dataesr/scientific-paragraphs-categorization huggingface dataset:
Samples Breast cancer. Discovery sample set: Sera from the UKCTOCS women who went on to develop invasive breast cancer were identified. Women with previous cancer history at recruitment were excluded. The cases were matched to controls (healthy women with no notification of cancer when the case was identified) 1 : 1 on age at donation (±1 year) and length of frozen storage ( ± 1 year). Validation sample sets: UKCTOCS case-control set: Sera from women who developed invasive breast cancer after randomisation to UKCTOCS and sera donation (not included in the discovery set, no previous cancer history and had physician-confirmed breast cancer with data on histological subtype and either stage/grade or both) were included. These were matched to controls, women from UKCTOCS with no cancer history either at recruitment or when the case was identified, on age ( ± 1 year), length of storage (±1 year) and trial centre.
The quality control of printed glycopeptides was visualised by staining with glycoform-specific lectins and antibodies as described previously  (Blixt et al, 2011). Human IgG was also printed as a positive control for the second antibody and to orientate the arrays. Sera were diluted 1 : 50 and the arrays screened as described by  Blixt et al (2011). The slides were scanned in a PerkinElmer Scanarray and the images quantified with ProScanArray Express software programme (PerkinElmer, Cambridge, UK). Spots were identified using automated spot finding or manual adjustments for occasional irregularities. All samples were screened in duplicate with blinding as to case or control. The same positive control serum from a breast cancer patient from the cohort used in our previous paper  (Blixt et al, 2011)  was used on every slide and where possible, cases and their controls were screened on the same slide.
ACKNOWLEDGEMENTS We are particularly grateful to women who contributed their samples to this study and to the medical, nursing and administrative staff who worked on the  UKCTOCS/ Guernsey  study. UKCTOCS was core funded by the  Medical Research Council, Cancer Research UK  and the  NIHR  with additional support from the  Eve Appeal, Special Trustees of Bart 's and the  London and Special Trustees of UCLH. This study was supported by an  EU   FP7  collaborative project grant,  O-PTM Biomarkers , grant agreement number  201381  and  NIH  grant  U01CA128437-03. We also acknowledge financial support from the  Experimental Cancer Medicine Centre at King's College London  and the  National Institute for Health Research via CBRC  funding (women's health theme) to  University College London Hospitals (UCLH)  in partnership with  University College London , and the  Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust and King's College London.
We also acknowledge financial support from the  Excellence programme at University of Copenhagen , the  Danish Research Council and National Research Foundation. The researchers are independent from the funders and the views expressed are those of the author(s) and not necessarily those of the  NHS , the  NIHR  or the Department of Health. UKCTOCS was core funded by the  Medical Research Council, Cancer Research UK , and the  NIHR  with additional support from the  Eve Appeal, Special Trustees of Bart 's and the  London, and Special Trustees of UCLH. This work is published under the standard license to publish agreement. After 12 months the work will become freely available and the license terms will switch to a Creative Commons Attribution-NonCommercial-Share Alike 3. 0 Unported License.
The energy concentrations of the S-transform of localized sounds are optimized by using a window width optimization algorithm. Then the SSE envelope is recalculated and a local adaptive threshold is applied to refine the estimated boundaries. To distinguish between S1 and S2, a feature extraction method based on the Singular Value Decomposition (SVD) of the S-matrix is applied in this study. The proposed segmentation module is evaluated at each block according to a database of 80 sounds, including 40 sounds with cardiac pathologies.
The proposed methods in each block can be summarized as follow: named SSE, calculates the Shannon Energy of the local spectrum obtained by the S-transform for each sample of the heart sound signal in order to extract its envelope. -The second block contains a novel approach for boundaries detection of S1 and S2. The energy concentrations of the S-transform of localized sounds are optimized by using a window width optimization algorithm then the SSE envelope is recalculated and a local adaptive threshold is applied to refine the estimated boundaries. -A feature extraction method based on the Singular Value Decomposition (SVD) of the S-matrix is applied to classify S1 and S2. The proposed methods are evaluated based on a database of 80 sounds (40 pathologic). This study is made under the control of an experienced cardiologist, in with the aim of validating the results of each method.
Data Base Several factors affect the quality of the acquired signal, above all, the type of the electronic stethoscope, its mode of use, the patient's position during auscultation, and the surrounding noise  [5]. According to the cardiologist's experience, it's preferable that the signals remain unrefined; filtration will only be applied subsequently in the purpose of signal analysis. For this reason we used prototype stethoscopes produced by Infral Corporation, and comprising an acoustic chamber in which a sound sensor is inserted. Electronics of signal conditioning and amplification are inserted in a case along with a Bluetooth standard communication module. Different cardiologists equipped with a prototype electronic stethoscope have contributed to a campaign of.
The second method calculates the time-varying parameter (so an optimal parameter for each sample) which is more suitable for signals with fast varying frequency components. The disadvantage of the second approach is its high computational complexity which makes it unsuitable for applications where time is an important factor. Based on the first approach, the optimization algorithm is applied on both parameters p and α, separately. The performance measure against each parameter is compared in section (5. 2). The performance measure is based on the concentration measure (CM) proposed in  [17]. For each α (or p) from a given set, the CM (α) can be expressed by:            dtdf f t S CM x ) , ( 1 ) (   (7) With ) , ( f t S x  is the normalized energy of the S-transform for each α; it's given by  [14] :            dtdf f t S f t S f t S x x x 2 ) , ( ) , ( ) , (    (8) The CM (α) and CM (p) are calculated and compared for all existing S1 and S2 sounds in the database.
The criterion of the performance is the concentration energy measure. The enhancement of energy concentration in the TF domain, influence clearly on the boundaries estimation results (Table  3 ). The "Reference" row in Table  3  represents the manual measures made by the cardiologists by using the software "Stetho" developed under the license of Alcatel-Lucent. Limits of heart sounds were defined with classical Phonocardiographic references; mitral closure initiating S1 with high frequency vibration, the aortic closure generating the high frequencies beginning S2 and the end of S1 and S2 are annotated by the end of the high amplitude vibrations. Results show the efficiency of optimizing the energy concentration of the S-transform in order to estimate more realistic boundaries for S1 and S2.
S1 and S2 classification The localization of heart sounds is established by using the SSE method. The boundaries of the heart sounds are determined by the OSSE algorithm. The results were visually inspected by a cardiologist and erroneously extracted heart sounds were excluded from the study. The feature extraction process extracts a feature vector per extracted sound S i (S1 or S2) and each of these vectors is averaged across available extracted sounds from each subject. So from each subject in the database, we obtain one S1 feature vector and one S2 feature vector to use in the training and classification process. A 3-Neirest Neighbor (KNN) classifier is used to evaluate the performance of the four feature vectors obtained and the 5-fold approach is used for cross validation. The choice of KNN classifier was based on its simplicity of and its robustness to a noisy training data.
measurements in the Hospital of Strasbourg (France); recruitment was made through clinical research project (HUS-PRI 4179) with the support of the clinical investigation center (INSERM). In parallel, 2 prototypes have dedicated to the Cardio-Psy experience as a part of the MARS500 project (IBMP-Russia) promoted by ESA (European Spatial Agency -PI. A. Aubert), in order to collect signals from 6 volunteers (astronauts). The use of prototype electronic stethoscopes by different cardiologists makes the database rich in terms of qualitative diversity of collected sounds. The sounds are recorded with 16 bits accuracy and 8000Hz sampling frequency in a wave format, using the software "Stetho" developed under Alcatel-Lucent license. The dataset contains 80 sounds, including 40 pathological cardiac sounds.
Background: Pseudogenes are traditionally considered "dead" genes, therefore lacking biological functions. This view has however been challenged during the last decade. This is the case of the Protein phosphatase 1 regulatory subunit 2 (PPP1R2) or inhibitor-2 gene family, for which several incomplete copies exist scattered throughout the genome. Results: In this study, the pseudogenization process of PPP1R2 was analyzed. Ten PPP1R2-related pseudogenes (PPP1R2P1-P10), highly similar to PPP1R2, were retrieved from the human genome assembly present in the databases. The phylogenetic analysis of mammalian PPP1R2 and related pseudogenes suggested that PPP1R2P7 and PPP1R2P9 retroposons appeared before the great mammalian radiation, while the remaining pseudogenes are primate-specific and retroposed at different times during Primate evolution.
Background In the past, pseudogenes were generally regarded as functionally inert, due to the presence of several disabling features that prevent their expression (e. g. premature stop codons, frameshift mutations, no promoter regions, etc. ), and therefore their evolution has been considered to be neutral  [1]. However, this view has been challenged by new evidences, which demonstrate that certain pseudogenes are functionally active  [1, 2]. The GENCODE, a sub-project of the ENCODE (ENCyclopedia Of DNA Elements from the National Human Genome Research Institute, NHGRI), has estimated the number of pseudogenes in the human genome to be near 14,000  [3]. From these, ~6% were identified has potentially transcribed by computational models and almost half of them validated by RT-PCR-Seq techniques  [3].
For PPP1R2, nine loci were found that present hallmark features of processed pseudogenes. These related sequences were collectively named PPP1R2 pseudogenes and were numbered from 1 to 9 (PPP1R2P1-P9)  [34]. These pseudogenes are found scattered in the genome due to random retrotransposition phenomena that consist on the reverse transcription of cellular RNAs and random insertion into the nuclear genome  [36, 37]. Past studies identified four PPP1R2 pseudogenes at the messenger RNA level using high throughput techniques. PPP1R2P1 and PPP1R2P2 were discovered in human  [38, 39] , PPP1R2P3 in human and crab-eating macaque (Macaca fascicularis) and PPP1R2P9 (also called I4) was found in human and mouse (Mus musculus)  [40] [41] [42] [43]. In this work we performed an exhaustive search for PPP1R2 pseudogenes in publicly available mammalian genome databases in order to infer their evolutionary history.
Results and discussion A total of 119 sequences were retrieved from the NCBI and Ensembl databases by blasting against the human PPP1R2 mRNA sequence. Ten pseudogenes were obtained from human sequences, increasing by one the previous number reported in the literature  [34]. All pseudogenes obtained are intronless and with a truncated 5'UTR meaning that are processed pseudogenes. The parental human PPP1R2 CDS (618 bp) covers 17% of the entire mRNA (3475 bp); even the pseudogenes with the lowest coverage contain the parental CDS, with the exception of PPP1R2P7 that only comprises part of the 3'UTR.
2-42. 6 Mya (Figures  2  and 4 ). The PPP1R2P7 (Glires) and PPP1R2P8 (gibbon) sequences were not retrieved from the databases, which suggest the later deletion of these pseudogenes (Figure  5 ). The fact that some genome annotations are early assemblies, might explain the missing of these and other sequences. However, the good quality of Glires (Mus, Rattus and Oryctolagus) genome assemblies reinforces the absence of PPP1R2P7 sequence and suggests that it occurred in the common ancestor. The absence of gibbon PPP1R2P8 sequence could also be explained by the several insertions present, similar to what happens in other species, virtually dismantling it and making the retrieval impossible (Figure  5 ). Moreover, the conserved linkage confirms the results of the phylogenetic analysis, being all pseudogenes flanked by the same respective genes in all species analyzed  (Figures 3, 4, 5, 6).
PPP1R2P3 The sequence of PPP1R2P3 is complete, without any frameshifts or element repeats disruptions (Figure  3 ). The sequence was truncated at the 5'UTR, as expected due to  the low processivity of the reverse transcriptase, and in the 3'UTR it lost two of the four polyA signals that may lead to a short ~1500-1600 nt message. We have previously found, by a yeast two hybrid screening of human testis cDNA, using as bait PPP1CC1, one clone assigned to PPP1R2P3  [43, 45]. A search for PPP1R2P3 ESTs in databases revealed that this is one of the most represented PPP1R2 pseudogenes (72 GEO, 313 GXA), being highly detected in testis (14 ESTs in Unigene). Together with our previous data, this strongly suggests that this pseudogene is transcribed. Two independent reports using mass spectrometry have also assigned peptides to PPP1R2P3  [46, 47].
Evidences of non-coding nature of PPP1R2-related pseudogenes Considering the other pseudogenes sequences, many insertions in PPP1R2P8 lead to a completely disrupted ORF and missense mutations in PPP1R2P4/P10 and PPP1R2P6 lead to premature stop codons (Figures  4  and 5 ). Also, since these pseudogenes showed low coverage to the parental gene, most of the 3'UTR is missing and so, regulatory elements such as polyadenylation signals that are important for the transcript cleavage and stability are absent. This indicates that no transcription or translation should be expected from these pseudogenes, which corroborates with the fact that no expression was found in ESTs and high-throughput databases with the exception of PPP1R2P4. Considering the pseudogenes with the highest coverage in relation to the parental gene, PPP1R2P2 and PPP1R2P5, no ORF disruptions were found but many missense mutations were found in all species analyzed that lead to premature stop codons (Figure  4 ).
PPP1CC2 is a sperm-specific protein phosphatase involved in spermatogenesis and sperm motility  [32, 33, 57]. Its inhibition in vivo, was associated with a PPP1R2-like activity since GSK3 was able to reverse the process  [32]. Recently, a report identified the PPP1R2 protein in heat-stable extracts of bull testis and mouse testis and sperm where it may account for this PPP1R2-like activity  [58]. It is well known that testis is one of the organs where most pseudogenes are expressed and their gene products were shown to have important roles in spermatogenesis and other germ cell related functions  [52] [53] [54]. This might be due, in part, to the hypertranscription state of the autosomal chromosomes in the meiotic and post-meiotic germ cells due to chromatin modifications  [13, 54, 59]. A recent study done by GENCODE has revealed that 64% of all validated expressed pseudogenes are expressed in testis  [3].
PPP1R2 is one of the PPP1C regulators with more related pseudogenes  [34]. We have previously identified PPP1R2P3 message and protein, in testis  [43, 45, 48]. We hypothesized that from the other pseudogenes, only PPP1R2P1 and PPP1R2P9 are capable of being also translated. In fact, the two pseudogenes, PPP1R2P3 and PPP1R2P9, were present in the mass spectrometry data obtained from a human sperm immunoprecipitation (Table  1 ). This analysis was based on the fact that the molecular weight of these PPP1R2-related proteins should be similar to the parental one (PPP1R2-23. 0kDa), being therefore present in the same region where the band was extracted to mass spectrometry analysis. The antibody used to immunoprecipitate PPP1R2-related proteins was raised against a peptide containing amino acid residues 134-147 from the mouse PPP1R2 sequence (Figure  7 ).
This antibody was used previously to detect PPP1R2  [48, 58]. In the 14-residue region, PPP1R2P1 and PPP1R2P9 have two and three substitutions respectively, when comparing to PPP1R2 sequence. We predicted that using this antibody, we were also able to detect the other PPP1R2-related proteins. Mass spectrometry data identified 23 MSMS (tandem mass spectrometry) spectra corresponding to 8 different peptides matching unequivocally to PPP1R2P9 (Figure  7  and Table  1 ) and 3 MSMS spectra corresponding to one peptide matching unequivocally to PPP1R2P3  [48]. The sequence coverage obtained for PPP1R2P9 was 36. 5% and the mascot score levels were 623. 41 (in addition, spectra were manually evaluated). This is the first time that PPP1R2P9 protein is detected, being clearly recovered from human ejaculated sperm.
Sequences retrieval The human PPP1R2 mRNA sequence (GenBank accession number NM_006241. 4) was used to detect orthologs and pseudogene-related sequences by performing a BLAST search on GenBank, from National Center for Biotechnology Information (NCBI, http://BLAST. ncbi. nlm. nih. gov/) and Ensembl (http://www. ensembl. org/Multi/blastview) databases against all available mammalian reference genomic sequences. Only sequences with more than 60% of sequence similarity and with query coverage of more than 35% were recovered. Genomic sequences flanking the retrieved sequences were also manually inspected for missing parts, especially at the 3'UTR.
Evolutionary tree reconstruction and divergence times The retrieved sequences (Additional file 1: Table  S1 ) were visually inspected and aligned using ClustalW implemented in BioEdit 7. 0. 9. 0  [65]. For phylogenetic reconstruction, and to improve accuracy, only sequences encompassing >85% coverage of the human PPP1R2 CDS (nucleotide positions 377-994 of the mRNA sequence) and with >60% of sequence similarity were included in the alignment. In order to determine the phylogenetic relationships between the PPP1R2 gene and related pseudogenes, the best-fit model of nucleotide substitution was first assessed using the program jModelTest v0. 1. 1  [66]  under the Akaike Information Criterion (AIC). A maximum likelihood (ML) phylogeny was inferred using the software GARLI v1. 0  [67]  by indicating the best nucleotide substitution model.
Pseudogene classification and conserved linkage Sequences obtained from the BLAST queries were analyzed in terms of presence of intronic regions, polyA traits (PolyApred, http://www. imtech. res. in/raghava/polyapred/), truncation of the 5'UTR and chromosomal location. Chromosomal locations were obtained from the GenBank database (Additional file 1: Table  S1 ). Pseudogenes located in the same chromosome and nearby and/or with intronic regions were classified as duplicated pseudogenes. Pseudogenes that were located in different chromosomes and had polyA traits, truncation of the 5'UTR and no introns were classified as processed pseudogenes. Furthermore, genes flanking each human PPP1R2 pseudogene and conserved among mammals were selected. Conserved linkage, meaning conservation of synteny and also conservation of the gene order, was then searched for in order to provide insights regarding their orthology.
Signatures of positive and negative selection were searched for in Datamonkey webserver (http://www. datamonkey. org) that uses the HyPhy package  [71]. The best-fitting nucleotide model (GTR + G) was determined using the automated tool provided by Datamonkey. Five models were used: single likelihood ancestor counting (SLAC), fixed-effect likelihood (FEL), random effect likelihood (REL), fast unbiased bayesian approximation (FUBAR) and mixed effects model of evolution (MEME). SLAC is based on the reconstruction of the ancestral sequences and the counts of dS and dN at each codon position of the phylogeny. FEL estimates the ratio of dN/dS on a siteby-site basis, without assuming an a priori distribution across sites while REL fits a distribution of rates across sites and then infers the substitution rate for individual sites.
Mass spectrometry For mass spectrometry analysis, the immunoprecipitate and the heat stable extract were resolved by 10% SDS-PAGE along with purified positive controls. Gels were stained with Coomassie blue colloidal (Sigma-Aldrich Química, S. A. , Sintra, Portugal) using standard procedures  [48]. Bands were then excised from the gel using commercial PPP1R2 band as control and destained. An overnight digestion with trypsin (Promega, Madison, Wisconsin, USA) was performed and resulting peptides were extracted and prepared for mass spectrometry analysis using an Orbitrap Velos mass spectrometer as described elsewhere  [48]. Subsequent generated data were imported to ProteinScapeTM (Bruker Daltonik GmbH, Bremen, Germany,  [77] ) and analyzed using MASCOT (version 2. 2. 0, Matrix Science, London, UK,  [78] ) search algorithm.
The experiments were repeated using the classical SPR method to analyze carefully the association phases on longer time scales (Fig. 4A ). In these experiments, three concentrations of Seq9110 were injected in duplicate. The regeneration step was performed at least 800 sec after the beginning of the dissociation phase. Either at low, 250 nM, or high concentrations, 5 µM, duplicate overlaid sensorgrams demonstrated that regeneration was efficient. Again, the data still did not fit to a 1:1 model. Other attempts consisting of lowering the level of immobilization of biotinylated 5BSL3. 2 to exclude problems related to a too high density of the immobilized target did not improve the curve fitting (data not shown). Seq9110 was loaded on a native gel to test a potential structural heterogeneity responsible for these findings.
Seq9110 is slightly heterogeneous compared to the immobilized target 5BSL3. 2 (Supplemental Fig. S1 ) but to a lesser extent than SL2 WT (Fig. 3 ). We actually cannot conclude if this is a reason good enough to explain why these data could not be fitted with a 1:1 model. Despite the problems in determining the corresponding rate constants, the observed kinetics are clearly slower than those obtained with SL2 kistem. In addition, the results show that the interaction between the internal loop of 5BSL3. 2 and Seq9110 is specific. Indeed, substitution of the three guanines by three adenines (Seq9110aaa) in the recognition sequence of Seq9110 (Fig. 1B ) drastically decreases the binding (Supplemental Fig. S3 ). An antisense oligonucleotide, AS 9110 (Fig. 1B ), was then injected over the immobilized 5BSL3.