Proton NMR studies of the electronic structure of ZrH/sub x/

The proton spin lattice relaxation times and Knight shifts were measured in f.c.c. (delta-phase) and f.c.t. (epsilon-phase) ZrH/sub x/ for 1.5 or = to x or = to 2.0. Both parameters indicate that N(E/sub F/) is very dependent upon hydrogen content with a maximum occurring at ZrH1 83. This behavior is ascribed to modifications in N(E/sub F/) through a fcc/fct distortion in ZrH/sub x/ associated with a Jahn-Teller effect

Optimal partial-arcs in VMAT treatment planning

Purpose: To improve the delivery efficiency of VMAT by extending the recently published VMAT treatment planning algorithm vmerge to automatically generate optimal partial-arc plans. Methods and materials: A high-quality initial plan is created by solving a convex multicriteria optimization problem using 180 equi-spaced beams. This initial plan is used to form a set of dose constraints, and a set of partial-arc plans is created by searching the space of all possible partial-arc plans that satisfy these constraints. For each partial-arc, an iterative fluence map merging and sequencing algorithm (vmerge) is used to improve the delivery efficiency. Merging continues as long as the dose quality is maintained above a user-defined threshold. The final plan is selected as the partial arc with the lowest treatment time. The complete algorithm is called pmerge. Results: Partial-arc plans are created using pmerge for a lung, liver and prostate case, with final treatment times of 127, 245 and 147 seconds. Treatment times using full arcs with vmerge are 211, 357 and 178 seconds. Dose quality is maintained across the initial, vmerge, and pmerge plans to within 5% of the mean doses to the critical organs-at-risk and with target coverage above 98%. Additionally, we find that the angular distribution of fluence in the initial plans is predictive of the start and end angles of the optimal partial-arc. Conclusions: The pmerge algorithm is an extension to vmerge that automatically finds the partial-arc plan that minimizes the treatment time. VMAT delivery efficiency can be improved by employing partial-arcs without compromising dose quality. Partial arcs are most applicable to cases with non-centralized targets, where the time savings is greatest

The physical activity experiences of men with serious mental illness: Three short stories

Objectives: Although a considerable amount of research has explored the effects of physical activity on mental health, the voices of people with mental illness have been largely excluded from published reports. Through this study we aim to foreground service users' voices in order to shed light on the personal and subjective nature of the relationship between physical activity and serious mental illness (SMI). Methods: An interpretive case study approach was used to explore in depth the physical activity experiences of three men with SMI. Creative analytic practice was used to write three creative non-fictions which, as first-person narratives, foreground the participants' voices. Results: We present three short stories in an effort to communicate participants' personal and subjective experiences of physical activity in an accessible, engaging, and evocative manner. We hope to: (i) provide potentially motivating physical activity success stories for others who live with SMI; (ii) increase awareness among mental health professionals of the possibilities of physical activity; and (iii) provide an empathetic understanding of possibilities and problems of living with SMI which may help challenge the stigma surrounding mental illness. Conclusions: For us, the stories communicate the diversity and difference inherent in the ways men with SMI experience physical activity. We reflect on how the short story form allows these differences to be preserved and respected. We resist making further interpretations of the stories preferring instead to encourage the reader to form her or his own conclusions. © 2007 Elsevier Ltd. All rights reserved

Glucose enhancement of memory is modulated by trait anxiety in healthy adolescent males

Glucose administration is associated with memory enhancement in healthy young individuals under conditions of divided attention at encoding. While the specific neurocognitive mechanisms underlying this ‘glucose memory facilitation effect’ are currently uncertain, it is thought that individual differences in glucoregulatory efficiency may alter an individual’s sensitivity to the glucose memory facilitation effect. In the present study, we sought to investigate whether basal hypothalamic–pituitary–adrenal axis function (itself a modulator of glucoregulatory efficiency), baseline self-reported stress and trait anxiety influence the glucose memory facilitation effect. Adolescent males (age range = 14–17 years) were administered glucose and placebo prior to completing a verbal episodic memory task on two separate testing days in a counter-balanced, within-subjects design. Glucose ingestion improved verbal episodic memory performance when memory recall was tested (i) within an hour of glucose ingestion and encoding, and (ii) one week subsequent to glucose ingestion and encoding. Basal hypothalamic–pituitary–adrenal axis function did not appear to influence the glucose memory facilitation effect; however, glucose ingestion only improved memory in participants reporting relatively higher trait anxiety. These findings suggest that the glucose memory facilitation effect may be mediated by biological mechanisms associated with trait anxiety

Asynchronous food-web pathways could buffer the response of Serengeti predators to El Niño southern oscillation

Understanding how entire ecosystems maintain stability in the face of climatic and human disturbance is one of the most fundamental challenges in ecology. Theory suggests that a crucial factor determining the degree of ecosystem stability is simply the degree of synchrony with which different species in ecological food webs respond to environmental stochasticity. Ecosystems in which all food-web pathways are affected similarly by external disturbance should amplify variability in top carnivore abundance over time due to population interactions, whereas ecosystems in which a large fraction of pathways are nonresponsive or even inversely responsive to external disturbance will have more constant levels of abundance at upper trophic levels. To test the mechanism underlying this hypothesis, we used over half a century of demographic data for multiple species in the Serengeti (Tanzania) ecosystem to measure the degree of synchrony to variation imposed by an external environmental driver, the El Niño Southern Oscillation (ENSO). ENSO effects were mediated largely via changes in dry-season vs. wet-season rainfall and consequent changes in vegetation availability, propagating via bottom-up effects to higher levels of the Serengeti food web to influence herbivores, predators and parasites. Some species in the Serengeti food web responded to the influence of ENSO in opposite ways, whereas other species were insensitive to variation in ENSO. Although far from conclusive, our results suggest that a diffuse mixture of herbivore responses could help buffer top carnivores, such as Serengeti lions, from variability in climate. Future global climate changes that favor some pathways over others, however, could alter the effectiveness of such processes in the future

A framework for applying natural language processing in digital health interventions

BACKGROUND: Digital health interventions (DHIs) are poised to reduce target symptoms in a scalable, affordable, and empirically supported way. DHIs that involve coaching or clinical support often collect text data from 2 sources: (1) open correspondence between users and the trained practitioners supporting them through a messaging system and (2) text data recorded during the intervention by users, such as diary entries. Natural language processing (NLP) offers methods for analyzing text, augmenting the understanding of intervention effects, and informing therapeutic decision making. OBJECTIVE: This study aimed to present a technical framework that supports the automated analysis of both types of text data often present in DHIs. This framework generates text features and helps to build statistical models to predict target variables, including user engagement, symptom change, and therapeutic outcomes. METHODS: We first discussed various NLP techniques and demonstrated how they are implemented in the presented framework. We then applied the framework in a case study of the Healthy Body Image Program, a Web-based intervention trial for eating disorders (EDs). A total of 372 participants who screened positive for an ED received a DHI aimed at reducing ED psychopathology (including binge eating and purging behaviors) and improving body image. These users generated 37,228 intervention text snippets and exchanged 4285 user-coach messages, which were analyzed using the proposed model. RESULTS: We applied the framework to predict binge eating behavior, resulting in an area under the curve between 0.57 (when applied to new users) and 0.72 (when applied to new symptom reports of known users). In addition, initial evidence indicated that specific text features predicted the therapeutic outcome of reducing ED symptoms. CONCLUSIONS: The case study demonstrates the usefulness of a structured approach to text data analytics. NLP techniques improve the prediction of symptom changes in DHIs. We present a technical framework that can be easily applied in other clinical trials and clinical presentations and encourage other groups to apply the framework in similar contexts