Extending Non-Ambiguity Range of Dual-Comb Ranging for a Mobile Target Based on FPGA.

Dual-comb ranging (DCR) is an important method in absolute distance ranging because of its high precision, fast acquisition rate, and large measuring range. DCR needs to obtain precise results during distance measurements for a mobile target. However, the non-ambiguity range (NAR) is a challenge when pushing the dual-comb ranging to the industry field. This paper presents a solution for extending NAR by designing an algorithm and realizing it on a field-programmable gate array (FPGA). The algorithm is robust when facing the timing jitter in the optical frequency comb. Without averaging, the Allan deviation of the results in 1 ms is ∼3.89 µm and the Allan deviation of the results is ∼0.37 µm at an averaging time of 100 ms when the target object is standstill near the NAR. In addition, several ranging experiments were conducted on a mobile target whose speed was from ∼5 mm/s to ∼10 mm/s. The experimental results verify the effectiveness and robustness of our design. The implemented design is an online and real-time data processing unit that shows great industrial potential for using the DCR system.

Bioluminescence Imaging of Carbon Monoxide in Living Cells and Nude Mice Based on Pd0-Mediated Tsuji-Trost Reaction.

Carbon monoxide (CO) is highly toxic and lethal to humans and animals because of its strong affinity for hemoglobin, while this "silent killer" is constantly generated in the body as a cell-signaling molecule of the gasotransmitter family in various pathological and physiological conditions. Up to now, designing fluorescent probes for real-time imaging of CO in living species is a continuous challenge due to background interference, light scattering, and photoactivation/photobleaching. Herein, a novel type of bioluminescence probe (allyl-luciferin) was synthesized and exploited to realize CO imaging with high signal-to-noise ratios. Based on Pd0-mediated Tsuji-Trost reaction, allyl-luciferin specifically reacted with CO to yield D-luciferin and thus generate a turn-on bioluminescence response, exhibiting high selectivity against bioactive small molecules such as reactive nitrogen, oxygen, and sulfur species. Furthermore, the new probe can be easily employed to detect exogenous CO in Huh7 cells and MDA-MB-231 cells, and CO production was enhanced greatly in these living cells after pretreatment with [Ru(CO)3Cl-(glycinate)] (CORM-3). Through the use of PdCl2-containing liposomes to improve poor membrane permeability of PdCl2, endogenous CO stimulated by heme was also seen clearly. In addition, the probe was successfully used to monitor exogenous and endogenous CO in nude mice. Overall, our data proved that the allyl-luciferin is a promising tool for exogenous and endogenous CO detection and imaging within living species. This is the first demonstration of bioluminescence imaging obtained by a probe for CO. We anticipate that the good imaging properties of allyl-luciferin presented in this study will provide a potentially powerful approach for illuminating CO functions in the future.

Design of a phosphinate-based bioluminescent probe for superoxide radical anion imaging in living cells.

Superoxide radical anion (O2 ˙- ) as an important member of reactive oxygen species (ROS) plays a vital role both in physiology and pathology. Herein we designed and synthesized a novel phosphinate-based bioluminescence probe for O2 ˙- detection in living cells, which exhibited good sensitivity for capturing O2 ˙- at the nanomole level and high selectivity against other ROS. The probe was further found to be of low toxicity for living cells and was then successfully employed for sensing endogenous O2 ˙- by using phorbol-12-myristate-13-acetate (PMA) as a traditional O2 ˙- stimulator in Huh7 cells. Moreover, the increasing production and use of nanoparticles, has given rise to many concerns and debates among the public and scientific authorities regarding their safety and final fate in biological systems. Herein it was found that mondisperse polystyrene particles could stimulate O2 ˙- generation in Huh7 cells. Overall, the probe was demonstrated to have a great potential as a novel bioluminescent sensor for detecting O2 ˙- in living cells. To our knowledge, this is the first small-molecule phosphinate-based bioluminescence probe that will open up great opportunities for unlocking the mystery of O2 ˙- in human health and disease.

Scene-aware Foveated Neural Radiance Fields.

Foveated rendering provides an idea for improving the image synthesis performance of neural radiance fields (NeRF) methods. In this paper, we propose a scene-aware foveated neural radiance fields method to synthesize high-quality foveated images in complex VR scenes at high frame rates. Firstly, we construct a multi-ellipsoidal neural representation to enhance the neural radiance field's representation capability in salient regions of complex VR scenes based on the scene content. Then, we introduce a uniform sampling based foveated neural radiance field framework to improve the foveated image synthesis performance with one-pass color inference, and improve the synthesis quality by leveraging the foveated scene-aware objective function. Our method synthesizes high-quality binocular foveated images at the average frame rate of 66 frames per second (FPS) in complex scenes with high occlusion, intricate textures, and sophisticated geometries. Compared with the state-of-the-art foveated NeRF method, our method achieves significantly higher synthesis quality in both the foveal and peripheral regions with 1.41-1.46× speedup. We also conduct a user study to prove that the perceived quality of our method has a high visual similarity with the ground truth.

Insight into N2O emission and denitrifier communities under different aeration intensities in composting of cattle manure from perspective of multi-factor interaction analysis.

Nitrous oxide (N2O) emission from composting is a significant contributor to greenhouse effect and ozone depletion, which poses a threat to environment. To address the challenge of mitigating N2O emission during composting, this study investigated the response of N2O emission and denitrifier communities (detected by metagenome sequencing) to aeration intensities of 6 L/min (C6), 12 L/min (C12), and 18 L/min (C18) in cattle manure composting using multi-factor interaction analysis. Results showed that N2O emission occurred mainly at mesophilic phase. Cumulative N2O emission (QN2O, 9.79 mg·kg-1 DW) and total nitrogen loss (TN loss, 16.40 %) in C12 composting treatment were significantly lower than those in the other two treatments. The lower activity of denitrifying enzymes and the more complex and balanced network of denitrifiers and environmental factors might be responsible for the lower N2O emission. Denitrification was confirmed to be the major pathway for N2O production. Moisture content (MC) and Luteimonas were the key factors affecting N2O emission, and nosZ-carrying denitrifier played a significant role in reducing N2O emission. Although relative abundance of nirS was lower than that of nirK significantly (P < 0.05), nirS was the key gene influencing N2O emission. Community composition of denitrifier varied significantly with different aeration treatments (R2 = 0.931, P = 0.001), and Achromobacter was unique to C12 at mesophilic phase. Physicochemical factors had higher effect on QN2O, whereas denitrifying genes, enzymes and NOX- had lower effect on QN2O in C12. The complex relationship between N2O emission and the related factors could be explained by multi-factor interaction analysis more comprehensively. This study provided a novel understanding of mechanism of N2O emission regulated by aeration intensity in composting.

Improving prediction of N2O emissions during composting using model-agnostic meta-learning.

Nitrous oxide (N2O) represents a significant environmental challenge as a harmful, long-lived greenhouse gas that contributes to the depletion of stratospheric ozone and exacerbates global anthropogenic greenhouse warming. Composting is considered a promising and economically feasible strategy for the treatment of organic waste. However, recent research indicates that composting is a source of N2O, contributing to atmospheric pollution and greenhouse effect. Consequently, there is a need for the development of effective, cost-efficient methodologies to quantify N2O emissions accurately. In this study, we employed the model-agnostic meta-learning (MAML) method to improve the performance of N2O emissions prediction during manure composting. The highest R2 and lowest root mean squared error (RMSE) values achieved were 0.939 and 18.42 mg d-1, respectively. Five machine learning methods including the backpropagation neural network, extreme learning machine, integrated machine learning method based on ELM and random forest, gradient boosting decision tree, and extreme gradient boosting were adopted for comparison to further demonstrate the effectiveness of the MAML prediction model. Feature analysis showed that moisture content of structure material and ammonium concentration during composting process were the two most significant features affecting N2O emissions. This study serves as proof of the application of MAML during N2O emissions prediction, further giving new insights into the effects of manure material properties and composting process data on N2O emissions. This approach helps determining the strategies for mitigating N2O emissions.

Investigation of underlying links between nitrogen transformation and microorganisms' network modularity in the novel static aerobic composting of dairy manure by "stepwise verification interaction analysis".

Conventional composting is a viable method treating agricultural solid waste, and microorganisms and nitrogen transformation are the two major components of this proces. Unfortunately, conventional composting is time-consuming and laborious, and limited efforts have been made to mitigate these problems. Herein, a novel static aerobic composting technology (NSACT) was developed and employed for the composting of cow manure and rice straw mixtures. During the composting process, physicochemical parameters were analyzed to evaluate the quality of compost products, and microbial abundance dynamics were determined using high-throughput sequencing technique. The results showed that NSACT achieved compost maturity within 17 days as the thermophilic stage (≥55 °C) lasted for 11 days. GI, pH, and C/N were 98.71 %, 8.38, and 19.67 in the top layer, 92.32 %, 8.24, and 22.38 in the middle layer, 102.08 %, 8.33, and 19.95 in the bottom layer. These observations indicate compost products maturated and met the requirements of current legislation. Compared with fungi, bacterial communities dominated NSACT composting system. Based on the stepwise verification interaction analysis (SVIA), the novel combination utilization of multiple statistical analyses (Spearman, RDA/CCA, Network modularity, and Path analyses), bacterial genera Norank Anaerolineaceae (-0.9279*), norank Gemmatimonadetes (1.1959*), norank Acidobacteria (0.6137**) and unclassified Proteobacteria (-0.7998*), and fungi genera Myriococcum thermophilum (-0.0445), unclassified Sordariales (-0.0828*), unclassified Lasiosphaeriaceae (-0.4174**), and Coprinopsis calospora (-0.3453*) were the identified key microbial taxa affecting NH4+-N, NO3--N, TKN and C/N transformation in the NSACT composting matrix respectively. This work revealed that NSACT successfully managed cow manure-rice straw wastes and significantly shorten the composting period. Interestingly, most microorganisms observed in this composting matrix acted in a synergistic manner, promoting nitrogen transformation.

Microecological insight to fungal structure and key fungal communities regulating nitrogen transformation based on spatial heterogeneity during cow manure composting by multi-angle and multi-aspect analyses.

Composting is the mainstream technology for the treatment of agricultural solid waste, but limited efforts were made to investigate fungal composition and its contributions to nitrogen transformation in different depths of compost. In this study, spatial distributions of fungi were analyzed using high throughput sequencing by multi-angle analyses, and the key fungal communities determining nitrogen transformation were quantified and identified by multi-aspect analyses during cow manure composting. Multi-angle analyses showed that fungal structure, biomarkers and trophic mode composition varied in different layers, revealing that spatial heterogeneity is the distinctive attribute of composting system. Ascomycota and Basidiomycota were dominant phyla during composting, the two phyla peaked in top and bottom layer respectively. At mesophilic stage, Tremellales, and unclassified Ascomycota (order) were biomarkers in top and middle layer respectively, and so were Remersonia, Pyrenochaetopsis, and Wallemia in bottom layer by LEfSe analysis. Based on multi-aspect analyses, Unclassified Dothideomycetes mainly affected NH4+-N transformation both in top (1.2816***) and middle layers (1.1726*). Trichocladium asperum (0.9536***) and Zopfiella (-0.9484***) mainly affected TN transformation in top layer. Guehomyces pullulans (-0.9684**) and Preussia (-1.0508**) regulated NO3--N transformation in middle layer. Thermomyces lanuginosus (0.7127***) and Typhula sp. UW973129 (0.7298***) were the key species promoting TN and C/N transformation in bottom layer, respectively. Interestingly, different fungal communities showed a complex network interaction driving nitrogen transformation, and the abundance of microbial community could be conducive to characterizing nitrogen transformation in the vertical space of composting.

Plant Disease Recognition: A Large-Scale Benchmark Dataset and a Visual Region and Loss Reweighting Approach.

Plant disease diagnosis is very critical for agriculture due to its importance for increasing crop production. Recent advances in image processing offer us a new way to solve this issue via visual plant disease analysis. However, there are few works in this area, not to mention systematic researches. In this paper, we systematically investigate the problem of visual plant disease recognition for plant disease diagnosis. Compared with other types of images, plant disease images generally exhibit randomly distributed lesions, diverse symptoms and complex backgrounds, and thus are hard to capture discriminative information. To facilitate the plant disease recognition research, we construct a new large-scale plant disease dataset with 271 plant disease categories and 220,592 images. Based on this dataset, we tackle plant disease recognition via reweighting both visual regions and loss to emphasize diseased parts. We first compute the weights of all the divided patches from each image based on the cluster distribution of these patches to indicate the discriminative level of each patch. Then we allocate the weight to each loss for each patch-label pair during weakly-supervised training to enable discriminative disease part learning. We finally extract patch features from the network trained with loss reweighting, and utilize the LSTM network to encode the weighed patch feature sequence into a comprehensive feature representation. Extensive evaluations on this dataset and another public dataset demonstrate the advantage of the proposed method. We expect this research will further the agenda of plant disease recognition in the community of image processing.

Coenzyme A-aptamer-facilitated label-free electrochemical stripping strategy for sensitive detection of histone acetyltransferase activity.

Abnormal histone acetyltransferases (HAT) activity gives rise to all kinds of cellular diseases. Herein, we first report a coenzyme A (CoA)-aptamer-facilitated label-free electrochemical stripping biosensor for sensitive detection of HAT activity via square wave voltammetry (SWV) technique. The presence of HAT can lead to the transfer of the acetyl group from acetyl coenzyme A (Ac-CoA) to lysine residues of substrate peptide, thus generating CoA molecule. Later, CoA, which acts as an initiator, can embrace its aptamer via the typical target-aptamer interaction, then arousing deoxynucleotide terminal transferase (TdT)-induced silver nanoclusters (AgNCs) as signal output. Under optimized conditions, the resultant aptasensor shows obvious electrochemical stripping signal and is employed for HAT p300 analysis in a wide concentration range from 0.01 to 100 nM with a very low detection limit of 0.0028 nM (3δ/slope). The good analytical performances of the biosensor depend on the strong interaction of CoA and its aptamer and abundant stripping resource rooted from AgNCs. Next, the proposed biosensor is used for screening HAT's inhibitors and the practical HAT detection with satisfactory results. Therefore, the new, simple and sensitive HAT biosensor presents a promising direction for HAT-targeted drug discovery and epigenetic research.