dataesr/scientific-paragraphs-categorization huggingface dataset:
Information on protein subcellular localization is important to understand the cellular functions of proteins. Currently, such information is manually curated from the literature, obtained from high-throughput microscopy-based screens and predicted from primary sequence. To get a comprehensive view of the localization of a protein, it is thus necessary to consult multiple databases and prediction tools. To address this, we present the COMPARTMENTS resource, which integrates all sources listed above as well as the results of automatic text mining. The resource is automatically kept up to date with source databases, and all localization evidence is mapped onto common protein identifiers and Gene Ontology terms. We further assign confidence scores to the localization evidence to facilitate comparison of different types and sources of evidence.
Introduction Determining the subcellular localization of a protein is a key step toward understanding the cellular function of a protein. Therefore, knowledge on protein subcellular localization is manually curated by UniProtKB  (1)  and model organism databases such as MGI  (2) , SGD (3), FlyBase  (4)  and WormBase  (5). These databases also integrate data from cDNA tagging projects  (6) (7) (8) , proteomics-based experiments  (9, 10)  and microscopy-based high-throughput localization studies  (11) (12) (13) (14). However, an ongoing effort like the Human Protein Atlas (HPA)  (15)  is only partially integrated in UniProtKB, and thus needs to be treated separately to obtain a comprehensive view of the currently available experimental data on localization. Despite the huge efforts by curators working for the databases mentioned above, it is impossible to fully keep up with the ever-growing literature.
However, this is not trivial. The databases and experimental data sets come in various file formats and use different identifiers/names for the same proteins and cellular compartments. The sequence-based prediction methods have different web interfaces, the prediction outputs consist of scores that are not directly comparable and local installation of the software is generally required for genomewide analyses. It is thus difficult and time-intensive to collect and evaluate the evidence pertaining to the subcellular localization of a protein of interest, not to mention for a large number of proteins. Several databases have attempted to address this data integration challenge. An early effort was DBSubLoc  (25) , which integrated annotations from knowledge bases such as UniProtKB and the major model organism databases.
Manual annotations were complemented by sequencebased predictions in eSLDB  (26)  and further by experimental data sets in LOCATE  (27) , locDB  (28)  and SUBA3  (29). The most recent versions of the first three of these resources (DBSubLoc, eSLDB and LOCATE) are >5 years old, and thus, they cannot be considered to reflect the current evidence. The last two resources (locDB and SUBA3) have been updated within the past 2 years; however, between them these two resources cover only human and Arabidopsis thaliana proteins. Whereas these resources are, or were, collecting evidence from a variety of sources in a single database, they generally do not address the challenge of putting the different types of evidence on a common confidence scale. An exception is the A. thaliana resource SUBA3, which assigns an overall confidence score; however, it is difficult for the user to trace these scores back to their origin.
Results The COMPARTMENTS web resource COMPARTMENTS holds subcellular localization information for 22 705 human and 6696 yeast proteins, and covers also other eukaryotes such as fruit fly, mouse and Caenorhabditis elegans. When querying the database for a protein of interest, the user is presented with an interactive schematic of a cell. These figures are color coded according to the confidence of the evidence supporting each of the 11 (12 in case of plants) labeled compartments (Figure  1 ). Interactive tables provide the user with more fine-grained localization information and the source of the underlying evidence. To provide a unified overview as described above, we map protein identifiers from the source databases to their corresponding identifiers in the STRING (Search Tool for the Retrieval of Interacting Genes) database  (30) , which for organisms in question come from Ensembl  (31).
Evidence channels and sources The evidence contained in COMPARTMENTS is logically partitioned into four channels of evidence. The first channel, called knowledge, is based on annotations from UniProtKB (1), MGI (2), SGD (3), FlyBase (4) and WormBase  (5). We assign confidence scores to these annotations based on the associated GO evidence codes  (34, 35) , which encode whether the annotation is based on a peer-reviewed publication, an experimental data set or sequence similarity (see Methods section). The knowledge channel provides localization information on 16 864 human and 5909 yeast proteins. HPA  (36)  is an ongoing effort to experimentally validate the tissue expression and subcellular localization for the entire set of human proteins. The latter data are captured by the experiments channel and currently contain The third channel provides associations between proteins and subcellular localizations derived from automatic text mining of the abstracts in Medline.
Moreover, these and the other methods mentioned earlier were developed on overlapping training sets, and thus cannot be considered independent evidence. The primary reason for including only two methods is thus to not present the user with a large number of redundant predictions. We applied both methods to 22 523 human, 23 443 mouse, 22 938 rat, 14 076 Drosophila melanogaster, 20 158 C. elegans, 6697 Saccharomyces cerevisiae and 31 280 A. thaliana protein sequences from STRING 9. 1  (30). The output scores from each tool were transformed to make them comparable with other evidence in the database (see Methods section). The number of human and yeast proteins assigned to each of the 11 labeled compartments based on each of these evidence channels are summarized in Tables  1  and 2 , respectively.
Discussion The COMPARTMENTS resource unifies complementary evidence on protein localization from curated knowledge, high-throughput experiments, text mining and sequencebased prediction methods. We go beyond merely integrating many sources of evidence into a single database by mapping all pieces of evidence onto the same set of identifiers and carefully assigning them comparable confidence scores. We derived these through a combination of manual inspection of each evidence source, a previous study of the reliabilities of GO evidence codes  (38) , the benchmark We counted protein-compartment associations separately for each of the 11 labeled compartments and for each evidence channel. The only exception is the predictions channel, for which we show the results from the two sequence-based methods (PSORT and YLoc) separately.
Dashes denote compartments for which a channel or prediction method cannot provide evidence. For details refer to the footnote of Table  1. The primary aim of COMPARTMENTS web interface is to provide the user with a simple overview of the localization of a protein of interest without losing the connection to the underlying evidence. The overview is provided through a schematic of a cell, which is color coded based on the strongest evidence supporting each compartment. This visualization is interactive and allows the user to see which evidence channels support a particular compartment and how strongly it supports. This directly informs the user about which of the tables below contain further details about the origin of the evidence. For the knowledge and experiments channels, the tables link out to the external databases from which the evidence was obtained.
Lastly, cross talk between these major trafficking pathways between intracellular organelles (  49 ) is captured by the connections between the Golgi apparatus, endosomes and lysosomes. Because COMPARTMENTS uses the same protein identifiers as the STRING database  (30) , it also facilitates large-scale analysis of protein localization in the context of interaction networks. COMPARTMENTS is the first resource to integrate subcellular localization evidence from manually curated annotations, high-throughput screens and sequence-based predictions with automatic text mining for all major model organisms. To avoid the common problem of bioinformatics databases not being maintained, we have from the beginning designed the resource to be automatically kept up-to-date with the constant changes in source databases and literature.
We address the challenge of making it easy for users to comprehend the heterogeneous evidence by projecting it onto a common reference both in terms of protein and compartment identifiers and in terms of reliability scores. This is complemented by the web interface, which provides an intuitive, interactive graphical overview of the unified evidence and simple tables with more detailed information, including links to the original sources. We also make the unified evidence available as bulk download files to facilitate large-scale computational studies of protein localization and integration with omics data sets.
Assembly of the knowledge and experiments channels We imported subcellular localization annotations from comments and database cross-reference fields of UniProtKB. We map these to the corresponding Ensembl identifiers using the STRING alias file  (30)  and GO terms using the UniProtKB controlled vocabulary of subcellular localizations. For S. cerevisiae, C. elegans, D. melanogaster and Mus musculus, we imported cellular component GO annotations from their respective model organism database  (2) (3) (4) (5). For the knowledge channel, we assigned the highest score of four stars for annotations with the following evidence codes: CURATED, IDA, TAS and NAS. We assigned three stars to the evidence codes PROBABLE, EXP, IPI, IMP, IGI, IEP, ISS, ISO, ISA, ISM, IBA, IBD, IKR, IMR, IRD and IC. We assigned two stars to the less reliable evidence codes POTENTIAL, IGC and IEA, while BY SIMILARITY, RCA and NR are assigned only one star.
Because we consider some sources to be more reliable than others, we upgraded annotations from UniProtKB and the model organism databases by one star, resulting in a maximum score of five stars for the knowledge channel. We also imported subcellular localization data from the Human Protein Atlas (HPA)  (15, 50) , which uses Ensembl identifiers, and manually mapped their locations to the corresponding GO terms. HPA uses two scoring schemes to classify the quality of its data. When a protein has been stained using two or more antibodies, HPA provides a reliability score based on the similarity of the staining patterns obtained with the different antibodies and the agreement with published literature. This scale has four levels of reliability: high (four stars), medium (three stars), low (two stars) and very low (one star).
Statistical analysis of compartments sharing proteins From the unified data set, we extracted localization information on human proteins with more than two stars to disregard weak text-mining and prediction evidence. The retrieved data set comprised 18 692 unique human proteins with 29 493 links to compartments: 20 021 were supported by curated knowledge, 4841 by high-throughput experimental evidence, 1468 by text mining and 15 788 by sequence-based predictions. We counted the number of proteins shared between any two compartments. To assess if this is higher than expected, we compared the counts to a null model that assumed no correlation between any compartments. To this end, we generated 1 000 000 random data sets in which links between proteins and compartments were permuted, thereby preserving the number of links per protein and per compartment.
ß The Author(s) 2014. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/ licenses/by/3. 0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. page number not for citation purposes) Database, Vol. 2014, Article ID bau012, doi:10. 1093/database/bau012.
Acknowledgements The authors thank  Andreas Bok Andersen  for contributions to the development of the web framework. Funding  Luxembourg Centre for Systems Biomedicine  and the  Novo Nordisk Foundation Center for Protein Research ;  EMBL   International PhD Programme  and an  EMBO  short-term fellowship ( 295-2012  to J. X. B);  CSIRO's Office of the Chief Executive and CSIRO's Computation and Simulation Sciences platform  (to C. S and S. I. D. ). Funding for open access charge: The  Novo Nordisk Foundation Center for Protein Research.
Page 1 of 9 ( 9. ß The Author(s) 2014. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/ licenses/by/3. 0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. page number not for citation purposes) Database, Vol. 2014, Article ID bau012, doi:10. 1093/database/bau012.
SSCA on Android Environment: Key Points In the following, we discuss the key points to properly mount an SSCA on Android smartphone: first, we describe the measurement requirements to properly access the Android smartphone; second we tackle the most important difficulties which arise when targeting such device; and third we outline the possible software modifications of the Android system that might be performed by an attacker to enhance his malicious analysis. Access to the Smartphone Basically, the SSCA measurement bench is composed of a PC-Target system (or client-server system) and a measurement device (an oscilloscope and an antenna) to record EM waveforms. Concerning the minimum features needed in the oscilloscope, four points should be taken into consideration: the sampling frequency, the memory depth, the placement of antennas and the trigger which provides a reference timing point necessary to synchronise waveforms.
Just In Time Compiler The Just In Time Compile (JIT) aims to compile on-the-fly the Android bytecode into native machine instructions. This significantly enhances the performance of the Dalvik Virtual Machine that is in charge of executing Android applications. From the side-channel point of view, the activity of the JIT compiler might slightly influence the analysis as reported by Aboulkassimi et al. in  [1]. In practice, the impact of the JIT can be seen through the first executions of an iterative process. For instance, in the case of a basic RSA process, the JIT activity may impact the first patterns related to the secret key bits manipulation. As only the first patterns are affected, this does not harm the overall efficiency of the SCA. Besides, even if some few secret bits are guessed incorrectly, some techniques  [5]  allows recovering the entire secret key.
Software Modifications for Better EM Acquisition 1. Under-clocking the CPU The higher the CPU clock frequency is, the higher the resolution of the oscilloscope is required. In case that the clock frequency is too high and the oscilloscope is unable to acquire enough samples to cover the necessary leakage, the attacker can decrease the CPU clock frequency for a better acquisition. Basically, lowering clock frequency is not always necessary. In fact, in our experiments we kept the original CPU clock frequency of 832 MHz. 2. Lowering Radio Emission When many applications are running on the Android smartphone, in particular that ones which are responsible for ensuring radio and cell communications, the noise generated might have a considerable impact on the effectiveness of SSCA. In such situation, the attacker may reduce the level of noise by turning on the airplane mode available on most smartphones.
Target Cryptographic Android Implementations There are several cryptographic libraries compatible with the Android environment such as Java Crypto Extension (JCE)  [19] , OpenSSL  [18] , Bouncy Castle  [6] , Crypto++ [9], RELIC [20] and so on. At first we intended to exhaustively evaluate the security of all libraries when RSA and ECC are being executed. Then we realised that most of libraries, particularly the non-native ones, are mainly based on the same low level native primitives to perform arithmetic calculations on big integers, necessary for asymmetric cryptography. This can be verified by checking the source code of libraries or more easily by analysing the real time processor activity through the debugging Trace View Android tool  [13]. We note that the comparison of these libraries is out of the scope of this paper.
Conclusion and Perspectives In this paper, we have proposed an efficient pre-processing composition to mount a powerful SSCA on RSA and ECC. We applied the proposed attack to recover the secret keys on an Android smartphone clocked at high frequency (832 MHz). We remind the reader that, in practice, the higher the frequency is, the harder to attack as more noise is generated and more sophisticated equipments are needed. Our scheme succeeded in recovering the secret keys from a single waveform. Therefore, we conclude that our technique is particularly efficient to perform the pattern discrimination as it deals with both types of noise (measurement and algorithmic noise) and both domain representations (time and frequency). The proposed attack is applied directly on baseband traces. Hence, we expect to further enhance our analysis with a Software-Defined Radio (SDR) demodulator.
More recently, with the emergence of next generation sequencing (NGS) technologies, new efforts were made to develop novel tools to detect TEs because previous methods are not directly applicable to reads produced by NGS data  [11, 12]. However, one of the most commonly used procedures to find occurrences of known TEs remains the homology-based method proposed by the RepeatMasker program  [13]  because it is easy to use, rapid, and efficient  [14, 15]. The main drawback of this program is its dependence on reference sequences and consequent inability to discover new TEs. This method however remains a must for identifying TE sequences in an assembly or after the identification of new consensus TE sequences using de novo methods. For example, this last approach (de novo TE libraries used with Repeat-Masker) was applied for the identification of TEs in the  12  Drosophila genomes  [16].
These programs usually have very specific aims. TSDFINDER was developed to refine the coordinates of long interspersed nuclear element (LINE) L1 insertions by identifying flanking target site duplication (TSD) sequences and the poly(A) tails of 3′ intact L1 insertions in the human genome  [21]. The LTR-MINER program was designed to specifically retrieve information concerning the age and distribution of LTR-retrotransposons  [22]. This program was then implemented in the REANNO-TATE program for use on all categories of TEs to estimate the temporal order of insertions in the case of nested elements and to estimate the age of LTR-retrotransposon copies  [23]. In this manuscript, we propose a perl tool (available at http://doua. prabi. fr/software/one-code-to-find-them-all) that parses the RepeatMasker.
Script 1: build_dictionary. pl This script builds a list of all of the LTR-retrotransposons found in the query sequence at least once by RepeatMasker to associate hits corresponding to the internal portion and those corresponding to LTR sequences. This module uses the RepeatMasker. out file or a directory path containing several RepeatMasker. out files as input. RepeatMasker files are recognized based on their. out extension, allowing the program to run recursively on large file structures without prior file sorting (for example, working on one organism by running the program on an entire directory downloaded from a genome database). Then, the program matches together internal and LTR portions, based on name similarity. The main issue with this step relies on heterogeneity in the annotation of LTR-retrotransposons in the library.
Three options can be specified by users: --help This option prints a summary of the different usages of the script. --fuzzy This option allows the script to associate more LTR names with internal counterparts to account for the possibility of LTR variants. In three successive passes, the program associates similar names differing by a single letter, a single number, or two characters. For example, in the human genome, the --fuzzy option allows for the association of MER66-int with its various counterparts MER66A, MER66B, MER66C, and MER66D or HERV1_I-int with HERV1_LTRa, HERV1_LTRb, HERV1_LTRc, HERV1_LTRd, and HERV1_LTRe. --unknown To be used in particular cases where the RepeatMasker program was run using a local TE library without the class/subclass specification (see below). Finally, the name and path of the output file should be specified using a redirection (>dictionary_output.
txt). Examples of command lines are detailed in the tutorial available on the program website. The second script uses the output file produced by build_dictionary. pl and a RepeatMasker. out file (or a directory containing several RepeatMasker. out files). The principle of this program is to compare the positions and orientation of each hit corresponding to the same TE family to determine whether the hits correspond to the same copy and can be merged or correspond to different copies. Two hits located on the same scaffold or chromosome are considered to be fragments of the same copy if they abide by the three following conditions: 1) they have the same orientation; 2) the extremities of the fragments respect a distance criterion: by default the furthest extremities should be separated by less than twice the length of the reference TE element (see the --insert option for non-default behavior); and 3) the second fragment starts and ends after the first one respectively starts and ends (that is, the two fragments can overlap but cannot be included in one another).
These steps are necessary for the merging of fragments into a copy. Once all copies are reconstructed from the RepeatMasker hits, the program assembles full-length LTR-retrotransposons by associating LTR copies and their corresponding internal copy located closely to one another. Conditions for associating a LTR sequence with an internal sequence include the following: the LTR sequence must be in the same orientation as the internal sequence, and it must be separated from the internal sequence by less than half the LTR length. The reconstruction of full-length 'LTR-I-LTR' elements is performed as a priority, and with the remaining copies, incomplete 'LTR-I' or 'I-LTR' elements are then built. All copies, assembled or solo, are reported. As solo-LTRs are of special evolutionary interest, they are reported separately from the full-length and partial LTRretrotransposon copies in the summary file.
Several options can be specified by users: --help This option prints the possible usages of the script. --strict This option makes the program use a rule based on the 80-80-80 rule  [5]  to select hits. In this case, the program provides copies with sizes greater than 80 bp long and which have greater than 80% identity to the reference element. By default, the program gives all hits found, regardless of the size or percentage of identity compared with the reference. --length 'length_file' This option allows users to work with their own file for the length of the reference elements, which will be used to determine the ratio of the length of a given copy compared with its reference. If not provided, the code computes the length of all elements (LTR and internal portions separated for the LTR-retrotransposons) present in all.