Search

Throughout the natural aging process from new to aged white tea, the flavor evolves into a 'stale flavor', despite the initial umami diminishes. The flowering process, inoculation of Eurotium cristatum to white tea, improves the flavor. The impact on sensory qualities and underlying chemical basis of flowering in aged white tea warrant investigation. Sensory analysis, non-targeted metabolomics and volatilomics together deciphered flavor modifications of flowering in aged white tea from different aging years (FAWTs). Findings indicate the flowering process can recover the umami of aged white tea, enhancing the 'stale flavor'. These changes primarily stem from oxidations of catechins and free amino acids, enrichments of flavonols and soluble sugars, and 16 pivotal aroma compounds from degradations of lipids and glycosides. Additionally, 15 volatile and 39 non-volatile compounds were identified as potential biomarkers for FAWTs. These findings offer a viable strategy to improving the quality of aged white tea., Competing Interests: Declaration of competing interest We declare that we have no commercial or financial interest that represents a conflict of interest in connection with the work submitted., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Non-invasive prenatal testing (NIPT) employs ultra-low-pass sequencing of maternal plasma cell-free DNA to detect fetal trisomy. Its global adoption has established NIPT as a large human genetic resource for exploring genetic variations and their associations with phenotypes. Here, we present methods for analyzing large-scale, low-depth NIPT data, including customized algorithms and software for genetic variant detection, genotype imputation, family relatedness, population structure inference, and genome-wide association analysis of maternal genomes. Our results demonstrate accurate allele frequency estimation and high genotype imputation accuracy (R 2 >0.84) for NIPT sequencing depths from 0.1× to 0.3×. We also achieve effective classification of duplicates and first-degree relatives, along with robust principal-component analysis. Additionally, we obtain an R 2 >0.81 for estimating genetic effect sizes across genotyping and sequencing platforms with adequate sample sizes. These methods offer a robust theoretical and practical foundation for utilizing NIPT data in medical genetic research., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Golden-flower fungus (Eurotiwm Cristatum, EC) is widely inoculated in dark tea to endow a typical fungal floral aroma. Recently, Golden Flower White Tea (GFWT), prepared by transplanting EC-mediated fermentation to white tea (Shoumei, SM) to reform the roughness and coarseness, has attracted much attention attributed to coordinated flavor. However, the bio-chemistry reactions between EC and SM, along with origination of composited aroma are still unclear. Thus, the rejected EC, GFWT leaves and stems after EC removal were separated by layer-by-layer stripping following sensory evaluation, volatiles and microstructure analysis to uncover aroma formation mechanism. In GFWT, EC presents fungal flower aroma rather than contribution of extracellular enzymes secreted by fungus in Fu brick tea. Moreover, the short "flowering process" (7 days) endows SM with a stale, jujube, and sweet aroma, which is regarded as the typical characteristic of aged white tea. This inspires EC-mediated fermentation as a promising rapid aging process., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Background and Aims: Observational studies have implicated the involvement of gut microbiome in stroke development. Conversely, stroke may disrupt the gut microbiome balance, potentially causing systemic infections exacerbated brain infarction. However, the causal relationship remains controversial or unknown. To investigate bidirectional causality and potential ethnic differences, we conducted a bidirectional two-sample Mendelian randomisation (MR) study in both East Asian (EAS) and European (EU) populations., Methods: Leveraging the hitherto largest genome-wide association study (GWAS) summary data from the MiBioGen Consortium (n=18 340, EU) and BGI (n=2524, EAS) for the gut microbiome, stroke GWAS data from the GIGASTROKE Consortium(264 655 EAS and 1 308 460 EU), we conducted bidirectional MR and sensitivity analyses separately for the EAS and EU population., Results: We identified nominally significant associations between 85 gut microbiomes taxa in EAS and 64 gut microbiomes taxa in EU with stroke or its subtypes. Following multiple testing, we observed that genetically determined 1 SD increase in the relative abundance of species Bacteroides pectinophilus decreased the risk of cardioembolic stroke onset by 28% (OR 0.72 (95% CI 0.62 to 0.84); p=4.22e-5), and that genetically determined 1 SD increase in class Negativicutes resulted in a 0.76% risk increase in small vessel stroke in EAS. No significant causal association was identified in the EU population and the reverse MR analysis., Conclusion: Our study revealed subtype-specific and population-specific causal associations between gut microbiome and stroke risk among EAS and EU populations. The identified causality holds promise for developing a new stroke prevention strategy, warrants further mechanistic validation and necessitates clinical trial studies., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Cellular senescence represents an irreversible state of cell cycle arrest induced by various stimuli strongly associated with aging and several chronic ailments. In recent years, studies have increasingly suggested that the accumulation of senescent cells is an important contributor to the decline of organ metabolism, ultimately resulting in metabolic diseases. Conversely, the elimination of senescent cells can alleviate or postpone the onset and progression of metabolic diseases. Thus, a close relationship between senescent cells and metabolic diseases is found, and targeting senescent cells has emerged as an alternative therapy for the treatment of metabolic diseases. In this review, we summarize the role of cellular senescence in metabolic diseases, explore relevant therapeutic strategies for metabolic diseases by removing senescent cells, and provide new insights into the treatment of metabolic diseases., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Zhang, Zhou, Shen, Lin, Zhang, Sun, Zhou, Zhang and Zhang.)

In this Letter, we present a novel, to the best of our knowledge, image-based approach to analyze the mode control ability of a photonic lantern employed in diode laser beam combining, aiming to achieve a stable beam output. The proposed method is founded on theories of power flow and mode coupling and is validated through experiments. The findings demonstrate that the analysis of the beam combining process is highly reliable when the main mode component of the output light is the fundamental mode. Moreover, it is experimentally demonstrated that the mode control performance of the photonic lantern significantly influences the beam combining loss and the fundamental mode purity. In the essence of the variation-based analysis, a key advantage of the proposed method is its applicability even in the situation of a poor combined beam stability. The experiment only requires the collection of the far-field light images of the photonic lantern to characterize the model control ability, achieving an accuracy greater than 98%.

Compressed white tea (CWT) is a reprocessed tea of white tea. Long-term storage has greatly changed its aroma characteristics, but the material basis and transformation mechanism of its unique aroma are still unclear. In this study, flavor wheel, headspace gas chromatography ion mobility spectroscopy, chemometrics, and microbiomics were applied to study the flavor evolution and important aroma components during long-term storage of CWT, and core functional bacteria were screened. During long-term storage, the aroma of CWT gradually changed from sweet, fruity and floral to stale flavor, woody and herbal. A total of 56 volatile organic compounds (VOCs) were identified, 54 of which were significantly differences during storage. The alcohols content was the highest during 1-5 years of storage, the esters content was the highest during 7-13 years of storage, and the aldehydes content was the highest during 16 years of storage. Twenty-nine VOCs were identified as important aroma components, which were significantly correlated with 6 aroma sub-attributes ( P < 0.05). The functional prediction of bacterial community reminded that bacterial community could participate in the transformation of VOCs during storage of CWT. Twenty-four core functional bacteria were screened, which were significantly associated with 29 VOCs. Finally, 23 characteristic differential VOCs were excavated, which could be used to identify CWT in different storage years. Taken together, these findings provided new insights into the changes in aroma characteristics during storage of CWT and increased the understanding of the mechanism of characteristic aroma formation during storage., Competing Interests: XL was employed by Fujian Ming Shan Tea Industry Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wang, Wang, Dai, Wu, Song, Lin, Huang, Lin and Sun.)

Recovering molecular information remains a grand challenge in the widely used holographic and computational imaging technologies. To address this challenge, we developed a computational mid-infrared photothermal microscope, termed Bond-selective Intensity Diffraction Tomography (BS-IDT). Based on a low-cost brightfield microscope with an add-on pulsed light source, BS-IDT recovers both infrared spectra and bond-selective 3D refractive index maps from intensity-only measurements. High-fidelity infrared fingerprint spectra extraction is validated. Volumetric chemical imaging of biological cells is demonstrated at a speed of ~20 s per volume, with a lateral and axial resolution of ~350 nm and ~1.1 µm, respectively. BS-IDT's application potential is investigated by chemically quantifying lipids stored in cancer cells and volumetric chemical imaging on Caenorhabditis elegans with a large field of view (~100 µm x 100 µm)., (© 2022. The Author(s).)

Background: Fractional vegetation cover (FVC) is an important basic parameter for the quantitative monitoring of the alpine grassland ecosystem on the Qinghai-Tibetan Plateau. Based on unmanned aerial vehicle (UAV) acquisition of measured data and matching it with satellite remote sensing images at the pixel scale, the proper selection of driving data and inversion algorithms can be determined and is crucial for generating high-precision alpine grassland FVC products., Methods: This study presents estimations of alpine grassland FVC using optimized algorithms and multi-dimensional features. The multi-dimensional feature set (using original spectral bands, 22 vegetation indices, and topographical factors) was constructed from many sources of information, then the optimal feature subset was determined based on different feature selection algorithms as the driving data for optimized machine learning algorithms. Finally, the inversion accuracy, sensitivity to sample size, and computational efficiency of the four machine learning algorithms were evaluated., Results: (1) The random forest (RF) algorithm (R 2 : 0.861, RMSE: 9.5%) performed the best for FVC inversion among the four machine learning algorithms driven by the four typical vegetation indices. (2) Compared with the four typical vegetation indices, using multi-dimensional feature sets as driving data obviously improved the FVC inversion accuracy of the four machine learning algorithms (R 2 of the RF algorithm increased to 0.890). (3) Among the three variable selection algorithms (Boruta, sequential forward selection [SFS], and permutation importance-recursive feature elimination [PI-RFE]), the constructed PI-RFE feature selection algorithm had the best dimensionality reduction effect on the multi-dimensional feature set. (4) The hyper-parameter optimization of the machine learning algorithms and feature selection of the multi-dimensional feature set further improved FVC inversion accuracy (R 2 : 0.917 and RMSE: 7.9% in the optimized RF algorithm)., Conclusion: This study provides a highly precise, optimized algorithm with an optimal multi-dimensional feature set for FVC inversion, which is vital for the quantitative monitoring of the ecological environment of alpine grassland., (© 2021. The Author(s).)

A dense spectral multiplexing structure based on the reflective volume Bragg gratings is introduced to realize the diode laser multiplexing with an ultra-narrow channel spacing of 1.5 nm. With the combination of polarization multiplexing and coarse spectral multiplexing, a diode laser source producing 2045 W power from an output fiber with a core diameter of 105 µm and NA of 0.2 is achieved at an injection current of 10 A. The electro-optical and optical-optical efficiency of the laser source is 42% and 76%, respectively. Experimental results demonstrate the ability of dense spectral multiplexing based on VBG to realize high output power and brightness.