Predicting Positive Education Outcomes for Emerging Adults in Mental Health Systems of Care

Emerging adults who receive services based on positive youth development models have shown an ability to shape their own life course to achieve positive goals. This paper reports secondary data analysis from the Longitudinal Child and Family Outcome Study including 248 culturally-diverse youth ages 17 through 22 receiving mental health services in systems of care. After 12 months of services, school performance was positively related to youth ratings of school functioning, and service participation and satisfaction. Regression analysis revealed ratings of young peoples’ perceptions of school functioning and their experience in services added to the significant prediction of satisfactory school performance, even controlling for sex and attendance. Finally, in addition to expected predictors, participation in planning their own services significantly predicted enrollment in higher education for those who finished high school. Findings suggest that programs and practices based on positive youth development approaches can improve educational outcomes for emerging adults

Anchored phosphatases modulate glucose homeostasis.

Endocrine release of insulin principally controls glucose homeostasis. Nutrient-induced exocytosis of insulin granules from pancreatic β-cells involves ion channels and mobilization of Ca(2+) and cyclic AMP (cAMP) signalling pathways. Whole-animal physiology, islet studies and live-β-cell imaging approaches reveal that ablation of the kinase/phosphatase anchoring protein AKAP150 impairs insulin secretion in mice. Loss of AKAP150 impacts L-type Ca(2+) currents, and attenuates cytoplasmic accumulation of Ca(2+) and cAMP in β-cells. Yet surprisingly AKAP150 null animals display improved glucose handling and heightened insulin sensitivity in skeletal muscle. More refined analyses of AKAP150 knock-in mice unable to anchor protein kinase A or protein phosphatase 2B uncover an unexpected observation that tethering of phosphatases to a seven-residue sequence of the anchoring protein is the predominant molecular event underlying these metabolic phenotypes. Thus anchored signalling events that facilitate insulin secretion and glucose homeostasis may be set by AKAP150 associated phosphatase activity

Primary and secondary scintillation measurements in a xenon Gas Proportional Scintillation Counter

NEXT is a new experiment to search for neutrinoless double beta decay using a 100 kg radio-pure high-pressure gaseous xenon TPC. The detector requires excellent energy resolution, which can be achieved in a Xe TPC with electroluminescence readout. Hamamatsu R8520-06SEL photomultipliers are good candidates for the scintillation readout. The performance of this photomultiplier, used as VUV photosensor in a gas proportional scintillation counter, was investigated. Initial results for the detection of primary and secondary scintillation produced as a result of the interaction of 5.9 keV X-rays in gaseous xenon, at room temperature and at pressures up to 3 bar, are presented. An energy resolution of 8.0% was obtained for secondary scintillation produced by 5.9 keV X-rays. No significant variation of the primary scintillation was observed for different pressures (1, 2 and 3 bar) and for electric fields up to 0.8 V cm-1 torr-1 in the drift region, demonstrating negligible recombination luminescence. A primary scintillation yield of 81 \pm 7 photons was obtained for 5.9 keV X-rays, corresponding to a mean energy of 72 \pm 6 eV to produce a primary scintillation photon in xenon.Comment: 16 pages, 10 figures, accepted for publication in JINS

Realizing the potential of artificial intelligence in healthcare: Learning from intervention, innovation, implementation and improvement sciences

Introduction: Artificial intelligence (AI) is widely seen as critical for tackling fundamental challenges faced by health systems. However, research is scant on the factors that influence the implementation and routine use of AI in healthcare, how AI may interact with the context in which it is implemented, and how it can contribute to wider health system goals. We propose that AI development can benefit from knowledge generated in four scientific fields: intervention, innovation, implementation and improvement sciences. Aim: The aim of this paper is to briefly describe the four fields and to identify potentially relevant knowledge from these fields that can be utilized for understanding and/or facilitating the use of AI in healthcare. The paper is based on the authors' experience and expertise in intervention, innovation, implementation, and improvement sciences, and a selective literature review. Utilizing knowledge from the four fields: The four fields have generated a wealth of often-overlapping knowledge, some of which we propose has considerable relevance for understanding and/or facilitating the use of AI in healthcare. Conclusion: Knowledge derived from intervention, innovation, implementation, and improvement sciences provides a head start for research on the use of AI in healthcare, yet the extent to which this knowledge can be repurposed in AI studies cannot be taken for granted. Thus, when taking advantage of insights in the four fields, it is important to also be explorative and use inductive research approaches to generate knowledge that can contribute toward realizing the potential of AI in healthcare

It’s Not as Simple as it Sounds: Problems and Solutions in Accessing and Using Administrative Child Welfare Data for Evaluating the Impact of Early Childhood Interventions

In recent years, there has been increasing interest in using administrative data collected by state child welfare agencies as a source of information for research and evaluation. The challenges of obtaining access to and using these data, however, have not been well documented. This study describes the processes used to access child welfare records in six different states and the approach to combining and using the information gathered to evaluate the impact of the Early Head Start program on children’s involvement with the child welfare system from birth through age eleven. We provide “lessons learned” for researchers who are attempting to use this information, including being prepared for long delays in access to information, the need for deep understanding of how child welfare agencies record and code information, and for considerable data management work for translating agency records into analysis-ready datasets. While accessing and using this information is not easy, and the data have a number of limitations, we suggest that the benefits can outweigh the challenges and that these records can be a useful source of information for policy-relevant child welfare research