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Mastering the changes in the planting area of crops are critical for formulating sustainable agricultural development strategies. The Landsat 9 satellite provides continuous observational data for crop mapping. It is necessary to evaluate the potential of Landsat 9 images for canola mapping. According to literatures, some canola indices have been proposed in relation to the spectral features of the yellow canola flower, such as the canola ratio index (CRI), normalised difference yellowness index (NDYI), canola index (CI), canola flower index (CFI), normalised difference rapeseed flower index (NRFI) and winter rapeseed index (WRI). However, it is unclear which index has more potential to extract canola based on the Landsat 9 images. Therefore, the potentials of these indices for canola mapping were quantitatively compared by using different classification methods including supervised and unsupervised classification methods. For supervised classification methods and the CFI, the overall accuracy of canola mapping was above 90% and the kappa coefficient was above 0.8. For unsupervised classification methods, CFI also performs best. It was demonstrated that the CFI outperformed the other five indices for canola mapping. It was also confirmed that Landsat 9 images and the CFI exhibit promising potential for canola mapping via quantitative comparison.

Landsat-9 is the latest satellite of the Landsat program and provides useful agricultural remote sensing data. The earlier the crop distribution data is obtained, the greater the value of the data. However, whether Landsat-9 image is suitable for the early-season mapping of winter garlic and winter wheat is a problem worthy of attention. Therefore, this study evaluates the potential to use two Landsat-9 images, acquired on 13 December 2021 and 30 January 2022, for the early-season identification of winter garlic and winter wheat in China. According to J-M (Jeffries-Matusita) distances, we evaluated the separability of winter garlic, winter wheat, and other ground objects based on the two Landsat-9 images. Then, winter garlic and winter wheat were extracted by using unsupervised classification method, i.e. the IsoData and K-means clustering algorithms, and supervised classification method, i.e. the Random Forest (RF), and Support Vector Machine (SVM) algorithms. The separability between garlic and wheat in January is stronger than that in December. The classification overall accuracy based on the Landsat-9 image on 30 January 2022 is 92.03% with a kappa coefficient of 0.87 using the SVM algorithm. This period is about 4 months earlier than the crop harvest period. Landsat-9 images have good potentiality for early-season mapping of winter garlic and winter wheat.

Abstract Hydrogel sensors are widely utilized in soft robotics and tissue engineering due to their excellent mechanical properties and biocompatibility. However, in high‐water environments, traditional hydrogels can experience significant swelling, leading to decreased mechanical and electrical performance, potentially losing shape, and sensing capabilities. This study addresses these challenges by leveraging the Hofmeister effect, coupled with directional freezing and salting‐out techniques, to develop a layered, high‐strength, tough, and antiswelling PVA/MXene hydrogel. In particular, the salting‐out process enhances the self‐entanglement of PVA, resulting in an S‐PM hydrogel with a tensile strength of up to 2.87 MPa. Furthermore, the S‐PM hydrogel retains its structure and strength after 7 d of swelling, with only a 6% change in resistance. Importantly, its sensing performance is improved postswelling, a capability rarely achievable in traditional hydrogels. Moreover, the S‐PM hydrogel demonstrates faster response times and more stable resistance change rates in underwater tests, making it crucial for long‐term continuous monitoring in challenging aquatic environments, ensuring sustained operation and monitoring.

Abstract Manufacturing fuels and chemicals from cellulose materials is a promising strategy to achieve carbon neutralization goals. In addition to the commonly used enzymatic hydrolysis by cellulase, rapid pyrolysis is another way to degrade cellulose. The sugar obtained by fast pyrolysis is not glucose, but rather its isomer, levoglucosan (LG). Here, we revealed that both levoglucosan kinase activity and the transportation of levoglucosan are bottlenecks for LG utilization in Saccharomyces cerevisiae, a widely used cell factory. We revealed that among six heterologous proteins that had levoglucosan kinase activity, the 1,6-anhydro-N-acetylmuramic acid kinase from Rhodotorula toruloides was the best choice to construct levoglucosan-utilizing S. cerevisiae strain. Furthermore, we revealed that the amino acid residue Q341 and W455, which were located in the middle of the transport channel closer to the exit, are the sterically hindered barrier to levoglucosan transportation in Gal2p, a hexose transporter. The engineered yeast strain expressing the genes encoding the 1,6-anhydro-N-acetylmuramic acid kinase from R. toruloides and transporter mutant Gal2pQ341A or Gal2pW455A consumed ~ 4.2 g L−1 LG in 48 h, which is the fastest LG-utilizing S. cerevisiae strain to date.

Because canola is a major oilseed crop, accurately determining its planting areas is crucial for ensuring food security and achieving UN 2030 sustainable development goals. However, when canola is extracted using remote-sensing data, winter wheat causes serious interference because it has a similar growth cycle and spectral reflectance characteristics. This interference seriously limits the classification accuracy of canola, especially in mixed planting areas. Here, a novel canola flower index (CFI) is proposed based on the red, green, blue, and near-infrared bands of Sentinel-2 images to improve the accuracy of canola mapping, based on the finding that spectral reflectance of canola on the red and green bands is higher than that of winter wheat during the canola flowering period. To investigate the potential of the CFI for extracting canola, the IsoData, support vector machine (SVM), and random forest (RF) classification methods were used to extract canola based on Sentinel-2 raw images and CFI images. The results show that the average overall accuracy and kappa coefficient based on CFI images were 94.77% and 0.89, respectively, which were 1.05% and 0.02, respectively, higher than those of the Sentinel-2 raw images. Then we found that a threshold of 0.14 on the CFI image could accurately distinguish canola from non-canola vegetation, which provides a solution for automatic mapping of canola. The overall classification accuracy and kappa coefficient of this threshold method were 96.02% and 0.92, which were very similar to those of the SVM and RF methods. Moreover, the advantage of the threshold classification method is that it reduces the dependence on training samples and has good robustness and high classification efficiency. Overall, this study shows that CFI and Sentinel-2 images provide a solution for automatic and accurate canola extraction.

The complete and efficient utilization of both glucose and xylose is necessary for the economically viable production of biofuels and chemicals using lignocellulosic feedstocks. Although recently obtained recombinant Saccharomyces cerevisiae strains metabolize xylose well when xylose is the sole carbon source in the medium (henceforth referred to as “X stage”), their xylose consumption rate is significantly reduced during the xylose-only consumption phase of glucose-xylose co-fermentation (“GX stage”). This post-glucose effect seriously decreases overall fermentation efficiency. We showed in previous work that THI2 deletion can alleviate this post-glucose effect, but the underlying mechanisms were ill-defined. In the present study, we profiled the transcriptome of a thi2Δ strain growing at the GX stage. Thi2p in GX stage cells regulates genes involved in the cell cycle, stress tolerance, and cell viability. Importantly, the regulation of Thi2p differs from a previous regulatory network that functions when glucose is the sole carbon source, which suggests that the function of Thi2p depends on the carbon source. Modeling research seeking to optimize metabolic engineering via TFs should account for this important carbon source difference. Building on our initial study, we confirmed that several identified factors did indeed increase fermentation efficiency. Specifically, overexpressing STT4, RGI2, and TFC3 increases specific xylose utilization rate of the strain by 36.9, 29.7, 42.8%, respectively, in the GX stage of anaerobic fermentation. Our study thus illustrates a promising strategy for the rational engineering of yeast for lignocellulosic ethanol production.