dataesr/scientific-paragraphs-categorization huggingface dataset:
Again, BLAST hits were associated with high levels of sequence similarity (98% to 100%). Group 8 (MLH 13 to MLH 18) was morphologically identified as the citrus mealybug, Planococcus citri (Risso). In total, 65 specimens were identified as Pl. citri in this work, making this species the most frequently observed in Brazilian vineyards. Genetic variation was particularly common in this group, with six multilocus haplotypes observed and differences detected for four of the five markers used. BLAST results revealed hits with very similar sequences (99 to 100%) to a sample previously identified as Pl. citri. Group 9 (MLH 19) was morphologically identified as Pseudococcus cryptus Hempel. The BLAST study revealed one hit (99% similarity) with haplotype C1-14 and a Ps. cryptus sequence from Genbank.
For LCO-19, 28S-09 and 16S-10, we observed hits with similarities of 94, 96 and 97%, respectively, with Pseudococcus comstocki Kuwana from Malausa et al. [23]  and Abd-Rabou et al. [17]. Group 10 (MLH 20 to MLH 26) was morphologically identified as the obscure mealybug, Pseudococcus viburni (Signoret), intragroup variation was observed, with two 28S haplotypes (28S-6 and 28S-8) and different haplotypes at LCO, C1 and ITS2 associated with each 28S haplotype. All the haplotypes observed displayed similarity to Ps. viburni sequences previously published in GenBank (Table  2 ). Group 11 (MLH 27) was morphologically identified as Pseudococcus near viburni. BLAST hits revealed similarities of 99% for 28S-07 and 16S-04 with Ps. viburni sequences identified in previous studies (Table  2 ). Groups 12 and 13 (MLH 28 and MLH 29) were identified morphologically as Pseudococcus near sociabilis Hambleton.
BLAST hits corresponding to 98% similarity were obtained between the 28S-10 and 16S-08 sequences and GenBank sequences assigned to Pseudococcus maritimus (Ehrhorn) by Gullan et al. [38]  and Malausa et al. [23]. However, relatively high levels of divergence (e. g. 5% (14/278) between 28S-10 and 28S-11 and 10% (36/362) between C1-15 and C1-16) were observed between groups 12 and 13, which may thus correspond to two different species that could not be clearly identified as Ps. sociabilis. Group 14 was morphologically identified as Dysmicoccus texensis (Tinsley). Only one specimen was sampled (displaying the MLH 30 haplotype). This was the first time that DNA from D. texensis has been sequenced. The BLAST hits with the highest scores were obtained for the 16S-03 and 28S-15 loci, with the species D.
This species is an important pest of pineapple Ananas comosus (L. ) Merrill, and, according to the scale insect database ScaleNet, it has previously been observed in several Brazilian states  [9] ,  [47]. F. meridionalis was observed in Table  3. Cont. Pernambuco and Rio Grande do Sul, the same multilocus haplotype being identified in both regions. This is the first record of this species in Brazil, but it has previously been found in Argentina, Chile, Paraguay and Uruguay  [35]. We identified 13 specimens from Pernambuco as Ph. solenopsis. This species was recently observed in Brazil on tomato Solanum lycopersicum Linnaeus, and then on plants from the Amaranthaceae and Caricaceae families in Espı ´rito Santo State  [48] ,  [49]. Ps. viburni, D. sylvarum, D. texensis, F. terani, Ph. baccharidis and Ps.
Conclusions The taxonomic identifications obtained in the DNA analyses were entirely consistent with the morphological characterization, allowing the clear identification of 17 species from Brazilian vineyards. Pl. citri, D. brevipes and Ps. viburni were the most frequently collected species. F. terani and F. meridionalis were reported for the first time in Brazil. The data and samples obtained from this survey were used to design an identification kit based on five multiplexed species-specific PCRs. This multiplex PCR proved useful for the rapid and cost-efficient identification of Ps. viburni, Pl. citri, D. brevipes, Ph. solenopsis and Pl. ficus.
This work was supported by the  European Union Seventh Framework Programme   FP7-KBBE #265865 ''PURE '',  FP7-IRSES #269196 ''IPRABIO ''and  FP7-IAPP #324475 ''COLBICS' ' and by the  French grants Agropolis Fondation  (http://www. agropolis-fondation. fr/) ( RTRA -Montpellier ,  BIOFIS  project number  1001-001 ) and ' Bibliotheque du Vivant '. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Mitochondrial G-quadruplex Motif Prediction and Correlation with Mitochondrial Deletions-Mitochondrial G4P sequences were searched using Pattern Finder software to predict G4 in a user-defined quadruplex pattern for a given input sequence  (15). Human mtDNA sequences used for analysis were taken from accession number NC_012920 (formerly, AC_000021). Mitochondrial L-strand (C-rich) and H-strand (G-rich) DNA was subjected to Pattern Finder analysis to assign G4 motifs with a stem size of 2-5 nucleotides (nt) and loop size of 1-7 nt. The mitochondrial deletion breakpoints are reported based on L-strand numbering. The mitochondrial deletion data were obtained from MitoMap and MitoBreak. Circular Dichroism Spectroscopy, UV Melting Curves, and Thermal Difference Spectra-Oligonucleotides used to prepare G4 DNA substrates for biophysical analysis were purchased from Eurogentec (Seraing, Belgium) without further purification.
DISCUSSION The source of mitochondrial DNA deletions and rearrangements is debated  (47). A prominent hypothesis for a number of mitochondrion-based genetic disorders is that a significant fraction of mtDNA deletions are attributed to defective replication of the organelle's genome. Given the interest in and growing evidence that G-quadruplexes exist in vivo and can be sources of genomic instability, we sought to investigate their potential abundance in mitochondria by performing a metaanalysis of predicted G-quadruplex-forming human mitochondrial sequences. Using a G4 algorithm and information from mitochondrial deletion databases, we mapped predicted G-quadruplexes in the human mitochondrial genome to known mitochondrial deletion breakpoints and correlated this information with mitochondrial deletion breakpoints prevalent in human disease, cancer, and aging.
* This research was supported, in whole or in part, by the  National Institutes of Health ,  NIA ,  Intramural Research Program. 1 Recipient of a  European Union Marie Curie International Incoming Fellowship  (IIF). 2 Supported by the  Academy of Finland (Centre of Excellence funding)  and the  Netherlands Organization for Scientific Research (NWO) ,  VICI Grant   865. 10. 004. 3Supported by  Fondation ARC , the  Aquitaine Regional Council , and the  Agence Nationale de la Recherche  ( ANR Grants Quarpdiem and Oligoswitch ).
One of the most challenging fields in vehicular communications has been the experimental assessment of protocols and novel technologies. Researchers usually tend to simulate vehicular scenarios and/or partially validate new contributions in the area by using constrained testbeds and carrying out minor tests. In this line, the present work reviews the issues that pioneers in the area of vehicular communications and, in general, in telematics, have to deal with if they want to perform a good evaluation campaign by real testing. The key needs for a good experimental evaluation is the use of proper software tools for gathering testing data, post-processing and generating relevant figures of merit and, finally, properly showing the most important results. For this reason, a key contribution of this paper is the presentation of an evaluation environment called AnaVANET, which covers the previous needs.
net/ evaluation software for outdoor scenarios (Figure  1  includes a preliminary screenshot of the visualization). Both V2V and V2I networks can be efficiently analyzed, thanks to the integrated features for collecting results, post-processing data, generate graphical figures of merit and, finally, publish the results in a dedicated web site (if desired). All tests and results are later available in the form of an animated webpage where both researchers and the general public can access the evaluations. AnaVANET has been successfully exploited for the moment in experimental evaluation campaigns in the GeoNet  [6]  and ITSSv6  [7]  projects.
The software evaluation tool should accommodate to all of these scenarios. Easiness for data collection. In order to compare the network performance obtained when using different network protocols, a lot of experiments could be needed. This may require installing data collector software on many devices, depending on the scenario. Thus, the easiness of the installation of these software modules is very important. Of course, the most favorable case is to employ common software in all of them, such as tcpdump or cat. Flexible experimental data format. The experimental data should be stored in a well-organized way. Therefore, the data format needs to be flexible for future extension. For example, the user of the system could require adding new attributes to the data format of evaluation results.
Evaluation methodology As it is later described, the evaluation tool presented in this work (AnaVANET) copes with the previous requirements, but first it is important to identify a generic testing methodology that allow a researcher to success in a testing campaign with a vehicular network. In general, the evaluation goals in computer networks are to analyze which testing conditions affect which data flows or network protocols. For achieving this end it is necessary to design a proper evaluation methodology. Within it, we should consider the tendency of results by repeating tests with the same settings or varying parameters under study, such as the network protocol, the mobility of nodes or the data volume. A proper evaluation tool, such as the later presented AnaVANET, should support the overall analysis.
Testing conditions Among the various testbed conditions, the hardware specifications (CPU, memory, etc), antenna and wireless settings are important factors for the evaluation, since they will highly affect the results. In our case, MRs are Alix3d3 embedded boxes provided with a Linux 2. 6. 29. 6 kernel. Each MR has a mini-pci wireless card Atheros AR5414 802. 11 a/b/g Rev 0, and an antenna 2. 4GHz 9dBi indoor OMNI RP-SMA6 is used. The frequency used has been 2. 422Ghz and the data rate has been fixed to 6 Mbits/s. Testing scenarios. Fixing the evaluation scenarios beforehand is essential in the planning of a testing campaign. In general, the main factors that determine the possible scenarios are: Mobility Vehicle mobility is a key issue to cope with realistic vehicular network conditions.
Packet Delivery Ratio (PDR) is the percentage of packets received by the target node as compared with the number of packets sent by the source. iperf, for instance, shows this value at the receiver side when using TCP in an end-to-end manner, but AnaVANET is also able to calculate the PDR on each hop between the sender and destination nodes. In this part of the work AnaVANET is put in the context of the evaluation scenario described in the previous section in Figure  7 , showing also the main inputs and outputs of the tool. The sender MNN (left most vehicle) is in charge of generating data traffic, and both the sender and the receiver (right most vehicle) MNNs record a high level log, according to the application used to generate network traffic (iperf and ping6 for the moment). All MRs record information about forwarded data packets by means of the tcpdump tool, and log the vehicle position continuously.
All this data is post-processed by the AnaVANET core software and then analyzed. The tool traces all the data packets transmitted from the sender node to detect packet losses and calculate statistics for each link and end-toend, and then merge all these per-hop information with transport level statistics of the traffic generator. As a result, AnaVANET outputs a JSON file with statistics on a one-second basis (see Section6. 2 for details), and a packet trace file with the path followed by each data packet.
In this line, a normalized way of calling the attributes is also important. If there is no rule of normalization, developers have to handle differences of an attribute name (e. g. , temperature, Temperature, temp). AnaVANET was initially developed to analyze the real operation of VANETs. The initial data format used as output of an evaluation had some problems regarding its flat format and the dynamic columns available per each data record. Hence it took several hours to check the results after carrying out new experiments. To solve these problems, we have designed a structured and normalized format, considering the features of vehicular networks. Our format is extensible and independent from concrete experimental environments and visualization tools. We have also adapted the initial visualization tool with an internal converter module within the web application, and an additional command line tool has been implemented to process the output logs of AnaVANET in a text-based basis.
The new data format and tools enable us to check the results in several seconds after carrying out experiments, considering that users could require a fast evaluation to continue with new experiments. The new structured and normalized data format of AnaVANET considers three layers, as it is shown in Figure  8. AnaVANET summarizes data on each time slot following the next scheme. The top layer is the "experiment" layer. This layer mainly manages static attributes (e. g. ID of experiment or the name of experiment). The second layer is the "data" layer. This layer manages results of an experiment on each time slot. This layer has time, total packet delivery ratio (PDR), total RTT and other attributes. The third layer is comprised by the "node" and "link" parts. The node part manages each node's statuses, whereas the link part manages each link's statuses.
Evaluation of NEMO over IPv6 GeoNetworking Early versions of AnaVANET were designed for evaluating infrastructure less network protocols, as used in our previous works for analyzing OLSR in vehicular environments  [32]  and later tests of IPv6 over C2CNet  [33]  in the FP7 GeoNet project. The current version of AnaVANET can also analyze infrastructurebased network protocols such as NEMO. In this section, we report a summary of the results collected in the evaluation of NEMO over IPv6 GeoNetworking when a vehicle connects with a node located in the Internet using two roadside units as access routers. The umip. org 2  implementation of NEMO is used, whereas the cargeo6. org 3 software is used for { "exp_id": 1303883952, "name": "[ICMP] 27/4/2011 5:59:12", "exp_type": "ICMP", "timezone": 9, "data": [ {"time": 1303883952, "jitter": 0, "bandwidth": 0, "bytes": 64, "rtt": 3.
e. 300Kbps), however the PDR with the two ARs reaches 100%. This is because the throughput is measured between end nodes (MNN and a node in the Internet) by iperf and the PDR in the wireless links are calculated hop-by-hop by AnaVANET. In this case, it shows that more than 70% of the UDP packets are dropped outside the wireless links. In fact, the CarGeo6 software experimented a bottleneck in the processing of so many UDP packets at that time. This also explains the phenomenon where the binding registration messages are lost while none of the UDP packets are lost (this can be seen in the straight road in the south part of the circuit). In this case, the BUs are lost in the CarGeo6 software and are not transmitted from the wireless interface. We can detect where a packet is lost, especially the loss in a wireless link, thanks to the AnaVANET system (although the cause of the packet losses was not in the wireless links in the As can be seen in Figure  11 , AR2 is available most of the test period (especially, around the square) except for the end of the test.
Conclusions and future work The paper has presented the peculiarities of evaluating vehicular networks experimentally, through presenting the most used protocols and detailing the needs of the software tools to be used for this task. After that, the importance of the testing methodology is described, and a reference design of a vehicular network evaluation is used to exemplify it. The testbed design and implementation, testing scenarios, routing protocols and data flows, are found essential to be fixed beforehand to avoid improvisation during the testing campaign. The AnaVANET platform is then presented.
Easiness for data collection The system does not require special software to gather experimental data. Packet dumps are taken with tcpdump, and GPS NMEA data is obtained directly from a serial interface, to finally generate results. Flexible experimental data format We have adopted a structured and normalized format defining a three-layer model in order to increase the flexibility for future extension. Table  1. Qualitative evaluation of the system as an efficient evaluation software to process the data gathered by common testing tools, and then generate lots of performance indicators of the trials. All of these performance parameters are put in the spatio-temporal context, through the collection and correlation of GPS information, and most important figures of merit can be exported in the form of graphics or showed interactively in a web front-end.
The capabilities of AnaVANET are exploited in a novel evaluation of NEMO over IPv6 GeoNetworking, using the tool to gather RTT, PDR and channel throughput information. The results reveal that mobile IPv6 connectivity can be maintained in a V2I case using On the Experimental Evaluation of Vehicular Networks: Issues, Requirements and Methodology Applied to a Real Use Case GeoNetworking over WiFi to pass NEMO IPv6 traffic between vehicles and infrastructure. Our future work includes, first, a link layer extension of the system to analyze the channel quality (RSSI) and load ratio. This data will allow the development of coverage maps for the communication nodes. Second, it is considered the support for multicast data flows, since it is essential for the dissemination of events in vehicular networks.
Implementation The proposed framework is evaluated by our experimental vehicle, SetCar (Figure  2 ), introduced in Section 3. 1. The installed stereo vision system (Bumblebee XB3) observes the surroundings by stereo image pairs (with a resolution of 640 × 480) in a frame-rate of 10 fps with baseline length of 0. 24 m. The whole framework is implemented in C++, based on the OpenCV library http://opencv. org/, without any acceleration technique. A desktop computer with a CPU Intel i7-3770 quad core 3. 40 GHz running Linux is used to run the software. We will firstly discuss the performances of sparse feature point-based motion analysis and, then, evaluate the dynamic grid mapping results. The data set contains more than 4000 images acquired by our SetCar platform when driving in the city of Belfort, France.
The repeatability score is defined as: S r = f -/f * , where f -is the number of features found in both images and f * is the number of features detected in the reference image. • Uniformity: To precisely estimate motion in dynamic scenes, the detected features should be distributed as uniformly as possible. To evaluate uniformity, we divide the 640 × 480 image into n u disjoint identical cells. If the number of feature points located in one grid reaches is more than 2, it would be marked as "filled". The uniformity score is: S u = n - u /n u , where n - u is the number of "filled" cells. Ten typical and different images from the database are selected for testing the feature detectors. To evaluate repeatability, we use a similar method as  [45] , by converting an image by a homography transformation.
When tracking or matching through four images, this phenomenon could be amplified and cause severe problems. The survival rate is calculated by survivalrate n lef t n whole , where n whole is the number of whole detected features and n lef t is the number of successfully tracked or matched features. • Accuracy: The accuracy of established point-to-point correspondences can be evaluated from the ratio between inliers and the whole features when applying RANSAC for ego-motion estimation. The accuracy factor is: n inlier n whole. To exclude distractions of independent moving objects, we only use images without moving objects. Forty images (10 groups of consecutive stereo image pairs) are selected from the image database. For a fair comparison, we control the number of detected feature points between 400-500.
Conclusions and Future Works In this paper, we present a framework of a dynamic occupancy grid mapping technique. The framework mainly consists of motion analyzing and dynamic occupancy mapping. The motion estimation is achieved based on circularly-tracked feature points. A U-V disparity map-based independent moving object segmentation is also presented. The dynamic occupancy grid mapping is performed with a 3D reconstructed point cloud. The combination of the segmentation of a moving object and the occupancy probability estimation results in the final dynamic occupancy grid map. For future works, we are planning to improve the performance when many pedestrians appear, to make dynamic occupancy grid mapping smoother and to integrate LiDAR measurements within the data fusion framework.
XSS. The XSS attacker is able to inject malicious script code into benign web pages. The code runs in the context of the benign domain and can impersonate the user. There is actually no difference between XSS attacks on mobile browsers and desktop browsers. Session Fixation. During a session fixation attack, the attacker sets a session identifier before the user logs in. The attack is successful if the SID is not changed during the login. The attacker's window of opportunity lasts until the user logs out. The mobile case is very similar to the desktop case. However, sessions in some mobile web applications expire later  [4] , or do not expire at all but only delete the client-side session cookie upon logout  [7]. This extends the attacker's control over the user's account.
Components The overall architecture consists of three main components: The actual Mo-bileAuthenticator that runs on the mobile device and provides the trusted UI, a server-side module that evaluates incoming requests and checks the integrity of the authentication token, and a JavaScript library that is delivered to the browser and takes care of delegation between all participants. MobileAuthenticator. The client-side component, the MobileAuthenticator, maintains a repository of preconfigured authorized actions (see Sec. 3. 3) including a human understandable description of each action's impact. Upon receiving a security critical request from the browser, it looks up the respective action's details in its repository, displays the description to the user, and asks for consent. The MobileAuthenticator signs the request using a shared secret (see Sec.
3. 3) with the web application, and passes it back to the browser, if the user agreed. Server-Side Module. On the web application's server side a counterpart is needed that maintains a trust relationship with the user's MobileAuthenticator instance and implements the challenge/response process to accept incoming authorized actions. AuthenticationBroker. The AuthenticationBroker is a small JavaScript library that provides the necessary interface to the application's web front-end to delegate authorized actions to the MobileAuthenticator for obtaining user consent. Upon receiving the MobileAuthenticator's response, the acknowledged request is routed to the web application for processing. It is evident that the AuthenticationBroker itself is not security critical. This is an important fact because otherwise malicious injected script code might be able to manipulate or disable the AuthenticationBroker and, thus, run an attack.