Using AI Methods for Health Behavior Change

Artificial intelligence (AI) has been applied to health behavior change research for over a decade. Current research programs include machine learning for delivering just-in-time adaptive interventions, computational modeling of behavior change processes, and the use of social AI for communication and persuasion. With new advances in AI, we propose an international workshop to bring together experts from all related disciplines to discuss and explore the potentials of AI for behavior change research. We discuss in this proposal the aims, planned activities, expected outcomes, and a promotion strategy for the workshop

Habitat use of urban-nesting lesser black-backed gulls during the breeding season

Increasing urbanisation is detrimental for some animal species and potentially advantageous for others. Urban-nesting populations of gulls have undergone rapid population increases worldwide, which has resulted in an increase in human-gull conflicts. In order to inform management and conservation decisions in relation to these populations, more information is needed about the behaviour of these birds in urban settings and how they utilise their environment. This study combined Global Positioning System (GPS) tracking data of 12 urban-nesting lesser black-backed gulls, Larus fuscus, with habitat and behaviour data over three breeding seasons (2016–2018). Despite the proximity of marine areas (~10 km), the birds only made significant use of terrestrial environments, spending two-thirds of their time away from the nest in suburban and urban areas, and one-third in rural green areas. The gulls utilised suburban and urban areas more as their chicks grew and appeared to use diverse foraging strategies to suit different habitats. These results indicate that the range of potential foraging areas available needs to be considered in management decisions and that urban bird populations may not use the resources they are expected to

Modifying Anthocyanins Biosynthesis in Tomato Hairy Roots:A Test Bed for Plant Resistance to Ionizing Radiation and Antioxidant Properties in Space

Gene expression manipulation of specific metabolic pathways can be used to obtain bioaccumulation of valuable molecules and desired quality traits in plants. A single-gene approach to impact different traits would be greatly desirable in agrospace applications, where several aspects of plant physiology can be affected, influencing growth. In this work, MicroTom hairy root cultures expressing a MYB-like transcription factor that regulates the biosynthesis of anthocyanins in Petunia hybrida (PhAN4), were considered as a testbed for bio-fortified tomato whole plants aimed at agrospace applications. Ectopic expression of PhAN4 promoted biosynthesis of anthocyanins, allowing to profile 5 major derivatives of delphinidin and petunidin together with pelargonidin and malvidin-based anthocyanins, unusual in tomato. Consistent with PhAN4 features, transcriptomic profiling indicated upregulation of genes correlated to anthocyanin biosynthesis. Interestingly, a transcriptome reprogramming oriented to positive regulation of cell response to biotic, abiotic, and redox stimuli was evidenced. PhAN4 hairy root cultures showed the significant capability to counteract reactive oxygen species (ROS) accumulation and protein misfolding upon high-dose gamma irradiation, which is among the most potent pro-oxidant stress that can be encountered in space. These results may have significance in the engineering of whole tomato plants that can benefit space agriculture

Insight into the evolution of the Solanaceae from the parental genomes of Petunia hybrida

Petunia hybrida is a popular bedding plant that has a long history as a genetic model system. We report the whole-genome sequencing and assembly of inbred derivatives of its two wild parents, P. axillaris N and P. inflata S6. The current assemblies include 91.3% and 90.2% coverage of their diploid genomes (1.4 Gb; 2n=14) containing 32,928 and 36,697 protein-coding genes, respectively. The Petunia lineage has experienced at least two rounds of paleohexaploidization, the older gamma hexaploidy event, which is shared with other Eudicots, and the more recent Solanaceae paleohexaploidy event that is shared with tomato and other Solanaceae species. Transcription factors that were targets of selection during the shift from bee- to moth pollination reside in particularly dynamic regions of the genome, which may have been key to the remarkable diversity of floral color patterns and pollination systems. The high quality genome sequences will enhance the value of Petunia as a model system for basic and applied research on a variety of unique biological phenomena

An artificial neural network stratifies the risks of reintervention and mortality after endovascular aneurysm repair:a retrospective observational study

Background Lifelong surveillance after endovascular repair (EVAR) of abdominal aortic aneurysms (AAA) is considered mandatory to detect potentially life-threatening endograft complications. A minority of patients require reintervention but cannot be predictively identified by existing methods. This study aimed to improve the prediction of endograft complications and mortality, through the application of machine-learning techniques. Methods Patients undergoing EVAR at 2 centres were studied from 2004-2010. Pre-operative aneurysm morphology was quantified and endograft complications were recorded up to 5 years following surgery. An artificial neural networks (ANN) approach was used to predict whether patients would be at low- or high-risk of endograft complications (aortic/limb) or mortality. Centre 1 data were used for training and centre 2 data for validation. ANN performance was assessed by Kaplan-Meier analysis to compare the incidence of aortic complications, limb complications, and mortality; in patients predicted to be low-risk, versus those predicted to be high-risk. Results 761 patients aged 75 +/- 7 years underwent EVAR. Mean follow-up was 36+/- 20 months. An ANN was created from morphological features including angulation/length/areas/diameters/ volume/tortuosity of the aneurysm neck/sac/iliac segments. ANN models predicted endograft complications and mortality with excellent discrimination between a low-risk and high-risk group. In external validation, the 5-year rates of freedom from aortic complications, limb complications and mortality were 95.9% vs 67.9%; 99.3% vs 92.0%; and 87.9% vs 79.3% respectively (p0.001) Conclusion This study presents ANN models that stratify the 5-year risk of endograft complications or mortality using routinely available pre-operative data

Optimising the use of bio-loggers for movement ecology research

1.The paradigm‐changing opportunities of bio‐logging sensors for ecological research, especially movement ecology, are vast, but the crucial questions of how best to match the most appropriate sensors and sensor combinations to specific biological questions, and how to analyse complex bio‐logging data, are mostly ignored. 2.Here, we fill this gap by reviewing how to optimise the use of bio‐logging techniques to answer questions in movement ecology and synthesise this into an Integrated Bio‐logging Framework (IBF). 3.We highlight that multi‐sensor approaches are a new frontier in bio‐logging, whilst identifying current limitations and avenues for future development in sensor technology. 4.We focus on the importance of efficient data exploration, and more advanced multi‐dimensional visualisation methods, combined with appropriate archiving and sharing approaches, to tackle the big data issues presented by bio‐logging. We also discuss the challenges and opportunities in matching the peculiarities of specific sensor data to the statistical models used, highlighting at the same time the large advances which will be required in the latter to properly analyse bio‐logging data. 5.Taking advantage of the bio‐logging revolution will require a large improvement in the theoretical and mathematical foundations of movement ecology, to include the rich set of high‐frequency multivariate data, which greatly expand the fundamentally limited and coarse data that could be collected using location‐only technology such as GPS. Equally important will be the establishment of multi‐disciplinary collaborations to catalyse the opportunities offered by current and future bio‐logging technology. If this is achieved, clear potential exists for developing a vastly improved mechanistic understanding of animal movements and their roles in ecological processes, and for building realistic predictive models