Realizing high-efficiency thermoelectric module by suppressing donor-like effect and improving preferred orientation in n-type Bi2(Te, Se)3.

Thermoelectric materials have a wide range of application because they can be directly used in refrigeration and power generation. And the Bi2Te3 stand out because of its excellent thermoelectric performance and are used in commercial thermoelectric devices. However, n-type Bi2Te3 has seriously hindered the development of Bi2Te3-based thermoelectric devices due to its weak mechanical properties and inferior thermoelectric performance. Therefore, it is urgent to develop a high-performance n-type Bi2Te3 polycrystalline. In this work, we employed interstitial Cu and the hot deformation process to optimize the thermoelectric properties of Bi2Te2.7Se0.3, and a high-performance thermoelectric module was fabricated based on this material. Our combined theoretical and experimental effort indicates that the interstitial Cu reduce the defect density in the matrix and suppresses the donor-like effect, leading to a lattice plainification effect in the material. In addition, the two-step hot deformation process significantly improves the preferred orientation of the material and boosts the mobility. As a result, a maximum ZT of 1.27 at 373 K and a remarkable high ZTave of 1.22 across the temperature range of 300-425 K are obtained. The thermoelectric generator (TEG, 7-pair) and thermoelectric cooling (TEC, 127-pair) modules were fabricated with our n-type textured Cu0.01Bi2Te2.7Se0.3 coupled with commercial p-type Bi2Te3. The TEC module demonstrates superior cooling efficiency compared with the commercial Bi2Te3 device, achieving a ΔT of 65 and 83.4 K when the hot end temperature at 300 and 350 K, respectively. In addition, the TEG module attains an impressive conversion efficiency of 6.5% at a ΔT of 225 K, which is almost the highest value among the reported Bi2Te3-based TEG modules.

Dataset Condensation via Expert Subspace Projection.

The rapid growth in dataset sizes in modern deep learning has significantly increased data storage costs. Furthermore, the training and time costs for deep neural networks are generally proportional to the dataset size. Therefore, reducing the dataset size while maintaining model performance is an urgent research problem that needs to be addressed. Dataset condensation is a technique that aims to distill the original dataset into a much smaller synthetic dataset while maintaining downstream training performance on any agnostic neural network. Previous work has demonstrated that matching the training trajectory between the synthetic dataset and the original dataset is more effective than matching the instantaneous gradient, as it incorporates long-range information. Despite the effectiveness of trajectory matching, it suffers from complex gradient unrolling across iterations, which leads to significant memory and computation overhead. To address this issue, this paper proposes a novel approach called Expert Subspace Projection (ESP), which leverages long-range information while avoiding gradient unrolling. Instead of strictly enforcing the synthetic dataset's training trajectory to mimic that of the real dataset, ESP only constrains it to lie within the subspace spanned by the training trajectory of the real dataset. The memory-saving advantage offered by our method facilitates unbiased training on the complete set of synthetic images and seamless integration with other dataset condensation techniques. Through extensive experiments, we have demonstrated the effectiveness of our approach. Our method outperforms the trajectory matching method on CIFAR10 by 16.7% in the setting of 1 Image/Class, surpassing the previous state-of-the-art method by 3.2%.

Prodigiosin inhibits the proliferation of glioblastoma by regulating the KIAA1524/PP2A signaling pathway.

Prodigiosin (PG), a member of a family of natural red pigments produced by a variety of bacteria, was first discovered in Serratia marcescens. PG has been reported to have an apoptosis-inducing effect in many cancers, such as lymphoma, colon cancer and nasopharyngeal carcinoma. For this study, we used three glioblastoma (GBM) cell lines (LN229, U251 and A172) to explore the effect of prodigiosin on GBM cells. A CCK8 assay was used to evaluate cell viability. We determinedthe cell cycle distribution by flow cytometry and measured proliferation by an EdU incorporation assay. The expression of different molecules was investigated by western blotting and RT-PCR. We further confirmed our results by plasmid transfection and lentiviral transduction. The LN229 xenograft model was used to study the effect of prodigiosin in vivo. We confirmed that prodigiosin played an anticancer role in several GBM cell lines through the KIAA1524/PP2A/Akt signalling pathway. Prodigiosin inhibited the protein expression of KIAA1524 by suppressing its transcription, which led to activation of PP2A. Afterward, PP2A inhibited the phosphorylation of Akt, thereby inducing increased expression of p53/p21. Furthermore, it was verified that prodigiosin inhibited the KIAA1524/PP2A/Akt axis in vivo in the LN229 xenograft model. These data improve the understanding of the anticancer effects of prodigiosin and further highlight the potential of prodigiosin for the development of anti-glioma drugs.

2,5-Dimethyl Celecoxib Inhibits Proliferation and Cell Cycle and Induces Apoptosis in Glioblastoma by Suppressing CIP2A/PP2A/Akt Signaling Axis.

2,5-Dimethyl-celecoxib (DMC) is a close structural analog of the selective COX-2 inhibitor celecoxib that lacks COX-2-inhibitory function. Thus, DMC is a promising drug for anti-tumor. In this study, we evaluated the efficacy and the molecular basis of DMC in the treatment of human glioblastoma multiforme (GBM). DMC inhibited the growth and proliferation of GBM cell lines (LN229, A172, U251, and U87MG) in a dose-dependent manner (P < 0.001). In GBM cells treated with DMC, detection by flow cytometry showed cell cycle arrest, and proteins involved in cell cycle such as P21 were increased. Compared with control group, Annexin-V/PI-staining in DMC-treatment group was increased, indicating that DMC could induce apoptosis in GBM cells. Also, associated proteins including cleaved caspase 3 and cleaved PARP-1 were increased. It was further explored whether DMC blocked cell cycle and induced apoptosis in GBM cells through CIP2A/PP2A/AKT signaling pathway. After treatment of DMC, the phosphorylation of Akt was reduced while the total Akt level was not affected. DMC suppressed the expression of CIP2A in a time-dependent manner, while the CIP2A overexpression group reversed cell cycle and apoptotic protein expression led by DMC. Finally, in a xenograft model in nude mice using LN229 cells, DMC suppressed tumor growth. These findings proved that DMC could block cell cycle and induce apoptosis in GBM cells by suppressing CIP2A/PP2A/Akt signaling axis, which indicated that DMC could be an effective option for GBM treatment.