dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We are indebted to  Dr. Anton Berns  (The  Netherlands Cancer Institute, Amsterdam, The Netherlands ) for providing us with the floxed Rb mice. We thank  Ms. Taija Poikkipuoli  for assistance with histology,  Jonna Palmu  and  Hannele Rekola  for their technical assistance with genotyping PCR,  Taina Kirjonen  for her technical help with hormone measurements,  Jouko Sandholm  for helping with imaging, and  Tiina Kyro  ¨la ¨for taking good care of the mouse colony. This work was supported financially by The  Academy of Finland , The Sigrid Juselius Foundation, The  Finnish Cultural Foundation ,  Turku Doctoral Programme of Molecular Medicine (TuDMM) , and The  Turku University Hospital  evo-funds.
tion. A nitrate concentration maximum was found in the spring of 2005. Isotopic data of nitrate combined with photochemical calculations suggest that the presence of this spring maximum was linked to a significantly weakened stratospheric ozone (O 3 ) layer. This weakened O 3 layer resulted in elevated UV-B (Ultraviolet B) radiation on the snow surface, where the productions of OH and NO x from the photolysis of their precursors were enhanced. A concentration maximum was then observed as the result of enhanced local nitrate production due primarily to the increased OH concentrations, as indicated by decreases in δ 18 O and ∆ 17 O of nitrate associated with the spring maximum. We further examined the nitrate concentration record from a shallow ice core covering the period from 1772 to 2006 and compared this record to satellite observations of springtime O 3 column density data from 1979 to 2006.
, 2008). In this study, the concentrations of major ions (Na + , NH + 4 , K + , Mg 2+ , Ca 2+ , Cl -, NO - 3 , and SO 2- 4 ) in a snowpit and a shallow ice core from central Greenland were measured. In additon, we measured the isotopic composition of nitrate with high temporal resolution from the snowpit. The concentration data were used to establish their temporal patterns and to identify spring nitrate concentration maxima. The isotopic data were 9405 Figures.
Back Close Full described by  Cole-Dai et al. (2006). Ice blanks were made from deionized water, processed and analyzed following the same procedure as that for the snow samples. High blank values of Ca 2+ (up to 5 µg L -1 ) indicated that the snowpit samples were probably contaminated with Ca 2+ during sample preparation. No evidence of contamination was found for the other ions. Therefore, the snowpit Ca 2+ data were not used in interpretation in this study. The 79 m shallow core was analyzed for the ionic species using the technique of continuous flow analysis with ion chromatography (CFA-IC) detection  (Cole-Dai et al. , 2006). One advantage of the CFA-IC technique is that it minimizes the potential contamination by eliminating sample preparation. Replicate analysis of blanks with CFA-IC showed no contamination of any of the ions including Ca 2+.
The SP-1 samples at LGGE were measured for nitrate concentration and isotopic composition (δ 15 N, δ 18 O and ∆ 17 O). A small portion of each sample was taken for nitrate concentration measurement using the well-established Griess method in continuous flow analysis (Seal Analytical, Method No 1000223-1784-09). The rest of samples were processed followed the similar procedure described above and the N and O isotope ratios were determined by the bacterial denitrifier method. Instrumentation details at the LGGE laboratory are similar to that described in  Erbland et al. (2013). International reference materials (USGS34, USGS35 and IAEA-NO-3) were used for data re-.
The temperature and relative humidity data at Summit is available from Greenland Climate Network Data (GCND, http://cires. colorado. edu/science/groups/steffen/gcnet/ order/admin/station. php). Using these data, the spring (February and March average) atmospheric water vapor mixing ratio in 2005 was calculated to be 40 % higher than that in 2006 due to high temperatures in the 2005 spring (-30 • C) relative to the 2006 spring (-34 • C). Assuming this high water vapor in spring 2005 will result in a maximum increase in OH production of 40 %, the increase in water vapor is not enough to account for the 60 % enhancement in the nitrate deposition flux in the spring of 2005. In fact, as discussed later, OH must be increased by 200 % to explain the spring nitrate maximum. Although the relatively high temperature in the spring of 2005 may have contributed around 20 % (40 % increase vs.
Funding statement. The study was supported by  Academy of Finland  to M. J. R. ,  Turku University Foundation  to U. M. M, strategic research grant of  University of Turku  to M. V. K,  Romanian National Authority for Scientific Research, CNCS-UEFISCDI  to O. A. G. ( PN-II-RU-PD-2011-3-0131 ) and  CONACyT  to J. C. G. ( 152666 ).
Several splice variants were identified during our cloning step and by database scanning (unpublished results Figure  1B ). Alternative splicing events were found in the intracellular loop linking domains 2 and 3 and in the carboxyterminus region. Interestingly, there is a long non-coding RNA FIGURE 1 | Predicted molecular make-up of NALCN. The predicted structure of NALCN is similar to α/α 1 subunits of voltage-gated Na + and Ca 2+ channels  (Lee et al. , 1999). (A) It has four homologs repeats (domains I-IV) each composed of six transmembrane segments (S1-6). Four pore-forming loops (P-loops) spanning from S5 to S6 make up the ion selectivity filter. The intracellular loop linking domains I and II of NALCN interacts with M3R and molecular determinants involved in the regulation by CaSR are located in the carboxy-terminus  (Swayne et al.
UNC-80 UNC-80 (also named KIAA1843, c2orf21) is located on human chromosome 2q34 and has at least 45 exons. UNC-80 is a large protein of about 3300 amino acids without any predicted transmembrane segments or particular functional domains. UNC-80 contributes to the neuronal localization and/or stabilization of NALCN channel in C. elegans and D. melanogaster  (Jospin et al. , 2007; Yeh et al. , 2008; Lear et al. , 2013). In addition, UNC-80 acts as a scaffold protein for the SFKs and UNC-79. In fact, NALCN can interact with UNC-80 in the absence of UNC-79 and UNC-79 can interact with UNC-80 in the absence of NALCN. The interaction of NALCN and UNC-80 is required for the NALCN activation/inhibition by GPCRs in neurons  (Wang and Ren, 2009; Lu et al. , 2010). Similar to NALCN, databases indicate the existence of several UNC-80 splice variants.
ARE THERE MORE PROTEINS IN THE NALCN CHANNELOSOME? The NALCN channelosome is probably not restricted to NALCN, UNC-79, UNC-80, NLF-1, SFKs, and GPCRs. As an example, there is another gene highly related to NLF-1, FAM155B in mammals (accession number NG_021282. 1). Whether the FAM155B gene product belongs to the NALCN channel complex should be investigated. Also, the mechanisms involved in the regulation of the NALCN channel by CaSR are not known and could imply a co-inclusion in the same protein complex. Some studies indicate genetic interactions between nca-1;nca-2 and other genes in C. elegans. As a matter of fact, null mutants of stomatins (unc-1 and unc-24) and innexins (unc-7 and unc-9) were found to restore the altered sensitivity to GAs and ethanol  (Sedensky and Meneely, 1987; Morgan et al.
ACKNOWLEDGMENTS The authors are grateful to  Stephanie Knapp  who participated to the early writing steps of this review article. We thank  Sarah England ,  Erin Reinl , and  Natalia Prevarskaya  for critical readings and comments on the manuscript and  Drs. Céline Lemmers ,  Isabelle Bidaud ,  Samer Khoury-Hanna ,  Hamid Gholamipour-Badie , and  Adriano Senatore  for their contribution to the work performed in the laboratory. Our laboratory is supported by a grant from " Agence Nationale de la Recherche "  11 BSV1 004 01  and is part of the  Laboratory of Excellence "Ion Channel Science and Therapeutics " (http://www. labex-icst. fr/en; ANR-11-LABX-0015-01_ICST ).
General genetic techniques and plasmid construction Standard molecular genetic techniques were used throughout this study following  Sambrook and Russell (2001). Restriction enzymes were obtained from New England Biolabs (Ipswich, England). Genomic DNA from yeast transformants was prepared as described by  Querol et al. (1992). PCR amplification was performed using an Eppendorf 2720 thermal cycler and employing both Taq (Promega, Madison, WI) and Pfu (Stratagene, La Jolla, CA) DNA polymerases. PCR fragments were then purified with a QIAgen Purification Kit (Qiagen, Hilden, Germany), and DNA fragments were recovered from agarose gels using a QIAquick Gel Extraction Kit (Qiagen, Hilden, Germany). The Staden software package was used for gene sequence analysis  (Dear and Staden, 1991). To quantify hexokinase gene expression, genes were amplified with the primer pairs ylHXK1-fwd and ylHXK1-rev (GAGAAGATC-TATGGTTCATCTTGGTCCCCGAAAACCC and GCGCCCTAGGCTAAA-TATCGTACTTGACACCGGGCTTG, respectively), and scHXK2-fwd and scHXK2-rev (GCGCGGATCCATGGTTCATTTAGGTCCAAAAAAACC and GCGCCCTAGGTTAAGCACCGATGATACCAACG, respectively), all of which contained BamHI(BglII)-AvrII restriction sites.
Fluorescence microscopy Images were obtained using a Zeiss Axio Imager M2 microscope (Zeiss, Le Pecq, France) with a 100 Â objective lens and Zeiss filter sets 45 and 46 for fluorescence microscopy. Axiovision 4. 8 software (Zeiss, Le Pecq, France) was used for image acquisition. To make the lipid bodies (LBs) visible, BodiPy s Lipid Probe (2. 5 mg mL À 1 in ethanol; Invitrogen) was added to the cell suspension (OD 600 ¼ 5) and the samples were incubated for 10 min at room temperature.
5 μM forward and reverse primers and 1 μg of cDNA template in a final reaction volume of 10 μL. Thermocycling was performed in the Eco Real-Time PCR System (Illumina, San Diego, CA, USA) with the following cycling parameters: 5 min incubation at 95 1C, followed by 40 cycles of 10 s at 95 1C, 10 s at 60 1C, and 8 s at 72 1C. Fluorescence data were acquired during each elongation step, and at the end of each run, specificity was controlled by melting curve analysis. Hexokinase expression was detected using the primers ylHXK1-qPCR-fwd (TCTCCCAGCTTGAAACCATC) and ylHXK1-qPCRrev (CTTGACAACTCGCAGGTTGG). The results were normalized to actin gene expression  (Lazar et al. , 2011)  and then analyzed using the ddCT method  (Schmittgen and Livak, 2008). All experiments in this paper were performed at least thrice.
Acknowledgments This work was funded by the  Agence Nationale de la Recherche  ( Investissements d'avenir program ; reference  ANR-11-BTBR-0003 ). Z. Lazar received financial support from the  European Union  in the form of an  AgreenSkills Fellowship  (Grant agreement no. 267196 ;  Marie-Curie   FP7 COFUND People Program ). We thank  Claude Gaillardin  for critical comments on the manuscript. We would also like to thank  Jessica Pearce  and  Lindsay Higgins  for their language editing services.
The data are based on that of a. (d) The frequency of crossresistance interactions between antibiotics is independent of whether they show physiological interactions (that is, synergy or antagonism), P ¼ 0. 35, N ¼ 45, Kruskal-Wallis test. Aminoglycosides are excluded from the analysis as they show an especially large number of synergistic interactions and are strongly depleted in cross-resistance interactions with other antibiotics). Box plot presents the median and first and third quartiles, with whiskers showing either the maximum (minimum) value or 1. 5 times the interquartile range of the data, whichever is smaller (larger). selected 63 independently evolved lines from the final day of experiments (5-6 lines per antibiotic). These lines were subjected to whole-genome sequencing using the Applied Biosystems SOLiD platform.
Furthermore, genes showing sequence similarity to known resistance genes are enriched among genes mutated in multiple lines compared with those mutated in a single line (17 out of 47 versus 11 out of 96, Po0. 005, Fisher's exact test). We identified genes showing significant sequence similarity to a set of genes curated in the Comprehensive Antibiotic Resistance Database 16 using BLASTP search. In brief, we used the standalone NCBI BLASTP þ tool to identify E. coli genes that show highly significant similarity to any of the curated resistance or target genes (a conservative E-value cutoff of 10 À 30 was applied). accumulated large numbers of mutations, including deletions of large genomic segments (Supplementary Fig. 2 ). Loss-of-function mutations are prevalent. Over 27% of the observed point mutations, small deletions and insertions generated in-frame stop codons, frameshifts or disruption of the start codon.
This yielded a matrix of evolutionary interactions between evolved strains and antibiotics (Supplementary Data 1). Where multiple independent experimental runs were available, we used Fisher's method to aggregate P-values. All statistical analyses were carried out in Matlab. The results were confirmed by E-test assays, using standard protocols. Finally we calculated the the frequency of cross-resistance (FCR) for each antibiotic pair as follows: FCR ¼ (NA-B þ NB-A)/(NA þ NB), where NA-B and NB-A are the number of populations adapted to antibiotic A showing enhanced resistance to B, and vice versa. NA and NB are the total number of populations adapted to antibiotic A and B, respectively. Chemical and chemogenomic profile similarities. Chemical similarities of antibiotics were computed using an R implementation of the cheminformatics library CDK (Chemistry Development Kit)  51.
coli K-12 substr. MG1655 chromosome (GenBank Accession No. NC000913; Version NC_000913. 2 GI:49175990) in colour space using Genomics Workbench 6. 5 (CLC Bio). Within a single read, the maximum gap and mismatch count was set to two and the similarity fraction was set to 0. 8. Two mappings were performed for each strain which differed in setting the length fraction to 0. 5 for relaxed or 0. 6 for stringent analysis. Minimum coverage of Z51-fold and Z44-fold was accomplished for each strain when using relaxed or stringent parameters, respectively. A minimum of six reads were required to call a point mutation or short indel (o15 bp) upon relaxed analysis; in contrast, 20 reads were required to call a structural variation (SV; for example, inversion, duplication, replacement, translocation) upon stringent analysis.
Acknowledgements This work was supported by grants from the  European Research Council  ( 202591 ) and the  EMBO Young Investigator Programme  (to C. P. ), the  Wellcome Trust , and the  'Lendu ¨let Program ' of the  Hungarian Academy of Sciences  (to C. P. and B. P. ). B. C. was supported by the  Hungarian Research Fund  ( OTKA PD 109572 ). We also acknowledge the use of the NGS platform established by the  ERC Advanced Grant 'SymBiotics' of Eva Kondorosi.
Infection by hepatitis C virus (HCV) leads to one of two outcomes; either the infection resolves within approximately 6 months or the virus can persist indefinitely. Host genetics are known to affect the likelihood of clearance or persistence. By contrast, the importance of the virus genotype in determining infection outcome is unknown, as quantifying this effect traditionally requires well-characterized transmission networks, which are rare. Extending phylogenetic approaches previously developed to estimate the virus control over set-point viral load in HIV-1 infections, we simulate inheritance of a binary trait along a phylogenetic tree, use this data to quantify how infection outcomes cluster and ascertain the effect of virus genotype on these. We apply our method to the Hepatitis C Incidence and Transmission Study in prisons (HITS-p) data set from Australia, as this cohort prospectively identified incident cases including viraemic subjects who ultimately clear the virus, thus providing us with a unique collection of sequences from clearing infections.
Subjects analysed in study Hepatitis C virus genome data used in this study were obtained as part of the Australian 'Hepatitis C Incidence and Transmission Study in prisons (HITS-p) cohort  (Dolan et al. 2010; Teutsch et al. 2010). From September 2005 to May 2009, adult prison inmates were recruited at 19 of 30 correctional centres housing adult male or females in New South Wales (NSW). The conditions to join the study were to report a lifetime history of injecting drug use (IDU); to be imprisoned within the last 12 months; and to have a serological test documenting negative anti-HCV antibody status during that time. Of the 500 subjects enrolled in HITS-p, this analysis included subjects who were HCV-RNA negative at enrolment and had completed at least one prospective follow-up visit while in prison (having either been continuously imprisoned or visited upon re-imprisonment after a period of release).
Sequences of E1-HVR1 The region encoding the last 171 bp of core, E1 and HVR1 (882 bp; nucleotides 723-1604, with reference to HCV strain H77; GenBank accession number AF009606) was amplified by nested RT-PCR as previously described  (Pham et al. 2010). In brief, viral RNA was extracted from 140 lL of plasma using the QIAmp Viral RNA kit according to manufacturers' instructions (Qiagen, Venlo, Limburg, the Netherlands). Five microlitres of extracted RNA was added to a 15 lL reaction mixture containing 10 lL of RT-PCR reaction mix (iScript One-Step RT-PCR with SYBR Green; Bio-Rad, Hercules, CA, USA), 500 nM of each primer (GV32 and GV33 in  Pham et al. (2010) ) and 0. 4 lL of 50x iScript reverse-transcriptase enzyme (Bio-Rad). Cycling conditions included a 10-min reverse transcription step at 50°C; reverse-transcription inactivation at 95°C for 5 min; 15 cycles at 95°C for 30 s, 55°C for 30 s and 72°C for 1 min, followed by a final extension of 72°C for 7 min.
Initial analysis of HCV genetic data Sequences of the E1-HVR1 region of the HCV genome were available for 92 subjects infected with genotype 1a, 1b or 3a viruses. Of these, 19 were from subjects who ultimately naturally cleared the HCV infection (sequencing was difficult in clearing infections due to lower viral loads). For the study, we only used the earliest-obtained HCV sequence for each patient and discarded subsequent sequences. Preliminary analysis showed that within-host evolution contributed minimally to the phylogenetic relatedness between subjects, as all the longitudinal samples present in the cohort clustered with the subject itself (result not shown). Due to some incomplete sequencing, subsequent phylogenies were built using a 657 bp length of sequence, covering the core and E1 regions.
Although  Gray et al. (2011)  found that this segment was hypervariable within hosts, they also showed that it evolved at a similar rate to the rest of the genome at the between-host level, so phylogenies should not reflect within-host selection at this segment. We performed an initial analysis on the complete data set. However, to prevent confounding of any detected genetic signal with the HCV genotype, we subsequently extracted the genotypes from the two major observed subtypes and performed analysis on these separate data sets, which we denoted as 'Genotype 1' (containing viruses of subtype 1a and 1b) and 'Genotype 3' (subtype 3a; Fig. 1 ). The application of these selection criteria resulted in 31 subjects in the Genotype 1, and 42 subjects in the Genotype 3 category with confirmed sequences amenable for further analyses.
Creation of phylogenetic trees For each data set, we used BEAST v1. 7. 3  (Drummond et al. 2012)  to create a set of phylogenetic trees for use in our Bayesian analysis, to account for sampling error in the phylogeny. As the inference of phylogenetic signal for a trait can be very sensitive to the underlying phylogeny, then by sampling from the posterior distribution and performing the analysis over several trees, we minimized the influence that phylogenetic sensitivity had on the final result  (Parker et al. 2008). The collection date of the HCV sample, as obtained from the HITS-p cohort, was used to align tree tips. Using the JMODELTEST software  (Guindon and Gascuel 2003; Darriba et al. 2012) , it was found that a GTR nucleotide model with estimated base frequencies, invariable sites and a Gamma site heterogeneity model with four categories (GTR + I + Γ) gave the lowest Bayesian information criterion (BIC) score.
We estimated phylogenies by comparing outputs consisting of a constant population size coalescent model; exponential population growth; or a Bayesian Skyline model with 10 groups. Using the Path-O-Gen software (version 1. 4, available from http://tree. bio. ed. ac. uk/software/pathogen/), we did not find a positive correlation between the sampling date and root-to-tip divergence for either subtype. Both estimates were not significantly different from zero (correlation coefficient for Genotype 1 was À0. 220, P = 0. 398; for Genotype 3, it was À0. 0863, P = 0. 600). Therefore, it appeared that no molecular clock signal is present in the data. We therefore proceeded using a strict clock model as the simplest possible scenario and checked afterwards that the estimated substitution rates were in line with previous findings for HCV.
The outputs seemed to make sense, as substitution rate estimates that we found lied around 10 À3 , as found by  Gray et al. (2011). The loglikelihoods of the exponential model and the Bayesian Skyline models were not significantly different when individually compared with the constant-size model, as inferred (A) (B) Figure  1  Phylogenies of the two HCV data sets. 'Genotype 1' (part A) consisted of viruses of subtype 1a or 1b, as denoted on the graph. 'Genotype 3' (part B) consisted of viruses of subtype 3a. Short-term acute outcomes are denoted with an A, chronic outcomes with a C. Grey labels indicate strains where the host had the homozygous IL28B-917 SNP corresponding to increased clearance rate. A scale bar is also included that shows the number of substitutions per site. Phylogenies were compiled for this figure using PhyML  (Guindon et al.
achieve this, we used the 'ace' function, provided as part of the 'ape' package (Analyses of Phylogenetics and Evolution) for R  (Paradis et al. 2004 ). This function implements Pagel's maximum-likelihood method  (Pagel 1994)  for estimating the rate of correlated evolution between several discrete traits along a phylogeny. It does so by estimating the transition probabilities q 0,1 and q 1,0 between the two traits; the probability that a 'clearer' genotype will switch to being a chronic genotype, and vice versa. There are two probabilities because it can be easier to switch from one state to the other, hence, q 0,1 is not necessarily equal to q 1,0. After estimating these transition probabilities, the inputted control value was saved, along with estimates of the switching rates (with standard errors) and the log-likelihood for the estimates.
This behaviour is due to the final trait allocations being more likely to reflect the initial state assigned to the tree root (Fig. S1A ). After removing extremely high outlier values (those that lie outside the 97. 5% upper quartile), the estimated switching rates were found to be significantly negatively correlated with the input trait correlation values (for q 1,0 , F 1,9748 = 135. 7, P < 2. 2 9 10 -16 ; for q 0,1 , F 1,9748 = 177. 4, P < 2. 2 9 10 -16 ; see also Fig. S1B, C ). This supported our intuition that with high levels of virus control on trait outcomes, similar outcomes tended to congregate around closely related tips. These results were quantitatively similar for the Genotype 3 data set, and also to results produced using a birth-death phylogeny.