Deep Semantic-Preserving Reconstruction Hashing for Unsupervised Cross-Modal Retrieval

Deep hashing is the mainstream algorithm for large-scale cross-modal retrieval due to its high retrieval speed and low storage capacity, but the problem of reconstruction of modal semantic information is still very challenging. In order to further solve the problem of unsupervised cross-modal retrieval semantic reconstruction, we propose a novel deep semantic-preserving reconstruction hashing (DSPRH). The algorithm combines spatial and channel semantic information, and mines modal semantic information based on adaptive self-encoding and joint semantic reconstruction loss. The main contributions are as follows: (1) We introduce a new spatial pooling network module based on tensor regular-polymorphic decomposition theory to generate rank-1 tensor to capture high-order context semantics, which can assist the backbone network to capture important contextual modal semantic information. (2) Based on optimization perspective, we use global covariance pooling to capture channel semantic information and accelerate network convergence. In feature reconstruction layer, we use two bottlenecks auto-encoding to achieve visual-text modal interaction. (3) In metric learning, we design a new loss function to optimize model parameters, which can preserve the correlation between image modalities and text modalities. The DSPRH algorithm is tested on MIRFlickr-25K and NUS-WIDE. The experimental results show that DSPRH has achieved better performance on retrieval tasks

Histopathological Image Retrieval Based on Asymmetric Residual Hash and DNA Coding

Histopathological image retrieval is a key technology for computer-aided diagnosis. However, patients are reluctant to reveal their privacy in histopathological image retrieval. In order to further improve the effectiveness and safety of histopathological image retrieval, this paper proposes a new histopathological retrieval scheme based on asymmetric residual hash (ARH) and DNA coding techniques. In this paper, we first present a novel ARH for histopathological image retrieval to improve the effectiveness of histopathological search scheme, and then we use the 5-D hyperchaotic system to protect patient privacy. Specifically, the contribution consists of four aspects: 1) A histopathology ciphertext domain search scheme was proposed to improve the performance of computer-aided diagnosis. 2) An asymmetric approach was implemented to process histopathological query points and database points, and a novel asymmetric residual hash algorithm was first proposed to improve the accuracy and speed of histopathological image retrieval. 3) The 5-D hyperchaotic system and DNA coding technique are applied to histopathological image retrieval to protect patient privacy. 4) The loss function is constructed and optimized to learn network parameters and hash codes. The simulation experiment was performed on three datasets (Kimia Path24, Kimia Path960, and Malaria), and the results proved the effectiveness of the ARH algorithm. In addition, our proposed search method can resist common types of attacks during histopathological data transmission. Specifically, the MAP of the ARH is 0.9678 on the KIMIA Path24 with the value of hyperparameter is 125 and the length of hash code is 32. The MAP of the ARH is 0.966 on the KIMIA Path960 with the value of hyperparameter is 225 and the length of hash code is 32. The MAP of the ARH is 0.9482 on the Malaria with the value of hyperparameter is 10 and the length of hash code is 24

CANet: A Combined Attention Network for Remote Sensing Image Change Detection

Change detection (CD) is one of the essential tasks in remote sensing image processing and analysis. Remote sensing CD is a process of determining and evaluating changes in various surface objects over time. The impressive achievements of deep learning in image processing and computer vision provide an innovative concept for the task of CD. However, existing methods based on deep learning still have problems detecting small changed regions correctly and distinguishing the boundaries of the changed regions. To solve the above shortcomings and improve the efficiency of CD networks, inspired by the fact that an attention mechanism can refine features effectively, we propose an attention-based network for remote sensing CD, which has two important components: an asymmetric convolution block (ACB) and a combined attention mechanism. First, the proposed method extracts the features of bi-temporal images, which contain two parallel encoders with shared weights and structures. Then, the feature maps are fed into the combined attention module to reconstruct the change maps and obtain refined feature maps. The proposed CANet is evaluated on the two publicly available datasets for challenging remote sensing image CD. Extensive empirical results with four popular metrics show that the designed framework yields a robust CD detector with good generalization performance. In the CDD and LEVIR-CD datasets, the F1 values of the CANet are 3.3% and 1.3% higher than those of advanced CD methods, respectively. A quantitative analysis and qualitative comparison indicate that our method outperforms competitive baselines in terms of both effectiveness and robustness

Parameter Identification for Lithium-Ion Battery Based on Hybrid Genetic–Fractional Beetle Swarm Optimization Method

This paper proposes a fractional order (FO) impedance model for lithium-ion batteries and a method for model parameter identification. The model is established based on electrochemical impedance spectroscopy (EIS). A new hybrid genetic–fractional beetle swarm optimization (HGA-FBSO) scheme is derived for parameter identification, which combines the advantages of genetic algorithms (GA) and beetle swarm optimization (BSO). The approach leads to an equivalent circuit model being able to describe accurately the dynamic behavior of the lithium-ion battery. Experimental results illustrate the effectiveness of the proposed method, yielding voltage estimation root-mean-squared error (RMSE) of 10.5 mV and mean absolute error (MAE) of 0.6058%. This corresponds to accuracy improvements of 32.26% and 7.89% for the RMSE, and 43.83% and 13.67% for the MAE, when comparing the results of the new approach to those obtained with the GA and the FBSO methods, respectively

Silencing of lncRNA UCA1 inhibited the pathological progression in PCOS mice through the regulation of PI3K/AKT signaling pathway

Abstract Background Polycystic ovary syndrome (PCOS) is the most common hormonal disorder among reproductive-aged women worldwide, however, the mechanisms and progression of PCOS still unclear due to its heterogeneous nature. Using the human granulosa-like tumor cell line (KGN) and PCOS mice model, we explored the function of lncRNA UCA1 in the pathological progression of PCOS. Results CCK8 assay and Flow cytometry were used to do the cell cycle, apoptosis and proliferation analysis, the results showed that UCA1 knockdown in KGN cells inhibited cell proliferation by blocking cell cycle progression and promoted cell apoptosis. In the in vivo experiment, the ovary of PCOS mice was injected with lentivirus carrying sh-UCA1, the results showed that knockdown of lncRNA UCA1 attenuated the ovary structural damage, increased the number of granular cells, inhibited serum insulin and testosterone release, and reduced the pro-inflammatory cytokine production. Western blot also revealed that UCA1 knockdown in PCOS mice repressed AKT activation, inhibitor experiment demonstrated that suppression of AKT signaling pathway, inhibited the cell proliferation and promoted apoptosis. Conclusions Our study revealed that, in vitro, UCA1 knockdown influenced the apoptosis and proliferation of KGN cells, in vivo, silencing of UCA1 regulated the ovary structural damage, serum insulin release, pro-inflammatory production, and AKT signaling pathway activation, suggesting lncRNA UCA1 plays an important role in the pathological progression of PCOS

Metabolic Mechanism of Plant Defense against Rice Blast Induced by Probenazole

The probenazole fungicide is used for controlling rice blast (Magnaporthe grisea) primarily by inducing disease resistance of the plant. To investigate the mechanism of induced plant defense, rice seedlings were treated with probenazole at 15 days post emergence, and non-treated plants were used for the control. The plants were infected with M. grisea 5 days after chemical treatment and incubated in a greenhouse. After 7 days, rice seedlings were sampled. The metabolome of rice seedlings was chemically extracted and analyzed using gas chromatography and mass spectrum (GC-MS). The GC-MS data were processed using analysis of variance (ANOVA), principal component analysis (PCA) and metabolic pathway elucidation. Results showed that probenazole application significantly affected the metabolic profile of rice seedlings, and the effect was proportionally leveraged with the increase of probenazole concentration. Probenazole resulted in a change of 54 metabolites. Salicylic acid, γ-aminobutyrate, shikimate and several other primary metabolites related to plant resistance were significantly up-regulated and some metabolites such as phenylalanine, valine and proline were down-regulated in probenazole-treated seedlings. These results revealed a metabolic pathway of rice seedlings induced by probenazole treatment regarding the resistance to M. grisea infection

Representative microPET images of mice with SKOV3 tumors at 30, 60, and 120 min post-injection of [¹⁸F]AlF-NOTA-C6, with or without an overdose of C6 to pre-block (top row: non-blocked; bottom row: blocked).

<p>Arrows indicate the tumor sites.</p

Representative microPET images of mice with PC3 tumors at 30, 60, and 120 min post-injection of [¹⁸F]AlF-NOTA-C6.

<p>Arrows indicate the tumor sites.</p

Cell uptake assays of [¹⁸F]AlF-NOTA-C6.

<p>Cell uptake assays of [¹⁸F]AlF-NOTA-C6.</p