dataesr/scientific-paragraphs-categorization huggingface dataset:
Instruments and Data Acquisition EMG signals were recorded from the following eight upper limb and trunk muscles: Deltoid Anterior (DA), Deltoid Posterior (DP), Pectoralis Major Clavicular (PMC), Pectoralis Major Sternal (PMS), Biceps Brachii (BB), Triceps Brachii (TB), Trapezius Descending (TD) and Latissimus Dorsi (LD). The skin area was cleaned with alcohol wipes and the electrodes were attached in pairs with a center-to-center distance of 25 mm, based upon recommendations reported in the previous literature  [44]. After similar skin preparations, a ground electrode was attached to the anterior aspect of the leg over the tibial bone. The EMG signals were recorded with a commercially available EMG system (TeleMyo 900, Noraxon, Scottsdale, Arizona, USA). All EMG signals and hand switch data were sampled at 1200 Hz.
First Strategy At first, all EMG signals were analog filtered using a band pass filter between 30 to 500 Hz by 6th order Butterworth filter, rectified and then low-pass filtered at 100 Hz  [45]. The mean and standard deviation (SD) of the signal were computed between 1. 5 to 0. 5 s immediately before the perturbation. Response onset latencies were Sensors 2023, 23, 1132 5 of 14 determined as the time at which the rectified EMG signal exceeded a threshold of 2×SD above the mean baseline for a period of at least 25 data points  [4, 46, 47]. EMG onset latencies were computed for all muscles and then the average and SD were calculated for all trials in all subjects.
Third Strategy (Cepstral Analysis) The feature of neutral delay-differential equations is mainly that the delay of the neutral part can be detected in the cepstrum of the output signal, which motivated one study  [42]  to estimate the delay of the acceleration feedback term in stick balancing tasks on kinematic data for healthy individuals. Thus, the cepstral analysis was used in this study as the third strategy for time delay estimation in SCI people. At first, the cepstral transformed signal of each EMG signal was obtained from the smoothed signals of the second strategy (TKEO) as shown in equation 2, in which F and F -1 represent Fourier transform and T(n) is the signal time series after performing the TKEO technique. The frequency domain of 0-0. 5 s was examined to find the sharp peaks.
Firstly, the full amplitude burst c-VEP sequences exhibited higher accuracy, ranging from 90. 5% (with 17. 6 s of calibration data) to 95. 6% (with 52. 8 s of calibration data), compared to its m-sequence counterpart (71. 4% to 85. 0%). The mean selection time for both types of codes (1. 5 s) compared favorably to reports from previous studies. Secondly, our findings revealed that lowering the intensity of the stimuli only slightly decreased the accuracy of the burst code sequences to 94. 2% while leading to substantial improvements in terms of user experience. Taken together, these results demonstrate the high potential of the proposed burst codes to advance reactive BCI both in terms of performance and usability. The collected dataset, along with the proposed CNN architecture implementation, are shared through open-access repositories.
None of the participants reported any of the exclusion criteria (neurological antecedents and being under psychoactive medication at the time of the study). The study was approved by the ethics committee of the University of Toulouse (CER approval number 2020-334) and was carried in accordance with the declaration of Helsinki. Participants gave informed written consent prior to the experiment. The anonymized data collected within the frame of this study are available at https://zenodo. org/record/ 8255618.
, 𝑡 𝑛 ] is the sequence of the variable part between each burst, including the minimal duration, shift is the phase of the code (circular right-shift), and R is the screen refresh rate the code is designed for (as the duration is expressed in frames rather than milliseconds). This notation contains all the necessary information to generate the code data array. Refer to Fig. 1  for an illustration of how they relate. In assessing the correlation across distinct burst sequences, we capitalize on the concept of a burst's ''neighborhood'' -instances where bursts from divergent codes are in closer proximity, thus exhibiting heightened correlation. By examining the neighborhood surrounding bursts within one code concerning another, we ascertain their relative closeness. In the context of a code 𝑐 1 , each individual burst undergoes scrutiny for its distance in relation to bursts within a separate 𝑐 2 code.
Experimental protocol Participants were comfortably seated and instructed to read and sign the informed consent. EEG data were recorded using a BrainProduct LiveAmp 32 active electrodes wet-EEG setup with a sample rate of 500 Hz to record the surface brain activity. The 32 electrodes were placed following the 10-20 international system on a BrainProduct Acticap. The ground electrode was placed at the FPz electrode location and all electrodes were referenced to the FCz electrode. The impedance of all electrodes was brought below 25kΩ prior to recording onset. Once equipped with the EEG system, volunteers were asked to focus on four targets that were cued sequentially in a random order for 0. 5 s, followed by a 2. 2 s stimulation phase, before a 0. 7 s inter-trial period. The cue sequence for each trial was pseudo-random and different for each block.
After each block, a pause was observed and subjects had to press the space bar to continue. The participants were presented with fifteen blocks of four trials for each of the four conditions (burst or msequence × 40% or 100%), see Fig. 2  -left. The task was implemented in Python using the Psychopy toolbox. 1 The four discs were all 150 pixels, without borders, and were presented on the following LCD monitor: Dell P2419HC, 1920 × 1080 pixels, 265 cd/m 2  , and 60 Hz refresh rate. After completing the experiment and removing the EEG equipment, the participants were asked to provide subjective ratings for the different stimuli conditions. These stimuli included burst c-VEP with 100% amplitude, burst c-VEP with 40% amplitude, m-sequences with 100% amplitude, and m-sequences with 40% amplitude.
EEG pre-processing EEG data and markers were synchronized during recording using Lab Streaming Layer. 2 EEG analyses were conducted only on a subset of specific electrodes: O1, O2, Oz, Pz, P3, P4, P8, and P9. The raw continuous EEG data were average re-referenced. An IIR cut-band filter between 49. 9 and 50. 1 Hz of order 16 was then applied to remove line noise. The raw continuous data were then epoched from 0 to 2. 2s around timestamps of flickers onset. Finally, a baseline removal was performed on the epochs to remove eventual slow drifts.
Convolutional neural network and pattern decoding The CNN architecture 3  used in this study is the same as in our previous work  (Darmet et al. , 2023). It is an improved version of EEG2Code  (Nagel and Spüler, 2019b) , which demonstrated superior performance in terms of selection time and accuracy compared to the original architecture. This CNN architecture comprises three blocks of convolutional layers and ends with dense layers block. Each convolutional block is defined as a sequence of the following layers: (1) a 2D convolutional layer, with the number of filters, kernel size, stride and dilation parameters dependent on the block's depth, (2) a batch normalization layer, scaled but not centered (3) a Leaky Rectified Linear Unit (LeakyReLU) activation function layer, with a small negative slope of 0.
Classification analysis In this study, we evaluated the number of needed calibration data with an iterative approach: starting with one block of calibration (2. 2 s × 4 stimuli × 1 block = 8. 8 s of data) and gradually increasing it to six blocks of calibration (2. 2 s × 4 × 6 = 52. 8 s of data). This approach allowed us to compare the effects of two stimuli (burst versus m-sequence) and amplitude depth reduction on CNN training time, 4class selection time, and 4-class decoding accuracy. To simulate an online setting, we performed sequential train/test splits in each of these computations. The epochs were segmented into windows of 250 ms with a stride of 2 ms (one data point of EEG data sampled at 500 Hz). We computed the standard deviation within each time window of the calibration dataset enabling standard deviation normalization.
VEP analysis The effect of experimental manipulations on Visual Evoked Potentials (VEP) responses was investigated to better understand the neural underpinnings that may explain differences in terms of classification performance. All the EEG analyses were ran using Matlab (R2021b) (The MathWorks Inc. , 2021) and EEGlab (2019. 1)  (Delorme and Makeig, 2004). For this purpose, the continuous EEG data was bandpass filtered between 0. 1 and 40 Hz (FIR, 16,501 filter order, cutoff frequencies at 0. 05 Hz and 40. 05 Hz) and then average rereferenced. The onset of singular visual stimuli (within the whole sequence) was defined based on the timings at which the code sequences alternated from the dark to the bright states. The continuous EEG data were then segmented into epochs around the onset of visual stimulation onset (from -0.
Statistical analyses Statistical analyses were carried out with JASP 0. 18 software (JASP Team, 2023). 2 × 2 repeated measure ANOVA, with factors of c-VEP type (burst, m-sequence) and amplitude depth (low, high), were computed for each subjective metrics (visual comfort, mental tiredness and intrusiveness) with types of codes and luminance as within factor. Similar 2 × 2 repeated measure ANOVA were computed for each objective metrics (classification accuracy, selection time, CNN training time, information transfert rate) when considering 6 blocks of calibration data (4 flickers × 2. 2 s × 6 blocks = 52. 8 s). We also ran some descriptive analyses to report the effect of the number of blocks of calibration trials (from one block to 6 blocks) on classification accuracy and CNN training time.
For both types of c-VEP stimulation, a CNNbased decoding method was applied. This CNN-based decoding approach was demonstrated to yield superior performance in terms of classification accuracy and selection time for the decoding of msequence c-VEP than traditional template-matching methods  (Nagel and Spüler, 2019b; Darmet et al. , 2023; Huang et al. , 2023; Li and Huang, 2021). Nonetheless, it is critical to consider alternative classification and decoding methods to explore solutions that will further enhance BCI performance. Future work should therefore focus on a more systematic comparison across state-of-the-art methods traditionally used for the decoding c-VEP signals (for a review see  Martínez-Cagigal et al. , 2021). By making the dataset publicly available, we encourage fellow researchers to apply their own classification and decoding methods to both types of c-VEP stimulation patterns.
KB + MCO Plan The trade-Off Exploration tool of Eclipse available in HSR radiotherapy research station (TBox Varian Eclipse, 16. 1) allowed real-time exploration and visual assessment of the range of trade-offs in target coverage and preservation of healthy tissue. Once a previously optimized plan, i. e. the KB-plan, was available, structures and targets for the trade-off examination were chosen. A collection of optimized and representative plans was then generated and used to examine the compromises among optimization objectives. The plan database was examined using the selected objective sliders to find the best-compromised plan for the purpose. Defined clinical objectives were also used to limit the range of examination. DVHs, isodoses and clinical objectives values were examined to assess the impact of the trade-offs; a figure explaining the system functionality is available in the Supplementary material (Figure  S1 ).
18. This group had a mean stature (1. 693-m) and body mass (65. 0-kg) equivalent to 19th the 30th percentiles respectively for US females 28 , giving a mean BMI of 22. 7-kg•m -2 , equivalent to the 18th percentile for US females  28. In comparison, the male astronauts (n = 30) in the study of Moore et al. 18  whose data were used by Scott et al. 9  had a mean stature of 1. 755-m and body mass of 81. 6-kg, equivalent to the 51st and 40th percentiles for US males, resulting in a mean BMI of 26. 5-kg•m -2 (36th percentile for US males)  28. As such, it is possible that this small group is not truly representative of the ISS female astronaut population (38 individuals as of March 2021)  44. Standardised (1. 50-m, 1. 60-m, 1. 70-m, 1. 80-m, 1. 90-m) stature intervals across the range accepted by the ESA, NASA and CSA for astronaut recruitment were used to make a direct comparison between theoretical female and male astronauts.
In this sense, two categories were considered: (1) Congruent scores: sum of congruent normal percentiles (<90 for both CEF-B and BRIEF) and congruent deficits (percentiles ≥90 for both CEF-B and BRIEF); (2) Incongruent deficits: BRIEF percentile ≥90 and CEF-B percentile <90 or BRIEF percentile <90 and CEF-B percentile ≥90. Based on these categories, we established an overall congruence percentage. The agreement rate was classified using Cohen's kappa  (Landis and Koch, 1977). Finally, we used Spearman's rho correlation coefficient and t-test to analyze the associations between sociodemographic and injury severity variables (gender, SES, age at injury, time since injury, and intelligence tests) and executive scores. Statistical analyses were performed using SPSS v. 20. 0  (IBM Corp. Released, 2011).
Protein identification and quantification The. raw files were analysed with MaxQuant  (Tyanova et al. , 2016)  version v 1. 5. 0. 0 using default settings. The minimal peptide length was set to seven. The criteria 'Trypsin/P' (which means C-terminus peptides of 'K/R' unless followed by 'P': 'K/R' followed by 'P' cannot be a cleavage site) was chosen as the digestion enzyme. Carbamidomethylation of cysteine was selected as a fixed modification and oxidation of methionine and acetylation (protein N terminus) as variable modifications. Up to two missed cleavages were allowed. The mass tolerance for the precursor was 20 and 4. 5 ppm for the first and the main searches, respectively, and for the fragment ions was 20 ppm. The files were searched and a global database was built (759,499 entries) compiled from Drosophila, Ceratitis, Glossina morsitans, Glossina palpalis, Trypanosoma brucei gambiense, Trypanosoma brucei brucei, Trypanosoma congolense, Wolbachia, Sodalis, Wigglesworthia, Spiroplasma, Homo sapiens, Sus scrofa, Bos Taurus and Ovis aeris protein databases from UniProtKb (2022-02).
Identified proteins were filtered according to the following criteria: at least two different trypsin peptides with at least one unique peptide, an E value below 0. 01 and a protein E value smaller than 0. 01 were required. Using the above criteria, the rate of false peptide sequence assignment and false protein identification were lower than 1%. Proteins were quantified by label-free method with MaxQuant software using unique and razor peptide intensities  (Cox & Mann, 2008). Statistical analyses were carried out using RStudio package software. The protein intensity ratio (protein intensity in infected fly/protein intensity in uninfected fly) was tested for significant differences in protein abundance using a t-test. Hits were retained if they were quantified in at least three of the four replicates in at least one experiment.
Functional annotation The Gene Ontology (GO) annotation of proteins was performed using the UNIPROT Mass Spectrometry Data Analysis Site (The UniProt Consortium, 2017). Protein denomination homogenising and alphabetical classifying allowed an easy visualisation of the isoforms of given proteins-they are distinguished from each other by distinct accession numbers. Further, this classifying process allowed discrimination of subunits for a given protein (e. g. , for the subunits of the eukaryotic translation initiation factor 3), or of proteins involved in multiproteic complexes (such as proteasome), or in the structure of biological particles (such as ribosomes).
Epidemiological data The overall epidemiological data were presented by Tsagmo  Ngoune et al. (2017). A total of 1991 flies were trapped (775 in Campo; 1216 in Bipindi); most of them belonged to the G. p. palpalis species (95. 61% of the flies trapped in Campo; 99. 33% of those from Bipindi). The other fly species identified in Campo were composed of Glossina caliginea (2. 06%), Glossina palicera (1. 87%) and Glossina nigrofusca (0. 52%), while the only other fly species identified in the Bipindi focus was G. palicera (0. 66%). Over 99% of the identified species were non-teneral. Out of the 1991 flies, 1245 were dissected, the others were either teneral (10) or desiccated (736). Among 1245 dissected flies, a total of 421 were screened by traditional PCR. Out of the 337 Campo flies analysed, 25 (7.
Identified proteins Based on mass spectrometry raw data analyses, 3291 proteins have been identified. As described in the Materials and Methods section, the proteins extracted from the flies' gut were identified with reference to the proteins listed in our compiled database that associates several UniProt organism-specific data. Table  1  shows the number of identified proteins with reference to the proteins listed in different databases. As expected, most of the proteins (2728) matched with reference proteins from the Glossina species; 297 and 104 proteins matched with Drosophila and Ceratitis reference proteins, respectively. Some proteins matched with Wigglesworthia reference proteins (Wigglesworthia being the obligate Glossina symbiont), and only a few with Spiroplasma, Sodalis, and Wolbachia (non-obligate Glossina symbionts) reference proteins.
Finally, some proteins were identified with reference to four mammals (Bos Taurus, Homo sapiens, Ovis aries, and Sus scrofa) chosen because tsetse flies feed frequently on these mammals, thus ingesting their blood and the proteins they contain. Table  S1  shows the 3291 identified proteins and their quantification (Label Free Quantification  [LQF] ) in each of the eight replicates (four from infected and four from non-infected flies). Proteins were alphabetically classified after previous homogenization of their nomenclature to avoid artefacts due to synonyms. Column C provides the name of the organisms for which reference proteins were listed and their corresponding database. This allowed the identification of proteins extracted from the tsetse flies. Overall, a very large number and diversity of proteins could be identified.
Functional annotation of proteins with significant abundance variations Functional annotation was carried out to determine the functions associated with the differentially expressed proteins, particularly regarding fly infection. We classified the differentially expressed proteins according to two main categories: Molecular function and T A B L E 1 Number of proteins identified with reference to the different databases and percentage of the total number of proteins. F I G U R E 3 Repartition of the proteins extracted from guts of trypanosome-infected or uninfected tsetse flies into different groups and subgroups: the overall proteins, whether or not they were quantified or characterised, detected in the tsetse fly gut extracts; where available they are identified by name and accession number.
Quantified proteins: proteins whose abundance met the threshold for the different quantification filters. Characterised proteins: those that could be identified with reference to proteins listed in diverse databases. Differentially expressed proteins: quantified proteins whose abundance in the four replicates from infected flies differed significantly from those recorded in the four replicates from non-infected flies, and whose abundance ratio of infected versus uninfected flies is higher than 1. 2 or less than 0. 8. 2  shows, for each group, the percentage of proteins involved in different activities or processes. Regarding molecular function, the annotation of the 236 differentially expressed proteins showed binding activity to be highly prominent since around 40% of the proteins were involved in this activity; 8.
Therefore, more variability in the results recorded on these field tsetse flies may be expected than in the recorded experimentally controlled experiments. Consequently, we recognise that more robust data with a higher number of replicates per condition would be necessary in the future to consolidate the following conclusions. Nevertheless, as reported in the results section, 3291 proteins (including isoforms) were identified, that is, about 6-fold more than those identified in extracts from the guts of insectary-reared flies  (Geiger et al. , 2015). The difference is likely due to the refinement of analytical techniques as well as database updates between the two studies. Regarding the overall identified proteins, special attention has been paid to those from trypanosomes, the fly-infecting parasite and from Wigglesworthia, Sodalis, and Wolbachia, the three tsetse fly symbionts involved in fly survival, fly infection by trypanosomes causing HAT or AAT and fly reproduction, respectively.
CONCLUSION To conclude, an important objective of the present study was to verify if the molecular events recorded during experimental infections are representative of those occurring in field flies. Our results validate exploring an alternative vector control strategy for fighting sleeping sickness. This approach would consist of reducing, if not altogether eliminating, the vector competence of the tsetse fly. These results provide further data and nuanced information from which to draw for future investigations including in the exploration of mammalian host immunisation or fly vector competence modification. Targeting the vector as a point of control via para-transgenic approaches is a promising direction given the compulsory relationship between the bacteria and the fly. Thus, using Sodalis as an in vivo expression vector, we could deliver important transgenic modified immune proteins, such as serpins, and trypanocidal compounds into the fly's gut or compounds stimulating the tsetse fly's immune defences, leading to the increase of the fly refractory rate and/or reduction of the fly's vector competence.
SUPPORTING INFORMATION Additional supporting information can be found online in the Supporting Information section at the end of this article. Supplementary Material S1: Sample size calculation. Table  S1. Listing of the proteins identified in gut extracts from trypanosome-infected and non-infected field-collected tsetse flies. Overall, 3291 proteins were identified among which 1429 were characterised with reference to databases; 1862 proteins were uncharacterized. Quantification (LFQ_Label Free Quantification) was performed on 4 replicates of extracts from infected and non-infected flies. p-value results from comparing protein abundance in the 4 replicates from infected versus the 4 replicates from uninfected flies. Among the 3291 proteins, 1818 crossed the quantifying filters (red fonts) and were significantly quantified while 1473 low abundant proteins could not be significantly quantified (black fonts).
Event selection and analysis technique The PbPb data used in this analysis were selected with two calorimeter-based triggers that use an anti-k T jet clustering algorithm  [29, 30]  with a radius parameter of R = 0. 4. These two triggers require at least one jet with p T greater than 80 GeV or 100 GeV, respectively, after subtracting the UE background contribution iteratively  [31]. The data selected by these two triggers combined are used to extract the P ( r) signals. Additional data collected with a minimum bias trigger  [32]  are used to correct for the limited jet and track acceptance via an event mixing technique, which correlates jets with tracks from different events and thus produces a reference correlation containing only detector and acceptance effects  [6]. Vertex and noise filters  [33, 34]  are applied to reduce contamination of noncollision events, including calorimeter noise and beam-gas collisions.