Uncovering the crystallography and formation mechanism of nanoscale clusters in Sb-rich SPPs of a p-type (Bi, Sb)2Te3 alloy.

Considering that the crystallographic characteristics of the Sb-rich secondary phase particles (SPPs) greatly affect the thermoelectric properties of Bi2Te3 based materials, it is of great significance to explore the mechanism behind the Sb-rich SPPs in the p-type (Bi, Sb)2Te3 material. Here a conventional TEM technique was used to characterize the composition, size and distribution of Sb-rich SPPs in a spark plasma sintered p-type (Bi, Sb)2Te3 alloy. The results indicated that two different morphologies of Sb-rich SPPs including elongated and circular Sb-rich SPPs were frequently observed. Combined with high-resolution transmission electron microscopy, this work provides atomic-scale evidence for the formation mechanism behind the Sb-rich SPPs in the (Bi, Sb)2Te3 material.

Transitional structure of {0001} twin in a deformed p-type (Bi,Sb)2Te3 alloy: a direct experimental basis for understanding the twinning mechanism.

Twin boundaries provide a strong phonon scattering center to hinder the lattice thermal conductivity in thermoelectric materials, but the underlying evolution process of deformation twinning remains to be figured out. By applying atomic resolution transmission electron microscope (TEM) observations, a novel type of transitional structure of {0001} twin was observed, for the first time, in the p-type (Bi,Sb)2Te3 alloy subjected to three-point bending deformation. The transformation from matrix to (0001) twin can be realized by the following path: matrix → transitional twin → (0001) twin, and this process was completed by the gliding of a total of four partial dislocations (b1 = 1/3[011̄0]) extended in the different (0001) planes. This new finding here will shed light on the nucleation and growth of deformation twins in the p-type (Bi, Sb)2Te3 alloy.

SALA-LSTM: a novel high-precision maritime radar target detection method based on deep learning.

Radar detection of maritime targets plays an important role in marine environment monitoring. For civil maritime detection in the areas of inshore coastal, pulse-compression radar is universally used owing to its low cost. The complex sea clutter in the practical application will greatly affect the received radar echoes. Due to the inability to accurately describe the differences in characteristics between sea clutter and maritime targets, the detection performance of methods based on mathematical derivation is not satisfactory in actual deployment. Recently, neural-based methods have made strides in many pattern recognition tasks, such as computer vision and natural language processing. The sophisticated deep neural models can be applied to different downstream tasks due to their powerful learning ability. Inspired by this idea, we propose a maritime radar target detection method in sea clutter based on deep learning. To better model the sequence correlation of radar echoes, we propose a Self-Adaption Local Augmented Long Short-Term Memory (SALA-LSTM) structure. The proposed SALA-LSTM integrates adaptive convolution into vanilla LSTM cells, which not only maintains the inherent overall sequence modeling ability of vanilla LSTM, but also strengthens its ability to perceive the correlation on a small scale in the local scope. Based on SALA-LSTM and other neural structures, we propose a radar target detection network. A measured dataset containing different typical scenarios is utilized to evaluate the detection probability and false alarm rate. The detection performance of our proposed network is superior to that of the existing methods.

Triple-Negative Breast Cancer-Derived Extracellular Vesicles Promote a Hepatic Premetastatic Niche via a Cascade of Microenvironment Remodeling.

Patients with triple-negative breast cancer (TNBC) often develop metastases in visceral organs including the liver, but the detailed molecular mechanisms of TNBC liver metastasis is not clearly understood. In this study, we tried to dissect the process of premetastatic niche formation in the liver by using patient-derived xenograft (PDX) models of TNBC with different metastatic propensity. RNA sequencing of TNBC PDX models that successfully metastasized to liver showed upregulation of the Cx3cr1 gene in the liver microenvironment. In syngeneic breast cancer models, the Cx3cr1 upregulation in liver preceded the development of cancer cell metastasis and was the result of recruitment of CX3CR1-expressing macrophages. The recruitment was induced by the CX3CL1 production from the liver endothelial cells and this CX3CL1-CX3CR1 signaling in the premetastatic niche resulted in upregulation of MMP9 that promoted macrophage migration and cancer cell invasion. In addition, our data suggest that the extracellular vesicles derived from the breast cancer cells induced the TNFα expression in liver, which leads to the CX3CL1 upregulation. Lastly, the plasma CX3CL1 levels in 155 patients with breast cancer were significantly associated with development of liver metastasis. IMPLICATIONS: Our data provides previously unknown cascades regarding the molecular education of premetastatic niche in liver for TNBC.

AKR1C2 Promotes Metastasis and Regulates the Molecular Features of Luminal Androgen Receptor Subtype in Triple Negative Breast Cancer Cells.

PURPOSE: Patients with triple-negative breast cancer (TNBC) have an increased risk of distant metastasis compared to those with other subtypes. In this study, we aimed to identify the genes associated with distant metastasis in TNBC and their underlying mechanisms. METHODS: We established patient-derived xenograft (PDX) models using surgically resected breast cancer tissues from 31 patients with TNBC. Among these, 15 patients subsequently developed distant metastases. Candidate metastasis-associated genes were identified using RNA sequencing. In vitro wound healing, proliferation, migration, and invasion assays and in vivo tumor xenograft and metastasis assays were performed to determine the functional importance of aldo-keto reductase family 1 member C2 (AKR1C2). Additionally, we used the METABRIC dataset to investigate the potential role of AKR1C2 in regulating TNBC subtypes and their downstream signaling activities. RESULTS: RNA sequencing of primary and PDX tumors showed that genes involved in steroid hormone biosynthesis, including AKR1C2, were significantly upregulated in patients who subsequently developed metastasis. In vitro and in vivo assays showed that silencing of AKR1C2 resulted in reduced cell proliferation, migration, invasion, tumor growth, and incidence of lung metastasis. AKR1C2 was upregulated in the luminal androgen receptor (LAR) subtype of TNBC in the METABRIC dataset, and AKR1C2 silencing resulted in the downregulation of LAR classifier genes in TNBC cell lines. The androgen receptor (AR) gene was a downstream mediator of AKR1C2-associated phenotypes in TNBC cells. AKR1C2 expression was associated with gene expression pathways that regulate AR expression, including JAK-STAT signaling or interleukin 6 (IL-6). The levels of phospho-signal transducer and activator of transcription and IL-6, along with secreted IL-6, were significantly downregulated in AKR1C2-silenced TNBC cells. CONCLUSION: Our data indicate that AKR1C2 is an important regulator of cancer growth and metastasis in TNBC and may be a critical determinant of LAR subtype features.

JAK2 regulates paclitaxel resistance in triple negative breast cancers.

We investigated the molecular mechanisms of paclitaxel resistance in TNBC using seven patient-derived xenograft (PDX) models and TNBC cell lines. Among the seven PDX models, four models showed resistance to paclitaxel. Dysregulation of JAK/STAT pathways and JAK2 copy number gains were observed in the four paclitaxel-resistant PDX tumors. In TNBC cell lines, silencing the JAK2 gene showed a significant but mild synergistic effect when combined with paclitaxel in vitro. However, JAK1/2 inhibitor treatment resulted in restoration of paclitaxel sensitivity in two out of four paclitaxel-resistant PDX models and JAK1/2 inhibitor alone significantly suppressed the tumor growth in one out of the two remaining PDX models. Transcriptome data derived from the murine microenvironmental cells revealed an enrichment of genes involved in the cell cycle processes among the four paclitaxel-resistant PDX tumors. Histologic examination of those PDX tumor tissues showed increased Ki67-positive fibroblasts in the tumor microenvironment. Among the four different cancer-associated fibroblast (CAF) subtypes, cycling CAF exhibiting features of active cell cycle was enriched in the paclitaxel-resistant PDX tumors. Additionally, fibroblasts treated with the conditioned media from the JAK2-silenced breast cancer cells showed downregulation of cell cycle-related genes. Our data suggest that the JAK2 gene may play a critical role in determining responses of TNBC to paclitaxel by modulating the intrinsic susceptibility of cancer cells against paclitaxel and also by eliciting functional transitions of CAF subtypes in the tumor microenvironment. KEY MESSAGES : We investigated the molecular mechanisms of paclitaxel resistance in TNBC. JAK2 signaling was associated with paclitaxel resistance in TNBC PDX models. Paclitaxel-resistant PDX tumors were enriched with microenvironment cCAF subpopulation. JAK2 regulated paclitaxel-resistant CAF phenotype transition.

An Efficient Fire Detection Method Based on Multiscale Feature Extraction, Implicit Deep Supervision and Channel Attention Mechanism.

Recent progress in vision-based fire detection is driven by convolutional neural networks. However, the existing methods fail to achieve a good tradeoff among accuracy, model size, and speed. In this paper, we propose an accurate fire detection method that achieves a better balance in the abovementioned aspects. Specifically, a multiscale feature extraction mechanism is employed to capture richer spatial details, which can enhance the discriminative ability of fire-like objects. Then, the implicit deep supervision mechanism is utilized to enhance the interaction among information flows through dense skip connections. Finally, a channel attention mechanism is employed to selectively emphasize the contribution between different feature maps. Experimental results demonstrate that our method achieves 95.3% accuracy, which outperforms the suboptimal method by 2.5%. Moreover, the speed and model size of our method are 3.76% faster on the GPU and 63.64% smaller than the suboptimal method, respectively.

Residual Strain-Mediated Multiferroic Properties of Ba0.85Ca0.15Zr0.9Ti0.1O3/La0.67Ca0.33MnO3 Epitaxial Heterostructures.

In this study, artificial multiferroic Ba0.85Ca0.15Zr0.1Ti0.9O3/La0.67Ca0.33MnO3(BCZT/LCMO) epitaxial heterostructures were deposited on Nb-doped SrTiO3 substrates using pulsed laser deposition. The epitaxial growth of the heterostructures on the substrate was demonstrated by XRD, RSM, and TEM analyses, which displayed decreasing residual strain with increasing BCZT layer thickness. The electrical, magnetic, and magnetoelectric properties of the epitaxial heterostructures were investigated in detail, and they were sensitive to the varying BCZT layer thickness in terms of residual strain. The multiferroic nature of the heterostructures was demonstrated by ferroelectric and ferromagnetic hysteresis loops. Strain-mediated magnetoelectric behavior was observed in the epitaxial heterostructures. Finally, dielectric properties were enhanced in the heterostructures over the BCZT single layer film, and the maximum magnetoelectric coefficient of the heterostructures was 206.5 mV/cm·Oe.