dataesr/scientific-paragraphs-categorization huggingface dataset:
Literature Search and Data Entry A computerized literature search [Agricola (http:// agricola. nal. usda. gov/), CAB Abstracts (http:// www. cabi. org/Default. aspx?site=170&page=1016&pid =125), and Web of Science (http://thomsonreuters. com/web-of-science/)] was conducted to identify papers where the effect of PA on pasture intake or milk production in lactating dairy cows was studied. These papers eventually also studied the effect of PA on milk composition or grazing behavior, or both, as additional measurements. The search was carried out using the following key words in different combinations: dairy cow, grazing, allowance, herbage, and pasture. More papers were then identified by reviewing the reference list in the publications resulting from the search. Papers were selected if they met the following criteria: (1) temperate regions and temperate sward species, (2) lactating dairy cows under strip-or rotational-grazing management, (3) a comparison of at least 2 PA under the same experimental conditions, particularly same pasture mass (i.
e. , where PA levels were only obtained through changes in daily offered area). After discarding publications with duplicated data (i. e. , results from the same experiment published several times), a starting database was obtained. The initial database included 61 papers with 140 PA comparisons. It was conceptualized with rows representing treatments within experiments and columns reporting treatment characteristics and least squares means of measured variables. Each paper was identified by author(s), year of publication, and country. Each PA comparison was allocated an individual code (study) and was characterized by grazing system, season, sward type, experimental design, experimental length, number of cows, preexperimental cow characteristics, EH, and the method to estimate pasture intake.
Calculations At least 2 of the 3 following variables were needed to calculate the remaining variable: pasture mass, PA, and offered area (PA = pasture mass × daily offered area). Data were standardized before quantitative and statistical analyses. Pregrazing pasture mass and PA were expressed in tonnes of DM per hectare and kilograms of DM per cow per day, respectively. The filtered database included papers where pasture mass and PA were estimated above ground level, 2. 5, 3, 3. 5, 4, or 5 cm. For analytical purposes, the database was divided into 3 subsets: PA estimated above ground level (PA 0 subset), PA estimated above 2. 5 to 3. 5 cm (PA 3 subset), and PA estimated above 4 to 5 cm (PA 5 subset). In the PA 3 subset, pasture mass and PA originally reported above 2. 5 and 3. 5 cm were standardized and recalculated above 3 cm.
In the PA 5 subset, pasture mass and PA above 4 cm were standardized and recalculated above 5 cm. For subsequent global database analyses with fixed EH, pregrazing pasture mass and PA were standardized and recalculated above ground level, 3, and 5 cm in all the studies included in the meta-analysis. This was done according to the following equations calibrated from pure perennial ryegrass (Lolium perenne L. ) and perennial ryegrass-white clover (Trifolium repens L. ) pastures, reported in or developed from  Delagarde et al. (2011) : PM 0 = 1. 06 × PM 2. 5 + 1,452, PM 0 = 1. 08 × PM 3 + 1,621, PM 0 = 1. 10 × PM 3. 5 + 1,759, PM 0 = 1. 13 × PM 4 + 1,903, PM 0 = 1. 17 × PM 5 + 2,142, PM 3 = 0. 92 × PM 0 -1,499, PM 5 = 0. 85 × PM 0 -1,811, where PM 0 , PM 2. 5 , PM 3 , PM 3. 5 , PM 4 , and PM 5 are pregrazing pasture mass (kg of DM/ha) above ground level, 2.
5, 3, 3. 5, 4, and 5 cm, respectively. To facilitate the description of results and comparisons between the 3 subsets, 3 theoretical PA classes were established: low, medium, and high. These 3 PA classes are reported for each EH in Table  1. According to previous studies  (Dalley et al. , 1999; Wales et al. , 1999) , low, medium, and high PA 0 correspond to 20, 40, and 60 kg of DM/d, respectively. The corresponding theoretical levels for PA 3 and PA 5 were recalculated from the daily offered area (fixed for each PA) and the average pasture mass above ground level, 3, and 5 cm of the whole database (i. e. , 4. 6, 2. 7, and 2. 1 t of DM/ha, respectively). Accordingly, the average pasture mass in the database is 1. 9 t of DM/ha between 0 and 3 cm, 0. 6 t of DM/ha between 3 and 5 cm, and 2. 5 t of DM/ha between 0 and 5 cm  (Delagarde et al.
Statistical Analyses Statistical analyses were conducted independently on the PA 0 , PA 3 , and PA 5 subsets and on the whole database (global analysis with fixed EH) by using the same models. Pasture intake and production of milk, milk solids, milk fat, and milk protein are recognized for presenting an exponential relation with PA, increasing at a declining rate to a maximum with increasing PA  (Stockdale, 2000; Delagarde et al. , 2001 Delagarde et al. , , 2011)). Accordingly, these variables were analyzed by running a nonlinear mixed model. The model was conditioned to a normal (Gaussian) distribution and the study effect was considered random, specifying that it follows a normal distribution with mean 0 and 1 variance. The model was forced to pass through the origin, assuming that intake and production are 0 when no pasture is on offer.
Data were analyzed by using the following model (PROC NLMIXED; SAS Institute, 2008): R y = a × (1 -exp (-b× PA) ) + study, where R y is the predicted variable y in response to PA change, a is the overall intercept, b is the overall exponential coefficient, and study is the random effect of the study. Grazing behavior, milk fat concentration, and milk protein concentration were analyzed by running a linear mixed model. The study effect was considered random and a structured variance-covariance matrix for the intercepts and slopes was included in the model, except when the random covariance was significant (P < 0. 05) and an unstructured variance-covariance matrix was used  (St-Pierre, 2001). Data were analyzed using the following model  (PROC MIXED; SAS Institute, 2008) : R y = a + study + b × PA + c × PA 2 , where R y is the predicted variable y in response to PA change, a is the overall intercept, study is the random effect of the study, b is the overall regression linear coefficient, and c is the overall regression quadratic coefficient.
This latter was tested for each variable and was not included in the final model if P > 0. 05. An additional global analysis was run on the whole database to determine, within the same relationship, the effects of both PA and EH. The model included the PA as originally estimated in each paper (i. e. , above ground level, 2. 5, 3, 3. 5, 4, or 5 cm) and the fixed effect of EH. This was carried out by replacing the overall exponential, linear, or quadratic coefficient in the mixed models presented above by a linear fixed effect of EH (PROC NLMIXED; SAS Institute, 2008): R y = a × (1 -exp [-(b+c× EH) × PA] ) + study and (PROC MIXED; SAS Institute, 2008) R y = a + study + (b + c × EH) × PA + (d + e × EH) × PA 2 , where d and e are, respectively, the overall intercept and overall linear regression coefficient of the linear effect of EH on the PA 2 variable.
Database Description The final database included 97 PA comparisons reported in 56 papers published between 1966 and 2011 (Table  2 ). Grazing method was strip (84) or rotational grazing (13), including grass-based swards (69), mixed legume/grass swards (  21 ), or legume-based swards (7). Pasture intake was determined by a sward-sampling technique (35), indirectly from fecal output and pasture digestibility (33), with the n-alkanes technique (24), or by an unreported method (5). Experimental design was either Latin square or switchback (37), or complete randomized design (60). The studies were from Argentina, Australia, Brazil, Finland, France, Ireland, Korea, the Netherlands, New Zealand, the United Kingdom, or the United States (Table  2 ). Most of the experiments were conducted in the spring and summer, or summer only.
Pregrazing pasture mass and PA were estimated above Equivalences between PA above ground level (PA 0 ), above 3 cm (PA 3 ), and above 5 cm (PA 5 ) are calculated from the offered area and the average pregrazing pasture mass above ground level, above 3 cm, and above 5 cm in the database (4. 6, 2. 7, and 2. 1 t of DM/ha, respectively), recalculated when necessary according to the equations of pasture vertical distribution in  Delagarde et al. (2011). ground level (47), above an intermediate height of 2. 5 to 3. 5 cm (11), or above 4 or 5 cm (39). Pregrazing pasture mass recalculated above ground level, 3 cm, and 5 cm averaged 4. 6, 2. 7, and 2. 1 t of DM/ha, respectively. In the PA 0 subset, the mean experiment lasted 6 wk and the average pregrazing pasture mass was 4. 4 t of DM/ha (Table  3 ).
Global Analysis Equations describing the exponential effect of PA on pasture intake, milk production, or milk solids production may be compared within each subset (Table  4 ) or considering the whole database (Table  5 ). In both cases, the equations differed mainly by the curvilinearity of the relationship (b parameter). This made the curves Table  2. Summary of the 56 papers included in the meta-analysis to determine the effect of pasture allowance (PA) on pasture intake, milk production, milk composition, and grazing behavior of grazing dairy cows according to the estimation height (EH): ground level (PA 0 subset); 2. 5, 3. 0, or 3. 5 cm (PA 3 subset); and 4. 0 or 5. 0 cm (PA 5 subset).
e. , EH) or to standardized pasture mass at any EH. In fact, the effect of PA on pasture intake may be represented by only one curve when plotting at equivalent PA (i. e. , by correcting PA according to EH). Increasing PA from low to medium, or from medium to high, results in the same increase in pasture intake whatever the EH. As EH in the database is, to some degree, linked to country, this suggests that no specific relationship exists for some parts of the world, and infers high portability of the global equations calculated here. This also suggests that the equations used to recalculate pasture mass and PA at any EH are well suited for swards mainly based on perennial ryegrass and perennial ryegrass-white clover, and that the global relationship is not biased.
e. , from medium to low PA). In our database, grazing time was rarely longer than 600 min/d under strip-or rotational-grazing management  (Bargo et al. , 2002; Kennedy et al. , 2007a) , with most data falling between 450 and 550 min/d. The lack of grazing time extension under severe grazing conditions has to be related to the limited area, which forces cows to graze lower into the sward profile. The cows' motivation is probably reduced due to the grazing of strata richer in pseudostem and dead material and grazing time is limited. The lack of extension of daily grazing time with low leaf availability has already been observed in dairy cows  (Delagarde et al. , 2010)  and sheep  (Penning et al. , 1994)  during the last days of residence time under rotational grazing management, particularly when compared with continuous stocking  (Penning et al.
A global analysis of international data will, however, require the calculation of equivalences between PA estimated at different EH. The equations calculating PA equivalenc-es in  Delagarde et al. (2011)  were developed from the vertical distribution of pure perennial ryegrass (Lolium perenne L. ) or ryegrass-white clover (Trifolium repens L. ) pastures  (Delagarde et al. , 2000b) , which may have biased the estimation of PA effect. However, recalculating PA either at ground level, above 3 cm, or above 5 cm in each subset did not affect the global relationships (Figure  2 ). This confirms the robustness of this approach for temperate pastures (i. e. , those included in our database: mainly grass or grass/clover mixtures). It can be hypothesized that these relationships would be different with others types of pasture, rich in alfalfa or chicory for instance.
The asymptotic parameter values reported in the exponential equations, however, correspond to the average grazing conditions and average animal characteristics registered in our database. For example, maximum pasture intake and milk production were approximately 18 kg of DM/d and 21 kg/d, respectively. Greater values have been already recorded with higher-genetic-merit dairy cows during the early stages of lactation or with high pasture  quality and ingestibility  (Bargo et al. , 2002; McCarthy et al. , 2007). The equations reported in this metaanalysis become universally applicable when maximum asymptotic values are considered as a potential value, related to animal and pasture quality characteristics. This is the case in previously published models where, for example, intake at grazing is usually reported as a proportion of voluntary intake (i.
ACKNOWLEDGMENTS The financial support of both the  Comisión Nacional de Investigación Científica y Tecnológica (CONICYT, Santiago, Chile)  and the  French Embassy (Santiago, Chile)  in the form of the first author's PhD scholarship are gratefully acknowledged. We thank  Luc Delaby  ( UMR1348   INRA-Agrocampus Ouest PEGASE, Saint-Gilles, France ) for statistical assistance during the preparation of the manuscript. InPuzzle ( Rennes, Bretagne, France ) post-edited the English style.
cRNAs were hybridized on Rat AGILENT 4644 K microarrays and, after adequate washing, scanned with Agilent Microarray Scanner. All steps were performed according to the manufacturer (Agilent Technologies, Santa Cruz, CA, USA). All individual samples were hybridized onto separate microarrays. Thus, 12 Agilent oligonucleotide microarrays were used to compare the gene expression profiles of 6-OHDA, HFS, DOPA and DOPA/HFS groups. Microarray data were extracted and quantified with Feature extraction software (Agilent). The AgiND R library was further used for data diagnosis and normalization (http://tagc. univ-mrs. fr/tagc/ index. php/software/16). Diagnosis was based on boxplot, colorcoded images and MA plot, while normalization was performed based on the quantile method. Statistical analysis was performed using the TIGR MeV (MultiExperiment Viewer) v4.
1 software (http://www. tm4. org/mev. html) and the GeneANOVA programs. First, a global ANOVA model gives an estimation of the contribution of HFS and DOPA in the total variation of the whole data set; gene was considered a factor in this model  [24]. Genes showing significant differential expression between the 4 experimental groups were determined by a multi-class analysis using Significance Analysis of Microarrays (SAM) of TMev with 100 permutations. Hierarchical cluster analysis was applied to these genes in order to identify samples with similar profiles. Genes with. 1. 2 or ,0. 8 fold change in the HFS, DOPA and DOPA/HFS groups compared to 6-OHDA were further analyzed using two statistical tests. A two-class unpaired SAM of TMev with 20 permutations was first performed, with adaptive false discovery rate (FDR) procedure  [25].
FDR was ,10% for HFS and DOPA (6 and 1%, respectively) and was 13% for DOPA/HFS due to individual variability in this group. From these genes, those which did not reach significance with a second statistical test, the nonparametric Wilcoxon test (with p,0. 05) were then subtracted. Annotation of the significantly altered genes was performed using DAVID database (http://david. abcc. ncifcrf. gov/) [26]. This program was used to assess whether gene ontology (GO) terms and the Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathways were overrepresented among the differentially expressed genes and within specific gene clusters. A score based on Fisher's exact test reflected the probability that the prevalence of a particular term within a cluster was due to chance alone, given the prevalence of that term in the population of all genes under study.
Global gene analysis of the four groups: 6-OHDA, HFS, DOPA and DOPA/HFS We performed a gene profiling study using 44000 oligonucleotide microarrays to investigate the striatal effect of STN-HFS, of DOPA treatment and of the combined STN-HFS/DOPA treatment. The data obtained have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE43375 (http://www. ncbi. nlm. nih. gov/ geo/query/acc. cgi?acc = GSE43375). The global gene ANOVA analysis demonstrated a very significant effect of the different treatments with p value less than 10 25 (Table  S2 ). Hierarchical cluster analysis (Figure  3 ) was used to test if changes in gene expression levels segregate the experimental groups. It showed two clusters of samples with similar gene profiles: one regrouped the samples from the 6-OHDA animals without treatment (L) and the other gathered together the samples from all the treated animals.
However, severe hypoglycaemia was analysed using a Poisson regression model, as the negative binomial regression model could not be fitted to the sparse data for severe hypoglycaemia. Efficacy variables assessed in the core and extension trial included HbA 1c , central laboratory-measured fasting plasma glucose, 9-point self-monitored blood glucose profile and a questionnaire based on patient-reported outcomes. Treatment differences in the efficacy variables and in body weight were analysed using analysis of variance (ANOVA) with treatment, anti-diabetic therapy at screening, sex and region as fixed factors, and age and baseline value as covariates. In addition to analysing glycaemic efficacy in the full analysis set, the extension trial set was analysed for HbA 1c and post hoc for fasting plasma glucose and self-monitored blood glucose.
Acknowledgements The authors would like to thank all the investigators, trial staff and participants. The authors also thank  Pei-Ling Chu , an employee of  Novo Nordisk Inc. , Princeton,  USA  for statistical analyses,  Abha Chandra , an employee of  Novo Nordisk Inc. , Princeton,  USA , for providing medical writing assistance, and  Watermeadow Medical  (sponsored by  Novo Nordisk ) for preparation of figures and submission assistance.
Cell culture Cells were taken from a patient with Stage III multiform glioblastoma undergoing a brain tumor resection for therapeutic purposes. These cells were selected from one particular patient out of four under study, since they showed the highest sensitivity to chemical and physical agents  [24]. The secondary use of the human specimens was approved by the hospital ethical board, and the patients' data were adequately anonymized. Primary cultures were grown in RPMI medium supplemented with 5% fetal calf serum and used in their early passages. Cells in exponential growth, in triplicate Petri dishes for each time-point under investigation, were exposed to SMFs and/or XRs, as reported below and illustrated in the scheme in Table  1. In order to submit all the samples to the same environmental conditions (i.
Cell capture and analysis As previously indicated we analyzed six Petri dishes for each experimental point (2 × 3 replicas). Randomly captured cells (150 cells) for each Petri dish (obtaining a total of 6 slides for each time-point) were examined at × 400 magnification using a fluorescent Axiolab Zeiss microscope (Carl Zeiss AG, Oberkochen, Germany) equipped with a Coolsnap cooled digital (CCD) camera (Roper Scientific, Princeton, NJ, USA). DNA migration was measured using the freely available CASP comet assay software program (http://www. casp. of. pl/). DNA migration was measured by analyzing the percentage of DNA in Tail (TD) and tail length (TL)  [27].
ACKNOWLEDGEMENTS The authors are indebted to  Prof. Zbygniew Darzinkiewicz  for stimulating discussions and for reviewing the manuscript. The authors thank also  Dr Dorota Halicka  for her collaborative scientific support,  Dr Matteo Manganelli  and  Dr Ing. Floreana Tramonti  for their excellent technical help and  Prof. Wolfgang Goehde  for providing the flow cytometry instrument.
OBJECTIVEdWe sought to determine levels of adherence in eight European countries to recommendations for the management of type 2 diabetes and to investigate factors associated with key intermediate outcomes. RESEARCH DESIGN AND METHODSdGUIDANCE was a cross-sectional study including retrospective data extraction from the medical records of people with type 2 diabetes recruited, using a shared protocol, from primary and specialist care sites in the following eight European countries: Belgium, France, Germany, Italy, Ireland, Sweden, the Netherlands, and the United Kingdom. The dataset for analysis comprised 7,597 cases. Proportions meeting process and outcome criteria were determined, including between-country variations. Logistic regression was used to investigate potential predictors of meeting targets for HbA 1c , blood pressure, and LDL cholesterol.
Observations regarding the quality of care of people with type 2 diabetes in specific geographic areas may be relevant not only to those locations but also more generally in terms of broad lessons and opportunities for comparison. We identified only a small number of previously published studies of this type conducted in more than one European country  (22) (23) (24) (25) (26). Data were collected for the GUIDANCE study from a large sample of patients with type 2 diabetes and their health care providers in the following eight European countries: Belgium, France, Germany, Italy, Ireland, Sweden, the Netherlands, and the United Kingdom. The broad aims of the study were to provide an overview of the quality of care of people with type 2 diabetes in a sample of European countries and to consider between-country similarities and differences.
Data collection The findings presented in this article are based mainly on data collected from participating patients' medical records; collection of survey data are, therefore, described only briefly. A standardized self-completion questionnaire was used to collect data from participating physicians and the survey instrument for patients comprised a study-specific questionnaire combined with two previously validated instruments, the Diabetes Treatment Satisfaction Questionnaire  (27)  and EQ5-D visual scale  (28). Data derived from these questionnaires used for the current article included primary versus specialist care management (from the physician questionnaire) and receipt of diabetes education and home glucose monitoring (from the study-specific patient questionnaire). Presentation of findings derived from the remainder of the questionnaire data are not within the scope of the current article.
6 mmol/L. Potential predictors that were included in the models were as follows: age (continuous variable); gender; BMI (continuous); recruitment from primary versus specialist care; self-reported status as current smoker versus nonsmoker; duration of diabetes (continuous); self-reported receipt of group or individual diabetes education versus neither; prescribing versus nonprescribing of medication relevant to the respective outcome; self-reported (blood or urine) home glucose monitoring versus none (as an indicator of self-management activity); one or more recorded microvascular complications versus none; and one or more recorded macrovascular complications versus none. Microvascular complications identifiable from the data collected were foot sensation abnormality, blindness or retinopathy present, and end-stage renal disease.
2% cases with missing data) or for the main outcome measure undergoing investigation. To account for the hierarchical dependencies in the study data (patients nested within physicians, physicians nested within countries), random effects for physicians and countries were included into the logistic regression models and parameters were estimated by penalized quasi-likelihood methods (SAS, GLIMMIX procedure). Results from these models are given as odds ratios (ORs). Sensitivity analysis was conducted with country included as a fixed, rather than random, effect. All statistical analysis was conducted using SAS version 9. 2 (SAS Institute, Cary, NC). Multiple imputation was performed by the MI and the MIANALYZE procedures with 20 imputed datasets and the default Markov Chain Monte Carlo (MCMC) imputation algorithm.