dataesr/scientific-paragraphs-categorization huggingface dataset:
Modal analysis The development of modern measurement technologies has led this study to focus on understanding the vibration characteristics of tidal turbines and to increasing attention to the problems of instability given that tidal current turbines have become increasingly larger than before. In this context, it is essential to perform vibration characteristics in the design and development process of the structure. Evidently, structural vibration control and modal analysis are essential tools to effectively monitoring the operational behavior and ensure the structural security of offshore wind turbines  (Alaoui et al. , 2019). In this regard, in our study, a modal analysis was calculated by the finite element method using ABAQUS software to identify the natural frequencies and modal shapes of a hybrid composite nozzle at different modes.
Conclusion Research on structural vibration characteristics and operational modal analysis of tidal current turbines offers technological support for the assessment of operational safety and the practical basis for the conception of the tidal turbine structures. A modal analysis was developed in Abaqus software using finite element method (FEM) to evaluate the vibration mode shapes and natural frequencies of the ducted tidal turbine. The effects of the reinforcement of the carbon/glass fiber hybridization of the behavioral aspects and stability issues of the tidal turbine nozzle were studied. Furthermore, to properly investigate the operational and environmental impacts on the mechanical properties of the nozzle, two cases of boundary conditions of Encastre type were investigated. The comparison of the results of the three laminates showed that the stacking sequence has a meaningful impact on the natural frequency, displacement, and resonance of the structure.
In P. falciparum and other Apicomplexan organisms as well as in Dictyostelium discoideum, the process appears highly dynamic, with different cLREs involved at different phases of the life cycle. For multicellular organisms, the same cLREs are involved in all tissues, but a dynamic behavior is observed along embryonic development stages. In P. falciparum, whose genome is known to be strongly depleted of transcription factors, cLREs are predictive of expression with an accuracy above 70%, and our analyses show that they are associated with both transcriptional and post-transcriptional regulation signals. Moreover, we assessed the biological relevance of one LRE discovered by DExTER in P. falciparum using an in vivo reporter assay. The source code (python) of DExTER is available at https://gite.
Next, we also learned series of models that use TF binding motif scores instead of DExTER variables (see Materials and methods), and another series that use both TF motif scores and DExTER variables (S2 Fig). For these experiments, we used the 23 ApiAP2 motifs identified in Campbell et al. [64]  for P. falciparum, and the JASPAR 2020  [65]  Fungi, Vertebrates, Plants, and Insects motifs databases for S. cerevisiae, human, A. thaliana and Drosophila, respectively. Here again, we observe a substantial drop when using only motif scores as predictive variables (above 20% for most species and conditions, except for human). On the other hand, adding motif scores to DExTER variables only marginally increases the accuracy. These results highlight the importance of long regions in comparison to (short) motifs for predicting gene expression.
We ran DExTER on these two types of data and for each available time point, and identified the 8 most important variables on each condition. Among the 15 different variables, 4 are clearly more correlated with nascent transcription than with stabilized transcript levels, while 5 others are more associated with stabilized transcripts (Fig  8b ) (the remaining variables cannot be clearly associated with one or the other type of data, see Materials and methods). Remarkably, all variables associated with nascent transcription are both upstream and non strand-specific, while variables associated with mRNA stabilization are downstream and strand-specific. This raises a question about the nature of the few variables that span the whole sequence. This is typically the case of the variable TTT[-2000,+2000] on IEC (see Fig  4a ) and of variable ATTA  [-1925,1925 ] on the whole parasite life cycle (see Fig  7c ).
Links with antisense transcript levels in P. falciparum It has been shown that P. falciparum produces numerous antisense RNAs. Former studies estimate that around 25% of coding genes have significant antisense levels during blood stage development  [73]. We thus asked whether cLREs could be associated with the regulation of antisense RNA level also. For this, we used DExTER to predict the expression level of the data of Siegel et al. [73] , who measured independently the level of sense and antisense transcript of all coding genes. For sense transcription data, we used 4000 bp sequences centered on the start codon (AUG) as in our previous analyses. For antisense data, we used the same sequences, as well as 4000 bp sequences centered on the stop codons. Results are summarized in S11 Fig. For  sense transcripts, we obtained similar results to those obtained on the data of Otto et al.
Links with genome AT content One striking observation is that P. falciparum and P. berghei, the two organisms for which cLREs have the strongest links with gene expression-both in terms of predictive performance and dynamic behavior-, are also the organisms with the most AT-biased genomes. One question is then to know if the importance of cLREs for the regulation of expression could be linked to the genome AT-content. To answer this question, we ran DExTER on gene expression datasets of Dictyostelium discoideum and Tetrahymena thermophila, two other organisms selected for the high AT content of their genome  [74]. For these experiments, we analysed the RNAseq data of time course development of Nichols et al. [75]  and Zhang et al. [76]. Results achieved on these two species contrast strongly (see  Fig 9).
Several studies provided evidence for a control of gene expression at the post-transcriptional RNA level in this parasite. Lack of coordination between active transcription and mRNA abundance has been reported in P. falciparum  [82, 83]  and bioinformatics analysis have indicated that a significant percentage of the Plasmodium genome encode RNA-binding proteins (4-10%)  [84, 85]. Finally, in vivo analysis of the promoter activity of a chimeric DNA fragment showed higher transcriptional activity when the region was enriched in one of the cLREs identified by DExTER as positively associated with RNA levels. These results should now be confirmed by additional experiments in order to precisely assess the role and importance of this LRE in P. falciparum biology. Notably, it would be interesting to replicate these experiments in varying the number of repetitions of the high ATA frequency region, as well as to monitor the transcriptional activity of these chimeric promoters along the entire erythrocytic cycle.
The work was supported by funding from  CNRS  ( International Associated Laboratory "miREGEN", C-H. L. & L. B. ),  INSERM-ITMO Cancer  ( BIO2015-04  " LIONS ", C-H. L. & S. L. & L. B. ),  Plan d'Investissement d'Avenir  (# ANR-11-BINF-0002  " Institut de Biologie Computationnelle ", C-H. L. & S. L. & L. B. and # ANR-11-LABX-0024-01  " ParaFrap ", J-J. L-R. & V. G. ),  Labex NUMEV (GEM   Flagship  project, C-H. L. & S. L. & L. B. ),  CNRS/INSERM funding Defi Santenumerique  (project  REGAI , C-H. L. ), the  Fondation pour la Recherche Medicale  ( DEQ2018033199 ,  J-J. L-R. & R. M. M. ) , and the program ATIP-Avenir (J-J. L-R. )The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Finally, following each musical trial, the participant was asked whether she/he was familiar with the song (3-point familiarity scale, either 2 [familiar], 1 [not sure], or 0 [unfamiliar]), and how much they liked the song (9-point Likert likability scale, ranging from 1 [not at all] to 9 [a lot]). Equipment. A stomp box (Acoustim8, Series 100 foot drum, UK; Fig. 1 , left) was used for the finger and toe-tapping tasks. It is a small box containing a contact microphone that is often used as a foot drum by musicians to create the sound percept of a bass drum. With a curved frontage, this piece of equipment offers a large and easy to access target zone. To record the pace of whole-body stepping movements, a MP150 Biopac system was used (Biopac Systems, Goleta, CA). Two heel and toe strike transducers (Model RX111) gathered press and release data specifically in the stepping task.
The heel and toe strike transducers were taped into place on the sole of the shoe. A Velcro elasticated band was used to attach the BioNomadix amplifier (Model BN-TX STRK2-T) around the ankles (Fig. 1, right ). The psychology software Superlab (Version 5; Cedrus Corporation, San Pedro, CA; https ://cedru s. com/index. htm) generated the randomized program for the auditory stimuli via a desktop computer that ran on a Windows platform. The metronome sound used was the standard Klopfgeist tone from LogicPro (Apple Inc. ), presented for 100 ms duration. A LG Flatron (Model L17108) screen was used to display instructions. To insure perfect synchronization across the sound tracks and the response recordings, all signals of interest were recorded simultaneously using separate channels on the Biopac system at a sampling frequency of 1000 Hz via a mixing desk (Behringer Xenyx502 premium 5-Input 2-Bus Mixer Mic Preamp British EQ).
Signal preprocessing. The acquisition files recorded by the Biopac MP150 were imported under Matlab (Matlab R2016b https ://www. mathw orks. com/) and analyzed with custom scripts to perform the automatic detection of the stimuli beats, finger/foot taps, and heel strikes. A description of the steps taken to perform these automatic detections follows. Extracting the beat within the metronome and song excerpts. Click track versions of the auditory stimuli consisting in oscillating bursts exponentially decreasing and vanishing after 30 ms were generated by Logic Pro. They were streamed in sync with the auditory stimuli to the Biopac, but not to the headphones of the participants, and continued during the whole 24 bars including the continuation section (see Fig. 2 ). They provided easy detection of the temporal position of the beats within the auditory stimuli.
After amplification by the PV6 Mixer, the oscillating bursts had a maximum amplitude of -1 to 1 V. In order to detect these oscillating bursts, we computed the envelope of the signal, which was then obtained by applying a second order Butterworth low pass filter, with a cut-off frequency of 100 Hz to the rectified signal. The filter was applied forward and backward using the Matlab "filtfilt" function to ensure that no temporal shift was introduced. A first coarse detection of the temporal onset of the beats was performed by searching the position at which the envelope was increasing through a threshold of 0. 02 V. Finally, for each detected coarse onset, researchers obtained the specific position by searching for the next sample for which the rectified signal went above the threshold of 0.
This was conducted as we postulated that it corresponded to the moment in time at which the participants exerted the most force with the foot and thus, it should correspond to the moment at which the participants wanted to materialize the beat. For both the tapping and the stepping data, a Matlab script was written to offer a visual inspection of the events detected with the automatic algorithms. Visual inspection was performed to remove those automaticallydetected taps that do not correspond to actual taps (i. e. , false detections). During this visual inspection, false detections were identified from the shape of the participants' taps. False detections were removed manually (less then 12% of the trials).
Statistical analyses. For all tasks, a 40% criterion (i. e. , deviation from the mean of that trial) was calculated to remove outliers from the IRI means based on Repp  51. For the ASYNC means, a 25% criterion was calculated to remove outliers based on and to enable comparison with Sowiński and Dalla Bella  52. Trials were removed from the analyses if less than 18 and more than 44 strikes were recorded, due to participant error and/ or equipment failure. These criterion resulted in a loss of 4. 3% data overall (28 trials from a possible total of 648 trials; 18 in the older group, and 10 in the younger group). As the assumptions for parametric analyses were met, age group comparisons for measures unrelated to action production were first analyzed using one-way ANOVAs, and corrected t-tests.
The entrainment and the synchronization data was analyzed following three steps. First, a multifactorial repeated-measures (RM) ANOVA (  1 ) IRI error = IRI -ISI ISI × 100. (2) CoV = SD IRI Mean IRI × 100. ( www. nature. com/scientificreports/ was conducted with section (synchronization, continuation), modality (finger tapping, toe tapping, stepping on the spot), and cue type (metronome, song) as the within-group RM. Then, a multifactorial RM ANOVA was performed on the synchronization data of the synchronization section only, with modality and cue type as the repeated measures. Finally, an independent analysis was done on the entrainment and the synchronization data to reveal the effects of song preferences (i. e. , familiarity and likability). In all analyses, the between group factor was age group (younger, older).
The analyses were conducted independently for each tempo (slow, medium, fast). Analyses were conducted using Statistica software (2018), with the significance level set to an alpha p of 0. 05, and with the application of Bonferroni corrections adjusted alpha p for multiple comparisons. Tukey post hoc measures were used when necessary. ) ASYNC = Strike start -Beat start. (4) ACoV = SD ASYNC Mean ASYNC × 100. 3.
Many nations are challenged by landscape fires. A confident knowledge of the area and distribution of burning is crucial to monitor these fires and to assess how they might best be reduced. Given the differences that arise using different detection approaches, and the uncertainties surrounding burned-area estimates, their relative merits require evaluation. Here we propose, illustrate, and examine one promising approach for Indonesia where recurring forest and peatland fires have become an international crisis. Drawing on Sentinel-2 satellite time-series analysis, we present and validate new 2019 burned-area estimates for Indonesia. The corresponding burned-area map is available at https://doi. org/10. 5281/zenodo. 4551243 (Gaveau et al. , 2021a). We show that > 3. 11 million hectares (Mha) burned in 2019.
This burned-area extent is double the Landsat-derived official estimate of 1. 64 Mha from the Indonesian Ministry of Environment and Forestry and 50 % more that the MODIS MCD64A1 burned-area estimate of 2. 03 Mha. Though we observed proportionally less peatland burning (31 % vs. 39 % and 40 % for the official and MCD64A1 products, respectively), in absolute terms we still observed a greater area of peatland affected (0. 96 Mha) than the official estimate (0. 64 Mha). This new burned-area dataset has greater reliability than these alternatives, attaining a user accuracy of 97. 9 % (CI: 97. 1 %-98. 8 %) compared to 95. 1 % (CI: 93. 5 %-96. 7 %) and 76 % (CI: 73. 3 %-78. 7 %), respectively. It omits fewer burned areas, particularly smaller-(< 100 ha) to intermediate-sized (100-1000 ha) burns, attaining a producer accuracy of 75.
Introduction Accurate burned-area maps are key to characterising landscape fires, clarifying emissions, and identifying the probable causes. Such information is needed to target interventions; to assess policies and practices intended to reduce or control fires, such as law enforcement and restoration of fire-prone degraded lands; and to measure progress towards international climate commitments  (Sloan et al. , 2021). Here, we focus on Indonesia where recurring forest and peatland fires have become an international crisis  (Tacconi, 2016). These concerns arise from the large carbon emissions associated with these fires and the impact of associated aerosol emissions for human health in the wider region (Van der Werf Published by Copernicus Publications. D. L. A. Gaveau et al. :  Refined burned-area mapping protocol using  Sentinel-2 data et al.
, 2018). This validation is based on independent globally distributed, visually interpreted reference satellite data, however none over Indonesia. These coarse-resolution datasets generally omit small-scale fires and, thus, the reported burned area is underestimated  (Ramo et al. , 2021). This has motivated research in the use of medium-resolution satellites (10 to 30 m) such as Sentinel-1  (Lohberger et al. , 2018, in Indonesia) , Sentinel-2  (Chuvieco et al. , 2018, in sub-Saharan Africa) , and the Landsat constellation  (Hawbaker et al. , 2020, in North America)  to produce more detailed burned-area maps. Lohberger et al. (2018)  reported 4. 6 Mha burned in 2015 in Indonesia, nearly double the estimate of 2. 6 Mha from the Indonesian Ministry of Environment and Forestry (MOEF), using visual interpretations of time-series Landsat 8 imagery  (Sipongi, 2020).
We also developed an independent reference dataset to compare the accuracy of our estimate against the official and MCD64A1 burned-area maps. Given the lack of objectively distributed ground truthing, we sought ways to extract reference sites by visually detecting a smoke plume, burn, or heat source (flaming front or hotspot) from the archive of original Sentinel-2 images. Finally, we exam-ined differences in terms of burn size frequency distributions among these three burned-area estimates to examine spatial patterns.
, 2021b). After the production of the pre-and post-fire composites, we used a "random forest" classification model (see Sect. 2. 3) trained on visually identified pairs of pre-and post-fire pixels to confirm if the spectral changes indicating vegetation damage corresponded to a burning event. Third, three independent interpreters assembled a reference dataset by visually identifying burns in the original time-series Sentinel-2 images. Fourth, we assessed our burned-area map, as well as the official and MCD64A1 burned-area maps, against the reference dataset to gauge the reliability and accuracy of the three burned-area products. Finally, we tested whether, and how, the three burned-area estimates differed in their tendencies to incorporate burns of different sizes.
We excluded the bands at 60 m spatial resolution (bands B1, B9, and B10) since these bands present a low spatial resolution for the aim of the study. Therefore, we used a total of 22 features: the NBR and bands B2, B3, B4, B5, B6, B7, B8, B8A, B11, and B12 of the pre and post-composites. We used a 10-fold cross-validation to assess the accuracy obtained with a set of different parameters in the random forest. The splitting "train-test" in the cross-validation was done only with the training dataset, since the reference dataset used for the final validation must be completely independent of the training and model parameterisation. The two parameters that we tuned were the number of trees and the minimum leaf size. Random forest is an ensemble classifier composed of several decision trees; the parameter number of trees represents the number of decision trees in the random forest.
The pixels that show a burn present a singular spectral signature, and, for this reason, it is necessary to collect a large number of training points. We collected training points until we were satisfied with the results of the classification by visually comparing the resulting burned-area map against the pre-and post-fire composites. We trained the random forest algorithm using 988 independent training pixels (Fig. S1  in the Supplement for locations), point coordinates labelled as either burned (317 points) or unburned (671 points). These pixels were selected by visual interpretation of the pre-and post-fire image composites. Burned areas show a distinctive dark (low albedo) brown/red colour in the SWIR-NIR-red composite image when displayed as red-green-blue channels (Fig. 1 ). The training pixels were collected across land cover types (Table  S1  in the Supplement for land cover types) to ensure the representa-tiveness of the training dataset and the satisfactory generalisation of the classification model across Indonesia.
Burned-area map validation The gold standard is to validate the map against a sufficiently large reference dataset developed based on ground visits to burned and unburned sites sampled objectively and randomly across the region of interest  (Olofsson et al. , 2014). We sought alternative ways to generate the reference dataset because the sample of GPS locations of burned locations collected by the Indonesian government was not available. Given the laborious scale of this validation exercise, we validated our burned-area estimates for only the seven provinces prioritised by the Indonesian government for restoration of fire-prone degraded lands (Kalimantan Barat, Kalimantan Tengah, Kalimantan Selatan, Papua, Jambi, Riau, and Sumatra Selatan). These provinces are also those that typically burn most extensively.
We employ the term "corrected burned-area" for the estimation of the burned area based on the validation of a given burned-area map against the reference dataset, following the practices in  Olofsson et al. (2014). For instance, a high omission rate in the burned class of a given burned-area estimate would potentially lead to a lower mapped area than a corrected area for that estimate, while a high commission rate would potentially lead to a higher mapped area than the corrected area. The corrected area represents an estimation of the actual burned area for the year 2019 computed for each of the three datasets separately. The accuracy of the burned-area map and the sample size of the reference dataset play a role in the confidence interval of the corrected area estimate. Lower map accuracy and smaller sample size mean wider confidence intervals.
Reference site sampling design Good practices for estimating area and assessing accuracy, as reported in  Olofsson et al. (2014) , assume a simple random sampling or a stratified random sampling in the generation of the reference dataset. In our study, we employed a stratifiedrandom sampling approach to ensure an acceptable sample of burned reference sites. Our stratified approach was necessary given that the burned class was rare over the study area: the area of seven provinces of interest is 87. 6 Mha and the combined area detected as burned by all three datasets represented only 3. 1 % of this area. For the generation of the 1298 reference sites (see Table  S4  for associated land cover types 1 year before fire), we randomly sampled (i) 419 sites across from the areas classified burned by the three datasets (red area in Fig.
3a ; Table  S2 ), and (ii) 879 sites in areas classified as unburned by all three datasets hereafter denoted U (grey area in Fig. 3a ). This sample size is deemed sufficient and comparable to other map assessments at a larger scale  (Stehman et al. , 2003; Olofsson et al. , 2014). This initial sample of 1298 total sites presents a shortcoming for direct pair-wise comparisons between the reference dataset and each of the three burned-area maps individually. Specifically, sampling densities in the reference dataset were greater in areas classified burned by the three datasets (red area in Fig. 3a ) compared to the area deemed unburned by all three datasets, hereafter denoted U (grey area in Fig. 3a ). Consequently, for the validation of a given burned-area dataset, its total number of unburned reference sites would be over-sampled upon defining unburned reference sites with reference to U as well as areas classified as burned uniquely by one of the other two maps (cyan areas in Fig.