dataesr/scientific-paragraphs-categorization huggingface dataset:
In the services world, the expected benefits are the fastest time to market, lower costs, greater consistency in the application, and increased agility. The reuse and sharing properties of software components are useful to address these challenges. However, to achieve this, it is necessary to be able to observe each service and to control the service composition. This article proposes to rethink the company's organisational process of application development and use the power of monitoring to help the application design. The proposed Monitoring as-a-service (MaaS), whose properties are detailed, will be used for the computation of the offered Quality of Service (QoS), for the services calibration during the service creation phase and to inform the QoS Controller during the operational phase.
An hypervisor controller checks the list of virtual execution environment (VEE) and add or remove a monitor according to the detected number  [9]. An extension of Nagios  [38]  allows the use of active verification method (pulling) by remote code execution. An extension of Nagios  [10]  offers a push-pull model. The monitoring information is sent by agents to a Manager (push) and information consumers can obtain data from it (pull). Monalytics  [32]  was designed for scaleability and efficiency in highly dynamic scenarios: discovery at runtime of resources to monitor and configuration at runtime of monitoring agents. Brokers at different hierarchical levels, collect process and transmit the monitoring information. To improve Intrusiveness and Comprehensiveness,  [11]  proposes an architecture based on agents that monitor directly the flow of information through the same workflow system.
They are connected with adapters, which abstract from data of a specific technology. [39]  monitors events at the VM level. Sensu  [26]  provides built-in metric translation that allows you to collect metrics in various formats from disparate data sources, and mutate them into a proprietary intermediate format that has been optimized for portability. In the literature, several works search for the reasons impacting Resilience: Resource Volatility  [30, 40] , virtualisation technology  [34]. To improve the Availability,  [41]  provides a publish-subscribe paradigm for communication and a set of redundant brokers for events management while providing tolerance to attacks and malfunctions. Consul  [23]  is a distributed, highly available system. The agents talk to one or more Consul servers. The Consul servers are where data is stored and replicated to avoid failure scenarios leading to data loss.
• Not intrusive because they are external to the functional component because they are inside the service component membrane and operate in parallel with the functional component. They have no effects on the second (Intrusiveness). 5. At each addition/removal of a functional component, a monitoring and controlling component is therefore added/removed (Scaleability, Elasticity). 6. The volume of data exchanged and thus the communication resources are extremely low since the analysis would be done on site. Only its result would be sent. 7. We measure a QoS (Section 3. 1) of each component (hardware of software) allowing better diagnostic of various malfunctions whereas most existing tools monitor network traffic or CPU usage when they should monitor the functional component performance. In the following section we propose a method compatible with the objectives of self-control, i.
Furthermore, VCE has already been used to build a real application called Springoo. It's a web application that conforms to  the three-tier Java Enterprise Edition (JEE) platform architecture, providing typical commercial web services through an Apache/Jonas/MySQL architecture. This application is one of the end-to-end case-studies of the OpenCloudware project  [5]. In self-test situation, each request is generated by the Self-test component, which is recorded by the InMonitor. The record consists of the request number and a time stamp. Note that several different metrics could be taken into account by the same monitor for a more complex QoS processing. The request is then processed by the business code and the result is intercepted by the OutMonitor, which records it. The request is based on a generic Interface: IRequest, which includes his number (requestId) and a list of functional parameters for the business code.
Acknowledgement. The authors would like to thank for their help and contribution: • Oleksandra Kulankhina:  INRIA, Sophia-Antipolis, France • Cristian Ruz: Pontificia Universidad Católica de Chile ,  Santiago de Chili , Chile This work is supported by the  OpenCloudware  project [6]. OpenCloudware is funded by the  French FSN (Fond national pour la Société Numérique ) and is supported by  Pôles Minalogic, Systematic , and  SCS.
, , 2014;; Coelho et al. , 2017) ]. The appearance of complex nuclear phenotypes prompted us to apply other techniques besides flow cytometry to get a more accurate picture of the relation nuclear volume/shape and ploidy. Indeed, although it is generally accepted that endoreduplication leads to an increase in nuclear volume, the outsized and highly irregular nuclear shapes observed in the lines studied here, suggest that other mechanisms might be responsible for the nuclear phenotypes observed. Therefore, we opted to accurately measure nuclear volumes in GCs induced by the RKN Meloidogyne incognita using thick slices of nematode infection sites and confocal microscopy imaging. Optical sections of galls were subjected to nuclear volume measurements that were performed by two independent softwares; the public domain Volumest plugin from ImageJ and the AMIRA 3D software for visualization and analysis.
Volumetric Measurement of Giant and Non-giant Cells Nuclei Image stacks of DAPI-stained galls were analyzed with the plugin Volumest 1  from the public domain ImageJ software (NIH, Bethesda, MD, United States). Volumest allows volume estimation using confocal data sets. Before estimating the volume of GCs nuclei, 3D projections of each image stack were made with ImageJ, to facilitate nuclei mapping. Then, original z-stacks were prepared as required for Volumest analysis. Volume measurements were performed on a selection of nuclei with bright DAPI staining as identified from maximum projection images. This selection was necessary to avoid measuring background noise. Well defined nuclei were subsequently measured using the cumulative nuclei volume tool and recorded as shown in the Supplementary File 1.
Individual nuclear volumes are depicted in Supplementary File 1 and the total volume of GC or NGC nuclei is divided by the number of measured nuclei resulting in the average nuclear volume per gall. To simplify the measuring procedure we assessed the nuclei volume from a single Col-0 image stack at different manners. First, the nuclear volumes were computed by summing the volumes of each individual image in the stack. Then volumes were measured by skipping one, two, three or four images in the same sequence taking into account the slice thickness. All measurements were carried out on at least 400 GC nuclei with a minimum of 22 GCs for each time point for each line. Also, the volume of NGC was measured with more than 100 per time point for each line. All data generated were analyzed statistically by Sigma Plot version 12 (Systat Software, Inc.
Confocal Image Stacks Processed with the AMIRA 3D Visualization and Analysis Software In order to confirm the reliability of the data obtained using the ImageJ Volumest software, the same dataset was also analyzed with the AMIRA software package (FEI, Hillsboro, OR, United States). For the latter, nuclei were segmented according to standard procedures and correspondingly volume rendered. Briefly, to enhance the signal to noise ratio while preserving edges, images were first filtered with a 2D-median rolling ball, then nuclei were segmented by applying a white Top-Hat filter. Sorted out nuclei from gall sections were then filled to avoid holes within the selection. GC and NGC nuclei were individually labeled via the AMIRA segmentation tool and analyzed for morphologic traits.
Measuring Volumes of Giant Cell Nuclei Using Volumest Herein, we describe a methodology to measure nuclear volumes from plant root cells, especially from GCs induced by M. incognita. Therefore, we used confocal image stacks to assess the GC nuclear volume using the ImageJ plugin, Volumest software (here on mentioned as Volumest). This plugin allows estimating volumes from confocal acquired z-stack. In this way, we were able to estimate the GC nuclear volume by measuring the nuclear area from different gall confocal optical sections within a z-stack (Supplementary File 1). As a result, we assessed the volume of each individual nucleus. First, we performed a z-projection of the dataset to map nuclei within galls. Then, we measured the cross section area for each image optical section of the stack to estimate the total nuclear volume.
Applying the Volumest Method to Investigate the Effect of the Ectopic KRP3 and KRP5 Expression on Giant Cell Nuclear Volume To acquire information other than nuclear size and morphology  (Coelho et al. , 2017) , we estimated nuclear volumes of Arabidopsis plants overexpressing the cell cycle inhibitors KRP3 and KRP5 compared to wild-type applying the Volumest software. We already observed that nuclei from GCs induced in plants overexpressing KRP3 and KRP5 have a peculiar morphology, presenting elongated and apparently connected GC nuclei in contrast with nuclei in wild-type GC that are mostly amoeboid-shaped and convoluted (Figures  1, 5 )  (Coelho et al. , 2017). In order to evaluate whether nuclear morphology in GCs upon ectopic KRP3 and KRP5 expression featured an effect on nuclei volume of both GCs and NGCs, we performed volumetric nuclear measurements in gall cells 14 and 21 DAI.
Applying the AMIRA Method to Investigate the Effect of the Ectopic KRP3 and KRP5 Expression on Giant Cell and Non-giant Cell Nuclear Volume To consolidate the data obtained with the Volumest software, we also analyzed the confocal datasets obtained from the different KRP lines (KRP3 OE , KRP5 OE , and wild-type lines 14 and 21 days after infection with the RKN M. incognita) using a 3D analysis dedicated software named AMIRA (Figure  7 ). GC and NGC nuclei were segmented (Figure  8 ) and investigated for their volume (Figure  9 ) via AMIRA imaging software according to the "Materials and Methods" session. Throughout the RKN infection process in the wild-type, we measured a slight but significant (p < 0. 01) 1. 8-fold increase of the GC nuclear volume between 14 and 21 DAI. This volume rise may be explained by the onset of the endoreduplication process.
We adapted this protocol as outlined in Figure  2. Fixed galls were mounted in agarose, sectioned with a vibroslicer, cleared and DAPI stained and finally mounted between two large coverslips while avoiding tissue compression. Thicker samples can, therefore, be imaged at both sides generating high-quality images even for very large galls. Applying the same methodology, galls in other hosts, as well as other complex plant organs for, e. g. , in tobacco, rice, soybean, and cotton, can be analyzed. Here, optical sections were generated according to  Coelho et al. (2017)  by confocal microscopy and image datasets were processed for volumetric measurements using two different softwares: the free Volumest plugin tool from ImageJ and the AMIRA 3D analysis package. These approaches enabled us to compare nuclear volume between galls at different time points, among two KRP OE transgenic lines compared to wild-type.
, 2017). Using the "Volumest" method we were unable to observe minor differences in nuclear volumes of NGC for the three different lines. As a second measuring approach we used AMIRA, a commercial software package which is based on morphological segmentation of the image data set. This segmentation relies on a white top-hat transformation to extract objects (here nuclei) from the data set. The size of the extracted object is conditioned by a self-chosen kernel size for a mathematical operation called "opening. " In digital image processing, an opening is the dilation of the erosion of the image by a structuring element named a kernel matrix. While opening removes small objects from the foreground, it can also be used to extract specific features from the image that fits the kernel matrix. Segmented objects are finally properly labeled and analyzed for volumetrics.
This error may affect the volume output and the observed differences between the two methods. Besides, the highly irregular nuclear shapes observed in the KRP lines may hamper the selection of a single nucleus which actually might consist of two entangled entities in very close proximity. A manual measuring procedure will be more prone to introducing this type of error. Since Volumest seems less accurate, it may also have prevented to observe the increase of NGC nuclear volume in KRP3 OE and KRP5 OE lines during the nematode infection process. Although our analysis revealed that the Volumest approach seems to provide somewhat less accurate results than AMIRA, the former still features some advantages: -it is a free public domain software, -there is no need for additional mathematical operation and, -it is easy in use relying on hand-made object delineation.
Since "Volumest" is supported by ImageJ, it is up to the user to implement additional data processing tools such as the "top-hat" approach prior to "Volumest" analysis. It is important to mention that recently Ohtsu et al. (  2017 ) have described a method for imaging CN-induced syncytia using the ClearSee clearing method  (Kurihara et al. , 2015). In this work, the authors have used the software IMARIS (Bitplane, United Kingdom) to assess syncytia nuclear volume. The authors were able to nicely image whole syncytia samples of Astragalus sinicus roots infected with Heterodera glycines. The strategy we propose here and which is based on root vibroslicing combined with nuclear volume measurement (ImageJ and AMIRA) can also be applied for much thicker roots infected with cyst or RKN. Currently, thick vibrosliced samples are being optimized using different clearing approaches for deep tissue imaging.
Summing up, "Volumest" ImageJ and AMIRA softwares have been applied to determine nuclear volumes of GC and NGC nuclei from transgenic lines overexpressing KRP3 and KRP5 compared to wild-type. Both lines presented exceptionally elongated and apparently connected nuclear morphology. These phenotypes are characteristically occurring in some KRP overexpressing lines and were enhanced in mature GC (21 DAI) compared to young feeding cells (5-10 DAI), most likely due to cumulative defective mitotic events  (de Almeida Engler et al. , 2012; Vieira et al. , 2012). Remarkably, differences observed in nuclear ploidy and volume in GC show that an increase in nuclear volume is not necessarily linked to ploidy levels. The evident accumulation of mitotic defects in GC nuclei upon overexpression of KRP3 and KRP5 could to a certain degree prevent optimal cell cycle progression.
31), where considering demographic details may be less relevant to predict the future species' range. How much modelling choices regarding demographic and genetic details matter for predicting species range may depend on the characteristics of the focal species. Nevertheless, our results strongly suggest that details of the evolutionary and demographic processes can deeply affect the interpretation and understanding of current and future species range variations. Our results, as a modelling exercise to project the future range of actual species in their environment, rely on the data available and model assumptions. First, we selected ecologically relevant and relatively independent bioclimatic variables and consequently assumed that the quantitative traits underlying adaptation to these variables are independent (Methods).
To this end, high-quality database on plant demography as compiled in the COMPADRE database  44  with precise information on species distribution would be particularly suitable to the present framework. Our main results are nevertheless qualitatively robust to the sensitivity analysis we performed on the key parameters for which no precise estimates were available, such as strength of local stabilizing selection (Fig. 3  and Supplementary Fig. 5 ), adult survival rate (Fig. 6 ) and mutation rate of the quantitative loci (Supplementary Fig. 7 ). In summary, our new approach highlights how eco-evolutionary dynamics at the population level may translate into variation of species' ranges under a warming climate. We show that the persistence of established long-lived adults combined with low dispersal capacities delays the contraction of Alpine plant species ranges while local maladaptation immediately increases.
The bioclimatic variables were chosen to (i) be as independent as possible in a principal component analysis using the 19 bioclimatic variables proposed by WorldClim, (ii) contain information about temperature and water availability and (iii) be ecologically relevant. With these criteria, we chose the mean annual temperature (Bio 1), the annual amount of precipitation (Bio 12) and the percentage of carbonate in the bedrock. The SENMs were performed with Biomod2 R package  47. An ensemble of forecasts of niche model models was obtained for each one of our selected species using the presence-absence and the environmental data (climate and soil) explained above. The ensemble included projections with generalized linear models (with linear and quadratic terms selected through a stepwise procedure), generalized additive models (with a maximum degree of smoothing of 3), surface range envelopes and random forests.
Acknowledgements O. C. , M. S. and F. G. acknowledge support from the  Swiss National Science Foundation  (grant  PPOOP3_144846 ). W. T. acknowledges support from the  European Research Council  under the European Community's  Seven Framework Programme FP7 /2007-2013 Grant Agreement no. 281422  (TEEMBIO). G. K. and S. D. were supported by the  Austrian Science Foundation  (project ' Who Is Next '  I-1443-B25 ). J. W. and S. D. acknowledge funding from the  University of Vienna 's rectorate. Parts of the simulations were performed on the CIMENT HPC infrastructure (https://ciment. ujf-grenoble. fr),supported by the  Rhone-Alpes region  (GRANT CPER07_13 CIRA: http://www. ci-ra. org)and  France-Grille  (http://www. france-grilles. fr). The  University of Zurich , S3IT team (http://www. s3it. uzh.
Data availability. All models were run using the BIOMOD package  47  in R (the complete script is available here: https://github. com/DamienGeorges/ endemicitysdmhub/blob/master/SCRIPTS/2_species_niche_modelling. R). The extended code of Nemo  48  and the corresponding input simulation files will be available upon request before publication of the version of Nemo we used in this study. A GitHub repository will then be updated: https://github. com/ oliviercotto/NCOMMS-16-18804A.
In metagenome analysis, computational methods for assembly, taxonomic profiling and binning are key components facilitating downstream biological data interpretation. However, a lack of consensus about benchmarking datasets and evaluation metrics complicates proper performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on datasets of unprecedented complexity and realism. Benchmark metagenomes were generated from ~700 newly sequenced microorganisms and ~600 novel viruses and plasmids, including genomes with varying degrees of relatedness to each other and to publicly available ones and representing common experimental setups. Across all datasets, assembly and genome binning programs performed well for species represented by individual genomes, while performance was substantially affected by the presence of related strains.
However, these results are extremely difficult to compare, due to the varying evaluation strategies, benchmark datasets, and performance criteria used in different studies. Users are thus not well informed about general and specific limitations of computational methods, and their applicability to different research questions and datasets. This may result in difficulties selecting the most appropriate software for a given task, as well as misinterpretations of computational predictions. Furthermore, due to lack of regularly updated benchmarks within the community, method developers currently need to individually evaluate existing approaches to assess the value of novel algorithms or methodological improvements. Due to the extensive activity in the field, performing such evaluations represents a moving target, and consumes substantial time and computational resources, and may introduce unintended biases.
We tackle these challenges with a new community-driven initiative for the Critical Assessment of Metagenome Interpretation (CAMI). CAMI aims to evaluate computational methods for metagenome analysis comprehensively and most objectively. To enable a comprehensive performance overview, we have organized a benchmarking challenge on datasets of unprecedented complexity and degree of realism. Although comparative benchmarking has been done before  3, 4  , this is the first time it has been performed as a community-driven effort. CAMI seeks to establish consensus on performance evaluation and to facilitate objective assessment of newly developed programs in the future through community involvement in the design of benchmarking datasets, evaluation procedures, choice of performance metrics, and specific questions to focus on.
To address these questions, we generated extensive metagenome benchmarking datasets employing newly sequenced genomes of approximately 700 microbial isolates and 600 complete plasmids, viruses, and other circular elements, which were not publicly available at the time of the challenge and include organisms that are evolutionarily distinct from strains, species, genera, or orders already represented in public sequence databases. Using these genomes, benchmark datasets were designed to mimic commonly used experimental settings in the field. They include frequent properties of real datasets, such as the presence of multiple, closely related strains, of plasmid and viral sequences, and realistic abundance profiles. For reproducibility, CAMI challenge participants were encouraged to provide their predictions together with an executable docker-biobox implementing their software with specification of parameter settings and reference databases used.
Overall 215 submissions representing 25 computational metagenomics programs and 36 biobox implementations of 17 participating teams from around the world were received with consent to publish. To facilitate future comparative benchmarking, all data sets are provided for download together with the current submissions in the CAMI benchmarking platform (https://data. camichallenge. org/), allowing to submit predictions for further programs and computation of a range of performance metrics. Our results supply users and developers with extensive data about the performance of common computational methods on multiple datasets. Furthermore, we provide guidance for the application of programs, their result interpretation and suggest directions for future work.