dataesr/scientific-paragraphs-categorization huggingface dataset:
Short-Circuit Current Assessment To assess CFTR function, differentiated first-passage HBE ALI cultures were mounted in Ussing chambers (Physiologic Instruments, San Diego, CA) to measure short-circuit currents (I sc ). HBEs were equilibrated in symmetrical apical and basolateral Krebs buffer solution (containing in mM: 115 NaCl, 25 NaHCO 3 , 5 KCl, 1 CaCl 2 , and 1 MgCl 2 , pH 7. 4, 37 C) and gassed with 5% CO 2 -95% O 2. The following reagents (all from Tocris Bioscience, Abingdon, UK) were sequentially added to the apical compartment and corresponding shortcircuit current (I sc ) changes recorded: amiloride (100 mM) to inhibit the epithelial sodium channel (ENaC), forskolin (10 mM) to stimulate CFTR, and CFTR inh -172 (20 mM) to inhibit CFTR. Data were recorded and digitized using Acquire and Analysis software (version 2.
Statistical Analysis Results are presented as individual data points with the mean (SD) for HBE culture inserts or Xenopus oocytes. Normality of data was assessed with Shapiro-Wilk testing. Statistical analysis was performed using an unpaired t test (unless specified otherwise) in GraphPad Prism software (version 8. 2; GraphPad, San Diego, CA). P values of <0. 05 were considered statistically significant.
ACKNOWLEDGMENTS We thank  Dr. Blanche Schwappach  ( University of Goettingen, Germany ) for providing plasmid DNA containing human CFTR, and  Dr. John Riordan  ( University of North Carolina at Chapel Hill ) and the  CFF  for providing the 596 antibody. We would also like to thank  Dr. Joseline Houwman  and  Dr. Ann Thomas  from the  Braakman group  ( Utrecht University, Utrecht, The Netherlands ) for thorough work in quantification of the biochemical data, and  Prof. T. C. Hwang  ( University of Missouri ) for valuable contribution to and expertise with this work.
SPIDERMASS EXPERIMENTS The laser sampling position of sarcoma samples was determined based on histopathological annotations. The samples were randomized to avoid batch effects. The acquisition was composed of 10 laser shots consecutively fired between 2-4 times with a 10 s pause. Ten laser shots result in 1 individual spectrum. The data were acquired in positive and negative sensitivity modes for rat brain tissue and positive mode for sarcoma, with 1 s scan time and 22 000 mass resolution. The mass range included m/z 50-2000. The acquired spectra were scaled using MassLynx software (v. 4. 2 SNC966, Waters Laboratory Informatics). MS/MS was performed with collision-induced dissociation with collision energy ranging between 25 and 40 V. The lipid annotations were performed manually through fragmentation spectra guidelines, and compared to LIPIDMAPS and the METLIN database  (26, 27).
BLIND TISSUE ANALYSIS To test the efficiency of our databank, we used the Abstract Model Builder Recognition plugin of the AMX software. The same preprocessing was applied to the unclassified (test) specimens. The discriminating molecular patterns of the test specimen were extracted by means of the PCA-LDA projection matrix. The Mahalanobis distances were calculated from each previously assigned class mean. The algorithm classified the novel spectrum into the closest class, unless the distance was larger than a user defined n Â standard deviation of that class. In this particular experiment, the outlier limit was set to 5 standard deviations. The other parameters set were 1e 3 TIC intensity threshold, 5 scans/spectrum, and 5 s timeout.
5 [PC (32:0)þH] þ , 760. 5 [PC(34:1)þH] þ , and m/z 782. 5 [PC(34:1)þNa] þ. The MS/MS identified lipid species are shown in Table  1  in the online Data Supplement. The calculated increase values and CVs for the selected ions are displayed in Supplemental Table  2. The addition of glycerol also increased the signal to noise ratio by a minimum of 3-fold. Parameters such as time of spray, number of laser shots, and IPA/glycerol ratios (data not shown) were also evaluated to produce maximum intensity mass spectrometry molecular profiles, minimal sample preparation time, and a homogeneous layer of matrix. The data are shown in Supplemental Figs  1 2 3. The tests revealed that 10 s and 20% glycerol/IPA were sufficient to obtain a homogeneous layer, reproducible and high intensity signals (up to 10e 5 ).
To reinforce the importance of analyzing nondewaxed FFPE samples, we investigated the influence of tissue dewaxing on the detected species, which revealed only 10 residual fatty acids remaining (Supplemental Fig. 4 ). The lipidomic screening of FFPE tissues was operated in a two-stage workflow (Fig. 2 ). First (Fig. 2, A ), following the optimized sample preparation protocol, a molecular database using histologically annotated samples and molecular data was constructed through machine learning algorithms (training). Second, these classification models were used to perform interrogation of blind samples within <5 min (Fig. 2, B ) (test). The first FFPE classification databank was generated by analyzing a cohort (29 patients; 76 samples; 235 spectra) of canine veterinary sarcoma patients. The collected FFPE blocks followed 4 main sarcoma classes from diverse breeds and tumor origin (Supplemental Table  3 ).
HISTOLOGICAL CLASSIFICATION OF FFPE HUMAN SARCOMA TISSUES A conceptual databank was created using a cohort of human sarcoma FFPE samples (29 patients, 37 samples, 116 spectra). Sarcomas were classified based on different subtypes and classes (malignant and benign) from optical images (Fig. 3, A ). The samples were collected from Fig. 2. A schematic workflow of the formalin-fixed paraffin-embedded (FFPE) screening experiment. (A), Steps to build the molecular database. The FFPE blocks are consecutively sectioned onto slides for SpiderMass analysis and hematoxylin-phloxine-saffron staining. The first section is hematoxylin-phloxine-saffron stained and annotated by the trained pathologist. The tissue heterogeneity is usually expressed as healthy, necrotic, or cancer region. Following the annotation, the consecutive slide is sprayed with the 20% glycerol/isopropyl alcohol solution and analyzed by the SpiderMass.
Each region is measured separately and the data is recorded as mass spectra. The data files are imported into Abstract Model Builder software and subjected to principal component analysis-linear discriminant analysis based on their type and heterogeneity. (B), Blind tissue screening (5 min). The fast screening of a blind sample interrogates the database and provides classification based on the type and tissue heterogeneity. In the case of FFPE sarcoma the sections are classified either as lipoma, liposarcoma, leiomyoma, or leiomyosarcoma. patients with different ages, sexes, and tumors (Supplemental Table  4 ). The acquired spectra for each subtype are shown in Supplemental Fig. 6. The first 3 PCs explained >80% of the total variance in the dataset (Supplemental Fig. 7 ) due to intra-and interpatient heterogeneity, particularly for lipoma and liposarcoma, which had a considerable number of subtypes and tumor origin (Supplemental Table  4 ).
For our proof-of-concept, we developed canine and human sarcoma lipido-histological databanks. Sarcomas are a rare and complex type of cancer and can, for canines and humans, form over 100 different heterogeneous mesenchymal lesions  (29). Current diagnostic methods include standard histological staining, immunostaining, and molecular genetics  (30, 31). However, without the new ancillary testing methods, the firsthand morphological diagnostic approach can be extremely challenging particularly for tumors of intermediate malignant potential  (32) (33) (34). Interestingly, soft tissue tumor subtypes known to be difficult to distinguish morphologically by the pathologist (lipoma versus well-differentiated liposarcoma or liposarcoma lipomalike)  (35)  were distinguished by the molecular fingerprints.
The most abundant triglycerides, TG (52:4)/ TG(50:1)Na þ , TG (54:5)/TG(52:2)Na þ , and TG (56:6)/TG(54:3)Na þ , found discriminant in benign lipomas, have also been previously shown to decrease in fresh-frozen high-grade myxoid liposarcomas  (36). Although PCA analysis reflected on tumor heterogeneity associated to the origin of the tumors, the databanks gave 99. 1% cross-validation accuracy using 5-fold and 98. 5% cross-validation accuracy using leave-one-patient out and 91. 2% cross-validation accuracy using 5-fold and 73. 8% cross-validation accuracy using leave-one-patient out for canine and human sarcoma subtyping, respectively. Although the leaving-one-patient-out  (24, 37)  method is the reference cross-validation technique, it also produces errors when dealing with under-represented classes.
However, hydrolysis is not expected to increase with the conservation time. Thus, we hypothesize the decrease of intensities arises from the increase of crosslinking in the older samples. However, no time dependent study was previously performed from FFPE tissues to systematically investigate the effect of conservation time. In our study, despite the decrease of certain lipid signals, statistical tests indicated that only 3 ions show a significant difference. To conclude, by demonstrating the analysis of nondewaxed FFPE samples, we have laid the groundwork for the creation of a rapid WALDI-MS platform to screen and classify sarcoma tissues. The sarcoma databanks are just one of the examples where the platform could be used for consolidation of histopathological diagnosis. Tissue banks with different types of cancer FFPE tissue could use this platform.
g. auto-correlations) to be corrected are viewed as inter-variable dependencies to be adjusted and an existing multivariate bias correction (MBC) method can then be used to answer this need. This approach is first applied and evaluated on synthetic data from a vector autoregressive (VAR) process. In a second evaluation, we work in a "perfect model" context where a regional climate model (RCM) plays the role of the (pseudo-)observations, and where its forcing global climate model (GCM) is the model to be downscaled or bias corrected. For both evaluations, the results show a large reduction of the biases in the temporal properties, while inter-variable and spatial dependence structures are still correctly adjusted. However, increasing the number of lags too much does not necessarily improve the temporal properties, and an overly strong increase in the number of dimensions of the dataset to be corrected can even imply some potential instability in the adjusted and/or downscaled results, calling for a reasoned use of this approach for large datasets.
To overcome those spatial or inherent biases, various statistical post-processing methods have been developed for downscaling and/or bias correction of the climate simulations. The common idea is to statistically transform the numerical simulations -over a historical periodin such a way that some properties become equal or close to those of a chosen reference dataset (e. g. Gudmundsson et al. , 2012). The statistical downscaling or correction transformation is then usually supposed valid under other climate conditions and applied to changed (e. g. future) climate. The obtained downscaled/bias corrected climate data can then serve as input into impact models (e. g. Teutschbein and Seibert, 2012; Galmarini et al. , 2019; Bartók et al. , 2019; Chen et al. , 2021, among many others). Over the last two decades, many such post-processing methods were developed, either in a "perfect prognosis" (PP) context, generally for downscaling (DS), or in a "model output statistics" (MOS) one, generally for bias correction (BC) -see e.
Data: climate simulations and VAR processes To apply any (M)BC method, it is necessary to dispose of a dataset of reference -i. e. it is supposed to be as close as possible to real observed climate -and a dataset of simulations (e. g. stemming from a GCM) that are biased with respect to the reference dataset. Here, the climate simulations to be corrected are daily temperature and precipitation times series over the southeast of France, for the time period 1951-2010, extracted at a 1. 25 • × 2. 5 • spatial resolution from the IPSL-CM5A-MR global climate model  (Marti et al. , 2010; Dufresne et al. , 2013)  developed at Institut Pierre Simon Laplace (IPSL). The simulations are forced by historical conditions up to 2005 and then by RCP8. 5 scenario conditions from 2006. The region of interest is presented in Fig.
1  and corresponds to a zone where correlations between precipitation and temperature as well as auto-correlations are difficult to model due to various geographical constraints, such as Mediterranean influences and three mountain ranges (the Pyrenees, the Massif Central, the Alps), which can generate high-precipitation events known as "Cevennol events" (see for example  Delrieu et al. , 2005). Regarding the reference dataset, regional climate simulations are used in this study, instead of observational or reanalysis data. Those are EURO-CORDEX daily temperature and precipitation from the KNMI-RACMO22E regional climate model (KNMI, 2017) developed at the Royal Netherlands Meteorological Institute (KNMI) and forced by the largerscale IPSL simulations. The same time period and region as for the IPSL simulations were extracted but with a 0.
, X d 1:N-s , X 1 , X 2 ,. , X d 2:N -s+1 ,. , X 1 , X 2 ,. , X d s+1:N (3) or equivalently M X (s) =           x 1 1 x 2 1. x d 1 x 1 2 x 2 2. x d 2. x 1 N -s x 2 N -s. x d N-s      ,      x 1 2 x 2 2. x d 2 x 1 3 x 2 3. x d 3. x 1 N-s+1 x 2 N -s+1. x d N-s+1      ,. ,      x 1 s+1 x 2 s+1. x d s+1 x 1 s+2 x 2 s+2. x d s+2. x 1 N x 2 N. x d N           , (4) which can then be grouped by variable (dimension) and thus reordered as M X (s) =           x 1 1 x 1 2. x 1 s+1 x 1 2 x 1 3. x 1 s+2. x 1 N -s x 1 N -s+1. x 1 N      ,      x 2 1 x 2 2. x 2 s+1 x 2 2 x 2 3. x 2 s+2. x 2 N -s x 2 N -s+1. x 2 N      ,. ,      x d 1 x d 2. x d s+1 x d 2 x d 3. x d s+2. x d N -s x d N -s+1. x d N          . ( 5 ) Such a transformation can be made to create M Y (s) and M X (s), the lagged matrices obtained from the reference dataset and the biased one to be corrected, respectively.
An MBC method can thus be applied to correct the multivariate properties of M X (s) with respect to M Y (s) as reference. The result of the MBC is therefore a matrix M Z (s) of the same size as M X (s). The question to answer now is, from this resulting MBC matrix, how can a N × d matrix Z be extracted or reconstructed that corresponds to proper multivariate (time included) corrected data? There are indeed various possibilities for that, and they may not be equivalent. In this study, we propose a method based on a reconstruction by rows. For illustration, let us take this example of a bivariate dataset X and its associated matrix M X (1), i. e. including a lag-1 shift:  1 10 2 20  2 20 3 30  3 30 4 40  4 40 5 50  5 50 6 60 X =         1 10 2 20 3 30 4 40 5 50 6 60         , M X (1) =            .
In this example, the bold rows of the matrix M X (1) (its rows 1, 3 and 5) can be used to reconstruct the original dataset X. More generally, a row s of M X (1) is a vector corresponding to a portion of X, which continues at row s +2, and which continues at row 2s +3, etc. Concatenating these rows, we can re-construct the dataset X. Applying this method to the matrix M Z (s), we can reconstruct a corrected dataset Z. Because the same operation can be applied by starting at the second row (and any row between 1 and s + 1), the reconstruction depends on the choice of the starting row. However, even if the values are repeated in the lagged matrix, no repeated values can appear in the final reconstruction (i. e. multivariate corrections). It is also worth noting the initial mapping (i. e. going from X to M X ) is injective, and that, in general, an inverse mapping is not.
However, with the suggested reconstruction (i. e. inverse mapping), when a starting row is chosen, the inverse mapping gives a unique time series and is thus injective. Moreover, the choice of a starting row r > 1 omits the first r -1 values in the final reconstruction. This leads us to wonder about the influence of the choice of the starting row. These points will be investigated with the VAR processes. Our approach is summarized in the Algorithm 1. Note that a reconstruction "by column" could also be performed: each column of M X (1) being a sub-column of X, it could then be used to reconstruct the original dataset. However, preliminary analyses showed that this approach does not allow auto-correlations to be corrected, as the temporal dependence structure in each row is not corrected and mostly corresponds to that of the model to be adjusted (not shown).
Hence, only the TSMBC approach "by rows" is investigated in the following. Finally, because TSMBC uses an underlying MBC, potentially any MBC method can be used, as MBCn (Cannon, 2018) or R2D2  (Vrac and Thao, 2020). Here, the MBC method used is the dynamical optimal transport correction (dOTC) developed by  Robin et al. (2019). While most of the bias correction methods build a mapping between the biased and the reference dataset, dOTC infers a probability distribution P such that P(x, y) is the probability that y is the correction of x. This probability distribution is inferred -with optimal transport methods; see Appendix A and  Villani (2008)  and  Santambrogio (2015)  -between the biased and reference dataset in calibration period (representing the bias), and between the biased dataset in calibration and projection periods (representing the evolution).
Results: synthetic VAR data In this section we test our TSMBC method on synthetic data, generated from two VAR processes (see Sect. 2. 1). A biased dataset and a reference one -denoted respectively as X and Y -are drawn from two VAR processes fitted from two time series of temperature of the GCM or RCM. TSMBC is applied to correct X with respect to Y, i. e. to generate a corrected dataset Z. Because the reconstruction step preserves the dependence structure, we propose to test which part of the correction is due to the underlying method (here dOTC), and which part is due to the reconstruction. To do so, a second underlying bias correction method is then used as a benchmark. It corresponds to a very naive method: the correction is randomly drawn from the reference dataset, i. e. for any x ∈ X, the correction is given by a random value y generated according to the distribution of Y.
In practice, values from Y are resam-Y. Robin and M. Vrac: Is time a variable like the others in multivariate statistical downscaling and bias correction? 1259 pled. Note that this naive method corresponds to dOTC with a transport plan given by the product probability distribution between the biased and reference datasets; see Appendix A. We denote this multivariate method a "random bias correction" (RBC), and TSMBC is thus available in two versions for this evaluation: TSMBC(dOTC) and TSMBC(RBC). For these two methods, we test in the first part the influence of the choice of the starting row, and in the second part the influence of the choice of the lag.
Influence of the choice of the starting row We fix the number of lags s = 10, and we compute the correction for each of the two methods with a starting row r ∈ {1, 3, 6, 9, 12}. Note that, since here s = 10, TSMC applied with r = 12 will provide the same results as for r = 1 but will ignore the first row. The corrections are denoted Z r. To measure the similarity of the corrections from different starting rows, we compute the matrix of Pearson correlations between the pair Z r 1 /Z r 2. We also compute the correlations with X and Y to compare the corrections with the biased and reference datasets. The results are presented in Fig. 3a  for TSMBC(dOTC) and in Fig. 3b  for TSMBC(RBC). Only the first dimension is represented; the second dimension -which gives similar results -and the associated p values are given in Fig.
Influence of the choice of the lag In this section the starting row is fixed at r = s+1 2 , and we vary the number of lags s ∈ {3, 5, 10, 20, 100}. This generates the corrections Z s for the two methods TSMBC(dOTC) and TSMBC(RBC). As for the previous sub-section, the correlations between corrections, reference and biased dataset are computed and represented in Fig. 3c  and d  for the first variable, and the second variable and p values are given in Fig. S2. We can see for TSMBC(dOTC) that all corrections are highly correlated (and correlated with the biased dataset), with correlations decreasing with s. This shows that the corrections are similar, even when increasing the lag to a reasonable extent. As for the previous section, the method TSMBC(RBC) provides many different corrections, different from each other and thus difficult to link together.
Furthermore, we have added in Fig. 2a  the density of the corrections with TSMBC(dOTC) and with TSMBC(RBC) in green and black respectively. The two methods correctly adjust the density and the dependence structure of the reference dataset. Therefore the differences seen in Fig. 3c ,d come only from their capability to adjust the temporal structure. The (cross-)auto-correlations between the 2 dimensions for various lags are also given in Fig. 2b-e. On each panel, we can see that the results for the lag 0 -which corresponds to classic correlations -are close to the references for all corrections, which is confirmed by the Fig. 2a. For nonzero lags, the (cross-)auto-correlations of corrections with TSMBC(dOTC) are close to the reference one, validating the method. For TSMBC(RBC), the (cross-)auto-correlations become close to the reference if s is large enough.
This shows that the ability to correct the (cross-)auto-correlation when s is small comes from dOTC, and not only from the reconstruction part. When s increases, TSMBC(RBC) tends to adjust the temporal properties correctly, but this is due to the reconstruction that replaces the biased dataset with the reference one for large s. Generally, from the synthetic VAR dataset, we can see the ability of the TSMBC approach to correct the (cross-)auto-correlations. The choice of the starting row has little influence on the final corrections, and we fix it now at s+1 2. We also highlighted the importance of dOTC as the underlying bias correction method (compared to the naive random approach), as the ability to properly correct the temporal structure does not come only from the reconstruction step of TSMBC.
6 and 1. Interestingly, the use of a TSMBC approach relying on dOTC implies bias reductions in means and standard deviations equivalent to those provided by the dOTC method, i. e. without "time-shifted" consideration. In other words, TSMBC does not degrade the basic marginal properties of the corrections from dOTC. Note that the ability of the methods to reduce the biases can be strongly affected by the evolution of the GCM variables between the calibration and validation period. Indeed, this evolution is different from that of the RCM (i. e. here the reference dataset) variables. As dOTC preserves the evolution of the GCM to be corrected, the resulting corrections for the projection period have necessarily properties different from the reference over the same period. The same boxplots are now represented in Fig.
This gives an auto-correlogram or a cross-auto-correlogram, respectively. Figure  7  presents the scatter plots of the DCP values, where correlations are cross-auto-correlations between temperature and precipitation for all pairs of grid points with lag 1 for the GCM data (Fig. 7a ), the RCM (Fig. 7 b ), the corrections with dOTC (S2V, Fig. 7c ) and with TSMBC-5 (S2V, Fig. 7d ). In each panel, the black line is the es-timated mean cross-auto-correlogram. It is clear that the cross-auto-correlograms -as mean conditional correlations given distances -are not quite representative of the DCP scatter-plot structures. Indeed, the DCP structures show large spreads that cannot be visible on simple lines. Moreover, those structures are different from one dataset to another, for example between the GCM data to be corrected and from the reference RCM data, while their associated cross-autocorrelograms (i.