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With the development of Internet of Things (IoT) technology,its application scenarios have exploded recently,and such applications are generally delay-sensitive and resource-constrained.It is a focused issue in the way of offloading the real-time tasks under the condition of limited resource.Besides,it is a NP-hard combinatorial optimization problem to allocate limited computational resources for the real-time tasks.To solve this problem,this paper proposes a real-time resource management algorithm based on Lyapunov optimization,aiming at stabilizing the virtual queues while optimizing the total power consumption and total utility.Firstly,the optimization model for the total power consumption and weighted total utility is proposed under the constraint of computation and communication resources.This model contains of two virtual buffer queues,and tasks are unloaded in a device-to-device (D2D) scheduling model.Then,an optimization algorithm is proposed based on Lyapunov optimization to decompose the joint long-term average sum energy consumption and sum utility optimization problem into a series of real-time optimization problems.To solve these problems,a greedy-based matching algorithm is proposed.Experimental results demonstrate that the performance of the proposed algorithm is 8.6% better than the best result of random method and can approximate the exhaustive attack method under different connection degrees.

Silver (Ag) nanocrystals are appealing due to their excellent antimicrobial properties. Synthesis of harmless Ag nanocrystals by low toxic reagents remains significant. Herein, an environmentally benign solvothermal process was applied to synthesize Ag nanocrystals in ethylene glycol (EG) dissolving silver nitrate and sodium oleate. Silver ions were reduced by EG to zero-valent Ag atoms that underwent nucleation and growth to form oleate-stabilized Ag nanocrystals. Multiple experimental parameters including the reaction time, temperature, concentration of Ag precursor, and the molar ratio of sodium oleate and silver nitrate were investigated to probe their effects on morphological structures and surface plasmon resonance (SPR) properties of resulting Ag nanocrystals. Results from UV–Vis absorption spectra and TEM images show that structural and SPR features of Ag nanocrystals are extremely sensitive to the reaction temperature as well as the molar ratio of sodium oleate and silver nitrate. Especially, two absorption bands appeared. The short wavelength (~ 450 nm) represents the characteristic SPR of Ag nanospheres. While, the plasmon absorption at long wavelength (500–700 nm depending on reaction conditions) indicates the anisotropic or heterogeneous structures of Ag nanocrystals that maybe pave their potential applications in asymmetric catalysis, selective growth of coating materials, nanomedicine and optoelectronics. [ABSTRACT FROM AUTHOR]

Due to the difficulty in self-degradation of waste polyethylene terephthalate (PET) products, in order to solve the environmental problems and energy resource waste caused by PET, it is necessary to explore effective recovery methods for the treatment of waste PET products. For the chemical recovery process, the chemical recovery and reuse methods of waste PET were introduced from the aspects of alkaline hydrolysis, neutral hydrolysis, acidic hydrolysis, methanol alcoholysis, ethylene glycol alcoholysis, and ammonia/amine hydrolysis, and various chemical depolymerization methods were compared. The studies show that chemical recovery can obtain the initial raw material of PET, maintain the quality of PET products, and recycle and upgrade PET waste to add value to chemicals. Improving the recycling rate of waste PET products and converting them into renewable green resources is one of the effective measures to promote the clean and low-carbon transformation of energy. [ABSTRACT FROM AUTHOR]

Object: The study aims to determine whether multimorbidity status is associated with cerebrospinal fluid (CSF) biomarkers for neurodegenerative disorders., Methods: A total of 827 patients were enrolled from the Parkinson's Progression Markers Initiative (PPMI) database, including 638 patients with early-stage Parkinson's disease (PD) and 189 healthy controls (HCs). Multimorbidity status was evaluated based on the count of long-term conditions (LTCs) and the multimorbidity pattern. Using linear regression models, cross-sectional and longitudinal analyses were conducted to assess the associations of multimorbidity status with CSF biomarkers for neurodegenerative disorders, including α-synuclein (αSyn), amyloid-β 42 (Aβ 42 ), total tau (t-tau), phosphorylated tau (p-tau), glial fibrillary acidic protein (GFAP), and neurofilament light chain protein (NfL)., Results: At baseline, the CSF t-tau (p = 0.010), p-tau (p = 0.034), and NfL (p = 0.049) levels showed significant differences across the three categories of LTC counts. In the longitudinal analysis, the presence of LTCs was associated with lower Aβ 42 (β < -0.001, p = 0.020), and higher t-tau (β = 0.007, p = 0.026), GFAP (β = 0.013, p = 0.022) and NfL (β = 0.020, p = 0.012); Participants with tumor/musculoskeletal/mental disorders showed higher CSF levels of t-tau (β = 0.016, p = 0.011) and p-tau (β = 0.032, p = 0.044) than those without multimorbidity., Conclusion: Multimorbidity, especially severe multimorbidity and the pattern of mental/musculoskeletal/ tumor disorders, was associated with CSF biomarkers for neurodegenerative disorders in early-stage PD patients, suggesting that multimorbidity might play a crucial role in aggravating neuronal damage in neurodegenerative diseases., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Objectives: We aimed to explore the shared and specific signalling pathways involved in diabetic retinopathy (DR), diabetic peripheral neuropathy (DPN) and diabetic nephropathy (DN)., Methods: Differentially expressed mRNAs and lncRNAs were identified by high-throughput sequencing. Subsequently, functional enrichment analysis, protein-protein interaction (PPI) analysis and lncRNAs-mRNAs networks were conducted to determine the pathogenic mechanisms underlying DR, DPN and DN., Results: Twenty-six biological pathways were shared among DR, DPN and DN groups compared to the type 2 diabetes mellitus (T2DM) group without complications, and most of the shared pathways and core proteins were involved in immune and inflammatory responses of microvascular damage. Cytokine‒cytokine receptor interactions and chemokine signalling pathway were the most significant and specific pathways for DR, and the lncRNA‒mRNA regulatory networks affected DR by targeting these pathways. Sphingolipid metabolism and neuroactive ligand-receptor pathways were found to be specific for the pathogenesis of DPN. Moreover, multiple amino acid metabolic pathways were involved in the occurrence and progression of DN., Conclusions: Diabetic retinopathy, DPN and DN exhibited commonality and heterogeneity simultaneously. The shared pathologic mechanisms underlying these diabetic complications are involved in diabetic microvascular damage via immune and inflammatory pathways. Our findings predict several biomarkers and therapeutic targets for these diabetic complications.

Liquid biopsy test has a better uptake for colorectal cancer (CRC) screening. However, suboptimal detection of early-staged colorectal neoplasia (CRN) limits its application. Here, we established an early-staged CRN blood test using error-corrected sequencing by comparing clonal hematopoiesis (CH) of 63 CRN patients and that of 32 controls. We identified 1,446 variants and classified the uniqueness in CRN patients. There was no significance difference in the amount of variant between CRNs and controls, but the uniqueness of variants with defective DNA mismatch repair-related mutational signature was addressed from peripheral blood in early-staged CRN patients. By machine learning approach, the early-staged CRNs was discriminated from controls with an AUC of 0.959 and an accuracy of 0.937 (95% CI, 0.863 to 0.968). The CRN predictive model was further validated by additional 20 CRNs and 10 controls and showed the accuracy, sensitivity, specificity, positive prediction value (PPV) and negative prediction value (NPV) of 0.933 (95% CI: 0.779 to 0.992), 0.95, 0.90, 0.95 and 0.90, respectively. In summary, we develop a CH-based liquid biopsy test with machine learning approach, which not only increase screening uptake but also improve the detection rate of early-staged CRN., Competing Interests: None., (AJCR Copyright © 2022.)