dataesr/scientific-paragraphs-categorization huggingface dataset:
Response rates have been high and we have representation from wide ranges of settings from low to high income involving countries with differing levels of eczema prevalence. GAN Phase I involved fewer centres than the ISAAC study. 15  Centres were self-selecting and there was an over-representation of urban settings with few rural centres; hence, findings may not be representative of the global population. Data availability meant that we were unable to consistently include the same centres across the two time periods (ISAAC Phase I to III and ISAAC Phase III to GAN Phase I), somewhat limiting our conclusions. There was also substantial between-centre heterogeneity in prevalence change, which is a meaningful finding but makes the global change estimates harder to interpret. There were many challenges affecting GAN Phase I, including COVID-19 pandemic-related disruption and revised approaches to ethical approval, specifically the need for active rather than passive consent.
12  We identified a substantial burden of eczema globally. Our data support an overall increase in the prevalence of eczema and severe eczema globally, but with substantial variation by geographical location and income as well as other factors unexplained by our modelling. Future studies on risk factors for eczema are planned for GAN Phase I to determine if changes observed relate to changing risk factors, following on from previous analyses in ISAAC Phase III. 26  Global research efforts are needed to address the burden related to eczema with continued international efforts to identify strategies to prevent the onset of eczema and to better manage the impact on individuals, their families, and health services.
Glaxo Wellcome International Medical Affairs supported the regional coordination and the ISAAC International Data Centre. The GAN Global Centre in Auckland was funded by The University of Auckland with additional funding from The Asthma and Respiratory Foundation of NZ, the Auckland Asthma Charitable Trust, The International Union Against Tuberculosis and Lung Disease, Boehringer Ingelheim NZ, Astra Zeneca Educational Grant. The London Data Centre was supported by a PhD studentship [to CR] from the UK Medical Research Council.
Results Tumors from patients with lung cancer who respond to FGFR inhibition exhibit tail-to-tail rearrangements within FGFR1. We first collected 52 squamous cell lung carcinomas that were positive for FGFR1 amplification, tested by FISH or Affymetrix SNP 6. 0 arrays, and performed deep genomic sequencing (Figure  1A ). We first analyzed treatment-naive biopsy specimens obtained from 10 patients with FGFR1-amplified SQLC (Figure  1, A  and B ). Nine of these patients had been treated with the FGFR inhibitor BGJ398 as part of a phase I clinical trial (NCT01004224), and 1 patient had been treated with pazopanib, a multi-kinase inhibitor that also inhibits FGFR  (5, 6). Of these 10 patients, 4 had experienced a partial response (PR) (defined as tumor shrinkage of at least 30% of the maximal tumor diameter) lasting for 9 to 17 months (5), and 6 patients had progressive disease (PD) under treatment (Figure  1,  A-E and Supplemental Figure  1 ; supplemental material available online with this article; https://doi.
However, although the BFB mechanism explains ples were reviewed by at least 2 independent expert pathologists, and the diagnosis of SQLC was histomorphologically confirmed by H&E staining and IHC  (21). Matching normal material was provided in the form of EDTA-anticoagulated blood or adjacent nontumorigenic lung tissue. The matched normal tissue was confirmed to be free of tumor contaminants by pathological assessment. Furthermore, tumor and matching normal material were confirmed to be acquired from the same patient by SNP 6. 0 array and sequencing analyses. Patient material was stored at -80°C. Genome and transcriptome sequencing. Next-generation sequencing data on whole exomes or whole genomes were analyzed using our the kinase as well as tail-to-tail rearrangements close to FGFR1 that drive FGFR1-centered amplification, may define SQLCs with therapeutically relevant FGFR dependency.
Xenograft model. IL-3-independent Baf3 cells transformed with ΔEC-FGFR1 or EML4-Alk were subcutaneously injected into both flanks of NSG mice. Mice with palpable tumors were randomly grouped and treated daily with either BGJ398 (Tocris Bioscience, 20 mg/kg, dissolved in 33% PEG 300, 5% glucose, with fresh stock prepared every week and stored at 4°C) or vehicle (33% PEG 300, 5% glucose). Patient samples. From the clinical trial reported by Nogova et al. , we were able to receive 8 formalin-fixed biopsies, taken before treatment (NCT01004224)  (5). Three of these patients had a PR and 5 showed stable or progressive disease. Given the small amount of tissue, we were limited to CAGE sequencing  (6). In addition, the University of Berne and Cantonal Hospital of Lucerne provided 1 sample from a patient who had a clear response following off-label treatment with pazopanib.
All samples were confirmed to be 8p-amplified by FGFR1 FISH and CAGE sequencing. Break validation by PCR. To verify expression over the breakpoint and to validate the tail-to-tail rearrangement, as well as the ΔEC-FGFR1 variant, we generated cDNA from 1 μg total RNA from sample S00674 and its corresponding normal sample. cDNA was generated using the SuperScripIII kit (Invitrogen, Thermo Fisher Scientific) in-house-developed pipeline, which has been used and described in previous large-scale cancer genome sequencing projects  (16, (22) (23) (24). Briefly, the data were processed by aligning sequencing reads to a reference genome (NCBI build 37/hg19) using bwa-mem (0. 7. 13-r1126; https://github. com/lh3/bwa), masking potential PCR duplicates and regions of overlapping read pairs, and collecting various count statistics, which are used to call mutations and, for whole genomes, genomic rearrangements.
Finally, Sclust  (24)  was used for sample purity estimation and copy number analysis. RNA-Seq data were analyzed using TRUP  (25). ARCHER sequencing. RNA was extracted from formalin-fixed, paraffin-embedded (FFPE) material using the Maxwell RSC in combination with the Maxwell RSC RNA FFPE Kit (Promega) according to the manufacturer's instructions. Removal of genomic DNA was performed with the TURBO DNA-Free Kit (Thermo Fisher Scientific), and both transfer RNA (tRNA) and RNA were quantified using the Qubit RNA HS Assay Kit (Thermo Fisher Scientific). Analysis of RNA integrity was done with the 4200 TapeStation System (Agilent Technologies). For fusion detection, the Archer FusionPlex Lung Panel (ArcherDX) was used according to the manufacturer's instructions, with 200 ng tRNA input for library preparation.
Purified libraries were quantified using the KAPA Library Quantification Kit (Roche). Sequencing was performed on the NextSeq System (Illumina), and results were analyzed using Archer Suite Analysis, version 6. 2. 7 (ArcherDX). Since the Archer Suite Analysis software is not able to label inversions, bam files were extracted and loaded directly into the Integrative Genomics Viewer (IGV) to visualize tail-to-tail breaks. Computational analysis. Copy numbers were visualized with GIS-TIC  (26) , web-based interactive builder from the MD Anderson Cancer Center and ROBOCOP. To summarize the copy number information in the FGFR1 region across samples, a view window of 2MB around the FGFR1 locus was split into 10,000 bins. The available chromosome segment-based copy number data were then mapped onto these bins for each of the samples.
Acknowledgments This work was supported by the  German Cancer Aid (Deutsche Krebshilfe , grant  ID :  7011273 , to RKT). Additional Funding for this work was provided through the  German Ministry of Science and Education (BMBF)  as part of the  e:Med program  ( SMOOSE , grant ID:  01ZX1603A , to RKT, MF, MP, HCR, RB, JW, and  InCa ,  01ZX1901A , to RKT, HCR, MP, and JG) and by the  German Research Foundation Deutsche Forschungsgemeinsaft (DFG)  as part of the  SFB1399 program  (grant ID:  413326622 , to RKT, MP, RB, JW, HW, MF, JB, and JG). Additional support for this work J Clin Invest. 2023;133(21): e170217  https: // doi. org /10. 1172/JCI170217  Address correspondence to:  Roman K. Thomas ,  Department of Translational Genomics ;  University of Cologne , Weyertal 115b, 50931 Cologne, Germany.
Fused-in sarcoma (FUS) gene mutations have been implicated in amyotrophic lateral sclerosis (ALS). This study aimed to investigate the impact of FUS mutations (R521H and P525L) on the transcriptome of induced pluripotent stem cells (iPSCs) and iPSC-derived motor neurons (iMNs). Using RNA sequencing (RNA Seq), we characterized differentially expressed genes (DEGs) and differentially expressed lncRNAs (DELs) and subsequently predicted lncRNA-mRNA target pairs (TAR pairs). Our results show that FUS mutations significantly altered the expression profiles of mRNAs and lncRNAs in iPSCs. Using this large dataset, we identified and verified six key differentially regulated TAR pairs in iPSCs that were also altered in iMNs. These target transcripts included: GPR149, NR4A, LMO3, SLC15A4, ZNF404, and CRACD.
Furthermore, FUS is necessary for the formation of these paraspeckles in some cells, suggesting that FUS-NEAT1 interactions play a role in the regulation of this subnuclear structure. Interestingly, proteins enriched in the pool of proteins affected by ALS-causative mutations are also found in paraspeckles. Here, we employed RNA-Seq to examine the effects of neurodegeneration-linked FUS mutations (R521H and P525L) on the transcriptional profiles of iPSCs and assessed whether any of these changes persisted into the differentiated motor neuron state. We first analyzed the expression profiles of both mRNAs and long non-coding RNAs (lncR-NAs) in control and FUS-mutant iPSCs and used software tools to predict the interaction between lncRNAs and their putative target mRNA transcripts. Using these datasets, we identified the top differentially regulated lncRNAs and mRNAs that were predicted to have a functional relationship and confirmed their expression trends using RT-PCR in iPSCs.
RNA Isolation, Library Construction, and Sequencing Approximately 2 × 10 6 cells were collected and centrifuged at 4 • C at 2000 rpm for 3 min for the extraction of RNA. Total RNA was isolated using Trizol (Thermo Scientific, Waltham, MA, USA) according to the manufacturer's instruction. RNA purity and quantification was determined using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). The extracted RNA was sent to BGI (Shenzhen, China) for transcriptome library construction and data analysis. During this process, RNA quality was measured using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CS, USA). The RNA samples were further purified using a Ribo-Zero rRNA Removal Kit before fragmentation occurred. Random primers and a TruSeq reverse transcriptase kit were used for first-strand cDNA synthesis, followed by DNA polymerase I and RNaseH for doublestranded cDNA synthesis.
The library was then sequenced using BGISEQ-500. Low-quality reads, adapter contamination, and unknown N bases were filtered. The remaining clean reads were mapped to the reference genome (GRCh37) using HISAT (v2. 0. 4) with default parameters. The resulting transcripts were assembled using StringTie (v1. 0. 4). lncRNA transcripts were further annotated using the NONCODE  [25]  database.
Coding Ability Prediction Novel transcripts were assessed for their coding ability to distinguish mRNA from lncRNA. The software tools CPC (Coding Potential Calculator)  [26] , txCdsPredict  [27] , and CNCI (Coding Non Coding Index)  [2]  were used to score the coding capacity of novel transcripts, requiring CPC and CNCI scores of <0 and txCdsPredict scores of <500. These results were also queried against the database pfam  [28]  to identify known protein-coding domains. Novel transcripts were designated as lncRNA or mRNA when at least three of the prediction methods were in agreement.
Identification of Differentially Expressed lncRNA and mRNA lncRNA and mRNA expression levels were calculated using the fragment per kilobase of transcript per million mapped reads (FPKM). To calculate the differential expression analysis of genes and transcripts, Bowtie2 was used to align clean reads to the reference sequence, and then RSEM was used to calculate gene and transcript expression levels. Subsequently, the PossionDis tool (PossionDisFoldChange ≥ 2. 00 and FDR ≤ 0. 001) was used to analyze the significance of differentially expressed lncRNAs (DELs) and differentially expressed mRNAs (DEGs) between the SA (control) samples and the SB (FUS-P525L) and SC (FUS-R521H) samples. Initial screening identified DELs and DEGs as those transcripts with p-values of <0. 05 and an absolute log 2 fold change of ≥1.
Functional Enrichment of DEGs and Target Genes of DELs The datasets of DEGs, DELs, and DEL targets were cross examined to identify TAR pairs containing DELs predicted to target DEGs; these pairs were further refined such that each DEL and DEG pair exhibited a 5-fold or greater increase/decrease relative to the control. These pairs were then selected for further study. Furthermore, the datasets of DEGs and DEL target genes were used for functional enrichment analysis using gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and ingenuity pathway analysis (IPA-QIAGEN). GO, KEGG, and IPA terms for which p < 0. 05 were accepted as significant.
Results of Sequencing and Characteristics of Transcripts The overall design of this study is depicted in Supplementary Figure  S1. Human derived iPSCs were grown under standard conditions before total RNA isolation. After verifying the quality of the extracted RNA, sequencing libraries were prepared (BGI). Short-read sequencing was used to analyze the transcriptomes of iPSCs derived from ALS patients carrying the FUS P525L or FUS R521H mutations and compared to control iPSCs. A total of 106,975,508 reads were collected from control samples, and 108,244,484 and 92,527,778 reads were collected from the P525L and R521H mutants, respectively (Table  1 ). These data were then filtered to remove low-quality reads and adaptor sequences, which yielded a total of 99,081,102 clean reads for the control and 99,842,418 and 85,427,768 clean reads for the R521H and P525L mutants, respectively.
A summary of the quantitative analysis is given in Table  2. Only known mRNA transcripts mapping to the reference sequence and lncRNAs identified in the NONCODE database were used for the downstream analysis of differential expression. Additional characteristics of the mapped transcripts are shown in Figure  1. Expectedly, analysis of the distribution of exon number across the transcripts revealed that lncRNAs tended to have two exons, while mRNAs contained more than ten (Figure  1C, D ). Similarly, transcripts with a length of 2-2. 5 kb made up the majority of mRNAs, while predicted lncR-NAs were comprised mostly of transcripts between 0 and 500 bp (Figure  1E ). Additionally, when we analyzed the number of DEGs and DELs between each comparison group, we discovered that FUS-P525L mutant cells contained 58% more DEGs and 31% more DELs than FUS-R521H mutant cells, relative to the controls (Figure  1F, G ).
Differential Expression Analysis LncRNA and mRNA expression levels were calculated using the fragment per kilobase of transcript per million mapped reads (FPKM). To perform a differential expression analysis of the genes and transcripts, Bowtie2  [29]  was used to align clean reads to the reference sequence, and then RSEM  [30]  was used to calculate gene and transcript expression levels. Subsequently, the PossionDis  [31]  tool (PossionDisFoldChange ≥ 2. 00 and FDR ≤ 0. 001) was used to analyze the significance of DELs and DEGs between the control, P525L, and R521H. The analyses of DEGs and DELs are depicted in Figure  2A-D. In total, 1734 significantly differentially expressed mRNAs and 1239 lncRNAs were identified in the control as compared to the P525L samples, while 1317 mRNAs and 1041 lncRNAs were detected in the control as compared to the R521H samples.
Identification of Differentially Regulated lncRNA-mRNA Target Pairs One of the mechanisms by which lncRNAs can alter gene expression is through cis-or trans-acting effects with target mRNAs (Figure  3A ). To this end, we questioned whether any relationship existed between the DEGs and DELs identified between the control and FUS mutant iPSCs. To accomplish this, we cross-referenced datasets containing lists of DEGs and DELs against a list of software-generated predictions of lncRNA-mRNA target pairs (TarPairs) classified as cis/trans-acting (Supplementary Table  S4 ) and overlapping (Supplementary Table  S5 ). The results of this analysis are summarized in Figure  3B. We refined the list of 7764 TarPairs by filtering those pairs whose members were differentially regulated by a factor of two or greater in either direction.
Identification of Differentially Regulated lncRNA-mRNA Target Pairs One of the mechanisms by which lncRNAs can alter gene expression is through cisor trans-acting effects with target mRNAs (Figure  3A ). To this end, we questioned whether any relationship existed between the DEGs and DELs identified between the control and FUS mutant iPSCs. To accomplish this, we cross-referenced datasets containing lists of DEGs and DELs against a list of software-generated predictions of lncRNA-mRNA target pairs (TarPairs) classified as cis/trans-acting (Supplementary Table  S4 ) and overlapping (Supplementary Table  S5 ). The results of this analysis are summarized in Figure  3B. We refined the list of 7764 TarPairs by filtering those pairs whose members were differentially regulated by a factor of two or greater in either direction.
The p values and model parameters were recovered from the 'summary. psem' procedure (package 'PiecewiseseM'). The stability of model parameters and the absence of overfitting of the SEMs were checked by simplifying the SEMs by removing variables not involved in direct/indirect effects on seed predation (Figure  S5 ; Table  S9 ). In addition, we checked the stability of the linear models alone and included in the SEM. All analyses and figures were conducted in R version 4. 2. 1 (R Core Team, 2022) and we used packages lMe4 (procedures 'glmer' and 'lmer'); MuMIn ('dredge';  Bartoń, 2022) , PiecewiseSEM ('psem'; Lefcheck, 2016) and ggeffect for the figures.
Modern astrometric and spectroscopic surveys have revealed a wealth of structure in the phase space of stars in the Milky Way, with evidence of resonance features and non-equilibrium processes. Using the third Gaia data release, we present evidence of a new resonance-like feature in the outer disc of the Milky Way. The feature is most evident in the angular momentum distribution of the young classical Cepheids, a population for which we can derive accurate distances over much of the Galactic disc. We then searched for similar features in the outer disc using a much larger sample of red giant stars, as well as a compiled list of over 31 million stars with spectroscopic line-of-sight velocity measurements. While much less evident in these two older samples, the distribution of stars in action-configuration space suggests that resonance features are present here as well.
Introduction The recent third public release of the Gaia survey (Gaia Collaboration 2016, 2022a, hereafter Gaia DR3) has provided the community with the largest ever homogeneous set of spectroscopic line-of-sight velocities (V los ) for nearly 33 million stars in the Milky Way, supplementing the high-precision astrometry provided already in the Gaia Early Data Release 3 (Gaia Collaboration 2021a, hereafter Gaia EDR3). Using the new astrophysical parameters also provided in Gaia DR3, Gaia Collaboration (2022b, hereafter GD22) selected a large sample of red giant branch (RGB) stars to map the kinematics of the Galactic disc over an unprecedented volume of the Galactic disc. The most remarkable feature of these velocity maps was the clear signature of a quadrupole pattern in the Galactocentric V R component in the inner disc (their Fig.
2018; Fragkoudi et al. 2019; Khanna et al. 2019b; Laporte et al. 2019; Bernet et al. 2022; Lucchini et al. 2022). Additionally,  Antoja et al. (2018)  also discovered an overdensity, known commonly as the phase-spiral, in the z-V Z plane, when mapped by V R or V Z. This spiral pattern, thought to be a result of a perturbation to the Galactic disc, has since been dissected in the chemo-dynamic space  (Bland-Hawthorn et al. 2019; Xu et al. 2020; Li 2021 ) in order to study its origins. With Gaia DR3, this feature can now be mapped over a much larger extent of the disc (Gaia Collaboration 2022c;  Hunt et al. 2022). Trick et al. (2019 Trick et al. ( , 2021) )  and  Trick (2022, hereafter T22 ) further explored the Gaia DR2 dataset in action-angle space. Actions are invariant quantities for a steady or slowly varying axisymmetric Galactic potential.
While the Cepheids constitute a relatively small sample, they have the advantage of having excellent relative distance errors that allow us to also accurately determine their individual velocities to large distances. In this sample, we note a resonance-like feature in their kinematics in the outer disc. We then turn to much larger samples of older stars with complete phase-space information to determine whether they also show evidence of a resonance feature in the outer disc. The paper is organised as follows. In Sect. 2 we briefly describe the three datasets we used and our analysis methods. In Sect. 3 we present evidence for a new resonance-like feature in the outer disc, then we further discuss its relation to known resonance features in Sect. 4. We summarise our findings in Sect. 5.
Dataset We restrict our analysis to three data subsets constructed primarily using Gaia DR3. We select both young and old stellar populations to illustrate the similarities and differences in their kinematics. Cepheids. To trace the young population, we adopted the sample of classical Cepheids published by GD22. This consists of 1948 stars with estimated ages younger than 200 Myr and that have spectroscopic line-of-sight velocities, and their distances are estimated based on the period-Wesenheit-metallicity relation  (Ripepi et al. 2019 (Ripepi et al. , 2022a)). The relative distance error of this sample is less than 6. 25% for 90% of this sample. The precision in the distances means that the uncertainties in the velocities perpendicular to the line of sight, derived from the proper motions and distances, are less than 10 km s - 1  for 90% of our sample to a distance of 6 kpc, while the median uncertainty at this distance is less than 5 km s -1.
Action estimation In general, actions can be computed analytically only for potentials for which the variables can be separated, in order to solve the Hamilton-Jacobi equation  (Sanders & Binney 2016). Spherical and Stäckel potentials (de Zeeuw 1985)  are examples where this is possible. For more general axisymmetric potentials, Binney (2012) developed a method in which the gravitational potential is assumed to be similar to a Stäckel potential. This so-called Stäckel-fudge method is implemented in the AGAMA code  (Vasiliev 2019)  to calculate the classical axisymmetric actions J = (J R , J φ , J Z ) mentioned in the Introduction. The actions estimated in this way remain conserved over time and serve as true integrals of motion only in an axisymmetric potential. For a more realistic Galactic potential (e.