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The identification of similar music segments is of great significance for the study of online music search, content relevance, emotional expression and many other aspects. In the overall structure of music, the extraction of key frames, the identification of similar key frames for different types of music, which is to obtain better music emotion data. This paper uses in-depth reinforcement learning algorithms to analyze the music data in detail to construct music similarity Intelligently identify the database and match the obtained music files with the music data in the database to find similar segments. Case analysis shows that this method can effectively analyze music fragments and provide a basis for subsequent music control.

Circular RNAs (circRNAs), are a covalently closed, single-stranded RNA without 5′- and 3′-termini, commonly stem from the exons of precursor mRNAs (pre-mRNAs). They have recently garnered interest, with studies uncovering their pivotal roles in regulating various aspects of cell functions and disease progressions. A notable feature of circRNA lies in the mechanism of its biogenesis involving a specialized form of splicing: back-splicing. A splicing process that relies on interactions between introns flanking the circularizing exon to bring the up and downstream splice sites in proximity through the formation of a prerequisite hairpin structure, allowing the spliceosomes to join the two splice sites together to produce a circular RNA molecule. Based on this mechanism, we explored the feasibility of facilitating the formation of such a prerequisite hairpin structure by utilizing a newly designed oligonucleotide, CircuLarIzation Promoting OligoNucleotide ( CLIP-ON ), to promote the production of circRNA in cells. CLIP-ON was designed to hybridize with and physically bridge two distal sequences in the flanking introns of the circularizing exons. The feasibility of CLIP-ON was confirmed in HeLa cells using a model pre-mRNA, demonstrating the applicability of CLIP-ON as a trans-acting modulator to upregulate the production of circRNAs in a cellular environment.

Developmental dyslexia is a common neurodevelopmental disorder that impairs reading ability despite adequate intelligence and education, affecting up to 17% of children worldwide. Advances in neuroscience have revealed complex mechanisms involving phonological, visual, and temporal processing, with cross-linguistic variability. At the same time, technological innovation is driving a shift toward AI-powered diagnostics, immersive learning tools, and neurostimulation-based interventions. This narrative review synthesizes evidence from recent research published between 2015 and 2025, focusing on four thematic areas: (1) neurobiological underpinnings of dyslexia, (2) diagnostic innovations using AI and eye- or handwriting-based deep learning, (3) neurostimulation and immersive VR/AR interventions, and (4) policy, equity, and ethical considerations. Studies were identified through major academic databases and thematically analyzed to highlight trends, strengths, and limitations. AI-based diagnostic tools using eye-tracking and handwriting features have achieved reported accuracies exceeding 80% in multiple pilot studies. VR/game-based programs and neurostimulation interventions (TMS, tDCS) have shown promising short-term effects on reading fluency and phonological processing, though evidence for long-term literacy transfer remains limited. Across studies, methodological heterogeneity and small sample sizes constrain generalizability. Significant disparities in access persist across socioeconomic, linguistic, and geographic contexts. While these technologies offer promising avenues for more personalized and scalable dyslexia care, their integration must be accompanied by stronger evidence, ethical safeguards, and equity-focused policies. Technology should augment, not replace, human interaction in inclusive education. Future research should prioritize larger trials, cross-linguistic validation, and sustainable implementation strategies.

The structural, mechanical, and electronic properties of the metastable β″ -Mg 5 Si 6  phase doped with six elements were calculated by using DFT. Our calculated lattice constants of pristine Mg 5 Si 6  are in good agreement with the experimental and other theoretical values. It is found that all doping elements preferentially occupy the Mg site in the Mg 5 Si 6 structure. The independent elastic constants of Mg 9 Si 12 X (X = Li, Ca, Al, Ge, Cu, Zn) structures are calculated and a series of mechanical moduli (bulk modulus, shear modulus, Young’s modulus, and Poisson’s ratio) are determined. The Debye temperature and the anisotropy of doped structures are also investigated. Doping Ge can significantly improve the toughness of Mg 9 Si 12 Ge structure, and the doping Li and Zn atoms are beneficial to enhance the strength and hardness of Mg 9 Si 12 X structures. Finally, the type of interatomic bonding and charge transfer in the materials are determined by the electronic structure analysis. Electron orbital hybridization will change the types of bonds between nearby atoms and, in turn, the mechanical properties of Mg 9 Si 12 X structures. Graphical abstract Effect of element doping on mechanical properties of β″ -Mg 5 Si 6 phase in Al–Mg–Si alloy.

Metabolic changes in T cells can affect the genomic methylation status of key transcription factors and regulate the fate decision between induced regulatory T cells and T helper 17 cells. (DING, Sheng , 23475; Biological Sciences - Letter) Sheng Ding and colleagues address the question of how metabolic changes in T cells can affect the genomic methylation status of key transcription factors and regulate the fate decision between regulatory T and T H 17 T cells. This study demonstrates the ability of a small molecule inhibitor to target a specific metabolic pathway, a finding which may lead to the development of novel therapeutics against T H 17 mediated auto-immune diseases. Metabolism has been shown to integrate with epigenetics and transcription to modulate cell fate and function 1 , 2 , 3 . Beyond meeting the bioenergetic and biosynthetic demands of T-cell differentiation 4 , 5 , 6 , 7 , 8 , whether metabolism might control T-cell fate by an epigenetic mechanism is unclear. Here, through the discovery and mechanistic characterization of a small molecule, (aminooxy)acetic acid, that reprograms the differentiation of T helper 17 (T H 17) cells towards induced regulatory T (iT reg ) cells, we show that increased transamination, mainly catalysed by GOT1, leads to increased levels of 2-hydroxyglutarate in differentiating T H 17 cells. The accumulation of 2-hydroxyglutarate resulted in hypermethylation of the Foxp3 gene locus and inhibited Foxp3 transcription, which is essential for fate determination towards T H 17 cells. Inhibition of the conversion of glutamate to α-ketoglutaric acid prevented the production of 2-hydroxyglutarate, reduced methylation of the Foxp3 gene locus, and increased Foxp3 expression. This consequently blocked the differentiation of T H 17 cells by antagonizing the function of transcription factor RORγt and promoted polarization into iT reg cells. Selective inhibition of GOT1 with (aminooxy)acetic acid ameliorated experimental autoimmune encephalomyelitis in a therapeutic mouse model by regulating the balance between T H 17 and iT reg cells. Targeting a glutamate-dependent metabolic pathway thus represents a new strategy for developing therapeutic agents against T H 17-mediated autoimmune diseases.

Previous studies have shown that waterlogging/ hypoxic conditions induce aerenchyma formation to facilitate gas exchange. Ethylene (ET) and reactive oxygen species (ROS), as regulatory signals, might also be involved in these adaptive responses. However, the interrelationships between these signals have seldom been reported. Herein, we showed that programmed cell death (PCD) was involved in aerenchyma formation in the stem of . Lysigenous aerenchyma formation in the stem was induced through waterlogging (WA), ethylene and ROS. Pre-treatment with the NADPH oxidase inhibitor diphenyleneiodonium (DPI) partially suppressed aerenchyma formation in the seedlings after treatment with WA, ET and 3-amino-1, 2, 4-triazole (AT, catalase inhibitor). In addition, pre-treatment with the ethylene perception inhibitor 1-methylcyclopropene (1-MCP) partially suppressed aerenchyma formation induced through WA and ET in the seedlings, but barely inhibited aerenchyma formation induced through ROS. These results revealed that ethylene-mediated ROS signaling plays a role in aerenchyma formation, and there is a causal and interdependent relationship during WA, ET and ROS in PCD, which regulates signal networks in the stem of .

High energy density and enhanced rate capability are highly sought-after for supercapacitors in today’s mobile world. In this work, polyaniline/titanium carbide (MXene) (PANI/Ti 3 C 2 T x ) nanohybrid is synthesized through a facile and cost-effective self-assembly of one-dimensional (1D) PANI nanofibers and two-dimensional (2D) Ti 3 C 2 T x nanosheets. PANI/Ti 3 C 2 T x delivers greatly improved specific capacitance, ultrahigh rate capability (67% capacitance retention from 1 to 100 A·g −1 ) as well as good cycle stability. Electrochemical kinetic analysis reveals that PANI/Ti 3 C 2 T x is featured with surface capacitance-dominated process and has a quasi-reversible kinetics at high scan rates, giving rise to an ultrahigh rate capability. By using PANI/Ti 3 C 2 T x as positive electrode, an 1.8 V aqueous asymmetric supercapacitor (ASC) is successfully assembled, showing a maximum energy density of 50.8 Wh·kg −1 (at 0.9 kW·kg −1 ) and a power density of 18 kW·kg −1 (at 26 Wh·kg −1 ). Moreover, an 3.0 V organic ASC is also elaborately fabricated by using PANI/Ti 3 C 2 T x , achieving an ultrahigh energy density of 67.2 Wh·kg −1 (at 1.5 kW·kg −1 ) and a power density of 30 kW·kg −1 (at 26.8 Wh·kg −1 ). The present work not only improves fundamental understanding of the structure-property relationship towards ultrahigh rate capability electrode materials, but also provides valuable guideline for the rational design of high-performance energy storage devices with both high energy and power densities.

The Hilbert-Huang transform (HHT) is widely used for time-frequency analysis of blasting seismic wave signals due to its unique adaptability. However, blasting seismic wave signals are typical non-stationary vibration signals that are susceptible to noise interference, leading to mode confusion and endpoint effects in empirical mode decomposition (EMD) in HHT, which in turn affects the accuracy of time-frequency analysis. In order to obtain accurate time-frequency characteristic parameters of blasting seismic wave signals, it is necessary to improve HHT. A time-frequency analysis algorithm called DEP- CEEMDAN-MPE-INHT was proposed. The first step of the algorithm is to perform dual endpoint processing (DEP) on the signal. The second step is to combine the advantages of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multi-scale permutation entropy (MPE) to obtain CEEMDAN-MPE, and perform CEEMDAN-MPE on the DEP processed signal to achieve synchronous suppression of high-frequency noise and low-frequency trend terms. The third step is to perform a normalized Hilbert transform (NHT) on the intrinsic mode function (IMF) obtained from DEP-CEEMDAN-MPE to achieve INHT. The above three steps can establish the time-frequency analysis algorithm of DEP-CEEMDAN-MPE-INHT. Through noisy simulation signal testing, the comparative study of DEP-CEEMDAN-MPE-INHT and HHT is carried out. Finally, the algorithm is applied to the time-frequency analysis of actual blasting seismic wave signals. The results show that DEP-CEEMDAN-MPE-INHT not only suppresses the EMD endpoint effect and mode confusion, but also obtains the time spectrum with high resolution in time domain and frequency domain. Through DEP-CEEMDAN-MPE-INHT time-frequency analysis, the time-frequency characteristic parameters of blasting seismic wave signal can be accurately extracted, which has important practical significance for the hazard identification and control of blasting seismic wave.

Steroid-refractory (SR) hepatic acute graft-versus-host disease (aGVHD) remains a life-threatening complication following allogeneic hematopoietic stem cell transplantation, characterized by limited responsiveness to both first- and second-line therapies and an overall poor prognosis. This study aimed to evaluate the efficacy and safety of cyclophosphamide (CTX) as a salvage treatment for SR- hepatic aGVHD. A total of 50 patients with SR-hepatic aGVHD who underwent CTX treatment were retrospectively included in the analysis. Seventeen patients (34.0%) received CTX as second-line therapy, whereas the majority (n=33, 66.0%) were administered CTX as salvage therapy following failure of prior second-line interventions. The overall response rate (ORR) at day 28 was 70.0%, with a durable ORR of 66.0% at day 56. Patients with the hepatitic variant of aGVHD showed a superior response compared to those with the classic variant (complete response: 6 of 8 [75.0%] vs. 14 of 42 [33.3%], P = 0.042). The probabilities of overall survival (OS) and nonrelapse mortality (NRM) at 3 years after CTX treatment were 36.9% (95% CI, 24.8%-54.9%) and 56.5% (95% CI, 41.4%-71.6%). Using propensity score matching (PSM), we compared 35 patients receiving CTX with 35 BAT (best available treatment) controls during the same study period. CTX initiation occurred later than BAT (median line: 3 vs 2, P < 0.001). Response rates and survival outcomes were comparable between two groups and CTX demonstrated consistent efficacy even when used as later-line therapy. Additionally, CTX did not significantly increase the risk of adverse events compared to the BAT group up to day 28. The most common adverse events in both groups were neutropenia (71.4% in the CTX group vs. 62.9% in the BAT group, P = 0.445), anemia (68.6% vs. 60.0%, P = 0.454), and cytomegalovirus infection (51.4% vs. 45.7%, P = 0.632). These findings suggest that CTX is a promising and well-tolerated treatment option for patients with SR-hepatic aGVHD.

While China’s clean air actions implemented since 2013 have been effective in mitigating PM2.5 air pollution, the large emission reductions during the COVID-19 lockdown period in early 2020 did not similarly alleviate PM2.5 pollution in North China, reflecting a distinct nonlinear chemical response of PM2.5 formation to emission changes. Here we apply emission-concentration relationships for PM2.5 diagnosed using the adjoint approach to quantitatively assess how chemical nonlinearity affects PM2.5 over Beijing in February 2020 in response to two emission reduction scenarios: the COVID-19 lockdown and 2013–2017 emission controls. We find that, in the absence of chemical nonlinearity, the COVID-19 lockdown would decrease PM2.5 in Beijing by 17.9 μg m–3, and the 2013–2017 emission controls resulted in a larger decrease of 54.2 μg m–3 because of greater reductions of SO2 and primary aerosol emissions. Chemical nonlinearity offset the decrease for Beijing PM2.5 by 3.4 μg m–3 during the lockdown due to enhanced sensitivity of aerosol nitrate to NOx emissions, but enhanced the efficiency of 2013–2017 emission controls by 11.9 μg m–3 due to the weakened heterogeneous reaction of sulfate. Such nonlinear chemical effects are important to estimate and consider when designing or assessing air pollution control strategies.