High-fat diet induces protein kinase A and G-protein receptor kinase phosphorylation of β2 -adrenergic receptor and impairs cardiac adrenergic reserve in animal hearts.

Key pointsPatients with diabetes show a blunted cardiac inotropic response to β-adrenergic stimulation despite normal cardiac contractile reserve. Acute insulin stimulation impairs β-adrenergically induced contractile function in isolated cardiomyocytes and Langendorff-perfused hearts. In this study, we aimed to examine the potential effects of hyperinsulinaemia associated with high-fat diet (HFD) feeding on the cardiac β2 -adrenergic receptor signalling and the impacts on cardiac contractile function. We showed that 8 weeks of HFD feeding leads to reductions in cardiac functional reserve in response to β-adrenergic stimulation without significant alteration of cardiac structure and function, which is associated with significant changes in β2 -adrenergic receptor phosphorylation at protein kinase A and G-protein receptor kinase sites in the myocardium. The results suggest that clinical intervention might be applied to subjects in early diabetes without cardiac symptoms to prevent further cardiac complications.AbstractPatients with diabetes display reduced exercise capability and impaired cardiac contractile reserve in response to adrenergic stimulation. We have recently uncovered an insulin receptor and adrenergic receptor signal network in the heart. The aim of this study was to understand the impacts of high-fat diet (HFD) on the insulin-adrenergic receptor signal network in hearts. After 8 weeks of HFD feeding, mice exhibited diabetes, with elevated insulin and glucose concentrations associated with body weight gain. Mice fed an HFD had normal cardiac structure and function. However, the HFD-fed mice displayed a significant elevation of phosphorylation of the β2 -adrenergic receptor (β2 AR) at both the protein kinase A site serine 261/262 and the G-protein-coupled receptor kinase site serine 355/356 and impaired adrenergic reserve when compared with mice fed on normal chow. Isolated myocytes from HFD-fed mice also displayed a reduced contractile response to adrenergic stimulation when compared with those of control mice fed normal chow. Genetic deletion of the β2 AR led to a normalized adrenergic response and preserved cardiac contractile reserve in HFD-fed mice. Together, these data indicate that HFD promotes phosphorylation of the β2 AR, contributing to impairment of cardiac contractile reserve before cardiac structural and functional remodelling, suggesting that early intervention in the insulin-adrenergic signalling network might be effective in prevention of cardiac complications in diabetes

Multi-Antenna Spectrum Sensing With Alpha-Stable Noise for Cognitive Radio-Enabled IoT

Cognitive radio-enabled Internet of Things (CR-IoT) is considered as a promising technology to handle spectrum scarcity for IoT applications. Spectrum sensing enables unlicensed secondary users to exploit spectrum holes under the condition of avoiding interference with primary users in CR-IoT networks. Previous studies often assume that the noise is Gaussian while ignoring the influence of non-Gaussian noise. Moreover, multi-antenna-based spectrum sensing algorithms only consider the partial information of covariance matrix. This paper develops two multi-antenna-based spectrum sensing schemes, using fractional low-order covariance matrices to address the issue of performance degradation in impulsive noise. Specifically, the first scheme, namely, diagonal element weighting detection, exploits the diagonal element weighting of the fractional low-order covariance matrix. The latter scheme is called off-diagonal element weighting detection, which adopts the diagonal matrix weighting strategy that exploits the off-diagonal elements of fractional low-order covariance matrices. The approximate analytical expressions of the false alarm probability and detection probability are derived. These developed schemes do not employ any priori knowledge of the primary user signal. Simulation results indicate that two proposed schemes achieve acceptable performance and are robust to the characteristic exponent of the alpha-stable noise, e.g., these proposed methods could achieve a detection probability of 90% with a false alarm probability of 0.1 at GSNR = -16dB, respectively

Enrichment of Polychlorinated Biphenyls from Aqueous Solutions Using Fe3O4 Grafted Multiwalled Carbon Nanotubes with Poly Dimethyl Diallyl Ammonium Chloride

In this paper, Fe3O4 nanoparticles (Fe3O4 NPs) grafted carboxyl groups of multiwalled carbon nanotubes with cationic polyelectrolyte poly (dimethyldiallylammonium chloride) (PDDA) (MWCNTs-COO−/PDDA@Fe3O4), are successfully synthesized and used for the extraction of six kinds of major toxic polychorinated biphenyls (PCBs) from a large volume of water solution. The hydrophilicity of the PDDA cage can enhance the dispersibility of sorbents in water samples, and the superparamagnetism of the Fe3O4 NPs facilitate magnetic separation which directly led to the simplification of the extraction procedure. With the magnetic solid-phase extraction (MSPE) technique based on the MWCNTs-COO−/PDDA@Fe3O4 sorbents, it requires only 30 min to extract trace levels of PCBs from 500 mL water samples. When the eluate condensed to 1.0 mL, concentration factors for PCBs became over 500. The spiked recoveries of several real water samples for PCBs were in the range of 73.3–98.9% with relative standard deviations varying from 3.8% to 9.4%, reflecting good accuracy of the method. Therefore, preconcentration of trace level of PCBs by using this MWCNTs-COO−/PDDA@Fe3O4 sorbent, which are stable for multiple reuses, from water solution can be performed

Multi-granularity Backprojection Transformer for Remote Sensing Image Super-Resolution

Backprojection networks have achieved promising super-resolution performance for nature images but not well be explored in the remote sensing image super-resolution (RSISR) field due to the high computation costs. In this paper, we propose a Multi-granularity Backprojection Transformer termed MBT for RSISR. MBT incorporates the backprojection learning strategy into a Transformer framework. It consists of Scale-aware Backprojection-based Transformer Layers (SPTLs) for scale-aware low-resolution feature learning and Context-aware Backprojection-based Transformer Blocks (CPTBs) for hierarchical feature learning. A backprojection-based reconstruction module (PRM) is also introduced to enhance the hierarchical features for image reconstruction. MBT stands out by efficiently learning low-resolution features without excessive modules for high-resolution processing, resulting in lower computational resources. Experiment results on UCMerced and AID datasets demonstrate that MBT obtains state-of-the-art results compared to other leading methods

Advances in basic research, clinical diagnosis and treatment of thyroid cancer in 2022

Thyroid cancer is a common malignancy of the endocrine system with a significantly increased incidence in recent years. Surgical treatment is the primary treatment, followed by endocrine therapy and radionuclide therapy according to the clinical evaluations, and in some cases, radiotherapy and targeted therapy are needed. However, the mortality of thyroid cancer also tends to increase, especially in advanced or low/dedifferentiated patients with poor prognosis and short survival time. With the advancement of basic and clinical research, new progress has been made in individualized treatment of thyroid cancer. This article reviewed the research progress of thyroid cancer in 2022

In Search of the Long-Tail: Systematic Generation of Long-Tail Knowledge via Logical Rule Guided Search

Since large language models have approached human-level performance on many tasks, it has become increasingly harder for researchers to find tasks that are still challenging to the models. Failure cases usually come from the long-tail distribution - data that an oracle language model could assign a probability on the lower end of its distribution. Current methodology such as prompt engineering or crowdsourcing are insufficient for creating long-tail examples because humans are constrained by cognitive bias. We propose a Logic-Induced-Knowledge-Search (LINK) framework for systematically generating long-tail knowledge statements. Grounded by a symbolic rule, we search for long-tail values for each variable of the rule by first prompting a LLM, then verifying the correctness of the values with a critic, and lastly pushing for the long-tail distribution with a reranker. With this framework we construct a dataset, Logic-Induced-Long-Tail (LINT), consisting of 200 symbolic rules and 50K knowledge statements spanning across four domains. Human annotations find that 84% of the statements in LINT are factually correct. In contrast, ChatGPT and GPT4 struggle with directly generating long-tail statements under the guidance of logic rules, each only getting 56% and 78% of their statements correct. Moreover, their "long-tail" generations in fact fall into the higher likelihood range, and thus are not really long-tail. Our findings suggest that LINK is effective for generating data in the long-tail distribution while enforcing quality. LINT can be useful for systematically evaluating LLMs' capabilities in the long-tail distribution. We challenge the models with a simple entailment classification task using samples from LINT. We find that ChatGPT and GPT4's capability in identifying incorrect knowledge drop by ~3% in the long-tail distribution compared to head distribution