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Nano-impact (NIE) (also referred to as collision) single-entity electrochemistry is an emerging technique that enables electrochemical investigation of individual entities, ranging from metal nanoparticles to single cells and biomolecules. To obtain meaningful information from NIE experiments, analysis and feature extraction on large datasets are necessary. Herein, a method is developed for the automated analysis of NIE data based on unsupervised machine learning and template matching approaches. Template matching not only facilitates downstream processing of the NIE data but also provides a more accurate analysis of the NIE signal characteristics and variations that are difficult to discern with conventional data analysis techniques, such as the height threshold method. The developed algorithm enables fast automated processing of large experimental datasets recorded with different systems, requiring minimal human intervention and thereby eliminating human bias in data analysis. As a result, it improves the standardization of data processing and NIE signal interpretation across various experiments and applications. Nano-impact (NIE) electrochemistry is an emerging technique for studying individual entities. Analyzing large NIE datasets, often with low signal-to-noise ratios, is challenging. Herein, an automated approach is introduced using unsupervised machine learning and template matching for accurate feature extraction from spike-shaped NIE signals. It improves data processing, accuracy and standardization, reducing human bias in signal interpretation across experiments.image (c) 2023 WILEY-VCH GmbH

Graph self-supervised learning is now a go-to method for pre-training graph foundation models, including graph neural networks, graph transformers, and more recent large language model (LLM)-based graph models. There is a wide variety of knowledge patterns embedded in the structure and properties of graphs which may be used for pre-training, but we lack a systematic overview of self-supervised pre-training tasks from the perspective of graph knowledge. In this paper, we comprehensively survey and analyze the pre-training tasks of graph foundation models from a knowledge-based perspective, consisting of microscopic (nodes, links, etc) and macroscopic knowledge (clusters, global structure, etc). It covers a total of 9 knowledge categories and 25 pre-training tasks, as well as various downstream task adaptation strategies. Furthermore, an extensive list of the related papers with detailed metadata is provided at https://github.com/Newiz430/Pretext., Comment: Work in progress

Masked graph autoencoders have emerged as a powerful graph self-supervised learning method that has yet to be fully explored. In this paper, we unveil that the existing discrete edge masking and binary link reconstruction strategies are insufficient to learn topologically informative representations, from the perspective of message propagation on graph neural networks. These limitations include blocking message flows, vulnerability to over-smoothness, and suboptimal neighborhood discriminability. Inspired by these understandings, we explore non-discrete edge masks, which are sampled from a continuous and dispersive probability distribution instead of the discrete Bernoulli distribution. These masks restrict the amount of output messages for each edge, referred to as "bandwidths". We propose a novel, informative, and effective topological masked graph autoencoder using bandwidth masking and a layer-wise bandwidth prediction objective. We demonstrate its powerful graph topological learning ability both theoretically and empirically. Our proposed framework outperforms representative baselines in both self-supervised link prediction (improving the discrete edge reconstructors by at most 20%) and node classification on numerous datasets, solely with a structure-learning pretext. Our implementation is available at https://github.com/Newiz430/Bandana., Comment: Full version (17 pages, 8 figures, 12 tables), accepted by TheWebConf 2024 (WWW 2024)

Plasmon-enhanced electrocatalysis (PEEC), based on a combination of localized surface plasmon resonance excitation and an electrochemical bias applied to a plasmonic material, can result in improved electrical-to-chemical energy conversion compared to conventional electrocatalysis. Here, we demonstrate the advantages of nano-impact single-entity electrochemistry (SEE) for investigating the intrinsic activity of plasmonic catalysts at the single-particle level using glucose electrooxidation and oxygen reduction on gold nanoparticles as model reactions. We show that in conventional ensemble measurements, plasmonic effects have minimal impact on photocurrents. We suggest that this is due to the continuous equilibration of the Fermi level (EF) of the deposited gold nanoparticles with the EF of the working electrode, leading to fast neutralization of hot carriers by the measuring circuit. The photocurrents detected in the ensemble measurements are primarily caused by photo-induced heating of the supporting electrode material. In SEE, the EF of suspended gold nanoparticles is unaffected by the working electrode potential. As a result, plasmonic effects are the dominant source of photocurrents under SEE experimental conditions., QC 20230713

Plasmon-enhanced electrocatalysis (PEEC), based on a combination of localized surface plasmon resonance excitation and an electrochemical bias applied to a plasmonic material, can result in improved electrical-to-chemical energy conversion compared to conventional electrocatalysis. Here, we demonstrate the advantages of nano-impact single-entity electrochemistry (SEE) for investigating the intrinsic activity of plasmonic catalysts at the single-particle level using glucose electrooxidation and oxygen reduction on gold nanoparticles as model reactions. We show that in conventional ensemble measurements, plasmonic effects have minimal impact on photocurrents. We suggest that this is due to the continuous equilibration of the Fermi level (EF) of the deposited gold nanoparticles with the EF of the working electrode, leading to fast neutralization of hot carriers by the measuring circuit. The photocurrents detected in the ensemble measurements are primarily caused by photo-induced heating of the supporting electrode material. In SEE, the EF of suspended gold nanoparticles is unaffected by the working electrode potential. As a result, plasmonic effects are the dominant source of photocurrents under SEE experimental conditions.

Plasmon-enhanced electrocatalysis (PEEC), based on a combination of localized surface plasmon resonance excitation and an electrochemical bias applied to a plasmonic material, can result in improved electrical-to-chemical energy conversion compared to conventional electrocatalysis. Here, we demonstrate the advantages of nano-impact single-entity electrochemistry (SEE) for investigating the intrinsic activity of plasmonic catalysts at the single-particle level using glucose electrooxidation and oxygen reduction on gold nanoparticles as model reactions. We show that in conventional ensemble measurements, plasmonic effects have minimal impact on photocurrents. We suggest that this is due to the continuous equilibration of the Fermi level (EF) of the deposited gold nanoparticles with the EF of the working electrode, leading to fast neutralization of hot carriers by the measuring circuit. The photocurrents detected in the ensemble measurements are primarily caused by photo-induced heating of the supporting electrode material. In SEE, the EF of suspended gold nanoparticles is unaffected by the working electrode potential. As a result, plasmonic effects are the dominant source of photocurrents under SEE experimental conditions.

Graph Contrastive Learning (GCL) is an effective way to learn generalized graph representations in a self-supervised manner, and has grown rapidly in recent years. However, the underlying community semantics has not been well explored by most previous GCL methods. Research that attempts to leverage communities in GCL regards them as having the same influence on the graph, leading to extra representation errors. To tackle this issue, we define ''community strength'' to measure the difference of influence among communities. Under this premise, we propose a Community-Strength-enhanced Graph Contrastive Learning (CSGCL) framework to preserve community strength throughout the learning process. Firstly, we present two novel graph augmentation methods, Communal Attribute Voting (CAV) and Communal Edge Dropping (CED), where the perturbations of node attributes and edges are guided by community strength. Secondly, we propose a dynamic ''Team-up'' contrastive learning scheme, where community strength is used to progressively fine-tune the contrastive objective. We report extensive experiment results on three downstream tasks: node classification, node clustering, and link prediction. CSGCL achieves state-of-the-art performance compared with other GCL methods, validating that community strength brings effectiveness and generality to graph representations. Our code is available at https://github.com/HanChen-HUST/CSGCL., Comment: Accepted to 23th International Joint Conferences on Artificial Intelligence(IJCAI)

The impact of microbial communities on ecosystem function varies due to the diverse biological attributes and sensitivities exhibited by different taxonomic groups. These groups can be classified as always rare (ART), conditionally rare (CRT), dominant, and total taxa, each affecting ecosystem function in distinct ways. Thus, understanding the functional traits of organisms within these taxa is crucial for comprehending their contributions to overall ecosystem function. In our study, we investigated the influence of climate warming on the biogeochemical cycles of the ecosystem in the Qinghai-Tibet Plateau, utilizing an open top chamber experiment. Simulated warming significantly lowered ecosystem function in the grassland but not in the shrubland. This discrepancy was due to the diverse responses of the various taxa present in each ecosystem to warming conditions and their differing roles in determining and regulating ecosystem function. The microbial maintenance of ecosystem function was primarily reliant on the diversity of bacterial dominant taxa and CRT and was less dependent on ART and fungal taxa. Furthermore, bacterial CRT and dominant taxa of the grassland ecosystem were more sensitive to changing climatic conditions than grassland ART, resulting in a more pronounced negative diversity response. In conclusion, the biological maintenance of ecosystem function during climate warming is dependent on microbiome composition and the functional and response characteristics of the taxa present. Thus, understanding the functional traits and response characteristics of various taxa is crucial for predicting the effects of climate change on ecosystem function and informing ecological reconstruction efforts in alpine regions of the plateau.

Seasonal rhythms in biological and ecological dynamics are fundamental in regulating the structuring of microbial communities. Evaluating the seasonal rhythms of microorganisms in response to climate change could provide information on their variability and stability over longer timescales (>20-year). However, information on temporal variability in microorganism responses to medium- and long-term global warming is limited. In this study, we aimed to elucidate the temporal dynamics of microbial communities in response to global warming; to this end, we integrated data on the maintenance of species diversity, community composition, temporal turnover rates (v), and community assembly process in two typical ecosystems (meadows and shrub habitat) on the Qinghai-Tibet Plateau. Our results showed that 21 years of global warming would increase the importance of the deterministic process for microorganisms in both ecosystems across all seasons (R2 of grassland (GL) control: 0.524, R2 of GL warming: 0.467; R2 of shrubland (SL) control: 0.556, R2 of SL warming: 0.543), reducing species diversity and altering community composition. Due to environmental filtration pressure from 21 years of warming, the low turnover rate (v of warming: −3.13/-2.00, v of control: −2.44/−1.48) of soil microorganisms reduces the resistance and resilience of ecological communities, which could lead to higher community similarity and more clustered taxonomic assemblages occurring across years. Changes to temperature might increase selection pressure on specialist taxa, which directly causes dominant species (v of warming: −1.63, v of control: −2.49) primarily comprising these taxa to be more strongly impacted by changing temperature than conditionally (v of warming: −1.47, v of control: −1.75) or always rare taxa (v of warming: −0.57, v of control: −1.33). Evaluation of the seasonal rhythms of microorganisms in response to global warming revealed that the variability and stability of different microbi

Changquan Fang,1,* Limin Xu,2,* Jiarong Tan,1 Hongyi Tan,1 Junhong Lin,1 Ziwen Zhao3 1Department of Pulmonary and Critical Care Medicine, Huizhou Central People’s Hospital, Huizhou, Guangdong Province, Peoples’ Republic of China; 2Department of Geriatrics, Huizhou First People’s Hospital, Huizhou, Guangdong Province, Peoples’ Republic of China; 3Department of Pulmonary and Critical Care Medicine, Guangzhou First People’s Hospital Affiliated to South China University of Technology, Guangzhou, Guangdong Province, Peoples’ Republic of China*These authors contributed equally to this workCorrespondence: Junhong Lin, Department of Pulmonary and Critical Care Medicine, Huizhou Central People’s Hospital, No. 41 Eling North Road, Huicheng District, Huizhou City, Guangdong Province, 516000, Peoples’ Republic of China, Tel +86 130-7528-2839, Email hz_ljh@163.com Ziwen Zhao, Department of Pulmonary and Critical Care Medicine, Guangzhou First People’s Hospital Affiliated to South China University of Technology, No. 1 Panfu Road, Yuexiu District, Guangzhou City, Guangdong Province, 510180, Peoples’ Republic of China, Tel +86 130-0687-2260, Email zhaozw@yeah.netAbstract: Psittacosis is a rare zoonotic disease caused by Chlamydia psittaci infection, and tetracyclines are the preferred treatment. Omadacycline is a novel tetracycline that has a strong in vitro antibacterial activity against atypical pathogens, including C. psittaci; however, clinical data for its usage are lacking. We report a patient with severe C. psittaci-induced pneumonia presenting with a high fever, muscle aches, severe hepatic and renal insufficiency, and acute respiratory failure requiring tracheal intubation and mechanical ventilation. The condition was diagnosed using metagenomic next-generation sequencing. The patient was discharged after treatment with omadacycline. The findings of this study suggest that metagenomic next-generation sequencing is valuable for th

The harmonics exacerbated by the integration of distributed energy such as wind power has been extensively studied. However, the interaction and propagation mechanism between harmonic sources in the hydro-wind complementary generation system are still not clear. To tackle this challenge, the presented study establishes the hydro-wind complementary generation system model and explores the harmonics propagation and interaction in all components. Then three operation mode of complementary system (scenario 1: stand-alone Hydroelectric Generating System, scenario 2: stand-alone Wind Farm (WF) and scenario 3: complementary generation system) are selected. The results demonstrate that the integration of HGS diminishes the harmonic at DFIG side but at the grid side. In complementary generation system, the THDu rises but the corresponding THDi declines due to the regulation of power grid. Furthermore, the odd harmonics interactions analysis reveal that the doubly-fed induction generator's (DFIG) side and the stator's side are the two high-risk sources in the complementary generation process. The presented results provide a basis for power quality evaluation of hydro-wind complementary generation system.

Zhuxiang Zhao,1,2 Changbin Jiang,1 Dongxing Zhao,1 Yujun Li,2 Chunxiao Liang,1 Weifeng Liu,2 Shuquan Wei,2 Yumin Zhou,1 Ziwen Zhao,2 Pixin Ran1 1The State Key Laboratory of Respiratory Diseases, The First Affliated Hospital, Guangzhou Medical University, Guangzhou, 2The Pulmonary Medicine,Guangzhou First People’s Hospital, Guangzhou Medical University, Guangzhou, Guangdong, People’s Republic of China Abstract: Quantitative computed tomography (CT) measures of emphysema have been shown to be associated with increased mortality in humans, but genetic variants affecting the quantitative parameters of chest CT that measure degree of emphysema have not yet been examined. In this study, using available chest CT data from a total of 344 emphysema patients, we assessed the correlations between five chronic obstructive pulmonary disease (COPD) susceptibility variants in the cholinergic receptor nicotinic (CHRN) genes and the degree of emphysema and chest CT manifestations. We verified that most of the parameters were significantly correlated with the degree of emphysema. Compared to rs76071148AA and TT genotype carriers, the rs76071148AT genotype carriers exhibited a decreased probability of having severe emphysema (odds ratio [OR] =0.63, 95% confidence interval [CI] =0.40–0.99), whereas the variant rs8040868C allele was negatively correlated with the emphysema index (P=0.002). Interestingly, further stratification analysis grouped by spirometry-diagnosed COPD status revealed that the variant rs8040868C (CT + CC) genotypes exerted a protective effect against severe emphysema with borderline significance (OR =0.41, 95% CI =0.16–1.05) and affected the mean lung density, emphysema index, ratio of airway wall thickness to airway dimensions (AWT/AD), and AWT grade in spirometry-diagnosed non-COPD subjects. The rs76071148 variant was also significantly associated with AWT/AD and AWT grade in those individuals. In summary, we determined that