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Abstract Background Tuberculosis (TB) remains a global public health event of great concern, however epidemic data on TB covering entire areas during the special period of the COVID-19 epidemic have rarely been reported. We compared the dissemination and multidrug-resistance patterns of Mycobacterium tuberculosis complex (MTBC) in the main urban area of Luoyang City, China (including six municipal jurisdictions) and nine county and township areas under its jurisdiction, aimed to establish the epidemiology of TB in this region and to provide reference for precision anti-TB in places with similar settings. Methods From 2020 to 2022, sputum samples were collected from 18,504 patients with confirmed, suspected and unexcluded TB in 10 designated TB medical institutions. Insertion sequence 6110 was amplified by PCR (rpoB gene detection if necessary) to confirm the presence of MTBC. PCR-positive specimens were analyzed by multicolor melting curve analysis to detect multidrug resistance. Results Among the 18,504 specimens, 2675 (14.5%) were MTBC positive. The positive rate was higher in the main urban area than in the county and township areas (29.8% vs. 10.9%, p 60 years were the largest group infected with MTBC in the main urban area, compared with individuals aged

The vaccine-induced innate immune response is essential for the generation of an antibody response. To date, how Ad5-vectored vaccines are influenced by preexisting anti-Ad5 antibodies during activation of the early immune response remains unclear. Here, we investigated the specific alterations in GP1,2-specific IgG-related elements of the early immune response at the genetic, molecular, and cellular levels on days 0, 1, 3, and 7 after Ad5-EBOV vaccination. In a causal multiomics analysis, distinct early immune responses associated with GP1,2-specific IgG were observed in Ad5-EBOV recipients with a low level of preexisting anti-Ad5 antibodies. This study revealed the correlates of the Ad5-EBOV-induced IgG response and provided mechanistic evidence for overcoming preexisting Ad5 immunity during the administration of Ad5-vectored vaccines.

This article presents a novel approach for reconstructing an equivalent underlying model and deriving a precise equivalent expression through the use of direct causality topology. Central to this methodology is the transfer entropy method, which is instrumental in revealing the causality topology. The polynomial fitting method is then applied to determine the coefficients and intrinsic order of the causality structure, leveraging the foundational elements extracted from the direct causality topology. Notably, this approach efficiently discovers the core topology from the data, reducing redundancy without requiring prior domain-specific knowledge. Furthermore, it yields a precise equivalent model expression, offering a robust foundation for further analysis and exploration in various fields. Additionally, the proposed model for reconstructing an equivalent underlying framework demonstrates strong forecasting capabilities in multivariate time series scenarios.

In the past, we haven't paid much attention to higher-dimensional models, which are actually more consistent with the real atmosphere. In this manuscript, we derive a high-dimensional Kadomtsev-Petviashvili equation from a fluid system based on the Gardner-Morikawa transformation and the small parameter perturbation method, which is the first time to characterize the propagation process of Rossby waves in Large-scale atmospheric. The exact solutions such as soliton, lump, rogue wave and breather of this equation are achieved according to Hirota bilinear method and symbolic computation approach. Further we utilize the composite diagram of these solutions to observe the propagation properties of Rossby waves with assistance of Mathematica. The results enrich the analysis of Rossby waves in marine engineering and fluid dynamics.

Recently, there has been a significant focus on the issue of pollution caused by livestock and poultry rearing, which is recognized as a prominent contributor to nonpoint source pollution in the agricultural sector. This study employed the life cycle assessment (LCA) methodology to evaluate the environmental impact of several pig manure processing scenarios with the aim of determining the appropriate solid–liquid separation tool for large-scale pig farms. The findings indicate that the utilization of a screw extruder for solid–liquid separation in Scenario 2 has a lower environmental impact. In contrast to Scenario 1, Scenario 2 exhibits reduced environmental potential in the areas of global warming, human toxicity, acidification, and eutrophication. Specifically, the global warming, human toxicity, acidification, and eutrophication impacts decreased by 56%, 81%, 83%, and 273%, respectively, due to the implementation of solid–liquid separation. The type of solid–liquid separation equipment used during the processing of swine manure, as well as the subsequent treatment, have a significant impact on environmental emissions. Compared to Scenario 2, Scenario 3, which utilizes a centrifugal microfilter for solid–liquid separation, exhibits a lower environmental impact in terms of human toxicity, resulting in a reduction of 0.736 kg DCB-eq. In general, solid–liquid separation is a viable and environmentally friendly method for the disposal of waste from large-scale pig farms. The adoption of this method is highly recommended. During its implementation, careful consideration should be given to factors such as separation efficiency and pollution emissions. It is crucial to select appropriate equipment for solid–liquid separation to effectively process the waste.

The newly identified complete ammonia oxidizer (comammox) that converts ammonia directly into nitrate has redefined the long-held paradigm of two-step nitrification mediated by two distinct groups of nitrifiers. However, exploration of the niche differentiation of canonical nitrifiers and comammox Nitrospira and their ecological importance in agroecosystems is still limited. Here, we adopted quantitative PCR (qPCR) and Illumina MiSeq sequencing to investigate the effects of five long-term fertilization regimes in the variations of ammonia-oxidizing bacteria (AOB), ammonia-oxidizing archaea (AOA), nitrite-oxidizing bacteria (NOB), and comammox Nitrospira abundances and comammox community composition in two soil layers (0–20 cm, topsoil; 20–40 cm, subsoil) in an Alfisol in Northeast China. The fertilization treatments included no fertilizer (CK); chemical nitrogen (N) fertilizer; chemical N; phosphorus (P) and potassium (K) fertilizers (NPK); recycled organic manure (M) and chemical N, P, K plus recycled manure (MNPK). Compared with CK, manure and/or chemical fertilizer significantly increased the AOB amoA gene abundance. Long-term recycled manure increased soil organic matter (SOM) contents and maintained the soil pH, but decreased the NH4+-N concentrations, which markedly promoted the nxrA and nxrB gene abundances of NOB and the amoA gene abundances of comammox Nitrospira clade A and AOA. Although the comammox Nitrospira clade B abundance tended to decrease after fertilization, the structural equation modeling analysis showed that comammox clade B had direct positive impacts on soil potential ammonia oxidation (PAO; λ = 0.59, p

Remediation of environmental toxic pollutants has attracted extensive attention in recent years. Microbial bioremediation has been an important technology for removing toxic pollutants. However, microbial activity is also susceptible to toxicity stress in the process of intracellular detoxification, which significantly reduces microbial activity. Electroactive microorganisms (EAMs) can detoxify toxic pollutants extracellularly to a certain extent, which is related to their unique extracellular electron transfer (EET) function. In this review, the extracellular and intracellular aspects of the EAMs’ detoxification mechanisms are explored separately. Additionally, various strategies for enhancing the effect of extracellular detoxification are discussed. Finally, future research directions are proposed based on the bottlenecks encountered in the current studies. This review can contribute to the development of toxic pollutants remediation technologies based on EAMs, and provide theoretical and technical support for future practical engineering applications.

Abstract Background Previous studies demonstrated the dependence of cancer on nerve. Recently, a growing number of studies reveal that cancer cells share the property and regulatory network with neural stem/progenitor cells. However, relationship between the property of neural stemness and cell tumorigenicity is unknown. Results We show that neural stem/progenitor cells, but not non-neural embryonic or somatic stem/progenitor cell types, exhibit tumorigenicity and the potential for differentiation into tissue types of all germ layers when they are placed in non-native environment by transplantation into immunodeficient nude mice. Likewise, cancer cells capable of tumor initiation have the property of neural stemness because of their abilities in neurosphere formation in neural stem cell-specific serum-free medium and in differentiation potential, in addition to their neuronal differentiation potential that was characterized previously. Moreover, loss of a pro-differentiation factor in myoblasts, which have no tumorigenicity, lead to the loss of myoblast identity, and gain of the property of neural stemness, tumorigenicity and potential for re-differentiation. By contrast, loss of neural stemness via differentiation results in the loss of tumorigenicity. These suggest that the property of neural stemness contributes to cell tumorigenicity, and tumor phenotypic heterogeneity might be an effect of differentiation potential of neural stemness. Bioinformatic analysis reveals that neural genes in general are correlated with embryonic development and cancer, in addition to their role in neural development; whereas non-neural genes are not. Most of neural specific genes emerged in typical species representing transition from unicellularity to multicellularity during evolution. Genes in Monosiga brevicollis, a unicellular species that is a closest known relative of metazoans, are biased toward neural cells. Conclusions We suggest that the property of neural stemness is the source of cell tumorigenicity. This is due to that neural biased unicellular state is the ground state for multicellularity and hence cell type diversification or differentiation during evolution, and tumorigenesis is a process of restoration of neural ground state in somatic cells along a default route that is pre-determined by an evolutionary advantage of neural state.

Crucial mechanical-chemical (MC) interactions occur during the cement hydration process in cement marine clay; however, the role of such an important element of the resulting strength has been subject to less investigation, particularly from the theoretical perspective. To overcome this scientific gap, an efficient strength-based model accounting for the coupled MC processes is proposed here. Based on the analysis of the cement hydration mechanism, the porosity was chosen as the main factor to characterize the influence of the MC interactions on the overall response. To verify the accuracy of the MC model, the unconfined compressive strength (UCS) experiment was conducted for the cement marine clay samples, and the corresponding simulation model was constructed using COMSOL multiphysics®. In addition, a comparison between the predicted results by the existing three strength models and the proposed MC model was performed. Subsequently, the sensitivity analysis and identification of mechanical parameters were carefully carried out. The obtained results show that the UCS strength for Taizhou clay ranges from 10.21 kPa to 354.2 kPa as the cement content increases from 10% to 20%, and the curing time varies from 3 days to 28 days. The mechanical parameters in the MC model can be obtained according to the porosity level. A reasonably good agreement between the UCS strength results of simulations and the experimentally observed data is reported. Additionally, the predicted UCS strength results by the MC model demonstrate the best correspondence with the measured values, indicating the high efficacy of the established model.

In this work, we deal with high-order solver for incompressible flow based on velocity correction scheme with discontinuous Galerkin discretized velocity and standard continuous approximated pressure. Recently, small time step instabilities have been reported for pure discontinuous Galerkin method, in which both velocity and pressure are discretized by discontinuous Galerkin. It is interesting to examine these instabilities in the context of mixed discontinuous Galerkin–continuous Galerkin method. By means of numerical investigation, we find that the discontinuous Galerkin–continuous Galerkin method shows great stability at the same configuration. The consistent velocity divergence discretization scheme helps to achieve more accurate results at small time step size. Since the equal order discontinuous Galerkin–continuous Galerkin method does not satisfy inf-sup stability requirement, the instability for high Reynolds number flow is investigated. We numerically demonstrate that fine mesh resolution and high polynomial order are required to obtain a robust system. With these conclusions, discontinuous Galerkin–continuous Galerkin method is able to achieve high-order spatial convergence rate and accurately simulate high Reynolds flow. The solver is tested through a series of classical benchmark problems, and efficiency improvement is proved against pure discontinuous Galerkin scheme.

Misexpression of chromatin modification factors and changed epigenetic modifications play crucial roles for tumorigenesis. Our previous studies demonstrated that inhibition of epigenetic modification enzymes EZH2, LSD1, DNMTs, and HDACs caused post-mitotic neuron-like differentiation in different cancer cells. However, how they regulate neuronal differentiation in cancer cells was unknown. Here, we show that EZH2, LSD1, DNMT1, and HDAC1 form interactions themselves, meanwhile, they also interact with SMAD proteins and β-CATENIN in cancer cells. Chemical inhibition of these enzymes leads to reduced level of proteins except HDAC1. The change in protein level and/or enzymatic activities further result in changed chromatin modifications on neuronal gene promoters, and activation of neuronal genes. Inhibition of these enzymes in neural progenitor cells (NPCs) also caused neuronal differentiation, similar to cancer cells. Particularly, EZH2 interacts with and required for the stability of LSD1, HDAC1, DNMT1, β-CATENIN, or SMAD2/4, via recruitment of deubiquitinase USP7. Reduced EZH2 leads to enhanced ubiquitination and degradation of these proteins, and decreased binding of LSD1, HDAC1, and DNMT1 to neuronal gene promoters, and lessened Wnt and TGFβ target gene activation. Hence, EZH2 sustains a series of proteins that promote tumorigenesis, in addition to its original function of histone methylation. Considering together with other studies, we conclude that these chromatin modification factors function in the same way in cancer cells as in neural progenitor/stem cells. The similarity between cancer cells and neural progenitor/stem cells provides an insight into the essence and unified framework for cancer initiation and progression, and are suggestive for novel strategies of cancer therapy.

The pressure equation, generated while solving the incompressible Navier−Stokes equations with the segregated iterative algorithm such as PISO, produces a series of linear equation systems as the time step advances. In this paper, we target at accelerating the iterative solution of these linear systems by improving their initial guesses. We propose a weighted group extrapolation method to obtain a superior initial guess instead of a general one, the solution of the previous linear equation system. In this method, the previous solutions that are used to extrapolate the predicted solutions are carefully organized to address the oscillatory solution on each grid. The proposed method uses a weighted average of the predicted solutions as the new initial guess to avoid over extrapolating. Three numerical test results show that the proposed method can accelerate the iterative solution of most linear equation systems and reduce the simulation time up to 61.3%.

Autonomous Underwater Vehicle (AUV) has become a hotspot in the field of robot in recent years. As a special kind of AUV, the robotic fish can achieve better propulsion efficiency and maneuverability than traditional AUVs. Studies show that robotic fish formation can save energy and perform more complex tasks than single robotic fish, but it is difficult to maintain a stable formation because the nearby environmental condition is hard to obtain. Inspired by the lateral line system (LLS) of fish, this paper constructs a predictive model of flow velocity and a judgement model of spacing between individual platforms for robotic fish formation through monitoring sensors on robotic fish surface. The models are built by methods of polynomial fitting and neural networks based on Computational Fluid Dynamics (CFD) simulation. The results show that the flow velocity predicted by our model could reduce the error to 0.4 % , and the spacing judgement accuracy could reach at least 80%. The findings are useful for maintaining a stable formation and will provide significant guidance for the control of robotic fish formation and sensor installation position on the robotic fish surface.

Artificial fish-like robot is an important branch of underwater robot research. At present, most of fish-like robot research focuses on single robot mechanism behavior, some research pays attention to the influence of the hydro-environment on robot crowds but does not reach a unified conclusion on the efficiency of fish-like robots swarm. In this work, the fish-like robots swarm is studied by numerical simulation. Four different formations, including the tandem, the phalanx, the diamond, and the rectangle are conducted by changing the spacing between fishes. The results show that at close spacing, the fish in the back can obtain a large wake from the front fish, but suffers large lateral power loss from the lateral fish. On the contrary, when the spacing is large, both the wake and pressure caused by the front and side fishes become small. In terms of the average swimming efficiency of fish swarms, we find that when the fish spacing is less than 1.25 L (L is the length of the fish body), the tandem swarm is the best choice. When the spacing is 1.25 L , the tandem, diamond and rectangle swarms have similar efficiency. When the spacing is larger than 1.25 L , the rectangle swarm is more efficient than other formations. The findings will provide significant guidance for the control of fish-like robots swarm.