Mesoporous silica nanoparticle delivery of biomolecules into plants

Well dispersed mesoporous silica nanoparticles (MSNs) were synthesized and their biosafety were confirmed in the plant system. Plasmid DNA was effectively imported into plant cells using MSNs as delivery vectors. The controlled release of agrochemicals inside plants was achieved using MSN-mediated delivery system with redox-responsive gatekeepers

Unstructured Mixed Grid and SIMPLE Algorithm based Model for 2D-SWE

AbstractA 2D depth-averaged flow model was developed using implicit schemes on unstructured mixed grid. The implicit time-marching algorithm is adopted to make the model much stable. To suppress the numerical oscillation, the TVD (total-variation diminishing) based second-order convection scheme is employed in the framework of finite volume method. The new model is validated using measured data and compared with YGLai model (newly developed by Lai (2010)). Results show that the new model is consistent with the measured data fairly well. The comparison with YGLai model indicates that our new model is generally better with respect to accuracy

Learning Disentangled Representation Implicitly via Transformer for Occluded Person Re-Identification

Person re-identification (re-ID) under various occlusions has been a long-standing challenge as person images with different types of occlusions often suffer from misalignment in image matching and ranking. Most existing methods tackle this challenge by aligning spatial features of body parts according to external semantic cues or feature similarities but this alignment approach is complicated and sensitive to noises. We design DRL-Net, a disentangled representation learning network that handles occluded re-ID without requiring strict person image alignment or any additional supervision. Leveraging transformer architectures, DRL-Net achieves alignment-free re-ID via global reasoning of local features of occluded person images. It measures image similarity by automatically disentangling the representation of undefined semantic components, e.g., human body parts or obstacles, under the guidance of semantic preference object queries in the transformer. In addition, we design a decorrelation constraint in the transformer decoder and impose it over object queries for better focus on different semantic components. To better eliminate interference from occlusions, we design a contrast feature learning technique (CFL) for better separation of occlusion features and discriminative ID features. Extensive experiments over occluded and holistic re-ID benchmarks (Occluded-DukeMTMC, Market1501 and DukeMTMC) show that the DRL-Net achieves superior re-ID performance consistently and outperforms the state-of-the-art by large margins for Occluded-DukeMTMC

Hybrid Message Passing Algorithm for Downlink FDD Massive MIMO-OFDM Channel Estimation

The design of message passing algorithms on factor graphs has been proven to be an effective manner to implement channel estimation in wireless communication systems. In Bayesian approaches, a prior probability model that accurately matches the channel characteristics can effectively improve estimation performance. In this work, we study the channel estimation problem in a frequency division duplexing (FDD) downlink massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. As the prior probability, we propose the Markov chain two-state Gaussian mixture with large variance difference (TSGM-LVD) model to exploit the structured sparsity in the angle-frequency domain of the massive MIMO-OFDM channel. In addition, we present a new method to derive the hybrid message passing (HMP) rule, which can calculate the message with mixed linear and non-linear model. To the best of the authors' knowledge, we are the first to apply the HMP rule to practical communication systems, designing the HMP-TSGM-LVD algorithm under the structured turbo-compressed sensing (STCS) framework. Simulation results demonstrate that the proposed HMP-TSGM-LVD algorithm converges faster and outperforms its counterparts under a wide range of simulation settings

Evaluation of Anti-tumor and Chemoresistance-lowering Effects of Pectolinarigenin from Cirsium japonicum Fisch ex DC in Breast Cancer

Purpose: To investigate the antitumor and chemoresistance-lowering effects of pectolinarigenin on breast cancer cells.Methods: Pectolinarigenin was purified by a combination of silica gel and Sephadex LH-20 column chromatography from ethanol extracts of the aerial parts of C. japonicum DC. Breast cancer selfrenewal properties were tested by colony formation and tumor sphere formation assays. Thereafter, real-time polymerase chain reaction (PCR) was used to detect breast cancer stem cell markers. Furthermore, the effect of pectolinarigenin on breast cancer cell was evaluated by chemoresistance using 3-(4,5-dimethyl-2 thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) assay. Finally, tumor formation in nude mice was used to test the effect of pectolinarigenin on tumorigenicity of breast cancer cells in vivo.Results: The results showed that pectolinarigenin, extracted from Cirsium japonicum Fisch. ex DC., inhibited tumor cell self-renewal in MCF-7 breast cancer cells. Pectolinarigenin (25 μM) caused significant inhibition of colony formation (61.23 %, p < 0.001) and tumor sphere formation (59.49 %, p < 0.01) in MCF-7. The inhibitory effects were associated with changes in breast cancer stem cell markers. Treatment of breast cancer cells with pectolinarigenin reduced the chemoresistance of the cells to doxorubicin. At the same time, mRNA expression of chemoresistance genes (ATP binding cassette subfamily G member 2, ABCG2 and ATP binding cassette subfamily B member 1, MDR1) was repressed by pectolinarigenin. The inhibition efficiency of MDR1 and ABCG2 by 10 μM pectolinarigenin treatment was about 59.29 (p < 0.01) and 46.48 % (p < 0.01), respectively. Furthermore, pectolinarigenin reduced tumor mass in nude mice xenograft model.Conclusion: Pectolinarigenin inhibits breast cancer stem cell-like properties and lowers the chemoresistance of the cancer cells to chemotherapy. The results provide an insight into the mechanism of the anti-breast tumor effects and an experimental basis for the use of pectolinarigenin to enhance treatment of patients with breast cancer.Keywords: Pectolinarigenin, Cancer stem cells, Breast cancer, Chemoresistance, Cirsium japonicum Fisch. ex D