dataesr/scientific-paragraphs-categorization huggingface dataset:
SYK Targets Are Rewired between the Breast Cancer and Burkitt Lymphoma Cell Networks To highlight the discriminating differences of the three SYK networks, we aligned the networks built using the DG75, MCF7 and MDA231 cell datasets. First, we merged the MCF7 and MDA231 cell networks, conserving the information on the origin of nodes and edges. We then compared this breast cancer SYK network with the Burkitt lymphoma SYK network. We defined the nodes and edges present in all networks as "common", those present only in the DG75 network as "DG75-exclusive'', and those present only in the MCF7 and/or MDA231 cell networks as "breast cancer-exclusive" (Figure  4A, B ). We initially focused on the SYK proximal network by selecting its 68 first neighbors (Figure  4D ). Almost all nodes were present in the three networks (but for 11 nodes absent from the MDA231 cell network).
This subnetwork was hyper-connected with 736 edges among which 177 were DG75-exclusive and 94 breast cancer-exclusive. This last group included 29 edges shared by the MCF7 and MDA231 networks, 45 MCF7-exclusive edges, and 20 MDA231-exclusive edges. Among the 47 edges arriving at SYK, 12 were DG75-exclusive edges and corresponded to the protein interactions involved in SYK activation in lymphocyte signaling. The proteins involved in these interactions are generally not expressed in breast cancer cells. Moreover, there was no breast cancer-exclusive SYK edge, most probably due to the lack of information in the pathway databases on SYK activation in breast cancer cells. We then selected amongst the identified SYK targets those that showed a major reconfiguration of their interactions in the breast cancer and Burkitt lymphoma SYK networks (Figure  4C ).
The serine/threonine kinase CDK1 plays a key role in cell cycle control  [45] [46] [47]. Although we identified these proteins as SYK targets in two different datasets, we merged their proximal networks (Figure  4F ) because they are both involved in cell cycle control, as reported for SYK  [48] [49] [50]. Almost all SKP1 and CDK1 interactors were present in the breast cancer and Burkitt lymphoma networks. However, almost all interactions of CDK1 and those of SKP1 with CDK1 neighboring proteins were breast cancer-exclusive. Nevertheless, some interactions of CDK1 and SKP1 were DG75-exclusive, notably with identified SYK targets (VCPIP1 for CDK1, and COPS8 and COPS4 for SKP1). Although it was composed of target proteins shared by the MCF7 and DG75 cell datasets, the CDK1/SKP1 proximal network had mostly interactions that were exclusive to the breast cancer or DG75 cell network, suggesting that its regulation by SYK could be different depending on the cell type.
Finally, we were interested in the regulation of the Hippo signaling pathway because we observed that this pathway was enriched in MCF7 cell SYK targets and in YAP1 (MDA231 cell SYK target) (Figure  S11 ). We included also the proximal STK4 network, another key component of the Hippo pathway  [51] , identified as an SYK target in the DG75 and MDA231 cell datasets. Half of the proteins were common to all three networks, and the other half were breast cancer-exclusive (Figure  4G ). Almost all interactions were breast cancer-exclusive, whereas two STK4 interactions originating from the Ras signaling pathway were DG75-exclusive. It should be noted that the other identified SYK targets (AMOTL2, PPP2R1A and PPP2CA) came from the MCF7 cell dataset. These observations strengthen our hypothesis that SYK regulates the Hippo signaling pathway specifically in breast cancer cells.
Signal Propagation from SYK to its Targets Identified in the Breast Cancer and Burkitt Lymphoma Cell Lines To connect SYK to its targets, we extracted the shortest paths between them for each cell line using our bioinformatics pipeline that combines the advantages of the random walk-based analysis methods and the weighted k-shortest paths  [20, 21]. We merged and colored the resulting subnetworks to compare them in the three cell lines. The first SYK neighbors were 22 proteins with an interaction with SYK documented in signaling pathway databases and that link SYK to experimentally identified targets (Figure  5A ). Among these 22 proteins, 16 were exclusive to only one cell line. Overall, the SYK subnetwork of the shortest pathways showed that many of them were exclusive to only one cell line (Figure  5B ).
Lymphoma Cell Lines To connect SYK to its targets, we extracted the shortest paths between them for each cell line using our bioinformatics pipeline that combines the advantages of the random walk-based analysis methods and the weighted k-shortest paths  [20, 21]. We merged and colored the resulting subnetworks to compare them in the three cell lines. The first SYK neighbors were 22 proteins with an interaction with SYK documented in signaling pathway databases and that link SYK to experimentally identified targets (Figure  5A ). Among these 22 proteins, 16 were exclusive to only one cell line. Overall, the SYK subnetwork of the shortest pathways showed that many of them were exclusive to only one cell line (Figure  5B ). This observation was confirmed when we restrained the paths to the targets involved in cell adhesion and motility, cell growth and death, differentiation, inflammation and transport and metabolism (Figure  S15 ).
Signal propagation analysis from SYK to its targets identified in the breast cancer and the Burkitt lymphoma cell line. The paths from SYK to its targets identified in the MCF7, MDA231 and/or the DG75 cell datasets were extracted from the same prior-knowledge network (step 4 of the workflow in Figure  1B ). Diamond-shaped nodes are SYK targets and are colored according to their presence in a specific experimental dataset; circle-shaped nodes are present in the prior-knowledge network and are colored according to their presence in the extracted paths (gray, MCF7; orange, MDA231; blue, DG75; pink, MCF7 and MDA231; yellow with red border, MCF7 and DG75; white with red border, MDA231 and DG75; purple, MCF7, MDA231 and DG75). Edges are colored depending on their presence in the extracted paths (gray, MCF7; orange, MDA231; blue, DG75; pink, MCF7 and MDA231; yellow, MCF7 and DG75; red, MDA231 and DG75; purple, MCF7, MDA231 and DG75).
Therefore, BTK could be crucial for SYK pro-oncogenic function in B-cell lymphomas. We also found other pathways downstream of immunoreceptors involved in lymphocyte and myelocyte lineage signaling, receptors that are not expressed in epithelial cells. Consistent with this observation, we observed that the BLK protein tyrosine kinase proximal network was specific to the SYK signaling network in DG75 cells. The alignment of the networks reconstructed from the different datasets allowed us to demonstrate that protein interactions of some SYK targets common to the networks were profoundly modified between the breast cancer and lymphoma networks. The paradigmatic example is the E3 ubiquitin-protein ligase CBL that is expressed in both hematopoietic and epithelial cells. CBL has been identified as a proto-oncogene in hematopoietic cells  [69].
An in-depth comparison of the SYK interactions that were rewired between the breast cancer and Burkitt lymphoma networks revealed the absence of breast cancer cellspecific interactions arriving at SYK. This is a consequence of the absence of defined SYK activation pathways in non-hematopoietic cells in the databases. SYK can be activated in epithelial cells by the engagement of beta1 integrin receptors, through reoxygenation and tumor necrosis factor alpha  [54, 74, 75] , but the activation mechanism has not been clearly established yet. The identification of SYK activation pathways in mammary epithelial cells would allow completing the signaling network and better understanding its breast tumor suppressor function. Overall, a better characterization of the SYK phosphorylation signaling pathways in different tissue types is crucial because pharmacological SYK inhibitors are now used in the clinic to treat immune thrombocytopenia, autoimmune hemolytic anemia and IgA nephropathy  [76].
M. B. is the recipient of an EpigenMed PhD studentship from the Labex EpiGenMed (an « Investissements d'avenir » program, reference ANR-10-LABX-12-01) and a PhD studentship from the Fondation ARC pour la recherche sur le Cancer. M. D. was supported by the ANR (#ANR-18-CE14-0019-01). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
This further confirms that diagnostic image-level variations in behavior could be further utilized to gain more insight into the mechanisms that drive the atypical facial emotion responses in IwA. Next, I quantified how stimuli selection based on high image-level differences can be leveraged to design more efficient behavioral experiments. To do this, I selected images based on the difference in behavior between the two groups ( ℎ : using data from four randomly selected individual subjects from each group) and tested the resulting correlation between the two groups' behavior (using the held-out subject population). This was repeated several times to get a mean measure of the cross-validated raw correlation (y-axis in Figure  1E ). A noise-ceiling was measured for each image-set selection based on image-level internal reliability of the held-out test population (see Methods).
I selected four different ANNs to test their behavioral predictions with respect to the facial emotional judgement task. These ANNs were pretrained to perform image classification (AlexNet 28 , CORnet-S 29 ), face recognition (VGGFace 30 ) and emotion recognition (Emotion-Net 31 ). I observed that, a 10-fold cross validated partial least square regression model (see Methods for details) could be used to train each model to perform the task. The variation of the behavioral responses of the model with parametric changes in the level of happiness in the faces qualitatively matched the patterns observed in the human data (Figure  2B ).
I observed that each of these populations of VF, VS, and mixed (equal number of VS and VF neurons) could significantly (p<0. 0001; permutation test for significance of correlation) predict the image-level facial emotion judgments measured in Controls. Figure  3C  shows how these three populations predict the image-level behavior measured in Controls as a function of the number of neurons sampled to build the neural population decoders. Given that all of these groups exhibit an increase in behavioral predictivity with the number of neurons, it is difficult to reject any of these decoding models (with the current neural dataset). Therefore, in the following analyses I have examined the VF and VS units separately. Next, I estimated how well the VS and VF population predicted the behavioral patterns measured in the Control and IwA respectively.
Facial emotion judgment task During the task, Wang and Adolphs 4 asked participants to discriminate between two emotions, fear and happiness. The image-set includes faces of four individuals (2 female) each posing fear and happiness expressions from the STOIC database (Roy et al. 2007), which are expressing highly recognizable emotions. To generate the morphed expression continua for the experiments, the authors interpolated pixel value and location between fearful exemplar faces and happy exemplar faces using a piece-wise cubic-spline transformation over a Delaunay tessellation of manually selected control points. They created 5 levels of fear-happy morphs, ranging from 30% fear/70% happy to 70% fear/30% happy in steps of 10% (Figure  1B ). Low-level image properties were equalized using the SHINE toolbox  46.
Depth recording in human amygdala In this study I have re-analyzed the neural data that was previously collected and used in a study by Wang et al. 15. The raw neural dataset was kindly shared via personal communication. Wang and colleagues recorded bilaterally from implanted depth electrodes in the amygdala from patients with pharmacologically intractable epilepsy. Target locations in the amygdala were verified using post-implantation structural MRIs. At each site, they recorded from eight 40 m microwires inserted into a clinical electrode. Bipolar wide-band recordings (0. 1-9 kHz), using one of the eight microwires as reference, were sampled at 32 kHz and stored continuously for off-line analysis with a Neuralynx system (Digital Cheetah; Neuralynx, Inc. ). The raw signal was filtered with a zero-phase lag 300-3000 Hz bandpass filter and spikes were sorted using a semiautomatic template matching algorithm.
Selection of neurons for analyses In the original study, only units with an average firing rate of at least 0. 2 Hz (entire task) were considered. Only single units were considered. In addition to that, in this study I have further restricted the neural dataset to neurons that have a significant visual response (both increase and decrease). To estimate that I compared the neural firing rates (per image) averaged across two specific time bins, [-1000 0] and [250 1250], where 0 is the onset of the image. If the paired Wilcoxon Signed Rank test between these two firing rate vectors were significant, the site was considered for further analyses. Thus, I considered 156 total neurons: 99 visually facilitated (VF) neurons and 57visually suppressed (VS) neurons.
Decoding facial emotion judgment from neural population activity To decode facial emotion judgments from the neural responses per image, I used a linear model that linked the neural responses to the levels of happiness (ground truth from image generation). Building the model, essentially involves solving a regression problem estimating the weights ( ''⃗) per neuron and a  term. I used a partial least squares (MATLAB command: plsregress) regression procedure, using 15 retained components. I also used 10-fold cross validation. For each fold, the model was trained (i. e. ,  ''⃗ and  were estimated) using the data from the other 9 folds (training data), and predictions were generated for the held-out fold (test images). This was repeated for each of the folds and the entire procedure was repeated 100 times.
Estimating model facial emotion judgment behavior To decode facial emotion judgments from the model responses per image, I used the same linear modeling approach as the neural data (see above), that linked the model feature activations to the level of happiness (ground truth from image generation). The model features, per layer, were extracted using the MATLAB command activations for AlexNet  28  , VGGFace 30 and EmotionNet 31 in MATLAB-R 2020b. For the CORnet-S 29 model, I used the code from: https://github. com/dicarlolab/CORnet.
Statistics All correlation scores reported in this study are Kendall rank coefficients (unless otherwise mentioned). For significance tests of correlations (between two variables of interest), I have used a bootstrapped permutation test. To do this, I first constructed a null hypothesis by mixing the two variables and then randomly drew (as many times as the number of elements in the original variable) with replacements two elements from the mixed dataset to create two vectors. These two vectors can be constructed multiple times (typically >100) and correlated. The resulting correlation distribution was considered as the null hypothesis. Then the true raw correlation was compared to this distribution to determine a p-value of rejecting the null distribution. !.
We introduce a new cell population score called SpecEnr (specific enrichment) and describe a method that discovers robust and accurate candidate biomarkers from flow cytometry data. Our approach identifies a new class of candidate biomarkers we define as driver cell populations, whose abundance is associated with a sample class (e. g. , disease), but not as a result of a change in a related population. We show that the driver cell populations we find are also easily interpretable using a lattice-based visualization tool. Our method is implemented in the R package flowGraph, freely available on GitHub (github. com/aya49/flowGraph) and on BioConductor.
| DISCUSSION In this article, we introduced a new cell population score, SpecEnr, and a method, flowGraph, that integrates SpecEnr to identify MDCPs. We showed that the results of flowGraph are statistically sound, accurate, and easily interpretable. In the FlowCAP-II challenge, the AML data set was used to evaluate how well methods are able to classify samples belonging to healthy and AML positive subjects. Among the competing methods, those that used cell population proportions for classification were DREAM-D, flowPeakssvm, Kmeanssvm, flowType, FeaLect, PBSC, BCB/SPADE, SWIFT  [10]. All of these methods assume that cell count and proportion may be used to differentiate between the two classes of samples. However, cell count and proportion do not account for relations between cell populations, making it difficult to F I G U R E 2 Cell hierarchy plots for synthetic data sets pos1-3 and real data sets flowcap and pregnancy show that SpecEnr q values accurately identify maximal differential cell populations (MDCPs) while proportion q values flag all differential cell populations (DCPs) but do not highlight which of the DCPs are MDCPs [Color figure can be viewed at wileyonlinelibrary.
We also observed this contrast in behavior between SpecEnr and proportion q values in the pregnancy data set. We hypothesized that flowGraph would be able to find MDCPs because  [17]  were able to use L 1 , L 2 , and cell signal pathway regularized regression to classify samples taken from women at different stages of pregnancy. The original authors also assumed that there exists MDCPs in the pregnancy data set  [6]. However, because these methods find candidate biomarkers as a byproduct of a sample classification method, there was no way of verifying whether the candidate biomarkers they inferred are simply DCPs or are also MDCPs. FlowGraph answers this question by providing users a way to differentiate between the two while verifying our hypothesis validating the existence of MDCPs in the pregnancy data set.
To provide an overview of the existing approaches, Table  1  lists all the available tools for investigation of neurite evolution, highlighting imaging modalities, the kind of measurements allowed, and the amount of user intervention. It can be noted that, except for NeuriteSegmentation,  20  the remaining tools have been applied on labeled images or on high-contrast-imaging modalities. 23 As a consequence, none of the tools allows dynamic modeling of the outgrowth process and only two of them present discriminant analysis via statistical tests performed on one  20  or a very few individual shape descriptors. 28 n this work, we present a novel software architecture, called NeuriTES, which works automatically in FLI sequences of living motor neuron cultures. The main strength of NeuriTES is to conjugate unlabeled bright-field images and time-lapse microscopy (frame rate in the order of minutes) with high performing image analysis and dynamic feature extraction tools.
CASE study 1 Source data In this study we used primary spinal cord cultures prepared from transgenic G1H mice, which overexpress a mutant human SOD1 gene containing a glycine93/alanine (G93A) substitution. These mice have been extensively validated as an animal model of ALS due to the development of symptoms and a motor neuron pathology that mimics those found in ALS patients. 39 We acquired videos of motor neurons from two distinct kind of cells, G93A and wild-type (WT), as control. The original videos were acquired using time-lapse microscopy at a frame rate of 1 frame/min over a day at the 6 th and 8 th days in vitro. Based on the focus and illumination conditions obtained during time-lapse microscopy acquisitions an expert biologist manually segmented 15 motor neurons at the initial frame using ImageJ.
40 To increase the training set for segmentation, we transfer the same ground truth images also to the corresponding next 9 frames (10 min in total), leading to 10 consecutive labeled frames. The assumption is that the motor neurons' motility is almost null within 10 min of observation and that the same ground truth (GT) image can be assumed as the truth segmentation for the subsequent frames. A total number of 150 labeled frames (15 motor neurons by 10 labeled frames) were then used to train the SS network and to assess the segmentation performance also with respect to comparative approaches. Such setting also allowed us to reduce the memory storage of the entire video sequence (see Table  3 ). The analyzed motor neurons derive from a total of nine distinct cultures. In addition, an expert located the center of additional motor neurons to increase the dataset for analysis.
According to the coordinates provided, each motor neuron was then automatically cropped and a distinct video was extracted from each motoneuron progression. Table  2 , lists the number of videos for each condition either in terms of day of acquisition (6 th or 8 th ) and in terms of the presence of the disease phenotype (WT versus G93A). In addition, we also detailed the number of videos with labeled and unlabeled cells. Videos Supplementary_video_G93A_6 th _day. avi, Supplementar-y_video_G93A_8 th _day. avi, Supplementary_video_WT_6 th _day. avi, and Supplementary_video_WT_8 th _day. avi provide four examples of videos for WT and G93A motor neurons, acquired on the 6 th and 8 th days, respectively (see data and code availability for the link to download the videos).
Case study 2 Source data In this case study, we considered prostate cancer cells (PC3)  31  treated with a chemotherapeutic drug (etoposide). Etoposide, inhibiting the action of topoisomerase II, thus impairing the ligation step of the cell cycle, has a replication blocking effect over cancer cells. The aim of the study was to demonstrate that the mutual evolution of shape and texture descriptors of cells is indicative of the phenotype changes in PC3 treated with etoposide versus untreated cells. For this task, we considered the experimental setup described in Giuseppe et al. ,  31  and applying NeuriTES modules to label-free time-lapse microscopy video sequences of living PC3 cultured in a Petri dish (1 frame/min, 0. 33 mm/pixel). The steps described in Figure  1  are sequentially applied to the analysis of PC3.
Since there are a few small cells in a single frame, cells are first automatically tracked using the software Cell-Hunter as described in Giuseppe et al. 31  A region of interest (ROI) is then extracted for each cell along its trajectory over time, thus leading to a sequence of ROIs for each moving cell. For the training of the SS network, a total of 24 cells (12 from control and 12 from treated cells) were manually segmented at the first frame. A total of 5,000 cell frames were extracted. Results in the automatic recognition of the effect of chemotherapy on PC3 phenotype The 5,000 cell frames were automatically segmented by ASS after a dedicated SS network was trained over the first manually segmented frames of each track. On average, trajectories were 6 h long (360 values at a frame rate of 1 frame/min).
Limitations of the study In the present release of NeuriTES, the preprocessing image algorithms are specifically designed for bright-field time-lapse microscopy imaging. In the future, NeuriTES could be adapted to a wider class of optical imaging techniques, such as phase contrast imaging and fluorescence time-lapse microscopy imaging. In the current release, NeuriTES does not provide an optimal visualization of the DTEG associated with the experiment. The user is asked to design their own optimal DTEG according to the result of the thresholded TE matrix. Future releases will include an additional module for the construction and the visual optimization of the DTEG representation. Regarding the biological aspects of the ALS study, the approach has two main limitations: the use of a mixed culture system, without a physical separation between the neuronal and the glial components, and the absence of a clear neuronal marker.