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We demonstrate a novel flat-field, dual-optic imaging EUV-soft X-ray spectrometer and monochromator that attains an unprecedented throughput efficiency exceeding 60% by design, along with a superb spectral resolution of λ/Δλ > 200 accomplished without employing variable line spacing gratings. Exploiting the benefits of the conical diffraction geometry, the optical system is globally optimized in multidimensional parameter space to guarantee optimal imaging performance over a broad spectral range while maintaining circular and elliptical polarization states at the first, second, and third diffraction orders. Moreover, our analysis indicates minimal temporal dispersion, with pulse broadening confined within 80 fs tail-to-tail and an FWHM value of 29 fs, which enables ultrafast spectroscopic and pump-probe studies with femtosecond accuracy. Furthermore, the spectrometer can be effortlessly transformed into a monochromator spanning the EUV-soft X-ray spectral region using a single grating with an aberration-free spatial profile. Such capability allows coherent diffractive imaging applications to be conducted with highly monochromatic light in a broad spectral range and extended to the soft X-ray region with minimal photon loss, thus facilitating state-of-the-art imaging of intricate nano- and bio-systems, with a significantly enhanced spatiotemporal resolution, down to the nanometer-femtosecond level.

The research investigated the nutritional physiology effect of ginseng saponins on Litopenaeus vannamei (L. vannamei) under low-fishmeal diets. In total, five experimental groups were arranged, with 21% fishmeal (high-fishmeal) serving as the positive control (PC), 11% fishmeal (low-fishmeal) serving as the negative control (NC), and 11% fishmeal serving as the addition in all three other groups. Similarly, ginseng saponins (GSP, purity of 2%) were added in the order of 0.1%, 0.3%, and 0.5% (GSP0.1, GSP0.3, and GSP0.5), with an 8-week growth cycle. Both GSP0.1 and GSP0.3 showed significantly higher growth performance (final body weight, FBW; weight gain rate, WGR; specific growth rate, SGR) than the NC group, but significantly lower growth performance than the PC group (P <0.05). However, it was found that there was no significant difference in the body composition of the whole shrimp between the experimental groups. Compared to the PC group, the GSP0.3 group exhibited significantly elevated levels of antioxidant enzymes, total antioxidant capacities (T-AOC), superoxide dismutase (SOD), and glutathione peroxidase (GSH-Px) (P <0.05). Additionally, significant differences were observed between the PC and GSP0.3 groups regarding the expression levels of sod , cat , and gsh-px (P<0.05). And there was a better morphological organization of shrimp hepatopancreas in the GSP0.3 group than in all other groups. In comparison with the PC group, there was no significant difference in shrimp survival rates after ammonia nitrogen stress with ginseng saponins added (P >0.05). Whereas, in terms of the relative expression levels of the corresponding genes, in shrimp of the GSP0.3 group, the relative expression of antioxidant-related genes sod , cat , and gsh-px were significantly higher than that of the PC group (P <0.05). Caspase3 and p53 , along with bcl-2 and bax , were found to be significantly more expressed in shrimp of the GSP0.3 group than in all other groups (P <0.05). These findings imply that in addition to improving growth performance, adding ginseng saponins at a concentration of 11% fishmeal could improve the antioxidant capacity of L. vannamei as well as its resistance to stress. Therefore, ginseng saponins can be utilized as a functional additive to increase L. vannamei growth performance, enhance antioxidant capacity, and reduce stress in low-fishmeal diets, 0.3% of ginseng saponins is optimal. [ABSTRACT FROM AUTHOR]

Grazing activities constitute the paramount challenge to grassland conservation over the Qinghai–Tibet Plateau (QTP), underscoring the urgency of obtaining detailed information regarding the extent, patterns, and trends of grazing to enable efficient grassland management and sustainable development. Here, to inform stakeholders about these issues, we provided the first annual Gridded Dataset of Grazing Intensity (GDGI), with a resolution of 100 m , from 1990 to 2020 for the QTP. The five most commonly used machine learning algorithms were leveraged to develop a livestock spatialization model, which spatially disaggregates the livestock census data at the county level into a detailed 100 m × 100 m grid based on seven key predictors from terrain, climate, vegetation, and socio-economic factors. Among these algorithms, the extreme-tree (ET) model performed the best in representing the complex nonlinear relationship between various environmental factors and livestock intensity, with an average absolute error of just 0.081 SUha-2 (where SU denotes sheep units), a rate outperforming the other models by 21.58 %–414.60 %. By using the ET model, we further generated the GDGI for the QTP to reveal the spatio-temporal heterogeneity and variations in grazing intensities. The GDGI indicates that grazing intensity remained high and largely stable from 1990 to 1997, followed by a sharp decline from 1997 to 2001 and fluctuations thereafter. Encouragingly, compared to other open-access datasets for grazing distribution on the QTP, the GDGI has the highest accuracy, with the determinant coefficient (R2) exceeding 0.8. Given its high resolution, recentness, and robustness, we believe that the GDGI dataset can significantly enhance our understanding of the substantial threats to grasslands emanating from overgrazing activities. Furthermore, the GDGI product holds considerable potential as a foundational source for other research, facilitating the rational utilization of grasslands, refined environmental impact assessments, and the sustainable development of animal husbandry. The GDGI product developed in this study is available at https://doi.org/10.5281/zenodo.10851119 (Zhou et al., 2024). [ABSTRACT FROM AUTHOR]