Phthalate Acid Esters (PAEs) in Indoor Dust from Decoration Material Stores: Occurrence, Sources, and Health Risks.

Phthalate acid esters (PAEs) are one of the most widely used plasticizers globally, extensively employed in various decoration materials. However, studies on the impact of these materials on indoor environmental PAE pollution and their effects on human health are limited. In this study, forty dust samples were collected from four types of stores specializing in decoration materials (flooring, furniture boards, wall coverings, and household articles). The levels, sources, exposure doses, and potential health risks of PAEs in dust from decoration material stores were assessed. The total concentrations of Σ9PAE (the sum of nine PAEs) in dust from all decoration-material stores ranged from 46,100 ng/g to 695,000 ng/g, with a median concentration of 146,000 ng/g. DMP, DEP, DBP, and DEHP were identified as the predominant components. Among all stores, furniture board stores exhibited the highest Σ9PAE (159,000 ng/g, median value), while flooring stores exhibited the lowest (95,300 ng/g). Principal component analysis (PCA) showed that decoration materials are important sources of PAEs in the indoor environment. The estimated daily intakes of PAEs through non-dietary dust ingestion and dermal-absorption pathways among staff in various decoration-material stores were 60.0 and 0.470 ng/kg-bw/day (flooring stores), 113 and 0.780 ng/kg-bw/day (furniture board stores), 102 and 0.510 ng/kg-bw/day (wall covering stores), and 114 and 0.710 ng/kg-bw/day (household article stores). Particularly, staff in wall-covering and furniture-board stores exhibited relatively higher exposure doses of DEHP. Risk assessment indicated that although certain PAEs posed potential health risks, the exposure levels for staff in decoration material stores were within acceptable limits. However, staff in wall covering stores exhibited relatively higher risks, necessitating targeted risk-management strategies. This study provides new insights into understanding the risk associated with PAEs in indoor environments.

Dietary ellagic acid therapy for CNS autoimmunity: Targeting on Alloprevotella rava and propionate metabolism.

BACKGROUND: Mediterranean diet rich in polyphenolic compounds holds great promise to prevent and alleviate multiple sclerosis (MS), a central nervous system autoimmune disease associated with gut microbiome dysbiosis. Health-promoting effects of natural polyphenols with low bioavailability could be attributed to gut microbiota reconstruction. However, its underlying mechanism of action remains elusive, resulting in rare therapies have proposed for polyphenol-targeted modulation of gut microbiota for the treatment of MS. RESULTS: We found that oral ellagic acid (EA), a natural polyphenol rich in the Mediterranean diet, effectively halted the progression of experimental autoimmune encephalomyelitis (EAE), the animal model of MS, via regulating a microbiota-metabolites-immunity axis. EA remodeled the gut microbiome composition and particularly increased the relative abundances of short-chain fatty acids -producing bacteria like Alloprevotella. Propionate (C3) was most significantly up-regulated by EA, and integrative modeling revealed a strong negative correlation between Alloprevotella or C3 and the pathological symptoms of EAE. Gut microbiota depletion negated the alleviating effects of EA on EAE, whereas oral administration of Alloprevotella rava mimicked the beneficial effects of EA on EAE. Moreover, EA directly promoted Alloprevotella rava (DSM 22548) growth and C3 production in vitro. The cell-free supernatants of Alloprevotella rava co-culture with EA suppressed Th17 differentiation by modulating acetylation in cell models. C3 can alleviate EAE development, and the mechanism may be through inhibiting HDAC activity and up-regulating acetylation thereby reducing inflammatory cytokines secreted by pathogenic Th17 cells. CONCLUSIONS: Our study identifies EA as a novel and potentially effective prebiotic for improving MS and other autoimmune diseases via the microbiota-metabolites-immunity axis. Video Abstract.

A Lateral Flow-Recombinase Polymerase Amplification Method for Colletotrichum gloeosporioides Detection.

The greater yam (Dioscorea alata), a widely cultivated and nutritious food crop, suffers from widespread yield reduction due to anthracnose caused by Colletotrichum gloeosporioides. Latent infection often occurs before anthracnose phenotypes can be detected, making early prevention difficult and causing significant harm to agricultural production. Through comparative genomic analysis of 60 genomes of 38 species from the Colletotrichum genus, this study identified 17 orthologous gene groups (orthogroups) that were shared by all investigated C. gloeosporioides strains but absent from all other Colletotrichum species. Four of the 17 C. gloeosporioides-specific orthogroups were used as molecular markers for PCR primer designation and C. gloeosporioides detection. All of them can specifically detect C. gloeosporioides out of microbes within and beyond the Colletotrichum genus with different sensitivities. To establish a rapid, portable, and operable anthracnose diagnostic method suitable for field use, specific recombinase polymerase amplification (RPA) primer probe combinations were designed, and a lateral flow (LF)-RPA detection kit for C. gloeosporioides was developed, with the sensitivity reaching the picogram (pg) level. In conclusion, this study identified C. gloeosporioides-specific molecular markers and developed an efficient method for C. gloeosporioides detection, which can be applied to the prevention and control of yam anthracnose as well as anthracnose caused by C. gloeosporioides in other crops. The strategy adopted by this study also serves as a reference for the identification of molecular markers and diagnosis of other plant pathogens.

Genome-Wide Identification and Evolutionary Analysis of Receptor-like Kinase Family Genes Provides Insights into Anthracnose Resistance of Dioscorea alata.

Dioscorea alata, commonly known as "greater yam", is a vital crop in tropical and subtropical regions of the world, yet it faces significant threats from anthracnose disease, mainly caused by Colletotrichum gloeosporioides. However, exploring disease resistance genes in this species has been challenging due to the difficulty of genetic mapping resulting from the loss of the flowering trait in many varieties. The receptor-like kinase (RLK) gene family represents essential immune receptors in plants. In this study, genomic analysis revealed 467 RLK genes in D. alata. The identified RLKs were distributed unevenly across chromosomes, likely due to tandem duplication events. However, a considerable number of ancient whole-genome or segmental duplications dating back over 100 million years contributed to the diversity of RLK genes. Phylogenetic analysis unveiled at least 356 ancient RLK lineages in the common ancestor of Dioscoreaceae, which differentially inherited and expanded to form the current RLK profiles of D. alata and its relatives. The analysis of cis-regulatory elements indicated the involvement of RLK genes in diverse stress responses. Transcriptome analysis identified RLKs that were up-regulated in response to C. gloeosporioides infection, suggesting their potential role in resisting anthracnose disease. These findings provide novel insights into the evolution of RLK genes in D. alata and their potential contribution to disease resistance.

Aqueous Developable and CO2-Sourced Chemical Amplification Photoresist with High Performance.

Seeking high-performance photoresists is an important item for semiconductor industry due to the continuous miniaturization and intelligentization of integrated circuits. Polymer resin containing carbonate group has many desirable properties, such as high transmittance, acid sensitivity and chemical formulation, thus serving as promising photoresist material. In this work, a series of aqueous developable CO2-sourced polycarbonates (CO2-PCs) were produced via alternating copolymerization of CO2 and epoxides bearing acid-cleavable cyclic acetal groups in the presence of tetranuclear organoborane catalyst. The produced CO2-PCs were investigated as chemical amplification resists in deep ultraviolet (DUV) lithography. Under the catalysis of photogenerated acid, the acetal (ketal) groups in CO2-PC were hydrolysed into two equivalents of hydroxyl groups, which change the exposed area from hydrophobicity to hydrophilicity, thus enabling the exposed area to be developed with water. Through normalized remaining thickness analysis, the optimal CO2-derived resist achieved a remarkable sensitivity of 1.9 mJ/cm2, a contrast of 7.9, a favorable resolution (750 nm, half pitch), and a good etch resistance (38 % higher than poly(tert-butyl acrylate)). Such performances outperform commercial KrF and ArF chemical amplification resists (i.e., polyhydroxystyrene-derived and polymethacrylate-based resists), which endows broad application prospects in the field of DUV (KrF and ArF) and extreme ultraviolet (EUV) lithography for nanomanufacturing.

Signal2signal: Pushing the Spatiotemporal Resolution to the Limit by Single Chemical Hyperspectral Imaging.

There is growing interest in developing a high-performance self-supervised denoising algorithm for real-time chemical hyperspectral imaging. With a good understanding of the working function of the zero-shot Noise2Noise-based denoising algorithm, we developed a self-supervised Signal2Signal (S2S) algorithm for real-time denoising with a single chemical hyperspectral image. Owing to the accurate distinction and capture of the weak signal from the random fluctuating noise, S2S displays excellent denoising performance, even for the hyperspectral image with a spectral signal-to-noise ratio (SNR) as low as 1.12. Under this condition, both the image clarity and the spatial resolution could be significantly improved and present an almost identical pattern with a spectral SNR of 7.87. The feasibility of real-time denoising during imaging was well demonstrated, and S2S was applied to monitor the photoinduced exfoliation of transition metal dichalcogenide, which is hard to accomplish by confocal Raman spectroscopy. In general, the real-time denoising capability of S2S offers an easy way toward in situ/in vivo/operando research with much improved spatial and temporal resolution. S2S is open-source at https://github.com/3331822w/Signal2signal and will be accessible online at https://ramancloud.xmu.edu.cn/tutorial.

Deep Learning-Assisted Spectrum-Structure Correlation: State-of-the-Art and Perspectives.

Spectrum-structure correlation is playing an increasingly crucial role in spectral analysis and has undergone significant development in recent decades. With the advancement of spectrometers, the high-throughput detection triggers the explosive growth of spectral data, and the research extension from small molecules to biomolecules accompanies massive chemical space. Facing the evolving landscape of spectrum-structure correlation, conventional chemometrics becomes ill-equipped, and deep learning assisted chemometrics rapidly emerges as a flourishing approach with superior ability of extracting latent features and making precise predictions. In this review, the molecular and spectral representations and fundamental knowledge of deep learning are first introduced. We then summarize the development of how deep learning assist to establish the correlation between spectrum and molecular structure in the recent 5 years, by empowering spectral prediction (i.e., forward structure-spectrum correlation) and further enabling library matching and de novo molecular generation (i.e., inverse spectrum-structure correlation). Finally, we highlight the most important open issues persisted with corresponding potential solutions. With the fast development of deep learning, it is expected to see ultimate solution of establishing spectrum-structure correlation soon, which would trigger substantial development of various disciplines.

Unraveling the Molecular Basis of Color Variation in Dioscorea alata Tubers: Integrated Transcriptome and Metabolomics Analysis.

Dioscorea alata L. (Dioscoreaceae) is a widely cultivated tuber crop with variations in tuber color, offering potential value as health-promoting foods. This study focused on the comparison of D. alata tubers possessing two distinct colors, white and purple, to explore the underlying mechanisms of color variation. Flavonoids, a group of polyphenols known to influence plant color and exhibit antioxidant properties, were of particular interest. The total phenol and total flavonoid analyses revealed that purple tubers (PTs) have a significantly higher content of these metabolites than white tubers (WTs) and a higher antioxidant activity than WTs, suggesting potential health benefits of PT D. alata. The transcriptome analysis identified 108 differentially expressed genes associated with the flavonoid synthesis pathway, with 57 genes up-regulated in PTs, including CHS, CHI, DFR, FLS, F3H, F3'5'H, LAR, ANS, and ANR. The metabolomics analysis demonstrated that 424 metabolites, including 104 flavonoids and 8 tannins, accumulated differentially in PTs and WTs. Notably, five of the top ten up-regulated metabolites were flavonoids, including 6-hydroxykaempferol-7-O-glucoside, pinocembrin-7-O-(6″-O-malonyl)glucoside, 6-hydroxykaempferol-3,7,6-O-triglycoside, 6-hydroxykaempferol-7-O-triglycoside, and cyanidin-3-O-(6″-O-feruloyl)sophoroside-5-O-glucoside, with the latter being a precursor to anthocyanin synthesis. Integrating transcriptome and metabolomics data revealed that the 57 genes regulated 20 metabolites within the flavonoid synthesis pathway, potentially influencing the tubers' color variation. The high polyphenol content and antioxidant activity of PTs indicate their suitability as nutritious and health-promoting food sources. Taken together, the findings of this study provide insights into the molecular basis of tuber color variation in D. alata and underscore the potential applications of purple tubers in the food industry and human health promotion. The findings contribute to the understanding of flavonoid biosynthesis and pigment accumulation in D. alata tubers, opening avenues for future research on enhancing the nutritional quality of D. alata cultivars.

Patch-Based Convolutional Encoder: A Deep Learning Algorithm for Spectral Classification Balancing the Local and Global Information.

Molecular vibrational spectroscopies, including infrared absorption and Raman scattering, provide molecular fingerprint information and are powerful tools for qualitative and quantitative analysis. They benefit from the recent development of deep-learning-based algorithms to improve the spectral, spatial, and temporal resolutions. Although a variety of deep-learning-based algorithms, including those to simultaneously extract the global and local spectral features, have been developed for spectral classification, the classification accuracy is still far from satisfactory when the difference becomes very subtle. Here, we developed a lightweight algorithm named patch-based convolutional encoder (PACE), which effectively improved the accuracy of spectral classification by extracting spectral features while balancing local and global information. The local information was captured well by segmenting the spectrum into patches with an appropriate patch size. The global information was extracted by constructing the correlation between different patches with depthwise separable convolutions. In the five open-source spectral data sets, PACE achieved a state-of-the-art performance. The more difficult the classification, the better the performance of PACE, compared with that of residual neural network (ResNet), vision transformer (ViT), and other commonly used deep learning algorithms. PACE helped improve the accuracy to 92.1% in Raman identification of pathogen-derived extracellular vesicles at different physiological states, which is much better than those of ResNet (85.1%) and ViT (86.0%). In general, the precise recognition and extraction of subtle differences offered by PACE are expected to facilitate vibrational spectroscopy to be a powerful tool toward revealing the relevant chemical reaction mechanisms in surface science or realizing the early diagnosis in life science.