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In this paper, neural network is used as a predictive network modeling method, with the support of MATLAB Neural Toolbox, based on the implementation of predictive research. A risk warning model is designed for sports events relying on neural network s to reduce the losses caused by risk accidents. First, the article introduces a literature review of sports event risk warning, combined with the sports event risk warning index system; summarizes the main advantages of using neural network and fuzzy theory; and establishes a sports event risk warning model relied on neural network. The article starts with the application of gray network in sports risk warning design, starting from the necessity of applying gray network in sports event risk warning; analyzes the risk warning model and operation process; and conducts sample data verification to verify this power of the model. Practice has proved that the application of gray neural network in sports events can play a role in risk warning.

Data-driven constitutive models, owing to their inherent flexibility, can outperform traditional plasticity-based models in certain aspects. When calibrating these models, ensuring adherence to fundamental mechanical principles allows the calibrated models, referred to as physics-encoded neural networks (PeNNs), to be effectively integrated into finite element method (FEM) software for boundary value problem simulations. However, calibration challenges arise when only limited data are available. Addressing this issue, this study employs transfer learning. Synthetic labeled data, derived from traditional constitutive models were used to pre-train PeNNs. Subsequently, these pre-trained PeNNs are fine-tuned using implicitly labeled data from high-fidelity experimental records. The fine-tuned models are integrated into FEM software as user materials to conduct extensive drained and undrained triaxial test simulations. An analysis of the simulation results highlights the impact of the available volume of experimental data, the quantity of synthetic data, and key configurations in the fine-tuning process, such as the architecture of the fine-tuning model, frozen parameters, and batch size. Results indicate that through robust PeNN models and meticulous modeling, transfer learning can establish a data-driven constitutive model with limited experimental records, achieving superior simulation performance compared to the synthetic model alone. This underscores the potential of combining cost-effective synthetic and experimental data to advance constitutive modeling.

Concerns regarding food security arise from population growth, global warming, and reduction in arable land. With advances in synthetic biology, food production by microbes is considered to be a promising alternative that would allow rapid food production in an environmentally friendly manner. Moreover, synthetic biology can be adopted to the production of healthier or specifically designed food ingredients (e.g., high-value proteins, lipids, and vitamins) and broaden the utilization of feedstocks (e.g., methanol and CO2), thereby offering potential solutions to high-quality food and the greenhouse effect. We first present how synthetic biology can facilitate the microbial production of various food components, and then discuss feedstock availability enabled by synthetic biology. Finally, we illustrate trends and key challenges in synthetic biology-driven food production. Microbially synthesized food can address challenges in global food security and deliver high-quality food products in an environmentally friendly manner.Synthetic biology is emerging as a powerful approach for engineering microbes to produce macronutrient and micronutrient compounds in food.Recent achievements in synthetic biology have enabled microbes to produce healthier or specifically designed food ingredients.Microbial fermentation from nonfood feedstocks offers the opportunity to alleviate economic, ecologic, and societal problems by recycling resources and greenhouse gases.

The next few years will be exciting as prototype universal quantum processors emerge, enabling the implementation of a wider variety of algorithms. Of particular interest are quantum heuristics, which require experimentation on quantum hardware for their evaluation and which have the potential to significantly expand the breadth of applications for which quantum computers have an established advantage. A leading candidate is Farhi et al.’s quantum approximate optimization algorithm, which alternates between applying a cost function based Hamiltonian and a mixing Hamiltonian. Here, we extend this framework to allow alternation between more general families of operators. The essence of this extension, the quantum alternating operator ansatz, is the consideration of general parameterized families of unitaries rather than only those corresponding to the time evolution under a fixed local Hamiltonian for a time specified by the parameter. This ansatz supports the representation of a larger, and potentially more useful, set of states than the original formulation, with potential long-term impact on a broad array of application areas. For cases that call for mixing only within a desired subspace, refocusing on unitaries rather than Hamiltonians enables more efficiently implementable mixers than was possible in the original framework. Such mixers are particularly useful for optimization problems with hard constraints that must always be satisfied, defining a feasible subspace, and soft constraints whose violation we wish to minimize. More efficient implementation enables earlier experimental exploration of an alternating operator approach, in the spirit of the quantum approximate optimization algorithm, to a wide variety of approximate optimization, exact optimization, and sampling problems. In addition to introducing the quantum alternating operator ansatz, we lay out design criteria for mixing operators, detail mappings for eight problems, and provide a compendium with brief descriptions of mappings for a diverse array of problems.

Background Abundant research indicates that increased extracellular matrix (ECM) stiffness significantly enhances the malignant characteristics of hepatocellular carcinoma (HCC) cells. Plectin, an essential cytoskeletal linker protein, has recently emerged as a promoter of cancer progression, particularly in the context of cancer cell invasion and metastasis. However, the responsiveness of plectin to changes in ECM stiffness and its impact on HCC progression remain unclear. In this study, we aimed to investigate whether plectin responds to variations in ECM stiffness and to explore its involved molecular mechanisms in regulating HCC cell migration. Results Our results showed that, when compared with control group (7 kPa), high ECM stiffness (53 kPa) boosts HCC cell migration by upregulating plectin and integrin β1 expression and increasing F-actin polymerization. Knockdown of integrin β1 negated the high stiffness-upregulated plectin expression. Furthermore, reducing either plectin or integrin β1 levels, or using latrunculin A, effectively prevented the high ECM stiffness-induced F-actin polymerization and HCC cell migration. Conclusions These findings demonstrate that integrin β1-plectin-F-actin axis is necessary for high matrix stiffness-driven migration of HCC cells, and provide evidence for the critical role of plectin in mechanotransduction in HCC cells.

BackgroundLight is the ultimate energy source of plant photosynthesis, which has an important impact on the growth, development, physiology and biochemistry of tea plant. Photosensitive etiolated tea plant belongs to a kind of colored leaf plant, which is a physiological response to light intensity. Compared with conventional green bud and leaf of tea plant, the accumulation of pigment compounds (chlorophyll and carotenoids, etc.) closely related to a series of reactions of photosynthesis in photosensitive etiolated tea plant is reduced, resulting in the difference of leaf color of tea. This specific tea resource has high application value, among which high amino acid is one of its advantages. It can be used to process high-quality green tea with delicious taste and attractive aroma, which has been widely attention. The mechanism of the color presentation of the etiolated mutant tea leaves has been given a high topic and attention, especially, what changes have taken place in the pigment compounds of tea leaves caused by light, which makes the leaves so yellow. At present, there have been a lot of research and reports.Purpose of the reviewWe describe the metabolism and differential accumulation of key pigment compounds affecting the leaf color of photosensitive etiolated tea that are triggered by light, and discuss the different metabolism and key regulatory sites of these pigments in different light environments in order to understand the “discoloration” matrix and mechanism of etiolated tea resources, answer the scientific question between leaf color and light. It provides an important strategy for artificial intervention of discoloration of colored tea plant.ConclusionThe differential accumulation of pigment compounds in tea plant can be induced phytochrome in response to the change of light signal. The synthesis of chlorophyll in photoetiolated tea plants is hindered by strong light, among which, the sites regulated by coproporphyrinogen III oxidase and chlorophyllide a oxidase is sensitive to light and can be inhibited by strong light, resulting in the aggravation of leaf etiolation. The phenomenon can be disappeared or weakened by shading or reducing light intensity, and the leaf color is greenish, but the increase of chlorophyll-b accumulation is more than that of chlorophyll-a. The synthesis of carotenoids is inhibited strong light, and high the accumulation of carotenoids is reduced by shading. Most of the genes regulating carotenoids are up-regulated by moderate shading and down-regulated by excessive shading. Therefore, the accumulation of these two types of pigments in photosensitive etiolated tea plants is closely related to the light environment, and the leaf color phenotype shape of photosensitive etiolated tea plants can be changed by different light conditions, which provides an important strategy for the production and management of tea plant.

The development of small-scale quantum devices raises the question of how to fairly assess and detect quantum speedup. Here, we show how to define and measure quantum speedup and how to avoid pitfalls that might mask or fake such a speedup. We illustrate our discussion with data from tests run on a D-Wave Two device with up to 503 qubits. By using random spin glass instances as a benchmark, we found no evidence of quantum speedup when the entire data set is considered and obtained inconclusive results when comparing subsets of instances on an instance-by-instance basis. Our results do not rule out the possibility of speedup for other classes of problems and illustrate the subtle nature of the quantum speedup question.

'Huangguogan' (Citrus reticulata × C. sinensis) is a new cultivar of mandarin citrus in China, and the research on fertilization of 'Huangguogan' is very limited. In this study, the effect of N fertilization on 'Huangguogan' fruit quality was determined at ripening. Sugars (sucrose, fructose, and glucose), organic acids (pyruvic, oxalic, citric acid, etc.), and vitamin components were measured at six stages of fruit development, and eight enzymes related to the glycolytic and Krebs cycle were assessed. The 1.81 kg N y-1 treatment group showed the highest total soluble solids concentration and total soluble solids/titratable acidity ratio but the lowest titratable acidity (acid content) at ripening, while the N1 treatment (0 kg N y-1) showed the opposite trend. Sucrose and citric acid accumulated to the largest extent during fruit development. Sucrose and ascorbic acid content increased (8.46 to 50.97 mg g-1 and 8.16 to 27.39 mg g-1, respectively), while citric acid content decreased (90.81 to 0.02 mg g-1). Aconitase was the key enzyme responsible for the observed changes in citric acid. The N concentrations in ripening fruit ranged from 2.25% to 4.15%. Curve estimation and principal component analysis revealed that fruit N was positively correlated with the sugars and vitamin components and negatively correlated with the organic acids. The accumulation of these metabolites seemed closely related to the dynamic changes in fruit N concentration at the five N levels tested. In conclusion, we suggest that the 1.81 kg N y-1 treatment represents the most suitable N fertilizer treatment for 'Huangguogan' citrus fruit.

China’s population has rapidly aged over the recent decades of social and economic development as neurodegenerative disorders have proliferated, especially Alzheimer’s disease (AD) and related dementias (ADRD). AD’s incidence rate, morbidity, and mortality have steadily increased to make it presently the fifth leading cause of death among urban and rural residents in China and magnify the resulting financial burdens on individuals, families and society. The ‘Healthy China Action’ plan of 2019–2030 promotes the transition from disease treatment to health maintenance for this expanding population with ADRD. This report describes related epidemiological trends, evaluates the economic burden of the disease, outlines current clinical diagnosis and treatment status and delineates existing available public health resources. More specifically, it examines the public health impact of ADRD, including prevalence, mortality, costs, usage of care, and the overall effect on caregivers and society. In addition, this special report presents technical guidance and supports for the prevention and treatment of AD, provides expertise to guide relevant governmental healthcare policy development and suggests an information platform for international exchange and cooperation.

• Leaf mass per area (LMA) is a key plant trait, reflecting tradeoffs between leaf photosynthetic function, longevity, and structural investment. Capturing spatial and temporal variability in LMA has been a long-standing goal of ecological research and is an essential component for advancing Earth system models. Despite the substantial variation in LMA within and across Earth’s biomes, an efficient, globally generalizable approach to predict LMA is still lacking. • We explored the capacity to predict LMA from leaf spectra across much of the global LMA trait space, with values ranging from 17 to 393 gm–2. Our dataset contained leaves from a wide range of biomes from the high Arctic to the tropics, included broad- and needleleaf species, and upper- and lower-canopy (i.e. sun and shade) growth environments. • Here we demonstrate the capacity to rapidly estimate LMA using only spectral measurements across a wide range of species, leaf age and canopy position from diverse biomes. Our model captures LMA variability with high accuracy and low error (R² = 0.89; root mean square error (RMSE) = 15.45 gm–2). • Our finding highlights the fact that the leaf economics spectrum is mirrored by the leaf optical spectrum, paving the way for this technology to predict the diversity of LMA in ecosystems across global biomes.