dataesr/scientific-paragraphs-categorization huggingface dataset:
-Direct comparisons of homogenized and uncorrected ECC concentrations in the stratosphere between 18 and 26 km show limited changes using a subset of 40 d, with LiO3St and ECC measurement time difference less than 6 h. The mean differences between 2005 and 2021 ECC and MLS or LIO3St ozone observations using a less restrictive time coincidence of < 12 h are either slightly positive (+2 %) for MLS or slightly negative (-2 %) for LiO3St, meaning that the homogenization is a good compromise for intercomparability of ECC with other stratospheric O 3 measurements below 26 km. -The comparisons with both lidar and satellite observations suggest that homogenization increases the negative bias of the ECC to values lower than -6 % above 26 km. While the objective of this paper is to discuss the impact of homogenization on the OHP dataset using lidar and satellite measurements, it is worth checking how such corrections have improved data quality at other sites.
Acknowledgements. The technical staff of  Observatoire de Haute Provence  are gratefully acknowledged for carrying out the lidar measurements and ECC ozone soundings. We acknowledge the  European Centre for Medium-Range Weather Forecasts (ECMWF)  for providing meteorological reanalysis for lidar data processing and dynamical tropopause altitude calculations. The authors acknowledge the  NASA/GSFC AURA Validation Data Center and EU-METSAT  for providing the satellite data used in this paper (MLS, GOME, OMI, OMPS). Financial support. This research has been supported by the  Centre National de la Recherche Scientifique/INSU  (project  NDACC-France and ACTRIS ). Review statement. This paper was edited by  Ja-Ho Koo  and reviewed by two anonymous referees.
Code and data availability. OHP ECC data are available at https://doi. org/10. 25326/293 (Ancellet, 2021). LiO3Tr data are in ohto*. anl files available at https://ftp. cpc. ncep. noaa. gov/ndacc/ station/ohp/ames/lidar (last access: 27 January 2022; NOAA/N-DACC, 2022b) and LiO3St data are in oho*. gol files available at https://ftp. cpc. ncep. noaa. gov/ndacc/station/ohp/ames/lidar (last access: 27 January 2022; NOAA/NDACC, 2022a). ML-S/Aura Level 2 Ozone (O 3 ) Mixing Ratio V005 data are available at https://doi. org/10. 5067/Aura/MLS/DATA2516 (Schwartz et al. , 2020). OMI/Aura Ozone (O 3 ) Total Column Daily L2 data are available at https://doi. org/10. 5067/Aura/OMI/DATA2025 (Bhartia, 2012). OMPS-NPP L2 NM Ozone (O 3 ) Total Column L2 data are available at https://doi. org/10. 5067/0WF4HAAZ0VHK (Jaross, 2017).
It has been licensed in South Korea since  August 31, 2009 , for active immunization against diphtheria, tetanus, pertussis, and poliomyelitis in infants and children. It has been evaluated for immunogenicity and safety in multiple randomized clinical trials including in South Korea  [41]. Per the regulations from the Ministry of Food and Drug Safety (MFDS) in South Korea, data were collected in the present study in South Korea to confirm its safety profile in real-world clinical practice as a post-authorization commitment and when used per licensed indications (i. e. , primary series and school-entry booster). This post-marketing surveillance (PMS) study aimed to monitor and evaluate the safety of the DTaP-IPV vaccine over a period of 6 years in children aged between 2 months and 6 years who were given the DTaP-IPV vaccine in realworld clinical practice.
Study Design This was a multicenter, observational PMS study, conducted in real-world practice for a duration of 6 years from the product approval date in accordance with the MFDS regulations (NCT01437423). The planned sample size was 600 participants in accordance with the MFDS notification. Participants were enrolled across eight centers from August 31, 2009, to August 30, 2015. The protocol for this study was approved by the MFDS and ethics committee before study initiation. The study was conducted in accordance with the guidelines established by the Declaration of Helsinki and MFDS Notification No. 2009-46 (basic standard for re-examination of new drug). Informed consent was obtained from all participants or their parents/legal representative before enrollment.
Data Collection Demographic and other baseline data were collected on a case report form (CRF). Immediate events, within 30 min of the observation period after the vaccination, were also collected on a CRF on the day of vaccination. Participants and/or parents were given a diary card to record safety information on all pre-listed solicited injection site and systemic reactions and all adverse events (AEs), including unsolicited AEs/ adverse drug reactions (ADRs) and serious AEs (SAEs) occurring up to 30 days after vaccination.
The intensity of the solicited and unsolicited AEs was measured by the participant's parent/ legal representative based on an intensity scale provided on the CRF. Solicited injection site reactions and systemic reactions were classified as grade 1 (mild), grade 2 (moderate), or grade 3 (severe). Unsolicited AEs were classified as grade 1 (mild-did not interfere with daily activities), grade 2 (moderate-interfered with daily activities), or grade 3 (severe-prevented daily activities). The causality of each AE was assessed for all events occurring up to 30 days post-vaccination. It was assessed by the investigator as certain (valid time relationship to vaccine administration and cannot be explained by other concomitant drugs or other disorders), probable/likely (pertinent time relationship to vaccine administration and was not likely to be caused by other concomitant drugs or disorders), possible (pertinent time relationship to vaccine administration, but could be caused by other concomitant drugs or disorders), unlikely (not likely to have causal relationship with vaccine administration; temporary cases), conditional/unclassified (requires more data for proper assessment), or unassessable/unclassifiable (insufficient information exists).
Disclosures. Emmanuel Vidor and Christine Manson are employees of Sanofi and may hold stocks in the company. The current affiliation of Young June Choe is Korea University Anam Hospital, 73 Goryeodae-ro Seongbuk-gu, Seoul 02841, South Korea. Young June Choe was an employee of Sanofi at the time of the study. Compliance with Ethics Guidelines. The protocol for this study was approved by the MFDS and ethics committee before study initiation. The study was conducted in accordance with the guidelines established by the Declaration of Helsinki and MFDS Notification No. 2009-46 (basic standard for re-examination of new drug). Informed consent was obtained from all participants or their parents/legal representative before enrollment. Data Availability. The datasets generated during and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.
ACKNOWLEDGEMENTS The authors would like to thank all participants who volunteered to take part in the study, the primary investigators, and their site staff. We thank  Roopsha Brahma, PhD , for editorial assistance and manuscript coordination on behalf of  Sanofi. The authors would also like to thank  Hyun Jung Kim  ( Sanofi ) and  Catherine Huoi (Sanofi)  for their contributions as the medical lead for South Korea and the global medical expert, respectively.
The endpoints of interest were overall survival, the completion of chemotherapy, adverse events, and quality of life. We screened for well-defined interventions targeting each specific domain of the CGA in order to review the current evidenced-based data on the impact of interventions targeting each domain. Every domain of the CGA was attributed to one of the authors. The designated author for each CGA domain reviewed all of the abstracts yielded by the search in terms of their relevance by screening their titles and abstracts. He selected the papers for which the full text was reviewed. Then, he discussed the inclusion (according to the inclusion criteria) and quality of the studies with V. T. In the case of non-agreement with the presence of inclusion criteria, the articles' full texts were reviewed by a third author (A.
1% vs. 24. 5%, respectively, p = 0. 009). No data regarding quality of life were included in this study. Pergolotti et al. (2019)  conducted a randomized controlled trial with 63 adults aged 65 years and older (mean age: 74) with a recent diagnosis or recurrence of cancer within 5 years and at least one functional limitation, as identified with a geriatric assessment  [13]. The intervention group received individualized occupational therapy and physical therapy adapted to their needs. Follow-up phone calls were made to ensure occupational/physical therapy appointments were held and to collect final post-assessment data 3 months later. Perceived possibilities for activity, rated with the Possibilities for Activity Scale, were significantly improved in the intervention group (evaluated with the Possibilities for Activity Scale scores (∆ = 3.
2 g protein/kg/day in a randomized controlled trial that involved 336 older patients receiving chemotherapy for solid or hematological malignancies. This study showed a significant increase in the dietary intake for the intervention group (p < 0. 01) but no difference in the 1-year (p = 0. 74) or 2-year (p = 0. 37) survival or in grade 3-4 infections. One-year and two-year mortality rates were similar in both groups (R = 1. 1, 95% CI = 0. 8-1. 5, p = 0. 74, and RR = 1. 1, 95% CI = 0. 9-1. 5, p = 0. 37, respectively). The response to chemotherapy was also similar between groups. No data on quality of life were included in this study. The observational study (n = 135) published by Kalsi et al. on the effect of a geriatric assessment and intervention plan compared to usual care showed an improvement in the chemotherapy completion rate (33.
(2019). Identification and counting of calcareous nannofossils were performed using a Leica DMP 750 petrographic microscope at 1000× magnification under crossed polars. Photographs were taken with a Leica MC 170 HD camera. For each sample 300 specimens were counted, thus ensuring that the probability of not recovering a rare species is below 5 %  (Fatela and Taborda, 2002). In the slides for which a low nannofossil abundance made it difficult to count 300 specimens, the counting stopped at 450 fields of view (FOV; the area of one FOV is 0. 0069 mm 2 ). All nannofossil data were integrated in a distribution chart (Fig. 2 ). The absolute abundance (as nannofossils per gram of sediment) and preservation index are also indicated. Preservation is a discrete scale based on the general aspect of the specimens  (Roth, 1984).
Micaela Chaumeil Rodríguez developed her work in Argentina with funding provided by the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). The stay of Micaela Chaumeil Rodríguez at the LGL-TPE (Université Lyon 1, France) was funded by an "Eiffel Excellence Bourse" awarded by the Ministère de l'Europe et des Affaires étrangères through Campus France. Micaela Chaumeil Rodríguez introduced the preparation technique in the Argentinian laboratory with the "Katharina von Salis Graduate Research Fellowship" awarded by the International Nannoplankton Association. This study benefited from the financial support of the Institut Universitaire de France (IUF) to Emanuela Mattioli.
Financial support. support Micaela Chaumeil Rodríguez developed her work in Argentina with funding provided by the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). The stay of Micaela Chaumeil Rodríguez at the LGL-TPE (Université Lyon 1, France) was funded by an "Eiffel Excellence Bourse" awarded by the Ministère de l'Europe et des Affaires étrangères through Campus France. Micaela Chaumeil Rodríguez introduced the preparation technique in the Argentinian laboratory with the "Katharina von Salis Graduate Research Fellowship" awarded by the International Nannoplankton Association. This study benefited from the financial support of the Institut Universitaire de France (IUF) to Emanuela Mattioli.
However, lorlatinib could act as a trigger after a prolonged D2R desensitization exerted by a first-and/or second-generation ALK TKI, although this remains a speculative explanation due to the lack of data regarding the use of lorlatinib (first-line versus subsequent lines) in the current FAERS analysis. On the other hand, the non-negligible proportion of psychiatric events with crizotinib deserves further discussion. Collectively, pharmacokinetic data could suggest the low BBB penetrance of crizotinib results in worst CNS activity, as compared to alectinib and lorlatinib  [23]. However, patient-related factors, especially CNS radiation, but also severe renal impairment as well as drug-drug interactions, can increase BBB penetrance  [24] [25] [26]  and possibly explain the observed neurocognitive toxicity.
Of note, the reported onset of psychiatric AEs with crizotinib in the present study was delayed and the proportion of subjects requiring discontinuation was lower as compared to lorlatinib. We acknowledge the limitations of our work. In particular, the retrospective nature of the study and the lack of exposure and clinical data (including missing data, comorbidities, and radiation therapy), which do not allow to infer firm causality and calculate real incidence. Moreover, FAERS is a self-reported database used for identifying potential relationship between drugs and AEs in post-marketing surveillance of drug safety, and submission to this platform is not mandatory for physicians. There are no reasons to support the existence of confounding by indication or channeling bias (i. e. , preferential prescription towards more severe patients), although other reporting biases cannot be ruled out with certainty due to different approval times and market penetration of the various ALK TKIs.
We also apply stringent exclusion criteria, which, together with the likely under-reporting, may potentially underestimate actual psychiatric events. Nonetheless, several strengths can be identified. We used a large-scale publicly accessible pharmacovigilance database, thus supporting generalizability of the results, and contributed to the cumulative knowledge about the safety of ALK inhibitors in an unselected real-world population, a topic still poorly investigated. Current evidence suggest that, with the appearance of psychiatric disorders, antipsychotic therapy must be started. Olanzapine is one of the preferred antipsychotic drugs because it can be safely administered with concomitant ALK TKIs. Co-adjuvant drugs, such as antidepressants and benzodiazepines, can be used; conversely, other antipsychotic drugs, including risperidone and quetiapine, should be used with caution due to drug-drug interactions  [27].
In this work, we present data for electrons in the range  [1. 5 , 13]  keV and for protons in  [2 , 13]  keV. The number of events in the electron runs is always larger than 30 × 10 3 while for protons it varies be-tween 3 × 10 3 and 13 × 10 3 , the disparity depending on the proportion of heavier species sent by Comimac. In this paper, we define the measured ionization energy in ADU (Analog-Digital Units) as the amplitude of the signal outputted by the charge sensitive preamplifier (CSP) after correction of the ballistic deficit. This ballistic deficit depends on the time constant of the CSP (measured to be 51. 8 µs) which is small compared to the signal duration of a few hundreds of microseconds. In other words, it means that the signal starts decaying before all the charges has been collected.
Comparison between Comimac electrons and fluorescence photoelectrons We used the X-ray generator to produce photoelectrons of 1. 49 keV and 3. 23 keV  [38]  by fluorescence as explained in the previous section. According to CXRO database  [44] , 94% of the fluorescence X-rays of the cadmium will be transmitted in 15 cm. While this setup does not produce photoelectrons everywhere in the volume, it does produce them at all possible radii within the detector, and so we assume they will integrate any potential volume effects. On the contrary, the Comimac electrons are always sent at the same position: at the SPC surface through the hole. The range of the particles remains low: 7 mm for 5 keV electrons (according to Casino) and 1. 4 mm for 5 keV protons (according to SRIM). Thus, comparing fluorescence photoelectrons and Comimac electrons is of particular interest for validating two assumptions: (1) that the electric field in the SPC is strong enough to collect all the charges, even from the surface; (2) that the kinetic energy of the electrons sent by Comimac is well determined.
We define R ≡ (E Comimac -E X-rays )/E X-rays , a ratio that quantifies the difference between the ionization energy measured for a fluorescence photoelectron and the one measured for a Comimac electron. We obtain R(1. 49 keV) = 3. 7% ± 0. 5% and R(3. 23 keV) = 3. 9% ± 0. 5%, respectively for the aluminium line and for the cadmium line, indicating that we measure slightly more ionization energy for the Comimac electrons than for the photoelectrons, excluding energy losses in the interface between Comimac and the SPC as a possible explanation for the difference. The measurements with the X-ray generator have been performed with a fresh charge of gas compared to the other measurements but we applied the same working conditions. The different charge of gas has a non-negligible influence on the gain of the detector, so we must extrapolate the results by comparing the position of the energy peaks in both dataset.
Data availability All the model data used in this study, together with the code used to analyse and plot the results are available to download via https:// doi. org/ 10. 5281/ zenodo. 58842 27. The full experimental dataset is available at https:// prima-dm1. jasmin. ac. uk/ and at the ESGF https:// esgf-index1. ceda. ac. uk/ search/ esgf-ceda/ (search for primwp5 or dcpp-for MPI-ESM). Please contact the authors for further help accessing these datasets.
e. ej ∼ N(0, 2 ) ). Hence a predictor ( e ) can then be assessed to see if it explains a significant fraction of the variance of variable X ej , with respect to the residual noise term ej. The similarity with linear regression is clear (e. g. y = c + mx + ). In this study our dataset X emrj , consists of two experi- ments (e: 2AMV + ,2AMV -), five models (m), two resolu- tions (r: LR, HR), and multiple ensemble members (j). We can propose the following statistical model for this dataset combining these predictors and their interactions: Here e represents the variation in X emrj due to the experi- ments (e: 2AMV + , 2AMV -) averaged across all models (m), resolutions (r) and ensemble members (j). A em is an interac- tion term, and represents the variation in X emrj due to both the experiments (e) and models (m).
In this way we can examine the impact of the AMV ( e ), and also both how this impact varies across the models (m: A em ), and changes with model resolution (r: G er ). The remaining terms describe other aspects of the dataset, for example, m describes the spread of climatologies between models (m) (see the appendix (A)). We do not consider these further terms in the remainder of this study, but focus on the impact of the experiment ( e ), the impact of the choice of model on the experiment ( A em ), and the impact of the choice of resolution on the experiment ( G er ).
A Analysis of Variance (ANOVA) In this section we outline the basis for Analysis of Variance (ANOVA) see  Storch and Zwiers (1999, p117) ,  Zwiers (1996)  or  Wilks (2019)  for detailed explanations). ANOVA aims to decompose the total variance of a dataset into contributions from different factors. The significance of each contribution can then be assessed. Consider a multi-model ensemble experiment with (m = 1. M) models and (e = 1. E) experiments, where each experiment was performed (j = 1. J) times. 1  Suppose we wish to examine the factors influencing Mean Sea Level Pressure (MSLP) in this ensemble. Assuming the models uses a common spatial grid, we can define MSLP at a grid point across all experiments (e), models (m) and ensemble members (j) to be X emj. We can then express X emj as a linear combination of factors: Here, is the average over all experiments, models and ensemble members: (5) X emj = + e + m + em + emj e is the part of MSLP that changes between experiment, but does not change between ensemble members or models.
| Field study To To survey these populations, we placed a 20 × 20 m square on each and mapped all individuals in each 10 × 10 cm cell using 1 × 1 m quadrats subdivided with a 100-cell grid. To analyse the data from the three natural populations, we grouped the cells four by four to obtain the total number of individuals per 20 × 20 cm cell to have a study area of 100 × 100 cells as for the virtual populations. We simulated the same sampling surveys on the counts of the three natural populations as on the virtual populations and computed the variance ratio of SYS and SBS relative to SRS. To test whether cluster diameter can be quickly assessed in the field, an observer surveyed the study area by splitting the 20 × 20 m square into 20 columns 1 m in width. He then walked through every second column and measured the largest width of five randomly selected clusters intersected by the column.
The fact that SYS and SBS achieved a similar precision could stem from the same phenomenon, but this requires further investigation. Further studies need to be conducted to identify the other characteristics of the distribution of individuals than the diameter of the clusters involved for natural populations. This will first require to better understand how individuals are distributed within natural populations. Currently, datasets containing the location of all individuals in a population, or more synthetic aggregation metrics, remain scarce (but see  Morrison et al. , 2008, and Law et al. , 2009) , making it difficult to build simulations with realistic distributions of individuals. Once this barrier is removed, simulation studies will have to be carried out to identify the characteristics other than cluster diameter that drive the relative precision of sampling methods.
Among them we underline spatio-temporal processing of information, frequency combs multiplexing in the same cavity  [51]  and speckle-free imaging with short pulses  [52]. Funding. The INPHYNI Group acknowledges project ANR-18-CE2»-0002 BLASON and funding of Région PACA OPTIMAL. J. J. acknowledges financial support from project MOVELIGHT (PGC2018-0996«7-B-100 AEI/FEDER UE). Supplemental document. See Supplement 1 for supporting content.
Acknowledgments The Jason-3 and Sentinel-3A data were obtained from the  Geophysical Data Record (GDR)  available on the AVISO website (https://www. aviso. altimetry. fr). The Mean Sea Surface Model CNES_CLS_2015 is also available on the same website. The authors would like to acknowledge the support from the  French Space Agency (CNES)  via the  French TOSCA 645 program  to conduct the present study.
Quantification of root-zone soil moisture (RZSM) is crucial for agricultural applications and soil sciences. RZSM impacts processes such as vegetation transpiration and water percolation. Surface soil moisture (SSM) can be assessed through active and passive microwave remote sensing methods, but no current sensor enables direct RZSM retrieval. Spatial maps of RZSM can be retrieved via proxy observations (vegetation stress, water storage change, and surface soil moisture) or via land surface model predictions. In this study, we investigated the combination of surface soil moisture information with process-based inferred features involving artificial neural networks (ANNs). We considered the infiltration process through the soil water index (SWI) computed with a recursive exponential filter and the evaporation process through the evaporative efficiency computed based on a MODIS remote sensing dataset and simplified analytical model, while vegetation growth was expressed through normalized difference vegetation index (NDVI) time series.