dataesr/scientific-paragraphs-categorization huggingface dataset:
Stereochemistry of C-5 and C-6 in CR-591 (1) and CR-777 (2) The ROESY spectrum of compounds 1 and 2 supported by the 3D structures obtained from Chem3D software (ChemBio3D, Perkin Elmer, Waltham, MA, US) allowed us to determine the configuration at C-5 and C6. ROE correlations were observed for both compounds between H-6 and H-4 and between H-6 and H-8. H-9 did not correlate with H-4, H-6, and H-8. According to these data, only the conformation shown in Figure  7  accounts for the observed ROE correlations, corresponding to (4S, 5R, 6S) stereochemistry.
Analysis The immunolabeled cultures were automatically examined with ImageXpress (Molecular Devices) equipped with a LED at ×10 (for dopaminergic neuron) and ×20 (for cortical neuron) magnification. For each condition (six culture wells), 20 automatic fields per well (for dopaminergic neurons) and 30 automatic fields per well (for cortical neurons) (representing ~80% of the total surface of the well) were analyzed. The total number of TH neurons, the MAP-2 positive neurons, the neurite network, the alpha-syn into dopaminergic neurons, and hyperphophorylation of T protein (pT) into neurons (pT/MAP-2) were automatically analyzed using MetaXpress software (Molecular Devices).
The articles were included in our database based on titles and abstract analysis. Statistical analysis was used to characterise citation number, citation index, Source journal, author frequency, type of study and levels of evidence. Results: The 200 most cited articles were published between 1923 and 2012. The decades of 2000's (36%) followed by 1990's (33%) were the most productive era of the articles. All the articles were written in English except for one in French and were published in forty journals. The majority of articles originated from United States, followed by France and Switzerland. Articles related to rotator cuff (18%) and Instability (17. 5%) were the most discussed topics. Most cited articles were clinical series, the most common level of evidence was level IV. Only a small percentage of articles utilized patient rated outcome measures.
Computational methods The subassembly design analysis employed the WIMS-10 lattice physics code using nuclear data from the JEF 2. 2 database available from the IAEA  (Newton et al. , 2008)  The advanced 3D nodal code PANTHER  (Hutt, 1992)  is used for whole-core analysis and design. PANTHER imports few-group nuclear data from WIMS. Using the LED output module, WIMS homogenizes the neutronic data for a subassembly and exports a 'flat file' that is readable by PANTHER. This allows the user to load subassemblies prepared in WIMS into PANTHER in order to perform whole-core calculations. WIMS and PANTHER are thus interconnected in this study in performing subassembly design and whole-core analysis  (Alam et al. , 2019c). Flow chart of sample input files  (Zainuddin and Lindley, 2014)  for the WIMSbuilder, WIMS and PANTHER route is shown in Fig.
5%. Furthermore, a recent SMR neutronic study by Oak Ridge National Lab  (Brown et al. , 2017)  showed that ⇠400 MWth SMR exhibits 6-8% leakage depending on the core loading patters and other input parameters. Since WIMS calculations assume an infinitely-large core and a small core is prone to larger leakage, we have assumed 7. 5% leakage in this study. In conventional PWR reactor, 4% leakage is considered while considering 2D lattice-level calculations  (Alam, 2018). We have estimated from our core sizing analyses that considering our marine propulsion SMR core (Power = 333 MWth, Volume = 5. 3 m 3 ), leakage of 7. 5% is considered conservative. This leakage has been checked with 3D whole-core nodal di↵usion code PANTHER  (Hutt, 1992). In the assembly level analysis for fresh fuel in WIMS (which assumes an infinitely-large core), discharge burnup is 95 GWd/tonne considering 7.
Microcomputerised Tomography Analysis: Skeleton Morphology For the analysis of whole-body bone morphology (Figure  1 ), anesthetised animals were introduced in a SkyScan µCT -1178 X-ray tomograph (Bruker) and analyzed as previously described  (11, 21). Mice were scanned using the following parameters: 104 µm pixel size, 49 kV, 0. 5 mm thick aluminum filter and a rotation step of 0. 9 •. 3D reconstructions and analysis of skeleton, vertebra and femur lengths as well as skeleton volume were performed using NRecon and CTAn software (Skyscan). Skeleton volume was calculated using CTan software by extracting from µCT scans all voxels exceeding a single global CT threshold value, ensuring that differences between study groups are due to experimental effects rather than image-processing effects. The representative skeleton reconstruction from one Fmr1-WT animal (Figure  1D ) was obtained by further processing of the 3D reconstructed sections using the ImageJ plugin 3D viewer and the BoneJ plugin  (22).
Microcomputerised Tomography Analysis: Bone Microstructure For measurements of bone mineral density (BMD) and microstructure, we proceeded according to the guidelines for Assessment of Bone Microstructure in Rodents Using Micro-Computed Tomography  (23). After necropsy, femora were individually scanned with a SkyScan µCT-1173 X-ray tomograph (Bruker) set with the following parameters: 7. 76 µm pixel size, 45 kV, 1 mm thick aluminum filter, 0. 5 • of rotation step, as previously described  (21). 3D reconstructions and bone microstructure analysis were performed using NRecon and CTAn software (Skyscan). Bone microstructure was reconstructed using CTan software by extracting from µCT scans all voxels exceeding a single global CT value. The region of interest (ROI) for calculations of femur cortical bone microstructure parameters was defined as a mid-shaft segment of 400 µm starting at 55% of the total femur length from the proximal epiphysis extremity (corresponding on average to a distance of 8.
Individual cortical (total cross-sectional object area, object area per slice, cortical area fraction, cortical thickness, mean polar moment of inertia (MMI), eccentricity) and trabecular (bone volume fraction, trabecular number, trabecular thickness, trabecular separation, total volume of trabecular pores) bone microstructure parameters were extracted on each section of the corresponding 3D ROI, using the CTAn software and then averaged as recommended in Bouxsein et al. (23). Their means and standard deviations (SD) for each group  are reported in Tables  1, 2. Representative cortical and trabecular ROI from one Fmr1-WT and one Fmr1-KO animal were obtained by further processing of the 3D reconstructed segments using the ImageJ plugin 3D viewer and the BoneJ plugin  (22).
Strains-Taxonomic characterization Isolates from the whole library were taxonomically characterized based on the sequencing of their housekeeping 16SrRNA encoding rrs gene. Genomic DNA from all bacteria was extracted using the NucleoSpin Microbial DNA kit (Ref-740235. 50, Macherey-Nagel, Germany). The rrs gene was amplified with primers pA/pH  [29]  and sequenced. The sequences were aligned with Muscle  [30]. The phylogenetic tree was inferred using Seaview and the Neighbor-Joining distance-based method with 1000 bootstraps. Other methods were implemented and gave similar tree topologies. The tree was represented using iTOL  [31]. The European Nucleotide Archive (ENA) accession number for the 203 rrs sequences reported in this paper is ERP115553. Other accession numbers used in this work are KT380501.
Acknowledgments Yoann-Besset Manzoni 's work was supported by a  CIFRE Ph. D grant  from the  Association Nationale de la Recherche et de la Technologie. We also thank the  Auvergne Rhone Alpes region  for funding the  DOPELARA  project. The authors would like to thank  Richard Blanc ,  Florence Cambon  and  Aurelie Wauquier  for their technical support during greenhouse experiments. This work made use greenhouses at the  GDEC INRA joint research Unit and technical devices from the 'Serre ' platform of FR41 at UniversiteLyon 1.
Data collection Baseline characteristics and FU data were prospectively collected, including measurement of VA (ETDRS chart), a 30-2 Swedish Interactive Threshold Algorithm (SITA) standard program on the Humphrey Field Analyzer (Carl Zeiss Meditec Inc. , Dublin, CA) and a Lanthony desaturated panel D-15 test for color vision under standardized conditions of ambient illumination, with calculation of the total error score. 16, 17 All patients had a reliable visual field test, defined as a false positive error < 15%, a false negative error <15%, and a fixation loss < 20%. Quality of life (QoL) was assessed using the cross-cultural adaptation of the NEI Visual Function Questionnaire (VFQ-25) in French. 18 atomical testing was based on FA, ICGA (Heidelberg, Germany) and optical coherence tomography (OCT) (Spectral domain Carl Zeiss Meditec Inc.
Mathematically, the first order kernel is obtained by adding all the records that follow the presentation of a white hexagon (luminance, 400 cd/m 2 ) and subtracting all the records that follow a black hexagon. The following data were collected: the root mean square values (RMS), implicit time (IT) and AMP of N1, P1, and N2 waves. The N1 response was measured from the starting baseline to the base of the N1 trough; the P1 response AMP was measured from the N1 trough to the P1 peak. IT was measured from the onset of the stimulation to the trough or peak. Only averaged data including five rings were presented in the Results section. Normative values (obtained from the manufacturer Métrovision) are (mean ±SD): -915 ±260 nV for N1 AMP, 1633 ±395 NV for P1 AMP, 23. 2 ±1. 3 msec for N1 IT and 42.
Statistical analysis Gender, treatments at baseline, evidence of photoreceptor alteration, ME and ERM on OCT, fluorescein and indocyanine green angiography findings were reported as numbers and percentages. Age, disease duration, VA, QoL score, foveal threshold, VF parameters, color vision score, CMT and ERG data (i. e. N1 and P1 implicit times and amplitudes) were summarized with means and standard deviation. Because of skewed distribution, disease duration and total error score were summarized with the median and 25th-75th percentiles. Time trends in continuous mfERG parameters were modeled using linear regression for longitudinal data with time to FU visit entered as an independent variable. The linear time trend reflected the long-run tendency of mfERG parameter values to increase or decrease over time.
Partners are excluded if they do not speak French sufficiently well to participate in assessments. All participants will be reimbursed for their time and effort. The allocation ratio of randomisation is 1:1. The randomisation sequence will be generated using a computergenerated block randomisation (StataCorp 2017. Stata Statistical Software: Release 15) using blocks of sizes 2, 4 and 6 over 144 participants per stratum (stratified by research centre). Opaque envelopes will be prepared in advance, numbered sequentially by alternating between stratums by block. After conducting the baseline assessment, the clinical midwife/nurse will open the envelope and announce the cognitive task to be carried out (ie, the cognitive visuospatial task or the attention-placebo control task) to the participant.
The scales are administered by a trained psychologist or paediatrician through a standard set of play tasks following a standardised protocol. The composite scores for the subscales are age standardised with a mean score of 100. 128  Sociodemographic, obstetric and neonatal characteristics Mothers will report demographic information, including marital status, nationality, profession, level of education  129  and previous and current psychiatric disease as well as any trauma history via a self-report questionnaire. Mothers will also report their height, weight (before pregnancy and current), menstrual cycle, smoking behaviours and alcohol/drug use. Obs etric data will be extracted from the hospital medical record, such as pregnancyrelated, labour-related and birth-related information, gravidity, parity, mode of previous childbirths, history of miscarriage, stillbirth and prematurity, pain, medication, birth control, sexual activity and psychological support.
PAtIEnt And PublIC InvolvEMEnt The acceptability of the intervention was assessed by participants during the previous proof-of-principle RCT. 4  A questionnaire of satisfaction and acceptability examined the burden of the intervention. An individual feedback and debriefing session will be offered to each participant. Results will be disseminated in written form to the participants and distributed to the public via social media and public events. They will also be discussed with clinical professionals involved in this project. dAtA MAnAgEMEnt And stAtIstICAl AnAlysEs All study data will be entered by research staff. Study data will be managed using Research Electronic Data.
Open access Capture (REDCap) tools hosted at Lausanne University Hospital. 131 132 REDCap is a secure, web-based software platform designed to support data capture for research studies. Double data entry will be done for the primary outcomes. For the rest of the data, a random 5% will be double checked. Access to the full final trial dataset will be restricted to the principal investigator. For the primary analyses, group differences regarding the mean subscale and total scores of the PCL-5 and CAPS-5 at 6 weeks will be analysed using separate linear regression analyses. Analyses will be adjusted for recruitment centre and the respective baseline values. Associations between primary outcomes and potential confounders, such as maternal and gestational age, will first be assessed applying univariate tests.
Subsequent analyses will be adjusted for significant covariates. For secondary aims, the analyses will be performed for differences in changes between groups and differences between groups at different time points in maternal and infant outcomes. Proportion of participants meeting the diagnostic criteria for PTSD at 6 weeks and 6 months between the two groups will also be compared using logistic regression analyses. The same procedure for identifying significant covariates described above will be applied here. Furthermore, post hoc exploratory analyses will be conducted but described as such in publications. All regression analyses regarding cortisol data will be adjusted for potential covariates, such as cosleeping, breastfeeding, menstrual cycle, and infant and maternal medication. For HRV analyses, interbeat intervals from baseline, stress task and recovery will be analysed by the extraction from ECG recordings.
Statistics The results are expressed as the mean +/-SEM. The significance of between-group differences was assessed by one-way analysis of variance (ANOVA) with the Tukey correction for multiple comparisons. A Student t-test was performed to compare two groups. GraphPad (La Jolla, CA) Prism 6 software was used for statistical analyses and graphical representations. p values < 0. 05 were considered to be statistically significant.
Acknowledgements This work was supported by grants from the  Institut National de la Santé et de la Recherche Médicale , the  Agence Nationale de la Recherche  ( ANR-16-CE14-0021-01 ), the  French Medical Research Foundation  ( DEQ20160334885 ), the  National Autonomous University of Mexico -DGAPA -PAPIIT  ( IA200117  and  IN202919  to JBC), and the  EU COST Action BM1301 European Cooperation in Science and Technology ADMIRE -Aldosterone and Mineralocorticoid Receptor (MR) Physiology and Pathophysiology  (ADMIRE http://www. admirecosteu. com). BB is supported by a PhD ARDoC grant from the  Region of Ile de France.
In a previous work  (Dahab et al. , 2018) , we conducted an interesting and promising research work to fully automate the definition of software measurement plans at runtime. Indeed, in most real case studies, that process is fixed in a sense that the expert measures all what he can and not necessarily what he needs. Then a huge amount of data are unnecessarily collected and analyzed. Our previous work has shown that measurement plans can be suggested and adjusted at runtime through an automatic learningbased methodology in reducing the amount of collected data. In this current novel work, we increase our efforts in the analysis and efficiency of software measurement by introducing unsupervised learning algorithm, and we specifically demonstrate the effectiveness of considering the clustering approach X-MEANS  (Pelleg and Moore, 2000)  to reduce further the management cost and improve the performance of such a process.
We herein use X-MEANS to automatically generate correlations of a sample of data through clustering by generating a training file as input to our previous suggestion approach. Software measurement is an empirical science which depends on the experience  (Fenton and Neil, 2000). It is impossible to define a generic measurement analysis model. It depends on the software project, the used language, the used computer. Thereby, to evaluate a software, it is needed to know the context of the measured object, as well as, to analyze a software evaluation is needed to know the context. That is what makes difficult to automate a software measurement analysis. And this is to handle this lack that we propose to use an unsupervised learning technique, which will learn from a measurement dataset of a software to generate the corresponding analysis model and reduce the expert load and the related time cost.
Software Measurements Model The analyzed data are software measurements, more precisely the measurements values. These values give information on a software at time t. Definition 1 Measurement Plan: A measurement plan mp is the ordered set of metrics considered for the global measurement process and the correlation between the metrics and the evaluated software properties y. This is defined by the expert at the beginning of the process and it never changes. This is the context of our measurement process and the basis for the suggestion (see 3. 1. 3). We define the ordered set m and the set of software properties y as: m = {m 1 ,. m n } (1) where n is the number of considered metrics and m i a unique metric. y = {y 1 ,. y l } (2) where l is the number of considered properties and y k a unique property.
From an unlabeled software measurements sample, a labeled one is generated. This output is then used as training file to train the classifier used for the analysis and suggestion steps. The purpose is to use a clustering algorithm. It will group in clusters the similar vectors of measurements then according to the clustering result, the expert will associate to each cluster a set of metrics to suggest (see 2. 2). Herein, the expert intervention only appears for determinating the correlation between classes corresponding to the clusters, and set of metrics. Our improved suggestion approach is based on three procedures: • The elaboration, through software measurements clustering based on unsupervised learning ap-proach X-MEANS, of an analysis model (or mapping system as called in our previous work), herein considered as the initial measurement plan mp.
• An analysis procedure, which aims to highlight a software property of interest through a software metrics classification, based on a learning technique, herein SVM. • A suggestion of metrics based on the mp, the analysis result and the features selection procedure, which aim to determine the needed metrics (nm), for the conservation of information and based on the learning technique RFE. In the next sections, we briefly describe our metrics suggestion approach that is improved and formalized. Then we introduce the X-MEANS technique.
Algorithm 1 Metrics Suggestion Input mp, f , {ì v} 1: Output mp 2: y ← array() 3: n f ← array() 4: for each ì v in {ì v} do 5: y ← f (ì v) 6: end for 7: if y == 0 or y without duplicate == 1 then 8: return mp 9: else 10: n f ← f s( f , {ì v}) 11: mp ← m f +mp[most common(y)]+mp[mm] 12: return mp 13: end if learning technique to generate automatically the training file. The advantage of using this latter is to reduce the expert cost, but also the dependency of an expert. Indeed, as the software measurement is an empirical science, it depends on the experience, on the software or on the property evaluated. There is as much model as there is software project. Thereby, our purpose is to use learning clustering algorithm X-MEANS as expert to generate automatically the training file according to a measurement dataset of the evaluated project.
Industrial MEASURE Platform Integration The MEASURE Platform 1  is the research result of the European project ITEA3 MEASURE 2. This project aims to improve the whole software measurement processes. For that, this platform proposes a database as storage of software metrics specified and developed according to the standard language SMM; a storage of measurement result; a coverage of all the software engineering processes; and analysis tools. As described in the Figure  2 , there is the main measure platform which enables the communication between the different stores and the analysis tool, the measurement tools and the agent platform. The measurement tools allow to use other external measurement tools. And the agent platform allows a local execution of the metrics with a storage of results in the platform.
Suggestion results Finally, the initial measurement plan is defined by the expert according the previous step result. In fact, the    expert will add to the MP presented in the Figure  1  the correlation between clusters and a metrics subset. Once the initial mp is defined, we train the classifier with the file 3. Then, as suggestion experiment, we use as input files to analyze, a dataset of 50000 unclassified vectors divided in 10 subsets of 5000 unclassified vectors. The objective is to see if the suggestion provides correct plans (of metrics). The Figure  6  shows the results of suggestions based on the previous analysis model. The results show a dynamic suggestion of mp. Each mp is between 5 and 13 metrics. There is no convergence (e. g. , deadlock or undesired fixity in the generated plans) and the suggested mp evolves continuously according to the dataset values.
Conclusion & Perspectives In this paper, we proposed to improve our previous work by reducing the expert dependency to the management of the analysis process. For that, we propose to use an unsupervised learning algorithm X-MEANS to take the place of the expert and to generate automatically an analysis model by learning from an historical database. The objective is to reduce the management cost, and the time cost. Well implemented and experimented, this approach shows the possibility to generate a reliable model with a low time cost, and also to verify the validity of manual models. The promising results demonstrate us the beneficial contribution of using learning techniques in the software measurement area. Thereby, as perspective, it could be interesting to analyze the differences between automated models and manual models and also to increase the independence to the expert by generating automatically the correlations between clusters and metrics subsets.
Simulations In order to understand the experimental observations, we have performed numerical simulations of the bus-waveguide/wedge microdisk system. These simulations were based on the finite elements method (FEM) engine by Comsol Multiphysics controlled by the mathematical programming environment of Matlab. As a first step, we made use of a full 3D model of the device to study the back-reflections connected to the vertical coupling between the bus-waveguide and the wedge shaped microdisk. As it is shown in Fig. 2(a) , almost all the coupled light propagates in the forward direction inside the resonator and back-reflections into oppositely propagating modes have an almost negligible amplitude. This is due to the fact that the light entering the coupling area experiences a smooth growth of the wedge profile (7 degree), so it progressively couples to the resonator with almost no reflections.