Proteome Fishing for CRISPR/Cas12a-Based Orthogonal Multiplex Aptamer Sensing.

Detection of serum protein biomarkers is extremely challenging owing to the superior complexity of serum. Here, we report a method of proteome fishing from the serum. It uses a magnetic nanoparticle-protein corona and a multiplexed aptamer panel, which we incubated with the nanoparticle-protein corona for biomarker recognition. To transfer protein biomarker detection to aptamer detection, we established a CRISPR/Cas12a-based orthogonal multiplex aptamer sensing (COMPASS) platform by profiling the aptamers of protein corona with clinical nonsmall cell lung cancer (NSCLC) serum samples. Furthermore, we determined the four out of nine (FOON) panel (including HE4, NSE, AFP, and VEGF165) to be the most cost-effective and accurate panel for COMPASS in NSCLC diagnosis. The diagnostic accuracy of NSCLC by the FOON panel with internal and external cohorts was 95.56% (ROC-AUC = 99.40%) and 89.58% (ROC-AUC = 95.41%), respectively. Our developed COMPASS technology circumvents the otherwise challenging multiplexed serum protein amplification problem and avoids aptamer degradation in serum. Therefore, this novel COMPASS could lead to the development of a facile, cost-effective, intelligent, and high-throughput diagnostic platform for large-cohort cancer screening.

Serum Protein Fishing for Machine Learning-Boosted Diagnostic Classification of Small Nodules of Lung.

Diagnosis of benign and malignant small nodules of the lung remains an unmet clinical problem which is leading to serious false positive diagnosis and overtreatment. Here, we developed a serum protein fishing-based spectral library (ProteoFish) for data independent acquisition analysis and a machine learning-boosted protein panel for diagnosis of early Non-Small Cell Lung Cancer (NSCLC) and classification of benign and malignant small nodules. We established an extensive NSCLC protein bank consisting of 297 clinical subjects. After testing 5 feature extraction algorithms and six machine learning models, the Lasso algorithm for a 15-key protein panel selection and Random Forest was chosen for diagnostic classification. Our random forest classifier achieved 91.38% accuracy in benign and malignant small nodule diagnosis, which is superior to the existing clinical assays. By integrating with machine learning, the 15-key protein panel may provide insights to multiplexed protein biomarker fishing from serum for facile cancer screening and tackling the current clinical challenge in prospective diagnostic classification of small nodules of the lung.

Identification of Rice Seed Varieties Based on Near-Infrared Hyperspectral Imaging Technology Combined with Deep Learning.

Rice is one of the most important food crops in the world, and rice seed varieties are related to the yield and quality of rice. This study used near-infrared (NIR) hyperspectral technology with conventional machine learning methods (support vector machine (SVM), logistic regression (LR), and random forest (RF)) and deep learning methods (LeNet, GoogLeNet, and residual network (ResNet)) to establish variety identification models for five common types of rice seeds. Among the deep learning methods, the classification accuracies of most models were higher than 95%. This study further used the deep learning methods to establish variety identification models for 10 varieties of rice seeds without considering their types. Among them, the ResNet model had the best classification results. The classification accuracy on the test set was 86.08%. This study used the saliency map method to visualize each convolutional neural network (CNN) model to find the band region that contributed the most to the data. The results showed that the bands with the largest data contribution were mainly concentrated at approximately 1300-1400 nm and secondarily concentrated at approximately 1050-1250 nm. The overall results showed that NIR hyperspectral imaging technology combined with deep learning could effectively distinguish rice seeds of different varieties. This method provided an effective way to identify rice seed varieties in a quick and nondestructive manner.

Mechanistic insights into ball milling enhanced montmorillonite modification with tetramethylammonium for adsorption of gaseous toluene.

Montmorillonite is widely used for pollutants adsorption due to its porous structure and low price. However, the low specific surface area and small porosity limit its application in gas adsorption field. In this study, montmorillonite was organically modified using a facile dry ball milling method by tetramethylammonium bromide. The adsorption behaviour of toluene as a model VOC compound on organic montmorillonite was systematically investigated through adsorption breakthrough curves, adsorption kinetics and isotherms. After modification by ball milling, the specific surface area of ball milling with tetramethylammonium bromide for montmorillonite modification (BMTMt) was increased from 20.6 m2/g to 186.4 m2/g, and the microporosity proportion was up to 47%. Dynamic adsorption experiments showed that the best performance of BMTMt for toluene (55.9 mg/g) was 6 times higher than that of original montmorillonite (8.8 mg/g). Compared with the water bath preparation method, ball milling method promoted the intercalation of tetramethylammonium bromide into the layers of montmorillonite, resulting in a higher proportion of micropores. Density functional theory calculations indicated that the interaction between tetramethylammonium bromide and montmorillonite was mainly electrostatic forces, and the enhanced adsorption performance for toluene was mainly through microporous filling. BMTMt was proved to be a promising adsorbent for VOCs removal.

Spatiotemporal variations of dissolved organic matter in a typical multi-source watershed in northern China: a fluorescent evidence.

The amount of dissolved organic matter (DOM) in a multi-source watershed is important for complete management and assessing the river basin's long-term safety. Based on this, we study the composition, spatiotemporal changes, and primary sources of DOM using the excitation-emission matrix (EEM) and parallel factor analysis (PARAFAC). The relationship between DOM composition and water quality was also discussed. It was found that the DOM in the North Canal River watershed was composed of two similar humic acid-like components (230, 335/400 nm and 260, 360/450 nm) and a tryptophan-like component (280/290-350 nm). The intensity of DOM shows obvious seasonal spatiotemporal variations. In terms of time, the relative concentration of DOM in winter is significantly higher than that in other seasons due to the influence of water volume, temperature, and photochemical degradation factors. As for the aspect of space, under the combined effect of land use and multiple sources of pollution, the relative concentration of tryptophan-like in the mainstream was significantly higher than tributaries, while the relative concentration of humic-like components in the tributaries was higher than that in the mainstream. The chief sources of DOM in the North Canal River watershed include human-derived point sources and agricultural non-point sources in the main channel, as well as terrestrial and microbiological sources in the tributaries. Moreover, the composition of DOM is significantly related to water quality indicators, especially nitrogen and phosphorus, which shows that DOM can have an indicative impact on the trophic status in the North Canal River. The findings of this study could have a predictive effect and provide a scientific foundation for water quality monitoring and pollution control in the North Canal River watershed.

A review on occurrence and risk of polycyclic aromatic hydrocarbons (PAHs) in lakes of China.

Polycyclic aromatic hydrocarbons (PAHs) residues have attracted attention worldwide. This study summarizes the current levels of PAH exposure in the water environments of lakes. In addition, the risk levels from individual PAHs and ΣPAHs in the water environments of lakes in China were evaluated by incremental lifetime cancer risk (ILCR) assessment, the toxic equivalent concentration (TEQBaP), the risk quotient (RQ), the effects range-low (ERL) and the effects range-median (ERM). The results showed that the concentrations of ∑PAHs in water and sediment ranged from 4.0 to 12,970.8 ng L-1 and 6.52 to 7935.21 ng g-1, respectively, and the highest concentrations of individual PAHs were of naphthalene (Nap) (6525 ng L-1), followed by indeno(1,2,3­cd)pyrene (IcdP) (3452.6 ng g-1). Concentrations in the Great Lakes region in China showed spatial difference, with the Qinghai-Tibet Plateau Lakes District and Mongolia-Xinjiang Lakes District being less polluted. However, the pollution level of PAHs in lakes is relatively high at the global scale. The ecological risk assessment found a moderate level of ∑PAHs in water, but benz(a)anthracene (BaA) and phenanthrene (Phe) had high RQ values, which might pose a significant risk to aquatic organisms in lakes. Although the contents of ∑PAHs in sediments are low, most individual PAHs pose potential risks, especially acenaphthene (Ace), fluorene (Flu) and dibenz(a,h)anthracene (DahA). This study revealed the pollution levels of PAHs across China and provided a scientific basis for PAH pollution control and environmental protection.