dataesr/scientific-paragraphs-categorization huggingface dataset:
Small-angle X-ray scattering (SAXS). SAXS experiments have been performed on the Xeuss 2. 0 SAXS instrument of LLB of the platform IRAMIS\SWAXSLab in CEA Saclay. The sample-detector distance was fixed to 2. 4 m in order to cover a scattering wave vector, q, ranging from 4. 5. 10 -3 to 2. 5. 10 -1 Å -1. Samples were measured in kapton capillary during three hours. Spectra were corrected from capillary and solvent. To analyse the scattered intensity obtained for gold nanoparticles, we used a form factor of a polydisperse sphere. Fits were performed with the help of SasView software (http://www. sasview. org). Polydisperse sphere model describes a sphere with a lognormal distribution on the radius rs (Equations 1 and 2) I(q, r ) = [3V(ρ -ρ )P (q, r )]² (equation 1) With P (q, r ) = [ ( ! " )# ! " $ ( ! " )] ( ! " ) % (equation 2) Where rs is the radius of the sphere, ρs and ρsolv are the scattering length density of respectively the gold nanoparticles and the solvent.
Indeed, the western blot is a laboratory method used to measure the amount of protein expression from among a mixture of proteins. On the right side of the figure  5  A, the protein ladder assesses the relative molecular sizes of proteins that have been separated by the gel. The transfected HEK without nanoparticles is considered as the positive control and corresponds to the (c-mip/GAPDH) ratio at 1. 6. Next to the positive control, the intensity of the c-mip band determines the reduced expression of c-mip protein. The GAPDH band should be equivalent in each condition in order to interpret the results, which is the case here. The use of 100 nm nanoparticles reveals a lighter c-mip but less lighter than the ones with 20 nm and above all the 60 nm nanoparticles. In order to quantify the reduce expression, an "Image J" software enables to estimate the intensity of the pixels of the band and gives the numeric expression of each band (c-mip expression has to be reported to the housekeeping protein GAPDH).
On the figure  5B , the numeric (c-mip/GAPDH) ratio expression can be analyzed, i. e. the 100 nm nanomaterial reduces 30% c-mip expression, the 20 nm nanomaterial inhibits 52% c-mip expression and the 60 nm nanomaterial inhibits 84% c-mip expression. From this result, the quantifications show clearly that 60 nm nanocapsules are the most efficient to inhibit c-mip expression than the other one (figure  5 ). The data of 4 independent experiments have been interpreted with statistical software (GraphPad®). Elbakry et al. [50]  presented gold nanoparticles with different diameters (20, 30, 50 and 80 nm), coated with a layer-by-layer approach with nucleic acid and poly(ethylene imine) (PEI)). These "nanosystems" are certainly different from the model presented herein but it could highlight a hypothesis to explain the results obtained according to which nanoparticles of 60 nm are the most effective.
5 on figure 6B. The intensity of the c-mip band still reveals the reduced expression of cmip protein according to the different conditions. Nanocapsules are associated with a less intense c-mip band than with gold nanoparticles. The ImageJ quantification on figure 6B reports a decrease of 41% and 86% of c-mip expression with gold-nanoparticles, and hollow particles respectively. The data of 4 independent experiments have been interpreted with statistical software (GraphPad®). The experiment was repeated 4 times with each condition in triplicate.
Acknowledgment The authors wish to sincerely thank  Prof. Saleem  ( University of Bristol, Bristol Royal Hospital for Children ,  BS2 8BJ  Bristol,  United Kingdom ,) for providing the podocyte cell line. Sabrina Belbekhouche thanks the  UPEC and ICMPE-CNRS  for financial help. The authors would like to thank the  SATT IDF Innov  for financial help.
at 1. 1 5 on figure 6B. The intensity of the c-mip band still reveals the reduced expression of cmip protein according to the different conditions. Nanocapsules are associated with a less intense c-mip band than with gold nanoparticles. The ImageJ quantification on figure 6B reports a decrease of 41% and 86% of c-mip expression with gold-nanoparticles, and hollow particles respectively. The data of 4 independent experiments have been interpreted with statistical software (GraphPad®). The experiment was repeated 4 times with each condition in triplicate.
Machine learning (ML) methods can be leveraged to prevent the spread of deadly infectious disease outbreak (e. g. ,. This can be done by applying machine learning methods in predicting and detecting the deadly infectious disease. Most reviews did not discuss about the machine learning algorithms, datasets and performance measurements used for various applications in predicting and detecting the deadly infectious disease. In contrast, this paper outlines the literature review based on two major ways (e. g. , prediction, detection) to limit the spread of deadly disease outbreaks. Hence, this study aims to investigate the state of the art, challenges and future works of leveraging ML methods to detect and predict deadly disease outbreaks according to two categories mentioned earlier. Specifically, this study provides a review on various approaches (e.
g. , individual and ensemble models), types of datasets, parameters or variables and performance measures used in the previous works. The literature review included all articles from journals and conference proceedings published from 2010 through 2020 in Scopus indexed databases using the search terms Predicting Disease Outbreaks and/or Detecting Disease using Machine Learning. The findings from this review focus on commonly used machine learning approaches, challenges and future works to limit the spread of deadly disease outbreaks through preventions and detections.
The aim of this study is to investigate the state of the art, challenges and future works of leveraging machine learning methods to control the spread of deadly disease outbreaks according to two categories mentioned earlier. This study provides a review on various approaches, types of datasets, types of parameters or variables, individual models, ensemble models, performance measures and approaches used in the previous works. The literature review included all articles from journals and conference proceedings published from 2010 through 2020 in Scopus indexed databases using the search terms Predicting Disease Outbreaks and/or Detecting Disease using Machine Learning. We categorized all articles and reports based on global health security issues addressedi. e. , whether it depicted prediction or detection strategies.
Types of datasets and parameters used Tables  7  and 8  summarize both the structured and unstructured data based on the type of datasets used and also number of studies conducted for predicting and detecting the spread of disease outbreaks in this work. Based on these findings obtained from all the studies, there are six sets of structured datasets and three sets of unstructured datasets identified as the most commonly used in predicting and detecting the spread of disease outbreaks. The structured databases include the Epidemiology Data, Spatial Data, Remotely Sensed Data, Meteorological Data, Physiological Data and finally Demographic Data. Epidemiology is a systematic study and analysis of the distribution, patterns and determinants of health and disease conditions in a particular predefined population.
The three most used epidemiology parameters in this review include the number of disease outbreak incidences, signs and symptoms of diseases, treatment information and seasonal information. Spatial data, also known as geospatial data, is information about a physical object that can be represented by numerical values in a geographic coordinate system  [60]. Other information includes digital elevations, distance and area. Remotely Sensed Data are derived from the remote sensing activities. Remote sensing is the process of detecting and monitoring the physical characteristics of an area by measuring its reflected and emitted radiation at a distance (typically from satellite or aircraft)  [61]. Three parameters that are commonly included in the prediction that include Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Land Surface Temperature (LST).
Meteorological data includes temperature, humidity, precipitation (Rain or Snow), air pressure, solar radiation and wind speed. They were collected regionally by surface and upper air meteorological stations  [62]. Physiological data represents physiological properties including blood pressure, cholesterol, obesity, heart Rate and many others variables  [63]. Finally, demographic data is statistical data collected about the characteristics of the population, e. g. age, gender, race, ethnicity, marital status, income, education and occupation for example. Next, unstructured databases are typically large collections of files that are not stored in a structured database format. In this work, three types of unstructured datasets are found to be useful that include Social Media Data, Search Keywords and News Articles.
Types of datasets and parameters used This section summarizes and discusses the findings in relation to the RQ2: What disease datasets in the literature have been used to build the models? and RQ3: What type of parameters or variables have been used? Table  13  shows the type of diseases, dataset sources and related studies working on the prediction and detection in order to limit the spread of disease outbreaks. For instance, for dengue disease, most studies have used the Epidemiology and Meteorological data in order to perform the predictions and detections of dengue outbreaks. For structured datasets, the most frequently used databases include the Meteorological and Epidemiology data. The temperature variable improves dengue outbreaks forecasts better than humidity and rainfall for the Meteorological data  [40].
Zika Epidemiology Data  [19, 49, 51, 58]  Meteorological Data  [49, 58]  Demographic Data  [49, 58]  HFMD Meteorological  [19, 42]  Spatial Data  [16]  Search Keywords [44] ILI Social Media Data  [21, 23, 29, 33, 37, 53, 68, 69, 70]  Meteorological  [17, 21, 23, 31, 33, 37, 68, 69]  Search Keywords  [21, 23, 33, 37, 68, 69]  Epidemiology Data  [22, 23, 24, 36, 59]  Spatial Data  [59]  Others Epidemiology Data  [13, 18, 27, 28, 35, 38, 39, 46, 50, 55, 57]  Demographic Data  [45, 50, 55, 57]  Meteorological Data  [25, 56]  Spatial Data & Remotely Sensed Data  [14]  Social Media Data  [46]  News Articles  [47]  Search Keywords [48]    * Dependent variable: Number of disease outbreak incidences (EP1) (see Table  7 ). In contrast, for unstructured datasets, Social Media data and Search Keyboards are the most frequently used dataset for forecasting disease outbreaks (e.
In machine learning approach, most of the best methods found to be more effective in predicting disease outbreaks are those related to neural network family. The experimental results showed the consistent performance improvements by the proposed deep learning approaches over other representative linear and non-linear methods on multiple real-world datasets. These algorithms include the Long Short Term Memory (LSTM)  [17, 23, 25, 31, 32, 33] , Convolutinal Neural Network (CNN)  [29] , Back Propagation Neural Network (BPNN)  [14, 42] , Multilayer Perceptron (MLP)  [20] , Neural Network  [22] , Hybrid Neural Network (HNN)  [15]  and combination of statistic and deep learning approaches  [38, 39]. LSTM (RNN), was also able to produce better predictive capability for predicting the morbidity incidence of 10 infectious diseases, compared to linear model (RR), time series analysis model (ARIMA), boosting tree model (XGBoost)  [23].
Conclusion In conclusion, the aim of this literature review is to identify and analyse various approaches, types of datasets, types of parameters or variables, individual models, ensemble models, performance measures and approaches used in the previous works on leveraging machine learning approaches to limit the spread of deadly disease outbreaks. In this work, there were six online digital libraries used to retrieve all related peer-reviewed articles and only forty-seven studies have been selected between the year of 2010 and 2020 publications in which seven main questions are used to assess the quality of these studies. This SLR was conducted to evaluate and select all relevant research studies related to the detection and prediction of disease outbreaks using machine learning based on the seven questions outlined earlier.
The contributions of this paper can be summarized as follows: • The type of databases and variables used are identified, and Meteorological and Epidemiology data are found to be the mostly useful datasets for predicting and detecting disease outbreaks. • Multi-sources data contributes to the improvement of the disease outbreaks predictions. • Algorithms belong to the Neural Network family are found to provide better performance compared to other linear and non-linear machine learning methods. • Ensemble and hybrid approaches performed better and are more appropriate to be applied for predicting and detecting disease outbreaks. • Exploring unstructured data (e. g. , news, blogs, search keyword trends) may improve the performance of the disease outbreaks prediction and detection. Several guidelines are generated based on the findings obtained from this SLR for future work.
Firstly, there is a need to explore further the capability of ensemble models or hybrid models based on deep learning methods using multi-source data, as these have been shown to improve the performance of the base model. Next, A limited number of investigations conducted in the area of disease outbreaks prediction based on multi-sources data as the findings from existing studies have shown that a more comprehensive understanding can be obtained about a particular disease outbreak by integrating multi-sources data. We can produce better modelling results comprehensively by analysing these complex relationships among multi-sources data. Finally, limited works are found in exploring unstructured data such as news articles, blogs and web social media sites, even though integrating structured and unstructured data, has been shown to improve the prediction of disease outbreaks.
Image Processing Images were processed using in-house scripts and functions developed with MATLAB (MathWorks, Natick, MA, United States). T 1 and T 2 maps were obtained after normalizing the data and fitting them pixel-wise using either an exponential build-up (S 1 ) or decay (S 2 ) model: S 1 = a • (1 -2 • e t / T 1 ) S 2 = b • e -t / T 2 With S i the normalized signal intensity in arbitrary units, t the time obtained from the echo train or inversion times, and a/b constants of the fitting models. Relaxation times are tied to the magnetic properties of the different tissue species and are expected to vary significantly between the different disc compartments.
Finite Element Model A finite element (FE) model was developed to perform a stress analysis from a simplified geometry extracted using MRI. Our aim was to use this specific FE model from the actual geometry and the actual boundary conditions in order to calculate the stress distribution in the nucleus and the contribution of the radial fissure. FE analysis was carried out using SolidWorks software (Dassault Systèmes Corporation, Waltham, MA, United States). The geometry of the model was defined using points extracted from the boundary of the annulus and nucleus segmented based on a threshold on the MR images acquired in the intact and fissured states (Figure  4 ). For this stress analysis, homogeneous   1 ). C np is the centroid of the nucleus pulpous; C sc is the centroid of the spinal cord; d 0 is the reference distance; d is the distance of the studied mechanical state.
The diffused practice of pre-training Convolutional Neural Networks (CNNs) on large natural image datasets such as ImageNet causes the automatic learning of invariance to object scale variations. This, however, can be detrimental in medical imaging, where pixel spacing has a known physical correspondence and size is crucial to the diagnosis, for example, the size of lesions, tumors or cell nuclei. In this paper, we use deep learning interpretability to identify at what intermediate layers such invariance is learned. We train and evaluate different regression models on the PASCAL-VOC (Pattern Analysis, Statistical modeling and ComputAtional Learning-Visual Object Classes) annotated data to (i) separate the effects of the closely related yet different notions of image size and object scale, (ii) quantify the presence of scale information in the CNN in terms of the layer-wise correlation between input scale and feature maps in InceptionV3 and ResNet50, and (iii) develop a pruning strategy that reduces the invariance to object scale of the learned features.
Results indicate that scale information peaks at central CNN layers and drops close to the softmax, where the invariance is reached. Our pruning strategy uses this to obtain features that preserve scale information. We show that the pruning significantly improves the performance on medical tasks where scale is a relevant factor, for example for the regression of breast histology image magnification. These results show that the presence of scale information at intermediate layers legitimates transfer learning in applications that require scale covariance rather than invariance and that the performance on these tasks can be improved by pruning off the layers where the invariance is learned. All experiments are performed on publicly available data and the code is available on GitHub.
Training from scratch is sometimes a preferred alternative to introduce desired invariances in the learned features  [13, 14]. Scratch training is adopted by scale covariant  [4]  and multi-scale designs  [15] [16] [17] [18]. This work is based on the assumption that the scale invariance implicitly learned from pretraining on ImageNet can be detrimental to the transfer to applications for which scale is a relevant feature. With a controlled viewpoint and known voxel spacing dimensions, scale is informative (and often decisive) in some medical imaging tasks (e. g. , size of lesions, tumoral regions or cell nuclei, as illustrated in Figure  1. The other transferred features such as shape, color and texture, however, are beneficial to the medical tasks, in particular for learning from limited data and improving the model accuracy and speed of convergence  [10, [19] [20] [21].
3). This is relevant not only in medical imaging but also in other applications with a controlled viewpoint. Considering this information about scale may help to build models that predict the magnification range of images for which the physical dimension of voxels is unknown, for example, magnification level not reported. For example, remote sensing, defect detection, material recognition and biometrics (e. g. , iris and face recognition with registered images)  [1]. In the medical context, these results may have a positive impact on the use of large and growing open-access biomedical data repositories such as PubMed Central (https://www. ncbi. nlm. nih. gov/pmc/tools/openftlist/, accessed on 2 April 2021) to extend existing medical datasets  [26].
Pruned networks achieve a similar performance to, if not better  [36]  than, that of the original network. The asset of network pruning is that even if not providing massive increases in network performance it improves training convergence and it reduces the number of parameters to be trained and thus the computational complexity of the models  [37]. This allows the training and fine-tuning of the models on smaller datasets, as shown by the study on PAP smears  [36]. Pruning methods mostly focus on identifying the importance of individual elements in the network, such as individual neurons  [34] , individual filters and/or feature maps  [36, 37]. Particularly in  [35] , the authors dealt with multiple object scales by specific-design observations that can make their pruning responsive to multiple object scales.
Materials and Methods This section outlines the proposed method and the setups used for the experiments. Section 3. 1 introduces the notations in the paper, while Sections 3. 2 and 3. 3 describe the datasets and network architectures, respectively. We outline the main approach in Section 3. 4, while the evaluation metrics are defined in Section 3. 5. The hypotheses, scope and methodologies of the multiple experiments are described in Section 3. 6.
Datasets The experiments in this paper involve two datasets since the scale analysis is performed on inputs of natural images and the proposed final architecture is evaluated on a medical image analysis task. For the scale quantification part, images with manual annotations of bounding boxes are selected from the publicly available PASCAL-VOC dataset  [43]. We restrict our analysis to three object categories and images containing a single bounding box, chosen among the available annotated classes. These are albatross (ID: n02058221, 441 images), kite (ID: n01608432, 406 images) and racing car (ID: n04037443, 365 images). For the histopathology application, the data consist of 141 Whole Slide Images (WSI) of Estrogen Receptor-positive Breast Cancer (ERBCa+). For these images of 2000 × 2000 pixels, manual annotations of 12,000 nuclei are available  [44].
Smaller image regions are extracted as image patches from the WSIs. A total of 69,019 patches with nuclei segmentation masks were split into training, validation and test partitions (approximately 60%, 20%, 20% respectively) as shown in Table  1. To not introduce bias, all the patches from a single image were assigned to the same data partition. The imbalance in the magnification categories is due to the area covered by each magnification level. The average nuclei area is extracted for each input image by computing the average number of pixels in the relative nuclei segmentation mask. Example images with overlaid segmentation masks are displayed in Figure  2.
This mapping approach was used since it outperforms the direct classification of the magnification in  [17]. The networks are implemented in Keras and trained for five epochs with an Adam optimizer and standard hyperparameters (learning rate 1 × 10 -4 , batch size 32 and default values of the exponential decay rates). The full pipeline is shown in Figure  3  and the source code is available on github for reproducibility (https://github. com/medgift/scale_covariant_pruning) (acessed on 2 April 2021).
The task analyzed in the final experiment for improving the transfer of the learned weights to histopathology data represents an important problem in this field. Many open access repositories (e. g. , PubMed Central) do not provide information about the magnification level of the images, which become thus difficult to integrate with other datasets. Data from open access repositories or social networks can provide examples of rare and under-represented cases since these images are often presented for visual comparison and discussion among experts  [26]. The proposed pruning strategy drops the layers with scale-invariant features to improve the transfer and better regress the magnification level of histopathology images. For InceptionV3, the pruned features are a result of a GAP on top of the mixed8 features.