A novel porous high entropy alloy (HEA) coating was prepared on the steel surface by vacuum sintering. The coating was then used as a transition layer during dissimilar laser Al/steel joining. Compared with the uncoated laser joints, the liquid alloy spread and infiltrated into porous structure, the contact angle of the weld reduced from 65.8 ° to 56.7°, and the brazed width increased from 5.1 mm to 5.9 mm, which improved the wettability and spreadability of the molten filler wire on the substrate. In the case of uncoated steel, the fusion zone/steel interfacial microstructure consisted of laminated Al7.2Fe1.8Si and Fe(Al,Si)3, while it changed to a composite-like structure containing a soft HEA skeleton and hard IMCs which was composed of Al7.2Fe1.8Si, Al3Ni, and (Al,Si)2Cr. In addition, due to the sluggish diffusion effect of HEAs, a layer of gradient nanocrystalline composed of Al7.2Fe1.8Si was generated, which significantly strengthened the dissimilar laser joints with improved both the fracture load (~26.5%) and the displacement (~101.8%). The fracture mode changed from brittle to ductile failure when the porous HEA coating was applied, with fracture propagating through the HEA skeleton. This work provides a novel solution for the strengthening of hard-to-join dissimilar combinations.
Abstract Plitidepsin, a marine-derived anticancer medicine, is being tested in phase III clinical trials for treating COVID-19. However, the current supply of plitidepsin relies on laborious chemical synthesis processes. Here, we present a new approach that combines microbial and chemical synthesis to produce plitidepsin. We screened a Tistrella strain library to identify a high-yield didemnin B producer, and then introduced a second copy of the didemnin biosynthetic gene cluster into its genome, resulting in the highest yield of didemnin B reported in the literature. Next, we developed two straightforward chemical strategies to convert didemnin B to plitidepsin, one of which involved a one-step synthetic route giving over 90% overall yield. We also synthesized two new didemnin analogues and assessed their anticancer and antiviral activities. Our findings offer a practical and sustainable solution for producing plitidepsin and its derivatives, potentially expediting didemnin drug development.
Abstract Introduction Major depressive disorder (MDD) affects about 17% population in the world. Although abnormal energy metabolism plays an important role in the pathophysiology of MDD, however, how deficiency of adenosine triphosphate (ATP) products affects emotional circuit and what regulates ATP synthesis are still need to be elaborated. Aims Our study aimed to investigate how deficiency of PGAM5‐mediated depressive behavior. Results We firstly discovered that PGAM5 knockout ( PGAM5 −/− ) mice generated depressive‐like behaviors. The phenotype was reinforced by the observation that chronic unexpected mild stress (CUMS)‐induced depressive mice exhibited lowered expression of PGAM5 in prefrontal cortex ( PFC), hippocampus (HIP), and striatum. Next, we found, with the using of functional magnetic resonance imaging (fMRI), that the functional connectivity between PFC reward system and the PFC volume were reduced in PGAM5 −/− mice. PGAM5 ablation resulted in the loss of dendritic spines and lowered density of PSD95 in PFC, but not in HIP. Finally, we found that PGAM5 ablation led to lowered ATP concentration in PFC, but not in HIP. Coimmunoprecipitation study showed that PGAM5 directly interacted with the ATP F 1 F 0 synthase without influencing the interaction between ATP F 1 F 0 synthase and Bcl‐xl. We then conducted ATP administration to PGAM5 −/− mice and found that ATP could rescue the behavioral and neuronal phenotypes of PGAM5 −/− mice. Conclusions Our findings provide convincing evidence that PGAM5 ablation generates depressive‐like behaviors via restricting neuronal ATP production so as to impair the number of neuronal spines in PFC.
Data is the cornerstone of deep learning. This paper reveals that the recently developed Diffusion Model is a scalable data engine for object detection. Existing methods for scaling up detection-oriented data often require manual collection or generative models to obtain target images, followed by data augmentation and labeling to produce training pairs, which are costly, complex, or lacking diversity. To address these issues, we presentDiffusionEngine (DE), a data scaling-up engine that provides high-quality detection-oriented training pairs in a single stage. DE consists of a pre-trained diffusion model and an effective Detection-Adapter, contributing to generating scalable, diverse and generalizable detection data in a plug-and-play manner. Detection-Adapter is learned to align the implicit semantic and location knowledge in off-the-shelf diffusion models with detection-aware signals to make better bounding-box predictions. Additionally, we contribute two datasets, i.e., COCO-DE and VOC-DE, to scale up existing detection benchmarks for facilitating follow-up research. Extensive experiments demonstrate that data scaling-up via DE can achieve significant improvements in diverse scenarios, such as various detection algorithms, self-supervised pre-training, data-sparse, label-scarce, cross-domain, and semi-supervised learning. For example, when using DE with a DINO-based adapter to scale up data, mAP is improved by 3.1% on COCO, 7.6% on VOC, and 11.5% on Clipart.
Partial nitritation is required to provide nitrite for the anammox reaction in an autotrophic nitrogen removal process, which has been considered crucial to achieving energy-positive mainstream sewage treatment. In this study, three lab-scale sequencing batch reactors were operated to treat wastewaters with low ammonium concentrations (65-80 mgNH 4 + -N/L) at high hydraulic loading rates (hydraulic retention time of 3-4.8 h, nitrogen loading rates of 0.36-0.43 kgN/d/m3 Â ). Among these three reactors, one was fed with influent without organic carbon, and the others were provided with low C/N wastewaters. Long-term experiments repeatedly demonstrated that a high hydraulic loading rate favoured the start-up of partial nitritation, as indicated by nitrite build-up and effluent nitrite accumulation ratio maintained above 95% over two months. Despite many advantages of high loading rates, a major drawback resides in the process instability, i.e., partial nitritation would shift to full nitrification after 3-4 months of operation. To elucidate the occurrence and disappearance of the partial nitritation, mathematical modelling was further implemented. An integrated fixed film activated sludge biofilm model was developed, calibrated, and validated, based on the observed co-existence of granules and floccular sludge. Model-based analysis suggested high hydraulic loading rates suppressed NOB likely via intensifying granule surface sloughing and restricting oxygen penetration. Based on the unravelled mechanisms, operational strategies employing high hydraulic loadings were proposed to achieve stable partial nitritation and tested with mathematical models. The mechanisms illustrated in this work can guide the development of new operational strategies to facilitate mainstream partial nitritation and anammox process.
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