dataesr/scientific-paragraphs-categorization huggingface dataset:
Immunohistochemistry. Tumors were fixed in 10% Paraformaldehyde (PFA) immediately after excision from the CAM. PFA-fixed and paraffin-embedded tumors were cut into 3. 5 μm-thick sections. Hematoxylin and eosin (H&E) staining was performed according to standard protocols. Tissue sections were rehydrated and immunohistochemical staining was performed on a BOND-MAX immunohistochemistry robot (Leica Biosystems) with BOND polymer refine detection solution for DAB, using anti-GATA3, cleaved caspase 3, phospho-Akt or phospho-S6 (Supplementary Data 3) primary antibodies as substrate. Estrogen receptor immunostain was performed as described previously  84. Photomicrographs of the tumors were acquired using an Olympus BX46 microscope. All stained sections were evaluated blindly by two independent pathologists.
RNA sequencing and pathway analysis. Biological duplicates were generated for all the samples analyzed. Total RNA was extracted from cells at 75% confluence using TRIZOL (Supplementary Data 3) according to the manufacturer's guidelines. RNA samples were treated with Turbo DNase (AM 1907, Thermo Fisher Scientific) and quantified using a Qubit Fluorometer (Life Technologies). RNA integrity was measured using the Agilent Bioanalyzer 2100 (Agilent Technologies). Library generation was performed using the TruSeq Stranded mRNA protocol (Illumina). Paired-end RNA sequencing was performed on the Illumina NovaSeq 6000 platform using the 2 × 100bp protocol according to the manufacturer's guidelines. Reads were aligned to the GRCh37 human reference genome using STAR 2. 7. 1  85  , and transcript quantification was performed using RSEM 1.
3. 2  86. Genes without at least ten assigned reads in at least two samples were discarded. Counts were normalized using the median of ratios method from the DESeq2 package  87  in R version 3. 6. 1 (https://www. R-project. org/). Differential expression analysis was performed using the DESeq2 Wald test. Gene set enrichment analysis was performed using the fgsea R package  88  and the Hallmark gene set from the Molecular Signatures Database 89 , using the ranked t statistics from the DESeq2 Wald test. Pathways with false discovery rate (FDR) < 0. 05 were considered to be significant. Results were visualized using ggplot2  90. Analysis of The Cancer Genome Atlas (TCGA) data. ER-positive breast cancer mutation annotation file for variant calling pipeline mutect2, FPKM gene expression data, and raw read counts of the TCGA BRCA project were downloaded using TCGAbiolinks 91 package.
Tumor samples were classified as GATA3-mutant (n = 122) and GATA3-wild type (n = 596) according to the GATA3 mutation status. Samples with GATA3 mRNA expression in the bottom and top quartile were classified as GATA3-low (n = 200) and GATA3-high (n = 204), respectively. edgeR package  92  was used for differential expression analysis and the genes with low expression (<1 log-counts per million in ≥30 samples) were filtered out. Normalization was performed using the "TMM" (weighted trimmed mean) method  93  and differential expression was assessed using the quasi-likelihood F-test. Gene set enrichment analysis of all analyzed genes ranked based on signed P value according to the direction of the log-fold change was performed using the fgsea package  88. Hallmark gene sets from Molecular Signatures Database 89 were used to identify significantly up-/down-regulated pathways.
Pathways with FDR < 0. 05 were considered significantly regulated. Analysis of mutually exclusive genetic alterations. ER-positive breast cancer mutational data for the GATA3, TP53, PIK3CA, and PTEN genes and copy number status for PTEN derived from the TCGA PanCancer Atlas 1 and the METABRIC dataset  4  were obtained using cBioportal  94. A total of 2379 samples were used for the analysis. Mutual exclusivity of somatic mutations in GATA3, TP53, PIK3CA, and PTEN and deep deletions for PTEN were calculated using one-sided Fisher's Exact and P < 0. 05 was considered statistically significant. Statistics and reproducibility. Statistical analyses were conducted using Prism software v8. 0 (GraphPad Software, La Jolla, CA, USA). For in vitro studies, statistical significance was determined by the two-tailed unpaired Student's t test.
Statistical analysis of differences in tumor volume among the groups was conducted using one-way ANOVA followed by individual comparisons using Games-Howell (equal variance not assumed). All data were analyzed in SPSS (Statistical Product and Service Solutions) version 16. 0. P values were rounded to three decimal places, with the exception that raw P-values less than 0. 001 were stated as P < 0. 001. All tests were two-sided. P < 0. 05 was considered to be statistically significant. Power calculation. For the in vivo experiments, the samples size was calculated using a G*Power calculation  95. For zebrafish experiments, assuming a difference of 20% in tumorigenic potential and type I error of 5%, 85 samples in each group would ensure >80% power to detect statistical differences between experimental groups using Fisher's exact test.
Acknowledgements We would like to thank  Stefano Medagli  who helped with the generation of the figures. We would like to thank  Dr. Rachael Natrajan  for sharing breast cancer cell lines. RNAsequencing analysis was performed at sciCORE scientific computing center at the  University of Basel. This work was supported by the  Swiss Centre for Applied Human Toxicology ; the  Swiss Cancer Research foundation  [ KFS-4543-08-2018  to C. K. Y. N. and  KFS-4988-02-2020-R  to S. P. ]; The  Professor Dr Max Cloëtta Foundation  (Medical research positions to S. P. ), the  Swiss National Science Foundation  [ 31003A_169352  to M. K. -d. J. ]; the  Dutch Cancer Society  [ KWF 2015_7599  to M. K. -d. J. ];  Novartis  [ 17B076  to M. K. -d. J. ]; and the  European Research Council  [Synergy Grant  609883  to N.
The summer solstice insolation and the caloric season were computed using the AnalySeries software  (Paillard et al. , 1996). The ISI for a threshold τ (noted as ISI above τ , or ISI(τ ) in this article) was defined by  Huybers (2006)  as the sum of the insolation on days exceeding this threshold. ISI(τ ) = i β i (W i • 86 400) Here, W i is the mean insolation of day i in W m -2 and β i equals one if W i > τ and zero otherwise. To compute ISI(τ ), one first needs to compute the daily insolation on day i, and then sum over the year for the days exceeding the threshold. Here, we developed a Python code based on the MATLAB code provided by  Huybers (2006). Unlike the insolation files provided by  Huybers (2006) , which used the solution from  Berger and Loutre (1991) , we use the orbital parameter value of the  Laskar et al.
Similar conceptual modelling on a wider temporal scope over the Cenozoic could help better explain the connections between astronomical forcing, the carbon cycle, ice sheets and the climate. This would help us imagine what the Anthropocene might be like. Code and data availability. The model code, insolation input files, spectral analysis and code needed to reproduce the figures are available for download: https://doi. org/10. 5281/zenodo. 6045532 (Leloup, 2022). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/cp-18-547-2022-supplement. Author contributions. GL and DP designed the study. GL performed the simulations, and also wrote the manuscript under the supervision of DP.
We introduce in this survey the major concepts, models, and algorithms proposed so far to infer causal relations from observational time series, a task usually referred to as causal discovery in time series. To do so, after a description of the underlying concepts and modelling assumptions, we present different methods according to the family of approaches they belong to: Granger causality, constraint-based approaches, noise-based approaches, score-based approaches, logic-based approaches, topology-based approaches, and difference-based approaches. We then evaluate several representative methods to illustrate the behaviour of different families of approaches. This illustration is conducted on both artificial and real datasets, with different characteristics. The main conclusions one can draw from this survey is that causal discovery in times series is an active research field in which new methods (in every family of approaches) are regularly proposed, and that no family or method stands out in all situations.
The main characteristics of representative methods of the above families are summarized in Section 7. We then turn in Section 8 to other approaches (logic-based, topology-based, and difference-based approaches) which differ from the previous ones on several aspects. The behaviour of representative methods of different families 3  is then illustrated in Section 9. This illustration, conducting on both artificial and real datasets, with different characteristics, shows that different families, and within a given family different methods, are adapted to different situations so that there isn't a single family or method that outperforms all the others in all situations. Lastly, Section 10 concludes the survey.
Samples are then divided into two subgroups, corresponding to normal samples and samples with local distribution shifts. The cost function finally considered corresponds to a weighted quadratic mean of these two subgroups. Another drawback of Granger causality is related to its underlying linear assumption as associations are highly likely to be non-linear on real datasets. To overcome this, several extensions have been proposed. For example,  Hiemstra and Jones (1994)  state that time series X p does not Granger-cause times series X q if for given values of a ≥ 1, b ≥ 1, m ≥ 1 and ε > 0 one has: Pr ∥X q t:m -X q s:m ∥ ∞ < ε | ∥X q t-a:a -X q s-a:a ∥ ∞ < ε, ∥X p t-b:b -X p s-b:b ∥ ∞ < ε = Pr (∥X q t:m -X q s:m ∥ ∞ < ε | ∥X q t-a:a -X q s-a:a ∥ ∞ < ε). with X t:m = (X t , • • • , X t+m-1 ); the infinite norm ∥.
FAST CAUSAL INFERENCE ALGORITHM FOR NON-TEMPORAL DATA FCI starts, as the PC algorithm, by initializing the skeleton with all possible edges and by removing the edges that are either independent or conditionally independent, first when conditioning with Sepsets and then with Possible-Dsep sets. Ten orientation rules, described by, e. g. ,  Zhang (2008) , are applied recursively 11. As in PC, all colliders are first identified by Rule 0. One then orients as many of the remaining undirected edges as possible, by repeating Rules 1 to 4. FCI-Rule 0 (Origin of causality) For each unshielded triple X p * -•X r •- * X q , if X r / ∈ Sepset(p, q), then orient the unshielded triple as a collider: X p * → X r ← * X q. FCI-Rule 1 In an unshielded triple X p * → X r •- * X q , if X r ∈ Sepset(p, q) then orient the unshielded triple as X p * → X r * → X q.
Experiments, conducted on data generated from random DPNs and the Yeast dataset, show that the CV-based scoring method leads to models that generalize better than those based on BIC and BDe for a wide range of sample sizes, in particular the range of sizes one usually encounters in practice. One can also note another extension of SEM for fMRI data, referred to as extended unified SEM, presented by  Gates and Molenaar (2012)  and based on a bilinear system to describe brain regions of interest. More recently,  Pamfil et al. (2020)  have proposed a method, named DYNOTEARS, which simultaneously estimates instantaneous and time-lagged relationships between time series through d × d adjacency matrices W , A 1 , • • • A τ max that respectively represent the importance of the relation between two time series with a time lag 0 (instantaneous relations, W ), 1 (A 1 ),.
Evaluated Methods From the Granger family (Section 3), we retain the pairwise and multivariate methods (referred to as PWGC and MVGC). The full model is compared to the restricted model using an F-test. We rely on our implementation of PWGC and use for MVGC the code available at http://www. sussex. ac. uk/sackler/mvgc/. In addition, we include in our comparison TCDF and rely for this method on the implementation available at https://github. com/M-Nauta/TCDF. For the hyper-parameters in this latter method, we used the values suggested by the authors: a kernel of size 4, a dilation coefficient equal to 4, 1 hidden layer, a learning rate of 0. 01, and 5000 epochs. From the constraint-based family (Section 4), we retain PCMCI using both partial correlation and mutual information to measure independence.
Both scores are available in the implementation provided at https://github. com/jakobrunge/tigramite. We also include oCSE, which we implemented. In all those methods, mutual information is estimated using the k-Nearest Neighbour method  (Runge, 2018)  and a permutation test is used to assess whether the mutual information scores are significantly different from 0 or not. Finally, we also consider tsFCI, with the implementation provided at https://sites. google. com/site/dorisentner/publications/tsfci, in which independence and conditional independence are tested respectively with tests of zero correlation and zero partial correlation. Among the noise-based approaches (Section 5), we retain VarLiNGAM and TiMINo, which are respectively available at https://github. com/cdt15/lingam and http://web.
math. ku. dk/~peters/code. html. For VarLiNGAM, the regularization parameter in the adaptive Lasso is selected using BIC, and no statistical test is performed as we directly use the value of the statistics. TiMINo uses a test based on cross-correlation that can be derived from  Brockwell and Davis (1986, Thm 11. 2. 3. ). We have retained the most recent score-based method, namely DYNOTEARS  (Pamfil et al. , 2020)  available at https://github. com/quantumblacklabs/causalnex, the hyperparameters of which are set to their recommended values (λ W = λ A = 0. 05 and α W = α A = 0. 01).
Fork V-structure Mediator Diamond 7TS2H X 1 X 2 X 3 X 1 X 2 X 3 X 1 X 2 X 3 X 1 X 2 X 3 X 4 X 1 X 2 X 3 X 4 X 5 X 6 X 7 Table  4 : Structures corresponding to the artificial datasets. A → B means that A causes B and A ← → B represents the existence of a hidden common cause between A and B. For all the methods, when doing a statistical test, we use a significance level of 0. 05. Our implementations of PWGC and oCSE are available at https://github. com/ckassaad/causal_ discovery_for_time_series; furthermore, all methods can be used through a Python routine available at https://github. com/ckassaad/causal_discovery_for_time_series.
Datasets The artificial datasets, available at https://dataverse. harvard. edu/dataverse/basic_causal_ structures_additive_noise, correspond to five basic causal structures presented in Table  4 : fork, v-structure, mediator, diamond, as well as to a nine nodes structure introduced by  Spirtes et al. (2001)  and referred to as 7ts2h. In 7ts2h, seven nodes correspond to observational time series and two to hidden common causes, represented by double arrows. The underlying generating process is based on nonlinear functions between time series and linear relations for self causation, as defined below: ∀q, X q 0 = 0; ∀t > 0, X q t = a qq t-1 X q t-1 + ∑ (p,γ) X p t-γ ∈Par(X q t ) a pq t-γ f (X p t-γ ) + 0. 1ξ q t , (3) where γ ≥ 0, a jq t are random coefficients chosen uniformly in U ([-1; -0.
1] ∪ [0. 1; 1]) for all 1 ≤ j ≤ d, ξ q t ∼ N (0, √ 15 ) and f is a non linear function chosen at random uniformly between absolute value, tanh, sine and cosine. To evaluate the performance of the inference with respect to the length of the time series, we make the length vary from 125 to 4000 time points. For each structure and each length, we generate 10 different datasets over which the performance of each method is averaged. The real-world benchmark we have retained here is FMRI (Functional Magnetic Resonance Imaging) which contains BOLD (Blood-oxygen-level dependent) datasets for 28 different underlying brain networks  (Smith et al. , 2011)   16. Each dataset contains the neural activity, based on the change of blood flow, of at most 50 different regions. Each region corresponds to a time series which contains between 50 and 5000 time points.
Numerical Results Here we assess how the retained methods behave on the artificial datasets corresponding to basic causal structures and on the FMRI benchmark. Figure  16 : Performance of all methods on the 5 artificial datasets for the inference of the summary causal graph with two settings: excluding self causes (left column) and focusing only on self causes (right column). The results are computed over 10 runs for which we report the mean (± the standard deviation) of the F1 score taking into account the orientation of the edges. The results are computed for various lengths of the time series: 125, 250, 500, 1000, 2000 and 4000 time points (a log-scale is used for the x-axis).
ARTIFICIAL DATASETS We are interested here in assessing the capacity of each method to identify correct summary and window causal graphs. The overall results obtained by the different methods are given in Figure  15 , for the skeleton of the summary causal graph (right column), the summary causal graph (middle column) and the window causal graph (right column). Note that the orientation in the window causal graph relies (except for instantaneous relations) on temporal information so that the accuracy on the skeleton of the window causal graph and on the window causal graph itself are almost identical. For this reason, we solely present here the results on the window causal graph. The first four datasets (fork, v-structure, mediator and diamond) are causally sufficient whereas the last one, 7ts2h, is not as it contains two hidden variables.
Only two methods, tsFCI and TCDF, are able to deal with this latter dataset. They can infer both a summary and a window causal graph. Among all methods, only five, namely TCDF, PCMCI (mi and pc), tsFCI and VarLiNGAM, aim at building both a summary and a window causal graph. The remaining 5 methods, PWGC, MVGC, oCSE, TiMINo and DYNOTEARS, directly infer a summary causal graph. As one can note from Figure  15 , for all methods and all datasets, the performance on the (skeleton of the) summary causal graph slightly increases with the number of time points considered and reaches a plateau after roughly 250 time points have been considered. We attribute this to the fact that the summary causal graph is a relatively simple structure in this case that can be inferred without much information. The increase with the number of time points considered is more important for the window causal graph which requires a certain amount of information for the inference to be correct.
For all the causally sufficient structures, the performance obtained on the summary causal graph (for both its skeleton and graph itself) is high (in between 0. 8 and 1 in terms of F1 score) and comparable for all methods, except for VarLiNGAM, TCDF and DYNOTEARS which do not perform as well as the other methods. This is not really surprising for VarLiNGAM and DYNOTEARS as both methods are based on a linear assumption whereas the generation process of the datasets considered is based on non-linear relations between different time series. Surprisingly, both the Granger pairwise method and its multivariate extension have good performance, whereas they do not aim at inferring true causality by definition. The best performing method overall on the summary graph is oCSE, but the difference with other methods is not significant (with respect to a two sided t-test with level 0.
01). The results obtained on the window causal graph are lower than the ones obtained on the summary causal graph and the difference between the methods are more marked. This is not really surprising as the former graph is more complex than the latter one. The best performing method here is PCMCI, and in particular the version based on mutual information (mi). Note however that this version is more computationally demanding  (Runge et al. , 2019)  than the one based on partial correlation (pc). Lastly, for 7ts2h, a dataset with hidden confounders, the applicable methods have difficulties in identifying both the summary and the window causal graphs. The problem is definitely more complex and no satisfying solution has been proposed yet, even though TiMINo reaches 0. 6 and 0. 5 in terms of F1 score on the summary causal graph and the window causal graph respectively.
Self causes Among the methods we have reviewed, some (PWGC, MVGC, TiMINo) assume that a time series always causes itself, which seems a reasonable assumption for time series, whereas others (DYNOTEARS, oCSE, PCMCI, TCDF, tsFCI and VarLiNGAM) do not make such an assumption and try to infer self causes as any other causes, which is more difficult a priori. As all the artificial datasets we have considered contain self-causes (which represent roughly 50% of the causal relations on each dataset), methods of the first type have an advantage over the ones of the second type. To further compare all methods, we have examined the performance of each method in two cases: one in which self causes are excluded and one in which only self causes are considered. The results obtained are displayed in Figure  16.
As one can note, the performance of all methods when excluding self causes are lower than when considering all causes and the differences between the methods are more important. oCSE outperforms, with a larger margin than before, all other methods on the causally sufficient structures. In addition, VarLiNGAM obtains poor results which makes sense as the relations between different time series in the datasets retained are not linear. On self causes only, all methods but DYNOTEARS and TCDF perform very well and make no mistake when the number of time points is sufficient. As mentioned before, an important difference between self causes and causes between different time series is that the former are linear whereas the latter are not (and are thus more difficult to identify). This explains the difference in performance between the left and right columns of Figure  16  as well as the fact that VarLiNGAM, which relies on a linear model, behaves well on self causes.
Limit cases We conclude this comparison by considering two limit cases, one in which the Markov equivalence class contains more than one graph and one in which the faithfulness assumption is no longer valid. For the first case, we generate a new fork structure in which all relations are instantaneous, so that one cannot differentiate between common and intermediate causes (by definition, the fork structure does not contain any collider). For the second case, we generate new mediator and diamond structures in which all relations are linear with coefficients set in such a way that different causal paths eliminate each other. This is obtained by setting, in Eq. (  3 ), a 13 = -a 12 a 23 for mediator, following  Zhalama et al. (2016) , and a 34 = -a 12 a 23 /a 13 for diamond. In each case, we exclude self causes and simulate 10 datasets each with 1000 time points.
As one can see, the results differ from the ones obtained before and methods that rely strongly on faithfulness and causal sufficiency (as PCMCI and oCSE) now perform poorly even though they were among the best methods before. Furthermore, all methods have difficulties in orienting edges as the -→ F1 score is systematically lower than the F1 score, the drop being particularly important for most methods on the unfaithful datasets. The best method overall for the -→ F1 score is TiMINo. For the F1 score, the best method on fork is tsFCI, followed by TiMINo, and VarLiNGAM on the two unfaithful datasets, followed by either TiMINo or tsFCI. The good behaviour of TiMiNo, and to a certain extent of TCDF which obtains consistent, good results on all datasets for both measures, can be explained by the fact that these methods are not restricted to the Markov equivalence class and do not rely on the faithfulness assumption.