Transactivation of Human Endogenous Retroviruses by Tumor Viruses and Their Functions in Virus-Associated Malignancies.

Human endogenous retroviruses (HERVs), viral-associated sequences, are normal components of the human genome and account for 8–9% of our genome. These original provirus sequences can be transactivated to produce functional products. Several reactivated HERVs have been implicated in cancers and autoimmune diseases. An emerging body of literature supports a potential role of reactivated HERVs in viral diseases, in particular viral-associated neoplasms. Demystifying studies on the mechanism(s) of HERV reactivation could provide a new framework for the development of treatment and prevention strategies targeting virus-associated tumors. Although available data suggest that coinfection by other viruses, such as Kaposi’s Sarcoma-associated herpesvirus (KSHV) and Epstein–Barr virus (EBV), may be a crucial driving force to transactivate HERV boom, the mechanisms of action of viral infection-induced HERV transactivation and the contributions of HERVs to viral oncogenesis warrant further studies. Here, we review viral coinfection contributes to HERVs transactivation with focus on human viral infection associated oncogenesis and diseases, including the abilities of viral regulators involved in HERV reactivation, and physiological effects of viral infection response on HERV reactivation

Transfer learning-based physics-informed convolutional neural network for simulating flow in porous media with time-varying controls

A physics-informed convolutional neural network is proposed to simulate two phase flow in porous media with time-varying well controls. While most of PICNNs in existing literatures worked on parameter-to-state mapping, our proposed network parameterizes the solution with time-varying controls to establish a control-to-state regression. Firstly, finite volume scheme is adopted to discretize flow equations and formulate loss function that respects mass conservation laws. Neumann boundary conditions are seamlessly incorporated into the semi-discretized equations so no additional loss term is needed. The network architecture comprises two parallel U-Net structures, with network inputs being well controls and outputs being the system states. To capture the time-dependent relationship between inputs and outputs, the network is well designed to mimic discretized state space equations. We train the network progressively for every timestep, enabling it to simultaneously predict oil pressure and water saturation at each timestep. After training the network for one timestep, we leverage transfer learning techniques to expedite the training process for subsequent timestep. The proposed model is used to simulate oil-water porous flow scenarios with varying reservoir gridblocks and aspects including computation efficiency and accuracy are compared against corresponding numerical approaches. The results underscore the potential of PICNN in effectively simulating systems with numerous grid blocks, as computation time does not scale with model dimensionality. We assess the temporal error using 10 different testing controls with variation in magnitude and another 10 with higher alternation frequency with proposed control-to-state architecture. Our observations suggest the need for a more robust and reliable model when dealing with controls that exhibit significant variations in magnitude or frequency

Generating Subsurface Earth Models using Discrete Representation Learning and Deep Autoregressive Network

Subsurface earth models (referred to as geo-models) are crucial for characterizing complex subsurface systems. Multiple-point statistics are commonly used to generate geo-models. In this paper, a deep-learning-based generative method is developed as an alternative to the traditional Geomodel generation procedure. The generative method comprises two deep-learning models, namely the hierarchical vector-quantized variational autoencoder (VQ-VAE-2) and PixelSNAIL autoregressive model. Based on the principle of neural discrete representation learning, the VQ-VAE-2 learns to massively compress the Geomodels to extract the low-dimensional, discrete latent representation corresponding to each Geomodel. Following that, PixelSNAIL uses the deep autoregressive network to learn the prior distribution of the latent codes. For the purpose of Geomodel generation, PixelSNAIL samples from the newly learned prior distribution of latent codes, and then the decoder of the VQ-VAE-2 converts the newly sampled latent code to a newly constructed geo-model. PixelSNAIL can be used for unconditional or conditional geo-model generation. In an unconditional generation, the generative workflow generates an ensemble of geo-models without any constraint. On the other hand, in the conditional geo-model generation, the generative workflow generates an ensemble of geo-models similar to a user-defined source image, which ultimately facilitates the control and manipulation of the generated geo-models. To better construct the fluvial channels in the geo-models, the perceptual loss is implemented in the VQ-VAE-2 model instead of the traditional mean squared error loss. At a specific compression ratio, the quality of multi-attribute geo-model generation is better than that of single-attribute geo-model generation

A dual-band slotted trapezoidal inverted-F antenna for indoor WLAN communications

This letter presents a new directional dual-band slotted trapezoidal inverted-F antenna (IFA) for indoor Wireless Local Area Network (WLAN) applications. The dual-band performance can be obtained by tuning the lengths of the inner symmetrical trapezoidal slots and the outer trapezoidal arms in a nearly independent manner. The measured results show that the proposed antenna can provide two separate impedance bandwidths (return loss better than 10 dB) around 180MHz and 750MHz for 2.4/5.1-5.8 GHz WLAN bands, respectively. Good radiation performance and roughly constant in-band antenna directivities are also observed

A Novel Method for ECG Signal Classification Via One-Dimensional Convolutional Neural Network

This paper develops an end-to-end ECG signal classification algorithm based on a novel segmentation strategy and 1D Convolutional Neural Networks (CNN) to aid the classification of ECG signals and alleviate the workload of physicians. The ECG segmentation strategy named R-R-R strategy (i.e., retaining ECG data between the R peaks just before and after the current R peak) is used for segmenting the original ECG data into segments to train and test the 1D CNN models. The novel strategy mimics physicians in scanning ECG to a greater extent, and maximizes the inherent information of ECG segments for diagnosis. The performance of the proposed end to end ECG signal classification algorithm was verified with the ECG signals from 48 records in the MIT-BIH arrhythmia database. When the heartbeat types were divided into the five classes recommended by clinicians, i.e., normal beat, left bundle branch block beat, right bundle branch block beat, premature ventricular contraction, and paced beat, the classification accuracy, the area under the curve (AUC), the sensitivity, and the F1-score achieved by the proposed model were 0.9924, 0.9994, 0.99 and 0.99, respectively. When the heartbeat types were divided into six classes recommended by clinicians, i.e., normal beat, left bundle branch block beat, right bundle branch block beat, premature ventricular contraction, paced beat and other beats, the beat classification accuracy, the AUC, the sensitivity, and the F1-score achieved by the model reached 0.9702, 0.9966, 0.97, and 0.97, respectively. When the heartbeat types were divided into five classes recommended by the Association for Advancement of Medical Instrumentation (AAMI), i.e., normal beat, supraventricular ectopic beat, ventricular ectopic beat, fusion beat, and unknown beat, the beat classification accuracy, the sensitivity, and the F1-score were 0.9745, 0.97, and 0.97, respectively. Experimental results show that the proposed method achieves better performance than the state-of-the-art methods

Palaeosedimentary Environment and Formation Mechanism of High-Quality Xujiahe Source Rocks, Sichuan Basin, South China

AbstractTriassic Xujiahe source rocks, the main gas source of shallow tight gas, are the most typical continental coal-bearing source rocks in the Sichuan Basin, South China. However, the organic matter enrichment section cannot be identified easily, leading to limited progress in the exploration of coal-bearing tight gas. This paper reveals the main controlling factors of the organic matter enrichment, reconstructs the evolution process of the Xujiahe palaeosedimentary environment, proposes a dynamic enrichment mechanism of the organic matter, and determines the organic matter enrichment section of the high-quality coal-bearing source rocks by geochemical characteristics of the source rocks, major elements, and trace elements. The results show that the Xujiahe sedimentary environment can be divided into a fluctuating stage of transitional sedimentation, stable stage of transitional sedimentation, fluctuating stage of continental sedimentation, and stable stage of continental sedimentation. The Xujiahe source rocks were featured with high-quality coal-bearing source rocks with high total organic carbon and maturity and good parent material in the stable stage of transitional sedimentation and fluctuating stage of continental sedimentation, in which the water was connected with the Palaeo-Tethys Ocean with abundant terrestrial organisms. The water was shallow in the fluctuating stage of transitional sedimentation with a low sedimentation rate, leading to poor organic matter enrichment. The Palaeo-Tethys Ocean withdrew westward from the Yangtze plate in the late period of the fluctuating stage of continental sedimentation, leading to the absence of algae and dinosteranes and a decrease in biological productivity in the stable stage of continental sedimentation. Therefore, high terrestrial inputs and biological productivity and high sedimentation rate were conducive to the organic matter preservation in the coal-bearing source rocks