dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analyses All personal data was removed from the dataset prior to analyses. Analyses were conducted using StataMP 17. 0 for Mac or IBM SPSS Statistics software for Windows (version 29). Frequency counts and percentages were used to show the sociodemographic characteristics and rates of exposure to modifiable risk factors for dementia among the sample. For all analyses, statistical significance was defined as p < 0. 05. To investigate differences in awareness levels for modifiable risk factors across groups, 2-way mixed ANOVAs with Greenhouse-Geisser correction were performed. Three 2way mixed ANOVAs were undertaken with gender, age, and education as respective between-subject factors. Each ANOVA had "risk factor" as a within-subject factor with 13 levels, each level representing one risk factor.
The outcome measure for each ANOVA was awareness level measured by a Likert scale. Age was categorized into three groups, defined as 50-59 years old, 60-69 years old, and 70 years and above for the purpose of analysis. Due to small numbers in this group (n = 3), those who responded that they would prefer not to disclose their gender were excluded from the dataset for analyses. Education was categorized into three groups for the purpose of analysis, defined as primary/secondary school, university undergraduate, and university postgraduate levels. Due to small numbers who had been educated to primary school level (n = 10), this group was combined with those who had been educated to secondary school level. Adjustment for post hoc comparisons was undertaken using the Sidak correction. Binary logistic regression models were used to investigate the associations between exposure to modifiable risk factors for dementia, age, gender, and education presenting odds ratios (OR) and adjusted odds ratios (AOR) for the likelihood of reporting exposure to risk factors.
Sample characteristics The study population ultimately comprised 551 participants. Of the 555 volunteers to whom the survey was originally administered, 3 participants were excluded as they did not disclose their gender. One further participant was excluded due to an incomplete dataset. Table  1  outlines the sociodemographic characteristics of the sample. The study population comprised 551 participants with an almost equal proportion of male and female respondents (50. 3% male; 49. 6% female). Mean age was 59. 7 years, with the majority of the sample ranging between 50 and 59 years of age (54. 3%). 98. 9% of respondents were of White ethnicity. Most participants were educated to secondary school level or higher (98. 2%), were employed or self-employed (52. 2%), and cohabited with one or more persons (74.
e. , RCP 4. 5 and RCP 8. 5). We used a large and recent occurrences dataset (2006 to 2021) that combined different sources (i. e. , the MigrenMer and ObsMer program, DATRAS, OBIS, GBIF), environmental data at a relatively fine resolution (i. e. , 11 km) and an ensemble modeling approach. Our results can serve as a basis for designing targeted conservation measures for Allis shad and European flounder, and can also be applied to other diadromous species worldwide. The information gathered through our approach can contribute to the development of sustainable management strategies for diadromous fish populations, ultimately helping to ensure their long-term survival and conservation.
The study area extends from the Portuguese coasts to southern Scandinavia (from 36 to 62°N and from -12 to 10. 2°E) to cover a large part of the species' distribution ranges (Figure  1 ). The Mediterranean and Baltic seas were excluded from the analysis because of their narrow mouths to connect with the Atlantic Ocean that likely prevent large-scale dispersal and favor local adaptations  (Hemmer-Hansen et al. , 2007). In order to enhance the accuracy of our models, we established a maximum depth of 150 m and 100 m for Allis shad and European flounder, respectively, in accordance with their known preferences for shallower habitats  (Taverny and Elie, 2001b). A B FIGURE 1 Map illustrating the distribution of presences/absences dataset for Allis shad (A) and European flounder (B). A comprehensive dataset comprising 710 and 7322 presences for Allis shad and European flounder, respectively.
The number of absences selected was the same as the number of presences for both species. One of the three distinct absence datasets used in model calibration was depicted while maintaining the prevalence of the occurrence dataset. To mitigate over-plotting, an additive transparency approach was adopted, resulting in areas darkening due to overlapping points. The marine sub-regions referred to in the text were categorized as follows: Iberian Coast -IC, Bay of Biscay -BoB, Celtic Sea -CS, Irish Sea -IS, English Channel -EC, and North Sea -NS.
Presence/absence dataset Species occurrences were collected from three datasets: (i) the MigrenMer database  (Elliott et al. , 2023)  that contained declarations of diadromous species collected by French fishery observers (i. e. , the ObsMer program) and some scientific surveys A B D E C FIGURE 2 Scheme of the species distribution modeling approach used in this study. (A) Input data were species occurrences and environmental data (three time periods: current, mid-century and end-century). (B) Choices concerning the calibration of species distribution models. (C) Resulting models were filtered based on their abilities to predict known presences and absences by selecting models with True Skill Statistic (TSS) values greater than the median of all models. (D) Selected models were combined to create an ensemble probabilistic density depicting environment suitability probabilities.
The dataset includes 710 presence and 710 absence records for Allis shad, spanning the period from 2006 to 2019, along with 7322 records for European flounder, covering the time span from 2006 to 2021 (see Figure  1 ; Tables  S1, S2  for more details on the temporal and spatial distribution of species dataset). The absence data were obtained from the ObsMer database (as it is the only one that contains data for the entire study area) on fishing gears that caught, at least once, the species. For both species, absence data from a particular year were retained if they were located more than 20 km away from presence data recorded in the same year. The prevalence (i. e. , proportion of presences in the dataset) was set to 0. 5 which gives as much weight to presences as to absences  (Jimenez-Valverde and Lobo, 2006).
Time step selected Although the environmental variables could be obtained at a monthly basis, we opted for an annual time step for both model construction and application (henceforth, the time step is referred to as a year). This choice, was driven by our research focus on species that inhabit the marine phase for extended periods, spanning multiple years. Thus, we extracted the annual average of each environmental variable based on the geographic coordinates and the corresponding year for both the presence and absence data of each species. As an example, a fish caught in 2006 was assigned the temperature value of the grid cell of capture for the year 2006. Consequently, the model calibration used environmental conditions spanning the years of presence/absence data availability:  2006-2019 and 2006-2021  for Allis shad and European flounder, respectively (Table  S2 ).
Species distribution modeling To ensure optimal model performances, the calibration process involved multiple presence/absence datasets, as well as the use of multiple cross-validations. In this study, we used the typical crossvalidation approach, based on 80% of the original dataset, with models evaluated using the remaining 20%  (Roberts et al. , 2017). Additionally, the biomod2 platform employed in this study enabled us to test nine algorithm classes using their default settings: generalized linear models (GLM), generalized additive models (GAM), Maximum Entropy (MaxEnt), multivariate adaptive regression splines (MARS), random forest (RF), boosted regression tree (BRT), classification tree analysis (CTA), flexible discriminant analysis (FDA) and artificial neural networks (ANN) (Figure  2B )  (Thuiller et al.
, 2009). To serve our modeling objectives, we created a total of 108 models for each species (9 algorithms x 3 absence datasets x 4 cross-validation runs). Statistical models must be developed with uncorrelated explanatory variables. To this end, we calculated the pairwise Pearson correlation coefficients between all environmental variables, and only included variables with a coefficient value of less than 0. 7 in the same model  (Dormann et al. , 2013). Then, to ensure that only relevant environmental variables were included in the model, we employed a performance criterion that provides an importance value for each variable involved in the model. The principle was to compute the Pearson correlation coefficient between the reference predictions and the predictions obtained from a shuffled dataset for each variable.
, 2004). Model efficiency is usually summarized into this scale: excellent (TSS > 0. 8), good (0. 8 > TSS > 0. 6), correct (0. 6 > TSS > 0. 4), and bad (TSS < 0. 4). Therefore, we ranked the selected models based on their TSS values, and chose those that had a TSS higher than the median value of all models' TSS scores, across all algorithms, datasets, and cross-validation runs. This allowed us to choose models that had a better performance than at least half of all models tested. In total, 54 model outputs for each of the two species were obtained.
Ensemble modeling The 54 selected models based on TSS were used to create the ensemble modeling using the R package biomod2, which takes into account the variability within each model and across different modeling methods  (Thuiller et al. , 2009 ; Figure  2D ). Ensemble maps of the suitable environment for both species were generated for each time step (i. e. , year). We also constructed response curves for each environmental variable in the ensemble model by setting the other variables to their mean values using the evaluation strip method  Elith et al. , 2005; Guisan et al. , 2017; Figures   S1, S2 ).
, 1994; Skerritt, 2010). Marine habitats can also be found, in a smaller proportion, along the coast of the Bay of Biscay but are absent along the Iberian coast although several studies mentioned the species' presence in adjacent Iberian rivers (e. g. , Minho, Lima, Vouga and Mondego -  Cabral et al. , 2007; Mota et al. , 2016; Nachoń et al. , 2016). Portugal is currently reported as the southern limit of the species' distribution  (Pedron et al. , 2017). The present study acknowledges that the model's predictive ability for European flounder and Allis shad along the Iberian coast may be inaccurate due to the scarcity of occurrence data in that area among the input dataset (0. 1% and 0. 4% of presence data, respectively, Table  S1 ). Although the model's performance was not evaluated based on the proportion of absence, we recognize the importance of achieving a balanced representation of absence data (0.
Misidentification of species is a common source of errors in studies using fishery data. For instance, Allis shad and Twaite shad (Alosa fallax), as well as European flounder and European plaice (Pleuronectes platessa), share similarities in morphology, and may be misidentified. Nevertheless, the MigrenMer database was carefully curated to reduce the risk of confusion, such as by renaming Twaite shads larger than 50 cm as Allis shad  (Elliott et al. , 2023). As our approach adheres to conventional frequentist statistical analysis, we were compelled to omit certain environmental variables due to their relatively weak associations. Nevertheless, it is imperative to recognize that high correlation does not necessarily imply strong causation. There are potential remedies to take into account the dynamic nature of variable correlations, with one such option being the adoption of Empirical Dynamic Modeling (EDM), accessible through the rEDM package in R  (Sugihara et al.
Acknowledgments We would like to thank  Ana Queiroś  and  Susan Kay  from the  Plymouth Marine Laboratory  for their support and guidance throughout the environmental data extraction process. We also thank the common unit  Management of Diadromous Fish in their Environment OFB-INRAE-Institut Agro-UPPA (Pole MIAME) , and especially  Sophie Elliott , for generously sharing the MigrenMer database. We are thankful to the reviewers for their relevant comments to improve the paper.
Study population All patients were evaluated between November 2021 and June 2022. All four patients were placed on V-A ECMO and enrolled in our local ECPR protocol after in-hospital cardiac arrest or as part of the Minnesota Resuscitation Consortium's previously published protocol  (8)  for out-of-hospital cardiac arrest. All patients had cannulation with the venous and arterial cannulae in the femoral vasculature (V f -A f ECMO). Their intensive care also followed a protocolized pathway that we have published previously  (9). All hemodynamic data were collected prospectively. The study was approved by our institution's Institutional Review Board (study number 00005355).
Funding The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This project was supported by a grant from the  American Heart Association  ( 23TPA1140962 ; awarded to RK), a grant from the  German Research Foundation  ( GA 3379/1-1 ; awarded to CG), a grant from  The Helmsley Charitable Trust  (awarded to DY) and a grant from the  National Institute of Health  ( R01HL160973-01 ; awarded to DY).
Acknowledgments S. W. acknowledges funding from  CY Initiative of Excellence (TALENT)  and  EUTOPIA Young Leaders Academy. Y. H thanks  National Natural Science Foundation of China  ( 62074054 ;  U21A20497 ) for funding support. J. W. A acknowledges funding from the  Independent Research Fund Denmark  through the  EPIC-OPV  grant no. 1032-00326B. K. L. W. acknowledges funding from  University Malaya Impact-Oriented Interdisplinary Research Grant  ( IIRG005A-19FNW ) computation time from A*CRC Fugaku Trial for  ASEAN High Performance  Computing. S. H. K. P. and C. M. acknowledge funding from the  Knut and Alice Wallenberg Foundation  through a  Wallenberg Academy Fellow Prolongation Grant. The authors thank  Mr. Alexandre Michaud  for NMR measurement and  Ms. Marion Rollet  for SEC measurement.
Statistical Analysis Statistical analyses were performed using MedCalc software (Version 20. 216). Distribution normality was assessed using the Shapiro-wilk test. Data were expressed as medians with [minimum to maximum range] for continuous variables and absolute numbers for categorical variables. Comparisons of paired medians were performed using the Wilcoxon signed-rank test and paired percentages using the McNemar test; correlations were assessed using the Spearman test (correlation coefficient, rho). A P-value <0. 05 was considered statistically significant. Reproducibility was assessed using intraclass correlation coefficients (ICCs), with mixed model analysis and absolute agreement option. ICC values were classified as null (=0), slight (>0 and <0. 20), fair (≥0. 20 and <0. 40), moderate (≥0.
We report on a detailed experimental and simulation study of the short-and medium-range order of a potential phase change materialglassy KSb 5 S 8. On the experimental side, diffraction techniques and EXAFS have been employed to record accurate structural data. Structural models have been generated by fitting multiple datasets simultaneously with the reverse Monte Carlo simulation technique. In addition, density functional theory was employed to study the structure and vibrational modes of selected clusters, representative of the glass structure. Unconstrained RMC simulation runs revealed that the average Sb-S coordination number is 3. 18 ± 0. 2, thus Sb is mostly threefold coordinated in the glassy state. The fraction of edge and corner sharing SbS n polyhedra and distribution of bridging S atoms (Q n distribution) have also been obtained.
While Ge-Sb-Te (GST) phase change materials have been extensively studied over the last two decades, much less attention has been paid for the non-crystalline phase of materials that belong to the R 2 S-Sb 2 S 3 family. The present study attempts to clarify the structure of glassy KSb 5 S 8 (K 2 S -5Sb 2 S 3 ) by neutron and high energy X-ray diffraction, Sb K-edge EXAFS measurements and Raman spectroscopy. To facilitate assignment of the vibrational modes, the Raman spectrum of glassy KSb 5 S 8 is compared with that of vitreous Sb 2 S 3. Structural changes accompanied with the addition of K 2 S are interpreted by means of density functional theory (DFT) calculations of various clusters with different sizes. Diffraction and EXAFS datasets are fitted simultaneously in the framework of reverse Monte Carlo (RMC) simulations  [3, 4].
Measurements The x-ray diffraction experiment ('XRD 1′) at room temperature was carried out at the high-energy x-ray diffraction beamline BL04B2  [5]  at Spring-8 using a two-axis diffractometer dedicated for glass, liquid, and amorphous materials  [6]. The energy of incident x-rays was 61. 6 keV. The beam size of x-rays was 0. 7 mm width and 2. 5 mm height. The diffraction patterns of powder sample in a thin walled (10 μm) silica glass tube of 0. 5 mm diameter (supplier: GLAS Müller Berlin) and an empty tube were measured in transmission geometry. The intensity of incident x-rays and scattered x-rays were detected by an ionization chamber filled with Ar gas and a Ge solid state detector, respectively. The collected data were corrected using standard programs. Absorption coefficients  [7] , incoherent scattering functions  [8]  and atomic scattering factors  [9]  were used for the corrections and normalization.
The fully corrected data were normalized to Faber-Ziman  [10]  total structure factor S(Q). An additional high energy x-ray diffraction measurement ('XRD 2′) was carried out at the BW5 high energy station  [11]  at Hasylab, DESY Hamburg. The energy of the radiation was 100 keV. Raw data were corrected for deadtime, background scattering and changes in detector solid angle  [12]. The neutron diffraction structure factor was measured at the 7C2 liquid and amorphous diffractometer at (LLB, CEA, Saclay). Powdered sample was filled into a thin walled vanadium can (5 mm diameter, 0. 1 mm wall thickness). Raw intensities were corrected for detector efficiency, background scattering, absorption and multiple scattering  [14, 13]. Neutron scattering lengths, absorption and incoherent scattering cross sections were taken from Ref.
[15]. Sb K-edge extended X-ray absorption fine structure was measured at beamline C of Hasylab, DESY Hamburg. Finely ground powder of KSb 5 S 8 was mixed with cellulose and pressed into a tablet. The transmission of the tablet was about 1/e around the absorption edge. Intensities were recorded by ionization chambers filled with Ar/N 2 mixtures. The Viper program  [16]  was used to obtain χ(k) EXAFS curves from raw data. k 3 -weighted χ(k) curves were first Fourier-transformed to real space (k-range: 2-15 Å -1 ). The r-space data was then back-transformed to k-space using a rectangular window over 1. 44 -2. 57 Å (see Fig. 1 ). The resulting filtered k 3 χ(k) curve was used as input in reverse Monte Carlo.
Reverse Monte Carlo simulation The reverse Monte Carlo simulation technique (RMC) is a general framework for modeling the structure of disordered materials by using experimental information (diffraction and/or EXAFS data) and physical constraints (density, minimum interatomic distances, coordination and bond angle constraints) as input information. RMC has been successfully applied several times to multicomponent covalent glasses  [17, 18]. For details of the simulation technique we refer to a paper describing rmcpp, the latest RMC version  [4]. Backscattering coefficients needed to calculate model EXAFS spectra from the atomic configuration were obtained by the feff8. 4 code  [19]. The four datasets were fitted simultaneously. Minimum atomic distances (cut offs) were determined by a series of test runs in which the cut offs were increased gradually until fit qualities got worse or one or more peak shapes become distorted.
81 g/cm 3. The corresponding number density is 0. 0355Å -3. Final simulations were started from two configurations: Then β was set to 90 • and the half box lengths were rescaled to 50. 676 Å to get the required number density. (iii) constrained: using the starting configuration of unconstrained 1 but eliminating unphysically low (0, 1 and 2) Sb-S coordination numbers up to 2. 9 Å. This constraint was satisfied by 97% of the atoms. The contribution of a given dataset to the total cost function is determined by its σ value  [4]. In the present study the initial values were 0. 01 for the neutron diffraction dataset, 0. 01 and 0. 014 for the XRD 2 and XRD 1 structure factors and 0. 21 for the k 3 -weighted EXAFS dataset. These values were reduced in 3 steps to 0. 0006, 0. 0006, 0. 00085 and 0.
014, respectively. The fits of the four experimental datasets and the partial pair correlation functions obtained by the simulation are shown in Figs. 2  and 3 , respectively. The first minima of K-S and Sb-S partial pair correlation functions were taken as the upper limits of the first coordination spheres. Coordination numbers and nearest neighbor distances are listed in Tables  2  and 3. Fit qualities and partial pair correlation functions were practically the same for all starting configurations. Only g KK (r) exhibits visible differences but this pair correlation function has a very low weight in diffraction datasets due to the low concentration of K. Only Sb-S distances were taken into account in the reverse Monte Carlo fit of Sb K-edge data. Sb-S pairs were taken into account up to 2.
DFT calculations The B3LYP hybrid density functional approach  [24] , which combines density functional and Hartree-Fock methods and uses Becke's three-parameter exchange functional  [25]  with the Lee-Yang-Parr correlation function  [26]  and its long-range corrected version using the Coulomb-attenuating method (CAM-B3LYP)  [27] , were employed as implemented in the GAUSSIAN 09 program package  [28]. These methods were selected for the calculation of the structural details and vibrational properties. The equilibrium geometries of structural models have been fully optimized at the CAM-B3LYP/LANL2DZ level of theory using the Berny algorithm as implemented in Gaussian09 program package. All optimized geometries are characterized as stationary points on the potential energy surface with vibrational frequency calculations.
This means that under the presence of several S atoms around K + , the influence of the latter, on the S t -atoms, decreases, which leads to strengthening of the Sb-S t -bonds. However, considering that DFT calculated harmonic frequencies are overestimated in relation to the experimental values by ~8-10%  [40] , the analysis presented above reveals that the existence of simple Sb-S t -terminal The large difference between the DFT-calculated Sb-S t -mode energy and the experimental band calls for modification of the structural role of K 2 S in the Sb 2 S 3 glass. The average number of S atoms around Sb is 3. 18 ± 0. 2 meaning that the majority of Sb atoms are threefold coordinated but a degree of hypervalency is also tolerated by our models obtained by the simultaneous fit of three diffraction structure factors and an EXAFS dataset.
Acknowledgment P. J. is indebted to  L. Temleitner  for helpful discussions. This work was supported by the  ELKH  project ' Structure of materials used in energy storage'  (Grant No. SA-89/2021 ). The synchrotron radiation experiments were performed at BL04B2 of SPring-8 with the approval of the  Japan Synchrotron Radiation Research Institute (JASRI)  and at  DORIS III (DESY, Hamburg) , a member of the  Helmholtz Association (HGF).