dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, it is necessary to correct such effects before further analysis  11. Several methods have been developed to correct batch effects in single-cell transcriptomics data, using different statistical and mathematical frameworks (see for example  5, 7, 12, 13  ). It is essential to benchmark all those methods properly in order to evaluate precisely their respective strengths and weaknesses. For instance, Haghverdi and colleagues  13  use different datasets to benchmark their mutual nearest-neighbor (MNN) algorithm: simulated data for a simple scenario with two batches of cells having varying proportions of three cell types, two hematopoietic datasets generated in different laboratories using different scRNA-seq protocols (SMART-seq2 and MARS-seq) and a complex dataset on human pancreatic tissue composed of four different sources of data, generated using two different scRNA-seq protocols (SMART-seq2 and CEL-seq2).
Obviously, datasets generated from biological samples are highly valuable for these type of benchmarking studies, preferably generated using a variety of scRNA-seq protocols. Hence the interest in our resting versus stimulated PBMC dataset. The 13,263 resting and stimulated cells of our dataset are clearly separated on the two dimensional UMAP projection (Fig. 2 ). Since the two populations are composed of the same cell types and originate from the same sample, data integration (i. e. batch correction) algorithms should be able to re-align and group the different cell types. To illustrate this, we applied three different methods to our dataset: the Seurat method CCA  12  , the reciprocal PCA method described in the Seurat package (RPCA) and the MultiMAP method implemented in the Python package of the same name  7.
The Seurat alignment workflow (CCA) is based on the canonical correlation analysis, which aims at finding linear combinations of features across datasets that are maximally correlated, thus identifying shared correlation structures. The first step of this method is to find cell pairs that are in a matched biological state (called 'anchors') that will be used to correct the differences between datasets. The reciprocal PCA (RPCA) is a slightly modified version of the CCA workflow, using reciprocal PCA to identify the 'anchors' between the datasets. This procedure is faster and constitute a more conservative approach where cells in different biological states are less likely to align after integration. The Seurat developers recommend using RPCA when a substantial fraction of the cells in one dataset have no matching type in the other, when datasets originate from the same platform, or when there are a large number of datasets or cells to integrate.
MultiMAP 7 is an algorithm for the dimensionality reduction and integration of multiple datasets. It constructs a nonlinear manifold on which diverse high dimensional data reside and then projects the manifold and data into a shared low-dimensional embedding space. MultiMAP is a generalization of the UMAP algorithm for multiple datasets with different dimensions. It can integrate any number of datasets, can leverage features that are not present in all datasets and is scalable to very large datasets. Figures  3 4 5 show UMAP graphs of the resting and stimulated datasets after integration with CCA, RPCA and MultiMAP methods respectively. We can see that after integration the cell clusters overlap to a large extend, with no large cluster composed of only one condition. Also visible on the different graphs is that the different methods have different performances.
For instance it is clear that the overlap of cells for the two different conditions is better in the case of CCA (Fig. 3 ) or MultiMAP (Fig. 5 ) than for RPCA (Fig. 4 ). After the integration step, we can cluster the cells according to their expression profile. Figure  6  shows the resting and stimulated datasets after integration with CCA and clustering. As expected, the different clusters display homogeneous cell types, indicating good integration performance. Datasets such as the one proposed here have already been used to develop and refine data integration methods and to investigate how human PBMCs vary in response to immune system stimulation  12. An interesting way to benchmark these data integration methods is to remove abundant or rare cells from one of the conditions (e. g. , the stimulated set) and run the alignment procedure to see how it handles the missing data.
More than 50 integration methods are currently available for single-cell data integration. In order to efficiently benchmark them, there is a need for an objective methodology and metrics, i. e. the ability of the methods to remove batch effects while preserving biological variation, and of course a diverse set of robust datasets that reflect the variety and complexity of nonlinear, nested batch effects that can occur in real experimental conditions  14. Once the cells have been integrated and the different cell types identified, it is possible to look for cell type markers that are conserved in both resting and stimulated conditions, or to compare the datasets to find cell type specific responses to stimulation. Here, we used the "FindConservedMarkers" function of the Seurat R package to identify canonical cell type marker genes that are conserved across the two conditions.
The Fig. 7  shows that this gene is indeed expressed in both resting and stimulated NK cells, but its expression is restricted to this cell type, thus defining a conserved marker. The contrast is quite striking with the gene IFI6 (Interferon alpha inducible protein 6  17  ), also shown in the Fig. 7 , which is a core interferon response gene and is upregulated in all cell types in the stimulated dataset. Using the "FindMarkers" function of the Seurat package, we can find out which genes are differentially expressed in resting and stimulated cells of the same type. For example, for B cells, we find 986 genes that are differentially expressed between resting and stimulated conditions with an adjusted p-value < 0. 05. Among them, CXCL10 is significantly differentially expressed with an adjusted p-value of 1.
45 × 10 -81 and an average log2 fold change of 4. 9, and indeed appears strongly upregulated in stimulated B cells on the Fig. 7 , but also in monocytes and some T cells. CXCL10 is a pro-inflammatory cytokine involved in a variety of processes including chemotaxis, differentiation and activation of peripheral immune cells. It plays an important role during viral infections by stimulating the activation and migration of immune cells to the infected sites  18. Note that more samples would be needed to strengthen the observations that we make here on this dataset. Fresh and frozen PBMCs samples. As described in the methods, we used technical replicates issued from the same individual to avoid biological variations which are quite common for PBMCs. In order to have realistic conditions for the frozen samples, they were kept in the freezer for seven days.
The establishment of free mobility in Europe has lowered barriers to movement and given rise to diversity in migration and integration patterns. However, in part due to data constraints, it is difficult to study migration motives, integration and return migration together. Using linked Finnish and Swedish register data covering the period 1988-2005, we address these processes within the same framework and study how the reason for migration and trajectories at the destination relate to return migration. In particular, we assess the migration motives of 13,948 Finnish migrants in Sweden using pre-and post-migration information. Finland and Sweden have been part of the common Nordic labour market since 1954, which has allowed Nordic citizens to move without barriers between the two countries.
In order to disentangle context-specific patterns from more general migration patterns, it is important to expand the literature to other settings. This study analyses the Finnish-Swedish migration context and examines how the reason for migration and trajectories at the destination relate to return migration. The Nordic setting provides insight into migration trends in a context of free mobility that has been in place for more than 50 years, which may be interpreted as a potential precursor to European migration. Using linked Finnish and Swedish register data that provide information on pre-and post-migration characteristics, we address four research aims. First, we approximate the reason for migration, distinguishing among labour, student and tied migrants, as well as a residual category in a data set where direct information on the reason for migration is not available.
However, return and circular migration risks are difficult to assess without population-register data. Linked Finnish and Swedish population-register data used in this study are at the individual level and cover the years 1988-2005. This allows us to address the reason for migration, patterns of integration, and risks of return and circular migration during a relatively long follow-up period. Disentangling the processes that drive migration and migrants' situation in the destination country is generally intricate as the majority of migrants are young adults. In 2019, half of all migrants coming to the EU were under the age of 29  (Eurostat, 2020). During early adulthood, several parallel processes, such as migration, family and employment trajectories, occur within a relatively short time span and influence each other.
Data Our data set was constructed by integrating records of Finnish immigrants in Sweden from population registers in both Sweden and Finland. 1  The two data sets were linked by the identification of migrants based on their unique personal identity numbers (PINs). Linkage was fully successful, but since Statistics Finland had a policy of not providing data on total populations, the data at hand constitute a 77. 5% random sample. Through the linkage we have detailed information on pre-and postmigration characteristics. We measure migration by registration and deregistration from the population registers in each country. Nordic citizens, who move between the Nordic countries, are required to register a move if they intend to stay abroad for more than six months  (FPA, 2022; Swedish Tax Agency, 2022).
Most register even shorter sojourns, as there are high incentives to do so. For instance, one needs a PIN to open a bank account, rent a flat, or to receive income. We can thus identify migrants who move back and forth between Finland and Sweden and assess the reliability of these records by verifying that migrants who deregister in Finland appear in the Swedish register, and vice versa. Comparing the month of exit from Finland and entry in Sweden, we find that for 98% of all moves, the timing of the migration in each country's register differs by less than two months. The raw data from Sweden cover the period 1985-2005 and contain rich information on socioeconomic, demographic and labour market characteristics of individuals who migrated to Sweden. The raw data from Finland cover the years 1987-2007 and contain information on similar variables of the same persons, who are linked to the Swedish registers.
Data Restrictions We make a number of data restrictions. First, we focus on Finnish-born individuals who made their first move to  Sweden between 1988 and 2004. This ensures that we have information from both countries and observe individuals for at least one year after immigration. Records in the Swedish register data on each individual's previous moves (to and from Sweden) allow us to establish the first move of each migrant, even if it occurred before 1985. We use this information to exclude persons who experienced a move from Finland to Sweden prior to 1988, in order to avoid problems of left truncation. Second, we exclude individuals who migrate and return migrate in the same year, as we cannot analyse their income trajectories and trajectories of family formation. In order to identify the reason for migration, we build on information from the Finnish register recorded in the calendar year before the move and information from the Swedish register recorded in the calendar year of the move.
(1) Y it = + reason mig i + years since mig it + reason mig i × years since mig it + X i + it , Pre-migration characteristics are measured one year before migration from Finland 3 Adjusted predictions are estimated using the Stata command: margins, at(year_since_mig = (1(1)15) reason_mig = (1 2 3 4)) atmeans post  (Williams, 2012). Trajectories of family formation are captured using a time-varying indicator that is equal to one in the year when an individual has entered the first marriage or parenthood and in the subsequent years, and zero in the years prior to family formation. 4 Swedish register data do not provide information on cohabitation without children. We are therefore restricted to measuring family formation based on marriage and parenthood. We observe migrants for 15 years, starting the year after their arrival in Sweden, and capture their trajectories of family formation in Sweden.
In the literature, student migrants have been considered to be a growing group who are difficult to identify in administrative data  (Kelo et al. , 2006; King, 2018; Wit et al. , 2013). We found that student migrants constitute nearly a third of all migrants analysed (29%). Our second research aim was to assess income and family trajectories across migration motives. Results revealed that labour migrants' income was initially higher than that of the other migrant groups. This may be due to the fact that labour migrants move for work and with the aim of increasing returns to their skills, as argued by the human capital model  (Harris & Todaro, 1970; Maskileyson et al. , 2021; Sjaastad, 1962). However, over time in the destination country, economic differences between the groups decreased. In particular, a steep earnings curve was observed among student migrants.
Second, we used individual-level information and thereby only proxied social contacts. We could not observe family members explicitly, nor did we have information on their personal characteristics. Including such information in similar data sets will be important for future research in the field. Nonetheless, the findings have a number of implications. Using longitudinal data and observing migrants over a 15-year period provided insight into detailed trajectories. These revealed considerable heterogeneity within the migrant population, especially when it comes to how young migrants structure their lives in the destination and/or origin country. This implies that it is important for policy makers to consider diversity across migrants, even from the same country of origin, when designing integration policies.
Our findings also indicated that information from both the origin and destination country is valuable for gaining a more complete picture of the heterogeneity in integration and migration risks across different migrant groups. In this way, it will be important to incorporate data from both the origin and destination country more prominently in empirical and theoretical research. Page 24 of 28 Funding Open access funding provided by Stockholm University. This work was supported by the Åbo Akademi University Foundation's funding of the DemSwed Internal Centre of Excellence 2019-2024 (J. S. and R. W. ); the Foundation for Baltic and East European Studies (Östersjöstiftelsen) (J. S. and R. W. ); Högskolestiftelsen i Österbotten (J. S. ); Aktiastiftelsen i Vasa (J. S. ); Svenska Litteratursällskapet i Finland (J.
Study population The study population was drawn from two mother-child cohorts: the PELAGIE (Perturbateurs endocriniens: Etude Longitudinale sur les anomalies de la grossesse, l'infertilité et l'enfance) and the INMA (Infancia y medio ambiente) cohorts (Supplemental material, Figure  A1 ). The PELAGIE cohort recruited 3421 women between 2002 and 2006, in Brittany (France)  (Chevrier et al. , 2013). Pregnant women were enrolled at the beginning of their pregnancy (<19 gestational weeks) by gynecologists, midwives, and ultrasonographers, and completed a self-administrated questionnaire. Data on newborn health and cord blood sample were collected at the maternity. Data on child development, health, and lifestyles were collected through self-administrated questionnaires at 2, 6 and 12 years of age.
At 12 years old, a subgroup of children was invited to participate in a more in-depth follow-up performed in hospital settings (CHU of Rennes, France) that included a clinical examination to study their growth, pubertal development, and behaviors. Of the participants invited to attend the clinical examination at 12 years, (60 %) agreed to participate. The INMA cohort is a multicenter cohort set up in Spain; INMA subcohorts from Gipuzkoa, Sabadell, and Valencia were considered in the present study. Between 2003 and 2008, these three sub-cohorts enrolled 2270 women at the beginning of their pregnancy (10th to 13th weeks of gestation)  (Guxens et al. , 2012). The women completed a questionnaire and provided a blood sample in the 1st trimester of pregnancy. Children were then followed up with questionnaires and clinical examination at 4-5, 7, 9, and 11 years of age, to collect data on their development, health, and lifestyles.
Of the 1487 participants invited to the clinical examination at 11 years, 1262 (85 %) agreed to participate. All participants from both cohorts provided informed consent. In each sub-cohort, only those mother-child pairs for whom both exposure biomarker data and at least one anthropometric measurement during preadolescence (defined here as 11 to 13 years old) were available were included in the present study: a total of 1667 mother-child pairs. The overall quality and accuracy for the analytical methods were monitored by the participation of each of the three laboratories to interlaboratory program including the Arctic Monitoring and Assessment Program (AMAP) for persistent organic pollutants in human serum  (Chevrier et al. , 2013; Tillaut et al. , 2022; Llop et al. , 2010; Goñi et al. , 2009).
5  (Browning et al. , 2010). Blood pressure was measured using an automatic oscillometric device. After 5 min at rest, systolic and diastolic blood pressure were measured at least twice in the sitting or lying position, depending on the sub-cohort. The average of 2 to 3 measures, expressed in mmHg, were calculated and used in the present analysis. Because blood pressure was not assessed in the INMA-Valencia sub-cohort, there was a study population of 1314 (79 %) for these outcomes. The six main outcomes retained for the analysis were zBMI (continuous), percentage of fat mass (continuous, expressed in %), abdominal obesity (binary), overweight/obese (binary) and systolic and diastolic blood pressure (continuous). Analyses with blood pressure zscores (standardized on age, sex and height) were also performed, and showed similar results (data not shown).
Covariates Data on sociodemographic and lifestyle characteristics of the mothers was collected at inclusionby self-administrated questionnaire for PELAGIE and by interview with a trained professional for INMAand considered in the analysis as follow: age (continuous), education (Primary or Secondary school; University), fish consumption (Less than twice a week; Between two and four times a week; More than four times a week), parity (Primiparous; Multiparous), smoking (Non-smoker; Smoker at conception; Smoker during the first trimester). Maternal height (self-reported for PELAGIE and measured for INMA) and selfreported weight of the mothers at the beginning of the pregnancy were used to calculate pre-pregnancy BMI (kilograms per square meter). Gestational weight gain was defined according to WHO recommendations (Less than recommended weight gain; Recommended weight gain; Higher than recommended weight gain) based on weight at inclusion (self-reported for PELAGIE and from medical record for INMA) and selfreported weight at the end of the pregnancy or gestational weight gain self-reported at birth or at the 2-year-old follow-up.
Child sex (male/ female) and birthweight (in grams) were collected from medical records. Lifestyle data of the children were collected by questionnaire administrated to the mother close to the 11-12-year-old clinical visit: fish consumption (Less than twice a week; Between two and four times a week; More than four times a week) and sedentary behavior (time spent using screens in hour/week). Child age at the time of the clinical visit was collected (year) (Supplemental material Figure  A2 ).
In addition, adjustment for sub-cohort (Pelagie; INMA-Gipuzkoa; INMA-Sabadell; INMA-Valencia), child sex, and child age was forced a priori in all models, as well as child height for the blood pressure outcomes. All models were performed for both sexes and separately for boys and girls since the literature suggested sex-specific associations between POP exposures and metabolic outcomes  (Stratakis et al. , 2021). For all analyses, all five imputed data sets were used, except for the BKMR method for which only the first imputed dataset was used. The threshold for statistical significance was set to 5 %.
Funding This study received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  825712  -the  OBERON  project, from the  French Agency for Environmental Health Safety (ANSES)  under grant agreement No  EST19-233 , and from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  874583 , the  ATHLETE  project. This publication reflects only the authors' view, and funding agencies are not responsible for any use that may be made of the information it contains. PELAGIE cohort: This study has been funded by  Inserm  (since the beginning), the  French Ministries of Health  ( 2003-2004 ),  Labor  ( 2002-2003 ), and  Research  ( ATC 2003-2004 ), the  French National Institute for Public Health Surveillance (InVS ,  2002 ( InVS , -2006)), the  National Agency for Research (ANR ,  2005 ( ANR, -2008(ANR, , 2010(ANR, -2012(ANR, , 2015 ( ANR, -2019 )), the  French Agency for Environmental Health Safety (Afsset/ ANSES ,  2007ANSES , -2009ANSES, , 2009ANSES, -2012)), the  French Agency for Drug Safety  ( 2013-2017 ), the  Fondation de France  ( 2014-2017 ,  2015-2018 ,  2017-2021 ), the  French Ministry of Ecology  ( PNRPE 2014-2016 ) and the  Research Institute of Public Health  (IResP 2011-2014), as well as the following European programs: Hi-WATE 2007-2009, and ENRIECO.
Ex vivo model and ventilation settings The ex vivo model was used under controlled volumetric ventilation without triggering, thanks to a medical ventilator (ICU Evita 4 ventilator (Dräger, Germany)). Systematic security tests were performed before experiments. An important data is the absence of bias ow for this ventilator according to manufacturer. This ventilation mode was selected because it is currently recommended to optimize RT deposition fraction of nebulization  (1)  and is widely used in ICUs  (27). The ventilation parameters were identical for each condition and adapted to be comparable to Erhmann et al. (28) with a tidal volume of 540mL, respiratory rate of 20/min, inspiratory ow of 35L/min with a constant ow pattern, inspiratory to expiratory ratio of 1/2, positive end expiratory pressure of 9cmH 2 O and inspired oxygen fraction of 21%.
Data sequencing was required to meet minimum quality standards, with an average of over ×35 depth of coverage and over 97% of the genome covered by at least 10 reads. Variants were identified using a computational platform of the FHU Translad, hosted by the University of Burgundy Computing Cluster (CCuB). Raw data quality was evaluated by FastQC software (v0. 11. 4-see web resources). Reads were aligned to the GRCh37/ hg19 human genome reference sequence using the Burrows-Wheeler Aligner (v0. 7. 15)  (Li and Durbin, 2009). Aligned read data underwent the following steps: (a) duplicate paired-end reads were marked by Picard software (v2. 4. 1-see web resources), and (b) base quality score was recalibrated using the Genome Analysis Toolkit (GATK v3. 8) Base recalibrator  (DePristo et al. , 2011).