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The rapid, reliable, and efficient characteristics of quantitative reverse transcription polymerase chain reaction (qRT-PCR) make it a highly advantageous method for assessing gene expression levels. The identification of stable reference genes is crucial for successful gene expression studies. Cultivated strawberry fruit has been extensively investigated as a model for studying the non-climacteric fruit ripening process. However, more research needs to be conducted on identifying suitable reference genes at different developmental stages of strawberry fruit. We selected the 'Yanli' and 'Chuliandeweidao' cultivars to screen potential reference genes in various tissues, organs, and developmental stages of strawberry fruit. Based on the analysis of high-quality haplotype-resolved genome and transcriptomic FPKM data, FaADPrf1 (ADP-ribosylation factor 1), FaGAPC2 (Glyceraldehyde-3-phosphate dehydrogenase), FaPPC1 (Peptidyl-prolyl cis-trans isomerase 1), and FaEF1-α (Elongation factor 1-alpha) were selected as candidate reference genes, along with the commonly used Fa26S rRNA, for normalization purposes. A qRT-PCR analysis showed 89.21% to 101.51% amplification efficiency for five candidate reference genes, with correlation coefficients (R2) exceeding 0.99. Reference genes' expression stability was assessed using GeNorm, NormFinder, BestKeeper, and Comparative delta-Ct method. RefFinder analysis determined that FaGAPC2 and FaADPrf1 were the most suitable reference genes, considering the results obtained from the abovementioned four methods. The calculated results were validated by studying the expression of FaMYB10, FaUGT1, and FaCHS in different developmental stages of 'Yanli' fruit. This validation confirmed that both FaGAPC2 and the combination of FaGAPC2 and FaADPrf1 could serve as suitable reference genes, with the combination of FaGAPC2 and FaADPrf1 being more suitable than the single FaGAPC2 in certain cases. In summary, we obtained suitable reference genes for research on cultivated strawberry fruit development, which will benefit further study on the ripening of non-climacteric fruits.

The existing kinase inhibitors for hepatocellular carcinoma (HCC) have conferred survival benefits but are hampered by adverse effects and drug resistance, necessitating the development of novel agents targeting distinct pathways. To discover potent new anti-HCC compounds, we leveraged scaffold hopping from Sorafenib and introduced morpholine/piperidine moieties to develop ureido-substituted 4-phenylthiazole analogs with optimized physicochemical properties and binding interactions. Notably, compound 27 exhibited potent cytotoxicity against HepG2 cells (IC50 = 0.62 ± 0.34 μM), significantly exceeding Sorafenib (IC50 = 1.62 ± 0.27 μM). Mechanistic investigations revealed that compound 27 potently inhibited HCC cell migration and colony formation, and it induced G2/M arrest and early-stage apoptosis. Kinase profiling revealed IGF1R as a key target, which compound 27 potently inhibited (76.84% at 10 μM). Molecular modeling substantiated compound 27’s strong binding to IGF1R via multiple hydrogen bonds. Computational predictions indicate favorable drug-like properties for compound 27. These findings provide a promising drug candidate for the treatment of HCC patients.

We have studied the degradation of both full-sized modules and minimodules with PERC and Al-BSF cell variations in fields while considering packaging strategies. We demonstrate the implementations of data-driven tools to analyze large numbers of modules and volumes of timeseries data to obtain the performance loss and degradation pathways. This data analysis pipeline enables quantitative comparison and ranking of module variations, as well as mapping and deeper understanding of degradation mechanisms. The best performing module is a half-cell PERC, which shows a performance loss rate (PLR) of −0.27 ± 0.12% per annum (%/a) after initial losses have stabilized. Minimodule studies showed inconsistent performance rankings due to significant power loss contributions via series resistance, however, recombination losses remained stable. Overall, PERC cell variations outperform or are not distinguishable from Al-BSF cell variations.

Degradation pathway models constructed using network structural equation modeling (netSEM) are used to study degradation modes and pathways active in photovoltaic (PV) system variants in exposure conditions of high humidity and temperature. This data-driven modeling technique enables the exploration of simultaneous pairwise and multiple regression relationships between variables in which several degradation modes are active in specific variants and exposure conditions. Durable and degrading variants are identified from the netSEM degradation mechanisms and pathways, along with potential ways to mitigate these pathways. A combination of domain knowledge and netSEM modeling shows that corrosion is the primary cause of the power loss in these glass/backsheet PV minimodules. We show successful implementation of netSEM to elucidate the relationships between variables in PV systems and predict a specific service lifetime. The results from pairwise relationships and multiple regression show consistency. This work presents a greater opportunity to be expanded to other materials systems.