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Power grids, due to their lack of network redundancy and structural interdependence, are particularly vulnerable to cascading failures, a phenomenon where a few failed nodes—having their loads exceeding their capacities—can trigger a widespread collapse of all nodes. Here, we extend the cascading failure (Motter-Lai) model to a more realistic perspective, where each node’s load capacity is determined to be nonlinearly correlated with the node’s centrality. Our analysis encompasses a range of synthetic networks featuring small-world or scale-free properties, as well as real-world network configurations like the IEEE bus systems and the US power grid. We find that fine-tuning this nonlinear relationship can significantly enhance a network’s robustness against cascading failures when the network nodes are under attack. Additionally, the selection of initial nodes and the attack strategies also impact overall network robustness. Our findings offer valuable insights for improving the safety and resilience of power grids, bringing us closer to understanding cascading failures in a more realistic context.

Summary: Rice stripe virus (RSV) establishes infection in the ovaries of its vector insect, Laodelphax striatellus. We demonstrate that RSV infection delays ovarian maturation by inhibiting membrane localization of the vitellogenin receptor (VgR), thereby reducing the vitellogenin (Vg) accumulation essential for egg development. We identify the host protein L. striatellus Rab1 protein (LsRab1), which directly interacts with RSV nucleocapsid protein (NP) within nurse cells. LsRab1 is required for VgR surface localization and ovarian Vg accumulation. RSV inhibits LsRab1 function through two mechanisms: NP binding LsRab1 prevents GTP binding, and NP binding LsRab1-GTP complexes stimulates GTP hydrolysis, forming an inactive LsRab1 form. Through this dual inhibition, RSV infection prevents LsRab1 from facilitating VgR trafficking to the cell membrane, leading to inefficient Vg uptake. The Vg-VgR pathway is present in most oviparous animals, and the mechanisms detailed here provide insights into the vertical transmission of other insect-transmitted viruses of medical and agricultural importance.

This study analyzed the effects of airflow characteristics on the temperature distribution and drying uniformity of paddy during convective drying. Simulations of the drying process with varying airflow inlet and outlet positions were conducted using COMSOL Multiphysics 6.1 software. The determination coefficient (R2) between the simulated data and experimental values of Sample1 (S1), Sample2 (S2), and Sample3 (S3) was calculated, and its average values were 0.964, 0.963, 0.963, and 0.967, respectively. This study demonstrates that the airflow direction and outlet location have a significant impact on the temperature uniformity of the paddy. The vortex structure generated by the obstruction of the sidewalls and paddy influences both the airflow and temperature distribution within the drying chamber. When the outlet was on the left side and the inlet airflow was in a vertical orientation (VO), the temperature distribution of the paddy exhibited higher temperatures in the edge regions and lower temperatures in the center, with a maximum temperature difference of around 16 °C. The time required for the temperature to reach equilibrium with the outlet positioned on the left was 28.6% shorter than with the outlets positioned in the center or on both sides. Moreover, the temperature uniformity of the three paddy samples was better under this condition. The developed model accurately reflected the paddy drying process. It could also be used to analyze the optimal heating uniformity, providing a technical basis for the design of grain dryers.

ObjectiveSpinal schwannomas are the most common intradural extramedullary tumors, and their complete removal is recommended to avoid tumor recurrence. Although laminoplasty provides a sufficient window for tumor resection, this approach may increase tissue trauma and cause postoperative instability compared with unilateral hemilaminectomy. This study aimed to compare the efficacy and clinical outcomes of the two approaches.Materials and methodsWe included 100 consecutive patients who underwent unilateral hemilaminectomy or laminoplasty for resection of spinal schwannomas between January 2015 and February 2023. The patients' baseline characteristics, including sex, age, tumor location, percentage of tumor occupying the intradural space, operative time, postoperative length of hospital stay, intraoperative bleeding volume, visual analog scale score, and neurologic results, were retrospectively analyzed.ResultsHemilaminectomy patients who underwent unilateral hemilaminectomy had smaller intraoperative bleeding (p = 0.020) volume, shorter operative time (p = 0.012), and shorter postoperative length of hospital stay (p = 0.044). The mean VAS scores at the last follow-up were similar between the two groups (p = 0.658). Although the postoperative McCormick and Karnofsky Performance scores were not significantly different between the laminoplasty and unilateral hemilaminectomy groups (p = 0.687 and p = 0.649, respectively), there was a statistically significant improvement based on postoperative neurological results compared to preoperative neurological results for both groups. The incidence of postoperative complications was 5% and 11.7% in the unilateral hemilaminectomy and laminoplasty groups, respectively (p = 0.308).ConclusionsFor spinal schwannoma resection, unilateral hemilaminectomy has more advantages than laminoplasty, including a shorter postoperative hospital stay, faster procedure, and less intraoperative blood loss while achieving the same desired result.

Abstract Although the origin of the fat-tail characteristic of the degree distribution in complex networks has been extensively researched, the underlying cause of the degree distribution characteristic across the complete range of degrees remains obscure. Here, we propose an evolution model that incorporates only two factors: the node’s weight, reflecting its innate attractiveness (nature), and the node’s degree, reflecting the external influences (nurture). The proposed model provides a good fit for degree distributions and degree ratio distributions of numerous real-world networks and reproduces their evolution processes. Our results indicate that the nurture factor plays a dominant role in the evolution of social networks. In contrast, the nature factor plays a dominant role in the evolution of non-social networks, suggesting that whether nodes are people determines the dominant factor influencing the evolution of real-world networks.

BackgroundRecent studies have suggested that the composition of gut microbiota (GM) may change after intracerebral hemorrhage. However, the causal inference of GM and hemorrhagic stroke is unknown. Mendelian Randomization (MR) is an effective research method that removes confounding factors and investigates the causal relationship between exposure and outcome. This study intends to explore the causal relationship between GM and hemorrhagic stroke with the help of MR.MethodsUnivariable and multivariable MR analyses were performed using summary statistics of the GM (n = 18,340) in the MiBioGen consortium vs. the FinnGen consortium R9 summary statistics (intracerebral hemorrhage, subarachnoid hemorrhage, and nontraumatic intracranial hemorrhage). Causal associations between gut microbiota and hemorrhagic stroke were analyzed using inverse variance weighted, MR-Egger regression, weighted median, weighted mode, simple mode, and MR-PRESSO. Cochran’s Q statistic, MR-Egger regression, and leave-one-out analysis were used to test for multiplicity and heterogeneity of instrumental variables. Separate reverse MR analyses were performed for microbiota found to be causally associated with hemorrhagic stroke in the forward MR analysis. Also, multivariate MR analyses were conducted after incorporating common confounders.ResultsBased on the results of univariable and multivariate MR analyses, Actinobacteria (phylum) (OR, 0.80; 95%CI, 0.66–0.97; p = 0.025) had a protective effect against hemorrhagic stroke, while Rikenellaceae RC9 gut group (genus) (OR, 0.81; 95%CI, 0.67–0.99; p = 0.039) had a potential protective effect. Furthermore, Dorea (genus) (OR, 1.77; 95%CI, 1.27–2.46; p = 0.001), Eisenbergiella (genus) (OR, 1.24; 95%CI, 1.05–1.48; p = 0.013) and Lachnospiraceae UCG008 (genus) (OR, 1.28; 95%CI, 1.01–1.62; p = 0.041) acted as potential risk factors for hemorrhagic stroke. The abundance of Dorea (genus) (β, 0.05; 95%CI, 0.002 ~ 0.101; p = 0.041) may increase, and that of Eisenbergiella (genus) (β, −0.072; 95%CI, −0.137 ~ −0.007; p = 0.030) decreased after hemorrhagic stroke according to the results of reverse MR analysis. No significant pleiotropy or heterogeneity was detected in any of the MR analyses.ConclusionThere is a significant causal relationship between GM and hemorrhagic stroke. The prevention, monitoring, and treatment of hemorrhagic stroke through GM represent a promising avenue and contribute to a deeper understanding of the mechanisms underlying hemorrhagic stroke.

Here the authors, by using data from three electrical companies in the USA, find that the recovery duration of an outage is connected with the downtime of its nearby outages and blackout intensity and present a cluster-based recovery framework.

Abstract It is essential to study the robustness and centrality of interdependent networks for building reliable interdependent systems. Here, we consider a nonlinear load-capacity cascading failure model on interdependent networks, where the initial load distribution is not random, as usually assumed, but determined by the influence of each node in the interdependent network. The node influence is measured by an automated entropy-weighted multi-attribute algorithm that takes into account both different centrality measures of nodes and the interdependence of node pairs, then averaging for not only the node itself but also its nearest neighbors and next-nearest neighbors. The resilience of interdependent networks under such a more practical and accurate setting is thoroughly investigated for various network parameters, as well as how nodes from different layers are coupled and the corresponding coupling strength. The results thereby can help better monitoring interdependent systems.

The move to the quantum internet demands developments in communication networks that are based on quantum entanglement. The authors discuss the phenomenon of entanglement percolation in a quantum network presenting solutions to significantly accelerate the intensive computation effort involved in the process.

Abstract In this paper, we present a mesh adaptation algorithm for the unsteady compressible Navier-Stokes equations under the framework of local discontinuous Galerkin methods coupled with implicit-explicit Runge-Kutta or spectral deferred correction time discretization methods. In both of the two high order semi-implicit time integration methods, the convective flux is treated explicitly and the viscous and heat fluxes are treated implicitly. The remarkable benefits of such semi-implicit temporal discretizations are that they can not only overcome the stringent time step restriction compared with time explicit methods, but also avoid the construction of the large Jacobian matrix as is done for fully implicit methods, thus are relatively easy to implement. To save computing time as well as capture the flow structures of interest accurately, a local mesh refinement (h-adaptive) technique, in which we present detailed criteria for selecting candidate elements and complete strategies to refine and coarsen them, is also applied for the Navier-Stokes equations. Numerical experiments are provided to illustrate the high order accuracy, efficiency and capabilities of the semi-implicit schemes in combination with adaptive local discontinuous Galerkin methods for the Navier-Stokes equations.

Quantum networks have experienced rapid advancements in both theoretical and experimental domains over the last decade, making it increasingly important to understand their large-scale features from the viewpoint of statistical physics. This review paper discusses a fundamental question: how can entanglement be effectively and indirectly (e.g., through intermediate nodes) distributed between distant nodes in an imperfect quantum network, where the connections are only partially entangled and subject to quantum noise? We survey recent studies addressing this issue by drawing exact or approximate mappings to percolation theory, a branch of statistical physics centered on network connectivity. Notably, we show that the classical percolation frameworks do not uniquely define the network’s indirect connectivity. This realization leads to the emergence of an alternative theory called “concurrence percolation”, which uncovers a previously unrecognized quantum advantage that emerges at large scales, suggesting that quantum networks are more resilient than initially assumed within classical percolation contexts, offering refreshing insights into future quantum network design.

Photodynamic therapy (PDT) is an effective noninvasive therapeutic strategy that has been widely used for anti-tumor therapy by the generation of excessive highly cytotoxic ROS. However, the poor water solubility of the photosensitizer, reactive oxygen species (ROS) depleting by high concentrations of glutathione (GSH) in the tumor microenvironment and the activation of DNA repair pathways to combat the oxidative damage, will significantly limit the therapeutic effect of PDT. Herein, we developed a photosensitizer prodrug (CSP) by conjugating the photosensitizer pyropheophorbide a (PPa) and the DNA-damaging agent Chlorambucil (Cb) with a GSH-responsive disulfide linkage and demonstrated a multifunctional co-delivery nanoplatform (CSP/Ola nanoparticles (NPs)) together with DSPE-PEG2000 and PARP inhibitor Olaparib (Ola). The CSP/Ola NPs features excellent physiological stability, efficient loading capacity, much better cellular uptake behavior and photodynamic performance. Specifically, the nanoplatform could induce elevated intracellular ROS levels upon the in situ generation of ROS during PDT, and decrease ROS consumption by reducing intracellular GSH level. Moreover, the CSP/Ola NPs could amplify DNA damage by released Cb and inhibit the activation of Poly(ADP-ribose) polymerase (PARP), promote the upregulation of γ-H2AX, thereby blocking the DNA repair pathway to sensitize tumor cells for PDT. In vitro investigations revealed that CSP/Ola NPs showed excellent phototoxicity and the IC50 values of CSP/Ola NPs against MDA-MB-231 breast cancer cells were as low as 0.05–01 μM after PDT. As a consequence, the co-delivery nanoplatform greatly promotes the tumor cell apoptosis and shows a high antitumor performance with combinational chemotherapy and PDT. Overall, this work provides a potential alternative to improve the therapeutic efficiency of triple negative breast cancer cell (TNBC) treatment by synergistically enhancing DNA damage and disrupting DNA damage repair.

Purpose: No established biomarkers currently exist for therapeutic efficacy and durability of anti–VEGF therapy in neovascular age-related macular degeneration (nAMD). This study evaluated radiomic-based quantitative OCT biomarkers that may be predictive of anti-VEGF treatment response and durability. Design: Assessment of baseline biomarkers using machine learning (ML) classifiers to predict tolerance to anti-VEGF therapy. Participants: Eighty-one participants with treatment-naïve nAMD from the OSPREY study, including 15 super responders (patients who achieved and maintained retinal fluid resolution) and 66 non–super responders (patients who did not achieve or maintain retinal fluid resolution). Methods: A total of 962 texture-based radiomic features were extracted from fluid, subretinal hyperreflective material (SHRM), and different retinal tissue compartments of OCT scans. The top 8 features, chosen by the minimum redundancy maximum relevance feature selection method, were evaluated using 4 ML classifiers in a cross-validated approach to distinguish between the 2 patient groups. Longitudinal assessment of changes in different texture-based radiomic descriptors (delta-texture features) between baseline and month 3 also was performed to evaluate their association with treatment response. Additionally, 8 baseline clinical parameters and a combination of baseline OCT, delta-texture features, and the clinical parameters were evaluated in a cross-validated approach in terms of association with therapeutic response. Main Outcome Measures: The cross-validated area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to validate the classifier performance. Results: The cross-validated AUC by the quadratic discriminant analysis classifier was 0.75 ± 0.09 using texture-based baseline OCT features. The delta-texture features within different OCT compartments between baseline and month 3 yielded an AUC of 0.78 ± 0.08. The baseline clinical parameters sub–retinal pigment epithelium volume and intraretinal fluid volume yielded an AUC of 0.62 ± 0.07. When all the baseline, delta, and clinical features were combined, a statistically significant improvement in the classifier performance (AUC, 0.81 ± 0.07) was obtained. Conclusions: Radiomic-based quantitative assessment of OCT images was shown to distinguish between super responders and non–super responders to anti-VEGF therapy in nAMD. The baseline fluid and SHRM delta-texture features were found to be most discriminating across groups.

Excessive milling of rice kernels will result in nutrient loss and grain waste. To avoid grain waste, multibreak milling systems have been widely used in large-scale commercial rice mills. However, there is still no reasonable breakpoint planning method to guide the multibreak milling process. To construct a reasonable multibreak milling system, in this research, taking rice milling, a typical heterogeneous cereal-kernel milling process, as an example, the multivariate analysis method was used to comprehensively analyze the characteristic changes of milled rice during the whole milling process. A breakpoint planning method was established, including planning the number of breakpoints, determining the degree of milling or milling time corresponding to each breakpoint, and estimating the actual breakpoint to which the milled rice belongs. The verification results showed the rationality and high accuracy of the planning method. The presented work will help operators to plan the multibreak milling system of rice efficiently and objectively so as to significantly improve the commercial value of milled rice.

The performance of distributing entanglement between two distant nodes in a large-scale quantum network (QN) of partially entangled bipartite pure states is generally benchmarked against the classical entanglement percolation (CEP) scheme. Improvements beyond CEP were only achieved by nonscalable strategies for restricted QN topologies. This paper explores and amplifies a new and more effective mapping of a QN, referred to as concurrence percolation theory (ConPT), that suggests using deterministic rather than probabilistic protocols for scalably improving on CEP across arbitrary QN topology. More precisely, we implement ConPT via a deterministic entanglement transmission (DET) scheme that is fully analogous to resistor network analysis, with the corresponding series and parallel rules represented by deterministic entanglement swapping and concentration protocols, respectively. The main contribution of this paper is to establish a powerful mathematical framework, which is applicable to arbitrary d-dimensional information carriers (qudits), that provides different natural optimality metrics in terms of generalized k-concurrences (a family of fundamental entanglement measures) for different QN topologies. In particular, we conclude that the introduced DET scheme (a) is optimal over the well-known nested repeater protocol for distilling entanglement from partially entangled qubits and (b) leads to higher success probabilities of obtaining a maximally entangled state than using CEP. The implementation of the DET scheme is experimentally feasible as tested on IBM's quantum computation platform.

Deoxynivalenol (DON), a mycotoxin that contaminates crops such as wheat and corn, can cause severe acute or chronic injury when ingested by animals or humans. This study investigated the protective effect of ferulic acid (FA), a polyphenolic substance, on alleviating the toxicity induced by DON (40 μM) in IPEC-J2 cells. The experiments results showed that FA not only alleviated the decrease in cell viability caused by DON (p < 0.05), but increased the level of superoxide dismutase (SOD) (p < 0.01), glutathione peroxidase (GSH-Px), (catalase) CAT and glutathione (GSH) (p < 0.05) through the nuclear factor erythroid 2-related factor 2 (Nrf2)-epoxy chloropropane Kelch sample related protein-1 (keap1) pathway, and then decreased the levels of intracellular oxidative stress. Additionally, FA could alleviate DON-induced inflammation through mitogen-activated protein kinases (MAPKs) and nuclear factor kappa-B (NF-κB) pathways, down-regulated the secretion of interleukin-6 (IL-6) (p < 0.0001), interleukin-8 (IL-8) (p < 0.05), interleukin-1β (IL-1β), interferon-γ (IFN-γ) and further attenuated the DON-induced intracellular apoptosis (10.7% to 6.84%) by regulating the expression of Bcl2-associated X protein (Bax) (p < 0.0001), B-cell lymphoma-2 (Bcl-2) (p < 0.0001), and caspase-3 (p < 0.0001). All these results indicate that FA exhibits a significantly protective effect against DON-induced toxicity.

To improve the classification results of high-resolution remote sensing images (RSIs), it is necessary to use feature transfer methods to mine the relevant information between high-resolution RSIs and low-resolution RSIs to train the classifiers together. Most of the existing feature transfer methods can only handle homogeneous data (i.e., data with the same dimension) and are susceptible to the quality of the RSIs, while RSIs with different resolutions present different feature dimensions and samples obtained from illumination conditions. To obtain effective classification results, unlike existing methods that focus only on the projection transformation in feature space, a joint feature-space and sample-space heterogeneous feature transfer (JFSSS-HFT) method is proposed to simultaneously process heterogeneous multi-resolution images in feature space using projection matrices of different dimensions and reduce the impact of outliers by adaptive weight factors in the sample space simultaneously to reduce the occurrence of negative transfer. Moreover, the maximum interclass variance term is embedded to improve the discriminant ability of the transferred features. To solve the optimization problem of JFSSS-HFT, the alternating-direction method of multipliers (ADMM) is introduced to alternatively optimize the parameters of JFSSS-HFT. Using different types of ship patches and airplane patches with different resolutions, the experimental results show that the proposed JFSSS-HFT obtains better classification results than the typical feature transferred methods.

Traditional machine-learning methods are inefficient in capturing chaos in nonlinear dynamical systems, especially when the time difference Δt between consecutive steps is so large that the extracted time series looks apparently random. Here, we introduce a new long-short-term-memory (LSTM)-based recurrent architecture by tensorizing the cell-state-to-state propagation therein, maintaining the long-term memory feature of LSTM, while simultaneously enhancing the learning of short-term nonlinear complexity. We stress that the global minima of training can be most efficiently reached by our tensor structure where all nonlinear terms, up to some polynomial order, are treated explicitly and weighted equally. The efficiency and generality of our architecture are systematically investigated and tested through theoretical analysis and experimental examinations. In our design, we have explicitly used two different many-body entanglement structures—matrix product states (MPS) and the multiscale entanglement renormalization ansatz (MERA)—as physics-inspired tensor decomposition techniques, from which we find that MERA generally performs better than MPS, hence conjecturing that the learnability of chaos is determined not only by the number of free parameters but also the tensor complexity—recognized as how entanglement entropy scales with varying matricization of the tensor.

Background: Recent studies have suggested that the composition of gut microbiota (GM) may change after intracerebral hemorrhage. However, the causal inference of GM and hemorrhagic stroke is unknown. Mendelian Randomization (MR) is an effective research method that removes confounding factors and investigates the causal relationship between exposure and outcome. This study intends to explore the causal relationship between GM and hemorrhagic stroke with the help of MR. Methods: Univariable and multivariable MR analyses were performed using summary statistics of the GM (n = 18,340) in the MiBioGen consortium vs. the FinnGen consortium R9 summary statistics (intracerebral hemorrhage, subarachnoid hemorrhage, and nontraumatic intracranial hemorrhage). Causal associations between gut microbiota and hemorrhagic stroke were analyzed using inverse variance weighted, MR-Egger regression, weighted median, weighted mode, simple mode, and MR-PRESSO. Cochran's Q statistic, MREgger regression, and leave-one-out analysis were used to test for multiplicity and heterogeneity of instrumental variables. Separate reverse MR analyses were performed for microbiota found to be causally associated with hemorrhagic stroke in the forward MR analysis. Also, multivariate MR analyses were conducted after incorporating common confounders. Results: Based on the results of univariable and multivariate MR analyses, Actinobacteria (phylum) (OR, 0.80; 95%CI, 0.66-0.97; p = 0.025) had a protective effect against hemorrhagic stroke, while Rikenellaceae RC9 gut group (genus) (OR, 0.81; 95%CI, 0.67-0.99; p = 0.039) had a potential protective effect. Furthermore, Dorea (genus) (OR, 1.77; 95%CI, 1.27-2.46; p = 0.001), Eisenbergiella (genus) (OR, 1.24; 95%CI, 1.05-1.48; p = 0.013) and Lachnospiraceae UCG008 (genus) (OR, 1.28; 95%CI, 1.01-1.62; p = 0.041) acted as potential risk factors for hemorrhagic stroke. The abundance of Dorea (genus) (β, 0.05; 95%CI, 0.002~ 0.101; p = 0.041) may increase, and that of Eisenbergiella (genus) (β, -0.072; 95%CI, -0.137~ -0.007; p = 0.030) decreased after hemorrhagic stroke according to the results of reverse MR analysis. No significant pleiotropy or heterogeneity was detected in any of the MR analyses. Conclusion: There is a significant causal relationship between GM and hemorrhagic stroke. The prevention, monitoring, and treatment of hemorrhagic stroke through GM represent a promising avenue and contribute to a deeper understanding of the mechanisms underlying hemorrhagic stroke. [ABSTRACT FROM AUTHOR]