dataesr/scientific-paragraphs-categorization huggingface dataset:
Study design, sources, and population In this nationwide, population-based cohort study, we used data from the Programme de Medicalisation des Systèmes d'Information (PMSI database), the French national hospital database in which administrative and medical data are systematically collected for acute (PMSI-MCO) and psychiatric (PMSI-PSY) hospitalizations. The PMSI database is based on diagnosis-related groups (DRGs), with all diagnoses coded according to the 10th revision of the International Classification of Diseases (ICD-10) and using procedural codes from the Classification Commune des Actes Medicaux (CCAM). The PMSI database is used to determine financial resource use and is frequently and carefully verified by its producer as well as the paying party, with possible financial and legal consequences.
Data from the PMSI database are anonymized and can be reused for research purposes. A unique anonymous identifier enables different inpatient stays of individual patients to be linked. The study was submitted to the French National Data Protection Commission (N˚2203797) for ethical approval. This manuscript follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines  [29]  (S1 STROBE Checklist). We included all hospital admissions between January 1, 2014, and December 31, 2018, using the following criteria: aged 18 years or older, admitted to the ICU, had a diagnosis of septic shock (ICD-10 code = R572 or a combination of codes corresponding to a severe infection associated with the use of vasopressors). We limited inclusion to patients with an ICU length of stay of at least 48 hours, unless the patient died within 48 hours, in order to avoid overestimating diagnoses of septic shock.
Results The database included a total of 187,587 patients with septic shock (flow chart, Fig  1 ). The main sociodemographic data of the patients are shown in Table  1. The mean age was 67. 1 (±14. 3) years and 63. 8% were men. A majority of patients (106,941 patients [57. 0%]) were socially deprived and most patients (167,738 patients [89. 4%]) were hospitalized in public hospitals. Among the 187,587 patients, 3,812 had schizophrenia (2. 0%), 2,258 had bipolar disorder (1. 2%), and 5,246 had major depressive disorder (2. 8%). A total of 3,269 patients with schizophrenia, 1,923 patients with bipolar disorder, and 4,432 patients with major depressive disorder were matched with 10,894, 6,303, and 14,452 controls, respectively.
Nonetheless, our findings at 30 and 90 days were similar to those reported in other studies  [62]. In addition, the evolution of the CFR between 30 and 90 days and between 90 and 365 days was similar in the patients with and without SMI, supporting a lack of bias to account for the different extrahospital mortality. Second, a weakness of administrative databases is the potential miscoding of diagnoses during hospital stays, which can underestimate important patient features (especially for overweight and obesity, which are insufficiently coded in administrative databases but which allow the most serious cases to be targeted for epidemiological research  [63, 64] ) and disease severity at ICU admission. Missing data are thus assumed to indicate no disease present. In addition, the key exposure in the present study (i.
The study uses survey data that were collected concurrently in four European countries: France, Germany, Norway, and the United Kingdom. These countries represent four key energy producing nations in Europe, and vary in significant ways with respect to energy production, vulnerability to the impacts of climate change, and background socio-political contexts  (Arnold et al. , 2016; Steentjes et al. , 2017). Their comparison promises to help inform future research on which socio-economic or cultural factors may be worthwhile exploring in more depth with respect to their role in climate emotions, underlying appraisals, and subsequent climate behaviors. To our knowledge, this is the first study that investigates several discrete emotions in an international comparison of representative samples of the adult population and which considers a broader range of both antecedents and consequences of emotions.
We will also explore whether different sociodemographic groups within countries differ in their emotional responses to climate change. We use a dataset from the European Perceptions of Climate Change project (EPCC:  Steentjes et al. , 2017)  to answer the two research questions. The EPCC project conducted an international survey among representative samples from four European countries investigating a broad range of perceptions and judgments on the themes of climate and energy. The nationally representative samples comprise about 1,000 respondents from France, Germany, Norway and the United Kingdom, respectively. Arnold et al. (2016)  describe our selection of the four countries to be surveyed and their contrasted energy policy profiles at that time. France is the world's largest nuclear power producer which means that it has relatively low greenhouse gas emissions per capita.
Specifically, in addition to measures of the five emotions already discussed, the questionnaire included ten appraisal items, and seven behavioral options. Our data analysis is exploratory and correlational. The first research question will be addressed using canonical correlation analysis techniques aimed at identifying regularities in appraisalemotion and emotion-behavior linkages. The second research question, using correspondence analysis techniques, focuses on national differences, but also explores variation across sociodemographic subgroups (defined by age, gender, education, and political orientation) within the national samples. The EPCC survey data will be complemented with external publicly available countrylevel data on cultural dimensions. Specifically, we will use Hofstede's cultural values  (Hofstede, 2001 ), Inglehart's cultural dimensions  (Inglehart and Welzel, 2023) , and Schwartz' cultural value orientations  (Schwartz, 2006).
These will be used to enrich the interpretation of country differences with respect to their cultural embeddedness. Representative national samples were drawn from four countries: France, Germany, Norway, and the United Kingdom with about 1,000 participants aged 15 or older per country (n France = 1,010; n Germany = 1,001; n Norway = 1,004; n UK = 1,033; total N = 4,048). Fieldwork took place in the period June 1-17, 2016. Data collection was commissioned to the international social research company Ipsos Mori. In France, Germany, and the United Kingdom, the questionnaire was administered as part of a weekly face-to-face omnibus survey that took place in respondents' own homes using Computer-Assisted Personal Interviewing (CAPI). The average length of the interviews across the three countries ranged from 22 to 28 min.
All analyses were done with the R computing environment (R Core Team, 2023), version 4. 2. 3, using the package ca  (Nenadic and Greenacre, 2007)  for correspondence analysis and the package CCA  (González and Déjean, 2021)  for canonical correlation analysis. Supplementary Table  S1  in Supplementary material shows for all measures the country means and confidence intervals, along with the number of cases and standard deviations.
Discussion In this study, we examined relationships between appraisals of climate change and emotions and between emotions and behaviors targeted toward climate change. Furthermore, we explored differences between respondents from four countries (France, Germany, Norway, United Kingdom) concerning emotional responses in the face of climate change while also investigating related country differences in climate appraisals and behaviors. Our data are a subset from an international survey on European Perceptions of Climate Change (EPCC:  Steentjes et al. , 2017). The first research question, approached by using canonical correlation analysis techniques, draws on appraisal-theoretic frameworks and attempts to identify regularities in appraisal-emotion and emotionbehavior linkages (Section 3. 1).
, 2016). More importantly, the five emotions measured are merely a small subset of possibly relevant emotions concerning climate change; in particular, discrete emotions such as despair, sadness, regret, or shame, that have been treated as relevant for climate change research, were not assessed. In fact, it is quite likely that the emotional response to climate change is more complex than can be inferred from the five emotions examined in this study. Furthermore, although we generally rely on an appraisal-theoretic background concerning the role of emotions, all analyses presented are exploratory and descriptive. The existing knowledge on climate change emotions and the nature of the data available do not allow us to formulate more precise statistical hypotheses, in particular, no causal hypotheses can be tested.
Funding This research was conducted as part of the " European Perceptions of Climate Change " project, which was funded by the  Joint Programme Initiative on Climate Change (JPI-Climate ) with associated grants from  Cardiff University Sustainable Places Research Institute, School of Psychology  and the  Economic & Social Research Council, ESRC  [grant number  ES/M009505/1 ],  France 's  Agence Nationale de la Recherche  [grant number  ANR-14-JCLI-0003 ], the  KLIMAFORSK programme  of the  Norwegian Research Council  [NFR; project number  244904 ], and the  German Federal Ministry of Education and Research  [grant number  01UV1403 ]. The project received co-funding from the cooperation agreement between  Equinor (formerly Statoil)  and the  University of Bergen [Akademiaavtale ; project number  803589 ].
Clade IV consists of Ficus tikoua (Sycomorus) and F. albipila (Oreosycea) in a grade, followed by two subclades-one containing the rest of Mixtiflores along with Malesian and Papuasian Synoecia and Sycomorus species and the other containing Australasian Oreosycea plus most of Eriosycea and Sycomorus. Analyses of the nuclear and chloroplast datasets together revealed substantial cyto-nuclear discordance. Scoring the plastome tree using a pruned set of nuclear gene trees resulted in a low normalized quartet score of 0. 61, indicating that nearly 40% of nuclear gene tree quartets were not reflected in the plastome tree. Similarly, coalescent-based simulations starting from the nuclear species tree revealed that the plastome tree contained dramatically more extra lineages (52 clades not appearing in nuclear species tree) than would be expected for a maternally inherited organellar genome (simulated plastome trees had a median of 6 clades not appearing in the nuclear species tree) and instead fell within the expected range of extra lineages present in simulated nuclear gene trees under incomplete lineage sorting (SI Appendix, Fig.
Materials and Methods Taxon Sampling. The complete study included a total of 627 samples, 245 species of Ficus (330 samples), 284 other Moraceae, and 13 outgroups (in Urticaceae and Cannabaceae) (SI Appendix, Table  S1 ). In Ficus, sampling represented all major lineages and about 30% of the recognized species. In the rest of Moraceae, sampling represented all genera, mostly with species-level sampling (exceptions include Dorstenia, with ca. 50% sampling, and the tribe Olmedieae, with ca. 60% sampling). Data sources included 330 samples newly sequenced for this study and 256 samples from previous studies by the authors  (27, 34, 54, 55) , supplemented with samples from the NCBI Sequence Read Archive, and outgroups from the Plant and Fungal Tree of Life project (PAFTOL). New samples were collected during fieldwork in Australia, Brunei, Cameroon, Madagascar, Papua New Guinea, Philippines, Reunion, Sabah and Sarawak in Malaysia, Thailand, Yunnan, South China, Ryukyu Islands, from curated living collections of botanic gardens (Bergen Botanical Garden, Fairchild Tropical Botanical Garden, National Tropical Botanical Garden, US Department of Agriculture Subtropical Horticulture Research Station, Xishuangbanna Tropical Botanical Garden), and from herbarium specimens (C, F, K, L, MO, SING).
For other samples, similar libraries were prepared using the blunt-end single-tube protocol "BEST"  (57)  with an additional column-c leaning step  (58). All newly prepared libraries were enriched for 1,315 single-c opy nuclear genes ("Ficus1315") identified by Bruun-Lund (31), and a majority were also enriched for the 333 Moraceae loci ("Moraceae333") identified by Gardner et al. (30). Enrichment was carried out using a custom myBaits kit (Daicel Arbor Biosciences, Ann Arbor, MI, USA) following the manufacturer's protocol, except that the RNA baits were diluted 1:1 with nuclease-f ree water. A subset of samples were subjected to technical replicates to verify the reliability and repeatability of laboratory processes. Datasets. We assembled four datasets: 1. All-Ficus: 1,751 genes for 330 Ficus samples (not including 29 technical replicates) plus five outgroups in the sister tribe, rooted with Broussonetia papyrifera.
Chloroplast Phylogeny. For each sample, reads were mapped to the Ficus carica chloroplast genome (GenBank accession number KY635880. 1) using BWA  (71). Variants were called using SAMtools, and a consensus sequence was generated using the mpileup command  (72). To ensure the reliability of the alignments, indels were not included in consensus sequences, as we found that these introduced errors into alignments, particularly in areas with lower depth. Samples with more than 50% undetermined bases across the entire plastome were discarded, and the remaining were concatenated into an alignment (no separate aligning step was necessary as the consensus sequences did not contain indels), which was visually inspected for artifacts in AliView. A sequence for Ficus albipila from Bruun-Lund et al. (19)  was added to the alignment.
A maximum-likelihood tree based on the unpartitioned alignment was inferred with RAxMLng under the "GTRCAT" model, with 1,000 bootstrap replicates. We also scored nodes with quartet scores based on the nuclear gene trees using the "-t 1" option in ASTRAL. The chloroplast tree was time calibrated and biogeographic areas were reconstructed following the methods outlined for the Moraceae tree below. The chloroplast tree was compared to the nuclear tree using the cophylo command in Phytools  (73)  in R after pruning F. albipila, which was not present in the nuclear dataset. To test whether the differences between the nuclear and chloroplast trees were consistent with incomplete lineage sorting or would be better explained by introgression, 1,000 chloroplast trees were simulated as described in ref.
Allowing simultaneous areas on such a large tree was made computationally feasible by parallelizing the DEC analysis on 32 threads. Finally, we reconstructed diversification rates using BAMM  (85, 86)  on the time-calibrated tree. Priors were optimized using BAMMtools  (86) , and less-than-complete sampling of certain lineages was specified using a sample probability table. The analysis was run for 100,000,000 generations, and the first 10% were discarded as burn-i n based on a visual inspection of a graphical output of the results. Data, Materials, and Software Availability. Raw reads have been deposited in the NCBI Sequence Read Archive under BioProject PRJNA956524  (87). Bait sequences, alignments, trees, and analysis scripts have been deposited in the Dryad Data Repository at https://doi.
ACKNOWLEDGMENTS. We thank the late  C. C. Berg ,  D. Dixon ,  E. Jousselin ,  O. Maurin ,  C. Michenaud , and  O. Ryding  for providing material or assistance in obtaining material for this study;  Fairchild Tropical Botanical Garden, Montgomery Botanical Center , and the  Ornamental program  at the  USDA-A RS Subtropical Horticulture Research Station  for access to living collections;  C. Hansen  for help in sample preparation; and  Postar Miun ,  Jeisin Jumian ,  Markus Gubilil ,  S. Brono ,  Aloyzius Laim, Salang anak Nyegang, Saini anak Rosalin, Jugah anak Tagi ,  Raihan Rashida binti Ruslan , and  Norazira binti Ramnor  for assistance in the field. We thank the  Sabah Biodiversity Centre  (permit nos. JKM/MBS. 1000-2/2 JLD. 9 (26 ) and  JKM/ MBS. 1000-2/2 JLD. 9 (27 ) and export license no.
We analyzed physical oceanic parameters gathered by a mooring array at mesoscale spatial sampling deployed in Argentine Basin within the Ocean Observatory Initiative, a National Science Foundation Major Research Facility. The array was maintained at 42°S 42°W, a historically sparsely sampled region with small ocean variability, during 34 months from March 2015 to January 2018. The data documented four anticyclonic extreme structures events in 2016. The four anticyclonic structures had different characteristics (size, vertical extension, origin, lifetime, Rossby Number). They all featured nearinertial waves (NIWs) trapped at depth and low Richardson values well below the mixed layer. Low Richardson values suggest favorable conditions for mixing. The anticyclonic features likely act as mixing structures at the pycnocline bringing heat and salt from the South Atlantic Central Water to the Antarctic Intermediate Waters.
The data processing of the physical variables is detailed in  Artana et al. (2020). We removed spikes from temperature and salinity and density inversions. Measurement quality of the mooring sensors, assessed by comparison with shipboard measurements (CTD profiles and bottle measurements) taken near the moorings during deployment and recovery cruises, was nominal. Table  2. Record length for deployment 1 (blue), deployment 2 (red) and deployment 3 (green). Grey squares correspond to missing data ADCP from mooring A (FLMA) and the surface mooring (SUMO) did not work during the third deployment (Table  2 ). Gaps in the upper layer occurred when FLMB and FLMA underwent vertical excursions due to strong currents. Data quality was overall very good at all depths, except for the velocity measurements close to the surface.
1 Velocity spectral content at high frequencies Rotary spectra of the ADCP velocities were produced considering 30-day long time series spanning each event, when the moorings did not experience vertical motions. SUMO data were used for the three events (A0, A1, A2") and FLMB data for A0 (Figure  10 ). A2' rotary spectra could not be built as A2' event was not sampled at SUMO and induced large vertical displacements in FLMA and FLMB. 4 , f ef f approximately corresponds to a period of about 18 h, consistent with the red shifts observed in Figure  10  spectra. in the water column during the three anticyclonic events between isopycnals 26. 65 and 27. 00 kg/m 3 (Figure  12d ). The energy from the trapped waves must then be transferred to smaller waves, which eventually break generating turbulence and mixing  (Kunze et al.
After a time life of 3, 6 and 2 months for A1, A2' and A2" respectively, the three eddies were reabsorbed by the STF. How often do the extreme events occur? The altimetry-derived EKE time series at the mooring location showed that those events were exceptional (Figure  6 ). In contrast, eddies detached from the subtropical front are frequent in the energetic Argentine Basin (Figure  1 ), in particular in the Brazil-Malvinas Confluence, in the vicinity of the STF and in a band centered at 49°S (e. g. Fu, 2006; Mason et al. , 2017; Artana et al. , 2019). Thus, OOI observations in 2016 are relevant and valuable for the data sparse Argentine Basin. Do the anticyclonic structures documented with OOI impact mixing? These anticyclonic structures are not subtropical "run- The OOI array provided unprecedented long time series with a high temporal sampling in the Southern Hemisphere and, for the first time documented near-inertial waves trapped at depth within anticyclones in the Argentine Basin.
Acknowledgements. We are deeply grateful to scientists and technicians from  Woods hole Oceanographic Institution and Scripps Institution of Oceanography  who developed and operated the  Argentine Basin  array within the  NSF  funded  Ocean Observatory Initiative. We are grateful to  CNES (Centre National d'Etudes Spatiales)  for constant support. This study is a contribution to the  CNES -funded  OSTST BACI   400  project. Camila Artana  acknowledges support from a  CNES   Postdoc Scholarship  and funding from the  Spanish government (AEI)  through the ' Severo Ochoa Centre of Excellence' accreditation  ( CEX2019-000928-S ).
The phase 3 BELIEVE study (NCT02604433) showed that luspatercept is effective in reducing RBCT burden,  14  but the impact of luspatercept on HRQoL has not yet been evaluated. This analysis of the BELIEVE study aimed to assess the effect of luspatercept + best supportive care (BSC) versus placebo + BSC on HRQoL using the generic 36-item Short Form Health Survey (SF-36) and the disease-specific Transfusiondependent Quality of Life questionnaire (TranQol). 15, 16 | METHODS.
| Study design The BELIEVE study (NCT02604433) was a phase 3, double-blind, randomized, placebo-controlled, multicenter study to determine the efficacy and safety of luspatercept (ACE-536) + BSC versus placebo + BSC in adult patients (≥18 years of age) who required regular RBCTs (6 to 20 units of packed red blood cells with no transfusion-free period of >35 days within 24 weeks of randomization) due to β-thalassemia as described previously. 14 The study was conducted at 65 sites in 15 countries across Australia, Europe, Middle East, North Africa, North America, and Southeast Asia. Patients were randomized in a 2:1 ratio to receive either luspatercept (starting dose of 1. 0 mg/ kg with titration up to 1. 25 mg/kg every 3 weeks) or placebo, subcutaneously for ≥48 weeks + BSC, which included RBCTs and iron chelation therapy as needed.
| Population The data cutoff for these HRQoL analyses was July 1, 2019. Two sets of study populations were created for the analyses: (1) the intent-totreat (ITT) population, which included all patients who were randomized in the study, and (2) the HRQoL-evaluable population. The disposition of randomized participants in the BELIEVE trial has been detailed previously. 14 The HRQoL-evaluable population included all patients who completed the HRQoL assessment at baseline and completed ≥1 postbaseline assessment visit. The TranQol was considered complete if ≥75% of items were answered at a given time point. SF-36 was considered complete if ≥50% of items were answered at a given time point.
ACKNOWLEDGMENTS This study was supported by  Celgene , a  Bristol Myers Squibb Company , in collaboration with  Acceleron Pharma Inc. , a subsidiary of  Merck & Co. , Inc. , Rahway, NJ,  USA. All authors directed the development, review, and approval of this manuscript and are fully responsible for all content and editorial decisions. The authors received writing support in the preparation of this manuscript from  Eilish McBurnie, PhD , of  Excerpta Medica , supported by  Bristol Myers Squibb.
Past work has shown that traffic patterns in the US and resulting NOX emissions vary by day of week, with NOX emissions typically higher on weekdays than weekends. This pattern of emissions leads to different levels of ozone on weekends versus weekdays and can be leveraged to understand how local ozone formation changes in response to NOX emissions perturbations in different urban areas. Specifically, areas with lower NOX but higher ozone on the weekends (the weekend effect) can be characterized as NOX -saturated and areas with both lower NOX and ozone on weekends (the weekday effect) can be characterized as NOX-limited. In this analysis we assess ozone weekend-weekday differences across US nonattainment areas using 18 years of observed and modeled data from 2002-2019 using two metrics: mean ozone and percentage of days > 70 ppb.