dataesr/scientific-paragraphs-categorization huggingface dataset:
Here, they are briefly presented and summarized in Fig 1. A 2-sec rolling window was used to select an input with fixed size every second. The input consisted therefore in 6 IMU variables taken for 2-sec at a sampling frequency of 208 Hz, which resulted in a 6x416 data matrix. A new input matrix was generated every second. The 6 IMU variables consisted in the three linear accelerations along the three IMU axes (a x , a y , and a z ) and the three angular velocities about the IMU axes (ω x , ω y , and ω z ). For each input matrix, the acceleration values were normalized on 9. 81 m/s 2 and the angular velocities were normalized by 500 rad/sec. The gold-standard foot-specific stride frequencies and duty factors were obtained by averaging the optoelectronic stride frequencies and duty factors provided in the same time window.
Therefore, the output of the machine learning algorithm has size of 4x1. To train the algorithm, the values of the labels were z-standardized by subtracting the average value and normalizing by the standard deviation. The standardization procedure is commonly performed to improve the stability of the training process. In more detail, the machine learning model consisted in a CNN developed in Python (3. 8) with the Keras library within the Tensorflow (2. 7) framework. Once the estimated stride frequency was available, both DFA-α and HG-D were computed as specified in the 'Temporal variables values' Section. More details about the structure of the CNN can be found in  [28]. All the Python scripts are available at the project repository (https://bitbucket. org/andrea_zignoli/ prissiv/src/np/src/).
This algorithm consists of a deep neural network developed in Python (3. 8) with dense layers provided by the Keras library in the Tensorflow (2. 7) framework. Three layers were included of 128, 64, and 32 neurons each, and the layers were interspersed with batch normalisation and dropout layers. The algorithm was trained to predict a single value of DFA-α from a 120-second window of IMU data. Considering that 120 seconds of data from an IMU collected at 208 Hz provides a large input matrix, a subset of temporal features were extracted from the linear accelerations a x , a y , a z and the angular velocities about the IMU axes ω x , ω y , and ω z. The selected temporal features for each temporal signal were as follows: skewness and Kurtosis indices (with scipy Python library), HG-D and DFA-α (with antropy Python library), standard deviation and average, spectral centroid, entropy and peak frequency, autocorrelation factors (with scipy Python library), and wavelet transform (with pywavelets Python library).
These features were computed for each IMU signal, resulting in a 6 x 23 input matrix for the deep neural network. The input data was provided by the IMU positioned on the runner's thorax, and the DFA-α data used to train the model were provided by the gold-standard optoelectronic device (average between right and left foot). To train the model, the same procedure detailed in the previous Section was repeated, and 120-second window data were taken from the same individuals included in the training dataset with steps of 20 seconds. Accuracy was evaluated for every running condition on individuals not included in the training dataset.
Statistical analysis Statistical analysis was conducted with custom scripts written in R programming language. Specifically, the graphical user interface RStudio (ver. 2021. 09. 1) was used to write the scripts and the ggstatsplot R library  [29]  was used to conduct the analyses and plot the results. First, running kinematic and variability data was organized into several groups based on one single-factor variable (i. e. , the running condition: different running speeds and treadmill inclinations). One-way ANOVA was used to test whether the averages of the different groups were equal/not equal to the others. Additional multiple pairwise-comparisons were conducted if the average difference between specific pairs of groups were statistically significant. For this analysis, all the data (training and test dataset) was used.
Second, the running temporal and variability data was also organized into several groups based on two single-factor variables (i. e. , running condition and estimation strategy). A two-way ANOVA was used to test whether the averages of the different groups were equal/not equal to the others. Additional multiple pairwise-comparisons were conducted if the average difference between specific pairs of groups were statistically significant. For this latter analysis, only data belonging to the test dataset was used. Also, the root mean square error (RMSE) was computed between the temporal parameters estimated with the IMU-based strategies and the gold standard optoelectronic device. Before conducting the analyses, a Shapiro-Wilk test was conducted on a group-by-group basis to check for normality.
If the assumption of normality was confirmed/violated in the majority of the groups, a parametric/non-parametric version of the analysis was conducted. By default, the ggstatsplot R library  [29]  reports the F-Welch's statistics, the corresponding pvalue, and the ω 2 effect size (with confidence intervals [CI 95% ]) for the parametric version of the ANOVA test, and conducts a Games-Howell's post-hoc analysis with Holm's correction. Conversely, the library reports the χ 2 -Kruskal-Wallis' statistics, the corresponding p-value, and the ε 2 effect size (with [CI 95% ]) for the non-parametric version of the ANOVA test and conducts a Dunn's post-hoc analysis with Holm's correction. For all the analyses, significance level was set at p = 0. 05. All the code used in this study to generate the statistics (R scripts) is available publicly in the project repository (https://bitbucket.
These results suggest that uphill treadmill running is characterized by stronger long-term relationships than downhill treadmill running. Consequently, uphill treadmill running looks more flexible and adaptable than downhill treadmill running. One limitation of using IMUs to assess running kinematics and variability is the physical distance between the point where the reaction force is generated (i. e. , the ground-foot interface) and the point where the accelerations are measured (e. g. , foot instep, tibia, wrist, hip, thorax). As a result, the kinematic data provided by an IMU only reflect the level ground movements and do not capture the full extent of the movements and forces generated during running. In line with what is reported in  [22] , IMUs placed at the foot instep level can provide better estimations for the stride frequency than for the duty factor.
Teleseismic back-projection We image the rupture process by teleseismic backprojection, a method that can image multi-fault ruptures without making strong assumptions on the fault system geometry (e. g. ,  Meng et al. , 2012)  and that can be applied rapidly after large earthquakes as soon as teleseismic P-wave data are available (the results reported here were ready for our analysis on February 7, see  Ampuero (2023). The method can be automated to deliver results within 1 hour of any large earthquake). We used data from the Alaska array (Figure  2 ), which   , 2023b). The aftershock catalog is also from the AFAD (AFAD, 2023a). Surface rupture traces (black curves with red trimming) are from the USGS (U. S. Geological Survey)  (Reitman et al. , 2023). EAFZ: East Anatolian Fault Zone. SFZ: SürgüFault Zone.
We discretize the simulation domain with a quadrilateral element mesh generated in a previous study  (Xu et al. , 2015)  with the software CUBIT (https:// cubit. sandia. gov/). The elements have a size of 200 m on average, which translates into a spatial resolution of about 50 m (there are multiple internal nodes within each element). The treatment of the fault junction follows the convention commonly adopted for fault branching problems  (DeDontney et al. , 2012; Xu et al. , 2015) , where split nodes run continuously across the junction along the main fault but converge to a single non-split node at the junction along the splay fault. Such treatment allows for through-going rupture along the main fault but terminated rupture along the splay fault (zero splay-fault slip at the junction). The rationale for such treatment is supported by the relative maturity of the main fault, making it more likely to exhibit a continuous fault geometry compared to the splay fault.
We carefully choose the values for other model parameters so that the simulated slip is comparable to the one found in source inversions. Unless mentioned otherwise, we assume model parameters are uniformly distributed along each fault. Their specific values may vary from one simulation case to another. Table  1  summarizes the key model parameters and their values. Superscripts sp and m indicate parameters along the splay fault and the main fault, respectively. We conduct the dynamic rupture simulations with the spectral-element-method software SEM2DPACK  (Ampuero, 2012).
Acknowledgments We are grateful to two anonymous reviewers and the Editor  Stephen Hicks  for comments that help improve the quality of the manuscript. We also thank  Han Yue ,  Lingling Ye ,  Chengli Liu  and  Jinlai Hao  for helpful discussion. SX was supported by the  National Key R&D Program of China   2021YFC3000700  and the  NSFC  grant  42074048. YX and JPA were supported by the  EU  project " DT-GEO ,  A Digital Twin for Geophysical Extremes " (No  101058129 ). JPA was also supported by the  French government  through the  UCA-JEDI Investments in the Future project  ( ANR-15-IDEX-01 ) managed by the  National SEISMICA |  volume 2. 3 |  2023 SEISMICA | RESEARCH ARTICLE | Special issue for 2023 Türkiye/Syria earthquakes | Backward rupture branching during the 2023 Kahramanmaraş earthquake Natural Resources Canada.
2i-l ). As expected, EGFR, HER2, CK8/18, CK5, CD44 and vimentin localized to cell membranes and cytoplasm (Fig. 2g-l ), Ag counterstain was visible throughout cells (Fig. 2m ) and HH3 was detected in nuclei (Fig. 2a-c ). Subsequent IMC of unmatched regions of the same samples showed the same patterns (Extended Data Fig. 4a-m ). These results confirmed that MEZ-XRF recapitulated the expected biological distributions of evaluated markers. We reasoned that MEZ-XRF could be used for quantitative profiling of marker intensities in the cell line models. We segmented individual whole cells and nuclei using cytoplasm and/or membrane and nuclear marker channels with the deep learning-based cell segmentation model Mesmer  29  , and then constructed matched cell and nuclear masks for single-cell comparisons and assessment of Z-tag subcellular localization (Fig.
4p-r ). The phenotypic separation of the nontumorigenic MCF10a cells from the three tumorigenic cell lines was also visible for both data types (Fig. 2r  and Extended Data Fig. 4r ). Per marker, for the top 10% of expressing cells across the four cell types, known nuclear and cytoplasm and/or membrane markers were enriched in the expected subcellular regions for both MEZ-XRF and IMC data (Fig. 2s  and Extended Data Fig. 4s ). Finally, we compared signal-to-noise between MEZ-XRF and IMC for each marker by measuring the difference between the mean signal of the top 10% of cells and the mean signal of the lowest 10% of cells across the four cell types (Fig. 2t ). There was comparable cellular signal between the two methods, with superior signal-to-noise for 1 Hz MEZ-XRF using the SDD for the lower mass channels.
3f ). HER2 was detected at low levels in epithelial cells of the HER2 + sample but not in the luminal A or luminal B HER2 -sample, as expected (Fig. 3g ). Higher sensitivity scans of the HER2 + region in the HER2 + sample confirmed that HER2 expression was localized to cell membranes (Fig. 3g ). The ER protein was detected at low levels in all three samples, with more intense staining in the luminal B HER2 -sample than in other samples (Fig. 3h ). Subsequent imaging of the same samples by IMC revealed the same intensity patterns and subcellular localization as observed in MEZ-XRF images (Extended Data Fig. 5 ). For markers with relatively low expression as measured by MEZ-XRF, subsequent IMC of the same  To evaluate the suitability of MEZ-XRF for tissue phenotyping, we quantified the frequencies of identified cellular phenotypes.
As expected, HH3, pHH3, ER and Ki-67 were located in nuclear regions; CK19, panCK, CD44 and HER2 were located in or near the membrane, and vimentin and SMA were detected in both nuclear and membrane regions, likely reflecting a cytoplasmic localization (Fig. 3k ). In summary, our data demonstrate that MEZ-XRF can be used to nondestructively and repetitively image tens of markers in parallel in tissue at multiple scales and can quantitatively discriminate different tissue phenotypes. G d _ K a |V im T m _ L 3 |E G F R L u _ L 3 |p a n C K L u _ K a |C K 8 / 1 8 E r _ L 3 |C K 1 9 T b _ K a |H E R 2 D y _ K a |C K 7 S m _ K a |C K 1 4 P r _ K a |C K 5 H o _ K a |K i-6 7 A g _ K a |T is s u e C e _ K a |p H H 3 Y _ K a |p H H 3 I r _ L 3 |D N A Y b _ L 3 |H H 3 I _ K a |I d U E u _ K a |H H 3 I n _ K a |H Epithelial 3 2 1 0 -1 E r _ K a |C K 1 9 L u _ K a |p a n C K G d _ K a |C D 4 4 N d _ K a |H E R 2 T m _ K a |V im L a _ K a |P R I n _ K a |S M A P r _ K a |K i-6 7 E u _ K a |p H H 3 C e _ K a |E R H o _ K a |H.
7c, d ). Using antibodies conjugated to standard Z-tags, markers detected using 10-Hz MEZ-XRF such as HER2 and CK7 yielded no signal by IMC (compare Fig. 4c, d  and Extended Data Fig. 7c ). Further, 10-Hz MEZ-XRF with GeCMOS2 showed superior signal-to-noise to IMC (Fig. 4l ) meaning that MEZ-XRF with GeCMOS2 achieved signal-to-noise equivalent to standard 200-Hz IMC. Use of SABERx2 increased signal-to-noise relative to the antibodies conjugated to standard Z-tags for both MEZ-XRF and IMC (Fig. 4m  and Extended Data Fig. 7 ). Thus, SABERx2 conferred higher sensitivity to both techniques for markers with otherwise borderline detection such as CK7 (Fig. 4d ,i and Extended Data Fig. 7c, d ). Given the increased signal-to-noise ratios of SABERx2-amplified MEZ-XRF, we stepwise increased imaging speed to assess the speed limits of our system.
Marker signal declined proportionally to dwell time (Fig. 4l-m ), but nevertheless markers were still clearly detectable at 1. 5 kHz (Fig. 4n-q  and Extended Data Fig. 8 ). With the setup available, we could not increase raster speed further due to software latency limitations of reading full spectra from the GeCMOS2 detector. In addition to dramatically improving imaging speed, SABERx2 combined with GeCMOS2 made detection of low-abundance molecular targets possible. In breast cancer tissue samples, several markers that were not detectable with standard Z-tags (Extended Data Fig. 9 ) were detectable with SABERx2 Z-tags (Fig. 5a-e ). These markers included PR (Fig. 5c  and Extended Data Fig. 9d ), the immune regulators PD1 (Fig. 5d  and Extended Data Fig. 9e ) and CTLA4 (Fig. 5n  and Extended Data Fig.
9f ), and the T cell markers CD3, CD4 and CD8 (Extended Data Fig. 10b-d  and Extended Data Fig. 9g-i ). Single-cell analysis of these data distinguished ten cell clusters including multiple epithelial cell types (ER + , PR + , HER2 + ) consistent with the original diagnosis (Fig. 5f-h ). In addition, T cell subtypes, including CD3 + CD4 + CTLA4 + PD1 + T cells (likely exhausted regulatory T cells) and CD3 + CD8 + PD1 + exhausted cytotoxic T cells, were identified in the immune-infiltrated HER2 + and luminal B samples (Fig. 5f-h ). Subsequent IMC of an overlapping region from the same samples demonstrated similar staining intensities to those in the MEZ-XRF data (Fig. 5i-m  and Extended Data Fig. 10k-s ). The analyzed regions did not completely overlap, and there were image resolution and channel sensitivity differences between IMC and MEZ-XRF that led to cell segmentation differences that complicated a one-to-one cell comparison between methods.
However, we observed consistencies between methods, notably that the same cell phenotypes were identified and that distributions of cell phenotypes across sample types was the equivalent in images from both methods (Fig. 5g, h, o, p ). Overall, our data support the use of MEZ-XRF as a cytometric tool. (f) and Ce_Ka|ER (g). Overview scans were imaged with a 500 nm focused 69 keV X-ray beam with 2 µm raster steps at 20 Hz. ROI (indicated by red box and shown magnified to the right) were imaged with a 500 nm focused 69 keV X-ray beam with 0. 5 µm raster steps at 5 Hz. XRF emissions were recorded with a prototype GeCMOS detector. Images are 0. 5-99. 5% gray levels of detector counts per element emission line matched across samples. h, Leiden clustering of single cells segmented from overview images colored by clusters as annotated by marker enrichment.
7 normalized transcripts per million (nTPM)  33  ). A potential issue for the germanium detectors is that escape peaks are more intense than with the SDD, which could introduce signal interference  34. However, this did not appear to be an issue in our analysis, possibly due to the low concentrations of element per Z-tag used in our experiments. Moreover, escape peaks for a germanium detector are accounted for in our PyMCA spectral deconvolution configuration  35. We showed that SABERx2 amplification of metal signals  25  increased sensitivity 10-100-fold relative to standard Z-tags, enabling detection of low-abundance epitopes (for example, T cell CTLA4 at 41. 9 nTPM and PD1 34. 7 at nTPM  33  ). For both MEZ-XRF and IMC, SABERx2 conferred improved marker sensitivity, which for MEZ-XRF, enabled faster imaging.
XRF image processing A Python (v. 3. 8. 8)-based pipeline (key packages listed in Supplementary Table  8 )  [45] [46] [47] [48] [49]  was assembled for processing. hdf file outputs for each scan into 2D element emission lines, which were stacked into a 3D array (x, y, emission_line). Full Jupyter notebooks  50  with details of the pipeline and a Conda environment. yml file to run the pipeline are available at https://github. com/BodenmillerGroup/MEZ_XRF. Notebook 1 includes instructions for stitching together scans collected in multiple. hdfs files due to interruption by a beamline-related event. Notebook 2 details the https://doi. org/10. 1038/s41592-023-01977-x method for deconvolution of per-pixel spectra with PyMCA to 3D image stacks (x, y, emission_line). Notebook 3 has instructions for normalization of image stacks to beam intensity fluctuations with an fpico_mask generated from the fpico3 diode recordings.
The remaining notebooks have the instructions necessary for replication of downstream analysis and reproduction of figures presented in this paper. Deconvolution was performed with a Python implementation of the PyMCA package with custom configuration files (detector calibration and elements to deconvolute). Detector spectra deconvolution configuration files and raw. hdf spectra are available at Zenodo (https:// doi. org/10. 5281/zenodo. 7949102)  51. For each scan, the deconvoluted raw image stack, a fpico_normalised image stack (to normalize within scan beam intensity fluctuations) and a Compton_scatter_normalised image stack (to facilitate between scan comparisons) were stored as 3D array datasets under their italicized names in the images node of a custom. hdf file with a structure that we term high_plex_hdf that includes images, sample metadata, a Pandas dataframe.
Single-cell and single-nucleus segmentation For single-cell analysis, XRF image stacks were segmented using the pretrained deep learning segmentation model Mesmer from the Deepcell v. 0. 9. 0 package  29. A nuclei_mask and a cell_mask were generated for each scan using Mesmer with default parameters and a specified pixel size. Mesmer operates on a two-channel 2D input (a nuclear and a cytoplasmic and/or membrane channel), with one channel or the average of multiple channels mean averaged for these two-channel inputs. The 0. 5-99. 5% pixel intensity values for each channel were normalized to between 0 and 1, then the average of these normalized channels was used for Mesmer segmentation. Channels that gave good nuclear or cell membrane staining were averaged in this manner for cell segmentation input.
Single-cell visualization and clustering In Scanpy, a uniform manifold approximation and projection (UMAP) was used to visualize high-dimension single-cell expression profiles  55  , and Leiden graph clustering was used to identify cell types  30. For cell pellet and breast cancer datasets, this analysis was applied to a set of informative markers normalized to the 2-98th percentile to remove outliers. For cell pellet scans, six informative markers were used: four cell-type specific markers (vimentin, HER2, CK19, EGFR), IdU and mitotic pHH3. Cell pellet UMAPs (k = 17) were Leiden clustered with resolution 0. 2 (code available at 5b_cell_pellet_XRF_analysis. pynb). For breast cancer scans, UMAPs and Leiden clustering were based on the nine markers detectable by XRF (ER, CD44, PR, SMA, HER2, panCK, Ki-67, CK19 and vimentin).
IMC Sample sections on Mylar film after XRF imaging were cut and taped flat onto a glass slide for staining. IMC was conducted using the Fluidigm Hyperion Imaging system using Fluidgm CyTOF IMC software (v. 7. 0. 8493). For direct comparison to XRF, the same Mylar film-mounted gelatin standards and tissue samples imaged by nondestructive XRF were imaged by destructive IMC. For IMC, Mylar film-mounted sections were cut from their acrylic windows and taped flat onto glass slides suitable for Hyperion loading and laser-ablation raster imaging. Samples were raster ablated with a 500-nm step size (200 Hz) to match the 500-nm focal spot used for 500 nm per-pixel XRF. The regions ablated by IMC were raster ablated with two or three rounds of imaging, ensuring that all regions scanned by IMC were completely ablated.
Multichannel images with different isotopes in each channel were extracted from the. mcd files output by IMC using imctools v. 2. 0 (https://github. com/Bodenmill-erGroup/imctools). The region imaged by XRF was manually identified in IMC scans (Extended Data Figs. 4  and 5 ). The horizontal dimension of IMC images was scaled 1/1. 3 to match XRF images (which were collected at a 40° angle). IMC overview scans matched to the SDD MEZ-XRF images were down-sampled 1/4 to simulate the 2-µm step scans used for MEZ-XRF overview images. For comparisons of IMC ROI to XRF ROI, there was no down-sampling. In all instances, at least a 67% overlap was achieved MEZ-XRF and subsequent IMC images. https://doi. org/10. 1038/s41592-023-01977-x Extended Data Fig. 1  | Overlap of X-ray fluorescent emissions. a, Upper: X-ray (red) displacement of a core shell electron creates a vacancy; high energy X-rays are able to displace electrons from central shells.
Acknowledgements We thank patients who donated tumor samples and  P. Schraml  and  S. Dettwiler  at the  Institute of Pathology and Molecular Pathology, University Hospital Zurich , for coordinating and assembling the breast cancer tissue microarray. We thank  N. de Souza  for thorough and critical evaluation of the paper,  S. Weidner  and  L. Lüchinger  at the  AddMan facility (University of Zurich ) for help designing and making custom scintillated nylon parts,  J. Windhager  for coding advice and  S. Zhao  for help conducting beamtime. We also acknowledge  Mirion Technologies (Canberra) SAS  for lending the demonstration GeCMOS detector and  C. Cohen  ( ESRF ) for making us aware of the GeCMOS detector, arranging the loan, and testing and calibration of the GeCMOS detector before the experiment.