dataesr/scientific-paragraphs-categorization huggingface dataset:
Resulting the HRQOL index was compared to reference values. An adjustment was made for missing data on the quality of life index for patients who did not answer the EQ5D questionnaire and for those who died before one year from the hospital release. For one year survivors who did not answer the questionnaire it was assumed that they had the mean value of the HRQOL index of the age and sex matched respondents. For one year non-survivors it was assumed they had the 75% of the mean HRQOL of respondents. This approach was previously taken in cost effectiveness analysis by Linko et al  [35]. Life time QALY was calculated as the product of life years saved and HQOL utility value.
Statistical analysis Discrete data are reported as frequencies and continuous data as means (confidence interval-CI) or medians [interquartile ranges-Interquartile range (IQR)], as appropriate. All hypotheses in the study were two-sided, with the level of significance 0. 05. Discrete variables were compared using the χ 2 test. Normally distributed continuous variables were compared using the Student's t test, and non-normally distributed variables were compared using the Mann Whitney test. Multiple groups were compared using the Kruskal Wallis test. Comparison of one year survivors EQ-5D index values to the reference values of the age and sex matched population of Slovenia was done using the Wilcoxon signed matched pair test. Statistical analysis was done using SPSS 19 and MS Excel 2010 programs.
Further, a higher incremental effectiveness of the ICU treatment of the critically ill at the Sarajevo University Clinical Center can be expected, simply because most interventions that are needed for vital function support are not available on general wards in our hospital. Data on patient survival in and out of ICU come from observational studies on treatment outcomes of ward care for the critically ill, when those patients were refused an ICU admission with careful admission triage due to the lack of beds. Ethical issues preclude conduction of randomized control trials on this subject. Therefore, the actual effectiveness of the ICU care can only be roughly estimated. Similarly, ethical and administrative reasons precluded data collection on a comparator patient cohort in our institution.
Defining genetic markers for linkage mapping In total 259,580,561 raw 100 bp reads were generated by sequencing (120,336,323 and 139,244,238 reads in the first and the second library, respectively). Overall read quality was inspected using FastQC (Babraham Bioinformatics, The Babraham Institute) confirming that per-base quality score exceeded 30 (with the exception of the last ten bases of parental library). A custom script (available upon request) coded in R  [23]  was used to sort raw reads according to unique barcodes into individual samples. Reads containing ambiguous bases and reads that did not feature valid barcode or restriction-site sequence were discarded from further analysis (23 % of the total raw reads). Moreover, the last ten bases were trimmed from the remaining reads due to a decrease in base-calling quality.
In both cases, individual SAM files were imported in Stacks and analysed with the ref_map. pl pipeline. Parental lines were used to construct a "catalog" of loci (3 mismatches allowed between reads mapping to the same locus, option -n in ref_map. pl). The minimum coverage depth (option -m) was set to 25 (parental library) and 15 (lower-coverage library) to call a stack (group of identical reads). Stacks uses error-bounded model for SNP identification however, since the prior information on sequencing error rate was not available, a lower and upper bound for the error rate were not specified (default between 0 and 1). Default chi-square significance level (0. 05) required to call a heterozyogote or homozygote was used. Custom MySQL scripts were used for merging the results from both libraries.
Deleveraged loci (see  [24] ), loci with more than three SNPs and loci with more than 2 alleles were excluded from the analysis. In addition, we were only interested in loci that were homozygous for alternative alleles (aa, bb) in the parental lines and heterozygous (ab) in the F1 hybrids. In total, haplotype and genotype data for 7183 putative markers were retrieved from the Stacks analysis. We inspected the distribution of missing values (per F2 line and per marker) among the obtained genotypes, since they are potential source of errors during linkage map construction. This resulted in the removal of six F2 lines from further analysis because they had more than 30 % of missing genotypes (in comparison, the remaining 52 F2 lines had on average less than 14 % of missing values). Furthermore, we removed markers exhibiting more than 20 % missing data across the F2 lines, as suggested by Catchen et al.
Linkage analysis JoinMap 4. 0  [26, 27]  was used as the main software for genetic map construction. However, several additional steps were taken (Additional file 1: Figure  S1 ) to maximize the number of markers that could be mapped and to avoid the reduction in mapping accuracy that is expected in large datasets (>1000 markers;  [28] ). First we selected a subset of 253 "anchor" markers (one or two markers per large scaffold that were successfully genotyped in more than 90 % of F2 lines) representing the 211 largest scaffolds of the D. magna draft genome assembly (v2. 4). Using the regression mapping algorithm with default parameters in JoinMap, these markers were grouped into 10 preliminary linkage groups (LGs) at LOD = 3. Assuming no assembly error at this point of the analysis, all other markers on the same scaffolds were attributed to the same preliminary LG as the respective anchor marker.
We then continued to expand the preliminary LGs by performing contingency table analyses of segregation patterns. More precisely, we compared terminal markers of scaffolds that were attributed to one of the preliminary LGs, against the dataset of so-far un-mapped markers. Only markers with very similar segregation patterns (< 3 different genotypes among the 52 F2 lines) were assigned to the same preliminary LG (Pearson's χ2 test, cut-off threshold P < 0. 0001), whereas markers showing ambiguous association to two or more LGs were discarded at this point of the analysis. Markers with the extreme segregation ratio distortion (SRD) that contradicted surrounding markers within the same scaffold were removed. Following this procedure, 4045 markers were assigned to one of the ten preliminary LGs, 75 markers were removed while 729 markers remained unattributed.
Many markers included in preliminary map showed identical segregation patterns across all F2 individuals (i. e. they did not show any evidence of CO recombination). In total, 756 segregation patterns could be distinguished within our preliminary dataset and the groups of co-segregating markers are hereafter referred to as "bins" (1 to 384 markers per bin). One of the markers exhibiting the lowest number of missing genotypes (i. e. , successfully genotyped in the largest number of F2 lines) from each bin was denoted as "frame marker" (unique segregation pattern within the framework map) and was used for creating a framework map, a non-redundant representation of all detected segregation patterns suitable for further analysis with JoinMap. The grouping of frame markers into  10  LGs was confirmed at LOD = 7 (maximum likelihood, ML, option and otherwise default parameter values of the program).
We then continued by iteratively adding sets of the remaining, unattributed markers to the preliminary map using same settings in JoinMap. After each round, newly grouped markers were inspected and designated as frame or non-frame markers, depending on whether their genotypes matched one of the previously defined bins. Non-frame markers were continuously omitted from the framework map and kept separately for later construction of a composite map. After several iterations, we managed to include a total of 4761 markers in the composite map while 13 markers did not map to any of the ten LGs and consequently, were omitted from the final dataset. Once all markers were included, the composite map was inspected visually, and the ordering of the markers within the LGs was corrected, based on the available information of physical position within the scaffolds (mostly applying to markers within the same bin, the position of which could not be determined based on segregation patterns).
The final composite map comprised 4037 markers, out of which 952 were defined as frame markers (952 bins with 1 to 354 markers). Grouping and ordering of markers within the framework map was confirmed using the ML algorithm at the LOD = 7 (JoinMap, default settings). Afterwards, each LG was analysed individually in JoinMap, with markers in fixed order and genetic distances were calculated using the Kosambi mapping function (Additional file 1). Furthermore, the mapping quality of the framework map was validated through an independent approach using the CheckMatrix program (http://www. atgc. org/XLinkage/).
Acknowledgements We would like to express our gratitude to  Walter Salzburger  for kindly sharing laboratory resources and infrastructure. We thank  Peter D. Fields  and  Roberto Arbore  for reading the previous versions of the manuscript and fruitful discussions. Jürgen Hottinger ,  Urs Stiefel  and  Nicolas Boileau  facilitated laboratory work while  Lukas Zimmerman  and sciCORE team ( University of Basel ) provided technical support for the data analysis. Illumina sequencing of RAD libraries was done by  Christian Beisel  and  Ina Niessen  at the  Quantitative Genomics Facility service platform, Deep Sequencing Unit Department of Biosystems Science and Engineering, ETH-Zurich.
11  The Curriculum 2010 covered a broad range of recommendations to be adopted by national educational and health authorities and to be implemented according to the resources and conditions of their countries. Furthermore, it was perceived that the diversity of health and educational systems around the world may have rendered some curriculum recommendations aspirational at the stage of its implementation, even for those systems with well-developed training programmes in medical oncology. Reflecting this aspirational nature of the recommendations, the former GC Task Force had changed the updated Curriculum title from 'Global Core Curriculum' to 'Global Curriculum'. An analysis of the ESMO GC European Landscape data still identified a high degree of heterogeneity, mainly at the organisational level as well as in the duration and structure of the internal medicine part of the training in medical oncology in Europe.
Data analysis and public databases: awareness, that is, in order to validate the hypothesis of translational studies, analysis of the data should be performed; awareness of the analysis options available as well of the databases that could be used to enrich translational research Knowledge • Cancer biology: it is fundamental in translational oncology to understand and integrate the wealth of alreadyexisting molecular oncology information, which in part was generated by translational oncology studies. The integration is challenging even for experts; hence integrative publications-reviews-which provide a critical overviews of the state of the art of the field are highly recommended. A good start is the Hallmarks of Cancer by Hanahan and Weinberg. This work not only conceptualises and integrates the wealth of cancer studies of the last 50 years but also provides a framework by which cancer can be viewed in all of its complexities.
• Knowledge on the basic physiological and pathophysiological mechanisms of normal and diseased tissues Data analysis and public databases: awareness, that is, in order to validate the hypothesis of translational studies, analysis of the data should be performed; awareness of the analysis options available as well of the databases that could be used to enrich translational research Knowledge • Cancer biology: it is fundamental in translational oncology to understand and integrate the wealth of alreadyexisting molecular oncology information, which in part was generated by translational oncology studies. The integration is challenging even for experts; hence integrative publications-reviews-which provide a critical overviews of the state of the art of the field are highly recommended. A good start is the Hallmarks of Cancer by Hanahan and Weinberg.
2. 4 Translational research Krisztian Homicsko Objectives • Cancer biology: to be able to conceptualise the most common alterations leading to cancer development • Molecular assays: to be able to describe the techniques, their potential uses and limitations • Biological sample collection: to understand the processes of sample collection and storage • Biomarkers: to be able to define the uses of biomarkers in clinical trials • Data analysis and public databases: to understand the importance of statistical planning and analyses of translational data; basic knowledge of access to databases for correlative studies • Transitioning results of translational research to clinical practice: to understand how translational oncology information can lead to pertinent clinical studies Awareness • Cancer biology: awareness of the tumour heterogeneity within a single patient as well as the population heterogeneity of the same type of cancer • Molecular assays: recognition that translational research is mainly based on the application of molecular biology techniques to proteins, RNA, DNA as well as metabolites; awareness of the main methods as a prerequisite in orderto understand and interpret results (see for details under chapter 4.
Data The measurements were taken at Col de Porte, which is located at 1325 m altitude in the Chartreuse mountain range, France. An exhaustive description of the study site and the long-term measurement instruments is provided in  Morin et al. (2012). The long-term measurements used in this study are 2-m air temperature, surface temperature, direct and diffuse broadband incident shortwave radiations, snow depth and snow water equivalent, snow and rain fall rates.
Albedo variations analysis method In order to relate variations of spectral albedo to variations of surface snow properties, we apply the following methodology to the measured albedo. Step 1 : Estimate the scaling factor A A seasonal value of A is estimated using Eq. 8 with r diff = 1 (selected fully cloudy days from visual inspection of the camera photographies and θ s smaller than 65 • ) and a nonlinear least squares method (provided by Python scipy. optimize. leastsq function). To avoid undetermination problem between A and c imp in case of moderate to high amount of impurities in snow, only the spectra from the beginning of season were used for this estimation. Quantiles 25 and 75 of A distribution are used to propagate uncertainties on the near surface and surface properties predicted from the spectra.
, 2006) , we apply the following method to distinguish wet from dry snow. The spectra are first filtered using a 20 nm moving window average in order to reduce noise before minimum calculation. We then compute the wavelength of minimum albedo in the 1000-1050 nm range and apply the following criteria to detect liquid water presence : argmin λ∈[1000,1050] nm (α(λ)) < λ water (9) λ water threshold has been set after studying the distribution of argmin(α) on the whole spectra dataset. only slightly lower than the ideal value of one. The spread of A values is small (quantile 25, q 25 : 0. 920 and quantile 75, q 75 : 0. 964). This spread could probably be attributed to residual measurement artifacts such as deposition of precipitation particles on the measurement head, presence of small direct incoming radiation, and reproductibility errors due to the optical switch  (Picard et al.
However, optimal near-surface SSA predicted from the spectra exhibits strong seasonal and diurnal cycles that can be related to snow and meteorological conditions. Near surface optimal effective impurity content also exhibits strong variations along the season that can be related to surface enrichment process due to melt and Saharan dust deposition events that frequently occur in the French Alps (Di  Mauro et al. , 2015). Such time series of spectral albedo are required to understand and quantify the evolution of snow albedo in relationship with snow surface variables such as SSA and surface impurity content. They are also a unique opportunity to better understand the evolution of near-surface SSA and effective impurity content during the snow season. They provide unique dataset to evaluate and refine detailed snowpack model as Crocus  (Vionnet et al.
Data availability Processed spectral albedo time series will be available through Pangaea database. Let ε 2 = 1 -V , when ε is small (e. g. θn is small), Eq. B5 leads to the following expression for αmeas(λ, θs): αmeas(λ, θs) A = (1r diff (λ, θs))α dir (λ, θs) + r diff (λ, θs)α diff (λ) +2ε tan θs cos φ(1r diff (λ, θs))α dir (λ, θs) +ε 2 (1r diff (λ, θs))(α diff (λ) -3)α dir (λ, θs) + r diff (λ, θs)(1α diff (λ)) 2 +o(ε 2 ) = K(1r diff (λ, θs))α dir (λ, θs) + r diff (λ, θs)α diff (λ) + ε 2 P + o(ε 2 ) (B6) where P = (1α diff (λ))(r diff (λ, θs)(1α diff (λ)) -(1r diff (λ, θs))α dir (λ, θs)) Figure  12  shows that ε 2 P can be neglected for slope angles lower than 10 • which is the case in the study (see Fig. 4 ). 16 The Cryosphere Discuss. , doi  :10. 5194/tc-2016-266, 2016  Manuscript under review for journal The Cryosphere Published: 18 November 2016 c Author(s) 2016.
Author contributions. M. Dumont coordinated the study and developed the retrieval algorithm. G. Picard and L. Arnaud developed and built the automatic albedometer. G. Picard, L. Arnaud, S. Morin and D. Voisin deployed it at Col de Porte. D. Voisin performed the impurity content measurements. P. Nabat provided the ALADIN-Climate simulations and Y. Lejeune provided the snow and meteorological measurements at Col de Porte. M. Dumont prepared the manuscript with contributions from the other authors. Acknowledgements. CNRM/CEN and LGGE are part of Labex OSUG@2020 (investissement d'avenir -ANR10 LABX56). This study was supported by the ANR program 1-JS56-005-01 MONISNOW and the LEFE programs BON and ASSURANCE. The authors are grateful to the Col de Porte staff for ensuring a proper working of all the instruments, to L.
Western blot, RNA isolations, qRT-PCR and stainings Protein and RNA isolations, reverse transcription, western blot and qRT-PCR analyses were performed as previously described  (20, 29). The list of antibodies used in this study is provided in Supplementary Table  S1. The primers are listed in Supplementary Table  S2. Alkaline phosphatase staining was performed following the manufacturer's instructions with the use of Leukocyte Alkaline Phosphatase Kit (Sigma). Pictures of stained wells were taken using an AxioScope. A1 microscope equipped with an AxioCam MRc under A-Plan 2. 5× magnification. For alizarin red staining cells were fixed with neutral formalin for 24 h and stained with alizarin red as previously described  (33). The stained 12-well plates were scanned and quantification of the stained areas was evaluated with the ImageJ software using the Threshold Color Plugin.
The sequencing Base-Caller bcl output files were further demultiplexed to fastq files using CASAVA 1. 8. 2. Raw and processed ChIP-Seq data generated by the Encyclopedia of DNA Elements (ENCODE) Consortium  (34)  from primary normal human osteoblast cells (NHOST) for H3K4me1, H3K4me3, H3K27ac, H3K27me3, DNAse-Hypersensitivity as well as published RUNX2 ChIP-Seq data  (35)  were downloaded from Gene Expression Omnibus and the European Nucleotide Archive, respectively. The accession numbers for each dataset are listed in Supplementary Table  S3.
RNA-sequencing bioinformatic analysis For RNA-Seq in hFOBs, seven conditions were used: undifferentiated hFOBs treated with DMSO and nontargeting siRNA (siCNTR); differentiated hFOBs treated with DMSO or JQ1; differentiated hFOBs transfected with a non-targeting siRNA (siCNTR) or two independent siRNAs (#3 and #4) against BRD4. The FASTQ files were mapped to the human genome (assembly hg19) using TopHat Gapped-read mapper  (36)  for RNA-seq data with very sensitive Bowtie2 settings on Galaxy Platform (Version 0. 9)  (37). The read counting was performed via HT-Seq (38) (version 0. 6. 0) with the following parameters: -f bam -r pos -s reverse -a 10 -t exon -m union. The count files were subsequently subjected for differential analysis using the DESeq2 Package (39) on R (Bioconductor version 3.
2 and FDR > 0. 8. For JQ1-regulated genes (DIF JQ1 versus DIF DMSO) the thresholds were set to 'average baseMean' >20, abs(log 2 fold change) ≥ 0. 8, FDR < 0. 05 and abs(log 2 fold change) ≤ 0. 2 and FDR > 0. 85 in case of unregulated genes. For siBRD4 regulated genes (DIF siBRD4 #3 + #4 versus DIF siCNTR), following thresholds were used: 'average baseMean' > 20, abs(log 2 fold change) ≥ 0. 7, FDR < 0. 05 and in case of siBRD4 unregulated genes the same thresholds as for JQ1 unregulated genes were used. The gene ontology (GO) analysis was performed using the 'goseq'  (40)  Bioconductor package version 3. 2. 2. The significantly enriched GO categories were identified based on the FDR values < 0. 05 using the Benjamini and Hochberg test. The resulting list of GO categories were further synthesized using REVIGO (41) which clusters the categories based on semantics.
For the gene set enrichment analysis (GSEA) (42) the variance stabilization transformed read counts obtained through DESeq2 analysis were used. GSEA was performed with default settings (1000 permutations of gene sets, Sig-nal2Noise ranking metric). For enrichment analysis GO sets (C5. all) were used. The RNA-Seq heatmaps were performed with heatmap. 2 from gplots R package  (43).
1. 0. 20140616. 0) using default settings with qcutoff < 0. 05. For BRD4, H3K27ac, H3K27me3, H2Bub1 and RNAPII -broad option was used, whereas for TF ChIP the narrow peak option was utilized. For each condition the corresponding input samples were used as control. To determine osteoblast-specific BRD4 binding regions differential binding analysis of all the BRD4 binding sites and regions among four different cell lines (differentiated hFOB, MCF7, MCF10A and L3. 6) was performed by using 'DiffBind' (49) R package version 3. 2. 2. To determine the regions bound by BRD4 that are specific to differentiated hFOB the DiffBind thresholds were set to: Fold Concentration Change (hFOB versus the rest of the cell lines) > 1 and FDR < 0. 05. These specific regions were further subdivided via Epigenome Count-based Segmentation (EpiCSeg) (50) software.
Mean ± SD, n = 2. One-way ANOVA followed by Turkey's test was performed for statistical analysis where ***P < 0. 001, **P < 0. 01, *P < 0. 05. (F). The relative expression of mouse osteocyte marker genes was evaluated via qRT-PCR and normalized to Gapdh and expression in the undifferentiated state. Mean ± SD, n = 2. One-way ANOVA followed by Turkey's test was performed for statistical analysis where ***P < 0. 001, **P < 0. 01, *P < 0. 05. (G). Heatmap depicting log 2 fold change values of RNA-Seq data from hFOBs. The genes for the heatmap were selected based on JQ1 regulation, grouped into JQ1 up-and downregulated genes and sorted according to their regulation during differentiation. (H and I). Venn diagrams showing overlap of significantly (p. adj<0. 05) up-(H) and downregulated (I) genes following JQ1 or siBRD4 treatment and regulation during differentiation (refer to the 'Experimental Procedures' section for description of thresholds used) (J).