Current evidence underscores a need to transform how we do clinical research, shifting from academic-driven priorities to co-led community partnership focused programs, accessible and relevant career pathway programs that expand opportunities for career development, and design of trainings and practices to develop cultural competence among research teams. Failures of equitable research translation contribute to health disparities. Drivers of this failed translation include lack of diversity in both researchers and participants, lack of alignment between research institutions and the communities they serve, and lack of attention to structural sources of inequity and drivers of mistrust for science and research. The Duke University Research Equity and Diversity Initiative (READI) is a program designed to better align clinical research programs with community health priorities through community engagement. Organized around three specific aims, READI-supported programs targeting increased workforce diversity, workforce training in community engagement and cultural competence, inclusive research engagement principles, and development of trustworthy partnerships.

Maladaptive daydreaming is a distinct syndrome in which the main symptom is excessive vivid fantasising that causes clinically significant distress and functional impairment in academic, vocational and social domains. Unlike normal daydreaming, maladaptive daydreaming is persistent, compulsive and detrimental to one’s life. It involves detachment from reality in favour of intense emotional engagement with alternative realities and often includes specific features such as psychomotor stereotypies (e.g. pacing in circles, jumping or shaking one’s hands), mouthing dialogues, facial gestures or enacting fantasy events. Comorbidity is common, but existing disorders do not account for the phenomenology of the symptoms. Whereas non-specific therapy is ineffective, targeted treatment seems promising. Thus, we propose that maladaptive daydreaming be considered a formal syndrome in psychiatric taxonomies, positioned within the dissociative disorders category. Maladaptive daydreaming satisfactorily meets criteria for conceptualisation as a psychiatric syndrome, including reliable discrimination from other disorders and solid interrater agreement. It involves significant dissociative aspects, such as disconnection from perception, behaviour and sense of self, and has some commonalities with but is not subsumed under existing dissociative disorders. Formal recognition of maladaptive daydreaming as a dissociative disorder will encourage awareness of a growing problem and spur theoretical, research and clinical developments.

Quantitative plant biology is an interdisciplinary field that builds on a long history of biomathematics and biophysics. Today, thanks to high spatiotemporal resolution tools and computational modelling, it sets a new standard in plant science. Acquired data, whether molecular, geometric or mechanical, are quantified, statistically assessed and integrated at multiple scales and across fields. They feed testable predictions that, in turn, guide further experimental tests. Quantitative features such as variability, noise, robustness, delays or feedback loops are included to account for the inner dynamics of plants and their interactions with the environment. Here, we present the main features of this ongoing revolution, through new questions around signalling networks, tissue topology, shape plasticity, biomechanics, bioenergetics, ecology and engineering. In the end, quantitative plant biology allows us to question and better understand our interactions with plants. In turn, this field opens the door to transdisciplinary projects with the society, notably through citizen science.

Many snow models have been developed for various applications such as hydrology, global atmospheric circulation models and avalanche forecasting. The degree of complexity of these models is highly variable, ranging from simple index methods to multi-layer models that simulate snow-cover stratigraphy and texture. In the framework of the Snow Model Intercomparison Project (SnowMIP), 23 models were compared using observed meteorological parameters from two mountainous alpine sites. The analysis here focuses on validation of snow energy-budget simulations. Albedo and snow surface temperature observations allow identification of the more realistic simulations and quantification of errors for two components of the energy budget: the net short- and longwave radiation. In particular, the different albedo parameterizations are evaluated for different snowpack states (in winter and spring). Analysis of results during the melting period allows an investigation of the different ways of partitioning the energy fluxes and reveals the complex feedbacks which occur when simulating the snow energy budget. Particular attention is paid to the impact of model complexity on the energy-budget components. The model complexity has a major role for the net longwave radiation calculation, whereas the albedo parameterization is the most significant factor explaining the accuracy of the net shortwave radiation simulation.

We have conducted a LABOCA 870 μm follow-up of ten massive lensing clusters of the Herschel Lensing Survey (HLS, Egami et al. 2010) aiming at unveiling the yet hidden part of dusty star formation in the distant Universe. Among these clusters, A2744 and AS1063 are part of the Frontier Fields HST program. We also obtained 2 mm bolometer observations of A2744 and A370 with the GISMO array at the IRAM 30 m. We detected sources that are undetected with Herschel (PACS and SPIRE) implying either a very high redshift (z > 4) or a very low dust temperature (T<25 K). Their flux also imply a low intrinsic luminosity, L FIR < 1012L⊙. Some of them are extended and could correspond to multiple sources or to multiple images of a lensed source. Substructures in the Sunyaev-Zeldovich effect could also contribute this very red submm emission. An ALMA program is ongoing to unveil the nature of these sources.

To extend the molecular gas measurements to typical L* star-forming galaxies (SFGs) at z ∼ 1.5 − 3, we have observed CO emission for five strongly-lensed galaxies selected from the Herschel Lensing Survey. The combined sample of our L* SFGs with CO-detected SFGs at z >1 from the literature shows a large spread in star formation efficiency (SFE). We find that this spread in SFE is due to variations of several physical parameters, primarily the specific star formation rate, but also stellar mass and redshift. An increase of the molecular gas fraction (fgas) is observed from z ∼ 0.2 to z ∼ 1.2, followed by a quasi non-evolution toward higher redshifts, as found in earlier studies. We provide the first measure of fgas of z >1 SFGs at the low-stellar mass end between 109.4 < M∗/M⊙ < 109.9, which shows a clear fgas upturn.