dataesr/scientific-paragraphs-categorization huggingface dataset:
Categories corresponding • "Family Composition". This table gives the number of family households by type. According to census data, there are categories of family household types, as elaborated in Table. It should be noted that couple families in the census data include same-sex families. While the dataset is consistent across all target areas with information highly useful for the purpose of population synthesising, there are mismatches in values between tables that characterise individual attributes and those that characterise household attributes. This is because in order to preserve the confidentiality of the census data, small random adjustments had been introduced into these tables before they were published. These mismatches need to be accounted for in the algorithm synthesising the population, as described in further detail in the following section.
Each pair of these RMS values represents the errors (between the resulting synthetic population and census data) associated with a possible choice of allocating the synthetic individual being considered to a feasible synthetic household. The best choice (i. e. the most suitable synthetic household this individual belongs to) is the one that results in both the smallest error in the distribution of individual counts by household type and the smallest error in the distribution of household counts by household size. In case such optimal choice is not available, i. e. not any one of the feasible choices strictly outperforms others, a set of choices that are not strictly dominated by any other are selected. These choices are represented by the Pareto front of RMS data points. The algorithm then randomly picks a choice out of this set that allocates the individual being considered into a household.
Cell Cycle Analysis One of the most common mechanisms in anticancer drug treatment is changes in cell cycle, which can be measured by DNA content  [37]. Cell cycle distribution in HCT-116, HeLa and MCF-7 cells was examined to determine cycle arrest and/or presence of sub-G1 after treatment with compounds 37 and 46. Cells were coincubated with trazines and a reference drug (cisplatin) for 24 h, respectively. Cell populations data are expressed as the mean ˘SD of at least three independent experiments.
Cell Cycle Analysis One of the most common mechanisms in anticancer drug treatment is changes in cell cycle, which can be measured by DNA content  [37]. Cell cycle distribution in HCT-116, HeLa and MCF-7 cells was examined to determine cycle arrest and/or presence of sub-G1 after treatment with compounds 37 and 46. Cells were coincubated with trazines and a reference drug (cisplatin) for 24 h, respectively. Cell populations data are expressed as the mean ± SD of at least three independent experiments. Sub-G1 cell population increased in dose dependent manner (HCT-116 from 3. 6% ± 1. 66% (control) to 48. 6% ± 13. 5%; HeLa from 5. 7% ± 3. 4% (control) to 21% ± 3. 4%) after treatment with compound 37 for 24 h. Figure  4  shows one representative experiment. Similar results were obtained for MCF-7 cells coincubated with compound 46 for 24 h (from 5.
X-ray Structure Determination Crystals of 59 were grown from DMF solution. All specimens obtained were in form of very thin needles, having poor diffraction power. The copper radiation (almost no diffraction was observed with the Mo lamp) and long exposure times (4 min) were applied. The obtained signal to noise ratio was still smaller than usually, so final R int and R 1 indices are above the regular standards for small molecules. Nevertheless, structure solution is with no doubts generally correct. Diffraction intensity data were collected on an IPDS 2T dual-beam diffractometer (STOE & Cie GmbH, Darmstadt, Germany) at 120. (2) K with Cu-Kα radiation of a microfocus X-ray source (50 kV, 0. 6 mA, λ = 154. 186 pm, GeniX 3D Cu High Flux, Xenocs, Sassenage, France), The crystal was thermostated in nitrogen stream at 120 K using CryoStream-800 device (Oxford CryoSystem, Oxford, UK) during the entire experiment.
Data collection and data reduction were controlled by X-Area 1. 75 program  [43]. An absorption correction was performed on the integrated reflections by a combination of frame scaling, reflection scaling and a spherical absorption correction. Outliers have been rejected according to Blessing's method  [44]. The structure was solved using direct methods with SHELXS-13 program and refined by SHELXL-2013  [45]  program run under control of WinGx  [46]. All C-H type hydrogen atoms were attached at their geometrically expected positions and refined as riding on heavier atoms with the usual constraints. The N-H hydrogen atoms were found in the differential Fourier electron density map and were refined without constraints. Crystallographic data for the analysis have been deposited with the Cambridge Crystallographic data Centre, CCDC reference number is 1471508.
Cell Cycle Analysis Cells were coincubated with compounds and a reference drug (cisplatin) for 24 h, respectively. Following treatment, cells were fixed in cold 70% ethanol for 30 min. Fixed cells were stained for 15 min in buffer containing 500 µg/mL RNAse A (EURx, Gdansk, Poland) and 2. 5 µg/mL of DAPI (Sigma-Aldrich, Munich, Germany). The fluorescence of DAPI was measured at 440 ˘40 nm with a flow cytometer (LSR II, BD Biosciences, Mountain View, CA, USA). Each experiment was performed at least in triplicate. Data were analyzed off-line using the BD FACSDiva (BD Biosciences).
However, rapamycin only binds mTORC1 via FKBP12 protein binding and in most of cases does not inhibit the mTORC2 complex that plays a key role in cellular survival and proliferation by upregulating AKT. Clinical trials using pharmacological agents targeting RAS-MAPK pathways (including MEK inhibitors) and AKT/mTORC1 pathways (rapamycin and rapalogs) are currently under evaluation for PNFs (http:// www. clinicaltrials. gov/ct2/results?term = nf1)  [10, 11]. In previous preclinical studies using NF1-tumor mouse models, both MEK and mTORC1 inhibitors showed tumors growth suppression properties but no cytolytic effect. Different mechanisms underlying resistance to rapamycin have been described and could explain this moderate activity: (i) the rapamycin-induced increase of PI3K activity, (ii) the lack of complete mTORC1 inhibition as attested by the sustained high level of 4E-BP1 phosphorylation, and (iii) the inefficiency of rapamycin towards mTORC2 activity.
AZD8055 impaired cell cycle progression in MPNST-derived cell lines To explore the AZD8055 cell growth inhibition mechanism, apoptosis/necrosis and cell cycle analyzes were performed in MPNST-derived cell lines using flow cytometry. Apoptosis/necrosis was not significantly induced by AZD8055 treatment (data not shown). In contrast, cell cycle progression was impaired. After 24h of treatment, AZD8055 significantly inhibited MPNST- derived cell lines proliferation via inducing G0/G1 phase arrest supported by S and G2 phase decrease combined to G0/G1 phase increase in both 90-8 and STS26T cell lines (Figure  4 ).
Furthermore, rapamycin induced only partial inhibition of mTORC1 signaling which correlates with inefficiency on protein translation (data not shown). We also showed that AZD8055 decreased proliferation in MPNST-derived cell lines via inducing G0/G1 phase arrest. These results provide the rational to directly target the mTOR catalytic activity with AZD8055 to alleviate these intrinsic mechanisms of resistance in NF1-associated PNFs and MPNSTs. Due to NF1 loss in MPNST, RAS proteins are activated. Inhibitions of MAPK and mTOR pathways have been evaluated in MPNST preclinical studies. As single agents, no tested inhibitor caused tumor regression, revealing the need for combinatorial approaches in MPNST  [2, 8, 9, 21, 26, 27, 31]. Crosstalk between the MAPK and AKT/mTOR pathways is well documented.
clinicaltrials. gov/ct2/results?term = nf1). We observed that these three therapeutic agents used as single molecules impaired cell proliferation and induced G0/G1 phase increase especially in MPNST-derived cell line 90-8 in which PRC2 is known to be inactivated  [12]. To a lesser extent 88-14 and 96-2 cell lines showed a slight response to OTX015 and GSK525762A/I-BET762 compounds. Combined to AZD8055, JQ1, OTX015 and GSK525762A/ I-BET762 inhibited 90-8 cell proliferation, soft agar colony formation and impaired cell cycle progression in a synergistic manner thus supporting that dual blockade of both PI3K pathway and chromatin structure regulation is also of interest in MPNST pharmacological treatment. Based on 2D and 3D in vitro results, AZD8055/ PD0325901 or AZD8055/BET bromodomain inhibitors combination efficacy now needs to be empirically tested in rigorous models in vivo to support these combination strategy for NF1-associated nerve sheath tumors  [43].
Microarray procedure and data analysis Genome-wide array-CGH was used to identify potential genetic rearrangements in MPNST cell lines (400K array-CGH) and PNFs-derived primary Schwann cells (60K array-CGH). The array-CGH labelling and hybridization were performed as recommended in the Agilent manual (Protocol v5. 0, June 2007, Agilent technologies, Palo Alto, CA, USA). Each cell line DNA (labelled with Cy5-dUTP) was individually hybridized on Agilent whole human genome 400K or 60K microarrays (Agilent Technologies) versus six pooled normal control DNAs (from six healthy donors' blood samples) (labelled with Cy3-dUTP) as reference, to detect cell line-specific aberrations. Arrays were scanned with an Agilent DNA microarray scanner (G2565BA). Log2 ratios were determined with Agilent Feature Extraction software.
ACKNOWLEDGMENTS We thank  Hilde Brems  ( Department of Human Genetics, KU Leuven-University of Leuven, Leuven, Belgium ) for helpful discussions concerning Schwann cells primary culture and  Magali Pederzoli-Ribeil  ( UMR8258 CNRS -U1022   Inserm ,  Faculté de Pharmacie de Paris, Université Paris Descartes ,  Sorbonne Paris Cité, Paris, France ) for helpful discussions concerning cell cycle analysis. This work was supported in part by grants from  Ligue Française Contre les Neurofibromatoses, Association Neurofibromatoses et Recklinghausen, Adebiopharm Association.
Accuracy of WOB calculation The ASL 5000 was set as a single-compartment model to resemble three respiratory mechanics: normal (R RS = 5 cmH 2 O/L/s, C RS = 60 mL/cmH 2 O), restrictive (R RS = 5 cmH 2 O/L/s, C RS = 20 mL/cmH 2 O) and obstruc- tive (R RS = 20 cmH 2 O/L/s, C RS = 60 mL/cmH 2 O). A semi-sinusoidal inspiratory waveform was selected with a rise time of 25 %, inspiratory holding time of 0 % and releasing time of 15 %. Each condition was tested under P tot = P aw + P mus = V ′ × R RS + V × E RS + PEEPt P aw /P mus = %Gain/(100 -%Gain). 2 RR (20 and 30 breaths/min) and across 2 levels of P mus (5 and 10 cmH 2 O). The flow triggering was 0. 8 L/min and PEEP was 5 cmH 2 O. Moreover, we also used a real esophageal pressure signal, derived from our patients' database, to drive the simulator and test PAV+.
Data collection Each test was recorded for 5 min, which was the time period for stabilization of the system. Then the last minute of the recording was selected and analyzed offline. Data acquisition from ASL 5000 was performed at 128 Hz and stored in a laptop computer for subsequent analysis with AcqKnowledge software (Biopac Systems, Goleta, CA, USA). V′, P mus and P aw meas curves over time were provided by the ASL 5000. V was derived from V′ integration over time. PEEPi was estimated as the pressure difference between the P mus at the onset of inspiration (defined as the first point where P mus started to decrease at end-expiration) and P mus at the start of inspiratory V′.
Accuracy of WOB calculation Data from the ventilator were recorded throughout a specific set of tests with the aid of a software provided by Covi-dien© and were stored. Data included ventilator settings and calculation of mean P aw , peak P aw , V, V′, minute-ventilation (V E′ ), RR, Ti, C vent , R vent , PEEPi, total PEEP and total displayed WOB in J/L (WOB displ ). These values were recorded every second and breath by breath. Based on the Campbell diagram, the patient and ventilator WOB were estimated on a breath-by-breath basis. Patient WOB and ventilator WOB were derived by integration of the area plotted between the P mus -V and P aw -V curves, respectively. Total WOB (WOBreal ) was the sum of patient and ventilator WOB. Work of breathing was calculated per liter (WOB per minute divided by V E′ ) and was expressed in joules per liter (J/L).
Acknowledgements FB was receiving a Grant from his own institution in France. EA received a Grant from  Covidien. NR was receiving a Grant from his own institution in Thailand. LB's laboratory received research Grants from the following companies:  Covidien (PAV) ,  General Electric  (lung volume measurement),  Draeger (SmartCare) ,  Vygon (CPAP) ,  Fisher Paykel  (Optiflow).
The reference electrode was attached to the patella of the right knee. Myoelectrical signals were amplified with a bandwidth frequency ranging from 10 Hz to 500 Hz (gain: VL = 500; RF and VM = 1000), digitized on-line at a sampling frequency of 2 kHz using a computer, and stored for analysis with commercially available software (Acqknowledge 4. 2 for MP Systems, Biopac Systems Inc. , Goleta, USA). Due to the pressure of the thigh on the dynamometer chair, the Biceps Femoris EMG signal quality was impaired (e. g. numerous artefacts, problems with electrodes) and therefore not analyzed. The EMG signals were filtered with a Butterworth band pass filter (cutoff frequencies 20 and 400 Hz). Then, the root mean square (RMS) of the EMG signal was automatically calculated with the software. During the incremental test, the EMG RMS was averaged for the last 5 EMG bursts of each step (at the end of each minute) and at exhaustion.
, Stoughton, USA). During the tests a two shoulder harnesses and a belt across the abdomen limited extraneous movement of the upper body. Torque signal and knee angle signal were digitized on-line at a sampling frequency of 1 kHz using a computer, and stored for analysis with commercially available software. Torque signal was filtered prior to data analysis (Butterworth low-pass filter at 100 Hz). Torque signal, knee angle signal and EMG signal were recorded with the same device (MP150, Biopac Systems Inc. , Goleta, USA) and analyzed with the same commercially available software (Acqknowledge 4. 2 for MP Systems, Biopac Systems Inc. , Goleta, USA).
Limit of agreement ratio (LOA) was also calculated from the log transformed data as follow: LOA = (1. 96 × SDdiff / grand mean) × 100; where "SDdiff " represents the SD of the differences between tests (S1 vs S2, S1 vs S3, S2 vs S3) and "grand mean" represents (mean S1 + mean S2 + mean S3)/3. As time to exhaustion data were heteroscedastic, we also calculated the coefficient of variation (CV) for each subject as follow: CV = 100×(SD of the three measurements)/(mean of the three measurements). Mean CV for all subjects were also calculated. We also calculated the smallest worthwhile change (0. 2 × between subjects SD)  [21]. Time course of normalized EMG RMS for all muscles was analyzed with fully repeated measures 3 (session) x 10 (time: from 10 to 100% of time to exhaustion) ANOVA. Fully repeated measures 3 (session) x 11 (time: warm-up and from 10 to 100% of time to exhaustion) ANO-VAs were used to analyze the time course of leg RPE, leg muscle pain, heart rate and cadence.
Significant effect of time was explored with planned comparison (pre vs exhaustion, exhaustion vs P20, P20 vs P40) adjusted with Holm-Bonferonni correction. Cohen's effect size f(V) was also calculated. Changes in isometric KF MVC torque was analyzed with fully repeated measures 3 (session) x 2 (time: pre and post exercise) ANOVA. As the time to exhaustion did not differ between sessions, only main effects of time are reported. Significance was set at 0. 05 (2-tailed) for all analyses, which were conducted using the Statistical Package for the Social Sciences, version 20 for Mac OS X (SPSS Inc. , Chicago, IL, USA). Cohen's effects size f(V) was calculated with G Ã Power software (version 3. 1. 6, Universität Düsseldorf, Germany).
001). As for cell proliferation, we then analyzed the activation of DDR2 in A204 sarcoma cells in collagen 3D matrices. As shown in the supplementary data 1B, DDR2 phosphorylation was also higher in adult collagen when compared to the old one. Since the cell proliferation rate was similar on adult and old 2D collagen coating, we investigated whether DDR2 activation was similar or not in these conditions. As shown in the supplementary data 4, DDR2 phosphorylation in the case of adult 2D collagen coating was similar to that observed in the case of the older one. This suggests that the 3D matrix model is crucial for the differential DDR2 activation with type I collagen aging.
The primary DDR2 downstream target SHP-2 is highly activated in adult collagen DDR2 has been shown to phosphorylate the tyrosine phosphatase SHP-2 when activated with fibrillar type I collagen  [29]. To elucidate whether SHP-2 is also involved as a downstream effector of DDR2 in our model, HT-1080 cells were seeded in adult and old  collagen matrices for 6 hours. Then, Western blotting experiments were performed in order to analyze SHP-2 expression and Tyr-542 phosphorylation. Figure  8A  shows that SHP-2 expression was similar for both collagens, whereas SHP-2 phosphorylation was increased 2-fold in adult collagen compared to the old one. As shown in the supplementary data 1C, SHP-2 phosphorylation also increased in A204 cells in adult collagen. When DDR2 expression was suppressed in HT-1080 cells using siRNA, SHP-2 phosphorylation decreased in adult collagen (supplementary data 5A).
JAK2 phosphorylation is downregulated in adult collagen The Tyr-1007 site of JAK2 has been reported to be dephosphorylated by tyrosine phosphatase SHP-2  [30]. In order to determine whether SHP-2 activation in the presence of adult collagen is able to downregulate JAK2 phosphorylation, JAK2 expression and phosphorylation were analyzed by Western blott. Figure  9A  shows that JAK2 phosphorylation level was 3-fold lower in adult collagen than in the old one. As shown in supplementary data 1C, JAK2 phosphorylation was also decreased in A204 cells in adult collagen. It is important to note that in both cell lines, JAK2 expression was significantly decreased in the presence of old collagen. In order to investigate whether kinase function of DDR2 was involved in the activation of JAK2, we analyzed JAK2 phosphorylation in wild-type and gatekeeper DDR2 expressing cells in the presence or not of 100 nM nilotinib.
As shown in supplementary data 3B, nilotinib was able to increase JAK2 phosphorylation in wildtype DDR2 expressing cells but not in gatekeeper ones. When SHP-2 expression was suppressed in HT-1080 cells using siRNA, JAK2 phosphorylation increased in adult collagen (supplementary data 6). To establish whether JAK2 phosphorylation was involved in the increase of cell proliferation in old collagen, we analyzed cell proliferation in the presence of the p-JAK2 inhibitor AG490 in both collagens. At 10 µM concentration, AG490 was able to decrease HT-1080 cell proliferation in old collagen but not in the adult one (Figure  9B ). To demonstrate that the inhibition of cell proliferation in the presence of AG490 was due to a decrease in JAK2 activation, JAK2 phosphorylation was analyzed after AG490 treatment.
ERK1/2 phosphorylation is downregulated by adult collagen ERK1/2 pathway has been shown to be phosphorylated by p-JAK2  [31]  and is known to promote cell proliferation in various cell models  [32]. To determine whether ERK1/2 was differentially activated in adult and old collagen, ERK1/2 phosphorylation was analyzed by Western blot. Figure  10A  indicates that ERK1/2 phosphorylation was 2-fold lower in adult collagen when compared to the old one (p < 0. 05). As shown in the supplementary data 1D, ERK1/2 phosphorylation was also decreased in A204 cells in adult collagen. In order to verify whether ERK1/2 function upregulates cell proliferation in old collagen, cells were treated with the ERK1/2 phosphorylation inhibitor U0126. Figure  10B  shows that U0126 was able to decrease cell proliferation in old collagen to a level close to that of the adult one.
In order to verify if there is a relationship between JAK2 activity and ERK1/2 phosphorylation in old collagen, cells were treated with the JAK2 inhibitor AG490 before analysis of ERK1/2 phosphorylation. As shown in Figure  11A , ERK1/2 phosphorylation was 2-fold lower when cells were treated with AG490 in old collagen. To establish a link between DDR2 activation and ERK1/2 pathway inhibition, DDR2 expression was suppressed in HT-1080 cells using siRNA and ERK1/2 pathway was analyzed in adult collagen. ERK1/2 phosphorylation was increased (supplementary data 5B). ERK1/2 activation was also analyzed after nilotinib treatment in adult collagen. As shown in Figure  11B , ERK1/2 phosphorylation was increased 3-fold in the presence of nilotinib compared to control cells. In order to show that nilotinib induced this effect by inhibiting specifically DDR2, we investigated whether ERK1/2 phosphorylation was increased by nilotinib in gatekeeper DDR2 expressing cells.
As shown in Figure  11A , p21 CIP1 expression was 2-fold higher when cells were treated with AG490 in old collagen. Finally, to establish a link between DDR2 activation and p21 CIP1 expression, DDR2 expression was suppressed in HT-1080 cells using siRNA and p21 CIP1 expression was analyzed in adult collagen. As shown in the supplementary data 5B, p21 CIP1 expression was decreased. p21 CIP1 expression was also analyzed after nilotinib treatment in adult collagen. As shown in Figure  11B , nilotinib induced a 2-fold decrease in p21 CIP1 expression. As for ERK1/2, and in order to show that nilotinib induced this effect by inhibiting specifically DDR2, we investigated whether p21 CIP1 expression was decreased by nilotinib in gatekeeper DDR2 expressing cells. As shown in the supplementary data 3C, p21 CIP1 expression level was similar when cells were treated or not with nilotinib.
ACKnoWlEDGMEnTS We would like to acknowledge the help of  Dr. Saltel F. for the critical review of the manuscript. We thank  Pr. Sockalingum G. D. and  Ricord S. for the English revision of the manuscript. We are grateful to  Dr. Hammerman P. S. for the DDR2 gatekeeper construct. We thank  Dr. Takeuchi A. for HT-1080 cells expressing dominant negative and wild-type RAGE. Finally, we thank  Dr. Rio M. C  and  Dr. Trent J. C. for the 293T and the A204 cells respectively.
METHODS Data selection and extraction. The Scottish Cervical Call Recall System (SCCRS) is a nationwide, population register-based computer system, populated with demographic data from the population register, in use since 2007 whose function is to manage all aspects of call and recall. It incorporates immunisation status, acts as a requesting and reporting system for cytology and records relevant histology and HPV results. It includes in its reports recommended management and refers women directly for colposcopy. The dates of screening invitations and reminders are recorded, as are the reasons for exclusion from screening-for example, pregnancy, no cervix, severe inter-current illness or a formal declaration to opt out. Invitations are sent to all eligible women at their current recorded address by GP registration.