Nudel functions in membrane traffic mainly through association with Lis1 and cytoplasmic dynein

Nudel and Lis1 appear to regulate cytoplasmic dynein in neuronal migration and mitosis through direct interactions. However, whether or not they regulate other functions of dynein remains elusive. Herein, overexpression of a Nudel mutant defective in association with either Lis1 or dynein heavy chain is shown to cause dispersions of membranous organelles whose trafficking depends on dynein. In contrast, the wild-type Nudel and the double mutant that binds to neither protein are much less effective. Time-lapse microscopy for lysosomes reveals significant reduction in both frequencies and velocities of their minus end–directed motions in cells expressing the dynein-binding defective mutant, whereas neither the durations of movement nor the plus end–directed motility is considerably altered. Moreover, silencing Nudel expression by RNA interference results in Golgi apparatus fragmentation and cell death. Together, it is concluded that Nudel is critical for dynein motor activity in membrane transport and possibly other cellular activities through interactions with both Lis1 and dynein heavy chain

Development of an efficient numerical method for wind turbine flow, sound generation, and propagation under multi-wake conditions

The propagation of aerodynamic noise from multi-wind turbines is studied. An efficient hybrid method is developed to jointly predict the aerodynamic and aeroacoustics performances of wind turbines, such as blade loading, rotor power, rotor aerodynamic noise sources, and propagation of noise. This numerical method combined the simulations of wind turbine flow, noise source and its propagation which is solved for long propagation path and under complex flow environment. The results from computational fluid dynamics (CFD) calculations not only provide wind turbine power and thrust information, but also provide detailed wake flow. The wake flow is computed with a 2D actuator disc (AD) method that is based on the axisymmetric flow assumption. The relative inflow velocity and angle of attack (AOA) of each blade element form input data to the noise source model. The noise source is also the initial condition for the wave equation that solves long distance noise propagation in frequency domain. Simulations were conducted under different atmospheric conditions which showed that wake flow is an important part that has to be included in wind turbine noise propagation

Kernel Flow:a high channel count scalable time-domain functional near-infrared spectroscopy system

Significance: Time-domain functional near-infrared spectroscopy (TD-fNIRS) has been considered as the gold standard of noninvasive optical brain imaging devices. However, due to the high cost, complexity, and large form factor, it has not been as widely adopted as continuous wave NIRS systems. Aim: Kernel Flow is a TD-fNIRS system that has been designed to break through these limitations by maintaining the performance of a research grade TD-fNIRS system while integrating all of the components into a small modular device. Approach: The Kernel Flow modules are built around miniaturized laser drivers, custom integrated circuits, and specialized detectors. The modules can be assembled into a system with dense channel coverage over the entire head. Results: We show performance similar to benchtop systems with our miniaturized device as characterized by standardized tissue and optical phantom protocols for TD-fNIRS and human neuroscience results. Conclusions: The miniaturized design of the Kernel Flow system allows for broader applications of TD-fNIRS.</p

Atypical radio pulsations from magnetar SGR 1935+2154

Magnetars are neutron stars with extremely strong magnetic fields, frequently powering high-energy activity in X-rays. Pulsed radio emission following some X-ray outbursts have been detected, albeit its physical origin is unclear. It has long been speculated that the origin of magnetars' radio signals is different from those from canonical pulsars, although convincing evidence is still lacking. Five months after magnetar SGR 1935+2154's X-ray outburst and its associated Fast Radio Burst (FRB) 20200428, a radio pulsar phase was discovered. Here we report the discovery of X-ray spectral hardening associated with the emergence of periodic radio pulsations from SGR 1935+2154 and a detailed analysis of the properties of the radio pulses. The complex radio pulse morphology, which contains both narrow-band emission and frequency drifts, has not been seen before in other magnetars, but is similar to those of repeating FRBs - even though the luminosities are many orders of magnitude different. The observations suggest that radio emission originates from the outer magnetosphere of the magnetar, and the surface heating due to the bombardment of inward-going particles from the radio emission region is responsible for the observed X-ray spectral hardening.Comment: 47 pages, 11 figure

Electrochemical properties of Ni(OH)2/MnO2 on hybrid N-doped carbon structure as high-performance electrode material

A hierarchical hybrid architectures are very crucial through assembly of various carbon-based materials such as graphene and carbon nanotubes to achieve high-performance supercapacitors for energy storage. In this study, we propose a new pyrolysis method to prepare nitrogen-doped hybrid carbon structure (N-HCS). Then, the binary metal hydroxides Ni(OH)2/oxide MnO2 deposited on N-HCS with multifunctionality between them, exhibits an excellent specific capacitance as high as1563F/g at 1A/g in 1mol/L Na2SO4 solution and stable cycling performance remaining 80% of the initial specific capacitance after 1000 cycles at current density of 10A/g. This work paves a pathway to prepare electrode materials of N-doped carbon structure/Ni(OH)2/MnO2 with high performance for applying in energy storage and conversion

Solar Radiation Prediction Based on Convolution Neural Network and Long Short-Term Memory

Photovoltaic power generation is highly valued and has developed rapidly throughout the world. However, the fluctuation of solar irradiance affects the stability of the photovoltaic power system and endangers the safety of the power grid. Therefore, ultra-short-term solar irradiance predictions are widely used to provide decision support for power dispatching systems. Although a great deal of research has been done, there is still room for improvement regarding the prediction accuracy of solar irradiance including global horizontal irradiance, direct normal irradiance and diffuse irradiance. This study took the direct normal irradiance (DNI) as prediction target and proposed a Siamese convolutional neural network-long short-term memory (SCNN-LSTM) model to predict the inter-hour DNI by combining the time-dependent spatial features of total sky images and historical meteorological observations. First, the features of total sky images were automatically extracted using a Siamese CNN to describe the cloud information. Next, the image features and meteorological observations were fused and then predicted the DNI in 10-min ahead using an LSTM. To verify the validity of the proposed SCNN-LSTM model, several experiments were carried out using two-year historical observation data provided by the National Renewable Energy Laboratory (NREL). The results show that the proposed method achieved nRMSE of 23.47% and forecast skill of 24.51% for the whole year of 2014, and it also did better than some published methods especially under clear sky and rainy days

Apple-Net: A Model Based on Improved YOLOv5 to Detect the Apple Leaf Diseases

Effective identification of apple leaf diseases can reduce pesticide spraying and improve apple fruit yield, which is significant to agriculture. However, the existing apple leaf disease detection models lack consideration of disease diversity and accuracy, which hinders the application of intelligent agriculture in the apple industry. In this paper, we explore an accurate and robust detection model for apple leaf disease called Apple-Net, improving the conventional YOLOv5 network by adding the Feature Enhancement Module (FEM) and Coordinate Attention (CA) methods. The combination of the feature pyramid and pan in YOLOv5 can obtain richer semantic information and enhance the semantic information of low-level feature maps but lacks the output of multi-scale information. Thus, the FEM was adopted to improve the output of multi-scale information, and the CA was used to improve the detection efficiency. The experimental results show that Apple-Net achieves a higher [email protected] (95.9%) and precision (93.1%) than four classic target detection models, thus proving that Apple-Net achieves more competitive results on apple leaf disease identification