Double-enzyme active MnO2@BSA mediated lab-on-paper dual-modality aptasensor for di(2-ethylhexyl)phthalate.

Di(2-ethylhexyl)phthalate (DEHP), as an environmental endocrine disruptor, has adverse effects on eco-environments and health. Thus, it is crucial to highly sensitive on-site detect DEHP. Herein, a double-enzyme active MnO2@BSA mediated dual-modality photoelectrochemical (PEC)/colorimetric aptasensing platform with the cascaded sensitization structures of ZnIn2S4 and TiO2 as signal generators was engineered for rapid and ultrasensitive detection of DEHP using an all-in-one lab-on-paper analytical device. Benefitting from cascaded sensitization effect, the ZnIn2S4/TiO2 photosensitive structures-assembled polypyrrole paper electrode gave an enhanced photocurrent signal. The MnO2@BSA nanoparticles (NPs) with peroxidase-mimic and oxidase-mimic double-enzymatic activity induced multiple signal quenching effects and catalyzed color development. Specifically, the MnO2@BSA NPs acted as peroxidase mimetics to generate catalytic precipitates, which not only obstructed interfacial electron transfer but also served as electron acceptors to accept photogenerated electrons. Besides, the steric hindrance effect from MnO2@BSA NPs-loaded branchy polymeric DNA duplex structures further decreased photocurrent signal. The target recycling reaction caused the detachment of MnO2@BSA NPs to increase PEC signal, realizing the ultrasensitive detection of DEHP with a low detection limit of 27 fM. Ingeniously, the freed MnO2@BSA NPs flowed to colorimetric zone with the aid of fluid channels and acted as oxidase mimetics to induce color intensity enhancement, resulting in the rapid visual detection of DEHP. This work provided a prospective paradigm to develop field-based paper analytical tool for DEHP detection in aqueous environment.

Ensemble latent assimilation with deep learning surrogate model: application to drop interaction in a microfluidics device.

A major challenge in the field of microfluidics is to predict and control drop interactions. This work develops an image-based data-driven model to forecast drop dynamics based on experiments performed on a microfluidics device. Reduced-order modelling techniques are applied to compress the recorded images into low-dimensional spaces and alleviate the computational cost. Recurrent neural networks are then employed to build a surrogate model of drop interactions by learning the dynamics of compressed variables in the reduced-order space. The surrogate model is integrated with real-time observations using data assimilation. In this paper we developed an ensemble-based latent assimilation algorithm scheme which shows an improvement in terms of accuracy with respect to the previous approaches. This work demonstrates the possibility to create a reliable data-driven model enabling a high fidelity prediction of drop interactions in microfluidics device. The performance of the developed system is evaluated against experimental data (i.e., recorded videos), which are excluded from the training of the surrogate model. The developed scheme is general and can be applied to other dynamical systems.

Alzheimer's disease diagnosis via multimodal feature fusion.

Alzheimer's disease (AD) is the most common neurodegenerative disorder in the elderly. Early diagnosis of AD plays a vital role in slowing down the progress of AD because there is no effective drug to treat the disease. Some deep learning models have recently been presented for AD diagnosis and have more satisfactory performance than classic machine learning methods. Nevertheless, most of the existing computer-aided diagnostic models used neuroimaging features for diagnosis, ignoring patients' clinical and biological information. This makes the AD diagnosis inaccurate. In this study, we propose a novel multimodal feature transformation and fusion model for AD diagnosis. The feature transformation aims to avoid the difference in feature dimensions between different modal data and further mine the significant features for AD diagnosis. A geometric algebra-based feature extension method is proposed to obtain different levels of high-dimensional features from patients' clinical and personal biological data. Then, an influence degree-based feature filtration algorithm is proposed to filtrate those features that have no apparent guiding significance for AD diagnosis. Finally, an ANN (Artificial Neural Network)-based framework is designed to fuse transformed features with neuroimaging features extracted by CNN (Convolutional Neural Network) for AD diagnosis. The more in-depth feature mining of patients' clinical information and biological information can significantly improve the performance of computer-aided AD diagnosis. The experiments are obtained on the ADNI dataset. Our proposed model can converge faster and achieves 96.2% accuracy in AD diagnostic task and 87.4% accuracy in MCI (Mild Cognitive Impairment) diagnostic task. Compared with other methods, our proposed approach has an excellent performance in AD diagnosis and surpasses SOTA (state-of-the-art) methods. Therefore, our model can provide more reasonable suggestions for clinicians to diagnose and treat disease.

[Automated mapping of urban forests' disturbance and recovery in Nanjing, China].

Using Landsat TM/ETM dense time series observations spanning from 1987 to 2011, taking Laoshan forest farm and Purple Mountain as the research objects, the landsat ecosystem disturbance adaptive processing system (Ledaps) algorithm was used to generate surface reflectance datasets, which were fed to the vegetation change tracker model (VCT) model to derive urban forest disturbance and recovery products over Nanjing, followed by an intensive validation of the products. The results showed that there was a relatively high spatial agreement for forest disturbance products mapped by VCT, ranging from 65.4% to 95.0%. There was an apparent fluctuating forest disturbance and recovery rate over time, and the change trend of forest disturbance occurring at the two sites was roughly similar, but forest recovery was obviously different. Forest coverage in Purple Mountain was less than that in Laoshan forest farm, but the forest disturbance and recovery rates in Laoshan forest farm were larger than those in Purple Mountain.

STR data for the AmpFlSTR SGM Plus loci from Shantou (south of China).

Allele frequencies for the ten STRs included in the AmpFlSTR SGM Plus kit were obtained from a sample of 132 unrelated Han individuals born in the region of Shantou (south of China).