Increased Renal Methylglyoxal Formation with Down-Regulation of PGC-1α-FBPase Pathway in Cystathionine γ-Lyase Knockout Mice

We have previously reported that hydrogen sulfide (H2S), a gasotransmitter and vasodilator has cytoprotective properties against methylglyoxal (MG), a reactive glucose metabolite associated with diabetes and hypertension. Recently, H2S was shown to up-regulate peroxisome proliferator-activated receptor-γ coactivator (PGC)-1α, a key gluconeogenic regulator that enhances the gene expression of the rate-limiting gluconeogenic enzyme, fructose-1,6-bisphosphatase (FBPase). Thus, we sought to determine whether MG levels and gluconeogenic enzymes are altered in kidneys of 6–22 week-old cystathionine γ-lyase knockout (CSE-/-; H2S-producing enzyme) male mice. MG levels were determined by HPLC. Plasma glucose levels were measured by an assay kit. Q-PCR was used to measure mRNA levels of PGC-1α and FBPase-1 and -2. Coupled-enzymatic assays were used to determine FBPase activity, or triosephosphate levels. Experimental controls were either age-matched wild type mice or untreated rat A-10 cells. Interestingly, we observed a significant decrease in plasma glucose levels along with a significant increase in plasma MG levels in all three age groups (6–8, 14–16, and 20–22 week-old) of the CSE-/- mice. Indeed, renal MG and triosephosphates were increased, whereas renal FBPase activity, along with its mRNA levels, were decreased in the CSE-/- mice. The decreased FBPase activity was accompanied by lower levels of its product, fructose-6-phosphate, and higher levels of its substrate, fructose-1,6-bisphosphate in renal extracts from the CSE-/- mice. In agreement, PGC-1α mRNA levels were also significantly down-regulated in 6-22 week-old CSE-/- mice. Furthermore, FBPase-1 and -2 mRNA levels were reduced in aorta tissues from CSE-/- mice. Administration of NaHS, a H2S donor, increased the gene expression of PGC-1α and FBPase-1 and -2 in cultured rat A-10 cells. In conclusion, overproduction of MG in CSE-/- mice is due to a H2S-mediated down-regulation of the PGC-1α-FBPase pathway, further suggesting the important role of H2S in the regulation of glucose metabolism and MG generation

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Numerical Investigation of Passive Vortex Generators on a Wind Turbine Airfoil Undergoing Pitch Oscillations

Passive vortex generators (VGs) are widely used to suppress the flow separation of wind turbine blades, and hence, to improve rotor performance. VGs have been extensively investigated on stationary airfoils; however, their influence on unsteady airfoil flow remains unclear. Thus, we evaluated the unsteady aerodynamic responses of the DU-97-W300 airfoil with and without VGs undergoing pitch oscillations, which is a typical motion of the turbine unsteady operating conditions. The airfoil flow is simulated by numerically solving the unsteady Reynolds-averaged Navier-Stokes equations with fully resolved VGs. Numerical modelling is validated by good agreement between the calculated and experimental data with respect to the unsteady-uncontrolled flow under pitch oscillations, and the steady-controlled flow with VGs. The dynamic stall of the airfoil was found to be effectively suppressed by VGs. The lift hysteresis intensity is greatly decreased, i.e., by 72.7%, at moderate unsteadiness, and its sensitivity to the reduced frequency is favorably reduced. The influences of vane height and chordwise installation are investigated on the unsteady aerodynamic responses as well. In a no-stall flow regime, decreasing vane height and positioning VGs further downstream can lead to relatively high effectiveness. Compared with the baseline VG geometry, the smaller VGs can decrease the decay exponent of nondimensionalized peak vorticity by almost 0.02, and installation further downstream can increase the aerodynamic pitch damping by 0.0278. The obtained results are helpful to understand the dynamic stall control by means of conventional VGs and to develop more effective VG designs for both steady and unsteady wind turbine airfoil flow

An Integration Optimization Method for Power Collection Systems of Offshore Wind Farms

The traditional power collection system design separately optimizes the connection topology and the cable cross sections, which may result in the inherent shortcoming of lacking the most economical solutions. In this pursuit, the present work envisages the development of an integrated design method for general wind farm power collection systems, which integrated the coupling random fork tree coding, union-find set loop identification, current and voltage drop calculation models, and a high performance optimization algorithm. The proposed coupling random fork tree coding, for the first time, realized the coupling code of the substation location, connection topology, and cable cross sections, providing the basis for the integration design of the power collection system. The optimization results for discrete and regular wind farms indicated that the proposed integration method achieved the best match of topology, substation location, and the cable cross sections, thus presenting the most economical scheme of the power collection system. Compared to the traditional two-step methods, the integration method used more branches while acquiring them, to maintain the lower number of wind turbines in each branch. Furthermore, it also employed large cross-section cables to reduce the energy loss caused by the impedance in the topology, thereby resulting in a slight increased cable cost; however, the total cost was minimized. The proposed method is very versatile and suitable for the optimization of power collection systems containing any number of wind turbines and substations, and can be combined with any evolutionary algorithm

Impact of Economic Indicators on the Integrated Design of Wind Turbine Systems

This article presents a framework to integrate and optimize the design of large-scale wind turbines. Annual energy production, load analysis, the structural design of components and the wind farm operation model are coupled to perform a system-level nonlinear optimization. As well as the commonly used design objective levelized cost of energy (LCoE), key metrics of engineering economics such as net present value (NPV), internal rate of return (IRR) and the discounted payback time (DPT) are calculated and used as design objectives, respectively. The results show that IRR and DPT have the same effect as LCoE since they all lead to minimization of the ratio of the capital expenditure to the energy production. Meanwhile, the optimization for NPV tends to maximize the margin between incomes and costs. These two types of economic metrics provide the minimal blade length and maximal blade length of an optimal blade for a target wind turbine at a given wind farm. The turbine properties with respect to the blade length and tower height are also examined. The blade obtained with economic optimization objectives has a much larger relative thickness and smaller chord distributions than that obtained for high aerodynamic performance design. Furthermore, the use of cost control objectives in optimization is crucial in improving the economic efficiency of wind turbines and sacrificing some aerodynamic performance can bring significant reductions in design loads and turbine costs

Prediction of Sea Surface Temperature by Combining Interdimensional and Self-Attention with Neural Networks

Sea surface temperature (SST) is one of the most important and widely used physical parameters for oceanography and meteorology. To obtain SST, in addition to direct measurement, remote sensing, and numerical models, a variety of data-driven models have been developed with a wealth of SST data being accumulated. As oceans are comprehensive and complex dynamic systems, the distribution and variation of SST are affected by various factors. To overcome this challenge and improve the prediction accuracy, a multi-variable long short-term memory (LSTM) model is proposed which takes wind speed and air pressure at sea level together with SST as inputs. Furthermore, two attention mechanisms are introduced to optimize the model. An interdimensional attention strategy, which is similar to the positional encoding matrix, is utilized to focus on important historical moments of multi-dimensional input; a self-attention strategy is adopted to smooth the data during the training process. Forty-three-year monthly mean SST and meteorological data from the fifth-generation ECMWF (European Centre for Medium-Range Weather Forecasts) reanalysis (ERA5) are collected to train and test the model for the sea areas around China. The performance of the model is evaluated in terms of different statistical parameters, namely the coefficient of determination, root mean squared error, mean absolute error and mean average percentage error, with a range of 0.9138–0.991, 0.3928–0.8789, 0.3213–0.6803, and 0.1067–0.2336, respectively. The prediction results indicate that it is superior to the LSTM-only model and models taking SST only as input, and confirm that our model is promising for oceanography and meteorology investigation

Minimizing Energy Loss by Coupling Optimization of Connection Topology and Cable Cross-Section in Offshore Wind Farm

The cable cross section of an offshore wind farm power system is conventionally determined on the basis of the maximum current carrying capacity. However, this criterion cannot be matched and optimized with connection topology, which may lead to a large overall resistance level in the topology, thereby causing severe energy loss. In this pursuit, the present work envisages the establishment of a coupling optimization method of connection topology and cable cross-section planning for the first time. Based on this method, the power system of a small discrete wind farm is optimized and its results are compared with the results of the traditional design methods. The results indicate that an optimal matching of connection topology and cable cross section can be achieved using the proposed method. Besides, the optimal topology obtained uses more branches, and the large cross-section cables are reasonably used on the large-current cable segments, thus dramatically reducing the energy loss and minimizing the total cost of the power system. The proposed method is very versatile and suitable for the optimization of power systems containing any number of wind turbines and substations. Moreover, it can be combined with any evolutionary algorithm

Prediction of Sea Surface Temperature by Combining Interdimensional and Self-Attention with Neural Networks

Sea surface temperature (SST) is one of the most important and widely used physical parameters for oceanography and meteorology. To obtain SST, in addition to direct measurement, remote sensing, and numerical models, a variety of data-driven models have been developed with a wealth of SST data being accumulated. As oceans are comprehensive and complex dynamic systems, the distribution and variation of SST are affected by various factors. To overcome this challenge and improve the prediction accuracy, a multi-variable long short-term memory (LSTM) model is proposed which takes wind speed and air pressure at sea level together with SST as inputs. Furthermore, two attention mechanisms are introduced to optimize the model. An interdimensional attention strategy, which is similar to the positional encoding matrix, is utilized to focus on important historical moments of multi-dimensional input; a self-attention strategy is adopted to smooth the data during the training process. Forty-three-year monthly mean SST and meteorological data from the fifth-generation ECMWF (European Centre for Medium-Range Weather Forecasts) reanalysis (ERA5) are collected to train and test the model for the sea areas around China. The performance of the model is evaluated in terms of different statistical parameters, namely the coefficient of determination, root mean squared error, mean absolute error and mean average percentage error, with a range of 0.9138–0.991, 0.3928–0.8789, 0.3213–0.6803, and 0.1067–0.2336, respectively. The prediction results indicate that it is superior to the LSTM-only model and models taking SST only as input, and confirm that our model is promising for oceanography and meteorology investigation

Study of Dynamic Response Characteristics of the Wind Turbine Based on Measured Power Spectrum in the Eyewall Region of Typhoons

The present research envisages a method for calculating the dynamic responses of the wind turbines under typhoon. The measured power spectrum and inverse Fourier transform are used to generate the fluctuating wind field in the eyewall of the typhoon. Based on the beam theory, the unsteady aerodynamic model and the wind turbine dynamic model are coupled to calculate the dynamic response. Furthermore, using this method, the aeroelastic responses of a 6 MW wind turbine at different yaw angles are studied, and a 2 MW wind turbine are also calculated to verify the applicability of the results for different sizes of wind turbines. The results show that the turbulence characteristics of the fluctuating wind simulated by the proposed method is in good agreement with the actual measurement. Compared with the results simulated by the recommended power spectrum like the Kaimal spectrum, the energy distribution and variation characteristics simulated by the proposed method represent the real typhoon in a superior manner. It is found that the blade vibrates most violently at the inflow yaw angle of 30 degrees under the coupled effect of the aerodynamic, inertial and structural loads. In addition, the load on the tower exceeds the design limit values at the yaw angles of both 30 degrees and 120 degrees