dataesr/scientific-paragraphs-categorization huggingface dataset:
The discriminator gives a probability of truth each time it receives an image, with higher values corresponding to images that are closer to reality. A probability close to 0. 5 is the optimal solution, as this means that the discriminator is not able to distinguish the real images from the fake ones  [19, 20]. The whole process is illustrated in Figure  1. Given the results that GANs have achieved in various tasks  [21, 22] , it will be possible to disseminate a larger dataset than the original one without protection restrictions once GANs have the ability to anonymise  [23]. Additionally, data augmentation with GANs is more robust than traditional data augmentation alone. This reduces the number of real magnetic resonance scans needed to train good medical DL models or even improve some existing models.
Sigma Dataset of Rat Brains An MRI dataset of the Wistar rat brain from the Sigma project was used to test the ability of GANs to produce synthetic scans. A total of 210 scanning sessions were performed using a Bruker (Bruker Corporation, Ettlingen, Germany) preclinical ultra-high field scanner 11. 7 Tesla and a 4 × 4 surface coil dedicated to the rat head. A T2-weighted Echo Planar Imaging sequence was implemented to acquire resting-state data, with a spatial resolution of 0. 375 mm × 0. 375 mm × 0. 5 mm over a matrix of 64 × 64 × 40, a TR of 2000 ms, a TE of 17. 5 ms, and nine averages. Figure  2  shows slices from three different planes-coronal, sagittal and axial-of a rat brain. The dataset consists of 210 scans with a resolution of 64 × 64 × 40, which have been preprocessed to avoid complex values.
For more information on the rat brain sigma dataset, see  Barrière et    In this work, a different number of scans and sources-real or synthetic-were used in some steps of the experiment. Therefore, each dataset is formally defined as D s N = (x i , y i ) i=1 N , where s indicates whether the scans are synthetic (s) or real (r), x indicates the scans, y indicates the respective labels-WM, GM and CSF masks-and N indicates the number of scans. The original sigma dataset of rat brains is formally defined as D r 210 = (x i , y i ) i=1 210.
Overall Process Workflow This work was developed in Python (version 3. 7. 7, Python Software Foundation), drawing on two important DL frameworks, PyTorch  [24]  (version 1. 7. 1+cu101, Facebook AI Research team) and MONAI  [25]  (version 0. 5. 0, Project MONAI). The specifications of the workstations are given in Table  1. The code is available at https://github. com/ShadowTwin41/alpha-WGAN-SigmaRat (accessed on 4 January 2022). The entire training and evaluation process flow is described in Figure  3. Figure  3A , the Image Resources block, represents the acquisition of the MRI scans and the creation of the dataset. Figure  3B , the Development Environment block, describes the development environment with all frameworks, libraries, and other dependencies for training the models. Figure  3C , the Preprocessing block, corresponds to the preprocessing of the dataset-always working with the whole 3D scan-such as resizing to 64 × 64 × 64 with constant padding of zero values, intensity normalisation between -1 and 1, and some conventional data augmentation.
Cropping is necessary because the input data were padded as mentioned above. Flipping was done because the input dataset was inverted. Finally, it is possible to generate a 3D NIfTI file from the dataset using the generated scan and metadata-headers and affines-from an original file-from the D r 210. Eleven models were created, each trained for 200,000 iterations, i. e. , 952 epochs, with a batch size of four, but only the two best and the baseline model for comparison are presented in this article. The first model was the baseline based on the Kwon et al. (  2019 )  [26]  model, where the generator, discriminator, encoder, and code discriminator networks are identical, and the latent space is of size 1000. The use of this architecture is more computationally intensive than some traditional GANs architectures because it involves four networks instead of just two.
However, this choice was justified because the α-GAN architecture avoids mode collapse and fuzziness by adding a VAE and a code discriminator to the GAN network  [26]. The loss functions used in this training process were (1) and (  2 ) for generator/encoder, (3) for code discriminator, and (4) for discriminator with λ 1 = λ 2 = 10-based on the experiments of  Kwon et al. (2019)   [26] : L GD = -E z e [D(G(z e ))] -E z r [D(G(z r ))] (1) L G1 = L GD -E z e [C(z e )] + λ 2 ||x real -G(z e )|| L1 (2) L C = E z e [C(z e )] -E z r [C(z r )] + λ 1 L GP-C (3) L D = -L GD -2E x real [D(x real )] + λ 1 L GP-D (4) where L GD denotes the feedback from the Discriminator, D denotes the Discriminator, G denotes the Generator, C denotes the Code Discriminator, z e denotes the latent vector of the encoder, z r denotes the input random vector, x real denotes a real scan, L GP-D denotes the gradient penalty of Discriminator, L GP-C denotes the gradient penalty of Code Discriminator, L1 denotes the L1 loss function, and E denotes the total distribution.
The mean and standard deviation were calculated for each metric. For MMD, MAE, and NCC metrics, 21,000-the number of scans in dataset *100-comparisons were made between a random real scan and a generated scan; for MS-SSIM, only 2100 comparisons were made for both real and generated data, since the computational power required was much higher than for the other metrics. The MS-SSIM value was calculated under the same conditions as in  Kwon et al. (2019)   [26] , i. e. , with a batch size of 8. The MS-SSIM value should be similar to the MS-SSIM value of the real dataset to prove that the distribution is similar. There is no consensus on which metrics should be used to evaluate the best GAN models; however, the use of the quantitative metrics helped to identify the worst models in the first step  [35] [36] [37].
From Table  2 , it can be seen that the new models outperformed the baseline model in all quantitative metrics. The α-WGANSigmaRat1 had the best overall quantitative results, but after a quick analysis of several scans by some specialists and the authors, the α-WGANSigmaRat2 model seems to produce more realistic scans with less blur and fewer structural anomalies, as shown in Figure  7. Therefore, these two models were used for the Turing test. The α-WGAN_ADNI model was discarded for the Turing test because it was too easy to distinguish between real and generated scans. This architecture, adapted from  [26] , was unable to properly learn the distribution of the dataset, and the generated scans contain artefacts, as seen in Figure  7. Table  2. Quantitative results. For each cell, the first value is the mean and the second is the standard deviation.
The best results are in bold for each metric. An up arrow ↑ next to the metric name means that larger values are better, and a down arrow ↓ means the opposite. The Turing test was performed by four MRI experts on 50 scans, of which 20 were real and 15 + 15 were generated by each model, α-WGANSigmaRat1 and α-WGANSigmaRat2. Only 50 scans were selected because each evaluation is very time consuming. Therefore, it was agreed with the evaluators that 50 scans were a reasonable number. In addition, the experts had to follow some rules, such as: do not open more than one scan at a time; do not change any answer; consider only the original slices-axial plane; do not look at the dataset D r 210 while performing the test. Table  3  presents the results of the Turing test and the respective expertise of each expert.
The level of expertise was categorised as a function of the amount of time each expert had spent working with MRI scans of rat brains. For each rater, the first three values are the number of failed answers, e. g. , α-WGANSigmaRat1 = 13 means that 13 scans generated by the α-WGANSigmaRat1 model were misclassified, and the last value is the number of correct answers. The generated scans were also evaluated in a segmentation task to determine if the synthetic data could improve the results and if it performed better than traditional data augmentation. This test is more important than tricking experts, since synthetic data should be created with a finality, and here, it is the segmentation task. Using a DL model developed by  Rodrigues (2018)   [38]  for GM, WM and CSF segmentation, an experiment was conducted using the following datasets: D r 174 = (x i , y i ) i=1 174 ; D r 87 = (x i , y i ) i=1 87 ; D s 174 = (x i , y i ) i=1 174 ; D s 87 = (x i , y i ) i=1 87 ; D s 261 = (x i , y i ) i=1 261 ; D s 348 = (x i , y i ) i=1 348.
The number of scans in each dataset is a multiple of 174, since the maximum of labelled real scans-with existing segmentation masks of GM, WM and CSF-is the dataset D r 174. Only the α-WGANSigmaRat2 model was used to generate synthetic scans because the raters involved in the Turing test, especially the first one, indicated that the scans generated with this model were more realistic, had less blur and more detail, and could perform better on the segmentation task than the α-WGANSigmaRat1 model. The same 25 real scans were used to test all models, with 80% of the remaining scans used for training and 20% used for validation. The Statistical Parametric Mapping software-SPM12, Welcome Centre for Human Neuroimaging  [39] -was used to generate the GM, WM and CSF labels for the generated scans.
SPM12 was unable to synthesise the labels of some synthetic scans, so these were discarded, and new scans were generated, i. e. , the SPM12 software was used in a final step to assess the quality of the generated scans before segmentation. Table  4  presents the results of this segmentation task performed on different combinations of the previously mentioned datasets. The Dice score results of the first test are shown, i. e. , segmentation using only real scans, such as the one presented by  Rodrigues (2018)   [38]. The use of the conventional data augmentation with the dataset D r 174 was also tested, and the results are presented in Table  5 , where tests 10 and 11, random mirroring, random rotation by 3 degrees in all three axes, random zooming between 1. 0 and 1. 1 and a translation range of 4 voxels were used.
Discussion The initial evaluation of the models was based on the quantitative results of Table  2  to discard the worse models more quickly, e. g. , the MS-SSIM value should not be very close to one, as it is important that the model can generate a large variety of scans. Table  2  shows that the α-WGANSigmaRat1 model has the second highest MS-SSIM value and the value closest to the real distribution that is higher than the real distribution, which means that the generated scans are more repetitive than the real ones. This is not necessarily a problem, as the value is far from one, but it does not correspond to the real distribution of the sigma dataset of rat brains. This value is even higher for the α-WGANSigmaRat2 model, but the generated scans appear to be more realistic after a qualitative visual inspection by the experts.
He was the expert best able to distinguish between synthetic and real scans, so his input and comments were very important. Raters 3 and 4 reported some distortions, some strange blurs and low signal homogeneity. Rater 2 is used to analyse MRI scans from humans but not from rats. This could be the reason why he rejected many responses because they were too homogeneous and symmetrical. After generating the scans for the final test, a selection was made to ensure that all scans had correct anatomy, i. e. , whether the SPM12 software could-or could not-generate the GM, WM, and CSF labels. About half of the randomly generated scans had structural defects, and the SPM software was not able to create the GM, WM and CSF labels correctly. This is the main weakness of this work and should be improved in a future work; however, it can easily be overcome by a quick visual inspection to discard the imperfect scans.
It was also tested whether SPM could create the labels of the synthetic data generated by the remaining models. It was found that almost all the scans generated by α-WGAN_ADNI had problems in creating the labels, so the proposed methods, i. e. , the new loss functions and the normalisation layer were able to improve the quality of the generated scans, as can be seen in Figure  10. The segmentation results using synthetic data are shown in Table  4. The best results for global and white matter segmentation were obtained when a dataset combining D r 174 and D s 348 was used, with an improvement in the Dice score of 0. 0172 and 0. 0129, respectively. For grey matter and CSF segmentation, the use of the D r 174 and the D s 87 resulted in an improvement of 0. 0038 and 0. 0764, respectively. The introduction of only 87 synthetic scans-D s 87 -into the D r 174 dataset had a big impact, particularly on CSF segmentation, evidencing that the use of GANs can significantly improve results without the need for more scanning sessions.
Conclusions In this work, improved alpha-GAN architectures were proposed to generate synthetic MRI scans of the rat brain. The new proposed loss functions used to train the generator and the new proposed normalisation layer applied to the discriminator enabled more realistic results on rat MRI data. The use of traditional data augmentation also helped to generate more diverse and realistic scans. Different techniques were used to evaluate the different architectures and strategies. The α-WGANSigmaRat1 model performed better than the others in the quantitative metrics. However, with the help of some MRI experts, it was determined that the best qualitative model was α-WGANSigmaRat2. The Turing test proved that it is possible to trick experts with moderate or low expertise, but it is very difficult to trick experts who are familiar with the dataset.
Unfortunately, it was not possible to use this test to check which model generated the best scans. Brain structure segmentation also improved when synthetic scans were added to the original dataset, with the global Dice score improving from 0. 8969 to 0. 9141. The improvements in CSF segmentation were even more significant, from 0. 7468 to 0. 8232. It was also found that using synthetic data generated by the new data augmentation model produced better than traditional data augmentation. The segmentation task proved that this method is capable of improving segmentation tasks and therefore other DL tasks. This work has shown that it is possible to adapt tools developed mainly for processing human MR images to preclinical research. From a methodological point of view, it was possible to extend methods that work with 2D images to 3D images, and it was shown that it is possible to obtain improved segmentation results by adding synthetic scans to the original dataset.
The use of data augmentation techniques in the context of preclinical research is interesting, particularly the ability to create larger datasets without scanning more animals, which contributes to the ethical 3R rule, i. e. , the reducing part in particular. It is important to note that the use of GANs for data augmentation is not a substitute for traditional data augmentation but a complement. Both together contribute to increasing the amount of data. Author Contributions: Conceptualization, A. F. , R. M. , S. M. and V. A. ; methodology, A. F. and V. A. ; software, A. F. ; validation, A. F. , R. M. , S. M. and V. A. ; formal analysis, A. F. and R. M. ; investigation, A. F. ; resources, R. M. , S. M. and V. A. ; data curation, A. F. , R. M. , S. M. and V. A. ; writing-original draft preparation, A.
Funding: The MRI dataset of rat brains was acquired in the context of the Sigma project with the reference FCT-ANR/NEU-OSD/0258/2012. This project was co-financed by the French public funding agency ANR (Agence Nationale pour la Recherche, APP Blanc International II 2012), the Portuguese FCT (Fundação para a Ciência e Tecnologia) and the Portuguese North Regional Operational Program (ON. 2-O Novo Norte) under the National Strategic Reference Framework (QREN), through the European Regional Development Fund (FEDER), as well as the Projecto Estratégico cofunded by FCT (PEst-C/SAU/LA0026/2013) and the European Regional Development Fund COMPETE (FCOMP-01-0124-FEDER-037298). France Life Imaging is acknowledged for its support in funding the NeuroSpin platform of preclinical MRI scanners. This work of André Ferreira and Victor Alves has been supported by FCT-Fundação para a Ciência e a Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.
Nature Microbiology published  20  , which may be related to different biological factors and inconsistent standards for metagenomic data generation and processing. Recently, several attempts were made to identify the core CRC-associated bacterial microbiome signatures by meta-analysis using published shotgun metagenomic sequencing datasets. These studies provide an unbiased evaluation of CRC-associated bacterial microbiomes across multiple cohorts  12, 13, 21. However, they did not determine the consistency, or potential inconsistency, of a multi-kingdom microbiome across different populations in CRC. Although traditional screenings reportedly aid in reducing CRC incidence and mortality, the high false positive rate of faecal occult blood or faecal immunochemical tests, as well as the risk and expense of gold standard colonoscopy, represent relevant concerns  [22] [23] [24] [25] [26].
Thus, major efforts have been made to explore complementary strategies of CRC diagnosis, including the potential application of gut microbiomes as non-invasive biomarkers for CRC. However, the clinical implementation of microbial-based diagnostic tools is challenging due to the heterogeneity of patient populations and associated high costs. For the time being, diagnostic tools based on multi-kingdom microbiome analysis should be used as a supplement to traditional CRC screening methods. Therefore, cross-cohort, multi-kingdom studies are urgently needed to provide integrated and robust assessment of CRC and multi-kingdom microbiome association. In this study, we performed a comprehensive analysis of metagenomic datasets to assess the collective predictability of single-and multi-kingdom microbiota across eight distinct geographical cohorts.
Results Characterization of multi-cohort CRC and processing of shotgun metagenomic sequencing data. We collected multi-cohort CRC metagenomic data from 1,368 samples, consisting of population data from 7 publicly available cohorts and one new Chinese (CHN_ SH) cohort (Supplementary Data 1 and Fig. 1a ). To identify reproducible microbial markers for diagnosing patients with CRC, the discovery dataset consisted of samples with broader geographical heterogeneity and genetic background, including 491 individuals with CRC and 494 tumour-free controls across 5 countries (Austria, France, Germany, China and Japan) (Fig. 1a ). Accordingly, the independent validation dataset consisted of 193 patients with CRC and 190 controls covering 3 countries (China, Italy and the USA) (Fig. 1a ). To reduce technical bias in the bioinformatics analysis, all raw shotgun sequencing data were processed consistently (Supplementary Data 2).
Integrated analysis of CRC-associated microbial species in four kingdoms. We first assessed changes in alpha diversity of patients with CRC and healthy controls. Decreased microbial alpha diversity assessed by the Shannon Index was observed for CRC (false discovery rate (FDR) = 2. 579 × 10 -4 ; Fig. 1b ). Notably, differences in beta diversity varied not only according to disease status (P = 0. 001, Fig. 1c ) but also across cohorts (P = 0. 001; Fig. 1c ). Regarding microbial composition, we found different microbial alterations across all four kingdoms for the CRC samples at the phylum level (Supplementary Discussion, Extended Data Fig. 1  and Supplementary Data 3). To identify specific microbial markers for potential CRC diagnosis, we next examined species composition. Although differential microbial species varied greatly in different cohorts, some species with consistent alterations were identified, such as the bacteria Alistipes onderdonkii, Parvimonas micra and Gemella morbillorum (Fig.
In particular, protective species from butyrate-producing bacteria, such as Clostridium butyricum, Roseburia intestinalis and Butyrivibrio fibrisolvens, were decreased in patients with CRC compared to controls. Apart from gut bacteria, emerging studies suggest the importance of other microbial kingdoms in gastrointestinal disease  17, 27. Fig. 1  | overview of the patient populations with CRC included in this study and their associated gut microbiome compositions. a, Global map representing a total of 1,368 samples from 8 patient populations with faecal shotgun metagenomic data. The discovery data populations included Austria (AUS, PRJEB7774), France (FRA, PRJEB6070), Germany (GER, PRJEB27928), China (CHN_HK, PRJEB10878) and Japan (JPN, PRJDB4176). The validation data populations included the United States (USA, PRJEB12449), Italy (ITA, SRP136711) and China (CHN_SH, in-house).
The numbers in brackets represents sample size. Details are shown in Supplementary Data 1. b, Alpha diversity measured by Shannon index of patients with CRC (red, n = 491) and control individuals (blue, n = 494). Adjusted P value (FDR = 2. 579 × 10 -4 , two-sided test) was calculated by MMUPHin. Data are shown via the interquartile ranges (IQRs) with the median as a black horizontal line and the whiskers extending up to the most extreme points within 1. 5× the IQR; outliers are represented as dots. c, Principal coordinate analysis (PCoA) of samples from all five cohorts based on Bray-Curtis distance, which shows that microbial composition was different between groups (P = 0. 001) and cohorts (P = 0. 001). P values of beta diversity based on Bray-Curtis distance were calculated with PERMANOVA by 999 permutations (two-sided test).
The group is colour-coded and the cohort is indicated by different shapes. d, UpSet plot showing the number of differential bacterial species identified via MaAsLin2 in each population and shared by combinations of datasets. The number above each column represents the size of differential species. The set size on the right represents the number of differential species in each cohort and the connected dots represent the common differential species across connected cohorts. e, UpSet plot showing the number of differential fungal species identified via MaAsLin2 in each population and shared by combinations of datasets. The number above each column represents the size of differential species. The set size on the right represents the number of differential species in each cohort and the connected dots represent the common differential species across connected cohorts.
To evaluate whether the above features could be applied universally for CRC diagnosis and overcome geographical heterogeneity, we performed cohort-to-cohort transfer analysis and leave-one-cohort-out (LOCO) analysis as described previously  11. Overall, the AUROC scores based on the cohort-to-cohort transfer analysis were slightly reduced compared to the cross-validation models, while the AUROC values of LOCO were increased compared to those from the cohort-to-cohort transfer analysis (Supplementary Discussion, Fig. 2c  and Extended Data Fig. 5a ), probably due to the larger size of the 'training' dataset. Collectively, our findings demonstrate that marker features from different kingdoms provide unbiased predictive capabilities for CRC diagnosis across various populations. Improved predictability based on combined multi-kingdom features.