Reducing the Risks: Reflections on Bridging Home and School Communication

Recent scholarship on literacy development has focused on studying young at-risk learners (Allen and Mason, 1989; Clay, 1982; Taylor and Dorsey-Gaines, 1988; Swap, 1990; Teale and Sulzby, 1986). As kindergarten and first grade teachers we worried about many of our students whose families were not in the cultural mainstream and whose literacy backgrounds appeared different from those of our more successful children. As we thought about how we might better teach our children we began to consider how we could improve our communication with the children\u27s parents to begin to build a partner ship between home and school literacy experiences. We wanted to be supportive and invitational with the parents. We hoped to provide the parents with information which they could use in helping their children interact with print, and, importantly, we wanted to learn from the parents. We valued their input and welcomed information that they could provide which would allow us to build our program to sup port the home. We wished to begin to build a two way bridge that would connect home and school literacy practice

Duration and exposure to virtual environments: Sickness curves during and across sessions

Although simulator sickness is known to increase with protracted exposure and to diminish with repeated sessions, limited systematic research has been performed in these areas. This study reviewed the few studies with sufficient information available to determine the effect-that exposure duration and repeated exposure have on motion sickness. This evaluation confirmed that longer exposures produce more symptoms and that total sickness subsides over repeated exposures. Additional evaluation was performed to investigate the precise form of this relationship and to determine whether the same form was generalizable across varied simulator environments. The results indicated that exposure duration and repeated exposures are significantly linearly related to sickness outcomes (duration being positively related and repetition negatively related to total sickness). This was true over diverse systems and large subject pools. This result verified the generalizability of-the relationships among sickness, exposure duration, and repeated exposures. Additional research is indicated to determine the optimal length of a single exposure and the optimal intersession interval to facilitate adaptation

Guidelines for Genome-Scale Analysis of Biological Rhythms

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding “big data” that are conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome-scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them

Guidelines for Genome-Scale Analysis of Biological Rhythms

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding ‘big data’ that is conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them