Wireless powered communication networks using peer harvesting

For an energy-constrained wireless network, energy harvesting (EH) is a promising technology to prolong the network life. Whether traditional near-field wireless power transfer (WPT) using inductive and resonant coupling or far-field WPT via radiated electromagnetic waves, both of them draw considerable research interests these years [1], [2]. In particular, the far-field WPT is meaningful for wireless powered communication (WPC) networks. A fundamental tradeoff was first studied for simultaneous wireless information and power transfer (SWIPT) in [3], [4]. These results aroused the interest of researchers. Subsequently, wireless communication with EH technology was presented in [5], [6]

Secured green communication scheme for interference alignment based networks

In this paper, a new security and green communication scheme is proposed to the Interference-Alignment (IA) based networks. To achieve a secured communication, full-duplex receivers are utilized to transmit artificial noise (AN). Both the signals and the ANs are used to harvest energy to realize green communication. For these reasons, the feasible conditions of this scheme are analyzed first. Secondly, the average transmission rate, the secrecy performance and the harvested energy are investigated. Thirdly, an optimization scheme of simultaneous wireless information and power transfer (SWIPT) is given to optimize the information transmission and the energy harvesting efficiency. Meanwhile, an improved IA iteration algorithm is designed to eliminate both the AN and the interference. Furthermore, relay cooperation is considered and its system performance is analyzed. The simulations show that the target average transmission rate is not affected by AN, while the secrecy performance can be greatly improved. The energy harvesting efficiency is also better than the traditional schemes. As expected, the average transmission rate further is improved with the relay cooperation

Analysis of Yarrowia lipolytica Growth, Catabolism, and Terpenoid Biosynthesis during Utilization of Lipid-derived Feedstock

This study employs biomass growth analyses and 13C-isotope tracing to investigate lipid feedstock utilization by Yarrowia lipolytica. Compared to glucose, oil-feedstock in the minimal medium increases the yeast\u27s biomass yields and cell sizes, but decreases its protein content (\u3c20% of total biomass) and enzyme abundances for product synthesis. Labeling results indicate a segregated metabolic network (the glycolysis vs. the TCA cycle) during co-catabolism of sugars (glucose or glycerol) with fatty acid substrates, which facilitates resource allocations for biosynthesis without catabolite repressions. This study has also examined the performance of a β-carotene producing strain in different growth mediums. Canola oil-containing yeast-peptone (YP) has resulted in the best β-carotene titer (121 ± 13 mg/L), two-fold higher than the glucose based YP medium. These results highlight the potential of Y. lipolytica for the valorization of waste-derived lipid feedstock

Covert Communication with Relay Selection

In this letter, we investigate covert communication in relay networks with relay selection. We consider the scenario that while forwarding the source’s message, the selected relay opportunistically transmits its own message to the destination covertly. We derive the probability of detection error (PDE) and the average covert rate (ACR) in a closed form, based on which we analyse the effects of system parameters on the performance of the covert communication. Our analysis indicates that applying relay selection causes a decrease in the PDE, however, it can provide an ACR gain when the transmission rate of the source increases

Deep learning‐based prediction of H3K27M alteration in diffuse midline gliomas based on whole‐brain MRI

Abstract Background H3K27M mutation status significantly affects the prognosis of patients with diffuse midline gliomas (DMGs), but this tumor presents a high risk of pathological acquisition. We aimed to construct a fully automated model for predicting the H3K27M alteration status of DMGs based on deep learning using whole‐brain MRI. Methods DMG patients from West China Hospital of Sichuan University (WCHSU; n = 200) and Chengdu Shangjin Nanfu Hospital (CSNH; n = 35) who met the inclusion and exclusion criteria from February 2016 to April 2022 were enrolled as the training and external test sets, respectively. To adapt the model to the human head MRI scene, we use normal human head MR images to pretrain the model. The classification and tumor segmentation tasks are naturally related, so we conducted cotraining for the two tasks to enable information interaction between them and improve the accuracy of the classification task. Results The average classification accuracies of our model on the training and external test sets was 90.5% and 85.1%, respectively. Ablation experiments showed that pretraining and cotraining could improve the prediction accuracy and generalization performance of the model. In the training and external test sets, the average areas under the receiver operating characteristic curve (AUROCs) were 94.18% and 87.64%, and the average areas under the precision‐recall curve (AUPRC) were 93.26% and 85.4%. Conclusions The developed model achieved excellent performance in predicting the H3K27M alteration status in DMGs, and its good reproducibility and generalization were verified in the external dataset

Biosynthesis of terpene compounds using the non-model yeast Yarrowia lipolytica: grand challenges and a few perspectives

Yarrowia lipolytica has emerged as an important non-model host for terpene production. However, three main challenges remain in industrial production using this yeast. First, considerable knowledge gaps exist in metabolic flux across multiple compartments, cofactor generation, and catabolism of non-sugar carbon sources. Second, many enzymatic steps in the complex-terpene synthesis pathway can pose rate-limitations, causing accumulation of toxic intermediates and increased metabolic burdens. Third, metabolic shifts, morphological changes, and genetic mutations are poorly characterized under industrial fermentation conditions. To overcome these challenges, systems metabolic analysis, protein engineering, novel pathway engineering, model-guided strain design, and fermentation optimization have been attempted with some successes. Further developments that address these challenges are needed to advance the Yarrowia lipolytica platform for industrial-scale production of high-value terpenes, including those with highly complex structures such as anticancer molecules withanolides and insecticidal limonoids

The ELAVL3/MYCN positive feedback loop provides a therapeutic target for neuroendocrine prostate cancer

Abstract Neuroendocrine prostate cancer is a rapidly progressive and lethal disease characterized by early visceral metastasis, poor prognosis, and limited treatment options. Uncovering the oncogenic mechanisms could lead to the discovery of potential therapeutic avenues. Here, we demonstrate that the RNA-binding protein ELAVL3 is specifically upregulated in neuroendocrine prostate cancer and that overexpression of ELAVL3 alone is sufficient to induce the neuroendocrine phenotype in prostate adenocarcinoma. Mechanistically, ELAVL3 is transcriptionally regulated by MYCN and subsequently binds to and stabilizes MYCN and RICTOR mRNA. Moreover, ELAVL3 is shown to be released in extracellular vesicles and induce neuroendocrine differentiation of adenocarcinoma cells via an intercellular mechanism. Pharmacological inhibition of ELAVL3 with pyrvinium pamoate, an FDA-approved drug, effectively suppresses tumor growth, reduces metastatic risk, and improves survival in neuroendocrine prostate cancer mouse models. Our results identify ELAVL3 as a critical regulator of neuroendocrine differentiation in prostate cancer and propose a drug repurposing strategy for targeted therapies