Gregory J. Norman

Bio: Gregory J. Norman is an academic researcher from University of Chicago. The author has contributed to research in topics: Medicine & Overweight. The author has an hindex of 70, co-authored 249 publications receiving 15544 citations. Previous affiliations of Gregory J. Norman include Group Health Research Institute & West Health.

A Text Message-Based Intervention for Weight Loss: Randomized Controlled Trial

Abstract: Background: To our knowledge, no studies have evaluated whether weight loss can be promoted in overweight adults through the use of an intervention that is largely based on daily SMS (Short Message Service: text) and MMS (Multimedia Message Service: small picture) messages transmitted via mobile phones. Objective: This paper describes the development and evaluation of a text message-based intervention designed to help individuals lose or maintain weight over 4 months. Methods: The study was a randomized controlled trial, with participants being exposed to one of the following two conditions, lasting 16 weeks: (1) receipt of monthly printed materials about weight control; (2) an intervention that included personalized SMS and MMS messages sent two to five times daily, printed materials, and brief monthly phone calls from a health counselor. The primary outcome was weight at the end of the intervention. A mixed-model repeated-measures analysis compared the effect of the intervention group to the comparison group on weight status over the 4-month intervention period. Analysis of covariance (ANCOVA) models examined weight change between baseline and 4 months after adjusting for baseline weight, sex, and age. Results: A total of 75 overweight men and women were randomized into one of the two groups, and 65 signed the consent form, completed the baseline questionnaire, and were included in the analysis. At the end of 4 months, the intervention group (n = 33) lost more weight than the comparison group (?1.97 kg difference, 95% CI ?0.34 to ?3.60 kg, P = .02) after adjusting for sex and age. Intervention participants' adjusted average weight loss was 2.88 kg (3.16%). At the end of the study, 22 of 24 (92%) intervention participants stated that they would recommend the intervention for weight control to friends and family. Conclusions: Text messages might prove to be a productive channel of communication to promote behaviors that support weight loss in overweight adults. Trial Registration: Clinicaltrials.gov NCT00415870; http://clinicaltrials.gov/ct2/show/NCT00415870 (Archived by WebCite at http://www.webcitation.org/5dnolbkFt) [J Med Internet Res 2009;11(1):e1]

Development and Evaluation of the Conceptual Inventory of Natural Selection

Abstract: Natural selection as a mechanism of evolution is a central concept in biology; yet, most nonbiology-majors do not thoroughly understand the theory even after instruction. Many alternative conceptions on this topic have been identified, indicating that the job of the instructor is a difficult one. This article presents a new diagnostic test to assess students' understanding of natural selection. The test items are based on actual scientific studies of natural selection, whereas previous tests have employed hypothetical situations that were often misleading or oversimplified. The Conceptual Inventory of Natural Selection (CINS) is a 20-item multiple choice test that employs common alternative conceptions as distractors. An original 12-item version of the test was field-tested with 170 nonmajors in 6 classes and 43 biology majors in 1 class at 3 community colleges. The test scores of one subset of nonmajors (n ¼ 7) were compared with the students' performances in semistructured interviews. There was a positive correlation between the test scores and the interview scores. The current 20-item version of the CINS was field-tested with 206 students in a nonmajors' general biology course. The face validity, internal validity, reliability, and readability of the CINS are discussed. Results indicate that the CINS will be a valuable tool for instructors. 2002 Wiley Periodicals, Inc. J Res Sci Teach 39: 952-978, 2002 Natural selection is the principal mechanism of evolution, and the theory of evolution is of great importance as a unifying theory in biology education according to the National Science Standards (National Research Council, 1996). Yet, natural selection is misunderstood by many students. The litany of alternative conceptions regarding natural selection and evolution is long (Mayr, 1982; Clough & Driver, 1986; Good, Trowbridge, Demastes, Wandersee, Hafner, & Cummins, 1992; Scharmann & Harris, 1992; Cummins, Demastes & Hafner, 1994). Some studies

Diet, Physical Activity, and Sedentary Behaviors as Risk Factors for Overweight in Adolescence

Abstract: Background The proportion of overweight adolescents has increased, but the behavioral risk factors for overweight youth are not well understood. Objective To examine how diet, physical activity, and sedentary behaviors relate to overweight status in adolescents. Design and Setting Baseline data from the Patient-Centered Assessment and Counseling for Exercise Plus Nutrition Project, a randomized controlled trial of adolescents to determine the effects of a clinic-based intervention on physical activity and dietary behaviors. Participants A total of 878 adolescents aged 11 to 15 years, 42% of whom were from minority backgrounds. Main Outcome Measure Centers for Disease Control and Prevention body mass index–for-age percentiles divided into 2 categories: normal weight ( Results Overall, 45.7% of the sample was classified as AR + O with a body mass index for age at the 85th percentile or higher. More girls from minority backgrounds (54.8%) were AR + O compared with non-Hispanic white girls (42%) (χ21= 7.6;P= .006). Bivariate analyses indicated that girls and boys in the AR + O group did fewer minutes per day of vigorous physical activity, consumed fewer total kilojoules per day, and had fewer total grams of fiber per day than those in the normal-weight group. Boys in the AR + O group also did fewer minutes per day of moderate physical activity and watched more minutes per day of television on nonschool days than normal-weight boys. Final multivariate models indicated that independent of socioeconomic status (as assessed by household education level), girls had a greater risk of being AR + O if they were Hispanic or from another minority background (odds ratio [OR] = 1.65; 95% confidence interval [CI], 1.09-2.49) and a reduced risk of being AR + O as minutes per day of vigorous physical activity increased (OR = 0.93; 95% CI, 0.89-0.97). A low level of vigorous physical activity was the only significant risk factor for boys being AR + O (OR = 0.92; 95% CI, 0.89-0.95). Analyses based on meeting behavioral guidelines supported these findings and showed that failing to meet the 60 min/d moderate to vigorous physical activity guideline was associated with overweight status for both girls and boys. In addition, boys who failed to meet sedentary behavior and dietary fiber guidelines were more likely to be overweight. Conclusions Of the 7 dietary and physical activity variables examined in this cross-sectional study, insufficient vigorous physical activity was the only risk factor for higher body mass index for adolescent boys and girls. Prospective studies are needed to clarify the relative importance of dietary and physical activity behaviors on overweight in adolescence.

Social isolation: Social isolation

Abstract: Social species, by definition, form organizations that extend beyond the individual. These structures evolved hand in hand with behavioral, neural, hormonal, cellular, and genetic mechanisms to support them because the consequent social behaviors helped these organisms survive, reproduce, and care for offspring sufficiently long that they too reproduced. Social isolation represents a lens through which to investigate these behavioral, neural, hormonal, cellular, and genetic mechanisms. Evidence from human and nonhuman animal studies indicates that isolation heightens sensitivity to social threats (predator evasion) and motivates the renewal of social connections. The effects of perceived isolation in humans share much in common with the effects of experimental manipulations of isolation in nonhuman social species: increased tonic sympathetic tonus and HPA activation; and decreased inflammatory control, immunity, sleep salubrity, and expression of genes regulating glucocorticoid responses. Together, these effects contribute to higher rates of morbidity and mortality in older adults.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).