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The digital economy is an important engine to promote sustainable economic growth. Exploring the mechanism by which the digital economy promotes economic development, industrial upgrading and environmental improvement is an issue worth studying. This paper takes China as an example for study and uses the data of 286 cities from 2011 to 2019. In the empirical analysis, the direction distance function (DDF) and the Global Malmquist-Luenberger (GML) productivity index methods are used to measure the green total factor productivity (GTFP), while Tobit, quantile regression, impulse response function and intermediary effect models are used to study the relationship among digital economy development, industrial structure upgrading and GTFP. The results show that: (1) The digital economy can significantly improve China’s GTFP; however, there are clear regional differences. (2) The higher the GTFP, the greater the promotion effect of the digital economy on the city’s GTFP. (3) From a dynamic long-term perspective, the digital economy has indeed positively promoted China’s GTFP. (4) The upgrading of industrial structures is an intermediary transmission mechanism for the digital economy to promote GTFP. This paper provides a good reference for driving green economic growth and promoting the environment.

Intelligent process control and automation systems require verification authentication through digital or handwritten signatures. Digital copies of handwritten signatures have different pixel intensities and spatial variations due to the factors of the surface, writing object, etc. On the verge of this fluctuating drawback for control systems, this manuscript introduces a Spatial Variation-dependent Verification (SVV) scheme using textural features (TF). The handwritten and digital signatures are first verified for their pixel intensities for identification point detection. This identification point varies with the signature’s pattern, region, and texture. The identified point is spatially mapped with the digital signature for verifying the textural feature matching. The textural features are extracted between two successive identification points to prevent cumulative false positives. A convolution neural network aids this process for layered analysis. The first layer is responsible for generating new identification points, and the second layer is responsible for selecting the maximum matching feature for varying intensity. This is non-recurrent for the different textures exhibited as the false factor cuts down the iterated verification. Therefore, the maximum matching features are used for verifying the signatures without high false positives. The proposed scheme’s performance is verified using accuracy, precision, texture detection, false positives, and verification time.

Lysine crotonylation has been discovered in histone and non-histone proteins and found to be involved in diverse diseases and biological processes, such as neuropsychiatric disease, carcinogenesis, spermatogenesis, tissue injury, and inflammation. The unique carbon–carbon π-bond structure indicates that lysine crotonylation may use distinct regulatory mechanisms from the widely studied other types of lysine acylation. In this review, we discussed the regulation of lysine crotonylation by enzymatic and non-enzymatic mechanisms, the recognition of substrate proteins, the physiological functions of lysine crotonylation and its cross-talk with other types of modification. The tools and methods for prediction and detection of lysine crotonylation were also described.

riboCIRC is a translatome data-oriented circRNA database specifically designed for hosting, exploring, analyzing, and visualizing translatable circRNAs from multi-species. The database provides a comprehensive repository of computationally predicted ribosome-associated circRNAs; a manually curated collection of experimentally verified translated circRNAs; an evaluation of cross-species conservation of translatable circRNAs; a systematic de novo annotation of putative circRNA-encoded peptides, including sequence, structure, and function; and a genome browser to visualize the context-specific occupant footprints of circRNAs. It represents a valuable resource for the circRNA research community and is publicly available at http://www.ribocirc.com .

Proton-exchange-membrane fuel cells demand highly efficient catalysts for the oxygen reduction reaction, and core-shell structures are known for maximizing precious metal utilization. Here, we reported a controllable “carbon defect anchoring” strategy to prepare Pt 1 Ni 1 @Pt/C core-shell nanoparticles with an average size of ~2.6 nm on an in-situ transformed defective carbon support. The strong Pt–C interaction effectively inhibits nanoparticle migration or aggregation, even after undergoing stability tests over 70,000 potential cycles, resulting in only 1.6% degradation. The stable Pt 1 Ni 1 @Pt/C catalysts have high oxygen reduction reaction mass activity and specific activity that reach 1.424 ± 0.019 A/mg Pt and 1.554 ± 0.027 mA/cm Pt 2 at 0.9 V, respectively, attributed to the optimal compressive strain. The experimental results are generally consistent with the theoretical predictions made by our comprehensive microkinetic model which incorporates essential kinetics and thermodynamics of oxygen reduction reaction. The consistent results obtained in our study provide compelling evidence for the high accuracy and reliability of our model. This work highlights the synergy between theory-guided catalyst design and appropriate synthetic methodologies to translate the theory into practice, offering valuable insights for future catalyst development. Efficient and stable catalysts for oxygen reduction reactions are difficult to achieve due to slow kinetics and degradation. Here, the authors use a ‘carbon defect anchoring’ strategy to create Pt 1 Ni 1 @Pt core-shell nanoparticles with high activity and durability.

The phenylpropanoid pathway remains a key target for most climate-resilient crop development, owing to it being a precursor to over 8000 metabolites, including flavonoids and lignin compounds, including their derivatives. These metabolites are involved in biotic and abiotic stress tolerance, inviting several studies into their roles in plant defense, drought, temperature, UV, and nutrient stress tolerance. Literature is currently inundated with cutting-edge reports on the phenylpropanoid pathways and their functions. Here, we provide a comprehensive update on the biosynthesis of phenylpropanoids, mainly lignin and flavonoids, their roles in biotic and abiotic interaction, and transcending topics, including pest and diseases, drought, temperature, and UV stress tolerance. We further reviewed the post-transcriptional, post-translational, and epigenetic modifications regulating phenylpropanoid metabolism and highlighted their applications and optimization strategies for large-scale production. This review provides an all-inclusive update on recent reports on the metabolism of phenylpropanoids in plants.

Color-changing melon is an ornamental and edible fruit. Aiming at the problems of slow detection speed and high deployment cost for Color-changing melon in intelligent agriculture equipment, this study proposes a lightweight detection model YOLOv8-CML.Firstly, a lightweight Faster-Block is introduced to reduce the number of memory accesses while reducing redundant computation, and a lighter C2f structure is obtained. Then, the lightweight C2f module fusing EMA module is constructed in Backbone to collect multi-scale spatial information more efficiently and reduce the interference of complex background on the recognition effect. Next, the idea of shared parameters is utilized to redesign the detection head to simplify the model further. Finally, the α-IoU loss function is adopted better to measure the overlap between the predicted and real frames using the α hyperparameter, improving the recognition accuracy. The experimental results show that compared to the YOLOv8n model, the parametric and computational ratios of the improved YOLOv8-CML model decreased by 42.9% and 51.8%, respectively. In addition, the model size is only 3.7 MB, and the inference speed is improved by 6.9%, while mAP@0.5, accuracy, and FPS are also improved. Our proposed model provides a vital reference for deploying Color-changing melon picking robots.

Background The zinc-finger transcription factor Krüppel-homolog 1 (Kr-h1) exerts a dual regulatory role during insect development by preventing precocious larval/nymphal metamorphosis and in stimulating aspects of adult reproduction such as vitellogenesis. However, how Kr-h1 functions both as a transcriptional repressor in juvenile metamorphosis and an activator in adult reproduction remains elusive. Here, we use the insect Locusta migratoria to dissect the molecular mechanism by which Kr-h1 functions as activator and repressor at these distinct developmental stages. Results We report that the kinase PKCα triggers Kr-h1 phosphorylation at the amino acid residue Ser 154 , a step essential for its dual functions. During juvenile stage, phosphorylated Kr-h1 recruits a corepressor, C-terminal binding protein (CtBP). The complex of phosphorylated Kr-h1 and CtBP represses the transcription of Ecdysone induced protein 93F ( E93 ) and consequently prevents the juvenile-to-adult transition. In adult insects, phosphorylated Kr-h1 recruits a coactivator, CREB-binding protein (CBP), and promotes vitellogenesis by inducing the expression of Ribosomal protein L36 . Furthermore, Kr-h1 phosphorylation with the concomitant inhibition of E93 transcription is evolutionarily conserved across insect orders. Conclusion Our results suggest that Kr-h1 phosphorylation is indispensable for the recruitment of transcriptional cofactors, and for its anti-metamorphic and vitellogenic actions in insects. Our data shed new light on the understanding of Kr-h1 regulation and function in JH-regulated insect metamorphosis and reproduction.

Background Increasing evidence suggests that multiple or long-time exposure to general anaesthesia (GA) could be detrimental to cognitive development in young subjects and might also contribute to accelerated neurodegeneration in the elderly. Iron is essential for normal neuronal function, and excess iron in the brain is implicated in several neurodegenerative diseases. However, the role of iron in GA-induced neurotoxicity and cognitive deficits remains elusive. Methods We used the primary hippocampal neurons and rodents including young rats and aged mice to examine whether GA impacted iron metabolism and whether the impact contributed to neuronal outcomes. In addition, a pharmacological suppression of iron metabolism was performed to explore the molecular mechanism underlying GA-mediated iron overload in the brain. Results Our results demonstrated that GA, induced by intravenous ketamine or inhalational sevoflurane, disturbed iron homeostasis and caused iron overload in both in vitro hippocampal neuron culture and in vivo hippocampus. Interestingly, ketamine- or sevoflurane-induced cognitive deficits, very likely, resulted from a novel iron-dependent regulated cell death, ferroptosis. Notably, iron chelator deferiprone attenuated the GA-induced mitochondrial dysfunction, ferroptosis, and further cognitive deficits. Moreover, we found that GA-induced iron overload was activated by NMDAR-RASD1 signalling via DMT1 action in the brain. Conclusion We conclude that disturbed iron metabolism may be involved in the pathogenesis of GA-induced neurotoxicity and cognitive deficits. Our study provides new vision for consideration in GA-associated neurological disorders.