dataesr/scientific-paragraphs-categorization huggingface dataset:
This again is undesirable because it not only increases the response time but also decreases the overall efficiency of the scintillation process. Ideally, it is preferrable when the 4f or 5d 1 level is situated close to the band edges, but not too close to excite the localized charge carriers back to the delocalized host bands by thermal vibrations. To make these more quantitative, the study utilized a well known and widely used scintillator material Cs 2 LiYCl 6 :Ce (CLYC) as a reference. A classification metric was defined that compared the relative positions of the activator levels to the band edges in a new compound with those in the reference compound. Using the above domain-knowledge-informed criterion for scintillator/non-scintillator classification, two different regression models were trained and validated using a database of accurate experimental measurements of two key spectroscopic quantities, namely the U and the D parameters  [285] ,  [286].
That is, at any given stage several samples may be predicted to have given properties along with the associated uncertainties. The tradeoff is between exploiting the results by choosing to perform the next computation on the material predicted to have the optimal property or further improving the model by performing the calculation on a material where the predictions have the largest uncertainties. By choosing the latter, the uncertainty in the property is expected to (given the learning model) decrease, model will improve (and its domain of applicability will expand) and this will influence the results of the next iteration in the loop (i. e. , exploration)  [290]. The new compounds proposed by the adaptive design strategy are then synthesized, characterized and the new data is used to augment the training database.
More than 160 kinds of scintillators and applications were presented during the SCINT22 conference, as summarized in Table  III. Recent work included inorganic and organic scintillator composites or heterostructures, liquid phase synthesis of perovskites and single-crystal micrometer-thick films, use of multi-physics models and lately data science to guide scintillator development, structural innovations such as photonic crystals, nanostructured scintillators enhanced by the Purcell effect, novel use of existing scintillators through heterostructural innovations (fibers), and multilayer configurations. Scintillator metrics such as light yield, decay time are discussed in light of RadIT metrics. RadIT, both photon and particle based, continue to aim for finer spatial and temporal resolution, the highest possible efficiency in conjunction with advances in high luminosity X-ray and particle sources, photodetectors, and efficient algorithms for data processing (mostly left out of this paper).
While X-ray and charged particle IT necessarily require faster, brighter scintillators, and the concerns with radiation damage are growing, neutron IT on the other hand is currently limited by the neutron source intensity, and, therefore, high efficiency scintillators with good spatial, energy resolution (for neutron recoils) would be desired. The scintillator requirements in RadIT overlap significantly with other applications such as in HEP. For example, the calorimeter applications at FCC at CERN, or CEPC in China will not only need excellent energy resolution, but also new scintillator functions including fast and precision timing (ps, driven by high luminosity above 10 34 cm -2 /s and corresponding high data rate and large data sets), outstanding radiation tolerance and finer granularity or spatial resolution of the active elements.
Dark matter search usually requires large volume and surface area of scintillators, which overlaps with requirements of RadIT in higher detection efficiencies, large field of view, and up to 4π solid angle signal coverage. Since there is no universal scintillator that can fit all needs, tradeoffs between, for example, cost and performance, spatial resolution and efficiency, light yield and decay time, are often necessary. Optimizing a scintillator for a specific application appears to be the next best option. Scintillator optimization can become a part of the 'global' optimization strategy in RadIT applications, which include cradle-to-grave analysis of an ionizing photon or particle. In addition to a growing number of successful empirical approaches, a new approach is optimization through data science for the maximal information yield.
For many years, the discovery and design of new scintillator materials relied on laborious, timeconsuming, trial-and-error approaches, yielding little physical insight sometimes, and leaving a vast space of potentially revolutionary materials to be explored. A closed-loop machinelearning-driven adaptive design framework based on data from literature, in-house experiments and first-principles (quantum mechanical) calculations has recently been demonstrated for fast screening of perovsikites, garnets and elpasolites. It is possible to extend such a framework to, for example, high entropy scintillators, even though it is well recognized that computation can become a bottleneck. Plenty of new opportunities exist that make RadIT and scintillator development mutually beneficial and dependent. Examples include optimization of RadIT performance with reduced radiation dose, data-driven measurements, photon/particle counting and tracking methods supplementing time-integrated measurements, multimodal RadIT, and novel applications of RadIT for scintillator discovery.
They account for the amplification of climate policy shocks, with the transmission channel given by the changes in the valuation of equity and debt securities conditional upon a shock on the asset side of the security issuer. Based on the findings referred to a dataset of financial exposures between the institutional sectors in the Euro Area, even a small shock on the banking system could create a great amplification in the banks-households chain and the consequent large gains (losses) for the banks would positively (negatively) affect the real economy. Battiston et al. (2019) 's CLIMAFIN provides a science-based approach to climate scenarios adjusted financial pricing models and risk metrics, which is built on the definition of a risk-adverse investor information set, risk management strategy and portfolio of risky financial contracts and securities.
Data To estimate our measures of systemic risk, we collect data on the daily values of the S&P 500 Banks Industry Group GICS Level 2, S&P 500 Insurance Industry Group GICS Level 2 and S&P 500 Financials Sector GICS Level 1 Index, which is used as proxy for the whole US financial system. We are strongly motivated to consider the GICS framework 7 because it has become widely recognized by market participants worldwide and enables meaningful comparisons of sectors and industries. Moreover, MSCI and Standard & Poor's review the entire framework annually to ensure an accurate representation of the marketplace. We consider the following two green indexes: (i) MSCI USA Select Green 50 3% Decrement Index, which is designed to track the performance of the largest 50 stocks from the MSCI USA Investable Market Indexes that offer products and services that contribute to an environmentally sustainable economy; and, (ii) the NASDAQ OMX Green Economy US Index, which aims to track the performance of companies across the spectrum of industries most closely associated with the economic model around sustainable development through every economic sector and involved in the reduction of fossil-sourced fuels, products, services, and lifestyles domiciled in the United States.
We calculate three capitalization weighted indexes of listed US polluters, as identified by the Political Economy Research Institute at the University of Massachusetts Amherst, defined as the Greenhouse 100 Polluters Index, Toxic 100 Air Polluters Index and Toxic 100 Water Polluters Index. They comprise the top greenhouse gas emitters and the top corporate air and water polluters using the most recent data available from the US Environmental Protection Agency. 8  Market data are all downloaded from Bloomberg. As argued by Monasterolo and De Angelis (2020) and  Ehlers et al. (2022) , financial markets started paying attention to climate issues only after the 2015 Paris Agreement. For this reason, we study both green and brown indexes over the period from December 12, 2015, when the final wording of the Paris Agreement was adopted by consensus by the 195 states and the European Union, to July 31, 2022.
: Histopathological evaluation according to region and intensity of inflammation. Author Contributions: J. E. d. S. S. : Project execution, data collection and curation, analysis of results, and writing the draft; G. G. J. : Proofreading and editing significant parts of the work; F. G. G. : Data collection and processing; M. C. C. P. : Data collection and processing; G. D. d. M. : Statistical analysis, critical revision of the text; T. T. N. W. : Critical revision of the text; V. F. d. L. : Data collection and processing; G. F. M. : Coordination of the research activity planning and execution, acquisition of financial support. All authors have read and agreed to the published version of the manuscript. Funding: This research project was partially funded by FAPESP No. 2016/02384-9. Institutional Review Board Statement: All the procedures and methods used in this study were performed in accordance with the Committee on Ethics and Animal Experimentation of School of Veterinary Medicine, São Paulo State University, UNESP, Araçatuba, Brazil (FOA-00633-2016).
Materials and methods Fig. 1a  shows the limits of the study area (yellow rectangular) on a quasitrue color daily MODIS composite for August 8, 2016. In this image, the SPM-rich Mackenzie River plume propagates along the coast and northward, partly into the MIZ (marginal ice zone). The variations of SPM concentrations (Fig. 1b ) from the delta to offshore waters are obvious, which highlights complex processes along this land-sea interface. The field and satellite datasets used in this study, so as the methods used to retrieve and analyze time series of river discharge and SPM concentrations, are detailed hereafter.
1d ). Over the studied period, some years are poorly represented (one measurement per year in  2003 , 2006 , and 2007; no SPM measurement in 2008). The lack of SPM in situ data implies the necessity to use satellite data. The ArcticGRO in situ datasets pass the quality control procedure and indicate less reliable measurements as "provisional data". Over the 2003-2022 period, it corresponds to river discharge data in 2020-2022 and SPM insitu data in 2019-2022 (shown with thinner lines in Fig. 1c, d ).
Ocean color satellite data As in  Doxaran et al. (2015) , a single satellite sensor (MODIS/Aqua) was considered for this study in order to avoid any bias between different sensor products when detecting temporal trends. Since 2003, MODIS provides high-quality ocean color observations at a good temporal resolution: several images per day of the study area during the Arctic summer months  (Doxaran et al. , 2012 (Doxaran et al. , , 2015;; Hilborn and Devred, 2022; Matsuoka et al. , 2022). The region of interest was defined as 68. 5 • N-72 • N, 142 • W-124 • W (yellow rectangle in Fig. 1a ). The initial dataset contained 10,608 swaths collected between May 1 and October 31 each year over the 2003-2022 period.
Processing of satellite data MODIS L1A satellite data were processed using the SeaWiFS Data Analysis System (SeaDAS 8. 1. 0) software (http://seadas. gsfc. nasa. gov/) and its l2gen function. The atmospheric correction was performed using the NIR-SWIR algorithm of  Wang and Shi (2007) ; this method was proved to be the most appropriate for the highly turbid waters at the mouth of the Mackenzie River to the less turbid waters offshore  (Doxaran et al. , 2012). SeaDAS l2gen flags were used to mask clouds and glint. In the work of  Doxaran et al. (2015) , two techniques were used to mask clouds: the default one in the coastal waters and an increased cloud albedo threshold value (0. 4 instead of 0. 027) over the specific area of the river delta zone to avoid masking the highly turbid water pixels.
This procedure was time-consuming and required an additional inspection of every image in the river delta zone. Here, the cloud-masking method preconized by  Ody et al. (2022)  for river mouths was used to process each satellite image only once: the 2130 nm shortwave-infrared waveband was used with a cloud threshold of 0. 018. The sea ice mask was computed from daily sea ice concentration (SIC) data at 3. 125 • spatial resolution distributed by the University of Bremen  (Spreen et al. , 2008). This dataset contains AMRS-E and AMSR2 (Advanced Microwave Scanning Radiometer -for Eos and -2) images. Due to several gaps in data acquisition over the 2003-2022 time period, the dataset was completed with the sea ice concentration from the SSMIS (Special Sensor Microwave Imager/Sounder) instrument.
The ice mask was created using the SIC above 0 %. All satellite data (Rayleigh-corrected reflectances and masks) were reprojected on a regular 250 m grid. The following criteria were then applied to exclude potentially contaminated images: • the number of valid pixels should exceed 15 % of the marine area of the study area in the downloaded MODIS image; • the distance between the central point of the study area and that of the MODIS image should be <1000 km to avoid aberrations on the border of the image; • only areas with a zenith solar angle lower than 74 • were conserved. This automatic filtering reduced the MODIS collection to 700 images. Finally, all images were inspected visually to filter out the remaining contaminated images because of not detected MIZ, clouds or cloud shadows, etc.
Reanalysis data A dataset from ERA5 Land reanalysis was extracted to better understand how environmental factors did impact the Mackenzie River discharge and SPM concentrations in the adjacent coastal waters. This dataset was selected as the most suitable tool for the studies of river discharge variability  (Winkelbauer et al. , 2022). ERA5 Land has a monthly temporal resolution and 9-km spatial resolution and is provided by Copernicus data center  (Hersbach et al. , 2023; Muñoz-Sabater et al. , 2021). The area of extraction approximately corresponds to the Mackenzie drainage basin (52-70 • N, 100-140 • W). The following parameters were used: river runoff (ro), surface river runoff (sro), evaporation (e), total precipitation (tp), snow depth (sd), and air temperature at 2 m (t2m), (see detailed description at https://confluence.
ecmwf. int/displa y/CKB/ERA5-Land%3A+data+document). The Pearson correlation matrix for the mean annual values of all described parameters (Fig. A. 3). To include a possible effect of storms on the SPM concentrations, we added to the analysis the wind data from ERA5 (marine area similar to that chosen for the MODIS study box). The Pearson correlation matrix (where the yearly parameters are compared) contains an additional parameter "the number of days of storms per year" (ndays_storm in Fig. A. 3). The day was considered stormy if over the study area there was a wind vector with a wind speed over 15 m/s.
To apply this model to our datasets, we used the segmented function in the R package segmented. To compute the trends, the in situ data time series of daily river discharge (Q insitu ) and SPM concentration (SPM insitu ) described above were used (Fig. 2a, b ). For the satellite data, the spatial sparsity of observations was taken into account. Thus, to obtain SPM sat time series, we calculated: • one daily spatial mean SPM value for all available SPM pixels over the study area, which resulted in SPM sat time series (Fig. 2c-d ); • one daily spatial mean SPM value for the area with the highest data density (where valid satellite pixels appear >200 times over the 2003-2022 period), which resulted in SPM sat200 (Fig. 2 e-f ). Then the segmented regression analysis technique was applied to all time series: Q insitu , SPM insitu , SPM sat , and SPM sat200.
In situ data show that the SPM seasonal cycle generally follows the river discharge cycle, with the summer maxima in late May-beginning of June. The SPM sat values vary from 0. 8 to 461 g/m 3 with a median value of 67. 7 ± 11 g/m 3. The breakpoint of SPM isitu trends is slightly shifted to 2018, and CIs contain zero, thus indicating that the segmented model is less reliable for this dataset. At the same time, the SPM isitu time series has the fewest amount of data, which is not homogeneous in time and is mainly available in summer. It makes it more difficult to interpret with any statistical model. SPM sat and SPM sat200 trend analyses show similar results: their negative slope1 values and corresponding SE are close to each other, which gives confidence in the observed SPM negative trend. The positive trends slope2 sat and slope2 sat200 are not statistically significant, and their SEs are of the order of the estimated coefficient of linear regression (Table  1 ).
Acknowledgments This study is part of the  Nunataryuk  project. The project has received funding under the  European Union 's  Horizon 2020 Research and Innovation Program  under grant agreement no. 773421. The study was cofunded by the  ArcticFlux TOSCA  project from the  French space agency (CNES). The authors want also to acknowledge the  NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group. Moderateresolution Imaging Spectroradiometer (MODIS) Aqua Data ;  NASA OB. DAAC, Greenbelt, MD, USA. doi: DOI. Accessed on 05/ 08/2023. No conflict of interest is stated.
Data availability In situ measurements described in Section 1 (river water discharge and TSS) are provided at https://arcticgreatrivers. org/data/. MODIS data is accessible from the NASA website https://oceancolor. gsfc. nasa. gov. Sea ice concentrations are available at https://seaice. uni-bremen. de/data-archive/. ERA5 LAND reanalysis data can be found at https://doi. org/10. 24381/cds. 68d2bb30 with a detailed description at https://confluence. ecmwf. int/display/CKB/ERA5% 3A+data+documentation, and ERA5 wind data is available at https://doi. org/10. 24381/cds. adbb2d47 with a detailed description at.
Individualised risk prediction models Whereas there is good evidence for the role of individual or simple combinations of prognostic factors, there has been relatively little research into the development and validation of multivariable prognostic models or individualised risk stratification scores for patients with cancer and a prognosis of a few months or less who are still receiving DMT. One study used routinely collected laboratory data in patients receiving ChT to develop six adaptable prognosis prediction models. 40 The combination of albumin, lactate dehydrogenase (LDH) and neutrophil count was predictive of death at every month within the last 6 months of life (each of the six models utilised a different set of coefficients in the same general regression equation). The area under the curve (AUC) for each model ranged from 0.
ACKNOWLEDGEMENTS Assistance with literature searching was provided by  Yuki Kataoka ,  Department of Internal Medicine, Kyoto Min-Iren Asukai Hospital, Kyoto, Japan , and  Akemi Shidaro Naito ,  Department of Palliative Care, Miyazaki Medical Association Hospital, Miyazaki, Japan. Manuscript editing support was provided by  Claire Bramley  and  Jennifer Lamarre  ( ESMO Guidelines staff ) and  Sarah Amir  and  Angela Corstorphine of Kstorfin Medical Communications Ltd (KMC) ; this support was funded by  ESMO.
| Data source Data were collected between 2015 and 2020 and followed the same protocol and methodology as the earlier ISAAC studies (Phase I from 1992 to 1995  8  and Phase III from 2001 to 2003  11  ) in order to facilitate comparison of the prevalence of symptoms across different time points. 10  All participating centres obtained ethical approval from their local ethics committees before commencing the study and the ethics committee determined the method of consent (although the ISAAC and GAN protocol recommended passive consent if possible). 12  Each centre was based on a defined geographical area from which a minimum of 10 schools were randomly selected (or all schools if there were 10 or fewer schools in the area).
| Data handling and analyses Each centre submitted data to the GAN Global Centre in Auckland, New Zealand. After initial checks, the data, along with a centre report, were forwarded to either the Murcia GAN Data Centre (Spain) for Spanish and Portuguese speaking countries or to the London GAN Data Centre (United Kingdom) for all other countries, for thorough data checking and cleaning. Both data centres used the same suite of Stata programs to perform checks, liaising with centre principal investigators or their delegate for any queries/amendments in data coding or data entry checks. Centres with serious deviations from protocol, for example, response rates <50%, were excluded from analyses. Less serious deviations from protocol are identified with footnotes in the results tables. 16, 17.
| Covariates Countries were allocated to four regions based on WHO regions of the world. The WHO regions of Africa and Eastern Mediterranean were combined and South-East Asia and Western Pacific were combined, because of the smaller number of centres that completed GAN Phase I compared to ISAAC. These four groups also correspond to the nine ISAAC regions with North America and Latin America combined (Americas), Western Europe and Northern and Eastern Europe combined, Africa and Eastern Mediterranean combined, and Asia-Pacific, Indian sub-continent and Oceania combined. Country income group was obtained from the World Bank which identifies countries as low-, lower middle-, upper middle-, and highincome. 19  The 2001 classification was used as a mid-point for the ISAAC-GAN studies. To model time trends of different types of countries and centres across the whole time period of ISAAC Phase I to GAN Phase I, for each age group (adolescents [aged 13-14] and children [aged 6-7]), we included data from ISAAC/GAN centres with at least two time points.
1%. Amongst children, the median prevalence of current eczema symptoms (19 centres) was 6. 0%, ranging from 1. 3% in Lucknow, India to 15. 7% in Taipei, Taiwan. The prevalence of severe eczema symptoms which disturbs sleep in this age group ranged from 0. 1% in Kottayam, India to 2. 1% in Taipei, Taiwan, with median prevalence of 0. 6%. The absolute change in current eczema symptom prevalence in adolescents per decade from the latest available ISAAC data to GAN Phase I ranged from a reduction of 8. 9% in Managua, Nicaragua to a rise of 4. 8% in Bilbao, Spain. We observed a >2 SE decrease in current eczema symptom prevalence from the latest available ISAAC data to GAN Phase I in six centres, with a 1-2 SE decrease in three centres, minimal change in six centres, a 1-2 SE increase in two centres and with the remaining 10 centres showing a >2 SE increase (Figure  1A  and Table  1a, Figure S2     current eczema symptoms prevalence of >2 SE in the remaining five centres (Figure  1B  and Table  1b , Figure  S2 ).
Recent data from the Global Burden of Disease (GBD) 2017 reported that eczema ranked 59th amongst all diseases based on disability adjusted life years (DALYS) and 15th amongst non-fatal disorders. 6  The GBD group reported that, while global DALYs were stable over three decades, there was substantial variation between countries, from 85. 14 to 326. 91 DALYs per 100,000. The age-standardized prevalence ranged from 1. 8% to 5. 0% with the highest reported prevalence in Andean Latin America in 2017. Concerns were also raised by authors about misclassification contributing to high burdens attributed to eczema in Andean Latin America, with a recommendation that future iterations use harmonized definitions and highlighting a need for more and higher quality data on eczema prevalence from settings outside Europe and North America.