dataesr/scientific-paragraphs-categorization huggingface dataset:
2016 PF02127 No MH 2 Zn 2+ M20 TGME49_213520 V 514 peptidase M20D, amidohydrolase 96,99%/ 5,80e-205 (M20D) M20 - - PF07687 PF01546 yes TGME49_248850 XII 416 methionine aminopeptidase 100%/ 8,20e-145 (M24A) M24 - - No TGME49_211330 IV 697 methionine aminopeptidase 95,93%/ 1,50e-129 (M24A) M24A - - No TGME49_257730 VIIb 484 methionine aminopeptidase, type i, putative 54,30%/ 2,10e-66 (M24A) M24B - - yes TGME49_305460 IX 480 methionine aminopeptidase 2, putative 61,21%/ 1,50e-106 (M24A) M24C - - No TGME49_279390 IX 462 proliferation-associated protein 2G4, putative 51,22%/ 3,00e-83 (M24X) M24D - - No TGME49_261600 VIIb 886 creatinase domain containing protein 35,00%/ 1,20e-97(M24B) M24E TgAPP Yang M. 2016 No TGME49_233310 VIII 599 peptidase D, putative 100%/ 4,20e-125 (M23B) M24F - - No 4 E M G T 9 _ 2 2 1 6 7 0 I I 1 1 9 8 t s n a r c t p i r o i a n l e o l a g n t o i n c a f t o r 1 T C A F 4 0 3 9 , 6 2 / % 9 , 0 0 e - 4 9 2 M ( 4 ) X 2 M 4 G o N TGME49_225850 X 1555 peptidase, M28 family protein 42,55%/ 3,10e-28 (M28X) M28 - - No TGME49_231130 VIII 711 hypothetical protein 36,60%/ 7,30e-25 (M28X) M28A - - No TGME49_202630 VIIa 1188 ATP-de pendent metallopeptidase HflB subfamily protein 99,59%/ 2,00e-125 (M41) M41A - - Ye s TGME49_300020 XII 1021 ATP--- No TGME49_285670 V 260 hypothtical protein 70,00%.
/ 6,50e-00 (M50B) M50A - - No TGME49_251500 XII 600 eukaryotic translation initiation factor 3 subunit 3, putative 29,17%/ 1,60e-32 (M67X) M67A - - Ye s TGME49_231970 VIII 2538 pre-mRNA splicing factor PRP8, putative 27,37%/ 1,50e-01 (M67C) M67B M1 PF01433 M3 PF01432 PF08439 M14 PF00246 PF00675 M16 PF05193 PF16187 PF08367 M24 PF00557 PF16188 2 MH M28 PF04389 M41 PF01434 M50 PF02163 PF13398 M67 TGME49_228190 TGME49_269250 X VIII 346 343 eukaryotic translation initiation factor 3 subunit 5, putative Mov34/ MPN/ PAD-1 family protein 66,67%/ 3,30e-45 (M67A) 57,00%/ 2,9e-39 (M67A) M67C M67D --- --- PF01398 PF05021 PF14464 No No No TGME49_269840 VIII 314 proteasome regulatory subunit 100%/ 1,70e-167 (M67A) M67E - - No TGME49_208590 XI 489 Mov34/ MPN/ PAD-1 family protein 94,05%/ 2,50e-134 (M67A) M67F - - Ye s MA(E) 1 Zn 2+ M76 TGME49_257110 VIIb 618 hypothetical protein 100%/ 3,50e-50 (M76) M76 - - PF09768 No.
In silico mining of the RNAseq data available through the public TomExpress web platform, identified several SlARFs as responsive to various pathogen infections induced by bacteria and viruses. Accordingly, sequence analysis revealed that 5' regulatory regions of these SlARFs are enriched in biotic and abiotic stress-responsive cis-elements. Moreover, quantitative qPCR expression analysis revealed that many SlARFs were differentially expressed in tomato leaves and roots under salt, drought and flooding stress conditions. Further pointing to the putative role of SlARFs in stress responses, quantitative qPCR expression studies identified some miRNA precursors as potentially involved in the regulation of their SlARF target genes in roots exposed to salt and drought stresses. These data suggest an active regulation of SlARFs at the post-transcriptional level under stress conditions.
Histochemical analysis of GUS expression Transgenic plants expressing pARF8A::GUS and pARF10A::GUS were generated by A. tumefaciens-mediated transformation according to  Wang et al. (2005)   [46]. For that, PCR was performed on the genomic DNA of tomato 'Micro-Tom' (10 ng. ml -1 ) using specific primers. The corresponding amplified fragment was cloned into the pMDC162 vector containing the GUS reporter gene using Gateway technology (Invitrogen). The cloned SlARF promoter was sequenced from both sides using vector primers in order to see whether the end of the promoter is matching with the beginning of the reporter gene. Sequence results were carried out using the Vector NTI (Invitrogen) and ContigExpress software by referring to ARF promoter sequences. GUS assays were conducted on DR5::GUS, pARF8A::GUS and pARF10::GUS tomato lines.
Analysis of Cis-acting regulatory elements Using database associated search tools, 2 kb of 5 0 regulatory region of each ARF transcription factor gene in tomato was scanned for the presence of putative cis-acting regulatory elements and motifs registered in Plant PLACE database (https://sogo. dna. affrc. go. jp/cgi-bin/sogo. cgi? lang=en) (consulted in March 22 nd , 2015)  [44]. The cis-acting elements analyzed are listed in S3 Table.
After 5 days of PEG treatment, a slight increase in GUS activity was detected in leaf veins and primary and lateral root tips while it scarcely appears in lateral root primordia of the untreated plants. Expression profiling of SlARF genes in response to salt stress. We analyzed the expression profiles of SlARF genes in tomato leaves and roots after 24 hours of salt treatment. The expression of Cold Inducible 7 gene (CI7), a known marker for salt stress  [48] , was significantly induced in both leaves and roots as expected, thus validating the efficiency of the applied stress in our condition. The data reveal that almost all SlARF genes were downregulated in leaves except SlARF1, SlARF4 and SlARF19 whose expression was significantly induced (Fig  3 ). Among the down-regulated genes, SlARF3, SlARF5, SlARF8A and SlARF18 were highly repressed.
Auxin signaling pathway is altered by pathogen infection in tomato The auxin response has emerged recently as an active actor in plant defense against pathogens  [72]. Auxin coordinates plant development essentially through two transcriptional regulators Aux/IAA and ARFs  [24]. In tomato, 22 SlARFs were previously isolated and characterized  [21]. In silico promoter analysis of SlARFs using Plant PLACE database have revealed that at least one copy of the pathogenesis induced element (GT1GMSCAM4) was found in SlARFs 5' regulatory regions. Furthermore, the expression of almost the entire SlARF gene family was altered in response to some of the pathogen tested based on the results related to biotic stresses that are available in TOMEXPRESS platform  [59]. This suggests that auxin might be involved in biotic stress responses through ARFs.
Acknowledgments The authors are grateful to  L. Lemonnier ,  D. Saint-Martin  and  O. Berseille  for genetic transformation and culture of tomato plants. This research was supported by the  "Laboratoire d'Excellence" (LABEX)  entitled  TULIP  ( ANR-10-LABX-41 ) and benefited from networking activities within the European  COST Action   FA1106 , from the  ANR TomEpiSet  ( ANR-16-CE20-0014 ) project, the  TomGEM H2020  project, by  Hubert-Curien partnership program  Volubilis ( MA/ 12/280-27105PL ) and  CMCU program  ( 08G0906 ) granted to  Dr Habib Khoudi.
This research was supported by the  "Laboratoire d'Excellence" (LABEX)  entitled  TULIP  ( ANR-10-LABX-41 ) and benefited from networking activities within the European  COST Action   FA1106 , from the  ANR TomEpiSet  ( ANR-16-CE20-0014 ) project, the  TomGEM H2020  project, by  Hubert-Curien partnership program  Volubilis ( MA/12/280-27105PL ) and  CMCU program  ( 08G0906 ) granted to  Dr Habib Khoudi. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
This is achieved after three 20-min exposures for targets with V 14. 0; for bright fields the exposure time is halved. In case of bad seeing the total exposure time is extended using additional exposures within the same night. 'A spectrum' in this paper refers to data from a given star collected during an uninterrupted sequence of scientific exposures of its field. Its effective time of observation is assumed to be mid-time of the sequence. GALAH is a magnitude limited survey of stars with V < 14. 0, where the V magnitude is computed from 2MASS magnitudes as discussed in  Martell et al. (2017). Stars are selected randomly with no preference for colour or other properties. To avoid excessive reddening or crowding most of the targets are located at least 10 degrees from the Galactic plane. The upper limit on the distance from the plane is set by the requirement that there should be ∼ 400 stars with V < 14.
(2018) , Table  5 ). GALAH DR2, which is based on The Cannon analysis with an internal release date of 8th January 2018, contains 213,231 spectra without such problems, i. e. with all warning flags set to 0. Here we publish RVs for 212,734 of these objects. Altogether our database contains 514,406 RVs (from these 323,949 with all flags set to 0) derived with the same approach. Additional spectra are either repeated observations of the same objects or objects observed as part of other programs than GALAH which will be published separately. We plan to add radial velocities for more spectra in the future, which will include new observations but also hot and cool stars, for which no Cannon-derived values of stellar parameters are available at this time. Figure  1  shows distributions in temperature, gravity, metallicity, and Galactic latitude for stars with our RVs.
This scheme is useful as it takes into account both the statistical errors of the RV measurements in individual intervals and their distance from the final averaged radial velocity. Table  1  reports averages of the final weights (w i ) assigned to any of the 20 intervals, where averaging is done over 323,949 spectra in our database with zero values of reduction, synt, and parameter flags. For tabulation purposes, weights were rescaled so that their average value is 1. 0. The table shows that different intervals do not contribute with the same weight to the final velocity. In particular, intervals of Hα (3b) and the ones in the blue and green arm have higher weights, while some intervals located at the edges of the wavelength range of particular arms, and especially the ones in the infra-red arm, have significantly lower weights.
DATA PRODUCTS The tabular results of this work are reported in four columns, which are published as part of the GALAH DR2 data release  (Buder et al. 2018). These are the values of RV_obst and RV_nogr_obst, and their errors e_RV_obst and e_RV_nogr_obst, all in units of km s -1. The GALAH DR2 catalogue and documentation is available at https://galah-survey. org and at https: //datacentral. aao. gov. au/docs/pages/galah/, through a search form or ADQL query. The catalogue is also available through TAP via https://datacentral. aao. gov. au/vo/tap. Some Python tools for processing the data are available online in an open-source repository 1. An additional result of this work is a table of 1181 median observed spectra, also published on the GALAH website https://galah-survey. org. Their median velocity (RV med ) and gravitational redshifts are taken into account, so these spectra are at rest with respect to the stellar barycentre.
(Kharchenko et al. 2013;usters. Table reports the number of spectra (N) of observed cluster member candidates, median observed radial velocity and metallicity, and standard deviations of all measurements around the median. The last column is RV from the literature(Kharchenko et al. 2013;  Mermilliod et al. 2009 or WEBDA database)]. All velocities are in km s -1.
ACKNOWLEDGMENTS We thank the referee for useful comments on the initial version of the manuscript. The GALAH survey is based on observations made at the  Australian Astronomical Observatory , under programmes  A/2013B/13 ,  A/2014A/25 , A/2015A/19, A/2017A/18. We acknowledge the traditional owners of the land on which the AAT stands, the Gamilaraay people, and pay our respects to elders past and present. TZ, GT, and KČ acknowledge financial support of the  Slovenian Research Agency  (research core funding No. P1-0188  and project  N1-0040 ). TZ acknowledges the grant from the distinguished visitor programme of the RSAA at the  Australian National University. JK is supported by a  Discovery Project grant  from the  Australian Research Council  ( DP150104667 ) awarded to  J. Bland-Hawthorn  and T.
Results Macedonian physician champions and US partners trained 97 physicians in the counseling methodology. Statistically significant improvements (p<0. 001) in knowledge over six months were demonstrated on multiple topics, including health risks, nicotine knowledge, addiction behavior, and vulnerable populations. Provider self-efficacy scores for conducting counseling activities increased by over 50%. Patient-level perception and outcome data are currently being collected and will be reported. To date, over 20,000 counseling sessions have been reported, lasting on average 8. 3 minutes, which represents 45% of an entire visit's length.
Methods A longitudinal study was conducted to analyse Indonesian adults (aged 15 years and above) data from two waves of Indonesian Family Life Survey (IFLS) in 2007 (n= 13,535 households; 44,103 individuals) and 2014 (n=16,204 households; 50,148 individuals). Regression analyses were performed to identify the socioeconomic factors associated with the change in smoking status -quit smoking and smoking initiation -occurred between 2007 and 2014.
Methods Pre-post evaluation of the self-reported performance of the 5A's by hospital workers from the three involved organizations. We assessed individual, behavioral, and organizational-level factors through a questionnaire that included 43 items (0=none to 10=most possible) completed before and six months after the training. Medians and interquartile ranges were computed. To examine changes, the non-parametric test for paired data (Wilcoxon) was used.
Methods The study used primary data collected from 285 tobacco farmers and 174 alternative crop growers, selected randomly from Kushtia, Chuadanga, and Jhenaidah districts of Bangladesh in 2015. Logistic regression was done to identify factors liable for tobacco cultivation over traditional crops. Results_ Result showed that TPCs grabbed some typical limitations of traditional agriculture. TPCs made formal agreement with farmers intend to grow tobacco, and offered them stimulus packages to cultivate tobacco. Statistically significant variables increasing probability of tobacco cultivation over traditional crops were TPCs offered sales guarantee, input subsidy and farming assistance, price stability which all are generally lack in tradition crops. These incentives led higher profitability in tobacco cultivation also.
Methods We conducted a windshield survey of six predefined regions in Macedonia with diverse socio-economic, geographic, and environmental characteristics. Windshield surveys are a qualitative research tool for rapid systematic collection of observational data. A project-specific community-based formative research toolkit was created with a dedicated observation form encompassing demographic, organizational data, community identity, economic elements, and use of tobacco in various socio-cultural settings, and personal perception of researchers. Observations were conducted by teams of two researchers by site: one detecting micro focus data (i. e. looking at particular individual's actions and characteristics) while the other focused on a broader macro perspective (i. e. larger focus on environment and total big-picture numbers).
Conclusions In Macedonia, tobacco is cheap, easily accessible, and a part of local cultures. These observations were combined with qualitative interview data to adapt the Certified Tobacco Treatment Specialist program for Macedonian physicians and the associated evaluation tools. Introduction This study had three basic aims: to develop a community activity in smoking, to implement it in a primary care centre and to teach new doctors about tobacco addiction by working brief interventions. Methods 1400 patients from general population were included. We selected people with diagnoses of smoking in their electronic clinical history (ECH). Codes 305. 1 and 989. 8 from CIE-9 were used. Resident doctors were taught about brief interventions in smoking cessation and they phoned patients to invite them to come to the health centre in order to talk about tobacco.
Methods Prospective, controlled non-randomized trial of 12 months of follow-up. 100 trained pharmacies will participate through the CESAR programme (intervention group) and 100 non-trained pharmacies (control group), that will recruit at least 5 patients per pharmacy. The trial has been classified as NO-EPA and approved by the Ethics Committee. The group assigned to the CESAR programme will carry out an initial visit, follow-up visits at days 7, 15, 30, 60, 90 and 180, and a final visit at month 12. The control group will conduct an initial visit, a follow-up visit at day 180 and a final visit at month 12. The endpoints of the trial will be: sociodemographic, motivation, dependence, cooximetry, quality of life, treatment adherence, direct and indirect cost of the health services used. The data will be recorded in the web platform: investigacionsefac.
Acknowledgments: This work was carried out as part of the knowledge-hub  Modelling European Agriculture with Climate Change for Food Security  (MACSUR). M. v. O. thanks the  Natural Environment Research Council in the U. K. for funding his participation in MACSUR and the  Norwegian Institute of Bioeconomy Research (NIBIO)  for additional support. G. B. thanks the metaprogramme Adaptation of Agriculture and Forests to Climate Change.
The clinical data of these patients are included in Supplementary Data 1. For CTCs, the tumor fraction corresponds to the fraction of clonal cells present within the normal mononuclear cells isolated during the CD138+ bead selection used to enrich for the tumor cells. We first examined tumor fractions in matched samples of cfDNA and enriched CTCs obtained from the same blood draw from 28 patients with MM (Fig. 1a, b ). Interestingly, beside one patient (MM_2205), we found a wide discrepancy in the tumor fraction obtained from cfDNA and enriched CTCs, including several patients with higher tumor fractions in cfDNA than enriched CTCs or vice versa. For instance, patient MM_2214 had a tumor fraction of 80% in the enriched CTCs but only 6. 7% in cfDNA. Conversely, patient MM_2242 had a tumor fraction of 91% in cfDNA and only 4% in the enriched CTCs.
Indeed, there was no correlation between the tumor fraction present in cfDNA or enriched CTCs in the 28 samples that were performed on the same liquid biopsy, Fig. 1b , (Pearson correlation 0. 081, p = 0. 680). When we use either cfDNA or CTC tumor fractions ≥10% (the % required for confidence in performing WES on the sample), we could detect tumor DNA in 35% of the samples, rescuing some of the samples that were not detected by CTC or cfDNA alone (Fig. 1b ). Our data suggest that analyzing both cfDNA and CTCs may broaden the applicability of liquid biopsies to patients with MM, provided that cfDNA and CTCs yield similar genomic profiles. Clinical correlation of CTCs and cfDNA in MM. To further confirm the variability in tumor fractions among patients and potential correlations with clinical stage, we examined all 107 samples from patients for which cfDNA was isolated and 56 samples from patients for which CTCs were collected.
Acknowledgements We would like to first and foremost acknowledge the courageous patients and their families for participation and contribution to this study. We are also grateful for all the members of  Van Allen lab  for their helpful discussions and feedback. We thank  Tiana Issa  for her graphic support. This work was supported in part by  NCI   R01 CA181683-01A1 , the  Multiple Myeloma Research Foundation (MMRF) , and the  Leukemia & Lymphoma Society (LLS). We would also like to acknowledge the generous support of the  Gerstner Family Foundation.
, 2017). To evaluate and tune the models, studies to date have relied upon satellite data from the Optical Spectrograph and Infrared Imaging System (OSIRIS) instrument to provide a global estimation of aerosol optical depth. However, comparison between OSIRIS and the models found a ≈ 1-month discrepancy in the timing of the SAOD maximum following the eruption. This was attributed to be likely due to deficiencies in the model aerosol microphysics, specifically the absence of nucleation processes  (Haywood et al. , 2010; Jégou et al. , 2013). In subsequent work,  Fromm et al. (2014)  identified that stratospheric AOD derived from OSIRIS under high aerosol loadings was likely underestimated following volcanic eruptions, due to a saturation effect and because the extinction profiles may terminate well above the tropopause (and therefore miss volcanic aerosol in the lowermost stratosphere).
Anthropogenic CH 4 emissions were added from the EDGAR v4. 2 database (available at http://edgar. jrc. ec. europa. eu); biogenic CO emissions were added from the Model of Emissions of Gases and Aerosols from Nature -Monitoring Atmospheric Composition and Climate (MEGAN-MACC) database  (Sindelarova et al. , 2014). Carbonyl sulfide (OCS) was prescribed using data from  Kettle et al. (2002). CH 2 O was prescribed according to the IPCC RCP8. 5 scenario  (Riahi et al. , 2011)  Simulations were started on 1 January 2009, using the CESM1(WACCM) initial atmosphere state file at that date. This enabled a 6-month model spin-up period before the eruption injection on 15 June 2009, after which the simulations were continued for 1 year, ending on 31 May 2010. The Sarychev Peak eruption was simulated by injecting volcanic SO 2 (and HCl) gases into model grid boxes corresponding to the location of the volcano (48 • N, 153 • E), over the duration of 15 June 2009, spread evenly between 11 and 15 km altitude a.
For this comparison, we use the IASI retrieval of SO 2 by  Clarisse et al. (2012). The IASI data set and retrieval algorithm used for this precise eruption can be considered as showing a lower threshold of around 0. 3 DU, and SO 2 loads can be expected to have a 10-20 % uncertainty. IASI altitude retrievals have a typical sensitivity of 1-2 km. We also compare our results to HadGEM2 model simulations of SO 2 and earlier IASI SO 2 retrievals reported by  Haywood et al. (2010). Comparisons of the modelled aerosols with in situ measurements are two-fold. First, we compare the model's output with size-resolved aerosol measurements carried out with the balloon-borne Stratospheric and Tropospheric Aerosol Counter (STAC) optical particle counter (OPC) instrument over Kiruna, Sweden, on 2, 7, and 18 August 2009, and 18 May 2010.
Comparison of the model SAOD to OSIRIS observations Extinction data from OSIRIS have been used for model and observational assessment of stratospheric aerosol impacts following the Sarychev 2009 eruption  (Haywood et al. , 2010; Kravitz et al. , 2011; O'Neill et al. , 2012; Jégou et al. , 2013). However, as mentioned in the introduction, biases in the  OSIRIS measurement following volcanic eruptions can affect the reported model-observation comparisons. In  Fromm et al. (2014) , a detailed analysis of OSIRIS's limitations was carried out. These authors have shown that two main factors affect the derivation of SAOD by OSIRIS: (i) an upper detection limit on the value of extinctions, above which the measured values saturate, and (ii) a latitude dependence in the minimal altitude above which extinctions are integrated to yield the SAOD.
A2 ). For this series of calculations, thermal tropopause heights were diagnosed in the model. We verify the broad consistency of these altitude limits for OSIRIS data during the 2009-2010 Sarychev post-eruption period in Fig. A2. Integrating the model-simulated saturated extinctions at 750 nm over the truncated altitude columns as defined above gives SAOD values that can be considered reasonably consistent with the measurements performed by OSIRIS. Figure  7a  shows the zonally averaged stratospheric sulfate AOD, through time, over the Northern Hemisphere, as computed by CESM1(WACCM) in the volcano-on simulation (with co-injection of HCl). A degradation of the model data was then performed following the method described above. The resulting estimation of the sulfate SAOD "as would be detected by OSIRIS" is shown in Fig.