dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Description Data provided in this article consists of near infrared spectra of 33 organic waste samples of various biochemical composition, acquired on four different optical systems: an FT-NIR laboratory spectrometer (NIRFlex N-500, Buchi), a FT-NIR compact spectrometer (Rocket, Arcoptics) with two configurations (a contact immersed probe and a polarized light system PoLiS), and an FT-NIR micro spectrometer (Neospectra-micro, Si-Ware) based on MEMS FT-NIR technology. Each sample was scanned in triplicates on each optical system. Depending on the system, absorbance or reflectance spectra were obtained ( Fig. 1  ). Each collected signals type was put into a separate file: the signals from the laboratory spectrometer ("lab_spectrometer_spectra_absorbance_nm. tab "), the signals from the compact spectrometer with the immersed probe ("immersed_probe_spectra_absorbance_nm.
Finally, a python script is provided to show how a partial least squares regression (PLS-R) can be applied on this data. The script runs a train/test random split, followed by a k-fold crossvalidation on train dataset to find optimal latent variable number of the PLS model and finally plots predictions and observed values on test set to evaluate obtained model performance. The python script name is "data_usage_example. py ", and a documentation on how to use this script can be found in the text file "README. md ". Fig. 1. Raw spectra obtained on the 33 organic waste samples with A) the laboratory spectrometer (absorbance, in nm, referred as A lab ), B) the compact spectrometer with an immersed probe (absorbance, in nm, referred as A ip ), C) the micro spectrometer (absorbance, in nm, referred as A μ), and the spectra obtained using the compact spectrometer plugged to the polarized light system PoLiS with D) the single scattered signals (reflectance, in nm, referred as R ss ), E) the multiscattered signal (reflectance, in nm, referred as R ms ) and F) the total backscattered signal (reflectance, in nm, referred as R bs ).
Sned1 is broadly expressed during development, in particular in neural-crest-cell and mesoderm derivatives  (18, 19) , and interrogation of RNA-seq databases indicate that SNED1 is also expressed in multiple adult tissues although at a very low level1. A decade after the cloning of this gene, we identified SNED1 in a proteomic screen comparing the ECM of poorly and highly metastatic mammary tumors and further reported the first function of this protein, as a promoter of mammary tumor metastasis  (20). 1.
Unpublished data from the Naba lab Intrigued by this novel protein, we sought to identify its physiological roles. To do so, we generated a Sned1 knockout mouse model and demonstrated that Sned1 is an essential gene, since knocking it out resulted in early neonatal lethality  (19). Furthermore, we showed that Sned1 contributed to the formation of craniofacial and skeletal structures, since the few surviving knockout mice presented craniofacial abnormalities, were smaller in size, and had shorter long bones  (19). However, the cellular and molecular mechanisms by which SNED1 regulates breast cancer metastasis or the development of craniofacial and skeletal structures remain unknown. Interrogation of the literature has revealed that the SNED1 gene is found in large -omic datasets as dysregulated in different pathophysiological contexts or in response to various stimulations and in different in-vivo or in-vitro systems, hinting at a broader role for SNED1  (19).
Yet, mining of gene expression, protein structure, and pathway databases revealed a paucity of data available on SNED1. This lack of tools and framework to study SNED1 creates an important gap in knowledge, given its pathophysiological significance. Here, we used a combination of computational approaches and classical biochemical assays to analyze in depth the amino acid sequence and protein domains of SNED1, predict its secondary and tertiary structures and post-translational modifications, and build the first ab-initio 3D model of this protein and of its N-terminal region that contains a NIDO domain, a domain only found in 4 other human proteins. We further predicted its domain-and protein-level interaction networks which revealed the potential roles for SNED1 in multiple signaling pathways and biological processes.
SNED1 is a glyco-phosphoprotein A key feature of ECM proteins is their high level of post-translational modifications (PTMs), including glycosylations  (38)  and phosphorylations (39-41), with these modifications potentially further supporting protein-protein interactions and scaffolding (especially of mineralized tissues in the case of phosphorylations). We thus used several algorithms and queried multiple databases to determine whether SNED1 was predicted to be subject to PTMs (Supplementary Table  S2 ) and further tested experimentally our findings.
Glycosylation and attachment of glycosaminoglycans 13 N-glycosylation sites were predicted in the sequence of human SNED1, including two sites in the Nterminal fragment of SNED1 at Asn145 and Asn204 (Figure  4A  and Supplementary Table  S2A ) as well as 97 O-glycosylation sites (Supplementary Table  S2B ). dbPTM, the database compiling and curating experimentally verified PTMs, listed Asn886 and Asn977 as being N-glycosylated in murine SNED1 and Ser1247 as being O-glycosylated (Supplementary Table  S2D ). Of note, NetOGlyc did not predict the glycosylation of Ser1247 while it was detected experimentally. We thus sought to determine whether SNED1 produced in our mammalian culture system was glycosylated. Apparent Mw shift upon PNGase F treatment, which removes N-linked oligosaccharides, was calculated in three independent experiments.
Phosphorylation Sequence analysis also revealed 133 predicted phosphorylation sites in SNED1 (Supplementary Table  S2C ), including 22 residues within the N-terminal domain of SNED1, and 8 within the NIDO domain. However, it is worth noting that most of these sites are predicted by intracellular kinases not known to modify secreted ECM proteins (Supplementary Table  S2C ). However, interestingly, 6 residues are predicted to be phosphorylated by casein kinase II which is known to phosphorylate ECM proteins including collagen XVII (ecto-caseinase 2) fibronectin, and vitronectin (41). Through database interrogation, we found experimental evidence showing the phosphorylation of twelve of these residues: 5 serine, 5 threonine, and 2 tyrosine residues, none of which lie within the N-terminal domain of SNED1 (Supplementary Table  S2D ).
Domain-domain interaction network of SNED1 A domain-domain interaction network of fl-SNED1 was built using 3did, the database of 3-dimensional interacting domains, and returned 106 unique interactions (Figure  5  and Supplementary Table  S3 ) when queried for domains present in SNED1. 49 domains were predicted to bind to the fibronectin type III domains, 24 to the sushi domain, 22 to EGF-like domains, 11 to calcium-binding EGF-like domains, and 2 to the follistatin domain (Figure  5 ). Two interactions were retrieved twice (Sushi/EGF and EGF/EGF_CA). Of note, the lack of knowledge about the NIDO domain was further exemplified here, since NIDO is absent from the database 3did and no other protein domain has ever been reported to interact with it.
Prediction of the protein-level SNED1 interactome The query of the interaction databases MatrixDB  (42)  and IntAct  (43)  returned only one partner of SNED1, the estrogen receptor beta  (44)  (see below). This prompted us to use computational approaches to predict interactions established by SNED1 with other proteins (see Materials and Methods). We focused on secreted proteins and membrane proteins, which resulted in the prediction of 114 unique interactions by at least one algorithm, including SNED1 auto-interaction (Figure  6A ). More than half of the protein partners of SNED1 were annotated as membrane proteins in UniProtKB (66/114, 57. 9%). Of these, 11 (ITGA1, ITGA10, ITGA11, ITGA4, ITGA7, ITGB1, ITGB2, ITGB3, ITGB4, ITGB5, ITGB7) were integrins, which constitute the major class of ECM receptors  (45).
Potential binding partners of SNED1 are involved in multiple signaling pathways The in-silico interaction network of SNED1 was then analyzed using Reactome (52) to identify associated biological pathways. No annotation could be retrieved for SNED1 itself, further highlighting the critical gap in knowledge about this protein (Reactome, version 73, released  June 17, 2020). The Reactome database included information on 94 of the 114 predicted SNED1 partners, and since the majority of them are either part of the matrisome or are transmembrane receptors, the processes most over-represented in SNED1's network were "signal transduction", "cell-cell communication", "ECM organization" and "developmental biology" (Figure  6B ). More specifically, the predicted SNED1 interactors were found to be part of, or to contribute significantly to, more defined pathways including "integrin cell surface interaction" and "ECM proteoglycans" (Figure  6C ).
Size-Exclusion Chromatography -Multi-Angle Laser Light Scattering (SEC-MALLS) Human fl-SNED1 and the N-terminal fragment of SNED1 were analyzed by SEC-MALLS on the SWING beamline at SOLEIL synchrotron (Saint-Aubin, France) at 20°C with a Wyatt Dawn Heleos light scattering instrument equipped with an Optilab rEX refractometer and a spectrophotometer for molecular mass measurements. 50 μl of the N-terminal fragment of SNED1 (13. 2 μM) and fl-SNED1 (4. 5 μM) were injected on Superdex 75 Increase 10-300 GL and Superdex 200 Increase 10/300 GL columns (GE Healthcare) respectively at a flow rate of 0. 5 ml/min at 20°C, see Figure  3B  and 3C. Data were analyzed with ASTRA software 6. 1. 7 (Wyatt Technology).
Dynamic Light Scattering (DLS) Three series of ten acquisitions were performed at 20°C and an angle of 90° for fl-SNED1 at a concentration of 4. 5 µM in HBS on a Zetasizer μV (Malvern Instruments Ltd, SOLEIL synchrotron, Saint-Aubin, France) in a disposable plastic cuvette. Data were analyzed with the Zetasizer software 7. 12 (Malvern Instruments Ltd). The theoretical hydrodynamic radii of fl-SNED1 and the N-terminal fragment of SNED1 were calculated using folded proteins parameters and the number of amino acid residues of each protein  (62).
ACKNOWLEDGMENTS SEC-SAXS ,  SEC-MALLS , and DLS experiments were performed on the  SWING beamline  ( French National Synchrotron Facility SOLEIL, Saint-Aubin, France , experiment n°20181074). The authors are grateful to  Aurélien Thureau  from  SWING  beamline and  Frank Gondelaud  ( ICBMS ,  UMR 5246, University Lyon 1 ) for their skillful assistance in SEC-SAXS/MALLS experiments and data interpretation. The authors are also grateful for the  UMS 3444/US8 Facility (Lyon, France)  for providing access to the Chirascan system. AN would also like to thank  Dr. Richard Hynes  ( Massachusetts Institute of Technology ) for his kind gift of the anti-SNED1 antibody and for supporting the initial phase of the development of this project.
Analytical methods During sample preparation, a colloidal silica-water solution (mixed in a 70:30 proportion) was used for 5 min following mechanical polishing, in order to reduce surface damage. Thin sections were then coated with a ~6-nm-thick carbon layer to prevent charging during analysis. Crystallographic orientations of minerals were acquired using an HKL NordlysNano EBSD detector attached to a Zeiss EVO MA15 scanning electron microscope in the Geochemical Analysis Unit (GAU) at GEMOC/CCFS, Macquarie University, Australia. For all analyses, a 15-μm step size and a 20-kV acceleration voltage were used; working distance ranged between 15 and 20 mm. Automatic indexing was performed using the AZtec software (Oxford Instruments). The HKL software was used to perform a standard noise reduction and to extrapolate missing data with at least 8, 7, 6 and then 5 identical neighbours.
Resulting EBSD data were then processed in MATLAB using the MTex toolbox  (Bachmann et al. 2010b (Bachmann et al. , 2011)). A value of 10°as the cut-off angle for the determination of the grains was used. Further details on the data processing protocol in MTex can be found in  Henry et al. (2017)  and  Henry (2018). To avoid oversampling of large grains, pole figures were plotted with one point per grain on a lower-hemisphere, equal-area stereographic projection. For strongly serpentinised samples (3 samples), pole figures were produced using every available measurements, which proved to yield results comparable to those of a standard one-point-per-grain model  (Achenbach et al. 2011 ) (see Supplementary Material A for the number of grains and the type of representation). When twinning in cpx was detected during data processing, pole figures of cpx were drawn using all the available measurements.
Acknowledgments We are grateful to  M. Bebbington  and  F. de Parseval  for providing high-quality polished thin sections for EBSD measurements. We would also like to thank  T. Murphy  for his assistance during lab work and  Patrick Launeau  for stimulating discussions on magmatic processes. We would like to thank  Károly Hidas  and four anonymous reviewers who greatly contributed in improving the quality and flow of the manuscript. This work was supported by the  Australian Research Council  grant for the  ARC Centre of Excellence for Core to Crust Fluid Systems (CCFS) , a  Macquarie University International Postgraduate Scholarship  (H. H. ), Macquarie postgraduate funds (H. H. ), and  CNRS-INSU funds  ( UMR 5563 , Géosciences Environnement Toulouse). The analytical data were obtained using instrumentation funded by  DEST Systemic Infrastructure Grants ,  ARC LIEF ,  NCRIS/AuScope , industry partners and  Macquarie University.
Neuro-Oncology Advances trials to test ONC201 in pediatric patients diagnosed with H3K27M-mutant DMG, including DIPG (NCT03416530). However, access to these trials is limited almost exclusively to patients in the United States or Japan. Signals of efficacy for patients diagnosed with DIPG receiving ONC201 have resonated throughout the international DIPG community, partly due to an alternative access pathway available under German compassionate care regulations. However, published details regarding the chemical characteristics of the German synthesized version of ONC201 (henceforth referred to as "GsONC201") are lacking, thus highlighting the critical necessity to characterize the authenticity of GsONC201, particularly given earlier reports of the availably of an inactive [4,3-d] linear isomer of ONC201.
High-Resolution Tandem Mass Spectrometry High-resolution tandem mass spectrometry was performed on GsONC201 and ONC201 (Oncoceutics) using direct infusion into an accurate mass Thermo Scientific Q Exactive Plus mass spectrometer (Thermo Fisher Scientific). Samples were resuspended in 1 mL of dimethyl sulfoxide (DMSO), diluted 1 in 100 in 50:50 methanol:acetonitrile/0. 1% formic acid, and analyzed using a resolution of 140 000 at 200 m/z for both MS1 and MS2. Data analysis was performed using Mass Frontier 7. 0 (Thermo Fisher Scientific).
Nuclear Magnetic Resonance Spectroscopy Nuclear magnetic resonance (NMR) spectroscopy was performed using either a Bruker Avance 300 (300. 13 MHz, 1 H; 75. 5 MHz,  13  C) or an Avance III 400 (400. 13 MHz, 1 H; 100. 6 MHz,  13  C) with or without a Prodigy cryoprobe CPPBBO. NMR spectra were processed using TopSpin 3. 5 software (Bruker). Chemical shifts are expressed in parts per million (ppm) on the δ scale. Chemical shifts in CDCl 3 were referenced relative to CHCl 3 (7. 26 ppm) for 1 H NMR and CDCl 3 (77. 16 ppm) for  13  C NMR and chemical shifts in (CH 3 ) 2 SO were referenced relative to (CH 3 ) 2 SO (2. 50 ppm) for 1 H NMR and (CD 3 ) 2 SO (39. 52 ppm) for  13  C NMR.
Importantly, 2-week treatment of PDX models with either ONC201 or GsONC201 decreased Ki67 staining (Figure  3Dii ), however, did not increase the cleavage of PARP and CASP3 (data not shown), analogous to in vitro results (Figure  2B -C, Supplementary Figure  4B ). Significantly, both ONC201 and GsONC201 extended the survival of PDX models compared to vehicle controls (P =. 0065 and P =. 0029, respectively), with no difference between the 2 chemicals (Figure  3Dii ).
Patient Experience Using German-Sourced ONC201 The median OS of 28 DIPG patients harboring H3K27M mutations who were not enrolled on clinical trials, but receiving GsONC201 between 2017 and 2020, was investigated. Clinical and pharmacological data were assessed, and survival was recorded as of April 20, 2021. Importantly, many other concomitant anticancer and complementary therapies were also used by these patients, therefore survival cannot be attributed to GsONC201 alone (Supplementary Table  1 ). Median OS for patients who received GsONC201 was 18 months (range 4-48 months) compared to 9-11 months historical records (P =. 0007, Figure  4A ). 2  For patients who commenced GsONC201 following radiation but prior to recurrence, median OS was also 18 months (n = 19 patients, range 4-48 months) (Figure  4B ).
Neuro-Oncology Advances Japan or gain expanded access. However, the chemical formulation GsONC201 has hitherto remained unknown. Herein, analysis of the constituents of GsONC201 capsules confirmed GsONC201 is indeed the active angular isomer (Figure  1 , Supplementary Figures  1  and 2A-C ) and not the inactive linear isomer that has plagued the use of the chemical. 14, 21 Our NMR analysis identified mannitol as the bulking agent (Figure  1B  and C ), potentially included as a sweetening agent,  33  with parents reporting GsONC201 to have a "sweet taste. " Mannitol is also under clinical investigation for the treatment of Parkinson's disease in a randomized, controlled Phase IIa clinical trial (NCT03823638) and has been used as an intravenous hyperosmolar solution to transiently permeabilize the BBB.
Prolonged and/or extreme heat has become a natural hazard that presents a significant risk to humans and the buildings, technologies, and infrastructure on which they have previously relied on to provide cooling. This paper presents a conceptual model of a resilient cooling system centred on people, the socio-cultural-technical contexts they inhabit, and the risks posed by the temperature hazard. An integrative literature review process was used to undertake a critical and comprehensive evaluation of published research and grey literature with the objective of adding clarity and detail to the model. Two databases were used to identify risk management and natural hazard literature in multiple disciplines that represent subcomponents of community resilience (social, economic, institutional, infrastructure and environment systems).
Integrative literature review A critical and comprehensive evaluation of published research and practice was undertaken with the objectives of (a) adding clarity and detail to various components of this resilient cooling system; (b) highlighting the commonalities and differences in the terms used by different disciplines to indicate resilience; and (c) inferring the implication of our model for practice and policy. An integrative literature review process was used as it allows for the inclusion of theoretical and empirical research and perspectives from diverse sources to more fully understand complex phenomena  [34]. A two-stage search approach was conducted using two databases (Science Direct and Scopus) for 1990 -2019. In the first stage, key search terms (disaster, exposure, hazard, resilience, risk, and vulnerability) were applied to titles and abstracts to find literature relating to the subcomponents of community resilience (social, eco-nomic, institutional, infrastructure and environment) inferred in the DROP model  [15]  and other community resilience frameworks, such as Ref.
Such targeted actions, however, may result in increasing the vulnerability of another part of the electricity system. For example, a database topological error caused the 2003 US-Canada Northeast blackout  [139]. The database was implemented to improve visibility of network, but this robustness improvement in the network caused a large-scale system failure resulting in billions of dollars in losses. Therefore, a resilient power system may need to be robust in quite a few areas and be flexible, agile, and adaptable at the same time. The focus of power system resilience frameworks such as those found in Refs. [138, 151, [153] [154] [155]  appears to remain on risk-assessment for high-impact low-probability events, with no indication of how power system design and operation is responding to natural hazards that may be high-impact and high-probability (i.
Acknowledgements This work was supported by the  Assistant Secretary for Energy Efficiency and Renewable Energy, Building Technologies Office of the U. S. Department of Energy  under Contract No. DE-AC02-05CH11231 ,  Det Energiteknologisk Udviklings-og Demonstrationsprogram (EUDP)  under grant  64018-0578 ,  Swedish Energy Agency Program Thermo 3  grant  48296-1 ,  Innovation Hub for Affordable Heating and Cooling (Australia).
Bathymetry is based on the ETOPO1 database  [38]. The vertical mixing is a turbulent kinetic energy closure scheme based on Gaspar et al. [39]  (like the 1D oceanic mixing layer used in this study), but with significant modifications introduced by Madec et al. [40]  in the implementation and formulation of the mixing length scale. The split-explicit free surface formulation of Shchepetkin and McWilliams  [41]  is used. The ocean-atmosphere interface is based on SURFEX and the OASIS3-MCT coupler  [42] -a full description of the interface and coupling strategy is detailed in Voldoire et al. [43]. A set of 3 simulations was carried out from the same initial state, but with differences in the treatment of the coupling between the atmosphere and the ocean: NOCPL (no coupling i. e. , SST constant), OML1D (1D ocean mixed layer) and CPL3D (full 3D ocean).
45), respectively. TC Bejisa thus tends to move slightly slower (resp. faster) in the OML1D (resp. CPL3D) experiment than in the NOCPL simulation, with slightly larger variations for the CPL3D experiment. The translation speed is a crucial parameter for controlling the underlying ocean response and the subsequent SST cooling feedback to the storm. A translation speed of 5 m s -1 seems to be a threshold below which the differences in cooling between a 1D and a 3D ocean model are greater compared to the differences with a faster translation speed  [11, 16]. The translation speed of TC Bejisa is close to this threshold value, which could limit the effect of the degree of coupling on the ocean cooling. The simulated intensity (MSLP, Figure  3b ) matches best-track data very well during the first 18 h of simulation.
Despite a slight intensity deficit (∼4 hPa) at initialization time, the three simulations are able to capture the first intensification of the TC (up to 962 hPa), and the following plateau. Between 18 and 24 h, none of them is nevertheless able to capture the storm weakening analyzed in the best-track (-9 hPa in 6 h). After 24 h, best-track data also show a re-intensification of the system (7 hPa in 12h), which is well reproduced in the 3 simulations. After 18 h, the uncoupled and coupled experiments start to diverge. Simulation NOCPL shows the highest intensity with MSLP values 7 hPa (resp. 4 hPa) higher than in the CPL3D (resp. OML1D) experiment at the end of the simulation.
Data analysis Responses were identified according to NHS Trusts and Boards, as opposed to individual maternity units, and where duplicate responses were received those were compared and one combined response created. All UK Trusts and Boards currently offering maternity services (n = 156) was used as the denominator to calculate response rate. Responses were exported from the survey site into IBM SPSS Statistics 23 software and descriptive statistics were produced in relation to the key issues. Free-text responses were analysed using a conventional content analysis approach to determine what themes are present and how often they reoccur in the data  [10]. This approach was employed, as it is more phenomenological in nature, best suited for research on a topic where there is limited pre-existing literature.