Automated microscopy has facilitated the large scale acquisition of live cell image data [Sig06,Gor07,Dav07,and Bah05]. In the case of low magnification imaging in transmission mode, the migration, morphology, and lineage development of large numbers of single cells in culture can be monitored. However, obtaining quantitative data related to single cell behavior requires image analysis methods that can accurately segment and track cells. When fluorescence protein gene reporters are used, the activity of specific genes can be related to phenotypic changes at a single cell level. The analysis of living, single cells also provides information on the variability that exists within homogeneous cell populations [Ras05 and Si206].Furthermore, multiple fluorescence protein reporters transfected into single cells can be used to understand the sequence of transcriptional changes that occurs in response to perturbations. In order to facilitate the extraction of quantitative data from live cell image sets, automated image analysis methods are needed.The diversity of both cell imaging techniques and the cell lines used in biological research is enormous making the task of developing reliable segmentation and cell tracking algorithms even harder. Many popular cell tracking techniques are based on complex probabilistic models. In [Bah05] Gaussian probability density functions are used to characterize the selected tracking criteria. In [Mar06] cells are tracked by fitting their tracks to a persistent random walk model based on mean square displacement. In [Lia08] the final cell

In order to facilitate the extraction of quantitative data from live cell image sets, automated image analysis methods are needed. This paper presents an introduction to the general principle of an overlap cell tracking software developed by the National Institute of Standards and Technology (NIST). This cell tracker has the ability to track cells across a set of time lapse images acquired at high rates based on the amount of overlap between cellular regions in consecutive frames. It is designed to be highly flexible, requires little user parameterization, and has a fast execution time.

S ample medications allow physicians to quickly initiate therapy to evaluate initial patient response. However, sample closets primarily contain newer, costly brand name medications and rarely have generic options available. Studies now suggest that sample availability may affect the medications that physicians choose to subsequently prescribe, 1,2 but little data exist regarding this effect in private practice. In the present study, we explored the effect of removing samples of 3 medication classes on prescribing patterns at a private clinic. Methods. The primary objective of this study was to measure the effect of removing free samples of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors (statins), levothyroxine products, and selective serotonin reuptake inhibitors (SSRIs) from a private clinic on the percentage of generic prescribing within each drug class and classes combined. Secondary objectives included the effect on generic prescribing for all medications and sustainability. This was a 150-day pre-post study (60-day run-in, 90day intervention) at Lakeview Internal Medicine in West Des Moines, Iowa. Prescribers participate in pay-forperformance and receive quarterly prescribing reports. Prior to the main intervention, all prescribers (n=5) attended an educational session presented by a clinical pharmacist reviewing current evidence on the study medication classes. Sample sign-out sheets were posted to demonstrate baseline sample use. All sampled statins, levothyroxine, and SSRIs were removed from the clinic for 90 days (December 1, 2007-February 28, 2008). No restriction was placed on the ability to prescribe these medicines. Data from a third-party payer were used to compare generic prescribing percentage during the samplefree period and a matched 90-day period prior to intervention (July 2, 2007-September 29, 2007). Data were analyzed using 2 as a test of proportions. PϽ.05 was considered statistically significant.

Introduction There is wide variability in prescribing practices among providers, even for patients undergoing the same operations. Our study aims to analyze the variation in opioid prescription practices using a patient-centered approach to establish more appropriate prescribing guidelines for health care providers. Methods We conducted phone surveys 30 days after surgery to assess patient-reported opioid use. Over a two-year collection period, we identified patients that had undergone common outpatient pediatric surgery procedures in our 4-surgeon group. Included in the survey tool was the narcotic prescribed (if any), the amount used, and patient/family rating of pain control. Results We collected data for 189 separate procedures (88 umbilical hernias, 30 laparoscopic inguinal hernias, 2 open inguinal hernias, 41 appendectomies, 15 laparoscopic cholecystectomies, and 13 pectus bar removals). Patient age ranged from less than 1 month to 246 months. 83.5% of patients had a narcotic prescribed. The average number of doses used was 4, ranging from 0 (11.3%) to 30 (1.5%). 72.6% of families surveyed felt pain control was appropriate. However, 19.6% did feel they received too much pain medication. 10.6% reported completing their entire prescription; however, only 13.6% of families with excess narcotics reported proper disposal. Conclusions Despite heightened awareness of the opioid epidemic, there is still a poor understanding of appropriate pain control regimens in the pediatric surgical population. We demonstrate that most patients are discharged home with excess opioids and that many families save the leftover pills/liquid. Further research and education are encouraged to limit the use of opioids in standard pediatric surgical procedures.