Adenocarcinoma developing from gastric heterotopic pancreas: a case report and short review

Heterotopic pancreas is a relatively rare condition that may be associated to clinical complaints or signs. Here, we report a case of gastric heterotopic pancreas assictaed to ductal adenocarcinoma. Obstructive jaundice was the initial symptom prompting medical intervention. A 73-year-old male patient presented with yellow staining of the skin and sclera, and dull epigastric pain. Contrast-enhanced computed tomography showed stenosis of the extrahepatic distal bile duct and mass lesions of the antrum. The patient underwent tumor resection, distal gastrectomy (Billroth II), and common bile duct exploration. Postoperative pathological examination revealed an adenocarcinoma located in the wall of the gastric antrum. Immunohistochemical results suggested that the tumor originated from the pancreas. Heterologous pancreatic tissue and a dilated pancreatic duct were found in the tumor. These findings suggest malignant transformation of the gastric heterotopic pancreas. Of note, jaundice as clinical complaint for adenocarcinoma associated to gastric heterotopic pancreas

Distributional Drift Adaptation with Temporal Conditional Variational Autoencoder for Multivariate Time Series Forecasting

Due to the nonstationary nature, the distribution of real-world multivariate time series (MTS) changes over time, which is known as distribution drift. Most existing MTS forecasting models greatly suffer from distribution drift and degrade the forecasting performance over time. Existing methods address distribution drift via adapting to the latest arrived data or self-correcting per the meta knowledge derived from future data. Despite their great success in MTS forecasting, these methods hardly capture the intrinsic distribution changes, especially from a distributional perspective. Accordingly, we propose a novel framework temporal conditional variational autoencoder (TCVAE) to model the dynamic distributional dependencies over time between historical observations and future data in MTSs and infer the dependencies as a temporal conditional distribution to leverage latent variables. Specifically, a novel temporal Hawkes attention mechanism represents temporal factors subsequently fed into feed-forward networks to estimate the prior Gaussian distribution of latent variables. The representation of temporal factors further dynamically adjusts the structures of Transformer-based encoder and decoder to distribution changes by leveraging a gated attention mechanism. Moreover, we introduce conditional continuous normalization flow to transform the prior Gaussian to a complex and form-free distribution to facilitate flexible inference of the temporal conditional distribution. Extensive experiments conducted on six real-world MTS datasets demonstrate the TCVAE's superior robustness and effectiveness over the state-of-the-art MTS forecasting baselines. We further illustrate the TCVAE applicability through multifaceted case studies and visualization in real-world scenarios.Comment: 13 pages, 6 figures, submitted to IEEE Transactions on Neural Networks and Learning Systems (TNNLS

Deep Coupling Network For Multivariate Time Series Forecasting

Multivariate time series (MTS) forecasting is crucial in many real-world applications. To achieve accurate MTS forecasting, it is essential to simultaneously consider both intra- and inter-series relationships among time series data. However, previous work has typically modeled intra- and inter-series relationships separately and has disregarded multi-order interactions present within and between time series data, which can seriously degrade forecasting accuracy. In this paper, we reexamine intra- and inter-series relationships from the perspective of mutual information and accordingly construct a comprehensive relationship learning mechanism tailored to simultaneously capture the intricate multi-order intra- and inter-series couplings. Based on the mechanism, we propose a novel deep coupling network for MTS forecasting, named DeepCN, which consists of a coupling mechanism dedicated to explicitly exploring the multi-order intra- and inter-series relationships among time series data concurrently, a coupled variable representation module aimed at encoding diverse variable patterns, and an inference module facilitating predictions through one forward step. Extensive experiments conducted on seven real-world datasets demonstrate that our proposed DeepCN achieves superior performance compared with the state-of-the-art baselines

Surface Stabilization of O3-type Layered Oxide Cathode to Protect the Anode of Sodium Ion Batteries for Superior Lifespan

Even though the energy density of O3-type layer-structured metal oxide cathode can fully reach the requirement for large-scale energy storage systems, the cycling lifespan still cannot meet the demand for practical application once it is coupled with a non-sodium-metal anode in full-cell system. Transition metal dissolution into the electrolyte occurs along with continuous phase transformation and accelerates deterioration of the crystal structure, followed by migration and finally deposition on the anode to form a vicious circle. Surface engineering techniques are employed to modify the interface between active materials and the electrolyte by coating them with a thin layer of AlPO4 ion conductor. This stable thin layer can stabilize the surface crystal structure of the cathode material by avoiding element dissolution. Meanwhile, it can protect the anode from increased resistance by suppressing the dissolution-migration-deposition process. This technique is a promising method to improve the lifetime for the future commercialization

ImmFusion: Robust mmWave-RGB Fusion for 3D Human Body Reconstruction in All Weather Conditions

3D human reconstruction from RGB images achieves decent results in good weather conditions but degrades dramatically in rough weather. Complementary, mmWave radars have been employed to reconstruct 3D human joints and meshes in rough weather. However, combining RGB and mmWave signals for robust all-weather 3D human reconstruction is still an open challenge, given the sparse nature of mmWave and the vulnerability of RGB images. In this paper, we present ImmFusion, the first mmWave-RGB fusion solution to reconstruct 3D human bodies in all weather conditions robustly. Specifically, our ImmFusion consists of image and point backbones for token feature extraction and a Transformer module for token fusion. The image and point backbones refine global and local features from original data, and the Fusion Transformer Module aims for effective information fusion of two modalities by dynamically selecting informative tokens. Extensive experiments on a large-scale dataset, mmBody, captured in various environments demonstrate that ImmFusion can efficiently utilize the information of two modalities to achieve a robust 3D human body reconstruction in all weather conditions. In addition, our method's accuracy is significantly superior to that of state-of-the-art Transformer-based LiDAR-camera fusion methods

Spatial distribution of job opportunities in China: Evidence from the opening of the high-speed rail

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The provision of sufficient job opportunities has traditionally been a primary objective for both local and central governments. In response to this concern, we investigate spatial dependence of job opportunities among 30 Chinese provincial capital cities (PCCs) from 2002 to 2016, giving special attention to the spatial spillovers of the opening of the high-speed rail (HSR). Using appropriate spatial panel data models, our findings suggest the presence of significant spatial autocorrelation of job opportunities among PCCs. Whilst the HSR has been found to increase job opportunities at the national level, which, however, is not confirmed at the regional level. The spatial spillover effects of the HSR are significant and positive only in the eastern/northeastern region. These findings can help the central government to more fully understand spatial dependence of job opportunities, better plan future HSR networks, and efficiently allocate transportation resources, encouraging cross-regional collaboration to promote regional employment

CXCL9 Is a Potential Biomarker of Immune Infiltration Associated With Favorable Prognosis in ER-Negative Breast Cancer

The chemokine CXCL9 (C-X-C motif chemokine ligand 9) has been reported to be required for antitumour immune responses following immune checkpoint blockade. In this study, we sought to investigate the potential value of CXCL9 according to immune responses in patients with breast cancer (BC). A variety of open-source databases and online tools were used to explore the expression features and prognostic significance of CXCL9 in BC and its correlation with immune-related biomarkers followed by subsequent verification with immunohistochemistry experiments. The CXCL9 mRNA level was found to be significantly higher in BC than in normal tissue and was associated with better survival outcomes in patients with ER-negative tumours. Moreover, CXCL9 is significantly correlated with immune cell infiltration and immune-related biomarkers, including CTLA4, GZMB, LAG3, PDCD1 and HAVCR2. Finally, we performed immunohistochemistry with breast cancer tissue samples and observed that CXCL9 is highly expressed in the ER-negative subgroup and positively correlated with the immune-related factors LAG3, PD1, PDL1 and CTLA4 to varying degrees. These findings suggest that CXCL9 is an underlying biomarker for predicting the status of immune infiltration in ER-negative breast cancer