Pharmacokinetics and tissue distribution of N-3- methoxybenzyl-palmitamide in rat: A macamide derived from Lepidium meyenii

Purpose: To study the pharmacokinetics and tissue distribution of N-3-methoxybenzyl-palmitamide (MPM) derived from Lepidium meyenii (Maca)Methods: MPM and N-benzylpalmitamide (BPM, as the internal standard, IS) were prepared by one-pot synthesis method and characterized. For the analysis of MPM in rat plasma and tissue samples, a rapid ultra-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) method was developed and validated by optimizing sample preparation conditions and UPLC conditions. Finally, the pharmacokinetics and biodistribution of MPM after oral administration in rats were studied.Results: The lower limit of quantification (LLOQ) and limit of detection (LOD) of the UPLC-MS/MS method were 1.2 and 5.0 ng/mL, respectively. Good linear relationship of calibration curve (r > 0.9951) was achieved over the range of 5 – 5000 ng/mL. In pharmacokinetics, plasma concentration-time curve of MPM showed double peaks. The highest distribution of MPM after absorption was in the stomach, followed by lung. The absorption and eliminate rate of MPM were slow in rats. In fact, MPM displayed a lung targeting property.Conclusion: The developed UPLC-MS/MS method is suitable for plasma and tissue distribution studies of MPM in rats. The present study can provide guidance for the further development and utilization of Maca tuber.Keywords: Macamide, Maca tuber, Lepidium meyenii, Pharmacokinetics, Tissue distribution, UPLCMS/M

Analytical Model for Multi-Hazard Resilient Prefabricated Concrete Frame Considering Earthquake and Column Removal Scenarios

Research on multi-hazard prevention and mitigation in building structures is the most recent developing trend in civil engineering. In this study, an analytical model is proposed to calculate the structural resistance of a type of multi-hazard resilient prefabricated concrete (MHRPC) frame under earthquake and column removal scenarios. The MHRPC frame is assembled using prefabricated RC beams and columns, unbonded post-tensioning (PT) tendons, energy-dissipating steel angles, and large rotational shear plates. According to the experimental results, the MHRPC frame exhibits the features of low damage and self-centering under seismic loading. Meanwhile, when subjected to column removal scenarios, the MHRPC frame is proven to demonstrate a high progressive collapse resistance. In order to calculate the seismic and progressive collapse resistance of the MHRPC frame, analytical models for the critical components in the MHRPC frame (PT tendons and steel angles) are compared and selected based on the experimental results and numerical simulations. Furthermore, calculation methods for the seismic and progressive collapse resistance of the MHRPC frame specimens are proposed. The calculation results are validated using the experimental results. This study could provide a reference for the design of MHRPC frame structures, considering both earthquake and progressive collapse

Chalcogenide Glass-on-Graphene Photonics

Two-dimensional (2-D) materials are of tremendous interest to integrated photonics given their singular optical characteristics spanning light emission, modulation, saturable absorption, and nonlinear optics. To harness their optical properties, these atomically thin materials are usually attached onto prefabricated devices via a transfer process. In this paper, we present a new route for 2-D material integration with planar photonics. Central to this approach is the use of chalcogenide glass, a multifunctional material which can be directly deposited and patterned on a wide variety of 2-D materials and can simultaneously function as the light guiding medium, a gate dielectric, and a passivation layer for 2-D materials. Besides claiming improved fabrication yield and throughput compared to the traditional transfer process, our technique also enables unconventional multilayer device geometries optimally designed for enhancing light-matter interactions in the 2-D layers. Capitalizing on this facile integration method, we demonstrate a series of high-performance glass-on-graphene devices including ultra-broadband on-chip polarizers, energy-efficient thermo-optic switches, as well as graphene-based mid-infrared (mid-IR) waveguide-integrated photodetectors and modulators

Integrate the Canopy SIF and Its Derived Structural and Physiological Components for Wheat Stripe Rust Stress Monitoring

Solar-induced chlorophyll fluorescence (SIF) has great advantages in the remote sensing detection of crop stress. However, under stripe rust stress, the effects of canopy structure and leaf physiology on the variations in canopy SIF are unclear, and these influencing factors are entangled during the development of disease, resulting in an unclear coupling relationship between SIFcanopy and the severity level (SL) of disease, which affects the remote sensing detection accuracy of wheat stripe rust. In this study, the observed canopy SIF was decomposed into NIRVP, which can characterize the canopy structure, and SIFtot, which can sensitively reflect the physiological status of crops. Additionally, the main factors driving the variations in canopy SIF under different disease severities were analyzed, and the response characteristics of SIFcanopy, NIRVP, and SIFtot to SL under stripe rust stress were studied. The results showed that when the severity level (SL) of disease was lower than 20%, NIRVP was more sensitive to variation in SIFcanopy than SIFtot, and the correlation between SIFtot and SL was 6.6% higher than that of SIFcanopy. Using the decomposed SIFtot component allows one to detect the stress state of plants before variations in vegetation canopy structure and leaf area index and can realize the early diagnosis of crop diseases. When the severity level (SL) of disease was in the state of moderate incidence (20% < SL ≤ 45%), the variation in SIFcanopy was affected by both NIRVP and SIFtot, and the detection accuracy of SIFcanopy for wheat stripe rust was better than that of the NIRVP and SIFtot components. When the severity level (SL) of disease reached a severe level (SL > 45%), SIFtot was more sensitive to the variation in SIFcanopy, and NIRVP reached a highly significant level with SL, which could better realize the remote sensing detection of wheat stripe rust disease severity. The research results showed that analyzing variations in SIFcanopy by using the decomposed canopy structure and physiological response signals can effectively capture additional information about plant physiology, detect crop pathological variations caused by disease stress earlier and more accurately, and promote crop disease monitoring and research progress

An Improved Approach to Monitoring Wheat Stripe Rust with Sun-Induced Chlorophyll Fluorescence

Sun-induced chlorophyll fluorescence (SIF) has shown potential in quantifying plant responses to environmental changes by which abiotic drivers are dominated. However, SIF is a mixed signal influenced by factors such as leaf physiology, canopy structure, and sun-sensor geometry. Whether the physiological information contained in SIF can better quantify crop disease stresses dominated by biological drivers, and clearly explain the physiological variability of stressed crops, has not yet been sufficiently explored. On this basis, we took winter wheat naturally infected with stripe rust as the research object and conducted a study on the responses of physiological signals and reflectivity spectrum signals to crop disease stress dominated by biological drivers, based on in situ canopy-scale and leaf-scale data. Physiological signals include SIF, SIFyield (normalized by absorbed photosynthetically active radiation), fluorescence yield (ΦF) retrieved by NIRvP (non-physiological components of canopy SIF) and relative fluorescence yield (ΦF-r) retrieved by near-infrared radiance of vegetation (NIRvR). Reflectance spectrum signals include normalized difference vegetation index (NDVI) and near-infrared reflectance of vegetation (NIRv). At the canopy scale, six signals reached extremely significant correlations (P < 0.001) with disease severity levels (SL) under comprehensive experimental conditions (SL without dividing the experimental samples) and light disease conditions (SL < 20%). The strongest correlation between NDVI and SL (R = 0.69) was observed under the comprehensive experimental conditions, followed by NIRv (R = 0.56), ΦF-r (R = 0.53) and SIF (R = 0.51), and the response of ΦF (R = 0.45) and SIFyield (R = 0.34) to SL was weak. Under lightly diseased conditions, ΦF-r (R = 0.62) showed the strongest response to disease, followed by SIFyield (R = 0.60), SIF (R = 0.56) and NIRv (R = 0.54). The weakest correlation was observed between ΦF and SL (R = 0.51), which also showed a result approximating NDVI (R = 0.52). In the case of a high level of crop disease severity, NDVI showed advantages in disease monitoring. In the early stage of crop diseases, which we pay more attention to, compared with SIF and reflectivity spectrum signals, ΦF-r estimated by the newly proposed ‘NIRvR approach’ (which uses SIF together with NIRvR (i.e., SIF/ NIRvR) as a substitute for ΦF) showed superior ability to monitor crop physiological stress, and was more sensitive to plant physiological variation. At the leaf scale, the response of SIF to SL was stronger than that of NDVI. These results validate the potential of ΦF-r estimated by the NIRvR approach to monitoring disease stress dominated by biological drivers, thus providing a new research avenue for quantifying crop responses to disease stress