Detecting the oxidative reactivity of nanoparticles: a new protocol for reducing artifacts

Understanding the oxidative reactivity of nanoparticles (NPs; <100 nm) could substantially contribute to explaining their toxicity. We attempted to refine the use of 2′7-dichlorodihydrofluorescein (DCFH) to characterize NP generation of reactive oxygen species (ROS). Several fluorescent probes have been applied to testing oxidative reactivity, but despite DCFH being one of the most popular for the detection of ROS, when it has been applied to NPs there have been an unexplainably wide variability in results. Without a uniform methodology, validating even robust results is impossible. This study, therefore, identified sources of conflicting results and investigated ways of reducing occurrence of artificial results. Existing techniques were tested and combined (using their most desirable features) to form a more reliable method for the measurement of NP reactivity in aqueous dispersions. We also investigated suitable sample ranges necessary to determine generation of ROS. Specifically, ultrafiltration and time-resolved scan absorbance spectra were used to study possible optical interference when using high sample concentrations. Robust results were achieved at a 5 µM DCFH working solution with 0.5 unit/mL horseradish peroxidase (HRP) dissolved in ethanol. Sonication in DCFH-HRP working solution provided more stable data with a relatively clean background. Optimal particle concentration depends on the type of NP and in general was in the µg/mL range. Major reasons for previously reported conflicting results due to interference were different experimental approaches and NP sample concentrations. The protocol presented here could form the basis of a standardized method for applying DCFH to detect generation of ROS by NPs

Physico-chemical characterization and oxidative reactivity evaluation of aged brake wear particles

Brake wear dust is a significant component of traffic emissions and has been linked to adverse health effects. Previous research found a strong oxidative stress response in cells exposed to freshly generated brake wear dust. We characterized aged dust collected from passenger vehicles, using microscopy and elemental analyses. Reactive oxygen species (ROS) generation was measured with acellular and cellular assays using 2′7-dichlorodihydrofluorescein dye. Microscopy analyses revealed samples to be heterogeneous particle mixtures with few nanoparticles detected. Several metals, primarily iron and copper, were identified. High oxygen concentrations suggested that the elements were oxidized. ROS were detected in the cell-free fluorescent test, while exposed cells were not dramatically activated by the concentrations used. The fact that aged brake wear samples have lower oxidative stress potential than fresh ones may relate to the highly oxidized or aged state of these particles, as well as their larger size and smaller reactive surface area

Detecting the oxidative reactivity of nanoparticles: a new protocol for reducing artifacts

Understanding the oxidative reactivity of nanoparticles (NPs;<100nm) could substantially contribute to explaining their toxicity. We attempted to refine the use of 2′7-dichlorodihydrofluorescein (DCFH) to characterize NP generation of reactive oxygen species (ROS). Several fluorescent probes have been applied to testing oxidative reactivity, but despite DCFH being one of the most popular for the detection of ROS, when it has been applied to NPs there have been an unexplainably wide variability in results. Without a uniform methodology, validating even robust results is impossible. This study, therefore, identified sources of conflicting results and investigated ways of reducing occurrence of artificial results. Existing techniques were tested and combined (using their most desirable features) to form a more reliable method for the measurement of NP reactivity in aqueous dispersions. We also investigated suitable sample ranges necessary to determine generation of ROS. Specifically, ultrafiltration and time-resolved scan absorbance spectra were used to study possible optical interference when using high sample concentrations. Robust results were achieved at a 5 µM DCFH working solution with 0.5unit/mL horseradish peroxidase (HRP) dissolved in ethanol. Sonication in DCFH-HRP working solution provided more stable data with a relatively clean background. Optimal particle concentration depends on the type of NP and in general was in the µg/mL range. Major reasons for previously reported conflicting results due to interference were different experimental approaches and NP sample concentrations. The protocol presented here could form the basis of a standardized method for applying DCFH to detect generation of ROS by NPs

Computational prediction of type III secreted proteins from gram-negative bacteria

Abstract Background Type III secretion system (T3SS) is a specialized protein delivery system in gram-negative bacteria that injects proteins (called effectors) directly into the eukaryotic host cytosol and facilitates bacterial infection. For many plant and animal pathogens, T3SS is indispensable for disease development. Recently, T3SS has also been found in rhizobia and plays a crucial role in the nodulation process. Although a great deal of efforts have been done to understand type III secretion, the precise mechanism underlying the secretion and translocation process has not been fully understood. In particular, defined secretion and translocation signals enabling the secretion have not been identified from the type III secreted effectors (T3SEs), which makes the identification of these important virulence factors notoriously challenging. The availability of a large number of sequenced genomes for plant and animal-associated bacteria demands the development of efficient and effective prediction methods for the identification of T3SEs using bioinformatics approaches. Results We have developed a machine learning method based on the N-terminal amino acid sequences to predict novel type III effectors in the plant pathogen Pseudomonas syringae and the microsymbiont rhizobia. The extracted features used in the learning model (or classifier) include amino acid composition, secondary structure and solvent accessibility information. The method achieved a precision of over 90% on P. syringae in a cross validation study. In combination with a promoter screen for the type III specific promoters, this classifier trained on the P. syringae data was applied to predict novel T3SEs from the genomic sequences of four rhizobial strains. This application resulted in 57 candidate type III secreted proteins, 17 of which are confirmed effectors. Conclusion Our experimental results demonstrate that the machine learning method based on N-terminal amino acid sequences combined with a promoter screen could prove to be a very effective computational approach for predicting novel type III effectors in gram-negative bacteria. Our method and data are available to the public upon request

Characterization of Tungsten Inert Gas (TIG) Welding Fume Generated by Apprentice Welders.

Tungsten inert gas welding (TIG) represents one of the most widely used metal joining processes in industry. Its propensity to generate a greater portion of welding fume particles at the nanoscale poses a potential occupational health hazard for workers. However, current literature lacks comprehensive characterization of TIG welding fume particles. Even less is known about welding fumes generated by welding apprentices with little experience in welding. We characterized TIG welding fume generated by apprentice welders (N = 20) in a ventilated exposure cabin. Exposure assessment was conducted for each apprentice welder at the breathing zone (BZ) inside of the welding helmet and at a near-field (NF) location, 60cm away from the welding task. We characterized particulate matter (PM4), particle number concentration and particle size, particle morphology, chemical composition, reactive oxygen species (ROS) production potential, and gaseous components. The mean particle number concentration at the BZ was 1.69E+06 particles cm(-3), with a mean geometric mean diameter of 45nm. On average across all subjects, 92% of the particle counts at the BZ were below 100nm. We observed elevated concentrations of tungsten, which was most likely due to electrode consumption. Mean ROS production potential of TIG welding fumes at the BZ exceeded average concentrations previously found in traffic-polluted air. Furthermore, ROS production potential was significantly higher for apprentices that burned their metal during their welding task. We recommend that future exposure assessments take into consideration welding performance as a potential exposure modifier for apprentice welders or welders with minimal training

Empowering Graph Representation Learning with Test-Time Graph Transformation

As powerful tools for representation learning on graphs, graph neural networks (GNNs) have facilitated various applications from drug discovery to recommender systems. Nevertheless, the effectiveness of GNNs is immensely challenged by issues related to data quality, such as distribution shift, abnormal features and adversarial attacks. Recent efforts have been made on tackling these issues from a modeling perspective which requires additional cost of changing model architectures or re-training model parameters. In this work, we provide a data-centric view to tackle these issues and propose a graph transformation framework named GTrans which adapts and refines graph data at test time to achieve better performance. We provide theoretical analysis on the design of the framework and discuss why adapting graph data works better than adapting the model. Extensive experiments have demonstrated the effectiveness of GTrans on three distinct scenarios for eight benchmark datasets where suboptimal data is presented. Remarkably, GTrans performs the best in most cases with improvements up to 2.8%, 8.2% and 3.8% over the best baselines on three experimental settings

Utilizing the nanosecond pulse technique to improve antigen intracellular delivery and presentation to treat tongue squamous cell carcinoma

Tongue squamous cell carcinoma is the most common squamous cell carcinoma of the head and neck. Immunotherapy has great potential in the treatment of tongue squamous cell carcinoma because of its unique advantages. However, the efficacy of immunotherapy is limited by the efficiency of antigen phagocytosis by immune cells. We extracted dendritic cells (DCs) from human peripheral blood. Utilizing a nanosecond pulsed electric field (nsPEF), we deliver the tumour lysate protein into DCs and then incubate the DCs with PBMCs to obtain specific T cells to kill tumour cells. The biosafety of nsPEF was evaluated by the ANNEXIN V-FITC/PI kit. The efficacy of lysate protein delivery was evaluated by flow cytometry. The antitumour efficacy was tested by CCK-8 assay. The nsPEF of the appropriate field strength can significantly improve the phagocytic ability of DCs to tumour lysing proteins and have good biosafety. The tumour cell killing rate of the nsPEF group was higher than the other group (p< 0.05). Utilizing nsPEF to improve the phagocytic and presenting ability of DCs could greatly activate the adaptive immune cells to enhance the immunotherapeutic effect on tongue squamous cell carcinoma

Hawkeye: Change-targeted Testing for Android Apps based on Deep Reinforcement Learning

Android Apps are frequently updated to keep up with changing user, hardware, and business demands. Ensuring the correctness of App updates through extensive testing is crucial to avoid potential bugs reaching the end user. Existing Android testing tools generate GUI events focussing on improving the test coverage of the entire App rather than prioritising updates and its impacted elements. Recent research has proposed change-focused testing but relies on random exploration to exercise the updates and impacted GUI elements that is ineffective and slow for large complex Apps with a huge input exploration space. We propose directed testing of App updates with Hawkeye that is able to prioritise executing GUI actions associated with code changes based on deep reinforcement learning from historical exploration data. Our empirical evaluation compares Hawkeye with state-of-the-art model-based and reinforcement learning-based testing tools FastBot2 and ARES using 10 popular open-source and 1 commercial App. We find that Hawkeye is able to generate GUI event sequences targeting changed functions more reliably than FastBot2 and ARES for the open source Apps and the large commercial App. Hawkeye achieves comparable performance on smaller open source Apps with a more tractable exploration space. The industrial deployment of Hawkeye in the development pipeline also shows that Hawkeye is ideal to perform smoke testing for merge requests of a complicated commercial App