Search Results (763)

Ceramides (Cer), as bioactive substances, hold significant research importance in fields such as biology, medicine, and daily chemicals. As a significant bioactive substance, Cer has garnered considerable attention in various fields in recent years. Especially in the field of daily chemicals, Cer serves as a core functional ingredient in skincare products, which ameliorate skin dryness and sensitivity and reinforce the barrier function of the stratum corneum. Among them, ultra-long-chain Cer assemble compact and ordered epidermal lipid layers to block exogenous oxidative stress, reduce intracellular ROS accumulation, and mitigate lipid-peroxidation-mediated skin barrier damage, while excess medium- and short-chain Cer disrupt the ordered arrangement of stratum corneum lipids and further aggravate cutaneous oxidative-stress imbalance. Such chain-length-dependent functional divergence reflects the core regulatory effect of structure–activity relationships on cutaneous physiological phenotypes. The acquisition of Cer is of great significance for advancing their research and applications. Currently, there is a lack of comprehensive reviews that introduce the acquisition pathways of Cer and discuss efficient preparation strategies for different methods. Cer and their related raw materials can be obtained through three main pathways: microbial fermentation, natural extraction, and chemical synthesis. This review provides a comprehensive comparison and analysis of these three approaches, focusing on recent advancements and developments in the field. The purpose of this review is to enable researchers to gain a comprehensive understanding of the current research status of the preparation strategies for biological Cer. And the discussion of these studies will be propitious to the future improvement of green synthesis and large-scale production, further promoting the wide application of Cer. Full article

Cuticular wax is a critical component of the plant cuticle, playing an indispensable role in plant growth, development, and defense against environmental stresses. ECERIFERUM3 (CER3), a very-long-chain fatty acyl-CoA reductase, serves as a pivotal hub channeling carbon resources into the alkane- and alcohol-forming pathways respectively. Here, we systematically characterized the protein structural features and evolutionary patterns of CER3. We also comprehensively reviewed the multiple regulatory mechanisms governing CER3, encompassing transcriptional, post-transcriptional, translational, and epigenetic modifications, with particular emphasis on the definitive finding that CER3 modulates cuticular wax biosynthetic flux by assembling distinct protein complexes. Finally, existing knowledge gaps and future research directions are also discussed. Full article

Coupling renewable energy with seawater electrolysis is a highly promising strategy for sustainable hydrogen production. However, the practical application of direct seawater electrolysis remains challenging due to severe anode corrosion and the competitive chlorine evolution reaction (CER) induced by chloride ions. Herein, we report a facile cation-etching strategy to synthesise Fe@ZIF-67 catalysts at room temperature, using ZIF-67 as the sacrificial template and Fe²⁺ salts as the etching agent. The as-prepared Fe@ZIF-67 exhibits superior electrocatalytic activity for the oxygen evolution reaction (OER) in a simulated alkaline saline electrolyte (1.0 M KOH + 0.5 M NaCl). Specifically, it achieves a current density of 10 mA cm⁻² at a low overpotential of 259 mV, outperforming commercial RuO₂. Furthermore, an alkaline saline electrolyser assembled with Fe@ZIF-67 as the anode and Pt/C as the cathode requires a cell voltage of only 1.57 V to reach 10 mA cm⁻², which is significantly lower than that of the RuO₂||Pt/C benchmark (1.65 V). This work demonstrates that the cation-doping strategy effectively modulates the surface electronic structure of metal–organic frameworks (MOF)-based catalysts, providing a new perspective for optimising their performance in seawater electrolysis. Full article

Aerosols exert significant impacts on Earth’s radiation balance through direct and indirect effects, with the latter representing the largest uncertainty in current climate models. To clarify aerosol–cloud interactions over China, this study synergized MODIS satellite retrievals (2015–2020) with in-situ MA60 aircraft observations across six representative regions. Satellite data provided aerosol optical depth (AOD), cloud optical depth (COD), cloud effective radius (CER), and cloud phase, while aircraft measurements delivered vertical profiles and microphysical properties of aerosols and cloud droplets. Results show that AOD exhibits a “high east, low west” pattern, with hotspots in the North China Plain and Sichuan Basin, and a significant decreasing trend over polluted regions. Cloud phase is spatially heterogeneous, with water clouds dominating the southeast and ice clouds prevailing in the northwest (>85% over the Tibetan Plateau). Water cloud COD shows a southeast-high–northwest-low distribution, with a clear inverse CER-COD correlation. Aircraft data reveal that polluted northern sites have high near-surface aerosol concentrations with small effective diameters (~0.3 μm), while cloud droplet number concentration and size spectra vary markedly across regions. These findings provide a robust observational foundation for improving aerosol–cloud interaction parameterizations in climate models. Full article

Background/Objectives: Myasthenia gravis (MG) is an immune-mediated neuromuscular disorder for which antibody-based assays have limited sensitivity, particularly in double-seronegative MG (dsNMG), highlighting the need for complementary biomarkers. Given their roles in immune regulation, membrane integrity, and metabolic stress responses, lipids represent promising candidates for biomarker discovery. Methods: We designed a prospective case–control study and systematically stratified 68 patients with myasthenia gravis (MG) according to clinical classification and autoantibody status. Using LC–MS/MS, we quantified 824 lipids in 136 serum samples collected from these patients and 68 healthy controls. The analyzed subtypes included ocular MG (OMG), generalized MG (GMG), acetylcholine receptor antibody-positive MG (AChR-MG), and dsNMG. Differential lipid analysis, correlation network construction, KEGG pathway enrichment, and multivariable logistic regression were performed. Diagnostic and subtype prediction models were developed using LASSO with 10 × 10 repeated cross-validation and interpreted using Shapley Additive exPlanations (SHAP) analysis. A longitudinal follow-up analysis was conducted to assess dynamic associations between lipid signatures and disease activity. Results: In total, 240 lipids were significantly altered in MG compared with controls. Lipids distinguishing GMG from OMG were enriched in ether lipid metabolism, necroptosis, and sphingolipid signaling pathways. AChR-MG and dsNMG shared lipid networks related to membrane remodeling and signaling regulation, whereas dsNMG exhibited marked elevations in acylcarnitines and bile acid-related metabolites, potentially reflecting a distinct phenotype characterized by altered energy metabolism. The lipid-based model achieved an AUC of 0.917 for distinguishing MG from controls, and AUCs of 0.77 and 0.71 for differentiating AChR-MG from dsNMG and GMG from OMG, respectively. Longitudinal analyses showed that SM(d18:1/23:0) and Cer(d24:1/18:0(2OH)) displayed dynamic changes consistent with disease activity. Conclusions: Serum lipidomics revealed subtype-specific metabolic features of MG, with stable disease-associated remodeling and dynamic sphingolipid changes potentially reflecting disease activity. By integrating systematic clinical and antibody-based subtype stratification with longitudinal follow-up, this study supports lipidomics as a complementary tool for precision diagnosis and disease stratification, particularly in antibody-negative dsNMG. Full article

The increasing emphasis on sustainability in the energy sector has highlighted the importance of understanding the factors that promote employees’ pro-environmental behavior (PEB). Although green training (GT) is widely regarded as an effective organizational practice for encouraging PEB, the psychological mechanisms underlying this relationship remain insufficiently understood. Drawing on Social Learning Theory, this study proposes a dual-pathway framework through which GT is associated with employees’ PEB via perceived corporate environmental responsibility (CER) and environmental self-identity (ESI). Using purposively sampled, cross-sectional survey data from 1028 employees of a large Chinese energy company, the proposed framework was tested through structural equation modeling and bootstrap mediation analysis. The results indicate that perceived CER alone does not independently mediate the relationship between GT and PEB. However, perceived CER plays an important role by serving as a bridge through which employees internalize organizational environmental values into their ESI. Furthermore, ESI emerged as an independent and robust mediator, highlighting the central role of the identity-based pathway. This study contributes context-specific evidence from a large Chinese energy company, providing a deeper understanding of the psychological mechanisms linking GT and PEB and offering practical implications for organizations seeking to foster sustainability-oriented behaviors among employees. Full article

Low-carbon transformation requires substantial investments, challenging capital-constrained manufacturers to adopt carbon emission reduction (CER) technologies. While external financing alleviates capital shortages, it cannot address potential profit imbalances that trigger fairness concerns. We investigate a low-carbon supply chain where a capital-constrained manufacturer adopts CER technologies via a preferential bank loan and sells to a capital-abundant retailer. Unlike prior studies treating CER investments as one-time costs, we model CER technology as a quadratic per-unit royalty licensing fee. We find that, given consumers’ willingness to pay for low-carbon products, financing encourages CER upgrades but creates profit disparities unfavorable to the retailer. Incorporating the retailer’s fairness concerns, results show that compared to the non-fairness scenario, the manufacturer sets a lower wholesale price and cannot earn more. Conversely, the retailer strategically maintains or increases its order quantity, attaining higher profits. Furthermore, the optimal CER level remains invariant regardless of fairness preferences. Finally, supply chain coordination is achievable under specific conditions, yielding a win–win outcome where the manufacturer adopts CER technologies and the retailer’s fairness concerns are accommodated. The quadratic per-unit technology licensing fee we investigated maintains the manufacturer’s motivation and ensures the retailer’s fairness, contributing to the stable and sustainable evolution of low-carbon supply chains. Full article

Automatic speech recognition (ASR) systems based on Conformer architectures achieve strong performance by jointly modeling local acoustic patterns and global contextual dependencies. However, their interleaved convolution–attention design leads to progressive entanglement of fine-grained acoustic features and global semantic representations, which weakens monotonic alignment in speech recognition and degrades performance in long utterances. To address this limitation, we propose an AS-Split Conformer–Mamba framework that decouples local and global modeling into two explicit stages. First, a stage-wise encoder is introduced, where a dedicated local modeling stage extracts phonetic-level acoustic features using SE-enhanced convolution, followed by a global modeling stage that captures long-range dependencies via multi-head self-attention and temporal convolution. Second, a Transition Fusion Block (TFB) is designed as an adaptive transition module that transforms local acoustic representations before they enter the global modeling stage. Third, intermediate CTC supervision is introduced to explicitly strengthen monotonic alignment at shallow representations. Finally, a hybrid Transformer–Mamba decoder is adopted, in which the Mamba block provides O(N) state-space computation within the replaced FFN sublayer while retaining Transformer attention mechanisms for acoustic–text alignment. Experiments conducted on AISHELL-1, THCHS-30, and ST-CMDS demonstrate that the proposed method achieves consistent improvements over strong baselines. On AISHELL-1, our model reduces Character Error Rate (CER) from 5.7% to 4.8% and Sentence Error Rate (SER) from 24.8% to 20.5%, while maintaining competitive computational efficiency. Full article

Sphingolipids, essential structural components of biological membranes, form a framework that maintains their stability and fluidity. In addition to their structural function, these lipids and their metabolites participate in regulating multiple cellular processes, including proliferation, differentiation, gene expression, and apoptosis, thereby contributing to the maintenance of oral homeostasis. Dysregulation of sphingolipid metabolism is involved in the pathogenesis of several major oral diseases: oral squamous-cell carcinoma (OSCC), periodontitis, oral candidiasis, Sjögren’s syndrome (SS), and periapical diseases. Accordingly, a deeper understanding of sphingolipid biology may provide new opportunities for developing therapeutic strategies targeting these disorders. This review provides a comprehensive analysis of the structural characteristics and principal metabolic pathways of key sphingolipids (e.g., ceramide, sphingosine-1-phosphate [S1P], and glucosylceramide [GlcCer]), discusses their diverse roles in oral diseases, and summarizes recent advances in pharmacological approaches targeting enzymes involved in sphingolipid metabolism. Full article

Straw incorporation generally improves soil nutrient availability, aggregate stability, microbial activity, as well as carbon dioxide (CO₂) emissions. However, the regulatory effects of tillage regimes on soil physicochemical properties, microbial community structure, and CO₂ emission rates (CER) remain largely unexplored in rice–rapeseed rotation systems incorporating straw residue in the upper Yangtze River basin. A two-year field experiment in Sichuan’s Xindu District adopted three tillage practices: rotary tillage combined with whole rice straw incorporation (RT), deep plowing combined with whole rice straw incorporation (DP), and no-tillage with whole rice straw mulching (NT). Compared with NT, both RT and DP significantly lowered soil bulk density (BD) and macroaggregate stability, and elevated soil available nitrogen (AN), available phosphorus (AP), and available potassium (AK). On average, the AN, AP, and AK were significantly increased by 7.4%, 9.0%, and 80.3% in RT and by 4.1%, 43.9%, and 42.2% in DP relative to NT, respectively. Compared with the NT, the CER was significantly decreased by 37.1% in RT and by 12.9% in DP. Notably, RT effectively mitigated CER. Microbial α-diversity was significantly higher in the NT relative to other tillage treatments. Proteobacteria dominated the bacterial community, and Ascomycota dominated the fungal community. RT and DP increased the relative abundance of Proteobacteria and suppressed Ascomycota simultaneously. Redundancy analysis revealed positive associations between Proteobacteria and both AN and AK, whereas Ascomycota positively correlated with BD, MWD, and CER. Mantel test confirmed significant correlation between soil microbial community compositions and the soil properties, including BD, AN, AK, and CER. Relative to the other tillage practices, RT is conducive to soil quality improvement and CER reduction, and can be recommended for sustainable rice-rapeseed production locally. Full article

This study presents an audio–visual speech recognition (AVSR) model for Kazakh that jointly exploits audio and visual channels. The study introduces QazAVSR, a 57 h dataset collected from 271 speakers, and extracts synchronized audio signals and lip-region video sequences using FFmpeg 7.0, Dlib 19.24, and OpenCV 4.9.0. The proposed architecture uses the self-supervised HuBERT_BASE model in the audio branch and an ImageNet-pretrained ViT-B/16 model in the visual branch. Audio and visual representations are fused by a three-layer BiModalHformer block, where intra- and cross-attention operations are performed at each level. Extensive experimental validation, supplemented by rigorous paired bootstrap resampling significance tests, demonstrates that the full multimodal BiModalHformer model achieves a highly robust average character error rate (CER) of 31.2% and a Word Error Rate (WER) of 43.1%. These results significantly outperform traditional audio-only, video-only, and standard representation-level fusion baselines. Furthermore, comparisons against powerful external baseline architectures—including Whisper-Small and AV-HuBERT configurations rigorously adapted for the Kazakh language—statistically validate the architectural efficacy of the BiModalHformer framework. Additional systematic evaluations utilizing extended metrics such as the Match Error Rate (MER), word information preserved (WIP), and the Multimodal Synergy Index (MSI) confirm that the full audio–visual configuration preserves lexical information significantly more effectively. Finally, extensive noise perturbation experiments confirm that the multimodal architecture exhibits superior structural robustness to complex acoustic distortions, including environmental noise, synthetic room reverberation, and overlapping speech topologies. Full article

Cooperative multi-agent reinforcement learning (MARL) is hard when rewards are sparse. This is an evaluation study built around a negative result. We study CHARM (Cooperative Hindsight-Augmented Role-conditioned Memory), a four-part memory layer that wraps a standard backbone: role-indexed episodic memory (JACM/RCMP), hindsight relabeling (HCR), an auxiliary memory loss (SMAL), and cooperative replay (CER). We test it on four backbones (DQN, QMIX, MADDPG, QPLEX) against the episodic-memory baselines EMC and EMU, five to ten seeds, on Google Research Football. The mean win count barely moves on any backbone, and the small five-seed differences reverse sign at ten seeds. Because every method trains under reward shaping, the task is not fully sparse; a shaping-free control and a 30,000-episode anchor keep the result flat. A random-action policy scores within the trained range; only two of eleven configurations exceed it before correction. Inter-seed variance drops with CHARM on all four, but this is not a memory effect: plain weight decay reproduces the reduction, and removing the recall path leaves training byte-identical, so the cause is generic regularization. Evaluation is fragile: a three-seed comparison ($g=2.07$) collapsed at five seeds, and no comparison survived correction. A three-tier memory added nothing: its top tier was never consumed. Full article

Elucidating the evolutionary trajectories and genetic basis of critical agronomic traits in regional rice germplasm is paramount for discovering elite allelic variations for crop improvement. Here, we systematically characterized a panel of 109 rice accessions from Zhejiang Province through whole-genome resequencing (~10× coverage) coupled with two years of rigorous field phenotypic evaluations. A total of 4,753,071 high-quality genomic variants, including 4,147,316 SNPs, were identified across the genome. Population structure and evolutionary analyses revealed sharp genetic differentiation at the subspecies level, partitioning the panel into distinct indica and japonica clusters accompanied by intricate subpopulation stratification and historical gene flow. Through a joint scanning of the fixation index (Fst) and nucleotide diversity (Pi) ratios, three prominent selective sweep regions (qSS1, qSS10, and qSS12) driving subspecific differentiation were captured on chromosomes 1, 10, and 12. Notably, the qSS12 locus harbors the sucrose transporter gene OsSUT2, indicating that carbohydrate transport and energy metabolism served as core genomic targets driving the indica–japonica divergence. Furthermore, genome-wide association studies (GWAS) successfully mapped 9 significant loci modulating heading date, effective tiller number, and grain size. Subsequent gene-based haplotype analyses within these target intervals pinpointed elite allelic variations in core candidate genes, including OsSPX1 (phosphate homeostasis, 1000-grain weight), Chl9 (chlorophyll synthesis, grain width), and OsCER1 (wax biosynthesis, panicle length). Collectively, this study deciphers the genomic landscape and subspecies differentiation patterns of Zhejiang rice germplasm, providing pivotal molecular targets and invaluable genomic resources for germplasm conservation and precision molecular breeding. Full article

Historical document collections are increasingly available as digitised images and PDFs, but their conversion into reliable text remains affected by optical character recognition (OCR) errors, degraded pages, heterogeneous layouts, and domain-specific terminology. This study proposes a pathway-level framework for documenting and comparing conventional OCR, OCR followed by large language model (LLM)-assisted correction, and direct multimodal transcription. The framework is demonstrated using the Portuguese Agricultural and Forestry Surveys (1950–1958). A stratified validation sample of 45 pages was selected by visual quality, page type, and geographic coverage. Outputs were evaluated against manually verified reference transcriptions using content-normalised character error rate (CER) and word error rate (WER), document-condition analysis, paired statistical tests, and an entity-level semantic preservation assessment focused on place names, agricultural terms, and measurement expressions. Under the evaluated model and interface conditions, both LLM-based pathways produced lower mean CER and WER than the conventional OCR baseline, with the lowest values observed for direct multimodal transcription. Semantic preservation was also higher for the LLM-based pathways, although measurement expressions remained the most persistent risk, particularly in table-based pages. Downstream tasks were not directly evaluated. The findings support the framework as a method for validating text-extraction pathways before reuse, rather than establishing a universal ranking of tools. Full article

Modernising historical literature into contemporary language is a form of diachronic intralingual translation that supports access to written cultural heritage. For low-resource languages such as Slovene, this task is hindered by orthographic, lexical and syntactic shifts, as well as the scarcity of parallel data. We present a two-stage pipeline that combines a fine-tuned No Language Left Behind (NLLB) model with Claude Opus 4.8 post-editing for the modernisation of 16th-century Slovene literature, retaining archaic words and phrases while normalising the alphabet, orthography and grammar. Using Jurij Dalmatin’s 1584 Bible and its 2017 modernised edition, we constructed an aligned parallel corpus of 14,876 sentence and clause-level pairs through Bohorič-to-Gaj normalisation and LaBSE-based embedding alignment. The hybrid pipeline achieved the best overall scores, reaching BLEU 45.78, CHRF 67.68 and METEOR 71.72, with TER 43.25 and CER 34.00. Its gains over the standalone LLM baseline were large and statistically significant across all the metrics, while its improvement over the fine-tuned NLLB model was smaller and significant mainly for overlap-based measures. We applied the pipeline further to Tulščak’s Kerszhanske leipe molitve (1579), producing the first preliminary modernisation of the earliest known Slovene prayer book assessed qualitatively and tested the generalisation on additional 16th-century texts, including an out-of-domain legal text. The results demonstrate that combining task-specific neural machine translation with controlled LLM post-editing offers a practical strategy for modernising low-resource historical texts and contributes a reusable methodology for digital cultural heritage preservation. Full article