Explore the vast range of titles available.

Chengkou mountain chicken, a Chinese indigenous breed, exhibits unique flavor characteristics. However, the genetic basis of its flavor precursor substances remains unexplored. We performed a genome-wide association study (GWAS) using low-coverage whole-genome sequencing (lcWGS) and genotype imputation to explore genetic markers linked to flavor precursors (nucleotides, amino acids, etc.) in Chengkou mountain chicken breast muscle. We identified 44 SNPs potentially or significantly associated with flavor precursor traits and localized 18 genes. Functional analysis revealed eight important candidate genes, including ZBTB20, RFX4, MAMLD1, SYN3, ABTB3, PRPF39, LRFN5, and DGCR14, which may play key roles in influencing flavor precursor substances. Moreover, two SNPs residing in the haplotype block (53,448,483 bp to 53,450,834 bp) on chromosome 1 were significantly associated with a di-unsaturated acyl chain (C20:2) residue and mapped to the ABTB3 gene. This study analyzes the composition of flavor precursor substances in the pectoralis major muscle of Chengkou mountain chicken. Moreover, the SNPs, haplotypes, and candidate genes identified in this study can be used to improve the accuracy of the marker-assisted selection of traits related to flavor precursor substances in the breast muscle of chicken. In addition, the candidate genes that are significantly associated with these traits will potentially lay the foundation for future genetic selection aimed at improving the flavor traits of chicken meat.

Coptisine is a natural small‐molecular compound extracted from Coptis chinensis (CC) with a history of using for thousands of years. This work aimed at summarizing coptisine's activity and providing advice for its clinical use. We analysed the online papers in the database of SciFinder, Web of Science, PubMed, Google scholar and CNKI by setting keywords as ‘coptisine’ in combination of ‘each pivotal pathway target’. Based on the existing literatures, we find (a) coptisine exerted potential to be an anti‐cancer, anti‐inflammatory, CAD ameliorating or anti‐bacterial drug through regulating the signalling transduction of pathways such as NF‐κB, MAPK, PI3K/Akt, NLRP3 inflammasome, RANKL/RANK and Beclin 1/Sirt1. However, we also (b) observe that the plasma concentration of coptisine demonstrates obvious non‐liner relationship with dosage, and even the highest dosage used in animal study actually cannot reach the minimum concentration level used in cell experiments owing to the poor absorption and low availability of coptisine. We conclude (a) further investigations can focus on coptisine's effect on caspase‐1‐involved inflammasome assembling and pyroptosis activation, as well as autophagy. (b) Under circumstance of promoting coptisine availability by pursuing nano‐ or microrods strategies or applying salt‐forming process to coptisine, can it be introduced to clinical trial.

In poultry, prolactin (PRL) plays a key role in the regulation of incubation behavior, hormone secretion, and reproductive activities. However, previous in vitro studies have focused on the actions of PRL in ovarian follicles of poultry, relying on the use of exogenous or recombinant PRL, and the true role of PRL in regulating ovarian granulosa cell (GC) functions in poultry awaits a further investigation using endogenous native PRL. Therefore, in this study, we first isolated and purified recombinant goose PRL protein (rPRL) and native goose PRL protein (nPRL) using Ni-affinity chromatography and rabbit anti-rPRL antibodies-filled immunoaffinity chromatography, respectively. Then, we analyzed and compared the effects of rPRL and nPRL at different concentrations (0, 3, 30, or 300 ng/mL) on the proliferation and apoptosis of both GCs isolated from goose ovarian pre-hierarchical follicles (phGCs) and from hierarchical follicles (hGCs). Our results show that rPRL at lower concentrations increased the viability and proliferation of both phGCs and hGCs, while it exerted anti-apoptotic effects in phGCs by upregulating the expression of Bcl-2. On the other hand, nPRL increased the apoptosis of phGCs in a concentration-dependent manner by upregulating the expressions of caspase-3 and Fas and downregulating the expressions of Bcl-2 and Becn-1. In conclusion, this study not only obtained a highly pure nPRL for the first time, but also suggested a dual role of PRL in regulating the proliferation and apoptosis of goose GCs, depending on its concentration and the stage of follicle development. The data presented here can be helpful in purifying native proteins of poultry and enabling a better understanding of the roles of PRL during the ovarian follicle development in poultry.

Accurate snow cover data are critical for understanding the Earth’s climate system, and exploring hydrological processes and regional water resource management over High Mountain Asia (HMA). However, satellite-based remote sensing observations of snow cover have inevitable data gaps originating from cloud cover, sensor, orbital limitations and other factors. Here an effective cloud-gap-filled (CGF) method was developed to fully fill the data gaps in Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference snow index (NDSI) product. The CGF method combines the respective strengths of the cubic spline interpolation method and the spatio-temporal weighted method for generating the CGF Terra-Aqua MODIS NDSI product over HMA from 2000 to 2021. Based on the validation results of in situ snow-depth observations, the CGF NDSI product achieves a high range overall accuracy (OA) of 93.54–98.08%, a low range underestimation error (MU) of 0.15–3.49% and an acceptable range overestimation error (MO) of 0.84–5.77%. Based on the validation results of high-resolution Landsat images, this product achieves the OA of 88.52–92.40%, the omission error (OE) of 1.42–10.28% and the commission error (CE) of 5.97–17.58%. The CGF MODIS NDSI product can provide scientific support for eco-environment sustainable management in the high mountain region.

Accurate snow cover data are critical for understanding the Earth’s climate system, and exploring hydrological processes and regional water resource management over High Mountain Asia (HMA). However, satellite-based remote sensing observations of snow cover have inevitable data gaps originating from cloud cover, sensor, orbital limitations and other factors. Here an effective cloud-gap-filled (CGF) method was developed to fully fill the data gaps in Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference snow index (NDSI) product. The CGF method combines the respective strengths of the cubic spline interpolation method and the spatio-temporal weighted method for generating the CGF Terra-Aqua MODIS NDSI product over HMA from 2000 to 2021. Based on the validation results of in situ snow-depth observations, the CGF NDSI product achieves a high range overall accuracy (OA) of 93.54–98.08%, a low range underestimation error (MU) of 0.15–3.49% and an acceptable range overestimation error (MO) of 0.84–5.77%. Based on the validation results of high-resolution Landsat images, this product achieves the OA of 88.52–92.40%, the omission error (OE) of 1.42–10.28% and the commission error (CE) of 5.97–17.58%. The CGF MODIS NDSI product can provide scientific support for eco-environment sustainable management in the high mountain region.

Real-time online simulation based on a real-time workshop (RTW) plays a vital role in the study and application of power electronics. However, restricted by the performance of equipment and hardware, the simulators so far available in the market mainly support simulation steps over 50 μs, while large step simulation may result in the action delay of pulse-width modulating (PWM), numerical oscillation and high-level non-characteristic harmonic distortion. In view of these problems, this paper puts forward a modeling method based on integral prediction and interpolation compensation. First of all, prediction is performed one step in advance by the implicit trapezoidal method to find out the accurate time when the triangle carrier wave intersects with the modulation wave. At the same time, a mathematic model is built for the insulated gate bipolar transistor (IGBT) to output equivalent voltage waveform according to the principle of area equivalent. Next, in MATLAB/Simulink, offline simulation is performed with the three-phase AC-DC-AC converter as the subject. By comparing the control accuracy, the content of harmonic wave and the simulation time, the simulation effects of the 50 μs fixed-step interpolation prediction model are the same as that for a 5 μs fixed-step standard model. Finally, the effectiveness and high efficiency of this algorithm are verified on a real-time simulator, marking the application of offline models on real-time simulators.

Supercapacitors have been extensively studied due to their advantages of fast-charging and discharging, high-power density, long-cycling life, low cost, etc. Exploring novel nanomaterial schemes for high-performance electrode materials is of great significance. Herein, a strategy to combine vertical graphene (VG) with MoO3 nanosheets to form a composite VG/MoO3 nanostructure is proposed. VGs as transition layers supply rich active sites for the growth of MoO3 nanosheets with increasing specific surface areas. The VG transition layer further improves the electric contact and adhesion of the MoO3 electrode, simultaneously stabilizing its volume and crystal structure during repeated redox reactions. Thus, the prepared VG/MoO3 nanosheets have been demonstrated to exhibit excellent electrochemical properties, such as high reversible capacitance, better cycling performance, and high-rate capability.

Recent advances in reasoning models have demonstrated remarkable success in text and vision domains through extended chain-of-thought deliberation. However, a perplexing phenomenon persists in audio language models: they consistently perform better with minimal or no reasoning, raising a fundamental question - can audio intelligence truly benefit from deliberate thinking? We introduce Step-Audio-R1, the first audio reasoning model that successfully unlocks reasoning capabilities in the audio domain. Through our proposed Modality-Grounded Reasoning Distillation (MGRD) framework, Step-Audio-R1 learns to generate audio-relevant reasoning chains that genuinely ground themselves in acoustic features rather than hallucinating disconnected deliberations. Our model exhibits strong audio reasoning capabilities, surpassing Gemini 2.5 Pro and achieving performance comparable to the state-of-the-art Gemini 3 Pro across comprehensive audio understanding and reasoning benchmarks spanning speech, environmental sounds, and music. These results demonstrate that reasoning is a transferable capability across modalities when appropriately anchored, transforming extended deliberation from a liability into a powerful asset for audio intelligence. By establishing the first successful audio reasoning model, Step-Audio-R1 opens new pathways toward building truly multimodal reasoning systems that think deeply across all sensory modalities.

This paper presents Step-Audio 2, an end-to-end multi-modal large language model designed for industry-strength audio understanding and speech conversation. By integrating a latent audio encoder and reasoning-centric reinforcement learning (RL), Step-Audio 2 achieves promising performance in automatic speech recognition (ASR) and audio understanding. To facilitate genuine end-to-end speech conversation, Step-Audio 2 incorporates the generation of discrete audio tokens into language modeling, significantly enhancing its responsiveness to paralinguistic information such as speaking styles and emotions. To effectively leverage the rich textual and acoustic knowledge in real-world data, Step-Audio 2 integrates retrieval-augmented generation (RAG) and is able to call external tools such as web search to mitigate hallucination and audio search to switch timbres. Trained on millions of hours of speech and audio data, Step-Audio 2 delivers intelligence and expressiveness across diverse conversational scenarios. Evaluation results demonstrate that Step-Audio 2 achieves state-of-the-art performance on various audio understanding and conversational benchmarks compared to other open-source and commercial solutions. Please visit https://github.com/stepfun-ai/Step-Audio2 for more information.