dataesr/scientific-paragraphs-categorization huggingface dataset:
BACKGROUND: Cervical cancer (CC) remains a leading cause of gynaecological cancer-related mortality world wide and constitutes the third most common malignancy in women. The RAIDs consortium (http://www. raids-fp7. eu/) conducted a prospective European study [BioRAIDs (NCT02428842)] with the objective to stratify CC patients for innovative treatments. A "metagene" of genomic markers in the PI3K pathway and epigenetic regulators had been previously associated with poor outcome  [2]. METHODS: To detect new, more specific, targets for treatment of patients who resist standard chemo-radiation, a high-dimensional Cox model was applied to define dominant molecular variants, copy number variations, and reverse phase protein arrays (RPPA). FINDINGS: Survival analysis on 89 patients with all omics data available, suggested loss-of-function (LOF) or activating molecular alterations in nine genes to be candidate biomarkers for worse prognosis in patients treated by chemo-radiation while LOF of ATRX, MED13 as well as CASP8 were associated with better prognosis.
Introduction Cervical cancer (CC) remains, after breast cancer, the second most common malignancy in women  [11]. Although patients with CC exhibit differences in their clinical course, infection by high-risk Human Papilloma Virus (HPV) remains an important initiating event in CC tumourigenesis  [12] , and one of the most important risk factors for developing CC  [13]. The global incidence is approximately 500 000 cases per year and the mortality of this virally initiated disease is in the order of 50% of patients worldwide  [11]. Advances in biomarkerdriven cancer therapy development are hampered by the complexity of the human genome and the high inter-and intra-patient variability in molecular alterations. BioRAIDs, a supervised longitudinal study with pretreatment sample collection and clinical annotation (NCT02428842)  [9]  allowed the identification of molecular pathways related to poor outcome.
The summary on present achievements of biomarker-driven studies is the low number of patients who benefit from this approach. This suggests that for heterogeneous cancers with multiple potential oncogenic drivers, biomarkers assessed only at the DNA level in a panel assay, may 1/ not establish the main tumour drivers and, 2/ not reliably predict drug responses. For that reason, we took-into-account not only the genotype but also the phenotype (e. g. gene expression/proteomic profiles) of cancer cells as well as the multiplicity of potential driver alterations. We initiated BioRAIDs, a supervised longitudinal study with pretreatment cervical cancer sample collection and clinical annotation (NCT02428842)  [9]. PIK3CA mutations and/or gene amplifications were the most frequently diagnosed oncogenic alteration, present in 40% of BioRAIDs patients  [2].
Added value of this study While current treatment strategies are still mostly based on tumour location and disease stage and very few on tumour biology  [10] , we set out to identify upfront the molecular alterations that will need innovative therapies by assessing a European cervical cancer patient population. To do this, we tested the lead molecular alterations based not only on DNA alterations but also on activated protein expression profiles associated with cure or progression following standard (chemo-radiation) therapy. Molecular testing for multiplicity of driver alterations was carried out on a supervised patient population with pre-therapy frozen and fixed tumour biopsies as well as six-monthly liquid biopsies, allowing to follow viral presence and persistent molecular alterations over time (NCT02428842).
Implication of all the available evidence The present findings are to our knowledge the first systematic approach towards the understanding of multiple governing alterations in cervical cancer, treated by the current best standard clinical approach, which has been developed in careful clinical trials over the past decades. Integration of individual tumour specific constellations based on multiple tumour cell genotypic and phenotypic driver alterations with outcome data leads to a better understanding of relevant mechanisms that govern clinical control (or not) of cervical cancer treated by chemo-radiation. Pre-treatment awareness of type and constellation of deleterious genetic alterations will assist the design of innovative umbrella type platform trials. markers on CC response and outcome following chemo-radiation was assessed using an integrative approach.
Patients Patients included in this study had been enrolled in the EUfunded prospective CC BioRAIDs study (NCT02428842) run by the RAIDs Network (Rational Molecular Assessment and Innovative Drug Selection, www. raids-fp7. eu). The clinical protocol together with tumour sampling procedures, quality control of samples and treatment was conducted in 18 European centers from seven European countries. Study results have been previously published  [2, 9, 14]. The study has been conducted in accordance with the ethics principles of the Declaration of Helsinki and a signed informed consent for the participation in the study was a prerogative. All patients had pretreatment mandatory frozen tumour, blood and serum sampling as well as mandatory magnetic resonance imaging. Pet scan imaging was optional and is available in half of the population.
DNA sequencing & bioinformatics Paired-end whole exome sequencing (WES) and paired-end targeted gene panel sequencing were performed on a HiSeq2500 platform. The sequencing was performed to reach an average depth of coverage of ~150 £ for whole exome sequencing and ~730 £ for targeted sequencing. The data were further processed by the Institut Curie bioinformatics pipeline. Somatic alterations (point mutations, insertions/deletions and copy number changes) were identified (Supplementary Table  2 ) from the aligned sequences of matched-samples using dedicated tools, detailed in Scholl et al. [2]  CoxBoost analysis  [15]  was used to fit a Cox proportional hazards model by component wise likelihood-based-boosting. This type of analysis is suited for models with high number predictors, typically omics covariates.
Patient characteristics with a complete dataset (n = 89) Clinical and patho-biological covariates on the complete BioRAIDS population (n = 376) have been previously published  [2]. Here we focused first on a patient population (n = 89) with complete information for all data types (mutations, amplifications/deletions and protein expression and phosphorylation patterns). For subsets of the initial population (Table  1 ), there were no significant changes in treatment allocation or in clinical and patho-biological parameters such as FIGO-2018 stage (I-II vs III-IV), HPV type  This led to close to 90% of patients having received radiation therapy as previously published  [2].
Furthermore, HPV positivity and TP53 loss are mutually exclusive patterns in cervical cancer patients (Kamal et al. , submitted). Novel cancer therapeutics that supposedly reactivate a variant tumour p53 protein include small molecule drug candidates (such as APR-246), presently in clinical development for myelodysplastic syndromes, acute myeloid leukemia, as well as solid tumours (NCT03268382)  [32]. A phase Ib/II clinical trial in TP53 mutated high-risk hematological cancers is ongoing, combining APR-246 and azacytidine (NCT03931291). Such a combination might be worth considering for chemoradiation resistant cervical cancer patients with loss of function in both p53 and epigenetic regulating genes. CREBBP was first isolated as a nuclear protein that binds to cAMPresponse element binding protein (CREB), and together with EP300 was shown to encode lysine acetyl-transferases (KAT) with critical roles in embryonic development, growth control, and homeostasis by coupling chromatin remodeling to transcription factor recognition  [33].
Fig. 5  details the patients with single or multiple molecular alterations (above the 5% frequency level) that were present in the population. These results may assist to define the patient population for future molecular targeting. Two different post-translational modifications on beta-catenin appeared to be high-profile parameters related to outcome, with opposite prognosis, depending on the protein isoform as distinguished by its specific phosphorylation pattern. The supposedly active nuclear form of beta catenin, which according to Goretsky et al. [18] , is phosphorylated in Ser 552 (pb-Cat552) appeared associated with good outcome, while the predominance of a phosphorylated epitope in Ser 675 (pb-Cat675), which is located just outside the armadillo repeat at the beginning of the C terminal domain, was associated with poor outcome in our dataset.
Acknowledgements The authors gratefully acknowledge the financial support from the  EU Graphene Flagship  project (no. 785219 ). This work was partly supported by the  Agence Nationale de la Recherche (ANR)  through the  LabEx  project  Chemistry of Complex Systems  ( ANR-10-LABX-0026_CSC ), and through the program " Investissements d'Avenir"  ( ANR-11-EQPX-022 ). We wish to acknowledge the  Centre National de la Recherche Scientifique (CNRS)  and the  International Center for Frontier Research in Chemistry (icFRC). The authors wish to thank  C. Royer  and  V. Demais  for TEM analyses at the  Plateforme Imagerie in Vitro at the INCI (Strasbourg, France ),  I. El Masoudi  for the ICP-MS and ICP-AES measurements, and  S. Guo  for the exfoliation of MoS2. Received: ((will be filled in by the editorial staff)) Revised: ((will be filled in by the editorial staff)) Published online: ((will be filled in by the editorial staff)).
Data Analysis Data were analyzed using SPSS 24 (IBM, Armonk, NY, 2016). Descriptive variables were analyzed for normal distribution using values of -2 to +2 for skewness and kurtosis. 32  Demographic variables were analyzed using means (± standard deviation) and frequency (%). Independent t-tests were used to analyze baseline differences between the CTDM-F and the control groups, and between universities. Analysis of covariance (ANCOVA) was conducted at baseline to control for significant differences between groups. A P a C -square test of independence analysis was used to analyze categorical variables. To assess differences in CT-DM score between groups, a two-stage hierarchical multiple regression was performed with CT-DM score as the dependent variable. University was entered at stage one of the regression to control for differences at baseline.
However, there were no differences between groups in recognizing the d. After controlling for university using repeated measures ANCOVA, there were no between group differences in CTD or GE over time. However, there was a significant withingroup change in CTD for both the CTDM-F group from a mean baseline value of 3. 52 (±. 6) to a post-intervention mean of 3. 63 (±. 6), p<. 01 and the control group from a baseline mean of 3. 54 (±. 6) to a post-intervention mean of 3. 69 (±. 6), p<. 001. There were no significant within group changes for GE behavior. For the exploratory SEM path analysis, three model versions were hypothesized and tested using EQS software  33  : direct, predictive, and mediational models. Findings revealed that compared to a direct model with only a single predictor from group to CT-DM score, and a mediational model with CTD and GE behavior as mediators, a third prediction model with paths from three predictors (group, CTD, and GE behavior) fit best.
Therefore, the objective of this paper is to determine the limitations of the isotopic labelling technique for ENPs in biological matrices, which should be an important step prior to any future fate and toxicity study. Multi-spiked quantum dots with a CdSe/ZnS core/shell structure were synthesized and dispersed at very low and relevant concentrations (ppt-ppb) in various biological matrices (blood plasma, urine, saliva and growth medium). Measurements of the isotopic compositions by HR-ICP-MS and statistical data treatment were used to accurately determine the limits of quantification of the method (QD-LOQ), based on the isotopically labelled element and the matrix composition. This work is expected to provide a valuable basis for all future studies aimed at using isotopically labelled ENPs to determine their fate or toxicity in biological media.
The TEM pictures were processed with the software ImageJ 1. 51n. Chemical analysis. The microscope used above was coupled with electron-dispersive X-ray spectroscopy (EDXS) using a JEOL detector with an ultrathin window allowing the detection of low atomic mass elements to perform a chemical analysis on the QDs. To measure the total concentrations of Cd, Se and Zn in the TGA-coated QD stock solution, diluted QD solutions (dilution factor: 500 and 1000) were analyzed by ICP-OES using the Thermo Scientific iCAP 6000 Series after complete acid digestion with HF/HNO 3 (SI) and then after simple acidification (2% nitric acid) to evaluate the best sample preparation (i. e. complete digestion) before analysis. Their isotopic composition was verified by HR-ICP-MS using Thermo Scientific Element II.
Acknowledgements This research study was completed thanks to the financial support from the  French Agency for Food, Environmental and Occupational Health & Safety (ANSES)  under the convention No. EST-2013/1/264 , as well as the  IPGP multidisciplinary program PARI and Paris-IdF region SESAME  Grant No. 12015908. Our thanks go to  Ms. Laure Cordier  for the assistance she provided during various multi-elemental analyses. Two anonymous reviewers as well as the associate editor are thanked for their critical comments.
, Lorain, OH, USA) treated with borax (sodium tetraborate; Sigma-Aldrich Chemie Gmbh, Munich, Germany) to provide boron, an essential micronutrient (1 : 100 dilution of a 1. 1 g l À1 stock solution), and adapted over 2 wk to the field conditions of high light intensity (over 2. 0 mmol m À2 s À1 of photosynthetically active radiation; see  Joo et al. , 2017)  and low relative humidity (often 8% relative air humidity during the day during planting; see Nicotiana Attenuata Data Hub: http://nadh. ice. mpg. de/NaDH/fielddata/ weather) by keeping seedlings first in shaded, closed translucent plastic 34-quart boxes (Sterilite, Townsend, MA, USA), then opening the boxes, and subsequently transferring open boxes to partial sunlight in mesh tents (Tatonka, Dasing, Germany). Adapted size-matched seedlings were transplanted into an irrigated field plot at the Lytle Ranch Preserve, Santa Clara, Utah, in April 2016, and fitness correlates and biomass were counted at harvest in June 2016.
Details regarding which outliers were removed, as well as further details of statistical analyses for each dataset, are given in Notes S1. Pairwise post-hoc comparisons were made using the R package EMMEANS  (Lenth et al. , 2018)  using Tukey HSD tests after significant results were observed in a two-way ANOVA. When a two-way ANOVA revealed significance in only one factor, and not in the interaction, the interaction was excluded from the model and the pairwise comparisons were extracted within the nonsignificant factor by performing pairwise t-tests. All ANOVAs and relevant contrasts are presented in Notes S1. For regression analyses, estimates of the slopes of the covariate trends were generated for each level of the factor using the function emtrends in the R package EMMEANS  (Lenth et al.
Acknowledgements We thank the glasshouse department at the  Max Planck Institute for Chemical Ecology  and the field team in 2015 and 2016 for support;  Brigham Young University  for the use of the Lytle Ranch Preserve field station in Utah,  USA , and  APHIS  for constructive regulatory oversight; the technical staff at the department of  Molecular Ecology  for providing seeds;  L. Cortes-Llorca  for helpful discussions;  K. Groten  for suggesting a triple response assay to test ethylene sensitivity; and both the  International Max Planck Research School (IMPRS)  on the Exploration of Ecological Interactions with Chemical and  Molecular Techniques  and the  Young Biodiversity Research Training Group (yDiv)  for their support of HV and EM.
Introduction Analysis of the three-dimensional (3D) structures of proteins, and in particular the examination of functional binding sites, is of importance in drug discovery and biological chemistry  (Skolnick and Fetrow, 2000; Ferreira et al. , 2015). Binding site comparison, also known as pocket matching, can be used to predict selectivity of ligand binding, as an approach for hit-finding in early drug discovery or to suggest the function of, as yet, uncharacterized proteins  (Ehrt et al. , 2016). Structural approaches for pocket matching have been shown to be more predictive of shared ligand binding between two proteins than global structure or sequence similarity  (Grishin, 2001; Illergård et al. , 2009). Increasingly, there is interest in applying pocket matching approaches to large datasets of protein structures to enable proteome-wide analysis  (Holm and Sander, 1996; Hou et al.
Conveniently,  Govindaraj and Brylinski (2018)  have recently compiled TOUGH-M1, a large-scale dataset of roughly one million pairs of pockets differentiated by whether or not they bind structurally similar ligands. In this paper, we rely on this collection for training and expect that the scale will help the method overcome the noise inherently present in automated strategies for gathering data. We introduce DeeplyTough, a pocket matching method that uses a convolutional neural network (CNN) to encode protein binding sites into descriptor vectors. Once computed, descriptors can be compared very efficiently in an alignment-free way by simply measuring their pairwise Euclidean distances. This efficiency makes the proposed approach especially suited to investigations on large datasets. Our main contribution is the formulation of pocket matching as a supervised learning problem with three goals: i) to provide similar pockets with similar descriptors, ii) to separate the descriptors of dissimilar pockets by a minimum margin, and iii) to achieve robustness to selected kinds of nuisance variability, such as specific delineations of binding sites.
System and Methods The problem of protein pocket matching is seen from the perspective of computer vision in our approach. The main idea is to regard pockets as 3D images and process them using a CNN to obtain their representation in a vector space where proximity indicates structural similarity, see Figure  1. In this section, we first discuss the choice of the training dataset and featurization. Then, we pose our method as a descriptor learning problem and describe a training strategy that encourages robustness to nuisance variability. Finally, we describe the architecture of the neural network and details relevant to its implementation.
Training Dataset Moving from intuition-based featurization schemes towards learned representations presumes the availability of a large training corpus of pocket pairs with associated 3D structures. To frame the task as a supervised machine learning problem, we assume each pocket pair to be of a certain similarity. Here, we restrict ourselves to the binary case of similar and dissimilar pocket pairs. Unfortunately, obtaining this information is not easy in practice and has been the underlying theme in a range of benchmarking papers. Indeed, the performance of pocket matching algorithms has been shown to depend strongly on the construction of particular datasets  (Lee and Im, 2017) , and we expect similar behavior to arise when using such datasets for training as well. While highly structurally similar pocket pairs can be easily obtained by considering sequence similar proteins, pairs of unrelated proteins binding similar ligands  (Barelier et al.
This uncertainty makes pocket pair datasets defined at the protein-level unfit for training a pocket-level similarity predictor directly. Nevertheless, such datasets can be used to evaluate pocket matching algorithms by estimating protein similarity as the maximum predicted pocket similarity computed over all pockets and all structures of the respective proteins  (Chen et al. , 2016). Pocket-level similarity is derived directly from 3D protein-ligand complexes such as those available in the Protein Data Bank (PDB)  (Berman et al. , 2000) , and is often enhanced by the heuristic assumption that similar ligands bind to similar pockets and vice versa. While providing detailed localization of protein-ligand binding, data acquisition is expensive and pockets are observed in a single bound (holo-) conformation, which means that training on such data may not warrant generalization to other induced fit or unbound (apo-) protein conformations.
Although studies have suggested that conformational rearrangement may be limited  (Brylinski and Skolnick, 2008). Historically, datasets have been constructed as classification experiments involving sets of protein structures bound to a small number of commonly occurring co-crystal ligands  (Kahraman et al. , 2007; Xie and Bourne, 2008; Hoffmann et al. , 2010). However, these datasets tend to be small (hundreds or a few thousand pairs) and may not be sufficiently representative of possible protein binding site space for training purposes. The APoc dataset  (Gao and Skolnick, 2013)  represents a step towards larger, more general datasets, comprising 34,970 positive and 20,744 negative pairs. Recently,  Govindaraj and Brylinski (2018)  proposed a large dataset, TOUGH-M1, of roughly one million pairs of protein-ligand binding sites curated from the PDB.
Specifically, the authors considered a subset of the PDB including protein structures binding a single "druglike" ligand. Structures were clustered based on sequence similarity and representative structures bound to a diverse set of ligands were chosen from each cluster. Since the dataset is designed with the prospective use case in mind, in which the location of ligand binding is not available, pocket definition was performed using Fpocket (Le  Guilloux et al. , 2009)  and predicted cavities having the greatest overlap with known binding residues were selected. Finally, bound ligands were also clustered and globally dissimilar protein pairs were identified either within (positive) or between (negative) each ligand cluster. The resulting TOUGH-M1 dataset consists of 505,116 positive pocket pairs and 556,810 negative pocket pairs.
In this work, we choose to train our approach on the TOUGH-M1 dataset. From a machine learning perspective, TOUGH-M1 has the advantages of being large, balanced and offers pocket-level similarities. Notwithstanding, this dataset represents a specific method for defining pocket similarities and it is thus unclear if a trained method can generalize to other datasets, constructed in possibly different ways. We will return to this question in Section 3 and answer it affirmatively. Finally, let us emphasize that while it is often functional binding sites that are of biological interest, we refer to protein cavities indiscriminately as pockets, since the method discussed is agnostic to the biological relevance of the pockets analyzed.
, 2016). A pocket is represented as tensor p ∈ R c×d×d×d created by sampling the corresponding protein structure image f over a grid of shape d × d × d with step sÅ. To denote the representation of a particular pocket in f centered at point µ ∈ R 3 and seen under angle φ, we use the functional notation p = p(f, µ, φ). In our datasets of interest, µ is either the geometric center of a pocket, i. e. the centroid of convex hull of alpha spheres in the case of pockets detected with Fpocket 2. 0 (Le  Guilloux et al. , 2009)  or the centroid of convex hull of surrounding residues laying within 8 Å of non-hydrogen ligand atoms in the case of pockets defined by their bound ligands.