University of Maine

About: University of Maine is a education organization based out in Orono, Maine, United States. It is known for research contribution in the topics: Population & Ice sheet. The organization has 8637 authors who have published 16932 publications receiving 590124 citations. The organization is also known as: University of Maine at Orono.

Bringing Evidence-Based Child Mental Health Services to the Schools: General Issues and Specific Populations

Abstract: Epidemiological research indicates a high prevalence of psychiatric disorders among children and adolescents. Approximately 21% of children and adolescents, ages 9 to 17, have a diagnosable psychiatric disorder (Costello et al., 1996; Shaffer et al., 1996; U.S. Public Health Service, 2000), and additional youngsters experience social and emotional difficulties that do not meet symptom criteria for a disorder but cause considerable distress and impairment in functioning. Unfortunately, there is a significant gap between the many youth who are in need of treatment and those who actually receive mental health care (Burns et al., 1995; Leaf et al., 1996). According to the Surgeon General's 1999 report on mental health, 6 to 9 million youngsters with emotional problems are not receiving the help they require (U. S. Department of Health and Human Services, 1999). The failure to provide treatment to youth represents a major public health concern (U.S. Public Health Service, 2000). Schools present a crucial avenue for ameliorating this problem, and have been designated as a key setting by the Surgeon General for identifying and addressing mental health needs in youth (U.S. Department of Health and Human Services, 1999). This proposed solution for increasing health care access is based on several observations. First, in a large study of children's mental health service use, of the only 16% of youth receiving mental health services, 75% received them at school (Burns et al., 1995). Second, schools provide unparalleled contact with youth (Adelman & Taylor, 1999; Weist, 1997), and therefore, represent a single location through which the majority can be reached (Anglin, 2003). Such ease of accessibility creates the optimal environment to launch prevention, early identification, and intervention efforts that may prevent the development of serious secondary dysfunction such as suicidal behavior or substance abuse (Weist, 1999). In addition, children and families may avoid seeking help partly due to the stigma associated with traditional mental health treatment. Offering services in a familiar setting like schools may make treatment more acceptable (Catron & Weiss, 1994; Weist, 1999) since many children already receive school-based services for non-mental health concerns. On a related note, school programs reduce barriers common to treatment in community mental health services such as cost, transportation, and family and demographic factors (Catron, Harris, & Weiss, 1998; Wu et al., 1999), and thus, may offer opportunities that would otherwise be unavailable. Moreover, psychiatric issues in children and adolescents are often not recognized, and adults frequently minimize problems experienced by youth (Clauss-Ehlers & Weist, 2002). Partnering with schools creates opportunities to educate and support school personnel and parents in identifying mental health issues and making appropriate referrals for treatment. Finally, treatment implemented within schools provides opportunities to practice skills in realistic contexts and with diverse individuals, thereby increasing the likelihood of generalization to the natural environment (Evans, 1999; Evans, Langberg, & Williams, 2003). Treatment progress can be further encouraged and reinforced by peers and teachers. Such a real-world approach reduces the division between the treatment setting and natural environment, and may enhance the effectiveness of school interventions compared to clinic-based treatments (Evans et al., 2003). Based on the many potential advantages, there has been a proliferation of school-based programs (Adelman & Taylor, 1998). Although some positive effects have been found for programs addressing anxiety disorders, depression, substance use, and conduct and emotional problems (Dadds et al., 1997, Dadds et al., 1999, Masia-Warner et al., 2005; Rones & Hoagwood, 2000), the effectiveness of the majority of school initiatives is largely unknown (Adelman & Taylor, 1998; Hoagwood & Erwin, 1997; Leff, Power, Manz, Costigan, & Nabors, 2001; Power, Manz, & Leff, 2003; Rones & Hoagwood, 2000). …

Comparison of nano- and microfibrillated cellulose films

Abstract: Nanocellulose is an interesting building block for functional materials and has gained considerable interest due to its mechanical robustness, large surface area and biodegradability. It can be formed into various structures such as solids, films and gels such as hydrogels and aerogels and combined with polymers or other materials to form composites. Mechanical, optical and barrier properties of nanofibrillated cellulose (NFC) and microfibrillated cellulose (MFC) films were studied in order to understand their potential for packaging and functional printing applications. Impact of raw material choice and nanocellulose production process on these properties was evaluated. MFC and NFC were produced following two different routes. NFC was produced using a chemical pretreatment followed by a high pressure homogenization, whereas MFC was produced using a mechanical treatment only. TEMPO-mediated oxidation followed by one step of high pressure (2,000 bar) homogenization seems to produce a similar type of NFC from both hardwood and softwood. NFC films showed superior mechanical and optical properties compared with MFC films; however, MFC films demonstrated better barrier properties against oxygen and water vapor. Both the MFC and NFC films were excellent barriers against mineral oil used in ordinary printing inks and dichlorobenzene, a common solvent used in functional printing inks. Barrier properties against vegetable oil were also found to be exceptionally good for both the NFC and MFC films.

Role of plutonism in low-pressure metamorphic belt formation

Abstract: Wickham and Oxburgh1 recently proposed that low-pressure/high-temperature (low-P/high-T) metamorphism in the eastern Pyrenees, and possibly all low-P/high-T metamorphic belts, resulted from anomalously high mantle heat flow brought about by rifting. Their model is largely constrained by the presence of nearby synmetamorphic rift-related sedimentary rocks and the interpretation that the migmatites and granites are the product of in situ melting in the presence of an anomalously steep geotherm. Here we present an alternative model, in which low-P/high-T metamorphism (pro-grade reactions at pressures near or below the Al2SiO5 triple point) results from contact effects near sill-like igneous intrusions at intermediate crustal levels. Low-P/high-T conditions can be achieved through this process in regions of continent–continent collision with normal mantle heat flux as well as in zones of extension. Our model is based on studies of the low-P/high-T metamorphic terrane in the New England Appalachians.

Uncertainty in the response of terrestrial carbon sink to environmental drivers undermines carbon-climate feedback predictions.

Abstract: Terrestrial ecosystems play a vital role in regulating the accumulation of carbon (C) in the atmosphere. Understanding the factors controlling land C uptake is critical for reducing uncertainties in projections of future climate. The relative importance of changing climate, rising atmospheric CO2, and other factors, however, remains unclear despite decades of research. Here, we use an ensemble of land models to show that models disagree on the primary driver of cumulative C uptake for 85% of vegetated land area. Disagreement is largest in model sensitivity to rising atmospheric CO2 which shows almost twice the variability in cumulative land uptake since 1901 (1 s.d. of 212.8 PgC vs. 138.5 PgC, respectively). We find that variability in CO2 and temperature sensitivity is attributable, in part, to their compensatory effects on C uptake, whereby comparable estimates of C uptake can arise by invoking different sensitivities to key environmental conditions. Conversely, divergent estimates of C uptake can occur despite being based on the same environmental sensitivities. Together, these findings imply an important limitation to the predictability of C cycling and climate under unprecedented environmental conditions. We suggest that the carbon modeling community prioritize a probabilistic multi-model approach to generate more robust C cycle projections.

Estimating parameters of a forest ecosystem C model with measurements of stocks and fluxes as joint constraints

Abstract: We conducted an inverse modeling analysis, using a variety of data streams (tower-based eddy covariance measurements of net ecosystem exchange, NEE, of CO2, chamber-based measurements of soil respiration, and ancillary ecological measurements of leaf area index, litterfall, and woody biomass increment) to estimate parameters and initial carbon (C) stocks of a simple forest C-cycle model, DALEC, using Monte Carlo procedures. Our study site is the spruce-dominated Howland Forest AmeriFlux site, in central Maine, USA. Our analysis focuses on: (1) full characterization of data uncertainties, and treatment of these uncertainties in the parameter estimation; (2) evaluation of how combinations of different data streams influence posterior parameter distributions and model uncertainties; and (3) comparison of model performance (in terms of both predicted fluxes and pool dynamics) during a 4-year calibration period (1997–2000) and a 4-year validation period (“forward run”, 2001–2004). We find that woody biomass increment, and, to a lesser degree, soil respiration, measurements contribute to marked reductions in uncertainties in parameter estimates and model predictions as these provide orthogonal constraints to the tower NEE measurements. However, none of the data are effective at constraining fine root or soil C pool dynamics, suggesting that these should be targets for future measurement efforts. A key finding is that adding additional constraints not only reduces uncertainties (i.e., narrower confidence intervals) on model predictions, but at the same time also results in improved model predictions by greatly reducing bias associated with predictions during the forward run.