The decrotonylase FoSir5 facilitates mitochondrial metabolic state switching in conidial germination of Fusarium oxysporum.

Fusarium oxysporum is one of the most important pathogenic fungi with a broad range of plant and animal hosts. The first key step of its infection cycle is conidial germination, but there is limited information available on the molecular events supporting this process. We show here that germination is accompanied by a sharp decrease in expression of FoSir5, an ortholog of the human lysine deacetylase SIRT5. We observe that FoSir5 decrotonylates a subunit of the fungal pyruvate dehydrogenase complex (FoDLAT) at K148, resulting in inhibition of the activity of the complex in mitochondria. Moreover, FoSir5 decrotonylates histone H3K18, leading to a downregulation of transcripts encoding enzymes of aerobic respiration pathways. Thus, the activity of FoSir5 coordinates regulation in different organelles to steer metabolic flux through respiration. As ATP content is positively related to fungal germination, we propose that FoSir5 negatively modulates conidial germination in F. oxysporum through its metabolic impact. These findings provide insights into the multifaceted roles of decrotonylation, catalyzed by FoSir5, that support conidial germination in F. oxysporum

In situ grown palladium nanoparticles on polyester fabric as easy-separable and recyclable catalyst for Suzuki-Miyaura reaction

Palladium nanoparticles supported on low-melting polyester (Pd/LMPET) fabric were prepared through a microwave irradiation assisted method. In this way, in situ growth of Pd nanoparticles onto an easy to handle material was initiated and proceeded. The results of the characterization revealed that the palladium nanoparticles were well-dispersed on the surfaces of the polyester fibers. The Pd/LMPET fabrics were then employed in the Suzuki-Miyaura coupling. They exhibited excellent catalytic activity in ethanol/water under air atmosphere at 50 °C. Importantly, the Pd/LMPET fabrics could be separated from reaction mixture conveniently and they can still maintain good activity after 8 cycles without Pd leaching. © 2021 The Author

Quantifying Nonradiative Recombination and Resistive Losses in Perovskite Photovoltaics: A Modified Diode Model Approach

Pinpointing the origin of inefficiency can expedite the process of optimizing the efficiency of perovskite photovoltaics. However, it is challenging to discern and quantify the different loss pathways in a complete perovskite photovoltaic device under operational conditions. To address this challenge, we propose a modified diode model that can quantify bulk/interface defect-assisted recombination and series/shunt resistive losses. By adopting drift-diffusion simulation as the benchmark, we explore the physical meanings of the modified diode model parameters and evaluate the performance of the model for simulation parameters spanning many orders of magnitude. Our evaluation shows that, in most practical cases, the proposed model can accurately quantify all the aforementioned losses, and in some special cases, it is possible to identify the predominant loss pathway. Moreover, we apply the modified diode model to our lab-produced devices (based on Cs0.05FA0.95PbI3 perovskites), demonstrating its effectiveness in quantifying entangled losses in practice. Finally, we provide a set of guidelines for applying the modified diode model and interpreting the results. Source code available at https://github.com/WPT-Lab124/Modified-Diode-Model.Comment: 26 pages, 6 figures, published in Solar RR

Generative Model for Models: Rapid DNN Customization for Diverse Tasks and Resource Constraints

Unlike cloud-based deep learning models that are often large and uniform, edge-deployed models usually demand customization for domain-specific tasks and resource-limited environments. Such customization processes can be costly and time-consuming due to the diversity of edge scenarios and the training load for each scenario. Although various approaches have been proposed for rapid resource-oriented customization and task-oriented customization respectively, achieving both of them at the same time is challenging. Drawing inspiration from the generative AI and the modular composability of neural networks, we introduce NN-Factory, an one-for-all framework to generate customized lightweight models for diverse edge scenarios. The key idea is to use a generative model to directly produce the customized models, instead of training them. The main components of NN-Factory include a modular supernet with pretrained modules that can be conditionally activated to accomplish different tasks and a generative module assembler that manipulate the modules according to task and sparsity requirements. Given an edge scenario, NN-Factory can efficiently customize a compact model specialized in the edge task while satisfying the edge resource constraints by searching for the optimal strategy to assemble the modules. Based on experiments on image classification and object detection tasks with different edge devices, NN-Factory is able to generate high-quality task- and resource-specific models within few seconds, faster than conventional model customization approaches by orders of magnitude

A novel botybirnavirus with a unique satellite dsRNA causes latent infection in Didymella theifolia isolated from tea plants

© 2023 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/The unique, recently discovered fungus Didymella theifolia specifically infects local varieties of tea plant Camellia sinensis in China, and therefore, the characterization of its mycoviruses is important. Three double-stranded (ds) RNAs (1, 2, and 3, with 6,338, 5,910, and 727 bp in size, respectively) were identified in the avirulent D. theifolia strain CJP4-1, which exhibits normal growth and morphology. Characterization of these double-stranded RNAs (dsRNAs) revealed that the two largest elements are the genomic components of a novel botybirnavirus, tentatively named Didymella theifolia botybirnavirus 1 (DtBRV1). Conversely, dsRNA3 shares no detectable similarity with sequences deposited in public databases but has high similarity with the 5′-terminal regions of dsRNAs 1 and 2 and contains a duplicated region encoding a putative small peptide. All three dsRNAs are encapsidated in isometric virions ca. 40 nm in diameter, supporting the notion that dsRNA3 is a DtBRV1 satellite. SDS-polyacrylamide gel electrophoresis in combination with peptide mass fingerprint analysis revealed that the DtBRV1 capsid protein consists of polypeptides encoded by the 5′-terminal regions of both genomic components dsRNA1 and dsRNA2. Vertical transmission of DtBRV1 through conidia is efficient, while its horizontal transmission from CJP4-1 to other strains was not detected. DtBRV1, with or without dsRNA3, has no obvious effects on fungal growth and virulence, as illustrated following transfection of the virulent D. theifolia strain JYC1-6. In summary, DtBRV1 exhibits unique molecular traits and contributes to our understanding of mycovirus diversity.Peer reviewe

On the use of an explicit chemical mechanism to dissect peroxy acetyl nitrate formation.

Peroxy acetyl nitrate (PAN) is a key component of photochemical smog and plays an important role in atmospheric chemistry. Though it has been known that PAN is produced via reactions of nitrogen oxides (NOx) with some volatile organic compounds (VOCs), it is difficult to quantify the contributions of individual precursor species. Here we use an explicit photochemical model--Master Chemical Mechanism (MCM) model--to dissect PAN formation and identify principal precursors, by analyzing measurements made in Beijing in summer 2008. PAN production was sensitive to both NOx and VOCs. Isoprene was the predominant VOC precursor at suburb with biogenic impact, whilst anthropogenic hydrocarbons dominated at downtown. PAN production was attributable to a relatively small class of compounds including NOx, xylenes, trimethylbenzenes, trans/cis-2-butenes, toluene, and propene. MCM can advance understanding of PAN photochemistry to a species level, and provide more relevant recommendations for mitigating photochemical pollution in large cities

Isobavachalcone Sensitizes Cells to E2-Induced Paclitaxel Resistance by Down-Regulating CD44 Expression in ER+ Breast Cancer Cells

Oestrogen receptor (ER) is expressed in approximately 60%‐70% of human breast cancer. Clinical trials and retrospective analyses have shown that ER‐positive (ER+) tumours are more tolerant to chemotherapeutic drug resistance than ER‐negative (ER−) tumours. In addition, isobavachalcone (IBC) is known as a kind of phytoestrogen with antitumour effect. However, the underlying mechanism of IBC in ER+ breast cancer needs to be elucidated further. Our in vitro experiments showed that IBC could attenuate 17β‐estradiol (E2)‐induced paclitaxel resistance and that E2 could stimulate CD44 expression in ER+ breast cancer cells but not in ER− cells. Meanwhile, E2 could promote ERα expression to render ER+ breast cancer cells resistant to paclitaxel. Furthermore, we established paclitaxel‐resistant breast cancer cell lines and determined the function of ERα in the enhancement of paclitaxel resistance via the regulation of CD44 transcription. IBC down‐regulated ERα and CD44 expression and thus inhibited tumour growth in paclitaxel‐resistant xenograft models. Overall, our data demonstrated for the first time that IBC could decrease CD44 expression level via the ERα pathway and make ER+ breast cancer cells sensitive to paclitaxel treatment