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Introduction: Patients undergoing hemodialysis (HD) are highly vulnerable during the COVID-19 pandemic. We aimed to investigate the risk factors associated with the severity of COVID-19 and death after the complete liberalization of epidemic control in China. Methods: We followed the outcomes of the HD patients of Central Hospital of Dalian University of Technology, from December 6, 2022 to January 8, 2023. The non-contrast enhanced chest computed tomography (CT) was performed on all COVID-19-infected hospitalized patients. We recorded the patient's clinical characteristics, demographic features, vaccination history, treatments, and lung lesions. Odds ratios and 95% confidence intervals were calculated using logistic regression models to identify independent risk factors for COVID-19-related severity and mortality. Results: This study included a total of 858 hemodialysis patients, of which 660 were infected with COVID-19. The mean age was (55.61±14.61) years, with a median (interquartile range) dialysis duration of 44.5 (69.5) months. Over half (60%) of the study participants were male, and the majority had hypertension as a comorbidity. Multivariable analysis revealed that age, pre-dialysis diastolic pressure, fever, white blood cell (WBC) count, potassium, β2-microglobulin level and calcium were independent risk factors for disease severity, while platelets, urea nitrogen and creatinine were identified as independent protective factors. Furthermore, total iron- binding capacity and vaccination were found to be independent protective factors against mortality, and WBC count was an independent risk factor for in-hospital mortality (p < 0.05). The most frequent CT finding among hospitalized patients with chest symptoms was patchy shadow or pleural effusion, observed in 64.8% of cases. More than half of the patients exhibited bilateral lung lesions, and over 60% involved two or more lobes. Conclusion: The majority of HD patients are susceptible to COVID-19. Demographic, clinical features and laboratory indicators can be used to predict the severity and mortality associated with COVID-19. Our findings will assist clinicians in identifying markers for the early detection of high mortality risk in HD patients with COVID-19.

We propose a network traffic prediction model based on linear and nonlinear model combination. Network traffic is modeled by an autoregressive moving average model, and the error between the measured and predicted network traffic values is obtained. Then, an echo state network is used to fit the prediction error with nonlinear components. In addition, an improved slime mold algorithm is proposed for reservoir parameter optimization of the echo state network, further improving the regression performance. The predictions of the linear (autoregressive moving average) and nonlinear (echo state network) models are added to obtain the final prediction. Compared with other prediction models, test results on two network traffic datasets from mobile and fixed networks show that the proposed prediction model has a smaller error and difference measures. In addition, the coefficient of determination and index of agreement is close to 1, indicating a better data fitting performance. Although the proposed prediction model has a slight increase in time complexity for training and prediction compared with some models, it shows practical applicability.

Abstract Artificial photosynthesis using carbon nitride (g-C3N4) holds a great promise for sustainable and cost-effective H2O2 production, but the high carrier recombination rate impedes its efficiency. To tackle this challenge, we propose an innovative method involving multispecies iodine mediators (I−/I3 −) intercalation through a pre-photo-oxidation process using potassium iodide (suspected deteriorated “KI”) within the g-C3N4 framework. Moreover, we introduce an external electric field by incorporating cationic methyl viologen ions to establish an auxiliary electron transfer channel. Such a unique design drastically improves the separation of photo-generated carriers, achieving an impressive H2O2 production rate of 46.40 mmol g−1 h−1 under visible light irradiation, surpassing the most visible-light H2O2-producing systems. Combining various advanced characterization techniques elucidates the inner photocatalytic mechanism, and the application potential of this photocatalytic system is validated with various simulation scenarios. This work presents a significative strategy for preparing and applying highly efficient g-C3N4-based catalysts in photochemical H2O2 production.

Brain apoptosis is one of the main causes of epileptogenesis. The antiapoptotic effect and potential mechanism of Q808, an innovative anticonvulsant chemical, have never been reported. In this study, the seizure stage and latency to reach stage 2 of pentylenetetrazol (PTZ) seizure rat model treated with Q808 were investigated. The morphological change and neuronal apoptosis in the hippocampus were detected by hematoxylin and eosin (HE) and terminal deoxynucleotidyl transferase-mediated dUTP nick end-labeling (TUNEL) staining, respectively. The hippocampal transcriptomic changes were observed using RNA sequencing (RNA-seq). The expression levels of hub genes were verified by quantitative reverse-transcription PCR (qRT-PCR). Results revealed that Q808 could allay the seizure score and prolong the stage 2 latency in seizure rats. The morphological changes of neurons and the number of apoptotic cells in the DG area were diminished by Q808 treatment. RNA-seq analysis revealed eight hub genes, including Map2k3, Nfs1, Chchd4, Hdac6, Siglec5, Slc35d3, Entpd1, and LOC103690108, and nine hub pathways among the control, PTZ, and Q808 groups. Hub gene Nfs1 was involved in the hub pathway sulfur relay system, and Map2k3 was involved in the eight remaining hub pathways, including Amyotrophic lateral sclerosis, Cellular senescence, Fc epsilon RI signaling pathway, GnRH signaling pathway, Influenza A, Rap1 signaling pathway, TNF signaling pathway, and Toll-like receptor signaling pathway. qRT-PCR confirmed that the mRNA levels of these hub genes were consistent with the RNA-seq results. Our findings might contribute to further studies exploring the new apoptosis mechanism and actions of Q808.

ObjectivesAiming at the challenge of accurate diving modeling of a smart float, an anti-saturation and noise least squares (ASNLS) algorithm is proposed in this paper to achieve diving multi-parameter identification and depth prediction. MethodsFirstly, the nonlinear motion characteristics of the smart float actuator were included in the gray box-based diving model to better fit the actual model, and the continuous diving motion equation was transformed into a discrete form to match the real-world discrete data sampling. Subsequently, the aforementioned discrete diving model was constructed into a correlation form to attenuate the influence of data noise. Finally, by adjusting the values of the covariance matrix, the designed diving parameter identification algorithm achieved resistance to data saturation. ResultsBased on the data of the South China Sea deep diving experiment of the smart float in 2021, diving model parameter identification and depth prediction are carried out. The results demonstrate that the ASNLS algorithm has faster convergence speed (31.8% higher than the least squares algorithm) and smaller depth prediction error (average absolute percentage errors less than 9% at different depths) than both the traditional least squares algorithm and supports the vector machine algorithm.ConclusionsConsequently, the ASNLS algorithm can provide effective support for the depth control and prediction of the smart float.

Abstract Progesterone and AdipoQ Receptor 3 (PAQR3) is a member of the AdipoQ receptor. Our previous studies have found that PAQR3 plays a role as a candidate inhibitor in cardiac adenocarcinoma, breast cancer, gastric cancer and colorectal cancer, but the systematic analysis of PAQR3 in tumors is currently lacking. The objective of this study was to investigate the prognostic and therapeutic value of PAQR3 in 31 tumors. Through the analysis of TCGA, UALCAN, GEO, GEPIA2, TIMER, Kaplan–Meier plotter, TISIDB and other databases, it was found that the expression level of PAQR3 changed significantly in different tumor types, and the expression level of Neuroblastoma was very high. And the level of Prostate adenocarcinoma is low. In addition, the expression level of PAQR3 in Cholangiocarcinoma, Esophageal carcinoma, Head and neck squamous carcinoma, Liver Hepatocellular Carcinoma, Lung Adenocarcinoma and Lung squamous cell carcinoma was significantly higher than that in normal tissues. However, the expression level of PAQR3 in Breast Cancer, Kidney Renal Clear Cell Carcinoma, Kidney renal papillary cell carcinoma, Prostate Adenocarcinoma, Rectum Adenocarcinoma, Thyroid Cancer and Uterine Corpus Endometrial Carcinoma was lower than that in normal tissues. Subsequently, we explored the value of PAQR3 as a prognostic indicator of cancer. In Acute Myeloid Leukemia, Lower-grade Glioma and Glioblastoma, Pediatric Low-grade Gliomas, Kidney Chromophobe, and Thyroid Cancer, PAQR3 expression was positively correlated with OS and DSS, while in Rectum Adenocarcinoma, PAQR3 expression was negatively correlated with OS. PAQR3 high expression group Lower-grade Glioma and Glioblastoma, Pediatric Low-grade Gliomas, Uveal Melanoma, Kidney Chromophobe and DFI were positively correlated. PAQR3 can be used as a risk factor for the prognosis of multiple tumors. Then, we discussed the correlation between PAQR3 and immunology, and found that PAQR3 has a wide range of mutations in various tumor types, the most common mutation type is missense mutation, and common mutation types also include amplification, depth deletion, splicing, truncation and structural variation. Among the tumor samples with PAQR3 alterations, mutation occurred in all tumor samples except prostate adenocarcinoma and adrenal cortical carcinoma, head and neck squamous cell carcinoma, brain low-grade glioma, and kidney clear cell carcinoma, while esophageal adenocarcinoma had the highest total alteration frequency. PAQR3 was strongly associated with CNV in 18 tumors, particularly in Ovarian cancer, Lung squamous cell carcinoma, and Adenoid cystic carcinoma. On the other hand, PAQR3 has a higher SNV frequency in Uterine Corpus Endometrial Carcinoma, Skin Cutaneous Melanoma and Lung Adenocarcinoma, among which Uterine Corpus Endometrial Carcinoma has the highest SNV frequency. These results showed that PAQR3 expression levels were significantly correlated with tumor mutation load, microsatellite instability, neoantigens, and purity. In summary, PAQR3 can affect the tumor microenvironment and has potential for chemotherapy. Finally, we investigated the role of PAQR3 in tumor resistance and found that the expression of PAQR3 affects the efficacy of multiple chemotherapy drugs. Based on these studies, we found that PAQR3 plays an important role in cancer and has potential in tumor diagnosis and prognosis.

With the opening of industrial networks in the information age, the characteristic of Industrial Control Protocols (ICPs) to transmit plaintext without encryption exposes serious security risks, threatening the safe and stable operation of Industrial Control Systems (ICSs). Exploring the work of mining vulnerabilities in ICPs can use fuzzing to mine potential vulnerabilities in protocols to ensure the safe operation of ICS. However, traditional fuzzing methods require the construction of test cases based on expert experience and the format syntax specification of ICPs. This process is time-consuming, labor-intensive, inefficient, and limited when facing unknown ICPs. In response to these issues, this paper proposes an automated fuzzing method for ICPs based on the Denoising Diffusion Probabilistic Model (DDPM). Specifically, DDPM achieves the conversion from noise to data samples, which can easily and quickly generate test cases. On this basis, we designed a universal fuzzing framework, DiffusionFuzz, that can be applied to most ICPs. The experimental results obtained on ICPs such as Modbus/TCP in the Industrial Attack-Defense Range of the Key Laboratory of Information Security for Petrochemical Industry in Liaoning Province demonstrate that the test cases generated by this method are diverse, and outperform other fuzzing methods in terms of acceptance rate and ability to trigger exceptions. Certainly, DiffusionFuzz can enhance the effectiveness of fuzzing, identify vulnerabilities in ICPs, and thereby reduce potential economic risks and impacts.

Network intrusion detection technology has always been an indispensable protection mechanism for industrial network security. The rise of new forms of network attacks has resulted in a heightened demand for these technologies. Nevertheless, the current models’ effectiveness is subpar. We propose a new Deformable Vision Transformer (DE-VIT) method to address this issue. DE-VIT introduces a new deformable attention mechanism module, where the positions of key-value pairs in the attention mechanism are selected in a data-dependent manner, allowing it to focus on relevant areas, capture more informative features, and avoid excessive memory and computational costs. In addition to using deformable convolutions instead of regular convolutions in embedding layers to enhance the receptive field of patches, a sliding window mechanism is also employed to utilize edge information fully. In Parallel, we use a layered focal loss function to improve classification performance and address data imbalance issues. In summary, DE-VIT reduces computational complexity and achieves better results. We conduct experimental simulations on the public intrusion detection datasets, and the accuracy of the enhanced intrusion detection model surpasses that of the Deep Belief Network with Improved Kernel-Based Extreme Learning (DBN-KELM). It reaches 99.5% and 97.5% on the CIC IDS2017 and UNSW-NB15 datasets, exhibiting an increase of 8.5% and 9.1%, respectively.

AbstractBackground Poor sleep quality is a common problem among hemodialysis (HD) patients. Dietary fiber is a key component of a healthy diet and is beneficial for a variety of health outcomes; however, evidence of an association between dietary fiber consumption and subjective sleep quality has not been established among HD patients. Therefore, we determined the association between dietary fiber consumption and the subjective sleep quality in Chinese maintenance HD patients, taking into account fiber type and source.Methods Dietary intake was assessed with a validated food frequency questionnaire in a cross-sectional study including 741 maintenance HD patients between December 2021 and January 2022. The daily intake of dietary fiber was categorized into three groups. The lowest tertile was used as the reference category. Sleep quality of patients was accurately calculated using the Pittsburgh sleep quality index standard questionnaire. Multivariable logistic regression model and restricted cubic spline analysis were performed to assess the relationship between dietary fiber consumption and poor sleep quality.Results Compared with the lowest tertile group of dietary fiber intake, the highest tertile group had a lower prevalence of poor sleep quality. After adjustment for potential confounders, a higher intake of total dietary fiber (ORtertile 3 (T3) to tertile 1 (T1)= 0.51, 95% CI: 0.31–0.85), total insoluble dietary fiber (ORT3 to T1 =0.54, 95% CI: 0.33–0.89), and soluble dietary fiber in vegetables (ORT3 to T1 =0.61, 95% CI: 0.40–0.93) were associated with a lower prevalence of poor sleep quality. Furthermore, significant linear trends were also observed (p

ObjectivesThis study focuses on how the sampling efficiency of a free-diving sediment trap (FST) is affected by its body posture, and uses quantitative analysis to describe the changes of the center of gravity and attitude angles. MethodsAn FST is designed which consists of a sampling module and a buoyancy control system. A 3D model of the FST is built using SolidWorks. Kinematic analysis is completed using Matlab, and the offset of the center of gravity at each moment in the whole working cycle is obtained by simulation. The principle of moments is used to derive its attitude changes caused by the offset of the center of gravity. The influence of the attitude changes on the effective collection area is then quantitatively analyzed using projective transformation. ResultsThe result shows that the offset of the center of gravity is limited to a circle with the origin of the body-fixed coordinate system as the center and a radius of \begin{document}$ 8 \times {10^{ - 3}}\;{\text{m}} $\end{document}. It is seen that the domains of x and y are \begin{document}${x_{\text{g}}} \in ( - 6 \times {10^{ - 3}}\;{\text{m}},{\text{ }}3 \times {10^{ - 3}}\;{\text{m}})$\end{document}, \begin{document}${y_{\text{g}}} \in ( - 1 \times {10^{ - 3}}\;{\text{m}},{\text{ }}7 \times {10^{ - 3}}\;{\text{m}})$\end{document}, and the domains of the attitude angles are \begin{document}$ \theta \in ( - 1.5^\circ ,{\text{ }}16.6^\circ ) $\end{document} and \begin{document}$\phi \in ( - {2.6^ \circ },{16.8^ \circ })$\end{document}. When \begin{document}$\theta = 14.7^\circ {\text{ and }}\phi {\text{ = 13}}^\circ$\end{document}, the effective collection area is reduced to 94.27% of the original value. Compared with the 100% effective collection area, it takes 1.06 times the original collection time to collect the same volume of sinking particles. ConclusionsThe results of this study can provide useful references for FST stability analysis and buoyancy regulation system design.