dataesr/scientific-paragraphs-categorization huggingface dataset:
Author contribution : contribution Projects were conceptualized and supervised by SR, MIR, HC-D, AK, MK and FP. The original draft of the manuscript was written by SR and MIR, and subsequently revised mainly by SR, MIR, HC-D, AK, MK and FP (all authors edited and read the final version). Experiments were planned and conducted by SR, MIR, HC-D, AD, BS, WT, TA, IS, MT and FP. Competing interests:The authors of this manuscript declare that they have no conflicts of interest. Data and materials availability:Sequences presented in this paper can be found in Genbank, with accession numbers as follows: Tcas Svb MG913606 Nvit mlpt MH181829 Nvit Svb MH181831 Nvit Ubr3 MH181828 Ofas mlpt MH181830 Ofas svb MH181832 Ofas Ubr3 MH 181827 Gbue Svb MH011417 Gbue mlpt MH011418 Gbue Ubr3 MH011418.
Young's modulus E was calculated referring the EA to the unit cross-sectional area (A) of the sample (E = EA/A), where A was determined for fresh microscopic slices using the ImageJ (v. 1. 51f) software. The tensile strength was defined as the ratio of the load at break to the cross-sectional area (A). Spectroscopic studies IR spectra of plant samples were measured at room temperature in the spectral range 400-4000 cm -1 using a Thermo Scientific Nicolet 6700 FT-IR spectrometer with 2 cm -1 resolution. The ATR-diamond equipment was used for recording the reflection spectra. Raman spectra were measured with a Bruker RFS 100/S FT-Raman spectrometer (Bruker, Karlsruhe, Germany). A diode-pumped Nd:YAG laser at 1064 nm with an output of 400 mW was used as the excitation source. The spectral resolution was 2 cm -1 and 250 scans were collected.
Three replicates of IR and Raman spectra were recorded for every sample. The procedures for spectroscopic measurements were standardized. The same spectral parameters, the power of excitation source, the acquisition and accumulation were applied to the all studied samples. The mathematical processing of the measured spectra was performed using the computer program ORIGIN 7. 5. The crystallinity index Icr was calculated as the integral intensity ratio of the 1380 and 2920 cm -1 IR bands  (Kruer-Zerhusen et al. 2018; Wrobel-Kwiatkowska et al. 2015).
Thus the genetic modification influenced the cell wall chemical composition. Further research concerning spectroscopic characteristic of generated flax plants was performed. Spectroscopic analysis of transgenic flax plants Spectroscopic methods, such as infrared (IR) and Raman spectroscopy were applied to investigate the structural properties of examined plants. The differences in cell wall composition were detected using these techniques. The contours in vibrational spectra typical for cellulose, lignin and pectin were observed (Fig. 5a, b )  (Socrates 2001). Comparison of the integral intensities of the bands characteristic for cellulose with integral intensity of the band at 2920 cm -1 gives the following relationships for these ratios: wt \ 7 \ 11 samples (Fig. 5c ). Such a result suggests that the wild-type sample exhibits the lowest content of cellulose in relation to the samples 7 and 11, which corresponds to biochemical data indicated that control plants had the lowest cellulose level, while transgenic plants were characterized by higher cellulose amount (Fig.
Additionally the detection of mcl-PHA in transgenic and control flax lines was determined by FTIR method. The differences in the integral intensity ratios of the bands 1730/2920 and 1655/2920 cm -1 result from differences in the level of carboxyl groups of the hydroxy acids and pectins  (Guo et al. 2013) (Fig. EMS 1C). The integral intensity ratio of the bands 1605/2920 cm -1 determines the level of pectins in the samples (Fig. EMS 1D ). The data may indicate that amount of hydroxy acids in transgenic line 11 is the highest, thus spectroscopic analyses confirm biochemical data, obtained by GC-FID method (Fig. 1D ).
Acknowledgements The authors wish to thank  Professor Jean Marc Nicaud  for the possibility of PHA measurements,  Professor Yves Poirier  for the construct used in this study, and  Professor Jan Szopa  for the Agrobacterium strain. Publication supported by  Wroclaw Centre of Biotechnology , programme The  Leading National Research Centre (KNOW)  for years 2014-2018. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http:// creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
2. 2]). In the absence of germline DNA, variants were called by the GATK unified genotyper. Variants were processed through vcf2maf-1. 6. 15 to obtain a final Mutant Annotation File (maf). The variants' biological effects predictions were carried out using Ensembl's VEP-annotator-v. 86. Variant annotation database versions were as follows: ExAC-r0. Variants that were present more than three times were removed, as well as variants with Global Allele Frequency in ExAC databases over 1% (with respect to ethnicity frequencies when known). Finally, clinically benign mutants, as annotated by ClinVar, were removed ("benign" or "likely benign"). Only protein-coding variants were used for subsequent analyses, and structural protein coding genes (actin, myosin, collagen, fibronectin, vitronectin, tenascin, laminin, titin, obscurin, plectin, aggrecan, and mucins) were removed.
Statistical analyses Analyses were performed under R 3. 4. 4. Fisher's test was carried out with the resampling of parameters for robustness. The somatic interaction plot code was adapted from Gerstung et al. [15]. Enrichment analyses were carried out by ReactomePA and clusterProfiler  [16, 17] , p values were adjusted for multiple testing by the false discovery rate (q = 0. 05). For the Reactome determination, KEGG and GO annotations were used. MAF manipulation was performed using the maftools packages  [18]. Oncoprints, heatmaps, and Chord-Diagrams were performed with ComplexHeatmap R-package. Considering the number of samples, the linear regressions between scores and drug responses were calculated by robust a linear regression using a M-estimator (rlm, MASS package) in order to discard outliers.
Abnormalities in cell cycle genes do not favor the response to CDK inhibition Approximately half (55%) of HMCLs showed impaired cell cycle regulation, mostly bi-allelic deletion of the CDKN2C locus (33% of HMCLs), RB1 alterations (12%) Fig. 3  Oncoprint of altered pathways in human myeloma cell lines. Oncoprint of frequently altered pathways was performed as described in the "Methods" section. Oncoprint was performed with ComplexHeatmap R-package or CDKN2A alterations (9%) (Additional file 1: Figures S3 & S5-"CellCycle"). Because deletions in CDKN2A or CDKN2C, by contrast to RB1 deletion/mutation, were reported to favor cell cycle inhibition by the CDK4/CDK6 inhibitor palbocicib, we assessed whether altered HMCLs were sensitive to the inhibitor  [36]. Palbociclib induced an inhibition of cell cycle that was correlated to pRb inhibition (β = -25, 95% CI = [-33.
The high TP53 abnormality frequency (67%) in HMCLs identified by WES in our study (and confirmed by direct RT-PCR sequencing  [1] ) was not in good agreement with a previous WES study reporting a rate of 21% in HMCLs  [40] , which was highly underestimated: indeed, well-known TP53 mutations in L-363, LP-1, and SKMM-2 (COSMIC database and p53. iarc. fr, Release = 18) were not reported in this study and at least three "HMCLs" were not of myeloma origin (ARH77, MC-CAR, CTV-1)  [41, 42]. Our results clearly confirmed a major alteration in both proliferation control, with either loss of suppressor (TP53, CDKN2C, RB1) or acquisition of activator (BRAF, RAS) and in tumor suppression/drug response (TP53, FAM46C), as in most if not all cancers  [43]. Because the loss of function of TP53, FAM46C, or CDKN2C are not directly targetable, drugs bypassing these proteins or exploiting their loss consequences are required.
Although all HMCLs without hit in RAS/BRAF/FGFR3 genes were resistant to trametinib, all HMCLs with NRAS mutation were not sensitive since four NRAS mutated HMCLs were resistant. These data collectively suggest that mutation in RAS/BRAF genes is required but not sufficient for eliciting response to trametinib. The BRAF/RAS impact will be assessed in an ongoing clinical trial (NCT03091257) evaluating dabrafenib and/or trametinib in patients with relapsed and/or refractory multiple myeloma patients according to their BRAF/RAS mutation. The high percentage of altered genes in DNA/chromatin repair/regulation, Fanconi pathway, and chromatin/ DNA modification might be related to the frequency in relapsing patients  [32]. Because of the lack of specific drugs, we could not directly assess the functionality/vulnerability of these pathways, which require a deep investigation.
Funding This work was supported by grant from  FFRMG ,  Action Cancer 44, DHU Oncogreffe, Ligue Grand Ouest contre le Cancer ,  SIRIC ILIAD (INCa-DGOS-Inserm_12558 ), and  L'Héma-NexT  (i-site NexT). BT was supported by  INSERM  (poste d'accueil) and  Fondation ARC. AG was supported by the  SyMeTRIC  project, funded by the  Région Pays de la Loire  Connect Talent research call.
Availability of data and materials All data and materials are provided in the Additional files; Mutant Annotation File (MAF) is hosted as a separate file at Journal of Hematology & Oncology. Raw data will be submitted at COSMIC website (https://cancer. sanger. ac. uk/ cosmic) after manuscript approval for publication, due to COSMIC restrictions. Gene Expression Profiling data are available at ArrayExpress public database (https://www. ebi. ac. uk/arrayexpress/) under accession numbers E-TABM-937 and E-TABM-1088.
We use data from 14 blood biomarkers representing four physiological systems (neuroendocrine, immune and inflammatory, metabolic, cardiovascular system) measured in 45 years old adults. We first examine the performance of the AL score in predicting mortality up to 11 years after blood collection, using models correcting for a priori life course confounders. In a second step we also evaluate the relative contribution of each physiological systems, and each individual biomarkers entering in the AL score definition, in the relationship linking AL and death.
Study design and participants We used data from the 1958 National Child Development Study (NCDS), which includes all live births during 1 week in 1958 (n = 18,555) in Great Britain. The NCDS has been described in detail elsewhere  [25]. Information on health, economic, social and developmental factors has been collected from participants at ages 7, 11, 16, 23, 33, 42, 44/45, 46, 50 and 55 years. Ethical approval has been obtained for this study and study participants have provided informed consent. The survey at age 44/45 years was a biomedical survey on a subsample of participants (n = 9,377) during which blood samples were collected  [26]. Venous blood samples were obtained without prior fasting and posted to the collaborating laboratory. The 45-year biomedical survey was approved by the South-East Multi-Centre Research Ethics Committee, and written consent for use of information in medical research studies was obtained from the participants.
Participants in this survey were found representative of the general cohort  [27]. Based on their representative nature and on the availability of biomarkers we used these participants to evaluate the allostatic load. A set of 1,264 participants was excluded from our analyses including pregnant women and those for whom blood was not obtained. Additionally, individuals who died before blood sample collection (N = 1,251) or those who died with missing biological data were excluded (N = 234) leaving 132 deaths for 7,981 living participants.
All-cause mortality Deaths were ascertained systematically by the Centre for Longitudinal Studies upon receipt of death certificates from the National Health Service Central Register (NHSCR). The mortality data most recently available to researchers provided information on date of death up to December 2013. Since information on death was obtained from the NHSCR, even when individuals were lost to follow-up in the cohort, information on their death will have been received. The follow-up time was calculated from the date of blood collection to the date of death for participants who died and up to 1 December 2013 for survivors.
The high-risk quartile was the highest quartile of all biomarkers, except for those found inversely related to health outcomes (HDL, salivary cortisol t1-t2, IGF1, peak expiratory flow). Descriptive information and high-risk cut-off values are reported in Supplementary Table  1. The AL score was calculated by summing the 14 dichotomous scores for each of the 14 markers. We considered a 3 group variable based on tertiles in the total population contrasting individuals with 'low [0:2]', 'mid  [3:4] ', and 'high  [5:12] ' AL. A conservative approach (maximum bias) was used for the individuals with missing data for each biomarker, systematically considering them to be not at risk for the missing biomarker. In the study population, 26 individuals had missing data on all biomarkers and were therefore excluded from the analyses.
Gender and birth weight were also included. A binary childhood pathologies variable was constructed using data collected at ages 7, 11, and 16 y based on both maternal reporting and medical examinations including congenital conditions, moderate/severe disabilities, chronic respiratory or circulatory conditions, sensory impairments, and special schooling. Adverse childhood experiences (ACEs) were defined as intra-familial events or conditions causing chronic stress responses in the child's immediate environment. ACEs were measured by counting the reports of: child in care, physical neglect, offenders, parental separation, mental illness and alcohol abuse. A three category variable was then constructed (0 adversity/1 adversity/more than one adversity). Young adulthood risk factors included the respondent's educational attainment at 23 y (A level/O level/no qualification) and a binary indicator of psychological malaise which was set to true if the participant reported to experience more than 7 (out of 24) symptoms of a ''malaise inventory'' assessing symptoms of depression and/or anxiety.
Missing data To control for possible bias due to missing data, we imputed data for covariates with missing data using the multiple imputation in the overall population (N = 18,558) using the MICE R package  [30]. Twenty imputations were conducted taking the missing-at-random assumption. Each variable with missing values was imputed including all covariates used in the models as well as variables from other sweeps correlated with the variable to impute, but excluding the AL and death outcome (Supplementary Table  2 ). To obtain Cox regression estimates from the multiply imputed data, Rubin's combination rules were used. Sensitivity analyses We conducted three different sensitivity analyses. The first sensitivity analysis was to ensure that our results were not biased by imputing the missing values; we ran our analyses on the non-imputed dataset.
Results We first compared the subsample of 132 deaths with biological data included in our analyses from the 234 deaths without blood collection. Individuals in each group did not differ in terms of adult covariates (Supplementary Table  3 ). However, participants without blood samples were more likely to come from overcrowded households (24. 3% vs 11. 11%, p = 0. 005) and their mothers were more likely to have left school before 14 years old (81. 2% vs 72. 4%, p = 0. 079, Supplementary Table  3 ). Table  1  presents the distribution of selected characteristics for NCDS participants according to death status and for the overall sample. During a median follow-up time of 10. 5 years (10-90 percentile: 10. 0-11. 1), 132 participants with blood collected in the cohort died. Compared with participants who did not die (N = 7,981), those who died (N = 132) were more likely to be men; to have suffered from childhood pathologies; their fathers were more likely to have had a 'manual' occupation; their mothers to have been smokers during pregnancy; and they were more likely to have a low birthweight.
Funding The Centre for Environment and Health is supported by the  Medical Research Council and Public Health England  ( MR/L01341X/ 1 ). Marc Chadeau-Hyam  acknowledges support from  Cancer Research UK ,  Population Research Committee Project Grant  (' Mechanomics ' Project # 22184  to MC-H). This work was funded by the  European Commission  as part of the  Lifepath  project [H2020 grant number  633666 ].
Since the signal strength of an ion in the APi-TOF depends not only on the abundance of the respective neutral molecules but also on the availability of other charge-competing species, it is very important to note that the APi-TOF cannot quantify the neutral species. One important virtue of APi-TOF is that it does not introduce extra energy during sampling, which ensures the sample is least affected when compared to other measurement techniques such as CIMS although fragmentation cannot be fully avoided inside the instrument  (Schobesberger et al. , 2013). Because of this, it is a well-suited instrument to directly measure the composition of weakly bonded clusters in the atmosphere. The APi-TOF data were processed with the tofTools package (version 6. 08)  (Junninen et al. , 2010). Since the ion signal in APi-TOF is usually weak, a 5 h integration time was used, after which the signals of H 2 SO 4 -NH 3 clusters and HOMs were fitted (see Fig.
Measurement of the meteorological parameter The meteorological variables used as supporting data in the present work were measured on a mast, all with a time resolution of 1 min. Temperature and relative humidity were measured at 16. 8 m using a PT-100 sensor and relative humidity sensors (Rotronic Hygromet MP102H with Hygroclip HC2-S3, Rotronic AG, Bassersdorf, Switzerland), respectively. Global radiation was measured at 18 m with a pyranometer (Middleton Solar SK08, Middleton Solar, Yarraville, Australia) and further used to calculate the cloudiness parameter, as done previously by  Dada et al. (2017, and references therein). This parameter is defined as the ratio of measured global radiation to theoretical global irradiance so that parameter values < 0. 3 correspond to a complete cloud coverage, while values > 0.
g. ,  Hoppel, 1985; Tammet and Kulmala, 2005; Franchin et al. , 2015). GR 1. 5-3 were calculated from NAIS data in ion mode using the "maximum" method introduced by  (Hirsikko et al. , 2005). Briefly, the peaking time of the ion concentration in each size bin of the selected diameter range was first determined by fitting a Gaussian to the concentration. The growth rate was then determined by a linear least square fit through the times. The uncertainty in the peak time determination was reported as the Gaussian's mean 67 % confidence interval and was further taken into account in the growth rate determination. A similar approach was used to estimate the early growth rate of the H 2 SO 4 -NH 3 clusters detected with the APi-TOF. Prior to growth rate calculation, we first converted cluster masses into diameters in order to get growth rate values in nm h -1 instead of amu h -1.
The effect of cluster size on the probability of IIN events We identified IIN events using data from the NAIS (ion mode) by observing an increase in the concentration of sub-2 nm ions  (Rose et al. , 2018)  and classified 67 IIN events out of the 134 days of measurements. We defined the IIN probability as the number of days when IIN events were identified out of the total number of days that were counted. For example, the overall IIN probably is 50 % (67 out of 134 days). We found that the maximum observed size of H 2 SO 4 -NH 3 clusters may affect the occurrence of IIN. Our conclusion is complementary to previous theories which stated that the critical  step of particle nucleation is the formation of initial clusters that are big enough for condensational growth to outcompete evaporation  (Kulmala et al.
Continuous growth from clusters to 3 nm particles Although the strong connection between the size of H 2 SO 4 -NH 3 clusters and the occurrence of IIN was confirmed, it is challenging to directly observe the growth of these clusters in the atmosphere, limited by the inhomogeneity of the ambient air and low concentrations of atmospheric ions. Combining APi-TOF and NAIS measurements, we were able to follow the very first steps of the cluster growth for eight of the detected events. In Fig. 4a  and b, we present two examples in which the continuous growth of H 2 SO 4 -NH 3 clusters to 3 nm (mobility diameter) particles was directly evaluated using the maximum-time method. The maximum times, determined from APi-TOF and NAIS data independently, fall nicely into the same linear fit. The continuity of the growth and the linearity of the fit suggests that the current mechanism (H 2 SO 4 -NH 3 , acid-base) explains the formation and growth of sub-3 nm ion clusters in these cases.
Acknowledgements. This work was partially funded by the  Academy of Finland  ( 1251427 ,  1139656 ,  296628 ,  306853 , Finnish centre of excellence 1141135), the  EC Seventh Framework Program and European Union's Horizon 2020 program  ( Marie Curie ITN  no. 316662  " CLOUD-TRAIN ", no. 656994  " Nano-CAVa ", www. atmos-chem-phys. net/18/13231/2018/Atmos. Chem. Phys. , 18,  13231-13243 , 2018.
From samples of wild and captive zebras, viral DNA was extracted from blood samples and nasal swabs using a commercially available kit (NucleoSpin ® Tissue, Macherey-Nagel, Düren, Germany) according to the manufacturer's instructions for the respective sample type (blood or buccal swab protocol, respectively). To test for EHV DNA, we performed a nested PCR as described by  46  with a modified thermocycling protocol  47. Amplified products were visualized on a 1. 5% agarose gel and purified using a kit (NucleoSpin ® Gel and PCR Clean-up, Macherey-Nagel, Düren, Germany) according to manufacturer's instructions, for subsequent Sanger sequencing. Purified PCR products were sequenced by LGC Genomics GmbH and virus strains identified by blast searches against GenBank entries. Animals were considered positive when at least one of the samples tested positive for herpesviruses.
Samples of plains zebras from Namibia and Tanzania were pooled because values in oxidative status markers between herpes positive and negative were similar in the two sampling areas for all the markers measured (sampling area × herpes, P ≥ 0. 29). A gamma error function and an identity-link function were applied to models of protein carbonyls. A normal error function and an identity-link function were applied to models of GPX and SOD. Functions were selected on how well they fitted with the data using the Akaike Information Criterion. All analyses were performed using STATISTICA 10 (StatSoft, Tulsa, OK, USA). Finally, the compute. es package  50  in R  51  was used to calculate the standardized effect size Hedges' g from test statistics. The forestplots function of the metafor package in R was used to visualise boxplots of effect size and 95% confidence interval.