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Objectives: This study aimed to survey the geriatric nursing competencies of clinical nursing staff in Chongqing City, China, and provide suggestions to enhance these competencies. Methods: This study was conducted in 204 hospitals in Southwest China from December 24, 2022 to January 7, 2023. The “Geriatric Nursing Competence of Clinical Nurse Investigation Tool” was used to explore factors that influence geriatric nurses’ competencies via stratified sampling. The survey was conducted by distributing and collecting questionnaires through the online platform Wenjuanxing. Results: A total of 10,692 nurses answered the questionnaires. Of these questionnaires, 9,442 were valid. The total geriatric nursing competence score of the clinical nursing staff was 2.29 ± 0.81, the secondary hospital score was 2.23 ± 0.78, and the tertiary hospital’s overall mean score was 2.33 ± 0.83. The factors that influenced secondary hospitals included the department of medicine, age of nurses and total length of career (P

The manual extraction of land disturbances associated with oil exploration, which normally includes resource roads, mining facilities, and well pads, presents significant challenges in terms of cost and time. Accurate monitoring and mapping of land disturbances resulting from oil exploration plays a crucial role in conducting comprehensive environmental assessments and facilitating effective land reclamation initiatives. However, prevailing deep learning methodologies in the realm of oil and gas exploration primarily focus on oil spill detection, neglecting the critical aspect of land disturbances resulting from oil exploration, thus overlooking the impact on land. Furthermore, given that the well sites are scattered and relatively diminutive compared to other land covers, their detection poses substantial difficulties. This paper proposes an automatic error-correcting (AEC) algorithm to address deficiencies in ground truth data quality. This AEC method was integrated into the deep-learning framework for land disturbance extraction, specifically tailored for land disturbances analysis associated with oil exploration. The efficacy of our method was validated on a dataset collected in Alberta covering an area of oil sand mining sites. The application of the AEC algorithm significantly enhanced the accuracy of land disturbance analysis, thereby contributing to a more effective hydrocarbon exploration impact analysis and facilitating the timely planning by the Alberta government. The results demonstrate notable improvements in both average pixel accuracy (AA) and mean intersection over union (mIoU), ranging from 8.3% to 15.4% and 0.5% to 5.8%, respectively. These enhancements, which have profound implications for the precision of land disturbance detection, prove that the proposed AEC algorithm can serve a dual purpose: correcting errors in the dataset and efficiently detecting land disturbance features in the oil exploration area.

Knot detection is an important aspect of timber grading. Reducing the false-positive frequency of knot detection will improve the accuracy of the predicted grade, as well as the utilization of the graded timber. In this study, a framework for timber knot detection was proposed. Faster R-CNN, a state-of-the-art defect identification algorithm, was first employed to detect timber knots because of its high true-positive frequency. Then, an overlapping bounding box filter was proposed to lower the false positive frequency achieved by Faster R-CNN, where a single knot is sometimes marked several times. The filter merges the overlapping bounding boxes for one actual knot into one box and ensures that each knot is marked only once. The main advantage of this framework is that it reduces the false positive frequency with a small computational cost and a small impact on the true positive frequency. The experimental results showed that the detection precision improved from 90.9% to 97.5% by filtering the overlapping bounding box. The framework proposed in this study is competitive and has potential applications for detecting timber knots for timber grading.

Abstract Graph contrastive learning has been developed to learn discriminative node representations on homogeneous graphs. However, it is not clear how to augment the heterogeneous graphs without substantially altering the underlying semantics or how to design appropriate pretext tasks to fully capture the rich semantics preserved in heterogeneous information networks (HINs). Moreover, early investigations demonstrate that contrastive learning suffer from sampling bias, whereas conventional debiasing techniques (e.g., hard negative mining) are empirically shown to be inadequate for graph contrastive learning. How to mitigate the sampling bias on heterogeneous graphs is another important yet neglected problem. To address the aforementioned challenges, we propose a novel multi-view heterogeneous graph contrastive learning framework in this paper. We use metapaths, each of which depicts a complementary element of HINs, as the augmentation to generate multiple subgraphs (i.e., multi-views), and propose a novel pretext task to maximize the coherence between each pair of metapath-induced views. Furthermore, we employ a positive sampling strategy to explicitly select hard positives by jointly considering semantics and structures preserved on each metapath view to alleviate the sampling bias. Extensive experiments demonstrate MCL consistently outperforms state-of-the-art baselines on five real-world benchmark datasets and even its supervised counterparts in some settings.

Nonalcoholic fatty liver disease (NAFLD) is a clinicopathological syndrome characterized by excessive deposition of fatty acids in the liver. Further deterioration leads to nonalcoholic steatohepatitis, cirrhosis, and hepatocellular carcinoma, creating a heavy burden on human health and the social economy. Currently, there are no effective and specific drugs for the treatment of NAFLD. Therefore, it is important to further investigate the pathogenesis of NAFLD and explore effective therapeutic targets for the prevention and treatment of the disease. Six-transmembrane epithelial antigen of prostate 3 (STEAP3), a STEAP family protein, is a metalloreductase. Studies have shown that it can participate in the regulation of liver ischemia-reperfusion injury, hepatocellular carcinoma, myocardial hypertrophy, and other diseases. In this study, we found that the expression of STEAP3 is upregulated in NAFLD. Deletion of STEAP3 inhibits the development of NAFLD in vivo and in vitro, whereas its overexpression promotes palmitic acid/oleic acid stimulation-induced lipid deposition in hepatocytes. Mechanistically, it interacts with transforming growth factor beta-activated kinase 1 (TAK1) to regulate the progression of NAFLD by promoting TAK1 phosphorylation and activating the TAK1-c-Jun N-terminal kinase/p38 signaling pathway. Taken together, our results provide further insight into the involvement of STEAP3 in liver pathology.

Abstract Novel Coronavirus disease 2019 (COVID‐19) has spread rapidly around the world. Individuals with immune dysregulation and/or on immunosuppressive therapy, such as rheumatic patients, are considered at greater risk for infections. However, the risks of patients with each subcategory of rheumatic diseases have not been reported. Here, we identified 100 rheumatic patients from 18,786 COVID‐19 patients hospitalized in 23 centers affiliated to Hubei COVID‐19 Rheumatology Alliance between January 1 and April 1, 2020. Demographic information, medical history, length of hospital stay, classification of disease severity, symptoms and signs, laboratory tests, disease outcome, computed tomography, and treatments information were collected. Compared to gout and ankylosing spondylitis (AS) patients, patients with connective tissue disease (CTD) tend to be more severe after COVID‐19 infection (p = 0.081). CTD patients also had lower lymphocyte counts, hemoglobin, and platelet counts (p values were 0.033,

To address the challenge of local optimization and step size factor parameter setting in the full attraction model of the firefly algorithm (FA), this paper introduces level-based attraction and variable step size to FA. In the level-based attraction model, fireflies are firstly grouped in different levels according to the brightness, and each firefly randomly selects two fireflies from a higher level to learn from. By using the variable step size strategy, the searching step size decreases with the number of iterations. Herein, the level-based attracting model can increase the diversity of individual learning and improve the ability to jump out of local optimization. Meanwhile, dynamic adjustment of step size can balance the detection and development ability of the algorithm and improve the optimization accuracy of the algorithm. To evaluate the effectiveness of the proposed algorithm, comparisons are drawn against optimized FA, particle swarm optimization algorithm, artificial bee colony algorithm and differential evolution algorithm on a variety of test function sets.

Abstract In this paper, we solve the variable-coefficient fractional diffusion-wave equation in a bounded domain by the Legendre spectral method. The time fractional derivative is in the Caputo sense of order γ∈(1,2) $\gamma \in (1,2)$. We propose two fully discrete schemes based on finite difference in temporal and Legendre spectral approximations in spatial discretization. For the first scheme, we discretize the time fractional derivative directly by the L1 $L_{1}$ approximation coupled with the Crank–Nicolson technique. For the second scheme, we transform the equation into an equivalent form with respect to the Riemann–Liouville fractional integral operator. We give a rigorous analysis of the stability and convergence of the two fully discrete schemes. Numerical examples are carried out to verify the theoretical results.

In this article, we consider spectral methods for solving the initial-boundary value problem of the space fractional-order Allen-Cahn equation. A fully discrete scheme based on the modified Crank-Nicolson scheme in time and the Legendre spectral method in space is established. The existence and uniqueness of the fully discrete scheme are derived, and the stability and convergence analysis of the fully discrete scheme are proved rigorously. By constructing a fractional duality argument, the corresponding optimal error estimates in $L^2$ and $H^\alpha$ norm are derived, respectively. Also, numerical experiments are performed to support the theoretical results.

Dihydroartemisinin (DHA), a widely used antimalarial agent, has clinical potential for the treatment of hepatic carcinoma. Although chemotherapy is indispensable for tumor therapy, it is generally limited by poor solubility, low efficiency, rapid clearance, and side effects. As an emerging treatment method, photothermal therapy (PTT) has many outstanding properties, but suffers from poor photostability of photosensitizer and incomplete ablation. Multimodal therapies could combine the advantages of different therapy methods to improve antitumor efficiency. Hence, we designed a nano-delivery system (ICG&DHA@ZIF-8) using zeolitic imidazolate framework-8 (ZIF-8) with a high porous rate and pH sensitivity property, to co-load DHA and indocyanine green (ICG). Dynamic light scattering and transmission electron microscopy were used to characterize the prepared nanoparticles. The photothermal conversion and drug release performances of ICG&DHA@ZIF-8 were investigated. In vitro antitumor efficacy and cellular uptake were studied. The mechanism of the combination treatment was studied by reactive oxygen species level detection and western blot assays. In vivo antitumor assays were then studied with the guidance of ex vivo imaging. The results showed that the ICG&DHA@ZIF-8 based combination therapy could efficiently kill hepatic carcinoma cells and suppress tumor growth. This research provides a potential nanodrug for the treatment of hepatic carcinoma.

Abstract In this paper, we consider the space-fractional reaction–advection–diffusion equation with fractional diffusion and integer advection terms. By treating the first-order integer derivative as the composition of two Riemann–Liouville fractional derivative operators, we construct a fully discrete scheme by Legendre spectral method in a spatial and Crank–Nicolson scheme in temporal discretizations. Using thee right Riemann–Liouville fractional derivative, a novel duality argument is established, the optimal error estimate is proved to be O(τ2+N−m) $O(\tau^{2}+N^{-m})$ in L2 $L^{2}$-norm. Numerical tests are carried out to support the theoretical results, and the coefficient matrix with respect to first-order derivative obtained here is compared with that of traditional Legendre spectral method.

Abstract In this paper, we consider a multi-term variable-order fractional diffusion equation on a finite domain, which involves the Caputo variable-order time fractional derivative of order α(x,t)∈(0,1) $\alpha(x,t) \in(0,1) $ and the Riesz variable-order space fractional derivatives of order β(x,t)∈(0,1) $\beta(x,t) \in (0,1)$, γ(x,t)∈(1,2) $\gamma(x,t)\in(1,2)$. Approximating the temporal direction derivative by L1-algorithm and the spatial direction derivative by the standard and shifted Grünwald method, respectively, a characteristic finite difference scheme is proposed. The stability and convergence of the difference schemes are analyzed via mathematical induction. Some numerical experiments are provided to show the efficiency of the proposed difference schemes.

Abstract Finding the minimum connected dominating set (MCDS) is a key problem in wireless sensor networks, which is crucial for efficient routing and broadcasting. However, the MCDS problem is NP-hard. In this paper, a new approximation algorithm with approximation ratio H(Δ)+3 in time O(n 2) is proposed to approach the MCDS problem. The key idea is to divide the sensors in CDS into core sensors and supporting sensors. The core sensors dominate the supporting sensors in CDS, while the supporting sensors dominate other sensors that are not in CDS. To minimize the number of both the cores and the supporters, a three-phased algorithm is proposed. (1) Finding the base-core sensors by constructing independent set (denoted as S 1), in which the sensors who have the largest |N2(v)||N(v)| $\frac {|N^{2}(v)|}{|N(v)|}$ (number of two-hop neighbors over the number of one-hop neighbors) will be selected greedily into S 1; (2) Connecting all base-core sensors in S 1 to form a connected subgraph, the sensors in the subgraph are called cores; (3) Adding the one-hop neighbors of the core sensors to the supporter set S 2. This guarantees a small number of sensors can be added into CDS, which is a novel scheme for MCDS construction. Extensive simulation results are shown to validate the performance of our algorithm.

Background and objective Gefitinib and Pemetrexed are drugs used as second-line therapy for advanced non-small cell lung cancer (NSCLC), although studies comparing the two drugs are limited. The aim of this study is to explore the effects, safety, and quality of life (QoL) of Gefitinib and Pemetrexed on patients with advanced non-squamous NSCLC. Methods Forty-six advanced non-squamous NSCLC patients who failed to first-line therapy were randomly divided into two groups with 23 patients each, one using oral Gefitinib (Gefitinib group) and the other using intravenous injection Pemetrexed (Pemetrexed group). The effects, safety, and QoL were determined and analyzed. Results For the Pemetrexed group, objective response rate (ORR) was 13.0% (3/23), disease control rate (DCR) was 30.4% (7/23), and median progression-free survival (mPFS) was 3.1 months. In the Gefitinib group, ORR was 17.3% (4/23), DCR was 39.1% (9/23), and mPFS was 4.4 months. Compared with the Pemetrexed group, the ORR, DCR, and mPFS in the Gefitinib group exhibited no statistical significance (P>0.05). Furthermore, the most common toxicities in the Pemetrexed group were neutropenia (n=9, 39.13%) and fatigue (n=8, 34.78%), whereas those in the in Gefitinib group were skin rash (n=8, 34.78%) and diarrhea (n=4, 17.39%). Compared with baseline, the QoL improved in both groups but to different degrees. Likewise, emotional, functional well-being, and QoL aspects specifically related to lung cancer were better improved in the Gefitinib group than in the Pemetrexed group (P

It has been considered that the detection methods for circulating tumor cells (CTCs) based on epithelial cell adhesion molecule (EpCAM) underestimate the number of CTCs and may miss a metastatic subpopulation with cancer stem cell (CSC) properties. Therefore, we investigated EpCAM-positive and -negative CTCs in non-small cell lung cancer (NSCLC) patients at different stages, assessed the clinical value of these CTCs and explored their capacity in the following CSC model.CTCs were enriched by the depletion of leukocytes with bi-antibodies using a magnetic bead separation technique and then identified by the expression of EpCAM and cytokeratin 7 and 8 using multi-parameter flow cytometry. We determined the distribution of CTCs classified by the expression of EpCAM in 46 NSCLC patients with stages I to IV, assessed the diagnostic value of these CTCs by longitudinal monitoring in 4 index patients during adjuvant therapy and characterized the stemness of these CTCs by the expression of CXCR4 and CD133 in 10 patients.EpCAM-negative (E-) CTCs were detected to be significantly higher than EpCAM-positive (E+) CTCs in stage IV (p = 0.003). The patients with the percentage of E-CTCs more than 95% (r > 95%) were detected to be significantly increased from 13.3% in stage I-II to 61.1% in stage IV (p = 0.006). Kaplan-Meier analysis indicated that the patients with r > 95% had significantly shorter survival time than those with r ≤ 0.95 (p = 0.041). Longitudinal monitoring of CTCs indicated that the patients with a high percentage of E-CTCs in the blood were not responsive to either chemotherapy or targeted therapy. Further characterization of CTCs revealed that a stem-like subpopulation of CXCR4+CD133+ CTCs were detected to be significantly more prevalent in E-CTCs than that in E+CTCs (p = 0.005).The enrichment of CTCs by the depletion of leukocytes with bi-antibodies is a valuable method for estimating the number of CTCs, which can be potentially applied in predicting the prognosis, monitoring the therapeutic effect of NSCLC patients and further analyzing the biology of CTCs.

Maximizing network lifetime is an important objective for the target-coverage problem. With practicable manufacture and cost reduction, directional sensor has been widely used in wireless sensor networks to save energy. In this paper, we address the target Q-coverage (TQC) problem to prolong the network lifetime with bounded service delay constraint in directional sensor networks. We propose a protocol to find a collection of coverage sets that satisfy the coverage quality requirement and the bounded service delay constraint, where the target in each coverage set may not be served continuously but can be served with tolerant service delay. By steering some sensors' directional antennas, our protocol could deal with the changes of network topology or monitoring tasks. Simulation results show that the performance of our protocol is close to the upper bound of the optimal solution.