Fifteen-minute consultation: Not the whole story-considering children's spirituality and advance care planning.

In a society of diverse views, faiths and beliefs, what can paediatric palliative care contribute to our understanding of children's spirituality? By failing to recognise and respond to their spirituality in this work, we risk missing something of profound importance to children and their families. We overlook their search for wholeness in the absence of cure and fall short of offering truly holistic care. This paper explores how developments in advance care planning and related documentation are addressing these issues. Since children's spirituality is elusive and rarely explored in practice, it aims to clarify our understanding of it with a variety of examples and contains suggestions for hearing the voice of the child amid the needs of parents and professionals.

Individual and demographic consequences of reduced body condition following repeated exposure to high temperatures.

Although the lethal consequences of extreme heat are increasingly reported in the literature, the fitness costs of exposure to sublethal high air temperatures, typically identified in the 30-40 degrees C range, are poorly understood. We examine the effect of high (> or = 35 degrees C) daily maxima on body condition of a semiarid population of White-plumed Honeyeaters, Ptilotula penicillatus, monitored between 1986 and 2012. During this 26-yr period, temperature has risen, on average, by 0.06 degrees C each year at the site, the frequency of days with thermal maxima > or = 35 degrees C has increased and rainfall has declined. Exposure to high temperatures affected body condition of White-plumed Honeyeaters, but only in low-rainfall conditions. There was no effect of a single day of exposure to temperatures > or = 35 degrees C but repeated exposure was associated with reduced body condition: 3.0% reduction in body mass per day of exposure. Rainfall in the previous 30 d ameliorated these effects, with reduced condition evident only in dry conditions. Heat-exposed males with reduced body condition were less likely to be recaptured at the start of the following spring; they presumably died. Heat-exposed females, regardless of body condition, showed lower survival than exposed males, possibly due to their smaller body mass. The higher mortality of females and smaller males exposed to temperatures > or = 35 degrees C may have contributed to the increase in mean body size of this population over 23 years. Annual survival declined across time concomitant with increasing frequency of days > or = 35 degrees C and decreasing rainfall. Our study is one of few to identify a proximate cause of climate change related mortality, and associated long-term demographic consequence. Our results have broad implications for avian communities living in arid and semiarid regions of Australia, and other mid-latitudes regions where daily maximum temperatures already approach physiological limits in regions affected by both decreased precipitation and warming.

Dynamic size responses to climate change: prevailing effects of rising temperature drive long-term body size increases in a semi-arid passerine.

Changes in animal body size have been widely reported as a correlate of contemporary climate change. Body size affects metabolism and fitness, so changing size has implications for resilience, yet the climatic factors that drive size variation remain poorly understood. We test the role of mean and extreme temperature, rainfall, and remotely sensed primary productivity (NDVI) as drivers of body size in a sedentary, semi-arid Australian passerine, Ptilotula (Lichenostomus)penicillatus, over 23 years. To distinguish effects due to differential growth from changes in population composition, we analysed first-year birds and adults separately and considered climatic variation at three temporal scales (current, previous, and preceding 5 years). The strongest effects related to temperature: in both age classes, larger size was associated with warmer mean temperatures in the previous year, contrary to Bergmann's Rule. Moreover, adults were larger in warmer breeding seasons, while first years was larger after heat waves; these effects are more likely to be mediated through size-dependent mortality, highlighting the role of body size in determining vulnerability to extinction. In addition to temperature, larger adult size was associated with lower primary productivity, which may reflect a trade-off between vegetative growth and nectar production, on which adults rely. Finally, lower rainfall was associated with decreasing size in first year and adults, most likely related to decreased food availability. Overall,body size increased over 23 years, strongly in first-year birds (2.7%) compared with adults (1%), with size outcomes a balance between competing drivers. As rainfall declined over time and productivity remained fairly stable, the temporal increase in body size appears largely driven by rising mean temperature and temperature extremes. Body size responses to environmental change are thus complex and dynamic, driven by effects on growth as well as mortality.

Translating building information modeling to building energy modeling using model view definition.

This paper presents a new approach to translate between Building Information Modeling (BIM) and Building Energy Modeling (BEM) that uses Modelica, an object-oriented declarative, equation-based simulation environment. The approach (BIM2BEM) has been developed using a data modeling method to enable seamless model translations of building geometry, materials, and topology. Using data modeling, we created a Model View Definition (MVD) consisting of a process model and a class diagram. The process model demonstrates object-mapping between BIM and Modelica-based BEM (ModelicaBEM) and facilitates the definition of required information during model translations. The class diagram represents the information and object relationships to produce a class package intermediate between the BIM and BEM. The implementation of the intermediate class package enables system interface (Revit2Modelica) development for automatic BIM data translation into ModelicaBEM. In order to demonstrate and validate our approach, simulation result comparisons have been conducted via three test cases using (1) the BIM-based Modelica models generated from Revit2Modelica and (2) BEM models manually created using LBNL Modelica Buildings library. Our implementation shows that BIM2BEM (1) enables BIM models to be translated into ModelicaBEM models, (2) enables system interface development based on the MVD for thermal simulation, and (3) facilitates the reuse of original BIM data into building energy simulation without an import/export process.

Improved prediction of prostate cancer recurrence through systems pathology.

We have developed an integrated, multidisciplinary methodology, termed systems pathology, to generate highly accurate predictive tools for complex diseases, using prostate cancer for the prototype. To predict the recurrence of prostate cancer following radical prostatectomy, defined by rising serum prostate-specific antigen (PSA), we used machine learning to develop a model based on clinicopathologic variables, histologic tumor characteristics, and cell type-specific quantification of biomarkers. The initial study was based on a cohort of 323 patients and identified that high levels of the androgen receptor, as detected by immunohistochemistry, were associated with a reduced time to PSA recurrence. The model predicted recurrence with high accuracy, as indicated by a concordance index in the validation set of 0.82, sensitivity of 96%, and specificity of 72%. We extended this approach, employing quantitative multiplex immunofluorescence, on an expanded cohort of 682 patients. The model again predicted PSA recurrence with high accuracy, concordance index being 0.77, sensitivity of 77% and specificity of 72%. The androgen receptor was selected, along with 5 clinicopathologic features (seminal vesicle invasion, biopsy Gleason score, extracapsular extension, preoperative PSA, and dominant prostatectomy Gleason grade) as well as 2 histologic features (texture of epithelial nuclei and cytoplasm in tumor only regions). This robust platform has broad applications in patient diagnosis, treatment management, and prognostication.

Nonverbal neurodevelopmental dysfunctions.

Although perhaps not as well-studied or well-known as other learning disorders, the possible academic and social sequelae of math, writing, motor, or visuospatial dysfunction, as well as difficulties with nonverbal processing and understanding, are readily identifiable and worthy of identification. While pediatricians should not feel compelled to attempt to identify all of the variables of these difficulties, an understanding of them will allow pediatricians to maintain their role as an advocate for patients and families and to feel comfortable communicating with others who are equally dedicated to children.

Systems pathology approach for the prediction of prostate cancer progression after radical prostatectomy.

PURPOSE: For patients with prostate cancer treated by radical prostatectomy, no current personalized tools predict clinical failure (CF; metastasis and/or androgen-independent disease). We developed such a tool through integration of clinicopathologic data with image analysis and quantitative immunofluorescence of prostate cancer tissue. PATIENTS AND METHODS: A prospectively designed algorithm was applied retrospectively to a cohort of 758 patients with clinically localized or locally advanced prostate cancer. A model predicting distant metastasis and/or androgen-independent recurrence was derived from features selected through supervised multivariate learning. Performance of the model was estimated using the concordance index (CI). RESULTS: We developed a predictive model using a training set of 373 patients with 33 CF events. The model includes androgen receptor (AR) levels, dominant prostatectomy Gleason grade, lymph node involvement, and three quantitative characteristics from hematoxylin and eosin staining of prostate tissue. The model had a CI of 0.92, sensitivity of 90%, and specificity of 91% for predicting CF within 5 years after prostatectomy. Model validation on an independent cohort of 385 patients with 29 CF events yielded a CI of 0.84, sensitivity of 84%, and specificity of 85%. High levels of AR predicted shorter time to castrate prostate-specific antigen increase after androgen deprivation therapy (ADT). CONCLUSION: The integration of clinicopathologic variables with imaging and biomarker data (systems pathology) resulted in a highly accurate tool for predicting CF within 5 years after prostatectomy. The data support a role for AR signaling in clinical progression and duration of response to ADT.