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. Due to the increasing spatial distance between two dyes, the FRET is attenuated, leading to a rapid recovery of FAM fluorescence and a reduction of TAMRA fluorescence. A discernible color response from orange to green could be observed by the naked eye, with a limit of detection (LOD) of 0.38 nM and 5.22 nM for spectrometer- and smartphone-based assays, respectively. The proposed ratiometric method transcends previous reports in its capacities in visualizing TK1 expression toward reliable nucleic acid biomarker analysis, which might establish a general strategy for ratiometric biosensing via strand displacement.

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. Methods: Enrolled adjuvant treatment decision stage early breast cancer cases were randomly assigned to different decision-making physician panels (each panel consisted of three different seniority physicians in different grades hospitals), each physician made an independent "Initial Decision" and then reviewed the CDSS report online and made a "Final Decision". In addition, the CDSS and guideline expert groups independently review all cases and generate "CDSS Recommendations" and "Guideline Recommendations" respectively. Based on the design framework, a multi-level multi-indicator system including "Decision Concordance", "Calibrated Concordance", " Decision Concordance with High-level Physician", "Consensus Rate", "Decision Stability", "Guideline Conformity", and "Calibrated Conformity" were constructed. Results: 531 cases containing 2124 decision points were enrolled; 27 different seniority physicians from 10 different grades hospitals have generated 6372 decision opinions before and after referring to the "CDSS Recommendations" report respectively. Overall, the calibrated decision concordance was significantly higher for CDSS and provincial-senior physicians (80.9%) than other physicians. At the same time, CDSS has a higher " decision concordance with high-level physician" (76.3%-91.5%) than all physicians. The CDSS had significantly higher guideline conformity than all decision-making physicians and less internal variation, with an overall guideline conformity variance of 17.5% (97.5% vs. 80.0%), a standard deviation variance of 6.6% (1.3% vs. 7.9%), and a mean difference variance of 7.8% (1.5% vs. 9.3%). In addition, provincial-middle seniority physicians had the highest decision stability (54.5%). The overall consensus rate among physicians was 64.2%. Conclusions: There are significant internal variation in the standardization treatment level of different seniority physicians in different geographical regions in the adjuvant treatment of early breast cancer. CDSS has a higher standardization treatment level than all physicians and has the potential to provide immediate decision support to physicians and have a positive impact on standardizing physicians' treatment behaviors.

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. The hierarchical generative model allows us to handle variations in sequence length and sampling interval gracefully. We also give nonparametric kernels not based on generative models, but rather on the reproducing kernel Hilbert space. We apply the methods to detecting cognitive decline from longitudinal clinical data on motor and neuropsychological tests. The likelihood ratio classifiers based on the neuropsychological tests perform better than than classifiers based on the motor behavior. Discriminant classifiers performed better than likelihood ratio classifiers for the motor behavior tests.

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. The dosages of wheat bran hydrolysate and persimmon juice in the medium were statistically optimized by response surface methodology (RSM). The yield of L(+)-lactic acid reached 45.78g/100g dry acorn with a final concentration of 57.61+/-1.37g/l and a productivity of 1.60+/-0.12g/lh when the batch fermentation was carried out in a 5l bioreactor under the optimal conditions of wheat bran hydrolysate 24.55g/l and persimmon juice 12.30g/l. Comparative batch fermentations using different raw materials such as acorn, cassava, corn and glucose showed that both the yield and the productivity of L(+)-lactic acid production were the highest when the hydrolyzed acorn starch was used as the carbon source. Therefore, the acorn could be used as a new substitute of grain raw material in L(+)-lactic acid production.

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. As a result, when the fermentation was carried out under the optimal conditions for wheat bran powder (29.10g/l) and yeast extract (2.50g/l), the d-lactic acid yield reached 731.50g/kg unpolished rice with a volumetric production rate of 1.50g/(lh). In comparison with fresh corn and polished rice, the d-lactic acid yield increased by 5.79% and 8.71%, and the raw material cost decreased by 65% and 52%, respectively, when the unpolished rice was used as a major nutrient source. These results might provide a reference for the industrial production of d-lactic acid.

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. Such fast dynamic surrogates will enable significant advances in ensemble forecasts in oceanography and weather.

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. We express clustering preferences in a prior distribution over assignments of data points to clusters. This prior penalizes cluster assignments according to the degree with which they violate the preferences. The model parameters are fit with the expectation-maximization (EM) algorithm. Our model provides a flexible framework that encompasses several other semisupervised clustering models as its special cases. Experiments on artificial and real-world problems show that our model can consistently improve clustering results when pairwise relations are incorporated. The experiments also demonstrate the superiority of our model to other semisupervised clustering methods on handling noisy pairwise relations.