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Rapid fabricating and harnessing stimuli-responsive behaviors of microscale bio-compatible hydrogels are of great interest to the emerging micro-mechanics, drug delivery, artificial scaffolds, nano-robotics, and lab chips. Herein, we demonstrate a novel femtosecond laser additive manufacturing process with smart materials for soft interactive hydrogel micro-machines. Bio-compatible hyaluronic acid methacryloyl was polymerized with hydrophilic diacrylate into an absorbent hydrogel matrix under a tight topological control through a 532 nm green femtosecond laser beam. The proposed hetero-scanning strategy modifies the hierarchical polymeric degrees inside the hydrogel matrix, leading to a controllable surface tension mismatch. Strikingly, these programmable stimuli-responsive matrices mechanized hydrogels into robotic applications at the micro/nanoscale (

A marine drilling riser at normal operation condition is required to disconnect the lower marine riser package (LMRP) and blow-out preventer (BOP) in case of severe weather. When the weather gets fine, it must reconnect the LMRP and the BOP. This process is called riser reentry. Marine drilling operations have been driven into extreme deep-waters characterized by severe weather which inevitably leads to a much higher incidence of disconnection. In addition, it requires to accomplish the reentry in a fast way owing to the capricious ocean environment. This study tries to develop a novel reentry control system based on model predictive control (MPC). First, the transverse governing equation of the hanging-off riser system with an end-mass is established based on the modified Hamilton’s principle. The optimization function and constraints in MPC are designed by use of the riser prediction model and the target location. A nonlinear disturbance observer is established for compensation of the model uncertainties and ocean environment disturbances. Finally, simulations are conducted after introducing the dynamic position system (DPS). The riser dynamics employing MPC are compared with that when adopting proportional-integral-derivative (PID) controller. It has found that the drilling riser system based on MPC has a higher response speed, which can complete the reentry process in a faster and more stable manner. It can handle the hydrodynamic force model uncertainties well and has a good robustness for current velocity disturbances. As the flexibility of the riser system is notably enhanced with the significant increase of aspect ratio, the higher-order mode of the flexible hanging-off riser can be triggered in the fast reentry process subjected to the excitations of the mother vessel and ocean environment.

Anthracnose, caused by Colletotrichum camelliae, poses a significant threat to the yield and quality of Camellia oleifera. Mitophagy, a selective form of autophagy, is crucial in maintaining mitochondrial quality and intracellular homeostasis. To date, an optimized experimental system for studying mitophagy in C. camelliae has yet to be established, and the role of mitophagy in the pathogenesis of C. camelliae remains unclear. Here, we characterized the function of CaEch1, a homolog of Magnaporthe oryza MoEch1, in the anthracnose fungus C. camelliae. Our findings indicate that mitochondria-localized CaEch1, plays a vital role for mitophagy in C. camelliae. Also, the CaEch11-29-GFP could serve as a reliable marker for monitoring mitophagy in C. camelliae. Additionally, the knockout of CaECH1 resulted in defects in fungal growth, conidiation, and appressorium formation. Pathogenicity assays further revealed that knockout of CaECH1 significantly reduced the virulence of C. camelliae. In summary, our research underscores the importance of CaEch1 in growth, conidiation, appressorium formation, and virulence in C. camelliae. This suggests its potential as a marker for mitophagy and provides valuable insights for developing new fungicides targeting anthracnose. [ABSTRACT FROM AUTHOR]

Phytophthora cinnamomi causes significant root rot in hickory, leading to substantial yield losses. While Bacillus spp. are recognized as beneficial rhizosphere microorganisms, their application against hickory root rot and their impact on rhizosphere microbial communities remain under-investigated. This study demonstrated that Bacillus velezensis TCS001 significantly inhibited P. cinnamomi ST402 growth in vitro, and achieved 71% efficacy in root rot disease management. Scanning electron microscopy (SEM) revealed that TCS001 fermentation filtrate induced mycelial deformities in P. cinnamomi. An analysis of α and β diversity indicated a significant impact of TCS001 on rhizosphere bacterial community richness and diversity, with minimal effects on the fungal community. Moreover, TCS001 altered the hickory rhizosphere microbiome co-occurrence network. The differential abundance analysis suggests that TCS001 promotes the recruitment of beneficial microbes associated with disease resistance, thereby suppressing disease development. These findings underscore the influence of TCS001 on the hickory rhizosphere microbiome in the presence of pathogens, providing valuable data for future research and the development of effective biocontrol strategies for hickory root rot. [ABSTRACT FROM AUTHOR]

Ocean resource exploration expands into deep and ultra-deep waters, which has posed great challenges to the 6-DOF parallel platform that requires to finish the long-span and high-velocity wave compensation task with high precision and anti-interference ability. The control strategy employed in the asymmetric hydraulic system of large aspect ratio requires more careful considerations when operating in the harsh and severe environment. An adaptive feedback linearization control strategy was proposed by employing the radial basis function neural network (RBFNN) for identification. First, a nonlinear model of the asymmetric hydraulic system was established. Then, an adaptive controller was designed based on RBFNN and feedback linearization. Finally, simulations were performed by using MATLAB/Simulink under the five-stage wave environment at a 90° wave encounter angle and under the external interference condition. The result shows that this method has a good traceability and robustness compared to classic PID and sliding mode control methods, which is more suitable in control of the wave compensation platform in complex sea conditions. The new controller can significantly increase the compensation accuracy and anti-interference ability, and provide a workbench for the 6-DOF parallel platform operation in deep waters.

Objective To investigate the protective effect and mechanism of dl-3-n-butylphthalide (NBP) on hydrogen peroxide (H2O2) - induced oxidative damage of vascular endothelial cells (VECs). Methods Human umbilical vein endothelial cells (HUVECs) and human umbilical artery endothelial cells (HUAECs) were cultured and MTT assay was used to find the appropriate dosage of H2O2 for establishment of oxidative stress injury model. Then the cells were pre-incubated with two different dosages of NBP (5 and 10 μmol/L) 2 h before exposure to H2O2. The cell viability was detected by MTT assay, mitochondrial membrane potential was measured by rhodamine 123, cell apoptosis was detected by Annexin-V/PI double staining, and intracellular free Ca2+ was measured by Fura-4/AM. Results H2O2-induced oxidative injury reduced the cell viability of HUVECs and HUAECs, increased the intracellular free Ca2+ concentration thus leading to mitochondrial membrane potential decline and cell apoptosis(P＜0.05). However, NBP treatment improved the above changes induced by H2O2. Conclusions NBP protects vascular endothelial cells by alleviating the oxidative damage induced by H2O2.

Rice false smut, caused by Ustilaginoidea virens, threatens rice production by reducing yields and contaminating grains with harmful ustiloxins. However, studies on resistance genes are scarce. In this study, the resistance level of IR28 (resistant cultivar) to U. virens was validated through artificial inoculation. Notably, a reactivation of resistance genes after transient down-regulation during the first 3 to 5 dpi was observed in IR28 compared to WX98 (susceptible cultivar). Cluster results of a principal component analysis and hierarchical cluster analysis of differentially expressed genes (DEGs) in the transcriptome exhibited longer expression patterns in the early infection phase of IR28, consistent with its sustained resistance response. Results of GO and KEGG enrichment analyses highlighted the suppression of immune pathways when the hyphae first invade stamen filaments at 5 dpi, but sustained up-regulated DEGs were linked to the 'Plant–pathogen interaction' (osa04626) pathway, notably disease-resistant protein RPM1 (K13457, CNLs, coil-coiled NLR). An analysis of CNLs identified 245 proteins containing Rx-CC and NB-ARC domains in the Oryza sativa Indica genome. Partial candidate CNLs were shown to exhibit up-regulation at both 1 and 5 dpi in IR28. This study provides insights into CNLs' responses to U. virens in IR28, potentially informing resistance mechanisms and genetic breeding targets. [ABSTRACT FROM AUTHOR]

The Southern Ocean connects the ocean's major basins via the Antarctic Circumpolar Current (ACC), and closes the global meridional overturning circulation (MOC). Observing these transports is challenging because three‐dimensional mesoscale‐resolving measurements of currents, temperature, and salinity are required to calculate transport in density coordinates. Previous studies have proposed to circumvent these limitations by inferring subsurface transports from satellite measurements using data‐driven methods. However, it is unclear whether these approaches can identify the signatures of subsurface transport in the Southern Ocean, which exhibits an energetic mesoscale eddy field superposed on a highly heterogeneous mean stratification and circulation. This study employs Deep Learning techniques to link the transports of the ACC and the upper and lower branches of the MOC to sea surface height (SSH) and ocean bottom pressure (OBP), using an idealized channel model of the Southern Ocean as a test bed. A key result is that a convolutional neural network produces skillful predictions of the ACC transport and MOC strength (skill score of ∼ ${\sim} $0.74 and ∼ ${\sim} $0.44, respectively). The skill of these predictions is similar across timescales ranging from daily to decadal but decreases substantially if SSH or OBP is omitted as a predictor. Using a fully connected or linear neural network yields similarly accurate predictions of the ACC transport but substantially less skillful predictions of the MOC strength. Our results suggest that Deep Learning offers a route to linking the Southern Ocean's zonal transport and overturning circulation to remote measurements, even in the presence of pronounced mesoscale variability. Plain Language Summary: Monitoring changes in the strengths of Southern Ocean current systems is challenging due to their vast size and the region's relative inaccessibility. This study explores the potential for remotely monitoring these currents via satellite measurements. Neural networks are used to "learn" the relationship between satellite‐measurable ocean properties and the strengths of Southern Ocean currents, using a simplified simulation as a test case. A key question is whether the circulation can be inferred from satellite measurements when the ocean hosts a vigorous field of mesoscale eddies—horizontal swirls of fluid that reach hundreds of kilometers in diameter. Three neural network (NN) frameworks are trained to predict the simulated ocean circulation strength from the simulated satellite measurements, and then their performance is evaluated using a separate segment of the simulation data. It is shown that this approach yields accurate predictions of all of the targeted components of the Southern Ocean circulation strength, provided that the NNs use a "convolutional" filter, which enhances their ability to identify spatial patterns in the simulated satellite measurements, and thus to infer movements of ocean water induced by the eddies. These findings serve to guide future indirect approaches to observing the Southern Ocean using remote sensing. Key Points: Deep Learning methods link sea surface height and ocean bottom pressure to transport variability in an eddying Southern Ocean channel modelConvolutional neural network captures sub‐annual and interannual variance in both circumpolar transport and overturning strengthPredicting overturning variability requires convolutional kernel to capture eddy‐induced meridional transports [ABSTRACT FROM AUTHOR]