dataesr/scientific-paragraphs-categorization huggingface dataset:
Complementary DNA (cDNA) was synthesized from 2 g of total RNA using RevertAid Premium Reverse Transcriptase (Fermentas) and primed with oligo-dT primers (Fermentas) and random primers (Fermentas). Quantitative polymerase chain reaction (qPCR) was performed using a LightCycler 480 Real-Time PCR System (Roche, Meylan, France). qPCRs were performed in duplicate for each sample using transcriptspecific primers, cDNA (4 ng), and LightCycler 480 SYBR Green I Master (Roche) in a final volume of 10 l. The PCR data were exported and analyzed in an informatics tool (Gene Expression Analysis Software Environment) developed at the NeuroCentre Magendie. For the determination of the reference gene, the geNorm method was used. Relative expression analysis was corrected for PCR efficiency and normalized against two reference genes.
Multiple-layer perceptrons Each MLP had the same architecture rule: three neurons as input, three neurons in the hidden layer, and three neurons as output. Activation function of neurons was the hyperbolic tangent. Each network was trained over 1000 presentations of a subset of the dataset. We used as error measure the mean square of differences between the expected output and the actual output. Our implementation comprises two parameters: a learning rate set at 0. 05 (regulating the learning speed) and a momentum set at 0. 05 (introducing purposefully a conservatism bias). Before learning, inputs were first scaled and centered (z scoring) to avoid dimensionality issues and then normalized between -0. 5 and 0. 5. For every combination of three variables used as inputs, 50 instances of MLP were trained with different subsets of the dataset.
Eighty percent of available data have been used for learning, and the remaining 20% have been used for testing the performance of the network [elements of each subset were randomly (and uniformly) drawn for each network]. The performance from a given set of input variables was the mean of the error of the 50 instances of MLP that had data for these variables as inputs. Code was written using Python (v. 3. 7. 3) and the Python scientific stack: numpy (v. 1. 17. 2), scipy (v. 1. 3. 1), and Cython.
The growing availability of large neuroimaging databases offers exceptional opportunities to train more and more efficient machine learning algorithms. Nevertheless, these databases may be prone to several sources of variability (e. g. , age, gender, acquisition parameters,. ). These nuisance variables can hamper the performance of a classification method and can even lead to misinterpret its behavior. We focus in this paper on how to account for data coming from different databases. First, we present experiments on simulated data that illustrate how interactions with other confounds such as age can be problematic for the adjustment of data from multiple databases. Then, we compare three strategies to adjust data and evaluate them in the real scenario of a Computer-Aided Diagnosis (CAD) system that discriminates healthy from Alzheimer's Disease (AD) subjects based on volumetric characteristics derived from structural MRI.
INTRODUCTION The growing availability of large neuroimaging databases offers a unique opportunity to collect considerable amount of data to train artificial intelligence algorithms. A side effect is that it inevitably leads to the introduction of new biases related especially to heterogeneous protocol guidelines in terms of subject enrollment and acquisition parameters  [1, 2]. In this paper, we investigate several strategies to account for data coming from different databases. This is exemplified through the design of a CAD system that discriminates healthy from Alzheimer's Disease (AD) subjects based on volumetric characteristics derived from structural MRI. A nuisance variable is an external factor which is not of interest for the study, but which causes an increase in variability within groups.
In our case, nuisance variables can be database, age, gender and Estimated Total Intracranial Volume (ETIV). A particular subcategory of nuisance variables are the confounding variables (or confounds) which also have a direct effect on the target variable (i. e. , the diagnosis for a CAD system). For instance, gender is a confounding variable for AD diagnosis since almost two-thirds of the individuals diagnosed with AD are women  [3]. We also define the concept of artificial confound, which is a nuisance variable that should theoretically not have an effect on the target variable, but which in practice has one because it has been sampled non uniformly. For instance, the database should not theoretically have an impact on AD diagnosis, but since the ratio of AD vs healthy subjects can be very different according to the database, an artificial link is created.
Note that such artificial confounds are as far as possible avoided in prospective study by collecting data stratified into groups that have the same distribution for the confounds. Nonetheless, it may frequently occur when merging several databases that may have heterogeneous recruitment schemes in terms of age distribution, gender ratio, or pathology prevalence. Particular attention should be paid to both natural and artificial confounds since they can introduce a positive bias into the performance of a classifier and can lead to misinterpret its behavior. Several papers already addressed how to handle confounds such as age and gender in neuroimaging study  [4, 5, 6]. Here, we investigate more specifically how to account for data coming from multiple databases and the interplay that occurs with other confounds such as age and gender.
Data simulation and adjustment procedure For simplicity, we consider a single feature x that is influenced by both age and database effect. We denote by x a the age of x (-1 ≤ x a ≤ 1). x is assumed to come from two databases (DB1 and DB2) that are modeled by two binary variables x c1 and x c2 (x j ci = 1 if subject j belongs to DBi, 0 otherwise). The generative model of feature x is expressed as x = f (x a ) + x c1 -x c2 + , where ∼ N (0, 0. 01 2 ). The terms x ci represent a bias in the measure and f (x a ) represents the influence of age. We consider two hypotheses for modeling the effect of age: a linear function f (x a ) = -x a , and a nonlinear one f (x a ) = 4 -(x a + 1) 2. We also investigate two scenarios concerning the distribution of ages within each database: either Similar Database (SD) age distribution (x a is drawn for both databases and each subject following a uniform distribution U(-1,1)) or Dissimilar Database (DD) age distribution (x a is drawn following U(-1,1) for the first database DB1 and following U(-0.
2,0. 2) for the second one DB2). In order to highlight only the potential biases induced by model misspecification without being impacted by estimation inaccuracies in the regression parameters, we consider a large number of subjects (10 6 for each database). These experiments aim at simulating a given population (e. g. , healthy subjects) acquired in two different studies and recruited according to different age sampling schemes. Under these conditions, the distributions of the observational measures from each database are different. The adjustment procedure should ideally make the two distributions similar, so that all data can be merged to efficiently learn a CAD system. We evaluate here the most commonly used way to suppress the effect of nuisance variables, namely to regress them out from the features of interest through a multilinear model.
The confound effects are modeled using the generalized linear model with a design matrix composed of three columns: the first one corresponds to the age, and the two others encode the database belonging. The parameter vector β = [β a , β 1 , β 2 ] T is estimated with a least square fit using half of the data. Then, the effects of the confounds are removed for the rest of the data. The adjusted value denoted x c is given by: x c = x -β a x a -β 1 x c1 -β 2 x c2. The distribution of x c is plotted in Fig. 1  for each database under four different conditions.
Results In the case of linear influence of age, the fitted model corresponds exactly to the data generating process: the nuisance variable effect can perfectly be regressed out. Consequently, the distributions of the adjusted data x c of the two databases are identical and reflect the true population variability (i. e. , x c ∼ N (0, 0. 01 2 )) (Fig. 1(a) ). The same behavior is observed for both SD and DD scenarios. When considering the nonlinear influence of age, the fitted model is misspecified and the effect of age cannot be regressed out properly. For the SD case (Fig. 1(b )), the distributions of x c for each database are no more reflecting the true population variability (the variability is higher), but the adjusted data of the two databases have still similar distributions. Consequently, although the age effect is not regressed out properly, the database effect is successfully corrected.
This conclusion is no more true for the DD case (Fig. 1(c )), which leads to different distributions of the adjusted data for each database. For the SD case, since the two databases share the same distribution of ages, the distribution of the non-adjusted features x of DB1 is just a translated version of the distribution of DB2. This translation (i. e. , the database effect) can be perfectly captured by the regression model even if the shape of the distributions (i. e. , the age effect) is not modeled properly. For the DD case (Fig. 1(c )), the distribution of ages varies across the databases: the distribution of the non-adjusted features x of DB1 is no more just a translated version of the distribution of DB2. Consequently, the effects of database and age cannot be properly disentangled to explain the shape variation of the distributions.
It may be surprising to notice on Fig. 1(c ) that the distribution of DB2 seems to reflect the true population variability (see Fig. 1(a) ). Indeed, the nonlinear function that models the effect of age is almost linear on the age interval [-0. 2, 0. 2], thus explaining why it has been well regressed out for DB2. Note finally that, when the effect of age cannot be modeled properly, the worst case appears when the learned model is applied on a population whose distribution of ages significantly differs from the distribution of the training population. In Fig. 1(d) , we use the model learned in the last experiment (Fig. 1(c ): nonlinear model of age, DD case) and we apply it to a population generated with the same model except that x a is now drawn following U(-0. 2,0. 2) for DB1 and following U(-1,1) for DB2 (distributions have been switched between databases for the testing phase).
AD VS CONTROLS DISCRIMINATION We focus here on a CAD system learned on multiple databases that discriminates healthy from AD subjects based on volumetric characteristics derived from structural MRI. Three public databases are considered: OASIS  [7, 8] , ADNI  [9]  and IXI (http://brain-development. org/ixi-dataset/). The IXI and ADNI datasets have been split into two parts, one with 1. 5T MRI, and the other one with 3T MRI. We extracted 116 volumetric characteristics from structural MRI using Freesurfer image analysis suite (http://surfer. nmr. mgh. harvard. edu/). As it can be observed in Tab1, age distribution, gender ratio and pathology prevalence vary across databases. The objective is to design a CAD system that does not exploit any prior knowledge about age or gender (natural confounds) in order to evaluate the potential for discrimination related to brain morphology.
We would like not to account for global shape difference of the head, which induces unwilling variability. To this end, the Estimated Total Intracranial Volume (ETIV) will be considered as a nuisance variable. we also want the CAD system not to be biased by any database effect (artificial confound). To achieve these specifications, we compare three strategies to adjust the data from confounds: • S1: no data adjustment (baseline method), • S2: effects of database, age, gender and ETIV are regressed out from each feature independently using the generalized linear model as described in Sec. 2. 1, • S3: data are adjusted using S2 and then each feature is linearly transformed so that its distribution for each database has similar median and Median Absolute Deviation (MAD). We propose the S3 strategy in order to compensate for potential residual variation in position and shape between the distributions of adjusted data (see experiments on simulated data, Fig.
Confound predictability In this section, only healthy subjects are considered. They are split into three datasets: 40% for computing the adjustment diag-std 0. 89 ± 0. 02 / 0. 89 ± 0. 03 0. 89 ± 0. 02 / 0. 88 ± 0. 03 0. 90 ± 0. 02 / 0. 87 ± 0. 02 diag-biased 0. 71 ± 0. 05 / 0. 69 ± 0. 05 0. 84 ± 0. 04 / 0. 83 ± 0. 04 0. 84 ± 0. 04 / 0. 84 ± 0. 05 Table  2. Prediction of the confounds and of the diagnosis for the three strategies. Each result is given as mean FUV or BAC ± standard deviation. The first score is obtained with a linear method (LSVM or LASSO) and the second with a nonlinear one (GBDT or RF). parameters (regression set), 40% for the learning step relative to the confound prediction (learning set), and 20% for testing (testing set). For the learning step, categorical confounds (gender and database) are predicted with a linear (linear support vector machine, LSVM) and a nonlinear (gradient boosted decision trees, GBDT) classifiers while accounting for imbalanced data.
Continuous data (ETIV and age) are predicted with a linear (LASSO method) and nonlinear (random forests, RF) regressors. Cross-validation is used to estimate the hyperparameters of the different learning methods. The testing set is finally used to assess the accuracy of the prediction, namely the balanced accuracy (BAC) for classification and the fraction of unexplained variance (FUV) for regression. To improve the reliability of the assessment, the whole procedure is repeated 10 times by splitting randomly the controls into the three datasets. Results are reported in the four first rows of Tab. 2. A good adjustment strategy should lead to a low BAC and to a high FUV. Without data adjustment (S1), all the confounds can be well predicted using both linear and nonlinear methods (except the gender which can only be predicted with the nonlinear method).
Strategies S2 and S3 are relatively efficient because it becomes much more difficult to predict the confounds. Indeed, except for database prediction with S2, linear approaches do not allow anymore to predict confounding variables properly: the classification (resp. regression) performance is similar to a random classifier (resp. a regression method that always predicts the mean). The failure of S2 to remove the database effect is related to the phenomenon described on simulated data (Sec. 2. 2, Fig. 1(c-d )). We can observe that the proposed S3 strategy can successfully overcome this limitation. However, in all cases except for the ETIV, nonlinear approaches are still able to predict confounds with above-chance accuracy, thus pointing out the need for further more sophisticated strategies to adjust data.
AD vs controls CAD system AD and healthy subjects are split into three datasets as follows: 40% of the controls in the regression set (only controls are used for computing the regression parameters to adjust data  [6] ), 40% of the controls and of 80% of the AD subjects in the learning step, and the rest of the data in the testing set. The same classifiers as before (LSVM and GBDT) are trained from the learning set. As previously, mean BAC is computed over 10 realizations. Results are presented in the second last row (diag-std) of Tab. 2. Surprisingly, similar results are obtained whatever the data adjustment strategy and the classifier used. Our intuition is that there is a positive bias in the results obtained without data adjustment (S1). Since some confounds are strongly correlated with the diagnosis and can be predicted with above chance accuracy, this may help to improve diagnosis accuracy.
Considering only age, gender, ETIV and database enables us to predict diagnosis with a BAC of 56% ± 4 (LSVM) and 63% ± 2 (GBDT). Classifiers learned with S2 and S3 strategies are expected to capture more relevant information related to brain morphology only, since confounds are less well predicted. Age seems to play a prominent role in the positive bias induced by S1. S1 tends classify old subject as AD since AD subjects are on average significantly older than controls. To fault this approach, we build a testing set composed of "young" AD subjects and "old" controls. Results are given in the last row (diag-biased) of Tab. 2. The performance obtained with the S1 strategy is largely degraded, thus confirming that the information of age (and probably also of the other confounds) is indirectly exploited to drive the decision process.
CONCLUSION In this paper, we demonstrate that database effect cannot be properly regressed out if the effect of another confound, whose distribution varies across databases, is not properly modeled. We propose a simple strategy that compensates for the residual variation in position and shape that can appear between the distributions of data adjusted with the generalized linear model. The benefit of this strategy has been highlighted in the context of a CAD system discriminating AD vs healthy subjects. However, the fact that confounds can still be predicted from adjusted data suggests that there is still some room for improvement in the adjustment procedure. The risk of processing data corrupted by several confounding variables is that the adjusted data may still permit the prediction of confounds with above chance accuracy.
Treatment of colonies was done on colonies grown from single cells as above, and then treating for 24 h. Cell transfections were carried out with Transit-LT1 (MirusBio) or with Lipofectamine RNAi-MAX (Thermo). Antibodies, immunofluorescence and stains. See Supplementary Table  2  for catalogue numbers, dilutions and validations. For the SREBP2 antibodies, independent lots were used with consistent results. Immunofluorescence was performed as in ref. 56  with minor modifications: 10 min 1. 5 mg ml -1 glycine in PBS before permeabilization to reduce background, and blocking in 1-2% BSA. Images were acquired sequentially with a Zeiss LSN700 or a Leica SP5 confocal microscope equipped with a charge-coupled device camera using ZEN 2 or Leica LAS AF software. Typical acquisition settings for IF were as follows: image size 1,024 × 1,024 pixels; acquisition mode xyz; pixel size 0.
Primer sequences are provided in Supplementary Table  1. Microarray probe synthesis, hybridization and detection were performed at CMB Trieste on HumanHT-12 v4 Expression BeadChips with an Illumina Hiscan system. Data analyses were performed in R (version 3. 0. 2) using Bioconductor libraries (BioC 2. 13) and R statistical packages. Probe level signals were converted to expression values using the Robust Multi-array Average procedure RMA  58  of the Bioconductor Affymetrix package. Differentially expressed genes were identified using the Significance Analysis of Microarray (SAM) algorithm coded in the same R package  59. In SAM, we estimated the percentage of false-positive predictions (that is, false discovery rate, FDR) with 100 permutations. Genes activated or inhibited upon YM treatment and used for gene list enrichment analysis were filtered based on P < 0.
Quantification of different phospholipids in positive ion mode was performed using a Synergi 4μ Hydro-RP (50 mm × 2. 0 mm, 4 μ m; Phenomenex) and in negative ion mode using a Cyano-phase LUNA column (50 mm × 4. 6 mm, 5 μ m; Phenomenex). The mobile phase for positive ion mode was 0. 1% formic acid in MeOH and 5 mM ammonium acetate pH 7 in MeOH for negative ion mode, both with a flow rate of 500 μ l min -1. MultiQuant software version 3. 0. 2 was used for data analysis and peak review of chromatograms. Semi-quantitative evaluation of different phospholipids was performed based on external standards, then the data were normalized on protein content assessed by the BCA (bicinchoninic acid assay) method. For quantification of the different diacylglycerol and triacylglycerol species the LC-MS/MS analysis was performed on an API-4000 triple quadrupole mass spectrometer (AB Sciex) coupled with an HPLC system (Agilent) and a CTC PAL HTS autosampler (PAL System).
Methanolic extracts were dried under nitrogen and resuspended in 100 µ l of 65% acetonitrile/30% isopropanol/5% water. Samples were then analysed with a 10 min run in positive ion mode with 92 MRM transitions. Quantification of different diacylglycerols and triacylglycerols was performed using an XBridge C-18 column (100 mm × 2. 1 mm, 3. 5 μ m; Waters). Column temperature was set at 55 °C. The mobile phases were as follows-phase A: 40% acetonitrile, 0. 1% formic acid and 10 mM ammonium acetate in water; phase B: isopropanol 90%, acetonitrile 10%, 0. 1% formic acid and 10 mM ammonium acetate; T 0 : 55%A; T 2min : 55%A; T 4min : 3%A; T 7min : 3%A; T 7. 1min : 55%A; T 10min : 55%A; with a flow rate of 260 μ l min -1. MultiQuant software version 3. 0. 2 was used for data analysis and peak review of chromatograms.
Semi-quantitative evaluation of different diacylglycerols and triacylglycerols was performed based on external standards, then data were normalized on protein content assessed by the BCA method. For isotopologue analysis, cells were exposed to 2. 5 mM [ 13 C 2 ]acetate (Sigma 282014) for 24 h. After removing medium, cells were washed in ice-cold PBS. Lipid extraction was performed by adding 500 μ l of MeOH/ACN (1:1 vol/vol) and then 2. 5 ml of chloroform-MeOH (1:1 vol/vol). Total fatty acids were obtained by acid hydrolysis by adding 1. 25 ml of HCl 1 M and 1. 25 ml of MeOH. After leaving samples for 1 h under agitation at 210 r. p. m. , 2. 5 ml of chloroform-water (1:1 vol/ vol) were added to the mixture and the lower organic phase was collected, split, transferred into tubes and dried under nitrogen flow.
Trapping was not possible on polyacrylamide hydrogels because of the excessive distance between the lens and cells in this set-up. The applied force F was deduced from the bead displacement relative to the trap centre, Δ x, after calibration of the trap stiffness k trap using F = k trap Δ x with k trap = 280 pN μ m -1. The output power of the infra-red laser at the objective aperture was 150 mW. Stage displacement was performed using a nanopositioning piezo-stage (Nanobio 200, Mad City Labs) controlled by NanoRoute3D software (Mad City Labs). The stage displacement consisted of five consecutive 0. 5 μ m steps with a 10 s pause between each step to allow viscoelastic relaxation of the bead position towards the trap centre. The total duration of optical trapping was limited to 1 min for a given cell to ensure cell viability.
The total intensities of the mCherry Golgi marker (I r ) and GFP-PKD-Kd (I g ) were measured as well as the mean intensity of the background for each channel ( ⟨ ⟩ I r back for the mCherry Golgi marker; ⟨ ⟩ I g back for GFP-PKD-KD) and the area of the Golgi apparatus, A Golgi. The total intensity of the background in the Golgi apparatus region for each channel was then estimated by multiplying the mean background intensity by the Golgi area: Statistics and reproducibility. All data are based on two or three independent experiments with independent biological replicates, except for metabolomics and microarrays, which were based on independent biological replicates collected in a single experiment.