dataesr/scientific-paragraphs-categorization huggingface dataset:
By contrast, recommendations in adults also encompass occupational groups and all older adults are recommended for vaccination, thereby expecting to dilute chronic conditions as a predictor of vaccination. The covariates available in the dataset used were insufficient for DAG closure; therefore, residual confounding cannot be ruled out. For some covariates on which we have no data, such as socioeconomic status, data collection may be feasible in IVE studies (even if practically challenging in some settings). Several of the other variables in the DAG are challenging to account for in IVE studies. For example, data collection on variables such as contact patterns and precautionary behavior, which require behavioral questionnaires ideally administered before symptom onset, is unlikely to be considered a priority in large IVE studies.
19, 27, 30 llivan et al have reviewed the inclusion (and definition) of key variables in the principal analysis of 85 IVE studies and described how many studies applied the restriction criteria and adjusted for specific covariates. 17 Clearly, there is important variation between studies. 2] [33] [34]  The US Flu VE Network additionally adjusted for sex, self-rated general health, race, and Hispanic ethnicity  31  ; the HAIVEN for sex and race/ethnicity  34  ; and I-MOVE for sex, chronic conditions, and study site. 32, 33 CSPSN found that additional adjustment for sex and presence of at least one comorbidity resulted in an absolute change of the IVE of ≤4% and, therefore, did not include these variables in their final model. 7 e present study has several limitations. This was a secondary analysis of an existing dataset.
Collection of data on chronic conditions (beyond "presence of at least one chronic condition") and prior healthcare use were not mandatory and were consequently not available for all sites, sometimes only for a subset of subjects within one site. To optimize the use of all existing data, a complete case analysis was performed for each model. The drawbacks are, first, that the set of subjects included in each analysis was not identical, and second, that the number of conditions eligible to be counted to build the variables "presence of at least two or three chronic conditions" was not the same across all sites. Furthermore, the lack of more granular information on chronic conditions in the dataset, such as on the precise condition, disease severity, and frailty for older adults, precludes the possibility to assess in more detail the potential chronic conditions/ diseases associated with the likelihood of being diagnosed with influenza.
The finding that adjustment for additional variables did not lead to a meaningful impact on the IVE (with one exception) can be because the evaluated covariates are not true confounders, because of the low magnitude of the effect of true confounders, and/or because of the low prevalence of the variable in the population. Omitting variables with low prevalence only has a relatively small impact on IVE compared to more prevalent conditions with the same effect on the exposure and outcome. 43 though most estimates of the relative change in IVE were close to 1. 0, indicating little to no impact of additional adjustment on the IVE estimates in the current dataset, variability was observed in the width of the CI. Multiple covariates analyzed in adults and older adults in the primary care setting and in children in the hospital setting had a CI that encompassed a > 10% relative change in the IVE, which represents an important side note when generalizing the findings to potential future IVE studies.
Since Bitcoin transactions are publicly reported to a decentralized ledger, it is possible to 'listen' to incoming transactions and record the IP address (and so location) of a transaction's sender. We do this by sourcing data from blockchain. info, which contains every Bitcoin transaction as well as IP address, including the most likely relaying IP address (similar data are used by  Parino, Beiró, and Gauvin (2018) ). Following the user identification methods of  Foley et al. (2019) , we identify the location of and activity behind each transaction. We annualize these Bitcoin data (number of trades and volume) into measures of Bitcoin trades and volume per capita. We compute these measures annually for 80 countries over the period  2009-2018. To examine the relationship between individualism and Bitcoin activity, we conduct a multivariate regression analysis controlling for other potential determinants of a country's Bitcoin activity.
6 Unlike Visa, a proposed transaction from a user is sent to that user's connections (between 8 and 150 other users) and is then propagated out to the entire network. Forcing a connection with every other user allowed this 'motivated' participant to listen to all transactions and, critically, to observe the IP address from which a message is sent. When connected to all (or most) users, the first participant from which a given transaction is received is recorded as the 'likely origin' of the transaction. 7 Similar to  Parino et al. (2018) , our Bitcoin data is obtained from blockchain. info, one of the oldest blockchain explorers, who operated such a node between 6 January 2012 and 4 April 2018. Blockchain. info stores a dynamically updated record of every transaction on the blockchain and made IP addresses during this time available to us.
8 aving determined the location of Bitcoin users, we construct our country-level measure of Bitcoin activity by aggregating transactions on the Bitcoin network (the number of transactions sent and received as well as the volume sent and received per country). 9 We obtain data on the number and volume of trades per day and aggregate these data to an annual frequency. Overall, this yields a sample of 770 observations (80 countries over the period January 2009-April 2018). 10 To assess the impact of culture on Bitcoin activity, we scale our Bitcoin metric on a per capita basis and use logarithms of this metric as our dependent variable. 11 Given the difficulty of attributing pan-national exchanges to a particular country (i. e. , Binance offers services to over 140 countries globally) as well as the issues regarding wash-trading (i.
Finally, we include legal origin dummies, controlling for English, French, and German. We find a positive coefficient on the German civil law dummy, significant for the volume-based metric, and negative for English and French legal origin. Columns 2 and 5 of Table  3  report the results for the regressions where we include the Capital Flows Restrictions Index. Data on this index are only available for the earlier part of the sample, up to and including 2013. Hence, this regression also serves as a subsample test focusing on the early years of Bitcoin's existence. While the coefficients on individualism reduce slightly in these regressions, they remain positive and significant for both the trade and volume specifications, and so our results are robust to the inclusion of the Capital Flows Restrictions Index, as well as over the first part of the sample.
Alternative measures for individualism The results reported in the main specification are based on Hofstede's individualism score. To assess the robustness of our results with regard to the choice of culture framework, we collect individualism scores from alternative sources. Specifically, we obtain data from the GLOBE project  (House et al. , 2004) , where we use both their In-Group Collectivism, as well as their Institutional Collectivism scores, noting that the collectivism scale is the reverse of the individualism scale (i. e. , a high score on collectivism equates to a low score on individualism). In-Group Collectivism captures the degree to which people express pride, loyalty, and cohesiveness in their organizations or families, while Institutional Collectivism captures the degree to which organizational and societal institutional practices encourage and reward collective distribution of resources and collective action.
Although we expect a stronger connection between Institutional Collectivism and Bitcoin activity, we include both metrics as a robustness test. We also employ the "as-is" scores based on practices within a country, as practices will be mostrelated to the actions of people. In addition to the GLOBE scores, we obtain data from  Schwartz (1994). Schwartz (1994)  constructs various bi-polar culture dimensions. We focus on the dimensions that most resemble individualism/collectivism. We obtain the Embeddedness score, which relates to collectivism, as well as the opposite end of the spectrum: Affective and Intellectual Autonomy, which both relate to individualism. Embeddedness captures the degree to which people view themselves as entities embedded in the collective, their commitment to the status quo, and their restraining of actions that may disrupt in-group solidarity or the traditional order.
, 2013)  or optimizing resistance surfaces  (Peterman, 2018; Peterman & Pope, 2021) , widened the potential to identifying relevant landscape components while optimizing their cost values from the genetic data itself. The overall goal of our study is to assess the effects of landscape on N. spinifolia's genetic diversity and population structure to inform conservation strategies. We used genomic data from reently collected specimens of N. spinifolia across most of its range, the LM region. We first tested whether its restricted geographic distribution resulted in a low genetic diversity, as expected under a neutral model  (Kimura, 1983) , or remained relatively high as for co-distributed primates [P. tattersalli and Microcebus tavaratra Rasoloarison,  Goodman & Ganzhorn (Aleixo-Pais et al.
, 2002; Van de Paer et al. , 2018). As we cannot directly verify this assumption on progenies of N. spinifolia, we here investigated the linkage disequilibrium [LD] among organellar markers using the package poppr [Method S2  (Agapow & Burt, 2001; Kamvar et al. , 2014 Kamvar et al. , , 2015)) ]. A catalog of nuclear tags (loci) was de-novo optimized (Methods S6) by iterating around the core parameters of Stacks  (Rochette et al. , 2019)  to maximize the amount of available biological information  (Paris et al. , 2017). The final catalog was further cleaned (Methods S6) for exogenous contaminants using DeconSeq  (Schmieder & Edwards, 2011)  and endogenous orthologs using MUMmer  (Kurtz et al. , 2004). To enable ploidy-adapted analyses (catalog assembly and variants genotyping), ploidy was inspected using minor allele frequency plots and statistically confirmed using nQuire  (Weiß et al.
In addition, to evaluate how effective dispersal is distributed geographically, we build Mantel correlograms  (Oden & Sokal, 1986; Sokal, 1986) , using the ecodist R package  (Goslee & Urban, 2007). We used the Bruvo's genetic distances  (Bruvo et al. , 2004)  to consider the specific mutation model of the cpSSR data. Additionally, we used Nei's genetic distance  [Nei's D (Nei, 1973) ], appropriate for small scale study of organellar material haplotypic data [  (Kohrn et al. , 2017)  Methods S3]. Finally, we estimated the covariance of nuclear RADseq GLs  (Meisner & Albrechtsen, 2018) , in PCAnsgd, as such metrics based on principal components analysis have been shown to be particularly fitted for landscape genetics  (Shirk et al. , 2017).
Movement model and analytical flow To determine which dispersal model best applies to N. spinifolia, we used both the Least Cost Path (LCP) and the Circuit Theory (CT). These two approaches, respectively, consider the least cost trajectory and the cost of all possible trajectories  (McRae & Beier, 2007). We computed landscape distances using the R package gdistance  (van Etten, 2012). We used a two-step procedure to assess the landscape components' relative contributions to the spatial structure of N. spinifolia's genetic diversity. First we selected landscape components, as well as their best fitting cost, resolution, and movement model. We estimated the correlation between geographic or landscape distance and genetic matrices (i. e. Landscape variables and Genetic distances as described above) using Mantel tests  (Mantel, 1967)  in the R Package vegan  (Dixon, 2003).
g. Binara and Antsahabe). Altogether, this pattern, though unclear, echoes the peculiarities of these forests that likely acted as refugia for numerous taxa during drier periods  (Goodman & Wilmé, 2006; Rakotoarisoa et al. , 2013b; Raxworthy & Nussbaum, 1995; Sgarlata et al. , 2019). Data accessibility Raw RADseq data and RADseq mtDNA alignments have been deposited at the Short Read Archive (SRA) NCBI database under the reference PRJNA632767. Organellar microsatellite genotypes and mtRAD variants are available in Tables  S9  and S10 , respectively. All additional daa, scripts and materials are available to readers at https://doi. org/10. 5281/zenodo. 7193354.
Acknowledgments We thank the  Direction Générale du Ministère de l'Environnement et des Forêts de Madagascar, Madagascar's Ad Hoc Committee for Fauna and Flora, and Organizational Committee for Environmental Research (CAFF/CORE)  for permission to perform this study (permits number: [ 49/17] & [127/18]/MEEF/SG/DGF/DSAP/SCB. Re ) and for their support. We thank the local communities of Daraina and of the LM region for their warm reception and support. We thank  E. Rasolondraibe  and the many local guides and cooks for sharing their incomparable expertise and help in the field, misaotra anareo jiaby. We thank  U. Suescun ,  C. Verbeke  and  M. A. Naranjo Arcos  for lab assistance,  L. Chikhi  for comments on an earlier version of the draft. This work was mostly funded through an  ERA-NET   BiodivERsA  project:  INFRAGECO (Inference, Fragmentation, Genomics , and  Conservation ,  ANR-16-EBI3-0014 ).
We also thank the  LABEX TULIP  ( ANR-10-LABX-0041 ) and  CEBA  ( ANR-10-LABX-25-01 ), and the  LIA BEEG-B (Laboratoire International Associé -Bioinformatics, Ecology, Evolution, Genomics and Behaviour, CNRS). We are grateful to the  Get-Plage sequencing, Genotoul bioinformatics (Bioinfo Genotoul ) and GenOuest bioinformatics platforms for sequencing services and providing computing resources. The fourth preprint version of this manuscript has been peer-reviewed and recommended by  Peer Community In Evolutionary Biology  (https://doi. org/10. 24072/pci. evolbiol. 100136).
, 2014). Despite efforts to look for more meaningful temporal scales in community-level time series, temporal compensation has remained surprisingly elusive in the field  (Houlahan et al. , 2007; Vasseur et al. , 2014) ; but see  Christensen et al. (2018)  and Morgan Ernest et al. (2008). Most datasets used so far to evaluate temporal compensation vs synchrony involve planktonic organisms  (Vasseur et al. , 2014; Vasseur & Gaedke, 2007)  or terrestrial plants  (Bai et al. , 2004; Gross et al. , 2014; Houlahan et al. , 2007 ; though see  Bell et al. , 2014 in fishes, Morgan Ernest et al. , 2008 in mammals and van Klink et al. , 2019 in beetles). Here, we take advantage of a long-term bird abundance time series in a natural reserve, with records every month for 35 years, allowing us to dig deeper into patterns of synchrony, at several temporal and taxonomic or functional scales.
Our dataset is ideally suited to examine the presence of synchrony or compensation at different scales given that (i) it is a highly temporally resolved time series with respect to the species typical generation times, but it also extends well beyond generation time (timespan of 35 years) and (ii) the reserve where the data have been collected was subjected to a major management change c. 2006 (change in water levels), favoring different types of wetland birds (so that over long timescales, there is a real potential for changes in community composition).
, 2016). This is particularly important here as we perform analyses at different taxonomic scales, and therefore with a different n in Equation 1. All analyses performed with abundance in the main text are performed with biomass in Appendix S4. We computed the synchrony index η over all available years, but separately for cold and warm seasons, using the codyn package in R  (Hallett et al. , 2016). That is, we constructed two communitylevel time series of species abundances, one for the cold season and one for the warm season. To do so, we averaged monthly bird abundances, for each species, over the season duration. In follow-up analyses, we also differentiated periods before and after 2006, given that a management change occurred within the reserve in 2006. We (1) = 1 n ∑ i Corr X i , ∑ j ≠ i X j considered both the synchrony within a given guild (e.
An intuitive explanation of this modeling result is that when there are two groups and many species within a group, a stronger forcing homogeneizes the dynamics within a group as much as it creates differences between groups. This might explain the low levels of compensation that we found in our empirical dataset, at the overall wetland bird community level (Figure  3 ), in spite of the clear presence of two guilds (waders and waterfowl) reacting F I G U R E 3 Synchrony indices for the whole community of frequently observed birds. Panel (a) presents yearly synchrony (η) for both seasons and (b) the wavelet modulus ratio (ρ). The latter index scales from 0 (compensation, blue color) to 1 (synchrony, red color). Blue and red lines respectively delineate regions of significantly lower and higher synchrony than the null model (independently fluctuating species, but conserving their original Fourier spectrum), at the 10% level suggested by earlier plant studies (e.
DATA AVA I L A B I L I T Y S TAT E M E N T All the code and data used for analyses are available at https://github. com/fbarr aquan d/BirdT imeSe ries_Teich and archived at Zenodo, https://doi. org/10. 5281/zenodo. 6368332 (Picoche et al. , 2022)  and the main data file deposited at Dryad https://doi. org/10. 5061/ dryad. zpc86 6t9v.
F Figure 6 that synchrony is in fact the rule around the annual scale and below, when considering the wavelet modulus ratio. We wondered if the patterns in Figure 5 Synchrony index (η) as a function of the season (cold and warm seasons), calculated within (top, a-b) and between (bottom, c-d) groups. The groups considered were different taxonomic groups (Anatini, Calidris, left a-c) or functional groups (waders vs waterfowl, right b-d). The index was computed in each panel on the whole dataset (black) or using two periods: before and after 2006 (light and dark blue), the year of the change in water level management. Boxplots indicate the distribution of η under the null hypothesis (independent species) and filled symbols correspond to the observed values. Red stars correspond to synchrony values significantly different from the null model, at the 10% threshold with a Benjamini-Hochberg correction.
Acknowledgements This work was funded by the  French National Research Agency (Agence Nationale de la Recherche)  through  the  project  EGOFLEX ANR-17-CE08-0025 , and by the  Swedish Research Council  (Project  2017-04474 ). The computations were performed on resources provided by the  Swedish National Infrastructure for Computing (SNIC)  at  NSC  and  HPC2N.
FDA is a classical branch of statistics dedicated to the analysis of functional data, in situations where each data object is considered a function. This is particularly appropriate for DECT image data, as each 3D image voxel is associated with a curve of image intensity decay over multiple reconstructed energy levels (more details are provided below in Section 2. 1). Thus, we adapt FDA statistical models for the clustering of 3D image voxels based on the full functional information provided by the decay curves associated with each voxel. More specifically, the architecture underpinning our proposed method is a functional mixture model, where the mixture component densities are built upon functional approximation of the spectral decay curves at each image voxel, and the mixture weights are constructed to integrate spatial constraints.
We develop unsupervised learning algorithms for clustering by incorporating full spectral information from DECT data; 3. To our knowledge, this is the first time that these models are applied to DECT; 4. The source codes of our algorithms are publicly available https://github. com/fchamroukhi/ DECT-CLUST (accessed on 28 October 2022), free of charge. The rest of this paper is organized as follows; First, as a background, we describe in Section 2 related work on dual-energy CT and dedicated segmentation methods. Then, in Section 3, we introduce the proposed methodology and present the developed algorithms. Section 4 is dedicated to the experimental study and the obtained quantitative results are provided in Section 5. Finally, in Section 6, we discuss the proposed approach and the obtained results.
Data In total, 91 head and neck DECT scans were evaluated, consisting of HNSCC tumors of different sizes and stages from different primary sites. In our dataset, 34% of tumors are located in the oral cavity, 26% in the oropharynx, 21% in the larynx, 8% in the hypopharynx and 11% in other locations. The tumors' T-stage  [43]  ranges from T1 to T4. Of the patients, 75% were coming for a first diagnostic while 25% were recurrent patients. Institutional review board approval was obtained for this study with a waiver of informed consent. Tumors were contoured by an expert head and neck radiologist. All scans were acquired using a fast kVp switching DECT scanner (GE Healthcare) after administration of IV contrast and reconstructed into 1. 25 mm sections of axial slices with a resolution of 0. 61 mm, as previously described  [11].
The number of clusters is picked to be also K = 40, the number of clusters being stable throughout the optimization. We use the Matlab k-means implementation for images with a reproducible initialization through the built-in 'imsegkmeans' function. Third and last, we implement selective search, a machine learning graph-based segmentation method for object recognition. Using a region merging hierarchical approach with an SVM classifier to select the hierarchical rank of the resulting regions, the authors published an open-source code  [44]. We apply selective search on low-, intermediate-and high-energy levels (resp. 40, 65 and 140 keV). These energy levels are used instead of the three RGB image channels. We note that selective search does not predetermine the number of clusters, but specifies a cluster minimum size or favors smaller or larger cluster sizes.
Several reasons may explain this finding. First, our clinical dataset of DECT scans is not uniform, i. e. , it includes tumors of highly variable characteristics, in highly variable sizes, locations and environments, which makes it particularly challenging. Moreover, as seen in Figure  4 , changes in parameter tuning can lead to substantial improvement in Dice scores for some tumors. Finally, because of their intricate morphology and often small sizes, HNSCC tumors are inherently difficult to segment. In a recent international challenge, Dice scores of head and neck tumor segmentation ranged across different competition entries between 0. 56 and 0. 76  [45]. Most importantly, as argued throughout this paper, the clinical value of recovering the manual segmentations of HNSCC tumors as an objective criterion for evaluating the algorithm is also not clear.