dataesr/scientific-paragraphs-categorization huggingface dataset:
Interestingly, among these 170 genes, 59 are common to our proteome dataset. As observed at the level of the protein expression changes, the hierarchical clustering of these 59 differentially expressed genes efficiently separated the tumor samples from the normal adrenal tissue (Figure  6A ). Moreover, the expression of 53 of these 59 genes (90%) shows the same variation trend as the protein expression, both in the cytosol and in the membranes (Figures 6B and Supplementary Figure  2 ). To visualize whether the deregulated proteins identified in this comparison were highly connected to each other, we performed a protein-protein interaction (PPI) network analysis using Cytoscape (  (Shannon et al, 2003) , Figure  7 ). Interestingly, one major significant cluster is composed of proteins that are overexpressed both at the protein and at the gene levels (blue squares).
Finally, to further validate our dataset, we analyzed the global expression change of some key overexpressed proteins present in the main exocytic cluster identified with Cytoscape. To do so, we performed a multiplexed MRM MS assay in total protein homogenates prepared from another independent cohort of 25 pairs of human Pheo and their matched non-tumor tissue  (Croise et al, 2016). As MRM is a targeted MS approach that uses synthetic peptide reference standards, it is used to confirm and quantify the presence of proteins of interest on smaller amounts of sample with high sensitivity, which eliminates the need for fractionation  (Keshishian et al, 2007). As observed for the subcellular fractions, using MRM MS we found that the expression of every protein identified in the PPI analysis was also significantly increased in Pheos compared with the matched adjacent non-tumor adrenal tissue (Figure  8 ).
Analysis of the amperometric recordings was performed as previously described with a macro (laboratory of Dr. R. Borges; http://webpages. ull. es/users/rborges/ ) written for Igor software (WaveMetrics), allowing automatic spike detection and extraction of spike parameters  (Segura et al, 2000). The spike parameters analysis was restricted to spikes with amplitudes higher than 5 pA, which were considered as exocytic events. All spikes identified by the program were visually inspected. Overlapping spikes and spikes with aberrant shapes were discarded for parameters analysis. Quantal size (spike charge, Q) of each individual spike was measured by calculating the spike area above the baseline. Spike area is defined as the time integral of each transient current, Imax as the height of each spike, half-width as the width of each spike at half its height (T1/2) and Tpeak as the spike rise time (Figure  1A ).
Peptides were separated using a linear gradient (92. 5% A to 84% A) for 26 min, followed by (84% A to 75% A) for 14 min and a wash at 60% B for 2 min. The flow rate was 1. 8 µL/min. Protein identification was accomplished using data acquired by LC-MS/MS. The MS/MS spectra were matched to the corresponding peptide sequences found in the UniProt human protein database using Mascot (Matrix Science, version 2. 2. 06. ) software.
An SRM transition was predicted for each peptide. The detection of this transition triggered the acquisition of a full MS/MS spectrum of the target peptide. The two most intense fragment ions (b or y fragment ions only) in the MS/MS spectrum for each acquired peptide were recorded by in-house developed software. The mass spectrometer collision energy (CE) was optimized for each transition with 5 different CE values automatically generated by in-house developed software. A solution containing all synthesized peptides at a concentration of 200 pmoL/mL was analyzed with the created MRM method. The two best peptides per proteins were selected to be monitored by the MRM assay. The processed samples were resolubilized with 11 µL of a reconstitution solution containing 5 internal standard (IS) peptides each at 100 ng/mL.
Eight (8) µL of material (~10 µg) was analyzed by LC/MRM-MS. Peptide separation was achieved using a BioBasic C18 column (Thermo) (320 µm x 150 mm, 5 µm particle size). The mobile phases were (A) 0. 2% formic acid in water and (B) 0. 2% formic acid in acetonitrile. Peptides were separated using a linear gradient (92. 5% A to 60% A) for 21 min, followed by a wash at 60% B for 2 min. The flow rate was 10 µL/min. The transition peak areas were integrated using Elucidator software (Rosetta Biosciences) in combination with software developed at CellCarta for automated MRM peak integration.
Data processing and statistical analysis For the analysis of the carbon fiber amperometry, the data was first standardized (mean=0 and variance=1). Data then was normalized by quantile method using preprocessCore R package. PCA analysis was performed using FactoMineR and factoextra R packages. Spearman's rank order correlation was performed using cor function from stats R package and plotted using corrplot and ggplot2 R packages. For comparison between paired Tumor (T) and Non-Tumor (NT) patients, paired Wilcoxon test was performed. For comparison between Tumor (T) patients, non-paired Wilcoxon test was performed. For the differential expression analysis by mass spectrometry, the intensity values for all detected components were log (base e) transformed with values < 0 replaced by 0. Intensity data was normalized to account for small differences in protein concentration between samples.
For Multiple reaction monitoring (MRM) analysis, differential intensity (DI) ratios were calculated in pair wise comparisons for each transition as the median of the ratio of the normalized intensities of each group. Paired Student's t-test was applied for the expression analysis. Protein-level statistics were also computed by linearly combining the transitions of a given protein into a single variable and then applying a t-test. All differential expression analysis and data visualization were done using R. PCA analysis was done using prcomp function from stats R package and plotted using ggplot2 R package. Volcano plot was performed using EnhancedVolcano function from R package.
Transcriptomic analyses Gene expression data were retrieved from the GSE19422 dataset (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE19422)  (Lopez-Jimenez et al. , 2010)  which includes 6 normal adrenal medulla and 61 Pheos. Consistently with the proteomic analysis, the 23 paraganglioma samples were not considered. The GSM483021 Pheo sample was excluded from our analysis because its transcriptomic profile, as revealed by clustering and UMAP analyses (data not shown), is similar to that of normal samples suggesting that it is misidentified. The expression level of each gene was calculated as the geometric mean of the significantly differentially expressed probes identified with the GEO2R web tool using a Benjamin-Hochberg FDR correction (q-values < 0. 05). Genes of the regulated exocytosis (#BPGO:0045055) and secretory granule (#CCGO:0030141) GO terms were then selected for further analysis.
Unsupervised hierarchical clustering was performed with Bioconductor (v. 3. 13) and R software (v. 4. 1. 0) using Pearson correlation as distance calculation and complete linkage. The Fold change (FC) for each gene was calculated from the median expression values for the normal and Pheo groups and only those with a FC greater than or equal to 2 were retained for further analysis. The correlation between the expression level of common genes and proteins was evaluated in both cytosolic and membrane fractions using Spearman correlation (GraphPad Prism v. 8. 1). In order to highlight functional clusters, an integrative analysis was conducted with Cytoscape software  (v. 3. 8. 2, (Shannon et al. , 2003) ) using expression data and protein interaction data retrieved from STRING-DB (v. 11. 5, https://string-db.
Linear regression analysis between the membrane-fold changes and the cytosolic-fold changes shows a positive correlation for both down-regulated and up-regulated proteins. Supplementary Figure 2: Comparison of genes and proteins of the exocytic pathway deregulated inPheo. The 59 common differentially expressed genes (GSE19422) and proteins (this study) are selected from the exocytosis pathway. Unsupervised hierarchical clustering highlights two clusters of up-and down-regulated genes and proteins. Except for 5 (A) and 3 (B) genes/proteins, expression of all the genes (top row) and their proteins (bottom row) varies in the same direction. The color scale illustrates the over-(blue) or under-(red) expression of genes and proteins in Pheo compared to non-tumor tissue.
Funding and acknowledgements This work was financially supported by  ITMO Cancer AVIESAN (Alliance Nationale pour les Sciences de la Vie et de la Santé, National Alliance for Life Sciences & Health)  within the framework of the Cancer Plan to SG and LB (Single Cell 2018 N°  19CS004-00 ); by the  University of Strasbourg Institute for Advanced Study (USIAS)  for a Fellowship, within the  French national programme  " Investment for the future" (IdEx-Unistra ) to SG; by the  Conseil Régional de Normandie  to CD; by grants from the  Agence Nationale pour la Recherche ("SecretoNET", N°   ANR-16-CE17-0022-01 ) and from the  Ligue contre le Cancer  to SG (CCIR Grand-Est) and to CD (Comité Normand); by a fellowship from la  Fondation pour la Recherche Médicale  (FRM;  FDM201806005916 ) to SM. INSERM is providing salary to SG and NV.
In the current study, these MS/MS landmarks seem to be taken into account by the molecular networking processing since two isomeric nodes, distinguished thanks to MZmine 2 data preprocessing  [14] , were tagged as vitexin (C-8 glucosyl apigenin) and isovitexin (C-6 glucosyl apigenin) as depicted in the cluster in ▶ Fig. 1. The MS/MS spectra recorded from these two precursors were examined to determine whether these assignments were supported by comparison with formerly reported tandem mass spectrometric data. At first, the different intensity ratios of the 0,2 X + / 0,1 X + signals in the low energy tandem mass spectrum (Collision Energy = 30 eV) of the [M + H] + ion of the two molecules of this isomeric pair were strongly reminiscent of those reported by Waridel and co-workers  [15] , supporting the tentative assignment proposed by the molecular networking workflow.
1816). Compared to the other bidesmosic flavones described so far, the MS² spectrum obtained from this ion showed a Y n + ion at m/z 447 that hinted the existence of a O-glycoside while also exhibiting a variety of fragment ions related to cross-ring cleavages of saccharidic residues, similar to those formerly observed in the tandem mass spectrum of swertisin/isoswertisin, evocative of a Cglycosidic residue. These spectroscopic features led us to surmise that this compound should be a C,O-diglycosylated flavone. The search of the generated molecular formula against the DNP database [23] revealed 10 different hits (accessed 14 th February 2021) among which nine were flavonoids. Yet, the flavonoids matching the elemental composition query either corresponded to O-diglycosylated flavones or to 3-methylglutaroylated O-glycosylflavones, which seemed to be inconsistent with the putative structural elements deduced from the cursory examination of the MS² data.
Inferring that this compound had important chances to be unknown, we decided to target it for isolation and full structure elucidation. The MS² landmarks outlined earlier made us gear our isolation efforts towards some specific compounds prioritized due to their presumed structural novelty. Accordingly, the isolation of the prioritized compounds, detected at m/z 609. 1816 and at 579. 1713, could be successfully performed using a suite of chromatographic separations. Due to this study being the first undertaken on E. anomala, we wished to complete this targeted pipeline with a more traditional, untargeted, phytochemical workflow. The isolation of the main constituents of the ethanolic extract shed light on structural classes not formerly identified in the molecular network. The structures of these compounds could be deduced from their spectroscopic data, including 1D and 2D NMR experiments and comparison with reported data as syringaresinol  [24] , lariciresinol [25], tanarifuranonol  [26] , vitexin  [27] , trematin  [28] , isocytisoside  [29] , and orcinol.
A second hexosyl unit could also be determined and the lack of any 13 C NMR glycosidation shift confirmed it being the terminal unit. spectra were acquired in CD 3 OD (S9-S14, Supplementary Material). The large coupling constants of the anomeric protons (J H- 1′-H-2′ = 10. 0 Hz and J H-1′-H-2′ = 7. 9 Hz) determined the β-configuration of both sugar moieties. Subsequently, the axial orientation of the remaining sugar protons H-2′ to H-5′ and H-2′ to H-5′ could be deduced from the large magnitude of their coupling constant values, as collated in ▶Table 1. These data confirmed the carbohydrate part of anomaloflavone as laminaribiose, in excellent agreement with the NMR data formerly reported by Hamburger and co-workers in both CD 3 OD and DMSO-d 6 on closely related C-glycosylflavones  [36]. Accordingly, anomaloflavone was determined as acacetin 6 -C-[β-d- glucopyranosyl-(1-> 3)-β-d-glucopyranoside].
30, 50, and 70 eV) were used for MS/ MS data generation. Purine (C 5 H 4 N 4 , m/z 121. 050873) and HP-0921 (hexakis (1 H, 1 H, 3 H-tetrafluoropropoxy)-phosphazene C 18 H 18 F 24 N 3 O 6 P 3 , m/z 922. 009798) were used as internal lock masses. Full scans were acquired at a resolution of 10. 000 (m/z 922) and 4. 000 (m/z 121). A permanent MS/MS exclusion list criterion was established to prevent oversampling of the internal calibrant.
MZmine data processing and Molecular Networking Parameters The MS/MS data files were converted from the. d in-house Agilent data format to. mzXML using the MS Convert software, as included in the ProteoWizard package  [43]. The. mzXML files were then processed using the MZmine 2 v. 52 workflow. The mass detection was performed using a threshold of 1. 0E4. The ADAP chromatogram builder was obtained using a minimum group scan size of 4, a group intensity threshold of 1. 0E4, a minimum highest intensity of 1. 0E5 and m/z tolerance of 0. 05 Da or 5 ppm  [44]. The ADAP wavelets deconvolution algorithm was performed using the following settings: S/N threshold = 10, minimum feature height = 3, coefficient/area threshold = 3, peak duration.
Modeling Approach The process of PBPK model development commenced with the literature search for the extraction of drug-specific and population-specific parameters. The obtained data, i. e. , molecular weight, fraction absorbed (f a ), blood to plasma ratio (B:P), etc. were incorporated into the program to simulate and evaluate the IV profiles in the healthy adults. After the IV model was successfully developed in the adult population, oral PK profiles were simulated by incorporating oral drug absorption parameters, i. e. , human jejunum permeability (P eff ), etc. After successful oral data assessment, the clinical PK profiles of other routes of administration for diazepam were evaluated, i. e. , intranasal and rectal. The layout for the developed diazepam PBPK model is given in Figure  1.
Pharmacokinetic Data A detailed online literature search was executed to categorize the PK profiles (plasma concentration vs. time profiles or data) of diazepam in the adult population for model development. The studies were selected based on the PK profiles, the route of adminis-tration (IV, oral, intranasal, and rectal), the dose administered, age, the female proportion, and the duration of the study. The observed data were extracted by scanning PK profiles through GetData Graph Digitizer V. 2. 26. 0 (http://getdata-graph-digitizer. com, accessed on 12 September 2021)  [45]. In conclusion, 8 clinical studies and 20 concentrations vs. time profiles were added for diazepam model development; out of these 20 profiles, 6 were based on IV administration (71 individuals), 5 were profiles of oral (88 individuals), 6 were of intranasal administration (66 individuals), and 3 were of rectal diazepam administration (38 individuals) were used.
Among all of these PK profiles, about one-third (two IV, two oral, two intranasal, and one rectal) were employed for diazepam PBPK model development and the remaining two-third (four IV, three oral, four intranasal, and two rectal) were utilized for verification of the model. For model evaluation, all of the observed clinical PK data were used. Table  2  shows the demographic data utilized for diazepam model construction and evaluation in the adult populations.
This algorithm is reactive; it does not use any forecaster; and it deals with SRE. We analyse the efficiency and robustness of this method by experimenting over a large variety of daily conditions, covering almost 2 years of solar and load conditions. We created a dataset with 2 years of activity of our experimental microgrid. A part of this dataset (training dataset) is used to train a DRL agent; then, its performance is assessed on the remaining part of the dataset (testing dataset). As EMS agent, we propose two versions of DRL agents that stem from the family of double deep Q-network (DDQN) algorithms. These two DRL agents are benchmarked against other methods: a RL decision tree  [14]  (RL-DT), a rule-based algorithm and dynamic programming (DP).
In DRL, the state vector feeds a neural network and an estimate of the Q-values is output for each action. Weights are updated following the Q-Learning update rule in Equation  (1). Because of the combination of the updates of the weights of the neural netowrk and the updates of Q estimates, Equation (1) can be simplified: the learning rate α can be removed as it is already used during the backward propagation phase resulting in: Qðs t ; a t ; wÞ ¼ r t þ γmax a Qðs tþ1 ; a; wÞ ð2Þ Supervised learning methods such as neural networks require a dataset of examples, that is a set of input-output pairs. In Deep Q Network, we create an experience replay memory D. An experience at each time step is defined by the tuple: exp t ¼ (s t , a t , r t , s tþ1 ) and is stored in D. Nevertheless, as each transition exp t is recorded, we have a problem of correlation between close experiences, which is inconvenient for training the neural network.
| MGSimulator We have created a microgrid simulator called MGSimulator and implemented it in Python. The simulator calls a preprocessing module which returns a training and testing net demand dataset. To make MGSimulator useful and easily used by the RL community, MGSimulator follows OpenAI Gym  [33]  design and API. This is part of a larger effort to create a generic, easy to use, simulator of microgrids that is available to the communities studying microgrids and RL. The main function of the simulator is named step(a), which takes the agent action a ∈ A as input and it returns three elements to the agent: the next state s tþ1 , the reward r t , and a Boolean value determining if the terminal state is reached or not (variable name: done). A function called reset( ) resets the environment to its initial state.
| Rare events Rare events are low probability events which significantly affect the expected performance. In our case, they are characterised by a combination of a rare state and a rare action. Figure  3  exhibits rare events (in red) present in our dataset, representing the discrete net demand observed at each time step. We can observe that the two points with a net demand below À 20 are not considered as rare events. The reason is that the agent takes the right decision during these two situations and the expected performance is therefore not affected. In our case study, we define rare events when the net demand P Net is equal to zero, that is when the power produced by the PV panels is equal to the power consumption. In Section 4, we have defined action Idle (a ¼ 3) to effectively manage these situations.
Altogether, these points raise the necessity of better managing SRE in DDQN. Rare events are under-represented in the replay memory D because there only represent 2. 66% of the dataset. This means that a rare event is less likely to be picked in the batch memory to train the neural network if it has been stored in the replay memory. A solution to unstuck an agent would be to increase its exploration rate, however as epochs pass, the probability to explore decrease. Therefore, as time passes, the agent capability to unstuck itself at a late stage of training becomes null. To address these problems, we propose two mechanisms that are embedded in the DDQN. the last G episodes. For instance, if the agent fails in step t ¼ 88, then 88 is added to C. At each time step t of an episode, a new ratio noted δ t is calculated.
| Experimental settings The dataset used in this study comes from different sources: the load measurements from an office building, the Drahi X Novation Center, and the PV generation measurements from the SIRTA atmospheric laboratory  [35]. Figure  4   https://bit. ly/3jmaasD. First the agent is trained. This period is called the training phase or the learning phase. When this phase is over, we test the learned agent on new conditions of the microgrid. This is called the testing phase. The training phase is conducted on simulating 43 active days of the microgrid, which are randomly selected over 43 successive weeks along the year. A time step of 30 min is taken during the training phase which represents 2 � 24 � 43 ¼ 2064 steps for one episode. We have chosen to set the time step to half an hour to increase the amount of training data, which is enough to have good performance without increasing too much the computing time.
The more data we have in an episode, the more time is needed to train our agent. The testing phase is performed on a new series of 52 days, over 52 successive weeks along the year, with a time step of 1 h. Note that the testing dataset represents a large variety of days, including seasonal variations, holidays, etc. The parameters of the objective function J obj (which are also used in the reward function) are set to m ¼ 0. 5 €, q ¼ 1. 5 €, and c ¼ 10 €. Table  1  provides the parameters of the simulated microgrid. The hyperparameters used for the two agents: DDQN-EMS a and DDQN-EMS p , are given in Table  2. The selection of these hyperparameters is sensitive because they affect directly the learning performance. DDQN-EMS p (with a priority list of actions) needs more episodes to converge because the estimation of Q is more difficult to learn.
| Testing phase In the testing phase, the trained agents are facing a new dataset, that is new conditions under which to control the microgrid. We measure their performance on these new conditions. The cumulative cost obtained to control our off-grid hybrid microgrid is reported. At test time, the agent does not perform exploration: actions are selected greedily. To assess DDQN-EMS a and the DDQN-EMS p agents, we compare their performance with those obtained by other methods. The first of these methods is a RL Decision Tree (RL-DT) algorithm, proposed in Reference  [14]. This method is a combination of a Q-Learning and a CART decision tree. The second method is a hand-crafted rule-based control algorithm. The third method is a DP algorithm called Value Iteration, as proposed in Reference  [9].
Experiments are based on a simulated microgrid with real data. As usual in machine learning, the data used for training is different from the data used to assess the performance of the algorithms: they have to generalise their knowledge from the training data and hence be able to cope with unseen situations. We have demonstrated that our proposed DDQN-EMS agents succeed to adapt themselves in a new testing year with good performances, outperforming standard methods. The proposed algorithm could be used without modification for an other off-grid hybrid microgrid. Nevertheless, an improvement of this work would be to design different microgrid architectures (islanded and connected to the grid with different dataset), with more or less complexity, in order to study the scalability and the robustness of this method.