dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements: Funding for the  NIHR-BioResource  was provided by the  National Institute for Health Research  ( NIHR , grant number  RG65966 ). We gratefully acknowledge the participation of all  NIHR BioResource volunteers , and thank the  NIHR BioResource centre  and staff for their contribution. JEDT is supported by the  MRC  ( RG95376  and  MR/L006197/1 ). AJT is supported by the  Wellcome Trust  ( 104807/Z/14/Z ) and the  NIHR Biomedical Research Centre at Great Ormond Street Hospital for Children NHS Foundation Trust and University College London. KGCS is supported by the  Medical Research Council  (program grant  MR/L019027 ) and is a Wellcome Investigator. AJC was supported by the  Wellcome  [ 091157/Z/10/Z ], [ 107212/Z/15/Z ], [ 100140/Z/12/Z ], [ 203141/Z/16/Z ]; JDRF [9-2011-253], [5-SRA-2015-130-A-N];  NIHR Oxford Biomedical Research Centre  and the  NIHR Cambridge Biomedical Research Centre.
EE has received funding from the  European Union Seventh Framework Programme  ( FP7-PEOPLE-2013-COFUND)  under grant agreement no 609020-Scientia Fellows. ER is supported supported by the  Wellcome Trust  [ 201250/Z/16/Z ]. DE is supported by the  German Federal Ministry of Education and Research (BMBF)  within the framework of the e:Med research and funding concept (SysInflame grant  01ZX1306A ;  GB-XMAP grant 01ZX1709 ) and funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany`s Excellence Strategy -EXC 2167-390884018. Author Contributions: JEDT, ES, JS, ZZ, WR, NSG, PT, ER, AJC carried out experiments. HLA, OSB, JEDT, JHRF, DG, IS, CP, SVVD, ASJ, JM, JS, PAL, AGL, KM, EE, DE, SFJ, THK, ET performed computational analysis of the data. HLA, IS, CP, MB, CrS, RL, PJRM, JS, KES conducted sample and data processing.
Data Availability WGS and phenotype data from participants is available from one of 3 data repositories determined by the informed consent of the participant. (1) Data from participants enrolled in the NIHR BioResource for the 100,000 Genomes Project-Rare Diseases Pilot can be accessed via Genomics England Limited: https://www. genomicsengland. co. uk/about-gecip/joining-research-community/. (2) data from the UK Biobank samples are available through a data release process overseen by UK Biobank (https://www. ukbiobank. ac. uk/). (  3 ) data from the remaining NIHR BioResource participants is available from the European Genome-phenome Archive (EGA) at the EMBL European Bioinformatics Institute (EGA accession code EGAD00001004523). Patients all fall into group (3) and controls into groups (1)-  (3).
Preliminary Results in Other Malignancies Tagraxofusp treatment is not specific to BPDCN. It has been tested in relapsed or refractory (R/R) CD123expressing myeloproliferative neoplasms, including AML and myeloproliferative neoplasms (MPN) such as chronic myelomonocytic leukemia (CMML) and myelofibrosis (MF). Related early-stage clinical trials are ongoing (NCT02268253, NCT02270463). Preliminary results have already been communicated. In 18 R/R CMML patients, tagraxofusp showed 3 complete responses in bone marrow and 100% responses in spleen, including a 67% of reduction of ≥ 50%. 28 n 27 R/R MF patients, tagraxofusp treatment significantly decreased symptoms in 45% of the patients, and reduced spleen size in 53% of the 17 evaluable patients with baseline splenomegaly. 29 n both cases, the most frequent treatment-related adverse events were hypoalbuminemia (31 to 43%), thrombocytopenia (24 to 30%) and nausea (24 to 38%).
, 2018). However, the intrinsic features of subterranean environments (e. g. , narrow passages, air moisture near saturation) represent a natural challenge for researchers  (MacNeil & Brcic, 2017) , making it very difficult to collect data on subterranean populations. Using noninvasive methods, such as photographing individuals for measurements and identification, may not only limit negative effects incurred by wildlife, but may also alleviate the complexities of prolonged surveys performed in "nonhuman friendly" conditions that characterize subterranean and forest environments where plethodontids are found. In a previous study,  Lunghi et al. (2020)  produced a photographic database of European Hydromantes, providing high-quality pictures of the dorsal pattern for more than 1,000 individuals of all eight species.
The authors photographed salamanders in situ placing them in a white soft box to obtain high-quality photograph  (Lunghi et al. , 2020). During this study, 22 individuals (~3 per species) were randomly selected and photographed ventrally (Figure  1a ). Before photographing the salamanders,  Lunghi et al. (2020)  held them for 30 s in hand to increase their body temperature, which caused a short thermal shock that made them calm for a few seconds (Lunghi   , 2016). These digital images were used to measure the 22 salamanders using the program ImageJ (Figure  1a ). Dorsal and ventral images of each individual were paired. We measured SVL of each individual using images from the ventral view and used it as reference of a "true" measure  (Margenau et al. , 2018). Then, we asked 31 volunteers with different backgrounds (students or experienced herpetologists) to use ImageJ and estimate the SVL e from the dorsal view (Figure  1b ).
We used linear mixed models (LMMs) (function lme of the R package nlme; Pinheiro, Bates, DebRoy, Sarkar, & Team, 2016; R Development Core Team, 2019) to evaluate the relationship between SVL and SVL e in European Hydromantes. The salamanders' SVL e was used as the dependent variable and the SVL as the independent variable. The experience of operators (yes/no), together with the identity of operators, salamanders, and species, were assigned as random factors. The correlation coefficient between SVL e and SVL was calculated with the function r. squaredGLMM of the R package MuMIn  (Bartoń, 2016). We calculated the mean squared error (MSE) between SVL and SVL e and assessed whether it significantly differed from zero. We used LMM to evaluate the relationship between the MSE (dependent factor) and SVL (independent factor); considering that the operator experience may affect measurement equality between SVL and SVL e , we added "Expert" as a further independent variable to assess whether there were differences in SVL e between students and experienced herpetologists.
| D ISCUSS I ON Besides the reliable estimation of the "true" SVL, the use of SVL e shows some noteworthy advantages. For example, the time available to measure SVL e from pictures is virtually endless, whereas data collection in the field is more time restrictive. As mentioned above, measuring SVL in the field is difficult and may be prone to increased error rate  (Guo, Chen, Zhang, Pan, & Wu, 2016; Luiselli, 2005; Setser, 2007). Once a salamander has been released, SVL cannot be measured again, so unnoticed errors that occurred while measuring will become part of the dataset  (Brown et al. , 2018). Furthermore, prolonged handling stress produces negative effects on animal health  (Allen-Blevins et al. , 2017; Bliley & Woodley, 2012; Caipang et al. , 2014; Lunghi et al. , 2016). Therefore, photographing salamanders and the post hoc measurement of SVL e facilitates easier measurements, requires shorter handling time with images of sufficient quality  (Bradley, 2018; Lunghi et al.
Oppositely to a previous statistical work using a single time resolution of the total ion density measured onboard the Detection of Electro-Magnetic Emissions Transmitted from Earthquake Regions (DEMETER) satellite, this work deals with statistical seismo-ionospheric influences by comparing different parameters and various time resolutions. The O + density and electron density recorded by the China Seismo-Electromagnetic Satellite (CSES) for more than one year and by the DEMETER satellite for about 6. 5 years have been utilized to globally search ionospheric perturbations with different time resolutions. A comparison is automatically done by software between the occurrence of these ionospheric perturbations determined by different data sets, and the occurrence of earthquakes under the conditions that these perturbations occur at less than 1,500 km and up to 15 days before the earthquakes.
Combined with statistical results given by both satellites, it is shown that the detection rate r of earthquakes increases as the data time resolution and the earthquake magnitude increase and as the focal depth decreases. On average, the number of perturbations is higher the day of the earthquake, and then smoothly decreases the days before, which is independent of either ionospheric parameters or time resolutions. The number of right alarms is high near the South Atlantic Magnetic Anomaly area but its relationship with seismic activities is weak. The ion density tends to be more sensitive to seismic activities than the electron density but this needs further investigations. This study shows that the CSES could effectively register ionospheric perturbations due to strong EQs as the DEMETER satellite does.
, 2016 Li et al. , , 2019;; Molchanov et al. , 2004; Molchanov & Hayakawa, 2008; Pulinets et al. , 1994 Pulinets et al. , , 1997 Pulinets et al. , , 2000;; Pulinets & Ouzounov, 2011; Sorokin et al. , 2015, and references therein). Irregularities in the ionospheric sounder data before the Alaskan earthquake taking place on 28 March 1964 were reported as early as 1965  (Davies & Baker, 1965). However, examples have been recently intensively reported about seismic influence on different ionospheric parameters as modern satellite-borne receivers develop, especially after the launch of the DEMETER (Detection of Electro-Magnetic Emissions Transmitted from Earthquake Regions) satellite in 2004 in France. Ionospheric variations have been confirmed before the L'Aquila M S 6. 2 EQ on 6 April 2009 on GPS TEC (total electron content) and DEMETER IAP (Instrument d'Analyse du Plasma) ion density and ISL (Instrument Sonde de Langmuir) electron density  (Akhoondzadeh et al.
, 2014). This shows that various ionospheric parameters measured by several scientific payloads onboard a satellite can response to a seismic event, especially for strong EQs. There has been many statistical works on ionospheric variations associated with strong seismic events using satellite measurements. Seismo-ionospheric disturbances within a few days before EQs have been registered in 73% of EQs with magnitude 5. 0, and in 100% of EQs with magnitude 6. 0  (Pulinets et al. , 2003). Liu, Chen, et al. (2009)  have performed a statistical analysis on GPS TEC and found that seismo-ionospheric variations above the epicentral area occurred 5 days prior to 16 EQs out of 20 events with M ≥ 6. 0 in the Taiwan area from September 1999 to December 2002. Statistical analyses have also been performed using DEMETER data sets.
0 during the entire operation period of the DEMETER satellite. However,  Akhoondzadeh et al. (2010)  statistically analyzed the ionospheric variations prior to four large EQs simultaneously using, on one hand, several satellite parameters of DEMETER: IAP ion density and ion temperature, ISL electron density and electron temperature, and on the other hand, GPS TEC. Their results show that there is a very good agreement between the different parameters and that, most positive and negative anomalies appeared 1 to 5 days before all studied EQs during quiet geomagnetic conditions, although their amplitude depends on the magnitude of the EQs involved. The CSES (China Seismo-Electromagnetic Satellite) has been launched for more than one year and effective payload data have been recorded. It is possible to check its response to seismic activities during this period.
So, in this paper, a primary statistical analysis on seismo-ionospheric influence of different parameters of ion density and electron density recorded by CSES and DEMETER satellites will be comparatively shown. The CSES and DEMETER satellites are briefly described in section 2. In section 3, the data processing method is retrospectively introduced. In section 4, automatic statistical results for different parameters recorded by CSES will be compared with that of DEMETER and confirmed. Discussion and conclusions are provided in section 5.
, 2006). DEMETER is a low-altitude satellite with an altitude of 710 km, which was decreased to 660 km in December 2005. The orbit of DEMETER is nearly sun-synchronous and the upgoing half-orbits correspond to night time (22. 30 LT) whereas the downgoing half-orbits correspond to day time  (10. 30 LT). The onboard experiments of DEMETER includes six scientific payloads, the IAP-plasma analyzer instrument, the ICEelectric field instrument, the ISL-Langmuir probe, the IMSC-search-coil magnetometer instrument, the IDP-particle detector instrument, and the BANT-an electronic unit. The variations of the ion density and the electron density are measured by the instrument IAP and ISL, respectively. These two experiments have different operational modes. The ISL is with an experimental resolution of 1 s for all data, while the IAP has two experimental data resolutions of 4 s in survey mode and 2 s in burst mode.
Data Processing Method The data processing method here is similar to the one used before by  Li and Parrot (2012, 2013). First of all, a software is used to automatically search global ionospheric perturbations in several data sets (issued from CSES or DEMETER). Only the perturbations which comply with the duration time between 20 and 120 s are kept in the perturbation database but without a limit on perturbation amplitudes. Here, the minimum duration time 20 s is calculated automatically by the improved software according to data sample rate instead of previously used ~23 s, five data points for survey mode in IAP DEMETER  (Li & Parrot, 2012 , 2013). The information for each perturbation in the perturbation database includes peak appearing time, orbit number, location (latitude and longitude), background value, amplitude, change trend (increase or decrease; if the amplitude is larger than the background value, it is increase, if not, it is decrease), increase or decrease percentage, duration time, and extension distance (km).
In order to examine the capability of recording seismic influence on the ionosphere with different data time resolutions using our software, the raw data are sampled at different time resolutions: 1 and 3 s for PAP O + density and 3 s for LAP electron density of CSES; 4 s for IAP O + density, and 3 and 4 s for ISL electron density of DEMETER. Thus, six data sets have been established: PAP-1 s, PAP-3 s, and LAP-3 s for CSES; IAP-4 s, ISL-3 s, and ISL-4 s for DEMETER. At the same time, the SAVGOL method is employed to smooth the data before searching for perturbations. The SAVGOL function returns the coefficients of a Savitzky-Golay smoothing filter  (Savitzky & Golay, 1964). So, at this stage, two O + perturbation databases (1 and 3 s data are used respectively, including 48,529 and 24,438 perturbations in total) and one electron database  (3 s data, 26,782 perturbations) for CSES, and one O + perturbation database (4 s data, 74,959 perturbations) and two electron databases (3 and 4 s data, 44,285 and 32,627 perturbations) for DEMETER have been established.
An example of ionospheric perturbation detected by the software and corresponding to an EQ is shown in Figure  1. It corresponds to an EQ occurring on 21 August 2018 at 21:31:47 UT with a magnitude equal to M W 7. 3 and a depth equal to 147 km. Its position was 10. 77°N, 62. 90°W. Figure  1a  shows variations of PAP parameters. From the top to the bottom, the panels show the densities of the H + , He + , and O + ion. The X axis represents UTC (Universal Time Coordinated)/BJT (Beijing time), latitude, longitude, and altitude of CSES. At 6:00 UT, about 15 h before this EQ, the orbit 03040 flew up this epicentral area and an increase of O + labeled by a black arrow in the bottom panel with red curve is observed in Figure  1a  (this variation seems not obvious at this period because it is formed automatically by the CSES data processing system).
For each group of EQs, they are detected under the conditions of D = 1,500 km, T = 15 days, and the focal depth d = 0-1,000 km with different ionospheric databases confirmed by data sets of PAP-1 s and PAP-3 s data and LAP-3 s from CSES. To check effects of the focal depth (d) of earthquakes on the detection rate, "crust" earthquakes with d ≤ 20 km have been comparatively detected for each group of EQs. The Kp index is kept to be less than 3 during this period in order to eliminate the effect from solar activities. Their corresponding detection rates are listed in Table  2. From Table  2 , except M > 6. 0 with contrary result due to its less examples (169 in total and 77 with the depth d ≤ 20 km), the detection rate r for "crust" EQs with d = 0-20 km is thoroughly higher than that of EQs with d = 0-1,000 km for each group of EQs in all data sets.
8 ≤ M W ≤ 5. 0, 5. 0 < M W ≤ 6. 0, and M W > 6. 0 groups of EQs tend to be all higher a little than that of LAP but this law are contrary for the two group EQs of Zone1 and Zone2. In order to confirm the results given by CSES, the 21863 EQs occurring during the DEMETER 6. 3. For a better comparison, the data of Table  3  is shown in Figure  3  under the form of histograms. From Table  3  and Figure  3 , one thing we can reconfirm is that the detection rate r increases as the time resolution of the same ionospheric parameter ISL electron density increases. Another point is that the detection rates for IAP density are clearly higher than that of ISL density for each group of EQs when the time resolution is the same, which probably gives a conclusion that the ion density is more sensitive to seismic activities than the electron density although this claim is not completely verified by the CSES data.
Discussion and Conclusions As CSES has run more than 1 year since it was launched on 2 February 2018 in China, this increases the probability to examine the effectiveness of seismo-ionospheric influences recorded by the scientific payloads of PAP O + density and LAP electron density. For comparison, two corresponding DEMETER parameters of IAP and ISL for about 6. 5 years have also been checked to gain some similar results. Oppositely to previous statistical works with DEMETER using a single time resolution of one parameter of IAP total ion density  (Li & Parrot, 2012 , 2013) , this work is associated with statistical seismo-ionospheric influences using two parameters recorded by the DEMETER satellite and CSES, respectively. At the same time, various time resolutions for a given parameter are also employed to survey the effects of seismic activities on the ionosphere and also to check the efficiency of our two software.
, 2010; Liu, Chen, et al. , 2009)  and with the statistical work conducted by  Li et al. (2019) , who found that ionospheric variations mainly happened 6 days and gained a high climax 3 days before the Wenchuan main event on 12 May 2008 in the light of different authors (can be referred to Figure  4  in  Li et al. , 2019). However, the abnormal range in the ionosphere arise from seismic activities has not been well established so far. The software in this paper is designed to only accept ionospheric perturbations with a duration of 20-120 s (about 160-840 km if the speed of the satellite 7. 0 km/s is considered) and positive results have been given. In order to further examine this design is effective for the investigations, variations with a duration of 200-300 s using PAP-1s data set from August 1 2018 to November 30 2019 have been accepted this time and 509 ionospheric perturbations have been attained in total.
This combined to the fact that Zone1 is the seismic zone which is at the highest geomagnetic latitude make the number of ionospheric perturbations higher than in another seismic region. Overall, the results given by CSES and DEMETER have shown the following: 1. The CSES ionospheric data can effectively respond to strong EQs. 2. The detection rate r increases as the time resolution of the satellite data, and the magnitude of EQs involved increase and decreases as the epicentral depth of seismic events increases. 3. On average, the occurring frequency of perturbations is higher the day of the EQ and then gradually decreases before the event. This is independent of the data sets we use, either the ion density or the electron density, and whatever are their time resolutions. 4. The ion density seems to be more sensitive to the seismic activities than the electron density, but it needs further investigations with more data.
5 year life time have been classified into six groups of EQs: 4. 8 ≤ M W ≤ 5. 0 12057 EQs, 5. 0 < M W ≤ 6. 0 8953 EQs, M W > 6. 0 853 EQs, Zone1 [70°S, 45°S] [150°W, 20°W] 615 EQs, and Zone2 [0°N, 30°N] [90°W, 150°W] 4,549 EQs. They are also detected with the same software under the conditions of D = 1,500 km, T = 15 days, and d = 0-1,000 km, and Kp < 3 with the different ionospheric perturbation databases named IAP-4 s, ISL-3 s, and ISL-4 s (see section 3. 2). Their corresponding detection rates r are listed in Table.
Acknowledgments This work uses data from the CSES mission, a project funded by the  China National Space Administration (CNSA)  and the  China Earthquake Administration (CEA). This work was also supported by the  Centre National d'Etudes Spatiales. It is based on observations with the plasma analyzer IAP and the Langmuir probe ISL embarked on DEMETER. This work was supported by the  National Key R&D Program of China  under Grant No. 2018YFC1503506  and the  National Natural Science Foundation of China (NSFC)  under Grant No. 41774084.
INTRODUCTION Valuable mechanistic and functional information can be obtained from protein structures modeled at atomistic resolution  (1) (2) (3). Due to the growth of experimentally determined structures deposited in the Protein Data Bank (PDB)  (4) , currently there are >160 000 3D structures of macromolecules available in the database  (4). As structural biology has mainly focused on determining the structure of globular proteins until the recent past, the presence of intrinsically disordered protein (IDP) regions (IDRs) have mostly been inferred either from unresolved or proteolytically digested tails or loops of these globular structures solved by X-ray crystallography, or from shorter regions yielding few structural constraints in nuclear magnetic resonance (NMR) spectroscopy measurements  (5).
As a result, determining an ensemble is a mathematically 'ill-posed' or 'underdetermined' problem that has more than one solution. We don't yet know how to select the 'best' ensemble from multiple alternatives, neither can we be sure if an actual ensemble is a faithful representation of the real physical state of the IDP/IDR, nor is only a reasonable fit to experiment observations. To help address these issues, IDP/IDR ensembles solved at the time were collected and made available in the dedicated Protein Ensemble database (published as pE-DB in 2014  (26) , renamed as PED in later versions). This first version was an ambitious attempt to fill the niche in the deposition of ensembles of fully disordered proteins and proteins with IDRs. At the time of the publication, it only stored data for a few dozens of ensembles for a limited set of proteins, which increased very slowly in the following years.
Manual deposition and validation of entry submissions used to hinder the smooth maintenance and increment of the database. A lot has happened, however, in the structural-functional characterization of IDPs/IDRs since the inception of PED. For example, it has been proven that structural ensembles can predict independent structural data  (24, 26) , i. e. they are realistic and do have predictive power. Based on novel, better suited force-fields  (29) (30) (31) (32)  the computational simulation of IDPs has also significantly advanced  (33, 34). Influential IDP-related databases have been either updated (e. g. DisProt  (35)  or MobiDB  (36) ) or created anew (e. g. MFIB  (37)  or DIBS  (38) ). Successful targeting of IDPs/IDRs by small molecules offers hope for a new class of effective drugs  (39, 40).