Exploratory assessment of dose proportionality: review of current approaches and proposal for a practical criterion

This article reviews currently used approaches for establishing dose proportionality in Phase I dose escalation studies. A review of relevant literature between 2002 and 2006 found that the power model was the preferred choice for assessing dose proportionality in about one-third of the articles. This article promotes the use of the power model and a conceptually appealing extension, i.e. a criterion based on comparing the 90% confidence interval for the ratio of predicted mean values from the extremes of the dose range (R(dnm)) to pre-defined equivalence criterion (theta(L),theta(U)). The choice of bioequivalence default values of theta(L)=0.8 and theta(U)=1.25 seems reasonable for dose levels only a doubling apart but are impractically strict when applied over the complete dose range. Power calculations are used to show that this prescribed criterion lacks power to conclude dose proportionality in typical Phase I dose-escalation studies. A more lenient criterion with values theta(L)=0.5 and theta(U)=2 is proposed for exploratory dose proportionality assessments across the complete dose range.     

Children and Young Adults' Recognition of Anxiety

Previous research has shown that decoders (judges) accurately identified state anxiety in peers when provided with brief, single channel (audio-only, video-only) stimuli, and appropriately differentiated trait anxiety. Using a similar approach in the present study with decoders and encoders from three distinct age groups, we discovered that the ability to detect state anxiety transcended age. Children and young adults showed an ability to identify level of state anxiety in young adult and elder encoders. Young adults differentiated levels of state and trait anxiety in young adult and elder encoders. Discussion is focused on the adaptive and practical value of cross-generational identification of state anxiety and trait anxiety, and the difficulty of inferring personality traits in individuals from different peer groups.     

Can Machine Learning Create an Advocate for Foster Youth?

ABSTRACT

Statistics are bleak for youth aging out of the United States foster care system. They are often left with few resources, are likely to experience homelessness, and are at increased risk of incarceration and exploitation. The Think of Us platform is a service for foster youth and their advocates to create personalized goals and access curated content specific to aging out of the foster care system. In this article, we propose the use of a machine learning algorithm within the Think of Us platform to better serve youth transitioning to life outside of foster care. The algorithm collects and collates publicly available figures and data to inform caseworkers and other mentors chosen by the youth on how to best assist foster youth. It can then provide valuable resources for the youth and their advocates targeted directly toward their specific needs. Finally, we examine machine learning as a support system and aid for caseworkers to buttress and protect vulnerable young adults during their transition to adulthood.     

Where the wild things are! Do urban green spaces with greater avian biodiversity promote more positive emotions in humans?

Urban Ecosystems - Urban green space can help mitigate the negative impacts of urban living and provide positive effects on citizens’ mood, health and well-being. Questions remain, however,...