Cross-species complementation reveals conserved functions for EARLY FLOWERING 3 between monocots and dicots

Plant responses to the environment are shaped by external stimuli and internal signaling pathways. In both the model plant Arabidopsis thaliana (Arabidopsis) and crop species, circadian clock factors are critical for growth, flowering, and circadian rhythms. Outside of Arabidopsis, however, little is known about the molecular function of clock gene products. Therefore, we sought to compare the function of Brachypodium distachyon (Brachypodium) and Setaria viridis (Setaria) orthologs of EARLY FLOWERING 3, a key clock gene in Arabidopsis. To identify both cycling genes and putative ELF3 functional orthologs in Setaria, a circadian RNA-seq dataset and online query tool (Diel Explorer) were generated to explore expression profiles of Setaria genes under circadian conditions. The function of ELF3 orthologs from Arabidopsis, Brachypodium, and Setaria was tested for complementation of an elf3 mutation in Arabidopsis. We find that both monocot orthologs were capable of rescuing hypocotyl elongation, flowering time, and arrhythmic clock phenotypes. Using affinity purification and mass spectrometry, our data indicate that BdELF3 and SvELF3 could be integrated into similar complexes in vivo as AtELF3. Thus, we find that, despite 180 million years of separation, BdELF3 and SvELF3 can functionally complement loss of ELF3 at the molecular and physiological level

Automated Coding of Job Descriptions From a General Population Study: Overview of Existing Tools, Their Application and Comparison

OBJECTIVES: Automatic job coding tools were developed to reduce the laborious task of manually assigning job codes based on free-text job descriptions in census and survey data sources, including large occupational health studies. The objective of this study is to provide a case study of comparative performance of job coding and JEM (Job-Exposure Matrix)-assigned exposures agreement using existing coding tools. METHODS: We compared three automatic job coding tools [AUTONOC, CASCOT (Computer-Assisted Structured Coding Tool), and LabourR], which were selected based on availability, coding of English free-text into coding systems closely related to the 1988 version of the International Standard Classification of Occupations (ISCO-88), and capability to perform batch coding. We used manually coded job histories from the AsiaLymph case-control study that were translated into English prior to auto-coding to assess their performance. We applied two general population JEMs to assess agreement at exposure level. Percent agreement and PABAK (Prevalence-Adjusted Bias-Adjusted Kappa) were used to compare the agreement of results from manual coders and automatic coding tools. RESULTS: The coding per cent agreement among the three tools ranged from 17.7 to 26.0% for exact matches at the most detailed 4-digit ISCO-88 level. The agreement was better at a more general level of job coding (e.g. 43.8-58.1% in 1-digit ISCO-88), and in exposure assignments (median values of PABAK coefficient ranging 0.69-0.78 across 12 JEM-assigned exposures). Based on our testing data, CASCOT was found to outperform others in terms of better agreement in both job coding (26% 4-digit agreement) and exposure assignment (median kappa 0.61). CONCLUSIONS: In this study, we observed that agreement on job coding was generally low for the three tools but noted a higher degree of agreement in assigned exposures. The results indicate the need for study-specific evaluations prior to their automatic use in general population studies, as well as improvements in the evaluated automatic coding tools

EEG Data Quality: Determinants and Impact in a Multicenter Study of Children, Adolescents, and Adults with Attention-Deficit/Hyperactivity Disorder (ADHD)

Electroencephalography (EEG) represents a widely established method for assessing altered and typically developing brain function. However, systematic studies on EEG data quality, its correlates, and consequences are scarce. To address this research gap, the current study focused on the percentage of artifact-free segments after standard EEG pre-processing as a data quality index. We analyzed participant-related and methodological influences, and validity by replicating landmark EEG effects. Further, effects of data quality on spectral power analyses beyond participant-related characteristics were explored. EEG data from a multicenter ADHD-cohort (age range 6 to 45 years), and a non-ADHD school-age control group were analyzed (ntotal = 305). Resting-state data during eyes open, and eyes closed conditions, and task-related data during a cued Continuous Performance Task (CPT) were collected. After pre-processing, general linear models, and stepwise regression models were fitted to the data. We found that EEG data quality was strongly related to demographic characteristics, but not to methodological factors. We were able to replicate maturational, task, and ADHD effects reported in the EEG literature, establishing a link with EEG-landmark effects. Furthermore, we showed that poor data quality significantly increases spectral power beyond effects of maturation and symptom severity. Taken together, the current results indicate that with a careful design and systematic quality control, informative large-scale multicenter trials characterizing neurophysiological mechanisms in neurodevelopmental disorders across the lifespan are feasible. Nevertheless, results are restricted to the limitations reported. Future work will clarify predictive value

Diagnoses, problems and healthcare interventions amongst older people with an unscheduled hospital admission who have concurrent mental health problems: a prevalence study

Background Frail older people with mental health problems including delirium, dementia and depression are often admitted to general hospitals. However, hospital admission may cause distress, and can be associated with complications. Some commentators suggest that their healthcare needs could be better met elsewhere. Methods We studied consecutive patients aged 70 or older admitted for emergency medical or trauma care to an 1800 bed general hospital which provided sole emergency medical and trauma services for its local population. Patients were screened for mental health problems, and those screening positive were invited to take part. 250 participants were recruited and a sub-sample of 53 patients was assessed by a geriatrician for diagnoses, impairments and disabilities, healthcare interventions and outstanding needs. Results Median age was 86 years, median Mini-Mental State Examination score at admission was 16/30, and 45% had delirium. 19% lived in a care home prior to admission. All the patients were complex. A wide range of main admission diagnoses was recorded, and these were usually complicated by falls, immobility, pain, delirium, dehydration or incontinence. There was a median of six active diagnoses, and eight active problems. One quarter of problems was unexplained. A median of 13 interventions was recorded, and a median of a further four interventions suggested by the geriatrician. Those with more severe cognitive impairment had no less medical need. Conclusions This patient group, admitted to hospital in the United Kingdom, had numerous healthcare problems, and by implication, extensive healthcare needs. Patients with simpler conditions were not identified, but may have already been rapidly discharged or redirected to non-hospital services by the time assessments were made. To meet the needs of this group outside the hospital would need considerable investment in medical, nursing, therapy and diagnostic facilities. In the meantime, acute hospitals should adapt to deliver comprehensive geriatric assessment, and provide for their mental health needs

Exposure to aircraft and road traffic noise and associations with heart disease and stroke in six European countries: a cross-sectional study

This work was supported by the Economic and Social Research Council (grant ES/F038763/1) with additional funding from the European Network for Noise and Health (ENNAH, EU FP7 grant number 226442)

An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge

There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. RESULTS: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. CONCLUSIONS: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups