dataesr/scientific-paragraphs-categorization huggingface dataset:
9a, 9b, 9e, 9f) and thrust faults (TF, Figs. 9c, 9d, 9g, 9h), two V S30 values (e. g. 300 m/s and 600 m/s) and two Joyner-Boore distances, equal to 10km and 30 km, respectively. For the comparison, the prediction of ITA18-ML has been made assuming a hypocentral depth equal to 10 km, which is very close to the event pseudo-depth of ITA18. The spectral shapes allow us to appreciate the difference in the predictions at all frequencies. We selected two reference magnitudes, 4. 5 and 6. 5, that are very well and scarcely sampled by the dataset, respectively. The larger differences are found for Mw 6. 5 for both fault mechanisms (Figs. 9a to 9d), where ITA18-ML predicts larger IMs, in particular in the period range (0. 1s -0. 8s). The two GMMs show minor differences, at all periods, for events with a magnitude equal to 4.
5 (Figs. 9e to 9h). Finally, we examine the prediction performance of the two models for three events that occurred after 2016, that are not part of the training dataset. One event occurred on 2019-11-07 at 17:35:21 UTC in Southern Italy with a normal faulting mechanism, a focal depth of 16 km and a magnitude Mw = 4. 4 (event id = EMSC-20191107_0000114 in http://esm-db. eu). The event has been recorded by 120 stations and we predict two intensity measures, PGA and SA at T = 1s, using ITA18 and ITA18-ML. Figure  10a  shows the difference between the observation and predictions of the two models. The machine learning algorithm and ITA18 predict with similar accuracy PGA (RMSE ML = 0. 24 and RMSE ITA18 = 0. 21, respectively) and SA at T = 1 s (RMSE ML = 0. 24 and RMSE ITA18 = 0. 22) with a slightly better performance of ITA18.
Discussion and conclusions In this study, we examine the advantages and disadvantages of the use of a functional form versus machine learning algorithms in ground-motion models. The dataset selected for the analysis is the  Lanzano et al (2019) , used to derive the most recent ground motion model for Italy (ITA18). The dataset consists of 5,607 records, relative to 146 earthquakes and 1657 stations, which is comparable, in terms of the number of waveforms, to the datasets used recently to calibrate models based on ML techniques worldwide. We have selected the most usual variables to calibrate GMMs with classical regression algorithms (e. g. magnitude, distance, hypocentre depth, style of faulting, V S30 ) to better compare the most recent model for Italy (ITA18) to our results (ITA18-ML) and explain the differences.
The ML algorithm with the best predicting capability is the Gaussian Process Regression available in Matlab, which signi cantly reduces the standard deviation of the predictions. Machine Learning allows for a reduction of the total standard deviation by about 30% for peak values and spectral acceleration and, in particular, the largest reduction (about 50%) is observed for the betweenevent standard deviation. The site-to-site standard deviation, although reduced, is affected by the largest variability. We found that the largest difference in ground motion prediction between models when the event hypocentral depth is considered a separate variable. In particular, in the case of deep events (e. g. depth > 10km) the predicted ground motion in the near-fault has lower values than traditional models (e.
g. ITA18) and higher values at long distances, probably because the machine learning algorithm is successful in accounting for the effect of multiple re ections of seismic waves. The size of the dataset is also crucial: our results suggest that machine learning and traditional regression have similar outcomes in the case of large datasets that adequately sample the variable space. Major differences arise when the sampling set is scarcely populated. Traditional regression-based models show a better performance than ML algorithms when the amount of observations is limited since they compensate the lack of data with prede ned linear or nonlinear equations that are based on physical considerations. Therefore the use of traditional regression is more appropriate when the available dataset is poor, like in the case of this study, although the uncertainty about the mean (e.
Derakhshani and Foruzan (2019) developed a model for peak ground acceleration (PGA), PGV, and PGD based on a deep neural network (DNN), using the Next Generation Attenuation dataset for the western United States (NGA-West2), which includes 21,336 ground-motion records, demonstrating the outperformance for traditional regression-based or machine learning models by the improvement of correlation coe cients between observed and predicted intensity measures. Khosravikia and Clayton (2021) applied Arti cial Neural Network, Random Forest and Support Vector Machine to a dataset of 4,528 ground motions of earthquakes in Oklahoma, Kansas, and Texas; Seo et al (2022) used three machine-learning techniques (e. g. , arti cial neural network, random forest, and gradient boosting) to 1,189 ground motions from 77 earthquakes in Korea; Mori et al (2022) applied ML techniques to produce shaking scenarios, in alternative to USGS Shakemap, using 15,779 records from the Engineering Strong Motion and the Italian Strong Motion databases (Luzi et al, 2016; Luzi et al, 2019).
Seismological datasets are constantly increasing in terms of available waveforms, due to the fast development of new networks and strong motion databases, therefore recent studies, such as Kim et al (2020) or Okazaki et al (2021) developed models using much larger datasets than previous studies. Kim et al (2020) used 115,501 records of the Kiban-Kyoshin network (KiK-Net) database to predict ground motion ampli cations, while Okazaki et al (2021) used 234,552 records of the same KiK-Net database, to derive peak parameters. In general, the use of ML algorithms results in a more accurate estimation of ground motion.
g Dynamics of electroencephalogram (EEG) power in 1 s bins in the wave band (5-9 Hz) surrounding seizure start and end times for seizures with continued licking (green = spared, n = 158 seizures) and no licking (black = impaired, n = 2988 seizures). Wave power was significantly greater for impaired versus spared SWD (p =4. 691×10 -8 ,ranksum test). See Methods for definitions of spared and impaired licking. h Dynamics of EEG power in 1 s bins in the spike band (15-100 Hz) around seizure start and end, comparing behaviorally spared (n = 158) and impaired (n =2988)seizures. Spike power was significantly greater for impaired seizures (p =2. 146×10 -7 ,ranksum test). For all plots (c-h), measures of center are mean, and error bars/regions denote standard error of the mean. Source data are provided in Source Data file.
These patterns were distinctive and consistent within groups (extended data slides and single neuron raster plots are available at https://doi. org/10. 5061/dryad. rfj6q57dz). Interestingly, analysis of thalamic unit activity around SWD revealed that the same four patterns of firing dynamics were apparent (Fig. 5a  lower), with each neuron showing either a Sustained Decrease (76 neurons, 47%), Sustained Increase (7 neurons, 4%), Onset Peak (27 neurons, 17%), or No Change (52 neurons, 32%). Three cortical and one thalamic neuron did not fall into any of these categories and were not included in the analysis. When quantifying changes in firing between the periods preceding and during SWDs, as for all neurons above, we observed that the SD groups were unsurprisingly responsible for the majority of the decrease in overall activity in both cortex and thalamus (Supplementary Fig.
h As in e but for thalamic neurons. i As in f but for thalamic neurons (p =8. 9×10  -17  ). j As in g but for thalamic neurons (p = 0. 0007). Data are from 165 cortical neurons in 7 animals and 163 thalamic neurons in 7 animals. All traces are mean and corresponding shading denotes standard error of the mean; all statistical tests are two-sided paired t-tests. Purple colors indicate cortical data (a, b, f) and green colors thalamic data (c, d, i). Source data are provided in Source Data file. undrugged state resembling human absence seizures including cortical fMRI decreases and thalamic increases  14, [20] [21] [22] [23] [24]. We then replicated behavioral deficits similar to the interruption of responsiveness and impaired directed activity seen in human absence epilepsy  14, 15, 19, 20. This was accomplished by reducing auditory stimulus intensity and increasing interstimulus intervals to minimize seizure interruption, revealing impaired responses on two tasks during seizures.
During this period the rat was also gradually habituated to intensity-matched audio recordings of the MR system. Recordings were acquired over a 2 week period following the end of habituation. All data were obtained on a 9. 4 T Bruker horizontalbore spectrometer, interfaced with Bruker ParaVision v6. 0. 1 software running on CentOS (Bruker, Billerica, MA, USA) using a custom-built 1 H radiofrequency transceiver quadrature coil (2 perpendicular coils with 35 mm diameter each). Magnetic field homogeneity was optimized by localized shimming (STEAM sequence using B 0 field map) to yield typical water spectrum line-width of less than 30 Hz for the whole brain. High spatial resolution anatomical images were acquired with 12 interlaced contiguous slices in the coronal plane using a T2-weighted TurboRARE sequence.
EEG was simultaneously acquired from the carbon filament electrodes between 0. 1 and 300 Hz (Grass Instruments Model 79D Data Recording System), and digitized via a Cambridge Electronic Design Micro 1401 with Spike2 software at 1000 samples/second. Each recording session lasted up to 1 h, to allow insertion and removal within the 90 min maximum training time (see above). SWDs began to appear after a mean of 25 ± 1. 8 min, and there was an average of 31. 3 ± 3 SWDs per hour, lasting on average 15. 1 ± 1. 7 s. The corresponding values for the behavioral recordings are first SWD at 30. 3 ± 1. 8 min, SWD rate of 60. 5 ± 2. 8 per hour, and SWD duration of 8. 7 ± 0. 2 s. As in our prior work with simultaneous EEG-fMRI in rodent models several measures were employed to reduce artifacts induced in the EEG recordings by high field gradient and radiofrequency pulses in the scanner, including selection of EEG amplifiers to reduce artifact, careful placement, and mechanical buffering of MRI-compatible recording leads, and post-acquisition digital smoothing of EEG signals to remove residual high frequency artifacts  35, 36, 44, 78.
Behavioral data acquisition with simultaneous EEG and behavioral data analysis Animals were food restricted to 90% of their initial body mass in order to increase motivation to work for a calorific reward. This 90% target mass was maintained through training, and food restriction ceased ~1 week before implantation to allow recovery for surgery. Animals were individually housed and weighed daily, and any animal exceeding 15% loss of initial mass was returned to ad libitum food access. Upon reaching 90% of initial mass animals were introduced (in their home cage) to 20% sucrose (weight per volume) in water, manually dispensed from a 1 ml syringe (Becton-Dickinson). When rats consumed 2 ml of sucrose water within a 10 min presentation session (training stage 0) they were deemed to have learned the association and proceeded to training in the behavioral paradigm.
Transistor-transistor logic (TTL) pulses from behavioral Med-PC V Software (Med Associates, Inc. ) were recorded as event channels in the same recordings for synchronization of behavioral and electrophysiological data. Animals recovered for 5 days following the implant and then underwent behavioral retraining, using the same criterion of reaching 50 rewards within 2 h. In all cases animals achieved this reward threshold in a single session. Behavioral testing for experimental data collection began 1 week post-implantation, with 1 daily session per animal. For the sensory tone detectiontask,thetonewasagaindelivered at random intervals varying from 150 to 210 s, and also whenever a SWD was detected by a custom-written online amplitude thresholdbased algorithm (implemented in Spike2, Cambridge Electronic Design).
35 mm, ML 2. 9 mm) and data were acquired via a head-mounted Plexon HST/32V-G20 VLSI-based preamplifier and digitized via a Plexon data acquisition system at 20 kHz. Thalamic data were acquired from 7 animals over 58 recording sessions, yielding a total of 163 sortable single units (27 ± 29 units/animal, mean ± SD). For both cortical and thalamic experiments data were collected during 2-4 h sessions during which rats were free to explore, rest, and seize in the recording chamber, and during which electrodes could be moved via the microdrive if no neurons were visible online. In the animals used for cortical neuronal activity recordings, frontoparietal EEG could not be collected by paired screw electrodes due to space constraints on the skull. Therefore, EEG was obtained by the differential of the voltage from a frontal epidural screw implanted as already described (see section on Behavioral Data Acquisition with Simultaneous EEG) and a single recording site on the silicon probe, acquired with the OpenEphys system as described above.
To analyze data in a common space across animals and across recording sessions, we selected high anatomical resolution images from a single animal and session in our cohort as a template, and coregistered all data to that template. First, rostrocaudal alignment was performed by visual inspection of each anatomical slice and comparison with both a standard rat brain atlas and the template anatomical image slices to find the best correspondence with the latter. A custom MATLAB script then allowed in-plane rigid body translation of the image by whole voxels in the dorsoventral and mediolateral planes to find the best correspondence with the template. The same translations applied to coregister the anatomical images withthetemplatewerethenappliedtothefunctionalimagesfromthe corresponding recording session.
fMRI analysis Analysis of fMRI signals during SWD was done with a general linear model approach using statistical parametric mapping (SPM12; https://www. fil. ion. ucl. ac. uk/spm/software/spm12/)a n di n -h o u s e software with methods similar to those used for fMRI analysis in human absence epilepsy. Data were pre-whitened using an autoregressive model, and a 128 s high-pass temporal filter was applied, both using SPM12. A first level fixed-effects model was used to generate animal-level activation maps by creating seizure regressors for each recording session in an animal and then concatenating them together across sessions. To create the seizure regressors, a boxcar function with onset, offset and duration times corresponding to each SWD as determined from EEG review (see "Spike-Wave Discharge Detection" above) was convolved with SPM's canonical double gamma hemodynamic response function.
Neuronal activity analyses Action potential spikes were extracted from continuous recordings including both non-seizure and seizure periods and clustered into individual neurons 10 (see Fig. 3b ). Briefly, the broadband signal was high-pass filtered and thresholded to extract action potential waveforms, and the first three principal components of each action potential on each channel were used to cluster action potentials, first via the unsupervised KlustaKwik program (Ken Harris, GNU General Public License) and then via supervised refinement. Spike trains attributed to individual neurons were labeled as cortical or thalamic based on probe location during acquisition and further analyzed. All analysis steps including deriving firing rates, averaging across seizures, plotting spike-triggered averages, and calculating rhythmicity indices as described below were carried out with custom-written scripts in Python (available on GitHub).
Briefly, after application of smooth and DC remove functions as described above, EEG signals were exported from Spike2 to MATLAB. An amplitude threshold was manually set in the negative direction, and all time points exceeding this threshold within previously-identified SWDs (see Spike-Wave Discharge Marking and EEG Artifact Rejection section above) were marked. All local minima (points of greater negative amplitude than their immediate neighbors) beyond the threshold were identified, and any of those local minima that were within 60 ms of the preceding minimum were discarded to prevent double-detection within a single spike-wave complex. Finally, the global minimum amplitude values within a sliding 100 ms window were marked as the times of the spike-wave complex spikes. The mean firing rate for each neuron across spike-wave cycles was next calculated in 1 ms bins centered in time around the EEG spike peaks during SWDs, and group analyses were performed by averaging across neurons (see Figs.
Neuronal categorization Raster plots and firing rate histograms were constructed across all SWD for each neuron (extended data slides are available at https://doi. org/10. 5061/dryad. rfj6q57dz), and neurons were sorted into the following categories based on visual inspection of their firing pattern around seizure initiation: Sustained decrease (SD) neurons showed reduced mean firing rate throughout the SWD relative to pre-seizure baseline; sustained increase (SI) neurons showed increased firing throughout the SWD; onset peak (OP) neurons showed a transient increase in firing within ±1 s of seizure onset (classified as OP regardless of increased, decreased or no change in firing for the remainder of the seizure); no change (NC) neurons showed no change in mean firing rate during SWD compared to baseline.
The resulting values (power/mean power) were log-transformed (10 × log 10 ) to obtain power expressed in dB. The log-transformed, baseline-corrected spectrograms were then averaged (mean) across seizures within an animal before averaging across animals. These were visualized using the imagesc function in MATLAB (Fig. 6a ). Time course of power in frequency bands of interest (Fig. 6b ) was calculated in the same way except that values were averaged over the frequency ranges in question after baseline normalizing and log transformation but before averaging across seizures and across animals.
(1998)  that prove asymptotic normality of the estimator for a stationary process. These results concerning discrete-time hidden Markov models (DTHMM) case are used in our CTHMM in order to provide asymptotic results for our estimators. Here, a continuous-time HMM (CTHMM) is considered, where X is defined by its generating matrix A and Y by its probability law, with conditional distribution G(i, B) given {X t = i}, and B a measurable set in R q , i. e. , P(Y t ∈ B|X t = i) = G(i, B), so the considered model is an M1-M0. Our results concern the consistency and asymptotic normality of the estimator of the parameter θ and applications to a real data set and simulated data. It is assumed that the generator A of X is a matrix depending on the parameter θ , i. e. , A = A(θ ) as well as the probability distribution of Y , i.
Scenario 1. Regular inspections in time As a illustration we consider a comparison study of the suicide-rate in the US and Japan during the period 1985-2015. The data have been taken from the data platform https://www. kaggle. com/. We focus on the following variables: the Suicide Rate, which is measured as the number of suicides/100k population, and, the Gross Domestic Product per capita (G D P_ per_capita).
Results We use the EM-Algorithm. The function Q(θ |θ (m) ) = E θ (m) [ln f ( X , Ŷ|θ)] will give us by successive iterations an approximation of the estimate of θ, where ln f ( X , Ŷ|θ) is the loglikelihood corresponding to the complete dataset, which would be obtained from the bivariate process ( X , Ŷ ). Q(θ |θ (m) ) = N -1 n=1 i, j∈E P θ (m) ( Xn = i, Xn+1 = j| Ŷ) ln P θ (i, j) + N n=1 i∈E P θ (m) ( Xn = i| Ŷ) K k=1 ln g( Ŷkn ; φ i ). (13) • E-Step For given θ (m) , compute the probabilities:  To solve this step we use of the forward-backward probabilities defined as in  Gamiz et al. (2023). P θ (m) ( Xn = i, Xn+1 = j| Ŷ), n ∈ {1,. , N -1}; i, j ∈ E and P θ (m) ( Xn = i| Ŷ), n ∈ {1,. , N }; i ∈ E. G(1, •) → N ( μ 1 = 11. 90, σ 1 = 0. 437) G(1, •) → N ( μ 1 = 19. 53, σ 1 = 2. 340) G(2, •) → N ( μ 2 = 13.
Acknowledgements This work was jointly supported by the  Spanish Ministry of Science and Innovation-State Research Agency  through grants numbered  PID2020-120217RB-I00  and  PID2021-123737NB-I00 , and the  IMAG-Maria de Maeztu  grant  CEX2020-001105-/AEI/10. 13039/501100011033 ; and by the  Spanish Junta de Andalucia  through grant number  B-FQM-284-UGR20. Funding Funding for open access publishing:  Universidad de Granada/CBUA.
In parts of North America, the marsh crane fly, the known target of ToNV, is considered as an invasive pest  21. Other nudiviruses can also cause extensive damage in crustacean or insect large-scale farming  13, 22. A greater understanding of OBs may allow for the best deployment of nudiviruses as biocontrol agents. In the present study, we sought to understand whether the structural features and lattice constraints observed are conserved in examples of OBs identified in nudiviruses. To do this we determined the structure of the ToNV polyhedrin from native 70-year-old polyhedra and recombinantly expressed and assembled protein. In an interesting example of convergent evolution, our data show a conservation of polyhedra design principles using a unique protein building block.
Results Determining the structure of the ToNV polyhedrin Previous characterisation of the native OB showed particles with sizes of 2-5 microns with an irregular morphology that still contained infectious viral particles  14. Initial X-ray experiments yielded highquality diffraction patterns that could be readily indexed and gave good data collection statistics to a high resolution. To generate experimental phases, the wild-type ToNV polyhedrin protein was cloned into an insect cell expression vector before three hydrophobic residues (F104/L105/L137) were mutated to methionine. The native sequence contains only an N-terminal methionine. The structure was determined using single wavelength anomalous diffraction (SAD) data measured using the nano/microfocus X-ray diffraction beamline VMXm at Diamond Light Source that employs a low-background, invacuum cryogenic sample environment.
To prepare samples for the VMXm beamline, a slurry of microcrystals was first applied to a single cryoEM grid that was then vitrified (Fig. 1a ). The crystals on a single grid were then used for diffraction experiments, enabling the efficient collection of data from many protein crystals. X-ray diffraction data for both recombinant selenomethionine derivatised and native polyhedra were measured from protein crystals mounted on cryoelectron microscopy grids (Fig. 1a ). SAD data were collected from selenomethionine crystals, with dimensions between 1-5 microns using a beam size of 3 by 4 (H × V) µm. Due to the small size of the crystals, small wedges of data were collected per crystal and then merged to form a final dataset using data from 55 crystals. The model for the native polyhedra was more complete and determined to a higher resolution and will be the dataset described unless otherwise stated.
In our structure we observe a single copy of the polypeptide in the asymmetric unit, with a solvent content of ~22% (Matthew's coefficient of 1. 59 Å 3 /Da) placing this in the bottom ~0. 1% of structures by decreasing crystal solvent content in the Protein Data Bank. The crystals belong to space group P3 2 21 with unit cell dimensions of a = b = 53. 7 Å, c = 105. 6 Å (Supplementary Table  Attempts to phase the datasets with molecular replacement using previously determined polyhedra structures, in silico structures (trRosetta and AlphaFold), or secondary structure fragments were trialled but ultimately unsuccessful (Supplementary Fig. 1a )  [23] [24] [25]. In silico models of oligomeric ToNV polyhedrin were generated, however these were also unsuccessful in molecular replacement.