dataesr/scientific-paragraphs-categorization huggingface dataset:
DHS Forms a Functional Tetramer The structure of apo DHS was determined by molecular replacement using the Protein Data Bank (PDB) entry 1DHS as a search model and refined to a resolution of 1. 52 Å with R work /R free values of 15. 42%/17. 62%. In comparison, the previously reported structures have been refined at 2. 2 and 3 Å for NAD-and GC7-bound structures respectively, thus the resolution of the structure presented by us constitutes a major improvement and allows for a more detailed analysis  [36, 37]. DHS crystallised in the P3 2 21 space group with one tightly associated homodimer per asymmetric unit (ASU), with two active sites located at the dimer interface (Figure  1B ). Each active site is formed by residues from both subunits. The DHS dimer is related by a two-fold axis with the identical dimer (Video S1), forming a dimer of dimers, which was previously identified as an active form of protein  [47]  and additionally confirmed by multiangle light scattering (MALS) measurements.
Discussion Much attention is currently paid to the molecular mechanism of the selective control of protein translation by the hypusinated eIF5A. The DHS-catalysed reaction is a rate-limiting step in the hypusination pathway. Hence, a detailed knowledge of the reaction mechanism will help to understand the basics of this unique post-translational modification. Furthermore, since the hypusinated eIF5A is indispensable for tumour growth, as well as being closely related to the aggressiveness of cancers, the discovery of novel specific inhibitors of hypusination is highly awaited. Thus, the availability of high-resolution crystal structures in complex with natural ligands is essential to perform future structure-guided drug discovery attempts. Low-resolution crystal structures of DHS have already been solved previously (PDB ID: 1RLZ, 1ROZ, 1RQD, 1DHS), yet our studies show not only more precise information due to significantly higher resolution, but also the enzyme in its apo form, as well as a detailed mode of binding of different polyamines.
Acknowledgments: X-ray data were collected at the BESSY II 14. 1 beamline at  Helmholtz-Zentrum Berlin für Materialien und Energie. We thank  HZB  for the allocation of synchrotron radiation beamtime. We would particularly like to acknowledge the support of  Christian Feiler ,  Martin Gerlach  and  Manfred S. Weiss. We would like to thank  Piotr Małecki  for help with light scattering measurements,  Klaudia Woś  for invaluable support in crystallization trials,  Marcin Kami  ński for stimulating discussions and inspiration and  Ban Judek  for proofreading and valuable comments.
Funding: The research has been supported by  National Science Centre  ( NCN, Poland ) research grant no. UMO-2019/33/B/NZ1/01839  to  P. G. This  project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  730872. We acknowledge the  MCB Structural Biology Core Facility  (supported by the  TEAM TECH CORE FACILITY/2017-4/6  grant from the  Foundation for Polish Science)  for valuable support.
The binding prevents membrane fusion and viral entry and clearly indicates the importance of the HNV prefusion F conformation for eliciting a robust immune response  [25]. Despite reports of nipah virus inhibitors, no systematic evaluation of the reported compounds is performed. A few antiviral inhibitor databases and studies exist, namely, The In uenza Research Database (IRD) [26]  [27] , The Virus Pathogen Database and Analysis Resource (ViPR) [28], enamine library (https://enamine. net/hit-nding/focusedlibraries/view-all/antiviral-library), etc. They contain very limited or no information with the focus on Nipah virus. The ones with focus on Nipah virus inhibitors are either scattered datasets or curated versions with limited or no evaluation of the anti-Nipah compounds as starting points for drug discovery and development of anti-Nipah inhibitor(s)  [29].
Towards this, we developed a combined evidence based strategy to systematically evaluate Nipah virus inhibitors. With this objective, we curated the anti-Nipah compounds using a crowdsourcing model and the data were made publicly available at -http://vinodscaria. rnabiology. org/nipah since the inception of the project (May 2018). Our two pronged strategy comprised of (i) prioritization of NVIs based on the robustness of the assays which were used to identify them and (ii) estimation of several drug likeness parameters for the NVIs. The drug likeness analysis includes diversity analysis, similarity to FDA approved compounds (as drug repurposing candidates) or reported antivirals (to identify new scaffolds), pro ling based on PAINS and DruLito lters and comparison to ZINC lead like libraries.
Data source and structure PubMed (https://www. ncbi. nlm. nih. gov/pubmed) and Google (https://www. google. com/) were the two main sources considered for collecting the inhibitors that showed activity against Nipah virus. Research papers relevant to the study were downloaded using the query as 'Nipah' and those with the keyword in their abstract/title were shortlisted. At the time of curation this keyword listed 838 papers which were considered for data-mining. This step was followed by a scoring system wherein papers that had con rmed information regarding the inhibitors against Nipah virus in their abstracts were assigned a score of '10', those that may or may not have information regarding the same were given a score of '5', those with information on peptides used as inhibitors were assigned a score of '4' and rest with no relevant information were given a score of '0'.
Data quality check In order to ensure data quality, a separate team of curators re-performed the curation independent of the initial team. Errors are marked in 'Red' in the curation sheet and was then marked 'Green' once the data is veri ed by both the teams. Data curation and quality check was done manually and the data is stored in NVIK-Open resource Sheet which served as the crowdsourcing platform. Each entry was tagged with its contributor's email Id.
Search tools In the NVIK web interface, three search options are available, namely, 'Simple Search', 'Query Builder' and 'Structure Search'. The Simple Search option is to retrieve information using a text-based query on all/selected elds of the database. User can selectively display the searched records. Overall, the database contains -unique elds as shown in Fig. 1. NVIK's query builder option enables search using complex queries with the help of logical operators such as AND & OR. With these options, the user can retrieve targeted results such as compounds tested in speci c cell lines with different IC50 values. An extensive 'Structure Search' interface is also available in NVIK. The user can query using SMILES, SDF and MOL formats. In addition, the users can also draw a compound in JSME browser that may be searched in NVIK.
Browsing interface Data on NVIs can be browsed for physicochemical properties like molecular weight, XLogP, polar surface area (PSA), hydrogen bond donor (HBD), hydrogen bond acceptor (HBA) and the number of rotatable bonds (http://bioinfo. imtech. res. in/anshu/nipah/physico_chem. php). The database can also be browsed from major elds as shown in Fig. 1.
Tools used for compound analysis and clustering Near neighbours (NNs) in NVIK are identi ed using NNeib (version 5. 12. 0) (https://docs. chemaxon. com/display/docs/Jarvis-Patrick+clustering). The clusters of NVIs have been obtained using Jarp (version 6. 0. 2) (https://docs. chemaxon. com/display/docs/Jarvis-Patrick+clustering). which is based on the dissimilarity threshold (1-Tanimoto coe cient). A threshold of 0. 15 is used as the dissimilarity coe cient. A total of 1875 descriptors were calculated using PaDel software  [30]. To prioritize drug-like compounds, Drulito (http://www. niper. gov. in/pi_dev_tools/ DruLiToWeb/DruLiTo_index. html) and PAINS lters were applied. Clustering is also performed with FDA approved compounds and the results are reported with known side-effects and predicted rat acute toxicity of NVIs similar to FDA approved compounds (http://bioinfo.
List of prioritized NVIs along with their biological activity, drug likeness pro le and structural similarity to antivirals / FDA approved small molecular libraries. Most compounds were found to be dissimilar to FDA approved small molecule with the exception of bortezomib (NVIC0027) and chloroquine (NVIC0048). among the three and has considerable overlap with the physicochemical properties of FDA approved drugs. The scope of the current study was to perform a systematic evaluation of physicochemical properties of NVIs with respect to FDA approved drugs and antiviral libraries, understand the diversity of chemical scaffolds and correlate these computed properties with the experimentally reported bioactivity datasets for proposing a list of prioritized candidates. In order to simplify data access and further enhancement of data libraries, a web-based platform is designed for reporting new compounds, further analysis and prioritization of potential NVIs.
The current probe is placed at the power converter stator phase output. A XCC1510P Schneider encoder, 360 points of resolution, is coupled to the camshaft to measure the shaft rotation. A DAQ NI 6143 device, 16-channels with 16 bits of resolution, is used to perform data acquisition. A 20 kHz sampling frequency is considered for stator motor current and encoder. The schematic representation of the experimental arrangement is depicted in Fig. 4. All processing procedures are implemented in Matlab on a standard i7-3. 5 GHz computer. In order to validate the proposed methodology, three different experimental scenarios were considered: healthy conditions, Hc, and two fault conditions created by inducing effort disturbances. The first fault condition, F1, consists of a 25% decrease in the effort pattern of the first cam, C1, through the adjustment of the thumbscrew for load grip using a dynamometric key.
2(e) or 2(f), the first novelty detection model would include the RMS values corresponding to regions 1 and 5, the second model would include the RMS values corresponding to regions 2 and 6, and so on. The novelty detection models provide quantification if a new sample diverges from the characterised distributions in which these models were trained. An MVKDE-based probabilistic approach is employed for this purpose, based on the calculation of the PDFs,   (), where X is the training dataset characterised by the arrays of features.
Data Source Molecular test results for EGFR, HER2, KRAS, BRAF, and PIK3CA mutations, and also ALK rearrangements and histologic types were provided directly to the Intergroupe Francophone de Cancérologie Thoracique by the certified molecular genetics platforms. At the same time, each patient's treating physician was given secure access to the patient's chart to complete the required data: sex, ethnicity, smoking history, family cancer history, Eastern Cooperative Oncology Group performance status, stage, type of treatment, the impact of molecular findings on the treatment decision, and outcomes. All patients with KRAS mutation with NSCLC of the Biomarkers France cohort were reviewed. In the Biomarker France cohort, all consecutive patients with NSCLC who were routinely screened for molecular alterations from April 2012 to April 2013 at one of the 28 certified molecular genetics centers were eligible for this study.
Only patients with advanced NSCLC were considered (relapse, noncurative stage III and stage IV). Clinical data were collected, as previously described. 9 Patients were treated according to the medical standard of 2012 to 2013. The effects of firstand second-line treatments (i. e. , chemotherapy or EGFR TKI) on the objective response, disease control, PFS, and OS were analyzed. An evaluation of response and survival was completed by each clinician according to the Response Evaluation Criteria in Solid Tumors 1. 1. The OS was defined as the time from diagnosis to the date of death or last follow-up (FU) censored on October 1, 2016. At the last FU, 299 patients (33. 2%) were censored (FU [mo], 95% CI: 15. 9 [14. 9-17. 4]). A KRAS mutation analysis was performed on available tissues through standard Sanger sequencing or a more sensitive validated allele-specific technique, as previously described.
Statistical Analysis Descriptive statistics were used and expressed in medians with ranges for continuous variables and percentages for categorical variables. Comparisons between categorical variables were conducted using the chisquare test. The significance level was set at p less than 0. 05. The OS, first-line PFS, and second-line PFS were previously defined. 9 Survival curves were estimated using the Kaplan-Meier method. The disease control rate was defined as the percentage of patients presenting stable disease, partial response, or complete response to treatment, and the overall response rate was defined as the percentage of patients with a partial or complete response. The analyses were carried out using the SAS software, Version 9. 3 (SAS Institute, Cary, NC).
Results In the Biomarkers France trial, 4894 (26. 2%) KRAS mutations from 4634 patients were detected out of 18,679 molecular analyses from 17,664 enrolled patients with NSCLC. Survival and treatment data were available for 1014 patients. Data on sex, tobacco status, and TNM stage were not declared for 31 patients. Three patients were excluded because they presented multiple biopsies with distinct molecular mutations; patients with stages I to II (31 patients) and stage III with curative treatment (chemoradiation therapy or surgery, 51 patients) were also excluded. Therefore, 901 patients treated for noncurative stage III or IV NSCLC were included in this analysis (Fig. 1 ).
It was a prospective nonrandomized cohort study with no information collected regarding the chemotherapy used in firstor second-line treatment. As data collection for this cohort occurred before the availability of immunotherapy, the impact of KRAS mutations on immune checkpoint inhibitors (ICIs) could not be assessed. Identifying other concurrent mutations is also critical as it may influence outcome and treatment response. Comutations of TP53, STK11, or CDKN2A/B seems promising as their inactivation permits defining different KRAS-mutated subgroups. 19 KRAS mutations and STK11 inactivation led to a more aggressive tumor phenotype and ICI resistance, whereas KRAS/TP53 or KRAS/ CDKN2a/B were ICI-sensitive. 20 KRAS alone neither seems to be a sufficient prognostic nor predictive biomarker, particularly for ICI.
Acknowledgments This work was supported by  Intergroupe Francophone de Cancérologie Thoracique  through unrestricted grants from the  Institut National Du Cancer, France  (grant number  2011-038 ) and by  Novartis, France. The authors thank  Quân Tran ,  Antoine Deroy ,  Sandy Dos Santos , and  Fabienne Hirchaud  ( French Cooperative Thoracic Intergroup, Paris, France ) for their participation in data collection, monitoring, and computing.
Introduction T 1 mapping aids clinicians in the assessment and diagnosis of myocardial disease. However, measurement needs to be stable over time with transferable values. Knowledge of normal reference ranges would benefit from not requiring local healthy subject scanning, and the pooling of multiscanner datasets would have advantages such as increasing available sample sizes for the detection of subtle effects or subgroup analysis and increasing result robustness and generalizability, lowering the chance of unforeseen bias when compared to single-center data  [1]. Combining results however introduces sequence, magnet, and field strength bias  [2]. The field of T 1 mapping would therefore benefit from a "T 1 standard" to enable cross-center T 1 mapping data pooling and delivery  [3] -like the international normalized ratio (INR) which makes it possible to adjust the dosing of vitamin K antagonists regardless of which laboratory has performed the test  [4].
Methods part 1-Evaluation of the phantom Structural integrity Gel integrity and aging were checked at each submission time point for participating sites through the manual inspection of localizers that formed part of the minimum dataset requirement for participation. In addition, a highresolution, isotropic, three-dimensional (3D) gradient echo sequence (0. 42 mm 3 ) was run on four phantoms (three 1. 5 T phantoms; one 3 T phantom) at baseline (October 2015) and at 2 years post manufacturing in each case using a 3 T MAGNETOM Skyra (Siemens Healthineers, Erlangen, Germany; software syngo MR D13C). The sequence acquired two overlapping slabs (due to scanner software constraints), each with two directions of phase encoding, a slow repetition time (repetition time, TR = 17 ms), and narrow sampling bandwidth (250 Hz/pixel) for better signalto-noise ratio (SNR).
This sequence had weak T 1 and T 2 image contrast and was only for structural examination. "Reference" rT 1 and rT 2 data Baseline (October 2015) "reference" T 1 and T 2 values (rT 1 , rT 2 ) were acquired at the Royal Brompton Hospital CMR Unit using basic single-slice TR = 10 s inversion recovery spin echo (IRSE, 8 inversion times [TI] from 25 to 3200 ms) and single-slice repetition time (TR) = 10 s SE (8 echo times [TE] from 10 to 640 ms)  [5]  respectively. These sequences were identically repeated at 2 years on the same three 1. 5 T phantoms and on the same three 3 T phantoms sampled from the production batch. The identifying serial numbers of the three 1. 5 T phantoms were 15E031, 15E033, and 15E034, and these phantoms were scanned on a 1. 5 T MAGNETOM Avanto [Siemens Healthineers; software syngo MR B17A].
The three 3 T phantoms were 30E001, 30E017, and 30E018, and they were scanned on a 3 T MAGNETOM Skyra [Siemens Healthineers; software syngo MR D13C]. Separate rT 1 and rT 2 data were acquired on the same 3 T phantom (30E021) at the German National Metrology Institute, Physikalisch-Technische Bundesanstalt (PTB) over a period of 1041 days (64 scans) commencing September 2015 (3 T MAGNETOM Verio (Siemens Healthineers; software syngo MR B17A). Sequences used for rT 1 and rT 2 were respectively basic single-slice TR = 8000 ms IRSE (IRSE, 7 TI from 25 to 4800 ms) and single-slice TR = 3000 ms SE (5 TE from 24 to 400 ms).
Temperature sensitivity The following three methods were used: First, controlled-temperature experiments over the range 10-30 °C were conducted at the United States National Institute of Standards and Technology (NIST) on six loose T1MES tubes at 1 year (Fig. 2i ). T 1 and T 2 were measured at 10, 17, 20, 23, and 30 °C on an VnmrJ4 Second, controlled-temperature experiments at 19, 21, and 25 °C were conducted at the PTB laboratory on T1MES phantom 30E012 at 1 year (also Fig. 2i ). T 1 and T 2 were measured on a 3 T MAGNETOM Verio scanner (Siemens Healthineers; software syngo MR B17A) using a Pt100 resistance thermometer. T 1 was measured by IRSE (TR = 8000 ms, TI = 25-4800 ms) and T 2 by SE (TR = 3000 ms, TE = 24-400 ms). Third, for each T1MES phantom scan at all centers, temperature was measured using liquid crystal thermometers adhered to every phantom.
Methods part 2-Multi-center phantom testing Serial, multi-center T 1 mapping data The T1MES user manual (https://doi. org/10. 6084/m9. figshare. c. 3610175_D1. v1) defined strict scanning instructions (scanning and shim volume strictly at isocenter, use of same supporting materials, etc. ). Each contributed T1MES dataset (localizers, sets of inversion recovery images, and inline scanner-generated T 1 maps, Fig. 3 ) underwent initial quality assurance, checking orientation, and isocenter (through visual inspection of localizers and maps and semi-automatically by inspecting metadata contained in Digital Imaging and COmmunications in Medicine [DICOM] headers "ImagePo-sitionPatient" and "ImageOrientationPatient") and to exclude Fig. 2  Temperature experiments (i) performed at two national metrology institutes: the US National Institute of Standards and Technology (NIST) laboratory after 1 year on six loose tubes from T1MES and at Physikalisch-Technische Bundesanstalt (PTB) on phantom 30E012.
ii and iii indicate to which field-strength device the tested tubes belong. At NIST, T 1 and T 2 were measured on a Varian/Agilent small bore scanner operating at 1. 5 T in a temperature-controlled environment. Temperatures were measured using a fiber optic probe. At PTB, T 1 and T 2 were measured on a 3 T MAGNETOM Verio scanner (Siemens Healthineers; software syngo MR B17A). The phantom was always stored, moved, and scanned while resting in a Styrofoam box to ensure that the temperatures picked at bottle hull reflects the tube temperature. At scan time, the box was placed in the head coil (12 ch) of the PTB 3 T scanner. Temperatures were measured using a Pt100 resistance thermometer. Similar to temperature dependency results immediately after phantom manufacture,  [5]  short-T 1 tubes (modeling post-GBCA myocardium and blood) are more stable with temperature than very long-T 1 tubes (native blood) where T 1 increases more significantly with temperature image artifacts.
All Siemens sequences except MyoMaps product variant and all Philips (Philips Healthcare, Best, the Netherlands) sequences except CardiacQuant product variant were prototypes. Any tubes with artifacts detected by operator inspection of the submitted T 1 maps were excluded from the analysis. Software version changes were captured automatically from DICOM headers ("StationName" and "SoftwareVersion"). The T 1 measurements from T1MES datasets (directly using only the parametric maps submitted, not by any T 1 fitting applied centrally to the submitted sets of T 1 recovery images) were carried out using a bespoke MATLAB pipeline (The MathWorks Inc. , Natick, Massachusetts, USA, R2012b) assembled in collaboration with the US National Institutes of Health. From the data, T 1 for each of the nine tubes was measured in identically sized regions of interest (ROI) occupying the central 50% by area of each tube (accommodating ~40 independent pixels) and collated in a dedicated research electronic data capture instrument (REDCap  [9, 10] ).
Software upgrades To explore whether software upgrades resulted in an abrupt "step" change in the temperature-adjusted T 1 reads, we performed piece-wise linear regression to check for any segmented relationship between the covariates "scan day" and "tube T 1 " (considering tube "F" at 1. 5 T and "M" at 3 T)  [21]. For any broken-line relationship discovered, we defined slope parameters and break points where the linear relation/s changed and temporally correlated these with DICOM software metadata.
Results part 1-Evaluation of the phantom Structural integrity Individual inspection of all the localizers submitted by sites with each phantom dataset revealed no visible gel rips or tears down any of the tubes. At baseline and at 2 years following batch manufacturing (Supplementary Movie 1), phantoms were free of air bubbles and susceptibility artifacts at both field strengths. High-resolution imaging showed no evidence of gel rips or tears down any of the tubes, and the gels were intact in the mid slice-the piloted location for serial mapping (Fig. 1 , left panel). T 1 maps collected through the midline of the phantom, using the specified T1MES scan setup, were free from off-resonance artifacts.
Temperature sensitivity Temperature experiments at both NIST and PTB using spin echo, long TR, and sequences (Fig. 2i ) consistently showed that short-T 1 tubes (modeling post-GBCA myocardium and blood) were more stable with temperature than very long-T 1 tubes (native blood), where T 1 increased more significantly with temperature. T 1 increased by ~11 ms/°C for a typical MOLLI 5s(3s)3s 1. 5 T dataset in tube "B" (normal native blood). Conversely, T 1 decreased by ~1. 2 ms/°C in tube "G" (short post-GBCA myocardium). Temperature corrections for each of the nine tubes at both field strengths from the pooled multi-center analysis are presented separately (Supplementary Table  S3 ) from temperature sensitivity experiments of NIST and PTB due to the latter's use of bespoke standard operating procedures.