Towards an AI-Based Objective Prognostic Model for Quantifying Wound Healing.

Chronic wounds affect millions of people worldwide every year. An adequate assessment of a wound's prognosis is critical to wound care, guiding clinical decision making by helping clinicians understand wound healing status, severity, triaging and determining the efficacy of a treatment regimen. The current standard of care involves using wound assessment tools, such as Pressure Ulcer Scale for Healing (PUSH) and Bates-Jensen Wound Assessment Tool (BWAT), to determine wound prognosis. However, these tools involve manual assessment of a multitude of wound characteristics and skilled consideration of a variety of factors, thus, making wound prognosis a slow process which is prone to misinterpretation and high degree of variability. Therefore, in this work we have explored the viability of replacing subjective clinical information with deep learning-based objective features derived from wound images, pertaining to wound area and tissue amounts. These objective features were used to train prognostic models that quantified the risk of delayed wound healing, using a dataset consisting of 2.1 million wound evaluations derived from more than 200,000 wounds. The objective model, which was trained exclusively using image-based objective features, achieved at minimum a 5% and 9% improvement over PUSH and BWAT, respectively. Our best performing model, that used both subjective and objective features, achieved at minimum an 8% and 13% improvement over PUSH and BWAT, respectively. Moreover, the reported models consistently outperformed the standard tools across various clinical settings, wound etiologies, sexes, age groups and wound ages, thus establishing the generalizability of the models.

Food begging and sharing in wild bonobos (Pan paniscus): assessing relationship quality?

Food transfers are often hypothesised to have played a role in the evolution of cooperation amongst humans. However, they also occur in non-human primates, though no consensus exists regarding their function(s). We document patterns of begging for food and success rates as well as associated factors that may influence them for wild bonobos at LuiKotale, Democratic Republic of Congo. Our data, collected over 1074 observation hours, focus on 260 begging events (outside mother-offspring dyads) of which 37 % were successful. We find no support for the "reciprocity hypothesis"-that food is exchanged for grooming and/or sexual benefits; and only weak support for the "sharing under pressure" hypothesis-that food is transferred as a result of harassment and pays off in terms of nutritional benefits for the beggar. Instead, our data support the "assessing-relationships" hypothesis, according to which beggars gain information about the status of their social relationship with the possessor of a food item. This seems to hold particularly true for the frequent, albeit unsuccessful begging events by young females (newly immigrated or hierarchically non-established) towards adult females, although it can be observed in other dyadic combinations independent of sex and age.