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Over the past decade, there has been a growing emphasis on investigating the role of immunotherapy in cancer treatment. However, it faces challenges such as limited efficacy, a diminished response rate, and serious adverse effects. Nanozymes, a subset of nanomaterials, demonstrate boundless potential in cancer catalytic therapy for their tunable activity, enhanced stability, and cost-effectiveness. By selectively targeting the metabolic vulnerabilities of tumors, they can effectively intensify the destruction of tumor cells and promote the release of antigenic substances, thereby eliciting immune clearance responses and impeding tumor progression. Combined with other therapies, they synergistically enhance the efficacy of immunotherapy. Hence, a large number of metabolism-regulating nanozymes with synergistic immunotherapeutic effects have been developed. This review summarizes recent advancements in cancer immunotherapy facilitated by nanozymes, focusing on engineering nanozymes to potentiate antitumor immune responses by disturbing tumor metabolism and performing synergistic treatment. The challenges and prospects in this field are outlined. We aim to provide guidance for nanozyme-mediated immunotherapy and pave the way for achieving durable tumor eradication. [ABSTRACT FROM AUTHOR]

The Compact thermal neutron imaging systems have become an important development trend. Compact thermal neutron imaging systems can use neutron tubes as neutron sources because of their low cost, small size, and portability. In order to achieve optimal imaging quality, the moderator and collimator in a neutron imaging system based on neutron tubes source are designed and simulated using SuperMC. The entrance position of the collimator is determined by analyzing the performance of different materials with their parameter's optimization. It is suggested that a cylinder-shaped structure made of carbon, hydrogenous materials, and heavy metals can significantly increase the thermal neutron flux at the sample location. Further dimension optimization is carried out to improve the neutron beam collimation performance. A vertically divergent collimator with a divergence angle of 5° and an air gap is selected, along with the addition of a B4C lining material. According to the modelling results, the sample position can be as far away from the neutron source as 77 cm with a neutron source yield of 1.14 × 109 n/s. The collimation ratio is 29, the thermal neutron flux is 1.08 × 103 n/cm2·s, and n/γ is 1.44 × 1012 n/cm2·Sv, which is suitable for thermal neutron radiography. [ABSTRACT FROM AUTHOR]

The demand for high-power density motors has been increasing due to their remarkable output capability and compact construction. To achieve a significant improvement in motor power density, lightweight design methods have been recognized as an effective enabler. Therefore, extensive investigations have been conducted to reduce motor mass and achieve lightweight configurations through the exploration of lightweight materials, structures and manufacturing techniques. This article provides a comprehensive review and summary of state-of-the-art lightweight implementation methods for electrical machines, including the utilization of lightweight materials, structural lightweight design, and incorporation of advanced manufacturing technologies, such as additive manufacturing techniques. The advantages and limitations of each approach are also discussed in this paper. Furthermore, some comments and forecasts on potential future methodologies for motor lightweighting are also provided. [ABSTRACT FROM AUTHOR]

From the perspective of principal-agent, the theory of financial acceleration holds that due to the defects of the financial market, external shocks will be amplified by the financial market and accelerate the transmission in the real economy, and the impact on small enterprises is greater than that on large enterprises. According to the theory of financial deceleration, the agency cost caused by financial friction is counter-cyclical, which restrains credit scale to some extent, prevents excessive debt, and thus alleviates external shocks. According to the empirical analysis of panel data of iron and steel enterprises and the existing empirical results of the real estate industry, it is found that there is no financial accelerator effect or financial reducer effect in the field of iron and steel enterprises. China’s financial accelerator is more focused on bubbly assets where growth is expected and continues to be overheated despite policy tightening. That would trigger a bigger debt crisis and greater economic volatility.

Minor enterprises have made an indelible contribution to economic development, while financing has always been a problem hindering the sustainable development of them. Under the influence of the epidemic, the business environment of minor enterprises has changed, and the financing demand has also changed. However, the degree and scope of assistance are limited. There is a contradiction between supply and demand in fund allocation. Based on this, this project puts forward policy suggestions by studying the changes of financing demand and fund supplier of minor enterprises in Wuhan in the post epidemic era, which will help to solve the financing difficulties and promote healthy and sustainable development of the national economy.

Background: BRAF non-V600 mutation occupies a relatively small but critical subset in colorectal cancer (CRC). However, little is known about the biological functions and impacts of BRAF class III mutation in CRC. Here, we aim to explore how D594A mutation impacts on biological behaviors and immune related signatures in murine CRC cells. Methods: BRAF V600E (class I), G469V (class II) and D594A (class III) mutant cell lines were established based on MC38 cells. The biological behaviors of cells were evaluated in respect of cell growth, cell proliferation, cell apoptosis, cell migration and invasion by the methods of colony-forming assay, CCK-8 assay, Annexin V/PI staining and transwell assay. The concentrations of soluble cytokines were detected by ELISA. The membrane expression of immuno-modulatory molecules and the pattern of tumor infiltrating lymphocyte were evaluated by flow cytometry. The molecular mechanism was explored by RNA sequencing. Immunohistochemistry (IHC) staining was used for the detection of CD8α in tumor tissues. qRT-PCR and western blot were performed to assess the mRNA and protein expression. Anti-PD-L1 treatment and cytokines neutralization experiments were conducted in in vivo models. Results: D594A mutant cells displayed lower grade malignancy characteristics than V600E (class I) and G469V (class II) mutant cells. Meanwhile, D594A mutation led to evident immuno-modulatory features including upregulation of MHC Class I and PD-L1. In vivo experiments displayed that the frequency of infiltrated CD8+ T cells was significantly high within D594A mutant tumors, which may provide potential response to anti-PD-L1 therapy. RNA sequencing analysis showed that D594A mutation led to enhanced expression of ATF3 and THBS1, which thus facilitated CXCL9 and CXCL10 production upon IFN-γ treatment. In addition, CXCL9 or CXCL10 neutralization reduced the infiltration of CD8+ T cells into THBS1-overexpressing tumors. Conclusions: D594A mutant CRC exhibited lower aggressiveness and immune-activated phenotype. ATF3-THBS1-CXCL9/CXCL10 axis mediated functional CD8+ T cells infiltration into the microenvironment of D594A mutant CRC. Our present study is helpful to define this mutation in CRC and provide important insights in designing effective immunotherapeutic strategies in clinic. [ABSTRACT FROM AUTHOR]

Nitrogen content is a crucial index for crop growth diagnosis and the exact estimation of nitrogen content is of great significance for grasping crop growth status in real-time. This paper takes winter wheat as the study object and the precision agriculture demonstration area of the Jiaozuo Academy of Agricultural and Forestry Sciences in Henan Province as the research area. The hyperspectral reflectance data of the wheat canopy in different growth periods are obtained with the ASD ground object hyperspectral instrument, and the original canopy spectral data are preprocessed by fractional differential and continuous wavelet transform; then, the vegetation indices are established, correlation analysis with nitrogen content is conducted, and the fractional differential spectra are selected; finally, based on the wavelet energy coefficient and the vegetation indices with strong correlations, the methods of support vector machine (SVM), ridge regression, stepwise regression, Gaussian process regression (GPR), and the BP neural network are used to construct the estimation model for nitrogen content in wheat at different growth stages. By adopting the  R 2  and root mean square error (RMSE) indices, the best nitrogen content estimation model at every growth stage is selected. The overall analysis of the nitrogen content estimation effect indicated that for the four growth periods, the maximum modeling and validation  R 2  of the nitrogen content estimation models of the SVM, ridge regression, stepwise regression, GPR, and BP neural network models reached 0.95 and 0.93, the average reached 0.76 and 0.71, and the overall estimation effect was good. The average values of the modeling and validation  R 2  of the nitrogen content estimation model at the flag picking stage were 0.85 and 0.81, respectively, which were 37.10% and 44.64%, 1.19% and 3.85%, and 14.86% and 17.39% higher than those at the jointing stage, flowering stage, and filling stage, respectively. Therefore, the model of the flag picking stage has higher estimation accuracy and a better estimation effect on the nitrogen content. For the different growth stages, the optimal estimation models of nitrogen content were different. Among them, continuous wavelet transform combined with the BP neural network model can be the most effective method for estimating the N content in wheat at the flagging stage. The paper provides an effective method for estimating the nitrogen content in wheat and a new idea for crop growth monitoring. [ABSTRACT FROM AUTHOR]

Leaf water content (LWC) is one of the important indicators of crop health. It plays an important role in the physiological process of leaves, participates in almost all physiological processes of crops, and is of great significance to the survival and growth of crops. Based on the hyperspectral (350–1350 nm) and LWC data (jointing, booting, flowering, and filling periods) of winter wheat in 2020 and 2021, this work proposed to transform and process the hyperspectral data by adopting fractional order differential and continuous wavelet transform, and took a differential spectrum, wavelet coefficients, and mixed variables (differential spectrum and wavelet coefficients) as input variables of the model and adopted Gaussian process regression (GPR), classification and regression decision tree (CART), and artificial neural network (ANN) methods to estimate the LWC of wheat in different growth periods. The results indicated that fractional differential and continuous wavelet transform could highlight the spectral characteristics of winter wheat canopy and improve its correlation with LWC. The three model variables had the best estimation effect on LWC in the flowering period, and the average values of R2 were 0.86 and 0.87 in modeling and verification, which indicated that the flowering period could be used as the best estimation period for LWC. Compared with the differential spectrum and wavelet coefficients, LWC estimation based on mixed variables performed best. The average values of R2 in modeling and verification were 0.78 and 0.79. Among them, the ANN model had the highest estimation accuracy, and the R2 in modeling and verification could reach 0.92 and 0.91. This showed that fractional differential and continuous wavelet transform could effectively promote the sensitivity of spectral information to LWC and enhance the prediction ability and stability of wheat LWC. The outcomes of the present study have the potential to provide new ideas for the water monitoring of crops. [ABSTRACT FROM AUTHOR]

Optoelectronic synapses are considered to be important cornerstones in the construction of neuromorphic computing systems because of their low power consumption, high operating speeds, and high scalability. In this work, we demonstrate an optoelectronic synaptic device based on a ZnO/HfOx heterojunction in which optical potentiation/electrical depression behaviors and nonvolatile high current state can be implemented. The heterojunction device exhibits conductance evolution with high linearity. The excellent optoelectronic memristive behavior of the device can be attributed to the interface barrier between ZnO and HfOx, which hinders the recombination of photo-excited electron–hole pairs to increase the carrier lifetime, and realizes the nonvolatile high current state. More importantly, the artificial vision system based on optoelectronic synaptic devices can achieved a high recognition accuracy of 96.1%. Our work provides a feasible pathway toward the development of optoelectronic synaptic devices for use in high-performance neuromorphic vision systems. [ABSTRACT FROM AUTHOR]

An efficient access to a large array of diarylmethane thioether derivatives is developed through catalyst‐free 1,6‐conjugate addition of 1,4‐dithiane‐2,5‐diols with para‐quinone methides in high to excellent yields. Moreover, this reaction system is also suitable for other sulfur‐based nucleophiles, and the resulting products can be readily converted into other valuable diarylmethane derivatives. [ABSTRACT FROM AUTHOR]

The existing semantic segmentation methods have some shortcomings in feature extraction of remote sensing images. Therefore, an image semantic segmentation method based on deep learning in UAV aerial remote sensing images is proposed. First, original remote sensing images obtained by S185 multirotor UAV are divided into smaller image blocks through sliding window and normalized to provide high-quality image set for subsequent operations. Then, the symmetric encoding-decoding network structure is improved. Bottleneck layer with 1 × 1 convolution is introduced to build ISegNet network model, and pooling index and convolution are used to fuse semantic information and image features. The improved encoding-decoding network gradually strengthens the extraction of details and reduces the number of parameters. Finally, based on ISegNet network, five-classification problem is transformed into five binary classification problems for network training, so as to obtain high-precision image semantic segmentation results. The experimental analysis of the proposed method based on TensorFlow framework shows that the accuracy value reaches 0.901, and the F1 value is not less than 0.83. The overall segmentation effect is better than those of other comparison methods. [ABSTRACT FROM AUTHOR]

Exploration of optoelectronic memristors with the capability to combine sensing and processing functions is required to promote development of efficient neuromorphic vision. In this work, the authors develop a plasmonic optoelectronic memristor that relies on the effects of localized surface plasmon resonance (LSPR) and optical excitation in an Ag–TiO2 nanocomposite film. Fully light‐induced synaptic plasticity (e.g., potentiation and depression) under visible and ultraviolet light stimulations is demonstrated, which enables the functional combination of visual sensing and low‐level image pre‐processing (including contrast enhancement and noise reduction) in a single device. Furthermore, the light‐gated and electrically‐driven synaptic plasticity can be performed in the same device, in which the spike‐timing‐dependent plasticity (STDP) learning functions can be reversibly modulated by visible and ultraviolet light illuminations. Thereby, the high‐level image processing function, i.e., image recognition, can also be performed in this memristor, whose recognition rate and accuracy are obviously enhanced as a result of image pre‐processing and light‐gated STDP enhancement. Experimental analysis shows that the memristive switching mechanism under optical stimulation can be attributed to the oxidation/reduction of Ag nanoparticles due to the effects of LSPR and optical excitation. The authors' work proposes a new type of plasmonic optoelectronic memristor with fully light‐modulated capability that may promote the future development of efficient neuromorphic vision. [ABSTRACT FROM AUTHOR]

Chronic neuroinflammation has been shown to exert adverse influences on the pathology of Alzheimer's disease (AD), associated with the release of abundant proinflammatory mediators by excessively activated microglia, causing synaptic dysfunction, neuronal degeneration, and memory deficits. Thus, the prevention of microglial activation‐associated neuroinflammation is important target for deterring neurodegenerative disorders. Peony seed oil (PSO) is a new food resource, rich in α‐linolenic acid, the precursor of long chain omega‐3 polyunsaturated fatty acids, including docosahexaenoic acid and eicosapentaenoic acid, which exhibit anti‐inflammatory properties by altering cell membrane phospholipid fatty acid compositions, disrupting lipid rafts, and inhibiting the activation of the proinflammatory transcription factor NF‐κB. However, few studies have examined the anti‐neuroinflammatory effects of PSO in AD, and the relevant molecular mechanisms remain unclear. Presenilin1/2 conditional double knockout (PS cDKO) mice display obvious AD‐like phenotypes, such as neuroinflammatory responses, synaptic dysfunction, and cognitive deficits. Here, we assessed the potential neuroprotective effects of PSO against neuroinflammation‐mediated cognitive deficits in PS cDKO using behavioral tests and molecular biologic analyses. Our study demonstrated that PSO suppressed microglial activation and neuroinflammation through the down‐regulation of proinflammatory mediators, such as inducible NOS, COX‐2, IL‐1β, and TNF‐α, in the prefrontal cortex and hippocampus of PS cDKO mice. Further, PSO significantly lessened memory impairment by reversing hyperphosphorylated tau and synaptic proteins deficits in PS cDKO mice. Importantly, PSO's therapeutic effects on cognitive deficits were due to inhibiting neuroinflammatory responses mediated by NF‐κB signaling pathway. Taken together, PSO may represent an effective dietary supplementation to restrain the neurodegenerative processes of AD. [ABSTRACT FROM AUTHOR]

The no‐reference image quality assessment (NR‐IQA) method can evaluate the distortions in an image without the reference image. However, due to the diversity of the image contents and distortion types, it is hard for the existing NR‐IQA algorithms to obtain competitive performance on both synthetically and authentically distorted images. To address the problem, a multiscale feature representation‐based NR‐IQA method that performs well for both synthetic and authentic distortions is proposed. This model consists of two parts: The feature extraction part and the feature fusion part. First, part of the Res2Net‐50 network is chosen as the feature extraction part due to its high ability in increasing the range of receptive fields. Then, the feature fusion part consisting of a novel residual block and two fully connected layers is designed to fuse the extracted features and realize the quality score mapping. After a series of stepwise optimization experiments, the most competitive network architecture consisting of the feature extraction part and the feature fusion part is obtained. Comprehensive experiments on the LIVE, TID2013, CSIQ, KADID‐10k, KonIQ‐10K, and LIVE challenge databases demonstrate that the proposed method can work powerfully on both the synthetic and authentic distortions and also has a strong generalization ability. [ABSTRACT FROM AUTHOR]