State University of New York System

About: State University of New York System is a education organization based out in Albany, New York, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 54077 authors who have published 78070 publications receiving 2985160 citations.

Metal (Ni, Co)‐Metal Oxides/Graphene Nanocomposites as Multifunctional Electrocatalysts

Abstract: Oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) along with hydrogen evolution reaction (HER) have been considered critical processes for electrochemical energy conversion and storage through metal-air battery, fuel cell, and water electrolyzer technologies. Here, a new class of multifunctional electrocatalysts consisting of dominant metallic Ni or Co with small fraction of their oxides anchored onto nitrogen-doped reduced graphene oxide (rGO) including Co-CoO/N-rGO and Ni-NiO/N-rGO are prepared via a pyrolysis of graphene oxide and cobalt or nickel salts. Ni-NiO/N-rGO shows the higher electrocatalytic activity for the OER in 0.1 m KOH with a low overpotential of 0.24 V at a current density of 10 mA cm−2, which is superior to that of the commercial IrO2. In addition, it exhibits remarkable activity for the HER, demonstrating a low overpotential of 0.16 V at a current density of 20 mA cm−2 in 1.0 m KOH. Apart from similar HER activity to the Ni-based catalyst, Co-CoO/N-rGO displays the higher activity for the ORR, comparable to Pt/C in zinc-air batteries. This work provides a new avenue for the development of multifunctional electrocatalysts with optimal catalytic activity by varying transition metals (Ni or Co) for these highly demanded electrochemical energy technologies.

Family-Based Obesity Treatment, Then and Now: Twenty-Five Years of Pediatric Obesity Treatment

Abstract: Family-based behavioral pediatric obesity treatment programs were developed over 25 years ago, and both short- and long-term results support their efficacy (Epstein, Myers, Raynor, & Saelens, 1998; Epstein, Valoski, Wing, & McCurley, 1990, 1994; Jelalian & Saelens, 1999). Obesity runs in families (Whitaker, Wright, Pepe, Seidel, & Dietz, 1997), and it has been hypothesized that targeting eating and activity change in the child and parent, along with teaching parents behavioral skills to facilitate child behavior change, could mobilize family resources to improve the efficacy of childhood obesity treatments. Simultaneously treating the parent and child benefits both, and creates positive relationships between child and parent weight change (Wrotniak, Epstein, Paluch, & Roemmich, 2004, 2005). The efficacy of family-based treatments has been replicated many times since the late 1970s (Epstein, 2003), but there has been no attempt to evaluate changes in efficacy over time. Several variables have changed over that period that may influence the effectiveness of family-based treatments. These include changes in the characteristics of youth who are being treated for pediatric obesity, changes in the environment and family structure that may require conceptual changes in components of family-based treatment, and changes in the analysis and reporting of clinical trials. There has been an increase in the incidence and prevalence of pediatric obesity over the last 20 years (Ogden, Flegal, Carroll, & Johnson, 2002; Troiano, Flegal, Kuczmarski, Campbell, & Johnson, 1995). The body mass index (BMI) distribution is positively skewed; the average overweight child today is more overweight than the average overweight child in the 1970s and 1980s. More overweight youth may show greater decreases in percentage overweight. It is also possible that the more overweight the child is, the more the environment has influenced the child and the poorer the child’s eating and exercise habits may be. Thus, treatment effectiveness may suffer. There is general agreement that the increase in obesity is due in part to changes in the environment (Hill, Wyatt, Reed, & Peters, 2003) that can lead to decreased energy expenditure and increased food intake (French, Story, & Jeffery, 2001). Almost all homes have at least one television, and there has been an increase in the percentage of homes with multiple televisions (Neilsen Media Research 2000, 2000) and in the percentage of children with televisions in their bedrooms (Dennison, Erb, & Jenkins, 2002). Television watching has been associated with obesity in youth (Crespo et al., 2001; Gortmaker et al., 1996). Eating is often paired with television watching in youth (Matheson, Killen, Wang, Varady, & Robinson, 2004; Saelens et al., 2002), and television watching is related to energy intake (Epstein, Roemmich, Paluch, & Raynor, 2005b; Taras et al., 1989). Television watching may shift time away from physical activity (Durant, Baranowski, Johnson, & Thompson, 1994; Epstein, Roemmich, Paluch, & Raynor, 2005a; Taras, Sallis, Patterson, Nader, & Nelson, 1989), reducing energy expenditure. Youth make the choice to be active or sedentary, and developments in behavioral choice theory provide a theoretical framework for interventions to reduce sedentary behaviors (Epstein & Roemmich, 2001; Epstein & Saelens, 2000). Two recent studies in our research program have focused on behavioral economic approaches to modify sedentary behavior as part of a comprehensive treatment for pediatric obesity (Epstein, Paluch, Gordy, & Dorn, 2000; Epstein, Paluch, Kilanowski, & Raynor, 2004). Changes in the environment also influence energy intake (French et al., 2001). There has been an increase in added fats and oils to the food supply (Kantor, 1999). Cheese and pizza consumption (Putnam & Gerrior, 1999) and soda consumption (Tippett & Cleveland, 1999) have increased while milk intake has decreased (Tippett & Cleveland, 1999). In combination with the increase in the number of working mothers and single-parent families (Bowers, 2000), there has been an increase in meals in restaurants (National Restaurant Association, 1998) and energy consumed from eating out (Biing-Hwan, Guthrie, & Frazao, 1999). As people eat out, they experience greater portion sizes (Rolls, 2003), which increases consumption. Behavioral economics also provides ideas for new approaches to reducing energy intake in obese youth. On the basis of research showing that obese youth and adults (Legerski & Epstein, 2006; Saelens & Epstein, 1996) are more motivated to eat than are their leaner peers, in one of our recent studies we attempted to identify nonfood alternatives to compete with the reinforcing value of food (Epstein, Roemmich, Stein, Paluch, & Kilanowski, 2005). There have been changes in the family since the 1950s. The divorce rate more than doubled from 1950 to 1970 (15/1,000 to 40/1,000 per year) and remained stable from 1970 to 1988 (Shiono & Quinn, 1994). The number of families in which both parents work has increased (Anderson & Butcher, 2006), which has resulted in greater income but made it more challenging for parents to allocate enough time for children in family-based behavioral treatment interventions that focus on teaching behavioral principles and modification of the environment. Changes in family life may increase parents’ distress, which also may affect treatment effectiveness (Zeller, Saelens, Roehrig, Kirk, & Daniels, 2004). We tested an intervention that taught problem-solving skills that may be useful in coping with changes in family life that have evolved over time (Epstein, Paluch, Gordy, Saelens, & Ernst, 2000). There are methodological reasons for reexamining studies completed in the 1970s and early 1980s. Older studies used height and weight charts for children (Jelliffe, 1966). When children became older than 18, their overweight status was evaluated with the use of adult height and weight charts (Metropolitan Life Insurance Company, 1959, 1983), which were derived using different methods from a different sample than the youth charts. BMI charts were introduced in 1991 (Must, Dallal, & Dietz, 1991) and have been updated (Kuczmarski et al., 2002). BMI charts use the same methods for parents and children, and the BMI curves represent smooth functions between child and young adult age ranges. It is possible that results obtained using older standards, or even different versions of BMI charts, would show different efficacy when current standards are applied. There have been changes in the reporting of randomized clinical trials in obesity during the last 25 years. Studies completed over 2 decades ago generally reported data for study completers, along with the rate of attrition. It is now common to consider intention to treat, whereby everyone who is randomized and begins the study is accounted for. There have also been changes in the analytical approaches to longitudinal data. Mixed-effects regression models can be used to analyze differences in the patterns of between-groups change over time, as well as predictors of the pattern of change over time (Bryk & Raudenbush, 1987; Goldstein, 1995). Mixed-effects regression models use all the data that are available, as these models do not delete participants with missing data and can analyze data obtained at different time points across studies. Mixed-effects regression models take into account serial correlation between repeated observations and changes in the variability over time, which is relevant because increases in variability for weight control over time are commonly observed in obesity treatment studies. The aims of this study included assessment of changes over time for treatments implemented 25 years ago or current family-based treatments across eight studies (Epstein, Paluch, Gordy, & Dorn, 2000; Epstein, Paluch, Gordy, Saelens, & Ernst, 2000; Epstein, Paluch, Kilanowski, & Raynor, 2004; Epstein, Wing, Koeske, Andrasik, & Ossip, 1981; Epstein, Wing, Koeske, & Valoski, 1984, 1985, 1986; Epstein, Wing, Valoski, & Gooding, 1987) using the same dependent measures (Kuczmarski et al., 2002) and the identification of participant characteristics related to treatment success. The family-based behavioral treatment program for overweight youth is well standardized, with a common core used across all the studies, facilitating the comparison of treatment effects over time. The research program represents a systematic approach to the design of family-based treatments that has focused on different aspects of treatment, including the influence of the family and parent weight, comparison of lifestyle and programmed aerobic activity, the influence of problem solving on treatment outcome, and the influence of methods to reduce sedentary behaviors such as watching television and playing computer games (Epstein, 2003). In addition to evaluating the period during which the study was implemented, we reevaluated long-term (10-year) results for the earlier studies using z-BMI standards and contemporary analytic methods. To identify clinical significance, we also assessed differences in the dichotomous outcomes of achieving BMI values below the overweight (95th BMI percentile) and at risk for overweight (85th BMI percentile) values, as well as reductions greater than 0.5 or 1.0 standard deviation units. This article is unique in the ability to (a) compare a standardized treatment for effectiveness over a long period that overlapped periods of environmental changes that have been hypothesized to increase the prevalence of obesity and to (b) use a large data set to assess how participant characteristics may be related to treatment outcome.

CoREST: A functional corepressor required for regulation of neural-specific gene expression

Abstract: Several genes encoding proteins critical to the neuronal phenotype, such as the brain type II sodium channel gene, are expressed to high levels only in neurons. This cell specificity is due, in part, to long-term repression in nonneural cells mediated by the repressor protein REST/NRSF (RE1 silencing transcription factor/neural-restrictive silencing factor). We show here that CoREST, a newly identified human protein, functions as a corepressor for REST. A single zinc finger motif in REST is required for CoREST interaction. Mutations of the motif that disrupt binding also abrogate repression. When fused to a Gal4 DNA-binding domain, CoREST functions as a repressor. CoREST is present in cell lines that express REST, and the proteins are found in the same immunocomplex. CoREST contains two SANT (SW13/ADA2/NCoR/TFIIIB B) domains, a structural feature of the nuclear receptor and silencing mediator for retinoid and thyroid human receptors (SMRT)-extended corepressors that mediate inducible repression by steroid hormone receptors. Together, REST and CoREST mediate repression of the type II sodium channel promoter in nonneural cells, and the REST/CoREST complex may mediate long-term repression essential to maintenance of cell identity.

Assessing and Managing the University Technology Business Incubator: An Integrative Framework

Abstract: Seeks to develop a framework for assessing and managing university-based technology business incubators (UTBI). Three related areas of knowledge are used to develop this framework: (1) business incubation support, (2) university's involvement in technology and business development support, and (3) organizational effectiveness approaches. The conceptual model that is created is based on three sets of variables: (1) performance outcomes, (2) management policies and their effectiveness, and (3) services and their value-added. Performance outcomes are further divided into program growth and sustainability, tenant firm's survival and growth, contributions to sponsoring-university's mission, and community-related impacts. Management policies are broken down by goals, structures and governance, financing and capitalization, operational policies, and target markets. Services are examined in terms of shared incubator services and university-related services. In order to test the validity of this model, data were gathered from 30 five-year and older U.S. facilities. Four of these facilities were analyzed using a comparative evaluation approach. Using these four facilities, the analysis shows that the proposed model is flexible and easy-to-use in assessing and managing the performance of UTBIs. This model will help those assessing UTBIs to better understand performance and areas for improvement. (SRD)