Explore the vast range of titles available.

Reconstruction of catalysts by reaction environments represents a viable approach to create highly performed active sites. Herein, we develop a reaction-induced regioselective reconstruction of Ni-doped Ce(OH) 3 /CeO 2 nanorods to form dual-active sites composed of carburized Ni clusters and frustrated Lewis pairs (FLPs), delivering exceptional activity, selectivity and stability for reverse water-gas shift reaction. Ni aggregation in the Ce(OH) 3 region, coupled with in-situ carbonization by catalytically generated CO during reaction, induces the formation of the carburized Ni clusters, which effectively promoted H 2 dissociation. Additionally, Ni doping in the CeO 2 region and Ce(OH) 3 -to-CeO 2 phase transition introduce more oxygen vacancies and thereby generated FLPs in CeO 2 , which facilitate CO 2 adsorption and subsequent hydrogenation by spilled *H species from the carburized Ni clusters. Weak CO adsorption on both the carburized Ni clusters and FLPs significantly suppresses the methanation side-reaction. This reaction-induced regioselective reconstruction strategy provides a new avenue for designing highly performed catalysts. Catalyst reconstruction via reaction environment is promising but complex. Here, Ni-doped Ce-based nanorods undergo regioselective reconstruction into dual-active carburized Ni clusters and frustrated Lewis pairs, enhancing CO₂ conversion efficiency.

Accurate power load forecasting is crucial for ensuring grid stability, optimizing economic dispatch, and facilitating renewable energy integration in modern smart grids. However, real load forecasting is often disturbed by the inherent non-stationarity and multi-factor coupling effects. To address this problem, a novel hybrid forecasting framework based on adaptive mode decomposition (AMD) and improved least squares support vector machine (ILSSVM) is proposed for effective short-term power load forecasting. First, AMD is utilized to obtain multiple components of the power load signal. In AMD, the minimum energy loss is used to adjust the decomposition parameter adaptively, which can effectively decrease the risk of generating spurious modes and losing critical load components. Then, the ILSSVM is presented to predict different power load components, separately. Different frequency features are effectively extracted by using the proposed combination kernel structure, which can achieve the balance of learning capacity and generalization capacity for each unique load component. Further, an optimized genetic algorithm is deployed to optimize model parameters in ILSSVM by integrating the adaptive genetic algorithm and simulated annealing to improve load forecasting accuracy. The real short-term power load dataset is collected from Guangxi region in China to test the proposed forecasting framework. Extensive experiments are carried out and the results demonstrate that our framework achieves an MAPE of 1.78%, which outperforms some other advanced forecasting models.

Background South and Southeast Asia face severe malnutrition in children under five, with coexisting forms of malnutrition (CFM) exacerbating mortality risks and posing greater challenges than isolated forms of malnutrition. We aimed to assess the prevalence, trends, and factors of CFM among children aged 6–59 months in South and Southeast Asian countries and the entire region. Methods We used anthropometric and hemoglobin data from 515,170 children aged 6–59 months in seven low- and middle-income South and Southeast Asian countries, based on Demographic and Health Surveys conducted between 1996 and 2022. Weighted multivariable logistic regression was performed to identify the sociodemographic factors associated with seven forms of CFM: coexistence of underweight with wasting, coexistence of underweight with stunting, coexistence of underweight with both wasting and stunting, coexistence of stunting with overweight/obesity (CSO), coexistence of anemia with overweight/obesity, coexistence of anemia with underweight, and coexistence of anemia with stunting (CAS). Results The overall pooled prevalence of child CFM ranged from 0.8% for CSO to 23.4% for CAS. Most countries showed a declining trend in CFM, except for Timor-Leste, India, and the Maldives. Higher maternal education and being male were associated with lower odds of CFM. Compared to children aged 6–23 months, those aged 24–59 months had a higher risk of CUS but a lower risk of CSO and CAO. Children in India had higher odds of experiencing CAU and CAS compared to those in the Maldives, Myanmar, Nepal, and Timor-Leste. Conclusion The coexistence of undernutrition and/or anemia in South and Southeast Asia remains a public health problem. Although the prevalence of child CFM has decreased in most countries, it remains higher in Timor-Leste and India. It is necessary to consider multi-faceted nutritional interventions for children with CFM in this region, taking into account the impact of children’s gender, age, and maternal education, to further reduce child CFM.

While deep learning techniques have been extensively employed in malware detection, there is a notable challenge in effectively embedding malware features. Current neural network methods primarily capture superficial characteristics, lacking in-depth semantic exploration of functions and failing to preserve structural information at the file level. Motivated by the aforementioned challenges, this paper introduces MalHAPGNN, a novel framework for malware detection that leverages a hierarchical attention pooling graph neural network based on enhanced call graphs. Firstly, to ensure semantic richness, a Bidirectional Encoder Representations from Transformers-based (BERT) attribute-enhanced function embedding method is proposed for the extraction of node attributes in the function call graph. Subsequently, this work designs a hierarchical graph neural network that integrates attention mechanisms and pooling operations, complemented by function node sampling and structural learning strategies. This framework delivers a comprehensive profile of malicious code across semantic, syntactic, and structural dimensions. Extensive experiments conducted on the Kaggle and VirusShare datasets have demonstrated that the proposed framework outperforms other graph neural network (GNN)-based malware detection methods.

Image inpainting approaches have made considerable progress with the assistance of generative adversarial networks (GANs) recently. However, current inpainting methods are incompetent in handling the cases with large masks and they generally suffer from unreasonable structure. We find that the main reason is the lack of an effective receptive field in the inpainting network. To alleviate this issue, we propose a new two-stage inpainting model called MD-GAN, which is a multi-scale diverse GAN. We inject dense combinations of dilated convolutions in multiple scales of inpainting networks to obtain more effective receptive fields. In fact, the result of inpainting large masks is generally not uniquely deterministic. To this end, we newly propose the multi-scale probabilistic diverse module, which achieves diverse content generation by spatial-adaptive normalization. Meanwhile, the convolutional block attention module is introduced to improve the ability to extract complex features. Perceptual diversity loss is added to enhance diversity. Extensive experiments on benchmark datasets including CelebA-HQ, Places2 and Paris Street View demonstrate that our approach is able to effectively inpaint diverse and structurally reasonable images.

Radiation-induced lung injury (RILI) is one of the most common and fatal complications of thoracic radiotherapy, whereas no effective interventions are available. Andrographolide, an active component extracted from Andrographis paniculate , is prescribed as a treatment for upper respiratory tract infection. Here we report the potential radioprotective effect and mechanism of Andrographolide on RILI. C57BL/6 mice were exposed to 18 Gy of whole thorax irradiation, followed by intraperitoneal injection of Andrographolide every other day for 4 weeks. Andrographolide significantly ameliorated radiation-induced lung tissue damage, inflammatory cell infiltration, and pro-inflammatory cytokine release in the early phase and progressive fibrosis in the late phase. Moreover, Andrographolide markedly hampered radiation-induced activation of the AIM2 inflammasome and pyroptosis in vivo. Furthermore, bone marrow-derived macrophages (BMDMs) were exposed to 8 Gy of X-ray radiation in vitro and Andrographolide significantly inhibited AIM2 inflammasome mediated-pyroptosis in BMDMs. Mechanistically, Andrographolide effectively prevented AIM2 from translocating into the nucleus to sense DNA damage induced by radiation or chemotherapeutic agents in BMDMs. Taken together, Andrographolide ameliorates RILI by suppressing AIM2 inflammasome mediated-pyroptosis in macrophage, identifying Andrographolide as a novel potential protective agent for RILI.

Most deep-learning-based object detection algorithms exhibit low speeds and accuracy in gear surface defect detection due to their high computational costs and complex structures. To solve this problem, a lightweight model for gear surface defect detection, namely STMS-YOLOv5, is proposed in this paper. Firstly, the ShuffleNetv2 module is employed as the backbone to reduce the giga floating-point operations per second and the number of parameters. Secondly, transposed convolution upsampling is used to enhance the learning capability of the network. Thirdly, the max efficient channel attention mechanism is embedded in the neck to compensate for the accuracy loss caused by the lightweight backbone. Finally, the SIOU_Loss is adopted as the bounding box regression loss function in the prediction part to speed up the model convergence. Experiments show that STMS-YOLOv5 achieves frames per second of 130.4 and 133.5 on the gear and NEU-DET steel surface defect datasets, respectively. The number of parameters and GFLOPs are reduced by 44.4% and 50.31%, respectively, while the mAP@0.5 reaches 98.6% and 73.5%, respectively. Extensive ablation and comparative experiments validate the effectiveness and generalization capability of the model in industrial defect detection.

Developing an energy-efficient process to convert chemically inert CO 2 to methanol is of great significance in sustainable chemistry. Herein, we report an indirect pathway for methanol synthesis below 100 °C, utilizing CO 2 -derived dimethyl carbonate (DMC) as a bridging molecule. By engineering oxygen vacancies in In 2 O 3 , we construct a Lewis acidic combination of In 5 sites and In 4 …In 4 ּ pairs that efficiently activate H 2 and DMC, respectively. The spatial intimacy of In 5 and In 4 …In 4 enables efficient transfer of generated *H, achieving a methanol generation rate of 31.6 mmol ּ g cat -1 h -1 with >99.99% selectivity at 100 °C. Integrating DMC synthesis from CO 2 with subsequent hydrogenation in a single reactor via alternating feedstreams from CO 2 to H 2 , the optimized In 2 O 3 catalysts yield a methanol production rate of 5.2 mmol ּ g cat -1 h -1 at 100 °C, outperforming the performance of previous catalysts through direct CO 2 hydrogenation even at temperatures over 200 °C. Developing an energy-efficient method to transform the chemically inert CO₂ into methanol is a key goal in sustainable chemistry. Here, the authors introduce an indirect route for methanol production at temperatures below 100 °C, using CO₂-derived dimethyl carbonate as an intermediate molecule.

Background Parkinson's disease (PD), characterized by the progressive loss of dopaminergic neurons in the substantia nigra and striatum of brain, seriously threatens human health, and is still lack of effective treatment. Dysregulation of N6-methyladenosine (m6A) modification has been implicated in PD pathogenesis. However, how m6A modification regulates dopaminergic neuronal death in PD remains elusive. Mesenchymal stem cell-derived exosomes (MSC-Exo) have been shown to be effective for treating central nervous disorders. We thus propose that the m6A demethylase FTO-targeted siRNAs (si-FTO) may be encapsulated in MSC-Exo (Exo-siFTO) as a synergistic therapy against dopaminergic neuronal death in PD. Methods In this study, the effect of m6A demethylase FTO on dopaminergic neuronal death was evaluated both in vivo and in vitro using a MPTP-treated mice model and a MPP + -induced MN9D cellular model, respectively. The mechanism through which FTO influences dopaminergic neuronal death in PD was investigated with qRT-PCR, western blot, immumohistochemical staining, immunofluorescent staining and flow cytometry. The therapeutic roles of MSC-Exo containing si-FTO were examined in PD models in vivo and in vitro. Results The total m6A level was significantly decreased and FTO expression was increased in PD models in vivo and in vitro. FTO was found to promote the expression of cellular death-related factor ataxia telangiectasia mutated (ATM) via m6A-dependent stabilization of ATM mRNA in dopaminergic neurons. Knockdown of FTO by si-FTO concomitantly suppressed upregulation of α-Synuclein (α-Syn) and downregulation of tyrosine hydroxylase (TH), and alleviated neuronal death in PD models. Moreover, MSC-Exo were utilized to successfully deliver si-FTO to the striatum of animal brain, resulting in the significant suppression of α-Syn expression and dopaminergic neuronal death, and recovery of TH expression in the brain of PD mice. Conclusions MSC-Exo delivery of si-FTO synergistically alleviates dopaminergic neuronal death in PD via m6A-dependent regulation of ATM mRNA.

Aberrant activation of NLRP3 inflammasome has an important function in the pathogenesis of various inflammatory diseases. Although many components and mediators of inflammasome activation have been identified, how NLRP3 inflammasome is regulated to prevent excessive inflammation is unclear. Here we show NLRP3 inflammasome stimulators trigger Src homology-2 domain containing protein tyrosine phosphatase-2 (SHP2) translocation to the mitochondria, to interact with and dephosphorylate adenine nucleotide translocase 1 (ANT1), a central molecule controlling mitochondrial permeability transition. This mechanism prevents collapse of mitochondrial membrane potential and the subsequent release of mitochondrial DNA and reactive oxygen species, thus preventing hyperactivation of NLRP3 inflammasome. Ablation or inhibition of SHP2 in macrophages causes intensified NLRP3 activation, overproduction of proinflammatory cytokines IL-1β and IL-18, and increased sensitivity to peritonitis. Collectively, our data highlight that, by inhibiting ANT1 and mitochondrial dysfunction, SHP2 orchestrates an intrinsic regulatory loop to limit excessive NLRP3 inflammasome activation. The NLRP3 inflammasome is central to a variety of inflammatory diseases, but how it is regulated to prevent excessive inflammation is not clear. Here the authors show that NLRP3 activation causes SHP2 translocation to the mitochondria to interact with and dephosphorylate ANT1, thus stabilizing the mitochondria and preventing release of proinflammatory mitochondrial DNA and ROS.