dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, because we used simulated data, the impact of sequencing depth in mutation discovery could be tested. We used SHRiMP2 because this mapper behaved well in the variant discovery experiments. The same procedure was applied with four different read datasets of 200 bases with mean depths of 20X, 80X, 160X, and 320X (results are shown in Table  S1  of Section 4. 2 in Additional file 1). The precision and recall values were lower with a mean sequencing depth of 20X and were equivalent for the other tested sequencing depths. These results showed that a mean sequencing depth of 40X was enough to call variations correctly. Increasing the depth of sequencing did not seem to improve the quality of variant calling. These experiments showed that most of the tested mappers gave correct results in mutation discovery even when used with their default settings.
Discussion Here, a benchmark procedure to compare mappers for HTS that can be applied to any sequencing platforms and any applications is described. The different steps involved in this procedure are shown in Figure  7. In step 1, a list of mappers is defined. Depending on the sequencing technology and the application, the most appropriate mapper can be selected for use. In step 2, real datasets are collected and simulated datasets are generated before being mapped onto the reference genome. Step 3 is a comparison step based on four criteria: mapper computational resource and time requirements; mapper robustness; mapper behavior with repetitive regions; and mapper mutation discovery ability. The benchmark procedure uses simulated and real datasets to provide the user with a robust method for mapper comparison.
This newly introduced definition more stringent than the previous ones the end of the alignment and the number of mutations were considered in addition to the start position. The mapper robustness results obtained with the CuReSim suite simulated data matched the results obtained with real datasets and RABEMA, demonstrating that the CuReSim suite simulated reads with characteristics similar to real reads. We performed completely independent experiments to evaluate the mutation discovery ability of the mappers and found that the results obtained for mapper robustness can also be used to predict the mutation discovery ability of the mappers. Variant calling efficiency is directly dependent on the alignment quality obtained by the mapping algorithms. Checking whether a mapped read is in its expected position is not sufficient because the position and number of edit operations in the produced alignment must also be as close as possible to the expected alignment.
For example, very or low GC content percentages create a higher probability of encountering homopolymers in the genome sequence, which can significantly increase the number of indels in homopolymers. Our benchmark procedure for Ion Torrent data with bacterial genomes did not reveal a single best mapper but rather indicated several options depending on the particular application and technology. When only a desktop computer with 4 GB of RAM is available, users can select a mapper that is not highly memoryconsuming; for example, Novoalign, SMALT, SRmapper, Bowtie2, MOSAIK, or segemehl. Novoalign, segemehl, and GSNAP require very long runtimes, while SNAP is very fast (around 2 minutes to deal with big datasets). Other mappers had runtimes shorter than 40 minutes for the bigger datasets. Concerning mapper robustness with varying error rates, all mappers manage to correctly map reads when the sequencing error rate was low; however, some mappers were clearly not suitable for use with datasets containing high error rates (PASS, BWA, GSNAP, SNAP, and SRmapper).
Segemehl presented good F-measure values with all the tested datasets even at high error rates. MOSAIK, SHRiMP2, and Bowtie2 also gave correct results. SMALT was well fitted to retrieve all hits for repeat-located reads and GSNAP, MOSAIK, and SHRiMP2 also give correct results in this task. One of these mappers is therefore suitable for the identification of unique and non-unique reads, whereas PASS, BWA, BWASW, SNAP, and SRmapper are not. Mapper behavior for mutation discovery with datasets with varying mutation rates using a close but not identical reference genome is of special interest because often only the genome of a closely-related species is available as a reference. Mutation discovery ability is also important when the genomes of two closely-related strains are compared to detect variants or mutated strains, for example.
In such cases, mappers need to produce accurate alignments so that true mutations can be detected. All the mappers tested here showed good precision and recall with all tested mutation rates and for all datasets, except BWA, Novoalign, PASS, and SRmapper. Our results show that some mappers dealt correctly with the Ion Torrent data although they were not initially designed for this technology. For example, SHRiMP2 which was designed for Illumina, SOLiD, and 454 reads, showed robust results with Ion Torrent data. The mapper default parameters were used deliberately in this study to mimic the general case of a non-expert user; therefore, different results could have been obtained with other parameter settings. Even with the default settings, several mappers that can be used with Ion Torrent data were identified.
Datasets Real and simulated datasets were used in this study. Three real datasets obtained from the Ion Torrent Community website (http://ioncommunity. lifetechnologies. com/) were used. The main features of the real datasets, RD_100, RD_200, and RD_400, are shown in Table  1. For some experiments, smaller datasets were required; therefore, 50,000 reads were extracted randomly from the real data to generate the smaller datasets. Four files were generated from each dataset and the mean value was computed. Simulated data were generated from the complete genome of Escherichia coli str. K-12 substr. DH10B [Gen-Bank:NC_010473] using CuReSim. The command lines used for read generation are available in Section 1 in Additional file 1. Three datasets were generated with read lengths specific to Ion Torrent technology: mean lengths of 100 bases, 200 bases, and 400 bases with a standard deviation in length of 10% for each dataset.
Ion Torrent technology produces reads with about 1% deletions, 0. 5% insertions, and 0. 5% substitutions  [12, 29, 30]. Each simulated dataset contained 9 files of 50,000 reads with varying indel and substitution rates: the indel rate varied from 0 to 3% and the substitution rate varied from 0 to 1%. Table  3  shows the 9 files that formed the simulated dataset. Each dataset contained, among the 50,000 reads, 2,500 randomly generated reads. The datasets each contained 50,000 reads with varying indel, substitution and error rates. The indel rate varied from 0 to 3%, the substitution rate varied from 0 to 1%, and the total error rate varied from 0 to 4%.
Mapper robustness To compare the mappers' robustness, several metrics were computed for the simulated datasets. A read was considered as correctly mapped if among the reported hits at least one hit fitted the following criteria: i) the original start position (i. e. the position from which the read is generated) was retrieved, ii) the end position was retrieved, and iii) the alignment produced by the mapper showed exactly the same number of insertions, deletions, and substitutions. Indels in homopolymers at the end of the alignment led to failure to find some correct alignments (the observed start and end positions are not the expected ones). To deal with this special case, a shift for the start and end positions was allowed. The shift gives the number of possible insertions and deletions not considered at the alignment ends.
Precision and recall values were computed as: precision = TP TP+FP and recall = TP TP+FN with TP: true positives being correctly mapped reads, FP: false positives being incorrectly mapped reads, and FN: false negatives being unmapped reads. The F-measure combines the precision and recall values and was computed as: Fmeasure = 2 * precision * recall precision+recall The script to compute these metrics with simulated datasets produced by CuReSim is freely available. To evaluate the mapper performances on real datasets, the reduced datasets containing 50,000 reads were mapped with each mapper using RABEMA  [6]  to obtain the percentage of NFI depending on the error rates. RABEMA was run for all the mappers in 'all-mode', except for BWASW, SNAP, and SRmapper for which the 'allmode' is not available.
Study of repeats A 250,000 bp long artificial genome was generated with five repeats of 500 bp and an error rate of 3%. Using CuReSim, we generated from this genome three sets of 50,000 reads with 0. 5% insertions, 1% deletions, 0. 5% substitutions, and a mean size of 100, 200, and 400 bases with a standard deviation in length of 10%. This artificial genome was used to evaluate the ability of a mapper to retrieve all locations for a read located in a repeat. A total of 479, 465, and 482 reads for the 100, 200, and 400-base datasets, respectively, were located in one of the 5 repetitions. The number of locations corresponding to a repeat was counted for each of the repeat-located reads.
Mutation discovery To evaluate the ability of each mapper to retrieve mutations (i. e. true genetic variations within the sample), real and simulated datasets were used with reference genomes in which mutations were introduced artificially at different rates. An in-house script that can take an entire genome as input and return a mutated genome with a given error rate and a file containing the introduced mutations with their type (substitution or indel) and their genome position was used. For the real datasets, three mutated genomes were generated from the complete genome of Escherichia coli str. K-12 substr. DH10B with 0. 05, 1, and 5% mutations (comprising 90% substitutions and 10% indels). These genomes were used as reference genomes with the real datasets RD_100 and a subset containing 830,000 reads from RD_200.
In the same way, three mutated genomes from Escherichia coli str. K-12 substr. MG16655 [GenBank:NC_000913] were generated to use as reference genomes with a sub-dataset extracted from the RD_400 dataset (595,000 reads). We used sub-datasets for read lengths of 200 and 400 bases to obtain a similar depth of 40X for each real dataset. For the simulated dataset, a sub-sequence of 250,000 bp from the Escherichia coli str. K-12 substr. DH10B complete genome(from 2,000,001 to 2,250,000) was extracted. From this sub-sequence, we generated three simulated read datasets with CuReSim, with mean lengths of 100, 200, and 400 bp with 10% standard deviation in length and 0. 5% insertions, 1% deletions and 0. 5% substitutions, and three mutated small genomes with 0. 05, 1, and 5% mutations. To obtain a mean depth of about 40X for each length set, these 100, 200, and 400 datasets contained 100,000; 50,000, and 25,000 reads respectively.
Acknowledgements This work was supported by  Univ Lille Nord de France, Institut Pasteur de Lille  and the  Gènes Diffusion company. We thank  Gaël Even  ( Gènes Diffusion ) for help on algorithm and computing aspects, and  Kristina Keidel ,  Anne Bouchard , and  Raymond Pierce  for proofreading the manuscript. We also thank  Wan-Ping Lee  for help with the MOSAIK algorithm and  Erik Garrison  for help with FreeBayes and the modifications he made to the program.
The results of our preliminary assays encouraged us to extend this technique to a series of 36 oligodendroglial tumors previously analyzed in our laboratory by standard FISH technique. To validate this technique we first studied the ImFISH reproducibility between two independent observers. Results obtained by ImFISH technique were then compared with those obtained by standard FISH technique which served as the reference in our study. To determine the reliability of this technique on a limited number of cells, we analyzed a decreasing number of cells (60 cells, 40 cells and 20 cells) by ImFISH and checked if the reported 1p and 19q status remained constant or not. In our series, chromosomal data were also compared with the clinical and surgical data, and with MIB-1 labeling indices to seek any correlation existing between these different parameters.
Statistical analyses All statistical analyses were carried out with the R statistical environment (http://www. R-project. org/). Inter-observer agreement of ImFISH analysis was estimated by calculating Cohen's kappa coefficient (k) with the Kappa function of the R package vcd. We considered a k value between 0. 6 and 0. 8, as good agreement and a value. 0. 8 as high concordance. ImFISH reliability was studied by calculation of the sensitivity and the specificity of the method compared to FISH independently for each of the two observers' values. Chi-square test was performed for group comparisons between FISH and ImFISH analysis for 20 cells, 40 cells and 60 cells. A significant correlation between two parameters was noted at the 95% confidence interval and a P value ,0. 05 was considered as a significant difference between two groups.
Interpretation of ImFISH results For a given sample area, the ImFISH technique generates two distinct images, MIB-1 IHC and 1p or 19q FISH, that are easily interpreted by the user. These images are easily stackable by image software for analysis together on the same screen or can be analyzed separately on two separate screens (Figure  2 ). We preferred the second solution for this study because of its simplicity, knowing that many people who examine chromosomal status by FISH do not necessarily use sophisticated image processing and image fusion software. A phosphatase alkaline system using an AEC chromogen proved superior to the widely-used peroxidase system with its brown diaminobenzidine (DAB) chromogen. DAB chromogen remained adherent to the target despite successive washes causing non specific fluorescence and difficulties of interpretation during the FISH analysis, whereas AEC chromogen was very easy to remove with an ethanol bath.
To our surprise, the results of normal and imbalanced cases, although less similar, remain statistically significant and underline the interest of the ImFISH. Unfortunately, for normal and imbalanced cases, the FISH literature does not offer clear quantitative data allowing us to more detailed comparisons between these two techniques. Nevertheless, our results highlight the benefits of practicing ImFISH on tumor samples of low cellularity (diffuse low grade gliomas, periphery of the tumor or biopsy material) since the analysis of 20 cells still allows to provide a satisfactory result on 1p and 19q chromosomal status (Table  3 ). Despite the preselected character of our series, the majority of our cases were codeleted as typically described in the literature  [13, 17, 22, 30, 59 ] and there was no chromosomal abnormalities differences between the primary and the recurrent tumoral population underlining the genetic stability of the majority of recurrent oligodendrogliomas  [23].
Among the possibilities we note the interest to combine immunohistochemical analysis of other known prognostic factors in gliomas such as TP53, MGMT, EGFR and IDH1 expression with the chromosome 1p and 19q status within a given sub-population of tumor cells  [9, 19, 34, 64, 65, 66]. Other oligodendroglial chromosomal abnormalities such as gain or loss of chromosome 10 or 7 could also been studied by this technique  [64, 67, 68]. Finally ImFISH analysis can be easily automated, which may lead to easier validation of automated image analysis software for FISH within each institution. By focusing analysis on a limited number of pre-selected cells, ImFISH removes a potential source of inter-observer variability whether a human or computer examines the cells.
Fingerprinting with Magnetic Anomalies A more recent method, which is growing in popularity, consists in navigating using only measured magnetic fields. At first, the indoor (even outdoor) magnetic fields are mapped during a calibration phase  [12, 13]. Following Wi-Fi receiver signal strength based fingerprinting techniques, the user's location is extracted from the database of magnetic anomalies. This method benefits from the singularities in the local magnetic field anomalies, but it is non-ergonomic and laborious. Up to now, its limitations have hardly been investigated. One limitation among others is the influence of soft-iron effects in a crowded environment, which has not yet been assessed.
The total walking path length was about 1. 5 km including successive periods in outdoor and indoor environments. The experiment starts outdoors with the magnetometer calibration and the initialization phase. This initialization must be performed in surroundings without magnetic disturbances where only the earth magnetic field is measured. The people continued the tests walking successively in the parking lot and inside a building. To evaluate the performances of the present algorithm, a reference system is needed. In outdoor spaces, where Global Navigation Satellite Signals (GNSS) are available, a post-processed differential trajectory was calculated using GrafNav software from NovAtel  [27]. The 5 cm accurate reference solution is post-processed at 5 Hz for the GNSS antenna attached on the pedestrian's cap, as shown in Figure  8.
Indoor Static Test For the static test, 5000 seconds of static IMU data were collected in an indoor space, where the local magnetic field is constant but different from the earth magnetic field. Three attitude estimators are applied to the recorded data. Two of them are state of the art estimators. All angle outcomes are shown in Figure  9. The angles in blue are estimated with the GyInt approach, for Gyroscope Integration. This state of the art estimator corresponds to a nominal gyroscope bias corrected integration only. In this strategy, gyroscope measurements are corrected with the nominal angular rate bias, extracted from the Allan Variance analysis, and then propagated in time. It can be observed that GyInt attitude estimator presents a large angular random walk drift. The second estimator, shown in green, corresponds to the well-known QUEST estimator.
Therefore, performance evaluation tools become very essential in the process of designing a wireless sensor network, and simulation is one of the most used tools for this evaluation. There exists a large number of WSN simulator tools. Some of them are described in  [12]. In this work we propose to separate these tools into two main categories: simulators and emulators and to introduce only some simulators that are of four different types. The first type represents simulators that are based on NS2  [7] , which is a simulator developed in general for traditional networks and adapted for WSN. It uses discrete event simulation. In this family we can find NRL Sensorsim  [13] , based on modules, and RTNS  [11]  for the real-time distributed systems. The second family is based on the OMNET++ simulator  [14].
The fourth family is specially developed for WSNs, such as WSNet  [4]  used to evaluate high-level designs, Atarraya  [16]  for teaching and research topology control algorithms and J-Sim  [10]  a framework for modeling and simulation. In the context of this study, we present a simulator named CupCarbon which is based on multi-agent and discrete event simulation. The current version belongs to the second family of simulators that is described above. It can be used to generate networks for OMNET++. It offers a simple and friendly graphical user interface for the modeling of the networks using the OpenStreetMap (OSM) framework. Each node is designed to be as close as possible to the real one. It is composed of four modules: mirco-controller the radio antenna, the capture unit and a battery. The current version of CupCarbon includes simulation of mobiles and it allows to represent the detailed energy diagram for each node versus the simulation time.
WIRELESS SENSOR NETWORKS Sensors are electronic components that operate in networks with autonomy. The term sensor is usually used to refer to a sensor node (having a dimensions of a few centimeters). It is composed of four components which are: a microcontroller (programmable integrated circuit), a radio antenna (for wireless communication), a battery and a set of sensors that we will call capture unit in order to avoid confusion. A capture unit (having a dimension of a few millimeters) captures or intercept environmental information (motion, temperature, humidity, gas, etc. ). Henceforth, we use the term sensor to refer to a sensor node and sensor unit to refer to a sensor. In order to be able to communicate (read or receive information), a sensor must be programmed. Figure  1  shows an example of a program for Arduino cards with an ATmega microcontroller.
Multi-agent Simulation Environment CupCarbon provides an environment for a multi-agent simulation, which allows to run simulations and monitor various events and changes over time. The use of a multi-agent system allows a reproduction of an environment similar to the real world system, wherein each object in the system operates autonomously while communicating with other objects in the same environment. The integration of OpenStreetMap in this environment provides an interface and a database of digitised data related to geolocation such as roads, the positions of buildings, etc. Among the main agents in the system, one can find sensors for sending and receiving packets. There is also gas, insects and mobiles. They do not necessarily communicate between them but they interact with the sensors.
Communication script CupCarbon allows to configure each agent of the simulated system using script files to program their communications. Such a file describes how an agent will communicate with its neighbours and its environment. It contains all the main actions to be performed by a sensor during the simulation. Each line of the file represents an event that is characterised by its type, date, value, signal power, and the list of agents involved in it. An event is a command that can be of five different types, as described below: • COM_SEND: for the packet (message) sending, • COM_DELAY, COM_WAIT: to force a waiting, • COM_BREAK: to mark the end of a communication, • COM_RECEIVE: to accept the reception of a packet (message), • COM_UNKNOWN: for unknown commands. This file can, in some cases, have a header part which allows to configure the communication protocol and describe the type of algorithm to use in the simulation, subject to the fact that the algorithm is already integrated into the simulator.
The WSN simulator: WiSeN WiSeN is the name of the module that represents the kernel of CupCarbon for simulating events related to sensors (sending, receiving, waiting, etc. ). It supports and manages the evolution of the state of each object in the system (energy, position, etc. ). Its implementation in a multi-agent environment allows for each agent to be executed independently and in parallel. Thus, it is possible to include mobility aspects and the detection of the target. Each agent generates events based on a existing saved script. The simulator organises events generated by agents (sensors, mobiles, etc. ) according to their creation dates and executes them in the same order, and it updates their status (energy, position, etc. ). Two types of files are automatically generated during the simulation: 1.