dataesr/scientific-paragraphs-categorization huggingface dataset:
Automation allows analysis of a larger cohort of compounds and avoids the high variability inherent to manual measurements, ensuring more reliable results than those obtained by manual methods. We provide a MATLAB toolbox with custom code for each step of the analysis (slide preprocessing, mwh hair detection, and genotoxicity characterization). Image acquisition can easily be adapted to any microscope equipped with a computer-controlled stage. Overall, the proposed method requires only standard, affordable instruments. We believe that the materials provided and methods described will provide a starting point for an automated SMART. Improvements may emerge over time, including faster and more exhaustive wing image acquisition, more sensitive hair phenotype detection, and the ability to recognize additional hair phenotypes.
Software and computation tools All data analyses were performed using custom MATLAB R2013a (Mathworks) software on a computer with an Intel core i7 3970X processor, 24 GB RAM, and a Windows 8 64-bit Professional operating system. The hair segmentation analysis, in particular, was performed with a custom-made watershed algorithm implemented in C# and compiled to a dll file accessed inMATLAB. Alternatively, MATLAB watershed function (in the image processing toolbox) can be used at the expense of longer processing time. We provide the MATLAB scripts and algorithms developed for the SMART automated readout, with further description of its contents provided in the S1 File. The ND2 file generated by our Nikon software is read using Nikon ND2 SDK library, distributed by Nikon upon request.
Slide preprocessing for image acquisition Each sample slide was preprocessed to determine a list of acquisition positions. We determined multiple positions for each wing on a slide as follows: A low-resolution, full image of the slide was acquired with a standard desktop scanner (Epson perfection V33), using Epson Scan software at 300 dpi, 8 bit grayscale. A threshold of 15% of the intensity range was applied to the inverted image to select the wing regions. In an optional step, the mask obtained was filtered to regularize its contours and improve the separation of wing area. A labeling filter was then applied to the filtered mask to identify the distinct regions. Regions that did not conform to normal wing parameter ranges were discarded. The parameters considered included region aspect ratio, area, and average position.
An axis was subsequently defined with an origin at the wing origin and orientation determined by a vector going from the origin of the wing to the center of mass of the wing. Regions (1) with a distance to origin length shorter than 400 mm, i. e. roughly 20% of average wing length ( ffiffiffiffi ffi l 1 p ) or (2) on the negative side of the wing axis, were not analyzed in the steps that follow. In cases in which wing detection and characterization were problematic, wing segmentation and origin were checked manually and, if necessary, updated using a custom-made MATLAB interface. Each of the wing regions was then partitioned with a k-mean classifier  [29]  using the wing's pixel positions as input. The average position of each class defined an acquisition position for the microscope. The number of classes was optimized to limit the overlap between regions of acquisition.
Image acquisition Image acquisition was performed using bright-field microscopy with a Nikon eclipse 90i transmission microscope equipped with a Nikon DS-1QMcooled to -30°C, sensitive black and white charge-coupled device (CCD), 1280 × 1024 pixel camera, and a Nikon APO Plan 20X, 0. 75NA, DIC N2 WD 1. 0lens. System planar sampling was performed at 0. 32 micron per pixel, while the axial sampling resolution was 1 micron. Samples were repositioned with a motorized XY stage and a Z focus. The system was controlled with Nikon NIS Element AR software version 3. 2, which includes a built-in interface for z-stack and multipoint acquisition. Z-stack parameters and a precalculated list of positions were loaded to the "ND experiment" module of the NIS Element software. The microscope was then manually initialized at the first position to be acquired.
This work was supported by  National Research Foundation of Korea (NRF)  grants funded by the  Korean government (MSIP)  (No. 2007-00559 ,  2009-0090095  and  2014R1A2A1A10050531 ),  Gyeonggi-do , and  KISTI. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Phantom and patients A 20 cm high cylinder (16 cm in diameter) was filled with water including 103. 3 MBq 18F-FDG. For image acquisitions, the cylinder axis was parallel to the axial direction of the PET/CT scanner. Patient data were retrospectively retrieved after approval by the local institution review board of Avicenne Hospital (Ile-de-France X), and the requirement to obtain informed consent was waived. Patient information was anonymized and de-identified prior to analysis. Eightyfive consecutive patients with NSCLC underwent a 18F-FDG PET/CT between July 2008 and June 2012 before surgery. Among them, only 48 patients were included in the study, whose characteristics are summarized in Table  1. As detailed in Fig  1 , the exclusion criteria were: 1) no clearly identifiable tumor in PET images (n = 9 patients), 2) delay between injection time and acquisition time departing from the [60-90] min range (n = 12 patients) and 3) metabolic volume after segmentation lower than 2.
Texture analysis For each VOI, metabolic volume (MV) and maximum SUV (SUVmax) were computed. After RR or AR resampling, three texture matrices were calculated as explained in  [14] : the co-occurrence matrix (CM), the gray-level run length matrix (GRLM) and the gray-level zone length matrix (GZLM). The co-occurrence matrix was computed in 13 directions using a distance of 1 voxel, and each TI calculated from this matrix corresponds to the average value in the 13 directions. Similarly, the GRLM was calculated for 13 directions while the GZLM was computed directly in 3 dimensions. Seven TI were derived per VOI using the home-made LIFEx (Local Image Features Extraction) software, by selecting one index robust to segmentation in each group of highly correlated indices previously identified in  [14] : homogeneity and entropy from CM, Short-Run Emphasis (SRE), Long-Run Emphasis (LRE) and Run Length Non-Uniformity (RLNU) based on GRLM, and Low Gray-Level Zone Emphasis (LGZE) and High Gray-level Zone Emphasis (HGZE) from GZLM.
29ЉN; longitude, 10°50=38. 32ЉE; altitude, 514 m). Data analysis. For the final analysis of the data, the results of all culture methods from soil were combined, and the rate of positivity by any culture method was compared with the rate of positivity by direct DNA extraction or DNA extraction postenrichment. Statistical analysis was performed using Stata/IC (v10) software (StataCorp, College Station, TX, USA). Comparisons were made by the use of McNemar's test (paired samples) or the Mann-Whitney U test, as appropriate. Significance was set at a P value of Ͻ0. 05. Nucleotide sequence accession numbers. Sequences were analyzed using NCBI-BLAST and subsequently submitted to GenBank and are accessible under accession numbers KM058066 to KM058079 (Table  1 ).
ACKNOWLEDGMENTS We are grateful to all staff and students at the  Microbiology Laboratory, Mahosot Hospital, Vientiane, Laos , especially  P. Panyanouvong ,  P. Phouminh ,  M. Seephonelee ,  D. Sengdetkha ,  A. Sengduangphachanh ,  A. Seupsavith ,  B. Sibounheuang ,  V. Sihalath ,  M. Simmalavong , and  Ian Ewing , and the farmers who granted permission to dig on their land, as well as  Oloth Sengtaheuanghoung  ( Department of Agriculture Land Management, Lao People's Democratic Republic ) for his help in organizing the field mission throughout the Sedone River catchment and liaising with local authorities. We thank  Direk Limmathurotsakul  from the  Mahidol-Oxford-Research Unit  for his useful suggestions and fruitful discussions during the analysis and write-up of this study. Further, we are very grateful to the  Minister of Health  and the director of the  Curative Department, Ministry of Health , for their support for this study.
This study was part of the work of the  Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit  funded by the  Wellcome Trust of Great Britain. Additional funding was provided by a seed award (Lee  Ka Shing Foundation) of the University of Oxford  (grant  SM40 ) and the  Institut de Recherche pour le Développement (IRD)  through the regional pilot program Soils, Waters,  Coastal Zones and Societies in Southern and Southeast Asia (SELTAR-RPP).
Ethical considerations All data were anonymous, with identifYing patient details removed at time of data collection. Data were collected from clinical records by a member of the research team and entered to an electronic database and stored on a password protected computer at the host institution. The researcher involved in data collection was nominated as an agent of each practice and GPs were involved in data collection to ensure that any issues requiring clinical follow up were reviewed by the GP with clinical responsibility for the patient's ongoing care and to minimise potential bias resulting from coding and interpretation of clinical problems. The study was reviewed and approved by the Irish College of General Practitioners Research Ethics Committee (9 1h August 2012). s Cambridge University Press.
Population characteristics and prevalence of mental disorders Data were collected on 690 patients attending general practice (mean age 47 years, range 18-95), of whom 355(52%) were male, 357(52%) were GMS-eligible (Ireland's means-tested free general practice system) and 443(64%) had been referred to or attended secondary care in the past year. A total of 139 people had a documented mental disorder in the previous two years (20% prevalence, 95% confidence interval 17-23%), with 37(27%) of the 139 patients having two or more disorders, and 88(63%) consulting more than once with a mental disorder. The most common disorder identified was stress 1 anxiety (73 cases), followed by depression (65 cases), problem alcohol use (17 cases), problem drug use (15 cases), psychosis (nine cases) and dementia 1 related problems (four cases).
In the first instance, senior medical students on clinical placement at three participating practices collected data in collaboration with the GP principal, supervised by two experienced researchers. To ensure consistency in data collection, issues and problems were reviewed at regular meetings of the research team during this stage and resulted in further development of the study instrument (see Appendix I -online documenmtation). Subsequently, one senior medical student collected data from a further four practices, again in collaboration with the respective GP principal and supervised by two experienced researchers. Anonymised data were entered to an Excel database by the research team and imported to PASW 18 for statistical analyses. Descriptive analytics were carried out on the key study measures, specifically: psychological morbidity, its treatment, referral to secondary care and other agencies, health service utilisation and how mental and substance use diagnoses were recorded in clinical records.
This is a concern because the choice of crop model is an important source of uncertainty in climate change impact assessments on crop yields (e. g. Mearns et al. , 1999; Bassu et al. , 2014). Therefore, having access to a tool capable of replicating yields from a wide ensemble of crop models would facilitate the analysis of crop model uncertainty in climate change impact assessments. To address the limitations of simulations based on processed-based models and to consider crop model uncertainty, we design an ensemble of simple statistical models able to accurately replicate the outcomes of process-based crop models at the grid cell level over the globe using only a limited set of weather variables. To this end, we use the recently released Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) Fast Track experiment dataset of global gridded crop models (GGCM) simulations.
Crop Yields and Growing Seasons Crop yields and growing season information are obtained from GGCMs members of the ISI-MIP Fast Track experiment. Based on data availability, we consider five crop models: the Geographic Information System (GIS)-based Environmental Policy Integrated Climate (GEPIC) model  (Williams, 1995; Liu et al. , 2007) , the Lund Potsdam-Jena managed Land (LPJmL) dynamic global vegetation and water balance model  (Bondeau et al. , 2007; Waha et al. , 2012) , the Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) with managed land model  (Bondeau et al. , 2007; Smith et al. , 2001; Lindeskog et al. , 2013) , the parallel Decision Support System for Agro-technology Transfer (pDSSAT) model  (Elliott et al. , 2013; Jones et al. , 2003) , and the Predicting Ecosystem Goods And Services Using Scenarios (PEGASUS) model  (Deryng et al.
Weather Bias-corrected weather data used as input into each crop model are obtained from the CMIP5 climate data simulations. This study uses daily weather data for three of the five climate models, or General Circulation Models (GCMs) included in CMIP5: HadGEM2-ES, NorESM1-M, and GFDL-ESM2M. As summarized in  Warszawski et al. (2014) , these GCMs project, respectively, high, medium and low level of global warming. GCM simulations are available for an 'historical' period of 1975 to 2005 and a 'future' period of 2006 to 2099. For the 'future' period, each GCM is run under four Representative Concentration Pathways (RCPs), each representative of different level of radiative forcing (RCP 2. 6, RCP 4. 5, RCP 6. 0 and RCP 8. 5). We selected the scenario with the highest level of global warming compared to historical conditions, RCP 8.
5, and the corresponding CO 2 concentrations data  (Riahi et al. , 2007). 3 As the maximum amount of warming induced under other RCPs is encompassed in this pathway, and a wide range of climate change patterns are represented by the three GCMs, the analyses consider the broadest possible range of climate change. Each GCM produces three variables that are used as inputs by crop models: daily minimum soil surface temperature (Tmin), daily maximum soil surface temperature (Tmax), and daily precipitation (Pr). We compute various composite variables based on these weather variables (which are summarized in Table  1 ). Mean daily temperature (Tmean) is calculated as: Tmean = (Tmin + Tmax)/2 (1) We also consider reference evapotranspiration (ETo) to represent the evaporative demand of the air. Following  Hargreaves and Samani (1985) , it is calculated daily as: ETo = 0.
VALIDATION To assess the ability of our regressions models to emulate maize yields simulated by GGCMs, we implement two validation exercises. First, we compare predicted yields with 'actual' yields using the same sample used to estimate the regression coefficients. This within-sample exercise facilitates validation using the largest available dataset. Second, we conduct an out-of-sample validation exercise by estimating the regression coefficients using a sample that includes data from all but one climate model and using these coefficients to estimates yields under the excluded climate model. Our validation analyses focuses on the S1int specification.
Out-of-Sample Validation As the purpose of this study is to provide a crop emulator capable of predicting crop yields under alternative climate change scenarios, we implement an out-of-sample validation exercise by re-estimating the S1int specification using yield simulations under two of the three GCMs. Using regression coefficients estimated using this sample, yields are then predicted under the GCM omitted from the training dataset. We reiterate the procedure three times in order to assess the predictive ability of our estimates for each omitted GCM. Table  7  reports RMSEs and NRMSEs for each GGCM and climate model for in-and out-of-sample predictions from our leave-one-GCM-out validation exercise. As expected, prediction errors are larger out-of-sample than in-sample. Out-of-sample RMSEs are between 0.
103t/ha (pDSSAT) and 0. 058t/ha (LPJmL) larger than corresponding in-sample values. In relative terms, the NRMSE differences between in-sample and out-of-sample predictions range between 0. 002 (PEGASUS) and 0. 008 (LPG-GUESS). To evaluate discrepancies between GGCM yields and out-of-sample statistical yields over time, Figures 8 to 12 show yield time series for each GGCM and leave-one-GCM-out combination. The figures indicate that predicted maize yields are underestimated for the NorESM1-M model when this GCM is excluded from the training dataset. This is because yield projections under the NorESM1-M model are higher than under other GCMs. Conversely, maize yields are smallest under the GFDL-ESM2M model. When the sample for this GCM is excluded from the training sample, yield predictions from the statistical models are over-estimated, especially toward the end of the century.
For the pDSSAT model, growing season information is only available for the HadGEM2-ES climate model, so data for other climate models is not included in the growing-season specific estimates for this model. Detailed regression results (see Appendix A) show that some weather coefficients are not significant for some months (e. g. , T_mean for February and March for the GEPIC model in the Southern Hemisphere). Goodness of fit measures for these regressions are presented in Table  9  and show that the R ¯2 and NRMSE values are generally more favorable for the Northern Hemisphere regressions than for the Southern Hemisphere. The overall R ¯2, calculated by weighting the Northern and Southern R ¯2 by the number of observations in each hemisphere, indicate that the summer-month regressions have a better goodness of fit for the GEPIC, LPJ-GUESS and PEGASUS models than the growing season-specific regressions.
The R ¯2 and NRMSE indicate that, in general, the statistical model fits that the data best for the AEZ-G4 and AEZ-G5 subsamples. Overall, the average R ¯2 is smaller for the AEZ group regressions than for the global regressions for all models. The average NRMSE is larger for the AEZ group regressions than for the global regressions, but only for the GEPIC, LPJ-GUESS, and pDSSAT models. These results indicate that there are only small differences in performance for the AEZ and global models. Also, the fact that the AEZ groups do not change over time as climate changes is a concern in using this subsampling strategy.
Managing risks related to OSS adoption is a must for organizations that need to smoothly integrate OSS-related practices in their development processes. Adequate tool support may pave the road to effective risk management and ensure the sustainability of such activity. In this paper, we present the RISCOSS platform for managing risks in OSS adoption. RISCOSS builds upon a highly configurable data model that allows customization to several types of scopes. It implements two different working modes: exploration, where the impact of decisions may be assessed before making them; and continuous assessment, where risk variables (and their possible consequences on business goals) are continuously monitored and reported to decision-makers. The blackboard-oriented architecture of the platform defines several interfaces for the identified techniques, allowing new techniques to be plugged in.
Introduction Risk management is a necessary and challenging task for organisations that adopt open source software (OSS) in their products and in their software development process  [1]   [2]. Risk management in OSS adoption can benefit from the huge amounts of data that are publicly available about the adopted OSS components, as well as data that describes the behavior of OSS communities. The complexity and heterogeneity of the involved data sources, the need to integrate this data with contextual information related to the organisation and its ecosystem, and the convenience of combining different expertise involved in the assessment, call for adequate tools in support of all the phases of risk assessment, from data gathering, to data statistical analysis, to the correlation of these data to the organisational strategic and business risks and assets.
In this paper, we present RISCOSS (www. riscoss. eu), a platform and related assessment methodology for managing risks in OSS adoption  [3]. It defines several interfaces to a portfolio of identified measurement and risk management techniques, allowing new techniques to be plugged in if they implement these interfaces and follow well-documented protocols. RISCOSS builds upon a highly configurable data model that allows customization to several types of scopes to support different risk assessment perspectives. It implements two working modes: exploration, where the impact of decisions may be assessed in advance; and continuous assessment, where risk variables (and their consequences on business goals) are monitored, analysed and reported. This allows RISCOSS to support a holistic decision making process inside the adopter organisation.
Related Work Several long-term projects, corporate programs and research initiatives have similar aims than the approach supported by RISCOSS. In Table  1  we summarize the characteristics of the most related European projects that propose methodological approaches and tool support for measuring several aspects of OSS projects, mainly to evaluate the quality of the OSS components and the communities behind them. In particular we refer to the projects with objectives:  FLOSSMetrics (www. flossmetrics. org): constructing, publishing and analysing a large scale OSS database with metrics on OSS development;  QualiPSO (qualipso. icmc. usp. br/OMM/): improving the quality & maturity of OSS projects;  QualOSS (www. libresoft. es/research/projects/qualoss): defining a method to assess the robustness and evolvability of OSS projects;  ALERT (www.
alert-project. eu): improving the quality of the software acting on the overall bug resolution process in OSS collaborative environments;  OSSMETER (www. ossmeter. com): supporting the process of discovering, comparing, assessing and monitoring the health, quality, and activity of OSS;  MARKOS (www. markosproject. eu): provides an integrated view on OSS projects, focusing on software functional, structural and license aspects.  SQO-OSS (www. sqo-oss. org): proposing methods and a supporting platform for OSS code quality and community measurement and analysis.  U-QASAR (www. uqasar. eu): providing a quality assurance methodology to assess the quality of software development projects for Internet applications. All these approaches and platforms focus on the gathering and analysis of OSS data but they are not specifically oriented to inform about the risks derived from these data nor to suggest possible mitigation strategies at the technical and business level.
ow2. org/view/About/SQuAT). Some approaches target specific aspects as license assessment, e. g. Palamida (http://www. palamida. com), which provides tools to verify if there is any intellectual property violation in a project adopting OSS; White Source (http://www. whitesourcesoftware. com) provides a solution for companies that need to manage their open source assets to ensure license compliance and reduce risk. Several recent works face with mining and analysis of OSS projects mainly to assess or predict their quality. For example, in  [4]  the GHTorent project is presented that had the purpose of collecting data related to different aspects of the quality of the source code for all public projects available on Github. In the area of defect prediction for quality improvement, Peters et al.