dataesr/scientific-paragraphs-categorization huggingface dataset:
Cortical Overlap of the Tumor with Yeo's Networks The functional impact of cortical disruption by the tumor was estimated using a parcellation of 17 functional networks described by  Yeo et al. [58]. Briefly, this parcellation was obtained by combining resting-state fMRI data of 1000 healthy subjects and detecting clusters of voxels with coherent functional activity. The resulting parcels represent wellknown functional networks such as the FTPN. For each of Yeo's 17 networks [58], the percentage of voxels overlapping the tumor segmentation was calculated, resulting in 17 predictors for each patient.
Disconets of the Tumor Segmentation The degree to which the white matter connections within each of Yeo's 17 networks  [58]  were disrupted by the tumor was calculated using Disconets  [59]  (https://github. com/ scilus/disconets_flow, accessed on 26 September 2022). Whole-brain tractograms of 20 Human Connectome Project  [60]  subjects in MNI space were used as representations of healthy brain connectomics. Tractograms were obtained by again using the constrained spherical deconvolution-based iFOD2 method as implemented in MRTrix, generating a set of two million streamlines uniformly seeded in white matter voxels with FOD cutoff set to 0. 15. The resulting tractograms were transformed to MNI space and filtered by selecting streamlines with both ends in the same Yeo network. Then, each patient's tumor segmentation was registered to MNI space with ANTs registration.
Z-scores were calculated to adjust for age, sex, and education level based on a Dutch normative control sample  [64]. Note that z-scores for the letter fluency test were adjusted for education level only, as age and sex were not found to significantly influence test performance  [62]. In the Paris center, neuropsychological assessment was performed in the week before surgery using the standard paper-and-pencil version of the digit span forward and backward test  [65] , the Stroop interference test (the difference in reaction time on Stroop task 3 -Stroop task 2), the Trail Making Test (TMT) and the letter fluency test  [66]. Z-scores were computed according to normative data in a reference sample of French healthy people, stratified by age and educational level. Scores were reversed for Stroop interference so that for all tests lower z-scores indicate worse performance.
Machine Learning A random forest algorithm (as implemented in the scikit-learn package https://scikitlearn. org/stable/, accessed on 3 October 2022) was used to predict preoperative executive function as measured by the neuropsychological assessments described above. As input variables we used four sets of predictors as described above: (i) minimal distance of the tumor to each of the ipsilateral SLF branches and the aforementioned control tracts (AF, CST, FAT), (ii) MD of the ipsilateral SLF branches and control tracts as measured with patient-specific DWI, (iii) cortical overlap between the tumor and each of Yeo's 17 networks [58], and (iv) Disconets of Yeo's 17 networks. On top of this, we used medical center, lesion side, and tumor volume as covariate predictors in each run, resulting in (i) 9, (ii) 15, (iii) 20, and (iv) 20 predictors, respectively.
Note that for distance we only considered the ipsilateral tracts. As targets, we used binary variables indicating for each neuropsychological test described above whether a patient was impaired (Z-score < 1. 5) or not impaired (Z-score ≥ 1. 5). Classification performance was assessed by calculating the average area under the classification receiver operating characteristic curve (ROC-AUC) for 100 cross-validation splits holding out 25% of the data for performance testing. Each split was stratified to preserve the group proportions in each cross-validation loop. Statistical significance was assessed by permutation testing, randomly shuffling the target variables 1000 times, and recomputing the ROC-AUC for each of the cross-validations splits in the same way as before. To investigate the importance of each feature variable, we used the permutation importance measure provided by the scikit-learn library and reported the variables with the highest scores.
To overcome these limitations, a multi-modal pushover analysis procedure is here tested and validated on the data from a non-symmetric structure (the SMART case study) under multi-component earthquakes. In detail, the procedure applies a linear combination of modal load patterns, defined according to the well-established Direct Vectorial Addition (DVA) method. With respect to other existing multi-modal pushover analysis techniques, elliptical response envelopes are employed to calculate the corresponding combination factors. Innovatively, the identification of the dominant modes for the load pattern is not limited to the classic maximization of the forces at the basis of a structure but rather generalized to shear and displacement maximization at different heights and locations throughout the whole structural frame.
2016 ). (14) ṽ = v * 1 2 √ v * T ⋅ X -1 v ⋅ v * ( The experimental results were then used by EDF in several test-cases of its Finite Element (FE) software Code Aster (EDF 2023) and the geometrical data as well as the mesh were made available. In this study, this calibrated FE model used for validation. According to its original formulation  (Richard et al. 2016) , the column, beams, and foundations were considered as linear elastic, while the walls and slabs were modeled with GLRC_HEGIS global nonlinear constitutive model for shell elements (refer to  (Huguet et al. 2017 ) for further details). To summarize, the model is formulated using an innovative analytical multiscale analysis (without any computational-costly numerical homogenization) using an RC strut between two constitutive cracks as the Representative Volume Element (RVE), which allows a formulation in the thermodynamics of irreversible processes framework at the global RC scale for various local nonlinear phenomena: concrete isotropic diffuse damage evolving in compression; concrete smeared cracking in tension; tension stiffening effect from concrete-reinforcement relative slip and stress; yielding of steel reinforcement bars.
Results of Step 2 for the maximization of shear forces between the floors 2-3 We consider V x and V y the horizontal components of the shear force (according to the global reference frame reported in Fig. 2b ) at these nodes. The shear force F = V x cos( ) + V y sin( ) is maximized for a total of eight cases, obtained considering = n ⋅ 45 • , with oriented as depicted in Fig. 2  and n ∈ [0, 7] increasing in unit step, thus resulting in = 0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315°. The corresponding eight load patterns Q given by the E-DVA method are applied to the structure in a non-linear static calculation run on Code Aster (EDF 2023). The response spectrum analysis of the shear forces in the different floor level is summarized in Table  2. The used spectrum if the average spectrum (indicated as 'moy' in the legend) of Fig 3.
1a ). Then, a signal-processing chain is applied on these images to remove biological tissues and to detect, localize precisely, and track MBs through time in order to accumulate all their trajectories and retrieve the vascular map with a micrometer resolution  [10] ,  [11]. A standard ULM algorithm from Performance Assessment of Localization Algorithm (PALA) toolbox is available online at https://github. com/AChavignon/PALA  [12]. The different signal-processing steps of the classic ULM are described below and illustrated in Fig. 1b-f. Step (b) of the standard ULM algorithm from PALA consists in tissue filtering (Fig. 1b ), i. e. , separating the tissue from the MBs. The most commonly used method is based on the singular value decomposition (SVD) of the Casorati matrix obtained from the sequence of ultrasound images  [13].
It is thus still not clear if the synthetic training datasets are realistic enough to allow the network to generalize well to experimental data  [37]. In this paper, an improvement of the MB detection step is proposed (Fig. 1c ). As explained previously, the standard MB detection method is based on the intensity of MBs. However, from in vivo ultrasound images it has been noted that MB intensity may be lower than tissue or noise in certain conditions. One can assume, for example, that MBs in capillaries may present a lower intensity due to their limited range of oscillation. Moreover, the SVD filter attenuates (and even removes) the slowest MBs  [41]. Consequently, these SVD-attenuated MBs are not detected by the algorithm. As the challenge of ULM is to image a maximum number of vessels in a minimum amount of time, it is very important to improve the MB detection rate, especially in capillaries where the passage of bubbles is not frequent.
To deal with this challenge, a detection method based on decision-theory tools, known as a Constant False Alarm Rate (CFAR) detection method in RADAR applications  [42] ,  [43] , is proposed in this work. The remainder of the paper is structured as follows. First, the standard detection methods of the literature are presented, then the problem of MB detection is modeled using decision theory concepts and the resulting solution based on the Neyman-Pearson criterion is explained. Second, the ultrasound data used to validate the proposed approach are described. Finally, the decision theory-based detection is compared with the intensity-based and normalized cross-correlation algorithms on simulated and in vivo ultrasound images of rat brain and kidney.
F. INTEGRATION OF THE DECISION MAP IN ULM FRAMEWORK The NP decision criterion is used to improve the detection step of ULM. The NP criterion is first applied on the beamformed images after SVD filtering, resulting in the detection maps C i,j (t) in  (18). Local maxima are then detected on these maps. Rectangular ROIs are defined around the retained maxima, used as input for the localization step. The final pseudocode is given in Algorithm 1, which takes as input the original (without any filtering) beamformed ultrasound image sequence denoted by I(i, j, t). MB detection methods are evaluated through three datasets available online at https://zenodo. org/record/4343435 from the PALA toolbox, which provides a benchmark for evaluating the ULM algorithm  [12]. The simulated dataset (named PALA_data_InSilicoFlow) was composed of 20,000 simulated ultrasound images of moving MBs at various constant velocities in crossing vessels of different sizes and shapes  [12].
This design of bright residual tissue is extremely simplified and does not correspond visually with what could be seen on ultrasound images. Nonetheless, it allows us to evaluate the effect of bright residual tissue on the detection methods. The results can be seen in Fig. 3 , where the MBs are scatterers with relatively large PSF, the residual bright tissues are the brightest pixels, and the granite pattern is the residual speckle. The second dataset (named PALA_data_InVivoRat BrainBolus) was composed of 213 blocks of 800 beamformed images of in vivo rat brain measurements with two bolus injections of MBs, which corresponds to an acquisition time of 171 s. In this study, only the data with the first bolus injection are used, i. e. , the first 85. 6 s of acquisition. This dataset is interesting for validating the NP detection since it contains MBs brighter than others for each given image, as can be seen in Fig.
4. It is used to analyze the NP detection in high (at the beginning of the bolus injection) and low MB (at the end of the bolus injection) concentration. A high MB concentration enables the evaluation of the robustness of the noise statistical moments estimator. The last dataset (PALA_data_InVivoRatKidney) was composed of 238 blocks of 790 beamformed images of in vivo rat kidney with a bolus injection, which corresponds to 185 s of   acquisition. In order to compare the results with the rat brain example in term of acquisition time, only the first 92. 8 s of the acquisition are used in this study. An example is given in Fig. 5 ; as can be seen, some MBs are moving in a low SNR area and thus it will be interesting to evaluate the results in such a context. The main acquisition parameters of all the datasets presented here are given in Tab.
B. COMPARISON METHOD AND HYPER-PARAMETER CHOICE Table  2  summarizes the most important hyper-parameter and its signification, of the proposed methods and the techniques used for comparison. For NCC-based method, the kernel choice is a key-point to provide correct results. For the simulation study, the PSF shape and size were estimated by selecting a PSF from a simulated ultrasound image of the dataset without noise. For the in vivo study, the extraction of a PSF directly from an image was not possible because of the noise, so the PSF was modeled as a Gaussian with a standard deviation chosen by analyzing the intensity drop between the main lobe and side lobes on experimental PSF examples. Thus, the retained standard deviation for the brain and the kidney datasets was 1. 75λ. The kernel size S K presents a trade-off since a too wide window will risk to take information from neighboring PSF and a too narrow one may lack information about the PSF shape.
The PSF size was empirically fixed at 5λ, for both in vivo studies, in order to ensure the best possible results. Then, to ensure a reliable reference, the ULM parameters in Tab. 3 are the same as those provided in the PALA toolbox script example. The main ULM parameters are: • SVD cutoff: number of singular values removed, • MaxDist: maximum linking distance between two localized MBs to enable pairing in the tracking step, • MinLength: minimum length of track, • N LM : number of local maxima allowed in an ROI. The localization algorithm used in this study was based on the radial symmetry due to its good performance/computational load compromise. For tracking, the Hungarian algorithm was used for the same reason and for its good performance within high frame rate acquisitions.
C. RESULTS FROM SIMULATED DATA By applying the NP detection to the first dataset, the η thresholds of Eq. (  15 ) are computed for each pixel (i, j) (called η i,j ),  which gives the normalized threshold map η i,j max(x i,j (t)) in Fig. 6. For this example, a false-alarm rate α 0 of 1% is chosen. When the signal is higher than this threshold, an MB is detected. As the threshold is low in the vessel area it will ease MB detection. On the contrary, outside the vessels, the normalized threshold is close to 1. In that case, there is a low probability of detecting any MB at any time. In order to evaluate and compare the intensity-based, the NCC and NP detection methods, standard detection metrics are used. A true positive (TP) is counted if an MB is detected and localized at a maximum range of λ/4 away from a real MB as per the ground truth, as done in  [12].
D. RESULTS FROM IN VIVO RAT BRAIN DATA The NP detection method is then applied to the in vivo dataset. The influence of α 0 on the threshold map η i,j is shown in Fig. 8. These maps are computed on 800 successive images as the acquisitions are continuous per block of 800 images. These 800 images enable the estimation of the noise statistical moments. It can be seen that the lower the false-alarm rate, the more constraining the threshold map. There is therefore a compromise between TP and FN detection. In the following results, the detection methods were applied to first bolus only, i. e. , to the first 107 blocks of 800 images. The effect of each method hyperparameters is studied in Fig. 9. To begin, it is important to note that the ULM maps provided by the intensity and NCC-based methods have a large pixel value dynamics.
E. RESULTS FROM IN VIVO RAT KIDNEY DATA In order to complete the study, the methods are evaluated on a rat kidney in vivo dataset. First, the spatially uniform detection benefits are clear in this example as it can be seen by comparing Fig. 15a  with Fig. 15d, f. The densities are more uniform with the NCC and NP detection methods with well chosen threshold parameters. Indeed, there are fewer tracks for large vessels but more details for small vessels in areas B and C. To detect these small vessels with the intensitybased method, the parameter N bubbles needs to be increased as in Fig. 15b. However, instead of detecting the vessels in these areas, it detects more FP in the circular areas and in area D. Note that only the NP method removes these punctual FP detections. From visual inspection of the results in Fig.
This work was supported in part by  LABEX CELYA  under Grant  ANR-10-LABX-0060 , in part by  LABEX PRIMES  under Grant  ANR-11-LABX-0063  both through  Université de Lyon  within the  Program ''Investissements d' Avenir' ' operated by the  French National Research Agency (ANR)  under Grant  ANR-11-IDEX-0007 , and in part by the  France Life Imaging   TRAIL  Project  ANR-11-INBS-0006.
Cluster 1 also displayed lower HLA-DR and CD11c on mDCs, and lower expression of CD278, an inducible T-cell co-stimulator, on monocytes, mDCs, pDCs and mature neutrophils and lower IL17Ra on memory T reg cells in comparison with Cluster 2. Children in Cluster 1 therefore show a more extreme immunophenotype than children in Cluster 2. Cluster membership was not associated with immunomodulator treatment prior to acute sampling. To extend our analysis of the immune cell changes over the disease trajectory in an unsupervised manner, we used multi-omic factor analysis (MOFA)  27. This is a computational method used to discover the principal axes of variability within high parameter datasets, and the resulting factors can be interpreted like a principal component analysis. We also explored membership of the previously identi ed Cluster 1 and Cluster 2 in the context of this analysis.
MOFA revealed four Factors which collectively accounted for 81. 5% of the variability in the dataset (Supplementary Fig. 3 ). Factor 1 accounted for 36% of the variability within the data and represented myeloid cell activation and T cell activation. Factor 1 was associated with CD64 and CD95/Fas upregulation in neutrophils, NK cells, monocytes, and CD38 upregulation on memory CD4 + and CD8 + and γδT cells. Factor 1 scores were positively associated with MIS-C at T1 and with Cluster 1 membership; and negative associated with MIS-C at T3, KD at T3 and Cluster 2 membership (Supplementary Fig. 3 ). For SBI, there was a non-signi cant trend of decreasing Factor 1 scores between T1 and T3. Factor 1 scores were positively associated with severity of in ammation (as determined by C-reactive protein concentration, adjusted R 2 0.
Immunophenotyping of cells from the stimulation assay We explored myeloid cell activation and impaired T cell function using samples following the cell stimulation assay from 12 children with MIS-C (T1:8, T3:6) and 3 HPC. Raw data were used for analysis of unstimulated samples, while foldchange from baseline was evaluated for stimulated samples. Three distinct groups were observed on the innate immune response heatmap: 1) MIS-C T1 2) MIS-C T3 and 3) HPC (Fig. 4a ), while the following groups were observed on the T cell immune response heatmap: 1) MIS-C T1 (nil, Αg1, mit), 2) MIS-C T3 (nil, Αg1) and HPC (nil, Αg1) and 3) MIS-C T3 (mit) and HPC (mit) (Fig. 4b ). In unstimulated samples, MIS-C T1 displayed innate immune cell in ammatory features: increased proportions of neutrophils, and non-classical monocytes compared to MIS-C T3 and HPC (Fig.
Of children with samples for which mass cytometry was acquired, half of children with MIS-C had been treated with a rst dose of corticosteroid or a rst dose of immunoglobulins. However, a sensitivity analysis showed no association with treatment and immune features. Finally, the use of a limited number of samples in mass cytometry experiments following cell stimulation assays limited statistical power for these experiments. In summary, we used a multi-omic systems biology approach to provide a comparative analysis of the trajectory immunology of severe febrile illness in children in the COVID-19 era. Our data support an important role for neutrophil and monocyte activation in the pathology of MIS-C and highlight T cell exhaustion upon presentation with MIS-C. Neutrophil activation, IFNγ signaling and NET production was shared with SBI, highlighting shared features of immune dysregulation in these disparate severe febrile illnesses of childhood.
Cell staining buffer (CSC), Fix and Perm Buffer (FPB) and Cell Acquisition Solution (CAS) are all Fluidigm Inc. (CA, USA, now Standard Biotools). A single standardized healthy control sample was stained and analyzed for each batch of samples. Cells were centrifuged and then resuspended in 50 µl Fc receptor blocking solution (45 µl cell staining buffer (CSB) / 5 U heparin / 5 µl FcX Trustain, Biolegend, CA, USA) and incubated for 10 min at 20 ºC. Barcoding antibody mastermixes to CD45 (Fluidigm, now Standard BioTools; Supplementary Fig. 2 ) were then added to individual samples to allow later deconvolution of data and incubated for 20 min at 20ºC with gentle mixing. Cells were washed twice in CSB and groups of samples were created by pooling six prede ned samples into a single tube for subsequent multiplexing.
Data was then immediately acquired using a Fluidigm Helios Mass Cytometer at the Advanced Cytometry Platform (Flow Core), Research and Development Department at Guy's and St Thomas' NHS Foundation Trust (https://sites. google. com/nihr. ac. uk/brc ow/home). Samples were acquired in cell acquisition solution (CAS) with 1:10 EQ™ four element calibration beads using a Helios mass cytometer (Fluidigm). Flow cytometry standard (FCS) les were then normalized with EQ beads for comparison between samples. Files were then concatenated using the CYTOF software (Fluidigm). We used CD45 labels ("barcodes") to enable pooling of six-plexed samples and subsequently debarcoding of samples from FCS les using a custom MATLAB script and manual gating on FlowJo based on positive then negative selection by CD45 barcodes (BD, version 10.
Supernatant cytokine data acquisition and analysis A BD LSR Fortessa ow cytometer was used for data acquisition with PE as the reporter channel (575-585 nm) and APC used as the classi cation channel (660 nm). Compensation was not required. The LEGENDplex (Biolegend) guide was followed to set up the PMT voltages with the set-up beads using the FACSDiva software (Version 6. 0). The HTS was used for rapid automated sample acquisition and the total events per well were limited to 10,000. The LEGENDplex Data Analysis Software Suite (Version 8) was used for initial quality control checks. The automated gating of bead population was reviewed and manually adjusted for any misclassi cations. A 5-parameter logistic (PL) equation was used to t the standard curves using logistic regression. The threshold for the R 2 value was set at > 0.
95. A minimum of 100 events were required for each bead ID and the threshold for the coe cient of variation between replicates was set at < 30%. Principle component analyses (PCA) were performed to detect any batch effects as the samples were run across 20 plates. Raw data were used for baseline, unstimulated samples. Percentage change from baseline was used for evaluating the stimulated samples. To address the skewness of data and to improve visualisation of data, the results were presented after applying log10 transformation. Pairwise comparisons using the Wilcoxon test were performed across clinical cohorts and timepoints with BH 44 correction for multiple testing. A sub-analysis of the MIS-C T1 (acute) cohort by the two clusters identi ed from the unsupervised hierarchical clustering analysis of the mass cytometry data was performed.