dataesr/scientific-paragraphs-categorization huggingface dataset:
It is usually of advantage to use a reference library consisting of all genetic regions with available sequence information, including loci that were not targeted during target capture, since these additional regions can act as filters that bind reads not desired in the dataset of enriched regions. De novo assembly: Few non-model organisms have suitable (closely related) reference sequences available for referencebased assembly. To generate longer sequences from short read data, a common first step in those cases is de novo assembly. During de novo assembly, reads with sequence overlap are assembled into continuously growing clusters of reads (contigs) which are then collapsed into a single contig consensus sequence for each cluster. There are different de novo assembly software packages, which differ in their specific target use (short or long DNA or RNA contigs).
Some of the commonly used software packages for assembling target capture data are ABySS  (Simpson et al. , 2009) , Trinity  (Grabherr et al. , 2011) , Velvet  (Zerbino and Birney, 2008) , and Spades  (Bankevich et al. , 2012). De novo assemblies are usually computationally very time intensive and generate large numbers of contig consensus sequences, only some of which represent the targeted loci. In order to extract and annotate the contig sequences that represent targeted loci, a common approach is to run a BLAST search between the contig sequences on the one hand and the bait sequences or some other collection of reference sequences on the other hand (e. g. ,  Faircloth et al. , 2015). The above mentioned short read pipelines aTRAM, HYBPIPER, PHYLUCE, and SECAPR all contain functions that employ some BLAST algorithm to match the assembled contigs with reference sequences of the desired target loci.
Yet another promising path for de novo generation of even longer sequences from short read data are reference-guided de novo assembly pipelines, such as implemented in aTRAM. In this iterative approach, clusters of reads are identified that align to a given reference (e. g. the bait sequences) and are then assembled de novo, separately within each read cluster (locus). This process is repeated, using the resulting consensus contig sequence for each locus as reference for identifying alignable reads, leading to growing numbers of reads assigned to each locus, as reference sequences become increasingly longer in each iteration. All following steps describe downstream considerations in case of reference-assembly data. If one decides to work with the contig data instead and omit reference-assembly, the contig sequences are ready to be aligned into MSAs and require no further processing.
If the read coverage is substantially lower than that, it is usually an indicator that either the enrichment process with the used baits did not work properly for the given locus (perhaps because the locus sequence in the sequenced individual is to divergent from the bait sequence), or it could be a result of the processing pipeline and the chosen reference sequence or mapping thresholds. In many cases the recovered read-coverage at many loci can be improved by testing different mapping and sequence similarity threshold settings for the specific dataset (see  Andermann et al. , 2018). However, if the sequencing experiment did not work as expected and coverage of all loci is low, it may still be possible to produce consensus sequences from loci with read coverage thresholds of at least 3, yet in these cases it will not be possible to extract allelic information (see  Andermann et al.
Extracting Sequences from Assembly Results With all target reads assembled, there are different ways of compiling the sequence data for downstream phylogenetic analyses. One possible approach is to compile full sequences for each locus in the reference library by extracting the bestsupported base-call at each position across all reads (e. g. the unphased SECAPR approach, see  Andermann et al. , 2018). This approach yields one consensus sequence for each given locus. Alternatively, to forcing a definite base-call at each position, those positions with multiple base-calls originating from allelic variation can be coded with IUPAC ambiguity characters (e. g. ,  Andermann et al. , 2019). In the latter case, it is important to check if the phylogenetic software that is used to analyze the resulting sequences can read these ambiguity characters, as some programs treat these characters as missing information.
Another approach is to separate reads belonging to different alleles through allele phasing  (He et al. , 2010; Andermann et al. , 2019). Subsequently, a separate sequence can be compiled for each allele, yielding N sequences per locus for an N-ploid individual. However, no general software solutions for allele phasing of more than two alleles have been established for short-read data at this point (but see  Rothfels et al. , 2017 , for long read solutions), which presents a major bottleneck for many studies working with polyploid organisms. A third approach is the extraction of SNPs from the reference assembly results. In this case, only variable positions within a taxon group are extracted for each sample. SNP datasets are commonly used in population genomic studies, since they contain condensed phylogenetic information, compared to full sequence data.
Even though large SNP datasets for population genomic studies are commonly produced with the RAD-seq genome subsampling approach, target capture produces data that can also be very useful for this purpose, as it usually provides thousands of unlinked genetic markers at high coverage that are present in all samples. This renders the extraction of genetically unlinked SNPs-a requirement for many downstream SNP applications-simple and straightforward (e. g. ,  Andermann et al. , 2019). Even though most phylogenetic methods are sequence based, some methods can estimate tree topology and relative divergence times using only SNPs instead (e. g. , SNAPP,  Bryant et al. , 2012).
Secondly, sequencing costs for full genomes of many samples are still prohibitively high for research groups in developing countries, even though sequencing costs are rapidly decreasing. Thirdly, the complexity of assembling and annotating full genomes, especially using short-fragment sequencing approaches, is still a major bottleneck and requires suitable references among closely related taxa, which is lacking in many cases. Other initiatives that are sequencing large groups of organisms with standardized target capture kits, such as the Plant and Fungi Tree of Life (PAFTOL, https://www. kew. org/science/our-science/ projects/plant-and-fungal-trees-of-life) constitute a promising alternative to the mentioned full genome initiatives. To further accelerate the use of target capture we advocate A) sequencing and annotation of high-quality reference genomes across a wider representation of the Tree of Life, B) the establishment of data quality and processing standards to increase comparability among studies, such as those put forward by the computational pipelines mentioned in this review, and C) the availability of published baitsets and target capture datasets on shared public platforms.
FUNDING MT and CB were funded by the  Swedish Research Council. PM-M was funded by the  European Union 's  Horizon 2020 Programme  under the  Marie Skłodowska-Curie  ( MARIPOSAS-704035 ) and the  PPLZ program  of the  Czech Academy of Sciences  (grant  L200961951 ). RB received a  Doctoral Fellowship and a Fellowship for Internship abroad  from  Coordination for the Improvement of Higher Education Personnel (CAPES ,  Processo 99999. 000566/2015-02 ). TA and AA were funded by a  Wallenberg Academy Fellowship. JB-P was funded by the  European Union 's  Seventh Framework Programme  under a  Marie Skłodowska-Curie fellowship  ( AlfalfaEvolution, Grant  Agreement n. 625308). AA was further funded by the  Swedish Research Council  ( B0569601 ), the  Swedish Foundation for Strategic Research , and the  Royal botanic Gardens, Kew.
The present work is a contribution to this understanding. It provides an effective method to construct adversarial images without prior knowledge of the features of the NN. The proof of concept of this method is achieved by a test against a concrete NN. More concretely, our work further substantiates our research program [3] to construct Evolutionary Algorithms (EA) which create images to deceive machines and humans at image classification. The present article, extending significantly [4], describes the construction of two such EAs (that actually differ only by their fitness function, and are variants of those used in [2]) in the following context. Trained on an image dataset, a given NN sorts images into categories. For any category c A , one chooses an image A classified by the NN as belonging to c A.
From there, two scenarios (see  [3]  for more) are considered. In the first "target" scenario, a target category c t = c A is fixed a priori. We construct the evolutionary algorithm EA target d , that evolves A to a modified image that the NN classifies as belonging to c t. In the second "flat" scenario, we construct the evolutionary algorithm EA flat d that evolves A to a modified image that the NN is unable to classify with certainty to any of the categories. In both scenarios, the EAs are used with two different similarity measures d (L 2 and SSIM), that assess differently the proximity between the created adversarial images and the image A. In each case, a human would likely classify the obtained adversarial images as still belonging to c A. The strategy being set, experiments are performed with the neural network  trained on the image dataset  to sort images into = 10 categories.
Neural Networks and Evolutionary Algorithms in a nutshell We give here the very minimum background about Neural Networks (NNs) and Evolutionary Algorithms (EAs) needed to position our contribution (section 3) in the context of some related work (2. 2). A Neural Network can be considered here as a black box (see  [10]  for a detailed description of NNs in the context of Deep Learning), which takes an image p as input and produces the corresponding output, consisting of a vector o p of length. Each position 1 ≤ i ≤ in the output vector represents a category c i of objects defined in a dataset. Since the vector components are probabilities, one has 0 ≤ o p [i] ≤ 1, and i=1 o p [i] = 1. A NN is first trained on images of the dataset. Once trained, the NN produces a label for an image p by extracting the category for which the probability is highest, e.
(9) 4 Dataset, Neural Network Architecture and Parameters of the two EAs VGG-16  [25]  is a convolutional neural network (CNN) that passes input images through 16 layers to produce a classification output. As shown in Figure  1 , the model consists of 5 groups of convolution layers and 1 group of fully-connected layers. Each convolution filter has a kernel size of 3 × 3 and a stride of 1. Meanwhile, pooling is applied on regions of size 2 × 2, with no overlap. Since VGG-16 was initially designed for the ImageNet dataset  [6] , a series of adjustments were necessary for its use with the CIFAR-10 dataset  [9]. The CNN used here was therefore an adapted VGG-16 architecture obtained through the steps described in  [17]. Specifically, VGG-16's input size was adjusted to 32 × 32 × 3, Batch Normalization layers were added before every nonlinearity and the first two fully-connected layers were reduced in size from 4096 to 100.
EA Parameters The implementation of EA target d and of EA flat d was done from scratch, using Python 3. 7 with the NumPy  [20]  library. Keras  [5]  was used to load and run the VGG-16  [25]  model, and Scikit-image  [32]  to compute SSIM (using Scikitimage's default options). We ran our experiments on nodes with Nvidia Tesla V100 GPGPUs of the Iris HPC cluster at the University of Luxembourg  [31]. Both EAs run with a population of size 160, the ancestor images being chosen from the CIFAR-10 [14] test set. For any source category out of the 10 categories of CIFAR-10, a random image was selected from the 1000 test images belonging to that category. This image was then set as the ancestor for both EAs. The specific ancestor images used in our experiments are referred to in Table  1 's last row, and pictured in the appendix section A.
Results Depending on the chosen source and target categories, reaching the probability of 0. 95 required between 5 and 124 generations, for the 90 altogether evolutions, as specified in Appendix section (in Figure  10  with L 2 and Figure  11  with SSIM). The average computation time per generation is 0. 03 ± 0. 01s for L 2 and 0. 15 ± 0. 02s for SSIM. Organizing the 10 categories of the CIFAR-10 dataset into the group of animal categories and the group of object categories, the animal →animal and object→object evolutions took fewer generations than the animal →object and object→animal ones. The required number of generations varied significantly not only with the category of an ancestor, but also with the particular image extracted from a given category. Table  3  presents the mean number of generations for the 4 combination types for the 90 evolutions of Figure  10  and of Figure  11  in Appendix.
Conclusions and Future Work Pursuing the research program announced in  [3] , this work substantially complements  [4]  by demonstrating the validity of an approach using evolutionary algorithms to produce adversarial samples that deceive neural networks performing image recognition, and that are likely to deceive humans as well. Our two evolutionary algorithms, EA target d and EA flat d , that differ by their fitness functions, successfully fool, for two "target" and "flat" scenarios, the neural network VGG-16  [25] , trained on the dataset CIFAR-10  [14]  to label images in 10 categories. The similarity between the adversarial images and the original ones, aiming at ensuring that humans would still classify the modified image as belonging to the original category, is measured by two "distances" d, namely d = L 2 and d = SSIM.
Towards this goal, several steps could be taken, such as optimizing the EAs with different mutations or using other datasets, notably with larger images. As a complement to this, one of the main advantages of our evolutionary algorithms is the ability to treat the neural network as a black box, with no required knowledge of its architecture or parameters. We hence intend to extend the present study to more CNNs (Inception  [27] , etc. ) trained on larger datasets (CIFAR-100  [14] , Ima-geNet  [6] , etc. ) with images of larger resolution. The third direction aims at confronting the resistance to filters (e. g. denoising filters like the median filter) of the adversarial images constructed by our EAs compared to adversarial images obtained by other methods, for instance those of  [26]. Once these studies performed, we will be armed to conduct a thorough comparison between our evolutionary algorithm approach and other adversarial image generation approaches listed for instance in the survey  [1].
In-silico prediction of aqueous solubility plays an important role during the drug discovery and development processes. For many years, the limited performance of in-silico solubility models has been attributed to the lack of high-quality solubility data for pharmaceutical molecules. However, some studies suggest that the poor accuracy of solubility prediction is not related to the quality of the experimental data and that more precise methodologies (algorithms and/or set of descriptors) are required for predicting aqueous solubility for pharmaceutical molecules. In this study a large and diverse database was generated with aqueous solubility values collected from two public sources; two new recursive machine-learning approaches were developed for data cleaning and variable selection, and a consensus model based on regression and classification algorithms was created.
In this respect, the in-silico prediction of aqueous solubility by Quantitative Structure-Property Relationship (QSPR) has been widely used in the early stage of the drug discovery and development process  [2]. Several QSPR models for predicting aqueous solubility have been developed in recent decades, but the performance of some of these models on various solubility datasets has demonstrated the poor reliability of the prediction methods  [2, 5, 7, 8]. One of the main reasons for these results is that published methods are derived from different data sources, for which the root-mean-square errors (RMSE) are around 0. 6-0. 7 log S units  [9]. However, some studies suggest that the poor accuracy of solubility prediction is not related to the quality of the experimental data and that more accurate methodologies (algorithms and/or set of descriptors) are required  [9, 10].
Recently, some machine learning (ML) algorithms such as random forests (RF), support vector machines (SVM), k-nearest neighbors (k-NN), and convolutional and recurrent networks have been applied for aqueous solubility prediction, and their performance matches or outperforms the previous results obtained  [11] [12] [13] [14] [15]. The Konstanz Information Miner (KNIME) is a free and public software tool that has become one of the main analytical platforms for innovation, discovery of the hidden nature of data and prediction of new features  [16]. KNIME, through the implementation of interconnected nodes, integrates several components of machine learning and data mining, which can be easily used in chemistry, biology, drug design and recently in the prediction of ADME properties  [17, 18]. The flexibility of workflows developed in KNIME to include different tools allows users to read, create, edit, train and test ML models, greatly facilitating the automation of predictions and application by any user.
Taking into consideration the limited predictive performance of many of the published solubility models, the main goal of this study is twofold: the development of an innovative QSPR model using new recursive algorithms for data and variable selection in machine learning approaches, and the implementation of an automatic workflow for a better prediction of aqueous solubility during the early stages of drug discovery and development. In order to achieve the goal, a large database of aqueous solubility and a new recursive machine learning algorithm for data cleaning and variable selection were used. The application of a consensus regression model and a classification model, using a Random Forest approach, showed good predictive performance in comparison to different models published in the recent literature.
Computational tool The open source software KNIME version 4. 0. 2 [19] , its free complementary extensions "KNIME Base Chemistry Types" and the "KNIME Chemistry Add-ons", were used in this study  [20]. Several node extensions such as "RDKit KNIME Integration" and "Indigo KNIME Integration" were applied for the curation of the databases and the conversion of the chemical structures to different formats, respectively  [21, 22]. For the generation of molecular descriptors from structures, the "Descriptor" node from "alvaDesc" extension was employed  [23]. AlvaDesc 1. 0. 16 is available with academic or commercial licenses, which can be obtained by requesting a quote online (registration required) or by contacting them directly by email (chm@kode-solutions. net). Other nodes, belonging to KNIME "Analytics" and "Manipulation" extensions, were used opportunely for the transformation, processing and modeling of data, according to the requirements of the automated workflow.
Database of aqueous solubility To develop in-silico solubility models, two large publicly accessible databases with experimental values of thermodynamic aqueous solubilities (log S, S in units of mol/L) were selected. The first database (AqSolDB) was generated by Sorkun et al. from nine publicly available datasets and included 9982 liquids and crystalline solid compounds, filtered by temperature for a range of 25 ± 5 °C  [24]. The total solubility range of this database is more than eleven orders of magnitude (-9. 7 to + 2. 1). AqSolDB is openly accessible at the Harvard Dataverse Repository (https://doi. org/10. 7910/DVN/OVHAW8). The second database was published by Cui et al. and consists of 9943 molecules with experimental solubility values measured at room temperature  [12] , and its solubility range was more than nineteen orders of magnitude (-18.
Data curation In order to create a single database containing only unique molecules matched to the most reliable aqueous solubility value, a general protocol for the curation of the database was developed. The curation protocol was divided into three parts: cleaning of chemical structures, standardization of the molecular representation and treatment of duplicates (see Figure  1 ). The cleaning of the molecular structures followed the procedure described by Fourches et al. [25]. To apply this approach, a sequence of nodes was created in KNIME. First, the valence of the atoms was checked to filter out structures with unusual values. Subsequently, different filters were applied to maintain organic molecules, keep molecules containing only the following elements (C, N, O, H, F, Cl, Br, I, P and S), remove salts and unconnected molecules and filter out molecules out of weight bounds (between 50 and 1250 g/mol).
Eventually the molecular representations were normalized and standardized. For the treatment of duplicate structures, the first step was the generation of their InChI (International Chemical Identifier) code  [26]. The duplicated molecules in both databases were identified and solubility values were collected. The standard deviation values for the solubility measurements (STD) were calculated and compared with the previous STD values recorded in both databases. To consider the variability in solubility measurement, the final reported value was the highest STD. Finally, these structures were concatenated with the unique molecules from both databases and care was taken to exclude from this set of molecules those belonging to the external test sets.
Recursive data cleaning Considering the high variability of the experimental solubility measurements and the unreliable solubility data in several databases  [28] , a new recursive clean-up procedure was devised and carried out before the development of prediction models. In a first instance, all molecules in databases were organized as RELIABLE/UNRELIABLE according to their known Solubility Standard Deviation (STD) value. Molecules with known STD lower than 1 were considered to have good experimental solubility (RELIABLE set), while the remaining molecules with unknown STD (single measure) or STD  1 were considered to have high experimental variability (UNRELIABLE set). In the case of the UNRELIABLE data, a recursive procedure was carried out to clean this set. The dataset cleaning consisted of discriminating all the molecules that were systematically outside of an allowed threshold of Predicted Solubility Variance (PSV, threshold  1) during a training/classification-based process of selection.
Selection of training and test sets To evaluate the performance and stability of the in-silico solubility models, a split of the whole dataset was made based on the quality of the available solubility experimental data. From the global curated solubility dataset, initially partitioned and tagged as RELIABLE and CLEANED subsets (Figure  3 ), CLEANED data was used as the training set for the development of the model and RELIABLE data as a first test set. This sampling strategy ensures that the testing and evaluation of the model is based on the most reliable data without adding an optimistic bias on the training of the model when using for it the less reliable CLEANED data. To validate the predictive ability of the models, an external set I of 181 molecules was used as an external validation.