dataesr/scientific-paragraphs-categorization huggingface dataset:
Annotation of Most Expressed Contigs Open reading frames (≥100 amino-acids length), found by translating RNAseq data, were extracted from the most abundant contigs and then submitted to the NCBI BLAST program against the Uniprot refseq protein database on the computational cluster of the Genotoul bioinformatic facility, INRA Toulouse, France (ncbi-blast-2. 6. 0 + package, command line: blastp, parameter: -matrix BLOSUM62).
Acknowledgments: We are grateful for  Andrea Yockey  for proofreading the manuscript. V. B. was the recipient of a PhD fellowship from the  French Ministry of Scientific Research. This study was performed in collaboration with the  GeT core facility, Toulouse, France  (http://get. genotoul. fr),and was aided by the  France Génomique National institutional  infrastructure, funded as part of the " Investissement d'avenir" program  managed by the  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ). Ant samples were collected under the authorization of the  Cameroon Ministry of Scientific Research and Innovation.
Tetraponera aethiops venom peptide precursor cDNA sequences were submitted to GenBank, with the following accession numbers: U 1 -PSDTX-Ta1a (MN607166), U 2 -PSDTX-Ta1a (MN607169), U 2 -PSDTX-Ta1b (MN607170), U 2 -PSDTX-Ta1c (MN607168), U 3 -PSDTX-Ta1a (MN607165), U 4 -PSDTX-Ta1a (MN607167), and U 5 -PSDTX-Ta1a (MN607171).
The pre-processing defense block can be associated with any classifier, without any change to their architecture or training procedure. In addition, the proposed method is a universal defense, since it does not require any knowledge about the attack, making it usable against any type of attack. The experimental results on MNIST and CIFAR-10 datasets have shown that the proposed DDSA defense provides a high robustness against a set of prominent attacks under white-, gray-and black-box settings, and outperforms state-of-the-art defense methods.
In addition, the proposed method is thought to be a universal defense, which can defend against any attack without requiring any a priori knowledge about it. The rest of this paper is organized as follows. Section II reviews several attack techniques and defense mechanisms that have been proposed in the literature. The proposed defense solution is detailed in Section III. The architectures and the performance of the proposed defense on both MNIST and CIFAR-10 datasets are provided and analysed in Section IV. Finally, Section V concludes this paper. It is important to note that the most notations and symbols used in this paper are provided in Table  1.
1) ADVERSARIAL TRAINING An obvious defense approach is to augment the training dataset with adversarial examples, which regularizes the network and reduce over-fitting, and therefore make the network more robust against adversarial attacks  [1] ,  [13] ,  [20]. This defense can be useful if it has already been trained with the same kind of attack as that exploited by an adversary. However, adversarial training defense not tend to generalize across different attack strategies, thus leaving the classifier vulnerable to new/unknown attack models. Moreover, an adversarial example can again be computed on an already brute force trained networks  [20].
2) DEFENSIVE DISTILLATION Papernot et al. [21]  exploited the notion of distillation introduced by Hinton et al. [22]  as a defense mechanism against adversarial examples. First, the network is normally trained to the exception of raising the temperature of the softmax function to a large value (40-50), so that it produces smooth probability vectors. Then, these probability vectors are used to label the training data and the same architecture is retrained using these new labeled datasets to obtain the new distilled network. Finally, the prediction is performed, and subsequently, the temperature has to be set back to 1 at test time. Therefore, instead of constraining the network to provide only the correct class, defensive distillation allows it to produce some scores for the other classes.
This assumption has been also verified by Carlini et al. in  [25]. In addition, in order to maintain a good accuracy on clean samples and to regularize the proposed DDSA block, we used a training dataset including a mixture of clean and perturbed samples. The training formulation is designed as follows. θ * = arg min θ E (x,f θ (x ))∈p data max δ∈S J x, f θ (x ) +E (x,f θ (x))∈p data J (x, f θ (x)) , ( 9 ) where pdata is the training data distribution. Moreover, by adding a sparsity constraint to the Fully Connected (FC) layers of the proposed DDSA block, we constraint the neurons to be inactive most of the time in order to extract only meaningful and relevant features. Specifically, the sparsity constraint allows to force the activations of hidden units to be equal to some target activation µ.
IV. EXPERIMENTAL RESULTS In order to evaluate the efficiency of the proposed defense method, we tested it against different set of attacks, including FGSM  [1] , Rand+FGSM  [19] , MIM  [17] , PGD  [18]  and C&W  [12]. These latter represent a broad range of attack models proposed in the literature. Additionally, the performance of the proposed DDSA defense have been compared to two state-of-the-art defenses, namely MagNet  [23]  and adversarial training  [1] , in addition to the trained Deep Denoising Autoencoder (DDA) corresponding to the proposed DDSA method without the sparsity constraint. The experiments were performed on the MNIST  [26]  and CIFAR-10 [27] datasets: • MNIST dataset  [26] : consists of handwritten digits. MNIST was split into training, validation and test sets with 50,000, 10,000 and 10,000 samples, respectively.
• CIFAR-10 dataset  [27] : consists of 60,000 images, where each image has the following dimension 32 × 32 × 3. CIFAR-10 contains ten different classes, and each class is divided into 5,000 training images and 1,000 test images. Our implementation is build on open-source software Clev-erHans library  [28]  based on TensorFlow  [29] , with NVIDIA GEFORCE GTX 1080 ROG. In the following, first, we describe the different classifiers and defenses architectures that we used for MNIST and CIFAR-10 datasets. Then, the performance of the DDSA defense under the three attack levels, namely black-box, gray-box and white-box, are presented.
A. CLASSIFIERS AND DEFENSES ARCHITECTURES 1) CLASSIFIERS ARCHITECTURES Table  2  describes classifiers architectures of MNIST and CIFAR-10 datasets. For these considered classifiers, we obtained accuracies of 98. 96% and 81. 42% on the MNIST and CIFAR-10 datasets, respectively, which is not far from state-of-the-art performance on these datasets. The two architectures are based on convolutional neural networks with the format Conv2D and fully connected layers with the format dense (number of hidden units). We used the rectified linear unit activation function and a dropout layer with a rate of 0. 25 to prevent overfitting. Table  3  presents the training parameters used to train our classifiers. We used the Adam optimization algorithm for the MNIST classifier and RMSprop for the CIFAR-10 classifier, which are the most commonly used learning algorithms.
2) DEFENSES ARCHITECTURES Table  4  summarizes the architectures of our DDSA defense for both MNIST and CIFAR-10 datasets. The two architectures are based on convolutional neural networks with Conv2D layer, dense layers and a dropout using the ReLU activation function. In addition, we used a Conv2DTranspose for the deconvolution process and BatchNormalization to normalize the hidden units activations and therefore speed up the learning. The network architecture of the reformer (encoder) used for the MagNet defense, as specified in  [30] , is provided in Table  5. Finally, for the adversarial training defense, in addition to the clean samples, also the training was performed using the FGSM adversarial samples. These latter have been generated using a perturbation magnitude of = 0. 3. These choices are the most adopted in the literature.
2) RESULTS ON GRAY-BOX ATTACKS In gray-box attack level, the attacker has knowledge of the classifier's parameters without the defense strategy. Table  7  presents our defense results for the gray-box settings as well as for the DDA defense on the MNIST dataset. From this table, we can see that the gray-box attacks are more efficient than the black-box attacks in reducing the classification accuracy, making them more challenging to defend. As with the previous black-box attacks, the proposed defense performs well against the gray-box attacks. Despite the classification accuracy is slightly smaller than the results of black-box attacks, however, compared to no defense case the DDSA increases the accuracy by up of 87%, which is more significant than the black-box results. As illustrated in table  7 , the obtained results confirm the importance of using a denoising sparse autoencoder instead of a normal DDA method with a mean squared error loss function.
In contrast to black-box results, where DDA defense provides an acceptable result, against gray-box attacks, the DDA is far from sufficient. However, the proposed DDSA method shows that it is also robust against gray-box attacks. Consequently, it is clear that our defense is performing way better than the DDA method under all considered gray-box attacks. In addition, a defense is considered effective, if it resists to a wide variety of attacks as well as different attack parameters. Thus, in order to assess the effect of different attack parameters on DDSA's performance, in Figure  5 , we show the accuracy of classifier on MNIST dataset with and without any defense against PGD, FGSM and MIM gray-box attacks, with different perturbation magnitudes. In Figure  5 (a), it can be seen that the accuracy of classifier gradually decreases while augmenting the magnitude of the perturbation.
Therefore, the attacks are more successful with higher as the accuracy dropped with nearly 60%, however, at the expense of being more detectable and visually perceptible. of DDSA defense, the classifier is now highly confident and robust against the attacks even for large values of. The enhancement is significant for all values, except for the = 0. 4 that generates very noisy image, making them difficult to classify, even by a human. However, this is not the case for PGD attack, which may be explained by the fact that our DDSA was trained including this attack. Some adversarial images defended by DDSA from MNIST dataset are shown in Figure  7. Furthermore, we investigated the effect of varying the number of PGD attack iterations, ranging from 10 to 1000, on the defense performance. Figure  6  shows that our defense is more robust than DDA defense, where the latter decreases rapidly with the increase of attack iterations, this is more   noticeable between 90 and 1000 iterations.
However, our defense approach provides somewhat stable results even under the utmost 1000-iteration PGD attack for which our DDSA has obtained 88. 93% accuracy compared to only 74. 14% accuracy achieved by the DDA. Table  8  reports the performance of our defense against different gray-box attacks on the CIFAR-10 dataset. FGSM, Rand+FGSM, MIM and PGD attacks have been performed with a perturbation magnitude of = 0. 2 that represents, according to  [31] , a high distortion that makes most images completely unrecognizable and hardly to classify. The obtained results on CIFAR-10 dataset are lower than those achieved on MNIST dataset, it is clear that the CIFAR-10 dataset is much more challenging to defend than the MNIST one. However, here again our DDSA provides an increase in the classification accuracy and surpassed the DDA method for all the considered attacks.
The proposed DDSA achieves the highest accuracy against the PGD attack with 61. 14% accuracy, while obtained the lowest accuracy against C&W attack. Because it is important to specify that the C&W attack is one of the most powerful attacks that generates adversarial samples that are visually very close to the clean image, i. e, adversarial images with very low noise. As evidence of its effectiveness, it reduces the classification accuracy to 0% and, by using the DDSA defense, the accuracy of classifier goes to 36. 7%. Furthermore, on the CIFAR-10 dataset, except for the C&W attack, the remaining attacks provide adversarial images that are highly perturbed and, therefore, are hardly classified even for the human. Some visual examples from CIFAR-10 dataset are illustrated in Figure  8. The achievements of our DSSA method are comparable to the state-of-the-art accuracy performance on the CIFAR-10 dataset  [12] ,  [33] ,  [34].
V. CONCLUSION In this paper, we proposed DDSA method, a defense against adversarial attacks. The proposed defense, which consists of applying sparsity constraint on a denoising autoencoder, is used as a preprocessing block applied to the input samples for removing the effect of adversarial noise. The proposed method allows to increase the adversarial robustness of DNNs and has been designed in such a way that can be deployed with any classifier without any change to its architecture or training stage. In addition, since it does not require any knowledge about the attack, the DDSA defense can be used against any type of attack. The proposed defense has been evaluated against FGSM, Rand+FGSM, C&W, MIM and PGD attacks under blackbox, gray-box and withe-box settings on two standard datasets. The experimental results demonstrated that the proposed defense can provide a significant improvement in the robustness of DNNs against adversarial attacks, and outperforms two state-of-the-art defenses.
The predator-prey system Phytoseiulus persimilis Athias-Henriot (Acari: Phytoseiidae) and Tetranychus urticae (Koch) (Acari: Tetranychidae) represents the most studied and the most known system among the Acari. Because of the agronomical importance of the two mite species, a wealth of modelling studies investigated the interactions of the two species at the individual level. However, regional or global level works on this system are lacking, which is impeding the investigation of climate change effects on biological control effectiveness. Here we compile and geo-locate worldwide occurrences for the two species considered, based on literature, collection and field survey data. The datasets presented in this document gather most of the literature records of both species for which locality data were available for geo-referencing (1,037 for T.
Introduction The development of effective biocontrol strategies relies on an in-depth understanding of predator-prey relationships  (McMurtry and Croft, 1997). However, while many modelling studies focus at fine scale interactions  (Takabayashi et al. , 2000; Ellner et al. , 2001) , few studies address interactions at coarser levels. Furthermore, the investigation of climate change effects on biological control requires studies that expand beyond the local to the regional and global level. Such studies depend however, on the availability of detailed geo-located data on the distribution of both prey and predators. Among the Acari, due to their economic importance, Tetranychus urticae (Koch, 1836) (Acari: Tetranychidae) and its predator Phytoseiulus persimilis Athias-Henriot, 1957 (Acari: Phytoseiidae) are the most studied, the most well-known and the most widely distributed preypredator pair globally.
For both species, databases recording host plants (for phytophagous mites) or plant support (for predatory mites) and country level distribution are available  (Demite et al. , 2018; Migeon and Dorkeld, 2018)  but geo-located occurrences are not included in these databases and remain scarce. This lack of information combined with a country scale mapping leads to an over-representation on a global scale as exemplified figures 1 and 2. Furthermore, some reports are made from glasshouses in countries with too harsh winters and unsuitable for outdoor surviving and have no ecological significance. Tetranychus urticae is the species that attracts the most interest and has generated the greatest number of works among the family Tetranychidae  (Vacante, 2015) ; the Google Scholar query "Tetranychus urticae" returns 50,600 records (Google Scholar October 2018).
urticae and belongs to the specialist class of Phytoseiidae predators, as it is closely associated with its "unique" prey T. urticae  (McMurtry & Croft, 1997; McMurtry et al. , 2015). Few datasets regarding Phytoseiidae or Tetranychidae have been published. GBIF (2018) gathered 10,572 occurrences of Phytoseiidae, of which only 69% are geo-located. In the GBIF (Global Biological Information Facility, https://www. gbif. org) dataset we found 121 P. persimilis records but only 74 with suitable coordinates (match of location and coordinates and 0. 1 or more degree precision) and 65 unique locations. Regarding the Tetranychidae, two datasets exist. The first one deals with the genus Mononychellus (Vásquez-Ordóñez and Parsa 2014); the second deals with the whole family  (Migeon 2015)  and provides 271 T.
urticae records but only 248 with suitable coordinates and 69 unique locations. To fill the gap in the availability of detailed geo-located data for both species, we publish four datasets. Two are issued from literature records and contain the occurrences retrieved for the two mite species considered. The two others contain new unpublished data from our collections and surveys and report occurrences and host plants or plant support for the two species. (Migeon and Dorkeld, 2018). Migeon (2015)  data (green dots) are also added. Table  1  Countries for which Tetranychus urticae occurrence was not accurate enough to determine the corresponding geographical coordinates and then including them in the dataset. with localities (see Supplementary materials). These references correspond to 29 countries.
Method step description The datasets pool two different data types. (i) All available literature for the two species was analysed. Geographical data (coordinates) were compiled when available. When only textual descriptions of locations were available, occurrence data were assigned using several geolocation tools like GoogleMaps, GeoNames and other gazetteers   were added for the two species. These latter records are also completed by host (or support) plant when available. To avoid confusions and provide solely "natural records", only records not reported, according to the literature, from glasshouses or indoors supports were used. The coordinates are in decimal degrees (DD) based on the WGS84 geodetic system. Geographic coordinates were visually verified using the Check Coordinates tool in Diva-GIS  (Hijmans et al.
Funding The work was carried out as part of the  FACCE ERA-NET Plus -FACCE JPI   GENOMITE  project, with funding from national and EC sources (EC contract  618105 ). The authors acknowledge funding from the  Cyprus Research Promotion Foundation of Cyprus  (Grant:  KOINA/ΠΚΠ/FACE/1013/10  to V. L. and M. S. ). This project was partially supported by the  French Agence Nationale de la Recherche  (Grant  ANR-14-JFAC-0006-01  to M. N. , A. M. and MS. T. ), the scientific conceal of  Montpellier SupAgro  (MS. T. ) and the  metaprogramme Adaptation of Agriculture and Forest  to  Climate Change  (A. M. and M. N. ) launched by the  French National Institute for Agricultural Research (INRA).
Data published through GBIF The datasets have been deposited in GBIF and are available under the following references: • Phytoseiulus persimilis literature occurrences https://www. gbif. org/dataset/6a5bca76-9c9b-4962-acda-c02ddc2292dc • Phytoseiulus persimilis collection records https://www. gbif. org/dataset/4367882e-714d-4268-a657-5dd7680eff15 • Tetranychus urticae literature occurrences https://www. gbif. org/dataset/a9c0892e-0991-4c73-826d-8629cd71d95a • Tetranychus urticae collection records https://www. gbif. org/dataset/f7bec8f5-55a8-4dde-9b2e-571fef74bb23.
Dataset • Object name -Darwin Core Archive Phytoseiulus persimilis (Acari: Phytoseiidae) and Tetranychus urticae (Acari: Tetranychidae): worldwide occurrences records. • Character encoding -UTF-8. • Format name -Darwin Core Archive Format. • Format version -1. 0. • Distribution -Phytoseiulus persimilis literature occurrences https://www. gbif. org/dataset/6a5bca76-9c9b-4962-acda-c02ddc2292dc -Phytoseiulus persimilis collection records https://www. gbif. org/dataset/4367882e-714d-4268-a657-5dd7680eff15 -Tetranychus urticae literature occurrences https://www. gbif. org/dataset/a9c0892e-0991-4c73-826d-8629cd71d95a -Tetranychus urticae collection records https://www. gbif. org/dataset/f7bec8f5-55a8-4dde-9b2e-571fef74bb23 • Publication date of data -2019-08-07 • Language -English. • License of use -Open Data Commons Attribution License (ODC-By).
Gene expression analysis RNA extraction from cells was performed by using RNeasy Mini Kit or miRNeasy Mini Kit (QIAGEN). qRT-PCR was performed using a Roche LightCycler II and 1-Step Brilliant II SYBR Green qRT-PCR master mix kit (Agilent Technologies). Data for qRT-PCR were expressed as mean ± SEM and statistical differences between groups were calculated by a two-tailed Student t-test.
Ethosuximide working solution was added to the culture medium within 10 min before exposure to HCCMF, RCF, or SHAM groups. Within 5 min after completion of the three-hour exposure time, media was removed from all dishes and replaced with fresh media without ethosuximide. On day seven, cell proliferation was assessed with the tritiated thymidine incorporation assay, flow cytometry markers were stained, or cells were cultured in tumour sphere media for sphere formation assays. Experiments were repeated at least twice. Statistics: One-way ANOVA was used to statistically compare the effect of experimental (HCCMF) and control groups (SHAM and randomly chosen frequencies). Post hoc testing was by the Tukey test. Data are expressed as mean ± SEM. Graphpad Prism was the software used for statistical analysis.
Flow cytometry analysis Cells cultured, divided and treated into HCCMF, RCF or SHAM groups. After seven days of treatment cells were labelled for CD44-APC (mouse anti-human 1:20,000(Huh7) and 1:10(Hep3B), Cat#103011 Biolegend) and CD133-PE (mouse anti-human 1:10 (Huh7) and 1:20(Hep3B), Cat# 130-098-826 Miltenyi Biotec) markers of cancer stem cell, fixed and analysed via flow cytometry. Data collection was performed on a C6 Accrui flow cytometer while analysis was performed on CFlow Plus software (Becton Dickinson).
K. ) from digital slide scans (1x and 20x magnification). IHC staining was performed by Dr. David Caudell. Visualization and quantification were performed blindly by Dr. Barry DeYoung, average number of positive staining cells was counted for both control and treated groups. Tumour samples were from mice with established tumour after subcutaneous xenograft injection of the Huh7 cells, having received HCCMF or SHAM treatment for ten weeks. Statistics: One-way Analysis of Variance (ANOVA) was used to statistically compare the effect of experimental (HCCMF) and control groups (SHAM & RCF). Post-Hoc testing was compared by a Tukey Test. Data for Immunohistochemistry were expressed as mean ± SEM. Graphpad Prism was the software used for statistical analysis. IHC was performed on a Leica Bond RX (Leica Biosystems, Buffalo Grove, IL) automated stainer.