dataesr/scientific-paragraphs-categorization huggingface dataset:
Cells expressing high level of immune markers (Cd163, Cd68, Clec4f, and Timd4) were found in hepatocyte datasets. These were excluded from downstream analysis. To evaluate the effects of liver dissociation to our scRNA-Seq datasets, AddModuleScore function was used to assess the module scores of stress response genes in hepatocytes. 17 ustering and trajectory inferring After quality control, 6,246 and 327 hepatocytes from 10X and Smart-seq2 pipeline left, respectively. For cells sequenced by 10X pipeline, we conducted re-normalization (SCTransform), RPCA-based integration (FindIntegrationAnchors and IntegrateData function), and subsequent clustering (FindClusters with res = 1. 5) analysis. The effect of total UMI and MT-gene content were regressed out when normalization. Four-thousand most variable genes were used for analysis.
Uniform manifold approximation and projection (UMAP) is used for visualization. Cellular trajectory was constructed using monocle3 (v1. 0. 0)  54, 79  R package. To ensure consistency between hepatocyte subpopulations and cellular trajectory, we conducted pseudo-temporal analysis in a semi-supervised manner that the principal component (PC) values generated from Seurat integrated dataset were used as the input of 'reduce_dimension' function in monocle3. To validate the cellular subpopulations and trajectory found in 10X dataset, Smart-seq2 data containing cells assessed in two batches was analyzed independently using the same pipeline descripted above.
Identification of hepatocytes subpopulations To increase the power, the 10X libraries were prepared pooling normal and DDC-diet hepatocytes together. To distinguish them, a public dataset (GSE125688)  40  and our Smart-seq2 dataset in which the normal and DDC-diet hepatocytes were assessed separately were used. The normal and DDC signature genes were identified as DEGs (|log 2 FC| > 1. 5, adjusted P value < 0. 05) between two conditions. Genes with high expression level in the opposite condition (rank of median expression level <= 500) were removed from signature. AddModuleScore function was used to score these two signatures of all cells in 10X datasets. By subtracting DDC signature score from normal signatures, we got N-score for each cell. Cells with N-score greater than 0 were regarded as normal hepatocytes.
5 in bulk RNA-Seq samples were retained. For the full list of RRGs, please refer to Table  S1. To evaluate whether Sox9 and RRGs were activated in APAP-induced liver injury, the processed Seurat R object was downloaded from a previously published study. 39 And the module scores of RRGs for individual hepatocytes were assessed by AddModuleScore function. scRNA-Seq data analysis of immune cells After normalization, 3,000 MVGs were selected. Principle component analysis was conducted based on the scaled expression level of MVGs. Robust PCs (PC1 to 15) were identified and used for further dimension reduction and neighbor detection. Clustering was conducted using FindClusters function with resolution set to 0. 5. Cells of different clusters were annotated according to expression of marker genes.
Pseudotime-based pathway activity analysis To unravel pathway dynamics along hepatocyte reprogramming, we assessed single-cell pathway activity by calculating normalized enrichment score (NES) on Reactome pathway (https://reactome. org) using GSVA R package (v1. 38. 2). 56  Log-scaled SCTransformnormalized UMI matrix was provided to gsva function. The limma R package (v3. 46. 0)  57  was used to perform pairwise comparisons of NES values between cell subpopulations. Pathways that showed significant changes (|log 2 FC| > 0. 1 and FDR adjusted P value < 0. 05).
ACKNOWLEDGMENTS We thank  Fuchou Tang  ( Peking University, China ) and  Yun Gao  ( Peking University, China ) for help with Smart-seq2 experiments. We thank  Yi Arial Zeng  ( CEMCS, CAS, China ),  Hongyan Wang  ( CEMCS, CAS, China ),  Jing Wang  ( Shanghai Institute of Immunology, China ),  Dangsheng Li  ( CEMCS, CAS, China ),  Ting Chen  ( National Institute of Biological Sciences, China ), and  Bing Gao  ( National Institutes of Health, USA ) for critical comments and suggestions. We also thank the core facilities at  CEMCS. This project was supported by the  National Key Research and Development Project  ( 2019YFA0801503 ), the  National Natural Science Foundation of China (NSFC)  ( 31930030 ), the  National Key Research and Development Project  ( 2019YFA0802001 ,  2021YFF1200902 ), the '' Strategic Priority Research Program '' of the  Chinese Academy of Sciences  (CAS,  XDA16020201 ), the  National Natural Science Foundation of China  ( 91942313 ,  92168202 ,  32221002 ,  T2122018 ),  Shanghai Science and Technology Committee  ( 22JC1403000 ),  Shanghai Municipal Science and Technology Major Project ,  CAS Youth Innovation Promotion Association  ( 2018307 ), and the  Shanghai Sailing Program  ( 22YF1458000 ).
Introduction Computer vision and natural language processing have immensely benefited from the emergence of deep learning neural networks (NNs) and the availability of huge datasets over the past 15 years. It has long been recognized that arbitrarily large and deep NNs represent functional models for non-linear regressors  [1]. But progresses in non-linear optimization due to a better derivation of the adjoint model and much richer datasets have allowed us to practically demonstrate the considerable potential of the approach  [2, 3]. However, climate sciences (or more generally sciences of the atmosphere, oceans, land surfaces, their respective constituents, and biosphere) are far more error-prone and far less idealized than computer vision and natural language processing, which slowed down transfers between disciplines.
The fabulous achievements of the methods nonetheless outweighed the hurdles, and the techniques finally percolated into the fields of geoscience. Yet, geosciences have specificities. Modelers rely on comprehensive, though imperfect, scale-depend, costly numerical models, such as the dicretisation of the primitive equations for the geofluids. Earth scientists acquire and exploit huge observation datasets, but which do not offer full coverage of the system, neither in space nor in time. This challenge led to the development of data assimilation (DA) techniques that aim to optimally merge information from the observations and the numerical models to obtain a more accurate representation of the physical fields and parameters of the models. Both machine learning (ML) to which deep learning belongs and DA are statistical estimation/inverse problems techniques which can offer predictions of the dynamical system under study and which share methodologies because they can both root in statistical and Bayesian principles.
The precision of the forecasts is driven by the number of observations, their instrumental errors, as well as the fidelity of the numerical models. It is impacted by the relevance and consistency of the employed DA technique  [7, 8]. It eventually depends on the accuracy of the sensitivity map established by the observation operator between the model variables and the observations, the so-called representation error  [9]. Data assimilation in geosciences is the counterpart to training in ML and vice versa; they are both parts of estimation theory or inverse problems and combine a numerical model of either physical or statistical origin and possibly huge datasets (the larger, the better)  [10]. There are known correspondences between them. For instance, the adjoint models  [11]  required by the gradients in variational DA draw from the branch of applied mathematics known as optimal control  [12]  and have been named backpropagation in deep learning  [13].
Deep learning revived the NN techniques of the 90's by proving their relevance, by the availability of very large training databases, by demonstrating that deeper, i. e. , multiple internal layers, and larger NNs can counter the curse of dimensionality of estimation theory, often met in ML and DA  [14]   [16] , Jax  [17] , numpy, and autograd  [18] , which are mainly used on top of python or entirely new programming languages such as Julia/Flux  [19]. In the meantime, over the past 20 years, progress has been slower in DA applied to the geosciences. Automatic differentiation was recognized as a critical challenge by this research community, especially since the advent and the remarkable success of the 4D-Var method and its implementation at the ECMWF, Météo-France, the UK MetOffice, Environment Canada, the Japan Meteorological office, and the Korean Meteorological Administration  [20].
As a result, deep learning techniques were welcomed into the geosciences. Among the first specialists to experiment with these techniques were the climatologists. They already have significant experience, if not advanced expertise in statistics and more traditional ML techniques. These techniques were applied to past events, reanalysis, and Coupled Model Intercomparison Project datasets, with decade-long forecast lead times, in contrast to the short-term geophysical forecasts where DA is deemed necessary. It was then proposed to apply advanced ML techniques to derive parameterizations of physical processes such as subgrid turbulent physics and convection, which are crucial for both accurate climate and shorter-term NWP models  [30] [31] [32] [33] [34]. Over the past few years, in the realm of NWP and DA applied to the geosciences, deep learning was also seriously considered, with the research community shifting gears under ML influence.
e. , with millions of parameters non-linearly related to the observations and based on combined DA and deep learning. Moreover, this endeavor blurred the lines between NWP DA and ML experts in the geo-and climate sciences, making the community larger and its impact deeper with faster communication streams. Even ML/deep learning specialists from the industry attracted by the challenges of NWP and climate modeling brilliantly joined the collective efforts (e. g. , Google, Nvidia, Huawei, and Microsoft  [35] [36] [37] [38] [39] [40] ). Despite these references and the amazing accurate surrogate skills of their deep learning weather models, they still crucially rely on re-analysis data of the ECMWF patiently produced from complex and large physical models, huge datasets of meteorological observations, and increasingly sophisticated DA methods to combine them  [41].
Deep learning techniques hence offer more systematic non-linear regression tools provided that the datasets are large enough. More generally, it could help us with model error correction of forecasting systems  [45] [46] [47]. Deep learning could help us build surrogate models of part or the whole dynamical systems either from pure observations or from data of high resolution simulations. In the first case, it bypasses the requirement for physical principles and modeling. In the second case, it can offer an acceleration to forecasting, or a way to simplify automatic differentiation of the represented physical model since its NN counterpart can easily be differentiated  [48] , or a fast means to generate ensembles, etc. It can also be a very appealing versatile tool to help with traditional DA methods, for instance in the tuning of statistical parameters needed to regularize covariances in ensemble DA methods, or help carry out non-linear adaptive transformation such as DA methods based on Gaussian anamorphosis.
It remains to be investigated whether the method can be applied to larger sets of parameters, or even deep learning NNs. This approach would likely call for model reduction, explicitly or implicitly, which would be implemented by deep learning. Figure  2  illustrates learning the dynamics of the global atmosphere from the ERA5 dataset  [41]. We used the setup, data, and ECMWF Integrated Forecasting System model runs provided by the WeatherBench platform  [85]. A residual deep NN model of about 1. 5 × 10 6 parameters is learned from 38 years of meteorology at coarse 5. 625 • × 5. 625 • spatial resolution. This surrogate model is independently tested on the years 2017-2018 over many forecast runs, on the 500 hPa geopotential fields using the root mean square error (RMSE) and the auto-correlation (ACC), as functions of the forecast lead time up to 14 days.
Provided the surrogate model is a good enough representation of the truth, this variability is not expected to depend much on the surrogate model solution, since it is mainly driven by the dynamics. Surprisingly, this uncertainty (or risk of the forecast scores) is usually not provided in the ML surrogate modeling literature, where the focus is only on deterministic forecasts without their uncertainty. In particular, this variability is not the uncertainty attached to the learning process from the dataset. However, it shows how easy it is to pick initial conditions with flattering forecast scores. Among the fundamental open problems in combining DA with ML is the ability of the ML+DA schemes to assimilate fresh observations. Upon receiving them, one would update our knowledge of not only the state and possibly a few physical model parameters (as in classical DA) but would also update any neural network used within the sequential algorithm, whether it is related to correcting the evolution model, the observation model, or any block of the assimilation scheme itself.
As recently proven by the industry artificial intelligence research teams and others, the potential of surrogate modeling for short-term meteorological forecasting is extraordinary but critically relies on the modelers' expertise through the reanalysis products. That is why a long-term challenge in the geoscience domain is to build surrogate models from large database of observations only, without relying on physical and numerical expertise which, for now, remains critical. Moreover, the potential of such dynamical surrogate modeling for longer time-scales, i. e. , for climate modeling, is far from established and the topic is nascent (e. g. ,  [114] ). Accounting for slow-evolving trends and limited time reanalysis data from a climate standpoint as opposed to numerical weather prediction, is likely to make the surrogate and model error correction challenges quite difficult.
Acknowledgments The author would like to acknowledge the many useful suggestions of two reviewers on the manuscript. The author would also like to express his gratitude to the many colleagues with whom he has collaborated on the topic or who offered their insight:  A. Farchi ,  A. Carrassi ,  J. Brajard ,  L. Bertino ,  M. Bonavita ,  P. Laloyaux ,  M. Chrust ,  T. Janjić ,  T. S. Finn ,  C. Durand ,  J. Dumont Le Brazidec ,  P. Vanderbecken ,  R. Fablet ,  S. Ouala ,  P. Tandéo ,  R. Arcucci ,  S, Gratton ,  S. Penny , and  I. Szunyogh. CEREA  is a member of the  Institut Pierre-Simon Laplace  ( IPSL ).
Acknowledgements This work was supported by the  INGV  project  Pianeta Dinamico-Working Earth  ( CUP 1466 D53J19000170001-" Fondo finalizzato al rilancio degli investimenti delle 1467 amministrazioni centrali dello Stato e allo sviluppo del Paese ", legge  145/2018 )-Task V3 (M. A. D. V. ), and with the financial support of  Presidenza del Consiglio dei Ministri and Dipartimento della Protezione Civile DPC-INGV  project. We really thank  Teng Wang  and  Joe Aslin  for the editorial work, and  Federico Galetto  and two anonymous reviewers for helping improve the quality of the manuscript. We also thank  Stefano Caliro  and  Pierangelo Romano  for useful discussions on retrospective gas geochemistry.
Acknowledgement This work was supported by the  Performance Improvement Project of EAST, Hefei Comprehensive National Science Centre , the  National Key R&D Program of China  (Grant No. 2017YFE0300401 ) and the  National Natural Science Foundation of China  (Nos. 11305211  and  11775259 ). The authors would like to thank the experts from  GYCOM and CPI  companies for their great efforts in the development of gyrotrons for EAST.
Altogether, accumulated evidence has shown high heterogeneity within ASD in the participation of functional brain networks and behavioral manifestations and in the longitudinal trajectories at the individual level. Moreover, with neuroimaging studies, recent work has shown additional sources of heterogeneity due to variations in diagnostic and inclusion criteria and differences in the processing neuroimaging pipeline  (32, 33). ASD is also a polygenic, highly heterogeneous condition, with 1010 genes associated with ASD as of July 8, 2022, according to the Simons Foundation Autism Research Initiative (SFARI) gene human database [see also  (34) ]. Of those, 213 have a relevance score of 1, meaning that they have maximum published pathophysiological evidence to ASD. This high genetic complexity is another manifestation of the heterogeneity of this condition on several scales.
Participants A total of 2156 participants from the ABIDE-I (54) and ABIDE-II (59) repositories were initially considered in this study, of which 1026 were individuals with ASD and 1130 were TDC participants. These data were collected across 35 different scanning cohorts. For each participant, both anatomical and functional magnetic resonance imaging (MRI) data were used. Acquisition parameters for each scanning site are found at http://fcon_1 000. projects. nitrc. org/indi/abide/. Additionally, we extracted several composite scores from the Autism Diagnostic Observation Schedule-Generic, Autism Diagnostic Interview-Revised, Vineland Adaptive Behavior Scales, Social Responsiveness Scale, Social Communication Questionnaire, and raw score of the Autism Quotient, and the verbal, performance, and Full Scale IQ scores to address cognitive performance and disorder severity.
Functional Connectivity Matrices After neuroimaging preprocessing using state-of-the-art methodology (see the Supplement), FreeSurfer version 5. 3. 0 was used for brain segmentation and cortical parcellation. A total of 82 regions were generated from the Desikan-Killiany atlas, with 68 cortical regions (34 in each hemisphere) and 14 subcortical regions segmented from FreeSurfer (left/right thalamus, caudate, putamen, pallidum, hippocampus, amygdala, and accumbens). Different brain parcellations were also considered for the analyses (see the Supplement). For each participant, the parcellations were projected to the individual functional data and the mean functional time series of each region was obtained. Finally, one connectivity matrix for each participant was built by Fisher's Z-transformation of the Pearson correlation coefficients between the region pairs of the time series.
Data Harmonization To harmonize our multi-institution functional connectivity data, and before performing subtyping, we used an in-house implementation of Combat (https://pypi. org/project/ pycombat), adjusting these multi-institution batch effects by linear mixed modeling and the use of empirical Bayes methods  (56). We also included in this model the diagnosis label (TDC or ASD) as a biological variable of interest, ensuring that grouplevel connectivity differences were preserved after harmonization. See the Supplement for details.
Association Between Subtypes and Transcriptomics We computed the association between functional connectivity alterations represented by pseudo-R 2  maps (larger R 2 scores Neurogenetics of Functional Brain Subtyping in Autism corresponding to greater alterations) (see the Supplement) and brain transcriptomics maps using spatial autoregressive models, well known to reduce the correlation bias produced by the similar transcriptomic expression in proximal brain regions  (49). This analysis was implemented by means of the maximum-likelihood estimator routine (ML_Lag) from the Python Spatial Analysis Library (pysal)  (61). As a result, for each gene we obtained one t-statistic and one p value, which allowed us to assess the association with the pseudo-R 2 maps while accounting for possible spatial autocorrelations.
For each of the surrogate maps, we computed the association with brain transcriptome maps using spatial autoregressive models and used those genes with a p FDR value ,. 05 for computing enrichment analysis. For each of the annotations of interest, we generated a distribution of the likelihood of that annotation being significant by these random maps. These distributions were used to compute a p value to evaluate false positive bias. For the protein interaction analysis, we used the tool STRING version 11. 5  (67)  to generate a physical protein-protein interaction network for each subtype, with experiments and databases as interaction sources. These networks were then analyzed using Cytoscape version 3. 9. 0.
Significant NEG genes, also present in the SFARI gene human database with a relevance score of 1, were GFAP, CHD7, SKI, SHANK3, ANK3, and CACNA1E, while POS genes were ASXL3, MAP1A, STXBP1, DPYSL2, KNCB1, SCN8A, RIMS1, and CDKL5. Similarly, for subtype 2, we found 142 NEG genes, of which GRIA2, RFX3, SHANK2, GRIN2B, DLG4, LRRC4C, ARX, and GABRB3 were also present in the SFARI list, and 180 POS genes, including MAGEL2 and IQSEC2. We next applied gene enrichment to the list of significant genes within each subtype, finding no significant enrichment for subtype 1, the type with brain hypoconnectivity. However, for subtype 2, the enrichment of the NEG genes included GO biological processes and Reactome pathways related to glutamate signaling (affecting both AMPA and NMDA receptors) and synapse organization in relation to the E/I imbalance occurring during the development of brain circuits (Figure  3A ).
We also assessed which NEG genes participated in each biological process and pathway (Figure  3B ), finding that genes DLG4, GRIN2B, GRIA2, and SHANK2 were participating in most of them; and, the gene DLG4 plays a role in all of them. Additionally, the DLG4 gene was the one with the highest degree in the protein interaction network. We found a significant enrichment with biological processes related to E/I imbalance for subtype 2 but not for subtype 1. To test whether these findings suggested that the functional connectivity in subtype 1 was different from that in previous studies of ASD, we calculated the similarity of the connectivity profiles of our 2 subtypes with typical connectivity alterations in ASD, represented by brain maps in  (69)  and calculated from 4 different ASD databases of resting functional MRI data.
Second, we used Combat, a rigorous data harmonization method to eliminate the variability between MRI scans across the 24 institutions, one of the largest sources of variability when combining imaging data from multiple institutions  (72). Third, our analysis of brain connectivity was carried out on a large scale, in which each brain region is represented by its connectivity pattern across the entire brain. Therefore, we did not consider a priori any brain region as more dominant or relevant than the others. Fourth, we made use of a consensus clustering approach that we developed  (43, 44) , and that has been successfully tested by others  (73) , to group participants in the same subtype if the connectivity profiles are similar across all the analyzed regions. Finally, we made use of the AHBA to describe the neurogenetic profiles of each subtype, which has been used before for morphometric information in ASD  (35)  but never for characterizing subtypes based on functional connectivity patterns of this condition.
ACKNOWLEDGMENTS We appreciate the contributions made by the NIPT-SCA-map study group for this research. Ellen Hollands Steffensen  is funded by the faculty of  Health at Aarhus University , the  Health Research Fund of Central Denmark Region  ( A2602 ), and  Helsefonden  ( 20-B-0065 ). Anne Skakkebaek  is funded by the  Independent Research Fund Denmark  ( 0134-00130B ). Olav Bjørn Petersen  holds a professorship funded by the  Novo Nordisk Foundation  (grant  NNFSA170030576 ). Heather Strange  is funded by  Health and Care Research Wales  ( HRG-18 1507 ). Ida Vogel  is funded by the  Novo Nordisk Foundation  ( NNF16OC0018772 ).