Miracle-dz/UniFAR-FTUs huggingface dataset:
Candidate gene discovery of Botrytis cinerea resistance in grapevine based on QTL mapping and RNA-seq

Grape gray mold disease (Botrytis cinerea) is widespread during grape production especially in Vitis vinifera and causes enormous losses to the grape industry. In nature, the grapevine cultivar ‘Beta ‘ (Vitis riparia × Vitis labrusca) showed high resistance to grape gray mold. Until now, the candidate genes and their mechanism of gray mold resistance were poorly understood. In this study, we firstly conducted quantitative trait locus (QTL) mapping for grape gray mold resistance based on two hybrid offspring populations that showed wide separation in gray mold resistance. Notably, two stable QTL related to gray mold resistance were detected and located on linkage groups LG2 and LG7. The phenotypic variance ranged from 6.86% to 13.70% on LG2 and 4.40% to 11.40% on LG7. Combined with RNA sequencing (RNA-seq), one structural gene VlEDR2 (Vitvi02g00982) and three transcription factors VlERF039 (Vitvi00g00859), VlNAC047 (Vitvi08g01843), and VlWRKY51 (Vitvi07g01847) that may be involved in VlEDR2 expression and grape gray mold resistance were selected. This discovery of candidate gray mold resistance genes will provide an important theoretical reference for grape gray mold resistance mechanisms, research, and gray mold-resistant grape cultivar breeding in the future.
Rgp1 contributes to craniofacial cartilage development and Rab8a-mediated collagen II secretion

Rgp1 was previously identified as a component of a guanine nucleotide exchange factor (GEF) complex to activate Rab6a-mediated trafficking events in and around the Golgi. While the role of Rgp1 in protein trafficking has been examined in vitro and in yeast, the role of Rgp1 during vertebrate embryogenesis and protein trafficking in vivo is unknown. Using genetic, CRISPR-induced zebrafish mutants for Rgp1 loss-of-function, we found that Rgp1 is required for craniofacial cartilage development. Within live rgp1-/- craniofacial chondrocytes, we observed altered movements of Rab6a+ vesicular compartments, consistent with a conserved mechanism described in vitro. Using transmission electron microscopy (TEM) and immunofluorescence analyses, we show that Rgp1 plays a role in the secretion of collagen II, the most abundant protein in cartilage. Our overexpression experiments revealed that Rab8a is a part of the post-Golgi collagen II trafficking pathway. Following loss of Rgp1, chondrocytes activate an Arf4b-mediated stress response and subsequently respond with nuclear DNA fragmentation and cell death. We propose that an Rgp1-regulated Rab6a-Rab8a pathway directs secretion of ECM cargoes such as collagen II, a pathway that may also be utilized in other tissues where coordinated trafficking and secretion of collagens and other large cargoes is required for normal development and tissue function.
Metagenomic next-generation sequencing for the etiological diagnosis of rabies virus in cerebrospinal fluid

Background Rabies is a highly fatal disease. Once symptoms develop, death usually occurs within days. Survivors were occasionally reported in the literatures. Ante-mortem diagnosis remains a challenge in most rabies endemic countries. A novel, accurate diagnostic assay is highly desirable. Methods We used metagenomic next-generation sequencing (mNGS) to examine the cerebrospinal fluid (CSF) samples of a 49-year-old patient with rabies and validated the results by TaqMan PCR and RT-PCR/Sanger sequencing. Results Metagenomic next-generation sequencing identified sequence reads uniquely aligned to the rabies virus (RABV). PCR confirmed the presence of the partial RABV N gene in the CSF. Phylogenetic analysis showed that the RABV grouped as an Asian clade, which is the most broadly distributed clade in China. Conclusion Metagenomic next-generation sequencing may be a useful screening tool for the etiological diagnosis of rabies, especially in the absence of timely rabies laboratory testing or in patients with no exposure history.
Numerical investigation of non-linear inverse Compton scattering in double-layer targets

Non-linear inverse Compton scattering (NICS) is of significance in laser-plasma physics and for application-relevant laser-driven photon sources. Given this interest, we investigated this synchrotron-like photon emission in a promising configuration achieved when an ultra-intense laser pulse interacts with a double-layer target (DLT). Numerical simulations with two-dimensional particle-in-cell codes and analytical estimates are used for this purpose. The properties of NICS are shown to be governed by the processes characterizing laser interaction with the near-critical and solid layers composing the DLT. In particular, electron acceleration, laser focusing in the low-density layer, and pulse reflection on the solid layer determine the radiated power, the emitted spectrum, and the angular properties of emitted photons. Analytical estimates, supported by simulations, show that quantum effects are relevant at laser intensities as small as ∼ 1 0 21 W/cm2 Target and laser parameters affect the NICS competition with bremsstrahlung and the conversion efficiency and average energy of emitted photons. Therefore, DLT properties could be exploited to tune and enhance photon emission in experiments and future applications.
Shift in vacuolar to cytosolic regime of infecting Salmonella from a dual proteome perspective

By applying dual proteome profiling to Salmonella enterica serovar Typhimurium (S. Typhimurium) encounters with its epithelial host (here, S. Typhimurium infected human HeLa cells), a detailed interdependent and holistic proteomic perspective on host-pathogen interactions over a time course of infection was obtained. Data-independent acquisition (DIA)-based proteomics was found to outperform data-dependent acquisition (DDA) workflows, especially in identifying the downregulated bacterial proteome response during infection progression infection by permitting quantification of low abundant bacterial proteins at early times of infection at low bacterial infection load. S. Typhimurium invasion and replication specific proteomic signatures in epithelial cells revealed interdependent host/pathogen specific responses besides pointing to putative novel infection markers and signalling responses.
Co-Ordinate: Reconstruction of Three Dimensional Geometric Diagrams from a Single Image

We designed a new algorithm for reconstructing three-dimensional (3D) geometric diagrams. By extracting the orthogonality of the coordinate axes, the contents of labels, the positional relationships between labels and other elements, and direct and indirect connection relationships between elements, we define a score function and derive the optimal parameters that minimize it. This allows the elements to be reconstructed in 3D space and allows the system to create diagrams with different viewpoints. In our system, the reconstructed elements are redrawn with explicit orthogonal coordinate axes. The user can directly move the axes as an interface for changing viewpoints. The system allows the user to edit and attach elements to the diagram interactively.
Surface effects on the elastic modulus of nanoporous materials

The mechanical properties of a nanoporous material depend not only on its porosity but also on its characteristic sizes of microstructure, e.g., the average sizes of ligaments. Classical continuum mechanics models cannot interpret this type of size dependence. We here present a unit-cell micromechanics model to predict the effective Young’s modulus of open-cell nanoporous materials. The theory of surface elasticity is adopted to incorporate the effects of surface energy and residual surface stress on the effective elastic property of nanoporous materials. This model can reasonably elucidate the relevant experimental results.
The effect of creep on magnetic domain structure of heat resistant steels

The magnetic domain and magnetic properties of heat resistant steels including 10CrMo910, P91 and 23CrMoNiWV88 are investigated in the present work. The magnetic properties characterized by magnetic hysteresis loop of the three materials under 500-600°C are measured by vibrating sample magnetometer (VSM). The magnetic domain structure of as-received and crept specimens is observed by magnetic force microscope (MFM). The magnetic domain of ferrite phase change from initial stripe pattern to maze pattern during creep. The black and white fringes and stripe-like pattern have also been found in the P91 and 23CrMoNiWV88 specimens, respectively. The experimental results reveal that the magnetic domain structure is strongly influenced by microstructures with different distributions of the carbides. It is shown that the coercivity and remanence of each material although has a remarkable decrease at 500-600°C especially for P91 almost 64% decrease, it’s still the same magnitude as the one at room temperature. All the short-term crept specimens with different creep damage have a linear increase in coercivity and remanence comparing to the as-received 10CrMo910 specimens. These results indicate that it should be possible to develop an in-situ monitoring technology for creep damage based on magnetism measurement.
Nonlinear system identification with lattice piecewise-linear functions

A lattice piecewise-linear function is introduced for modeling nonlinear systems. A systematic procedure is proposed for building a lattice piecewise-linear model from the given input-output data. The objective of the paper is to demonstrate that modeling a nonlinear system using a lattice piecewise-linear function is a practically viable and very effective method of applying linear techniques to nonlinear dynamical systems.
Channel Estimation of OFDM – MIMO

To address in-phase and quadrature-phase imbalances in uplink massive multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, Channel Estimation of OFDM-MIMO examines the estimation of channel and robust detection (IQI). The received signal is modelled using a real-valued approach, and the wireless channel and IQI combination is taken into account as the effective channel. The effective channel is then determined using the least mean square error (MSE) criterion based on the real-valued model. In order to lower pilot overhead and demonstrate that the MSE of the effective channel estimate may be reduced by appropriately timing the pilot phase shifts, this experimental work employs adjustable phase shift pilots (APSPs). Numerous numerical examples are shown to show the performance of the resilient data detection and the channel estimation with APSPs.
ISAR Target Recognition Based on Capsule Net

Inverse synthetic aperture radar (ISAR) has been widely applied in automatic recognition of sea, air, and space targets thanks to its all-day, all-weather, and long-range operational abilities. But because of the inherent distinct characteristics such as the time-varying unknown effective rotation vector and the changing range and azimuth sampling rates, the field of ISAR ATR is facing many challenging problems such as image rotation-sensitivity, scaling-sensitivity, and translation-sensitivity, etc. This paper proposes a robust recognition method based on Capsule Net. In this structure, Capsule Net can extract the robust features of the deformable ISAR image. For the ISAR image database generated from electromagnetic (EM) echoes of four satellites, the proposed structure achieves higher recognition accuracy than traditional methods.
Compound Current Control Based on Active Disturbance Rejection Control for The LCL-Type Grid-Connected Inverter

A current control method based on linear active disturbance rejection control (LADRC) is proposed for the LCL-type grid-connected inverter to suppress the resonance characteristics and to handle the strong coupling problem in the current dq frames. The coupling and resonance dynamics are considered as a component of the total disturbance by using a linear extended state observer (LESO) for real-time observation, which simplifies the sophisticated decoupling process of the static decoupling approach and decreases the required sensors for active damping. At the same time, the grid voltage feedforward loop is adopted to reduce the negative impact on the dynamic performance of current control caused by grid voltage fluctuations. Therefore, a compound current control strategy is formed by LADRC combined with grid voltage feedforward for solving resonance and coupling problems. For the proposed strategy, the feasibility and efficiency are verified by the simulation.
Performance Analysis of E-Learning System Using Data Mining Techniques

The way we learn has changed as a result of advances in science and technology and the growing influence of information technology on daily life. E-learning is a relatively new concept in educational settings, but it has progressively earned a spot in modern training techniques. Data mining methods are used in many applications, particularly in elearning. Comparing direct communications difficult to track the student involvement in online courses. To overcome the above mentioned issues the existing methods are analyzed various techniques of data mining and optimization methods which can be applied to bring out hidden knowledge from the educational data? In the e-learning work, preprocessing, feature selection and classification process are performed to improve the Kalboard 360 educational dataset accuracy significantly. This study focused on educational data mining (EDM) and employed decision trees, support vector machines (SVM), multilayer perceptrons, and the naive bayesian C4.5 algorithm among other classification techniques to assess the features for the prediction of students’ behaviour and academic achievement. This paper analyzes the advantages and shortcomings of each algorithm applied to data set on the basis of accuracy for various classification techniques like Multilayer Perceptron based ANN algorithm (MP-ANN), Decision Tree (DT) C4.5 and Hybrid Regression model with Multi Label Classification (HR-MLC) approach. The experimental result shows that the HR-MLC algorithm provides better performance in terms of higher accuracy, precision, recall and f-measure rather than the other existing methods.
Fast-moving Target Detection and Imaging in Microwave photonic SAR

Microwave photonic radar realizes ultra-wideband radar waveform, which can break through the technical bottleneck of high-resolution SAR. However, there still exists several problem in fast-moving target detection and imaging in Microwave photonic SAR, included moving targets defocus and Doppler spectrum split. In this paper, one method combining azimuth frequency modulation compensation with azimuth deramp is presented to refocus fast-moving targets. Finally, simulation results are provided to demonstrate the effectiveness of the proposed method.
Student’s Acceptance of Video Lecture: An Extension of the Technology Acceptance Model (TAM)

Since the emergence of COVID-19, virtual learning has been the norm at institutions of higher education. Video lecture have emerged as a useful tool for academics in delivering distance education courses. Furthermore, video lecture are expected to keep the educational system running smoothly despite the pandemic. Therefore, the purpose of this study is to assess university students' acceptance of video lecture by extending the Technology Acceptance Model (TAM) with internet self-efficacy and learner-content interaction as the extended variable. Data from 390 students were gathered and analysed by Partial Least Squares-Structural Equation Modelling (PLS-SEM). The result shows that both internet self-efficacy and learner-content interaction plays an important role to determine the acceptance of video lecture and all path relationships in TAM were significant. Compared to internet self-efficacy, learner-content interaction have a bigger impact on the perceived usefulness of video lecture. This demonstrates that interaction between the learner and the content is important in deciding the acceptance of video lecture, and that educators should make video lecture brief and engaging so that students would enjoy using them.
Precision-Aware Workload Distribution and Dataflow for a Hybrid Digital-CIM Deep CNN Accelerator

SRAM-based Computing-in-memory (CIM) circuits have been demonstrated as a promising solution to effectively accelerate the inference of convolutional neural networks (CNNs) by shifting computation into the memory arrays. However, the advantages of CIM accelerators will disappear as increasing the bit precision and adopting advanced process technology due to the overhead caused by ADC/DAC and poor technology scaling capability of analog circuits. In this paper, a hybrid digital-CIM accelerator was proposed to solve above problems and the weights and activations of different layers are quantized to different precision (high, medium, and low precision). Moreover, precision-aware workload distribution and dataflow are proposed for the hybrid digital-CIM accelerator. Overall, the proposed accelerator can achieve 12.481 TOPS/W.
Bio-Inspired Algorithm for Speed Control of Electric Vehicle

The Electric Vehicle (EV) is a cutting-edge invention that helps save natural resources and minimize environmental damage. As more people become concerned about global warming, more attention is being paid to EVs in India. Advanced and intelligent control technologies have combined to create smart automobiles, which feature Artificial Intelligence, control systems and sensors. The study of EV systems has been increasingly popular in recent years. The speed management technique represents the most essential for achieving EV stability and safety. The sensor data collected from an EV could be used to regulate the vehicle’s speed for enhancing performance. In this study, a bio-inspired algorithm is used to tune the Proportional, Integral, and Derivative (PID) controller for an EV. Bio-inspired algorithms like the Elephant Search Algorithm (ESA) and the Whale Optimization Algorithm (WOA) are employed to find optimal values for P, I, and D. Simulation is used for developing the mathematical model of the EV system and for analysing and implementing the speed control. The simulation outcome indicates that the WOA was the most effective controller for regulating EV speed. Integral Square Error (ISE), Integral Absolute Error (IAE), and Integral Time Absolute Error (ITAE) is used to verify the effectiveness of the controllers.
Optimal Scheduling and Reserves Benefit allocation Model for the Regional Cascade Hydropower-wind Power Alliance

The large-scale access to renewable energy resource (RES) poses a challenge to the energy consumption of power and the ability to allocate system reserves. To use electricity in a clean and integrated manner, a multi-energy complementary system considering wind power uncertainty and transmission section restriction is studied for cascade hydropower and wind farms in the same basin. Firstly, a day-ahead scheduling model for cascade hydro-wind power joint operation, which aims to maximize wind power grid-connected power, is proposed. Among them, surplus output from regional hydro plants can be utilized as reserves for wind farms once the hydro plants have met their production plan. Afterwards, a cascade hydro-wind power alliance model is built. The Nash negotiation theory is introduced to distribute the profits of alliance members to realize the optimization of the internal compensation price and the improvement of benefits of each member. Then the dispatched reserves are optimally allocated among the various hydro plants. Finally, a case study is performed to evaluate the performance of the proposed method. Simulations show that the proposed method can effectively improve wind power consumption and the benefits of alliance members.
Improving the SSMVEP Detection Performance by Introducing a Secondary Discriminant Mechanism

This study demonstrates that the detection performance of the steady-state motion visual evoked potential (SSMVEP) can be improved by introducing a secondary discriminant mechanism. An extended canonical correlation analysis (CCA)-based method was applied to the detection of SSMVEPs. The target corresponding to the maximum coefficient was not identified as the focused target after calculating the coefficients of the obtained electroencephalogram (EEG) at all stimulation frequencies and a maximum contrast fusion method was performed on the EEG data. A secondary analysis was performed on the fused signal. A radial contraction-expansion motion paradigm was used and 40 targets in the frequency range of 7-14.8 Hz were designed. The introduction of the secondary discriminant mechanism resulted in higher accuracy and information transfer rate (ITR) than the extended CCA-based method. The online accuracy increased by 5.17% and the online ITR increased by 10.41%, respectively. The experimental results showed that the second discriminant mechanism significantly improved the recognition accuracy when the extended CCA-based method was applied to detect the SSMVEPs with a single harmonic component.
Vehicle Classification via Multi-dimension Feature Extraction with Millimeter Wave Radar

Accurate vehicle classification is an important issue in autonomous driving and traffic monitoring technology. In order to improve the classification accuracy of road vehicles in passive traffic monitoring scenario with millimeter wave (MMW) radar, a vehicle classification algorithm based on multi-dimension feature extraction and support vector machine (SVM) is proposed in this paper. According to the scattering points distribution of the vehicle in range-doppler-angle domain, 9 statistical features are mined and extracted to obtain the feature vector, and then an SVM classifier is used to realize vehicle classification. The real data experimental results based on a 77GHz MMW radar verify the effectiveness and superiority of the proposed method in road vehicle classification over the classification method based on point cloud feature and SVM.
Research of Land Surface Segmentation based on Convolutional Netword and Transformer Network

Throughout the decades, various applications have utilized semantic segmentation of spatial sensing imagery. As computer vision has benefited from deep learning methods, researchers have focused their efforts on transferring their superior performance to remote sensing image analysis. To achieve semantic segmentation of ultra-high resolution images, almost all neural networks have been using different methods for multi-scale fusion of image features in recent years. Although there have been numerous studies comparing the overall performance of these networks, there seems to be no study of the performance of these networks on a single class of datasets, especially for the Transformer networks that have emerged in recent years. In this paper, five networks using different multiscale fusion approaches were selected for experiments on two different datasets and similar results were obtained. We found that Transformer can achieve good results in the case where segmented objects occupy a large proportion of the image due to its focus on global image changes. However, Transformer also has some limitations, especially the image hue has a great influence on it. Our experiments found that if the color of the roof in the segmented image is similar to the color of the water surface, Transformer has a high probability to incorrectly confuse the roof as the water surface, while the traditional Convolutional Networks will work much better in this case.