dataesr/scientific-paragraphs-categorization huggingface dataset:
5 • C. After completion of the IEF, proteins on the strip were equilibrated for 15 min in a buffer containing 6 M urea, 1% DTT, 30% glycerol, 50 mM Tris base, 2% Sodium dodecyl sulfate (SDS) and DTT, and then for an additional 15 min in the same solution, except DTT was replaced by 5% iodoacetamide. The immobilized pH gradient strips were then transferred onto 12% T, with 2. 6% C separating polyacrylamide gels, and proteins were separated in the second dimension using a Protean Plus Dodeca cell system (BioRad, Hercules, CA, USA). Gels were stained with G250 colloidal Comassie Blue. Protein spot detection was realized with SameSpots V4. 5 software (Nonlinear Dynamics, Newcastle, UK). Protein spots from the two ATs (30 gels, 15 for perirenal and 15 from omental ATs) or the 2 muscles (30 gels, 15 from Semitendinosus and 15 from Longissimus thoracis muscles) were matched to a reference gel chosen from the 30 gels from one tissue type.
For MS/MS, the isolation width for the ion precursor was fixed at 2 m/z and single charged species were rejected; fragmentation used 37% normalized collision energy as the default activation of 0. 25. At the end of each LC-MS/MS analysis, the raw files were loaded into the Proteome Discoverer v1. 4 (Thermo Fisher Scientific, Illkirch-Graffenstaden, France) and identification was performed with Mascot v2. 5 (http://www. matrixscience. com, Matrix Science Ltd, London, UK). The database used for the search was UniProt Bos taurus (37,513 sequences, FASTA file version: 7 November 2019), with the following parameters: peptide mass tolerance was set to 1. 5 Da, fragment mass tolerance was set to 0. 5 Da, and a maximum of two missed cleavages were allowed. Variable modifications were methionine oxidation (M) and carbamidomethylation (C) of cysteine.
Gene Ontology (GO) Analysis Identifiers for adipose and muscular proteins were launched independently for a basic GO analysis (default options within ProteINSIDE, http://www. proteinside. org) in order to identify biological pathways associated with fetal adipogenesis and myogenesis. The GO enrichment analyses were achieved in the human species using human orthologs. The Benjamini-Hochberg (BH) adjusted p-values were considered to establish lists of significant enriched pathways in each dataset as compared to the whole genome. Only GO terms with BH p-values < 0. 01 that had annotated at least 2 proteins were considered to describe the adipose and muscular fetal datasets. Additionally, biological functions related to adipogenesis and myogenesis were searched from results of the IDresume module provided by ProteINSIDE, gathering descriptions of protein functions from Uniprot and NCBI databases.
5, and TargetP score ≤ 2. The second prediction analysis aimed to identify proteins that are secreted through unconventional pathways (i. e. , membrane flip-flop or vesicule secretion  [82] ), through a local version of TargetP  [81]  and the use of GO terms related to extracellular components. For this prediction, we used the identifiers and protein sequences from the bovine species that were launched in the "basic analysis" with the default options. The following criteria were used to declare a protein as putatively secreted through pathways that do not involve a SignalP sequence: TargetP annotation "other secretory pathway", plus TargetP score ≤ 2, plus at least one GO term related to an extracellular location. In order to confirm the results, the prediction of proteins secreted both by a signal peptide or by others pathways was compared to the lists of proteins declared in the human plasma database  [18] ), of proteins identified in bovine plasma  [19] , and proteins proposed to be secreted by AT or muscle in bovine  [20].
Computational Prediction of Protein-Protein Interactions between Cell Surface Proteins and Secreted Proteins Interactions between proteins secreted by a tissue and cell surface proteins from another tissue were searched with the Protein-Protein Interactions (PPI) module of ProteINSIDE. PPI between the proteins were identified thank to the PPI referenced within UniProt (https://www. uniprot. org) [83] , IntAct (https://www. ebi. ac. uk/intact/main. xhtml) [84] , and BioGrid (https://thebiogrid. org)  [85]  databases. The input data were the gene names of the human orthologs of bovine proteins in order to take advantage of the well-known PPI characterized in humans. As already described  [79] , these PPI databases were chosen as a default option in the PPI analysis module of ProteINSIDE because these are updated daily and reviewed by curators.
Moreover, they are included in curation processes of the IMEx (which ensures reliable interaction data using experts and curation rules shared between many interaction databases  [86] ) or MIMIx projects (a guideline of the minimum information required for reporting a molecular interaction experiment, thus advising the user on how to use the interaction data  [87] ). Only PPI that were proven by experiments are considered by ProteINSIDE. Lastly, and to ascertain the biological significance of these interactions in the context of AT and muscle biology, we verified the synthesis of a protein by a tissue by using the experimental results reported in the Expression Atlas database (https://www. ebi. ac. uk/gxa/home)  [88].
Acknowledgments: The authors acknowledge L. E. Camacho  for conducting the animal portion of the project,  M. Reichstadt  for the maintenance of ProteINSIDE,  C. Barboiron  for the 2-DE analysis and spot preparation,  A. Delavaud  for their help in figure preparation and data analysis, and  D. Chadeyron  for their help in the shipment and reception of fetal tissue.
Funding: The PhD grant for N. Kaspric was funded by the  Regional Council of Auvergne (France)  and  Apis-Gene. The animal portion of the project was supported by  Agriculture and Food Research Initiative Competitive  Grant no. 2009-65203-05812  from the  USDA National Institute of Food and Agriculture  awarded to K. V. and  Hatch  Project number  ND10782  awarded to K. V. This study was realized in the framework of the  French government IDEX-ISITE  initiative  16-IDEX-0001  ( CAP 20-25 ).
Human Activity Recognition requires very high accuracy to be effectively employed into practical applications, ranging from elderly care to microsurgical devices. The highest accuracies are achieved by Deep Learning models, but these are not easily deployable in handheld or wearable devices with very constrained resources. We therefore present a new HAR system suitable for a compact FPGA implementation. A new Binarized Neural Network (BNN) architecture achieves the classification based on data from a single tri-axial accelerometer. From our experiments, the effect of gravity and the unknown orientation of the sensor cause a degradation of the accuracy. In order to compensate for these issues, we propose a HW-friendly algorithm to pre-process the raw acceleration signal. Moreover, the very low power and hardware friendly BNN has been trained and validated on the PAMAP2 dataset, for which the pre-processing operations increase the accuracy from 51% to 99% in the best case.
Unfortunately, the price of these benefits is a significant loss in accuracy, which is an obstacle to the use of reduced precision DL models in actual systems  [11]. In this work, the performance of a partially-binarized hybrid neural network (HNN)  [16]  is evaluated when it is used to build a HAR system designed to overcome the above limitations. We evaluate the accuracy of the model when data is acquired from only one tri-axial accelerometer, along with the amount of physical resources for its HW implementation. Next, we evaluate the accuracy advantage of a custom pre-processing stage considering its HW resource requirements. Results show that the HAR system, made of accelerometer, pre-processing stage, and HNN, exhibits an average recall up to 99% when trained on the PAMAP2 dataset  [17].
II. HYBRID NEURAL NETWORK The HNN is similar to a BNN, but while all weights are binarized, i. e. constrained to +1 and -1, the output activations for some layers are not. In accordance with  [16] , the HNN can classify between 5 different human activities. To test, train, and validate the model, we used the PAMAP2 public dataset  [17] , selecting 5 standard activities. The HNN model, as schematized in Fig. 1 , has been built using Lasagne  [18].
B. Training and Accuracy Results The accuracy of the proposed HAR system has been evaluated by using data from the PAMAP2 dataset. The dataset provides data from 9 users performing 12 daily human activities. Three Inertial Measurement Units (IMUs) and a heart rate sensor are used to collect the data. Each IMU was located in a different part of the body, thus 3 sensor positions can be chosen: ankle, hand, and chest. In our system, the input comes from a single accelerometer. Therefore, only the samples from the accelerometers have been considered. Also, for each position, two different ranges can be selected for the accelerometer: ±16g, and ±6g, but in both cases the sensor use 13 bits to represent the data. Thus, a different sensitivity is related to the two ranges. In total, data from 6 different sensors can be considered, namely: ankle16g, ankle6g, hand16g, hand6g, chest16g, chest6g.
The data have been down-sampled from 100 Hz to 25 Hz, considering the usual frequencies in HAR  [20]. To be compliant with the topology of the HNN model in  [16] , 5 human activities have been selected among the 12 standard activities provided by the PAMAP2 dataset, namely: standing, walking, running, biking, and rope jumping. The method presented in  [21]  has been used to train the model, by setting the number of epochs to 30 and the batch size to 500. For training and testing purposes, the dataset is split in sets of consecutive samples that are associated to the same activity. The number of samples in each set is called FL. During training process, we sequentially feed the HNN with these sets. As the batch size is 500, parameters are updated every 500 sets during each epoch. The number of samples used during training, testing, and validation are reported in Table  II  for each FL.
Note that the FL refers to the number of samples before the down-sampling process. Thus, a FL equal to 512 corresponds to a time window of 5. 12 s. Training results are summarized in Table  III , where the average recall is reported for each one of the 6 sensors and considering 3 different Frame Lengths (FLs), namely 5120, 1024, and 512. The average recall tends to increase with the FL in most cases. The HNN model has poor performance in recognizing the human activities from the raw data provided by the dataset. In fact, the average recall is lower than 90% in 88. 89% of the cases. Better results are achieved only when the sensor position is chest and the FL is equal to 5120. This can be explained by considering the effect of the sensor orientation when performing human daily activities, such as walking, running, and the like.
A. Training results on 5 classes from PAMAP2 We demonstrate the effectiveness of the proposed preprocessing method in removing the noise deriving from the sensor orientation, by training the HNN model using preprocessed data from the PAMAP2 dataset according to the scheme explained in the previous paragraph. The results are summarized in Table V, when the 5 activities mentioned above are classified. A significant improvement is obtained thanks to the pre-processing operations when data come from sensors in ankle or hand position. The best case is for the sensor hand16g when the FL = 5120, where the average recall increases from 51. 32% to 99. 92%, thus gaining the huge value of 48. 60 percentage points. On the other hand, no improvement is obtained when the sensor is in the chest position, and the average recall decreases from 98.
C. Training Results on the 12 PAMAP2 classes Considering that up to 12 standard activities can be selected from the PAMAP2 dataset, we also made a version of the HNN with 12 neurons in the last FC layer. In this case, the memory required to store the weights for this layer increases from 40 bytes to 96 bytes, with a total increase in the memory requirement of 5. 6%. Also, the number of ADD operations increases from 320 to 768, with a total increase in the ADD/SUB operations of 1. 8%. Results are summarized in Table  VII  for FL = 1024, with and without pre-processing data. The number of neurons of the first FC layer is equal to 64. The HNN shows poor performance in recognizing the 12 activities from the dataset, with a reduction of roughly 50% with respect to the 5 classes case. This can be explained considering that only one accelerometer is used as input sensor in our system, and some classes cannot be distinguished with such a configuration.
Genetically encoded ratiometric fluorescent probes are cutting-edge tools in biology. They allow precise and dynamic measurement of various physiological parameters within cell compartments. Because data extraction and analysis are time consuming and may lead to inconsistencies between results, we describe here a standardized pipeline for • Semi-automated treatment of time-lapse fluorescence microscopy images. • Quantification of individual cell signal. • Statistical analysis of the data. First, a dedicated macro was developed using the FIJI software to reproducibly quantify the fluorescence ratio as a function of time. Raw data are then exported and analyzed using R and MATLAB softwares. Calculation and statistical analysis of selected graphic parameters are performed. In addition, a functional principal component analysis allows summarizing the dataset.
To record a cell parameter in real time with these probes, either simultaneous excitation at two different wavelengths or simultaneous signal recovery at two emission wavelengths is performed depending if the shift deals with the excitation or the emission spectrum. The ratio of both signals is then calculated, which is independent of the probe local concentration. These probes are thus called ratiometric. Image acquisition with ratiometric probes generates a tremendous amount of data. Data processing and analysis are laborious, yet crucial for a reproducible and efficient exploitation of these biological tools. Macros, like FluoQ  [19]  and softwares, like Friker's one  [18]  are available for analysis of multidimensional images. Fricker's software, for example, enables to quantify ratio data from 4Dimages (x,y,z,t) with up to 5 fluorescence channels in a single analysis round  [18].
However, this software is rather dedicated to optical sections of entire plants or fungi and does not perform individual cell segmentation. In the same way, the FluoQ macro allows the rapid processing of large quantities of images, but the cells are segmented on a t-projection of the timelapse, which is efficient only for cells with no or little mobility  [19]. Consequently, both tools do not allow easy tracking of individual moving mammalian cells in culture. To circumvent this drawback, we developed a complementary approach that considers morphological changes and movements of individual cells over time during image acquisition, in response to an exogenous stimulus. Derived from Belousov's methods related to a redox ratiometric probe  [20 , 21]  , we developed an automated FIJI-encoded, user-friendly macro that preprocesses and quantifies data from any ratiometric probe.
Secondly, we developed a set of tools for statistical analyses of the generated data using R software. This method paper describes this pipeline step-by-step, as summarized in Fig. 1. As a proof-of-concept, we used time-lapse data collected from cells treated with varying H 2 O 2 concentrations and expressing the ratiometric H 2 O 2 -specific HyPer probe, which is routinely used in our laboratory  [22 , 23]. HyPer is a widely used, genetically encoded fluorescent redox biosensor based Fig. 1. Schematic diagram of the analysis pipeline. Starting from raw images, a two-step analysis procedure is shown within the gray rectangle. The FIJI macro extracts quantitative data from images, while R and MATLAB scripts perform and display data statistical analysis.
3. With HyPer, perform excitation near 420 nm first for each frame, as this reverses photo-conversion that occurs when exciting the probe at 500 nm  [20]. 4. Acquisition parameters optimization was based on previous work  [20]. Initial fluorescence intensities in both channels must be similar and the ratio curve of cells exposed to 0 μM H 2 O 2 has to remain flat. 5. Save or format the acquired image as a 2-channel stack. Channel 1 must correspond to the near-420-nm acquisition and channel 2 to the near-500-nm acquisition. ( Fig. 2 A ). 6. For quantification, images must not be compressed and a high dynamic range (at least 12 bits) is preferable. Any image format supported by FIJI is acceptable, such as tiff or czi. Required softwares for image quantification and data analysis FIJI or ImageJ : Use either ImageJ (imagej.
nih. gov/) or FIJI ( https://fiji. sc/ ) to run the macro. R and MATLAB: Most of statistical analyses are performed using R (cran. r-project. org) through the Rstudio interface ( https://rstudio. com/ ). Parameters calculation is performed using MATLAB ( https:// uk. mathworks. com/products/matlab. html ), but free analysis softwares like Scilab ( https://www. scilab. org/ ) or spreadsheet programs like LibreOffice Calc can also be used. Further details regarding softwares are described in the Materials and Methods section.
Data extraction from raw acquired images We developed a macro, based on the image analysis protocol established by the creators of the HyPer probe  [20 , 21]. Briefly, the raw 2-channel image is split into two images (STEP 1), which are then processed to improve their signal-to-noise ratio as well as dynamic range (STEP 2). The ratio of the two denoised images is then calculated. For each single cell, a rectangle containing the cell on each slice is duplicated on each image (STEP 3). The cell is then segmented, and both fluorescence intensities are quantified within the cell for each time step, as well as ratio values (STEP 4). The macro eventually exports (i) a summary table containing the time values in minutes and the fluorescence intensities for each analyzed cell and (ii) a report including all the choices made by the user (STEP 5).
At every step, the macro saves regions of interest (ROIs) and/or intermediates images. These files are referred as "control data" in the following text. STEP 0: Macro installation 1. Download a recent version of FIJI or ImageJ software. 2. Save the provided macro in the file of your choice. 3. Open the macro using the Macros → Edit menu and select the IJ1 macro Language ( Fig. 2 B ). Alternatively, the macro can simply be run using the Macros → Run… menu. 4. If desired, edit the macro parameters. Then launch it by clicking run ( Fig. 2 C ). STEP 1: Image import and working folder selection 1. Click "OK" on the 1 st dialog box and choose the folder to save the control data (ROIs positions, intermediates images…), later referred as "working folder" ( Fig. 3 A ). 2. Click "OK" on the 2 nd dialog box and open the image to analyze.
6 G ). Summing both binary images usually optimizes cell segmentation (Figure S 3A). However, better results may be obtained with one of the two binary images only, e. g. if the fluorescence of the probe on a whole channel is insufficient to achieve a clear segmentation. It is recommended to exclude edge-touching cells, to ensure that the whole cell is considered for all measurements. The default parameters for cell detection are: -Size (μm ²): > 100 μm ². -Circularity: 0. 00 -1. 00 (1 corresponding to a perfectly circular particle). -No hole inclusion (to exclude the nucleus region). Depending on cell size, cell morphology and probe localization, these parameters can be modified in the script of the macro before running it (Figure S 3B). We advise to perform segmentation tests by varying size and circularity parameters to check whether these settings are accurate for a proper segmentation of the user's cells.
Data analysis STEP 0: R configuration 1. Download a recent version of R ( www. r-project. org ) and Rstudio ( https://rstudio. com/ ). 2. Save the provided R script in the folder of your choice. 3. Open the script using the File → open menu ( Fig. 8  A to C). 4. Install the required packages: {tidyr}  [26]  , {xlsx}  [27]  , {reshape2}  [28]  , {ggplot2}  [29]  , {ggpubr}  [30]  , {gridExtra}  [31]  , {FactoMineR}  [32]  and {factoextra}  [33]. This step can be performed either by using the Packages tab ( Fig. 8 D ) or the install. packages function. The installed packages will stay installed for your next working session using R. 5. Load the required packages by running the 1st lines of the script ( Fig. 8 C ), for example by using the Ctrl + Enter shortcut. This step needs to be performed at each new working session.
STEP 1: Qualitative variable implementation and data compilation Qualitative variables are required for data analysis. They must be implemented in each raw data file exported by the macro. Here, we added the following variables: H 2 O 2 concentration, date, replicate number, cell passage number, image number, and a unique identifier for each cell ( Table  2  ). Some variables like H 2 O 2 concentration are expected to be important parameters for the analysis. Others like the replicate number or the cell passage number can be added to examine their potential implication in the final results. Based on these variables, data can easily be filtered and analyzed accordingly. The cell identifier is useful to retrieve the initial image. Then, these completed datasets must be compiled together. We provide a method to perform these actions using R ( Fig.
9  ). 1. Open a new R script using the Ctrl + Shift + n shortcut. 2. Set as working directory the folder where the raw data are saved using the setwd function. setwd(''folder_where_your_data_are_saved'') # Sets the proper working directory 3. Import the 1 st raw data file using the read. table function ( Fig. 9  A and B). rawdata < -read. table(''data1. csv'', sep = '','') # Imports the dataset and names it rawdata 4. Add the 1 st required qualitative variable as shown on the following example; it adds a column entitled C_H2O2 which is filled of "10". Quotation marks around "10" allow the software to consider the variable "C_H2O2" as a multi-level factor rather than a quantitative variable. rawdata [''C_H2O2''] < -''10'' # Adds a full column of ''10'' entitled C_H2O2 5. Repeat step 4 to add any other desired qualitative variable ( Fig.
9 A ). 6. One of these qualitative variables must be a unique identifier for each cell (Id_cell column, Fig. 9  C and E). This allows (i) to represent the results as curves using the {ggplot2} package, and (ii) to easily retrieve the cell and experiment associated to a particular curve. 7. Rename this 1 st completed data frame using the following script line, which creates a new data frame called data1 that equals the rawdata data frame ( Fig. 9  A): data1 < -rawdata 8. Perform steps 3 to 7 with the other raw data files, renaming each completed data file with a new name (for example data2, data3, …, datak). 9. Compile these completed data files using the rbind function as follows ( Fig. 9  D and E): dataset < -rbind (data1, data2, …,datak) 10. Export the complete dataset in the chosen folder using the write.