dataesr/scientific-paragraphs-categorization huggingface dataset:
Historical data The historical data have been processed as has been done for observatory time series. Since data are sparser and sparser before ∼ 1800, for London, Paris, Rome, and Edinburgh a cubic B-spline interpolation of early data is used to obtain a plot with continuous annual values, after removing the evident outliers. The latter can be a result of less precise mea- surements and/or less precise reduction to the location of the present-day observatory contributing with data over the most recent periods. Due to the temporal distance of several years between historical data at the beginning of the reconstructed time series, the spline line might show artifact wiggles. For that part of the time series a linear or quadratic interpolation could be more appropriate. The Munich series has already been filtered by  Korte et al.
Observatory data The two constituents of the secular variation for the 24 observatories considered in this study as obtained by an HP filtering, namely the trend and the decadal cyclic variations, are shown in Fig. 6. The trends are referred to as the average value for the time interval in which they are defined. The two constituents of the trend, the inter-decadal and the sub- centennial variations as obtained by a Butterworth filtering, are also plotted. We superimpose the time series from the 24 observatories corresponding to each of the timescales in order to emphasize common features and differences. We also indicate in Fig. 6  the accepted occurrence time of geomagnetic jerks (e. g. ,  Mandea et al. , 2010; Brown et al. , 2013). The cyclic constituent obtained from an HP filtering (Fig.
Phase differences between individual time series explain differences in the occurrence time and geographical distribution of geomagnetic jerks. Once the external contributions to the first differences in the observatory annual means of comparable amplitude with the trend variations are minimized, the core contribution to the observed secular variation no longer exhibits the very sharp appearance of geomagnetic jerks. According to our quantitative analysis of recorded data presented in Figs. 6  and 7  (see also  Demetrescu and Dobrica, 2014) , the geomagnetic jerk might be seen as a result of a more general phenomenon, namely the evolution of the secular variation as a result of a superposition of two (or several) waves describing the effects of processes in the Earth's core at two (or several) timescales.
Historical data Demetrescu and Dobrica (2014) have previously analyzed three of the long time series of historical magnetic declination data (London, Munich, Rome) and showed that the variation is present back in time to the 15th century. Here, we define and characterize the sub-centennial variation in the case of the secular variation of declination for five available time series. The time series showing the declination at the five locations are plotted in Fig. 8. We also superimpose time series obtained from gufm1  (Jackson et al. , 2000)  for the corresponding location. The spot measurements before the observatory era are affected by much larger errors than observatory measurements due on the one hand to the equipment accuracy and on the other to the non-corrected external variations and/or reduction to the location of the present-day observatory.
As mentioned in Sect. 2, for London, Paris, Rome, and Edinburgh a cubic B-spline interpolation of early data is used to obtain a plot with continuous annual values. The spline curve shows artifact wiggles before ∼ 1700. For that part of time series a linear or quadratic interpolation could be more appropriate to describe the long-term evolution. The Munich time series published by  Korte et al. (2009)  had been smoothed with an 11-year filter and a 2. 5-year knot space spline. Figure  9  shows, for the five locations, only the first time derivative of the HP trend together with the corresponding HP trend time derivative in gufm1 values. In the following, only the sub-centennial constituent, which is less affected by noise than the decadal and interdecadal constituents, is discussed. The five curves in Fig.
10 , showing the sub-centennial constituent of the trend plotted in Fig. 9 , demonstrate that the latter is not restricted to the last 150 years. So does the sub-centennial constituent derived from gufm1. The same conclusion based on empirical mode decomposition applied to the Munich data has been invoked by  Jackson and Mound (2010). The latter also found a longer period of 160 years. Referring to the appearance of the curves in Figs. 8  and 9 , a variation at a much larger timescale of 400 years or longer could, however, be present in the data. Several maxima and minima are evident in the subcentennial constituent time-derivative plots (Fig. 10 ) before 1900, in addition to the known ones over the 20th century. By comparing the five time series of the sub-centennial constituent time derivatives with each other and with the corresponding gufm1 model, a few interesting observations can be emphasized.
Firstly, all curves as derived from the data and/or model show the same maxima and minima of the sub-centennial constituent after 1850, namely maxima at  1850-1880 and 1920-1930 and minima at around 1900 and 1960 ; however, the sub-centennial signal is noisier at the beginning of the five time series and mismatches to gufm1 are evident at 1650 and 1700 for Munich and Rome, while the gufm1 time series are consistent with each other. Taking into account the noise in the historical data (Fig. 9 ) and the fact that gufm1 is based on a much larger number of measurements, not only on the five time series considered here, the gufm1 is conceivably able to satisfactorily represent the sub-centennial variation at the beginning of the time series. Secondly, the amplitude of the sub-centennial constituent before and after 1900 seems to be comparable, in spite of the lower quality of the data for the first 300 years of the time series.
A good agreement characterizes the time interval 1770-2000 and, as the authors stated, surprisingly consistent secular variation and acceleration between the smoothed curves from the two locations is found for the time span 1400-1580, in spite of the rather low quality of the data over this time span. Significant differences between the two locations exist, however, between 1580 and 1770. Korte et al. (2009)  also note that in both curves the time interval 1765-1865 seems to be devoid of strong rapid secular changes. When comparing the possible geomagnetic jerks (called "events" by  Korte et al. , 2009)  in the Munich curve with the maxima and minima of the first time derivative of the sub-centennial constituent for the same location plotted in Fig. 10 , we find that most of them  (1448, 1508, 1558, 1693, 1741, 1861, 1889, 1932 ) coincide within 0-3 years with the maxima and minima of our sub-centennial constituent  (1460, 1510, 1560, 1690, 1740, 1780, 1800, (1850) (1851) (1852) (1853) (1854) (1855) (1856) (1857) (1858) (1859) (1860) 1900 ).
Conclusions Our results underline the importance of the time perspective one has on geomagnetic data: besides the contribution of the sub-centennial constituent in defining geomagnetic jerks, what we called "steady variation" based on 150 years of observatory data  (Demetrescu and Dobrica, 2014)  proves to be only part of a larger timescale variation when 400 years of data are available. Declination annual means time series longer than 1 century provided by 24 geomagnetic observatories worldwide, together with 5 reconstructed declination series over the last 4 centuries in Western Europe, have been analyzed in terms of the frequency constituents of the secular variation at inter-decadal and sub-centennial timescales of 20-35 and 70-80 years. Observatory time series until 2015 have been processed by several types of filtering, namely Hodrick-Prescott, running averages, and Butterworth.
Once the external contributions to the first differences in the observatory annual means -of comparable amplitude with the inter-decadal and sub-centennial constituents -are minimized, the observed secular variation no longer exhibits a clear V-shape at the time of geomagnetic jerks. We are aware, however, that in doing so, some possibly impor-tant internal signal could have also been removed, and further work is necessary to elucidate this problem. The detected extrema in the historical data have been compared with events interpreted in terms of geomagnetic jerk occurrence dates proposed by other authors  (Alexandrescu et al. , 1997; Korte et al. , 2009). It appears that possible "events" in jerk terms, at 1700, 1730, 1750, 1770, and 1785, considered with a question mark by  Alexandrescu et al.
(1997)  because of too-noisy or unreliable data, are occurring close to the maxima and minima of the sub-centennial constituent. The sub-centennial constituent has comparable amplitudes before and after 1900, in spite of the lower quality of the data in the first 300 years of the analyzed time series, making it a reliable tracer of geomagnetic jerks in the past. Unfortunately, because of noise in the reconstructed time series, the inter-decadal variation, a constituent of the secular variation, could not be recovered and complete information on these phenomena is limited. According to our results, epochs of geomagnetic jerks may vary as much as a couple of years from one series to another. Over the investigated period, some very clear long periods exist between two successive and well-defined jerks.
Acknowledgements. The historical geomagnetic declination has been compiled from papers by the following authors:  Stuart R. C. Malin  and  Edward C. Bullard ;  Lili Cafarella ,  Angelo De Santis , and  Antonio Meloni ;  David R. Barraclough ,  Mioara Alexandrescu ,  Vincent Courtillot , and  Jean-Louis Le Mouël ; and  Monika Korte ,  Mioara Mandea , and  Jurgen Matzka  (op cit). The study has been carried out in the framework of the project  IDEI-UEFISCDI 93/2011. Partial results were presented at MagNetE5 (Rome, 2011) and  IUGG  (Melbourne, 2011) meetings. We are indebted to  Lili Cafarella  ( INGV-Rome ), who kindly provided historical geomagnetic data for Italy, and to the anonymous geomagnetic observatory staff and the  World Data Centers on Geomagnetism  for obtaining and keeping the data used in this study.
A blood smear was made in the morning when parasites are in the ring stage, and parasites counted under optical microscopy (×10,000 red blood cells). We also assessed the number of red blood cells per µl of blood, which allowed us to estimate the number of parasites per µl of blood. This standard blood was serially diluted to have a range of parasite doses and use them as standards in the real-time PCR. Crossing point values (Cp) were estimated for each sample using the second derivative maximum method implemented in StepOne software (Thermo Fisher scientific, Waltham, MA, USA). Mean Cp values of three replicates were used. The number of parasites per µl of blood was calculated by linear interpolation using the regression between the log 10 number of parasites per µl in the standard and the associated Cp.
Additionally, these doses are safe, as children with hepatitis C are known to tolerate doses of 30-400 mg/day. Moreover, it is well documented that ribavirin penetrates well into the CSF of patients with SSPE and HIV, achieving 74% (ranging 50-89%) of the concentration in the serum  [47, 48]. This leads us to believe that in a shorter period of time, higher concentrations could be achieved in order to maximize the inhibitory effects of ribavirin on AT/ RT cell growth. In conclusion, AT/RT research is an area of unmet need, standard treatments do not exist, and current treatments fail to cure. Here, we report that ribavirin has the ability to reduce AT/RT cell tumorigenesis in vitro and more importantly, in vivo (Figure  5 ). Ribavirin is currently being investigated in numerous clinical trials for its antineoplastic activity in various cancers, particularly acute myeloid leukemia (NCT02109744, NCT02073838)  [46] , head and neck cancer (NCT01268579) and metastatic breast cancer (NCT01056757).
Flow cytometry At 24, 48, 72, or 96 hrs after treatment with ribavirin (50 µM), cells were collected, washed in PBS and prepared for flow cytometry. For cell death, we used reagents from the APC-AnnexinV/Dead Cell Apoptosis Kit (Invitrogen). For cell cycle analysis, cells were labeled with FITC-anti-Ki67 antibody (Abcam, Cambridge, MA, USA) and run on a flow cytometer (FACS Calibur, Becton-Dickinson, Franklin Lakes, NJ, USA). Subsequent analyses were performed using FlowJo software (FlowJo LLC, Ashland, OR, USA).
Migration / adhesion assay Migration and Adhesion assays were performed as described previously  [52]. For the adhesion assay, ribavirin-pretreated (48 hrs) or control cells were seeded into 24-well plates coated with gelatin (2 mg/ml, Sigma-Aldrich), Matrigel™ (10 mg/ml, Corning, NY, USA), or laminin (10 µg/ml, Sigma-Aldrich) and incubated for 1hr at 37°C. Cells were then carefully washed 3 times with PBS, fixed, and counted. The number of remaining adherent cells was counted in at least 4 different wells for each condition using a Zeiss Observer Z. 1 AX10 microscope (Zeiss, Thornwood, NY, USA) and ImageJ software (Windows 1. 47, Research Services Branch, NIH, Bethesda, MD, USA).
Invasion assay The invasion assay was performed using 24-well plates with Matrigel-coated inserts (8μm pores, BD Biosciences). Ribavirin-pretreated and control cells (1x10 5 ) were placed in the top chamber containing medium without serum. The lower chamber contained medium with 10% fetal bovine serum. After invasion, non-invasive cells were removed from the top of the insert using cotton swabs. The underside of each membrane was fixed and stained with DAPI (Invitrogen). The number of cells was counted for each condition using a Zeiss Observer Z. 1 AX10 microscope (Zeiss). The data are representative of three independent experiments.
Western blot analysis Cells treated with ribavirin or control were lysed and Western blots were performed using the following rabbit and mouse antibodies: anti-phospho (S209) eIF4E (1/1000, #9742, Cell Signaling, Beverly, MA, USA), anti-eIF4E (1/1000, #9741, Cell Signaling), anti-EZH2 (1/500, #MA5-18108, ThermoFisher Scientific, Waltham, MA USA), anti-phospho (T202/Y204) ERK (1/1000, #4370, Cell Signaling), anti-ERK (1/1000, #9102, Cell Signaling), and anti-p21 (1/1000, #2947, Cell Signaling). Data were normalized to their respective loading controls using a rabbit anti-β-tubulin antibody (1/1000, #2146, Cell Signaling). Gel quantification was performed using ImageJ software.
Orthotopic xenograft experiments Mice (NU/NU athymic) were purchased from Charles River (Cambridge, MA, USA) and intracranially implanted with 5x10 5 BT12 AT/RT cells as previously described  [53, 54]. Animals were given intraperitoneal injections (200µL) of pharmaceutical-grade anesthesia, analgesia, and study agents (water vehicle or ribavirin, 100 mg/kg/day). At day 20 after implantation, mouse brains from vehicle-and ribavirin-treated groups were collected, fixed in formalin, paraffin-embedded, sectioned, and H&E stained. Images were taken using a Zeiss Observer Z. 1 AX10 microscope (Zeiss), and quantification of tumor sizes was performed using ImageJ software. All procedures were performed in accordance with the guidelines set forth by the Johns Hopkins University Animal Care and Use Committee.
Results Translation of MK2 from an alternative start codon in the 5'UTR of its mRNA MK2 is a ubiquitously expressed p38 MAPK -activated protein kinase  (10). When analyzing its expression in different cell lines of murine and human origin or in mouse tissues by Western blotting, a multiple band pattern is detected (Fig. 1A, B, S1 ). Of note, the ratio of isoform expression is distinct and the intensities of the lower and upper band vary between tissues and cell lines. In murine cells and tissues, two predominant bands of approximately 50 and 60 kDa can be detected in Western Blot by using an anti-MK2 antibody directed towards the C-Terminus of human MK2 (Fig. 1A, B ). The low molecular weight isoform corresponds to the hitherto known open reading frame (ORF) of the gene, whereas the origin and function of the slower migrating isoform is obscure, and only one transcript existed in the NCBI database (NM_008551.
2). Importantly, both bands were found to be absent in cells from MK2 knockout (KO, described in  (12) ) and MK2/3 double knockout mice (DKO, described in (  13 )) (Fig. 1A, S1A ). In human cell lines two dominating bands of around 55 kDa together with two less intense bands at around 65 kDa were detected (Fig. 1A, S1C, D ). Two similar human MK2 transcripts (NM_004759. 4 and NM_032960. 3) can be found in the database and arise from alternative splicing. shRNA-mediated knockdown of human MK2 in HT29 cells removed all of these bands confirming that they were derived from the same gene (Fig. S1D ). For the murine MK2-related protein kinase MK3 only one specific protein product is detected and this is absent in MK2/3 DKO cells (Fig. S1A, B ). Taken together, we concluded that the different MK2 protein bands can be assigned to distinct protein isoforms that may originate from post-translational modifications of MK2, alternative splicing or alternative translation events.
Overview scans were acquired at a resolution of 60k in a mass range of m/z 300-1600 in the orbitrap analyzer and stored in profile mode. The top 10 most intensive ions of charges two or three and a minimum intensity of 2000 counts were selected for CID fragmentation with a normalized collision energy of 38. 0, an activation time of 10 ms and an activation Q of 0. 250 in the LTQ. Fragment ion mass spectra were recorded in the LTQ at normal scan rate and stored as centroid m/z value and intensity pairs. Active exclusion was activated so that ions fragmented once were excluded from further fragmentation for 70 s within a mass window of 10 ppm of the specific m/z value. Raw data were processed using Proteome discoverer (Thermo Fisher Scientific) and murine uniprot/swissprot data bases containing common contaminants.
Raw MS data were analyzed using MaxQuant software (version 1. 6. 0. 17). Proteins were identified with parameters previously described (  52 ) and quantified using the LFQ algorithm integrated in MaxQuant  (53). Match between runs function was used. Data analysis was performed using Perseus software  (54). Missing values were imputed in all samples using Perseus default settings. Volcano plots were generated by plotting the -log10 transformed FDR-adjusted p-values derived from a two-sided t-test versus log2 transformed fold changed. Significance was determined based on a hyperbolic curve threshold of s0=0. 1 using Perseus.
RNA extraction and qPCR Total RNA from stimulated cells was extracted using Trizol (Thermo Fisher Scientific/Invitrogen) according to the instructions. 500 ng RNA were then reverse transcribed to cDNA using random hexameric primers and Reverse Transcriptase (Thermo Afterwards, cells on coverslips were washed three times with 1x PBS before dipping them in MilliQ water and drying them. After complete drying coverslips were mounted on a glass slide in 2. 5µl DAPI-containing Vector Shield (Vector Laboratories) mounting medium and fixed on the slide to avoid dissolution. Glass slides were then kept at -20°C until use. Pictures were taken on a Leica DM4 B device using the LasX Software (Leica Microsystems).
Accession numbers Nucleotide sequence data reported in this work is available in the Third Party Annotation (TPA) Section of the DDBJ/ENA/GenBank databases. E. Purification of endogenous MK2 isoforms form RAW264. 7 cells. Endogenous MK2 was purified from 1 x 10 8 RAW264. 7 cells using an agarose MK2-Trap (Chromotek). Input (lysate), column flow-through and precipitates (beads, eluted using 2x SDS loading buffer) were separated on a 10% SDS gel that was Coomassie blue stained afterwards. Bands were excised and further processed for mass-spectrometry analysis. Figure Legends F. Identification of MK2 peptides from mass-spectrometry (1E. ). The three translation initiation sites are marked in bold and italics. Red sequences depict detected peptides and black ones the non-detected. Table S1  G-rich quadruplexes in GC-rich sequences in the MK2 5'UTR Analysis of putative G-rich quadruplex forming sequences within the 5'UTR of murine and human MK2 using the QGRS online tool  (1).
Acknowledgement The following people and institutions are thanked for providing expertise and reagents: Dr. Mathias Ballmaier  and the sorting facility of the  Hannover Medical School (MHH) ,  Prof. Andreas Pich  and the mass-spectrometry facility at the  Hannover Medical School (MHH)  for performing initial mass-spectrometry to identify isoforms. C. T. thanks  Katrin Laaß  for excellent technical assistance.
Residue interaction networks (RINs) have been shown to be relevant representations of the tertiary or quaternary structures of proteins, in particular thanks to network centrality analyses. We recently developed the RINspector 1. 0. 0 Cytoscape app, which couples centrality analyses with backbone flexibility predictions. This combined approach permits the identification of crucial residues for the folding or function of the protein that can constitute good targets for mutagenesis experiments. Here we present an application programming interface (API) for RINspector 1. 1. 0 that enables interplay between Cytoscape, RINspector and external languages, such as R or Python. This API provides easy access to batch centrality calculations and flexibility predictions, and allows for the easy comparison of results between different structures.
The CyREST technology  5  , which is now included as a core app of Cytoscape 3. 3 (and higher versions), enables further analyses, letting the user the possibility to complement analyses done in Cytoscape with scripting developments through external languages like R or Python. We recently developed the RINspector 1. 0. 0 6 app for Cytoscape, which combines centrality analyses of residue interaction networks with flexibility predictions of a protein from its associated sequence through a call to the DynaMine flexibility prediction server  7, 8. Centrality analyses have been shown to identify residues important for functions, folding or allostery, as well as long range interactions (e. g. Refs 9,10). Coupled with flexibility predictions, the app enables users to highlight a subset of these central residues, which might affect the dynamics upon mutation  6.
90GHz, 32Go RAM, 1TB SSD, for RCA: 3m20s for use case 1, 1m47s for use case 2 ; for BCA: 10s for use case 1, 20s for use case 2; for CCA: 7s for use case 1, 17s for use case 2). Automation for RINspector requires Cytoscape 3. 6. 0 (or higher) and RINspector 1. 1. 0. The scripts we provide were developed and tested for Python 3. 5. 2 and R 3. 2. 3, with Cytoscape 3. 6. 1. ○ RCA (.
Use cases We present here two examples that benefit from the API. The first one considers NMR data with 10 conformers of a yeast N-acetylglucosamine transferase, all grouped in the PDB ID 2JZC. The second one is the tetratricopeptide repeat (TPR) domain of a human O-GlcNAc transferase (OGT) (PDB ID 4GYW). Use cases files including scripts, inputs and results are available in the 'use_cases+scripts' folder of the git repository. README. txt files in each use case folder detail the steps to follow to run them. Use case 1: 2JZC NMR data are well suited for automation treatment because the associated PDB files usually contain multiple conformers for which RINs can be generated. We built the RINs in Cytoscape from Chimera through the structureViz2 app. Once these are  created, the RINspector API can be queried to calculate centralities in batch.