dataesr/scientific-paragraphs-categorization huggingface dataset:
This is an issue which is limiting the potential physics output of the LHC programme, and may get significantly worse at HL-LHC, where the projected computing needs of the experiments exceed the resources that are expected to be available  [14] , despite the fact that the most aggressive HL-LHC physics projections  [20] [21] [22] [23]  assume no uncertainty due to the limited size of simulated samples. When the HEP Software Foundation prepared its CWP  [14]  in 2017, the fraction of the ATLAS CPU resources in WLCG used for event generation was estimated  [24]  at around 20%. Beyond the existing projections, which assume the same level of theoretical precision as in the current event generation campaigns, concern was also raised that event generation would become computationally more expensive at the HL-LHC, where more complex calculations (e.
In terms of absolute CPU time spent for event generation, the ratio between ATLAS and CMS is actually larger, as the overall ATLAS budget for compute resources is larger than that of CMS. To understand what causes this difference, detailed benchmarking of the computational costs of Sherpa  [29]  and Mad-Graph5 aMC@NLO  [30]  (in the following abbreviated as MG5 aMC) have also started  [31, 32] , as these are the two generators used for some of the most expensive event generation productions in ATLAS and CMS, respectively. The WG has also been active in other areas, such as in discussing the possible sharing of common parton-level samples by ATLAS and CMS  [33] , and in reviewing and supporting the efforts for porting generators to modern architectures, notably GPUs. This last activity is particularly important, as it has become increasingly clear that being able to run computeintensive WLCG software workloads on GPUs  [34]  would allow the exploitation of modern GPU-based supercomputers at High Performance Computing (HPC) centers, and generators look like a natural candidate for this, as discussed later on in Sec.
Accounting of compute budgets for generators While progress has been made in the HSF generator WG to better understand which areas of generator software have the highest computational cost, more detailed accounting of the experiment workloads and profiling of the main generator software packages would help to further refine R&D priorities. Accounting of CPU budgets in ATLAS/CMS. Thanks to a large effort from the generator teams in both experiments, a lot of insight into the settings used to support each experiment's physics programme was gained within the WG. It is now clear that the fraction of CPU that ATLAS spends for event generation is somewhat higher than that in CMS, although the difference is lower than previously thought: the latest preliminary estimates of these numbers are 12% and 5%, respectively.
A more detailed study of the different strategies is ongoing, in particular by analysing individually the CPU costs of the main processes simulated (notably, V+jets, t t, diboson and multijet). This effort aims at providing these figures also as absolute numbers in normalized HEP-SPEC06 (HS06) seconds  [148, 149] , to allow a more meaningful comparison of the compute budgets for event generation in ATLAS and CMS. A practical issue is that these figures had to be harvested from logs and production system databases a posteriori. Deriving precise numbers for CMS has been particularly difficult, requiring significant person hours to extract the required information, as until recently the event generation (GEN) and detector simulation (SIM) steps were mixed in a single software application, and no separate accounting figures for GEN and SIM could be recovered from past job logs, therefore Grid costs had to be extrapolated from ad-hoc local tests.
In other words, event-level parallelism looks like an appropriate approach to try and exploit efficiently these architectures  [155]. In this respect, generators should be somewhat easier to reengineer efficiently for GPUs than detector simulation software (notably Geant4  [156] ), where the abundance of conditional branching of a stochastic nature may lead to "thread divergence" and poor software performance (see, for examples, Refs. [157] [158] [159] [160] [161] [162] ). Porting and optimizing generators on GPUs is especially important to be able to exploit modern GPUbased HPCs (such as SUMMIT  [163] , where 95% of the compute capacity comes from GPUs  [164] ). Some work in this direction was done in the past on MG5 aMC, including both a port to GPUs (HEGET  [165] [166] [167] ) of the library that was used in MG5 aMC, before ALOHA  [168]  was introduced, for the automatic generation of matrix element code (HELAS  [169, 170] ), and a port to GPUs of VEGAS and BASES (gVE-GAS and gBASES  [171, 172] ).
Generic code optimizations. A speedup of generators may also be achievable by more generic optimizations, not involving concurrency. For instance, one could study if different compilers and build strategies  [206]  may lead to any improvements. Another possible strategy is to search for redundant calculations, i. e. to investigate if some numerical results can be reused more than once, for instance via data caching. Recent studies  [152]  on the way LHAPDF6 is used in Pythia8 have indeed resulted in significant speedups through better data caching, and similar studies are in progress for Sherpa  [153]. Ongoing studies  [209]  on MG5 aMC have similarly shown that important speedups may obtained through "helicity recycling", i. e. by avoiding the recomputation of some building blocks of Feynman diagrams which are needed in more than one matrix element calculation.
Physics challenges (increasing precision) In addition to software issues, important physics questions should also be addressed about more accurate theoretical predictions, above all NNLO QCD calculations, but also electroweak (EW) corrections, and their potential impact on the computational cost of event generators at HL-LHC. For a recent review of these issues, see for example Ref. [25]. Some specific NNLO calculations are already available and used today by the LHC experiments in their data analysis. For example, the measurements of fiducial t t cross sections, extrapolated to the full phase space, are compared to the predictions of TOP++  [80] , accurate to NNLO: this program, however, does not use MC methods and cannot be used to generate unweighted events. Research on NNLO matching has also made significant progress, for example on the NNLOPS  [210] , GENEVA  [211] , UN2LOPS  [140]  and MINNLOPS  [212, 213]  prescriptions.
Acknowledgements This work received funding from the  European Union 's  Horizon 2020 research and innovation programme  as part of the  Marie Sk lodowska-Curie Innovative Training Network MCnetITN3  (grant agreement no. 722104 ). This research used resources of the  Fermi National Accelerator Laboratory (Fermilab) , a  U. S. Department of Energy, Office of Science, HEP User Facility. Fermilab  is managed by  Fermi Research Alliance, LLC (FRA) , acting under Contract No. DE-AC02-07CH11359. This work was supported by the  Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory  under  U. S. Department of Energy  Contract No. DE-AC02-05CH11231. The work at  Argonne National Laboratory  was supported by the  U. S. Department of Energy, Office of Science, High Energy Physics Center for Computational Excellence  ( HEP-CCE) program  under Award Number  0000249313.
F. Krauss acknowledges funding as  Royal Society Wolfson  Research fellow. M. Schönherr is funded by the  Royal Society  through a  University Research Fellowship. E. Yazgan acknowledges funding from  National Taiwan University  grant  NTU 109L104019. A. Siódmok  acknowledges support from the  National Science Centre, Poland  Grant No. 2019/34/E/ST2/00457.
17, 18 Different methods used to assess anticholinergic use and variation in diagnostic tools for delirium may have contributed to these diverging study findings. 19 he availability of a database with patient characteristics from different European countries provides a unique opportunity to investigate the effect of anticholinergic drugs on delirium prevalence in a large NH population. The aim of this study is to investigate to what extent the use of drugs with anticholinergic properties in NH patients is associated with prevalence of delirium, particularly in people with dementia and to explore whether such an association would allow for clear recommendations with respect to clinical diagnosis and management of delirium in NH patients.
Setting Between May 2009 and July 2010, study partners in each country identified NHs willing to participate. All patients were assessed on baseline, 6 months, and 1 year, if still in the facility, using the interRAI LTCF. If no longer in the facility, reason (death, hospitalization, discharge to home or another institution) and date of death or discharge were recorded. The study ended in July 2011. Medical ethical approval was obtained following local regulations for all facilities in all participating countries according to local ethical regulations. Residents were invited to take part in the study and were free to decline participation. Consent was obtained with assurance of data confidentiality. 20 For the present study, we used data from the baseline assessments only.
Delirium Model On baseline, a diagnosis of delirium was approximated based on the following model using criteria recorded in the SHELTER database: an acute change in mental status deviating from usual functioning (ie, restlessness, lethargy, difficult to arouse, altered environmental perception) within the 3 days before the assessment or a new onset or worsening of 1 or more of the following symptoms: easily distracted (ie, episodes of difficulty paying attention; person gets sidetracked), episodes of disorganized speech (ie, speech is nonsensical, irrelevant, or rambling from subject to subject, person loses train of thought), and mental function variation over the course of the day. 24.
Statistical Analysis Patients' baseline characteristics, dementia diagnosis, prevalence of delirium, and use of anticholinergic drugs characterized according to the ARS and ACB were assessed using frequency analyses. Logistic regression analyses were performed to calculate the odds ratio (OR) and corresponding 95% confidence interval (CI) for prevalence of delirium in patients with and without dementia according to different measures of anticholinergic drug exposure (total number of anticholinergic drugs and categories of anticholinergic burden with the ARS and ACB). The number of anticholinergic drugs was treated as a continuous variable, and the anticholinergic burden divided into 3 categories: none or minimal, 0-1; moderate, 2; and strong, 3-10. For the SHELTER database, 10 was the highest score for both ACB and ARS.
All analyses were adjusted for age, cognitive function according to the CPS and comorbidity using the Charlson Comorbidity Index. For distribution of the prevalence of delirium in relation to anticholinergic drug burden according to ACB, we calculated the percentage of those with delirium within the ACB drug burden categories in patients with and without dementia, with corresponding 95% CIs. For stratification of the anticholinergic risk score according to the cognitive impairment severity levels, we calculated the percentage of patients with delirium within the ACB drug burden for categories of patients with a specific CPS score (1 ¼ borderline intact, 2 ¼ mild impairment, 3 ¼ moderate impairment, 4 ¼ moderate or severe impairment, 5 ¼ severe impairment, 6 ¼ very severe impairment). All analyses were conducted using IBM SPSS Statistics 26 for Windows (IBM Corp, Armonk, NY).
The data available did not allow to establish a follow-up for incidence of delirium in relation to drug prescription. Further, it was not known how long drugs were taken, as drug use was recorded for only 3 days prior to the assessment. Similarly, the SHLETER database did not allow to characterize the exact temporal relation between drug prescription and any mental changes during this period or even before. Another limitation concerning the anticholinergic burden is the fact that both the ACB and ARS list are based only on dichotomous (yes/no) information on use of drugs with or without anticholinergic properties but both do not incorporate dosing information to further characterize in detail the anticholinergic burden. The diagnosis of delirium, especially in people with dementia, is challenging and concerns a clinical diagnosis supported by a diagnostic tool like the confusion assessment method (CAM).
Construction of the genomic relationship matrix A GRM was established for 358 genotypes by combining two single nucleotide polymorphism (SNP) panels (Supplemental   S4 ). Panel 1 with 94 genotypes was a 770 SNP array  (Raatz et al. , 2019)  and more than 20,000 SNPs were available in Panel 2 with 298 genotypes based on diversity array technology analyzed by the Integrated Genotyping Service and Support platform, a genotyping service facility based in Nairobi, Kenya (as used by  Nkhata et al. , 2020). Thirty-four genotypes were common to both panels (Figure  2 ). FImpute software  (Sargolzaei et al. , 2014)  was used to impute missing values. The physical map position of each SNP locus in Panel 1 and Panel 2 was used to form a consensus SNP panel (defined as the African bean panel) based on 358 genotypes.
OCS Optimal contributions selection was used to generate a crossing design that balanced genetic gain for the GBLUPindex i under the constraint of a maximum permissible target inbreeding rate  (Kinghorn, 2011; Kinghorn & Kinghorn, 2021; Woolliams et al. , 2015). We used selection and cross allocation methods developed previously  (Cowling et al. , 2019; Cowling et al. , 2017). Optimal contributions selection was implemented in program "MateSel"  (Kinghorn & Kinghorn, 2021) , which is based on an evolutionary algorithm with constraints easily invoked to ensure practical relevance and precise control of selection response and other outcomes. MateSel dictates which individuals to select and the actual crossing allocations and/or selfings to be made. The Plant Genome.
Genomic relationships Cluster analysis based on SNP data for 358 genotypes revealed groups and subgroups that conformed with the known Andean or Mesoamerican origins of the African bean panel. Several genotypes in a previous study  (Raatz et al. , 2019)  acted as reference genotypes to assist in classifying the clusters (Supplemental Figure  S2 , Supplemental Table  S9 ). The reference genotypes in the upper branch belonged to the Andean gene pool and defined this cluster as "Andean" (Supplemental Figure  S2 ). The uppermost cluster of the lower branch had several Andean reference genotypes, and this subgroup was classified as "Andean admixture. " The lower branch of the lower group had many Mesoamerican reference genotypes and hence defined this cluster as "Mesoamerican. " A branch above the Mesoamerican cluster included progenies of a cross between a Mesoamerican line (MLB-49-89A, GID 121058, in this branch) and an Andean genotype CAL96 (C.
Growth habit of plot varied widely within and between trials in several genotypes such as MLB-49-89A, KK8, and CNF5520 in our dataset, and this variation in GHP may be responsible for the significant GHP × genotype interaction for Fe and GY. The African bean panel included candidates from the Andean and Mesoamerican gene pools. Genetic differentiation between the Andean and Mesoamerican bean accessions has been well documented  (Delfini et al. , 2021; Lobaton et al. , 2018; Raatz et al. , 2019). Many Andean beans have higher Fe concentration than Mesoamerican beans  (Blair, 2013; Blair et al. , 2010). The Plant Genome The moderate-to-high genomic heritability values for GY (0. 45), CKT (0. 50), Fe (0. 57), and Zn (0. 61) in our study confirms the potential for future genetic improvement in these traits in the African bean panel.
However, the alliance of bean breeders and researchers in Africa (PABRA) considers that it is important to breed for MGs based on seed color and size  (Buruchara et al. , 2011; Mukankusi et al. , 2019). Optimal contributions selection can guide breeders toward a favorable outcome in terms of breeding goals and rates of inbreeding and helps to answer questions, such as whether crossing should be done between different MG (mixing seed color and size), different GH (mixing bush and climbing types), or different GP (mixing Andean and Mesoamerican types). White et al. (1992)  suggested exploitation of genetic variance in climbing types to improve potential yield in bush types. MateSel can be used as a tool to discover the best outcome in the breeding program based on various technical and logistical constraints  (Kinghorn & Kinghorn, 2021).
One technical constraint may be potentially poor field performance in progeny of Andean bush × Mesoamerican bush types due to a low frequency of shared domestication genes in the two GP  (Schmutz et al. , 2014). If this occurs, then several cycles of recurrent selection under OCS should re-establish a high frequency of favorable domestication alleles. The best result from OCS with MateSel, aligned with the breeding goals of PABRA, was obtained when genotypes were grouped based on MGs and some intergroup crossings were permitted in OCS Scenario 6 (Supplemental Table  S10 ). Scenario 6 resulted in several crossings between bush and climbing types (Table  5 ). Segregation will occur in progeny of Scenario 6 for GH, seed color, and seed size. This project is linked into regional breeding programs in six partner countries in East Africa where selection for these traits will occur during selfing generations, and segregation for these traits in early generations may be an advantage because each breeding program has slightly different goals and target agroecological environments and target markets.
Multivariate Statistics Morphological variation of Kapentagyrus spp. was visualized based on a PCA performed on 20 standardised haptoral morphometric parameters. The first PCA included morphometric data on haptoral structures of specimens of all host species analysed in this study. The seventh pair of hooks was excluded due to the large number of missing data. The first two PC axes explained 41. 7% and 20. 8% of the variation, respectively (Figure  3a ). Separation of species infecting members of Odaxothrissa Boulenger, 1899 and Kapentagyrus verbisti n. sp. ex P. leonensis from Lambaréné (Gabon) was visible along the first PC axis mainly driven by parameters from both dorsal and ventral anchors. Clustering with rather a continuous gradient along both PC axes among the rest of the species of Kapentagyrus suggests close morphological similarity among the species within Kapentagyrus with the first PC axis explaining 26.
2% and the second 22. 4% of the variation present in the dataset (Figure  3b ). In the NMDS plots (Figure  4 ), clusters and the length and direction of the vectors of the most important parameters in PCA are similar. While these parameters clearly contribute a great deal to differentiating species of Kapentagyrus, these metrics alone do not allow complete separation between species. Likewise, the length of the copulatory tube (Figure  5a ) and accessory piece (AP) (Figure  5b ) of the male copulatory organ (MCO) substantially overlap between various species of Kapentagyrus.
Acknowledgments:  Tine Huyse  is thanked for making this research possible by hosting N. K. at the  Royal Museum for Central Africa. Miguël Parrent ,  Jos Snoeks , and  Emmanuel J. Vreven  are gratefully acknowledged for providing access to the ichthyological collections at the  Royal Museum for Central Africa ,  Jean-François Agnèse  for providing host samples from  Lake Itezhi-Tezhi , and  James Maclaine  for granting access to specimens from the ichthyological collections of the  Natural History Museum (London, UK ).
Funding: This research was funded by  Czech Science Foundation  ( GA ČR)  standard project  GA19-13573S  and by the  SYNTHESYS  Project (http://www. synthesys. info/)( BE-TAF-5731 ), which is financed by  European Community Research Infrastructure Action  under the  FP7 Integrating Activities Programme. Part of the research leading to results presented in this publication was carried out with infrastructure funded by the  European Marine Biological Research Centre (EMBRC) Belgium, FWO-Vlaanderen  project  GOH3817N. M. P. M. V. and N. K. are financed by the  Special Research Fund of Hasselt University  ( BOF20TT06  and  BOF21PD01 , respectively). The funding agencies had no role in the design, execution, interpretation, or writing of the study.
Data Availability Statement: Type material was deposited in the invertebrate collection of the RMCA (RMCA_VERMES_43430-459), the collection of the research group Zoology: Biodiversity and Toxicology of Hasselt University (Diepenbeek, Belgium) (HU 779-820), the Finnish Museum of Natural History (Helsinki, Finland) (MZH https://id. luomus. fi/KV. 670-https://id. luomus. fi/KV. 685), and the Iziko South African Museum (Cape Town, South Africa) (SAMC-A094487-504). Raw morphometric data are available in Supplementary Materials.
Introduction The gains in life expectancy observed worldwide have increased the risk of living with chronic conditions  [1]. Over the past 2 decades, multimorbidity, defined as the co-occurrence of at least 2 chronic or recurrent conditions  [2] , has been increasingly investigated and estimated in various populations, settings, and geographical locations  [3] [4] [5] [6]. The broad variations observed in the available estimates of the burden of multimorbidity can be attributed, at least partly, to heterogeneous study methodologies in terms of the number and type of conditions (e. g. , diseases, risk factors, symptoms, specific conditions) as well as the thresholds used (e. g. , two or three)  [7, 8]. In particular, the identification of conditions for inclusion in multimorbidity assessments has largely relied on the prevalence, or more commonly, the availability of conditions in existing datasets  [8].
CIs of RERI were computed according to Zou  [28]. All the analyses (descriptive and analytic) were performed separately for the 2 surveys using SAS, version 9. 2 software (SAS Institute, Cary, NC, USA) and R software, version 3. 6. 1 (lasso methods). Appropriate weights were used to provide valid estimates for the French population, while taking into account the unequal probabilities of selection resulting from sample design, nonresponse, and noncoverage in the 2 surveys  [13, 14]. Due to the data collection methodology used (mostly personal interviews in the 2 surveys), there were very few missing data (<0. 005) either for conditions, standard sociodemographic variables, or health status measures. Due to the complex sampling designs, however, classical standard goodness of fit and predictive ability statistics including the Hosmer-Lemeshow statistic and c-index were not available.
Policy and research implications The lack of methodological consensus or consistent approaches for studying multimorbidity has been a serious impediment for the development of appropriate control and prevention strategies. The proposed approach reported in this paper can guide future contributions with the aim to gain a better understanding and awareness of the different aspects of multimorbidity. Although undertaken in a single country (France), this study is replicable, as exemplified by the use of several survey datasets. It could therefore be readily implemented in many countries with existing systems for the surveillance of chronic diseases. Multimorbidity is definitely not only a problem of old age; identification and monitoring should be implemented as early as midlife, possibly earlier in disadvantaged groups.