Human infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19) and there is no cure currently. The 3CL protease (3CLpro) is a highly ...conserved protease which is indispensable for CoVs replication, and is a promising target for development of broad-spectrum antiviral drugs. In this study we investigated the anti-SARS-CoV-2 potential of Shuanghuanglian preparation, a Chinese traditional patent medicine with a long history for treating respiratory tract infection in China. We showed that either the oral liquid of Shuanghuanglian, the lyophilized powder of Shuanghuanglian for injection or their bioactive components dose-dependently inhibited SARS-CoV-2 3CLpro as well as the replication of SARS-CoV-2 in Vero E6 cells. Baicalin and baicalein, two ingredients of Shuanghuanglian, were characterized as the first noncovalent, nonpeptidomimetic inhibitors of SARS-CoV-2 3CLpro and exhibited potent antiviral activities in a cell-based system. Remarkably, the binding mode of baicalein with SARS-CoV-2 3CLpro determined by X-ray protein crystallography was distinctly different from those of known 3CLpro inhibitors. Baicalein was productively ensconced in the core of the substrate-binding pocket by interacting with two catalytic residues, the crucial S1/S2 subsites and the oxyanion loop, acting as a "shield" in front of the catalytic dyad to effectively prevent substrate access to the catalytic dyad within the active site. Overall, this study provides an example for exploring the in vitro potency of Chinese traditional patent medicines and effectively identifying bioactive ingredients toward a specific target, and gains evidence supporting the in vivo studies of Shuanghuanglian oral liquid as well as two natural products for COVID-19 treatment.
The current four‐dimensional variational (4D‐Var) data assimilation (DA) algorithm, available in the community Weather Research and Forecasting (WRF) model's DA (WRFDA) system, can run only at the ...same resolution as that of the WRF model forecast, which makes it computationally prohibitive for operational applications at convective scale. The Multi‐Resolution Incremental 4D‐Var (MRI‐4DVar) has been developed in this study in order to speed up WRF 4D‐Var through a three‐stage procedure in each outer loop. One key aspect of WRF MRI‐4DVar is the introduction of the inverse control variable transform within WRFDA that allows the proper resolution change between different outer loops for the control variables projected in the vertical empirical orthogonal function (EOF) space. MRI‐4DVar's computational efficiency and forecast performance are demonstrated by applying it to an afternoon thunderstorm event over northern Taiwan with a 2 km model resolution setting. Comparing to the full‐resolution 4D‐Var experiment, two MRI‐4DVar configurations with a speed‐up of 4.5 and 7.5 times performed similarly well in terms of Fractions Skill Score (FSS) of 6‐hr accumulated rainfall forecasts and hourly variation of total rainfall amount, indicating MRI‐4DVar's potential for operational applications at convective scale.
A Multi‐Resolution Incremental 4D‐Var (MRI‐4DVar) has been developed for the WRF model's data assimilation system to substantially speed up the existing 4D‐Var by introducing an inverse control variable transform within WRFDA in a three‐stage procedure. MRI‐4DVar performed as well as the full‐resolution 4D‐Var for convective‐scale rainfall forecasts for an afternoon thunderstorm event over northern Taiwan.
During the past one to two decades, the probabilistic forecasting of a wind-power generation has been regarded as a necessary input to decisions made for the purpose of reliable and economic power ...systems operations, especially since the penetration of the renewable energy has begun to grow rapidly. Probabilistic forecasting differs from traditional deterministic forecasting in which it takes uncertainty into account. This paper proposes a modified nonparametric method for constructing reliable prediction intervals (PIs). The lower upper bound estimation (LUBE) method is adapted to construct PIs for the wind-power generation, based on the ensemble wind-speed data from the numerical weather prediction system of the Central Weather Bureau of Taiwan. The charged system search (CSS) is used to adjust parameters in LUBE. The performance of the proposed method is examined using datasets from several wind farms in Taiwan. Simulation results demonstrate that the quality of PIs output by the proposed model significantly exceeded that of those constructed using the persistence model with a 1-h-ahead time horizon.
Background and Purpose
New remedies are required for the treatment of diabetic neuropathic pain (DNP) due to insufficient efficacy of available therapies. Here, we used chemogenetic approaches ...combined with in vivo pharmacology to elucidate the role of basolateral amygdala (BLA) astrocytes in DNP pathogenesis and provide new insights into therapeutic strategies for DNP.
Experimental Approach
A streptozotocin‐induced DNP model was established. Designer receptors exclusively activated by designer drugs (DREADDs) were used to regulate astrocyte activity. Mechanical hyperalgesia was assessed using the electronic von Frey test. Anxiety‐like behaviours were detected using open field and elevated plus maze tests. Astrocytic activity was detected by immunofluorescence, and cytokine content was determined by ELISA.
Key Results
BLA astrocytes were regulated by DREADDs, and inhibition of BLA astrocytes attenuated mechanical allodynia and pain‐related negative emotions in DNP rats. In contrast, temporary activation of BLA astrocytes induced allodynia without anxious behaviours in naive rats. In addition, koumine (KM) alleviated mechanical allodynia and anxiety‐like behaviours in DNP rats, inhibited the activation of BLA astrocytes and suppressed the inflammatory response. Furthermore, persistent activation of BLA astrocytes through chemogenetics mimicked chronic pain, and KM alleviated the pain hypersensitivity and anxiety‐like behaviours.
Conclusion and Implications
DREADDs bidirectionally regulate the activity of BLA astrocytes, which proves for the first time the role of BLA astrocyte activation in the pathogenesis of DNP and represents a novel therapeutic strategy for DNP. KM ameliorates DNP, perhaps by inhibiting the activation of BLA astrocytes and reveal KM as a potential candidate for treating DNP.
This paper proposes a novel stratification-based wind power forecasting method and develops a hybrid forecasting model at different stratifications using charged system search algorithm. The proposed ...model applies the concept of segmentation from the theory of optimal stratification to forecast short-term wind power outputs. Additionally, the proposed method elucidates different weighting values of each individual model at different segmentation blocks. Based on the forecasting results, the proposed stratification-based hybrid model outperforms traditional stand-alone models and unstratified hybrid models in terms of forecasting accuracy, which verifies the proposed forecasting model for accurate wind power forecasting.
It was reported that the Wnt/β-catenin pathway is involved in the regulation of aerobic glycolysis and that brain glycolytic dysfunction results in the development of Alzheimer's disease (AD). ...Icariin (ICA), an active component extracted from Epimedii Folium, has been reported to produce neuroprotective effects in multiple models of AD, but its underlying mechanism remains to be fully described. We aimed to investigate the protective effects of ICA on animal and cell models of AD and confirm whether the Wnt/β-catenin pathway has functions in the neuroprotective function of ICA. The 3 × Tg-AD mice were treated with ICA. HT22 cells, the Aβ
peptide and Dickkopf-1 (DKK1) agent (a specific inhibitor of the Wnt/β-catenin pathway) were used to further explore the underlying mechanism of ICA that produces anti-AD effects. Behavioral examination, western blotting assay, staining analysis, biochemical test, and lactate dehydrogenase (LDH) assays were applied. We first demonstrated that ICA significantly improved cognitive function and autonomous behavior, reduced neuronal damage, and reversed the protein levels and activities of glycolytic key enzymes, and expression of protein molecules of the canonical Wnt signaling pathway, in 3 × Tg-AD mice back to wild-type levels. Next, we further found that ICA increased cell viability and effectively improved the dysfunctional glycolysis in HT22 cells injured by Aβ
. However, when canonical Wnt signaling was inhibited by DKK1, the above effects of ICA on glycolysis were abolished. In summary, ICA exerts neuroprotective effects in 3 × Tg-AD animals and AD cellular models by enhancing the function of glycolysis through activation of the Wnt/β-catenin pathway.
With the increasing proportion of renewable energy, some problems have gradually emerged. To reduce the operating cost and improve system reliability, renewable power forecasting is an indispensable ...part. Compared with the deterministic prediction, the probabilistic forecast considers the uncertainty, which helps manage the power system operations. This study proposes a novel hour-ahead probabilistic forecasting method for wind power generation. It includes data preprocessing, adaptive neuro fuzzy inference system training model with fuzzy C-means clustering algorithm, and postprocessing of predicted interval (PI). The input data of the proposed forecasting model include the numerical weather prediction (NWP) ensemble wind speeds, NWP spot wind speeds, and historical wind power measurements. The research results demonstrate that the proposed model supports better performance and prediction stability. Furthermore, this work reveals that the data preprocessing and postprocessing of PI are essential for wind power forecasting. These processes greatly improve the performance of the probabilistic wind power forecasts.
Abstract This paper investigates a wind speed oscillation event that occurred near the coastline of central Taiwan in the afternoon of 17 February 2018, using data from observations and numerical ...simulations. The observed wind speeds at 100-m altitude displayed a fast-oscillating pattern of about 6 cycles between strong winds of approximately 21 m s −1 and weak winds of around 2 m s −1 , with periods of about 10 min. The pressure anomalies fluctuated in antiphase with the wind speed anomalies. The synoptic analysis revealed the influence of a continental high pressure system, resulting in a cold-air outbreak over Taiwan. The cold north-northeasterly winds split into two branches upon encountering Taiwan’s topography, with ridging off the east coast and a lee trough off the west coast of Taiwan. Wind oscillations were detected in the low-level cold air offshore the west coast of Taiwan, depicted by wavelike structures in wind speeds, sea level pressure, and potential temperature. The perturbations were identified as Kelvin-Helmholtz billows characterized by regions of strong wind speeds, warm and dry air, sinking motions, and low pressure collocated with each other, while regions of weaker wind speeds, cooler and moister air, ascending motions, and high pressure were associated with each other. With terrain contributing to favorable conditions, the large vertical and horizontal wind shears resulted from the southward acceleration of low-level cold air and the northward movement of the lee trough played an important role in initiating the wind oscillations.
Abstract
Radar and surface rainfall observations are two sources of operational data crucial for heavy rainfall prediction. Their individual values on improving convective forecasting through data ...assimilation have been examined in the past using convection-permitting numerical models. However, the benefit of their simultaneous assimilations has not yet been evaluated. The objective of this study is to demonstrate that, using a 4D-Var data assimilation system with a microphysical scheme, these two data sources can be assimilated simultaneously and the combined assimilation of radar data and estimated rainfall data from radar reflectivity and surface network can lead to improved short-term heavy rainfall prediction. In our study, a combined data assimilation experiment is compared with a rainfall-only and a radar-only (with or without reflectivity) experiments for a heavy rainfall event occurring in Taiwan during the passage of a mei-yu system. These experiments are conducted by applying the Weather Research and Forecasting (WRF) 4D-Var data assimilation system with a 20-min time window aiming to improve 6-h convective heavy rainfall prediction. Our results indicate that the rainfall data assimilation contributes significantly to the analyses of humidity and temperature whereas the radar data assimilation plays a crucial role in wind analysis, and further, combining the two data sources results in reasonable analyses of all three fields by eliminating large, unphysical analysis increments from the experiments of assimilating individual data only. The results also show that the combined assimilation improves forecasts of heavy rainfall location and intensity of 6-h accumulated rainfall for the case studied.
Because of climate changes, natural disasters are becoming more serious. For instance, the intensity of typhoons has been increasing in recent years. Typhoons and other natural disasters have ...high-impact low-probability characteristics. Thus, procedures for preparing for natural disasters and increasing power system resilience are important issues. This article proposes an all-inclusive process for system operators to make decisions for enhancing power system resilience and economic value during a severe weather event. This process first considers the typhoon track, the fragility curve and the recovery time of transmission lines. After collecting these data, system simulations and a calculated resilience index are implemented according to cases with and without disaster prevention. Next, the probability threshold and calculated economic value index are obtained based on numerical weather prediction wind speeds and the cost-loss ratio. Finally, the two indexes are considered in combination to obtain the highest resilience with the greatest benefit. The proposed process helps system operators make decisions for appropriate preventive actions at the least cost. An actual Taiwan power system and a severe weather event are used as an example to demonstrate the proposed decision analysis. The simulation results indicate the feasibility of the proposed method, which can reduce potential risks caused by extreme weather events with the maximum economic benefits.
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