MXenes have been widely studied for their excellent specific surface area, high conductivity and composition tunability, which have been used as a highly efficient electrode material for lithium-ion ...batteries（LIBs）.However, limited storage capacity and severe lattice expansion caused by Li-ions diffusion restrict the application of MXenes as electrode materials. Here, Ti3C2 MXenes with surface halogenation（fluorination, chlorination and bromination） as representative MXene materials were designed. Effects of surface functionalization on the atomic structures, electronic properties, mechanical properties, and electrochemical performance of Ti3C2T2（T = F, Cl and Br）anode in LIBs were investigated using first-principles calculations based on density functional theory with van der Waals correction. The results reveal that Ti3C2T2 MXenes exhibit metallic conductivity with improved structural stability and mechanical strength. Compared with Ti3C2F2 and Ti3C2Br2, Ti3C2Cl2 exhibits the large elastic modulus（321.70 and 329.43 N/m along x and y directions, respectively）, low diffusion barrier（0.275 eV）, high open circuit voltage（0.54 eV）, and storage capacity（674.21 mA·h/g） with stoichiometric ratio of Ti3C2Cl2Li6, which renders the enhanced rate performance and endures the repeated lattice expansion and contraction during the charge/discharge process. Moreover, surface chlorination yields expanded interlayer spacing, which can improve Li-ion accessibility and fast charge–discharge rate in Ti3C2Cl2. The research demonstrates that Cl– terminated Ti3C2 is a promising anode material, and provides effective and reversible routes to engineering other MXenes as anode materials for LIBs.
Osteoporosis is one of the important bone abnormalities in chronic kidney disease-mineral and bone disorder (CKD-MBD) and still lacks a sensitive biomarker to diagnose. Fibroblast growth factor 21 ...(FGF21) can stimulate bone loss in patients with diabetes and increase in CKD patients. In this study, we investigated whether FGF21 could serve as a biomarker to predict osteoporosis in a haemodialysis cohort.
We recorded demographic information, biochemical data, and serum FGF21 and FGF23 levels and measured the CT attenuation values of 339 haemodialysis patients from two large medical centres. We assessed the correlation of CT attenuation values with serum FGF21 and FGF23 levels and tested whether they were independent factors for osteoporosis. ROC curves were constructed to compare the prognostic value of FGF21 and FGF23 for osteoporosis.
Based on the CT attenuation value, serum FGF21 levels were higher in our osteoporosis group (median 640.86 pg/ml vs. 245.46 pg/ml, P ˂ 0.01). Meanwhile, FGF21 (r = -0.136, P < 0.05) and FGF23 (r = -0.151, P < 0.05) were both negatively associated with osteoporosis. Moreover, FGF21 (β = -0.067, P < 0.05) was an independent factor for osteoporosis. Furthermore, FGF21 combined with age yielded a marked specificity (90.5 %) and sensitivity (61.8 %) in predicting osteoporosis of haemodialysis patients with less residual renal function.
FGF21 has a positive relationship with the incidence of osteoporosis in patients on haemodialysis. FGF21 combined with age is a good predictive biomarker for osteoporosis in patients on haemodialysis, especially those with less residual renal function.
Epigenetic status of fetal fibroblasts (FFs) is one of the crucial factors accounted for the success of somatic cell nuclear transfer and gene editing, which might inevitably be affected by ...passaging. But few systematic studies have been performed on the epigenetic status of passaged aging cells. Therefore, FFs from large white pig were in vitro passaged to the 5, 10, and 15 (F5, F10, and F15) passages in the present study to investigate the potential alteration of epigenetic status. Results indicated the senescence of FFs occurs with the passaging, as assessed by the weakened growth rate, increased β-gal expression, and so on. For the epigenetic status of FFs, the higher level both of DNA methylation and H3K4me1, H3K4me2, H3K4me3 was observed at F10, but the lowest level was observed at F15. However, the fluorescence intensity of m6A was significantly higher in F15, but lower (p < 0.05) in F10, and the related mRNA expression in F15 was significantly higher than F5. Further, RNA-Seq indicated a considerable difference in the expression pattern of F5, F10, and F15 FFs. Among differentially expressed genes, not only the genes involved in cell senescence were changed, but also the upregulated expression of Dnmt1, Dnmt3b, Tet1 and dysregulated expression of histone methyltransferases-related genes were detected in F10 FFs. In addition, most genes related to m6A such as METTL3, YTHDF2, and YTHDC1 were significantly different in F5, F10, and F15 FFs. In conclusion, the epigenetic status of FFs was affected by being passaged from F5 to F15.
Federated multi-task learning (FMTL) is a promising edge learning framework to fit the data with non-independent and non-identical distribution (non-i.i.d.) by exploiting the correlations of ...personalized models. In many practical systems, the sensory data distribution in wireless systems is not only heterogeneous but also non-stationary due to the mobility of terminals and the randomness of link connections. The non-stationary heterogeneous data may lead to model divergence and staleness in the training stage and poor accuracy in the inference stage. In this paper, we design an adaptive FMTL framework, which can work in a non-stationary environment. We propose to optimize the model update scheme and cluster splitting scheme in the training stage to accelerate model convergencse when the training data are non-stationary. We further design a low-complexity model selection scheme in both the training and the inference stages to choose the best model for fitting the current data. The proposed framework is validated in two scenarios, linear regression and graph neural network (GNN)-based power control in wireless device-to-device (D2D) networks. Both sets of numerical results demonstrate that the proposed framework can accelerate the model training convergence and reduce the computation complexity while ensuring model accuracy.
Introduction:
Intracranial stents are of paramount importance in managing cerebrovascular disorders. Nevertheless, the currently employed drug-eluting stents, although effective in decreasing ...in-stent restenosis, might impede the re-endothelialization process within blood vessels, potentially leading to prolonged thrombosis development and restenosis over time.
Methods:
This study aims to construct a multifunctional bioactive coating to enhance the biocompatibility of the stents. Salvianolic acid B (SALB), a bioactive compound extracted from Salvia miltiorrhiza, exhibits potential for improving cardiovascular health. We utilized dopamine as the base and adhered chitosan-coated SALB microspheres onto nickel-titanium alloy flat plates, resulting in a multifunctional drug coating.
Results:
By encapsulating SALB within chitosan, the release period of SALB was effectively prolonged, as evidenced by the
in vitro
drug release curve showing sustained release over 28 days. The interaction between the drug coating and blood was examined through experiments on water contact angle, clotting time, and protein adsorption. Cellular experiments showed that the drug coating stimulates the proliferation, adhesion, and migration of human umbilical vein endothelial cells.
Discussion:
These findings indicate its potential to promote re-endothelialization. In addition, the bioactive coating effectively suppressed smooth muscle cells proliferation, adhesion, and migration, potentially reducing the occurrence of neointimal hyperplasia and restenosis. These findings emphasize the exceptional biocompatibility of the newly developed bioactive coating and demonstrate its potential clinical application as an innovative strategy to improve stent therapy efficacy. Thus, this coating holds great promise for the treatment of cerebrovascular disease.
With crucial roles on the differentiation of anterior pituitary and the regulation of the prolactin (PRL), growth hormone (GH) and thyroid-stimulating hormone-beta (TSH-beta) genes, the chicken PIT1 ...gene is regarded as a key candidate gene for production traits. In this study, five reported polymorphisms (MR1-MR5) of the PIT1 gene were genotyped in a full sib F2 resource population to evaluate their effects on growth, carcass and fatty traits in chickens.
Marker-trait association analyses showed that, MR1 was significantly associated with shank diameters (SD) at 84 days (P < 0.05), hatch weight (HW) and shank length (SL) at 84 days (P < 0.01), MR2 was significantly associated with BW at 28, 42 days and average daily gain (ADG) at 0-4 weeks (P < 0.05), and MR3 was significantly associated with ADG at 4-8 weeks (P < 0.05). MR4 was associated with SL at 63, 77, 84 days and BW at 84 days (P < 0.05), as well as SD at 77 days (P < 0.01). Significant association was also found of MR5 with BW at 21, 35 days and SD at 63 days (P < 0.05), BW at 28 days and ADG at 0-4 weeks (P < 0.01). Both T allele of MR4 and C allele of MR5 were advantageous for chicken growth. The PIT1 haplotypes were significantly associated with HW (P = 0.0252), BW at 28 days (P = 0.0390) and SD at 56 days (P = 0.0400). No significant association of single SNP and haplotypes with chicken carcass and fatty traits was found (P > 0.05).
Our study found that polymorphisms of PIT1 gene and their haplotypes were associated with chicken growth traits and not with carcass and fatty traits.
Existing deep learning-enabled semantic communication systems often rely on shared background knowledge between the transmitter and receiver that includes empirical data and their associated semantic ...information. In practice, the semantic information is defined by the pragmatic task of the receiver and cannot be known to the transmitter. The actual observable data at the transmitter can also have non-identical distribution with the empirical data in the shared background knowledge library. To address these practical issues, this paper proposes a new neural network-based semantic communication system for image transmission, where the task is unaware at the transmitter and the data environment is dynamic. The system consists of two main parts, namely the semantic coding (SC) network and the data adaptation (DA) network. The SC network learns how to extract and transmit the semantic information using a receiver-leading training process. By using the domain adaptation technique from transfer learning, the DA network learns how to convert the data observed into a similar form of the empirical data that the SC network can process without retraining. Numerical experiments show that the proposed method can be adaptive to observable datasets while keeping high performance in terms of both data recovery and task execution.
Mobile-edge computing (MEC) has recently emerged as a cost-effective paradigm to enhance the computing capability of hardware-constrained wireless devices (WDs). In this paper, we first consider a ...two-user MEC network, where each WD has a sequence of tasks to execute. In particular, we consider task dependency between the two WDs, where the input of a task at one WD requires the final task output at the other WD. Under the considered task-dependency model, we study the optimal task offloading policy and resource allocation (e.g., on offloading transmit power and local CPU frequencies) that minimize the weighted sum of the WDs' energy consumption and task execution time. The problem is challenging due to the combinatorial nature of the offloading decisions among all tasks and the strong coupling with resource allocation. To tackle this problem, we first assume that the offloading decisions are given and derive the closed-form expressions of the optimal offloading transmit power and local CPU frequencies. Then, an efficient bi-section search method is proposed to obtain the optimal solutions. Furthermore, we prove that the optimal offloading decisions follow an one-climb policy, based on which a reduced-complexity Gibbs Sampling algorithm is proposed to obtain the optimal offloading decisions. We then extend the investigation to a general multi-user scenario, where the input of a task at one WD requires the final task outputs from multiple other WDs. Numerical results show that the proposed method can significantly outperform the other representative benchmarks and efficiently achieve low complexity with respect to the call graph size.
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