Monitoring Alaskan Arctic shelf ecosystems through collaborative observation networks

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Danielson, S. L., Grebmeier, J. M., Iken, K., Berchok, C., Britt, L., Dunton, K. H., Eisner, L., V. Farley, E., Fujiwara, A., Hauser, D. D. W., Itoh, M., Kikuchi, T., Kotwicki, S., Kuletz, K. J., Mordy, C. W., Nishino, S., Peralta-Ferriz, C., Pickart, R. S., Stabeno, P. S., Stafford. K. M., Whiting, A. V., & Woodgate, R. Monitoring Alaskan Arctic shelf ecosystems through collaborative observation networks. Oceanography, 35(2), (2022): 52, https://doi.org/10.5670/oceanog.2022.119.Ongoing scientific programs that monitor marine environmental and ecological systems and changes comprise an informal but collaborative, information-rich, and spatially extensive network for the Alaskan Arctic continental shelves. Such programs reflect contributions and priorities of regional, national, and international funding agencies, as well as private donors and communities. These science programs are operated by a variety of local, regional, state, and national agencies, and academic, Tribal, for-profit, and nongovernmental nonprofit entities. Efforts include research ship and autonomous vehicle surveys, year-long mooring deployments, and observations from coastal communities. Inter-program coordination allows cost-effective leveraging of field logistics and collected data into value-added information that fosters new insights unattainable by any single program operating alone. Coordination occurs at many levels, from discussions at marine mammal co-management meetings and interagency meetings to scientific symposia and data workshops. Together, the efforts represented by this collection of loosely linked long-term monitoring programs enable a biologically focused scientific foundation for understanding ecosystem responses to warming water temperatures and declining Arctic sea ice. Here, we introduce a variety of currently active monitoring efforts in the Alaskan Arctic marine realm that exemplify the above attributes.Funding sources include the following: ALTIMA: BOEM M09PG00016, M12PG00021, and M13PG00026; AMBON: NOPP-NA14NOS0120158 and NOPP-NA19NOS0120198; Bering Strait moorings: NSF-OPP-AON-PLR-1758565, NSF-OPP-PLR-1107106; BLE-LTER: NSF-OPP-1656026; CEO: NPRB-L36, ONR N000141712274 and N000142012413; DBO: NSF-AON-1917469 and NOAA-ARP CINAR-22309.07; HFR, AOOS Arctic glider, and Passive Acoustics at CEO and Bering Strait: NA16NOS0120027; WABC: NSF-OPP-1733564. JAMSTEC: partial support by ArCS Project JPMXD1300000000 and ArCS II Project JPMXD1420318865; Seabird surveys: BOEM M17PG00017, M17PG00039, and M10PG00050, and NPRB grants 637, B64, and B67. This publication was partially funded by the Cooperative Institute for Climate, Ocean, & Ecosystem Studies (CICOES) under NOAA Cooperative Agreement NA20OAR4320271, and represents contribution 2021-1163 to CICOES, EcoFOCI-1026, and 5315 to PMEL. This is NPRB publication ArcticIERP-43

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

(Presentation Unavailable) Integrating Technology & Content to Engage Students

Students are digitally connected. Keep them connected in your course with eInstruction\u27s Classroom Performance Systems, ExamView and the MOBI. Rarely is a technology tool so easily implemented and so broadly applicable. Come and experience for yourself the variety of strategies that are being used in classrooms to capture not only the student\u27s scores but also their attention and interest. This session will look at what the research shows about using student response systems as an instructional tool in your course and practical uses of student response systems to guide instruction through formative assessment, start or focus discussion, require interaction with peers, survey opinions before and after debate and other instructional strategies

Predictors of primary care referrals to a vascular disease prevention lifestyle program among participants in a cluster randomised trial

Background\ud Cardiovascular disease accounts for a large burden of disease, but is amenable to prevention through lifestyle modification. This paper examines patient and practice predictors of referral to a lifestyle modification program (LMP) offered as part of a cluster randomised controlled trial (RCT) of prevention of vascular disease in primary care.\ud \ud Methods\ud Data from the intervention arm of a cluster RCT which recruited 36 practices through two rural and three urban primary care organisations were used. In each practice, 160 eligible high risk patients were invited to participate. Practices were randomly allocated to intervention or control groups. Intervention practice staff were trained in screening, motivational interviewing and counselling and encouraged to refer high risk patients to a LMP involving individual and group sessions. Data include patient surveys; clinical audit; practice survey on capacity for preventive care; referral records from the LMP. Predictors of referral were examined using multi-level logistic regression modelling after adjustment for confounding factors.\ud \ud Results\ud Of 301 eligible patients, 190 (63.1%) were referred to the LMP. Independent predictors of referral were baseline BMI ≥ 25 (OR 2.87 95%CI:1.10, 7.47), physical inactivity (OR 2.90 95%CI:1.36,6.14), contemplation/preparation/action stage of change for physical activity (OR 2.75 95%CI:1.07, 7.03), rural location (OR 12.50 95%CI:1.43, 109.7) and smaller practice size (1–3 GPs) (OR 16.05 95%CI:2.74, 94.24).\ud \ud Conclusions\ud Providing a well-structured evidence-based lifestyle intervention, free of charge to patients, with coordination and support for referral processes resulted in over 60% of participating high risk patients being referred for disease prevention. Contrary to expectations, referrals were more frequent from rural and smaller practices suggesting that these practices may be more ready to engage with these programs

Health improvement and prevention study (HIPS) - evaluation of an intervention to prevent vascular disease in general practice

<p>Abstract</p> <p>Background</p> <p>The Health Improvement and Prevention Study (HIPS) study aims to evaluate the capacity of general practice to identify patients at high risk for developing vascular disease and to reduce their risk of vascular disease and diabetes through behavioural interventions delivered in general practice and by the local primary care organization.</p> <p>Methods/Design</p> <p>HIPS is a stratified randomized controlled trial involving 30 general practices in NSW, Australia. Practices are randomly allocated to an 'intervention' or 'control' group. General practitioners (GPs) and practice nurses (PNs) are offered training in lifestyle counselling and motivational interviewing as well as practice visits and patient educational resources. Patients enrolled in the trial present for a health check in which the GP and PN provide brief lifestyle counselling based on the 5As model (ask, assess, advise, assist, and arrange) and refer high risk patients to a diet education and physical activity program. The program consists of two individual visits with a dietician or exercise physiologist and four group sessions, after which patients are followed up by the GP or PN. In each practice 160 eligible patients aged between 40 and 64 years are invited to participate in the study, with the expectation that 40 will be eligible and willing to participate. Evaluation data collection consists of (1) a practice questionnaire, (2) GP and PN questionnaires to assess preventive care attitudes and practices, (3) patient questionnaire to assess self-reported lifestyle behaviours and readiness to change, (4) physical assessment including weight, height, body mass index (BMI), waist circumference and blood pressure, (5) a fasting blood test for glucose and lipids, (6) a clinical record audit, and (7) qualitative data collection. All measures are collected at baseline and 12 months except the patient questionnaire which is also collected at 6 months. Study outcomes before and after the intervention is compared between intervention and control groups after adjusting for baseline differences and clustering at the level of the practice.</p> <p>Discussion</p> <p>This study will provide evidence of the effectiveness of a primary care intervention to reduce the risk of cardiovascular disease and diabetes in general practice patients. It will inform current policies and programs designed to prevent these conditions in Australian primary health care.</p> <p>Trial Registration</p> <p>ACTRN12607000423415</p

Trends in GP prescribing of psychotropic medications among young patients aged 16-24 years: A case study analysis

Background: Current clinical guidelines recommend non-pharmacological interventions as first-line treatments for young patients aged 16-24 years with a mental health condition (MHC). However, several studies have noted increasing trends in psychotropic prescribing for this age group, especially in antidepressant prescribing. In Australia, the vast majority of psychotropic medications prescribed to young people come from the general practice setting. To assess whether Australian General Practitioners (GPs) are prescribing in accordance with clinical guideline recommendations, this study examined trends in GP prescribing of psychotropic medications to young patients aged 16-24 years. Methods: We performed a retrospective analysis of routine general practice data from 9112 patients aged 16-24 years with a MHC. Data were extracted from the Melbourne East Monash General Practice Database from 1/01/2009 to 31/12/2014. The main outcome measures included the number of consultations for patients with MHCs, psychotropic prescribing by GPs, and patient characteristics associated with the likelihood of being prescribed a psychotropic. Results: In total, 9112 out of a total of 77,466 young patients were identified as having a MHC in this study, and 11,934 psychotropic prescriptions were provided to 3967 (43.5%) of them over the study period. Antidepressants accounted for 81.4% of total psychotropic prescriptions, followed by anxiolytics (9.6%) and antipsychotics (9.0%). The number of prescriptions issued to individuals with MHCs increased over time. Women and patients aged 21-24 years had higher incidence rates for prescription than men and those aged 16-17 (IRR: 1.15, 95% CI 1.08-1.22, IRR: 1.93, 95% CI 1.750-2.11). Conclusions: Our findings demonstrate an increasing trend in GP prescribing of psychotropics to young people over the study period with higher levels of prescribing to women and those 21-24 years of age. Although GP prescribing corresponded with guideline recommendations on the whole, there were discrepancies between GP's antidepressant prescribing and guideline recommendations, reasons for which were unclear. Research is needed to investigate GPs decision-making processes underlying their prescribing, to target interventions to improve existing data in GP records to improve management, and to identify areas of further training if needed to facilitate greater concordance between clinical practice and guideline recommendations