dataesr/scientific-paragraphs-categorization huggingface dataset:
III. AB INITIO SIMULATIONS In order to obtain the electronic level structure, the wave functions and the orientation of the main-anisotropy axes of the Ln(III) ions in the "butterfly" {Fe 3 LnO 2 } molecules, relativistic ab initio calculations were performed. Post-Hartree-Fock calculations were carried out using the CASSCF/CASPT2-RASSI-SO  [21]  method as implemented in the MOLCAS 7. 4 package  [22]. Such a relativistic quantum-chemistry method has proven to be suitable for the study of the magnetic anisotropy of trivalent lanthanide ions and, in particular, to predict with good accuracy the direction of the easy anisotropy axis for lanthanide ions with uniaxial magnetic anisotropy  [23] [24] [25]. Computations were done on a [Fe 3 Ln(μ 3 -O) 2 (CCl 3 COO) 8 (H 2 O)(THF) 3 ] molecular unit, in which the -CCl 3 groups were replaced by -CH 3 groups and the -OC 4 H 8 groups linked to the Fe(III) ions by water molecules in order to reduce the computation time.
A. Magnetic contributions of the lanthanide and Fe 3 subclusters From the results of XMCD and M(H ), the contribution of the two subcluster components, Ln and Fe 3 , which make up the total magnetization of the cluster M tot (H ), were determined separately. On the one hand, the total magnetization of the cluster, M tot = M Ln + M Fe 3 was obtained from the VSM measurements in absolute scale; i. e. , in μ B per formula unit (f. u. ). On the other hand, the XMCD measured intensity, I XMCD (H ), was obtained in arbitrary units. From these two sets of data, the magnetization contributed by the Ln ions, M Ln , can be determined in μ B /f. u. by scaling I XMCD (H ) of Fig. 3 , as explained below. At the highest magnetic field B = 14 T, where both VSM and XMCD magnetization data were available, the M Fe 3 subcluster is completely saturated M s Fe 3 = 4.
ACKNOWLEDGMENTS discussions with P. J. Alonso are acknowledged. The projects  MINECO  ( MAT2011/23791 ,  MAT2011/27233-C02-02 , and  MAT2014/53921-R ),  DGA IMANA   E34 , and  Alexander Von Humboldt Foundation  (D. P. ) are acknowledged for financial support. The XMCD data were taken during the training period of L. B. carried out in ID12 at the  ESRF. The authors would like to acknowledge the use of  Servicio General de Apoyo a la Investigación (SAI) ,  Universidad de Zaragoza. We also thank resources from the supercomputer "Caesaraugusta," technical expertise and assistance provided by  BIFI-Universidad de Zaragoza.
Material and methods The 14 human PI3K protein sequences were retrieved from UniProtKB  [51]  (Table  1 ). According to homology relationships, we built four phylogenies corresponding to: i) all catalytic subunits; ii) class IA regulatory subunits; iii) class IB regulatory subunits; and iv) the class III regulatory protein. Metazoan homologs were retrieved from Ensembl  [52]  and other eukaryotic homologs were retrieved from a local database of complete proteomes. Similarity searches were performed on the two databases using BLASTP  [53]  with default settings and a cut-off set to E ≤ 10 -30. Because PI3K proteins have distant homologs, the retrieved homologs were used as the seed for new BLASTP runs with the same parameters (Additional file 1). To keep only one protein sequence per genomic locus, we grouped alternative transcripts together using the E-utilities  [54]  and the ACNUC sequence retrieval system  [55].
Then, we manually selected the most conserved and/or the better aligned peptides. In order to decrease noise and phylogenetic redundancy, we defined two taxonomic samplings. For the catalytic dataset, a subset of 44 representative species was selected for in-depth phylogenetic analysis. Among them, we kept only ten mammals over the 42 available in Ensembl. This choice was driven by three constraints: i) having a good representative diversity for the main eukaryotic groups; ii) limiting the number of fast-evolving sequences; and iii) including model organisms such as S. cerevisiae and C. elegans. For regulatory datasets, we only made a selection among mammals and kept all the other species. For the multiple alignments we compared the results returned by PRANK  [56]  and MAFFT  [57]  using NorMD  [58].
As its scores were consistently better, we chose to use the alignments computed by MAFFT. According to author recommendations, we set the maximum number of iterations at 100 and used the localpair options (equivalent to the linsi option). Alignments were then trimmed using BMGE  [59]. Several sets of parameters were tested for this program in order to get a balance between the number of sites selected and the quality of the resulting multiple alignments (Additional file 2). Number of gaps per sequence after site selection are listed in Additional files 3 to 8. The selection of evolutionary models used for the phylogenetic inference was carried out using ProtTest  [60]  and the Bayesian Information Criterion (BIC)  [61]. In addition to the standard amino acids substitution models implemented in ProtTest we also performed the BIC test with UL3  [62]  and CAT20  [63]  models.
The JTT+ 4 model  [64]  was proposed for the regulatory class IA and IB proteins, the subset for Opisthokonta homologs of class II and the complete catalytic subunits dataset. The UL3+ 4 model  [62]  was suggested for the regulatory class III, and for the reduced catalytic subunits datasets. Finally, the subset for Opisthokonta homologs of class I was inferred using the LG+ 4 model  [65]. Maximum likelihood trees were built with PhyML  [66]. Shape parameter of the Gamma distribution was estimated by PhyML with four categories for substitution rates. Branch statistical supports were estimated by the Shimodaira-Hasegawa-like test (SH) and non-parametric bootstrap (BS) with 1000 replicates. A Bayesian approach was also used to infer the phylogeny of the eukaryotic dataset of catalytic subunits.
For that purpose we used MrBayes  [67]. Default parameters were used with the exception of the substitution model for amino acids, which was set to mixed. Seven million MCMC (Markov Chain Monte-Carlo) iterations were required to reach convergence. Burn-in values were set at 50 % of the iterations and we built a 50 % majority rule consensus tree after sampling one thousand trees from the posterior distribution. This sample was also used to establish the clades posterior probabilities (PP). Domain composition analysis of all sequences was performed using HMMScan from the HMMER package  [68, 69]. We searched for domains in both PfamA and PfamB databases and used all default parameters. For class IA regulatory proteins we also used Batch CD-Search  [70]  to confirm the presence of the p110 binding domain in non-Euteleostomi sequences.
We also found sequences from Haptophyta (Emiliana huxleyi), Cryptophyta (Guillardia theta) and Apusozoa (Thecamonas trahens). It is worth noting that no PI3K catalytic subunit was detected in red algae, while complete proteomes of three species were present in our database. The second cluster gathered sequences of classes I and II from all eukaryotic lineages with the exception of Fungi and most Archaeplastida (Fig. 1  and Additional files 9 and 10). Specific similarity searches in Archaeplastida and Fungi in the Non-Redundant NCBI database (NR) confirmed these absences (data not shown). Regarding other eukaryotic lineages, only one copy was present in Bikonta lineages (SAR, Excavata and Haptophyta), while two copies (corresponding to class I and class II) were found in Unikonta (Amoebozoan and Opisthokonta).
Catalytic subunits First, our results confirmed that all eukaryotic catalytic subunits shared three common domains in the same order: PI3KC2 (accession number PF00792), PI3KA Fig. 2  Maximum likelihood phylogenetic tree of class III regulatory PI3Ks subunits. The tree was inferred with the UL3+ 4 model (839 sites, 117 sequences). Sequences are colored according to their taxonomic classification. Branch statistical supports and duplication events are shown using the same symbols as in Fig. 1. The scale bar represents the average number of substitutions per site Fig. 3  Maximum likelihood phylogenetic tree of class IA regulatory PI3Ks subunits. The tree was inferred with the JTT+ 4 model (539 sites, 126 sequences). Branch statistical supports and duplication events are shown using the same symbols as in Fig.
1. The scale bar represents the average number of substitutions per site (PF00613) and PI3K kinase (PF00454) (Fig. 5 ). Homologs of class III catalytic subunit did not harbour additional domains, while class I and II proteins possessed, in addition, the Ras Binding Domain (RBD, PF00794). This domain appeared after the class III catalytic diverged from the ancestral protein of classes I and II (i. e. , before LECA). It is essential for the activation of PI3K catalytic proteins by the Ras protein  [71, 72]. This suggests that a functional change occurred after the duplication at the origin of classes III and I/II. Amoebozoan class I and II proteins as well as Bikonta ancestor of class I/II protein shared exactly the same domain composition (i. e. , the four previously mentioned domains), precluding any conclusion regarding functional changes.
In contrast the Opisthokonta subunits of classes I and II differed in their domain composition. First, we confirmed the specific presence of PX (PF00787) and C2 (PF00168) domains in class II catalytic proteins  [73]. Furthermore, we detected two additional domains specific to class II PI3K-C2α and PI3K-C2β proteins: PB011861 was found at the N-terminal part of the PI3K-C2α homologs, whereas PB008942 was located in-between the RBD and PI3KC2 domains of PI3K-C2β homologs. For class I, we confirmed the presence of the p85 binding domain (PF02192) in all IA homologs and its absence in IB homologs. Interestingly, the acquisition of the p85 binding domain by class IA proteins occurred in the last common ancestor of Metazoa, Ichthyosporea and Choanoflagellida, i. e. , while class IB diverged from class IA (see before).
Regulatory subunits All class IA regulatory subunits harboured the same C-terminal domain composition, i. e. , a ρ-gap domain (PF00620) followed by two SH2 (PF00017) domains Fig. 4  Maximum likelihood phylogenetic tree of class IB regulatory PI3Ks subunits. The tree was inferred with the JTT+ 4 model (599 sites, 67 sequences). Branch statistical supports and duplication events are shown using the same symbols as in Fig. 1. The scale bar represents the average number of substitutions per site intercut by a PB011403 (p110-binding) domain (Fig. 6 ). Three exceptions were the p55γ homologs that lacked domain but had a PB019399 domain, and the short Ichthyosporea and the Choanoflagellida teins that lacked the p110-binding domain. This result is puzzling given that we detected class IA catalytic subunits in Ichthyosporea and Choanoflagellida (ancestor of p110α-β-δ).
This suggested that in these species, the p110 binding domain is not required for the interaction between the regulatory and catalytic subunits. Because Ichthyosporea were represented by a single species in our databases, we could wonder if the very short protein detected is real or is artifactual because of sequencing errors. In addition to the three conserved domains (ρ-gap, SH2 and PB011403), additional domains are present in the N-terminal of some sequences (Fig. 6 ). For instance, the p85α and p85β proteins contained a PB000584 domain, while the copy present in Mollusca, Cnidaria and Ichtyosporea have a C1 (PF00130) domain at this location. We detected SAM_1 (PF00536) or SAM_2 (PF07647) domains in Mollusca, Arthropoda and Choanoflagellida, whereas Cnidaria and Ichtyospoera harboured an additional SH2 domain.
Finally, we detected SH3 (PF00018) in Choanoflagellida and in p85α Actinopterygii homologs. Concerning class IB regulatory proteins, no domain was previously described. Our analysis detected only one PfamB domain named PI3K_1B_p101 (PF10486) in all dataset proteins (data not shown). Finally, class III regulatory proteins showed a diverse domain composition (Fig. 7 ). The main information was that all proteins possessed a well-conserved Pkinase domain (PF00069) located at the N-terminal part of the proteins and two or more WD40 domains (PF00400) at.
This suggested that these processes could have been already established in LECA, which could be further investigated by the phylogenetic study of the other proteins involved in this crucial function. In agreement with the previous studies  [49, 50] , we found that two major duplication events affected the evolutionary history of catalytic subunits. As Brown and Auger, we inferred that the first duplication leading to the separation of class III and classes I/II proteins occurred before LECA. In the case of the Kawashima et al. study, the data set was only made of sequences that came from five Opisthokonta species (Homo sapiens, D. melanogaster, C. elegans, Ciona intestinalis and S. pombe), therefore, their taxonomic sample was too restricted to conclude precisely the timing of this duplication.
We detected neither catalytic nor regulatory PI3Ks proteins in red algae, suggesting three independent gene losses in this lineage. Among the three complete proteomes present in our database (Chondrus crispus, Cyanidioschyzon merolae and Galdieria sulphuraria) the first harbour an unusual structure  [79] , and the others are very small for eukaryotic genomes  [80, 81]. Further analyses are needed to confirm and explain these absences. The taxonomic distribution and the phylogeny of class I/II catalytic subunits suggested that these two classes originated in the Unikonta lineage. This implied the presence of an ancestral class I/II protein in Bikonta lineages. In our study, as in Brown and Auger, these sequences grouped with the class I homologs. But unlike them, we found that this branch of the tree was not significantly supported (BS = 7 % and SH = 0.
87). All non-Opisthokonta proteins from classes I and II have the same domain composition which do not help to infer different biochemical or molecular functions for these paralogs. Moreover, the ancestral class I/II protein present in LECA might have the same biological function as the protein present in all present day Bikonta organisms. In agreement with previous studies, we confirmed that S. cerevisiae possess only class III PI3K proteins  [7, 50]. We extended this observation to all Fungi and showed that this resulted from independent losses of class I and II catalytic proteins in this lineage. Similarly, no Archaeplastida class I and II sequences were detected except in Selaginella moellendorffii in which a class I catalytic subunit was found. S. moellendorffii was not among the three Archaeplastida species of Brown and Auger dataset, so they did not find homologs in Archaeplastida in their study.
The two previous published phylogenies disagreed regarding the pattern of duplications. Brown and Auger found that p110α and p110γ (class IA and IB, respectively) grouped together, while Kawashima et al. found the three class IA proteins (p110α, p110β and p110δ) in the same cluster. Our analyses agreed with the result of Kawashima et al. , but provided a more precise picture because we used 117 Opisthokonta complete proteomes, while they analyzed less than 30 Opisthokonta species. In fact, we found that a first duplication occurred before the last common ancestor of Metazoa, Ichthyosporea and Choanoflagellida and led to the separation of class IA and IB. Due to the low number of proteomes available for Nucleariidae and Apusozoa (only one of each in our database) and weak statistical branch supports, we could not date more precisely this gene duplication event.
This 'wait and see' management may also avoid performing CAG in patients with the lowest probability of an acute coronary lesion. These two strategies (early versus delayed CAG) were evaluated in patients with VF arrest and without shock in an RCT that showed no difference in 90-day survival, the primary outcome [odds ratio 0. 89; 95% confidence interval (CI) 0. 62-1. 27; P = 0. 51]  [10] , In this study, the median time to target temperature was 5. 4 h in the immediate angiography group and 4. 7 h in the delayed angiography group (ratio of geometric means 1. 19; 95% CI 1. 04-1. 36). Another recently published pilot RCT comparing early with delayed CAG also showed no difference in the primary outcome, which was a composite of efficacy and safety measures  [116]. Further trials testing the same hypothesis are ongoing (DISCO NCT02309151, COUPe NCT02641626, TOMAHAWK NCT02750462, EMERGE NCT02876458).
Acknowledging noradrenaline as the most commonly used vasopressor, their suggestion was that major changes in clinical practice were not needed  [159]. As noradrenaline is the most widely used vasoactive agent for post-cardiac arrest patients, we suggest using noradrenaline as the first-line vasoactive agent in hypotensive post-cardiac arrest patients. A recent RCT comparing noradrenaline with adrenaline in 57 patients with acute myocardial infarction and cardiogenic shock was terminated early because of significantly more refractory shock in patients treated with adrenaline  [160]. The COMACARE and NEUROPROTECT pilot trials also used noradrenaline as the drug of choice to achieve higher MAP targets  [131, 132]. None of the studies showed any evidence of relevant tachycardia, arrhythmias or recurrent shock in the higher MAP group, despite the use of significantly higher doses of noradrenaline compared with the lower MAP group.
Steroids ILCOR performed an evidence update on use of steroids for post-cardiac arrest patients for the 2020 guidelines  [9]. Three small RCTs and a large observational study have addressed the use of steroids in post-cardiac arrest patients  [163] [164] [165] [166]. Two of the RCTs used steroids both during CPR for IHCA and after ROSC  [163, 164]. The first of these RCTs showed improved survival to discharge with a combination of methylprednisolone, vasopressin and adrenaline during cardiac arrest and hydrocortisone after ROSC for those with shock, compared with the use of only adrenaline and placebo  [9/48 (19%)   One trial has recently been completed but is not yet published (NCT02790788). ILCOR recommended a systematic review be undertaken once the recently completed trial is published and, therefore, left the treatment recommendation unchanged from 2010 [167]: • There is insufficient evidence to support or refute the use of corticosteroids for patients with ROSC following cardiac arrest.
Other therapies to improve neurological outcome In contrast to a number of positive results from studies in experimental settings  [18] , several neuroprotective drugs failed to demonstrate a positive clinical effect  [164, 193, 194, [244] [245] [246] [247]. More recently, erythropoietin  [248] , cyclosporine  [249]  and exenatide  [250] , used alone, or as an adjunct to mild induced hypothermia, have also not been shown to increase neurologically intact survival when included in the post arrest treatment of cardiac arrest patients. The combination of xenon and mild induced hypothermia, which is beneficial and superior to mild induced hypothermia alone in experimental settings  [18, 251] , has been studied in several trials with no convincing effects  [252] [253] [254]  and is undergoing further clinical evaluation (XePOHCAS, EudraCT Number 2017-00251432).
Gamper G, Havel C, Arrich J et al (2016) Vasopressors for hypotensive shock. Cochrane Database Syst Rev 2:CD003709 160. Levy B, Clere-Jehl R, Legras A et al (2018) Epinephrine versus norepinephrine for cardiogenic shock after acute myocardial infarction. J Am Coll Cardiol 72:173-182 161. Kern KB, Hilwig RW, Berg RA et al (1997) Postresuscitation left ventricular systolic and diastolic dysfunction: treatment with dobutamine. Circulation 95:2610-2613 162. Vasquez A, Kern KB, Hilwig RW, Heidenreich J, Berg RA, Ewy GA (2004) Optimal dosing of dobutamine for treating post-resuscitation left ventricular dysfunction. Resuscitation 61:199-207 163. Mentzelopoulos SD, Malachias S, Chamos C et al (2013) Vasopressin, steroids, and epinephrine and neurologically favorable survival after in-hospital cardiac arrest: a randomized clinical trial.
According to CAZY database, β-(1→3)-glucanases could be associated with five different families of CBM (Table  1 ). Only five modules were successfully crystallized and their structure resolved with or without ligand. [113] [114] [115]. They are described as lectin like β-jelly roll with shallow cleft on the concave face. Recently, their specificity was studied and it is believed that it arises from the conformation of the cleft. For example, family 4 CBM from Thermotoga maritima (TmCBM4) was co-crystallized with laminariheptaose and its three-dimensional structure resolved by molecular replacement with Cellulomonas fimi CBM (CfCBM4), a β-(1→4)-glucan receptor (Figure  2 )  [116]. As CfCBM4, TmCBM4 shows a β-jelly roll structure but differs from the first protein in the geometry of the groove that accommodates the polysaccharide.
X-Ray data of the co-crystallized complex laminarihexaose-BhCBM6 was resolved. In contrast to TmCBM4, the oligosaccharide first interacts within a small cleft bounded by a glutamine. This cleft can accommodate only one glucose unit, the non-reducing end that is sandwiched between two tryptophan residues. From there, the oligomer curled around the protein and again adopted a natural "U shape. " At least six binding sub-sites are identified along the trail routed for the sugar and stabilize the whole by direct or water mediated H-bond. Three cations (certainly sodium) reinforce the interaction. As β-(1→4)-glucans adopted in solution a linear conformation, it could not curl like β-(1→3)-glucans but instead it will stick out of the cleft and extend into the solvent. This explains the very low affinity of BhCBM6 for xylans or mixed glucans.
The GROG network The GROG is a French countrywide influenza sentinel surveillance network based on voluntary primary care physicians (general practitioners [GPs] and paediatricians), covering 21 of 22 French regions  [7] [8] [9]. The GROG was established in 1984 and has been collecting and linking clinical, virological and demographic data since then. Active surveillance of influenza runs every season from week 40 to week 15. The number of physicians participating to the GROG network has been constantly increasing since its establishment: in 2012-2013, 0. 64 % of general practitioners and 4. 44 % of paediatricians in France participated in the GROG network, with a high participation rate (73 % of general practitioners and 76 % of paediatricians swabbed at least one person per season), and with a distribution on the French territory representative of the general population of French GPs and paediatricians.
Selection of patients and data collection The sentinel physicians participating in the GROG network reported the weekly number of acute respiratory infections (ARI) patients presenting at their practice, and collected information and provided, on a judgmental sampling basis, nasal/pharyngeal swabs from a subset of ARI patients presenting within 48 h of onset of symptoms. The definition of ARI adopted was as follows: sudden onset of at least one respiratory sign (cough, sore throat, shortness of breath, coryza…) AND at least one systemic sign suggestive of an acute infectious disease (fever, fatigue, headache, malaise…). The information being collected included socio-demographic data, clinical symptoms, and whether the patient was vaccinated in the current and (from season 2009-2010) previous season.
Ethical aspects and informed consent Surveillance forms were routinely used in the influenza seasons, and oral informed consent was obtained from the ARI patient at the moment of swab taking in accordance with national regulations. All swab results and forms were anonymized by the laboratories before they were sent to the GROG network coordination, and only identified by the number given by each laboratory for virological tests. In accordance with applicable laws and regulations, no clearance of an Ethics Committee is required in France for the retrospective analysis of anonymized data collected within routine influenza surveillance schemes.