dataesr/scientific-paragraphs-categorization huggingface dataset:
Available data from the literature The room temperature structure of butamben was recognized to be monoclinic by Watanabe in 2002 and solved in 2004 by Caira et al. 6, 7  The latter found a monoclinic P21/c structure with parameters a = 8. 3508(5) Å, b = 5. 2093(4) Å, c = 25. 626(2) Å, β = 95. 372(2)°, and a density of 1. 156 g⋅cm -3 at 298(2) K. 7  The CIF of the structure is available in the Cambridge Structural Database (CSD) under the reference EZAVIK. According to Schmidt, 1 the commercial powder of this monoclinic butamben (form I) melts at 330. 5 K (onset) with a melting enthalpy of 23. 9 kJ⋅mol -1 (123. 68 J⋅g  -1  ). In addition, this author reported that on cooling the commercial sample at a rate of 5 K⋅min -1 , a phase transition to another form, thus named form II, was observed.
Densitometry of the melt as a function of temperature Liquid density as a function of temperature was measured with a DMA-5000 Density Meter from Anton-Paar. A melted specimen was introduced in the apparatus equilibrated at a temperature above the temperature of fusion. Data were obtained at isothermal steps while slowly cooling in the temperature range from 363 to 323 K. Dry air and bi-distilled water were used as calibration standards in the temperature range. The temperature was controlled at ±1 mK and measurements were performed when temperature fluctuations were smaller than ± 0. 5 mK.
9, 10 utamben samples were introduced in a Lindemann capillary (0. 5-mm diameter). The capillaries were rotated perpendicularly to the X-ray beam during the experiments to improve the averaging of the crystallite orientations. Before each isothermal data acquisition, the samples were allowed to equilibrate for about 10 min, and each acquisition time was no less than 1 h. The heating rate in-between data collection was 1. 33 K min -1. Patterns were recorded on heating in the temperature range from 100 K up to the melting point.
Structure Solution from Powder Diffraction For the structure solution of the low temperature form II, 24 hours of diffraction intensity was accumulated at 120 K. The program DASH 3. 4. 5 was employed and the powder pattern was truncated from 6. 12 to 55. 5• in 2θ (λ-Cu-Kα1), corresponding to a real space resolution of 1. 65 A�. The background was subtracted with a Bayesian high-pass filter. Twenty peak positions for indexing were obtained by fitting with an asymmetrycorrected pseudo-Voigt function. 13, 14 They were indexed with the program DICVOL91. 15  monoclinic unit cell was obtained. The figures of merit given by DICVOL were M(20) = 11. 4 and F(20) = 18. 3 (0. 0176, 62). Although these values were relatively low, the unit cell was selected due to its similarity with the high temperature phase.
Pawley refinement was used to extract integrated intensities and their correlations, from which the space group was determined using Bayesian statistical analysis. 16 The extinction symbol C1-1 had the highest probability directly followed by P21/a. Despite the high probability, it was not possible to fit the pattern against space group C2 as several reflections violate the extinction rules for this space group. The high temperature form of butamben has space group P21/c, 7 so P21/c while inverting the a and c axes of the unit cell was chosen for further investigation. It resulted in a Pawley χ 2 of 6. 154. Simulated annealing was used to solve the crystal structure from the powder pattern in direct space. The starting molecular geometry was taken from the published room temperature form from the Cambridge Structural Database (reference code EZAVIK).
7 In 30 simulated annealing runs, the same crystal structure was found 30 times. The profile χ 2 of the best solution was 25. 89, which is four times the Pawley χ 2 ; this is a good indication that the correct solution has been found. For the Rietveld refinement, data up to 64° 2θ were used, which corresponds to 1. 45 A� real-space resolution. The Rietveld refinement was carried out with TOPAS-Academic. 17 ond lengths, bond angles, and planar groups were subjected to suitable restraints, including bonds to H atoms. A global Biso (isotropic temperature parameter) was refined for all non-hydrogen atoms, with the Biso of the hydrogen atoms constrained at 1. 2 times the value of the global Biso. The inclusion of a preferred-orientation correction with the March-Dollase formula was tried for directions (100), (010), and (001).
18 None of the directions made a significant difference to the Rwp value (coefficient for comparison between measured and calculated powder pattern). The molecular geometry was checked with Mogul, which compares each bond length and bond angle with corresponding distributions from single-crystal data. 19 No unusual bond lengths, angles, torsion angles or rings were found. Supplementary crystallographic data can be found in the CCDC, deposit number 2087260, and obtained free of charge from the Cambridge Crystallographic Data Centre via www. ccdc. cam. ac. uk/data request/cif/.
Data and methods We use primarily aggregate fertility data from European countries disseminated by Eurostat's database 1  and the Human Fertility Database (Max Plank Institute for Demographic Research and Vienna Institute of Demography)  (Jasilioniene et al. , 2007). Wherever this aggregate European data is insufficient, we use country-specific data provided directly from national institutes. Aside from the standard fertility indicators of TFR and mean age at birth 2 that contextualize our study, we use age-specific fertility rates (ASFRs) to better detail the effects of delayed births on fertility in recent decades. Specifically, we detail fertility at ages 15-29 and 30-49 by measuring the sum of the ASFRs in these age groups and the partial TFRs at ages below and above 30. We also compare changes in the timing of fertility and changes in fertility rates between birth cohorts, with a focus on average age at the birth of the first child and average number of children within cohorts.
Breaking these trends down further, we identify three groups of countries according to decrease in number of children born per woman and delay in childbearing. Italy and Spain are characterized by a sharp increase in the mean age at first birth and a sharp decline in completed fertility, by which the highly significant fertility decline at younger ages is not offset by an increase at older ages (see Fig. 9 ). A similar trend occurs in Poland and, to a lesser extent, in Hungary and Austria. This group constitutes a small Fig. 5  Average maternal age at first birth and average number of children among women born in  1952, 1962, and 1972, various  European countries. Source: Human Fertility Database, the UK's Office for National Statistics (ONS), and Italy's Istat. Mean age at first birth for Austria  (1952 and 1962 cohort)  and Spain (1952 cohort) is extrapolated from the median values calculated in  Mamolo (2006)  and  Martín-García and Baizán (2006).
Initially, these indications were not restricted by the level of PD-L1 expression, but preliminary data from an ongoing phase III trial in patients with UC led to a restriction in the UC indication to cisplatinineligible patients whose tumours have 5% PD-L1 expression. Still, the role of PD-L1 expression as predictive biomarker for atezolizumab therapy remains inconclusive and further research is needed. Data in this paper came from the scientific review leading to the initial regulatory approval of atezolizumab in the EU and its complementary application for indication (EMEA/H/C/004143/II/0010). The full scientific assessment report and product information are available on the EMA website (www. ema. europa. eu).
The safety database for NSCLC included 1636 patients. Overall, 65. 7% of patients reported at least one treatmentrelated adverse event, of which 13. 3% were of grade 3. The most common immune-related events in this population were hypothyroidism (4. 6%) and pneumonitis (3. 4%). Safety was also evaluated across all TC/IC subpopulations, but no noteworthy differences were observed.
The primary endpoint was OS but tested in a hierarchical fixed sequence in prespecified populations: IC2/3, followed by IC1/2/3, followed by the entire population. Statistical significance was required at each step before formal testing of the subsequent population. The main secondary endpoints included ORR, PFS, and DOR. At the CCOD of 13 March 2017, the HR for OS in the IC2/3 population was 0. 87 (95% CI: 0. 63-1. 21), precluding further formal statistical comparisons. The exploratory analysis of the entire population showed a HR of 0. 85 (95% CI: 0. 73-0. 99), which was maintained (HR below 1) in all PD-L1 expression subgroups (Table  2 ). The ORR was 13. 4% in both treatment arms. The safety profile of atezolizumab in UC patients was assessed with data from 524 patients. Overall, 69. 1% of the patients reported at least one treatment-related adverse event (16.
Direct evaluation of background CO 2 via longterm observation is needed to assess the accuracy of our urban inversion systems. The goal of this paper is to document the effects of background choice on CO 2 enhancements above background measured with an urban tower-based network. We first consider the differences between background towers as a function of wind direction and season. Two potential background towers were available for January 2013-December 2018, and an additional one was available for April 2017-December 2018. We calculated 31-day median afternoon enhancements above each of the primary background towers, and composited over 5. 7 years of data to determine yearly cycles of enhancements at each tower. We compared the results from each of the background towers, and made a simple prediction to explain the difference between the towers based on their land cover types.
Wind measurements The wind data used to characterize overall synoptic patterns in the city were measured at the Indianapolis International Airport, outside the southwest corner of the city. The data are part of the Integrated Surface Dataset (https ://www. ncdc. noaa. gov/isd). The weather station at the airport uses the Automated Surface Observing System (http://www. nws. noaa. gov/asos/pdfs/aum-toc. pdf ). The accuracy of wind speed is ± 1. 0 ms -1 or 5% (whichever is greater) and the accuracy of wind direction is 5° when the wind speed is ≥ 2. 6 ms -1. Wind directions are not reported for periods in which the wind speed is less than 1. 6 ms -1. The height of the wind instrument is about 10 m AGL. The wind data are reported at a single point in time recorded within the last 10 min of each hour.
We compared the CO 2 measured at the INFLUX sites by calculating the 31-day running median of the enhancement in CO 2 for each of the towers from Tower 01 and from Tower 09. Here we use the term enhancement although the difference can be negative, and we note that we have not isolated the anthropogenic enhancement with this calculation. The 31-day running median was chosen to focus on seasonal variability. We excluded data points for which any of the background towers were downwind of the urban area (afternoon average wind directions of 20-65° (Tower 01 in urban plume) and 235-280° (Tower 09 in urban plume). To determine these wind directions, we considered the angle between the background towers and the geometric edges of the urban area, as defined as the region within the expressway encircling Indianapolis (I-465) and the differences between background towers as a function of wind direction in the dormant season.
The urban plume was not apparent at Tower 14. Furthermore, median results centered within 10 days of extended data gaps attributable to instrument malfunctions were excluded. We averaged these results for each day of the year for the available years between January 2013 and December 2018 to form annual cycles of smoothed composited CO 2 differences. The standard deviation of the data from each of the available years was used as an estimate of the variability. We compared the results using Tower 09 as a background to those using Tower 01 as a background. The enhancements at each tower using these two different Fig. 3  Percentage landcover types for a 10-km radius circle encompassing approximately 80% of the footprint for each tower using the NASS database  [47, 48]. The grass category also includes hay/pasture.
Hestia emissions were aggregated from the initial building-level product to 1-km resolution, covering Marion County and the eight surrounding counties. For biogenic fluxes, we used the urban Vegetation Photosynthesis and Respiration Model (VPRM,  [55, 56] , driven by greenness data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite product and climate data from the North America Regional Reanalysis (NARR). The fraction of impervious surface area from the National Land Cover Database  [47]  within each pixel was used to adjust the carbon fluxes for the impact of urbanization on ecosystem function. Nonpaved portions of the city were defined as deciduous broadleaf forest. Distributions for four land cover types (corn, soy, grassland/pasture, and forest) were derived from the United States Department of Agriculture National Agriculture Statistics Service (NASS;  [47, 48] ), and the VPRM parameters were optimized for these land cover types  [57]  to produce hourly carbon fluxes at 1-km resolution as the weighted average of carbon fluxes from each type.
The Tower 09 CO 2 was predicted to be its greatest magnitude lower than Tower 01 in mid-July, only a 2-week shift from the observed pattern. That is, by mid-July, the agricultural fluxes in the footprint of Tower 09 were maximally larger than the forest fluxes in the footprint of Tower 01. While the predicted CO 2 difference was small in November and December, as observed, the prediction indicated an additional minimum in September (about one half the magnitude of the minimum in May) that was not observed in the tower data. According to the fluxes in Fig. 2a , the forest was expected to continue to draw down CO 2 in September, whereas the agricultural fluxes indicated small magnitude respiration. The tower results, however, indicated the both forest and agricultural fluxes were roughly balanced by mid-September, averaged over the time period of the dataset.
1 ± 0. 5 ppm during the dormant season. Developing and testing robust CO 2 flux estimates for the rural ecosystems upwind of cities is therefore critical to year-round urban anthropogenic CO 2 flux estimates. Further tuning of the biogenic model response, or a more advanced vegetation model in order to more fully capture the timing and productivity differences between the forested and agricultural sites considered here would likely improve the inversion results. The sensitivity of the inverse fluxes to the biogenic fluxes is of course dependent upon the fossil fuel emissions for the study area. The growing season forward model to data mismatch was larger than the dormant season mismatch, suggesting that biogenic fluxes were a larger source of mismatch than the fossil fuel fluxes. The inversion of these data, separately optimizing fossil fuel and biogenic CO 2 emissions,  [52]  indicated very little adjustment to the fossil fuel CO 2 emissions from Hestia.
Tower 14 is on the predominately downwind edge of the U. S. corn belt, and while there is corn grown in the area surrounding Tower 09 it is a less productive area overall. Persistent differences in CO 2 between two background sites with similar land cover presents an additional challenge for vegetation models. The biogenic model may need to be further tuned to capture the differences between these agricultural sites with differing productivity. INFLUX is unique being on the edge of the U. S. corn belt, but in general, potential gradients in production and differing landcover type within domains for each study should be considered. Additional measurements, including flux tower eddy covariances are planned to learn more about the differences in CO 2 drawdown between these locations. Although the number of towers and timespan of the dataset is unprecedented for a study of CO 2 mole fractions in and around a city, there were some limitations of this study.
The magnitude of potential background differences depends on time of year and land cover types in the region. Indianapolis is a large city, but not a mega city, and determination of fluxes for larger cities with larger CO 2 fluxes may be less affected by land cover-based differences in background towers. Still, careful consideration of land cover types is necessary in order to interpret CO 2 tower network data throughout the growing season. Each city, depending on topography, climate, population, surrounding land cover and other factors, has unique challenges for the estimation of greenhouse gas emissions.
Availability of data Miles NL, Richardson SJ, Davis KJ, Haupt BJ. In-situ tower atmospheric measurements of carbon dioxide, methane and carbon monoxide mole fraction for the Indianapolis Flux (INFLUX) project, Indianapolis, IN, USA. Data set available on-line from The Pennsylvania State University Data Commons, 2017b; http:// dx. doi. org/10. 18113 /D37G6 P. For further information, see http://sites. psu. edu/ INFLU X.
Medical, Anthropometric, and Lifestyle Assessment Detailed medical history was obtained including personal/family medical history and medication. Body weight was measured to the nearest 0. 1 kg. Height was measured to the nearest millimeter and BMI was computed as weight (kg) / height (m)  2. Total diabetes risk was assessed using the validated Finnish Diabetic Risk Score (FINDRISK) questionnaire. [20] Dietary intake was assessed using a 24-h recall record (three random days) and data was analyzed using Nutritionist Pro™ (Axxya Systems) software for the estimation of caloric intake. Physical activity level was evaluated via the International Physical Activity Questionnaire (IPAQ)  [21]  and Metabolic Equivalent Task minutes per week (MET-min wk -1 ) were derived according to the IPAQ scoring protocol.
Sequencing and Analysis of 16S rRNA Amplicons DNA from fecal samples was extracted using the MagNA Pure LC DNA isolation kit II (Roche Life Science, Basel, Switzerland). The V3-V4 region of the 16S rRNA gene was amplified to construct amplicon libraries that were sequenced using the Reagent Kit v3 (2 × 300 cycles) in a MiSeq platform (Illumina, San Diego, CA, USA). The authors used the DADA2 pipeline  [27]  (R package) to generate amplicon sequence variants (ASV) from raw sequences. Chimeras and sequences matching the human genome were filtered out to generate the final ASV abundance tables. The taxonomic information of the ASVs was obtained by comparison with the SILVA reference database (v. 132). [28] The sequences have been deposited in the European Nucleotide Archive under accession number PRJEB40538.
Liquid Chromatography-High Resolution Mass Spectrometry (LC-HRMS) Based Metabolomics LC-HRMS analysis was performed as described in the Supporting Information. All samples were analyzed in duplicate, in a random order. QC pooled samples were used and mass accuracy was maintained ≤5 ppm. Raw data file preprocessing was achieved using the MZMine 2. 53 software. [29]  generic streamline was employed, including mass detection, chromatogram building, chromatogram deconvolution, isotopic peak grouping, spectral alignment, and gap filling to generate the peak list. Metabolite annotation was performed comparing the recorded HRMS and HRMS/MS spectra with online databases such as METLIN  [30]  and the Human Metabolome Database (HMDB). [31] Further details in Supporting information. The bacterial group decreases with the associated MRI variables; b) The bacterial group increases with the associated MRI variables.
The authors obtained levels for 65 annotated metabolites and calculated the mean values per metabolite per sample per time point, based on duplicate measurements. Baseline and post-treatment metabolite levels have been transformed and normalized, via the "bestNormalise" package in R, prior to the analysis. A list of all metabolites and the transformation applied is shown in Table  S1 , Supporting Information. As part of the exploratory analysis, a PCA including all 65 metabolites at baseline and post-treatment was performed, but we did not observe any significant clustering between the intervention groups (Fig. S1 , Supporting Information). In order to prioritize metabolites for further statistical analysis, all metabolites with a log2fold change >1. 5 between the Mastiha and Placebo groups post-treatment have been selected.
These included 24 metabolites, as shown in Fig. S2 , Supporting Information. The selection of the microbiota and metabolites variables is described in more detail in the Supporting Information. In total, 58 variables under this group of outcomes have been considered, including anthropometric, biochemical, liver MRI (20 variables), microbiota (14 variables), and metabolites (24 variables). To avoid overcorrection for multiple testing, the Pearson's coefficient for all pairwise correlations among these 58 variables have been calculated (Fig S3, Supporting Information). Then, the effective number of tests,  [33]  as estimated via the "meff" function of the "poolr" package in R, has been ascertained. The authors performed 34 independent tests and accordingly set the multiple testing threshold of significance at 0.
In the stratified analysis, ANCOVA models (adjusted for age, sex, and center) have been applied to assess differences in the mean values of the MRI outcomes, separately for each BMI category (i. e. , BMI ≤ 35 kg m -2 or BMI > 35 kg m -2 ). All ANCOVA models were implemented in R. The "lsmeans" package in R was also used, in combination with the ANCOVA models, to calculate adjusted means of selected outcomes post-treatment. In order to assess the improvement within individual treatment for the MRI outcomes, the mean difference between post-treatment and baseline was also estimated and tested for significance between the Mastiha and the Placebo group, per BMI category, through AN-COVA models (adjusted for age, sex, and center). Even though the body weight has not considered as an outcome, the changes between baseline and the end of trial, separately in each group, via paired t-tests in R, have been assessed.
Committees approvals were obtained from all centers, HUA (Bioethics Committee 49/29-10-2015), CNR (Ethical Clearance by Commissione per l'Etica e l'Integrità nella Ricerca, February 2016), and Niguarda Hospital Ethics Committee 230-052017 (Comitato Etico Milano Area 3-11. 05. 2017), UNS (Faculty of Medicine Novi Sad, The Human Research Ethics Commission No. 01-39/58/1-27. 06. 2016). The trial was conducted following the Helsinki declaration and the Data Protection Act 1998, and was registered with ClinicalTrials. gov (Identifier: NCT03135873).
Acknowledgements We are grateful to patients for participating in this study and to all secondees and researchers involved in the recruitment process. The authors gratefully acknowledge all members of the  MAST4HEALTH  project participating in this study. More specifically, from HUA:  Stamatia-Angeliki Kleftaki ,  Magdalini Tzouma ,  Afroditi Gkini ,  Niki Zolota ,  Anastasia Diolintzi ,  Marina Plyta ,  Vasiliki Mpoukorou ,  Evdokia K. Mitsou ,  Adamantini Kyriacou ,  Andrea Georgiou  and  Ioanna Tzoulaki. From CNR Elisabetta Spagnolo ,  Cinzia Dellanoce. Marina Parolini ,  Luca Belli  and  Angelo Vanzulli. From UNS:  Natasa Milosevic ,  Maja Milanovic ,  Bojan Vukovic ,  Iva Barjaktarovic ,  Ivana Bajkin ,  Tijana Icin ,  Ljilja Torovic  and  Dusko Kozic. From INSERM: Satish Kumar ,  Christine Masson ,  Vesna Gorenjak  and  Maria-Spyridoula Kontoe.