dataesr/scientific-paragraphs-categorization huggingface dataset:
Several analyses of bulk RNA-seq data derived from human brain tissues have discovered transcription originating from intronic or intergenic regions (henceforth termed unannotated)  (9) (10) (11). For example, Jaffe and colleagues found that as much as 41% of transcription in the human frontal cortex was unannotated  (11). In combination, these factors lead to specific challenges in fully capturing the transcriptome of the human brain and suggest that improvements to gene annotation may have a disproportionate impact on the understanding of neurological diseases. In this study, we address this issue by leveraging transcriptomic data available through the Genotype-Tissue Expression (GTEx) Consortium to identify previously unannotated exons of known genes. Distinct from existing de novo assembly approaches, such as that implemented by Pertea and colleagues leading to the development of the CHESS database, our analytic approach was focused on the detection of unannotated exons among known genes rather than the assembly of previously unidentified transcripts  (12).
Optimizing the tissue-specific, annotation-agnostic detection of transcription Pervasive transcription of the human genome, the presence of pre-mRNA even within polyA-selected RNA-seq libraries and variability in read depth complicates the identification of exons and transcripts using RNA-seq data  (15, 16). With this in mind, we used a set of exons with the most reliable boundaries [namely, all exons from Ensembl v92 that did not overlap with any other exon (4)] to calibrate the detection of transcription from 41 GTEx tissues  (17). Of available annotation databases, Ensembl was selected as it is one of the most commonly used and comprehensive annotation providers. We used the tool derfinder to perform this analysis  (18). However, we noted that while derfinder enables the detection of continuous blocks of transcribed bases termed expressed regions (ERs) in an annotation-agnostic manner, the mean coverage cutoff (MCC) applied to determine transcribed bases is difficult to define and variability in read depth even across an individual exon can result in false segmentation of blocks of expressed sequence.
Validation of unannotated transcription We recognize that a proportion of unannotated transcription may originate from technical variability or pre-mRNA contamination. Therefore, we assessed the reliability of detecting unannotated ERs across different versions of Ensembl and within an independent dataset. First, we measured how many Kb of the transcription that we detected would have been classified as unannotated with respect to Ensembl v87 but was now annotated in Ensembl v92 and found that across all tissues, an average of 68 Kb (43 to 127 Kb) had changed status. This value was 5. 3 times (3. 2 to 10. 1 times) greater in every tissue compared to the Kb of ERs overlapping exons in Ensembl v87 that had become purely intronic or intergenic in Ensembl v92 (Fig. 3A ). To further assess whether this was greater than what would be expected by chance, we compared the total Kb of unannotated ERs entering v92 annotation for each tissue to 10,000 sets of random length-matched intronic and intergenic regions.
Therefore, we investigated whether our GTEx frontal cortex-derived ERs could also be discovered in an independent frontal cortex dataset reported by Labadorf and colleagues  (19). As expected, ERs that overlapped with annotated exons had near-complete validation (≥89%), but importantly, 62% of intergenic and 70% of intronic ERs, respectively, were also detected in the second independent frontal cortex dataset (Fig. 3B ). While this high validation rate implied that most of all ERs were reliably detected, we investigated whether a subset of ERs supported with evidence of RNA splicing as well as transcription would have even better rates of validation. Evidence of transcription is provided by the coverage data derived using derfinder, while junction reads, which are reads with a gapped alignment to the genome, provide evidence of the splicing out of an intron (fig.
S1B ). With this in mind, we focused our attention on the putative spliced ERs as indicated by the presence of an overlapping junction read. Consistent with expectation, we found that ERs with junction read support had higher validation rates than ERs lacking this additional feature. This increase in validation rate for ERs with junction read support was greatest for intergenic and intronic ERs with the validation rate rising to 87% for intergenic ERs and 88% for intronic ERs (as compared to 99% for ERs overlapping exons, Fig. 3B ). Even when considering this set of highly validated ERs with junction read support, 1. 7 to 3. 8 Mb of intronic and 0. 5 to 2. 2 Mb of intergenic transcription was detected across all 41 tissues. Thus, in summary, most of the unannotated ERs were reliably detected and validated in an independent dataset.
Tissues are color-coded as indicated in the x axis, with GTEx brain regions highlighted with bold font. (B) Bar plot represents the percentage of ERs seeding from the GTEx frontal cortex that validated in an independent frontal cortex RNA-seq dataset. ERs defined in the seed tissue were requantified using coverage from the validation dataset, after which the optimized MCC was applied to determine validated ERs. Colors represent the different annotation features that the ERs overlapped, and the shade indicates whether the ER was supported by junction read(s). enriched for cell-type specific genes when compared to the background list of 2422 brain-specific genes without reannotations. Of the 13 brain-specific cell types considered, genes specifically expressed by oligodendrocytes had the largest difference in enrichment (reannotated = <2 × 10 -16 ; not reannotated = 0.
These observations led us to postulate whether incomplete annotation could also be hindering our understanding of complex disorders. We assessed whether our list of reannotated genes was enriched for genes associated with complex forms of neurodegenerative, neuropsychiatric, or other neurological conditions. This analysis was performed by using the Systematic Target OPportunity assessment by Genetic Association Predictions (STOPGAP) database, which provides an extensive catalog of human genetic associations mapped to effector gene candidates (see detailed Materials and Methods)  (25). We found that genes associated with neurodegenerative disorders were significantly overrepresented within our reannotated set (P = 0. 004; table  S3 ). In particular, important neurodegenerative disease genes such as SNCA, APOE, and CLU were among those reannotated, suggesting that despite being extensively studied, the annotation of these genes remains incomplete (complete list found in table  S4 ).
6C ). Similarly, we detected a brain-specific, unannotated ER in the long intron of the gene SNCA, which encodes -synuclein protein implicated in the pathogenesis of Mendelian and complex Parkinson's disease. This ER connected to two flanking proteincoding exons through junction reads (Fig. 6D ) and appeared to also have coding potential. While the ER sequence is not conserved within mammals (phastcons7 score: 0. 09) or primates (phastcons20 score: 0. 21), it is in the top 19% of most constrained regions in the genome, suggesting that it is of functional importance in humans. We validated the existence of this ER both in silico and experimentally. The expression of this ER was confirmed in silico using an independent frontal cortex dataset reported by Labadorf and colleagues  (19). Using Sanger sequencing, we validated the junctions intersecting the ER and the flanking exons in RNA samples originating from pooled human frontal cortex samples (fig.
We believe that a key factor is the high cellular heterogeneity of human brain combined with the high cellular specificity of some transcripts. Lowly expressed, tissuespecific isoforms or those that are only transcribed within a cell type of proportionally low abundance may be missed from bulk RNA-seq datasets. Accordingly, we find that most of the putative unannotated exons that we detect have a restricted expression pattern across tissues and that the highest numbers are derived from human brain. Even within human brain, there appeared to be cellular biases influencing a gene's likelihood of being reannotated. Among reannotated genes, we found a significant enrichment of genes with a cell-specific expression pattern, and this was most evident for genes specifically expressed by oligodendrocytes.
Thus, our analyses suggest that incomplete annotation is a substantial limiting factor in our understanding of both Mendelian and common complex neurological diseases. Last, we release our results through a dedicated web resource, vizER (http://rytenlab. com/browser/app/vizER), which enables individual genes to be queried for incomplete annotation as well as the download of all the definitions of putative exons discovered in this study. We believe that vizER will be an important resource for clinical scientists in the diagnosis of Mendelian disorders, neuroscientists studying individual gene structures and functions, and, together with the emergence of larger long-read sequencing datasets, will accelerate transcript discovery particularly in human brain.
Optimizing the detection of transcription Transcription was detected across 41 GTEx tissues using the package derfinder version 1. 14. 0  (18). The MCC, defined as the number of reads supporting each base above which bases were considered to be transcribed, and MRG, defined as the maximum number of bases between ERs below which adjacent ERs will be merged, were optimized. Optimization was performed using 156,674 nonoverlapping exons (defined by Ensembl v92) as the gold standard (4). Exon biotypes of all Ensembl v92 exons were compared to this set of nonoverlapping exons to ensure that we were not preferentially optimizing for one particular biotype (fig. S6 ). Nonoverlapping exons were selected as these definitions would be least likely to be influenced by ambiguous reads. For each tissue, we generated ERs using MCCs increasing from 1 to 10 in steps of 0.
Validation of detected transcription Transcription was validated across different versions of Ensembl and within an independent dataset. ERs that overlapped purely intronic or intergenic regions according to Ensembl v87, but fell within exons according to v92, were counted as unannotated transcription that was validated in later versions of Ensembl. Furthermore, ERs overlapping exonic regions in Ensembl v87 now classified as intronic or intergenic in v92 were measured to control for expected corrections in gene definitions. To assess whether the total Kb of validated unannotated ERs entering v92 annotation was greater than what would be expected by chance, we generated 10,000 random sets of length-matched regions for each tissue that were intronic or intergenic with respect to Ensembl. Using a one sample Wilcoxon test, we compared the total Kb of intronic and intergenic ERs entering annotation to the total Kb distribution of the randomized intronic and intergenic regions, respectively.
Validation within an independent dataset was performed using RNA-seq coverage data from 49 control frontal cortex (BA9) samples originally reported by Labadorf and colleagues and available via the recount R package version 1. 4. 6  (19, 28). ERs derived from the GTEx frontal cortex (BA9) data were requantified using this independent frontal cortex dataset, and those that had a mean coverage of at least 1. 4 (the optimized MCC for the GTEx frontal cortex data) were counted as unannotated transcription that was validated.
Checking ER protein coding potential Intronic and intergenic ERs that were intersected by two junction reads were extracted. The junction reads were used to determine the precise boundaries of the ER. The R package Biostrings version 2. 46. 0 was used to extract the DNA sequence corresponding to the ER genetic coordinates from the genome build hg38  (31). Since the translation frame was ambiguous without knowledge of the other exons that are part of the transcript that included the unannotated ER, we converted the DNA sequence to amino acid sequence for all three possible frames starting from the first, second, or third base. Any ER that had at least one frame that did not include a stop codon was considered to be potentially protein coding.
Gene properties influencing reannotation All Ensembl v92 genes were marked with a 1 or a 0 depending on whether we detected a reannotation for that gene in the form of an ER connected to the gene using a junction read, with 1 representing a detected reannotation event. Details of gene length, biotype, and transcript count and whether the gene overlapped another gene were retrieved from the Ensembl v92 database. Brain specificity was assigned using the Finucane dataset and selecting the top 10% of brainspecific genes when compared to nonbrain tissues  (32). Mean gene transcripts per million (TPM) was calculated by downloading tissuespecific TPM values from the GTEx portal and summarized by calculating the mean across all tissues. The list of OMIM genes (May 2018) was used to assign whether a gene was known to cause disease or not.
Expression-weighted cell-type enrichment: Evaluating enrichment of theta-correlated genes Expression-weighted cell-type enrichment (EWCE) was used to determine whether brain-specific genes (both reannotated and not reannotated) have higher expression within particular cell types than expected by chance  (33). As our input, we used (i) neuronal and glial clusters of the CNS identified in the Linnarsson single-cell RNA-seq dataset (amounting to a subset of 114 of the original 265 clusters identified) and (ii) lists of genes split by whether or not they were reannotated, and if reannotated, by their overlap with Ensembl v92 annotation features (see table S6 for the full list of CNS neuronal clusters and genes used)  (34). For each gene in the Linnarsson dataset, we estimated its cell-type specificity (the proportion of a gene's total expression in one cell type compared to all cell types) using the "generate.
Enrichment of reannotated genes for neurological disorder-associated genes The STOPGAP database detailing all genes associated with 4684 GWASs was downloaded. To select which genes were associated to a GWAS, the "best gene" as determined by STOPGAP using functional evidence was used  (25). The medical subject heading for each disease was used to further subgroup GWASs into four categories; neurodegenerative, neuropsychiatric, other neurological conditions, and the remaining as other (table  S7 ). For each of the subgroups, we generated a contingency table, counting the number of genes that were reannotated or not in relation to whether they fell into that particular subgroup. For genes that were overlapping between GWASs, we classified a gene to be part of a subgroup if it was associated with at least one GWAS contained in that subgroup.
Acknowledgments Funding: S. G. was supported through the award of an  Alzheimer's Research UK  PhD fellowship. R. H. R. was supported through the  award of a Leonard Wolfson Doctoral Training Fellowship  in Neurodegeneration. J. H. and M. R. were supported by the  UK Medical Research Council (MRC) , with J. H. supported by a grant ( MR/N026004/ ) and M. R. through the award of a  Tenure Track Clinician Scientist Fellowship  ( MR/N008324/1 ). J. H. was also supported by the  UK Dementia Research Institute , The  Wellcome Trust  ( 202903/Z/16/Z ), the  Dolby Family Fund , and the  NIHR. A. E. J. and L. C. -T. were supported by the  R21MH109956  grant (https:// projectreporter. nih. gov/project_info_description. cfm?aid=9093092). Author contributions: D. Z. , S. G. , and M. R. conceived and designed the study.
A recent review summarized novel developments regarding patents for PPAR modulators and possible novel clinical indications  [3]. Clinical evidence for the use of PPAR agonists and antagonists is reviewed in  [4]. Toxicological aspects and side effects of PPAR modulators have been reviewed recently  [5]. Increasing interest focuses on potential implications of PPARs in cancer. The major clinical trials database (https://clinicaltrials. gov) lists one clinical trial for a PPARα antagonist for treatment of multiple kinds of cancer, 24 trials for modulators of PPARγ for cancer treatment, but none for PPARβ/δ. The human protein atlas (https://www. proteinatlas. org/ENSG00000112033-PPARD/pathology) lists low cancer type specificity, but detection of PPARβ/δ in all cancer types. A current major limitation for the investigation of PPARβ/δ expression in human cancer samples compared to healthy tissues is the quality of commercially available antibodies.
Clinical data collection. We collected 40 clinical relevant measurements as shown in Supplementary Table  S1. All dogs were examined fasted (12 hours for food and 2 hours for water). A complete history was taken and each dog underwent a thorough physical examination. During this physical exam several morphometric parameters and stress responses were measured as well. Stress phenotype was assessed by the same dog handler within each country (5 dog handlers in total) using the same 4-criteria scale (stress absent, mild stress, moderate stress and heavy stress). This scale was subjective, and though handler differences were not controlled, all of them were experienced vets that were used to interact with dogs. Furthermore, within breeds, the same handler performed the tests on all dogs, except for Labradors and Belgian Shepherds, which were shared between different dog handlers.
Tubes were centrifuged within 30 minutes of blood sample collection. Plasma and serum were harvested, transferred into plastic cryotubes and samples were frozen and stored at -80 °C. All samples were later transported frozen to different accredited laboratories. All analyses were performed using commercially available assays validated for dogs, according to manufacturers' instructions. Insulin, cortisol and cardiovascular hormones samples were analyzed in duplicate by personnel blinded to dog identity, and the mean of the two results was used for data analysis. Data were tested for normality using Shapiro Wilk test. Outliers were individually checked and some of them were excluded from the analysis if their results seemed inappropriate. Descriptive statistics for the 40 phenotypes are listed in Supplementary Table  S1.
In France, data was obtained before the creation of the Local Ethical Committee dedicated to clinical research (ComERC-ENVA). As the data were from client owned dogs undergoing normal veterinary exams, there was no animal experiment according to legal definitions in France. However, all local regulations related to clinical procedures were observed. In all countries, informed owners consent was obtained for use of samples and data for scientific research. All undertaken procedures were part of routine veterinary clinical exam in all countries and the local responsible co-investigator, who was also licensed to practice veterinary medicine within the EU, was responsible for collection of the samples.
Acknowledgements This work was funded by Grant from the  European Commission  ( FP7-LUPA ,  GA-201370 ) to M. G. ,  Mochida Memorial Foundation for Medical and Pharmaceutical Research  to Y. M. , and  Jane and Aatos Erkko Foundation , The  Academy of Finland  and the  Sigrid Juselius Foundation  to H. L. Y. M. benefitted from fellowship from  Grantin-Aid for JSPS Fellows and Postdoctoral Fellowship for Research Abroad  from  JSPS. We thank all dogs and their owners for joining in this project.
Literature review We reviewed the literature since 1971 to date using the terms "Hormographiella aspergillata" or "Coprinus cinereus" and "infection" in MEDLINE database (Tables  1  and 2 ). For each strain, antifungals MIC with the method used were reported in Table  1  when available. According to the 2008 EORTC/MSG criteria, all probable or proven IFI due to H. aspergillata were reported in Table  2  with significant clinical details.
Literature review of Hormographiella aspergillata infections in humans published since 1971 Search for previously published cases using the terms "Hormographiella aspergillata" or "Coprinus cinereus infection" in MEDLINE database *ALL Acute lymphoid leukemia, AML Acute myeloid leukemia, BAL Biphenotypic acute leukemia, X-ALD X-linked adrenoleukodystrophy, CML Chronic myeloid leukemia, MDS Myelodysplasia syndrome, MPAL Mixed phenotype acute leukemia, CNS Central nervous system, AmB Deoxycholate amphotericin B, ITZ Itraconazole, CSF Caspofungin, VRZ Voriconazole, PSZ Posaconazole, lAmB Liposomal amphotericin B, MCF Micafungin, IV Intravenous, RFLP Restriction fragment length polymorphism, DE Direct examination, PF-PCR Pan-fungal-polymerization chain reaction, βDG 1, 3-beta-D glucan.
PKA activity assay The PepTag Nonradioactive Protein Kinase Assay Kit (Promega, Madison, USA) was used according to the manufacturer's instructions. Imaging of the phosphorylated peptide substrate was performed using a ChemiDoc MP (Bio-Rad) and densitometric quantification was performed using Bio-Rad ImageLab 4. 1 software. Plasmid transfection, RNA interference, and reporter assays GH3 cells were transfected using SuperFect (Qiagen)  [32]. Single-interfering RNAs (siRNAs) were against rat HIF-1α (Santa Cruz Cat. sc-45919) and rat Creb1 (Santa Cruz Cat. sc-72030) and nonspecific scramble control (Santa Cruz Cat. sc-37007). The following expression plasmids were used: M7 pdn-PKA-GFP (Randall Moon; Addgene plasmid # 16716), RSV CREB (Marc Montminy; Addgene plasmid # 22394), RSV CREB-M1 (Marc Montminy; Addgene plasmid # 22395), PRKAR2B (Origene, SKU SC125501), and pCMV-3xFLAG-HIF-1α (gift of Eduardo Arzt (Buenos Aires Argentina)).
Statistical analysis Statistical analysis was performed using SPSS (PASW Statistics) 18. Differences in human tissue samples were assessed using the nonparametric Mann-Whitney U-Test with P < 0. 05 considered as significant in samples with similar variance. Differences in cell line experiments were assessed using the Student's t test with P < 0. 05 considered as significant. Analysis of patient data was performed using linear regression analysis and Pearson's rho with P < 0. 05. Experiments with full data/replicate values were included for statistical analysis.
They then performed a (pseudo) energy minimization, optimizing a tradeoff between the thermodynamic stability and its compatibility with the reactivity profile. Popular packages for secondary structure prediction, such as RNAStructure  (2)  or the Vienna package  (31) , now routinely accept SHAPE-like reactivity profiles as input of their main structure prediction methods. Besides free-energy optimization, such methods notably include the joint folding of aligned RNAs  (32) , partition function, statistical sampling and Maximal Expected Accuracy predictions  (33). However, independent computational predictions for the same RNA using probing data obtained with different probes often yield substantially different models, none of which are fully consistent with all the probing data. It follows that, while theoretically informative, a multiprobing strategy often leaves a user with different models, from which one cannot objectively decide.
Datasets To validate our computational method quantitatively, we consider several datasets, depending on the availability of probing data for one or several reagents, restricted to the wild type or produced for several point-wise mutants. Each dataset consists of sequences and individual reactivities to one or several probes, at each position in the RNA, completed with one or several functionally-relevant secondary structures. Hajdin dataset. A dataset was gathered by Hajdin et al. (43)  to validate the predictive capacities of probing datadriven predictions. It consists of 24 RNA sequences with known secondary structures for which a single chemical probing reactivity profile (1M7-SHAPE) was made available. This dataset includes sequences originating from a variety of organisms, and spans lengths ranging from 34 nts to 530 nts, with a focus on riboswitches and complex RNA architectures (full list in Supp.