dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2013). A connectivity matrix between atlas regions was calculated using Pearson product moment correlation with PANDAS. We further reduced the number of features by only selecting 148 ROI regions that are part of known networks including default mode (DMN), ventral attention (VAN), frontal-parietal (FPN), salience (SN), subcortical (SCN), dorsal attention (DAN), cingulo-opercular networks (CON)  (Gao et al. , 2018). The final functional connectivity feature set contained 10,878 measures (Supplemental Table  6 ). D-MRI: DTI data were preprocessed following ENIGMA-DTI protocols and quality control procedures at  (Power et al. , 2011). Processing steps include Eddy current correction, echo-planar imaging-induced distortion correction, motion correction, and tensor fitting. Fractional Anisotropy (FA) images generated from the estimated tensors were mapped to the ENIGMA DTI FA template and projected onto the skeleton FA template (FMRIB58_FA standard-space).
Data analysis Overview: The overall analysis procedure is presented in Fig. 2. The analysis followed the structure of the three main aims and 2 supplementary aims of the paper. Goals 1 and 2 used data that was pooled across sites/scanners whereas goal 3 used site information to facilitate generalization performance assessment. Goal 1 investigated both Support vector machine (SVM) and random forest (RF) for classification of s-MRI data, which was repeated for rs-fMRI, and then for D-MRI. Goal investigated DVAE in conjunction with SVM and in conjunction with RF (DVAE+SVM/RF), first using s-MRI features and then using rs-fMRI features. Goal 3 investigated performance of SVM or RF on single site data that was tested separately on s-MRI, rs-fMRI, and D-MRI brain features. Goal 3 also investigated performance of LOSOCV tested separately on s-MRI (SVM, DVAE+SVM), and rs-fMRI (SVM, DVAE+SVM) brain features.
Classification We built three models for classifying PTSD relative to 1) all controls (HC and TEHC), 2) healthy controls with no trauma history (HC), and 3) those previously exposed to trauma who did not develop PTSD (TEHC) for each modality. SVM and RF algorithms were used for classification (Supplemental Material Methods). Machine learning algorithms and Gridsearch were implemented in Python, and are available as part of the scikit-learn library (https://scikit-learn. org/stable/about. html#citing-scikit-learn). Our first task was to train classifiers that can differentiate patients with PTSD from control subjects using pooling methods. We randomly split all imaging data into two subsets: 70 % of the data was used for training and validation (cross-validation), and the remaining 30 % was used as a hold-out test dataset.
The labeled training+validation data is used to train a machine learning model through cross-validation. The validation data, which is separate from the training data, is used for hyperparameter tuning and assessing the model's performance during the cross-validation training process. The independent-test data is entirely separate from the training+validation data and is never involved in model training phase. Brain features with 30 % of missing data were dropped from further analysis  (Madley-Dowd et al. , 2019). RobustScaler from the scikit-learn library was used to scale the data for each modality, and missing values were imputed with the mean of the training dataset. The same scaling method was applied to the test set  (Pedregosa et al. , 2011). Gridsearch with stratified 10-fold cross-validation was used to select hyperparameters for both classifiers and to validate performance.
Based on previous research  (Gao et al. , 2018) , we used 10-fold cross validation, which generally provides better and more stable performance across different datasets, compared to Leave-One-Out Cross-Validation (LOOCV). To achieve an equal number of samples for each group, random under-sampling was applied to the imbalanced groups, with the under-sampling transform applied to the training dataset on each split of a repeated 10-fold cross-validation. The model's performance during training phase was evaluated by averaging across its the performance in the 10 fold. After model training, and the selection of the best hyperparameters, its performance is also assessed on the independent test set. Classification performance was measured using standard metrics including accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (ROC-AUC), which summarizes sensitivity and specificity at different thresholds.
Training of the DVAE model: The model was trained with ADAM optimizer  (Kingma, 2014)   The resulting VAE model learned to encode healthy patterns from the input brain features into its latent representation. Later, the brain features from patients with PTSD (PTSD test set) were input into the same VAE model, and the latent variables were extracted as new features for classification analysis (Fig. 3 ). Convergence was measured by evaluating the per-feature loss, which we defined as L/n where n is the number of features of the input. This was done so as to be able to roughly compare the loss for models from different modalities, as each had a different number of features. Encoding and Classification: After training the model, we used it to encode and reconstruct the data from both control subjects and the individuals with PTSD.
The training set was further randomly partitioned into 10 folds for cross-validation. Model performance was evaluated on the data from the hold-out site. The goal of this procedure was to assess the generalizability of the classifier to a totally independent data set that was sampled from a different sample and scanner. The LOSOCV performance was compared with an aggregated pooling method, in which data from all sites were included in training process. ComBat: For a large multi-site study, it is important to consider whether a classifier can generalize well to new data coming from a different scanner or site. We used the ComBat method  (Radua et al. , 2020)  to remove the site-specific information from the data and to test the generalizability of our classifier. The ComBat method models each imaging measure as a combination of three parts: variation of Y captured by the covariates such as age and sex, mean differences across sites, and the error term that contributes a different normal from each sites.
Leave one site out cross validation (LOSOCV) The LOSOCV performance was compared with an aggregated pooling method (as in Results Section 1). For all three MRI modalities (s-MRI, rs-fMRI, and D-MRI), LOSOCV provided chance level classification (s-MRI: test AUC=0. 56, Cohen's d = 0. 212; rs-fMRI: test AUC=0. 47, Cohen's d = 0; D-MRI: test AUC=0. 49, Cohen's d = 0) (Supplemental Table  10 ), and performed worse than the aggregate pooling method (Fig. 8 ). In s-MRI and rs-fMRI, we also compared LOSOCV and pooling methods using DVAE features, and assessed their generalizability. The DVAE achieved the same performance between LOSOCV and the pooling method, indicating a good generalization to unseen dataset using DVAE. Specifically, the LOSOCV method using SVM yielded an averaged test AUC of 0. 61 (std:0.
, 2020)  for rs-fMRI, and between 73 %  (Im et al. , 2017)  and 80 %  (Li et al. , 2014)  for studies focusing on multimodal biomarkers. Our single-site performance is comparable to other single-site studies (Fig. 7 ). Yet, single-site studies show poor generalization to independent datasets  (Pereira et al. , 2009) , suggesting that performance might be adversely affected by small sample sizes, high-dimensional features, and use of complex models with a large number of parameters. Good performance on training data, with poor performance on test data, suggests overfitting, as most machine-learning studies are evaluated only on the basis of cross validation. Therefore, while our accuracy is relatively low  (Y Li et al. , 2020) , the strength of our methods and sample size support the importance of our findings.
Conversely, the present results are comparable with machine-learning studies using large scale imaging datasets in other psychiatric disorders based on s-MRI data -65 % accuracy when classifying MDD from HC  (Gao et al. , Fig. 5. Compare classification performance between PTSD and Controls using all features (labeled as RF or SVM) and DVAE-based latent variables (labeled as DVAE+RF or DVAE+SVM) in s-MRI (A) and rs-fMRI (B). Compared with the performance (CV AUC) using SVM of all features, the performance of DVAE+SVM (CV AUC) significantly improved for both s-MRI and rs-fMRI. X. Zhu et al. 2018); 65. 2 % accuracy in differentiating bipolar disorder from controls  (Nunes et al. , 2020) ; and a CV AUC of 0. 57-0. 61 when classifying OCD from controls  (Bruin et al. , 2020). Exploring the utility of a DVAE, improved classification results emerged as compared to traditional ML approaches.
The DVAE successfully reduced feature dimensions, e. g. reduced the rs-fMRI features from 10,878 features to 10 latent variables, without losing information important for classification (SVM test AUC=59 %, Cohen's d = 0. 325 using 10,878 features; test AUC=62 %, Cohen's d = 0. 424 using 10 latent variables). Thus, the present results have the potential to provide a baseline classification performance for PTSD when using large scale imaging datasets. When considering HCs and TEHCs as separate control groups, our results yielded a markedly improved discrimination standard (test AUC in the range of 72 % to 78 %) across the three modalities, with the discrimination between PTSD and HC outperforming that of PTSD and TEHC. These findings are in line with previous studies showing greater similarity in underlying neural circuits between PTSD and TEHC participants  (Belleau et al.
Previous literature suggests that statistical harmonization methods developed to reduce data heterogeneity have the potential to improve accuracy, but at the cost of generalizability. Such approaches may compromise the train/test separation and introduce additional assumptions. Our findings demonstrate that DVAE may be able to capture differences across sites, and can be better generalized to new sites data. More importantly, the DVAE model does not require a priori knowledge of site information. Taken together, our findings support reproducibility of the DVAE across heterogeneous datasets from multiple sites. Testing generalizability, we also assessed the effect of age and sex on performance by adding them as features to the model  (Bruin et al. , 2020) , which did not affect classification performance.
Neither did they emerge as informative features in classifying patients with PTSD from controls. Future studies should further assess the specific effects of age and sex on PTSD classification. Several challenges still remain to be explored. First, combining biomarkers from different modalities with data fusion approaches is still in its infancy, and should be considered in future analyses to better detect potentially weak or latent effects hidden within high-dimensional datasets. Most deep-learning models are still being applied as black boxes, but serious efforts are underway to visualize latent variables and therefore improve the interpretability of results. Second, neuropsychiatric comorbidity was not consistently recorded across participating sites, so we could not evaluate it in the present study.
Acknowledgements Dr. Zhu  is supported by  NIH   K01MH122774  and by a  NARSAD Young Investigator Grant  from the  Brain & Behavior Research Foundation   27040 ;  Dr. Dennis  is supported by  NIH   R61NS120249 ;  Dr. Jahanshad  is supported by  NIH   R01MH117601 ;  Dr. Thompson  is supported by  NIH   U54 EB020403 ;  Dr. Fani  is supported by  NIH   AT011267  and  MH111671 ;  Dr. Bomyea  is suppprted by  NIH   R61MH127005  and  CX001600 ;  Dr. Lebois  is supported by  NIH   K01MH118467 ;  Dr. Daniels  is supported by  German Research Foundation   DA 1222/4-1 ;  Dr. Disner  is supported by  VA RR&D  Award  IK2RX002922 ;  Dr. Bruce  is supported by  NIH   K23 MH090366 ;  Dr. Bryant  is supported by  National Health and Medical Research Council  # 1073041 ;  Dr. Ross  is supported by the  NIH   T32MH018931 ,  F31MH122047  and  T32GM007507 ;  Dr.
In this article, the sensitivity of C-RIVE  (Vilmin et al. , 2012; Wang et al. , 2018) , the biogeochemical module of ProSe-PA  (Wang et al. , 2019) , is investigated against the background of the aforementioned parameters based on 45-and 5-day DO simulations. After incorporation of new parameters to account for repartition and degradation of OM, SA is conducted on a synthetic river system mimicking the Seine. Finally, the parameters influencing the evolution of DO and those governing the degradation and repartition of OM are selected. On the basis of the results, proposals are made for better integration of the influential parameters in data assimilation where the model is coupled with observation data to make an optimal estimate of the temporal evolution of the parameters as well as to produce better simulation results  (Cho et al.
ProSe-PA ProSe-PA results from the coupling of the ProSe hydro-biogeochemical model with a particle filter  (Wang et al. , 2019 (Wang et al. , , 2021)). It was developed to assimilate high-frequency observation data for a better estimation of the model parameters and an improvement in ProSe simulation results. The ProSe model is developed to simulate the hydro-biogeochemical evolution of the Seine from upstream of Paris until Poses (close to the estuary) and has been applied and validated numerous times  (Even et al. , 1998 (Even et al. , , 2007;; Flipo et al. , 2004 Flipo et al. , , 2007;; Polus et al. , 2011; Raimonet et al. , 2015; Vilmin et al. , 2015b, a; Wang et al. , 2021). In this model, the river is divided into longitudinal cells of specific length, where three set of equations corresponding to the three modules of ProSe-PA are solved (hydrodynamics, transport, and biogeochemical).
C-RIVE Biogeochemical model C-RIVE is a C ANSI library that implements RIVE  (Billen et al. , 1994)  concepts. It is the biogeochemical module of ProSe-PA  (Wang et al. , 2019) , which simulates the cycles of carbon, O 2 , and other nutrients both in the water and sediment columns of the river. The exchange of dissolved and particulate material between the two layers occurs through diffusion  (Boudreau, 1997)  and sedimentation-resuspension (due to shear flow and river navigation)  (Martin, 2001; Even et al. , 2004; Vilmin et al. , 2015b) , respectively. Numerous applications of RIVE exist for the ProSe and RIVERSTRAHLER softwares (See for instance  Garnier et al. (1995 Garnier et al. ( , 2005)) ;  Even et al. (1998) ;  Flipo et al. (2004 Flipo et al. ( , 2007)) ;  Thieu et al. (2009) ;  Vilmin et al.
therefore the net growth parameter is fixed at its lowest value. The settings for the three SAs are as follows: 1. First SA (All parameters included): There are 17 defined parameters ( Furthermore, to decrease the computational cost of the model, the three OM share parameters (t, b 1 and b 2 ) from the first SA are narrowed to BDOM and BPOM whose variation ranges are calculated based on the following Eq. (  12 )-(  15 ) as follows: BDOM min = T OC * t ref * b 1,min (12) BDOM max = T OC * t ref * b 1,max (13) BP OM min = T OC * (1 -t ref ) * b 2,min (14) BP OM max = T OC * (1 -t ref ) * b 2,max (15) Here TOC varies between 1-10, therefore, similar to the first SA, this SA is repeated 10 times corresponding to each TOC case. The pre-analysis also demonstrated that BDOM or precisely the substrate (DOM 1 ) is consumed in less than 5 days under the high net growth condition (simulations 1, 3, 5 & 7) , therefore imposing a 5 days simulation period.
Sensitivity analysis methodology Each of the three aforementioned SAs is implemented based on an innovative SA methodology initially proposed in Wang et al. (2018) and adopted in this study, where the influence of input parameters (X) is evaluated on the C-RIVE model according to the variations of a large set of DO simulations (model output, Y). The followings steps are pursued in this approach: 1. Input parameter identification: Initially, a set of D input parameters (Table  4 ) are identified with their corresponding ranges of variation (Table  1 ). 2. Parameter sampling and model input creation: Saltelli's extension of the Sobol sequence  (Saltelli, 2002)  implemented in PYTHON SALIB package  (Herman and Usher, 2017 ) is employed to create different combinations of the input   the output data from one coordinate system into another by introducing new uncorrelated (orthogonal) variables (principal components)  (Jolliffe and Cadima, 2016).
In addition, to facilitate the inclusion of BDOM in any data assimilation tool, it is necessary to explicitly include the model of OM sharing (Fig. 2 ) as part of the software, which will read TOC as an input data from now on. Moreover, BDOM from each organic matter source (tributary river, WWTP, and CSO) should be independently represented in the DA scheme due to its distinct role and contribution of organic matter in urban rivers  (Servais and Garnier, 1993; Seidl et al. , 1998b). The fact that the effluents from WWTP and CSO contain large bacteria which have significantly different physiological properties than the small autochtonous ones  (Garnier et al. , 1992; Servais and Garnier, 1993)   implemented in the DA framework such as fixing a few parameters values of each community, for instance, their growth and mortality rates assuming that the net growth of small bacteria is slow while that of large bacteria is fast.
The effect of this parameter prevails at higher net growth rates occurring during summer low-flow periods when the organic matter attributable to human pressure is abundant in the river; -The river metabolism is dominated by bacterial activity at low flow during summer non-bloom periods; -Water quality monitoring networks should be densified to encompass all important BDOM contributing boundary conditions. BDOM data should be collected at high frequency; -More frequent sampling of autochtonous bacteria communities upstream and downstream of major urban areas and in majors WWTP and CSO effluents will be of considerable interest to validate time varying parameter values estimated by data assimilation frameworks; -The results of this study provide us with a list of influential and non-influential parameters.
Limited knowledge about a substantial portion of protein coding genes, known as "dark" proteins, hinders our understanding of their functions and potential therapeutic applications. To address this, we leveraged Reactome, the most comprehensive, open source, open-access pathway knowledgebase, to contextualize dark proteins within biological pathways. By integrating multiple resources and employing a random forest classifier trained on 106 protein/gene pairwise features, we predicted functional interactions between dark proteins and Reactome-annotated proteins. We then developed three scores to measure the interactions between dark proteins and Reactome pathways, utilizing enrichment analysis and fuzzy logic simulations. Correlation analysis of these scores with an independent single-cell RNA sequencing dataset provided supporting evidence for this approach.
Furthermore, systematic natural language processing (NLP) analysis of over 22 million PubMed abstracts and manual checking of the literature associated with 20 randomly selected dark proteins reinforced the predicted interactions between proteins and pathways. To enhance the visualization and exploration of dark proteins within Reactome pathways, we developed the Reactome IDG portal, deployed at https://idg. reactome. org, a web application featuring tissue-specific protein and gene expression overlay, as well as drug interactions. Our integrated computational approach, together with the user-friendly web platform, offers a valuable resource for uncovering potential biological functions and therapeutic implications of dark proteins.
Placing Dark Proteins in the Context of Reactome Pathways via Predicted Functional Interactions The high quality, manually curated pathways in Reactome provide a framework to understand the functions of proteins and their action mechanisms via biochemical reactions. Reactome has annotated a small portion of proteins categorized as dark (i. e. Tdark) proteins according to the Reactome database (Release 84, March 2023) and Pharos web site (April 2023): 1,351 of total 5,679 dark proteins. To place those dark proteins that have not been annotated in Reactome into the context of Reactome pathways, we first collected a variety of pairwise relationship features from multiple data sources and then trained a random forest using functional interactions extracted from annotated Reactome complexes and reactions as positive data points and random pairs as negative data points.
After that, we predicted whether or not a pair of proteins could functionally interact with each other based on the trained random forest model. Based on the predicted functional interactions (FIs), we inferred how likely a dark protein could potentially functionally interact with pathways annotated in Reactome (Figure  1 ). Though this workflow was originally designed for dark proteins, it can be applied to any proteins, including proteins that have not been annotated in Reactome or proteins that have been annotated but not for specific pathways. In total we have collected 106 gene/protein pairwise features, including 48 tissue specific gene co-expressions from GTEx  [9] , 31 cancer specific gene co-expressions from TCGA  [10] , 20 gene similarities from Harmonizome  [11] , 5 physical protein-protein interaction datasets in human and mapped from mouse, fly, worm and yeast based on protein orthologous mappings, 1 protein domain-domain interaction data from pFam  [12] , and 1 biological process annotation from GO  [13].
Validating Interacting Pathway Scores by Analyzing a scRNA-seq Dataset Single cell omics technologies, especially single cell RNA-sequencing (scRNA-seq) technology, are generating unbiased extremely large datasets at the single cell level, allowing researchers to study molecular interactions and pathways with unprecedented details inside and between cells. To validate the predicted scores for interacting pathways, we conducted a gene expression correlation analysis using a blood scRNA-seq dataset generated by the Tabular Sapiens project  [15]. This dataset is independent from gene co-expression features based on the bulk RNA-seq GTEx dataset we used to train the random forest to predict functional interactions. We used the BigScale workflow  [16]  to calculate gene co-expression first and then selected the top 0.
We also analyzed the correlation between interacting pathway scores from scRNA-seq and average_activation_scores and average_inhibition_scores, and found a similar pattern (Figures  S3  and S4  in Supplemental Figures). To check if the scRNA-seq results are biased to annotated and not-dark proteins, we compared the distributions of Pearson correlations between annotated and not-annotated proteins and dark proteins and not-dark proteins (Figure  4B , two left panels). The results show no significant difference between them, indicating scRNA-seq results are unbiased to both Reactome annotations and research bias. To calculate the correlation between FI-based enrichment score and co-expression-based enrichment score for the scRNA-seq dataset for interacting pathways, we chose proteins having at least 10 interacting pathways having both scores.