Sudden-onset asthma exacerbations may have different triggers and responses to treatment than slower-onset exacerbations. The authors studied this hypothesis among patients with severe asthma exacerbations.The Multicenter Airway Research Collaboration prospectively enrolled patients presenting to 64 North American emergency departments with asthma exacerbations. Of 1,847 patients aged 18±54 yrs, 900 had severe exacerbations (peak expiratory flow rate (PEFR) <50% predicted or hospitalized without PEFR). These patients were divided into sudden-onset (#3 h of symptoms) and slower-onset (>3 h of symptoms) groups.Fourteen per cent (95% confidence interval, 11±16%) of patients with severe asthma exacerbations had sudden-onset exacerbations. Sudden-onset patients were similar to slower-onset patients, except triggers of their exacerbations were more often respiratory allergens, exercise or psychosocial stress and less often respiratory infections. Sudden-onset patients were more likely to have used oral b-agonists and salmeterol in the preceding 4 weeks. Although initial PEFRs and management were similar, sudden-onset patients had a greater improvement in PEFR (35 versus 28% p<0.001). Sudden-onset patients were less often discharged on systemic corticosteroids, but had similar 2-week relapse rates compared with slower-onset patients.Among patients presenting with severe asthma exacerbations, sudden-onset exacerbations had a different pattern of triggers and greater improvement with treatment than slower-onset exacerbations. Eur Respir J 2000; 15: 266±273.

Small RNA-seq is increasingly being used for profiling of small RNAs. Quantitative characteristics of long RNA-seq have been extensively described, but small RNA-seq involves fundamentally different methods for library preparation, with distinct protocols and technical variations that have not been fully and systematically studied. We report here the results of a study using common references (synthetic RNA pools of defined composition, as well as plasma-derived RNA) to evaluate the accuracy, reproducibility and bias of small RNA-seq library preparation for five distinct protocols and across nine different laboratories. We observed protocol-specific and sequence-specific bias, which was ameliorated using adapters for ligation with randomized end-nucleotides, and computational correction factors. Despite this technical bias, relative quantification using small RNA-seq was remarkably accurate and reproducible, even across multiple laboratories using different methods. These results provide strong evidence for the feasibility of reproducible cross-laboratory small RNA-seq studies, even those involving analysis of data generated using different protocols. (Introduction without separate heading below)RNA-seq using next generation sequencing has been a transformative technology that has been widely used as a method for characterizing the transcriptome in a wide range of biological contexts 1,2 . Applications of RNA-seq fall into two categories: long RNA-seq and small RNA-seq, distinguished not only by the size of the targeted RNAs, but also by the technical methods used and the resulting biases of the different approaches in the quantitative data produced 3 . For example, the production of the libraries for long RNA-seq, by virtue of having sufficiently long target RNA lengths, commonly utilizes primers (e.g., random primers or oligo-dT) for direct generation of cDNA from RNA. However, small RNA-seq library construction methods typically require an RNA ligation or polyA tailing step to overcome the challenge of performing reverse transcription and subsequent PCR-based amplification from extremely short (e.g., 16-30 nt) target RNA sequences.Multiple approaches have been developed to overcome the challenge of uniformly and robustly generating cDNA from small RNAs for the purpose of small RNA-seq library preparation 4-9 . Protocols in use for small RNA-seq therefore vary more widely than those used for long RNA-seq, creating significantly more potential for variation across small RNA-seq results using different library preparation protocols and by different labs. In addition, small RNA-seq is increasingly used to study small RNAs present in very low input concentration samples (e.g., in exosomes and other types of extracellular vesicles (EV) 10-19 , or in RNA-protein complexes present in biofluids 20-26 ). Normalization methods 27-29 developed to correct for variation in long RNA-seq data are typically not well-suited for such small RNA-seq data, making it even more important to