dataesr/scientific-paragraphs-categorization huggingface dataset:
Regarding the optical characteristics, continuous wave PL measurements were carried out at room temperature (RT) and low temperature (LT), i. e. , at 300 K and 12 K, in a closed-cycle Helium (He) cryostat using a frequency-doubled Argon (Ar) laser at 244 nm (5. 08 eV) with an excitation power of 20 mW. Atomic force microscopy (AFM) EDGE-DIMENSION (BRUKER, Billerica, MA, USA), operating in tapping mode with a silicon tip with a radius between 5 and 10 nm, was used to study the surface morphologies of h-BN and AlN. In addition, a diamond-coated tip with a typical radius between 5 and 10 nm was also used to investigate the morphology of the surface QDs plane, and all data were processed using WSxM software  [18]. Furthermore, X-ray diffraction (XRD) measurements were conducted using a PANalytical X'Pert PRO MRD four-circle diffractometer (Malvern Panalytical, Malvern, United Kingdom) to assess the crystalline quality of the AlxGa1-xN/AlN layers and examine the impact of h-BN on the subsequent layers.
Data Analysis Our primary outcome will be the changes in the level of anxiety evaluated with a repeated scale. We will graph and analyze data for each participant and each target behavior. We will first visually inspect the analyses as per the guidelines for visual analysis in single-case studies  [23]. Within-condition analysis (baseline and intervention phases) will include the evaluation of the stability of the level of data by condition by calculating means, medians, ranges, and stability envelopes. The absolute level of change will be evaluated by calculating the difference between the first and last values in each condition. Between-condition analysis of data will allow for comparisons of adjacent data in the study. The change in the trend direction between phases A and B will be evaluated by identifying the trend as accelerating, decelerating, or zero-celerating.
The therapeutic or contra-therapeutic direction will also be reported. The level of change between conditions will be evaluated by calculating the median level of change. A comparative table of data will be edited for each repeated measure, patient, and step of the protocol. The means, medians, range, stability envelope, and absolute level of change will be presented in this table. We will use the Tau-U to examine the within-phase trend and between-phase difference to estimate the treatment effect both within and between phases. We prefer the Tau-U to alternative statistics (ie, proportion of nonoverlapping data) because it provides a baseline trend-corrected estimation of trends in treatment and follow-up phases by combining nonoverlap between phases and trends within phases. The following 3 Tau-U indices will be calculated: (1) Tau-U A-to-A compares XSL • FO RenderX a phase-A score with another phase-A score, (2) Tau-U B-to-B compares a phase-B score with another phase-B score, and (3) Tau-U A-to-B compares a phase-A score with a phase-B score.
The generated events were passed through the detailed Geant4 simulation of the ATLAS detector  [85, 86]  and processed with the same software as used to reconstruct the data. Conditions in the ATLAS detector, such as the average number of 𝑝 𝑝 interactions per bunch crossing, changed throughout Run 2. Separate samples were produced with 2015-2016 conditions, 2017 conditions, and 2018 conditions, where each sample is normalised according to the integrated luminosity of the corresponding run period. The effect of multiple interactions in the same or neighbouring bunch crossings (pile-up) was modelled by overlaying each simulated hard-scattering event with inelastic 𝑝 𝑝 events generated with Pythia 8. 186 using the NNPDF2. 3lo parton distribution functions and the A3 tune  [87]. The production rates for the SM Higgs boson with 𝑚 𝐻 = 125 GeV, obtained from the CERN Yellow Reports  [37, 45] , are assumed throughout this analysis.
Exclusive background The Drell-Yan production of dimuons with a highly energetic photon, typically from final-state radiation, 𝑞 q → 𝛾 * /𝑍 * → 𝜇 + 𝜇 -𝛾, constitutes a significant background contribution, exhibiting a characteristic resonant structure in the 𝑚 𝜇 + 𝜇 -𝛾 distribution. The shapes of this background in 𝑚 𝜇 + 𝜇 -𝛾 and 𝑚 𝜇 + 𝜇 -are modelled using events simulated with Sherpa 2. 2. 10 [107] at leading order with the NNPDF3. 0 parton distribution function set in the 𝜓(𝑛𝑆) and Υ(𝑛𝑆) 𝑚 𝜇 + 𝜇 -regions. The normalisation of this background is determined from a fit to the data in the signal region following the selection described in Section 3. The resonant shape in 𝑚 𝜇 + 𝜇 -𝛾 is modelled analytically as the sum of a Voigtian function and a threshold function defined as 𝑓 (𝑥) = √ 𝑥 -𝑥 0 e -𝐴( 𝑥-𝑥 0 ) , where 𝐴 and 𝑥 0 are constants, 𝑓 (𝑥) = 0 for 𝑥 < 𝑥 0 , and 𝑥 is the three-body mass 𝑚 𝜇 + 𝜇 -𝛾.
Potential contamination of the GR sample from signal events is expected to be negligible. Signal injection tests were performed where a significant amount of signal, much larger than the signal branching fraction excluded in the presented analysis, was added to the GR to investigate the effect of such a potential signal contamination on the model; it was found that the presence of the signal is largely inconsequential to the shape of the background model and does not lead to any peaking structures in the background templates. The ability to predict data using the background model is studied in several validation regions (VRs), defined by selections looser than the nominal signal requirements and tighter than the generation region requirements. The requirements imposed in each of the selection regions used in the analysis are summarised in Table  4.
[110]. When setting limits for one of the signals, the other potential signal contributions are treated as nuisance parameters and are profiled in the fit. Only the decays Q → 𝜇 + 𝜇 -are considered in the limit setting, feed-down from 𝑄 ′ → 𝑄 + 𝑋 transitions are not included. The ability of the fit to identify potential signal contributions was verified through signal injection tests. Signals corresponding to a branching fraction of 5 × 10 -4 and 5 × 10 -7 for the Higgs and 𝑍 boson decays, respectively, were injected into an Asimov dataset  [110]  constructed from a background-only fit in the signal region. The full fit accurately recovered the injected signals.
1 fb -1 of √ 𝑠 = 13 TeV 𝑝 𝑝 collision data. Further, constraints are set on the ratio 𝜅 𝑐 /𝜅 𝛾 and 𝜅 𝑏 /𝜅 𝛾 of the Higgs boson coupling modifiers. An observed 95% CL interval of (-133, 175) is obtained for 𝜅 𝑐 /𝜅 𝛾 , and a 95% CL interval of (-37, 40) is obtained for 𝜅 𝑏 /𝜅 𝛾. Bern and Geneva, Switzerland; MOST, Taiwan; TENMAK, Türkiye; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, CANARIE, Compute Canada and CRC, Canada; PRIMUS 21/SCI/017 and UNCE SCI/013, Czech Republic; COST, ERC, ERDF, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and MINERVA, Israel; Norwegian Financial Mechanism 2014-2021, Norway; NCN and NAWA, Poland; La Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Göran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom.
For this purpose it was evaluated the genetic variability and structure of populations from different host plants along crucial areas of occurrence, including that potentially of origin and the invaded ones, based on DNA sequences of mitochondrial (Cytochrome c oxidase subunit I) and nuclear (Internal Transcribed spacer, D2 28S) genomic regions. Specimens from South America (Brazil) and Europe (France, Italy, Poland, The Netherlands) were collected from tomato and other solanaceous species from the Solanum and Physalis genera. Final TRM datasets were composed by 101, 82 and 50 sequences from the COI (672 bp), ITS (553 bp) and D2 (605 bp) regions, respectively. Distributions and frequencies of haplotypes (COI) and sequence variants (D2 and ITS1) were inferred; pairwise genetic distances, and phylogenetic analysis were performed, including Bayesian Inference (BI) combined analysis.
Sequences, Genetic Diversity, And Phylogenetic Analyses The software Staden Package v. 1. 6. 0  (Staden et al. 2000)  was used for checking, editing and assembling the raw data into sequence contigs. The COI, D2 and ITS sequences were aligned using the ClustalW multiple alignment procedure  (Thompson et al. 1994)   Different primer combinations were used to generate sequences of the ITS region, producing dissimilarities in the size of the segment sequenced. One spanning the ITS1, 5. 8S and ITS2 region with about 900 bp and the other the ITS1 fragment of about 514 bp. The latter region was present in all accessions and was selected for the subsequent analysis. Analyses of the pairwise genetic distances between and within nucleotide sequences, as well the choice of the most appropriate evolutionary models for estimation of inter-and intra-lineage genetic variation were performed using MEGA v.
7  (Tamura et al. 2013 ). The Hasegawa-Kishino-Yano (HKY) model  (Tamura et al. 2013 ) was applied to the COI dataset; the Kimura 2-Parameters (K2P) model  (Kimura 1980 ) was applied to the D2 datasets; and Tamura 3-parameter (T92) was applied to the ITS datasets. Standard error estimates were obtained by a bootstrap procedure (1000 replicates). The phylogenetic analyses were conducted using the maximum likelihood (ML) optimality criterion. The best-t models of nucleotide substitution for the three fragments were selected using the jModeltest v. To perform the Bayesian Inference (BI) combined analysis of COI, D2 and ITS fragments, the sequence les were individually organized using MEGA v. 6. Alignments of the three fragments were performed separately by the CLUSTAL W multiple alignment method  (Thompson et al.
COI The nal dataset consisted of 102 aligned sequences of 672 bps, 99 sequences representing twenty-ve TRM populations associated with solanaceous plants obtained in this study (Table  3 ); two TRM sequences available in/extracted from the GenBank (as mentioned in M&M); ans one Abacarus mite sequence as an outgroup. No insertions or deletions were found. The translation of the mtDNA COI nucleotide sequences resulted in 230 amino acids in length. Alignment of these amino acid sequences revealed differences at four positions (variable sites), one was parsimony informative. The G+C and A+T composition of the entire data set ranged from 36. 79 to 38,57% and 61. 47 to 63. 21%, respectively. Polymorphism indices such as the number of variable sites (S), the haplotype number (h) and diversity (Hd), the average number of differences in nucleotides (K), and the diversity of nucleotides (Pi) for mitochondrial and nuclear sequences of A.
ITS1 The nal ITS1 dataset consisted of 84 sequences of 553 bps, including 81 sequences of TRM populations from Brazil and Europe, obtained in this study (Table  3 ); one TRM genome sequence and two sequences of Ab. neosacchari (as an outgroup) recovered from GenBank. In the alignment, ve sites were parsimoniously informative, and nine sites were variable (Table  4 ). The average mean divergence over all sequence pairs, including the outgroup taxa, was 13. 81% (SE = 1. 1%) and ranged from 0. 28 to 33. 52%. The average mean divergence over the A. lycopersici sequences was 0. 65% (SE = 0. 38%) and ranged from 0. 28 to 1. 13% (Table  6 ).
D2 The nuclear sequence data of the D2 region of the 28S rDNA comprised 49 aligned sequences of 605 bp (26 sequences from ve TRM populations from Brazil and 20 sequences from Europe); one TRM genome sequence; and three outgroup sequences-two species of Aculops species and one of Ab. neosacchari four available in the GenBank). The D2 sequences obtained during this study are the rst TRM sequences available in the GenBank for this fragment. No polymorphic sites and variable regions were detected within the TRM D2 sequences. The average mean divergence over all sequence pairs (including the outgroup taxa) was 30. 78% (SE = 2. 10%) and ranged from 0. 0 to 36. 73%. The average mean divergence over the TRM sequences was 0. 0% (SE = 0. 0) (Table  7 ).
For instance, no studies investigated the occurrence of cryptic species on Calacarus citrifolii  Keifer, 1955 , the species with the wider host range of all superfamily reported on 21 plant families (de Lillo and Amrine -Computerized Database for Eriophyoidea, Filemaker Pro). Among species of economic importance, TRM does stand out to be an oligophagous species. TRM genetic homogeneity in Europe revealing a highly invasive haplotype Analysis of the diversity of sequences of the COI mitochondrial region showed that haplotype 1 (cH1) is dominant in Brazil, is associated with all host plants considered, and that it is the only one present in European populations (France, Italy, Poland and The Netherlands). The genetic homogeneity among TRM European populations highlights that cH1 is a highly invasive and not host-speci c haplotype.
Another junior synonym of A. lycopersici was described in 2005 in China as Tetra lycopersici Xue and Hong, 2005 (Amrine and de Lillo, personal communication). In this context of historical untraceability patterns genetic data can be explored for tracing invasion routes  (Estoup and Guillemaud 2010). Although the objective of this work was not to trace invasion routes, since a better representation of populations worldwide would be necessary for this purpose, genetic diversity and a phylogeographical analysis based on both mitochondrial and nuclear markers analyzed jointly with the main host plant history corroborate the hypothesis of a South American origin for TRM. In general, the highest genetic diversity.
São Sebastião Al5_2* cH-1 D2-1 I-1 MW173893* MT652189* MT652117* aethiopicum BR: Distrito Federal. São Sebastião Al5_10* cH-1 D2-1 I-1 MW173894* MT652190* MT652118* BR: Distrito Federal. São Sebastião Al5_11 cH-1 D2-1 I-1 MW148528 MT652191 MT652119 BR: Distrito Federal. São Sebastião Al5_12 cH-1 - - MW173895 - - BR: Distrito Federal. São Sebastião Al5_13 cH-1 - I-1 MW173896 - MT652120 BR: Distrito Federal. São Sebastião Al5_16* cH-1 D2-1 I-1 MW173897* MT652192* MT652121* BR: Distrito Federal. São Sebastião Al5_18 cH-1 - - MW173898 - - The COI A. lycopersici sequence available in GenBank was included in the analyses (JX298841) (Bouneb et al. 2014) , as well as one fragment extracted from the complete genome (WNKI00000000) (Greenhalgh et al. 2020 ). In addition, sequences of Aculops strobilaceae Xue and Hong, 2006 (KF782528) and Aculops cajanusis Xue and Hong, 2011 (KF782527) (Li et al.
2014) were included in the D2 analyses as internal group. Sequences of Abacarus neosacchari Duarte and Navia, 2019 (belonging to the Phyllocoptinae, Anthocoptini similarly to Aculops) were included as outgroup in COI (KX892640), D2 (KX855705) and ITS (KX855740 and KX855741) analyses. Sequences were also checked against Aculops sequences retrieved from GenBank (Bouneb et al. 2014; Li et al. 2014; Duarte et al. 2019, Greenhalgh et al. 2020 ) and used to edit a neighbor joining tree to validate their reliability. The alignment of COI sequences was con rmed by translating the aligned DNA into amino acids using MEGA v. 6. To initially identify candidate protein-coding regions in DNA sequences searching start and stop codons, an open reading frame was determined using a graphical analysis tool (ORF FINDER) available at http://www.
Introduction: Phenotypic spectrum of SLC6A1-related neurodevelopmental disorders (SLC6A1-NDD) includes intellectual disability (ID), autistic spectrum disorders (ASD), epilepsy, developmental delay, beginning from early infancy or after seizure onset, and other neurological features such as hypotonia and movement disorders. Data on familial phenotypic heterogeneity have been rarely reported, thus in our study we aimed to investigate intrafamilial phenotypic variability in families with SLC6A1 variants. Methods: We collected clinical, laboratory and genetic data on 39 individuals, including 17 probands, belonging to 13 families harboring inherited variants of SLC6A1. Data were collected through an international network of Epilepsy and Genetic Centers.
Methods Patients with inherited variants of SLC6A1 were collected through an international network of Epilepsy and Genetic Centers. Patient clinical, neurophysiological, and genetics data were collected and registered in a RedCap database  (Harris et al. , 2019)  hosted at the Department of Epilepsy Genetics and Personalized Medicine at the Danish Epilepsy Center in Dianalund (Denmark). Probands were defined as the first person in a family to receive genetic counseling and/or testing for suspected hereditary risk; in some families two individuals (always siblings) were considered probands because both raised suspicion for a genetic disease. Carriers were defined as both the probands and the first-and second-degree relatives harboring a SLC6A1 variant. Clinical data, mainly on epilepsy, neurodevelopment, behavioral features, and genotype were retrieved for this study.
Seizures were classified according to the International League Against Epilepsy (ILAE) classification. Cognitive development, including intellectual and learning disabilities, was based on the assessment of the referring physicians. Variants are classified according to the ACMG criteria  (Richards et al. , 2015). Patients were included if they had a variant of unknown significance (VUS), a likely pathogenic or a pathogenic variant. One of the variants identified was found in a likely mosaic state in Genome Aggregation Database (gnomAD, https://gnomad. broadinstitute. org/). The local ethical committees approved this study. All patients or parents/legal guardians in case of minors gave the consent to participate in the study.
Thus, they will go undiagnosed unless a family member develops a more severe phenotype prompting genetic testing. Additionally, some of the families presented here were detected as part of genetic trio WES studies, which in general is the preferred analysis when suspecting a genetic disorder. However, the risk of trio analysis is that with lack of sufficient information of more subtle symptoms in parents or relatives, inherited variants may be overlooked during the data analysis, particularly when the search is for de novo variants, and thus the patient may be classified as genetically undiagnosed. Secondly, we acknowledge that the SLC6A1 variant found in these families might not be the sole cause of disease in the most affected family members. It is well known that other genetic factors, as well as environmental factors may influence the phenotypic outcome.
Resting-state fMRI (rs-fMRI) has also been used to identify differences in local, regional, and network activity between DOC patients and healthy controls. Machine learning models trained to distinguish between conscious wakefulness and anaesthetic-induced unconsciousness were investigated for their ability to identify pathologically induced unconsciousness  ( Campbell et al. , 2020 ). The models achieved reliable performance within and across datasets and demonstrated potential for discriminating between degrees of pathological unconsciousness in clinical patients. Analysing rs-fMRI from the perspective of a trajectory in a state space, where the states were defined by clustering instantaneous patterns of phase coherence between brain areas detected over time, revealed that UWS/VS patients show primarily a brain pattern of low interareal phase coherence, with reduced transition probabilities (meaning that this state is more stable) when compared with healthy individuals and minimally conscious patients  ( Demertzi et al.
First, several of the previous works do not fully validate the models on new datasets and lack performance estimates beyond the initial cross-validated performances, which are often overestimated  ( Varoquaux, 2018 ). Second, their utility as part of clinical decision systems remains unknown. While the models provide novel insights into the DOC population, it is not yet clear what is the cost versus benefit of using such models. Further holistic analysis considering the human, technical and economic cost are required. Finally, and most importantly, statistical models of DOC are well suited to identify relevant features out of many possible candidates, but they do not explicitly model the process by which such features come to be relevant, i. e. , they do not, on their own, provide insight on how to act on them, so they provide little insight about clinical interventions.
For instance, fMRI and EEG differ in terms of both spatial and temporal resolution, so a model that can simulate both would imply the ability to capture both slow and fast dynamics. Additionally, models that can provide a good fit to multiple modalities (e. g. , both fMRI and EEG data) provide intrinsic validation for their biological plausibility. This avenue of addressing the first challenge to generalisation can capitalise on the existence of rich datasets about patients, spanning not only neuroimaging but also neurotransmitter expression, transcriptomics and proteomics, which are now becoming increasingly available. Of course, the success of this endeavour depends on the data being compatible and of high enough quality, to make sure that the additional data are not merely adding noise.
General recommendations In addition to the Desiderata outlined above, there are also a number of more general recommendations that we believe would help both the study of DOCs as a whole, and the more specific endeavour of modelling DOCs. First, adding to the call for data-sharing and harmonisation, we believe that DOC research should take inspiration from the non-human primate neuroscience. Like most DOC datasets, non-human primate neuroimaging datasets tend to comprise a relatively small number of individuals, due to the difficulties of data collection. In non-human primate neuroscience, one way to overcome this limitation has been by repeated data acquisition from the same individual, to boost the signal-to-noise ratio. To the extent that this is feasible for DOC patients, we believe that availability of multiple scanning sessions, both with the same and with different neuroimaging modalities, would be of great help to the modelling effort.
Acknowledgements This Working Group was brought together under the auspices of the international Curing Coma Campaign. AIL was supported by the  Gates Cambridge Trust  ( OPP 1144 ). RC acknowledge the support the  Human Brain Project ,  H2020-945539. AK was supported by  US National Institutes of Health  grants  R01NS102646  and  RF1MH123232. JC was supported by  La Caixa Foundation  ( LCF/BQ/PR22/ 11920014 ). JMS was supported by the  Australian National Health and Medical Research Council  ( 1193857 ). PO is supported by grants  Fondecyt 1211750  and  Centro Basal   AC3E FB0008  ( ANID, Chile ). YSP is supported by  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie grant   896354. MLK is supported by the Centre for Eudaimonia and  Human Flourishing  (funded by the  Pettit and Carlsberg Foundations)  and  Center for Music in the Brain  (funded by the  Danish National Research Foundation ,  DNRF117 ).