dataesr/scientific-paragraphs-categorization huggingface dataset:
2b ) and IASI (Fig. 2c ) CO maps provides additional perspective that although both datasets show CO enhancement in the region of the ASM anticyclone, the appearance and detail of the enhancement are quite different. These differences are contributed to by several factors. For example, the missing data in IASI (due to cloud contamination) and the larger grid size in MLS may both contribute to the difference in the spatial pattern of the enhancement between 90-120 • E and 20-30 • N. Similarly, the filamentary structure in IASI CO near 150 • E and 30 • N, although hinted at in the MLS orbital data, is represented differently in the MLS CO map. Additionally, IASI signal-to-noise ratio is likely degraded over the region of elevated terrain, which will be discussed in later examples. The two datasets are also obtained at slightly different sampling times, as the MLS and the IASI cross the Equator at around 01:30 and 09:30 local time, respectively.
Comparisons of MLS and IASI CO Although the focus of this study is to characterize chemical tracers' space-time variability and dynamical consistency, we make quantitative comparisons between the MLS and IASI CO data in this section. The comparisons focus on the consistency between the two datasets in representing CO UT variability in the study domain and their representation of the well-demonstrated large-scale spatial pattern associated with the ASM anticyclone on the seasonal scale. Vertical ranges of the data were chosen to optimize the overlap of information from nadir-and limb-viewing instruments with the vertical extent of the anticyclone. Based on the analyses of the dynamical fields and trajectory calculations, the maximum chemical confinement in the anticyclone in the vertical range is between 200-100 hPa or 12-16 km  (Randel and Park, 2006) , although elevated levels of tropospheric tracers in the ASM anticyclone are evident at up to 68 hPa in the MLS data  (Park et al.
, 2007). Moreover, the strongest closed circulation of the anticyclone occurs at ∼ 14-15 km, above the main convective outflow level (∼ 12 km)  (Park et al. , 2008). Based on this structure and the vertical information content of IASI CO retrieval  (George et al. , 2009) , we choose to use the IASI UT layer CO mixing ratio and the 147 hPa MLS CO retrieval product. The June-July-August (JJA) season of a single year of 2008 is examined. Figure  3  shows a scatter plot of IASI CO in the UT layer versus the MLS 147 hPa CO level 2 product. Each point rep- resents a co-located daily average in a 10 • × 6 • longitudelatitude bin in the study domain for all days in the JJA 2008 period. The scatter plot shows that variations in CO in the two datasets are generally consistent and correlated (r = 0. 8), although the IASI CO shows a smaller range of variability than MLS (indicated by the slope of the linear fit, 0.
55). The smaller variability in IASI CO is likely contributed to by a weaker detection sensitivity in the UT and the use of a single a priori profile in CO retrieval  (George et al. , 2015). Figure  4  shows JJA seasonal averages for (a) MLS 147 hPa CO, (b) the MLS CO average of the 147 and 215 hPa products, and (c) IASI UT layer CO for 2008. Note that different ranges are used in the MLS and IASI color bar to adjust for the smaller range of variability in IASI CO as indicated in Fig. 3. Selected GPH contours and wind vectors at 150 hPa for the same period are shown on all three maps to indicate the seasonal mean location of the anticyclone. The chemical signature of the ASM anticyclone is evident for all three seasonal averages. The clear chemical signature indicates that, despite the relatively weak UT sensitivity, the IASI data are capable of showing the impact of the ASM circulation on UT CO.
This factor likely contributed to the difference between MLS 147 hPa and IASI UT CO databased maps over the plateau (see Figs. 5a  and 8a ). The cross sections in Fig. 9b  and d  are two examples of the CO enhancement over the western Pacific high. In both cases, the enhanced layers are shown in the UT. Similarly, Fig. 9c  shows an example of an enhanced UT CO layer near the southern edge of the Iranian plateau. In all three cases (Fig. 9b-d  over the middle east and the western Pacific are not a result of local vertical transport but are produced by UT redistribution via westward and eastward eddy shedding. Figure  9  not only provides observational evidence supporting the model-based hypothesis on transport structure, it also provides evidence supporting the ability of the IASI retrieval to resolve independent variability in the UT CO.
This figure is similar to the CO scatter plot in Fig. 3 , but the correlation between the OMI and MLS O 3 is much better with both the slope (0. 94) and the correlation coefficient (0. 96) near unity. Figures 11 and 12 characterize the good overall agreement between OMI and MLS O 3 on seasonal and ASM regional scales. We now proceed to examine the daily and subseasonal variability represented by the two datasets. Here the 105 hPa tropopause contour appears to correlate well with the O 3 and wind field gradients. Note that the tropopause pressure here is from the GFS final analysis product, which is based on the World Meteorological Organization thermal tropopause definition. Since this quantity is derived from the vertical gradient and is not analyzed on the pressure surface, its intersection with the pressure surface can appear noisy.
All three Hovmöller diagrams exhibit westward propagation in 10-20-day timescales. The correlation in the variability along the longitudinal dimension is 0. 90 between the tropopause pressure and MLS O 3 and 0. 76 between the tropopause pressure and OMI O 3. In both cases, the interpolated fields are used to calculate the correlations. The strong correlation between the tropopause structure and O 3 supports the conceptual model that the higher tropopause over the ASM forms a region of tropospheric bubble above the mean level of tropical tropopause for the season. This structure enables a unique transport pathway for air masses in the bubble to enter the LS via horizontal eddy shedding, bypassing the equatorial tropical tropopause (e. g. ,  Garny and Randel, 2016; Ploeger et al. , 2017). While the two O 3 datasets provide generally consistent large-scale ozone structure, there are visible differences between MLS and OMI in small-scale structures.
Conclusions and discussions We have examined space-time variability in chemical tracers in the UTLS associated with the ASM represented by nadirviewing (IASI and OMI) satellite instruments in comparison with a widely used limb-viewing (MLS) dataset. Using CO (a boundary layer pollution tracer) and O 3 (a stratospheric tracer), we focus on the strengths and limitations of these data for representing the distribution of and variability in  horizontal winds (gray arrows), and 105 hPa tropopause pressure contour (pink) are superimposed. MLS maps are interpolated using the natural neighbor method on 5 • × 5 • longitude-latitude grids while OMI maps are interpolated on 1 • × 1 • longitude-latitude grids. The tropopause pressure is from the GFS product. A Gaussian smoothing is applied to all maps.
11 and 12). The dynamical consistency of OMI O 3 mixing ratios in the layer 18 product (centered near 100 hPa) on seasonal and sub-seasonal timescales demonstrates the sufficient information for the nadir-viewing datasets to contribute to the ASM dynamically driven UTLS O 3 variability. Both MLS and OMI O 3 variability in the region exhibit good correlations with the tropopause pressure, supporting the conceptual model that the ASM creates a tropospheric bubble above the season's average tropopause in the tropics  (Pan et al. , 2016). The CO maps from different layers (Fig. 7 ) and selected cross sections (Fig. 9 ) both provide strong evidence that IASI has sufficient information content to discriminate UT CO variability from that in the lower troposphere to MT. This result is consistent with and complementary to the model estimates of retrieval information content, which show that the DOFS for the interested region is approximately 2 (Fig.
Acknowledgements. This work is part of  Jiali Luo 's PhD research, funded by the  National Science Foundation of China  ( 41705021 ,  41630421 , and  41575038 ). The work was in part conducted at the  National Center for Atmospheric Research , operated by the  University Corporation for Atmospheric Research  under sponsorship of the  United States National Science Foundation. The IASI mission is a joint mission of  EUMETSAT  and the  Centre National d'Etudes Spatiales (CNES, France). We thank the  ULB team (Daniel Hurtmans, Pierre Coheur)  for the development of the FORLI-CO retrieval algorithm and  Mijeong Park  for helpful discussions. We also thank the three anonymous reviewers for their helpful comments and suggestions. Edited by: Federico Fierli Reviewed by: three anonymous referees.
Acknowledgments: This research was conducted as part of the  EcoDesign Network ,  a Cross-Border Network  project aimed at the engineering of eco-compatible products, part of the intra-regional Alcotra Program 2007-2013. The authors would like to thank the  Mattioli company , the case study for this research, for the challenge set regarding eco-jewelry and for testing new materials and finishes on its Yin Yang ring. The authors also thank  Eleonora Buiatti  for setting up the analysis on the perceptions of jewelry and for suggestions that greatly helped to improve the research. Many thanks are also due to all the participants who took part in the test. Lastly, the authors thank the reviewer for insights.
Methodology The PHARMINE questionnaire was based on that used in a first survey of pharmacy departments carried out by the EAFP in the 1990s  [13]. The project ran from spring 2009 through summer 2011. All country profiles were updated in the spring of 2017 for publication. The survey form was sent out to at least one department in each country of the European Higher Education Area. Data for the survey were collected by electronic means  [14]. This was backed up by telephone calls and/or by on-site visits to help and encourage respondents to fill out the form.
Conclusions Country profiles for European PET will be published in 'pharmacy'. This article serves as an introduction to these surveys and as a vade mecum for the reader of such articles. The information contained in the country profile articles is of a descriptive nature; it will serve as a tool for those wishing to do educational research on European PET and/or promote student and staff mobility. Data for individual countries are compared with European averages already published  [15]. For such comparisons we used a European linear regression estimation. Data were taken from the 25 EU members of the EHEA that had at least one pharmacy department. The calculation was as follows: estimations of numbers of pharmacies, etc. (X, dependent variable) were calculated from the linear regression equation with the country population as the independent (Y) variable.
The fit-for purpose approach (an umbrella term used to describe distinct stages of the validation process) for biomarker development and validation addresses the proper assay tailored to meet the intended purpose of the biomarker. The Society for Immunotherapy of Cancer (SITC) Immune Biomarkers Task Force convened to address this need in this two-volume series; preanalytical and analytical (Volume I) as well as clinical and regulatory (Volume II) aspects of the validation process as applied to predictive biomarkers for cancer immunotherapy  [1, 2]. Clinical study design in which biomarker analysis is one of the primary objectives/endpoints needs to be promoted. A good example of such a study was CA184-004 (NCT00261365), a phase II trial to determine predictive markers of response to ipilimumab (MDX-010).
In this study, the primary endpoint was to identify candidate markers predictive of response and/or serious toxicity to ipilimumab. Tissue and blood samples were collected at different time points from enrolled patients and the subsequent biomarker analyses generated interesting data. The findings of this study could have been considered as the first evidence for biomarker association with outcome, and could potentially have been confirmed and prospectively validated in subsequent studies. However, study results can often not sufficiently meet expectations or may be contradictory, which may be in part due to the constraints of underpowered cohort size, variables in the time and type of sample collection, and differences in procedures, data generation and tools used at different sites. The opportunity to design parallel biomarker studies that are integrated as key components of important randomized clinical trials could offer a solution.
Inclusion of patients with diverse treatment history (previous antitumoral treatment), histological and radiographic conditions. 4. Limited and/or suboptimal correlative study design due to funding and/or regulatory constraints. 5. Heterogeneity in the types of biological samples with different time points of collection, storage conditions, platforms used for data generation, lab-driven SOPs and data analysis algorithms and tools. 6. Heterogeneity in the clinical data collected and the length of follow-up, 7. Lack of international joint initiatives. 8. Potential intellectual property. Previous programs that utilized treatment stratification biomarkers had higher success rates at each phase of development versus the overall dataset  [5]. Moreover, the importance of determining biomarkers for both patient selection for treatment and selection of treatment for the patient is of utmost importance.
A survey to establish the need and willingness of different institutions to participate with time, reagents, and sample sets. This would involve development of a subcommittee to explore issues and strategies for sharing licensing and royalty income for IPs developed from the consortium with centers who participate in providing tissue samples and scientific expertise. 2. A database and registry of ongoing international clinical trials; clinical information including outcome; survey of sample type and availability; clinical and experimental data have already been completed  [4]. This exercise will help identify gaps, unify standards and understand the willingness of participants. 3. A committee to propose overall direction and identify projects to pursue, based on data-based information and sample availability.
Other responsibilities of this committee would include: a. Announcing survey results and providing database access to all participants. b. Requesting proposals from participants and the broader community. c. Seeking sponsorship from venture capital, pharma, society, and private funding (with the possibility to have exclusive rights to develop companion diagnostics). d. Review proposals and grant-selected projects with sample access for phase I discovery up to phase III validation studies. The consortium will develop policies and procedures to ensure that samples will be used in the most effective, ethical way and to maximize data generation and knowledge generation using state-of-the-art methodology germline and somatic genetic analysis (SNP, NGS, CNV, TCR). i. Epigenetics (transcription, methylation, miRNA, splicing variation, fusion transcripts, transcription regulation).
ii. Proteomics (serum/plasma protein profiling, cytokine assays, antibody screen, exosome analysis, iii. Metabolomic profiling. iv. Microbiome/virome analysis. v. Flow cytometry, Single cell network analysis, sorted cells, functional and phenotypic profiling. vi. Evaluation of the tumor and relationship of immune cells with image analysis and machine learning (Multiplex IHC, Immunoscore, CyTOF). vii. Pharmacogenomics. viii. ImmunoPET. e. Monitor project progression and outcome. f. Present updates to the consortium at international meetings. g. Publish results of discovery as a consortium in an open access journal and make databases available in format similar to TCGA. h. Coordinate biomarker validation.
Objectives Objectives of this consortium are outlined below. • The creation of a database of available samples, with linked key information would be a top priority. This effort could start with the trial titles/therapeutics tested and basic trial design, number of patients, timepoints and the nature of biospecimens obtained, for a first review. • Every participant would keep the samples on site but provide the information on this centralized database. An app that facilitates the collection of the data preserving the deidentified nature of the information is essential. This could also be designed to allow a relevant search engine to find samples based on specific characteristics. • A committee would evaluate proposals after samples have been identified that may be suitable for a specific project.
2  Reconstruction from Fig. 1  participate in correlative biomarker science in clinical trials and also post-trial analyses in a systematic manner. This consortium could be very closely connected with clinical trials in immunotherapy including existing networks (e. g. the NCI supported Cancer Immunotherapy Trial Network, CITN) and all other ongoing trials in the US and Europe. Most trials will likely be small cohort studies. The consortium could combine several types of trials and analyze them across studies in an interdisciplinary approach. It is important to keep in mind possible bottlenecks. For instance, the US National Clinical Trials Network (NCTN, formerly the cooperative groups) has been attempting to develop a shared database across multiple specimen banks for some time and has found it to be a very complex task.
Different institutes track different sample parameters and sharing any patient identifiers will be a concern with regard to privacy regulations. This is an issue that might be solved by applying similar parameters to categorize samples collected at each institution. Another example is the database for the pediatric brain cancer consortium, run at CHOP (Children's Hospital of Philadelphia) using RedCap software with restricted consortium-only access  [10]. While this is feasible, it requires expertise and personnel with dedicated effort. In order to maintain solid communication and upto-date information flow, the consortium could have monthly discussions and generate white papers summarizing promising data and outlining validation that could become high priority research initiatives to be taken on by the consortium or by other larger clinical trial groups with the capacity for such larger studies.
Acetone and carbon monoxide (CO) are two important trace gases controlling the oxidation capacity of the troposphere; enhancement ratios (EnRs) are useful in assessing their sources and fate between emission and sampling, especially in pollution plumes. In this study, we focus on in situ data from the upper troposphere recorded by the passenger-aircraft-based IAGOS-CARIBIC (In-service Aircraft for a Global Observing System-Civil Aircraft for the Regular Investigation of the atmosphere Based on an Instrument Container) observatory over the periods 2006-2008  and 2012-2015. This dataset is used to investigate the seasonal and spatial variation of acetone-CO EnRs. Furthermore, we utilize a box model accounting for dilution, chemical degradation and secondary production of acetone from precursors.
In former studies, increasing acetone-CO EnRs in a plume were associated with secondary production of acetone. Results of our box model question this common presumption and show increases of acetone-CO EnR over time without taking secondary production of acetone into account. The temporal evolution of EnRs in the upper troposphere, especially in summer, is not negligible and impedes the interpretation of EnRs as a means for partitioning of acetone and CO sources in the boundary layer. In order to ensure that CARIBIC EnRs represent signatures of source regions with only small influences by dilution and chemistry, we limit our analysis to temporal and spatial coherent events of high-CO enhancement. We mainly focus on North America and Southeast Asia because of their different mix of pollutant sources and the good data coverage.
To tackle this problem, efforts have been made to retrieve acetone from ACE-FTS  (Coheur et al. , 2007; Harrison et al. , 2011; Tereszchuk et al. , 2013; Dufour et al. , 2016)  and MIPAS  (Moore et al. , 2012)  satellite data, but the signature of acetone is hard to detect  (Stiller et al. , 2004; Waterfall et al. , 2004)  and the vertical resolution of the respective instruments is limited to 2-3 km  (Moore et al. , 2012; Dufour et al. , 2016). Therefore, limited acetone data have been provided this way. Given the poor understanding of the oceans as an acetone reservoir  (Marandino et al. , 2005; Fischer et al. , 2012; Dixon et al. , 2013)  and the strong temporal and spatial variability of other sources, constraining the global acetone source clearly requires more extended datasets. Current global source estimates range from 42.
5 Tg a -1  (Arnold et al. , 2005)  to 127 Tg a -1  (Elias et al. , 2011). In this study, we adopt the approach of  Zahn et al. (2002) , who identified correlations between carbon monoxide (CO) and ozone (O 3 ) from small to regional scales in the CARIBIC (Civil Aircraft for the Regular Investigation of the atmosphere Based on an Instrument Container) dataset, and investigate the relationship of acetone and CO using 7 years of IAGOS (In-service Aircraft for a Global Observing System)-CARIBIC measurements covering large parts of the Northern Hemisphere. The paper is organized as follows. In Sect. 2, we introduce the concept of enhancement ratios (EnRs). The IAGOS-CARIBIC project and the applied measurement techniques are described in the Sect. 3. 1 to 3. 3. In Sect. 3. 4, we explain how to analyse the data.
IAGOS-CARIBIC project In the CARIBIC project, regular atmospheric measurements are conducted on board a commercial passenger aircraft  (Brenninkmeijer et al. , 2007). The present aircraft is a Lufthansa Airbus A340-600 equipped with a multi-line air inlet system (installed below the forward cargo bay) to supply the instruments with sample air. Currently, 15 instruments for in situ and 1 for remote sensing measurements of trace gases and aerosols as well as sample collecting systems for trace gas and aerosol are installed in a modified airfreight container (1. 6 t). Since May 2005, the CARIBIC laboratory has been monthly deployed during regular service for 4-6 consecutive long-range flights. Detailed meteorological analysis for the CARIBIC flights (including backward and forward trajectories) is based on ECMWF (European Centre for Medium-Range Weather Forecasts) model data and provided by van Velthoven (2016).
(2004)  and verified by  Thouret et al. (2006). Air masses with an ozone concentration above the threshold value of [O 3 ] TP ppb = 97 + 26 sin 2π doy -30 365 , (4) where doy denotes day of the year, are identified as stratospheric and excluded. In the rare event of ozone data being unavailable, we use potential vorticity (PV) calculated from the ECMWF model and discard measurements with a PV > 2 pvu, a threshold commonly used to define the dynamical tropopause (e. g. Hoskins et al. , 1985; Holton et al. , 1995). In this way, 42 % of the acetone-CO data were identified as stratospheric. In the remaining dataset, we search for physically meaningful correlations in all possible subsets of data fulfilling the following two requirements adapted from  Zahn et al. (2002)  and  Brito, 2012: (i)  The subset consists of at least 10 successive measurements that are no further apart from each other than 50 km and cover less than 500 km flight path; (ii) The range of CO VMRs in the subset is greater than 10 times the average measurement uncertainty of CO.