dataesr/scientific-paragraphs-categorization huggingface dataset:
The basic idea behind identification of a cluster is the notion that clustering indicates an ongoing or recent transmission, while unique patterns indicate reactivation events  [23, 29]. Nonetheless, spoligotyping is known to overestimate clustering, and only a systemic second-line typing, such as MIRUs, couples with a better tuberculosis registry and epidemiological investigations would allow recognizing the rate of ongoing transmission. Among 39 spoligotyping patterns observed, 33 patterns matched preexisting shared types in the international database SITVIT2. One SIT was newly created after matching with an orphan strain from Iran; this strain was recovered from a patient from Misan Province, which is close to the Iranian borders. Additionally, 5 patterns were new and unique (orphan). Although orphan strains did not match any preexisting SIT, nevertheless, a comparison of their patterns based on the absence and presence of spacers with that found in other SITs revealed that these orphans were closely related to major lineages.
9%). The T family genotype prevails in Africa, Central and South America and Europe  [36]. Although there is no geographic link between Iraq and these regions, both of the two largest surrounding countries to Iraq, Turkey and Iran are known to share active borders and historic links with European countries; therefore, it may be suggested that the T-family (except SIT1144, see below) may have first spread in Turkey and/or Iran and then transmitted to Iraq. Several SITs (clusters) belonging to the T-family were recorded in this study; notably, SIT1144 was the biggest cluster recorded in this study (n = 20, 14. 93% of all cases). SIT1144/T1 showed the following distribution pattern in the SITVIT2 database (n = 24 strains in total: Iraq n = 20, USA n = 2, Venezuela n = 1, Greece n = 1). It is not known whether there is a link between political displacements and/or war and the presence of those sporadic strains in USA, Venezuela and Greece.
In this study, SIT1144 is mostly isolated from younger age groups; A finding that may indicate the recent spread of this shared type. In addition, 18/20 SIT1144 strains were MDR, suggesting that this shared type has more propensity to develop multi-drug resistance. SIT284/T1 was represented in this study by 7 isolates (5. 22%). This SIT is remarkable by the absence of spacers 1-4, 12-13, and 33-36. Considering the total number of strains in the SITVIT2 database (n = 167), the global proportion of SIT284 strains is more predominant in Bulgaria (23. 95%), followed by Turkey (20. 96%) and Saudi Arabia (13. 77%). However, this new Iraqi study revealed that considering the proportion of the SIT number per country, SIT284 is present in Iraq in a greater proportion than the neighboring countries.
In addition, as shown in Fig. 2A , certain shared types (such as SIT26, SIT54, SIT284) are located in the upper layouts and thus they potentially represent the oldest spoligotype in this forest. Furthermore, the SIT26 has a large node size yet positioned at a tip of the spoligoforest; this may indicate that the strain within this spoligotype are being transmitted faster than other strains in this data set  [51]. The SIT1144, SIT50, and SIT1198 are present in the down layouts and could therefore be ''emerging strains''. In line with this finding, MIRU-VNTR showed a high strain homogeneity among the strains of the SIT1144; this suggests once again that SIT1144 may be associated with a higher transmission rate than other strains. However, further quantitative analyses are required to verify this point.
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) ;  SFI (Ireland) ;  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 ).
Methods All clinical Enterobacteriaceae isolated and studied here were collected from hospitalized patients between October and December 2011 in the Hôpital Principal de Dakar, Senegal. All of them were isolated from blood cultures, urine samples, superficial pus, protected bronchial samples, or catheters. Bacteria were grown on trypticase soy or M€ uller-Hinton media for 24 h at 37°C. Bacterial identification was performed using both standard phenotypic methods including API 20E gallery (BioM erieux, Marcy l'Etoile, France) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) (Autoflex, Bruker Daltonic, Bremen, Germany) with the FLEX CONTROL software (Bruker Daltonic). MALDI-TOF MS identification to species level was validated with score values ≥1.
Following incubation, cultures were washed again and kept in ACSF. During recordings, cultures were kept at 37uC. Time lapse data were taken with an Olympus upright microscope (BX51WI) fitted with an EMCCD camera (Andor Ixon-885) and a 620 water immersion objective (Olympus, UMPLFLN 20XW NA 0. 5). This setup allowed the visualization of cells residing on top of non-transparent electrodes. Fluorescent excitation was delivered by a 120 W mercury lamp (EXFO x-cite 120PC) coupled with a dichroic mirror with a filter to match the dye spectrum (Chroma T495LP). Camera control utilized Andor propriety SOLIS software. Time-lapse recordings were performed at 262 binning mode for resolution of 5006502 and 51. 948 frames per second. Time lapse sequences were collected via a dedicated 12 bit Andor data acquisition card installed on a personal computer, spooled to a high capacity hard drive and stored as uncompressed multi-page TIFF file libraries.
Analysis of Ca 2+ data Ca 2+ imaging data was stored as uncompressed TIFF library, where pixel values represented fluorescence intensity. Boundaries of astrocyte somata were semi-automatically segmented from the time-averaged Ca 2+ image using a custom code implemented in MATLAB (The MathWorks Inc. , Natick, Massachussetts, USA), followed by manual adjustments. Ca 2+ variations in the astrocyte cell bodies were estimated as normalized changes of fluorescence signal from baseline(DF/F 0 ). Local baseline fluorescence (F 0 ) was evaluated from the histograms of the signal within a running time window. Time windows without cellular activity were best fit by a single Gaussian (due to white noise), whereas those with cellular activity were best fit by two Gaussians (due to white noise and activity).
For display purposes, the signal was smoothed by convolution with a 50-data point-large Savitsky-Golay filter of polynomial degree 7  [26, 27]. Neuronal signals were distinguished from astrocytic signals based on typical dynamic time scales, and physiological properties of their calcium signals  [28]. Neurons were characterized by fast variations in Ca 2+ activity during spike onset, whereas astrocytes exhibited slowly varying signals (S2 Fig. ). Neuronal activation probability was calculated over several stimulations at different amplitudes. The probability is defined for each neuron as the portion of times it responded by an action potential to an electrical stimulation at a specific amplitude. We neglected neurons with no response throughout the whole stimulation range. The stimulation threshold was defined for each neuron as the amplitude which activated it with 0.
This study was funded by the  High Council for Scientific and Technological Cooperation between France  and Israel (GW, JL, NH, MDP, EBJ, HB, YH), by the  ERCIM ''AlainBensoussan'' Fellowship (MDP) , by the  Italian-Israeli Joint Neuroscience Lab (MDP, EBJ)  and the  ERC  (grant FuNMaNIA, YH). The authors also acknowledge the support of the computing center IN2P3 at CNRS (http://cc. in2p3. fr),where the simulations were performed. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data analysis Data were analyzed by one-way analysis of variance (ANOVA). The Newman-Keuls test was used for post hoc comparisons. Induction of hyperlocomotion by MK-801 (MK-801 vs. saline) was confirmed by Student's t test. The reversal of MK-801-induced hyperactivity by antipsychotic drugs was analyzed by ANOVA. In the case of the passive avoidance test, data were not normally distributed so stepthrough latencies were analyzed with the aid of the Kruskal-Wallis and Mann-Whitney non-parametric tests. p values ≤0. 05 were considered significant. The Statistica 8. 0 software package for Windows (StatSoft, Tulsa, OK, USA) was used to analyze all data. The lowest drug dose eliciting a significant effect was defined as a minimal effective dose (MED). Drugs MK-801 (Sigma-Aldrich, Poznan, Poland) was dissolved in sterile physiological saline (0.
Conclusions and perspectives To summarize, all the antipsychotics possess sub-optimal profiles, mostly by eliciting memory-impairment or sedation at antipsychotic-like range of doses-side effects that may be of particular concern for elderly dementia patients with preexisting cognitive deficits and motor difficulties. Improved management of BPSD may be achieved by use of drugs that incisively oppose glutamate-related psychosis but do not elicit cognitive deficits or motor impairment. If such drugs also possessed accentuated antidepressant properties, they could constitute promising therapeutics for treatment of dementiarelated disturbances in elderly patients. While further investigation would be desirable to determine the effects of drugs under other treatment conditions (e. g. , following repeated administration or in conjunction with antidepressants), the present comparative study provides a reference dataset by which future drugs candidates can be evaluated at an early stage of in vivo testing.
Materials and Methods The gene sequence for Ccace2 was obtained using a cDNA sequence (GenBank FJ480224. 1). The SNP causing the G328A mutation is at position 984 of this sequence. Blast searches of the short read archive (SRA: SRX016529) available at GenBank produced multiple hits, which were compiled for a sequence covering parts of the intron region between exon 5 and exon 6 of the Ccace2 gene. The resulting sequences were mapped against the recently released Mediterranean fruit ßy genome (http://www. ncbi. nlm. nih. gov/gene/101457905) and revealed a scaffold (scaffold 242) covering the Ccace2 gene. The intron boundaries found matched the genome annotations (citations? http://www. ncbi. nlm. nih. gov/Traces/wgs/ ?val ϭ AOHK01). The genome sequence of the region was used for primer design and ampliÞcation of the target.
Acknowledgments We thank  R. Pereira ,  J. Hendricks ,  P. Rendon ,  A. Malavasi ,  B. Sabater-Munoz ,  A. Zacharopoulo , and  B. Barnes  for kindly providing Mediterranean fruit ßy samples. This project was funded by the  Natural Environment Research Council (NERC)  small research grant  NE/J014389/1  awarded to A. P. V. S. E. was supported by the  United Nations Educational, ScientiÞc, and Cultural Organization (UNESCO)-LÕOreal  fellowship and the  European Molecular Biology Organization  (EMBO) short-term fellowship ( ASTF-42-2010 ). We gratefully acknowledge the  Medßy Whole Genome Sequencing Project  for providing the NCBI sequence data used.
Sequence alignment and modelling studies Using BLAST (blast. ncbi. nlm. nih. gov), protein sequences and structures similar to PttTrHb were searched from the nonredundant protein sequence database and protein structure database (Protein Data Bank, PDB), respectively. Twenty-five plant sequences (as presented in Fig. 1 ) with 66-83% sequence identity to PttTrHb were selected. The BLAST search against PDB revealed five bacterial structures: B. subtilis (1UX8;  [10] ), M. tuberculosis (1NGK;  [8] ), G. stearothermophilus (2BKM;  [11] ), T. fusca (2BMM;  [9] ) and A. tumefaciens (2XYK;  [1] ), which all have less than 33% sequence identity to PttTrHb. To aid sequence alignment, five more bacterial HbO sequences from Verrucosispora maris, Salinispora tropica, Arthrobacter sp. FB24, Actinoplanes sp.
SE50/ 110 and Leptospira licerasiae were selected from the BLAST search against the non-redundant protein sequence database. To identify structurally conserved regions, we superimposed the structures using the program VERTAA in the Bodil suite  [31] , and pairwise sequence alignments were then combined to generate a structurebased sequence alignment. Other bacterial and plant sequences together with PttTrHb were aligned to the structure-based sequence alignment. The sequence comparisons were done using the program MALIGN  [32] [33]. Based on the multiple sequence alignment, the structural model of PttTrHb was constructed with MODELLER  [34]  using the default parameters and refinement procedure. The crystal structure of Bs-HbO (1UX8;  [10] ) was used as the structural template. The PttTrHb model with the lowest value of the objective function derived by MODELLER was visualized in Bodil, and it shows the best heme group coordination.
Phylogenetic analyses For studying the evolution of plant TrHbs, the phylogenetic analyses were performed using maximum likelihood, neighbourjoining and maximum parsimony algorithms  [36]. The amino acid sequences for the analyses were obtained by BLAST searches against the PttTrHb sequence in NCBI databases (http://www. ncbi. nlm. nih. gov). Additionally, the TrHb sequences of peach (Prunus persica L. ) and rose gum (Eucalyptus grandis W. Hill ex Maiden) were taken from the Phytozome database (http://www. phytozome. net). Phylogenetic analyses were conducted in MEGA 5. 10 (build 5121019). The bootstrap method  [37]  with 500 replicates was used to evaluate the confidence of the reconstructed trees. Bootstrap values between 70% and 100% have been suggested to indicate significant support for a branch  [38].
With both reference genes the calculated relative expression values were comparable (data not shown) and, therefore, only the results obtained with TUA are presented. The differences between the control line V617 and the RNAi lines were statistically examined either with the two-sample t-test or the Wilcoxon rank-sum test using the R software package 2. 5. 1  [44]  with the graphical user interface, R Commander  [45].
3D model of PttTrHb and evolution of plant TrHb In order to examine the possible ligand-binding residues in plant TrHbs, we constructed a homology model of PttTrHb. Firstly, a sequence alignment was constructed using ten group II bacterial TrHb sequences, which included five structures (PDB codes: 1UX8, 2BKM, 2BMM, 2XYK, 1NGK; see M & M for details). To evaluate sequence conservation and to assist the comparative modelling of PttTrHb, an extensive multiple sequence alignment was then performed by adding 25 plant sequences (Fig. 1 ). Of the possible template structures, Bs-HbO showed the highest sequence identity, 33%, with PttTrHb. Therefore, the cyanide-bound X-ray structure of Bs-HbO was chosen as a template to construct the 3D model of PttTrHb. B9Phe, B10Tyr, G8Trp, F8His and three glycine motifs are well conserved both in bacterial and plant HbO sequences (Fig.
Conclusions: The use of allozymes and RAPD are of very limited value to determine population structuring at small spatial (i. e. landscape) scales, microsatellites giving much higher estimate resolution. The use of RAPD data is also limited for evidencing inbreeding. However, coarse-grain spatial structure (i. e. regional scale), and gene flow estimates based on RAPD and microsatellites data gave congruent results. At a time with increasing development of new molecular methods and markers, dominant markers may still be worthwhile to consider in organisms for which no genomic information is available, and for which limited resources are available.
Population genetics has also been used to assess effective connectivity between populations  [29, 30]. These studies were essentially based on RAPD (Random Amplified Polymorphic DNA) markers, because allozymes revealed very little polymorphism in the study area. However, RAPD's revealed low population differentiation within the study landscape, which confirmed that populations were functionally connected, as expected from ecological studies. The genetic structure of the populations inferred from the RAPD data set was indeed remarkably congruent with the geographic configuration of the populations within the landscape and the observed movements among populations  [29]. Following the successful isolation of 15 microsatellites loci for B. aquilonaris  [24] , here we analyze the genetic diversity and structure of eight populations located in four different landscapes.
However, at the landscape scale, the use of microsatellites enabled the detection of a more fine-scale spatial structure compared to RAPD. Indeed, using RAPD, two genetically distinct clusters were detected, while three clusters were detected using microsatellites (Figure  2 ). For both markers, the Mirenne population formed a separate genetic cluster. In addition, the microsatellite data identified the Logbiermé population as a distinct cluster from the remaining populations of the Tailles upland. The majority of the individuals in this last cluster (7 out of 9) had a high probability of membership (>0. 75).
Dispersal and gene flow Genetic similarity decreased with increasing distance for both the RAPD (Mantel test: r =0. 706, P =0. 004) and microsatellite datasets (Mantel test: r =0. 599, P =0. 022) at the regional scale. It was similar for allozymes, despite not significantly (Mantel test: r =0. 414, P =0. 092;  [29] ). However, at the landscape scale (i. e. within the Tailles upland) isolation by distance (IBD) was not significant (P RAPD =0. 189, P microsatellite =0. 223). MRR-based estimates of dispersal at the landscape scale were highly correlated to both the effective migration rate estimate (r =0. 919, P =0. 015) and the maximum likelihood estimate (r =0. 912, P =0. 019). Using the Akaike information criteria (AIC) in the maximum likelihood analyses of dispersal, the full model (i. e.
Conclusions As previously mentioned, allozymes remain of very limited value at small spatial scales; microsatellites giving much higher estimate resolution (e. g. [52] ). This is especially true in species such as B. aquilonaris where enzyme variation is extremely limited and hence impedes the correct and accurate estimate of genetic population parameters (  [29] ; but see contradictory results in  [53, 54] ). However, here we show that dominant markers may still be worthwhile to consider in organisms for which no genomic information is available, and for which limited (financial and technical) resources are available. Indeed, coarse-grain spatial structure, genetic diversity, and gene flow estimates based on previously published RAPD data gave congruent results with both demographic data and microsatellite-based estimates.
Detection of enzyme polymorphism was carried out as in Vandewoestijne et al. [64]. Only four out of 17 enzyme systems proved to be polymorphic  [29]. Due to this extremely low level of polymorphism (e. g. only four polymorphic loci with a mean number of 2. 31 alleles), the allozyme data was not re-analyzed in this study; results obtained previously being included for the sake of a broader comparison of markers. Four RAPD primers produced 28 reproducible bands of which 18 were polymorphic. Analyses were carried out as in Vandewoestijne and Baguette  [29] , i. e. allele frequencies were calculated from null homozygote frequencies assuming panmixia and corrected for dominance according to  Lynch & Milligan [31]  using TFPGA 1. 3 (http://www. marksgeneticsoftware. net/tfpga. htm). Fifteen microsatellite loci were amplified in three multiplex reactions  [24].
Genetic diversity estimates Genetic diversity of the sampled populations was characterized through the number of polymorphic loci using the 95% criterion (P95) and unbiased expected heterozygosity (H e ), calculated using TFPGA 1. 3 for RAPD data and GENETIX 4. 05 (http://www. genetix. univ-montp2. fr/genetix/intro. htm) for microsatellites data. From microsatellites data, we also calculated i) Wright's inbreeding coefficient f and its significance by permutating alleles within each population (1000 permutations) using GENETIX 4. 05, ii) the mean number of alleles per locus (A) using GENETIX 4. 05 and iii) the allelic richness (AR) using FSTAT 2. 9. 3. 2 (http://www2. unil. ch/ popgen/softwares/fstat. htm). Effective population size (N e ) was only calculated for populations with a sample size greater than 25 individuals with microsatellites data (as Luikart et al.
e. the parental generation). Nc for each population was estimated using Jolly-Seber models fitted on MRR data implemented in the Mark software  [72]  as in Baguette  [28]  and following the method described in Schtickzelle et al. [73]. Finally, we tested for a disproportional decrease in allelic diversity compared to heterozygosity due to founder effects following population bottlenecks with BOTTLENECK 1. 2. 02  [74].
Structure of the populations Population differentiation for microsatellite loci was estimated using F st sensu Weir & Cockerham  [75]. Genotype data was used to derive F st from the variance components (AMOVA) using ARLEQUIN for RAPD data  [76]. Discriminant analysis of principal components (DAPC;  [77] ) and the Bayesian clustering method implemented in STRUCTURE  [78]  were used to identify and describe clusters of related individuals on both the RAPD and microsatellite data sets. The DAPC method has the advantage that it does not make any assumptions about the underlying population genetic model, although most conservation geneticists are acquainted with, and use the Bayesian clustering method. DAPC analyses were implemented in R 2. 12. 2 (R Development Core Team 2009) using the adegenet package  [79].
A principal component (PCA) was carried out on the genetic data sets. The number of PC's maintained in the discriminant analyses was determined so as to retain 90% of the genetic variation. The number of clusters was chosen based on the lowest BIC value. STRUCTURE analyses were run with admixture and correlated allele frequencies, 300. 000 iterations after an initial burn-in of 100. 000 iterations and 10 repetitions for each K. The number of clusters K was determined based on the probability ln P(D)parameter  [78]. The second order rate of change of lnP(D) with respect to K  [80]  was also used to determine the number of clusters using the Structure-sum 2010 package  [81]  in R 2. 12. 2 (R Development Core Team 2009). For both clustering approaches, analyses were run on the entire data set to detect the regional structure (i.
Those MRR data were collected in populations of the Tailles upland only in 1997. Number of migrants based on MRR data was calculated by multiplying the probability of dispersal by the estimated population size. This dispersal measure was compared with several genetic estimates of gene flow. First, the effective migration rate  [82]  between site pairs was calculated as F st ≈ 1/(4 Nm +1), where N is the effective population size of each population and m is the migration rate between populations. It should be mentioned that this island model makes a large number of simplifying assumptions. Nonetheless, performance evaluations using both simulations and analytical theory, suggest that the approach gives reasonable estimates of Nm even when certain assumptions are violated  [10]. Next, a maximum likelihood estimator based on the coalescent for unequal migration rates and different subpopulation sizes was used  [83] , using the Brownian motion microsatellite data model, as implemented in MIGRATE v2.