Effect of glaucocalyxin B on the protein expressions of PTEN, Beclin1 and LC3 in a mouse model of transplanted cervical cancer, and its significance

Purpose: To determine the effect of glaucocalyxin B (GLB) on the protein expressions of PTEN, Beclin1 and LC3 in a mouse model of transplanted cervical cancer, and its significance.Methods: A mouse model of transplanted cervical cancer was established in female BALB/C mice. The model mice were divided into control group, low-dose GLB group and high-dose GLB group. Mice in low-dose and high-dose groups were given intraperitoneal injection of low-dose GLB and high-dose GLB, respectively. The volume and weight of transplanted tumor were measured and compared between the two groups. Serum levels of CEA and CA125 were assayed by enzyme-linked immunosorbent assay (ELISA). The expressions of phosphatase and tensin homolog (PTEN), autophagy-related factor microtubule-associated protein-1 (Beclin-1), microtubule-associated protein 1 light chain 3 (LC3), apoptosis-related protein p53, and Bax were determined using SABC immunohistochemical operation.Results: On days 5, 10 and 15, the volume and weight of transplanted tumor, and levels of CA125 and CEA in low- and high-dose GLB groups were significantly and dose-dependently lower than those in control group (p < 0.05). Results from immunohistochemistry showed that the protein expression levels of PTEN, Beclin-1, LC3, p53 and Bax were significantly and dose-dependently higher in low- and highdose GLB groups than in the control group (p < 0.05).Conclusion: Glaucocalyxin B significantly and dose-dependently induces apoptosis of cervical cancer cells and inhibits their growth by regulating the protein expressions of PTEN, Beclin1 and LC3. Thus, glaucocalyxin B is a potential adjunct therapy in the management of cervical cancer

Constant-Size Unbounded Multi-Hop Fully Homomorphic Proxy Re-Encryption from Lattices

Proxy re-encryption is a cryptosystem that achieves efficient encrypted data sharing by allowing a proxy to transform a ciphertext encrypted under one key into another ciphertext under a different key. Homomorphic proxy re-encryption (HPRE) extends this concept by integrating homomorphic encryption, allowing not only the sharing of encrypted data but also the homomorphic computations on such data. The existing HPRE schemes, however, are limited to a single or bounded number of hops of ciphertext re-encryptions. To address this limitation, this paper introduces a novel lattice-based, unbounded multi-hop fully homomorphic proxy re-encryption (FHPRE) scheme, with constant-size ciphertexts. Our FHPRE scheme supports an unbounded number of reencryption operations and enables arbitrary homomorphic computations over original, re-encrypted, and evaluated ciphertexts. Additionally, we propose a potential application of our FHPRE scheme in the form of a non-interactive, constant-size multi-user computation system for cloud computing environments

Radar Target Classification Using an Evolutionary Extreme Learning Machine Based on Improved Quantum-Behaved Particle Swarm Optimization

A novel evolutionary extreme learning machine (ELM) based on improved quantum-behaved particle swarm optimization (IQPSO) for radar target classification is presented in this paper. Quantum-behaved particle swarm optimization (QPSO) has been used in ELM to solve the problem that ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. But the method for calculating the characteristic length of Delta potential well of QPSO may reduce the global search ability of the algorithm. To solve this issue, a new method to calculate the characteristic length of Delta potential well is proposed in this paper. Experimental results based on the benchmark functions validate the better performance of IQPSO against QPSO in most cases. The novel algorithm is also evaluated by using real-world datasets and radar data; the experimental results indicate that the proposed algorithm is more effective than BP, SVM, ELM, QPSO-ELM, and so on, in terms of real-time performance and accuracy

Body fat ratio as a novel predictor of complications and survival after rectal cancer surgery

BackgroundThe present study aimed to evaluate the association between body fat ratio (BFR), visceral fat area (VFA), body mass index (BMI) and visceral fat density (VFD) and assess their reliability in assessing risk of postoperative complications and survival status in patients with rectal cancer (RC).Materials and methodsThe present study retrospectively included 460 patients who underwent surgical treatment for RC at the First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College, Wuhu, China) between September 2018 and July 2021. BFR, VFA, BMI, and VFD were measured and basic information, clinical data, complications and survival were recorded.ResultsStatistical analysis was performed to determine optimal BFR cut-off and evaluate group differences. BFR demonstrated a significant positive correlation with VFA (R = 0.739) and BMI (R = 0.783) and significant negative correlation with VFD (R = −0.773). The areas under the receiver operating characteristic curve of BFR, VFA, BMI, and VFD in predicting postoperative complications in RC were all >0.7 and the optimal cut-off value of BFR was 24.3. Patients in the BFR-low group had fewer postoperative complications, lower intraoperative indices, shorter hospitalization times and lower costs than those in the BFR-high group. BFR predicted complications with high diagnostic significance and was validated by multiple models. Furthermore, patients in the BFR-high group had a longer overall survival compared with patients in the BFR-low group.ConclusionBFR was associated with BMI, VFA, and VFD. A BFR threshold of 24.3 was correlated with decreased complications and enhanced long-term survival

Silencing MicroRNA-134 Alleviates Hippocampal Damage and Occurrence of Spontaneous Seizures After Intraventricular Kainic Acid-Induced Status Epilepticus in Rats

Epilepsy is a disorder of abnormal brain activity typified by spontaneous and recurrent seizures. MicroRNAs (miRNAs) are short non-coding RNAs, critical for the post-transcriptional regulation of gene expression. MiRNA dysregulation has previously been implicated in the induction of epilepsy. In this study, we examined the effect of silencing miR-134 against status epilepticus (SE). Our results showed that level of miR-134 was significantly up-regulated in rat brain after Kainic acid (KA)-induced SE. TUNEL staining showed that silencing miR-134 alleviated seizure-induced neuronal apoptosis in the CA3 subfield of the hippocampus. Western blot showed that a miR-134 antagonist suppressed lesion-induced endoplasmic reticulum (ER) stress and apoptosis related expression of CHOP, Bim and Cytochrome C, while facilitated the expression of CREB at 24 h post KA-induced lesion in the hippocampus. Consistently, silencing miR-134 significantly diminished loss of CA3 pyramidal neurons using Nissl staining as well as reducing aberrant mossy fiber sprouting (MFS) in a rat epileptic model. In addition, the results of EEG and behavior analyses showed seizures were alleviated by miR-134 antagonist in our experimental models. These results suggest that silencing miR-134 modulates the epileptic phenotype by upregulating its target gene, CREB. This in turn attenuates oxidative and ER stress, inhibits apoptosis, and decreases MFS long term. This indicates that silencing miR-134 might be a promising intervention for the treatment of epilepsy

Informative scene decomposition for crowd analysis, comparison and simulation guidance

Crowd simulation is a central topic in several fields including graphics. To achieve high-fidelity simulations, data has been increasingly relied upon for analysis and simulation guidance. However, the information in real-world data is often noisy, mixed and unstructured, making it difficult for effective analysis, therefore has not been fully utilized. With the fast-growing volume of crowd data, such a bottleneck needs to be addressed. In this paper, we propose a new framework which comprehensively tackles this problem. It centers at an unsupervised method for analysis. The method takes as input raw and noisy data with highly mixed multi-dimensional (space, time and dynamics) information, and automatically structure it by learning the correlations among these dimensions. The dimensions together with their correlations fully describe the scene semantics which consists of recurring activity patterns in a scene, manifested as space flows with temporal and dynamics profiles. The effectiveness and robustness of the analysis have been tested on datasets with great variations in volume, duration, environment and crowd dynamics. Based on the analysis, new methods for data visualization, simulation evaluation and simulation guidance are also proposed. Together, our framework establishes a highly automated pipeline from raw data to crowd analysis, comparison and simulation guidance. Extensive experiments and evaluations have been conducted to show the flexibility, versatility and intuitiveness of our framework

Radar Target Recognition Based on Stacked Denoising Sparse Autoencoder

Feature extraction is a key step in radar target recognition. The quality of the extracted features determines the performance of target recognition. However, obtaining the deep nature of the data is difficult using the traditional method. The autoencoder can learn features by making use of data and can obtain feature expressions at different levels of data. To eliminate the influence of noise, the method of radar target recognition based on stacked denoising sparse autoencoder is proposed in this paper. This method can extract features directly and efficiently by setting different hidden layers and numbers of iterations. Experimental results show that the proposed method is superior to the K-nearest neighbor method and the traditional stacked autoencoder

A Radar Target Classification Algorithm Based on Dropout Constrained Deep Extreme Learning Machine

Radar target classification is very important in military and civilian fields. Extreme Learning Machines (ELMs) are widely used in classification because of their fast learning speed and good generalization performance. However, because of their shallow architecture, ELMs may not effectively capture the data high level abstractions. Although many researchers have proposed the Deep Extreme Learning Machine (DELM), which can be used to automatically learn high level feature representations, the model easily falls into overfitting when the training sample is limited. To address this issue, Dropout Constrained Deep Extreme Learning Machine (DCDELM) is proposed in this paper. The experimental results on the measured radar data show that the accuracy of the proposed algorithm can reach 93.37%, which is 5.25% higher than that of the stacked autoencoder algorithm, and 8.16% higher than that of the traditional DELM algorithm

Improved production of levoglucosan and levoglucosenone from acid-impregnated cellulose via fast pyrolysis

In this research, the production of levoglucosan (LG) and levoglucosenone (LGO) was improved from acid-impregnated cellulose via fast pyrolysis. Thermogravimetric and kinetic analysis disclosed the production formation mechanism. The impregnation of acid could reduce the activation energy and lower the reaction temperature. Furthermore, the yield of LG from H3PO4- and H2SO4-impregnated cellulose increased significantly (26.4-35.8 wt%) compared with that of pure cellulose (7.5 wt%) pyrolysis at 300 degrees C. At 350 degrees C, 0.1 wt% H3PO4-impregnated cellulose gave a maximum LGO yield (18.3 wt%) via pyrolysis, which increased 36 times concerning that of cellulose without acid impregnation (0.5 wt%). This study exhibited great potential for industrial LG and LGO production from cellulose at low temperatures