Target recycling-oriented amplification has been widely applied for sensitive detection of DNA, RNA, and proteins due to its successful overcoming the inherent limitation of target-to-signal ratio of 1:1 in the traditional hybridization assay. Exonuclease III (Exo III) is usually used as the cleavage enzyme in the target recycling-oriented amplification because of its easy availability, high catalytic activity, and wide applicability. Even though Exo III is assumed to be double-stranded DNA (dsDNA) specific exonuclease in most literature, its cleavage of single-strand DNA (ssDNA) does occur, resulting in the target-independent degradation of probes. Herein, we design an intramolecular displacement probe with the capability of resistance to the nonspecific digestion of Exo III and fast hybridization kinetics. Through the substitution of 2-aminopurine for adenine in the intramolecular displacement probes, we develop a rapid and label-free approach to monitor Exo III-assisted target recycling amplification. We further demonstrate that this method can be used for the detection of DNA and proteins with excellent specificity and high sensitivity. Importantly, this method can be extended to rapid, label-free and multiplexed detection of various nucleic acids, proteins, and small molecules using different kinds of fluorescent nucleotide analogues and specific aptamers.

Given a financial time series such as S&P 500, or any historical data in stock markets, how can we obtain useful information from recent transaction data to predict the ups and downs at the next moment? Recent work on this issue shows initial evidence that machine learning techniques are capable of identifying (non-linear) dependency in the stock market price sequences. However, due to the high volatility and non-stationary nature of the stock market, forecasting the trend of a financial time series remains a big challenge. In this paper, we introduced a new method to simplify noisy-filled financial temporal series via sequence reconstruction by leveraging motifs (frequent patterns), and then utilize a convolutional neural network to capture spatial structure of time series. The experimental results show the efficiency of our proposed method in feature learning and outperformance with 4%-7% accuracy improvement compared with the traditional signal process methods and frequency trading patterns modeling approach with deep learning in stock trend prediction. INDEX TERMS Trend prediction, convolutional neural network, financial time series, motif extraction.

Evaluation of plasma renin activity is essential to the assessment of renin-related diseases such as hypertension, congestive heart failure, and cancers. Here, we develop a single quantum dot (QD) based nanosensor for sensitive detection of renin activity. This single-QD-based nanosensor consists of a streptavidin-coated QD and multiple biotinylated and Cy5-labeled peptide substrates, which form a QD/substrate/Cy5 complex where fluorescence resonance energy transfer (FRET) occurs with the QD as the donor and Cy5 as the acceptor. The presence of renin leads to the cleavage of the substrate and the separation of Cy5 from the QD and consequently the decrease of FRET efficiency and the reduction of Cy5 counts. Through the measurement of Cy5 counts by total internal reflection fluorescence (TIRF) microscopy, the renin activity can be quantitatively evaluated at the single-molecule level. This single-QD-based nanosensor can measure not only the renin concentration, but also the enzymatic velocity and the Michaelis-Menten kinetic parameters, and has significant advantages of simplicity, low cost with minimum sample consumption, and high sensitivity with a detection limit of 25 pM. This single-QD-based nanosensor might be further applied to monitor a variety of important enzymatic biomarkers such as kinases and endonuleases.

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Increased glucose utilization is a hallmark of cancer, and tumor metabolism is emerging as anticancer target for therapeutic intervention. Triple-negative breast cancers TNBC are highly glycolytic and show poor clinical outcomes. We previously identified hexokinase 2, the major glycolytic enzyme, as a target gene of miR-143 in TNBC. Here, we developed a therapeutic formulation using cholesterol-modified miR-143 agomir encapsulated in a neutral lipid-based delivery agent that blocked tumor growth and glucose metabolism in TNBC tumor-bearing mice when administered systemically. The antioncogenic effects were accompanied by a reduction in the direct target hexokinase 2 and [18F]-fluorodeoxyglucose (18F-FDG) uptake based on positron emission tomography/computed tomography. Treatment with miR-143 formulation has minimal toxic effects and mice tolerated it well. Thus, we demonstrated that miR-143 is a robust inhibitor of the Warburg effect and an effective therapeutic target for TNBC. In addition, 18F-FDG positron emission tomography/computed tomography can be used to specifically monitor the response of TNBC to miR-143-based therapeutics by targeting tumor glycolysis.

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The ring-opening polymerization of δ-valerolactone catalyzed by a thermophilic esterase from the archaeon Archaeoglobus fulgidus was successfully conducted in organic solvents. The effects of enzyme concentration, temperature, reaction time and reaction medium on monomer conversion and product molecular weight were systematically evaluated. Through the optimization of reaction conditions, poly(δ-valerolactone) was produced in 97% monomer conversion, with a number-average molecular weight of 2225 g/mol, in toluene at 70 °C for 72 h. This paper has produced a new biocatalyst for the synthesis of poly(δ-valerolactone), and also deeper insight has been gained into the mechanism of thermophilic esterase-catalyzed ring-opening polymerization.