dataesr/scientific-paragraphs-categorization huggingface dataset:
Additional controls were recruited by the  Singapore Consortium of Cohort Studies-Multi-ethnic cohort (SCCS-MEC ), which was funded by the  Biomedical Research Council , grant number:  05/1/21/19/425 ; SKKDKFZS: SKKDKFZS is supported by the DKFZ; SZBCS: The SZBCS was supported by Grant  PBZ_KBN_122/P05/2004 ; TBCS: The TBCS was funded by The  National Cancer Institute Thailand ; TNBCC: The TNBCC was supported by: a  Specialized Program of Research Excellence (SPORE) in Breast Cancer  ( CA116201 ), a grant from the  Breast Cancer Research Foundation , a generous gift from the  David F. and Margaret T. Grohne Family Foundation , the  Stefanie Spielman Breast Cancer  fund and the  OSU Comprehensive Cancer Center , the  Hellenic Cooperative Oncology Group research grant  ( HR R_BG/04 ) and the  Greek General Secretary for Research and Technology (GSRT) Program ,  Research Excellence II , the  European Union (European Social Fund -ESF) , and Greek national funds through the  Operational Program "Education and Lifelong Learning " of the National Strategic Reference Framework (NSRF)-ARISTEIA; TWBCS: The TWBCS is supported by the  Taiwan Biobank  project of the  Institute of Biomedical Sciences, Academia Sinica , Taiwan; UKBGS: The UKBGS is funded by  Breast Cancer Now  and the  Institute of Cancer Research (ICR), London.
Assessments The participants will be assessed at three time points: baseline (after consent, before randomization), six weeks (the mid-point when the control group crosses over to receive the intervention); 12 weeks (the final endpoint). At baseline (week 0), information will be gathered from carers on socio-demographic background, history of the patient's illness and treatment, as well as use of existing support and information services. The selection of outcome measures was informed by practical factors including administration time, with a preference for self-report measures, validated scales, and instruments available in the three working languages (English, French, and German). Data will be gathered by face-to-face or telephone interview (Table  1 ).
Program feasibility, user satisfaction, and computer literacy will be investigated using the Technology Acceptance Model (TAM)  (Venkatesh and Bala, 2008). This assesses perceived usefulness, ease of use, intention to use, computer self-efficacy, and computer anxiety. To assess acceptability of the intervention, qualitative information on user experiences and satisfaction with program content and mode of delivery will be obtained through individual interviews with all participants using a semi-structured interview guide after week 12. Since no appropriate and validated instrument is available for the assessment of dementia-and caring-related knowledge, a questionnaire was developed by RHAPSODY members with special expertise in education on dementia. Engagement with the intervention will be assessed through utilization data (e.
Statistical analysis Data will be entered at each site into a common central database using an electronic case record form. The primary end point will be the six-week assessment. Descriptive statistics will be conducted to detail the study population characteristics at baseline. The impact of the intervention on each outcome will be assessed by comparing the change score (week 0 to week 6) between the intervention group and the control group using ttest or non-parametric tests as appropriated. An intention-to-treat procedure (ITT analysis) will be applied. The possible additional benefit of another six weeks of access to the program will be determined by a global ANOVA including the following fixed effects: group (intervention or control), period (period 1: weeks 0-6; period 2: weeks 6-12), and the interaction between group and period.
Health economic analysis An embedded health economic analysis will assess the costs and outcomes of the e-learning intervention in a cost-consequences framework at the six-week follow-up point. The costing exercise will focus on direct intervention delivery costs, including the time spent by technical staff in managing the website when it is live, and time spent monitoring the online forum. The resources devoted to the development of the e-learning program and its translation will not be considered since these are fixed costs and costs per user will fall the more widely the program is accessed. An initial estimate of the incremental mean cost per QALY gained from access to the intervention will be performed using data collected from EQ-5D-5L in preparation for the full cost-effectiveness analysis in the larger trial.
Acknowledgments The authors would like to thank  Dr. Florence Gauzy  at  Bavarian Research Alliance  for advice and assistance with the preparation and submission of the RHAPSODY proposal to the JPND. RHAPSODY is an  EU Joint Program -Neurodegenerative Disease Research (JPND)  project. The project is supported through the following funding organizations under the aegis of JPND (www. jpnd. eu). France:  National Research Agency; Germany: Ministry of Education and Research ; the Netherlands: the  Netherlands Organization for Health Research and Development; Portugal: Foundation for Science and Technology; Sweden: The Swedish Research Council ;  United Kingdom :  Economic and Social Research Council. The RHAPSODY Study Group also includes the following individuals: England: Wendy Parker; France:  Sarah Boucault ,  Sophie Tezenas du Montcel ; Germany:  Johannes Mayer ;  Silvia Egert ,  Edith Heimsch ,  Alfred Zollner  ( Münchner Studienzentrum );  Uta Schwertel  (IM-C); The Netherlands: Raymond Koopmans; Portugal:  Helena Barrios ,  Maria Graça Melo ,  Tiago Mendes ; Sweden:  Petra Lilja Anderson ,  Anna-Karin Edberg.
Mass spectrometry analyses and protein identification Eluted peptides from the C18 analytical column were analysed by a Q-Exactive mass spectrometer (Thermo Scientific, Bremen, Germany) with a spray voltage et up at 1. 9 kV, and capillary temperature set up at 275 °C. Data acquisition was performed in a data-independent mode consisting of a full scan MS over the range m/z 230-2000 with resolution of 70,000 at m/z 200 and a full scan MS/MS of the ten most intense peaks. MS/MS data were acquired using a 2 m/z units ion isolation window and a collision energy of 24. These experiments performed in triplicate for each sample were fully controlled by Thermo Xcalibur 3. 0 (Thermo Fisher Scientific). All data files (*. raw) collected during nanoLC-MS/MS analyses 2. 9. 4. Label-free quantification of desmin Desmin abundance was quantified by calculating the area under the curve for each precursor ion identified as a peptide mapping to desmin at 2 ppm with Proteome Discoverer.
Levels of desmin in plasma To verify for the presence of desmin in plasma, we used different antibodies from 3 different clones that recognized both human, rat and mouse species (Fig. 2A ). The desmin antibodies (clone Y66, raised in rabbit and used in Fig. 1  and clone DE-U-10, raised in mouse) recognized the C-terminal part of the sequence corresponding respectively to the desmin tail (412-469 AA) and the Col2B region (325-372 AA). The third one (clone D33, raised in mouse) was previously used for detecting desmin in heart and plasma from mouse and human species  [6]. First, we analysed crude plasma from rats and humans for the presence of desmin and did not find any specific bands (data not shown). We then treated plasma from rat, mouse and human species either for CPPL or Alb/IgG depletion in order to access lowabundance proteins that we analysed directly or after immunoprecipitation (IP) with the desmin antibody (clone DE-U-10) to increase the sensitivity of detection.
Funding sources This work was supported by grants from the  National Agency for Research (ANR) ,  ANR  blanc -SVSE1  ( ANR-10-BLAN-1104 ), the  E. U. FP7 HOMAGE  ( 305 , 507 ), the  Laboratoire d'Excellence (LabEx)   ParaFrap  from  ANR ,  ANR-11-LABX-0024 , the  "Fonds Européen de Développement Economique Régional" (FEDER) , the  "Métropole Européenne de Lille" (MEL)  and the " Région Nord Pas de Calais ".
As a consequence, anticancer peptides appear as promising candidates for cancer therapy. Indeed, these small molecules are expected to become efficient anticancer drugs because of their high selectivity for cancerous cells  [6]. Moreover, most of these bioactive peptides interact directly with the cell membrane of the target cells, thus are supposed to induce less resistance mechanisms  [6]. During the last two decades, a growing number of studies reported the cytotoxic activity against cancer cells of antimicrobial peptides (for reviews see  [6] [7] [8] [9] ). In 2016, 198 peptides displaying anticancer or antitumor activity were listed in the Antimicrobial Peptide Database  [10]  available on http://aps. unmc. edu/AP/database/antiC. php. These multifunction antimicrobial peptides are used to fight against microbial invaders and constitute the first level of immune defense  [11]  that can be found in numerous eukaryotic organisms (plants, insects, reptiles, mammals,.
Membrane permeabilization assays Cells were washed and resuspended in phosphate buffer saline (PBS) at a concentration of 10 6 cells /mL. The cells suspension was treated with various concentrations (25, 12. 5, 6, 3, 1 and 0 μg/mL), of peptides during 45 min at 37°C. The cells were then treated during 15 min with 5 μl of propidium iodide (PI), a fluorescent dye which only penetrates the cells with damaged cell membrane, before flow cytometry analysis. Flow cytometric measurements were performed on a FACSConto II flow cytometer (BD Bosciences) with a 488 nm argon excitation laser. A total of 100 000 events were analyzed in each sample, using BD FACSDiVa 6 software (BD Biosciences) for data acquisition and analysis. LD 50 were calculated from the linear regression of the rate of permeabilized cells as a function of the peptide concentration expressed in μM.
However, Jurkat are T-lymphocytes which originated from an acute T-cell leukemia. We tested the WRK toxicity on two other different leukocytes cell lines KG1 and K562. Both are macrophages cell types, corresponding to an acute myelogenous leukemia and a chronic myelogenous leukemia respectively. Indeed, the bacterial anticancer peptide, microcin E492, was found to be cytotoxic for Jurkat cells but inactive against KG1 cells  [20]. The rates of permeabilized leukemic cells by WRK are presented in Fig 2. Even if the Jurkat cells appeared the most sensitive to the peptide, with 95% of permeabilized cells at 25 μg/mL, compared to about 80% and 65% for KG1 and K562 cells respectively, all the leukemia cell lines were sensitive, in a dose-response manner to WRK. KG1 and K562 cells were found insensitive to PSMα (data not shown).
(LLW)  [16]  model, and the Quadratic Loss Multi-Class Support Vector Machine (M-SVM 2 ) model  [10]  introduced by Guermeur and Monfrini. The current study proposes a new hybrid structure based on two stages, with supervised and unsupervised learning. This approach is validated on EEG database of Keirn and Aunon  [13] , that consists on EEG recordings of 7 subjects performing five different mental tasks namely: Baseline, Multiplication, Letter, Rotation, and Counting. The first stage consists of a Self Organizing Map (SOM) that reduces the original variable set (150 components obtained from DWT decomposition) into a smaller feature set, enabling to cluster the redundant irrelevant features and select the best descriptors to enhance the performance of the final classifier. The second stage uses a Quadratic Loss Multi-Class Support Vector Machine (M-SVM 2 ).
It receives as input the obtained feature set from the first stage and outputs class posterior probability estimates. The remainder of the paper is organized as follows. Section 2 summarizes some previous works using Keirn and Aunon database. Section 3 gives a description of the used database, explains the methodology adopted to build the first feature set, and presents details of our proposed hybrid structure. Section 4 exposes the obtained results. Section 5 discusses the results, and Section 6 concludes the study.
Related works One can find many models proposed in the literature for the interpretation of mental tasks using ElectroEncephaloGram (EEG) signals. Hereafter, we have selected those whose database is similar to ours (see Section 3. 1). Keirn and Aunon  [14]  proposed a method based on AR features and BQ classifier. They demonstrate the necessity to use (δ, θ, α, and β) bands and AR features to enhance the classification results. First, the EEG signals are subdivided into 1/4 s segments, and 36 AR features are estimated for each segment. Then, BQ classifier is proposed to classify combinations case of task pairs for five subjects, and achieved an average accuracy of 84. 6% across all task pairs and over all the five subjects. Palaniappan  [21]  divided the EEG signals into 20 segments with a length of 1/2 s.
Description of the database The EEG database used in this study was assembled by Keirn  [13] , and distributed online by Anderson  [2]. It consists of 325 EEG recordings from seven subjects 21-48 years old performing five distinct mental tasks, namely: Baseline Task (BT), the subjects were invited to relax and think of nothing in particular. Multiplication Task (MT), all subjects were asked to solve a nontrivial multi- plication problem (eg. 28 * 62). The subjects were not vocalizing nor making movements when they were solving the problem. Letter Task (LT), subjects were instructed to mentally compose a letter to another person without vocalizing. Rotation Task (RT), the subjects were given 30 s to see a rotating 3D object. After removing the object, they were invited to imagine that object being rotating around an axis.
Counting Task (CT), all subjects were asked to imagine a blackboard and to visualize numbers being written on it sequentially. An electro-cap was used to obtain each EEG recording from six channels: C3, C4, P3, P4, O1 and O2, according to the 10-20 systems. The obtained signals were recorded for 10 s at 250 Hz during each task. We used all the subjects of the database except subject four because of the invalidation of the signals in several trials as reported by Liang et al. [17]. Each subject attended (1 session, 2 sessions, or 3 sessions) of five trials per session. Each trial contains five recordings, and each recording represents one of the five distinct mental tasks. We note the existence of a seventh channel named ElectroOculoGraphy (EOG), that helps to locate the eye blinks, by performing a signal processing on it.
Classification using probabilistic M-SVM 2 The M-SVM 2 is a Kernel machines Method  [26, 25] , that operates in the functional class induced by a Mercer kernel  [5]. In what follows, X is the description space, Y is the set of the categories A 11; Q U with 3 rQ o þ1; κ is a kernel of Mercer on X 2 ; H κ ; 〈Á; Á〉 Hκ À Á is the Reproducing kernel Hilbert Space (RKHS) with kernel κ, and C is the penalty parameter. For m A N Ã , let z m ¼ x i ; y i À Á À Á 1 r i r m A X Â 11; Q U À Á m ; y m ¼ y i À Á 1 r i r m ; ξAR Qm y m À Á. A Q-category M-SVM 2 with a kernel κ, a RKHS H κ ; 〈Á; Á〉 Hκ À Á , and a "training set" z m is a large margin discriminant model trained by solving a convex quadratic programming problem of the form: (for more detail see  [10] ). Learning problem of an M-SVM 2 , primal formulation min h;ξ 1 2 X Q k ¼ 1 w k 2 þ Cξ T Mξ o s:t: ( 8 iA 11; mU; 8 k A 11; Q U⧹ y i È É ; h k ðx i Þ r À 1 Q À 1 þ ξ ik P Q k ¼ 1 h k ¼ 0 8 < : where M A M Qm;Qm is a matrix of rank Q À 1 ð Þm, with a general term defined as follows: m ð2Þ ik;jl ¼ 1 À δ y i ;k À Á 1 À δ y j ;l δ k;l þ ffiffiffiffi Q p À 1 Q À 1 ! δ i;j ; In our experiments we used the M-SVM package (M-SVMpack)  [15] , which allowed us to implement all the different M-SVM models (WW model, CS model, LLW model, and M-SVM 2 model).
Results As mentioned in Section 3. 1, we have used all the subjects of the database except subject four. Each subject was evaluated separately, session by session, because the mental state and reasoning of persons performing the same task can be different. Then, to illustrate the results, five-fold cross-validation procedure was performed using five distinct trials. We trained on three trials (15 tasks*46 segments ¼ 690 segments), used a fourth trial on validation (5 tasks*46 segments ¼ 230 segments), and tested on the fifth trial (5 tasks*46 segments ¼230 segments).
Conclusion In this study, we have proposed and implemented a new hybrid structure based on two stages to discriminate between five distinct tasks taken from the Keirn and Aunon database. In the first stage, we have proposed a new alternative of feature selection using SOM algorithm with unsupervised learning. The map received as input the 150 components obtained from wavelet decomposition. In the second stage, we have used a Probabilistic M-SVM 2 with supervised learning for the final classification. The M-SVM 2 received as input the vector parameters that was reduced by the SOM map. The proposed approach surpassed the results from the previous systems proposed in this framework, with a best average accuracy across sessions per subject between 81. 73% and 91. 90%. These observations conclude that our method can be an excellent alternative to enhance the performances of BCI systems.
88 Å per pixels on the specimen level. Images were recorded using a back-thinned direct electron detector (Falcon II) 4096 Â 4096 camera with dose fractionation (seven individual frames were collected, starting from the second one). Total exposure time was 1 s with a dose of 60 e ¯Å À 2 (or 3. 5 e ¯Å À 2 per frame). Images in the stack were aligned using the whole image motion correction method described in ref. 16. Particles (159,000) were picked automatically using the in-house software gEMpicker  17  and the contrast transfer function of every image was determined using CTFFIND4 (ref. 18) in the RELION workflow  19. For the first steps of refinement, images were coarsened by 2 (C2 images) and 4 (C4 images) using EMAN2. First, we applied 3D classification to remove bad particles (119,386 particles left), followed by two-dimensional (2D) classification to remove images with ice or noise (95,917 good particles; for 2D and 3D classifications see also ref.
1 Å resolution in the 80S/CHX complex. The cryo-EM maps were interpreted using Chimera  25  , COOT  26  to derive an atomic model of the human ribosome obtained by model building and structure refinement using Phenix  27. For this, the atomic model of the human ribosome  9  was used as starting point and refined including the CHX ligand. The atomic model was refined against the experimental cryo-EM map by iterative manual model building and restrained parameter refinement protocols (real space refinement, positional refinement, grouped B-factor refinement and simulated annealing as described in Khatter et al. 9  and Natchiar et al. (manuscript in preparation). Feature-enhanced maps  28  were used occasionally to facilitate assignment of side-chain conformations and the atomic model was then refined against the cryo-EM map as previously described  9.
8% of outliers). Figures were prepared using the software Chimera  25  and Pymol (DeLano, 2006). Cell cultures assays. The murine KO99L cell line was established from a tPTEN À / À tumour. Human leukaemia cell lines used are: Jurkat (T-ALL: T-cell acute lymphoblastic leukaemia); NALM-6 and 697 (B-ALL: B-cell acute lymphoblastic leukaemia); Kasumi-1 and U937 (AML: Acute Myeloid Leukaemia). Cell lines were annually checked for mycoplasma infection using MycoAlert (Lonza). Cells were grown in Roswell Park Memorial Institute (RPMI) 1,640 medium (invitrogen) supplemented with 10% or 20% (KO99L) foetal calf serum and 50 units per ml penicillin, 50 mg ml À 1 streptomycin and 1. 0 mM sodium pyruvate (Sigma-Aldrich). Cell cultures were maintained at 37 °C under 5% CO 2. Peripheral blood lymphocytes were obtained from the Heparin-treated blood from healthy donors after isolation by Ficoll-Paque-Plus density gradient (Amersham Biosciences).
Cell death was assessed after staining with 4,6 diamidino-2-phenylindole followed by FACS analyses on MACSQuant or MACSQuant VYB analysers (Miltenyi Biotec). Absolute numbers of cells were also measured for each condition. All statistical data from functional assays are presented as the mean±s. d. RNA and DNA isolation and quantitative real time PCR analysis. Total RNAs from treated KO99 cells were extracted using Trizol reagent (Invitrogen) and 4 mg of RNA was reverse transcribed using GoScript reverse transcriptase (Promega). Complementary DNAs (50 ng) were amplified in triplicate on a StepOne Plus Real Time PcR system using SYBR Green I Dye (Applied Biosystems). Primers against 36B4, Cox2 and Mcl-1 were designed using PRIMER Express Software (Applied Biosystems), and sequences are available upon request.
Primers against mouse c-Myc were purchased from OriGene. Fluorescence differences were collected by the thermal cycler during each 60 °C cycle. Semi-logarithmic plots were constructed of delta fluorescence versus cycle number. The 2 À qqCt method was used to determine the change in expression of each gene relative to 36B4 expression. Data availability. Atomic coordinates and the cryo-EM map have been deposited in the Protein Data Bank and EMDB under accession codes 5LKS. pdb and EMD-4070. The data that support the findings of this study are available from the corresponding author upon request.
Acknowledgements We thank  Jonathan Michalon ,  Remy Fritz  and  Romaric David  for IT support,  Jean-Francois Me  ´netret for technical support, the IGBMC cell culture facilities for HeLa cell production, and  Grigory Sharov  and  Gabor Papai  for scripting the initial work flow. This work was supported by the  Centre Nationale pour la Recherche Scientifique (CNRS) ,  Association pour la Recherche sur le Cancer (ARC) ,  Institut National du Cancer (INCa)  and the  European Research Council  ( ERC Starting Grant  No. 243296   TRANSLA-TIONMACHINERY ). The electron microscope facility was supported by the  Alsace Region , the  Fondation pour la Recherche Medicale (FRM) , the  IBiSA platform programme, INSERM ,  CNRS  and  ARC , and by the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01 , and Instruct as part of the  European Strategy Forum on Research Infrastructures (ESFRI).
Thus, nominally the pulsed delivery relative to the equivalent continuous delivery is 0. 016 L × 60 s/min =0. 96≈1 L/min. Figure  2  shows examples of flow versus time pulses for  setting 1, indicating two different rise times with the pulse width and time delay. The pulse volume can be found by integrating the flow versus time curve. Tests were performed at settings 1, 5, and 9 (specified pulse volumes of 16 mL, 80 mL, and 192 mL, respectively) at the fast and medium rise times and two different breathing conditions (described later). Data were collected for ten successive pulses in each condition/setting.
5  A COPD. Type A patients are characterized by the presence of anatomical emphysema or an increased total lung capacity (TLC), while type B patients are characterized by evidence of chronic inflammation or smaller TLC. 6 Minute ventilation and respiratory rate were recorded minute by minute using a calibrated Wright spirometer. Low dead space, low resistance, and nonrebreathing valves were used to collect the expired gas through a heated-mixing box, at rest (no 1,500; Hans Rudolph Inc. ) or during exercise (E Jaeger, Wurzburg, Germany) to measure oxygen uptake. Tidal volume is calculated by dividing the minute ventilation by the breathing frequency. Functional residual capacity (FRC) is 40% of TLC for normal healthy subjects and 50% for COPD subjects. 7 TLC for COPD is based on data from Wagner et al.