dataesr/scientific-paragraphs-categorization huggingface dataset:
Image Acquisition and Analysis Stained slices were visualized with a QImaging Retiga EXI camera (Qimaging Surrey, BC, Canada), and scanned with an Optigrid II (Thales Optem, Qioptik, Rochester, NY, USA) on an inverted Olympus IX81 microscope. The Optigrid system permitted the acquisition of structured images Stacks of 50-80 images (zstep, 0. 7 μm) were acquired per slice, using an oil immersion objective (20x, NA 0. 9). Presubicular layers and borders were defined using specific cytoarchitectonic features identified by DAPI staining. Images were uniformly adjusted for contrast and brightness. For each brain slice, fluorescent tdTomato+ or GFP+ cells from all layers of the presubiculum were identified visually by complete scans of optical sections. Cell counting was performed using Volocity software (Improvision, Perkin-Elmer, Coventry, UK) to measure cell numbers in defined volumes.
Electrophysiological Analysis Recorded signals were analyzed with Axograph X and routines written in MATLAB (The Mathwork). Cellular parameters were measured at least 3-5 min after whole-cell records were established. RMP was the mean potential over at least 10 s. Most electrophysiological parameters were measured from responses to step current injections of 800 ms duration applied from a fixed membrane potential of -65 mV. Injected currents increased from negative to positive values, with a range of amplitudes that resulted in hyperpolarization to about -100 mV during the first step and depolarizations to maximum sustained firing frequency. Depending on the resistance of the recorded neuron, the first hyperpolarizing step was in the range of -50 pA to -500 pA with a step change of 5-50 pA.
Slices were mounted on coverslips using anti-fade Prolong Gold medium (Life technologies). Filled cells were visualized with a QImaging Retiga EXI camera on an inverted Olympus IX81 microscope. Structured images were acquired with an Optigrid system and Volocity software (Improvision, Perkin-Elmer, Coventry, UK). Stacks of 75-250 images (z-step 0. 7 μm) were acquired with a 20X, 0. 9NA oil immersion objective. Stacks were then imported to the Neurolucida software (Microbrightfield, Williston, VT, USA) for three-dimensional reconstruction. The "layer length" analysis feature of the Neurolucida software was used to measure dendritic and axonal lengths in specific layers of the presubiculum as previously  (Simonnet et al. , 2013). DAPI staining was used to define boundaries and layers of the presubiculum.
Statistics Results are given as mean ± SEM. Statistical analysis was performed with Prism (GraphPad Software, Inc. ) and MATLAB (The Mathwork). The Wilcoxon signed rank test for matched pairs was used to compare non-parametric data in matched samples. The Kruskal-Wallis one-way analysis of variance (ANOVA) test followed by Dunn's post hoc comparison was used for comparison between more than two groups. Significance levels are indicated as p values.
Discussion This study provides a first classification of mouse presubicular interneurons. Our data show about 11% of mouse presubicular neurons are GABAergic. We used transgenic animals to identify subtypes of these cells that express the peptide SOM or the Ca-binding protein PV. We measured physiological parameters and somato-dendritic form and also verified the chemical content of labeled neurons from Pvalb-Cre, Sst-Cre and X98 mice. Immunohistochemical verification of these interneuron markers revealed both a true overlap -about 5% of presubicular interneurons were co-labeled by antibodies against SOM and PV -and an imperfect specificity for one of the animal lines -only about 70% of labeled cells in Sst-Cre animals were immunopositive for SOM. We therefore used physiological parameters alone for a cluster analysis.
Acknowledgments We thank  Alberto Bacci  and  Caroline Mailhes  for help and support, and  Yoshiyuki Kubota  for comments on an earlier version of the manuscript. This work was supported by  ANR  Grant  JCJC R10206DD  (DF); the  French Ministry for Research (MN) ; the  Région Ile-de-France  and the  Fondation pour la Recherche Médicale  (JS),  INSERM  and  Grant-in-Aids for Scientific Research  from the  MEXT, Japan (YY). The research leading to these results also benefitted from the program " Investissements d'avenir "  ANR-10-IAIHU-06.
Data collection Clinical and biological data were collected between the 4th and the 10th day after admission, as soon as the patient met the inclusion criteria. Urines were sampled over 24 h for measuring urinary creatinine concentration, and SCr was measured during that same period (modified kinetic Jaffe colorimetric reaction). Measured CrCl was then calculated using the standard formula: CrCl = (UCr × V)/SCr, where UCr (urine creatinine concentration) and SCr were expressed in µmol/l and V corresponded to the urinary flow rate (diuresis) in ml/min. At the same time, the CrCl was calculated using different formulas, i. e. , the Cockcroft formula CrCl = (140 -age) × weight 0. 8 × SCr for men, with age in years and weight in kg  [2]. A correcting factor of 0. 85 was used for women. We adjusted the Cockcroft formula on body surface area (BSA) of 1.
Statistical analysis Data are presented as mean ± standard deviation or ratio. Differences between groups were calculated using parametric and non-parametric tests as appropriate. The agreement between the individual eGFRs by the CG, Robert, sMDRD, and CKD-EPI formulas and the measured CrCl was analyzed by residual plots according to the method of Bland and Altman  [27]. The bias and the precision of the different formulas compared with the measured CrCl were evaluated according to Sheiner et Beal by the following equations  [28] : The results are expressed as a percentage of the mean measured CrCl. The diagnostic accuracy of the 4 used formulas to estimate GFR and other significant variables in predicting ARC was assessed by measuring the area under the receiver operating characteristic (ROC) curves.
Nevertheless, if the characteristics of the study population produce a 95 % CI of the best thresholds larger than the inconclusive zone, the values obtained during the first step were retained as gray zone. A logistic regression was performed to determine if polytrauma or any continuous variable or their cut-off with a p value of less than 0. 20 in the univariate analysis was independently able to predict the presence of Bias = 1 N N i=1 CrC estimated -CrCl Precision = 1 N N i=1 (CrCl estimated -CrCl) 2 ARC. The odds ratio (OR) and 95 % confidence intervals (CI) were calculated. Goodness of fit of the model was assessed using the Hosmer-Lemeshow test  [30]. Statistical analysis was performed using Medcalc (MedCalc Software, Ostend, Belgium). A p value ≤0. 05 was considered as statistically significant.
1991). In order to achieve assignment of the aliphatic side chains, additional spectra were obtained: HCCH-COSY  (Kay et al. 1993 ), H(CCO)NH, C(CO)NH  (Montelione et al. 1992 ) and hCCH-TOCSY  (Kay et al. 1993). All NMR spectra were processed using Bruker Topspin 3. 1 or 3. 2 software. The resonance assignment was carried out manually in the CARA  (Keller 2004 ) and Sparky programs  (Goddard and Kneller 2000). Secondary structure was analyzed using the online version of the Talos N program  (Shen and Bax 2013) , where the torsion angles u, w and v are estimated from experimental backbone chemical shifts (HN, H a , C a , C b , CO, N).
Assignments and data deposition Assignments First NMR experiments were run at 293 K due to the low stability of the protein sample. However, 1 H-15 N HSQC as well as preliminary triple resonance experiments were hampered by high signal overlap (see Fig. 2 ) and severe broadening of some resonances, which precluded any significant number of assignments. To overcome this problem, all NMR experiments for assignment were then recorded at elevated temperature of 313 K after optimizing sample stability by a protease-inhibitor stabilized preparation (see preceding Section). This temperature increase led to a drastic improvement of resolution through narrowing of many broad lines presumably from regions of protein subject to internal dynamics. When the sample is cooled down to 293 K again, identical poorly resolved spectra are obtained implicating that the broadening effects are reversible and due to the dynamic properties of the protein.
This assignment enables us to proceed with the resolving of the solution structure of PsbP and to provide a basis for interaction studies between the Psb proteins. The assigned resonances have been deposited in the Biological Magnetic Resonance Data Bank under the Accession number 25379. The central framed regions are magnified on the left sides of the spectra for better comparison. The spectra were recorded with two scans per increment and 2048 9 256 data points in the 1 H and 15 N dimensions, respectively.
Secondary structure Chemical shift based analysis of secondary structure elements was performed using the online platform version of Talos N  (Shen and Bax 2013). The respective propensities of the three main secondary structure categories-a-helix, b-sheet and random coil were compared with the available crystallographic structure of PsbP (PDB ID: 2VU4)  (Kopecky ´et al. 2012)  as represented in Fig. 3. The juxtapositioning yielded almost perfect agreement within the crystallographically well-resolved b-sheet core and a-helices. From the NMR chemical shifts the secondary structures of the segments which were not deducible from the crystallographic electron densities, were estimated to be random coil in solution by Talos N. These results are corroborated by experimental NMR data at lower temperatures (293 K), where only resonances of these flexible regions appear well resolved in 1 H-15 N HSQC.
Acknowledgments All NMR spectra were recorded at the  NMR laboratory of the Upper Austrian-South Bohemian Research Infrastructure Center  in Linz which was co-financed by the  European Union  via the project '' RERI-uasb ''  EFRE RU2-EU-124/100-2010  (ETC Austria-Czech Republic 2007-2013, Project  M00146 ). AR and PR acknowledge 3-and 4-months AKTION scholarships from OeAD, ICM, respectively. NM, RE, and JK were supported through AKTION projects (Projects 69p26 and 64p1). KC and NM acknowledge a  Lise Meitner Grant  from  FWF , Project  M1404-N28.
The S-wave fraction (i. e. not a K * 0 meson) of the Kπ system within the selected Kπ mass range is (4 ± 4)%  [42]. The yield of the normalization mode is N (B 0 → K * 0 µ + µ -) = 506 ± 33, where the uncertainty includes both statistical and systematic contributions. Probability density functions, obtained from the data using splines, are used to generate simulated data sets under the no-signal hypothesis from which the global significance of any χ signal is obtained  [38]. For this the data are collected in the prompt region into wide bins with a width of 200 MeV, and into a total of three bins in the displaced region. Simulated events show that the presence of a narrow χ signal anywhere in the m(χ)-τ (χ) plane, whose local significance is 5σ, would not produce a significant excess in these wide-binned data.
Acknowledgments We express our gratitude to our colleagues in the CERN accelerator departments for the excellent performance of the LHC. We thank the technical and administrative staff at the LHCb institutes. We acknowledge support from  CERN  and from the national agencies:  CAPES ,  CNPq ,  FAPERJ  and  FINEP (Brazil) ;  NSFC (China) ;  CNRS/IN2P3  (France);  BMBF ,  DFG ,  HGF  and  MPG (Germany) ;  INFN (Italy) ;  FOM  and  NWO  (The Netherlands);  MNiSW  and  NCN (Poland) ;  MEN/IFA (Romania) ;  MinES  and  FANO (Russia) ;  MinECo (Spain) ;  SNSF  and  SER (Switzerland) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  NSF (USA). The Tier1 computing centres are supported by  IN2P3 (France) ,  KIT  and  BMBF (Germany) ,  INFN (Italy) ,  NWO  and SURF (The Netherlands),  PIC (Spain) ,  GridPP  ( United Kingdom ).
The bino, winos and Higgsinos mix to form four charged states called charginos χ± 1,2 , and four neutral states known as neutralinos χ0 i (where the index i lies in the range 1 to 4, ordered by increasing neutralino mass). The charginos and neutralinos are collectively referred to as electroweakinos. Since no statistically significant signals consistent with supersymmetry have yet been observed at the LHC, searches have been used to constrain the allowed supersymmetric model space. In the case of searches for supersymmetry, this typically results in setting lower limits on the masses of the pertinent supersymmetric partner particles (sparticles). This paper presents the combined sensitivity and constraints from 22 separate ATLAS analyses of the Run 1 LHC dataset, using centre-of-mass energies of 7 and 8 TeV and an integrated luminosity of up to 20.
Minimum cross Fraction of models generated  strong production, electroweak production (encompassing electroweakino and slepton pair production) and finally mixed production (e. g. of an electroweakino in association with a squark or gluino). Model points with cross sections for any of those processes larger than the minimum values in Table  5  are subsequently retained, and any such processes are investigated in more detail as described below. For strong and mixed production, the minimum cross-section corresponds to just five signal events produced in the full √ s = 8 TeV dataset. Sensitivity to such small cross-sections occurs only for model points with a very high fraction of events with four leptons in the final state, for which the 4-leptons analysis has a high acceptance (up to 50%). Production of electroweakinos is most effectively observed by using decays to leptons, which are often suppressed by the leptonic branching fractions of W, Z and h bosons, explaining the higher cross-sections limits.
Heavy Higgs boson search Cross sections and branching ratios for heavy Higgs bosons are calculated for gluon fusion, or for production in association with b-quark(s)  [127] [128] [129]. The high-m A category (m A > 200 GeV) of the ATLAS search  [75]  is used since this regime is relevant to all the model points in this study. It is assumed that b-quark associated production dominates and this calculation is performed for each model point in the pMSSM parameter space using the software SusHi 1. 3. 0 [103, [130] [131] [132] [133] [134] [135] [136] [137] [138] [139] [140] [141]. The large value of m A in all the model points has the effect that the A and H bosons are nearly mass degenerate, so both must be simulated. The quantity σ(bbH) × BR(H → ττ) + σ(bbA) × BR(A → ττ) is calculated and is compared to the ATLAS 95% CL upper limits  [75]  for a scalar particle produced in association with b-quark(s) and decaying to ττ.
The two techniques have different sensitivities across the pMSSM-19 parameter space, with the ATLAS searches having more sensitivity at lower LSP mass (with significant sensitivity for m( χ0 1 ) < ∼ 800 GeV) and the direct detection experiments providing bounds at larger LSP-nucleon scattering cross-section. Similarly, the ATLAS experiment provides constraints which are complementary to those of precision measurements of BR(B s → µµ), BR(b → sγ), and ∆(g -2) µ. Model points with relatively low fine-tuning (of order 50) remain viable after LHC Run 1. Those with the lowest fine-tuning have relatively light top squarks, which indicates that one would expect them to be accessible by ATLAS searches with the LHC Run 2 dataset. out to be uncommon. Therefore MadGraph5_aMC@NLO is employed to calculate the decays of any righthanded sfermion satisfying the following selection criteria (designed to select all model points for which the multi-body decays would be significant while minimising the number of model points selected): 1) The bino content of the LSP is less than 10%.
Here use was made of the MadWidth package in MadGraph5_aMC@NLO  [150]  by calling the compute_widths function; this function automatically determines whether three-body and four-body decays are required and includes an automatic mechanism for removing the degeneracy. It is found that MadWidth reproduces the results obtained using the procedure described above for several test cases with three-body decays. In total the three-body decays are recalculated for at least 1 sparticle decay in 160482 model points. For 86109 model points, ũR decays are recalculated, while for 86791 model points the dR decays are recalculated and for 122127 model points the ẽR decays are recalculated. The four-body decays are calculated for 11633 model points, although this ends up making a significant contribution only for a few model points.
Acknowledgements The ATLAS Collaboration expresses its sincere thanks to  Matthew Cahill-Rowley ,  JoAnne Hewett  and  Ahmed Ismail , for their sustained efforts in generating the dedicated set of pMSSM points, without which this paper would not have been possible. We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia;  ARC, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC , China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF,  DNSRC  and  Lundbeck Foundation , Denmark; EPLANET,  ERC  and  NSRF ,  European Union ;  IN2P3-CNRS ,  CEA-DSM/IRFU , France; GNSF, Georgia; BMBF,  DFG ,  HGF ,  MPG  and  AvH Foundation, Germany ;  GSRT  and  NSRF, Greece ;  RGC, Hong Kong SAR, China ;  ISF, MINERVA, GIF, I-CORE and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; FOM and  NWO, Netherlands ;  BRF  and  RCN, Norway ;  MNiSW  and  NCN , Poland; GRICES and FCT, Portugal; MNE/IFA, Romania;  MES of Russia  and  NRC KI , Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain;  SRC and Wallenberg Foundation, Sweden ;  SER ,  SNSF  and  Cantons of Bern and Geneva, Switzerland ;  NSC, Taiwan ;  TAEK, Turkey ;  STFC , the  Royal Society and Leverhulme Trust ,  United Kingdom ;  DOE  and  NSF ,  United States of America.
Background: DNA replication and transcription are dynamic processes regulating plant development that are dependent on the chromatin accessibility. Proteins belonging to the Agenet/Tudor domain family are known as histone modification "readers" and classified as chromatin remodeling proteins. Histone modifications and chromatin remodeling have profound effects on gene expression as well as on DNA replication, but how these processes are integrated has not been completely elucidated. It is clear that members of the Agenet/Tudor family are important regulators of development playing roles not well known in plants. Methods: Bioinformatics and phylogenetic analyses of the Agenet/Tudor Family domain in the plant kingdom were carried out with sequences from available complete genomes databases. 3D structure predictions of Agenet/ Tudor domains were calculated by I-TASSER server.
Methods In silico analyses of proteins containing Agenet/Tudor domain Agenet/Tudor family proteins were searched by TBLASTN using the following databases: Phytozome  [18] , the National Center for Biotechnology Information (NCBI) database  [19] , The Arabidopsis Information Resource (TAIR) database  [20]  and Congenie databases  [21]. The partlength (Agenet/Tudor domain) sequence of At2g17950 (FSSGTVVEVSSDEEGFQGCWFAAKVVEPVGEDKFLV EYRDLREKDGIEPLKEETDFLHIRPPPPR) was used as a query sequence for TBLASTN. The e-value of all the sequences selected was below 1e -5. The presence of conserved domains in all the sequences was checked using the Pfam  [22] , the SMART  [23]  and the NCBI databases  [19]  with e-value below 1e -3. Multiple sequence alignments were carried out by using MUSCLE 3. 6 (http://www.
ebi. ac. uk/Tools/msa/ muscle/) with the default parameter setting. A phylogenetic tree using neighbor joining method was constructed with the sequences of the members of the Agenet/Tudor protein family aligned by MEGA (version 3. 0;)  [24]. NJ analyses were done using the following parameters: poisson correction methods, pairwise deletion of gaps, and bootstrap (1000 replicates; random seed). For Domain assiniture we used WebLogo (Web-based sequence logo generating application; Weblogo. berkeley. edu) [25]. See Additional file 13 for sequences used to build Agenet/Tudor signature in plant via WebLogo. The in silico analysis to find a peptide signal of cellular localization in AIP1 amino acid sequence was performed using iPSORT on line software according to  [26].
Protein structural modeling Structural modeling and visualization of Agenet/Tudor domains were performed using the I-TASSER server for protein 3D structure prediction  [27]. The three models generated were visualized and handled using the PyMol package  [28]. The structures of the Agenet/Tudor domain were aligned using PyMol, and their primary multiple sequence alignments were calculated using Multalin server  [29]. The alignment image with the secondary structure of the most significant model adjusted in it was produced using ESPript  [30]. PDBeFOLD  [31]  was used to evaluate the folding of the Agenet domains and to identify structural homologies in the PDB. The likely function of proteins was predicted using ProFunc  [32].
Tandem Affinity Purification (TAP) AIP1 CDS was cloned for N-terminal fusion to the TAP tag system under the control of the constitutive cauliflower mosaic virus 35S promoter into the NGSrhino vector. Transformation of Arabidopsis cell suspension cultures were then performed as described in  [35]. Tandem affinity purification of protein complexes was done using the protein G and streptavidin binding peptide tag followed by protein precipitation and separation, according to  [36]. The protocols of proteolysis and peptide isolation, acquisition of mass spectra by a 4800 Proteomics Analyzer (Applied Biosystems), and MSbased protein homology identification based on The Arabidopsis Information Resource 8. 0 genomic database were performed according to  [37]. Experimental background proteins were subtracted based on approximately 40 TAP experiments on wild-type cultures and cultures expressing the TAP tagged mock proteins Beta-glucuronidase, red fluorescent protein, and green fluorescent protein  [38].
Results Agenet/Tudor family members have expanded with the evolution of plants Most proteins containing Agenet/Tudor domain are still poorly characterized in plants. In order to get more insights into the evolution and possible biological role of these proteins, an in silico analysis of the Agenet/Tudor domain in the plant kingdom was performed. To search for proteins belonging to Agenet/Tudor domain family in plants, we used an Agenet/Tudor sequence from the gene At1g09320 to perform TBLASTN query against available genome sequences in Phytozome, NCBI, TAIR and Congenie databases  [18] [19] [20] [21]. The search included genomes of unicellular green algae (4 species), nonvascular plants (Bryophyte -1 species), seedless plants (Lycopodiophyta -1 species), and seeded plants: Gymnosperms (Gnetophyta -1 species; Coniferophyta -1 species; Ginkgophyta -1 species) and Angiosperms (22 species).
Redundant sequences were removed manually. In addition, the putative orthologs in Arabidopsis of each protein containing Agenet/Tudor Domain were identified by TBLASTN in TAIR (Additional file 7). In total, 31 species were studied, from green algae to angiosperms, as it was summarized in Additional file 1. The analysis revealed that lower plants such as green algae and moss have none or fewer Agenet/Tudor genes compared to those of higher plants. Only one member of the Agenet/ Tudor family was found in Coccomyxa, four members were found in Physcomitrella patens, and above ten members were identified in most of the higher plants. This data suggested that the number of Agenet/Tudor family members expanded in plant genomes with the evolution of plants.