Strand displacement-triggered FRET nanoprobe tracking TK1 mRNA in living cells for ratiometric fluorimetry of nucleic acid biomarker.

A precisely designed dual-color biosensor has realized a visual assessment of thymidine kinase 1 (TK1) mRNA in both living cells and cell lysates. The oligonucleotide probe is constructed by hybridizing the antisense strand of the target and two recognition sequences, in which FAM serves as the donor and TAMRA as the acceptor. Once interacting with the target, two recognition strands are replaced, and then the antisense complementary sequence forms a more stable double-stranded structure.

Assessing the decision quality of artificial intelligence and oncologists of different experience in different regions in breast cancer treatment.

Background: AI-based clinical decision support system (CDSS) has important prospects in overcoming the current informational challenges that cancer diseases faced, promoting the homogeneous development of standardized treatment among different geographical regions, and reforming the medical model. However, there are still a lack of relevant indicators to comprehensively assess its decision-making quality and clinical impact, which greatly limits the development of its clinical research and clinical application. This study aims to develop and application an assessment system that can comprehensively assess the decision-making quality and clinical impacts of physicians and CDSS.

Kernels for longitudinal data with variable sequence length and sampling intervals.

We develop several kernel methods for classification of longitudinal data and apply them to detect cognitive decline in the elderly. We first develop mixed-effects models, a type of hierarchical empirical Bayes generative models, for the time series. After demonstrating their utility in likelihood ratio classifiers (and the improvement over standard regression models for such classifiers), we develop novel Fisher kernels based on mixture of mixed-effects models and use them in support vector machine classifiers.

Fermentative production of L(+)-lactic acid using hydrolyzed acorn starch, persimmon juice and wheat bran hydrolysate as nutrients.

The use of hydrolyzed acorn starch as a novel carbon source for L(+)-lactic acid production was proposed. The effects of carbon-nitrogen ratio and growth factor on the fermentations were studied by single factor experiments. A lower carbon-nitrogen ratio could enhance L(+)-lactic acid production, and the expensive yeast extract could be replaced by the cheap persimmon juice providing growth factor for L(+)-lactic acid production when wheat bran hydrolysate was used as the nitrogen source.

An economical approach for d-lactic acid production utilizing unpolished rice from aging paddy as major nutrient source.

In order to reduce the raw material cost of d-lactic acid fermentation, the unpolished rice from aging paddy was used as major nutrient source in this study. The unpolished rice saccharificate, wheat bran powder and yeast extract were employed as carbon source, nitrogen source and growth factors, respectively. Response surface methodology (RSM) was applied to optimize the dosages of medium compositions.

Fast neural network surrogates for very high dimensional physics-based models in computational oceanography.

We present neural network surrogates that provide extremely fast and accurate emulation of a large-scale circulation model for the coupled Columbia River, its estuary and near ocean regions. The circulation model has O(10(7)) degrees of freedom, is highly nonlinear and is driven by ocean, atmospheric and river influences at its boundaries. The surrogates provide accurate emulation of the full circulation code and run over 1000 times faster.

Penalized probabilistic clustering.

While clustering is usually an unsupervised operation, there are circumstances in which we believe (with varying degrees of certainty) that items A and B should be assigned to the same cluster, while items A and C should not. We would like such pairwise relations to influence cluster assignments of out-of-sample data in a manner consistent with the prior knowledge expressed in the training set. Our starting point is probabilistic clustering based on gaussian mixture models (GMM) of the data distribution.