Effect of Exogenous Nitric Oxide on Postharvest Storage Quality of Hyacinth Bean

In order to study the effect of nitric oxide (NO) on the storage quality of hyacinth bean after harvest, sodium nitroprusside (SNP) was used as an exogenous NO donor in this study. Hyacinth bean was soaked in 0.2 mmol/L SNP solution or distilled water as control for 10 min and then stored at (20 ± 1) ℃ and 80%–90% relative humidity. Decay incidence, rust incidence, hardness, the contents of total soluble solids (TSS), malondialdehyde (MDA), flavonoids, total phenols and chlorophyll, and the activities of antioxidant enzymes (peroxidase (POD), polyphenol oxidase (PPO), phenylalanine ammoniase (PAL), catalase (CAT) and ascorbate peroxidase (APX)) were observed during the storage period. The results showed that exogenous NO treatment could inhibit the rot and rust, keep the color and hardness, and inhibit the degradation of TSS and chlorophyll in hyacinth bean, so that hyacinth bean could maintain good sensory quality. Exogenous NO treatment could also prevent the accumulation of MDA and increase the contents of total phenols and flavonoids. In addition, exogenous NO treatment maintained the activities of PAL, CAT and APX during storage, and inhibited the increase in the activities of POD and PPO, thereby enhancing the antioxidant capacity and delaying the maturation and senescence of hyacinth bean. In conclusion, exogenous NO treatment can delay the postharvest maturation and senescence, maintain the physiological quality during storage, and effectively prolong the shelf life of hyacinth bean

Glycyrrhizin Protects Mice Against Experimental Autoimmune Encephalomyelitis by Inhibiting High-Mobility Group Box 1 (HMGB1) Expression and Neuronal HMGB1 Release

The inflammatory mediator high-mobility group box 1 (HMGB1) plays a critical role in the pathogenesis of human multiple sclerosis (MS) and mouse experimental autoimmune encephalomyelitis (EAE). Glycyrrhizin (GL), a glycoconjugated triterpene extracted from licorice root, has the ability to inhibit the functions of HMGB1; however, GL’s function against EAE has not been thoroughly characterized to date. To determine the benefit of GL as a modulator of neuroinflammation, we used an in vivo study to examine GL’s effect on EAE along with primary cultured cortical neurons to study the GL effect on HMGB1 release. Treatment of EAE mice with GL from onset to the peak stage of disease resulted in marked attenuation of EAE severity, reduced inflammatory cell infiltration and demyelination, decreased tumor necrosis factor-alpha (TNF-α), IFN-γ, IL-17A, IL-6, and transforming growth factor-beta 1, and increased IL-4 both in serum and spinal cord homogenate. Moreover, HMGB1 levels in different body fluids were reduced, accompanied by a decrease in neuronal damage, activated astrocytes and microglia, as well as HMGB1-positive astrocytes and microglia. GL significantly reversed HMGB1 release into the medium induced by TNF-α stimulation in primary cultured cortical neurons. Taken together, the results indicate that GL has a strong neuroprotective effect on EAE mice by reducing HMGB1 expression and release and thus can be used to treat central nervous system inflammatory diseases, such as MS

Seasonal expressions of prolactin, prolactin receptor and STAT5 in the scented glands of the male muskrats (Ondatra zibethicus)

Prolactin (PRL) production in mammals has been demonstrated in extrapituitary gland, which can activate autocrine/paracrine signaling pathways to regulate physiological activity. In the current study, we characterized the gene expression profiles of PRL, prolactin receptor (PRLR) and signal transducers and activators of transcription 5 (STAT5) in the scented glandular tissues of the muskrats, to further elucidate the relationship between PRL and the scented glandular functions of the muskrats. The weight and volume of the scented glands in the breeding season were significantly higher than those of the non-breeding season. Immunohistochemical data showed that PRL, PRLR and STAT5/phospho-STAT5 (pSTAT5) were found in the glandular and epithelial cells of the scented glands in both seasons. Furthermore, we found that PRL, PRLR and STAT5 had higher immunoreactivities in the scented glands during the breeding season when compared to those of the non-breeding season. In parallel, the gene expressions of PRL, PRLR and STAT5 were significantly higher in the scented glands during the breeding season than those of the non-breeding season. The concentrations of PRL in scented glandular tissues and sera were measured by enzyme-linked immunosorbent assay (ELISA), and their levels were both notably higher in the breeding season than those of the non-breeding season. These findings suggested that the scented glands of the muskrats were capable of extrapituitary synthesis of PRL, which might attribute PRL a specific function to an endocrine or autocrine/paracrine mediator

3D Cu Pyramid Array Grown on Planar Cu Foil for Stable and Dendrite-free Lithium Deposition

Lithium metal is recognized as the anticipated anode for rechargeable batteries because of its inherent physicochemical properties. Unfortunately, the industrialization of Li metal anodes (LMAs) has been entangled in some intractable problems stemming from the uncontrollable growth of Li dendrites, which could result in the issue of short-circuit, thereby leading to cell failure. Here, a three-dimensional structured Cu pyramid array (CPA@CF) is constructed on planar Cu foil (CF) by the simple electrodeposition method. Owing to the features of large surface area and 3D porous structure, the proposed CPA@CF not only can promote Li-ion diffusion and charge transfer, but also effectively slow down the volume change of Li. Consequently, an even and steady Li plating/stripping process up to 360 h is realized using such a CPA@CF current collector. The Li@CPA@CF|LiFePO4 full cell achieves an excellent Coulombic efficiency (CE) of 99.3 % for 160 cycles at 0.3 C with a superior capacity retention of 84.2 %

Quantifying the Effect of Index-Based Operation Logic for Building Environmental Control System—Taking Shading as Example

Dynamic control of building environment control systems (BECSs) is an important procedure to realize energy consumption reduction while ensuring the occupant’s comfort. Two types of BECSs operation logic exist: parameter-based and index-based. This research concluded that based on the literature review and argumentation, index-based operation logic, advanced from parameter-based operation logic, can better fit the dynamic and complex needs of occupants. However, existing index-based operation logic is generally based on a single performance index, while the BECS operation affects the indoor environment in multiple dimensions, thus, a single index cannot describe the operation comprehensively and accurately. Therefore, this study takes shading as an example, summarizes the performance indices of index-based operation logic for shading from two dimensions, and sorts out six typical control strategies according to different control objectives. The operation effect was analyzed and quantified through simulation. The results demonstrate that the index-based operation strategy has positive effects. It is not sensitive to changes in boundary conditions and the control effect is not affected by individual factors. Meanwhile, advice on the index selection for shading is proposed

Quantifying the Effect of Index-Based Operation Logic for Building Environmental Control System—Taking Shading as Example

Dynamic control of building environment control systems (BECSs) is an important procedure to realize energy consumption reduction while ensuring the occupant’s comfort. Two types of BECSs operation logic exist: parameter-based and index-based. This research concluded that based on the literature review and argumentation, index-based operation logic, advanced from parameter-based operation logic, can better fit the dynamic and complex needs of occupants. However, existing index-based operation logic is generally based on a single performance index, while the BECS operation affects the indoor environment in multiple dimensions, thus, a single index cannot describe the operation comprehensively and accurately. Therefore, this study takes shading as an example, summarizes the performance indices of index-based operation logic for shading from two dimensions, and sorts out six typical control strategies according to different control objectives. The operation effect was analyzed and quantified through simulation. The results demonstrate that the index-based operation strategy has positive effects. It is not sensitive to changes in boundary conditions and the control effect is not affected by individual factors. Meanwhile, advice on the index selection for shading is proposed

A combined transformation of ordering SPECT sinograms for signal extraction from measurements with Poisson noise

A theoretically based transformation, which reorders SPECT sinograms degraded by the Poisson noise according to their signal-to-noise ratio (SNR), has been proposed. The transformation is equivalent to the maximum noise fraction (MNF) approach developed for Gaussian noise treatment. It is a two-stage transformation. The first stage is the Anscombe transformation, which converts Poisson distributed variable into Gaussian distributed one with constant variance. The second one is the Karhunen-Loeve (K-L) transformation along the direction of the slices, which simplifies the complex task of threedimensional (3D) filtering into 2D spatial process slice-by-slice. In the K-L domain, the noise property of constant variance remains for all components, while the SNR of each component decreases proportional to its eigenvalue, providing a measure for the significance of each components. The availability of the noise covariance matrix in this method eliminates the difficulty of separating noise from signal. Thus we can construct an accurate 2D Wiener filter for each sinogram component in the K-L domain, and design a weighting window to make the filter adaptive to the SNR of each component, leading to an improved restoration of SPECT sinograms. Experimental results demonstrate that the proposed method provides a better noise reduction without sacrifice of resolution

Deep Reinforcement Learning for Intersection Signal Control Considering Pedestrian Behavior

Using deep reinforcement learning to solve traffic signal control problems is a research hotspot in the intelligent transportation field. Researchers have recently proposed various solutions based on deep reinforcement learning methods for intelligent transportation problems. However, most signal control optimization takes the maximization of traffic capacity as the optimization goal, ignoring the concerns of pedestrians at intersections. To address this issue, we propose a pedestrian-considered deep reinforcement learning traffic signal control method. The method combines a reinforcement learning network and traffic signal control strategy with traffic efficiency and safety aspects. At the same time, the waiting time of pedestrians and vehicles passing through the intersection is considered, and the Discrete Traffic State Encoding (DTSE) method is applied and improved to define the more comprehensive states and rewards. In the training of the neural network, the multi-process operation method is adopted, and multiple environments are run for training simultaneously to improve the model’s training efficiency. Finally, extensive simulation experiments are conducted on actual intersection scenarios using the simulation software Simulation of Urban Mobility (SUMO). The results show that compared to Dueling DQN, the waiting time due to our method decreased by 58.76% and the number of people waiting decreased by 51.54%. The proposed method can reduce both the number of people waiting and the waiting time at intersections

Deep Reinforcement Learning for Intersection Signal Control Considering Pedestrian Behavior

Using deep reinforcement learning to solve traffic signal control problems is a research hotspot in the intelligent transportation field. Researchers have recently proposed various solutions based on deep reinforcement learning methods for intelligent transportation problems. However, most signal control optimization takes the maximization of traffic capacity as the optimization goal, ignoring the concerns of pedestrians at intersections. To address this issue, we propose a pedestrian-considered deep reinforcement learning traffic signal control method. The method combines a reinforcement learning network and traffic signal control strategy with traffic efficiency and safety aspects. At the same time, the waiting time of pedestrians and vehicles passing through the intersection is considered, and the Discrete Traffic State Encoding (DTSE) method is applied and improved to define the more comprehensive states and rewards. In the training of the neural network, the multi-process operation method is adopted, and multiple environments are run for training simultaneously to improve the model’s training efficiency. Finally, extensive simulation experiments are conducted on actual intersection scenarios using the simulation software Simulation of Urban Mobility (SUMO). The results show that compared to Dueling DQN, the waiting time due to our method decreased by 58.76% and the number of people waiting decreased by 51.54%. The proposed method can reduce both the number of people waiting and the waiting time at intersections

Seasonal Change in Adiponectin Associated with Ovarian Morphology and Function in Wild Ground Squirrels (<i>Citellus dauricus</i> Brandt)

The goal of this study is to explore the relationship between altered circulating adiponectin concentration, ovarian tissue morphology, ovarian steroidogenesis, and sex hormone production in ovaries of wild ground squirrels. The ovarian mass differed significantly during the breeding and non-breeding seasons, and the circulating estradiol and progesterone concentrations were significantly higher in the breeding season, while the circulating adiponectin level was significantly lower. The expression levels of gonadotropin receptors (FSHR and LHR) and steroidogenic enzymes (StAR, P450scc, P450arom, and 3β-HSD) were significantly higher during the breeding season. Comparing the ovarian transcriptome data of wild ground squirrels between the two periods, we found that some differentially expressed genes were enriched for ovarian steroidogenesis and the adipocytokine signaling pathway, which correlated with our present results. Notably, the MAPK signaling pathway was also enriched and its related genes (Erk1, p38 Mapk, Jnk) were up-regulated by qPCR during the non-breeding season. These findings suggested that adiponectin may be involved in the regulation of seasonal changes in the ovarian function of wild ground squirrels, possibly by acting on the MAPK signaling pathway to regulate sex steroidogenesis in the ovaries