Identification of an Oxidative Stress-Related LncRNA Signature for Predicting Prognosis and Chemotherapy in Patients With Hepatocellular Carcinoma

Background: Oxidative stress plays a critical role in oncogenesis and tumor progression. However, the prognostic role of oxidative stress-related lncRNA in hepatocellular carcinomas (HCC) has not been fully explored.Methods: We used the gene expression data and clinical data from The Cancer Genome Atlas (TCGA) database to identify oxidative stress-related differentially expressed lncRNAs (DElncRNAs) by pearson correlation analysis. A four-oxidative stress-related DElncRNA signature was constructed by LASSO regression and Cox regression analyses. The predictive signature was further validated by Kaplan–Meier (K–M) survival analysis, receiver operating characteristic (ROC) curves, nomogram and calibration plots, and principal component analysis (PCA). Single-sample gene set enrichment analysis (ssGSEA) was used to explore the relationship between the signature and immune status. Finally, the correlation between the signature and chemotherapeutic response of HCC patients was analyzed.Results: In our study, the four-DElncRNA signature was not only proved to be a robust independent prognostic factor for overall survival (OS) prediction, but also played a crucial role in the regulation of progression and chemotherapeutic response of HCC. ssGSEA showed that the signature was correlated with the infiltration level of immune cells. HCC patients in high-risk group were more sensitive to the conventional chemotherapeutic drugs including Sorafenib, lapatinib, Nilotinib, Gefitinib, Erlotinib and Dasatinib, which pave the way for targeting DElncRNA-associated treatments for HCC patients.Conclusion: Our study has originated a prognostic signature for HCC based on oxidative stress-related DElncRNAs, deepened the understanding of the biological role of four key DElncRNAs in HCC and laid a theoretical foundation for the choice of chemotherapy

Proxima: Near-storage Acceleration for Graph-based Approximate Nearest Neighbor Search in 3D NAND

Approximate nearest neighbor search (ANNS) plays an indispensable role in a wide variety of applications, including recommendation systems, information retrieval, and semantic search. Among the cutting-edge ANNS algorithms, graph-based approaches provide superior accuracy and scalability on massive datasets. However, the best-performing graph-based ANN search solutions incur tens of hundreds of memory footprints as well as costly distance computation, thus hindering their efficient deployment at scale. The 3D NAND flash is emerging as a promising device for data-intensive applications due to its high density and nonvolatility. In this work, we present the near-storage processing (NSP)-based ANNS solution Proxima, to accelerate graph-based ANNS with algorithm-hardware co-design in 3D NAND flash. Proxima significantly reduces the complexity of graph search by leveraging the distance approximation and early termination. On top of the algorithmic enhancement, we implement Proxima search algorithm in 3D NAND flash using the heterogeneous integration technique. To maximize 3D NAND's bandwidth utilization, we present customized dataflow and optimized data allocation scheme. Our evaluation results show that: compared to graph ANNS on CPU and GPU, Proxima achieves a magnitude improvement in throughput or energy efficiency. Proxima yields 7x to 13x speedup over existing ASIC designs. Furthermore, Proxima achieves a good balance between accuracy, efficiency and storage density compared to previous NSP-based accelerators

FHEmem: A Processing In-Memory Accelerator for Fully Homomorphic Encryption

Fully Homomorphic Encryption (FHE) is a technique that allows arbitrary computations to be performed on encrypted data without the need for decryption, making it ideal for securing many emerging applications. However, FHE computation is significantly slower than computation on plain data due to the increase in data size after encryption. Processing In-Memory (PIM) is a promising technology that can accelerate data-intensive workloads with extensive parallelism. However, FHE is challenging for PIM acceleration due to the long-bitwidth multiplications and complex data movements involved. We propose a PIM-based FHE accelerator, FHEmem, which exploits a novel processing in-memory architecture to achieve high-throughput and efficient acceleration for FHE. We propose an optimized end-to-end processing flow, from low-level hardware processing to high-level application mapping, that fully exploits the high throughput of FHEmem hardware. Our evaluation shows FHEmem achieves significant speedup and efficiency improvement over state-of-the-art FHE accelerators

Obesity and clinical outcomes in COVID-19 patients without comorbidities, a post-hoc analysis from ORCHID trial

ObjectiveLarge body of studies described individuals with obesity experiencing a worse prognosis in COVID-19. However, the effects of obesity on the prognosis of COVID-19 in patients without comorbidities have not been studied. Therefore, the current study aimed to provide evidence of the relationship between obesity and clinical outcomes in COVID-19 patients without comorbidities.MethodsA total of 116 hospitalized COVID-19 patients without comorbidities from the ORCHID study (Patients with COVID-19 from the Outcomes Related to COVID-19 Treated with Hydroxychloroquine among Inpatients with Symptomatic Disease) were included. Obesity is defined as a BMI of ≥30 kg/m2. A Cox regression analysis was used to estimate the hazard ratio (HR) for discharge and death after 28 days.ResultsThe percentage of obesity in COVID-19 patients without comorbidities was 54.3% (63/116). Discharge at 28 days occurred in 56/63 (84.2%) obese and 51/53 (92.2%) non-obese COVID-19 patients without comorbidities. Four (3.4%) COVID-19 patients without any comorbidities died within 28 days, among whom 2/63 (3.2%) were obese and 2/53 (3.8%) were non-obese. Multivariate Cox regression analyses showed that obesity was independently associated with a decreased rate of 28-day discharge (adjusted HR: 0.55, 95% CI: 0.35–0.83) but was not significantly associated with 28-day death (adjusted HR: 0.94, 95% CI: 0.18–7.06) in COVID-19 patients without any comorbidities.ConclusionsObesity was independently linked to prolonged hospital length of stay in COVID-19 without any comorbidity. Larger prospective trials are required to assess the role of obesity in COVID-19 related deaths

High Performance Indium-Doped ZnO Gas Sensor

Gas sensors for ethanol and acetone based on ZnO nanobelts with doping element indium were fabricated. Excellent sensitivity accompanied with short response time (10 s) and recovery time (23 s) to 150 ppm ethanol is obtained. For In-doped sensors, a minimum concentration of 37.5 ppm at 275°C in acetone was observed with an average sensitivity of 714.4, which is 7 times larger than that of the pure sensors and much larger than that reported response (16) of Co-doped ZnO nanofibers to acetone. These results indicate that doping elements can improve gas sensitivity, which is associated with oxygen space and valence ions. In-doped ZnO nanobelts exhibit higher sensitivity to acetone than that to ethanol. These results indicate that doped ZnO nanobelts can successfully distinguish acetone and ethanol, which can be put into various practical applications

Application of Hidden Markov Models in Financial Time Series: Inspection of the Capital Asset Pricing Model

In this thesis, we propose two Gaussian hidden Markov models: univariate Gaussian hidden Markov models with covariate and bivariate Gaussian hidden Markov models. After that they are applied to stock market returns to inspect the return-beta relationship stated inthe capital asset pricing model (CAPM). The relationship is examined under 3 definitions of regimes: market regimes, idiosyncratic regimes and co-regimes. Results show that betas are larger under bullish market regime compared to bearish. Although no consistent patternsin beta are discovered under different idiosyncratic regimes and co-regimes, for each stock the betas do seem to vary considerably across regimes. Our model is also able to capture volatility clustering exhibited in return series

Tripartite motif containing 26 retards steatohepatitis by suppressing C/EBPD signaling activation

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Efficient Model Assisted Probability of Detection Estimations in Eddy Current NDT with ACA-SVD Based Forward Solver

Model assisted probability of detection (MAPoD) is crucial for quantifying the inspection capability of a nondestructive testing (NDT) system which uses the coil or probe to sense the size and location of the cracks. Unfortunately, it may be computationally intensive for the simulation models. To improve the efficiency of the MAPoD, in this article, an efficient 3D eddy current nondestructive evaluation (ECNDE) forward solver is proposed to make estimations for PoD study. It is the first time that singular value decomposition (SVD) is used as the recompression technique to improve the overall performance of the adaptive cross approximation (ACA) algorithm-based boundary element method (BEM) ECNDE forward solver for implementation of PoD. Both the robustness and efficiency of the proposed solver are demonstrated and testified by comparing the predicted impedance variations of the coil with analytical, semi-analytical and experimental benchmarks. Calculation of PoD curves assisted by the proposed simulation model is performed on a finite thickness plate with a rectangular surface flaw. The features, which are the maximum impedance variations of the coil for various flaw lengths, are obtained entirely by the proposed model with selection of the liftoff distance as the uncertain parameter in a Gaussian distribution. The results show that the proposed ACA-SVD based BEM fast ECNDE forward solver is an excellent simulation model to make estimations for MAPoD study.This article is published as Bao, Yang, Minxuan Xu, Jiahao Qiu, and Jiming Song. "Efficient Model Assisted Probability of Detection Estimations in Eddy Current NDT with ACA-SVD Based Forward Solver." Sensors 22, no. 19 (2022): 7625. DOI: 10.3390/s22197625. Copyright 2022 by the authors. Attribution 4.0 International (CC BY 4.0). Posted with permission