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Aims.JWST/NIRCam provides rest-frame near-IR photometry of galaxies up toz = 2.5 with exquisite depth and accuracy. This affords us an unprecedented view of the evolution of the UV/optical/near-IR color distribution and its interpretation in terms of the evolving dust attenuation,A_V. Methods.We used the value-added data products (photometric redshift, stellar mass, rest-frameU − VandV − Jcolors, andA_V) provided by the public DAWN JWST Archive. These data products derive from fitting the spectral energy distributions obtained from multiple NIRCam imaging surveys, augmented with preexisting HST imaging data. Our sample consists of a stellar-mass-complete sample of ≈28 000M_⋆ >  10⁹ M_⊙galaxies in the redshift range 0.5 <  z <  2.5. Results.TheV − Jcolor distribution of star-forming galaxies evolves strongly, in particular for high-mass galaxies (M_⋆ >  3 × 10¹⁰ M_⊙), which have a pronounced tail of very red galaxies reachingV − J >  2.5 atz >  1.5 that does not exist atz <  1. Such redV − Jcan only be explained by dust attenuation, with typical values forM_⋆ ≈ 10¹¹ M_⊙galaxies in the rangeA_V ≈ 1.5 − 3.5 atz ≈ 2. This redshift evolution went largely unnoticed before. Today, however, photometric redshift estimates for the reddest (V − J >  2.5), most attenuated galaxies have markedly improved thanks to the new, precise photometry, which is in much better agreement with the 25 available spectroscopic redshifts for such galaxies. The reddest population readily stands out as the independently identified population of galaxies detected at submillimeter wavelengths. Despite the increased attenuation,U − Vcolors across the entire mass range are slightly bluer at higherz. A well-defined and tight color sequence exists at redshifts 0.5 <  z <  2.5 forM_⋆ >  3 × 10¹⁰ M_⊙quiescent galaxies, in bothU − VandV − J, but inV − Jit is bluer rather than redder compared to star-forming galaxies. In conclusion, whereas the rest-frame UV-optical color distribution evolves remarkably little fromz = 0.5 toz = 2.5, the rest-frame optical/near-IR color distribution evolves strongly, primarily due to a very substantial increase with redshift in dust attenuation for massive galaxies. 

Anthropogenic stressors to marine ecosystems from climate change and human activities increase extinction risk of species, disrupt ecosystem integrity, and threaten important ecosystem services. Addressing these stressors requires understanding where and to what extent they are impacting marine biological and functional diversity. We model cumulative risk of human impact upon 21,159 marine animal species by combining information on species-level vulnerability and spatial exposure to a range of anthropogenic stressors. We apply this species-level assessment of human impacts to examine patterns of species-stressor interactions within taxonomic groups. We then spatially map impacts across the global ocean, identifying locations where climate-driven impacts overlap with fishing, shipping, and land-based stressors to help inform conservation needs and opportunities. Comparing species-level modeled impacts to those based on marine habitats that represent important marine ecosystems, we find that even relatively untouched habitats may still be home to species at elevated risk, and that many species-rich coastal regions may be at greater risk than indicated from habitat-based methods alone. Finally, we incorporate a trait-based metric of functional diversity to identify where impacts to functionally unique species might pose greater risk to community structure and ecosystem integrity. These complementary lenses of species, function, and habitat provide a richer understanding of threats to marine biodiversity to help inform efforts to meet conservation targets and ensure sustainability of nature’s contributions to people. 

Socially assistive robots can be used as therapeutic technologies to address depression symptoms. Through three sets of workshops with individuals living with depression and clinicians, we developed design guidelines for a personalized therapeutic robot for adults living with depression. Building on the design of Therabot™, workshop participants discussed various aspects of the robot’s design, sensors, behaviors, and a robot connected mobile phone app. Similarities among participants and workshops included a preference for a soft textured exterior and natural colors and sounds. There were also differences – clinicians wanted the robot to be able to call for aid, while participants with depression differed in their degree of comfort in sharing data collected by the robot with clinicians. 

Deploying socially assistive robots (SARs) at home, such as robotic companion pets, can be useful for tracking behavioral and health-related changes in humans during lifestyle fluctuations over time, like those experienced during CoVID-19. However, a fundamental problem required when deploying autonomous agents such as SARs in people’s everyday living spaces is understanding how users interact with those robots when not observed by researchers. One way to address that is to utilize novel modeling methods based on the robot’s sensor data, combined with newer types of interaction evaluation such as ecological momentary assessment (EMA), to recognize behavior modalities. This paper presents such a study of human-specific behavior classification based on data collected through EMA and sensors attached onboard a SAR, which was deployed in user homes. Classification was conducted using generative replay models, which attempt to use encoding/decoding methods to emulate how human dreaming is thought to create perturbations of the same experience in order to learn more efficiently from less data. Both multi-class and binary classification were explored for comparison, using several types of generative replay (variational autoencoders, generative adversarial networks, semi-supervised GANs). The highest-performing binary model showed approximately 79% accuracy (AUC 0.83), though multi-class classification across all modalities only attained 33% accuracy (AUC 0.62, F1 0.25), despite various attempts to improve it. The paper here highlights the strengths and weaknesses of using generative replay for modeling during human–robot interaction in the real world and also suggests a number of research paths for future improvement. 

Cocrystallization of long-chain bromoalkanes and lipid-like ionic liquids, revealing distinctive host–guest complex structures reminiscent of biomembranes. 

Artificial Intelligence (AI)-driven Digital Health (DH) systems are poised to play a critical role in the future of healthcare. In 2021, $57.2 billion was invested in DH systems around the world, recognizing the promise this concept holds for aiding in delivery and care management. DH systems traditionally include a blend of various technologies, AI, and physiological biomarkers and have shown a potential to provide support for individuals with various health conditions. Digital therapeutics (DTx) is a more specific set of technology-enabled interventions within the broader DH sphere intended to produce a measurable therapeutic effect. DTx tools can empower both patients and healthcare providers, informing the course of treatment through data-driven interventions while collecting data in real-time and potentially reducing the number of patient office visits needed. In particular, socially assistive robots (SARs), as a DTx tool, can be a beneficial asset to DH systems since data gathered from sensors onboard the robot can help identify in-home behaviors, activity patterns, and health status of patients remotely. Furthermore, linking the robotic sensor data to other DH system components, and enabling SAR to function as part of an Internet of Things (IoT) ecosystem, can create a broader picture of patient health outcomes. The main challenge with DTx, and DH systems in general, is that the sheer volume and limited oversight of different DH systems and DTxs is hindering validation efforts (from technical, clinical, system, and privacy standpoints) and consequently slowing widespread adoption of these treatment tools. 

Oceans play critical roles in the lives, economies, cultures, and nutrition of people globally, yet face increasing pressures from human activities that put those benefits at risk. To anticipate the future of the world's ocean, we review the many human activities that impose pressures on marine species and ecosystems, evaluating their impacts on marine life, the degree of scientific uncertainty in those assessments, and the expected trajectory over the next few decades. We highlight that fundamental research should prioritize areas of high potential impact and greater uncertainty about ecosystem vulnerability, such as emerging fisheries, organic chemical pollution, seabed mining, and the interactions of cumulative pressures, and deprioritize research on areas that demonstrate little impact or are well understood, such as plastic pollution and ship strikes to marine fauna. There remains hope for a productive and sustainable future ocean, but the window of opportunity for action is closing. 

Abstract Glucocorticoids are regularly used as biomarkers of relative health for individuals and populations. Around the Western Antarctic Peninsula (WAP), baleen whales have and continue to experience threats, including commercial harvest, prey limitations and habitat change driven by rapid warming, and increased human presence via ecotourism. Here, we measured demographic variation and differences across the foraging season in blubber cortisol levels of humpback whales (Megaptera novaeangliae) over two years around the WAP. Cortisol concentrations were determined from 305 biopsy samples of unique individuals. We found no significant difference in the cortisol concentration between male and female whales. However, we observed significant differences across demographic groups of females and a significant decrease in the population across the feeding season. We also assessed whether COVID-19-related reductions in tourism in 2021 along the WAP correlated with lower cortisol levels across the population. The decline in vessel presence in 2021 was associated with a significant decrease in humpback whale blubber cortisol concentrations at the population level. Our findings provide critical contextual data on how these hormones vary naturally in a population over time, show direct associations between cortisol levels and human presence, and will enable comparisons among species experiencing different levels of human disturbance.