Small interfering RNA targeting mcl-1 enhances proteasome inhibitor-induced apoptosis in various solid malignant tumors

<p>Abstract</p> <p>Background</p> <p>Targeting the ubiquitin-proteasome pathway is a promising approach for anticancer strategies. Recently, we found Bik accumulation in cancer cell lines after they were treated with bortezomib. However, recent evidence indicates that proteasome inhibitors may also induce the accumulation of anti-apoptotic Bcl-2 family members. The current study was designed to analyze the levels of several anti-apoptotic members of Bcl-2 family in different human cancer cell lines after they were treated with proteasome inhibitors.</p> <p>Methods</p> <p>Different human cancer cell lines were treated with proteasome inhibitors. Western blot were used to investigate the expression of Mcl-1 and activation of mitochondrial apoptotic signaling. Cell viability was investigated using SRB assay, and induction of apoptosis was measured using flow cytometry.</p> <p>Results</p> <p>We found elevated Mcl-1 level in human colon cancer cell lines DLD1, LOVO, SW620, and HCT116; human ovarian cancer cell line SKOV3; and human lung cancer cell line H1299, but not in human breast cancer cell line MCF7 after they were treated with bortezomib. This dramatic Mcl-1 accumulation was also observed when cells were treated with other two proteasome inhibitors, MG132 and calpain inhibitor I (ALLN). Moreover, our results showed Mcl-1 accumulation was caused by stabilization of the protein against degradation. Reducing Mcl-1 accumulation by Mcl-1 siRNA reduced Mcl-1 accumulation and enhanced proteasome inhibitor-induced cell death and apoptosis, as evidenced by the increased cleavage of caspase-9, caspase-3, and poly (ADP-ribose) polymerase.</p> <p>Conclusions</p> <p>Our results showed that it was not only Bik but also Mcl-1 accumulation during the treatment of proteasome inhibitors, and combining proteasome inhibitors with Mcl-1 siRNA would enhance the ultimate anticancer effect suggesting this combination might be a more effective strategy for cancer therapy.</p

Multimodal Emotion Recognition From EEG Signals and Facial Expressions

Emotion recognition has attracted attention in recent years. It is widely used in healthcare, teaching, human-computer interaction, and other fields. Human emotional features are often used to recognize different emotions. Currently, there is more and more research on multimodal emotion recognition based on the fusion of multiple features. This paper proposes a deep learning model for multimodal emotion recognition based on the fusion of electroencephalogram (EEG) signals and facial expressions to achieve an excellent classification effect. First, a pre-trained convolution neural network (CNN) is used to extract the facial features from the facial expressions. Next, the attention mechanism is introduced to extract more critical facial frame features. Then, we apply CNNs to extract spatial features from original EEG signals, which use a local convolution kernel and a global convolution kernel to learn the features of left and right hemispheres channels and all EEG channels. After feature-level fusion, the fusion features of the facial expression features and EEG features are fed into the classifier for emotion recognition. This paper conducted experiments on the DEAP and MAHNOB-HCI datasets to evaluate the performance of the proposed model. The accuracy of valence dimension classification is 96.63&#x0025;, and arousal dimension classification is 97.15&#x0025; on the DEAP dataset, while 96.69&#x0025; and 96.26&#x0025; on the MAHNOB-HCI dataset. The experimental results show that the proposed model can effectively recognize emotions

Potential functions of histone H3.3 lysine 56 acetylation in mammals

H3K56 acetylation (H3K56Ac) was first identified in yeast and has recently been reported to play important roles in maintaining genomic stability, chromatin assembly, DNA replication, cell cycle progression and DNA repair. Although H3.1K56Ac has been relatively well studied, the function of H3.3K56Ac remains mostly unknown in mammals. In this study, we used H3.3K56Q and H3.3K56R mutants to study the possible function of H3.3K56 acetylation. The K-to-Q substitution mimics a constitutively acetylated lysine, while the K-to-R replacement mimics a constitutively unmodified lysine. We report that cell lines harbouring mutation of H3.3K56R exhibit increased cell death and dramatic morphology changes. Using a Tet-Off inducible system, we found an increased population of polyploid/aneuploid cells and decreased cell viability in H3.3K56R mutant cells. Consistent with these results, the H3.3K56R mutant had compromised H3.3 incorporation into several pericentric and centric heterochromatin regions we tested. Moreover, mass spectrometry analysis coupled with label-free quantification revealed that biological processes regulated by the H3.3-associating proteins, whose interaction with H3.3 was markedly increased by H3.3K56Q mutation but decreased by H3.3K56R mutation, include sister chromatid cohesion, mitotic nuclear division, and mitotic nuclear envelope disassembly. These results suggest that H3.3K56 acetylation is crucial for chromosome segregation and cell division in mammals

Large Data Set-Driven Machine Learning Models for Accurate Prediction of the Thermoelectric Figure of Merit

The figure of merit (zT) is a key parameter to measure the performance of thermoelectric materials. At present, the prediction of zT values via machine leaning has emerged as a promising method for exploring high-performance materials. However, the machine learning-based predictions still suffer from unsatisfactory accuracy, and this is related to the size of the data set, the hyperparameters of models, and the quality of the data. In this work, 5038 pieces of data of thermoelectric materials were selected, and several regression models were generated to predict zT values. This large data set-driven light gradient boosting (LGB) model with 57 features performed with an excellent accuracy, achieving a coefficient of determination (R2) value of 0.959, a root mean squared error (RMSE) of 0.094, a mean absolute error (MAE) of 0.057, and a correlation coefficient (R) of 0.979. Owing to the large size of the data set, the prediction accuracy exceeds that of most reported zT predictions via machine learning. The “ME Lattice Parameter” was verified as the most important feature in the zT prediction. Furthermore, nine potential candidates were screened out from among one million pieces of data. This study solves the problem of the data set size, adjusts the hyperparameters of the models, uses feature engineering to improve data quality, and provides an efficient strategy to perform wide-ranging screening for promising materials