WebServices and GIS-based Management System for QingShuihai Reservoir

The development of a modern integrated water quality model of Qingshuihai Reservoir and its watershed would permit decision-makers and water quality managers to address mechanisms underlying observed trends in water quality within Qingshuihai Reservoir, to assess the potential benefits of reductions in point source, non-point source inputs of nutrients, and to provide the Authority with a user-friendly management software for basin management and drinking water security system

Circadian regulation of night feeding and daytime detoxification in a formidable Asian pest Spodoptera litura

Voracious feeding, trans-continental migration and insecticide resistance make Spodoptera litura among the most difficult Asian agricultural pests to control. Larvae exhibit strong circadian behavior, feeding actively at night and hiding in soil during daytime. The daily pattern of larval metabolism was reversed, with higher transcription levels of genes for digestion (amylase, protease, lipase) and detoxification (CYP450s, GSTs, COEs) in daytime than at night. To investigate the control of these processes, we annotated nine essential clock genes and analyzed their transcription patterns, followed by functional analysis of their coupling using siRNA knockdown of interlocked negative feedback system core and repressor genes (SlituClk, SlituBmal1 and SlituCwo). Based on phase relationships and overexpression in cultured cells the controlling mechanism seems to involve direct coupling of the circadian processes to E-boxes in responding promoters. Additional manipulations involving exposure to the neonicotinoid imidacloprid suggested that insecticide application must be based on chronotoxicological considerations for optimal effectiveness

Lepidopteran wing scales contain abundant cross-linked film-forming histidine-rich cuticular proteins

Scales are symbolic characteristic of Lepidoptera; however, nothing is known about the contribution of cuticular proteins (CPs) to the complex patterning of lepidopteran scales. This is because scales are resistant to solubilization, thus hindering molecular studies. Here we succeeded in dissolving developing wing scales from Bombyx mori, allowing analysis of their protein composition. We identified a distinctive class of histidine rich (His-rich) CPs (6%–45%) from developing lepidopteran scales by LC-MS/MS. Functional studies using RNAi revealed CPs with different histidine content play distinct and critical roles in constructing the microstructure of the scale surface. Moreover, we successfully synthesized films in vitro by crosslinking a 45% His-rich CP (BmorCPR152) with laccase2 using N-acetyl- dopamine or N-β-alanyl-dopamine as the substrate. This molecular study of scales provides fundamental information about how such a fine microstructure is constructed and insights into the potential application of CPs as new biomaterials

A NAC-EXPANSIN module enhances maize kernel size by controlling nucellus elimination

Maize early endosperm development is initiated in coordination with elimination of maternal nucellar tissues. However, the underlying mechanisms are largely unknown. Here, we characterize a major quantitative trait locus for maize kernel size and weight that encodes an EXPANSIN gene, ZmEXPB15. The encoded β-expansin protein is expressed specifically in nucellus, and positively controls kernel size and weight by promoting nucellus elimination. We further show that two nucellus-enriched transcription factors (TFs), ZmNAC11 and ZmNAC29, activate ZmEXPB15 expression. Accordingly, these two TFs also promote kernel size and weight through nucellus elimination regulation, and genetic analyses support their interaction with ZmEXPB15. Importantly, hybrids derived from a ZmEXPB15 overexpression line have increased kernel weight, demonstrates its potential value in breeding. Together, we reveal a pathway modulating the cellular processes of maternal nucellus elimination and early endosperm development, and an approach to improve kernel weight

Simulation of Drainage Capacity in a Coastal Nuclear Power Plant under Extreme Rainfall and Tropical Storm

To ensure the safety of coastal nuclear power plants, accurately simulating water depth due to flooding resulting from heavy rainfall and tropical storms is important. In this paper, a combined model is developed to analyze and simulate the drainage capacity in a coastal nuclear power plant under the combined action of extreme rainfall and wave overtopping. The combined model consist of a surface two-dimensional flood-routing model, a pipe network model, and an offshore wave model. The method of predictive correction calculation is adopted to calculate the node return flow. The inundated water depth varying with time for different design rainstorm return periods (p = 0.1 and 1%) was simulated and analyzed by the combined model. The maximum inundated water depth is calculated for the important entrances of the workshop. The model was validated and calibrated with the data of the rainfall, outflow discharge, and flow velocity measured on 23 June 2016 in plant. Modeling indicates that the simulated depths are consistent with the observed depths. The results show that the water depths in the left and right of the nuclear power plant are 0.2⁻0.4 m and 0.3⁻0.8 m, respectively. The water depth increases of Monitoring Point 22 are the largest in different design rainstorm return periods (p = 0.1 and 1%), which increase by 16% for a rainstorm once every thousand years compared to events occurring once in one hundred years. The main factor influencing water accumulation is wave overtopping, and the seawall, revetments, and pipe system play an important role in decreasing the inundated water depth. Through scientific analysis, a certain decision-making basis has been provided for flood disaster management and a certain security guarantee has also been provided for regional sustainable development

Robust Nonlinear Control Scheme for Electro-Hydraulic Force Tracking Control with Time-Varying Output Constraint

This paper presents a robust nonlinear control scheme with time-varying output constraint for the electro-hydraulic force control system (EHFCS). Two typical double-rod symmetrical hydraulic cylinders are employed to simulate force environments in the EHFCS. Therefore, in order to improve the performance of the EHFCS, firstly, the model of the EHFCS is established with taking external disturbances, parameter uncertainties as well as structural vibrations into consideration. Secondly, in order to estimate external disturbances, parameter uncertainties and structural vibrations in the EHFCS and compensate them in the following robust controller design, two disturbance observers (DOs) are designed according to the nonlinear system model. Thirdly, with two estimation values from two DOs, a time-varying constraint-based robust controller (TVCRC) is presented in detail. Moreover, the stability of the proposed controller is analyzed by defining a proper Lyapunov functions. Finally, in order to validate the performance of the proposed controller, a series of simulation studies are conducted using the MATLAB/Simulink software. These simulation results give a fine proof of the efficiency of the proposed controller. What’s more, an experimental setup of the EHFCS is established to further validate the performance. Comparative experimental results show that the proposed controller exhibits better performance than the TVCRC without two DOs and a conventional proportional integral (PI) controller

PPARα Agonist WY-14643 Relieves Neuropathic Pain through SIRT1-Mediated Deacetylation of NF-κB

Inflammation caused by neuropathy contributes to the development of neuropathic pain (NP), but the exact mechanism still needs to be understood. Peroxisome proliferator-activated receptor α (PPARα), an important inflammation regulator, might participate in the inflammation in NP. To explore the role of PPARα in NP, the effects of PPARα agonist WY-14643 on chronic constriction injury (CCI) rats were evaluated. The results showed that WY-14643 stimulation could decrease inflammation and relieve neuropathic pain, which was relative with the activation of PPARα. In addition, we also found that the SIRT1/NF-κB pathway was involved in the WY-14643-induced anti-inflammation in NP, and activation of PPARα increased SIRT1 expression, thus reducing the proinflammatory function of NF-κB. These data suggested that WY-14643 might serve as an inflammation mediator, which may be a potential therapy option for NP

Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM), and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro) and NFIS-WPM (Ave) are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy