dataesr/scientific-paragraphs-categorization huggingface dataset:
Actual data analysis 3. 2. 1 Computational speed analysis and biological enrichment Supervised dsMTL. We first explored the efficiency of dsMTL using real molecular data of patients with schizophrenia, given the actual latency of a distributed network, and then investigated the biological relevance of the identified gene signatures. The efficiency was quantified for a three-server scenario (see Supplementary Table  S3 , two servers at the Central Institute of Mental Health, Mannheim; one server at BioQuant, Heidelberg University). We analyzed four casecontrol gene expression datasets of patients with schizophrenia and controls (median n ¼ 80; 8013 genes). Table  1  shows the comparison between dsLasso and mean-regularized dsMTL_Net, which were trained (cross-validation þ training) and tested in approximately 8 min and 10 min, respectively.
The time-difference might be due to the high heterogeneity of the brain gene expression that can be disentangled by dsMTL_Net but that is ignored by dsLasso. Thus, a larger number of iterations were used by dsMTL_Net. To demonstrate the cross-cohort prediction performance of dsMTL_Net and dsLasso, we predicted the models on an independent brain expression dataset (HBCC). As shown in Table  1 , the prediction accuracy of dsMTL_Net (misclassification rate: 0. 29) was slightly higher than that of dsLasso (0. 34), consistent with our previous study  (Cao et al. , 2018). To explore biological interpretability and cross-tissue reproducibility, respectively, we performed pathway-related enrichment analysis of the top-ranked gene signatures and predicted the signatures in data from blood samples of patients with schizophrenia.
Reproducibility analysis of gene signature identification Table  2  shows that the gene signatures found by dsMTL_Net were more reproducible in independent data cohorts than the ones found by the glmnet-bagging method. As indicated in Table  2 , four datasets were categorized into two distinct dataset groups, and the gene signatures were compared across these groups. For glmnet-bagging, on an average, around nine gene signatures were reproducibly found in the two distinct dataset groups. The number of overlapping genes with consistent association direction was slightly lower (8 on an average). However, for dsMTL_Net, on an average 13 genes were found simultaneously in the two dataset groups, and 12 genes on an average demonstrated the same direction. This showed that federated MTL had a stronger ability to identify reproducible gene signatures among heterogeneous datasets.
Discussion We here present dsMTL-a secure, federated multi-task learning package for the programming language R, building on DataSHIELD as an ecosystem for privacy-preserving and distributed analysis. Multi-task learning allows the investigation of research questions that are difficult to address using conventional ML, such as the identification of heterogeneous, albeit related, signatures across datasets. The implementation of a privacy-preserving framework for the distributed application of MTL is an essential requirement for the large-scale adoption of MTL. Using such a distributed server setup, we demonstrate the applicability and utility of dsMTL to identify biomarker signatures in different settings. For applications where the target biomarker signatures are different, but relate to an overlapping set of features (explored here as the 'heterogeneous' case), conventional machine learning would not be a meaningful algorithm choice.
Surprisingly, we found that an increasing subject number reduces the communication cost for federated MTL methods. This might be due to the fact that the network communication in federated MTL is aimed at identifying the shared effect among tasks, and such effect would be more efficiently identified in a large sample. In the actual data analysis, we showed that federated MTL was able to capture reproducible and biologically plausible gene signatures. Despite this reproducibility, the number of reproduced genes (e. g. overlapping genes: 13 on an average) was comparatively low, likely a reflection of the pronounced heterogeneity of the brain tissue expression datasets. We show that unsupervised MTL could disentangle the shared from cohort-specific effects, demonstrating its potential utility for comorbidity analysis.
Other applications for this method include the analysis of biological patterns shared across or specific to diverse single-cell measurements  (Welch et al. , 2019). We selected R as the major implementation language to make dsMTL able to cooperate with other DataSHIELD-based or CRAN packages, based on the concept of a 'freely composing script'. This means the dsMTL workflow was formed as a composition of dsMTL, DataSHIELD, and local R commands (e. g. R base functions, customer-defined functions and CRAN packages) into a script, such that the geo-distribution of datasets and the federated computation are transparent to users. This concept is similar to that of the 'freely composing apps' used in a recently presented federated ML application  (Matschinske et al. , 2021) , which allows flexible scheduling of functions in the form of apps and improves the federated data analysis flexibility for users.
Considering there are thousands of well-established packages already on CRAN for different analysis aims, this will significantly broaden the potential applicability of dsMTL. All DataSHIELD-based packages are listed here  (Consotia, 2019). Interesting future developments of the dsMTL approach could include the implementation of asynchronous gradient descent, which provides a probabilistically approximate solution but faster convergence  (Xie et al. , 2017; Zhang and Liu, 2020). Furthermore, the integration of other popular systems for ML, such as tensorflow  (Dahl et al. , 2018) , for which interfaces with the R language already exist, would provide valuable additions to the DataSHIELD system. In addition, the re-implementation of gradient calculation functions on the server side into a low-level language, i.
e. Cþþ would significantly improve the efficiency. Finally, a noteworthy consideration is an architecture underlying the distributed data infrastructure. DataSHIELD builds on a centralized ('client-server') architecture and each data provider needs to install a well-configured data warehouse. Such infrastructure is suitable for long-term collaboration scenarios and large consortia projects that conduct a broad spectrum of complex analyses requiring high flexibility. However, in other scenarios that require more temporary and easy-compute collaboration setups, a server-free or decentralized architecture  (Warnat-Herresthal et al. , 2020)  might be more suitable, because the cost of data provider for participating is low. In conclusion, the dsMTL library for R programming provides an easy-to-use framework for privacy-preserving, federated analysis of geographically distributed heterogeneous datasets.
Due to its ability to disentangle shared and cohort-specific effects across these datasets, dsMTL has numerous interesting application areas, including comorbidity modeling and translational research focused on the simultaneous prediction of different outcomes across datasets. Note: Four datasets were split into two distinct dataset groups. The model was trained on each dataset group individually. Then the top selected genes (200) from both models were compared to assess the signature reproducibility. The 'overlapping genes' referred to the gene signatures identified in all dataset groups. The 'consistent direction' referred to the overlapping genes demonstrating a consistent direction of outcome-associations. To address sampling variability, the analysis was repeated 100 times and results were averaged across repetitions.
Funding This study was supported by the  Deutsche Forschungsgemeinschaft (DFG) ;  SCHW 1768/1-1  and the  German Federal Ministry of Education and Research (BMBF , eMed COMMITMENT grants  01KU1905A  and  01ZX1904A ). Meanwhile, this project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreements No. 826078  (J. B. , J. M. and J. S. ) and No. 777111  (J. B. and J. S. ). Research related to the HBCC dataset used in this study (dbGAP ID: phs000979. v3. p2) was supported by the  Intramural Research Program  of the  NIMH  ( NCT00001260 ,  900142 ).
We investigated the contributions and the evolution of organic aerosol (OA) sources at the Marseille-Longchamp supersite (MRS-LCP, France) based on Time-of-flight Aerosol Chemical Speciation Monitor (ToF-ACSM) measurements of non-refractory PM 1 over a fourteen-month period (1 February -3 April 2018). The OA source apportionment was performed by positive matrix factorization (PMF) using the novel "rolling window" approach implemented in the Source Finder Professional (SoFi Pro). Here, PMF is performed over a 14-days window moving over the entire OA dataset, in order to account for the temporal variability of the source profiles. Six factors were resolved, including hydrocarbon-like organic aerosol (HOA) which is related to traffic exhausts, cooking-like organic aerosol (COA), biomass burning aerosol (BBOA), less oxidized organic aerosol (LOOA), more oxidized organic aerosol (MOOA) and a new defined source related to the mix between shipping and industrial plumes (Sh-IndOA).
This fraction can be classified as primary OA (POA) corresponding to particles directly emitted in the atmosphere, or secondary OA (SOA) which are formed via condensation or uptake of oxidized gas-phase precursors. The aerosol mass spectrometer (AMS)  (Canagaratna et al. , 2007; Jayne et al. , 2000)  has been extensively used for the online analyses of the non-refractory submicron aerosol (NR-PM 1 ) composition. While the studies using AMS are limited to short-term campaigns, the aerosol chemical speciation monitor (ACSM)  (Fröhlich et al. , 2013; Ng et al. , 2011b) , based on the same technology, is easily deployed for unattended long-term monitoring (up to multiple years). A great advantage is to allow the investigation of the seasonality and yearly variations of the dataset. AMS and ACSM are widely deployed worldwide in order to quantify and identify the OA emission sources  (Fröhlich et al.
, 2015a; Zhang et al. , 2011). Source apportionment of OA is commonly performed using positive matrix factorization (PMF;  Paatero and Tapper, 1994). The PMF algorithm can be solved with the multilinear engine (ME-2;  Paatero, 1999)  which allows to introduce a-priori information to the factor profiles and/or time series  (Canonaco et al. , 2013; Crippa et al. , 2014; Lanz et al. , 2008). In Europe, several studies conducted OA source apportionment over large dataset (>1 year) using AMS  (Ovadnevaite et al. , 2014; Young et al. , 2015) , offline-AMS  (Bozzetti et al. , 2017b; Daellenbach et al. , 2016 Daellenbach et al. , , 2017;; Vlachou et al. , 2018 Vlachou et al. , , 2019) )  and ACSM  (Bressi et al. , 2016; Canonaco et al. , 2015; Fröhlich et al. , 2015b; Heikkinen et al. , 2020; Minguillón et al.
, 2015; Ripoll et al. , 2015; Schlag et al. , 2016; Stavroulas et al. , 2019; Via et al. , 2021; Zhang et al. , 2019). As factors seasonal variations can't be properly modelled with static profiles generated with a year-long PMF, most of the studies had to apply multi-seasonal PMF over their dataset. However, this method is likely to produce several seasonal factors representations leading to discontinuities between each PMF solutions. To overcome this limitation, a novel approach was developed and consists in applying PMF on a smaller and moving time window  (Canonaco et al. , 2021; Parworth et al. , 2015). This so-called "rolling window" method allows to capture the temporal variability of the factor profiles and results in shorter computational times for each PMF run. and was successfully applied to two sites in Switzerland (Zurich;  Canonaco et al.
, 2021 and Magadino; Chen et al. , 2021)  and one in Poland (Krakow;  Tobler et al. , 2021). This study presents a fourteen-month long dataset measured with a ToF-ACSM in Marseille, a coastal city in the south of France, between February 2017 and April 2018. We conducted a complete PMF analysis on organic aerosol based on the "rolling window" technique. To our knowledge, this is the first study using this novel PMF approach over an up-to-1 year ToF-ACSM dataset. We compare the rolling results to standard seasonal PMF analysis and further, estimate and investigate the BBOA and SOA origins over the study period.
(2013)  and  Timonen et al. (2016). The NR-PM 1 aerosol is sampled at a flow rate of 3 L min -1 and dried using a Nafion dryer system (Perma Pure, New Jersey, USA) which allowed to keep the relative humidity (RH) below 40%. The ToF-ACSM was operated between m/z 12 and 214 and produced averaged scans every 15 min. The data were acquired using Igor-DAQ v. 2. 1. 4 software and by Tofware v. 2. 5. 13 written in Igor Pro (Wave Metric inc. , Lake Oswego, Oregon, USA). Necessary calibrations and data corrections are detailed in  Chazeau et al. (2021). Calibrations of ionization efficiency (IE) of nitrate and relative ionization efficiency (RIE) of ammonium and sulfate were performed three times over the 2017-2018 period, and blank measurements were achieved one time for determining detection limits of ToF-ACSM species.
The collection efficiency (CE) values were corrected using algorithms described by  Middlebrook et al. (2012)  and an averaged value of 0. 47 was used for this dataset. According to  Pieber et al. (2016)  ammonium nitrate can contribute to m/z 44 and cause an overestimation of the organic signal. This interference was corrected and showed negligible contribution of NH 4 NO 3 on the organic m/z (CO 2.
, Minnesota, USA) providing measurements from 20 to 1000 nm with six channels of size resolution. Furthermore, a Scanning Mobility Particle Sizer system (SMPS, L-DMA, CPC5403, GRIMM) was deployed over two periods: from 23 June to August 12, 2017 (summer period) and from November 6, 2017 to January 11, (winter period). The particle size distribution was measured in the size range 10. 25-1084 nm. In addition, all data including O 3 , NO x , SO 2 , PM 1 , PM 2. 5 and PM 10 15 min-averaged concentrations were also measured with the standard real-time equipment of AtmoSud available directly on site. Finally, temperature, wind directions and speeds were recorded with an anemometer sonic 3D since June 2017. Non-parametric wind regressions (NWR) were performed to estimate the OA source concentrations at given wind direction and speed  (Henry et al.
Source apportionment method Source apportionment was performed on ToF-ACSM OA mass spectra using Positive Matrix Factorization (PMF;  Paatero and Tapper, 1994)  for the 14 months period in Marseille. PMF algorithm is solved by the multi-linear engine (ME-2;  Paatero, 1999)  with the software package SoFi (Source Finder;  Canonaco et al. , 2013)  coded in Igor Pro. PMF is a bilinear unmixing model that describes the variability of a multivariate dataset as a linear combination of factor profiles times their time series as described in the following: x i,j = ∑ p k=1 g i, k. f k,j + e i, j (1) Here, x i,j corresponds to the measurement matrix, g i,k the factor time series, f k,j the factor profiles and e i,j the model residuals. The index i, j, k and p correspond to the time elements, variables (ToF-ACSM OA fragments), factor numbers and total number of factors selected, respectively.
ME-2 solver fits the non-negative entries in g i,k and f k,j using a least squares algorithm to iteratively minimize the object function Q (Eq. (  2 )) defined as the sum of the squared residuals (e ij ) weighted by their respective uncertainties (σ ij ): Q = Σ i Σ j ( e ij σ ij ) 2 (2) The error matrix σ ij was calculated by Tofware v. 2. 5. 13 software and includes ion counting statistics  (Allan et al. , 2004) , background errors and electronic noise. A minimum error corresponding to the measurement of a single ion was also applied to the error matrix. To inspect the object function Q for different PMF runs, Q/Q exp is calculated and defined as Q normalized by the degree of freedom of the model solution (Q exp ). Following the recommendations of  Paatero and Hopke (2003) , a step function is applied for removing variables with signal-to-noise ratio (S/N) less than 0.
The ME-2 solver enables efficient exploration of the rotational ambiguity and offers high rotational control. Here, the solutions were oriented toward environmentally meaningful rotations by constraining elements of factor profiles (f k,j ) and factor time series (g i, k ). Constraints are achieved with known inputs profile/time series and the scalar a defined as the extent to which the outputs f' k,j /g' i,k are allowed to vary during the model iteration: f kj = f ′ kj ± a⋅f ′ kj (3) g ik = g ′ ik ± a⋅g ′ ik (4) The index j represents the m/z variable and i the measured point in time of the kth factor. The a-value approach is a successfully applied and well documented method for unmixing sources and it removes environmentally unreasonable factors for AMS/ACSM datasets  (Canonaco et al. , 2013; Lanz et al.
, 2008). PMF was performed using a fixed rolling PMF window, which moves over the entire input dataset. Instead of modelling static factor profiles this dynamic PMF allows catching source profiles variability over time. The technique is available in the additional module 'SoFi Pro', recently implemented in the SoFi software package  (Canonaco et al. , 2021). With this tool the user can easily select the width of the PMF window, the shift and the number of repeats per PMF window adapted to the dataset. Due to the large number of generated PMF runs, a criteria-based panel was built to either automatically accept or reject environmentally reasonable solutions. The results are then explored through several statistical analyses and graphic visualisations available in the current version of the module.
The residual did not exhibit strong diurnal structures (Figure  A3 ), which also suggests that the model captures the major sources  (Crippa et al. , 2014). However, HOA and COA factors were mixed in a single factor in all the seasonal unconstrained solutions and did not have typical diurnal profiles (Figure  A4 ). Unconstrained PMF does not allow a clear separation as their mass spectra profiles are very similar (contributions of alkyl fragments like m/z 43, m/z 55 and m/z 57) and thus the use of constraint from reference mass spectra is required. In previous studies an industrial related OA factor was resolved at the MRS-LCP station with online and offline AMS datasets  (Bozzetti et al. , 2017a; El Haddad et al. , 2013). This factor was attributed to plumes (associated with high ultrafine particle number and SO 2 concentrations) emitted from the industrial area of Fos-sur-mer located 40 km northwest of the city.
Rotational ambiguity exploration Both HOA and COA factor profiles were constrained using the avalue approach, with reference profiles derived from several datasets from  Ng et al. (2011a)  and  Crippa et al. (2013) , respectively. The identification of COA is based on its diurnal cycles with maxima at lunch and dinner times. We have considered that the increase in COA concentrations during traffic rush hours to be an indication of the mixing between HOA and COA. We conducted an a-value sensitivity analysis over the whole study period by scanning a-value combinations for HOA and COA with an a-value step size of 0. 1 in the range between 0 and leading to 121 possible outcomes  (Bozzetti et al. , 2017a; Daellenbach et al. , 2016; Elser et al. , 2016). A-value combinations were retained based on statistical criteria as described in the following.
BC WB was used to evaluate BBOA. The R Pearson correlation between BC WB and BBOA time series is inspected. If BBOA has not been identified according to this criterion, then a 5-factor solution was selected instead of the 6-factor solution. The scores of the three criteria are presented in Fig. 1 (d, e, f ) for all the 53,298 runs. Statistical thresholds must be applied for each score in order to differentiate the "good" and "bad" runs. A significant drop of the lowest score plots (displayed in descending order in Fig. 1d  and e ) indicates some rare unstable solutions which might include mixing within the factors. As first data cleansing, the corresponding runs are removed. For the remaining runs of HOA and COA criteria thresholds have to be chosen based on a statistical assessment. The time series of HOA and BC FF were resampled together by bootstrap and the Pearson correlation coefficient was evaluated for each resample.
Furthermore, the MOOA diurnal profile was mostly flat suggesting the long-range transport origin for this factor, except for winter profile which shows, as for all OA factors, increased concentrations at night under a shallow boundary layer (Figure  A21 ). In overall LOOA does not show significant correlation with semi volatile species such as particulate nitrate. The fact that LOOA variability is not driven by its volatility prevents here to use the SVOOA denomination like other studies  (Jimenez et al. , 2009; Lanz et al. , 2007). MOOA displayed a stronger correlation with nitrate (R 2 = 0. 43) than LOOA (R 2 = 0. 15). It has to be noted that organic nitrate concentrations (NO 3,Org ) of this dataset were previously estimated for the nitrate functionality only following  Farmer et al. (2010)  and  Kiendler-Scharr et al.
An underestimation of the m/z 43 signal was recorded after an unknown technical issue with the ACSM for this one-month period (see SI text). These data points were excluded from the f44/f43 analysis. The data points in Fig. 7  lie in different location in f44/f43 space depending on the seasons similarly to other studies  (Canonaco et al. , 2015; Crippa et al. , 2014; Freney et al. , 2011; Reyes-Villegas et al. , 2016). Furthermore, the points from the modelled factor profiles display high variability in space mostly for LOOA. This implies that a yearly or seasonal PMF solution, unless going to higher number of factors, would fail to capture the entire variability of SOA, unlike rolling PMF solution, which provides continuous observations of physico-chemical processes leading to LOOA and MOOA formation.
Summary and conclusion The study presented the PMF source apportionment of OA measured with a ToF-ACSM from February 2017 to April 2018 (14 months) at the MRS-LCP site in Marseille. A novel approach was performed by applying PMF on a rolling window of 14 days using the new designed SoFi Pro. This method provided the decomposition of time dependant factor profiles which were more able to capture the OA sources variability through seasons compared to batch PMF analysis for the full dataset. However, it remained complex to model the OA composition with the rolling method during fast environmental changes. Some improvements are needed to deeply investigate these periods not well captured with a 14-days window and could require additional factors. The a-value space for the applied PMF constraints was fully explored to determine the best OA sources separation.
In winter, spring and autumn SOA concentrations may arise from oxidized combustion sources such as biomass burning or road traffic. Additionally, the more oxygenated SOA (MOOA) was partly driven by long-range transport with potential anthropogenic emissions from the Rhône valley (mostly in winter) and a strong origin from the west Mediterranean Sea (for every season). This study emphasized the importance of dynamic factor profiles to assess the variability of long-term dataset, notably for the secondary origin. It would be necessary to apply the rolling method to more yearlong ACSM datasets to corroborate its improved performances compared to the conventional seasonal PMF. Within the framework of these conclusions, the rolling approach constitutes a first step for automated analysis of continuous growing ACSM dataset.