Optical simulation of the detector module level is highly desired for Position Emission Tomography (PET) system design. Commonly used simulation toolkits such as GATE are not efficient in the optical simulation of detector modules with complicated light-sharing configurations, where a vast amount of photons need to be tracked. We present a fast approach based on a simplified specular reflectance model and a structured light-tracking algorithm to speed up the photon tracking in detector modules constructed with polished finish and specular reflector materials. We simulated conventional block detector designs with different slotted light guide patterns using the new approach and compared the outcomes with those from GATE simulations. While the two approaches generated comparable flood maps, the new approach was more than 200-600 times faster. The new approach has also been validated by constructing a prototype detector and comparing the simulated flood map with the experimental flood map. The experimental flood map has nearly uniformly distributed spots similar to those in the simulated flood map. In conclusion, the new approach provides a fast and reliable simulation tool for assisting in the development of light-sharing-based detector modules with a polished surface finish and using specular reflector materials.

Early detection has the potential to reduce cancer mortality, but an effective screening test must demonstrate asymptomatic cancer detection years before conventional diagnosis in a longitudinal study. In the Taizhou Longitudinal Study (TZL), 123,115 healthy subjects provided plasma samples for long-term storage and were then monitored for cancer occurrence. Here we report the preliminary results of PanSeer, a noninvasive blood test based on circulating tumor DNA methylation, on TZL plasma samples from 605 asymptomatic individuals, 191 of whom were later diagnosed with stomach, esophageal, colorectal, lung or liver cancer within four years of blood draw. We also assay plasma samples from an additional 223 cancer patients, plus 200 primary tumor and normal tissues. We show that PanSeer detects five common types of cancer in 88% (95% CI: 80-93%) of post-diagnosis patients with a specificity of 96% (95% CI: 93-98%), We also demonstrate that PanSeer detects cancer in 95% (95% CI: 89-98%) of asymptomatic individuals who were later diagnosed, though future longitudinal studies are required to confirm this result. These results demonstrate that cancer can be non-invasively detected up to four years before current standard of care.

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.