Topoisomerase II trapping agent teniposide induces apoptosis and G2/M or S phase arrest of oral squamous cell carcinoma

BACKGROUND: Teniposide (VM-26) has been widely used in the treatment of small cell lung cancer, malignant lymphoma, breast cancer, etc. However, there are few reports on VM-26 against oral cancers. The present study was designed to identify the effect of VM-26 against oral squamous cell carcinoma in vitro, and to provide evidence for the feasibility and effectiveness of VM-26 for application to the patients with oral cancer. METHODS: Human tongue squamous cell carcinoma cell line, Tca8113, was used. Cells were incubated with different concentrations of VM-26 for a variety of time span. Cisplatin (CDDP) was employed as a control reagent. MTT assay was used to assess the inhibitory rate of Tca8113 growth. Flow cytometer (FCM), transmission electronic microscope (TEM) and fluorescence staining were employed for determining the cell apoptotic rate. Cell cycle distribution of Tca8113 incubated with VM-26 was examined by flow cytometer assay. Statistic software (SAS 6.12, USA) was used for one-way ANOVA. RESULTS: The IC50 of VM-26 against Tca8113 cells was 0.35 mg/l and that of CDDP was 1.1 mg/l. The morphological changes of Tca8113 cells were observed with fluorescence microscope and TEM. Apoptotic morphological feature could be found in the nucleus. Apoptotic rate of Tca8113 cells incubated with 5.0 mg/l of VM-26 for 72 hours was 81.67% and cells waere arrested at S phase. However, when exposed to 0.15 mg/l of VM-26 for 72 hours, G2/M phase increased from 12.75% to 98.71%, while the apoptotic rate was 17.38%, which was lower than that exposed to 5.0 mg/l of VM-26. CONCLUSION: VM-26 could significantly induce apoptosis of oral squamous cell carcinoma and inhibit cell growth. There may be another pathway to induce apoptosis of oral squamous cell carcinoma cells except for G2/M phase arrest

The influence of passivation and photovoltaic properties of α-Si:H coverage on silicon nanowire array solar cells

Silicon nanowire (SiNW) arrays for radial p-n junction solar cells offer potential advantages of light trapping effects and quick charge collection. Nevertheless, lower open circuit voltages (V(oc)) lead to lower energy conversion efficiencies. In such cases, the performance of the solar cells depends critically on the quality of the SiNW interfaces. In this study, SiNW core-shell solar cells have been fabricated by growing crystalline silicon (c-Si) nanowires via the metal-assisted chemical etching method and by depositing hydrogenated amorphous silicon (α-Si:H) via the plasma-enhanced chemical vapor deposition (PECVD) method. The influence of deposition parameters on the coverage and, consequently, the passivation and photovoltaic properties of α-Si:H layers on SiNW solar cells have been analyzed

SCARA: Scalable Graph Neural Networks with Feature-Oriented Optimization

Recent advances in data processing have stimulated the demand for learning graphs of very large scales. Graph Neural Networks (GNNs), being an emerging and powerful approach in solving graph learning tasks, are known to be difficult to scale up. Most scalable models apply node-based techniques in simplifying the expensive graph message-passing propagation procedure of GNN. However, we find such acceleration insufficient when applied to million- or even billion-scale graphs. In this work, we propose SCARA, a scalable GNN with feature-oriented optimization for graph computation. SCARA efficiently computes graph embedding from node features, and further selects and reuses feature computation results to reduce overhead. Theoretical analysis indicates that our model achieves sub-linear time complexity with a guaranteed precision in propagation process as well as GNN training and inference. We conduct extensive experiments on various datasets to evaluate the efficacy and efficiency of SCARA. Performance comparison with baselines shows that SCARA can reach up to 100x graph propagation acceleration than current state-of-the-art methods with fast convergence and comparable accuracy. Most notably, it is efficient to process precomputation on the largest available billion-scale GNN dataset Papers100M (111M nodes, 1.6B edges) in 100 seconds

SpOctA: A 3D Sparse Convolution Accelerator with Octree-Encoding-Based Map Search and Inherent Sparsity-Aware Processing

Point-cloud-based 3D perception has attracted great attention in various applications including robotics, autonomous driving and AR/VR. In particular, the 3D sparse convolution (SpConv) network has emerged as one of the most popular backbones due to its excellent performance. However, it poses severe challenges to real-time perception on general-purpose platforms, such as lengthy map search latency, high computation cost, and enormous memory footprint. In this paper, we propose SpOctA, a SpConv accelerator that enables high-speed and energy-efficient point cloud processing. SpOctA parallelizes the map search by utilizing algorithm-architecture co-optimization based on octree encoding, thereby achieving 8.8-21.2x search speedup. It also attenuates the heavy computational workload by exploiting inherent sparsity of each voxel, which eliminates computation redundancy and saves 44.4-79.1% processing latency. To optimize on-chip memory management, a SpConv-oriented non-uniform caching strategy is introduced to reduce external memory access energy by 57.6% on average. Implemented on a 40nm technology and extensively evaluated on representative benchmarks, SpOctA rivals the state-of-the-art SpConv accelerators by 1.1-6.9x speedup with 1.5-3.1x energy efficiency improvement.Comment: Accepted to ICCAD 202

Large Trajectory Models are Scalable Motion Predictors and Planners

Motion prediction and planning are vital tasks in autonomous driving, and recent efforts have shifted to machine learning-based approaches. The challenges include understanding diverse road topologies, reasoning traffic dynamics over a long time horizon, interpreting heterogeneous behaviors, and generating policies in a large continuous state space. Inspired by the success of large language models in addressing similar complexities through model scaling, we introduce a scalable trajectory model called State Transformer (STR). STR reformulates the motion prediction and motion planning problems by arranging observations, states, and actions into one unified sequence modeling task. With a simple model design, STR consistently outperforms baseline approaches in both problems. Remarkably, experimental results reveal that large trajectory models (LTMs), such as STR, adhere to the scaling laws by presenting outstanding adaptability and learning efficiency. Qualitative results further demonstrate that LTMs are capable of making plausible predictions in scenarios that diverge significantly from the training data distribution. LTMs also learn to make complex reasonings for long-term planning, without explicit loss designs or costly high-level annotations

SIRT1-FOXO3a Regulate Cocaine Actions in the Nucleus Accumbens

Previous studies have shown that chronic cocaine administration induces SIRT1, a Class III histone deacetylase, in the nucleus accumbens (NAc), a key brain reward region, and that such induction influences the gene regulation and place conditioning effects of cocaine. To determine the mechanisms by which SIRT1 mediates cocaine-induced plasticity in NAc, we used chromatin immunoprecipitation followed by massively parallel sequencing (ChIP-seq), 1 d after 7 daily cocaine (20 mg/kg) or saline injections, to map SIRT1 binding genome-wide in mouse NAc. Our unbiased results revealed two modes of SIRT1 action. First, despite its induction in NAc, chronic cocaine causes depletion of SIRT1 from most affected gene promoters in concert with enrichment of H4K16ac (itself a deacetylation target of SIRT1), which is associated with increased expression of these genes. Second, we deduced the forkhead transcription factor (FOXO) family to be a downstream mechanism through which SIRT1 regulates cocaine action. We proceeded to demonstrate that SIRT1 induction causes the deacetylation and activation of FOXO3a in NAc, which leads to the induction of several known FOXO3a gene targets in other systems. Finally, we directly establish a role for FOXO3a in promoting cocaine-elicited behavioral responses by use of viral-mediated gene transfer: we show that overexpressing FOXO3a in NAc enhances cocaine place conditioning. The discovery of these two actions of SIRT1 in NAc in the context of behavioral adaptations to cocaine represents an important step forward in advancing our understanding of the molecular adaptations underlying cocaine action.National Institute on Drug AbuseNational Alliance for Research on Schizophrenia and Depression (U.S.)UNCF-Merc

Protection against SHIV-KB9 Infection by Combining rDNA and rFPV Vaccines Based on HIV Multiepitope and p24 Protein in Chinese Rhesus Macaques

Developing an effective vaccine against HIV infection remains an urgent goal. We used a DNA prime/fowlpox virus boost regimen to immunize Chinese rhesus macaques. The animals were challenged intramuscularly with pathogenic molecularly cloned SHIV-KB9. Immunogenicity and protective efficacy of vaccines were investigated by measuring IFN-γ levels, monitoring HIV-specific binding antibodies, examining viral load, and analyzing CD4/CD8 ratio. Results show that, upon challenge, the vaccine group can induce a strong immune response in the body, represented by increased expression of IFN-γ, slow and steady elevated antibody production, reduced peak value of acute viral load, and increase in the average CD4/CD8 ratio. The current research suggests that rapid reaction speed, appropriate response strength, and long-lasting immune response time may be key protection factors for AIDS vaccine. The present study contributes significantly to AIDS vaccine and preclinical research