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Spiking Neural Network (SNN) is a promising energy-efficient neural architecture when implemented on neuromorphic hardware. The Artificial Neural Network (ANN) to SNN conversion method, which is the most effective SNN training method, has successfully converted moderately deep ANNs to SNNs with satisfactory performance. However, this method requires a large number of time-steps, which hurts the energy efficiency of SNNs. How to effectively covert a very deep ANN (e.g., more than 100 layers) to an SNN with a small number of time-steps remains a difficult task. To tackle this challenge, this paper makes the first attempt to propose a novel error analysis framework that takes both the "quantization error" and the "deviation error" into account, which comes from the discretization of SNN dynamicsthe neuron's coding scheme and the inconstant input currents at intermediate layers, respectively. Particularly, our theories reveal that the "deviation error" depends on both the spike threshold and the input variance. Based on our theoretical analysis, we further propose the Threshold Tuning and Residual Block Restructuring (TTRBR) method that can convert very deep ANNs (100 layers) to SNNs with negligible accuracy degradation while requiring only a small number of time-steps. With very deep networks, our TTRBR method achieves state-of-the-art (SOTA) performance on the CIFAR-10, CIFAR-100, and ImageNet classification tasks.

This work is aimed to study the effects of component size and solubility on the surface composition of spray dried (SD) uniform two-component particles fabricated by micro-fluidic spray dryer. Various precursor liquid consisting of small molecular of methionine (Met, 33 g/L) or lysine (Lys, 739 g/L) and large-sized silica (12 nm) were prepared by adjusting the mass ratio of components. X-ray energy dispersive results showed that the respective enrichment degree (De) of Met and Lys on the surface of SD-M1S9 and -L1S9 prepared at 150 °C were 182 ± 9% and 125 ± 14%. The De of hydrophobic Met for SD-M1S1 and -M9S1 were 46 ± 9% and 4 ± 2%, respectively, whereas relative hydrophilic Lys mainly distributed internal of the particle meanwhile the De of silica on the surface for SD-L1S1 and -L9S1 were 17 ± 4% and 12 ± 1%, respectively. Drying temperature (120 and 180 °C) showed more apparent effect on the De of amino acid for the particles of less amino acid. The possible formation mechanism of surface composition and the surface composition impact on the wettability of particles were explored. These results provide new guidance for manufacturing functional SD powders with various components.

Renewable energy sources, especially wind power, were believed to be able to slow down global warming; however, evidence in recent years shows that wind farms may also induce climate change. With the rapid development of wind power industry, the number of wind farms installed in mountains has gradually increased. Therefore, it is necessary to study the impact of wind farms in mountainous areas on local climate. The Suizhou and Dawu wind farms in northern Hubei Province were chosen for the present study on the impact of wind farm operations on the local climate in mountainous areas. The mesoscale meteorological numerical model Weather Research and Forecasting Model (WRF) and the Fitch model, together with turbulence correction factor, were used to simulate wind farm operations and study their effects on local climate. The results showed the characteristics of wind speed attenuation in mountainous wind farms: the amplitude and range of wind speed attenuation were stronger in the nighttime than in the daytime, and stronger in summer than in winter. The surface temperature increased and became more significant in summer. However, a cooling variation was observed above the surface warming center. The height of this center was higher in the daytime than it was in the nighttime. The latent heat flux in the wind farms decreased at night, accompanied by an increase in sensible heat flux. However, these changes were not significant. Some differences were observed between the impact of wind farms on the climate in the plains and the mountains. Such differences are more likely to be related to complex terrain conditions, climate conditions, and the density of wind turbines. The present study may provide support for the development and construction of wind farms in mountainous areas.

This article presents the design of an event-trigger-based networked robust dissipative tracking control systems. An event-triggered mechanism (ETM) is proposed by introducing the probability density distribution of communication delay. Thus, it can yield less conservative results. The tracking error system is transferred by a distributed communication delay system wherein the delay probability density is viewed as the kernel of the distributed delay. The threshold of the proposed ETM is designed as a dynamic parameter to adapt the transmission of the control signal. By applying the Lyapunov method, sufficient conditions are achieved to ensure the stability of the tracking error system with strictly dissipative performance, which includes $H_{\infty }$ performance and passive performance. Based on the dissipativity analysis condition, a co-design method with both tracking control and the ETM is derived in a united framework. Experiments on networked dc motor tracking system are provided to show the effectiveness of the presented method.

Background Single-cell RNA sequencing (scRNA-seq) measurements of gene expression show great promise for studying cellular heterogeneity of rice root. How precisely annotating cell identity is a major unresolved problem in plant scRNA-seq analysis due to the inherent high dimensionality and sparsity.Results To address this challenge, we present NRTPredictor, an ensemble-learning system, to predict rice root cell stage and mine biomarkers through complete model interpretability. The performance of NRTPredictor was evaluated using an external dataset, with 98.01% accuracy and 95.45% recall. With the power of the interpretability provided by NRTPredictor, our model recognizes 110 important marker genes, partially involved in the phenylpropanoid biosynthesis, that domain knowledge does not consider. Expression patterns of rice root could be mapped by the above-mentioned candidate genes, showing the superiority of NRTPredictor. Integrative bulk RNA-seq analysis we revealed aberrant expression of Epidermis and Cortex cell subpopulations in flooding, Pi stress and salt stress.Conclusion Taken together, our results demonstrate that NRTPredictor is a useful tool for automated prediction of rice root cell stage and provides a valuable resource for deciphering the rice root cellular heterogeneity and the molecular mechanisms of flooding, Pi stress and salt stress. Based on the proposed model, a free webserver has been established, which is available at http://bio.germplasmai.com.

International audience; The Bloom filter, answering whether an item is in a set, has achieved great success in various fields, including networking, databases, and bioinformatics. However, the Bloom filter has two main shortcomings: no support of item deletion and no support of expansion. Existing solutions either support deletion at the cost of using additional memory, or support expansion at the cost of increasing the false positive rate and decreasing the query speed. Unlike existing solutions, we propose the Elastic Bloom filter (EBF) to address the two shortcomings simultaneously. Importantly, when EBF expands, the false positives decrease. Our key technique is Elastic Fingerprints, which dynamically absorb and release bits during compression and expansion. To support deletion, EBF can first delete the corresponding fingerprint and then update the corresponding bit in the Bloom filter. To support expansion, Elastic Fingerprints release bits and insert them to the Bloom filter. Our experimental results show that the Elastic Bloom filter significantly outperforms existing works.

Burst is a common pattern in data streams which is characterized by a sudden increase in terms of arrival rate followed by a sudden decrease. Burst detection has attracted extensive attention from the research community. In this paper, we propose a novel sketch, namely BurstSketch, to detect bursts accurately in real time. BurstSketch first uses the technique Running Track to select potential burst items efficiently, and then monitors the potential burst items and capture the key features of burst pattern by a technique called Snapshotting. Experimental results show that our sketch achieves a 1.75 times higher recall rate than the strawman solution.

Introduction This study aims to explore the effect and related molecular mechanism of miR-153-3p on high glucose-stimulated human glomerular mesangial cells. Materials and methods The quantitative real-time polymerase chain reaction (qPCR) assay was employed to check miR-153-3p and PAQR3 expression levels in diabetic nephropathy patients. (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) MTT assay was applied to investigate the effects of miR-153-3p transfection or PAQR3 administration on mesangial cell (MC) activity. ELISA assays were used to check the expression levels of extracellular matrix (ECM) related proteins. The bioinformatics method and dual-luciferase reporter assay were employed together to anticipate and check the targeting relationship between miR-153-3p and PAQR3. Western blot assays were applied to check the PAQR3, PI3K and AKT expression after miR-153-3p transfection or PAQR3 administration. Results The expression level of miR-153-3p was lower in diabetic nephropathy patients, while the expression of PAQR3 was concomitantly higher. Upregulation of miR-153-3p can reduce MC proliferation and ECM accumulation. Further research indicated that miR-153-3p directly regulated PAQR3 expression via coupling with the 3’-UTR of PAQR3. Finally, the fact that miR-153-3p regulates the PI3K/AKT pathway by PAQR3 was confirmed. Conclusion MiR-153-3p regulates the PI3K/AKT pathway through PAQR3, thereby playing a role in regulating cell proliferation and ECM accumulation in high glucose-stimulated MCs.

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Background: The p21-activated kinases 4 (PAK4) refer to a promising target for cancer treatment. Currently, a wide range of PAK4 inhibitors has been reported. Objective: The objective of this study is to study the structural requirements of quinoline derivatives as PAK4 inhibitors and to design novel PAK4 inhibitors. Methods: In the present study, a set of quinazoline PAK4 inhibitors underwent CoMFA, CoMSIA, molecular docking, as well as molecular dynamics simulations. Results: The built CoMFA (q2=0.595, r2=0.986, r2 pred =0.689) and CoMSIA (q2=0.762, r2=0.984, r2 pred=0.822) models exhibited high robustness and prominent predicting ability. As revealed from the results of molecular docking and molecular dynamics simulations, hydrogen bond and hydrophobic interactions primarily impact the affinity of PAK4 inhibitors, and Leu398 acts as an amino acid that leads to significant stabilization of the mentioned inhibitors. Moreover, the present study developed five novel molecules exhibiting high biological activity predicted and satisfactory ADME properties. Conclusion: The structural basis of PAK4 with respect to the activities of its inhibitors was revealed, which may be conducive to designing novel PAK4 inhibitors.

Crystalline lactose microparticles with uniform size and spherical shape are greatly desirable for various applications, but still challenging. Herein, uniform lactose microspheres with high crystallinity were fabricated via spray drying lactose/polyethylene glycol (200 Da) (PEG 200) combined with ethanol/water post-treatment process. The effects of feed solution composition and ethanol/water volume ratio on particle size, morphology, density, crystal property and hygroscopicity were investigated. The obtained spherical lactose/PEG 200 microparticles were partially crystallized (30-50%), exhibiting dominant crystal forms of anhydrous Lα and Lβ. After removing PEG 200 by ethanol/water, the resulting microparticles remained uniform and spherical with enhanced crystallinity (92.90 ± 1.40%), high Lα·H2O content (68.57%) and ultra-low hygroscopicity (0.29%). Possible mechanism of “micro-zone competitive dissolution-reprecipitation” was proposed to reveal the influence of PEG 200 and ethanol/water volume ratio on promoting crystallinity and maintaining spherical morphology of lactose microparticles. This work provides a promising and up-scalable way to manufacture spherical crystalline microparticles.

The "data pipe" model used by the existing Internet protocol stack is no longer ideal for many emerging applications, due to multimedia, multicast, mobility, machine learning, and network management challenges. A new learning-oriented network architecture is required to deal with these challenges and serve learning-centric applications in data centers, around network edges, and on mobile devices. This article focuses on the network for AI and AI for network for learning-oriented network architecture. This is done by leveraging, improving, and creating new learning techniques to determine and optimize protocol mechanisms and control policies. The new network architecture can provide ample research opportunities in network topology control, protocol design, and performance evaluation, aiming to network a truly dependable cyber-infrastructure. The learning-oriented network can also learn from applications and communications automatically and continuously while running on different infrastructures to support diverse requirements. In addition, the network can keep evolving its protocol mechanisms and control policies in an online manner. It does this while maintaining protocol security and preserving user privacy, to learn and perform more effectively and efficiently. Finally, the main challenges and opportunities of learning-oriented network are discussed, encouraging further research.

We address the event-based control of nonlinear cyber-physical systems subject to deception attacks. In particular, an improved Takagi–Sugeno (T–S) fuzzy model is employed to solve the mismatch problem between the fuzzy system and fuzzy controllers. From the perspective of attack detection, we construct a novel queuing model to depict deception attacks. Then, a switched event-based communication scheme is proposed, which dynamically converts with different attack modes. The idea is to appropriately reduce the number of triggers according to the severity level of attacks, which can further save network resources. By using piecewise Lyapunov functional methods, we find a solution to the co-design of fuzzy controllers and event-triggering parameters while the concerned system is guaranteed to be exponentially stable. Finally, we apply the proposed approach to a mass–spring–damper system, where the effectiveness is well verified.

This article investigates the problem of resilient control for the Takagi–Sugeno (T–S) fuzzy systems against bounded cyberattack. A novel memory-based event triggering mechanism (ETM) is developed, by which the past information of the physical process through the window function is utilized. Using such an ETM cannot only lead to a lower data-releasing rate but also reduce the occurrence of wrong triggering event. Furthermore, the frequency of event generation is relatively smoother than existing ETMs. From the current releasing instant to the next, two periods are designed. The ETM works only when the first period ends, thereby avoiding the Zeno behavior that commonly exists in continuous ETM designs. The control system is then formulated as a switched fuzzy control system with two modes in each releasing period. Based on an assumption of secure control, and the proposed ETM, sufficient conditions are obtained to guarantee the exponential stability of networked T–S fuzzy systems in the presence of deception attacks in secure sense. Finally, a single-link rigid robot is taken as an example to illustrate the advantages of theoretical results.

This paper mainly focuses on the problem of networked fault estimation (FE) and fault-tolerant control (FTC). A novel adaptive memory-based mechanism is proposed by introducing the latest piece of historical output information. The historical information at each instant is matched with a corresponding weight such that the closer information is, the more contribution to the releasing event. Many unexpected triggering events can be avoided under this communication protocol, especially for the scenarios of the system with jitter disturbance or random noise. Moreover, to make the instantaneous data releasing rate adapt to the requirement of the control system, a time-varying threshold of the event-triggered mechanism is designed. Therefore, the burden of network bandwidth can be greatly decreased. Based on this proposed communication protocol, a new fault and state estimation model is developed. The fault-tolerant controller uses the estimations to compensate for the influence induced by the network and the fault. Sufficient conditions are derived to co-design the parameters of FE, FTC, and adaptive memory-based event-triggered mechanism. Finally, the performance of the proposed communication mechanism, FE, and FTC is evaluated on an example of the F-404 engine system.

Conventional image retrieval techniques for Structure-from-Motion (SfM) suffer from the limit of effectively recognizing repetitive patterns and cannot guarantee to create just enough match pairs with high precision and high recall. In this paper, we present a novel retrieval method based on Graph Convolutional Network (GCN) to generate accurate pairwise matches without costly redundancy. We formulate image retrieval task as a node binary classification problem in graph data: a node is marked as positive if it shares the scene overlaps with the query image. The key idea is that we find that the local context in feature space around a query image contains rich information about the matchable relation between this image and its neighbors. By constructing a subgraph surrounding the query image as input data, we adopt a learnable GCN to exploit whether nodes in the subgraph have overlapping regions with the query photograph. Experiments demonstrate that our method performs remarkably well on the challenging dataset of highly ambiguous and duplicated scenes. Besides, compared with state-of-the-art matchable retrieval methods, the proposed approach significantly reduces useless attempted matches without sacrificing the accuracy and completeness of reconstruction.

In this study, the event-triggered problem of networked control systems (NCSs) is investigated, and a novel information transmission scheme is established. Under this scheme, the segment-weighted information (SWI) in a sliding historical window (SHW) is calculated and then sampled. Compared with the traditional direct sampling method, in this approach, the control input includes historical information in the SHW, thereby leading to less information loss due to sampling. This study also emphasizes on designing an SWI-based event-triggered mechanism (ETM) for scheduling network transmission. Different from most of the existing ETMs, the proposed SWI-based ETM leverages historical information to determine which data are necessary for the whole control system. Our approach can greatly reduce the number of unexpected triggering events of a control system with stochastic disturbances owing to the introduction of the SWI in the ETM. Moreover, Zeno phenomena are prevented thanks to periodic sampling. Sufficient conditions are derived based on the Lyapunov functional approach, and a numerical simulation example is provided to demonstrate the effectiveness of the proposed method.

The finely tuned integration of intra- and extracellular cues by components of the mitogen-activated protein kinase (MAPK) signaling pathways controls the mutually exclusive phenotypic manifestations of uncontrolled growth and tumor cell dissemination. The Ser/Thr kinase MAP4K4 is an upstream integrator of extracellular cues involved in both proliferation and cell motility control. Initially identified as an activator of the c-Jun N-terminal kinase (JNK), the discovery of diverse functions and additional effectors of MAP4K4 beyond JNK signaling has considerably broadened our understanding of this complex kinase. The implication of MAP4K4 in the regulation of cytoskeleton dynamics and cell motility provided essential insights into its role as a pro-metastatic kinase in cancer. However, the more recently revealed role of MAP4K4 as an activator of the Hippo tumor suppressor pathway has complicated the understanding of MAP4K4 as an oncogenic driver kinase. To develop a better understanding of the diverse functions of MAP4K4 and their potential significance in oncogenesis and tumor progression, we have collected and assessed the current evidence of MAP4K4 implication in molecular mechanisms that control proliferation and promote cell motility. A better understanding of these mechanisms is particularly relevant in the brain, where MAP4K4 is highly expressed and under pathological conditions either drives neuronal cell death in neurodegenerative diseases or cell dissemination in malignant tumors. We review established effectors and present novel interactors of MAP4K4, which offer mechanistic insights into MAP4K4 function and may inspire novel intervention strategies. We discuss possible implications of novel interactors in tumor growth and dissemination and evaluate potential therapeutic strategies to selectively repress pro-oncogenic functions of MAP4K4.

Rhizosphere bacteria play important role in soil nutrient cycling and plant growth, and their richness and diversity are influenced by soil management systems. However, the specific changes in tobacco rhizosphere bacterial community structure in continuous and tobacco-rice rotation cropping systems remain uninvestigated. In this study, soil properties and the composition of the rhizosphere bacterial community in tobacco monocropping and tobacco-rice rotation cropping systems were analyzed. Moreover, the comparison of rhizosphere bacterial community structure between tobacco continuous and tobacco-rice rotation cropping systems was performed via high-throughput sequencing. The changes in the composition of the rhizosphere bacterial community were investigated at different tobacco growth stages. The results showed that continuous tobacco cropping increased the soil soluble organic carbon (SOC), total nitrogen (TN), and the content of other nutrients (e.g., available phosphorus and available potassium) compared to tobacco-rice rotation cropping. However, monocropping decreased bacterial alpha-diversity and altered the community composition when compared to the rotation cropping system. At the phylum level, the relative abundance of Proteobacteria, Gemmatimonadetes, and Bacteroidetes increased in the continuous cropping soil, while that of Acidobacteria, Firmicutes, and Actinobacteria decreased. At the genera level, the average abundance of the dominant genus Bacillus varied from 12.96% in continuous cropping libraries to 6.33% in the rotation cropping libraries (p < 0.05). Additionally, several other taxa, such as o_Acidobacteriales and Candidatus_Solibacter decreased from 7.63 to 6.62% (p < 0.05) and 4.52 to 2.91% (p < 0.05), respectively. However, the relative abundance of f_Gemmatimonadaceae and c_Subgroup_6 showed an increase of 1.46% (p < 0.05) and 1.63% (p < 0.05) in the tobacco-rice rotation cropping system, respectively. The results of NMDS indicated that the rhizobacteria community structure differed in the two cropping systems. In tobacco, the rhizosphere bacterial community structure showed no significant changes in the prosperous long-term stage and topping stage, but the composition changed significantly in the mature stage.

Improper disposal of antibiotic fermentation dregs poses a risk of releasing antibiotics and antibiotic resistant bacteria to the environment. Therefore, this study evaluated the effects of biochar addition to lincomycin fermentation dregs (LFDs) composting. Biochar increased compost temperature and enhanced organic matter decomposition and residual antibiotics removal. Moreover, a 1.5- to 17.0-fold reduction in antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs) was observed. Adding biochar also reduced the abundances of persistent ARGs hosts (e.g., Streptomyces, Pseudomonas) and ARG-related metabolic pathways and genes (e.g., ATP-binding cassette type-2 transport, signal transduction and multidrug efflux pump genes). By contrast, compost decomposition improved due to enhanced metabolism of carbohydrates and amino acids. Overall, adding biochar into LFDs compost reduced the proliferation of ARGs and enhanced microbial community metabolism. These results demonstrate that adding biochar to LFDs compost is a simple and efficient way to decrease risks associated with LFDs composting.

Active pharmaceutical ingredient (API)-embedded dry powder for inhalation (AeDPI) is highly desirable for pulmonary delivery of high-dose drug. Herein, a series of spray freeze-dried (SFD) ciprofloxacin hydrochloride (CH)-embedded dry powders were fabricated via a self-designed micro-fluidic spray freeze tower (MFSFT) capable of tuning freezing temperature of cooling air as the refrigerant medium. The effects of total solid content (TSC), mass ratio of CH to