT1 expression in 3 WT (WT1, WT2, WT3) and 3 affected (V. 1 (F1), V. 5 (F1), IV. 1 (F2)) primary fibroblasts treated with cycloheximide (CHX). CHX was used to block nonsense mediated decay (NMD). Our results showed a time dependent increase in the level of TAPT1 transcripts in all 3 patient cells while  secretory pathway was supported by TAPT1 partner protein SUCO localization in various human cell lines  (Hein et al, 2015). We then carried out a series of functional tests by comparing WT and patient fibroblasts in order to gain a better understanding of TAPT1's cellular function.
Transcriptional compensation of ECM and collagen-related genes in parent cells In heterozygous parent cells, the TAPT1 protein level was reduced but not completely abolished as in patient cells (Fig  4C ). The remaining level of TAPT1 seemed to be sufficient for cell functions, thereby protecting the parents from a clinical manifestation. To gain insights into the underlying protection mechanism, we performed SI-NET-seq experiments on parent fibroblasts. SI-NETseq revealed that nascent transcription was changed for fewer genes (170 genes) in parent cells (Fig EV5C ; Dataset EV3) when compared with patient cells (317 genes). Surprisingly, an integrated Reactome pathway analysis of parent and patient fibroblasts uncovered a high overlap of affected pathways (Fig  EV5D ). However, from the 24 genes that were linked to the pathogenic pathways (Fig EV5E ), only five genes (COL3A1, COL5A2, COL8A1, COMP, ELN) exhibited a significant change in nascent Pol II transcription in both parent and patient cells.