ABSTRACT
Plant growth-promoting rhizobacteria (PGPR) are known for their role in ameliorating plant stress, including alkaline stress, yet the mechanisms involved are not fully understood. This study investigates the impact of various inoculum doses of Bacillus licheniformis Jrh14-10 on Arabidopsis growth under alkaline stress and explores the underlying mechanisms of tolerance enhancement. We found that all tested doses improved the growth of NaHCO3-treated seedlings, with 109 cfu/mL being the most effective. Transcriptome analysis indicated downregulation of ethylene-related genes and an upregulation of polyamine biosynthesis genes following Jrh14-10 treatment under alkaline conditions. Further qRT-PCR analysis confirmed the suppression of ethylene biosynthesis and signaling genes, alongside the activation of polyamine biosynthesis genes in NaHCO3-stressed seedlings treated with Jrh14-10. Genetic analysis showed that ethylene signaling-deficient mutants (etr1-3 and ein3-1) exhibited greater tolerance to NaHCO3 than the wild type, and the growth-promoting effect of Jrh14-10 was significantly diminished in these mutants. Additionally, Jrh14-10 was found unable to produce 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase, indicating it does not reduce the ethylene precursor ACC in Arabidopsis. However, Jrh14-10 treatment increased the levels of polyamines (putrescine, spermidine, and spermine) in stressed seedlings, with spermidine particularly effective in reducing H2O2 levels and enhancing Fv/Fm under NaHCO3 stress. These findings reveal a novel mechanism of PGPR-induced alkaline tolerance, highlighting the crosstalk between ethylene and polyamine pathways, and suggest a strategic redirection of S-adenosylmethionine towards polyamine biosynthesis to combat alkaline stress.
Subject(s)
Arabidopsis , Bacillus licheniformis , Ethylenes , Polyamines , Arabidopsis/genetics , Arabidopsis/drug effects , Arabidopsis/metabolism , Arabidopsis/microbiology , Arabidopsis/physiology , Ethylenes/metabolism , Polyamines/metabolism , Bacillus licheniformis/metabolism , Bacillus licheniformis/genetics , Gene Expression Regulation, Plant/drug effects , Signal Transduction/drug effects , Stress, Physiological , Seedlings/drug effects , Seedlings/genetics , Seedlings/physiology , Seedlings/metabolism , Alkalies/pharmacology , Arabidopsis Proteins/metabolism , Arabidopsis Proteins/geneticsABSTRACT
Alternaria species are fungal pathogens that can infect maize, causing leaf blight disease and significant economic losses. This study aimed to determine the baseline sensitivity to prochloraz of A. alternata isolates obtained from diseased maize leaves collected from Heilongjiang province by assessing the half-maximal effective concentration (EC50) values. The EC50 values of prochloraz ranged from 0.0550 µg/mL to 2.3258 µg/mL, with an average of 0.9995 ± 0.5192 µg/mL. At EC50 (1.2495 µg/mL) and 2EC50 (2.4990 µg/mL), prochloraz increased the number of mycelial offshoots, disrupted the cell membrane integrity of conidia and mycelia, and resulted in a reduced ergosterol content in the mycelia. Prochloraz significantly affected the mycelial cell membrane permeability and increased the malondialdehyde (MDA) content and superoxide dismutase (SOD) activity. No cross-resistance was detected between prochloraz and other fungicides. These data demonstrate that prochloraz is a promising fungicide for managing maize leaf blight caused by A. alternata and provide novel insights into understanding the mechanism of prochloraz toxicity against A. alternata isolates.
ABSTRACT
Renewable electricity-powered nitrate/carbon dioxide co-reduction reaction toward urea production paves an attractive alternative to industrial urea processes and offers a clean on-site approach to closing the global nitrogen cycle. However, its large-scale implantation is severely impeded by challenging C-N coupling and requires electrocatalysts with high activity/selectivity. Here, cobalt-nanoparticles anchored on carbon nanosheet (Co NPs@C) are proposed as a catalyst electrode to boost yield and Faradaic efficiency (FE) toward urea electrosynthesis with enhanced C-N coupling. Such Co NPs@C renders superb urea-producing activity with a high FE reaching 54.3% and a urea yield of 2217.5 µg h-1 mgcat. -1, much superior to the Co NPs and C nanosheet counterparts, and meanwhile shows strong stability. The Co NPs@C affords rich catalytically active sites, fast reactant diffusion, and sufficient catalytic surfaces-electrolyte contacts with favored charge and ion transfer efficiencies. The theoretical calculations reveal that the high-rate formation of *CO and *NH2 intermediates is crucial for facilitating urea synthesis.
ABSTRACT
This paper presents an energy-efficient wireless power receiver for implantable electrical stimulation applications, which can achieve one-step adiabatic bipolar-supply that is generated by a hybrid single-stage dual-output regulating (SSDOR) rectifiers. The structure using only four switches overcomes the disadvantages that the two output voltage values in the traditional dual-output rectifiers are close to each other. A constant-current (CC) controlled adiabatic dynamic voltage scaling (DVS) technique is proposed to minimize the voltage headroom of the stimulating drivers and improve the stimulation efficiency significantly. In addition, the receiver adopts only one general constant on-time (COT) low-frequency control to adjust the stimulation current, reducing both the power consumption and the complexity of the control circuits. The proposed receiver has been fabricated in a 0.18 µm BCD process with ±6 V voltage compliance and 2.5 mA maximum stimulating current. With a current range from ±1.5 mA to ±2.5 mA, the measured maximum average headroom voltage is only 80 mV and the peak total efficiency of the receiver is 85.6%. The functionalities of the proposed receiver have been successfully verified through in vitro experiments.
ABSTRACT
This paper 5 presents a novel charge balancing (CB) with a current-control (CC) mode and a voltage-control (VC) mode for implantable biphasic stimulators, which can achieve one-step accurate anodic pulse generating. Compared with the conventional short-pulse-injection-based CB, the proposed method could reduce the balancing time and avoid inducing undesired artifact. The CC operation compensates the majority stimulation charge at high speed, while the VC operation guarantees a high CB precision. In order to eliminate the oscillation during the mode transition, a smooth CC-VC transition method is adopted. In addition, a digital auxiliary monitoring loop is introduced against the variations of the tissue-electrode interface impedance during the stimulation process to meet long-term CB requirement. The proposed stimulator has been fabricated in a 0.18 µm BCD process with 10 V voltage compliance, and the measured CB precision is less than 3 mV. The functionalities of the proposed CB have been verified successfully through in vitro experiments.
Subject(s)
Equipment Design , Humans , Electrodes, Implanted , Electric Impedance , Electric Stimulation/instrumentation , Signal Processing, Computer-Assisted/instrumentation , Implantable Neurostimulators , AnimalsABSTRACT
Virtual reality (VR)-based rehabilitation training holds great potential for post-stroke motor recovery. Existing VR-based motor imagery (MI) paradigms mostly focus on the first-person perspective, and the benefit of the third-person perspective (3PP) remains to be further exploited. The 3PP is advantageous for movements involving the back or those with a large range because of its field coverage. Some movements are easier to imagine from the 3PP. However, the 3PP training efficiency may be unsatisfactory, which may be attributed to the difficulty encountered when generating a strong sense of ownership (SOO). In this work, we attempt to enhance a visual-guided 3PP MI in stroke patients by eliciting the SOO over a virtual avatar with VR. We propose to achieve this by inducing the so-called out-of-body experience (OBE), which is a full-body illusion (FBI) that people misperceive a 3PP virtual body as his/her own (i.e., generating the SOO to the virtual body). Electroencephalography signals of 13 stroke patients are recorded while MI of the affected upper limb is being performed. The proposed paradigm is evaluated by comparing event-related desynchronization (ERD) with a control paradigm without FBI induction. The results show that the proposed paradigm leads to a significantly larger ERD during MI, indicating a bilateral activation pattern consistent with that in previous studies. In conclusion, 3PP MI can be enhanced in stroke patients by eliciting the SOO through induction of the "OBE" FBI. This study offers more possibilities for virtual rehabilitation in stroke patients and can further facilitate VR application in rehabilitation.
Subject(s)
Stroke Rehabilitation , Stroke , Virtual Reality , Humans , Male , Female , Ownership , Electroencephalography , Upper ExtremityABSTRACT
Electrochemical conversion of nitrite (NO2-) contaminant to green ammonia (NH3) is a promising approach to achieve the nitrogen cycle. The slow kinetics of the complex multi-reaction process remains a serious issue, and there is still a need to design highly effective and selective catalysts. Herein, we report that molybdenum doped cobalt oxide nanoarray on titanium mesh (Mo-Co3O4/TM) acts as a catalyst to facilitate electroreduction of NO2- to NH3. Such a catalyst delivers an extremely high Faradaic efficiency of 96.9 % and a corresponding NH3 yield of 651.5 µmol h-1 cm-2 at -0.5 V with strong stability. Density functional theory calculations reveal that the introduction of Mo can induce the redistribution of electrons around Co atoms and further strengthen the adsorption of NO2-, which is the key to facilitating the catalytic performance. Furthermore, the assembled battery based on Mo-Co3O4/TM suggests its practical application value.
ABSTRACT
The respiratory rate (RR) serves as a critical physiological parameter in the context of both diagnostic and prognostic evaluations. Due to the challenges of direct measurement, RR is still predominantly measured through the traditional manual counting-breaths method in clinic practice. Numerous algorithms and machine learning models have been developed to predict RR using physiological signals, such as electrocardiogram (ECG) or/and photoplethysmogram (PPG) signals. Yet, the accuracy of these existing methods on available datasets remains limited, and their prediction on new data is also unsatisfactory for actual clinical applications. In this paper, we proposed an enhanced Transformer model with inception blocks for predicting RR based on both ECG and PPG signals. To evaluate the generalization capability on new data, our model was trained and tested using subject-level ten-fold cross-validation using data from both BIDMC and CapnoBase datasets. On the test set, our model achieved superior performance over five popular deep-learning-based methods with mean absolute error (1.2) decreased by 36.5% and correlation coefficient (0.85) increased by 84.8% compared to the best results of these models. In addition, we also proposed a new pipeline to preprocess ECG and PPG signals to improve model performance. We believe that the development of the TransRR model is expected to further expedite the clinical implementation of automatic RR estimation.
ABSTRACT
Electrochemical nitrate (NO3-) reduction is a sustainable pathway for ambient ammonia (NH3) synthesis while eliminating NO3- pollutants in water. However, the NO3- reduction reaction (NO3-RR) involves a complicated eight-electron transfer process, which needs highly selective and efficient electrocatalysts. This work describes the synthesis of Fe3O4 nanoparticle-decorated 3D pinewood-derived carbon (Fe3O4/PC) as a high-efficiency catalyst for the electroreduction of NO3- to NH3 at ambient reaction conditions. When tested in 0.1 M NaOH containing 0.1 M NO3-, the Fe3O4/PC obtains a large NH3 yield of 394.8 µmol h-1 cm-2 and high faradaic efficiency (FE) of 91.6% at -0.4 V. Significantly, Fe3O4/PC also delivers high stability.
ABSTRACT
This paper attempts to induce the third-person perspective full body illusion (3PP-FBI) with virtual reality (VR) in stroke patients. Nineteen individuals with stroke were recruited. The 3PP-FBI induction method, which was well-established in healthy individuals, using synchronous visual-tactile stimulation on one body part was used. Questionnaire scores and proprioceptive drift values were collected under different conditions for characterizing the induced 3PP-FBI. Results showed that synchronous visual-tactile stimulation of a single body part (back or upper limb) was sufficient to elicit 3PP-FBI in stroke patients, forming a sense of ownership (SOO) over the entire virtual body. Moreover, the intensity of 3PP-FBI was stronger when the back was stimulated, compared to stimulating the impaired upper limb. This study demonstrated the viability of visual-guided rehabilitation training while having a SOO to a virtual body from the third-person perspective, in anticipation of achieving better rehabilitation outcome for movements beyond the first-person perspective.
Subject(s)
Illusions , Stroke , Touch Perception , Virtual Reality , Humans , Illusions/physiology , Touch , Touch Perception/physiologyABSTRACT
A 3D cauliflower-like Ni foam on titanium plate (Ni foam/TP) shows high electrocatalytic performance for ambient ammonia (NH3) synthesis via nitrite (NO2-) reduction. In 0.1 M phosphate-buffered saline solution with 0.1 M NO2-, such Ni foam/TP attains a high NH3 Faradaic efficiency (FE) of 95.9% and a large NH3 yield of 742.7 µmol h-1 cm-2 at -0.8 V. Its Zn-NO2- battery offers a high power density of 6.2 mW cm-2 and an NH3 FE of 90.1%.
ABSTRACT
Electrochemical nitrite (NO2-) reduction is recognized as a promising strategy for synthesizing valuable ammonia (NH3) and degrading NO2- pollutants in wastewater. The six-electron process for the NO2- reduction reaction is complex and necessitates a highly selective and stable electrocatalyst for efficient conversion of NO2- to NH3. Herein, a FeP nanoparticle-decorated TiO2 nanoribbon array on a titanium plate (FeP@TiO2/TP) is proposed as an efficient catalyst for NH3 production under ambient conditions. In 0.1 M NaOH with 0.1 M NO2-, such a FeP@TiO2/TP affords a large NH3 yield of 346.6 µmol h-1 cm-2 and a high Faradaic efficiency of 97.1%. Additionally, it demonstrates excellent stability and durability during long-term cycling tests and electrolysis experiments.
ABSTRACT
Ammonia (NH3 ) is an indispensable feedstock for fertilizer production and one of the most ideal green hydrogen rich fuel. Electrochemical nitrate (NO3 - ) reduction reaction (NO3 - RR) is being explored as a promising strategy for green to synthesize industrial-scale NH3 , which has nonetheless involved complex multi-reaction process. This work presents a Pd-doped Co3 O4 nanoarray on titanium mesh (Pd-Co3 O4 /TM) electrode for highly efficient and selective electrocatalytic NO3 - RR to NH3 at low onset potential. The well-designed Pd-Co3 O4 /TM delivers a large NH3 yield of 745.6 µmol h-1 cm-2 and an extremely high Faradaic efficiency (FE) of 98.7% at -0.3 V with strong stability. These calculations further indicate that the doping Co3 O4 with Pd improves the adsorption characteristic of Pd-Co3 O4 and optimizes the free energies for intermediates, thereby facilitating the kinetics of the reaction. Furthermore, assembling this catalyst in a Zn-NO3 - battery realizes a power density of 3.9 mW cm-2 and an excellent FE of 98.8% for NH3 .
ABSTRACT
Electroreduction of nitrite (NO2 - ) to ammonia (NH3 ) provides a sustainable approach to yield NH3 , whilst eliminating NO2 - contaminants. In this study, Ni nanoparticles strutted 3D honeycomb-like porous carbon framework (Ni@HPCF) is fabricated as a high-efficiency electrocatalyst for selective reduction of NO2 - to NH3 . In 0.1â M NaOH with NO2 - , such Ni@HPCF electrode obtains a significant NH3 yield of 12.04â mg h-1 mgcat. -1 and a Faradaic efficiency of 95.1 %. Furthermore, it exhibits good long-term electrolysis stability.
ABSTRACT
Electrochemical reduction of nitrite (NO2-) not only removes NO2- contaminant but also produces high-added value ammonia (NH3). This process, however, needs efficient and selective catalysts for NO2--to-NH3 conversion. In this study, Ruthenium doped titanium dioxide nanoribbon array supported on Ti plate (Ru-TiO2/TP) is proposed as an efficient electrocatalyst for the reduction of NO2- to NH3. When operated in 0.1 M NaOH containing NO2-, such Ru-TiO2/TP achieves an ultra-large NH3 yield of 1.56 mmol h-1 cm-2 and a super-high Faradaic efficiency of 98.9%, superior to its TiO2/TP counterpart (0.46 mmol h-1 cm-2, 74.1%). Furthermore, the reaction mechanism is studied by theoretical calculation.
ABSTRACT
Electroreduction of nitrite (NO2 - ) to valuable ammonia (NH3 ) offers a sustainable and green approach for NH3 synthesis. Here, a Cu3 P@TiO2 heterostructure is rationally constructed as an active catalyst for selective NO2 - -to-NH3 electroreduction, with rich nanosized Cu3 P anchored on a TiO2 nanoribbon array on Ti plate (Cu3 P@TiO2 /TP). When performed in the 0.1 m NaOH with 0.1 m NaNO2 , the Cu3 P@TiO2 /TP electrode obtains a large NH3 yield of 1583.4 µmol h-1 cm-2 and a high Faradaic efficiency of 97.1%. More importantly, Cu3 P@TiO2 /TP also delivers remarkable long-term stability for 50 h electrolysis. Theoretical calculations indicate that intermediate adsorption/conversion processes on Cu3 P@TiO2 interfaces are synergistically optimized, substantially facilitating the conversion of NO2 - -to-NH3 .
ABSTRACT
Synthesis of green ammonia (NH3 ) via electrolysis of nitric oxide (NO) is extraordinarily sustainable, but multielectron/proton-involved hydrogenation steps as well as low concentrations of NO can lead to poor activities and selectivities of electrocatalysts. Herein, it is reported that oxygen-defective TiO2 nanoarray supported on Ti plate (TiO2- x /TP) behaves as an efficient catalyst for NO reduction to NH3 . In 0.2 m phosphate-buffered electrolyte, such TiO2- x /TP shows competitive electrocatalytic NH3 synthesis activity with a maximum NH3 yield of 1233.2 µg h-1 cm-2 and Faradaic efficiency of 92.5%. Density functional theory calculations further thermodynamically faster NO deoxygenation and protonation processes on TiO2- x (101) compared to perfect TiO2 (101). And the low energy barrier of 0.7 eV on TiO2- x (101) for the potential-determining step further highlights the greatly improved intrinsic activity. In addition, a Zn-NO battery is fabricated with TiO2- x /TP and Zn plate to obtain an NH3 yield of 241.7 µg h-1 cm-2 while providing a peak power density of 0.84 mW cm-2 .
ABSTRACT
The non-coding RNA secondary structure largely determines its function. Hence, accuracy in structure acquisition is of great importance. Currently, this acquisition primarily relies on various computational methods. The prediction of the structures of long RNA sequences with high precision and reasonable computational cost remains challenging. Here, we propose a deep learning model, RNA-par, which could partition an RNA sequence into several independent fragments (i-fragments) based on its exterior loops. Each i-fragment secondary structure predicted individually could be further assembled to acquire the complete RNA secondary structure. In the examination of our independent test set, the average length of the predicted i-fragments was 453 nt, which was considerably shorter than that of complete RNA sequences (848 nt). The accuracy of the assembled structures was higher than that of the structures predicted directly using the state-of-the-art RNA secondary structure prediction methods. This proposed model could serve as a preprocessing step for RNA secondary structure prediction for enhancing the predictive performance (especially for long RNA sequences) and reducing the computational cost. In the future, predicting the secondary structure of long-sequence RNA with high accuracy can be enabled by developing a framework combining RNA-par with various existing RNA secondary structure prediction algorithms. Our models, test codes and test data are provided at https://github.com/mianfei71/RNAPar .
Subject(s)
Deep Learning , RNA , RNA/genetics , RNA/chemistry , Algorithms , Protein Structure, Secondary , Computational Biology/methods , Nucleic Acid ConformationABSTRACT
Electrochemical nitrate (NO3 - ) reduction reaction (NO3 - RR) is a potential sustainable route for large-scale ambient ammonia (NH3 ) synthesis and regulating the nitrogen cycle. However, as this reaction involves multi-electron transfer steps, it urgently needs efficient electrocatalysts on promoting NH3 selectivity. Herein, a rational design of Co nanoparticles anchored on TiO2 nanobelt array on titanium plate (Co@TiO2 /TP) is presented as a high-efficiency electrocatalyst for NO3 - RR. Density theory calculations demonstrate that the constructed Schottky heterostructures coupling metallic Co with semiconductor TiO2 develop a built-in electric field, which can accelerate the rate determining step and facilitate NO3 - adsorption, ensuring the selective conversion to NH3 . Expectantly, the Co@TiO2 /TP electrocatalyst attains an excellent Faradaic efficiency of 96.7% and a high NH3 yield of 800.0 µmol h-1 cm-2 under neutral solution. More importantly, Co@TiO2 /TP heterostructure catalyst also presents a remarkable stability in 50-h electrolysis test.
ABSTRACT
Electrochemical nitrite (NO2-) reduction is a potential and sustainable route to produce high-value ammonia (NH3), but it requires highly active electrocatalysts. Herein, Cu nanoparticles anchored on a TiO2 nanobelt array on a titanium plate (Cu@TiO2/TP) are reported as a high-efficiency electrocatalyst for NO2--to-NH3 conversion. The designed Cu@TiO2/TP catalyst exhibits outstanding catalytic performance toward the NO2-RR, with a high NH3 yield of 760.5 µmol h-1 cm-2 (237.7 µmol h-1 mgcat.-1) and an excellent faradaic efficiency of 95.3% in neutral solution. Meanwhile, it also presents strong electrochemical stability during cyclic tests and long-term electrolysis.