Dynamical Analysis of an Improved Bidirectional Immunization SIR Model in Complex Network.

In order to investigate the impact of two immunization strategies-vaccination targeting susceptible individuals to reduce their infection rate and clinical medical interventions targeting infected individuals to enhance their recovery rate-on the spread of infectious diseases in complex networks, this study proposes a bilinear SIR infectious disease model that considers bidirectional immunization. By analyzing the conditions for the existence of endemic equilibrium points, we derive the basic reproduction numbers and outbreak thresholds for both homogeneous and heterogeneous networks. The epidemic model is then reconstructed and extensively analyzed using continuous-time Markov chain (CTMC) methods. This analysis includes the investigation of transition probabilities, transition rate matrices, steady-state distributions, and the transition probability matrix based on the embedded chain. In numerical simulations, a notable concordance exists between the outcomes of CTMC and mean-field (MF) simulations, thereby substantiating the efficacy of the CTMC model. Moreover, the CTMC-based model adeptly captures the inherent stochastic fluctuation in the disease transmission, which is consistent with the mathematical properties of Markov chains. We further analyze the relationship between the system's steady-state infection density and the immunization rate through MCS. The results suggest that the infection density decreases with an increase in the immunization rate among susceptible individuals. The current research results will enhance our understanding of infectious disease transmission patterns in real-world scenarios, providing valuable theoretical insights for the development of epidemic prevention and control strategies.

Spatial-Temporal EEG Fusion Based on Neural Network for Major Depressive Disorder Detection.

In view of the major depressive disorder characteristics such as high mortality as well as high recurrence, it is important to explore an objective and effective detection method for major depressive disorder. Considering the advantages complementary of different machine learning algorithms in information mining process, as well as the fusion complementary of different information, in this study, the spatial-temporal electroencephalography fusion framework using neural network is proposed for major depressive disorder detection. Since electroencephalography is a typical time series signal, we introduce recurrent neural network embedded in long short-term memory unit for extract temporal domain features to solve the problem of long-distance information dependence. To reduce the volume conductor effect, the temporal electroencephalography data are mapping into a spatial brain functional network using phase lag index, then the spatial domain features were extracted from brain functional network using 2D convolutional neural networks. Considering the complementarity between different types of features, the spatial-temporal electroencephalography features are fused to achieve data diversity. The experimental results show that spatial-temporal features fusion can improve the detection accuracy of major depressive disorder with a highest of 96.33%. In addition, our research also found that theta, alpha, and full frequency band in brain regions of left frontal, left central, right temporal are closely related to MDD detection, especially theta frequency band in left frontal region. Only using single-dimension EEG data as decision basis, it is difficult to fully explore the valuable information hidden in the data, which affects the overall detection performance of MDD. Meanwhile, different algorithms have their own advantages for different application scenarios. Ideally, different algorithms should use their respective advantages to jointly address complex problems in engineering fields. To this end, we propose a computer-aided MDD detection framework based on spatial-temporal EEG fusion using neural network, as shown in Fig. 1. The simplified process is as follows: (1) Raw EEG data acquisition and preprocessing. (2) The time series EEG data of each channel are input as recurrent neural network (RNN), and RNN is used to process and extract temporal domain (TD) features. (3) The BFN among different EEG channels is constructed, and CNN is used to process and extract the spatial domain (SD) features of the BFN. (4) Based on the theory of information complementarity, the spatial-temporal information is fused to realize efficient MDD detection. Fig. 1 MDD detection framework based on spatial-temporal EEG fusion.

Impact of contact rate on epidemic spreading in complex networks.

Abstract: Contact reduction is an effective strategy to mitigate the spreading of epidemic. However, the existing reaction-diffusion equations for infectious disease are unable to characterize this effect. Thus, we here propose an extended susceptible-infected-recovered model by incorporating contact rate into the standard SIR model, and concentrate on investigating its impact on epidemic transmission. We analytically derive the epidemic thresholds on homogeneous and heterogeneous networks, respectively. The effects of contact rate on spreading speed, scale and outbreak threshold are explored on ER and SF networks. Simulations results show that epidemic dissemination is significantly mitigated when contact rate is reduced. Importantly, epidemic spreads faster on heterogeneous networks while broader on homogeneous networks, and the outbreak thresholds of the former are smaller.

Vital node identification based on cycle structure in a multiplex network.

Abstract: Multiplex networks frame the heterogeneous nature of real systems, where the multiple roles of nodes, both functionally and structurally, are well represented. We identify these vital nodes in a multiplex network so that we can control a pandemic outbreak like COVID-19, eliminate damage from a network attack, maintain traffic, and so on. Vital node identification has attracted scientists in various fields for decades. In this paper, we propose a hybrid supra-cycle number and hybrid supra-cycle ratio based on the cycle structure, and present an extensive experimental analysis by comparing our indexes and several different indexes in four real multiplex networks on layer nodes and multiplex nodes. The experimental results show that these proposed indexes have good robustness, synchronization, and transmission dynamics. Finally, we provide an in-depth understanding of multiplex networks and cycle structure, and we sincerely hope more valuable academic achievements are proposed in the future.

Feature-level fusion based on spatial-temporal of pervasive EEG for depression recognition.

BACKGROUND AND OBJECTIVE: In view of the depression characteristics such as high prevalence, high disability rate, high fatality rate, and high recurrence rate, early identification and early intervention are the most effective methods to prevent irreversible damage of brain function over time. The traditional method of depression recognition based on questionnaires and interviews is time-consuming and labor-intensive, and heavily depends on the doctor's subjective experience. Therefore, accurate, convenient and effective recognition of depression has important social value and scientific significance. METHODS: This paper proposes a depression recognition framework based on feature-level fusion of spatial-temporal pervasive electroencephalography (EEG). Time series EEG data were collected by portable three-electrode EEG acquisition instrument, and mapped to a spatial complex network called visibility graph (VG). Then temporal EEG features and spatial VG metric features were extracted and selected. Based on the correlation between features and categories, the differences in contribution of individual feature are explored, and different contribution coefficients are assigned to different features as the data basis of feature-level fusion to ensure the diversity of data. A cascade forest model based on three different decision forests is designed to realize the efficient depression recognition using spatial-temporal feature-level fusion data. RESULTS: Experimental data were obtained from 26 depressed patients and 29 healthy controls (HC). The results of multiple control experiments show that compared with single type feature, feature-level fusion without contribution coefficient, and independent classifiers, the feature-level method with contribution coefficient of spatial-temporal has a stronger recognition ability of depression, and the highest accuracy is 92.48%. CONCLUSION: Feature-level fusion method provides an effective computer-aided tool for rapid clinical diagnosis of depression.

Circ_0018168 inhibits the proliferation and osteogenic differentiation of fibroblasts in ankylosing spondylitis via regulating miR-330-3p/DKK1 axis.

Background: Circular RNAs (circRNAs) play a crucial regulatory role in human diseases. However, the roles of circRNAs in ankylosing spondylitis (AS) are barely known. In this study, the functions of circ_0018168 in AS were investigated. Methods: Quantitative real-time polymerase chain reaction (qRT-PCR) and western blot assay were used for circ_0018168, microRNA-330-3p (miR-330-3p), dickkopf-1 (DKK1), alkaline phosphatase (ALP), osteocalcin (OCN), Runt-related transcription factor 2 (Runx2) levels. Cell Counting Kit-8 (CCK-8) assay and 5'-ethynyl-2'-deoxyuridine (EdU) assay were conducted to analyze cell proliferation ability. Flow cytometry analysis was manipulated for cell cycle process. ALP activity was examined with a commercial kit. RNA immunoprecipitation (RIP) assay, RNA pull-down assay and dual-luciferase reporter assay were used to analyze the relationships of circ_0018168, miR-330-3p and DKK1. Results: Circ_0018168 and DKK1 levels were lowly expressed in AS hip capsule specimens. Circ_0018168 overexpression repressed cell proliferation, cell cycle process as well as reduced ALP activity and ALP, OCN and Runx2 protein levels in AS fibroblasts. DKK1 silencing ameliorated the impact of circ_0018168 on AS progression. In addition, circ_0018168 served as the sponge for miR-330-3p, which could target DKK1. MiR-330-3p inhibition suppressed the proliferation, cell cycle and osteogenic differentiation in AS fibroblasts, but DKK1 silencing reversed the impacts. Besides, the effect of circ_0018168 on AS development was abolished by miR-330-3p upregulation. Conclusion: Circ_0018168 overexpression restrained fibroblast proliferation and osteogenic differentiation in AS by elevating DKK1 through adsorbing miR-330-3p.

Li-ESWT treatment reduces inflammation, oxidative stress, and pain via the PI3K/AKT/FOXO1 pathway in autoimmune prostatitis rat models.

BACKGROUND: Due to limited data on the pathogenesis of chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) and the suboptimal therapeutic effect, the development of new and effective treatment modalities was needed urgently. Low-intensity extracorporeal shock wave therapy (Li-ESWT) has been reported for the treatment of CP/CPPS. However, the underlying mechanism remains to be elucidated. OBJECTIVE: To interrogated the efficacy and the mechanism of Li-ESWT in the treatment of CP/CPPS. MATERIALS AND METHODS: According to different treatments, RWPE-1 cells (human prostate epithelial cells) were randomly divided into three groups: control group, LPS (lipopolysaccharide) group, or Li-ESWT group (LPS-induced RWPE-1 managed by Li-ESWT). Following the Li-ESWT treatment, the levels of oxidative stress were assayed. We then established a rat model of experimental autoimmune prostatitis (EAP) by injecting prostatic protein homogenate mixed with complete Freund's adjuvant. The Sprague-Dawley rats were randomly divided into the control group, EAP group, or Li-ESWT group. Von Frey Filament was used to quantify pelvic hyperalgesia in the rats. Prostates tissues from each group were collected for immunohistochemistry, oxidation stress, and Western blot analysis. RESULTS: Histological analysis showed reduced inflammation and expression of cytokines (TNF-α, IL-1ß, IL-6, COX-2, SP) in prostate tissues from the Li-ESWT group compared with those from the EAP group (all p < 0.05). Similarly, there was reduced pelvic pain and allergic symptoms in the Li-ESWT group compared with the EAP group (all p < 0.05). Besides, Li-ESWT treatment could decrease oxidative stress in the prostate and in RWPE-1 cells, respectively (both p < 0.05). Moreover, the Li-ESWT upregulated the expression of CAT through the inhibition of phosphorylation of AKT/FOXO1 signaling pathway. DISCUSSION AND CONCLUSIONS: Li-ESWT may reduce inflammation, oxidative stress, and pain in rats with autoimmunity-induced prostatitis via the PI3 K/AKT/FOXO1 pathway. It implies that Li-ESWT can present a potential therapeutic option for the treatment of CP/CPPS.

EEG based functional connectivity analysis of human pain empathy towards humans and robots.

Humans can show emotional reactions toward humanoid robots, such as empathy. Previous neuroimaging studies have indicated that neural responses of empathy for others' pain are modulated by an early automatic emotional sharing and a late controlled cognitive evaluation process. Recent studies about pain empathy for robots found humans present similar empathy process towards humanoid robots under painful stimuli as well as to humans. However, the whole-brain functional connectivity and the spatial dynamics of neural activities underlying empathic processes are still unknown. In the present study, the functional connectivity was investigated for ERPs recorded from 18 healthy adults who were presented with pictures of human hand and robot hand under painful and non-painful situations. Functional brain networks for both early and late empathy responses were constructed and a new parameter, empathy index (EI), was proposed to represent the empathy ability of humans quantitatively. We found that the mutual dependences between early ERP components was significantly decreased, but for the late components, there were no significant changes. The mutual dependences for human hand stimuli were larger than to robot hand stimuli for early components, but not for late components. The connectivity weights for early components were larger than late components. EI value shows significant difference between painful and non-painful stimuli, indicating it is a good indicator to represent the empathy of humans. This study enriches our understanding of the neurological mechanisms implicated in human empathy, and provides evidence of functional connectivity for both early and late responses of pain empathy towards humans and robots.

Brain Functional Networks Based on Resting-State EEG Data for Major Depressive Disorder Analysis and Classification.

If the brain is regarded as a system, it will be one of the most complex systems in the universe. Traditional analysis and classification methods of major depressive disorder (MDD) based on electroencephalography (EEG) feature-levels often regard electrode as isolated node and ignore the correlation between them, so it's difficult to find alters of abnormal topological architecture in brain. To solve this problem, we propose a brain functional network framework for MDD of analysis and classification based on resting state EEG. The phase lag index (PLI) was calculated based on the 64-channel resting state EEG to construct the function connection matrix to reduce and avoid the volume conductor effect. Then binarization of brain function network based on small world index was realized. Statistical analyses were performed on different EEG frequency band and different brain regions. The results showed that significant alterations of brain synchronization occurred in frontal, temporal, parietal-occipital regions of left brain and temporal region of right brain. And average shortest path length and clustering coefficient in left central region of theta band and node betweenness centrality in right parietal-occipital region were significantly correlated with PHQ-9 score of MDD, which indicates these three network metrics may be served as potential biomarkers to effectively distinguish MDD from controls and the highest classification accuracy can reach 93.31%. Our findings also point out that the brain function network of MDD patients shows a random trend, and small world characteristics appears to weaken.

Liberation enhancement and copper enrichment improvement for waste printed circuit boards by heating pretreatment.

Crushing is the key part for the recycling technology of waste printed circuit boards (WPCBs). In this study, the breakage and liberation effects of WPCBs was improved by heat pretreatment technology before crushing (HPBC). Based on the test results, 200 °C was found as the safe temperature for the HPBC of WPCBs. The fracture mode, particle size distribution, and enrichment characteristics of WPCBs were systematically studied. The experimental results show that the HPBC changed the breakage mode from longitudinal fracture to horizontal fracture and improved the liberation of metal from non-metal components. During the crushing process, the increase in the heat pretreatment time (30-120 min) and temperature (100-200 °C) can improve the crushing effect of WPCBs and increase the content of crushing products of -0.3 mm by 3.16% and 5.64%, respectively. Compared to the non-metallic components, the metal components have ductility and are difficult to break into -0.3 mm during the crushing process. HPBC can promote copper enrichment to narrow grain size. In the 0.3-1 mm range, the content of copper increased from 47.87% to 57.61%, an increase by 9.74%. The initial enrichment of copper was achieved by adjusting the crushing time. The recovery rate of copper can reach 85.66%, and the enrichment rate is 1.74 when 0.3-2 mm breaking product is used as enrichment. Therefore, HPBC can effectively improve the crushing and liberation effect of WPCBs and improve the enrichment rate, and thus is an effective pretreatment method.

Denosumab compared to bisphosphonates to treat postmenopausal osteoporosis: a meta-analysis.

BACKGROUND: The standard treatment for osteoporosis was controversial. Denosumab and bisphosphonates were two most common drugs. The purpose of this study was to compare the efficacy and safety of denosumab with bisphosphonates to treat osteoporosis. METHODS: Published literatures, only including randomized controlled trials (RCTs), were searched in the following electronic databases: PubMed, Embase, Web of Science, Cochrane Library, and Google database from inception to April 20 2018. Studies that compared denosumab with bisphosphonates to treat osteoporosis were included. Random-effect model was used for meta-analysis due to the unavoidable clinical heterogeneity. We used the risk of fracture as the primary outcome. Stata 12.0 was used for meta-analysis. RESULTS: Eleven studies involving 5446 patients (denosumab = 2873, bisphosphonates = 2573) were included in the present meta-analysis. There was no significant difference between the risk of fracture (risk ratio (RR), 1.13; 95% confidence interval (CI), 0.82-1.55; P = 0.466), adverse events (AEs) (RR 1.00; 95% CI 0.96-1.04; P = 0.957) and withdrawn due to AEs (RR 0.68; 95% CI 0.34-137; P = 0.280). Denosumab compared with bisphosphonates significantly increased change in total hip, femoral neck, lumbar spine, and one-third radius bone mineral density (BMD) for postmenopausal osteoporosis patients (P < 0.05). CONCLUSIONS: Our meta-analysis suggested that denosumab but not bisphosphonates significantly increased change in total hip, femoral neck, lumbar spine, and one-third radius BMD for postmenopausal osteoporosis patients. Current evidence suggested no benefit of denosumab for reducing risk of fracture than bisphosphonates. More long-term follow-up RCTs are needed to identify the potential complications of denosumab.

The stripping effect of using high voltage electrical pulses breakage for waste printed circuit boards.

In this study, high voltage electrical pulses were utilized to process waste printed circuit boards to cost effectively liberate metal and nonmetal materials. Relative mass ωiand particles content ηiindexes were defined to assess the stripping effect produced by high voltage electrical pulses breakage. For relative mass level in the 0-10% range, in the -6+3 mm fraction, particles content accounted for 84.84% of the total particles, while the mechanical crushing only occupied 8.84%. Voltage and pulse experiments were carried out to investigate the crushing effect of high voltage electrical pulse breakage for printed circuit boards. It was found that when the voltage and pulse number was at 160 kV and 300, the stripping rate of copper was 98.56% and 92.58% in the -25+13 mm fraction respectively. The measured bending strength of the material revealed the selective crushing effect of high voltage electrical pulses in the different material interfaces. A liberation mechanism was elaborated by using the energy band theory, and a process model was utilized to reveal the mode of crushing. Furthermore, the microscopic appearance of the resulting product confirmed that copper underwent high-temperature melting, while the resin was decomposed during the crushing process. Compared to conventional mechanical crushing process, high voltage electrical pulses can better liberate metal-bearing than mechanical comminution technology.

Clean Desulphurization of High-Sulfur Coal Based on Synergy Effect between Microwave Pretreatment and Magnetic Separation.

Coal is the world's second largest energy resource and the primary energy resource in China. Coal-fired power generation is the dominant source for obtaining power both in China and the world. This situation is considered to remain unchanged for the coming few decades. The paper is to study the mechanism of microwave absorption capacity of the magnetic medium. A mechanism for the microwave energy absorption of fine coal is proposed by obtaining the parameters of dielectric properties of the magnetic medium. The prediction model of electromagnetic parameters during the process of microwave dielectric enhancement of fine coal is established by using the neural network method. The content of magnetic medium, by increasing the electromagnetic parameters to improve the energy utilization rate and reduce energy loss, can effectively improve the absorption and broaden bandwidth of the coal sample, improving the frequency adaptive design of coal by the microwave pretreatment process. A neural network model for the prediction of the electromagnetic parameters of fine coal is established by using the neural network method. The desulfurization rates were 23.99 and 45.78% for Weinan and Lu'an coals, respectively.

Effects of phosphorylation on the intrinsic propensity of backbone conformations of serine/threonine.

Each amino acid has its intrinsic propensity for certain local backbone conformations, which can be further modulated by the physicochemical environment and post-translational modifications. In this work, we study the effects of phosphorylation on the intrinsic propensity for different local backbone conformations of serine/threonine by molecular dynamics simulations. We showed that phosphorylation has very different effects on the intrinsic propensity for certain local backbone conformations for the serine and threonine. The phosphorylation of serine increases the propensity of forming polyproline II, whereas that of threonine has the opposite effect. Detailed analysis showed that such different responses to phosphorylation mainly arise from their different perturbations to the backbone hydration and the geometrical constraints by forming side-chain-backbone hydrogen bonds due to phosphorylation. Such an effect of phosphorylation on backbone conformations can be crucial for understanding the molecular mechanism of phosphorylation-regulated protein structures/dynamics and functions.

Prevalence and associated factors of corneal blindness in Ningxia in northwest China.

AIM: To describe the prevalence and demographic characteristics of corneal blindness in an urban and rural region of Ningxia, located in the northwest part of China. METHODS: A stratified, randomized sampling procedure was employed in the study, including urban and rural area of all age group. Visual acuity, anterior segment and ocular fundus were checked. Related factor of corneal disease, including age, gender, education status, ethnic group, location and occupation, were identified according to uniform customized protocol. An eye was defined to be corneal blindness if the visual acuity was <20/400 due to a corneal disease. RESULTS: Three thousand individuals (1290 from urban area and 1710 from rural area) participated in the investigation, with a response rate of 80.380%. The prevalence of corneal blindness was 0.023% in both eyes and 0.733% in at least one eye. The blindness in at least one eye with varied causes was present in 106 participants (3.533%) and in bilateral eyes in 34 participants (1.133%). The corneal diseases accounted for 20.754% of blindness in at least one eye and 20.588% of bilateral blindness. The prevalence of corneal disease was higher in older and Han ethnic group, especially those who occupied in agriculture and outdoor work. People with corneal blindness were more likely to be older and lower education. Rural population were more likely to suffer from bilateral corneal blindness than the urban population in ≥59-year group (χ (2)=6.716, P=0.019). Infectious, trauma and immune corneal disease were the three leading causes of corneal disease. Trauma corneal disease was more likely leading to blindness in one eye. However, infectious and immune corneal diseases make more contribution to the bilateral corneal blindness. CONCLUSION: Corneal blindness is a significant burden of in Ningxia population, encompassing a variety of corneal infections and trauma; the majority of those were avoidable. Health promotion strategies and good hygienic conditions have to be developed.

Range of motion of lumbar pedicle screw entrance point under physiological weight bearing / 中华骨科杂志

Objective To measure the range of motion (ROM) of the lumbar pedicle screw entrance point (LPSEP) in vivo during unrestricted motion under physiological weight bearing.Methods Eleven healthy volunteers aged 45-60 years underwent MRI scans in a supine position.Three-dimensional (3D) models of L2-5 were constructed.Next,each volunteer was asked to stand and was positioned in the following sequence:standing,45° flexion,maximal extension,maximal left-right twisting,while two orthogonal fluoroscopic images were taken simultaneously at each position.The MRI models were matched to the osseous outlines of the images from the two orthogonal views to determine the position of the vertebrae in 3D at each position.Coordinate systems were established to study the ROM of the LPSEP.Results The predominant translations were along anteroposterior and craniocaudal axis from supine to standing position (average,2.44mm and 2.35 mm).Rotation of the LPSEP occurred mainly around the mediolateral axis(average,3.91 °).During flexion-extension movements of the trunk,the predominant translation were along anteroposterior and craniocaudal axis and rotation were around mediolateral axis.During lateral bending and twisting,ROM of LPSEP did not rotate or translate in one dominant direction.Instead,the resulting motion represented a combination of rotation and translation in different directions.There was no significantly difference in translation and rotation between the three axis in L4,5segment (P＞0.05).Conclusion The kinematic behaviors of the LPSEP of the upper lumbar spine (L2,3 and L3,4) are similar which are different from that of the lower lumbar spine (L4,5).