dataesr/scientific-paragraphs-categorization huggingface dataset:
Usage Notes Gravitational wave detectors are complex instruments, and their data reflect this complexity. Precaution needs to be taken when searching for GW signals in the LIGO and Virgo data. The background noise is not stationary and evolves over multiple time scales. The detectors are not always in science mode and, even when they are, it is possible that data quality does not meet the requirements of a given analyses. For these reasons it is necessary to work with segments of data. On the GWOSC website a tool called Timeline 4 provides a visual representation of the duty cycle of each data-quality (or injection) bit across a prescribed time interval. From the timeline page it is also possible to select specific segments and download the corresponding data. The data are not calibrated in the extreme parts of the observed frequency range (below 10 Hz and above 5 kHz).
In most searches for astrophysical sources, data below 20 Hz are not used because the noise is too high. Moreover, the data quality and calibration are less well studied above 2 kHz. Simulated signals, called hardware injections, are also present in the Advanced LIGO data. Their times can be retrieved in the GWOSC files by the injection data quality information. The GWOSC website contains many tutorials 5 that can be used to understand how to read and analyze the data. To ease these analyses, the LIGO Scientific Collaboration and Virgo Collaboration have developed various software packages 6 , mainly python based. The tutorials help also in using these packages, especially the on-line courses obtained from recordings of the two Open Data Workshops 5 that the GWOSC team have organized in 2018 and 2019.
Acknowledgement This research was supported European Union's Horizon 2020 research and innovation programme under grant agreement No 653477 (ASTERICS). It has made use of data, software and/or web tools obtained from the Gravitational Wave Open Science Center (https://www. gw-openscience. org), a service of LIGO Laboratory, the LIGO Scientific Collaboration and the Virgo Collaboration. LIGO is funded by the U. S. National Science Foundation. Virgo is funded by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale della Fisica Nucleare (INFN) and the Dutch institute Nikhef, with contributions by Polish and Hungarian institutes.
This algorithm is particularly promising for large datasets as well as images having a low signal to noise ratio. In addition, it has limited system requirements. Over the past few years, deep learning methods have been successfully introduced for biological data processing. For instance, Liu et al. 11  suggested a convolutional encoder-decoder neural network method for denoising of cell nuclei in fluorescence microscopy images. This method based on using the stochastic characteristics of noise as well as the shape of nuclei for learning step, and then regenerating the clean nuclei image based on the learned priori knowledge. Weigert et al. 12  proposed a method to enhance the axial resolution in 3D microscopy images by reconstructing isotropic 3D data from non-isotropic acquisitions using a convolutional neural network.
As a result, identifying a proper and robust approach for various datasets regarding high variation in cell nuclei volume, shape, and stain distribution as well as high cell density has become a major challenge in image analysis. The contribution of this paper is to design a generic method for denoising of 3D cell nuclei images based on a sparse representation model. Consequently, a classical segmentation method can be used to segment cell nuclei without the need for more complicated segmentation approaches. The concept of dictionary learning, and sparse representation is already well established by M. Elad and M. Aharon  31  as well as it is implemented in different application such as image denoising  [32] [33] [34]  , and image classification  35, 36. However, the added value of the proposed approach is employing the advantage of the sparse representation to find the potential locations of nuclei in microscopy image.
Datasets description. Since the main motivation of our work is to automate the detection and segmentation of cell nuclei in time-lapse fluorescence microscopy images, we focused on applying our algorithm to development biology datasets, where only a limited number of existing methods had provided satisfactory results. The proposed framework is extensively tested on three real datasets for embryonic cells and one dataset of synthetic images with different values for the signal to noise ratio (SNR) and the object size. SNR is a performance measure for the sensitivity of imaging systems which is defined as the ratio of the average signal level (μ signal ) to the standard deviation (σ noise ) of the background noise level: SNR = μ signal /σ noise and expressed in logarithmic function as SNR(dB) = 20 × log 10 (μ signal /σ noise )  37.
Synthetic dataset. In order to measure the robustness of the proposed method, we generated synthetic images of size (XYZ) equal (100 × 100 × 20) voxels containing spheres of two radii: 7 and 9 voxels. As it is common in fluorescence microscopy images to have low contrast and low signal to noise ratio (SNR), as a consequence of weak fluorescent staining or microscope properties, the images are distorted with different levels of Poisson-Gaussian noise, resulting in SNRs of 2 dB, -1 dB, -5 dB and -7 dB, respectively. Furthermore, the images include touching spheres where these conditions simulate the same characteristics existing in the real datasets as shown in Supplementary Fig. 1. Real dataset. The first dataset comes from the work of Gul-Mohammed  17  and it is named as CE-UPMC. The other two datasets come from the cell tracking challenge  38, 39  , namely Fluo-N3DH-CE and Fluo-N3DL-DRO.
The last two datasets are proven to be the hardest to be fully segmented automatically  38. Each dataset from the cell tracking challenge contains 2 sequences. For the cell tracking challenge datasets, all pixels belonging to objects including the centroid are labelled as object by the ground truth. However, for the other dataset (i. e. CE-UPMC), only the centroid of each object is labelled. The datasets are described as follows: CE-UPMC dataset: It involves the C. elegans embryonic cells. The size (XYZT) of dataset is 512 × 512 ×31 × 160. The cells were acquired every 1 minute using a spinning-disk confocal microscope. This dataset is very challenging, as the intensity of the images is decaying over time due to the labelling technique and acquisition system. Thus, the quality of the acquired images is low.
Fluo-N3DH-CE dataset: It includes the C. elegans embryonic cells. The size (XYZT) of the first sequence is 708 × 512 × 35 × 250 and of the second sequence is 712 × 512 × 31 × 250. Both sequences are 8-bit images with cells imaged every 1. 5 minutes. The cells are acquired using a Zeiss LSM 510 Meta Confocal Microscope. This dataset is challenging as well, since it has a low signal to noise ratio (SNR = 6. 74 dB), in addition, the fluorescence can fade when the cells divide. Furthermore, the cells become smaller over time. Fluo-N3DL-DRO dataset: It contains the Drosophila melanogaster embryonic cells. The size (XYZT) of each sequence is 1272 × 603 × 125 × 49. Both sequences are 8-bit images with cells imaged every 30 second. The cells are acquired using a SIMView light-sheet microscope. This dataset is very challenging as it has a large number of densely packed cells.
In addition, it has a low signal to noise (SNR = 2. 46 dB). Experimental setup and suitable parameters selection. Synthetic datasets are generated to study the effect of parameters (described in Table  1 ) on cell nuclei detection and segmentation, as well as to understand the overall mechanism for selecting and tuning the significant parameters of various datasets (summarised in Table  2 ). Our approach is based on the building of a dictionary (small patches of the image) that will be eventually used for denoising and detection of cell nuclei. We investigated the optimal size of the patches and the number of patches (called atoms) in the dictionary. Then, the randomly created dictionary is updated, and so we investigated the number of iteration for the update. Finally, we investigated the sparsity level (i.
Segmentation of 3D cell nuclei images. Following the denoising step, a local adaptive thresholding  42  is applied to the denoised image to get the segmentation mask of candidates regions. In order to obtain the candidates locations of cell nuclei centres, we used a novel representation called the detection map. Each voxel in this map is computed as the summation of the patch coefficients that are used to reconstruct the denoised image. We then define a maximum response image by multiplying the denoised image with the detection map. This maximum response image is used to detect the local maxima (Fig. 1 ). Afterwards, the obtained local maxima are used as an input for a 3D marker-controlled watershed segmentation of the cell nuclei (Fig. 2 ). For the synthetic dataset, great performance is observed on very low signal to noise ratios (2 dB, -1 dB, -5 dB and -7 dB), in which our method is capable of correctly identifying and segmenting all cell nuclei at the various noise levels as presented in Supplementary Fig.
8. Furthermore, our method has similar performance as the top-ranked KTH algorithm from the cell tracking challenge  38, 39  , as shown in Supplementary Fig. 5. Regarding the CE-UPMC dataset, there is an intensity decay over time owing to the labelling technique and acquisition system. As a result, the acquired image quality is low. Table  3  and Supplementary Fig. 9 , illustrate the results obtained at certain time points. For instance, at early time points (40, 60, 80 and 100) all cell nuclei are correctly detected. In addition, few false positives are also detected (2 objects out of 119 cell nuclei). Even though the image quality at advanced time points (120, 140 and 160) is low, only 11 cell nuclei out of 247 are not detected, also there exists a small number of false positives (3 objects out of 247 cell nuclei which is displayed as yellow arrows in Table  2.
Denoising and segmentation parameters. When the values of parameters differ between the first and the advanced time points, the value for the advanced time points is given in round brackets. Supplementary Fig. 9(f) ). Typically, the reason for missing cell nuclei is the detection of clustered cell nuclei (indicated by red arrows in Supplementary Fig. 9 (f)) rather than detecting them separately. Supplementary Fig. 10  shows the segmentation results of our method and the results from the original paper  17  for the CE-UPMC dataset. In the Fluo-N3DH-CE dataset, the proposed approach is able to identify and segment correctly more than 96% of total cell nuclei. Furthermore, it detects a small number of false positives (9 objects out of 876 cell nuclei) as well as a small number of false negative (29 cell nuclei).
The achieved F-measure of approximately 97. 8%, which is comparable to the competing algorithm, i. e. KTH  38, 39  (Table  4 , Supplementary Figs  11  and 12 ). For the Fluo-N3DL-DRO dataset, despite our method succeeds to detect more cell nuclei (99% recall), it has low precision (3%) due to the annotated ground truth, which considered only the cell nuclei located in the early nervous system and all other nuclei are deemed as false positives. As a result, the obtained F-measure is low with an approximate value of 6. 4% Table  4. Furthermore, our method has a comparable segmentation accuracy with KTH competing approach  38, 39  (Table  4 , Supplementary Figs  13  and 14 ). The results achieved by our method for the two datasets obtained from cell tracking challenge are compared with the top-ranked KTH algorithm  38, 39.
12(b ). In contrast, our method succeeds to identify and segment each nucleus individually as shown in Supplementary Fig. 12(a, c ). For the Fluo-N3DL-DRO dataset, although our method succeeds to detect more cell nuclei than KTH approach (Table:4 and Supplementary Fig. 14 ), the evaluation method considered those cell nuclei as false positives, due to the annotation method which considered only the cell nuclei located in the early nervous system. We have found that, the proposed method is less sensitive to some parameters such as dictionary size (K), sparsity level (L) and number of iterations (N). All these parameters are being fixed for different datasets and experiments with the subsequent values K = 64, L = 3 and N = 15. However, the proposed method is more sensitive to fundamental parameters, i.
e. , such as patch-size, and in a less critical manner to Sensitivity Factor and Nuclei Seed Dilation. As these parameters are easy to understand, this makes them easier to tune-up if needed. We need to stress that all parameters, except patch size, are quite robust, as we only need to use three sets of parameters for all datasets. The set of empirically determined parameter values being applied to the datasets are listed in the Table  2. Concerning the Fluo-N3DH-CE dataset, although the average cell nuclei size in sequence (  2 ) is slightly greater than the average size in sequence (1), we have decided to use the same parameter (i. e. , patch size) for denoising of both sequences. As a result, we tuned Nuclei Seed Dilation to avoid detection of multiple local maxima for the same object as explained in section Marker points detection.
We have also presented a Supplementary Table  1  to show the detection and segmentation results among various datasets considering the patch size percentage (related to average cell nuclei volume). In order to test the genericity of the algorithm, we have conducted additional experiments on real datasets coming from various tissues such as thymus tissue (provided by J. Sheridan, Walter and Eliza Hall Institute of Medical Research (WEHI)), lymphoid tissue (provided by JR. Groom, WEHI), and islets of Langerhans tissue (from Tran et al. 43  ), where robust cell segmentation is still challenging. Despite the noisy and crowded environment, the obtained results from our method are quite encouraging as presented in Supplementary Fig. 15.
Aharon  31  , we have only changed the construction of the initial dictionary as depicted in the following steps. Firstly, the patches are extracted by moving a window with a step size of one pixel over the raw image. For each extracted patch, pixels' intensities are summed up. Then, the average intensity over all patches is calculated. Secondly, an initial dictionary is constructed by selecting random patches from extracted patches among those having intensities greater than the obtained average intensity. By doing that, we are ensuring the presence of cell nuclei patches in the initial dictionary. Thirdly, a technique based on K-clustering with singular value decomposition (K-SVD)  Table  4. Segmentation performance of our method (SRS) and the KTH algorithm  38, 39  , for datasets from cell tracking challenge.
Implementation details. The image analysis framework is developed using MATLAB (R2017b) on a Windows-based computer (Intel Core i 7, 3. 07 GHz, and 16 GB RAM). Furthermore, the 3D ImageJ viewer plugin  53  along with tools from the 3D ImageJ suite  54  are used for three-dimensional visualisation of the final segmentation result. The source code, as well as datasets, are available upon request. Data processing using the complete framework took 5 mins for the synthetic dataset, 11 mins for the CE-UPMC dataset, 35 mins for Fluo-N3DH-CE dataset and 48 mins for Fluo-N3DL-DRO dataset to process only one time point of 3D image from the complete dataset. Regarding cell tracking challenge, the web site (http://www. codesolorzano. com/celltrackingchallenge) provides access to the datasets with the ground truth.
Over the last few years, deep learning approaches have achieved promising results in several domains, including denoising. However, they have some limits, for example, they are implemented to solve a specific problem, i. e. , any new dataset will require a new training step. Furthermore, deep learning approaches required ground truth labels. Similarly to machine learning approaches, including deep learning, our method is based on a learning technique, but in dictionnary-based methods the learning is completely unsupervised and hence can be performed for any new data without any change. We also showed that our algorithm can also lead to accurate detection of nuclei centroids, we coupled this detection to a classical segmentation method and showed very good results on challenging datasets. We believe than coupling our algorithm with a more powerful segmentation method may lead to even better results, but this was not the purpose of this article.
As a future work, we will investigate an online learning method to handle the time issue to reduce the processing time needed for dictionary learning. The proposed method can handle the most challenging cases involving noisy, densely packed and multiple touching cell nuclei. In addition, it can produce the denoised image and simultaneously the potential locations of cell nuclei. The proposed method is adapted to the segmentation of cell nuclei in 3D time-lapse fluorescence microscopy images, nevertheless, it can be employed to detect and segment the nearly interacting intracellular organelles, including the endosomes, lysosomes, and lipid droplets. Our method is successfully evaluated on two embryo models, the C. elegans, and the Drosophila datasets. The overall detection and segmentation results are comparable to the existing methods, which is a good starting point for automated cell nuclei tracking process.
Finally, it is impossible to ignore that there are many initiatives around the world that aim to enhance the participation of girls and women in science and mathematics. Which ones work? What is the evidence for effectiveness? Can effective practices developed in one place be used in other contexts? These are some of the questions that will structure an online database of good practices (Task 3). The project's part focussing on publication patterns is based on methods and research questions from a recent study in Mathematics  Mihaljević-Brandt, Santamaría, and Tullney [2016]. Based on four decades of data from Zentralblatt Math, the authors showed a systemic gender imbalance in the publication distribution of mathematicians: women mathematicians tripled their number since 1970, but they publish less than men at the beginning of their careers and leave academia at a higher rate; high-ranked journals publish fewer articles by women, some showing less than 5% authorships by women with no change over time; women publish fewer single authored papers, although their coauthor networks are similar in size to those of men.
1b. Although after reanalyzing published data, 1. 3-fold and 1. 4fold increases in PRS detection were observed when switching from one orientation to the other (i. e. Tag1-X/Tag2-Y to Tag1-Y/ Tag2-X), or from one configuration to another (i. e. N1N2 to C1N2), respectively (Supplementary Fig. 3a, b ), the impact of permuting expression systems remained unclear as no assay had been used to systematically explore this parameter. However, several high-quality PPI datasets have been obtained using heterologous expression of protein pairs in yeast  12, 34  , in mammalian cells  35, 36  and via cell-free coupled transcriptiontranslation systems in vitro  37, 38. N C N C N C X Y X Y N C N C N N C C N N Y X Y X N N C C X-Y X-Y N-N N-N X-Y GPCA KISS Y2H NanoBiT After surveying existing binary interaction assays, it appeared that split-protein reconstitution was the most suitable technology to cover the three experimental parameters presented in Fig.
The same trend was observed when combining all tested versions and a 1. 6-fold increase in PRS detection obtained by extending assays from a single to multiple configurations (Fig. 5b , union). These results illustrate the gain of sensitivity achieved when performing the same assay with different combinations of tagging configurations, and confirm the preliminary observations made after reanalyzing published data  23, 25  (Supplementary Fig. 3b ). To evaluate the impact of different protein expression systems on binary PPI detection, we implemented different versions of the N2H assay in yeast, in mammalian cells and in vitro (cell-free). All four tagging configurations (Fig. 1b, c ) were systematically tested in each environment. For the N1N2 version, an average PRS detection rate of 24% was reached when performing the assay in a single environment.
Methods Processing publicly available hsPRS-v1 and hsRRS-v1 data. Publicly available protein interaction datasets used in this study came from the original publications  23, 24, 26  or were provided by the authors upon request  25, 27. Analyses were restricted to datasets for which a straightforward titration at a threshold of no RRS pair scoring positive could be applied. To evaluate the recovery rates of hsPRS-v1 when none of the RRS pairs are scored positive, data were calibrated such that the provided score for a given PRS pair must be strictly higher than the highest score of any of the RRS pairs. When authors clearly identified a particular pair as not interacting (negative) in their experiments, this assignment was also considered to generate the final dataset at a threshold where no RRS pair was scored positive.
2b ). After applying these filters, at total of 60 PRS pairs and 78 RRS pairs were thus assembled into hsPRS-v2 and hsRRS-v2, respectively (Supplementary Table  4 ). Discarded pairs from hsPRS-v1 and hsRRS-v1 are presented in Supplementary Data 1, and PubMed IDs supporting remaining hsPRS-v2 pairs are presented in Supplementary Data 2. The performances of the updated reference sets were compared to the older sets by repeating four assays previously benchmarked with hsPRS-v1 and hsRRS-v1 (Supplementary Fig. 2c ). Cloning ORFs into Gateway-compatible expression plasmids. Human hsPRS-v2 and hsRRS-v2 ORFs are available in pDONR221 or pDONR223 vectors, which make them compatible with the Gateway-cloning technology. Each ORF was introduced into the different assay-specific expression vectors used in this study via an LR clonase-mediated Gateway reaction (Life Technologies).
Construction of Gateway Scoring hsPRS-v2 pairs. Data for each assay version performed in this study are presented in Source Data. All binary PPI experiments were independently performed three times. For the different versions of N2H experiments, the average signal of the three replicates was used to determine a raw luminescence value for each protein pair X/Y. Control experiments were performed similarly where protein X or Y fused to the NanoLuc fragment F1 or F2 was co-expressed with the matching NanoLuc fragment alone (X/empty or empty/Y). For each protein pair X-Y, we calculated a normalized luminescence ratio (NLR) corresponding to the raw luminescence value of the tested pair (X-Y) divided by the maximum luminescence value from one of the two controls (X-empty or empty-Y), as summarized in Eq.
Acknowledgements The authors would like to thank all members of the  Vidal and Jacob laboratories  for helpful discussions throughout this project. The authors thank  Pascal Braun , and  Mikko Taipale  for providing inputs on the experimental procedures of various binary PPI assays, and  Steffi de Rouck  for technical assistance. This work was supported by a  Claudia Adams Barr Award  to S. G. C. , a  Fonds de la Recherche Scientifique (F. R. S. -FNRS) -Télévie Grant  and a  Wallonia-Brussels International  ( WBI)-World Excellence Fellowship  to J. O. ,  NHGRI  grants  U41HG001715  awarded to M. V. , D. E. H. and M. A. C, and  P50HG004233  awarded to M. V. We acknowledge the support of  Labex IBEID  ( 10-LABX-0062 ). M. V. is a  Chercheur Qualifié Honoraire  from the  Fonds de la Recherche Scientifique (FRS-FNRS ,  Wallonia-Brussels Federation, Belgium).