dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Viridot was developed using PRNT data of genetically diverse DENV strains in >100,000 plaque well images from experiments conducted over many years. Most well images were of plaques on clear 96 well plates immunostained with peroxidase-labeled antibodies with peroxide substrate TrueBlue. Plates were imaged with CTL ImmunoSpot Analyzer and saved as square. TIF and. JPG images of more than 500 by 500 pixels. Examples of well images that capture the range of plaque phenotypes used for developing Viridot and optimizing the automatic parameter identification mode, along with corresponding images of counted plaques, are shown in Fig 1. To enable the user to optimize the Viridot plaque counting program (Fig 2 ) to count diverse virus plaque phenotypes, we incorporate two main features: 1) an automatic parameter identification mode to help the user have the best 'first pass' parameters for counting particular well images and 2) user adjustment of parameter settings, with the option of seeing the effect of each step in the image processing pipeline, to enable the user to fine-tune parameters for that particular virus or experiment.
Evaluation of the automatic Viridot plaque counter parameteridentification mode The automatic parameter-identification mode was developed using a training set (n = 1521 well images) of plaques of genetically diverse DENV1-4 strains. The automatic mode missed <10 plaques/well for 84% of the training set and achieved a plaque count �80% of the analystclassified count for 86% of the training set (Fig  3 ). The quality of the Viridot automatic program was evaluated for a different randomly selected well from the same 1521 plates (test set 1) and for 421 additional well images of plaques from 107 DENV strains not used in the training set (test set 2). For test set 1 and 2, 87% and 87%, respectively, of automatically-counted wells missed <10 plaques and 90% and 83%, respectively, of wells had �80% the number of plaques of the analyst-classified count (Fig  3 ).
Adjustment of parameter settings can help but may still be imperfect. Users should check Viridot counts of well images against their Viridot provides some advantages over other existing tools. CTL Analyzer software and ImageJ require more extensive parameter optimization (clicking through menu-bars to identify all modifiable parameters) for each plaque phenotype, making them efficient programs for protocols using a few prototype strains but inconvenient for working with large numbers of diverse viral strains with distinct plaque phenotypes. Viridot has parameters readily accessible and organized in a single scroll bar, making optimization easier, and can be used for analysis of a diverse array of plaque phenotypes as well as for other immunostained spots. Further, the source code for the CTL ImmunoSpot Analyzer software is proprietary, and ImageJ is written in JavaScript, a language with which many laboratory-based researchers may not be familiar.
Viridot is open-source and written in R, a statistical language used widely by biologists, potentially making it easier for researchers to modify the program for their own means. Further, while commercial counters have high-quality hardware for acquiring images of each well, they are expensive. We show that Viridot can analyze camera phone images, making it potentially useful in low-resource settings as part of an inexpensive plaque counting pipeline. With further development and standardization, Viridot may be useful in settings that involve stringent requirements such as the assay validation required for clinical trials or hospital settings. Viridot has some limitations. The Viridot plaque counter was not fully optimized for all light and cellular conditions, and thus may perform better under some conditions than others.
Acknowledgments We thank  Judy Fonville  and  Michael Baumer  for helpful comments on the  Viridot program. Thanks to  Julia Misplon  of the  U. S. Food and Drug Administration, Paulina Andrade of University of California, Berkeley , and  Christopher Ziegler  of  University of University of Vermont Larner College of Medicine  for provided well images of T cell responses, B cell responses, and LCMV plaques, respectively.
On-line continuous monitoring of water bodies produces large quantities of high frequency data. Long-term quality control and applicability of these data require rigorous storage and documentation. To carry out these activities successfully, a database has to be built. Such a database should provide the simplicity to store and document all relevant data and should be easy to use for further data evaluation and interpretation. In this paper, a comprehensive database structure for water quality data is proposed. Its goal is to centralize the data, standardize their format, provide easy access, and, especially, document all relevant information (metadata) associated with the measurements in an efficient way. The emphasis on data documentation enables the provision of detailed information not only on the history of the measurements (e.
). As personnel are changing over time, this diversification hinders data interpretation, understanding and reproduction leading to inconsistencies in further studies. Thus, to successfully manage these large amounts of heterogeneous data, a systematic and efficient storage system is needed  (Rieger et al. ). In this respect,  Camhy et al. ()  and  Horsburgh et al. ()  identified several data management challenges: the collected raw data have a highly variable format; the database has to be flexible and adaptable because it is growing continuously: monitoring programs are modified, additional variables are measured and different sensors are used; the personnel involved in collecting and managing the data changes. It is thus critical that one is documenting the collected data with all relevant metadata (data about data).
Some commercial databases to store water quality and hydrological data in a structured way are offered on the market. Nevertheless, accessing the raw data or making a modification of the metadata is sometimes limited or not possible, and can only be done through a predefined graphical user interface (GUI)  (Camhy et al. ). Using their experience with high frequency data collection, the modelEAU research group at Université Laval in Québec City (Canada), developed a database structure to be applied to water quality data from rivers, sewer systems and water resource recovery facilities (WRRFs). The main objectives of this database are to centralize data storage from on-line measurements, laboratory analysis and data post-treatments, and deal with the challenges presented above, especially regarding the storage of metadata.
DATABASE DESIGN The database structure that was designed, named datEAUbase (water database, 'eau' is water in French), offers robustness, data format uniformity, flexibility if modifications are needed, efficient storage of relevant metadata, and the possibility to comprehensively document a monitoring program. The datEAUbase has been designed to store all relevant data, i. e. , the raw, filtered and validated data, laboratory measurements and corresponding metadata (see Figure  1 ). The storage of the raw, filtered and laboratory data in the same database has been considered essential since all of them are related, and crucial to validate the data series and assure their quality.
Primary tables The general structure is based on primary and lookup tables. The primary tables (Metadata, Value and Comments tables presented in Figure  3 ) are the main tables of the database. Each measured value, its corresponding time stamp and its replicate identification (under 'Number_of_experiment') are stored in the Value table. Through its Metadata_ID each value is linked to a specific set of metadata in the Metadata table. Moreover, any comment can be added next to a value if needed. The Metadata table contains a list of all existing metadata combinations. This list only consists of IDs that represent links to more detailed information in the lookup tables. Hence, the Metadata table is directly or indirectly linked to all other lookup tables (see Table  2 ). To illustrate the database's structure, an example follows.
Project information In the Project table, information about the project is detailed. This table is linked to other parts of the database by a number of tables containing n-to-m links. These linking tables contain information about who is working in a project, where a project takes place and which equipment is used, and vice versa, in how many projects someone is working, for how many projects a location is used, and in how many projects a piece of equipment is used. For example, the monEAU project deals with the usefulness of automatic monitoring stations (AMS) to study the water quality. The measurements are located at the inlet of Grandes-Piles F/AL. The following equipment is used: con-ductivity_001, pH_003 and ammolyser_001. The personnel involved are Alferes, Plana and Vanrolleghem.
The interface allows an easy check as to whether a certain combination of metadata is already present in the database or if it should be created. Once the metadata_ID for an online sensor is created, online data from this sensor can easily be stored in the database through coupling with its metadata_ID. • Non-automated data (such as laboratory results) can be entered in the datEAUbase through the user interface. This also consists of a simple coupling of the measured values to their corresponding metadata_ID. • One of the main features of the interface is its appli- cation to search the database and extract a specific data set of interest or information on sensor or project history. • During the search process, an internal quality check is also performed. Data will only be available for extraction if all internal links are present.
This paper presents a comprehensive database structure (the datEAUbase) that offers a data storage system with an emphasis on metadata. It provides a robust, large storage capacity with flexibility for future modifications and possible improvements. Its specific structure, consisting of a combination of three primary tables interlinked with 20 lookup tables, allows for very efficient storage of huge amounts of information while avoiding redundancy. Moreover, this rigorous documentation of all measured values with their metadata allows to build memory on sensor history, project history and so on, in a reliable way. Since this tool is meant for large data users to store and exchange water quality data, easy access and maintenance is ensured through a user-friendly interface.
Moreover, data have to be continuously transformed to the proprietary format of the software. In addition, any modification relies on the vendor support, thus placing important restraints on customized use. Also, some organizations have proposed standards to exchange environmental data including data description, analysis and reporting, e. g. , the Environmental Data Standards Council (EDSC) presented a manual on Environmental Sampling, Analysis and Results Data Standards (EDSC ), the National Water Quality Monitoring Council (NWQMC) developed a similar standard but specific for water quality (NWQMC ), and the Open Geospatial Consortium (OGC) presented the 'Observations and Measurements' best practices document (OGC ). Despite that, these standards are focused on the elements to transfer and exchange the data rather than how to structure the data in a relational database.
In recent years, some hydrological and water quality databases have been developed, e. g. , the Observations Data Model (ODM) database from the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) (Horsburgh et al. ), or the STOrage and RETrieval (STORET) database developed by the US Environmental Protection Agency (EPA) (EPA ). However, storage and access to metadata is still a challenge. Most of the published databases focus on measurement and location details, providing priority to data collection activities and data set characteristics rather than information about monitoring programs. Moreover, some limitations are also observed on the control of the data quality (Horsburgh et al. ).
cient way. Moreover, it ensures that each value stored in the datEAUbase is unique, being linked to a specific time stamp and a complete set of metadata. Although these features represent the core functionality of the datEAUbase, in reality such a large-scale database will only be useful if the information contained within is easily accessible for all users. Hence, tools should be in place to facilitate interaction with the database. External interaction with the datEAUbase currently consists of two different parts (see Figure4): automatic read-in of online data from data loggers and a user-friendly web interface (programmed in PHP) which allows further manual entry as well as a comprehensive search, viewing and export of the stored data. Data transmission from on-line monitoring stations into the datEAUbase is ensured through a secure VPN connection.
ACKNOWLEDGEMENTS This work has been supported by the  Natural Sciences and Engineering Research Council of Canada (NSERC)  discovery grant, the  Urban Science Joint Research Unit (Unité Mixte de Recherche en sciences urbaines, UMRsu) , the  Quebec consortium  for industrial bioprocess research and innovation (CRIBIQ), and  MITACS. Peter A. Vanrolleghem  holds the  Canada Research Chair in Water Quality Modelling.
Tomato greenhouses are a crucial element in the Equadorian economy. Wireless sensor networks (WSNs) have received much attention in recent years in specialized applications such as precision farming. The energy consumption in WSNs is relevant nowadays for their adequate operation, and attention is being paid to analyzing the affecting factors, energy optimization techniques working on the network hardware or software, and characterizing the consumption in the nodes (especially in the ZigBee standard). However, limited information exists on the analysis of the consumption dynamics in each node, across different network technologies and communication topologies, or on the incidence of data transmission speed. The present study aims to provide a detailed analysis of the dynamics of the energy consumption for tomato greenhouse monitoring in Ecuador, in three types of WSNs, namely, ZigBee with star topology, ZigBee with mesh topology (referred to here as DigiMesh), and WiFi with access point topology.
Specifically, the CO 2 sensor has the highest power consumption because its chemical composition requires preheating to start logging measurements. The ZigBee network is more efficient in energy saving independently of the transmission rate, since the communication modules have lower average consumption in data transmission, in contrast to the DigiMesh network, whose consumption is high due to its topology. Results also show that the average energy consumption in WiFi networks is the highest, given that the coordinator node is a Meshlium TM router with larger energy demand. The transmission duration in the ZigBee network is lower than in the other two networks. In conclusion, the ZigBee network with star topology is the most energy-suitable one when designing wireless monitoring systems in greenhouses.
Experiments and Results In this section, we first describe the configuration parameters and the connections of the measurement equipment used in the experimental stage to acquire the energy consumption of each node of the three networks. The data collection experiments were repeated ten times, all under the same scenario, to characterize the statistical behavior of the energy consumption of each element of the networks. Next, we present the dynamics of the consumption signals that were analyzed with custom-developed software in Matlab TM. Selected and representative graphs of time series, probability distributions, and SAF, were studied in each node and at different transmission rates. The scheme of the comparative study was the following. Initially, we defined some relevant characteristics of the different graphical representations of the DigiMesh network, as given by its trend, mean, standard deviation, probability distribution, and seasonality, at a fixed and low rate of 9600 bauds.
Setup of Energy Consumption Measurements The efficient energy consumption in the nodes of WSNs is relevant for their performance, because it increases the operating time of the batteries and reduces the risk of data loss due to lack of energy in a network node. In this research, the energy consumption measurements of each node were acquired and stored in the laboratory according to the scheme in Figure  1. An experiment was developed for each network, and in all cases the link between the nodes was with line of sight and 2-m length. The battery of each node was connected to the Agilent TM 34410A multimeters for the current consumption measurements, and the recordings were transmitted through a local area network to the computer. The multimeter was configured from the computer using SCPI commands, and current data were transferred to a Microsoft Excel TM database with Agilent IntuiLink TM software, where data were subsequently processed and analyzed with custom-made Matlab TM functions.
Discussion and Conclusions In this paper, we addressed the detailed study of the dynamics of energy consumption time processes in three types of WSNs, namely, ZigBee with star topology, ZigBee with mesh topology (DigiMesh), and WiFi with access point topology, at different transmission rates, for the monitoring of tomato greenhouses in Ecuador. The electrical current measurements of the nodes were acquired at a fast sampling rate, using high resolution multimeters that were connected to the data storage station through a local area network. The experiments were conducted with all the network elements being active, distributed in line of sight, and at the same distance. For the three networks, the test was repeated several times for each transmission rate, and without charging the battery between runs to analyze the impact of battery discharge over the reliability of the acquired consumption data.
The consumption dynamics was scrutinized by using stochastic process analysis techniques, such as time series, pdf, histograms, and autocorrelation estimation, complemented with bootstrap resampling and CIs, and the results of all the realizations were simultaneously represented by the M-mode. The power consumption of the WSNs nodes was evaluated considering the type of sensors, the network topology, the transmission speed, and the data transmission duration. According to the type of sensor, we conclude that the nodes with the CO 2 , wind speed, and wind direction sensors were the ones with the highest consumption, independently of the network communication technology. Regarding the network topology, we verify that the consumption is associated with the role of each node. The consumption of the sensor nodes was lower in the ZigBee and WiFi networks (star topology), where each node only communicates with its coordinator, in contrast to the DigiMesh network (mesh topology), where the links are redundant and the nodes communicate simultaneously with more than one network element.
6 and 1. 7 s. The dynamics of consumption signals in the time domain was studied by using three of the methods described in Section 3. The M-mode proposed in this work provided us with simultaneous graphical representation of the empirical pdf for the energy consumption of the ten realizations performed for each node. This way, the similarity of the signals obtained between one and another realization was analyzed to validate the acquired data, discriminate the erroneous signals, and repeat the data collection process if necessary. The regions with the highest concentration of consumption measures for each node, as well as their variability and dispersion, were scrutinized with the histograms, so that an approximate view of the probability distributions was obtained. Finally, the graphs of the CI allowed analyzing the variability of the probability distributions, and, for this purpose, bootstrap techniques for each node yielded the CI for its estimated average, with 95% of confidence level.
Funding: This research received no external funding. Acknowledgments: This work was partly supported by  Research Grants PRINCIPIAS and FINALE  ( TEC2013-48439-C4-1-R  and  TEC2016-75161-C2-1-R ) from  Spanish Government ;  Research Grant PRICAM  ( S2013/ICE-2933 ) from  Comunidad de Madrid , supported in part by the  Universidad de las Fuerzas Armadas ESPE  under  Research Grant Energy Efficiency in Wireless Sensor Networks  ( 2016-PIC-043 ), and  2015-PIT-004 ; and the  Thematic Network: RiegoNets  ( CYTED  project  514RT0486 ), and  PLATANO  project from  Universidad Politécnica Salesiana, Cuenca, Ecuador. The authors are grateful to  Wendy Parra-Narváez.
Therefore, the results obtained are hardly repeatable or comparable to others. LEPTON is an emulation platform that can help bridge the gap between pure simulation and fully operational implementation, thus allowing developers to observe how the software they develop (instead of pseudo-code that simulates its behavior) performs in controlled, repeatable conditions. In this paper we present LEPTON, an emulation platform we developed, and we show how existing opportunistic networking systems can be adapted to run with this platform. Taking two existing middleware systems as use cases, we also demonstrate that running demanding scenarios with LEPTON constitute an excellent stress test and a powerful tool to improve the opportunistic systems under test.
In order to meet this challenge developers must follow a rigorous procedure, that ideally should involve all the steps shown in Figure  1. First comes the initial idea, the conception of the mechanisms. After this initial phase, the idea must be reified into a particular model, which can then be analyzed formally. During this analysis, the mechanisms and procedures can be checked, some theoretical results can be obtained, and limitations can be identified. The next stage is typically simulation. In a simulator, the system can be tested in a given set of scenarios. Even if these scenarios involve datasets that come from the real world (e. g. , traces of real taxi cabs, or positions of real people evacuating a stadium), or even if the simulator is assumed to simulate very accurately all the layers of the protocol stack, the system under evaluation is usually executed based on pseudo-code.
In this paper we present LEPTON (Lightweight Emulation PlatTform for Opportunistic Networking), an emulation platform that has been primarily designed to allow the developers of opportunistic networking software (i. e. , middleware and/or applications) to run their real software systems with simulated mobility. With LEPTON, an implementation can run in real time, either on a real device (e. g. smartphone, tablet, laptop) or on a virtual one. LEPTON also constitutes an interesting demonstration tool, since participants in a demo session can use an opportunistic application deployed on smartphones or tablets, while a display screen shows the simulated mobility of all devices. In this paper our main contribution is the description of LEPTON, our lightweight emulation platform for opportunistic networking.