Background:
Drosophila gene expression pattern images document the spatiotemporal dynamics of gene expression during embryogenesis. A comparative analysis of these images could provide a fundamentally important way for studying the regulatory networks governing development. To facilitate pattern comparison and searching, groups of images in the Berkeley Drosophila Genome Project (BDGP) high-throughput study were annotated with a variable number of anatomical terms manually using a controlled vocabulary. Considering that the number of available images is rapidly increasing, it is imperative to design computational methods to automate this task.

Results:
We present a computational method to annotate gene expression pattern images automatically. The proposed method uses the bag-of-words scheme to utilize the existing information on pattern annotation and annotates images using a model that exploits correlations among terms. The proposed method can annotate images individually or in groups (e.g., according to the developmental stage). In addition, the proposed method can integrate information from different two-dimensional views of embryos. Results on embryonic patterns from BDGP data demonstrate that our method significantly outperforms other methods.

Conclusion:
The proposed bag-of-words scheme is effective in representing a set of annotations assigned to a group of images, and the model employed to annotate images successfully captures the correlations among different controlled vocabulary terms. The integration of existing annotation information from multiple embryonic views improves annotation performance.

Background: We describe the study design and methods used in a 9-month pedometer-based worksite intervention called “ASUKI Step” conducted at the Karolinska Institutet (KI) in Stockholm, Sweden and Arizona State University (ASU) in the greater Phoenix area, Arizona. Methods/Design: “ASUKI Step” was based on the theory of social support and a quasi-experimental design was used for evaluation. Participants included 2,118 faculty, staff, and graduate students from ASU (n = 712) and KI (n = 1,406) who participated in teams of 3–4 persons. The intervention required participants to accumulate 10,000 steps each day for six months, with a 3-month follow-up period. Steps were recorded onto a study-specific website. Participants completed a website-delivered questionnaire four times to identify socio-demographic, health, psychosocial and environmental correlates of study participation. One person from each team at each university location was randomly selected to complete physical fitness testing to determine their anthropometric and cardiovascular health and to wear an accelerometer for one week. Study aims were: 1) to have a minimum of 400 employee participants from each university site reach a level of 10, 000 steps per day on at least 100 days (3.5 months) during the trial period; 2) to have 70% of the employee participants from each university site maintain two or fewer inactive days per week, defined as a level of less than 3,000 steps per day; 3) to describe the socio-demographic, psychosocial, environmental and health-related determinants of success in the intervention; and 4) to evaluate the effects of a pedometer-based walking intervention in a university setting on changes in self-perceived health and stress level, sleep patterns, anthropometric measures and fitness. Incentives were given for compliance to the study protocol that included weekly raffles for participation prizes and a grand finale trip to Arizona or Sweden for teams with most days over 10,000 steps. Discussion: “ASUKI Step” is designed to increase the number of days employees walk 10,000 steps and to reduce the number of days employees spend being inactive. The study also evaluates the intra- and interpersonal determinants for success in the intervention and in a sub-sample of the study, changes in physical fitness and body composition during the study.

Background:
Many pharmaceutical drugs are known to be ineffective or have negative side effects in a substantial proportion of patients. Genomic advances are revealing that some non-synonymous single nucleotide variants (nsSNVs) may cause differences in drug efficacy and side effects. Therefore, it is desirable to evaluate nsSNVs of interest in their ability to modulate the drug response.

Results:
We found that the available data on the link between drug response and nsSNV is rather modest. There were only 31 distinct drug response-altering (DR-altering) and 43 distinct drug response-neutral (DR-neutral) nsSNVs in the whole Pharmacogenomics Knowledge Base (PharmGKB). However, even with this modest dataset, it was clear that existing bioinformatics tools have difficulties in correctly predicting the known DR-altering and DR-neutral nsSNVs. They exhibited an overall accuracy of less than 50%, which was not better than random diagnosis. We found that the underlying problem is the markedly different evolutionary properties between positions harboring nsSNVs linked to drug responses and those observed for inherited diseases. To solve this problem, we developed a new diagnosis method, Drug-EvoD, which was trained on the evolutionary properties of nsSNVs associated with drug responses in a sparse learning framework. Drug-EvoD achieves a TPR of 84% and a TNR of 53%, with a balanced accuracy of 69%, which improves upon other methods significantly.

Conclusions:
The new tool will enable researchers to computationally identify nsSNVs that may affect drug responses. However, much larger training and testing datasets are needed to develop more reliable and accurate tools.