dataesr/scientific-paragraphs-categorization huggingface dataset:
The issues surrounding electrochemical signal identification The rapidly increasing number of scientific publications involving the study of capacitive materials over the last decade points to the importance of this field of study (as shown in Figure  4 ). It was found that the 3,300 papers contain around 5,600 CVs and 3,000 GCDs, which generates a massive amount of data and thereby renders human-based interpretation extremely challenging. Furthermore, the CV signals measured by these experiments are mostly performed in complex situations, and thus to not produce the perfect curves obtained in theoretical demonstrations using various common types of electrode materials (Figures 4). including MnO2,  [32]  V2C,  [33]  RuOx,  [34]  LaMnO3,  [35]  Ti3C2Tx,  [15]  H2TiNb6O18, [36] Ag1-3xLax□2xNbO3,  [37]  Nb2O5,  [38]  nano-MnS2,  [39]  bulk-MoS2, [39] TiO2  [40]  and NaFePO4  [41]  , In this study, these CVs and GCDs were analyzed via supervised ML trained with datasets extracted from over 4,000 scientific papers (see DOI in Supplementary Information).
Validation of architectures To select the Convolutional Neural Network architecture best suited to our datasets, the validation of a total of five models (ResNet50, MobileNetV2, VGG16, Xception, and 8-Layer CNN) was first performed using Processes 2 and 3 with different types of input and output (Table  ESI 1 ). These architectures were chosen based on the reported accuracy ranking ascribed to the models' performance from ImageNet validation. [42, 43]  In this step, the prediction was governed by binary classification to obtain only two different outputs, namely (i) battery or (ii) pseudocapacitor, since the model had been trained and supervised with CV and GCD datasets without ambiguity. ResNet50 was found to be the most accurate and precise one out of all the models (Table  ESI  2) and was thus selected to further prediction in the next step.
Moreover, ResNet50 is more adapted to the variety of data that will be input by the users, for example, plot with different frame and font styles and different color curves. To demonstrate the efficiency of the model, 5598 CVs and 2979 GCDs were randomly selected and entered into the classifier according to Processes 2 and 3. Figure ESI 9 clearly demonstrates that the majority of predicted datasets showed a 100 % confidence rating, which would suggest that our ML model displays a high level of precision and reliability with a negligible risk of error.
Data and code availability Machine-learning models and datasets are made publicly available at GitHub repository: https://github. com/ice555mee/TB-robot_code-data or contact the author (olivier. fontaine@vistec. ac. th) for more information. The instruction is provided in both supporting information and on Github repository. The website is available via the link: http://supercapacitor-battery-artificialintelligence. vistec. ac. th/.
II. METHODS Polycrystalline samples and single crystals were synthezized using the method described in  [26]. Note that the samples were fully protonated (no deuteration). Powder neutron diffraction experiments were performed on the two-axis diffractometer D1B at Institut Laue Langevin (ILL, France), from 1. 5 K to 300 K using the neutron wavelength 2. 52 Å. Neutron data were analyzed by a profile matching analysis using the FULLPROF suite program  [28]. Quasielastic neutron scattering measurements were carried out on the indirect-geometry time-of-flight spectrometer OSIRIS at ISIS facility (UK). Graphite PG002 analyzers were used to produce a fixed final energy E f = 1. 84 meV. With an incident white beam of neutrons on the sample, the default dynamic range of the instrument is ± 0. 5 meV.
Quasielastic neutron data were collected in the temperature range of 5-290 K using a powder sample. The time-of-flight neutron spectrum was corrected for detector efficiency and then normalized against the monitored beam flux at the sample space. Powder inelastic neutron scattering experiment was done on the thermal-neutron direct-geometry time-of-flight (TOF) spectrometer PANTHER at the ILL  [29]. TOF neutron scattering data were measured in the temperature range of 1. 5-300 K with different incident energies E i = 19, 40, and 76 meV. Complementary measurements were carried out on a large single crystal in the (H 0 L) scattering plane, using the thermal triple-axis spectrometer IN8 with a constant energy E f = 14. 7 meV  [30]. Magnetic excitations were investigated using inelastic neutron scattering on two single crystals oriented in the (H 0 L) and the (H K H) scattering planes respectively, using the cold-triple axis spectrometer MACS located at NIST (United States).
E. Magnetic excitations and modeling In order to get further insight into the magnetic dynamical properties of MA-Co, we have measured the spin wave excitations, associated with the magnetic order using neutron spectroscopy. The magnetic excitation spectra measured on the cold spectrometer MACS (NIST, US) using two single crystals oriented in the (H 0 L) and the (H K H) scattering planes respectively, are presented in Figs. 6(c )-6(e). The measured neutron spectra show dispersive branches along the (1 0 L), (H 1 H), and (0. 5 K 0. 5) directions. An energy gap of about 3 meV can also be observed in the three directions, which indicates a rather strong spin anisotropy. To model the spin dynamics, spin-wave calculations were performed using the SPINWAVE software  [37]  developed at Laboratoire Léon Brillouin (LLB).
The simultaneous recording session was carried out at the end of the long-term video-SEEG recording, at J11 from electrodes implantation once all clinical data were acquired. The ethical approval was obtained at the XXXX under ID RCB 2020-A01830-39 and the patient gave informed consent. intracranially with robotic assistance (ROSA, Zimmer Biomet) and intraoperative Mobius-Airo CT-scan (Stryker, US) verification. Small insertion screws (2023-VG-C-10 or -15, Alcis Besancon) were used to minimize the bulk of the electrodes around the skull and to allow for simultaneous recordings. The electrodes had a diameter of 0. 8 mm, contained 10 to 15 contacts. Each contact consists of 2 mm long platinum-iridium and separated from each other by 1. 5 mm of insulated material (Alcis, Besançon, France). To accurately define the anatomical position of each SEEG contact along the electrode trajectory, a CTscan/MRI data fusion was performed using in-house software (to be specified).
Data processing: Noise correction: A noise compensation was performed for SQUIDs thanks to recordings from reference sensors (magnetometers and gradiometers). Noise compensation is obtained by subtracting the contribution of the noise measured by the references for each sensor. As there are not enough sensors to perform it, there is no such compensation for the OPM. In that case noise cancellation is only performed by filtering (band-pass filter 2Hz-70Hz and a notch filter at 50Hz). Downsampling and temporal registration: since the data were acquired with different devices, post-processing was necessary to temporally register the simultaneous recordings with the same sampling rate and the same number of samples. To do so, randomly distributed triggers were sent to both kind of MEG sensors (SQUID or OPM) and SEEG.
Data were then processed by an in-house Matlab routine to match the sequence of triggers and to resample the data (from SEEG to MEG time frame). The resampling procedure did not result in differences in delays larger than 1 sample. The end results for each session (SQUID-SEEG and OPM-SEEG) were two files that contained the same number of samples with synchronized SEEG and MEG data. Filtering: all data were band-pass filtered at 2-70Hz; a notch filter at 50Hz was added. SNR computation: to investigate the amplitude of events, the SNR was computed as the following ratio: the max amplitude of the event divided by the standard deviation of a baseline (2s of signal before the event for the single event presented in Fig. 2  and 500ms for the averages in Fig. 3  and Fig. 4 ). Automatic extraction of events of interest: We had to compare data recorded in the two separate sessions.
To do so, we used a procedure to identify identical events across recordings. As a first step, an expert neurologist (FB) manually selected four kinds of events of interest from the SEEG recording of the SQUID session. The selection was done using a montage of one bipolar SEEG derivation per explored brain areas. These "reference" events were chosen to be representative of IEDs involving lateral temporal structures (Spike I) and IEDs involving both medial and lateral temporal structures (Spike II). We used the Matlab routine « findsignal » (Matlab 2020b) to find similar intracerebral events for both the SQUID session and the OPM session. Three to ten events were kept for averaging. From this set of events, we chose a representative occurrence of Spike II to illustrate a single event (See Fig.
, 2023]. In our study, we observed that 4 He-OPM can record IEDs with a better SNR compared to SQUIDs. This result altogether with the recent data from 18 volunteers shows that 4 He-OPMs are able to deliver high quality brain recordings  [Gutteling et al. , 2023]. Some limitation of this work comes from the limited number of OPM sensors. Nevertheless, the full coverage of the SQUID system allowed selecting of the co-localized SQUID sensors and ensuring a reasonable comparison. We found that the amplitude of the OPM signals was higher than that of SQUID signals, as expected from both simulations and real data  [Boto et al. , 2016; Boto et al. , 2017; Iivanainen et al. , 2017]. Still, the MEG IEDs, measured with 4 He-OPMs and SQUIDs, arose from neocortical structures. We could not show activity from deep structures -such as the hippocampus -on either type of sensor.
Conflict of Interest The authors declare the following competing interests: MLP, APL and EL hold founding equity in Mag4Health SAS, a French start-up company which is developing, and commercializing MEG systems based on He-OPM technology. RR and SM are employees of Mag4Health SAS. Mag4Health SAS provided technical support to the data acquisition. At the time the recordings were made, RR, SS, MLP, APL, and EL were still employees of CEA LETI. averaged spike involves only the bipolar TP'3-TP'4 recording (Temporal pole). b, Simultaneous SQUID-MEG averaged data on the four sensors closest to the 4 HeOPM sensors: a small interictal spike appears around 25ms with a peak-topeak amplitude of 8 pT. c, Bipolar averaged SEEG data for the OPM session. Note the similarity between the intracerebral spikes of the two sessions, disclosing the same anatomical location and time course.
Statistical analyses All statistical analyses were done by linear mixed-effects models (LMM) using the nlme package  (Pinheiro et al. 2022)  of the software R, version 4. 3 (R Core Team 2023). We included 'year' as a random intercept factor to account for potential among-year (n = 8) variation. We analyzed the effects of sex and season (factors with 2 levels) on organ masses (response variables) by multifactorial LMMs. To check whether seasonal differences were sexspecific, we included the 2-way interaction between these factors. All models included the individual body mass and mandible length (covariates) to account for possible body mass or body size-dependent differences in organ masses  (Lazic et al. 2020). We also considered the 2-way interactions between body mass or mandible length and season to check whether the slope of the potential allometric association between the different organ masses and the individual body mass or body size were season-specific.
When non-significant, interaction terms were stepwise removed from the models before these were re-calculated  (Engqvist 2005). Using the plot_model function of the R package sjplot (Lüdecke 2022), we plotted predicted values of organ masses dependent on sex and season, which were based on the model estimates of multifactorial LMMs, thus correcting for the possible effects of the covariates body mass and mandible length  (Lazic et al. 20, 220). Post hoc comparisons carried out after significant interactions (sex × season) were also done by LMMs. P-values were calculated by 10,000 Monte-Carlo permutations using the package pgirmess  (Giraudoux 2022). Such non-parametric permutation tests do not require normal distribution of model residuals (Good 2005). However, by plotting fitted values versus the residuals we verified for all full models that variances were homogenous  (Faraway 2006) , as unequal variances in permutation testing can lead to inflated type I error rates  (Huang et al.
Seasonal differences in food availability, body mass and mandible length Food availability was significantly higher in the wet season [5. 1 food plants per 4 m 2 on average; CI 95% = (4. 4, 5. 9)] than during the dry season [2. 3 food plants per 4 m 2 on average; CI 95% = (2. 0, 2. 6); LMM: P < 0. 001]. For body mass, we found a significant interaction between sex and season (LMM: P = 0. 039). Post hoc Data collected from N = 53 adult African striped mice sampled over 8 years. Analysis by multifactorial LMMs including year as a random factor. Standardized estimates with 95% confidence intervals are given. P-values were calculated by 10,000 Monte Carlo permutations, highlighted in bold when statistically significant 1 Log[x] transformation for statistical analysis to obtain variance homogeneity 2 The sample size for adrenal gland masses was smaller as the data from one individual could not be obtained  The mandible length did not differ significantly between dry and wet season (LMM: P = 0.
Acknowledgements Funding was provided by the  University of the Witwatersrand. We are grateful to  Ulrich Schneppat  and  Elizabeth A. Flaherty  for important advice on how to perform the dissections. Sven Krackow  made important comments on previous versions of the manuscript. The  Succulent Karoo Research Station (SKRS)  provided technical support. Ethical clearance for the study was provided by the  Animal, Ethics and Screening Committee, University of the Witwatersrand, South Africa.
Statistical Analysis All data were verified through a normal distribution and analysed using a general analysis of variance (Genstat v. 18th VSN International, Hemel Hempstead, UK). For respiration rate, rectal temperature, and skin temperature, data were analysed for the main effect of temperature (TN vs. C HS), diet (CON vs. r SOD), time of the day, and the interactions, and then the results were presented graphically. For blood gas parameters, plasma and urine metabolites, TER, FD4 permeability, oxidative stress biomarker, heat shock protein, and inflammatory markers, the effects of temperature (TN vs. C HS) and diet (CON vs. r SOD) were analysed using individual pigs as experimental units and experimental replication as a blocking factor. If data were skewed, a log 10 transformation was conducted to reduce the heterogeneity of variances before ANOVA analysis.
Phylogenetic analysis of Epidendroideae genera and divergence times In order to produce a well-sampled, robust, time-calibrated phylogenetic reconstruction of the Epidendroideae, we used a molecular dataset of three plastid genes  (matK, psaB, rbcL). Sequences were retrieved from GenBank for 203 genera of Epidendroideae listed in Genera Orchidacearum  (Pridgeon et al. , 2005 (Pridgeon et al. , , 2009 (Pridgeon et al. , , 2014)). Each of the genera was represented in the dataset by sequences obtained from the same voucher specimen (69% of the genera), from different specimens of the same species (14%), or from different species (17%). GenBank accession numbers are provided in Supplementary DataTable S1. Name acceptance by the WCSP (2020) of vouchers and genera was systematically checked. Entirely synonymised genera were merged with the corresponding accepted genus, except for Cadetia and Epigeneium (synonym: Dendrobium) which were kept for the tree calibration (see below).
Sequences of each gene were aligned using MAFFT in Geneious Prime 2021. 2, manually checked, and then concatenated, resulting in a molecular dataset of 203 Epidendroideae genera and 4649 characters. Best-fitting partition scheme was selected using ModelFinder in IQ-TREE 1. 6. 12 (Kalyaanamoorthy et al. , 2017). A phylogenetic analysis based on these three genes is unlikely to provide strong support for the deep relationships between the genera of Epidendroideae, but tribe relationships in Epidendroideae have been studied several times using 74 to 78 plastid genes  (Givnish et al. , 2015; Li et al. , 2019; Pérez-Escobar et al. , 2021; Serna-Sánchez et al. , 2021). Here we used the multispecies coalescent tree inferred from 78 plastid genes by  Pérez-Escobar et al. (2021)  as a backbone tree for tribe relationships.
Plant Material and Treatment Solanum lycopersicum L. seeds (cv. St Pierre) were sterilized with ethanol and sodium hypochlorite and cultivated on agar 1  2 MS medium at 24 • C (80-90% relative humidity (RH), 16 h to 8 h day and night cycle), in a growth chamber for 13 days. The seedlings were elicited with water, 1% (w/v) standard laminaran from L. digitata (LAM std) or 1% (w/v) laminaran LAM2 (from L. hyperborea) three days after sprouting. Root and aerial parts lengths were measured using the ImageJ software (1. 54f version). The standard laminaran (LAM std) was obtained from Sigma-Aldrich (L9634; Saint-Quentin-Fallavier, France). The laminaran LAM2 fraction was isolated using an industrial targeted Laminaria hyperborea extraction process involving ethanol precipitation. Precipitated laminaran was resuspended in water and recovered from fucoidans using an ultrafiltration step.
Two other genes were used as reference Ef1 (forward primer 5 GGAACTTGAGAAGGAGCCTAAG 3 , reverse primer 5 CAACACCGACAGCAACAGTCT 3 , amplicon size 158 bp) and PHD (forward primer 5 ATTCGTGGCTGCTCTCTGTC 3 , reverse primer 5 CCCTGTCACGGCTTCAAAGA 3 , amplicon size 116 bp). The quantitative real-time PCR was performed using the CFX96 real-time system (Bio-Rad, Hercules, CA, USA). The following parameters were used: 20 s at 95 • C then 40 cycles of 5 s at 95 • C, 20 s at 60 • C followed by the melt-curve analysis: 15 s at 95 • C, 6 min at 60 • C and 15 s at 95 • C. Data were analyzed using the CFX Maestro software (Bio-Rad, Hercules, CA, USA). The relative expression was normalized with the 2 -∆∆Cq method to Ef1 and PHD as reference genes using GENorm analysis (provided in CFX Maestro software 2.
Image Analysis and Statistical Test Fluorescence intensity measurements were performed on a region of interest (ROI) containing all the tissues of the stem and used to obtain a mean of fluorescence intensity value of the ROI. To automate the measurements, an ImageJ macro was developed. It manages the opening of an image, the projection of average intensity along the Z axis and the retrieval and saving of intensities along a line in a result table. Z-average projection is used to smooth out cutting effects, as each point on the line is itself an average of a user-defined orthogonal segment. The macro code is provided in Supplementary Materials. Statistical analysis was performed using a Kruskal-Wallis test followed by multiple pairwise comparisons using Dunn's procedure at the significance level 0.
Immunity Gene Expression Analysis After application of LAM2, LAM std or water, root and aerial part lengths were measured using the ImageJ software. As illustrated in Figure  3 , no treatment affected the growth of tomato seedlings. As laminarans are known to induce defense reactions in various plants  [4] [5] [6] [7] [8] , we investigated the gene expression of six key genes in plant defense: LOXD (a lipoxygenase D in the jasmonic acid pathway), PAL5 (Phenylalanine ammonia-lyase in the phenylpropanoid pathway), PPO-D (a polyphenol oxidase D in the phenylpropanoid pathway), Pti-5 (a pattern-triggered immunity marker), Worky28 and Worky70-80 (Pti marker and tran- As laminarans are known to induce defense reactions in various plants  [4] [5] [6] [7] [8] , we investigated the gene expression of six key genes in plant defense: LOXD (a lipoxygenase D in the jasmonic acid pathway), PAL5 (Phenylalanine ammonia-lyase in the phenylpropanoid pathway), PPO-D (a polyphenol oxidase D in the phenylpropanoid pathway), Pti-5 (a pattern-triggered immunity marker), Worky28 and Worky70-80 (Pti marker and transcription factor defense response-salicylic acid related, respectively).
Author Contributions: Project conceptualization: I. B. , A. D. , F. H. and J. B. ; formal analysis and investigation: C. M. -N. , Q. A. , G. D. , C. P. , S. B. , C. C. , J. G. , N. M. -B. , A. D. , J. B. , F. H. , P. L. and I. B. ; writing-original draft preparation: C. M. -N. , P. L. , S. B. , A. D. , J. G. and I. B. ; writing-review and editing: C. MN. , S. B. , C. C. , J. G. , N. M. -B. , A. D. , J. B. , F. H. , P. L. and I. B. ; project supervision, I. B. ; funding acquisition, I. B. , A. D. and F. H. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by ANR (Agence Nationale de la Recherche) through LabCom SEASIDES project, grant number ANR-18-LCV3-0010. Institutional Review Board Statement: Authors complied with the Convention on Biological Diversity guidelines regarding the Solanum lycopersicum (Tomato) plants used in this study.
e. symptomatic areas with latency) for 19. 14 °C ≤ MST < 23 °C and MAT < 14 °C, and symptomatic areas (without latency) corresponded to areas where MST ≥ 19. 14 °C and MAT ≥ 14 °C and MST ≥ 23 °C & MAT < 14 °C. For this purpose, we used the daily data of 'Météo France' (SAFRAN dataset, which is at an 8 km × 8 km resolution).
Data analysis The data used in this research were collected from the Regional Directorate for Food, Agriculture and Forestry (French Ministry of Agriculture) and the laboratories that analysed samples in the framework of PWN monitoring (NRL and official laboratories). They consisted of information on the location of sampling and the country of origin of the commodity sampled (in the case of imported wood materials), identity of the host species (in the case of tree sampling) and the sample's status i. e. detection or not of B. xylophilus. Moreover, we had information on the other Bursaphelenchus species identified in the samples during the first part of monitoring pine forest stands  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011). Finally, the maps of PWD expression were created with the software R (version 4.
Acknowledgements We would like to thank all the people involved in the  French PWD monitoring program , whether in coordination, inspection and sampling (local services of the  French Ministry of Agriculture  and their delegated agencies, including FREDON, agents of the DSF and their corresponding observers), with a particular mention for Jean-Luc Flot (DSF) and  Thierry End (DRAAF). We are also grateful to the people who allowed us to collect the data used in this article, notably  Robin Guillem  ( LDA33 ),  Marie-Pierre Cornec (Labocea).
In this study, a mathematical model for studying the dynamics of monkeypox virus transmission with non-pharmaceutical intervention is created, examined, and simulated using real-time data. Positiveness, invariance, and boundedness of the solutions are thus examined as fundamental features of mathematical models. The equilibrium points and the prerequisites for their stability are achieved. The basic reproduction number and thus the virus transmission coe cient R were determined and quantitatively used to study the global stability of the model's steady state. Furthermore, this study considered the sensitivity analysis of the parameters according to R. The most sensitive variables that are important for infection control are determined using the normalized forward sensitivity index. Data from the United Kingdom collected between May and August , which also aid in demonstrating the usefulness and practical application of the model to the spread of the disease in the United Kingdom, were used.