dataesr/scientific-paragraphs-categorization huggingface dataset:
R package (cluster profiler, ggplot…) R package cluster profiler (version 3. 14. 3) was used to calculate the GO enrichment and KEGG enrichment  [36]. The R package DOSE (version 3. 12. 0) was used to perform the DO enrichment  [37]. All the enrichment functions were used with the adjustment method "Bonferroni" and the p-value cutoff set at 0. 1. The R package org. Hs. eg. db  [38]  (version 3. 10. 0) was used to translate "EntrezID" [unique gene ID from the Entrez Gene database at the National Center for Biotechnology Information, (http:// www. ncbi. nlm. nih. gov/ gene)] to SYMBOL (Gene Name) and GO term.
Network pharmacology building The heterogeneous sources of data were integrated into a network pharmacology database. First, we only selected compounds that have at least information on one bioassay (5,03,000 molecules) and integrated them in two main nodes of our network: "Molecule", containing InchiKey and SMILES information and "Compound-Name", containing the chemical name and the database Fig. 1  Illustration of the cutting process of Scaffold Hunter, from the whole molecule to one ring from which that name was extracted. We added 3 types of nodes related to assays: "Result" which mainly contains the value of the assay (from IC50, Ki…), linked to an "AssayParameter" providing the type of assay (IC50, Ki…) and unit of the value. The type of assay between A (ADME), B (Binding), F (Functional), U (Unassigned) and the confidence score defined by ChEMBL with a scale between 0: uncurated data and 9: direct target assigned were included.
Network pharmacology development A representation of the final graph database developed with Neo4J is shown in Fig. 2. Globally, 1,61,468 molecules that have a Ki/IC50 activity below 1 μM, a confidence score of 9 among bioassays of type B and bioactive in mouse, rat and human were integrated into the network. This ensemble of compounds modulates 1975 targets which will be considered for further filtering steps. A direct link between the node "Molecule" and "Target" called actifConf9 was created to facilitate the database manipulation. From this set of bioactive compounds, 1,13,853 distinct scaffolds were generated and integrated into the network. For the protein classes, ChEMBL has defined 1073 distinct classes distributed in 7 main protein families. They represent the main area of drug discovery investigation, notably the membrane receptor [with the G protein-coupled receptors (GPCR)] and the enzyme.
Morphological profile integration Finally, we integrated the morphological profiles for compounds in common between the ChEMBL and the BBBC dataset. We found 2473 compounds common to both datasets. It means that for this set of compounds, proteins are annotated and can be suggested to the morphological perturbations observed in the U20S cell line. Morphological features are included in the network according to the 3 cellular components described in Cell Painting: the nucleus, the cytoplasm and the whole cell itself (respectively named "nucl. ", "cyto. " and "cell"). This phenotypic information could highlight links between the target compartment and the phenotypic variations associated with the molecule. Among others, features may be a measure of the mean radius of the cytoplasm area shape ("Cytoplasm_AreaShape_MeanRadius"), the location of the centre of the cell according to the X-axis ("Cells_Location_Center_X") or the entropy in the nucleus of the cell ("Nuclei_Texture_Entropy").
Chemogenomics library development Based on our graph database, we decided to develop a chemogenomic library of 5000 molecules that would cover the chemogenomic space and could be used for phenotypic screening. A workflow of the protocol is shown in Fig. 5. In the first step, from the set of bioactive molecules, we selected sub-scaffolds at level 2. Such selection allowed to remove too specific scaffolds of a molecule observed at level 1, but still capturing selectivity of molecules associated with some proteins. The main objective is to avoid a general scaffold (i. e. , only a ring) that would not be specific enough to discriminate between molecules when trying to select active ones for a target. Then, to limit promiscuity, all scaffolds that were linked to more than 6 targets were removed, (being the beginning of the curves' elbow in Additional file 1: Figure  S3 ), retaining 32,038 scaffolds.
Then, we performed a Pareto multiobjective optimisation which selected the best subsets of 5000 molecules satisfying the criteria defined in the method section. The Pareto optimisation created multiple "fronts" which correspond to a dataset containing multiple subsets of 5000 molecules. They had the same range of results concerning the criteria and we decided to select the one maximising both the biological profile and the number of scaffolds. As such the 43rd out of 170 subsets from the 1st front matched those criteria and was chosen to represent the 5000 compounds (Additional file 1: Figure  S4 ). We figured out that by selecting protein classes at level one, some proteins (94 proteins) were not targeted by one of the 5000 compounds or their scaffolds. This is due to the ChEMBL proteins classification schema for Fig.
e. , polypharmacology. Interestingly, only a few chemicals from this library also had information on the Cell painting data and around 10% of the compounds in the phenotypic data is also present in ChEMBL. In addition, many of these compounds did not pass the confidence score (score of 9) applied in ChEMBL and a bioactivity threshold (< 1 μM) that allow selecting highly active compounds. It means that only a few chemicals are shared between the two databases. Nevertheless, for these chemicals a relation between their morphological profiles and molecular mechanisms could be proposed.
The integration of chemical-protein activity from ChEMBL with chemical-morphological profile from Cell Painting, can help to identify proteins that could explain the morphological change of a cell by a chemical and so the potential phenotypic and/or disease impact. The drug-targets-pathways-diseases relationships might help in the investigation of repurposing drugs or a combination of bioactive drugs on two complementary proteins involved in the same pathway. The system pharmacology network is not fully accomplished and phenotypic outcomes could be caused by some targets not yet determined for a compound. Other databases could be integrated. Among them, PubChem  [41] , ChemProt, DrugCentral  [42]  databases would be useful to enrich drug-target interactions. Furthermore, with microarray and next-generation sequencing technology, deregulation of genes and pathways caused by a compound in specific conditions (dose, time, cell type, organ, species) like for example in LINCS  [43]  would be beneficial for obtaining a more comprehensive chemogenomic network.
Also, in addition to scaffolds that can help to recognize certain chemotypes, other methods based on activity cliffs could be interesting to integrate as it consists of interpreting a set of structurally similar compounds with a large difference in potency against their target  [51]. Overall, our systems pharmacology network captures a large ensemble of drug-target interactions with high confidence and based on a state-of-the-art NOSQL graphics database (Neo4J) facilitating the manipulation of large sets of data in a fast and efficient manner. The integration of biological data such as pathways, diseases and phenotypic screening allows to study the effect of a molecule not only at the molecular level but also in more complex layers of a systems biology and can reveal novel repurposing and synergistic therapeutic opportunities or drug safety issues.
There are four commonly used sequences: T1-weighted (T1), contrast-enhanced T1-weighted (T1c), Fluid Attenuation Inversion Recovery (FLAIR) and T2-weighted (T2) images. In this work, we refer to different sequences of images as modalities. Different MRI modalities can highlight different sub-regions, which can provide the complimentary information to analyze the brain tumor. To make it clear, we visualize a case from BraTS 2018 dataset in Figure  1. It can be observed that the FLAIR highlights the whole tumor region, and T1c provides more information about tumor core. Intuitively, it's possible to obtain the best segmentation result using full modalities. However, it's common to have one or more missing modalities in clinical practice, due to motion artefacts, scan corruption and limited scan time.
(3) The proposed correlation constraint block can guide, on the one hand, the generator to synthesize the feature-enhanced modality via satisfying the multi-source correlation with other available modalities; On the other hand, the following segmentation network to learn useful feature representations so as to achieve a better segmentation performance. (4) The experimental results evaluated on the public multi-modal brain tumor segmentation dataset demonstrated the effectiveness of each proposed components, and the proposed method can obtain the significant improvements compared with the baseline methods and state-of-the-art methods. The paper is organized as follows: Section 2 reviews the previous work, Section 3 offers an overview of this work and details of our network architecture. Section 4 describes experimental setup and used datasets.
Motivation of the proposed method In clinical diagnosis and treatment planning, the patient undergoes multimodal MRI scans because each modality can provide specific information. Our hypothesis is that there is some correlation among multimodal MRIs in the tumor regions. We take an example from BraTS 2018 dataset to present joint Then the complete modalities are input to the segmentation network for the final prediction. In addition, the correlation constraint block are used to guide both the feature-enhanced generator and segmentation network via exploiting the multi-source correlation. intensity distributions of the MR images. From Figure  4 , we can observe that there is a strong correlation in intensity distribution between each pair of modalities. To this end, it's reasonable to assume that a strong correlation also exists in the latent representation between modalities  [14].
Dataset and pre-processing To evaluate our proposed method, we used BraTS 2018 dataset  [43]. The enhancing tumor region (ET). The provided data have been pre-processed by organisers: co-registered to the same anatomical template, interpolated to the same resolution (1mm 3 ) and skull-stripped. The ground-truth have been manually labeled by experts. We did additional pre-processing with a standard procedure. To exploit the spatial contextual information of the image, we used 3D image, we cropped and resized them from 155×240×240 to 128×128×128. The N4ITK [44] method is used to do the bias field correction for MRI data, and intensity normalization is applied to normalize each modality to a zero-mean, unit-variance space.
Implementation details The proposed network is implemented using Keras with a single Nvidia Tesla V100 (32G). The model is trained using Nadam optimizer, the initial learning rate is 0. 0005, it will reduce with a factor 0. 5 with patience of 5 epochs. To avoid over-fitting, early stopping is used if the validation loss is not improved over 10 epochs. We randomly split the dataset into 80% training and 20% testing. All the results are obtained by online evaluation platform 2.
For all the tumor regions, our method achieves the best results in most of the cases. Compared to the current stateof-the-art method  [34] , our method can achieve the average Dice Score of 82. 9, 74. 9 and 59. 1 across all the situations, which outperforms the best method by 3. 5%, 17% and 18. 2%. We explain that the proposed feature-enhanced generator compensates the missing modality with the correlation constraint block. It indicated the importance of the complete dataset, since it can provide the full information for the network to learn the effective features for segmentation. We also compared with the method trained on missing-one modality  [38]. Our method can obtain much better results with a large margin, we can achieve the improvement of 6. 7%, 4. 4%, 7. 4% and 12. 6% in the terms of average Dice Score when T2, T1c, T1, Flair is missing, respectively.
Qualitative analysis To further demonstrate the performance of our proposed method, we first randomly select an example on BraTS 2018 dataset and visualize the results in Figure  6  and Figure  7. In Figure  6 , the first row shows the four MR modalities. The last three rows show the segmentation results. Here, 'Ours' denotes the method 'Baseline + FeG + CC'. From Figure  6 , we can observe that in each column, with the help of our proposed components, the segmentation results can be gradually improved. Especially when only FLAIR is available, the baseline method produces many false predictions on the tumor core region. When the FeG is applied, these false predictions are corrected gradually. This improvement is mainly attributed to the new generated average modality, it can provide more rich information for the network to achieve more useful feature  In addition, thanks to the generated modality, the segmentation network can utilize more data information to achieve the promising results when one or more modalities are missing.
Therefore, we design a specific multi-encoder based network to generate the missing modalities. To investigate the importance of the proposed components of our network, However, there are some limitations of the proposed method. (i) The proposed method is only validated on multi MR modalities. (ii) There are still some rooms to improve the proposed correlation constraint block. (iii) The proposed segmentation network is evaluated on the public brain tumor dataset. To overcome the above limitations, in our future work, we would like to expand our method to other multi-modal segmentation problem, such as CT and MRI. And it would be interesting to compare different multi-source correlation expressions (e. g. nonlinear) and to improve the performance of the method. In addition, we plan to validate our method on other public or private multi-modal segmentation datasets to investigate the robustness of our method.
several comparison experiments are implemented with regard to the featureenhanced generator and the correlation constraint block. The experimental results demonstrate the proposed feature-enhanced generator enable the network to generate a relevant feature-enhanced missing modality, and the correlation constraint block can aide the network to achieve the promising segmentation results. In addition, the comparison results on BraTS 2018 dataset show that out method can outperform the state-of-the-art methods despite missing modalities. There are several advantages of our proposed method. (i) The architec-ture are trained by an end-to-end fashion and fully automatic without any user interventions. (ii) The segmentation results evaluated on two metrics (Dice similarity coefficient and Hausdorff distance) are promising.
327 In what follows, we present the datasets used in this study in       et al. , 2012)  between the proposed method (CMCNet with VGG16 and contrastive loss) and state-of-the-art approaches. 44 patches can be further eliminated because there is no corre-565 sponding detection in the other view. These results confirm that 566 our contributions can also provide reliable detection results in a 567 setup more similar to a screening process in real life.
Contrary to (Ma et al. , 2019; Perek et al. , 2018), our 96 method can provide both classification and matching results. 97 Specifically, our contributions are two-folds. First, we propose 98 a new deep learning algorithm that capitalizes on multi-view fu-99 sion and multi-task learning to improve breast mass detection. 100 To the best of our knowledge, our framework is the first that ex-101 ploits multi-tasking abilities of deep learning models to improve 102 mass detection using multi-view matching. Second, we conduct 103 a comprehensive evaluation of various networks towards multi-104 task learning on public datasets. Both quantitative and visual 105 results prove the effectiveness of the proposed strategy. 106 This paper is organized as follows. We present background 107 material and previous works related to mass classification, de-108 tection and matching using deep learning in Sect.
In this paper, we propose a novel multi-tasking approach 570 that combines breast mass/non-mass classification with dual-571 view mass matching between complementary CC/MLO mam-572 mograms. We prove the effectiveness of integrating multi-view 573 information within the breast mass detection pipeline by ex-574 tensive experiments on public datasets. Based on Siamese net-575 works and contrastive learning, our method generalizes well us-576 ing different deep networks and shows impressive results as an 577 integrated CAD system. We can thus easily address the prob-578 lem of false detections without struggling with difficult whole-579 image detection schemes. More globally, the proposed contri-580 butions pave the way for robust automatic second opinions in 581 breast cancer diagnosis. 582 Even if multiple masses can still be detected using the classi-583 fication network, dealing with more than one mass with respect 584 to matching purposes should deserve further investigation.
569 In this paper, we propose a novel multi-tasking approach 570 that combines breast mass/non-mass classification with dual-571 view mass matching between complementary CC/MLO mam-572 mograms. We prove the effectiveness of integrating multi-view 573 information within the breast mass detection pipeline by ex-574 tensive experiments on public datasets. Based on Siamese net-575 works and contrastive learning, our method generalizes well us-576 ing different deep networks and shows impressive results as an 577 integrated CAD system. We can thus easily address the prob-578 lem of false detections without struggling with difficult whole-579 image detection schemes. More globally, the proposed contri-580 butions pave the way for robust automatic second opinions in 581 breast cancer diagnosis. 582 Even if multiple masses can still be detected using the classi-583 fication network, dealing with more than one mass with respect 584 to matching purposes should deserve further investigation.
Deep models for patch classification and match-331 ing are implemented using pytorch. Experiments are performed 332 with a single Nvidia GeForce GTX 1080Ti GPU (11GB/s). 333 4. 1. Imaging datasets 334 We focus on mass detection from high-resolution 2048×1024 335 mammograms arising from two publicly-available mammo-336 gram datasets: INbreast (Moreira et al. , 2012) and DDSM-337 CBIS (Lee et al. , 2017). Here are the dataset characteristics: 346 work, we selected 35 CC/MLO pairs (70 images) out of 347 107 examinations with mass. 348 -DDSM-CBIS (Digital Database for Screening Mammog-349 raphy) (Lee et al. , 2017) is a larger database which con-350 tains approximately 2,500 mammograms including nor-351 mal, benign, and malignant cases with verified pathology 352 information and coarse ground truth manual delineations.
5. In practice, 436 437 Improvements obtained by VGG16 using the contrastive loss are also significant (acc = 0. 9084, p < 1e -6 ), followed by 382 K = 5 (respectively 10) for DDSM-CBIS (INbreast) since the 438 ResNet50 (acc = 0. 9049), InceptionV3 (acc = 0. 90) and 383 INbreast dataset is much smaller. Likewise, we choose K 439 EfficientNet-B3 (acc = 0. 8979), showing that using deeper 384 negative patches from false YOLO predictions. We thus use networks is not necessary to reach better performance. a very small threshold (<10 -4 ) on detection probabilities to 393 355During training, the principle followed for CC/MLO pair se-356 lection is that both views should contain only one mass. The 357 reason is that if the image contains two or more masses, multi-358 view correspondences become unknown without the help of an 359 expert.
To minimize these effects, 385 retain as many predictions as possible and select the K false 386 candidates with the highest scores. All patches are resized to 387 64 × 64 pixels, as in Han et al. (2015) and Perek et al. (2018). 388 Random rotations of 25 degree, random horizontal flips and 389 random resized crops are applied for data augmentation. For 390 matching, we consider a pair of positive patches of the same 391 mass from the two views as a matching sample. If one of the 392 patches is labeled negative, they are considered as a negative 411 All deep models are initialized using pre-trained weights 412 (Litjens et al. , 2017) from the ImageNet dataset (Russakovsky 413 et al. , 2015) and trained using the SGD optimizer. Optimal 414 hyper-parameters vary depending on the network. For loss 415 functions, we choose α = β = 1 and γ = 1 for cross-entropy 416 and γ = 0.
In this implementation, we use three PHYs: FSK 868 MHz option 1 at 50 kbps (offering the lowest bit-rate), OFDM 868 MHz option 1 MCS 3 at 800 kbps (offering the highest bit-rate), and O-QPSK 2. 4 GHz at 250 kbps as an intermediate option. 1 As an online addition to this article, the source code is available under an open-source license at http://github. com/openwsn-berkeley/. We experimentally compare the performance of g6TiSCH against the performance of the 6TiSCH architecture with each PHY individually (as previously studied  [17] ). The comparison is based on the Key Performance Indicators (KPIs) recommended in by Vucinic et al. [18] : network formation time, end-to-end latency, end-to-end reliability, and battery lifetime. The remainder of this article is organized as follows. Section II provides an overview of the related work.
The following measurements are captured and sent every 60 s from each mote: • a counter, that is used to detect loss of data packets. • the time at which the data packet was created (expressed in Asynchronous Slot Number, ASN), • the DAG rank of the sender, • T on : how long the sender's radio has been on since the previous data packet transmission, • T TX : how long the sender's radio has been on and transmitting since the previous data packet transmission, • T FSK on , T OFDM on , and T OQPSK on : the breakdown of T on for each PHY, : the breakdown of T TX for each PHY, • T total : the amount of time since the previous data packet transmission, • the maximum and minimum number of packets in the packet buffer since the previous packet. After the experiment, we use the logged data to compute the following KPIs, as recommended by Vučinić et al.
For a given T TX and T on for a specific PHY, we use Eq. 6 to compute the duty cycle of transmission DC TX and the duty cycle in receive mode DC RX. From the data series of the DC measurements, we compute the global DC for the steady state (last 60 mn in the network). Fig. 15  shows the expected battery lifetime at steady state for g6TiSCH compared to each single-PHY network. FSK 868 MHz has the shortest lifetime (compared to O-QPSK 2. 4 GHz and OFDM 868 MHz), which was expected as it has the lowest bit-rate.            T RX = T on -T TX DC TX = T TX T total DC RX = T RX T total (6) The battery-lifetime of g6TiSCH is estimated based on the duty cycle breakdown between the three PHYs. It demonstrates generally better lifetime than the FSK 868 MHz network thanks to the overall lower duty cycle.
Data and Methodology We use annual data within an unbalanced panel. We classify 172 countries into 95 upper-middle-or high-Income (developed) countries and 77 low and lower-middle-income (developing) countries for the period 1975-2017 (see Table  A0  for the full list of countries), following the 2019 World Development Indicators classification. With this data in hand, we investigate the income inequality-corruption nexus within and across homogenous groups of countries. We depart from standard specifications  (Andres and Ramlogan-Dobson, 2011; Barro, 2000; Lundberg and Squire, 2003)  by accounting for the reverse causality between our regressors and the dependent variable. Indeed, one can intuitively assume that education inequality and income inequality are positively correlated, although the direction of causality remains unclear.