Structure and magnetic properties of Ni/NiO, Co/CoO composite films

Ph.DDOCTOR OF PHILOSOPH

Unraveling the Regulatory Mechanism of Color Diversity in Camellia japonica Petals by Integrative Transcriptome and Metabolome Analysis

Camellia japonica petals are colorful, rich in anthocyanins, and possess important ornamental, edible, and medicinal value. However, the regulatory mechanism of anthocyanin accumulation in C. japonica is still unclear. In this study, an integrative analysis of the metabolome and transcriptome was conducted in five C. japonica cultivars with different petal colors. Overall, a total of 187 flavonoids were identified (including 25 anthocyanins), and 11 anthocyanins were markedly differentially accumulated among these petals, contributing to the different petal colors in C. japonica. Moreover, cyanidin-3-O-(6″-O-malonyl) glucoside was confirmed as the main contributor to the red petal phenotype, while cyanidin-3-O-rutinoside, peonidin-3-O-glucoside, cyanidin-3-O-glucoside, and pelargonidin-3-O-glucoside were responsible for the deep coloration of the C. japonica petals. Furthermore, a total of 12,531 differentially expressed genes (DEGs) and overlapping DEGs (634 DEGs) were identified by RNA sequencing, and the correlation between the expression level of the DEGs and the anthocyanin content was explored. The candidate genes regulating anthocyanin accumulation in the C. japonica petals were identified and included 37 structural genes (especially CjANS and Cj4CL), 18 keys differentially expressed transcription factors (such as GATA, MYB, bHLH, WRKY, and NAC), and 16 other regulators (mainly including transporter proteins, zinc-finger proteins, and others). Our results provide new insights for elucidating the function of anthocyanins in C. japonica petal color expression

Artificial intelligence-aided rapid and accurate identification of clinical fungal infections by single-cell Raman spectroscopy

Integrating artificial intelligence and new diagnostic platforms into routine clinical microbiology laboratory procedures has grown increasingly intriguing, holding promises of reducing turnaround time and cost and maximizing efficiency. At least one billion people are suffering from fungal infections, leading to over 1.6 million mortality every year. Despite the increasing demand for fungal diagnosis, current approaches suffer from manual bias, long cultivation time (from days to months), and low sensitivity (only 50% produce positive fungal cultures). Delayed and inaccurate treatments consequently lead to higher hospital costs, mobility and mortality rates. Here, we developed single-cell Raman spectroscopy and artificial intelligence to achieve rapid identification of infectious fungi. The classification between fungi and bacteria infections was initially achieved with 100% sensitivity and specificity using single-cell Raman spectra (SCRS). Then, we constructed a Raman dataset from clinical fungal isolates obtained from 94 patients, consisting of 115,129 SCRS. By training a classification model with an optimized clinical feedback loop, just 5 cells per patient (acquisition time 2 s per cell) made the most accurate classification. This protocol has achieved 100% accuracies for fungal identification at the species level. This protocol was transformed to assessing clinical samples of urinary tract infection, obtaining the correct diagnosis from raw sample-to-result within 1 h

Polyploidy underlies co-option and diversification of biosynthetic triterpene pathways in the apple tribe

Whole-genome duplication (WGD) plays important roles in plant evolution and function, yet little is known about how WGD underlies metabolic diversification of natural products that bear significant medicinal properties, especially in nonmodel trees. Here, we reveal how WGD laid the foundation for co-option and differentiation of medicinally important ursane triterpene pathway duplicates, generating distinct chemotypes between species and between developmental stages in the apple tribe. After generating chromosome-level assemblies of a widely cultivated loquat variety and Gillenia trifoliata, we define differentially evolved, duplicated gene pathways and date the WGD in the apple tribe at 13.5 to 27.1 Mya, much more recent than previously thought. We then functionally characterize contrasting metabolic pathways responsible for major triterpene biosynthesis in G. trifoliata and loquat, which pre- and postdate the Maleae WGD, respectively. Our work mechanistically details the metabolic diversity that arose post-WGD and provides insights into the genomic basis of medicinal properties of loquat, which has been used in both traditional and modern medicines