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"Li, Xiaowei"
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Molecular immune pathogenesis and diagnosis of COVID-19
Coronavirus disease 2019 (COVID-19) is a kind of viral pneumonia which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The emergence of SARS-CoV-2 has been marked as the third introduction of a highly pathogenic coronavirus into the human population after the severe acute respiratory syndrome coronavirus (SARS-CoV) and the Middle East respiratory syndrome coronavirus (MERS-CoV) in the twenty-first century. In this minireview, we provide a brief introduction of the general features of SARS-CoV-2 and discuss current knowledge of molecular immune pathogenesis, diagnosis and treatment of COVID-19 on the base of the present understanding of SARS-CoV and MERS-CoV infections, which may be helpful in offering novel insights and potential therapeutic targets for combating the SARS-CoV-2 infection.
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•.The highly pathogenic SARS-CoV-2 appearing in December 2019 can cause COVID-19 and even death in infected persons.•.Coronavirus infections led to the damage of lung, while imbalanced and excessive immune responses may cause pneumonia.•.RT-PCR and CT scans are significant for the diagnosis of SARS-CoV-2 infection, and drugs and vaccines against SARS-CoV-2 are being developed.
Journal Article
Rewiring carbon metabolism in yeast for high level production of aromatic chemicals
The production of bioactive plant compounds using microbial hosts is considered a safe, cost-competitive and scalable approach to their production. However, microbial production of some compounds like aromatic amino acid (AAA)-derived chemicals, remains an outstanding metabolic engineering challenge. Here we present the construction of a
Saccharomyces cerevisiae
platform strain able to produce high levels of
p
-coumaric acid, an AAA-derived precursor for many commercially valuable chemicals. This is achieved through engineering the AAA biosynthesis pathway, introducing a phosphoketalose-based pathway to divert glycolytic flux towards erythrose 4-phosphate formation, and optimizing carbon distribution between glycolysis and the AAA biosynthesis pathway by replacing the promoters of several important genes at key nodes between these two pathways. This results in a maximum
p
-coumaric acid titer of 12.5 g L
−1
and a maximum yield on glucose of 154.9 mg g
−1
.
Microbial production of aromatic amino acid (AAA)-derived chemicals remains an outstanding metabolic engineering challenge. Here, the authors engineer baker’s yeast for high levels
p
-coumaric acid production by rewiring the central carbon metabolism and channeling more flux to the AAA biosynthetic pathway.
Journal Article
Synergy of ferroelectric polarization and oxygen vacancy to promote CO2 photoreduction
Solar-light driven CO
2
reduction into value-added chemicals and fuels emerges as a significant approach for CO
2
conversion. However, inefficient electron-hole separation and the complex multi-electrons transfer processes hamper the efficiency of CO
2
photoreduction. Herein, we prepare ferroelectric Bi
3
TiNbO
9
nanosheets and employ corona poling to strengthen their ferroelectric polarization to facilitate the bulk charge separation within Bi
3
TiNbO
9
nanosheets. Furthermore, surface oxygen vacancies are introduced to extend the photo-absorption of the synthesized materials and also to promote the adsorption and activation of CO
2
molecules on the catalysts’ surface. More importantly, the oxygen vacancies exert a pinning effect on ferroelectric domains that enables Bi
3
TiNbO
9
nanosheets to maintain superb ferroelectric polarization, tackling above-mentioned key challenges in photocatalytic CO
2
reduction. This work highlights the importance of ferroelectric properties and controlled surface defect engineering, and emphasizes the key roles of tuning bulk and surface properties in enhancing the CO
2
photoreduction performance.
Solar-driven CO
2
reduction into value-added chemicals and fuels is attracting worldwide attention. Here, substantially enhanced photocatalytic CO
2
reduction activity is achieved via the synergy of surface oxygen vacancies and ferroelectric polarization over Bi
3
TiNbO
9
photocatalyst.
Journal Article
A Deep Learning Approach for Mild Depression Recognition Based on Functional Connectivity Using Electroencephalography
Early detection remains a significant challenge for the treatment of depression. In our work, we proposed a novel approach to mild depression recognition using electroencephalography (EEG). First, we explored abnormal organization in the functional connectivity network of mild depression using graph theory. Second, we proposed a novel classification model for recognizing mild depression. Considering the powerful ability of CNN to process two-dimensional data, we applied CNN separately to the two-dimensional data form of the functional connectivity matrices from five EEG bands (delta, theta, alpha, beta, and gamma). In addition, inspired by recent breakthroughs in the ability of deep recurrent CNNs to classify mental load, we merged the functional connectivity matrices from the three EEG bands that performed the best into a three-channel image to classify mild depression-related and normal EEG signals using the CNN. The results of the graph theory analysis showed that the brain functional network of the mild depression group had a larger characteristic path length and a lower clustering coefficient than the healthy control group, showing deviation from the small-world network. The proposed classification model obtained a classification accuracy of 80.74% for recognizing mild depression. The current study suggests that the combination of a CNN and functional connectivity matrix may provide a promising objective approach for diagnosing mild depression. Deep learning approaches such as this might have the potential to inform clinical practice and aid in research on psychiatric disorders.
Journal Article
Trajectory tracking for agricultural tractor based on improved fuzzy sliding mode control
Trajectory tracking is one of the key technologies for tractor automatic navigation. Its main purpose is to adjust the steering mechanism of the tractor to follow the planned trajectory. Thus, in this paper a trajectory tracking control system is designed for an agricultural tractor with the electric power steering mechanism. A DC brush motor is added on the steering column of the tractor and the hardware circuits for the steering controller are designed to control the front wheel angel. The three degrees of freedom model of the tractor is established, and a trajectory tracking control system is proposed including a fuzzy sliding mode controller and a steering angle tracking controller designed according to the internal mode control and minimized sensitivity theory. The effectiveness of the designed trajectory tracking control system is demonstrated by simulation analyses in reference to the planed trajectory.
Journal Article
Multichannel vectorial holographic display and encryption
Since its invention, holography has emerged as a powerful tool to fully reconstruct the wavefronts of light including all the fundamental properties (amplitude, phase, polarization, wave vector, and frequency). For exploring the full capability for information storage/display and enhancing the encryption security of metasurface holograms, smart multiplexing techniques together with suitable metasurface designs are highly demanded. Here, we integrate multiple polarization manipulation channels for various spatial phase profiles into a single birefringent vectorial hologram by completely avoiding unwanted cross-talk. Multiple independent target phase profiles with quantified phase relations that can process significantly different information in different polarization states are realized within a single metasurface. For our metasurface holograms, we demonstrate high fidelity, large efficiency, broadband operation, and a total of twelve polarization channels. Such multichannel polarization multiplexing can be used for dynamic vectorial holographic display and can provide triple protection for optical security. The concept is appealing for applications of arbitrary spin to angular momentum conversion and various phase modulation/beam shaping elements.
Journal Article
Aridity-driven shift in biodiversity–soil multifunctionality relationships
Relationships between biodiversity and multiple ecosystem functions (that is, ecosystem multifunctionality) are context-dependent. Both plant and soil microbial diversity have been reported to regulate ecosystem multifunctionality, but how their relative importance varies along environmental gradients remains poorly understood. Here, we relate plant and microbial diversity to soil multifunctionality across 130 dryland sites along a 4,000 km aridity gradient in northern China. Our results show a strong positive association between plant species richness and soil multifunctionality in less arid regions, whereas microbial diversity, in particular of fungi, is positively associated with multifunctionality in more arid regions. This shift in the relationships between plant or microbial diversity and soil multifunctionality occur at an aridity level of ∼0.8, the boundary between semiarid and arid climates, which is predicted to advance geographically ∼28% by the end of the current century. Our study highlights that biodiversity loss of plants and soil microorganisms may have especially strong consequences under low and high aridity conditions, respectively, which calls for climate-specific biodiversity conservation strategies to mitigate the effects of aridification.
Biodiversity-ecosystem functioning relationships may vary with climate. Here, the authors study relationships of plant and soil microbial diversity with soil nutrient multifunctionality in 130 dryland sites in China, finding a shift towards greater importance of soil microbial diversity in arid conditions.
Journal Article
Multi-scale attention network (MSAN) for track circuits fault diagnosis
As one of the three major outdoor components of the railroad signal system, the track circuit plays an important role in ensuring the safety and efficiency of train operation. Therefore, when a fault occurs, the cause of the fault needs to be found quickly and accurately and dealt with in a timely manner to avoid affecting the efficiency of train operation and the occurrence of safety accidents. This article proposes a fault diagnosis method based on multi-scale attention network, which uses Gramian Angular Field (GAF) to transform one-dimensional time series into two-dimensional images, making full use of the advantages of convolutional networks in processing image data. A new feature fusion training structure is designed to effectively train the model, fully extract features at different scales, and fusing spatial feature information through spatial attention mechanisms. Finally, experiments are conducted using real track circuit fault datasets, and the accuracy of fault diagnosis reaches 99.36%, and our model demonstrates better performance compared to classical and state-of-the-art models. And the ablation experiments verified that each module in the designed model plays a key role.
Journal Article
Amorphous organic-hybrid vanadium oxide for near-barrier-free ultrafast-charging aqueous zinc-ion battery
Fast-charging metal-ion batteries are essential for advancing energy storage technologies, but their performance is often limited by the high activation energy (
E
a
) required for ion diffusion in solids. Addressing this challenge has been particularly difficult for multivalent ions like Zn
2+
. Here, we present an amorphous organic-hybrid vanadium oxide (AOH-VO), featuring one-dimensional chains arranged in a disordered structure with atomic/molecular-level pores for promoting hierarchical ion diffusion pathways and reducing Zn
2+
interactions with the solid skeleton. AOH-VO cathode demonstrates an exceptionally low
E
a
of 7.8 kJ·mol
−1
for Zn
2+
diffusion in solids and 6.3 kJ·mol
−1
across the cathode-electrolyte interface, both significantly lower than that of electrolyte (13.2 kJ·mol
−1
) in zinc ion battery. This enables ultrafast charge-discharge performance, with an Ah-level pouch cell achieving 81.3% of its capacity in just 9.5 minutes and retaining 90.7% capacity over 5000 cycles. These findings provide a promising pathway toward stable, ultrafast-charging battery technologies with near-barrier-free ion dynamics.
Promoting solid ion-diffusion is essential for fast-charging battery. Here, authors present near-barrier-free ion dynamics in an amorphous organic-hybrid vanadium oxide-based zinc ion battery and developed Ah-level fast-charging pouch cell.
Journal Article