dataesr/scientific-paragraphs-categorization huggingface dataset:
0, 105. 9, 149. 7, 52. 6, 57. 0, 19. 7, 23. 6, 7. 4, 27. 9, 34. 5, 67. 2, and 31. 4 ppm. [14] Searching acd_lotusv7 in the same conditions as those used for compound 1, requesting at least 43 chemical shift matches lead to 3 hits. Only the first one accounted for the molecular formula C43H52N4O7 associated to the list of chemical shift values in the original publication. The molecular formula of the two other hits was C43H54N2O8 and corresponded to the first hit compound to which of a water molecule was added, resulting from the opening of an epoxide ring. The first hit corresponded to a compound isolated from Ervatamia peduncularis whose QID is Q96375411. Searching this QID in Wikidata revealed that this compound was named conofoline while our group published it in 1995 as pedunculin. [15] Conofiline was isolated from Tabernaemontana divaricata and published shortly before pedunculin.
These compounds belong to the family of triterpene saponins, as indicated by the to classyfire data embedded in acd_lotusv7. The best hit, referenced Q105155240 in Wikidata, was associated to "liquorice" in this database and to the chemical study of Glycyrrhiza uralensis. The publication from which the  13  C NMR data were taken referred to Glycyrrhiza glabra, or sweet liquorice, and to Glycyrrhiza uralensis. [16] The corresponding compound is called glycyrrhizic acid, also known as glycyrrhizin. Its representation as a "flat" molecule is due to lack of configuration data in the original document that was imported in LOTUS. A few other compounds were tested for dereplication from their  13  C NMR data but their number is certainly too small yet in order to ensure that the acd_lotusv7 would be the definitive solution (if any exists) to the problem of the quick identification of already known natural products.
Conclusion This article reports the building process of the acd_lotusv7 database using the structural data contained in the LOTUS database, version 7, and using  13  C NMR chemical shift predictions provided by the ACD/Labs predictor in the validation mode. Database usage is illustrated by examples of various structural complexity. Future works may involve the creation of a protocol intended to minimize the amount of calculation required by the updating for the acd_lotus database subsequently to the publication of new LOTUS versions. The actual structure search engine, based on the ACD/Labs software, does not take into account the highly useful multiplicity information bound to  13  C NMR resonances. [5] A remedy to this situation will be necessary to propose to potential users, as well as an integrated graphical interface for interactive solution structure browsing and evaluation.
Results Demographic Information. There was no significant difference between groups with regards to participants' age, gender, levels of autism symptoms severity, trait anxiety, sensory difficulties, and IQ at baseline. Results of the independent sample t tests at baseline are provided in Table  1  for the entire dataset (n = 36). All of the participants were right handed, and none of the participants met the criteria for a learning disability. Drumming Performance. The drumming performance analysis was carried out on a subset of 32 participants. Full details about this subsample are provided in Table  2  and in Materials and Methods. An ANOVA showed a significant time*group interaction for the timing error measure (F (1,30) = 4. 678, P = 0. 039, effect size η 2 = 0. 14). Paired t tests revealed that the drum group exhibited a significant improvement in relation to timing error over time (t (16) = 3.
Future studies could also consider adding an active control group participating in nonmusical motor activities such as archery. This would contribute to distinguishing between motor actions and higher-order functions involved in music-based training. Finally, even though the groups were not found to differ significantly with respect to any of the variables at baseline, it was observed that the difference in IQ between groups was close to significance for the entire dataset (n = 36; P = 0. 065) with a medium effect size (Cohen's D = 0. 638). Given the potential impact of IQ on learning new skills, we recommend that future studies investigating the effect of drum training on behavior and brain function consider using stratified sampling in order to guarantee more-balanced groups with respect to IQ.
Assessment and Analysis. Drumming assessment and practice. Drumming ability was assessed at the Centre for Neuroimaging Sciences before and after drumming tuition. All before and after drum tests were performed on the same volume-controlled, electronic drum kit (TD9, Roland) with a standard right-handed five-piece configuration comprising a snare drum, three tom-toms, hi-hat, ride cymbal, crash cymbal, and bass drum. The drum tests were recorded using a Musical Instrument Digital Interface (MIDI) sequencer (Cubase Pro-9. 5) and converted to exportable MIDI files, each containing a single exercise. Matlab MIDI Toolbox 1. 1  (103)  was used to import the files into the Matlab environment, and custom code was used for extracting data from the performance. The drumming assessment consisted of nine exercises which involved playing on the snare drum with the right hand, the left hand, and alternate hands to the metronome set at three tempi: 60, 90, and 120 beats per minute (bpm).
A total of 18 drum tutors were recruited, and one tutor taught two participants. The lessons comprised individualized learning following the standardized Rockschool drumming syllabus. All tutors were carefully selected based on their previous experience of teaching individuals with neurodevelopmental disorders and carrying out tuition following the Rockschool syllabus for beginners. The complexity of drumming tuition was increased on a weekly basis in line with participants' demonstration of improved drumming coordination and technique. The control participants were asked not to take part in any musical activities. After the 8 wk, all of the participants came back for a second drumming assessment, neuropsychological testing, questionnaires, and an MRI scan. Statistical analyses of the drumming data extracted in Matlab were performed using SPSS (SPSS version 18).
Independent sample t tests and paired t tests were used to determine baseline differences and the effects of drum training within groups. To test whether there was a significant interaction of group over time (time*group), we also carried out a repeated-measures ANOVA by specifying "time" as a within-subjects contrast (i. e. , before vs. after drum training) and "group" as a between-subjects contrast (drum group vs. control group). Significance was set at P < 0. 05. Finally, in order to further evaluate observed effects, we also calculated effect sizes (Cohen's D for paired t tests and partial eta squared η 2 for ANOVAs) using SPSS (SPSS version 18). Psychological measures. Two separate measures were used to investigate participants' social communication abilities and levels of restricted, repetitive behaviors.
More specifically, participants filled in the State-Trait Anxiety Inventory -State subscale (100), and a chartered psychologist administered the Brief Ataxia Rating Scale (106) and the Cerebellar Cognitive Affective Syndrome scale  (107). However, in this paper, we will specifically focus on the SSIS and the RBS described above. Statistical analyses of the psychological data were performed on all 36 participants, using SPSS (SPSS version 18). Data were assessed for normality using the Shapiro-Wilk test and, as they were acceptably normal, a repeated-measures ANOVA was used for investigating the interaction of group over time (time*group); and independent sample t tests were used to ensure there were no baseline differences between the two groups. Significance was set at P < 0. 05. MRI data acquisition.
All participants were scanned in a 3T MR scanner (Discovery MR750, General Electric). All participants underwent an anatomical T1-weighted MRI using a gradient-echo sequence with the following scan parameters: 196 sagittal slices, time repetition = 7. 3 ms, time echo = 3 ms, time to inversion = 400 ms, flip angle = 11°, field of view = 270 mm 2 , matrix size = 256 × 256, voxel size = 1 × 1 × 1 mm 3 , and slice thickness = 1. 2 mm. The rs-fMRI data were collected using an echo-planar imaging sequence with the following scan parameters: 180 volumes, descending slice order, time repetition = 2 s, time echo = 30 ms, flip angle = 75°, field of view = 211 mm 2 , matrix size = 64 × 64, voxel size = 3 × 3 × 3 mm 3 , and slice thickness = 3 mm. During acquisition of the rs sequence, the participants were asked to look at a cross on the screen in a wakeful resting state and were provided with headphones and earplugs in order to reduce the acoustic noise generated by the scanner.
MRI data preprocessing. Statistical Parametric Mapping (SPM12) and the CONN functional connectivity toolbox Version 18b (https://www. nitrc. org/ projects/conn)  (108)  were used to preprocess and analyze the anatomical and functional data. Preprocessing of the functional data included a slice-timing correction, realignment (motion correction), registration to structural images, spatial normalization into the Montreal Neurological Institute (MNI) standardized space, and smoothing with a Gaussian filter of 5. 0 mm spatial full width at half maximum value. A conventional band-pass filter over a low-frequency window of interest (0. 008 to 0. 09) was also applied to the rs time series. A CompCor strategy (109) was then carried out, extracting signal to noise from the white matter and cerebrospinal fluid by principal component analysis (PCA) without affecting intrinsic FC  (110).
e. , before drum training vs. after drum training scores) within the drum group. In order to explore whether clusters of increased FC overlapped with the dorsal attention network, the dorsal attention network mask was extracted from the CONN toolbox. MRIcroGL (https://www. nitrc. org/projects/mricrogl/) was used to create the statistical overlay images for both seed-to-voxel and voxel-to-voxel analyses. Voxel-to-voxel analysis. Finally, the last step of the analysis consisted in investigating how drumming performance related to changes in FC between the two groups over time (time*group interaction). For that purpose, a data-driven MVPA (multivariate pattern analysis) approach (108) was performed, using timing error as a regressor. For this MRI analysis specifically, it is important to note that the drumming performance data were used, and, therefore, this analysis was performed on the same subset of 32 participants.
ACKNOWLEDGMENTS. We thank  The Waterloo Foundation  for their financial support and guidance during the course of this project. We acknowledge  Clem Burke  and  Mark Richardson  from the  Clem Burke Drumming Project  for their continued support and enthusiasm for our drum-related research. We thank all of the participants as well as their carers and drum tutors, for their dedication to this research. We thank the  Wellcome Trust  and the  National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London  for their continued support of our neuroimaging research endeavors. The views expressed are those of the author(s) and not necessarily those of the  NHS , the  NIHR , or the  Department of Health and Social Care.
We also hypothesized that functional guilds would respond differently to forest dieback intensity; in particular the diversity of parasitoid and floricolous insects in areas of high forest dieback with greater canopy openness. As expected, we found no change in species richness but variations in community compositions across dieback levels and management practices. We performed zeta order analyses  28, 29  to further investigate the dataset structure and identify the nature of these community changes, as well as the species compositional and functional turnover. We also used zeta analyses in multi-site generalized dissimilarity modelling (zeta. msgdm)  30 to study the impact of environmental features (i. e. geographic distance to the nearest plot, altitude, canopy openness, total amount of deadwood, basal area, tree diversity, density of very large trees and both Tree-related Microhabitats (TreM) diversity and density) in driving these community structural changes and further discuss their associated consequences for forest management and conservation.
Results Diverse insect community with high temporal and spatial turnover. A total of 59,321,436 raw sequencing reads (individual forward and reverse reads) were obtained from the sequencing, and 11,802,769 sequences were recovered after complete demultiplexing and data processing including applying our criteria of at least 3 reads present in each of the 3 PCR replicates for each retained MOTU. Overall, the mean number of reads per sample was 55,937 ± 22,120 (SD) (max. 157,012; min. 12,669). Of the 222 Malaise-trap samples studied, 211 samples yielded results after demultiplexing. Metabarcoding recovered thousands of locally rare species and unnamed "dark taxa" (i. e. species-level 'arthropod' taxa in DNA reference libraries without a species name assigned and/ or species-rich taxa, often small in body size and underdescribed)  32.
Two orders, Diptera and Hymenoptera, together represented 73% of all MOTUs, as expected with Malaise-trapped samples (Fig. 3a ), with 641 (49. 7%) and 371 MOTUs (41. 4%) identified to species, respectively (Supplementary Fig. 3 ). As with the whole dataset (Fig. 2a ), the accumulation curves of the five most represented insect orders approached asymptotes (Fig. 3b ). We observed a high rate of temporal species turnover across months, as evidenced by only 360 MOTUs (12% of the total MOTUs diversity) being detected in all 4 months (i. e. from mid-May to mid-September) (Fig. 2d ). Thus, even though our sampling was efficient at recovering high biodiversity, sampling throughout the complete growing season is likely to increase the total diversity occurring in the region (Fig. 2d ). After grouping monthly temporal replicates into sample sites, geographic turnover was also observed, with only 45% of the total insect species sampled occurring in both of the sampled districts Aure valley (Central French Pyrenees) and Sault plateau (Eastern French Pyrenees) (Fig.
for each plot. Insect species richness was compared across the three dieback categories but also across three defined stand types including management practice (healthy, stands expressing dieback but not salvaged-hereafter 'disturbed'-, stands expressing dieback and being salvage logged-hereafter 'salvaged'). No response could be detected in terms of changes in species richness from all insect or functional groups tested, neither for the three levels of forest dieback nor the stand types (Table  1 ). However, a significant difference in species richness was found between the two sampled districts for Coleoptera only, with more species of beetles detected in Sault plateau (Table  1 ). As the dataset was geographically structured (i. e. Aure valley and Sault plateau districts), we analysed the dataset using the nearest-neighbour (NN) sample-selection scheme (Fig.
Representativeness of each insect order in the dataset was consistent with the known bias of Malaise-trap sampling, especially toward dipterans and hymenopterans. b Incidence frequency-based accumulation curves (solid lines) with species richness extrapolations (dashed lines) for each insect order. Shaded area represents 95% confidence interval. As all accumulation curves were nearly plateauing, this indicated that for each represented insect order, almost all Malaise-trappable diversity over the sampling period had been successfully recovered. retention rates across plots for the five main insect orders represented and for functional groups (floricolous/non-floricolous and parasitoid/non-parasitoid species) within each dieback category and stand type. We detected that species were retained differently within both dieback intensity gradient and stand types, regardless of the model scheme used (Figs.
Fit to exponential regression implies equal and stochastic turnover among species whereas fit to power-law regression as represented here indicates turnover driven by niche differentiation processes and rare species less likely to be found across the whole dataset. significantly with the level of dieback, driven mainly by turnover among rare insect species. Comparable changes in insect species composition but stable insect species richness have been observed in response to pest-induced forest dieback of other tree species  22, 33  , and the fact that similar observations have been made in aquatic insect communities over time  17  suggests that our findings are indicative of a broad ecological pattern. While species richness remained similar across the forest dieback gradient, community composition of the orders Lepidoptera, Hemiptera, Diptera and Hymenoptera changed significantly.
e. a time-delayed negative response of a taxon to an environmental disturbance) was observed in Diptera after 29 years in Quercus spp. dominated forests of Southern France  37. The lack of variation of species richness of insects with dieback level found in our study contrasts with other studies that show a positive effect of forest dieback on insect biodiversity  14, 15. However, those studies are based on the response of well-studied groups, i. e. "those insects that we love and cherish"  38. Indeed, Moretti et al. 15  highlighted that insect diversity could respond both negatively and positively to fire-induced forest diebacks after examining the datasets at lower taxonomic level. They found that the winners were among the most studied groups of insects Fig. 5  Variation in community composition across dieback gradient, stand types and geographical districts.
Green line with plain dots represents mean species retention rate of the total dataset in each respective dieback category. Increasing curves express that common MOTUs are more likely to be retained in additional samples than rare ones (with presence of common species over all plots if zeta ratio = 1) and decreasing curves indicate species turnover. In our study, no core of common species could be sampled for Coleoptera at low dieback level as well as for Lepidoptera and Hemiptera throughout the entire dataset for each dieback category, respectively. For both Diptera and Hymenoptera, common core of species was observed within all dieback level plots except between high dieback level ones. Similarly, drop and lack of structure in common species was detected for floricolous and parasitoid functional assemblages within high dieback level plots while stable for the other two dieback levels, with this functional turnover being driven by dipteran and hymenopteran species turnovers at high dieback.
only on species richness metrics and letting be forest diebacks to favour biodiversity may hold potential risks by driving a catastrophic decline of insects through a potential homogenization of dieback levels and associated species assemblages with more generalist species. Albeit Borges and collaborators  40  highlighted the issue in the very distinct island ecosystem, their concern can be extended to freshwater streams  17  and disturbed forest environments as in the present case. Looking at community compositional changes in a more specific way, zeta decline fitting a power-law regression implied a compositional turnover of the total dataset driven by environmental factors rather than stochastic events  29. The zeta ratio analyses showed that species are indeed retained differently within each dieback category.
Green line with plain dots represents mean species retention rate of the total dataset in each respective dieback category. Increasing curves express that common MOTUs are more likely to be retained in additional samples than rare ones (with the presence of common species over all plots if zeta ratio = 1) and decreasing curves indicate species turnover. In the present case, we found no core of common species for Hemiptera within each studied stand type. For clarity purposes, Hemiptera are hereafter not considered. Within healthy stands, only Coleoptera had no common species retained, while Diptera had a slight drop in the probability of common species occurring, but yet not a complete compositional turnover. Lepidoptera, Diptera and Hymenoptera were all impacted by disturbed stands with complete compositional turnovers within studied plots, and similar observation could be noticed for parasitoid and floricolous functional assemblages.
Furthermore, it highlights the need for more work to investigate whether climate-induced forest dieback can deeply impact functional efficiency and ecosystem services through these taxa  3. While these broad functions remain, the observed complete change in their respective species cohorts through the dieback gradient might impact trophic relationship at finer scales and species-dependent interactions. In a more detailed view, within each studied order or functional group, the winner and loser taxa were relatively rarely occurring species in our dataset. This pattern is comparable to previously reported taxonomic dissimilarities driven by rare species  34  and consistent with models showing that selective perturbations directed towards rare species generate more dynamical effects on the ecosystem  44.
Instead, both TreM diversity and density, as well as the amount of deadwood were the main factors influencing community composition when considering the whole dataset. Interestingly, we highlight that TreM diversity, TreM density and amount of deadwood impacting ranges on community changes at high zeta orders have similar impacts as canopy openness and altitude at low zeta orders, respectively. This finding may result in various management strategies to promote biodiversity, with both canopy openness and TreM diversity manipulated in similarly limited way (i. e. around 20% increase maximum) in accordance with the size of the managed area, but without threshold in regards to deadwood amount on-site for wide areas. Here, we also highlight that TreM diversity had more impact than TreM density on insect diversity  48  , similarly to deadwood diversity and amount in other studies  47, 49.
This may potentially be extended to most aerial insects with higher dispersal ability, which are the main representatives of our Malaise-trapped dataset. Hence, while species-specific response to environmental factors must be accounted for, geographic scale of the conservation area, especially through stand and landscape scales  37  , as well as dieback intensity should also be considered when managing stand heterogeneity and environmental factors to favour forest insect biodiversity  47. Finally, our study confirms DNA metabarcoding of Malaisetrapped samples to be an efficient approach for biomonitoring Fig. 9  Environmental variables driving species community changes (zeta diversity changes). a I-splines mean values of predictors from 30-rounds calculation with Sørensen-equivalent metric for seven environmental variables with their respective contributions to zeta diversity.