Objectives
Tanshinone I (Tan‐I) is one of the vital fatsoluble monomer components, which extracted from Chinese medicinal herb Salvia miltiorrhiza Bunge. It has been shown that Tan‐I exhibited ...anti‐tumour activities on different types of cancers. However, the underlying mechanisms by which Tan‐Ⅰ regulates apoptosis and autophagy in ovarian cancer remain unclear. Thus, this study aimed to access the therapy effect of Tan‐Ⅰ and the underlying mechanisms.
Methods
Ovarian cancer cells A2780 and ID‐8 were treated with different concentrations of Tan‐Ⅰ (0, 1.2, 2.4, 4.8 and 9.6 μg/mL) for 24 hours. The cell proliferation was analysed by CCK8 assay, EdU staining and clone formation assay. Apoptosis was assessed by the TUNEL assay and flow cytometry. The protein levels of apoptosis protein (Caspase‐3), autophagy protein (Beclin1, ATG7, p62 and LC3II/LC3I) and PI3K/AKT/mTOR pathway were determined by Western blot. Autophagic vacuoles in cells were observed with LC3 dyeing using confocal fluorescent microscopy. Anti‐tumour activity of Tan‐Ⅰ was accessed by subcutaneous xeno‐transplanted tumour model of human ovarian cancer in nude mice. The Ki67, Caspase‐3 level and apoptosis level were analysed by immunohistochemistry and TUNEL staining.
Results
Tan‐Ⅰ inhibited the proliferation of ovarian cancer cells A2780 and ID‐8 in a dose‐dependent manner, based on CCK8 assay, EdU staining and clone formation assay. In additional, Tan‐Ⅰ induced cancer cell apoptosis and autophagy in a dose‐dependent manner in ovarian cancer cells by TUNEL assay, flow cytometry and Western blot. Tan‐Ⅰ significantly inhibited tumour growth by inducing cell apoptosis and autophagy. Mechanistically, Tan‐Ⅰ activated apoptosis‐associated protein Caspase‐3 cleavage to promote cell apoptosis and inhibited PI3K/AKT/mTOR pathway to induce autophagy.
Conclusions
This is the first evidence that Tan‐Ⅰ induced apoptosis and promoted autophagy via the inactivation of PI3K/AKT/mTOR pathway on ovarian cancer and further inhibited tumour growth, which might be considered as effective strategy.
Prime editing is a novel and universal CRISPR/Cas-derived precision genome-editing technology that has been recently developed. However, low efficiency of prime editing has been shown in transgenic ...rice lines. We hypothesize that enhancing pegRNA expression could improve prime-editing efficiency. In this report, we describe two strategies for enhancing pegRNA expression. We construct a prime editing vector harboring two pegRNA variants for W542L and S621I double mutations in ZmALS1 and ZmALS2. Compared with previous reports in rice, we achieve much higher prime-editing efficiency in maize. Our results are inspiring and provide a direction for the optimization of plant prime editors.
This study aimed at predicting the survival status on non-small cell lung cancer patients with the phenotypic radiomics features obtained from the CT images.
A total of 186 patients' CT images were ...used for feature extraction via Pyradiomics. The minority group was balanced via SMOTE method. The final dataset was randomized into training set (n = 223) and validation set (n = 75) with the ratio of 3:1. Multiple random forest models were trained applying hyperparameters grid search with 10-fold cross-validation using precision or recall as evaluation standard. Then a decision threshold was searched on the selected model. The final model was evaluated through ROC curve and prediction accuracy.
From those segmented images of 186 patients, 1218 features were obtained via feature extraction. The preferred model was selected with recall as evaluation standard and the optimal decision threshold was set 0.56. The model had a prediction accuracy of 89.33% and the AUC score was 0.9296.
A hyperparameters tuning random forest classifier had greater performance in predicting the survival status of non-small cell lung cancer patients, which could be taken for an automated classifier promising to stratify patients.
Due to its storage and retrieval efficiency, cross-modal hashing (CMH) has been widely used for cross-modal similarity search in many multimedia applications. According to the training strategy, ...existing CMH methods can be mainly divided into two categories: relaxation-based continuous methods and discrete methods. In general, the training of relaxation-based continuous methods is faster than that of discrete methods, but the accuracy of relaxation-based continuous methods is not satisfactory. On the contrary, the accuracy of discrete methods is typically better than that of the relaxation-based continuous methods, but the training of discrete methods is very time-consuming. In this paper, we propose a novel CMH method, called Discrete Latent Factor model-based cross-modal Hashing (DLFH), for cross modal similarity search. DLFH is a discrete method which can directly learn the binary hash codes for CMH. At the same time, the training of DLFH is efficient. Experiments show that the DLFH can achieve significantly better accuracy than existing methods, and the training time of DLFH is comparable to that of the relaxation-based continuous methods which are much faster than the existing discrete methods.
The super augmented nested array (SANA) is a new sparse array (SA) proposed by expanding, changing the inter-element spacing (IES), and splitting the nested array (NA). The proposed SANA further ...enhances the sparsity of the NA to achieve lower mutual coupling (MC) and higher uniform degrees of freedom (uDOF) than the existing SAs. Specifically, the proposed SANA contains five uniform linear arrays (ULAs), and all sensor positions can be obtained from a closed-form expression. Moreover, the weight functions and achievable uDOF of the proposed SANA are analyzed in detail. It has been shown through simulations that SANA has a significant advantage over existing SAs for direction-of-arrival (DOA) estimation.
Remote difunctionalization of unactivated alkenes is challenging but a highly attractive tactic to install two functional groups across long distances. Reported herein is the first remote ...difunctionalization of alkenes with CO2. This visible‐light photoredox catalysis strategy provides a facile method to synthesize a series of carboxylic acids bearing valuable fluorine‐ or phosphorus‐containing functional groups. Moreover, this versatile protocol shows mild reaction conditions, broad substrate scope, and good functional‐group tolerance. Based on DFT calculations, a radical adds to an unactivated alkene to smoothly form a new carbon radical, followed by a 1,5‐hydrogen atom‐transfer process, the rate‐limiting step, generating a more stable benzylic radical. The reduction of the benzylic radicals by an IrII species generates the corresponding benzylic carbanions as the key intermediates, which further undergo nucleophilic attack with CO2 to generate carboxylates.
Reported is the first remote difunctionalization of unactivated alkenes with CO2 by visible‐light photoredox catalysis. Mechanistic studies indicate that a 1,5‐hydrogen atom‐transfer process is the rate‐limiting step and reduction of radical intermediates generates the corresponding carbanions. Other electrophiles, including aldehydes, ketones, and benzylic bromides, are also applicable in this process, demonstrating a general strategy for redox‐neutral remote difunctionalization of unactivated alkenes.
Hashing has been widely used for large-scale search due to its low storage cost and fast query speed. By using supervised information, supervised hashing can significantly outperform unsupervised ...hashing. Recently, discrete supervised hashing and feature learning based deep hashing are two representative progresses in supervised hashing. On one hand, hashing is essentially a discrete optimization problem. Hence, utilizing supervised information to directly guide discrete (binary) coding procedure can avoid sub-optimal solution and improve the accuracy. On the other hand, feature learning based deep hashing, which integrates deep feature learning and hash-code learning into an end-to-end architecture, can enhance the feedback between feature learning and hash-code learning. The key in discrete supervised hashing is to adopt supervised information to directly guide the discrete coding procedure in hashing. The key in deep hashing is to adopt the supervised information to directly guide the deep feature learning procedure. However, most deep supervised hashing methods cannot use the supervised information to directly guide both discrete (binary) coding procedure and deep feature learning procedure in the same framework. In this paper, we propose a novel deep hashing method, called deep discrete supervised hashing (DDSH). DDSH is the first deep hashing method which can utilize pairwise supervised information to directly guide both discrete coding procedure and deep feature learning procedure and thus enhance the feedback between these two important procedures. Experiments on four real datasets show that DDSH can outperform other state-of-the-art baselines, including both discrete hashing and deep hashing baselines, for image retrieval.
Deep Cross-Modal Hashing Qing-Yuan Jiang; Wu-Jun Li
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
2017-July
Conference Proceeding
Due to its low storage cost and fast query speed, cross-modal hashing (CMH) has been widely used for similarity search in multimedia retrieval applications. However, most existing CMH methods are ...based on hand-crafted features which might not be optimally compatible with the hash-code learning procedure. As a result, existing CMH methods with hand-crafted features may not achieve satisfactory performance. In this paper, we propose a novel CMH method, called deep cross-modal hashing (DCMH), by integrating feature learning and hash-code learning intothe same framework. DCMH is an end-to-end learning framework with deep neural networks, one for each modality, to perform feature learning from scratch. Experiments on three real datasets with image-text modalities show that DCMH can outperform other baselines to achieve the state-of-the-art performance in cross-modal retrieval applications.
The Moon has a magmatic and thermal history that is distinct from that of the terrestrial planets
. Radioisotope dating of lunar samples suggests that most lunar basaltic magmatism ceased by around ...2.9-2.8 billion years ago (Ga)
, although younger basalts between 3 Ga and 1 Ga have been suggested by crater-counting chronology, which has large uncertainties owing to the lack of returned samples for calibration
. Here we report a precise lead-lead age of 2,030 ± 4 million years ago for basalt clasts returned by the Chang'e-5 mission, and a
U/
Pb ratio (µ value)
of about 680 for a source that evolved through two stages of differentiation. This is the youngest crystallization age reported so far for lunar basalts by radiometric dating, extending the duration of lunar volcanism by approximately 800-900 million years. The µ value of the Chang'e-5 basalt mantle source is within the range of low-titanium and high-titanium basalts from Apollo sites (µ value of about 300-1,000), but notably lower than those of potassium, rare-earth elements and phosphorus (KREEP) and high-aluminium basalts
(µ value of about 2,600-3,700), indicating that the Chang'e-5 basalts were produced by melting of a KREEP-poor source. This age provides a pivotal calibration point for crater-counting chronology in the inner Solar System and provides insight on the volcanic and thermal history of the Moon.
High peak-to-average power ratio (PAPR) is a major drawback of orthogonal frequency division multiplexing (OFDM) systems. Among the various PAPR reduction techniques, companding transform appears ...attractive for its simplicity and effectiveness. This paper proposes a new companding algorithm. Compared with the others, the proposed algorithm offers an improved bit error rate and minimized out-of-band interference while reducing PAPR effectively. Theoretical analysis and numerical simulation are presented.
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