Purification and characterization of recombinant human superoxide dismutase integrated with resilin-like polypeptide.

Human superoxide dismutase (hSOD1) plays an important role in the aerobic metabolism and free radical eliminating process in the body. However, the production of existing SOD faces problems such as complex purification methods, high costs, and poor product stability. This experiment achieved low-cost, rapid, and simple purification of hSOD1 through ammonium sulfate precipitation method and heat resistance of recombinant protein. We constructed a recombinant protein hSOD1-LR containing a resilin-like polypeptide tag and expressed it. The interest protein was purified by ammonium sulfate precipitation method, and the results showed that the purification effect of 1.5 M (NH4)2SO4 was the best, with an enzyme activity recovery rate of 80 % after purification. Then, based on its thermal stability, further purification of the interest protein at 60 °C revealed a purification fold of up to 24 folds, and the purification effect was similar to that of hSOD1-6xHis purified by nickel column affinity chromatography. The stability of hSOD1-LR showed that the recombinant protein hSOD1-LR has better stability than hSOD-6xHis. hSOD1-LR can maintain 76.57 % activity even after 150 min of reaction at 70 °C. At same time, hSOD1-LR had activity close to 80 % at pH < 5, indicating good acid resistance. In addition, after 28 days of storage at 4 °C and 40 °C, hSOD1-LR retained 92 % and 87 % activity, respectively. Therefore, the method of purifying hSOD1-LR through salt precipitation may have positive implications for the study of SOD purification.

Analyzing the robustness of LEO satellite networks based on two different attacks and load distribution methods.

Low earth orbit (LEO) satellite constellations have emerged as a promising architecture integrated with ground networks, which can offer high-speed Internet services to global users. However, the security challenges faced by satellite networks are increasing, with the potential for a few satellite failures to trigger cascading failures and network outages. Therefore, enhancing the robustness of the network in the face of cascading failures is of utmost importance. This paper aims to explore the robustness of LEO satellite networks when encountering cascading failures and then proposes a modeling method based on virtual nodes and load capacity. In addition, considering that the ground station layout and the number of connected satellites together determine the structure of the final LEO satellite network, we here propose an improved ground station establishment method that is more suitable for the current network model. Finally, the robustness of the LEO satellite networks is deeply studied under two different attacks and cost constraints. Simulations of LEO satellite networks with different topologies show that the maximum load attacks have a destructive impact on the network, which can be mitigated by adjusting the topology and parameters to ensure normal network operation. The current model and related results provide practical insights into the protection of LEO satellite networks, which can mitigate cascading risks and enhance the robustness of LEO systems.

Quantifying the potential of cascade outbreaks via early infected nodes using network percolation.

In many fields, accurate prediction of cascade outbreaks during their early stages of propagation is of paramount importance. Based on percolation theory, we propose a global propagation probability algorithm that effectively estimates the probability of information spreading from source nodes to the giant component. Building on this, we further introduce an early prediction method for cascade outbreaks, which provides quantitative predictions of both the probability and scope of cascade outbreaks by fully considering the network structure data and propagation dynamics. Through our research, we observe that cascade outbreaks resemble a phase transition. When approaching the critical point of an outbreak, a few specific activating nodes typically facilitate the transmission of information throughout the entire network, thus enabling early inference of a cascading outbreak. To validate our findings, we conducted experiments on diverse network structures using a classical propagation model and applied our proposed method to analyze a real microblog cascade dataset. The experimental results robustly demonstrate the superiority of our approach over baseline methods in terms of effectively predicting cascade outbreaks with high precision and early detection capability.

Heterogeneous decision-making dynamics of threshold-switching agents on complex networks.

In the classical two-player decision-making scenario, individuals may have different tendencies to take a certain action, given that there exists a sufficient number of neighbors adopting a particular option. This is ubiquitous in many real-life contexts including traffic congestion, crowd evacuation, and minimal vertex cover problem. Under best-response dynamics, we investigate the decision-making behaviors of heterogeneous agents on complex networks. Results of the networked games are twofold: for networks of uniform degree distribution (e.g., the lattice) and fraction of the strategy is of a linear function of the threshold setting. Moreover, the equilibrium analysis is provided and the relationship between the equilibrium dynamics and the change of the threshold value is given quantitatively. Next, if the games are played on networks with non-uniform degree distribution (e.g., random regular and scale-free networks), influence of the threshold-switching will be weakened. Robust experiments indicate that it is not the value of the average degree, but the degree distribution that influences how the strategy evolves affected by the threshold settings. Our result shows that the decision-making behaviors can be effectively manipulated by tuning the parameters in the utility function (i.e., thresholds) of some agents for more regular network structures.

Researchers' transfer network reveals the evolution of national science and technology capabilities.

Science and technology capability refers to the comprehensive capability of all factors that affect the development of science and technology, mainly referring to human and material factors related to science and technology, among which human resources are the foundation and driving force. Therefore, researchers become a unique research perspective for the evaluation of national science and technology capabilities. Taking the integrated circuit field as the analysis case, this article proposed a researchers' transfer network model based on the online open source literature database. From the published literature information, the model obtains the researchers' transfer network that has a core-periphery structure. The core nodes are the European Union, the United States, China, etc., and these nodes are the most closely connected. A country/region role evolution model is also proposed, which reveals the characteristics of the role evolution of the European Union, the United States, China, and other countries from the perspective of researchers' transfer, especially their transfer between countries.

Prediction and Optimization Algorithm for Intersection Point of Spatial Multi-Lines Based on Photogrammetry.

The basic theory of photogrammetry is mature and widely used in engineering. The environment in engineering is very complex, resulting in the corners or multi-line intersections being blocked and unable to be measured directly. In order to solve this problem, a prediction and optimization algorithm for intersection point of spatial multi-lines based on photogrammetry is proposed. The coordinates of points on space lines are calculated by photogrammetry algorithm. Due to the influence of image point distortion and point selection error, many lines do not strictly intersect at one point. The equations of many space lines are used to fit their initial value of intersection point. The initial intersection point is projected onto each image, and the distances between the projection point and each line on the image plane are used to weight the calculated spatial lines in combination with the information entropy. Then the intersection point coordinates are re-fitted, and the intersection point is repeatedly projected and recalculate until the error is less than the threshold value or reached the set number of iterations. Three different scenarios are selected for experiments. The experimental results show that the proposed algorithm significantly improves the prediction accuracy of the intersection point.

Identification of multiple influential spreaders on networks by percolation under the SIR model.

Identification of multiple influential spreaders on complex networks is of great significance, which can help us speed up information diffusion and prevent disease from spreading to some extent. The traditional top-k strategy to solve an influence maximization problem based on node centrality is unsuitable for selecting several spreaders simultaneously because of influence overlapping. Besides, other heuristic methods have a poor ability to keep the balance between efficiency and computing time. In this paper, an efficient method is proposed to identify the decentralized influential spreaders on networks by edge percolation under the Susceptible-Infected-Recovered (SIR) model. Thanks to the average size of the connected component where one node is located under the edge percolation equivalent to the final spread range of this node under the SIR model approximately, it inspires us to choose suitable spreaders maximize the spread of influence. The experimental results show that our method has high efficiency compared with other benchmark methods on three synthetic networks and six empirical networks, and it also requires less time and cost.

Event detection in temporal social networks using a higher-order network model.

Event detection is one of the most important areas of complex network research. It aims to identify abnormal points in time corresponding to social events. Traditional methods of event detection, based on first-order network models, are poor at describing the multivariate sequential interactions of components in complex systems and at accurately identifying anomalies in temporal social networks. In this article, we propose two valid approaches, based on a higher-order network model, namely, the recovery higher-order network algorithm and the innovation higher-order network algorithm, to help with event detection in temporal social networks. Given binary sequential data, we take advantage of chronological order to recover the multivariate sequential data first. Meanwhile, we develop new multivariate sequential data using logical sequence. Through the efficient modeling of multivariate sequential data using a higher-order network model, some common multivariate interaction patterns are obtained, which are used to determine the anomaly degree of a social event. Experiments in temporal social networks demonstrate the significant performance of our methods finally. We believe that our methods could provide a new perspective on the interplay between event detection and the application of higher-order network models to temporal networks.

Identifying highly influential nodes in multilayer networks based on global propagation.

Based on percolation theory and the independent cascade model, this paper considers the selection of the optimal propagation source when the propagation probability is greater than the percolation threshold. First, based on the percolation characteristics of real networks, this paper presents an iterative algorithm of linear complexity to solve the probability of the propagation source transmitting information to the network's giant component, that is, the global propagation probability. Compared with the previous multiple local simulation algorithm, this algorithm eliminates random errors and significantly reduces the operation time. A sufficient and necessary condition is provided, and it is proved that the final propagation range of the propagation source obeys the bimodal distribution. Based on this sufficient and necessary condition, we extend the efficient iterative algorithm proposed in this article to multi-layer networks and find that for two-layer networks, the final propagation range of the propagation source follows a four-peak distribution. Through iterations and calculations, the probability of each peak and the number of nodes included can be directly obtained, and the propagation expectations of the nodes in the multi-layer network can then be calculated, which can result in a better ranking of the propagation influence of the nodes. In addition, to maximize the influence of multi-propagation sources, this paper also presents a de-overlapping method, which has evident advantages over traditional methods.

Robustness of partially interdependent networks under combined attack.

We thoroughly study the robustness of partially interdependent networks when suffering attack combinations of random, targeted, and localized attacks. We compare analytically and numerically the robustness of partially interdependent networks with a broad range of parameters including coupling strength, attack strength, and network type. We observe the first and second order phase transition and accurately characterize the critical points for each combined attack. Generally, combined attacks show more efficient damage to interdependent networks. Besides, we find that, when robustness is measured by the critical removing ratio and the critical coupling strength, the conclusion drawn for a combined attack is not always consistent.

Community discovering in temporal network with spectral fusion.

With the deep understanding of the time-varying characteristics of real systems, research studies focusing on the temporal network spring up like mushrooms. Community detection is an accompanying and meaningful problem in the temporal network, but the analysis of this problem is still in its developing stage. In this paper, we proposed a temporal spectral clustering method to detect the invariable communities in the temporal network. Through integrating Fiedler's eigenvectors of normalized Laplacian matrices within a limited time window, our method can avoid the inaccurate partition caused by the mutation of the temporal network. Experiments demonstrated that our model is effective in solving this problem and performs obviously better than the compared methods. The results illustrated that taking the historical information of the network structure into consideration is beneficial in clustering the temporal network.

Parent-Child Relationships: A Shield Against Maternal Depression in the Midst of Household Chaos.

Background: Depression, a severe mental disorder, not only jeopardizes the health of mothers but also significantly negative impacts on families and their children. This study investigates the correlation between household chaos and maternal depression. Methods: This study adopted a cross-sectional design and used the Confusion, Hubbub, and Order Scale, Dyadic Adjustment Scale, Parent-Child Relationship Scale, and Beck Depression Inventory to assess 1947 mothers of children in seven kindergartens in Shanghai, China. Results: The findings revealed a significant positive correlation between household chaos, marital conflict, and maternal depression. Marital conflict also showed a significantly positively correlated with maternal depression. Marital conflict mediates the relationship between household chaos and maternal depression. Parent-child relationships moderated the direct effect of household chaos on maternal depression. When parent-child relationships were low, household chaos had a greater predictive effect on maternal depression. Conversely, when parent-child relationships were high, the predictive effect of household chaos on maternal depression was reduced. Conclusion: This study reveals that parent-child relationships play a protective role in the impact of household chaos on maternal depression. This study significantly contributes to enriching the social support buffering model.

Sex differences of signal complexity at resting-state functional magnetic resonance imaging and their associations with the estrogen-signaling pathway in the brain.

Sex differences in the brain have been widely reported and may hold the key to elucidating sex differences in many medical conditions and drug response. However, the molecular correlates of these sex differences in structural and functional brain measures in the human brain remain unclear. Herein, we used sample entropy (SampEn) to quantify the signal complexity of resting-state functional magnetic resonance imaging (rsfMRI) in a large neuroimaging cohort (N = 1,642). The frontoparietal control network and the cingulo-opercular network had high signal complexity while the cerebellar and sensory motor networks had low signal complexity in both men and women. Compared with those in male brains, we found greater signal complexity in all functional brain networks in female brains with the default mode network exhibiting the largest sex difference. Using the gene expression data in brain tissues, we identified genes that were significantly associated with sex differences in brain signal complexity. The significant genes were enriched in the gene sets that were differentially expressed between the brain cortex and other tissues, the estrogen-signaling pathway, and the biological function of neural plasticity. In particular, the G-protein-coupled estrogen receptor 1 gene in the estrogen-signaling pathway was expressed more in brain regions with greater sex differences in SampEn. In conclusion, greater complexity in female brains may reflect the interactions between sex hormone fluctuations and neuromodulation of estrogen in women. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-023-09954-y.

Higher-order Granger reservoir computing: simultaneously achieving scalable complex structures inference and accurate dynamics prediction.

Recently, machine learning methods, including reservoir computing (RC), have been tremendously successful in predicting complex dynamics in many fields. However, a present challenge lies in pushing for the limit of prediction accuracy while maintaining the low complexity of the model. Here, we design a data-driven, model-free framework named higher-order Granger reservoir computing (HoGRC), which owns two major missions: The first is to infer the higher-order structures incorporating the idea of Granger causality with the RC, and, simultaneously, the second is to realize multi-step prediction by feeding the time series and the inferred higher-order information into HoGRC. We demonstrate the efficacy and robustness of the HoGRC using several representative systems, including the classical chaotic systems, the network dynamical systems, and the UK power grid system. In the era of machine learning and complex systems, we anticipate a broad application of the HoGRC framework in structure inference and dynamics prediction.

Tipping Point Detection Using Reservoir Computing.

Detection in high fidelity of tipping points, the emergence of which is often induced by invisible changes in internal structures or/and external interferences, is paramountly beneficial to understanding and predicting complex dynamical systems (CDSs). Detection approaches, which have been fruitfully developed from several perspectives (e.g., statistics, dynamics, and machine learning), have their own advantages but still encounter difficulties in the face of high-dimensional, fluctuating datasets. Here, using the reservoir computing (RC), a recently notable, resource-conserving machine learning method for reconstructing and predicting CDSs, we articulate a model-free framework to accomplish the detection only using the time series observationally recorded from the underlying unknown CDSs. Specifically, we encode the information of the CDS in consecutive time durations of finite length into the weights of the readout layer in an RC, and then we use the learned weights as the dynamical features and establish a mapping from these features to the system's changes. Our designed framework can not only efficiently detect the changing positions of the system but also accurately predict the intensity change as the intensity information is available in the training data. We demonstrate the efficacy of our supervised framework using the dataset produced by representative physical, biological, and real-world systems, showing that our framework outperforms those traditional methods on the short-term data produced by the time-varying or/and noise-perturbed systems. We believe that our framework, on one hand, complements the major functions of the notable RC intelligent machine and, on the other hand, becomes one of the indispensable methods for deciphering complex systems.

Estimation of Human Mobility Patterns for Forecasting the Early Spread of Disease.

Human mobility data are indispensable in modeling large-scale epidemics, especially in predicting the spatial spread of diseases and in evaluating spatial heterogeneity intervention strategies. However, statistical data that can accurately describe large-scale population migration are often difficult to obtain. We propose an algorithm model based on the network science approach, which estimates the travel flow data in mainland China by transforming location big data and airline operation data into network structure information. In addition, we established a simplified deterministic SEIR (Susceptible-Exposed-Infectious-Recovered)-metapopulation model to verify the effectiveness of the estimated travel flow data in the study of predicting epidemic spread. The results show that individual travel distance in mainland China is mainly within 100 km. There is far more travel between prefectures within the same province than across provinces. The epidemic spatial spread model incorporating estimated travel data accurately predicts the spread of COVID-19 in mainland China. The results suggest that there are far more travelers than usual during the Spring Festival in mainland China, and the number of travelers from Wuhan mainly determines the number of confirmed cases of COVID-19 in each prefecture.

USF1-induced upregulation of LINC01048 promotes cell proliferation and apoptosis in cutaneous squamous cell carcinoma by binding to TAF15 to transcriptionally activate YAP1.

Previous studies have revealed that dysregulation of long non-coding RNAs (lncRNAs) can facilitate carcinogenesis. This study aims to investigate the biological role of a certain lncRNA in cutaneous squamous cell carcinoma (CSCC). According to the data of TCGA database, high expression of long intergenic non-protein coding RNA 1048 (LINC01048) is an unfavorable prognostic factor for patients with CSCC. Therefore, we further detected the expression pattern of LINC01048 in CSCC tissues. Obviously, LINC01048 was expressed higher in the CSCC tissues and recurrence tissues compared with that in adjacent normal tissues and non-recurrence tissues. Furthermore, Kaplan-Meier analysis revealed the negative correlation between LINC01048 expression and the overall survival and disease-free survival of CSCC patients. Subsequently, functional assays were conducted to prove the inhibitory effect of silenced LINC01048 on the proliferation and apoptosis of CSCC cells. Mechanistically, LINC01048 was proved to be transcriptionally activated by USF1. Pathway analysis and western blot assay showed that knockdown of LINC01048 led to the activation of Hippo pathway. Moreover, YAP1, a Hippo pathway factor, was positively regulated by LINC01048. Further mechanism investigation revealed that LINC01048 increased the binding of TAF15 to YAP1 promoter to transcriptionally activate YAP1 in CSCC cells. Finally, rescue assays demonstrated that YAP1 involved in LINC01048-mediated CSCC cell proliferation and apoptosis. In conclusion, USF1-induced upregulation of LINC01048 promoted CSCC by interacting with TAF15 to upregulate YAP1.

[Study on sustentaculum tali screw placement for constant fragment of calcaneal fracture].

Objective: To investigate the anatomical characters of the sustentaculum tali (ST), accurate entry point and direction for the placement of ST screw from posterior subtalar joint facet to the constant fragment (CF) in calcaneal fractures. Methods: A total of 100 patients with calcaneal fractures performed ankle CT scans were enrolled between January 2016 and April 2016. According to the inclusion criteria, the clinical data of 33 patients were analyzed, including 18 males and 15 females, with a median age of 41.0 years (range, 18-60 years). There were 16 cases on left side and 17 cases on the right side. Three-dimensional (3D) calcaneal model was reconstructed by Mimics 17.0 software, and the ST anatomical references were measured, including the length of upper and lower edge, the length and height of the midline, the horizontal angle between the midline and foot plantar surface. The parameters of the optimal entry point position (P' point) and placement angle of the ST screw were determined. The length of ST screw was also measured. The differences between males and females or left and right sides were compared. Results: The length of upper edge of the ST was (16.60±2.23) mm, lower edge (20.65±2.90) mm, midline (20.56±2.62) mm, and the height of midline was (9.61±1.36) mm. The horizontal angle between the midline and foot plantar surface was (23.43±3.36)°. The vertical distance from P' point to the lowest point of the tarsal sinus was (3.09±1.65) mm, while the horizontal distance was (14.29±2.75) mm. The distance from P' point to the apex of the lateral talus, subchondral bone of subtalar joint, calcaneocuboid joint was (11.41±3.22), (6.59±2.22), (34.58±3.75) mm, respectively. The horizontal angle between the ST screw and foot plantar surface was (-1.17±2.07)°. The anteversion angle of ST screw was (16.18±2.05)° and the length was (41.64 ± 3.09) mm. There were significant differences in the length of upper and lower edge, the length and height of the midline, the distance from P' point to the apex of the lateral talus, subchondral bone of subtalar joint, and calcaneocuboid joint, and the anteversion angle and length of the ST screw between males and females ( P<0.05). There was no significant difference in above all parameters between left and right sides ( P>0.05). Conclusion: After appropriate reduction of the calcaneal fractures, the entry point of ST screw was recommended at about 14 mm posterior and about 3 mm upper related to the foot horizontal line through the lowest tarsal sinus point; and the direction of ST screw placement was about 17° anteversion for males and 15° anteversion for females.

Maximizing the Spread of Influence via Generalized Degree Discount.

It is a crucial and fundamental issue to identify a small subset of influential spreaders that can control the spreading process in networks. In previous studies, a degree-based heuristic called DegreeDiscount has been shown to effectively identify multiple influential spreaders and has severed as a benchmark method. However, the basic assumption of DegreeDiscount is not adequate, because it treats all the nodes equally without any differences. To consider a general situation in real world networks, a novel heuristic method named GeneralizedDegreeDiscount is proposed in this paper as an effective extension of original method. In our method, the status of a node is defined as a probability of not being influenced by any of its neighbors, and an index generalized discounted degree of one node is presented to measure the expected number of nodes it can influence. Then the spreaders are selected sequentially upon its generalized discounted degree in current network. Empirical experiments are conducted on four real networks, and the results show that the spreaders identified by our approach are more influential than several benchmark methods. Finally, we analyze the relationship between our method and three common degree-based methods.

[Repair and reconstruction of femoral pseudoaneurysm caused by drug injection].

OBJECTIVE: To evaluate repair and reconstruction of the femoral pseudoaneurysm caused by drug injection. METHODS: From May 2000 to May 2005, 15 cases of femoral pseudoaneurysm caused by drug injection underwent operation treatment. All patients were male, aging 20-36 years. The disease course was 18-52 days (mean 35 days) and the course of drug injection was 3-17 months. The locations were the left side in 5 cases and the right side in 10 cases. After having been bandaged with pressure and supported with nutrition, they had been all operated. One case received fistula repair, and 14 cases received vascular grafting with ePTFE man-made blood vessel. RESULTS: The wounds healed by the first intention in 14 cases. All limbs survived. The complexion, temperature and response of involved leg were in gear. The postoperative color ultrasound Doppler detection showed that all the vascular grafts were of patency. The function of the involved limbs restored to normal. CONCLUSION: Complete debridement, vascular reconstruction and better microsurgery skill were the key factors of treating successfully the femoral pseudoaneurysm caused by drug injection.