Antibiotic fermentation residue (AFR) is nutrient-rich solid waste generated from fermentative antibiotic production process. It is demonstrated that AFR contains high-concentration of remaining antibiotics, and thus may promote antibiotic resistance development in receiving environment or feeding farmed animals. However, the dominate microorganisms and antibiotic resistance genes (ARGs) in AFRs have not been adequately explored, hampering understanding on the potential antibiotic resistance risk development caused by AFRs. Herein, seven kinds of representative AFRs along their production, storage, and treatment processes were collected, and multiple methods including amplicon sequencing, metagenomic sequencing, and bioinformatic approaches were adopted to explore the biological characteristics of AFRs. As expected, antibiotic fermentation producer was found as the predominant species in raw AFRs, which were collected at the outlet of fermentation tanks. However, except for producer species, more environment-derived species persisted in stored AFRs, which were temporarily stored at a semi-open space. Lactobacillus genus, classified as Firmicutes phylum and Bacilli class, became predominant bacterial taxa in stored AFRs, which might attribute to its tolerance to high concentration of antibiotics. Results from metagenomic sequencing together with assembly and binning approaches showed that these newly-colonizing species (e.g., Lactobacillus genus) tended to carry ARGs conferring resistance to the remaining antibiotic. However, after thermal treatment, remaining antibiotic could be efficiently removed from AFRs, and microorganisms together with DNA could be strongly destroyed. In sum, the main risk from the AFRs was the remaining antibiotic, while environment-derived bacteria which tolerate extreme environment, survived in ARFs with high content antibiotics, and may carry ARGs. Thus, hydrothermal or other harmless treatment technologies are recommended to remove antibiotic content and inactivate bacteria before recycling of AFRs in pharmaceutical industry.
Animal manures have been demonstrated to enhance antibiotic resistance in agricultural soils. However, little is known about the effects of plant-derived fertilizer on soil antibiotic resistome. Herein, metagenomic sequencing was used to investigate the effects of a plant-derived fertilizer processed from sugarcane and beet on soil antibiotic resistance genes (ARGs) in a soybean field along crop growth stages. ARG profiles in the soils amended by plant-derived fertilizer were compared with those in the soils amended by chicken manure. The abundance and diversity of total ARGs in the soils amended by plant-derived fertilizer were significantly (P < 0.05) elevated at the sprout stage, to a level comparable to that in the manured soils. Whereas, unlike chicken manure mainly introducing manure-borne ARGs to soil, the plant-derived fertilizer was indicated to mainly enrich multidrug resistance genes in soil by nourishing indigenous bacteria. ARGs with abundances in amended soils significantly (P < 0.05) higher than in unamended soils at the sprout stage of soybean were considered as enriched ARGs. Decrease in the abundance of the enriched ARGs was observed in both the amended soils from the sprout to the harvest. Network analysis further identified Proteobacteria and Bacteroidetes as the primary bacterial taxa involved in the temporal variation of the enriched ARGs in the soils amended by plant-derived fertilizer, while in manured soils were Firmicutes and Actinobacteria. As revealed by multivariate statistical analyses, variation of the enriched ARGs in the soils amended by plant-derived fertilizer was majorly attributed to the response of co-occurred bacteria to depleting nutrients, which was different from the failed establishment of manure-borne bacteria in the manured soils. Our study provided field-based evidence that plant-derived fertilizer stimulated the intrinsic antibiotic resistome, and proposed attention to the un-perceived risk since some clinically relevant ARGs originate and evolve from natural resistome.
Traditional cancer therapy methods, especially those directed against specific intracellular targets or signaling pathways, are not powerful enough to overcome tumor heterogeneity and therapeutic resistance. Oncolytic peptides that can induce membrane lysis-mediated cancer cell death and subsequent anticancer immune responses , has provided a new paradigm for cancer therapy. However, the clinical application of oncolytic peptides is always limited by some factors such as unsatisfactory bio-distribution, poor stability, and off-target toxicity. To overcome these limitations, oncolytic polymers stand out as prospective therapeutic materials owing to their high stability, chemical versatility, and scalable production capacity, which has the potential to drive a revolution in cancer treatment. This review provides an overview of the mechanism and structure-activity relationship of oncolytic peptides. Then the oncolytic peptides-mediated combination therapy and the nano-delivery strategies for oncolytic peptides are summarized. Emphatically, the current research progress of oncolytic polymers has been highlighted. Lastly, the challenges and prospects in the development of oncolytic polymers are discussed.
The tin (IV) oxide (SnO2) electron transport layer (ETL) has been widely employed to fabricate high-performance perovskite solar cells (PSCs). It has been reported that carbon quantum dots (CQDs) can be used to enhance electron mobility of SnO2. However, an in-depth understanding of the driving force in this process is still lacking. Here, a high-angle annular dark-field scanning transmission electron microscope (HAADF-STEM) is employed, for the first time, to reveal the SnO2 crystal face changes with one new type of CQD doping. Synchrotron-based grazing incidence wide-angle X-ray scattering (GIWAXS) can penetrate the flexible substrate to detect the buried region of the perovskite layer, showing the crystallinity and phase purity of the perovskite are significantly improved with CQD-modified SnO2. The flexible n-i-p PSCs delivers a power conversion efficiency (PCE) up to 23.57% (22.75%, certificated), which is one of the highest values for single-junction n-i-p flexible PSCs. The corresponding n-i-p flexible modules achieve a PCE of 17.79% with aperture area ~ 24 cm². Furthermore, the flexible PSCs show excellent stability, preserving ≈95% of their initial efficiency after 1200 h under 40% relative humidity and 1-sun light irradiation at 25 °C, and maintained > 90% of initial efficiency after 2500 bending cycles at a bending radius of 6 mm.
The transportation sector will be critical to the successful pursuit of the carbon neutrality goal in China. Electric vehicles (EVs) have seemed to be an important solution, for reducing carbon emissions, and residents’ support will be essential to achieve the extensive use of EVs. Thus, this study aimed to explore Macao residents’ willingness to buy (WTB) and pay for (WTP) EVs through 406 questionnaires, and to determine the influencing factors. The results showed that convenience, actual cost and environmental benefits are the consideration factors for residents to purchase EVs. It was found that 90.14% of respondents were willing to buy an EV. For WTP, however, the proportion fell to 56.71%. The environmental benefits, battery range, charging convenience and safety of EVs, and consumer income are all positively correlated with respondents’ WTB. For their WTP, potential pollution from fuel vehicles, driving frequency and environmental benefits of EVs were more important. Using the contingent valuation method (CVM), it was estimated that respondents’ WTP value is about 15041.10 MOP (much lower than market value gap: 50 000 MOP). In summary, the existing policies in Macao are insufficient to guide and improve the residents’ WTB and WTP. In the future, the government should strengthen publicity, especially to young consumers, and give EV owners some privileges, to stimulate residents’ WTB and WTP. In addition, in the context of carbon neutrality, more attention should be paid to cleaner power generation in Macao.
Soft magnetic skins have been widely adopted for tactile perception due to their high accuracy and simple wiring advantages. However, the perceptual properties of magnetic skins are limited by information mapping relationships with weak interpretation. To overcome existing limitations, dynamic Young's modulus (DYM) is proposed in this paper based on strain energy density function to precisely describe the compression stiffness of magnetic skins. Furthermore, a highly interpretable and broadly applicable method is derived using DYM to analyze a cylindrical magnetic skin's deformation process as the skin deformed under external mechanic load. Extensive experiments in simulated and real situations with different deformations are carried out to verify the proposed method. Experimental results demonstrate that 0.14% and 0.43% relative errors in simulated and real environments, respectively, can be reached. Moreover, the proposed method can achieve minimum errors in almost all situations than data-driven or state-of-the-art analysis approaches. And the generality of the proposed method is validated by experiments conducted on skins with two different shapes. These promising results indicate the potential of the proposed method in establishing practical information mapping relationships for magnetic skins, probably addressing the significant challenges for magnetic skin application in complex scenarios.
The path tracking of the robotic fish is a hotspot with its high maneuverability and environmental friendliness. However, the periodic oscillation generated by bionic fish-like propulsion mode may lead to unstable control. To this end, this paper proposes a novel framework involving a newly-designed platform and multi-agent reinforcement learning (MARL) method. Firstly, a bionic robotic fish equipped with a reaction wheel is developed to enhance the stability. Secondly, a MARL-based control framework is proposed for the cooperative control of tail-beating and reaction wheel. Correspondingly, a hierarchical training method including initial training and iterative training is designed to deal with the control coupling and frequency difference between two agents. Finally, extensive simulations and experiments indicate that the developed robotic fish and proposed MARL-based control framework can effectively improve the accuracy and stability of path tracking. Remarkably, the head-shaking is reduced about 40%. It provides a promising reference for the stability optimization and cooperative control of bionic swimming robots featuring oscillatory motions.
All-solid-state batteries (ASSBs) with solid-state electrolytes and lithium-metal anodes have been regarded as a promising battery technology to alleviate range anxiety and address safety issues due to their high energy density and high safety. Understanding the fundamental physical and chemical science of ASSBs is of great importance to battery development. To confirm and supplement experimental study, theoretical computation provides a powerful approach to probe the thermodynamic and kinetic behavior of battery materials and their interfaces, resulting in the design of better batteries. In this review, we assess recent progress in the theoretical computations of solid electrolytes and the interfaces between the electrodes and electrolytes of ASSBs. We review the role of theoretical computation in studying the following: ion transport mechanisms, grain boundaries, phase stability, chemical and electrochemical stability, mechanical properties, design strategies and high-throughput screening of inorganic solid electrolytes, mechanical stability, space-charge layers, interface buffer layers and dendrite growth at electrode/electrolyte interfaces. Finally, we provide perspectives on the shortcomings, challenges and opportunities of theoretical computation in regard to ASSBs.
As a member of the pattern recognition receptors (PRRs) involving in the innate immune system, Toll-like receptors (TLRs) can sense a wide range of microbial pathogens and combat infections by producing antimicrobial products, inflammatory cytokines, and chemokines. All TLRs, with the exception of TLR3, activate a signalling cascade via the myeloid differentiation primary response gene 88 (MyD88). Therefore, the activation of MyD88-dependent signalling pathway must be finely controlled. Herein, we identified that cyclin-dependent kinase 5 (CDK5) negatively regulated TLR-MyD88 signalling pathway by targeting MyD88. Overexpression of CDK5 reduced the production of interferons (IFNs), while a deficiency in CDK5 increased the expression of IFNs in response to vesicular stomatitis virus (VSV) infection. Mechanistically, CDK5 suppressed the formation of MyD88 homodimers, resulting in the attenuated production of IFNs induced by VSV infection. Surprisingly, its kinase activity does not play a role in this process. Therefore, CDK5 can act as an internal regulator to prevent excessive production of IFNs by restricting TLR-MyD88-induced activation of antiviral innate immunity in A549 cells.
Dark current behaviors of the 2.6 μm cutoff wavelength In <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.83</sub> Ga <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.17</sub> As photodetectors are investigated as a function of the mesa etching depth. The total dark current monotonically declines from 2.0×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-6</sup> A/cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> to 8.3×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-7</sup> A/cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> at 180 K and -10 mV as the mesa etching depth decreases from 2.6 to 0.9 μm. Meanwhile, an order of magnitude lower surface leakage current from 4.56 to 0.47 nA/cm, and a narrower statistical distribution are observed simultaneously. Moreover, the 300 K peak detectivity and quantum efficiency increase from 2.6×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> to 5.4×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> cmHz <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/2</sup> /W and from 67.1% to 71.2%, respectively, as the mesa etching depth decreases from 2.6 to 0.9 μm, benefit from the lateral carrier collection effect. These results suggest shallow mesa structures are indispensable towards surface leakage free In <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.83</sub> Ga <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.17</sub> As photodetectors.
Aerosol optical properties were studied over Chisinau in Moldova, one of the longest running AERONET sites in Eastern Europe. During two decades (September 1999-November 2018), the mean aerosol optical depth (AOD) and Angstrom exponent (AE) were observed as 0.21 ± 0.13 and 1.49 ± 0.29, respectively. The highest AOD (0.24 ± 0.13) and AE (1.60 ± 0.26) were observed during the summer. More than half (∼55%) of the share was occupied by clean continental aerosols with seasonal order of winter (74.8%) > autumn (62%) > spring (48.9%) > summer (44.8%) followed by mixed aerosols with a respective contribution of 30.7% (summer), 28.4% (spring), 22.5 (autumn) and 16.4% (winter). A clear dominance of volume size distribution in the fine mode indicated the stronger influence of anthropogenic activities resulting in fine aerosol load in the atmosphere. The peak in the fine mode was centered at 0.15 μm, whereas that of the coarse mode was centered either at 3.86 μm (summer and autumn) or 5.06 μm (spring and winter). 'Extreme' aerosol events were observed during 21 days with a mean AOD (AE) of 0.99 ± 0.32 (1.43 ± 0.43), whereas 'strong' events were observed during 123 days with a mean AOD (AE) of 0.57 ± 0.07 (1.44 ± 0.40), mainly influenced by anthropogenic aerosols (during 19 and 101 days of each event type) from urban/industrial and biomass burning indicated by high AE and fine mode fraction. During the whole period (excluding events days), the fine and coarse mode peaks were observed at the radius of 0.15 and 5.06 μm, which in the case of extreme (strong) events were at 0.19 (0.15) and 3.86 (2.24) μm respectively. The fine mode volume concentration was 4.78 and 3.32 times higher, whereas the coarse mode volume concentration was higher by a factor of 1.98 and 2.27 during extreme and strong events compared to the whole period.
Agricultural drought posing a significant threat to agricultural production is subject to the complex influence of ocean, terrestrial and meteorological multi-factors. Nevertheless, which factor dominating the dynamics of agricultural drought characteristics and their dynamic impact remain equivocal. To address this knowledge gap, we used ERA5 soil moisture to calculate the standardized soil moisture index (SSI) to characterize agricultural drought. The extreme gradient boosting model was then adopted to fully examine the influence of ocean, terrestrial and meteorological multi-factors on agricultural drought characteristics and their dynamics in China. Meanwhile, the Shapley additive explanation values were introduced to quantify the contribution of multiple drivers to drought characteristics. Our analysis reveals that the drought frequency, severity and duration in China ranged from 5–70, 2.15–35.02 and 1.76–31.20, respectively. Drought duration is increasing and drought intensity is intensifying in southeast, north and northwest China. In addition, potential evapotranspiration is the most significant driver of drought characteristics at the basin scale. Regarding the dynamic evolution of drought characteristics, the percentages of raster points for drought duration and severity with evapotranspiration as the dominant factor are 30.7 % and 32.7 %, and the percentages with precipitation are 35.3 % and 35.0 %, respectively. Precipitation in northern regions has a positive effect on decreasing drought characteristics, while in southern regions, evapotranspiration dominates the dynamics in drought characteristics due to increasing vegetation transpiration. Moreover, the drought severity is exacerbated by the Atlantic Multidecadal Oscillation in the Yangtze and Pearl River basins, while the contribution of the North Atlantic Oscillation to the drought duration evolution is increasing in the Yangtze River basin. Generally, this study sheds new insights into agricultural drought evolution and driving mechanism, which are beneficial for agricultural drought early warning and mitigation.
This paper confronts the formation control problem for a multi-robotic fish system with event-triggered communication mechanism. A three-dimensional (3-D) distributed formation control framework is proposed to drive the robotic fish agents to an anticipated configuration aligning with a moving target. In particular, a consensus-based formation control law is intended to realize the two-stage formation control process. Taking the energy-constrained occasions into consideration, the communication structure and event-triggered protocols are initially tailored. Meanwhile, the Lyapunov function is employed and the globally asymptotic stability of the proposed method is fully demonstrated. Afterwards, making use of the local measurements of triggering times, the unscented Kalman filter (UKF) is introduced and a novel model-based event-triggered mechanism is put forward to further mitigate otiose communication consumption. Finally, adequate simulations and experiments are carried out to verify the effectiveness and robustness of the proposed scheme. Thereby, the proposed formation control frame offers great potential for future practical marine operations of the underwater multi-agent systems.
To realize high-quality robotic welding, an efficient and robust complex weld seam feature point extraction method based on a deep neural network (Shuffle-YOLO) is proposed for seam tracking and posture adjustment. The Shuffle-YOLO model can accurately extract the feature points of butt joints, lap joints and irregular joints, and the model can also work well despite strong arc radiation and spatters. Based on the nearest neighbor algorithm and cubic B-spline curve fitting algorithm, the position and posture models of the complex spatially curved weld seams are established. The robot welding postures adjustment and high-precision seam tracking of complex spatially curved weld seams are realized. Experiments show that the method proposed in this paper can extract weld seam feature points quickly and robustly, which enables welding robots to accurately track the weld seams and adjust the welding torch postures simultaneously.
With the rapid development and wide applications of industrial manipulators, a vital concern rises regarding a manipulator's absolute positioning accuracy. The manipulator calibration models have proven to be highly efficient in improving the absolute positioning accuracy of an industrial manipulator. However, existing calibration models commonly suffer from the low calibration accuracy caused by the ignorance of non-geometric errors. To address this critical issue, this paper proposes an E xtended Kalman Filter-incorporated R esidual Neural Network-based C alibration (ERC) model for kinematic calibration. Its main ideas are two-fold: a) adopting an e xtended Kalman filter to address a manipulator's geometric errors; and b) adopting a r esidual neural network to cascade with the e xtended Kalman filter for eliminating the remaining non-geometric errors. Detailed experiments on three real datasets collected from industrial manipulators demonstrate that the proposed ERC model has achieved significant calibration accuracy gain over several state-of-the-art models.
Recently, visuotactile sensors have shown promising potential in robotics due to their high-resolution sensing ability. Unfortunately, the majority of available visuotactile sensors are limited to flat shapes, which severely limits their application possibilities. In this paper, we propose a novel curved visuotactile sensor, the GelStereo Palm, which senses the 3D contact geometry on a curved surface using a binocular vision system. Meanwhile, to solve the light refraction problem in the binocular stereo vision system under a curved medium, a Refractive Stereo Ray Tracing model for GelStereo Palm (GP-RSRT) is presented. Moreover, a 3D tactile point cloud sensing pipeline is introduced to reconstruct the 3D contact geometry in real-time. Finally, extensive experiments are conducted to verify the accuracy and robustness of the 3D contact geometry sensing of our GelStereo Palm sensor.
Optical format conversion technologies have attracted a lot of attention to bridge the different optical networks employing different formats. In this paper, the advances of optical format conversion technologies are introduced and reviewed. Through analyzing the optical network architecture and modulation technology development, we focus on the format conversions between the signals with one dimensional and two dimensional constellations. The important enabling technologies for flexible constellation manipulation are introduced in detail, including the linear and nonlinear optical effects-based functions. Then the key format conversion researches are reviewed in four categories: one-to-one conversions, optical aggregations, optical de-aggregations, and optical interconversions. Finally, the current issue and future trend of this research field is analyzed and discussed.
Structured illumination microscopy (SIM) provides an enhanced resolving power surpassing the optical diffraction limit by optical modulation of patterned illuminations. Although end-to-end deep learning techniques have recently advanced the reconstruction of SIM images, the reconstruction fidelity of existing networks is still moderate. We experimentally point out the crux lies in the inability of these models for faithful frequency learning. As a remedy, we propose a dual-domain learning strategy for SIM reconstruction, namely DDL-SIM, which learns to reconstruct SIM images from raw images in the spatial domain and raw image spectra in the frequency domain simultaneously, with the goal of narrowing the reconstruction gaps in both domains, thereby better recovering modulated frequencies and resolving more fine structures. Reconstruction experiments across various biological structures demonstrate the proposed DDL-SIM significantly improves the reconstruction fidelity of SIM images and shows great robustness against reconstruction artifacts.
Baseflow is pivotal in maintaining catchment ecological health and improving sustainable economic development. The Yellow River Basin (YRB) is northern China's most important water supplier. However, it faces water shortage due to synergistic effects between natural conditions and anthropogenic activities. Investigating baseflow characteristics quantitively is, therefore, beneficial to promoting the sustainable development of the YRB. In this study, daily ensemble means baseflow data derived from four revised baseflow separation algorithms (i.e., the United Kingdom Institute of Hydrology (UKIH), Lyne-Hollick, Chapman-Maxwell, and Eckhardt methods)-was obtained from 2001 to 2020. Thirteen baseflow dynamics signatures were extracted to investigate baseflow spatiotemporal variations and their determinants across the YRB. The main findings were: (1) There were significant spatial distribution patterns of baseflow signatures, and most signatures had higher values in upstream and downstream reaches than in the middle reaches. There were also mixing patterns with higher values in middle and downstream reaches simultaneously. (2) The magnitude of temporal variation in baseflow signatures was most strongly correlated with catchment terrain (r = − 0.4), vegetation growth (r > 0.3), and cropland coverage (r > 0.4). (3) There was a strong synergistic effect of multiple factors (e.g., soil textures, precipitation and vegetation conditions) on baseflow signature values. This study provided a heuristic evaluation of baseflow characteristics in the YRB, contributing to water resources management in the YRB and similar catchments.
Superconducting magnetic levitation rotors have good application prospects in fields such as gravimeters, accelerometers, and inertial instruments. Aiming at the little research on the heat transfer characteristics of superconducting magnetic levitation rotor, this paper introduces the structure of the superconducting magnetic levitation rotor and the heat conduction model. Then, the temperature relationship of the rotor at steady state is obtained and the surface radiation distribution of the rotor during temperature rise is calculated. The results show that the temperature accuracy of superconducting magnetic levitation rotor is critical to the drift accuracy. The drift caused by temperature change mainly depends on the accuracy of mechanical components and the stability of temperature control system, which can provide some reference for the safe operation of the rotor.
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