dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments-We would like to thank  Alessandro Nonis  and  Federica Cugnata  in the group of  Prof. Clelia di Serio  at  University Centre for Statistics in the Biomedical Sciences (CUSSB) at Universita`Vita Salute San Raffaele, Milan, Italy , for the valuable discussion on the issues arisen from the statistical analysis of this paper. LM designed and carried out all the behavioral procedures with the help of VB. BK and AB performed fMRI. LY and ST performed electrophysiology. AM and EF performed dopamine release experiments. SG participated in the scientific discussion and paper revision. LM and PD wrote the manuscript.
Beijing lineage of Mycobacterium tuberculosis constitutes the most predominant lineage in East Asia. Beijing epidemiology, evolutionary history, genetics are studied in details for years revealing probable origin from China followed by worldwide expansion, partially linked to higher mutation rate, hypervirulence, drug-resistance, and association with cases of mixed infections. Considering huge amount of data available for 24-loci Mycobacterial Interspersed Repetitive Units-Variable Number of Tandem Repeats, we performed detailed phylogenetic and Bayesian population structure analyses of Beijing lineage strains in mainland China and Taiwan using available 24-loci MIRU-VNTR data extracted from publications or the SITVIT2 database (n = 1490). Results on genetic structuration were compared to previously published data.
A total of three new Beijing clonal complexes tentatively named BSP1, BPS2 and BSP3 were revealed with surprising phylogeographical specificities to previously unstudied regions in Sichuan, Chongqing and Taiwan, proving the need for continued investigations with extended datasets. Such geographical restriction could correspond to local adaptation of these "ecological specialist" Beijing isolates to local human host populations in contrast with "generalist pathogens" able to adapt to several human populations and to spread worldwide. Tuberculosis (TB) is one of the main public health problems in the world and its morbidity and mortality rank first among infectious diseases. According to the World Health Organization (WHO), TB caused an estimated 10. 4 million new (incident) cases in 2015, including 480,000 cases of multidrug-resistant TB (MDR-TB), and 1.
Nonetheless, considering the relatively smaller numbers of Chinese Beijing strains in the above studies (615 and 302 isolates, respectively), we decided to evaluate these findings on an enlarged 24-loci MIRU-VNTR dataset (n = 1490 isolates from mainland China and Taiwan; data recovered from the SITVIT2 database and/or from published literature). Following phylogenetic and Bayesian population structure analyses performed on this dataset led to the characterization of tentatively three new Beijing clonal complexes with phylogeographical specificities to previously unstudied geographical regions, proving the need for continued investigations with extended datasets worldwide. Lastly, as a spin-off of this larger study, we also studied the phenomenon of clonal heterogeneity (CH) defined as "inpatient" microevolution of an infecting clone, to explore if any given clonal complex could be more prone to variability (and subsequent geographical adaptability) among the involved isolates  21, 22.
Briefly, the highest mean allelic richness (mean value, 2. 68) was seen in Eastern Asia (which included 615 strains from China). Nonetheless the values observed in Eastern Asia were not statistically different than those observed in Africa, North America, Pacific and Southern Asia, most likely as the strain collection from Eastern Asia was devoid of the BSP1 strains identified for the first time in the present study. Considering significantly higher mean allelic richness for BSP1 in our study than values obtained in each of the geographic regions in Merker's dataset (P < 0. 01, Supplementary Figure  S2a ), it seems crucial to extend future studies to cover all Chinese provinces to fully comprehend the evolution of Beijing strains in China.
Main patterns of tandem repeats (WebLogos) and allelic diversity. Based on its allelic diversity, each loci was classified as highly discriminatory (HGDI > 0. 6, shown in bold font), moderately discriminatory (0. 3 ≤ HGDI ≤ 0. 6, shown as normal font), or poorly discriminatory (HGDI < 0. 3, shown in italic font). As illustrated in Supplementary Fig. S3 , it allowed to define a total of 6 highly discriminatory loci (loci 4052, 424, 1955, 3192, 2163b, 4156), 8 moderately discriminatory loci (loci 3690, 802, 4348, 2996, 2059, 1644, 960, 580), and 10 poorly discriminatory loci (loci 2401, 2461, 2687, 2531, 3007, 3171, 2165, 154, 577 and 2347) in the Chinese dataset. Next, we drew WebLogos to visualize main patterns of tandem repeats for 24-loci MIRU-VNTRs in each BSP grouping in our dataset (Fig.
2b ), as well as on Merker's global Beijing data in function of different geographic regions worldwide (Supplementary Figure  S2b ). When the two datasets (Chinese vs. global) were compared, one could notice that: (i) among the 6 highly discriminatory loci in China, locus 4052 was also highly discriminatory in North America, 2163b in Africa, Eastern Asia, North America and Southern Asia, and 4156 only in Eastern Asia. The three remaining loci (424, 1955, and 3192) showed variable HGDI for the geographic regions in the global study but none was highly discriminatory; (ii) among the loci with moderately discriminatory power in China, 3/8 loci were poorly discriminatory in each geographic region in the global sample (580, 1644, and 2059), while the remaining 5 loci showed variable discriminatory power; (iii) among the 10 poorly discriminatory loci in China, 7 loci (2461, 2687, 3007, 3171, 154, 577, and 2347), were also poorly discriminatory worldwide, while 3 others were moderately discriminatory (2401 in South America, 2531 in South and North America, and 2165 in North America).
20  were scattered over distant branches of loosely-knit BSP1, or close to BL7; nonetheless the number of isolates was too small (n = 29) to conclude. The phenomenon of clonal heterogeneity. We further decided to focus on the phenomenon of clonal heterogeneity (CH) defined as "inpatient" microevolution of an infecting clone, as a spin-off of the larger study by adding 24-loci MIRU data on a total of 106 isolates that were defined as cases of clonal heterogeneity  20, 24, 25. This aimed to explore if a given clonal complex could be more prone to variability (and subsequent geographical adaptability) among the involved isolates. Consequently, a new MST (Supplementary Figure  S4 ) was drawn to investigate the evolutionary relationships of different Beijing subpopulations in the worldwide dataset (n = 6779 strains, including the present study from China) as shown in Fig.
4. Thus, the CH isolates from Yin et al. 20  provide a better comprehension of modern vs. ancient Beijing isolates among CH cases, and further suggest why most of CH isolates (without information on modern vs. ancient Beijing strains) appear as connected with BSP5. Lastly, the detection of CH cases among our dataset (n = 92 entries corresponding to 46 strains, Supplementary Figure  S3 ) was optimal with 6 highly discriminatory loci (loci  4052, 424, 1955, 3192, 2163b, 4156); the values being 60. 88% for this category vs. 23  entries for the group "clonal heterogeneity" representing 53 isolates. Among the latter, 46/53 strains were from our dataset while 7/53 strains were from a recent study by Yin et al. 20  , and described as Modern (10 entries from 5 strains), Ancient (2 entries from 1 strain), and Early ancient (2 entries from 1 strain).
Overall, more people poured in than went out in its history, resulting in the total of 55 ethnic groups with a population of more than 4 million now in Sichuan, probably accounting for BSP1 phylogeographical specificity and genetic diversity. Chongqing, as the only one municipality in inland China sharing the fertile "Sichuan basin", was separated from Sichuan in 1997. Similar to Sichuan, it is striking that both BSP4 and BSP5 are not represented in Chongqing, and that BSP2 represent around 97. 49% of Beijing isolates in this region. Moreover, VNTR 580, as one of the poorly discriminatory loci worldwide (Supplementary Figure  S2b ), showed a moderate discriminatory power in our dataset just caused by BSP1 and BSP2 isolates (Fig. 2b  and Supplementary Figure  S3 ). One may notice that BSP1 and BSP2 shared same primary pattern for VNTR 580 with 3 repeats at this marker (84.
19  (mainly represented in Eastern Asia and North America), as well as with the Ancient Beijing isolates identified by Yin et al. 20  (also reportedly prevalent in Tibet). Concerning the Early Ancient Beijing lineage, these isolates were absent in Tibet, an observation that could be linked to the early history of Tibet being devoid of any significant Han Chinese human influx  20, 26. Regarding BSP5, these strains were scattered all over major nodes of typical/modern Beijing clonal complexes CC1 to CC5, and corresponded to the only clonal complex characterized primarily by 2 copies in VNTR 4156 in the global dataset (Fig. 2b , Supplementary Figures S2b). Interestingly, BSP5 strains were clearly associated with CH isolates in phylogenetic analysis (Supplementary Figure  S4 ), with whom they also shared the characteristic 2 copies in VNTR 4156 (97.
Methods Data collection. The study is based on genotyping data of an initial collection of MTBC clinical isolates (n = 16090) classified as Beijing lineage from mainland China and Taiwan, recovered either from the SITVIT2 database  11  or from published literature (detailed in Supplementary Table  S1 ). Briefly, our collection contained unpublished genotyping data of 193 isolates from our laboratory; genotyping data of 420 isolates from the SITVIT2 database; and 15477 from published literature, including 2652 also provided in the SITVIT2 database. Available genotyping data comprised spoligotyping and/or MIRU-VNTRs  [41] [42] [43]. The 24-loci MIRU-VNTR data (n = 1490 Beijing isolates) were recovered from 6 regions, including Tibet, Sichuan, Chongqing, Beijing, Xinjiang and Taiwan. In addition, 68 isolates with clonal heterogeneity were collected in this study, which were identified from a Chinese national survey including 3929 cases from all over the country  24, 25.
Acknowledgements The author CZ thanks for the financial support from  China Scholarship Council  ( CSC ,  File NO. 201506240129 ). This work was supported by a  FEDER grant , financed by the  European Union and Guadeloupe Region  ( Programme Opérationnel FEDER-Guadeloupe-Conseil Régional 2014-2020 , Grant number  2015-FED-192 ). YR was awarded a Calmette and Yersin postdoctoral fellowship by the  Institut Pasteur International Network. Help of David Couvin  for the construction of the SITVIT2 database is gratefully acknowledged.
Here, we extend this study by solving the crystal structures of human BChE in complex with five reversible ligands, namely, decamethonium, thioflavin T, propidium, huprine, and ethopropazine. We compare these structures to equivalent AChE complexes when available in the protein data bank and supplement this comparison with kinetic data and observations from isothermal titration calorimetry. This new information now allows us to define the binding mode of various ligand families and will be of importance in designing specific reversible ligands of BChE that behave as inhibitors or reactivators.
Molecular Docking to the AChE and BChE Active Site Gorges Molecular docking has become an important tool for the design of AChE inhibitors with improved affinity and selectivity. But docking is quite challenging in the AChE active site because multiple X-ray structures have revealed a large mobility of aromatic P-site and A-site residues to accommodate ligands, especially Trp286 and Tyr337 in hAChE. Therefore, the flexibility of aromatic side chains must be taken into account in docking strategies, either by docking a candidate ligand to various representative conformations of AChE or by allowing side chain flexibility during the docking search as performed by software like Autodock Vina  [53]. This complication leads to a considerable increase in calculation time and prevents the screening of large virtual ligand databases.
Furthermore, changes in main chain positions are often observed upon ligand binding and cannot be predicted by current docking software. Time-consuming molecular dynamics simulations of the AChE ligand complex is the sole method overcoming this issue, but this approach is unsuitable for large database screening. Another factor that alters docking accuracy in AChE is the consideration of the highly-conserved network of water molecules that significantly contribute to the gorge environment  [54]  (Figure  1A ). Omitting these water molecules during docking could lead to unrealistic binding poses. Consideration of the protonation state of Glu202 and His447 may also have a serious impact on docking pertinence. Indeed, both residues could be either protonated or deprotonated at physiological pH  [31] , thus affecting the electrostatic field in the A-site and changing an H-bond acceptor to a donor and vice-versa.
X-ray Data Collection and Structure of BChE-Ligand Complexes Diffraction data were collected at the European Synchrotron Radiation Facility (ESRF, Grenoble, France) at different beam lines (ID14-4, ID23-1, ID23-2 and ID29-1). All data sets were processed with XDS  [62] , intensities of integrated reflections were scaled using XSCALE, and structure factors were calculated using XDSCONV. The structures were solved with the CCP4 suite  [63]  using the recombinant BChE structure (PDB entry 1P0I and 4AQD) as the starting model. The initial models were refined by iterative cycles of model building with Coot  [64] , then restrained and subjected to TLS refinement with Phenix  [65]. The ligands and their descriptions were built using phenix. elbow included in Phenix. Data collection and refinement statistics are reported in Table  S1.
Inhibition of Second-Order Substrate Hydrolysis Inhibition constants for inhibitors of BChE were determined using a modification of a previously described method  [15]. Briefly, varying amounts of inhibitor (in 50 µL of 50% CH 3 CN (aq) ) were added to 1. 60 mL of buffer (0. 09 M phosphate buffer, pH 8. 0), 5,5 -dithio-bis (2-nitrobenzoic acid) (0. 32 mM), and butyrylthiocholine substrate (5 µM) in a quartz cuvette of 1 cm path length, and the mixture was zeroed at 412 nm. The reaction was initiated by the addition of 50 µL of BChE (to 0. 68 nM) in 0. 1% aqueous gelatin. Assays were conducted at 23 • C. The absorbance (A), reflecting the cumulative hydrolysis of the substrate, was recorded every 5-20 s using an Ultraspec 2100 pro ultra violet (UV)-visible spectrophotometer (Fisher Scientific, Toronto, ON, Canada) equipped with Swift II application software.
The fitting of kinetic data with inhibitors was conducted by nonlinear regression with SigmaPlot 12. 0 (Systat Software, San Jose, CA, USA), and analyses were either unweighted (Equation (1)) or weighted assuming that the dependent variable had constant percent error [67] (Equations (  2 )-(  5 )). Second order rate constants for substrate hydrolysis (k E ) were obtained from Equation (1) at low initial substrate concentration ([S] 0 ) (i. e. , [S] 0 ≤ ~0. 2 K app , [68]) where K app is the apparent Michaelis constant)  [15]  and [S] 0 is the initial concentration of substrate. A 412 = A 412(final) -∆A 412 e -k obs t (1) In Equation (1), A 412 is absorbance, [E] tot is the total enzyme concentration, k obs = k E [E] tot , and ∆A 412 = A 412(final) -A 412(t = 0). With substrates that are hydrolyzed by AChE or BChE at nearly diffusion controlled rates, inhibitor binding to either the A-or the P-site, but not both, can be analyzed with Equation (2)  [43] , k E[I]=0 k E+I = 1 + I K I ( 2 ) where k E[I] = 0 is k E measured in the absence of inhibitor, k E + I is k E measured with a fixed concentration of inhibitor I, and K I is the inhibition constant (equilibrium dissociation constant) for the enzyme-inhibitor complex.
Acknowledgments: The authors gratefully acknowledge the  Direction Générale de l'Armement (DGA)  and  Service de Santé des Armées (SSA)  of the  French Ministry of Armed Forces  for sustained financial support (currently under grant  PDH-2-NRBC-3-C-3201 ),  Canadian Institutes of Health Research  ( MOP-119343 ), Faculty and  Department of Medicine of Dalhousie University, Gunn Family Research Prize ,  DeWolfe Graduate Studentship , the  Dalhousie Medical Research Foundation Irene MacDonald Sobey Endowed Chair in Curative Approaches  to Alzheimer's Disease. We thank the  ESRF  for beam-time under long-term projects ( IBS BAG ) and the beam staff for help during data collection. We dedicate this article to our dear colleague  Marie Trovaslet-Leroy  who passed away before his time.
Parameterization of data from LHAASO hybrid detectors The schematic of LHAASO Hybrid detectors is shown in Fig. 1. The WCDA is located in the central area of the observatory, covering a 300 m × 260 m rectangular area. It consists of three large water pools, which are segmented into 5 m × 5 m grids as single detector units. WCDA has a large duty cycle (>90%) and a wide field of view (∼1. 5 sr) in the detection of EAS. In cosmicray measurements, one of the WCDA pools (150 × 150 m 2 ) is used for the detection of shower geometries. Each cell in this pond is equipped additionally with a 1-inch small photomultiplier tube (PMT), which can extend the dynamic range for cosmic-ray detection  [7]. The telescopes of WFCTA are deployed alongside this shower core detector array providing calorimetric energy measurements.
Each telescope has a field of view (FoV) of 14 • × 16 •. The KM2A, surrounding the central area, is a complex array composed of electromagnetic particle detectors (ED) and muon detectors (MD). The KM2A detectors are uniformly distributed in the remaining area of the observatory, covering nearly 1 km 2. In the EAS detection, these detector arrays can be combined together for hybrid measurements, yielding a precise reconstruction of the shower parameters. A shower library simulated by using the CORSIKA package  [6]  with a slope of -2. 3 for the energy spectrum over the energy range from 100 TeV to 1 PeV, covering a FoV from 22 • to 38 • for zenith (θ ) and from 77 • to 103 • for azimuth (ϕ), is used in this work. The shower library consists of five mass compositions for the cosmic rays: p, He, CNO, MgAlSi and Iron.
The responses of the three detector arrays are simulated for the shower parameters saved in binary files. In the detector simulation, one WFCTA telescope, pointing to (θ , ϕ) = (30 • , 90 • ) is employed to work together with WCDA and KM2A-MD. The shower events triggered by both the WCDA-core detector array and the WFCTA telescope are selected for further analysis. Parameterization of the WCDA data Fig. 2  shows the signals in p. e. numbers measured in each cell of WCDA for a shower event. The core position and the arrival direction of the shower can be obtained with a precision of ∼4. 2 m by fitting the hump of S WCDA. Fig. 2  shows the lateral distribution of S WCDA (binned in each 5 m). By fitting the lateral distribution of S WCDA with an NKG-type function (Eq. 2. 1), the reference parameter of WCDA signals, S re f , can be obtained to describe the shower size.
Results and discussion The BDTG classifiers for the separation of Iron/p and heavy nuclide/(p+He) are trained and applied to the simulation data of LHAASO hybrid detection. The responses of the two separations are shown in Fig. 6 -left and Fig. 6 right. The cut values for Iron/p and heavy nuclide/(p+He) separations are set to 0 and to -0. 1, respectively, based on the analysis of Signal-to-Background ratio. The cut efficiency and the contamination of each separation are calculated based on the response of each event weighted by its proportion of flux in the cosmic-  ray spectrum following the Höerandel model  [10]  (see in Fig. 7 ). For the separation of iron/p, nearly perfect results approximating the theoretical limit are obtained with the cut efficiency > 95% and the contamination < 5% over the energy range from 100 TeV to 1 PeV.
Summary Primary particle identification based on the simulations of the LHAASO hybrid detection of the EAS has been implemented by using MVA methods over the energy range from 100 TeV to 1 PeV. Simulated data for various detector arrays of LHAASO are parameterized and tuned for the training of the MVA classifiers. The first results show perfect separations for iron/p and good separations for heavy nuclide/(p+He) with the BDTG classifier. Further studies will be performed with better statistics for mass composition and for a larger energy range of cosmic rays.
Introduction Near the phylogenetic root of the Kingdom Fungi lies a branch that unites Microsporidia and a group variably known as Rozellomycota or Cryptomycota. This clade of intracellular parasites are considered energy thieves, because they import ATP from their hosts. The clade encompasses a great diversity of morphologies from the fungus-like Rozella spp. to the highly derived Microsporidia which are best known for the reductive nature of their cells  (Hirt et al. , 1997; Williams et al. , 2002; Burri et al. , 2006). The biology of Rozellomycota is unclear, being almost exclusively documented from diverse DNA sequences commonly found in environmental sequencing datasets from terrestrial and aquatic ecosystems  (Lara et al. , 2010; Jones et al. , 2011; Livermore and Mattes, 2013; Lazarus and James, 2015).
(B) Topo map overlaid with contig median coverage; the most common P. saccamoebae coverage class (50-59x) is highlighted in pink; this alone was not enough to identify P. saccamoebae-specific contigs. (C) Contigs with blastn hits to the de novo Trinity assembly are in yellow, and found in many parts of the map including concentrations in the P. saccamoebae area, whereas those contigs with no hit to the assembly (in white) are not found in P. saccamoebae area. (D) Phylogenetic classes mapped onto the topology (based on best blastn to GenBank 'nr' database). P. saccamoebae region contains contigs with hits to mostly Fungi, animals, NO HIT, plants, Nuclearia, Microsporidia, and the P. saccamoebae rDNA. There are a few hits to bacteria that map within the genome, which were further tested to insure they belong on contigs which have multiple fungal proteins on them as well.
SmrtPortal v2. 2. 0 was used for assembly of the PacBio data, with the RS_HGAP_Assembly. 3 program. This genome assembly with 437 scaffolds, 16. 7 Mb, and an average 40x coverage, was exclusively bacterial in nature, and used to remove 'non-target' bacterial raw reads from the Illumina data. Raw Illumina reads were trimmed (of first 5 bases and last 16 bases for a total length of 80 bp) and filtered (all bases q-score !20) for quality using scripts as a part of the Fastx toolkit  (Gordon, 2011) , and then aligned to the PacBio assembly using Bowtie2  (Langmead and Salzberg, 2012)  with default settings, resulting in 127,660,572 reads. These remaining reads (those that did not align) were assembled with Velvet v1. 2. 10 (  Zerbino and Birney, 2008)  with a kmer size of 53 and binned based on coverages estimated with MetaVelvet v1.
2. 02  (Namiki et al. , 2012)  before subsequent rerun of velvetg that resulted in a final metagenome assembly size of 72 Mb on 3843 contigs. To identify target genome contigs, analysis and visualization of tetramer frequencies was conducted using the emergent self-organizing map (ESOM) program  (Ultsch and Mo ¨rchen, 2005)  and associated scripts developed for metagenomic binning  (Dick et al. , 2009; Anderson et al. , 2010)  (Figure  1 ), with default settings and window size of 3000. Visualization within ESOM used the default settings except for the following: K-batch training algorithm, 150 rows, 210 columns, and a start radius value of 50. The resulting subsection of contigs is the one referred to in the results was used for all downstream analyses. The CEGMA program (v 2. 5) was used to find core eukaryotic genes for assessment of genome assembly completeness  (Parra et al.