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Visual stimuli design plays an important role in brain-computer interfaces (BCIs) based on visual evoked potentials (VEPs). Variations in stimulus parameters have been shown to affect both decoding accuracy and subjective perception experience, implying the need for a trade-off in design. In this study, we comprehensively and systematically compared various combinations of amplitude contrast and spectral content parameters in the stimulus design to quantify their impact on decoding performance and subject comfort. Specifically, three parameters were investigated: 1) contrast level, 2) temporal pattern (periodic steady-state or pseudo-random code-modulated), and 3) frequency range. We collected electroencephalogram (EEG) data and subjective perception ratings from ten subjects and evaluated the decoding accuracy and subject comfort rating for different combinations of the stimulus parameters. Our results indicate that while high-frequency steady-state VEP (SSVEP) stimuli were rated the most comfortable, they also had the lowest decoding accuracy. Conversely, low-frequency SSVEP stimuli were rated the least comfortable but had the highest decoding accuracy. Standard and high-frequency M-sequence code-modulated VEPs (c-VEPs) produced intermediates between the two. We observed a consistent trade-off relationship between decoding accuracy and subjective comfort level across all parameters. Based on our findings, we offer c-VEP as a preferable stimulus for achieving reliable decoding accuracy while maintaining a reasonable level of comfortability.

Alfalfa ( Medicago sativa L.) is one of the most extensively cultivated forage crops globally, and its nutritional quality critically influences the productivity of dairy cows. Silage fermentation is recognized as a crucial technique for the preservation of fresh forage, ensuring the retention of its vital nutrients. However, the detailed microbial components and their functions in silage fermentation are not fully understood. This study integrated large-scale microbial culturing with high-throughput sequencing to thoroughly examine the microbial community structure in alfalfa silage and explored the potential pathways of nutritional degradation via metagenomic analysis. The findings revealed an enriched microbial diversity in silage, indicated by the identification of amplicon sequence variants. Significantly, the large-scale culturing approach recovered a considerable number of unique microbes undetectable by high-throughput sequencing. Predominant genera, such as Lactiplantibacillus , Leuconostoc , Lentilactobacillus , Weissella , and Liquorilactobacillus , were identified based on their abundance and prevalence. Additionally, genes associated with Enterobacteriaceae were discovered, which might be involved in pathways leading to the production of ammonia-N and butyric acid. Overall, this study offers a comprehensive insight into the microbial ecology of silage fermentation and provides valuable information for leveraging microbial consortia to enhance fermentation quality., Importance: Silage fermentation is a microbial-driven anaerobic process that efficiently converts various substrates into nutrients readily absorbable and metabolizable by ruminant animals. This study, integrating culturomics and metagenomics, has successfully identified core microorganisms involved in silage fermentation, including those at low abundance. This discovery is crucial for the targeted cultivation of specific microorganisms to optimize fermentation processes. Furthermore, our research has uncovered signature microorganisms that play pivotal roles in nutrient metabolism, significantly advancing our understanding of the intricate relationships between microbial communities and nutrient degradation during silage fermentation., Competing Interests: The authors declare no conflict of interest.

Visual stimuli design plays an important role in brain-computer interfaces (BCIs) based on visual evoked potentials (VEPs). Variations in stimulus parameters have been shown to affect both decoding accuracy and subjective perception experience, implying the need for a trade-off in design. In this study, we comprehensively and systematically compared various combinations of amplitude contrast and spectral content parameters in the stimulus design to quantify their impact on decoding performance and subject comfort. Specifically, three parameters were investigated: 1) contrast level, 2) temporal pattern (periodic steady-state or pseudo-random code-modulated), and 3) frequency range. We collected electroencephalogram (EEG) data and subjective perception ratings from ten subjects and evaluated the decoding accuracy and subject comfort rating for different combinations of the stimulus parameters. Our results indicate that while high-frequency steady-state VEP (SSVEP) stimuli were rated the most comfortable, they also had the lowest decoding accuracy. Conversely, low-frequency SSVEP stimuli were rated the least comfortable but had the highest decoding accuracy. Standard and high-frequency M-sequence code-modulated VEPs (c-VEPs) produced intermediates between the two. We observed a consistent trade-off relationship between decoding accuracy and subjective comfort level across all parameters. Based on our findings, we offer c-VEP as a preferable stimulus for achieving reliable decoding accuracy while maintaining a reasonable level of comfortability.