dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This work was supported by the  Bill & Melinda Gates Foundation  ( GCGH GCE OPP1151010 , KB and WPH),  NIH -National Institute of Allergy and Infectious Diseases  ( R01 AI106786-05 , KB), the  Canadian Institutes of Health Research  ( MFE 152448 , RSL), the  Canadian Institutes for Health Research  (a fellowship grant, DRM), and the  David and Lucile Packard Foundation  (MB). This paper presents independent research funded by the  National Institute for Health Research (NIHR)  under its  Programme Grants for Applied Research Programme  (Reference Number RP-PG-0514-20018, JOG), the  UK Antimicrobial Resistance Cross Council Initiative  ( MR/N013956/1 , JOG),  Rosetrees Trust  ( A749 , JOG), the  University of East Anglia (JOG, TC) , and  Oxford Nanopore Technologies  (JOG, TC).
Supplementary Figure 1 :Supplementary Figure 2 : 12 Size and memory footprint of the S. pneumoniae and N. gonorrhoeae RASE databases. The graph compares the size of the ProPhyle RASE index to the size of the original sequences: original draft assemblies (seq-fa), original draft assemblies compressed using gzip (seq-fagz), memory footprint of ProPhyle with the RASE index (ind-mem), and size of the ProPhyle RASE index compressed for transmission (ind-transm). Timeline of resistance genes. Number of occurrences of individual resistance genes in reads of SP02, as a function of time for the first hour of nanopore sequencing.
: Delays in prediction based on the k-mer length. The plot displays delays in prediction as a function of the used k-mer length, for selected experiments and all possible k-mer lengths. Each horizontal panel displays times required for stabilization of one of the three predictions: the lineage, the alternative lineage, and the closest strain. Every column within a panel corresponds to a single k-mer length. When the required time exceeded 1 hour, the point is displayed at the top. Experiments where lineage could not be identified are plotted in red. The highlighted column corresponds to the k-mer length used for constructing the RASE databases in this paper. 50.
5281/zenodo. 3346055');forthemetagenomic experiments, only the filtered datasets (i. e. , after removing the remaining human reads in silico) were made publicly available. (R01AI46645),and to Brian J Arnold, Taj Azarian and Cristina M Herren for useful comments in various stages of this project. 3113. Feldgarden, M. et al. Using the NCBI AMRFinder Tool to Determine Antimicrobial Resistance Genotype-Phenotype Correlations Within a Collection of NARMS Isolates. bioRxiv(2019). doi:10. 1101/55070714. Gupta, S. K. et al. ARG-ANNOT, a New Bioinformatic Tool To Discover Antibiotic Resistance Genes in Bacterial Genomes. Antimicrob. Agents Chemother. 58,212-220 (2014). 15. Rowe, W. et al. Search Engine for Antimicrobial Resistance: A Cloud Compatible Pipeline and Web Interface for Rapidly Detecting Antimicrobial Resistance Genes Directly from Sequence Data.
PLoS One 10, e0133492 (2015). 16. Kaminski, J. et al. High-Specificity Targeted Functional Profiling in Microbial Communities with ShortBRED. PLOS Comput. Biol. 11,e1004557 (2015). 17. de Man, T. J. B. & Limbago, B. M. SSTAR, a Stand-Alone Easy-To-Use Antimicrobial Resistance Gene Predictor. mSphere 1, 1-10 (2016). 18. Clausen, P. T. L. C. , Zankari, E. , Aarestrup, F. M. & Lund, O. Benchmarking of methods for identification of antimicrobial resistance genes in bacterial whole genome data. J. Antimicrob. Chemother. 71, 2484-2488 (2016). 19. Yang, Y. et al. ARGs-OAP: online analysis pipeline for antibiotic resistance genes detection from metagenomic data using an integrated structured ARG-database. Bioinformatics 32, 2346-2351 (2016). 20. Garner, E. , Pruden, A. , Heath, L. S. & Vikesland, P.
DeepARG : A deep learning approach for predicting antibiotic resistance genes from metagenomic data. (2017). 21. Antonopoulos, D. A. et al. PATRIC as a unique resource for studying antimicrobial resistance. Brief. Bioinform. 1-9 (2017). doi:10. 1093/bib/bbx08322. Boolchandani, M. , D'Souza, A. W. & Dantas, G. Sequencing-based methods and resources to study antimicrobial resistance. Nat. Rev. Genet. 2019 1 (2019). doi:10. 1038/s41576-019-0108-423. Croucher, N. J. et al. Population genomics of post-vaccine changes in pneumococcal epidemiology. Nat. Genet. 45, 656-63 (2013). 24. Croucher, N. J. et al. Population genomic datasets describing the post-vaccine evolutionary 33errors were comparable (10 vs. 12 and 11), in the RaDAR-Go dataset it increased from 1 to 8 and 10. Overall, the obtained results suggest that Kraken is less suited for the use in genomic neighbor typing than ProPhyle.
Hardware Nanomites As demonstrated in previous sections, topological and Boolean function analyses are powerful tools that can extract crucial information from obfuscated FSM circuits. Even though the state transition graph may not be recovered by Boolean function analysis in some cases, topological analysis can be used to yield sufficient information to deobfuscate a protected FSM (e. g. , for Active Hardware Metering). Analogy: Software Obfuscation. In the domain of software protection, selfmodifying code, i. e. code that alters its own instructions during execution, is typically leveraged to mitigate static analyses [Auc96, KMNM03, MAM + 06]. To additionally protect against dynamic analysis by use of debugging or emulation, alternative methods can be utilized  [GTG07]. A well-known example is Armadillo Nanomites Technology which protects software from dynamic analysis by exploiting the fact that only one ring-3 (user land) debugger can be attached to a debugged process at a time  [Inc].
To determine the size of the largest partial module, the designer synthesizes each partial module and obtains a count of the required resources from the synthesis report to allocate a suitable pBlock that is large enough to fit all partial modules. On-Demand Partial Bitstream Streaming. Our physically-implemented prototype includes a UART core to receive partial bitstreams and store them in DRAM at runtime to provide on-demand design reconfiguration. This setup not only accelerates the implementation, but it may be of further interest for Internet of Things (IoT) applications where obfuscated hardware designs may be directly streamed to the FPGA to increase the effort of reverse engineering (similar to advanced software protection via code streaming [AME + 06]). We now consider the hardware area overhead of Hardware Nanomites FSM obfuscation of the AES core implemented on a Xilinx KCU105 evaluation board  [Xil17a]  containing a Xilinx UltraScale FPGA.
Depending on the implementation, partial bitstreams are either stored in FPGA internal or external memory. In the former case, the adversary must identify and reverse engineer the contents of FPGA memory structures that hold the partial bitstreams. There are no automated techniques reported so far to the best of our knowledge that perform this activity. In the latter case (which we use in our implementation), the adversary must first analyze the chunk-level permutations of partial bitstreams which requires an analysis of the partial reconfiguration controller (≈ 1. 7k LUTs and ≈ 1. 4k FFs) as there are numerous variants for the Xilinx Microblaze processor and significant parts of the firmware data flow (≈ 4. 4M B), i. e. combined hardware and software reverse engineering. These numbers for the effort depict the worst case of reverse engineering all information.
Circular dichroism (CD) spectroscopy is a widely used method to study the protein secondary structure. However, for decades, the general opinion was that the correct estimation of ␤-sheet content is challenging because of the large spectral and structural diversity of ␤-sheets. Recently, we showed that the orientation and twisting of ␤-sheets account for the observed spectral diversity, and developed a new method to estimate accurately the secondary structure (PNAS, 112, E3095). BeStSel web server provides the Beta Structure Selection method to analyze the CD spectra recorded by conventional or synchrotron radiation CD equipment. Both normalized and measured data can be uploaded to the server either as a single spectrum or series of spectra. The originality of BeStSel is that it carries out a detailed secondary structure analysis providing information on eight secondary structure components including parallel-␤ structure and antiparallel ␤-sheets with three different groups of twist.
However, each basis spectrum set is optimized separately for one of the secondary structure components, e. g. Helix1 content is determined using the basis spectra optimized to provide the best results on Helix1, and taken as the Helix1 fraction from the fitting. The other seven secondary structure contents are determined similarly from the other seven fittings. In each case, the basis spectra were calculated by least squares method on an optimized reference spectrum and wavelength data subset. The entire optimization process was separately executed for the different wavelength ranges offered by the BeStSel server (175-250, 180-250, 190-250 and 200-250 nm). The optimization procedure to get the basis spectra sets is described in details by Micsonai et al. (11).
The search for the protein fold corresponding to a secondary structure composition is based on the CATH classifications of the protein structures deposited in the PDB, i. e. we search for structures in the PDB that have similar secondary structure composition to the result of the CD analysis and then discover their folds. Even in the case of using the BeStSel secondary structure components, protein folds can be overlapping in the eight-dimensional secondary structure space making the fold prediction challenging. Depending on the secondary structure composition (see below), different prediction methods can be effective. If the protein of interest is a multidomain protein containing heterodomains, there is much less chance for a clear prediction. The BeStSel package offers various different methods for fold recognition.
The simplest method makes a search on the entire PDB and sorts out the twenty closest structures regarding the Euclidean distance from the secondary structure of interest in the eight-dimensional space. The CATH classification of the resulting structures are listed out and the user can examine these structures by using additional external information, e. g. chain length and sequence information. In the case of single domain proteins, we use a reference database that contains the non-redundant (95% sequence identity is allowed) single domain reference subset of CATH 4. 2 (  16 ) and the corresponding secondary structure compositions calculated from the respecting PDB structures. This database contains 55350 chains (domains) covering the four fold classes, 41 architectures, 1310 topologies and 5398 homologies.
The operation of the BeStSel web server The BeStSel web server is freely available for academic users. A detailed description of the use of the server is provided in the tutorial. pdf file, which is attached as Supplementary Material. The homepage also provides short helps, explanations and important tips for users. Error messages explain when data format is inappropriate. The homepage provides four program blocks: Single spectrum analysis, Fold recog-nition, Multiple spectra analysis and Secondary structure and ␤-sheet decomposition for PDB structures. The block diagram showing the modules and functions of the web server is presented in Figure  2. In single spectrum analysis, a single CD spectrum can be analyzed for the secondary structure composition and the protein fold can be predicted in a second step.
The input is a CD spectrum either in normalized form ( ε or mean residue ellipticity, [Θ]) or as measured data. In the latter case, protein concentration, number of residues and pathlength data should be given, as well. The spectral data can be copied into the BeStSel text box or uploaded from a text file. Text file outputs of various instruments are usable. The program recognizes automatically the file header and the data columns. Column separator can be space, comma, semicolon or tab. The order of wavelength data is handled properly. The data step can be different from 1 nm/data, the program will convert it automatically to data in 1 nm steps. For data upload, a captcha should be provided to prevent automatic attacks from the internet. After uploading, the Data Examination page appears, where the user can verify the correct handling of the data.
A list is given for the CATH classes in the order of frequency and the 10 most frequent architectures and topologies are shown. Representative graphical images for the most frequent CATH topologies are presented. (iv) The weighted K-nearest neighbors' (WKNN) method predicts the class, architecture, topology and homology of the protein. The description of the prediction methods is presented above and in the 'Materials and Methods' section. The second module, the 'Fold recognition' module of the server is used to predict the fold of a protein from secondary structure contents provided by the user. The calculation can be initiated after the manual input of the eight secondary structure components with the condition they sum up to 100. 0% and the chain length is provided. The protein fold will be predicted by the four methods described in the first module.
The output is a list of the highest ranked 1, 5, 10, and 15 CATH classes, architectures, topologies and homologies, respectively. Although the WKNN method provides the most reliable fold predictions on accurate secondary structure contents, its performance declines when there is an estimation error in the secondary structure composition. However, it is excellent for structural studies to find out the fold of model structures or structures originated from the PDB. The third block is to analyze a series of CD spectra simultaneously, which can be useful when recording CD spectra as the function of temperature, ligand concentration, etc. Data can be copied into the text box (e. g. from a spreadsheet) or opened from a text file. Similarly to the single spectrum analysis, data can be normalized or measured.
After data examination, the analysis is carried out by a single clicking. Graphical or text output both are available. Wavelength range and a scaling factor can be set for recalculation. The fourth, 'Secondary structure and ␤-sheet decomposition for PDB structure' module calculates the secondary structure composition of known protein structures deposited in the PDB on the basis of the eight structural elements of BeStSel. For comparison, DSSP data  (13)  and Sel-con3 (5) composition are also calculated. As input, a PDB ID should be given in four letters code format.
NEW FEATURES The first version of BeStSel was released in 2015 as described by Micsonai et al. (11)  and was optimized for Firefox. The current version works on any web browser. The background databases were updated; originally it recognized 87356 PDB structures for secondary structure analysis and fold classification which is up-to-date now with 130254 structures. The updated single domain dataset used as a basis for fold prediction contains now 55350 single domains versus 10433 and based on CATH 4. 2 instead of CATH 3. 5. It covers 41 architectures, 1310 topologies and 5398 homologies (previously were 38, 783 and 1490, respectively). There is a fourth module for data input, users can provide arbitrary secondary structure contents and the searches for similar structures and fold prediction is carried out on these data.
(C) Block diagram showing the modules and functions of the BeStSel package. Input data are on the left, output data presented on the right. From a single CD spectrum the secondary structure contents are estimated and then, based on these, the protein fold can be predicted. A series of CD spectra as input can be evaluated at once to get the secondary structure contents. Users can provide arbitrary secondary structure contents and carry out the fold prediction for that secondary structure composition. Users can also enter PDB IDs as input to find out the corresponding secondary structure contents and fold classification.
For the OpenNESS project as a whole, stakeholders self-reported their 'membership of the CAB' as follows: very applicable (39%), applicable (14%), somewhat applicable (3%), little bit applicable (6%) and not applicable (38%). Partial involvement in the CAB reflects the dynamic nature of CAB membership, with individuals leaving and new members joining during the lifetime of the project in some case studies  (Dick et al. , 2018). The stakeholder questionnaire was structured into four main topics (i) self-characterisation of stakeholders, (ii) perception of the participatory process followed in the case study, (iii) perceived impact, and (iv) practical usefulness of appraisal methods, allowing the stakeholders to feed back their experiences anonymously. We use a selection of the stakeholder survey data for our analysisin particular, the stakeholders' degree of participation in the case study advisory board; the extent to which they participated in study design; method selection; knowledge co-production and how informed they were about results.
Fig. 5  illustrates the relative importance -as assessed by case study leaders -of data, time and budget constraints in the selection of methods used in the case studies. Data constraints within the project are more important in relative terms than time and budget constraints for 'modelling', and to some extent for 'monetary valuation'. But more striking is the fact that the mean importance of data, time and budget constraints in method selection is low, varying from 'to some extent' to 'definitely not'. In other words, none of these constraints -as judged by case study leaders -were 'definitely' important in selecting the methods they used in case studies. We also looked at whether stakeholder-researcher familiarity increased the likelihood of decisive and technical design purposes of studies, using as a proxy the number of years they had worked with stakeholders before initiating OpenNESS.
Conclusions We developed a theory of cumulative uncertainty in integrated ES apprisal based on a 'stairway' framework of ES appraisal. Successive ES appraisals -from mapping, modelling to benefit assessment and valuation -lead stakeholders to expect higher cumulative uncertainty in outcomes. Given an expectation about uncertainty, the more demanding the decision-support purpose of the ES appraisal, the lower the likelihood that the ES appraisal will be considered appropriate for decision-support. We defined an information gap hypothesis based on particular governance requirements for accuracy and reliability, combined with increasing uncertainty of the ES appraisal methods' outcomes. In order to test the information gap hypothesis, we evaluated responses to survey data of case study coordinators and stakeholders from 26 case studies and 80 ecosystem services appraisals in a large integrated EU research project.
Packet Captures A standard installation of the Carelife system was done to obtain packet captures. Several software modules were installed on the Televes gateway in order to capture and parse (in a PCAP format) the data packets exchanged with various sensors which were previously paired and registered by the gateway. The gateway was also connected to the Internet using a 3G SIM card. Packets were captured for a complete weekend on all the network interfaces of the gateway (see Fig. 1 ). The packet captured during the experiment were analyzed using Scapy. Scapy is a packet manipulation tool for computer networks, written in Python by Philippe Biondi. It can forge or decode packets, send them on the wire, capture them, and match requests and replies. It can also handle tasks like scanning, tracerouting, probing, unit tests, attacks, and network discovery.
Dataset Construction Starting with the captured packets, statistical data (e. g. , the number of UDP ports opened per time slot) in time series can be obtained as explained in Sect. 3. We extract samples from the time-series statistical data by setting a sliding window with length l. If a sample X n ∈ R l×1 is extracted in the non-attack case, then we assign the label of this sample denoted as y n as 0; otherwise, if it is extracted in the attack case, the label of this sample is assigned as y n = 1. Then, we have a dataset {(X n , y n )|n = 1, • • • , N}, where the input is the statistical data extracted from captured packet data and the output is a binary value.
Experimental Results In this section, we present the empirical results obtained for the detection of TCP SYN attacks. Using Scapy, we wrote a Python script for generating such attacks. The resulting pcap files can be used to train the learning algorithm, in addition to the "normal traffic" captured during the experiment described in Sect. 3. Moreover, using the utility tool mergecap, it is possible to insert a SYN attack into the files containing the packets captured during the experiment, thereby allowing to test the learning algorithm. As an example, Fig. 6a  plots the time-series for the difference between the numbers of initiated and established TCP connections per time slot (10 s) which was extracted from a pcap file obtained using the above procedure. As can be observed in Fig. 6b , the Dense RNN models correctly predicts that there was an attack.
[13,14,16]aining dataset {(X n , y n )|n = 1, • • • , N}, the works in[13,14,16]have developed an efficient training procedure for DenseRNN to determine the values of W 1 , • • • , W L , W L+1 , which combines unsupervised and supervised learning techniques. , • • • , L + 1)and O is the final DenseRNN output.
This paper presents the automation of the thin-layer chromatography technique whose separation and identification of molecules present in a mixture are currently done manually and laboriously. We have therefore found an interest in automating this technique. In this part, the method implemented comprises 2 steps. First we proceeded to the segmentation of the spots obtained on the chromatographic plate. We then developed a program to identify families of molecules such as coumarins, terpenes, tannins, flavonoids, polyphenols, etc. by calculating segmentation parameters such as standard deviation, entropy, mean pixel intensity and frontal ratio from an algorithm on the matlab software. Finally our results have been compared to the results obtained by the traditional identification technique in laboratories.
In these laboratories all research is based on plants (stem, leaves, roots, seeds) and mostly works in the context of the revaluation of traditional medicine based on plants  (Adjanohoun and Aké-Assi, 2008). However, the separation and identification of molecules is done manually, always referring to the data in the literature, which is slow and very laborious. We have therefore found an interest in automating the identification technique based on algorithms run on Matlab software version 2016. For this work, we used three (03) plants that are: Alchornea cordifolia, Paullinia pinnata (Sapindaceae), Trema guineensis (Ulmaceae)  (Djeddi et al. , 2013). We have first described the material and methods in which we present the segmentation of the digital color images of the extracts acquired on the plate as well as the device of experimentation acquisition of these images.
Acquisition of Images on the Chromatographic Plate For each extract, we obtained images on the chromatographic plates. The Fig. 4  shows some of the images obtained. We tested the presence of coumarins, tannins and terpenes in the hexane fraction of leaves of Blighia unijugata Bak (Sapindaceae). The protocol of the thin layer chromatography being the same, we made the segmentation of the different spots on the chromatographic plate. We then calculated the different parameters of the image processing namely entropy, front ratio (Rf), standard deviation (s) and mean pixel intensity (at) (Fig. 11  and 12 ). We obtained the results recorded in Table  3. These values correspond to molecules in the database. Since the values of the various parameters are defined, we have compared these results with the values in the database.