dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Data are expressed as means ± S. E. M. ; n represents the number of patients from whom lung explants were obtained. Concentration-effect curves were plotted and analyzed using GraphPad Prism® software (GraphPad Software Inc. , San Diego, CA). The potency (pD 2 ) was defined as the negative log 10 of the concentration producing 50% of the maximum inhibition (EC 50 ). Data were compared in a one-way or repeated-measures analysis of variance, followed by Bonferroni's post-hoc test. Statistical significance was defined as p<0. 05.
This study was sponsored in part by  Nycomed. AB received a post-doctoral fellowship from the  Conseil Régional d'Ile-de-France , under its  Domaine d'Intérêt Majeur -Santé Environnement program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. SGD has received speaker's fees from  Nycomed/Takeda ; PD has received consulting fees from  GlaxoSmithKline ,  Takeda ,  Schering-Plough ,  Astra Zeneca ,  Almirall ,  Boehringer-Ingelheim ,  Chiesi ,  Stallergenes ,  Sandoz , and research grants from  Nycomed ,  Novartis ,  Boehringer-Ingelheim ,  Sanofi-Aventis, Servier ,  Pierre Fabre  and  Top Pharm ; HT is an employee of  Nycomed/Takeda. This study was funded in part by  Nycomed. There are no patents, products in development, or.
Patients Data on 451 patients with ML-DS were collected from 13 collaborative study groups participating in the International AML-BFM Study Group. For comparison, a reference cohort of non-DS AML patients (n=543) from the same treatment era, kindly provided by the AML-BFM Study Group, was used. This study was approved by the Institutional Review Boards in accordance with local legislation and guidelines. Patients were eligible if diagnosed between January 1, 1995 and January 1, 2005. Patients who were not treated with curative intent from diagnosis were excluded. The data collected at diagnosis comprised karyotype, sex, age, white blood cell (WBC) count, hemoglobin level, platelet count, immunophenotypic data and FAB morphology. In addition, we collected data on treatment, such as therapy protocol, including stem-cell transplantation, and all events during follow-up.
Statistical analyses Continuous variables were categorized according to cut-off points; age < or ≥3 years, WBC count < or ≥20x10  9  and Ara-C < or ≥20,000 mg/m 2. The χ 2 or Fisher exact test was used to compare discrete variables among groups; the Mann-Whitney U test was used for continuous variables. All P values are descriptive and explorative, and were considered statistically significant if ≤0. 05. Statistical analyses were performed using SAS software (SAS-PC, Version 9. 1). More details on the methods are provided in the Online Supplementary Material.
Acknowledgments This study was a  Berlin-Frankfurt-Münster Study Group-collaboration. The project of MB was funded by the  Sophia Foundation , and the  Stichting Kinderen Kankervrij  ( KIKA , project n. 15). The Japanese part of the study was partially supported by a  Grant-in-Aid  from the  Ministry of Health, Labour and Welfare of Japan. The Nordic part of the study was supported by the  Swedish Cancer Society , the  Swedish Childhood Cancer Foundation , and the  Swedish Research Council. The authors would like to thank all of the above mentioned bodies for the funds.
Non-symmetric connectivity matrices We here include a discussion of anatomical connectivity matrices of the sort found in the CoCoMac database  (Stephan et al. , 2001). This important database contains data from numerous tracing studies of the macaque brain and includes connectivity matrices that retain directionality features. These matrices are non-symmetric and comprise excitatory long range and excitatory and inhibitory short range connections. Cortical networks characterised by non-symmetric connectivity can exhibit an oscillatory and a non-oscillatory instability,   the former depending critically on time-delays  (Jirsa and Ding, 2004). We examined the impact of directed connections by repeating the numerical simulations described above but using an asymmetric anatomical connectivity.
Further details have been described previously  [13]. The present analysis focused on men with lung cancer and their population controls, restricting the dataset to 4,658 males with full smoking histories. Of these 1,995 are cases. We also separately consider the male dataset stratified by histological cell type. For the histological analyses, we use all the controls, but only the relevant cases, resulting in datasets of size 3,365 for adenocarcinoma (702 cases), 3,359 for squamous (696 cases) and 2,933 for small cell cancers (270 cases). The smoking covariates that we study are: intensity (cigarettes per day), duration (years as a smoker), time since smoking cessation (years) and pack-years. For each covariate we categorise the data into 5 categories (summarised in Table  1 ), chosen to contain approximately balanced numbers of individuals as well as being easily interpretable.
Sensitivity In this section, we explore how "sensitive" profile regression is to changes in its implementation. The ability of the Dirichlet process model to fit a dataset is clearly dependent on the number of components allowed by the prior structure. In fact, the implied prior on the number of clusters in the partition of the Dirichlet Process is completely determined by the size of the sample N and the parameter α  [15, 26]. Liu  [27]  showed that given a fixed α, E[K|α, N] ≈ αlog(1 + N α ). All the results presented in this paper are obtained with α = 1. In order to investigate the impact of the priors on α in our analysis, we applied profile regression to our data with different values of α (3. 6 and 10 : these values have been studied previously in other epidemiological contexts  [10, 26] ).
We applied our approach to two different discretisations of our exposure data and found similar conclusions in terms of shifts in the posterior distribution of the log odds as smoking intensity and duration increase. As expected, the finer discretisation leads to increased uncertainty for some combinations, highlighting that, in this case, the information in the data on some of the covariate patterns may be sparse. We provide a freely available R package PreMiuM to implement our approach. This package can also cluster vectors of continuous exposures, but it cannot accommodate, at present, mixtures of categorical and continuous covariates (as would be required if our analysis included intensity and duration as continuous covariates). Epidemiological analysis is often faced with the need to estimate risks associated with multicomponent exposure data.
e. , V i = V f 1 - K i K jam T i = L i /V i (1) where K jam and V f are the traffic jam density and the free-flow speed for road segment i, respectively; and T i and L i are the estimated travel time and length for the same segment, respectively. The free-flow speed V f is defined as the average speed at which a motorist would travel if there were no congestion or other adverse conditions. To simplify our implementation, we consider that the free-flow speed is the road speed limit. Basically, K i /K jam is the ratio between the current number of vehicles and the maximum number of vehicles. The current number of vehicles is obtained from the traffic data collected by the service, whereas the maximum number of vehicles is equal to length of road/(avg vehicle length + min gap).
A. Simulation Setup We employed SUMO 15. 0  [7]  and TraCI  [41]  for our simulations. SUMO is an open-source highly portable microscopic road traffic simulation package designed to handle large road networks. TraCI is a library providing extensive commands to control the behavior of the simulation, including the vehicle state, the road configuration, and traffic lights. We implemented the rerouting strategy algorithms using TraCI. Essentially, when SUMO is called with the option to use TraCI, SUMO starts up, loads the scenario, and then waits for a command. Thus, variables in the simulation can be changed (e. g. , new paths assigned to certain vehicles). Then, a new command can be sent with how many seconds to run the simulation before stopping and waiting for another command. We downloaded two urban road maps from OpenStreetMap  [16]  in OSM format.
For clarity, we list in Table III the main differences between these two cases and the experiments for compliance rate. Fig. 15 (a) shows the average travel time for various penetration rates, when traffic data are collected only from vehicles (i. e. , no support from roadside sensors). When the penetration rate is low, the performance of the proposed methods is the same as "no-reroute. " In this case, the service does not have enough data to accurately detect signs of congestion. Once the penetration rate is greater than 0. 4, the system is able to improve the travel time. For penetration rates above 0. 6, the EBkSP, the FBkSP, and the AR * start to perform better than DSP and RkSP since a larger number of vehicles are rerouted, which requires a more advanced load balancing mechanism. Compared with our methods, DTA performs better under low penetration rates since the DTA rerouting is not triggered by the congestion detection, as in our approach.
To boost the adoption of the system, we believe that data from roadside sensors (in conjunction with data from vehicles) can be leveraged to detect congestion more accurately in the case of low penetration rates. When road sensors are present, the road traffic density can be measured, the congestion can be detected, and the travel time can be estimated more accurately. Fig. 15(b)  demonstrates that most of our methods can significantly improve the travel time even under low penetration rates if roadside sensor information is available. Furthermore, the EBkSP, the FBkSP, and the AR * perform better than DTA in this case. When the penetration rate is low (x%), DTA distributes evenly the x% vehicles without considering the rest, which can still create congestion. Therefore, the alternative routes proposed by DTA are not as effective in alleviating congestion.
The results indicate that our system can still significantly improve the travel time even under low compliance rates and that our approach is more robust than DTA. In addition, if accurate traffic data (e. g. , collected by roadside sensors) can be provided to the system, then our strategies exhibit good robustness with various penetration rates. To summarize the performance of the proposed strategies, we rank them according to four criteria in Table  IV , where A > B means that strategy A is better than strategy B. While each method has its own advantages and shortcomings, the experimental results made us conclude that the entropy method (EBkSP) and the local optimization method (FBkSP), which led to very similar results, should be the preferred strategies as they offer the best tradeoff between performance and computation cost.
Felyx is currently under development and is the latest evolution of a generalised High Resolution Diagnostic Data Set system funded by ESA. It draws on previous prototype developments and experience in the GHRSST, Medspiration, GlobColour and GlobWave projects. In this paper, we outline the design and implementation of the system, and illustrate using the Ocean Colour demonstration activities. Felyx is fundamentally a tool to facilitate the analysis of EO data: it is being developed by IFREMER, PML and Pelamis. It will be free software written in python and javascript. The aim is to provide Earth Observation data producers and users with an opensource, flexible and reusable tool to allow the quality and performance of data streams from satellite, in situ and model sources to be easily monitored and studied.
New to this project, is the ability to establish and incorporate multi-sensor match-up database capabilities. The systems will be deployable anywhere and even include interaction mechanisms between the deployed instances. The primary concept of Felyx is to work as an extraction tool. It allows for the extraction of subsets of source data over predefined target areas(which can be static or moving). These data subsets, and associated metrics, can then be accessed by users or client applications either as raw files or through automatic alerts. These data can then be used to generate periodic reports or be used for statistical analysis and visualisation through a flexible web interface. Felyx can be used for subsetting, the generation of statistics, the generation of reports or warnings/alerts, and in-depth analyses, to name a few.
There are many potential applications but important uses foreseen are : * monitoring and assessing the quality of Earth observations (e. g. satellite products and time series) through statistical analysis and/or comparison with other data sources * assessing and inter-comparing geophysical inversion algorithms * observing a given phenomenon, collecting and cumulating various parameters over a defined area * crossing different sources of data for synergy applications The services provided by felyx will be generic, deployable at users own premises, and flexible allowing the integration and development of any kind of parameters. Users will be able to operate their own felyx instance at any location, on datasets and parameters of their own interest, and the various instances will be able to interact with each other, creating a web of felyx systems enabling aggregation and cross comparison of miniProds and metrics from multiple sources.
INTRODUCTION 1. 1 Capability felyx is, primarily, a set of data extraction tools used to subset source data over predefined target areas which can be static or moving. These subsets, and any associated metrics, are accessible to users or machines as raw files, automatic alerts and periodic reports. felyx tools are open-source and provide both back-end and front-end software components to: • subset large local or remote collections of Earth Observation data over predefined sites (geographical boxes) or moving targets (ship, buoy, hurricane), storing locally the extracted data (referred as miniProds). These data can be directly accessed by users, they constitute a much smaller representative subset of the original collection on which one can perform any kind of processing or assessment without having to cope withheavy volumes of data.
• compute statistical (or any kind of) metrics over these extracted subset using for instance a set of classic statistical operators (mean, median, rms,. ) that is fully xtensible over some parameters of each dataset. These metrics are stored in a database and coupled with a fast search engine (ElasticSearch) from which they can be later queried by users or automated applications. • provide periodic reports and raise alerts based on a userdefined set of inference rules through various media (email, twitter feed,. ) and devices. The content and conditions on which this information is sent to the user is fully configurable through a web interface. • analyse the content of the miniProds and metrics through a dedicated web interface allowing the information to be examined and useful knowledge to be extracted through multidimensional interactive display functions (time series, scatterplots, histograms, maps, etc.
Applicability Among several potential applications, users will be able to use felyx for: • monitoring and assessing the quality of Earth observations (e. g. satellite products and their time series) through statistical analysis and/or comparison with other data sources, in NRT or over longer periods • assessing and inter-comparing geophysical inversion algorithms or different datasets • alerting and reporting on performance degradation or specific conditions • performing geophysical analysis (variability, trends, etc. ) • monitor a given phenomenon, collecting and accumulating various parameters over a defined area or time period. • comparing and combining different sources of data for synergy applications.
Utility In this context, felyx will serve different kind of users: • instrument engineers • calibration engineers • quality control engineers • project validation scientists • external validation scientists • algorithm developers • scientific validation community • ocean/atmosphere scientific community. The target users and validators of the system will also be part of the project Reference User Group. felyx will provide services deployable at the users' own premises and adaptable enough to integrate potentially any kind of parameters. Users will be able to operate their own felyx instance at any location, on datasets and parameters of their own interest. The different instances of felyx will be able to interact with each other, creating a web of systems enabling aggregation and cross comparison of data subsetsand metrics from multiple sources.
Requirements State-of-the-art: Build on, and learn from, previous HR-DDS system pilot projects (GHRSST, GLOBWAVE, GLOBCOLOUR, etc. ). Sustainability: Developing for the sustained long-termoperation and maintenance. Universally applicable: The HR-DDS system concept shall apply to any types of quantities (sea surface temperature, ocean colour, etc. ). Reliable, quality, tool: Need to support cal/val activities of upcoming missions (Sentinel) Prevent Redundancy: Avoid multiple system implementation for such activities. High precision, high definition: Concentrate on the small subset extraction principle, and do not duplicate existing global frameworks. Flexible: It shall be easy for users to deploy their own HRDDS system instance (configure it on their local datasets). It shall be possible and easy to tailor the system to each user need -parameter, datasets, metrics, front-end.
Data Flow and Processing Input data can be of any form: satellite data in point source, swath or gridded format; model output; ancillary field information; climatologies, covering the atmosphere, ocean, and land. Static Miniprods: Data processing is based on the idea of 'miniprods' which are local areas or sites. Data from the core datasets are then extracted, processed, and aligned in these miniprods. Static miniprods are predefined areas of interest, allowing, for example, long-term trends to be examined.
Dynamic Miniprods: The miniprods can be dynamic, allowing for the tracking of objects, phenomena or events, such as buoys, ships, hurrricanes. In these cases, data is extracted based on moving space and time windows allowing data to be 'matched-up'. Multiple datasets: are extracted over the miniprods. Additional filtering provides a multisensor matchup capability selecting miniprods close to each other. Rules of collocation take into account work done by CDR-TAG and CCI-SST. MMDB behaviour is through a special kind of query. Metrics: can be custom made by the user and are extensible. There are reallly too numerous to mention here completely but a core set includes: Statistical operators (mean, median, rmse, etc. ). Bias indicators. Cloud coverage. Day/night tagging. ….
Scenario In this scenario, the felyx system can be used to extract minProds for several datasets (including L1b and one or more derived L2 products, other products for comparison) over a selection of geographical sites. These miniProds are stored in the system, together with metrics computed from their content, and can be queried by the user: > either through the web interface for visual inspection, display of statistical plots (see example in outputs and next scenarii) > or through FTP (in NetCDF4 format)/OpenDAP download : they can then be analysed or processed locally (for instance to test again various algorithm versions over these data subsets instead of complete orbit files) This approach allows quick comparisons to be made of processing algorithms (L1, L2); the inter-comparison of parameter performance; and the detection and analysis of processing issues.
Scenario: In this scenario, the HRDDS system can be used to extract minProds for several datasets (waves, sst,. ) over a selection of geographical sites. Metrics are computed from their content. Users or system administrators can define inference rule to trigger alerts base on the range of the parameter(s) value over specific sites. and can be queried by the user. These inference rules are periodically run over the latest metrics. The alerts raised when applying these inference rules are broadcast to subscribed users.
Scenario: In this scenario a particular observation appears to be erroneous. Using the social aspects of felyx service, this analysis can be shared with other researchers using simple email services, social media such as Facebook or Twitter; or can be bookmarked and labeled for later analysis. Bookmarked cases can gather several plots selected as relevant by the user and rearranged within a single page that can be restored at any time. Users may categorize a particular view on the database as "<hot/cold> bias observed in <product_name> at site <site_name>". Such entries could be searched to provide a list of already studied events, assisting new users and researchers.
Scenario: For example, a study on the small Mediterreanean islands to assess the climate change in the Mediterranean basin, using a synergy of various parameters (SST, winds, SAR-derived radial current,. ). Each island could be defined as a custom site The user would start by loading the default set of sites defined by the system. Alternatively, with the proper permissions (granted by the felyx administrator), the user could define their own custom set of sites. These sites can be defined by: • uploading a CSV-type file in a comprehensible format for felyx. • defining the sites (sqare box or polygon) interactively on a map with high-resolution zoom capability. Custom sites defined by users implies the development of new miniprods so the user has the capability to process the associated miniProd and extract metrics for these sites, for a selection of datasets and parameters of their choice.
Amidolytic Assays The amidolytic activity of MASP-3 SP, thrombin and trypsin on selected peptidyl-AMC substrates was determined using a fluorometric assay based on the measurement of AMC released upon cleavage. Varying enzyme amounts were added to the substrate (0. 1 mM) in 2 ml of 20 mM Hepes, 5 mM CaCl 2 , pH 8. 5. Samples were excited at 360 nm and emission was read at 440 nm every 30 s for 30 min at 37uC using an Aminco-Bowman Series 2 fluorometer. The initial rates of AMC release were calculated from a calibration curve obtained with varying AMC concentrations (1-10 mM) diluted in 2 ml of the above buffer. Rates were expressed in pmol AMC released/min/mg of enzyme to allow comparison with previously published data. For selected substrates, the Michaelis constant (K m ) and the maximum velocity (V max ) were determined for MASP-3 SP and thrombin, using nonlinear regression analysis (SigmaPlot software).
Surface Plasmon Resonance Spectroscopy and Data Evaluation Surface plasmon resonance analyses were performed using a BIAcore 3000 or a Biacore X instrument (GE Healthcare). Ecotin (1,000 RU) was immobilized using the amine coupling chemistry by injecting the protein (diluted to 20 mg/ml in 10 mM sodium acetate, pH 5. 0) on the surface of a CM5 sensor chip (GE Healthcare) in 150 mM NaCl, 5 mM EDTA, 10 mM HEPES, pH 7. 4 containing 0. 005% surfactant P20 (GE Healthcare). Binding was measured at a flow rate of 20 ml/min/min in 145 mM NaCl, 50 mM triethanolamine-HCl, pH 7. 4 containing 0. 005% surfactant P20. Regeneration of the surface was achieved by 10-ml injections of 10 mM HCl. Equivalent volumes of each protein sample were injected over a reference surface without immobilised protein for subtraction of the bulk refractive index background.