dataesr/scientific-paragraphs-categorization huggingface dataset:
Sources of evidence: NICE Healthcare Databases Advanced Search website (https://hdas. nice. org. uk/) was used as a tool for multiple searches, with a content analysis and charting of data extracted. Results: 711 records were identified, 35 were included in the review. Studies evaluated serum micronutrient status was determined on admission day in majority of patients. A content analysis identified (n ¼ 49) initial codes, (n ¼ 14) sub-categories and (n ¼ 5) overarching themes during critical illness, which were identified as: i) low levels of micronutrients, ii) causes of aberrant micronutrient levels, iii) associations between micronutrients levels and outcome, iv) supplementation of micronutrients. Conclusion: During critical illness, micronutrients should be provided in sufficient amounts to meet reference nutrient intakes for age.
Data sources e stage 1 After finalising the objectives and research questions a literature search was completed to identify relevant studies. NICE Healthcare Databases Advanced Search website (https://hdas. nice. org. uk/) was used as a tool for multiple searches within multiple databases including the PsycInfo, Cumulative Index to Nursing and Allied Health Literature (CINAHL) and Medline. PubMed, the Cochrane Library and NHS Evidence were also searched, with searches adapted for each database. Forward and backward citation searching was completed on studies exploring.
Image acquisition and reconstruction Scans were obtained at constant electron acceleration energy of 120 kV, an electric current of 300 µA and a rotation speed of 4. 99 degrees/s resulting in a scan duration of 14 m34 s. A total of 2,164 images (or radiographs) in a. tif format were used for reconstructing each 3D image using North Star Imaging© reconstruction software EFX-CT (version 1. 9. 5. 12) where the resulting 3D images were exported in a. nsihdr format with a resolution of 992 × 992 × 2991 voxels (voxel size of 0. 1 × 0. 1 × 0. 1 mm). After 3D reconstruction, a multi-stage workflow was applied to all CT images in order to eventually achieve a quantitative study. This workflow consists of three key steps as illustrated in Fig. 1d-f : preprocessing steps, walnuts individualization, and morphological traits extraction and quantification.
The optimum threshold τ and greyscale ranges of the principal parts of the walnuts were estimated experimentally based on the analysis of the histogram, using K Nearest Neighbor clustering method, that corresponds to the distribution of the intensities of the images (Fig. 3 ). Cropping and exporting take 1m30s/walnut. An additional movie file shows this in more detail (Additional file 2). A fully automated in-house image processing pipeline was developed using the Thermo Scientific Avizo© software V9. 0. 0 built-in functions, the MATLAB© version 7. 7. 0 R2008b image processing toolbox  [54]  from The MathWorks©, Inc. (Massachusetts, USA), the TCL scripting language and Spyder Python IDE. To use this pipeline, walnuts that have no damage on the shell are required. Measurements of a total of 14 morphological and shape descriptors were obtained: the nut length, the nut face diameter, the nut profile diameter, the nut volume, the nut shape VA3D, the nut Feret shape 3D (defined by D/d where d is the minimum Feret diameter and D is the maximum Feret diameter in the orthogonal direction, so 90° from the minimum Feret diameter; the maximum Feret diameter is the maximum diameter of an object as if it were freely rotating in three dimensions using a caliper  [55] ), the nut surface area, the shell volume, shell thickness, the kernel volume, the kernel filling ratio, and the empty space volume (Table  1 ).
Quantification takes 2 min/walnut. In addition, 2 supplementary traits, the nut sphericity (close to roundness) (3) and the shell rugosity (or surface roughness) (4) indexes, were measured using a nonstandard calculation since they are not supported by the Avizo© software: where V n is the nut volume and A is its surface area. The sphericity of a sphere is 1 and any object which is not a sphere will have sphericity less than 1. The nut shape VA3D is defined by A 3 36π V n 2. Experiments were run on a workstation equipped with an Intel ® Xeon ® dual-core processor running at 3 GHz using 64 MB of RAM and running Windows ® version 10. The dataset was assessed using R software  [56]  with the package "tidyverse"  [57]. Pearson correlation matrices were performed using the package "corrplot"  [58]  and Principal Component Analysis (PCA) using the package "FactoMineR"  [59].
The X-ray CT method for the selection of superior genotypes In French walnut industry, both producers and consumers have particular expectations. For example, they would prefer large round-shaped walnuts, easy to crack and having a high kernel filling ratio. A Principal Component Analysis using the dataset obtained permitted us to select interesting genotypes for the previous important traits (Fig. 5 ). The first two dimensions of the PCA explain 75. 5% of the total variance (Fig. 5b ). The dimension 1 corresponds to the morphometric traits (Nut Length, Nut Face Diameter, Nut Profile Diameter), the volumes (Nut Volume, Shell Volume, Kernel Volume, Empty Space Volume) and the Nut Surface Area (Fig. 5a  and d ). The dimension 2 is linked to the Nut Shape VA3D, the Nut Sphericity and the Shell Rugosity (Fig.
Discussion Primarily developed for medical purposes, X-ray CT is now widely applied in food science, for instance to track the microstructural evolution of dairy products or to quantify salt concentrations in pork meat  [60]. An overview of applications related to various food products showed the use of X-ray CT in chestnut for postharvest assessment of internal decay and in pecan nuts for components screening  [61] [62] [63]. We showed for the first time that X-ray CT is a method of choice also for walnut germplasm analysis of morphological traits. We obtained quantitative data of high accuracy on 14 traits including volumes and shell thickness which are classically obtained by cracking the nuts. The obtained dataset includes information that is crucial for the INRAE walnut germplasm collection, allowing a precise and relevant characterization of the nuts of each accession.
Conclusions We presented a method for better resolution phenotyping of walnuts based on X-ray CT compared to classical measurement methods. The data will be used for INRAE walnut germplasm management, but also for GWAS purposes and for selecting superior genotypes in a future breeding program. This method could be easily adapted for any nut species and potentially moved towards the identification of the first steps of infection by pathogens. • thorough peer review by experienced researchers in your field • rapid publication on acceptance • support for research data, including large and complex data types • gold Open Access which fosters wider collaboration and increased citations maximum visibility for your research: over 100M website views per year.
Acknowledgements First, we would like to thank the late  Eric Germain , former head of the breeding program at the  INRAE of Bordeaux  from  1977  to 2007. His remarkable work, then continued by  Francis Delort , has given us the opportunity to study a rich set of plant material. We thank the  Fruit Tree Experimental Unit of the INRAE in Toulenne and the Prunus/Juglans Genetic Resources Center  for the maintenance of the collection and for helping us to collect the samples.
These programs have been commonly claimed as one of the main drivers which increased the enrollment rate. However, to the best of our knowledge, the effect of the programs on the children rate of enrollment in Vietnam has not been empirically investigated thoroughly. In this paper, we attempt to fill this gap by considering the case of Vietnam and relying our empirical investigation on a unique dataset from the Vietnam Household Living Standard Surveys (VHLSS) over the most recent 12-year period of the program implementation. While it is commonly argued that tuition fees reduction as well as cash transfers can reduce the direct education cost to households, the effect of these policies on school enrollment of children is still ambiguous. World  Bank (2000)  shows that, in addition to tuition fees, households might have to pay other fees for children such as contribution to schools.
The effect of the two policies is furthermore not alike among different groups of children with greater effect on children who are either minor ethnic groups, or in poor households, or living in rural areas. Our finding thus implies that tuition fee exemption and reduction policy, as well as the education subsidy program, are still an effective way to encourage children to enroll school. Policymakers could align these policies with other most recent surveys 2016, 2018, it is impossible to combine construct meaningful panel data for all surveys from 2006 to 2018. Thus, we use two pairs of survey datasets in year  2006, 2008 and 2016, 2018  to examine the impact of these policies over the most recent decade. The first set of two surveys in years 2006 and 2008 cover the data for the same cohort of children aged from 6 to 18 years old and enrolled schools in 2006.
The second set of two surveys in years 2016 and 2018 provide the data for the cohort of children aged 6-18 years old and enrolled in 2016. These two surveys 2016 and 2018 are also the most recent surveys available. complementary encouraging measures for households having younger children such as the reduction in poverty and distance to schools, the development of microcredit/finance programs, and the alleviation in credit constraints. The rest of the paper is structured as follows. Section 2 describes the dataset used in the empirical investigation. Section 3 reviews child education and the tuition fee exemption and reduction and the education subsidy program in Vietnam. Section 4 presents the estimation method. Section 5 reports and discusses the empirical findings. Section 6 summarizes the paper and provides some concluding remarks.
Data We use four of Vietnam Household Living Standard Surveys (VHLSS) which were conducted by the General Statistics Office of Vietnam  (GSO) in 2006 (GSO) in , 2008 (GSO) in , 2016 (GSO) in and 2018. The surveys contain standardized questionnaires developed by the World Bank. The VHLSS data has long been considered to be of high quality and they have been widely used in recent studies (see, e. g. ,  McCaig & Pavcnik, 2015; Bui, Dungey, Nguyen, & Pham, 2014; Nguyen & Nguyen, 2015). The 2006 and 2008 VHLSS have the same sample size, at 9189 households for each survey. There are 4090 households who were surveyed in both the surveys. The 2016 and 2018 VHLSSs sampled 9399 and 9168 households. The panel data from the 2016 and 2018 VHLSSs are contained for 4005 households. The VHLSSs are representative for the whole country, urban/rural areas, and the 8 regions.
In other words, we use lagged treatment variables instead of current treatment ones. In this study, we use panel data from VHLSSs 2006 and 2008, and panel data from VHLSSs 2016 and 2018 for impact evaluation. We regress the  We estimate the model (1) using linear probability and probit models. Linear probability models can be used for binary outcomes  (Angrist & Pischke, 2008). In addition, we use the probit model which fits Eq. (  1 ) using a cumulative probability function of the standard normal distribution: = + + + + Y Reducation Subsidy X H ( ) i j t i j t i j t i j t j t , , , , 1 , , 1 ' , , ' , (2) where denotes the cumulative probability function of standard normal distribution. The interpretation of the coefficient in the probit model is not straightforward. Thus, we estimate the marginal effect of the tuition fee reduction and education subsidy variables on student's enrollment as follows: = ME reduction Y Reducation ( ) / i j t i j t i j t , , , , , , 1 = + + + + Reducation Subsidy X H ( ), i j t i j t i j t j t , , 1 , , 1 ' , , ' , (3) = ME subsidy Y Subsidy ( ) / i j t i j t i j t , , , , , , 1 = + + + + Reducation Subsidy X H ( ) i j t i j t i j t j t , , 1 , , 1 ' , , ' , (4) whether is the standard normal density function.
The above marginal effect varies across students. Using Stata software, we estimate the marginal effect evaluated at the mean of explanatory variables. It should be noted that although we use the lagged treatment variables to avoid the reverse causality, there is still a problem of endogenous problem. Children receiving and those not receiving tuition fee reduction and education subsidy can differ in unobserved characteristics, which affect both school enrollment and the receipt of tuition fee reduction and education subsidy. To the extent that we are seeking evidence of a causal effect of these education policies, we are acutely aware of the difficulties in estimating causal effects when lacking randomization and are therefore cautious in interpreting our findings. We expect that the estimation bias is small since we control for a large number of explanatory variables including the poverty status and ethnic minorities, which are the key eligibility criteria for tuition fee reduction and education subsidy.
Finally, since students in the same commune share similar unobservable characteristics such as quality of education, infrastructure, job opportunity for young children, the assumption that observations are independent and identical distributed is violated. To overcome the problem, we estimate standard errors clustered by communes so that our estimation results are robust to both heteroskedasticity and Notes: Robust standard errors in parentheses. The standard errors are corrected for sampling weight and cluster correlation. Other control variables are the same as the model in Table  2. These variables include characteristics of household heads, household composition, and regional dummies. ***, **, and * indicate significance at 1%, 5%, and 10% levels. Source: authors' estimation using data from VHLSSs 2016 and 2018.
Impact of the tuition fee reduction and education subsidy on school enrollment Table  3  presents the estimation of the impact of tuition fee reduction and education subsidy on school enrollment of students. We focus on the effect of children in secondary schools because almost all children attend primary schools in Vietnam and primary students are eligible for tuition fee exemption. We estimate both OLS and probit models. For each model, two sets of data are deployed: one set of panel data from VHLSSs 2006 and 2008 and another set of panel data from VHLSSs 2016 and 2018. The results show similar estimates for the 2006-2008 and the 2016-2018 periods. The point estimate of the effect of education subsidy from the OLS model is higher than that of tuition fee reduction. However, the difference is not statistically significant.
Membranes were washed three times with TBS-Tween 1X during 10 min. Specific bands were revealed with Clarity Western ECL Substrate (BioRad) and membranes were observed with a ChemiDoc XRS + system (BioRad). For the revelation step, anti-GFP blots were exposed for 60s except for GFP controls (3s) to avoid overexposition. The 60s exposition for the GFP control is shown on Supp fig 1. Microscopy. Microscopy observations were realized at the platform FR AIB (Toulouse, France) at 5 days postinfiltration. GFP fluorescence (λ excitation = 488 nm; λ emission = 510 nm) was observed with a confocal laser scanning microscope (CLSM) TCS SP8 (Leica) and a HC Fluotar L25X/0. 95 W VISIR (Leica) lens. Pictures were taken with the software LAS (Leica) and were processed with the software ImageJ (1. 52a). Activity of recombinant proteins.
Briefly, 12 ml of culture was centrifuged, the pellet was resuspended in 120 l of PBS, and 35 l was loaded in a microcapillary tube. After centrifugation for 15 min at 800 ϫ g, the height of the pellet within the tube was measured with a ruler. Electron microscopy of mid-log-phase S. pneumoniae fixed in 3% paraformaldehyde (PAF) was performed using a ruthenium red and London resin capsule-preserving protocol as previously described  (33). Capsule thickness was calculated by direct measurement of the surface layer for 30 randomly chosen S. pneumoniae bacteria/strain using ImageJ software. Confocal microscopy on bacteria was performed using an Olympus FV1000 confocal laser scanning microscope with a 63ϫ objective. Bacteria were fixed for 30 min with 4% PFA (Sigma) on slides (Thermo Scientific; SuperFrost Plus 10149870) and subsequently stained with anti-serotype 2 antibody (Statens Serum Institute) plus Alexa Fluor 546-conjugated anti-rabbit antibody.
pneumoniae incubated for 30 min with human serum (25%), human heat-inactivated serum (25%), or just Hanks balanced salt solution (HBSS) medium. For macrophage phagocytosis, THP-1 monocytes cultured in suspension in RPMI medium supplemented with 10% fetal bovine serum (FBS) were treated for 24 h with 10 nM phorbol 12-myristate 13-acetate (PMA) to induce cell adhesion and macrophage differentiation. Flow cytometry was performed using a fluorescence-activated cell sorting (FACS) Verse machine (BD), and the data were analyzed with FlowJo software. For neutrophil killing assays, fresh human neutrophils were purified using a magnetically activated cell sorting (MACS) neutrophil isolation kit (Miltenyi Biotec) and resuspended in HBSS medium at a concentration of 1 ϫ 10 6 cells/ml. S. pneumoniae previously incubated with human sera was incubated with the neutrophils at a multiplicity of infection (MOI) of 1:500 (bacteria to neutrophils) in a 48-well plate for 45 min at 37°C.
Sites were filtered to a minimum depth of five reads at each and a single-nucleotide variant quality of 25, and the Integrated Genome Viewer was used to visualize mapping and coverage. Gene transcriptome microarrays were performed as described previously  (34). Briefly, RNA was extracted with the RNeasy minikit (Qiagen), and labeled cDNA was prepared using Cy3-dCTP (GE Healthcare, United Kingdom) and SuperScript II reverse transcriptase with random hexamer primers (Life Technologies). Labeled cDNA was hybridized overnight to the BG@S SPv1. 4. 0 Agilent SurePrint platform (Agilent Technologies) microarray designed by the Bacterial Microarray Group at St. George's, University of London. After hybridization, washed slides were scanned immediately, using an Agilent high-resolution microarray scanner, at 5-m resolution; scanned images were quantified using Feature Extraction software v 10.
PCR products were digested using 1 U DraI, 2 U PleI, and 1ϫ CutSmart Buffer (all from New England Biolabs, United Kingdom) in a 20-l volume. Each FAM-labeled SpnIII variant has a unique size that can be distinguished through capillary electrophoresis on an ABI Prism gene analyzer (Applied Biosystems, USA) and analyzed using Peak Scanner v1. 0 software. Genomic DNA for transformation using locked SpnD39III (ST5556II) alleles due to an inactivated creX gene was obtained from preexisting strains  (14). Animal models of infection. All animal experiments conformed to institutional and United Kingdom Home Office guidelines and regulations. Outbred CD1 sex-matched white mice were used for infection experiments using established models of infection  (50) (51) (52). For the nasopharyngeal colonization model, 10 7 CFU of bacteria in 10 l was administered by intranasal inoculation under light halothane general anesthesia, and nasal washes were obtained after 5 days.
ACKNOWLEDGMENTS This work was undertaken at  UCLH/UCL , which received a proportion of funding from the  Department of Health's NIHR Biomedical Research Centre 's funding scheme. G. E. was supported by  MRC  (grant  MR/R001871/1 ), M. O. was supported by grants from the  MRC  and  BBSRC  ( MR/M003078/1  and  BB/N002903/1 ), and S. C. was supported by the  Wellcome Trust  (grant  WT076442 ).
Background: Knowledge about cancer-related malnutrition and the use of clinical nutrition (CN) in the real-world setting are lacking. We investigated diagnosis and treatment frequency of malnutrition in a multinational survey to identify unmet needs in cancer patients' care. Methods: Retrospective analyses were conducted on data from three administrative healthcare datasets from France (n = 570,727), Germany (n = 4642) and Italy (n = 58,468). Data from France described frequency and timing of malnutrition diagnosis in hospitalized gastrointestinal cancer patients. The German data detailed home parenteral nutrition (HPN) use in cancer patients with stage III/IV cancers. The Italian data analysed three cohorts: metastatic with CN, metastatic without CN, and patients without metastatic disease.
17 Despite the increasing awareness of the negative impact of malnutrition on clinical outcomes in cancer patients, the understanding of malnutrition rates, as well as the use of CN in the real-world setting, is lacking. Our overall objective in this research investigation was to describe the management of malnutrition among cancer patients in current clinical practice, using realworld data from large administrative healthcare datasets from three European countries. Several analytical methodologies were applied to characterize and demonstrate meaningful insights into different aspects of cancer patients' care. Specifically, (a) diagnosis, frequency, and rates of malnutrition, and (b) the use of CN in hospitalized cancer patients and in the homecare setting were examined.
France Malnutrition diagnosis frequency and timing were investigated, based on data from the French Hospital database PMSI (Program for the Medicalization of Information Systems), and Medicine, Surgery (Chirurgie), Obstetrics. The database contained information on reported diagnosis, procedures and included mean values for drugs used in each diagnosis group. 18 The study period ranged from 1 January 2013 to 31 December 2016 and inclusion criteria were: a first gastrointestinal (GI; colon-rectal-anal, liver/ biliary tract, pancreas, stomach, and esophagus) cancer-related hospital stay, as defined by the algorithm for attribution of hospitalizations related to cancer developed jointly by the Hospital Information Technology Agency, the National Cancer Institute (INCA) and representatives of public and private hospitals from the INCA during the study period 2013-2016.
Italy The use of CN, defined as enteral nutrition (EN) with or without parenteral nutrition (PN) in oncology patients was assessed, based on an integrated administrative database using data from 10 Italian Local Health Units (LHUs). The administrative database for healthcare resource use included information from a pharmaceutical database, a hospitalization database, laboratory tests, diagnostic tests and specialist visits database. The database covers 5. 9 million people from various regions in the northern, central and southern Italy, representing around 10% of the total Italian population. The study period was from 1 January 2009 to 31 December 2015. All patients with a diagnosis of H&N, gastrointestinal, respiratory, genitourinary, or haematologic malignancies defined by ICD-9-CM during the study period and a 12-month, available, pre-and postindex observation period were included.
France The study population identified 570,727 GI cancer patients with a first related hospital stay between 2013 and 2016. The distribution of patients by cancer types is shown in Figure  1. Malnutrition diagnosis at first hospitalization was reported in the PMSI database for 10% of patients with GI cancer, 13% were diagnosed after the first hospitalization and 77% had no diagnosis of malnutrition during the study period. The proportion of patients for whom malnutrition was reported ranged from 14% in CRC cancer patients to 38% in patients with pancreatic cancer. For all types of cancer, the 2016 PMSI data reports about 1. 2 million cancer patients in France, of which one third are reported with a malnutrition code (as defined by ICD-10 criteria), which is substantially lower than previous prevalence estimates in the available literature  4  (Figure  2 ).