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Herein, the aptamer–antibody sandwich module was first introduced to accurately recognize a low molecular weight compound (mycotoxin). Impressively, compared with the large steric hindrance of a traditional dual-antibody module, the aptamer–antibody sandwich with low Gibbs free energy and a low dissociation constant has high recognition efficiency; thus, it could reduce false positives and false negatives caused by a dual-antibody module. As a proof of concept, a sensitive electrochemiluminescence (ECL) biosensor was constructed for detecting mycotoxin zearalenone (ZEN) based on an aptamer–antibody sandwich as a biological recognition element and porous ZnO nanosheets (Zn NSs) supported Cu nanoclusters (Cu NCs) as the signal transduction element, in which the antibody was modified on the vertex of a tetrahedral DNA nanostructure (TDN) with a rigid structure to increase the kinetics of target recognition for promoting the detection sensitivity. Moreover, the Cu NCs/Zn NSs exhibited an excellent ECL response that was attributed to the aggregation-induced ECL enhancement through electrostatic interactions. The sensing platform achieved trace detection of ZEN with a low detection limit of 0.31 fg/mL, far beyond that of the enzyme-linked immunosorbent assay (ELISA, the current rapid detection method) and high-performance liquid chromatography (HPLC, the national standard detection method). The strategy has great application potential in food analysis, environmental monitoring, and clinical diagnosis.

In this study, we further develop the data assimilation framework proposed for probabilistic Machine Learning (ML) models, named Probabilistic Optimal Interpolation (POI), in nonstationary environments with missing data which are common in real-world situations. The dataset is based on a multi-scale Lorenz 96 chaos system. Three types of nonstationary environments (i.e., trend, heteroscedasticity, and random walk) are introduced in the dataset. In addition, the test datasets are masked with different missingness rates to evaluate the POI performance under scenarios with missing values. This study utilizes several filters to identify background noises for observation covariance initialization, and the covariance is updated along the real-time data assimilation specifically for nonstationary environments. The results show that heteroscedastic noises can be well identified while random-walk noises are very difficult to analyze. Overall, the results show that the POI implementation can lead to reduced uncertainty, but POI performance can also be significantly affected due to the limitation of ML models accuracy in the nonstationary environments. The impact from missing values is then examined and compared between stationary and nonstationary environments. Both prediction and POI updates are more accurate with smaller missingness rates as expected, and whether POI is bypassed or not at missing points does not affect the overall performance significantly. Finally, input evolution can perform well with POI under high noise level and missingness rates in stationary environments, but it always yields worse results in nonstationary environments and thus is not recommended. [ABSTRACT FROM AUTHOR]

In this study, we propose a new framework for Data Assimilation (DA) named Probabilistic Optimal Interpolation (POI) to combine the predictions from Machine Learning (ML) models trained with historical data and real-time observations, with the key objective to improve the estimate on the state of system. The framework utilizes the heteroscedastic uncertainty of the ML predictions as well as the residual-based uncertainty of the observations and integrates the two through the technique of optimal interpolation. The quantification of the respective uncertainties is directly included within the framework itself. As an application example, we test the performance of POI using a multi-scale Lorenz 96 chaos system with various added noise levels. The ML model is based on a Long Short-Term Memory (LSTM) neural network and the technique of Monte Carlo (MC) dropout is adopted for the uncertainty quantification. The computational results show that the POI implementation can lead to improved predictions of the state of the system with less uncertainty and it can also filter the added level of noises effectively when the historical data are reasonably accurate. However, if the noise level is high, using the updated POI predictions as sequential inputs for the next time step does not guarantee better performance than using the real-time observations directly. Furthermore, under very noisy conditions, the average ML predictions after the MC dropout can already reduce the noises substantially, and these predictions might even be better than the POI updates. Therefore, the POI implementation (or data assimilation in general) is not recommended with a ML-based surrogate model in a noisy environment. • A probabilistic optimal interpolation (POI) framework is proposed for data assimilation. • Uncertainty estimation is embedded in analysis of machine learning model predictions and observations. • POI performance is evaluated with the Lorenz 96 chaos system coupled with added noises. • Predictions improve with POI in general, but not recommended when the noise level is high. [ABSTRACT FROM AUTHOR]

Die Beweglichkeit linearer Polymereändert sich beim Verzweigen, was die Prozessierbar und Ziehfähigkeit stark beeinflusst. Regelmäßig verzweigte Modellpolyolefine wurden mit modernen FestkörperNMRTechniken untersucht und Verdrillungen um die Verzweigungen in den kristallinen Bereichen identifiziert. Bei wenigen Verzweigungen sind die Verdrillbewegungen entkoppelt, bei hoher Verzweigung findet eine kollektive Bewegung statt siehe Bild.

Branching out: The mobility of linear polymers changes upon branching, which has a pronounced effect on processability and drawability. Regularly branched model polyolefins were studied by advanced solidstate NMR spectroscopy, and twist defects around the branches in the crystalline regions are identified. For lower branch content, the twisting motions are decoupled; for higher content, collective motion is found see picture.