dataesr/scientific-paragraphs-categorization huggingface dataset:
These geometries were equally distributed into the different conformers. For example, in the case of molecules having equilibrium geometries in the ground and first excited-state or having only one cis and one trans isomers, 50000 geometries were sampled from each configurational subspace (S 0 /S 1 and cis/trans). Urocanic acid is the molecule with the most fragmented sampling in the database, 12500 geometries per conformation. To parallelize and speed up the sampling process, the configurational subspace of each molecule was further divided into smaller datasets of equal size. For all molecules with two equilibrium geometries, for instance, the 50000 geometries generated for each configurational subspace were sampled from the Wigner distribution in five chunks of 10000 geometries. In this case, a different random seed was used to generate each smaller dataset to guarantee the statistical diversity of the sampled geometries.
The broad span of configurations for each molecular dataset can be observed in the clustering structure of the principal component analysis (PCA) projections shown in Fig. 3 , which were generated using the unique elements of the nucleus-nucleus distance matrix (off-diagonal lower triangle) as a descriptor. The Wigner sampling calculations used to build all datasets were performed with the initial conditions program integrated into the Newton-X (version 2. 2-B08) package  51. To extend the coverage of the configurational space beyond the vibrational degrees of freedom probed by Wigner sampling, we augmented the datasets by performing a series of geometry interpolations between every possible combination of stable conformers. The method we used for the interpolations acts directly on the Cartesian coordinates space by finding the optimal geodesic curve on a Riemannian manifold as detailed in reference 39.
As demonstrated in that paper, these geodesic curves capture the topological structure of the molecular PES, thus providing a good approximation for the true minimum energy path between reactants and products in chemical reactions. In our case, the geodesic paths create a smooth link between two different conformational spaces (e. g. , from cis to trans conformations) by acting mainly on the molecular rotational degrees of freedom. In this way, we included in the datasets molecular structures far from equilibrium, corresponding to regions near transition states on the PESs, which are inaccessible via Wigner sampling. The data augmentation process used here consists of two steps. First, we randomly picked two molecular geometries from different conformational spaces, corresponding to the endpoint geometries in the geodesic curve search.
Then, the Python program provided by reference 39 [https://github. com/virtualzxnad/geodesic-interpolate] was used to generate 20 interpolated geometries along the geodesic path. This procedure was repeated multiple times for each configurational dataset of WS22 until 20,000 new molecular geometries have been sampled. Note that the interpolated geometries are distributed within all the unique pairs of conformation types. For example, in the case of the alanine dataset, we have six possible combinations of conformers where two of them, M1 and M3, correspond to relatively similar structures (see the overlapping clusters in Fig. 3 ). Thus, for the M1-M3 path, we generated 2000 molecular geometries, while the other five combinations of conformers contributed with 3600 interpolated geometries. This counting of the number of interpolated geometries per conformation pair can be easily done by selecting the specific string identifier in the CONF variable of the dataset, as described in the Data Records section (see Table  2 ).
Single-point DFT calculations Once the molecular geometries have been generated, we moved to the last and most computationally intensive step of our database construction pipeline, which is the single-point (SP) electronic structure calculations. In total, we performed 1. 18 million SPs to label (using ML terminology) all molecular geometries in the database with the respective quantum chemical properties. The atomic forces were calculated for every geometry in the datasets via analytic derivatives by requesting the Force keyword in the input of the electronic structure program. These calculations were carried out with the same DFT method and basis set as in the geometry optimizations and frequency calculations, i. e. , PBE0/6-311G*.
Data Records All the relevant electronic structure information available in the Gaussian 09 output files are collected and stored in independent NumPy npz format for each compound in the database. This is a compact binary file that internally has a dictionary-like structure whereby the molecular configurations and the calculated chemical properties can be accessed by querying the dictionary with a string key used as a shortcut for the property's name. Each query returns a NumPy array object with the number of molecular configurations corresponding to the first dimension of the array. For example, using the key R as the dictionary entry, one can gather information on all molecular geometries stored in the dataset as a NumPy 3D tensor, where the first dimension corresponds to the sample indices and the other two dimensions store the XYZ Cartesian coordinates (in Angstroms) of each sample (see Fig.
5 ). A full description of the data records with information on the dictionary entries, units, and NumPy array shape is provided in Table  2. The recorded data corresponds to all single-point calculations performed for Wigner sampled geometries and interpolated geometries as well as the minimum energy structures of each conformation obtained via geometry optimization. To access the minimum energy structures in the full dataset, one can search for the index corresponding to the lowest energy value in the E array, as exemplified in the Python code of Fig. 5. For completeness, these minimum energy structures corresponding to the optimized geometries for each conformation are made available in the WS22 database as separate XYZ files per molecule. In addition to the calculated quantities, we also provide an array of strings that tags each geometry with a label corresponding to the conformation identifier (CI) and a dash separated pair of CI labels in the case of the interpolated geometries.
Conformational diversity Our database was designed to cover as wide as possible a range of the conformational degrees of freedom of flexible organic molecules without relying on expensive ab initio MD simulations. To this end, we combined a Wigner sampling approach-from which we obtain a dense grid of geometries spread around the equilibrium regions of the PES(s)-together with a geometry interpolation strategy to construct the transition paths between every pair of conformations, thus also covering critical points of the PES far from the equilibrium. The configurational diversity of the datasets generated with our sampling strategy is testified by the two-dimensional projection of the geometries space using principal component analysis (PCA), as shown in Fig. 3. In fact, one can see in the PCA diagram the formation of well-separated clusters for most of the molecular datasets, which indicate the successful sampling of largely different regions of the PESs.
Moreover, by including interpolated geometries in the dataset (see gray stars in Fig. 3 ), we ensure that important rotational degrees of freedom non-accessible via Wigner sampling are also covered. These interpolated geometries (20,000 points, corresponding to ∼16% of each dataset) create a smooth path connecting different conformational spaces, thus mapping energy barrier regions of the PES that might require long MD simulations to be satisfactorily sampled.
Energy and forces coverage In addition to the demonstrated structural diversity of our database, we also validated the broad coverage of the PES in our dataset with respect to the potential energy and atomic forces by comparing the present results with the MD17 database. Taking toluene as an example, we can see from Fig. 4  that the energy distribution in the WS22 dataset obtained via Wigner sampling is not only broader than that of MD17 but is also centered on a much higher energy value, 44. 8 kcal mol -1 , and 20. 0 kcal mol -1 , respectively, as given by the median values. The spread in the energy distribution measured by the standard deviation (excluding outliers) is almost three times larger in the WS22 dataset, 12. 9 kcal mol -1 than in MD17, 4. 8 kcal mol -1. This difference is a consequence of the quantum effects on the vibrational amplitudes that is present in the Wigner distribution by construction but absent in the ab initio MD simulations used to build the MD17 dataset  48.
Indeed, a key distinction between sampling the nuclear coordinates via quantum distributions and classical dynamics is the amount of energy deposited in each degree of freedom. In the quantum distribution within harmonic approximation at 0 K, this energy is the harmonic zero-point energy. For a normal mode vibrating at 1000 cm -1 , it amounts to 0. 12 eV. On the other hand, in classical dynamics of a canonical system equilibrated at temperature T, the equipartition principle ensures that each degree must have k B T , which corresponds to 0. 04 eV at 500 K (as employed in the MD17 dataset). Thus, nuclear geometries sampled from a quantum distribution have a much broader distribution than when sampled from classical dynamics. A similar tendency is observed when comparing the data distribution in the atomic forces space.
In this case, the histograms in Fig. 4  show that the norm (Frobenius) of the atomic forces matrices has a broad Gaussian shape centered on 298 kcal mol -1 Å -1 in the WS22 dataset, while in MD17, the data is narrowly distributed around 178 kcal mol -1 Å -1. As for the standard deviation, we found the values of 29 kcal mol -1 Å -1 for MD17 and 57 kcal mol -1 Å -1 for the WS22 dataset. It is worth stressing that, for the toluene dataset, we have considered only one equilibrium geometry (S 0 minimum) as a reference to apply the Wigner sampling. Hence, the coverage of chemical properties in the other nine datasets composing the WS22 database should be even larger since our sampling strategy is applied to different conformational spaces.
Usage Notes As part of our database, we provide an interactive dashboard written in Python that is publicly available in the Streamlit cloud [share. streamlit. io/maxjr82/ws22-dashboard/main/app/dashboard. py]. The dashboard was designed to facilitate a preliminary data exploration and visualization of the main statistical features contained in the molecular datasets. Using this tool, the users can also visualize the molecular structures and download the geometries in the standard XYZ format.
Acknowledgements MPJ and MPJ are financially supported by the  European Union 's  Horizon 2020 research and innovation program  under  ERC advanced grant  (grant agreement No  832237 , SubNano). They also acknowledge the  Centre de Calcul Intensif d'Aix-Marseille. POD acknowledges funding by the  National Natural Science Foundation of China  ( No 22003051 ) and the  Fundamental Research Funds for the Central Universities  ( No 20720210092 ).
Code availability All the data annotations with the chemical properties were obtained from density functional theory calculations performed with the Gaussian 09 program  43. The set of molecular geometries used as input for these calculations was generated using the Newton-X (version 2. 2-B08) package  51  [https://newtonx. org/] for Wigner sampling together with a Python code for geometry interpolations [https://github. com/virtualzx-nad/geodesic-interpolate] as described in the Methods section. Both programs are open access. A custom Python script was written to extract the relevant information from the Gaussian 09 output files and it is publicly available in the GitHub repository https://github. com/maxjr82/QCDP. Finally, the Python script to perform the dimension reduction of the molecular geometries with the PCA method is available to download from https://github.
3i ). In silico analysis of the DGAT2 mRNA secondary structures using the RNAfold Web server  27  predicted that DGAT2 contains IRE1α consensus sequence 5′-CUG↓CAG-3′ at the position 258-263 of the coding sequence, located within a stem-loop secondary structure similar to the one observed in XBP1 (Fig. 3i, j ). Therefore, we tested the ability of IRE1α to directly cleave DGAT2 mRNA (CCDS21477. 1). In vitro processing of DGAT2 and XBP1 transcripts by recombinant hIRE1α (465-977) resulted in the appearance of a low molecular weight cleavage fragment, detected by agarose gel electrophoresis (Fig. 3k, l ). Inhibition of IRE1α or introduction of a single point mutation (c. 260G>A), in the DGAT2 transcript prevented IRE1α cleavage of DGAT2 mRNA (Fig. 3k ). Next, we generated MDA-MB-231 cells stably overexpressing either the FLAG-tagged wild type DGAT2 (WT DGAT2) or the RIDD resistant (c.
05). b Summary of a functional analysis of DEGs at 8 and 24 h time points. Overrepresented Gene Ontology (GO) terms were summarized into broader biological groups based on semantic similarity with cateGOrizer to facilitate biological interpretation. c Genes termed significantly changing at either of the two time points and annotated to lipid metabolism on the GO database. d Heatmap displaying average expression levels for DEG annotated to TAG metabolism in the GO database. The expression for each gene (row) was centered and scaled so the mean expression is zero and standard deviation is one. e Schematic representation of TAG metabolism, showing TAG metabolism-regulating genes whose expression is altered by IRE1α. G3P glycerol-3phosphate, LPA lysophosphatidic acid, PA phosphatidic acid, DAG diacylglycerol, TAG triacylglycerol, FFA free fatty acid.
Source data are provided as a Source Data file. Lipid extraction and quantification by LC-MS. Cells were seeded at density 10 × 10 4 , 6 × 10 4 and 3 × 10 3 cells/mm 2. Cells were collected at 24, 48, and 72 h after treatment, washed in ice-cold phosphate-buffered saline (PBS) and snap frozen in liquid nitrogen. Samples were ultrasonicated for 15 min in a 1 ml mixture of methyl-tert-butyl ether (MTBE)/methanol (3:1 v/v) at -15 °C. 2-diheptadecanoyl-sn-glycero-3-phosphocholine was added as an internal standard. Phase separation was obtained by adding 500 µl of water:methanol (3:1 v/v) and centrifugation at 20,000×g for 5 min at RT. The upper (organic) phase was moved to a new microfuge tube and evaporated in a vacuum centrifuge. After drying, the samples were dissolved in acetonitrile. The samples were measured with a Waters ACQUITY reversed phase ultra performance liquid chromatography (RP-UPLC) coupled to a Thermo-Fisher Q Exactive Plus mass spectrometer (UPLC-MS).
Data dependent fragmentation (dd-MS/MS) parameters for each polarity as follows: TopN: 10, resolution 17,500 units, maximum injection time: 25 ms, automatic gain control target: 5e5 and normalized collision energy of 20 and 25 (arbitrary units) in positive polarity. TopN: 5, resolution 17,500 units, maximum injection time: 80 ms automatic gain control target: 5e5 and normalized collision energy of 20 and 30 (arbitrary units) in negative polarity. The instrument was externally calibrated to <1 ppm using ESI positive and negative calibration solutions (Thermo Fisher Scientific, Waltham, MA, USA). Data processing and analysis. Extraction of the LC-MS data presented in Fig. 1 , Supplementary Fig. 1  and 2a-c  was accomplished with the software REFINER MS ® 10. 5 (GeneData, http://www. genedata.
com). After extraction of the peak list from the chromatograms, the data were processed, aligned and filtered with in-house software. Only those features were selected which were present in at least four out of the five replicates of at least one of the sample groups. The annotation of the extracted features was performed by querying the metaSysX in-house database (619 unique lipid species). The database contains mass-to-charge ratio (m/z) and retention time information of complex lipids that were previously annotated based on measurements of reference standards or a MS/MS spectra, monoisotopic mass and a retention time shift of consecutive m/z values. A 5 ppm and 0. 06 min deviation from the database mass-to-charge ratio and retention time, respectively, were used as matching criteria. Additionally, lipid annotation was extended with MS/MS fragments spectra of pooled samples using metaSysX-developed R-based algorithm.
Pooled samples were measured in dd-MS2 Top 3 mode (Data Dependent tandem mass spectrometry) with the settings: Full Scan MS mode (mass range [100-1500]), NCE 25 (Normalized Collision Energy). Signal intensities were converted into relative abundances by MSTUS normalization  48  , filtered (unannotated, detected in at least half of samples), corrected for batch effect with sva package, log 2 transformed and row-scaled across samples before performing exploratory multivariate analysis. All data transformations and visualizations were performed in R 4. 0. 4. Scripts available at: https://github. com/ASAGlab/. For data presented in Fig. 4a -d peak detection and integration from Raw data were processed using Compound Discoverer 3. 0 (Thermo Fisher Scientific, Waltham, MA, USA). Files were also converted to mgf format using MSConvert software and MS2 files were searched against LipidBlast database  49  (408 unique lipid species) using LipiDex software (Supplementary Data 1 and 2).
RNAseq libraries were prepared and sequenced unidirectionally (50 nucleotides read length) with a read depth of 25-50 million reads/library in Illumina sequencing platform at the EMBL Genomics Core Facility in Heidelberg. RNA-seq data analysis. Resulting sequenced libraries were assessed in a quality control step with FastQC (available from https://www. bioinformatics. babraham. ac. uk/projects/fastqc/), followed by adapter sequences trimming with TrimGalore (http://www. bioinformatics. babraham. ac. uk/projects/trim_galore). Reads were aligned with reference genome using STAR. Reads/counts per gene were summarized with FeatureCounts  50. The computational-heavy steps were performed using the cloud-computing infrastructure of SevenBridges. Genes with low counts were filtered out with limma  51.
Counts were normalized between samples by the method of trimmed mean of M-values (TMM), dataset corrected for batch effect with sva package  52. Statistical inference of differentially expressed genes was carried out with edgeR  53  by performing pairwise comparison between DMSO and MKC8866 treated samples at both time points. Genes with a p value below 0. 05 and a minimum log 2-fold change of the mean of 0. 25 were considered as significantly changing. Ggplot2, pheatmap packages at R/Bioconductor were used for graphical representation. Functional analysis of the significantly changing genes was performed with BioInfominer, utilizing Gene Ontology Biological Processes annotation corpus  24. Overrepresented GO terms were summarized into broader biological groups based on a semantic similarity with cateGOrizer  25.
All data transformations and visualizations were performed in R 4. 0. 4. Scripts available at: https://github. com/ASAGlab/. TurboFectin transfection reagent (OriGene #TF81001) was used according to the manufacturer's instructions with a 1:2 ratio of DNA to transfection reagent. Pooled cells were selected using 500 ng/ml puromycin (Sigma, P8833). Single clones were isolated and positive clones were confirmed by Western blot. In vitro DGAT2 mRNA cleavage assay. Mito stress test. Cells were seeded at a density of 8 × 10 3 cells/mm 2 and incubated with drugs for up to 6 days. MDA-MB-231 cells were reseeded and treated at day 3 and 5. At day 5, MDA-MB-231 cells were reseeded into XFp Cell Culture Miniplates (Agilent, 103025-100) at density 8 × 10 3 cells per well. HCC1806 and MCF10A were treated every second day and reseeded at day 4.
XF Cell Mito stress test (Agilent, 103010-100) was performed at day 6 according the manufacturer's protocol. The oxygen consumption rate (OCR) was measured using a Seahorse XFp analyzer (Agilent) under basal conditions and after injection of 1 µM oligomycin, 0. 5 μM carbonyl-cyanide-4-(trifluoromethoxy)phenylhydrazone (FCCP), and 0. 5 µM rotenone/antimycin A. Mitochondrial respiration parameters were calculated using the Seahorse Bioscience Wave software. The calculated parameters included: basal respiration (last OCR measurement before first oligomycin injection minus minimum OCR measurement post rotenone/antimycin A injection), ATP production (last OCR measurement before first oligomycin injection minus minimum OCR measurement after oligomycin injection), maximal respiratory capacity (post FCCP respiration minus OCR after rotenone/antimycin A injection), spare respiratory capacity (SCR, maximal respiration/basal respiration × 100), nonmitochondrial oxygen consumption (minimum rate measurement after rotenone/ antimycin A injection) and proton leak (minimum rate measurement after oligomycin injection minus non-mitochondrial respiration).
The percentage of Sytox Greenpositive cells was quantified using IncuCyte v2019B software. Statistical analysis. Assumptions concerning the data (normal distribution and similar variation between experimental groups) were examined for appropriateness before statistical tests were conducted. Statistical analysis was carried out using one-way or two-way ANOVA with Bonferroni multiple comparisons post hoc test, unpaired two-tailed Student's t-test and one-sample t-test (comparison value of zero) as indicated. Values with p < 0. 05 are considered statistically significant.
Acknowledgements This study utilized  NUI Galway Genomics and Screening Core Facility , which is supported by funds from  NUI Galway ,  Science Foundation Ireland , the  Irish Government 's  Program for Research in Third Level Institutions , Cycle 5 and the  European Regional Development Fund. We also would like to thank the  Metabolomics unit of the Cancer Research UK Beatson Institute , with particular thanks to  Dr. David Sumpton. This work was funded by  Precision Oncology Ireland , which is part-funded by the  Science Foundation Ireland (SFI) Strategic Partnership Program  (grant number  18/SPP/3522 ),  EU   H2020 MSCA RISE-734749  (INSPIRED),  EU   H2020 MSCA ITN-675448  ( TRAIN-ERS ) to A. M. G. and A. S. , SFI  Industry Fellowship  to K. M. ( 18/IF/6247 ). We are grateful to the contributions of all our team members, in particular  Dr.
Data availability All data supporting the findings of this study are available at: https://github. com/ ASAGlab. https://github. com/ASAGlab/Regulated-IRE1-dependent-decay-RIDDmediated-reprograming-of-lipid-metabolism-in-cancer or from the corresponding authors upon request. The RNA sequencing data have been deposited and are available in the Gene Expression Omnibus (GEO) database under accession number GSE176454. Metabolomics data are included in this article as excel tables in Supplementary Data. Source data are provided with this paper.
Discussion Here, we studied the risk of incident SARS-CoV-2 infection among HCWs residing in quarantine hospitals, relying on detailed longitudinal data collected during the first wave of COVID-19 in Egypt. We found that most diagnosed infections (70%) occurred during what we defined as healthcare-associated outbreaks (as compared to isolated infections). We observed high variability in nosocomial incidence, ranging from 0. 45 to 2. 25 infections per 100 PD across the three hospitals. Using a model-based approach, we estimated the risk of both diagnosed and undiagnosed SARS-CoV-2 infections among HCWs, by hospital and by hospital unit (ICU and non-ICU). We further estimated that a substantial proportion of infections may have remained undetected, ranging from 45. 0 to 59. 2% across the three hospitals.