dataesr/scientific-paragraphs-categorization huggingface dataset:
Data collection Demographic, clinical, therapeutic and biological data were retrieved from the medical record. All participants answered an individual score of deprivation, the Evaluation de la précarité et des inégalités de santé dans les centres d'examens de santé (EPICES; Evaluation of the Deprivation and Inequalities of Health in Healthcare Centres), that takes into account the multiple dimensions of socioeconomic conditions including psychological, social and economic aspects  [9, 10]. The EPICES score is based on the responses to 11 binary questions (marital status, health insurance status, economic status, family support and leisure activity). It varies from 0 (the least deprived) to 100 (the most deprived)  [9, 10]  with a deprivation state defined as a score � 30. 17, a threshold established in a large cohort study carried out by the Centre technique d'appui et de formation des centres d'examens de sante(CETAF; Technical Centre of Support and Training for Health Centres)  [9].
Results All our MD simulations were performed using the Gromacs software package  30  and the MARTINI 2. 2 Coarse-Grained (CG) model for ions, lipids  23  , proteins  31, 32  and the PW (Polarizable Water) model  29  for water. The systems were simulated either in the NVT or the NPT ensemble at a temperature of T = 320 K, where PW water presents a density of 1020 kg/m 329. MARTINI CG ions consist in particles of the same size and mass as a PW particle, carrying positive or negative charge. Although they are called "Na + " and "Cl -" in the force field parameters files, they do not represent a realistic sodium or chlorine ion  33. Since the MARTINI salt is perfectly symmetrical, we considered that it represents the KCl salt instead of NaCl. Methodological details of the simulations can be found in the Methods.
Ionic current through the nanopore In this section, we present the results of the simulations for the system composed by the α-hemolysin (PDB entry: 7AHL  44  ) inserted in a DPPC bilayer and surrounded by PW water, ions and counter-ions in the presence of an external electric field. A value of 1,1 M± 0. 1 M ionic concentration was measured at the bulk region. Further details of these procedures and the parameters used can be found in Methods. The initial condition for the ensemble α-hemolysin suspended in a lipid bilayer membrane was taken from final state of Prof. Sansom simulations  45  , where the lipid bilayer grows spontaneously around the protein. This is the reason for the observed membrane curvature in the pore vicinity in our system (see Figure  4 ). MD simulations of α-hemolysin in DMPC lipid membrane using MARTINI force field were recently studied  46  and showed a considerable distortion of the protein stem.
Simulation details We performed Molecular Dynamics (MD) using the Gromacs software package  30  and the MARTINI 2. 2 CG model for ions, lipids  23  , proteins  31, 32  and the PW (Polarizable Water) model  29  for water. Conductivity simulations systems contained only water and ions at different concentrations, whereas membrane simulations systems contained a DPPC bilayer, water, and ions at different concentrations. In the nanopore simulations, the protein nanopore was inserted in the lipid membrane. A timestep of 20 fs was used for all MD simulations, which is the recommended timestep for CG MD simulations using MARTINI force field. Periodic boundary conditions in the three dimensions were applied. All MD were performed using the PME method for electrostatic interactions, with a 2 Å spacing for the Fourier grid and a direct space cut-off radius of 13 Å.
Nanopore simulations The crystallographic structure of α-hemolysin was taken from the PDB (entry 7AHL  44  ). The coordinates of the missing atoms were added using the pdb2pqr software and the crystallographic water molecules were remove. The protein was reduced in coarse grains using the martinize. py script. The ElNeDyn MARTINI force field was applied on the protein  47. It is composed of an elastic network model that maintains the secondary structures during the dynamics using springs (500 kJ. mol -1. nm -  elastic bond strength and a 0. 9 nm cutoff). After a short energy minimization, the resulting CG protein structure was inserted in a DPPC bilayer using the following procedure: the PDB structure of a CG α-hemolysin inserted in a DPPC bilayer of 756 lipid molecules obtained by Prof. Sansom  45  was downloaded from CGDB (coarse grain database, a database of inserted membrane proteins inside DPPC bilayers  53.
Acknowledgements This work was granted access to the  HPC resources of CINES  under the allocations 2015 -c2015077139, 2016 -c2016077139 and 2017 -c2017077139 made by  GENCI. Part of this work was funded by  CNRS ,  Défi InFIniTi  for the  DYNANO  project (2016 and 2017).
Acknowledgments The present research project Water Budget over Dome C ( H2O-DC ) has been approved by the  Year of Polar Prediction (YOPP) international committee. The HAMSTRAD programme (910) was supported by the  French Polar Institute, Institut polaire français Paul-Emile Victor (IPEV) , the  Institut National des Sciences de l'Univers (INSU) / Centre National de la Recherche Scientifique (CNRS) ,  Météo-France  and the  Centre National d'Etudes Spatiales (CNES). The permanently manned Concordia station is jointly operated by  IPEV  and the  Italian Programma Nazionale Ricerche in Antartide (PNRA). The tropospheric LIDAR operates at Dome C from 2008 within the framework of several Italian national (PNRA) projects. We would like to thank https://doi. org/10. 5194/acp-2019-607Preprint. Discussion started: 28 August 2019 c Author(s)  2019.
Data availability HAMSTRAD data are available at http://www. cnrm. meteo. fr/spip. php?article961&lang=en (last access: 28 August 2019). The CALIOP images are accessible at http://wwwcalipso. larc. nasa. gov/ (last access: 28 August 2019). The tropospheric depolarization LIDAR https://doi. org/10. 5194/acp-2019-607 Preprint. Discussion started: 28 August 2019 c Author(s) 2019. CC BY 4. 0 License.
Article Search Strategy Article selection was based on a search for publications following the PRISMA guidelines  [71]  of the following scientific databases: IEEE Xplore, Embase, PubMed, Science Direct, Scopus, and Web of Science. All publications from January 2009 to December 2018 were included in the search. The electronic databases were searched by the first author (D. S. ) for relevant articles using the following search terms mentioned in the title or abstract or keywords: "Parkinson" AND ("cue" OR "cues" OR "cueing" OR "freezing"). In addition to the search terms, the following search filters were applied: (i) full journal article; (ii) written in English; (iii) appeared in a peer-reviewed academic source. To identify additional articles, a snowball technique was used through additional hand-searching of references and citation tracking from relevant articles.
Results The outcome of the systematic search is summarized in Figure  2. The initial search identified 4480 records. Reference manager software EndNote X5 (Thomson Reuters, Philadelphia, PA, USA) was used to collate results. Duplicates were removed and a screening process of both the title and abstract of the remaining records was subsequently conducted. The full-text of the remaining records were then assessed for relevance to the review. Following this procedure, 18 articles remained for the systematic review.
The algorithm was implemented with a lag time (time between the occurrence of a FoG episode and its detection) of less than 2 s. The main computational part of the algorithm used an fast Fourier transform (FFT) calculation to determine the power content of the acceleration sensor data between 0. 5 and 3 Hz (walking band) and 3 and 8 Hz (freeze band), as suggested by Moore et al. [77]. A method of thresholding was used to determine if the user was in a standing, walking or FoG state. By implementing their algorithm within the Linux operating system of the small wearable computer, the system would produce a rhythmic ticking sound at a tempo of 60 beats/minute whenever a FoG episode was identified, and this lasted until the user resumed walking. For all users, this provided a cue every one second, during the period the user was in FoG.
The main computational part of the algorithm used an fast Fourier transform (FFT) calculation to determine the power content of the acceleration sensor data between 0. 5 and 3 Hz (walking band) and 3 and 8 Hz (freeze band), as suggested by Moore et al. [77]. A method of thresholding was used to determine if the user was in a standing, walking or FoG state. By implementing their algorithm within the Linux operating system of the small wearable computer, the system would produce a rhythmic ticking sound at a tempo of 60 beats/minute whenever a FoG episode was identified, and this lasted until the user resumed walking. For all users, this provided a cue every one second, during the period the user was in FoG. Bachlin et al. did not explain why they choose to use auditory cueing with a fixed tempo of 60 beats/minute.
Although, video footage was analyzed for outcome measures by multiple assessors, inter-assessor agreement was not reported, therefore, this study failed to meet WWC single-case design standards. In conclusion, Bunting-Perry et al. acknowledged the many pitfalls that exist when studying FoG and provided instructions for future researchers. These included; (1) minimizing dual tasking by using continuous visual cueing; (2) ensuring adequate training with visual cueing devices; (3) test various lasers colors and (4) collect data regarding other features of the participants' disease, with the goal of characterizing any subgroup that may respond.
The research and development department of EDF had begun this work in 1998 and it was continued between 2009 and 2012 as part of the ANR-SeARCH programme. In 2012-2013 the aim of the campaigns was to continue the virtual reconstitution work on the pair of colossal royal statues that once stood next to the lighthouse. This work demonstrated that photogrammetry was the new solution for rapidly providing quality "digital duplicates" on the scale of the elements, whether sunken or lifted  (Reuter, Mellado, Granier, Hairy, Vergnieux, Couture, 2011). With around 30 blocks removed from the sea and more than 3000 pieces still underwater, some of which weighing almost 40 tons, this was an important step forward. From 2013, with the support of the Honor Frost Foundation (HFF), the gathering and processing of photographic data in this research were improved and led to a clear development in data gathering techniques and the consequent improvement in the quality of results  (Abd Elaziz, Elsayed, Hairy, Soubias, 2016).
Photography The first step towards creating an accurate three-dimensional model of the site is to take a series of good photos using a good camera. The most important thing for the precision and the accuracy of the work when using manual methods is the diver's competence in, firstly orientating underwater and secondly in respecting the level of the flight plan, which must not exceed 3 m height above the site. The data capture method is generally inspired by the classic approach used in the aerial domain. The flight plan and photographic work for obtaining the DSM was undertaken by Mohamed Elsayed using a DSLR camera (Nikon D700 fixed 24mm lens) in a hemispheric dome housing, which allows for a wide coverage area and close proximity to the subject especially in low visibility. All the captures were taken in a manual mode without flash whatever the sea conditions: f/stop and shutter speed varied according to underwater visibility and luminosity, sometimes using f/ 7.
These photos cannot be used efficiently without prior enhancement, thus image processing is necessary for colour correction, contrast, removing shadows and reducing highlights in order to get more matching points between pairs of images (Fig. 9 ). For this we used Adobe Lightroom and Photoshop for editing photos in such an environment (Fig. 8 ) and for batch processing. All photos were captured in a RAW format and then converted into JPG to be accepted by the photogrammetry software. , Castro, 2016). In order to improve the results some actions in the software were added, such as picture masking, deleting erroneous points. Beginning by feeding uncalibrated overlapping images into the software, the camera auto-calibration runs automatically through mathematical algorithms; the software detects and then tracks how many points move throughout the series of images.
Building a dense point cloud and mesh with low computer capability Metashape uses a so-called "feature detection algorithm" to automatically identify and match features in overlapping pictures. The software automatically aligns the pictures relevant to one another based on the detected features and the camera calibration parameters  (Van Damme, 2015). The next step is to build a dense point cloud, respecting the low capabilities of the actual computer, avoiding ultra-high resolution and medium subsetting because once started we found that it took about two weeks of processing, and then the software could crash after hours of processing. To resolve this problem, Metashape produces a python script called "split in chunks" (http://wiki. agisoft. com/wiki/Split_in_chunks. py), which we used. Since we must process huge numbers of photos, it was impossible to treat all of them at one time, so a dense cloud and then a mesh can generate smaller boundary boxes, which is a box containing the aligned photos, which the software can then merged back again into a single chunk.
Background: The amount of publicly available cancer-related "omics" data is constantly growing and can potentially be used to gain insights into the tumour biology of new cancer patients, their diagnosis and suitable treatment options. However, the integration of different datasets is not straightforward and requires specialized approaches to deal with heterogeneity at technical and biological levels. Methods: Here we present a method that can overcome technical biases, predict clinically relevant outcomes and identify tumour-related biological processes in patients using previously collected large discovery datasets. The approach is based on independent component analysis (ICA)an unsupervised method of signal deconvolution. We developed parallel consensus ICA that robustly decomposes transcriptomics datasets into expression profiles with minimal mutual dependency.
Results: By applying the method to a small cohort of primary melanoma and control samples combined with a large discovery melanoma dataset, we demonstrate that our method distinguishes cell-type specific signals from technical biases and allows to predict clinically relevant patient characteristics. We showed the potential of the method to predict cancer subtypes and estimate the activity of key tumour-related processes such as immune response, angiogenesis and cell proliferation. ICA-based risk score was proposed and its connection to patient survival was validated with an independent cohort of patients. Additionally, through integration of components identified for mRNA and miRNA data, the proposed method helped deducing biological functions of miRNAs, which would otherwise not be possible.
Background Genomic and transcriptomic research has accumulated a vast collection of publicly available cancer related data. Data have been continuously collected using massive financial and scientific efforts. For example, The Cancer Genome Atlas (TCGA, https://www. cancer. gov/tcga) holds over 10,000 patient-derived samples including various levels of omics data: DNA, RNA, and proteins. Now, the question arises if these resources can also be used to support clinicians in making rapid and accurate assessments leading to tailored treatments for individual cancer patients. Integrating this information still poses a considerable obstacle as genomic and transcriptomic data from cancer patients are characterised by significant heterogeneity at three levels. First, results are generally collected using different sample preparation protocols and transcriptome analysis platforms and are then interrogated by constantly changing techniques.
Melanoma arises through the malignant transformation of melanocytes and presents a very aggressive form of skin cancer with increasing global case numbers. Melanoma's extremely high mutation rate (> 10 somatic mutations/Mb) and the concomitant genetic heterogeneity make it difficult to distinguish true cancer driver genes from noise in bulk samples using current technologies  [16, 17]. Nevertheless, the analysis of gene expression data resulted in three patient subtypes or clusters: "immune", "keratin" and "MITF-low", which have implications for patient survival  [18]. Interestingly, the majority of primary melanomas belonged to the "keratin" cluster having a worse prognosis than the other two subtypes. In this study, we used the skin cutaneous melanoma (SKCM) TCGA cohort with over 470 patients diagnosed with cutaneous melanoma as the discovery dataset.
Two layers of "omics" data were considered and integrated: mRNA and microRNA (miRNA). The investigation dataset included a small cohort of three primary melanoma tumours and two controls: matched cancer patientderived normal skin and normal melanocytes. First, for the discovery cohort, we demonstrated that ICA deconvolution can be successfully applied to classify patients based on their tumour subtypes and to build the risk score that predicts patient survival. The risk score was then tested using an independent validation cohort of 44 patients, obtained by microarray gene expression technology. The strong technical differences between discovery RNA-seq data and microarray-derived validation datasets were resolved by our method. Next, the investigation dataset was studied in depth and key processes involved in cancer aetiology were detected and quantified: immune response and inflammation, angiogenesis, self-sufficient cell proliferation among others.
Discovery, validation and investigation datasets Discovery and validation datasets As a discovery dataset, we used two SKCM TCGA datasets: RNA-seq (472 samples) and miRNA-seq (452 samples) data from the Genomic Data Commons (GDC) data portal of the National Cancer Institute of the National Institutes of Health (NIH, https://portal. gdc. cancer. gov/). Soft filtering as in  [19]  was used to reduce number of RNA features from 60,446 to 16,579 (see Additional file 3: Fig. S1 ): only genes with more than 1000 counts in at least one sample among 472 were considered. For miRNA we used less strict filtering and required at least one read to be presented. Four metrics of gene expression were considered for mRNA: raw counts, DESeq2-normalized counts  [20] , FPKM and TPM. All expression values were log2 transformed.
From TCGA clinical data we extracted survival time, gender and sample type (primary tumour or metastatic). We also added data of tumour subtype based on RNA-cluster (immune, keratin, MITF-low) as it is relevant for prognosis  [18]. The extracted survival and clinical datasets are provided in Additional file 4: Tables  S1  and S2 , respectively. A validation gene expression dataset was taken from  [21] , available from ArrayExpress under E-GEOD-19234. This microarray dataset consists of 44 metastatic samples from melanoma patients accompanied by survival information. The samples were collected from different metastatic sites, mainly from lymph nodes, from patients with grade III (39 samples) and IV (5 samples). As microarray expression data have very different dynamic range compared to RNA-seq  [22] , array expression were linearly transformed to fit RNA-seq distribution as described in Additional file 1: Supplementary Methods.
Investigation dataset The investigation dataset, represented by RNA-seq and miRNA qPCR array data, originated from three primary tumour samples of melanoma patients (entitled P2PM, P4PM, P6PM) and two control samples: one matched normal skin P4NS and a healthy melanocyte cell line NHEM (see Additional file 4: Table  S3 ). Melanoma biopsies of three Caucasian patients were collected after surgical resection at the Dermatology Department of the University Clinic Freiburg, Germany. All patients signed an informed written consent. Ethical approval of this study was obtained from the Comité National d'Ethique de Recherche Luxembourg (CNER-No. 201201/05) and from the German Ethik-Kommission der Albert-Ludwigs-Universität Freiburg (EK-Freiburg 196/09). Histological examination and estimation of the percentage of tumour cells within the lesion was performed by two independent pathologists (normal skin and NHEM cell line were free of tumour cells).
Tissues from snap frozen biopsies were lysed in RLT buffer with a Qiagen TissueLyser (50 Hz, 5 min). DNA and total RNA were extracted using the Qiagen's AllPrep Mini Kit according to supplied protocols. Quality and quantity of samples were measured with Nanodrop, gel electrophoresis and Qubit High Sensitivity Kit. RNA integrity was determined using the Agilent Bioanalyzer Nano chip. RNA-seq data for these samples are available by GEO accession number GSE116111 and Ct-values for all quantified miRNAs are available in Additional file 4: Table  S4. MiRNA names were harmonised using miR-Base v. 21 and Ct-values were inverted and expression was calculated as 36-Ct.
Data analysis Consensus ICA ICA was applied to the combined discovery and investigation datasets for unsupervised separation of signals and feature extraction (Additional file 3: Fig. S2  and S3 ). By combining the datasets, we expect that technical biases between the discovery and investigation data are estimated by the method and isolated within some of the components. Each layer of omics data: mRNA and miRNA was analysed separately at this stage. ICA implementation from thefastICA`package of R was used  [23]. Let us denote E nm the expression matrix of n genes or miRNAs measured in m bulk samples. ICA decomposed such a matrix into a product of k statistically independent transcriptional signals S nk (addressed as matrix of metagenes) and a weight or mixing matrix M km (matrix of metasamples)  [11].
ICA may also suffer from reduced reproducibility for at least some components. To mitigate these drawbacks, we ran the analysis multiple times (100 runs during the exploratory steps and 1000 for the final analysis) following  [13]. In brief, the algorithm used for consensus ICA is described below. For the defined number of tries (nt), a random sample was excluded from the expression matrix and ICA was performed on this reduced dataset. As a result, we obtained nt matrices M (1) and S (1). Next, one of the decompositions was selected as "standard" and all the others were compared to it by correlation of metagenes. The sign and order of components was adjusted to fit the "standard" decomposition. Consensus S and M matrices were calculated by averaging all reordered S (1) and M (1). The squared correlation between corresponding metagenes was used as a measure of stability (R 2 ).
p-value) < 0. 01 were reported as topcontributing (see Additional file 2: Supplementary Results). Two lists of top-contributing genes resulted from the analysispositively and negatively involved. The lists of top-contributing genes of each RIC were afterwards used for over-representation (enrichment) analysis. The 16,579 genes, with expression above the selected threshold in at least one sample, were used as a background gene list and significantly enriched (adj. p-value< 0. 01) GO terms were investigated. In order to simplify the interpretation and to increase the robustness for runs on different datasets, we reoriented the components in order to have the most significantly enriched categories associated with positive topcontributing genes (see Additional file 1: Supplementary Methods). For MICs, the direction could not be identified by enrichment analysis, therefore we reoriented only those MICs that showed strong negative correlation with RICs.