dataesr/scientific-paragraphs-categorization huggingface dataset:
The beam parameters are listed in Table  I. The core of the setup was a 300 μm-thick silicon diode (exposed silicon surface: 0. 5 cm 2 ) produced by Canberra (model: FD 50-14-300 RM), shown in Fig. 4. The detector was operated under a reverse bias of 60 V, sufficient for a full silicon depletion. The signal was amplified through the Cividec C1-HV current sensitive preamplifier (21. 9 dB amplification), and further attenuated by 6 dB, resulting in the effective amplification of g = 15. 9 dB. The data acquisition was performed using the 1 GS/s Caen digitzer (model DT5751). The deposited energy E dep of each event was calculated as the integral of the measured diode current I(t) over time [see  (1) ], with the assumption that on average k = 3. 91 eV energy is needed to create one electron-hole pair via ionization.
In this equation, R exp would typically correspond to the SEE rate retrieved experimentally using the fragmented heavy ion beam and in the scope of this work is calculated as per  (5). In terms of response functions, the literature dataset is not fully homogeneous, i. e. , in some cases only the saturation cross-section σ sat and onset LET (LET 0 ) are known, but not the Weibull shape parameter W and exponent s. When the latter are not available, for a first calculation, average values of 30 MeVcm 2 /mg and 3 are taken based on the data from the devices for which these parameters were available. σ(LET) = σ sat • (1 -exp(-(LET -LET 0 )/W) s ) (2) R GCR = Φ Z≥40 (LET) • σ(LET) • dLET (3) R GSI = R exp • ∞ 40 Φ Z≥40 (LET) • dLET ∞ 40 Φ exp (LET) • dLET (4) R exp = Φ exp (LET) • σ(LET) • dLET (5) Fig.
Data were collected through 7 focus group discussions (FGDs) and 2 in-depth interviews, all of which were conducted online because of the constraints imposed by the COVID-19 pandemic. This web-based format allowed remote participation and ensured the safety and convenience of participants. Data collection was conducted between April 2020 and December 2021, allowing a comprehensive exploration of stakeholder perspectives over an extended period. By conducting this study within the specific context of the Indonesian health system, where the understanding of regulatory sandboxes is limited, we aimed to provide valuable insights that can inform policy development and decision-making processes related to health sector regulation and supervision.
Data Collection The data collection process involved in-depth interviews and FGDs to gather comprehensive insights into stakeholders' perspectives regarding the introduction of a regulatory sandbox for e-malaria. The collected data were systematically coded and organized according to the topics discussed to facilitate analysis and interpretation. The main points and concerns of stakeholders regarding the implementation of the regulatory sandbox were identified and described. These FGDs and seminars were virtually held. In addition, we held a talk show at the launch of the e-malaria regulatory sandbox guidelines, which was attended by stakeholders and the media. All sessions, including FGDs, interviews, and talk shows, were recorded and transcribed to ensure accuracy during the data analysis phase.
Acknowledgments The authors would like to thank the  Directorate of Infectious Disease Prevention and Control at the Ministry of Health, Indonesia ; the malaria working team, the  Center for Data and Information , and the  Digital Transformation Office of the Ministry of Health, Indonesia; United Nations Children's Fund (UNICEF) Indonesia Country Office ;  World Health Organization Indonesia Country Office ;  Association of Healthtech Indonesia ; and the  Yogyakarta Provincial Health Office  for their support throughout this study. They would also like to thank all reviewers and participants of the e-malaria regulatory sandbox. In addition, they would also like to acknowledge their former research team members  Hanung Adi Nugroho ,  Eka Legya Frannita ,  Anisa Pratita Kirana Mantovani ,  Ratna Lestari Budiani , and  Nur Rosyid.
Decreased Susceptibility of Noncapsulated Strains Antimicrobial Agents and Chemotherapy Month YYYY Volume XX Issue XX 10. 1128/aac. 00127- 23 3  antimicrobials, while increasing the spread of plasmid-borne antibiotic resistance  (31). Finally, these results challenge the view that capsules systematically protect against antimicrobials. Instead, we advise that the effects of capsules on resistance be systematically tested, since they may depend on the type of antimicrobial agent and on the species. Data availability. Data used in this study are available at the following link: https:// doi. org/10. 6084/m9. figshare. 21400050. v1.
ACKNOWLEDGMENTS We thank  Jean-Marc Ghigo  and  Christophe Beloin  for plasmid pKOBEG199 and the original KmFRT cassette, and we thank  Didier Mazel  for the gift of plasmid pMPIII, containing a flippase to excise kanamycin resistance, necessary for building the Dcps mutant in K. pneumoniae  CIP 52. 145. We are grateful to  Manuel Ares Arroyo  and  Javier Pizarro Cerda  for critical reading of the manuscript and to the  Microbial Evolutionary Genomics lab  for helpful discussions. F. D. was funded by an  ANR  ( Agence National de la Recherche )  JCJC grant  ( ANR 18 CE12 0001 01 ENCAPSULATION ) awarded to O. R. The laboratory is funded by a  Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases" grant  ( ANR-10-LABX-62-IBEID ) and the  FRM  ( EQU201903007835 ). The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Statistical analyses Duration of larval developmental was analysed with Log-rank tests. For each experimental treatment, outliers were removed when the mass of the abdominal segments A1-A4, the protein mass, and the specific activity were outside the range mean ± 1. 5*interquartile range, as defined in  (Crawley, 2007). Shapiro and Bartlett tests were applied to check for data normality and homoscedasticity. When data distribution was normal and homoscedastic, one-way ANOVA and Tukey post-hoc tests were performed to examine the effects of plasticizers (BPA, DEHP, or DEHP + BPA) on protein amounts and enzyme activities. Distribution of the mass of the abdominal segments A1-A4 was not gaussian in some experimental groups, even after data transformation; in these situations, Kruskal-Wallis tests were computed to compare the masses among the experimental treatments.
Time from data publication in high impact journals to approval and market release, the good and the ugly Abaloparatide received positive opinion from the Committee for Medicinal Products for Human Use in October 2022, not yet commercialised in Europe. †For the treatment of symptomatic chronic heart failure with reduced ejection fraction. on July 11, 2023 by guest. Protected by copyright. http://rmdopen. bmj. com/ RMD Open: first published as 10. 1136/rmdopen-2022-002973 on 9 February 2023. Downloaded from.
Funding The study was funded by Becton Dickinson, and Company (BD). All authors were free to express their own views and provided independent approval of the final version; BD approval for authorship was not required. Funding was unrestricted to individuals relating to research tasks. No funding was provided for authorship related activities. Competing interests MW, HG, AT, and TE were employees of BD at the time this study was developed and conducted. MP received consultancy fees from BD for providing analytical support. Alkemi LLC (BJL and KAC) received consultancy fees from BD for providing analytical medical writing support. Alkemi LLC did not receive budget for publication writing. JFHE does not have any conflicts of interest to declare.
GIMMS NDVI3g data have been widely used in monitoring changes in vegetation activity  48, 49  , vegetation phenology  50, 51  and productivity  52  both regionally and globally. Regions with multiyear mean annual NDVI values <0. 2 during 1982-2015 were discarded from the final analyses. The global gridded CSIF dataset was obtained from https://osf. io/8xqy6/ (ref. 53). The CSIF dataset was trained by a machine learning approach based on surface reflectance from the moderate resolution imaging spectroradiometer (MODIS) aiming at reproducing SIF from the Orbiting Carbon Observatory-2. Two products are available, including clear-sky instantaneous SIF and the all-sky daily SIF. In this study, we used the all-sky daily SIF product, which covers 2000-2016 and has a 0. 05° spatial resolution and 4 d temporal resolution.
umt. edu/data/LPDR_v2/)  56. The LPDR was generated using calibrated, multifrequency daily brightness temperature retrievals from the AMSR for end of growing season (EOS) (AMSR-E, 2003-2010) and AMSR2 (2013-2015). The resulting LPDR dataset provides a long-term ( June 2002-December 2015) gridded VOD record with a daily temporal resolution and a spatial resolution of 0. 25°. In this study, we analysed VOD data during 2003-2015. The three vegetation growth proxies were resampled into a spatial resolution of 0. 5° to match the other datasets.
Climate, drought and soil datasets Gridded monthly climate data for the period 1982-2015 were obtained from the Climatic Research Unit (CRU TS4. 02, http://www. cru. uea. ac. uk/cru/data/hrg/), including precipitation (Precip), temperature (Temp) and potential evapotranspiration (PET) at a spatial resolution of 0. 5° (ref. 57). The vapour pressure deficit (VPD) was calculated based on the CRU climate datasets, following the method of Wu et al. 7. The gridded monthly SWE during 1982-2015 with a spatial resolution of 25 km was obtained from the European Space Agency (ESA) Glob-Snow dataset (https://www. globsnow. info/). GlobSnow combines SWE retrieved from multisatellite microwave observations with forward snow emission model simulations and ground-based weather station observations for non-mountainous regions of the Northern Hemisphere.
5° was extracted from the Global SPEI dataset (https://spei. csic. es). In our main analyses, we defined extreme droughts using SPEI at timescale of 3 months (SPEI3) because vegetation growth in the Northern Hemisphere responds predominantly to SPEI within timescales of 2-4 months  7, 48. The findings derived from different definitions of extreme droughts using SPEI at timescales of 1, 6, 9 and 12 months were further compared. Gridded soil sand content data were obtained from the Harmonized World Soil Database v. 1. 2 (https://www. fao. org/soils-portal/data-hub/ soil-maps-and-databases/harmonized-world-soil-database-v12/ en/)  60. This database has 30 arcsec spatial resolution and >15,000 different soil mapping units that combine existing regional and national updates of soil information worldwide.
Climate zones and vegetation types Six major climate zones were defined from the Köppen-Geiger climate classification  61  , including arid (AR), warm dry (WD), warm humid (WH), cold dry (CD), cold humid (CH) and polar regions (Supplementary Table  1  and Supplementary Fig. 4 ). The classifications of four vegetation types, including evergreen forests, deciduous forests, shrubs and grasses, are based on the GLC 2000 land use/land cover dataset from the European Commission Joint Research Center (https://forobs. jrc. ec. europa. eu/products/glc2000/glc2000. php). The land cover map discriminates 17 land cover types on the basis of the IGBP (International Geosphere-Biosphere Programme) classification scheme. In this study, the evergreen broadleaf forests and evergreen needleleaf forests were grouped into a single 'evergreen forests' class; and the deciduous needleleaf forests and deciduous broadleaf forests were grouped into a 'deciduous forests' class.
1a  and Supplementary Fig. 1 ). To avoid lengthening the drought recovery duration due to algorithm design, in the R MGS , the drought recovery was calculated as the total length of the recovery period minus the length of the dormant season. Severe drought events and subsequent drought recovery were pixel specific and calculated for every combination of NDVI and SPEI3. Only drought and recovery events that were fully contained in the period 1982-2015 were analysed. The drought recovery derived from CSIF and VOD using the same protocol was additionally compared (Extended Data Fig. 3 ). The comparison of three different vegetation metrics allows for validating the NDVI-based findings and confirming whether the drought recovery patterns are consistent for different vegetation proxies.
Red and blue colours in a and b indicate positive and negative correlation coefficients between predicting variables and drought recovery, and darker colours indicate stronger correlation. Only factors with contribution >2% (averaged contribution of all factors) are displayed. The predrought period represents the six months preceding the extreme drought events. We did not consider the vegetation recovery under late-growing season drought for the R SGS scenario, to prevent the problem of overfitting caused by insufficient training dataset samples. We regrouped the extreme drought events into early-growing season (Early-GS) droughts, mid-growing season (Mid-GS) droughts, and late-growing season (Late-GS) droughts. Four kinds of vegetation types were considered, including evergreen forests (Evergreen), deciduous forests (Deciduous), shrubs, and grasses.
Acknowledgements X. W. was financially supported by the  National Natural Science Foundation of China  (grant nos. 41922001  and  42171050 ), the  Second Tibetan Plateau Scientific Expedition and Research Program  (grant no. 2019QZKK0306 ) and the  National Key Research and Development Program of China  (grant no. 2022YFF0801800 ). S. A. K. was supported by the  US Department of Energy Environmental System Science program  grant no. DE-SC0022052. A. G. is supported by the  Ramon y Cajal Program of the Spanish MICINN  under grant  RyC2020-030647-I , and by  CSIC  under grant  PIE-20223AT003. W. K. S. and W. Z. were supported by the  NASA   Carbon Cycle and Ecosystems Program  under grant  80NSSC21K1709.
Data availability All gridded vegetation growth proxies and climate data are freely accessible at the following websites: the GIMMS NDVI3g, https://climatedataguide. ucar. edu/climate-data/ndvi-normalizeddifference-vegetation-index-3rd-generation-nasagfsc-gimms; the gridded contiguous SIF dataset, https://osf. io/8xqy6/; the microwave-based VOD data, http://files. ntsg. umt. edu/data/LPDR_v2/; the gridded monthly temperature, precipitation, VPD and potential evapotranspiration data, http://www. cru. uea. ac. uk/cru/data/hrg/; the gridded monthly SWE data, https://www. globsnow. info/; and the SPEI data, https://spei. csic. es/. The world map of Köppen-Geiger climate classification was freely obtained from http://koeppen-geiger. vu-wien. ac. at/ https://doi. org/10. 1038/s41558-022-01584-2 present.
htm. The global vegetation map GLC 2000 was freely accessible from https://forobs. jrc. ec. europa. eu/products/glc2000/glc2000. php. The gridded soil sand content data were obtained from the Harmonized World Soil Database v. 1. 2, https://www. fao. org/soils-portal/data-hub/ soil-maps-and-databases/harmonized-world-soil-database-v12/en/. The shapefile of the world continents was publicly obtained from https://hub. arcgis. com/datasets/esri::world-continents/about. Source data are provided with this paper.
Acknowledgements The authors would like to acknowledge  Sabrina Marchal  for her excellent administration and her outstanding practical contributions keeping the overview during the realisation of the 1st EFORT European Consensus, as well as for her perfect communication to the  Scientific Committee Members , the  Expert Delegates  and to the 42 National Societies and 3 Speciality Societies within EFORT,  Dr Cornelia Lützner  for her perfect organisation of the 1st EFORT European Consensus Conference as a Hybrid Meeting with a well-functioning real-time electronic voting system in Dresden,  Dr Ronja Alissa Schierjott-Hermle  for hard ongoing work within the Editorial team, her knowledgeable contributions and critical review during the definition and consolidation of the research questions and her editorial work on the 32 Consensus papers and  Prof Klaus-Peter Günther  enabling the 1st EFORT European Consensus within the framework of the  EFORT Implant & Patient Safety Initiative (IPSI)  from the initial ideas towards the final publication with his profound knowledge and outstanding expertise and for his generous hosting of the 1st EFORT European Consensus Conference in Dresden 2021.
Acknowledgements This study was undertaken as part of the  Seine-Aval programme  ( SARTRE  project), coordinated by the  GIPSA (Groupement d'Intérêt Public Seine-Aval)  and the  Haute-Normandie region of France  and supervised by  Prof. Michèle Tackx ,  Toulouse University. The authors thank  Michel Simon ,  Romain Levaillant  and  Frédéric Azémar  for their valuable help during the sampling survey, and  Michael Carpenter  for correcting the English style and grammar. The authors would also like to express their sincere gratitude to three anonymous reviewers for their valuable advice and comments, which greatly contributed to the improvement of this paper.
Study design This prospective and consecutive cohort study was conducted over a period of 13 months at Gustave Roussy Comprehensive Cancer Center in France. Clinical and biological data of cancer patients who were presented to the CSMs were prospectively collected in four departments (three units of the Medical Oncology Department and the Department of Therapeutic Innovation and Early Trials  [DITEP] ). We also completed data retrospectively in order to retrieve discharge information about our patients (place of discharge, possible date of death, date of the last chemotherapy in relation to date of death. ). In order to complete the assessment of the organization, we developed a survey for the health-caregivers of the units involved in the CSM in order to obtain their qualitative assessments of these meetings.
Statistical analysis Analysis was performed on all enrolled patients. Quantitative data were summarized using number of observations with interquartile range. Qualitative data were summarized using number of observations and proportion by modality. Proportions were compared univariately using a Fisher exact test. Survival comparisons were performed using a Log-rank test. This study was reviewed and approved by the Gustave Roussy Scienti c Commission (Institutional Review Board), which did not identify any unethical elements.
On the other hand, an earlier and stronger involvement of patients and proxies themselves in the decision-making process is certainly a promising pathway forward. This observational study has several limitations. First neither emergency room, surgery nor hematology patients were included (although we consider that CSMs should be implemented in most of these settings). Above all, only 5% of hospitalized patients were discussed, with a very high short-term mortality rate (one third of patients included, while the hospital mortality rate was 11. 5% in these units): this may represent a bias of under-selection of patients eligible for CSM discussions. Finally, the impact of the CSMs on subsequent decisions and outcomes was only assessed retrospectively. On the other hand, some strengths should be noted: all patients presented in CSM were prospectively identi ed and consecutively included during a 13-mo period; data collection (CP, FB) and analysis methodology were homogeneous; short and mid-term analyses were performed.
reduce uncertainty in ligament-balanced planning. In a separate study, Van Oevelen et al. used a principal polynomial autoencoder model to nonlinearly encode cartilage wear patterns in a dataset of osteoarthritis (OA) images. They converted 3D joint space width data into 2D pixel images and compared healthy and OA cases using this approach. The study presented a novel concept of converting anatomically corresponding distance maps into pixel images for neural-based learning. The study also reconstructed a healthy virtual twin using a statistical shape model (SSM) to predict pre-morbid bone geometries of the femur and tibia bones. Using the healthy SSM-derived geometries for comparisons, the study identified four dominant orthogonal wear components (posteromedial, anteromedial, bicompartmental, and lateral) in OA that are highly correlated with relative limb geometry.
They introduced a novel technique incorporating shared boundaries within PSM for modeling multi-organ anatomies. The study included a toy dataset of parameterized shapes and a clinical dataset of biventricular heart models, specifically modeling the right ventricle, left ventricle wall, and interventricular septum. The illustrations demonstrated that the integrity of shared boundaries between multiple organs was preserved, and pathological changes could be effectively detected using this approach. Another interesting study by Adams et al. presented a spatiotemporal SSM to assess non-linear dynamic anatomies such as left atrium shape changes over the cardiac cycle. They combined an entropy-based correspondence optimization with nonlinear regression-called regularized principal component polynomial regression.
minutum compared to EBR controls, only L. alexandrii and M. adhaerens significantly enhanced the growth of this species (Tukey, Table  S1 ). This is in line with previous findings revealing data that L. alexandrii and M. adhaerens support the growth of Symbiodiniaceae, potentially via the provision of the growth promoting hormone indole-3 acetic acid, while no such interaction was detected with M. aquimarina  21. While Symbiodiniaceae heterotrophic feeding on the bacteria  29  could also have provided a nutritional source leading to an increase in the growth of the algal cells, the concomitant increase in bacterial cell densities suggests these bacteria were able to remain in culture alongside the Symbiodiniaceae (Table  S2 ). We examined the photophysiology of the untreated, EBR and bacteria-Symbiodiniaceae co-cultures during early and mid-exponential growth, to capture the effect of bacterial interactions at different Symbiodiniaceae growth stages.
Acknowledgements This work was supported by  Human Frontier Science Programme   Long-term Postdoctoral fellowship   LT000625/2018-L  (awarded to J. L. M. ),  University of Technology Sydney Chancellors Postdoctoral Research Fellowship  (awarded to J. L. M. ),  Australian Research Council Discovery  Project grants  DP180100838  (awarded to J-B. R. and J. R. S) and  DP180100074  (awarded to D. J. S. ) and  DP200100091  (awarded to D. J. S. and J. L. M. ).
Electricity load Load data were obtained from the online database of Waite et al. [6]  or were collected from National Electricity Distribution Companies of each country (Table  A1  & A2, Appendix). Hourly load data were available for Libreville, Yaoundé, Lomé, Accra, Dakar and Mindelo. For all the other cities, hourly loads were estimated from country-level hourly loads and monthly ratios of peak consumption at city-level feeders compared to peak consumption for the national grid as a whole. Measured/estimated hourly data were then processed to produce, for each city, time series of daily per-capita consumption. A comprehensive description of collected load data is given in SM (Table  SM2   & SM3). As a result of different practices of the national electricity companies, consumption data can be quite variable in terms of definition, quality and integrity.