dataesr/scientific-paragraphs-categorization huggingface dataset:
A. State of The Art Protocol The protocol proposed by Islam et al. [2]  is presented in Figure  1. Listing 1 gives an excerpt of the Verifpal script that models this protocol. The upgrade request (lines 1-10) and data reception (lines  [11] [12] [13] [14] [15] [16]  phases are shown. The first message transmission represents the request for SoC configuration modification and the adequate payment by Bob. To get this request written into the Blockchain, Bob uses his own node, which broadcasts it to all other nodes (including Alice's node). The actions performed by the Blockchain are represented at line 8 in the code by a hash operation on all the constants it has received, namely: the list of available configurations (previously initialized by Alice), and the state of the configuration requested by Bob.
When the SoC makes a request to retrieve its configuration state (line 11), it is this hash that is provided to it, as well as the data it needs to check it. The check is performed line 15 with the ASSERT primitive, which compares the hash provided by Bob's node to the SoC's own hash reconstruction. If an attacker is able to fool the SoC, then this highlights a flaw in the protocol or in our model. Verifpal will verify this thanks to the authentication query. This query is used to ensure that an attacker is not able to modify the content of the message sent to the SoC. The execution of this Verifpal script gives the output shown in Figure  2. The execution of this Verifpal model gives a counterexample. It shows that an attacker is able to create an alternative version of the Blockchain content that will considered as valid by the SoC.
We could also imagine that Alice and Bob agree to use a node from a trusted third party. But in this situation, the benefits of decentralization is lost. 2) Add a Contract-like Signature System: One way to allow the SoC to trust the data received from Bob's node is to have Alice countersign Bob's reconfiguration requests. Figure  3  presents the sequence diagram showing one way to achieve this. Listing 2 shows the Verifpal code that models the upgrade request (lines 4-20) and data reception phases (lines 21-28). With this protocol, when Bob makes a configuration request via the Blockchain, the payment made to Alice is blocked. Alice is notified and generates the certificate that unlocks the configuration. By sending this configuration via Blockchain the payment can be unlocked (the Blockchain is able to verify the certificate).
VI. THREATS TO VALIDITY There are several points in our approach that may be critical to the validity of our results. First, the used tool is not perfect. Indeed, Verifpal is still beta software, and the author does not guarantee that verification is exhaustive  [6]. The models are nonetheless simple enough to have high confidence in the abilities of Verifpal to verify them. Second, the Verifpal models have been simplified to the extreme, and mainly express the trust relationships. This means that one could blame them for pointing out the obvious. In the case of the flaw demonstration in the studied protocol, this can indeed be the case, as the counterexample can be found without tools. However, the validity of the proposed fixes is, in this situation, more difficult and the use of a protocol verification tool is an improvement.
Bacterial community characterization The bacterial community of B. tabaci collected in Burkina Faso was characterized on 72 individuals by a metabarcoding approach, without a priori assumptions, using universal bacterial primers targeting the 16S rRNA gene and an Illumina sequencing technology. Between 107,000 to 927,000 reads were obtained per sample (average: 215,000 reads ± 9,420). The majority of the reads belonged to the known B. tabaci Pand S-symbionts and, in most field individuals, sequences from Ca. Portiera aleyrodidarum constituted the majority of reads (up to 98. 1%; 71. 2% on average ± 2. 9%; see "table_level6" in Dryad, https://doi. org/10. 5061/dryad. 547d7wm91, and Figure  1 ). We detected bacterial taxa belonging to the genera Acinectobacter in few samples at low abundances since they represented between 0 and 7,4% of the sequences obtained per individual, both in field and lab samples.
e. 8 MED-Q1, 7 MED-Q3 and 5 ASL). This sequence showed 100% similarity with the ones of Ca. Hemipteriphilus asiaticus endosymbiont and Rickettsia of B. tabaci available in the genbank database (Blast done in December 2021). 16S rRNA gene is highly conserved  (36)  we thus designed new primers on two other loci, GltA and GroEL, using the sequences of the Ca. Hemipteriphilus asiaticus isolate YH-ZHJ available in Genbank  (20). The 190bp sequences of GltA were identical in all our 20 samples, but one substitution was found in the GroEL sequences (269bp) between MED individuals (Q1 and Q3) and ASL individuals, whatever their sampling locality. Analyses of the concatenated sequences obtained for the three loci (942bp in total) revealed ~97% identity between the Ca. Hemipteriphilus asiaticus strains identified in Burkina Faso and the YH-ZHJ reference isolate from the China B.
However, our results did not reveal any influence of Ca. Hemipteriphilus asiaticus on the Ca. Portiera aleyrodidarum density. Clearly, life history traits should be measured on Ca. Hemipteriphilus asiaticus-infected and -free B. tabaci individuals. Anyway, the high frequency of these two newly reported S-symbionts in some field populations of these phloemophagous insects could suggest they bring benefit to their hosts. Further research on the infection dynamics of these S-symbionts, and their role on their host phenotype and adaptation are needed. Compared to the previous field survey done in Burkina Faso by Gnankiné et al. (27) , data on the infection with S-symbionts are highly similar except for the presence of Ca. Hemipteriphilus asiaticus, which was not described at that time. In particular, the infection rate involving at least one S-symbiont were higher than 90% in the two studies.
B. The pooled library was PE-sequenced using the Illumina MiSeq reagent kit version 3 for 600 cycles (2x300pb) by Biofidal (Vaulx enVelin, France). The sequencing data in FASTQ format were processed and analyzed with the QIIME2 software suite version 2021. 11 The raw Illumina reads were imported into QIIME2, demultiplexed, and then denoised, trimmed and filtered with DADA2 pipeline to remove noisy and chimeric sequences, to construct denoised paired-end sequences and to dereplicate them  (51). This produced a table containing representative sequences also called amplicon sequence variant or ASV. The taxonomy assignment was then performed by using feature-classifier classify-sklearn. For that, reads from the Silva 138 SSURef NR99 reference database were extracted to match on the primer set 341F/805R.
The Naive Bayes classifier has been trained before the classification  (52) (53). A taxa barplot has been done on all the data with "qiime taxa bar plot" (except chloroplast sequences that have been removed; Dryad, https://doi. org/10. 5061/dryad. 547d7wm91) and a heatmap has been produced only on field samples for major taxa with "qiime feature table heatmap" (Figure  1 ).
Primer design We designed new primers for Ca. Hemipteriphilus asiaticus using sequences available in Genbank (20) with the Primer3 software (https://bioinfo. ut. ee/primer3-0. 4. 0/). We aligned sequences of Ca. Hemipteriphilus asiaticus with sequences of Rickettsia from B. tabaci in order to design primers specific to Ca. Hemipteriphilus asiaticus that do not amplify sequences from Rickettsia. These primers were tested on individuals harbouring Rickettsia but not Ca. Hemipteriphilus asiaticus and we didn't detect any amplification. Multiple sequence alignments were done using the MUSCLE algorithm (54) implemented in CLC DNA Workbench 8. 0 (CLC Bio). Three sets of primers targeting three different genes of Ca. Hemipteriphilus asiaticus, 16S rRNA, the citrate synthase GltA and the chaperonin GroEL were designed (Table  2 ).
0 (CLC Bio) and inspecting them by eye. Moreover, we studied the phylogenetic relationships of the Ca. Hemipteriphilus asiaticus strains found in our samples with the reference strain described in B. tabaci (isolate YH-ZHJ; 20) as well as other symbionts belonging to the Rickettsiales family, Rickettsia, Orientia tsutsumagushi and Sitobion miscanthi L Type Symbiont (SMLS). Phylogenetic trees were constructed with CLC DNA Workbench 8. 0 (CLC Bio) using maximumlikelihood method for each sequence separately and for the concatenated data set (substitution model: GTR + G + T, choosed by using the "Model Testing" tool of CLC DNA Workbench 8. 0). The robustness of nodes was assessed with 100 bootstrap replicates. We also constructed a phylogenetic tree with Bayesian inference using the program MrBayes (version 3.
Acknowledgements This study was funded  Campus France , the  Agence Nationale de la Recherche  (HMicMac,  ANR16-CE02-00014 ) and the  Scientific Breakthrough  Project Micro-be-have (Microbial impact on insect behavior) of  Université de Lyon , within the  programme 'Investissements d'Avenir'  ( ANR-11-IDEX-0007 ;  ANR-16-IDEX-0005 ). The authors thank  Agnès Nguyen  from the  Biofidal company  for her precious advice and collaboration on the metabarcoding part. A preprint version of this article has been peer-reviewed and recommended by  Peer Community In Zoology  (https://doi. org/10. 24072/pci. zool. 100011).
Introduction Behavioral assays are commonly used to measure various types of learning. Previously available assays incorporate apparatuses such as shuttle-boxes  [1] [2] [3] [4] [5] , forced choice Tmazes  [6, 7] , three compartment mazes  [8, 9]  for studying the effects of nicotine  [10, 11] , and plus-mazes  [12]  to conduct behavioral research in different species. Most of these assays require some level of human intervention, which can make the experiments cumbersome, time-consuming and sometimes costly. Most importantly, animal handling can stress the animals and introduce noise in the data  [13, 14]. Conditioned place preference assays  [15, 16]  are simple and fast, but typically require fear conditioning, which is itself stressful to the animal. Here, we describe a new type of behavioral assay that minimizes stress and provides a fast, reliable way to train and test an animal's ability to discriminate between sensory stimuli.
The custom software generates event-synchronized, short (10 to 30 s) video data files that can be analyzed using automated imaging software developed by others  [18]. The training and testing apparatus together with scheduling and control software, dubbed "NemoTrainer" (vers. 1. 0), was developed specifically to communicate with an external microprocessor (Arduino UNO r3; www. arduino. cc, accessed on 20 December 2022). One way we implemented NemoTrainer was through Java (https://www. oracle. com/java/, accessed on 20 December 2022) running on a personal computer (PC), to allow high-resolution video recording and cloud storage capabilities that are well beyond that of either an Arduino or a Raspberry Pi microcontroller. An Arduino provides a convenient interface to integrate the analog functionality of a robotic setup.
Design Philosophy Our goal here was to create a user-friendly, low-cost, automated behavioral training system that could be easily replicated and expanded for high throughput use with multiple animals. As such, we designed a space-efficient system, and employed readily available, cost-effective technology and materials. Using our system, it is possible to perform both auditory and visual training by means of a fully programmable, schedule-based combination of proximity sensors, LEDs, and an output transducer. Using either a unique food delivery setup in combination with aversive stimulation, reward-and fear-based conditioning, respectively, can be implemented to entrain and test fish behavior related to learning and decision making. Here, we focus on an exclusively reward-based paradigm. To present a comprehensive package for data collection and analysis, we also briefly report our findings on video analysis and animal tracking software available at the time of this study.
Hardware and Software Design Features We used Arduino, a dynamic microcontroller, for controlling external devices such as stepper motors, servos, sensors, LEDs, etc. that are ordinarily not configurable by a Another way to effectively use NemoTrainier is to employ an Arduino script (https:// github. com/SinghB13/NemoTrainer, URL accessed on 20 December 2022) in combination with GoBetwino software (http://mikmo. dk/gobetwino. html, accessed on 15 January 2019)-an event-driven application controller running on the PC to synchronize timing of Arduino-connected device functions with actions by PC-based software. Functions defining arm movements, feeder movements, and power to LEDs, were written according to the type of training to be conducted. Gobetwino-specific commands defining PC-based keystrokes were incorporated into functions to control PC-based software, such as Debut for video capture, thereby allowing specific recording and sensory stimulus presentation durations.
Additional user-specifiable interface settings include record-end delay, LED selection and duration, auditory stimulus selection (by specifying the location of a saved sound file on the PC) and duration, degrees of arm rotation, and simple or enhanced feed. Reps conclude with the end of a video recording, the length of which is based on the sum of all delays and triggers plus an additional delay to extend the recording if desired. Once the number of reps specified per run is reached, the run is concluded and the software waits until the time of the next scheduled run to begin the same.
Computer Interface and Operation The user-defined scheduling interface (Figure  3A ) was designed for manual entry of all run and trial control parameters prior to starting an experimental trial. This information sets up the design of the experiment by setting up time delays for various devices, video recordings, etc. to be activated over several days. Each scheduled run begins with onset of video recording for a defined time interval. Launching NemoTrainer software package from the PC opens a window with four dropdown menus at the top and the user-defined scheduling interface in the space below (Figure  3A ). Details of each dropdown menu are as follows: File: contains options to save a trial, load an existing trial, and quit Nemo Trainer. Ardulink: contains an option to establish or terminate a link between Nemo Trainer, and Arduino, and associated hardware (motors, sensors, LEDs).
This interface enables any user to comprehensively plan and automatically begin multiple trials. Below the scheduler-interface space are two buttons on the left to configure trial parameters and two buttons on the right to turn sensors on and off. In addition, two dropdown selectors above the sensor buttons are used to specify sensor pin connections. The two sensors can be connected to any of the numbered pins on an Arduino, but the numbers selected here should match the physical connections. These sensor activity monitors enable quick and simultaneous access to live feedback from each of the two sensors. The pins specified here also specifies within the NemoTrainer software which pins to monitor during a trial. Clicking each button enables live feedback on the status of the sensors, which can be viewed regardless of whether a trial is ongoing or not.
During online feedback, when a sensor is triggered, the circle above the button corresponding to that sensor becomes bright and text changes to "High" to indicate that it was triggered. Conversely, when a sensor is not triggered, the circle above the corresponding button remains dark and the text on its side changes to "Low". Finally, at the bottom far-right corner of the interface panel is an icon indicating the status (connected/disconnected) of the link between NemoTrainer and Arduino. An Ardulink sketch must be uploaded to the Arduino prior to launching NemoTrainer to establish the link. Since the Arduino communicates with the PC via a USB port, the custom program must establish a connection to the USB port, and for the duration of a trial, maintain an uninterrupted line of communication with the Arduino.
This enables live sensor and event feedback without any additional hardware normally required for feedback. Furthermore, it allows users to pause, make changes to parameters, and resume trials without having to end and start a new trial. Due to the precision needed for arm placement, food delivery, and all other training parameters, it is critical to ensure that all functions work as expected prior to starting a trial. As such, we have developed a testing window within NemoTrainer through which users can independently test LEDs, sound presentation, video recording software, and motor movement. In addition, users can specify the parameters of a run and conduct a test run, which in addition to the previously mentioned functions, also tests delays and sequence of events, to ensure that all values specified for a run meet their requirements.
Example Training Paradigm Zebrafish can demonstrate visual learning in as few as 4 training sessions  [8]. In one of the training paradigms, we conducted four days of training consisting of six training sessions per day. Given the well-established visual acuity in zebrafish  [34] , we paired different colored LEDs with different sound stimuli when testing their discrimination ability. Three distinct sounds were synthesized using software available at Wavtones. com (https://www. wavtones. com/functiongenerator. php, accessed on 20 December 2022), and filtered and normalized (RMS) using Audacity (https://www. audacityteam. org/, accessed on 20 December 2022). Sounds 1 and 2 were paired with specific food delivery locations, and sound 3 was played without food delivery to discourage nonspecific association of sound with food delivery.
Lighting is a critical factor in the successful application of automated tracking software. For videos with less-than-optimal lighting, one can resort to manual tracking. This provided verifiable quantitative data on fish position. Image-J allows for automated as well as manual tracking of multiple zebrafish. Although this method is slow and involves manual marking of fish in each video frame, it offers a reliable choice for any critical short-duration (<10 s) videos. Additional information on use of software packages is included in Appendix B. Behavior was recorded prior to and post-sound and LED onset to compare arousal using swim-speed and darts. To measure learning during classical conditioning, three zones were specified with increasing distance from the feeder location. We selected two time-windows, a window prior to and another post-sound/LED onset for analysis.
Results We successfully performed automated training and testing of zebrafish as well as recorded and analyzed their social and swimming behavior using available animal tracking software. We describe both exemplary and quantitative data indicating that zebrafish can be conditioned to respond to audiovisual stimuli via classical and operant conditioning. To test our setup and training paradigm, we trained fourteen zebrafish (seven for classical, and seven for operant conditioning) over a relatively short time period (three to four days). Generally, from the first feeding on day 1 of training and onwards, zebrafish showed a significant increase in arousal following sound onset. This was reflected in the increase in the frequency of turns and changes in swim speed.
This is a focus of some of our ongoing work. Examples of three separate "runs" recorded at the beginning, during and at the end of training illustrate navigational learning in zebrafish (Figure  5 ). Swim direction and trajectories were visualized using manual data entry from Image-J, from individual frames of a 30 frames/s video recording. In the "untrained" condition (Figure  5A ), the animal continues to swim away from the sensor and around the tank and sensors. In the "partially trained" condition, the animal responds to the sound and moves to the right location to trigger the sensor, but fails in its attempt (Figure  5B ), and in the "trained" condition, it succeeds in triggering the sensor twice: first, swimming in front of it (red arrows) to trigger a food reward, and then circling back (red arrows) to trigger it once again before proceeding to the feeding location (Figure  5C ).
Notable Program Features The scheduling interface is an important and convenient feature of our Java-based training software. It facilitates a high level of customization and allows one to set up and visualize the training paradigm for as many days as required. Our training of animals is incentive-rather than fear-based. Fear-conditioning can lead to stress and deviations from natural behavior. In contrast to the classical conditioning methods, operant conditioning builds on each instance of observation/response and reinforces an overall goal that the animal must learn to accomplish. Testing for associative learning typically entails the presentation of the conditioned stimulus without a reward at the end of training. Each such test can lead to extinction effects, which can confound the results over a series of tests, i.
Imaging and Tracking Options Excellent packages have been designed to track small freely moving organisms, such as fruitflies and round worms. idTracker was designed to specifically track zebrafish, although it has been tested on fruit flies, ants, and mice as well  [18]. Using various program features, idTracker can be used to identify territoriality and leadership attributes of an individual, making it a particularly useful tool in studying social behavior. One of the major challenges with tracking zebrafish is accurately identifying individuals once they swim past or over one another. idTracker resolves this issue by determining and assigning a specific fingerprint to each individual fish and using that fingerprint to identify the individual throughout the tracking process. The fingerprint of each fish is used in all frames of the recording, allowing idTracker to track with a 99.
It is also important that training starts as soon as fish are introduced into the tank and are still engaged in active swimming to explore their environment. During this active swimming period, it is critical for fish to learn that swimming in certain locations, i. e. , near sensors, can result in stimulus and/or reward presentation. This, in effect, establishes that the environment is dynamic rather than static and thus leads to further exploratory behavior during a trial. Otherwise, if a fish has exhaustively explored its environment without any stimulus or reward presentation during this critical exploration period, its behavior becomes more sedate and subsequently leads to decreased responsiveness during a trial. NemoTrainer can be further modified to incorporate this feature. Troubleshooting is an important part of a custom hardware-software setup.
Conclusions Our results suggest that our training apparatus and software (NemoTrainer) can be used for either classical or operant conditioning of freely moving animals. This can allow experimenters to test sensory discrimination capabilities of any species as well as allow high-throughput screening of drugs to study their effect on learning and memory, or for any other type of behavioral assessment. Here, we used NemoTrainer to test the ability of zebrafish to discriminate between different colored LEDs and sound patterns. We demonstrated that adult zebrafish can learn to respond to food reward in a navigational memory task in <5 days and sometimes in <3 days without any human intervention.