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To regulate nanostructure synthesis is of crucial importance for developing various applications, including catalysis, bioanalysis, and optical devices. Herein, the morphology and peroxidase (POD)-mimicking activity of peptide-templated copper nanoassemblies (Cu NAs) are regulable with peptide types. The Cu NAs templated with peptide containing single cysteine are uniform nanoclusters with strong POD-like activity. However, the Cu NAs templated with peptide containing two cysteines are fusiform-like with very weak POD-like activity. Unexpectedly, the POD-like activity of Cu NAs templated with peptide containing two cysteines with lysine between the cysteines is significantly enhanced when trypsin is incubated, which is unchanged for the Cu NAs templated with peptide containing two cysteines without lysine between the cysteines. The remarkably enhanced POD-mimicking activity originates from trypsin specifically shearing the peptide bond on the lysine, thereby allowing the aggregated Cu NAs to unravel into individual nanoclusters. Therefore, a robust colorimetric sensing platform was constructed for sensitive and selective detection of trypsin, which showed a linear concentration range of 3–1000 nM and a detection limit of 0.82 nM (S/N = 3). More interestingly, featured by trypsin inhibitor restraining trypsin activity, it enabled us to screen trypsin inhibitors as well. Subsequently, the developed assay was applied to detect trypsin in serum samples with good accuracy and reproducibility. Thus, this strategy shows great potential application in the clinic for diagnosis of trypsin-indicating diseases as well as the screening of trypsin inhibitor-based anti-cancer drugs.

ABSTRACTLung adenocarcinoma (LUAD) is originated from the mucus-producing glands of the lungs. The involvement of long noncoding RNAs (lncRNAs) has been discovered in multiple diseases. In the present research, we aimed to unmask the role of C2CD4D and THEM5 antisense RNA 1 (C2CD4D-AS1) in LUAD. RT-qPCR or western blot analysis was respectively applied in the detection of RNA or protein expressions. The function of C2CD4D-AS1 in LUAD was assessed by functional assays. Through ChIP, RNA pull down, DNA pull down, RIP and luciferase reporter assays, the in-depth regulatory mechanism of C2CD4D-AS1 in LUAD was explored. C2CD4D-AS1 was dramatically overexpressed in LUAD tissues and cell lines. As a result, depletion of C2CD4D-AS1 significantly repressed cell proliferation, migration, invasion and stimulated cell apoptosis in LUAD. Mechanistically, ETS variant transcription factor 4 (ETV4) activated the transcription of C2CD4D-AS1 and stimulated its up-regulation in LUAD cells, thus affecting LUAD cell biological functions. Furthermore, C2CD4D-AS1 sponged microRNA-3681-3p (miR-3681-3p) and regulated NIMA-related kinase 2 (NEK2), thus participating in modulating LUAD cell biological behaviors. To conclude, C2CD4D-AS1 up-regulation induced by ETV4 enhanced NEK2 expression by sequestering miR-3681-3p to contribute to the malignant behaviors of LUAD cells.

Nanozymes are artificial enzymes, which can substitute traditional biological enzymes for multifield applications. However, to date, it remains challenging to search novel mimic enzymes or multienzyme mimics. Herein, a facile and green method for preparing monodisperse, homogeneous copper nanoclusters (Cu NCs) with smaller size was developed, which used cysteamine as a template and hydrazine hydrate as a reductant to reduce Cu2+. The as-prepared Cu NCs exhibited excellent tetraenzyme-like activities, including peroxidase (POD)-, catalase (CAT)-, superoxide dismutase (SOD)-, and ascorbic acid oxidase (AAO)-mimic activities. The mechanisms, kinetics, and catalytic performances of Cu NCs were systematically studied. Moreover, based on the POD-like activity of Cu NCs, sensitive and simple colorimetric sensing glutathione (GSH) was explored, with the low limit of detection of 0.89 μM GSH (S/N= 3). Additionally, a novel fluorimetric ascorbic acid (AA) sensor was developed with the linear range of 0.5–30 μM and limit of detection (LOD) of 0.144 μM, on the basis of the principle that AA is oxidized to dehydroascorbic acid (DHAA) specifically catalyzed by the AAO-like activity of Cu NCs, while DHAA can further react with o-phenylenediamine (OPDA) to generate a highly fluorescent quinoxaline (DFQ) derivative. The as-proposed colorimetric GSH sensor and the fluorimetric AA sensor were capable of detecting GSH and AA, respectively, in real samples accurately and reproducibly. Thus, the Cu NCs-based multienzyme mimic is a promising candidate for biocatalysis and biosensing.

It is important to detect pathogens rapidly, sensitively, and selectively for clinical medicine, homeland security, food safety, and environmental control. We report here a specific and sensitive colorimetric assay that incorporated a bovine serum albumin-templated Co3O4magnetic nanozyme (Co3O4MNE) with a novel specific fusion phage protein and magnetophoretic chromatography to detect Staphylococcus aureus. The Co3O4MNE was conjugated to S. aureus-specific fusion-pVIII (Co3O4MNE@fusion-pVIII), screened from the S. aureus-specific phage AQTFLGEQD (the phage monoclone is denoted by the peptide sequence). The as-prepared triple-functional Co3O4MNE@fusion-pVIII particles were capable of capturing S. aureusin sterile milk, which were then isolated from milk magnetically. Assisted by polyethylene glycol, the Co3O4MNE@fusion-pVIII@S. aureuscomplex was separated from the free Co3O4MNE@fusion-pVIII by magnetophoretic chromatography in an external magnetic field. After transferring the isolated Co3O4MNE@fusion-pVIII@S. aureuscomplexes into a 96-well plate, diammonium salt of 2,2′-azino-bis(3-ethylbenzo-thiazoline-6-sulfonic acid) and H2O2were added to develop color because of the peroxidase mimetics activity of the Co3O4MNE. A S. aureusconcentration within 10–10,000 cfu/mL in milk can be detected (detection limit: 8 cfu/mL). The as-developed method is simple, cost-efficient, and sensitive, which is useful for rapidly diagnosing pathogenic bacteria and helpful to prevent disease outbreaks induced by pathogens in developing countries.

An optically pumped two-dimensional organic quasi-crystal microcavity laser is demonstrated based on conjugated polymer poly(2-methoxy, 5-(2^′-3ethylhexyloxy)-1,4-phenylene vinylene) (MEH-PPV). The optical resonator consists of the octagonal quasi-crystal for light localization in-plane by the bandgap effect and the distributed Bragg reflector introduced between the slab-substrate interface by inhibiting the scattering and absorption of light in the substrate to achieve vertical confinement of the light. A modified point-defect traps and localizes photons into the microcavity, forcing the wave oscillation along the vertical waveguide. The experimental results show that the single-mode lasing action by optical pumping is observed at 602.2 nm with an FWHM of 0.7 nm. The threshold of lasing is lowered to 6.9 μJ/pulse.

A high-throughput and sensitive fluorimetric analysis method has been developed with wettable microwells array for probing short-chain microRNAs (miRNAs) in blood using silver nanoclusters (AgNCs) with red fluorescence amplified and stabilized by metal organic frameworks (MOFs). Glass slides were first spotted with polyacrylic acid to form hydrophilic microdots and then patterned with hydrophobic hexadecyltrimethoxysilane, followed by etching the microdots to yield the microwells array. Furthermore, the DNA capture probes with silver-binding G sequences were covalently bound onto the functionalized microwells to hybridize with targeting miRNAs. Exonuclease I catalytic digestion was then conducted to remove any single-strand DNA probes unhybridized. Eventually, AgNCs were applied to specifically recognize the G sequences of DNA probes survived to be further coated with MOFs of zeolitic imidazolate framework (ZIF-8). Unexpectedly, the red fluorescence of AgNCs probes could be dramatically enhanced due to the “electron-donor effect” of nitrogen-containing ligands of ZIF-8 coatings, together with improved sensing stability. High detection sensitivity and reproducibility could thereby be expected for detecting miRNAs in the blood with the concentrations linearly ranging from 0.175 to 500 pM. Markedly different from the common sandwiched DNA detection methods, the developed fluorimetric strategy using AgNCs coated with MOFs and DNA probes designed with silver-recognizing sequences would be tailored for quantifying various kinds of short-chain miRNAs with low levels in the complicated blood media.

Recent years, textual semantic similarity measurements play an important role in Natural Language Processing. The semantic similarity between concepts or terms can be measured by various resources like corpora, ontologies, taxonomies, etc. With the development of deep learning, distributed vector models are constructed for extracting the latent semantic information from corpora. Most of existing models create a single prototype vector to represent the meaning of a word such as CBOW. However, due to lexical ambiguity, encoding word meaning with a single vector is problematic. In this work, the authors propose a knowledge-augmented multiple-prototype model by using corpora and ontologies. Based on the distributed word vector learned by the CBOW model, the authors append the concept definition and the relational knowledge vector into the target word vector to enrich the semantic information of the word. Finally, the authors perform the experiments on well-known datasets to verify the efficiency of the authors' approach.

C22H43.33N2O3.67S, monoclinic, P21/c, a= 9.5273(1) Å, b= 14.8850(2) Å, c= 17.5820(2) Å, V= 2492.84(5) Å3, Z= 4, Rgt(F) = 0.0526, wRref(F2) = 0.1459, T= 296.

As an information carrier with rich semantics, image plays an increasingly important role in real-time monitoring of logistics management. Abnormal objects are typically closely related to the specific region. Detecting abnormal objects in the specific region is conducive to improving the accuracy of detection and analysis, thereby improving the level of logistics management. Motivated by these observations, we design the method called abnormal object detection in a specific region based on Mask R-convolutional neural network: Abnormal Object Detection in Specific Region. In this method, the initial instance segmentation model is obtained by the traditional Mask R-convolutional neural network method, then the region overlap of the specific region is calculated and the overlapping ratio of each instance is determined, and these two parts of information are fused to predict the exceptional object. Finally, the abnormal object is restored and detected in the original image. Experimental results demonstrate that our proposed Abnormal Object Detection in Specific Region can effectively identify abnormal objects in a specific region and significantly outperforms the state-of-the-art methods.

C14H26N2O3S, monoclinic, Pc(no. 7), a= 7.1396(4) Å, b= 9.2946(5) Å, c= 13.4298(7) Å, β= 115.688(4)°, V= 803.12(8) Å3, Z= 2, Rgt(F)= 0.0341, wRref(F2)= 0.0961, S= 1.054, T= 296(2) K.

Hot mix asphalt (HMA) mixture is a major building material in paving engineering. To decrease energy consumption in HMA, a prediction model of energy consumption was investigated systematically in this paper, employing kernel principal component analysis (KPCA) and a support vector machine (SVM). The purpose of the work is to optimize production and structure parameters of an HMA plant. A prediction model of energy consumption cannot only be used for understanding but can also be used to develop new asphalt mixing plants and to achieve optimization. The main relationships between energy consumption and production parameters are studied in some field tests. To build a multi-parameter model of energy consumption, eigenvectors of many factors are optimized employing KPCA. The three kernel principal components of higher contribution rates are chosen. A prediction model of energy consumption is built using KPCA and SVM. Energy consumption of aggregate drying is predicted using the constructed model. The prediction model is optimized by particle swarm optimization (PSO). Influence coefficients of energy consumption are obtained by SVM and piecewise least-squares regression (PLSR) and are found to be consistent with test results. There is about a 5% error in energy consumption between the model prediction and the test. The prediction error can meet engineering requirements in the hot mix asphalt mixing plant.

Hot mix asphalt (HMA) mixture is a major building material in paving engineering. To decrease energy consumption in HMA, a prediction model of energy consumption was investigated systematically in this paper, employing kernel principal component analysis (KPCA) and a support vector machine (SVM). The purpose of the work is to optimize production and structure parameters of an HMA plant. A prediction model of energy consumption cannot only be used for understanding but can also be used to develop new asphalt mixing plants and to achieve optimization. The main relationships between energy consumption and production parameters are studied in some field tests. To build a multi-parameter model of energy consumption, eigenvectors of many factors are optimized employing KPCA. The three kernel principal components of higher contribution rates are chosen. A prediction model of energy consumption is built using KPCA and SVM. Energy consumption of aggregate drying is predicted using the constructed model. The prediction model is optimized by particle swarm optimization (PSO). Influence coefficients of energy consumption are obtained by SVM and piecewise least-squares regression (PLSR) and are found to be consistent with test results. There is about a 5% error in energy consumption between the model prediction and the test. The prediction error can meet engineering requirements in the hot mix asphalt mixing plant.