BACKGROUND AND OBJECTIVES: Kangaroo mother care (KMC) is a multifaceted intervention for preterm and low birth weight infants and their parents. Short- and mid-term benefits of KMC on survival, neurodevelopment, breastfeeding, and the quality of mother–infant bonding were documented in a randomized controlled trial (RCT) conducted in Colombia from 1993 to 1996. The aim of the present study was to evaluate the persistence of these results in young adulthood. METHODS: From 2012 to 2014, a total of 494 (69%) of the 716 participants of the original RCT known to be alive were identified; 441 (62% of the participants in the original RCT) were re-enrolled, and results for the 264 participants weighing ≤1800 g at birth were analyzed. The KMC and control groups were compared for health status and neurologic, cognitive, and social functioning with the use of neuroimaging, neurophysiological, and behavioral tests. RESULTS: The effects of KMC at 1 year on IQ and home environment were still present 20 years later in the most fragile individuals, and KMC parents were more protective and nurturing, reflected by reduced school absenteeism and reduced hyperactivity, aggressiveness, externalization, and socio-deviant conduct of young adults. Neuroimaging showed larger volume of the left caudate nucleus in the KMC group. CONCLUSIONS: This study indicates that KMC had significant, long-lasting social and behavioral protective effects 20 years after the intervention. Coverage with this efficient and scientifically based health care intervention should be extended to the 18 million infants born each year who are candidates for the method.

Brain research typically requires large amounts of data from different sources, and often of different nature. The use of different software tools adapted to the nature of each data source can make research work cumbersome and time consuming. It follows that data is not often used to its fullest potential thus limiting exploratory analysis. This paper presents an ancillary software tool called BRAVIZ that integrates interactive visualization with real-time statistical analyses, facilitating access to multi-facetted neuroscience data and automating many cumbersome and error-prone tasks required to explore such data. Rather than relying on abstract numerical indicators, BRAVIZ emphasizes brain images as the main object of the analysis process of individuals or groups. BRAVIZ facilitates exploration of trends or relationships to gain an integrated view of the phenomena studied, thus motivating discovery of new hypotheses. A case study is presented that incorporates brain structure and function outcomes together with different types of clinical data.

Virtual reality (VR) is a computer-based technology that can be used by professionals of many different fields to simulate an environment with a high feeling of presence and immersion. Nonetheless, one main issue when designing such environments is to provide user interactions that are adapted to the tasks performed by the users. Thus, we propose here a task-centred methodology to design and evaluate these user interactions. Our methodology allows for the determination of user interaction designs based on previous VR studies, and for user evaluations based on a task-related computation of usability. Here, we applied it on the hazard identification case study, since VR can be used in a preventive approach to improve worksite safety. Once this task and its related user interactions were analysed with our methodology, we obtained two possible designs of interaction techniques for the worksite exploration subtask. About their usability evaluation, we proposed in this study to compare our task-centred evaluation approach to a non-task-centred one. Our hypothesis was that our approach could lead to different interpretations of user study results than a non-task-centred one. Our results confirmed our hypothesis by comparing weighted usability scores from our task-centred approach to unweighted ones for our two interaction techniques.

In many applications, visual analytics (VA) has developed into a standard tool to ease data access and knowledge generation. VA describes a holistic cycle transforming data into hypothesis and visualization to generate insights that enhance the data. Unfortunately, many data sources used in the VA process are affected by uncertainty. In addition, the VA cycle itself can introduce uncertainty to the knowledge generation process but does not provide a mechanism to handle these sources of uncertainty. In this manuscript, we aim to provide an extended VA cycle that is capable of handling uncertainty by quantification, propagation, and visualization, defined as uncertainty-aware visual analytics (UAVA). Here, a recap of uncertainty definition and description is used as a starting point to insert novel components in the visual analytics cycle. These components assist in capturing uncertainty throughout the VA cycle. Further, different data types, hypothesis generation approaches, and uncertainty-aware visualization approaches are discussed that fit in the defined UAVA cycle. In addition, application scenarios that can be handled by such a cycle, examples, and a list of open challenges in the area of UAVA are provided.

The kangaroo mother care (KMC) method is a “45-year-old innovation” in health care to care for premature or low birth weight (LBW) infants: the KMC position with direct skin-to-skin contact on the chest of the mother, KMC nutrition based on breast milk and early home discharge in KMC position with close monitoring are the three key elements of this intervention. Created in 1978 to compensate for the lack of incubators in a huge maternity hospital in Colombia, it has always met with resistance from health professionals. Rigorously studied, it has shown benefits far beyond the reduction in mortality, to ensure a proper quality of life for these fragile babies. KMC has become an example of evidence-based care for premature or LBW infants, and it is no longer ethical not to apply it. Exploratory analysis results on brain growth according to the duration in days of KMC position are presented in this article and should help accelerate the systematic introduction of KMC as early as possible and for as long as possible from birth, as published by the WHO in 2022.