Arctic air pollution: Challenges and opportunities for the next decade

The Arctic is a sentinel of global change. This region is influenced by multiple physical and socio-economic drivers and feedbacks, impacting both the natural and human environment. Air pollution is one such driver that impacts Arctic climate change, ecosystems and health but significant uncertainties still surround quantification of these effects. Arctic air pollution includes harmful trace gases (e.g. tropospheric ozone) and particles (e.g. black carbon, sulphate) and toxic substances (e.g. polycyclic aromatic hydrocarbons) that can be transported to the Arctic from emission sources located far outside the region, or emitted within the Arctic from activities including shipping, power production, and other industrial activities. This paper qualitatively summarizes the complex science issues motivating the creation of a new international initiative, PACES (air Pollution in the Arctic: Climate, Environment and Societies). Approaches for coordinated, international and interdisciplinary research on this topic are described with the goal to improve predictive capability via new understanding about sources, processes, feedbacks and impacts of Arctic air pollution. Overarching research actions are outlined, in which we describe our recommendations for 1) the development of trans-disciplinary approaches combining social and economic research with investigation of the chemical and physical aspects of Arctic air pollution; 2) increasing the quality and quantity of observations in the Arctic using long-term monitoring and intensive field studies, both at the surface and throughout the troposphere; and 3) developing improved predictive capability across a range of spatial and temporal scales

Identifying component modules

A computer-based system for modelling component dependencies and identifying component modules is presented. A variation of the Dependency Structure Matrix (DSM) representation was used to model component dependencies. The system utilises a two-stage approach towards facilitating the identification of a hierarchical modular structure. The first stage calculates a value for a clustering criterion that may be used to group component dependencies together. A Genetic Algorithm is described to optimise the order of the components within the DSM with the focus of minimising the value of the clustering criterion to identify the most significant component groupings (modules) within the product structure. The second stage utilises a 'Module Strength Indicator' (MSI) function to determine a value representative of the degree of modularity of the component groupings. The application of this function to the DSM produces a 'Module Structure Matrix' (MSM) depicting the relative modularity of available component groupings within it. The approach enabled the identification of hierarchical modularity in the product structure without the requirement for any additional domain specific knowledge within the system. The system supports design by providing mechanisms to explicitly represent and utilise component and dependency knowledge to facilitate the nontrivial task of determining near-optimal component modules and representing product modularity

Effects Of Length, Complexity, And Grammatical Correctness On Stuttering In Spanish-Speaking Preschool Children

Purpose: To explore the effects of utterance length, syntactic complexity, and grammatical correctness on stuttering in the spontaneous speech of young, monolingual Spanish-speaking children. Method: Spontaneous speech samples of 11 monolingual Spanish-speaking children who stuttered, ages 35 to 70 months, were examined. Mean number of syllables, total number of clauses, utterance complexity (i.e., containing no clauses, simple clauses, or subordinate and/or conjoined clauses), and grammatical correctness (i.e., the presence or absence of morphological and syntactical errors) in stuttered and fluent utterances were compared. Results: Findings revealed that stuttered utterances in Spanish tended to be longer and more often grammatically incorrect, and contain more clauses, including more subordinate and/or conjoined clauses. However, when controlling for the interrelatedness of syllable number and clause number and complexity, only utterance length and grammatical incorrectness were significant predictors of stuttering in the spontaneous speech of these Spanish-speaking children. Use of complex utterances did not appear to contribute to the prediction of stuttering when controlling for utterance length. Conclusions: Results from the present study were consistent with many earlier reports of English-speaking children. Both length and grammatical factors appear to affect stuttering in Spanish-speaking children. Grammatical errors, however, served as the greatest predictor of stuttering.Communication Sciences and Disorder

Precision health: A nursing perspective

Precision health refers to personalized healthcare based on a person's unique genetic, genomic, or omic composition within the context of lifestyle, social, economic, cultural and environmental influences to help individuals achieve well-being and optimal health. Precision health utilizes big data sets that combine omics (i.e. genomic sequence, protein, metabolite, and microbiome information) with clinical information and health outcomes to optimize disease diagnosis, treatment and prevention specific to each patient. Successful implementation of precision health requires interprofessional collaboration, community outreach efforts, and coordination of care, a mission that nurses are well-positioned to lead. Despite the surge of interest and attention to precision health, most nurses are not well-versed in precision health or its implications for the nursing profession. Based on a critical analysis of literature and expert opinions, this paper provides an overview of precision health and the importance of engaging the nursing profession for its implementation. Other topics reviewed in this paper include big data and omics, information science, integration of family health history in precision health, and nursing omics research in symptom science. The paper concludes with recommendations for nurse leaders in research, education, clinical practice, nursing administration and policy settings for which to develop strategic plans to implement precision health

Biomarkers as Common Data Elements for Symptom and Selfâ Management Science

PurposeBiomarkers as common data elements (CDEs) are important for the characterization of biobehavioral symptoms given that once a biologic moderator or mediator is identified, biologically based strategies can be investigated for treatment efforts. Just as a symptom inventory reflects a symptom experience, a biomarker is an indicator of the symptom, though not the symptom per se. The purposes of this position paper are to (a) identify a â minimum setâ of biomarkers for consideration as CDEs in symptom and selfâ management science, specifically biochemical biomarkers; (b) evaluate the benefits and limitations of such a limited array of biomarkers with implications for symptom science; (c) propose a strategy for the collection of the endorsed minimum set of biologic samples to be employed as CDEs for symptom science; and (d) conceptualize this minimum set of biomarkers consistent with National Institute of Nursing Research (NINR) symptoms of fatigue, depression, cognition, pain, and sleep disturbance.Design and MethodsFrom May 2016 through January 2017, a working group consisting of a subset of the Directors of the NINR Centers of Excellence funded by P20 or P30 mechanisms and NINR staff met bimonthly via telephone to develop this position paper suggesting the addition of biomarkers as CDEs. The full group of Directors reviewed drafts, provided critiques and suggestions, recommended the minimum set of biomarkers, and approved the completed document. Best practices for selecting, identifying, and using biological CDEs as well as challenges to the use of biological CDEs for symptom and selfâ management science are described. Current platforms for sample outcome sharing are presented. Finally, biological CDEs for symptom and selfâ management science are proposed along with implications for future research and use of CDEs in these areas.FindingsThe recommended minimum set of biomarker CDEs include proâ and antiâ inflammatory cytokines, a hypothalamicâ pituitaryâ adrenal axis marker, cortisol, the neuropeptide brainâ derived neurotrophic factor, and DNA polymorphisms.ConclusionsIt is anticipated that this minimum set of biomarker CDEs will be refined as knowledge regarding biologic mechanisms underlying symptom and selfâ management science further develop. The incorporation of biological CDEs may provide insights into mechanisms of symptoms, effectiveness of proposed interventions, and applicability of chosen theoretical frameworks. Similarly, as for the previously suggested NINR CDEs for behavioral symptoms and selfâ management of chronic conditions, biological CDEs offer the potential for collaborative efforts that will strengthen symptom and selfâ management science.Clinical RelevanceThe use of biomarker CDEs in biobehavioral symptoms research will facilitate the reproducibility and generalizability of research findings and benefit symptom and selfâ management science.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143764/1/jnu12378.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/143764/2/jnu12378_am.pd

Tryptophan degradation in women with breast cancer: a pilot study

<p>Abstract</p> <p>Background</p> <p>Altered tryptophan metabolism and indoleamine 2,3-dioxygenase activity are linked to cancer development and progression. In addition, these biological factors have been associated with the development and severity of neuropsychiatric syndromes, including major depressive disorder. However, this biological mechanism associated with both poor disease outcomes and adverse neuropsychiatric symptoms has received little attention in women with breast cancer. Therefore, a pilot study was undertaken to compare levels of tryptophan and other proteins involved in tryptophan degradation in women with breast cancer to women without cancer, and secondarily, to examine levels in women with breast caner over the course of chemotherapy.</p> <p>Findings</p> <p>Blood samples were collected from women with a recent diagnosis of breast cancer (<it>n </it>= 33) before their first cycle of chemotherapy and after their last cycle of chemotherapy. The comparison group (<it>n </it>= 24) provided a blood sample prior to breast biopsy. Plasma concentrations of tryptophan, kynurenine, and tyrosine were determined. The kynurenine to tryptophan ratio (KYN/TRP) was used to estimate indoleamine 2,3-dioxygenase activity. On average, the women with breast cancer had lower levels of tryptophan, elevated levels of kynurenine and tyrosine and an increased KYN/TRP ratio compared to women without breast cancer. There was a statistically significant difference between the two groups in the KYN/TRP ratio (<it>p </it>= 0.036), which remained elevated in women with breast cancer throughout the treatment trajectory.</p> <p>Conclusions</p> <p>The findings of this pilot study suggest that increased tryptophan degradation may occur in women with early-stage breast cancer. Given the multifactorial consequences of increased tryptophan degradation in cancer outcomes and neuropsychiatric symptom manifestation, this biological mechanism deserves broader attention in women with breast cancer.</p

Using and Reporting the Delphi Method for Selecting Healthcare Quality Indicators: A Systematic Review

OBJECTIVE: Delphi technique is a structured process commonly used to developed healthcare quality indicators, but there is a little recommendation for researchers who wish to use it. This study aimed 1) to describe reporting of the Delphi method to develop quality indicators, 2) to discuss specific methodological skills for quality indicators selection 3) to give guidance about this practice. METHODOLOGY AND MAIN FINDING: Three electronic data bases were searched over a 30 years period (1978-2009). All articles that used the Delphi method to select quality indicators were identified. A standardized data extraction form was developed. Four domains (questionnaire preparation, expert panel, progress of the survey and Delphi results) were assessed. Of 80 included studies, quality of reporting varied significantly between items (9% for year's number of experience of the experts to 98% for the type of Delphi used). Reporting of methodological aspects needed to evaluate the reliability of the survey was insufficient: only 39% (31/80) of studies reported response rates for all rounds, 60% (48/80) that feedback was given between rounds, 77% (62/80) the method used to achieve consensus and 57% (48/80) listed quality indicators selected at the end of the survey. A modified Delphi procedure was used in 49/78 (63%) with a physical meeting of the panel members, usually between Delphi rounds. Median number of panel members was 17(Q1:11; Q3:31). In 40/70 (57%) studies, the panel included multiple stakeholders, who were healthcare professionals in 95% (38/40) of cases. Among 75 studies describing criteria to select quality indicators, 28 (37%) used validity and 17(23%) feasibility. CONCLUSION: The use and reporting of the Delphi method for quality indicators selection need to be improved. We provide some guidance to the investigators to improve the using and reporting of the method in future surveys

Internet-based guided self-help for glioma patients with depressive symptoms: a randomized controlled trial

Depressive symptoms are common in glioma patients, and can negatively affect health-related quality of life (HRQOL). We performed a nation-wide randomized controlled trial to evaluate the effects of an online guided self-help intervention for depressive symptoms in adult glioma patients. Glioma patients with depressive symptoms were randomized to a 5-week online course based on problem-solving therapy, or a waiting list control group. After having received the intervention, the glioma patient groups combined were compared with patients with cancer outside the central nervous system (non-CNS cancer controls), who also received the intervention. Sample size calculations yielded 63 participants to be recruited per arm. The primary outcome [depressive symptoms (CES-D)] and secondary outcomes [fatigue (Checklist Individual Strength (CIS)) and HRQOL (Short Form-36)], were assessed online at baseline, post-intervention, and 3 and 12 months follow-up. In total, 89 glioma patients (intervention N = 45; waiting list N = 44) and 26 non-CNS cancer controls were included, of whom 35 and 54% completed the intervention, respectively. Recruitment could not be extended beyond 3.5 years due to funding. On depression, no statistically significant differences between the groups were found. Fatigue decreased post-treatment in the glioma intervention group compared with the waiting list group (p = 0.054, d = 0.306). At 12 months, the physical component summary (HRQOL) remained stable in glioma patients, while scores improved in non-CNS cancer controls (p = 0.035, d = 0.883). In this underpowered study, no evidence for the effectiveness of online guided self-help for depression or HRQOL in glioma patients was found, but it may improve fatigue