dataesr/scientific-paragraphs-categorization huggingface dataset:
(G and H) LUZP1 and EPLIN depletion leads to increased ciliary length in RPE-1 cells. The graphs show the length of cilia measured in the indicated populations treated wither with control siRNA or siRNAs directed against LUZP1 or EPLIN. n > 200 cilia per condition (data pooled from three independent experiments). Bars correspond to the average and SD. ***, P < 0. 001 (Student's twotailed t test); ns, nonsignificant. S2 ) containing appropriate restriction sites. The PCR products were digested and ligated into the following vectors: pCMV-TO/FRT-Emerald (GFP), pcDNA5-TO/ FRT-mCherry (only LUZP1), pcDNA5-TO/FRT-FLAG, and pPUR 59/ FRT/TO-FLAG-BirA* (only LUZP1). The GFP-LUZP1, GFP-EPLIN, and mCherry-LUZP1 fusion coding sequences were amplified by PCR and subcloned into the lentiviral vector pHR-SIN-SFFV.
Fragment ions were detected in the Linear Trap Quadropole. Dynamic exclusion was activated such that MS/MS of the same m/z (within a range of 15 ppm; exclusion list size, 500) detected twice within 15 s were excluded from analysis for 30 s. For protein identification, raw files were converted to the. mzXML format using Proteowizard  (Kessner et al. , 2008)  and then searched using X! Tandem  (Craig and Beavis, 2004)  and COMET  (Eng et al. , 2013)  against the human RefSeq version 45 database (containing 36,113 entries). Data were analyzed using the transproteomic pipeline  (Deutsch et al. , 2010; Pedrioli, 2010)  via the ProHits software suite  (v3. 3; Liu et al. , 2010). Search parameters specified a parent ion mass tolerance of 15 ppm and an MS/MS fragment ion tolerance of 0. 4 D, with up to two missed cleavages allowed for trypsin.
33463 µm). Images were analyzed with FIJI (ImageJ; National Institutes of Health; cilia length measurements and line profile fluorescence measurements) or CellProfiler (MyosinVa fluorescence intensity measurements) software. For confocal imaging, the images were acquired using Nikon Ti2E/AIR-HD25 confocal microscope using 60× oil-immersion Plan-Apochromat lambda objective and are shown as maximum intensity projection. Confocal images were analyzed using the IMARIS software suite (Bitplane) for the quantitation of fluorescence intensity. Three-dimensional images were reconstructed using z-stacks in the IMARIS software suite. For performing intensity quantification, a sphere of 2 µm diameter (average size of the pole is 2 µm;  Colello et al. , 2012; Lecland and Lüders, 2014)  was drawn around the pericentrin to calculate the integrated fluorescence intensity of actin and ARP2 around the centrosome.
Acknowledgments We are grateful to  Dr. Johnny Tkach ,  Dr. Ladan Gheiratmand , and  Dr. Mikhail Bashkurov  for very fruitful discussions during the development of this work. J. Gonçalves was partially funded by  Fundação para a Ciência e a Tecnologia  (postdoctoral fellowship  SFRH/BPD/75847/2011 ). This work was funded by operating grants from the  Canadian Institutes of Health Research  (MOP# 142492  and FDN# 167279 ) and the  Krembil Foundation  to  L. Pelletier. The authors declare no competing financial interests. Author contributions:  J. Gonçalves  conceived the project, designed the research plan, and performed the experiments and data analyses. A. Sharma analyzed the centrosomal levels ARP2 and actin in RPE-1 stable lines. Sample preparation for MS was carried out by  E. M. N. Laurent ,  E.
The model was confronted with experimental data extracted from the literature and it was demonstrated that the model was able to reproduce the dependence of the apparent moduli on the tensile prestrain. It was also observed that the evolution of the apparent moduli was predicted even if it was not an explicit input of the experimental dataset used to identify the input parameters of the model. It was then deduced that the shape of the hardening and the dependency of moduli on the prestrain were two manifestations of a single cause: the heterogeneous yield stress in DP steels.
The compartmentalized model A compartmentalized model is a random heterogeneous finite element model in which the material properties of every element arise from those of a substructure called a compartment. Each compartment has unique material properties. In the presented model each element is a compartment. Compartments are not designed to represent a specific physical structure or scale such as grains. Their only purpose is to introduce a controlled amount of heterogeneity in the model to produce specific effects on the macroscopic mechanical behavior. The implementation of the compartmentalized model described in this section is made available by the authors (python libraries : Compmod2  (Charleux and Roux, 2019b)  and Argiope  (Charleux et al. , 2019) , full code example : Compdmod2 Documentation (Charleux and Roux, 2019a)).
On the other hand, since the optimization of the input parameters P opt is carried out with the EXP-A dataset, which contains only one loading/unloading/reloading cycle, it follows that, in the context of the compartmentalized model, the decay of the apparent moduli is related to the shape of this cycle. These results indicate that strain-hardening, as well as the evolution of apparent moduli, are only two manifestations of a single cause: the heterogeneity of the material. It also shows that the compartmentalized model allows a physically realistic representation of the mechanisms involved in the plastic deformation of DP steels.
Background: An estimated 12. 8% of children and adolescents experience chronic health conditions that lead to poor quality of life, adjustment and coping issues, and concurrent mental health problems. Digital health deployment of psychosocial interventions to support youth with chronic illness has become increasingly popular with the advent of the technological advances in the digital age. Objective: Our objectives were to systematically review published efficacy studies of eHealth and mHealth (mobile health) psychosocial interventions for youths with chronic illnesses and review intervention theory and treatment components. Methods: PubMed, Embase, Web of Science, PsycInfo, and Cochrane Database of Systematic Reviews were searched for studies published from 2008 to 2019 of eHealth and mHealth psychosocial interventions designed for children and adolescents with chronic illnesses in which efficacy outcomes were reported.
Literature Search The search was executed by a research librarian in five databases for articles published from 2008 to 2019: PubMed/MEDLINE, Embase, Web of Science, PsycINFO, and Cochrane Database of Systematic Reviews. We used keywords and Boolean operators  [34]  to identify original articles on eHealth and mHealth psychosocial interventions designed for youths or young adults with chronic illnesses. Inclusion criteria were (1) available in English; (2) published in peer-reviewed journal; (3) experimental, quasi-experimental, and observational studies in which efficacy outcome(s) were reported; (4) eHealth or mHealth psychosocial interventions (with technology as the primary mode of content delivery, either entirely self-guided or human-assisted); and (5) designed for children and adolescents aged 18 years and younger with chronic disease (ie, a long-term medical condition lasting 3 months or longer  [35] ).
Fourth, there exists the possibility of publication bias because nonsignificant findings are often difficult to publish. Fifth, although our search was executed by a medical librarian in five well-established library databases, other databases to which our university does not have access (eg, Scopus) may have uncovered additional relevant publications. Finally, given the heterogeneity of disease groups, measurement time points, and study outcomes measures, it was not possible to conduct a meta-analysis and provide synthesized results of the efficacy studies included in our systematic review.
(a) Integrated Carbon Observation System ecosystem and atmospheric observations In this study, we use the homogeneous and standardized dataset from the ICOS network, supplemented by European stations engaged in long-term greenhouse gas monitoring programmes. Eddy-covariance flux measurements are made at the ecosystem sites and CO 2 mole fractions are measured at the atmospheric sites (see http://www. icos-ri. eu). The current ICOS network consists of 81 ecosystem stations (22 of which have completed the ICOS labelling process) and 36 atmospheric stations (23 fully ICOS-labelled) in 12 countries. In our study, we use the local carbon flux measurements and partitioning thereof into NEE, GPP and TER and compare these with the simulated values from the biosphere model SiB4 (see §2c)  [28]. We select ecosystem sites inside and outside the drought region (see electronic supplementary material, table  S1  and figure  S1 ).
[23] , in which we coupled SiB4 to the hydrological model PCR-GLOBWB, a two-soil-layer model with full routing scheme and human water use (e. g. irrigation). PCR-GLOBWB provides soil water storage as well as several types of run-off and exchange of water between vertical model components. Full details of the model can be found in Sutanudjaja et al. [24]. We have used PCR-GLOBWB to simulate the hydrological system of Europe with a spatial resolution of 0. 1°× 0. 1°and a daily time step. The meteorological driver data were taken from the ERA5 reanalysis dataset. SiB4 was run with the upper soil layers (0-0. 26 m) driven by the top-layer soil moisture output of PCR-GLOBWB (0-0. 3 m), instead of its internally calculated soil moisture, following  [23]. A comparison of 2018 anomalies of observed and simulated soil water content (SWC) is shown in electronic supplementary material, §SD.
This comparison shows that the drought effect in PCR-GLOBWB SWC is limited. Simulated biosphere fluxes therefore do not show an improved drought response relative to the default SiB4 run (electronic supplementary material, figure  S4 ). In §3, we will show results from SiB4 with modified rooting depths and using the default SiB4 soil moisture scheme  [48]. (e) Atmospheric CO 2 inverse modelling CTE is a global atmospheric inverse system that estimates the biosphere and oceans' fluxes of CO 2 , described in full in van der Laan-Luijkx et al. [25]. Here, we use atmospheric observations of CO 2 concentrations from the GLOBALVIEWplus v. 5. 0 ObsPack product  [49]  and the European 2018 drought dataset  [15, 30] , made available on the ICOS Carbon Portal, to estimate net carbon fluxes using an ensemble Kalman smoother method.
Prior ocean fluxes are taken from the ocean inversion of Jacobson et al. [52]. Scaling factors are optimized for these fluxes on a 1°× 1°resolution with exponentially decaying covariances across each ecoregion, and a length scale of 200 km (see  [25] ). Fossil fuel and biomass burning emissions are taken from the EDGAR 4. 0  [53]  and SiBCASA-GFED4  [54, 55]  datasets respectively and are not optimized. In 2018, this product exhibits peaks in biomass burning emissions across the south of Sweden, where relatively large wildfires broke out during the dry, hot conditions (see electronic supplementary material, §SF. ) We test the sensitivity of the inversion results to the changes we make from our standard CTE setup to one more suitable for investigating extreme drought events. We vary the biosphere prior and observation dataset used.
Discussion The contrasting carbon cycle anomalies between the spring and summer periods of 2018 strongly resemble the 2012 drought in North America described extensively in Wolf et al. [57]. Similarly, during spring 2018 anomalous heat and solar radiation in Europe triggered extra photosynthesis and corresponding evapotranspiration at a time when soil moisture was available to vegetation, leading to extra net carbon uptake. This positive spring anomaly was also detected independently in eddy-covariance data, remote sensing products, biosphere model results, and in the CarbonTracker data assimilation system as constrained by CO 2 observations from an extensive tower network. As warm conditions persisted, and Table  2. Changes in the European carbon balance (GPP, TER, NEE) in TgC during the period July-September 2018 (JAS), integrated over the northwest European drought-affected area (figure  1 ), for SIF, NIRv, SiB4 and CTE.
Acknowledgements. The authors thank all data providers for the atmospheric CO 2 mole fraction observations from the  ICOS atmosphere network  and the further global network. We also thank the data providers for the eddy-covariance flux observations from the  ICOS network. We specifically acknowledge the following organizations for their contributions to the data from sites across the ICOS network, leading to the freely available atmospheric and ecosystem data sets that made this study possible. The authors thank the  Swedish Research Council  for co-financing the Swedish ICOS data. We thank the  University of Goettingen, Deutsche Forschungsgemeinschaft , and  German Federal Ministry of Education and Research for funding the operation of DE-Hai. The funding by  EU  projects EUROFLUX, CARBOEUROFLUX and CARBOEUROPE-IP, by  German BMBF  project  ICOS-D and by the state of Saxony  ( TU Dresden, LfULG ) is greatly appreciated.
Data from the Sorø beech forest site (DK-Sor) were measured, evaluated and provided by  Kim Pilegaard  and  Andreas Ibrom  and the station team. The work was funded by the  Technical University of Denmark (DTU) , the  Danish Research Council  ( DFF -1323-00182 ), the  Danish Ministry of higher Education and Science  ( 5072-00008B ) and the  EU  research infrastructure projects RINGO and ICOS. Atmospheric CO 2 concentrations from the French sites are supported by the  Service National d'Observation ICOS-France-Atmosphére. FR-Hes  acknowledges the support by successive European projects, by European regional development programmes with the Region Lorraine, by  GIP Ecofor  and  SOERE F-ORE -T, by  ADEME , and by the  INRA Department of Forest, Grassland and Freshwater Ecology (EFPA). DE-Geb site installation and operation was conducted by Thünen Institute of Climate-Smart Agriculture  and supported by the  Max-Planck Institute for Biogeochemistry (MPI-BGC) , the  German Federal Ministries of Education and Research (BMBF) ,  Transport and Digital Infrastructure (BMVI) , and  Food and Agriculture (BMEL).
Data accessibility. ICOS ecosystem and atmospheric site data are avail- able at the ICOS Carbon Portal (https://www. icos-cp. eu/) [28, 30] , and the GLOBALVIEWplus v5 ObsPack can be downloaded from the NOAA Earth System Research Laboratory website (https:// www. esrl. noaa. gov/gmd/ccgg/obspack/data. php). SIF data are available from the NASA website for the GOME-2B satellite (https://gs614-avdc1-pz. gsfc. nasa. gov/pub/data/satellite/MetOp/ GOME˙F/v28/MetOp-B/). NIRv, SiB4 and CarbonTracker Europe data created for this study will be available from the ICOS Carbon Portal. The CTDAS code (current revision r1479) is open access under GNU General Public License version 3 and available from the authors. The model PCR-GLOBWB is freely available at https://github. com/UU-Hydro/PCR-GLOBWB_model.
Objectives Determine the impact of infections on direct costs and healthcare resource use in England for patients undergoing intramedullary nailing (IMN) for tibial shaft fractures. Design Non-concurrent cohort based on retrospectively collected data with 2-year follow-up. Setting England. Participants The study population included adult patients (≥18 years) in England with a diagnosis of tibial shaft fracture (International Classification of Diseases-10, S822) in the inpatient setting between May 2003 and June 2017 followed by a procedure for IMN for tibial shaft fracture within 30 days. Patient data were derived from the Clinical Practice Research Datalink linked to National Health Service Hospital Episode Statistics datasets. Primary independent variable Infection. Primary and secondary outcome measures The primary outcome was total inpatient costs from index stay admission through 1 year of follow-up.
Strengths and limitations of this study ► This is the first study to quantify the healthcare resource burden of infections following tibial shaft fractures treated with intramedullary nailing in England. ► The study had a long-term and cross-sector perspective that included inpatient, hospital outpatient and primary care parameters. ► This study only considered patients with complete follow-up, thus, excluding very severe patients with short life expectancy. ► Some costs were not directly available from the Clinical Practice Research Datalink dataset and were sourced from published national sources. ► The study relied on clinical codes to identify superficial and deep infections which may be subject to coding errors and misclassifications. Open access of 2% after IMN for closed fractures compared with 7.
Study design and setting This was a non-concurrent cohort study based on retrospectively collected data of patients in England who underwent IMN for tibial shaft fracture (open or closed) and were followed-up for 2 years. Data derived from the Clinical Practice Research Datalink (CPRD) linked to National Health Service (NHS) Hospital Episode Statistics (HES) and NHS reference costs were used to calculate costs and healthcare resource utilisation associated with infections (superficial or deep) following IMN. The CPRD database is an anonymised longitudinal dataset of over 11. 3 million medical records from over 600 primary care practices across the UK. 12  It includes all visits to primary care and other healthcare professionals, reasons for visits, diagnoses observations, medical history, test results, referrals and prescriptions.
Data collection The primary outcome of this study was total inpatient costs (Healthcare Resource Group (HRG), unbundled HRG and specialised care) accrued beginning from index stay admission through 1 year of follow-up postdischarge from the index stay. Secondary endpoints included cumulative total healthcare costs and resource utilisation for 30 days, 90 days, 1 year and 2 years of follow-up postdischarge of the index stay. Total healthcare costs comprised inpatient, hospital outpatient and primary care costs (consisting of consultations, prescriptions and tests/investigations). Healthcare resource utilisation included LOS, readmissions, reoperations, days in intensive care unit, hospital outpatient visits, diagnostic tests and primary care visits. Time to infection was an additional secondary outcome.
Resource use and costs Healthcare cost data were estimated based on the healthcare resource utilisation reported in CPRD/HES and the unit cost associated with each service from an NHS perspective. In England, NHS provides preventive medicine, primary care and hospital services to 88% of the citizens. Responsibility for publicly funded healthcare remains with the Secretary of State for Health, supported by the Department of Health. 13 Hospitals are reimbursed by NHS according to the amount and type of activity that they perform using HRGs. 14.
Inpatient costs The 2017/2018 HRG Reference Costs Grouper software was used to generate HRG codes for each inpatient admission. 15 16 Each HRG code was assigned an appropriate cost from NHS Reference Costs, 17 using admission method, LOS, trim point and the patient classification to associate the relevant costs. 15 18 19 Inpatient stays were considered as long stays for admissions lasting ≥2 days in line with NHS reference costs. 18 20 Unbundled HRGs were automatically generated by the Grouper software and assigned relevant costs. 17 Specialised care episodes were identified using the Prescribed Specialised Services Tool 2017/18 software and top-up costs were applied as a percentage increase to the HRG cost. 21 pen access were sourced from the Unit Costs of Health and Social Care. 22 aboratory and diagnostic tests from the CPRD tests file were manually matched to the closest NHS test category and assigned NHS Reference Costs.
18 edication categories were based on British National Formulary classifications as recorded in the CPRD therapy file, and unit costs were obtained using the 23 Prescription Cost Analysis 2017 using the mean subparagraph cost associated with each medication. 23 llow-up period and cohort definitions Follow-up time was calculated as the difference between the index discharge date and the last date of observation. Only patients with follow-up data at the relevant time point were included in the analysis.
Covariates were identified a priori as risk factors for the study outcomes based on clinical knowledge. A backwards stepwise procedure was applied according to the Akaike information criterion. Missing data were not imputed. Except for in the sensitivity analyses, patients with missing data were excluded from analyses. Sensitivity analyses at all time points were conducted using data from the subgroup of patients who had completed 2-year follow-up for total costs, LOS, readmission (rate and mean count) and reoperation (rate and mean count).
DISCUSSION This study used CPRD-linked HES data to determine the impact of infection on English healthcare costs and resource utilisation associated with patients undergoing IMN for tibial fracture. Infection rates at 1 year and 2 years (11. 7% and 13. 4%, respectively) were comparable with  Open access the 10. 5% rate reported in a 2014 meta-analysis. 5 Mean inpatient costs measured after 1 year were predicted to be 80% higher (£6056) for patients with infection compared with those without infection, while overall costs were 78% higher. The greatest cost drivers were hospital LOS (109% increase at 1 year), readmissions (odds of being readmitted increased by 5. 18 times at 1 year) and reoperations (odds of reoperation increased by 2. 47 times at 1 year). The 2-year follow-up in this study meant that we were able to capture changes in resource use over time associated with infection, such as readmission and reoperation.
34 35 Recently it became clear that the CDC definition for infection probably is not sufficient to define fracture-related infections. One important reason is the fact that the subdivision of infection into superficial and deep infection is arbitrary. 36 owever, the use of the CDC definition was standard during our study period (2003-2017); (3) medication use was costed as recorded in CPRD, that is, averaged to the cost of the drug family/British National Formulary sub-paragraph; (4) dispensing costs were not included; (5) outpatient specialties from CPRD did not always exactly match outpatient specialty categories from NHS Reference Costs; when there was not an exact match, the closest matching specialty was chosen; (6) costs were not directly available from the CPRD dataset and hence unit costs had to be sourced from published national sources for primary and secondary care and for drug prices; (7) economic assessment was limited to direct healthcare Open costs while infections could lead to permanent functional loss and potentially increase in secondary costs;  25  and (8) all potential confounders could not be adjusted for, limiting the association between increased healthcare resource utilisations and costs with surgical site infections.
Ethics approval The study protocol was approved by the  Independent Scientific Advisory Committee for Medicines and Healthcare products Regulatory Agency database research (ISAC)  on 27 November 2017 (ISAC Protocol:  17-132R ). General ethical approval for observational research using the CPRD with approval from the ISAC was granted by a  Health Research Authority Research Ethics Committee (East Midlands-Derby ; reference number:  05/MRE04/87 ). Provenance and peer review Not commissioned; externally peer reviewed.