dataesr/scientific-paragraphs-categorization huggingface dataset:
The ligation product was enriched with 15 PCR cycles and the final library was validated on an Agilent 2100 Bioanalyzer with the DNA 7500 assay. Each library was sequenced on HiSeq4000 (Illumina) in a fraction of a HiSeq 4000 PE Cluster kit sequencing flow cell lane, following the manufacturer's protocol for dual indexing. Image analysis, base calling and quality scoring of the run were processed using the manufacturer's software Real Time Analysis (RTA 2. 7. 7) and followed by generation of FASTQ sequence files. Bioinformatic analysis. RNAseq reads were mapped against the mouse reference genome (GRCm38) using STAR version 2. 5. 3a  47 with ENCODE parameters for long RNA. Annotated genes (gencode version M21) were quantified using RSEM version 1. 3. 0 with default parameters  48. PCA was done using the top 500 most variable genes with the "prcomp" R function and "ggplot2" R library after the regularized log (rlog) transformation of the counts.
Differential expression analysis was performed with DESeq2 version 1. 18. 1  49  with a prefiltering on lowly expressed genes (at least ten normalized reads for each sample in one group). Differentially expressed genes were considered those with FDR < 0. 05 and absolute shrunken fold-change > 1. 5. Heatmaps with the top 50 differentially expressed genes were performed with the pheatmap R package  50  with the scaled rlog counts. Functional enrichment analysis of the differentially expressed genes was performed with Gprofiler 51. Mitochondria assays. DS and Acb were dissected and stored at -80 °C until further use. Tissue (n = 20-22/brain area) was homogenized and processed as previously described  52  in order to measure CI (EC 1. 6. 5. 3) and CS (E. C. 2. 3. 3. 1). Assay measurements were performed in duplicate.
Videos were analyzed using Noldus Ethovision software, and total distance traveled and average speed were calculated. Horizontal and vertical activity was measured in a circular corridor (Imetronic, Pessac, France) for 30 min. Counts for horizontal activity were incremented by consecutive interruption of two adjacent beams placed at a height of 1 cm per 90°sector of the corridor (mice moving through 1/4 of the circular corridor) and counts for vertical activity (rearings) corresponding to interruption of beams placed at a height of 7. 5 cm along the corridor (mice stretching upward) were used as an additional measure for exploratory activity. For amphetamine administration, mice were first habituated for 3 consecutive days in the same circular corridor used to measure locomotor activity. In this habituation phase, mice were placed in the activity box for 30 min, received a saline injection, and returned in the box for 2 h.
At the end of the 10 min sociability test, a new unfamiliar mouse (Stranger#2, gender matched) was placed in the previously unoccupied wire cage, and test mice were examined for an additional 10 min to assess preference for social novelty. The time spent sniffing the Stranger#1, Stranger#2, or empty wire cages were manually scored. The discrimination index for sociability and social novelty were calculated as follows, respectively: (time exploring Stranger#1-time exploring empty wire cage)/(total exploration time) × 100 and (time exploring Stranger#2-time exploring Stranger#1)/(total exploration time) × 100. Statistical analyses. GraphPad Prism v6. 0 software was used for statistical analyses. Data are shown as the means ± SEM. For normally distributed parameters, Student's t test (unpaired, two-sided) was used for all the tests except the rotarod, which was analyzed by two-way ANOVA repeated measures.
Acknowledgements We thank  Gabriel Mel de Fontenay  for critical reading of the manuscript and  Denis Hervé  and  Pierre-François Mery  for their insightful comments. We thank  Elisabeth Poole  for the anti-Peg10 antibody. We thank iExplore and  MRI Platforms. This work was supported by  Inserm ,  Fondation pour la Recherche Médicale  ( DEQ20160334919 ) (E. V. ) and ( DPA20140629798 ) (J-A. G. and E. V. ),  La Marato de TV3 Fundacio  (# 113-2016 ) (E. V. and M. M. ), and  Agence National de la Recherche  ( EPITRACES ,  ANR-16-CE16-0018 ) (E. V. and J-A. G. ),  NARSAD   Young Investigator Grant  from the  Brain and Behavior Research Foundation  (E. P. ),  NS091144  and  NS103037  from  NIH/NINDS  (J. B. D. ),  European Research Council  ( ERC-2014-StG-638106 ),  MINECO   Ramon y Cajal fellowship  ( RyC-2012-11873 ) and  AGAUR  ( 2017SGR-323 ) (A.
Q. ), and  MICIU Proyectos I+D RETOS-JIN  ( RTI2018-101838-J-I00 ) (E. S. ). A. E-C. is funded by  ISCIII/ MINECO  ( PT17/0009/0019 ), which is co-funded by  FEDER. E. P. was a  recipient of Marie Curie Intra-European Fellowship IEF327648 and Visiting Scholar Junior Fellowship (France-Stanford Center for Interdisciplinary Studies ). E. P. is currently a recipient of  Beatriu de Pinós  fellowship (# 2017BP00132 ) from  University and Research Grants Management Agency (Government of Catalonia ). L. C. is supported by the  Labex Epi-GenMed  ( «Investissements d'avenir» ,  ANR-10-LABX-12-01 ).
The phylogeny was inferred based on 781 loci from target capture data of 42 arsenurine species representing all 10 recognized genera. We found there are evolutionary trade-offs between body size, wing shape, and the interaction of fore-and hindwing shape. Namely, body size decreases with increasing hindwing length, but increases as forewing shape becomes more complex. Additionally, hindwing shape has a significant effect on forewing shape complexity. The complex wing shapes that make Arsenurinae, and silkmoths as a whole, so charismatic are likely driven by the strong forces of natural selection and genomic constraints. One other important outcome was discovering within our data one of the most vexing problems in phylogenetic inference -a region of a tree that possesses short branches and no "support" for relationships (i.
Following the filtering steps in the bioinformatics pipeline (i. e. , site orthology, and density and saturation evaluation), the flanking partition is viewed as a SNP supermatrix, where each site is homologous, but uninformative sites, saturated sites, or sites with large amounts missing data have been removed. All pipeline analyses, including phylogenomic analyses (below) were conducted on the University of Florida High-Performance Computing Center HiPerGator2 (http://www. hpc. ufl. edu/).
Phylogenetics. CC-BY-NC-ND 4. 0 International license made available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprint this version posted May 14, 2020. ; https://doi. org/10. 1101/2020. 05. 12. 092197 doi: bioRxiv preprint  Hamilton, Winiger, Rubin, Breinholt, Rougerie, Kitching, Barber, Kawahara 10  Phylogenomic inferences were conducted under likelihood optimality criteria in IQ-TREE multicore version 1. 6. 9 (Nguyen et al. 2015). To examine phylogenetic signal, we evaluated nucleotide and amino acid datasets. Four supermatrix datasets were built for phylogeny inference: 1) AA = an amino acid supermatrix (777 loci) composed of translated probe region loci; 2) Pr = probe region-only, as nucleotides (778 loci; each locus modeled by sites); 3) Pr+Fl = a probe + flanking supermatrix (782 loci; each locus modeled by sites); and 4) Fl = flanking supermatrix only (modeled by sites).
Because concatenation has been shown to fail when there are high levels of incomplete lineage sorting (see  Mendes and Hahn 2017) , we assessed the impact of potential gene-tree discordance (  (Maddison 1997) ,  (Slowinski and Page 1999) ,  (Edwards 2009 )) by inferring a phylogeny for each individual locus from the Pr+Fl supermatrix using IQ-TREE, with subsequent species tree estimation performed in  ASTRAL-III version 5. 6. 3 ((Mirarab et al. 2014) ,  (Mirarab and Warnow 2015) ,  (Zhang et al. 2017 )) (see Supplemental Trees). For both nucleotide and amino acid datasets, the '-m TEST' command was used in IQ-TREE to perform a search for the most appropriate model of amino acid or nucleotide substitution. Traditional branch support values were computed via 1000 random addition sequence (RAS) replicates, and 1000 replicates each for both ultrafast bootstraps (UFBS) ('-bb' command), as well as SH-aLRT tests ('-alrt' command).
Nodes were classified as "robust" if they were recovered with support values of UFBS ≥ 95 and SH-aLRT ≥ 80 (  (Minh et al. 2013) ,  (Guindon et al. 2010)). ASTRAL support values (ASV) -local posterior probabilities  (Sayyari and Mirarab 2016) , were used to evaluate node support on the species tree. ASTRAL support values were determined to be "robust" if nodes were recovered with local posterior probabilities ≥ 0. 95. Because phylogenomic datasets can produce incongruent topologies and artificially inflate traditional nodal support values (e. g. , due to the increase in total number of sites, the sampling variance for a particular branch is low -see  Reddy et al. 2017) , we evaluated additional modes of topological support. To identify potential rogue taxa or unstable regions in the tree, we ran RogueNaRok  ((Aberer and Stamatakis 2011), (Aberer et al.
Quartet testing was carried out using Likelihood Mapping  (Strimmer and vonHaeseler 1997)  in IQ-TREE  (Nguyen et al. 2015). In this hypothesis-testing framework, we computed the likelihood of possible relationships that can be constructed from possible quartets of the taxa. For example, we looked at the subfamily level by testing whether Arsenurinae was more closely related to the Agliinae, Salassinae, or Saturniinae. We also investigated the genus level by grouping lineages, according to the phylogeny, and evaluating (from a quartet standpoint) whether lineages were found more closely related to another. Quartet likelihood mapping is also a way to inspect the information (i. e. phylogenetic) content of a dataset. To investigate potential gene tree/species tree incongruence, we computed a phylogenetic network in SplitsTree4 version 4.
To test whether one topology was better than another, we ran Shimodaira-Hasegawa tests in the R package 'phangorn'  (Schliep 2010) , under 10,000 bootstrap replicates. All trees were rooted with six outgroup species from the subfamilies Saturniinae, Salassinae, and Agliinae (Supp. Table  1 ). All consensus alignment FASTA files, loci information, partition files, tree files, and other essential data files used for phylogenetic inference are available as supplementary materials on the Dryad Data Repository (doi:???). All data matrices and resulting trees are available in TreeBASE (???).
2009) ,  (Goolsby 2015) ,  (Goolsby 2017b) ,  (Goolsby 2017a) ). In order to probe our dataset for possible evolutionary tradeoffs, we asked these questions: 1. Is HW length a predictor of FW_L [body size]?; 2. Is FW_L [body size] a predictor of HW length?; 3. Is HW length a predictor of FW shape complexity?; and 4. Is FW shape complexity a predictor of HW length? (see Table  1 ). Additionally, phylogenetic ANOVA were conducted using the R package 'phytools'  (Revell 2011)  to test whether the type of hindwing (as multistate categorical variables) had an effect on another trait (HW shape, FW shape, or body size). We coded the HW categories into discrete states (based on the results of  Rubin et al. 2018 ): none (i. e. , no HW tail) = 0, a small lobe = 1, a long lobe = 2, a small tail = 3, medium tail = 4, or extra long tail = 5 (Fig.
2 ). To do this, we reduced our dataset to only our ingroup, the Arsenurinae, by removing the outgroup tips from our preferred tree, the constrained tree (#1). The tree was then converted into a relative-rate scaled ultrametric tree using the 'chronopl' command in the R package 'ape'  (Paradis and Schliep 2019). This approach produces a tree whose branches are scaled to evolutionary rates, rather than a dated tree, and provides a means to understand evolutionary changes over relative "time" in the group being investigated. Our quantitative dataset was then matched to the ingroup species and tips in the tree. Trait distributions were also visualized using the contMap function in 'phytools'  (Revell 2011).
Phylogenetics All initial datasets contained 42 taxa (36 ingroup Arsenurinae and 6 outgroup Saturniidae lineages). We evaluated how museum specimens performed in sequencing (i. e. , papered or pinned), and did not observe any qualitatively significant differences in recovery of genetic data (Supp. Table 1, Supp. Text 2). Importantly, our datasets do not possess GCbias  (Bossert et al. 2017) , with 41. 7% GC content. The sequence information found within the 'Pr' dataset comprised 185,007 bp, of which 55,209 were informative sites, and a -1479454. 703 log-likelihood score for the consensus tree. The sequence information found within the 'AA' dataset comprised 60,425 sites, of which 4,709 were informative sites, and a -294666. 796 log-likelihood score for the consensus tree. The sequence information found within the 'Pr+Fl' dataset comprised 267,958bp, of which were 93,428 informative sites, and a -2273571.
993 log-likelihood score for the consensus tree. The sequence information found within the 'Fl' dataset comprised 80,276 bp, of which 35,647 were informative sites, and a -817959. 106 log-likelihood score for the consensus tree. See Supplemental Trees for these material. In the initial phylogenetic inferences, we only see two recurring outcomes: 1) All genera are monophyletic entities and well-supported; and 2) Almeidaia -the only representative of tribe Almeidaiini -is always sister to the rest of the Arsenurinae, a clade forming the Arsenurini tribe. Generic relationships within this latter tribe are rather unresolved, except for the sister group relationship between Dysdaemonia and Titaea. We often found Copiopteryx and Rhescyntis as sister lineages, with Paradaemonia as the sister lineage to those two; the 'AA' and 'Pr' inferences identify the same three lineages as a monophyletic group, but with differing lineages as the sister to the other two (see Supplemental Trees).
From the 'AA' and 'Fl' datasets, we recovered a "well-supported" placement of Loxolomia as the sister to the rest of the Arsenurini, whereas in the 'Pr', 'Pr+Fl', and ASTRAL datasets Arsenura is found in that position. The lineages whose topological locations change depending on the dataset are Caio, Grammopelta, and Loxolomia. In the ASTRAL tree, these three genera were never found as sister to a single other genus, but always form independent lineages branching from the tree stem (see Supplemental Trees). In the 'AA' tree, Caio is sister to Arsenura, and Grammopelta is sister to Copiopteryx, Paradaemonia and Rhescyntis. In the 'Pr' tree, Caio is sister to Titaea and Dysdaemonia, and Grammopelta and Loxolomia are sister lineages. In the 'Fl' tree, Caio is sister to Paradaemonia, and Grammopelta is sister to Copiopteryx and Rhescyntis (see Supplemental Trees).
46) was again only slightly higher than other well supported taxa (e. g. , Dysdaemonia sister to Titaea = 76089. 91). Nevertheless, we decided to remove the taxon and rerun the phylogeny inference to investigate how topologies or support changed. After removing Grammopelta, the sequence information found within the 'AA minus Grammopelta' dataset comprised 60,425 sites, of which 4,670 were informative sites, and a -291394. 450 log-likelihood score for the consensus tree. The sequence information found within the 'Pr+Fl minus Grammopelta' dataset comprised 267,958 bp, of which 92,647 were informative sites, and a -2214908. 564 log-likelihood score for the consensus tree. When we compare the topological outcomes, we see that removing Grammopelta generally increases "traditional" support values, placing Paradaemonia and Caio as sister lineages, and provides good support for a clade comprising these two lineages as sister to Copiopteryx + Rhescyntis.
We also find support for Almeidaia as sister to the rest of the Arsenurinae, but very little support for any between-genera relationships, outside of Titaea and Dysdaemonia. Unfortunately, this measure of support again confirms the high levels of incongruence amongst the individual gene trees. Likelihood mapping was used to examine the clustering of user-defined subgroups, in a hypothesis-testing framework, as well as to visualize the phylogenetic content of the AHE dataset  (Strimmer and vonHaeseler 1997). By looking at the distribution of quartets, we see there is significant signal to expect a well-resolved tree, with signal split evenly among the quartets (Supp. Fig. 3 ). To reconstruct a better understanding of the Arsenurinae backbone, we computed the likelihood of relationships that could be constructed from possible quartets of the taxa.
While our data are comprehensive, they are not representative of the entire genome. But interestingly, an independent phylogenomic dataset (co-author RR, in prep) using ultraconserved elements (UCE) also faces the exact same problem, supporting the hypothesis of a hard polytomy. There are multiple additional reasons for why we find this polytomy, for example the phylogenetically-informative sites may be scattered in the "noisy" genes, such that individual gene trees do not possess much useful information. This would help answer why gCF values are low; our ability to resolve single locus gene trees is limited due to their short length (i. e. , lack of molecular evolutionary signal). For example, the gCF value can be as low as 0% if no single gene tree contains a branch present in the reference tree.
ACKNOWLEDGEMENTS We thank  Vincent Bonhomme  for his help with Momocs. We are indebted to  Charles Mitter  and  Kim Mitter  for the use of their specimens in the UMD collection, as well as  Jon Heppner  for the use of specimens in his collections (FLMNH) -this work would absolutely not have been done without their help. We thank those who helped collect specimens (too many to list here). Kelly Dexter  assisted with DNA extractions and additional lab work; Ryan.
Data collection In addition to the quantitative measures previously described participants in the MiCBT group will be asked some additional qualitative questions at the end of the 8-week program evaluating the program and asking about any adverse events they may experience with their meditation practices during the MiCBT program. Additionally, at the 6-month follow up, participants in the MiCBT group will be asked questions relating to adherence to meditation practices and any changes to treatment. Participants in the control condition will complete all measures at the same four time points as the treatment group and receive treatment as usual during this time.
Statistical analysis Statistical analysis will be conducted using SPSS version 24 statistical software package. The dependent variables are scores on the two primary outcomes (DASS-21 and the K10), the secondary outcomes (SWLS and FS) and the other mediators (EQ, the MSES-R, the MAIA, and the NAS). The expected outcomes are that the MiCBT group will show significantly changed scores as compared to the wait-list control group, with lower scores on the DASS-21, the K10 and higher scores on the SWLS, FS, the MSES-R, the MAIA, the EQ, and the NAS. The differential changes in the MiCBT group compared to the control group is the primary focus of the analysis. Analysis will use mixed-model repeated measures (MMRM), the method recommended for clinical trial data  [77]. Evidence for the effectiveness of MiCBT will be demonstrated by a significant two-way interaction demonstrating greater change in the outcome measures in the MiCBT group from the control group pre to post intervention.
Baseline to follow-up interactions for the MiCBT group will also be examined and changes compared with post intervention data. Results of the trial will be made available to referring clinicians and to participants in the form of a summarized report. Results may be published in journal articles, student thesis and conference presentations. Intention-to-treat (ITT) analysis will be used because this approach uses all available information. Subjects with incomplete data are not discarded and missing data are not replaced with estimated values. Missing data will be examined and if found to be missing completely at random (MCAR) then the analysis will proceed without replacing missing data with estimated values. If pattern of missingness is not MCAR then multiple imputation will be applied using the strata that defined the nonrandom missingness.
This is consistent with the CONSORT standard  [78]. If data are non-normal, then it will be analyzed using generalized linear MMRM. The mediational impact of pre to post MAIA, MSES, EQ and NAS will be assessed using the Sobel test, a method for measuring indirect effects  [79]. The test assesses the significance of the product of the coefficients for treatment-mediator and mediator-outcome effects. A bootstrapped multivariate extension of the Sobel test will be used to examine both the total indirect effect and the individual effect and the effect of each mediator. The first model will comprise: group (MiCBT vs wait-list control) as the independent variable; clinical DASS-21 and K10 measures as the dependent variables; and MAIA (interoceptive awareness), EQ (metacognitive awareness), MSES-R (Emotion Regulation, Distress Tolerance, Equanimity subscales and total) and NAS (equanimity), scales that reveal significant group x time interactions, as mediators.
Delivery through private psychology practices utilizes the well-established therapeutic collaboration in Australia between general medical practitioners and psychologists in private practice, which is supported by the National Health Insurance Scheme (Medicare). While the intervention is of short duration, it comprehensively teaches specific self-regulation skills, both in personal and interpersonal contexts. Its short duration enables the program to be cost-efficient, and in Australia, to be delivered within the limits of government-subsidized group psychological treatments. The datasets used and analyzed during the current study will be available from the corresponding author on reasonable request.
elementascience. org/toar). While TOAR provided a wide range of analysis on global ozone trends with relevance to climate [see  Gaudel et al. , 2018 , also known as TOAR-Climate] it could not cover every aspect in detail. Fortunately, TOAR was specifically designed to enable new research, independent of the original TOAR effort, allowing future studies to explore unaddressed science questions. This paper is part of that growing legacy, joining several new independent studies that have been enabled by the wide range of ozone metrics archived in the TOAR Surface Ozone Database  [Lu et al. , 2018; Jaffe et al. , 2018; Strode et al. , 2018; Chang et al. , 2019; Shen et al. , 2019; Seltzer et al. , 2020]. Our analysis has several goals: 1) identify the most useful sites in the TOAR database for understanding longterm ozone trends at remote locations worldwide, for the purpose of evaluating global chemistry-climate models; 2) document and display the wide range of ozone trends and multi-year ozone fluctuations recorded at remote sites around the world using the most recently available data (most sites have data through  [2017] [2018] ; 3) demonstrate that the ozone trends recorded at high altitude Alpine sites in central Europe do not match the ozone increases observed by commercial aircraft in the lower free troposphere above Europe; and 4) use the TOAR database and the recent peer-reviewed literature to place these remote ozone time series in the context of ozone trends observed in the polluted boundary layer and the free troposphere.