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Numerical study of coal reservoir and borehole parameters evaluation considering temperature and multi-borehole superposition effects
The Big Bang of an epidemic: a metapopulation approach to identify the spatiotemporal origin of contagious diseases and their universal spreading pattern
Abstract In this paper, we propose a mathematical framework that governs the evolution of epidemic dynamics, encompassing both intra-population dynamics and inter-population mobility within a meta-population network. By linearizing this dynamical system, we can identify the spatial starting point(s), namely the source(s) and the initiation time of the epidemic, which we refer to as the “Big Bang” of the epidemic. Furthermore, we introduce a novel concept of effective distance to track disease spread within the network. Our analysis reveals that the contagion geometry can be represented as a line with a universal slope, for any disease type (R0) or mobility network configuration. The mathematical derivations presented in this framework are corroborated by empirical data, including observations from the COVID-19 pandemic in Iran and the US and the H1N1 outbreak worldwide. Within this framework, to detect the Big Bang of an epidemic we require two types of data: (1) A snapshot of the active infected cases in each subpopulation during the linear phase. (2) A coarse-grained representation of inter-population mobility. Also even with access to only the first type of data, we can still demonstrate the universal contagion geometric pattern. Additionally, we can estimate errors and assess the precision of the estimations. This comprehensive approach enhances our understanding of when and where epidemics began and how they spread. It equips us with valuable insights for developing effective public health policies and mitigating the impact of infectious diseases on populations worldwide.
Electrochemical determination of uric acid in the presence of dopamine and riboflavin using a poly(resorcinol)-modified carbon nanotube sensor
IR thermography & NN models for damaged component thickness detection
Comparative analysis of the effects of Nano-SiO2 and carbon nanotubes on mechanical properties of polyethylene fibre reinforced cementitious composites
High-performance terahertz biosensor utilizing a hybrid one-dimensional photonic crystal with liquid crystal and graphene components
Hypoglycemia and hyperinsulinemia induced by phenolic uremic toxins in CKD and DKD patients
Prosopagnosia and the role of face-sensitive areas in race perception
α-spinasterol isolated from Achyranthes aspera L. ameliorates inflammation via NF-κB and Nrf2/HO-1 pathways
Abstract On the basis of previous studies, the low-polar part of Achyranthes aspera L. (A. aspera) had strong anti-inflammatory activity. Three compounds were isolated from the low polarity fraction of A. aspera, and their structures were identified as α-spinasterol (1), 7,8-dihydrospinasterol (2), 22,23-dihydrospinasterol (3). Among them, the content of α-spinasterol (1) in A. aspera was higher in the spring and winter seasons through HPLC methods, ranging from 0.0085 to 0.0157%. Futhermore, in the LPS-induced RAW264.7 cells inflammation model, α-spinasterol significantly reduced the levels of cytokines such as IL-6, PGE2 and TNF-α, inhibited the expression of COX-2, 5-LOX, p-IKKβ, p-NFκB and p-IkBα proteins, and promoted the expression of Nrf2, HO-1 and NQO1 proteins. Therefore, this study showed that α-spinasterol can inhibit LPS-induced RAW264.7 cells inflammation, and its mechanism may be related to the inhibition of NF-κB pathway, activation of Nrf2 pathway, and reduction of excessive release of inflammatory factors.
Metabolic dysfunction-associated steatotic liver disease increases cardiovascular disease risk in young adults
A fuzzy system based self-adaptive memetic algorithm using population diversity control for evolutionary multi-objective optimization
Abstract Simulated by nature’s evolution, numerous evolutionary algorithms had been proposed. These algorithms perform better for a particular problem domain and extensive parameter fine tuning and adaptations are required in optimizing problems of varied domain. This paper aims to develop robust and self-adaptive memetic algorithm by combining Differential Evolution based algorithm, a popular population based global search method with the Controlled Local search procedure to solve multi-objective optimization problems. Memetic Algorithm is an enhanced evolutionary algorithm, it combines global search method with local search techniques for faster convergence. Memetic algorithm improves both exploration and exploitation, preventing premature convergence and also refines the current best solutions efficiently. Proposed algorithm is named as Fuzzy based Memetic Algorithm using Diversity control (F-MAD). In F-MAD, population diversity is controlled through the control parameters self-adaptation of Differential Evolution algorithm (DE) such as, crossover rate and scaling factor by using two fuzzy systems. A controlled local search procedure is adapted for guiding convergence process thus balancing explore-exploit cycle. The control parameter self-adaptation and enhanced selection method with controlled local search method aid population diversity control in decision space and attaining optimal solutions with uniform distribution in terms of diversity and convergence metrics in objective space. These characteristics help the proposed method suitable to be extended to different application domain without the need of trial-and-error fine tuning of the parameters. The performance is tested through standard benchmark test problems-CEC 2009 test problems and DTLZ test problem and further validated through performance metrics and statistical test. It is compared with popular optimization algorithms and experiment results indicate that F-MAD perform well than State of-The-Art (SOTA) algorithms taken for comparison. F-MAD algorithm attains better results for 8 out of 10 CEC 2009 test problems (UF1-UF10) when compared to 20 other algorithms taken for comparison. For DTLZ problems, F-MAD attains better results for ALL 7 problems (DTLZ 1-DTLZ7) when compared to 8 other SOTA algorithms. The performance is further evaluated using Friedman rank test and the proposed F-MAD significantly outperformed other algorithms.
PDE4B promotes JNK/NLRP3 activation in the nucleus pulposus and mediates intervertebral disc degeneration
A new approach to treatment of stress urinary incontinence using non-ablative transurethral laser
Acidification potential estimation for small hydropower using LCA methodology in India
What are the best AI tools for research? Nature’s guide
Optimizing oil properties and asphaltene management using Fe3O4-based nanohybrids under microwave radiation
Scalable intracellular delivery via microfluidic vortex shedding enhances the function of chimeric antigen receptor T-cells
Abstract Adoptive chimeric antigen receptor T-cell (CAR-T) therapy is transformative and approved for hematologic malignancies. It is also being developed for the treatment of solid tumors, autoimmune disorders, heart disease, and aging. Despite unprecedented clinical outcomes, CAR-T and other engineered cell therapies face a variety of manufacturing and safety challenges. Traditional methods, such as lentivirus transduction and electroporation, result in random integration or cause significant cellular damage, which can limit the safety and efficacy of engineered cell therapies. We present hydroporation as a gentle and effective alternative for intracellular delivery. Hydroporation resulted in 1.7- to 2-fold higher CAR-T yields compared to electroporation with superior cell viability and recovery. Hydroporated cells exhibited rapid proliferation, robust target cell lysis, and increased pro-inflammatory and regulatory cytokine secretion in addition to improved CAR-T yield by day 5 post-transfection. We demonstrate that scaled-up hydroporation can process 5 × 10 8 cells in less than 10 s, showcasing the platform as a viable solution for high-yield CAR-T manufacturing with the potential for improved therapeutic outcomes.