dataesr/scientific-paragraphs-categorization huggingface dataset:
Young Adult Carers (YAC) are informal carers aged -years. This is an unrecognized population. The present systematic review aims to respond to: ( ) how YAC are identified in research; ( ) the prevalence of YAC; ( ) the characteristics of YAC; and ( ) how to support YAC. Five electronic databases (Google Scholar, PsycArticle, PsycInfo, Psychology and Behavioral Sciences Collection, and PubMed) were searched for studies, scientific articles, and gray literature on YAC published prior to January ,. The quality of included studies was assessed using the Crow Critical Appraisal Tool, and a narrative method was used to underline major findings. Twenty-three studies were included and revealed that there were several ways to identify YAC, resulting in heterogeneous prevalence. Nine themes were highlighted for YAC characteristics (way into caring; care receiver; caring responsibilities; amount of caring; self-identification as a carer; living arrangement; physical, psychological, and adaptative outcomes; interpersonal relationships; education and employment); and three for YAC support (needs, available support services, and recommendations).
Study selection, characteristics, and quality The database search and identification procedure led to 10,414 studies. After screening, 40 studies were examined in full. During full-text screening, 17 studies were excluded for the following reasons: thesis that became the subject of published articles (n = 2;  Abraham, 2010; Joshi, 2014) ; studies on young adults who had an ill/disabled relative but were not identified as providing informal care (n = 7;  Abraham and Stein, 2013, 2015; Mechling, 2015; Petrowski, 2015; Hinojosa et al. , 2018; Nuttall, 2018) ; a study on young adults identified as YAC but without an ill/disabled relative (van der  Werf et al. , 2020b) ; studies exploring young adults' willingness to serve as informal caregivers in the future (n = 4;  Alva, 2013; Joshi et al. , 2015; Trujillo et al.
A clear understanding of nuclear parton distribution functions (nPDFs) plays a crucial role in the interpretation of collider data taken at the Relativistic Heavy Ion Collider, the Large Hadron Collider (LHC), and in the near future at the Electron-Ion Collider. Even with the recent inclusions of vector boson and light meson production data, the uncertainty of the gluon PDF remains substantial and limits the interpretation of heavy ion collision data. To obtain new constraints on the nuclear gluon PDF, we extend our recent nCTEQ15WZ þ SIH analysis to inclusive quarkonium and open heavy-flavor meson production data from the LHC. This vast new data set covers a wide kinematic range and puts strong constraints on the nuclear gluon PDF down to x ≲ 10 -5. The theoretical predictions for these data sets are obtained from a data-driven approach, where proton-proton data are used to determine effective scattering matrix elements.
This approach is validated with detailed comparisons to existing next-to-leading order calculations in nonrelativistic QCD for quarkonia and in the general-mass variable-flavor-number scheme for the open heavy-flavored mesons. In addition, the uncertainties from the data-driven approach are determined using the Hessian method and accounted for in the PDF fits. This extension of our previous analyses represents an important step toward the next generation of PDFs not only by including new data sets, but also by exploring new methods for future analyses.
Even the vector boson production and single inclusive hadron production data sets, that were included in our recent nCTEQ15WZðþSIHÞ studies  [31, 32] , do not constrain the gluon below x ≈ 10 -3. In this paper, we perform a global analysis of nuclear PDFs in the nCTEQ framework, including open heavy flavor meson and heavy quarkonium data. Heavy quarkonium and open heavy-flavor meson production data (in the following collectively called heavy quark production, HQ) has the potential to yield new constraints on the gluon PDF, because the gluon-gluon channel contributes the dominant part to the overall cross section of these processes. This was first shown in the case of proton PDFs in Ref. [33] , before the reweighting study presented in Ref. [34]  was the first to demonstrate that the LHC heavy-quark data is also useful to constrain the nuclear gluon PDF at small x.
A. The nCTEQ framework The nCTEQ project expands upon the foundation of the proton PDF global fitting analysis by including the nuclear dimension. In early proton PDF analyses (e. g. , Ref. [60] ), the nuclear data was used to calculate correction factors which were then applied to the proton PDF fit without any uncertainties. In contrast, the nCTEQ framework provides full nuclear PDFs from dedicated global analyses with uncertainties given in the Hessian framework  [61]. The details of the nCTEQ15 nPDFs are presented in Ref. [15]. The current analysis, along with the other recent nCTEQ analyses, such as nCTEQ15WZ  [31] , nCTEQ15HIX  [62] , and nCTEQ15WZ þ SIH  [32] , is performed with a new C++-based code nCTEQ++. This allows us to easily interface external programs such as HOPPET  [63] , APPLgrid  [64] , and INCNLO  [65].
This includes the proton PDF baseline, which is fixed to the parameters obtained in Ref. [67]. While the nCTEQ++ integrates both the proton and nuclear degrees of freedom into the same framework, in the present study we only vary the nuclear degrees of freedom. Although recent progress has significantly reduced the nuclear PDF uncertainties, these still dominate the proton uncertainties. For example, Ref. [68]  has performed a comprehensive study investigating the interplay of the nuclear and proton uncertainties for the case of W production in pPb collisions. Using the methods of theoretical covariance matrices and Hessian PDF reweighting, they find that impact of the freeproton PDF uncertainties is small for the current data sets. However, given the improved statistics anticipated from the LHC Run 3, they expect it will be essential to include the proton uncertainties for future analyses.
Therefore, keeping the normalization of this bin the same as the remainder of the data set causes the χ 2 =N d:o:f: for the entire ϒð1SÞ fit to increase from 0. 92 to 1. 6. This particular bin is also not described by other models based on the color-octet mechanism  [73]. We also limit the study to LHC data only, as the available RHIC data taken at very different energies is not guaranteed to work with the same fit parameters and would also likely not provide additional strong constraints.
Overall the two methods are in very good agreement with only minor discrepancies seen in the highest p T bins. It should be noted, however, that Ref. [35]  shows that the x-dependence of the GMVFNS and the Crystal Ball predictions do not match exactly. They show that the Crystal Ball approach depends on a more narrow x-region, but the increased flexibility in our updated Crystal Ball parametrization should mitigate this to some extent. Furthermore, constraints from the quarkonium data dominates over the ones from open heavy flavor data like D-meson production. At the same time in case of quarkonium data we do not have reliable perturbative calculations and we need to resort to the data driven method which further limits the impact of using the data driven or perturbative calculation for D meson data.
IV. IMPACT OF HQ DATA ON nPDF FITS Using the Crystal Ball parameters determined in the previous section we can now perform a new global nPDF fit using the available heavy-quark data. The new fits are using the same framework as nCTEQ15WZ þ SIH, including all settings like open parameters, scales and cuts for the previously included data. The Hessian tolerance is also kept at a value of T ¼ 35. We do not include the changes made for nCTEQ15HIX  [62]  and nCTEQ15ν  [103]  as these developments are mostly orthogonal to those made in this study and do not affect the low-x gluon PDF. One minor change from the previous analyses is the treatment of normalizations. Previously, χ 2 penalties were assigned individually for each affected data set, whereas now they are applied only once per normalization parameter.
A. Data selection We add the heavy-quark data sets shown in Tables VII-X to the new PDF fit for a total of 1484 (548 new, 936 old) data points. Similar to the fragmentation function uncertainties of the SIH data in Ref. [32] , we can compensate for the theoretical uncertainty of the data-driven approach by adding the uncertainty from the Crystal Ball fit as a systematic uncertainty to all new data sets. For the new HQ data, we use the same cuts as in the proton-proton baseline and additionally exclude D 0 data points with p T > 15 GeV, because there is no baseline data. Furthermore, we remove two individual points from the 2018 LHCb ϒð1SÞ data set that are described very FIG. 3. Comparison between prompt D 0 production in pp collisions as predicted in the GMVFNS (red) and with the data-driven approach (blue).
Finally, the new nCTEQ15HQ fit gives a good χ 2 for all processes with only minor increases for DIS and WZ production compared to both nCTEQ15WZ and nCTEQ15WZ þ SIH, which are outweighed by a significant improvement in the large sample of new heavy-quark data. Interestingly, the χ 2 =N d:o:f: value for ψð2SÞ production barely changes between the four fits, but since it is the smallest of the new data sets with large theory uncertainties from the baseline fit, the small impact is to be expected. To estimate the impact of the new data on the PDF parameters, it is instructive to look at the Δχ 2 profiles along specific parameters. Figure  9  shows parameter scans for all of the seven gluon parameters included in the fit with the Δχ 2 split into processes. It is immediately evident that the new heavy-quark data is the dominant constraint on most of the parameters with the WZ production always constraining one side, because its minimum is somewhat off center.
This indicates some slight tensions with the WZ production data, but looking back at Table XI this tension largely comes from the SIH data and is still well within expectations. The only parameter that is not constrained mostly by vector boson and heavy quark production is b g 0. This parameter determines the A dependence of the gluon normalization and minor shifts can therefore be compensated through the normalization parameters of the data sets. Additionally, since parameters with b indices are related to the nuclear A dependence, they may cancel out when looking only at a singular A as is the case in the LHC data, which is only taken in proton-lead collisions.
V. CONCLUSIONS In conclusion, we have incorporated a large new data set of heavy quarkonium and open heavy-flavor production in the nCTEQ++ framework and extended our PDF analysis of the gluon to significantly lower x values than was previously possible. We employed a data-driven approach to determine the theory and investigated the advantages and limitations of this approach in detail. In particular, we determined the kinematic range where the approach is applicable and verified the predictions with those from rigorous pQCD calculations for J=ψ and D 0 production. We obtained good χ 2 =N d:o:f: values for the new data sets without compromising those of the established sets. The new data has a tremendous impact on the gluon PDF, especially in the region x < 0. 01, and lowers the uncertainty considerably.
Future studies can build upon the present work in a variety of ways. Most notably, the proton PDF baseline will be updated and the related uncertainties can be taken into account. Secondly, it would be interesting to use perturbative calculations in future analyses of the D-meson data to compare the resulting PDFs. Finally, additional data with complementary kinematics, e. g. , the CMS dijet data or data on prompt photon production can be included to cross-check the impact of the heavy quark data.
ACKNOWLEDGMENTS The work of P. D. , T. J. , M. K. , and K. K. was funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation )-Project No. 273811115-SFB 1225. We thank  Peter Braun-Munzinger  and  Johanna Stachel  for stimulating this research and all our SFB Collaborators for very useful discussions. P. D. , T. J. , M. K. , K. K. , and K. F. M. also acknowledge support of the DFG through the  Research Training Group GRK 2149. The work of I. S. was supported in part by the  French National Centre for Scientific Research CNRS  through  IN2P3  Project  GLUE@NLO. F. O. acknowledges support through  US DOE  Grant No. DE-SC0010129. A. K. acknowledges the support of  Narodowe Centrum Nauki  under  Sonata Bis Grant  No. 2019/34/E/ST2/00186. R. R. acknowledges the support of the  Polska Akademia Nauk  (Grant Agreement No.
Background: Drugs are a frequent cause of severe anaphylactic reactions. Here, we analyze a large dataset on drug induced anaphylaxis regarding elicitors, risk factors, symptoms, and treatment. Methods: Data from the European Anaphylaxis Registry (2007-2019) with 1815 reported cases of drug-induced anaphylaxis were studied accordingly. Results: Drugs are the third most frequent cause of anaphylaxis reported in the Anaphylaxis Registry. Among the eliciting groups of drugs analgesics and antibiotics were far most often reported. Female and senior patients were more frequently affected, while the number of children with DIA was low. DIA patients had symptoms affecting the skin and mucous membranes (n = 1525, 84. 02%), the respiratory (n = 1300, 71. 63%), the cardiovascular (n = 1251, 68. 93%) and the gastrointestinal system (n = 549, 30.
| INTRODUC TI ON Anaphylaxis is a potentially life-threatening systemic hypersensitivity reaction, defined as any acute onset illness with typical skin features, plus involvement of respiratory and/or cardiovascular and/ or persistent severe gastrointestinal symptoms; or any acute onset of hypotension or bronchospasm or upper airway obstruction where anaphylaxis is considered possible, even if typical skin features are not present. 1 The most common culprits for anaphylaxis are drugs, insects, and foods. 2  For decades, drug consumption has been rising, in part driven by changes in clinical practice in age-related and chronic diseases. 3 The purpose of this manuscript is to analyze the clinical epidemiology of drug-induced anaphylaxis (DIA) in the European Anaphylaxis Registry (EAR). There are numerous data sources on DIA worldwide, with geographical differences in prevalence, emergency treatment, and postreaction care.
Current data show that the diagnosis is often missed, with underuse of the few available specific test methods for some drugs and a lack of specific and sensitive test methods for many drug groups. 12 However, appropriate management of patients with anaphylaxis is fundamentally dependent on a correct diagnosis and immediate treatment. This study unravels the most frequent elicitors, symptoms, and possible cofactors of anaphylactic reactions triggered by drugs in a large cohort from the European Anaphylaxis Registry.
| ME THOD The Anaphylaxis Registry collects data regarding the culprit causing anaphylaxis through a web-based data entry system. The multilingual online questionnaire with an assembly of 375 variables was filled out by allergists from 75 tertiary referral centers in 11 countries. Experienced allergy centers are participating on a voluntary basis in the register. Study centers were asked to enter their most severe cases, usually with respiratory and/or cardiovascular symptoms, but more moderate anaphylaxis could be recorded as well. After the allergic work-up in the questionnaire, the clinical specialist decided whether the respective trigger was confirmed or highly suspected. Confirmed cases were those that could be clearly attributed to one specific trigger (clear history of exposure and onset of symptoms and/skin tests and/or provocation test).
Highly suspected were the cases in which experts assumed with a high degree of probability that there was an anaphylactic reaction that could be attributed to one specific trigger but were not able to confirm this with absolute certainty (no skin and/or provocation test). Cases with more than one suspected trigger were not included into this analysis. Originally developed for the German Anaphylaxis Registry, the questionnaire has been translated for international use. Interrater reliability was assessed by using repeated data entry by two independent pro-.
| Drugs are the third most frequent cause of anaphylaxis The entire dataset includes 12,848 cases of anaphylactic reactions, recorded between July 2007 and March 2019. Of these, 4350 cases are food related (37. 37%), 4945 are insect related (42. 48%), and 2346 (20. 15%) cases are drug-induced, making DIA the third most frequent elicitor of anaphylaxis. The flowchart in Figure  1  illustrates the detailed case selection process and the number of remaining samples after each step. In the first step, we excluded all nondrug-related cases (n = 10, 138). Subsequently, we sorted out cases with more than one suspected trigger (n = 144) and did not include moderate cases (Ring & Messmer grade I, n = 233) or reactions that occurred due to specific immunotherapy (SIT). After data processing, 1815 cases remained for further analysis.
| Analgesics Analgesics are the most common culprits for drug-induced anaphylaxis in the European Anaphylaxis Registry (n = 749, 41. 27%). According to the Anatomical Therapeutic Chemical (ATC) classification system,  14  analgesics are classified into three subgroups: Nonsteroidal antiinflammatory drugs (NSAIDs), other analgesics and antipyretics (pyrazolones and anilids) and opioids. Opioids were not analyzed any further since opioid-triggered anaphylaxis was reported only four times in the dataset. The results obtained from this analysis are shown in Table  4  and reveal that analgesics as a trigger for anaphylaxis occurred significantly However, no significance regarding other emergency drugs, such as antihistamines, beta-2-agonists, and corticosteroids was observed.
| DISCUSS ION In this study, we analyzed a large dataset of drug-induced anaphylaxis covering more than one decade. Drugs are the third most common cause of anaphylaxis after foods and insect venom in the European Anaphylaxis Registry. However, severe reactions and reactions that needed to be treated in hospital and ICU occurred significantly more often with DIA compared with other elicitors, which is consistent with other studies. 15 Furthermore, hospitalization rates in DIA reported within the European Anaphylaxis Registry were significantly higher than in anaphylaxis caused by other triggers, eventually linked to the most common location of occurrence: medical facilities.
| Sex as a factor 2] [23] [24]  Interestingly enough, data from Korean databases show different sex proportions. 17, 25 The hormone estradiol has been discussed as a potentiator for sex differences in anaphylaxis,  26  as it may reinforce mast cell releasability. 27 We found that male patients, however, developed significantly more severe reactions than female patients. 16 A possible cause might be that with increasing age men tend to be multimorbid compared with women of the same age. 28.
| Symptoms In concordance with other studies, we found that the most frequent symptoms in DIA were skin symptoms and the least frequent were gastrointestinal symptoms. 29 Nevertheless, these symptoms still occurred in one in three patients in our database, contradicting other studies from Portugal or Korea, where the proportion of gastrointestinal (GIT) symptoms was lower. 23, 25, 30 This may partly be explained by the greater use of analgesics  23  in the regions covered by the European Anaphylaxis Registry (EAR). We showed that children presented more often with skin symptoms than adults and seniors, which is in concordance with previous studies. 29, 31 However, we found no significant difference in the occurrence of cardiovascular symptoms compared with other culprits-which contradicts other studies.
In this database, most of the reported DIA to NSAIDs in children were due to propionic acid derivatives and in seniors due to acetic acid derivatives-both do not seem to cause severe reactions. For salicylic acid, however, we found that people with respiratory diseases are more susceptible to hypersensitivity reactions. 47 is should be considered when prescribing medications. For the group of pyrazolones, we found that they lead to severe reactions. However, this could be attributed to the fact they are often given after surgery, that is, to patients in a state of physical vulnerability.
81) p <. 05 Adrenaline 421 (23. 20) 1721 (17. 82) p <. 0001 Postreaction care Hospital admission 574 (31. 63) 2663 (27. 58) p <. 05 ICU 215 (11. 85) 526 (5. 45) p <. 0001 Note: The differences between groups were assessed with the Chi-squared or the Fisher's exact test. p-values<. 05wereconsideredstatisticallysignificant. TA B L E 2 Demographics of druginduced anaphylaxis: characteristics of drug-induced anaphylaxis versus anaphylaxis caused by other triggers (foods or insects)3. 9. 1 | Local anestheticsBeyond analgesics and antibiotics, local anesthetics were the third largest ATC drug group given as an elicitor of anaphylaxis in our dataset (n = 134, 7. 38%). However, only 49. 25% of all registered DIA to local anesthetics were confirmed (n = 66), while all others were only suspected (see Table1).
During sampling and sample handling, all precautionary measures (e. g. , carefully cleaning all the equipment used prior to sampling, covering samples and equipment in use), recently suggested by GESAMP (2019) guidelines, were taken into account in order to avoid potential environmental contamination. The use of a flow-meter was not applied in all sampling areas, since flow-meters do not always give reliable results  (Gago et al. , 2018). For all sampling areas, the filtered volume (m 3 ) was calculated by multiplying the area of the net mouth by the distance covered during the tow, between two positions registered by GPS starts and stops  (Gago et al. , 2018; Ourmieres et al. , 2018). The MPs abundance was then calculated as items/m 3 , namely the number of particles per volume. These data were also converted to items/m 2 and items/Km 2 in order to be comparable with the simulated MPs abundance and also the majority of available in situ data in the Mediterranean (see Table  1 ).
Model Description A basin-scale Mediterranean (Figure  1 ) 3-D hydrodynamic model (∼5 Km horizontal resolution) has been coupled with a Lagrangian drift model (see also  Tsiaras et al. , 2021) , describing the transport and fate of different size MPs from major land based sources (rivers and wastewater effluents). The hydrodynamic model is based on the Princeton Ocean Model (POM;  Blumberg and Mellor, 1983 ) that is currently operational within the POSEIDON forecasting system (Korres et al. ,  2007 1 ). A hybrid ensemble data assimilation algorithm  (Tsiaras et al. , 2017)  was implemented to correct the simulated near surface circulation, based on satellite altimetry and sea surface temperature data, obtained from the European Copernicus database. 2 The hydrodynamic model is also coupled with a comprehensive biogeochemical model  (Kalaroni et al.
Fine-Resolution Model Simulations and Comparison With in situ Data In this study, a 2-year simulation over the 2018-2019 period was performed with the fine-resolution models in the four key study areas (Figures  3 4 5 6 ). As mentioned above, sources of MPs were slightly modified from those adopted in the Mediterranean basin-scale simulation in order to obtain a better fit of simulated microplastics with in situ data in this study (Figures  7 8 9 ) and other available data in the Ligurian Sea and the Gulf of Lion (see Supplementary Figures  8, 9 ). More specifically, untreated wastewater in the Ligurian Sea was decreased (× 0. 35), as simulated MPs abundance with the Mediterranean model was clearly overestimated in coastal areas (Figures  7 8 9 and Supplementary Figures  3, 8, 9 ). This initial overestimation in sources could be related to a possible upgrade of treatment facilities along the Italian coast, with regard to those reported in the UNEP/MAP (2011) report.