dataesr/scientific-paragraphs-categorization huggingface dataset:
2. 0. 11). Accordingly, aerosol number size distribution data from the SMPS measurements were used to classify individual days as NPF event, undefined, or non-event days. The classification procedure, presented in Dal  Maso et al. (2005) , follows the decision criteria based on the presence of UFP (D p < 25 nm) and their subsequent growth to Aitken mode (D p < 80 nm). Briefly, event days are identified when sub-25 nm particle formation and growth are observed. Undefined days correspond to days when sub-25 nm particle formation is observed for more than 1 h, but those particles are not growing so their diameter remains below 25 nm. On nonevent days the nucleation mode is absent. SMPS particle number size distributions were also used for CS (Eq. 1) and GR (Eq. 3) calculations. The CS estimates the loss rate of the condensable vapors  (Kulmala et al.
eu/ particle-light-absorption. html, last access: 1 October 2022), σ abs coefficients at each wavelength have been recalculated by (1) multiplying equivalent black carbon (eBC) by the mass-specific absorption coefficient (MAC) and then (2) dividing by the suitable harmonization factor to account for the filter multiple scattering effect, i. e. , 2. 21 (M8020 filter tape) in 2017 and 1. 76 (M8060 filter tape) afterwards. The aethalometer samples at 5 L min -1 downstream of a PM 1 cyclone  (BGI SCC1. 197, Mesa Labs). The spectral dependency of σ abs was used to determine the contributions of traffic (fossil fuel -BC ff ) and wood burning (BC wb ) to eBC via a source apportionment model  (Sandradewi et al. , 2008). Meteorological data including temperature and water vapor mixing ratio were also measured every minute at the sampling site using a weather station (DAVIS Inc weather station, Vantage Pro 2).
Aerosol number size distribution Hourly averaged median particle number size distributions (PNSDs) obtained from the SMPS are shown in Fig. 3  separately for NPF event (around 800 PNSD), undefined (around 2300 PNSD), and non-event (around 1700 PNSD) days observed during the warm period (only spring and summer). For all event days, the PNSD were first sorted for each hour of the day. Then, the median PNSD was calculated for each hour of the day. PNSD shown in Fig. 3a  is then representative of a "typical" NPF event day  (Kulmala et al. , 2022). The same data filtering was done for PNSD observed during undefined (Fig. 3b ) and non-event days (Fig. 3c ). Atmospheric NPF and subsequent particle growth are seen in Fig. 3a  as an appearance of new aerosol particles with small diameters followed by the growth of these particles toward larger sizes.
Environmental conditions The cloud fraction was calculated from the sky imager dataset following the method by  Shukla et al. (2016)  and sorted as a function of event, undefined, and non-event days. The effect of cloudiness on NPF event occurrence is shown in Fig. 6a , with a specific focus on measurements collected between 09:00 and 14:00 UTC, i. e. , the period of time where most NPF events tended to start. There is a clear inverse correlation between cloud fraction and NPF occurrences. The average cloud fraction is around 0. 47 during event days, 0. 68 during undefined days, and 0. 74 during non-event days. Moreover, the 25th percentiles of the cloud fractions for NPF event (0. 06), undefined (0. 47), and non-event days (0. 63) clearly show that the absence of clouds (lower cloud fraction) is mostly associated with NPF event days.
Conclusions This study was based on a 4-year (2017-2020) dataset collected at the ATOLL site, in close vicinity of the city of Lille, northern France, to study NPF occurrence over a periurban site. The results highlight a strong seasonal variation of the NPF event frequency, with a maximum occurrence observed during spring (15 %) and summer (19 %). The undefined cases, which correspond to bursts of UFP that do not grow, are much more frequent (40 % on average) than NPF events all year long. The highest frequency (68 %) is observed in August and the lowest one (17 %) in February. The interruption of particle growth during undefined events can be mostly attributed to changes in environmental conditions (irradiance and wind direction). The seasonal variation of NPF parameters was also clearly observed and associated with environmental parameters.
In future studies, the impact of local vertical dynamics such as the effect of the boundary layer dynamics as in  Lampilahti et al. (2021 Lampilahti et al. ( , 2020) )  as well as the CCN enhancement factor will be analyzed. Data availability. ATOLL measurements are available through the EBAS database (https://ebas-data. nilu. no/Pages/DataSetList. aspx?key=F9603747DC7D43F092A6CA18C91BDF09, last access: 2 January 2023;  Crumeyrolle, 2022a)  and SMPS data before 2020 through https://doi. org/10. 5281/zenodo. 6794562  (Crumeyrolle, 2022b). GDAS files for back trajectory calculations are available on demand at https://www. arl. noaa. gov/hysplit/ (last access: 22 September 2022; NOAA, 2022). NO x data can be requested through the ATMO website: https://data-atmo-hdf. opendata. arcgis. com (last access: 3 March 2022; Atmo Hauts-de-France, 2022).
Competing interests. The contact author has declared that none of the authors has any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Acknowledgements. This research was supported by the French National Research Agency (ANR) under the MABCaM (ANR-16-CE04-0009) contract. Part of the instrumental system has been financially supported by the CaPPA project (Chemical and Physical Properties of the Atmosphere), which is funded by the French National Research Agency (ANR) through the PIA (Programme d'Investissement d'Avenir) under contract "ANR-11-LABX-0005-01" and by the Regional Council "Hauts-de-France". ATOLL is a French component of the Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS, https://www.
actris. eu/, last access: 1 October 2022), and the particle chemical composition measurements are also supported by the CARA program of the LCSQA funded by the French Ministry of Environment. The authors also thank the Région Hauts-de-France, the Ministère de l'Enseignement Supérieur et de la Recherche (CPER Climibio), and the European Fund for Regional Economic Development for their financial support. The authors gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model and/or READY website (https://www. ready. noaa. gov, last access: 29 June 2021) used in this publication. We thank François Thieuleux for ECMWF data sharing during this work. Financial support. This research has been supported by the Agence Nationale de la Recherche (grant nos.
Aims: To describe glucose-lowering treatment regimens and glycated haemoglobin (HbA1c) trajectories in individuals with type 2 diabetes (T2D) over 36 months of follow-up from the start of second-line therapy. Materials and Methods: This data analysis from the 3-year, observational DISCOVER study programme included 14 687 participants from 37 countries with T2D initiating second-line glucose-lowering therapy. Treatment and HbA1c data were collected at baseline (start of second-line therapy) and at 6, 12, 24 and 36 months. Treatment regimen changes over follow-up were analysed using the McNemar test, with carryforward imputation for intermediate missing values. Results: A total of 11 592 participants had treatment data at baseline and 36 months, and 11 882 had HbA1c data at baseline. At baseline and 36 months, respectively, rates of oral monotherapy use were 12.
7] [8]  However, longitudinal data on glucose-lowering regimens and clinical outcomes after second-line treatment are lacking. DISCOVER was a 3-year, prospective, observational study programme including individuals with T2D initiating second-line glucoselowering therapy in six regions and 38 countries, including 778 clinical sites at rural and urban locations (NCT02322762 and NCT02226822). 9, 10 At the start of second-line treatment, the median HbA1c level was 60 to 67 mmol/mol (7. 6-8. 3%) across regions, and the most commonly prescribed second-line therapies were metformin and DPP-4 inhibitor combinations (23. 5%) and metformin and SU combinations (20. 9%). The baseline analysis showed that at the start of second-line therapy, >50% and >30% of participants had HbA1c levels >64 mmol/mol (>8.
| Data collection Data were collected at baseline (initiation of second-line therapy) and at 6, 12, 24 and 36 months using a standardized electronic case report form that was transferred to a central database via a web-based data capture system. Information on further treatment changes after second-line therapy was collected during follow-up. Some data were extracted from existing electronic health records in Canada, Denmark, France, Norway and Sweden. Participant demographic data (such as sex, age, self-reported ethnicity, body mass index and duration of T2D) were collected at study baseline. Data collected at baseline and during follow-up included clinical variables (such as HbA1c and fasting plasma glucose [FPG] levels), and first-and second-line treatment. Data were measured and recorded according to routine clinical practice at each site.
1, 4, 7, 8 In the previous baseline analysis, 17. 0% of participants started second-line therapy because of hypoglycaemia with first-line therapy. 11 However, the DISCOVER programme included participants enrolled between 2014 and 2016, so may not reflect contemporary treatment patterns for SGLT2 inhibitors. In a DISCOVER analysis of 14 668 participants, SUs and DPP-4 inhibitors were widely used at first and second line, yet a SGLT2 inhibitor in combination with metformin was only used at second line (4. 3%), and only 1. 3% of participants received a GLP-1RA in combination with metformin. 13 These data suggested that for economic and other nonmedical reasons, many patients are prescribed SUs rather than newer agents, such as SGLT2 inhibitors or GLP-1RAs when initiating secondline therapy. In our analysis, the median (IQR) time from diagnosis to the initiation of second-line therapy was 4.
Because participants enrolled in DISCOVER were all initiating secondline glucose-lowering therapy, our findings do not represent the entire T2D population. Given the observational nature of the study, participants were not required to attend every study visit and there was no requirement to record data for all study variables, meaning that a complete dataset was not available for all participants. Overall, 2270 of the 11 592 participants (19. 6%) who had treatment data available at baseline and 36 months did not have a recorded baseline HbA1c level. However, 1495 of these participants did have a recorded baseline FPG level that, given the similar trend in HbA1c and FPG levels during follow-up, can be considered as a substitute measurement of the pattern of glycaemic control in these participants.
Missing sensor data is a common problem associated with the Internet of Things (IoT) ecosystems, which affect the accuracy of the associated services such as adequate medical intervention for older adults living at home. This problem is caused by many factors, power down is one of them, communication failure and sensor failure are another two reasons. Multiple missing data imputation methods have been developed to solve this issue. However, irregular temporal missing data locations is challenging to handle, due to lack of knowledge of their occurrence probability and their random temporal location. In this paper, we propose a Bayesian Gaussian Process based imputation technique that accounts for temporal forcing to fill in the missing sensor data. Our approach; Bayesian Gaussian Process (BGaP); can impute missing data efficiently at any missing rate and for any temporal location using prior knowledge gathered on past observations.
INTRODUCTION The Internet of Things now makes it possible to deploy a large number of sensory nodes in diverse environments, e. g. , to monitor older adults' behavior. In retirement homes, sensory data helps in the decision-making process about the status of older adults. In retirement homes, long-term behavior monitoring, and detection change is important so that physical and cognitive decline can be captured early and, when properly managed, can increase the well-being of older adults for longer periods. In addition, long-term behavior monitoring also provides valuable information for eventual medical intervention. However, long-term behavior monitoring, and detection change needs to be carried out continuously to obtain the best results; a constraint that is fulfilled by the Internet of Things (IoT) ecosystems.
Such information is valuable when assessing a patients' health status. Frequent short bathroom visits could indicate urinary tract infection while frequent long bathroom visits could indicate diarrhea. In short, while broad descriptions to assess a patient's physical abilities are valuable, they are incomplete. In this respect, the IoT technology can be used to monitor ambient environments unobtrusively and continuously with sensors and sensor nodes  [3]. Hence, patient movements can be assessed using IoT sensors and translated into meaningful behavioral data. IoT ecosystems consist of sensors and actuators which are used to harvest physical data from the environment  [4]  such as ambient temperature and/or pressure. Though the three-layer model is considered the basic IoT model, in this paper, we assume a five-layer architecture model is used to realize the IoT ecosystems  [5] ,  [6]  as presented in Figure  1.
A three-layer model is composed of perception layer (the sensors), the communication (or transmission) layer and the application layer. The role of the perception layer is to collect data from the environment, while the role of the transmission layer is to securely transmit the raw data collected by sensors. The application layer stores and retrieves collected pre-or postprocessed data to and from databases, while also providing special services to be performed on the data including missing data recovery, anomalous data detection and decision-making. In the perception layer, there exist several causes that lead to missing sensor data such as power and hardware failure. However, loss of data is not limited to the perception layer, it can also occur because of data exchange problems with the communication layer  [3].
As a starting point, let us use a vector of data Y as a reference point. This vector Y is composed of observed and missing values and can thus be partitioned in two parts: the observed values (Y observed ) and the missing values (Y missing ). The complete array of the IoT data including the missing data can be defined as: Y = (Y observed , Y missing ) (1) Using these two parts of Y, we can calculate the missingness value r for each value of Y, meaning that r has the same dimension as Y. Depending on the statistical mechanism considered, the way the missingness is calculated will change. structure the collected data. Although there could be variables structuring the missingness, such as punctual meteorological events like a storm that could cause electrical surge preventing a sensor to work, they are unrelated to the observation (movement of a patient or behavior of a sensor).
b) Missing At Random (MAR) Data missing at random occurs when the probability of data missing depends on the observed data and not on the missing data itself. In more technical terms, for MAR the missingness is conditional on the observed values P(r|Y observed , Y missing ) = P(r|Y observed ) (3) In MAR, it is assumed that there are variables of important for Y that also define the missingness. As in  [20]  and  [21] , it is impossible to test whether MAR assumption is valid for data or not solely with the prior knowledge of observed data. However, it is possible to inspect the tenability of MAR assumption using a t-test that test the difference between the means of the complete dataset and that of the missing dataset  [16] ,  [19].
In the next lines, we briefly present the practical implementation of the DLM and of the data we applied it to in the study. For the practical implementation, ten different older adults were monitored in their residence for periods ranging from one month to several years to identify their long-term behavior over the monitoring period. The monitoring system relies on motion and door sensors spread all over the residence to follow the subjects' activity levels day and night  [2]. Figure  3  presents the locations of the sensors in a typical monitored room with pictures of the sensors that were deployed. Motion sensors are used to detect subjects' daily activities in the bedroom and bathroom while door sensors detected outing and visiting activities. Motion sensors were used to detect the subjects' movements within the sensors' line-of-sight, as described by Figure  4.
Acknowledgments We acknowledge the  World Climate Research Programme , which, through its Working Group on Coupled Modelling, coordinated and promoted  CMIP6. We thank the climate modeling groups for producing and making available their model output, the  Earth System Grid Federation (ESGF)  for archiving the data and providing access, and the multiple funding agencies who support  CMIP6  and  ESGF. We also acknowledge IGAC,  SPARC , and  CEDA  for their support of CCMI. We acknowledge  NOAA/OAR/ ESRL PSL, Boulder, Colorado, USA , for.
Data Availability Statement CMIP6 data are available at https://esgf-node. llnl. gov/search/cmip6/. Specifically, the following datasets are used:  Dix et al. (2019) ;  Danabasoglu (2019b Danabasoglu ( , 2019a Danabasoglu ( , 2019d Danabasoglu ( , 2019c)) ; Séférian (2018); Voldoire (2018); Guo et al. (2018); Krasting et al. (2018); Yukimoto, Koshiro, et al. (2019); Wieners et al. (2019); Tang et al. (2019); Byun (2020); Ridley et al. (2019a, 2019b). CCMI2 data are downloaded from ftp://ftp. ceda. ac. uk/badc/ccmi/data/ post-cmip6/ccmi-2022. Specifically, the following datasets have been used: Dennison and Woodhouse (2021); Rozanov et al. (2021); Abraham and Keeble (2021). NCEP-DOE2 data were provided by the NOAA/OAR/ESRL PSL, Boulder, Colorado, USA, from their web site at https://psl. noaa.
gov/data/gridded/data. ncep. reanalysis2. pressure. html. Hersbach et al. (2018, 2019) was downloaded from the Copernicus Climate Change Service (C3S) Climate Data Store. The results contain modified Copernicus Climate Change Service information. Neither the European Commission nor ECMWF are responsible for any use that may be made of the Copernicus information or data it contains. MSR-2 data are available at https://www. temis. nl/protocols/O3global. php (van der A et al. , 2015b). Scripts and intermediate data used in the generation of the figures of this paper may be downloaded at Morgenstern et al. (2022).
Image acquisition and quantification was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which this version posted May 17, 2022. ; https://doi. org/10. 1101/2022. 05. 17. 492111 doi: bioRxiv preprint Images were acquired using a spinning disk confocal microscope (SDCM) (Ti2 CSU-W1, Nikon, France) with a 60x objective (CFI Plan Apo Lambda NA = 1. 40, MRD1605, Nikon) or using a confocal laser scanning microscope (CLSM) (LSM 800 airyscan, Zeiss, Oberkochen, Germany) with a 10x objective (Plan-Apochromat 10x/0. 45 M27, Zeiss). Image analysis was performed with the General Analysis 3 module of the NIS software (v5. 30, Nikon) or Fiji software (v1. 52p, NIH, USA). In HEK293 cells, YAP immunolabeling was quantified using an automatic macro developed with the NIS software to measure MFI in the cytoplasmic and nuclear areas and to calculate the NC ratio by dividing the nuclear MFI by the cytoplasmic MFI.
CytoID quantification was also performed using the NIS software. Briefly, the images were denoised and binarized in 3D. The CytoID vesicle count and volume as well as the S. aureus volume were measured, and the colocalization between the S. aureus volume and CytoID vesicle volume was assessed. The cell area was determined using an extended area of DAPI labeling. For each cell, the count and mean volume of the CytoID vesicles were measured. The same method was used to measure the S. aureus volume per cell. Quantifications were performed by analyzing 2 to 3 fields per well using a 60x objective. For organoid lining layer thickness, quantification was performed with the Fiji software using cryosections stained with DAPI and dye-labeled phalloidin. Two slices per organoid were assessed. Quantification was performed on tile images acquired with a 10x objective, allowing quantification of the entire structure.
Transcriptomic analysis using nCounter Host Response Panel The nCounter Host Response panel (Nanostring technology), which includes 770 genes involved in host response processes, was performed with the nCounter Sprint instrument following the manufacturer's recommendations. Briefly, we used 50 ng of RNA extracted from WT or YAP -/-HEK293 infected (or not) with the HG001 strain at MOI 10 for 7 h (n = 3 per group). All quality controls were performed according to the manufacturer's instructions. Normalization was performed using the housekeeping genes identified by the geNorm analysis using the NanoString advance software. The count detection limit was determined using a threshold based on the negative controls. Data analysis was performed using the nSolver package (version 3. 0) and the Advanced Analysis module (version 1.
Computational analyses of ChIP-seq and DIP-seq datasets Heatmaps and quantifications of the ChIP-seq and DIPseq data were performed running seqMINER  (38) , using datasets normalized to 10 million uniquely mapped reads. As reference coordinates, we used the Ensembl 67 database (for coding genes) of the mouse genome (mm9), the annotated RepeatMasker (RMSK) database (for DNA repeats) and the piRNA cluster database generated by the small RNA group Mainz University (http://www. smallrnagroup. uni-mainz. de/piRNAclusterDB. html) after having translated mm10 coordinates to mm9 using liftOver (UCSC: https://genome. ucsc. edu/cgi-bin/hgLiftOver). For chromosome coverage analysis, the number of reads for each non-overlapping 40 kb windows of the Mouse genome (mm9/NCBI37) was computed for each sample using BEDtools v2.
The region corresponding to those genes was extended to 3kb upstream of the transcription start site and 10 kb downstream of the transcript end site, in order to minimize signal from polymerase read-through from genic transcripts. The overlap was performed on the opposite strand as the library preparation protocol used to construct these RNA-seq libraries leads to sequence the strand generated during first strand cDNA synthesis. Quantification of piRNA cluster expression has been performed using IntersectBed from BEDTools release v2. 26. 0q. piRNA read counts were added to those of gene expression's and used as follows: read counts were normalized across libraries with the method proposed by Anders and Huber  (40). Comparisons of interest were performed using the method proposed by Love et al.
(41)  implemented in the DESeq2 Bioconductor library (DESeq2 v1. 14. 1). The following thresholds have been used to select significantly differentially expressed piRNA clusters: • P-value adjusted for multiple testing < 0. 01 • |log 2 Fold-Change| > 0. 5 DNA repeat expression analysis. Repeat analysis was performed following the methodology described in Papin et al. (23). Data were normalized based upon library size. Significance of the difference of repeat read counts between RNA samples was assessed using the Bionconductor package DESeq. To avoid over-or underestimating fold enrichments due to low sequence representation, repeat families with less than 5 RPM (reads per million mapped reads) per RNA sample were excluded from further analysis. The following thresholds have been used to select significantly differentially expressed DNA repeats: • P-value adjusted for multiple testing < 0.