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AI tools intend to transform mental healthcare by providing remote estimates of depression risk using behavioral data collected by sensors embedded in smartphones. While these tools accurately predict elevated depression symptoms in small, homogenous populations, recent studies show that these tools are less accurate in larger, more diverse populations. In this work, we show that accuracy is reduced because sensed-behaviors are unreliable predictors of depression across individuals: sensed-behaviors that predict depression risk are inconsistent across demographic and socioeconomic subgroups. We first identified subgroups where a developed AI tool underperformed by measuring algorithmic bias, where subgroups with depression were incorrectly predicted to be at lower risk than healthier subgroups. We then found inconsistencies between sensed-behaviors predictive of depression across these subgroups. Our findings suggest that researchers developing AI tools predicting mental health from sensed-behaviors should think critically about the generalizability of these tools, and consider tailored solutions for targeted populations.

 

Mobile sensing data processed using machine learning models can passively and remotely assess mental health symptoms from the context of patients’ lives. Prior work has trained models using data from single longitudinal studies, collected from demographically homogeneous populations, over short time periods, using a single data collection platform or mobile application. The generalizability of model performance across studies has not been assessed. This study presents a first analysis to understand if models trained using combined longitudinal study data to predict mental health symptoms generalize across current publicly available data. We combined data from the CrossCheck (individuals living with schizophrenia) and StudentLife (university students) studies. In addition to assessing generalizability, we explored if personalizing models to align mobile sensing data, and oversampling less-represented severe symptoms, improved model performance. Leave-one-subject-out cross-validation (LOSO-CV) results were reported. Two symptoms (sleep quality and stress) had similar question-response structures across studies and were used as outcomes to explore cross-dataset prediction. Models trained with combined data were more likely to be predictive (significant improvement over predicting training data mean) than models trained with single-study data. Expected model performance improved if the distance between training and validation feature distributions decreased using combined versus single-study data. Personalization aligned each LOSO-CV participant with training data, but only improved predicting CrossCheck stress. Oversampling significantly improved severe symptom classification sensitivity and positive predictive value, but decreased model specificity. Taken together, these results show that machine learning models trained on combined longitudinal study data may generalize across heterogeneous datasets. We encourage researchers to disseminate collected de-identified mobile sensing and mental health symptom data, and further standardize data types collected across studies to enable better assessment of model generalizability. 

The Hawaiian monk seal (HMS) is the single extant species of tropical earless seals of the genus Neomonachus. The species survived a severe bottleneck in the late 19th century and experienced subsequent population declines until becoming the subject of a NOAA-led species recovery effort beginning in 1976 when the population was fewer than 1000 animals. Like other recovering species, the Hawaiian monk seal has been reported to have reduced genetic heterogeneity due to the bottleneck and subsequent inbreeding. Here, we report a chromosomal reference assembly for a male animal produced using a variety of methods. The final assembly consisted of 16 autosomes, an X, and portions of the Y chromosomes. We compared variants in this animal to other HMS and to a frequently sequenced human sample, confirming about 12% of the variation seen in man. To confirm that the reference animal was representative of the HMS, we compared his sequence to that of 10 other individuals and noted similarly low variation in all. Variation in the major histocompatibility (MHC) genes was nearly absent compared to the orthologous human loci. Demographic analysis predicts that Hawaiian monk seals have had a long history of small populations preceding the bottleneck, and their current low levels of heterozygosity may indicate specialization to a stable environment. When we compared our reference assembly to that of other species, we observed significant conservation of chromosomal architecture with other pinnipeds, especially other phocids. This reference should be a useful tool for future evolutionary studies as well as the long-term management of this species. 

Digital biomarkers of mental health, created using data extracted from everyday technologies including smartphones, wearable devices, social media and computer interactions, have the opportunity to revolutionise mental health diagnosis and treatment by providing near-continuous unobtrusive and remote measures of behaviours associated with mental health symptoms. Machine learning models process data traces from these technologies to identify digital biomarkers. In this editorial, we caution clinicians against using digital biomarkers in practice until models are assessed for equitable predictions (‘model equity’) across demographically diverse patients at scale, behaviours over time, and data types extracted from different devices and platforms. We posit that it will be difficult for any individual clinic or large-scale study to assess and ensure model equity and alternatively call for the creation of a repository of open de-identified data for digital biomarker development. 

Sea turtles represent an ancient lineage of marine vertebrates that evolved from terrestrial ancestors over 100 Mya. The genomic basis of the unique physiological and ecological traits enabling these species to thrive in diverse marine habitats remains largely unknown. Additionally, many populations have drastically declined due to anthropogenic activities over the past two centuries, and their recovery is a high global conservation priority. We generated and analyzed high-quality reference genomes for the leatherback ( Dermochelys coriacea ) and green ( Chelonia mydas ) turtles, representing the two extant sea turtle families. These genomes are highly syntenic and homologous, but localized regions of noncollinearity were associated with higher copy numbers of immune, zinc-finger, and olfactory receptor (OR) genes in green turtles, with ORs related to waterborne odorants greatly expanded in green turtles. Our findings suggest that divergent evolution of these key gene families may underlie immunological and sensory adaptations assisting navigation, occupancy of neritic versus pelagic environments, and diet specialization. Reduced collinearity was especially prevalent in microchromosomes, with greater gene content, heterozygosity, and genetic distances between species, supporting their critical role in vertebrate evolutionary adaptation. Finally, diversity and demographic histories starkly contrasted between species, indicating that leatherback turtles have had a low yet stable effective population size, exhibit extremely low diversity compared with other reptiles, and harbor a higher genetic load compared with green turtles, reinforcing concern over their persistence under future climate scenarios. These genomes provide invaluable resources for advancing our understanding of evolution and conservation best practices in an imperiled vertebrate lineage. 

Industrial hog operation (IHO) workers are at increased risk of carryingStaphylococcus aureusin their nares, particularly strains that are livestock-associated (LA) and multidrug-resistant. The pathogenicity of LA-S. aureusstrains remains unclear, with some prior studies suggesting reduced transmission and virulence in humans compared to community-associated methicillin-resistant (CA-MRSA)S. aureus. The objective of this study was to determine the degree to which LA-S. aureusstrains contracted by IHO workers cause disease relative to a representative CA-MRSA strain in a mouse model of skin and soft tissue infection (SSTI). Mice infected with CC398 LA-S. aureusstrains (IHW398-1 and IHW398-2) developed larger lesion sizes with higher bacterial burden than mice infected with CA-MRSA (SF8300) (p < 0.05). The greatest lesion size and bacterial burden was seen with a CC398 strain that produced a recurrent SSTI in an IHO worker. The LA-S. aureusinfected mice had decreased IL-1β protein levels compared with CA-MRSA-infected mice (p < 0.05), suggesting a suboptimal host response to LA-S. aureusSSTIs. WGSA revealed heterogeneity in virulence factor and antimicrobial resistance genes carried by LA-S. aureusand CA-MRSA strains. The observed pathogenicity suggest that more attention should be placed on preventing the spread of LA-S. aureusinto human populations.