dataesr/scientific-paragraphs-categorization huggingface dataset:
Response shift The ICUROS study was not originally designed to measures response shift but the data were collected for other purposes. Therefore, the study at hand aims to evaluate response shift from secondary data and the analyses were informed by guidelines for this specific study type  [14]. We focused on response shift from the measurement time-point immediately after fracture to subsequent time points. The reason is twofold: Changes to health domains are likely most rapid in the first months after fracture, making the potential for response shift larger during this time-period. Less drastic changes to health may not produce important changes in outcomes on the group level and therefore no measurable response shift  [14]. Secondly, it has been argued that respondents rating hypothetical health states imagine themselves transitioning to the health state in question and therefore may value it differently than if they would have been accustomed to or better informed about the health state in question  [7].
Statistical analysis Patient characteristics were described using frequencies and percentages for dichotomous variables and median and interquartile ranges for continuous variables. In the regression models, we excluded data for the prefracture recall as outcome given that they are not based on the patient's current experience. However, pre-fracture recall was used for stratification of patients in the analysis of impact of prior impairment. We excluded phase 1 data for patients who reported higher TTO at enrollment than before the fracture given that this indicated that patients may have misunderstood the TTO task. We also excluded data from contacts where patients reported (i) "severe problems" with mobility but "no problems" with usual activities or self-care, and "severe problems" in usual activities but "no problems" with self-care.
The rationale for this exclusion is that these combinations of impairment may be considered inconsistent with each other and could therefore indicate data entry errors: either on part of the patient or the study nurse. Given that single patients could contribute with multiple observations and that patients may be more similar within hospitals, countries, and phases than between hospitals, countries, and phases, we modeled random effects  [20]  for patients, hospitals, countries, and phases. We derived the value set by fitting a linear mixed effects regression model with ten dichotomous variables-one for each level of impairment in the five EQ-5D dimensionon TTO. The regression model is presented in the online supplement. We assessed the model using split sample validation. Observations were assigned with equal probability to the estimation and validation samples.
In Russia, the following team members provided valuable contributions to the study: Dr. Natalia Toroptsova, Dr. Oxana Nikitinskaya, Dr. Olga Dobrovolskaya (Institute of Rheumatology, RAS, Moscow), Prof. Author contributions FB, AT, BJ, and JAK: designed the study. EM, PC, MDL, HPD, MJ, MLB, OL, KMS, SS, MT, TT, and VA: led the data collection. AS and EH: designed and executed the statistical analyses. AS: led the interpretation of findings with inputs from the other authors. AS: drafted the manuscript. All authors except MB reviewed the manuscript and approved the submission for publication. Funding Open access funding provided by Karolinska Institute. On its international level, the ICUROS was sponsored by Amgen, Eli Lilly, Medtronic, Novartis, Sanofi-Aventis, Servier, and Pfizer. In Australia, the study was supported by project Grant No.
BDOC is estimated as the difference between the DOC on the sampling day (T0) and the DOC after incubation (T45). A total of 97 DOC and 61 BDOC samples from the subsurface water drained by the giant lysimeter were analyzed (Table  1 ). Overall, 51 DOC and BDOC samples were analyzed from the sub-root water in the suction cups. Combined with the flow measurements, these data made it possible to estimate the flux of organic carbon leaching. As also shown in Table  1 , soil samples and farmer interviews for 52 plots allowed us to determine the three carbon fractions for the AMG model.
, 2003; Baudin et al. , 2015). Moreover, this innovative approach using RE data can quantify the proportion of centennially stable soil organic carbon (OC s ) and active soil OC pools for each plot (OF or CF), in any temperate soils  (Cécillon et al. , 2018; Cécillon et al. , 2021). A statistical model based on Rock-Eval® thermal analysis (PARTYSOC  Cécillon et al. , 2021)  optimally partitions the active and stable pools of AMG  (Kanari et al. , 2021). This statistical model uses Rock-Eval thermal analyses data as entry variables and provides the proportion of C stable in the analyzed sample. The proportion of stable C was then multiplied by TOC to calculate the C stable content. The content of active C was determined by the difference between the two.
Acknowledgments This study was partially funded by the  EC2CO CNRS program  in the interdisciplinary framework offered by the FIRE-FR3020 research federation. The work was also supported by the  PIREN-Seine program  and  ABAC  project. We grateful to  Anun Martinez ,  Benjamin Mercier ,  Cédric Chaumont ,  Florence Savignac , and  Sophie Mery de Bellegarde  for their kind and valuable contribution in the field and/or in the lab. We.
Data Invalidation These portable instruments were submitted to harsh conditions, which induced significant artefacts. Languille et al. [30]  showed the impact of environmental changes. An in-depth data curation was conducted manually. More precisely, abrupt changes in humidity, temperature, or atmospheric pressure (monitored by the sensors) were tracked. Single measurements with clearly unexpected value (obvious outliers exceeding the neighbour values by one order of magnitude), even without simultaneous environmental change recorded, were invalidated (because most likely due to an artefact). As a result, several data points in the range of a few thousand were invalidated, which represents about 1% of the total measurements. The limits of quantification are not specified by the manufacturer for the AE51 and Canarin.
First, the Spearman correlation was considered in all these works (even if the normality of the data sets are not systematically discussed). Unlike the Pearson correlation, it does not quantify the linear relation between the datasets, but the ranking order consistency of the elements. For instance, Spearman correlation would not detect some disagreements between ambient and PE measurements, such as discrepancies for extreme values. In the previous sections, we showed that portable sensors were able to monitor quick environment changes. This fine temporal resolution was possible because of the high measurement frequency (time step of one minute). However, the correlation studies mentioned above considered hourly  [43]  or daily averages  [15, 16, 45, 51]. Increasing the averaging time is expected to artificially increase the correlation.
Lastly, the representativeness of the sample size was sometimes questionable (only six volunteers in winter in  [43]  and a total number of only 25 data points in  [15] ). For comparison purposes, similar correlation calculations were conducted in the present work and, since the previously mentioned studies considered the whole available PE data set (including indoors), it was necessary to apply the same calculation protocol for a relevant comparison. Comprehensive correlations were computed for both Spearman and Pearson, whereby each single volunteer was considered separately, and each campaign was considered as a whole for time resolutions of 1 h and 1 day, as well as for the three measured pollutants (BC, NO 2 , and PM 2. 5 ). These comprehensive correlation results are presented in Table  4.
Acknowledgments: The authors thank all the volunteers who accepted to participate the experiments. Christophe Debert and Laurent Gauvin (Airparif) are acknowledged for the work they conducted regarding the sensors assessment. We thank Salim Srairi (CEREMA) for his help in recruiting volunteers. Thank you to Clément Pommier and Patricia Masson for the very productive work they achieved during their internship. More generally, the authors greatly acknowledge everyone who was involved in the Polluscope project, thank you all. Additional support from CEA and CNRS is acknowledged.
Funding: The Polluscope project was funded by the French research agency (ANR-Available online: https://anr. fr/Projet-ANR-15-CE22-0018 (accessed on 5 January 2022)). Part of the equipment was funded by iDEX Paris-Saclay, in the framework of the project ACE-ICSEN. Measurements from the SIRTA station were performed within the ACTRIS research infrastructure under the H2020 grant agreement 654109. Institutional Review Board Statement: Ethical review and approval were waived for this study because no medical data was used or even recorded. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
0 Peking Union NCT04996602 GBq (55mCi) 177 Lu-EB-PSMA in Patients Medical College Phase 1 China With mCRPC Hospital An International Prospective Open-label, NCT04720157 Randomized, Phase III Study Comparing 177 Lu-PSMA-617 in Combination With SOC, Versus SOC Alone, in Adult Male Patients Novartis Pharmaceuticals Phase 3 Multinational With mHSPC (PSMAddition) Abemaciclib Before 177 Lu-PSMA-617 for the NCT05113537 Treatment of Metastatic Castrate Resistant Vadim S Koshkin Phase 1 California, USA Prostate Cancer (UPLIFT) NCT04946370 Maximizing Responses to Anti-PD1 Immunotherapy With PSMA-targeted Alpha Therapy in mCRPC Weill Medical College of Cornell University Phase 1/2 New York, USA 64 Cu-SAR-bisPSMA and NCT04868604 67 Cu-SAR-bisPSMA for Identification and Treatment of PSMA-expressing Metastatic Castrate Resistant Prostate Cancer Clarity Pharmaceuticals Ltd.
Phase 1/2 USA (SECuRE) NCT05230251 Radioligand fOr locAl raDiorecurrent proStaTe cancER (ROADSTER) Glenn Bauman, Research Institute Lawson Health Phase 2 Canada NCT04576871 Re-treatment 225 Ac-J591 for mCRPC Weill Medical College of Cornell University Phase 1 New York, USA NCT04726033 64 Cu-TLX592 Phase I Safety, PK, Biodistribution and Dosimetry Study (CUPID Study) (CUPID) Telix International Pty Ltd. Phase 1 Australia NCT04506567 Fractionated and Multiple Dose 225 Ac-J591 for Progressive mCRPC Weill Medical College of Cornell University Phase 1/2 New York, USA.
G3/4 treatment-related AEs occurred in 60%, 55%, and 54% of patients in arms A, B, and C, respectively. The most common G3/4 AEs were hypertension (19, 12, and 20 patients), fatigue (20, 14, and 15 patients), hypokalemia (11, 7, and 10 patients), and handefoot skin reaction (8, 7, and 3 patients). Median overall survival was 7. 4 (IQR 4. 0e13. 7) months in arm A, 8. 6 (IQR 3. 8e13. 4) in arm B, and 7. 1 (IQR 4. 4e12. 4) in arm C. Conclusions: The alternative regorafenib dosing schedules were feasible and safe in patients with mCRC who had been previously treated with standard therapy. There was a higher numerical improvement on the most clinically relevant AEs in the intermittent dosing arm, particularly during the relevant first two cycles. Clinicaltrials.
Statistical analysis Based on the safety data from the CORRECT trial  [8] , we assumed that 54% of patients in arm A would develop G3/G4 treatment-related AEs. A sample size of 93 patients per treatment group would provide at least 80% of power to detect a 20%-point difference in the percentage of patients with G3/G4 treatment-related   AEs in arm B and arm C, assuming a 0. 05 one-sided significance level and after adjusting for multiple comparisons using the Bonferroni method. Considering a 5. 5% drop-out rate, a total of 295 patients should be included. The protocol did not plan for nor was it powered for comparisons between arm B versus arm C. Patients who received at least one dose of study treatment comprised the safety population. The intention-to-treat population was defined as all randomised patients, irrespective of whether they receive study medication.
Acknowledgements The authors thank the medical and nursing staff from all the participating institutions, the patients, and their families for participating in the study, and the study investigators for their cooperation and support. The REARRANGE investigators are as follows: Principal investigator:  Guillem Argile  ´s and  Josep Tabernero. Steering Committee :  Josep Tabernero ,  Guillem Argile  ´s,  Alfredo Falcone ,  Fortunato Ciardiello ,  Richard Goldberg , and  Jaafar Bennouna. Investigators: Argiles and  J. Tabernero  and  N. Mulet , H. Universitari Vall d'Hebron, Barcelona, Spain ;  M. L. Limo  ´n and  M. Valladares. H. Virgen del Rocı  ´o, Sevilla, Spain;  P. Jime  ´nez and J. M a Vieitez, H. Universitario Central de Asturias, Spain ;  C. Gra  ´valos. H. 12 de Octubre, Madrid, Spain;  P.
Conflict of interest statement The authors declare the following financial interests/ personal which may be considered as potential competing interests: G. Argiles received advisory honoraria, from Gadetta BV. Amgen, Bayer and Servier; M. Valladares-Ayerbes received honoraria, travel grants, and research grants from Hoffman La Roche, Bristol-Myers Squibb, Bayer, Servier, Amgen, Merck Serono and Sanofi. N. Mulet received grants and nonfinancial support from Merck Serono, Amgen and Roche, outside the submitted work; M. Toben ˜a received personal fees from Amgen, Kyowa Kirin and Grunenthal, non-financial support from Merck, Roche and Rovi, outside the submitted work; B. Garcı ´a received personal fees from Advanced Accelerator Applications (a Novartis company), Amgen, Bayer Hispania, Eisai, Lilly, MSD and Roche Farma, personal fees and nonfinancial support from Ipsen, Merck, Novartis, Sanofi-Aventis and Servier, outside the submitted work; E.
Transplant centers used different tools to contact their patients, e. g. , emails, text messages, phone calls, social media, etc. This prompted all centers to create a complete database and a simple tool for contacting all patients. This tool is now available so that information pertaining to transplant patients can be procured rapidly. Secondly, we needed to organize the process of vaccinating patients. In our hospital, it was decided that the vaccine should be administered in a dedicated place within the hospital. In addition, as recommended by the Francophone Society for Transplantation, patients were asked to control their anti-SARS-CoV-2 serology after vaccination. Based on the serology results, we initially administered two doses  [63] , and thereafter three doses  [64, 65]  to all patients.
They applied the G2PSR model to the Alzheimer's Disease Neuroimaging Initiative data and relating SNPs from more than 3,500 genes to clinical and multi-variate brain volumetric information. They showed that their method can provide an accurate selection of relevant genes in the dataset with a large ratio of SNPs to samples, thus overcoming the main limitations of current genome-tophenome association methods. Despite the fact that multi-omics integration remains challenging especially in the field of RD, new modeling and statistical tools employing artificial intelligence methodologies are being developed to this end. We believe that this Research Topic in Front. Mol. Biosci (https://www. frontiersin. org/research-topics/22827/ai-andmulti-omics-for-rare-diseases-challenges-advances-and-perspectivesvolume-ii) has recruited some in-depth computational approaches that dissect the fundamental principles of the role of multi-omics in RD.
Third, the PARADIGM-HF study included a biomarker sub-study in 2080 patients that, together with data from other studies, provided sufficient evidence to support the use of NT-proBNP as a bridging biomarker. Finally, NT-proBNP was measured in the pediatric PANORAMA-HF study, including as an early readout at Week 12. The Prentice criteria (30-32) provided a useful framework to assess whether NT-proBNP could be a viable bridging biomarker. The first two criteria were fulfilled in adults based on the PARADIGM-HF study demonstrating sacubitril/valsartan's effect on the FFS endpoint, (time-to-first event of a composite of hospitalization for HF and cardiovascular death), and on the biomarker (corresponding to Level II, criterion ii, in Table  2 )  (42, 43). The third Prentice criterion, including evidence of a significant relationship between the biomarker and the FFS endpoint (Level II, criterion iii, Table  2 ), was covered in a PARADIGM-HF biomarker sub-study  (42)  and was demonstrated in other settings, both in adult  (44, 45)  and pediatric patients  (46, 47).
New molecules were also tested and remained susceptible, except for ceftolozane/tazobactam (Table ). We identified β-lactamase activity by using B. hinzii CHAR-1 crude protein extracts as described  (13). In silico analysis showed a β-lactamase, Hinzii Bordetella lactamase (HBL-1), which had all canonical boxes of a functional broad-spectrum class A penicillinase (13) (Appendix Figure, https://wwwnc. cdc. gov/EID/ article/28/4/21-2564-App1. pdf). HBL-1 had 62. 7% amino acid identity with BOR-1 β-lactamase from B. parapertussis  (13). Highly related sequences (99. 7%-100% amino acid identity) were identified in genome sequences of B. hinzii available in public databases, suggesting that HBL-1-like enzymes might be native to that species (Appendix Figure). MICs of aminopenicillins and carboxypenicillins might be explained by expression of HBL-1, but, as suggested for B.
parapertussis, additional nonenzymatic β-lactam resistance mechanisms, such as impermeability, efflux, or penicillin-binding protein affinities, might be associated for B. hinzii. The complete genome and HBL-1 sequences have been deposited in DDBJ/EMBL (accession no. JAJTJL000000000) and GenBank (accession nos. OM212391). The lung microbiota of deceased patients who had COVID-19 showed complex bacterial and fungal colonization by opportunistic pathogens  (14). SARS-CoV-2 infection, antimicrobial drug pressure, alveolar damage, persistent lymphocytic depletion, mechanical ventilation, corticosteroid therapy, and prolonged hospital stays might predispose critically ill COVID-19 patients to opportunistic bacterial or fungal superinfection  (2, 14). Critically ill COVID-19 patients have the highest percentage of secondary pulmonary infections (34.
Acknowledgments We thank  Panya Wissa  and  Emna Warzele  for providing helpful discussions and the  Institut Pasteur PIBNet  for performing whole-genome sequencing of the bacterial isolate. This study was supported by a grant from the  Ministère de l'Education Nationale et de la Recherche  ( Université Paris-Saclay ),  Assistance Publique-Hôpitaux de Paris , and the  Centre Hospitalier de Chartres.
The current understanding of the carbonation and the prediction of the carbonation rate of alkali-activated concretes is complicated inter alia by the wide range of binder chemistries used and testing conditions adopted. To overcome some of the limitations of individual studies and to identify general correlations between mix design parameters and carbonation resistance, the RILEM TC 281-CCC 'Carbonation of Concrete with Supplementary Cementitious Materials' Working Group 6 compiled and analysed carbonation data for alkali-activated concretes and mortars from the literature. For comparison purposes, data for blended Portland cementbased concretes with a high percentage of SCMs (C 66% of the binder) were also included in the database. The analysis indicates that water/CaO ratio and water/binder ratio exert an influence on the carbonation resistance of alkali-activated concretes; however, these parameters are not good indicators of the carbonation resistance when considered.
This has been done with the aim of resolving the shortcomings of individual studies and to identify general correlations between mix design parameters of alkali-activated concretes and mortars, and their carbonation resistance. For comparison purposes, data for blended Portland cement-based concretes and mortars with a high percentage of supplementary cementitious materials (SCMs) in the binder were included in the database. In this report, the database, the assumptions made in the evaluation of the data, the analysis results and their implications are presented. 2 Data inventory and processing.
Data inventory To create the database, the literature was searched for studies that report carbonation depth (d c )-versuscarbonation duration (t) data for alkali-activated concretes or mortars, for concretes or mortars based on blended Portland cements with at least 70%  1  SCMs in the binder, as well as for concretes or mortars based on CEM III/B [66-80% ground granulated blast furnace slag (GGBS) in the binder according to. Only data obtained at controlled relative humidity (conditions approximating indoor or ''sheltered'' natural carbonation, and accelerated carbonation) were included in the database. Results for unsheltered natural exposure were not considered, because the degree of saturation of the pore systems of the exposed concretes is not controlled in these experiments, complicating the comparison and interpretation of the results.
Carbonation data for pastes were not considered, as the potential effects of aggregates (particularly, but not solely, due to the presence of the microstructurally distinct interfacial transition zone between paste and aggregate) on the carbonation coefficient would not be observed for these. Studies with incomplete or internally inconsistent information about the mix design or the test conditions were also excluded from the database. In most of the studies, the carbonation depths were determined by spraying a phenolphthalein solution (usually with a concentration of 1%) on fracture surfaces; in one case alizarin yellow R was used as the indicator, and in another case thin sections were examined. It is noted that there are difficulties and open questions regarding the phenolphthalein-spraying method for the determination of the carbonation depths of alkali-activated concretes, which may influence the outcomes of carbonation testing  [8].
However, this method is currently the most widely used and, thus, the present analysis had to rely on the results obtained with this method to be able to include a significant number of studies. The mix designs of the concretes or mortars, the chemical compositions of the binders (solid precursors and activators), the physical properties of the constituents, the compressive strengths of the cured materials as well as curing, preconditioning and carbonation conditions were included in the database together with the respective carbonation-depth data. The full database is provided in the Electronic Supplementary Material. For the present purpose, 'GGBS-based AAC' were defined as alkali-activated concretes/mortars in which the most abundant (by mass) component of the binder was GGBS. 'FA-and MK-based AAC' are correspondingly defined as alkali-activated concretes/mortars with fly ash (FA) or metakaolin (MK) as the most abundant binder component; only one of the twelve pertinent mixes in the database was based on MK.
Mixes in which GGBS and FA were present in equal amounts in the binder, were assigned to the GGBSbased AAC. The term 'other AAC' is used to denote alkali-activated concretes/mortars with binders based on municipal solid waste incineration (MSWI) bottom ash, silico-manganese slag or natural pozzolans. Two hybrid cements, i. e. cements containing Portland clinker as well as a second activator, were assigned to this class as well. Most of the AAC in the database were activated with sodium silicate solutions, while fewer were activated with NaOH, Na 2 CO 3 , or a mixture of sodium silicate and Na 2 CO 3. In some cases, portlandite [Ca(OH) 2 ] or gypsum [CaSO 4 Á2H 2 O] were employed as additional activators. The compressive strengths of the alkali-activated concretes and mortars in the database differed widely.