dataesr/scientific-paragraphs-categorization huggingface dataset:
Synthetic Chenopodium data set Synthetic plant point clouds (Figures  8B-E ) were also generated to comparatively evaluate our approach using virtual plants according to the method described in  (Chaudhury and Godin, 2020). The development of Chenopodium plants was modelled using L-systems and the L-Py software  (Boudon et al. , 2012). The simulated virtual plants were then sampled to obtain ground-truth labelled point clouds. By construction, three semantic classes were available in the models: petiole, stem and leaf blade. This means the ground truth segmentation is a semantic segmentation limited to 3 semantic classes. The model does not contain the apex class. This class was introduced in our segmentation algorithm to represent the aggregated set of small leaves at the extremities of growing stems.
An entity with the apex class in the segmentation thus corresponds to a set of terminal aggregated small leaves in the ground truth. We thus considered that apices in the segmented data correspond to the leaf class in the ground truth. Similarly, we merged the branch and main stem classes in the segmented data. 24 synthetic Chenopodium point clouds were generated to evaluate our semantic segmentation. Each point cloud contains 10,000 points. Evaluations were conducted on a 2. 6 GHz Intel Core i7 processor and 32 GB of memory. Our segmentation method processes the whole data set of 24 point clouds containing 10 000 points each in an average of 12 minutes in total.
Semantic segmentation evaluation We first evaluate the semantic segmentation result of our pipeline on the different data sets. Figure  9  shows several examples of these results on different plant individuals. In general leaf blades are correctly detected and accurately delineated and the stem is robustly detected. However, petioles are sometimes merged with the corresponding leaf blade or with the stem they are attached to. To quantify these tendencies, we assessed how well each point has been clustered in the category indicated by an expert (or by the simulation algorithm in the case of virtual plants). For this, we used the following classical metrics used in similar works [e. g  (Turgut et al. , 2020) ]. (5).
Results on our data sets We first tested our approach on our synthetic ground truth data as a control experiment. The pipeline was run on the synthetic Chenopodium data set with and without the refinement stage of our approach (see Section 2. 4). This produced segmented point clouds as an output, i. e. points labelled with one of the classes: leaf blade, main stem, branches, petioles and apex. We then applied the above metrics to evaluate these results (remember that to carry out this analysis of the synthetic data set results, we had to merge the main stem and branch classes to a unique 'stem' class, and apex and leaf blade to a unique 'leaf blade' class), see Table  2. We note that leaf blades were overall well segmented with high recall and precision values and a segmentation score (IoU) of 90%.
The refinement however improves the results, as shown in Figures  10E, F ), when the petiole has been labelled as part of a leaf blade. Altogether, for synthetic plants, the refinement procedure did not have a good balance between correcting errors and producing new ones. However, we will see that this procedure may have a positive impact on the segmentation of different organs for real plants. We then tested our algorithm on five Chenopodiums from the real data set for which a ground truth segmentation was manually made. Here, this manual ground truth made it possible to reuse the exact same classes as the ones produced by the algorithms. We, therefore, did not have to merge any of the original classes as we did in the case of synthetic plants. Quantitative results are given in Table  3. In general, results are comparable to the ones obtained on the synthetic plant data set, if we consider classes that appear in both tables (note that the 'Stem' class from the previous table is split here into 'Main stem' and 'Branches').
These results can be compared to those obtained in  (Schunck et al. , 2021)  where the authors used the whole set of tomato plants available and segmented the data set with three different deep neural networks: PointNet  (Qi et al. , 2017) , PointNet++  (Qi et al. , 2017)  and LatticeNet  (Rosu et al. , 2019). The IoU for the classes leaf and stem were respectively 84 % and 12 % for PointNet, 89 % and 20 % for PointNet++ and 99 % and 87 % for LatticeNet. While our algorithm performs better than the PointNet and PointNet++ networks for stem detection, it is not as accurate as LatticeNet on this dataset. We also checked how these scores depended on the stage of development of a tomato plant (Figure  1A ). The 14 individuals in the tomato plant data set actually correspond to 2 plants observed at 7 stages of development.
Due to the assumed far-field limit for the observation point r , it is useful to examine the behavior of the scattered field in terms of the scattering amplitude E sca 1 given from Eq. (  2 ) E sca (r ) = exp (ikr) r E sca 1 ( ˆ r ). ( ) 3 Note that E sca 1 depends only on direction (θ , φ) , i. e. , ˆ r , and not on the distance r. The DDA method can be used to find E int accurately within the aggregate. Indeed, the DDA can be thought of as a numerical solution of Eq. (2)  [13]. Here, the code DDSCAT developed by Draine and Flatau  [37]  is used to numerically determine E int. An aggregate's physical 3D shape is discretized on a cubic lattice of volume elements V that are d × d × d in size. Each element is then assigned an electric dipole moment, which are excited by the incident field and couple to each other.
The reader can download a python script for calculating the corrected RDG-FA based on the current work by using the link https://gitlab. coria-cfd. fr/c-rdgfa/C-RDGFA. As a pursuit of this work, the current methodology will be applied to more realistic aggregates by considering, for example, monomer polydispersity, necking, overlap and different fractal dimension and prefactor. It could be also adapted to larger range of refractive indices in order to be applicable to other aggregate materials.
Acknowledgments This work was financed by  ANR ASTORIA  (N •  ANR-18-CE05-0015 ). The authors thank the CRIANN numerical resources supported by the  Normandy, France region. The authors also thank  José Moran  for providing them with a database of fractal aggregates. MB acknowledges support from the  National Science Foundation , awards  1453987  and  1665456 , the  U. S. Air Force Office of Scientific Research  award  FA9550-19-1-0078 , and the  U. S. Army Research Office  award  FA9550-21-1-0339. RC acknowledges support from  ONERA  ( PROMETE  project).
, 2012)  and FTLD  (Cruts et al. , 2013)  mutation databases, the C9orf72 repeat expansion is the most common mutation documented and thus constitutes one of the most important known risk factors for an ALS/FTLD spectrum disorder. Recently, the first mouse model has been developed that reproduces the human form of C9orf72 repeat expansion  (Liu et al. , 2016). A mouse line with $500 G 4 C 2 repeats (c9-500) demonstrates some behavioral and neuropathological features of ALS/FTLD  (Liu et al. , 2016; Batra and Lee, 2017; Nguyen et al. , 2020a Nguyen et al. , , 2020b;; Zu et al. , 2020) , depending on genetic and environmental factors  (Mordes et al. , 2020; Le Bras, 2021). Interestingly, similar to data obtained on human patients with C9orf72 repeat expansion  (Verde and Ticozzi, 2016; Curtis et al.
Behavioral screening in young c9-500 female mice To investigate the effect of C9orf72 genetic expansion before the potential onset of ALS/FTLD-like symptoms in c9-500 mice, we conducted a comprehensive study on young adult c9-500 females (6-12 weeks of age). We probed ALS-related traits using the grip test as a robust test for muscle strength  (Olivan et al. , 2015; Takeshita et al. , 2017)  and the open field test (OFT) as the most sensitive assay for early detection of chronic motor deficits in rodents  (Hayworth and Gonzalez-Lima, 2009). Young c9-500 females showed no deficit in the grip test (16 mice/genotype, p = 0. 4619, Wilcoxon test; Figure  1A ) and no changes in their body weight (20-21 mice/genotype, p = 0. 4365, two-way unpaired t test; Figure  1B ). Moreover, in the OFT, there were no impairments in animal locomotion as most parameters, including the total ambulatory distance, the locomotion track patterns, (F-G) Summary data for rearing activity (F) and self-grooming (G) in WT versus c9-500 females.
Predicting C9orf72 expansion from M1 output and locomotion speed To examine whether M1 output at a specific animal speed contains information predictive of mutation, we trained deep-learning algorithms to take the M1 output and the animal-speed samples as the inputs and to calculate the probability of the animal belonging to the mutant group (see STAR Methods and supplemental information for more details). We first explored the datasets comprising both M1 output and speed time series using the massive time series feature-extraction approach  (Christ et al. , 2018). Using the feature importance scores from a random-forest model to condense the feature set to the 30 most discriminative ones, we then performed a two-dimensional embedding of the computed 30-dimensional feature vectors using a linear principal-component analysis (PCA) and non-linear t-distributed stochastic neighbor embedding (t-SNE) as well as uniform manifold approximation and projection (UMAP) embedding techniques.
STAR+METHODS Detailed methods are provided in the online version of this paper and include the following: mesh was released. Three trials were performed for each animal. The highest force generated by pulling the animal away from the wire mesh was recorded, and the average value across the three trials was used for analysis between genotypes. d KEY RESOURCES To monitor locomotor, rearing and self-grooming activity, the open field test (OFT) was performed on freely behaving animals in the open arena (36 3 29 cm) during which mice stayed in the arena for 5 min, and their activity was recorded using a video tracking camera (Digital USB 2. 0 CMOS Camera, 60516, Stoelting Co). The animal locomotion track, total distance, mean and maximum speed, total absolute (body and head) turn angle and angular speed were analyzed automatically using the ANY-maze software, whereas rearing and grooming behaviors were analyzed manually.
Starting from two weeks post-injection of AAV1. Syn. GCaMP6f. WPRE. SV40, mice were exposed to daily handling and habituation sessions for 4-5 consecutive days, during which a dummy head-stage was attached to the implanted cannula and animals were placed in the home cage for 5 min. Following this habituation period, mice were connected with the TeleFipho transmitter head-stage and placed in the OFT arena where they were allowed to explore for 5 min. During this period, fiber photometry Ca 2+ -signal was acquired wireless by the TeleFipho receiver at a sampling frequency of 100 Hz. Behavior of mice in the arena was video recorded using a camera (Digital USB 2. 0 CMOS Camera, 60516, Stoelting) and animal tracking was performed using ANY-maze software (Stoelting Co. ). Between animals the arena was cleaned with 70% ethanol.
Data acquisition (10 kHz sampling rate) was performed using the pCLAMP10 software (Molecular Devices). Data were analyzed using Clampfit 10. 7 and IgorPro software. For analysis of mEPSCs and mIPSCs, 100-200 events/cell were extracted using automatic detection in Clampfit 10. 7. The tonic GABA A R current was calculated as a difference in the holding current at 0 mV between control condition (in the presence of GABA, 5 mM), and after perfusion with Ptx or a5-GABA A R subunit inverse agonists (L-655,708 and MRK-016).
Images were collected using a 20x (NA 0. 8) and 63x (NA 1. 4) objectives and a laser-scanning Leica SP5 confocal microscope (Leica Microsystems). Z-stacks of biocytin-filled cells were acquired with a 1-mm step size, which after were used for morphological reconstruction in Neurolucida software. Post-hoc validation of in vivo imaging site After the fiber photometry experiments, mice were perfused with PFA-PA and brain sectioned (50 mm). Sections containing the cannula track were processed for immunolabeling using a primary chicken GFP antibody (1:1000, Aves labs, cat. #GFP-1020) and a secondary donkey anti-chicken AlexaFluor-488 antibody (1:500, Jackson ImmunoResearch). Confocal images were collected using the oil-immersion 20x (NA 0. 8) objective and a laser-scanning Leica SP5 confocal microscope, and the expression of GCaMP6f (GFP) as well as the cannula position were confirmed.
For both high-speed locomotion and immobility periods, the time frames were extracted from the animal speed and corresponding Ca 2+ -imaging data using scripts written in MATLAB. Time frames corresponding to the Cell Reports 40, 111043, July 5, 2022 e4 Report start and end of the behavioral state, such as self-grooming, were obtained by manual analysis based on video files recorded. The mean and maximum DF/F during a particular behavioral state was obtained using a MATLAB script detecting the dynamic threshold for calculation of DF/F.
Statistical analysis Summary group data are presented as box plots with the minimum, first quartile, median, third quartile and the maximum indicated. The outliers were kept in all datasets. For statistical analysis, distributions of the data were tested for randomness using Runs test or for normality using the Jarque-Bera test in IgorPro. Statistical significance was assessed using both parametric (unpaired t-test) and non-parametric (Mann-Whitney or Wilcoxon signed-rank test) statistical analyses as appropriate. p-values < 0. 05 were considered significant.
Machine learning modeling We formulated a binary time-series classification problem based on the in vivo recorded Ca 2+ -imaging data and animal speed and trained deep neural networks (DNNs) to predict whether an animal belongs to the WT or the mutant (c9-500) group. To align the Ca 2+ -imaging data (100-Hz sampling rate) with animal speed (10-Hz sampling rate), the speed signal was up-sampled using linear one-dimensional interpolation with an up-sampling factor of 10 to match the number of timestamps in the imaged Ca 2+ -signal. The experimental dataset consisted of recordings from 7 WT and 7 c9-500 animals, with a single signal of 30,000 timestamps per animal in both neural activity and speed modalities. We applied standard scaling along the time axis to signals of both modalities (subtracting the mean and dividing by the standard deviation value).
We further applied a de-noising procedure to both channels in the signal by convolving them with a Gaussian kernel with s = 10 timestamps. We then applied a rolling window of size equal to 500 timestamps with a step of 100 timestamps to produce a dataset of fixed-length time series of both activity classes (WT and mutant mice). This procedure resulted in a dataset of 3,645 samples with 2 channels (modalities) of 500 timestamps each. The objective was to train a machine learning model to predict the class label (0 for WT, 1 for mutant) for each sample. This essentially boiled down to a multivariate binary time-series classification problem with a balanced class distribution. Due to the small size of the full dataset, we did not perform a single train/test split, but rather utilized a leave-one-out cross-validation scheme, whereby performing 14 training experiments for each model corresponding to every one of the animals being consecutively put into the test set while the rest of the data comprised the train set.
All computational experiments were done using the Python programming language. The original code has been archived at lzrvch/ als_marker_detection: v0. 1 (https://zenodo. org/record/6596555). The implementations of time-series feature extraction routines we used were from the tsfresh  (Christ et al. , 2018)  and sktime  (Loning et al. , 2019)  packages with the random forest classifier implementation taken from scikit-learn  (Pedregosa et al. , 2011). Deep learning model implementations for time-series we used were taken from the tsai package based on the PyTorch framework  (Paszke et al. , 2019). Benchmarking DNN architectures for time-series data We compared performance of a range of different neural network architectures commonly used for sequence modeling in general and time-series classification in particular such as various one-dimensional convolutional architectures including the FCN  (Wang et al.
, 2016) , XCM  (Fauvel et al. , 2021)  and InceptionTime  (Ismail Fawaz et al. , 2020)  as well as non-convolutional architectures such as the fully-connected MLP  (Fawaz et al. , 2019) , Transformers and more recent Time-Series Transformers (TSTs)  (Zerveas et al. , 2021)  as well as an array of different recurrent architectures like LSTMs and GRUs  (Chung et al. , 2014) (Figure S2B and S3). Surprisingly, we found that the simple FCN baseline appears quite hard to beat with more advanced approaches. The default hyper-parameters for the training procedure used throughout the work are a constant learning rate schedule with a learning rate of 1e-3 and 50 training epochs and an SGD optimizer, with a PyTorch-based implementation of the training procedure provided in the tsai package. First, we selected a single animal to first evaluate a whole zoo of architectures available in the tsai package on.
We further checked whether increased accuracy variance due to reduced channel count was present across all animals during the leave-one-out validation, which turned out to be true (Figure  S4A ), with an increased variance in predictions in spite of the same number of misclassified animals as for the vanilla FCN model. We have additionally carried out experiments with a simpler validation scheme (Figure  S4D ) whereby we split the dataset into training and validation in a 70/30 ratio. The split was grouped such that animals from the validation and the training set did not overlap. We have found values of our classification metric of choice (F1 score) to be significantly above the chance level in this case as well for different random training/validation splits of the dataset.
ACKNOWLEDGMENTS We acknowledge the  Genie Program  and the  Janelia Farm Research Campus  for GCaMP6f:  L. L. Looger, PhD ,  J. Akerboom, PhD , and  D. S. Kim, PhD. This work was supported by the  ALS Canada/Brain Canada Discovery Grant , the  Canadian Institutes of Health Research (CIHR  grant nos. MOP-137072  and  MOP-142447 ), and the  Natural Sciences and Engineering Research Council of Canada  ( NSERC  grant no. 342292-2012 ) to L. T.
This work benefited from the support of the  ANR PhotoBiofilm Explorer  ( ANR-20-CE43-0008 ) This work benefited from the financial support of the LabeX LaSIPS project Greenbelt managed by the  French National Research Agency (ANR)  under the " Investissements d'avenir" program , to O. B. & F. L. This work also benefited from the support of the  Treilles Foundation , to O. B. & F. L. The authors are grateful to the  OPAL infrastructure from UniversiteCote d'Azur  for providing resources and support, to O. B. & B. P. The funders had no role in study design data collection and analysis decision to publish or preparation of the manuscript.
Acknowledgements The authors would like to thank the three anonymous reviewers for their insightful comments which improved the manuscript. We thank  G. Myhre ,  M. Prather ,  K. Shine , and  D. Stevenson  for helpful discussions. This study was partly funded by the  Research Council of Norway  under project No. 320240  " HYDROGEN: Climate and environmental impacts of hydrogen emissions " and by the  Direction Générale de l'Aviation Civile (DGAC)  under the  "Climaviation" project  (DGAC agreement  N°2021-39 ). The simulations were performed using HPC resources from  GENCI  ( Grand Equipement National de Calcul Intensif)  under project  gen2201.
Data availability The GFDL-AM4. 1 model results used in this study are available at https://drive. google. com/drive/u/1/folders/1JNDcA8tdaVfHNmr1OuZzJnFPX7uqOZ-N. The H 2 and CH 4 climate metrics calculated in this study as a function of the time horizon are available at https://doi. org/10. 5281/zenodo. 7244779.
Breast cancer metastases are most commonly found in bone, an indication of poor prognosis. Pathway-based biomarkers identification may help elucidate the cellular signature of breast cancer metastasis in bone, further characterizing the etiology and promoting new therapeutic approaches. We extracted gene expression profiles from mouse macrophages from the GEO dataset, GSE152795 using the GEO2R webtool. The differentially expressed genes (DEGs) were filtered by log2 fold-change with threshold 1. 5 (FDR < 0. 05). STRING database and Enrichr were used for GO-term analysis, miRNA and TF analysis associated with DEGs. Autodock Vienna was exploited to investigate interaction of anti-cancer drugs, Actinomycin-D and Adriamycin. Sensitivity and specificity of DEGs was assessed using receiver operating characteristic (ROC) analyses.
Materials The expression profile information of mouse macrophages was extracted from the GEO dataset, GSE152795 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE152795). The annotation was performed using Affymetrix GeneChip ® Mouse Gene 2. 0ST Array. The dataset included breast cancer bone metastasis in healthy mouse strains, with a sample size of five in each group. Bone metastasis was generated through intracardiac injection of 1 × 10 5 tumor cells into 4-week-old female mice, while healthy mice were not subjected to intra-cardiac injection.
Identification of differential expressed genes (DEGs) The GEO2R (http://www. ncbi. nlm. nih. gov/geo/geo2r/) web tool was used to compare mRNA expression between breast cancer metastasis and normal tissue samples in mice. The Benjamini and Hochberg false discovery rate (FDR) method was used to filter out false-positive results, and the adjusted p values were calculated. The differentially expressed genes were filtered based on log2 fold change, with a threshold value ≥1 and p < 0. 05. To determine the difference among samples and graphically depict the data, we used a built-in tool of GEO2R Uniform Manifold Approximation and Projection (UMAP). A volcano plot showing differentially expressed genes was constructed using the R Language ggplot package. The receiver operating characteristics (ROC) curve was plotted to determine the specificity and sensitivity of the identified DEGs towards controls and tumor samples using Stata v17.
Protein preparation: The 3D structure of the targets FABP4 (PDB ID: 3P6D) and IRF4 (PDB ID: 7JM4) were extracted from the Protein Databank. The protein structure of ADGRG7 was extracted from the AlphaFold Protein Structure Database (https://alphafold. ebi. ac. uk/entry/Q96K78). Local minimization of the structure was done by the GROMOS96 (43B1 parameter set) and the Swiss-pdbViewer used for implementation  [14]. The CASTp (Computed Atlas of Surface Topography of proteins) web tool was used to extract ligand binding site information  [15].
Binding in silico: The protein preparation in pdbqt-format was executed using AutodockTools 1. 5. 4 suite  [18]. The molecular docking analysis was performed to develop an improved modeled structure by Autodock Vina Software  [19]. After docking analysis, the obtained structures were detected by the Protein-Ligand Interaction Profiler (PLIP) web server (Technical University of Dresden) and tabularized for further analysis  [20]. Figure  1  presents an overview of methods used in present differential expression analysis of macrophages of healthy bone & breast cancer bone metastasis.