dataesr/scientific-paragraphs-categorization huggingface dataset:
Future endeavors in advancement of nuclear reactor designs builds upon contemporary and historic efforts to understand and characterize our nuclear world via experimentation. The ICSBEP and IRPhEP serve as an international omnibus of curated and evaluated experimental data from around the world. The benchmarks contained within their handbooks serve as the backbone for validation testing of contemporary neutronics methodologies, simulation, and nuclear data. This manuscript serves as only a snapshot of the current culmination of international collaborations to utilize our nuclear heritage to enable our nuclear future. It does not include numerous evaluations that are currently being considered or that are in the initial phases of the evaluation process.
Acknowledgments The  CNRS  and the  University of Strasbourg  are acknowledged for supporting and funding partially this work. The  University of Strasbourg  and the  French national program  " Investment for the future" (IdEx-Unistra 2019 ) are thanked for a PhD fellowship (H. P. ). This work has also benefitted from support provided by the  University of Strasbourg Institute for Advanced Study (USIAS)  for a Fellowship to C. M, within the  French national program  " Investment for the future" (IdEx-Unistra 2021 ).
Exposure HBM Children Adolescents  BBzP, DiBP, DnBP, DCHP, DnPeP, DEHP, DiNP, DiDP, DnOP)  and the substitute DINCH to answer the open policy relevant questions which were defined by HBM4EU partner countries and EU institutions as the starting point of the programme. The exposure dataset includes ~5,600 children (6-11 years) and adolescents (12-18 years) from up to 12 countries per age group and covering the North, East, South and West European regions. Study data from participating studies were harmonised with respect to sample size and selection of participants, selection of biomarkers, and quality and comparability of analytical results to provide a comparable perspective of European exposure. Phthalate and DINCH exposure were deduced from urinary excretions of metabolites, where concentrations were expressed as their key descriptor geometric mean (GM) and 95th percentile (P95).
Urinary metabolite concentrations can be directly compared to these HBM-GVs enabling a simple and quick risk assessment  (Apel et al. , 2020a). Although various national and regional HBM programs are conducted in Europe, the direct comparison between these studies is complicated by the unique set-up of each study with respect to target population, sampling design, choice of biomarkers, questionnaires, analysing laboratories using individual analytical methodologies, data bases, data protection and availability, statistical analyses, and the health-based interpretation. A first European HBM project that has intended to overcome such obstacles has been the COPHES/DEMO-COPHES project (COnsortium to Perform Human biomonitoring on a European Scale/DEMOnstration of a study to COordinate and Perform Human biomonitoring on a European Scale project; Den  Hond et al.
, 2015). This HBM program included analyses of five phthalates in morning or spot urine samples collected in 2011-12 from mother-child pairs in 17 countries. To obtain more recent exposure data, the HBM4EU Aligned Studies were launched within the European Human Biomonitoring Initiative (HBM4EU;  Ganzleben et al. , 2017)  to collect new and provide existing European HBM data for selected prioritized substances as harmonised as possible  (Gilles et al. , 2021). Overall, these HBM4EU Aligned Studies comprise more than 10,000 individuals (about 4,100 young adults, and 3,600 children and 3,100 adolescents) from 25 different studies and provide HBM data on various substances, including phthalates and DINCH  (Gilles et al. , 2022; Govarts et al. , in review in this special issue). The aim of this study was to answer open policy relevant questions as defined by HBM4EU partner countries and EU institutions by supplying recent data on the exposure of children and adolescents in Europe to phthalates and the substitute compound DINCH, using data harmonised under the HBM4EU Aligned Studies protocol.
Study population and sample collections The HBM4EU Aligned Studies are a survey aimed at collecting HBM samples and data as harmonised as possible from (national) studies to derive current internal biomarker data reflective of the internal exposure for the European population across a geographic spread. The process of gathering and harmonising data collections from European countries in the HBM4EU Aligned Studies and their characteristics are described elsewhere  (Gilles et al. , 2022; Gilles et al. , 2021; Govarts et al. ,  in review in this special issue). Briefly, data protection and ethical approval to provide individual data were requested beforehand from each individual study. Specifically, among phthalates and DINCH, 2 to countries per European region were asked to provide data on samples each for children (6-11 years) and adolescents (12-18 years) collected between 2014 and 2021, either from an existing national HBM study or from newly collected samples.
The children group also included 3% (n = 89) 12-year-olds from three studies. This circumstance was due to delay between recruitment and urine sampling (ESTEBAN, FR: n = 18; GerES V-sub, DE: n = 19; PCB cohort, SK: n = 52). Studies targeting hotspots, or institutionalized, patient or occupational populations were excluded. Studies providing data on phthalates and DINCH metabolites for children were: NEB II (Norway), OCC (Denmark), InAirQ (Hungary), PCB cohort (Slovakia), POLAES (Poland), CROME (Greece), NAC II (Italy), SLO CRP (Slovenia), 3 × G (Belgium), ESTEBAN (France), GerES V-sub unweighted (Germany), SPECIMEn-NL (The Netherlands). For adolescents, the following studies participated in the phthalates and DINCH program: NEB II (Norway), Riksmaten Adolescents 2016-2017 (Sweden), PCB cohort follow-up (Slovakia), CELSPAC: Teenagers (Czech Republic), POLAES (Poland), BEA (Spain), CROME (Greece), SLO CRP (Slovenia), ESTEBAN (France), FLEHS IV (Belgium), GerES V-sub unweighted (Germany).
The results and extended information of the phthalates and DINCH exercises are described in  Mol et al. (2022). All studies used liquid chromatography-tandem mass spectrometry (LC-MS/MS). For the current analyses, only phthalate/-DINCH metabolite data which were rated "quality assured by HBM4EU QA/QC program" or "generated before HBM4EU QA/QC program but deemed comparable by HBM4EU Quality Assurance Unit (QAU)" were included. Deemed comparable means that data was generated outside (before) the HBM4EU QA/QC program but the analysing laboratory proved its competence for the biomarkers certified by passing the HBM4EU QA/QC. In order to control the dilution of the urine samples, creatinine and specific gravity were also measured. These parameters were not included in the QA/QC programme.
Statistical analysis Values below limits of quantification (LOQ) or limits of detection (LOD) were imputed for each biomarker in each data collection by single random imputation from a truncated lognormal distribution (Henningsen and Toomet, 2011) if at least 30% of values were detected. Sum parameters were only calculated if all biomarkers needed to calculate the sum were measured and if at least 60% of values were detected for at least one of the biomarkers that constitute the sum. For the sum parameters, metabolite concentrations below LOD or LOQ were replaced by LOD/2 or LOQ/2. This method was used because in some cases not enough values were quantified for all metabolites constituting the sum to estimate a truncated lognormal distribution to allow random imputation. Sums (μg/L) of metabolites were available for DEHP (OH-MEHP, oxo-MEHP, and cx-MEPP), DiDP (cx-MiDP, OH-MiDP), DiNP (OH-MiNP, cx-MiNP), and DINCH (OH-MINCH, cx-MINCH) which will be presented as ∑ DEHPm, ∑ DiDPm, ∑ DiNPm, and ∑ DINCHm, respectively, for simplification reasons in this study.
To be able to use sums, studies not having measured all metabolites for the sums were excluded. All statistical analyses were performed in RStudio  (RStudio Team, 2008)  with pooled data from all data collections, and run separately for the metabolites or summed metabolites. We calculated sample proportion equal to or exceeding LOQ, geometric mean (GM, based on log-transformed data) and 95th percentile (P95) and their 95th confidence interval (CI), median (50th percentile, P50) and 90th percentile (P90) for the pooled data and stratified by sex, region, degree of urbanization, and household education level (for data protection reasons only presented for subgroups of n ≥ 50). Since higher similarity can be assumed within a study, survey methods were applied when pooling individual data from data collections (per suggestion in HBM4EU) to account for the nesting of individuals within studies (applied technically by using a survey design with data collection as identifier).
We also checked with multiple linear regression models separately for each sum parameter or metabolite whether there are differences between children and adolescents and whether there is an association across age in years in a pooled data set. Data were compared to urinary biomarker concentrations (GMs and P95) for both children (6-11 years old) and adolescents (12-19 years old) from two national surveys in North America in the overlapping time frame: U. S. National Health and Nutrition Examination Survey (NHANES; cycle 2015-2016; CDC, 2019) and the Canadian Health Measures Survey  (CHMS; cycle 2016 -2017; Health Canada, 2019). To compare exposure biomarker concentrations expressed as GMs of each HBM4EU Aligned Study to the European geometric mean (GM Eu ) heat maps were created separately for each age group (cf.
Results and discussion Phthalate and DINCH metabolites provided by each of the studies for children (n = 2,880) and adolescents (n = 2,799) can be found in Supplemetary Table  S1  and Table  S2. Most of the chemical analyses was performed within the project time of HBM4EU and thus quality assured by the HBM4EU QA/QC. 11% (adolescents) and 13% (children) were phthalate metabolite data generated before the HBM4EU QA/QC. These data were included only if the analysing laboratory had received the HBM4EU certificates for the respective biomarkers (biomarker data generated before HBM4EU QA/QC program but deemed comparable). On average, MCHP, MnOP, and MnPeP were only found in 1-5% of all urine samples (with differing levels of detection, however) indicating only low-level exposures to DCHP, DnPeP and DnOP.
Because of the low detection frequencies these three biomarkers were not included as imputed concentrations in further statistical analyses. Metabolites of DEP, BBzP, DiBP, DnBP, DEHP, DiDP, DiNP, and DINCH had very high detection frequencies, ranging from 65% for cx-MiNP in children to 100% for metabolites of DEP, BBzP, DiBP, DnBP, DEHP, and DINCH in most studies. The high detection frequencies indicate that despite various (strict) regulations, phthalates are still ubiquitous. Given the short elimination half-lives, it is likely that children and adolescents are exposed to phthalates and DINCH on a regular basis. Since not all studies provided data on both children and adolescents (NEB II in Northern Europe, POLAES and PCB cohort/PCB cohort followup in Eastern, SLO CRP and CROME in Southern, and ESTEBAN and GerES V-sub in Western Europe), comparisons between children and adolescents might be confounded with possibly different study characteristics in each age group.
Therefore, all results are reported separately for children and adolescents and comparisons between these age groups need to be interpreted with caution. Descriptive statistics for children's and adolescents' samples with quality assured phthalate metabolites data can be found in DiNPm. This could be an indication towards different exposure sources for the age groups, where adolescents for example use more cosmetics and other personal care products, a possible source of DEP and DnBP  (Zota et al. , 2014). A separate manuscript investigating the role of exposure determinants with HBM4EU Aligned Studies data collections is currently being prepared.
We chose to focus our data evaluation on the GM and P95 concentrations as they are the key descriptors of exposure used in HBM studies worldwide. European exposure levels dropped compared to DEMOCOPHES studies in 2011/2012  (Den Hond et al. , 2015)  for all phthalates (Fig. 1 ). For the substitute ∑ DINCHm, exposures increased. Comparisons of the GMs in HBM4EU Aligned Studies with NHANES (cycle 2015-2016; CDC, 2019) and CHMS  (cycle 2016 -2017; Health Canada, 2019)  for children indicate roughly similar urinary metabolite concentrations between North America and Europe for MnBP, MEP, and DEHP metabolites. Higher concentrations in North America can be seen for MBzP, while the opposite was seen for MiBP. Also, higher concentrations in the U. S. study compared to HBM4EU Aligned Studies can be found for cx-MiNP, cx-MiDP, while the exposure level was lower for DINCH metabolites OH-MINCH & cx-MINCH.
2 , bottom panel), and the quantified factor of differences between studies was two to seven. The highest levels compared to the GM Eu were found in ESTEBAN (FR), PCB cohort (SK), and NEB II (NO), and the lowest for FLEHS IV (BE), POLAES (PL), FLEHS IV (BE),  Although some subsets of the HBM4EU Aligned Studies were derived from nationally representative studies (ESTEBAN, GerES V), the data collections included here were not necessarily representative of their country (e. g. targeting mostly citizens from cities, distribution of education does not reflect the country's, ESTEBAN and GerES V-sub not weighted to be nationally representative). In addition, up to 300 participants per study make it difficult to balance major demographics even in a single age group and include sufficient and representative variation in participants to be able to draw conclusions for a whole country.
In addition, participants from Western data collections (ESTE-BAN from France, FLEHS IV from Belgium, GerES V-sub from Germany) had higher mean urinary concentrations than participants from the Eastern data collection. For adolescents (Supplementary Fig. 5 ), no differences between European regions were found for ∑ DEHPm and ∑ DINCHm. For MiBP, there was no data for Eastern Europe and only three regions could be compared. For the other sum parameters or metabolites, differences between regions differed by compound. The example of DiBP exposure in children illustrates that although 2 to 4 studies per region provided data on phthalates, depending on the metabolites, only one study might have contributed to the observed differences for a region (e. g. in Eastern Europe). The composition of a region is important to keep in mind (see Supplementary Tables  1  and 2 ), also with respect to the degree of representativity participating countries (which in turn are represented by the respective studies).
Most exceedances of HBM-GVs were observed for DiBP and DnBP (up to 5%) indicating unacceptable exposure levels of these two substances in European children and adolescents and that further analysis of exposure determinants is needed to inform on major exposure sources, especially for DnBP and DiBP. In addition, as HBM4EU Aligned Studies included only little data from recent exposure years (2021), more harmonised HBM at EU level is warranted to evaluate the effectiveness of recent phthalate regulation.
A search for pairs of dijet resonances with the same mass is conducted in final states with at least four jets. Results are presented separately for the case where the four jet production proceeds via an intermediate resonant state and for nonresonant production. The search uses a data sample corresponding to an integrated luminosity of 138 fb -1 collected by the CMS detector in proton-proton collisions at √ s = 13 TeV. Modelindependent limits, at 95% confidence level, are reported on the production cross section of four-jet and dijet resonances. These first LHC limits on resonant pair production of dijet resonances via high mass intermediate states are applied to a signal model of diquarks that decay into pairs of vector-like quarks, excluding diquark masses below 7. 6 TeV for a particular model scenario.
Two modes of production of the pair of new particles are possible, resonant production with an intermediate resonance Y and non-resonant production, both shown in figure  1 , and each mode presents a unique opportunity for discovery. We present a generic search for pairs of narrow dijet resonances in each production mode with data corresponding to an integrated luminosity of 138 fb -1 collected in 2016-2018 with the CMS detector at the LHC, and we use results obtained from each mode to constrain a benchmark model, as discussed below. Prior searches were for Lorentz-boosted low mass resonance pairs, produced via both resonant  [20]  and nonresonant  [21]  mechanisms, and have been conducted using jet substructure techniques to identify boosted resonances inside of jets. This analysis considers pairs of resolved dijet resonances, X, where both jets within each dijet resonance are individually reconstructed, allowing the resonant and nonresonant searches to be sensitive to high resonance masses: X masses greater than 0.
Event reconstruction and selection A particle-flow (PF) event algorithm aims to reconstruct and identify each individual particle in an event, with an optimized combination of information from the various elements of the CMS detector  [45]. Particles are classified as muons, electrons, photons, charged or neutral hadrons. To reconstruct jets from the input particles, the anti-k T algorithm  [46, 47]  is used with a distance parameter of 0. 4 as implemented in the FastJet package  [48]. The primary vertex (PV) is taken to be the vertex corresponding to the hardest scattering in the event, evaluated using tracking information alone, as described in section 9. 4. 1 of ref. [49]. Charged PF candidates not originating from the primary vertex are removed prior to the jet finding. Events are selected using a two-tier trigger system  [50].
Events satisfying loose jet requirements at the first-level (L1) trigger are examined by the high-level trigger (HLT) system. At L1, single-jet triggers that require at least one jet in the event to exceed a predefined transverse momentum (p T ) threshold are used. Triggers that require H T to exceed a threshold, where H T is the scalar sum of jet p T for all jets in the event with p T > 30 GeV and |η| < 3. 0, are also used. The HLT requires H T > 1050 GeV or at least one jet reconstructed with an anti-k T distance parameter of 0. 8 and p T > 550 GeV. The jet momenta and energies are corrected using calibration factors obtained from simulation, test beam results, and pp collision data at √ s = 13 TeV. The methods described in ref. [51]  are used and all in-situ calibrations are obtained from the current data.
There are two events in the tails of the distributions, with a four-jet mass of 8 TeV and an average dijet mass of 2 TeV. They result in a local significance of 3. 9 standard deviations and a global significance of 1. 6 standard deviations, when interpreted as a signal of a four-jet resonance with mass 8. 6 TeV and a dijet resonance with mass 2. 15 TeV. More data are needed to establish if these events are the first hints of a signal, and LHC data with the highest possible collision energy would be very effective. Model-independent upper limits at 95% confidence level (CL) are presented on the production cross section times branching fraction and acceptance. For resonant production, the limits are presented as a function of the four-jet resonance mass between 2 and 9 TeV, for all accessible values of the ratio of the dijet to four-jet resonance masses.
Acknowledgments We thank  Bogdan Dobrescu  and  Josh Isaacson  for supplying the code and cross section for the diquark model and for their work developing a benchmark model specifically for this search. We congratulate our colleagues in the  CERN accelerator departments  for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the  Worldwide LHC Computing Grid  and other centers for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the  LHC , the CMS detector, and the supporting computing infrastructure provided by the following funding agencies:  BMBWF  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ ,  FAPERGS , and  FAPESP (Brazil) ;  MES  and  BNSF (Bulgaria) ;  CERN ; CAS, MoST, and  NSFC (China) ;  MINCIENCIAS (Colombia) ;  MSES and CSF (Croatia) ;  RIF (Cyprus) ;  SENESCYT (Ecuador) ;  MoER ,  ERC PUT and ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3 (France) ;  BMBF ,  DFG , and  HGF (Germany) ;  GSRI (Greece) ;  NK-FIH (Hungary) ;  DAE and DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP and NRF (Republic of Korea) ;  MES (Latvia) ;  LAS (Lithuania) ;  MOE and UM (Malaysia) ;  BUAP ,  CINVESTAV ,  CONACYT ,  LNS ,  SEP , and  UASLP-FAI (Mexico) ;  MOS (Montenegro) ;  MBIE (New Zealand) ;  PAEC (Pakistan) ;  MES and NSC (Poland) ;  FCT (Portugal) ;  MESTD (Serbia) ;  MCIN/AEI and PCTI (Spain) ;  MOSTR (Sri Lanka) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ;  MHESI and NSTDA (Thailand) ;  TUBITAK and TENMAK (Turkey) ;  NASU (Ukraine) ;  STFC (United Kingdom) ;  DOE  and  NSF (USA).
"72 Therefore, one would administer any one of the many nourishing life [0419b20] in the Kissa Yōjōki 0175. Although Benn's translation of this passage may be interpreted as a movement of disease along the line of mutual generation, because of Eisai's knowledge of the five-phase paradigm, it is possible that Eisai may be referring to the more complicated movement of disease along the controlling cycle within the fivephases visceral interface. Here, Eisai uses the verb ke 尅 (restrain, control, overcome). The term ke 尅 is normally used to represent the controlling movement along the "system of correspondence" within the five-phases. However, a character analysis conducted of the Chinese medical classics on the online digital database Chinese Text Project (ctext. org), revealed a surprising dearth of the term of ke 尅 in both the Huangdi Neijing Suwen Lingshu (Yellow Emperor's Classic of Simple Questions and Needle Therapy) and the Nan jing (Classic of Difficulties); the classics seem to prefer the term sheng 勝 (overcome, keep in check).
The Chinese characters of 近代 jindai (recent, modern times), very much suggest that Eisai was exposed to something very unique while in China. Taken from the online database CBETA, Taisho Tripitaka. 0175, Kissa Yōjōki 喫茶 養生記西; paragraph [0419a05] http://tripitaka. cbeta. org/B32n0175_001. 22 Benn, Tea in China: A Religious and Cultural History, 158. 23 Translation of titles adopted from James Benn. 24 Translation of titles adopted from James Benn.
Article The diverse meteorology of Jezero crater over the first 250 sols of Perseverance on Mars NASA's Perseverance rover's Mars Environmental Dynamics Analyzer is collecting data at Jezero crater, characterizing the physical processes in the lowest layer of the Martian atmosphere. Here we present measurements from the instrument's first 250 sols of operation, revealing a spatially and temporally variable meteorology at Jezero. We find that temperature measurements at four heights capture the response of the atmospheric surface layer to multiple phenomena. We observe the transition from a stable night-time thermal inversion to a daytime, highly turbulent convective regime, with large vertical thermal gradients. Measurement of multiple daily optical depths suggests aerosol concentrations are higher in the morning than in the afternoon.