One-Pot Visual Detection of African Swine Fever Virus Using CRISPR-Cas12a

African swine fever virus (ASFV) is a leading cause of worldwide agricultural loss. ASFV is a highly contagious and lethal disease for both domestic and wild pigs, which has brought enormous economic losses to a number of countries. Conventional methods, such as general polymerase chain reaction and isothermal amplification, are time-consuming, instrument-dependent, and unsatisfactorily accurate. Therefore, rapid, sensitive, and field-deployable detection of ASFV is important for disease surveillance and control. Herein, we created a one-pot visual detection system for ASFV with CRISPR/Cas12a technology combined with LAMP or RPA. A mineral oil sealing strategy was adopted to mitigate sample cross-contamination between parallel vials during high-throughput testing. Furthermore, the blue fluorescence signal produced by ssDNA reporter could be observed by the naked eye without any dedicated instrument. For CRISPR-RPA system, detection could be completed within 40 min with advantageous sensitivity. While CRISPR-LAMP system could complete it within 60 min with a high sensitivity of 5.8 × 102 copies/μl. Furthermore, we verified such detection platforms display no cross-reactivity with other porcine DNA or RNA viruses. Both CRISPR-RPA and CRISPR-LAMP systems permit highly rapid, sensitive, specific, and low-cost Cas12a-mediated visual diagnostic of ASFV for point-of-care testing (POCT) applications

Stochastic Loss Function

Training deep neural networks is inherently subject to the predefined and fixed loss functions during optimizing. To improve learning efficiency, we develop Stochastic Loss Function (SLF) to dynamically and automatically generating appropriate gradients to train deep networks in the same round of back-propagation, while maintaining the completeness and differentiability of the training pipeline. In SLF, a generic loss function is formulated as a joint optimization problem of network weights and loss parameters. In order to guarantee the requisite efficiency, gradients with the respect to the generic differentiable loss are leveraged for selecting loss function and optimizing network weights. Extensive experiments on a variety of popular datasets strongly demonstrate that SLF is capable of obtaining appropriate gradients at different stages during training, and can significantly improve the performance of various deep models on real world tasks including classification, clustering, regression, neural machine translation, and objection detection

An Approach of Binary Neural Network Energy-Efficient Implementation

Binarized neural networks (BNNs), which have 1-bit weights and activations, are well suited for FPGA accelerators as their dominant computations are bitwise arithmetic, and the reduction in memory requirements means that all the network parameters can be stored in internal memory. However, the energy efficiency of these accelerators is still restricted by the abundant redundancies in BNNs. This hinders their deployment for applications in smart sensors and tiny devices because these scenarios have tight constraints with respect to energy consumption. To overcome this problem, we propose an approach to implement BNN inference while offering excellent energy efficiency for the accelerators by means of pruning the massive redundant operations while maintaining the original accuracy of the networks. Firstly, inspired by the observation that the convolution processes of two related kernels contain many repeated computations, we first build one formula to clarify the reusing relationships between their convolutional outputs and remove the unnecessary operations. Furthermore, by generalizing this reusing relationship to one tile of kernels in one neuron, we adopt an inclusion pruning strategy to further skip the superfluous evaluations of the neurons whose real output values can be determined early. Finally, we evaluate our system on the Zynq 7000 XC7Z100 FPGA platform. Our design can prune 51 percent of the operations without any accuracy loss. Meanwhile, the energy efficiency of our system is as high as 6.55 × 105 Img/kJ, which is 118× better than the best accelerator based on an NVDIA Tesla-V100 GPU and 3.6× higher than the state-of-the-art FPGA implementations for BNNs

HBCA: A Toolchain for High-Accuracy Branch-Fused CNN Accelerator on FPGA with Dual-Decimal-Fused Technique

The programmability of FPGA suits the constantly changing convolutional neural network (CNN). However, several challenges arise when the previous FPGA-based accelerators update CNN. Firstly, although the model of RepVGG can balance accuracy and speed, it solely supports two types of kernels. Meanwhile, 8-bit integer-only quantization of PyTorch which can support various CNNs is seldom successfully supported by the FPGA-based accelerators. In addition, Winograd F(4 × 4, 3 × 3) uses less multiplication, but its transformation matrix contains irregular decimals, which could lead to accuracy problems. To tackle these issues, this paper proposes High-accuracy Branch-fused CNN Accelerator (HBCA): a toolchain and corresponding FPGA-based accelerator. The toolchain proposes inception-based branch–fused technique, which can support more types of kernels. Meanwhile, the accelerator proposes Winograd-quantization dual decimal–fuse techniques to balance accuracy and speed. In addition, this accelerator supports multi-types of kernels and proposes Winograd decomposed-part reuse, multi-mode BRAM & DSP and data reuse to increase power efficiency. Experiments show that HBCA is capable of supporting seven CNNs with different types of kernels and more branches. The accuracy loss is within 0.1% when compared to the quantized model. Furthermore, the power efficiency (GOPS/W) of Inception, ResNet and VGG is up to 226.6, 188.1 and 197.7, which are better than other FPGA-based CNN accelerators

A novel memristor-based rSRAM structure for multiple-bit upsets immunity

A radiation hardened resistive SRAM structure (rSRAM) is proposed for the SRAM-based FPGAs in this paper. The rSRAM extends the conventional 6T SRAM structure by connecting memristors between the information nodes and drains of the transistors which compose cross-coupled invertors. With memristors connected to drains of OFF transistors configured to high resistance state while others configured to low resistance state forming stable voltage dividing path, the rSRAM structure is immune to both multiple-node upsets and multiple-bit upsets (MBUs). The simulation result demonstrates that rSRAM cell can tolerate simultaneous disruptions affecting all sensitive nodes with an LET (Liner Energy Transfer) of 100Mev-cm2/mg.Published Versio

Biogenic volatile organic compound emissions from Pinus massoniana and Schima superba seedlings: Their responses to foliar and soil application of nitrogen

Increasing nitrogen (N) deposition is one of the main drivers of global change, while the emission of biogenic volatile organic compounds (BVOCs) from plant in response to elevated N deposition is poorly understood, especially with respect to the response to foliar application of N. In this study, BVOC emissions from two tree species (Pious massoniana Lamb. and Schima superba Gardn. et Champ.) were determined by dynamic chamber coupled with a proton transfer reaction-time of flight-mass spectrometer. Two N application methods, namely soil application of N (SAN) and foliar application of N (FAN), and three N levels (5.6. 15.6 and 20.6 g N m(-2) yr(-1)) were employed by applying NH4NO3 every week for 1.5 years. The results showed that: (1) oxygenated volatile organic compounds (OVOCs, mainly acetaldehyde, methyl alcohol, ethenone and acetone) and non-methane hydrocarbons (NMHCs, mainly monoterpenes, propyne, 1,3-butadiene and propylene) were the dominant BVOCs for all the treatments, accounting for 32.40-65.72% and 1921-47.39% of total 100 determined BVOC compounds, respectively: (2) for s superba seedlings, both SAN and FAN treatments significantly decreased total BVOC emissions (11.83% to 66.23%). However, total BVOCs from P. massoniana significantly increased with N addition for SAN treatment, while no difference were found in the FAN treatment: (3) BVOC emission rates for FAN treatment were significantly lower than those for SAN treatment, indicating that previous studies which simulated N deposition by adding N directly to soil might have imprecisely estimated their effects on plant BVOC emissions. Considering the inconsistent responses of BVOC emissions to different N application methods for different plant species, dose attention should be paid on the effects of N deposition or even global change on plant BVOC emissions in the future. (C) 2019 Elsevier B.V. All rights reserved

Cytotoxic Pentaketide-Sesquiterpenes from the Marine-Derived Fungus <i>Talaromyces variabilis</i> M22734

Talaromyces, a filamentous fungus widely distributed across terrestrial and marine environments, can produce a diverse array of natural products, including alkaloids, polyketones, and polyketide-terpenoids. Among these, chrodrimanins represented a typical class of natural products. In this study, we isolated three previously undescribed pentaketide-sesquiterpenes, 8,9-epi-chrodrimanins (1–3), along with eight known compounds (4–11). The structures of compounds 1–3 were elucidated using nuclear magnetic resonance (NMR) and mass spectrometry (MS), while their absolute configurations were determined through X-ray crystallography and electronic circular dichroism (ECD) computations. The biosynthetic pathways of compounds 1–3 initiate with 6-hydroxymellein and involve multiple stages of isoprenylation, cyclization, oxidation, and acetylation. We selected four strains of gastrointestinal cancer cells for activity evaluation. We found that compound 3 selectively inhibited MKN-45, whereas compounds 1 and 2 exhibited no significant inhibitory activity against the four cell lines. These findings suggested that 8,9-epi-chrodrimanins could serve as scaffold compounds for further structural modifications, potentially leading to the development of targeted therapies for gastric cancer