dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We thank  Jutta Stober ,  Rebecca Seeger ,  Selma Huber ,  Sabine Mu ¨ller , and  Joanna Diak  for their excellent technical assistance. This work was supported by the  Prostate Cancer Foundation  (MB, ACBC) and the  Wilhelm Sander-Stiftung  (No. 2018. 142. 1  to ACBC),  Department of Defense Prostate Cancer Research   Program Early Investigator Researcher Award  (IIL), the  Helmholtz program Biointerfaces in Technology and Medicine (BIFTM) , and the  Helmholtz Program  " Materials Systems Engineering (MSE) ". We acknowledge support by  Deutsche Forschungsgemeinschaft  through the  DFG-core facility Molecule Archive  ( BR1750/ 40-1  and  JU2909/5-1 ) to SB and NJ. The data was analyzed at four different developmental stages (24, 48, 72 and 96 hpf) for two endpoints: (1). Mortality, which was calculated as: (number of dead embryos/total number of exposed embryos) x 100; (2).
Section 7 summarizes the main results and findings. The following notations are adopted; compression is positive, tensor indices are located at the lower position, bold non italic letters are used for vectors, bold italic letters for matrices/tensors,  is the zero vector or zero second order tensor. Dots denote derivatives with respect to time and the superscript  the transpose. The study is conducted using the small strain hypothesis and inertia forces as well as body forces are neglected. All calculations are done using a finite element code based on Matlab, initially developed by  Bonnet and Frangi (2006). GMSH  (Geuzaine & Remacle, 2009)  is used for the mesh generator and post processing of the results.
Identification With Raman Microspectroscopy Filters were analyzed using a micro-Raman Xplora Plus (HORIBA Scientific ® , France) equipped with a closing chamber that prevent filters from external airborne contamination. Zones of presence of potential MPs marked previously were observed with an objective ×10 and then ×100 by using a microscope (Olympus, France). Particles detected were analyzed with two lasers with a wavelength of 532 nm and 785 nm depending on particle on the range of 200-3,300 cm -1 to obtain spectra of main polymers  (Käppler et al. , 2016). For each suspected MP, Raman spectrum was compared to a polymer identification database (KnowItAll, BioRad ® ) and a personal library made with standard polymers obtained from Goodfellow (France). For the samples prepared from copepods collected in the Seine estuary, photos of items were taken using Labspec 6 software (Horiba Scientific, Japan).
ACKNOWLEDGMENTS We are grateful to  Michele Tackx  from  EcoLab laboratory  for the environmental samples of copepods and  Jérôme Cachot  from  EPOC laboratory  for the samples of microplastics. We also thank  Urania Christaki  from  LOG laboratory  to let us use her epifluorescence microscope for our experiments. SB acknowledge supports by the  European Community (FEDER)  for general support in the frame of LCFM-BIOMAT. J-MR is a FRS-FNRS Research Associate. We are grateful to the past and current members of the group of SS for maintaining the continuous plankton cultures during 2 decades. We are thankful to  Shagnika Das  for her help with the English and for her comments on the MS.
Bone tissue engineering (BTE) strategies are increasingly investigated to overcome the limitations of currently used bone substitutes and to improve the bone regeneration process. Among the natural polymers used for tissue engineering, dextran and pullulan appear as natural hydrophilic polysaccharides that became promising biomaterials for BTE. This systematic review aimed to present the different published applications of pullulan and dextran-based biomaterials for BTE. An electronic search in Pubmed, Scopus, and Web of Science databases was conducted. Selection of articles was performed following PRISMA guidelines. This systematic review led to the inclusion of 28 articles on the use of pullulan and/or dextran-based biomaterials to promote bone regeneration in preclinical models. Sixteen studies focused on dextran-based materials for bone regeneration, six on pullulan substitutes and six on the combination of pullulan and dextran.
Search Outcomes Database screening yielded to 713 publications after duplicates removal. Among these publications, 35 articles were selected for potential inclusion after title and abstract reading. Full texts of these 35 articles were reviewed. Seven of the 35 studies were excluded for not meeting the inclusion criteria. No additional study was added for selection after manually screening the list of references of selected publications. Finally, 28 studies were included for this systematic review: 16 used dextran-derived biomaterials  (Lafont et al. , 2004; Maire et al. , 2005a; Chen et al. , 2005; Chen et al. , 2006; Chen et al. , 2007; Degat et al. , 2009; Abbah et al. , 2012; Bölgen et al. , 2014; Ritz et al. , 2018; Chen et al. , 2019; Ding et al. , 2019; Fang et al. , 2019)  for bone regeneration, six used pullulan-derived biomaterials  (Hayashi et al.
2b ). For others (for example, Sulfolobus acidocaldarius), we retrieved only candidates that are much less abundant than known NAPs in the same organism. In contrast, we also find species where novel candidates make up a substantial portion of the overall investment in NAPs, rivalling or even dwarfing the abundance of known NAPs. Notably, this list includes the model archaeon Haloferax volcanii, where the two candidate NAPs (HVO_1577 and HVO_2029) are considerably more abundant than either histones or MC1 (Fig. 2b ), a finding we confirm in an independently generated proteomics dataset (Supplementary Fig. 9 ). Intrigued by this finding, we carried out nucleoid enrichment assays in H. volcanii (Methods). Proteins with a Pfam DNA-binding domain are strongly enriched in the nucleoid fraction (Wilcoxon test, P = 2.
Raw mass spectrometry data processing Data were processed using the MaxQuant software platform (v1. 6. 10. 43)  41 , with database searches carried out by the in-built Andromeda search engine against various organism-specific databases from the National Center for Biotechnology Information (NCBI) GenBank. A reverse decoy database approach was used at a 1% false discovery rate for peptide spectrum matches. Search parameters were as follows: maximum missed cleavages set to 2, fixed modification of cysteine carbamidomethylation and variable modifications of methionine oxidation, protein N-terminal acetylation, asparagine de-amidation and cyclization of glutamine to pyro-glutamate. Label-free quantification (LFQ) was enabled with a LFQ minimum ratio count of 1. The 'Match between runs' function was used with match and alignment time limits of 0.
Processing of public proteomics data We included only proteomes that were (1) derived from whole cell extract, (2) without size selection and (3) comprised more than 500 identified proteins. Data for H. volcanii was obtained from ref. 19  and, for Supplementary Fig. 9 , from ref. 44  , T. kodakarensis from ref. 18  , Natrialba magadii from ref. 45  and Nitrosopumilus maritimus from ref. 46. Data for all other species were obtained from ref. 6. For each dataset, measurements that did not correspond to the Genbank complete genome of the strain/species were discarded. Correspondence between Uniprot and Genbank ID was established using the Uniprot Retrieve/ID mapping tool. For each dataset, normalized intensity (in %) was computed as the ratio of each protein intensity over the total intensity for all quantified proteins in a given species.
DNA binding prediction DNA binding was predicted using DNAbinder (https://webs. iiitd. edu. in/ raghava/dnabinder/) using the support vector machine model trained on a realistic dataset  50. Proteins whose score was higher than 0 were considered as possible DNA-binding proteins. Protein structures of NAP candidates were predicted as homodimers using Colabfold 51 , and functional inference was carried out using the ProteInfer webserver  52. Similar folds were found using FoldSeek 53 with default settings.
Acknowledgements We thank the  LMS Proteomics Facility  for generating mass spectrometry data and the  Molecular Systems Group  for discussions. This work was funded by  Medical Research Council core funding  ( MC-A658-5TY40 , T. W. ) and an  EMBO   Short-Term Fellowship 8472  (A. H. ). We further acknowledge funding from the  French National Agency for Research for Archaevol  ( ANR-16-CE02-0005-01 , S. G. and G. B. ) and  Methevol  ( ANR-19-CE02-0005-01 , S. G. and G. B. ) grants, as well as the  French Government's Investissement d'Avenir programme for Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases"  ( ANR-10-LABX-62-IBEID , S. G. and G. B. ) grant.
Data availability All data generated or analysed in this manuscript is publicly available as follows: OGTs were obtained from bacdive-DSMZ (https:// bacdive. dsmz. de/). Genomes and predicted proteomes were obtained from https://www. ncbi. nlm. nih. gov/assembly. HMM models were downloaded from Pfam and TIGRFAMs 15. 0. Gene ontologies were obtained from http://current. geneontology. org/ontology/exter-nal2go/pfam2go. Archaeal trees were obtained from https://gtdb. ecogenomic. org. Mass spectrometry data generated as part of this study have been deposited in the PRIDE repository with accession code PXD034568 (https://www. ebi. ac. uk/pride/archive/projects/ PXD034568/). Previously published data that were re-analysed here and support the findings of this study are available as detailed in the original publications.
Acknowledgments The authors acknowledge the  FEDER-Region Basse-Normandie (France)-CNRS  for a Chaire d'Excellence for MAB, which resulted in the implementation of the "IRRaman" rig and a research stay allowing to perform these investigations. This work was also supported by the  Spanish Ministry of Economy and Competitiveness  and  FEDER  ( CTQ2012-36408 ,  CTQ2015-68654-R  and  CTM2017-82335-R ). J. J. Ternero-Hidalgo  acknowledges the assistance of the  Ministry of Economy and Competitiveness of Spain  for the award of a  FPI Grant  ( BES-2013-064425 ) and the award for performing his stay at LCS ( EEBB-I-16-10749 ).
Fundamental Properties of Polymer-Based Drug Delivery Systems DFT computations may be used to derive several characteristics of polymer-based drug delivery systems  [11]. To obtain the desired feature under examination, researchers might use a variety of strategies. However, Figure  1  presents a common procedure for applying DFT calculations. The creation of the drug's and excipient polymer's molecular structures often marks the beginning of the procedure  [38, 39]. Additionally, these structures may be imported from both public and commercial molecular structure databases, including the Protein Data Bank  [40] , PubChem Structure  [41, 42] , and databases for polymer materials. The energy of each particular molecular structure can then be reduced by performing an initial structural relaxation.
Charge Decomposition Analysis (CDA) and Density of State (DOS) Spectrum In a drug delivery complex, the charge decomposition analysis (CDA) aids in understanding orbital intrinsic properties and throws light on the mode of charge transfer between fragments of molecular orbitals, as well as the charge transfer transition in its isolated state for achieving charge equilibrium. The density of states (DOS) analysis determines the number of states in a unit energy interval of a system; it is the most important metric for analyzing fragment interactions and determining their molecular orbital (MO) energy level contributions in the complex. The CDA and DOS spectra both offer a visual representation of the interacting components  [75] [76] [77] [78]. Multiwfn is a multifunctional software designed to make visual exploration of electron and molecule structures in wavefunction analysis easier  [79].
These interactions are based on electron density characteristics  [82]. Farzad and coworkers also reported distinct forms of non-covalent interactions based on the color-filled RDG isosurface during the investigation of the probing impact of polyethylene glycol (PEG) on the adsorption mechanisms of Gemcitabine (Gem) drug on the hexagonal boron nitride (h-BN). The RDG vs. sign(λ2)ρ and RDG-based NCI isosurface plots of two configurations of the drug-carrier complex were predicted by the Multiwfn program as shown in Figure  9. Because of the formation of π-π stacking between the drug Farzad and coworkers also reported distinct forms of non-covalent interactions based on the color-filled RDG isosurface during the investigation of the probing impact of polyethylene glycol (PEG) on the adsorption mechanisms of Gemcitabine (Gem) drug on the hexagonal boron nitride (h-BN).
The RDG vs. sign(λ 2 )ρ and RDG-based NCI isosurface plots of two configurations of the drug-carrier complex were predicted by the Multiwfn program as shown in Figure  9. Because of the formation of π-π stacking between the drug and the carrier, the green region in the first configuration is more than the green region in the functionalized complex, as shown in Figure  9a ,b, respectively. While in Figure  9b , in the second configuration, the formation of the hydrogen bonds causes the blue area to be increased  [11].
Conclusions and Outlook In this work, we have explored different fundamental properties that are used in the calculation and prediction of drug delivery performance in the biological system. As mentioned in this article, we highlighted how DFT has been widely utilized to investigate the interaction, thermodynamic, electronic, charge transfer, and release mechanism aspects of polymer-based drug delivery systems. Despite the use of various DFT software to solve drug delivery behavior, the Gaussian software has been widely used, particularly with the B3LYP and 6-31G(d, p) functional and basic sets, respectively. It is envisaged that the logic of rational drug delivery systems and the continuous improvement in computing power of DFT software will spur increased multidisciplinary efforts for the future of nanomedicine from the atomic perspective at a low cost with accurate and reliable quantum mechanic calculations.
Software packages such as Gaussian, Orca, Schrodinger Suite, and Materials Studio, among others, offer robust quantum-mechanical code for the simulation of polymer-drug systems. Meanwhile, a library for reading and interacting with basis sets may be found in Basis Set Exchange (BSE)  [85] , an open-source data repository for basis sets used in quantum chemistry. The integration of machine learning (ML) with DFT, although presently in its infancy, can accelerate the discovery and predict the suitability of polymeric nanocarriers for drug delivery. New theories and a step toward the development of effective hybrid nanomaterials might be provided by ML  [86]. Useful ML algorithms have been reviewed in our previous work  [1]  which can pave the way for the development of a robust ML workflow for screening and predicting the properties of the drug-polymer complex.
Funding:  Adekoya Clara Chinyere 's financial assistance and motherly care are much appreciated by O. C. A. and G. J. A. In addition, the  National Research Foundation of South Africa  (Grant Number:  138768 ) provided financial assistance to G. J. A. S. S. R. acknowledges financial support from the  South African Department of Science and Innovation  (Grant Number:  C6ACH20 ) and the  Council for Scientific and Industrial Research  (Grant Number:  086ADMI ). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Longer irradiation does not enhance the isomerization yield or rotation performance, but leads to some photobleaching. The thermal helix inversion (THI) behavior of the motor inside the SURMOF was explored by measuring the transient UV/Vis spectra. In the transient spectra (Figure  S4 , left) and in the differential spectra (Figure  3a  and S4, middle), it can be clearly seen that the light-induced changes of the absorption bands disappear with time, in agreement with the thermal isomerization of the metastable diastereomer. Please note, the data show that a quantitative switching back to the pristine state by light or THI was not realized. The absorbance differences at the characteristic bands of the overcrowded alkene (Figure  3b  and S4) show monoexponential decay behaviors with characteristic time constants τ (= 1/k), indicating that the THI-relaxation is a firstorder reaction.
Acknowledgements We thank  Volkswagen Foundation , the  German Research Foundation (DFG)  and the  China Scholarship Council (CSC)  for financial support. B. L. F. and W. D. acknowledges financial support from the  Netherlands Organization for Scientific Research (NWO-CW) , the  European Research Council  (ERC; advanced grant No. 694345  to B. L. F. ), the  Dutch Ministry of Education, Culture and Science (Gravitation program  No. 024. 001. 035 ) and the  Horizon 2020 Framework Program  ( ERC Advanced Investigator Grant  No. 694345  to B. L. F. ). W. D. is grateful for financial support from  Marie Skłodowska-Curie Actions  (Individual Fellowship No. 101027639 ). Open Access funding enabled and organized by Projekt DEAL.
34. For NiO x electrodes, the spectra were measured at the potential of 1. 6 V vs. RHE (1. 59 V vs. RHE in the case of NiFeO x ) close to the "onset" potential of the OER where reaction intermediates cover all active sites at NiO x without interruptions from O 2 bubble formation. Each spectrum contains data at 198 frequencies from 30 kHz to 10 Hz measured using a probing signal amplitude of 10 mV, and the data fitting was done using EIS Data Analysis 1. 3 software  73, 74. Chronoamperometry measurements were conducted in 0. 25 M CO 2 saturated K 2 CO 3 (prepared from Milli-Q water and Potassium carbonate powder, anhydrous, 99. 99% trace metals basis, Sigma-Aldrich) to reduce the decomposition of H 2 O 2. Each chronoamperometry measurement lasted 30 min at 2. 5 V vs. RHE to have sufficient production of H 2 O 2  46, 49.
Acknowledgements We heartily thank  Prof. Jean-Philippe Ansermet  for many helpful discussions and  Prof. Hugo Dil  for sharing his input on the discussion of the CISS effect in terms of spin polarization. S. L. acknowledges financial support by the  Marie Skłodowska-Curie Fellowship  (No. 838367  to S. L. ) under the European Union's  Horizon 2020 research. K. M. , N. Z. , N. V. , and N. A. acknowledge the support from the  CNRS , the  University of Angers , and the  RFI LUMOMAT  (grant to K. M. ) in France. M. L. and N. A. thank the  Swiss Academies of Arts and Sciences (SATW)  and the  French Ministry of Foreign Affairs  for financial support through the bilateral  Germaine de Staël  ( PHC  Project  47931VB ) project. J. R. G. M. and F. A. G. P. thank the financial support of the  FEDER/Ministerio de Ciencia e Innovación ,  Agencia Estatal de Investigación  ( RTI2018-095618-B-I00 ); the  Generalitat de Catalunya  ( 2017-SGR-1406 ), the  CERCA Programme/Generalitat de Catalunya.
These four actuators can provide independently tension or compression speeds up to 530 mm/s and the maximum elongation is 200 mm via two face to face actuators. With these conditions, if one uses a 40 × 40mm2 specimen, elongation up to 5 could be obtained as well as strain rate up to 26. 5s-1. Biaxial tensile tests that are close to the stretch blow molding process may then be conducted. A cruciform sample is used and connected to four Festo electric actuators which are piloted independently with LABVIEW software via the Modbus TCP/IP protocol. The LABVIEW software is used to control the actuators but also to provide data acquisition of the tensile forces. Between each actuator and the specimen grip, a force sensor is installed. In addition, a high-speed camera is used to capture image for DIC.
VHE model identification The simulation of equal-biaxial tensile test is carried out using finite element code in software Matlab. The well-known inf-sup condition or the LBB condition  [37, 38]  guaranties the stability of a finite element velocity -pressure calculation as a quadratic interpolation for velocity and linear for pressure. By analogy, we choose a quadratic interpolation for velocity V and linear interpolation for B e. Considering the thermal effect, Mechanical and thermal balance equations are fully non-linear and solved together with implicit schemes on the current deformed configuration, which is updated at each time step. Newton-Raphson method is used to solve this nonlinear problem. 'fminsearch' in the toolbox Matlab is used to manage the identification. This tool can identify the characteristics of VHE model from minimizing the difference between the curves experimental and numerical simulation for different point of measurement.
Data deposited in the Cambridge Structural Database (CSD) for compounds of scandium, yttrium, lanthanum and lutetium(III) have been analysed to assess the structural similarities of complexes of the different metal ions. 29 sets of compounds of Sc, Y, La and Lu where at least three of the elements form compounds with the same ligands have been identified and their crystal structures analysed. In 14 of them, scandium and lutetium have the same coordination number; in the remaining 15 they do not. Similarly, there are 10 examples where there is a difference in coordination number between Lu and Y for compounds with the same ligands. For the other 19 either the coordination numbers are the same or that compounds for both the elements under consideration have not been reported. Overall structural differences correlate well with the size of the metal ions and provide no true chemical basis for arranging Lu rather than La in one triad with Sc and Y.
The Radii of the +3 ions of Sc, Y, La and Lu The largest single repository of structural data for lanthanide complexes is the Cambridge Structural Database (CSD)  [23]  which contains entries for 45,191 compounds of La and Ce to Lu (CSD Version 5. 41(November 2019) + 3 updates). In the same version of the CSD there are 1490 entries for Sc-containing compounds and 3959 entries for Y-containing compounds. It should be noted that all these complexes contain at least one "organic" carbon atom so that simple salts such as [Ln(H2O)9][CF3SO3]3, the oxides Ln2O3 or the binary halides [LnX3; X = F, Cl, Br, I ] are omitted unless accompanied in the crystal structure by a species containing an organic carbon. However information on these salts can be found in the Inorganic Crystal Structure Database (ICSD)  [24]  provided by FIZ Karlsruhe GmbH.
| Field Testing in the Adriatic Sea The immersion of the samples was performed in collaboration with the Marine Biology Station (MBS) of the National Institute of Biology in Piran, Slovenia. The MBS infrastructural center operates a research vessel (12 m), a boat (5 m), and a diving base with SCUBA facilities. Oceanographic buoy Vida provides real-time meteorological (air T, humidity, wind) and physical and chemical oceanographic data (seawater T, salinity, currents, waves, dissolved oxygen). Figure  2 (c) presents the image of the Vida buoy and its geographical position approximately 2 nautical miles away from Piran at the Adriatic coast in Slovenia (coordinates 45°32. 925′ N, 13°33. 042′ E) (Figures  2[d]  and [e] ). The framework carrying the racks with samples was lowered from the boat and positioned at a depth of 20 m (video given in Supplemental Video SV1).
The frame with racks was secured at the sea bottom and tied to the buoy (Figure  2 [f]). Therefore, the samples were exposed to the natural sea movements at this depth but firmly attached to the frame. The test duration was 8 months, from July 25, 2017 to March 13, 2018. Samples were taken out on August 25, 2017 (after 1 month), October 26, 2017 (after 3 months), and March 13, 2018 (after 8 months). Data on sea temperature at the sea bottom and the surface, salinity at the surface, and concentration of oxygen at the sea bottom were provided by MBS based on the data collected at the Vida buoy (Figure  S1 ). From July 2017 to March 2018 (32 weeks), the surface temperature of seawater gradually changed from summer to winter conditions decreasing from ca. 26°C to 27°C to 8°C. The bottom temperature reached the maximum of ca.