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Shorter length of stay for postpartum mothers and their newborns necessitates careful community follow-up after hospital discharge. The vast amount of information given during the initial postpartum period can be overwhelming. New parents often need considerable support to understand the nuances of newborn care including newborn feeding. Primary health care and community services need to ensure there is a seamless continuum of care to support, empower, and educate new mothers and their families to prevent unnecessary hospital readmission and other negative health outcomes. The Healthy & Home postpartum community nursing program provides clinical communication and supports to bridge the gap between acute hospital and community follow-up care through home visits, a primary health care clinic, a breastfeeding center, a breastfeeding café, a postpartum anxiety and depression support group, bereavement support, and involvement in a Baby-Friendly Initiative™ coalition. Nurses working in the program have the acute care skills and resources to complete required health care assessments and screening tests. They are also international board-certified lactation consultants able to provide expert breastfeeding and lactation care. This paper describes how the Healthy & Home program has evolved over the past 25 years and offers suggestions to other organizations wanting to develop a postpartum program to meet the physical and mental health needs of postpartum families to promote maternal and infant wellbeing.
Accurately identifying and extracting clusters from atom probe tomography (APT) reconstructions is extremely challenging, yet critical to many applications. Currently, the most prevalent approach to detect clusters is the maximum separation method, a heuristic that relies heavily upon parameters manually chosen by the user. In this work, a new clustering algorithm, Gaussian mixture model Expectation Maximization Algorithm (GEMA), was developed. GEMA utilizes a Gaussian mixture model to probabilistically distinguish clusters from random fluctuations in the matrix. This machine learning approach maximizes the data likelihood via expectation maximization: given atomic positions, the algorithm learns the position, size, and width of each cluster. A key advantage of GEMA is that atoms are probabilistically assigned to clusters, thus reflecting scientifically meaningful uncertainty regarding atoms located near precipitate/matrix interfaces. GEMA outperforms the maximum separation method in cluster detection accuracy when applied to several realistically simulated data sets. Lastly, GEMA was successfully applied to real APT data.
An automated procedure has been developed for the reconstruction of field ion microscopy (FIM) data that maintains its atomistic nature. FIM characterizes individual atoms on the specimen’s surface, evolving subject to field evaporation, in a series of two-dimensional (2D) images. Its unique spatial resolution enables direct imaging of crystal defects as small as single vacancies. To fully exploit FIM’s potential, automated analysis tools are required. The reconstruction algorithm developed here relies on minimal assumptions and is sensitive to atomic coordinates of all imaged atoms. It tracks the atoms across a sequence of images, allocating each to its respective crystallographic plane. The result is a highly accurate 3D lattice-resolved reconstruction. The procedure is applied to over 2000 tungsten atoms, including ion-implanted planes. The approach is further adapted to analyze carbides in a steel matrix, demonstrating its applicability to a range of materials. A vast amount of information is collected during the experiment that can underpin advanced analyses such as automated detection of “out of sequence” events, subangstrom surface displacements and defects effects on neighboring atoms. These analyses have the potential to reveal new insights into the field evaporation process and contribute to improving accuracy and scope of 3D FIM and atom probe characterization.
The National Early Care and Education Learning Collaboratives (ECELC) Project aims to promote healthy physical activity and nutrition environments, policies and practices in early care and education (ECE) programmes across multiple states. The present pilot study sought to assess changes to the physical activity and nutrition practices in a sub-sample of ECE programmes participating in the ECELC using the Environment and Policy Assessment and Observation (EPAO). Additionally, it sought to compare results with the Nutrition and Physical Activity Self-Assessment for Child Care (NAP SACC).
Quasi-experimental pre–post pilot study where paired-sample t tests examined changes to physical activity and nutrition practices from pre-assessment to post-assessment (P<0·05). Pearson correlation coefficients examined change scores from EPAO compared with NAP SACC with statistical significance set at a two-sided α level of P<0·10 to account for sample size.
The study occurred among ECE programmes.
Pre-school classrooms in nineteen ECE programmes across four US states were observed.
EPAO data demonstrated an increase in total score from pre-assessment to post-assessment (150 (sd 30) to 176 (sd 35)). NAP SACC change scores demonstrated little relationship with EPAO domain change scores, with exceptions in Nutrition Policy and Physical Activity Policy (r=−0·4 and −0·6, respectively).
The overall improvements reported through the EPAO suggest participation in the ECELC resulted in changes in critical nutrition- and physical activity-related practices. However, considerable differences in data reported using the NAP SACC compared with the EPAO suggest subjective data should be interpreted with caution and objective measurement should be used when feasible.
Hip and knee arthroplasty infections are associated with considerable healthcare costs. The merits of reducing the postoperative surveillance period from 1 year to 90 days have been debated.
To report the first pan-Canadian hip and knee periprosthetic joint infection (PJI) rates and to describe the implications of a shorter (90-day) postoperative surveillance period.
Prospective surveillance for infection following hip and knee arthroplasty was conducted by hospitals participating in the Canadian Nosocomial Infection Surveillance Program (CNISP) using standard surveillance definitions.
Overall hip and knee PJI rates were 1.64 and 1.52 per 100 procedures, respectively. Deep incisional and organ-space hip and knee PJI rates were 0.96 and 0.71, respectively. In total, 93% of hip PJIs and 92% of knee PJIs were identified within 90 days, with a median time to detection of 21 days. However, 11%–16% of deep incisional and organ-space infections were not detected within 90 days. This rate was reduced to 3%–4% at 180 days post procedure. Anaerobic and polymicrobial infections had the shortest median time from procedure to detection (17 and 18 days, respectively) compared with infections due to other microorganisms, including Staphylococcus aureus.
PJI rates were similar to those reported elsewhere, although differences in national surveillance systems limit direct comparisons. Our results suggest that a postoperative surveillance period of 90 days will detect the majority of PJIs; however, up to 16% of deep incisional and organ-space infections may be missed. Extending the surveillance period to 180 days could allow for a better estimate of disease burden.
Objectives: White matter (WM) integrity within the mesial temporal lobe (MTL) is important for episodic memory (EM) functioning. The current study investigated the ability of diffusion tensor imaging (DTI) in MTL WM tracts to predict 3-year changes in EM performance in healthy elders at disproportionately higher genetic risk for Alzheimer’s disease (AD). Methods: Fifty-one cognitively intact elders (52% with family history (FH) of dementia and 33% possessing an Apolipoprotein E ε4 allelle) were administered the Rey Auditory Verbal Learning Test (RAVLT) at study entry and at 3-year follow-up. DTI scanning, conducted at study entry, examined fractional anisotropy and mean, radial and axial diffusion within three MTL WM tracts: uncinate fasciculus (UNC), cingulate-hippocampal (CHG), and fornix-stria terminalis (FxS). Correlations were performed between residualized change scores computed from RAVLT trials 1–5, immediate recall, and delayed recall scores and baseline DTI measures; MTL gray matter (GM) and WM volumes; demographics; and AD genetic and metabolic risk factors. Results: Higher MTL mean and axial diffusivity at baseline significantly predicted 3-year changes in EM, whereas baseline MTL GM and WM volumes, FH, and metabolic risk factors did not. Both ε4 status and DTI correlated with change in immediate recall. Conclusions: Longitudinal EM changes in cognitively intact, healthy elders can be predicted by disruption of the MTL WM microstructure. These results are derived from a sample with a disproportionately higher genetic risk for AD, suggesting that the observed WM disruption in MTL pathways may be related to early neuropathological changes associated with the preclinical stage of AD. (JINS, 2016, 22, 1005–1015)
The concepts of nature, culture and heritage are deeply entwined; their threads run together in some of our finest museums, in accounts of exploration and discovery, in the work of artists, poets andwriters, and in areas that are cherished and protected because of their landscapes and wildlife. The conservation ethic - placing a value on the natural environment - lies at the heart of the notion of "natural heritage", but we need to question how those values originated, were consolidated and ultimately moulded and changed over time. In a contemporary context the connections between nature andculture have sometimes become lost, fragmented, dislocated or misunderstood; where did "natural heritage" begin and how do we engage with the idea of "nature" today? The essays collected here re-evaluate the role of culture in developing the concept of natural heritage, reflecting on the shifts in its interpretation over the last 300 years.
Contributors: Martin Holdgate, Marie Addyman,E. Charles Nelson, Darrell Smith, Andrew Ramsey, Viktor Kouloumpis, Richard Milner, Gina Douglas, Penny Bradshaw, Arthur MacGregor, Chiara Nepi, Hannah Paddon, Stephen Hewitt, Gordon McGregor Reid, Ghillean T Prance, Peter Davis, Christopher Donaldson, Lucy McRobert, Sophie Darlington, Keith Scholey, Paul A. Roncken, Angus Lunn, Juliet Clutton-Brock, Tim Sands, Robert A. Lambert, James Champion,Erwin van Maanen, Heather Prince, Chris Loynes, Julie Taylor, Sarah Elmeligi, Samantha Finn, Owen Nevin, Jared Bowers, Kate Hennessy, Natasha Lyons, Mike Jeffries.